  Mutual information is a general statistical dependency measure which has
found applications in representation learning, causality, domain generalization
and computational biology. However, mutual information estimators are typically
evaluated on simple families of probability distributions, namely multivariate
normal distribution and selected distributions with one-dimensional random
variables. In this paper, we show how to construct a diverse family of
distributions with known ground-truth mutual information and propose a
language-independent benchmarking platform for mutual information estimators.
We discuss the general applicability and limitations of classical and neural
estimators in settings involving high dimensions, sparse interactions,
long-tailed distributions, and high mutual information. Finally, we provide
guidelines for practitioners on how to select appropriate estimator adapted to
the difficulty of problem considered and issues one needs to consider when
applying an estimator to a new data set.


  On behalf of the International Astronomical Consortium for High Energy
Calibration (IACHEC), we present results from the cross-calibration campaigns
in 2012 on 3C 273 and in 2013 on PKS 2155-304 between the then active X-ray
observatories Chandra, NuSTAR, Suzaku, Swift and XMM-Newton. We compare
measured fluxes between instrument pairs in two energy bands, 1-5 keV and 3-7
keV and calculate an average cross-normalization constant for each energy
range. We review known cross-calibration features and provide a series of
tables and figures to be used for evaluating cross-normalization constants
obtained from other observations with the above mentioned observatories.


  Anomaly detection is an active research topic in many different fields such
as intrusion detection, network monitoring, system health monitoring, IoT
healthcare, etc. However, many existing anomaly detection approaches require
either human intervention or domain knowledge, and may suffer from high
computation complexity, consequently hindering their applicability in
real-world scenarios. Therefore, a lightweight and ready-to-go approach that is
able to detect anomalies in real-time is highly sought-after. Such an approach
could be easily and immediately applied to perform time series anomaly
detection on any commodity machine. The approach could provide timely anomaly
alerts and by that enable appropriate countermeasures to be undertaken as early
as possible. With these goals in mind, this paper introduces ReRe, which is a
Real-time Ready-to-go proactive Anomaly Detection algorithm for streaming time
series. ReRe employs two lightweight Long Short-Term Memory (LSTM) models to
predict and jointly determine whether or not an upcoming data point is
anomalous based on short-term historical data points and two long-term
self-adaptive thresholds. Experiments based on real-world time-series datasets
demonstrate the good performance of ReRe in real-time anomaly detection without
requiring human intervention or domain knowledge.


  We experimentally investigate the dynamics of a sphere rolling up a granular
slope. During the rolling-up motion, the sphere experiences slipping and
penetration (groove formation) on the surface of the granular layer. The former
relates to the stuck motion of the rolling sphere, and the latter causes energy
dissipation due to the deformation of the granular surface. To characterize
these phenomena, we measured the motion of a sphere rolling up a granular slope
of angle $\alpha$. The initial velocity $v_0$, initial angular velocity
$\omega_0$, angle of slope $\alpha$, and density of the sphere $\rho_s$ were
varied. As a result, the penetration depth can be scaled solely by the density
ratio between the sphere and granular layer. By considering the rotational
equation of motion, we estimate the friction due to the slips. Besides, by
considering energy conservation, we define and estimate the friction due to
groove formation. Moreover, the translational friction is proportional to the
penetration depth. Using these results, we can quantitatively predict the
sphere's motion including stuck behavior.


  The dynamical classification of rational maps is a central concern of
holomorphic dynamics. Much progress has been made, especially on the
classification of polynomials and some approachable one-parameter families of
rational maps; the goal of finding a classification of general rational maps is
so far elusive. Newton maps (rational maps that arise when applying Newton's
method to a polynomial) form a most natural family to be studied from the
dynamical perspective. Using Thurston's characterization and rigidity theorem,
a complete combinatorial classification of postcritically finite Newton maps is
given in terms of a finite connected graph satisfying certain explicit
conditions.


  A nonequilibrium statistical operator method is developed for ensembles of
particles obeying non-Hamiltonian equations of motion in classical phase space.
The main consequences of non-zero compressibility of phase space are examined
in terms of time-dependent dynamic quantities. The generalized transport
equations involve the phase-space compressibility in a non-trivial way. Our
results are useful in molecular dynamics simulation studies as well as
nonequilibrium or quasiclassical approximations of quantum-classical dynamics.


  In low concentration Tm$^{3+}$:YAG, we observe efficient adiabatic rapid
passage (ARP) of thulium nuclear spin over flipping times much longer than
$T_2$. Efficient ARP with long flipping time has been observed in monoatomic
solids for decades and has been analyzed in terms of spin temperature and of
the thermodynamic equilibrium of a coupled spin ensemble. In low-concentration
impurity-doped crystals the spin temperature concept may be questioned. A
single spin model should be preferred since the impurity ions are weakly
coupled together but interact with the numerous off-resonant matrix ions that
originate the spin-spin relaxation. The experiment takes place in the context
of quantum information investigation, involving impurity-doped crystals, spin
hyperpolarization by optical pumping, and optical detection of the spin
evolution.


  While camera and LiDAR are widely used in most of the assisted and autonomous
driving systems, only a few works have been proposed to associate the temporal
synchronization and extrinsic calibration for camera and LiDAR which are
dedicated to online sensors data fusion. The temporal and spatial calibration
technologies are facing the challenges of lack of relevance and real-time. In
this paper, we introduce the pose estimation model and environmental robust
line features extraction to improve the relevance of data fusion and instant
online ability of correction. Dynamic targets eliminating aims to seek optimal
policy considering the correspondence of point cloud matching between adjacent
moments. The searching optimization process aims to provide accurate parameters
with both computation accuracy and efficiency. To demonstrate the benefits of
this method, we evaluate it on the KITTI benchmark with ground truth value. In
online experiments, our approach improves the accuracy by 38.5\% than the soft
synchronization method in temporal calibration. While in spatial calibration,
our approach automatically corrects disturbance errors within 0.4 second and
achieves an accuracy of 0.3-degree. This work can promote the research and
application of sensor fusion.


  This article surveys the physics of systems proximate to Mott insulators, and
presents a classification using conventional and topological order parameters.
This classification offers a valuable perspective on a variety of conducting
correlated electron systems, from the cuprate superconductors to the heavy
fermion compounds. Connections are drawn, and distinctions made, between
collinear/non-collinear magnetic order, bond order, neutral spin 1/2
excitations in insulators, electron Fermi surfaces which violate Luttinger's
theorem, fractionalization of the electron, and the fractionalization of
bosonic collective modes. Two distinct categories of Z_2 gauge theories are
used to describe the interplay of these orders. Experimental implications for
the cuprates are briefly noted, but these appear in more detail in a companion
review article (S. Sachdev, cond-mat/0211005).


  We present new observations of the binary Sirius A / Sirius B performed with
HST-GHRS. Two interstellar clouds are detected on this sightline, one of them
being identified as the Local Interstellar Cloud (LIC). The interstellar
structure of this sightline is constrained by high spectral resolution data of
the species OI, NI, SiII, CII, FeII and MgII. Lyman alpha interstellar lines
are also observed toward the two stars. But whereas the deuterium Lyman alpha
line is well detected in the LIC with an abundance in agreement with that
obtained by Linsky et al. (1993 & 1995), no significant DI line is detected in
the other cloud.
  However, the Lyman alpha lines toward Sirius A and Sirius B are not trivial.
An excess of absorption is seen in the blue wing of the Sirius A Lyman alpha
line and interpreted as the wind from Sirius A. In its white dwarf companion,
an excess in absorption is seen in the red wing and interpreted as the core of
the Sirius B photospheric Lyman alpha line. A composite Lyman alpha profile can
nonetheless be constructed, and allows one to measure the deuterium abundance
in the second cloud 0 < D/H_ISM < 1.6 10^-5, which is marginally in agreement
with the Linsky et al. (1993 & 1995) value. This sightline appears consequently
as a good candidate for a low D/H_ISM.


  The ultimate control of magnetic states of matter at femtosecond (or even
faster) timescales defines one of the most pursued paradigm shifts for future
information technology. In this context, ultrafast laser pulses developed into
extremely valuable stimuli for the all-optical magnetisation reversal in
ferrimagnetic and ferromagnetic alloys and multilayers, while this remains
elusive in elementary ferromagnets. Here we demonstrate that a single laser
pulse with sub-picosecond duration can lead to the reversal of the
magnetisation of bulk nickel, in tandem with the expected demagnetisation. As
revealed by realistic time-dependent electronic structure simulations, the
central mechanism is ultrafast light-induced torques acting on the
magnetisation, which are only effective if the laser pulse is circularly
polarised on a plane that contains the initial orientation of the
magnetisation. We map the laser pulse parameter space enabling the
magnetisation switching and unveil rich intra-atomic orbital-dependent
magnetisation dynamics featuring transient inter-orbital non-collinear states.
Our findings open further perspectives for the efficient implementation of
optically-based spintronic devices.


  We propose a selective dynamical decoupling scheme on a chain of permanently
coupled qubits with XX type interactions, which is capable of dynamically
suppressing any coupling in the chain by applying sequences of local pulses to
the individual qubits. We demonstrate that high-fidelity single- and two-qubit
gates can be achieved by this procedure and that sequences of gates can be
implemented by this pulse control alone. We discuss the applicability and
physical limitations of our model specifically for strongly coupled
superconducting flux qubits. Since dynamically modifying the couplings between
flux qubits is challenging, they are a natural candidate for our approach.


  We review the most recent experimental progress in the hadron spectroscopy,
up to bottomonium mass. This covers the search for the J^PC=1^-+ exotic state,
the states in J/psi decays, the spin-singlets of heavy quarkonium, and the
charmoniumlike XYZ states.


  We classify all the phenomenologically viable fermion mass matrices coming
from a spontaneously broken abelian symmetry $ U(1)_X, $ with one and two
additional chiral fields of opposite charges $ X=\pm 1. $ We find that the
non-trivial K\"ahler metric can fill zeroes of the fermion mass matrices up to
phenomenologically interesting values. A general anomaly analysis shows that
for one additional chiral field the only way to achieve anomaly cancellation is
by use of the Green-Schwarz mechanism. For two additional fields with $X=\pm 1$
and negative charge differences in the lepton sector the anomalies can however
be directly put to zero. This case gives a unique prediction for the ratio of
the two Higgs scalars of MSSM, ${\rm tg} \beta \sim { m_t \over m_b} (sin
\theta_c)^ 2$, where $\theta_c$ is the Cabibbo angle.


