  Event detection (ED), aiming to detect events from texts and categorize them,
is vital to understanding actual happenings in real life. However, mainstream
event detection models require high-quality expert human annotations of
triggers, which are often costly and thus deter the application of ED to new
domains. Therefore, in this paper, we focus on low-resource ED without triggers
and aim to tackle the following formidable challenges: multi-label
classification, insufficient clues, and imbalanced events distribution. We
propose a novel trigger-free ED method via Derangement mechanism on a machine
Reading Comprehension (DRC) framework. More specifically, we treat the input
text as Context and concatenate it with all event type tokens that are deemed
as Answers with an omitted default question. So we can leverage the
self-attention in pre-trained language models to absorb semantic relations
between input text and the event types. Moreover, we design a simple yet
effective event derangement module (EDM) to prevent major events from being
excessively learned so as to yield a more balanced training process. The
experiment results show that our proposed trigger-free ED model is remarkably
competitive to mainstream trigger-based models, showing its strong performance
on low-source event detection.


  We demonstrate that the flavor oscillation when a neutrino travels through
spacetime, is equivalent to permanent changes on the vacuum state condition
perceived by the same particle. This can be visualized via the Quantum Yang
Baxter equations (QYBE). From this perspective, the neutrino never breaks the
symmetry of the ground state because it never selects an specific vacuum
condition. Then naturally the Higgs mechanism cannot be the generator of the
neutrino masses. The constraints emerging from this model predict a normal mass
hierarchy and some specific values for the mass eigenvalues once we fix the
mixing angles. Interestingly, the model suggests that the sum of the mix angles
is equal to $\pi/2$.


  Distributed multi-target tracking (DMTT) is addressed for sensors having
different fields of view (FoVs). The proposed approach is based on the idea of
fusing the posterior Probability Hypotheses Densities (PHDs) generated by the
sensors on the basis of the local measurements. An efficient and robust
distributed fusion algorithm combining the Generalized Covariance Intersection
(GCI) rule with a suitable Clustering Algorithm (CA) is proposed. The CA is
used to decompose each posterior PHD into well-separated components (clusters).
For the commonly detected targets, an efficient parallelized GCI fusion
strategy is proposed and analyzed in terms of $L_1$ error. For the remaining
targets, a suitable compensation strategy is adopted so as to counteract the
GCI sensitivity to independent detections while reducing the occurrence of
false targets. Detailed implementation steps using a Gaussian Mixture (GM)
representation of the PHDs are provided. Numerical experiments clearly confirms
the effectiveness of the proposed CA-GCI fusion algorithm.


  Relativistic invariant projectors of states in a complex bispinor space on a
complex spinor space are constructed. An expression for sections of bundle with
connection on group SU(4) in an explicit form has been obtained. Within the
framework of the proposed geometrical approach the rule of summation over
polarizations of states in a complex bispinor space has been derived. It has
been shown that states in a complex bispinor space always describe a pair of
Dirac's particles.


  New solution to the six-dimensional vacuum Einstein's equations is
constructed as a non-linear superposition of two five-dimensional solutions
representing the Melvin-Gibbons-Maeda Universe and its S-dual. Then using
duality between D=8 vacuum and a certain class of D=11 supergravity
configurations we generate M2 and M5 fluxbranes as well as some of their
intersections also including waves and KK-monopoles.


  Text-to-image diffusion models have an unprecedented ability to generate
diverse and high-quality images. However, they often struggle to faithfully
capture the intended semantics of complex input prompts that include multiple
subjects. Recently, numerous layout-to-image extensions have been introduced to
improve user control, aiming to localize subjects represented by specific
tokens. Yet, these methods often produce semantically inaccurate images,
especially when dealing with multiple semantically or visually similar
subjects. In this work, we study and analyze the causes of these limitations.
Our exploration reveals that the primary issue stems from inadvertent semantic
leakage between subjects in the denoising process. This leakage is attributed
to the diffusion model's attention layers, which tend to blend the visual
features of different subjects. To address these issues, we introduce Bounded
Attention, a training-free method for bounding the information flow in the
sampling process. Bounded Attention prevents detrimental leakage among subjects
and enables guiding the generation to promote each subject's individuality,
even with complex multi-subject conditioning. Through extensive
experimentation, we demonstrate that our method empowers the generation of
multiple subjects that better align with given prompts and layouts.


  This paper presents a mixed finite element framework for coupled
hydro-mechanical-chemical processes in heterogeneous porous media. The
framework combines two types of locally conservative discretization schemes:
(1) an enriched Galerkin method for reactive flow, and (2) a three-field mixed
finite element method for coupled fluid flow and solid deformation. This
combination ensures local mass conservation, which is critical to flow and
transport in heterogeneous porous media, with a relatively affordable
computational cost. A particular class of the framework is constructed for
calcite precipitation/dissolution reactions, incorporating their nonlinear
effects on the fluid viscosity and solid deformation. Linearization schemes and
algorithms for solving the nonlinear algebraic system are also presented.
Through numerical examples of various complexity, we demonstrate that the
proposed framework is a robust and efficient computational method for
simulation of reactive flow and transport in deformable porous media, even when
the material properties are strongly heterogeneous and anisotropic.


  The presence of an additive conserved quantity imposes a limitation on the
measurement process. According to the Wigner-Araki-Yanase theorem, the perfect
repeatability and the distinguishability on the apparatus cannot be attained
simultaneously. Instead of the repeatability, in this paper, the
distinguishability on both systems is examined. We derive a trade-off
inequality between the distinguishability of the final states on the system and
the one on the apparatus. The inequality shows that the perfect
distinguishability of both systems cannot be attained simultaneously.


  Adversarial examples resulting from instability of current computer vision
models are an extremely important topic due to their potential to compromise
any application. In this paper we demonstrate that instability is inevitable
due to a) symmetries (translational invariance) of the data, b) the categorical
nature of the classification task, and c) the fundamental discrepancy of
classifying images as objects themselves. The issue is further exacerbated by
non-exhaustive labelling of the training data. Therefore we conclude that
instability is a necessary result of how the problem of computer vision is
currently formulated. While the problem cannot be eliminated, through the
analysis of the causes, we have arrived at ways how it can be partially
alleviated. These include i) increasing the resolution of images, ii) providing
contextual information for the image, iii) exhaustive labelling of training
data, and iv) preventing attackers from frequent access to the computer vision
system.


  In recent years, spectral clustering has become one of the most popular
modern clustering algorithms. It is simple to implement, can be solved
efficiently by standard linear algebra software, and very often outperforms
traditional clustering algorithms such as the k-means algorithm. On the first
glance spectral clustering appears slightly mysterious, and it is not obvious
to see why it works at all and what it really does. The goal of this tutorial
is to give some intuition on those questions. We describe different graph
Laplacians and their basic properties, present the most common spectral
clustering algorithms, and derive those algorithms from scratch by several
different approaches. Advantages and disadvantages of the different spectral
clustering algorithms are discussed.


  The problem of sampling from the stationary distribution of a Markov chain
finds widespread applications in a variety of fields. The time required for a
Markov chain to converge to its stationary distribution is known as the
classical mixing time. In this article, we deal with analog quantum algorithms
for mixing. First, we provide an analog quantum algorithm that given a Markov
chain, allows us to sample from its stationary distribution in a time that
scales as the sum of the square root of the classical mixing time and the
square root of the classical hitting time. Our algorithm makes use of the
framework of interpolated quantum walks and relies on Hamiltonian evolution in
conjunction with von Neumann measurements.
  There also exists a different notion for quantum mixing: the problem of
sampling from the limiting distribution of quantum walks, defined in a
time-averaged sense. In this scenario, the quantum mixing time is defined as
the time required to sample from a distribution that is close to this limiting
distribution. Recently we provided an upper bound on the quantum mixing time
for Erd\"os-Renyi random graphs [Phys. Rev. Lett. 124, 050501 (2020)]. Here, we
also extend and expand upon our findings therein. Namely, we provide an
intuitive understanding of the state-of-the-art random matrix theory tools used
to derive our results. In particular, for our analysis we require information
about macroscopic, mesoscopic and microscopic statistics of eigenvalues of
random matrices which we highlight here. Furthermore, we provide numerical
simulations that corroborate our analytical findings and extend this notion of
mixing from simple graphs to any ergodic, reversible, Markov chain.


  We combine Newton's variational method with ideas from eigenvector
continuation to construct a fast & accurate emulator for two-body scattering
observables. The emulator will facilitate the application of rigorous
statistical methods for interactions that depend smoothly on a set of free
parameters. Our approach begins with a trial $K$ or $T$ matrix constructed from
a small number of exact solutions to the Lippmann--Schwinger equation.
Subsequent emulation only requires operations on small matrices. We provide
several applications to short-range potentials with and without the Coulomb
interaction and partial-wave coupling. It is shown that the emulator can
accurately extrapolate far from the support of the training data. When used to
emulate the neutron-proton cross section with a modern chiral interaction as a
function of 26 free parameters, it reproduces the exact calculation with
negligible error and provides an over 300x improvement in CPU time.


  We report the discovery from the WASP survey of two exoplanetary systems,
each consisting of a Jupiter-sized planet transiting an 11th magnitude (V)
main-sequence star. WASP-104b orbits its star in 1.75 d, whereas WASP-106b has
the fourth-longest orbital period of any planet discovered by means of transits
observed from the ground, orbiting every 9.29 d. Each planet is more massive
than Jupiter (WASP-104b has a mass of $1.27 \pm 0.05 \mathrm{M_{Jup}}$, while
WASP-106b has a mass of $1.93 \pm 0.08 \mathrm{M_{Jup}}$). Both planets are
just slightly larger than Jupiter, with radii of $1.14 \pm 0.04$ and $1.09 \pm
0.04 \mathrm{R_{Jup}}$ for WASP-104 and WASP-106 respectively. No significant
orbital eccentricity is detected in either system, and while this is not
surprising in the case of the short-period WASP-104b, it is interesting in the
case of WASP-106b, because many otherwise similar planets are known to have
eccentric orbits.


