  Currently there exists no known way to construct the Stress-Energy Tensor
$(T_{\mu \nu})$ of the produced medium in heavy ion collisions at strong
coupling from purely theoretical grounds. In this paper, some steps are taken
in that direction. In particular, the evolution of $T_{\mu \nu}$ at strong
coupling and at high energies is being studied for early proper times $(\tau)$.
This is achieved in the context of the AdS/CFT duality by constructing the
evolution of the dual geometry in an AdS$_5$ background. Improving the earlier
works in the literature, the two incident nuclei have an impact parameter $b$
and a non-trivial transverse profile. The nuclear matter is modeled by two
shock waves corresponding to a non-zero five dimensional bulk Stress-Energy
Tensor $J_{MN}$. An analytic formula for $T_{\mu \nu}$ at small $\tau$ is
derived and is used in order to calculate the momentum anisotropy and spatial
eccentricity of the medium produced in the collision as a function of the ratio
$\frac{\tau}{b}$. The result for eccentricity at intermediate $\frac{\tau}{b}$
agrees qualitatively with the results obtained in the context of perturbation
theory and by using hydrodynamic simulations. Finally, the problem of the
negative energy density and its natural connection to the eikonal approximation
is discussed.


  Atom interferometers employing optical cavities to enhance the beam splitter
pulses promise significant advances in science and technology, notably for
future gravitational wave detectors. Long cavities, on the scale of hundreds of
meters, have been proposed in experiments aiming to observe gravitational waves
with frequencies below 1 Hz, where laser interferometers, such as LIGO, have
poor sensitivity. Alternatively, short cavities have also been proposed for
enhancing the sensitivity of more portable atom interferometers. We explore the
fundamental limitations of two-mirror cavities for atomic beam splitting, and
establish upper bounds on the temperature of the atomic ensemble as a function
of cavity length and three design parameters: the cavity g-factor, the
bandwidth, and the optical suppression factor of the first and second order
spatial modes. A lower bound to the cavity bandwidth is found which avoids
elongation of the interaction time and maximizes power enhancement. An upper
limit to cavity length is found for symmetric two-mirror cavities, restricting
the practicality of long baseline detectors. For shorter cavities, an upper
limit on the beam size was derived from the geometrical stability of the
cavity. These findings aim to aid the design of current and future
cavity-assisted atom interferometers.


  Modern nucleon-nucleon (NN) potentials, which accurately fit the
nucleon-nucleon scattering phase shifts, contain terms which break isospin
symmetry. The effects of these symmetry violating terms on the bulk properties
of nuclear matter are investigated. The predictions of the charge symmetry
breaking (CSB) terms are compared with the Nolen-Schiffer (NS) anomaly
regarding the energies of neighboring mirror nuclei. We find that, for a
quantitative explanation of the NS anomaly, it is crucial to include CSB in
partial waves with $L>0$ (besides $^1S_0$) as derived from a microscopic model
for CSB of the NN interaction.


  This paper explores the use of low-dimensional parametric representations of
neutron-star equations of state that include discontinuities caused by phase
transitions. The accuracies of optimal piecewise-analytic and spectral
representations are evaluated for equations of state having first- or
second-order phase transitions with a wide range of discontinuity sizes. These
results suggest that the piecewise-analytic representations of these non-smooth
equations of state are convergent, while the spectral representations are not.
Nevertheless, the lower-order (2 <= N_parms <= 7) spectral representations are
found to be more accurate than the piecewise-analytic representations with the
same number of parameters.


