  The ability to seamlessly switch between the macro networks and femtocell
networks is a key driver for femtocell network deployment. The handover
procedures for the integrated femtocell/macrocell networks differ from the
existing handovers. Some modifications of existing network and protocol
architecture for the integration of femtocell networks with the existing
macrocell networks are also essential. These modifications change the signal
flow for handover procedures due to different 2-tier cell (macrocell and
femtocell) environment. The handover between two networks should be performed
with minimum signaling. A frequent and unnecessary handover is another problem
for hierarchical femtocell/macrocell network environment that must be
minimized. This work studies the details mobility management schemes for small
and medium scale femtocell network deployment. To do that, firstly we present
two different network architectures for small scale and medium scale WCDMA
femtocell deployment. The details handover call flow for these two network
architectures and CAC scheme to minimize the unnecessary handovers are proposed
for the integrated femtocell/macrocell networks. The numerical analysis for the
proposed M/M/N/N queuing scheme and the simulation results of the proposed CAC
scheme demonstrate the handover call control performances for femtocell
environment.


  Holographic communications are gaining ground among emerging eXtended-Reality
(XR) applications due to their potential to revolutionize human communication.
However, these technologies are characterized by higher requirements in terms
of Quality of Service (QoS), such as high transmission data rates, very low
latency, and high computation capacity, challenging current achievable
capabilities. In this context, computation offloading techniques are being
investigated, where resource-intensive computational tasks, like rendering XR
experiences, are shifted from user devices to a separate processor,
specifically an Edge Computing instance. This paper introduces an Edge
Rendering architecture for multiuser XR experiences, implements it on top of
widely employed XR and Web technologies, and proposes a method based on image
and audio processing to evaluate its performance in terms of end-to-end media
streaming latency, inter-device, and intra-media synchronization when employing
different access networks.


  In [4] and [5], we generalized the concept of completion of an infinitesimal
group action $\zeta : {\mathfrak g} \to \mathfrak X (M)$ to an actual group
action on a (non-compact) manifold $M$, originally introduced by R. Palais [9],
and showed by examples that this completion may have quite pathological
properties (much like the leaf space of a foliation). In the present paper, we
introduce and investigate a tamer class of $\mathfrak g$-manifolds, called
orbifold--like, for which the completion has an orbifold structure. This class
of $\mathfrak g$-manifolds is reasonably well-behaved with respect to its local
topological and smooth structure to allow for many geometric constructions to
make sense. In particular, we investigate proper $\mathfrak g$-actions and
generalize many of the usual properties of proper group actions to this more
general setting.


  We present a theory-inspired visual narrative generator that incorporates
comic-authoring idioms, which transfers the conceptual principles of comics
into system layers that integrate the theories to create comic content. The
generator creates comics through sequential decision-making across layers from
panel composition, object positions, panel transitions, and narrative elements.
Each layer's decisions are based on narrative goals and follow the respective
layer idioms of the medium. Cohn's narrative grammar provides the overall story
arc. Photographic compositions inspired by the rule of thirds is used to
provide panel compositions. McCloud's proposed panel transitions based on focus
shifts between scene, character, and temporal changes are encoded in the
transition layer. Finally, common overlay symbols (such as the exclamation) are
added based on analyzing action verbs using an action-verb ontology. We
demonstrate the variety of generated comics through various settings with
example outputs. The generator and associated modules could be a useful system
for visual narrative authoring and for further research into computational
models of visual narrative understanding.


  We compute the on-shell wave function renormalization constant to four-loop
order in QCD and present numerical results for all coefficients of the
SU$(N_c)$ colour factors. We extract the four-loop HQET anomalous dimension of
the heavy quark field and also discuss the application of our result to QED.


  We consider the nonlinear ion-acoustic wave induced by the orbiting charged
space debris in the plasma environment generated at Low Earth Orbital (LEO)
region. The generated nonlinear ion-acoustic wave is shown to be governed by
the forced Korteweg-de Vries equation with the forcing function dependent on
the charged space debris function. For a specific relationship between the
forcing debris function and the nonlinear ion-acoustic wave, the forced KdV
equation turns to be a completely integrable system where the debris function
obeys a definite non-holonomic constraint. A special exact accelerated soliton
solution (velocity of the soliton changes over time whereas its amplitude
remains constant) has been derived for the ion-acoustic wave for the first
time. On the other hand, the amplitude of the solitonic debris function varies
with time, and its shape changes during propagation. Approximate ion-acoustic
solitary wave solutions with time-varying amplitude and velocity, have been
derived for different weak localized charged debris functions. Possible
applications of the obtained results in space plasma physics are stated along
with future the direction of research.


  Space-time covariance modeling under the Lagrangian framework has been
especially popular to study atmospheric phenomena in the presence of transport
effects, such as prevailing winds or ocean currents, which are incompatible
with the assumption of full symmetry. In this work, we assess the dimple
problem (Kent et al., 2011) for covariance functions coming from transport
phenomena. We work under two important cases: the spatial domain can be either
the $d$-dimensional Euclidean space $\mathbb{R}^d$ or the spherical shell of
$\mathbb{R}^d$. The choice is relevant for the type of metric chosen to
describe spatial dependence. In particular, in Euclidean spaces, we work under
very general assumptions with the case of radial symmetry being deduced as a
corollary of a more general result. We illustrate through examples that, under
this framework, the dimple is a natural and physically interpretable property.


  Classical objectivity as a property of quantum states---a view proposed to
explain the observer-independent character of our world from quantum theory, is
an important step in bridging the quantum-classical gap. It was recently
derived in terms of spectrum broadcast structures for small objects embedded in
noisy photon-like environments. However, two fundamental problems have arisen:
a description of objective motion and applicability to other types of
environments. Here we derive an example of objective states of motion in
quantum mechanics by showing a formation of dynamical spectrum broadcast
structures in the celebrated, realistic model of decoherence---Quantum Brownian
Motion. We do it for realistic, thermal environments and show their
noise-robustness. This opens a potentially new method of studying
quantum-to-classical transition.


  The complexity of biological systems and processes, spanning molecular to
macroscopic scales, necessitates the use of multiscale simulations to get a
comprehensive understanding. Quantum mechanics/molecular mechanics (QM/MM)
molecular dynamics (MD) simulations are crucial for capturing processes beyond
the reach of classical MD simulations. The advent of exascale computing offers
unprecedented opportunities for scientific exploration, not least within life
sciences, where simulations are essential to unravel intricate molecular
mechanisms underlying biological processes. However, leveraging the immense
computational power of exascale computing requires innovative algorithms and
software designs. In this context, we discuss the current status and future
prospects of multiscale biomolecular simulations on exascale supercomputers
with a focus on QM/MM MD. We highlight our own efforts in developing a
versatile and high-performance multiscale simulation framework with the aim of
efficient utilization of state-of-the-art supercomputers. We showcase its
application in uncovering complex biological mechanisms and its potential for
leveraging exascale computing.


  We present Column2Vec, a distributed representation of database columns based
on column metadata. Our distributed representation has several applications.
Using known names for groups of columns (i.e., a table name), we train a model
to generate an appropriate name for columns in an unnamed table. We demonstrate
the viability of our approach using schema information collected from open
source applications on GitHub.


  This paper presents two sufficient conditions to ensure a faithful evaluation
of polynomial in IEEE-754 floating point arithmetic. Faithfulness means that
the computed value is one of the two floating point neighbours of the exact
result; it can be satisfied using a more accurate algorithm than the classic
Horner scheme. One condition here provided is an apriori bound of the
polynomial condition number derived from the error analysis of the compensated
Horner algorithm. The second condition is both dynamic and validated to check
at the running time the faithfulness of a given evaluation. Numerical
experiments illustrate the behavior of these two conditions and that associated
running time over-cost is really interesting.


  We propose a novel vector representation that integrates lexical contrast
into distributional vectors and strengthens the most salient features for
determining degrees of word similarity. The improved vectors significantly
outperform standard models and distinguish antonyms from synonyms with an
average precision of 0.66-0.76 across word classes (adjectives, nouns, verbs).
Moreover, we integrate the lexical contrast vectors into the objective function
of a skip-gram model. The novel embedding outperforms state-of-the-art models
on predicting word similarities in SimLex-999, and on distinguishing antonyms
from synonyms.


  We study rigidity properties of lattices in terms of invariant means and
commensurating actions (or actions on CAT(0) cube complexes). We notably study
Property FM for groups, namely that any action on a discrete set with an
invariant mean has a finite orbit.


  A deep survey of the European Large Area ISO Survey (ELAIS) field in the
southern celestial hemisphere (hereinafter S1) has been carried out with the
Australia Telescope Compact Array (ATCA) at 1.4 GHz. The S1 region, covering
about 4 square degrees, has been also surveyed in the mid- and far-infrared
(5-200 micron) with the Infrared Space Observatory (ISO). The radio survey
provides uniform coverage of the entire S1 region, with a sensitivity (5 sigma)
of 0.4 mJy over the whole area and 0.2 mJy in the center. To this sensitivity,
virtually all the radio counterparts of the far-infrared extragalactic ISO
sources should be detected. This paper presents a radio sample - complete at
the 5 sigma level - consisting of 581 sources detected at 1.4 GHz. Of these
radio sources, 349 have peak flux density in the range 0.2-1 mJy, forming a new
homogeneous sample of sub-mJy radio sources. Due to its size, depth and
multi-waveband coverage, the sample will allow us to study in greater detail
the sub-mJy radio source population. The full catalogue will be available from
http://athena.ph.ac.uk/


  It has been known for a long time that Morris-Thorne wormholes can only be
held open by violating the null energy condition, which can be expressed in the
form $\tau-\rho c^2>0$, where $\tau$ is the radial tension. Matter that
violates this condition is usually referred to as "exotic." For any wormhole
having a moderately-sized throat, the radial tension is equal to that at the
center of a massive neutron star. Attributing this outcome to exotic matter
seems reasonable enough, but it ignores the fact that exotic matter was
introduced for a completely different reason. Moreover, its problematical
nature suggests that the amount of exotic matter be held to a minimum, but this
would make the high radial tension harder to explain. If the amount is
infinitely small, this explanation breaks down entirely. By invoking $f(R)$
modified gravity, the need for exotic matter at the throat could actually be
eliminated, but the negation of the above condition, i.e., $\tau <\rho c^2$,
shows that we have not necessarily eliminated the high radial tension. This
survey discusses various ways to account for the high radial tension and, in
some cases, the possible origin of exotic matter. We conclude with some
comments on a possible multiply-connected Universe.


