  The policy debate around researchers' geographic mobility has been moving
away from a theorized zero-sum game in which countries can be winners (brain
gain) or losers (brain drain), and toward the concept of brain circulation,
which implies that researchers move in and out of countries and everyone
benefits. Quantifying trends in researchers' movements is key to understanding
the drivers of the mobility of talent, as well as the implications of these
patterns for the global system of science, and for the competitive advantages
of individual countries. Existing studies have investigated bilateral flows of
researchers. However, in order to understand migration systems, determining the
extent to which researchers have worked in more than two countries is
essential. This study focuses on the subgroup of highly mobile researchers whom
we refer to as peripatetic researchers or super-movers.
  More specifically, our aim is to track the international movements of
researchers who have published in more than two countries through changes in
the main affiliation addresses of researchers in over 62 million publications
indexed in the Web of Science database over the 1956-2016 period. Using this
approach, we have established a longitudinal dataset on the international
movements of highly mobile researchers across all subject categories, and in
all disciplines of scholarship. This article contributes to the literature by
offering for the first time a snapshot of the key features of highly mobile
researchers, including their patterns of migration and return migration by
academic age, the relative frequency of their disciplines, and the relative
frequency of their countries of origin and destination. Among other findings,
the results point to the emergence of a global system that includes the USA and
China as two large hubs, and England and Germany as two smaller hubs for highly
mobile researchers.


  We show that when the induced parity breaking part of the effective action
for the low-momentum region of U(1) x ... x U(1) Maxwell gauge field theory
with massive fermions in 3 dimensions is coupled to a \phi^4 scalar field
theory, it is not possible to eliminate the screening of the long-range Coulomb
interactions and get external charges confined in the broken Higgs phase. This
result is valid for non-zero temperature as well.


  We study the geometry of the Lagrangian Batalin--Vilkovisky theory on an
antisymplectic manifold. We show that gauge symmetries of the BV-theory are
essentially the symmetries of an even symplectic structure on the stationary
surface of the master action.


  Resource tradeoffs can often be established by solving an appropriate robust
optimization problem for a variety of scenarios involving constraints on
optimization variables and uncertainties. Using an approach based on sequential
convex programming, we demonstrate that quantum gate transformations can be
made substantially robust against uncertainties while simultaneously using
limited resources of control amplitude and bandwidth. Achieving such a high
degree of robustness requires a quantitative model that specifies the range and
character of the uncertainties. Using a model of a controlled one-qubit system
for illustrative simulations, we identify robust control fields for a universal
gate set and explore the tradeoff between the worst-case gate fidelity and the
field fluence. Our results demonstrate that, even for this simple model, there
exist a rich variety of control design possibilities. In addition, we study the
effect of noise represented by a stochastic uncertainty model.


  Specifying legal requirements for software systems to ensure their compliance
with the applicable regulations is a major concern to requirements engineering
(RE). Personal data which is collected by an organization is often shared with
other organizations to perform certain processing activities. In such cases,
the General Data Protection Regulation (GDPR) requires issuing a data
processing agreement (DPA) which regulates the processing and further ensures
that personal data remains protected. Violating GDPR can lead to huge fines
reaching to billions of Euros. Software systems involving personal data
processing must adhere to the legal obligations stipulated in GDPR and outlined
in DPAs. Requirements engineers can elicit from DPAs legal requirements for
regulating the data processing activities in software systems. Checking the
completeness of a DPA according to the GDPR provisions is therefore an
essential prerequisite to ensure that the elicited requirements are complete.
Analyzing DPAs entirely manually is time consuming and requires adequate legal
expertise. In this paper, we propose an automation strategy to address the
completeness checking of DPAs against GDPR. Specifically, we pursue ten
alternative solutions which are enabled by different technologies, namely
traditional machine learning, deep learning, language modeling, and few-shot
learning. The goal of our work is to empirically examine how these different
technologies fare in the legal domain. We computed F2 score on a set of 30 real
DPAs. Our evaluation shows that best-performing solutions yield F2 score of
86.7% and 89.7% are based on pre-trained BERT and RoBERTa language models. Our
analysis further shows that other alternative solutions based on deep learning
(e.g., BiLSTM) and few-shot learning (e.g., SetFit) can achieve comparable
accuracy, yet are more efficient to develop.


  Stimulated Emission Depletion Microscopy (STED) can achieve a spatial
resolution as high as several nanometers. As a point scanning imaging method,
it requires 3D scanning to complete the imaging of 3D samples. The
time-consuming 3D scanning can be compressed into a 2D one in the
non-diffracting Bessel-Bessel STED (BB-STED) where samples are effectively
excited by an optical needle. However, the image is just the 2D projection,
i.e., there is no real axial resolution. Therefore, we propose a method to
encode axial information to axially dense emitters by using a detection optical
path with single-helix point spread function (SH-PSF), and then predicted the
depths of the emitters by means of deep learning. Simulation demonstrated that,
for a density 1~ 20 emitters in a depth range of 4 nm, an axial precision of
~35 nm can be achieved. Our method also works for experimental data, and an
axial precision of ~63 nm can be achieved.


  We investigate molecular mechanisms of resistant or sensitive response of
cancer drug combination therapies in an inductive and interpretable manner.
Though deep learning algorithms are widely used in the drug synergy prediction
problem, it is still an open problem to formulate the prediction model with
biological meaning to investigate the mysterious mechanisms of synergy (MoS)
for the human-AI collaboration in healthcare systems. To address the
challenges, we propose a deep graph neural network, IDSP (Interpretable Deep
Signaling Pathways), to incorporate the gene-gene as well as gene-drug
regulatory relationships in synergic drug combination predictions. IDSP
automatically learns weights of edges based on the gene and drug node
relations, i.e., signaling interactions, by a multi-layer perceptron (MLP) and
aggregates information in an inductive manner. The proposed architecture
generates interpretable drug synergy prediction by detecting important
signaling interactions, and can be implemented when the underlying molecular
mechanism encounters unseen genes or signaling pathways. We test IDWSP on
signaling networks formulated by genes from 46 core cancer signaling pathways
and drug combinations from NCI ALMANAC drug combination screening data. The
experimental results demonstrated that 1) IDSP can learn from the underlying
molecular mechanism to make prediction without additional drug chemical
information while achieving highly comparable performance with current
state-of-art methods; 2) IDSP show superior generality and flexibility to
implement the synergy prediction task on both transductive tasks and inductive
tasks. 3) IDSP can generate interpretable results by detecting different
salient signaling patterns (i.e. MoS) for different cell lines.


  The aim of this work is to analyse the radio properties of the massive and
dynamical disturbed clusters Abell 1451 and Zwcl 0634.1+4750, especially
focusing on the possible presence of diffuse emission. We present new GMRT 320
MHz and JVLA 1.5 GHz observations of these two clusters. We found that both
Abell 1451 and Zwcl 0634.1+4750 host a radio halo with a typical spectrum
($\alpha\sim1-1.3$). Similarly to a few other cases reported in the recent
literature, these radio halos are significantly fainter in radio luminosity
with respect to the current radio power-mass correlations and they are smaller
than classical giant radio halos. These underluminous sources might contribute
to shed light on the complex mechanisms of formation and evolution of radio
halos. Furthermore, we detected a candidate radio relic at large distance from
the cluster center in Abell 1451 and a peculiar head tail radio galaxy in Zwcl
0634.1+4750, which might be interacting with a shock front.


  Discrete quantum phase space formalism is used to discuss some basic aspects
of the spin tunneling occurring in Fe8 magnetic cluster by means of Wigner
functions as well as Husimi distributions. Those functions were obtained for
sharp angle states and symmetric combinations of the lowest energy doublet with
the application of an external magnetic field. The time evolution of those
functions carried out numerically allows one to extract valuable information
about the dynamics of the states under consideration, in particular, the
coherent oscillations associated with the spin tunneling. It is also shown that
an entropy functional constructed out of the discrete Husimi distribution
function allows to characterize the spin tunneling.


  We propose a new class of method for solving nonlinear systems of equations,
which, among other things,has four nice features: (i) it is inspired by the
mathematical property of damped oscillators, (ii) it can be regarded as a
simple extention to the Newton-Raphson(NR) method, (iii) it has the same local
convergence as the NR method does, (iv) it has a significantly wider
convergence region or the global convergence than that of the NR method. In
this article, we present the evidence of these properties, applying our new
method to some examples and comparing it with the NR method.


  Trace alignment algorithms have been used in process mining for discovering
the consensus treatment procedures and process deviations. Different alignment
algorithms, however, may produce very different results. No widely-adopted
method exists for evaluating the results of trace alignment. Existing
reference-free evaluation methods cannot adequately and comprehensively assess
the alignment quality. We analyzed and compared the existing evaluation
methods, identifying their limitations, and introduced improvements in two
reference-free evaluation methods. Our approach assesses the alignment result
globally instead of locally, and therefore helps the algorithm to optimize
overall alignment quality. We also introduced a novel metric to measure the
alignment complexity, which can be used as a constraint on alignment algorithm
optimization. We tested our evaluation methods on a trauma resuscitation
dataset and provided the medical explanation of the activities and patterns
identified as deviations using our proposed evaluation methods.


  Permanental sequences with non-symmetric kernels that are generalization of
the potentials of a Markov chain with state space $\{0,1/2, \ldots,
1/n,\ldots\}$ that was introduced by Kolmogorov, are studied. Depending on a
parameter in the kernels we obtain an exact rate of divergence of the sequence
at $0$, an exact local modulus of continuity of the sequence at $0$, or a
precise bounded discontinuity for the sequence at $0$.


  Recent huge technological development of Unmanned Aerial Vehicles (UAVs) can
provide breakthrough means of fighting wildland fires. We propose an innovative
forest firefighting system based on the use of a swarm of hundreds of UAVs able
to generate a continuous flow of extinguishing liquid on the fire front,
simulating rain effect. Automatic battery replacement and refilling of the
extinguishing liquid ensure the continuity of the action, and fire-resistant
materials protect drones exposed to possible high temperatures. We demonstrate
the validity of the approach in Mediterranean scrub first computing the
critical water flow rate according to the main factors involved in the
evolution of a fire, then estimating the number of linear meters of active fire
front that can be extinguished depending on the number of drones available and
the amount of extinguishing fluid carried. A fire propagation cellular automata
model is also employed to study the evolution of the fire. Simulation results
suggest that the proposed system can successfully integrate, or in case of
low-intensity and limited extent fires completely replace, current forest
firefighting techniques.


