  Online firestorms on Twitter are seemingly arbitrarily occurring outrages
towards people, companies, media campaigns and politicians. Moral outrages can
create an excessive collective aggressiveness against one single argument, one
single word, or one action of a person resulting in hateful speech. With a
collective "against the others" the negative dynamics often start. Using data
from Twitter, we explored the starting points of several firestorm outbreaks.
As a social media platform with hundreds of millions of users interacting in
real-time on topics and events all over the world, Twitter serves as a social
sensor for online discussions and is known for quick and often emotional
disputes. The main question we pose in this article, is whether we can detect
the outbreak of a firestorm. Given 21 online firestorms on Twitter, the key
questions regarding the anomaly detection are: 1) How can we detect the
changing point? 2) How can we distinguish the features that cause a moral
outrage? In this paper we examine these challenges developing a method to
detect the point of change systematically spotting on linguistic cues of
tweets. We are able to detect outbreaks of firestorms early and precisely only
by applying linguistic cues. The results of our work can help detect negative
dynamics and may have the potential for individuals, companies, and governments
to mitigate hate in social media networks.


  We present the theory of interaction between a polar vibration in a
semiconductor and an electromagnetic mode of a surrounding cavity. Tuning the
cavity frequency near the transverse optical phonon frequency couples the
phonon-induced polarization field within the dielectric and the electromagnetic
field in the cavity. Depending on engineering parameters, we predict that this
cavity quantum electrodynamic interaction may reach the strong coupling regime.
The resonances of the system are in the terahertz spectral region, and while
spectroscopic measurements are a possible route for the detection of such a
system, we emphasize the possibility of measuring the Rabi oscillation directly
in the time domain with femtosecond optical pulses.


  Hardware-enclaves that target complex CPU designs compromise both security
and performance. Programs have little control over micro-architecture, which
leads to side-channel leaks, and then have to be transformed to have worst-case
control- and data-flow behaviors and thus incur considerable slowdown. We
propose to address these security and performance problems by bringing enclaves
into the realm of accelerator-rich architectures. The key idea is to construct
software-defined enclaves (SDEs) where the protections and slowdown are tied to
an application-defined threat model and tuned by a compiler for the
accelerator's specific domain. This vertically integrated approach requires new
hardware data-structures to partition, clear, and shape the utilization of
hardware resources; and a compiler that instantiates and schedules these
data-structures to create multi-tenant enclaves on accelerators. We demonstrate
our ideas with a comprehensive prototype -- Sesame -- that includes
modifications to compiler, ISA, and microarchitecture to a decoupled access
execute (DAE) accelerator framework for deep learning models. Our security
evaluation shows that classifiers that could distinguish different layers in
VGG, ResNet, and AlexNet, fail to do so when run using Sesame. Our
synthesizable hardware prototype (on a Xilinx Pynq board) demonstrates how the
compiler and micro-architecture enables threat-model-specific trade-offs in
code size increase ranging from 3-7 $\%$ and run-time performance overhead for
specific defenses ranging from 3.96$\%$ to 34.87$\%$ (across confidential
inputs and models and single vs. multi-tenant systems).


  For a quasi-smooth hyper-surface $X$ in a projective simplicial toric variety
$P$, the morphism $i:H^p(P) \to H^p(X)$ induced by the inclusion is injective
for $p=d$ and an isomorphism for $p<d-1$, where $d=dim\ P$. This allows one to
define the Noether-Lefschetz locus $NL_{\beta}$ as the locus of quasi-smooth
hypersurfaces of degree $\beta$ such that $i$ acting on the middle algebraic
cohomology is not an isomorphism. In this paper we prove that, under some
assumptions, if $dim P =2k+1$ and $k\beta-\beta_0=n\eta$ $(n\in\mathbb N)$,
where $\eta$ is the class of a 0-regular ample divisor, and $\beta_0$ is the
anticanonical class, then every irreducible component $V$ of the
Noether-Lefschetz locus quasi-smooth hypersurfaces of degree $\beta$ satifies
the bounds $n+1\leq codim\ V \leq h^{k-1,k+1}(X)$.


  Far-infrared vibrational spectroscopy by multiple photon dissociation has
proven to be a very useful technique for the structural fingerprinting of small
metal clusters. Contrary to previous studies on cationic V, Nb and Ta clusters,
measured vibrational spectra of small cationic cobalt clusters show a strong
dependence on the number of adsorbed Ar probe atoms, which increases with
decreasing cluster size. Focusing on the series Co_4^+ to Co_8^+ we therefore
use density-functional theory to analyze the nature of the Ar-Co_n^+ bond and
its role for the vibrational spectra. In a first step, energetically low-lying
isomer structures are identified through first-principles basin-hopping
sampling runs and their vibrational spectra computed for a varying number of
adsorbed Ar atoms. A comparison of these fingerprints with the experimental
data enables in some cases a unique assignment of the cluster structure.
Independent of the specific low-lying isomer, we obtain a pronounced increase
of the Ar binding energy for the smallest cluster sizes, which correlates
nicely with the observed increased influence of the Ar probe atoms on the IR
spectra. Further analysis of the electronic structure motivates a simple
electrostatic picture that not only explains this binding energy trend, but
also why the influence of the rare-gas atom is much stronger than in the
previously studied systems.


  A pair of coupled quantum harmonic oscillators, one subject to a gain one to
a loss, is a paradigmatic setup to implement PT-symmetric, non-Hermitian
Hamiltonians in that one such Hamiltonian governs the mean-field dynamics for
equal gain and loss strengths. Through a full quantum description (so as to
account for quantum noise) here is shown that when the system starts in any
two-mode coherent state, including vacuum, there appear quantum correlations
(QCs) without entanglement, as measured by the Gaussian discord. When the loss
rate is above a threshold, once generated QCs no more decay. This occurs in a
wide region of parameters, significantly larger than that where the full
quantum dynamics is stable. For equal gain and loss rates, in particular, QCs
decay in the exact phase (including the exceptional point) and are stable in
the broken phase.


  Probabilistic Circuits (PCs) are a general and unified computational
framework for tractable probabilistic models that support efficient computation
of various inference tasks (e.g., computing marginal probabilities). Towards
enabling such reasoning capabilities in complex real-world tasks, Liu et al.
(2022) propose to distill knowledge (through latent variable assignments) from
less tractable but more expressive deep generative models. However, it is still
unclear what factors make this distillation work well. In this paper, we
theoretically and empirically discover that the performance of a PC can exceed
that of its teacher model. Therefore, instead of performing distillation from
the most expressive deep generative model, we study what properties the teacher
model and the PC should have in order to achieve good distillation performance.
This leads to a generic algorithmic improvement as well as other
data-type-specific ones over the existing latent variable distillation
pipeline. Empirically, we outperform SoTA TPMs by a large margin on challenging
image modeling benchmarks. In particular, on ImageNet32, PCs achieve 4.06
bits-per-dimension, which is only 0.34 behind variational diffusion models
(Kingma et al., 2021).


  In many fields, including biology, medicine, physics, chemistry, economy and
actuarial science, data can be represented as the time-to-event of a finite
state Markov chain model. The distribution of time intervals between successive
recorded events is known as a phase-type distribution. We demonstrate that in
cases where the eigenvalues of the Markov chain transition matrix are distinct
(non-degenerate), the phase-type distribution is multi-exponential. We then
pose and solve an inverse problem: given knowledge of the phase-type
distribution, can we determine the transition rate parameters of the underlying
Markov chain? To tackle this challenge, we initially convert the inverse
problem into a computer algebraic task, involving the solution of a system of
polynomial equations. These equations are symmetrized with respect to the
parameters of the phase-type distribution. For a specific subset of Markov
models that we refer to as "solvable," the inverse problem yields a unique
solution, up to transformations by finite symmetries. We outline a recursive
approach to compute these solutions for specific families of models, regardless
of their number of states. Additionally, we use the Thomas decomposition
technique to calculate solutions for models possessing 2, 3, or 4 states.
Interestingly, we show that models having the same number of states but
different transition graphs can yield identical phase-distributions. In such
"Rashomon effect" scenarios, time-to-event data permits the formulation of
multiple models and interpretations, all of which are consistent with the
observed experimental data. To differentiate among these models, we propose
additional distinguishing properties beyond just the time until the next event.
In order to prove its applicability, we discuss how this method can be used to
infer models of transcription regulation from transcriptional bursting data.


  The vast progress in synthetic image synthesis enables the generation of
facial images in high resolution and photorealism. In biometric applications,
the main motivation for using synthetic data is to solve the shortage of
publicly-available biometric data while reducing privacy risks when processing
such sensitive information. These advantages are exploited in this work by
simulating human face ageing with recent face age modification algorithms to
generate mated samples, thereby studying the impact of ageing on the
performance of an open-source biometric recognition system. Further, a real
dataset is used to evaluate the effects of short-term ageing, comparing the
biometric performance to the synthetic domain. The main findings indicate that
short-term ageing in the range of 1-5 years has only minor effects on the
general recognition performance. However, the correct verification of mated
faces with long-term age differences beyond 20 years poses still a significant
challenge and requires further investigation.


  We experimentally study tunneling of Bose-condensed $^{87}$Rb atoms prepared
in a quasi-bound state and observe a non-exponential decay caused by
interatomic interactions. A combination of a magnetic quadrupole trap and a
thin $1.3\mathrm{\mu m}$ barrier created using a blue-detuned sheet of light is
used to tailor traps with controllable depth and tunneling rate. The escape
dynamics strongly depend on the mean-field energy, which gives rise to three
distinct regimes--- classical spilling over the barrier, quantum tunneling, and
decay dominated by background losses. We show that the tunneling rate depends
exponentially on the chemical potential. Our results show good agreement with
numerical solutions of the 3D Gross-Pitaevskii equation.


  Recent works of Costa-Dalai, Christandl-Gesmundo-Zuiddam,
Blatter-Draisma-Rupniewski, and Bri\"et-Christandl-Leigh-Shpilka-Zuiddam have
investigated notions of discreteness and gaps in the possible values that
asymptotic tensor ranks can take. In particular, it was shown that the
asymptotic subrank and asymptotic slice rank of any nonzero 3-tensor is equal
to 1, equal to 1.88, or at least 2 (over any field), and that the set of
possible values of these parameters is discrete (in several regimes). We
determine exactly the next gap, showing that the asymptotic subrank and
asymptotic slice rank of any nonzero 3-tensor is equal to 1, equal to 1.88,
equal to 2, or at least 2.68.