  Audio deepfake detection (ADD) is crucial to combat the misuse of speech
synthesized from generative AI models. Existing ADD models suffer from
generalization issues, with a large performance discrepancy between in-domain
and out-of-domain data. Moreover, the black-box nature of existing models
limits their use in real-world scenarios, where explanations are required for
model decisions. To alleviate these issues, we introduce a new ADD model that
explicitly uses the StyleLInguistics Mismatch (SLIM) in fake speech to separate
them from real speech. SLIM first employs self-supervised pretraining on only
real samples to learn the style-linguistics dependency in the real class. The
learned features are then used in complement with standard pretrained acoustic
features (e.g., Wav2vec) to learn a classifier on the real and fake classes.
When the feature encoders are frozen, SLIM outperforms benchmark methods on
out-of-domain datasets while achieving competitive results on in-domain data.
The features learned by SLIM allow us to quantify the (mis)match between style
and linguistic content in a sample, hence facilitating an explanation of the
model decision.


  We investigate the influence of the phase-front curvature of an input light
beam on the transverse localization of light by choosing an evanescently
coupled disordered one-dimensional semi-infinite waveguide lattice as an
example. Our numerical study reveals that a finite phase front curvature of the
input beam indeed plays an important role and it could degrade the quality of
light localization in a disordered dielectric structure. More specifically, a
faster transition from ballistic mode of beam propagation due to diffraction to
a characteristic localized state is observed in case of a continuous wave (CW)
beam, whose phase-front is plane as compared to one having a curved phase
front.


  We present a method for finding individual excited states' energy stationary
points in complete active space self-consistent field theory that is compatible
with standard optimization methods and highly effective at overcoming
difficulties due to root flipping and near-degeneracies. Inspired by both the
maximum overlap method and recent progress in excited state variational
principles, our approach combines these ideas in order to track individual
excited states throughout the orbital optimization process. In a series of
tests involving root flipping, near-degeneracies, charge transfers, and double
excitations, we show that this approach is more effective for state-specific
optimization than either the naive selection of roots based on energy ordering
or a more direct generalization of the maximum overlap method. Furthermore, we
provide evidence that this state-specific approach improves the performance of
complete active space perturbation theory. With a simple implementation, a low
cost, and compatibility with large active space methods, the approach is
designed to be useful in a wide range of excited state investigations.


  We extend our investigation on a possible de Sitter symmetry breaking
mechanism in non-linear sigma models. The scale invariance of the quantum
fluctuations could make the cosmological constant time dependent signaling the
de Sitter symmetry breaking. To understand such a symmetry breaking mechanism,
we investigate the energy-momentum tensor. We show that the leading infra-red
logarithms cancel to all orders in perturbation theory in a generic non-linear
sigma model. When the target space is an N sphere, the de Sitter symmetry is
preserved in the large N limit. For a less symmetric target space, the
infra-red logarithms appear at the three loop level. However there is a counter
term to precisely cancel it. The leading infra-red logarithms do not cancel for
higher derivative interactions. We investigate such a model in which the
infra-red logarithms first appear at the three loop level. A nonperturbative
investigation in the large N limit shows that they eventually grow as large as
the one loop effect.


  In this work, we employ hybrid and generalized gradient approximation (GGA)
level density functional theory (DFT) calculations to investigate the
convergence of surface properties and band structure of rutile titania
(TiO$_2$) nanoparticles with particle size. The surface energies and band
structures are calculated for cuboidal particles with minimum dimension ranging
from 3.7 \r{A} (24 atoms) to 10.3 \r{A} (384 atoms) using a highly-parallel
real-space DFT code to enable hybrid level DFT calculations of larger
nanoparticles than are typically practical. We deconvolute the geometric and
electronic finite size effects in surface energy, and evaluate the influence of
defects on band structure and density of states (DOS). The electronic finite
size effects in surface energy vanish when the minimum length scale of the
nanoparticles becomes greater than 10 \r{A}. We show that this length scale is
consistent with a computationally efficient numerical analysis of the
characteristic length scale of electronic interactions. The surface energy of
nanoparticles having minimum dimension beyond this characteristic length can be
approximated using slab calculations that account for the geometric defects. In
contrast, the finite size effects on the band structure is highly dependent on
the shape and size of these particles. The DOS for cuboidal particles and more
realistic particles constructed using the Wulff algorithm reveal that defect
states within the bandgap play a key role in determining the band structure of
nanoparticles and the bandgap does not converge to the bulk limit for the
particle sizes investigated.


  We examine the structural relaxation of glassy materials at finite
temperatures, considering the effect of activated rearrangements and long-range
elastic interactions. Our three-dimensional mesoscopic relaxation model shows
how the displacements induced by localized relaxation events can result in
faster-than-exponential relaxation. Thermal activation allows for local
rearrangements, which generate elastic responses and possibly cascades of new
relaxation events. To study the interplay between this elastically-dominated
and thermally-dominated dynamics, we introduce tracer particles that follow the
displacement field induced by the local relaxation events and also incorporate
Brownian motion. Our results reveal that the dynamic exponents and shape
parameter of the dynamical structure factor depend on this competition and
display a crossover from faster-than-exponential to exponential relaxation as
temperature increases, consistent with recent observations in metallic glasses.
Additionally, we find the distribution of waiting times between activations to
be broadly distributed at low temperatures, providing a measure of dynamical
heterogeneities characteristic for to glassy dynamics.


  We use the framework of the HIJING/B\=B v2.0 model to simulate
high-multiplicity (HM) $p+p$ collision events at the Large Hadron Collider
(LHC) to study observables sensitive to possible collective phenomena, such as
strong longitudinal color fields (SLCF) modeled by an enhanced string tension
($\kappa$). We focus on the hyperon/meson yield ratios at center-of-mass (c.m.)
energy $\sqrt{s}$ = 7 TeV, in the transverse momentum region, $1 < p_T < 4 $
GeV/{\it c}. For minimum bias events these ratios are well described assuming
an energy dependence $\kappa = \kappa(s)= \kappa_{0} (s/s_{0})^{0.04} {\rm
GeV/fm}$ ($\kappa_{0}$= 1 GeV/fm), giving a value $\kappa = 2$ GeV/fm at
$\sqrt{s}$ = 7 TeV. We compare minimum bias (MB) events to simulated HM events
assuming that $\kappa(MB)=2$ GeV/fm could grow to an extreme value of
$\kappa(HM)=5$ GeV/fm that saturates the strangeness suppression factor. With
this assumption the model predicts a very strong enhancement of (multi)strange
baryon/meson ratios in HM events. If observed, such an enhancement could be
also interpreted as a possible signature for formation in HM $p+p$ collision
events of a deconfined but out of local thermal equilibrium {\em mini
quark-gluon plasma} (mQGP).


  We analyze the problem of quantifying entanglement in pure and mixed states
of fermionic systems with fixed number parity yet not necessarily fixed
particle number. The "mode entanglement" between one single-particle level and
its orthogonal complement is first considered, and an entanglement entropy for
such a partition of a particular basis of the single-particle Hilbert space
$\mathcal H$ is defined. The sum over all single-particle modes of this entropy
is introduced as a measure of the total entanglement of the system with respect
to the chosen basis and it is shown that its minimum over all bases of
$\mathcal H$ is a function of the one-body density matrix. Furthermore, we show
that if minimization is extended to all bases related through a Bogoliubov
transformation, then the entanglement entropy is a function of the generalized
one-body density matrix. These results are then used to quantify entanglement
in fermion systems with four single-particle levels. For general pure states of
such a system a closed expression for the fermionic concurrence is derived,
which generalizes the Slater correlation measure defined in [J. Schliemann et
al, Phys. Rev. A 64, 022303 (2001)], implying that "particle entanglement" may
be seen as minimum "mode entanglement". It is also shown that the entanglement
entropy defined before is related to this concurrence by an expression
analogous to that of the two-qubit case. For mixed states of this system the
convex roof extension of the previous concurrence and entanglement entropy are
evaluated analytically, extending the results of previous ref. to general
states.


  We propose an analytical method for solving the problem of electronic
relaxation in solution in time domain, modelled by a particle undergoing
diffusion under the influence of two coupled potentials. The coupling between
the two potentials is assumed to be represented by a Dirac delta function of
arbitrary position and strength. Smoluchowskii equation is used model the
diffusion motion on both the potentials. We report an analytical expression for
survival probability in time domain. This is the first time analytical solution
in time domain is derived and this method can be used to solve problems
involving other potentials.


  This is Part II of our paper in which we prove finite time blowup of the 2D
Boussinesq and 3D axisymmetric Euler equations with smooth initial data of
finite energy and boundary. In Part I of our paper [ChenHou2023a], we establish
an analytic framework to prove stability of an approximate self-similar blowup
profile by a combination of a weighted $L^\infty$ norm and a weighted $C^{1/2}$
norm. Under the assumption that the stability constants, which depend on the
approximate steady state, satisfy certain inequalities stated in our stability
lemma, we prove stable nearly self-similar blowup of the 2D Boussinesq and 3D
Euler equations with smooth initial data and boundary. In Part II of our paper,
we provide sharp stability estimates of the linearized operator by constructing
space-time solutions with rigorous error control. We also obtain sharp
estimates of the velocity in the regular case using computer assistance. These
results enable us to verify that the stability constants obtained in Part I
[ChenHou2023a] indeed satisfy the inequalities in our stability lemma. This
completes the analysis of the finite time singularity of the axisymmetric Euler
equations with smooth initial data and boundary.


  The main challenge of online multi-object tracking is to reliably associate
object trajectories with detections in each video frame based on their tracking
history. In this work, we propose the Recurrent Autoregressive Network (RAN), a
temporal generative modeling framework to characterize the appearance and
motion dynamics of multiple objects over time. The RAN couples an external
memory and an internal memory. The external memory explicitly stores previous
inputs of each trajectory in a time window, while the internal memory learns to
summarize long-term tracking history and associate detections by processing the
external memory. We conduct experiments on the MOT 2015 and 2016 datasets to
demonstrate the robustness of our tracking method in highly crowded and
occluded scenes. Our method achieves top-ranked results on the two benchmarks.


