  Quantum mechanics dominates various effects in modern research from
miniaturizing electronics, up to potentially ruling solid-state physics,
quantum chemistry and biology. To study these effects experimental quantum
systems may provide the only effective access. Seminal progress has been
achieved in a variety of physical platforms, highlighted by recent
applications. Atomic ions are known for their unique controllability and are
identical by nature, as evidenced, e.g., by performing among the most precise
atomic clocks and providing the basis for one-dimensional simulators. However,
controllable, scalable systems of more than one dimension are required to
address problems of interest and to reach beyond classical numerics with its
powerful approximative methods. Here we show, tunable, coherent couplings and
interference in a two-dimensional ion microtrap array, completing the toolbox
for a reconfigurable quantum simulator. Previously, couplings and entangling
interactions between sites in one-dimensional traps have been realized, while
coupling remained elusive in microtrap approaches. Our architecture is based on
well isolatable ions as identical quantum entities hovering above scalable CMOS
chips. In contrast to other multi-dimensional approaches, it allows individual
control in arbitrary, even non-periodic, lattice structures. Embedded control
structures can exploit the long-range Coulomb interaction to configure
synthetic, fully connected many-body systems to address multi-dimensional
problems.


  Self-propelling micromotors are emerging as a promising microscale and
nanoscale tool for single-cell analysis. We have recently shown that the field
gradients necessary to manipulate matter via dielectrophoresis can be induced
at the surface of a polarizable active (self-propelling) metallo-dielectric
Janus particle (JP) under an externally applied electric field, acting
essentially as a mobile floating microelectrode. Here, we successfully
demonstrated for the first time, that the application of an external electric
field can singularly trap and transport bacteria and can selectively
electroporate the trapped bacteria. Selective electroporation, enabled by the
local intensification of the electric field induced by the JP, was obtained
under both continuous alternating current and pulsed signal conditions. This
approach is generic and is applicable to bacteria and JP, as well as a wide
range of cell types and micromotor designs. Hence, it constitutes an important
and novel experimental tool for single-cell analysis and targeted delivery.


  Interactive simulations allow students to discover the underlying principles
of a scientific phenomenon through their own exploration. Unfortunately,
students often struggle to learn effectively in these environments. Classifying
students' interaction data in the simulations based on their expected
performance has the potential to enable adaptive guidance and consequently
improve students' learning. Previous research in this field has mainly focused
on a-posteriori analyses or investigations limited to one specific predictive
model and simulation. In this paper, we investigate the quality and
generalisability of models for an early prediction of conceptual understanding
based on clickstream data of students across interactive simulations. We first
measure the students' conceptual understanding through their in-task
performance. Then, we suggest a novel type of features that, starting from
clickstream data, encodes both the state of the simulation and the action
performed by the student. We finally propose to feed these features into
GRU-based models, with and without attention, for prediction. Experiments on
two different simulations and with two different populations show that our
proposed models outperform shallow learning baselines and better generalise to
different learning environments and populations. The inclusion of attention
into the model increases interpretability in terms of effective inquiry. The
source code is available on Github
(https://github.com/epfl-ml4ed/beerslaw-lab.git).


  The inherent challenge of detecting symmetries stems from arbitrary
orientations of symmetry patterns; a reflection symmetry mirrors itself against
an axis with a specific orientation while a rotation symmetry matches its
rotated copy with a specific orientation. Discovering such symmetry patterns
from an image thus benefits from an equivariant feature representation, which
varies consistently with reflection and rotation of the image. In this work, we
introduce a group-equivariant convolutional network for symmetry detection,
dubbed EquiSym, which leverages equivariant feature maps with respect to a
dihedral group of reflection and rotation. The proposed network is built
end-to-end with dihedrally-equivariant layers and trained to output a spatial
map for reflection axes or rotation centers. We also present a new dataset,
DENse and DIverse symmetry (DENDI), which mitigates limitations of existing
benchmarks for reflection and rotation symmetry detection. Experiments show
that our method achieves the state of the arts in symmetry detection on LDRS
and DENDI datasets.


  The proliferation of data in recent years has led to the advancement and
utilization of various statistical and deep learning techniques, thus
expediting research and development activities. However, not all industries
have benefited equally from the surge in data availability, partly due to legal
restrictions on data usage and privacy regulations, such as in medicine. To
address this issue, various statistical disclosure and privacy-preserving
methods have been proposed, including the use of synthetic data generation.
Synthetic data are generated based on some existing data, with the aim of
replicating them as closely as possible and acting as a proxy for real
sensitive data. This paper presents a systematic review of methods for
generating and evaluating synthetic longitudinal patient data, a prevalent data
type in medicine. The review adheres to the PRISMA guidelines and covers
literature from five databases until the end of 2022. The paper describes 17
methods, ranging from traditional simulation techniques to modern deep learning
methods. The collected information includes, but is not limited to, method
type, source code availability, and approaches used to assess resemblance,
utility, and privacy. Furthermore, the paper discusses practical guidelines and
key considerations for developing synthetic longitudinal data generation
methods.


  The training process of neural networks usually optimize weights and bias
parameters of linear transformations, while nonlinear activation functions are
pre-specified and fixed. This work develops a systematic approach to
constructing matrix activation functions whose entries are generalized from
ReLU. The activation is based on matrix-vector multiplications using only
scalar multiplications and comparisons. The proposed activation functions
depend on parameters that are trained along with the weights and bias vectors.
Neural networks based on this approach are simple and efficient and are shown
to be robust in numerical experiments.


  We show that all quantum states that do not have a positive partial transpose
are distillable via channels, which preserve the positivity of the partial
transpose. The question whether NPT bound entanglement exist is therefore
closely related to the connection between the set of separable superoperators
and PPT-preserving maps.


  We investigate effects of the Coulomb force on the nuclear pairing properties
by performing the Gogny Hartree-Fock-Bogolyubov calculations for the $N=20$,
$28$, $50$, $82$ and $126$ nuclei. The Coulomb force reduces the proton pair
energy and the even-odd mass difference by about 25%, except for nuclei at and
around the proton shell or subshell closure. We then propose a renormalization
scheme via a reduction factor $\gamma_p$ for the proton pairing channel. It is
found that a single value of $\gamma_p(=0.90)$ well takes account of the
Coulombic effect, for nuclei covering wide range of the mass number and the
neutron excess including the nuclei around the shell or subshell closure.


  Additional material supporting the article "Target atmospheric CO2: Where
should humanity aim?"


  We show that correlation functions for branched polymers correspond to those
for $\phi^3$ theory with a single mass insertion, not those for the $\phi^3$
theory themselves, as has been widely believed. In particular, the two-point
function behaves as 1/p^4, not as 1/p^2. This behavior is consistent with the
fact that the Hausdorff dimension of the branched polymer is four.


  Context: The physical origin behind organic emission in embedded low-mass
star formation has been fiercely debated in the last two decades. A multitude
of scenarios have been proposed, from a hot corino to PDRs on cavity walls to
shock excitation.
  Aims: The aim of this paper is to determine the location and the
corresponding physical conditions of the gas responsible for organics emission
lines. The outflows around the small protocluster NGC 2071 are an ideal testbed
to differentiate between various scenarios.
  Methods: Using Herschel-HIFI and the SMA, observations of CH3OH, H2CO and
CH3CN emission lines over a wide range of excitation energies were obtained.
Comparisons to a grid of radiative transfer models provide constraints on the
physical conditions. Comparison to H2O line shape is able to trace gas-phase
synthesis versus a sputtered origin.
  Results: Emission of organics originates in three spots: the continuum
sources IRS 1 ('B') and IRS 3 ('A') as well as a outflow position ('F').
Densities are above 10$^7$ cm$^{-3}$ and temperatures between 100 to 200 K.
CH3OH emission observed with HIFI originates in all three regions and cannot be
associated with a single region. Very little organic emission originates
outside of these regions.
  Conclusions: Although the three regions are small (<1,500 AU), gas-phase
organics likely originate from sputtering of ices due to outflow activity. The
derived high densities (>10$^7$ cm$^{-3}$) are likely a requirement for organic
molecules to survive from being destroyed by shock products. The lack of
spatially extended emission confirms that organic molecules cannot (re)form
through gas-phase synthesis, as opposed to H2O, which shows strong line wing
emission. The lack of CH3CN emission at 'F' is evidence for a different history
of ice processing due to the absence of a protostar at that location and recent
ice mantle evaporation.


  The famous superdense coding protocol of Bennett and Wiesner demonstrates
that it is possible to communicate two bits of classical information by sending
only one qubit and using a shared EPR pair. Our first result is that an
arbitrary protocol for achieving this task (where there are no assumptions on
the sender's encoding operations or the dimension of the shared entangled
state) is locally equivalent to the canonical Bennett-Wiesner protocol. In
other words, the superdense coding task is rigid. In particular, we show that
the sender and receiver only use additional entanglement (beyond the EPR pair)
as a source of classical randomness.
  We also investigate several questions about higher-dimensional superdense
coding, where the goal is to communicate one of $d^2$ possible messages by
sending a $d$-dimensional quantum state, for general dimensions $d$. Unlike the
$d=2$ case (i.e. sending a single qubit), there can be inequivalent superdense
coding protocols for higher $d$. We present concrete constructions of
inequivalent protocols, based on constructions of inequivalent orthogonal
unitary bases for all $d > 2$. Finally, we analyze the performance of
superdense coding protocols where the encoding operators are independently
sampled from the Haar measure on the unitary group. Our analysis involves
bounding the distinguishability of random maximally entangled states, which may
be of independent interest.


  The global regularity for the incompressible magnetohydrodynamic equations
(MHD) in three dimensions is a long standing open problem of fluid dynamics and
PDE theory. The Navier-Stokes equations can be viewed as a special case of MHD
with a constant magnetic field, whose global regularity problem is known as a
Clay Millennium Prize Problem. In this article, we prove the global regularity
of axially symmetric solutions to the ideal MHD in three dimensions for a
family of non-trivial magnetic fields. The proofs are based on the special
structures of MHD and can of course also applied to the resistive MHD. Our
result might indicate that there are richer fantastic research topics in MHD
than Navier-Stokes equations.