  Despite the increasing interest in multi-agent reinforcement learning (MARL)
in multiple communities, understanding its theoretical foundation has long been
recognized as a challenging problem. In this work, we address this problem by
providing a finite-sample analysis for decentralized batch MARL with networked
agents. Specifically, we consider two decentralized MARL settings, where teams
of agents are connected by time-varying communication networks, and either
collaborate or compete in a zero-sum game setting, without any central
controller. These settings cover many conventional MARL settings in the
literature. For both settings, we develop batch MARL algorithms that can be
implemented in a decentralized fashion, and quantify the finite-sample errors
of the estimated action-value functions. Our error analysis captures how the
function class, the number of samples within each iteration, and the number of
iterations determine the statistical accuracy of the proposed algorithms. Our
results, compared to the finite-sample bounds for single-agent RL, involve
additional error terms caused by decentralized computation, which is inherent
in our decentralized MARL setting. This work appears to be the first
finite-sample analysis for batch MARL, a step towards rigorous theoretical
understanding of general MARL algorithms in the finite-sample regime.


  The van der Waals material CrSBr stands out as a promising two-dimensional
magnet. Especially, its high magnetic ordering temperature and versatile
magneto-transport properties make CrSBr an important candidate for new devices
in the emergent field of two-dimensional magnetic materials. To date, the
magnetic and structural properties of CrSBr have not been fully elucidated.
Here, we report on the detailed temperature-dependent magnetic and structural
properties of this material, by comprehensively combining neutron scattering,
muon spin relaxation spectroscopy, synchrotron X-ray diffraction, and
magnetization measurements. We evidence that this material undergoes a
transition to an A-type antiferromagnetic state below $T_{\rm N} \approx$ 140
K, with a pronounced two-dimensional character as deduced from the determined
critical exponent of $\beta \approx $ 0.18. In our analysis of the
field-induced metamagnetic transition, we find that the ferromagnetic
correlations within the monolayers persist clearly above the N\'eel temperature
in this material. Furthermore, we unravel the low-temperature (i.e. $T < T_{\rm
N}$) magnetic hidden order within the long-range magnetically ordered state. We
find that it is associated to a slowing down of the magnetic fluctuations,
accompanied by a continuous reorientation of the internal magnetic field. These
take place upon cooling below $T_s$ $\approx$ 100 K, until a spin freezing
process occurs at $T$* $\approx$ 40 K. We argue this complex dynamic behavior
to reflect a magnetic crossover driven by the in-plane uniaxial anisotropy,
which is ultimately caused by the mixed-anion character of the material. Our
findings indicate that the magnetic and structural properties of CrSBr widen
its potential application as a component for spin-based electronic devices.


  We present a study of certain singular one-parameter subfamilies of
Calabi-Yau threefolds realized as anticanonical hypersurfaces or complete
intersections in toric varieties. Our attention to these families is motivated
by the Doran-Morgan classification of variations of Hodge structure which can
underlie families of Calabi-Yau threefolds with $h^{2,1} = 1$ over the
thrice-punctured sphere. We explore their torically induced fibrations by
$M$-polarized K3 surfaces and use these fibrations to construct an explicit
geometric transition between an anticanonical hypersurface and a nef complete
intersection through a singular subfamily of hypersurfaces. Moreover, we show
that another singular subfamily provides a geometric realization of the missing
"14th case" variation of Hodge structure from the Doran-Morgan list.


  Quadratic nodal point (QNP) in two dimensions has so far been reported only
in nonmagnetic materials and in the absence of spin-orbit coupling. Here, by
first-principles calculations and symmetry analysis, we predict stable QNP near
Fermi level in a two-dimensional kagome metal-organic framework material,
Cr$_3$(HAB)$_2$, which features noncollinear antiferromagnetic ordering and
sizable spin-orbit coupling. Effective kp and lattice models are constructed to
capture such magnetic QNPs. Besides QNP, we find Cr$_3$(HAB)$_2$ also hosts six
magnetic linear nodal points protected by mirror as well as $C_{2z}T$ symmetry.
Properties associated to these nodal points, such as topological edge states
and quantized optical absorbance, are discussed.


  Quantum algorithms are a promising framework for a proper treatment of
Feynman loop integrals due to the existence of a manifestly causal
representation scenario. Particularly, unfolding causal configurations of
multiloop Feynman diagrams is understood as querying \textit{directed acyclic
graph} (DAG) configurations of undirected graphs in graph theory. In this paper
we present a quantum algorithm for querying causality of multiloop Feynman
diagrams using an ingenious change in the logic of the design of the oracle
operator. The construction of the quantum oracle is surprisingly based
exclusively on multicontrolled Toffoli gates and XNOT gates. The efficiency of
the algorithm is evaluated performing a comparison with a quantum algorithm
based on binary clauses. Additionally, we explicitly analise several three-,
four- and five-eloop topologies, which have not been previously explored due to
their higher complexity.


  High levels of penetration of distributed generation and aggressive reactive
power compensation with modern power electronics may result in the reversal of
active and reactive power flows in future distribution grids. The voltage
stability of these operating conditions may be very different from the more
traditional power consumption regime. We study the stability characteristics of
distribution networks with reversed power flow. After introducing a universal
algebraic approach to characterize all the solutions of the power flow
equations, we show that new solutions appear in the reversed power flow regime
even in the simplest three bus systems. We show that the some of these
solutions are stable and the system may exhibit a phenomenon of multistability,
where multiple stable equilibria co-exist at the given set of parameters, and
the system may converge to an undesirable equilibrium after a disturbance.
These predictions are validated with dynamic simulations of two different
systems. Under certain conditions the new states are viable and may be
characterized by relatively high voltages. Possible approaches towards reactive
power/voltage regulation as well as permissible distributed generation capacity
in future power systems are proposed and discussed in the end of the paper.


  This paper describes a new code for simulating astrophysical plasmas that
solves a hybrid model composed of gyrokinetic ions (GKI) and an isothermal
electron fluid (ITEF) [A. Schekochihin et al., Astrophys. J. Suppl.
\textbf{182}, 310 (2009)]. This model captures ion kinetic effects that are
important near the ion gyro-radius scale while electron kinetic effects are
ordered out by an electron-ion mass ratio expansion. The code is developed by
incorporating the ITEF approximation into {\tt AstroGK}, an Eulerian $\delta f$
gyrokinetics code specialized to a slab geometry [R. Numata et al., J. Compute.
Pays. \textbf{229}, 9347 (2010)]. The new code treats the linear terms in the
ITEF equations implicitly while the nonlinear terms are treated explicitly. We
show linear and nonlinear benchmark tests to prove the validity and
applicability of the simulation code. Since the fast electron timescale is
eliminated by the mass ratio expansion, the Courant--Friedrichs--Lewy condition
is much less restrictive than in full gyrokinetic codes; the present hybrid
code runs $\sim 2\sqrt{m_\mathrm{i}/m_\mathrm{e}} \sim 100$ times faster than
{\tt AstroGK}\ with a single ion species and kinetic electrons where
$m_\mathrm{i}/m_\mathrm{e}$ is the ion-electron mass ratio. The improvement of
the computational time makes it feasible to execute ion scale gyrokinetic
simulations with a high velocity space resolution and to run multiple
simulations to determine the dependence of turbulent dynamics on parameters
such as electron--ion temperature ratio and plasma beta.


  We discuss the equation of state of neutron stars in the dense interior
considering hyperons and the possible onset of kaon condensation within the
relativistic mean field model. We find that hyperons are favoured in dense
matter and that their appearance make the existence of a kaon condensed phase
quite unlikely. Implications for the recent measurements of kaons in heavy ion
collisions at subthreshold energy are also given.


  Energy-Based Models (EBMs) are an important class of probabilistic models,
also known as random fields and undirected graphical models. EBMs are
un-normalized and thus radically different from other popular self-normalized
probabilistic models such as hidden Markov models (HMMs), autoregressive
models, generative adversarial nets (GANs) and variational auto-encoders
(VAEs). Over the past years, EBMs have attracted increasing interest not only
from the core machine learning community, but also from application domains
such as speech, vision, natural language processing (NLP) and so on, due to
significant theoretical and algorithmic progress. The sequential nature of
speech and language also presents special challenges and needs a different
treatment from processing fix-dimensional data (e.g., images). Therefore, the
purpose of this monograph is to present a systematic introduction to
energy-based models, including both algorithmic progress and applications in
speech and language processing. First, the basics of EBMs are introduced,
including classic models, recent models parameterized by neural networks,
sampling methods, and various learning methods from the classic learning
algorithms to the most advanced ones. Then, the application of EBMs in three
different scenarios is presented, i.e., for modeling marginal, conditional and
joint distributions, respectively. 1) EBMs for sequential data with
applications in language modeling, where the main focus is on the marginal
distribution of a sequence itself; 2) EBMs for modeling conditional
distributions of target sequences given observation sequences, with
applications in speech recognition, sequence labeling and text generation; 3)
EBMs for modeling joint distributions of both sequences of observations and
targets, and their applications in semi-supervised learning and calibrated
natural language understanding.


  The field of robotics is a quickly evolving feat of technology that accepts
contributions from various genres of science. Neuroscience, Physiology,
Chemistry, Material science, Computer science, and the wide umbrella of
mechatronics have all simultaneously contributed to many innovations in the
prosthetic applications of robotics. This review begins with a discussion of
the scope of the term robotic prosthetics and discusses the evolving domain of
Neuroprosthetics. The discussion is then constrained to focus on various
actuation and control strategies for robotic prosthetic limbs. This review
discusses various soft robotic actuators such as EAP, SMA, FFA, etc., and the
merits of such actuators over conventional hard robotic actuators. Options in
control strategies for robotic prosthetics, that are in various states of
research and development, are reviewed. This paper concludes the discussion
with an analysis regarding the prospective direction in which this field of
robotic prosthetics is evolving in terms of actuation, control, and other
features relevant to artificial limbs. This paper intends to review some of the
emerging research and development trends in the field of robotic prosthetics
and summarize many tangents that are represented under this broad domain in an
approachable manner.


