  We present an analysis of high latitude $\delta$ Scuti stars
($\left|b\right|> 1^{\circ}$) in the Galactic bulge region ($-8^{\circ}.3<
l<9^{\circ}.4$) using a clean sample of the photometric data of $7,440$ stars
recently released by the OGLE-IV project. The geometrical parameters of the
bulge are determined based on Maximum Likelihood (ML) analysis in
five-dimensional parameter space. More refined values of these parameters as
well as their uncertainties are obtained from a fully Bayesian Markov Chain
Monte Carlo (MCMC) analysis. Approximating the bulge as an ellipsoid, the
distribution of the number density of stars as a function of Galacto-centric
distance has been modelled using three distribution functions: two Exponential
($\rm E_{1},\rm E_{2}$) types and one Gaussian ($\rm G$) type. Based on the AIC
and BIC values, the exponential model $\rm E_{1}$ is chosen as the best
statistical model for the parameter values obtained from the MCMC analysis. The
MCMC analysis yields the following results: the mean distance to the Galactic
center (GC) is found to be $R_{0}=8.034\pm0.012_{\rm stat}\pm0.586_{\rm sys}$
kpc; the bulge $\delta$ Scuti distribution has a triaxial shape with normalized
($a\equiv1$) axes ratios ($a:b:c$) as $1.000\pm
0.005:0.348\pm0.002:0.421\pm0.002$. Here $a$ is the semi-major axis lying in
the Galactic plane and pointing towards us; $b$ and $c$ are the two semi-minor
axes, the former lying in the Galactic plane and the later perpendicular to it.
Smaller values of $b$ as compared to $a$ obtained for Galacto-centric distances
$R\ge 2.0$~kpc indicate the presence of a bar-like structure of the bulge with
a bar angle of $22^{\circ}.006\pm2^{\circ}.078$.


  Headline generation aims to summarize a long document with a short, catchy
title that reflects the main idea. This requires accurately capturing the core
document semantics, which is challenging due to the lengthy and background
information-rich na ture of the texts. In this work, We propose using a unified
semantic discourse structure (S3) to represent document semantics, achieved by
combining document-level rhetorical structure theory (RST) trees with
sentence-level abstract meaning representation (AMR) graphs to construct S3
graphs. The hierarchical composition of sentence, clause, and word
intrinsically characterizes the semantic meaning of the overall document. We
then develop a headline generation framework, in which the S3 graphs are
encoded as contextual features. To consolidate the efficacy of S3 graphs, we
further devise a hierarchical structure pruning mechanism to dynamically screen
the redundant and nonessential nodes within the graph. Experimental results on
two headline generation datasets demonstrate that our method outperforms
existing state-of-art methods consistently. Our work can be instructive for a
broad range of document modeling tasks, more than headline or summarization
generation.


  Ratchet effect -- a {\it dc} current induced by the electromagnetic wave
impinging on the spatially modulated two-dimensional (2D) electron liquid --
occurs when the wave amplitude is spatially modulated with the same wave vector
as the 2D liquid but is shifted in phase. The analysis within the framework of
the hydrodynamic model shows that the ratchet current is dramatically enhanced
in the vicinity of the plasmonic resonances and has nontrivial polarization
dependence. In particular, for circular polarization, the current component,
perpendicular to the modulation direction, changes sign with the inversion of
the radiation helicity. Remarkably, in the high-mobility structures, this
component might be much larger than the the current component in the modulation
direction. We also discuss the non-resonant regime realized in dirty systems,
where the plasma resonances are suppressed, and demonstrate that the
non-resonant ratchet current is controlled by the Maxwell relaxation in the 2D
liquid.


  The estimation of implicit cross-frame correspondences and the high
computational cost have long been major challenges in video semantic
segmentation (VSS) for driving scenes. Prior works utilize keyframes, feature
propagation, or cross-frame attention to address these issues. By contrast, we
are the first to harness vanishing point (VP) priors for more effective
segmentation. Intuitively, objects near VPs (i.e., away from the vehicle) are
less discernible. Moreover, they tend to move radially away from the VP over
time in the usual case of a forward-facing camera, a straight road, and linear
forward motion of the vehicle. Our novel, efficient network for VSS, named
VPSeg, incorporates two modules that utilize exactly this pair of static and
dynamic VP priors: sparse-to-dense feature mining (DenseVP) and VP-guided
motion fusion (MotionVP). MotionVP employs VP-guided motion estimation to
establish explicit correspondences across frames and help attend to the most
relevant features from neighboring frames, while DenseVP enhances weak dynamic
features in distant regions around VPs. These modules operate within a
context-detail framework, which separates contextual features from
high-resolution local features at different input resolutions to reduce
computational costs. Contextual and local features are integrated through
contextualized motion attention (CMA) for the final prediction. Extensive
experiments on two popular driving segmentation benchmarks, Cityscapes and
ACDC, demonstrate that VPSeg outperforms previous SOTA methods, with only
modest computational overhead.


  Any scientific attempt to explain consciousness is tasked with reconciling
the third person objective perspective of science with our first person
subjective experience of the world. A good point of departure is to consider
situations in which these two perspectives are correlated. We note that
behaviors conducive to our well-being, i.e. conducive to maintaining
homeostasis, tend to be associated with feelings of pleasure while actions that
threaten our homeostasis tend to coincide with unpleasant feelings. We choose a
materialist/physicalist approach and find the simplest explanation for this
correlation arises from the assumption that we possess agency. If a system has
the agency to choose an outcome then presumably it would choose a pleasant over
an unpleasant one. Agency implementing preferences manifests itself as a force
in a third person theory. If a preference is strong it gives rise to a force
causing a deterministic evolution, while weak preferences give rise to
non-deterministic evolutions. Quantum physics assigns a high degree of
non-determinism to large systems of qubits, a Knightian uncertainty, in which
one can not even assign probabilities to observed outcomes. This can give
engineered systems the freedom to act on their preferences in ways not always
predictable by an outside observer. Our considerations lead us to propose a
three part design for an engineered animat for which one may provocatively
argue that it is conscious and possesses agency and feelings.


  The variable sdB known as V46 in the globular cluster M4 has remained
enigmatic since its discovery almost 10 years ago. We present here radial
velocity measurements obtained from medium-resolution VLT/FORS2 spectra that
show variations at twice the period of the luminosity changes. This implies
that the system is an ellipsoidal variable. Unlike the other sdB binaries of
this nature, the fundamental parameters of this star we derive suggest that it
lies below the Zero Age Extreme Horizontal Branch. From the cluster distance
and the gravity we determine the mass of V46 to be ~0.19Msun. This is too low
to sustain core helium burning. From the mass function we derive a lower limit
for the companion of only 0.26Msun. We discuss the star's origin in the context
of close binary evolution in the field and globular clusters.


  Earthquake occurrence in nature is thought to result from correlated elastic
stresses, leading to clustering in space and time. We show that occurrence of
major earthquakes in California correlates with time intervals when
fluctuations in small earthquakes are suppressed relative to the long term
average. We estimate a probability of less than 1% that this coincidence is due
to random clustering.


  Rule-based modelling allows to represent molecular interactions in a compact
and natural way. The underlying molecular dynamics, by the laws of stochastic
chemical kinetics, behaves as a continuous-time Markov chain. However, this
Markov chain enumerates all possible reaction mixtures, rendering the analysis
of the chain computationally demanding and often prohibitive in practice. We
here describe how it is possible to efficiently find a smaller, aggregate
chain, which preserves certain properties of the original one. Formal methods
and lumpability notions are used to define algorithms for automated and
efficient construction of such smaller chains (without ever constructing the
original ones). We here illustrate the method on an example and we discuss the
applicability of the method in the context of modelling large signalling
pathways.


  Two novel visual cryptography (VC) schemes are proposed by combining VC with
single-pixel imaging (SPI) for the first time. It is pointed out that the
overlapping of visual key images in VC is similar to the superposition of pixel
intensities by a single-pixel detector in SPI. In the first scheme, QR-code VC
is designed by using opaque sheets instead of transparent sheets. The secret
image can be recovered when identical illumination patterns are projected onto
multiple visual key images and a single detector is used to record the total
light intensities. In the second scheme, the secret image is shared by multiple
illumination pattern sequences and it can be recovered when the visual key
patterns are projected onto identical items. The application of VC can be
extended to more diversified scenarios by our proposed schemes.


  We analyze an unconventional inter-subband paired phase in a 2D doped
superconductor considering both systems with the inversion symmetry and with
the inversion symmetry broken. We find that for a centro-symmetric system the
inter-subband pairing can appear in the high concentration regime when the
repulsive Coulomb interaction leads to the nearly degenerate symmetric and
antisymmetric state. We discuss in detail the mutual competition between the
intra- and inter-subband paired phase. For systems with broken inversion
symmetry, we find that the critical temperature has a characteristic domelike
shape as a function of the asymmetry parameter, which is explained as resulting
form the inter-subbband pairing. This results is discussed in the context of
the domelike shape of $T_c$ in the LaAlO$_3$/SrTiO$_3$ interface.


  Comparison of appropriate models to describe observational data is a
fundamental task of science. The Bayesian model evidence, or marginal
likelihood, is a computationally challenging, yet crucial, quantity to estimate
to perform Bayesian model comparison. We introduce a methodology to compute the
Bayesian model evidence in simulation-based inference (SBI) scenarios (also
often called likelihood-free inference). In particular, we leverage the
recently proposed learnt harmonic mean estimator and exploit the fact that it
is decoupled from the method used to generate posterior samples, i.e. it
requires posterior samples only, which may be generated by any approach. This
flexibility, which is lacking in many alternative methods for computing the
model evidence, allows us to develop SBI model comparison techniques for the
three main neural density estimation approaches, including neural posterior
estimation (NPE), neural likelihood estimation (NLE), and neural ratio
estimation (NRE). We demonstrate and validate our SBI evidence calculation
techniques on a range of inference problems, including a gravitational wave
example. Moreover, we further validate the accuracy of the learnt harmonic mean
estimator, implemented in the HARMONIC software, in likelihood-based settings.
These results highlight the potential of HARMONIC as a sampler-agnostic method
to estimate the model evidence in both likelihood-based and simulation-based
scenarios.