  The entanglement entropy of a subsystem of a quantum system is expressed, in
the replica approach, through analytic continuation with respect to n of the
trace of the n-th power of the reduced density matrix. This trace can be
thought of as the vacuum expectation value of a suitable observable in a system
made with n independent copies of the original system. We use this property to
numerically evaluate it in some two-dimensional critical systems, where it can
be compared with the results of Calabrese and Cardy, who wrote the same
quantity in terms of correlation functions of twist fields of a conformal field
theory. Although the two calculations match perfectly even in finite systems
when the analyzed subsystem consists of a single interval, they disagree
whenever the subsystem is composed of more than one connected part. The reasons
of this disagreement are explained.


  Multi-component modelling of galaxies is a valuable tool in the effort to
quantitatively understand galaxy evolution, yet the use of the technique is
plagued by issues of convergence, model selection and parameter degeneracies.
These issues limit its application over large samples to the simplest models,
with complex models being applied only to very small samples. We attempt to
resolve this dilemma of "quantity or quality" by developing a novel framework,
built inside the Zooniverse citizen science platform, to enable the
crowdsourcing of model creation for Sloan Digitial Sky Survey galaxies. We have
applied the method, including a final algorithmic optimisation step, on a test
sample of 198 galaxies, and examine the robustness of this new method. We also
compare it to automated fitting pipelines, demonstrating that it is possible to
consistently recover accurate models that either show good agreement with, or
improve on, prior work. We conclude that citizen science is a promising
technique for modelling images of complex galaxies, and release our catalogue
of models.


  The knowledge of the physical conditions occurring in the relativistic jet of
radio-loud active galactic nuclei (AGNs) is important to understand the
mechanisms at the basis of their multiband emission. From parsec-scale radio
observations of blazar objects it has been suggested a connection between the
ejection of new jet features and strong gamma-ray flares. We present results
from multi-epoch Very Long Baseline Interferometer (VLBI) and Space-VLBI
observations of the Flat Spectrum Radio Quasar (FSRQ) PKS 1510-089. The
comparison of the radio structure observed at different epochs shows the
presence of jet features moving with highly superluminal apparent velocity.
Radio flux density variability and changes in the source structure and in the
polarization properties are then compared with the information on the gamma-ray
emission in order to find a possible connection between radio and gamma-ray
emission.


  An enormous variety of quantum nanoobjects and nanosystems calls for the
development of new approaches to their description and parametrization.
Corresponding methods should be simple, universal enough and ensuring the
retention of essential part of information at small number of parameters.
  A universal effective quantum number was introduced earlier and successfully
used for describing centrally symmetric systems. But in most cases nanosystems
own a lower symmetry. In the present article we generalize and adapt this
method for such systems.


  Machine learning has emerged as an invaluable tool in many research areas. In
the present work, we harness this power to predict highly accurate molecular
infrared spectra with unprecedented computational efficiency. To account for
vibrational anharmonic and dynamical effects -- typically neglected by
conventional quantum chemistry approaches -- we base our machine learning
strategy on ab initio molecular dynamics simulations. While these simulations
are usually extremely time consuming even for small molecules, we overcome
these limitations by leveraging the power of a variety of machine learning
techniques, not only accelerating simulations by several orders of magnitude,
but also greatly extending the size of systems that can be treated. To this
end, we develop a molecular dipole moment model based on environment dependent
neural network charges and combine it with the neural network potentials of
Behler and Parrinello. Contrary to the prevalent big data philosophy, we are
able to obtain very accurate machine learning models for the prediction of
infrared spectra based on only a few hundreds of electronic structure reference
points. This is made possible through the introduction of a fully automated
sampling scheme and the use of molecular forces during neural network potential
training. We demonstrate the power of our machine learning approach by applying
it to model the infrared spectra of a methanol molecule, n-alkanes containing
up to 200 atoms and the protonated alanine tripeptide, which at the same time
represents the first application of machine learning techniques to simulate the
dynamics of a peptide. In all these case studies we find excellent agreement
between the infrared spectra predicted via machine learning models and the
respective theoretical and experimental spectra.


  This paper considers low-density parity-check (LDPC) decoders affected by
deviations introduced by the electronic device on which the decoder is
implemented. Noisy density evolution (DE) that allows to theoretically study
the performance of these LDPC decoders can only consider symmetric deviation
models due to the all-zero codeword assumption. A novel DE method is proposed
that admits the use of asymmetric deviation models, thus widening the range of
faulty implementations that can be analyzed. DE equations are provided for
three noisy decoders: belief propagation, Gallager B, and quantized min-sum
(MS). Simulation results confirm that the proposed DE accurately predicts the
performance of LDPC decoders with asymmetric deviations. Furthermore,
asymmetric versions of the Gallager B and MS decoders are proposed to
compensate the effect of asymmetric deviations. The parameters of these
decoders are then optimized using the proposed DE, leading to better ensemble
thresholds and improved finite-length performance in the presence of asymmetric
deviations.


  In this work, we propose extropy measures based on density copula,
distributional copula, and survival copula, and explore their properties. We
study the effect of monotone transformations for the proposed measures and
obtain bounds. We establish connections between cumulative copula extropy and
three dependence measures: Spearman's rho, Kendall's tau, and Blest's measure
of rank correlation. Finally, we propose estimators for the cumulative copula
extropy and survival copula extropy with an illustration using real life
datasets.


  In this paper, we propose a single-actuator gripper that can lift thin
objects lying on a flat surface, in addition to the ability as a standard
parallel gripper. The key is a crawler on the fingertip, which is underactuated
together with other finger joints and switched with a passive and spring-loaded
mechanism. While the idea of crawling finger is not a new one, this paper
contributes to realize the crawling without additional motor. The gripper can
passively change the mode from the parallel approach mode to the pull-in mode,
then finally to the power grasp mode, according to the grasping state. To
optimize the highly underactuated system, we take a combination of black-box
optimization and physics simulation of the whole grasp process. We show that
this simulation-based approach can effectively consider the precontact motion,
in-hand manipulation, power grasp stability, and even failure mode, which is
difficult for the static-equilibrium-analysis-based approaches. In the last
part of the paper, we demonstrate that a prototype gripper with the proposed
structure and design parameters optimized under the proposed process
successfully power-grasped a thin sheet, a softcover book, and a cylinder lying
on a flat surface.


  In theoretical machine learning, the statistical complexity is a notion that
measures the richness of a hypothesis space. In this work, we apply a
particular measure of statistical complexity, namely the Rademacher complexity,
to the quantum circuit model in quantum computation and study how the
statistical complexity depends on various quantum circuit parameters. In
particular, we investigate the dependence of the statistical complexity on the
resources, depth, width, and the number of input and output registers of a
quantum circuit. To study how the statistical complexity scales with resources
in the circuit, we introduce a resource measure of magic based on the $(p,q)$
group norm, which quantifies the amount of magic in the quantum channels
associated with the circuit. These dependencies are investigated in the
following two settings: (i) where the entire quantum circuit is treated as a
single quantum channel, and (ii) where each layer of the quantum circuit is
treated as a separate quantum channel. The bounds we obtain can be used to
constrain the capacity of quantum neural networks in terms of their depths and
widths as well as the resources in the network.


  We generalize a work of Iovita-Zaharescu on the Galois theory of B_dR^+ to
the imperfect residue field case. The proof is based on a structure theorem of
Colmez's higher Kahler differentials.


  The superfluid/normal-fluid interface of liquid 4He is investigated in
gravity on earth where a small heat current Q flows vertically upward or
downward. We present a local space- and time-dependent renormalization-group
(RG) calculation based on model F which describes the dynamic critical effects
for temperatures T near the superfluid transition T_lambda. The model-F
equations are rewritten in a dimensionless renormalized form and solved
numerically as partial differential equations. Perturbative corrections are
included for the spatially inhomogeneous system within a self-consistent
one-loop approximation. The RG flow parameter is determined locally as a
function of space and time by a constraint equation which is solved by a Newton
iteration. As a result we obtain the temperature profile of the interface.
Furthermore we calculate the average order parameter <psi>, the correlation
length xi, the specific heat C_Q and the thermal resistivity rho_T where we
observe a rounding of the critical singularity by the gravity and the heat
current. We compare the thermal resistivity with an experiment and find good
qualitative agreement. Moreover we discuss our previous approach for larger
heat currents and the self-organized critical state and show that our theory
agrees with recent experiments in this latter regime.


  For $n\geq 3,$ let $\Gamma=SL_n(\mathbb Z).$ We prove the following
superridigity result for $\Gamma$ in the context of operator algebras. Let
$L(\Gamma)$ be the von Neumann algebra generated by the left regular
representation of $\Gamma.$ Let $M$ be a finite factor and let $U(M)$ be its
unitary group. Let $\pi: \Gamma\to U(M)$ be a group homomorphism such that
$\pi(\Gamma)''=M.$ Then
  \begin{itemize}
  \item[(i)] either $M$ is finite dimensional, or
  \item [(ii)] there exists a subgroup of finite index $\Lambda$ of $\Gamma$
such that $\pi|_\Lambda$ extends to a homomorphism $U(L(\Lambda))\to U(M).$
  \end{itemize}
  The result is deduced from a complete description of the tracial states on
the full $C^*$--algebra of $\Gamma.$
  As another application, we show that the full $C^*$--algebra of
  $\Gamma$ has no faithful tracial state.


  We consider the Cohen-Macaulay compactification of the space of twisted
cubics in projective n-space. This compactification is the fine moduli scheme
representing the functor of CM-curves with Hilbert polynomial 3t+1. We show
that the moduli scheme of CM-curves in projective 3-space is isomorphic to the
twisted cubic component of the Hilbert scheme. We also describe the
compactification for twisted cubics in n-space.


  Red supergiants (RSGs) are an evolved stage in the life of intermediate
massive stars (than than 25 solar masses). For many years, their location in
the H-R diagram was at variance with the evolutionary models. Using the MARCS
stellar atmospheres, we have determined new effective temperatures and
bolometric luminosities for RSGs in the Milky Way, LMC, and SMC, and our work
has resulted in much better agreement with the evolutionary models. We have
also found evidence of significant visual extinction due to circumstellar dust.
Although in the Milky Way the RSGs contribute only a small fraction (than than
1 percent) of the dust to the interstellar medium (ISM), in starburst galaxies
or galaxies at large look-back times, we expect that RSGs may be the main dust
source. We are in the process of extending this work now to RSGs of higher and
lower metallicities using the galaxies M31 and WLM.