  We demonstrate guiding of cold neutral atoms along a current carrying wire.
Atoms either move in Kepler-like orbits around the wire or are guided in a
potential tube on the side of the wire which is created by applying an
additional homogeneous bias field. These atom guides are very versatile and
promising for applications in atom optics.


  Handwritten character recognition is one of the most challenging and ongoing
areas of research in the field of pattern recognition. HCR research is matured
for foreign languages like Chinese and Japanese but the problem is much more
complex for Indian languages. The problem becomes even more complicated for
South Indian languages due to its large character set and the presence of
vowels modifiers and compound characters. This paper provides an overview of
important contributions and advances in offline as well as online handwritten
character recognition of Malayalam scripts.


  In physics and engineering literature, the distribution of the
excursion-above-zero time distribution (exceedance distribution) for a
stationary Gaussian process has been approximated by a stationary switching
process with independently distributed switching times. The approach matched
the covariance of the clipped Gaussian process with the one for the stationary
switching process and the distribution of the latter was used as the so-called
independent interval approximation (IIA). The approach successfully assessed
the persistency exponent for many physically important processes but left an
unanswered question when such an approach leads to a mathematically meaningful
and proper exceedance distribution. Here we address this question by proposing
an alternative matching of the expected values of the clipped Slepian process
and the corresponding switched process initiated at the origin. The method has
allowed resolving the mathematical correctness of the matching method for a
large subclass of the Gaussian processes with monotonic covariance, for which
we provide a sufficient condition for the validity of the IIA. Within this
class, the IIA produces a valid distribution for the excursion time and is
represented in an explicit stochastic form that connects directly to the
covariance of the underlying Gaussian process. We compare the excursion level
distributions as well as the corresponding persistency exponents obtained
through the IIA method with numerically computed exact distributions, and the
simulated distribution for several important Gaussian models. We also argue
that for stationary Gaussian processes with a non-monotonic covariance, the IIA
fails and should not be used.


  "M-Bias," as it is called in the epidemiologic literature, is the bias
introduced by conditioning on a pretreatment covariate due to a particular
"M-Structure" between two latent factors, an observed treatment, an outcome,
and a "collider." This potential source of bias, which can occur even when the
treatment and the outcome are not confounded, has been a source of considerable
controversy. We here present formulae for identifying under which circumstances
biases are inflated or reduced. In particular, we show that the magnitude of
M-Bias in linear structural equation models tends to be relatively small
compared to confounding bias, suggesting that it is generally not a serious
concern in many applied settings. These theoretical results are consistent with
recent empirical findings from simulation studies. We also generalize the
M-Bias setting (1) to allow for the correlation between the latent factors to
be nonzero, and (2) to allow for the collider to be a confounder between the
treatment and the outcome. These results demonstrate that mild deviations from
the M-Structure tend to increase confounding bias more rapidly than M-Bias,
suggesting that choosing to condition on any given covariate is generally the
superior choice. As an application, we re-examine a controversial example
between Professors Donald Rubin and Judea Pearl.


  We theoretically investigate the joint photodetection probabilities of the
biexciton-exciton cascade in single semiconductor quantum dots and analytically
derive the density matrix and the Bell's inequalities of the entangled state.
Our model includes different mechanisms that may spoil or even destroy
entanglement such as dephasing, energy splitting of the relay excitonic states
and incoherent population exchange between these relay levels. We explicitly
relate the fidelity of entanglement to the dynamics of these processes and
derive a threshold for violation of Bell's inequalities. Applied to standard
InAs/GaAs self-assembled quantum dots, our model indicates that spontaneous
emission enhancement of the excitonic states by cavity effects increases the
fidelity of entanglement to a value allowing for violation of Bell's
inequalities.


  Using a numerical simulation of the evolution of a qubit interacting with the
environment we show that quantum error detection and correction can work
effectively even when the recovery procedure introduces errors.


  Binary black hole mergers are analyzed using concepts from introductory
physics. Drawing upon Newtonian mechanics, dimensional considerations, and
analogies between gravitational and electromagnetic waves, we are able to
explain the principal features of LIGO's data and make order of magnitude
estimates of key parameters of the event by inspection of the data. Our
estimates of the black hole masses, the distance to the event, the initial
separation of the pair, and the stupendous total amount of energy radiated are
all in good agreement with the best fit values of these parameters obtained by
the LIGO-VIRGO collaboration.


  We study probabilistic single-item second-price auctions where the item is
characterized by a set of attributes. The auctioneer knows the actual
instantiation of all the attributes, but he may choose to reveal only a subset
of these attributes to the bidders. Our model is an abstraction of the
following Ad auction scenario. The website (auctioneer) knows the demographic
information of its impressions, and this information is in terms of a list of
attributes (e.g., age, gender, country of location). The website may hide
certain attributes from its advertisers (bidders) in order to create thicker
market, which may lead to higher revenue. We study how to hide attributes in an
optimal way. We show that it is NP-hard to solve for the optimal attribute
hiding scheme. We then derive a polynomial-time solvable upper bound on the
optimal revenue. Finally, we propose two heuristic-based attribute hiding
schemes. Experiments show that revenue achieved by these schemes is close to
the upper bound.


  The ICM plasma is subject to firehose and mirror instabilities at scales of
order the ion Larmor radius. The mirror instability generates fluctuations of
magnetic-field strength $\delta B / B \sim 1$. These fluctuations act as
magnetic traps for the heat-conducting electrons, suppressing their transport.
We calculate the effective parallel thermal conductivity in the ICM in the
presence of the mirror fluctuations for different stages of the evolution of
the instability. The mirror fluctuations are limited in amplitude by the
maximum and minimum values of the field strength, with no large deviations from
the mean value. This key property leads to a finite suppression of thermal
conduction at large scales. We find suppression down to $\approx 0.2$ of the
Spitzer value for the secular phase of the perturbations' growth, and $\approx
0.3$ for their saturated phase. The effect operates in addition to other
suppression mechanisms and independently of them. Globally, fluctuations
$\delta B / B \sim 1$ can be present on much larger scales, of the order of the
scale of turbulent motions. However, we do not expect large suppression of
thermal conduction by these, because their scale is considerably larger than
the collisional mean free path of the ICM electrons. The obtained suppression
of thermal conduction by a factor of $\sim 5$ appears to be characteristic and
potentially universal for a weakly collisional mirror-unstable plasma.


  We provide evidence that the Hausdorff dimension is 4 and the spectral
dimension is 2 for two-dimensional quantum gravity coupled the matter with a
central charge $c \leq 1$. For $c > 1$ the Hausdorff dimension and the spectral
dimension monotonously decreases to 2 and 1, respectively.


  Recent experimental observations of anisotropic conductivity in stretched
polymer electrolytes films of the polyethylene oxide family are discussed. The
main experimental observations, enhancement of the ionic diffusion and
conductivity in the stretch direction and decrease in these transport
coefficients in the normal direction are interpreted in terms of an effective
two-phase model. This two-phase model is based on the idea that a highly
conducting phase is associated with oriented molecular structures which are
surrounded by poorly conducting boundary regions. This model is evaluated
within the framework of differential effective medium theory (DEMT). Under
stretching these regions change from spherical to prolate-spheroidal shapes.
The computed dependence of the DC conductivity tensor and its AC counterpart on
the stretch parameters is in good agreement with experimental results.


  In recent years, deep learning models have revolutionized medical image
interpretation, offering substantial improvements in diagnostic accuracy.
However, these models often struggle with challenging images where critical
features are partially or fully occluded, which is a common scenario in
clinical practice. In this paper, we propose a novel curriculum learning-based
approach to train deep learning models to handle occluded medical images
effectively. Our method progressively introduces occlusion, starting from
clear, unobstructed images and gradually moving to images with increasing
occlusion levels. This ordered learning process, akin to human learning, allows
the model to first grasp simple, discernable patterns and subsequently build
upon this knowledge to understand more complicated, occluded scenarios.
Furthermore, we present three novel occlusion synthesis methods, namely
Wasserstein Curriculum Learning (WCL), Information Adaptive Learning (IAL), and
Geodesic Curriculum Learning (GCL). Our extensive experiments on diverse
medical image datasets demonstrate substantial improvements in model robustness
and diagnostic accuracy over conventional training methodologies.


  We present the application of the SU($N$) ($N>2$) spin-wave theory to
spin-orbital Mott insulators whose ground states exhibit magnetic orders. When
taking both the spin and orbital degrees of freedom into account rather than
projecting onto the Kramers doublet, the lowest spin-orbital locking energy
levels, due to the inevitable spin-orbital multipole exchange interactions, the
SU($N$) spin-wave theory should take the place of the SU($2$) one. To implement
the application, we introduce an efficient general local mean field approach
which involves all the local fluctuations into the SU($N$) linear spin-wave
theory. Our approach is tested firstly by calculating the multipolar spin-wave
spectra of the SU($4$) antiferromagnetic model. Then we apply it to
spin-orbital Mott insulators. It is revealed that the Hund's coupling would
influence the effectiveness of the isospin-$1/2$ representation when the spin
orbital coupling is not large enough. Besides, we also calculate the spin-wave
spectra based on the first principle calculations for two concrete materials,
$\alpha$-RuCl$_3$ and Sr$_2$IrO$_4$. The SU($N$) spin-wave theory appropriately
depicts the low-energy magnons and the spin-orbital excitations qualitatively.


