  We present further analysis of the [CII] 158$\mu$m fine structure line and
thermal dust continuum emission from the archetype extreme starburst/AGN group
of galaxies in the early Universe, BRI 1202-0725 at $z=4.7$, using the Atacama
Large Millimeter Array. The group is long noted for having a closely separated
(26kpc in projection) FIR-hyperluminous quasar host galaxy and an optically
obscured submm galaxy (SMG). A short ALMA test observation reveals a rich
laboratory for the study of the myriad processes involved in clustered massive
galaxy formation in the early Universe. Strong [CII] emission from the SMG and
the quasar have been reported earlier by Wagg et al. (2012) based on these
observations. In this letter, we examine in more detail the imaging results
from the ALMA observations, including velocity channel images,
position-velocity plots, and line moment images. We present detections of [CII]
emission from two Ly$\alpha$-selected galaxies in the group, demonstrating the
relative ease with which ALMA can detect the [CII] emission from lower star
formation rate galaxies at high redshift. Imaging of the [CII] emission shows a
clear velocity gradient across the SMG, possibly indicating rotation or a more
complex dynamical system on a scale $\sim 10$kpc. There is evidence in the
quasar spectrum and images for a possible outflow toward the southwest, as well
as more extended emission (a 'bridge'), between the quasar and the SMG,
although the latter could simply be emission from Ly$\alpha$-1 blending with
that of the quasar at the limited spatial resolution of the current
observations. These results provide an unprecedented view of a major merger of
gas rich galaxies driving extreme starbursts and AGN accretion during the
formation of massive galaxies and supermassive black holes within 1.3 Gyr of
the Big Bang.


  The complete positivity vs positivity correspondence in the
Choi-Jamio{\l}kowski-Kraus-Sudarshan quantum channel-state isomorphism depends
on the choice of basis. Instead of the "canonical" basis, if we use, e.g., the
Pauli spin matrices along with the identity as the basis for the space of
bounded operators on the two-dimensional complex Hilbert space, this
correspondence breaks down. A sufficient condition on the basis for validity of
this correspondence is provided in the work of Paulsen and
Shult~\cite{Paulsen}, which was later proven to be necessary by Kye~\cite{Kye}.
A correspondence is also present between the space of super-maps and the tensor
product of the spaces of the inputs and outputs of the same. In particular, a
super-map is completely CP-preserving if and only if its Choi-type
representation is completely positive (CP). This correspondence also depends on
a specific choice of basis. In this work, we find the necessary and sufficient
condition on a basis such that this correspondence holds true.


  We consider numerical schemes for root finding of noisy responses through
generalizing the Probabilistic Bisection Algorithm (PBA) to the more practical
context where the sampling distribution is unknown and location-dependent. As
in standard PBA, we rely on a knowledge state for the approximate posterior of
the root location. To implement the corresponding Bayesian updating, we also
carry out inference of oracle accuracy, namely learning the probability of
correct response. To this end we utilize batched querying in combination with a
variety of frequentist and Bayesian estimators based on majority vote, as well
as the underlying functional responses, if available. For guiding sampling
selection we investigate both Information Directed sampling, as well as
Quantile sampling. Our numerical experiments show that these strategies perform
quite differently; in particular we demonstrate the efficiency of randomized
quantile sampling which is reminiscent of Thompson sampling. Our work is
motivated by the root-finding sub-routine in pricing of Bermudan financial
derivatives, illustrated in the last section of the paper.


  We theoretically give an infinite number of metastable crystal structures for
the superconducting sulfur hydride H$_{x}$S under pressure. It has been thought
that theoretically predicted structures of H$_{2}$S and H$_{3}$S exhibit low
and high $T_{\rm c}$ in the experiment, respectively. The newly found
structures are long-period modulated crystals where slab-like H$_{2}$S and
H$_{3}$S regions intergrow in a microscopic scale. The extremely small
formation enthalpy for the H$_{2}$S--H$_{3}$S boundary indicated with the
first-principles calculations suggests possible alloying of these phases
through formation of local H$_{3}$S regions. The modulated structures and
gradual alloying transformations between them not only explain the peculiar
pressure dependence of $T_{\rm c}$ in sulfur hydride observed experimentally,
but also could prevail in the experimental samples under various compression
schemes.


  Smart contracts are Turing-complete programs that are executed across a
blockchain. Unlike traditional programs, once deployed, they cannot be
modified. As smart contracts carry more value, they become more of an exciting
target for attackers. Over the last years, they suffered from exploits costing
millions of dollars due to simple programming mistakes. As a result, a variety
of tools for detecting bugs have been proposed. Most of these tools rely on
symbolic execution, which may yield false positives due to over-approximation.
Recently, many fuzzers have been proposed to detect bugs in smart contracts.
However, these tend to be more effective in finding shallow bugs and less
effective in finding bugs that lie deep in the execution, therefore achieving
low code coverage and many false negatives. An alternative that has proven to
achieve good results in traditional programs is hybrid fuzzing, a combination
of symbolic execution and fuzzing. In this work, we study hybrid fuzzing on
smart contracts and present ConFuzzius, the first hybrid fuzzer for smart
contracts. ConFuzzius uses evolutionary fuzzing to exercise shallow parts of a
smart contract and constraint solving to generate inputs that satisfy complex
conditions that prevent evolutionary fuzzing from exploring deeper parts.
Moreover, ConFuzzius leverages dynamic data dependency analysis to efficiently
generate sequences of transactions that are more likely to result in contract
states in which bugs may be hidden. We evaluate the effectiveness of ConFuzzius
by comparing it with state-of-the-art symbolic execution tools and fuzzers for
smart contracts. Our evaluation on a curated dataset of 128 contracts and 21K
real-world contracts shows that our hybrid approach detects more bugs (up to
23%) while outperforming state-of-the-art in terms of code coverage (up to
69%), and that data dependency analysis boosts bug detection up to 18%.


  Federated learning (FL) is a new paradigm for distributed machine learning
that allows a global model to be trained across multiple clients without
compromising their privacy. Although FL has demonstrated remarkable success in
various scenarios, recent studies mainly utilize shallow and small neural
networks. In our research, we discover a significant performance decline when
applying the existing FL framework to deeper neural networks, even when client
data are independently and identically distributed (i.i.d.). Our further
investigation shows that the decline is due to the continuous accumulation of
dissimilarities among client models during the layer-by-layer back-propagation
process, which we refer to as "divergence accumulation." As deeper models
involve a longer chain of divergence accumulation, they tend to manifest
greater divergence, subsequently leading to performance decline. Both
theoretical derivations and empirical evidence are proposed to support the
existence of divergence accumulation and its amplified effects in deeper
models. To address this issue, we propose several technical guidelines based on
reducing divergence, such as using wider models and reducing the receptive
field. These approaches can greatly improve the accuracy of FL on deeper
models. For example, the application of these guidelines can boost the
ResNet101 model's performance by as much as 43\% on the Tiny-ImageNet dataset.


  The AGILE satellite, operating since mid 2007 and primarily devoted to
high-energy astrophysics, is one of the only three currently operating space
instruments capable of detecting Terrestrial Gamma-Ray Flashes (TGFs), together
with RHESSI and $Fermi$-GBM. Thanks to the AGILE Mini-Calorimeter instrument
energy range extended up to 100MeV and its flexible trigger logic on
sub-millisecond time scales, AGILE is detecting more than 10 TGFs/month, adding
a wealth of observations which pose severe constrains on production models. The
main AGILE discoveries in TGF science during two and a half years of
observations are the following: 1) the TGF spectrum extends well above 40 MeV,
2) the high energy tail of the TGF spectrum is harder than expected and cannot
be easily explained by previous theoretical models, 3) TGFs can be localized
from space using high-energy photons detected by the AGILE gamma-ray imaging
detector. In this presentation we will describe the characteristics of the
2.5-years AGILE TGF sample, focusing on the recent results concerning the TGF
high-energy spectral characteristics.


  Resonant optical systems have widespread applications in science and
technology. However, their quality ($Q$) factors can be significantly
deteriorated, if some of their parts exhibit optical absorption. Here, we show
that by coupling a lossy mode of such a structure to two independent lossless
modes, one can create a nonradiating and absorption-free bound state in the
continuum (BIC). The $Q$ factor of such a BIC is theoretically unlimited
despite interaction with an absorbing structure. We use this mechanism to
design a plasmonic metasurface with $Q$ factors that are close to $10^7$ in the
visible spectral range. The proposed mechanism is general and can be used to
engineer ultrahigh-$Q$ resonances in various absorbing structures.


  Synthesized medical images have several important applications, e.g., as an
intermedium in cross-modality image registration and as supplementary training
samples to boost the generalization capability of a classifier. Especially,
synthesized computed tomography (CT) data can provide X-ray attenuation map for
radiation therapy planning. In this work, we propose a generic cross-modality
synthesis approach with the following targets: 1) synthesizing realistic
looking 3D images using unpaired training data, 2) ensuring consistent
anatomical structures, which could be changed by geometric distortion in
cross-modality synthesis and 3) improving volume segmentation by using
synthetic data for modalities with limited training samples. We show that these
goals can be achieved with an end-to-end 3D convolutional neural network (CNN)
composed of mutually-beneficial generators and segmentors for image synthesis
and segmentation tasks. The generators are trained with an adversarial loss, a
cycle-consistency loss, and also a shape-consistency loss, which is supervised
by segmentors, to reduce the geometric distortion. From the segmentation view,
the segmentors are boosted by synthetic data from generators in an online
manner. Generators and segmentors prompt each other alternatively in an
end-to-end training fashion. With extensive experiments on a dataset including
a total of 4,496 CT and magnetic resonance imaging (MRI) cardiovascular
volumes, we show both tasks are beneficial to each other and coupling these two
tasks results in better performance than solving them exclusively.


  We study spectral properties of the Laplacians on Schreier graphs arising
from Grigorchuk's group acting on the boundary of the infinite binary tree. We
establish a connection between the action of $G$ on its space of Schreier
graphs and a subshift associated to a non-primitive substitution and relate the
Laplacians on the Schreier graphs to discrete Schroedinger operators with
aperiodic order. We use this relation to prove that the spectrum of the
anisotropic Laplacians is a Cantor set of Lebesgue measure zero. We also use it
to show absence of eigenvalues both almost-surely and for certain specific
graphs. The methods developed here apply to a large class of examples.