  Object detection is a comprehensively studied problem in autonomous driving.
However, it has been relatively less explored in the case of fisheye cameras.
The standard bounding box fails in fisheye cameras due to the strong radial
distortion, particularly in the image's periphery. We explore better
representations like oriented bounding box, ellipse, and generic polygon for
object detection in fisheye images in this work. We use the IoU metric to
compare these representations using accurate instance segmentation ground
truth. We design a novel curved bounding box model that has optimal properties
for fisheye distortion models. We also design a curvature adaptive perimeter
sampling method for obtaining polygon vertices, improving relative mAP score by
4.9% compared to uniform sampling. Overall, the proposed polygon model improves
mIoU relative accuracy by 40.3%. It is the first detailed study on object
detection on fisheye cameras for autonomous driving scenarios to the best of
our knowledge. The dataset comprising of 10,000 images along with all the
object representations ground truth will be made public to encourage further
research. We summarize our work in a short video with qualitative results at
https://youtu.be/iLkOzvJpL-A.


  We study the application of iterative first-order methods to the problem of
computing equilibria of large-scale two-player extensive-form games.
First-order methods must typically be instantiated with a regularizer that
serves as a distance-generating function for the decision sets of the players.
For the case of two-player zero-sum games, the state-of-the-art theoretical
convergence rate for Nash equilibrium is achieved by using the dilated entropy
function. In this paper, we introduce a new entropy-based distance-generating
function for two-player zero-sum games, and show that this function achieves
significantly better strong convexity properties than the dilated entropy,
while maintaining the same easily-implemented closed-form proximal mapping.
Extensive numerical simulations show that these superior theoretical properties
translate into better numerical performance as well. We then generalize our new
entropy distance function, as well as general dilated distance functions, to
the scaled extension operator. The scaled extension operator is a way to
recursively construct convex sets, which generalizes the decision polytope of
extensive-form games, as well as the convex polytopes corresponding to
correlated and team equilibria. By instantiating first-order methods with our
regularizers, we develop the first accelerated first-order methods for
computing correlated equilibra and ex-ante coordinated team equilibria. Our
methods have a guaranteed $1/T$ rate of convergence, along with linear-time
proximal updates.


  We introduce a distance-based neural network model for regression, in which
prediction uncertainty is quantified by a belief function on the real line. The
model interprets the distances of the input vector to prototypes as pieces of
evidence represented by Gaussian random fuzzy numbers (GRFN's) and combined by
the generalized product intersection rule, an operator that extends Dempster's
rule to random fuzzy sets. The network output is a GRFN that can be summarized
by three numbers characterizing the most plausible predicted value, variability
around this value, and epistemic uncertainty. Experiments with real datasets
demonstrate the very good performance of the method as compared to
state-of-the-art evidential and statistical learning algorithms.
\keywords{Evidence theory, Dempster-Shafer theory, belief functions, machine
learning, random fuzzy sets.


  In this work, a method is described to extend the iterative Hirshfeld-I
method, generally used for molecules, to periodic systems. The implementation
makes use of precalculated pseudo-potential based charge density distributions,
and it is shown that high quality results are obtained for both molecules and
solids, such as ceria, diamond, and graphite. The use of such grids makes the
implementation independent of the solid state or quantum chemical code used for
studying the system. The extension described here allows for easy calculation
of atomic charges and charge transfer in periodic and bulk systems.


  In this article, we discuss the idea of gravitational lensing, from a
systematic, historical and didactic point of view. We show how the basic
lensing equation together with the concepts of geometrical optics opens a space
of implications that can be explored along different dimensions. We argue that
Einstein explored the idea along different pathways in this space of
implication, and that these explorations are documented by different
calculational manuscripts. The conceptualization of the idea of gravitational
lensing as a space of exploration also shows the feasibility of discussing the
idea in the classroom using some of Einstein's manuscripts.


  We investigate spin transport in diffusive graphene nanoribbons with both
clean and rough zigzag edges, and long-range potential fluctuations. The
long-range fields along the ribbon edges cause the local doping to come close
to the charge neutrality point forming $p$-$n$ junctions with localized
magnetic moments, similar to the predicted magnetic edge of clean zigzag
graphene nanoribbons. The resulting random edge magnetization polarizes charge
currents and causes sample-to-sample fluctuations of the spin currents obeying
universal predictions. We show furthermore that, although the average spin
conductance vanishes, an applied transverse in-plane electric field can
generate a finite spin conductance. A similar effect can also be achieved by
aligning the edge magnetic moments through an external magnetic field.


  In particle physics, workflow management systems are primarily used as
tailored solutions in dedicated areas such as Monte Carlo production. However,
physicists performing data analyses are usually required to steer their
individual, complex workflows manually, frequently involving job submission in
several stages and interaction with distributed storage systems by hand. This
process is not only time-consuming and error-prone, but also leads to
undocumented relations between particular workloads, rendering the steering of
an analysis a serious challenge. This article presents the Luigi Analysis
Workflow (Law) Python package which is based on the open-source pipelining tool
Luigi, originally developed by Spotify. It establishes a generic design pattern
for analyses of arbitrary scale and complexity, and shifts the focus from
executing to defining the analysis logic. Law provides the building blocks to
seamlessly integrate with interchangeable remote resources without, however,
limiting itself to a specific choice of infrastructure. In particular, it
introduces the concept of complete separation between analysis algorithms on
the one hand, and run locations, storage locations, and software environments
on the other hand. To cope with the sophisticated demands of end-to-end HEP
analyses, Law supports job execution on WLCG infrastructure (ARC, gLite,
CMS-CRAB) as well as on local computing clusters (HTCondor, Slurm, LSF), remote
file access via various protocols using the Grid File Access Library (GFAL2),
and an environment sandboxing mechanism with support for sub-shells and virtual
environments, as well as Docker and Singularity containers. Moreover, the novel
approach ultimately aims for analysis preservation out-of-the-box. Law is
developed open-source and independent of any experiment or the language of
executed code, and its user-base increased steadily over the past years.


  Starting from the Maldacena-Nunez supergravity dual of N=1 super Yang-Mills
theory we study the inclusion of a supersymmetry breaking gaugino mass term. We
consider a class of non supersymmetric deformations of the MN solutions which
have been recently proposed in the literature. We show that they can be
interpreted as corresponding to the inclusion of both a mass and a condensate.
We calculate the vacuum energy of the supergravity solutions showing that the
N-fold vacuum degeneracy of the N=1 theory is lifted by the inclusion of a mass
term.


  Considering a two-user multi-antenna Broadcast Channel, this paper shows that
linearly precoded Rate-Splitting (RS) with Successive Interference Cancellation
(SIC) receivers is a flexible framework for non-orthogonal transmission that
generalizes, and subsumes as special cases, four seemingly different
strategies, namely Space Division Multiple Access (SDMA) based on linear
precoding, Orthogonal Multiple Access (OMA), Non- Orthogonal Multiple Access
(NOMA) based on linearly precoded superposition coding with SIC, and
physical-layer multicasting. The paper studies the sum-rate and shows
analytically how RS unifies, outperforms, and specializes to SDMA, OMA, NOMA,
and multicasting as a function of the disparity of the channel strengths and
the angle between the user channel directions.


  Given a family of subsets $\mathcal S$ over a set of elements~$X$ and two
integers~$p$ and~$k$, Max k-Set Cover consists of finding a subfamily~$\mathcal
T \subseteq \mathcal S$ of cardinality at most~$k$, covering at least~$p$
elements of~$X$. This problem is W[2]-hard when parameterized by~$k$, and FPT
when parameterized by $p$. We investigate the parameterized approximability of
the problem with respect to parameters~$k$ and~$p$. Then, we show that Max
Sat-k, a satisfiability problem generalizing Max k-Set Cover, is also FPT with
respect to parameter~$p$.


  A search for narrow, low-mass, scalar and pseudoscalar resonances decaying to
bottom quark-antiquark pairs is presented. The search is based on events
recorded in $\sqrt{s} =$ 13 TeV proton-proton collisions with the CMS detector
at the LHC, collected in 2016, and corresponding to an integrated luminosity of
35.9 fb$^{-1}$. The search selects events in which the resonance would be
produced with high transverse momentum because of the presence of initial- or
final-state radiation. In such events, the decay products of the resonance
would be reconstructed as a single large-radius jet with high mass and
two-prong substructure. A potential signal would be identified as a narrow
excess in the jet invariant mass spectrum. No evidence for such a resonance is
observed within the mass range from 50 to 350 GeV, and upper limits at 95%
confidence level are set on the product of the cross section and branching
fraction to a bottom quark-antiquark pair. These constitute the first
constraints from the LHC on exotic bottom quark-antiquark resonances with
masses below 325 GeV.


  Infinite types and formulas are known to have really curious and unsound
behaviors. For instance, they allow to type {\Omega}, the auto- autoapplication
and they thus do not ensure any form of normalization/productivity. Moreover,
in most infinitary frameworks, it is not difficult to define a type R that can
be assigned to every {\lambda}-term. However, these observations do not say
much about what coinductive (i.e. infinitary) type grammars are able to
provide: it is for instance very difficult to know what types (besides R) can
be assigned to a given term in this setting. We begin with a discussion on the
expressivity of different forms of infinite types. Then, using the
resource-awareness of sequential intersection types (system S) and tracking, we
prove that infinite types are able to characterize the order (arity) of every
{\lambda}-terms and that, in the infinitary extension of the relational model,
every term has a "meaning" i.e. a non-empty denotation. From the technical
point of view, we must deal with the total lack of productivity guarantee for
typable terms: we do so by importing methods inspired by first order model
theory.


