  Attributed network data is becoming increasingly common across fields, as we
are often equipped with information about nodes in addition to their pairwise
connectivity patterns. This extra information can manifest as a classification,
or as a multidimensional vector of features. Recently developed methods that
seek to extend community detection approaches to attributed networks have
explored how to most effectively combine connectivity and attribute information
to identify quality communities. These methods often rely on some assumption of
the dependency relationships between attributes and connectivity. In this work,
we seek to develop a statistical test to assess whether node attributes align
with network connectivity. The objective is to quantitatively evaluate whether
nodes with similar connectivity patterns also have similar attributes. To
address this problem, we use a node sampling and label propagation approach. We
apply our method to several synthetic examples that explore how network
structure and attribute characteristics affect the empirical p-value computed
by our method. Finally, we apply the test to a network generated from a single
cell mass cytometry (CyTOF) dataset and show that our test can identify markers
associated with distinct sub populations of single cells.


  The quantum predictions for a single nonrelativistic spin-1/2 particle can be
reproduced by noncontextual hidden variables. Here we show that quantum
contextuality for a relativistic electron moving in a Coulomb potential
naturally emerges if relativistic effects are taken into account. The
contextuality can be identified through the violation of noncontextuality
inequalities. We also discuss quantum contextuality for the free Dirac electron
as well as the relativistic Dirac oscillator.


  Recently, there has been provided two chaotic models based on the
twist-deformation of classical Henon-Heiles system. First of them has been
constructed on the well-known, canonical space-time noncommutativity, while the
second one on the Lie-algebraically type of quantum space, with two spatial
directions commuting to classical time. In this article, we find the direct
link between mentioned above systems, by synchronization both of them in the
framework of active control method. Particularly, we derive at the canonical
phase-space level the corresponding active controllers as well as we perform
(as an example) the numerical synchronization of analyzed models.


  Particle radiation from black holes has an observed emission power depending
on the surface gravity $\kappa = c^4/(4GM)$ as \begin{equation}\nonumber
P_{\textrm{black hole}} \sim \frac{\hbar \kappa^2}{6\pi c^2} = \frac{\hbar
c^6}{96\pi G^2 M^2}\,,\end{equation} while both the radiation from accelerating
particles and moving mirrors (accelerating boundaries) obey similar
relativistic Larmor powers, \begin{equation}\nonumber P_{\textrm{electron}}=
\frac{q^2\alpha^2}{6\pi \epsilon_0 c^3}\,, \quad P_{\textrm{mirror}}
=\frac{\hbar \alpha^2}{6\pi c^2}\,, \end{equation} where $\alpha$ is the
Lorentz invariant proper acceleration. This equivalence between the Lorentz
invariant powers suggests a close relation that could be used to understand
black hole radiation. We show that an accelerating mirror with a prolonged
metastable acceleration plateau can provide a unitary, thermal,
energy-conserved analog model for black hole decay.


  We construct smooth supergravity solutions describing a BPS black ring with a
BPS black hole centered at an arbitrary distance above the ring. We find that
as one moves the black hole the entropy of the ring remains constant, but the
angular momentum coming from the supergravity fluxes changes. Our solutions
also show that in order to merge a BPS black ring with a BPS black hole one has
to increase one of the angular momenta of the ring, and that the result of the
merger is always a BMPV black hole. We also find a class of mergers that are
thermodynamically reversible, and comment on their physics.


  In order to better understand a complex theory like quantum mechanics, it is
sometimes useful to take a step back and create alternative theories, with more
intuitive foundations, and examine which features of quantum mechanics can be
reproduced by such a foil theory. A prominent example is Spekkens' toy theory,
which is based off a simple premise: "What if we took a common classical theory
and added the uncertainty principle as a postulate?" In other words, the theory
imposes an epistemic restriction on our knowledge about a physical system: only
half of the variables can ever be known to an observer. Like good science
fiction, from this simple principle a rich behaviour emerges, most notoriously
when we compose several systems. The toy theory emulates some aspects of
quantum non-locality, although crucially it is still a non-contextual model. In
this pedagogical review we consolidate different approaches to Spekkens' toy
theory, including the stabilizer formalism and the generalization to arbitrary
dimensions, completing them with new results where necessary. In particular, we
introduce a general characterization of measurements, superpositions and
entanglement in the toy theory.


  In modern healthcare, addressing the complexities of accurate disease
prediction and personalized recommendations is both crucial and challenging.
This research introduces MLtoGAI, which integrates Semantic Web technology with
Machine Learning (ML) to enhance disease prediction and offer user-friendly
explanations through ChatGPT. The system comprises three key components: a
reusable disease ontology that incorporates detailed knowledge about various
diseases, a diagnostic classification model that uses patient symptoms to
detect specific diseases accurately, and the integration of Semantic Web Rule
Language (SWRL) with ontology and ChatGPT to generate clear, personalized
health advice. This approach significantly improves prediction accuracy and
ensures results that are easy to understand, addressing the complexity of
diseases and diverse symptoms. The MLtoGAI system demonstrates substantial
advancements in accuracy and user satisfaction, contributing to developing more
intelligent and accessible healthcare solutions. This innovative approach
combines the strengths of ML algorithms with the ability to provide
transparent, human-understandable explanations through ChatGPT, achieving
significant improvements in prediction accuracy and user comprehension. By
leveraging semantic technology and explainable AI, the system enhances the
accuracy of disease prediction and ensures that the recommendations are
relevant and easily understood by individual patients. Our research highlights
the potential of integrating advanced technologies to overcome existing
challenges in medical diagnostics, paving the way for future developments in
intelligent healthcare systems. Additionally, the system is validated using 200
synthetic patient data records, ensuring robust performance and reliability.


  We identify a route for the production of $^{87}$Rb$^{133}$Cs molecules in
the $\textrm{X} \, ^1\Sigma^+$ rovibronic ground state that is compatible with
efficient mixing of the atoms in optical lattices. We first construct a model
for the excited-state structure using constants found by fitting to
spectroscopy of the relevant $\textrm{a} \, ^3\Sigma^+ \rightarrow \textrm{b}
\, ^3\Pi_1$ transitions at 181.5 G and 217.1 G. We then compare the predicted
transition dipole matrix elements from this model to those found for the
transitions that have been successfully used for STIRAP at 181.5 G. We form
molecules by magnetoassociation on a broad interspecies Feshbach resonance at
352.7 G and explore the pattern of Feshbach states near 305 G. This allows us
to navigate to a suitable initial state for STIRAP by jumping across an avoided
crossing with radiofrequency radiation. We identify suitable transitions for
STIRAP at 305 G. We characterize these transitions experimentally and
demonstrate STIRAP to a single hyperfine level of the ground state with a
one-way efficiency of 85(4)%.


  We analyze a scheme to manipulate quantum states of neutral atoms at
individual sites of optical lattices using focused laser beams. Spatial
distributions of focused laser intensities induce position-dependent energy
shifts of hyperfine states, which, combined with microwave radiation, allow
selective manipulation of quantum states of individual target atoms. We show
that various errors in the manipulation process are suppressed below $10^{-4} $
with properly chosen microwave pulse sequences and laser parameters. A similar
idea is also applied to measure quantum states of single atoms in optical
lattices.


  One of the major problems in the study of the dynamics of proteins is the
visualization of changing conformations that are important for processes
ranging from enzyme catalysis to signaling. A protein exhibiting conformational
dynamics is the soluble blood protein beta 2-glycoprotein I (beta2GPI), which
exists in two conformations: the closed (circular) form and the open (linear)
form. It is hypothesized that an increased proportion of the open conformation
leads to the autoimmune disease antiphospholipid syndrome (APS). A
characteristic feature of beta2GPI is the high content of lysine residues.
However, the potential role of lysine in the conformational dynamics of
beta2GPI has been poorly investigated. Here, we report on a strategy to
permanently open up the closed protein conformation by chemical acetylation of
lysine residues using acetic acid N-hydroxysuccinimide ester (NHS-Ac). Specific
and complete acetylation was demonstrated by the quantification of primary
amino groups with fluoraldehyde o-phthalaldehyde (OPA) reagent, as well as
western blot analysis with an anti-acetylated lysine antibody. Our results
demonstrate that acetylated beta2GPI preserves its secondary and tertiary
structures, as shown by circular dichroism spectroscopy. We found that after
lysine acetylation, the majority of proteins are in the open conformation as
revealed by atomic force microscopy high-resolution images. Using this
strategy, we proved that the electrostatic interaction of lysine residues plays
a major role in stabilizing the beta2GPI closed conformation, as confirmed by
lysine charge distribution calculations. We foresee that our approach will be
applied to other lysine-rich proteins (e.g. histones) undergoing conformational
transitions.


  In 1917 Einstein initiated modern cosmology by postulating, based on general
relativity, a homogeneous, static, spatially curved universe. To counteract
gravitational contraction he introduced the cosmological constant. In 1922
Alexander Friedman showed that Einstein's fundamental equation also allowed
dynamical worlds, and in 1927 Geroges Lemaitre, backed by observational
evidence, concluded that our universe was expanding. Einstein impetuously
rejected Friedman's as well as Lemaitre's findings. However, in 1931 he
retracted his former static model in favour of a dynamic solution. This
investigation follows Einstein on his hesitating path from a static to the
expanding universe. Contrary to an often repeated belief the primary motive for
his switch was not observational evidence, but the realisation that his static
model was unstable.


  In this paper we review algorithms for checking diagnosability of
discrete-event systems and timed automata. We point out that the diagnosability
problems in both cases reduce to the emptiness problem for (timed) B\"uchi
automata. Moreover, it is known that, checking whether a discrete-event system
is diagnosable, can also be reduced to checking bounded diagnosability. We
establish a similar result for timed automata. We also provide a synthesis of
the complexity results for the different fault diagnosis problems.


  We present results from a detailed numerical study of the small-scale and
loop production properties of cosmic string networks, based on the largest and
highest resolution string simulations to date. We investigate the non-trivial
fractal properties of cosmic strings, in particular, the fractal dimension and
renormalised string mass per unit length, and we also study velocity
correlations. We demonstrate important differences between string networks in
flat (Minkowski) spacetime and the two very similar expanding cases. For high
resolution matter era network simulations, we provide strong evidence that
small-scale structure has converged to `scaling' on all dynamical lengthscales,
without the need for other radiative damping mechanisms. We also discuss
preliminary evidence that the dominant loop production size is also approaching
scaling.