  The evolution with redshift of the temperature-luminosity relation of X-ray
galaxy clusters is a key ingredient to break degeneracies in the interpretation
of X-ray clusters redshift number counts. We therefore take advantage of the
recent measurements of the temperature-luminosity relation of distant clusters
observed with XMM-Newton and Chandra satellites to examine theoretical number
counts expected for different available X-rays cluster samples, namely the
RDCS, EMSS, SHARC, 160deg^2 and the MACS at redshift greater than 0.3. We
derive these counts without any adjustment, using models previously normalized
to the local temperature distribution function and to the high-z (z = 0.33)
TDF. We find that these models having Omega_M in the range [0.85-1.] predict
counts in remarkable agreement with the observed counts in the different
samples. We illustrate that this conclusion is weakly sensitive to the various
ingredients of the modeling. Therefore number counts provide a robust evidence
of an evolving population. A realistic flat low density model (Omega_M = 0.3),
normalized to the local abundance of clusters is found to overproduce cluster
abundance at high redshift (above z = 0.5) by nearly an order of magnitude.
This result is in conflict with the popular concordance model. The conflict
could indicate a deviation from the expected scaling of the M-T relation with
redshift.


  This paper studies the capacity of an $n$-dimensional vector Gaussian noise
channel subject to the constraint that an input must lie in the ball of radius
$R$ centered at the origin. It is known that in this setting the optimizing
input distribution is supported on a finite number of concentric spheres.
However, the number, the positions and the probabilities of the spheres are
generally unknown. This paper characterizes necessary and sufficient conditions
on the constraint $R$ such that the input distribution supported on a single
sphere is optimal. The maximum $\bar{R}_n$, such that using only a single
sphere is optimal, is shown to be a solution of an integral equation. Moreover,
it is shown that $\bar{R}_n$ scales as $\sqrt{n}$ and the exact limit of
$\frac{\bar{R}_n}{\sqrt{n}}$ is found.


  In this article we define a Poincare series on a subspace of a complex
analytic germ, induced by a multi-index filtration on the ambient space. We
compute this Poincare series for subspaces defined by principal ideals. For
plane curve singularities and nondegenerate singularities this Poincare series
yields topological and geometric information. We compare this Poincare series
with the one introduced in [E,G-Z]. In few cases they are equal and we show
that the Poincare series we consider in this paper in general yields more
information.


  The process of morphogenesis is an evolution of the shape of an organism
together with the differentiation of its parts. This process encompasses
numerous biological processes ranging from embryogenesis to regeneration
following crisis such as amputation or transplantation. A fundamental
theoretical question is where exactly do these instructions for
(re-)construction reside and how are they implemented? We have recently
proposed a set of concepts, aiming to respond to these questions and to provide
an appropriate mathematical formalization of the geometry of morphogenesis [1].
First, we consider the possibility that evolution of shape is determined by
epigenetic information, responsible for realization of different types of cell
events. Second, we suggest a set of rules for converting this epigenetic
information into instructive signals for cell events for each cell, as well as
for transforming it after each cell event. Next we give notions of cell state,
determined by its epigenetic array, and cell event, which is a change of cell
state, and formalize development as a graph (tree) of cell states connected by
5 types of cell events, corresponding to the processes of cell division, cell
growth, cell death, cell movement and cell differentiation. Here we present a
Morphogenesis Software capable of simulating the evolution of a 3D embryo
starting from zygote, following a set of rules based on our theoretical
assumptions, and thus to provide a proof-of-concept for the hypothesis of
epigenetic code regulation. The software creates a developing embryo and a
corresponding graph of cell events according to the zygotic epigenetic spectrum
and chosen parameters of the developmental rules. Variation of rules
influencing the resulting shape of an embryo may help elucidating the principal
laws underlying pattern formation.


  We present an analytical calculation of the $\alpha_s$ corrections
  for the coefficient of $\rho_D/m_Q^3$ term in the heavy quark expansion
  for the inclusive semileptonic decays of heavy hadrons, such as
  $B \to X_c \ell \bar{\nu}$. The full dependence of the coefficient
  on the final-state quark mass is taken into account.
  Our result leads to further improvement of
  the theoretical predictions for the precision determination of
  CKM matrix element $|V_{cb}|$.


  Due to their compatibility to existing telecom technology, continuous
variable (CV) weak coherent state protocols are promising candidates for a
broad deployment of quantum key distribution (QKD) technology. We demonstrate
how an existing simulation framework for modelling classical optical systems
can be utilized for simulations of weak-coherent CV-QKD links. The quantum
uncertainties for the measured characteristics of coherent signals are modelled
in the electrical domain by shot noise, while a coherent signal in the optical
domain is described by its quadrature components. We simulate various
degradation effects such as attenuation, laser RIN, Raman noise (from classical
channels in the same fibre), and device imperfections and compare the outcome
with analytical theory. Having complemented the physical simulation layer by
the post-processing layer (reconciliation and privacy amplification), we are
able to estimate secure key rates from simulations, greatly boosting the
development speed of practical CV-QKD schemes and implementations.


  The X-ray satellite XMM-Newton has so far revealed coronal cycles in seven
solar-like stars. In this sample, the youngest stars $\epsilon$ Eridani (400
Myr) and $\iota$ Horologii (600 Myr) display the shortest X-ray cycles and the
smallest cycle amplitudes. The corona of $\epsilon$ Eridani was modelled in
terms of solar magnetic structures (active regions, cores of active regions and
flares) at varying filling factors. The study revealed that 65-95% of its
corona is covered with magnetic structures, and this was held responsible for
the low X-ray cycle amplitude. It was also hypothesized that the basal surface
coverage with magnetic structures may be higher on the corona of younger
solar-like stars. To investigate this hypothesis, we study the solar-like star
Kepler-63 in the X-rays. With an age of 210 Myr and a photospheric cycle of
1.27 yr, it is so far the youngest star observed in X-rays with the aim of
revealing a coronal cycle. In the long-term X-ray lightcurve we do not reveal a
periodic variation of the X-ray luminosity, but a factor two change would be
possible. As for the case of $\epsilon$ Eridani, we modelled the corona of
Kepler-63 with magnetic structures observed on the Sun. The study suggests that
100% of the corona is composed of cores and flares of Class M, justifying the
absence of an X-ray cycle and confirming the analogous results derived for
$\epsilon$ Eridani. Finally, we establish an empirical relation between the
cycle amplitude and the X-ray surface flux . From the absence of a coronal
cycle in Kepler-63 we infer that stars with higher X-ray flux than Kepler-63
must host a significant fraction of higher-energetic flares than those
necessary to model the corona of Kepler-63. Our study opens new ground for
studies of the solar-stellar analogy and the joint exploration of resolved and
unresolved variability in stellar X-ray lightcurves.


  Deep-inelastic ep scattering data, taken with the H1 detector at HERA, are
used to study event shape variables over a large range of "relevant energy" Q
between 7 GeV and 100 GeV. Previously published analysis on thrust, jet
broadening, jet mass and C parameter are substantially refined and updated;
differential two-jet rates treated as event shapes are presented for the first
time.
  The Q dependence of the mean values is fit to second order calculations of
perturbative QCD applying power law corrections proportional to 1/Q^p to
account for hadronization effects. The concept of these power corrections is
tested by a systematic investigation in terms of a non-perturbative parameter
\bar{alpha}_{p-1} and the strong coupling constant.


  We report on the main results obtained thanks to an observation campaign with
XMM-Newton of four persistent, low-luminosity (Lx ~ 10^34 erg/s) and
long-period (P > 200 s) Be accreting pulsars. We found that all sources
considered here are characterized by a spectral excess that can be described
with a blackbody component of high temperature (kTbb > 1 keV) and small area
(Rbb < 0.5 km). We show that: 1) this feature is a common property of several
low-luminosity X-ray binaries; 2) for most sources the blackbody parameters
(radius and temperature) are within a narrow range of values; 3) it can be
interpreted as emission from the NS polar caps.


  The local classification of Kaehler submanifolds $M^{2n}$ of the hyperbolic
space $\mathbb{H}^{2n+p}$ with low codimension $2\leq p\leq n-1$ under only
intrinsic assumptions remains a wide open problem. The situation is quite
different for submanifolds in the round sphere $\mathbb{S}^{2n+p}$, $2\leq
p\leq n-1$, since Florit, Hui and Zheng have shown that the codimension has to
be $p=n-1$ and then that any submanifold is just part of an extrinsic product
of two-dimensional umbilical spheres in
$\mathbb{S}^{3n-1}\subset\mathbb{R}^{3n}$. The main result of this paper is a
version for Kaehler manifolds isometrically immersed into the hyperbolic
ambient space of the result for spherical submanifolds. Besides, we generalize
several results obtained by Dajczer and Vlachos.


  Generalized $1+0$-dimensional Liouvillean dynamics describing deformations of
the Sachdev-Ye-Kitaev (SYK) model, as well as the various $1+1$-dimensional
dilaton and Horava-Lifshitz gravity theories, can all be mapped onto
single-particle quantum mechanics of a non-relativistic charge propagating in a
(generally, curved) $2d$ space and subject to a (generally, non-uniform)
magnetic field. The latter description provides a standard playground for the
phenomenon of Quantum Hall Effect (QHE), thereby elucidating the intrinsically
topological nature of pertinent gravity theories and demystifying their
(pseudo)holographic connection to a broad class of the SYK-like models.