  Effects of bond randomness and site dilution are systematically investigated
for the Kitaev model describing a quantum spin liquid with fractional
excitations of itinerant Majorana fermions and localized fluxes. We find that,
in the high-temperature region where the itinerant Majorana fermions release
their entropy, both types of disorders suppress the longitudinal thermal
conductivity while keeping the specific heat almost unchanged. This suggests
that both disorders reduce the mean-free path of the Majorana fermions. On the
other hand, in the low-temperature region, the other specific heat peak
associated with the entropy release from the localized fluxes is suppressed for
both cases, but it is broadened and shifted to the lower-temperature side by
the bond randomness, while the position and the width are almost unchanged
against the site dilution. Contrasting behavior is also found in the thermal
Hall effect under a magnetic field; the half quantization of the thermal Hall
conductivity is fragile against the site dilution, while it remains for the
bond randomness despite the reduced onset temperature. We discuss the
contrasting behavior from the stability of the topological nature by
calculating flux condensation and Majorana excitation gap.


  The possibility of finite-time, dispersive blow up for nonlinear equations of
Schroedinger type is revisited. This mathematical phenomena is one of the
possible explanations for oceanic and optical rogue waves. In dimension one,
the possibility of dispersive blow up for nonlinear Schroedinger equations
already appears in [9]. In the present work, the existing results are extended
in several ways. In one direction, the theory is broadened to include the
Davey-Stewartson and Gross-Pitaevskii equations. In another, dispersive blow up
is shown to obtain for nonlinear Schroedinger equations in spatial dimensions
larger than one and for more general power-law nonlinearities. As a by-product
of our analysis, a sharp global smoothing estimate for the integral term
appearing in Duhamel's formula is obtained.


  ESO 148-IG002 represents a transformative stage of galaxy evolution,
containing two galaxies at close separation which are currently coalescing into
a single galaxy. We present integral field data of this galaxy from the ANU
Wide Field Spectrograph (WiFeS). We analyse our integral field data using
optical line ratio maps and velocity maps. We apply active galactic nucleus
(AGN), star-burst and shock models to investigate the relative contribution
from star-formation, shock excitation and AGN activity to the optical emission
in this key merger stage. We find that ESO 148-IG002 has a flat metallicity
gradient, consistent with a recent gas inflow. We separate the line emission
maps into a star forming region with low velocity dispersion that spatially
covers the whole system as well as a southern high velocity dispersion region
with a coherent velocity pattern which could either be rotation or an
AGN-driven outflow, showing little evidence for pure star formation. We show
that the two overlapping galaxies can be separated using kinematic information,
demonstrating the power of moderate spectral resolution integral field
spectroscopy.


  Programmable management framework have paved the way for managing devices in
the network. Lately, emerging paradigm of Software Defined Networking (SDN)
have revolutionized programmable networks. Designers of networking applications
i.e. Internet of things (IoT) have started investigating potentials of SDN
paradigm in improving network management. IoT envision interconnecting various
embedded devices surrounding our environment with IP to enable internet
connectivity. Unlike traditional network architectures, IoT are characterized
by constraint in resources and heterogeneous inter connectivity of wireless and
wired medium. Therefore, unique challenges for managing IoT are raised which
are discussed in this paper. Ubiquity of IoT have raised unique security
challenges in IoT which is one of the aspect of management framework for IoT.
In this paper, security threats and requirements are summarized in IoT
extracted from the state of the art efforts in investigating security
challenges of IoT. Also, SDN based security service provisioning framework for
IoT is proposed.


  We investigate the effect of external stresses on structural and mechanical
properties of a strained damaged thin layer by developing a continuum
phase-field mesoscale model based on the introduction of an order parameter
field, the defect concentration, coupled with a displacement field. We find
that even in the case of an initially uniform distribution of point defects
external stresses drive the nucleation of local regions with higher
concentration of vacancies or self-interstitials than their average value over
the film. The effect can explain our experimental findings relating generation
of highly heterogeneous regions in cobalt disilicide film fabricated in
self-aligned processing on a silicon surface as well as improvement of fracture
toughness in a tetragonal zirconia ceramics with oxygen vacancies.


  First-principles calculations combining density functional theory and
many-body perturbation theory can provide microscopic insight into the dynamics
of electrons and phonons in materials. We review this theoretical and
computational framework, focusing on perturbative treatments of scattering,
dynamics and transport of coupled electrons and phonons. We discuss application
of these first-principles calculations to electronics, lighting, spectroscopy
and renewable energy.


  In this paper we survey Eckardt points on a smooth complex cubic threefold
with an approach aimed at computing all Eckardt points of a cubic threefold. In
addition, we construct cubic threefolds with no Eckardt points but containing
triple lines.


  The abundance of dark matter haloes is a key cosmological probe in
forthcoming galaxy surveys. The theoretical understanding of the halo mass
function (HMF) is limited by our incomplete knowledge of the origin of
non-universality and its cosmological parameter dependence. We present a deep
learning model which compresses the linear matter power spectrum into three
independent factors which are necessary and sufficient to describe the $z=0$
HMF from the state-of-the-art AEMULUS emulator to sub-per cent accuracy in a
$w$CDM$+N_\mathrm{eff}$ parameter space. Additional information about growth
history does not improve the accuracy of HMF predictions if the matter power
spectrum is already provided as input, because required aspects of the former
can be inferred from the latter. The three factors carry information about the
universal and non-universal aspects of the HMF, which we interrogate via the
information-theoretic measure of mutual information. We find that
non-universality is captured by recent growth history after matter-dark-energy
equality and $N_\mathrm{eff}$ for $M\sim 10^{13} \, \mathrm{M_\odot}\, h^{-1}$
haloes, and by $\Omega_{\rm m}$ for $M\sim 10^{15} \, \mathrm{M_\odot}\,
h^{-1}$. The compact representation learnt by our model can inform the design
of emulator training sets to achieve high emulator accuracy with fewer
simulations.


  Contemporary Virtual Reality (VR) setups often include an external source
delivering content to a Head-Mounted Display (HMD). "Cutting the wire" in such
setups and going truly wireless will require a wireless network capable of
delivering enormous amounts of video data at an extremely low latency. The
massive bandwidth of higher frequencies, such as the millimeter-wave (mmWave)
band, can meet these requirements. Due to high attenuation and path loss in the
mmWave frequencies, beamforming is essential. In wireless VR, where the antenna
is integrated into the HMD, any head rotation also changes the antenna's
orientation. As such, beamforming must adapt, in real-time, to the user's head
rotations. An HMD's built-in sensors providing accurate orientation estimates
may facilitate such rapid beamforming. In this work, we present coVRage, a
receive-side beamforming solution tailored for VR HMDs. Using built-in
orientation prediction present on modern HMDs, the algorithm estimates how the
Angle of Arrival (AoA) at the HMD will change in the near future, and covers
this AoA trajectory with a dynamically shaped oblong beam, synthesized using
sub-arrays. We show that this solution can cover these trajectories with
consistently high gain, even in light of temporally or spatially inaccurate
orientational data.


  In view of the recent interest in the pMSSM with light third generation
squarks, we consider a hitherto neglected scenario where the lighter bottom
squark ($\widetilde{b}_1$) is the next lightest supersymmetric particle (NLSP)
which co-annihilates with the lightest supersymmetric particle (LSP), the dark
matter (DM) candidate. Since the co-annihilation cross section receives
contributions from both electroweak and strong vertices, it is relatively
large. As a result relatively large NLSP-LSP mass difference (25 - 35 GeV) is
consistent with the PLANCK data. This facilitates the LHC signatures of this
scenario. We consider several variants of the sbottom NLSP scenario with and
without light stops and delineate the parameter space allowed by the PLANCK
data. We point out several novel signal (e.g., $\widetilde{t}_1 \rightarrow
\widetilde{b}_1 W$) which are not viable in the stop NLSP scenario of DM
production. Finally, we consider gluino ($\widetilde g$) decays in this
scenario and using the current ATLAS data in the jets (with or without
b-tagging) + $\not \!\! E_T$ channel, obtain new limits in the
$m_{\widetilde{b}_1} - m_{\widetilde g}$ mass plane. We find that for
$m_{\widetilde{b}_1}$ upto 500 GeV, $m_{\widetilde g} \geq$ 1.1 - 1.2 TeV in
this scenario.


  The genome of bacterial species is much more flexible than that of
eukaryotes. Moreover, the distributed genome hypothesis for bacteria states
that the total number of genes present in a bacterial population is greater
than the genome of every single individual. The pangenome, i.e. the set of all
genes of a bacterial species (or a sample), comprises the core genes which are
present in all living individuals, and accessory genes, which are carried only
by some individuals. In order to use accessory genes for adaptation to
environmental forces, genes can be transferred horizontally between
individuals. Here, we extend the infinitely many genes model from Baumdicker,
Hess and Pfaffelhuber (2010) for horizontal gene transfer. We take a
genealogical view and give a construction -- called the Ancestral Gene Transfer
Graph -- of the joint genealogy of all genes in the pangenome. As application,
we compute moments of several statistics (e.g. the number of differences
between two individuals and the gene frequency spectrum) under the infinitely
many genes model with horizontal gene transfer.


  Two topics in soft collinear effective theory (SCET) for gravitational
interactions are explored. First, the collinear Wilson lines---necessary
building blocks for maintaining multiple copies of diffeomorphism invariance in
gravity SCET---are extended to all orders in the SCET expansion parameter
$\lambda$, where it has only been known to $O(\lambda)$ in the literature.
Second, implications of reparametrization invariance (RPI) for the structure of
gravity SCET lagrangians are studied. The utility of RPI is illustrated by an
explicit example in which $O(\lambda^2)$ hard interactions of a collinear
graviton are completely predicted by RPI from its $O(\lambda)$ hard
interactions. It is also pointed out that the multiple diffeomorphism
invariances and RPI together require certain relations among $O(\lambda)$
terms, thereby reducing the number of $O(\lambda)$ terms that need to be fixed
by matching onto the full theory in the first place.