  We study the impact of leading soft-collinear effects on threshold and
joint-resummed calculations for the production of prompt photons via parton
fragmentation, complementing a previous study for direct production. We assess
these effects for both fixed-target and collider kinematics. We find them to be
small, but noticeable and comparable to the direct case.


  A consistent description of the radiative polarization for relativistic
electrons in intense laser fields is derived by generalizing the Sokolov-Ternov
effect in general field structure. The new form together with the
spin-dependent radiation-reaction force provides a complete set of dynamical
equations for electron momentum and spin in strong fields. When applied to
varying intense fields, e.g. the laser fields, the generalized Sokolov-Ternov
effect allows electrons to gain or lose polarization in any directions other
than along the magnetic field in the rest frame of the electron. The
generalized theory is applied to the collision process between initially
polarized/unpolarized high energy electrons with linearly polarized
ultra-intense laser pulse, showing results that eliminate the dependence on
specific choices of a quantization axis and spin initialization existing in
spin-projection models.


  The task of unpaired image-to-image translation is highly challenging due to
the lack of explicit cross-domain pairs of instances. We consider here diverse
image translation (DIT), an even more challenging setting in which an image can
have multiple plausible translations. This is normally achieved by explicitly
disentangling content and style in the latent representation and sampling
different styles codes while maintaining the image content. Despite the success
of current DIT models, they are prone to suffer from bias. In this paper, we
study the problem of bias in image-to-image translation. Biased datasets may
add undesired changes (e.g. change gender or race in face images) to the output
translations as a consequence of the particular underlying visual distribution
in the target domain. In order to alleviate the effects of this problem we
propose the use of semantic constraints that enforce the preservation of
desired image properties. Our proposed model is a step towards unbiased diverse
image-to-image translation (UDIT), and results in less unwanted changes in the
translated images while still performing the wanted transformation. Experiments
on several heavily biased datasets show the effectiveness of the proposed
techniques in different domains such as faces, objects, and scenes.


  The definition of Q-conditional symmetry for one PDE is correctly generalized
to a special case of systems of PDEs and involutive families of operators. The
notion of equivalence of Q-conditional symmetries under a group of local
transformation is introduced. Using this notion, all possible single
Q-conditional symmetry operators are classified for the n-dimensional (n >= 2)
linear heat equation and for the Euler equations describing the motion of an
incompressible ideal fluid.


  We perform a global fit to neutrino oscillation and coherent neutrino-nucleus
scattering data, using both timing and energy information from the COHERENT
experiment. The results are used to set model-independent bounds on
four-fermion effective operators inducing non-standard neutral-current neutrino
interactions. We quantify the allowed ranges for their Wilson coefficients, as
well as the status of the LMA-D solution, for a wide class of new physics
models with arbitrary ratios between the strength of the operators involving up
and down quarks. Our results are presented for the COHERENT experiment alone,
as well as in combination with the global data from oscillation experiments. We
also quantify the dependence of our results for COHERENT with respect to the
choice of quenching factor, nuclear form factor, and the treatment of the
backgrounds.


  Language models (LMs), including large language models (such as ChatGPT),
have the potential to assist clinicians in generating various clinical notes.
However, LMs are prone to produce ``hallucinations'', i.e., generated content
that is not aligned with facts and knowledge. In this paper, we propose the
Re$^3$Writer method with retrieval-augmented generation and knowledge-grounded
reasoning to enable LMs to generate faithful clinical texts. We demonstrate the
effectiveness of our method in generating patient discharge instructions. It
requires the LMs not to only understand the patients' long clinical documents,
i.e., the health records during hospitalization, but also to generate critical
instructional information provided both to carers and to the patient at the
time of discharge. The proposed Re$^3$Writer imitates the working patterns of
physicians to first \textbf{re}trieve related working experience from
historical instructions written by physicians, then \textbf{re}ason related
medical knowledge. Finally, it \textbf{re}fines the retrieved working
experience and reasoned medical knowledge to extract useful information, which
is used to generate the discharge instructions for previously-unseen patients.
Our experiments show that, using our method, the performance of five
representative LMs can be substantially boosted across all metrics. Meanwhile,
we show results from human evaluations to measure the effectiveness in terms of
fluency, faithfulness, and comprehensiveness.


  We report X-ray diffraction and magnetic susceptibility studies of the
structural phase transition in $\alpha$-RuCl$_{3}$. By utilizing a single
crystal sample with predominantly single twin domain, we show that
$\alpha$-RuCl$_{3}$ goes from high-temperature C2/m structure to a rhombohedral
structure with R$\bar{3}$ symmetry at low temperature. While the defining
feature of the structural transition is changing the stacking direction from
the monoclinic a-axis to the b-axis, bond-anisotropy disappears when the
structural change occurs, indicating that the local $C_3$ symmetry is restored
within the honeycomb layer. The symmetry change is corroborated by the
vanishing magnetic anisotropy in the low-temperature structure. Our study
demonstrates that magnetic interaction is extremely sensitive to structural
details in $\alpha$-RuCl$_{3}$, which could explain the sample dependence found
in this material.


  Mobile devices apply neighbor discovery (ND) protocols to wirelessly initiate
a first contact within the shortest possible amount of time and with minimal
energy consumption. For this purpose, over the last decade, a vast number of ND
protocols have been proposed, which have progressively reduced the relation
between the time within which discovery is guaranteed and the energy
consumption. In spite of the simplicity of the problem statement, even after
more than 10 years of research on this specific topic, new solutions are still
proposed even today. Despite the large number of known ND protocols, given an
energy budget, what is the best achievable latency still remains unclear. This
paper addresses this question and for the first time presents safe and tight,
duty-cycle-dependent bounds on the worst-case discovery latency that no ND
protocol can beat. Surprisingly, several existing protocols are indeed optimal,
which has not been known until now. We conclude that there is no further
potential to improve the relation between latency and duty-cycle, but future ND
protocols can improve their robustness against beacon collisions.


  Light passing through scattering media will be strongly scattered and
diffused into complex speckle pattern, which however contains almost all the
spatial information and color information of the objects. Although various
technologies have been proposed to realize color imaging through the scattering
media, current technologies are still complex with long sequence of measurement
for each imaging pixel or spectral point spread functions of optical system.
Here we theoretically prove the spatial averaging of triple correlation
technique can be used to retrieve the Fourier phase of object, and
experimentally demonstrate it can be applied in color imaging through
scattering media. Compared to other phase retrieval techniques, the phase
retrieval with triple correlation technique can retain the orientation
information of objects, and can composite color image without rotation
operation. Furthermore, our approach has the potential of realizing spectral
imaging through scattering media.


  In this paper, we construct a new topological quantum field theory of
cohomological type and show that its partition function is a crossing number.


  While the phenomenon of metal substrate adatom incorporation into molecular
overlayers is generally believed to occur in several systems, the experimental
evidence for this relies on the interpretation of scanning tunnelling
microscopy (STM) images, which can be ambiguous and provides no quantitative
structural information. We show that surface X-ray diffraction (SXRD) uniquely
provides unambiguous identification of these metal adatoms. We present the
results of a detailed structural study of the Au(111)-F4TCNQ system, combining
surface characterisation by STM, low energy electron diffraction and soft X-ray
photoelectron spectroscopy with quantitative experimental structural
information from normal incidence X-ray standing waves (NIXSW) and SXRD,
together with dispersion corrected density functional theory (DFT)
calculations. Excellent agreement is found between the NIXSW data and the DFT
calculations regarding the height and conformation of the adsorbed molecule,
which has a twisted geometry rather than the previously supposed inverted bowl
shape. SXRD measurements provide unequivocal evidence for the presence and
location of Au adatoms, while the DFT calculations show this reconstruction to
be strongly energetically favoured.


  Based on density functional theory calculations, we here show that the
formation of a fullerene C$_{60}$ carbon "nanobud" (CNB) on carbon nanotube
(CNT) caps is energetically more favorable than that on CNT sidewalls. The
dominant CNB formation mode for CNT caps is found to be the [2+2] cycloaddition
reaction as in the conventional CNT sidewall case. However, it is identified to
be exothermic in contrast to the endothermic reaction on CNT sidewalls.
Computed reaction pathways further demonstrate that the formation
(dissociation) barrier for the CNT cap-based CNB is slightly lower
(significantly higher) than that of the CNT sidewall-based CNB, indicating an
easier CNB formation as well as a higher structural stability. Additionally,
performing matrix Green's function calculations, we study the charge transport
properties of the CNB/metal electrode interfaces, and show that the C$_{60}$
bonding to the CNT cap or open end induces resonant transmissions near the
Fermi level. It is also found that the good electronic linkage in the CNT
cap-C$_{60}$ cycloaddition bonds results in the absence of quantum interference
patterns, which contrasts the case of the CNB formed on an open-ended CNT that
shows a Fano resonance profile.


  The regularity theory for pluriclosed flow hinges on obtaining $C^{\alpha}$
regularity for the metric assuming uniform equivalence to a background metric.
This estimate was established in \cite{StreetsPCFBI} by an adaptation of ideas
from Evans-Krylov, the key input being a sharp differential inequality
satisfied by the associated `generalized metric' defined on $T \oplus T^*$. In
this work we give a sharpened form of this estimate with a simplified proof. To
begin we show that the generalized metric itself evolves by a natural curvature
quantity, which leads quickly to an estimate on the associated Chern
connections analogous to, and generalizing, Calabi-Yau's $C^3$ estimate for the
complex Monge Ampere equation.


  This article studies the possibility of controlling the surge impedance of
the electric motor in motor drives. The existing solution to suppress (or
eliminate) the reflected wave impact on the motor insulation run by a Si-IGBT
or SiC-MOSFET-based drive is to use either a sinewave filter or dv/dt filter.
The alternative solution suggested in this article is to implement a
high-bandwidth electronic circuit at the end of the cable or at the motor
terminals to match the surge impedance of the cable and motor. The
high-frequency voltage ringing due to the reflected waves in motor drives is
around 1 MHz, depending on the cable parameters and the length of the cable. In
the proposed method, the electronic circuit can quickly detect the dv/dt rise
and fall edges and adjust the electronic circuit equivalent impedance when
pulses arrive the motor terminals. Thus, the cable and motor surge impedances
can be matched over a short time to prevent reflected waves. As a result, the
leakage currents passing through the ball bearing and overvoltage stress on the
motor insulation can be suppressed significantly.


