  Intelligent resident surveillance is one of the most essential smart
community services. The increasing demand for security needs surveillance
systems to be able to detect anomalies in surveillance scenes. Employing
high-capacity computational devices for intelligent surveillance in residential
societies is costly and not feasible. Therefore, we propose anomaly detection
for intelligent surveillance using CPU-only edge devices. A modular framework
to capture object-level inferences and tracking is developed. To cope with
partial occlusions, posture deformations, and complex scenes, we employed
feature encoding and trajectory association governed by two metrices
complementing to each other. The elements of an anomaly detection framework are
optimized to run on CPU-only edge devices with sufficient frames per second
(FPS). The experimental results indicate the proposed method is feasible and
achieves satisfactory results in real-life scenarios.


  In this work, we propose a new stochastic domain decomposition method for
solving steady-state partial differential equations (PDEs) with random inputs.
Based on the efficiency of the Variable-separation (VS) method in simulating
stochastic partial differential equations (SPDEs), we extend it to stochastic
algebraic systems and apply it to stochastic domain decomposition. The
resulting Stochastic Domain Decomposition based on the Variable-separation
method (SDD-VS) effectively addresses the ``curse of dimensionality" by
leveraging the explicit representation of stochastic functions derived from
physical systems. The SDD-VS method aims to obtain a separated representation
of the solution for the stochastic interface problem. To enhance efficiency, an
offline-online computational decomposition is introduced. In the offline phase,
the affine representation of stochastic algebraic systems is obtained through
the successive application of the VS method. This serves as a crucial
foundation for the SDD-VS method. In the online phase, the interface unknowns
of SPDEs are estimated using a quasi-optimal separated representation, enabling
the construction of efficient surrogate models for subproblems. The
effectiveness of the proposed method is demonstrated via the numerical results
of three concrete examples.


  The aim of this work is to understand the influence of chaotic states in
control problems involving strong fields. Towards this end, we numerically
construct and study the strong field control landscape of a bichromatically
driven double well. A novel measure based on correlating the overlap
intensities between Floquet states and an initial phase space coherent state
with the parametric motion of the quasienergies is used to construct and
interpret the landscape features. "Walls" of no control, robust under
variations of the relative phase between the fields, are seen on the control
landscape and associated with multilevel interactions involving chaotic Floquet
states.


  Manipulating the way in which colloidal particles self-organise is a central
challenge in the design of functional soft materials. Meeting this challenge
requires the use of building blocks that interact with one another in a highly
specific manner. Their fabrication, however, is limited by the complexity of
the available synthesis procedures. Here, we demonstrate that, starting from
experimentally available magnetic colloids, we can create a variety of complex
building blocks suitable for hierarchical self-organisation using a simple
scalable process. Using computer simulations, we compress spherical and cubic
magnetic colloids in spherical confinement, and investigate their suitability
to form small clusters with reproducible structural and magnetic properties. We
find that, while the structure of these clusters is highly reproducible, their
magnetic character depends on the particle shape. Only spherical particles have
the rotational degrees of freedom to produce consistent magnetic
configurations, whereas cubic particles frustrate the minimisation of the
cluster energy, resulting in various magnetic configurations. To highlight
their potential for self-assembly, we demonstrate that already clusters of
three magnetic particles form highly nontrivial Archimedean lattices, namely
staggered kagome, bounce and honeycomb, when viewing different aspects of the
same monolayer structure. The work presented here offers a conceptually
different way to design materials by utilizing pre-assembled magnetic building
blocks that can readily self-organise into complex structures.


  In the world of multivariate extremes, estimation of the dependence structure
still presents a challenge and an interesting problem. A procedure for the
bivariate case is presented that opens the road to a similar way of handling
the problem in a truly multivariate setting. We consider a semi-parametric
model in which the stable tail dependence function is parametrically modeled.
Given a random sample from a bivariate distribution function, the problem is to
estimate the unknown parameter. A method of moments estimator is proposed where
a certain integral of a nonparametric, rank-based estimator of the stable tail
dependence function is matched with the corresponding parametric version. Under
very weak conditions, the estimator is shown to be consistent and
asymptotically normal. Moreover, a comparison between the parametric and
nonparametric estimators leads to a goodness-of-fit test for the semiparametric
model. The performance of the estimator is illustrated for a discrete spectral
measure that arises in a factor-type model and for which likelihood-based
methods break down. A second example is that of a family of stable tail
dependence functions of certain meta-elliptical distributions.


  Nearest neighbors is a successful and long-standing technique for anomaly
detection. Significant progress has been recently achieved by self-supervised
deep methods (e.g. RotNet). Self-supervised features however typically
under-perform Imagenet pre-trained features. In this work, we investigate
whether the recent progress can indeed outperform nearest-neighbor methods
operating on an Imagenet pretrained feature space. The simple nearest-neighbor
based-approach is experimentally shown to outperform self-supervised methods
in: accuracy, few shot generalization, training time and noise robustness while
making fewer assumptions on image distributions.


  This research article introduces AIOptimizer, a prototype for a software
performance optimisation tool based on cost reduction. AIOptimizer uses a
recommendation system driven by reinforcement learning to improve software
system efficiency and affordability. The paper highlights AIOptimizer's design
factors, such as accuracy, adaptability, scalability, and user-friendliness. To
provide effective and user-centric performance optimisation solutions, it
emphasises the use of a modular design, data gathering techniques, continuous
learning, and resilient integration. The article also investigates AIOptimizer
features such as fault identification, cost optimisation recommendations,
efficiency prediction, and cooperation. Furthermore, it explores several
software development life cycle models and introduces AIOptimizer uses a
reinforcement learning-based recommendation engine for cost optimisation. The
purpose of this research study is to highlight AIOptimizer as a prototype that
uses advanced optimisation techniques and smart recommendation systems to
continually enhance software performance and save expenses. The research
focuses on various software development life cycle models, such as the
Waterfall model, Iterative model, Spiral model, V-Model, Big Bang model and
Agile Model. Each model has advantages and disadvantages, and their usefulness
is determined by the project's specifications and characteristics. The
AIOptimizer tool is a theoretical prototype for such software performance
optimizers.


  The paper reviews a novel class of phenomena observed recently in the
two-dimensional (2D) electron system formed on the free surface of liquid
helium in the presence of a magnetic field directed normally and exposed to
microwave radiation. The distinctive feature of these nonequilibrium phenomena
is magnetoconductivity oscillations induced by inter-subband (out-of-plane) and
intra-subband (in-plane) microwave excitations. The conductivity
magneto-oscillations induced by intra-subband excitation are similar to
remarkable microwave-induced resistance oscillations (MIRO) reported for
semiconductor heterostructures. Investigations of microwave-induced
conductivity oscillations (MICO) on liquid helium helped with understanding of
the origin of MIRO. Much stronger microwave-induced conductivity oscillations
were observed and well described theoretically for resonant inter-subband
microwave excitation. At strong powers, such excitation leads to
zero-resistance states (ZRS), the in-plane redistribution of electrons,
self-generated audio-frequency oscillations, and incompressible states. These
phenomena are caused by unusual current states of the 2D electron system formed
under resonant microwave excitation.


  In this report I discuss physical problems for future Photon Colliders (PLC),
which can be stated AFTER 10 years of work of LHC and few years of work of
e^+e^- ILC. I discuss mainly the unfavorable case when these colliders will
give us only Higgs boson(s) and perhaps some charged particles of unclear
nature. I focus my attention for the case of PLC based on the second stage of
ILC (about 1 TeV) or CLIC (1-3 TeV). It offers opportunity to study new series
of fundamental physical problems. Among them - multiple production of gauge
bosons, hunt for strong interaction in Higgs sector, search of exotic
interactions in the process $\gamma\gamma\to\gamma\gamma$ with final photons
having transverse momenta (0.5 - 0.7)E_e.


  Measurements of the energy spectra of multiply charged positive and negative
carbon ions recoiling from graphite surface under 100 and 150 keV argon and
krypton ion bombardment are presented. With the energy spectrometer set at
recoil angle of 79.5 degrees, direct recoil (DR) peaks have been observed with
singly as well as multiply charged carbon ions , where n = 1 to 6. These
monatomic and cluster ions have been observed recoiling with the characteristic
recoil energy E(DR) . We have observed sharp DR peaks. A collimated projectile
beam with small divergence is supplemented with a similar collimation before
the energy analyzer to reduce the background of sputtered ions due to scattered
projectiles.


  We applied machine learning to predict whether a gene is involved in axon
regeneration. We extracted 31 features from different databases and trained
five machine learning models. Our optimal model, a Random Forest Classifier
with 50 submodels, yielded a test score of 85.71%, which is 4.1% higher than
the baseline score. We concluded that our models have some predictive
capability. Similar methodology and features could be applied to predict other
Gene Ontology (GO) terms.


  ASVspoof, now in its third edition, is a series of community-led challenges
which promote the development of countermeasures to protect automatic speaker
verification (ASV) from the threat of spoofing. Advances in the 2019 edition
include: (i) a consideration of both logical access (LA) and physical access
(PA) scenarios and the three major forms of spoofing attack, namely synthetic,
converted and replayed speech; (ii) spoofing attacks generated with
state-of-the-art neural acoustic and waveform models; (iii) an improved,
controlled simulation of replay attacks; (iv) use of the tandem detection cost
function (t-DCF) that reflects the impact of both spoofing and countermeasures
upon ASV reliability. Even if ASV remains the core focus, in retaining the
equal error rate (EER) as a secondary metric, ASYspoof also embraces the
growing importance of fake audio detection. ASVspoof 2019 attracted the
participation of 63 research teams, with more than half of these reporting
systems that improve upon the performance of two baseline spoofing
countermeasures. This paper describes the 2019 database, protocols and
challenge results. It also outlines major findings which demonstrate the real
progress made in protecting against the threat of spoofing and fake audio.