  We consider solutions to the Cauchy problem for the incompressible Euler
equations on the 3-dimensional torus which are continuous or H\"older
continuous for any exponent $\theta<\frac{1}{16}$. Using the techniques
introduced in \cite{DS12} and \cite{DS12H}, we prove the existence of
infinitely many (H\"older) continuous initial vector fields starting from which
there exist infinitely many (H\"older) continuous solutions with preassigned
total kinetic energy.


  These are notes about the theory of Fibred Categories as I have learned it
from Jean Benabou. I also have used results from the Thesis of Jean-Luc Moens
from 1982 in those sections where I discuss the fibered view of geometric
morphisms. Thus, almost all of the contents is not due to me but most of it
cannot be found in the literature since Benabou has given many talks on it but
most of his work on fibered categories is unpublished. But I am solely
responsible for the mistakes and for misrepresentations of his views. And
certainly these notes do not cover all the work he has done on fibered
categories. I just try to explain the most important notions he has come up
with in a way trying to be as close as possible to his intentions and
intuitions. I started these notes in 1999 when I gave a course on some of the
material at a workshop in Munich. They have developed quite a lot over the
years and I have tried to include most of the things I want to remember.


  With rapid advancements in electronic gadgets, the security and privacy
aspects of these devices are significant. For the design of secure systems,
physical unclonable function (PUF) and true random number generator (TRNG) are
critical hardware security primitives for security applications. This paper
proposes novel implementations of PUF and TRNGs on the RRAM crossbar structure.
Firstly, two techniques to implement the TRNG in the RRAM crossbar are
presented based on write-back and 50% switching probability pulse. The
randomness of the proposed TRNGs is evaluated using the NIST test suite. Next,
an architecture to implement the PUF in the RRAM crossbar is presented. The
initial entropy source for the PUF is used from TRNGs, and challenge-response
pairs (CRPs) are collected. The proposed PUF exploits the device variations and
sneak-path current to produce unique CRPs. We demonstrate, through extensive
experiments, reliability of 100%, uniqueness of 47.78%, uniformity of 49.79%,
and bit-aliasing of 48.57% without any post-processing techniques. Finally, the
design is compared with the literature to evaluate its implementation
efficiency, which is clearly found to be superior to the state-of-the-art.


  We show that atoms trapped in micro-cavities that interact via exchange of
virtual photons can model an anisotropic Heisenberg spin-1/2 chain in an
external magnetic field. All parameters of the effective Hamiltonian can
individually be tuned via external lasers. Since the occupation of excited
atomic levels and photonic states are strongly suppressed, the effective model
is robust against decoherence mechanisms, has a long lifetime and its
implementation is feasible with current experimental technology. The model
provides a feasible way to create cluster states in these devices.


  Spin Transfer Torque Random Access Memory (STT-RAM) is an emerging
Non-Volatile Memory (NVM) technology that has garnered attention to overcome
the drawbacks of conventional CMOS-based technologies. However, such
technologies must be evaluated before deployment under real workloads and
architecture. But there is a lack of available open-source STT-RAM-based system
evaluation framework, which hampers research and experimentation and impacts
the adoption of STT- RAM in a system. This paper proposes a novel, extendable
STT-RAM memory controller design integrated inside the gem5 simulator. Our
framework enables understanding various aspects of STT-RAM, i.e., power, delay,
clock cycles, energy, and system throughput. We will open-source our HOPE
framework, which will fuel research and aid in accelerating the development of
future system architectures based on STT-RAM. It will also facilitate the user
for further tool enhancement.


  The power grid is rapidly transforming, and while recent grid innovations
increased the utilization of advanced control methods, the next-generation grid
demands technologies that enable the integration of distributed energy
resources (DERs)---and consumers that both seamlessly buy and sell electricity.
This paper develops an optimization model and blockchain-based architecture to
manage the operation of crowdsourced energy systems (CES), with peer-to-peer
(P2P) energy trading transactions. An operational model of CESs in distribution
networks is presented considering various types of energy trading transactions
and crowdsourcees. Then, a two-phase operation algorithm is presented: Phase I
focuses on the day-ahead scheduling of generation and controllable DERs,
whereas Phase II is developed for hour-ahead or real-time operation of
distribution networks. The developed approach supports seamless P2P energy
trading between individual prosumers and/or the utility. The presented
operational model can also be used to operate islanded microgrids. The CES
framework and the operation algorithm are then prototyped through an efficient
blockchain implementation, namely the IBM Hyperledger Fabric. This
implementation allows the system operator to manage the network users to
seamlessly trade energy. Case studies and prototype illustration are provided.


  We prove that the automorphism group of a topological parallelism on real
projective 3-space is compact. In a preceding article it was proved that at
least the connected component of the identity is compact. The present proof
does not depend on that earlier result.


  Graphic designers often get inspiration through the recombination of
references. Our formative study (N=6) reveals that graphic designers focus on
conceptual keywords during this process, and want support for discovering the
keywords, expanding them, and exploring diverse recombination options of them,
while still having room for designers' creativity. We propose CreativeConnect,
a system with generative AI pipelines that helps users discover useful elements
from the reference image using keywords, recommends relevant keywords,
generates diverse recombination options with user-selected keywords, and shows
recombinations as sketches with text descriptions. Our user study (N=16) showed
that CreativeConnect helped users discover keywords from the reference and
generate multiple ideas based on them, ultimately helping users produce more
design ideas with higher self-reported creativity compared to the baseline
system without generative pipelines. While CreativeConnect was shown effective
in ideation, we discussed how CreativeConnect can be extended to support other
types of tasks in creativity support.


  The performance of many medical image analysis tasks are strongly associated
with image data quality. When developing modern deep learning algorithms,
rather than relying on subjective (human-based) image quality assessment (IQA),
task amenability potentially provides an objective measure of task-specific
image quality. To predict task amenability, an IQA agent is trained using
reinforcement learning (RL) with a simultaneously optimised task predictor,
such as a classification or segmentation neural network. In this work, we
develop transfer learning or adaptation strategies to increase the adaptability
of both the IQA agent and the task predictor so that they are less dependent on
high-quality, expert-labelled training data. The proposed transfer learning
strategy re-formulates the original RL problem for task amenability in a
meta-reinforcement learning (meta-RL) framework. The resulting algorithm
facilitates efficient adaptation of the agent to different definitions of image
quality, each with its own Markov decision process environment including
different images, labels and an adaptable task predictor. Our work demonstrates
that the IQA agents pre-trained on non-expert task labels can be adapted to
predict task amenability as defined by expert task labels, using only a small
set of expert labels. Using 6644 clinical ultrasound images from 249 prostate
cancer patients, our results for image classification and segmentation tasks
show that the proposed IQA method can be adapted using data with as few as
respective 19.7% and 29.6% expert-reviewed consensus labels and still achieve
comparable IQA and task performance, which would otherwise require a training
dataset with 100% expert labels.


  Multimodal learning has shown great potentials in numerous scenes and
attracts increasing interest recently. However, it often encounters the problem
of missing modality data and thus suffers severe performance degradation in
practice. To this end, we propose a general framework called MMANet to assist
incomplete multimodal learning. It consists of three components: the deployment
network used for inference, the teacher network transferring comprehensive
multimodal information to the deployment network, and the regularization
network guiding the deployment network to balance weak modality combinations.
Specifically, we propose a novel margin-aware distillation (MAD) to assist the
information transfer by weighing the sample contribution with the
classification uncertainty. This encourages the deployment network to focus on
the samples near decision boundaries and acquire the refined inter-class
margin. Besides, we design a modality-aware regularization (MAR) algorithm to
mine the weak modality combinations and guide the regularization network to
calculate prediction loss for them. This forces the deployment network to
improve its representation ability for the weak modality combinations
adaptively. Finally, extensive experiments on multimodal classification and
segmentation tasks demonstrate that our MMANet outperforms the state-of-the-art
significantly. Code is available at: https://github.com/shicaiwei123/MMANet


  This article presents our unimodal privacy-safe and non-individual proposal
for the audio-video group emotion recognition subtask at the Emotion
Recognition in the Wild (EmotiW) Challenge 2020 1. This sub challenge aims to
classify in the wild videos into three categories: Positive, Neutral and
Negative. Recent deep learning models have shown tremendous advances in
analyzing interactions between people, predicting human behavior and affective
evaluation. Nonetheless, their performance comes from individual-based
analysis, which means summing up and averaging scores from individual
detections, which inevitably leads to some privacy issues. In this research, we
investigated a frugal approach towards a model able to capture the global moods
from the whole image without using face or pose detection, or any
individual-based feature as input. The proposed methodology mixes
state-of-the-art and dedicated synthetic corpora as training sources. With an
in-depth exploration of neural network architectures for group-level emotion
recognition, we built a VGG-based model achieving 59.13% accuracy on the VGAF
test set (eleventh place of the challenge). Given that the analysis is unimodal
based only on global features and that the performance is evaluated on a
real-world dataset, these results are promising and let us envision extending
this model to multimodality for classroom ambiance evaluation, our final target
application.


  We obtain some new characterizations of a variable version of Lipschitz
spaces in terms of the boundedness of commutators of sharp maximal functions,
fractional maximal functions or fractional maximal commutators in the context
of the variable Lebesgue spaces, where the symbols of the commutators belong to
the variable Lipschitz space. A useful tool is that a symbol $b$ belongs a
variable Lipschitz space of pointwise type if and only if $b$ belongs to a
variable Lipschitz space of integral type.


  It is shown that in one-particle Schr\"{o}dinger quantum mechanics a small
perturbation of a one-dimensional potential can produce a large change in the
ground state, the effect becoming more pronounced with growing typical length
of the potential, contrary to what standard stationary perturbation theory
could make one believe.The possible consequences of this result for solid state
physics and other fields are also discussed, and examples of such non-standard
perturbations are furnished.


  We describe a novel crystal growth instability that enhances the development
of thin edges, promoting the formation of plate-like or hollow columnar
morphologies. This instability arises when diffusion-limited growth is coupled
with structure-depdendent attachment kinetics, specifically when the nucleation
barrier on a facet surface decreases substantially as the facet width
approaches atomic dimensions. Experimental data are presented confirming the
presence of this instability in the growth of ice from water vapor at -15 C. We
believe this edge-enhancing effect plays an important role in determining the
growth morphologies of atmospheric ice crystals as a function of temperature, a
phenomenon that has been essentially unexplained for over 75 years. Our model
of structure-dependent attachment kinetics appears to be related to surface
melting, and thus may be present in other material systems, whenever crystal
growth from the vapor phase occurs near the material melting point.