  In a previous paper we attached to classical complex newforms $f$ of weight
$2$ certain $\delta_p$-modular forms $f^{\sharp}$ of order $2$ and weight $0$;
the forms $f^{\sharp}$ can be viewed as "dual" to $f$ and played a key role in
some of the applications of the theory. The aim of this paper is to provide a
higher weight version of this "$\sharp$-duality," by attaching to classical
newforms mod $p$, $\overline{f}$, of weight $\kappa$ between $3$ and $p$,
$\delta_{\pi}$-modular forms $f^{\sharp}$ of order $2$ and weight $-\kappa'$,
with $\kappa'$ between $1$ and $p-2$.


  Background: Modern code review is expected to facilitate knowledge sharing:
All relevant information, the collective expertise, and meta-information around
the code change and its context become evident, transparent, and explicit in
the corresponding code review discussion. The discussion participants can
leverage this information in the following code reviews; the information
diffuses through the communication network that emerges from code review.
Traditional time-aggregated graphs fall short in rendering information
diffusion as those models ignore the temporal order of the information
exchange: Information can only be passed on if it is available in the first
place.
  Aim: This manuscript presents a novel model based on time-varying hypergraphs
for rendering information diffusion that overcomes the inherent limitations of
traditional, time-aggregated graph-based models.
  Method: In an in-silico experiment, we simulate an information diffusion
within the internal code review at Microsoft and show the empirical impact of
time on a key characteristic of information diffusion: the number of reachable
participants.
  Results: Time-aggregation significantly overestimates the paths of
information diffusion available in communication networks and, thus, is neither
precise nor accurate for modelling and measuring the spread of information
within communication networks that emerge from code review.
  Conclusion: Our model overcomes the inherent limitations of traditional,
static or time-aggregated, graph-based communication models and sheds the first
light on information diffusion through code review. We believe that our model
can serve as a foundation for understanding, measuring, managing, and improving
knowledge sharing in code review in particular and information diffusion in
software engineering in general.


  The majority of cataclysmic variable (CV) stars contain a stochastic noise
component in their light curves, commonly referred to as flickering. This can
significantly affect the morphology of CV eclipses and increases the difficulty
in obtaining accurate system parameters with reliable errors through eclipse
modelling. Here we introduce a new approach to eclipse modelling, which models
CV flickering with the help of Gaussian processes (GPs). A parameterised
eclipse model - with an additional GP component - is simultaneously fit to 8
eclipses of the dwarf nova ASASSN-14ag and system parameters determined. We
obtain a mass ratio $q$ = 0.149 $\pm$ 0.016 and inclination $i$ = 83.4
$^{+0.9}_{-0.6}$ $^{\circ}$. The white dwarf and donor masses were found to be
$M_{w}$ = 0.63 $\pm$ 0.04 $M_{\odot}$ and $M_{d}$ = 0.093 $^{+0.015}_{-0.012}$
$M_{\odot}$, respectively. A white dwarf temperature $T_{w}$ = 14000
$^{+2200}_{-2000}$ K and distance $d$ = 146 $^{+24}_{-20}$ pc were determined
through multicolour photometry. We find GPs to be an effective way of modelling
flickering in CV light curves and plan to use this new eclipse modelling
approach going forward.


  Living systems often exhibit internal driving: active, molecular processes
drive nonequilibrium phenomena such as metabolism or migration. Active gels
constitute a fascinating class of internally driven matter, where molecular
motors exert localized stresses inside polymer networks. There is evidence that
network crosslinking is required to allow motors to induce macroscopic
contraction. Yet a quantitative understanding of how network connectivity
enables contraction is lacking. Here we show experimentally that myosin motors
contract crosslinked actin polymer networks to clusters with a scale-free size
distribution. This critical behavior occurs over an unexpectedly broad range of
crosslink concentrations. To understand this robustness, we develop a
quantitative model of contractile networks that takes into account network
restructuring: motors reduce connectivity by forcing crosslinks to unbind.
Paradoxically, to coordinate global contractions, motor activity should be low.
Otherwise, motors drive initially well-connected networks to a critical state
where ruptures form across the entire network.


  We explore the physics of the quantum Hall effect using the Haldane mapping
of dimerised $SU(N+M)$ spin chains, the large $N$ expansion and the density
matrix renormalization group technique. We show that while the transition is
first order for $N+M >2$, the system at zero temperature nevertheless displays
a continuously diverging length scale $\xi$ (correlation length). The numerical
results for $(M, N) = (1,3), ~ (2, 2),~(1, 5)$ and $(1, 7)$ indicate that $\xi$
is a directly observable physical quantity, namely the spatial width of the
edge states. We relate the physical observables of the quantum spin chain to
those of the quantum Hall system (and, hence, the $\vartheta$ vacuum concept in
quantum field theory). Our numerical investigations provide strong evidence for
the conjecture of super universality which says the dimerised spin chain quite
generally displays all the basic features of the quantum Hall effect,
independent of the specific values of $M$ and $N$. For the cases at hand we
show that the singularity structure of the quantum Hall plateau transitions
involves a universal function with two scale parameters that may in general
depend on $M$ and $N$. This includes not only the Hall conductance but also the
ground state energy as well as the correlation length $\xi$ with varying values
of $\vartheta \sim \pi$.


  Assembly systems constitute one of the most important fields in today
industry. In this paper we propose an open distributed architecture for the
engineering of evolvable flexible hybrid assembly systems. The proposed
architecture is based on the model driven development paradigm. Models are used
to represent structure and behavior and a domain specific engineering tool is
defined to facilitate the assembly system engineer in the engineering process
of the assembly system. Specific meta models are defined to capture domain
knowledge to guide the engineer in the construction of the models required to
construct the assembly system. This work is a specialization of our previous
work that defined a SOA based framework for embedded industrial automation
systems. It adapts and extends, in the assembly systems domain, the 3+1
SysML-view model architecture defined for the engineering of mechatronics
Manufacturing systems. The proposed architecture can be used to develop a
framework for evolvable flexible and reconfigurable assembly systems that would
exploit the benefits the Cyber Physical paradigm utilizing web technologies,
the IoT, the Cloud computing and Big Data.


  Sensitivity of linear continuous-time control systems, subject to control and
measurement noise, is analyzed by deriving the lower bounds of Bode-like
integrals via an information-theoretic approach. Bode integrals of four
different sensitivity-like functions are employed to gauge the control
trade-offs. When the signals of the control system are stationary Gaussian,
these four different Bode-like integrals can be represented as differences
between mutual information rates. These mutual information rates and hence the
corresponding Bode-like integrals are proven to be bounded below by the
unstable poles and zeros of the plant model, if the signals of the control
system are wide-sense stationary.


  The heavy fermion system CeNi9Ge4 exhibits a paramagnetic ground state with
remarkable features such as: a record value of the electronic specific heat
coefficient in systems with a paramagnetic ground state, \gamma = C/T \simeq
5.5 J/molK^2 at 80 mK, a temperature-dependent Sommerfeld-Wilson ratio,
R=\chi/\gamma, below 1 K and an approximate single ion scaling of the
4f-magnetic specific heat and susceptibility. These features are related to a
rather small Kondo energy scale of a few Kelvin in combination with a
quasi-quartet crystal field ground state. Tuning the system towards long range
magnetic order is accomplished by replacing a few at.% of Ni by Cu or Co.
Specific heat, susceptibility and resistivity studies reveal T_N \sim 0.2 K for
CeNi8CuGe4 and T_N \sim 1 K for CeNi8CoGe4. To gain insight whether the
transition from the paramagnetic NFL state to the magnetically ordered ground
state is connected with a heavy fermion quantum critical point we performed
specific heat and ac susceptibility studies and utilized the \mu SR technique
and quasi-elastic neutron scattering.


  We define the Chern character of the index class of a $G$-invariant family of
$G$-transversally elliptic operators, see [6]. Next we study the Berline-Vergne
formula for families in the elliptic and transversally elliptic case.


  Recent precise observations of the 2.7 K CMB by the Planck mission toward the
Coma cluster are not in agreement with X-ray measurements. To reconcile both
types of measuring techniques we suggest that unstable dark matter is the cause
of this mismatch. Decaying dark matter, which gravitationally dominates the
galaxy cluster, can affect the estimated hot plasma content, which is then
missing in the measured SZ effect from exactly the same place in the sky. The
model independent lifetime of dark matter decaying entirely to X-rays is
estimated to be about 6x10^{24} sec; this lifetime scales down with the
fraction of the radiatively decaying dark matter. In addition, it is shown that
the potential of such dark matter investigations in space is superior to the
largest volume Earth-bound dark matter decay searches. Other clusters might
provide additional evidence for or against this suggestion.


  A conventional context for supersymmetric problems arises when we consider
systems containing both boson and fermion operators. In this note we consider
the normal ordering problem for a string of such operators. In the general
case, upon which we touch briefly, this problem leads to combinatorial numbers,
the so-called Rook numbers. Since we assume that the two species, bosons and
fermions, commute, we subsequently restrict ourselves to consideration of a
single species, single-mode boson monomials. This problem leads to elegant
generalisations of well-known combinatorial numbers, specifically Bell and
Stirling numbers. We explicitly give the generating functions for some classes
of these numbers. In this note we concentrate on the combinatorial graph
approach, showing how some important classical results of graph theory lead to
transparent representations of the combinatorial numbers associated with the
boson normal ordering problem.


  We present the highly significant detection of a quasi-periodic flux
modulation with a period of 22.2 min seen in the X-ray data of the Sgr A* flare
of 2004 August 31. This flaring event, which lasted a total of about three
hours, was detected simultaneously by EPIC on XMM-Newton and the NICMOS
near-infrared camera on the HST. Given the inherent difficulty in, and the lack
of readily available methods for quantifying the probability of a periodic
signal detected over only several cycles in a data set where red noise can be
important, we developed a general method for quantifying the likelihood that
such a modulation is indeed intrinsic to the source and does not arise from
background fluctuations. We here describe this Monte Carlo based method, and
discuss the results obtained by its application to a other XMM-Newton data
sets. Under the simplest hypothesis that we witnessed a transient event that
evolved, peaked and decayed near the marginally stable orbit of the
supermassive black hole, this result implies that for a mass of 3.5 x 10^{6}
Msun, the central object must have an angular momentum corresponding to a spin
parameter of a=0.22.