  Federated edge learning (FEEL) is a popular distributed learning framework
for privacy-preserving at the edge, in which densely distributed edge devices
periodically exchange model-updates with the server to complete the global
model training. Due to limited bandwidth and uncertain wireless environment,
FEEL may impose heavy burden to the current communication system. In addition,
under the common FEEL framework, the server needs to wait for the slowest
device to complete the update uploading before starting the aggregation
process, leading to the straggler issue that causes prolonged communication
time. In this paper, we propose to accelerate FEEL from two aspects: i.e., 1)
performing data compression on the edge devices and 2) setting a deadline on
the edge server to exclude the straggler devices. However, undesired gradient
compression errors and transmission outage are introduced by the aforementioned
operations respectively, affecting the convergence of FEEL as well. In view of
these practical issues, we formulate a training time minimization problem, with
the compression ratio and deadline to be optimized. To this end, an
asymptotically unbiased aggregation scheme is first proposed to ensure zero
optimality gap after convergence, and the impact of compression error and
transmission outage on the overall training time are quantified through
convergence analysis. Then, the formulated problem is solved in an alternating
manner, based on which, the novel joint compression and deadline optimization
(JCDO) algorithm is derived. Numerical experiments for different use cases in
FEEL including image classification and autonomous driving show that the
proposed method is nearly 30X faster than the vanilla FedAVG algorithm, and
outperforms the state-of-the-art schemes.


  Distances to stars are key to revealing a three-dimensional view of the Milky
Way, yet their determination is a major challenge in astronomy. Whilst the
brightest nearby stars benefit from direct parallax measurements, fainter stars
are subject of indirect determinations with uncertainties exceeding 30%. We
present an alternative approach to measuring distances using
spectroscopically-identified twin stars. Given a star with known parallax, the
distance to its twin is assumed to be directly related to the difference in
their apparent magnitudes. We found 175 twin pairs from the ESO public HARPS
archives and report excellent agreement with Hipparcos parallaxes within 7.5%.
Most importantly, the accuracy of our results does not degrade with increasing
stellar distance. With the ongoing collection of high-resolution stellar
spectra, our method is well-suited to complement Gaia.


  Sunspots are prominent manifestations of the solar cycle and provide key
constraints for understanding its operation. Also, knowing internal structure
of sunspots allows us to gain insights on the energy transport in strong
magnetic fields and, thus, on the processes inside the convection zone, where
solar magnetic fields are generated and amplified before emerging at the
surface on various scales, even during solar minima. In this paper, we present
results of a spectropolarimetric analysis of a sunspot observed during the
declining phase of the solar cycle 23. By inversion of full Stokes spectra
observed in several spectral regions in the optical at the THEMIS facility we
infer the height dependence of physical quantities such as the temperature and
the magnetic field strength for different sunspot regions. The simultaneous use
of atomic (Fe{\sc i} 5250.2 and 5250.6 \AA) and highly temperature sensitive
molecular (TiO 7055 \AA and MgH 5200 \AA) lines allow us to improve a model of
the sunspot umbra.


  Large-scale turbulence in fluid layers and other quasi-two-dimensional
compressible systems consists of planar vortices and waves. Separately, wave
turbulence usually produces a direct energy cascade, while solenoidal planar
turbulence transports energy to large scales by an inverse cascade. Here, we
consider turbulence at finite Mach numbers when the interaction between
acoustic waves and vortices is substantial. We employ solenoidal pumping at
intermediate scales and show how both direct and inverse energy cascades are
formed starting from the pumping scale. We show that there is an inverse
cascade of kinetic energy up to a scale $\ell$, where a typical velocity
reaches the speed of sound; this creates shock waves, which provide for a
compensating direct cascade. When the system size is less than $\ell$, the
steady state contains a system-size pair of long-living condensate vortices
connected by a system of shocks. Thus turbulence in fluid layers processes
energy via a loop: Most energy first goes to large scales via vortices and is
then transported by waves to small-scale dissipation.


  The idea of using a separately trained target model (or teacher) to improve
the performance of the student model has been increasingly popular in various
machine learning domains, and meta-learning is no exception; a recent discovery
shows that utilizing task-wise target models can significantly boost the
generalization performance. However, obtaining a target model for each task can
be highly expensive, especially when the number of tasks for meta-learning is
large. To tackle this issue, we propose a simple yet effective method, coined
Self-improving Momentum Target (SiMT). SiMT generates the target model by
adapting from the temporal ensemble of the meta-learner, i.e., the momentum
network. This momentum network and its task-specific adaptations enjoy a
favorable generalization performance, enabling self-improving of the
meta-learner through knowledge distillation. Moreover, we found that perturbing
parameters of the meta-learner, e.g., dropout, further stabilize this
self-improving process by preventing fast convergence of the distillation loss
during meta-training. Our experimental results demonstrate that SiMT brings a
significant performance gain when combined with a wide range of meta-learning
methods under various applications, including few-shot regression, few-shot
classification, and meta-reinforcement learning. Code is available at
https://github.com/jihoontack/SiMT.


  Theoretical study of heavy ion acceleration from an ultrathin (20 nm) gold
foil irradiated by sub-picosecond lasers is presented. Using two dimensional
particle-in-cell simulations we identified two highly efficient ion
acceleration schemes. By varying the laser pulse duration we observed a
transition from Radiation Pressure Acceleration to the Breakout Afterburner
regime akin to light ions. The underlying physics and ion acceleration regimes
are similar to that of light ions, however, nuances of the acceleration process
make the acceleration of heavy ions more challenging. Two laser systems are
studied in detail: the Texas Petawatt Laser and the Trident laser, the former
having pulse duration 180 fs, intermediate between very short femtosecond
pulses and picosecond pulses. Both laser systems generated directional gold
ions beams (~10 degrees half-angle) with fluxes in excess of 1011 ion/sr and
normalized energy >10 MeV/nucleon.


  In this paper, by using two different techniques we derive an explicit
formula for the mean first-passage time (MFPT) between any pair of nodes on a
general undirected network, which is expressed in terms of eigenvalues and
eigenvectors of an associated matrix similar to the transition matrix. We then
apply the formula to derive a lower bound for the MFPT to arrive at a given
node with the starting point chosen from the stationary distribution over the
set of nodes. We show that for a correlated scale-free network of size $N$ with
a degree distribution $P(d)\sim d^{-\gamma}$, the scaling of the lower bound is
$N^{1-1/\gamma}$. Also, we provide a simple derivation for an eigentime
identity. Our work leads to a comprehensive understanding of recent results
about random walks on complex networks, especially on scale-free networks.


  The generation of outflows from the Sun known as solar winds is coupled with
the heating of the solar corona, and both processes are operated in magnetic
structures formed on the Sun. To study the magnetic configuration responsible
for these processes, we use three-dimensional magnetohydrodynamic simulations
to reproduce magnetic structures via flux emergence and investigate their
configurations. We focus on two key quantities characterizing a magnetic
configuration: the force-free parameter alpha and the flux expansion rate fex,
the former of which represents how much a magnetic field is twisted while the
latter represents how sharply a magnetic field expands. We derive distributions
of these quantities in an emerging flux region. Our result shows that an
emerging flux region consists of outer part where a magnetic loop takes a large
flux expansion rate but a small value of alpha at their photospheric
footpoints, and inner part occupied by those loops where a strong electric
current flows. We also investigate the expansion profile of a magnetic loop
composing an emerging flux region. The profile is given by an exponential
expansion type near the solar surface while it is given by a quadratic
expansion type in an outer atmosphere. These detailed magnetic configurations
obtained by this study contribute to developing a realistic model for the
coronal heating and solar wind generation.


  Under correlation-type conditions, we derive upper bounds of order
$\frac{1}{\sqrt{n}}$ for the Kolmogorov distance between the distributions of
weighted sums of dependent summands and the normal law.


  Design, epitaxial growth, and resonant spectroscopy of CdSe Quantum Dots
(QDs) embedded in an innovative (Zn,Cd)Se barrier are presented. The (Zn,Cd)Se
barrier enables shifting of QDs energy emission down to 1.87 eV, that is below
the energy of Mn$^{2+}$ ions internal transition (2.1 eV). This opens a
perspective for implementation of epitaxial CdSe QDs doped with several Mn ions
as, e. g., the light sources in high quantum yield magnetooptical devices.
Polarization resolved Photoluminescence Excitation measurements of individual
QDs reveal sharp ($\Gamma <$ 150 $\mu$eV) maxima and transfer of optical
polarization to QD confining charged exciton state with efficiency attaining 26
%. The QD doping with single Mn$^{2+}$ ions is achieved.


  Spin measurements of supermassive black holes (SMBHs) provide crucial
constraints on the accretion processes that power active galactic nuclei (AGN),
fuel outflows, and trigger black hole growth. However, spin measurements are
mainly limited to a few dozen nearby sources for which high quality, high S/N
spectra (e.g., from Chandra, XMM-Newton, Suzaku, NuSTAR) are available. Here we
measure the average SMBH spin of $\sim$1900 AGN in the Chandra COSMOS-Legacy
survey using spectral stacking analysis. We find broad Fe K$\alpha$ line
emission in the average COSMOS spectrum (Gaussian width $\sigma=0.27\pm0.05$
keV), and by fitting this emission line profile with relativistic line models,
we measure the average black hole spin parameter $a=0.62~\substack{+0.07 \\
-0.17}$. The sample size, availability of multiwavelength data, and spatial
resolution of the COSMOS Legacy field also provide a unique environment to
investigate the average SMBH spin as a function of other observables (e.g.,
redshift, luminosity) up to $z\sim5.3$. We find that optically classified Type
1 sources have broader Fe K$\alpha$ line emission than Type 2 sources. X-ray
unobscured and obscured sources, as defined by their column densities, have
widths that are consistent with the optically defined unobscured and obscured
sources, respectively. There is some evidence for evolution of the Fe K$\alpha$
width and black hole spin parameter with luminosity, but not conclusively with
redshift. The results of this work provide insights into the average spins of
SMBHs in AGN, shedding light on their growth mechanisms and observed
co-evolution with their host galaxies.


  We investigate a phase transition in an SU(3) symmetric spin-1 chain. We
identify the universality class of the critical trimer liquid phase, as well as
the phase boundary between these two phases.


  In this paper we study the properties of the chaotic wave fields generated in
the frame of the Kundu-Eckhaus equation (KEE). Modulation instability results
in a chaotic wave field which exhibits small-scale filaments with a free
propagation constant, k. The average velocity of the filaments is approximately
given by the average group velocity calculated from the dispersion relation for
the plane-wave solution however direction of propagation is controlled by the
$\beta$ parameter, the constant in front of the Raman-effect term. We have also
calculated the probabilities of the rogue wave occurrence for various values of
propagation constant k and showed that the probability of rogue wave occurrence
depends on k. Additionally, we have showed that the probability of rogue wave
occurrence significantly depends on the quintic and the Raman-effect nonlinear
terms of the KEE. Statistical comparisons between the KEE and the cubic
nonlinear Schrodinger equation have also been presented.