  Colloidal 2D monolayers sliding in an optical lattice are of recent
importance as a frictional system. In the general case when the monolayer and
optical lattices are incommensurate, we predict two important novelties, one in
the static equilibrium structure, the other in the frictional behavior under
sliding. Structurally, realistic simulations show that the colloid layer should
possess in full equilibrium a small misalignment rotation angle relative to the
optical lattice, an effect so far unnoticed but visible in some published
experimental moir\'e patterns. Under sliding, this misalignment has the effect
of boosting the colloid monolayer friction by a considerable factor over the
hypothetical aligned case discussed so far. A frictional increase of similar
origin must generally affect other incommensurate adsorbed monolayers and
contacts, to be sought out case by case.


  We study how to efficiently manipulate and store quantum information between
optical fields and atomic ensembles. We show how various non-dissipative
transfer schemes can be used to transfer and store quantum states such as
squeezed vacuum states or entangled states into the long-lived ground state
spins of atomic ensembles.


  The last few years have witnessed substantial progress in the field of
embodied AI where artificial agents, mirroring biological counterparts, are now
able to learn from interaction to accomplish complex tasks. Despite this
success, biological organisms still hold one large advantage over these
simulated agents: adaptation. While both living and simulated agents make
decisions to achieve goals (strategy), biological organisms have evolved to
understand their environment (sensing) and respond to it (physiology). The net
gain of these factors depends on the environment, and organisms have adapted
accordingly. For example, in a low vision aquatic environment some fish have
evolved specific neurons which offer a predictable, but incredibly rapid,
strategy to escape from predators. Mammals have lost these reactive systems,
but they have a much larger fields of view and brain circuitry capable of
understanding many future possibilities. While traditional embodied agents
manipulate an environment to best achieve a goal, we argue for an introspective
agent, which considers its own abilities in the context of its environment. We
show that different environments yield vastly different optimal designs, and
increasing long-term planning is often far less beneficial than other
improvements, such as increased physical ability. We present these findings to
broaden the definition of improvement in embodied AI passed increasingly
complex models. Just as in nature, we hope to reframe strategy as one tool,
among many, to succeed in an environment. Code is available at:
https://github.com/sarahpratt/introspective.


  If error distribution has heteroscedasticity, it voliates the assumption of
linear regression. Expectile regression is a powerful tool for estimating the
conditional expectiles of a response variable in this setting. Since multiple
levels of expectile regression modelhas been well studied, we propose composite
expectile regression by combining different levels of expectile regression to
improve the efficacy. In this paper, we study the sparse composite expectile
regression under high dimensional setting. It is realized by implementing a
coordinate descent algorithm. We also prove its selection and estimation
consistency. Simulations are conducted to demonstrate its performance, which is
comparable to or better than the alternatives. We apply the proposed method to
analyze Lung adenocarcinoma(LUAD) real data set, investigating the G-E
interaction.


  As the popularity of Location-based Social Networks (LBSNs) increases,
designing accurate models for Point-of-Interest (POI) recommendation receives
more attention. POI recommendation is often performed by incorporating
contextual information into previously designed recommendation algorithms. Some
of the major contextual information that has been considered in POI
recommendation are the location attributes (i.e., exact coordinates of a
location, category, and check-in time), the user attributes (i.e., comments,
reviews, tips, and check-in made to the locations), and other information, such
as the distance of the POI from user's main activity location, and the social
tie between users. The right selection of such factors can significantly impact
the performance of the POI recommendation. However, previous research does not
consider the impact of the combination of these different factors. In this
paper, we propose different contextual models and analyze the fusion of
different major contextual information in POI recommendation. The major
contributions of this paper are: (i) providing an extensive survey of
context-aware location recommendation (ii) quantifying and analyzing the impact
of different contextual information (e.g., social, temporal, spatial, and
categorical) in the POI recommendation on available baselines and two new
linear and non-linear models, that can incorporate all the major contextual
information into a single recommendation model, and (iii) evaluating the
considered models using two well-known real-world datasets. Our results
indicate that while modeling geographical and temporal influences can improve
recommendation quality, fusing all other contextual information into a
recommendation model is not always the best strategy.


  Generative AI is expected to have transformative effects in multiple
knowledge industries. To better understand how knowledge workers expect
generative AI may affect their industries in the future, we conducted
participatory research workshops for seven different industries, with a total
of 54 participants across three US cities. We describe participants'
expectations of generative AI's impact, including a dominant narrative that cut
across the groups' discourse: participants largely envision generative AI as a
tool to perform menial work, under human review. Participants do not generally
anticipate the disruptive changes to knowledge industries currently projected
in common media and academic narratives. Participants do however envision
generative AI may amplify four social forces currently shaping their
industries: deskilling, dehumanization, disconnection, and disinformation. We
describe these forces, and then we provide additional detail regarding
attitudes in specific knowledge industries. We conclude with a discussion of
implications and research challenges for the HCI community.


  In a quantum electrodynamics theory that is realized by critical scaling and
anomalous dimensions, the action is not chiral invariant and there are no
dynamical Goldstone or Higgs boson bound states. In the mean-field
approximation to a chiral invariant four-fermion theory the associated
mean-field sector action is not chiral invariant either and it also possesses
no dynamical bound states, with Goldstone and Higgs bosons instead being
generated by an accompanying four-fermion residual interaction. In this paper
we show that if a critical scaling electrodynamics in which the dimension
$d_{\theta}$ of $\bar{\psi}\psi$ is reduced from three to two is augmented with
a four-fermion interaction, precisely because it possesses no dynamical bound
states the electrodynamic sector can be reinterpreted as a mean-field
approximation to a larger theory that is chiral symmetric. And with
$d_{\theta}=2$ we show in this larger theory there is a residual interaction
that then does generate dynamical Goldstone and Higgs bosons in scattering
amplitudes that are completely finite. While the dynamically generated
Goldstone boson is massless, the dynamically induced Higgs boson is found to be
a narrow resonance just above threshold, with its width being a diagnostic that
could potentially enable one to distinguish between a dynamical Higgs boson and
an elementary one.


  We study ideals in, and continuity of, quantaloid-enriched categories
(Q-categories for short) as a 'many-valued and many-typed' generalization of
domain theory. Abstractly, for any (saturated) class Phi of presheaves, we
define and study the Phi-continuity of Q-categories. Concretely, we compute
three examples of such saturated classes of presheaves - the class of flat
ideals, the class of irreducible ideals and the class of conical ideals - which
are proper generalizations of ideals in domain theory.


  Every day, countless surgeries are performed worldwide, each within the
distinct settings of operating rooms (ORs) that vary not only in their setups
but also in the personnel, tools, and equipment used. This inherent diversity
poses a substantial challenge for achieving a holistic understanding of the OR,
as it requires models to generalize beyond their initial training datasets. To
reduce this gap, we introduce ORacle, an advanced vision-language model
designed for holistic OR domain modeling, which incorporates multi-view and
temporal capabilities and can leverage external knowledge during inference,
enabling it to adapt to previously unseen surgical scenarios. This capability
is further enhanced by our novel data augmentation framework, which
significantly diversifies the training dataset, ensuring ORacle's proficiency
in applying the provided knowledge effectively. In rigorous testing, in scene
graph generation, and downstream tasks on the 4D-OR dataset, ORacle not only
demonstrates state-of-the-art performance but does so requiring less data than
existing models. Furthermore, its adaptability is displayed through its ability
to interpret unseen views, actions, and appearances of tools and equipment.
This demonstrates ORacle's potential to significantly enhance the scalability
and affordability of OR domain modeling and opens a pathway for future
advancements in surgical data science. We will release our code and data upon
acceptance.


  We consider the response of a one-dimensional system with friction. S.W. Shaw
(Journal of Sound and Vibration, 1986) introduced the set up of different
coefficients for the static and dynamic phases (also called stick and slip
phases). He constructs a step by step solution, corresponding to an harmonic
forcing. In this paper, we show that the theory of variational inequalities
provides an elegant and synthetic approach to obtain the existence and
uniqueness of the solution, avoiding the step by step construction. We then
apply the theory to a real structure with real data and show that the model is
quite accurate. In our case, the forcing motion comes from dilatation, due to
temperature.


  Deformed special relativity (DSR) is one of the possible realizations of a
varying speed of light (VSL). It deforms the usual quadratic dispersion
relations so that the speed of light becomes energy dependent, with preferred
frames avoided by postulating a non-linear representation of the Lorentz group.
The theory may be used to induce a varying speed of sound capable of generating
(near) scale-invariant density fluctuations, as discussed in a recent Letter.
We identify the non-linear representation of the Lorentz group that leads to
scale-invariance, finding a universal result. We also examine the higher order
field theory that could be set up to represent it.


  A plentitude of applications in scientific computing requires the
approximation of mappings between Banach spaces. Recently introduced Fourier
Neural Operator (FNO) and Deep Operator Network (DeepONet) can provide this
functionality. For both of these neural operators, the input function is
sampled on a given grid (uniform for FNO), and the output function is
parametrized by a neural network. We argue that this parametrization leads to
1) opaque output that is hard to analyze and 2) systematic bias caused by
aliasing errors in the case of FNO. The alternative, advocated in this article,
is to use Chebyshev and Fourier series for both domain and codomain. The
resulting Spectral Neural Operator (SNO) has transparent output, never suffers
from aliasing, and may include many exact (lossless) operations on functions.
The functionality is based on well-developed fast, and stable algorithms from
spectral methods. The implementation requires only standard numerical linear
algebra. Our benchmarks show that for many operators, SNO is superior to FNO
and DeepONet.


  The measurement of bias in machine learning often focuses on model
performance across identity subgroups (such as man and woman) with respect to
groundtruth labels. However, these methods do not directly measure the
associations that a model may have learned, for example between labels and
identity subgroups. Further, measuring a model's bias requires a fully
annotated evaluation dataset which may not be easily available in practice. We
present an elegant mathematical solution that tackles both issues
simultaneously, using image classification as a working example. By treating a
classification model's predictions for a given image as a set of labels
analogous to a bag of words, we rank the biases that a model has learned with
respect to different identity labels. We use (man, woman) as a concrete example
of an identity label set (although this set need not be binary), and present
rankings for the labels that are most biased towards one identity or the other.
We demonstrate how the statistical properties of different association metrics
can lead to different rankings of the most "gender biased" labels, and conclude
that normalized pointwise mutual information (nPMI) is most useful in practice.
Finally, we announce an open-sourced nPMI visualization tool using TensorBoard.