  In this article we provide a review of geometrical methods employed in the
analysis of quantum phase transitions and non-equilibrium dissipative phase
transitions. After a pedagogical introduction to geometric phases and geometric
information in the characterisation of quantum phase transitions, we describe
recent developments of geometrical approaches based on mixed-state
generalisation of the Berry-phase, i.e. the Uhlmann geometric phase, for the
investigation of non-equilibrium steady-state quantum phase transitions
(NESS-QPTs ). Equilibrium phase transitions fall invariably into two markedly
non-overlapping categories: classical phase transitions and quantum phase
transitions, whereas in NESS-QPTs this distinction may fade off. The approach
described in this review, among other things, can quantitatively assess the
quantum character of such critical phenomena. This framework is applied to a
paradigmatic class of lattice Fermion systems with local reservoirs,
characterised by Gaussian non-equilibrium steady states. The relations between
the behaviour of the geometric phase curvature, the divergence of the
correlation length, the character of the criticality and the gap - either
Hamiltonian or dissipative - are reviewed.


  The phase-field system is a nonlinear model that has significant applications
in material sciences. In this paper, we are concerned with the uniqueness of
determining the nonlinear energy potential in a phase-field system consisted of
Cahn-Hilliard and Allen-Cahn equations. This system finds widespread
applications in the development of alloys engineered to withstand extreme
temperatures and pressures. The goal is to reconstruct the nonlinear energy
potential through the measurements of concentration fields. We establish the
local well-posedness of the phase-field system based on the implicit function
theorem in Banach spaces. Both of the uniqueness results for recovering
time-independent and time-dependent energy potential functions are provided
through the higher order linearization technique.


  In this paper we investigate Lie bialgebra structures on the deformative
Schr\"{o}dinger-Virasoro algebras mainly using the techniques introduced
recently by Liu, Pei and Zhu, which indicate that all cases considered in this
paper except one behave different from their centerless ones.


  Massive multiplayer online role-playing games (MMORPGs) are very popular in
past few years. The profit of an MMORPG company is proportional to how many
users registered, and the instant number of online avatars is a key factor to
assess how popular an MMORPG is. We use the on-off-line logs on an MMORPG
server to reconstruct the instant number of online avatars per second and
investigate its statistical properties. We find that the online avatar number
exhibits one-day periodic behavior and clear intraday pattern, the fluctuation
distribution of the online avatar numbers has a leptokurtic non-Gaussian shape
with power-law tails, and the increments of online avatar numbers after
removing the intraday pattern are uncorrelated and the associated absolute
values have long-term correlation. In addition, both time series exhibit
multifractal nature.


  The purpose of this paper is to obtain a fundamental $L^p-L^{p'}$ estimate
for a class of a strongly damped wave equations where the damping operator is
given by $-\delta \Delta$ with $\delta \geq 0$ and the constant in the estimate
is independent of the damping parameter $\delta.$ The method used here is based
on an estimate of the semigroup associated to the linear equation on the Besov
spaces and their elementary properties.


  One of the major characteristics of living organisms is metabolic rate -
amount of energy produced per unit time. When the size of organisms increases,
the metabolic rate also increases in a certain regular way. This regularity,
whose causes are considered unknown, is what puzzles researchers for many
decades. The effect is called metabolic allometric scaling. It has important
implications for the individual and population organismal development. Here, we
study the causes of this phenomenon for unicellular organisms considering
metabolic properties of Amoeba Proteus, fission yeast S. pombe, E. coli, B.
subtilis, Staphylococcus. We found that the metabolic properties of unicellular
organisms are defined by natural selection, acting within the food chain,
composed of microorganisms sharing nutritional resources within the common
habitat. This sharing of resources is optimized in such a way that bigger
microorganisms have progressively bigger nutrient influx per unit of surface.
Such an evolutionary arrangement provides metabolic advantage, which bigger
organisms need in order to support their metabolic requirements. On the other
hand, this advantage cannot be too strong, since this could lead to destruction
of smaller species, bigger organisms feed on or compete with for nutrients
within the food chain. Accounting for this regular increase of nutrient influx
with mass increase, we obtained allometric exponents and their range close to
experimental values. These findings supports an argument that the metabolic
properties of unicellular organisms are formed by natural selection in a way
that preserves a dynamic balance in sharing resources between populations of
different species within the food chain, while quantitatively this selection
and optimization manifest as a regular increase of nutrient influx per unit
surface, when mass of unicellular organisms increases.


  We present numerical simulations of fully nonlinear drift wave-zonal flow
(DW-ZF) turbulence systems in a nonuniform magnetoplasma. In our model, the
drift wave (DW) dynamics is pseudo-three-dimensional (pseudo-3D) and accounts
for self-interactions among finite amplitude DWs and their coupling to the
two-dimensional (2D) large amplitude zonal flows (ZFs). The dynamics of the 2D
ZFs in the presence of the Reynolds stress of the pseudo-3D DWs is governed by
the driven Euler equation. Numerical simulations of the fully nonlinear coupled
DW-ZF equations reveal that shortscale DW turbulence leads to nonlinear
saturated dipolar vortices, whereas the ZF sets in spontaneously and is
dominated by a monopolar vortex structure. The ZFs are found to suppress the
cross-field turbulent particle transport. The present results provide a better
model for understanding the coexistence of short- and large-scale coherent
structures, as well as associated subdued cross-field particle transport in
magnetically confined fusion plasmas.


  Monoclinic scheelite bismuth vanadate (BiVO4) is a promising photoanode for
water splitting yet the PEC performance is limited due to its relatively higher
(2.4 eV) band gap. Here, we successfully decreased its the band gap to 1.72 eV
by controlled antimony alloying. Low bandgap antimony alloyed bismuth vanadium
oxide (Sb:BiVO4) thin film was prepared by RF sputtering of high purity
homemade target, fabricated by solid-state reaction using a mixture of Sb2O3,
Bi2O3, and V2O5 powders with desired stoichiometric ratios. Several growth
parameters, powder crystallography, post-deposition effects, and surface
treatments, thickness dependence, effect of electrolytes on photocorrosion were
studied along with its optical and electrochemical characterization. We
discovered that Sb:BiVO4 is a direct band gap material in the visible light
range (1.72 eV) and a valence band position suitable for driving water
oxidation reaction under illumination. Furthermore, hole diffusion length is
increased with antimony alloying and achieved optimum thickness of 400 nm for
higher photocurrent. The controllably prepared Sb:BiVO4 particles are having
the sizes of 10-15 nm in room temperature deposition and can be grown up to 0.5
microns under air annealing.


  Language models have demonstrated impressive ability in context understanding
and generative performance. Inspired by the recent success of language
foundation models, in this paper, we propose LMTraj (Language-based Multimodal
Trajectory predictor), which recasts the trajectory prediction task into a sort
of question-answering problem. Departing from traditional numerical regression
models, which treat the trajectory coordinate sequence as continuous signals,
we consider them as discrete signals like text prompts. Specially, we first
transform an input space for the trajectory coordinate into the natural
language space. Here, the entire time-series trajectories of pedestrians are
converted into a text prompt, and scene images are described as text
information through image captioning. The transformed numerical and image data
are then wrapped into the question-answering template for use in a language
model. Next, to guide the language model in understanding and reasoning
high-level knowledge, such as scene context and social relationships between
pedestrians, we introduce an auxiliary multi-task question and answering. We
then train a numerical tokenizer with the prompt data. We encourage the
tokenizer to separate the integer and decimal parts well, and leverage it to
capture correlations between the consecutive numbers in the language model.
Lastly, we train the language model using the numerical tokenizer and all of
the question-answer prompts. Here, we propose a beam-search-based most-likely
prediction and a temperature-based multimodal prediction to implement both
deterministic and stochastic inferences. Applying our LMTraj, we show that the
language-based model can be a powerful pedestrian trajectory predictor, and
outperforms existing numerical-based predictor methods. Code is publicly
available at https://github.com/inhwanbae/LMTrajectory .


  In this paper, KdV-type equations with time- and space-dependent coefficients
are considered. Assuming that the dispersion coefficient in front of $u_{xxx}$
is positive and uniformly bounded away from the origin and that a primitive
function of the ratio between the anti-dissipation and the dispersion
coefficients is bounded from below, we prove the existence and uniqueness of a
solution $u$ such that $h u$ belongs to a classical Sobolev space, where $h$ is
a function related to this ratio. The LWP in $H^s(\mathbb{R})$, $s>1/2$, in the
classical (Hadamard) sense is also proven under an assumption on the
integrability of this ratio. Our approach combines a change of unknown with
dispersive estimates. Note that previous results were restricted to
$H^s(\mathbb{R})$, $s>3/2$, and only used the dispersion to compensate the
anti-dissipation and not to lower the Sobolev index required for
well-posedness.


  We present a fully differential next-to-next-to-leading order calculation of
t-channel single top-quark production and decay at the LHC under narrow-width
approximation and neglecting cross-talk between incoming protons. We focus on
the fiducial cross sections at 13 TeV, finding that the next-to-next-to-leading
order QCD corrections can reach the level of -6%. The scale variations are
reduced to the level of a percent. Our results can be used to improve
experimental acceptance estimates and the measurements of the single top-quark
production cross section and the top-quark electroweak couplings.


  Digital topology is part of the ongoing endeavour to understand and analyze
digitized images. With a view to supporting this endeavour, many notions from
algebraic topology have been introduced into the setting of digital topology.
But some of the most basic notions from homotopy theory remain largely absent
from the digital topology literature. We embark on a development of homotopy
theory in digital topology, and define such fundamental notions as function
spaces, path spaces, and cofibrations in this setting. We establish digital
analogues of basic homotopy-theoretic properties such as the homotopy extension
property for cofibrations, and the homotopy lifting property for certain
evaluation maps that correspond to path fibrations in the topological setting.
We indicate that some depth may be achieved by using these homotopy-theoretic
notions to give a preliminary treatment of Lusternik-Schnirelmann category in
the digital topology setting. This topic provides a connection between digital
topology and critical points of functions on manifolds, as well as other topics
from topological dynamics.