  The rapid advance of deep reinforcement learning techniques enables the
oversight of safety-critical systems through the utilization of Deep Neural
Networks (DNNs). This underscores the pressing need to promptly establish
certified safety guarantees for such DNN-controlled systems. Most of the
existing verification approaches rely on qualitative approaches, predominantly
employing reachability analysis. However, qualitative verification proves
inadequate for DNN-controlled systems as their behaviors exhibit stochastic
tendencies when operating in open and adversarial environments. In this paper,
we propose a novel framework for unifying both qualitative and quantitative
safety verification problems of DNN-controlled systems. This is achieved by
formulating the verification tasks as the synthesis of valid neural barrier
certificates (NBCs). Initially, the framework seeks to establish almost-sure
safety guarantees through qualitative verification. In cases where qualitative
verification fails, our quantitative verification method is invoked, yielding
precise lower and upper bounds on probabilistic safety across both infinite and
finite time horizons. To facilitate the synthesis of NBCs, we introduce their
$k$-inductive variants. We also devise a simulation-guided approach for
training NBCs, aiming to achieve tightness in computing precise certified lower
and upper bounds. We prototype our approach into a tool called $\textsf{UniQQ}$
and showcase its efficacy on four classic DNN-controlled systems.


  Fourier single-pixel imaging (FSI) is a data-efficient single-pixel imaging
(SPI). However, there is still a serious challenge to obtain higher imaging
quality using fewer measurements, which limits the development of real-time
SPI. In this work, a uniform-sampling foveated FSI (UFFSI) is proposed with
three features, uniform sampling, effective sampling and flexible fovea, to
achieve under-sampling high-efficiency and high-quality SPI, even in a
large-scale scene. First, by flexibly using the three proposed foveated pattern
structures, data redundancy is reduced significantly to only require high
resolution (HR) on regions of interest (ROIs), which radically reduces the need
of total data number. Next, by the non-uniform weight distribution processing,
non-uniform spatial sampling is transformed into uniform sampling, then the
fast Fourier transform is used accurately and directly to obtain under-sampling
high imaging quality with further reduced measurements. At a sampling ratio of
0.0084 referring to HR FSI with 1024*768 pixels, experimentally, by UFFSI with
255*341 cells of 89% reduction in data redundancy, the ROI has a significantly
better imaging quality to meet imaging needs. We hope this work can provide a
breakthrough for future real-time SPI.


  3D object detection is one of the most important tasks in autonomous driving
and robotics. Our research focuses on tackling low efficiency issue of
point-based methods on large-scale point clouds. Existing point-based methods
adopt farthest point sampling (FPS) strategy for downsampling, which is
computationally expensive in terms of inference time and memory consumption
when the number of point cloud increases. In order to improve efficiency, we
propose a novel Instance-Centroid Faster Point Sampling Module (IC-FPS) , which
effectively replaces the first Set Abstraction (SA) layer that is extremely
tedious. IC-FPS module is comprised of two methods, local feature diffusion
based background point filter (LFDBF) and Centroid-Instance Sampling Strategy
(CISS). LFDBF is constructed to exclude most invalid background points, while
CISS substitutes FPS strategy by fast sampling centroids and instance points.
IC-FPS module can be inserted to almost every point-based models. Extensive
experiments on multiple public benchmarks have demonstrated the superiority of
IC-FPS. On Waymo dataset, the proposed module significantly improves
performance of baseline model and accelerates inference speed by 3.8 times. For
the first time, real-time detection of point-based models in large-scale point
cloud scenario is realized.


  Over the past decade, Internet centralization and its implications for both
people and the resilience of the Internet has become a topic of active debate.
While the networking community informally agrees on the definition of
centralization, we lack a formal metric for quantifying centralization, which
limits research beyond descriptive analysis. In this work, we introduce a
statistical measure for Internet centralization, which we use to better
understand how the web is centralized across four layers of web infrastructure
(hosting providers, DNS infrastructure, TLDs, and certificate authorities) in
150~countries. Our work uncovers significant geographical variation, as well as
a complex interplay between centralization and sociopolitically driven
regionalization. We hope that our work can serve as the foundation for more
nuanced analysis to inform this important debate.