  We consider a variant of so called power-law random graph. A sequence of
expected degrees corresponds to a power-law degree distribution with finite
mean and infinite variance. In previous works the asymptotic picture with
number of nodes limiting to infinity has been considered. It was found that an
interesting structure appears. It has resemblance with such graphs like the
Internet graph. Some simulations have shown that a finite sized variant has
similar properties as well. Here we investigate this case in more analytical
fashion, and, with help of some simple lower bounds for large valued
expectations of relevant random variables, we can shed some light into this
issue. A new term, 'communication range random graph' is introduced to
emphasize that some further restrictions are needed to have a relevant random
graph model for a reasonable sized communication network, like the Internet. In
this case a pleasant model is obtained, giving the opportunity to understand
such networks on an intuitive level. This would be beneficial in order to
understand, say, how a particular routing works in such networks.


  We investigate the origin of the Hubble sequence by analysing the evolution
of the kinematic morphologies of central galaxies in the EAGLE cosmological
simulation. By separating each galaxy into disk and spheroidal stellar
components and tracing their evolution along the merger tree, we find that the
morphology of galaxies follows a common evolutionary trend. We distinguish
three phases of galaxy formation. These phases are determined primarily by
mass, rather than redshift. For M_star < 10^{9.5} M_sun galaxies grow in a
disorganised way, resulting in a morphology that is dominated by random stellar
motions. This phase is dominated by in-situ star formation, partly triggered by
mergers. In the mass range 10^{9.5} M_sun < M_star < 10^{10.5} M_sun galaxies
evolve towards a disk-dominated morphology, driven by in-situ star formation.
The central spheroid (i.e. the bulge) at z = 0 consists mostly of stars that
formed in-situ, yet the formation of the bulge is to a large degree associated
with mergers. Finally, at M_star > 10^{10.5} M_sun growth through in-situ star
formation slows down considerably and galaxies transform towards a more
spheroidal morphology. This transformation is driven more by the buildup of
spheroids than by the destruction of disks. Spheroid formation in these
galaxies happens mostly by accretion at large radii of stars formed ex-situ
(i.e. the halo rather than the bulge).


  In this paper, we give some results concerning atomic decompositions for
operators on reproducing kernel Hilbert spaces, using frame theory techniques.
We provide also generalizations for atomic decompositions of some theorems for
reproducing kernel Hilbert spaces. In particular, we obtain atomic
decomposition results for operators on Bergman spaces and Fock spaces in a
simple manner.


  We propose a small extension to the Hanoi Omega-Automata format to define
reactive-synthesis problems. Namely, we add a "controllable-AP" header item
specifying the subset of atomic propositions which is controllable. We describe
the semantics of the new format and propose an output format for synthesized
strategies. Finally, we also comment on tool support meant to encourage fast
adoption of the extended Hanoi Omega-Automata format for synthesis.


  We study the problem of allocating indivisible chores to agents under the
Maximin share (MMS) fairness notion. The chores are embedded on a graph and
each bundle of chores assigned to an agent should be connected. Although there
is a simple algorithm for MMS allocations of goods on trees, it remains open
whether MMS allocations of chores on trees always exist or not, which is a
simple but annoying problem in chores allocation. In this paper, we introduce a
new method for chores allocation with connectivity constraints, called the
group-satisfied method, that can show the existence of MMS allocations of
chores on several subclasses of trees. Even these subcases are non-trivial and
our results can be considered as a significant step to the open problem. We
also consider MMS allocations of chores on cycles where we get the tight
approximation ratio for three agents. Our result was obtained via the linear
programming (LP) method, which enables us not only to compute approximate MMS
allocations but also to construct tight examples of the nonexistence of MMS
allocations without complicated combinatorial analysis. These two proposed
methods, the group-satisfied method and the LP method, have the potential to
solve more related problems.


  We propose XPipe, an efficient asynchronous pipeline model parallelism
approach for multi-GPU DNN training. XPipe is designed to use multiple GPUs to
concurrently and continuously train different parts of a DNN model. To improve
GPU utilization and achieve high throughput, it splits a mini-batch into a set
of micro-batches. It allows the overlapping of the pipelines of multiple
micro-batches, including those belonging to different mini-batches. Most
importantly, the novel weight prediction strategy adopted by XPipe enables it
to effectively address the weight inconsistency and staleness issues incurred
by the asynchronous pipeline parallelism. As a result, XPipe incorporates the
advantages of both synchronous and asynchronous pipeline model parallelism
approaches. Concretely, it can achieve very comparable (even slightly better)
model accuracy as its synchronous counterpart while obtaining higher throughput
than it. Experimental results show that XPipe outperforms other
state-of-the-art synchronous and asynchronous model parallelism approaches.


  Abstract. The Set Intersection Problem (SIP) is the problem of finding a
point in the intersection of convex sets. This problem is typically solved by
the method of alternating projections. To accelerate the convergence, the idea
of using Quadratic Programming (QP) to project a point onto the intersection of
halfspaces generated by the projection process was discussed in earlier papers.
This paper looks at how one can integrate projection algorithms together with
an active set QP algorithm. As a byproduct of our analysis, we show how to
accelerate an SIP algorithm involving box constraints, and how to extend a
version of the Algebraic Reconstruction Technique (ART) while preserving finite
convergence. Lastly, the warmstart property of active set QP algorithms is a
valuable property for the problem of projecting onto the intersection of convex
sets.


  The solution-space structure of the 3-Satisfiability Problem (3-SAT) is
studied as a function of the control parameter alpha (ratio of number of
clauses to the number of variables) using numerical simulations. For this
purpose, one has to sample the solution space with uniform weight. It is shown
here that standard stochastic local-search (SLS) algorithms like "ASAT" and
"MCMCMC" (also known as "parallel tempering") exhibit a sampling bias.
Nevertheless, unbiased samples of solutions can be obtained using the
"ballistic-networking approach", which is introduced here. It is a
generalization of "ballistic search" methods and yields also a cluster
structure of the solution space. As application, solutions of 3-SAT instances
are generated using ASAT plus ballistic networking. The numerical results are
compatible with a previous analytic prediction of a simple solution-space
structure for small values of alpha and a transition to a clustered phase at
alpha_c ~ 3.86, where the solution space breaks up into several non-negligible
clusters. Furthermore, in the thermodynamic limit there are, for values of
alpha close to the SATUNSAT transition alpha_s ~ 4.267, always clusters without
any frozen variables. This may explain why some SLS algorithms are able to
solve very large 3-SAT instances close to the SAT-UNSAT transition.


  Carefully designed nanostructures can inspire new type of optomechanical
interactions and allow surpassing limitations set by classical diffractive
optical elements. Apart from strong near-field localization, nanostructured
environment allows controlling scattering channels and might tailor many-body
interactions. Here we investigate an effect of optical binding, where several
particles demonstrate a collective mechanical behaviour of bunching together in
a light field. In contrary to classical binding, where separation distances
between particles are diffraction limited, an auxiliary hyperbolic metasurface
is shown here to break this barrier by introducing several controllable
near-field interaction channels. Strong material dispersion of the hyperbolic
metamaterial along with high spatial confinement of optical modes, which it
supports, allow achieving superior tuning capabilities and efficient control
over binding distances on the nanoscale. In addition, a careful choice of the
metamaterial slabs thickness enables decreasing optical binding distances by
orders of magnitude compared to free space scenarios due to the multiple
reflections of volumetric modes from the substrate. Auxiliary tunable
metamaterials, which allow controlling collective optomechanical interactions
on the nanoscale, open a venue for new investigations including collective
nanofluidic interactions, triggered bio-chemical reactions and many others.


  Recent work of Bettin and Conrey on the period functions of Eisenstein series
naturally gave rise to the Dedekind-like sum \[
  c_{a}\left(\frac{h}{k}\right) \ = \
  k^{a}\sum_{m=1}^{k-1}\cot\left(\frac{\pi
mh}{k}\right)\zeta\left(-a,\frac{m}{k}\right), \] where $a\in\mathbb{C}$, $h$
and $k$ are positive coprime integers, and $\zeta(a,x)$ denotes the Hurwitz
zeta function. We derive a new reciprocity theorem for these Bettin--Conrey
sums, which in the case of an odd negative integer $a$ can be explicitly given
in terms of Bernoulli numbers. This, in turn, implies explicit formulas for the
period functions appearing in Bettin--Conrey's work. We study generalizations
of Bettin--Conrey sums involving zeta derivatives and multiple cotangent
factors and relate these to special values of the Estermann zeta function.


  Flum and Grohe define a parameter (parameterization) as a function $\kappa$
which maps words over a given alphabet to natural numbers. They require such
functions to be polynomial-time computable. We show how this technical
restriction can be lifted without breaking the theory. More specifically,
instead of $\kappa$ we consider the set of languages that it bounds as
parameterization and define the basic notions of parameterized complexity in
terms of promise problems, which completely replace slices. One advantage of
this formalization is that it becomes possible to interpret any
complexity-theoretic concept which can be considered on a restricted set of
inputs as a parameterized concept. Moreover, this formalization provides a
unified way to apply the parameterization paradigm to other kinds of complexity
such as enumeration or approximation by simply defining promise problems.


  Deep neural representations of 3D shapes as implicit functions have been
shown to produce high fidelity models surpassing the resolution-memory
trade-off faced by the explicit representations using meshes and point clouds.
However, most such approaches focus on representing closed shapes. Unsigned
distance function (UDF) based approaches have been proposed recently as a
promising alternative to represent both open and closed shapes. However, since
the gradients of UDFs vanish on the surface, it is challenging to estimate
local (differential) geometric properties like the normals and tangent planes
which are needed for many downstream applications in vision and graphics. There
are additional challenges in computing these properties efficiently with a
low-memory footprint. This paper presents a novel approach that models such
surfaces using a new class of implicit representations called the closest
surface-point (CSP) representation. We show that CSP allows us to represent
complex surfaces of any topology (open or closed) with high fidelity. It also
allows for accurate and efficient computation of local geometric properties. We
further demonstrate that it leads to efficient implementation of downstream
algorithms like sphere-tracing for rendering the 3D surface as well as to
create explicit mesh-based representations. Extensive experimental evaluation
on the ShapeNet dataset validate the above contributions with results
surpassing the state-of-the-art.