  Foraging is constrained by the energy within resources and the mechanics of
acquisition and assimilation. Thick molar enamel, a character trait
differentiating hominins from African apes, is predicted to mitigate the
mechanical costs of chewing obdurate foods. The classic expression of
hyperthick enamel together with relatively massive molars, termed megadontia,
is most evident in {\it Paranthropus}, a lineage of hominins that lived ca. 2.7
to 1.2 million years ago. Among contemporary primates, thicker molar enamel
corresponds with the consumption of stiffer, deformation-resistant foods,
possibly because thicker enamel can better resist cracking under high
compressive loads. Accordingly, plant underground storage organs (USOs) are
thought to be a central food resource for hominins such as {\it Paranthropus}
due to their abundance, isotopic composition, and mechanical properties. Here,
we present a process-based model to investigate foraging constraints as a
function of energetic demands and enamel wear among human ancestors. Our
framework allows us to determine the fitness benefits of megadontia, and to
explore under what conditions stiff foods such as USOs are predicted to be
chosen as fallback, rather than preferred, resources. Our model predictions
bring consilience to the noted disparity between functional interpretations of
megadontia and microwear evidence, particularly with respect to {\it
Paranthropus boisei}.


  Multi-task learning in text classification leverages implicit correlations
among related tasks to extract common features and yield performance gains.
However, most previous works treat labels of each task as independent and
meaningless one-hot vectors, which cause a loss of potential information and
makes it difficult for these models to jointly learn three or more tasks. In
this paper, we propose Multi-Task Label Embedding to convert labels in text
classification into semantic vectors, thereby turning the original tasks into
vector matching tasks. We implement unsupervised, supervised and
semi-supervised models of Multi-Task Label Embedding, all utilizing semantic
correlations among tasks and making it particularly convenient to scale and
transfer as more tasks are involved. Extensive experiments on five benchmark
datasets for text classification show that our models can effectively improve
performances of related tasks with semantic representations of labels and
additional information from each other.


  We discuss four-fermion contact-interaction searches in the processes
e^+e^-\to\mu^+\mu^-, c{\bar c} and b{\bar b} at a future e^+e^- Linear Collider
with c.m. energy \sqrt{s}=0.5 TeV and with both beams longitudinally polarized.
Our analysis is based on the measurements of familiar polarized observables
such as the total cross section and the forward-backward/left-right
asymmetries, and accounts for the general set of contact interaction couplings
as independent, non-zero, parameters thus avoiding simplifying,
model-dependent, assumptions. We derive the corresponding model-independent
constraints on the above-mentioned coupling constants, and evaluate the
corresponding reach at the Linear Collider, emphasizing the role of beam
polarization. We compare the results with a model-dependent procedure where
only one coupling is varied at a time.


  We investigate the charge-transfer interfaces between metal and redox
arylamine molecular films through studying the current-voltage characteristics
of single-layer organic diodes with the aid of anode interfacial engineering
method. The diode turn-on voltage is shown to be highly sensitive to the
arylamine/metal charge-transfer interfaces and thus can serve as a probe in
detecting such organic/metal interfaces. We show that the diode electrical
performance could be tuned through engineering the arylamine/metal interfaces
via controlling the factors of anode work function, arylamine substitute
groups, and active layer surface morphology etc. The conduction mechanism of
the diodes is shown to be injection limited, which could be well described with
Richardson-Schottky thermonic emission model. Our work may provide some insight
into the use of single-layer organic diode and interfacial engineering method
to rapidly probe the organic/metal and even organic/organic charge-transfer
interfaces.


  Multiple-antenna "based" transmitter (TX) cooperation has been established as
a promising tool towards avoiding, aligning, or shaping the interference
resulting from aggressive spectral reuse. The price paid in the form of
feedback and exchanging channel state information (CSI) between cooperating
devices in most existing methods is often underestimated however. In reality,
feedback and information overhead threatens the practicality and scalability of
TX cooperation approaches in dense networks. Hereby we addresses a "Who needs
to know what?" problem, when it comes to CSI at cooperating transmitters. A
comprehensive answer to this question remains beyond our reach and the scope of
this paper. Nevertheless, recent results in this area suggest that CSI overhead
can be contained for even large networks provided the allocation of feedback to
TXs is made non-uniform and to properly depend on the network's topology. This
paper provides a few hints toward solving the problem.


  SplitFed Learning, a combination of Federated and Split Learning (FL and SL),
is one of the most recent developments in the decentralized machine learning
domain. In SplitFed learning, a model is trained by clients and a server
collaboratively. For image segmentation, labels are created at each client
independently and, therefore, are subject to clients' bias, inaccuracies, and
inconsistencies. In this paper, we propose a data quality-based adaptive
averaging strategy for SplitFed learning, called QA-SplitFed, to cope with the
variation of annotated ground truth (GT) quality over multiple clients. The
proposed method is compared against five state-of-the-art model averaging
methods on the task of learning human embryo image segmentation. Our
experiments show that all five baseline methods fail to maintain accuracy as
the number of corrupted clients increases. QA-SplitFed, however, copes
effectively with corruption as long as there is at least one uncorrupted
client.


  As robots become more prevalent, optimizing their design for better
performance and efficiency is becoming increasingly important. However, current
robot design practices overlook the impact of perception and design choices on
a robot's learning capabilities. To address this gap, we propose a
comprehensive methodology that accounts for the interplay between the robot's
perception, hardware characteristics, and task requirements. Our approach
optimizes the robot's morphology holistically, leading to improved learning and
task execution proficiency. To achieve this, we introduce a Morphology-AGnostIc
Controller (MAGIC), which helps with the rapid assessment of different robot
designs. The MAGIC policy is efficiently trained through a novel PRIvileged
Single-stage learning via latent alignMent (PRISM) framework, which also
encourages behaviors that are typical of robot onboard observation. Our
simulation-based results demonstrate that morphologies optimized holistically
improve the robot performance by 15-20% on various manipulation tasks, and
require 25x less data to match human-expert made morphology performance. In
summary, our work contributes to the growing trend of learning-based approaches
in robotics and emphasizes the potential in designing robots that facilitate
better learning.


  Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.


  Classes in natural images tend to follow long tail distributions. This is
problematic when there are insufficient training examples for rare classes.
This effect is emphasized in compound classes, involving the conjunction of
several concepts, such as those appearing in action-recognition datasets. In
this paper, we propose to address this issue by learning how to utilize common
visual concepts which are readily available. We detect the presence of
prominent concepts in images and use them to infer the target labels instead of
using visual features directly, combining tools from vision and
natural-language processing. We validate our method on the recently introduced
HICO dataset reaching a mAP of 31.54\% and on the Stanford-40 Actions dataset,
where the proposed method outperforms that obtained by direct visual features,
obtaining an accuracy 83.12\%. Moreover, the method provides for each class a
semantically meaningful list of keywords and relevant image regions relating it
to its constituent concepts.


  Video captioning targets interpreting the complex visual contents as text
descriptions, which requires the model to fully understand video scenes
including objects and their interactions. Prevailing methods adopt
off-the-shelf object detection networks to give object proposals and use the
attention mechanism to model the relations between objects. They often miss
some undefined semantic concepts of the pretrained model and fail to identify
exact predicate relationships between objects. In this paper, we investigate an
open research task of generating text descriptions for the given videos, and
propose Cross-Modal Graph (CMG) with meta concepts for video captioning.
Specifically, to cover the useful semantic concepts in video captions, we
weakly learn the corresponding visual regions for text descriptions, where the
associated visual regions and textual words are named cross-modal meta
concepts. We further build meta concept graphs dynamically with the learned
cross-modal meta concepts. We also construct holistic video-level and local
frame-level video graphs with the predicted predicates to model video sequence
structures. We validate the efficacy of our proposed techniques with extensive
experiments and achieve state-of-the-art results on two public datasets.


  The bispectrum stands out as a revolutionary tool in frequency domain
analysis, leaping the usual power spectrum by capturing crucial phase
information between frequency components. In our innovative study, we have
utilized the bispectrum to analyze and decode complex grasping movements,
gathering EEG data from five human subjects. We put this data through its paces
with three classifiers, focusing on both magnitude and phase-related features.
The results highlight the bispectrum's incredible ability to delve into neural
activity and differentiate between various grasping motions with the Support
Vector Machine (SVM) classifier emerging as a standout performer. In binary
classification, it achieved a remarkable 97\% accuracy in identifying power
grasp, and in the more complex multiclass tasks, it maintained an impressive
94.93\% accuracy. This finding not only underscores the bispectrum's analytical
strength but also showcases the SVM's exceptional capability in classification,
opening new doors in our understanding of movement and neural dynamics.


  Coronal Holes (CHs) are large-scale, low-density regions in the solar
atmosphere which may expel high-speed solar wind streams that incite hazardous,
geomagnetic storms. Coronal and solar wind models can predict these high-speed
streams and the performance of the coronal model can be validated against
segmented CH boundaries. We present a novel method named Sub-Transition Region
Identification of Ensemble Coronal Holes (STRIDE-CH) to address prominent
challenges in segmenting CHs with Extreme Ultraviolet (EUV) imagery.
Ground-based, chromospheric He I 10830 {\AA} line imagery and underlying Fe I
photospheric magnetograms are revisited to disambiguate CHs from filaments and
quiet Sun, overcome obscuration by coronal loops, and complement established
methods in the community which use space-borne, coronal EUV observations.
Classical computer vision techniques are applied to constrain the radiative and
magnetic properties of detected CHs, produce an ensemble of boundaries, and
compile these boundaries in a confidence map that quantifies the likelihood of
CH presence throughout the solar disk. This method is science-enabling towards
future studies of CH formation and variability from a mid-atmospheric
perspective.


  We consider the exploration process associated to the continuous random tree
(CRT) built using a Levy process with no negative jumps. This process has been
studied by Duquesne, Le Gall and Le Jan. This measure-valued Markov process is
a useful tool to study CRT as well as super-Brownian motion with general
branching mechanism. In this paper we prove this process is Feller, and we
compute its infinitesimal generator on exponential functionals and give the
corresponding martingale.