  We describe a formula for computing the product of the Young symmetrizer of a
Young tableau with the Young symmetrizer of a subtableau, generalizing the
classical quasi-idempotence of Young symmetrizers. We derive some consequences
to the structure of ideals in the generic tensor algebra and its partial
symmetrizations. Instances of these generic algebras appear in the work of Sam
and Snowden on twisted commutative algebras, as well as in the work of the
author on the defining ideals of secant varieties of Segre-Veronese varieties,
and in joint work of Oeding and the author on the defining ideals of tangential
varieties of Segre-Veronese varieties.


  Graph Neural Networks (GNNs) are popular models for graph learning problems.
GNNs show strong empirical performance in many practical tasks. However, the
theoretical properties have not been completely elucidated. In this paper, we
investigate whether GNNs can exploit the graph structure from the perspective
of the expressive power of GNNs. In our analysis, we consider graph generation
processes that are controlled by hidden (or latent) node features, which
contain all information about the graph structure. A typical example of this
framework is kNN graphs constructed from the hidden features. In our main
results, we show that GNNs can recover the hidden node features from the input
graph alone, even when all node features, including the hidden features
themselves and any indirect hints, are unavailable. GNNs can further use the
recovered node features for downstream tasks. These results show that GNNs can
fully exploit the graph structure by themselves, and in effect, GNNs can use
both the hidden and explicit node features for downstream tasks. In the
experiments, we confirm the validity of our results by showing that GNNs can
accurately recover the hidden features using a GNN architecture built based on
our theoretical analysis.


  This past decade has seen a noticeable uptick in asymmetric election results
along with the inevitable claims of gerrymandering and litigation. Research,
too, has followed, giving rise to intense scrutiny of elections, where the goal
is to understand not only what goes into gerrymandering, but how to measure
what comes out. Perhaps the most cited symmetry measure of this decade is the
EG. The EG has been commonplace in gerrymandering litigation nationwide and the
focus of numerous articles, both popular and scholarly. This article shows how
the EG can be represented as a weighting function. This is not the full WDM
article though it shows up in Google searches. For the full WDM article, please
see https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3308888


  Language model (LM) pretraining has led to consistent improvements in many
NLP downstream tasks, including named entity recognition (NER). In this paper,
we present T-NER (Transformer-based Named Entity Recognition), a Python library
for NER LM finetuning. In addition to its practical utility, T-NER facilitates
the study and investigation of the cross-domain and cross-lingual
generalization ability of LMs finetuned on NER. Our library also provides a web
app where users can get model predictions interactively for arbitrary text,
which facilitates qualitative model evaluation for non-expert programmers. We
show the potential of the library by compiling nine public NER datasets into a
unified format and evaluating the cross-domain and cross-lingual performance
across the datasets. The results from our initial experiments show that
in-domain performance is generally competitive across datasets. However,
cross-domain generalization is challenging even with a large pretrained LM,
which has nevertheless capacity to learn domain-specific features if fine-tuned
on a combined dataset. To facilitate future research, we also release all our
LM checkpoints via the Hugging Face model hub.


  We experimentally report perfect acoustic absorption through the interplay of
the inherent losses and transparent modes with high $Q$ factor. These modes are
generated in a two-port, one-dimensional waveguide which is side-loaded by
isolated resonators of moderate $Q$ factor. In symmetric structures, we show
that in the presence of small inherent losses, these modes lead to coherent
perfect absorption associated with one-sided absorption slightly larger than
0.5. In asymmetric structures, near perfect one-sided absorption is possible
(96 \%) with a deep sub-wavelength sample ($\lambda/28$). The control of strong
absorption by the proper tuning of few resonators with weak losses will open
new possibilities in various wave-control devices.


  A new model of gamma bursts (GB) generated by MACHO's in the form of
noncompact dark matter halo objects (NO) is proposed. The model explain well
the spherical symmetry, $log N - log S$ curve of GB absolutely identically to
the well-known giant dark matter halo (GDMH) model. The main difficulty of GDMH
model -- the lack of neutron stars in our galactic halo is eliminated because
of the large number of NO ($\sim 10^{13}$) which is quite close to a total
number of bursts during the life time of the Universe. The proposed mechanism
of the burst is the instability and explosion of the baryonic core (BC) of NO.
The structure of BC and possible mechanism of BC explosion are discussed.
  The intensive infrared emission from BC of neutralino stars (NeS) at the
wavelength $(3 - 8)\mu$ is predicted. The possibility to discover NeS by
observation of infrared emission from the recently distinguished point-like
gamma sources is indicated.


  The aim of this paper is to study self-similar solutions to the symplectic
cuvature flow on 6-dimensional nilmanifolds. For this purpose, we focus our
attention in the family of symplectic Two- and Three-step nilpotent Lie
algebras admitting a "minimal compatible metric" and we give a complete
classification of these algebras together with their respective metric. Such
classification is given by using our generalization of Nikolayevsky's nice
basis criterium (arXiv:1301.4949), which will be repeated here in the context
of canonical compatible metrics for geometric structures on nilmanifolds, for
the convenience of the reader.
  By computing the Chern-Ricci operator $P$ in each case, we show that the
above distinguished metrics define a soliton almost K\"{a}hler structure.
  Many illustrative examples are carefully developed.


  The background geometries of the AdS/CFT and the Randall-Sundrum theories are
locally similar, and there is strong evidence for some kind of
"complementarity" between them; yet the global structures of the respective
manifolds are very different. We show that this apparent problem can be
understood in the context of a more complete global formulation of AdS/CFT. In
this picture, the brane-world arises within the AdS/CFT geometry as the
inevitable consequence of recent results on the global structure of manifolds
with "infinities". We argue that the usual coordinates give a misleading
picture of this global structure, much as Schwarzschild coordinates conceal the
global form of Kruskal-Szekeres space.


  In this paper, we study the statistical properties of weak lensing peaks in
light-cones generated from cosmological simulations. In order to assess the
prospects of such observable as a cosmological probe, we consider simulations
that include interacting Dark Energy (hereafter DE) models with coupling term
between DE and Dark Matter. Cosmological models that produce a larger
population of massive clusters have more numerous high signal-to-noise peaks;
among models with comparable numbers of clusters those with more concentrated
haloes produce more peaks. The most extreme model under investigation shows a
difference in peak counts of about $20\%$ with respect to the reference
$\mathrm{\Lambda}$CDM model. We find that peak statistics can be used to
distinguish a coupling DE model from a reference one with the same power
spectrum normalisation. The differences in the expansion history and the growth
rate of structure formation are reflected in their halo counts, non-linear
scale features and, through them, in the properties of the lensing peaks. For a
source redshift distribution consistent with the expectations of future
space-based wide field surveys, we find that typically seventy percent of the
cluster population contributes to weak-lensing peaks with signal-to-noise
ratios larger than two, and that the fraction of clusters in peaks approaches
one-hundred percent for haloes with redshift z$\leq$0.5. Our analysis
demonstrates that peak statistics are an important tool for disentangling DE
models by accurately tracing the structure formation processes as a function of
the cosmic time.


  The energy levels of a quantum graph with time reversal symmetry and
unidirectional classical dynamics are doubly degenerate and obey the spectral
statistics of the Gaussian Unitary Ensemble. These degeneracies, however, are
lifted when the unidirectionality is broken in one of the graph's vertices by a
singular perturbation. Based on a Random Matrix model we derive an analytic
expression for the nearest neighbour distribution between energy levels of such
systems. As we demonstrate the result agrees excellently with the actual
statistics for graphs with a uniform distribution of eigenfunctions. Yet, it
exhibits quite substantial deviations for classes of graphs which show strong
scarring.


  We provide observational evidence that galaxy mergers significantly affect
stellar kinematics of early-type galaxies (ETGs) such as specific stellar
angular momentum within the half-light radius ($\lambda_{R_e}$) and kinematic
misalignment ($\psi_\mathrm{mis}$), using MaNGA integral field unit
spectroscopic data that are in the Stripe 82 region of the Sloan Digital Sky
Survey. In this study, tidal features around ETGs, which are detected in deep
coadded images, are used as direct evidence for mergers that occurred recently.
In the case of ETGs that do not have dust lanes, $\lambda_{R_e}$ is lower in
ETGs with tidal features than in those without tidal features (median
$\lambda_{R_e}$: $0.21$ versus $0.39$) in all stellar mass and S\'ersic index
ranges except the most massive bin, so that the fraction of ETGs with tidal
features in slow rotators is more than twice as large as that in fast rotators
($42\%$ versus $18\%$). Moreover, ETGs with tidal features have larger
$\psi_\mathrm{mis}$ than those without tidal features (mean
$\psi_\mathrm{mis}$: $28^\circ$ versus $15^\circ$). By contrast, ETGs with dust
lanes are fast rotators, and ETGs with both dust lanes and tidal features have
the highest $\lambda_{R_e}$ (median $\lambda_{R_e}$: $0.59$) among all ETG
categories. In addition, ETGs with dust lanes have small $\psi_\mathrm{mis}$
regardless of the existence of tidal features ($\psi_\mathrm{mis}<7.5^\circ$).
Our results can be explained if mergers with different gas fractions generate
merger remnants that have different kinematic properties.


  We present an image reconstruction algorithm for the Inverse Conductivity
Problem based on reformulating the problem in terms of integral equations. We
use as data the values of injected electric currents and of the corresponding
induced boundary potentials, as well as the boundary values of the electrical
conductivity.
  We have used a priori information to find a regularized conductivity
distribution by first solving a Fredholm integral equation of the second kind
for the Laplacian of the potential, and then by solving a first order partial
differential equation for the regularized conductivity itself. Many of the
calculations involved in the method can be achieved analytically using the
eigenfunctions of an integral operator defined in the paper.