  The topological properties of many materials are central to their behavior,
with the dynamics of topological defects being particularly important to
intrinsically out-of-equilibrium, active materials. In this paper, local
manipulation of the ordering, dynamics, and topological properties of
microtubule-based extensile active nematic films is demonstrated in a joint
experimental and simulation study. Hydrodynamic stresses created by
magnetically actuated rotation of disk-shaped colloids in proximity to the
films compete with internal stresses in the active nematic, enabling local
control of the motion of the +1/2 charge topological defects that are intrinsic
to spontaneously turbulent active films. Sufficiently large applied stresses
drive the formation of +1 charge topological vortices in the director field
through the merger of two +1/2 defects. The directed motion of the defects is
accompanied by ordering of the vorticity and velocity of the active flows
within the film that is qualitatively unlike the response of passive viscous
films. Many features of the film's response to the disk are captured by Lattice
Boltzmann simulations, leading to insight into the anomalous viscoelastic
nature of the active nematic. The topological vortex formation is accompanied
by a rheological instability in the film that leads to significant increase in
the flow velocities. Comparison of the velocity profile in vicinity of the
vortex with fluid-dynamics calculations provides an estimate of film viscosity.


  The entanglement entropy of the $\nu = 1/3$ and $\nu = 5/2$ quantum Hall
states in the presence of short range random disorder has been calculated by
direct diagonalization. A microscopic model of electron-electron interaction is
used, electrons are confined to a single Landau level and interact with long
range Coulomb interaction. For very weak disorder, the values of the
topological entanglement entropy are roughly consistent with expected
theoretical results. By considering a broader range of disorder strengths, the
fluctuation in the entanglement entropy was studied in an effort to detect
quantum phase transitions. In particular, there is a clear signature of a
transition as a function of the disorder strength for the $\nu = 5/2$ state.
Prospects for using the density matrix renormalization group to compute the
entanglement entropy for larger system sizes are discussed.


  We construct a bulk geometry with Q-lattice structure, which is implemented
by two gauge fields and a coupling between the lattice and the Maxwell field.
This gravity dual model can describe a novel insulator which exhibits some key
features analogous to Mott insulator. In particular, a hard gap in insulating
phase as well as vanishing DC conductivity can be simultaneously achieved. In
addition, we discuss the non-Drude behavior of the optical conductivity in low
frequency region in insulating phase, which exhibits some novel characteristics
different from ordinary Mott insulator.


  Biometric security is a fast growing area. Protecting biometric data is very
important since it can be misused by attackers. In order to increase security
of biometric data there are different methods in which watermarking is widely
accepted. A more acceptable, new important development in this area is
reversible watermarking in which the original image can be completely restored
and the watermark can be retrieved. But reversible watermarking in biometrics
is an understudied area. Reversible watermarking maintains high quality of
biometric data. This paper proposes Rotational Replacement of LSB as a
reversible watermarking scheme for biometric images. PSNR is the regular method
used for quality measurement of biometric data. In this paper we also show that
SSIM Index is a better alternate for effective quality assessment for
reversible watermarked biometric data by comparing with the well known
reversible watermarking scheme using Difference Expansion.


  We present the performance of the Integral Field Spectrograph (IFS) of
SPHERE, the high-contrast imager for the ESO VLT telescope designed to perform
imaging and spectroscopy of extrasolar planets, obtained from tests performed
at the Institute de Plan\'etologie et d'Astrophysique de Grenoble facility
during the integration phase of the instrument.} {The tests were performed
using the instrument software purposely prepared for SPHERE. The output data
were reduced applying the SPHERE data reduction and handling software, adding
an improved spectral deconvolution procedure. To this aim, we prepared an
alternative procedure for the spectral subtraction exploiting the principal
components analysis algorithm. Moreover, a simulated angular differential
imaging procedure was also implemented to estimate how the instrument performed
once this procedure was applied at telescope. The capability of the IFS to
faithfully retrieve the spectra of the detected faint companions was also
considered.} {We found that the application of the updated version of the
spectral deconvolution procedure alone, when the algorithm throughput is
properly taken into account, gives us a $5\sigma$ limiting contrast of the
order of 5$\times$$10^{-6}$ or slightly better. The further application of the
angular differential imaging procedure on these data should allow us to improve
the contrast by one order of magnitude down to around 7$\times$$10^{-7}$ at a
separation of 0.3 arcsec. The application of a principal components analysis
procedure that simultaneously uses spectral and angular data gives comparable
results. Finally, we found that the reproducibility of the spectra of the
detected faint companions is greatly improved when angular differential imaging
is applied in addition to the spectral deconvolution.


  Trickle is a polite gossip algorithm for managing communication traffic. It
is of particular interest in low-power wireless networks for reducing the
amount of control traffic, as in routing protocols (RPL), or reducing network
congestion, as in multicast protocols (MPL). Trickle is used at the network or
application level, and relies on up-to-date information on the activity of
neighbors. This makes it vulnerable to interference from the media access
control layer, which we explore in this paper. We present several scenarios how
the MAC layer in low-power radios violates Trickle timing. As a case study, we
analyze the impact of CSMA/CA with ContikiMAC on Trickle's performance.
Additionally, we propose a solution called Cleansing that resolves these
issues.


  We construct a gluing map for stable affine vortices over the upper half
plane with the Lagrangian boundary condition at a rigid, regular, codimension
one configuration. This construction plays an important role in establishing
the relation between the gauged linear sigma model and the nonlinear sigma
model in the presence of Lagrangian branes.


  We introduce a fault-tolerant construction to implement a composite quantum
operation of four overlapping Toffoli gates. The same construction can produce
two independent Toffoli gates. This result lowers resource overheads in designs
for quantum computers by more than an order of magnitude. The procedure uses
Clifford operations and 64 copies of the non-Clifford gate $T = \exp[i \pi (I -
\sigma^z) /8]$. Quantum codes detect errors in the circuit. When the dominant
source of error is $T$-gate failure with probability $p$, then the composite
Toffoli circuit has postselected failure rate of $3072p^4$ to lowest order.


  Atomistic modelling of phase transitions, chemical reactions, or other rare
events that involve overcoming high free energy barriers usually entails
prohibitively long simulation times. Introducing a bias potential as a function
of an appropriately-chosen set of collective variables can significantly
accelerate the exploration of phase space, albeit at the price of distorting
the distribution of microstates. Efficient re-weighting to recover the unbiased
distribution can be nontrivial when employing adaptive sampling techniques such
as Metadynamics, Variationally Enhanced Sampling or Parallel Bias Metadynamics,
in which the system evolves in a quasi-equilibrium manner under a
time-dependent bias. We introduce an iterative unbiasing scheme that makes
efficient use of all the trajectory data, and that does not require the
distribution to be evaluated on a grid. The method can thus be used even when
the bias has a high dimensionality. We benchmark this approach against some of
the existing schemes, on models systems with different complexities and
dimensionalities.


  Learning representations of geographical space is vital for any machine
learning model that integrates geolocated data, spanning application domains
such as remote sensing, ecology, or epidemiology. Recent work embeds
coordinates using sine and cosine projections based on Double Fourier Sphere
(DFS) features. These embeddings assume a rectangular data domain even on
global data, which can lead to artifacts, especially at the poles. At the same
time, little attention has been paid to the exact design of the neural network
architectures with which these functional embeddings are combined. This work
proposes a novel location encoder for globally distributed geographic data that
combines spherical harmonic basis functions, natively defined on spherical
surfaces, with sinusoidal representation networks (SirenNets) that can be
interpreted as learned Double Fourier Sphere embedding. We systematically
evaluate positional embeddings and neural network architectures across various
benchmarks and synthetic evaluation datasets. In contrast to previous
approaches that require the combination of both positional encoding and neural
networks to learn meaningful representations, we show that both spherical
harmonics and sinusoidal representation networks are competitive on their own
but set state-of-the-art performances across tasks when combined. The model
code and experiments are available at
https://github.com/marccoru/locationencoder.


  In the last couple of years, there has been big progress in finite
temperature QCD on the lattice. Large-scale dynamical simulations of 2+1 flavor
QCD with various improved staggered quark actions have been started to produce
results for various thermodynamic quantities which are extrapolated to the
continuum limit at around physical quark masses, and thus are capable for a
direct comparison with experiment. At the same time, the theoretical uneasiness
with staggered-type lattice quarks motivated several groups to accelerate
studies with Wilson-type quarks and lattice chiral quarks. In this review, I
discuss these important developments in finite temperature QCD made in the past
year.


  We consider the problem of determining when the difference of two ribbon
Schur functions is a single Schur function. We fully classify the five infinite
families of pairs of ribbon Schur functions whose difference is a single Schur
function with corresponding partition having at most two parts at least $2$. We
also prove an identity for differences of ribbon Schur functions and we
determine some necessary conditions for such a difference to be Schur-positive,
depending on the distribution of $1$'s and the end row lengths.


  The main focus of this paper is document image classification and retrieval,
where we analyze and compare different parameters for the RunLeght Histogram
(RL) and Fisher Vector (FV) based image representations. We do an exhaustive
experimental study using different document image datasets, including the MARG
benchmarks, two datasets built on customer data and the images from the Patent
Image Classification task of the Clef-IP 2011. The aim of the study is to give
guidelines on how to best choose the parameters such that the same features
perform well on different tasks. As an example of such need, we describe the
Image-based Patent Retrieval task's of Clef-IP 2011, where we used the same
image representation to predict the image type and retrieve relevant patents.


  The main goal of the present lectures is to outline the key particle
interactions and energy loss mechanisms in the Galactic medium that high-energy
particles are subject to. These interactions are an important ingredient
entering the cosmic ray propagation equation, contributing to shape cosmic ray
spectra. They also source the so-called secondary species, like gamma rays,
neutrinos, "fragile" nuclei not synthesised in stars, and antiparticles, all
routinely used as diagnostic tools in a multi-messenger context. These lectures
are complementary to Denise Boncioli's ones, focusing instead on processes
happening at ultra-high energies in the extragalactic environment. They include
propaedeutic material to Felix Aharonian's and, to some extent, Stefano
Gabici's and Carmelo Evoli's lectures.