  Frameworks have begun to emerge to categorize Virtual, Augmented, and Mixed
Reality (VAM) technologies that provide immersive, intuitive interfaces to
facilitate Human-Robot Interaction. These frameworks, however, fail to capture
key characteristics of the growing subfield of VAM-HRI and can be difficult to
consistently apply due to continuous scales. This work builds upon these prior
frameworks through the creation of a Tool for Organizing Key Characteristics of
VAM-HRI Systems (TOKCS). TOKCS discretizes the continuous scales used within
prior works for more consistent classification and adds additional
characteristics related to a robot's internal model, anchor locations,
manipulability, and the system's software and hardware. To showcase the tool's
capability, TOKCS is applied to the ten papers from the fourth VAM-HRI workshop
and examined for key trends and takeaways. These trends highlight the
expressive capability of TOKCS while also helping frame newer trends and future
work recommendations for VAM-HRI research.


  Localization technology is important for the development of indoor
location-based services (LBS). Global Positioning System (GPS) becomes invalid
in indoor environments due to the non-line-of-sight issue, so it is urgent to
develop a real-time high-accuracy localization approach for smartphones.
However, accurate localization is challenging due to issues such as real-time
response requirements, limited fingerprint samples and mobile device storage.
To address these problems, we propose a novel deep learning architecture:
Tensor-Generative Adversarial Network (TGAN).
  We first introduce a transform-based 3D tensor to model fingerprint samples.
Instead of those passive methods that construct a fingerprint database as a
prior, our model applies artificial neural network with deep learning to train
network classifiers and then gives out estimations. Then we propose a novel
tensor-based super-resolution scheme using the generative adversarial network
(GAN) that adopts sparse coding as the generator network and a residual
learning network as the discriminator. Further, we analyze the performance of
tensor-GAN and implement a trace-based localization experiment, which achieves
better performance. Compared to existing methods for smartphones indoor
positioning, that are energy-consuming and high demands on devices, TGAN can
give out an improved solution in localization accuracy, response time and
implementation complexity.


  A $k$-page linear graph layout of a graph $G = (V,E)$ draws all vertices
along a line $\ell$ and each edge in one of $k$ disjoint halfplanes called
pages, which are bounded by $\ell$. We consider two types of pages. In a stack
page no two edges should cross and in a queue page no edge should be nested by
another edge. A crossing (nesting) in a stack (queue) page is called a
conflict. The algorithmic problem is twofold and requires to compute (i) a
vertex ordering and (ii) a page assignment of the edges such that the resulting
layout is either conflict-free or conflict-minimal. While linear layouts with
only stack or only queue pages are well-studied, mixed $s$-stack $q$-queue
layouts for $s,q \ge 1$ have received less attention. We show NP-completeness
results on the recognition problem of certain mixed linear layouts and present
a new heuristic for minimizing conflicts. In a computational experiment for the
case $s, q = 1$ we show that the new heuristic is an improvement over previous
heuristics for linear layouts.


  In this paper we investigate the zero dielectric constant limit to the
non-isentropic compressible Euler-Maxwell system. We justify this singular
limit rigorously in the framework of smooth solutions and obtain the
non-isentropic compressible magnetohydrodynamic equations as the dielectric
constant tends to zero.


  Adversarial robust models have been shown to learn more robust and
interpretable features than standard trained models. As shown in
[\cite{tsipras2018robustness}], such robust models inherit useful interpretable
properties where the gradient aligns perceptually well with images, and adding
a large targeted adversarial perturbation leads to an image resembling the
target class. We perform experiments to show that interpretable and
perceptually aligned gradients are present even in models that do not show high
robustness to adversarial attacks. Specifically, we perform adversarial
training with attack for different max-perturbation bound. Adversarial training
with low max-perturbation bound results in models that have interpretable
features with only slight drop in performance over clean samples. In this
paper, we leverage models with interpretable perceptually-aligned features and
show that adversarial training with low max-perturbation bound can improve the
performance of models for zero-shot and weakly supervised localization tasks.


  Experimental measurements and numerical simulations are made on fluid flow
through cross-slot devices with a range of aspect (depth:width) ratios, 0.4 <
alpha < 3.87. For low Reynolds numbers Re, the flow is symmetric and a sharp
boundary exists between fluid streams entering the cross-slot from opposite
directions. Above an alpha-dependent critical value Re_c, the flow undergoes a
symmetry-breaking bifurcation (though remains steady and laminar) and a spiral
vortex structure develops about the central axis of the outflow channel. An
order parameter characterizing the instability grows according to a sixth-order
Landau potential, and shows a progression from second order to first order
transitions as alpha increases. A tricritical point occurs for alpha ~ 0.55.
The spiral vortex acts as a mixing region in the flow field and this phenomenon
can be used to drive enhanced mixing in microfluidic devices.


  The Anomalous X-ray Pulsars (AXPs) are a small class of pulsars with spin
periods in the 6-12 s range, very soft X-ray spectra, secular spin down on time
scales of ~10^3-10^5 years, and lack of bright optical counterparts. Two,
possibly three, of them are close to the centre of shell-like supernova
remnants. AXPs are one of the most enigmatic classes of galactic high-energy
sources. Isolated neutron stars powered by the loss of rotational energy can be
excluded on energetic grounds. The models based on neutron stars involve either
accretion (perhaps from a fossil disk around an isolated neutron star) or the
decay of a very strong magnetic field (10^14-10^15 G). We review the models and
the observational properties of AXPs, including recent XMM-Newton and Chandra
observations. We also present some unpublished ASCA data.


  In this paper, we investigate how to learn to control a group of cooperative
agents with limited sensing capabilities such as robot swarms. The agents have
only very basic sensor capabilities, yet in a group they can accomplish
sophisticated tasks, such as distributed assembly or search and rescue tasks.
Learning a policy for a group of agents is difficult due to distributed partial
observability of the state. Here, we follow a guided approach where a critic
has central access to the global state during learning, which simplifies the
policy evaluation problem from a reinforcement learning point of view. For
example, we can get the positions of all robots of the swarm using a camera
image of a scene. This camera image is only available to the critic and not to
the control policies of the robots. We follow an actor-critic approach, where
the actors base their decisions only on locally sensed information. In
contrast, the critic is learned based on the true global state. Our algorithm
uses deep reinforcement learning to approximate both the Q-function and the
policy. The performance of the algorithm is evaluated on two tasks with simple
simulated 2D agents: 1) finding and maintaining a certain distance to each
others and 2) locating a target.


  A quantum system is usually measured through observations performed on a
second quantum system, or meter, to which it is coupled. In this scenario,
fundamental limitations arise as stated by the celebrated Wigner-Araki-Yanase
theorem and its generalizations, predicting an upper-bound on the measurement's
accuracy (Ozawa's bound). Here, we show it is possible to saturate this
fundamental bound. We propose a simple interferometric setup, arguably within
reach of present technology, in which a flying particle (the quantum meter) is
used to measure the state of a qubit (the target system). We show that the
bound can be saturated and that this happens only if the flying particle is
prepared in a Gaussian wavepacket.


  We propose a new fast word embedding technique using hash functions. The
method is a derandomization of a new type of random projections: By
disregarding the classic constraint used in designing random projections (i.e.,
preserving pairwise distances in a particular normed space), our solution
exploits extremely sparse non-negative random projections. Our experiments show
that the proposed method can achieve competitive results, comparable to neural
embedding learning techniques, however, with only a fraction of the
computational complexity of these methods. While the proposed derandomization
enhances the computational and space complexity of our method, the possibility
of applying weighting methods such as positive pointwise mutual information
(PPMI) to our models after their construction (and at a reduced dimensionality)
imparts a high discriminatory power to the resulting embeddings. Obviously,
this method comes with other known benefits of random projection-based
techniques such as ease of update.


  The Magellanic System (MS) encompasses the nearest neighbors of the Milky
Way, the Large (LMC) and Small (SMC) Magellanic Clouds, and the Magellanic
Bridge (MBR). This system contains a diverse sample of star clusters. Their
parameters, such as the spatial distribution, chemical composition and age
distribution yield important information about the formation scenario of the
whole Magellanic System. Using deep photometric maps compiled in the fourth
phase of the Optical Gravitational Lensing Experiment (OGLE-IV) we present the
most complete catalog of star clusters in the Magellanic System ever
constructed from homogeneous, long time-scale photometric data. In this second
paper of the series, we show the collection of star clusters found in the area
of about 360 square degrees in the MBR and in the outer regions of the SMC. Our
sample contains 198 visually identified star cluster candidates, 75 of which
were not listed in any of the previously published catalogs. The new
discoveries are mainly young small open clusters or clusters similar to
associations.


  In this paper we introduce the notion of Einstein-type structure on a
Riemannian manifold $\varrg$, unifying various particular cases recently
studied in the literature, such as gradient Ricci solitons, Yamabe solitons and
quasi-Einstein manifolds. We show that these general structures can be locally
classified when the Bach tensor is null. In particular, we extend a recent
result of Cao and Chen.


  We investigate online network topology identification from smooth nodal
observations acquired in a streaming fashion. Different from non-adaptive batch
solutions, our distinctive goal is to track the (possibly) dynamic adjacency
matrix with affordable memory and computational costs by processing signal
snapshots online. To this end, we leverage and truncate dual-based proximal
gradient (DPG) iterations to solve a composite smoothness-regularized,
time-varying inverse problem. Numerical tests with synthetic and real
electrocorticography data showcase the effectiveness of the novel lightweight
iterations when it comes to tracking slowly-varying network connectivity. We
also show that the online DPG algorithm converges faster than a primal-based
baseline of comparable complexity. Aligned with reproducible research
practices, we share the code developed to produce all figures included in this
paper.


  In this paper, we study the value-distributions of $L$-functions of
holomorphic primitive cusp forms in the level aspect. We associate such
automorphic $L$-functions with probabilistic models called the random Euler
products. First, we prove the existence of probability density functions
attached to the random Euler products. Then various mean values of automorphic
$L$-functions are expressed as integrals involving the density functions.
Moreover, we estimate the discrepancies between the distributions of values of
automorphic $L$-functions and those of the random Euler products.