  Green function techniques for studying nonequilibrium processes in dirty
two-band superconductors are discussed. Perturbation expansions and Green
function equations are developed. A time dependent modification of the Usadel
equation is given.


  This paper is devoted to study a class of stochastic Volterra equations
associated with fractional Brownian motion. We first prove the Driver type
integration by parts formula and the shift Harnack type inequalities. As a
direct application, we provide an alternative method to describe the
regularities of the law of the solution. Secondly, by using the Malliavin
calculus, the Bismut type derivative formula is established, which is then
applied to the study of the gradient estimate and the strong Feller property.
Finally, we establish the Talagrand type transportation cost inequalities for
the law of the solution on the path space with respect to both the uniform
metric and the $L^2$-metric.


  In order to satisfy limits on the allowed variation of particle masses from
big bang nucleosynthesis (BBN) until today, the chameleon scalar field is
required to reach its attractor solution early on in its cosmological
evolution. Brax et al. (2004) have shown this to be possible for certain
specific initial conditions on the chameleon field at the end of inflation.
However the extreme fine-tuning necessary to achieve this, poses a problem if
the chameleon is to be viewed a natural candidate for dark energy. In this
article we revisit the behaviour of the chameleon in the early Universe,
including the additional coupling to electromagnetism proposed by Brax et al.
(2011). Solving the chameleon evolution equations in the presence of a
primordial magnetic field, we find that the strict initial conditions on the
chameleon field at the end of inflation can be relaxed, and we determine the
associated lower bound on the strength of the primordial magnetic field.


  The energy dissipation mechanism in blazar jet is unknown. Blazar's flares
could provide insights on this problem. Here we report statistical results of
XMM-Newton X-ray flares of Mrk 421. We analyze all public XMM-Newton X-ray
observations for Mrk 421, and construct the light curves. Through fitting light
curves, we obtain the parameters of flare-profiles, such as peak flux ($F_{\rm
p}$) and flaring time duration ($T_{\rm fl}$). It is found that both the
distributions of $F_{\rm p}$ and $T_{\rm fl}$ obey a power-law form, with the
same index of $\alpha_{\rm F}=\alpha_{\rm T}\approx1$. The statistical
properties are consistent with the predictions by a self-organized criticality
(SOC) system with energy dissipation in one-dimensional space. This is similar
to solar flare, but with different space dimensions of the energy dissipation
domain. This suggests that X-ray flaers of Mrk 421 are possibly driven by a
magnetic reconnection mechanism. Moreover, in the analysis, we find that
variability on timescale of $\sim1000\ $s frequently appears. Such rapid
variability indicates a magnetic field of $\geq 2.1\delta_{\rm D}^{-1/3}$ G
($\delta_{\rm D}$ is the Doppler factor) in emission region.


  We define a class of stochastic processes based on evolutions and
measurements of quantum systems, and consider the complexity of predicting
their long-term behavior. It is shown that a very general class of decision
problems regarding these stochastic processes can be efficiently solved
classically in the space-bounded case. The following corollaries are implied by
our main result: (1) Any space O(s) uniform family of quantum circuits acting
on s qubits and consisting of unitary gates and measurement gates defined in a
typical way by matrices of algebraic numbers can be simulated by an unbounded
error space O(s) ordinary (i.e., fair-coin flipping) probabilistic Turing
machine, and hence by space O(s) uniform classical (deterministic) circuits of
depth O(s^2) and size 2^(O(s)). The quantum circuits are not required to
operate with bounded error and may have depth exponential in s. (2) Any
(unbounded error) quantum Turing machine running in space s, having arbitrary
algebraic transition amplitudes, allowing unrestricted measurements during its
computation, and having no restrictions on running time can be simulated by an
unbounded error space O(s) ordinary probabilistic Turing machine, and hence
deterministically in space O(s^2).


  The tunneling approach, for entropy generation in quantum gravity, is shown
to be valid when applied to 3-D general relativity. The entropy of de Sitter
and Reissner-Nordstr\"om external event horizons and of the 3-D black hole
obtained by Ba\~nados et. al. is rederived from tunneling of the metric to
these spacetimes. The analysis for spacetimes with an external horizon is
carried out in a complete analogy with the 4-D case. However, we find
significant differences for the black hole. In particular the initial
configuration that tunnels to a 3-D black hole may not to yield an infinitely
degenerate object, as in 4-D Schwarzschild black hole. We discuss the possible
relation to the evaporation of the 3-D black hole.


  In this article, we introduce a new paradigm of control, called hands-off
control, which can save energy and reduce CO2 emissions in control systems. A
hands-off control is defined as a control that has a much shorter support than
the horizon length. The maximum hands-off control is the minimum support (or
sparsest) control among all admissible controls. With maximum hands-off
control, actuators in the feedback control system can be stopped during time
intervals over which the control values are zero. We show the maximum hands-off
control is given by L1 optimal control, for which we also show numerical
computation formulas.


  Wireless Energy Transfer (WET) is emerging as a potential green enabler for
massive Internet of Things (IoT). Herein, we analyze Channel State Information
(CSI)-free multi-antenna strategies for powering wirelessly a large set of
single-antenna IoT devices. The CSI-free schemes are AA-SS (AA-IS), where all
antennas transmit the same (independent) signal(s), and SA, where just one
antenna transmits at a time such that all antennas are utilized during the
coherence block. We characterize the distribution of the provided energy under
correlated Rician fading for each scheme and find out that while AA-IS and SA
cannot take advantage of the multiple antennas to improve the average provided
energy, its dispersion can be significantly reduced. Meanwhile, AA-SS provides
the greatest average energy, but also the greatest energy dispersion, and the
gains depend critically on the mean phase shifts between the antenna elements.
We find that consecutive antennas must be $\pi$ phase-shifted for optimum
average energy performance under AA-SS. Our numerical results evidenced that
correlation is beneficial under AA-SS, while a greater line of sight (LOS)
and/or number of antennas is not always beneficial under such scheme.
Meanwhile, both AA-IS and SA schemes benefit from small correlation, large LOS
and/or large number of antennas.


  We describe how to evaluate approximately various physical interesting
quantities in random Ising systems by direct renormalization of a finite
system. The renormalization procedure is used to reduce the number of degrees
of freedom to a number that is small enough, enabling direct summing over the
surviving spins. This procedure can be used to obtain averages of functions of
the surviving spins. We show how to evaluate averages that involve spins that
do not survive the renormalization procedure. We show, for the random field
Ising model, how to obtain the "connected" 2-spin correlation function and the
"disconnected" 2-spin correlation function. Consequently, we show how to obtain
the average susceptibility and the average energy. For an Ising system with
random bonds and random fields we show how to obtain the average specific heat.
We conclude by presenting our numerical results for the average susceptibility
and the "connected" 2-spin correlation function along one of the principal
axes. (We believe this to be the first time, where the full three dimensional
correlation is calculated and not just parameters like Nu or Eta.) The results
for the average susceptibility are used to extract the critical temperature and
critical exponents of the 3D random field Ising system.


  To improve atomically controlled chemical vapor deposition (CVD) growth of
graphene, understanding the evolution from various carbon species to a graphene
nuclei on various catalyst surfaces is essential. Experimentally, an
ultra-stable carbon cluster on Ru(0001), Rh(111) surfaces was observed, while
its structure and formation process were still under highly debate. Using ab
initio calculations and kinetic analyses, we disclosed a specific type of
carbon clusters, composed of a C21 core and a few dangling C atoms around, were
exceptional stable in the size range from 21 to 27. The most stable one of
them, an isomer of C24 characterized as three dangling C atoms attached to the
C21 (denoted as C21-3C), is the most promising candidate for the experimental
observation. The ultra-stability of C21-3C originates from both the stable core
and the appropriate passivation of dangling carbon atoms by the catalyst
surface.


  This review article consists of two parts. In the first part we use the
formalism of (exceptional) generalized geometry to derive the scalar field
space of SU(2)xSU(2)-structure compactifications. We show that in contrast to
SU(3)xSU(3) structures, there is no dynamical SU(2)xSU(2) structure
interpolating between an SU(2) structure and an identity structure.
Furthermore, we derive the scalar manifold of the low-energy effective action
for consistent Kaluza-Klein truncations as expected from N=4 supergravity.
  In the second part we then determine the general conditions for the existence
of stable Minkowski and AdS N=1 vacua in spontaneously broken gauged N=2
supergravities and construct the general solution under the assumption that two
appropriate commuting isometries exist in the hypermultiplet sector.
Furthermore, we derive the low-energy effective action below the scale of
partial supersymmetry breaking and show that it satisfies the constraints of
N=1 supergravity. We then apply the discussion to special quaternionic-Kahler
geometries which appear in the low-energy limit of SU(3)xSU(3)-structure
compactifications and construct Killing vectors with the right properties.
Finally we discuss the string theory realizations for these solutions.


  We present the first stereoscopic and Doppler observations of simultaneous
transverse oscillations of a prominence and a filament and longitudinal
oscillation of another filament launched by a single shock wave. Using H-alpha
Doppler observations, we derive the three-dimensional oscillation velocities at
different heights along the prominence axis. The results indicate that the
prominence has a larger oscillation amplitude and damping time at higher
altitude, but the periods at different heights are the same (i.e., 13.5
minutes). This suggests that the prominence oscillates like a linear vertical
rigid body with one end anchored on the Sun. One of the filaments shows weak
transverse oscillation after the passing of the shock, which is possibly due to
the low altitude of the filament and the weakening (due to reflection) of the
shock wave before the interaction. Large amplitude longitudinal oscillation is
observed in the other filament after the passing of the shock wave. The
velocity amplitude and period are about 26.8 km/s and 80.3 minutes,
respectively. We propose that the orientation of a filament or prominence
relative to the normal vector of the incoming shock should be an important
factor for launching transverse or longitudinal filament oscillations. In
addition, the restoring forces of the transverse prominence are most likely due
to the coupling of gravity and magnetic tension of the supporting magnetic
field, while that for the longitudinal filament oscillation is probably the
resultant force of gravity and magnetic pressure.