  This letter reports results from the MINOS experiment based on its initial
exposure to neutrinos from the Fermilab NuMI beam. The rate and energy spectra
of charged current muon neutrino interactions are compared in two detectors
located along the beam axis at distances of 1 km and 735 km. With 1.27 x
10^{20} 120 GeV protons incident on the NuMI target, 215 events with energies
below 30 GeV are observed at the Far Detector, compared to an expectation of
336 \pm 14.4 events. The data are consistent with muon neutrino disappearance
via oscillation with |\Delta m^2_{23}| = 2.74^{+0.44}_{-0.26} x 10^{-3}
eV^2/c^4 and sin^2(2\theta_{23}) > 0.87 (at 60% C.L.).


  The growing complexity and capacity demands for mobile networks necessitate
innovative techniques for optimizing resource usage. Meanwhile, recent
breakthroughs have brought Reinforcement Learning (RL) into the domain of
continuous control of real-world systems. As a step towards RL-based network
control, this paper introduces a new framework for benchmarking the performance
of an RL agent in network environments simulated with ns-3. Within this
framework, we demonstrate that an RL agent without domain-specific knowledge
can learn how to efficiently adjust Radio Access Network (RAN) parameters to
match offline optimization in static scenarios, while also adapting on the fly
in dynamic scenarios, in order to improve the overall user experience. Our
proposed framework may serve as a foundation for further work in developing
workflows for designing RL-based RAN control algorithms.


  We present creep experiments on fiber composite materials with controlled
heterogeneity. Recorded strain rates and acoustic emission rates exhibit a
power law relaxation in the primary creep regime (Andrade law) followed by a
power law acceleration up to rupture over up to four decades in time. We
discover that the failure time is proportional to the duration of the primary
creep regime, showing the interplay between the two regimes and offering a
method of rupture prediction. These experimental results are rationalized by a
mean-field model of representative elements with nonlinear visco-elastic
rheology and with a large heterogeneity of strengths.


  We present the final legacy version of stellar photometry for the
Panchromatic Hubble Andromeda Treasury (PHAT) survey. We have reprocessed all
of the Hubble Space Telescope (HST) Wide Field Camera 3 (WFC3) and Advanced
Camera for Surveys (ACS) near ultraviolet (F275W, F336W), optical (F475W,
F814W), and near infrared (F110W, F160W) imaging from the PHAT survey using an
improved method that optimized the survey depth and chip gap coverage by
including all overlapping exposures in all bands in the photometry. An
additional improvement was gained through the use of charge transfer efficiency
(CTE) corrected input images, which provide more complete star finding as well
as more reliable photometry for the NUV bands, which had no CTE correction in
the previous version of the PHAT photometry. While this method requires
significantly more computing resources and time than earlier versions where the
photometry was performed on individual pointings, it results in smaller
systematic instrumental completeness variations as demonstrated by cleaner maps
in stellar density, and it results in optimal constraints on stellar fluxes in
all bands from the survey data. Our resulting catalog has 138 million stars,
18% more than the previous catalog, with lower density regions gaining as much
as 40% more stars. The new catalog produces nearly seamless population maps
which show relatively well-mixed distributions for populations associated with
ages older than 1-2 Gyr, and highly structured distributions for the younger
populations.


  We report new measurements of the parity-violating asymmetry A_PV in elastic
scattering of 3 GeV electrons off hydrogen and 4He targets with <theta_lab>~6.0
degrees. The 4He result is A_PV = (+6.40 +/- 0.23 (stat) +/- 0.12 (syst))
x10^-6. The hydrogen result is A_PV = (-1.58 +/- 0.12 (stat) +/- 0.04 (syst))
x10^-6. These results significantly improve constraints on the electric and
magnetic strange form factors G_E^s and G_M^s. We extract G_E^s = 0.002 +/-
0.014 +/- 0.007 at <Q^2> = 0.077 GeV^2, and G_E^s + 0.09 G_M^s = 0.007 +/-
0.011 +/- 0.006 at <Q^2> = 0.109 GeV^2, providing new limits on the role of
strange quarks in the nucleon charge and magnetization distributions.


  The interaction of waves and flows is a challenging topic where a complete
resolution has been frustrated by the essential nonlinear features in the
hydrodynamic case. Even in the case of EM waves in flowing media, the results
are subtle. For a simple shear flow of constant n fluid, incident radiation is
shown to be reflected and refracted in an analogous manner to Snell's law.
However, the beam intensities differ and the system has an asymmetry in that an
internal reflection gap opens at steep incident angles nearly oriented with the
shear. For EM waves these effects are generally negligible in real systems but
they introduce the topic at a reduced level of complexity of the more
interesting acoustic case. Acoustic streaming is suggested, both from theory
and experimental data, to be associated with vorticity generation at the driver
itself. Bounds on the vorticity in bulk and nonlinear effects demonstrate that
the bulk sources, even with attenuation, cannot drive such a strong flow. A
review of the velocity scales in the problem suggest that a variation of the
Scholte wave at the driver-fluid interface with local cavitation on the
decompression phase creates a lateral flow of fluid that generates the stream
and imparts the vorticity from the driver plate. In the case of Darcy flow in
sintered media, an analysis of data suggests that acoustically enhanced flow
may be the result of surface effects that narrow channels that are cleared by
ultrasonic cavitation.


  We highlight two recent studies of circumstellar disks in the Taurus and
Orion star forming regions. Using the JCMT and CSO, we measure disk fluxes in
Taurus over a wide range of submillimeter wavelengths and determine the
frequency dependence of the dust opacity. We find clear evidence for a
systematic change in its behavior with time, most readily explained by grain
growth. Using the SMA, we observed the protoplanetary disks (proplyds) in the
Orion Trapezium cluster. The combination of high resolution, high frequency,
and high sensitivity that this instrument provides made it possible to resolve
disks from one another, distinguish their emission from background cloud
material and surrounding ionized gas, and to detect thermal dust emission. This
allowed us to make the first mass measurements of the proplyds and to assess
their viability for planet formation.


  In this paper, we obtain several extensions of semi-Fredholm theory on
Hilbert modules by generalizing in this setting their classical counterparts
regarding Weyl operators and Drazin invertible operators.


  When robots are deployed in the field for environmental monitoring they
typically execute pre-programmed motions, such as lawnmower paths, instead of
adaptive methods, such as informative path planning. One reason for this is
that adaptive methods are dependent on parameter choices that are both critical
to set correctly and difficult for the non-specialist to choose. Here, we show
how to automatically configure a planner for informative path planning by
training a reinforcement learning agent to select planner parameters at each
iteration of informative path planning. We demonstrate our method with 37
instances of 3 distinct environments, and compare it against pure (end-to-end)
reinforcement learning techniques, as well as approaches that do not use a
learned model to change the planner parameters. Our method shows a 9.53% mean
improvement in the cumulative reward across diverse environments when compared
to end-to-end learning based methods; we also demonstrate via a field
experiment how it can be readily used to facilitate high performance deployment
of an information gathering robot.


  We propose a new supervised dimensionality reduction technique called
Supervised Linear Centroid-Encoder (SLCE), a linear counterpart of the
nonlinear Centroid-Encoder (CE) \citep{ghosh2022supervised}. SLCE works by
mapping the samples of a class to its class centroid using a linear
transformation. The transformation is a projection that reconstructs a point
such that its distance from the corresponding class centroid, i.e.,
centroid-reconstruction loss, is minimized in the ambient space. We derive a
closed-form solution using an eigendecomposition of a symmetric matrix. We did
a detailed analysis and presented some crucial mathematical properties of the
proposed approach. %We also provide an iterative solution approach based
solving the optimization problem using a descent method. We establish a
connection between the eigenvalues and the centroid-reconstruction loss. In
contrast to Principal Component Analysis (PCA) which reconstructs a sample in
the ambient space, the transformation of SLCE uses the instances of a class to
rebuild the corresponding class centroid. Therefore the proposed method can be
considered a form of supervised PCA. Experimental results show the performance
advantage of SLCE over other supervised methods.


  The next generation of galaxy surveys will attempt to measure the baryon
oscillations in the clustering power spectrum with high accuracy. These
oscillations encode a preferred scale which may be used as a standard ruler to
constrain cosmological parameters and dark energy models. In this paper we
present simple analytical fitting formulae for the accuracy with which the
preferred scale may be determined in the tangential and radial directions by
future spectroscopic and photometric galaxy redshift surveys. We express these
accuracies as a function of survey parameters such as the central redshift,
volume, galaxy number density and (where applicable) photometric redshift
error. These fitting formulae should greatly increase the efficiency of
optimizing future surveys, which requires analysis of a potentially vast number
of survey configurations and cosmological models. The formulae are calibrated
using a grid of Monte Carlo simulations, which are analyzed by dividing out the
overall shape of the power spectrum before fitting a simple decaying sinusoid
to the oscillations. The fitting formulae reproduce the simulation results with
a fractional scatter of 7% (10%) in the tangential (radial) directions over a
wide range of input parameters. We also indicate how sparse-sampling strategies
may enhance the effective survey area if the sampling scale is much smaller
than the projected baryon oscillation scale.


  The form of the leptonic mixing matrix emerging from experiment has, in the
last few years, generated a lot of interest in the so-called tribimaximal type.
This form may be naturally associated with the possibility of a discrete
permutation symmetry ($S_3$) among the three generations. However, trying to
implement this attractive symmetry has resulted in some problems and it seems
to have fallen out of favor. We suggest an approach in which the $S_3$ holds to
first approximation, somewhat in the manner of the old SU(3) flavor symmetry of
the three flavor quark model. It is shown that in the case of the neutrino
sector, a presently large experimentally allowed region can be fairly well
described in this first approximation.
  We briefly discuss the nature of the perturbations which are the analogs of
the Gell-Mann Okubo perturbations but confine our attention for the most part
to the $S_3$ invariant model. We postulate that the $S_3$ invariant mass
spectrum consists of non zero masses for the $(\tau,b,t)$ and zero masses for
the other charged fermions but approximately degenerate masses for the three
neutrinos. The mixing matrices are assumed to be trivial for the charged
fermions but of tribimaximal type for the neutrinos in the first approximation.
It is shown that this can be implemented by allowing complex entries for the
mass matrix and spontaneous breakdown of the $S_3$ invariance of the
Lagrangian.