  The crossover from a BEC (Bose-Einstein condensation) to a BCS
(Bardeen-Cooper-Schrieffer) superfluid in dilute gases of ultracold Fermi atoms
creates an ideal environment to enrich our knowledge of strongly correlated
many-body systems. These experiments are relevant to a wide range of fields
from condensed matter to astrophysics. The nature of pairing in strongly
interacting Fermi gases can be readily studied, thus aiding our understanding
of related problems in high-T_{c} superconductors, whose mechanism is still
under debate. These are not well-understood due to the large interaction
parameter. Here, we calculate the dynamical properties of a normal, trapped,
and strongly correlated Fermi gas, by developing a quantum cluster expansion.
In ultra-cold atomic physics one can measure the elementary excitations, using
rf or Bragg spectroscopy. Our calculations for the single-particle spectral
function agree with the recent measurements, and clearly demonstrate pseudogap
pairing in the strongly interacting regime.


  We discuss the paper "Citation Statistics" by the Joint Committee on
Quantitative Assessment of Research [arXiv:0910.3529]. In particular, we focus
on a necessary feature of "good" measures for ranking scientific authors: that
good measures must able to accurately distinguish between authors.


  A Howe curve is a curve of genus $4$ obtained as the fiber product of two
genus-$1$ double covers of $\mathbf{P}^1$. In this paper, we present a simple
algorithm for testing isomorphism of Howe curves, and we propose two main
algorithms for finding and enumerating these curves: One involves solving
multivariate systems coming from Cartier--Manin matrices, while the other uses
Richelot isogenies of curves of genus $2$. Comparing the two algorithms by
implementation and by complexity analyses, we conclude that the latter
enumerates curves more efficiently. Using these algorithms, we show that there
exist superspecial curves of genus $4$ in characteristic $p$ for every prime
$p$ with $7 < p < 20000$.


  Distributions of charged leptons in semileptonic $\Lambda_c$ and $\Lambda_b$
decays can be used as spin analysers for the corresponding charm and bottom
quarks. QCD corrections to the energy spectra of charged leptons have been
given in the literature as well as the corrections to the angular dependence
for polarised up-type quarks. The analogous formulae are derived also for
polarised down-type quarks. These results are applied to the decays of
polarised charm and bottom quarks. The corrections to the asymmetries are found
to be small for charm decays. For bottom decays they exhibit a non-trivial
dependence on the energy of the charged lepton.


  Discriminatively trained, deterministic neural networks are the de facto
choice for classification problems. However, even though they achieve
state-of-the-art results on in-domain test sets, they tend to be overconfident
on out-of-distribution (OOD) data. For instance, ReLU networks - a popular
class of neural network architectures - have been shown to almost always yield
high confidence predictions when the test data are far away from the training
set, even when they are trained with OOD data. We overcome this problem by
adding a term to the output of the neural network that corresponds to the logit
of an extra class, that we design to dominate the logits of the original
classes as we move away from the training data.This technique provably prevents
arbitrarily high confidence on far-away test data while maintaining a simple
discriminative point-estimate training. Evaluation on various benchmarks
demonstrates strong performance against competitive baselines on both far-away
and realistic OOD data.


  Aims. The data set of the long-term XMM-Newton monitoring program of HD 81809
is analyzed to study its X-ray cycle, to investigate if the latter is related
to the chromospheric one, to infer the structure of the corona of HD 81809, and
to explore if the coronal activity of HD 81809 can be ascribed to phenomena
similar to the solar ones and, therefore, considered an extension of the solar
case. Methods. We analyze the observations of HD 81809 performed with
XMM-Newton with a regular cadence of 6 months from 2001 to 2016 and
representing one of the longest available observational baseline ($\sim 15$~yr)
for a solar-like star with a well-studied chromospheric cycle (with a period of
$\sim 8$~yr). We investigate the modulation of coronal luminosity and
temperature and its relation with the chromospheric cycle. We interpret the
data in terms of a mixture of solar-like coronal regions, adopting a
methodology originally proposed to study the Sun as an X-ray star. Results. The
observations show a well-defined regular cyclic modulation of the X-ray
luminosity that reflects the activity level of HD 81809. The data covers
approximately two cycles of coronal activity; the modulation has an amplitude
of a factor of $\sim 5$ (excluding evident flares, as in the June 2002
observation) and a period of $7.3\pm 1.5$~yr, consistent with that of the
chromospheric cycle. We demonstrate that the corona of HD 81809 can be
interpreted as an extension of the solar case and it can be modeled with a
mixture of solar-like coronal regions along the whole cycle. The activity level
is mainly determined by a varying coverage of very bright active regions,
similar to cores of active regions observed in the Sun. Evidence of unresolved
significant flaring activity is present especially in proximity of cycle
maxima.


  We propose a phenomenological study of the Balitsky-Fadin-Kuraev-Lipatov
(BFKL) approach applied to the data on the proton structure function $F_2$
measured at HERA in the small-$x_{Bj}$ region ($x_{Bj}<0.01$) and $Q^2$ in the
range 5 to 120 GeV$^2$. With a simplified ``effective kernel'' approximation,
we present a comparison between leading-logs (LO) and next-to-leading logs
(NLO) BFKL approaches in the saddle-point approximation, using known resummed
NLO-BFKL kernels. The LO result gives a very good description of the data with
a three parameters fit of $F_2$ but an unphysical value of the strong coupling
constant, whereas the NLO two parameters fit leads to a qualitatively
satisfactory account of the running coupling constant effect but
quantitatively, for $Q^2 < 10$ GeV$^2$, it fails to reproduce properly the
data.


  We present a general framework for modelling the dynamics of limit order
books, built on the combination of two modelling ingredients: the order flow,
modelled as a general spatial point process, and market clearing, modelled via
a deterministic mass transport operator acting on distributions of buy and sell
orders. At the mathematical level, this corresponds to a natural decomposition
of the infinitesimal generator describing the evolution of the limit order book
into two operators: the generator of the order flow and the clearing operator.
Our model provides a flexible framework for modelling and simulating order book
dynamics and studying various scaling limits of discrete order book models. We
show that our framework includes previous models as special cases and yields
insights into the interplay between order flow and price dynamics.


  We apply the method of orbit harmonics to the set of break divisors and
orientable divisors on graphs to obtain the central and external zonotopal
algebras respectively. We then relate a construction of Efimov in the context
of cohomological Hall algebras to the central zonotopal algebra of a graph
$G_{Q,\gamma}$ constructed from a symmetric quiver $Q$ with enough loops and a
dimension vector $\gamma$. This provides a concrete combinatorial perspective
on the former work, allowing us to identify the quantum Donaldson-Thomas
invariants as the Hilbert series of the space of $S_{\gamma}$-invariants of the
Postnikov-Shapiro slim subgraph space attached to $G_{Q,\gamma}$. The
connection with orbit harmonics in turn allows us to give a manifestly
nonnegative combinatorial interpretation to numerical Donaldson-Thomas
invariants as the number of $S_{\gamma}$-orbits under the permutation action on
the set of break divisors on $G_{Q,\gamma}$. We conclude with several
representation-theoretic consequences, whose combinatorial ramifications may be
of independent interest.


  The paper is devoted to the investigation of Segal's entropy in semifinite
von Neumann algebras. The following questions are dealt with: semicontinuity,
the 'ideal-like' structure of the linear span of the set of operators with
finite entropy, and topological properties of the set of operators with finite
as well as infinite entropy. In our analysis, full generality is aimed at, in
particular, the operators for which the entropy is considered are not assumed
to belong to the underlying von Neumann algebra, instead, they are arbitrary
positive elements in the space $L^1$ over the algebra.


  The Gaussian mechanism is an essential building block used in multitude of
differentially private data analysis algorithms. In this paper we revisit the
Gaussian mechanism and show that the original analysis has several important
limitations. Our analysis reveals that the variance formula for the original
mechanism is far from tight in the high privacy regime ($\varepsilon \to 0$)
and it cannot be extended to the low privacy regime ($\varepsilon \to \infty$).
We address these limitations by developing an optimal Gaussian mechanism whose
variance is calibrated directly using the Gaussian cumulative density function
instead of a tail bound approximation. We also propose to equip the Gaussian
mechanism with a post-processing step based on adaptive estimation techniques
by leveraging that the distribution of the perturbation is known. Our
experiments show that analytical calibration removes at least a third of the
variance of the noise compared to the classical Gaussian mechanism, and that
denoising dramatically improves the accuracy of the Gaussian mechanism in the
high-dimensional regime.


  We construct a plethora of Anosov-Katok diffeomorphisms with non-ergodic
generic measures and various other mixing and topological properties. We also
construct an explicit collection of the set containing the generic points of
the system with interesting values of its Hausdorff dimension.


  After disasters, distribution networks have to be restored by repair,
reconfiguration, and power dispatch. During the restoration process, changes
can occur in real time that deviate from the situations considered in
pre-designed planning strategies. That may result in the pre-designed plan to
become far from optimal or even unimplementable. This paper proposes a
centralized-distributed bi-level optimization method to solve the real-time
restoration planning problem. The first level determines integer variables
related to routing of the crews and the status of the switches using a genetic
algorithm (GA), while the second level determines the dispatch of
active/reactive power by using distributed model predictive control (DMPC). A
novel Aitken- DMPC solver is proposed to accelerate convergence and to make the
method suitable for real-time decision making. A case study based on the IEEE
123-bus system is considered, and the acceleration performance of the proposed
Aitken-DMPC solver is evaluated and compared with the standard DMPC method.