  The "edge of chaos" phase transition in artificial neural networks is of
renewed interest in light of recent evidence for criticality in brain dynamics.
Statistical mechanics traditionally studied this transition with connectivity
$k$ as the control parameter and an exactly balanced excitation-inhibition
ratio. While critical connectivity has been found to be low in these model
systems, typically around $k=2$, which is unrealistic for natural systems, a
recent study utilizing the excitation-inhibition ratio as the control parameter
found a new, nearly degree independent, critical point when connectivity is
large. However, the new phase transition is accompanied by an unnaturally high
level of activity in the network.
  Here we study random neural networks with the additional properties of (i) a
high clustering coefficient and (ii) neurons that are solely either excitatory
or inhibitory, a prominent property of natural neurons. As a result, we observe
an additional critical point for networks with large connectivity, regardless
of degree distribution, which exhibits low activity levels that compare well
with neuronal brain networks.


  We discuss space-time evolution of ultrarelativistic quantum dipole in QED.
We show that the space-time evolution can be described, in a certain
approximation, by means of a regularized wave function, whose parameters are
determined by the process of the dipole creation by a local current. We derive
using these wave functions the dipole expansion law, that is found to coincide
parametrically in the leading order with the one suggested by Farrar,
Frankfurt,Liu and Strikman.


  Active triggering and manipulation of ultrafast flux dynamics in
superconductors are demonstrated in films of Nb. Controlled amounts of magnetic
flux were injected from a point along the edge of a square sample, which at 2.5
K responds by nucleation of a thermomagnetic avalanche. Magneto-optical imaging
was used to show that when such films are cooled in the presence of in-plane
magnetic fields they become anisotropic, and the morphology of the avalanches
change systematically, both with the direction and magnitude of the field. The
images reveal that the avalanching dendrites consistently bend towards the
direction perpendicular to that of the in-plane field. The effect increases
with the field magnitude, and at 1.5 kOe the triggered avalanche becomes
quenched at the nucleation stage. The experimental results are explained based
on a theoretical model for thermomagnetic avalanche nucleation in
superconducting films, and by assuming that the frozen-in flux generates
in-plane anisotropy in the film thermal conductance. The results demonstrate
that applying in-plane magnetic fields to film superconductors can be a
versatile external tool for controlling their ultrafast flux dynamics.


  Harnessing the rich nonlinear dynamics of highly-deformable materials has the
potential to unlock the next generation of functional smart materials and
devices. However, unlocking such potential requires effective strategies to
spatially design optimal material architectures for desired nonlinear dynamic
responses such as guiding of nonlinear elastic waves, energy focusing, and
cloaking. Here, we introduce an inverse-design framework for the discovery of
flexible mechanical metamaterials with a target nonlinear dynamic response. The
desired dynamic task is encoded via optimal tuning of the full-scale
metamaterial geometry through an inverse-design approach powered by a
custom-developed fully-differentiable simulation environment. By deploying such
strategy, we design mechanical metamaterials tailored for energy focusing,
energy splitting, dynamic protection, and nonlinear motion conversion.
Furthermore, we illustrate that our design framework can be expanded to
automatically discover reprogrammable architectures capable of switching
between different dynamic tasks. For instance, we encode two strongly competing
tasks -- energy focusing and dynamic protection -- within a single
architecture, utilizing static pre-compression to switch between these
behaviors. The discovered designs are physically realized and experimentally
tested, demonstrating the robustness of the engineered tasks. All together, our
approach opens an untapped avenue towards designer materials with tailored
robotic-like reprogrammable functionalities.


  The Mullins-Sekerka problem is numerically solved in $\mathbb{R}^2$ with the
aid of the charge simulation method. This is an expansion of the numerical
scheme by which Sakakibara and Yazaki computed the Hele-Shaw flow. We
investigate a sufficient condition for the number of collocation points to
ensure that the length of the generated approximate polygonal curves gradually
decreases. We propose a new benchmark function for the Mullins-Sekerka flow to
confirm that the scheme works well. Moreover, by changing the fundamental
solutions of the charge simulation method, we are successful to establish a
numerical scheme that can be used to treat the Mullins-Sekerka problem with the
contact angle condition.


  We calculate the fraction of B^8 solar neutrinos that arrive at the Earth as
a nu_2 mass eigenstate as a function of the neutrino energy. Weighting this
fraction with the B^8 neutrino energy spectrum and the energy dependence of the
cross section for the charged current interaction on deuteron with a threshold
on the kinetic energy of the recoil electrons of 5.5 MeV, we find that the
integrated weighted fraction of nu_2's to be 91 \pm 2 % at the 95% CL. This
energy weighting procedure corresponds to the charged current response of the
Sudbury Neutrino Observatory (SNO). We have used SNO's current best fit values
for the solar mass squared difference and the mixing angle, obtained by
combining the data from all solar neutrino experiments and the reactor data
from KamLAND. The uncertainty on the nu_2 fraction comes primarily from the
uncertainty on the solar delta m^2 rather than from the uncertainty on the
solar mixing angle or the Standard Solar Model. Similar results for the
Super-Kamiokande experiment are also given. We extend this analysis to three
neutrinos and discuss how to extract the modulus of the Maki-Nakagawa-Sakata
mixing matrix element U_{e2} as well as place a lower bound on the electron
number density in the solar B^8 neutrino production region.


  This paper studies the task of Visual Question Answering (VQA), which is
topical in Multimedia community recently. Particularly, we explore two critical
research problems existed in VQA: (1) efficiently fusing the visual and textual
modalities; (2) enabling the visual reasoning ability of VQA models in
answering complex questions. To address these challenging problems, a novel
Question Guided Modular Routing Networks (QGMRN) has been proposed in this
paper. Particularly, The QGMRN is composed of visual, textual and routing
network. The visual and textual network serve as the backbones for the generic
feature extractors of visual and textual modalities. QGMRN can fuse the visual
and textual modalities at multiple semantic levels. Typically, the visual
reasoning is facilitated by the routing network in a discrete and stochastic
way by using Gumbel-Softmax trick for module selection. When the input reaches
a certain modular layer, routing network newly proposed in this paper,
dynamically selects a portion of modules from that layer to process the input
depending on the question features generated by the textual network. It can
also learn to reason by routing between the generic modules without additional
supervision information or expert knowledge. Benefiting from the dynamic
routing mechanism, QGMRN can outperform the previous classical VQA methods by a
large margin and achieve the competitive results against the state-of-the-art
methods. Furthermore, attention mechanism is integrated into our QGMRN model
and thus can further boost the model performance. Empirically, extensive
experiments on the CLEVR and CLEVR-Humans datasets validate the effectiveness
of our proposed model, and the state-of-the-art performance has been achieved.


  The antiferromagnetic semiconductor CuFeS$_2$ belongs to a magnetic symmetry
class that is of interest for spintronics applications. In addition, its
crystal lattice is compatible with Si, making it possible to integrate it with
non-magnetic semiconducting structures. Therefore, we investigate this material
by finding the effective $\boldsymbol{k}\cdot\boldsymbol{p}$ Hamiltonian for
the electron- and hole bands. We base this description on \textit{ab initio}
calculations and classify the electronic bands by their symmetry. As a result,
we find that CuFeS$_2$ exhibits spin-polarized bands and an anomalous Hall
effect. Finally, we suggest using cyclotron resonance to verify our proposed
effective mass tensors at the conduction band minimum and valence band maximum.


  In these proceedings, we report on recent results related to vector
boson-tagged jet production in heavy ion collisions and the related
modification of jet substructure, such as jet shapes and jet momentum sharing
distributions. $Z^0$-tagging and $\gamma$-tagging of jets provides new
opportunities to study parton shower formation and propagation in the
quark-gluon plasma and has been argued to provide tight constrains on the
energy loss of reconstructed jets. We present theoretical predictions for
isolated photon-tagged and electroweak boson-tagged jet production in Pb+Pb
collisions at $\sqrt{s_{NN}} = 5.02$ TeV at the LHC, addressing the
modification of their transverse momentum and transverse momentum imbalance
distributions. Comparison to recent ATLAS and CMS experimental measurements is
performed that can shed light on the medium-induced radiative corrections and
energy dissipation due to collisional processes of predominantly
quark-initiated jets. The modification of parton splitting functions in the QGP
further implies that the substructure of jets in heavy ion collisions may
differ significantly from the corresponding substructure in proton-proton
collisions. Two such observables and the implication of tagging on their
evaluation is also discussed.


  With the progress of information technology, large amounts of asymmetric,
leptokurtic and heavy-tailed data are arising in various fields, such as
finance, engineering, genetics and medicine. It is very challenging to model
those kinds of data, especially for extremely skewed data, accompanied by very
high kurtosis or heavy tails. In this paper, we propose a class of novel skewed
generalized t distribution (SkeGTD) as a scale mixture of skewed generalized
normal. The proposed SkeGTD has excellent adaptiveness to various data, because
of its capability of allowing for a large range of skewness and kurtosis and
its compatibility of the separated location, scale, skewness and shape
parameters. We investigate some important properties of this family of
distributions. The maximum likelihood estimation, L-moments estimation and
two-step estimation for the SkeGTD are explored. To illustrate the usefulness
of the proposed methodology, we present simulation studies and analyze two real
datasets.


  We study the effects of internal symmetries on the decay by bubble nucleation
of a metastable false vacuum. The zero modes about the bounce solution that are
associated with the breaking of continuous internal symmetries result in an
enhancement of the tunneling rate into vacua in which some of the symmetries of
the initial state are spontaneously broken. We develop a general formalism for
evaluating the effects of these zero modes on the bubble nucleation rate in
both flat and curved space-times.


  A new emerging paradigm of Uncertain Risk of Suspicion, Threat and Danger,
observed across the field of information security, is described. Based on this
paradigm a novel approach to anomaly detection is presented. Our approach is
based on a simple yet powerful analogy from the innate part of the human immune
system, the Toll-Like Receptors. We argue that such receptors incorporated as
part of an anomaly detector enhance the detector's ability to distinguish
normal and anomalous behaviour. In addition we propose that Toll-Like Receptors
enable the classification of detected anomalies based on the types of attacks
that perpetrate the anomalous behaviour. Classification of such type is either
missing in existing literature or is not fit for the purpose of reducing the
burden of an administrator of an intrusion detection system. For our model to
work, we propose the creation of a taxonomy of the digital Acytota, based on
which our receptors are created.