  The current generation of air shower radio arrays has demonstrated that the
atmospheric depth of the shower maximum Xmax can be reconstructed with high
accuracy. These experiments are now contributing to mass composition studies in
the energy range where a transition from galactic to extragalactic cosmic-ray
sources is expected. However, we are still far away from an unambiguous
interpretation of the data. Here we propose to use radio measurements to derive
a new type of constraint on the mass composition, by reconstructing the shower
length L. The low-frequency part of the Square Kilometer Array will have an
extremely high antenna density of roughly 60.000 antennas within one square
kilometer, and is the perfect site for high-resolution studies of air showers.
In this contribution, we discuss the impact of being able to reconstruct L, and
the unique contribution that SKA can make to cosmic-ray science.


  With the rapid expansion of e-commerce, more consumers have become accustomed
to making purchases via livestreaming. Accurately identifying the products
being sold by salespeople, i.e., livestreaming product retrieval (LPR), poses a
fundamental and daunting challenge. The LPR task encompasses three primary
dilemmas in real-world scenarios: 1) the recognition of intended products from
distractor products present in the background; 2) the video-image heterogeneity
that the appearance of products showcased in live streams often deviates
substantially from standardized product images in stores; 3) there are numerous
confusing products with subtle visual nuances in the shop. To tackle these
challenges, we propose the Spatiotemporal Graphing Multi-modal Network (SGMN).
First, we employ a text-guided attention mechanism that leverages the spoken
content of salespeople to guide the model to focus toward intended products,
emphasizing their salience over cluttered background products. Second, a
long-range spatiotemporal graph network is further designed to achieve both
instance-level interaction and frame-level matching, solving the misalignment
caused by video-image heterogeneity. Third, we propose a multi-modal hard
example mining, assisting the model in distinguishing highly similar products
with fine-grained features across the video-image-text domain. Through
extensive quantitative and qualitative experiments, we demonstrate the superior
performance of our proposed SGMN model, surpassing the state-of-the-art methods
by a substantial margin. The code is available at
https://github.com/Huxiaowan/SGMN.


  We define auto- and cross-correlation functions capable to capture dynamical
characteristics induced by local phase space structures in a general dynamical
system. These correlation functions are calculated in the Standard Map for a
range of values of the non-linearity parameter $k$. Using a model of
non-interacting particles, each evolving according to the same Standard Map
dynamics and located initially at specific phase space regions, we show that
for $0.6 < k \leq 1.2$ long-range cross-correlations emerge. They occur as an
ensemble property of particle trajectories by an appropriate choice of the
phase space cells used in the statistical averaging. In this region of
$k$-values the single particle phase space is either dominated by local chaos
($k \leq k_c$ with $k_c \approx 0.97$) or it is characterized by the transition
from local to global chaos ($k_c < k \leq 1.2$). Introducing suitable symbolic
dynamics we demonstrate that the emergence of long-range cross-correlations can
be attributed to the existence of an effective intermittent dynamics in
specific regions of the phase space. Our findings further support the recently
found relation of intermittent dynamics with the occurrence of
cross-correlations (F.K. Diakonos, A.K. Karlis, and P. Schmelcher, Europhys.
Lett. {\bf 105}, 26004 (2014)) in simple one-dimensional intermittent maps,
suggesting its validity for two-dimensional Hamiltonian systems.


  The pion electromagnetic form factor has been calculated for the first time
from the solutions of the Bethe-Salpeter equation, obtained directly in
Minkowski space by using the Nakanishi integral representation of the
Bethe-Salpeter amplitude. The one-gluon exchange kernel contains as inputs the
quark and gluon masses, as well as a scale parameter featuring the extended
quark-gluon vertex. The range of variability of these parameters is suggested
by lattice calculations. After presenting a very successful comparison with the
existing data in the whole range of the momentum transfer, we show how
inserting the light-front (LF) formalism in our approach allows to achieve
further interesting results, like the evaluation of the LF valence form factor
and a first investigation of the end-points effects.


  As for semantic role labeling (SRL) task, when it comes to utilizing parsing
information, both traditional methods and recent recurrent neural network (RNN)
based methods use the feature engineering way. In this paper, we propose Syntax
Aware Long Short Time Memory(SA-LSTM). The structure of SA-LSTM modifies
according to dependency parsing information in order to model parsing
information directly in an architecture engineering way instead of feature
engineering way. We experimentally demonstrate that SA-LSTM gains more
improvement from the model architecture. Furthermore, SA-LSTM outperforms the
state-of-the-art on CPB 1.0 significantly according to Student t-test
($p<0.05$).


  We report current progress on the synthesis of methods to alleviate two major
difficulties in implementing a Monte Carlo Renormalization Group (MCRG) for
quantum systems. In particular, we have utilized the loop-algorithm to reduce
critical slowing down, and we have implemented an MCRG method in which the
symmetries of the classical equivalent model need not be fully understood,
since the Renormalization Group is given by the Monte Carlo simulation. We
report preliminary results obtained when the resulting MCRG method is applied
to the d=1 XXZ model. Our results are encouraging. However, since this model
has a Kosterlitz-Thouless transition, it does not yet provide a full test of
our MCRG method.


  Passing-Bablok regression is a standard tool for method and assay comparison
studies thanks to its place in industry guidelines such as CLSI. Unfortunately,
its computational cost is high as a naive approach requires O(n2) time. This
makes it impossible to compute the Passing-Bablok regression estimator on large
datasets. Additionally, even on smaller datasets it can be difficult to perform
bootstrap-based inference. We introduce the first quasilinear time algorithm
for the equivariant Passing-Bablok estimator. In contrast to the naive
algorithm, our algorithm runs in O(n log(n)) expected time using O(n) space,
allowing for its application to much larger data sets. Additionally, we
introduce a fast estimator for the variance of the Passing-Bablok slope and
discuss statistical inference based on bootstrap and this variance estimate.
Finally, we propose a diagnostic plot to identify influential points in
Passing-Bablok regression. The superior performance of the proposed methods is
illustrated on real data examples of clinical method comparison studies.


  Access resistance indicates how well current carriers from a bulk medium can
converge to a pore or opening, and is an important concept in nanofluidic
devices and in cell physiology. In simplified scenarios, when the bulk
dimensions are infinite in all directions, it depends only on the resistivity
and pore radius. These conditions are not valid in all-atom molecular dynamics
(MD) simulations of transport, due to the computational cost of large
simulation cells, and can even break down in micro- and nano-scale systems due
to strong confinement. Here, we examine a scaling theory for the access
resistance that predicts a special simulation cell aspect ratio -- the golden
aspect ratio -- where finite size effects are eliminated. Using both continuum
and all-atom simulations, we demonstrate that this golden aspect ratio exists
and that it takes on a universal value in linear response and moderate
concentrations. Outside of linear response, it gains an apparent dependence on
characteristics of the transport scenario (concentration, voltages, etc.) for
small simulation cells, but this dependence vanishes at larger length scales.
These results will enable the use of all-atom molecular dynamics simulations to
study contextual properties of access resistance -- its dependence on protein
and molecular-scale fluctuations, the presence of charges, and other functional
groups -- and yield the opportunity to quantitatively compare computed and
measured resistances.


  We introduce and implement a method to compute stationary states of nonlinear
Schr\''odinger equations on metric graphs. Stationary states are obtained as
local minimizers of the nonlinear Schr\''odinger energy at fixed mass. Our
method is based on a normalized gradient flow for the energy (i.e. a gradient
flow projected on a fixed mass sphere) adapted to the context of nonlinear
quantum graphs. We first prove that, at the continuous level, the normalized
gradient flow is well-posed, mass-preserving, energy diminishing and converges
(at least locally) towards stationary states. We then establish the link
between the continuous flow and its discretized version. We conclude by
conducting a series of numerical experiments in model situations showing the
good performance of the discrete flow to compute stationary states. Further
experiments as well as detailed explanation of our numerical algorithm are
given in a companion paper.


  Motivated by recent research on Physics-Informed Neural Networks (PINNs), we
make the first attempt to introduce the PINNs for numerical simulation of the
elliptic Partial Differential Equations (PDEs) on 3D manifolds. PINNs are one
of the deep learning-based techniques. Based on the data and physical models,
PINNs introduce the standard feedforward neural networks (NNs) to approximate
the solutions to the PDE systems. By using automatic differentiation, the PDEs
system could be explicitly encoded into NNs and consequently, the sum of mean
squared residuals from PDEs could be minimized with respect to the NN
parameters. In this study, the residual in the loss function could be
constructed validly by using the automatic differentiation because of the
relationship between the surface differential operators $\nabla_S/\Delta_S$ and
the standard Euclidean differential operators $\nabla/\Delta$. We first
consider the unit sphere as surface to investigate the numerical accuracy and
convergence of the PINNs with different training example sizes and the depth of
the NNs. Another examples are provided with different complex manifolds to
verify the robustness of the PINNs.


  A fundamental aspect of the quantum-to-classical limit is the transition from
a non-commutative algebra of observables to commutative one. However, this
transition is not possible if we only consider unitary evolutions. One way to
describe this transition is to consider the Gamow vectors, which introduce
exponential decays in the evolution. In this paper, we give two mathematical
models in which this transition happens in the infite time limit. In the first
one, we consider operators acting on the space of the Gamow vectors, which
represent quantum resonances. In the second one, we use an algebraic formalism
from scattering theory. We construct a non-commuting algebra which commutes in
the infinite time limit.


  This brief article reviews stochastic processes as relevant to dynamical
models of wave-function collapse, and is supplemental material for the review
article arXiv:1204.4325


  We study the $Y=0$ triplet extended supersymmetric model in the light of the
recent Higgs boson discovery. We calculate full one loop Higgs mass spectrum in
this model where the possible doublet-triplet mixing is considered in the
charged Higgs sector. This mixing changes the prediction of
$\mathcal{B}r(B_s\to X_s \gamma)$ in this model, compared to the MSSM. The
constraints from the $\sim 125$ GeV Higgs along with $\mathcal{B}r(B_s\to X_s
\gamma)$ are incorporated to find out the allowed parameter space. The lower
bounds on the third generation squark masses coming from 125 GeV Higgs are
rather week in this scenario compared to most constrained supersymmetric
scenarios, e.g. a 200 GeV squark mass is still possible.