  The exchange of knowledge across different areas and disciplines plays a key
role in the process of knowledge creation, and can stimulate innovation and the
emergence of new fields. We develop here a quantitative framework to extract
significant dependencies among scientific disciplines and turn them into a
time-varying network whose nodes are the different fields, while the weighted
links represent the flow of knowledge from one field to another at a given
period of time. Drawing on a comprehensive data set on scientific production in
modern physics and on the patterns of citations between articles published in
the various fields in the last thirty years, we are then able to map, over
time, how the ideas developed in a given field in a certain time period have
influenced later discoveries in the same field or in other fields. The analysis
of knowledge flows internal to each field displays a remarkable variety of
temporal behaviours, with some fields of physics showing to be more
self-referential than others. The temporal networks of knowledge exchanges
across fields reveal cases of one field continuously absorbing knowledge from
another field in the entire observed period, pairs of fields mutually
influencing each other, but also cases of evolution from absorbing to mutual or
even to back-nurture behaviors.


  We have developed a tunable colloidal system and a corresponding simulation
model for studying the phase behavior of particles assembling under the
influence of long-range magnetic interactions. A monolayer of paramagnetic
particles is subjected to a spatially uniform magnetic field with a static
perpendicular component and rapidly rotating in-plane component. The sign and
strength of the interactions vary with the tilt angle $\theta$ of the rotating
magnetic field. For a purely in-plane field, $\theta=90^{\circ}$, interactions
are attractive and the experimental results agree well with both equilibrium
and out-of-equilibrium predictions based on a two-body interaction model. For
tilt angles $50^{\circ}\lesssim \theta\lesssim 55^{\circ}$, the two-body
interaction gives a short-range attractive and long-range repulsive (SALR)
interaction, which predicts the formation of equilibrium microphases. In
experiments, however, a different type of assembly is observed. Inclusion of
three-body (and higher-order) terms in the model does not resolve the
discrepancy. We thus further characterize the anomalous behavior by measuring
the time-dependent cluster size distribution.


  We present both binarity and pulsation of KIC 6220497 from the {\it Kepler}
observations. The light curve synthesis shows that the eclipsing system is a
semi-detached Algol with parameters of $q$ = 0.243$\pm$0.001, $i$ =
77.3$\pm$0.3 deg, and $\Delta T$ = 3,372$\pm$58 K, in which the detached
primary component fills its Roche lobe by $\sim$87\%. A multiple frequency
analysis of the eclipse-subtracted light residuals reveals 33 frequencies in
the range of 0.75$-$20.22 d$^{-1}$ with amplitudes between 0.27 and 4.56 mmag.
Among these, four are pulsation frequencies in fundamental ($f_1$, $f_5$) and
$p$ ($f_2$, $f_7$) modes, and six are orbital frequency ($f_8$, $f_{31}$) and
its harmonics ($f_6$, $f_{11}$, $f_{20}$, $f_{24}$), which can be attributed to
tidally excited modes. For the pulsation frequencies, the pulsation constants
of 0.16$-$0.33 d and the period ratios of $P_{\rm pul}/P_{\rm orb}$ =
0.042$-$0.089 indicate that the primary component is a $\delta$ Sct pulsating
star and, thus, KIC 6220497 is an oscillating eclipsing Algol (oEA) star. The
dominant pulsation period of 0.1174051$\pm$0.0000004 d is significantly longer
than that expected from empirical relations that link the pulsation period with
the orbital period. The surface gravity of $\log g_1$ = 3.78$\pm$0.03 is
clearly smaller than those of the other oEA stars with similar orbital periods.
The pulsation period and the surface gravity of the pulsating primary
demonstrate that KIC 6220497 would be the more evolved EB, compared with normal
oEA stars.


  In this paper, we consider discrete time quantum walks on graphs with coin
focusing on the decentralized model, where the coin operation is allowed to
change with the vertex of the graph. When the coin operations can be modified
at every time step, these systems can be looked at as control systems and
techniques of geometric control theory can be applied. In particular, the set
of states that one can achieve can be described by studying controllability.
Extending previous results, we give a characterization of the set of reachable
states in terms of an appropriate Lie algebra. We then prove general results
and criteria relating controllability to the algebraic and topological
properties of the walk. As a consequence of these results, we prove that if the
degree of the underlying graph is larger than $\frac{N}{2}$, where $N$ is the
number of nodes, the quantum walk is always completely controllable, i.e., it
is possible to having it to evolve according to an arbitrary unitary evolution.
Another result is that controllability for decentralized models only depends on
the graph and not on the particular quantum walk defined on it. We also provide
explicit algorithms for control and quantify the number of steps needed for an
arbitrary state transfer. The results of the paper are of interest in quantum
information theory where quantum walks are used and analyzed in the development
of quantum algorithms.


  In order to develop trustworthy healthcare artificial intelligence (AI)
prospective and ergonomics studies that consider the complexity and reality of
real-world applications of AI systems are needed. To achieve this, technology
developers and deploying organisations need to form collaborative partnerships.
This entails access to healthcare data, which frequently might also include
potentially identifiable data such as audio recordings of calls made to an
ambulance service call centre. Information Governance (IG) processes have been
put in place to govern the use of personal confidential data. However,
navigating IG processes in the formative stages of AI development and
pre-deployment can be challenging, because the legal basis for data sharing is
explicit only for the purpose of delivering patient care, i.e., once a system
is put into service. In this paper we describe our experiences of managing IG
for the assurance of healthcare AI, using the example of an
out-of-hospital-cardiac-arrest recognition software within the context of the
Welsh Ambulance Service. We frame IG as a socio-technical process. IG processes
for the development of trustworthy healthcare AI rely on information governance
work, which entails dialogue, negotiation, and trade-offs around the legal
basis for data sharing, data requirements and data control. Information
governance work should start early in the design life cycle and will likely
continue throughout. This includes a focus on establishing and building
relationships, as well as a focus on organisational readiness deeper
understanding of both AI technologies as well as their safety assurance
requirements.


  We engineer a configuration of branes in type IIB string theory whose
mechanical structure is that of a DNA molecule. We obtain it by considering a
T-dual description of the quantum Hall soliton. Using a probe analysis, we
investigate the dynamics of the system and show that it is stable against
radial perturbations. We exercise a certain amount of restraint in discussing
applications to biophysics.


  This paper addresses a theory of R(p,q)-deformed combinatorics in discrete
probability. It mainly focuses on R(p,q)-deformed factorials, binomial
coefficients, Vandermonde's formula, Cauchy's formula, binomial and negative
binomial formulae, factorial and binomial moments, and Stirling numbers.
Moreover, the R(p,q)-Stirling numbers of the second kind and the R(p,q)-Bell
numbers for graphs are also derived. Related relevant properties are
investigated and discussed. Finally, as a concrete illustration, the developed
formalism is displayed for the well known generalized q-Quesne deformed quantum
algebra to construct the corresponding deformed combinatorics, as a particular
case.


  A Virtual Patient (VP) is a powerful tool for training medical students to
take patient histories, where responding to a diverse set of spoken questions
is essential to simulate natural conversations with a student. The performance
of such a Spoken Language Understanding system (SLU) can be adversely affected
by both the presence of Automatic Speech Recognition (ASR) errors in the test
data and a high degree of class imbalance in the SLU training data. While these
two issues have been addressed separately in prior work, we develop a novel
two-step training methodology that tackles both these issues effectively in a
single dialog agent. As it is difficult to collect spoken data from users
without a functioning SLU system, our method does not rely on spoken data for
training, rather we use an ASR error predictor to "speechify" the text data.
Our method shows significant improvements over strong baselines on the VP
intent classification task at various word error rate settings.


  Recently it has been speculated that the S-matrix elements on the world
volume of D$_3$-branes should satisfy the ward identity corresponding to the
S-duality transformations. For a single brane and in the low energy limit, this
indicates that in the abelian Dirac-Born-Indeld-Chern-Simons theory the
combination of contact terms and tree-level massless poles in an $n$-point
function should satisfy the S-duality. In this paper we examine in details the
S-matrix element of three gauge bosons and one two-form and the S-matrix
element of six gauge bosons in favor of the above proposal.


  In this paper, we study the $F(R, G)$ gravity model with an interacting model
by flat-FRW metric in a viscous fluid. We consider that the universe dominates
with components of dark matter and dark energy. This means that the dark matter
component derives from Extended Bose-Einstein Condensate (EBEC) and the
components of dark energy arise from the $F(R, G)$ gravity. After obtaining the
Einstein equation, the energy density and the pressure of dark energy are
written in terms of the geometries of the curvature and the Gauss-Bonnet terms,
and components of dark matter and viscous fluid. Also, the corresponding
continuity equations are written with the presence of interaction terms. In
what follows, we employ the EBEC regime instead of the normal dark matter by
the dark matter Equation of State (EoS) as $p_{dm} = \alpha \rho_{dm} + \beta
\rho_{dm}^2$, which arises from the gravitational form. The EoS can be
expressed from the perspective of the virial expansion, in which the first and
second terms represent normal dark matter and quantum ground state. Next, the
corresponding Friedmann equations reconstruct in terms of the redshift
parameter, then by using the scenario of the power-law cosmology for the scale
factor, we fit the present model with the Hubble amounts of 51 supernova data
by the likelihood analysis. In that case, we acquire the cosmological
parameters of dark energy in terms of the redshift parameter, and by plotting
these graphs, we see that the universe is currently undergoing an accelerated
expansion phase. Finally, we investigate the stability of the present model
with the sound speed parameter.