  Recently Chen et al. (2018, ApJ, 861, 58) accurately determined the volume
weighted halo velocity bias in simulations, and found that the deviation of
velocity bias from unity is much weaker than the peak model prediction. Here we
present a possible explanation of this vanishing velocity bias. The starting
point is that, halos are peaks in the low redshift {\it non-Gaussian} density
field with smoothing scale $R_{\Delta}$ (virial radius), instead of peaks in
the high redshift initial {\it Gaussian} density field with a factor of
$\mathcal{O}(\Delta^{1/3})$ larger smoothing scale. Based on the approximation
that the density field can be Gaussianized by a local and monotonic
transformation, we extend the peak model to the non-Gaussian density field and
derive the analytical expression of velocity dispersion and velocity power
spectrum of these halos. The predicted deviation of velocity bias from unity is
indeed much weaker than the previous prediction, and the agreement with the
simulation results is significantly improved.


  We explicitly analyse $O(\alpha')$ corrections to heterotic supergravity on
toroidal orbifolds and their resolutions, which play important roles in string
phenomenology as well as moduli stabilisation. Using a conformal factor ansatz
that is valid only for four dimensional geometries, we obtain a closed
expression for the $O(\alpha')$ metric corrections in the case of several
orbifold limits of K3, namely $T^4/\mathbb{Z}_n$ where $n=2,3,4,6$. However, we
find that non-standard embedding requires the inclusion of five-branes on such
orbifolds. We also numerically investigate the behaviour around orbifold fixed
points by considering the metric correction on the resolution of a
$\mathbb{C}^2/\mathbb{Z}_2$ singularity. In this case, a non-trivial conformal
factor can be obtained in non-standard embedding even without five-branes. In
the same manner, we generalise our analysis to study metric corrections on
$T^6/\mathbb{Z}_3$ and its resolution described by a complex line bundle over
$\mathbb{CP}^2$. Further prospects of utilising these $O(\alpha')$ corrected
metrics as a novel approach in obtaining realistic or semi-realistic Yukawa
couplings are discussed.


  Understanding the microscopic origin of the rheological behavior of soft
matter is a long-lasting endeavour. While early efforts concentrated mainly on
the relationship between rheology and structure, current research focuses on
the role of microscopic dynamics. We present in two companion papers a thorough
discussion of how Fourier space-based methods may be coupled to rheology to
shed light on the relationship between the microscopic dynamics and the
mechanical response of soft systems. In this first companion paper, we report a
theoretical, numerical and experimental investigation of dynamic light
scattering coupled to rheology. While in ideal solids and simple viscous fluids
the displacement field under a shear deformation is purely affine, additional
non-affine displacements arise in many situations of great interest, for
example in elastically heterogeneous materials or due to plastic
rearrangements. We show how affine and non-affine displacements can be
separately resolved by dynamic light scattering, and discuss in detail the
effect of several non-idealities in typical experiments.


  Detailed modeling of the millisecond brightness oscillations during
thermonuclear bursts from low mass X-ray binaries can provide important
information about neutron star structure. Until now the implementation of this
idea has not been entirely successful, largely because of the negligible
harmonic content in burst oscillation lightcurves. However, the recent
discovery of non-sinusoidal burst oscillation lightcurves from the accreting
millisecond pulsar XTE J1814-338 has changed this situation. We, therefore, for
the first time, make use of this opportunity to constrain neutron star
parameters. In our detailed study of the lightcurves of 22 bursts, we fit the
burst oscillation lightcurves with fully general relativistic models that
include light-bending and frame-dragging for lightcurve calculation, and
compute numerically the structure of neutron stars using realistic equations of
state. We find that for our model and parameter grid values, at the 90%
confidence level, Rc^2/GM > 4.2 for the neutron star in XTE J1814-338. We also
find that the photons from the thermonuclear flash come out through the layers
of accreted matter under conditions consistent with Thomson scattering, and
show that the secondary companion is a hydrogen burning main sequence star,
with possible bloating (probably due to X-ray heating).