  Multi-task indoor scene understanding is widely considered as an intriguing
formulation, as the affinity of different tasks may lead to improved
performance. In this paper, we tackle the new problem of joint semantic,
affordance and attribute parsing. However, successfully resolving it requires a
model to capture long-range dependency, learn from weakly aligned data and
properly balance sub-tasks during training. To this end, we propose an
attention-based architecture named Cerberus and a tailored training framework.
Our method effectively addresses the aforementioned challenges and achieves
state-of-the-art performance on all three tasks. Moreover, an in-depth analysis
shows concept affinity consistent with human cognition, which inspires us to
explore the possibility of weakly supervised learning. Surprisingly, Cerberus
achieves strong results using only 0.1%-1% annotation. Visualizations further
confirm that this success is credited to common attention maps across tasks.
Code and models can be accessed at https://github.com/OPEN-AIR-SUN/Cerberus.


  Solutions of RG equations for $\beta(\alpha)$ and $\alpha(Q)$ are found in
the class of meromorphic functions satisfying asymptotic conditions at large
$Q$ (resp. small $\alpha)$, and analyticity properties in the $Q^2$ plane. The
resulting $\alpha_R(Q)$ is finite in the Euclidean $Q^2$ region and agrees well
at $Q\geq 1$ GeV with the $\bar{MS} \alpha_s(Q)$.


  The ground state energy is great importance for studying the properties of a
material. In this study, we computed both the Hartree-Fock approximation and
the random phase approximation of the ground state energy. Considering the
effect of the electrostatic shielding potential, we utilized the Thomas-Fermi
dielectric function to obtain the Thomas-Fermi formula for the total potential
energy. We subsequently calculated the total potential energy of the metallic
and non-metallic elements in the periodic table using Wigner correlation energy
and Hedin-Lundqvist correlation energy, considering the changes in the
correlation energies after considering electrostatic shielding effects.The
exchange correlation potential including electrostatic shielding effect can be
used in the measurement of SIM experiments.


  In this paper, we investigate basic geometric quantities of a random
hyperbolic surface of genus $g$ with respect to the Weil-Petersson measure on
the moduli space $\mathcal{M}_g$. We show that as $g$ goes to infinity, a
generic surface $X\in \mathcal{M}_g$ satisfies asymptotically: (1) the
separating systole of $X$ is about $2\log g$; (2) there is a half-collar of
width about $\frac{\log g}{2}$ around a separating systolic curve of $X$; (3)
the length of shortest separating closed multi-geodesics of $X$ is about $2\log
g$. As applications, we also discuss the asymptotic behavior of the extremal
separating systole, the non-simple systole and the expectation value of lengths
of shortest separating closed multi-geodesics as $g$ goes to infinity.


  We provide a status report on our search for dark matter in our Galaxy in the
form of massive compact halo objects (MACHOs), using gravitational microlensing
of background stars. This search uses a very large CCD camera on the dedicated
1.27m telescope at Mt.~Stromlo, Australia, and has been taking data for 2
years. At present, we have analysed data for 8 million stars in the Large
Magellanic Cloud over 1 year, resulting in one strong candidate event and two
lower-amplitude candidates. We have also analysed 5 million stars in the
Galactic Bulge for 0.5 years, yielding approximately 27 microlensing events.


  We use the ROSAT public data archive to study the X-ray emission of a sample
of supposedly single A0-F6 spectral type stars from the Bright Star Catalogue.
We detected X-ray emission from 19 A and 33 F-type stars. However, our results
are not sufficient to associate with certainty the X-ray emission to the A-type
stars themselves, since the usual argument that it may originate from a binary
companion can not be excluded. A spectral analysis was conducted for 14 sources
(3 A and 11 F), finding that for 12 of them a two temperature thermal plasma
model is needed to reproduce the observed spectra. The two temperatures are
centered at 0.13 and 0.54 keV, respectively. The values found for the higher
temperature are lower than that ones of X-ray selected single late-type stars.
The X-ray luminosities are in the range 1E28 < L_X < 1E30 erg/s, with a
distribution similar to that of active late-type stars. No correlation is found
between L_X and B-V color, Vsin(i) and L_bol, while a positive correlation is
found between the X-ray luminosity and the hardness ratio.


  We propose a generalized framework for block-structured nonconvex
optimization, which can be applied to structured subgraph detection in
interdependent networks, such as multi-layer networks, temporal networks,
networks of networks, and many others. Specifically, we design an effective,
efficient, and parallelizable projection algorithm, namely Graph
Block-structured Gradient Projection (GBGP), to optimize a general non-linear
function subject to graph-structured constraints. We prove that our algorithm:
1) runs in nearly-linear time on the network size; 2) enjoys a theoretical
approximation guarantee. Moreover, we demonstrate how our framework can be
applied to two very practical applications and conduct comprehensive
experiments to show the effectiveness and efficiency of our proposed algorithm.


  Our main result (Theorem A) shows the incompleteness of any consistent
sequential theory T formulated in a finite language such that T is axiomatized
by a collection of sentences of bounded quantifier-alternation-depth. Our proof
employs an appropriate reduction mechanism to rule out the possibility of
completeness by simply invoking Tarski's Undefinability of Truth theorem. We
also use the proof strategy of Theorem A to obtain other incompleteness results
(as in Theorems A+; B and B+).


  We calculate the one-loop effective potential for the toroidal non-abelian
D-brane in the constant magnetic SU(2) gauge field. The study of its properties
shows that the potential is unbounded below. This fact indicates the
instability of the non-abelian D-brane in the background under consideration
like the instability of chromomagnetic vacuum in SU(2) gauge theory.


  The maximum supported context length is a critical bottleneck limiting the
practical application of the Large Language Model (LLM). Although existing
length extrapolation methods can extend the context of LLMs to millions of
tokens, these methods all have an explicit upper bound. In this work, we
propose LongCache, a training-free approach that enables LLM to support an
infinite context with finite context scope, through full-context cache
selection and training-free integration. This effectively frees LLMs from the
length extrapolation issue. We validate LongCache on the LongBench and L-Eval
and demonstrate its performance is on par with traditional full-attention
mechanisms. Furthermore, we have applied LongCache on mainstream LLMs,
including LLaMA3 and Mistral-v0.3, enabling them to support context lengths of
at least 400K in Needle-In-A-Haystack tests. We will improve the efficiency of
LongCache by GPU-aware optimization soon.


  This paper extends Edgeworth-Cornish-Fisher expansions for the distribution
and quantiles of nonparametric estimates in two ways. Firstly it allows
observations to have different distributions. Secondly it allows the
observations to be weighted in a predetermined way. The use of weighted
estimates has a long history including applications to regression, rank
statistics and Bayes theory. However, asymptotic results have generally been
only first order (the CLT and weak convergence). We give third order
asymptotics for the distribution and percentiles of any smooth functional of a
weighted empirical distribution, thus allowing a considerable increase in
accuracy over earlier CLT results.
  Consider independent non-identically distributed ({\it non-iid}) observations
$X_{1n}, ..., X_{nn}$ in $R^s$. Let $\hat{F}(x)$ be their {\it weighted
empirical distribution} with weights $w_{1n}, ..., w_{nn}$. We obtain cumulant
expansions and hence Edgeworth-Cornish-Fisher expansions for $T(\hat{F})$ for
any smooth functional $T(\cdot)$ by extending the concepts of von Mises
derivatives to signed measures of total measure 1. As an example we give the
cumulant coefficients needed for Edgeworth-Cornish-Fisher expansions to
$O(n^{-3/2})$ for the sample variance when observations are non-iid.


  Gait recognition is an important recognition technology, because gait is not
easy to camouflage and does not need cooperation to recognize subjects.
However, many existing methods are inadequate in preserving both temporal
information and fine-grained information, thus reducing its discrimination.
This problem is more serious when the subjects with similar walking postures
are identified. In this paper, we try to enhance the discrimination of
spatio-temporal gait features from two aspects: effective extraction of
spatio-temporal gait features and reasonable refinement of extracted features.
Thus our method is proposed, it consists of Spatio-temporal Feature Extraction
(SFE) and Global Distance Alignment (GDA). SFE uses Temporal Feature Fusion
(TFF) and Fine-grained Feature Extraction (FFE) to effectively extract the
spatio-temporal features from raw silhouettes. GDA uses a large number of
unlabeled gait data in real life as a benchmark to refine the extracted
spatio-temporal features. GDA can make the extracted features have low
inter-class similarity and high intra-class similarity, thus enhancing their
discrimination. Extensive experiments on mini-OUMVLP and CASIA-B have proved
that we have a better result than some state-of-the-art methods.


  The position and strength of the boson peak in silica glass vary considerably
with temperature $T$. Such variations cannot be explained solely with changes
in the Debye energy. New Brillouin scattering measurements are presented which
allow determining the $T$-dependence of unrelaxed acoustic velocities. Using a
velocity based on the bulk modulus, scaling exponents are found which agree
with the soft-potential model. The unrelaxed bulk modulus thus appears to be a
good measure for the structural evolution of silica with $T$ and to set the
energy scale for the soft potentials.


  Coherent causal memory (CCM) is causal memory in which prefixes of an
execution can be mapped to global memory states in a consistent way. While CCM
requires conflicting pairs of writes to be globally ordered, it allows writes
to remain unordered with respect to both reads and nonconflicting writes.
Nevertheless, it supports assertional, state-based program reasoning using
generalized Owicki-Gries proof outlines (where assertions can be attached to
any causal program edge). Indeed, we show that from a reasoning standpoint, CCM
differs from sequentially consistent (SC) memory only in that ghost code added
by the user is not allowed to introduce new write-write races.
  While CCM provides most of the formal reasoning leverage of SC memory, it is
much more efficiently implemented. As an illustration, we describe a simple
programming discipline that provides CCM on top of x86-TSO. The discipline is
considerably more relaxed than the one needed to ensure SC; for example, it
introduces no burden whatsoever for programs in which at most one thread writes
to any variable.


