  This paper investigates analytic properties of American option prices under
the finite moment log-stable (FMLS) model. Under this model the price of
American options is characterised by the free boundary problem of a fractional
partial differential equation (FPDE) system. Using the technique of
approximation we prove that the American put price under the FMLS model is
convex with respect the underlying price, and specify the impact of the tail
index on option prices.


  The coherent superposition of states, in combination with the quantization of
observables, represents one of the most fundamental features that mark the
departure of quantum mechanics from the classical realm. Quantum coherence in
many-body systems embodies the essence of entanglement and is an essential
ingredient for a plethora of physical phenomena in quantum optics, quantum
information, solid state physics, and nanoscale thermodynamics. In recent
years, research on the presence and functional role of quantum coherence in
biological systems has also attracted a considerable interest. Despite the
fundamental importance of quantum coherence, the development of a rigorous
theory of quantum coherence as a physical resource has only been initiated
recently. In this Colloquium we discuss and review the development of this
rapidly growing research field that encompasses the characterization,
quantification, manipulation, dynamical evolution, and operational application
of quantum coherence.


  Electrodynamic Dust Shield (EDS) technology is a dust mitigation strategy
that is commonly studied for applications such as photovoltaics or thermal
radiators where soiling of the surfaces can reduce performance. The goal of the
current work was to test the performance of a patterned nanocomposite EDS
system produced through spray-coating and melt infiltration of chemically
modified reduced graphene oxide (CMrGO) traces with thermoplastic high-density
polyethylene (HDPE). The EDS performance was tested for a dusting of lunar
regolith simulant under high vacuum conditions (~10-6 Torr) using both 2-phase
and 3-phase configurations. Uncapped (bare) devices showed efficient dust
removal at moderate voltages (1000 V) for both 2-phase and 3-phase designs, but
the performance of the devices degraded after several sequential tests due to
erosion of the traces caused by electric discharges. Further tests carried out
while illuminating the dust surface with a UV excimer lamp showed that the EDS
voltage needed to reach the maximum cleanliness was reduced by almost 50% for
the 2-phase devices (500 V minimum for rough and 1000 V for smooth), while the
3-phase devices were unaffected by the application of UV. Capping the CMrGO
traces with low-density polyethylene (LDPE) eliminated breakdown of the
materials and device degradation, but larger voltages (3000 V) coupled with UV
illumination were required to remove the grains from the capped devices.


  Mainly used to set constraints on cosmological parameters, CMB anisotropies
can give information on primordial universe. Unfortunately, initial conditions
and late time cosmology are degenerated. I focus on the effect of the shape of
the initial power spectrum of fluctuations on the cosmological parameter
estimation from CMB temperature anisotropies. Strong constraints obtained using
a priori knowledge of the initial spectrum disappear when the latter is let
free.


  This research paper delves deeply into the intricate interplay between
quintessence and a cloud of strings, exploring their influence on critical
points, behaviors, and fractional order phase transitions in AdS black holes.
We conducted a meticulous analysis of the various thermodynamic properties
associated with AdS black holes surrounded by quintessence and a cloud of
strings, with a specific focus on understanding how thermal fluctuations affect
their thermodynamics. Using critical conditions, we systematically derived an
approximate analytical expressions for critical points that are applicable to
all types of these black holes. Furthermore, we embarked on an exploration of
how changes in the quintessence parameter and the intensity of the cloud of
strings impact critical behaviors, using three-dimensional visualizations to
vividly depict these effects across different values of omegaq. Our examination
illuminated how these factors shape the locations of phase transitions and the
regions where both phases coexist. Additionally, we delved into the realm of
fractional order phase transitions, which indicate significant transformations
within the black hole system. This work delves into examining how the behaviors
of both small and large black holes are influenced by the combined effects of
quintessence and a string cloud. The study employs two distinct approximations
of the horizon radius for Ads black holes, providing a nuanced analysis of
their dynamic responses.


  We focus on the audio-visual video parsing (AVVP) problem that involves
detecting audio and visual event labels with temporal boundaries. The task is
especially challenging since it is weakly supervised with only event labels
available as a bag of labels for each video. An existing state-of-the-art model
for AVVP uses a hybrid attention network (HAN) to generate cross-modal features
for both audio and visual modalities, and an attentive pooling module that
aggregates predicted audio and visual segment-level event probabilities to
yield video-level event probabilities. We provide a detailed analysis of
modality bias in the existing HAN architecture, where a modality is completely
ignored during prediction. We also propose a variant of feature aggregation in
HAN that leads to an absolute gain in F-scores of about 2% and 1.6% for visual
and audio-visual events at both segment-level and event-level, in comparison to
the existing HAN model.


  The Hartree-Fock approximation is applied to study the "high-colour pomerons"
in the system of many reggeized gluons with a running QCD coupling constant. It
is shown that, contrary to the fixed coupling case, the high-colour pomerons
result supercritical, although with a smaller intercept than the multipomeron
states.


  As the volatility of electricity demand increases owing to climate change and
electrification, the importance of accurate peak load forecasting is
increasing. Traditional peak load forecasting has been conducted through time
series-based models; however, recently, new models based on machine or deep
learning are being introduced. This study performs a comparative analysis to
determine the most accurate peak load-forecasting model for Korea, by comparing
the performance of time series, machine learning, and hybrid models. Seasonal
autoregressive integrated moving average with exogenous variables (SARIMAX) is
used for the time series model. Artificial neural network (ANN), support vector
regression (SVR), and long short-term memory (LSTM) are used for the machine
learning models. SARIMAX-ANN, SARIMAX-SVR, and SARIMAX-LSTM are used for the
hybrid models. The results indicate that the hybrid models exhibit significant
improvement over the SARIMAX model. The LSTM-based models outperformed the
others; the single and hybrid LSTM models did not exhibit a significant
performance difference. In the case of Korea's highest peak load in 2019, the
predictive power of the LSTM model proved to be greater than that of the
SARIMAX-LSTM model. The LSTM, SARIMAX-SVR, and SARIMAX-LSTM models outperformed
the current time series-based forecasting model used in Korea. Thus, Korea's
peak load-forecasting performance can be improved by including machine learning
or hybrid models.


  For the first time, the Emirates Mars Infrared Spectrometer (EMIRS)
instrument on board the Emirates Mars Mission (EMM) "Hope", is providing us
with the temperature measurements of Mars at all local times covering most of
the planet. As a result, it is now possible to compare surface temperature
measurements made from orbit with those from the surface by rovers during the
same time period. We use data of diurnal temperature variation from the Rover
Environmental Monitoring Station (REMS) suite on board the Mars Science
Laboratory (MSL) "Curiosity" rover, and the Mars Environmental Dynamics
Analyzer (MEDA) suite on board the Mars 2020 "Perseverance" rover, between June
and August 2021 and compare them with EMIRS observations and estimates of the
Mars Climate Database (MCD) model. We show that although the overall trend of
temperature variation is in excellent agreement across missions, EMIRS
measurements are systematically lower at night compared to Mars 2020. The lower
spatial resolution of EMIRS compared to the rovers and consequently lower
average thermal inertia of the observed regions in this particular case
primarily contributed to this discrepancy, among other factors. We discuss the
implications of these results in improving our understanding of the Martian
climate which would lead to better modeling of local weather prediction, useful
for future robotic and crewed missions.


  Visual Question Answering (VQA) and Image Captioning (CAP), which are among
the most popular vision-language tasks, have analogous scene-text versions that
require reasoning from the text in the image. Despite their obvious
resemblance, the two are treated independently and, as we show, yield
task-specific methods that can either see or read, but not both. In this work,
we conduct an in-depth analysis of this phenomenon and propose UniTNT, a
Unified Text-Non-Text approach, which grants existing multimodal architectures
scene-text understanding capabilities. Specifically, we treat scene-text
information as an additional modality, fusing it with any pretrained
encoder-decoder-based architecture via designated modules. Thorough experiments
reveal that UniTNT leads to the first single model that successfully handles
both task types. Moreover, we show that scene-text understanding capabilities
can boost vision-language models' performance on general VQA and CAP by up to
2.69% and 0.6 CIDEr, respectively.


  Reinforcement learning (RL) requires either manually specifying a reward
function, which is often infeasible, or learning a reward model from a large
amount of human feedback, which is often very expensive. We study a more
sample-efficient alternative: using pretrained vision-language models (VLMs) as
zero-shot reward models (RMs) to specify tasks via natural language. We propose
a natural and general approach to using VLMs as reward models, which we call
VLM-RMs. We use VLM-RMs based on CLIP to train a MuJoCo humanoid to learn
complex tasks without a manually specified reward function, such as kneeling,
doing the splits, and sitting in a lotus position. For each of these tasks, we
only provide a single sentence text prompt describing the desired task with
minimal prompt engineering. We provide videos of the trained agents at:
https://sites.google.com/view/vlm-rm. We can improve performance by providing a
second "baseline" prompt and projecting out parts of the CLIP embedding space
irrelevant to distinguish between goal and baseline. Further, we find a strong
scaling effect for VLM-RMs: larger VLMs trained with more compute and data are
better reward models. The failure modes of VLM-RMs we encountered are all
related to known capability limitations of current VLMs, such as limited
spatial reasoning ability or visually unrealistic environments that are far
off-distribution for the VLM. We find that VLM-RMs are remarkably robust as
long as the VLM is large enough. This suggests that future VLMs will become
more and more useful reward models for a wide range of RL applications.


  Density-based clustering aims to find groups of similar objects (i.e.,
clusters) in a given dataset. Applications include, e.g., process mining and
anomaly detection. It comes with two user parameters ({\epsilon}, MinPts) that
determine the clustering result, but are typically unknown in advance. Thus,
users need to interactively test various settings until satisfying clusterings
are found. However, existing solutions suffer from the following limitations:
(a) Ineffective pruning of expensive neighborhood computations. (b) Approximate
clustering, where objects are falsely labeled noise. (c) Restricted parameter
tuning that is limited to {\epsilon} whereas MinPts is constant, which reduces
the explorable clusterings. (d) Inflexibility in terms of applicable data types
and distance functions. We propose FINEX, a linear-space index that overcomes
these limitations. Our index provides exact clusterings and can be queried with
either of the two parameters. FINEX avoids neighborhood computations where
possible and reduces the complexities of the remaining computations by
leveraging fundamental properties of density-based clusters. Hence, our
solution is effcient and flexible regarding data types and distance functions.
Moreover, FINEX respects the original and straightforward notion of
density-based clustering. In our experiments on 12 large real-world datasets
from various domains, FINEX frequently outperforms state-of-the-art techniques
for exact clustering by orders of magnitude.