  Major mergers between galaxies are predicted to fuel their central
supermassive black holes (SMBHs), particularly after coalescence. However,
determining the prevalence of active galactic nuclei (AGNs) in mergers remains
a challenge, because AGN diagnostics are sensitive to details of the central
structure (e.g., nuclear gas clouds, geometry and orientation of a dusty torus)
that are partly decoupled from SMBH accretion. X-rays, expected to be
ubiquitous among accreting systems, are detectable through non-Compton-thick
screens of obscuring material, and thus offer the potential for a more complete
assessment of AGNs in mergers. But, extant statistical X-ray studies of AGNs in
mergers have been limited by either sparse, heterogeneous, or shallow on-sky
coverage. We use new X-ray observations from the first SRG/eROSITA all-sky data
release to characterize the incidence, luminosity, and observability of AGNs in
mergers. Combining machine learning and visual classification, we identify 923
post-mergers in Dark Energy Camera Legacy Survey (DECaLS) imaging and select
4,565 interacting galaxy pairs (with separations <120 kpc and mass ratios
within 1:10) from the Sloan Digital Sky Survey. We find that galaxies with
X-ray AGNs are 2.0+/-0.24 times as likely to be identified as post-mergers
compared to non-AGN controls, and that post-mergers are 1.8+/-0.1 times as
likely to host an X-ray AGN as non-interacting controls. A multi-wavelength
census of X-ray, optical, and mid-IR-selected AGNs suggests a picture wherein
the underlying AGN fraction increases during pair-phase interactions, that
galaxy pairs within ~20 kpc become heavily obscured, and that the obscuration
often clears post-coalescence.


  This paper presents a rigorous optimization technique for wireless power
transfer (WPT) systems enhanced by passive elements, ranging from simple
reflectors and intermedi- ate relays all the way to general electromagnetic
guiding and focusing structures, such as metasurfaces and metamaterials. At its
core is a convex semidefinite relaxation formulation of the otherwise nonconvex
optimization problem, of which tightness and optimality can be confirmed by a
simple test of its solutions. The resulting method is rigorous, versatile, and
general -- it does not rely on any assumptions. As shown in various examples,
it is able to efficiently and reliably optimize such WPT systems in order to
find their physical limitations on performance, optimal operating parameters
and inspect their working principles, even for a large number of active
transmitters and passive elements.


  In this paper, we investigate the boundedness of good minimal models with
intermediate Kodaira dimensions, which has a natural Iitaka fibration to the
canonical models. We prove that good minimal models are bounded modulo crepant
birational equivalence when the base (canonical models) are bounded and the
general fibers of the Iitaka fibration are in a bounded family of polarized
Calabi-Yau pairs.


  We present a low frequency study of the diffuse radio emission in the galaxy
cluster A754. We present new 150 MHz image of the galaxy cluster A754 made with
the Giant Metrewave Radio Telescope (GMRT) and discuss the detection of 4
diffuse features. We compare the 150 MHz image with the images at 74, 330 and
1363 MHz; one new diffuse feature is detected. The flux density upperlimits at
330 and 1363 MHz imply a synchrotron spectral index, $\alpha > 2$, ($S\propto
\nu^{-\alpha}$) for the new feature. The 'west relic' detected at 74 MHz
(Kassim et al 2001) is not detected at 150 MHz and is thus consistent with its
non-detection at 1363 MHz (Bacchi et al 2003) and 330 MHz(Kassim et al 2001).
Integrated spectra of all the diffuse features are presented. The fourth
diffuse feature is located along the proposed merger axis (Zabludoff et al
1995) in A754 and 0.7 Mpc away from the peak of X-ray emission. We have made
use of the framework of adiabatic compression model (Ensslin & Gopal-Krishna
2001) to obtain spectra. We show that the spectrum of the fourth diffuse
feature is consistent with that of a cocoon of a radio galaxy lurking for about
$9\times10^{7}$ yr; no shock compression is required. The other three diffuse
emission have spectra steeper than 1.5 and could be cocoons lurking for longer
time. We discuss other possibilities such as shocks and turbulent
reacceleration being responsible for the diffuse emission in A754.


  Recent experiments in the topological Weyl semimetal TaAs have observed
record-breaking second-harmonic generation, a non-linear optical response at
$2\omega$ generated by an incoming light source at $\omega$. However, whether
second-harmonic generation is enhanced in topological semimetals in general is
a challenging open question because their band structure entangles the
contributions arising from trivial bands and topological band crossings. In
this work, we circumvent this problem by studying RhSi, a chiral topological
semimetal with a simple band structure with topological multifold fermions
close to the Fermi energy. We measure second-harmonic generation (SHG) in a
wide frequency window, $\omega\in [0.27,1.5]$eV and, using first principle
calculations, we establish that, due to their linear dispersion, the
contribution of multifold fermions to SHG is subdominant as compared with other
regions in the Brillouin zone. Our calculations suggest that parts of the bands
where the dispersion is relatively flat contribute significantly to SHG. As a
whole, our results suggest avenues to enhance SHG responses.


  It has recently been suggested that many contact mechanics problems between
solids can be accurately studied by mapping the problem on an effective one
dimensional (1D) elastic foundation model. Using this 1D mapping we calculate
the contact area and the average interfacial separation between elastic solids
with nominally flat but randomly rough surfaces. We show, by comparison to
exact numerical results, that the 1D mapping method fails even qualitatively.
We also calculate the normal interfacial stiffness $K$ and compare it with the
result of an analytical study. We attribute the failure of the elastic
foundation model to the neglect of the long-range elastic coupling between the
asperity contact regions.


  I study a game of strategic exploration with private payoffs and public
actions in a Bayesian bandit setting. In particular, I look at cascade
equilibria, in which agents switch over time from the risky action to the
riskless action only when they become sufficiently pessimistic. I show that
these equilibria exist under some conditions and establish their salient
properties. Individual exploration in these equilibria can be more or less than
the single-agent level depending on whether the agents start out with a common
prior or not, but the most optimistic agent always underexplores. I also show
that allowing the agents to write enforceable ex-ante contracts will lead to
the most ex-ante optimistic agent to buy all payoff streams, providing an
explanation to the buying out of smaller start-ups by more established firms.


  We investigate the average minimum cost of a bipartite matching between two
samples of n independent random points uniformly distributed on a unit cube in
d $\ge$ 3 dimensions, where the matching cost between two points is given by
any power p $\ge$ 1 of their Euclidean distance. As n grows, we prove
convergence, after a suitable renormalization, towards a finite and positive
constant. We also consider the analogous problem of optimal transport between n
points and the uniform measure. The proofs combine sub-additivity inequalities
with a PDE ansatz similar to the one proposed in the context of the matching
problem in two dimensions and later extended to obtain upper bounds in higher
dimensions.


  A vector field X is called a star flow if every periodic orbit, of any vector
field C1-close to X, is hyperbolic. It is known that the chain recurrence
classes of a generic star flow X on a 3 or 4 manifold are either hyperbolic or
singular hyperbolic (see [MPP] for 3-manifolds and [GLW] on 4-manifolds). As it
is defined, the notion of singular hyperbolicity forces the singularities in
the same class to have the same index. However, in higher dimension (i.e $\geq
5$) \cite{BdL} shows that singularities of different indices may be robustly in
the same chain recurrence class of a star flow. Therefore the usual notion of
singular hyperbolicity is not enough for characterizing the star flows.
  We present a form of hyperbolicity (called multi-singular hyperbolic) which
makes compatible the hyperbolic structure of regular orbits together with the
one of singularities even if they have different indices. We show that
multisingular hyperbolicity implies that the flow is star, and conversely,
there is a C1-open and dense subset of the an open set of star flows which are
multisingular hyperbolic.
  More generally, for most of the hyperbolic structures (dominated splitting,
partial hyperbolicity etc...) well defined on regular orbits, we propose a way
for generalizing it to a compact set containing singular points.


  In bilayer systems electron-hole (e-h) pairs with spatially separated
components (i.e., with electrons in one layer and holes in the other) can be
condensed to a superfluid state when the temperature is lowered. This article
deals with the influence of randomly distributed inhomogeneities on the
superfluid properties of such bilayer systems in a strong perpendicular
magnetic field. Ionized impurities and roughenings of the conducting layers are
shown to decrease the superfluid current density of the e-h pairs. When the
interlayer distance is smaller than or close to the magnetic length, the
fluctuations of the interlayer distance considerably reduce the superfluid
transition temperature.


  Princeton WordNet is one of the most important resources for natural language
processing, but is only available for English. While it has been translated
using the expand approach to many other languages, this is an expensive manual
process. Therefore it would be beneficial to have a high-quality automatic
translation approach that would support NLP techniques, which rely on WordNet
in new languages. The translation of wordnets is fundamentally complex because
of the need to translate all senses of a word including low frequency senses,
which is very challenging for current machine translation approaches. For this
reason we leverage existing translations of WordNet in other languages to
identify contextual information for wordnet senses from a large set of generic
parallel corpora. We evaluate our approach using 10 translated wordnets for
European languages. Our experiment shows a significant improvement over
translation without any contextual information. Furthermore, we evaluate how
the choice of pivot languages affects performance of multilingual word sense
disambiguation.


  In conformal differential geometry, there are some distinguished curves,
often known as 'conformal circles,' since, on the round sphere, they are the
round circles (and these are conformally invariant). But on the two-sphere, the
curves of constant compass bearing are also conformally invariant. These
'loxodromes' admit a curved analogue in the realm of Moebius geometry. In this
article, these curved analogues are explained and the fifth order invariant ODE
that they satisfy is derived.


  We introduce OpenFlamingo, a family of autoregressive vision-language models
ranging from 3B to 9B parameters. OpenFlamingo is an ongoing effort to produce
an open-source replication of DeepMind's Flamingo models. On seven
vision-language datasets, OpenFlamingo models average between 80 - 89% of
corresponding Flamingo performance. This technical report describes our models,
training data, hyperparameters, and evaluation suite. We share our models and
code at https://github.com/mlfoundations/open_flamingo.


  We address the question: Given an arbitrary initial state and a general
physical interaction what is the minimum time for reaching a target entangled
state? We show that the minimum time is inversely proportional to the quantum
mechanical uncertainty in the non-local Hamiltonian. We find that the presence
of initial entanglement helps to minimize the waiting time. Furthermore, we
find that in a bi-local rotating frame the entangling capability is actually a
geometric quantity. We give an universal bound for the time average of
entanglement rate for general quantum systems. The time average of entanglement
rate does not depend on the particular Hamiltonian, rather on the fluctuation
in the Hamiltonian. There can be infinite number of nonlocal Hamiltonians which
may give same average entanglement rate. We also prove a composition law for
minimum time when the system evolves under a composite Hamiltonian.