  Measuring presence is critical to improving user involvement and performance
in Mixed Reality (MR). \emph{Presence}, a crucial aspect of MR, is
traditionally gauged using subjective questionnaires, leading to a lack of
time-varying responses and susceptibility to user bias. Inspired by the
existing literature on the relationship between presence and human performance,
the proposed methodology systematically measures a user's reaction time to a
visual stimulus as they interact within a manipulated MR environment. We
explore the user reaction time as a quantity that can be easily measured using
the systemic tools available in modern MR devices. We conducted an exploratory
study (N=40) with two experiments designed to alter the users' sense of
presence by manipulating \emph{place illusion} and \emph{plausibility
illusion}. We found a significant correlation between presence scores and
reaction times with a correlation coefficient -0.65, suggesting that users with
a higher sense of presence responded more swiftly to stimuli. We develop a
model that estimates a user's presence level using the reaction time values
with high accuracy of up to 80\%. While our study suggests that reaction time
can be used as a measure of presence, further investigation is needed to
improve the accuracy of the model.


  When analyzing microarray data, hierarchical models are often used to share
information across genes when estimating means and variances or identifying
differential expression. Many methods utilize some form of the two-level
hierarchical model structure suggested by Kendziorski et al. [Stat. Med. (2003)
22 3899-3914] in which the first level describes the distribution of latent
mean expression levels among genes and among differentially expressed
treatments within a gene. The second level describes the conditional
distribution, given a latent mean, of repeated observations for a single gene
and treatment. Many of these models, including those used in Kendziorski's et
al. [Stat. Med. (2003) 22 3899-3914] EBarrays package, assume that expression
level changes due to treatment effects have the same distribution as expression
level changes from gene to gene. We present empirical evidence that this
assumption is often inadequate and propose three-level hierarchical models as
extensions to the two-level log-normal based EBarrays models to address this
inadequacy. We demonstrate that use of our three-level models dramatically
changes analysis results for a variety of microarray data sets and verify the
validity and improved performance of our suggested method in a series of
simulation studies. We also illustrate the importance of accounting for the
uncertainty of gene-specific error variance estimates when using hierarchical
models to identify differentially expressed genes.


  Unlike IPv4 addresses, which are typically masked by a NAT, IPv6 addresses
could easily be correlated with user activity, endangering their privacy.
Mitigations to address this privacy concern have been deployed, making existing
approaches for address-to-user correlation unreliable. This work demonstrates
that an adversary could still correlate IPv6 addresses with users accurately,
even with these protection mechanisms. To do this, we propose an IPv6 address
correlation model - SiamHAN. The model uses a Siamese Heterogeneous Graph
Attention Network to measure whether two IPv6 client addresses belong to the
same user even if the user's traffic is protected by TLS encryption. Using a
large real-world dataset, we show that, for the tasks of tracking target users
and discovering unique users, the state-of-the-art techniques could achieve
only 85% and 60% accuracy, respectively. However, SiamHAN exhibits 99% and 88%
accuracy.


  We show that strong magnetic fields can be generated at shock waves
associated with formation of galaxies or clusters of galaxies by the Weibel
instability, an instability in collisionless plasmas. The estimated strength of
the magnetic field generated through this mechanism is close to the order of
values observed in galaxies or clusters of galaxies at present, which indicates
that strong amplification of magnetic fields after formation of galaxies or
clusters of galaxies is not required. This mechanism could have worked even at
a redshift of ~10, and therefore the generated magnetic fields may have
affected the formation of stars at the early universe. This model will be
confirmed by future observations of nearby clusters of galaxies. In this
context, we also present the Japanese X-ray missions.


  We present BeppoSAX follow-up observations of GRB980425 obtained with the
Narrow Field Instruments (NFI) in April, May, and November 1998. The first NFI
observation has detected within the 8' radius error box of the GRB an X-ray
source positionally consistent with the supernova 1998bw, which exploded within
a day of GRB980425, and a fainter X-ray source, not consistent with the
position of the supernova. The former source is detected in the following NFI
pointings and exhibits a decline of a factor of two in six months. If it is
associated with SN 1998bw, this is the first detection of X-ray emission from a
Type I supernova above 2 keV. The latter source exhibits only marginally
significant variability. The X-ray spectra and variability of the supernova are
compared with thermal and non-thermal models of supernova high energy emission.
Based on the BeppoSAX data, it is not possible to firmly establish which of the
two detected sources is the GRB X-ray counterpart, although probability
considerations favor the supernova.


  We discuss four recycled pulsars found in Arecibo drift-scan searches. PSR
J1944+0907 has a spin period of 5.2 ms and is isolated. The 5.8-ms pulsar
J1453+19 may have a low-mass companion. We discuss these pulsars in the context
of isolated millisecond pulsar formation and the minimum spin period of neutron
stars. The isolated 56-ms pulsar J0609+2130 is possibly the remnant of a
disrupted double neutron star binary. The 41-ms pulsar J1829+2456 is in a
relativistic orbit. Its companion is most likely another neutron star, making
this the eighth known double neutron star binary system.


  Making a linguistic theory is like making a programming language: one
typically devises a type system to delineate the acceptable utterances and a
denotational semantics to explain observations on their behavior. Via this
connection, the programming language concept of delimited continuations can
help analyze natural language phenomena such as quantification and polarity
sensitivity. Using a logical metalanguage whose syntax includes control
operators and whose semantics involves evaluation order, these analyses can be
expressed in direct style rather than continuation-passing style, and these
phenomena can be thought of as computational side effects.


  By bootstrap percolation we mean the following deterministic process on a
graph $G$. Given a set $A$ of vertices "infected" at time 0, new vertices are
subsequently infected, at each time step, if they have at least
$r\in\mathbb{N}$ previously infected neighbors. When the set $A$ is chosen at
random, the main aim is to determine the critical probability $p_c(G,r)$ at
which percolation (infection of the entire graph) becomes likely to occur. This
bootstrap process has been extensively studied on the $d$-dimensional grid
$[n]^d$: with $2\leq r\leq d$ fixed, it was proved by Cerf and Cirillo (for
$d=r=3$), and by Cerf and Manzo (in general), that
\[p_c([n]^d,r)=\Theta\biggl(\frac{1}{\log_{(r-1)}n}\biggr)^{d-r+1},\] where
$\log_{(r)}$ is an $r$-times iterated logarithm. However, the exact threshold
function is only known in the case $d=r=2$, where it was shown by Holroyd to be
$(1+o(1))\frac{\pi^2}{18\log n}$. In this paper we shall determine the exact
threshold in the crucial case $d=r=3$, and lay the groundwork for solving the
problem for all fixed $d$ and $r$.


  In this paper, the influence of aggregate size and volume fraction on
shrinkage induced micro-cracking and permeability of concrete and mortar was
investigated. Nonlinear finite element analyses of model concrete and mortar
specimens were performed. The aggregate diameter was varied between 2 and 16
mm. Furthermore, a range of volume fractions between 0.1 and 0.5 was studied.
The nonlinear analyses were based on a 2D lattice approach in which aggregates
were simplified as monosized cylindrical inclusions. The analysis results were
interpreted by means of crack width and change of permeability. The results
show that increasing aggregate diameter (at equal volume fraction) and
decreasing volume fraction (at equal aggregate diameter) greatly increases
permeability.


  The Stieltjes constants $\gamma_n$ appear in the coefficients in the Laurent
expansion of the Riemann zeta function $\zeta(s)$ about the simple pole $s=1$.
We present an asymptotic expansion for $\gamma_n$ as $n\rightarrow \infty$
based on the approach described by Knessel and Coffey [Math. Comput. {\bf 80}
(2011) 379--386]. A truncated form of this expansion with explicit coefficients
is also given. Numerical results are presented that illustrate the accuracy
achievable with our expansion.


  We show the consistent interactions in the generalized electrodynamics gauge
theory with higher derivative matter fields by means of the order reduction
method. We deduce the BRST deformations in the reduced Lagrangian and using the
equations of motion of the auxiliary fields in the antighost number zero part
in the resulting deformed action, we are able to obtain the consistent coupling
terms added into the original Lagrangian density which are compatible with the
deformation master equations. We emphasize that the order of deformations is
truncated at four and the corresponding higher-order deformations are equal to
zero precisely. Moreover, the local Abelian gauge symmetry turns out to be
non-Abelian after the deformation procedure.


  Tumor-induced angiogenesis is the formation of new sprouts from preexisting
nearby parent blood vessels. Computationally, tumor-induced angiogenesis can be
modeled using cellular automata (CA), partial differential equations, etc. In
this present study, a realistic physiological approach has been made to model
the process of angiogenesis by using 3D CA model. CA technique uses various
neighborhoods like Von-Neumann neighborhood, Moore neighborhood, and Margolus
neighborhood. In our model Von-Neumann neighborhood has used for distribution
of some significant chemical and non-chemical tumor angiogenic factors like
vascular endothelial growth factor, endothelial cells, O2, extracellular
matrix, fibronectin, etc., and Moore neighborhood is used for distribution of
matrix metalloproteinase. In vivo tumor environment all the factors are not
distributed equally in the extracellular matrix. Distributions of those
chemical and nonchemical factors depend on their source, nature and function.
To keep similarity with the biological tumor environment, we have formulated
initial distributions of the chemical and non-chemical factors accordingly. We
have started the simulation in MATLAB with this initial distribution. Number of
sprouts randomly varies from one run to another. We observed that sprouts are
not originating from the same locations in each simulation. A sprout has high
sensitivity of VEGF and fibronectin concentrations. sVEGFR-1 always tries to
regress the sprout. When two or more sprouts come closer, they merge with each
other leading to anastomosis. Sufficient number of tip cells may cause sprout
towards tumor.


  We study 6-dimensional nearly Kahler manifolds admitting a Killing vector
field of unit length. In the compact case it is shown that up to a finite cover
there is only one geometry possible, that of the 3--symmetric space $S^3 \times
S^3$.