  The Standard Model (SM) has no flavor-changing neutral current (FCNC)
processes at the tree level. Therefore, processes featuring FCNC in new physics
are tightly constrained by data. Typically, the lower bounds on the scale of
new physics obtained from $K-\bar{K}$ or $B-\bar{B}$ mixing lie well above 10
TeV, surpassing the reach of current and future colliders. In this paper, we
demonstrate, using a specific Z' model, that such limits can be severely
weakened by applying certain parametrizations of the quark mixing matrices with
no prejudice while maintaining the CKM matrix in agreement with the data. We
highlight the valuable role of the often-overlooked D0 mixing in deriving
robust FCNC limits and show that the LHC and HL-LHC are promising probes for
flavor-changing interactions mediated by a Z' boson.


  It is frequently of interest to jointly analyze multiple sequences of
multiple tests in order to identify simultaneous signals, defined as features
tested in multiple studies whose test statistics are non-null in each. In many
problems, however, the null distributions of the test statistics may be
complicated or even unknown, and there do not currently exist any procedures
that can be employed in these cases. This paper proposes a new nonparametric
procedure that can identify simultaneous signals across multiple studies even
without knowing the null distributions of the test statistics. The method is
shown to asymptotically control the false discovery rate, and in simulations
had excellent power and error control. In an analysis of gene expression and
histone acetylation patterns in the brains of mice exposed to a conspecific
intruder, it identified genes that were both differentially expressed and next
to differentially accessible chromatin. The proposed method is available in the
R package github.com/sdzhao/ssa.


  The quantum speed limits (QSLs) set fundamental lower bounds on the time
required for a quantum system to evolve from a given initial state to a final
state. In this work, we investigate CP violation and the mass hierarchy problem
of neutrino oscillations in matter using the QSL time as a key analytical tool.
We examine the QSL time for the unitary evolution of two- and three-flavor
neutrino states, both in vacuum and in the presence of matter. Two-flavor
neutrino oscillations are used as a precursor to their three-flavor
counterparts. We further compute the QSL time for neutrino state evolution and
entanglement in terms of neutrino survival and oscillation probabilities, which
are experimentally measurable quantities in neutrino experiments. A difference
in the QSL time between the normal and inverted mass hierarchy scenarios, for
neutrino state evolution as well as for entanglement, under the effect of a CP
violation phase is observed. Our results are illustrated using energy-varying
sets of accelerator neutrino sources from experiments such as T2K, NOvA, and
DUNE. Notably, three-flavor neutrino oscillations in constant matter density
exhibit faster state evolution across all these neutrino experiments in the
normal mass hierarchy scenario. Additionally, we observe fast entanglement
growth in DUNE assuming a normal mass hierarchy.


  The $\sigma$ mesons may be considered as the Higgs boson of strong
interaction. While the observation of the electroweak Higgs boson is the
primary goal in ongoing experiments at the LHC, the $\sigma$ meson is by now
well studied both as an on-shell particle and as a virtual particle while being
part of the constituent quark. This makes it timely to give an overview of the
present status of the Higgs sector of strong interaction which includes the
scalar mesons $\sigma(600)$, $\kappa(800)$, $f_0(980)$ and $a_0(980)$ together
with the pseudo Goldstone bosons $\pi$, $K$ and $\eta$.


  General intelligence, the ability to solve arbitrary solvable problems, is
supposed by many to be artificially constructible. Narrow intelligence, the
ability to solve a given particularly difficult problem, has seen impressive
recent development. Notable examples include self-driving cars, Go engines,
image classifiers, and translators. Artificial General Intelligence (AGI)
presents dangers that narrow intelligence does not: if something smarter than
us across every domain were indifferent to our concerns, it would be an
existential threat to humanity, just as we threaten many species despite no ill
will. Even the theory of how to maintain the alignment of an AGI's goals with
our own has proven highly elusive. We present the first algorithm we are aware
of for asymptotically unambitious AGI, where "unambitiousness" includes not
seeking arbitrary power. Thus, we identify an exception to the Instrumental
Convergence Thesis, which is roughly that by default, an AGI would seek power,
including over us.


  In this paper, we redefine the Graph Fourier Transform (GFT) under the
DSP$_\mathrm{G}$ framework. We consider the Jordan eigenvectors of the directed
Laplacian as graph harmonics and the corresponding eigenvalues as the graph
frequencies. For this purpose, we propose a shift operator based on the
directed Laplacian of a graph. Based on our shift operator, we then define
total variation of graph signals, which is used in frequency ordering. We
achieve natural frequency ordering and interpretation via the proposed
definition of GFT. Moreover, we show that our proposed shift operator makes the
LSI filters under DSP$_\mathrm{G}$ to become polynomial in the directed
Laplacian.


  The landscape of high- and ultra-high-energy astrophysics has changed in the
last decade, largely due to the inflow of data collected by large-scale
cosmic-ray, gamma-ray, and neutrino observatories. At the dawn of the
multimessenger era, the interpretation of these observations within a
consistent framework is important to elucidate the open questions in this
field. CRPropa 3.2 is a Monte Carlo code for simulating the propagation of
high-energy particles in the Universe. This version represents a major leap
forward, significantly expanding the simulation framework and opening up the
possibility for many more astrophysical applications. This includes, among
others: efficient simulation of high-energy particles in diffusion-dominated
domains, self-consistent and fast modelling of electromagnetic cascades with an
extended set of channels for photon production, and studies of cosmic-ray
diffusion tensors based on updated coherent and turbulent magnetic-field
models. Furthermore, several technical updates and improvements are introduced
with the new version, such as: enhanced interpolation, targeted emission of
sources, and a new propagation algorithm (Boris push). The detailed description
of all novel features is accompanied by a discussion and a selected number of
example applications.


  We find new examples of complex surfaces with countably many non-isomorphic
algebraic structures. Here is one such example: take an elliptic curve $E$ in
$\mathbb P^2$ and blow up nine general points on $E$. Then the complement $M$
of the strict transform of $E$ in the blow-up has countably many algebraic
structures. Moreover, each algebraic structure comes from an embedding of $M$
into a blow-up of $\mathbb P^2$ in nine points lying on an elliptic curve
$F\not\simeq E$. We classify algebraic structures on $M$ using a Hopf
transform: a way of constructing a new surface by cutting out an elliptic curve
and pasting a different one. Next, we introduce the notion of an analytic
K-theory of varieties. Manipulations with the example above lead us to prove
that classes of all elliptic curves in this K-theory coincide. To put in
another way, all motivic measures on complex algebraic varieties that take
equal values on biholomorphic varieties do not distinguish elliptic curves.


  The Cartan's method of equivalence and moving coframe method has been applied
to solve the local equivalence problem for KDV-type equations under the action
of a pseudo-group of contact transformations. The structure equations, the sets
of differential invariants for symmetry groups and equivalent conditions of
these equations are found.


  We consider a quantum dot weakly tunnel coupled to superconducting
reservoirs. A finite superconducting pair amplitude can be induced on the dot
via the proximity effect. We investigate the dynamics of the induced pair
amplitude after a quench and under periodic driving of the system by means of a
real-time diagrammatic approach. We find that the quench dynamics is dominated
by an exponential decay towards equilibrium In contrast, the periodically
driven system can sustain coherent oscillations of both the amplitude and the
phase of the induced pair amplitude in analogy to Higgs and Nambu-Goldstone
modes in driven bulk superconductors.


  The vortical susceptibility of chiral condensate is derived in two ways.
First we obtain this using the spectrum of the Dirac operator in a metric of a
curved space-time, which corresponds to rotation. Second we come to the
expression for the susceptibility via the holographic approach to QCD.


  For a slowly rotating non-magnetized white dwarf the accretion disk extends
all the way to the star. Here the matter impacts and spreads towards the poles
as new matter continuously piles up behind it. We have solved the 3d
compressible Navier-Stokes equations on an axisymmetric grid to determine the
structure of this boundary layer for different viscosities corresponding to
different accretion rates. The high viscosity cases show a spreading BL which
sets off a gravity wave in the surface matter. The accretion flow moves
supersonically over the cusp making it susceptible to the rapid development of
gravity wave and/or Kelvin-Helmholtz instabilities. This BL is optically thick
and extends more than 30 degrees to either side of the disk plane after 3/4 of
a Keplerian rotation period (t=19s). The low viscosity cases also show a
spreading BL, but here the accretion flow does not set off gravity waves and it
is optically thin.


  We consider a project that consists of activities to be performed in parallel
under various temporal constraints, which include start-start, start-finish and
finish-start precedence relationships, release times, deadlines, and due dates.
Scheduling problems are formulated to find optimal schedules for the project
with respect to different objective functions to be minimized, such as the
project makespan, the maximum deviation from the due dates, the maximum
flow-time, and the maximum deviation of finish times. We represent these
problems as optimization problems in terms of tropical mathematics, and then
solve them by applying direct solution methods of tropical optimization. As a
result, new direct solutions of the scheduling problems are obtained in a
compact vector form, which is ready for further analysis and practical
implementation. The solutions are illustrated by simple numerical examples.


  BES II data for J/Psi->K*(890)Kpi reveal a strong kappa peak in the Kpi
S-wave near threshold. Both magnitude and phase are determined in slices of Kpi
mass by interferences with strong Ko(1430), K1(1270) and K1(1400) signals. The
phase variation with mass agrees within errors with LASS data for Kpi elastic
scattering. A combined fit is presented to both BES and LASS data. The fit uses
a Breit-Wigner amplitude with an s-dependent width containing an Adler zero.
The kappa pole is at 760+-20(stat)+-40(syst) - i(420+-45+-60syst) MeV. The
S-wave I=0 scattering length a_0 = 0.23+-0.04 (in units of m(pi)) is close to
the prediction 0.19+-0.02 of Chiral Perturbation Theory.


  A straightforward relationship between the two approaches to 3-dimensional
topological invariants, one of them put forward by Witten in the framework of
topological quantum field theory, and the second one proposed by Kohno in terms
of rational conformal field theory, is established.


  We consider adaptations of the Mumford-Shah functional to graphs. These are
based on discretizations of nonlocal approximations to the Mumford-Shah
functional. Motivated by applications in machine learning we study the random
geometric graphs associated to random samples of a measure. We establish the
conditions on the graph constructions under which the minimizers of graph
Mumford-Shah functionals converge to a minimizer of a continuum Mumford-Shah
functional. Furthermore we explicitly identify the limiting functional.
Moreover we describe an efficient algorithm for computing the approximate
minimizers of the graph Mumford-Shah functional.