  A measurement of the expansion rate of the Universe (that is the Hubble
constant, H0) is derived here using the gamma-ray attenuation observed in the
spectra of gamma-ray sources produced by the interaction of extragalactic
gamma-ray photons with the photons of the extragalactic background light (EBL).
The Hubble constant that is determined with our technique, for a Lambda CDM
cosmology, is H0=71.8_{-5.6}^{+4.6}(stat)_{-13.8}^{+7.2}(syst) km s^{-1}
Mpc^{-1}. This value is compatible with present-day measurements using well
established methods such as local distance ladders and cosmological probes. The
recent detection of the cosmic gamma-ray horizon (CGRH) from multiwavelength
observation of blazars, together with the advances in the knowledge of the EBL,
allow us to measure the expansion rate of the Universe. This estimate of the
Hubble constant shows that gamma-ray astronomy has reached a mature enough
state to provide cosmological measurements, which may become more competitive
in the future with the construction of the Cherenkov Telescope Array. We find
that the maximum dependence of the CGRH on the Hubble constant is approximately
between redshifts 0.04 and 0.1, thus this is a smoking gun for planning future
observational efforts. Other cosmological parameters, such as the total dark
matter density Omega_m and the dark energy equation of state w, are explored as
well.


  The Busemann-Petty problem asks whether origin-symmetric convex bodies in
$\mathbb{R}^n$ with smaller central hyperplane sections necessarily have
smaller $n$-dimensional volume. It is known that the answer is affirmative if
$n\le 4$ and negative if $n\ge 5$. In this article we modify the assumptions of
the original Busemann-Petty problem to guarantee the affirmative answer in all
dimensions.


  Data Visualization has been receiving growing attention recently, with
ubiquitous smart devices designed to render information in a variety of ways.
However, while evaluations of visual tools for their interpretability and
intuitiveness have been commonplace, not much research has been devoted to
other forms of data rendering, eg, sonification. This work is the first to
automatically estimate the cognitive load induced by different acoustic
parameters considered for sonification in prior studies. We examine cognitive
load via (a) perceptual data-sound mapping accuracies of users for the
different acoustic parameters, (b) cognitive workload impressions explicitly
reported by users, and (c) their implicit EEG responses compiled during the
mapping task. Our main findings are that (i) low cognitive load-inducing (ie,
more intuitive) acoustic parameters correspond to higher mapping accuracies,
(ii) EEG spectral power analysis reveals higher $\alpha$ band power for low
cognitive load parameters, implying a congruent relationship between explicit
and implicit user responses, and (iii) Cognitive load classification with EEG
features achieves a peak F1-score of 0.64, confirming that reliable workload
estimation is achievable with user EEG data compiled using wearable sensors.


  We report first-principles calculations, within density functional theory, on
the lead halide compounds PbCl2, PbBr2, and CH3NH3PbBr3-xClx, taking into
account spin-orbit coupling. We show that, when the modified Becke-Johnson
exchange potential is used with a suitable choice of defining parameters,
excellent agreement between calculations and experiment is obtained. The
computational model is then used to study the effect of replacing the
methylammonium cation in CH3NH3PbI3 and CH3NH3PbBr3 with either N2H+5 or N2H+3,
which have slightly smaller ionic radii than methylammonium. We predict that a
considerable downshift in the values of the band gaps occurs with this
replacement. The resulting compounds would extend optical absorption down to
the near-infrared region, creating excellent light harvesters for solar cells.


  This article documents the main design choices and the close to 20 years of
preparation, detector R&D, construction and installation of ALICE, the
dedicated heavy ion experiment at the CERN LHC accelerator.


  Deep neural networks have achieved remarkable performance across various
tasks when supplied with large-scale labeled data. However, the collection of
labeled data can be time-consuming and labor-intensive. Semi-supervised
learning (SSL), particularly through pseudo-labeling algorithms that
iteratively assign pseudo-labels for self-training, offers a promising solution
to mitigate the dependency of labeled data. Previous research generally applies
a uniform pseudo-labeling strategy across all model layers, assuming that
pseudo-labels exert uniform influence throughout. Contrasting this, our
theoretical analysis and empirical experiment demonstrate feature extraction
layer and linear classification layer have distinct learning behaviors in
response to pseudo-labels. Based on these insights, we develop two
layer-specific pseudo-label strategies, termed Grad-ReLU and Avg-Clustering.
Grad-ReLU mitigates the impact of noisy pseudo-labels by removing the gradient
detrimental effects of pseudo-labels in the linear classification layer.
Avg-Clustering accelerates the convergence of feature extraction layer towards
stable clustering centers by integrating consistent outputs. Our approach,
LayerMatch, which integrates these two strategies, can avoid the severe
interference of noisy pseudo-labels in the linear classification layer while
accelerating the clustering capability of the feature extraction layer. Through
extensive experimentation, our approach consistently demonstrates exceptional
performance on standard semi-supervised learning benchmarks, achieving a
significant improvement of 10.38% over baseline method and a 2.44% increase
compared to state-of-the-art methods.


  It was proposed recently that different chiralities of the dark matter (DM)
fermion under a broken dark U(1) gauge group can lead to distinguishable
signatures at the LHC through shower patterns, which may reveal the mass origin
of the dark sector. We study this subject further by examining the dark shower
of two simplified models, the dubbed Chiral Model and the Vector Model. We
derive a more complete set of collinear splitting functions with power
corrections, specifying the helicities of the initial DM fermion and including
the contribution from an extra degree of freedom, the dark Higgs boson. The
dark shower is then implemented with these splitting functions, and the new
features resulting from its correct modelling are emphasized. It is shown that
the DM fermion chirality can be differentiated by measuring dark shower
patterns, especially the DM jet energy profile, which is almost independent of
the DM energy.


  In optimization-based approaches to inverse problems and to statistical
estimation, it is common to augment criteria that enforce data fidelity with a
regularizer that promotes desired structural properties in the solution. The
choice of a suitable regularizer is typically driven by a combination of prior
domain information and computational considerations. Convex regularizers are
attractive computationally but they are limited in the types of structure they
can promote. On the other hand, nonconvex regularizers are more flexible in the
forms of structure they can promote and they have showcased strong empirical
performance in some applications, but they come with the computational
challenge of solving the associated optimization problems. In this paper, we
seek a systematic understanding of the power and the limitations of convex
regularization by investigating the following questions: Given a distribution,
what is the optimal regularizer for data drawn from the distribution? What
properties of a data source govern whether the optimal regularizer is convex?
We address these questions for the class of regularizers specified by
functionals that are continuous, positively homogeneous, and positive away from
the origin. We say that a regularizer is optimal for a data distribution if the
Gibbs density with energy given by the regularizer maximizes the population
likelihood (or equivalently, minimizes cross-entropy loss) over all
regularizer-induced Gibbs densities. As the regularizers we consider are in
one-to-one correspondence with star bodies, we leverage dual Brunn-Minkowski
theory to show that a radial function derived from a data distribution is akin
to a ``computational sufficient statistic'' as it is the key quantity for
identifying optimal regularizers and for assessing the amenability of a data
source to convex regularization.


  The behavioral specification of an object-oriented grammar model is
considered. The model is based on full lexicalization, head-orientation via
valency constraints and dependency relations, inheritance as a means for
non-redundant lexicon specification, and concurrency of computation. The
computation model relies upon the actor paradigm, with concurrency entering
through asynchronous message passing between actors. In particular, we here
elaborate on principles of how the global behavior of a lexically distributed
grammar and its corresponding parser can be specified in terms of event type
networks and event networks, resp.


  This article is based on a talk delivered at the RIMS--OCAMI Joint
International Conference on Geometry Related to Integrable Systems in
September, 2007. Its aim is to review a recent progress in the Hitchin
integrable systems and character varieties of the fundamental groups of Riemann
surfaces. A survey on geometric aspects of these character varieties is also
provided as we develop the exposition from a simple case to more elaborate
cases.


  In this paper, we analyze downlink non-orthogonal multiple access (NOMA)
networks with limited feedback. Our goal is to derive appropriate transmission
rates for rate adaptation and minimize outage probability of minimum rate for
the constant-rate data service, based on distributed channel feedback
information from receivers. We propose an efficient quantizer with
variable-length encoding that approaches the best performance of the case where
perfect channel state information is available everywhere. We prove that in the
typical application with two receivers, the losses in the minimum rate and
outage probability decay at least exponentially with the minimum feedback rate.
We analyze the diversity gain and provide a sufficient condition for the
quantizer to achieve the maximum diversity order. For NOMA with $K$ receivers
where $K > 2$, we solve the minimum rate maximization problem within an
accuracy of $\epsilon$ in time complexity of
$O\left(K\log\frac{1}{\epsilon}\right)$, then, we apply the previously proposed
quantizers for $K = 2$ to the case of $K > 2$. Numerical simulations are
presented to demonstrate the efficiency of our proposed quantizers and the
accuracy of the analytical results.


  We classify graded pre-Nichols algebras of diagonal type with finite
Gelfand-Kirillov dimension. The characterization is made through an isomorphism
of posets with the family of appropriate subsets of the set of positive roots
coming from central extensions of Nichols algebras of diagonal type,
generalizing the corresponding extensions for small quantum groups in de
Concini-Kac-Procesi forms of quantum groups.
  On the way to achieving this result, we also classify graded quotients of
algebras of functions of unipotent algebraic groups attached to semisimple Lie
algebras.


  We propose a novel mechanism to realize two-component asymmetric dark matter
of very different mass scales through bound state formation and late freeze-in
decay. Assuming a particle-antiparticle asymmetry is initially shared by SM
baryons and two dark matter components, we demonstrate that the existence of
bound states formed by the heavy component can efficiently transfer the
asymmetry from the heavy to the light component via late decay. In this case,
the energy densities of the two components can be comparable, and the correct
relic density is reproduced.


  The aim of this note is to study the spectrum of a linearized Liouville-type
problem, characterizing the case in which the first eigenvalue is zero.
Interestingly enough, we obtain also point-wise information on the associated
first eigenfunction. To this end, we refine the Alexandrov-Bol inequality
suitable for our problem and characterize its equality case.