  DNA supercoiling is central to many fundamental processes of living
organisms. Its average level along the chromosome and over time reflects the
dynamic equilibrium of opposite activities of topoisomerases, which are
required to relax mechanical stresses that are inevitably produced during DNA
replication and gene transcription. Supercoiling affects all scales of the
spatio-temporal organization of bacterial DNA, from the base pair to the large
scale chromosome conformation. Highlighted in vitro and in vivo in the 1960s
and 1970s, respectively, the first physical models were proposed concomitantly
in order to predict the deformation properties of the double helix. About
fifteen years later, polymer physics models demonstrated on larger scales the
plectonemic nature and the tree-like organization of supercoiled DNA. Since
then, many works have tried to establish a better understanding of the multiple
structuring and physiological properties of bacterial DNA in thermodynamic
equilibrium and far from equilibrium. The purpose of this essay is to address
upcoming challenges by thoroughly exploring the relevance, predictive capacity,
and limitations of current physical models, with a specific focus on structural
properties beyond the scale of the double helix. We discuss more particularly
the problem of DNA conformations, the interplay between DNA supercoiling with
gene transcription and DNA replication, its role on nucleoid formation and,
finally, the problem of scaling up models. Our primary objective is to foster
increased collaboration between physicists and biologists. To achieve this, we
have reduced the respective jargon to a minimum and we provide some explanatory
background material for the two communities.


  Physics Informed Neural Networks (PINNs) is a promising application of deep
learning. The smooth architecture of a fully connected neural network is
appropriate for finding the solutions of PDEs; the corresponding loss function
can also be intuitively designed and guarantees the convergence for various
kinds of PDEs. However, the rate of convergence has been considered as a
weakness of this approach. This paper proposes Sobolev-PINNs, a novel loss
function for the training of PINNs, making the training substantially
efficient. Inspired by the recent studies that incorporate derivative
information for the training of neural networks, we develop a loss function
that guides a neural network to reduce the error in the corresponding Sobolev
space. Surprisingly, a simple modification of the loss function can make the
training process similar to \textit{Sobolev Training} although PINNs is not a
fully supervised learning task. We provide several theoretical justifications
that the proposed loss functions upper bound the error in the corresponding
Sobolev spaces for the viscous Burgers equation and the kinetic Fokker--Planck
equation. We also present several simulation results, which show that compared
with the traditional $L^2$ loss function, the proposed loss function guides the
neural network to a significantly faster convergence. Moreover, we provide the
empirical evidence that shows that the proposed loss function, together with
the iterative sampling techniques, performs better in solving high dimensional
PDEs.


  Aortic dissection progresses via delamination of the medial layer of the
wall. Notwithstanding the complexity of this process, insight has been gleaned
by studying in vitro and in silico the progression of dissection driven by
quasi-static pressurization of the intramural space by fluid injection, which
demonstrates that the differential propensity of dissection can be affected by
spatial distributions of structurally significant interlamellar struts that
connect adjacent elastic lamellae. In particular, diverse histological
microstructures may lead to differential mechanical behavior during dissection,
including the pressure--volume relationship of the injected fluid and the
displacement field between adjacent lamellae. In this study, we develop a
data-driven surrogate model for the delamination process for differential strut
distributions using DeepONet, a new operator--regression neural network. The
surrogate model is trained to predict the pressure--volume curve of the
injected fluid and the damage progression field of the wall given a spatial
distribution of struts, with in silico data generated with a phase-field finite
element model. The results show that DeepONet can provide accurate predictions
for diverse strut distributions, indicating that this composite branch-trunk
neural network can effectively extract the underlying functional relationship
between distinctive microstructures and their mechanical properties. More
broadly, DeepONet can facilitate surrogate model-based analyses to quantify
biological variability, improve inverse design, and predict mechanical
properties based on multi-modality experimental data.


  Several multi-core fibers (MCF) were characterized using Correlation Optical
Time Domain Reflectometry (C-OTDR) in terms of propagation delay and
polarization mode dispersion (PMD). The results show that the propagation delay
in the cores depends on the position of the core in the fiber and that the
differential delay between the cores varies with temperature.


  This study sequentially investigates the enhancement of fracture behavior in
adhesive joints. Initially, mechanical sanding techniques were explored,
revealing significant improvements of 55% in fracture energy and 38% in
load-bearing capacity with optimal 240-grit sanding. Subsequently, in a second
stage, Multi-Walled Carbon Nanotubes (MWCNTs) were introduced at varying weight
percentages (0.1%, 0.3%, and 0.5% by weight) into the adhesive joints. This
combined approach aimed to synergistically examine the impact of surface
pretreatments and nanoparticle integration on fracture behavior. Results
highlight the potential for fracture resistance and enhanced load-carrying
capabilities in reinforced specimens, with maximum load and fracture energy
improvements of up to 92 % and 50 %, respectively, compared to unreinforced
specimens and those subjected solely to sanding treatments. Furthermore, the
fracture mode shifted from adhesive failure to cohesive substrate failure in
reinforced specimens. This comprehensive investigation underscores the
intricate interplay between mechanical preparation and nanomaterial
incorporation in significantly improving adhesive joint performance


  We present a mathematical study of two-dimensional electrostatic and
electromagnetic shielding by a cage of conducting wires (the so-called `Faraday
cage effect'). Taking the limit as the number of wires in the cage tends to
infinity we use the asymptotic method of multiple scales to derive continuum
models for the shielding, involving homogenized boundary conditions on an
effective cage boundary. We show how the resulting models depend on key cage
parameters such as the size and shape of the wires, and, in the electromagnetic
case, on the frequency and polarisation of the incident field. In the
electromagnetic case there are resonance effects, whereby at frequencies close
to the natural frequencies of the equivalent solid shell, the presence of the
cage actually amplifies the incident field, rather than shielding it. By
appropriately modifying the continuum model we calculate the modified resonant
frequencies, and their associated peak amplitudes. We discuss applications to
radiation containment in microwave ovens and acoustic scattering by perforated
shells.


  Based on {\em ab initio} density functional calculations, we propose
$\gamma$-P and $\delta$-P as two additional stable structural phases of layered
phosphorus besides the layered $\alpha$-P (black) and $\beta$-P (blue)
phosphorus allotropes. Monolayers of some of these allotropes have a wide band
gap, whereas others, including $\gamma$-P, show a metal-insulator transition
caused by in-layer strain or changing the number of layers. An unforeseen
benefit is the possibility to connect different structural phases at no energy
cost. This becomes particularly valuable in assembling heterostructures with
well-defined metallic and semiconducting regions in one contiguous layer.


  Recently, automatic code comment generation is proposed to facilitate program
comprehension. Existing code comment generation techniques focus on describing
the functionality of the source code. However, there are other aspects such as
insights about quality or issues of the code, which are overlooked by earlier
approaches. In this paper, we describe a mining approach that recommends
insightful comments about the quality, deficiencies or scopes for further
improvement of the source code. First, we conduct an exploratory study that
motivates crowdsourced knowledge from Stack Overflow discussions as a potential
resource for source code comment recommendation. Second, based on the findings
from the exploratory study, we propose a heuristic-based technique for mining
insightful comments from Stack Overflow Q & A site for source code comment
recommendation. Experiments with 292 Stack Overflow code segments and 5,039
discussion comments show that our approach has a promising recall of 85.42%. We
also conducted a complementary user study which confirms the accuracy and
usefulness of the recommended comments.


  1973 Schinzel proved that the standard logarithmic height h on the maximal
totally real field extension of the rationals is either zero or bounded from
below by a positive constant. In this paper we study this property for
canonical heights associated to rational functions and the corresponding
dynamical system on the affine line. At the end, we will give a few remarks on
the behavior of h on finite extensions of the maximal totally real field.


  To a quiver $Q$ and choices of nonzero scalars $q_i$, non-negative integers
$\alpha_i$, and integers $\theta_i$ labeling each vertex $i$,
Crawley-Boevey--Shaw associate a "multiplicative quiver variety"
$\mathcal{M}_\theta^q(\alpha)$, a trigonometric analogue of the Nakajima quiver
variety associated to $Q$, $\alpha$, and $\theta$. We prove that the pure
cohomology, in the Hodge-theoretic sense, of the stable locus
$\mathcal{M}_\theta^q(\alpha)^s$ is generated as a $\mathbb{Q}$-algebra by the
tautological characteristic classes. In particular, the pure cohomology of
genus $g$ twisted character varieties of $GL_n$ is generated by tautological
classes.


  We consider the vector wave equation on the Schwarzschild spacetime, which
can be considered as coming from the harmonic (or Lorenz) gauge fixed Maxwell
equations. After a separation of variables, the radial mode equations form a
complicated system of coupled linear ODEs. We outline a precise abstract
strategy to decouple this system into triangular form, where the diagonal
blocks consist of spin-$s$ scalar Regge-Wheeler equations, with $s=0$ or $1$.
This strategy is then implemented to give an explicit transformation of the
radial mode equations (with nonzero frequency and angular momentum) into this
triangular form. Our decoupling goes a step further than previous results in
the literature by making the triangular form explicit and reducing it as much
as possible. Also, with the help of our abstractly formulated decoupling
strategy, we have significantly streamlined both the presentation of the final
results and the intermediate calculations. Finally, we note that the vector
wave equation is a simple model for more complicated equations, like harmonic
(or de Donder) gauge fixed linearized gravity, and backgrounds, like Kerr,
where we expect the same abstract decoupling strategy to work as well.


  The time-resolved spectra of gamma-ray bursts (GRBs) remain in conflict with
many proposed models for these events. After proving that most of the bursts in
our sample show evidence for spectral "shape-shifting", we discuss what
restrictions that BATSE time-resolved burst spectra place on current models. We
find that the synchrotron shock model does not allow for the steep low-energy
spectral slope observed in many bursts, including GRB 970111. We also determine
that saturated Comptonization with only Thomson thinning fails to explain the
observed rise and fall of the low-energy spectral slope seen in GRB 970111 and
other bursts. This implies that saturated Comptonization models must include
some mechanism which can cause the Thomson depth to increase intially in
pulses.