  We propose a framework that elucidates the input-output characteristics of
flows with complex dynamics arising from nonlinear interactions between
different time scales. More specifically, we consider a periodically
time-varying base flow, and perform a frequency-domain analysis of periodic
perturbations about this base flow; the response of these perturbations is
governed by the harmonic resolvent, which is a linear operator similar to the
harmonic transfer function introduced by Wereley (1991). This approach makes it
possible to explicitly capture the triadic interactions that are responsible
for the energy transfer between different time scales in the flow. For
instance, perturbations at frequency $\alpha$ are coupled with perturbations at
frequency $\omega$ through the base flow at frequency $\omega-\alpha$. We draw
a connection with resolvent analsyis, which is a special case of the harmonic
resolvent when evaluated about a steady base flow. We show that the left and
right singular vectors of the harmonic resolvent are the optimal response and
forcing modes, which can be understood as full spatio-temporal signals that
reveal space-time amplification characteristics of the flow. We illustrate the
method on examples, including a three-dimensional system of ordinary
differential equations and the flow over an airfoil at near-stall angle of
attack.


  There is an ever growing need to ensure the quality of food and assess
specific quality parameters in all the links of the food chain, ranging from
processing, distribution and retail to preparing food. Various imaging and
sensing technologies, including X-ray imaging, ultrasound, and near infrared
reflectance spectroscopy have been applied to the problem. Cost and other
constraints restrict the application of some of these technologies. In this
study we test a novel Multiplexing Electric Field Sensor (MEFS), an approach
that allows for a completely non-invasive and non-destructive testing approach.
Our experiments demonstrate the reliable detection of certain foreign objects
and provide evidence that this sensor technology has the capability of
measuring fat content in minced meat. Given the fact that this technology can
already be deployed at very low cost, low maintenance and in various different
form factors, we conclude that this type of MEFS is an extremely promising
technology for addressing specific food quality issues.


  We propose new confinement structures similar to heterostructures and
superlattices. The new heteostructures can be obtained by applying strain to a
single material in a periodic or aperiodic way. The conversion of an indirect
gap into an optical active direct or quasi-direct gap problem has also been
investigated together with the role of zone folding in this phenomenon.


  Zigzag edges of the honeycomb structure of graphene exhibit magnetic
polarization making them attractive as building blocks for spintronic devices.
Here, we show that devices with zigzag edged triangular antidots perform
essential spintronic functionalities, such as spatial spin-splitting or spin
filtering of unpolarized incoming currents. Near-perfect performance can be
obtained with optimized structures. The device performance is robust against
substantial disorder. The gate-voltage dependence of transverse resistance is
qualitatively different for spin-polarized and spin-unpolarized devices, and
can be used as a diagnostic tool. Importantly, the suggested devices are
feasible within current technologies.


  We show that on every Spin(7) manifold there always exists a unique linear
connection with totally skew-symmetric torsion preserving a nontrivial spinor
and the Spin(7) structure. We express its torsion and the Riemannian scalar
curvature in terms of the fundamental 4-form. We present an explicit formula
for the Riemannian covariant derivative of the fundamental 4-form in terms of
its exterior differential. We show the vanishing of the (\hat)-A genus and
obtain a linear relation between Betti numbers of a compact Spin(7) manifolds
which are locally but not globally conformally equivalent to a space with
closed fundamental 4-form. A general solution to the Killing spinor equations
is presented


  In this lecture we show that the study of hadronic resonances is severely
hampered by the lack of accurate data and, moreover, that for similar reason
the study of Weak substructure does not make sufficient progress. We
furthermore report on an unexplained high statistics signal that may indicate
the existence of a superlight scalar boson.


  We present a novel method to determine eccentricity constraints of extrasolar
planets in systems with multiple transiting planets through photometry alone.
Our method is highly model independent, making no assumptions about the stellar
parameters and requiring no radial velocity, transit timing or occultation
events. Our technique exploits the fact the light curve derived stellar density
must be the same for all planets transiting a common star. Assuming a circular
orbit, the derived stellar density departs from the true value by a predictable
factor, Psi, which contains information on the eccentricity of the system. By
comparing multiple stellar densities, any differences must be due to
eccentricity and thus meaningful constraints can be placed in the absence of
any other information. The technique, dubbed "Multibody Asterodensity
Profiling" (MAP), is a new observable which can be used alone or in combination
with other observables, such as radial velocities and transit timing
variations. An eccentricity prior can also be included as desired. MAP is most
sensitive to the minimum pair-combined eccentricity e.g. (e1 + e2)[min].
Individual eccentricity constraints are less stringent but an empirical
eccentricity posterior is always derivable and is freely available from transit
photometry alone. We present a description of our technique using both analytic
and numerical implementations, followed by two example analyses on synthetic
photometry as a proof of principle. We point out that MAP has the potential to
constrain the eccentricity, and thus habitability, of Earth-like planets in the
absence of radial velocity data, which is likely for terrestrial-mass objects.


  $^{12}CO(J=1 \to 0)$ observations of 34 blue compact and star burst galaxies
are presented. Although these galaxies are experiencing vigorous star formation
at the current epoch, CO has been detected in only five of them. The five
detections reported in this paper are all in galaxies with relatively red
colours, (B-V)_0 > 0.4.
  The new observations, when combined with previously published data on CO in
BCGs, indicate that CO luminosity decreases with absolute luminosity of BCGs.
Since the absolute luminosity of a galaxy is correlated with its metallicity,
these results confirm that low metallicity BCGs have low abundances of CO gas.
We also show that the star formation rate determined from the $H_{\beta}$
luminosity is lower than that determined from the far infrared luminosity.


  A search for a light charged Higgs boson produced in decays of the top quark,
$t \to H^\pm b$ with $H^\pm \to cs$, is presented. This search targets the
production of top-quark pairs $t\bar{t} \to Wb H^\pm b$, with $W \to \ell\nu$
($\ell = e, \mu$), resulting in a lepton-plus-jets final state characterised by
an isolated electron or muon and at least four jets. The search exploits
$b$-quark and $c$-quark identification techniques as well as multivariate
methods to suppress the dominant $t\bar{t}$ background. The data analysed
correspond to 140 $\text{fb}^{-1}$ of $pp$ collisions at $\sqrt{s} = 13$ TeV
recorded with the ATLAS detector at the LHC between 2015 and 2018. Observed
(expected) 95% confidence-level upper limits on the branching fraction
$\mathscr{B}(t\to H^\pm b)$, assuming $\mathscr{B}(t\to Wb) + \mathscr{B}(t \to
H^\pm (\to cs)b)=1.0$, are set between 0.066% (0.077%) and 3.6% (2.3%) for a
charged Higgs boson with a mass between 60 GeV and 168 GeV.


  We rigorously justify the bilayer shallow-water system as an approximation to
the hydrostatic Euler equations in situations where the flow is
density-stratified with close-to-piecewise constant density profiles, and
close-to-columnar velocity profiles. Our theory accommodates with continuous
stratification, so that admissible deviations from bilayer profiles are not
pointwise small. This leads us to define refined approximate solutions that are
able to describe at first order the flow in the pycnocline. Because the
hydrostatic Euler equations are not known to enjoy suitable stability
estimates, we rely on thickness-diffusivity contributions proposed by Gent and
McWilliams. Our strategy also applies to one-layer and multilayer frameworks.


  We find an intriguing relation between neutrino and charged fermion masses,
$|m_{\nu_3^{}}^2- m_{\nu_1^{}}^2| : (m_{\nu_2^{}}^2- m_{\nu_1^{}}^2):: V_{tb}^4
m_\tau^2 m_b^2/m_t^2 : V_{cs}^4 m_\mu^2 m_s^2/m_c^2$. We further indicate this
relation can be predicted by a left-right symmetric model.


  In this paper, using exclusively homotopy theoretical methods, we study
degrees of maps between $(n-2)$-connected $(2n-1)$-dimensional Poincar\' e
complexes which have torsion free integral homology. Necessary and sufficient
algebraic conditions for the existence of map degrees between such Poincar\' e
complexes are established.
  We calculate the set of all map degrees between certain two $(n-2)$-connected
$(2n-1)$-dimensional torsion free Poincar\'e complexes.
  For low $n$, using knowledge of possible degrees of self maps, we classify,
up to homotopy, torsion free $(n-2)$-connected $(2n-1)$-dimensional Poincar\' e
complexes.


  Recent modeling of hydrogen-rich Type II supernova (SN II) light curves
suggests the presence of dense circumstellar material (CSM) surrounding the
exploding progenitor stars. This has important implications for the activity
and structure of massive stars near the end of their lives. Since previous work
focused on just a few events, here we expand to a larger sample of twenty
well-observed SNe II. For each event we are able to constrain the progenitor
zero-age main-sequence (ZAMS) mass, explosion energy, and the mass and radial
extent of dense CSM. We then study the distribution of each of these properties
across the full sample of SNe. The inferred ZAMS masses are found to be largely
consistent with a Salpeter distribution with minimum and maximum masses of 10.4
and 22.9 Msun, respectively. We also compare the individual ZAMS masses we
measure with specific SNe II that have pre-explosion imaging to check their
consistency. Our masses are generally comparable to or larger than the
pre-explosion imaging masses, potentially helping ease the red supergiant
problem. The explosion energies vary from (0.1-1.3)x10^51 erg, and for ~70% of
the SNe we obtain CSM masses in the range between 0.18-0.83 Msun. We see a
potential correlation between the CSM mass and explosion energy, which suggests
that pre-explosion activity has a strong impact on the structure of the star.
This may be important to take into account in future studies of the ability of
the neutrino mechanism to explode stars. We also see a possible correlation
between the CSM's radial extent and ZAMS mass, which could be related to the
time with respect to explosion when the CSM is first generated.


  We show that the matrix embeddings in Bratteli diagrams are iterated direct
sums of Hopf-Galois extensions (quantum principle bundles) for certain abelian
groups. The corresponding strong universal connections are computed. We show
that $ M_{n}(\mathbb{C})$ is a trivial quantum principle bundle for the Hopf
algebra $ \mathbb{C}[\mathbb{Z}_{n} \times \mathbb{Z}_{n}] $. We conclude with
an application relating known calculi on groups to calculi on matrices.