  We analyse complementary HST and SUBARU data for the globular cluster NGC
2419. We make a detailed analysis of the horizontal branch (HB), that appears
composed by two main groups of stars: the luminous blue HB stars ---that extend
by evolution into the RR Lyrae and red HB region--- and a fainter, extremely
blue population. We examine the possible models for this latter group and
conclude that a plausible explanation is that they correspond to a significant
(~30 %) extreme second generation with a strong helium enhancement (Y~0.4). We
also show that the color dispersion of the red giant branch is consistent with
this hypothesis, while the main sequence data are compatible with it, although
the large observational error blurs the possible underlying splitting. While it
is common to find an even larger (50 -- 80) percentage of second generation in
a globular cluster, the presence of a substantial and extreme fraction of these
stars in NGC 2419 might be surprising, as the cluster is at present well inside
the radius beyond which the galactic tidal field would be dominant. If a
similar situation had been present in the first stages of the cluster life, the
cluster would have retained its initial mass, and the percentage of second
generation stars should have been quite small (up to ~10 %). Such a large
fraction of extreme second generation stars implies that the system must have
been initially much more massive and in different dynamical conditions than
today. We discuss this issue in the light of existing models of the formation
of multiple populations in globular clusters.


  Deep models must learn robust and transferable representations in order to
perform well on new domains. While domain transfer methods (e.g., domain
adaptation, domain generalization) have been proposed to learn transferable
representations across domains, they are typically applied to ResNet backbones
pre-trained on ImageNet. Thus, existing works pay little attention to the
effects of pre-training on domain transfer tasks. In this paper, we provide a
broad study and in-depth analysis of pre-training for domain adaptation and
generalization, namely: network architectures, size, pre-training loss, and
datasets. We observe that simply using a state-of-the-art backbone outperforms
existing state-of-the-art domain adaptation baselines and set new baselines on
Office-Home and DomainNet improving by 10.7\% and 5.5\%. We hope that this work
can provide more insights for future domain transfer research.


  Based on an analogy with electromagnetic metamaterials, we develop an
effective medium description for the propagation of electron matter waves in
bulk semiconductors with a zincblende structure. It is formally demonstrated
that even though departing from a different starting point, our theory gives
results for the energy stationary states consistent with Bastard's envelope
function approximation in the long-wavelength limit. Using the proposed
approach, we discuss the time evolution of a wave packet in a bulk
semiconductor with a zero-gap and linear energy-momentum dispersion.


  Designing applications for use in a hybrid cloud has many features. These
include dynamic virtualization management and an unknown route switching
customers. This makes it impossible to evaluate the query and hence the optimal
distribution of data. In this paper, we formulate the main challenges of
designing and simulation offer installation for processing.


  This paper is an extended version of a report from a student-developed study
to compare Microsoft SQL Server and PostgreSQL, two widely-used
enterprise-class relational database management systems (RDBMSs). The study
followed an introductory undergraduate course in relational systems and was
designed to help gain practical understanding of specific DBMSs. During this
study, we implemented three non-trivial schemas in each system, identified 26
common database design, development, and administration activities while
implementing the schemas, and compared the support each system offers to carry
out the identified activities. Where relevant, we also compared each system
against the SQL standard.
  In this report, we present a summary of the similarities and differences we
found between the two systems, and we provide a quantitative measure ranking
both systems' implementations of the 26 activities. We also briefly discuss the
"technical suitability" of PostgreSQL to enterprise applications. Although this
report is not comprehensive and is too general to comment on the suitability of
either system to a specific enterprise application, it can nevertheless provide
an initial set of considerations and criteria to choose a system for most
enterprise applications.


  The notions of asymptotic amplitude for signals, and Cauchy gain for
input/output systems, and an associated small-gain principle, are introduced.
These concepts allow the consideration of systems with multiple, and possibly
feedback-dependent, steady states. A Lyapunov-like characterization allows the
computation of gains for state-space systems, and the formulation of sufficient
conditions insuring the lack of oscillations and chaotic behaviors in a wide
variety of cascades and feedback loops. An application in biology (MAPK
signaling) is worked out in detail.


  We show that quantum diffusion near the quantum critical point can provide a
highly very efficient mechanism of open-system quantum annealing. It is based
on the diffusion-mediated recombination of excitations. For an Ising spin chain
coupled to a bosonic bath, excitation diffusion in a transverse field sharply
slows down as the system moves away from the quantum critical region. This
leads to spatial correlations and effective freezing of the excitation density.
We find that obtaining an approximate solution via the diffusion-mediated
quantum annealing can be faster than via closed-system quantum annealing or
Glauber dynamics.


  Rig inversion is the problem of creating a method that can find the rig
parameter vector that best approximates a given input mesh. In this paper we
propose to solve this problem by first obtaining a differentiable rig function
by training a multi layer perceptron to approximate the rig function. This
differentiable rig function can then be used to train a deep learning model of
rig inversion.


  Proof formats for SAT solvers have diversified over the last decade, enabling
new features such as extended resolution-like capabilities, very general
extension-free rules, inclusion of proof hints, and pseudo-boolean reasoning.
Interference-based methods have been proven effective, and some theoretical
work has been undertaken to better explain their limits and semantics. In this
work, we combine the subsumption redundancy notion from (Buss, Thapen 2019) and
the overwrite logic framework from (Rebola-Pardo, Suda 2018). Natural
generalizations then become apparent, enabling even shorter proofs of the
pigeonhole principle (compared to those from (Heule, Kiesl, Biere 2017)) and
smaller unsatisfiable core generation.


  Many speech processing methods based on deep learning require an automatic
and differentiable audio metric for the loss function. The DPAM approach of
Manocha et al. learns a full-reference metric trained directly on human
judgments, and thus correlates well with human perception. However, it requires
a large number of human annotations and does not generalize well outside the
range of perturbations on which it was trained. This paper introduces CDPAM, a
metric that builds on and advances DPAM. The primary improvement is to combine
contrastive learning and multi-dimensional representations to build robust
models from limited data. In addition, we collect human judgments on triplet
comparisons to improve generalization to a broader range of audio
perturbations. CDPAM correlates well with human responses across nine varied
datasets. We also show that adding this metric to existing speech synthesis and
enhancement methods yields significant improvement, as measured by objective
and subjective tests.


  The subtree prune-and-regraft (SPR) distance metric is a fundamental way of
comparing evolutionary trees. It has wide-ranging applications, such as to
study lateral genetic transfer, viral recombination, and Markov chain Monte
Carlo phylogenetic inference. Although the rooted version of SPR distance can
be computed relatively efficiently between rooted trees using
fixed-parameter-tractable maximum agreement forest (MAF) algorithms, no MAF
formulation is known for the unrooted case. Correspondingly, previous
algorithms are unable to compute unrooted SPR distances larger than 7.
  In this paper, we substantially advance understanding of and computational
algorithms for the unrooted SPR distance. First we identify four properties of
optimal SPR paths, each of which suggests that no MAF formulation exists in the
unrooted case. Then we introduce the replug distance, a new lower bound on the
unrooted SPR distance that is amenable to MAF methods, and give an efficient
fixed-parameter algorithm for calculating it. Finally, we develop a
"progressive A*" search algorithm using multiple heuristics, including the TBR
and replug distances, to exactly compute the unrooted SPR distance. Our
algorithm is nearly two orders of magnitude faster than previous methods on
small trees, and allows computation of unrooted SPR distances as large as 14 on
trees with 50 leaves.


  In recent years, deep-learning-based approaches have been introduced to
solving time-series forecasting-related problems. These novel methods have
demonstrated impressive performance in univariate and low-dimensional
multivariate time-series forecasting tasks. However, when these novel methods
are used to handle high-dimensional multivariate forecasting problems, their
performance is highly restricted by a practical training time and a reasonable
GPU memory configuration. In this paper, inspired by a change of basis in the
Hilbert space, we propose a flexible data feature extraction technique that
excels in high-dimensional multivariate forecasting tasks. Our approach was
originally developed for the National Science Foundation (NSF) Algorithms for
Threat Detection (ATD) 2022 Challenge. Implemented using the attention
mechanism and Convolutional Neural Networks (CNN) architecture, our method
demonstrates great performance and compatibility. Our models trained on the
GDELT Dataset finished 1st and 2nd places in the ATD sprint series and hold
promise for other datasets for time series forecasting.


  Big data systems address the challenges of capturing, storing, managing,
analyzing, and visualizing big data. Within this context, developing benchmarks
to evaluate and compare big data systems has become an active topic for both
research and industry communities. To date, most of the state-of-the-art big
data benchmarks are designed for specific types of systems. Based on our
experience, however, we argue that considering the complexity, diversity, and
rapid evolution of big data systems, for the sake of fairness, big data
benchmarks must include diversity of data and workloads. Given this motivation,
in this paper, we first propose the key requirements and challenges in
developing big data benchmarks from the perspectives of generating data with 4V
properties (i.e. volume, velocity, variety and veracity) of big data, as well
as generating tests with comprehensive workloads for big data systems. We then
present the methodology on big data benchmarking designed to address these
challenges. Next, the state-of-the-art are summarized and compared, following
by our vision for future research directions.


  Discoveries of new biomarkers for frequently occurring diseases are of
special importance in today's medicine. While fully developed type II diabetes
(T2D) can be detected easily, the early identification of high risk individuals
is an area of interest in T2D, too. Metagenomic analysis of the human bacterial
flora has shown subtle changes in diabetic patients, but no specific microbes
are known to cause or promote the disease. Moderate changes were also detected
in the microbial gene composition of the metagenomes of diabetic patients, but
again, no specific gene was found that is present in disease-related and
missing in healthy metagenome. However, these fine differences in microbial
taxon- and gene composition are difficult to apply as quantitative biomarkers
for diagnosing or predicting type II diabetes. In the present work we report
some nucleotide 9-mers with significantly differing frequencies in diabetic and
healthy intestinal flora. To our knowledge, it is the first time such short DNA
fragments have been associated with T2D. The automated, quantitative analysis
of the frequencies of short nucleotide sequences seems to be more feasible than
accurate phylogenetic and functional analysis, and thus it might be a promising
direction of diagnostic research.