  We prove the Boundary Harnack Principle related to fractional powers of
Laplacian for some natural regions in the two-dimensional Sierpinski carpet.
This is a natual application of a probabilistic method based on the
Ikeda-Watanabe formula


  (ABRIDGED) Recent near-IR surveys have uncovered a plethora of new globular
cluster (GC) candidates towards the Milky Way bulge. These new candidates need
to be confirmed as real GCs and properly characterised. We investigate the
physical nature of FSR 1776. This object was originally classified as an
intermediate-age open cluster and has recently been re-discovered independently
and classified as a GC candidate (Minni 23). Firstly, we aim at confirming its
GC nature; secondly we determine its physical parameters. The confirmation of
the cluster existence is checked using the radial velocity (RV) distribution of
a MUSE data cube centred at FSR 1776. The cluster parameters are derived from
isochrone fitting to the RV-cleaned colour-magnitude diagrams (CMDs) from
visible and near-infrared photometry. The predicted RV distribution for the FSR
1776 coordinates, considering only contributions from the bulge and disc field
stars, is not enough to explain the observed MUSE RV distribution. The extra
population (12\% of the sample) is FSR 1776 with an average RV of $-103.7\pm
0.4~{\rm km}\,{\rm s}^{-1}$. The CMDs reveal that it is 10$\pm$1~Gyr old and
metal-rich, with [Fe/H]$_{phot}\approx + 0.2\pm$0.2,
[Fe/H]$_{spec}=~+0.02\pm0.01~(\sigma~=~0.14$~dex), located at the bulge
distance of 7.24$\pm$0.5~kpc with A$_{\rm V}$ $\approx$ 1.1~mag. The mean
cluster proper motions are
($\langle\mu_{\alpha}\rangle,\langle\mu_{\delta}\rangle$) $=$
($-2.3\pm1.1,-2.6\pm0.8$) ${\rm mas\, yr^{-1}}$.} FSR 1776 is an old GC located
in the Galactic bulge with a super-solar metallicity, among the highest for a
Galactic GC. This is consistent with predictions for the age-metallicity
relation of the bulge, being FSR 1776 the probable missing link between typical
GCs and the metal-rich bulge field. High-resolution spectroscopy of a larger
field of view and deeper CMDs are now required for a full characterisation.


  The Wald vector potential is an exact solution of the source-less Maxwell
equations regarding an electromagnetic field of a vacuum uncharged black hole
like the Kerr background black hole in an asymptotically uniform magnetic
field. However, it is not if the black hole is a nonvacuum solution in a theory
of modified gravity with extra fields or a charged Kerr-Newman spacetime. To
satisfy the source-less Maxwell equations in this case, the Wald vector
potential must be modified and generalized appropriately. Following this idea,
we derive an expression for the vector potential of an electromagnetic field
surrounding a hairy black hole in the Horndeski modified gravity theory.
Explicit symplectic integrators with excellent long-term behaviour are used to
simulate the motion of charged particles around the hairy black hole immersed
in the external magnetic field. The recurrence plot method based on the
recurrence quantification analysis uses diagonal structures parallel to the
main diagonal to show regular dynamics, but adopts no diagonal structures to
indicate chaotic dynamics. The method is efficient to detect chaos from order
in the curved spacetime, as the Poincare map and the fast Lyapunov indicator
are.


  We classify $n$-hereditary monomial algebras in three natural contexts:
First, we give a classification of the $n$-hereditary truncated path algebras.
We show that they are exactly the $n$-representation-finite Nakayama algebras
classified by Vaso. Next, we classify partially the $n$-hereditary quadratic
monomial algebras. In the case $n=2$, we prove that there are only two
examples, provided that the preprojective algebra is a planar quiver with
potential. The first one is a Nakayama algebra and the second one is obtained
by mutating $\mathbb A_3\otimes_k \mathbb A_3$, where $\mathbb A_3$ is the
Dynkin quiver of type $A$ with bipartite orientation. In the case $n\geq 3$, we
show that the only $n$-representation finite algebras are the
$n$-representation-finite Nakayama algebras with quadratic relations.


  We study fragmentation of small atomistic clusters via molecular dynamics. We
calculate the time scales related to fragment formation and emission. We also
show that some degree of thermalization is achieved during the expansion
process, which allows the determination of a local temperature. In this way we
can calculate the break-up temperature as a function of excitation energy, i.e.
the fragmentation caloric curve. Fragmentation appears as a rather constant
temperature region of the caloric curve. Furthermore, we show that different
definitions of temperature, related to different degrees of freedom, yield very
similar values.


  Henri Poincar\'e formulated the mathematics of the Lorentz transformations,
known as the Poincar\'e group. He also formulated the Poincar\'e sphere for
polarization optics. It is shown that these two mathematical instruments can be
combined into one mathematical device which can address the internal space-time
symmetries of elementary particles, decoherence problems in polarization
optics, entropy problems, and Feynman's rest of the universe.


  Adversarial robustness studies the worst-case performance of a machine
learning model to ensure safety and reliability. With the proliferation of
deep-learning-based technology, the potential risks associated with model
development and deployment can be amplified and become dreadful
vulnerabilities. This paper provides a comprehensive overview of research
topics and foundational principles of research methods for adversarial
robustness of deep learning models, including attacks, defenses, verification,
and novel applications.


  We consider the "partial information decomposition" (PID) problem, which aims
to decompose the information that a set of source random variables provide
about a target random variable into separate redundant, synergistic, union, and
unique components. In the first part of this paper, we propose a general
framework for constructing a multivariate PID. Our framework is defined in
terms of a formal analogy with intersection and union from set theory, along
with an ordering relation which specifies when one information source is more
informative than another. Our definitions are algebraically and axiomatically
motivated, and can be generalized to domains beyond Shannon information theory
(such as algorithmic information theory and quantum information theory). In the
second part of this paper, we use our general framework to define a PID in
terms of the well-known Blackwell order, which has a fundamental operational
interpretation. We demonstrate our approach on numerous examples and show that
it overcomes many drawbacks associated with previous proposals.


  Object region mining is a critical step for weakly-supervised semantic
segmentation. Most recent methods mine the object regions by expanding the seed
regions localized by class activation maps. They generally do not consider the
sizes of objects and apply a monotonous procedure to mining all the object
regions. Thus their mined regions are often insufficient in number and scale
for large objects, and on the other hand easily contaminated by surrounding
backgrounds for small objects. In this paper, we propose a novel multi-miner
framework to perform a region mining process that adapts to diverse object
sizes and is thus able to mine more integral and finer object regions.
Specifically, our multi-miner leverages a parallel modulator to check whether
there are remaining object regions for each single object, and guide a
category-aware generator to mine the regions of each object independently. In
this way, the multi-miner adaptively takes more steps for large objects and
fewer steps for small objects. Experiment results demonstrate that the
multi-miner offers better region mining results and helps achieve better
segmentation performance than state-of-the-art weakly-supervised semantic
segmentation methods.


  Horizontally aligned carbon nanotube (HACNT) arrays hold significant
potential for various applications in nanoelectronics and material science.
However, their high-throughput characterization remains challenging due to the
lack of methods with both high efficiency and high accuracy. Here, we present a
novel technique, Calibrated Absolute Optical Contrast (CAOC), achieved through
the implementation of differential principles to filter out stray signals and
high-resolution calibration to endow optical contrast with physical
significance. CAOC offers major advantages over previous characterization
techniques, providing consistent and reliable measurements of HACNT array
density with high throughput and non-destructive assessment. To validate its
utility, we demonstrate wafer-scale uniformity assessment by rapid density
mapping. This technique not only facilitates the practical evaluation of HACNT
arrays but also provides insights into balancing high throughput and high
resolution in nanomaterial characterization.


  To deploy machine learning-based algorithms for real-time applications with
strict latency constraints, we consider an edge-computing setting where a
subset of inputs are offloaded to the edge for processing by an accurate but
resource-intensive model, and the rest are processed only by a less-accurate
model on the device itself. Both models have computational costs that match
available compute resources, and process inputs with low-latency. But
offloading incurs network delays, and to manage these delays to meet
application deadlines, we use a token bucket to constrain the average rate and
burst length of transmissions from the device. We introduce a Markov Decision
Process-based framework to make offload decisions under these constraints,
based on the local model's confidence and the token bucket state, with the goal
of minimizing a specified error measure for the application. Beyond isolated
decisions for individual devices, we also propose approaches to allow multiple
devices connected to the same access switch to share their bursting allocation.
We evaluate and analyze the policies derived using our framework on the
standard ImageNet image classification benchmark.


  In contrast to our recent finding of an X-point band gap in divalent
hexaborides, we report here that angle resolved photoemission spectroscopy
(ARPES) data shows that the gap is absent for trivalent LaB$_6$ and is absent
or nearly so for mixed valent SmB$_6$. This finding demonstrates a nontrivial
evolution of the band structure from divalent to trivalent hexaborides.


  In this paper we consider a new mathematical extension of the Black-Scholes
model in which the stochastic time and stock share price evolution is described
by two independent random processes. The parent process is Brownian, and the
directing process is inverse to the totally skewed, strictly \alpha-stable
process. The subordinated process represents the Brownian motion indexed by an
independent, continuous and increasing process. This allows us to introduce the
long-term memory effects in the classical Black-Scholes model.


  We consider a dynamical method of storage of quantum states based on the
spin-1/2 systems with the dipole-dipole interactions in a strong external
magnetic field { supplemented with the special time-reversion procedure}. The
stored information can be extracted at certain time instants.


  I overview the status of the Electroweak Symmetry Breaking problem, paying
special attention to the possible signals of new physics at the Large Hadron
Collider (and at a Linear Collider)


  We show that a well behaved Noetherian, finite dimensional, stable, monoidal
model category is equivalent to a model built from categories of modules over
completed rings in an adelic fashion.
  For abelian groups this is based on the Hasse square, for chromatic homotopy
theory this is based on the chromatic fracture square, and for rational
torus-equivariant homotopy theory this is the model of Greenlees-Shipley
arXiv:1101.2511.


  In recent years, there has been a growing interest in travel applications
that provide on-site personalized tourist spot recommendations. While generally
helpful, most available options offer choices based solely on static
information on places of interest without consideration of such dynamic factors
as weather, time of day, and congestion, and with a focus on helping the
tourist decide what single spot to visit next. Such limitations may prevent
visitors from optimizing the use of their limited resources (i.e., time and
money). Some existing studies allow users to calculate a semi-optimal tour
visiting multiple spots in advance, but their on-site use is difficult due to
the large computation time, no consideration of dynamic factors, etc. To deal
with this situation, we formulate a tour score approach with three components:
static tourist information on the next spot to visit, dynamic tourist
information on the next spot to visit, and an aggregate measure of satisfaction
associated with visiting the next spot and the set of subsequent spots to be
visited. Determining the tour route that produces the best overall tour score
is an NP-hard problem for which we propose three algorithms on the greedy
method. To validate the usefulness of the proposed approach, we applied the
three algorithms to 20 points of interest in Higashiyama, Kyoto, Japan, and
confirmed that the output solution was superior to the model route for Kyoto,
with computation times of the three algorithms of $1.9\pm0.1$, $2.0\pm0.1$, and
$27.0\pm1.8$ s.