  We investigate the density of compactly supported smooth functions in the
Sobolev space $W^{k,p}$ on complete Riemannian manifolds. In the first part of
the paper, we extend to the full range $p\in [1,2]$ the most general results
known in the Hilbertian case. In particular, we obtain the density under a
quadratic Ricci lower bound (when $k=2$) or a suitably controlled growth of the
derivatives of the Riemann curvature tensor only up to order $k-3$ (when
$k>2$). To this end, we prove a gradient regularity lemma that might be of
independent interest. In the second part of the paper, for every $n \ge 2$ and
$p>2$ we construct a complete $n$-dimensional manifold with sectional curvature
bounded from below by a negative constant, for which the density property in
$W^{k,p}$ does not hold for any $k \ge 2$. We also deduce the existence of a
counterexample to the validity of the Calder\'on-Zygmund inequality for $p>2$
when $\mathrm{Sec} \ge 0$, and in the compact setting we show the impossibility
to build a Calder\'on-Zygmund theory for $p>2$ with constants only depending on
a bound on the diameter and a lower bound on the sectional curvature.


  The observed 21-cm signal from the epoch of reionization will be distorted
along the line-of-sight by the peculiar velocities of matter particles. These
redshift-space distortions will affect the contrast in the signal and will also
make it anisotropic. This anisotropy contains information about the
cross-correlation between the matter density field and the neutral hydrogen
field, and could thus potentially be used to extract information about the
sources of reionization. In this paper, we study a collection of simulated
reionization scenarios assuming different models for the sources of
reionization. We show that the 21-cm anisotropy is best measured by the
quadrupole moment of the power spectrum. We find that, unless the properties of
the reionization sources are extreme in some way, the quadrupole moment evolves
very predictably as a function of global neutral fraction. This predictability
implies that redshift-space distortions are not a very sensitive tool for
distinguishing between reionization sources. However, the quadrupole moment can
be used as a model-independent probe for constraining the reionization history.
We show that such measurements can be done to some extent by first-generation
instruments such as LOFAR, while the SKA should be able to measure the
reionization history using the quadrupole moment of the power spectrum to great
accuracy.


  Femtosecond pulsed lasers have been widely used over the past decades due to
their capability to fabricate precise patterns at the micro- and nano- lengths
scales. A key issue for efficient material processing is the determination of
the laser parameters used in the experimental set ups. Despite a systematic
investigation that has been performed to highlight the impact of every
parameter independently, little attention has been drawn on the role of the
substrate material on which the irradiated solid is placed. In this work, the
influence of the substrate is emphasised for films of various thicknesses which
demonstrates that both the optical and thermophysical properties of the
substrate affect the thermal fingerprint on the irradiated film while the
impact is manifested to be higher at smaller film sizes. Two representative
materials, silicon and fused silica have been selected as typical substrates
for thin films of different optical and thermophysical behaviour (gold and
nickel) and the thermal response and damage thresholds are evaluated for the
irradiated solids. The pronounced influence of the substrate is aimed to pave
the way for new and more optimised designs of laser-based fabrication set ups
and processing schemes.


  We develop an effective spin theory to describe magnetic properties of the
$\nu=2$ Quantum Hall bilayer systems. In the absence of disorder this theory
gives quantitative agreement with the results of microscopic Hartree-Fock
calculations, and for finite disorder it predicts the existence of a novel spin
Bose glass phase. The Bose glass is characterized by the presence of domains of
canted antiferromagnetic phase with zero average antiferromagnetic order and
short range mean antiferromagnetic correlations. It has infinite
antiferromagnetic transverse susceptibility, finite longitudinal spin
susceptibility and specific heat linear in temperature. Transition from the
canted antiferromagnet phase to the spin Bose glass phase is characterized by a
universal value of the longitudinal spin conductance.


  Understanding how language and linguistic constructions are processed in the
brain is a fundamental question in cognitive computational neuroscience. In
this study, we explore the representation and processing of Argument Structure
Constructions (ASCs) in a recurrent neural language model. We trained a Long
Short-Term Memory (LSTM) network on a custom-made dataset consisting of 2000
sentences, generated using GPT-4, representing four distinct ASCs: transitive,
ditransitive, caused-motion, and resultative constructions.
  We analyzed the internal activations of the LSTM model's hidden layers using
Multidimensional Scaling (MDS) and t-Distributed Stochastic Neighbor Embedding
(t-SNE) to visualize the sentence representations. The Generalized
Discrimination Value (GDV) was calculated to quantify the degree of clustering
within these representations. Our results show that sentence representations
form distinct clusters corresponding to the four ASCs across all hidden layers,
with the most pronounced clustering observed in the last hidden layer before
the output layer. This indicates that even a relatively simple,
brain-constrained recurrent neural network can effectively differentiate
between various construction types.
  These findings are consistent with previous studies demonstrating the
emergence of word class and syntax rule representations in recurrent language
models trained on next word prediction tasks. In future work, we aim to
validate these results using larger language models and compare them with
neuroimaging data obtained during continuous speech perception. This study
highlights the potential of recurrent neural language models to mirror
linguistic processing in the human brain, providing valuable insights into the
computational and neural mechanisms underlying language understanding.


  Recent progress in music generation has been remarkably advanced by the
state-of-the-art MusicLM, which comprises a hierarchy of three LMs,
respectively, for semantic, coarse acoustic, and fine acoustic modelings. Yet,
sampling with the MusicLM requires processing through these LMs one by one to
obtain the fine-grained acoustic tokens, making it computationally expensive
and prohibitive for a real-time generation. Efficient music generation with a
quality on par with MusicLM remains a significant challenge. In this paper, we
present MeLoDy (M for music; L for LM; D for diffusion), an LM-guided diffusion
model that generates music audios of state-of-the-art quality meanwhile
reducing 95.7% or 99.6% forward passes in MusicLM, respectively, for sampling
10s or 30s music. MeLoDy inherits the highest-level LM from MusicLM for
semantic modeling, and applies a novel dual-path diffusion (DPD) model and an
audio VAE-GAN to efficiently decode the conditioning semantic tokens into
waveform. DPD is proposed to simultaneously model the coarse and fine acoustics
by incorporating the semantic information into segments of latents effectively
via cross-attention at each denoising step. Our experimental results suggest
the superiority of MeLoDy, not only in its practical advantages on sampling
speed and infinitely continuable generation, but also in its state-of-the-art
musicality, audio quality, and text correlation.
  Our samples are available at https://Efficient-MeLoDy.github.io/.


  The emerging field of quantum computing has shown it might change how we
process information by using the unique principles of quantum mechanics. As
researchers continue to push the boundaries of quantum technologies to
unprecedented levels, distributed quantum computing raises as an obvious path
to explore with the aim of boosting the computational power of current quantum
systems. This paper presents a comprehensive survey of the current state of the
art in the distributed quantum computing field, exploring its foundational
principles, landscape of achievements, challenges, and promising directions for
further research. From quantum communication protocols to entanglement-based
distributed algorithms, each aspect contributes to the mosaic of distributed
quantum computing, making it an attractive approach to address the limitations
of classical computing. Our objective is to provide an exhaustive overview for
experienced researchers and field newcomers.


  We determine the solvent mediated contribution to the effective potentials
for model colloidal or nano- particles dispersed in a binary solvent that
exhibits fluid-fluid phase separation. Using a simple density functional theory
we calculate the density profiles of both solvent species in the presence of
the `colloids', which are treated as external potentials, and determine the
solvent mediated (SM) potentials. Specifically, we calculate SM potentials
between (i) two colloids, (ii) a colloid and a planar fluid-fluid interface,
and (iii) a colloid and a planar wall with an adsorbed wetting film. We
consider three different types of colloidal particles: colloid A which prefers
the bulk solvent phase rich in species 2, colloid C which prefers the solvent
phase rich in species 1, and `neutral' colloid B which has no strong preference
for either phase, i.e. the free energies to insert the colloid into either of
the coexisting bulk phases are almost equal. When a colloid which has a
preference for one of the two solvent phases is inserted into the disfavored
phase at statepoints close to coexistence a thick adsorbed `wetting' film of
the preferred phase may form around the colloids. The presence of the adsorbed
film has a profound influence on the form of the SM potentials.


  Graph-structured data arise ubiquitously in many application domains. A
fundamental problem is to quantify their similarities. Graph kernels are often
used for this purpose, which decompose graphs into substructures and compare
these substructures. However, most of the existing graph kernels do not have
the property of scale-adaptivity, i.e., they cannot compare graphs at multiple
levels of granularities. Many real-world graphs such as molecules exhibit
structure at varying levels of granularities. To tackle this problem, we
propose a new graph kernel called Tree++ in this paper. At the heart of Tree++
is a graph kernel called the path-pattern graph kernel. The path-pattern graph
kernel first builds a truncated BFS tree rooted at each vertex and then uses
paths from the root to every vertex in the truncated BFS tree as features to
represent graphs. The path-pattern graph kernel can only capture graph
similarity at fine granularities. In order to capture graph similarity at
coarse granularities, we incorporate a new concept called super path into it.
The super path contains truncated BFS trees rooted at the vertices in a path.
Our evaluation on a variety of real-world graphs demonstrates that Tree++
achieves the best classification accuracy compared with previous graph kernels.


  We give an algebraic characterization of the tree languages that are defined
by logical formulas using certain Lindstr\"om quantifiers. An important
instance of our result concerns first-order definable tree languages. Our
characterization relies on the usage of preclones, an algebraic structure
introduced by the authors in a previous paper, and of the block product
operation on preclones. Our results generalize analogous results on finite word
languages, but it must be noted that, as they stand, they do not yield an
algorithm to decide whether a given regular tree language is first-order
definable.


  A direct orbital optimization method is presented for density functional
calculations of excited electronic states using either a real space grid or a
plane wave basis set. The method is variational, provides atomic forces in the
excited states, and can be applied to Kohn-Sham (KS) functionals as well as
orbital-density dependent functionals (ODD) including explicit self-interaction
correction. The implementation for KS functionals involves two nested loops:
(1) An inner loop for finding a stationary point in a subspace spanned by the
occupied and a few virtual orbitals corresponding to the excited state; (2) an
outer loop for minimizing the energy in a tangential direction in the space of
the orbitals. For ODD functionals, a third loop is used to find the unitary
transformation that minimizes the energy functional among occupied orbitals
only. Combined with the maximum overlap method, the algorithm converges in
challenging cases where conventional self-consistent field algorithms tend to
fail. The benchmark tests presented include two charge-transfer excitations in
nitrobenzene and an excitation of CO to degenerate $\pi^\ast$ orbitals where
the importance of complex orbitals is illustrated. An application of the method
to several metal-to-ligand charge-transfer and metal-centred excited states of
an Fe$^{\rm II}$ photosensitizer complex is described and the results compared
to reported experimental estimates. The method is also used to study the effect
of Perdew-Zunger self-interaction correction on valence and Rydberg excited
states of several molecules, both singlet and triplet states, and the
performance compared to semilocal and hybrid functionals.