  Non-Fungible Tokens (NFTs) are crypto assets with a unique digital identifier
for ownership, powered by blockchain technology. Technically speaking, anything
digital could be minted and sold as an NFT, which provides proof of ownership
and authenticity of a digital file. For this reason, it helps us distinguish
between the originals and their copies, making it possible to trade them. This
paper focuses on art NFTs that change how artists can sell their products. It
also changes how the art trade market works since NFT technology cuts out the
middleman. Recently, the utility of NFTs has become an essential issue in the
NFT ecosystem, which refers to the owners' usefulness, profitability, and
benefits. Using recent major art NFT marketplace datasets, we summarize and
interpret the current market trends and patterns in a way that brings insight
into the future art market. Numerical examples are presented.


  Contributions of the Fluorescence detector Array of Single-pixel Telescopes
(FAST) to the 35th International Cosmic Ray Conference, 12-20 July 2017, Busan,
Korea


  We re-consider the self tuning idea in brane world models of finite volume.
We notice that in a large class of self tuning models, the four dimensional
physics is sensitive to the vacuum energy on the brane. In particular the
compactification volume changes each time the tension of the brane is modified:
consequently, observable constants, as the effective Planck mass or masses of
matter fields, change as well. We notice that the self tuning mechanism and the
stabilization mechanism of the size of the extra dimensions are generically in
apparent conflict. We focus on a self tuning model in six spacetime dimensions
to analyze how the above considerations are explicitely realized.


  Results of an exhaustive search for minimum peak sidelobe level binary
sequences are presented. Several techniques for efficiency implementation of
search algorithm are described. A table of number of non-equivalent optimal
binary sequences with minimum peak sidelobe (MPS) level up to length 68 is
given. This number can be used in prediction of the longest length for a given
sidelobe level of binary sequences. The examples of optimal binary MPS
sequences having high merit factor are shown.


  The supporting instrument on board the Fermi Gamma-ray Space Telescope, the
Gamma-ray Burst Monitor (GBM) is a wide-field gamma-ray monitor composed of 14
individual scintillation detectors, with a field of view which encompasses the
entire unocculted sky. Primarily designed as transient monitors, the
conventional method for background determination with GBM-like instruments is
to time interpolate intervals before and after the source as a polynomial. This
is generally sufficient for sharp impulsive phenomena such as Gamma-Ray Bursts
(GRBs) which are characterised by impulsive peaks with sharp rises, often
highly structured, and easily distinguishable against instrumental backgrounds.
However, smoother long lived emission, such as observed in solar flares and
some GRBs, would be difficult to detect in a background-limited instrument
using this method. We present here a description of a technique which uses the
rates from adjacent days when the satellite has approximately the same
geographical footprint to distinguish low-level emission from the instrumental
background. We present results from the application of this technique to GBM
data and discuss the implementation of it in a generalised background limited
detector in a non-equatorial orbit.


  We investigate two well known dynamical systems that are designed to find
roots of univariate polynomials by iteration: the methods known by Newton and
by Ehrlich-Aberth. Both are known to have found all roots of high degree
polynomials with good complexity. Our goal is to determine in which cases which
of the two algorithms is more efficient. We come to the conclusion that Newton
is faster when the polynomials are given by recursion so they can be evaluated
in logarithmic time with respect to the degree, or when all the roots are all
near the boundary of their convex hull. Conversely, Ehrlich-Aberth has the
advantage when no fast evaluation of the polynomials is available, and when
roots are in the interior of the convex hull of other roots.


  Under a mild Lipschitz condition we prove a theorem on the existence and
uniqueness of global solutions to delay fractional differential equations.
Then, we establish a result on the exponential boundedness for these solutions.


  We study light (current) quark masses in framework of chiral constitutent
quark model. In our calculation the current quark masses are defined uniquely,
and all order effects of the light quark masses are considered. The results at
energy scale $\mu=m_\rho$ are $m_s=(160\pm 15)$MeV, $m_s/m_d=20.2\pm 3.0$,
$m_u/m_d=0.5\pm 0.09$.


  This review treats asymmetric colloidal particles moving through their host
fluid under the action of some form of propulsion. The propulsion can come from
an external body force or from external shear flow. It may also come from
externally-induced stresses at the surface, arising from imposed chemical,
thermal or electrical gradients. The resulting motion arises jointly from the
driven particle and the displaced fluid. If the objects are asymmetric, every
aspect of their motion and interaction depends on the orientation of the
objects. This orientation in turn changes in response to the driving. The
objects' shape can thus lead to a range of emergent anisotropic and chiral
motion not possible with isotropic spherical particles. We first consider what
aspects of a body's asymmetry can affect its drift through a fluid, especially
chiral motion. We next discuss driving by injecting external force or torque
into the particles. Then we consider driving without injecting force or torque.
This includes driving by shear flow and driving by surface stresses, such as
electrophoresis. We consider how time-dependent driving can induce collective
orientational order and coherent motion. We show how a given particle shape can
be represented using an assembly of point forces called a Stokeslet object. We
next consider the interactions between anisotropic propelled particles, the
symmetries governing the interactions, and the possibility of bound pairs of
particles. Finally we show how the collective hydrodynamics of a suspension can
be qualitatively altered by the particles' shapes. The asymmetric responses
discussed here are broadly relevant also for swimming propulsion of active
micron-scale objects such as microorganisms.


  Motion planning against sensor data is often a critical bottleneck in
real-time robot control. For sampling-based motion planners, which are
effective for high-dimensional systems such as manipulators, the most
time-intensive component is collision checking. We present a novel spatial data
structure, the collision-affording point tree (CAPT): an exact representation
of point clouds that accelerates collision-checking queries between robots and
point clouds by an order of magnitude, with an average query time of less than
10 nanoseconds on 3D scenes comprising thousands of points. With the CAPT,
sampling-based planners can generate valid, high-quality paths in under a
millisecond, with total end-to-end computation time faster than 60 FPS, on a
single thread of a consumer-grade CPU. We also present a point cloud filtering
algorithm, based on space-filling curves, which reduces the number of points in
a point cloud while preserving structure. Our approach enables robots to plan
at real-time speeds in sensed environments, opening up potential uses of
planning for high-dimensional systems in dynamic, changing, and unmodeled
environments.


  First-principles calculations and analysis of the thermodynamic, structural,
and electronic properties of liquid SiO$_2$ characterize the bonded-to-atomic
transition at 0.1--1.6 TPa and 10$^4$--10$^5$ K (1--7 eV), the
high-energy-density regime relevant to understanding planetary interiors. We
find strong ionic bonds that become short-lived due to high kinetics during the
transition, with sensitivity of the transition temperature to pressure, and our
calculated Hugoniots agree with past experimental data. These results reconcile
previous experimental and theoretical findings by clarifying the nature of the
bond dissociation process in early Earth and "rocky" (oxide) constituents of
large planets.


  Synthetic data generation is increasingly recognized as a crucial solution to
address data related challenges such as scarcity, bias, and privacy concerns.
As synthetic data proliferates, the need for a robust evaluation framework to
select a synthetic data generator becomes more pressing given the variety of
options available. In this research study, we investigate two primary
questions: 1) How can we select the most suitable synthetic data generator from
a set of options for a specific purpose? 2) How can we make the selection
process more transparent, accountable, and auditable? To address these
questions, we introduce a novel approach in which the proposed ranking
algorithm is implemented as a smart contract within a permissioned blockchain
framework called Sawtooth. Through comprehensive experiments and comparisons
with state-of-the-art baseline ranking solutions, our framework demonstrates
its effectiveness in providing nuanced rankings that consider both desirable
and undesirable properties. Furthermore, our framework serves as a valuable
tool for selecting the optimal synthetic data generators for specific needs
while ensuring compliance with data protection principles.


  Second-order necessary conditions for optimal control problems are
considered, where the ``second-order" is in the sense of that Pontryagin's
maximum principle is viewed as a first-order necessary optimality condition. A
sufficient condition for a local minimizer is also given.


  We show that the heterotic supersymmetry (Killing spinor equations) and the
anomaly cancellation imply the heterotic equations of motion in dimensions
five, six, seven, eight if and only if the connection on the tangent bundle is
an instanton. For heterotic compactifications in dimension six this reduces the
choice of that connection to the unique SU(3) instanton on a manifold with
stable tangent bundle of degree zero.


  We report muon spin rotation (${\mu}$SR) experiments on the microscopic
properties of superconductivity and magnetism in the kagome superconductor
CeRu$_{2}$ with $T_{\rm c}$~${\simeq}$~5~K. From the measurements of the
temperature-dependent magnetic penetration depth ${\lambda}$, the
superconducting order parameter exhibits nodeless pairing, which fits best to
an anisotropic $s$-wave gap symmetry. We further show that the $T_{\rm
c}$/$\lambda^{-2}$ ratio is comparable to that of unconventional
superconductors. Furthermore, the powerful combination of zero-field
(ZF)-${\mu}$SR and high-field ${\mu}$SR has been used to uncover magnetic
responses across three characteristic temperatures, identified as
$T_1^*$~${\simeq}$~110~K, $T_2^*$~${\simeq}$~65~K, and $T_3^*$~${\simeq}$~40~K.
Our experiments classify CeRu$_{2}$ as an exceedingly rare nodeless magnetic
kagome superconductor.