  Davydov--Yetter (DY) cohomology classifies infinitesimal deformations of the
monoidal structure of tensor functors and tensor categories. In this paper we
provide new tools for the computation of the DY cohomology for finite tensor
categories and exact functors between them. The key point is to realize DY
cohomology as relative Ext groups. In particular, we prove that the
infinitesimal deformations of a tensor category $\mathcal{C}$ are classified by
the 3-rd self-extension group of the tensor unit of the Drinfeld center
$\mathcal{Z}(\mathcal{C})$ relative to $\mathcal{C}$. From classical results on
relative homological algebra we get a long exact sequence for DY cohomology and
a Yoneda product for which we provide an explicit formula. Using the long exact
sequence and duality, we obtain a dimension formula for the cohomology groups
based solely on relatively projective covers which reduces a problem in
homological algebra to a problem in representation theory, e.g. calculating the
space of invariants in a certain object of $\mathcal{Z}(\mathcal{C})$. Thanks
to the Yoneda product, we also develop a method for computing DY cocycles
explicitly which are needed for applications in the deformation theory. We
apply these tools to the category of finite-dimensional modules over a
finite-dimensional Hopf algebra. We study in detail the examples of the
bosonization of exterior algebras $\Lambda\mathbb{C}^k \rtimes
\mathbb{C}[\mathbb{Z}_2]$, the Taft algebras and the small quantum group of
$\mathfrak{sl}_2$ at a root of unity.


  Let $X$ be a Hadamard manifold with pinched negative curvature
$-b^2\leq\kappa\leq -1$. Suppose $\Sigma\subseteq X$ is a totally geodesic,
codimension-1 submanifold and consider the geodesic flow $\Phi^\nu_t$ on $X$
generated by a unit normal vector field $\nu$ on $\Sigma$. We say the normal
growth exponent of $\Sigma$ in $X$ is at most $\beta$ if \[ \lim_{t \rightarrow
\pm \infty} \frac{ \Vert d \Phi_t^\nu \Vert_{\infty} }{ e^{\beta \vert t
\vert}} < \infty, \] where $\Vert d \Phi_t^\nu \Vert_{\infty} $ is the supremum
of the operator norm of $d \Phi_t^\nu $ over all points of $\Sigma$. We show
that if $\Sigma$ is bi-Lipschitz to hyperbolic $n$-space $\mathbb{H}^n$ and the
normal growth exponent is at most 1, then $X$ is bi-Lipschitz to
$\mathbb{H}^{n+1}$. As an application, we prove that if $M$ is a closed,
negatively curved $(n+1)$-manifold, and $N\subset M$ is a totally geodesic,
codimension-1 submanifold that is bi-Lipschitz to a hyperbolic manifold and
whose normal growth exponent is at most 1, then $\pi_1(M)$ is isomorphic to a
lattice in $\text{Isom}(\mathbb{H}^{n+1})$. Finally, we show that the
assumption on the normal growth exponent is necessary in dimensions at least 4.


  The dynamics of geometrically non-linear flexible filaments play an important
role in a host of biological processes, from flagella-driven cell transport to
the polymeric structure of complex fluids. Such problems have historically been
computationally expensive due to numerical stiffness associated with the
inextensibility constraint, as well as the often non-trivial boundary
conditions on the governing high-order PDEs. Formulating the problem for the
evolving shape of a filament via an integral equation in the tangent angle has
recently been found to greatly alleviate this numerical stiffness. The
contribution of the present manuscript is to enable the simulation of non-local
interactions of multiple filaments in a computationally efficient manner using
the method of regularized stokeslets within this framework. The proposed method
is benchmarked against a non-local bead and link model, and recent code
utilizing a local drag velocity law. Systems of multiple filaments (1) in a
background fluid flow, (2) under a constant body force, and (3) undergoing
active self-motility are modeled efficiently. Buckling instabilities are
analyzed by examining the evolving filament curvature, as well as by
coarse-graining the body frame tangent angles using a Chebyshev approximation
for various choices of the relevant non-dimensional parameters. From these
experiments, insight is gained into how filament-filament interactions can
promote buckling, and further reveal the complex fluid dynamics resulting from
arrays of these interacting fibers. By examining active moment-driven
filaments, we investigate the speed of worm- and sperm-like swimmers for
different governing parameters. The MATLAB(R) implementation is made available
as an open-source library, enabling flexible extension for alternate
discretizations and different surrounding flows.


  In this paper we discuss the formation of peculiar narrow emission line
components observed in the spectra of a few novae. We aim to identify the
physical source responsible for transient narrow components that present
orbital radial velocity modulations, first observed in the post-outburst
recombination lines of Nova U Sco 2010. A search for candidate novae showing
similar narrow components is presented. Photoionization simulations indicate
that the forming region cannot be restricted to the primary Roche Lobe, but
could be located around the outer Lagrangian point L3. Further analysis
disfavors an origin at the accretion disk and the companion star. In addition,
we analyze possible correlations between the presence of the narrow components,
the basic nova parameters and the spectral classification in the initial
permitted phase.


  It is well-known that any maximal Cohen-Macaulay module over a hypersurface
has a periodic free resolution of period 2. Auslander, Reiten and Buchweitz
have used this periodicity to explain the existence of periodic projective
resolutions over certain finite-dimensional algebras which arise as stable
endomorphism rings of Cohen-Macaulay modules. These algebras are in fact
periodic, meaning that they have periodic projective resolutions as bimodules
and thus periodic Hochschild cohomology as well. The goal of this article is to
generalize this construction of periodic algebras to the context of Iyama's
higher AR-theory. We start by considering projective resolutions of functors on
a maximal (d-1)-orthogonal subcategory C of an exact Frobenius category B. If C
is fixed by the d-th syzygy functor of B, then we show that this d-th syzygy
functor induces the (2+d)-th syzygy on the category of finitely presented
functors on the stable category of C. If C has finite type, i.e., if C = add(T)
for a d-cluster tilting object T, then we show that the stable endomorphism
ring of T has a quasi-periodic resolution over its enveloping algebra.
Moreover, this resolution will be periodic if some higher syzygy functor is
isomorphic to the identity on the stable category of C. It follows, in
particular, that 2-C.Y. tilted algebras arising as stable endomorphism rings of
Cohen-Macaulay modules over curve singularities, as in the work of Burban,
Iyama, Keller and Reiten have periodic bimodule resolutions of period 4.


  Galaxy clusters are promising targets for indirect detection of dark matter
thanks to the large dark matter content. Using 14 years of Fermi-LAT data from
seven nearby galaxy clusters, we obtain constraints on the lifetime of decaying
very heavy dark matter particles with masses ranging from $10^3$ GeV to
$10^{16}$ GeV. We consider a variety of decaying channels and calculate prompt
gamma rays and electrons/positrons from the dark matter. Furthermore, we take
into account electromagnetic cascades induced by the primary gamma rays and
electrons/positrons, and search for the resulting gamma-ray signals from the
directions of the galaxy clusters. We adopt a Navarro-Frenk-White profile of
the dark matter halos, and use the profile likelihood method to set lower
limits on the dark matter lifetime at a 95% confidence level. Our results are
competitive with those obtained through other gamma-ray observations of galaxy
clusters and provide complementary constraints to existing indirect searches
for decaying very heavy dark matter.


  We investigate the contribution of higher spin particles in the signal of
direct detection experiments searching for dark matter. We consider a bosonic
or fermionic higher spin dark matter (HSDM) candidate which interacts with the
Standard Model via a dark U(1) mediator. For a particular subclass of
interactions, spin-polarized targets may be used for spin determination: The
angular dependence of scatterings can distinguish integer (spin-$s$) vs.
half-integer (spin-$s + 1/2$), while the recoil energy dependence of the signal
determines $s$. We consider also the signal of a supersymmetric higher spin
dark sector, which suggests a characteristic signal (''SUSY Rilles'') for
directional direct detection.


  Graph representation learning (also called graph embeddings) is a popular
technique for incorporating network structure into machine learning models.
Unsupervised graph embedding methods aim to capture graph structure by learning
a low-dimensional vector representation (the embedding) for each node. Despite
the widespread use of these embeddings for a variety of downstream transductive
machine learning tasks, there is little principled analysis of the
effectiveness of this approach for common tasks. In this work, we provide an
empirical and theoretical analysis for the performance of a class of embeddings
on the common task of pairwise community labeling. This is a binary variant of
the classic community detection problem, which seeks to build a classifier to
determine whether a pair of vertices participate in a community. In line with
our goal of foundational understanding, we focus on a popular class of
unsupervised embedding techniques that learn low rank factorizations of a
vertex proximity matrix (this class includes methods like GraRep, DeepWalk,
node2vec, NetMF). We perform detailed empirical analysis for community labeling
over a variety of real and synthetic graphs with ground truth. In all cases we
studied, the models trained from embedding features perform poorly on community
labeling. In constrast, a simple logistic model with classic graph structural
features handily outperforms the embedding models. For a more principled
understanding, we provide a theoretical analysis for the (in)effectiveness of
these embeddings in capturing the community structure. We formally prove that
popular low-dimensional factorization methods either cannot produce community
structure, or can only produce ``unstable" communities. These communities are
inherently unstable under small perturbations.