  The optimization of the electrode manufacturing process is important for
upscaling the application of Lithium Ion Batteries (LIBs) to cater for growing
energy demand. In particular, LIB manufacturing is very important to be
optimized because it determines the practical performance of the cells when the
latter are being used in applications such as electric vehicles. In this study,
we tackled the issue of high-performance electrodes for desired battery
application conditions by proposing a powerful data-driven approach supported
by a deterministic machine learning (ML)-assisted pipeline for bi-objective
optimization of the electrochemical performance. This ML pipeline allows the
inverse design of the process parameters to adopt in order to manufacture
electrodes for energy or power applications. The latter work is an analogy to
our previous work that supported the optimization of the electrode
microstructures for kinetic, ionic, and electronic transport properties
improvement. An electrochemical pseudo-two-dimensional model is fed with the
electrode properties characterizing the electrode microstructures generated by
manufacturing simulations and used to simulate the electrochemical
performances. Secondly, the resulting dataset was used to train a deterministic
ML model to implement fast bi-objective optimizations to identify optimal
electrodes. Our results suggested a high amount of active material, combined
with intermediate values of solid content in the slurry and calendering degree,
to achieve the optimal electrodes.


  We prove compactness of a restricted set of real-valued, compactly supported
potentials $V$ for which the corresponding Schr\"odinger operators $H_V$ have
the same resonances, including multiplicities. More specifically, let $B_R(0)$
be the ball of radius $R > 0$ about the origin in $R^d$, for $d=1,3$. Let
$\mathcal{I}_R (V_0)$ be the set of real-valued potentials in $C_0^\infty(
\overline{B}_R(0); R)$ so that the corresponding Schr\"odinger operators have
the same resonances, including multiplicities, as $H_{V_0}$. We prove that the
set $\mathcal{I}_R (V_0)$ is a compact subset of $C_0^\infty
(\overline{B}_R(0))$ in the $C^\infty$-topology. An extension to Sobolev spaces
of less regular potentials is discussed.


  We study surjective (not necessarily regular) rational endomorphisms $f$ of
smooth del Pezzo surfaces $X$. We prove that $f$ can have degree bigger than
$1$ only when $(-K_X^2) > 5$. Some structural properties of $f$ in the case $X
= \mathbb{P}^2$ are also established.


  Let P and Q be relatively prime integers greater than 1, and f a real valued
discretely supported function on a finite dimensional real vector space V. We
prove that if f_{P}(x)=f(Px)-f(x) and f_{Q}(x)=f(Qx)-f(x) are both
\Lambda-periodic for some lattice \Lambda\subset V, then so is f (up to a
modification at 0). This result is used to prove a theorem on the arithmetic of
elliptic function fields. In the last section we discuss the higher rank
analogue of this theorem and explain why it fails in rank 2. A full discussion
of the higher rank case will appear in a forthcoming work.


  We consider ringed accretion disks (RADs), representing models of aggregates
of corotating and counterrotating toroids orbiting a central Kerr super-massive
black hole (SMBH). We comment on system of two-tori governed by the polytropic
equation of state and including a toroidal magnetic field. We found the RADs
leading function describing the RAD inner structure and governing the
distribution of orbiting toroidal structures and the emergence of the
(hydro-mechanical) instabilities in the disk. We perform this analysis first in
pure hydrodynamical models by considering one-specie perfect fluid toroids and
then by considering the contribution of toroidal magnetic field.


  Using a recently developed effective field theory for the interactions of
nucleons at non-relativistic energies, we calculate the Coulomb corrections to
proton-proton scattering. Including the dimension-eight derivative interaction
in PDS regularization scheme, we obtain a modified Jackson-Blatt relation for
the scattering lengths which is found to be phenomenologically satisfactory.
The effective range is not modified by Coulomb effects to this order in the
calculation.


  As part of the Spaghetti Project Survey (SPS) we have detected a
concentration of giant stars well above expectations for a smooth halo model.
The position (l~350, b~50) and distance (~50 kpc) of this concentration match
those of the Northern over-density detected by SDSS (Yanny et al. 2000, Ivezic
et al. 2000). We find additional evidence for structure at ~80 kpc in the same
direction. We present radial velocities for many of these stars, including the
first published results from the 6.5m Magellan telescope. The radial velocities
for stars in these structures are in excellent agreement with models of the
dynamical evolution of the Sgr dwarf tidal debris, whose center is 60 degrees
away. The metallicity of stars in these streams is lower than that of the main
body of the Sgr dwarf, which may indicate a radial metallicity gradient prior
to disruption.


  The emergence of low-cost sensor architectures for diverse modalities has
made it possible to deploy sensor arrays that capture a single event from a
large number of vantage points and using multiple modalities. In many
scenarios, these sensors acquire very high-dimensional data such as audio
signals, images, and video. To cope with such high-dimensional data, we
typically rely on low-dimensional models. Manifold models provide a
particularly powerful model that captures the structure of high-dimensional
data when it is governed by a low-dimensional set of parameters. However, these
models do not typically take into account dependencies among multiple sensors.
We thus propose a new joint manifold framework for data ensembles that exploits
such dependencies. We show that simple algorithms can exploit the joint
manifold structure to improve their performance on standard signal processing
applications. Additionally, recent results concerning dimensionality reduction
for manifolds enable us to formulate a network-scalable data compression scheme
that uses random projections of the sensed data. This scheme efficiently fuses
the data from all sensors through the addition of such projections, regardless
of the data modalities and dimensions.


  Image restoration, which aims to retrieve and enhance degraded images, is
fundamental across a wide range of applications. While conventional deep
learning approaches have notably improved the image quality across various
tasks, they still suffer from (i) the high storage cost needed for various
task-specific models and (ii) the lack of interactivity and flexibility,
hindering their wider application. Drawing inspiration from the pronounced
success of prompts in both linguistic and visual domains, we propose novel
Prompt-In-Prompt learning for universal image restoration, named PIP. First, we
present two novel prompts, a degradation-aware prompt to encode high-level
degradation knowledge and a basic restoration prompt to provide essential
low-level information. Second, we devise a novel prompt-to-prompt interaction
module to fuse these two prompts into a universal restoration prompt. Third, we
introduce a selective prompt-to-feature interaction module to modulate the
degradation-related feature. By doing so, the resultant PIP works as a
plug-and-play module to enhance existing restoration models for universal image
restoration. Extensive experimental results demonstrate the superior
performance of PIP on multiple restoration tasks, including image denoising,
deraining, dehazing, deblurring, and low-light enhancement. Remarkably, PIP is
interpretable, flexible, efficient, and easy-to-use, showing promising
potential for real-world applications. The code is available at
https://github.com/longzilicart/pip_universal.


  In this paper, we first give the analytical solution of the general lag-lead
compensator design problem. Then, we show why a series of more than 5
phase-lead/phase-lag compensator cannot be solved analytically using the Galois
Theory.


  By conducting three-dimensional (3D) hydrodynamical simulations we find that
jets that a main sequence companion launches as it orbits inside the wind
acceleration zone of an asymptotic giant branch (AGB) star can efficiently
remove mass from that zone. We assume that during the intensive wind phase a
large fraction of the gas in the acceleration zone does not reach the escape
velocity. Therefore, in the numerical simulations we blow the wind with a
velocity just below the escape velocity. We assume that a main sequence
companion accretes mass from the slow wind via an accretion disk, and launches
two opposite jets perpendicular to the equatorial plane. This novel flow
interaction shows that by launching jets a companion outside a giant star, but
close enough to be in the acceleration zone of a slow intensive wind, can
enhance the mass loss rate from the giant by ejecting some gas that would
otherwise fall back onto the giant star. The jets are bent inside the wind
acceleration zone and eject mass in a belt on the two sides of the equatorial
plane. The jet-wind interaction contains instabilities that mix shocked jets'
gas with the wind, leading to energy transfer from the jets to the wind. As
well, our new simulations add to the rich variety of jet-induced outflow
morphologies from evolved stars.


  In genomic applications, there is often interest in identifying genes whose
time-course expression trajectories exhibit periodic oscillations with a period
of approximately 24 hours. Such genes are usually referred to as circadian, and
their identification is a crucial step toward discovering physiological
processes that are clock-controlled. It is natural to expect that the
expression of gene i at time j might depend to some degree on the expression of
the other genes measured at the same time. However, widely-used rhythmicity
detection techniques do not accommodate for the potential dependence across
genes. We develop a Bayesian approach for periodicity identification that
explicitly takes into account the complex dependence structure across
time-course trajectories in gene expressions. We employ a latent factor
representation to accommodate dependence, while representing the true
trajectories in the Fourier domain allows for inference on period, phase, and
amplitude of the signal. Identification of circadian genes is allowed through a
carefully chosen variable selection prior on the Fourier basis coefficients.
The methodology is applied to a novel mouse liver circadian dataset. Although
motivated by time-course gene expression array data, the proposed approach is
applicable to the analysis of dependent functional data at broad.


  The discovery of group IV and V elemental Xene's which exhibit topologically
non-trivial characters natively in their honeycomb lattice structure (HLS) has
led to extensive efforts in realising analogous behaviour in group VI elemental
monolayers. Although; it was theoretically concluded that group VI elemental
monolayers cannot exist as HLS but recent experimental evidence suggests
otherwise. In this letter we report that, HLS of group VI elemental monolayer
(such as, Tellurene) can be realised to be dynamically stable when
functionzalised with Oxygen. The functionalization leads to, peculiar orbital
filtering effects (OFE) and broken spatial inversion symmetry which gives rise
to the non-trivial topological character. The exotic quantum behaviour of this
system is characterized by, spin-orbit coupling induced large-gap $\approx$
0.36 eV with isolated Dirac cone along the edges indicating perspective room
temperature spin-transport applications. Further investigations of spin Hall
conductivity and the Berry curvatures unravel high conductivity as compared to
previously explored Xene's. The non-trivial topological character is quantified
in terms of the $\mathbb{Z}_2$ invaraint as $\nu =$ 1 and Chern number
$\mathit{C} =$ 1. Also, for practical purposes, we report that,
\textit{h}BN/TeO/\textit{h}BN quantum-wells can be strain engineered to realize
a sizable non-trivial gap ($\approx$ 0.11 eV). We finally conclude that,
functionalization of group VI elemental monolayer with Oxygen gives rise to,
exotic quantum properties which are robust against surface oxidation and
degradations while providing viable electronic degrees of freedom for
spintronic applications.