  A neutron-energy dependent angular distribution was measured for individual
$\gamma$-rays from the 3.2 eV $p$-wave resonance of $^{131}$Xe+$n$, that shows
enhanced parity violation owing to a mixing between $s$- and $p$-wave
amplitudes. The $\gamma$-ray transitions from the $p$-wave resonance were
identified, and the angular distribution with respect to the neutron momentum
was evaluated as a function of the neutron energy for 7132 keV $\gamma$-rays,
which correspond to a transition to the 1807 keV excited state of $^{132}$Xe.
The angular distribution is considered to originate from the interference
between $s$- and $p$-wave amplitudes, and will provide a basis for a
quantitative understanding of the enhancement mechanism of the fundamental
parity violation in compound nuclei.


  We study the possibility that further advancement in the understanding of the
order of chaos may demand a certain reconsideration of the approach to the
classical mechanics. For this we suggest to consider the viewpoint that
spatio-temporal relations between objects are emergent and that they are but a
result of complex interactions of objects. Such an approach is a natural
continuation of the revision of the notions of space and time started by the
general theory of relativity. It leads to a possible extension of the structure
of the classical mechanics. Namely, the result of the objects interactions can
be wider than the spatio-temporal relations. In this case interactions form a
generalized space ("Field-Space") wider than its spatio-temporal section (while
the known interactions are embedded within that section). The study of such
hypothesis demands constructing a theory, not relying on space and time as
primitive notions. As primary elements of such a theory we consider objects and
purely informational connections between them, not expressed in spatio-temporal
terms. Following the logic of this theory, the world constitutes a complex
information web. The Web is in the state of a constant flux (of a more general
category than the one of the quantum fields) - an incessant change of
connections, the complex order of which comprises the order of chaos. Reminding
the space build-up from the Regge skeleton, the Web builds a unified
Field-Space of pure information, manifested in space as energy distribution. We
describe the emergence of spatio-temporal laws from the viewpoint of purely
informational physics. The suggested construction of physics on the purely
informational basis is a candidate for the theory of quantum gravity.


  Deep Learning (DL) models excel in computer vision tasks but can be
susceptible to adversarial examples. This paper introduces Triple-Metric
EvoAttack (TM-EVO), an efficient algorithm for evaluating the robustness of
object-detection DL models against adversarial attacks. TM-EVO utilizes a
multi-metric fitness function to guide an evolutionary search efficiently in
creating effective adversarial test inputs with minimal perturbations. We
evaluate TM-EVO on widely-used object-detection DL models, DETR and Faster
R-CNN, and open-source datasets, COCO and KITTI. Our findings reveal that
TM-EVO outperforms the state-of-the-art EvoAttack baseline, leading to
adversarial tests with less noise while maintaining efficiency.


  The view provided by Z theory, based on its quantum-relativistic operators,
is an integrated picture of the micro and macro quantities relationships. The
axiomatic formulation of the theory is presented in this paper. The theory
starts with the existence of the wave function, the existence of three
fundamental constants h, c and G as well as the physical quantity Rc (the
radius of the space-time continuum) plus the definition of a general form for
the quantum-relativistic functional operators. Using such starting point the
relationships between relativity, quantum mechanics and cosmological quantities
can be clarified.


  In this paper, we present the acoustic transceiver developed for the
positioning system in underwater neutrino telescopes. These infrastructures are
not completely rigid and need a positioning system in order to monitor the
position of the optical sensors of the telescope which have some degree of
motion due to sea currents. To have a highly reliable and versatile system in
the infrastructure, the transceiver has the requirements of reduced cost, low
power consumption, high intensity for emission, low intrinsic noise, arbitrary
signals for emission and the capacity of acquiring and processing the received
signal on the board. The solution proposed and presented here consists of an
acoustic transducer that works in the 20-40 kHz region and withstands high
pressures (up to 500 bars). The electronic-board can be configured from shore
and is able to feed the transducer with arbitrary signals and to control the
transmitted and received signals with very good timing precision. The results
of the different tests done on the transceiver in the laboratory are described
here, as well as the change implemented for its integration in the
Instrumentation Line of ANTARES for the in situ tests. We consider the
transceiver design is so versatile that it may be used in other kinds of marine
positioning systems, alone or combined with other marine systems, or integrated
in different Earth-Sea Observatories, where the localization of the sensors is
an issue.


  The Kondo effect arises due to the interaction between a localized spin and
the electrons of a surrounding host. Studies of individual magnetic impurities
by scanning tunneling spectroscopy have renewed interest in Kondo physics;
however, a quantitative comparison with theoretical predictions remained
challenging. Here we show that the zero-bias anomaly detected on an organic
radical weakly coupled to a Au (111) surface can be described with astonishing
agreement by perturbation theory as originally developed by Kondo 60 years ago.
Our results demonstrate that Kondo physics can only be fully conceived by
studying both temperature and magnetic field dependence of the resonance. The
identification of a spin 1/2 Kondo system is of relevance not only as a
benchmark for predictions for Kondo physics but also for correlated electron
materials in general.


  In \cite{cheung2019optimally}, the authors presented two finite element
methods for approximating second order boundary value problems on polytopial
meshes with optimal accuracy without having to utilize curvilinear mappings.
This was done by enforcing the boundary conditions through judiciously chosen
polynomial extension operators. The $H^1$ error estimates were proven to be
optimal for the solutions of both the Dirichlet and Neumann boundary value
problems. It was also proven that the Dirichlet problem approximation converges
optimally in $L^2$. However, optimality of the Neumann approximation in the
$L^2$ norm was left as an open problem. In this work, we seek to close this
problem by presenting new analysis that proves optimal error estimates for the
Neumann approximation in the $W^1_\infty$ and $L^2$ norms.


  We prove, using optimal transport tools, weighted Poincar'e inequalities for
log-concave random vectors satisfying some centering conditions. We recover by
this way similar results by Klartag and Barthe-Cordero-Erausquin for
log-concave random vectors with symmetries. In addition, we prove that the
variance conjecture is true for increments of log-concave martingales.


  Tuning the durations of the Hamiltonian flow in Hamiltonian Monte Carlo (also
called Hybrid Monte Carlo) (HMC) involves a tradeoff between computational cost
and sampling quality, which is typically challenging to resolve in a
satisfactory way. In this article we present and analyze a randomized HMC
method (RHMC), in which these durations are i.i.d. exponential random variables
whose mean is a free parameter. We focus on the small time step size limit,
where the algorithm is rejection-free and the computational cost is
proportional to the mean duration. In this limit, we prove that RHMC is
geometrically ergodic under the same conditions that imply geometric ergodicity
of the solution to underdamped Langevin equations. Moreover, in the context of
a multi-dimensional Gaussian distribution, we prove that the sampling
efficiency of RHMC, unlike that of constant duration HMC, behaves in a regular
way. This regularity is also verified numerically in non-Gaussian target
distributions. Finally we suggest variants of RHMC for which the time step size
is not required to be small.


  The STCP (Stream Control Transmission Protocol) is a candidate for a new
transport layer protocol that may replace the TCP (Transmission Control
Protocol) and the UDP (User Datagram Protocol) protocols in future IP networks.
Currently, the SCTP is implemented in, or can be added to, many popular
operating systems (Windows, BSD, Linux, HPUX or Sun Solaris). This paper
identifies and presents all possible "places" where hidden information can be
exchanged using an SCTP. The paper focuses mostly on proposing new
steganographic methods that can be applied to an SCTP and that can utilise new,
characteristic SCTP features, such as multi-homing and multi-streaming.
Moreover, for each method, the countermeasure is covered. When used with
malicious intent, a method may pose a threat to network security. Knowledge
about potential SCTP steganographic methods may be used as a supplement to
RFC5062, which describes security attacks in an SCTP protocol. Presented in
this paper is a complete analysis of information hiding in an SCTP, and this
analysis can be treated as a "guide" when developing steganalysis (detection)
tools.


  The book gives a detailed exposition of basic concepts and results of a
theory of processes. The presentation of theoretical concepts and results is
accompanied with illustrations of their application to solving various problems
of verification of processes. Along with well-known results there are presented
author's results related to verification of processes with message passing, and
there are given examples of an application of these results.


  Predictive models make mistakes. Hence, there is a need to quantify the
uncertainty associated with their predictions. Conformal inference has emerged
as a powerful tool to create statistically valid prediction regions around
point predictions, but its naive application to regression problems yields
non-adaptive regions. New conformal scores, often relying upon quantile
regressors or conditional density estimators, aim to address this limitation.
Although they are useful for creating prediction bands, these scores are
detached from the original goal of quantifying the uncertainty around an
arbitrary predictive model. This paper presents a new, model-agnostic family of
methods to calibrate prediction intervals for regression problems with local
coverage guarantees. Our approach is based on pursuing the coarsest partition
of the feature space that approximates conditional coverage. We create this
partition by training regression trees and Random Forests on conformity scores.
Our proposal is versatile, as it applies to various conformity scores and
prediction settings and demonstrates superior scalability and performance
compared to established baselines in simulated and real-world datasets. We
provide a Python package clover that implements our methods using the standard
scikit-learn interface.


  A recently introduced concept of complexity for relativistic fluids is
extended to the vacuum solutions represented by the Bondi metric. A complexity
hierarchy is established, ranging from the Minkowski spacetime (the simplest
one) to gravitationally radiating systems (the more complex). Particularly
interesting is the possibility to differentiate between natural non--radiative
(NNRS) and non--natural non--radiative (NNNRS) systems, the latter appearing to
be simpler than the former. The relationship between vorticity and the degree
of complexity is stressed.


  An EMCCD camera, designed from the ground up for extreme faint flux imaging,
is presented. CCCP, the CCD Controller for Counting Photons, has been
integrated with a CCD97 EMCCD from e2v technologies into a scientific camera at
the Laboratoire d'Astrophysique Experimentale (LAE), Universite de Montreal.
This new camera achieves sub-electron read-out noise and very low Clock Induced
Charge (CIC) levels, which are mandatory for extreme faint flux imaging. It has
been characterized in laboratory and used on the Observatoire du Mont Megantic
1.6-m telescope. The performance of the camera is discussed and experimental
data with the first scientific data are presented.


  The first and most compelling evidence of the universe's expansion was, and
continues to be, the observed redshift of spectra from distant objects. This
paper plays "devil's advocate" by providing an alternative explanation with
elementary physics. I assume a steady-state universe that is infinite in both
expanse and age, with the observed redshifts caused by particle interactions
creating an overall index of refraction of the universe. The cumulative effects
of these interactions over long distances cause not only the shifts that we
observe, but also the monotonically increasing redshifts as more distant
objects are observed. This is a novel explanation for the phenomenon known as
"tired light" which has been discussed for decades.