  We study the problem of gradually representing a complex graph as a sequence
of drawings of small subgraphs whose union is the complex graph. The sequence
of drawings is called \emph{storyplan}, and each drawing in the sequence is
called a \emph{frame}. In an outerplanar storyplan, every frame is outerplanar;
in a forest storyplan, every frame is acyclic. We identify graph families that
admit such storyplans and families for which such storyplans do not always
exist. In the affirmative case, we present efficient algorithms that produce
straight-line storyplans.


  Using polarization-entangled photons from spontaneous parametric
downconversion, we have implemented Ekert's quantum cryptography protocol. The
near-perfect correlations of the photons allow the sharing of a secret key
between two parties. The presence of an eavesdropper is continually checked by
measuring Bell's inequalities. We investigated several possible eavesdropper
strategies, including pseudo-quantum non-demolition measurements. In all cases,
the eavesdropper's presence was readily apparent. We discuss a procedure to
increase her detectability.


  We show that gauge-transformation properties of correlation functions in
chiral gauge theories on the finite lattice are determined in a general way.


  Invited "News & Views" for the journal, SCIENCE CHINA: Physics, Mechanics &
Astronomy, on a recently new search for the doubly charmed baryon $\Xi_{cc}^+$
at the LHC, which is done by the LHCb Collaboration, arXiv:1909.12273.


  Automatic segmentation of liver tumors in medical images is crucial for the
computer-aided diagnosis and therapy. It is a challenging task, since the
tumors are notoriously small against the background voxels. This paper proposes
a new three-stage curriculum learning approach for training deep networks to
tackle this small object segmentation problem. The learning in the first stage
is performed on the whole input to obtain an initial deep network for tumor
segmenta-tion. Then the second stage of learning focuses the strength-ening of
tumor specific features by continuing training the network on the tumor
patches. Finally, we retrain the net-work on the whole input in the third
stage, in order that the tumor specific features and the global context can be
inte-grated ideally under the segmentation objective. Benefitting from the
proposed learning approach, we only need to em-ploy one single network to
segment the tumors directly. We evaluated our approach on the 2017 MICCAI Liver
Tumor Segmentation challenge dataset. In the experiments, our approach exhibits
significant improvement compared with the commonly used cascaded counterpart.


  We obtain analytical formulas which connect the neutrino masses and the
leptonic mixing matrix with the entries in the mass matrix for the
approximation in which the charged lepton mixing matrix is the unit matrix. We
also extract the CP violation phase and determine the conditions in which this
is present.


  This paper develops a novel control synthesis method for safe stabilization
of control-affine systems as a Differential Complementarity Problem (DCP). Our
design uses a control Lyapunov function (CLF) and a control barrier function
(CBF) to define complementarity constraints in the DCP formulation to certify
stability and safety, respectively. The CLF-CBF-DCP controller imposes
stability as a soft constraint, which is automatically relaxed when the safety
constraint is active, without the need for parameter tuning or optimization. We
study the closed-loop system behavior with the CLF-CBF-DCP controller and
identify conditions on the existence of local equilibria. Although in certain
cases the controller yields undesirable local equilibria, those can be confined
to a small subset of the safe set boundary by proper choice of the control
parameters. Then, our method can avoid undesirable equilibria that CLF-CBF
quadratic programming techniques encounter.


  I review the studies, which were reported after the last Linear Collider
Workshop, on top quark physics and QCD physics at a future e+e- linear
collider.


  We report cosmic ray ionization rates towards ten reddened stars studied
within the framework of the EDIBLES (ESO Diffuse Interstellar Bands Large
Exploration Survey) program, using the VLT-UVES. For each sightline, between 2
and 10 individual rotational lines of OH$^+$ have been detected in its (0,0)
and (1,0) $A^3\Pi-X^3\Sigma^-$ electronic band system. This allows constraining
of OH$^+$ column densities towards different objects. Results are also
presented for 28 additional sightlines for which only one or rather weak
signals are found. An analysis of these data makes it possible to derive the
primary cosmic ray ionization rate $\zeta_p$ in the targeted diffuse
interstellar clouds. For the ten selected targets, we obtain a range of values
for $\zeta_p$ equal to $(3.9-16.4) \times 10^{-16}~\mathrm{s}^{-1}$. These
values are higher than the numbers derived in previous detections of
interstellar OH$^+$ in the far-infrared / sub-millimeter-wave regions and in
other near-ultraviolet studies. This difference is a result of using new OH$^+$
oscillator strength values and a more complete picture of all relevant OH$^+$
formation and destruction routes (including the effect of proton recombinations
on PAHs), and the relatively high $N$(OH$^+$) seen toward those ten targets.


  We theoretically study specific schemes for performing a fundamental
two-qubit quantum gate via controlled atomic collisions by switching
microscopic potentials. In particular we calculate the fidelity of a gate
operation for a configuration where a potential barrier between two atoms is
instantaneously removed and restored after a certain time. Possible
implementations could be based on microtraps created by magnetic and electric
fields, or potentials induced by laser light.


  Point cloud segmentation, which helps us understand the environment of
specific structures and objects, can be performed in class-specific and
class-agnostic ways. We propose a novel region-based transformer model called
Region-Transformer for performing class-agnostic point cloud segmentation. The
model utilizes a region-growth approach and self-attention mechanism to
iteratively expand or contract a region by adding or removing points. It is
trained on simulated point clouds with instance labels only, avoiding semantic
labels. Attention-based networks have succeeded in many previous methods of
performing point cloud segmentation. However, a region-growth approach with
attention-based networks has yet to be used to explore its performance gain. To
our knowledge, we are the first to use a self-attention mechanism in a
region-growth approach. With the introduction of self-attention to
region-growth that can utilize local contextual information of neighborhood
points, our experiments demonstrate that the Region-Transformer model
outperforms previous class-agnostic and class-specific methods on indoor
datasets regarding clustering metrics. The model generalizes well to
large-scale scenes. Key advantages include capturing long-range dependencies
through self-attention, avoiding the need for semantic labels during training,
and applicability to a variable number of objects. The Region-Transformer model
represents a promising approach for flexible point cloud segmentation with
applications in robotics, digital twinning, and autonomous vehicles.


  For a finite-index $\mathrm{II}_1$ subfactor $N \subset M$, we prove the
existence of a universal Hopf $\ast$-algebra (or, a discrete quantum group in
the analytic language) acting on $M$ in a trace-preserving fashion and fixing
$N$ pointwise. We call this Hopf $\ast$-algebra the quantum Galois group for
the subfactor and compute it in some examples of interest, notably for
arbitrary irreducible finite-index depth-two subfactors. Along the way, we
prove the existence of universal acting Hopf algebras for more general
structures (tensors in enriched categories), in the spirit of recent work by
Agore, Gordienko and Vercruysse.


  The sum of the first n energy levels of the planar Laplacian with constant
magnetic field of given total flux is shown to be maximal among triangles for
the equilateral triangle, under normalization of the ratio (moment of
inertia)/(area)^3 on the domain. The result holds for both Dirichlet and
Neumann boundary conditions, with an analogue for Robin (or de Gennes) boundary
conditions too. The square similarly maximizes the eigenvalue sum among
parallelograms, and the disk maximizes among ellipses. More generally, a domain
with rotational symmetry will maximize the magnetic eigenvalue sum among all
linear images of that domain. These results are new even for the ground state
energy (n=1).


  This paper introduces tunable and switchable Perfect Absorbers (PAs)
operating within the mid-infrared spectrum, specifically targeting the 3 to 5
um range at 0.25 um intervals. This spectrum is engineered for minimal
atmospheric absorption and unique transmission characteristics. Our approach
uses graphene-based nanophotonic aperiodic multilayer structures, optimized
through micro-genetic algorithms within an inverse design framework. This
combination broadens the design space, enabling highly accurate absorption
control. Using the Transfer-Matrix-Method for simulations, we tailor absorption
characteristics while keeping the structures under 2 um thick. Our results
demonstrate PA tunability and switchability by adjusting graphene layers'
chemical potentials. For instance, a 4 um peak can shift to 4.22 um by changing
the graphene potential from 0 eV to 1 eV, without reducing efficiency. Our
research also reveals PA adaptability to incident angles, maintaining 90%
absorption up to 52 degrees, demonstrating versatility for applications like
thermal photovoltaics, sensors, and stealth technology. This research deepens
our understanding of nanophotonic materials and advances optical devices for
the mid-IR range.


  In a recent report, Carballo-Rubio [1] utilizes the semi-classical theory of
gravity to obtain a generalized Tolman-Oppenheimer-Volkoff (TOV) equation. This
model has a new coupling constant $l_p$ implying two different modified TOV
equation forms characterized by the sign of $p'$. The negative branch reduces
to the ordinary GR-TOV in the limit of $l_p\to0$, while the positive one does
not. In the positive branch, Carballo-Rubio was able to find the exact
solutions using the constant-$\lambda$ trick. In this work, we investigate
whether this theory's negative branch can also provide a different feature of
the ultra-compact object compared to those obtained from the GR-TOV equation.
We study ultra-compact objects with an isotropically ideal fluid matter where
we use a simple but physically motivated equation of state $\rho=p/w+\rho_0$
with $w$=1 and $w$=1/3. In general, we obtain that the range of $l_p$ is very
restricted and must not be equal to $r_c$. Here $r_c$ is the starting point of
integration located at the center of the star. While $l_p$ should be set to be
much larger than Planck length $L_\text{Pl}$. Consequently, the mass-radius
curves for the various value of $l_p$ for both $w$ cases are still
indistinguishable from the standard GR-TOV results. Hence from the negative
branch of $p'(r)$, the additional free parameter $l_p$ does not provide a
significant effect compared to the standard GR-TOV equation results, even
though $l_p$ is not in the limit of $l_p\to0$ anymore. Therefore, similar to
the conclusion in Ref. [3] with GR theory that the ultra-compact objects from
negative branch of semi-classical gravity with a linear equation of state are
unable to generate demanding gravitational echoes.