  Variation of positional information, measured by the two-body excess entropy
$\mathsf{S}_\mathrm{2}$, is studied across the liquid-solid equilibrium
transition in a simple two-dimensional system. Analysis reveals a master
relation between $\mathsf{S}_\mathrm{2}$ and the freezing temperature
$T_{\mathrm{f}}$, from which a scaling law is extracted:
$-\mathsf{S}_\mathrm{2}\sim |T_{\mathrm{f}} - T| ^{-1/3}$. Theoretical and
practical implications of the observed universality are discussed.


  We analyze the stellar mass-to-light ratio (M/L) gradients in a large sample
of local galaxies taken from the Sloan Digital Sky Survey, spanning a wide
range of stellar masses and morphological types. As suggested by the well known
relationship between M/L ratios and colors, we show that M/L gradients are
strongly correlated with colour gradients, which we trace to the effects of age
variations. Stellar M/L gradients generally follow patterns of variation with
stellar mass and galaxy type that were previous found for colour and metallicty
gradients. In late-type galaxies M/L gradients are negative, steepening with
increasing mass. In early-type galaxies M/L gradients are shallower while
presenting a two-fold trend: they decrease with mass up to a characteristic
mass of \M* \sim 10^10.3 M_sun and increase at larger masses. We compare our
findings with other analyses and discuss some implications for galaxy formation
and for dark matter estimates.


  Let $G$ be a group and $S\subseteq G$ its subset such that $S=S^{-1}$, where
$S^{-1}=\{s^{-1}\mid s\in S\}$. Then {\it the Cayley graph ${\rm Cay}(G,S)$} is
an undirected graph $\Gamma$ with the vertex set $V(\Gamma)=G$ and the edge set
$E(\Gamma)=\{(g,gs)\mid g\in G, s\in S\}$. A graph $\Gamma$ is said to be {\it
integral} if every eigenvalue of the adjacency matrix of $\Gamma$ is integer.
In the paper, we prove the following theorem: {\it if a subset $S=S^{-1}$ of
$G$ is normal and $s\in S\Rightarrow s^k\in S$ for every $k\in \mathbb{Z}$ such
that $(k,|s|)=1$, then ${\rm Cay}(G,S)$ is integral.} In particular, {\it if
$S\subseteq G$ is a normal set of involutions, then ${\rm Cay}(G,S)$ is
integral.} We also use the theorem to prove that {\it if $G=A_n$ and
$S=\{(12i)^{\pm1}\mid i=3,\dots,n\}$, then ${\rm Cay}(G,S)$ is integral.} Thus,
we give positive solutions for both problems 19.50(a) and 19.50(b) in "Kourovka
Notebook".


  We report on the theoretical investigation of the plasmonic wave propagation
in the coaxial cylindrical cables fabricated of both right-handed medium (RHM)
[with $\epsilon >0$, $\mu >0$] and left-handed medium (LHM) [with
$\epsilon(\omega) <0$, $\mu(\omega) <0$], using a Green-function (or response
function) theory in the absence of an applied magnetic field. The
Green-function theory generalized to be applicable to such quasi-one
dimensional systems enables us to derive explicit expressions for the
corresponding response functions (associated with the EM fields), which can in
turn be used to derive various physical properties of the system. The confined
plasmonic wave excitations in such multi-interface structures are characterized
by the electromagnetic fields that are localized at and decay exponentially
away from the interfaces. A rigorous analytical diagnosis of the general
results in diverse situations leads us to reproduce exactly the previously
well-known results in other geometries, obtained within the different
theoretical frameworks. As an application, we present several illustrative
examples on the dispersion characteristics of the confined (and extended)
plasmonic waves in single- and double-interface structures made up of
dispersive metamaterials interlaced with conventional dielectrics. These
dispersive modes are also substantiated through the computation of local as
well as total density of states. It is observed that the dispersive components
enable the system to support the simultaneous existence of s- and
p-polarization modes in the system. Such effects as this one are solely
attributed to the negative-index metamaterials and are otherwise impossible...


  The following questions are germane to our understanding of gauge-(in)variant
quantities and physical possibility: how are gauge transformations and
spacetime diffeomorphisms understood as symmetries, in which ways are they
similar, and in which are they different? To what extent are we justified in
endorsing different attitudes -- nowadays called sophistication, haecceitism,
and eliminativism -- towards each? This is the first of four papers taking up
this question.
  This first paper will discuss notions of symmetries and isomorphisms that
will be used in the remaining papers in the series. There are several such
notions in the literature and the question of how they mesh with empirical
discernibility is a delicate one; even the orthodox view that symmetries are
empirically unobservable (even in principle) has recently been challenged by
Belot. Focusing on local field theories, I will provide a precise definition of
dynamical symmetries in terms of the space of states of the theory at hand. I
will then apply the definition to Yang-Mills theories and general relativity
and show that these symmetries correspond to automorphisms of `natural'
geometric structures: the small diffeomorphisms of the spacetime manifold and
the small fiber-preserving diffeomorphisms of a fibered space. Finally, I will
show that these automorphisms can be given a passive gloss, since they
correspond 1-1 to the coordinate transformations of the underlying manifolds.


  The Han-Li conjecture states that: Let $(M,g_0)$ be an $n$-dimensional
$(n\geq 3)$ smooth compact Riemannian manifold with boundary having positive
(generalized) Yamabe constant and $c$ be any real number, then there exists a
conformal metric of $g_0$ with scalar curvature $1$ and boundary mean curvature
$c$. Combining with Z. C. Han and Y. Y. Li's results, we answer this conjecture
affirmatively except for the case that $n\geq 8$, the boundary is umbilic, the
Weyl tensor of $M$ vanishes on the boundary and has a non-zero interior point.


  In the QCD factorization (QCDF) approach we study the direct $CP$ violation
in
$\bar{B}^{0}\rightarrow\rho^0(\omega)\rho^0(\omega)\rightarrow\pi^+\pi^-\pi^+\pi^-$
via the $\rho-\omega$ mixing mechanism. We find that the $CP$ violation can be
enhanced by double $\rho-\omega$ mixing when the masses of the $\pi^+\pi^-$
pairs are in the vicinity of the $\omega$ resonance, and the maximum $CP$
violation can reach 28{\%}. We also compare the results from the naive
factorization and the QCD factorization.


  Global demand for donated blood far exceeds supply, and unmet need is
greatest in low- and middle-income countries; experts suggest that large-scale
coordination is necessary to alleviate demand. Using the Facebook Blood
Donation tool, we conduct the first large-scale algorithmic matching of blood
donors with donation opportunities. While measuring actual donation rates
remains a challenge, we measure donor action (e.g., making a donation
appointment) as a proxy for actual donation. We develop automated policies for
matching donors with donation opportunities, based on an online matching model.
We provide theoretical guarantees for these policies, both regarding the number
of expected donations and the equitable treatment of blood recipients. In
simulations, a simple matching strategy increases the number of donations by
5-10%; a pilot experiment with real donors shows a 5% relative increase in
donor action rate (from 3.7% to 3.9%). When scaled to the global Blood Donation
tool user base, this corresponds to an increase of around one hundred thousand
users taking action toward donation. Further, observing donor action on a
social network can shed light onto donor behavior and response to incentives.
Our initial findings align with several observations made in the medical and
social science literature regarding donor behavior.


  The point of this note is to prove that a language is in the complexity class
PP if and only if the strings of the language encode valid inferences in a
Bayesian network defined using function-free first-order logic with equality.


  We today live in the era of dynamic and mobile wireless enabled platforms.
This kind of stringent communication capability in the face of volatile and
turbulent mobility demands a fresh look at physical layer in general and
antenna design in particular. The dimension of the antenna is 30x35x1.6 mm^3.
Multiplicity of bands is very useful for compatibility purposes where legacy
robotic platforms generally operate in MHz range while latest robotic platforms
are capable to handle GHz communication regimes and can pump data very at much
greater speeds. Seven frequency bands are obtained at 700 MHz, 2.4 GHz, 3.6
GHz, 4.37 GHz, 5.8 GHz, 6.93 GHz and 7.7 GHz with bandwidth of 1.1 GHz, 0.7
GHz, 0.8 GHz, 0.23 GHz, 0.90 GHz, 0.19 GHz and 0.12 GHz respectively.


  When designing 3D applications it is necessary to find a compromise between
cost (e.g. money, time) and achievable realism of the virtual environment.
Reusing existing assets has an impact on the uniqueness of the application and
creating high quality 3D assets is very time consuming and expensive. We aim
for a low cost, high quality and minimal time effort solution to create virtual
environments. This paper's main contribution is a novel way of creating a
virtual meeting application by utilizing augmented spherical images for photo
realistic virtual environments.


  Growing network models with both heterogeneity of the nodes and topological
constraints can give rise to a rich phase structure. We present a simple model
based on preferential attachment with rewiring of the links. Rewiring
probabilities are modulated by the negative fitness of the nodes and by the
constraint for the network to be a simple graph. At low temperatures and high
rewiring rates, this constraint induces a Bose-Einstein condensation of paths
of length 2, i.e. a new phase transition with an extended condensate of links.
The phase space of the model includes further transitions in the scaling of the
connected component and the degeneracy of the network.


  Why do models often attend to salient words, and how does this evolve
throughout training? We approximate model training as a two stage process:
early on in training when the attention weights are uniform, the model learns
to translate individual input word `i` to `o` if they co-occur frequently.
Later, the model learns to attend to `i` while the correct output is $o$
because it knows `i` translates to `o`. To formalize, we define a model
property, Knowledge to Translate Individual Words (KTIW) (e.g. knowing that `i`
translates to `o`), and claim that it drives the learning of the attention.
This claim is supported by the fact that before the attention mechanism is
learned, KTIW can be learned from word co-occurrence statistics, but not the
other way around. Particularly, we can construct a training distribution that
makes KTIW hard to learn, the learning of the attention fails, and the model
cannot even learn the simple task of copying the input words to the output. Our
approximation explains why models sometimes attend to salient words, and
inspires a toy example where a multi-head attention model can overcome the
above hard training distribution by improving learning dynamics rather than
expressiveness. We end by discussing the limitation of our approximation
framework and suggest future directions.


  In a paper of Ashtekar and Campiglia, residual diffeomorphisms have been used
to single out the standard representation of the reduced holonomy-flux algebra
in homogeneous loop quantum cosmology (LQC). We show that, in the homogeneous
isotropic case, unitarity of the translations w.r.t. the extended
$\mathbb{R}$-action (exponentiated reduced fluxes in the standard approach)
singles out the Bohr measure on both the standard quantum configuration space
$\mathbb{R}_\mathrm{Bohr}$ as well as on the Fleischhack one. Thus, in both
situation, the same condition singles out the standard kinematical Hilbert
space of LQC.