  We simulate a variety of optical systematics for Taurus, a balloon-borne
cosmic microwave background (CMB) polarisation experiment, to assess their
impact on large-scale E-mode polarisation measurements and constraints of the
optical depth to reionisation {\tau}. We model a one-month flight of Taurus
from Wanaka, New Zealand aboard a super-pressure balloon (SPB). We simulate
night-time scans of both the CMB and dust foregrounds in the 150GHz band, one
of Taurus's four observing bands. We consider a variety of possible systematics
that may affect Taurus's observations, including non-gaussian beams, pointing
reconstruction error, and half-wave plate (HWP) non-idealities. For each of
these, we evaluate the residual power in the difference between maps simulated
with and without the systematic, and compare this to the expected signal level
corresponding to Taurus's science goals. Our results indicate that most of the
HWP-related systematics can be mitigated to be smaller than sample variance by
calibrating with Planck's TT spectrum and using an achromatic HWP model, with a
preference for five layers of sapphire to ensure good systematic control.
However, additional beam characterization will be required to mitigate
far-sidelobe pickup from dust on larger scales.


  In this article, the existence of Ulrich bundles on projective bundles
$\mathbb P(E) \to X$ is discussed. In the case, that the base variety $X$ is a
curve or surface, a close relationship between Ulrich bundles on $X$ and those
on $\mathbb P(E)$ is established for specific polarisations. This yields the
existence of Ulrich bundles on a wide range of projective bundles over curves
and surfaces.


  It is shown that in the sequential decay $H\to ZZ\to (f_1\bar{f_1})+
(f_2\bar{f_2})$, the energy distribution of the final state particles provides
a simple and powerful test of the $HZZ$ vertex. For a standard Higgs boson, the
energy spectrum of any final fermion, in the rest frame of $H$, is predicted to
be $d\Gamma /dx\sim 1+\beta^4-2(x-1)^2$, with $\beta = \sqrt{1-4m^2_Z/m^2_H}$
and $1-\beta \le x=4E/m_H\le 1+\beta $. By contrast, the spectrum for a
pseudoscalar Higgs is $d\Gamma /dx \sim \beta^2+(x-1)^2$. There are
characteristic energy correlations between $f_1$ and $f_2$ and between $f_1$
and $\bar{f_2}$. These considerations are applied to the ``gold--plated''
reaction $H\to ZZ\to \mu^+\mu^-\mu^+\mu^-$, including possible effects of
CP--violation in the $HZZ$ coupling. Our formalism also yields the energy
spectra and correlations of leptons in the decay $H\to W^+W^-\to l^+\nu_ll^-
\bar{\nu_l}$.


  We discuss some contradictions found in the literature concerning the problem
of stability of collisionless spherical stellar systems which are the simplest
anisotropic generalization of the well-known polytrope models. Their
distribution function $F(E,L)$ is a product of power-low functions of the
energy $E$ and the angular momentum $L$, i.e. $F\propto L^{-s}(-E)^q$. On the
one hand, calculation of the growth rates in the framework of linear stability
theory and N-body simulations show that these systems become stable when the
parameter $s$ characterizing the velocity anisotropy of the stellar
distribution is lower than some finite threshold value, $s<s_\textrm{crit}$. On
the other hand Palmer & Papaloizou (1987) showed that the instability remained
up to the isotropic limit $s=0$.
  Using our method of determining the eigenmodes for stellar systems, we show
that the growth rates in weakly radially-anisotropic systems are indeed
positive, but decrease exponentially as the parameter $s$ approaches zero, i.e.
$\gamma\propto \exp(-s_{\ast}/s)$. In fact, for the systems with finite
lifetime this means stability.


  The paper is devoted to (combinations of) Bessel cross-products that arise
from oblique derivative boundary value problems for the Laplacian in a circular
annulus. We show that like their Neumann-Laplacian counterpart (and unlike the
Dirichlet-Laplacian), they possess two kinds of zeros: those that can be
derived by McMahon series and diverge to infinity in the limit, and exceptional
ones that remain finite. For both cases we find asymptotic expressions for a
fixed oblique angle and vanishing thickness of the annulus. We further present
plots of numerically computed zeros and discuss their behaviour when the
oblique angle changes and the thickness remains fixed.


  Detecting nanomechanical motion has become an important challenge in Science
and Technology. Recently, electromechanical coupling to focused electron beams
has emerged as a promising method adapted to ultra-low scale systems. However
the fundamental measurement processes associated with such complex interaction
remain to be explored. Here we report highly sensitive detection of the
Brownian motion of um-long semiconducting nanowires (InAs). The measurement
imprecision is found to be set by the shot noise of the secondary electrons
generated along the electromechanical interaction. By carefully analysing the
nano-electromechanical dynamics, we demonstrate the existence of a radial
backaction process which we identify as originating from the momentum exchange
between the electron beam and the nanomechanical device, which is also known as
radiation pressure.


  In this letter, we consider an intelligent reflecting surface (IRS)-assisted
multiple input multiple output (MIMO) communication and we optimize the joint
active and passive beamforming by exploiting the geometrical structure of the
propagation channels. Due to the inherent Kronecker product structure of the
channel matrix, the global beamforming optimization problem is split into lower
dimensional horizontal and vertical sub-problems. Based on this factorization
property, we propose two closed-form methods for passive and active beamforming
designs, at the IRS, the base station, and user equipment, respectively. The
first solution is a singular value decomposition (SVD)-based algorithm
independently applied on the factorized channels, while the second method
resorts to a third-order rank-one tensor approximation along each domain.
Simulation results show that exploiting the channel Kronecker structures yields
a significant improvement in terms of computational complexity at the expense
of negligible spectral efficiency (SE) loss. We also show that under imperfect
channel estimation, the tensor-based solution shows better SE than the
benchmark and proposed SVD-based solutions.


  Bose condensation is responsible for many of the most spectacular effects in
physics because it can promote quantum behavior from the microscopic to the
macroscopic world. Bose condensates can be distinguished by the condensing
object; electron-electron Cooper-pairs are responsible for superconductivity,
Helium atoms for superfluidity, and ultracold alkali atoms in vapors for
coherent matter waves. Electron-hole pair (exciton) condensation has maintained
special interest because it has been difficult to realize experimentally, and
because exciton phase coherence is never perfectly spontaneous. Although ideal
condensates can support an exciton supercurrent, it has not been clear how such
a current could be induced or detected, or how its experimental manifestation
would be altered by the phase-fixing exciton creation and annhilation processes
which are inevitably present. In this article we explain how to induce an
exciton supercurrent in separately contacted bilayer condensates, and predict
electrical effects which enable unambiguous detection.


  Image generation has rapidly evolved in recent years. Modern architectures
for adversarial training allow to generate even high resolution images with
remarkable quality. At the same time, more and more effort is dedicated towards
controlling the content of generated images. In this paper, we take one further
step in this direction and propose a conditional generative adversarial network
(GAN) that generates images with a defined number of objects from given
classes. This entails two fundamental abilities (1) being able to generate
high-quality images given a complex constraint and (2) being able to count
object instances per class in a given image. Our proposed model modularly
extends the successful StyleGAN2 architecture with a count-based conditioning
as well as with a regression sub-network to count the number of generated
objects per class during training. In experiments on three different datasets,
we show that the proposed model learns to generate images according to the
given multiple-class count condition even in the presence of complex
backgrounds. In particular, we propose a new dataset, CityCount, which is
derived from the Cityscapes street scenes dataset, to evaluate our approach in
a challenging and practically relevant scenario.


  Global optimization of access point (AP) assignment to user terminals
requires efficient monitoring of user behavior, fast decision algorithms,
efficient control signaling, and fast AP reassignment mechanisms. In this
scenario, software defined networking (SDN) technology may be suitable for
network monitoring, signaling, and control. We recently proposed embedding
virtual switches in user terminals for direct management by an SDN controller,
further contributing to SDN-oriented access network optimization. However,
since users may restrict terminal-side traffic monitoring for privacy reasons
(a common assumption by previous authors), we infer user traffic classes at the
APs. On the other hand, since handovers will be more frequent in dense
small-cell networks (e.g., mmWave-based 5G deployments will require dense
network topologies with inter-site distances of ~150-200 m), the delay to take
assignment decisions should be minimal. To this end, we propose taking fast
decisions based exclusively on extremely simple network-side application
flow-type predictions based on past user behavior. Using real data we show that
a centralized allocation algorithm based on those predictions achieves network
utilization levels that approximate those of optimal allocations. We also test
a distributed version of this algorithm. Finally, we quantify the elapsed time
since a user traffic event takes place until its terminal is assigned an AP,
when needed.


  The Soudan 2 deep underground tracking calorimeter has recorded cosmic ray
muon tracks from the direction of the galactic x-ray binary Cygnus X-3 on most
transits during the interval 1989-1998. We analyze these events in the context
of previous reports of Cygnus X-3-related muon flux during major radio flares
of that source. We find some evidence for excess flux during a small number of
transits coincident with major radio flares. We also find an indication that
these events may be distributed around the source with a Gaussian point spread
function with $\sigma = 1.3^{\circ}$, larger than the instrumental angular
spread of $\le 0.3^{\circ}$, verified by observation of the shadow of the moon.


  At high latitudes, many cities adopt a centralized heating system to improve
the energy generation efficiency and to reduce pollution. In multi-tier
systems, so-called district heating, there are a few efficient approaches for
the flow rate control during the heating process. In this paper, we describe
the theoretical methods to solve this problem by deep reinforcement learning
and propose a cloud-based heating control system for implementation. A
real-world case study shows the effectiveness and practicability of the
proposed system controlled by humans, and the simulated experiments for deep
reinforcement learning show about 1985.01 gigajoules of heat quantity and
42276.45 tons of water are saved per hour compared with manual control.


  Two representations of the extended gamma functions
$\Gamma^{2,0}_{0,2}[(b,x)]$ are proved. These representations are exploited to
find a transformation relation between two Fox's $H$-functions. These results
are used to solve Fox's $H$-function in terms of Meijer's $G$-function for
certain values of the parameters. A closed form representation of the kernel of
the Bessel type integral transform is also proved.