  A role of the scalar-matter direct coupling in the evolution of scalar-tensor
gravity was studied. If the coupling functions in the generalized scalar-tensor
gravity satisfy a definite equation the scalar-tensor gravity is reduced to
Einstein's tensor gravity. We consider the issue in Jordan frame and then
Einstein frame. It is shown that the coupling strength in the right hand side
of the master equation obtained in Damour and Nordtvedt(1993) must be added by
a coupling strength $\hat{\beta}$ which characterizes the scalar-matter direct
coupling. If a minimum of potential $\ln C(\varphi)$ does not coincide with one
of the potential $\ln A(\varphi)$ or the potential $\ln C(\varphi)$ does not
have any minimum and is a monotonic function, then the attractor mechanism of
scalar-tensor gravity toward pure general relativity should be modified, and
the combined potential $\ln A(\varphi)+\ln C(\varphi)$ will determine the
attractor mechanism.


  The production of top quark-antiquark pair events in $p\bar{p}$ collisions at
$\sqrt{s}=1.96$ TeV is studied as a function of the transverse momentum and
absolute value of the rapidity of the top quarks as well as of the invariant
mass of the $t\bar{t}$ pair. We select events containing an isolated lepton, a
large imbalance in transverse momentum, and four or more jets with at least one
jet identified to originate from a $b$ quark. The data sample corresponds to
9.7 fb$^{-1}$ of integrated luminosity recorded with the D0 detector during Run
II of the Fermilab Tevatron Collider. Observed differential cross sections are
consistent with standard model predictions.


  Vector space models of words have long been claimed to capture linguistic
regularities as simple vector translations, but problems have been raised with
this claim. We decompose and empirically analyze the classic arithmetic word
analogy test, to motivate two new metrics that address the issues with the
standard test, and which distinguish between class-wise offset concentration
(similar directions between pairs of words drawn from different broad classes,
such as France--London, China--Ottawa, ...) and pairing consistency (the
existence of a regular transformation between correctly-matched pairs such as
France:Paris::China:Beijing). We show that, while the standard analogy test is
flawed, several popular word embeddings do nevertheless encode linguistic
regularities.


  By constructing a Landau-like energy functional, we investigate the molecular
organization of a three-component mixture in cell membranes. In the strongly
interacting limit, we model the interaction between molecules using pseudospin
variables and convert them into non-interacting variables using a mean-field
theory. Next, we construct the two-order parameter Landau-type energy
functional through the Helmholtz free energy. By analyzing the Landau free
energy, we map out the phase diagram focusing on homogeneous and various phase
separated states on the cell membrane.


  Chemically-reduced graphene-oxide-supported gold nanoparticles are considered
here as catalytic materials for the reduction of oxygen in alkaline medium.
Gold nanoparticles are prepared by the chemical reduction method, in which the
NaBH4-prereduced Keggin-type phosphomolybdate heteropolyblue acts as the
reducing agent for the precursor (HAuCl4). Polyoxmetallate (PMo12O403-) capping
ligands stabilize gold nanoparticle deposits, facilitate their dispersion and
attachment to carbon supports. Indeed, it is apparent from the independent
diagnostic voltammetric experiments (in 0.5 mol dm-3 H2SO4) that
heteropolymolybdates form readily stable adsorbates on nanostructures of both
gold and carbon (reduced graphene oxide and Vulcan). It is reasonable to expect
that the polyoxometallate-assisted nucleation of gold has occurred in the
proximity of oxygenated defects existing on carbon substrates. Under conditions
of electrochemical diagnostic experiments (performed in 0.1 mol dm-3 KOH): (i)
the phosphomolybdate adsorbates are removed from the interface as they undergo
dissolution in alkaline medium; and (ii) the Au nanoparticles (Au loading, 30
{\mu}g cm-2) remain well-dispersed on the carbon as evident from transmission
electron microscopy. High electrocatalytic activity of the reduced-graphene
oxide-supported Au nanoparticles toward reduction of oxygen in alkaline medium
is demonstrated using cyclic and rotating ring-disk voltammetric experiments.
Among important issues are possible activating interactions between gold and
the support, as well as presence of structural defects existing on poorly
organized graphitic structure of reduced graphene oxide (as evident from Raman
spectroscopy).


  We analyze the gravitational wave (GW) emission from our recently published
set of relativistic neutron star (NS) merger simulations and determine
characteristic signal features that allow one to link GW measurements to the
properties of the merging binary stars. We find that the distinct peak in the
GW energy spectrum that is associated with the formation of a hypermassive
merger remnant has a frequency that depends strongly on the properties of the
nuclear equation of state (EoS) and on the total mass of the binary system,
whereas the mass ratio and the NS spins have a weak influence. If the total
mass can be determined from the inspiral chirp signal, the peak frequency of
the postmerger signal is a sensitive indicator of the EoS.


  Electromagnetic field confinement is crucial for nanophotonic technologies,
since it allows for enhancing light-matter interactions, thus enabling light
manipulation in deep sub-wavelength scales. In the terahertz (THz) spectral
range, radiation confinement is conventionally achieved with specially designed
metallic structures - such as antennas or nanoslits - with large footprints due
to the rather long wavelengths of THz radiation. In this context, phonon
polaritons - light coupled to lattice vibrations - in van der Waals (vdW)
crystals have emerged as a promising solution for controlling light beyond the
diffraction limit, as they feature extreme field confinements and low optical
losses. However, experimental demonstration of nanoscale-confined phonon
polaritons at THz frequencies has so far remained elusive. Here, we provide it
by employing scattering-type scanning near-field optical microscopy (s-SNOM)
combined with a free-electron laser (FEL) to reveal a range of low-loss
polaritonic excitations at frequencies from 8 to 12 THz in the vdW
semiconductor ${\alpha}-MoO_3$. We visualize THz polaritons with i) in-plane
hyperbolic dispersion, ii) extreme nanoscale field confinement (below
${\lambda}_o/75$) and iii) long polariton lifetimes, with a lower limit of > 2
ps.


  Thermodynamic perturbation theory is employed to derive analytical
expressions for the equilibrium linear susceptibility and specific heat of
lattices of anisotropic classical spins weakly coupled by the dipole-dipole
interaction. The calculation is carried out to the second order in the coupling
constant over the temperature, while the single-spin anisotropy is treated
exactly. The temperature range of applicability of the results is, for weak
anisotropy (A/kT << 1), similar to that of ordinary high-temperature
expansions, but for moderately and strongly anisotropic spins (A/kT > 1) it can
extend down to the temperatures where the superparamagnetic blocking takes
place (A/kT \sim 25), provided only the interaction strength is weak enough.
Besides, taking exactly the anisotropy into account, the results describe as
particular cases the effects of the interactions on isotropic (A = 0) as well
as strongly anisotropic (A \to \infty) systems (discrete orientation model and
plane rotators).


  We consider fragments of first-order logic and as models we allow finite and
infinite words simultaneously. The only binary relations apart from equality
are order comparison < and the successor predicate +1. We give
characterizations of the fragments Sigma2 = Sigma2[<,+1] and FO2 = FO2[<,+1] in
terms of algebraic and topological properties. To this end we introduce the
factor topology over infinite words. It turns out that a language L is in the
intersection of FO2 and Sigma2 if and only if L is the interior of an FO2
language. Symmetrically, a language is in the intersection of FO2 and Pi2 if
and only if it is the topological closure of an FO2 language. The fragment
Delta2, which by definition is the intersection of Sigma2 and Pi2 contains
exactly the clopen languages in FO2. In particular, over infinite words Delta2
is a strict subclass of FO2. Our characterizations yield decidability of the
membership problem for all these fragments over finite and infinite words; and
as a corollary we also obtain decidability for infinite words. Moreover, we
give a new decidable algebraic characterization of dot-depth 3/2 over finite
words. Decidability of dot-depth 3/2 over finite words was first shown by
Gla{\ss}er and Schmitz in STACS 2000, and decidability of the membership
problem for FO2 over infinite words was shown 1998 by Wilke in his habilitation
thesis whereas decidability of Sigma2 over infinite words was not known before.


  Depth estimation from stereo images remains a challenge even though studied
for decades. The KITTI benchmark shows that the state-of-the-art solutions
offer accurate depth estimation, but are still computationally complex and
often require a GPU or FPGA implementation. In this paper we aim at increasing
the accuracy of depth map estimation and reducing the computational complexity
by using information from previous frames. We propose to transform the
disparity map of the previous frame into the current frame, relying on the
estimated ego-motion, and use this map as the prediction for the Kalman filter
in the disparity space. Then, we update the predicted disparity map using the
newly matched one. This way we reduce disparity search space and flickering
between consecutive frames, thus increasing the computational efficiency of the
algorithm. In the end, we validate the proposed approach on real-world data
from the KITTI benchmark suite and show that the proposed algorithm yields more
accurate results, while at the same time reducing the disparity search space.


  The most energetic gamma-ray bursts (GRBs) are remarkable sources releasing
huge amounts of energy on short timescales. Their prompt emission, which
usually lasts few seconds, is so bright that it is visible across the whole
observable universe. Studying these extreme events may provide clues on the
nature of GRB progenitors and on the physical processes at work in relativistic
jets.
  In this paper, we study the bright end of the isotropic energy distribution
of long GRBs. We use two samples of long GRBs with redshift detected by
Fermi/GBM or Konus-Wind, two instruments which measure the spectral shape and
the energetics of the prompt emission accurately. We focus on GRBs within a
range of redshifts z = 1 -- 5, a volume that contains a large number of
energetic GRBs, and we propose a simple method to reconstruct the bright end of
the GRB energy distribution from the observed one. We find that the GRB energy
distribution cannot be described by a simple power law but requires a strong
cutoff above $1-3 \times 10^{54}$ erg. We attribute this feature to an
intrinsic limit on the energy per unit of solid angle radiated by gamma-ray
bursts.