  This paper presents a profound analysis of the robust job scheduling problem
with uncertain release dates on unrelated machines. Our model involves
minimizing the worst-case makespan and interval uncertainty where each release
date belongs to a well-defined interval. Robust optimization requires
scenario-based decision-making. A finite subset of feasible scenarios to
determine the worst-case regret (a deviation from the optimal makespan) for a
particular schedule is indicated. We formulate a mixed-integer nonlinear
programming model to solve the underlying problem via three (constructive)
greedy algorithms. Polynomial-time solvable cases are also discussed in detail.
The algorithms solve the robust combinatorial problem using the makespan
criterion and its non-deterministic counterpart. Computational testing compares
both robust solutions and different decomposition strategies. Finally, the
results confirm that a decomposition strategy applied to the makespan criterion
is enough to create a competitive robust schedule.


  We have studied the mass distribution in the lensing galaxy 2237+0305 using
constraints from both gravitational lensing and photometric and spectroscopic
observations. We find that with sufficient dynamical information we can
constrain the mass-to-light ratio of the luminous components and determine the
dynamical contribution of the disk. In addition, future observations should
allow us to place constraints on the shape of the dark matter halo and its
inner slope.


  Recently, implicit graph neural networks (GNNs) have been proposed to capture
long-range dependencies in underlying graphs. In this paper, we introduce and
justify two weaknesses of implicit GNNs: the constrained expressiveness due to
their limited effective range for capturing long-range dependencies, and their
lack of ability to capture multiscale information on graphs at multiple
resolutions. To show the limited effective range of previous implicit GNNs, We
first provide a theoretical analysis and point out the intrinsic relationship
between the effective range and the convergence of iterative equations used in
these models. To mitigate the mentioned weaknesses, we propose a multiscale
graph neural network with implicit layers (MGNNI) which is able to model
multiscale structures on graphs and has an expanded effective range for
capturing long-range dependencies. We conduct comprehensive experiments for
both node classification and graph classification to show that MGNNI
outperforms representative baselines and has a better ability for multiscale
modeling and capturing of long-range dependencies.


  The K^+ \to \pi^0 \mu^+ \nu \gamma ($K_{\mu 3 \gamma}$) decay has been
measured with stopped positive kaons at the KEK 12 GeV proton synchrotron. A
$K_{\mu 3 \gamma}$ sample containing 125 events was obtained. The partial
branching ratio $Br(K_{\mu 3 \gamma}, E_{\gamma}>30 {\rm MeV}, \theta_{\mu^+
\gamma}>20^{\circ})$ was found to be $[2.4 \pm 0.5(stat) \pm 0.6(syst)]\times
10^{-5}$, which is in good agreement with theoretical predictions.


  Point cloud is a crucial representation of 3D contents, which has been widely
used in many areas such as virtual reality, mixed reality, autonomous driving,
etc. With the boost of the number of points in the data, how to efficiently
compress point cloud becomes a challenging problem. In this paper, we propose a
set of significant improvements to patch-based point cloud compression, i.e., a
learnable context model for entropy coding, octree coding for sampling centroid
points, and an integrated compression and training process. In addition, we
propose an adversarial network to improve the uniformity of points during
reconstruction. Our experiments show that the improved patch-based autoencoder
outperforms the state-of-the-art in terms of rate-distortion performance, on
both sparse and large-scale point clouds. More importantly, our method can
maintain a short compression time while ensuring the reconstruction quality.


  Over the last decade {\it ab initio} modeling of material properties has
become widespread in diverse fields of research. It has proved to be a powerful
tool for predicting various properties of matter under extreme conditions. We
apply modern computational chemistry and materials science methods, including
density functional theory (DFT), to solve lingering problems in the modeling of
the dense atmospheres of cool white dwarfs ($T_{\rm eff}\rm <7000 \, K$). Our
work on the revision and improvements of the absorption mechanisms in the
hydrogen and helium dominated atmospheres resulted in a new set of atmosphere
models. By inclusion of the Ly-$\rm \alpha$ red wing opacity we successfully
fitted the entire spectral energy distributions of known cool DA stars. In the
subsequent work we fitted the majority of the coolest stars with hydrogen-rich
models. This finding challenges our understanding of the spectral evolution of
cool white dwarfs. We discuss a few examples, including the cool companion to
the pulsar PSR J0437-4715. The two problems important for the understanding of
cool white dwarfs are the behavior of negative hydrogen ion and molecular
carbon in a fluid-like, helium dominated medium. Using {\it ab initio} methods
we investigate the stability and opacity of these two species in dense helium.
Our investigation of $\rm C_2$ indicates that the absorption features observed
in the ``peculiar'' DQp white dwarfs resemble the absorption of perturbed $\rm
C_2$ in dense helium.


  There is a knowledge gap regarding which types of failures robots undergo in
domestic settings and how these failures influence customer experience. We
classified 10,072 customer reviews of small utilitarian domestic robots on
Amazon by the robotic failures described in them, grouping failures into twelve
types and three categories (Technical, Interaction, and Service). We identified
sources and types of failures previously overlooked in the literature,
combining them into an updated failure taxonomy. We analyzed their frequencies
and relations to customer star ratings. Results indicate that for utilitarian
domestic robots, Technical failures were more detrimental to customer
experience than Interaction or Service failures. Issues with Task Completion
and Robustness & Resilience were commonly reported and had the most significant
negative impact. Future failure-prevention and response strategies should
address the technical ability of the robot to meet functional goals, operate
and maintain structural integrity over time. Usability and interaction design
were less detrimental to customer experience, indicating that customers may be
more forgiving of failures that impact these aspects for the robots and
practical uses examined. Further, we developed a Natural Language Processing
model capable of predicting whether a customer review contains content that
describes a failure and the type of failure it describes. With this knowledge,
designers and researchers of robotic systems can prioritize design and
development efforts towards essential issues.


  The cross section for anti-deuteron photoproduction is measured at HERA at a
mean centre-of-mass energy of W_{\gamma p} = 200 GeV in the range 0.2 < p_T/M <
0.7 and |y| < 0.4, where M, p_T and y are the mass, transverse momentum and
rapidity in the laboratory frame of the anti-deuteron, respectively. The
numbers of anti-deuterons per event are found to be similar in photoproduction
to those in central proton-proton collisions at the CERN ISR but much lower
than those in central Au-Au collisions at RHIC. The coalescence parameter B_2,
which characterizes the likelihood of anti-deuteron production, is measured in
photoproduction to be 0.010 \pm 0.002 \pm 0.001, which is much higher than in
Au-Au collisions at a similar nucleon-nucleon centre-of-mass energy. No
significant production of particles heavier than deuterons is observed and
upper limits are set on the photoproduction cross sections for such particles.


  We introduce a basis of the Orlik-Solomon algebra labeled by chambers, so
called chamber basis. We consider structure constants of the Orlik-Solomon
algebra with respect to the chamber basis and prove that these structure
constants recover D. Cohen's minimal complex from the Aomoto complex.


  We show that valley degeneracy in rotationally faulted multilayer graphene
may be broken in the presence of a magnetic field and interlayer
commensurations. This happens due to a simultaneous breaking of both
time-reversal and inversion symmetries leading to a splitting of Landau levels
linear in the field. Our theoretical work is motivated by an experiment [Y. J.
Song et al., Nature 467, 185 (2010)] on epitaxially grown multilayer graphene
where such linear splitting of Landau levels was observed at moderate fields.
We consider both bilayer and trilayer configurations and, although a linear
splitting occurs in both cases, we show that the latter produces a splitting
that is in quantitative agreement with the experiment.


  Vortical blood flow in the human left ventricular (LV) inflow initiates from
the mitral valve (MV) and evolves within the LV during diastolic E-filling.
Hence, vortical flow links MV and LV hemodynamics. This study sought to
elucidate and quantitatively characterize the in vivo 3D dynamics of LV
vortical flow over E-filling and relation to MV-LV hemodynamic coupling using
4D Flow MRI flow field. 34 healthy volunteers and 5 example patients underwent
4D Flow MRI. Vortical blood flow evolution was mapped in the LV over E-wave
using enstrophy density. A new dimensionless profile PMV-LV was derived as a
function of both MV vortex formation time (VFT) and LV volumetric enstrophy
density. Results reveal that 3D vortical flow evolution in the healthy LV
follows a bi-phasic behavior with a vortical growth phase followed by a
vortical decay phase. In healthy LVs studied, the PMV-LV profile showed that
the vortical growth and decay phases are characterized by a vortical growth
time T_growth= 1.23+/-0.25, growth rate {\alpha}=0.80+/-0.17, decay time
T_decay= 0.96+/-0.39 and decay rate \b{eta}=-1.02+/-0.49. Distinctly altered
parameters were found in the pilot patients studied. The derived PMV-LV profile
quantitatively characterizes MV-LV hemodynamic coupling by vortical flow
dynamics. Results herein unravel new insights into cardiac physiology and could
enable a novel standardized methodology to study MV-LV hemodynamic coupling and
association to cardiac function in health and disease.


  Argument Mining (AM) is a relatively recent discipline, which concentrates on
extracting claims or premises from discourses, and inferring their structures.
However, many existing works do not consider micro-level AM studies on
discussion threads sufficiently. In this paper, we tackle AM for discussion
threads. Our main contributions are follows: (1) A novel combination scheme
focusing on micro-level inner- and inter- post schemes for a discussion thread.
(2) Annotation of large-scale civic discussion threads with the scheme. (3)
Parallel constrained pointer architecture (PCPA), a novel end-to-end technique
to discriminate sentence types, inner-post relations, and inter-post
interactions simultaneously. The experimental results demonstrate that our
proposed model shows better accuracy in terms of relations extraction, in
comparison to existing state-of-the-art models.


  The field of mobile health aims to leverage recent advances in wearable
on-body sensing technology and smart phone computing capabilities to develop
systems that can monitor health states and deliver just-in-time adaptive
interventions. However, existing work has largely focused on analyzing
collected data in the off-line setting. In this paper, we propose a novel
approach to learning shallow detection cascades developed explicitly for use in
a real-time wearable-phone or wearable-phone-cloud systems. We apply our
approach to the problem of cigarette smoking detection from a combination of
wrist-worn actigraphy data and respiration chest band data using two and three
stage cascades.