  Heap layout manipulation is integral to exploiting heap-based memory
corruption vulnerabilities. In this paper we present the first automatic
approach to the problem, based on pseudo-random black-box search. Our approach
searches for the inputs required to place the source of a heap-based buffer
overflow or underflow next to heap-allocated objects that an exploit developer,
or automatic exploit generation system, wishes to read or corrupt. We present a
framework for benchmarking heap layout manipulation algorithms, and use it to
evaluate our approach on several real-world allocators, showing that
pseudo-random black box search can be highly effective. We then present SHRIKE,
a novel system that can perform automatic heap layout manipulation on the PHP
interpreter and can be used in the construction of control-flow hijacking
exploits. Starting from PHP's regression tests, SHRIKE discovers fragments of
PHP code that interact with the interpreter's heap in useful ways, such as
making allocations and deallocations of particular sizes, or allocating objects
containing sensitive data, such as pointers. SHRIKE then uses our search
algorithm to piece together these fragments into programs, searching for one
that achieves a desired heap layout. SHRIKE allows an exploit developer to
focus on the higher level concepts in an exploit, and to defer the resolution
of heap layout constraints to SHRIKE. We demonstrate this by using SHRIKE in
the construction of a control-flow hijacking exploit for the PHP interpreter.


  We consider the inhomogeneous Dirichlet problem on product domains. The main
result is the asymptotic expansion of the solution in terms of increasing
smoothness up to the boundary. In particular, we show the exact nature of the
singularities of the solution at singularities of the boundary by constructing
singular functions which make up an asymptotic expansion of the solution.


  We consider a qubit initalized in a superposition of its pointer states,
exposed to pure dephasing due to coupling to a quasi-static environment, and
subjected to a sequence of single-shot measurements projecting it on chosen
superpositions. We show how with a few of such measurements one can
significantly diminish one's ignorance about the environmental state, and how
this leads to increase of coherence of the qubit interacting with a properly
post-selected environmental state. We give theoretical results for the case of
a quasi-static environment that is a source of an effective field of Gaussian
statistics acting on a qubit, and for a nitrogen-vacancy center qubit coupled
to a nuclear spin bath, for which the Gaussian model applies qualitatively
provided one excludes from the environment nuclei that are strongly coupled to
the qubit. We discuss the reason for which the most probable sequences of
measurement results are the ones consisting of identical outcomes, and in this
way we shed light on recent experiment (D. D. Bhaktavatsala Rao et al.,
arXiv:1804.07111) on nitrogen-vacancy centers.


  The existence of glueballs within QCD is uncontroversial but their
experimental verification is still in doubt. We discuss the new possibilities
for a search of glueballs as the leading object in gluon jets at the LHC. We
summarize previous results from LEP which demonstrate a significant excess rate
of electrically neutral leading clusters in comparison with MC models.


  We address scaling in inhomogeneous and anisotropic turbulent flows by
decomposing structure functions into their irreducible representation of the
SO(3) symmetry group which are designated by $j,m$ indices. Employing
simulations of channel flows with Re$_\lambda\approx 70$ we demonstrate that
different components characterized by different $j$ display different scaling
exponents, but for a given $j$ these remain the same at different distances
from the wall. The $j=0$ exponent agrees extremely well with high Re
measurements of the scaling exponents, demonstrating the vitality of the SO(3)
decomposition.


  In this paper we propose a new Eulerian modeling and related accurate and
robust numerical methods, describing polydisperse evaporating sprays, based on
high order moment methods in size. The main novelty of this model is its
capacity to describe some geometrical variables of the droplet-gas interface,
by analogy with the liquid-gas interface in interfacial flows. For this
purpose, we use fractional size-moments, where the size variable is taken as
the droplet surface. In order to evaluate the evaporation of the polydisperse
spray, we use a smooth reconstruction which maximizes the Shannon entropy.
However, the use of fractional moments introduces some theoretical and
numerical difficulties, which need to be tackled. First, relying on a study of
the moment space, we extend the Maximum Entropy (ME) reconstruction of the size
distribution to the case of fractional moments. Then, we propose a new accurate
and realizable algorithm to solve the moment evolution due to evaporation,
which preserves the structure of the moment space. This algorithm is based on a
mathematical analysis of the kinetic evolution due to evaporation, where it
shown that the evolution of some negative order fractional moments have to be
properly predicted, a peculiarity related to the use of fractional moments. The
present model and numerical schemes yield an accurate and stable evaluationof
the moment dynamics with minimal number of variables, as well as a minimal
computational cost as with the EMSM model, but with the very interesting
additional capacity of coupling with diffuse interface model and transport
equation of averaged geometrical interface variables, which are essential in
oder to describe atomization.


  Dynamic searchable symmetric encryption (DSSE) enables users to delegate the
keyword search over dynamically updated encrypted databases to an
honest-but-curious server without losing keyword privacy. This paper studies a
new and practical security risk to DSSE, namely, secret key compromise (e.g., a
user's secret key is leaked or stolen), which threatens all the security
guarantees offered by existing DSSE schemes. To address this open problem, we
introduce the notion of searchable encryption with key-update (SEKU) that
provides users with the option of non-interactive key updates. We further
define the notion of post-compromise secure with respect to leakage functions
to study whether DSSE schemes can still provide data security after the
client's secret key is compromised. We demonstrate that post-compromise
security is achievable with a proposed protocol called ``Bamboo".
Interestingly, the leakage functions of Bamboo satisfy the requirements for
both forward and backward security. We conduct a performance evaluation of
Bamboo using a real-world dataset and compare its runtime efficiency with the
existing forward-and-backward secure DSSE schemes. The result shows that Bamboo
provides strong security with better or comparable performance.


  The first Advanced LIGO observing run detected two black hole merger events
with confidence and likely a third. Many groups organized to followup the
events in the optical even though the strong theoretical prior that no optical
emission should be seen. We carry through the logic of this by asking about the
experimental upper limits to the optical light from Advanced LIGO black hole
mergere events. We inventory the published optical searches for transient
events associated with the black hole mergers. We describe the factors that go
into a formal limit on the visibility of an event (sky area coverage, the
coverage factor of the camera, the fraction of sky not covered by intervening
objects), and list what is known from the literature of the followup teams
quantitative assessment of each factor. Where possible we calculate the total
probability from each group that the source was imaged. The calculation of
confidence level is reviewed for the case of no background. We find that an
experimental 95% upper limit on the magnitude of a black hole requires the sum
of the total probabilities over all events to be more than 3. In the first
Advanced LIGO observing run we were far from reaching that threshold.


  We reconsider the theory of scattering for the Wave-Schr\"odinger system and
more precisely the local Cauchy problem with infinite initial time, which is
the main step in the construction of the wave operators. Using a method due to
Nakanishi, we eliminate a loss of regularity between the Schr\"odinger
asymptotic data and the Schr\"odinger solution in the treatment of that
problem, in the special case of vanishing asymptotic data for the wave field.


  This paper extends our previous quantum Fisher information (QFI) based
analysis of the problem of separating a pair of equal-brightness incoherent
point sources in three dimensions to the case of a pair of sources that are
unequally bright. When the pair's geometric center is perfectly known in
advance, QFI with respect to the estimation of the three separation coordinates
remains independent of the degree of brightness asymmetry. For the
experimentally more relevant case of perfect prior knowledge of the pair's
brightness centroid, however, such QFI becomes dependent on the pair separation
vector in a way that is controlled by the degree of its brightness asymmetry.
This study yields potentially useful insights into the analysis of a more
general superresolution imaging problem involving extended incoherent sources
with nontrivial brightness distributions.


  A problem of mass flow in the immediate vicinity of a planet embedded in a
protoplanetary disk is studied numerically in two dimensions. Large differences
in temporal and spatial scales involved suggest that a specialized
discretization method for solution of hydrodynamical equations may offer great
savings in computational resources, and can make extensive parameter studies
feasible. Preliminary results obtained with help of Adaptive Mesh Refinement
technique and high-order explicit Eulerian solver are presented. This
combination of numerical techniques appears to be an excellent tool which
allows for direct simulations of mass flow in vicinity of the accretor at
moderate computational cost. In particular, it is possible to resolve the
surface of the planet and to model the process of planet growth with minimal
set of assumptions. Some issues related to visualization of the results and
future prospects are discussed briefly.


  The contribution of Resistive Plate Chamber (RPC) to muon reconstruction in
CMS has been studied on a sample of muons collected in proton-proton collisions
at sqrt(s) = 7 TeV at the LHC in 2011. Muon reconstruction is performed using
the all-silicon inner tracker and with up to four stations of gas-ionization
muon detectors. Drift Tubes and Cathode Strip Chambers detect muons in the
barrel and endcap regions, respectively, and are complemented by the RPC
system. Measured distributions of reconstructed hits in the RPCs crossed by
muons from Z decays with a transverse momentum pT above 20 GeV/c are well
reproduced by the Monte Carlo simulation. From the samples of J/psi and Z
events, the efficiencies for muons with and without the inclusion of the RPC
hits in the muon track reconstruction are measured and compared with the
simulation. Using RPC information in track reconstruction improves up to about
3% of offline reconstruction efficiency for the muons in the region of pT above
7 GeV/c, in good agreement with simulation.


  We present a method to create storytelling visualization with time series
data. Many personal decisions nowadays rely on access to dynamic data
regularly, as we have seen during the COVID-19 pandemic. It is thus desirable
to construct storytelling visualization for dynamic data that is selected by an
individual for a specific context. Because of the need to tell data-dependent
stories, predefined storyboards based on known data cannot accommodate dynamic
data easily nor scale up to many different individuals and contexts. Motivated
initially by the need to communicate time series data during the COVID-19
pandemic, we developed a novel computer-assisted method for meta-authoring of
stories, which enables the design of storyboards that include feature-action
patterns in anticipation of potential features that may appear in dynamically
arrived or selected data. In addition to meta-storyboards involving COVID-19
data, we also present storyboards for telling stories about progress in a
machine learning workflow. Our approach is complementary to traditional methods
for authoring storytelling visualization, and provides an efficient means to
construct data-dependent storyboards for different data-streams of similar
contexts.