  We have developed an efficient numerical method to investigate light
scattering from plasmonic nanospheres on a substrate covered by a shell, based
on the half-space Greens function approach. We use this method to study optical
scattering from DNA molecules attached to metallic nanoparticles on a substrate
and compare with experiment. We obtain fairly good agreement between
theoretical predictions and the measured ellipsometric spectra. The metallic
nanoparticles were used to detect the binding with DNA molecules in a
microfluidic setup via spectroscopic ellipsometry (SE), and a detectable change
in ellipsometric spectra was found when DNA molecules are captured on Au
nanoparticles surface. Our theoretical simulation indicates that the coverage
of Au nanosphere by a submonolayer of DNA molecules, which is modeled by a thin
layer of dielectric material, can indeed lead to a small but detectable change
in ellipsometric spectra. Our studies demonstrated the ultra-sensitive
capability of SE for sensing submonolayer coverage of DNA molecules on Au
nanospheres.


  When solving partial differential equations numerically, usually a high order
spatial discretisation is needed. Model order reduction (MOR) techniques are
often used to reduce the order of spatially-discretised systems and hence
reduce computational complexity. A particular MOR technique to obtain a reduced
order model (ROM) is balanced truncation (BT), a method which has been
extensively studied for deterministic linear systems. As so-called type I BT it
has already been extended to bilinear equations, an important subclass of
nonlinear systems. We provide an alternative generalisation of the linear
setting to bilinear systems which is called type II BT. The Gramians that we
propose in this context contain information about the control. It turns out
that the new approach delivers energy bounds which are not just valid in a
small neighbourhood of zero. Furthermore, we provide an $H_\infty$-error bound
which so far is not known when applying type I BT to bilinear systems.


  Magnetic Resonance Imaging (MRI) experiments have been performed in
conjunction with Direct Numerical Simulations (DNS) to study neutrally buoyant
particle-laden pipe flows. The flows are characterized by the suspension liquid
Reynolds number ($Re_s$), based on the bulk liquid velocity and suspension
viscosity obtained from Eilers' correlation, the bulk solid volume fraction
($\phi_b$) and the particle-to-pipe diameter ratio ($d/D$). Six different cases
have been studied, each with a unique combination of $Re_s$ and $\phi$, while
$d/D$ is kept constant at 0.058. These cases ensure that the comparison is
performed across different flow regimes, each exhibiting characteristic
behavior. In general, an excellent agreement is found between experiment and
simulation for the average liquid velocity and solid volume fraction profiles.
This study presents, for the first time, accurate and quantitative velocity and
volume fraction profiles of semi-dilute up to dense suspension flows using both
experimental and numerical methods. The discrepancy between the experiments and
simulations can be explained by various reasons, including a difference in
particle size distribution, uncertainty in experimental parameters used as an
input for the DNS, slight variations in particle roughness and frictional
collisions in the simulations. Eventually, three different flow regimes are
identified. For low bulk solid volume fractions a drag increase is observed.
For moderate $\phi_b$ the drag is found to decrease, due to particle
accumulation at the pipe centre. For high volume fractions the drag is found to
decrease further. For solid volume fractions of 0.4 a drag reduction higher
than 25% is found. This drag reduction is linked to the strong viscosity
gradient in the radial direction, where the relatively low viscosity near the
pipe wall acts as a lubrication layer between the pipe wall and the dense core.


  Recently, analysis of near-infrared broad-band photometry and Spitzer IRS
spectra has led to the identification of a new "pre-transitional disk" class
whose members have an inner optically thick disk separated from an outer
optically thick disk by an optically thin gap. This is in contrast to the
"transitional disks" which have inner disk holes (i.e. large reductions of
small dust from the star out to an outer optically thick wall). In LkCa 15, one
of these proposed pre-transitional disks, detailed modeling showed that
although the near-infrared fluxes could be understood in terms of optically
thick material at the dust sublimation radius, an alternative model of emission
from optically thin dust over a wide range of radii could explain the
observations as well. To unveil the true nature of LkCa 15's inner disk we
obtained a medium-resolution near-infrared spectrum spanning the wavelength
range 2-5 microns using SpeX at the NASA Infrared Telescope Facility. We report
that the excess near-infrared emission above the photosphere of LkCa 15 is a
black-body continuum which can only be due to optically thick material in an
inner disk around the star. When this confirmation of a primordial inner disk
is combined with earlier observations of an inner edge to LkCa 15's outer disk
it reveals a gapped structure. Forming planets emerge as the most likely
mechanism for clearing the gap we detect in this evolving disk.


  The increasing numbers of rocky, terrestrial exoplanets known to orbit nearby
stars (especially M dwarfs) has drawn increased attention to the possibility of
studying these planets' surface properties, and atmospheric compositions &
escape histories. Here we report the detection of the secondary eclipse of the
terrestrial exoplanet GJ1252b using the Spitzer Space Telescope's IRAC2 4.5
micron channel. We measure an eclipse depth of 149(+25/-32) ppm, corresponding
to a day-side brightness temperature of 1410(+91/-125) K and consistent with
the prediction for no atmosphere. Comparing our measurement to atmospheric
models indicates that GJ1252b has a surface pressure of <10 bar, substantially
less than Venus. Assuming energy-limited escape, even a 100 bar atmosphere
would be lost in <1 Myr, far shorter than estimated age of 3.9+/-0.4 Gyr. The
expected mass loss could be overcome by mantle outgassing, but only if the
mantle's carbon content were >7% by mass - over two orders of magnitude greater
than that found in Earth. We therefore conclude that GJ1252b has no significant
atmosphere. Model spectra with granitoid or feldspathic surface composition,
but with no atmosphere, are disfavored at >2 sigma. The eclipse occurs just
+1.4(+2.8/-1.0) min after orbital phase 0.5, indicating e cos
omega=+0.0025(+0.0049/-0.0018), consistent with a circular orbit. Tidal heating
is therefore likely to be negligible to GJ1252b's global energy budget.
Finally, we also analyze additional, unpublished TESS transit photometry of
GJ1252b which improves the precision of the transit ephemeris by a factor of
ten, provides a more precise planetary radius of 1.180+/-0.078 R_E, and rules
out any transit timing variations with amplitudes <1 min.


  An approach based on the Fisher information (FI) is developed to quantify the
maximum information gain and optimal experimental design in neutron
reflectometry experiments. In these experiments, the FI can be analytically
calculated and used to provide sub-second predictions of parameter
uncertainties. This approach can be used to influence real-time decisions about
measurement angle, measurement time, contrast choice and other experimental
conditions based on parameters of interest. The FI provides a lower bound on
parameter estimation uncertainties and these are shown to decrease with the
square root of measurement time, providing useful information for the planning
and scheduling of experimental work. As the FI is computationally inexpensive
to calculate, it can be computed repeatedly during the course of an experiment,
saving costly beam time by signalling that sufficient data has been obtained;
or saving experimental datasets by signalling that an experiment needs to
continue. The approach's predictions are validated through the introduction of
an experiment simulation framework that incorporates instrument-specific
incident flux profiles, and through the investigation of measuring the
structural properties of a phospholipid bilayer.


  We uncover a novel dynamical quantum phase transition, using random matrix
theory and its associated notion of planar limit. We study it for the isotropic
XY Heisenberg spin chain. For this, we probe its real-time dynamics through the
Loschmidt echo. This leads to the study of a random matrix ensemble with a
complex weight, whose analysis requires novel technical considerations, that we
develop. We obtain three main results: 1) There is a third order phase
transition at a rescaled critical time, that we determine. 2) The third order
phase transition persists away from the thermodynamic limit. 3) For times below
the critical value, the difference between the thermodynamic limit and a finite
chain decreases exponentially with the system size. All these results depend in
a rich manner on the parity of the number of flipped spins of the quantum state
conforming the fidelity.


  The unitary operator which transforms a harmonic oscillator system of
time-dependent frequency into that of a simple harmonic oscillator of different
time-scale is found, with and without an inverse-square potential. It is shown
that for both cases, this operator can be used in finding complete sets of wave
functions of a generalized harmonic oscillator system from the well-known sets
of the simple harmonic oscillator. Exact invariants of the time-dependent
systems can also be obtained from the constant Hamiltonians of unit mass and
frequency by making use of this unitary transformation. The geometric phases
for the wave functions of a generalized harmonic oscillator with an
inverse-square potential are given.


  This paper proposes a novel, rigorous and simple Fourier-transformation
approach to study resonances in a perfectly conducting slab with finite number
of subwavelength slits of width $h\ll 1$. Since regions outside the slits are
variable separated, by Fourier transforming the governing equation, we could
express field in the outer regions in terms of field derivatives on the
aperture. Next, in each slit where variable separation is still available, wave
field could be expressed as a Fourier series in terms of a countable basis
functions with unknown Fourier coefficients. Finally, by matching field on the
aperture, we establish a linear system of infinite number of equations
governing the countable Fourier coefficients. By carefully asymptotic analysis
of each entry of the coefficient matrix, we rigorously show that, by removing
only a finite number of rows and columns, the resulting principle sub-matrix is
diagonally dominant so that the infinite dimensional linear system can be
reduced to a finite dimensional linear system. Resonance frequencies are
exactly those frequencies making the linear system rank-deficient. This in turn
provides a simple, asymptotic formula describing resonance frequencies with
accuracy ${\cal O}(h^3\log h)$. We emphasize that such a formula is more
accurate than all existing results and is the first accurate result especially
for slits of number more than two to our best knowledge. Moreover, this
asymptotic formula rigorously confirms a fact that the imaginary part of
resonance frequencies is always ${\cal O}(h)$ no matter how we place the slits
as long as they are spaced by distances independent of width $h$.


  We investigate the general properties of expanding cosmological models which
generate scale-invariant curvature perturbations in the presence of a variable
speed of sound. We show that in an expanding universe, generation of a
super-Hubble, nearly scale-invariant spectrum of perturbations over a range of
wavelengths consistent with observation requires at least one of three
conditions: (1) accelerating expansion, (2) a speed of sound faster than the
speed of light, or (3) super-Planckian energy density.