  We argue that a generic instability afflicts vacua that arise in theories
whose moduli space has large dimension. Specifically, by studying theories with
multiple scalar fields we provide numerical evidence that for a generic local
minimum of the potential the usual semiclassical bubble nucleation rate, Gamma
= A e^{-B}, increases rapidly as function of the number of fields in the
theory. As a consequence, the fraction of vacua with tunneling rates low enough
to maintain metastability appears to fall exponentially as a function of the
moduli space dimension. We discuss possible implications for the landscape of
string theory. Notably, if our results prove applicable to string theory, the
landscape of metastable vacua may not contain sufficient diversity to offer a
natural explanation of dark energy.


  The share of wind energy in total installed power capacity has grown rapidly
in recent years around the world. Producing accurate and reliable forecasts of
wind power production, together with a quantification of the uncertainty, is
essential to optimally integrate wind energy into power systems. We build
spatio-temporal models for wind power generation and obtain full probabilistic
forecasts from 15 minutes to 5 hours ahead. Detailed analysis of the forecast
performances on the individual wind farms and aggregated wind power are
provided. We show that it is possible to improve the results of forecasting
aggregated wind power by utilizing spatio-temporal correlations among
individual wind farms. Furthermore, spatio-temporal models have the advantage
of being able to produce spatially out-of-sample forecasts. We evaluate the
predictions on a data set from wind farms in western Denmark and compare the
spatio-temporal model with an autoregressive model containing a common
autoregressive parameter for all wind farms, identifying the specific cases
when it is important to have a spatio-temporal model instead of a temporal one.
This case study demonstrates that it is possible to obtain fast and accurate
forecasts of wind power generation at wind farms where data is available, but
also at a larger portfolio including wind farms at new locations. The results
and the methodologies are relevant for wind power forecasts across the globe as
well as for spatial-temporal modelling in general.


  The results of x-ray reflectivity studies of two oil/water (liquid/liquid)
interfaces are inconsistent with recent predictions of the presence of a
vapor-like depletion region at hydrophobic/aqueous interfaces. One of the oils,
perfluorohexane, is a fluorocarbon whose super-hydrophobic interface with water
provides a stringent test for the presence of a depletion layer. The other oil,
heptane, is a hydrocarbon and, therefore, is more relevant to the study of
biomolecular hydrophobicity. These results are consistent with the sub-angstrom
proximity of water to soft hydrophobic materials.


  Near-field beam training is essential for acquiring channel state information
in 6G extremely large-scale multiple input multiple output (XL-MIMO) systems.
To achieve low-overhead beam training, existing method has been proposed to
leverage the near-field beam split effect, which deploys true-time-delay arrays
to simultaneously search multiple angles of the entire angular range in a
distance ring with a single pilot. However, the method still requires
exhaustive search in the distance domain, which limits its efficiency. To
address the problem, we propose a distance-dependent beam-split-based beam
training method to further reduce the training overheads. Specifically, we
first reveal the new phenomenon of distance-dependent beam split, where by
manipulating the configurations of time-delay and phase-shift, beams at
different frequencies can simultaneously scan the angular domain in multiple
distance rings. Leveraging the phenomenon, we propose a near-field beam
training method where both different angles and distances can simultaneously be
searched in one time slot. Thus, a few pilots are capable of covering the whole
angle-distance space for wideband XL-MIMO. Theoretical analysis and numerical
simulations are also displayed to verify the superiority of the proposed method
on beamforming gain and training overhead.


  In order to be practically useful, quantum cryptography must not only provide
a guarantee of secrecy, but it must provide this guarantee with a useful,
sufficiently large throughput value. The standard result of generalized privacy
amplification yields an upper bound only on the average value of the mutual
information available to an eavesdropper. Unfortunately this result by itself
is inadequate for cryptographic applications. A naive application of the
standard result leads one to incorrectly conclude that an acceptable upper
bound on the mutual information has been achieved. It is the pointwise value of
the bound on the mutual information, associated with the use of some specific
hash function, that corresponds to actual implementations. We provide a fully
rigorous mathematical derivation that shows how to obtain a cryptographically
acceptable upper bound on the actual, pointwise value of the mutual
information. Unlike the bound on the average mutual information, the value of
the upper bound on the pointwise mutual information and the number of bits by
which the secret key is compressed are specified by two different parameters,
and the actual realization of the bound in the pointwise case is necessarily
associated with a specific failure probability. The constraints amongst these
parameters, and the effect of their values on the system throughput, have not
been previously analyzed. We show that the necessary shortening of the key
dictated by the cryptographically correct, pointwise bound, can still produce
viable throughput rates that will be useful in practice.


  Application Tracking Systems (ATS) have allowed talent managers, recruiters,
and college admissions committees to process large volumes of potential
candidate applications efficiently. Traditionally, this screening process was
conducted manually, creating major bottlenecks due to the quantity of
applications and introducing many instances of human bias. The advent of large
language models (LLMs) such as ChatGPT and the potential of adopting methods to
current automated application screening raises additional bias and fairness
issues that must be addressed. In this project, we wish to identify and
quantify the instances of social bias in ChatGPT and other OpenAI LLMs in the
context of candidate screening in order to demonstrate how the use of these
models could perpetuate existing biases and inequalities in the hiring process.


  As a contribution to the ongoing controversy about the role of higher-order
Kerr effect (HOKE) in laser filamentation, we first provide thorough details
about the protocol that has been employed to infer the HOKE indices from the
experiment. Next, we discuss potential sources of artifact in the experimental
measurements of these terms and show that neither the value of the observed
birefringence, nor its inversion, nor the intensity at which it is observed,
appear to be flawed. Furthermore, we argue that, independently on our values,
the principle of including HOKE is straightforward. Due to the different
temporal and spectral dynamics, the respective efficiency of defocusing by the
plasma and by the HOKE is expected to depend substantially on both incident
wavelength and pulse duration. The discussion should therefore focus on
defining the conditions where each filamentation regime dominates.


  In this paper, an energy efficient passive optical network (PON) architecture
is proposed for backhaul connectivity in indoor visible light communication
(VLC) systems. The proposed network is used to support a fog computing
architecture allowing users with processing demands to access dedicated fog
nodes and idle processing resources in other user devices within a building.
The fog resources within a building complement fog nodes at higher layers of
the access and metro networks and the central cloud data center. A mixed
integer linear programming (MILP) model is developed to minimize the total
power consumption associated with serving demands over the proposed
architecture. The results show that the PON backhaul network improves the
energy efficiency of fog computing by 66% for networking and 12% for processing
compared to an architecture based on state-of-the-art Spine-and-Leaf
connectivity.


  Using fast off-lattice Monte Carlo simulations with experimentally accessible
fluctuations, we report the first systematic study unambiguously quantifying
the shift of the order-disorder transition (ODT) $\chi^*$ of symmetric diblock
copolymers from the mean-field prediction $\chi^*_{\rm MF}$. Our simulations
are performed in a canonical ensemble with variable box lengths to eliminate
the restriction of periodic boundary conditions on the lamellar period. Exactly
the same model system (Hamiltonian) is used in both our simulations and
mean-field theory; the ODT shift is therefore due to the
fluctuations/correlations neglected by the latter. While $\chi^* / \chi^*_{\rm
MF} - 1 \propto \bar{\mathcal{N}}^{-k}$ is found with $\bar{\mathcal{N}}$
denoting the invariant degree of polymerization, $k$ decreases around the
$\bar{\mathcal{N}}$-value corresponding to the close packing of polymer
segments as hard spheres, indicating the short-range correlation effects.


  We present 'CongNaMul', a comprehensive dataset designed for various tasks in
soybean sprouts image analysis. The CongNaMul dataset is curated to facilitate
tasks such as image classification, semantic segmentation, decomposition, and
measurement of length and weight. The classification task provides four classes
to determine the quality of soybean sprouts: normal, broken, spotted, and
broken and spotted, for the development of AI-aided automatic quality
inspection technology. For semantic segmentation, images with varying
complexity, from single sprout images to images with multiple sprouts, along
with human-labelled mask images, are included. The label has 4 different
classes: background, head, body, tail. The dataset also provides images and
masks for the image decomposition task, including two separate sprout images
and their combined form. Lastly, 5 physical features of sprouts (head length,
body length, body thickness, tail length, weight) are provided for image-based
measurement tasks. This dataset is expected to be a valuable resource for a
wide range of research and applications in the advanced analysis of images of
soybean sprouts. Also, we hope that this dataset can assist researchers
studying classification, semantic segmentation, decomposition, and physical
feature measurement in other industrial fields, in evaluating their models. The
dataset is available at the authors' repository. (https://bhban.kr/data)


  Superconducting behavior has been observed in the Sr2RuO4-Sr3Ru2O7 eutectic
system as grown by the flux-feeding floating zone technique. A supercurrent
flows across a single interface between Sr2RuO4 and Sr3Ru2O7 areas at distances
that are far beyond those expected in a conventional proximity scenario. The
current-voltage characteristics within the Sr3Ru2O7 macrodomain, as extracted
from the eutectic, exhibit signatures of superconductivity in the bilayered
ruthenate. Detailed microstructural, morphological and compositional analyses
address issues on the concentration and the size of Sr2RuO4 inclusions within
the Sr3Ru2O7 matrix. We speculate on the possibility of inhomogeneous
superconductivity in the eutectic Sr3Ru2O7 and exotic pairing induced by the
Sr2RuO4 inclusions.