  The phosphorus-doped single wall carbon nanotube (PSWCNT) is studied by using
First-Principle methods based on Density Function Theory (DFT). The formation
energy, total energy, band structure, geometry structure and density of states
are calculated. It is found that the formation energy of the P-doped single
carbon nanotubes increases with diameters; the total energy of carbon nanotubes
with the same diameter decreases as the doping rate increases. The effects of
impurity position on the im-purity level are discussed. It illustrates that the
position of the impurity level may depend on the C-P-C bond angle. According to
the above results, it is feasible to substitute a carbon atom with a phosphorus
atom in SWCNT. It is also found that P-doped carbon nanotubes are N type
semiconductor.


  In this work we develop a general tensor network decoder for 2D codes.
Specifically, we propose a decoder that approximates maximally likelihood
decoding for 2D stabiliser and subsystem codes subject to Pauli noise. For a
code consisting of $n$ qubits our decoder has a runtime of $O(n\log
n+n\chi^3)$, where $\chi$ is an approximation parameter. We numerically
demonstrate the power of this decoder by studying four classes of codes under
three noise models, namely regular surface codes, irregular surface codes,
subsystem surface codes and colour codes, under bit-flip, phase-flip and
depolarising noise. We show that the thresholds yielded by our decoder are
state-of-the-art, and numerically consistent with optimal thresholds where
available, suggesting that the tensor network decoder well approximates optimal
decoding in all these cases. Novel to our decoder is an efficient and effective
approximate contraction scheme for arbitrary 2D tensor networks, which may be
of independent interest. We have also released an implementation of this
algorithm as a stand-alone Julia package: SweepContractor.jl.


  We find that in parameter regimes relevant to the recently observed
alternating zonal jets in oceans, the formation of these jets can be explained
as due to an arrest of the turbulent inverse-cascade of energy by {\em free}
Rossby waves (as opposed to Rossby {\em basin} modes) and a subsequent
redirection of that energy into zonal modes. This mechanism, originally studied
in the context of alternating jets in Jovian atmospheres and two dimensional
turbulence in zonally-periodic configurations survives in spite of the presence
of the meridional boundaries in the oceanic context.


  Based on the Hilb type formula between Jacobi polynomials and Bessel
functions, optimal decay rates on Jacobi expansion coefficients are derived, by
applying van der Corput type lemmas, for functions of logarithmatic
singularities, which leads to the optimal convergence rates on the Jacobi,
Gegenbauer and Chebyshev orthogonal projections. It is interesting to see that
for boundary singularities, one may get faster convergence rate on the Jacobi
or Gegenbauer projection as $(\alpha,\beta)$ and $\lambda$ increases. The
larger values of parameter, the higher convergence rates can be achieved. In
particular, the Legendre projection has one half order higher than Chebyshev.
Moreover, if $\min\{\alpha,\beta\}>0$ and $\lambda>\frac{1}{2}$, the Jacobi and
Gegenbauer orthogonal projections have higher convergence orders compared with
Legendre. While for interior singularity, the convergence order is independent
of $(\alpha,\beta)$ and $\lambda$.


  This volume contains the papers presented at LINEARITY 2016, the Fourth
International Workshop on Linearity, held on June 26, 2016 in Porto, Portugal.
The workshop was a one-day satellite event of FSCD 2016, the first
International Conference on Formal Structures for Computation and Deduction.
  The aim of this workshop was to bring together researchers who are developing
theory and applications of linear calculi, to foster their interaction and
provide a forum for presenting new ideas and work in progress, and enable
newcomers to learn about current activities in this area. Of interest were new
results that made a central use of linearity, ranging from foundational work to
applications in any field. This included: sub-linear logics, linear term
calculi, linear type systems, linear proof-theory, linear programming
languages, applications to concurrency, interaction-based systems, verification
of linear systems, and biological and chemical models of computation.


  Active nematics are conceptually the simplest orientationally ordered phase
of self-driven particles, but have proved to be a perennial source of
surprises. We show here through numerical solution of coarse-grained equations
for order parameter and density that the growth of the active nematic phase
from the isotropic phase is necessarily accompanied by a clumping of the
density. The growth kinetics of the density domains is shown to be faster than
the 1/3-law expected for variables governed by a conservation law. Other
results presented include the suppression of density fluctuations in the
stationary ordered nematic by the imposition of an orienting field. We close by
posing some open questions.


  We present a comprehensive statistical analysis of the properties of Type Ia
SN light curves in the near infrared using recent data from PAIRITEL and the
literature. We construct a hierarchical Bayesian framework, incorporating
several uncertainties including photometric error, peculiar velocities, dust
extinction and intrinsic variations, for coherent statistical inference. SN Ia
light curve inferences are drawn from the global posterior probability of
parameters describing both individual supernovae and the population conditioned
on the entire SN Ia NIR dataset. The logical structure of the hierarchical
model is represented by a directed acyclic graph. Fully Bayesian analysis of
the model and data is enabled by an efficient MCMC algorithm exploiting the
conditional structure using Gibbs sampling. We apply this framework to the
JHK_s SN Ia light curve data. A new light curve model captures the observed
J-band light curve shape variations. The intrinsic variances in peak absolute
magnitudes are: sigma(M_J) = 0.17 +/- 0.03, sigma(M_H) = 0.11 +/- 0.03, and
sigma(M_Ks) = 0.19 +/- 0.04. We describe the first quantitative evidence for
correlations between the NIR absolute magnitudes and J-band light curve shapes,
and demonstrate their utility for distance estimation. The average residual in
the Hubble diagram for the training set SN at cz > 2000 km/s is 0.10 mag. The
new application of bootstrap cross-validation to SN Ia light curve inference
tests the sensitivity of the model fit to the finite sample and estimates the
prediction error at 0.15 mag. These results demonstrate that SN Ia NIR light
curves are as effective as optical light curves, and, because they are less
vulnerable to dust absorption, they have great potential as precise and
accurate cosmological distance indicators.


  When approximating the joint distribution of the component counts of a
decomposable combinatorial structure that is `almost' in the logarithmic class,
but nonetheless has irregular structure, it is useful to be able first to
establish that the distribution of a certain sum of non-negative integer valued
random variables is smooth. This distribution is not like the normal, and
individual summands can contribute a non-trivial amount to the whole, so its
smoothness is somewhat surprising. In this paper, we consider two coupling
approaches to establishing the smoothness, and contrast the results that are
obtained.


  CTB 109 (G109.1-1.0) is a Galactic supernova remnant (SNR) with a
hemispherical shell morphology in X-rays and in the radio band. In this work we
report the detection of {\gamma}-ray emission coincident with CTB 109, using 37
months of data from the Large Area Telescope on board the Fermi Gamma-ray Space
Telescope. We study the broadband characteristics of the remnant using a model
that includes hydrodynamics, efficient cosmic ray acceleration, nonthermal
emission and a self-consistent calculation of the X-ray thermal emission. We
find that the observations can be successfully fit with two distinct parameter
sets, one where the {\gamma}-ray emission is produced primarily by leptons
accelerated at the SNR forward shock and the other where {\gamma}-rays produced
by forward shock accelerated cosmic-ray ions dominate the high-energy emission.
Consideration of thermal X-ray emission introduces a novel element to the
broadband fitting process, and while it does not rule out either the leptonic
or the hadronic scenarios, it constrains the parameter sets required by the
model to fit the observations. Moreover, the model which best fits the thermal
and nonthermal emission observations is an intermediate case, where both
radiation from accelerated electrons and hadrons contribute almost equally to
the {\gamma}-ray flux observed.


  This paper proposes a dual-band frequency scanning meandering microstrip
leaky-wave antenna with linear polarization in the Ku-band and circular
polarization in the K-band. This is achieved by making use of two spatial
harmonics for radiation. The unit cell of the periodic microstrip antenna
contains three meanders with mitred corners. To ensure circular polarization, a
theoretical formulation is developed taking into account the delay caused by
microstrip length intervals. It defines the unit cell geometry by determining
the length of the meanders to ensure that axial ratio remains below 3 dB
throughout the operational band. Moreover, the meanders are used to provide
better control over scanning rate (the ratio of change of angle of maximum
radiation with frequency) and reduce spurious radiation of harmonics by
ensuring single harmonic operation within the operational band. To guarantee
continuous scanning through broadside direction, open stopband is suppressed
using mitered angles. The antenna is designed on a 0.254-mm substrate making it
suitable for conformal applications. The fabricated antenna shows a backward to
forward beam steering range of 72 deg (-42 deg to 30 deg) in the K-band
(19.4-27.5 GHz) with circular polarization and of 75 deg (-15 deg to 60 deg) in
the Ku-band (11-15.5 GHz) with linear polarization.


  Image reconstruction-based anomaly detection models are widely explored in
industrial visual inspection. However, existing models usually suffer from the
trade-off between normal reconstruction fidelity and abnormal reconstruction
distinguishability, which damages the performance. In this paper, we find that
the above trade-off can be better mitigated by leveraging the distinct
frequency biases between normal and abnormal reconstruction errors. To this
end, we propose Frequency-aware Image Restoration (FAIR), a novel
self-supervised image restoration task that restores images from their
high-frequency components. It enables precise reconstruction of normal patterns
while mitigating unfavorable generalization to anomalies. Using only a simple
vanilla UNet, FAIR achieves state-of-the-art performance with higher efficiency
on various defect detection datasets. Code: https://github.com/liutongkun/FAIR.


  During the lifetime of AR 12673, its magnetic field evolved drastically and
produced numerous large flares. In this study, using full maps of the Sun
observed by the Solar Dynamics Observatory and the Solar Terrestrial Relations
Observatory, we identified that AR 12673 emerged in decayed AR 12665, which had
survived for two solar rotations. Although both ARs emerged at the same
location, they possessed different characteristics and different flare
productivities. Therefore, it is important to study the long-term magnetic
evolution of both ARs to identify the distinguishing characteristics of an AR
that can produce large solar flares. We used the Spaceweather Helioseismic and
Magnetic Imager Active Region Patch data to investigate the evolution of the
photospheric magnetic field and other physical properties of the recurring ARs
during five Carrington rotations. All these investigated parameters dynamically
evolved through a series of solar rotations. We compared the long-term
evolution of AR 12665 and AR 12673 to understand the differences in their
flare-producing properties. We also studied the relation of the long-term
evolution of these ARs with the presence of active longitude. We found that the
magnetic flux and complexity of AR 12673 developed much faster than those of AR
12665. Our results confirmed that a strong emerging flux that emerged in the
pre-existing AR near the active longitude created a very strong and complex AR
that produced large flares.