  Large language modules (LLMs) have great potential for auto-grading student
written responses to physics problems due to their capacity to process and
generate natural language. In this explorative study, we use a prompt
engineering technique, which we name "scaffolded chain of thought (COT)", to
instruct GPT-3.5 to grade student written responses to a physics conceptual
question. Compared to common COT prompting, scaffolded COT prompts GPT-3.5 to
explicitly compare student responses to a detailed, well-explained rubric
before generating the grading outcome. We show that when compared to human
raters, the grading accuracy of GPT-3.5 using scaffolded COT is 20% - 30%
higher than conventional COT. The level of agreement between AI and human
raters can reach 70% - 80%, comparable to the level between two human raters.
This shows promise that an LLM-based AI grader can achieve human-level grading
accuracy on a physics conceptual problem using prompt engineering techniques
alone.


  While bacterial swimming has been well characterized in uniform liquid
environments, only little is known about how bacteria propagate through complex
environments, such as gel-like matrices or porous media that are typically
encountered in tissue or soil. Here, we study swimming motility of the soil
bacterium Pseudomonas putida (P. putida) in polysaccharide matrices formed by
different concentrations of agar. P. putida cells display intermittent
run-motility in the gel, where run times are exponentially distributed and
intermittently occurring dwell times follow a waiting-time distribution with a
power-law decay. An analysis of the turn angle distribution suggests that both,
flagella mediated turning as well as mechanical trapping in the agar matrix
play a role in the overall swimming pattern. Based on the experimentally
observed motility pattern and measured waiting-time distributions, we propose a
minimal active particle model which correctly describes the observed time
dependence of the mean square displacement of the bacterial swimmers.


  This paper defines a distance function that measures the dissimilarity
between planar geometric figures formed with straight lines. This function can
in turn be used in partial matching of different geometric figures. For a given
pair of geometric figures that are graphically isomorphic, one function
measures the angular dissimilarity and another function measures the edge
length disproportionality. The distance function is then defined as the convex
sum of these two functions. The novelty of the presented function is that it
satisfies all properties of a distance function and the computation of the same
is done by projecting appropriate features to a cartesian plane. To compute the
deviation from the angular similarity property, the Euclidean distance between
the given angular pairs and the corresponding points on the $y=x$ line is
measured. Further while computing the deviation from the edge length
proportionality property, the best fit line, for the set of edge lengths, which
passes through the origin is found, and the Euclidean distance between the
given edge length pairs and the corresponding point on a $y=mx$ line is
calculated. Iterative Proportional Fitting Procedure (IPFP) is used to find
this best fit line. We demonstrate the behavior of the defined function for
some sample pairs of figures.


  We establish a boundary Harnack principle for a large class of subordinate
Brownian motion, including mixtures of symmetric stable processes, in bounded
$\kappa$-fat open set (disconnected analogue of John domains). As an
application of the boundary Harnack principle, we identify the Martin boundary
and the minimal Martin boundary of bounded $\kappa$-fat open sets with respect
to these processes with their Euclidean boundary.


  We use Monte Carlo simulations to investigate the thermodynamical behaviour
of aggregates consisting of few superparamagnetic particles in a colloidal
suspension. The potential energy surface of this classical two-level system
with a stable and a metastable `ring' and `chain' configuration is tunable by
an external magnetic field and temperature. We determine the complex `phase
diagram' of this intriguing system and analyze thermodynamically the nature of
the transition between the ring and the chain `phase'.


  We propose Seq2Edits, an open-vocabulary approach to sequence editing for
natural language processing (NLP) tasks with a high degree of overlap between
input and output texts. In this approach, each sequence-to-sequence
transduction is represented as a sequence of edit operations, where each
operation either replaces an entire source span with target tokens or keeps it
unchanged. We evaluate our method on five NLP tasks (text normalization,
sentence fusion, sentence splitting & rephrasing, text simplification, and
grammatical error correction) and report competitive results across the board.
For grammatical error correction, our method speeds up inference by up to 5.2x
compared to full sequence models because inference time depends on the number
of edits rather than the number of target tokens. For text normalization,
sentence fusion, and grammatical error correction, our approach improves
explainability by associating each edit operation with a human-readable tag.


  We study the chiral flat space higher-spin algebra, which is the global
symmetry algebra of the chiral higher-spin theory in the 4d Minkowski space. We
find that it can be constructed as the universal enveloping algebra of a
certain chiral deformation of the Poincare algebra quotiented by a set of
quadratic identities. These identities allow us to identify a representation of
the latter algebra, which by analogy with the AdS space higher-spin holography,
we interpret as the flat space singleton representation. We provide two
explicit realisations of this singleton representation -- in terms of
$sl(2,\mathbb{C})$ spinors and in terms of oscillator-like variables -- as well
as briefly discuss its properties.


  We report on experiments for the fingerprint modality conducted during the
First BioSecure Residential Workshop. Two reference systems for fingerprint
verification have been tested together with two additional non-reference
systems. These systems follow different approaches of fingerprint processing
and are discussed in detail. Fusion experiments I volving different
combinations of the available systems are presented. The experimental results
show that the best recognition strategy involves both minutiae-based and
correlation-based measurements. Regarding the fusion experiments, the best
relative improvement is obtained when fusing systems that are based on
heterogeneous strategies for feature extraction and/or matching. The best
combinations of two/three/four systems always include the best individual
systems whereas the best verification performance is obtained when combining
all the available systems.


  We study the performance of spin-component-scaled second-order
M{\o}ller-Plesset perturbation theory (SCS-MP2) for the prediction of the
lattice constant, bulk modulus, and cohesive energy of 12 simple,
three-dimensional, covalent and ionic semiconductors and insulators. We find
that SCS-MP2 and the simpler scaled opposite-spin MP2 (SOS-MP2) yield
predictions that are significantly improved over the already good performance
of MP2. Specifically, when compared to experimental values with zero-point
vibrational corrections, SCS-MP2 (SOS-MP2) yields mean absolute errors of 0.015
(0.017) {\AA} for the lattice constant, 3.8 (3.7) GPa for the bulk modulus, and
0.06 (0.08) eV for the cohesive energy, which are smaller than those of leading
density functionals by about a factor of two or more. We consider a
reparameterization of the spin scaling parameters and find that the optimal
parameters for these solids are very similar to those already in common use in
molecular quantum chemistry, suggesting good transferability and reliable
future applications to surface chemistry on insulators.


  Future spintronics requires the realization of thin film of half-metallic
ferromagnets having high Curie temperature and 100\% spin polarization at the
Fermi level for potential spintronics applications. In this paper, we report
the epitaxial thin films growth of half-metallic CoFeMnGe Heusler alloy on MgO
(001) substrate using pulsed laser deposition system, along with the study of
structural, magnetic and transport properties. The magnetic property
measurements of the thin film suggest a soft ferromagnetic state at room
temperature with an in-plane magnetic anisotropy and a Curie temperature well
above the room temperature. Anisotropic magnetoresistance (AMR) ratio and
temperature dependent electrical resistivity measurements of the thin film
indicate the compound to be half-metallic in nature and therefore suitable for
the fabrications of spintronics devices.


  In this paper, the application of hierarchical wireless sensor networks in
water quality monitoring is investigated. Adopting a hierarchical structure,
the set of sensors is divided into multiple clusters where the value of the
sensing parameter is almost constant in each cluster. The members of each
cluster transmit their sensing information to the local fusion center (LFC) of
their corresponding cluster, where using some fusion rule, the received
information is combined, and then possibly sent to a higher-level central
fusion center (CFC). A two-phase processing scheme is also envisioned, in which
the first phase is dedicated to detection in the LFC, and the second phase is
dedicated to estimation in both the LFC and the CFC. The focus of the present
paper is on the problem of decision fusion at the LFC: we propose hard- and
soft-decision maximum a posteriori (MAP) algorithms, which exhibit flexibility
in minimizing the total cost imposed by incorrect detections in the first
phase. The proposed algorithms are simulated and compared with conventional
fusion techniques. It is shown that the proposed techniques result in lower
cost. Furthermore, when the number of sensors or the amount of contamination
increases, the performance gap between the proposed algorithms and the existing
methods also widens.


  We consider the optimal design of sequential transmission over broadcast
channel with nested feedback. Nested feedback means that the channel output of
the outer channel is also available at the decoder of the inner channel. We
model the communication system as a decentralized team with three decision
makers---the encoder and the two decoders. Structure of encoding and decoding
strategies that minimize a total distortion measure over a finite horizon are
determined. The results are applicable for real-time communication as well as
for the information theoretic setup.


  We rigorously establish the existence of many free boundary minimal annuli
with boundary in a geodesic sphere of $\mathbb{S}^3$. These arise as compact
subdomains of a one-parameter family of complete minimal immersions of
$\mathbb{R} \times \mathbb{S}^1$ into $\mathbb{S}^3$ described by do Carmo and
Dajczer [1]. While the free boundary minimal annuli we exhibit may have
self-intersections and may not be contained in a geodesic ball, we show that
there is at least a one-parameter family of embedded examples that are
contained in geodesic balls whose radius may be less than, equal to or greater
than $\pi$/2. After explaining the connection to Otsuki tori [13], we establish
lower bounds on the number of free boundary minimal annuli contained in each
Otsuki torus in terms of the corresponding rational number. Finally, we show
that some of the recent work of Lee and Seo [7] on isoperimetric inequalities
and of Lima and Menezes [10] on index bounds extends to geodesic balls equal to
or larger than a hemisphere.


  To predict the mean temperature profiles in turbulent thermal convection, the
thermal boundary layer (BL) equation including the effects of fluctuations has
to be solved. In Shishkina et al., Phys. Rev. Lett. 114 (2015), the thermal BL
equation with the fluctuations taken into account as an eddy thermal
diffusivity has been solved for large Prandtl-number fluids for which the eddy
thermal diffusivity and the velocity field can be approximated respectively as
a cubic and a linear function of the distance from the plate. In the present
work we make use of the idea of Prandtl's mixing length model and relate the
eddy thermal diffusivity to the stream function. With this proposed relation,
we can solve the thermal BL equation and obtain a closed-form expression for
the dimensionless mean temperature profile in terms of two independent
parameters for fluids with a general Prandtl number. With a proper choice of
the parameters, our predictions of the temperature profiles are in excellent
agreement with the results of our direct numerical simulations for a wide range
of Prandtl numbers from 0.01 to 2547.9 and Rayleigh numbers from 10^7 to 10^9.