  We develop a framework for certifying randomness from Bell-test trials based
on directly estimating the probability of the measurement outcomes with
adaptive test supermartingales. The number of trials need not be predetermined,
and one can stop performing trials early, as soon as the desired amount of
randomness is extractable. It can be used with arbitrary, partially known and
time-dependent probabilities for the random settings choices. Furthermore, it
is suitable for application to experimental configurations with low Bell
violation per trial, such as current optical loophole-free Bell tests. It is
possible to adapt to time-varying experimental parameters. We formulate the
framework for the general situation where the trial probability distributions
are constrained to a known set. Randomness expansion with logarithmic settings
entropy is possible for many relevant configurations. We implement probability
estimation numerically and apply it to a representative settings-conditional
probability distribution of the outcomes from an atomic loophole-free Bell test
[Rosenfeld et al., Phys. Rev. Lett. 119:010402 (2017), arXiv:1611.04604 (2016)]
to illustrate trade-offs between the amount of randomness, error, settings
entropy, unknown settings biases, and number of trials. We then show that
probability estimation yields more randomness from the loophole-free Bell-test
data analyzed in [Bierhorst et al., arXiv:1702.05178 (2017)] and tolerates
adversarial settings probability biases.


  We used new ALMA $^{13}$CO and C$^{18}$O(3-2) observations obtained at high
angular resolution ($\sim$0.2") together with previous CO(3-2) and (6-5) ALMA
data and continuum maps at 1.3 and 0.8 mm in order to determine the gas
properties (temperature, density, and kinematics) in the cavity and to a lesser
extent in the outer disk of GG Tau A, the prototype of a young triple T Tauri
star that is surrounded by a massive and extended Keplerian outer disk. By
deprojecting, we studied the radial and azimuthal gas distribution and its
kinematics. We also applied a new method to improve the deconvolution of the CO
data and in particular better quantify the emission from gas inside the cavity.
We perform local and nonlocal thermodynamic equilibrium studies in order to
determine the excitation conditions and relevant physical parameters inside the
ring and in the central cavity. Residual emission after removing a smooth-disk
model indicates unresolved structures at our angular resolution, probably in
the form of irregular rings or spirals. The outer disk is cold, with a
temperature $<20$ K beyond 250 au that drops quickly (r$^{-1}$). The kinematics
of the gas inside the cavity reveals infall motions at about 10% of the
Keplerian speed. We derive the amount of gas in the cavity, and find that the
brightest clumps, which contain about 10% of this mass, have kinetic
temperatures 40$-$80 K, CO column densities of a few 10$^{17}$ cm$^{-2}$, and
H$_2$ densities around 10$^7$ cm$^{-3}$. Although the gas in the cavity is only
a small fraction of the disk mass, the mass accretion rate throughout the
cavity is comparable to or higher than the stellar accretion rate. It is
accordingly sufficient to sustain the circumstellar disks on a long timescale.


  This paper presents BELT, a novel model and learning framework for the
pivotal topic of brain-to-language translation research. The translation from
noninvasive brain signals into readable natural language has the potential to
promote the application scenario as well as the development of brain-computer
interfaces (BCI) as a whole. The critical problem in brain signal decoding or
brain-to-language translation is the acquisition of semantically appropriate
and discriminative EEG representation from a dataset of limited scale and
quality. The proposed BELT method is a generic and efficient framework that
bootstraps EEG representation learning using off-the-shelf large-scale
pretrained language models (LMs). With a large LM's capacity for understanding
semantic information and zero-shot generalization, BELT utilizes large LMs
trained on Internet-scale datasets to bring significant improvements to the
understanding of EEG signals.
  In particular, the BELT model is composed of a deep conformer encoder and a
vector quantization encoder. Semantical EEG representation is achieved by a
contrastive learning step that provides natural language supervision. We
achieve state-of-the-art results on two featuring brain decoding tasks
including the brain-to-language translation and zero-shot sentiment
classification. Specifically, our model surpasses the baseline model on both
tasks by 5.45% and over 10% and archives a 42.31% BLEU-1 score and 67.32%
precision on the main evaluation metrics for translation and zero-shot
sentiment classification respectively.


  Let W be the germ of a smooth complex surface around an exceptional curve and
let E be a rank 2 vector bundle on W. We study the cohomological properties of
a finite sequence $E_i, 1 \leq i \leq t$ of rank 2 vector bundles canonically
associated to E. We calculate numerical invariants of E in terms of the $E_i.$
If S is a compact complex smooth surface and E is a rank 2 bundle on the blow-
up of S at a point, we show that all values of $c_2(E)-c_2(\pi_* E^{\vee\vee})$
that are numerically possible are actually attained.


  We study base field extensions of ordinary abelian varieties defined over
finite fields using the module theoretic description introduced by Deligne. As
applications we give algorithms to determine the minimal field of definition of
such a variety and to determine whether two such varieties are twists.


  In this colloquium-level account, I describe the cosmological constant
problem: why is the energy of empty space at least 60 orders of magnitude
smaller than several known contributions to it from the Standard Model of
particle physics? I explain why the "dark energy" responsible for the
accelerated expansion of the universe is almost certainly vacuum energy. The
second half of the paper explores a more speculative subject. The vacuum
landscape of string theory leads to a multiverse in which many different
three-dimensional vacua coexist, albeit in widely separated regions. This can
explain both the smallness of the observed vacuum energy and the coincidence
that its magnitude is comparable to the present matter density.


  Mapping and self-localization in unknown environments are fundamental
capabilities in many robotic applications. These tasks typically involve the
identification of objects as unique features or landmarks, which requires the
objects both to be detected and then assigned a unique identifier that can be
maintained when viewed from different perspectives and in different images. The
\textit{data association} and \textit{simultaneous localization and mapping}
(SLAM) problems are, individually, well-studied in the literature. But these
two problems are inherently tightly coupled, and that has not been
well-addressed. Without accurate SLAM, possible data associations are
combinatorial and become intractable easily. Without accurate data association,
the error of SLAM algorithms diverge easily. This paper proposes a novel
nonparametric pose graph that models data association and SLAM in a single
framework. An algorithm is further introduced to alternate between inferring
data association and performing SLAM. Experimental results show that our
approach has the new capability of associating object detections and localizing
objects at the same time, leading to significantly better performance on both
the data association and SLAM problems than achieved by considering only one
and ignoring imperfections in the other.


  The aim of this paper is to prove the boundedness of the oscillation and
variation operators for the multilinear singular integrals with Lipschitz
functions on weighted Morrey spaces.


  It is found that a large deviation function for frequency of events of size
not equal to the system size in the one dimensional forest-fire model with a
single ignition site at an edge is independent of the system size, by using an
exact "decomposition" of the modified transition matrix of a master equation.
An exchange in the largest eigenvalue of the modified transition matrix may not
occur in the model.


  Access to combination antiretroviral treatment (ART) has improved greatly
over recent years. At the end of 2011, more than eight million HIV infected
people were receiving antiretroviral therapy in low-income and middle-income
countries. ART generally works well in keeping the virus suppressed and the
patient healthy. However, treatment only works as long as the virus is not
resistant against the drugs used. In the last decades, HIV treatments have
become better and better at slowing down the evolution of drug resistance, so
that some patients are treated for many years without having any resistance
problems. However, for some patients, especially in low-income countries, drug
resistance is still a serious threat to their health. This essay will review
what is known about transmitted and acquired drug resistance, multi-class drug
resistance, resistance to newer drugs, resistance due to treatment for the
prevention of mother-to-child transmission, the role of minority variants
(low-frequency drug-resistance mutations), and resistance due to pre-exposure
prophylaxis.


  The International Fingerprint Liveness Detection Competition is an
international biennial competition open to academia and industry with the aim
to assess and report advances in Fingerprint Presentation Attack Detection. The
proposed "Liveness Detection in Action" and "Fingerprint representation"
challenges were aimed to evaluate the impact of a PAD embedded into a
verification system, and the effectiveness and compactness of feature sets for
mobile applications. Furthermore, we experimented a new spoof fabrication
method that has particularly affected the final results. Twenty-three
algorithms were submitted to the competition, the maximum number ever achieved
by LivDet.


  The dependence of the rescaled equilibration time on different parameters of
the field theories with a holographic dual has been investigated in this paper.
We consider field theories with nonzero chemical potential at finite
temperature which are dual to asymptotically AdS charged black holes. We
examine a dynamical probe scalar operator where its dynamics is due to a
time-dependent source, quantum quench, or out-of-equilibrium initial condition
in field theory with fixed or varying temperature and chemical potential. We
observe that the behavior of the scalar operator equilibration time with
respect to temperature or chemical potential can not be predicted merely by
field theory parameters and depends on how fast the energy is injected into the
system. It is shown that in field theories with critical point the rescaled
equilibration time is shorter for thermodynamically stable systems. We also
observe that the rescaled equilibration time as one approaches the critical
point enhances and acquires an infinite slope though its value remains finite.
We show that for fast quenches, even though the system is far from equilibrium,
the dynamical critical exponent is the same as the one reported for
quasi-normal modes in the same background. However for slow quenches the
dynamical critical exponent picks up a different value.


  We study inelastic processes in the hybrid quantum system constituted by a
carbon nanotube (CNT) in contact with an ultracold quantum gas, such as a cloud
of thermal atoms or a Bose-Einstein condensate (BEC). We present a
parameter-free ab-initio approach for the loss rate based on the underlying
scattering process, considering the two-dimensional character of the system as
well as the exact Casimir-Polder potential. The predicted loss rates are in
perfect agreement with recent experimental results, obtained both for a thermal
cloud of rubidium atoms and for a BEC. For the trap loss of a thermal cloud, we
find that retardation effects become important and contribute significantly,
which emphasises the crucial role of the exact interaction potential.


  Magnetic null points can develop near the ergosphere boundary of a rotating
black hole by the combined effects of strong gravitational field and the
frame-dragging mechanism. The electric component does not vanish in the
magnetic null and an efficient process or particle acceleration can occur. The
situation is relevant for starving (low-accretion-rate, such as the Milky Way's
supermassive black hole) nuclei of some galaxies which exhibit only episodic
accretion events. The presence of the magnetic field field of an external
origin is an important aspect. We propose that such conditions can develop when
a magnetized neutron star approaches the supermassive black hole during late
stages of its inspiral motion. The field lines of the neutron star dipole
thread the black hole's event horizon and change rapidly their connectivity. We
put in comparison the case of a dipole-type magnetic field of a sinking and
orbiting star near a non-rotating black hole, and the near-horizon structure of
an asymptotically uniform magnetic field of a distant source near a
fast-rotating black hole. Although the two cases are qualitatively different
from each other, they both develop magnetically neutral null points near the
event horizon.