  Index Coding has received considerable attention recently motivated in part
by real-world applications and in part by its connection to Network Coding. The
basic setting of Index Coding encodes the problem input as an undirected graph
and the fundamental parameter is the broadcast rate $\beta$, the average
communication cost per bit for sufficiently long messages (i.e. the non-linear
vector capacity). Recent nontrivial bounds on $\beta$ were derived from the
study of other Index Coding capacities (e.g. the scalar capacity $\beta_1$) by
Bar-Yossef et al (2006), Lubetzky and Stav (2007) and Alon et al (2008).
However, these indirect bounds shed little light on the behavior of $\beta$:
there was no known polynomial-time algorithm for approximating $\beta$ in a
general network to within a nontrivial (i.e. $o(n)$) factor, and the exact
value of $\beta$ remained unknown for any graph where Index Coding is
nontrivial.
  Our main contribution is a direct information-theoretic analysis of the
broadcast rate $\beta$ using linear programs, in contrast to previous
approaches that compared $\beta$ with graph-theoretic parameters. This allows
us to resolve the aforementioned two open questions. We provide a
polynomial-time algorithm with a nontrivial approximation ratio for computing
$\beta$ in a general network along with a polynomial-time decision procedure
for recognizing instances with $\beta=2$. In addition, we pinpoint $\beta$
precisely for various classes of graphs (e.g. for various Cayley graphs of
cyclic groups) thereby simultaneously improving the previously known upper and
lower bounds for these graphs. Via this approach we construct graphs where the
difference between $\beta$ and its trivial lower bound is linear in the number
of vertices and ones where $\beta$ is uniformly bounded while its upper bound
derived from the naive encoding scheme is polynomially worse.


  In this paper, we introduce a model of a distributed storage system that is
locally recoverable from any single server failure. Unlike the usual local
recovery model of codes for distributed storage, this model accounts for the
fact that each server or storage node in a network is connectible to only some,
and not all other, nodes. This may happen for reasons such as physical
separation, inhomogeneity in storage platforms etc. We estimate the storage
capacity of both undirected and directed networks under this model and propose
some constructive schemes. From a coding theory point of view, we show that
this model is approximately dual of the well-studied index coding problem.
  Further in this paper, we extend the above model to handle multiple server
failures. Among other results, we provide an upper bound on the minimum
pairwise distance of a set of words that can be stored in a graph with the
local repair guarantee. The well-known impossibility bounds on the distance of
locally recoverable codes follow from our result.


  We describe the structure of bimodules (over finite dimensional algebras)
which have the property that the functor of tensoring with such a bimodule
sends any module to a projective module. The main result is that all such
bimodules are $\Bbbk$-split in the sense that they factor (inside the tensor
category of bimodules) over $\Bbbk$-vector spaces. As one application, we show
that any simple $2$-category has a faithful $2$-representation inside the
$2$-category of $\Bbbk$-split bimodules. As another application, we classify
simple transitive $2$-representations of the $2$-category of projective
bimodules over the algebra $\Bbbk[x,y]/(x^2,y^2,xy)$.


  Let $x,y\in(0,1]$ and let $A,B,C$ be disjoint nonempty subsets of a graph
$G$, where every vertex in $A$ has at least $x|B|$ neighbours in $B$, and every
vertex in $B$ has at least $y|C|$ neighbours in $C$. We denote by $\phi(x,y)$
the maximum $z$ such that, in all such graphs $G$, there is a vertex $v$ in $C$
that is joined to at least $z|A|$ vertices in $A$ by two-edge paths. The
function $\phi$ is interesting, and we investigate some of its properties. For
instance, we show that it is symmetric in $x$ and $y$, and that it has a
discontinuity at $x=y=1/k$ for all integers $k>1$. We raise a number of
questions and conjectures.


  In this paper we discuss the development of Rayleigh-Taylor filaments in
axisymmetric simulations of Pulsar wind nebulae (PWN). High-resolution adaptive
mesh refinement magnetohydrodynamic (MHD) simulations are used to resolve the
non-linear evolution of the instability. The typical separation of filaments is
mediated by the turbulent flow in the nebula and hierarchical growth of the
filaments. The strong magnetic dissipation and field-randomization found in
recent global three-dimensional simulations of PWN suggests that magnetic
tension is not strong enough to suppress the growth of RT filaments, in
agreement with the observations of prominent filaments in the Crab nebula. The
long-term axisymmetric results presented here confirm this finding.


  In the evolving era of Unmanned Aerial Vehicles (UAVs), the emphasis has
moved from mere data collection to strategically obtaining timely and relevant
data within the Internet of Drones (IoDs) ecosystem. However, the unpredictable
conditions in dynamic IoDs pose safety challenges for drones. Addressing this,
our approach introduces a multi-UAV framework using spatial-temporal clustering
and the Frechet distance for enhancing reliability. Seamlessly coupled with
Integrated Sensing and Communication (ISAC), it enhances the precision and
agility of UAV networks. Our Multi-Agent Reinforcement Learning (MARL)
mechanism ensures UAVs adapt strategies through ongoing environmental
interactions and enhancing intelligent sensing. This focus ensures operational
safety and efficiency, considering data capture and transmission viability. By
evaluating the relevance of the sensed information, we can communicate only the
most crucial data variations beyond a set threshold and optimize bandwidth
usage. Our methodology transforms the UAV domain, transitioning drones from
data gatherers to adept information orchestrators, establishing a benchmark for
efficiency and adaptability in modern aerial systems.


  Infrared spectroscopy has been used to investigate the temperature dependence
and c-axis magnetic field dependence of the Josephson plasma resonance in
optimally doped Tl2Ba2CaCu2O8 thin films, and underdoped YBa2Cu3O6.5 ortho - II
single crystals. The resonances are directly related to the c-axis penetration
depths, yielding low temperature, zero fields values of about 20 micrometer,
and 7 micrometer respectively. While the temperature dependencies of the
resonances in the two compounds are very similar, the magnetic field dependence
in YBCO is much weaker. We attribute this weak magnetic field dependence to the
lower anisotropy of YBCO, and discuss the observed behaviours in terms of
thermal fluctuations and pinning of pancake vortices, valid for highly
anisotropic layered superconductors.


  Emission of solar flares across the electromagnetic spectrum is often
observed in the form of two expanding ribbons. The standard flare model
explains the flare ribbons as footpoints of magnetic arcades, emitting due to
interaction of energetic particles with the chromospheric plasma. However, the
physics of this interaction and properties of the accelerated particles are
still unknown. We present results of multiwavelength observations of C2.1 flare
of August 15, 2013, observed with New Solar Telescope (NST) of Big Bear Solar
Observatory, Solar Dynamics Observatory (SDO), GOES and FERMI spacecraft. The
observations reveal previously unresolved sub-arcsecond structure of the flare
ribbons in regions of strong magnetic field consisting from numerous
small-scale bright knots. We observe red-blue asymmetry of H alpha flare
ribbons with a width as small as 100 km. We discuss the relationship between
the ribbons and vertical electric currents estimated from vector magnetograms,
and show that Joule heating can be responsible for energization of H alpha
knots in the ribbons.


  We consider heterotic Calabi-Yau compactifications with S(U(4)xU(1))
background gauge fields. These models lead to gauge groups with an additional
U(1) factor which, under certain conditions, can combine with hypercharge to a
B-L symmetry. The associated gauge boson is automatically super-massive and,
hence, does not constitute a phenomenological problem. We illustrate this class
of compactifications with a model based on the monad construction, which leads
to a supersymmetric standard model with three families of quarks and leptons,
one pair of Higgs doublets, three right-handed neutrinos and no exotics charged
under the standard model group. The presence of the B-L symmetry means that the
model is safe from proton decay induced by dimension four operators. Due to the
presence of a special locus in moduli space where the bundle structure group is
Abelian and the low-energy symmetry enhances we can also show the absence of
dimension five proton-decay inducing operators.


  Building socially-intelligent AI agents (Social-AI) is a multidisciplinary,
multimodal research goal that involves creating agents that can sense,
perceive, reason about, learn from, and respond to affect, behavior, and
cognition of other agents (human or artificial). Progress towards Social-AI has
accelerated in the past decade across several computing communities, including
natural language processing, machine learning, robotics, human-machine
interaction, computer vision, and speech. Natural language processing, in
particular, has been prominent in Social-AI research, as language plays a key
role in constructing the social world. In this position paper, we identify a
set of underlying technical challenges and open questions for researchers
across computing communities to advance Social-AI. We anchor our discussion in
the context of social intelligence concepts and prior progress in Social-AI
research.


  It is commonly admitted that non-reversible Markov chain Monte Carlo (MCMC)
algorithms usually yield more accurate MCMC estimators than their reversible
counterparts. In this note, we show that in addition to their variance
reduction effect, some non-reversible MCMC algorithms have also the undesirable
property to slow down the convergence of the Markov chain. This point, which
has been overlooked by the literature, has obvious practical implications. We
illustrate this phenomenon for different non-reversible versions of the
Metropolis-Hastings algorithm on several discrete state space examples and
discuss ways to mitigate the risk of a small asymptotic variance/slow
convergence scenario.


  In this note, we revisit an iterative scheme, due to Abedin and Kitagawa
(Inverse Iteration for the Monge-Amp\`ere Eigenvalue Problem, Proc. Amer. Math.
Soc. 148 (2020), no. 11, 4875--4886), to solve the Monge-Amp\`ere eigenvalue
problem on a general bounded convex domain. Using a nonlinear integration by
parts, we show that the scheme converges for all convex initial data having
finite and nonzero Rayleigh quotient to a nonzero Monge-Amp\`ere eigenfunction.


  We calculate the strength of the effective onsite Coulomb interaction
(Hubbard $U$) in two-dimensional (2D) transition-metal (TM) dihalides MX$_2$
and trihalides MX$_3$ (M=Ti, V, Cr, Mn, Fe, Co, Ni; X=Cl, Br, I) from first
principles using the constrained random-phase approximation. The correlated
subspaces are formed from $t_{2g}$ or $e_g$ bands at the Fermi energy.
Elimination of the efficient screening taking place in these narrow bands gives
rise to sizable interaction parameters U between the localized $t_{2g}$ ($e_g$)
electrons. Due to this large Coulomb interaction, we find $U/W >1$ (with the
band width $W$) in most TM halides, making them strongly correlated materials.
Among the metallic TM halides in paramagnetic state, the correlation strength
$U/W$ reaches a maximum in NiX$_2$ and CrX$_3$ with values much larger than the
corresponding values in elementary TMs and other TM compounds. Based on the
Stoner model and the calculated $U$ and $J$ values, we discuss the tendency of
the electron spins to order ferromagnetically.