  Nowadays, the Hierarchical Storage System (HSS) is considered as an ideal
model to meet the cost-performance demand. The data migration between storing
tiers of HSS is the way to achieve the cost-performance goal. The bandwidth
control is to limit the maximum amount of data migration. Most of previous
research about HSS focus on studying the data migration policy instead of
bandwidth control. However, the recent research about cache and networking
optimization suggest that the bandwidth control has significant impact on the
system performance. Few previous work achieves a satisfactory bandwidth control
in HSS since it is hard to control bandwidth for so many data migration tasks
simultaneously. In this paper, we first give a stochastic programming model to
formalize the bandwidth control problem in HSS. Then we propose a
learning-aided bandwidth control policy for HSS, named \Pascal{}, which learns
to control the bandwidth of different data migration task in an cooperative
way. We implement \Pascal{} on a commercial HSS and compare it with three
strong baselines over a group of workloads. Our evaluation on the physical
system shows that \Pascal{} can effectively decrease 1.95X the tail latency and
greatly improve throughput stability (2X $\downarrow$ throughput jitter), and
meanwhile keep the throughput at a relatively high level.


  In this paper we show an example of two differential graded algebras that
have the same derivator K-theory but non-isomorphic Waldhausen K-theory. We
also prove that Maltsiniotis's comparison and localization conjectures for
derivator K-theory cannot be simultaneously true.


  An industrial scheme, to simulate the two compressible phase flow in porous
media, consists in a finite volume method together with a phase-by-phase
upstream scheme. The implicit finite volume scheme satisfies industrial
constraints of robustness. We show that the proposed scheme satisfy the maximum
principle for the saturation, a discrete energy estimate on the pressures and a
function of the saturation that denote capillary terms. These stabilities
results allow us to derive the convergence of a subsequence to a weak solution
of the continuous equations as the size of the discretization tends to zero.
The proof is given for the complete system when the density of the each phase
depends on the own pressure.


  This paper introduces a quantitative evaluation of filters that seek to
separate an image into its large-scale variations, the base layer, and its
fine-scale variations, the detail layer. Such methods have proliferated with
the development of HDR imaging and the proposition of many new tone-mapping
operators. We argue that an objective quality measurement for all methods can
be based on their artifacts. To this aim, the four main recurrent artifacts are
described and mathematically characterized. Among them two are classic, the
luminance halo and the staircase effect, but we show the relevance of two more,
the contrast halo and the compartmentalization effect. For each of these
artifacts we design a test-pattern and its attached measurement formula. Then
we fuse these measurements into a single quality mark, and obtain in that way a
ranking method valid for all filters performing a base+detail decomposition.
This synthetic ranking is applied to seven filters representative of the
literature and shown to agree with expert artifact rejection criteria.


  The period changes of six eclipsing binaries have been studied with focus on
the light-time effect. With the least squares method we also calculated
parameters of such an effect and properties of the unresolved body in these
systems. With these results we discussed the probability of presence of such
bodies in the systems with respect to possible confirmation by another method.
In two systems we also suggested the hypothesis of fourth body or magnetic
activity for explanation of the "second-order variability" after subtraction of
the light-time effect of the third body.


  In this paper, we continue our development of algorithms used for topological
network discovery. We present native P system versions of two fundamental
problems in graph theory: finding the maximum number of edge- and node-disjoint
paths between a source node and target node. We start from the standard
depth-first-search maximum flow algorithms, but our approach is totally
distributed, when initially no structural information is available and each P
system cell has to even learn its immediate neighbors. For the node-disjoint
version, our P system rules are designed to enforce node weight capacities (of
one), in addition to edge capacities (of one), which are not readily available
in the standard network flow algorithms.


  Bellantoni and Cook have given a function-algebra characterization of the
polynomial-time computable functions via an unbounded recursion scheme which is
called safe recursion. Inspired by their work, we characterize the
exponential-time computable functions with the use of a safe variant of nested
recursion.


  Magnetic topological states refer to a class of exotic phases in magnetic
materials with their non-trivial topological property determined by magnetic
spin configurations. An example of such states is the quantum anomalous Hall
(QAH) state, which is a zero magnetic field manifestation of the quantum Hall
effect. Current research in this direction focuses on QAH insulators with a
thickness of less than 10nm. The thick QAH insulators in the
three-dimensional(3D) regime are limited, largely due to inevitable bulk
carriers being introduced in thick magnetic TI samples. Here, we employ
molecular beam epitaxy (MBE) to synthesize magnetic TI trilayers with a
thickness of up to ~106 nm. We find these samples exhibit well-quantized Hall
resistance and vanishing longitudinal resistance at zero magnetic field. By
varying magnetic dopants, gate voltages, temperature, and external magnetic
fields, we examine the properties of these thick QAH insulators and demonstrate
the robustness of the 3D QAH effect. The realization of the well-quantized 3D
QAH effect indicates that the nonchiral side surface states of our thick
magnetic TI trilayers are gapped and thus do not affect the QAH quantization.
The 3D QAH insulators of hundred-nanometer thickness provide a promising
platform for the exploration of fundamental physics, including axion physics
and image magnetic monopole, and the advancement of electronic and spintronic
devices to circumvent Moore's law.


  Finding methods for the most efficient and fastest detection and control of
magnetic domains in antiferromagnets is presently among the main challenges of
magnetic research at large. We analyse the problem of optical read-out and
control of the antiferromagnetic Neel vector using symmetry analysis and the
principles of equilibrium thermodynamics. Following the pioneering approach of
Dzyaloshinksii, we divide all antiferromagnets in three classes. It is shown
that, using the magneto-optical Faraday effect or other effects which scale
linearly with the antiferromagnetic Neel vector, it is possible to distinguish
antiferromagnetic domains with mutually opposite N\'eel vectors in two of the
three classes. Symmetry properties of one of these two classes are similar to
those of altermagnets. The analysis also reveals multiple mechanisms to
directly excite spins with light for practically every type of antiferromagnet.


  We derive the first positivity bounds for low-energy Effective Field Theories
(EFTs) that are not invariant under Lorentz boosts. "Positivity bounds" are the
low-energy manifestation of certain fundamental properties in the UV -- to date
they have been used to constrain a wide variety of EFTs, however since all of
the existing bounds require Lorentz invariance they are not directly applicable
when this symmetry is broken, such as for most cosmological and condensed
matter systems. From the UV axioms of unitarity, causality and locality, we
derive an infinite family of bounds which (derivatives of) the $2\to2$ EFT
scattering amplitude must satisfy even when Lorentz boosts are broken (either
spontaneously or explicitly). We apply these bounds to the leading-order EFT of
both a superfluid and the scalar fluctuations produced during inflation,
comparing in the latter case with the current observational constraints on
primordial non-Gaussianity.


  The stellar binary $\eta$ Carinae has been observed during its full orbital
period in gamma rays by the Fermi-Large Area Telescope (LAT). The
shock-accelerated electrons in the colliding winds of the two stars radiate
synchrotron photons in the magnetic field of the shocked region and inverse
Compton photons, where the target photons are from the thermal emissions by the
more massive and luminous of the two stars. The inverse Compton emission
dominates the gamma-ray flux data from the $\eta$ Carinae, however the spectral
energy distribution shows signature of a hadronic component in the $\sim
10$-300 GeV range during the periastron passage. Current and future air
Cherenkov telescopes will be able to constrain this component at TeV energies.
Acceleration of cosmic-ray protons to $\gg 1$ TeV energies in the colliding
winds, required to explain the hadronic emission component through photopion
interactions, can lead to detectable signal of $\gtrsim 10$ TeV neutrino events
in large kilometer scale neutrino telescopes.


  We give new arguments in support of \emph{signed quantum key establishment},
where quantum cryptography is used in a public-key infrastructure that provides
the required authentication. We also analyze more thoroughly than previous
works the benefits that quantum key establishment protocols have over certain
classical protocols, motivated in part by the various objections to quantum key
establishment that are sometimes raised. Previous knowledge of quantum
cryptography on the reader's part is not required for this article, as the
definition of "quantum key establishment" that we use is an entirely classical
and black-box characterization (one need only trust that protocols satisfying
the definition exist).


  We propose a new method to enforce priors on the solution of the nonnegative
matrix factorization (NMF). The proposed algorithm can be used for denoising or
single-channel source separation (SCSS) applications. The NMF solution is
guided to follow the Minimum Mean Square Error (MMSE) estimates under Gaussian
mixture prior models (GMM) for the source signal. In SCSS applications, the
spectra of the observed mixed signal are decomposed as a weighted linear
combination of trained basis vectors for each source using NMF. In this work,
the NMF decomposition weight matrices are treated as a distorted image by a
distortion operator, which is learned directly from the observed signals. The
MMSE estimate of the weights matrix under GMM prior and log-normal distribution
for the distortion is then found to improve the NMF decomposition results. The
MMSE estimate is embedded within the optimization objective to form a novel
regularized NMF cost function. The corresponding update rules for the new
objectives are derived in this paper. Experimental results show that, the
proposed regularized NMF algorithm improves the source separation performance
compared with using NMF without prior or with other prior models.


  The kind of supersymmetry that can be discovered at the LHC must be very much
flavor-blind, which used to require very special intelligently designed models
of supersymmetry breaking. This led to the pessimism for some in the community
that it is not likely for the LHC to discover supersymmetry. I point out that
this is not so, because a garden-variety supersymmetric theories actually can
do this job.


  As AI models and services are used in a growing number of highstakes areas, a
consensus is forming around the need for a clearer record of how these models
and services are developed to increase trust. Several proposals for higher
quality and more consistent AI documentation have emerged to address ethical
and legal concerns and general social impacts of such systems. However, there
is little published work on how to create this documentation. This is the first
work to describe a methodology for creating the form of AI documentation we
call FactSheets. We have used this methodology to create useful FactSheets for
nearly two dozen models. This paper describes this methodology and shares the
insights we have gathered. Within each step of the methodology, we describe the
issues to consider and the questions to explore with the relevant people in an
organization who will be creating and consuming the AI facts in a FactSheet.
This methodology will accelerate the broader adoption of transparent AI
documentation.