  Analog-to-digital (quantization) and digital-to-analog (de-quantization)
conversion are fundamental operations of many information processing systems.
In practice, the precision of these operations is always bounded, first by the
random mismatch error (ME) occurred during system implementation, and
subsequently by the intrinsic quantization error (QE) determined by the system
architecture itself. In this manuscript, we present a new mathematical
interpretation of the previously proposed redundant sensing (RS) architecture
that not only suppresses ME but also allows achieving an effective resolution
exceeding the system's intrinsic resolution, i.e. super-resolution (SR). SR is
enabled by an endogenous property of redundant structures regarded as "code
diffusion" where the references' value spreads into the neighbor sample space
as a result of ME. The proposed concept opens the possibility for a wide range
of applications in low-power fully-integrated sensors and devices where the
cost-accuracy trade-off is inevitable.


  Particle swarm optimization (PSO) is a search algorithm based on stochastic
and population-based adaptive optimization. In this paper, a pathfinding
strategy is proposed to improve the efficiency of path planning for a broad
range of applications. This study aims to investigate the effect of PSO
parameters (numbers of particle, weight constant, particle constant, and global
constant) on algorithm performance to give solution paths. Increasing the PSO
parameters makes the swarm move faster to the target point but takes a long
time to converge because of too many random movements, and vice versa. From a
variety of simulations with different parameters, the PSO algorithm is proven
to be able to provide a solution path in a space with obstacles.


  We consider the problem of reconstructing rank-one matrices from random
linear measurements, a task that appears in a variety of problems in signal
processing, statistics, and machine learning. In this paper, we focus on the
Alternating Least Squares (ALS) method. While this algorithm has been studied
in a number of previous works, most of them only show convergence from an
initialization close to the true solution and thus require a carefully designed
initialization scheme. However, random initialization has often been preferred
by practitioners as it is model-agnostic. In this paper, we show that ALS with
random initialization converges to the true solution with
$\varepsilon$-accuracy in $O(\log n + \log (1/\varepsilon)) $ iterations using
only a near-optimal amount of samples, where we assume the measurement matrices
to be i.i.d. Gaussian and where by $n$ we denote the ambient dimension. Key to
our proof is the observation that the trajectory of the ALS iterates only
depends very mildly on certain entries of the random measurement matrices.
Numerical experiments corroborate our theoretical predictions.


  Dielectric measurements on 0.65[Pb(Ni_1/3Nb_2/3)O_3]-0.35PbTiO_3 ceramic in
the temperature range 90K to 470K shows a relaxor ferroelectric transition
around 350K with a Vogel-Fulcher freezing temperature of 338K and appearance of
a non-ergodic relaxor ferroelectric phase of tetragonal structure at room
temperature. This non-ergodic phase reenters into the relaxor state at low
temperatures as evidenced by the appearance of a frequency dependent anomaly in
the imaginary part of the dielectric constant around 160K, similar to those
reported in other relaxor ferroelectric based morphotropic phase boundary
ceramics. The polarization relaxation time for the 160K anomaly also follows
Vogel-Fulcher type temperature dependence. Temperature dependent magnetization
measurements show that this low temperature anomaly is not linked with any
magnetic transition. Elastic modulus and low temperature x-ray diffraction
(XRD) measurements reveal a tetragonal to monoclinic phase transition around
225K. It is argued that the low temperature dielectric dispersion around 160K
results from the freezing of mesoscopic conformally miniaturized monoclinic
domains formed inside the parent tetragonal domains below the structural phase
transition temperature of 225K.


  We resolve problems posed by Kesten and Erd\"os-Sz\"usz-Tur\'an on
probabilistic Diophantine approximation via methods of homogeneous dynamics.
Our methods allows us to generalize the problem to the setting of general
measure-valued processes in $\mathbb{R}^n$, and obtain applications to the
distribution of point sets which occur in higher dimensional Diophantine
approximation and the geometry of translation surfaces.


  Using first-principles calculations we show that the band gap of bilayer
sheets of semiconducting transition metal dichalcogenides (TMDs) can be reduced
smoothly by applying vertical compressive pressure. These materials undergo a
universal reversible semiconductor to metal (S-M) transition at a critical
pressure. S-M transition is attributed to lifting the degeneracy of the bands
at fermi level caused by inter-layer interactions via charge transfer from
metal to chalcogens. The S-M transition can be reproduced even after
incorporating the band gap corrections using hybrid functionals and GW method.
The ability to tune the band gap of TMDs in a controlled fashion over a wide
range of energy, opens-up possibility for its usage in a range of applications.


  We present a combined theoretical and experimental study of the electronic
structure of stoichiometric Bi$_4$Te$_3$, a natural superlattice of alternating
Bi$_2$Te$_3$ quintuple layers and Bi bilayers. In contrast to the related
semiconducting compounds Bi$_2$Te$_3$ and Bi$_1$Te$_1$, density functional
theory predicts Bi$_4$Te$_3$ to be a semimetal. In this work, we compute the
quasiparticle electronic structure of Bi$_4$Te$_3$ in the framework of the $GW$
approximation within many-body perturbation theory. The quasiparticle
corrections are found to modify the dispersion of the valence and conduction
bands in the vicinity of the Fermi energy, leading to the opening of a small
indirect band gap. Based on the analysis of the eigenstates, Bi$_4$Te$_3$ is
classified as a dual topological insulator with bulk topological invariants
$\mathbb{Z}_2$ (1;111) and magnetic mirror Chern number $n_M=1$. The bulk $GW$
results are used to build a Wannier-functions based tight-binding Hamiltonian
that is further applied to study the electronic properties of the (111)
surface. The comparison with our angle-resolved photoemission measurements
shows excellent agreement between the computed and measured surface states and
indicates the dual topological nature of Bi$_4$Te$_3$.


  We report the entanglement of topological features, namely, isolated, linked
optical vortex loops in the light from spontaneous parametric down-conversion
(SPDC). In three dimensions, optical vortices are lines of phase singularity
and vortices of energy flow which percolate through all optical fields. This
example of entanglement is between features that extend over macroscopic and
finite volumes, furthermore, topological features are robust to perturbation .
The entanglement of photons in complex three-dimensional(3D) topological states
suggests the possibility of entanglement of similar structures in other quantum
systems describable by complex scalar functions, such as superconductors,
superfluids and Bose-Einstein condensates.


  A series of the lead chalcogenide clusters PbnXn (X=S,Se; n=4,8,16,32) with
structures as fragments of the bulk crystalline lattice are calculated at DFT
level with B3LYP functional and ECP basis set. Optical absorption spectra are
simulated through the TDDFT method. The results are in consistence with
experimental data PbS and PbSe for magic size clusters of this size range.


  The Reheating era of inflationary universe can be parameterized by various
parameters like reheating temperature \(T_{\text{re}}\), reheating duration
\(N_{\text{re}}\) and average equation of state parameter \(\overline{\omega
}_{\text{re}}\), which can be constrained by observationally feasible values of
scalar power spectral amplitude \(A_{\text{s}}\) and spectral index
\(n_{\text{s}}\). In this work, by considering the quadratic chaotic
inflationary potential with logarithmic-correction in mass, we examine the
reheating era in order to place some limits on model's parameter space. By
investigating the reheating epoch using Planck 2018+BK18+BAO data, we show that
even a small correction can make the quadratic chaotic model consistent with
latest cosmological observations. We also find that the study of reheating era
helps to put much tighter constraints on model and effectively improves
accuracy of model.


  The theory of quantum cryptography aims to guarantee unconditional
information-theoretic security against an omnipotent eavesdropper. In many
practical scenarios, however, the assumption of an all-powerful adversary is
excessive and can be relaxed considerably. In this paper we study secret key
distillation across a lossy and noisy quantum wiretap channel between Alice and
Bob, with a separately parameterized realistically lossy quantum channel to the
eavesdropper Eve. We show that under such restricted eavesdropping, the key
rates achievable can exceed the secret key distillation capacity against an
unrestricted eavesdropper in the quantum wiretap channel. Further, we show
upper bounds on the key rates based on the relative entropy of entanglement.
This simple restricted eavesdropping model is widely applicable, e.g., to
free-space quantum optical communication, where realistic collection of light
by Eve is limited by the finite size of her optical aperture. Future work will
include calculating bounds on the amount of light Eve can collect under various
realistic scenarios.


  Let $p$ and $q$ be distinct primes. Consider the Shimura curve $\mathcal{X}$
associated to the indefinite quaternion algebra of discriminant $pq$ over
$\mathbb{Q}$. Let $J$ be the Jacobian variety of $\mathcal{X}$, which is an
abelian variety over $\mathbb{Q}$. For an odd prime $\ell$, we provide
sufficient conditions for the non-existence of rational points of order $\ell$
on $J$. As an application, we find some non-trivial subgroups of the kernel of
an isogeny from the new quotient of $J_0(pq)$ to $J$.


  BinSym is a framework for symbolic program analysis of software in binary
form. Contrary to prior work, it operates directly on binary code instructions
and does not require lifting them to an intermediate representation (IR). This
is achieved by formulating the symbolic semantics on top of a formal
description of binary code instruction semantics. By building on existing
formal descriptions, BinSym eliminates the manual effort required by prior work
to implement transformations to an IR, thereby reducing the margin for errors.
Furthermore, BinSym's symbolic semantics can be directly related to the binary
code, which improves symbolic execution speed by reducing solver query
complexity.


  Following the study of complex elliptic-function-type asymptotic behaviours
of the Painlev\'e equations by Boutroux and Joshi and Kruskal for $P_I$ and
$P_{II}$, we provide new results for elliptic-function-type behaviours admitted
by $P_{III}$, $P_{IV}$, and $P_{V}$, in the limit as the independent variable
$z$ approaches infinity. We show how the Hamiltonian $E_{\rm J}$ of each
equation $\rm P_{\rm J}$, $\rm J=I, \ldots , V$, varies across a local period
parallelogram of the leading-order behaviour, by applying the method of
averaging in the complex $z$-plane. Surprisingly, our results show that all the
equations $P_I-P_{V}$ share the same modulation of $E$ to the first two orders.