  When quantum states are used to send classical information, the receiver
performs a measurement on the signal states. The amount of information
extracted is often not optimal due to the receiver's measurement scheme and
experimental apparatus. For quantum non-demolition measurements, there is
potentially some residual information in the post-measurement state, while part
of the information has been extracted and the rest is destroyed. Here, we
propose a framework to characterize a quantum measurement by how much
information it extracts and destroys, and how much information it leaves in the
residual post-measurement state. The concept is illustrated for several
receivers discriminating coherent states.


  We show that, in a model based exclusively on constituent-quark degrees of
freedom interacting via a potential, the full axial current is conserved if the
spectrum of \bar QQ states contains a massless pseudoscalar. The current
conservation emerges nonperturbatively if the model satisfies certain
constraints on (i) the axial coupling g_A of the constituent quark and (ii) the
\bar QQ potential at large distances. We define the chiral point of the
constituent quark model as that set of values of the parameters (such as the
masses of the constituent quarks and the couplings in the \bar QQ potential)
for which the mass of the lowest pseudoscalar \bar QQ bound state vanishes. At
the chiral point the main signatures of the spontaneously broken chiral
symmetry are shown to be present, namely: the axial current is conserved, the
decay constants of the excited pseudoscalar bound states vanish, and the pion
decay constant has a nonzero value.


  The problem of selecting a small, yet high quality subset of patterns from a
larger collection of itemsets has recently attracted lot of research. Here we
discuss an approach to this problem using the notion of decomposable families
of itemsets. Such itemset families define a probabilistic model for the data
from which the original collection of itemsets has been derived from.
Furthermore, they induce a special tree structure, called a junction tree,
familiar from the theory of Markov Random Fields. The method has several
advantages. The junction trees provide an intuitive representation of the
mining results. From the computational point of view, the model provides
leverage for problems that could be intractable using the entire collection of
itemsets. We provide an efficient algorithm to build decomposable itemset
families, and give an application example with frequency bound querying using
the model. Empirical results show that our algorithm yields high quality
results.


  The coalescence of massive black hole binaries (BHBs) in galactic mergers is
the primary source of gravitational waves (GWs) at low frequencies. Current
estimates of GW detection rates for the Laser Interferometer Space Antenna and
the Pulsar Timing Array vary by three orders of magnitude. To understand this
variation, we simulate the merger of equal-mass, eccentric, galaxy pairs with
central massive black holes and shallow inner density cusps. We model the
formation and hardening of a central BHB using the Fast Multiple Method as a
force solver, which features a $O(N)$ scaling with the number $N$ of particles
and obtains results equivalent to direct-summation simulations. At $N \sim
5\times 10^5$, typical for contemporary studies, the eccentricity of the BHBs
can vary significantly for different random realisations of the same initial
condition, resulting in a substantial variation of the merger timescale. This
scatter owes to the stochasticity of stellar encounters with the BHB and
decreases with increasing $N$. We estimate that $N \sim 10^7$ within the
stellar half-light radius suffices to reduce the scatter in the merger
timescale to $\sim 10$\%. Our results suggest that at least some of the
uncertainty in low-frequency GW rates owes to insufficient numerical
resolution.


  We consider the rewiring of a bipartite graph using a mixture of random and
preferential attachment. The full mean field equations for the degree
distribution and its generating function are given. The exact solution of these
equations for all finite parameter values at any time is found in terms of
standard functions. It is demonstrated that these solutions are an excellent
fit to numerical simulations of the model. We discuss the relationship between
our model and several others in the literature including examples of Urn,
Backgammon, and Balls-in-Boxes models, the Watts and Strogatz rewiring problem
and some models of zero range processes. Our model is also equivalent to those
used in various applications including cultural transmission, family name and
gene frequencies, glasses, and wealth distributions. Finally some Voter models
and an example of a Minority game also show features described by our model.


  We recently found that Gamma Ray Burst energies and luminosities, in their
comoving frame, are remarkably similar. This, coupled with the clustering of
energetics once corrected for the collimation factor, suggests the possibility
that all bursts, in their comoving frame, have the same peak energy E'peak (of
the order of a few keV) and the same energetics of the prompt emission E'gamma
(of the order of 2e48 erg). The large diversity of bursts energies is then due
to the different bulk Lorentz factor Gamma and jet aperture angle theta_jet. We
investigated, through a population synthesis code, what are the distributions
of Gamma and theta_jet compatible with the observations. Both quantities must
have preferred values, with log-normal best fitting distributions and <Gamma0>
~ 275 and <\theta_jet> ~ 8.7 degree. Moreover, the peak values of the Gamma and
theta_jet distributions must be related - theta_jet^2.5 Gamma =const: the
narrower the jet angle, the larger the bulk Lorentz factor. We predict that ~6%
of the bursts that point to us should not show any jet break in their afterglow
light curve since they have sin(theta_jet)<1/Gamma. Finally, we estimate that
the local rate of GRBs is ~0.3% of all local SNIb/c and ~2.5% of local
hypernovae, i.e. SNIb/c with broad absorption lines.


  The recently sharpened H_0 tension is argued not to be a result of data
calibration or other systematic but an indication for the common nature of dark
matter and dark energy. This conclusion is devised within modified weak-field
General Relativity where the accelerated expansion of the Universe and the
dynamics of galaxy groups and clusters are described by the same parameter, the
cosmological constant. The common nature of the dark sector hence will result
in intrinsic discrepancy/tension between the local and global determinations of
values of the Hubble constant.


  The Haldane-Shastry spin chain has a myriad of remarkable properties,
including Yangian symmetry and, for spin $1/2$, explicit highest-weight
eigenvectors featuring (the case $\alpha = 1/2$ of) Jack polynomials. This
stems from the spin-Calogero-Sutherland model, which reduces to Haldane-Shastry
in a special `freezing' limit.
  In this work we clarify various points that, to the best of our knowledge,
were missing in the literature. We have two main results. First, we show that
freezing the $\mathit{fermionic}$ spin-1/2 Calogero-Sutherland model naturally
accounts for the precise form of the Haldane-Shastry wave functions, including
the Vandermonde factor squared. Second, we use the fermionic framework to prove
the claim of Bernard-Gaudin-Haldane-Pasquier that the Yangian highest-weight
eigenvectors of the $SU(r)$-version of the Haldane-Shastry chain arise by
freezing $SU(r-1)$ spin-Calogero-Sutherland eigenvectors at $\alpha = 1/2$.


  The clebsch potential approach to fluid lagrangians is developed in order to
establish contact with other approaches to fluids. Three variants of the
perfect fluid approach are looked at. The first is an explicit linear
lagrangian constructed directly from the clebsch potentials, this has fixed
equation of state and explicit expression for the pressure but is less general
than a perfect fluid. The second is lagrangians more general than that of a
perfect fluid which are constructed from higher powers of the comoving vector.
The third is lagrangians depending on two vector fields which can represent
both density flow and entropy flow.


  Using neutron elastic and inelastic scattering and high-energy x-ray
diffraction, we present a comparison of 40% Pb(Mg$_{1/3}$Nb$_{2/3}$)O$_{3}$-60%
PbTiO$_{3}$ (PMN-60PT) with pure Pb(Mg$_{1/3}$Nb$_{2/3}$)O$_{3}$ (PMN) and
PbTiO$_{3}$ (PT). We measure the structural properties of PMN-60PT to be
identical to pure PT, however, the lattice dynamics are exactly that previously
found in relaxors PMN and PZN. PMN-60PT displays a well-defined macroscopic
structural transition from a cubic to tetragonal unit cell at 550 K. The
diffuse scattering is shown to be weak indicating that the structural
distortion is long-range in PMN-60PT and short-range polar correlations (polar
nanoregions) are not present. Even though polar nanoregions are absent, the
soft optic mode is short-lived for wavevectors near the zone-centre. Therefore,
PMN-60PT displays the same waterfall effect as prototypical relaxors PMN and
PZN. We conclude that it is random fields resulting from the intrinsic chemical
disorder which is the reason for the broad transverse optic mode observed in
PMN and PMN-60PT near the zone centre and not due to the formation of
short-ranged polar correlations. Through our comparison of PMN, PMN-60PT, and
pure PT, we interpret the dynamic and static properties of the PMN-xPT system
in terms of a random field model in which the cubic anisotropy term dominates
with increasing doping of PbTiO$_{3}$.


  This short note is on a property of the Kullback-Leibler (KL) divergence
which indicates that independent Gaussian distributions minimize the KL
divergence from given independent Gaussian distributions. The primary purpose
of this note is for the referencing of papers that need to make use of this
property entirely or partially.


  We theoretically investigate bulk photovoltaic effects, with a specific focus
on shift-current and injection-current. Initially, we perform a numerical
analysis of the direct current (dc) induced by a laser pulse with a
one-dimensional model, utilizing mean-field theories such as time-dependent
Hartree--Fock and time-dependent Hartree methods. Our numerical results,
obtained with mean-field theories, reveal that the dc component of the current
exists even after irradiation with linearly polarized light as a second-order
nonlinear effect, indicating the generation of injection current. Conversely,
when we employ the independent particle approximation, no injection current is
generated by linearly polarized light. To develop the microscopic understanding
of injection current within the mean-field approximation, we further analyze
the dc component of the current with the perturbation theory, employing the
mean-field approximations, the independent-particle approximation, and the
exact solution of the many-body Schr\"odinger equation. The perturbation
analysis clarifies that the injection current induced by linearly polarized
light under the mean-field approximations is an artifact caused by population
imbalance, created through quantum interference from unphysical self-excitation
pathways. Therefore, investigation of many-body effects on the bulk
photovoltaic effects have to be carefully conducted in mean-field schemes due
to potential contamination by unphysical dc current. Additionally, we perform
the first-principles electron dynamics calculation for BaTiO$_3$ based on the
time-dependent density functional theory, and we confirm that the above
findings from the one-dimensional model calculation and the perturbation
analysis apply to realistic systems.