  In the field of affective computing, where research continually advances at a
rapid pace, the demand for user-friendly tools has become increasingly
apparent. In this paper, we present the AffectToolbox, a novel software system
that aims to support researchers in developing affect-sensitive studies and
prototypes. The proposed system addresses the challenges posed by existing
frameworks, which often require profound programming knowledge and cater
primarily to power-users or skilled developers. Aiming to facilitate ease of
use, the AffectToolbox requires no programming knowledge and offers its
functionality to reliably analyze the affective state of users through an
accessible graphical user interface. The architecture encompasses a variety of
models for emotion recognition on multiple affective channels and modalities,
as well as an elaborate fusion system to merge multi-modal assessments into a
unified result. The entire system is open-sourced and will be publicly
available to ensure easy integration into more complex applications through a
well-structured, Python-based code base - therefore marking a substantial
contribution toward advancing affective computing research and fostering a more
collaborative and inclusive environment within this interdisciplinary field.


  Gravitational wave signals from coalescing compact binaries in the LIGO and
Virgo interferometers are primarily detected by the template based matched
filtering method. While this method is optimal for stationary and Gaussian data
scenarios, its sensitivity is often affected by non stationary noise transients
in the detectors. Moreover, most of the current searches do not account for the
effects of precession of black hole spins and higher order waveform harmonics,
focusing solely on the leading order quadrupolar modes. This limitation impacts
our search for interesting astrophysical sources, such as intermediate mass
black hole binaries and hierarchical mergers. Here we show for the first time
that deep learning can be used for accurate waveform reconstruction of
precessing binary black hole signals with higher order modes. This approach can
also be adapted into a rapid trigger generation algorithm to enhance online
searches. Our model, tested on simulated injections in real LIGO noise from the
third observing run achieved high-degree of overlap with injected signals. This
accuracy was consistent across a wide range of black hole masses and spin
configurations chosen for this study. When applied to real gravitational wave
events, our reconstructions achieved between 0.85 and 0.98 overlaps with those
obtained by Coherent WaveBurst (unmodeled) and LALInference (modeled) analyses.
These results suggest that deep learning is a potent tool for analyzing signals
from a diverse catalog of compact binaries.


  We construct the duality-symmetric actions for a large class of
six-dimensional models describing hierarchies of non-Abelian scalar, vector and
tensor fields related to each other by first-order (self-)duality equations
that follow from these actions. In particular, this construction provides a
Lorentz invariant action for non-Abelian self-dual tensor fields. The class of
models includes the bosonic sectors of the 6d (1,0) superconformal models of
interacting non-Abelian self-dual tensor, vector, and hypermultiplets.


  Large objects, immersed in a homogeneous granular system, migrate when
subjected to vibrations. Under certain conditions large heavy objects rise and
similar light ones sink to the bottom. This is called reverse buoyancy. We
report an experimental study of this singular behavior, for a large sphere
immersed in a deep granular bed. A simple mechanism is proposed to describe the
motion of a sphere, inside a vertically vibrated granular system. When reverse
buoyancy is observed, the measured vertical velocity of the immersed object, as
a function of its density, shows a simple behavior. With a one-dimensional
mechanical model that takes into account a buoyancy force and the frictional
drag, we obtain the rising velocity for heavy objects and the sinking rate for
light ones. The model yields a very good qualitative and quantitative agreement
with the experiment.


  Given a compact surface $\mathcal{M}$ with a smooth area form $\omega$, we
consider an open and dense subset of the set of smooth closed 1-forms on
$\mathcal{M}$ with isolated zeros which admit at least one saddle loop
homologous to zero and we prove that almost every element in the former induces
a mixing flow on each minimal component. Moreover, we provide an estimate of
the speed of the decay of correlations for smooth functions with compact
support on the complement of the set of singularities. This result is achieved
by proving a quantitative version for the case of finitely many singularities
of a theorem by Ulcigrai (ETDS, 2007), stating that any suspension flow with
one asymmetric logarithmic singularity over almost every interval exchange
transformation is mixing. In particular, the quantitative mixing estimate we
prove applies to asymmetric logarithmic suspension flows over rotations, which
were shown to be mixing by Sinai and Khanin.


  In this paper, we introduce a methodology for predicting intent and slots of
a query for a chatbot that answers career-related queries. We take a
multi-staged approach where both the processes (intent-classification and
slot-tagging) inform each other's decision-making in different stages. The
model breaks down the problem into stages, solving one problem at a time and
passing on relevant results of the current stage to the next, thereby reducing
search space for subsequent stages, and eventually making classification and
tagging more viable after each stage. We also observe that relaxing rules for a
fuzzy entity-matching in slot-tagging after each stage (by maintaining a
separate Named Entity Tagger per stage) helps us improve performance, although
at a slight cost of false-positives. Our model has achieved state-of-the-art
performance with F1-score of 77.63% for intent-classification and 82.24% for
slot-tagging on our dataset that we would publicly release along with the
paper.


  Let F be a fibration on a simply-connected base with symplectic fibre (M,
\omega). Assume that the fibre is nilpotent and T^{2k}-separable for some
integer k or a nilmanifold. Then our main theorem, Theorem 1.8, gives a
necessary and sufficient condition for the cohomology class [\omega] to extend
to a cohomology class of the total space of F. This allows us to describe
Thurston's criterion for a symplectic fibration to admit a compatible
symplectic form in terms of the classifying map for the underlying fibration.
The obstruction due to Lalond and McDuff for a symplectic bundle to be
Hamiltonian is also rephrased in the same vein. Furthermore, with the aid of
the main theorem, we discuss a global nature of the set of the homotopy
equivalence classes of fibrations with symplectic fibre in which the class
[\omega] is extendable.


  Using Laplace transform techniques, I calculate the longitudinal structure
function $F_{L}(x,Q^{2})$ from the scaling violations of the proton structure
function $F_{2}(x,Q^{2})$, and make a critical study of this relationship
between the structure functions at leading order (LO) up to next-to-next-to
leading order (NNLO) analysis at small $x$. Furthermore, I consider heavy quark
contributions to the relation between the structure functions, which leads to
compact formula for $N_{f}=3+\mathrm{Heavy}$. The nonlinear corrections to the
longitudinal structure function at LO up to NNLO analysis are shown in the
$N_{f}=4$ (light quark flavor) based on the nonlinear corrections at $R=2$ and
$R=4 GeV^{-1}$. The results are compared to experimental data of the
longitudinal proton structure function $F_{L}$ in the range of $6.5 \leq Q^{2}
\leq 800 GeV^{2}$.


  The quantum mechanical two - particle problem is considered in hot dense
nuclear matter under the influence of a strong electric field such as the field
of the residual nucleus in heavy - ion reactions. A generalized
Galitskii-Bethe-Salpeter equation is derived and solved which includes
retardation and field effects. Compared with the in-medium properties in the
zero-field case, bound states are turned into resonances and the scattering
phase shifts are modified. Four effects are observed due to the applied field:
(i) A suppression of the Pauli-blocking below nuclear matter densities, (ii)
the onset of pairing occurs already at higher temperatures due to the field,
(iii) a field dependent finite lifetime of deuterons and (iv) the imaginary
part of the quasiparticle self-energy changes its sign for special values of
density and temperatures indicating a phase instability. The latter effect may
influence the fragmentation processes. The lifetime of deuterons in a strong
Coulomb field is given explicitly.


  This paper is concerned with the intrinsic geometric structure of interior
transmission eigenfunctions arising in wave scattering theory. We numerically
show that the aforementioned geometric structure can be much delicate and
intriguing. The major findings can be roughly summarized as follows. If there
is a cusp on the support of the underlying potential function, then the
interior transmission eigenfunction vanishes near the cusp if its interior
angle is less than $\pi$, whereas the interior transmission eigenfunction
localizes near the cusp if its interior angle is bigger than $\pi$.
Furthermore, we show that the vanishing and blowup orders are inversely
proportional to the interior angle of the cusp: the sharper the angle, the
higher the convergence order. Our results are first of its type in the spectral
theory for transmission eigenvalue problems, and the existing studies in the
literature concentrate more on the intrinsic properties of the transmission
eigenvalues instead of the transmission eigenfunctions. Due to the limitedness
of the computing resources, our study is by no means exclusive and complete. We
consider our study only in a certain geometric setup including corner, curved
corner and edge singularities. Nevertheless, we believe that similar results
hold for more general cusp singularities and rigorous theoretical
justifications are much desirable. Our study enriches the spectral theory for
transmission eigenvalue problems. We also discuss its implication to inverse
scattering theory.


  Supersymmetric extensions of the Standard Model that incorporate the axion
solution to the strong CP problem necessarily contain also the axino, the
fermionic partner of the axion. In contrast to the neutralino and the
gravitino, the axino mass is generically not of the order of the
supersymmetry-breaking scale and can be much smaller. The axino is therefore an
intriguing candidate for a stable superpartner. In a previous Letter [1] it was
shown that axinos are a natural candidate for cold dark matter in the Universe
when they are generated non-thermally through out-of-equilibrium neutralino
decays. Here, we extend the study of non-thermal production and include a
competing thermal production mechanism through scatterings and decays of
particles in the plasma. We identify axino masses in the range of tens of MeV
to several GeV (depending on the scenario) as corresponding to cold axino
relics if the reheating temperature $\treh$ is less than about
$5\times10^4\gev$. At higher $\treh$ and lower mass, axinos could constitute
warm dark matter. In the scenario with axinos as relics, the gravitino problem
finds a natural solution. The lightest superpartner of the Standard Model
spectrum remains effectively stable in high-energy detectors but may be either
neutral or charged. The usual highly restrictive constraint $\abundchi\lsim1$
on the relic abundance of the lightest neutralino becomes void.


  Geodesic acoustic modes (GAM) are oscillating zonal structures unique to
toroidal plasmas, and have been extensively studied in the past decades due to
their potential capabilities of regulating microscopic turbulences and
associated anomalous transport. This article reviews linear and nonlinear
theories of GAM; with emphases on kinetic treatment, system nonuniformity and
realistic magnetic geometry, in order to reflect the realistic experimental
conditions. Specifically, in the linear physics, the resonant wave-particle
interactions are discussed, with the application to resonant excitation by
energetic particles (EPs). The theory of EP-induced GAM (EGAM) is applied to
realistic devices for the interpretation of experimental observations, and
global effects due to coupling to GAM continuum are also discussed. Meanwhile,
in the nonlinear physics, the spontaneous GAM excitation by microscale
turbulences is reviewed, including the effects of various system
nonuniformities. A unified theoretical framework of GAM/EGAM is then
constructed based on our present understandings. The first-principle-based
GAM/EGAM theories reviewed here, thus, provide the tools needed for the
understanding and interpretation of experimental/numerical results.