  RuY1.5Ce0.5Sr2Cu2O10, is not superconducting (SC) and orders magnetically at
TM =152 K. The Ru moment at 5K is 0.75 mB. Ferromagnetic hysteresis loops are
observed below Tirr= 100 K. At 100 K, the remanent magnetization (Mrem) and the
coercive field (HC) become zero. Surprisingly, at Tirr <T< TM a reappearance of
antiferromagnetic (AFM) hysteresis loops are observed with a peak at 120 K for
both Mrem and HC. The paramagnetic constants are: Peff =2.06 mB (greater than
the 1.73 mB expected for low spin Ru5+) and q= 136 K which agrees well with TM.
Substitution of Zn (2.5% atm.) for Cu in RuEu1.5Ce0.5Sr2Cu2O10 (TC=38 K),
suppresses TC but does not affect the magnetic features such as: TM and Tirr,
Mrem and HC. This indicates that both SC and magnetic states are practically
decoupled. We argue that the magnetic structure of this system becomes (i) AFM
ordered at TM. (ii) Due to the tilting of the RuO6 octahedra away from the
crystallographic c axis, at Tirr < TM, weak-ferromagnetic (W-FM) order is
induced by the canting of the Ru moments. (iii) At TC (TM/TC~4), the specific
materials (M=Eu, Gd) become SC. Below TC, both SC and W-FM states coexist.


  Tensile creep tests, tensile relaxation tests and a tensile test with a
constant rate of strain are performed on injection-molded isotactic
polypropylene at room temperature in the vicinity of the yield point. A
constitutive model is derived for the time-dependent behavior of
semi-crystalline polymers. A polymer is treated as an equivalent network of
chains bridged by permanent junctions. The network is modelled as an ensemble
of passive meso-regions (with affine nodes) and active meso-domains (where
junctions slip with respect to their positions in the bulk medium with various
rates). The distribution of activation energies for sliding in active
meso-regions is described by a random energy model. Adjustable parameters in
the stress--strain relations are found by fitting experimental data. It is
demonstrated that the concentration of active meso-domains monotonically grows
with strain, whereas the average potential energy for sliding of junctions and
the standard deviation of activation energies suffer substantial drops at the
yield point. With reference to the concept of dual population of crystalline
lamellae, these changes in material parameters are attributed to transition
from breakage of subsidiary (thin) lamellae in the sub-yield region to
fragmentation of primary (thick) lamellae in the post-yield region of
deformation.


  We chart out the possible phases of laser driven Rydberg atoms in the
presence of a hypercubic optical lattice. We define a pseudospin degree of
freedom whose up(down) components correspond to the excited(ground) states of
the Rydberg atoms and use them to demonstrate the realization of a canted Ising
antiferromagnetic (CIAF) Mott phase of the atoms in these systems. We also show
that on lowering the lattice depth, the quantum melting of the CIAF and
density-wave (DW) Mott states (which are also realized in these systems) leads
to supersolid (SS) phases of the atoms. We provide analytical expressions for
the phase boundaries and collective excitations of these phases in the hardcore
limit within mean-field theory and discuss possible experiments to test our
theory.


  In this paper, we define a $\gamma$-factor for generic representations of
$\RU(1,1)\times \Res_{E/F}(\GL_1)$ and prove a local converse theorem for
$\RU(1,1)$ using the $\gamma$-factor we defined. We also give a new proof of
the local converse theorem for $\GL_2$ using a $\gamma$-factor of $\GL_2\times
\GL_2$ type which was originally defined by Jacquet in \cite{J}.


  We study the local behavior of special cycles on Shimura varieties for
$\mathbf{U}(2, 1) \times \mathbf{U}(1, 1)$ in the setting of the
Gan-Gross-Prasad conjectures at primes $\tau$ of the totally real field of
definition of the unitary spaces which are split in the corresponding totally
imaginary quadratic extension. We establish a local formula for their fields of
definition, and prove a distribution relation between the Galois and Hecke
actions on them. This complements work of \cite{jetchev:unitary} at inert
primes, where the combinatorics of the formulas are reduced to calculations on
the Bruhat--Tits trees, which in the split case must be replaced with
higher-dimensional buildings.


  The human brain is the central hub of the neurobiological system, controlling
behavior and cognition in complex ways. Recent advances in neuroscience and
neuroimaging analysis have shown a growing interest in the interactions between
brain regions of interest (ROIs) and their impact on neural development and
disorder diagnosis. As a powerful deep model for analyzing graph-structured
data, Graph Neural Networks (GNNs) have been applied for brain network
analysis. However, training deep models requires large amounts of labeled data,
which is often scarce in brain network datasets due to the complexities of data
acquisition and sharing restrictions. To make the most out of available
training data, we propose PTGB, a GNN pre-training framework that captures
intrinsic brain network structures, regardless of clinical outcomes, and is
easily adaptable to various downstream tasks. PTGB comprises two key
components: (1) an unsupervised pre-training technique designed specifically
for brain networks, which enables learning from large-scale datasets without
task-specific labels; (2) a data-driven parcellation atlas mapping pipeline
that facilitates knowledge transfer across datasets with different ROI systems.
Extensive evaluations using various GNN models have demonstrated the robust and
superior performance of PTGB compared to baseline methods.


  Quantum dots in magnetic fields are studied within the current spin density
functional formalism avoiding any spatial symmetry restrictions of the
solutions. We find that the maximum density droplet reconstructs into states
with broken internal symmetry: The Chamon-Wen edge co-exists with a modulation
of the charge density along the edge. The phase boundaries between the
polarization transition, the maximum density droplet and its reconstruction are
in agreement with recent experimental results.


  The Gene Ontology (GO) provides biologists with a controlled terminology that
describes how genes are associated with functions and how functional terms are
related to each other. These term-term relationships encode how scientists
conceive the organization of biological functions, and they take the form of a
directed acyclic graph (DAG). Here, we propose that the network structure of
gene-term annotations made using GO can be employed to establish an alternate
natural way to group the functional terms which is different from the
hierarchical structure established in the GO DAG. Instead of relying on an
externally defined organization for biological functions, our method connects
biological functions together if they are performed by the same genes, as
indicated in a compendium of gene annotation data from numerous different
experiments. We show that grouping terms by this alternate scheme is distinct
from term relationships defined in the ontological structure and provides a new
framework with which to describe and predict the functions of experimentally
identified sets of genes.


  Usual approaches for image recoloring, such as local filtering by transfer
functions and global histogram remapping, lack of accurate control or miss
small groups of important pixels. In this paper, we introduce a triangle-based
structuring of the colors of an image in the RGB space. We present an analysis
of image colors in the RGB space showing the theoretical motivation of our
triangular abstraction. We illustrate the usefulness of our structure to
recolor images.


  An abdominal aortic aneurysm (AAA) is an enlargement of the abdominal aorta
which, if left untreated, can progressively widen and may rupture with fatal
consequences. In this paper, we determine an optimal treatment policy using
Markov decision process modeling. The policy is optimal with respect to the
number of quality adjusted life-years (QALYs) that are expected to be
accumulated during the remaining life of a patient. The new policy takes into
account factors that are ignored by the current clinical policy (e.g. the
life-expectancy and the age-dependent surgical mortality). The resulting
optimal policy is structurally different from the current policy. In
particular, the policy suggests that young patients with small aneurysms should
undergo surgery. The robustness of the policy structure is demonstrated using
simulations. A gain in the number of expected QALYs is shown, which indicates a
possibility of improved care for patients with AAAs.


  We compare different actual forms of democracy and analyse in which way they
are variations of a 'natural consensus decision process'. We analyse how
'consensus decision followed by majority voting' is open to 'false play' by the
majority, and investigate how other types of false play appear in alternative
types of democratic decision procedures. We introduce the combined notion of
'quantum parliament' and 'quantum decision procedure', and prove it to be the
only one, when applied after consensus decision, that is immune to false play.


  There are multiple sides to every story, and while statistical topic models
have been highly successful at topically summarizing the stories in corpora of
text documents, they do not explicitly address the issue of learning the
different sides, the viewpoints, expressed in the documents. In this paper, we
show how these viewpoints can be learned completely unsupervised and
represented in a human interpretable form. We use a novel approach of applying
CorrLDA2 for this purpose, which learns topic-viewpoint relations that can be
used to form groups of topics, where each group represents a viewpoint. A
corpus of documents about the Israeli-Palestinian conflict is then used to
demonstrate how a Palestinian and an Israeli viewpoint can be learned. By
leveraging the magnitudes and signs of the feature weights of a linear SVM, we
introduce a principled method to evaluate associations between topics and
viewpoints. With this, we demonstrate, both quantitatively and qualitatively,
that the learned topic groups are contextually coherent, and form consistently
correct topic-viewpoint associations.


  In the present paper, the Tsallis statistics in the grand canonical ensemble
was reconsidered in a general form. The thermodynamic properties of the
nonrelativistic ideal gas of hadrons in the grand canonical ensemble was
studied numerically and analytically in a finite volume and the thermodynamic
limit. It was proved that the Tsallis statistics in the grand canonical
ensemble satisfies the requirements of the equilibrium thermodynamics in the
thermodynamic limit if the thermodynamic potential is a homogeneous function of
the first order with respect to the extensive variables of state of the system
and the entropic variable $z=1/(q-1)$ is an extensive variable of state. The
equivalence of canonical, microcanonical and grand canonical ensembles for the
nonrelativistic ideal gas of hadrons was demonstrated.


  This paper discusses recent research progress in robotic grasping and
manipulation in the light of the latest Robotic Grasping and Manipulation
Competitions (RGMCs). We first provide an overview of past benchmarks and
competitions related to the robotics manipulation field. Then, we discuss the
methodology behind designing the manipulation tasks in RGMCs. We provide a
detailed analysis of key challenges for each task and identify the most
difficult aspects based on the competing teams' performance in recent years. We
believe that such an analysis is insightful to determine the future research
directions for the robotic manipulation domain.


  Numerical studies are reported to support the idea to explain the particle
production in late inflationary era based on the parametric resonance with a
noise effect. Two nonperturbative renormalization group formulations are used
to numerically calculate the time evolution of particle numbers. Firstly, the
dynamical renormalization group (DRG) method is applied to sum up the secular
contributions. Secondly, we derive an exact evolution equation of the particle
number which turned out to be possible surprisingly owing to the presence of
the noise effect. Our numerical results show a drastic enhancement of the
particle production in agreement with earlier works qualitatively. A comparison
is made of numerical results based on two methods. Our work provides
nonperturbative and quantitative methods to evaluate the particle production
for the inflationary cosmology.