  Cosmic microwave background (CMB) experiments that constrain the
tensor-to-scalar ratio $r$ are now approaching the sensitivity at which
delensing---removing the $B$ modes induced by the gravitational lensing of
large-scale structure---is necessary. We consider the improvement in delensing
that maps of large-scale structure from tomographic line intensity mapping (IM)
experiments targeting $2 < z < 10$ could provide. Compared to a nominal
baseline of cosmic infrared background and internal delensing at CMB-S4
sensitivity, we find that the addition of high-redshift IM data could improve
delensing performance by ~11%. Achieving the requisite sensitivity in the IM
data is feasible with next-generation experiments that are now being planned.
However, these results are contingent on the ability to measure low-$k$ modes
along the line of sight. Without these modes, IM datasets are unable to to
correlate with the lensing kernel and do not aid in delensing.


  We present simple conditions under which the limiting genealogical process
associated with a class of interacting particle systems with non-neutral
selection mechanisms, as the number of particles grows, is a time-rescaled
Kingman coalescent. Sequential Monte Carlo algorithms are popular methods for
approximating integrals in problems such as non-linear filtering and smoothing
which employ this type of particle system. Their performance depends strongly
on the properties of the induced genealogical process. We verify the conditions
of our main result for standard sequential Monte Carlo algorithms with a broad
class of low-variance resampling schemes, as well as for conditional sequential
Monte Carlo with multinomial resampling.


  We study theoretically the interplay of spontaneous emission and interactions
for the multiple-excited quasistationary eigenstates in a finite periodic array
of multilevel atoms coupled to the waveguide. We develop an analytical approach
to calculate such eigenstates based on the subradiant dimer basis. Our
calculations reveal the peculiar multimerization effect driven by the
anharmonicity of the atomic potential: while a general eigenstate is an
entangled one, there exist eigenstates that are products of dimers, trimers, or
tetramers, depending on the size of the system and the fill factor. At
half-filling, these product states acquire a periodic structure with all-to-all
connections inside each multimer and become the most subradiant ones.


  While large-scale knowledge graphs provide vast amounts of structured facts
about entities, a short textual description can often be useful to succinctly
characterize an entity and its type. Unfortunately, many knowledge graph
entities lack such textual descriptions. In this paper, we introduce a dynamic
memory-based network that generates a short open vocabulary description of an
entity by jointly leveraging induced fact embeddings as well as the dynamic
context of the generated sequence of words. We demonstrate the ability of our
architecture to discern relevant information for more accurate generation of
type description by pitting the system against several strong baselines.


  We introduce Autoverse, an evolvable, domain-specific language for
single-player 2D grid-based games, and demonstrate its use as a scalable
training ground for Open-Ended Learning (OEL) algorithms. Autoverse uses
cellular-automaton-like rewrite rules to describe game mechanics, allowing it
to express various game environments (e.g. mazes, dungeons, sokoban puzzles)
that are popular testbeds for Reinforcement Learning (RL) agents. Each rewrite
rule can be expressed as a series of simple convolutions, allowing for
environments to be parallelized on the GPU, thereby drastically accelerating RL
training. Using Autoverse, we propose jump-starting open-ended learning by
imitation learning from search. In such an approach, we first evolve Autoverse
environments (their rules and initial map topology) to maximize the number of
iterations required by greedy tree search to discover a new best solution,
producing a curriculum of increasingly complex environments and playtraces. We
then distill these expert playtraces into a neural-network-based policy using
imitation learning. Finally, we use the learned policy as a starting point for
open-ended RL, where new training environments are continually evolved to
maximize the RL player agent's value function error (a proxy for its regret, or
the learnability of generated environments), finding that this approach
improves the performance and generality of resultant player agents.


  This paper comprises the written version of the lectures on string theory
delivered at the 31st British Universities Summer School on Theoretical
Elementary Particle Physics which was held in Manchester, England, August 28 -
September 12 2001.


  Manifold-valued functional data analysis (FDA) recently becomes an active
area of research motivated by the raising availability of trajectories or
longitudinal data observed on non-linear manifolds. The challenges of analyzing
such data come from many aspects, including infinite dimensionality and
nonlinearity, as well as time-domain or phase variability. In this paper, we
study the amplitude part of manifold-valued functions on $\mathbb{S}^2$, which
is invariant to random time warping or re-parameterization. Utilizing the nice
geometry of $\mathbb{S}^2$, we develop a set of efficient and accurate tools
for temporal alignment of functions, geodesic computing, and sample mean
calculation. At the heart of these tools, they rely on gradient descent
algorithms with carefully derived gradients. We show the advantages of these
newly developed tools over its competitors with extensive simulations and real
data and demonstrate the importance of considering the amplitude part of
functions instead of mixing it with phase variability in manifold-valued FDA.


  We introduce the concept of a class of graphs, or more generally, relational
structures, being locally tree-decomposable. There are numerous examples of
locally tree-decomposable classes, among them the class of planar graphs and
all classes of bounded valence or of bounded tree-width. We also consider a
slightly more general concept of a class of structures having bounded local
tree-width.
  We show that for each property P of structures that is definable in
first-order logic and for each locally tree-decomposable class C of graphs,
there is a linear time algorithm deciding whether a given structure A in C has
property P. For classes C of bounded local tree-width, we show that for every
k\ge 1 there is an algorithm that solves the same problem in time
O(n^{1+(1/k)}) (where n is the cardinality of the input structure).


  Federated learning (FL) has attracted vivid attention as a privacy-preserving
distributed learning framework. In this work, we focus on cross-silo FL, where
clients become the model owners after training and are only concerned about the
model's generalization performance on their local data. Due to the data
heterogeneity issue, asking all the clients to join a single FL training
process may result in model performance degradation. To investigate the
effectiveness of collaboration, we first derive a generalization bound for each
client when collaborating with others or when training independently. We show
that the generalization performance of a client can be improved only by
collaborating with other clients that have more training data and similar data
distribution. Our analysis allows us to formulate a client utility maximization
problem by partitioning clients into multiple collaborating groups. A
hierarchical clustering-based collaborative training (HCCT) scheme is then
proposed, which does not need to fix in advance the number of groups. We
further analyze the convergence of HCCT for general non-convex loss functions
which unveils the effect of data similarity among clients. Extensive
simulations show that HCCT achieves better generalization performance than
baseline schemes, whereas it degenerates to independent training and
conventional FL in specific scenarios.


  Minimum cut/maximum flow (min-cut/max-flow) algorithms solve a variety of
problems in computer vision and thus significant effort has been put into
developing fast min-cut/max-flow algorithms. As a result, it is difficult to
choose an ideal algorithm for a given problem. Furthermore, parallel algorithms
have not been thoroughly compared. In this paper, we evaluate the
state-of-the-art serial and parallel min-cut/max-flow algorithms on the largest
set of computer vision problems yet. We focus on generic algorithms, i.e., for
unstructured graphs, but also compare with the specialized GridCut
implementation. When applicable, GridCut performs best. Otherwise, the two
pseudoflow algorithms, Hochbaum pseudoflow and excesses incremental breadth
first search, achieves the overall best performance. The most memory efficient
implementation tested is the Boykov-Kolmogorov algorithm. Amongst generic
parallel algorithms, we find the bottom-up merging approach by Liu and Sun to
be best, but no method is dominant. Of the generic parallel methods, only the
parallel preflow push-relabel algorithm is able to efficiently scale with many
processors across problem sizes, and no generic parallel method consistently
outperforms serial algorithms. Finally, we provide and evaluate strategies for
algorithm selection to obtain good expected performance. We make our dataset
and implementations publicly available for further research.


  Procedural texts often describe processes (e.g., photosynthesis and cooking)
that happen over entities (e.g., light, food). In this paper, we introduce an
algorithm for procedural reading comprehension by translating the text into a
general formalism that represents processes as a sequence of transitions over
entity attributes (e.g., location, temperature). Leveraging pre-trained
language models, our model obtains entity-aware and attribute-aware
representations of the text by joint prediction of entity attributes and their
transitions. Our model dynamically obtains contextual encodings of the
procedural text exploiting information that is encoded about previous and
current states to predict the transition of a certain attribute which can be
identified as a span of text or from a pre-defined set of classes. Moreover,
our model achieves state of the art results on two procedural reading
comprehension datasets, namely ProPara and npn-cooking


  We study thermalization and non-equilibrium dynamics in a dissipative quantum
many-body system -- a chain of ions with two points of the chain driven by
thermal bath under different temperature. Instead of a simple linear
temperature gradient as one expects from the classical heat diffusion process,
the temperature distribution in the ion chain shows surprisingly rich patterns,
which depend on the ion coupling rate to the bath, the location of the driven
ions, and the dissipation rates of the other ions in the chain. Through
simulation of the temperature evolution, we show that these unusual temperature
distribution patterns in the ion chain can be quantitatively tested in
experiments within a realistic time scale.


  Polymer adsorption on fractally rough walls of varying dimensionality is
studied by renormalization group methods on hierarchical lattices. Exact
results are obtained for deterministic walls. The adsorption transition is
found continuous for low dimension $d_w$ of the adsorbing wall and the
corresponding crossover exponent $\phi$ monotonically increases with $d_w$,
eventually overcoming previously conjectured bounds. For $d_w$ exceeding a
threshold value $d_w^*$, $\phi$ becomes 1 and the transition turns
first--order. $d_w^*>d_{saw}$, the fractal dimension of the polymer in the
bulk. An accurate numerical approach to the same problem with random walls
gives evidence of the same scenario.