  The community may be on the verge of detecting low-frequency gravitational
waves from massive black hole binaries (MBHBs), but no examples of binary
active galactic nuclei (AGN) have been confirmed. Because MBHBs are
intrinsically rare, the most promising detection methods utilize photometric
data from all-sky surveys. Recently D'Orazio & Di Stefano 2018
(arXiv:1707.02335) suggested gravitational self-lensing as a method of
detecting AGN in close separation binaries. In this study we calculate the
detectability of lensing signatures in realistic populations of simulated
MBHBs. Within our model assumptions, we find that VRO's LSST should be able to
detect 10s to 100s of self-lensing binaries, with the rate uncertainty
depending primarily on the orientation of AGN disks relative to their binary
orbits. Roughly a quarter of lensing detectable systems should also show
detectable Doppler boosting signatures. If AGN disks tend to be aligned with
the orbit, lensing signatures are very nearly achromatic, while in misaligned
configurations the bluer optical bands are lensed more than redder ones.
Whether substantial obscuring material (e.g.~a dusty torus) will be present in
close binaries remains uncertain, but our estimates suggest that a substantial
fraction of systems would still be observable in this case.


  Semiconducting nanowire (NW) devices have garnered attention in self-powered
electronic and optoelectronic applications. This work explores and exhibits,
for the first time for visible light, a clear evidence of the zero-biased
optoelectronic switching in randomly dispersed Ge and Si NW networks. The test
bench, on which the NWs were dispersed for optoelectronic characterization, was
fabricated using standard CMOS fabrication process, and utilized metal contacts
with dissimilar work functions - Al and Ni. The randomly dispersed NWs respond
to light by exhibiting substantial photocurrents and, most remarkably,
demonstrate zero-bias photo-switching. The magnitude of the photocurrent is
dependent on the NW material, as well as the channel length. The photocurrent
in randomly dispersed GeNWs was found to be higher by orders of magnitude
compared to SiNWs. In both of these material systems, when the length of the
NWs was comparable to the channel length, the currents in sparse NW networks
were found to be higher than that in dense NW networks, which can be explained
by considering various possible arrangements of NWs in these devices.


  We describe an efficient numerical method for simulating the dynamics and
steady states of collective spin systems in the presence of dephasing and
decay. The method is based on the Schwinger boson representation of spin
operators and uses an extension of the truncated Wigner approximation to map
the exact open system dynamics onto stochastic differential equations for the
corresponding phase space distribution. This approach is most effective in the
limit of very large spin quantum numbers, where exact numerical simulations and
other approximation methods are no longer applicable. We benchmark this
numerical technique for known superradiant decay and spin-squeezing processes
and illustrate its application for the simulation of non-equilibrium phase
transitions in dissipative spin lattice models.


  For an attracting periodic orbit (limit cycle) of a deterministic dynamical
system, one defines the isochron for each point of the orbit as the
cross-section with fixed return time under the flow. Equivalently, isochrons
can be characterized as stable manifolds foliating neighborhoods of the limit
cycle or as level sets of an isochron map. In recent years, there has been a
lively discussion in the mathematical physics community on how to define
isochrons for stochastic oscillations, i.e. limit cycles or heteroclinic cycles
exposed to stochastic noise. The main discussion has concerned an approach
finding stochastic isochrons as sections of equal expected return times versus
the idea of considering eigenfunctions of the backward Kolmogorov operator. We
discuss the problem in the framework of random dynamical systems and introduce
a new rigorous definition of stochastic isochrons as random stable manifolds
for random periodic solutions with noise-dependent period. This allows us to
establish a random version of isochron maps whose level sets coincide with the
random stable manifolds. Finally, we discuss links between the random dynamical
systems interpretation and the equal expected return time approach via averaged
quantities.


  We have studied resistive bistability (memory) effects in junctions based on
metal oxides, with a focus on sample-to-sample reproducibility which is
necessary for the use of such junctions as crosspoint devices of hybrid
CMOS/nanoelectronic circuits. Few-nm-thick layers of NbOx, CuOx and TiOx have
been formed by thermal and plasma oxidation, at various deposition and
oxidation conditions, both with or without rapid thermal post-annealing (RTA).
The resistive bistability effect has been observed for all these materials,
with particularly high endurance (over 1000 switching cycles) obtained for
single-layer TiO2 junctions, and the best reproducibility reached for
multi-layer junctions of the same material. Fabrication optimization has
allowed us to improve the OFF/ON resistance ratio to about 1000, but the
sample-to-sample reproducibility is so far lower than that required for large
scale integration.


  A wealth of observations have long suggested that the vast majority of
isolated classical dwarf galaxies ($M_*=10^7$-$10^9$ M$_\odot$) are currently
star-forming. However, recent observations of the large abundance of
"Ultra-Diffuse Galaxies" beyond the reach of previous large spectroscopic
surveys suggest that our understanding of the dwarf galaxy population may be
incomplete. Here we report the serendipitous discovery of an isolated quiescent
dwarf galaxy in the nearby Universe, which was imaged as part of the PEARLS GTO
program. Remarkably, individual red-giant branch stars are visible in this
near-IR imaging, suggesting a distance of $30\pm4$ Mpc, and a wealth of
archival photometry point to an sSFR of $2\times10^{-11}$ yr$^{-1}$ and SFR of
$4\times10^{-4}$ M$_\odot$ yr$^{-1}$. Spectra obtained with the Lowell
Discovery Telescope find a recessional velocity consistent with the Hubble Flow
and ${>}1500$ km/s separated from the nearest massive galaxy in SDSS,
suggesting that this galaxy was either quenched from internal mechanisms or had
a very high-velocity ($>1000$ km/s) interaction with a nearby massive galaxy in
the past. This analysis highlights the possibility that many nearby quiescent
dwarf galaxies are waiting to be discovered and that JWST has the potential to
resolve them.


  We describe an automated method for assigning the most likely physical
parameters to the components of an eclipsing binary (EB), using only its
photometric light curve and combined color. In traditional methods (e.g. WD and
EBOP) one attempts to optimize a multi-parameter model over many iterations, so
as to minimize the chi-squared value. We suggest an alternative method, where
one selects pairs of coeval stars from a set of theoretical stellar models, and
compares their simulated light curves and combined colors with the
observations. This approach greatly reduces the EB parameter-space over which
one needs to search, and allows one to determine the components' masses, radii
and absolute magnitudes, without spectroscopic data. We have implemented this
method in an automated program using published theoretical isochrones and
limb-darkening coefficients. Since it is easy to automate, this method lends
itself to systematic analyses of datasets consisting of photometric time series
of large numbers of stars, such as those produced by OGLE, MACHO, TrES, HAT,
and many others surveys.


  The introduction of large language models (LLMs) like ChatGPT and Google
Palm2 for smart contract generation seems to be the first well-established
instance of an AI pair programmer. LLMs have access to a large number of
open-source smart contracts, enabling them to utilize more extensive code in
Solidity than other code generation tools. Although the initial and informal
assessments of LLMs for smart contract generation are promising, a systematic
evaluation is needed to explore the limits and benefits of these models. The
main objective of this study is to assess the quality of generated code
provided by LLMs for smart contracts. We also aim to evaluate the impact of the
quality and variety of input parameters fed to LLMs. To achieve this aim, we
created an experimental setup for evaluating the generated code in terms of
validity, correctness, and efficiency. Our study finds crucial evidence of
security bugs getting introduced in the generated smart contracts as well as
the overall quality and correctness of the code getting impacted. However, we
also identified the areas where it can be improved. The paper also proposes
several potential research directions to improve the process, quality and
safety of generated smart contract codes.


  The low-energy limits of the string theory lead to the higher-order curvature
corrections for Einstein gravity. Also, they give the higher-order derivative
corrections for the Maxwell or linear electrodynamics, which suggests the
nonlinear electrodynamics. Inspired by this, in this paper we investigate
$d$-dimensional holographic superconductors in the probe limit in the framework
of Einstein-Gauss-Bonnet gravity and exponential nonlinear electrodynamics.
Based on the Sturm-Liouville eigenvalue method, we compute the critical
temperature, the condensation value, and the critical exponent. It is observed
that the critical temperature decreases when the Gauss-Bonnet (GB) parameter or
the nonlinear parameter increases, but it increases with the higher dimension
of the spacetime at the efficiently low charge density. In addition, we found
that the condensation value becomes larger as increasing the GB parameter, the
nonlinear parameter as well as the spacetime dimension. Finally, we calculate
the optical conductivity and study the effects of the GB term and exponential
nonlinear electrodynamics on superconducting energy gap.


  Kernel smoothing is a highly flexible and popular approach for estimation of
probability density and intensity functions of continuous spatial data. In this
role it also forms an integral part of estimation of functionals such as the
density-ratio or "relative risk" surface. Originally developed with the
epidemiological motivation of examining fluctuations in disease risk based on
samples of cases and controls collected over a given geographical region, such
functions have also been successfully employed across a diverse range of
disciplines where a relative comparison of spatial density functions has been
of interest. This versatility has demanded ongoing developments and
improvements to the relevant methodology, including use spatially adaptive
smoothers; tests of significantly elevated risk based on asymptotic theory;
extension to the spatiotemporal domain; and novel computational methods for
their evaluation. In this tutorial paper we review the current methodology,
including the most recent developments in estimation, computation and
inference. All techniques are implemented in the new software package sparr,
publicly available for the R language, and we illustrate its use with a pair of
epidemiological examples.


  Generative models have enabled intuitive image creation and manipulation
using natural language. In particular, diffusion models have recently shown
remarkable results for natural image editing. In this work, we propose to apply
diffusion techniques to edit textures, a specific class of images that are an
essential part of 3D content creation pipelines. We analyze existing editing
methods and show that they are not directly applicable to textures, since their
common underlying approach, manipulating attention maps, is unsuitable for the
texture domain. To address this, we propose a novel approach that instead
manipulates CLIP image embeddings to condition the diffusion generation. We
define editing directions using simple text prompts (e.g., "aged wood" to "new
wood") and map these to CLIP image embedding space using a texture prior, with
a sampling-based approach that gives us identity-preserving directions in CLIP
space. To further improve identity preservation, we project these directions to
a CLIP subspace that minimizes identity variations resulting from entangled
texture attributes. Our editing pipeline facilitates the creation of arbitrary
sliders using natural language prompts only, with no ground-truth annotated
data necessary.