  Colorectal cancer is among the most common cause of cancer worldwide. Removal
of precancerous polyps through early detection is essential to prevent them
from progressing to colon cancer. We develop an advanced deep learning-based
architecture, Transformer based Residual Upsampling Network (TransRUPNet) for
automatic and real-time polyp segmentation. The proposed architecture,
TransRUPNet, is an encoder-decoder network consisting of three encoder and
decoder blocks with additional upsampling blocks at the end of the network.
With the image size of $256\times256$, the proposed method achieves an
excellent real-time operation speed of 47.07 frames per second with an average
mean dice coefficient score of 0.7786 and mean Intersection over Union of
0.7210 on the out-of-distribution polyp datasets. The results on the publicly
available PolypGen dataset suggest that TransRUPNet can give real-time feedback
while retaining high accuracy for in-distribution datasets. Furthermore, we
demonstrate the generalizability of the proposed method by showing that it
significantly improves performance on out-of-distribution datasets compared to
the existing methods. The source code of our network is available at
https://github.com/DebeshJha/TransRUPNet.


  Nearby sources of cosmic rays up to a ZeV(=10^21 eV) could be observed with a
multi-messenger approach including secondary gamma-rays and neutrinos. If
cosmic rays above ~10^18 eV are produced in magnetized environments such as
galaxy clusters, the flux of secondary gamma-rays below ~1 TeV can be enhanced
up to several orders of magnitudes compared to unmagnetized sources. A
particular source of enhancement are synchrotron and cascade photons from
e^+e^- pairs produced by protons from sources with relatively steep injection
spectra proportional to E^-2.6. Such sources should be visible at the same time
in ultra-high energy cosmic ray experiments and gamma-ray telescopes.


  The basic concepts of non-commutative probability theory are reviewed and
applied to the large $N$ limit of matrix models. We argue that this is the
appropriate framework for constructing the master field in terms of which large
$N$ theories can be written. We explicitly construct the master field in a
number of cases including QCD$_2$. There we both give an explicit construction
of the master gauge field and construct master loop operators as well. Most
important we extend these techniques to deal with the general matrix model, in
which the matrices do not have independent distributions and are coupled. We
can thus construct the master field for any matrix model, in a well defined
Hilbert space, generated by a collection of creation and annihilation
operators---one for each matrix variable---satisfying the Cuntz algebra. We
also discuss the equations of motion obeyed by the master field.


  This paper presents a plug-and-play approach for translation with terminology
constraints. Terminology constraints are an important aspect of many modern
translation pipelines. In both specialized domains and newly emerging domains
(such as the COVID-19 pandemic), accurate translation of technical terms is
crucial. Recent approaches often train models to copy terminologies from the
input into the output sentence by feeding the target terminology along with the
input. But this requires expensive training whenever the underlying language
model is changed or the system should specialize to a new domain. We propose
Cascade Beam Search, a plug-and-play terminology-forcing approach that requires
no training. Cascade Beam Search has two parts: 1) logit manipulation to
increase the probability of target terminologies and 2) a cascading beam setup
based on grid beam search, where beams are grouped by the number of
terminologies they contain. We evaluate the performance of our approach by
competing against the top submissions of the WMT21 terminology translation
task. Our plug-and-play approach performs on par with the winning submissions
without using a domain-specific language model and with no additional training.


  This note is concerned with isometries on the spaces of self-adjoint
traceless matrices. We compute the group of isometries with respect to any
unitary similarity invariant norm. This completes and extends the result of
Nagy on Schatten $p$-norm isometries. Furthermore, we point out that our proof
techniques could be applied to obtain an old result concerning isometries on
skew-symmetric matrices.


  Completely unmanned autonomous vehicles have been anticipated for a while.
Initially, these are expected to drive only under certain conditions on some
roads, and advanced functionality is required to cope with the ever-increasing
challenges of safety. To enhance the public's perception of road safety and
trust in new vehicular technologies, we investigate in this paper the effect of
several interaction paradigms with vulnerable road users by developing and
applying algorithms for the automatic analysis of pedestrian body language. We
assess behavioral patterns and determine the impact of the coexistence of AVs
and other road users on general road safety in a shared space for VRUs and
vehicles. Results showed that the implementation of visual communication cues
for interacting with VRUs is not necessarily required for a shared space in
which informal traffic rules apply.


  Thyroid cancer, the most prevalent endocrine cancer, has gained significant
global attention due to its impact on public health. Extensive research efforts
have been dedicated to leveraging artificial intelligence (AI) methods for the
early detection of this disease, aiming to reduce its morbidity rates. However,
a comprehensive understanding of the structured organization of research
applications in this particular field remains elusive. To address this
knowledge gap, we conducted a systematic review and developed a comprehensive
taxonomy of machine learning-based applications in thyroid cancer pathogenesis,
diagnosis, and prognosis. Our primary objective was to facilitate the research
community's ability to stay abreast of technological advancements and
potentially lead the emerging trends in this field. This survey presents a
coherent literature review framework for interpreting the advanced techniques
used in thyroid cancer research. A total of 758 related studies were identified
and scrutinized. To the best of our knowledge, this is the first review that
provides an in-depth analysis of the various aspects of AI applications
employed in the context of thyroid cancer. Furthermore, we highlight key
challenges encountered in this domain and propose future research opportunities
for those interested in studying the latest trends or exploring
less-investigated aspects of thyroid cancer research. By presenting this
comprehensive review and taxonomy, we contribute to the existing knowledge in
the field, while providing valuable insights for researchers, clinicians, and
stakeholders in advancing the understanding and management of this disease.


  Association rule mining plays vital part in knowledge mining. The difficult
task is discovering knowledge or useful rules from the large number of rules
generated for reduced support. For pruning or grouping rules, several
techniques are used such as rule structure cover methods, informative cover
methods, rule clustering, etc. Another way of selecting association rules is
based on interestingness measures such as support, confidence, correlation, and
so on. In this paper, we study how rule clusters of the pattern Xi - Y are
distributed over different interestingness measures.


  The computational characterization of game-theoretic solution concepts is a
central topic in artificial intelligence, with the aim of developing
computationally efficient tools for finding optimal ways to behave in strategic
interactions. The central solution concept in game theory is Nash equilibrium
(NE). However, it fails to capture the possibility that agents can form
coalitions (even in the 2-agent case). Strong Nash equilibrium (SNE) refines NE
to this setting. It is known that finding an SNE is NP-complete when the number
of agents is constant. This hardness is solely due to the existence of
mixed-strategy SNEs, given that the problem of enumerating all pure-strategy
SNEs is trivially in P. Our central result is that, in order for a game to have
at least one non-pure-strategy SNE, the agents' payoffs restricted to the
agents' supports must, in the case of 2 agents, lie on the same line, and, in
the case of n agents, lie on an (n - 1)-dimensional hyperplane. Leveraging this
result, we provide two contributions. First, we develop worst-case instances
for support-enumeration algorithms. These instances have only one SNE and the
support size can be chosen to be of any size-in particular, arbitrarily large.
Second, we prove that, unlike NE, finding an SNE is in smoothed polynomial
time: generic game instances (i.e., all instances except knife-edge cases) have
only pure-strategy SNEs.


  I discuss a recent analytic proof of bypassing the no-hair conjecture for two
interesting (and quite generic) cases of four-dimensional black holes: (i)
black holes in Einstein-Yang-Mills-Higgs (EYMH) systems and (ii) black holes in
higher-curvature (Gauss-Bonnet (GB) type) string-inspired gravity. Both systems
are known to possess black-hole solutions with non-trivial scalar hair outside
the horizon. The `spirit' of the no-hair conjecture, however, seems to be
maintained either because the black holes are unstable (EYMH), or because the
hair is of secondary type (GB), i.e. it does not lead to new conserved quantum
numbers.


  A continuum density-field formulation with particle-scale resolution is
constructed to simultaneously incorporate the orientation dependence of
interparticle interactions and the rotational invariance of the system, a
fundamental but challenging issue in modeling structure and dynamics of a broad
range of material systems across variable scales. This generalized phase field
crystal type approach is based upon the complete expansion of particle direct
correlation functions and the concept of isotropic tensors. Through
applications to the modeling of various two- and three-dimensional crystalline
structures, our study demonstrates the capability of bond angle control in this
continuum field theory and its effects on the emergence of ordered phases, and
provides a systematic way of tunable angle analysis for crystalline
microstructures.


  Long-time high-resolution simulations of the dynamics of a coronal loop in
cartesian geometry are carried out, within the framework of reduced
magnetohydrodynamics (RMHD), to understand coronal heating driven by motion of
field lines anchored in the photosphere. We unambiguously identify MHD
anisotropic turbulence as the physical mechanism responsible for the transport
of energy from the large scales, where energy is injected by photospheric
motions, to the small scales, where it is dissipated. As the loop parameters
vary different regimes of turbulence develop: strong turbulence is found for
weak axial magnetic fields and long loops, leading to Kolmogorov-like spectra
in the perpendicular direction, while weaker and weaker regimes (steeper
spectral slopes of total energy) are found for strong axial magnetic fields and
short loops. As a consequence we predict that the scaling of the heating rate
with axial magnetic field intensity $B_0$, which depends on the spectral index
of total energy for given loop parameters, must vary from $B_0^{3/2}$ for weak
fields to $B_0^{2}$ for strong fields at a given aspect ratio. The predicted
heating rate is within the lower range of observed active region and quiet Sun
coronal energy losses.


  Sending multiple messages on qubits encoded in different vibrational modes of
cold atoms or ions along a transmission waveguide requires to merge first and
then separate the modes at input and output ends. Similarly, different qubits
can be stored in the modes of a trap and be separated later. We design the fast
splitting of a harmonic trap into an asymmetric double well so that the initial
ground vibrational state becomes the ground state of one of two final wells,
and the initial first excited state becomes the ground state of the other well.
This might be done adiabatically by slowly deforming the trap. We speed up the
process by inverse engineering a double-function trap using dynamical
invariants. The separation (demultiplexing) followed by an inversion of the
asymmetric bias and then by the reverse process (multiplexing) provides a
population inversion protocol based solely on trap reshaping.