  This paper is the second in a series exploring the properties of 51 {\it
optically} selected, single-nuclei merger remnants. Spectroscopic data have
been obtained for a sub-sample of 38 mergers and combined with previously
obtained infrared photometry to test whether mergers exhibit the same
correlations as elliptical galaxies among parameters such as stellar luminosity
and distribution, central stellar velocity dispersion ($\sigma$$_{\circ}$), and
metallicity. Paramount to the study is to test whether mergers lie on the
Fundamental Plane. Measurements of $\sigma$$_{\circ}$ have been made using the
Ca triplet absorption line at 8500 {\AA} for all 38 mergers in the sub-sample.
Additional measurements of $\sigma$$_{\circ}$ were made for two of the mergers
in the sub-sample using the CO absorption line at 2.29 $\micron$. The results
indicate that mergers show a strong correlation among the parameters of the
Fundamental Plane but fail to show a strong correlation between
$\sigma$$_{\circ}$ and metallicity (Mg$_{2}$). In contrast to earlier studies,
the $\sigma$$_{\circ}$ of the mergers are consistent with objects which lie
somewhere between intermediate-mass and luminous giant elliptical galaxies.
However, the discrepancies with earlier studies appears to correlate with
whether the Ca triplet or CO absorption lines are used to derive
$\sigma$$_{\circ}$, with the latter almost always producing smaller values.
Finally, the photometric and kinematic data are used to demonstrate for the
first time that the central phase-space density of mergers are equivalent to
elliptical galaxies. This resolves a long-standing criticism of the merger
hypothesis.


  Temporal logics have been studied as an approach to the specification of
hyperproperties, resulting in the conception of "hyperlogics". With a few
recent exceptions, the hyperlogics thus far developed can only relate different
traces of a transition system synchronously. However, important information is
contained in the relation between different points in their asynchronous
interaction. To specify such "asynchronous hyperproperties", new trace
quantifier based hyperlogics have been developed. Yet, hyperlogics with trace
quantification cannot express certain requirements that describe the
relationships between all executions of a system. Also, these logics induce
model checking problems (MC) with prohibitively high complexity costs in the
number of quantifier alternations. We study an alternative approach to
asynchronous hyperproperties by introducing a novel foundation of temporal team
semantics. Team semantics is a logical framework that specifies properties of
sets of traces of unbounded size directly, and thus does not have the same
limitation as the quantifier based logics mentioned above. We consider temporal
team logics which employ quantification over so-called "time evaluation
functions" (TEFs) controlling the asynchronous progress of traces instead of
quantification over traces. TEFs constitute a novel approach to defining
expressive logics for hyperproperties where diverse asynchronous interactions
between computations can be formalised and enforced. We show embeddings of
synchronous TeamLTL into our new logics. We show that MC for some TeamCTL
fragment is highly undecidable. We present a translation from TeamCTL* to
Alternating Asynchronous B\"uchi Automata, and obtain decidability results for
the path checking problem and restrictions of MC and SAT. Our translation
constitutes the first approach to team semantics based on automata-theoretic
methods.


  Estimation of the Fisher Information Metric (FIM-estimation) is an important
task that arises in unsupervised learning of phase transitions, a problem
proposed by physicists. This work completes the definition of the task by
defining rigorous evaluation metrics distMSE, distMSEPS, and distRE and
introduces ClassiFIM, a novel machine learning method designed to solve the
FIM-estimation task. Unlike existing methods for unsupervised learning of phase
transitions, ClassiFIM directly estimates a well-defined quantity (the FIM),
allowing it to be rigorously compared to any present and future other methods
that estimate the same. ClassiFIM transforms a dataset for the FIM-estimation
task into a dataset for an auxiliary binary classification task and involves
selecting and training a model for the latter. We prove that the output of
ClassiFIM approaches the exact FIM in the limit of infinite dataset size and
under certain regularity conditions. We implement ClassiFIM on multiple
datasets, including datasets describing classical and quantum phase
transitions, and find that it achieves a good ground truth approximation with
modest computational resources. Furthermore, we independently implement two
alternative state-of-the-art methods for unsupervised estimation of phase
transition locations on the same datasets and find that ClassiFIM predicts such
locations at least as well as these other methods. To emphasize the generality
of our method, we also propose and generate the MNIST-CNN dataset, which
consists of the output of CNNs trained on MNIST for different hyperparameter
choices. Using ClassiFIM on this dataset suggests there is a phase transition
in the distribution of image-prediction pairs for CNNs trained on MNIST,
demonstrating the broad scope of FIM-estimation beyond physics.


  Galaxy clusters are the most massive systems in the known universe. They host
relativistic cosmic ray populations and are thought to be gravitationally bound
by large amounts of Dark Matter, which under the right conditions could yield
to a detectable $\gamma$-ray flux. Prior to the launch of the Fermi satellite,
predictions were optimistic that Galaxy clusters would be established as
$\gamma$-ray bright objects by observations through its prime instrument, the
Large Area Telescope (LAT). Yet, despite numerous efforts, even a single
cluster detection is still pending.


  Older users population is rapidly increasing all over the World. Presently,
we observe efforts in the human-computer interaction domain aiming to improve
life quality of age 65 and over through the use of mobile apps. Nonetheless,
these efforts focus primary on interface and interaction de- sign. Little work
has focused on the study of motivation to use and adherence to, of elderly to
technology. Developing specific design guidelines for this population is
relevant, however it should be parallel to the study of desire of elderly to
embrace specific technology in their life. Designers should not be limited to
technology design but consider as well how to fully convey the value that
technology can bring to the lives of the users and motivate adoption. This
position paper discusses techniques that might nudge elderly towards the use of
new technology.


  We summarize some of the difficulties that confront lattice calculations of
non-leptonic kaon decay matrix elements. We review some of the methods that
have been proposed to overcome these difficulties, and discuss the importance
of one-loop ChPT in this respect, including the role of O(p^4) operators.


  Using computer algorithms we establish that the Ramsey number $R(3,K_{10}-e)$
is equal to 37, which solves the smallest open case for Ramsey numbers of this
type. We also obtain new upper bounds for the cases of $R(3,K_k-e)$ for $11 \le
k \le 16$, and show by construction a new lower bound $55 \le R(3,K_{13}-e)$.
  The new upper bounds on $R(3,K_k-e)$ are obtained by using the values and
lower bounds on $e(3,K_l-e,n)$ for $l \le k$, where $e(3,K_k-e,n)$ is the
minimum number of edges in any triangle-free graph on $n$ vertices without
$K_k-e$ in the complement. We complete the computation of the exact values of
$e(3,K_k-e,n)$ for all $n$ with $k \leq 10$ and for $n \leq 34$ with $k = 11$,
and establish many new lower bounds on $e(3,K_k-e,n)$ for higher values of $k$.
  Using the maximum triangle-free graph generation method, we determine two
other previously unknown Ramsey numbers, namely $R(3,K_{10}-K_3-e)=31$ and
$R(3,K_{10}-P_3-e)=31$. For graphs $G$ on 10 vertices, %besides $G=K_{10}$,
this leaves 6 other open besides $G=K_{10}$, this leaves 6 open cases of the
form $R(3,G)$. The hardest among them appears to be $G=K_{10}-2K_2$, for which
we establish the bounds $31 \le R(3,K_{10}-2K_2) \le 33$.


  Visual Grounding (VG) in Visual Question Answering (VQA) systems describes
how well a system manages to tie a question and its answer to relevant image
regions. Systems with strong VG are considered intuitively interpretable and
suggest an improved scene understanding. While VQA accuracy performances have
seen impressive gains over the past few years, explicit improvements to VG
performance and evaluation thereof have often taken a back seat on the road to
overall accuracy improvements. A cause of this originates in the predominant
choice of learning paradigm for VQA systems, which consists of training a
discriminative classifier over a predetermined set of answer options.
  In this work, we break with the dominant VQA modeling paradigm of
classification and investigate VQA from the standpoint of an information
retrieval task. As such, the developed system directly ties VG into its core
search procedure. Our system operates over a weighted, directed, acyclic graph,
a.k.a. "lattice", which is derived from the scene graph of a given image in
conjunction with region-referring expressions extracted from the question.
  We give a detailed analysis of our approach and discuss its distinctive
properties and limitations. Our approach achieves the strongest VG performance
among examined systems and exhibits exceptional generalization capabilities in
a number of scenarios.


  In conventional metal-oxide semiconductor (CMOS) electronics, the logic state
of a device is set by a gate voltage (VG). The superconducting equivalent of
such effect had remained unknown until it was recently shown that a VG can tune
the superconducting current (supercurrent) flowing through a nanoconstriction
in a superconductor. This gate-controlled supercurrent (GCS) effect can lead to
superconducting logics like CMOS logics, but with lower energy dissipation. The
physical mechanism underlying the GCS effect, however, remains under debate. In
this review article, we illustrate the main mechanisms proposed for the GCS
effect, and the material and device parameters that mostly affect it based on
the evidence reported. We will come to the conclusion that different mechanisms
are at play in the different studies reported so far. We then outline studies
that can help answer open questions on the effect and achieve control over it,
which is key for applications. We finally give insights into the impact that
the GCS effect can have towards high-performance computing with low-energy
dissipation and quantum technologies.


  We demonstrate how the metallicities of red supergiant (RSG) stars can be
measured from quantitative spectroscopy down to resolutions of ~3000 in the
J-band. We have obtained high resolution spectra on a sample of the RSG
population of h and chi Persei, a double cluster in the solar neighborhood. We
show that careful application of the MARCS model atmospheres returns
measurements of Z consistent with solar metallicity. Using two grids of
synthetic spectra--one in pure LTE and one with NLTE calculations for the most
important diagnostic lines--we measure Z = +0.04 +/- 0.10 (LTE) and Z = -0.04
+/- 0.08 (NLTE) for the sample of eleven RSGs in the cluster. We degrade the
spectral resolution of our observations and find that those values remain
consistent down to resolutions of less than R of 3000. Using measurements of
effective temperatures we compare our results with stellar evolution theory and
find good agreement. We construct a synthetic cluster spectrum and find that
analyzing this composite spectrum with single-star RSG models returns an
accurate metallicity. We conclude that the RSGs make ideal targets in the near
infrared for measuring the metallicities of star forming galaxies out to 7-10
Mpc and up to ten times farther by observing the integrated light of unresolved
super star clusters.