  Aims. With the accumulation of polarization data in the gamma-ray burst (GRB)
prompt phase, polarization models can be tested. Methods. We predicted the
time-integrated polarizations of 37 GRBs with polarization observation. We used
their observed spectral parameters to do this. In the model, the emission
mechanism is synchrotron radiation, and the magnetic field configuration in the
emission region was assumed to be large-scale ordered. Therefore, the predicted
polarization degrees (PDs) are upper limits. Results. For most GRBs detected by
the Gamma-ray Burst Polarimeter (GAP), POLAR, and AstroSat, the predicted PD
can match the corresponding observed PD. Hence the synchrotron-emission model
in a large-scale ordered magnetic field can interpret both the moderately low
PDs ($\sim10\%$) detected by POLAR and relatively high PDs ($\sim45\%$)
observed by GAP and AstroSat well. Therefore, the magnetic fields in these GRB
prompt phases or at least during the peak times are dominated by the ordered
component. However, the predicted PDs of GRB 110721A observed by GAP and GRB
180427A observed by AstroSat are both lower than the observed values. Because
the synchrotron emission in an ordered magnetic field predicts the upper-limit
of the PD for the synchrotron-emission models, PD observations of the two
bursts challenge the synchrotron-emission model. Then we predict the PDs of the
High-energy Polarimetry Detector (HPD) and Low-energy Polarimetry Detector
(LPD) on board the upcoming POLAR-2. In the synchrotron-emission models, the
concentrated PD values of the GRBs detected by HPD will be higher than the LPD,
which might be different from the predictions of the dissipative photosphere
model. Therefore, more accurate multiband polarization observations are highly
desired to test models of the GRB prompt phase.


  Optics and photonics has recently captured interest as a platform to
accelerate linear matrix processing, that has been deemed as a bottleneck in
traditional digital electronic architectures. In this paper, we propose an
all-photonic artificial neural network processor wherein information is encoded
in the amplitudes of frequency modes that act as neurons. The weights among
connected layers are encoded in the amplitude of controlled frequency modes
that act as pumps. Interaction among these modes for information processing is
enabled by non-linear optical processes. Both the matrix multiplication and
element-wise activation functions are performed through coherent processes,
enabling the direct representation of negative and complex numbers without the
use of detectors or digital electronics. Via numerical simulations, we show
that our design achieves a performance commensurate with present-day
state-of-the-art computational networks on image-classification benchmarks. Our
architecture is unique in providing a completely unitary, reversible mode of
computation. Additionally, the computational speed increases with the power of
the pumps to arbitrarily high rates, as long as the circuitry can sustain the
higher optical power.


  We show that a metasurface-coated two-dimensional (2D) slab waveguide enables
the generation of arbitrary complex light fields by combining the extreme
versatility and freedom on wavefront control of optical metasurfaces with the
compactness of photonic integrated circuits. We demonstrated off-chip 2D
focusing and holographic projection with our metasurface-dressed photonic
integrated devices. This technology holds the potential for many other optical
applications requiring 2D light field manipulation with full on-chip
integration, such as solid-state LiDAR and near-eye AR/VR displays.


  High-dimensional expanders generalize the notion of expander graphs to
higher-dimensional simplicial complexes. In contrast to expander graphs, only a
handful of high-dimensional expander constructions have been proposed, and no
elementary combinatorial construction with near-optimal expansion is known. In
this paper, we introduce an improved combinatorial high-dimensional expander
construction, by modifying a previous construction of Liu, Mohanty, and Yang
(ITCS 2020), which is based on a high-dimensional variant of a tensor product.
Our construction achieves a spectral gap of $\Omega(\frac{1}{k^2})$ for random
walks on the $k$-dimensional faces, which is only quadratically worse than the
optimal bound of $\Theta(\frac{1}{k})$. Previous combinatorial constructions,
including that of Liu, Mohanty, and Yang, only achieved a spectral gap that is
exponentially small in $k$. We also present reasoning that suggests our
construction is optimal among similar product-based constructions.


  This paper introduces the first dataset of satellite images labeled with
forage quality by on-the-ground experts and provides proof of concept for
applying computer vision methods to index-based drought insurance. We also
present the results of a collaborative benchmark tool used to crowdsource an
accurate machine learning model on the dataset. Our methods significantly
outperform the existing technology for an insurance program in Northern Kenya,
suggesting that a computer vision-based approach could substantially benefit
pastoralists, whose exposure to droughts is severe and worsening with climate
change.


  Data of Pierre Auger Observatory show a proton-dominated chemical composition
of ultrahigh-energy cosmic rays spectrum at (1 - 3) EeV and a steadily heavier
composition with energy increasing. In order to explain this feature we assume
that (1 - 3) EeV protons are extragalactic and derive their maximum
acceleration energy, E_p^{max} \simeq 4 EeV, compatible with both the spectrum
and the composition. We also assume the rigidity-dependent acceleration
mechanism of heavier nuclei, E_A^{max} = Z x E_p^{max}. The proposed model has
rather disappointing consequences: i) no pion photo-production on CMB photons
in extragalactic space and hence ii) no high-energy cosmogenic neutrino fluxes;
iii) no GZK-cutoff in the spectrum; iv) no correlation with nearby sources due
to nuclei deflection in the galactic magnetic fields up to highest energies.


  We formulate an exact result, which we refer to as the pumping restriction
theorem (PRT). It imposes strong restrictions on the currents generated by
periodic driving in a generic dissipative system with detailed balance. Our
theorem unifies previously known results with the new ones and provides a
universal nonperturbative approach to explore further restrictions on the
stochastic pump effect in non-adiabatically driven systems.


  Prediction is a complex notion, and different predictors (such as people,
computer programs, and probabilistic theories) can pursue very different goals.
In this paper I will review some popular kinds of prediction and argue that the
theory of competitive on-line learning can benefit from the kinds of prediction
that are now foreign to it.


  Street networks may be planned according to clear organizing principles or
they may evolve organically through accretion, but their configurations and
orientations help define a city's spatial logic and order. Measures of entropy
reveal a city's streets' order and disorder. Past studies have explored
individual cases of orientation and entropy, but little is known about broader
patterns and trends worldwide. This study examines street network orientation,
configuration, and entropy in 100 cities around the world using OpenStreetMap
data and OSMnx. It measures the entropy of street bearings in weighted and
unweighted network models, along with each city's typical street segment
length, average circuity, average node degree, and the network's proportions of
four-way intersections and dead-ends. It also develops a new indicator of
orientation-order that quantifies how a city's street network follows the
geometric ordering logic of a single grid. A cluster analysis is performed to
explore similarities and differences among these study sites in multiple
dimensions. Significant statistical relationships exist between city
orientation-order and other indicators of spatial order, including street
circuity and measures of connectedness. On average, US/Canadian study sites are
far more grid-like than those elsewhere, exhibiting less entropy and circuity.
These indicators, taken in concert, help reveal the extent and nuance of the
grid. These methods demonstrate automatic, scalable, reproducible tools to
empirically measure and visualize city spatial order, illustrating complex
urban transportation system patterns and configurations around the world.


  In this contribution to the special issue on magnetoelectrics and their
applications, we focus on some single phase multiferroics theoretically
predicted and/or experimentally discovered by the authors in recent years. In
these materials, iron is the common core element. However, these materials are
conceptually different from the mostly-studied BiFeO$_3$, since their
ferroelectricity is improper. Our reviewed materials are not simply repeating
one magnetoelectric mechanism, but cover multiple branches of improper
ferroelectricity, including the magnetism-driven ferroelectrics, geometric
ferroelectric, as well as electronic ferroelectric driven by charge ordering.
In this sense, these iron-based improper ferroelectrics can be an encyclopaedic
playground to explore the comprehensive physics of multiferroics and
magnetoelectricity. Furthermore, the unique characteristics of iron's $3d$
orbitals make some of their magnetoelectric properties quite prominent,
comparing with the extensively-studied Mn-based improper multiferroics. In
addition, these materials establish the crossover between multiferroics and
other fields of functional materials, which enlarges the application scope of
multiferroics.


  We present a simple quantitative model that can describe the photometric B
and V band light curves of AM Herculis obtained during a high state. The
double-humped shape of the V band light curve is dominated by cyclotron
emission from a region at the main accreting pole with an area of ~5x10e16 cm^2
and sustaining an inflow of ~0.06 g/cm^2/s. The almost unmodulated B band is
dominated by emission from the accretion stream. The contribution of the heated
white dwarf to the optical emission is small in the B band, but comparable to
that of the accretion stream in the V band. The emission of the secondary star
is negligible both in B and in V.


  We present near-infrared observations of 35 of the most massive early-type
galaxies in the local universe. The observations were made using the infrared
channel of the Hubble Space Telescope Wide Field Camera 3 in the F110W (1.1
$\mu$m) filter. We measured surface brightness profiles and elliptical
isophotal fit parameters from the nuclear regions out to a radius of ~10 kpc in
most cases. We find that 37% (13) of the galaxies in our sample have isophotal
position angle rotations greater than 20 degrees over the radial range imaged
by WFC3/IR, which is often due to the presence of neighbors or multiple nuclei.
Most galaxies in our sample are significantly rounder near the center than in
the outer regions. This sample contains six fast rotators and 28 slow rotators.
We find that all fast rotators are either disky or show no measurable deviation
from purely elliptical isophotes. Among slow rotators, significantly disky and
boxy galaxies occur with nearly equal frequency. The galaxies in our sample
often exhibit changing isophotal shapes, sometimes showing both significantly
disky and boxy isophotes at different radii. The fact that parameters vary
widely between galaxies and within individual galaxies is evidence that these
massive galaxies have complicated formation histories, and some of them have
experienced recent mergers and have not fully relaxed. These data demonstrate
the value of high spatial resolution IR imaging of galaxies and provide
measurements necessary for determining stellar masses, dynamics, and black hole
masses in high mass galaxies.


  We develop a technique to present Higgs coupling measurements, which decouple
the poorly defined theoretical uncertainties associated to inclusive and
exclusive cross section predictions. The technique simplifies the combination
of multiple measurements and can be used in a more general setting. We
illustrate the approach with toy LHC Higgs coupling measurements and a
collection of new physics models.