  For two-scale homogenization of a general class of asymptotically
degenerating %uniformly strongly elliptic symmetric PDE systems with a
critically scaled high contrast in periodic coefficients of a small period
$\varepsilon$, we derive a two-scale limit resolvent problem under a single
generic decomposition assumption for the `stiff' part. We show that this key
assumption does hold for a large number of examples with a high contrast, both
studied before and some recent ones, including those in linear elasticity and
electromagnetism. Following ideas of V.V. Zhikov, under very mild restrictions
on the regularity of the domain $\Omega$ we prove that the limit resolvent
problem is well-posed and turns out to be a pseudo-resolvent problem for a
well-defined non-negative self-adjoint two-scale limit operator. A key novel
technical ingredient here is a proof that the linear span of product test
functions in the functional spaces corresponding to the degeneracies is dense
in associated two-scale energy space for a general coupling between the scales.
As a result, we establish (both weak and strong) two-scale resolvent
convergence, as well as some of its further implications for the spectral
convergence and for convergence of parabolic and hyperbolic semigroups and of
associated time-dependent initial boundary value problems.


  The quest for nonequilibrium quantum phase transitions is often hampered by
the tendency of driving and dissipation to give rise to an effective
temperature, resulting in classical behavior. Could this be different when the
dissipation is engineered to drive the system into a nontrivial quantum
coherent steady state? In this work we shed light on this issue by studying the
effect of disorder on recently-introduced dissipation-induced Chern topological
states, and examining the eigenmodes of the Hermitian steady state density
matrix or entanglement Hamiltonian. We find that, similarly to equilibrium,
each Landau band has a single delocalized level near its center. However, using
three different finite size scaling methods we show that the critical exponent
$\nu$ describing the divergence of the localization length upon approaching the
delocalized state is significantly different from equilibrium if disorder is
introduced into the non-dissipative part of the dynamics. This indicates a
different type of nonequilibrium quantum critical universality class accessible
in cold-atom experiments.


  The thermal transport properties of single-wall carbon nanotubes (SWCNTs) are
re-investigated using the iterative solution of the Boltzmann transport
equation by including four-phonon scattering. Using only three-phonon
scattering, the flexural and twisted phonon modes are found to remain
non-scattered via Umklapp processes, resulting in the literature-reported
divergence of thermal conductivity with tube length. However, with four-phonon
scattering, while longitudinal modes remain unaffected, the otherwise
non-scattered transverse modes undergo Umklapp scattering and result in a
non-ballistic transport, thus settling a decades-long debate on the length
dependence of thermal conductivity of SWCNTs. The predicted thermal
conductivity of SWCNTs using both three- and four-phonon scatterings is 3700
W/m-K at 300 K and stays highest amongst all known materials.


  There is growing interest in being able to run neural networks on sensors,
wearables and internet-of-things (IoT) devices. However, the computational
demands of neural networks make them difficult to deploy on
resource-constrained edge devices.
  To meet this need, our work introduces a new recurrent unit architecture that
is specifically adapted for on-device low power acoustic event detection (AED).
The proposed architecture is based on the gated recurrent unit (`GRU') but
features optimizations that make it implementable on ultra-low power
micro-controllers such as the Arm Cortex M0+.
  Our new architecture, the Embedded Gated Recurrent Unit (eGRU) is
demonstrated to be highly efficient and suitable for short-duration AED and
keyword spotting tasks. A single eGRU cell is 60x faster and 10x smaller than a
GRU cell. Despite its optimizations, eGRU compares well with GRU across tasks
of varying complexities.
  The practicality of eGRU is investigated in a wearable acoustic event
detection application. An eGRU model is implemented and tested on the Arm
Cortex M0-based Atmel ATSAMD21E18 processor. The Arm M0+ implementation of the
eGRU model compares favorably with a full precision GRU that is running on a
workstation. The embedded eGRU model achieves a classification accuracy 95.3%,
which is only 2% less than the full precision GRU.


  The prime divisors of a polynomial $P$ with integer coefficients are those
primes $p$ for which $P(x) \equiv 0 \pmod{p}$ is solvable. Our main result is
that the common prime divisors of any several polynomials are exactly the prime
divisors of some single polynomial. By combining this result with a theorem of
Ax we get that for any system $F$ of multivariate polynomial equations with
integer coefficients, the set of primes $p$ for which $F$ is solvable modulo
$p$ is the set of prime divisors of some univariate polynomial. In addition, we
prove results on the densities of the prime divisors of polynomials. The
article serves as a light introduction to algebraic number theory and Galois
theory.


  It is shown that the thermodynamic Rutgers relation for the second order
phase transitions can be used for the analysis of the superfluid density data
irrespective of complexities of the Fermi surface, structure of the
superconducting gap, pairing strength, or scattering. The only limitation is
that critical fluctuations should be weak, so that the mean field theory of the
second order phase transitions is applicable. By using the Rutgers relation,
the zero temperature value of the London penetration depth, $\lambda(0)$, is
related to the specific heat jump $\Delta C$ and the slope of upper critical
field $dH_{c2}/dT$ at the transition temperature $T_c$, provided the data on
$\Delta\lambda=\lambda(T)-\lambda(0)$ are available in a broad temperature
domain. We then provide a new way of determination of $\lambda(0)$, the
quantity difficult to determine within many techniques.


  Motivated by the work [Phys. Rev. Lett. 89, 220404 (2002)] for detecting the
vacuum-induced Berry phases with two-mode Jaynes-Cummings models (JCMs), we
show here that, for a parameter-dependent single-mode JCM, certain atom-field
states also acquire the photon-number-dependent Berry phases after the
parameter slowly changed and eventually returned to its initial value. This
geometric effect related to the field quantization still exists, even the filed
is kept in its vacuum state. Specifically, a feasible Ramsey interference
experiment with cavity quantum electrodynamics (QED) system is designed to
detect the vacuum-induced Berry phase.


  We have performed a search for scalar top quark (stop) pair production in the
inclusive electron-muon-missing transverse energy final state, using a sample
of $p \bar{p}$ events corresponding to 108.3 pb$^{-1}$ of data collected with
the D{\O}detector at Fermilab. The search is done in the framework of the
minimal supersymmetric standard model assuming that the sneutrino is the
lightest supersymmetric particle. For the dominant decays of the lightest stop,
$\bc$ and $\bls$, no evidence for signal is found. We derive cross-section
limits as a function of stop (\stt), chargino (\ca), and sneutrino (\snu)
masses.


  The Large Observatory For X-ray Timing (LOFT) is one of the candidate
missions selected by the European Space Agency for an initial assessment phase
in the Cosmic Vision programme. It is proposed for the M3 launch slot and has
broad scientific goals related to fast timing of astrophysical X-ray sources.
LOFT will carry the Large Area Detector (LAD), as one of the two core science
instruments, necessary to achieve the challenging objectives of the project.
LAD is a collimated detector working in the energy range 2-50 keV with an
effective area of approximately 10 m^2 at 8 keV.
  The instrument comprises an array of modules located on deployable panels.
Lead-glass microchannel plate (MCP) collimators are located in front of the
large-area Silicon Drift Detectors (SDD) to reduce the background contamination
from off-axis resolved point sources and from the diffuse X-ray background. The
inner walls of the microchannel plate pores reflect grazing incidence X-ray
photons with a probability that depends on energy. In this paper, we present a
study performed with an ad-hoc simulator of the effects of this capillary
reflectivity on the overall instrument performance. The reflectivity is derived
from a limited set of laboratory measurements, used to constrain the model. The
measurements were taken using a prototype collimator whose thickness is similar
to that adopted in the current baseline design proposed for the LAD.
  We find that the experimentally measured level of reflectivity of the pore
inner walls enhances the off-axis transmission at low energies, producing an
almost flat-top response. The resulting background increase due to the diffuse
cosmic X-ray emission and sources within the field of view does not degrade the
instrument sensitivity.


  Congestion in router buffer increases the delay and packet loss. Active Queue
Management (AQM) methods are able to detect congestion in early stage and
control it by packet dropping. Effective Random Early Detection (ERED) method,
among many other AQM methods, gives a good performance in detect and control
congestion and preserve packet loss. However, the ERED neglect the delay
factor, which is effect the performance of the network. Moreover, ERED has a
real parameterization problem. Several parameters have to be initialized to
optimal values to obtain satisfactory results. This paper proposed an extended
ERED method that considers the delay in its process and combines the extended
ERED method with a Fuzzy Inference Process that eases the problem of parameter
initialization. The results show that the parametric-based form of the proposed
work gives a better performance results, according to the performance measures,
delay, dropping and packet loss. The loss has been enhanced by 10-100%. Delay
has been enhanced by 30-60%. The performance of the fuzzy-based form of the
proposed method is better than the parametric-based form and ERED in terms of
delay and packet loss.


  High-resolution three-dimensional (3D) cardiovascular magnetic resonance
(CMR) is a valuable medical imaging technique, but its widespread application
in clinical practice is hampered by long acquisition times. Here we present a
novel compressed sensing (CS) reconstruction approach using shearlets as a
sparsifying transform allowing for fast 3D CMR (3DShearCS). Shearlets are
mathematically optimal for a simplified model of natural images and have been
proven to be more efficient than classical systems such as wavelets. Data is
acquired with a 3D Radial Phase Encoding (RPE) trajectory and an iterative
reweighting scheme is used during image reconstruction to ensure fast
convergence and high image quality. In our in-vivo cardiac MRI experiments we
show that the proposed method 3DShearCS has lower relative errors and higher
structural similarity compared to the other reconstruction techniques
especially for high undersampling factors, i.e. short scan times. In this
paper, we further show that 3DShearCS provides improved depiction of cardiac
anatomy (measured by assessing the sharpness of coronary arteries) and two
clinical experts qualitatively analyzed the image quality.