  In 2021, the Sombor index was introduced by Gutman, which is a new
degree-based topological molecular descriptors. The Sombor index of a graph $G$
is defined as $SO(G) =\sum_{uv\in E(G)}\sqrt{d^2_G(u)+d^2_G(v)}$, where
$d_G(v)$ is the degree of the vertex $v$ in $G$. Let $\mathscr{T}_{n,m}$ and
$\mathscr{U}_{n,m}$ be the set of trees and unicyclic graphs on $n$ vertices
with fixed matching number $m$, respectively. In this paper, the tree and the
unicyclic graph with the maximum Sombor index are determined among
$\mathscr{T}_{n,m}$ and $\mathscr{U}_{n,m}$, respectively.


  We study Gaussian random fields on certain Banach spaces and investigate
conditions for their existence. Our results apply inter alia to spaces of Radon
measures and H\"older functions. In the former case, we are able to define
Gaussian white noise on the space of measures directly, avoiding, e.g., an
embedding into a negative-order Sobolev space. In the latter case, we
demonstrate how H\"older regularity of the samples is controlled by that of the
covariance kernel and, thus, show a connection to the Theorem of
Kolmogorov-Chentsov.


  We introduce a spectrum for arbitrary varieties. This generalizes the
definition by Steenbrink for hypersurfaces. In the isolated complete
intersection singularity case, it coincides with the one given by Ebeling and
Steenbrink except for the coefficients of integral exponents. We show a
relation to the graded pieces of the multiplier ideals by using a relation to
the filtration $V$ of Kashiwara and Malgrange. This implies a partial
generalization of a theorem of Budur in the hypersurface case. The point is to
consider the direct sum of the graded pieces of the multiplier ideals as a
module over the algebra defining the normal cone of the subvariety. We also
give a combinatorial description in the case of monomial ideals.


  The proliferation of space debris in LEO has become a major concern for the
space industry. With the growing interest in space exploration, the prediction
of potential collisions between objects in orbit has become a crucial issue. It
is estimated that, in orbit, there are millions of fragments a few millimeters
in size and thousands of inoperative satellites and discarded rocket stages.
Given the high speeds that these fragments can reach, even fragments a few
millimeters in size can cause fractures in a satellite's hull or put a serious
crack in the window of a space shuttle. The conventional method proposed by
Akella and Alfriend in 2000 remains widely used to estimate the probability of
collision in short-term encounters. Given the small period of time, it is
assumed that, during the encounter: (1) trajectories are represented by
straight lines with constant velocity; (2) there is no velocity uncertainty and
the position exhibits a stationary distribution throughout the encounter; and
(3) position uncertainties are independent and represented by Gaussian
distributions. This study introduces a novel derivation based on first
principles that naturally allows for tight and fast upper and lower bounds for
the probability of collision. We tested implementations of both probability and
bound computations with the original and our formulation on a real CDM dataset
used in ESA's Collision Avoidance Challenge. Our approach reduces the
calculation of the probability to two one-dimensional integrals and has the
potential to significantly reduce the processing time compared to the
traditional method, from 80% to nearly real-time.


  Cosmological limits on neutrino masses are softened, by more than a factor 2,
if Cold Dark Matter (CDM) and Dark Energy (DE) are coupled. In turn, a neutrino
mass yielding $\Omega_\nu$ up to $\sim0.20$ allows coupling levels $\beta
\simeq 0.15, $ or more, already easing the coincidence problem. The coupling,
in fact, displaces both $P(k)$ and $C_l$ spectra in a fashion opposite to
neutrino mass. Estimates are obtained through a Fisher--matrix technique.


  To improve the capacity and coverage of current cellular networks, many
advanced technologies such as massive MIMO, inter-cell coordination, small
cells, device-to-device communications, and so on, are under studying. Many
proposed techniques have been shown to offer significant performance
improvement. Thus, the enabler of those techniques is of great importance. That
is the necessary signaling which guarantee the operation of those techniques.
The design and transmission of those signaling, especially the over-the-air
(OTA) signaling, is challenging. In this article, we provide an overview of the
OTA signaling in cellular networks to provide insights on the design of OTA
signaling. Specifically, we first give a brief introduction of the OTA
signaling in long term evolution (LTE), and then we discuss the challenges and
requirements in designing the OTA signaling in cellular networks in detail. To
better understand the OTA signaling, we give two important classifications of
OTA signaling and address their properties and applications. Finally, we
propose a signature-based signaling named (single-tone signaling, STS) which
can be used for inter-cell OTA signaling and is especially useful and robust in
multi-signal scenario. Simulation results are given to compare the detection
performance of different OTA signaling.


  Information geometry is an emergent branch of probability theory that
consists of assigning a Riemannian differential geometry structure to the space
of probability distributions. We present an information geometric investigation
of gases following the Fermi-Dirac and the Bose-Einstein quantum statistics.
For each quantum gas, we study the information geometry of the curved
statistical manifolds associated with the grand canonical ensemble. The
Fisher-Rao information metric and the scalar curvature are computed for both
fermionic and bosonic models of non-interacting particles. In particular, by
taking into account the ground state of the ideal bosonic gas in our
information geometric analysis, we find that the singular behavior of the
scalar curvature in the condensation region disappears. This is a
counterexample to a long held conjecture that curvature always diverges in
phase transitions.


  The study of Reynolds algebras has its origin in the well-known work of O.
Reynolds on fluid dynamics in 1895 and has since found broad applications. It
also has close relationship with important linear operators such as algebra
endomorphisms, derivations and Rota-Baxter operators. Many years ago
G.~Birkhoff suggested an algebraic study of Reynolds operators, including the
corresponding free algebras. We carry out such a study in this paper. We first
provide examples and properties of Reynolds operators, including a
multi-variant generalization of the Reynolds identity. We then construct the
free Reynolds algebra on a set. For this purpose, we identify a set of
bracketed words called Reynolds words which serves as the linear basis of the
free Reynolds algebra. A combinatorial interpretation of Reynolds words is
given in terms of rooted trees without super crowns. The closure of the
Reynolds words under concatenation gives the algebra structure on the space
spanned by Reynolds words. Then a linear operator is defined on this algebra
such that the Reynolds identity and the desired universal property are
satisfied.


  Plasmonic nanoparticles from unconventional materials can improve or even
bring some novel functionalities into the disciplines inherently related to
plasmonics such as photochemistry or (spectro)electrochemistry. They can, for
example, catalyze various chemical reactions or act as nanoelectrodes and
optical transducers in various applications. Silver amalgam is the perfect
example of such an unconventional plasmonic material, albeit it is well-known
in the field of electrochemistry for its wide cathodic potential window and
strong adsorption affinity of biomolecules to its surface. In this study, we
investigate in detail the optical properties of nanoparticles and
microparticles made from silver amalgam and correlate their plasmonic
resonances with their morphology. We use optical spectroscopy techniques on the
ensemble level and electron energy loss spectroscopy on the single-particle
level to demonstrate the extremely wide spectral range covered by the silver
amalgam localized plasmonic resonances, ranging from ultraviolet all the way to
the mid-infrared wavelengths. Our results establish silver amalgam as a
suitable material for introduction of plasmonic functionalities into
photochemical and spectroelectrochemical systems, where the plasmonic
enhancement of electromagnetic fields and light emission processes could
synergistically meet with the superior electrochemical characteristics of
mercury.


  Every open-system dynamics can be associated to infinitely many stochastic
pictures, called unravelings, which have proved to be extremely useful in
several contexts, both from the conceptual and the practical point of view.
Here, focusing on quantum-jump unravelings, we demonstrate that there exists
inherent freedom in how to assign the terms of the underlying master equation
to the deterministic and jump parts of the stochastic description, which leads
to a number of qualitatively different unravelings. As relevant examples, we
show that a fixed basis of post-jump states can be selected under some definite
conditions, or that the deterministic evolution can be set by a chosen
time-independent non-Hermitian Hamiltonian, even in the presence of external
driving. Our approach relies on the definition of rate operators, whose
positivity equips each unraveling with a continuous-measurement scheme and is
related to a long known but so far not widely used property to classify quantum
dynamics, known as dissipativity. Starting from formal mathematical concepts,
our results allow us to get fundamental insights into open quantum system
dynamics and to enrich their numerical simulations.


  With the recent advances in natural language processing (NLP), a vast number
of applications have emerged across various use cases. Among the plethora of
NLP applications, many academic researchers are motivated to do work that has a
positive social impact, in line with the recent initiatives of NLP for Social
Good (NLP4SG). However, it is not always obvious to researchers how their
research efforts are tackling today's big social problems. Thus, in this paper,
we introduce NLP4SG Papers, a scientific dataset with three associated tasks
that can help identify NLP4SG papers and characterize the NLP4SG landscape by:
(1) identifying the papers that address a social problem, (2) mapping them to
the corresponding UN Sustainable Development Goals (SDGs), and (3) identifying
the task they are solving and the methods they are using. Using
state-of-the-art NLP models, we address each of these tasks and use them on the
entire ACL Anthology, resulting in a visualization workspace that gives
researchers a comprehensive overview of the field of NLP4SG. Our website is
available at https://nlp4sg.vercel.app. We released our data at
https://huggingface.co/datasets/feradauto/NLP4SGPapers and code at
https://github.com/feradauto/nlp4sg


  We prove that the additive group of the rationals does not have an automatic
presentation. The proof also applies to certain other abelian groups, for
example, torsion-free groups that are $p$-divisible for infinitely many primes
$p$, or $Q/Z$. The proof is combinatorial and uses most notably Freiman's
theorem on sets with small doubling.