  The body of research on (III,Mn)V diluted magnetic semiconductors initiated
during the 1990's has concentrated on three major fronts: i) the microscopic
origins and fundamental physics of the ferromagnetism that occurs in these
systems, ii) the materials science of growth and defects and iii) the
development of spintronic devices with new functionalities. This article
reviews the current status of the field, concentrating on the first two, more
mature research directions. From the fundamental point of view, (Ga,Mn)As and
several other (III,Mn)V DMSs are now regarded as textbook examples of a rare
class of robust ferromagnets with dilute magnetic moments coupled by
delocalized charge carriers. Both local moments and itinerant holes are
provided by Mn, which makes the systems particularly favorable for realizing
this unusual ordered state. Advances in growth and post-growth treatment
techniques have played a central role in the field, often pushing the limits of
dilute Mn moment densities and the uniformity and purity of materials far
beyond those allowed by equilibrium thermodynamics. In (III,Mn)V compounds,
material quality and magnetic properties are intimately connected. In the
review we focus on the theoretical understanding of the origins of
ferromagnetism and basic structural, magnetic, magneto-transport, and
magneto-optical characteristics of simple (III,Mn)V epilayers, with the main
emphasis on (Ga,Mn)As. The conclusions we arrive at are based on an extensive
literature covering results of complementary ab initio and effective
Hamiltonian computational techniques, and on comparisons between theory and
experiment.


  Fractional delay filters are digital filters to delay discrete-time signals
by a fraction of the sampling period. Since the delay is fractional, the
intersample behavior of the original analog signal becomes crucial. In contrast
to the conventional designs based on the Shannon sampling theorem with the
band-limiting hypothesis, the present paper proposes a new approach based on
the modern sampled-data H-infinity optimization that aims at restoring the
intersample behavior beyond the Nyquist frequency. By using the lifting
transform or continuous-time blocking the design problem is equivalently
reduced to a discrete-time H-infinity optimization, which can be effectively
solved by numerical computation softwares. Moreover, a closed-form solution is
obtained under an assumption on the original analog signals. Design examples
are given to illustrate the advantage of the proposed method.


  Computer-aided diagnosis (CAD) techniques for lung field segmentation from
chest radiographs (CXR) have been proposed for adult cohorts, but rarely for
pediatric subjects. Statistical shape models (SSMs), the workhorse of most
state-of-the-art CXR-based lung field segmentation methods, do not efficiently
accommodate shape variation of the lung field during the pediatric
developmental stages. The main contributions of our work are: (1) a generic
lung field segmentation framework from CXR accommodating large shape variation
for adult and pediatric cohorts; (2) a deep representation learning detection
mechanism, \emph{ensemble space learning}, for robust object localization; and
(3) \emph{marginal shape deep learning} for the shape deformation parameter
estimation. Unlike the iterative approach of conventional SSMs, the proposed
shape learning mechanism transforms the parameter space into marginal subspaces
that are solvable efficiently using the recursive representation learning
mechanism. Furthermore, our method is the first to include the challenging
retro-cardiac region in the CXR-based lung segmentation for accurate lung
capacity estimation. The framework is evaluated on 668 CXRs of patients between
3 month to 89 year of age. We obtain a mean Dice similarity coefficient of
$0.96\pm0.03$ (including the retro-cardiac region). For a given accuracy, the
proposed approach is also found to be faster than conventional SSM-based
iterative segmentation methods. The computational simplicity of the proposed
generic framework could be similarly applied to the fast segmentation of other
deformable objects.


  Colloidal Cadmium Selenide (CdSe) nanoplatelets (NPLs) are a recently
developed class of efficient luminescent nanomaterial suitable for
optoelectronic device applications. A change in temperature greatly affects
their electronic bandstructure and luminescence properties. It is important to
understand how-and-why the characteristics of NPLs are influenced, particularly
at elevated temperature, where both reversible and irreversible quenching
processes come into picture. Here we present a study on the effect of elevated
temperature on the characteristics of colloidal CdSe NPLs. We used an
effective-mass envelope function theory based 8-band k$\cdot$p model and
density-matrix theory considering exciton-phonon interaction. We observed the
photoluminescence (PL) spectra at various temperatures for their photon
emission energy, PL linewidth and intensity by considering the exciton-phonon
interaction with both acoustic and optical phonons using Bose-Einstein
statistical factors. With rise in temperature we observed a fall in the
transition energy (emission redshift), matrix element, Fermi factor and quasi
Fermi separation, with reduction in intraband state gaps and increased
interband coupling. Also, there was a fall in the PL intensity, along with
spectral broadening due to an intraband scattering effect. The predicted
transition energy values and simulated PL spectra at varying temperatures
exhibit appreciable consistency with experimental results. Our findings have
important implications for application of NPLs in optoelectronic devices, such
as NPL lasers and LEDs, operating much above room temperature.


  We examine the envelope properties of asymptotic giant branch (AGB) stars as
they evolve on the upper AGB and during the early post-AGB phase. Due to the
high mass loss rate, the envelope mass decreases by more than an order of
magnitude. This makes the density profile below the photosphere much shallower,
and the entropy profile much steeper. We discuss the possible role of these
changes in the profiles in the onset of the high mass loss rate (superwind) and
the large deviation from spherical mass loss at the termination of the AGB. We
concentrate on the idea that the shallower density profile and steeper entropy
profile allow the formation of cool magnetic spots, above which dust forms much
more easily.


  The amount of information generated by a discrete time stochastic processes
in a single step can be quantified by the entropy rate. We investigate the
differences between two discrete time walk models, the discrete time quantum
walk and the classical random walk in terms of entropy rate. We develop
analytical methods to calculate and approximate it. This allows us to draw
conclusions about the differences between classical stochastic and quantum
processes in terms of the classical information theory.


  We determine, for all $k\geq 6$, the typical structure of graphs that do not
contain an induced $2k$-cycle. This verifies a conjecture of Balogh and
Butterfield. Surprisingly, the typical structure of such graphs is richer than
that encountered in related results. The approach we take also yields an
approximate result on the typical structure of graphs without an induced
$8$-cycle or without an induced $10$-cycle.


  Three-particle azimuthal correlation measurements with a high transverse
momentum trigger particle are reported for pp, d+Au, and Au+Au collisions at
200 GeV by the STAR experiment. Dijet structures are observed in pp, d+Au and
peripheral Au+Au collisions. An additional structure is observed in central
Au+Au data, signaling conical emission of correlated charged hadrons. The
conical emission angle is found to be 1.37 +- 0.02(stat) +0.06-0.07(syst),
independent of pt.


  We show that the commonly adopted hot dark matter (HDM) bound on the axion
mass $m_a \lesssim$ 1 eV is not reliable, since it is obtained by extrapolating
the chiral expansion in a region where the effective field theory breaks down.
This is explicitly shown via the calculation of the axion-pion thermalization
rate at the next-to-leading order in chiral perturbation theory. We finally
advocate a strategy for a sound extraction of the axion HDM bound via lattice
QCD techniques.


  We evaluate the three point function for arbitrary states in bosonic minimal
models on the sphere coupled to quantum gravity in two dimensions. The validity
of the formal continuation in the number of Liouville screening charge
insertions is shown directly from the Liouville functional integral using
semi-classical methods.


  Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) and
functional magnetic resonance imaging (fMRI), enable us to model the human
brain as a brain network or connectome. Capturing brain networks' structural
information and hierarchical patterns is essential for understanding brain
functions and disease states. Recently, the promising network representation
learning capability of graph neural networks (GNNs) has prompted many GNN-based
methods for brain network analysis to be proposed. Specifically, these methods
apply feature aggregation and global pooling to convert brain network instances
into meaningful low-dimensional representations used for downstream brain
network analysis tasks. However, existing GNN-based methods often neglect that
brain networks of different subjects may require various aggregation iterations
and use GNN with a fixed number of layers to learn all brain networks.
Therefore, how to fully release the potential of GNNs to promote brain network
analysis is still non-trivial. To solve this problem, we propose a novel brain
network representation framework, namely BN-GNN, which searches for the optimal
GNN architecture for each brain network. Concretely, BN-GNN employs deep
reinforcement learning (DRL) to train a meta-policy to automatically determine
the optimal number of feature aggregations (reflected in the number of GNN
layers) required for a given brain network. Extensive experiments on eight
real-world brain network datasets demonstrate that our proposed BN-GNN improves
the performance of traditional GNNs on different brain network analysis tasks.


  We study a model for the metal-insulator (MI) transition in the rare-earth
nickelates RNiO$_3$, based upon a negative charge transfer energy and coupling
to a rock-salt like lattice distortion of the NiO$_6$ octahedra. Using exact
diagonalization and the Hartree-Fock approximation we demonstrate that
electrons couple strongly to these distortions. For small distortions the
system is metallic, with ground state of predominantly $d^8\ligand$ character,
where $\ligand$ denotes a ligand hole. For sufficiently large distortions
($\delta d_{\rm Ni-O} \sim 0.05 - 0.10\AA$), however, a gap opens at the Fermi
energy as the system enters a periodically distorted state alternating along
the three crystallographic axes, with $(d^8\ligand^2)_{S=0}(d^8)_{S=1}$
character, where $S$ is the total spin. Thus the MI transition may be viewed as
being driven by an internal volume "collapse" where the NiO$_6$ octahedra with
two ligand holes shrink around their central Ni, while the remaining octahedra
expand accordingly, resulting in the ($1/2,1/2,1/2$) superstructure observed in
x-ray diffraction in the insulating phase. This insulating state is an example
of a new type of charge ordering achieved without any actual movement of the
charge.


  In the present study, we investigate the multifractal nature of a
long-cadence time series observed by the \textit{Kepler} mission for a sample
of 34 M dwarf stars and the Sun in its active phase. Using the Multifractal
Detrending Moving Average algorithm (MFDMA), which enables the detection of
multifractality in nonstationary time series, we define a set of multifractal
indices based on the multifractal spectrum profile as a measure of the level of
stellar magnetic activity. This set of indices is given by the ($A$,$\Delta
\alpha$,$C$,$H$)-quartet, where $A$, $\Delta \alpha$ and $C$ are related to
geometric features from the multifractal spectrum and the global Hurst exponent
$H$ describes the global structure and memorability of time series dynamics. As
a test, we measure these indices and compare them with a magnetic index defined
as $S_{ph}$ and verify the degree of correlation among them. First, we apply
the Poincar\'e plot method and find a strong correlation between the
$\left\langle S_{ph}\right\rangle$ index and one of the descriptors that
emerges from this method. As a result, we find that this index is strongly
correlated with long-term features of the signal. From the multifractal
perspective, the $\left\langle S_{ph}\right\rangle$ index is also strongly
linked to the geometric properties of the multifractal spectrum except for the
$H$ index. Furthermore, our results emphasize that the rotation period of stars
is scaled by the $H$ index, which is consistent with Skumanich's relationship.
Finally, our approach suggests that the $H$ index may be related to the
evolution of stellar angular momentum and a star's magnetic properties.