  In this paper we investigate the production of charmonium states in two and
three photon fusion processes in nucleus -- nucleus collisions at the CERN
Large Hadron Collider (LHC) energies. Our results indicate that the
experimental study of these processes is feasible and can be used to constrain
the theoretical decay widths and give information on the non $c - \bar{c}$
components of these states.


  We use a toy model to illustrate how to build effective theories for singular
potentials. We consider a central attractive 1/r^2 potential perturbed by a
1/r^4 correction. The power-counting rule, an important ingredient of effective
theory, is established by seeking the minimum set of short-range counterterms
that renormalize the scattering amplitude. We show that leading-order
counterterms are needed in all partial waves where the potential overcomes the
centrifugal barrier, and that the additional counterterms at next-to-leading
order are the ones expected on the basis of dimensional analysis.


  In this paper we present a technique for fusion of optical and thermal face
images based on image pixel fusion approach. Out of several factors, which
affect face recognition performance in case of visual images, illumination
changes are a significant factor that needs to be addressed. Thermal images are
better in handling illumination conditions but not very consistent in capturing
texture details of the faces. Other factors like sunglasses, beard, moustache
etc also play active role in adding complicacies to the recognition process.
Fusion of thermal and visual images is a solution to overcome the drawbacks
present in the individual thermal and visual face images. Here fused images are
projected into an eigenspace and the projected images are classified using a
radial basis function (RBF) neural network and also by a multi-layer perceptron
(MLP). In the experiments Object Tracking and Classification Beyond Visible
Spectrum (OTCBVS) database benchmark for thermal and visual face images have
been used. Comparison of experimental results show that the proposed approach
performs significantly well in recognizing face images with a success rate of
96% and 95.07% for RBF Neural Network and MLP respectively.


  We demonstrate the phenomenon of blockade in two-photon excitations to the
Rydberg state in thermal vapor. A technique based on optical heterodyne is used
to measure the dispersion of a probe beam far off resonant to the D2 line of
rubidium in the presence of a strong laser beam that couples to the Rydberg
state via two-photon resonance. Density dependent suppression of the dispersion
peak is observed while coupling to the Rydberg state with principal quantum
number, n = 60. The experimental observation is explained using the phenomenon
of Rydberg blockade. The blockade radius is measured to be about 2.2 {\mu}m
which is consistent with the scaling due to the Doppler width of 2-photon
resonance in thermal vapor. Our result promises the realization of single
photon source and strong single photon non-linearity based on Rydberg blockade
in thermal vapor.


  In this corrigendum, an error in the proof of a theorem in [Linear Algebra
and its Applications 509 (2016) 82--113] is pointed out. This theorem states
that every graph $T_n$ consisting of $n-2$ triangles sharing a common base is
SPN. An alternative proof is given here for the case $n=5$, but for all $n>5$
it remains open whether $T_n$ is SPN. As a result, the question whether
$K_{2,n}$, $n>4$, is SPN also remains open.


  We investigate the relationship between geometric, analytic and probabilistic
indices for quotients of the Cayley graph of the free group ${\rm Cay}(F_n)$
endowed with variable edge lengths, by an arbitrary subgroup $G$ of $F_n$. Our
main result, which generalizes Grigorchuk's cogrowth formula to variable edge
lengths, provides a formula relating the bottom of the spectrum of weighted
Laplacian on $G \backslash {\rm Cay}(F_n)$ to the Poincar\'e exponent of $G$.
Our main tool is the Patterson-Sullivan theory for Cayley graphs with variable
edge lengths.


  Efficient path planning and navigation is critical for animals, robotics,
logistics and transportation. We study a model in which spatial navigation
problems can rapidly be solved in the brain by parallel mental exploration of
alternative routes using propagating waves of neural activity. A wave of
spiking activity propagates through a hippocampus-like network, altering the
synaptic connectivity. The resulting vector field of synaptic change then
guides a simulated animal to the appropriate selected target locations. We
demonstrate that the navigation problem can be solved using realistic, local
synaptic plasticity rules during a single passage of a wavefront. Our model can
find optimal solutions for competing possible targets or learn and navigate in
multiple environments. The model provides a hypothesis on the possible
computational mechanisms for optimal path planning in the brain, at the same
time it is useful for neuromorphic implementations, where the parallelism of
information processing proposed here can fully be harnessed in hardware.


  Given a composition of Bely\u{\i} maps $\beta \circ \gamma: X \rightarrow Z$,
paths between edges of $\beta$ are extended to form loops, then lifted by
$\gamma$. These liftings are then studied to understand how loops in $Z$ act on
edges of $\beta \circ \gamma$, demonstrating the group operation in
$\mathop{\mathrm{Mon}} \beta \circ \gamma \unlhd \mathop{\mathrm{Mon}} \gamma
\wr \mathop{\mathrm{Mon}} \beta$. Abstracting away the specific Bely\u{\i} map
$\gamma$ and finding the image of $\pi_{1}(Z)$ in $\pi_{1}(Y) \wr
\mathop{\mathrm{Mon}} \beta$ instead allows subsequently determining
$\mathop{\mathrm{Mon}} \beta \circ \gamma$, for any $\gamma$, using only the
monodromy representation of $\gamma$.


  In this work, we develop efficient solvers for linear inverse problems based
on randomized singular value decomposition (RSVD). This is achieved by
combining RSVD with classical regularization methods, e.g., truncated singular
value decomposition, Tikhonov regularization, and general Tikhonov
regularization with a smoothness penalty. One distinct feature of the proposed
approach is that it explicitly preserves the structure of the regularized
solution in the sense that it always lies in the range of a certain adjoint
operator. We provide error estimates between the approximation and the exact
solution under canonical source condition, and interpret the approach in the
lens of convex duality. Extensive numerical experiments are provided to
illustrate the efficiency and accuracy of the approach.


  Food image classification is a fundamental step of image-based dietary
assessment, enabling automated nutrient analysis from food images. Many current
methods employ deep neural networks to train on generic food image datasets
that do not reflect the dynamism of real-life food consumption patterns, in
which food images appear sequentially over time, reflecting the progression of
what an individual consumes. Personalized food classification aims to address
this problem by training a deep neural network using food images that reflect
the consumption pattern of each individual. However, this problem is
under-explored and there is a lack of benchmark datasets with individualized
food consumption patterns due to the difficulty in data collection. In this
work, we first introduce two benchmark personalized datasets including the
Food101-Personal, which is created based on surveys of daily dietary patterns
from participants in the real world, and the VFNPersonal, which is developed
based on a dietary study. In addition, we propose a new framework for
personalized food image classification by leveraging self-supervised learning
and temporal image feature information. Our method is evaluated on both
benchmark datasets and shows improved performance compared to existing works.
The dataset has been made available at:
https://skynet.ecn.purdue.edu/~pan161/dataset_personal.html


  semanticSBML 2.0 is an online collection of services for the work with
biochemical network models in SBML format.


  Our concern in this paper is to describe the p-rank statification on the
Siegel moduli space with Iwahori level structure over fields of positive
characteristic. We calculate the dimension of the strata and describe the
closure of a given stratum in terms of p-rank strata. We also examine the
relationship between the p-rank stratification and the Kottwitz-Rapoport
stratification.


  Much effort has been devoted to understanding the decisions of deep neural
networks in recent years. A number of model-aware saliency methods were
proposed to explain individual classification decisions by creating saliency
maps. However, they are not applicable when the parameters and the gradients of
the underlying models are unavailable. Recently, model-agnostic methods have
also received attention. As one of them, \textit{Prediction Difference
Analysis} (PDA), a probabilistic sound methodology, was proposed. In this work,
we first show that PDA can suffer from saturated classifiers. The saturation
phenomenon of classifiers exists widely in current neural network-based
classifiers. To explain the decisions of saturated classifiers better, we
further propose Contextual PDA, which runs hundreds of times faster than PDA.
The experiments show the superiority of our method by explaining image
classifications of the state-of-the-art deep convolutional neural networks.


  We study phase space transport in a 2D caldera potential energy surface (PES)
using techniques from nonlinear dynamics. The caldera PES is characterized by a
flat region or shallow minimum at its center surrounded by potential walls and
multiple symmetry related index one saddle points that allow entrance and exit
from this intermediate region.We have discovered four qualitatively distinct
cases of the structure of the phase space that govern phase space transport.
These cases are categorized according to the total energy and the stability of
the periodic orbits associated with the family of the central minimum, the
bifurcations of the same family, and the energetic accessibility of the index
one saddles. In each case we have computed the invariant manifolds of the
unstable periodic orbits of the central region of the potential and the
invariant manifolds of the unstable periodic orbits of the families of periodic
orbits associated with the index one saddles. We have found that there are
three distinct mechanisms determined by the invariant manifold structure of the
unstable periodic orbits govern the phase space transport. The first mechanism
explains the nature of the entrance of the trajectories from the region of the
low energy saddles into the caldera and how they may become trapped in the
central region of the potential. The second mechanism describes the trapping of
the trajectories that begin from the central region of the caldera, their
transport to the regions of the saddles, and the nature of their exit from the
caldera. The third mechanism describes the phase space geometry responsible for
the dynamical matching of trajectories originally proposed by Carpenter and
described in Collins et al. (2014) for the two dimensional caldera PES that we
consider.