  We calculate the drag coefficient of a circular liquid domain, which is put
in a flat fluid membrane composed of a binary fluid mixture lying in the
homogeneous phase near the demixing critical point. Assuming a sufficiently
small correlation length, we regard the domain dynamics as independent of the
critical fluctuation and use the Gaussian free-energy functional for the
mixture. Because of the near-criticality, the preferential attraction between
the domain component and one of the mixture components generates the
composition gradient outside the domain significantly and can affect the drag
coefficient. We first consider a domain having the same membrane viscosity as
the domain exterior. The drag coefficient is expanded with respect to a
dimensionless strength of the preferential attraction. It is numerically shown
that the magnitude of the expansion coefficient decreases much as the order of
the strength increases and that the first-order term of the series usually
gives a good approximation for practical material constants. The effect of the
preferential attraction is shown to be able to become significantly large in
practice. We second consider cases where the membrane viscosities of the domain
interior and exterior are different. The first-order term of the expansion
series decreases to approach zero as the domain viscosity increases to
infinity. This agrees with previous numerical results showing that the
hydrodynamics makes the effect of the preferential attraction negligibly small
for a rigid disk.


  Spatial characteristics of the optical-vortex (OV) beams created during the
Gaussian beam diffraction by a grating with groove bifurcation are analyzed
theoretically and numerically. In contrast to previous works, condition of
small-angle diffraction is no longer required and the diffracted beam can be
strongly deformed. This causes the intensity profile rotation and the
high-order OV decomposition into a set of secondary single-charged OVs. These
effects are studied quantitatively and confronted with similar properties of a
Laguerre-Gaussian beam that undergoes astigmatic telescopic transformation. In
contrast to the latter case, the secondary OVs do not lie on a single straight
line within the beam cross section, and morphology parameters of the individual
secondary OVs carried by the same beam are, in general, different. Conditions
for maximum relative separation of the secondary OVs with respect to the beam
transverse size are specified. The results can be used for practical generation
of OV beams and OV arrays with prescribed properties.


  The purpose of this study was to develop a method for tissue elasticity
imaging using tagged magnetic resonance imaging (MRI). First, we developed a
cyclic pressure device that used air to remotely transmit the power to generate
cyclic deformation in an object. The pressure induced by the cyclic pressure
device was measured by MRI-compatible force sensors. Second, we developed a
software to calculate Young's modulus from tagged MRI data using the harmonic
phase (HARP) method and the finite element method (FEM). We also developed a
software to extract tag-cross points from tagged MRI data. Finally, we
evaluated the usefulness of our method using three homogeneous silicone gel
phantoms with different degrees of stiffness in comparison with Young's moduli
measured by a material testing machine. The coefficient of variation of the
pressure data measured by MRI-compatible force sensors was within 5 %,
indicating that the reproducibility of the pressure generated by our cyclic
pressure device was good. The Young's moduli obtained by the material testing
machine, the HARP method, and the FEM increased with increasing stiffness of
the phantoms. There were relatively good agreements among the three methods.
These results suggest that our method is useful for tissue elasticity imaging
and for quantifying the stiffness of tissues.


  Over the past decade, advancement of observational capabilities, specifically
the Atacama Large Millimeter/submillimeter Array (ALMA) and SPHERE instrument,
alongside theoretical innovations like pebble accretion, have reshaped our
understanding of planet formation and the physics of protoplanetary disks.
Despite this progress, mysteries persist along the winded path of
micrometer-sized dust, from the interstellar medium, through transport and
growth in the protoplanetary disk, to becoming gravitationally bound bodies.
This review outlines our current knowledge of dust evolution in circumstellar
disks, yielding the following insights:
  $\bullet$ Theoretical and laboratory studies have accurately predicted the
growth of dust particles to sizes that are susceptible to accumulation through
transport processes like radial drift and settling.
  $\bullet$ Critical uncertainties in that process remain the level of
turbulence, the threshold collision velocities at which dust growth stalls, and
the evolution of dust porosity.
  $\bullet$ Symmetric and asymmetric substructure are widespread. Dust traps
appear to be solving several long-standing issues in planet formation models,
and they are observationally consistent with being sites of active planetesimal
formation.
  $\bullet$ In some instances, planets have been identified as the causes
behind substructures. This underlines the need to study earlier stages of disks
to understand how planets can form so rapidly.
  In the future, better probes of the physical conditions in optically thick
regions, including densities, turbulence strength, kinematics, and particle
properties will be essential for unraveling the physical processes at play.


  Context. Stellar shells, which form axially symmetric systems of arcs in some
elliptical galaxies, are most likely remnants of radial minor mergers. They are
observed up a radius of $\sim$100 kpc. The stars in them oscillate in radial
orbits. The radius of a shell depends on the free-fall time at the position of
the shell and on the time since the merger. We previously verified the
consistency of shell radii in the elliptical galaxy NGC 3923 with its most
probable MOND potential. Our results implied that an as~yet undiscovered shell
exists at the outskirts of the galaxy.
  Aims. We here extend our study by assuming more general models for the
gravitational potential to verify the prediction of the new shell and to
estimate its position.
  Methods. We tested the consistency of the shell radial distribution observed
in NGC 3923 with a wide variety of MOND potentials of the galaxy. The
potentials differed in the mass-to-light ratio and in distance to the galaxy.
We considered different MOND interpolation functions, values of the
acceleration constant $a_0$, and density profiles of the galaxy. We verified
the functionality of our code on a Newtonian self-consistent simulation of the
formation of a shell galaxy.
  Results. Our method reliably predicts that exactly one new outermost shell
exists at a galactocentric radius of about 1900$^{\prime\prime}$ ($\sim$210
kpc) on the southwestern side of the galaxy. Its estimated surface brightness
is about 28 mag arcsec$^{-2}$ in $B$ - a value accessible by current
instruments. This prediction enables a rare test of MOND in an elliptical down
to an acceleration of $a_0/10$. The predictive power of our method is verified
by reconstructing the position of the largest known shell from the distribution
of the remaining shells.


  Dark matter density can be significantly enhanced by the supermassive black
hole at the galactic center, leading to a structure called dark matter spike.
Dark matter spike may change the spacetime properties of black holes that
constitute deviations from GR black holes. Based on these interesting
background, we construct a set of solutions of black holes in a dark matter
spike under the Newtonian approximation and full relativity. Combining the mass
model of M87, we study the quasinormal modes of black holes in the scalar field
and axial gravitational perturbation, then compared them with Schwarzschild
black hole. Besides, the impacts of dark matter on the quasinormal mode of
black holes have been studied in depth. In particular, in the axial
gravitational perturbation, our results show that the impacts of dark matter
spike on the quasinormal mode of black holes can reach up to $10^{-4}$. These
new features from quasinormal mode of black holes under the Newtonian
approximation and full relativity may provide some help for the establishment
of the final dark matter model, and provide a new thought for the indirect
detection of dark matter.


  We performed calibrations of four different commercial picoammeters using the
Ultrastable Low-noise Current Amplifier (ULCA) as a calibrator current source
operated in the range between 1 femtoampere and 1 microampere. The results
allow the comprehensive characterization of the devices under test regarding
noise, settling and burden voltage behavior as well as stability of the gain
factor, and confirm the performance of the ULCA for use as small-current
calibrator standard. Also, we present a further advanced setup for the
calibration of transimpedance amplifiers. Accuracy limits for best electrometer
calibrations in the current range between 1 femtoampere and 1 microampere and
possible implications on corresponding calibration and measurement capabilities
are discussed.


  We prove that there is off-diagonal long-range order in the symmetrised
version of the one-dimensional many-body problem presented by Jain and Khare
(Phys. Lett. A262 (1999)35). This model is related to the short-range Dyson
model employed to study intermediate statistics in systems like the Anderson
model in three dimensions at the metal-insulator transition point and
pseudointegrable billiards. To the best of our knowledge, this is the only
example showing quantum phases and possibility of Bose-Einstein condensation in
one-dimensional statistical mechanics.


  A method of consideration of gaussian fluctuations of the effective mean
field within the framework of the GRPA scheme is applied to investigation of
thermodynamical properties of a pseudospin-electron model (PEM).
  The grand canonical potential, pseudospin mean value, as well as the mean
squares of fluctuations are calculated.
  Obtained results are compared with corresponding ones obtained by other
approximations.
  An influence of the gaussian fluctuations of mean field on the thermodynamic
properties of PEM is discussed.


  The transport properties of Chern insulator junctions generated by bipolar
junctions in quantum Hall graphene are theoretically studied in the coherent
regime. Coherent transport across the junction exhibits two mesoscopic
features: valley-isospin dependence of the quantum Hall conductance, and the
Aharonov-Bohm (AB) effects with the interface channels. We demonstrate that the
valley-isospin dependence can be measured in a graphene sample with perfect
edge terminations, resulting in conductance oscillation for the smallest Chern
number case. On the other hand, while conductance plateaus are found to be
unclear for larger Chern numbers, the conductance exhibits an oscillatory
behavior of which period is relatively longer than the valley-isospin dependent
oscillation. This conductance oscillation is ascribed to the AB effect, which
is implicitly created by the split metallic channels near the junction
interface. We point out that a possible origin of the unclear plateaus
previously speculated to be incompleteness in realistic devices is the
low-visibility conductance oscillation due to unequal beam splitting.


  Vision-based cooperative motion estimation is an important problem for many
multi-robot systems such as cooperative aerial target pursuit. This problem can
be formulated as bearing-only cooperative motion estimation, where the visual
measurement is modeled as a bearing vector pointing from the camera to the
target. The conventional approaches for bearing-only cooperative estimation are
mainly based on the framework distributed Kalman filtering (DKF). In this
paper, we propose a new optimal bearing-only cooperative estimation algorithm,
named spatial-temporal triangulation, based on the method of distributed
recursive least squares, which provides a more flexible framework for designing
distributed estimators than DKF. The design of the algorithm fully incorporates
all the available information and the specific triangulation geometric
constraint. As a result, the algorithm has superior estimation performance than
the state-of-the-art DKF algorithms in terms of both accuracy and convergence
speed as verified by numerical simulation. We rigorously prove the exponential
convergence of the proposed algorithm. Moreover, to verify the effectiveness of
the proposed algorithm under practical challenging conditions, we develop a
vision-based cooperative aerial target pursuit system, which is the first of
such fully autonomous systems so far to the best of our knowledge.