  Beta-detected nuclear quadrupole resonances (\bnqr) at zero field are
observed using a beam of low energy highly polarized radioactive
\nuc{8}{Li}$^+$. The resonances were detected in SrTiO$_3$, Al$_2$O$_3$ and
Sr$_2$RuO$_4$ single crystals by monitoring the beta-decay anisotropy as a
function of a small audio frequency magnetic field. The resonances show clearly
that \nuc{8}{Li} occupies one site with non-cubic symmetry in SrTiO$_3, two in
Al$_2$O$_3$ and three sites in Sr$_2$RuO$_4$. The resonance amplitude and width
are surprisingly large compared to the values expected from transitions between
the $|\pm 2> \leftrightarrow |\pm 1>$ spin states, indicating a significant
mixing between the $|\pm m>$ quadrupolar split levels.


  We present results for Delta I=3/2 and Delta S=2 matrix elements relevant for
CP violation in K->Pi Pi decays and for the K_S-K_L mass difference in the
standard model and beyond. They were obtained with Neuberger fermions on
quenched gauge configurations generated with the Wilson plaquette action at
beta=6.0 on an 18^3x64 lattice.


  We give another derivation of quark confinement in QCD from the viewpoint of
the low-energy effective Abelian gauge theory of QCD obtained via Abelian
projection. It is based on the recently discovered
Berezinskii-Kosterlitz-Thouless phase transition in the four-dimensional
Abelian gauge theory. Moreover, we show that there exists a critical gauge
coupling constant in QCD, above which confinement set in and below which there
is no confinement. In a SU(N) gauge theory with $N_f$ flavored fermions, we
argue that this leads to a critical value of fermion flavors $N_f^c$ for the
confinement as well as the chiral symmetry breaking, which separates the
deconfinement and chiral symmetric phase. A finite-temperature deconfinement
transition is also discussed briefly.


  The nonlinear dust acoustic waves in unmagnetized dusty plasma which consists
of two temperature Boltzmann distributed ions and Boltzmann distributed
electrons in spherical dimension investigated and obtained spherical Kadomtsev
Petviashvili (SKP) equation and shown that the dust acoustic solitary wave can
exist in the SKP equation.


  The radiative electroweak symmetry breaking, the unification of
third-generation Yukawa couplings, and flavor-changing rare decay are
investigated in two types of supersymmetric SO(10) scenarios taking into
account of the effects of neutrino physics, i.e. the observed large generation
mixing and tiny mass scale. The first scenario is minimal, including
right-handed neutrinos at intermediate scale with the unification of
third-generation Yukawa couplings. Another is the case that the large mixing of
atmospheric neutrinos originates from the charged-lepton sector. Under the
SO(10)-motivated boundary conditions for supersymmetry-breaking parameters,
typical low-energy particle spectrum is discussed and the parameter space is
identified which satisfies the conditions for successful radiative electroweak
symmetry breaking and the experimental mass bounds of superparticles. In
particular, the predictions of the bottom quark mass and the b \to s gamma
branching ratio are fully analyzed. In both two scenarios, new types of
radiative electroweak symmetry breaking are achieved with the effects of
neutrino couplings. The Yukawa unification becomes compatible with the bottom
quark mass and the experimental constraints from flavor-violating rare
processes, and the hierarchical superparticle mass spectrum is obtained.


  Given $d\in \mathbb{N}$, $g\in \mathbb{N} \cup\{0\}$, and an integral vector
$\kappa=(k_1,\dots,k_n)$ such that $k_i>-d$ and $k_1+\dots+k_n=d(2g-2)$, let
$\Omega^d\mathcal{M}_{g,n}(\kappa)$ denote the moduli space of meromorphic
$d$-differentials on Riemann surfaces of genus $g$ whose zeros and poles have
orders prescribed by $\kappa$. We show that $\Omega^d\mathcal{M}_{g,n}(\kappa)$
carries a canonical volume form that is parallel with respect to its affine
complex manifold structure, and that the total volume of
$\mathbb{P}\Omega^d\mathcal{M}_{g,n}(\kappa)=\Omega^d\mathcal{M}_{g,n}/\mathbb{C}^*$
with respect to the measure induced by this volume form is finite.