  Using a partonic transport model based on the Nambu-Jona-Lasinio model, we
study the effect of scalar and vector mean fields on the elliptic flows of
quarks and antiquarks in relativistic heavy ion collisions in Au+Au collisions
at $\sqrt{s_{\rm NN}}=7.7~$GeV and impact parameter $b=8~{\rm fm}$ that leads
to the production of a baryon-rich matter. Although the scalar mean field,
which is attractive for both quarks and antiquarks, reduces both their elliptic
flows, the vector mean field, which is repulsive for quarks and attractive for
antiquarks, leads to a splitting of their elliptic flows, and this effect
increases with the strength of the vector coupling in the baryon-rich quark
matter. Converting quarks and antiquarks at hadronization to hadrons via the
quark coalescence model, we further study the dependence of the transverse
momentum integrated relative elliptic flow differences between protons and
antiprotons, lambda and anti-lambdas, and positively and negatively charged
kaons on the strength of the quark vector coupling. These results are then
compared with the experimental data measured by the STAR Collaboration in the
Beam Energy Scan program at the Relativistic Heavy Ion Collider.


  The Leftover Hash Lemma states that the output of a two-universal hash
function applied to an input with sufficiently high entropy is almost uniformly
random. In its standard formulation, the lemma refers to a notion of randomness
that is (usually implicitly) defined with respect to classical side
information. Here, we prove a (strictly) more general version of the Leftover
Hash Lemma that is valid even if side information is represented by the state
of a quantum system. Furthermore, our result applies to arbitrary delta-almost
two-universal families of hash functions. The generalized Leftover Hash Lemma
has applications in cryptography, e.g., for key agreement in the presence of an
adversary who is not restricted to classical information processing.


  We study the non-equilibrium dynamics of the O(N) model in classical and
quantum field theory in 1+1 dimensions, for N > 1. We compare numerical results
obtained using the Hartree approximation and two next to leading order
approximations, the bare vertex approximation and the 2PI-1/N expansion. The
later approximations differ through terms of order g^2, where g is the scaled
coupling constant, g=\lambda/N. In this paper we investigate the statement
regarding the convergence with respect to g. We find that the differences
between these twoapproximation schemes diminish for larger values of N, when
\lambda is fixed.


  The $K^-pp$ system is investigated using a variational approach with
realistic two-body interactions: the Argonne v18 $NN$ potential and an energy
dependent $\bar{K}N$ effective interaction derived from chiral SU(3)
coupled-channel dynamics. Uncertainties in subthreshold extrapolations of the
$\bar{K}N$ interaction are considered. A weakly bound $K^-pp$ state is found,
with a binding energy $B = (19\pm 3)$ MeV substantially smaller than suggested
in previous calculations. The decay width $\Gamma(K^-pp\to \pi\Sigma N)$ is
estimated to range between about 40 and 70 MeV.


  Desert mirages were simulated in the laboratory by heating a flat surface of
sand. This showed that a boundary layer of air, of only a few millimeters
immediately covering a heated surface has nearly the same thickness over a wide
range of temperatures. It consists of a region producing an inverted image, and
another producing an erect image depending on the relative distances between
observer, heated surface and object. Measurement of distances and heights show
angles of incidence greater than angles of reflection. Air refractive index
measurements agree with involvement of the critical angle in ray bending. An
analogy with density gradient sucrose solutions also shows double images,
inverted and erect. It is assumed that a similar situation exists outdoors.
Specular reflection over rough surfaces can be described as a mirage, but it is
seen at a wider range of viewing angles, and it does not require a temperature
gradient. It is always present, but it can be obliterated by a higher intensity
of diffuse reflection.


  New isoperimetric inequalities for lower order eigenvalues of the Laplacian
on closed hypersurfaces, of the biharmonic Steklov problems and of the
Wentzell-Laplace on bounded domains in a Euclidean space are proven. Some open
questions for further study are also proposed.


  For natural and artificial systems with some symmetry structure,
computational understanding and manipulation can be achieved without learning
by exploiting the algebraic structure. Here we describe this algebraic
coordinatization method and apply it to permutation puzzles. Coordinatization
yields a structural understanding, not just solutions for the puzzles.


  Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis
and treatment. However, variations in MRI acquisition protocols result in
different appearances of normal and diseased tissue in the images.
Convolutional neural networks (CNNs), which have shown to be successful in many
medical image analysis tasks, are typically sensitive to the variations in
imaging protocols. Therefore, in many cases, networks trained on data acquired
with one MRI protocol, do not perform satisfactorily on data acquired with
different protocols. This limits the use of models trained with large annotated
legacy datasets on a new dataset with a different domain which is often a
recurring situation in clinical settings. In this study, we aim to answer the
following central questions regarding domain adaptation in medical image
analysis: Given a fitted legacy model, 1) How much data from the new domain is
required for a decent adaptation of the original network?; and, 2) What portion
of the pre-trained model parameters should be retrained given a certain number
of the new domain training samples? To address these questions, we conducted
extensive experiments in white matter hyperintensity segmentation task. We
trained a CNN on legacy MR images of brain and evaluated the performance of the
domain-adapted network on the same task with images from a different domain. We
then compared the performance of the model to the surrogate scenarios where
either the same trained network is used or a new network is trained from
scratch on the new dataset.The domain-adapted network tuned only by two
training examples achieved a Dice score of 0.63 substantially outperforming a
similar network trained on the same set of examples from scratch.


  We investigate chemical bond formation and conductance in a molecular
C60-junction under finite bias voltage using first-principles calculations
based on density functional theory and nonequilibrium Green's functions
(DFT-NEGF). At the point of contact formation we identify a remarkably strong
coupling between the C60-motion and the molecular electronic structure. This is
only seen for positive sample bias, although the conductance itself is not
strongly polarity dependent. The nonequilibrium effect is traced back a sudden
shift in the position of the voltage drop with a small C60-displacement.
Combined with a vibrational heating mechanism we construct a model from our
results that explain the polarity-dependent two-level conductance fluctuations
observed in recent scanning tunneling microscopy (STM) experiments [N. N\'eel
et al., Nano Lett. 11, 3593 (2011)]. These findings highlight the significance
of nonequilibrium effects in chemical bond formation/breaking and in
electron-vibration coupling in molecular electronics.


  Tactile data and kinesthetic cues are two important sensing sources in robot
object recognition and are complementary to each other. In this paper, we
propose a novel algorithm named Iterative Closest Labeled Point (iCLAP) to
recognize objects using both tactile and kinesthetic information.The iCLAP
first assigns different local tactile features with distinct label numbers. The
label numbers of the tactile features together with their associated 3D
positions form a 4D point cloud of the object. In this manner, the two sensing
modalities are merged to form a synthesized perception of the touched object.
To recognize an object, the partial 4D point cloud obtained from a number of
touches iteratively matches with all the reference cloud models to identify the
best fit. An extensive evaluation study with 20 real objects shows that our
proposed iCLAP approach outperforms those using either of the separate sensing
modalities, with a substantial recognition rate improvement of up to 18%.


  We propose a novel hands-free method to interactively segment 3D medical
volumes. In our scenario, a human user progressively segments an organ by
answering a series of questions of the form "Is this voxel inside the object to
segment?". At each iteration, the chosen question is defined as the one halving
a set of candidate segmentations given the answered questions. For a quick and
efficient exploration, these segmentations are sampled according to the
Metropolis-Hastings algorithm. Our sampling technique relies on a combination
of relaxed shape prior, learnt probability map and consistency with previous
answers. We demonstrate the potential of our strategy on a prostate
segmentation MRI dataset. Through the study of failure cases with synthetic
examples, we demonstrate the adaptation potential of our method. We also show
that our method outperforms two intuitive baselines: one based on random
questions, the other one being the thresholded probability map.


  Through a redefinition of patterns in an Hopfield-like model, we introduce
and develop an approach to model discrete systems made up of many, interacting
components with inner degrees of freedom. Our approach clarifies the intrinsic
connection between the kind of interactions among components and the emergent
topology describing the system itself; also, it allows to effectively address
the statistical mechanics on the resulting networks. Indeed, a wide class of
analytically treatable, weighted random graphs with a tunable level of
correlation can be recovered and controlled. We especially focus on the case of
imitative couplings among components endowed with similar patterns (i.e.
attributes), which, as we show, naturally and without any a-priori assumption,
gives rise to small-world effects. We also solve the thermodynamics (at a
replica symmetric level) by extending the double stochastic stability
technique: free energy, self consistency relations and fluctuation analysis for
a picture of criticality are obtained.


  This paper presents the results of full polarization observations of the
massive star-forming region W75N, conducted with 3 arcsec spatial resolutions
at 345 GHz using the Submillimeter Array (SMA). The magnetic field structures
in the dense cores of the region are derived using the linearly polarized
continuum emission. The overall magnetic field strength and orientation are
found to agree with those from the previous observations. The plane-of-sky
(POS) component of the magnetic field in the region was calculated to be 0.8
\pm 0.1 mG using the angular dispersion function (ADF) method. Further analyses
involving the polarization-intensity gradient-local gravity method and H13CO+
(4-3) line data indicated that the cloud is undergoing global gravitational
collapse and the magnetic field is shaped by gravity and outflows in the dense
core regions.


  Assuming $\diamondsuit$, we construct a $T_2$ example of a hereditarily
Lindel\"of space of size $\omega_1$ which is not a $D$-space. The example has
the property that all finite powers are also Lindel\"of.