  Astrometry covers a parameter space that cannot be reached by RV or transit
methods to detect terrestrial planets on wide orbits. In addition, high
accuracy astrometric measurements are necessary to measure the inclination of
the planet's orbits. Here we investigate the principles of an artefact of the
astrometric approach. Namely, the displacement of the photo-centre due to
inhomogeneities in a dust disc around the parent star. Indeed, theory and
observations show that circumstellar discs can present strong asymmetries. We
model the pseudo-astrometric signal caused by these inhomogeneities, asking
whether a dust clump in a disc can mimic the astrometric signal of an
Earth-like planet. We show that these inhomogeneities cannot be neglected when
using astrometry to find terrestrial planets. We provide the parameter space
for which these inhomogeneities can affect the astrometric signals but still
not be detected by mid-IR observations. We find that a small cross section of
dust corresponding to a cometary mass object is enough to mimic the astrometric
signal of an Earth-like planet. Astrometric observations of protoplanetary
discs to search for planets can also be affected by the presence of
inhomogeneities. Some further tests are given to confirm whether an observation
is a real planet astrometric signal or an impostor. Eventually, we also study
the case where the cross section of dust is high enough to provide a detectable
IR-excess and to have a measurable photometric displacement by actual
instruments such as Gaia, IRAC or GRAVITY. We suggest a new method, which
consists in using astrometry to quantify asymmetries (clumpiness) in inner
debris discs that cannot be otherwise resolved.


  We show the existence of non-unique stationary weak solutions for forced
surface quasi-geostrophic (SQG) equation via a convex integration scheme. The
scheme is implemented for the sum-difference system of two distinct solutions.
Through this scheme, one observes the external forcing is naturally generated
accompanying the flexibility in means of lack of uniqueness. It thus provides a
transparent way to reveal the flexibility of the system with the presence of a
forcing.


  Neural network on Riemannian symmetric space such as hyperbolic space and the
manifold of symmetric positive definite (SPD) matrices is an emerging subject
of research in geometric deep learning. Based on the well-established framework
of the Helgason-Fourier transform on the noncompact symmetric space, we present
a fully-connected network and its associated ridgelet transform on the
noncompact symmetric space, covering the hyperbolic neural network (HNN) and
the SPDNet as special cases. The ridgelet transform is an analysis operator of
a depth-2 continuous network spanned by neurons, namely, it maps an arbitrary
given function to the weights of a network. Thanks to the coordinate-free
reformulation, the role of nonlinear activation functions is revealed to be a
wavelet function, and the reconstruction formula directly yields the
universality of the proposed networks.


  We study Nevanlinna theory of meromorphic mappings from a geodesic ball of a
general complete K\"ahler manifold with non-negative Ricci curvature into a
complex projective manifold by introducing a heat kernel method. When dimension
of a target manifold is not greater than one of a source manifold, we establish
a second main theorem which is a generalization of the classical second main
theorem for a ball of $\mathbb C^m.$ If a source manifold is non-compact and it
carries a positive global Green function, then we establish a global second
main theorem for the source manifold. As a result, we obtain a Picard's theorem
for complete K\"ahler manifolds with non-negative Ricci curvature.


  For q > 2, Carlitz proved that the group of permutation polynomials (PPs)
over F_q is generated by linear polynomials and x^{q-2}. Based on this result,
this note points out a simple method for representing all PPs with full cycle
over the prime field F_p, where p is an odd prime. We use the isomorphism
between the symmetric group S_p of p elements and the group of PPs over F_p,
and the well-known fact that permutations in S_p have the same cycle structure
if and only if they are conjugate.


  In Diffusion-based Molecular Communications, the channel between Transmitter
Nano-machine (TN) and Receiver Nano-machine (RN) can be modeled by Additive
Inverse Gaussian Channel, that is the first hitting time of messenger molecule
released from TN and captured by RN follows Inverse Gaussian distribution. In
this channel, a quantity-based modulation embedding message on the different
quantity levels of messenger molecules relies on a time-slotted system between
TN and RN. Accordingly, their clocks need to synchronize with each other. In
this paper, we discuss the approaches to make RN estimate its timing offset
between TN efficiently by the arrival times of molecules. We propose many
methods such as Maximum Likelihood Estimation (MLE), Unbiased Linear Estimation
(ULE), Iterative ULE, and Decision Feedback (DF). The numerical results shows
the comparison of them. We evaluate these methods by not only the Mean Square
Error, but also the computational complexity.


  Existing data on Cu-nuclear spin relaxation reveal two independent relaxation
processes: the one that is temperature independent we link to incommensurate
peaks seen by neutrons, while the ''universal'' temperature dependent
contribution coincides with 1/{63}^T_{1}(T) for two-chain YBCO 124. We argue
that this new result substitutes for a ''pseudogap'' regime in a broad class of
high-T_c cuprates and stems from the 1st order phase transition that starts
well above the superconductivity T_c but becomes frustrated because of broken
electroneutrality in the CuO_2 plane.


  The notion of nonlocality implicitly implies there might be some kind of
spooky action at a distance in nature, however, the validity of quantum
mechanics has been well tested up to now. In this work it is argued that the
notion of nonlocality is physically improper, the basic principle of locality
in nature is well respected by quantum mechanics, namely, the uncertainty
principle. We show that the quantum bound on the Clauser, Horne, Shimony, and
Holt (CHSH) inequality can be recovered from the uncertainty relation in a
multipartite setting. We further argue that the super-quantum correlation
demonstrated by the nonlocal box is not physically comparable with the quantum
one. The origin of the quantum structure of nature still remains to be
explained, some post-quantum theory which is more complete in some sense than
quantum mechanics is possible and might not necessarily be a hidden variable
theory.


  We have investigated electromagnetically induced transparency in the spectrum
of selective reflection at the interface of Rb atom vapors and a dielectric
nanocell window. A nanocell with atomic vapor column thicknesses ranging from
150 to 1200 nm, as well as a 50~$\mu$m thickness microcell were used. We have
compared electromagnetically induced transparency observed for the cases of the
selective reflection and transmission. It was demonstrated that for the
thicknesses of below $\leq 1000$ nm selective reflection technique is more
favorable. In contrast, for wider cells and microcells, using transmitted
radiation as probe field is more effective.


  This paper is motivated by a Eurobarometer survey on science knowledge. As
part of the survey, respondents were asked to rank sources of science
information in order of importance. The official statistical analysis of these
data however failed to use the complete ranking information. We instead propose
a method which treats ranked data as a set of paired comparisons which places
the problem in the standard framework of generalized linear models and also
allows respondent covariates to be incorporated. An extension is proposed to
allow for heterogeneity in the ranked responses. The resulting model uses a
nonparametric formulation of the random effects structure, fitted using the EM
algorithm. Each mass point is multivalued, with a parameter for each item. The
resultant model is equivalent to a covariate latent class model, where the
latent class profiles are provided by the mass point components and the
covariates act on the class profiles. This provides an alternative
interpretation of the fitted model. The approach is also suitable for paired
comparison data.


  We show that the recently proposed scheme of teleportation of continuous
variables [S.L. Braunstein and H.J. Kimble, Phys. Rev. Lett. 80, 869 (1998)]
can be improved by a conditional measurement in the preparation of the
entangled state shared by the sender and the recipient. The conditional
measurement subtracts photons from the original entangled two-mode squeezed
vacuum, by transmitting each mode through a low-reflectivity beam splitter and
performing a joint photon-number measurement on the reflected beams. In this
way the degree of entanglement of the shared state is increased and so is the
fidelity of the teleported state.


  This paper proposes a simple and efficient estimation procedure for the model
with non-ignorable missing data studied by Morikawa and Kim (2016). Their
semiparametrically efficient estimator requires explicit nonparametric
estimation and so suffers from the curse of dimensionality and requires a
bandwidth selection. We propose an estimation method based on the Generalized
Method of Moments (hereafter GMM). Our method is consistent and asymptotically
normal regardless of the number of moments chosen. Furthermore, if the number
of moments increases appropriately our estimator can achieve the semiparametric
efficiency bound derived in Morikawa and Kim (2016), but under weaker
regularity conditions. Moreover, our proposed estimator and its consistent
covariance matrix are easily computed with the widely available GMM package. We
propose two data-based methods for selection of the number of moments. A small
scale simulation study reveals that the proposed estimation indeed out-performs
the existing alternatives in finite samples.


  We study direct limits $(G,K) = \varinjlim (G_n,K_n)$ of compact Gelfand
pairs. First, we develop a criterion for a direct limit representation to be a
multiplicity--free discrete direct sum of irreducible representations. Then we
look at direct limits $G/K = \varinjlim G_n/K_n$ of compact riemannian
symmetric spaces, where we combine our criterion with the Cartan--Helgason
Theorem to show in general that the regular representation of $G = \varinjlim
G_n$ on a certain function space $\varinjlim L^2(G_n/K_n)$ is multiplicity
free. That method is not applicable for direct limits of nonsymmetric Gelfand
pairs, so we introduce two other methods. The first, based on ``parabolic
direct limits'' and ``defining representations'', extends the method used in
the symmetric space case. The second uses some (new) branching rules from
finite dimensional representation theory. In both cases we define function
spaces $\cA(G/K)$, $\cC(G/K)$ and $L^2(G/K)$ to which our multiplicity--free
criterion applies.


  On the basis of a suggestive definition of a classical extension of quantum
mechanics in terms of statistical models, we prove that every such classical
extension is essentially given by the so-called Misra-Bugajski reduction map.
We consider how this map enables one to understand quantum mechanics as a
reduced classical statistical theory on the projective Hilbert space as phase
space and discuss features of the induced hidden-variables model. Moreover,
some relevant technical results on the topology and Borel structure of the
projective Hilbert space are reviewed.


  We present a new magnetohydrodynamic (MHD) simulation code with the aim of
providing accurate numerical solutions to astrophysical phenomena where
discontinuities, shock waves, and turbulence are inherently important. The code
implements the HLLD approximate Riemann solver, the
fifth-order-monotonicity-preserving interpolation (MP5) scheme, and the
hyperbolic divergence cleaning method for a magnetic field. This choice of
schemes significantly improved numerical accuracy and stability, and saved
computational costs in multidimensional problems. Numerical tests of one- and
two-dimensional problems showed the advantages of using the high-order scheme
by comparing with results from a standard second-order TVD MUSCL scheme. The
present code enabled us to explore long-term evolution of a three-dimensional
accretion disk around a black hole, in which compressible MHD turbulence caused
continuous mass accretion via nonlinear growth of the magneto-rotational
instability (MRI). Numerical tests with various computational cell sizes
exhibited a convergent picture of the early nonlinear growth of the MRI in a
global model, and indicated that the MP5 scheme has more than twice the
resolution of the MUSCL scheme in practical applications.