  Background
  Nucleotide sequences contain multiple codes responsible for organism's
functioning and structure. They can be investigated by various signal
processing methods. These techniques are well suited for indication of
frequently encountered sequence motifs (i.e., repeats). However, if there are
two or more codes containing the same motif, the local nucleotide distribution
(i.e., profile), resulting from sequence alignment by the motif position, will
represent overlapping of the code patterns.
  Results
  The novel algorithm for decomposition of pattern overlapping is proposed. It
is capable to work with dispersed repeats as well. The algorithm is based on
cross-correlation procedure applied locally in a cumulative fashion. Its
sensitivity was tested on human genomic sequences.
  Conclusions
  Cumulative Local Cross-Correlation was successfully used to decompose
overlapping of nucleotide patterns in human genomic sequences. Being very
general technique (as general as cross-correlation), it can be easily adopted
in other signal processing applications and naturally extended for
multidimensional cases.
  Software implementation of the algorithm is available on request from the
authors.


  We show that in addition to providing effective and competitive closures,
when analysed in terms of dynamics and physically-relevant diagnostics,
artificial neural networks (ANNs) can be both interpretable and provide useful
insights in the on-going task of developing and improving turbulence closures.
In the context of large-eddy simulations (LES) of a passive scalar in
homogeneous isotropic turbulence, exact subfilter fluxes obtained by filtering
direct numerical simulations (DNS) are used both to train deep ANN models as a
function of filtered variables, and to optimise the coefficients of a turbulent
Prandtl number LES closure. \textit{A-priori} analysis of the subfilter scalar
variance transfer rate demonstrates that learnt ANN models out-perform
optimised turbulent Prandtl number closures and Clark-type gradient models.
Next, \textit{a-posteriori} solutions are obtained with each model over several
integral timescales. These experiments reveal, with single- and multi-point
diagnostics, that ANN models temporally track exact resolved scalar variance
with greater accuracy compared to other subfilter flux models for a given
filter length scale. Finally, we interpret the artificial neural networks
statistically with differential sensitivity analysis to show that the ANN
models feature dynamics reminiscent of so-called "mixed models", where mixed
models are understood as comprising both a structural and functional component.
Besides enabling enhanced-accuracy LES of passive scalars henceforth, we
anticipate this work to contribute to utilising neural network models as a tool
in interpretability, robustness and model discovery.


  We prove some asymptotic results for the radius and the profile of large
random rooted planar maps with faces of arbitrary degrees. Using a bijection
due to Bouttier, Di Francesco and Guitter between rooted planar maps and
certain four-type trees with positive labels, we derive our results from a
conditional limit theorem for four-type spatial Galton-Watson trees.


  Using highly resolved direct numerical simulations we examine the statistical
properties of the local energy flux rate $\Pi_\ell(x)$ towards small scales for
three isotropic turbulent magnetohydrodynamic flows, which differ in strength
and structure of the magnetic field. We analyse the cascade process both in the
kinetic and magnetic energy, disentangling the different flux contributions to
the overall energy dynamics. The results show that the probability distribution
of the local energy flux develops long tails related to extreme events, similar
to the hydrodynamic case. The different terms of the energy flux display
different properties and show sensitivity on the type of the flow examined. We
further examine the joint pdf between the local energy flux and the gradients
of the involved fields. The results point out a correlation with the magnetic
field gradients, showing however a dispersion much stronger than what is
observed in hydrodynamic flows. Finally, it is also shown that the local energy
flux shows some dependence on the local amplitude of the magnetic field. The
present results have implications for subgrid scale models that we discuss.


  Motivated by the hyper-holomorphic case we introduce and study rational
functions in the setting of Hida's white noise space. The Fueter polynomials
are replaced by a basis computed in terms of the Hermite functions, and the
Cauchy-Kovalevskaya product is replaced by the Wick product.


  In this paper, we study a class of nonsmooth fractional programs {\rm (FP,
for short)} with SOS-convex semi-algebraic functions. Under suitable
assumptions, we derive a strong duality result between the problem (FP) and its
semidefinite programming (SDP) relaxations. Remarkably, we extract an optimal
solution of the problem (FP) by solving one and only one associated SDP
problem. Numerical examples are also given.


  Neutrino astronomy offers the possibility to perform extra-galactic
observations well beyond the photon absorption cutoff above 50 TeV. Based on
observations of cosmic rays, we already know that astrophysical sources produce
particles with at least a million times more energy than this photon cutoff.
Once discovered, either the nature of the sources themselves or the cross
sections of ultra-high energy neutrinos with terrestrial matter may reveal
exotic physical processes that are inaccessible to modern accelerators. Some of
these processes may be due to as-yet unknown physics at the grand unification
scale or beyond. Neutrino telescopes based on optical techniques currently
operating and under construction have apertures measured in several km^3-sr.
Radio and acoustic detection techniques have been demonstrated in laboratory
experiments and are currently used for instrumentation of apertures 10 to
10,000 times larger than optical techniques for neutrinos above 10 PeV. I
discuss the status of current and proposed neutrino telescope projects based on
these techniques. These telescopes have already ruled out some of the more
exotic predictions for neutrino intensity. The upcoming generation of
radio-based and acoustic-based detectors should be sensitive to cosmic
neutrinos above 1 EeV originating through the so-called GZK process. A
comparison of different neutrino telescopes using a common aperture variable
shows how they are complementary in the trade-off of volume versus threshold. I
include a proposal for how neutrino telescopes should report their
sensitivities to facilitate direct comparisons among them and to allow testing
of neutrino brightness models that appear even after publication of the
experimental results.


  The last ten years a number of observational advances have substantially
increased our knowledge of shock phenomena in supernova remnants. This progress
has mainly been made possible by the recent improvements in X-ray and Gamma-ray
instrumentation. It has become clear that some shell-type supernova remnants,
e.g. SN 1006, have X-ray emission dominated by synchrotron radiation, proving
that electrons are accelerated up to 100 TeV. This is still an order of
magnitude below 3E15 eV, at which energy the ion cosmic ray spectrum at earth
shows a spectral break. So one of the major goals is to prove that supernova
remnants are capable of accelerating ions at least up that energy.
  Here I review the evidence that ions and electrons are accelerated up to
energies ~100 TeV in supernova remnants, and, in addition, the recent progress
that has been made in understanding the physics of collisionless shock fronts
and the magnetic fields inside supernova remnants.


  Caching at the network edge has emerged as a viable solution for alleviating
the severe capacity crunch in modern content centric wireless networks by
leveraging network load-balancing in the form of localized content storage and
delivery. In this work, we consider a cache-aided network where the cache
storage phase is assisted by a central server and users can demand multiple
files at each transmission interval. To service these demands, we consider two
delivery models - $(1)$ centralized content delivery where user demands at each
transmission interval are serviced by the central server via multicast
transmissions; and $(2)$ device-to-device (D2D) assisted distributed delivery
where users multicast to each other in order to service file demands. For such
cache-aided networks, we present new results on the fundamental cache storage
vs. transmission rate tradeoff. Specifically, we develop a new technique for
characterizing information theoretic lower bounds on the storage-rate tradeoff
and show that the new lower bounds are strictly tighter than cut-set bounds
from literature. Furthermore, using the new lower bounds, we establish the
optimal storage-rate tradeoff to within a constant multiplicative gap. We show
that, for multiple demands per user, achievable schemes based on repetition of
schemes for single demands are order-optimal under both delivery models.


  The cops-and-robber (CR) game has been used in mobile robotics as a
discretized model (played on a graph G) of pursuit/evasion problems. The
"classic" CR version is a perfect information game: the cops' (pursuer's)
location is always known to the robber (evader) and vice versa. Many variants
of the classic game can be defined: the robber can be invisible and also the
robber can be either adversarial (tries to avoid capture) or drunk (performs a
random walk). Furthermore, the cops and robber can reside in either nodes or
edges of G. Several of these variants are relevant as models or robotic pursuit
/ evasion. In this paper, we first define carefully several of the variants
mentioned above and related quantities such as the cop number and the capture
time. Then we introduce and study the cost of visibility (COV), a quantitative
measure of the increase in difficulty (from the cops' point of view) when the
robber is invisible. In addition to our theoretical results, we present
algorithms which can be used to compute capture times and COV of graphs which
are analytically intractable. Finally, we present the results of applying these
algorithms to the numerical computation of COV.


  Cross sections for quasi-free Compton scattering from the deuteron were
measured for incident energies of 236--260 MeV at the laboratory angle -135
degrees. The recoil nucleons were detected in a liquid-scintillator array
situated at 20 degrees. The measured differential cross sections were used,
with the calculations of Levchuk et al., to determine the polarizabilities of
the bound nucleons. For the bound proton, the extracted values were consistent
with the accepted value for the free proton. Combining our results for the
bound neutron with those from Rose et al., we obtain one-sigma constraints of
alpha_n = 7.6-14.0 and beta_n = 1.2-7.6.


  Nature-Inspired Computing or NIC for short is a relatively young field that
tries to discover fresh methods of computing by researching how natural
phenomena function to find solutions to complicated issues in many contexts. As
a consequence of this, ground-breaking research has been conducted in a variety
of domains, including synthetic immune functions, neural networks, the
intelligence of swarm, as well as computing of evolutionary. In the domains of
biology, physics, engineering, economics, and management, NIC techniques are
used. In real-world classification, optimization, forecasting, and clustering,
as well as engineering and science issues, meta-heuristics algorithms are
successful, efficient, and resilient. There are two active NIC patterns: the
gravitational search algorithm and the Krill herd algorithm. The study on using
the Krill Herd Algorithm (KH) and the Gravitational Search Algorithm (GSA) in
medicine and healthcare is given a worldwide and historical review in this
publication. Comprehensive surveys have been conducted on some other
nature-inspired algorithms, including KH and GSA. The various versions of the
KH and GSA algorithms and their applications in healthcare are thoroughly
reviewed in the present article. Nonetheless, no survey research on KH and GSA
in the healthcare field has been undertaken. As a result, this work conducts a
thorough review of KH and GSA to assist researchers in using them in diverse
domains or hybridizing them with other popular algorithms. It also provides an
in-depth examination of the KH and GSA in terms of application, modification,
and hybridization. It is important to note that the goal of the study is to
offer a viewpoint on GSA with KH, particularly for academics interested in
investigating the capabilities and performance of the algorithm in the
healthcare and medical domains.