  We propose a new all-fiber source of polarization-entangled photon pairs for
quantum communications. Fiber birefringence is compensated using Faraday
rotator mirror, resulting in enhanced stability against random polarization
drifts compared to existing schemes.


  In the past decade we have witnessed the failure of traditional polls in
predicting presidential election outcomes across the world. To understand the
reasons behind these failures we analyze the raw data of a trusted pollster
which failed to predict, along with the rest of the pollsters, the surprising
2019 presidential election in Argentina which has led to a major market
collapse in that country. Analysis of the raw and re-weighted data from
longitudinal surveys performed before and after the elections reveals clear
biases (beyond well-known low-response rates) related to mis-representation of
the population and, most importantly, to social-desirability biases, i.e., the
tendency of respondents to hide their intention to vote for controversial
candidates. We then propose a longitudinal opinion tracking method based on
big-data analytics from social media, machine learning, and network theory that
overcomes the limits of traditional polls. The model achieves accurate results
in the 2019 Argentina elections predicting the overwhelming victory of the
candidate Alberto Fern\'andez over the president Mauricio Macri; a result that
none of the traditional pollsters in the country was able to predict. Beyond
predicting political elections, the framework we propose is more general and
can be used to discover trends in society; for instance, what people think
about economics, education or climate change.


  Machine learning algorithms are increasingly provided as black-box cloud
services or pre-trained models, without access to their training data. This
motivates the problem of zero-shot out-of-distribution (OOD) detection.
Concretely, we aim to detect OOD objects that do not belong to the classifier's
label set but are erroneously classified as in-distribution (ID) objects. Our
approach, RONIN, uses an off-the-shelf diffusion model to replace detected
objects with inpainting. RONIN conditions the inpainting process with the
predicted ID label, drawing the input object closer to the in-distribution
domain. As a result, the reconstructed object is very close to the original in
the ID cases and far in the OOD cases, allowing RONIN to effectively
distinguish ID and OOD samples. Throughout extensive experiments, we
demonstrate that RONIN achieves competitive results compared to previous
approaches across several datasets, both in zero-shot and non-zero-shot
settings.


  We study generalized uncertainty principle through the basic concepts of
limit and Fourier transformation and analyze both the quantum theory of gravity
and string theory from the perspective of complex function theory. Motivated
from the noncommutative nature of string theory, we have proposed a UV/IR
mixing dependent function $ \tilde{\delta}(\Delta x,\Delta k, \epsilon) $. For
a given $ \tilde{\delta}(\Delta x,\Delta k, \epsilon) $, we arrived at the
string uncertainty principle from the analyticity condition of a complex
function, which depends upon UV cut-off of the theory. This non trivially
modifies the quantum measurements, black hole physics and short distance
geometries. The present analysis is based on the postulate that the Planck
scale is the minimal length scale in nature. Furthermore, our consideration is
in perfect agreement with the existence of the maximum length scale in nature.
Both of the above length scales rely only upon the analysis of $
\tilde{\delta}(\Delta x,\Delta k, \epsilon) $ and do not directly make use of
any specific structure of the theory or Hamiltonian. The Regge behavior of the
string spectrum and the quantization of the horizon area of a black hole are
natural consequences of the function $ \tilde{\delta}(\Delta x,\Delta k,
\epsilon) $. It is hereby anticipated that $ \tilde{\delta}(\Delta x,\Delta k,
\epsilon) $ contains all possible corrections operating in nature, and thus a
promising possibility to reveal important clues towards the geometric origin of
$M$-theory.


  The status of CP violation in $B^0\to \pi^+\pi^-$ and the determination of
$\sin 2\phi_2 (\alpha)$ from the $B$ factories is described.


  This paper presents results of a study investing effects of grip rings on
text entry. Results revealed that grip rings do not affect text entry
performance in terms of speed, accuracy, or keystrokes per character. It then
reflects on future research directions based on the results and observations
from the study. The purpose of this work is to stress the necessity of
classifying and evaluating low-cost mobile phone accessories.


  In this study, we develop a method for multi-task manifold learning. The
method aims to improve the performance of manifold learning for multiple tasks,
particularly when each task has a small number of samples. Furthermore, the
method also aims to generate new samples for new tasks, in addition to new
samples for existing tasks. In the proposed method, we use two different types
of information transfer: instance transfer and model transfer. For instance
transfer, datasets are merged among similar tasks, whereas for model transfer,
the manifold models are averaged among similar tasks. For this purpose, the
proposed method consists of a set of generative manifold models corresponding
to the tasks, which are integrated into a general model of a fiber bundle. We
applied the proposed method to artificial datasets and face image sets, and the
results showed that the method was able to estimate the manifolds, even for a
tiny number of samples.


  Let $\mathcal C$ be a category of a set of (small) categories. This paper
concerns with the ${\mathbf {Cat}}$-valued presheaves and sieves over category
$\mathcal C.$ Since ${\mathbf {Cat}}$ is not a concrete category, existing
definition of presheaves can not deal with the situation. This paper proposes a
new framework for the purpose. The main result is a version of Yoneda embedding
for ${\mathbf {Cat}}$-valued presheaves, and construction of the ${\mathbf
{Cat}}$-valued sieves over the category ${\mathcal O}(\mathbf B)$ of
subcategories of a given topological category $\mathbf B.$


  Ultrafast extreme ultraviolet (XUV) sources with a controllable polarization
state are powerful tools for investigating the structural and electronic as
well as the magnetic properties of materials. However, such light sources are
still limited to only a few free-electron laser facilities and, very recently,
to high-order harmonic generation from noble gases. Here we propose and
numerically demonstrate a laser-plasma scheme to generate bright XUV pulses
with fully controlled polarization. In this scheme, an elliptically-polarized
laser pulse is obliquely incident on a plasma surface, and the reflected
radiation contains pulse trains and isolated circularly- or
highly-elliptically-polarized attosecond XUV pulses. The harmonic polarization
state is fully controlled by the laser-plasma parameters. The mechanism can be
explained within the relativistically oscillating mirror model. This scheme
opens a practical and promising route to generate bright attosecond XUV pulses
with desirable ellipticities in a straightforward and efficient way for a
number of applications.


  We show that for any $\epsilon>0$, $\alpha\in[0,\frac{1}{2})$, as
$g\to\infty$ a generic finite-area genus g hyperbolic surface with
$n=O\left(g^{\alpha}\right)$ cusps, sampled with probability arising from the
Weil-Petersson metric on moduli space, has no non-zero eigenvalue of the
Laplacian below $\frac{1}{4}-\left(\frac{2\alpha+1}{4}\right)^{2}-\epsilon$.
For $\alpha=0$ this gives a spectral gap of size $\frac{3}{16}-\epsilon$ and
for any $\alpha<\frac{1}{2}$ gives a uniform spectral gap of explicit size.


  The Maunder Minimum (1645-1715) is currently considered the only grand
minimum within telescopic sunspot observations since 1610. During this epoch,
the Sun was extremely quiet and unusually free from sunspots. However, despite
reduced frequency, candidate aurorae were reported in the mid-European sector
during this period and have been associated with occurrences of interplanetary
coronal mass ejections (ICMEs), whereas some of them have been identified as
misinterpretations. Here, we have analysed reports of candidate aurorae on 1
June 1680 with simultaneous observations in mid-Europe, and compared their
descriptions with visual accounts of early modern aurorae. Most contemporary
sunspot drawings from 22, 24, and 27 May 1680 have shown that this apparent
sunspot may have been a source of ICMEs, which caused the reported candidate
aurorae. On the other hand, its intensity estimate shows that the magnetic
storm during this candidate aurora was probably within the capability of the
storms derived from the corotating interaction region (CIR). Therefore, we
accommodate both ICMEs and CIRs as their possible origin. This interpretation
is probably applicable to the candidate aurorae in the often-cited Hungarian
catalogue, on the basis of the reconstructed margin of their equatorward
auroral boundary. Moreover, this catalogue itself has clarified that the
considerable candidates during the MM were probably misinterpretations.
Therefore, frequency of the auroral visibility in Hungary was probably lower
than previously considered and agree more with the generally slow solar wind in
the existing reconstructions, whereas sporadic occurrences of sunspots and
coronal holes still caused occasional geomagnetic storms.


  A new telescope design based on Baker-Nunn optics is proposed for observation
of ultra high-energy cosmic rays (UHECRs). The optical system has an image
resolution of smaller than 0.02$^\circ$ within a wide field of view of
50$^\circ$ angular diameter. When combined with a high-quality imaging device,
the proposed design enables the directions of UHECRs and high-energy neutrinos
to be determined with accuracy of better than 1 arcmin. The outstanding
resolution of this telescope allows charge-separated cosmic-rays to be resolved
and the source to be determined accurately. This marked improvement in angular
resolution will allow the multi-wavelength and ``multi-particle'' observations
of astronomical objects through collaboration with established astronomical
observations.


  We show that dry scalar-order active field theories (AFTs) are universally
hyperuniform, i.e., density fluctuations are anomalously suppressed in the
long-time limit regardless of the integrability or functional form of the
active contributions up to third order in gradient terms. These AFTs include
Active model B, Active model B+, and effective Cahn-Hilliard models. Moreover,
density variances and spectral densities are virtually indistinguishable from
that of passive phase-separated hyperuniform fields. Higher moments of the
density fluctuations, however, reveal activity-dependent higher-order
correlations that are not captured by conventional two-point measures that
characterize hyperuniformity.


  Inspired by the Statistical Physics of complex networks, wireless multihop ad
hoc communication networks are considered in abstracted form. Since such
engineered networks are able to modify their structure via topology control, we
search for optimized network structures, which maximize the end-to-end
throughput performance. A modified version of betweenness centrality is
introduced and shown to be very relevant for the respective modeling. The
calculated optimized network structures lead to a significant increase of the
end-to-end throughput. The discussion of the resulting structural properties
reveals that it will be almost impossible to construct these optimized
topologies in a technologically efficient distributive manner. However, the
modified betweenness centrality also allows to propose a new routing metric for
the end-to-end communication traffic. This approach leads to an even larger
increase of throughput capacity and is easily implementable in a
technologically relevant manner.