  We analyze the critical transitions (a) to endemic states in an SIS
epidemiological model, and (b) to full synchronization in an ensemble of
coupled chaotic maps, on networks where, at any given time, each node is
connected to just one neighbour. In these "monogamous" populations, the lack of
connectivity in the instantaneous interaction pattern -that would prevent both
the propagation of an infection and the collective entrainment into
synchronization- is compensated by occasional random reconnections which
recombine interacting couples by exchanging their partners. The transitions to
endemic states and to synchronization are recovered if the recombination rate
is sufficiently large, thus giving rise to a bifurcation as this rate varies.
We study this new critical phenomenon both analytically and numerically.


  We recall the concept of Baxterisation of an R-matrix, or of a monodromy
matrix, which corresponds to build, from one point in the $ R$-matrix parameter
space, the algebraic variety where the spectral parameter(s) live. We show that
the Baxterisation, which amounts to studying the iteration of a birational
transformation, is a ``win-win'' strategy: it enables to discard efficiently
the non-integrable situations, focusing directly on the two interesting cases
where the algebraic varieties are of the so-called ``general type'' (finite
order iteration) or are Abelian varieties (infinite order iteration). We
emphasize the heuristic example of the sixteen vertex model and provide a
complete description of the finite order iterations situations for the Baxter
model. We show that the Baxterisation procedure can be introduced in much
larger frameworks where the existence of some underlying Yang-Baxter structure
is not used: we Baxterise L-operators, local quantum Lax matrices, and quantum
Hamiltonians.


  The second-type derivative nonlinear Schr\"odinger (DNLSII) equation was
introduced as an integrable model in 1979. Very recently, the DNLSII equation
has been shown by an experiment to be a model of the evolution of optical
pulses involving self-steepening without concomitant self-phase-modulation. In
this paper the $n$-fold Darboux transformation (DT) $T_n$ of the coupled DNLSII
equations is constructed in terms of determinants. Comparing with the usual DT
of the soliton equations, this kind of DT is unusual because $T_n$ includes
complicated integrals of seed solutions in the process of iteration. By a
tedious analysis, these integrals are eliminated in $T_n$ except the integral
of the seed solution. Moreover, this $T_n$ is reduced to the DT of the DNLSII
equation under a reduction condition. As applications of $T_n$, the explicit
expressions of soliton, rational soliton, breather, rogue wave and multi-rogue
wave solutions for the DNLSII equation are displayed.


  In this work, we present a general framework for building a biometrics system
capable of capturing multispectral data from a series of sensors synchronized
with active illumination sources. The framework unifies the system design for
different biometric modalities and its realization on face, finger and iris
data is described in detail. To the best of our knowledge, the presented design
is the first to employ such a diverse set of electromagnetic spectrum bands,
ranging from visible to long-wave-infrared wavelengths, and is capable of
acquiring large volumes of data in seconds. Having performed a series of data
collections, we run a comprehensive analysis on the captured data using a
deep-learning classifier for presentation attack detection. Our study follows a
data-centric approach attempting to highlight the strengths and weaknesses of
each spectral band at distinguishing live from fake samples.


  The convolutional layer and loss function are two fundamental components in
deep learning. Because of the success of conventional deep learning kernels,
the less versatile Gabor kernels become less popular despite the fact that they
can provide abundant features at different frequencies, orientations, and
scales with much fewer parameters. For existing loss functions for multi-class
image segmentation, there is usually a tradeoff among accuracy, robustness to
hyperparameters, and manual weight selections for combining different losses.
Therefore, to gain the benefits of using Gabor kernels while keeping the
advantage of automatic feature generation in deep learning, we propose a fully
trainable Gabor-based convolutional layer where all Gabor parameters are
trainable through backpropagation. Furthermore, we propose a loss function
based on the Pearson's correlation coefficient, which is accurate, robust to
learning rates, and does not require manual weight selections. Experiments on
43 3D brain magnetic resonance images with 19 anatomical structures show that,
using the proposed loss function with a proper combination of conventional and
Gabor-based kernels, we can train a network with only 1.6 million parameters to
achieve an average Dice coefficient of 83%. This size is 44 times smaller than
the original V-Net which has 71 million parameters. This paper demonstrates the
potentials of using learnable parametric kernels in deep learning for 3D
segmentation.


  We investigate learning difficulties among second-year students on
electromagnetism courses when they apply Amp\`ere-Maxwell's law. Using
phenomenography, we analysed written answers from 65 undergraduate physics
students to four questions on Amp\`ere's and Amp\`ere-Maxwell's laws. We
complemented our research by interviewing twelve students. To design the
questionnaire, we ran an epistemological analysis of classical electromagnetism
which helped us to identify a set of key essential concepts to understand this
theory and guided the definition of learning objectives and to draw up the
questions. The results revealed that the students found it hard to recognise
the validity framework from Amp\`ere's law and to apply Amp\`ere-Maxwell's law.
They face particular difficulties to recognise the appearance of the
displacement current and the relationship between the circulation of the
magnetic field and an electric field that is variable over time.


  Knowledge graph (KG) embedding is a fundamental task in natural language
processing, and various methods have been proposed to explore semantic patterns
in distinctive ways. In this paper, we propose to learn an ensemble by
leveraging existing methods in a relation-aware manner. However, exploring
these semantics using relation-aware ensemble leads to a much larger search
space than general ensemble methods. To address this issue, we propose a
divide-search-combine algorithm RelEns-DSC that searches the relation-wise
ensemble weights independently. This algorithm has the same computation cost as
general ensemble methods but with much better performance. Experimental results
on benchmark datasets demonstrate the effectiveness of the proposed method in
efficiently searching relation-aware ensemble weights and achieving
state-of-the-art embedding performance. The code is public at
https://github.com/LARS-research/RelEns.


  A 25 $\rm deg^2$ region, including the M81 complex (M81, M82, NGC 3077), NGC
2976 and IC2574, was mapped during ~3000 hours with the DRAO synthesis
telescope. With a physical resolution of ~1 kpc, these observations allow us to
probe a large region down to column density levels of ~$\rm 1\times10^{18}\,
cm^{-2}$ over 16 $\rm km\, s^{-1}$, mapping the extent of the HI arm connecting
the system and NGC 2976, and resolving the HI clouds adjacent to the arm. The
observations also reveal a few clouds located between the system and IC 2574,
probably tidally stripped from a past interaction between the two systems.
Given the regular velocity distribution in the HI envelope of the system, we
attempt and derive an idealised large-scale rotation curve of the system. We
observe a flat trend for the rotation velocity of the overall system from 20
kpc out to 80 kpc, well beyond the outskirts of the M81 disk, although with
asymmetries like a wiggle at the vicinity of M82. This supports the assumption
that intergalactic gas and galaxies in the system participate to a large-scale
ordered rotation motion which is dominated by M81. Also, our HI analysis of the
group further supports the hypothesis that the galaxies forming the system
moved closer from afar, in agreement with numerical simulations.


  We recall the notion of abstract bornology, and connect it with topological
spaces and size functions. As a generalization of measures of non-compactness,
we show how every size function can be mapped to a maxitive measure.


  Data centers have been evolved from a passive element of compute
infrastructure to become an active, core part of any ICT solution. In
particular, modular data centers (MDCs), which are a promising design approach
to improve resiliency of data centers, can play a key role in deploying ICT
infrastructure in remote and inhospitable environments in order to take
advantage of low temperatures and hydro- and wind-electric capabilities. This
is because of capability of the modular data centers to survive even in lack of
continuous on-site maintenance and support. The most critical part of a data
center is its network fabric that could impede the whole system even if all
other components are fully functional, assuming that other analyses has been
already performed to ensure the reliability of the underlying infrastructure
and support systems. In this work, a complete failure analysis of modular data
centers using failure models of various components including servers, switches,
and links is performed using a proposed Monte-Carlo approach. The proposed
Monte-Carlo approach, which is based on the concept of snapshots, allows us to
effectively calculate the performance of a design along its lifespan even up to
the terminal stages. To show the capabilities of the proposed approach, various
network topologies, such as FatTree, BCube, MDCube, and their modifications are
considered. The performance and also the lifespan of each topology design in
presence of failures of their components are studied against the topology
parameters.


  Unopposed radiative cooling in clusters of galaxies results in excessive mass
deposition rates. However, the cool cores of galaxy clusters are continuously
heated by thermal conduction and turbulent heat diffusion due to minor mergers
or the galaxies orbiting the cluster center. These processes can either reduce
the energy requirements for AGN heating of cool cores, or they can prevent
overcooling altogether. We perform 3D MHD simulations including field-aligned
thermal conduction and self-gravitating particles to model this in detail.
Turbulence is not confined to the wakes of galaxies but is instead
volume-filling, due to the excitation of large-scale g-modes. We systematically
probe the parameter space of galaxy masses and numbers. For a wide range of
observationally motivated galaxy parameters, the magnetic field is randomized
by stirring motions, restoring the conductive heat flow to the core. The
cooling catastrophe either does not occur or it is sufficiently delayed to
allow the cluster to experience a major merger that could reset conditions in
the intracluster medium. Whilst dissipation of turbulent motions is negligible
as a heat source, turbulent heat diffusion is extremely important; it
predominates in the cluster center. However, thermal conduction becomes
important at larger radii, and simulations without thermal conduction suffer a
cooling catastrophe. Conduction is important both as a heat source and to
reduce stabilizing buoyancy forces, enabling more efficient diffusion.
Turbulence enables conduction, and conduction enables turbulence. In these
simulations, the gas vorticity---which is a good indicator of trapped
g-modes--increases with time. The vorticity growth is approximately mirrored by
the growth of the magnetic field, which is amplified by turbulence.


  For a sample of ten nearby barred galaxies, images at 6.75\um and 15\um of
circumnuclear starbursts are being taken by ISOCAM on board of the ISO
satellite. We first recall the main goals of our project, give its current
status, and finally, as an example, preliminary results for NGC 4321 (M100) are
presented.