  Despite the rapid growth of smart contracts, they are suffering numerous
security vulnerabilities due to the absence of reliable development and
testing. In this article, we apply the metamorphic testing technique to detect
smart contract vulnerabilities. Based on the anomalies we observed in
vulnerable smart contracts, we define five metamorphic relations to detect
abnormal gas consumption and account interaction inconsistency of the target
smart contract. Through dynamically executing transactions and checking the
final violation of metamorphic relations, we determine whether a smart contract
is vulnerable. We evaluate our approach on a benchmark of 67 manually annotated
smart contracts. The experimental results show that our approach achieves a
higher detection rate (TPR, true positive rate) with a lower misreport rate
(FDR, false discovery rate) than the other three state-of-the-art tools. These
results further suggest that metamorphic testing is a promising method for
detecting smart contract vulnerabilities.


  In the framework of dielectric theory the static non-local self-energy of an
electron near an ultra-thin polarizable layer has been calculated and applied
to study binding energies of image-states near free-standing graphene. The
corresponding series of eigenvalues and eigenfunctions have been obtained by
solving numerically the one-dimensional Schr{\"o}dinger equation.
Image-potential-state wave functions accumulate most of their probability
outside the slab. We find that a Random Phase Approximation (RPA) for the
non-local dielectric function yields a superior description for the potential
inside the slab, but a simple Fermi-Thomas theory can be used to get a
reasonable quasi-analytical approximation to the full RPA result that can be
computed very economically. Binding energies of the image-potential states
follow a pattern close to the Rydberg series for a perfect metal with the
addition of intermediate states due to the added symmetry of the potential. The
formalism only requires a minimal set of free parameters; the slab width and
the electronic density. The theoretical calculations are compared to
experimental results for work function and image-potential states obtained by
two-photon photoemission.


  Vortex or spin is an important and ubiquitous form of motions existing in
almost all scale ranges of the universe and its dynamics is still an active
research theme in the classical as well as modern physics. As a novel attempt
of such studies, here we show that a class of vortex dynamics generated by
newly defined Clebsch parametrised (CP) flows parallel to geodesics exhibits an
intriguing property that it is isomorphic to the spacetime structure itself on
which it is defined in the sense that its energy-momentum conservation equation
automatically assumes exactly the same form as the Einstein field equation.
Implications of the existence of such a model is briefly discussed from the
view point of a current hot cosmological interest on dark energy together with
elusive concept on gravitational energy radiation.


  In this article, we concern the concept of nearly Frobenius algebra, which
corresponds to most 2D-TQFT of which each cobordism admits no critical points
of index 0 or 2. We prove that any nearly Frobenius algebra over a principal
ideal domain with surjective multiplication and injective comultiplication is
indeed a Frobenius algebra. The motivation of this study mainly emanates from
the investigation of potential constructions of link homology.


  We consider the nondynamical Chern-Simons (nCS) modified gravity, which is
regarded as a parity-odd theory of massive gravity in four dimensions. We first
find polarization modes of gravitational waves for $\theta=x/\mu$ in nCS
modified gravity by using the Newman-Penrose formalism where the null complex
tetrad is necessary to specify gravitational waves. We show that in the
Newman-Penrose formalism, the number of polarization modes is one in addition
to an unspecified $\Psi_4$, implying three degrees of freedom for
$\theta=x/\mu$. This compares with two for a canonical embedding of
$\theta=t/\mu$. Also, if one introduces the Ricci tensor formalism to describe
a massive graviton arising from the nCS modified gravity, one finds one massive
mode after making second-order wave equations, which is compared to five found
from the parity-even Einstein-Weyl gravity.


  The Kerr spacetime of spinning black holes is one of the most intriguing
predictions of Einstein's theory of general relativity. The special role this
spacetime plays in the theory of gravity is encapsulated in the no-hair
theorem, which states that the Kerr metric is the only realistic black-hole
solution of the vacuum field equations. Recent and anticipated advances in the
observations of black holes throughout the electromagnetic spectrum have
secured our understanding of their basic properties while opening up new
opportunities for devising tests of the Kerr metric. In this paper, we argue
that imaging and spectroscopic observations of accreting black-holes with
current and future instruments can lead to the first direct test of the no-hair
theorem.


  ASV (automatic speaker verification) systems are intrinsically required to
reject both non-target (e.g., voice uttered by different speaker) and spoofed
(e.g., synthesised or converted) inputs. However, there is little consideration
for how ASV systems themselves should be adapted when they are expected to
encounter spoofing attacks, nor when they operate in tandem with CMs (spoofing
countermeasures), much less how both systems should be jointly optimised.
  The goal of the first SASV (spoofing-aware speaker verification) challenge, a
special sesscion in ISCA INTERSPEECH 2022, is to promote development of
integrated systems that can perform ASV and CM simultaneously.


  The data scarcity of user preferences and the cold-start problem often appear
in real-world applications and limit the recommendation accuracy of
collaborative filtering strategies. Leveraging the selections of social friends
and foes can efficiently face both problems. In this study, we propose a
strategy that performs social deep pairwise learning. Firstly, we design a
ranking loss function incorporating multiple ranking criteria based on the
choice in users, and the choice in their friends and foes to improve the
accuracy in the top-k recommendation task. We capture the nonlinear
correlations between user preferences and the social information of trust and
distrust relationships via a deep learning strategy. In each backpropagation
step, we follow a social negative sampling strategy to meet the multiple
ranking criteria of our ranking loss function. We conduct comprehensive
experiments on a benchmark dataset from Epinions, among the largest publicly
available that has been reported in the relevant literature. The experimental
results demonstrate that the proposed model beats other state-of-the art
methods, attaining an 11.49% average improvement over the most competitive
model. We show that our deep learning strategy plays an important role in
capturing the nonlinear correlations between user preferences and the social
information of trust and distrust relationships, and demonstrate the importance
of our social negative sampling strategy on the proposed model.


  In this paper, we present a topology optimization (TO) framework to enable
automated design of mechanical components while ensuring the result can be
manufactured using multi-axis machining. Although TO improves the part's
performance, the as-designed model is often geometrically too complex to be
machined and the as-manufactured model can significantly vary due to machining
constraints that are not accounted for during TO. In other words, many of the
optimized design features cannot be accessed by a machine tool without
colliding with the part (or fixtures). The subsequent post-processing to make
the part machinable with the given setup requires trial-and-error without
guarantees on preserving the optimized performance. Our proposed approach is
based on the well-established accessibility analysis formulation using
convolutions in configuration space that is extensively used in spatial
planning and robotics. We define an 'inaccessibility measure field' (IMF) over
the design domain to identify non-manufacturable features and quantify their
contribution to non-manufacturability. The IMF is used to penalize the
sensitivity field of performance objectives and constraints to prevent
formation of inaccessible regions. Unlike existing discrete formulations, our
IMF provides a continuous spatial field that is desirable for TO convergence.
Our approach applies to arbitrary geometric complexity of the part, tools, and
fixtures, and is highly parallelizable on multi-core architecture. We
demonstrate the effectiveness of our framework on benchmark and realistic
examples in 2D and 3D. We also show that it is possible to directly construct
manufacturing plans for the optimized designs based on the accessibility
information.


  Two results are proved for $\mathrm{nul} \mathbb{P}_A$, the dimension of the
kernel of the Pauli operator $\mathbb{P}_A = \bigl\{\bbf{\sigma} \cdotp
\bigl(\frac{1}{i} \bbf{\nabla} + \vec{A} \bigr) \bigr\} ^2 $ in $[L^2
(\mathbb{R}^3)]^2$: (i) for $|\vec{B}| \in L^{3/2} (\mathbb{R}^3),$ where
$\vec{B} = \mathrm{curl} \vec{A}$ is the magnetic field, $\mathrm{nul} \
\mathbb{P}_{tA} = 0$ except for a finite number of values of $t$ in any compact
subset of $(0, \infty)$; (ii) $\bigl\{\vec{B}: \mathrm{nul} \mathbb{P}_{A} = 0,
| \vec{B} | \in L^{3/2}(\mathbb{R}^3) \bigr\} $ contains an open dense subset
of $[L^{3/2}(\mathbb{R}^3)]^3$.


  Algorithmic composition is the partial or total automation of the process of
music composition by using computers. Since the 1950s, different computational
techniques related to Artificial Intelligence have been used for algorithmic
composition, including grammatical representations, probabilistic methods,
neural networks, symbolic rule-based systems, constraint programming and
evolutionary algorithms. This survey aims to be a comprehensive account of
research on algorithmic composition, presenting a thorough view of the field
for researchers in Artificial Intelligence.


  Nitrogen vacancy (NV) centers in diamond have developed into a powerful
solid-state platform for compact quantum sensors. However, high sensitivity
measurements usually come with additional constraints on the pumping intensity
of the laser and the pulse control applied. Here, we demonstrate high
sensitivity NV ensemble based magnetic field measurements with low-intensity
optical excitation. DC magnetometry methods like, e.g., continuous-wave
optically detected magnetic resonance and continuously excited Ramsey
measurements combined with lock-in detection, are compared to get an
optimization. Gradiometry is also investigated as a step towards unshielded
measurements of unknown gradients. The magnetometer demonstrates a minimum
detectable field of 0.3-0.7 pT in a 73 s measurement by further applying a flux
guide with a sensing dimension of 2 mm, corresponding to a magnetic field
sensitivity of 2.6-6 pT/Hz^0.5. Combined with our previous efforts on the
diamond AC magnetometry, the diamond magnetometer is promising to perform wide
bandwidth magnetometry with picotesla sensitivity and a cubic-millimeter
sensing volume under ambient conditions.


  Variable selection for sparse linear regression is the problem of finding,
given an m x p matrix B and a target vector y, a sparse vector x such that Bx
approximately equals y. Assuming a standard complexity hypothesis, we show that
no polynomial-time algorithm can find a k'-sparse x with ||Bx-y||^2<=h(m,p),
where k'=k*2^{log^{1-delta} p} and h(m,p)<=p^(C_1)*m^(1-C_2), where delta>0,
C_1>0,C_2>0 are arbitrary. This is true even under the promise that there is an
unknown k-sparse vector x^* satisfying Bx^*=y. We prove a similar result for a
statistical version of the problem in which the data are corrupted by noise.
  To the authors' knowledge, these are the first hardness results for sparse
regression that apply when the algorithm simultaneously has k'>k and h(m,p)>0.


  In this short review article, we consider some aspects of the spin-polaron
concept as they relate to high-Tc superconductivity. The article is intended to
be an overview in which a flavor of the research on spin polarons is given.