  How can we effectively engineer a computer vision system that is able to
interpret videos from unconstrained mobility platforms like UAVs? One promising
option is to make use of image restoration and enhancement algorithms from the
area of computational photography to improve the quality of the underlying
frames in a way that also improves automatic visual recognition. Along these
lines, exploratory work is needed to find out which image pre-processing
algorithms, in combination with the strongest features and supervised machine
learning approaches, are good candidates for difficult scenarios like motion
blur, weather, and mis-focus -- all common artifacts in UAV acquired images.
This paper summarizes the protocols and results of Track 1 of the UG^2+
Challenge held in conjunction with IEEE/CVF CVPR 2019. The challenge looked at
two separate problems: (1) object detection improvement in video, and (2)
object classification improvement in video. The challenge made use of the UG^2
(UAV, Glider, Ground) dataset, which is an established benchmark for assessing
the interplay between image restoration and enhancement and visual recognition.
16 algorithms were submitted by academic and corporate teams, and a detailed
analysis of how they performed on each challenge problem is reported here.


  Loops of inflationary gravitons are known to induce large temporal and
spatial logarithms which can cause perturbation theory to break down. Nonlinear
sigma models possess the same kind of derivative interactions and induce the
same sorts of large logarithms, without the complicated index structure and
potential gauge problem. Previous studies have examined models with zero field
space curvature which can be reduced to free field theories by local,
invertible field redefinitions. Here we study a model which cannot be so
reduced and still shows the same sorts of large logarithms. We compute the
evolution of the background at 1-loop and 2-loop orders, and we find the 1-loop
$\beta$ and $\gamma$ functions.


  We present a supersymmetric model with two dark matter (DM) components
explaining the galactic positron excess observed by PAMELA/HEAT and
ATIC/PPB-BETS: One is the conventional (bino-like) lightest supersymmetric
particle (LSP) \chi, and the other is a TeV scale meta-stable neutral singlet
N_D, which is a Dirac fermion (N,N^c). In this model, N_D decays dominantly
into \chi e^+e^- through an R parity preserving dimension 6 operator with the
life time \tau_N\sim 10^{26} sec. We introduce a pair of vector-like superheavy
SU(2) lepton doublets (L,L^c) and lepton singlets (E,E^c). The dimension 6
operator leading to the N_D decay is generated from the leptophilic Yukawa
interactions by W\supset Ne^cE+Lh_dE^c+m_{3/2}l_1L^c with the dimensionless
couplings of order unity, and the gauge interaction by {\cal L}\supset \sqrt{2}
g'\tilde{e}^{c*}e^c\chi + h.c. The superheavy masses of the vector-like leptons
(M_L, M_E\sim 10^{16} GeV) are responsible for the longevity of N_D. The low
energy field spectrum in this model is just the MSSM fields and N_D. Even for
the case that the portion of N_D is much smaller than that of \chi in the total
DM density [{\cal O}(10^{-10}) \lesssim n_{N_D}/n_\chi], the observed positron
excess can be explained by adopting relatively lighter masses of the
vector-like leptons (10^{13} GeV \lesssim M_{L,E} \lesssim 10^{16} GeV). The
smallness of the electron mass is also explained. This model is easily embedded
in the flipped SU(5) grand unification, which is a leptophilic unified theory.


  We investigate the joint primary mass, mass ratio, and redshift observed
distribution of astrophysical black holes using the gravitational wave events
detected by the LIGO-Virgo-KAGRA collaboration and included in the third
gravitational wave transient catalogue. We reconstruct this distribution using
Bayesian non-parametric methods, which are data-driven models able to infer
arbitrary probability densities under minimal mathematical assumptions. We find
evidence for the evolution with redshift of both the primary mass and mass
ratio distribution: our analysis shows the presence of two distinct
sub-populations in the primary mass - redshift plane, with the lighter
population, $\lesssim$ 20 $M_\odot$, disappearing at higher redshifts, $z >
0.4$. The mass ratio distribution shows no support for symmetric binaries. The
observed population of coalescing binary black holes evolves with look-back
time, suggesting a trend in metallicity with redshift and/or the presence of
multiple, redshift-dependent formation channels.


  We review recent work on the effects of quark loops on the gluon propagator
in the Landau gauge, relying mainly on the Schwinger-Dyson equations that
describe the two-point sector of QCD. Particularly important in this context is
the detailed study of how the standard gluon mass generation mechanism, which
is responsible for the infrared finiteness of the quenched gluon propagator, is
affected by the inclusions of dynamical quarks. This issue is especially
relevant and timely, given the qualitative picture that emerges from recent
unquenched lattice simulations. Our results demonstrate clearly that the gluon
mass generation persists, and that the corresponding saturation points of the
unquenched gluon propagators are progressively suppressed, as the number of
quark flavors increases.


  We consider the holographic dark energy (HDE) model in the framework of DGP
braneworld with Granda-Oliveros infrared (IR) cutoff, $L=(\alpha \dot{H}+\beta
H^2)^{-1/2}$. With this choice for IR cutoff, we are able to derive evolution
of the cosmological parameters such as the equation of state and the
deceleration parameters, $w$ and $q$, as the functions of the redshift
parameter $z$. As far as we know, most previous models of HDE presented in the
literatures, do not gives analytically $\omega=\omega(z)$ and $q=q(z)$. We plot
the evolution of these parameters versus $z$ and discuss that the results are
compatible with the recent observations. With suitably choosing the parameters,
this model can exhibit a transition from deceleration to the acceleration
around $z\approx 0.6$. Then, we suggest a correspondence between the
quintessence and tachyon scalar fields and HDE in the framework of DGP
braneworld. This correspondence allows us to reconstruct the evolution of the
scalar fields and the scalar potentials. We also investigate stability of the
presented model by calculating the squared sound speed, $v^2_s$, whose sign
determines the stability of the model. Our study shows that $v^2_s$ could be
positive provided the parameters of the model are chosen suitably. In
particular, for $\alpha>1$, $\beta>0$, and $\alpha<1$, $\beta<0$, we have
$v^2_s>0$ during the history of the universe, and so the stable dark energy
dominated universe can be achieved. This is in contrast to the HDE in standard
cosmology, which is unstable against background perturbations and so cannot
lead to a stable dark energy dominated universe.


  This document presents an introduction of two commonly used power system
differential algebraic equations for studying electromechanical oscillation and
transient stability. Two types of generator models are used to formulate the
power system model, respectively: the second-order classical model and the
fourth-order generator model. An example is provided on the IEEE 9-bus system.


  It is not currently clear how important it will be to include conservative
self-force (SF) corrections in the models for extreme-mass-ratio inspiral
(EMRI) waveforms that will be used to detect such signals in LISA (Laser
Interferometer Space Antenna) data. These proceedings will address this issue
for circular-equatorial inspirals using an approximate EMRI model that includes
conservative corrections at leading post-Newtonian order. We will present
estimates of the magnitude of the parameter estimation errors that would result
from omitting conservative corrections, and compare these to the errors that
will arise from noise fluctuations in the detector. We will also use this model
to explore the relative importance of the second-order radiative piece of the
SF, which is not presently known.


  Doing research is fighting, what any other thing the human being could do?
Fight against powers or to get powers, that depends on us. Science can be a
revolution or deadlocked idleness. Still waters, without hitting the stones
along their history, trend to form bogs.


  We study a class of deterministic finite-horizon two-player nonzero-sum
differential games where players are endowed with different kinds of controls.
We assume that Player 1 uses piecewise-continuous controls, while Player 2 uses
impulse controls. For this class of games, we seek to derive conditions for the
existence of feedback Nash equilibrium strategies for the players. More
specifically, we provide a verification theorem for identifying such
equilibrium strategies, using the Hamilton-Jacobi-Bellman (HJB) equations for
Player 1 and the quasi-variational inequalities (QVIs) for Player 2. Further,
we show that the equilibrium number of interventions by Player 2 is upper
bounded. Furthermore, we specialize the obtained results to a scalar two-player
linear-quadratic differential game. In this game, Player 1's objective is to
drive the state variable towards a specific target value, and Player 2 has a
similar objective with a different target value. We provide, for the first
time, an analytical characterization of the feedback Nash equilibrium in a
linear-quadratic differential game with impulse control. We illustrate our
results using numerical experiments.


  Subsonic, compressive turbulence transfers energy to cosmic rays (CRs), a
process known as non-resonant reacceleration. It is often invoked to explain
observed ratios of primary to secondary CRs at $\sim \rm GeV$ energies,
assuming wholly diffusive CR transport. However, such estimates ignore the
impact of CR self-confinement and streaming. We study these issues in stirring
box magnetohydrodynamic (MHD) simulations using Athena++, with field-aligned
diffusive and streaming CR transport. For diffusion only, we find CR
reacceleration rates in good agreement with analytic predictions. When
streaming is included, reacceleration rates depend on plasma $\beta$. Due to
streaming-modified phase shifts between CR and gas variables, they are slower
than canonical reacceleration rates in low-$\beta$ environments like the
interstellar medium (ISM) but remain unchanged in high-$\beta$ environments
like the intracluster medium (ICM). We also quantify the streaming energy loss
rate in our simulations. For sub-Alfv\'{e}nic turbulence, it is
resolution-dependent (hence unconverged in large scale simulations) and heavily
suppressed -- by an order of magnitude -- compared to the isotropic loss rate
$v_{A} \cdot \nabla P_{\rm CR} / P_{\rm CR} \sim v_{A}/L_{0}$, due to
misalignment between the mean field and isotropic CR gradients.
Counterintuitively, and unlike acceleration efficiencies, CR losses are almost
independent of magnetic field strength over $\beta \sim 1-100$ and are,
therefore, not the primary factor behind lower acceleration rates when
streaming is included. While this paper is primarily concerned with how
turbulence affects CRs, in a follow-up paper (Bustard and Oh, in prep), we
consider how CRs affect turbulence by diverting energy from the MHD cascade,
altering the pathway to gas heating and steepening the turbulent power
spectrum.


  Recently, Garthwaite-Penniston have shown that the coefficients of
Ramanujan's mock theta function $\omega$ satisfy infinitely many congruences of
Ramanujan-type. In this work we give the first explicit examples of congruences
for Ramanujan's mock theta function $\omega$ and another mock theta function
$\mathcal{C}$.