  Thermal properties of glueballs in SU(3) Yang-Mills theory are investigated
in a large temperature range from $0.3T_c$ to $1.9T_c$ on anisotropic lattices.
The glueball operators are optimized for the projection of the ground states by
the variational method with a smearing scheme. Their thermal correlators are
calculated in all 20 symmetry channels. It is found in all channels that the
pole masses $M_G$ of glueballs remain almost constant when the temperature is
approaching the critical temperature $T_c$ from below, and start to reduce
gradually with the temperature going above $T_c$. The correlators in the
$0^{++}$, $0^{-+}$, and $2^{++}$ channels are also analyzed based on the
Breit-Wigner $\emph{Ansatz}$ by assuming a thermal width $\Gamma$ to the pole
mass $\omega_0$ of each thermal glueball ground state. While the values of
$\omega_0$ are insensitive to $T$ in the whole temperature range, the thermal
widths $\Gamma$ exhibit distinct behaviors at temperatures below and above
$T_c$. The widths are very small (approximately few percent of $\omega_0$ or
even smaller) when $T<T_c$, but grow abruptly when $T>T_c$ and reach values of
roughly $\Gamma\sim \omega_0/2$ at $T\approx 1.9T_c$.


  The relative sensitivity of different direct detection experiments depends
sensitively on the astrophysical distribution and particle physics nature of
dark matter, prohibiting a model-independent comparison. The situation changes
fundamentally if two experiments employ the same target material. We show that
in this case one can compare measurements of an annual modulation and exclusion
bounds on the total rate while making no assumptions on astrophysics and no (or
only very general) assumptions on particle physics. In particular, we show that
the dark matter interpretation of the DAMA/LIBRA signal can be conclusively
tested with COSINUS, a future experiment employing the same target material. We
find that if COSINUS excludes a dark matter scattering rate of about
$0.01\,\text{kg}^{-1}\,\text{days}^{-1}$ with an energy threshold of $1.8\,$keV
and resolution of $0.2\,$keV, it will rule out all explanations of DAMA/LIBRA
in terms of dark matter scattering off sodium and/or iodine.


  We have developed a numerically exact approach to compute real-time path
integral expressions for quantum transport problems out of equilibrium. The
scheme is based on a deterministic iterative summation of the path integral
(ISPI) for the generating function of nonequilibrium observables of interest,
e.g., the charge current or dynamical quantities of the central part.
Self-energies due to the leads, being nonlocal in time, are fully taken into
account within a finite memory time, thereby including non-Markovian effects.
Numerical results are extra-polated first to vanishing (Trotter) time
discretization and, second, to infinite memory time...


  DIRECT is a project to obtain directly the distances to two Local Group
galaxies, M31 and M33, which occupy a crucial position near the bottom of the
cosmological distance ladder. As the first step of the DIRECT project we have
searched for detached eclipsing binaries (DEBs) and new Cepheids in the M31 and
M33 galaxies with 1m-class telescopes. In this eighth paper we present a
catalog of variable stars discovered in the data from the followup observations
of DEB system D33J013337.0+303032.8 in field M33B [(RA,Dec)= (23.48, 30.57),
J2000.0], collected with the Kitt Peak National Observatory 2.1m telescope. In
our search covering an area of 108 sq. arcmin. we have found 895 variable
stars: 96 eclipsing binaries, 349 Cepheids, and 450 other periodic, possible
long period or non-periodic variables. Of these variables 612 are newly
discovered. Their light curves were extracted using the ISIS image subtraction
package. For 77% of the variables we present light curves in standard V and B
magnitudes, with the remaining 23% expressed in units of differential flux. We
have discovered a population of first overtone Cepheid candidates and for six
of them we present strong arguments in favor of this interpretation. The
catalog of variables, as well as their photometry (about 9.2*10^4 BV
measurements) and finding charts, is available electronically via anonymous ftp
and the World Wide Web. The complete set of the CCD frames is available upon
request.


  The recent HEGRA observations of the blazar Mkn 501 show strong curvature in
the very high energy gamma-ray spectrum. Applying the gamma-ray opacity derived
from an empirically based model of the intergalactic infrared background
radiation field (IIRF), to these observations, we find that the intrinsic
spectrum of this source is consistent with a power-law: dN/dE~ E^-alpha with
alpha=2.00 +/- 0.03 over the range 500 GeV - 20 TeV. Within current synchrotron
self-Compton scenarios, the fact that the TeV spectral energy distribution of
Mkn 501 does not vary with luminosity, combined with the correlated, spectrally
variable emission in X-rays, as observed by the BeppoSAX and RXTE instruments,
also independently implies that the intrinsic spectrum must be close to
alpha=2. Thus, the observed curvature in the spectrum is most easily understood
as resulting from intergalactic absorption.


  A dynamical approach, rather than the usual statistical approach, is taken to
explore the physical mechanisms underlying the nonlinear transfer of energy,
the damping of the turbulent fluctuations, and the development of coherent
structures in kinetic plasma turbulence. It is argued that the linear and
nonlinear dynamics of Alfven waves are responsible, at a very fundamental
level, for some of the key qualitative features of plasma turbulence that
distinguish it from hydrodynamic turbulence, including the anisotropic cascade
of energy and the development of current sheets at small scales. The first
dynamical model of kinetic turbulence in the weakly collisional solar wind
plasma that combines self-consistently the physics of Alfven waves with the
development of small-scale current sheets is presented and its physical
implications are discussed. This model leads to a simplified perspective on the
nature of turbulence in a weakly collisional plasma: the nonlinear interactions
responsible for the turbulent cascade of energy and the formation of current
sheets are essentially fluid in nature, while the collisionless damping of the
turbulent fluctuations and the energy injection by kinetic instabilities are
essentially kinetic in nature.


  The high internal quantum efficiency observed in higher plants remains an
outstanding problem in understanding photosynthesis. Several approaches such as
quantum entanglement and quantum coherence have been explored. However, none
has yet drawn an analogy between superlattices and the geometrical structure of
granal thylakoids in leaves. In this paper, we calculate the transmission
coefficients and perform numerical simulations using the parameters relevant to
a stack of thylakoid discs. We then show that quantum resonant tunneling can
occur at low effective mass of particles for 680 nm and 700 nm incident
wavelengths corresponding to energies at which photosynthesis occurs.


  We study the superconductor to insulator transition at zero temperature in a
Josephson-junction array model on a honeycomb lattice with $f$ flux quantum per
plaquette. The path integral representation of the model corresponds to a
(2+1)-dimensional classical model, which is used to investigate the critical
behavior by extensive Monte Carlo simulations on large system sizes. In
contrast to the model on a square lattice, the transition is found to be first
order for $f=1/3$ and continuous for $f=1/2$ but in a different universality
class. The correlation-length critical exponent is estimated from finite-size
scaling of vortex correlations. The estimated universal conductivity at the
transition is approximately four times its value for $f=0$. The results are
compared with experimental observations on ultrathin superconducting films with
a triangular lattice of nanoholes in a transverse magnetic field.


  Zinc-based batteries offer good volumetric energy densities and are
compatible with environmentally friendly aqueous electrolytes. Zinc-ion
batteries (ZIBs) rely on a lithium-ion-like Zn$^{2+}$-shuttle, which enables
higher roundtrip efficiencies and better cycle life than zinc-air batteries.
Manganese-oxide cathodes in near-neutral zinc sulfate electrolytes are the most
prominent candidates for ZIBs. Zn$^{2+}$-insertion, H$^+$-insertion, and
Mn$^{2+}$-dissolution are proposed to contribute to the charge-storage
mechanism. During discharge and charge, two distinct phases are observed.
Notably, the pH-driven precipitation of zinc-sulfate-hydroxide is detected
during the second discharge phase. However, a complete and consistent
understanding of the two-phase mechanism of these ZIBs is still missing. This
paper presents a continuum full cell model supported by DFT calculations to
investigate the implications of these observations. We integrate the
complex-formation reactions of near-neutral aqueous electrolytes into the
battery model and, in combination with the DFT calculations, draw a consistent
picture of the cycling mechanism. We investigate the interplay between
electrolyte pH and reaction mechanisms at the manganese-oxide cathodes and
identify the dominant charge-storage mechanism. Our model is validated with
electrochemical cycling data, cyclic voltammograms, and in-situ pH measurments.
This allows us to analyse the influence of cell design and electrolyte
composition on cycling and optimize the battery performance.


  The fixed energy scattering matrix is defined on a perturbed stratified
medium, and for a class of perturbations, its main part is shown to be a
Fourier integral operator on the sphere at infinity. This is facilitated by
developing a refined limiting absorption principle. The symbol of the
scattering matrix is shown to determine the asymptotics of a large class of
perturbations.


  It is well-known that the dynamical spectrum of an ergodic measure dynamical
system is related to the diffraction measure of a typical element of the
system. This situation includes ergodic subshifts from symbolic dynamics as
well as ergodic Delone dynamical systems, both via suitable embeddings. The
connection is rather well understood when the spectrum is pure point, where the
two spectral notions are essentially equivalent. In general, however, the
dynamical spectrum is richer. Here, we consider (uniquely) ergodic systems of
finite local complexity and establish the equivalence of the dynamical spectrum
with a collection of diffraction spectra of the system and certain factors.
This equivalence gives access to the dynamical spectrum via these diffraction
spectra. It is particularly useful as the diffraction spectra are often simpler
to determine and, in many cases, only very few of them need to be calculated.


  In the process algebra community it is sometimes suggested that, on some
level of abstraction, any distributed system can be modelled in standard
process-algebraic specification formalisms like CCS. This sentiment is
strengthened by results testifying that CCS, like many similar formalisms, is
Turing powerful and provides a mechanism for interaction. This paper counters
that sentiment by presenting a simple fair scheduler---one that in suitable
variations occurs in many distributed systems---of which no implementation can
be expressed in CCS, unless CCS is enriched with a fairness assumption.
  Since Dekker's and Peterson's mutual exclusion protocols implement fair
schedulers, it follows that these protocols cannot be rendered correctly in CCS
without imposing a fairness assumption. Peterson expressed this algorithm
correctly in pseudocode without resorting to a fairness assumption, so it
furthermore follows that CCS lacks the expressive power to accurately capture
such pseudocode.


  The finite momentum meson spectral function (MSF) in the pseudoscalar channel
is evaluated, adopting for the fermionic propagators HTL expressions. The
different contributions to the meson spectral functions are clearly displayed.
Our analysis may be of relevance for lattice studies of MSF based so far on the
Maximum Entropy Method. As a further step the correlation function along the
(imaginary-) temporal direction is evaluated.