  We use the spectral lag data of 32 long GRBs detected by Fermi/GBM, which has
been recently collated in Liu et al (2022) to quantify the statistical
significance of a transition in the spectral lag data based on Lorentz
invariance violation (LIV) (for both sub-luminal and super-luminal propagation)
using Bayesian model selection. We use two different parametric functions to
model the null hypothesis of only intrinsic emission: a smooth broken power law
model (SBPL) (proposed in Liu et al) as well as a simple power law model, which
has been widely used before in literature. We find that for sub-luminal
propagation, when we use the SBPL model as the null hypothesis, five GRBs show
``decisive evidence'' based on Jeffreys' scale for linear LIV and quadratic
LIV. When we use the simple power-law model as the null hypothesis, we find
that 10 and GRBs show Bayesian ``decisive evidence'' for linear and quadratic
LIV, respectively. However these results should not be construed as evidence
for LIV, as they would be in conflict with the most stringent upper limits.
When we did a test for super-luminal LIV, we find that only four and two GRBs
show Bayesian ``decisive evidence'' for linear and quadratic LIV, respectively,
assuming a simple power law for the intrinsic emission. When we use the SBPL
model, one GRB shows Bayesian ``decisive evidence'' for linear and quadratic
LIV. This underscores the importance of adequately modelling the intrinsic
emission while obtaining constraints on LIV using spectral lags, since
inadequate modelling could masquerade as a signature of LIV.


  Velocity-dependent interactions in a fundamental-string dominated universe
lead quite naturally, with reasonable assumptions on initial conditions, to an
accelerating expanding universe without assuming the existence of a
cosmological constant. This result also holds generically for a universe
dominated by moving extremal black holes, owing to a repulsive velocity-
dependent force. This interaction, however, does not preclude structure
formation. Here we discuss a toy model including both ordinary and extremal
matter, in which the latter accounts for dark matter while simultaneously
acting as effective dark energy. Eternal acceleration is once more seen to
arise in this case.


  In the following paper we consider a simulation technique for stochastic
trees. One of the most important areas in computational genetics is the
calculation and subsequent maximization of the likelihood function associated
to such models. This typically consists of using importance sampling (IS) and
sequential Monte Carlo (SMC) techniques. The approach proceeds by simulating
the tree, backward in time from observed data, to a most recent common ancestor
(MRCA). However, in many cases, the computational time and variance of
estimators are often too high to make standard approaches useful. In this paper
we propose to stop the simulation, subsequently yielding biased estimates of
the likelihood surface. The bias is investigated from a theoretical point of
view. Results from simulation studies are also given to investigate the balance
between loss of accuracy, saving in computing time and variance reduction.


  We discuss the non-Boltzmannian nature of quasi-stationary states in the
Hamiltonian Mean Field (HMF) model, a paradigmatic model for long-range
interacting classical many-body systems. We present a theorem excluding the
Boltzmann-Gibbs exponential weight in Gibbs $\Gamma$-space of microscopic
configurations, and comment a paper recently published by Baldovin and
Orlandini (2006). On the basis of the points here discussed, the ongoing debate
on the possible application, within appropriate limits, of the generalized
$q$-statistics to long-range Hamiltonian systems remains open.


  Federated Learning (FL) is a novel approach enabling several clients holding
sensitive data to collaboratively train machine learning models, without
centralizing data. The cross-silo FL setting corresponds to the case of few
($2$--$50$) reliable clients, each holding medium to large datasets, and is
typically found in applications such as healthcare, finance, or industry. While
previous works have proposed representative datasets for cross-device FL, few
realistic healthcare cross-silo FL datasets exist, thereby slowing algorithmic
research in this critical application. In this work, we propose a novel
cross-silo dataset suite focused on healthcare, FLamby (Federated Learning
AMple Benchmark of Your cross-silo strategies), to bridge the gap between
theory and practice of cross-silo FL. FLamby encompasses 7 healthcare datasets
with natural splits, covering multiple tasks, modalities, and data volumes,
each accompanied with baseline training code. As an illustration, we
additionally benchmark standard FL algorithms on all datasets. Our flexible and
modular suite allows researchers to easily download datasets, reproduce results
and re-use the different components for their research. FLamby is available
at~\url{www.github.com/owkin/flamby}.


  The control over the spectral broadening of an ensemble of emitters, mainly
attributable to the size and shape dispersion and the homogenous broadening
mechanisms, is crucial to several applications of quantum dots. We present a
convenient self-assembly approach to deliver strain-free GaAs quantum dots with
size distribution below 15%, due to the control of the growth parameters during
the preliminary formation of the Ga droplets. This results in an ensemble
photoluminescence linewidth of 19 meV at 14 K. The narrow emission band and the
absence of a wetting layer promoting dot-dot coupling allow us to deconvolve
the contribution of phonon broadening in the ensemble photoluminescence and
study it in a wide temperature range.


  In his recent investigation of a super Teichm\"uller space, Sachse (2007),
based on work of Molotkov (1984), has proposed a theory of Banach
supermanifolds using the `functor of points' approach of Bernstein and Schwarz.
We prove that the the category of Berezin-Kostant-Leites supermanifolds is
equivalent to the category of finite-dimensional Molotkov-Sachse
supermanifolds. Simultaneously, using the differential calculus of
Bertram-Gl\"ockner-Neeb (2004), we extend Molotkov-Sachse's approach to
supermanifolds modeled on Hausdorff topological super-vector spaces over an
arbitrary non-discrete Hausdorff topological base field of characteristic zero.
We also extend to locally k-omega base fields the `DeWitt' supermanifolds
considered by Tuynman in his monograph (2004), and prove that this leads to a
category which is isomorphic to the full subcategory of Molokov-Sachse
supermanifolds modeled on locally k-omega spaces.


  Task-oriented dialogue (TOD) systems have been widely deployed in many
industries as they deliver more efficient customer support. These systems are
typically constructed for a single domain or language and do not generalise
well beyond this. To support work on Natural Language Understanding (NLU) in
TOD across multiple languages and domains simultaneously, we constructed
MULTI3NLU++, a multilingual, multi-intent, multi-domain dataset. MULTI3NLU++
extends the English only NLU++ dataset to include manual translations into a
range of high, medium, and low resource languages (Spanish, Marathi, Turkish
and Amharic), in two domains (BANKING and HOTELS). Because of its multi-intent
property, MULTI3NLU++ represents complex and natural user goals, and therefore
allows us to measure the realistic performance of TOD systems in a varied set
of the world's languages. We use MULTI3NLU++ to benchmark state-of-the-art
multilingual models for the NLU tasks of intent detection and slot labelling
for TOD systems in the multilingual setting. The results demonstrate the
challenging nature of the dataset, particularly in the low-resource language
setting, offering ample room for future experimentation in multi-domain
multilingual TOD setups.


  We report measurements of enhanced tensor polarization on solid-state
targets. The results here represent an increase in tensor polarization over
that previously achieved in high energy and nuclear scattering experiments that
focused on the measurement of tensor polarized observables. Enhancement
techniques are used which require RF produced close to the Larmor frequency of
the target spins and use selective semi-saturation resulting from two sources
of irradiation, microwave for the DNP process and the additional RF used to
manipulate the population of the energy levels in the target material. The spin
dynamics of the solid target are used to align the spins enhancing the ensemble
average to improve the figure of merit of the scattering experiment. Target
rotation at an optimized rate can lead to additional enhancement by applying
selective semi-saturation in polycrystalline materials that possess a Pake
doublet in their NMR signal.


  Using numerical cosmological simulations completed under the "Cosmic
Reionization On Computers" (CROC) project, I explore theoretical predictions
for the faint end of the galaxy UV luminosity functions at $z\geq 6$. A
commonly used Schechter function approximation with the magnitude cut at
$M_{\rm CUT}\sim-13$ provides a reasonable fit to the actual luminosity
function of simulated galaxies. When the Schechter functional form is forced on
the luminosity functions from the simulations, the magnitude cut $M_{\rm CUT}$
is found to vary between $-12$ and $-14$ with a mild redshift dependence. An
analytical model of reionization from Madau, Haardt & Rees (1997), as used by
Robertson et al. (2015), provides a good description of the simulated results,
but only if the redshift dependence of the effective escape fraction (induced
by physical processes not captured by the Madau, Haardt & Rees model) is
accounted for.


  We show that when one drives a charge current across noncollinear magnetic
layers the two electron Coulomb scattering creates a spin flip potential at
interfaces. This scattering is found when the interface potential is updated
due to the spin accumulation attendant to charge flow, and it contributes in
linear response to the current. With this scattering there is an injection of
transverse spin distributions in a layer that propagate, and that in the steady
state lead to spin currents transverse to the magnetization.


  Given a closed subvariety X in a projective space, the rank with respect to X
of a point p in this projective space is the least integer r such that p lies
in the linear span of some r points of X. Let W_k be the closure of the set of
points of rank with respect to X equal to k. For small values of k such loci
are called secant varieties. This article studies the loci W_k for values of k
larger than the generic rank. We show they are nested, we bound their
dimensions, and we estimate the maximal possible rank with respect to X in
special cases, including when X is a homogeneous space or a curve. The theory
is illustrated by numerous examples, including Veronese varieties, the Segre
product of dimensions (1,3,3), and curves. An intermediate result provides a
lower bound on the dimension of any GL_n orbit of a homogeneous form.


  Ab initio computation of two-dimensional electronic spectra is an expanding
field, whose goal is improving upon simple, few-dimensional models often
employed to explain experiments. Here, we propose an accurate and
computationally affordable approach, based on the single-trajectory
semiclassical thawed Gaussian approximation, to evaluate two-dimensional
electronic spectra. Importantly, the method is exact for arbitrary harmonic
potentials with mode displacement, changes in the mode frequencies, and
inter-mode coupling (Duschinsky effect), but can also account partially for the
anharmonicity of the involved potential energy surfaces. We test its accuracy
on a set of model Morse potentials and use it to study anharmonicity and
Duschinsky effects on the linear and two-dimensional electronic spectra of
phenol. We find that in this molecule, the anharmonicity effects are weak,
whereas the Duschinsky rotation and the changes in the mode frequencies must be
included in accurate simulations. In contrast, the widely used displaced
harmonic oscillator model captures only the basic physics of the problem but
fails to reproduce the correct vibronic lineshape.