  We use the Spitzer SAGE survey of the Magellanic Clouds to evaluate the
relationship between the 8-mic PAH emission, 24-mic hot dust emission, and HII
region radiative transfer. We confirm that in the higher-metallicity Large
Magellanic Cloud, PAH destruction is sensitive to optically thin conditions in
the nebular Lyman continuum: objects identified as optically thin candidates
based on nebular ionization structure show 6 times lower median 8-mic surface
brightness (0.18 mJy arcsec^-2) than their optically thick counterparts (1.2
mJy arcsec^-2). The 24-mic surface brightness also shows a factor of 3 offset
between the two classes of objects (0.13 vs 0.44 mJy arcsec^-2, respectively),
which is driven by the association between the very small dust grains and
higher density gas found at higher nebular optical depths. In contrast, PAH and
dust formation in the low-metallicity Small Magellanic Cloud is strongly
inhibited such that we find no variation in either 8-mic or 24-mic emission
between our optically thick and thin samples. This is attributable to extremely
low PAH and dust production together with high, corrosive UV photon fluxes in
this low-metallicity environment. The dust mass surface densities and
gas-to-dust ratios determined from dust maps using Herschel HERITAGE survey
data support this interpretation.


  In this paper, we compute a number of cross sections for the production of
multiple particles at mid-rapidity in the semi-dilute / dense regime of the
color-glass condensate (CGC) effective field theory. In particular, we present
new results for the production of two quark-antiquark pairs (whether the same
or different flavors) and for the production of one quark-antiquark pair and a
gluon. We also demonstrate the existence of a simple mapping which transforms
the cross section to produce a quark-antiquark pair into the corresponding
cross section to produce a gluon, which we use to obtain various results and to
cross-check them against the literature. We also discuss hadronization effects
in the heavy flavor sector, writing explicit expressions for the production of
various combinations of $D$ and $\bar D$ mesons, $J/\psi$ mesons, and light
hadrons. The various multiparticle cross sections presented here contain a
wealth of information and can be used to study heavy flavor production,
charge-dependent correlations, and "collective" flow phenomena arising from
initial-state dynamics.


  HST observations show that the surface brightness profiles of early-type
galaxies have central cusps. I summarize here the results of van der Marel
(1999), which show that the observed characteristics of these cusps are
consistent with the hypothesis that all early-type galaxies have central black
holes (BHs) that grew adiabatically in homogeneous isothermal cores. The models
suggest a roughly linear correlation between BH mass and V-band galaxy
luminosity, log M_BH = -1.83 + log L in solar units, similar to the relation
suggested by kinematical BH detections in nearby galaxies.


  We propose to realize a two-dimensional chiral topological superconducting
(TSC) state from the quantum anomalous Hall plateau transition in a magnetic
topological insulator thin film through the proximity effect to a conventional
$s$-wave superconductor. This state has a full pairing gap in the bulk and a
single chiral Majorana mode at the edge. The optimal condition for realizing
such chiral TSC is to have inequivalent superconducting pairing amplitudes on
top and bottom surfaces of the doped magnetic topological insulator. We further
propose several transport experiments to detect the chiral TSC. One unique
signature is that the conductance will be quantized into a half-integer plateau
at the coercive field in this hybrid system. In particular, with the point
contact formed by a superconducting junction, the conductance oscillates
between $e^2/2h$ and $e^2/h$ with the frequency determined by the voltage
across the junction. We close by discussing the feasibility of these
experimental proposals.


  This article uses data of subjective Life Satisfaction aggregated to the
community level in Canada and examines the spatial interdependencies and
spatial spillovers of community happiness. A theoretical model of utility is
presented. Using spatial econometric techniques, we find that the utility of
community, proxied by subjective measures of life satisfaction, is affected
both by the utility of neighbouring communities as well as by the latter's
average household income and unemployment rate. Shared cultural traits and
institutions may justify such spillovers. The results are robust to the
different binary contiguity spatial weights matrices used and to the various
econometric models. Clusters of both high-high and low-low in Life Satisfaction
communities are also found based on the Moran's I test


  We show that if a disc triangulation has all internal vertex degrees at least
6, then the full triangulation may be determined from the pairwise graph
distance between boundary vertices. A similar result holds for quadrangulations
with all internal degrees at least 4. This confirms a conjecture of Itai
Benjamini. Both degree bounds are best possible, and correspond to local
non-positive curvature. However, we show that a natural conjecture for a
"mixed" version of the two results is not true.


  We give an exact solution of the quadratic gravity in D dimensions. The
solution is a plane fronted wave metric with a cosmological constant. This
metric solves not only the full quadratic gravity field equations but also the
linearized ones which include the linearized equations of the recently found
critical gravity. A subset of the solutions change the asymptotic structure of
the anti-de Sitter space due to their logarithmic behavior.


  We present a joint analysis of the galaxy S04590 at $z=8.496$ based on
NIRSpec, NIRCam, and NIRISS observations obtained through as part of Early
Release Observations programme of the James Webb Space Telescope (JWST) and the
far-infrared [CII]-$158\mu$m emission line detected by dedicated Atacama Large
Millimeter/submillimeter Array (ALMA) observations. We determine the physical
properties of S04590 from modelling of the spectral energy distribution (SED)
and through the redshifted optical nebular emission lines detected with
JWST/NIRSpec. The best-fit SED model reveals a low-mass ($M_\star =
10^{7.2}-10^{8}\,M_{\odot}$) galaxy with a low oxygen abundance of $12+\log{\rm
(O/H)} = 7.16^{+0.10}_{-0.12}$ derived from the strong nebular and auroral
emission lines. Assuming that [CII] effectively traces the interstellar medium
(ISM), we estimate the total gas mass of the galaxy to be $M_{\rm gas} =
(8.0\pm 4.0)\times 10^{8}\,M_\odot$ based on the luminosity and spatial extent
of [CII]. This yields an exceptionally high gas fraction, $f_{\rm gas} = M_{\rm
gas}/(M_{\rm gas} + M_\star) \gtrsim 90\%$, though still consistent within the
range expected for its low metallicity. We further derive the metal mass of the
galaxy based on the gas mass and gas-phase metallicity, which we find to be
consistent with the expected metal production from Type II supernovae. Finally,
we make the first constraints on the dust-to-gas (DTG) and dust-to-metals (DTM)
ratios of galaxies in the epoch of reionization at $z\gtrsim 6$, showing
overall low mass ratios of logDGT $<-3.8$ and logDTM $<-0.5$, though consistent
with local scaling relations and in particular the local metal-poor galaxy I
Zwicky 18. Our analysis highlights the synergy between ALMA and JWST in
characterizing the gas, metal, and stellar content of the first generation of
galaxies.


  Unsupervised semantic segmentation (USS) aims to achieve high-quality
segmentation without manual pixel-level annotations. Existing USS models
provide coarse category classification for regions, but the results often have
blurry and imprecise edges. Recently, a robust framework called the segment
anything model (SAM) has been proven to deliver precise boundary object masks.
Therefore, this paper proposes a universal organizer based on SAM, termed as
UO-SAM, to enhance the mask quality of USS models. Specifically, using only the
original image and the masks generated by the USS model, we extract visual
features to obtain positional prompts for target objects. Then, we activate a
local region optimizer that performs segmentation using SAM on a per-object
basis. Finally, we employ a global region optimizer to incorporate global image
information and refine the masks to obtain the final fine-grained masks.
Compared to existing methods, our UO-SAM achieves state-of-the-art performance.


  Given two positive definite forms $f,\,g\in\mathbb{R}[x_1,\dots,x_n]$, we
prove that $fg^r$ lies in the interior of the sums of squares cone for large
$r$.


  We calculate zeta functions for certain orders of rank $3$ defined by
standard integral table algebras and integral fusion rings that have
irrational-valued irreducible characters. The calculations are obtained from
explicit calculations of zeta integrals.


  We report the observation of the decay mode $B^{\pm}\to p \bar{p} K^{\pm}$
based on an analysis of 29.4 fb$^{-1}$ of data collected by the Belle detector
at KEKB. This is the first example of a $b\to s$ transition with baryons in the
final state. The $p \bar{p}$ mass spectrum in this decay is inconsistent with
phase space and is peaked at low mass. The branching fraction for this decay is
measured to be ${\cal B}(B^{\pm}\to p \bar{p} K^{\pm})
  =(4.3^{+1.1}_{-0.9}({\rm stat})\pm 0.5({\rm syst}))\times 10^{-6}.$ We also
report upper limits for the decays $B^0\to p \bar{p} K_S$ and $B^{\pm}\to p
\bar{p} \pi^{\pm}$.


  We review both theoretical and experimental developments in the area of
quantum games since the inception of the subject circa 1999. We will also offer
a narrative on the controversy that surrounded the subject in its early days,
and how this controversy has affected the development of the subject.


  Earthquake size-frequency distributions commonly follow a power law, with the
b value often used to quantify the relative proportion of small and large
events. Laboratory experiments have found that the b value of microfractures
decreases with increasing stress. Studies have inferred that this relationship
also holds for earthquakes based on observations of earthquake b values varying
systematically with faulting style, depth, and for subduction zone earthquakes,
plate age. However, these studies are limited by small sample sizes despite
aggregating events over large regions, which precludes the ability to control
for other variables that might also affect earthquake b values such as rock
heterogeneity and fault roughness. Our natural experiment in a unique seafloor
laboratory on Axial Seamount involves analyzing the size-frequency distribution
of ~60,000 microearthquakes which delineate a ring-fault system in a 25 km3
block of crust that experiences periodic tidal loading of +/-20 kPa. We find
that above a threshold stress amplitude, b value is inversely correlated with
tidal stress. The earthquake b value varies by ~0.09 per kPa change in Coulomb
stress. Our results support the potential use of b values to estimate small
stress variations in the Earth's crust.


  We show that the induced representations of the de Sitter isometry group
proposed many years ago by Nachtmann are equivalent to those derived from our
general theory of external symmetry. These methods complete each other leading
to a coherent theory of covariant fields with spin on the de Sitter spacetime.
Some technical details of these representations are presented here for the
first time.