  We use a combination of original light scattering techniques and particles
with unique optical properties to investigate the behavior of suspensions of
attractive colloids under gravitational stress, following over time the
concentration profile, the velocity profile, and the microscopic dynamics.
During the compression regime, the sedimentation velocity grows nearly linearly
with height, implying that the gel settling may be fully described by a
(time-dependent) strain rate. We find that the microscopic dynamics exhibit
remarkable scaling properties when time is normalized by strain rate, showing
that the gel microscopic restructuring is dominated by its macroscopic
deformation.


  Silicene and germanene derivatives constructed from periodic dumbbell units
play a crucial role in multilayers of these honeycomb structures. Using
first-principles calculations based on density functional theory, here we
investigate the dumbbell formation mechanisms and energetics of Group IV atoms
adsorbed on graphene, silicene, germanene and stanene monolayer honeycomb
structures. The stabilities of the binding structures are further confirmed by
performing ab-initio molecular dynamics calculations at elevated temperatures,
except for stanene which is subject to structural instability upon the
adsorption of adatoms. Depending on the row number of the adatoms and
substrates we find three types of binding structures, which lead to significant
changes in the electronic, magnetic, and optical properties of substrates. In
particular, Si, Ge and Sn adatoms adsorbed on silicene and germanene form
dumbbell structures. Furthermore, dumbbell structures occur not only on single
layer, monatomic honeycomb structures, but also on their compounds like SiC and
SiGe. We show that the energy barrier to the migration of a dumbbell structure
is low due to the concerted action of atoms. This renders dumbbells rather
mobile on substrates to construct new single and multilayer Si and Ge phases.


  The detection of earthquakes is a fundamental prerequisite for seismology and
contributes to various research areas, such as forecasting earthquakes and
understanding the crust/mantle structure. Recent advances in machine learning
technologies have enabled the automatic detection of earthquakes from waveform
data. In particular, various state-of-the-art deep-learning methods have been
applied to this endeavour. In this study, we proposed and tested a novel phase
detection method employing deep learning, which is based on a standard
convolutional neural network in a new framework. The novelty of the proposed
method is its separate explicit learning strategy for global and local
representations of waveforms, which enhances its robustness and flexibility.
Prior to modelling the proposed method, we identified local representations of
the waveform by the multiple clustering of waveforms, in which the data points
were optimally partitioned. Based on this result, we considered a global
representation and two local representations of the waveform. Subsequently,
different phase detection models were trained for each global and local
representation. For a new waveform, the overall phase probability was evaluated
as a product of the phase probabilities of each model. This additional
information on local representations makes the proposed method robust to noise,
which is demonstrated by its application to the test data. Furthermore, an
application to seismic swarm data demonstrated the robust performance of the
proposed method compared with those of other deep learning methods. Finally, in
an application to low-frequency earthquakes, we demonstrated the flexibility of
the proposed method, which is readily adaptable for the detection of
low-frequency earthquakes by retraining only a local model.


  In these lectures I start by briefly reviewing the status of the electroweak
theory, in the Standard Model and beyond. I then discuss the motivation and the
possible avenues for new physics, on the brink of the LHC start.


  Atomic interferometers measure phase differences along paths with exceptional
precision. Tweezer interferometry is a novel approach for performing this task.
It allows to guide the particles along pre-defined trajectories. We explore the
feasibility of using condensed bosons in tweezer interferometry. As opposed to
factor $\sqrt{N}$ enhancement with ${N \sim 100}$ fermions, here we expect
factor ${N}$ enhancement using NOON state interferometry. The proposed protocol
involves adiabatic splitting and merging, followed by adiabatic branching of
condensed bosons. The purpose of the latter step is phase encoding. Spontaneous
symmetry breaking should be avoided. We estimate the time that is required for
performing those sweep processes.


  Using the notion of Mahonian statistic on acyclic posets, we introduce a
$q$-analogue of the $h$-polynomial of a simple generalized permutohedron. We
focus primarily on the case of nestohedra and on explicit computations for many
interesting examples, such as $S_n$-invariant nestohedra, graph associahedra,
and Stanley--Pitman polytopes. For the usual (Stasheff) associahedron, our
generalization yields an alternative $q$-analogue to the well-studied Narayana
numbers.


  SYN-flooding attack uses the weakness available in TCP's three-way handshake
process to keep it from handling legitimate requests. This attack causes the
victim host to populate its backlog queue with forged TCP connections. In other
words it increases Ploss (probability of loss) and Pa (buffer occupancy
percentage of attack requests) and decreases Pr (buffer occupancy percentage of
regular requests) in the victim host and results to decreased performance of
the host. This paper proposes a self-managing approach, in which the host
defends against SYN-flooding attack by dynamically tuning of its own two
parameters, that is, m (maximum number of half-open connections) and h (hold
time for each half-open connection). In this way, it formulates the defense
problem to an optimization problem and then employs the learning automata (LA)
algorithm to solve it. The simulation results show that the proposed defense
strategy improves performance of the under attack system in terms of Ploss, Pa
and Pr.


  The rapid advancements in Large Language Models (LLMs) have opened new
avenues for automating complex tasks in AI research. This paper investigates
the efficacy of different LLMs-Mistral 7B, Llama-2, GPT-4-Turbo and GPT-4.o in
extracting leaderboard information from empirical AI research articles. We
explore three types of contextual inputs to the models: DocTAET (Document
Title, Abstract, Experimental Setup, and Tabular Information), DocREC (Results,
Experiments, and Conclusions), and DocFULL (entire document). Our comprehensive
study evaluates the performance of these models in generating (Task, Dataset,
Metric, Score) quadruples from research papers. The findings reveal significant
insights into the strengths and limitations of each model and context type,
providing valuable guidance for future AI research automation efforts.


  We study continuous stochastic games with inhomogeneous mean field
interactions on large networks and explore their graphon limits. We consider a
model with a continuum of players, where each player's dynamics involve not
only mean field interactions but also individual jumps induced by a Poisson
random measure. We examine the case of controlled dynamics, with control terms
present in the drift, diffusion, and jump components.
  We introduce the graphon game model based on a graphon controlled stochastic
differential equation (SDE) system with jumps, which can be regarded as the
limiting case of a finite game's dynamic system as the number of players goes
to infinity. Under some general assumptions, we establish the existence and
uniqueness of Markovian graphon equilibria. We then provide convergence results
on the state trajectories and their laws, transitioning from finite game
systems to graphon systems. We also study approximate equilibria for finite
games on large networks, using the graphon equilibrium as a benchmark. The
rates of convergence are analyzed under various underlying graphon models and
regularity assumptions.


  A controllable energy method, which considers the undersampling issue of the
transfer function and valid spectral energy of a source signal, is proposed to
implement angular spectrum diffraction calculation in near and far fields. The
proposed method provides an optimized frequency boundary $f_{CE}$ within which
it always keeps controllable energy to be diffracted. The controllable energy
angular spectrum method significantly reduces the number of samples while
having the same accuracy as previous angular spectrum methods, implying a
higher calculation efficiency. The new perspective of analyzing spectral energy
is shown to improve the performance of relevant diffraction calculations.


  A new Adaptive Neuro Particle Swarm Optimization (ANPSO) combined with a
fuzzy inference system for diagnosing disorders is presented in this paper. The
main contributions of the novel proposed method can be a global search across
the whole search space with faster convergence rate. Moreover, it shows a
better exploration and exploitation by applying the adaptive control
parameters, automatic control of inertia weight and coefficient of personal and
social behaviours. Utilizing such attributes lead to a fast and smart diagnosis
mechanism which is able to diagnosis the diseases by the high accuracy. The
ANPSO is associated with tuning the characteristics of the inference system to
achieve the minimum diagnosis error as far as the optimized model is obtained.
As a case study, we use liver disorders dataset called Bupa. According to the
preliminary ramifications, the suggested adaptive PSO performance can overcome
the traditional inference system and combined with other optimization methods
substantially.


  This study compared the effectiveness of COVID-19 control policies, including
wearing masks, and the vaccine rates through proportional infection rate in 28
states of the United States using the eSIR model. The effective rate of
policies was measured by the difference between the predicted daily infection
proportion rate using the data before the policy and the actual daily infection
proportion rate. The study suggests that both mask and vaccine policy had a
significant impact on mitigating the pandemic. We further explored how
different social factors influenced the effectiveness of a specific policy
through the linear regression model. Out of 9 factors, the population density,
number of hospital beds per 1000 people, and percent of the population over 65
are the most substantial factors on mask policy effectiveness, while public
health funding per person, percent of immigration have the most significant
influence on vaccine policy effectiveness. This study summarized the
effectiveness of different policies and factors they associated with. It can be
served as a reference for future covid-19 related policy.


  We analyze the kinematics of multigrid Monte Carlo algorithms by
investigating acceptance rates for nonlocal Metropolis updates. With the help
of a simple criterion we can decide whether or not a multigrid algorithm will
have a chance to overcome critial slowing down for a given model. Our method is
introduced in the context of spin models. A multigrid Monte Carlo procedure for
nonabelian lattice gauge theory is described, and its kinematics is analyzed in
detail.


  We address the problem of continuous-variable quantum phase estimation in the
presence of linear disturbance at the Hamiltonian level, by means of Gaussian
probe states. In particular we discuss both unitary and random disturbance, by
considering the parameter which characterizes the unwanted linear term present
in the Hamiltonian as fixed (unitary disturbance) or random with a given
probability distribution (random disturbance). We derive the optimal input
Gaussian states at fixed energy, maximizing the quantum Fisher information over
the squeezing angle and the squeezing energy fraction, and we discuss the
scaling of the quantum Fisher information in terms of the output number of
photons $n_{out}$. We observe that in the case of unitary disturbance the
optimal state is a squeezed vacuum state and the quadratic scaling is
conserved. As regards the random disturbance, we observe that the optimal
squeezing fraction may not be equal to one, and, for any non-zero value of the
noise parameter, the quantum Fisher information scales linearly with the
average number of photons. We finally discuss the performance of homodyne
measurement, comparing the achievable precision with the ultimate limit posed
by the quantum Cram\'er-Rao bound.