  Let $C$ be a unital AH-algebra and let $A$ be a unital separable simple
C*-algebra with tracial rank no more than one. Suppose that $\phi, \psi: C\to
A$ are two unital monomorphisms. With some restriction on $C,$ we show that
$\phi$ and $\psi$ are approximately unitarily equivalent if and only if
  [\phi]=[\psi] in KL(C,A)
  \tau\circ \phi=\tau\circ \psi for all tracial states of A and
  \phi^{\ddag}=\psi^{\ddag}, here \phi^{\ddag} and \psi^{\ddag} are
homomorphisms from $U(C)/CU(C)\to U(A)/CU(A) induced by \phi and \psi,
respectively, and where CU(C) and CU(A) are closures of the subgroup generated
by commutators of the unitary groups of C and B.


  Topic models have proven to be a useful tool for discovering latent
structures in document collections. However, most document collections often
come as temporal streams and thus several aspects of the latent structure such
as the number of topics, the topics' distribution and popularity are
time-evolving. Several models exist that model the evolution of some but not
all of the above aspects. In this paper we introduce infinite dynamic topic
models, iDTM, that can accommodate the evolution of all the aforementioned
aspects. Our model assumes that documents are organized into epochs, where the
documents within each epoch are exchangeable but the order between the
documents is maintained across epochs. iDTM allows for unbounded number of
topics: topics can die or be born at any epoch, and the representation of each
topic can evolve according to a Markovian dynamics. We use iDTM to analyze the
birth and evolution of topics in the NIPS community and evaluated the efficacy
of our model on both simulated and real datasets with favorable outcome.


  Humans exhibit complex motions that vary depending on the task that they are
performing, the interactions they engage in, as well as subject-specific
preferences. Therefore, forecasting future poses based on the history of the
previous motions is a challenging task. This paper presents an innovative
auxiliary-memory-powered deep neural network framework for the improved
modelling of historical knowledge. Specifically, we disentangle
subject-specific, task-specific, and other auxiliary information from the
observed pose sequences and utilise these factorised features to query the
memory. A novel Multi-Head knowledge retrieval scheme leverages these
factorised feature embeddings to perform multiple querying operations over the
historical observations captured within the auxiliary memory. Moreover, our
proposed dynamic masking strategy makes this feature disentanglement process
dynamic. Two novel loss functions are introduced to encourage diversity within
the auxiliary memory while ensuring the stability of the memory contents, such
that it can locate and store salient information that can aid the long-term
prediction of future motion, irrespective of data imbalances or the diversity
of the input data distribution. With extensive experiments conducted on two
public benchmarks, Human3.6M and CMU-Mocap, we demonstrate that these design
choices collectively allow the proposed approach to outperform the current
state-of-the-art methods by significant margins: $>$ 17\% on the Human3.6M
dataset and $>$ 9\% on the CMU-Mocap dataset.


  Among $\sim 160$ of the multiple exoplanetary systems confirmed, about $30\%$
of them have neighboring pairs with a period ratio $\leq 2$. A significant
fraction of these pairs are around mean motion resonance (MMR), more
interestingly, peak around 2:1 and 3:2, with a clear absence of more closely
packed MMRs with period ratios less than 4:3, regardless of planet masses. Here
we report numerical simulations demonstrating that such MMR behavior places
important constraints on the disk evolution stage out of which the observed
planets formed. Multiple massive planets (with mass $\geq 0.8$ $M_{\rm Jup}$)
tend to end up with a 2:1 MMR mostly independent of the disk masses but
low-mass planets (with mass $\leq 30$ $M_{\oplus}$) can have MMRs larger than
4:3 only when the disk mass is quite small, suggesting that the observed
dynamical architecture of most low-mass-planet pairs was established late in
the disk evolution stage, just before it was dispersed completely.


  We consider the class of Piecewise Deterministic Markov Processes (PDMP),
whose state space is $\R\_{+}^{*}$, that possess an increasing deterministic
motion and that shrink deterministically when they jump. Well known examples
for this class of processes are Transmission Control Protocol (TCP) window size
process and the processes modeling the size of a "marked" {\it Escherichia
coli} cell. Having observed the PDMP until its $n$th jump, we construct a
nonparametric estimator of the jump rate $\lambda$. Our main result is that for
$D$ a compact subset of $\R\_{+}^{*}$, if $\lambda$ is in the H{\''{o}}lder
space ${\mathcal H}^s({\mathcal D})$, the squared-loss error of the estimator
is asymptotically close to the rate of $n^{-s/(2s+1)}$. Simulations illustrate
the behavior of our estimator.


  A FORTRAN program to simulate the operation of infinite servers queues is
presented in this work. Poisson arrivals processes are considered but not only.
For many parameters of interest in queuing systems study or application, either
there are not theoretical results or, existing, they are mathematically
intractable what makes their utility doubtful. In this case a possible issue is
to use simulation methods in order to get more useful results. Indeed, using
simulation, some experiences may be performed and the respective results used
to conjecture about certain queue systems interesting quantities. In this paper
this procedure is followed to learn something more about quantities of interest
for those infinite servers queue systems, in particular about busy period
parameters and probability distributions.


  Sharing rides could drastically improve the efficiency of car and taxi
transportation. Unleashing such potential, however, requires understanding how
urban parameters affect the fraction of individual trips that can be shared, a
quantity that we call shareability. Using data on millions of taxi trips in New
York City, San Francisco, Singapore, and Vienna, we compute the shareability
curves for each city, and find that a natural rescaling collapses them onto a
single, universal curve. We explain this scaling law theoretically with a
simple model that predicts the potential for ride sharing in any city, using a
few basic urban quantities and no adjustable parameters. Accurate
extrapolations of this type will help planners, transportation companies, and
society at large to shape a sustainable path for urban growth.


  Photoluminesce (PL) spectroscopy offers excellent methods for mapping the PL
decay on the nanosecond time scale. However, capturing maps of emission
dynamics on the microsecond time scale can be highly time-consuming. We present
a new approach to fluorescence lifetime imaging FLIM, which combines the
concept of random temporal speckles excitation (RATS) with the concept of a
single-pixel camera based on spatial speckles. The spatio-temporal speckle
pattern makes it possible to map PL dynamics with unmatched simplicity.
Moreover, the method can acquire all data necessary to map PL decays in the
microsecond timescale within minutes. We present proof of principle
measurements for two samples and compare the reconstructed decays to the
non-imaging measurements. Finally, we discuss the effect of the preprocessing
routine and other factors on the reconstruction noise level. The presented
method is suitable for lifetime imaging processes in several samples, including
charge carrier dynamics in perovskites or monitoring solid-state luminophores
with a long lifetime of PL.


  The dynamics of the C($^{3}$P) + O$_{2}$($^3\Sigma_{g}^{-}$) $\rightarrow$
CO($^{1}\Sigma^{+}$)+ O($^{1}$D) reaction on its electronic ground state is
investigated by using time-dependent wave packet propagation (TDWP) and
quasi-classical trajectory (QCT) simulations. For the moderate collision
energies considered ($E_{\rm c} = 0.001$ to 0.4 eV, corresponding to a range
from 10 K to 4600 K) the total reaction probabilities from the two different
treatments of the nuclear dynamics agree very favourably. The undulations
present in $P(E)$ from the quantum mechanical treatment can be related to
stabilization of the intermediate CO$_2$ complex with lifetimes of on the 0.05
ps time scale. This is also confirmed from direct analysis of the QCT
trajectories. Product diatom vibrational and rotational level resolved
state-to-state reaction probabilities from TDWP and QCT simulations also agree
well except for the highest product vibrational states $(v' \geq 15)$ and for
the lowest product rotational states $(j' \leq 10)$. Opening of the product
vibrational level CO$(v' = 17)$ requires $\sim 0.2$ eV from QCT and TDWP
simulations with O$_2$($j=0$) and decreases to 0.04 eV if all initial
rotational states are included in the QCT analysis, compared with $E_{\rm c} >
0.04$ eV obtained from experiments. It is thus concluded that QCT simulations
are suitable for investigating and realistically describe the C($^{3}$P) +
O$_{2}$($^3\Sigma_{g}^{-}$) $\rightarrow$ CO($^{1}\Sigma^{+}$)+ O($^{1}$D)
reaction down to low collision energies when compared with results from a
quantum mechanical treatment using TDWPs.


  Many patterning events in multi-cellular organisms rely on cell-to-cell
contact signaling, such as the Notch pathway in metazoans. A particularly
interesting phenomenon in this form of communication is lateral inhibition
where a cell that adopts a particular fate inhibits its immediate neighbors
from doing the same. Dynamical models are of great interest for understanding
the circuit topologies involved in lateral inhibition and for predicting the
associated patterns. Several simplified models have been employed for Notch
signalling pathways in the literature. The objective of this paper is to
present an abstract dynamical model that captures the essential features of
lateral inhibition and to demonstrate with dynamical systems techniques that
these features indeed lead to patterning.


  This paper considers a Markovian model of a limit order book where
time-dependent rates are allowed. With the objective of understanding the
mechanisms through which a microscopic model of an orderbook can converge to
more general diffusion than a Brownian motion with constant coefficient, a
simple time-dependent model is proposed. The model considered here starts by
describing the processes that govern the arrival of the different orders such
as limit orders, market orders and cancellations. In this sense, this is a
microscopic model rather than a ``mesoscopic'' model where the starting point
is usually the point processes describing the times at which the price changes
occur and aggregate in these all the information pertaining to the arrival of
individual orders. Furthermore, several empirical studies are performed to shed
some light into the validity of the modeling assumptions and to verify whether
certain stocks satisfy the conditions for their price process to converge to a
more complex diffusion.


  The condition assessment of road surfaces is essential to ensure their
serviceability while still providing maximum road traffic safety. This paper
presents a robust stereo vision system embedded in an unmanned aerial vehicle
(UAV). The perspective view of the target image is first transformed into the
reference view, and this not only improves the disparity accuracy, but also
reduces the algorithm's computational complexity. The cost volumes generated
from stereo matching are then filtered using a bilateral filter. The latter has
been proved to be a feasible solution for the functional minimisation problem
in a fully connected Markov random field model. Finally, the disparity maps are
transformed by minimising an energy function with respect to the roll angle and
disparity projection model. This makes the damaged road areas more
distinguishable from the road surface. The proposed system is implemented on an
NVIDIA Jetson TX2 GPU with CUDA for real-time purposes. It is demonstrated
through experiments that the damaged road areas can be easily distinguished
from the transformed disparity maps.