  In a recent letter Machida et al. [Phys. Rev. Lett. 93, 200402 (2004)]
concluded that in a trapped gas of fermions with repulsive interactions a
superfluid phase appears around the Mott-insulator at the center of the trap.
They base their conclusion on a negative binding energy, and a large weight for
a singlet formed by particles located at opposite sides of the Mott-insulator.
We show here that the observed effects are not related to superfluidity.


  Data imbalance is a well-known issue in the field of machine learning,
attributable to the cost of data collection, the difficulty of labeling, and
the geographical distribution of the data. In computer vision, bias in data
distribution caused by image appearance remains highly unexplored. Compared to
categorical distributions using class labels, image appearance reveals complex
relationships between objects beyond what class labels provide. Clustering deep
perceptual features extracted from raw pixels gives a richer representation of
the data. This paper presents a novel method for addressing data imbalance in
machine learning. The method computes sample likelihoods based on image
appearance using deep perceptual embeddings and clustering. It then uses these
likelihoods to weigh samples differently during training with a proposed
$\textbf{Generalized Focal Loss}$ function. This loss can be easily integrated
with deep learning algorithms. Experiments validate the method's effectiveness
across autonomous driving vision datasets including KITTI and nuScenes. The
loss function improves state-of-the-art 3D object detection methods, achieving
over $200\%$ AP gains on under-represented classes (Cyclist) in the KITTI
dataset. The results demonstrate the method is generalizable, complements
existing techniques, and is particularly beneficial for smaller datasets and
rare classes. Code is available at:
https://github.com/towardsautonomy/DatasetEquity


  Results on the transverse spherocity dependence of light-flavor particle
production ($\pi$, K, p, $\phi$, ${\rm K^{*0}}$, ${\rm K}^{0}_{\rm{S}}$,
$\Lambda$, $\Xi$) at midrapidity in high-multiplicity pp collisions at
$\sqrt{s} = 13$ TeV were obtained with the ALICE apparatus. The transverse
spherocity estimator ($S_{{\rm O}}^{{\it p}_{\rm T}=1}$) categorizes events by
their azimuthal topology. Utilizing narrow selections on $S_{\text{O}}^{{\it
p}_{\rm T}=1}$, it is possible to contrast particle production in collisions
dominated by many soft initial interactions with that observed in collisions
dominated by one or more hard scatterings. Results are reported for two
multiplicity estimators covering different pseudorapidity regions. The $S_{{\rm
O}}^{{\it p}_{\rm T}=1}$ estimator is found to effectively constrain the
hardness of the events when the midrapidity ($\left | \eta \right |< 0.8$)
estimator is used. The production rates of strange particles are found to be
slightly higher for soft isotropic topologies, and severely suppressed in hard
jet-like topologies. These effects are more pronounced for hadrons with larger
mass and strangeness content, and observed when the topological selection is
done within a narrow multiplicity interval. This demonstrates that an important
aspect of the universal scaling of strangeness enhancement with final-state
multiplicity is that high-multiplicity collisions are dominated by soft,
isotropic processes. On the contrary, strangeness production in events with
jet-like processes is significantly reduced. The results presented in this
article are compared with several QCD-inspired Monte Carlo event generators.
Models that incorporate a two-component phenomenology, either through
mechanisms accounting for string density, or thermal production, are able to
describe the observed strangeness enhancement as a function of $S_{{\rm
O}}^{{\it p}_{\rm T}=1}$.


  In any bosonic lattice system, which is not dominated by local interactions
and thus "frozen" in a Mott-type state, numerical methods have to cope with the
infinite size of the corresponding Hilbert space even for finite lattice sizes.
While it is common practice to restrict the local occupation number basis to
$N_c$ lowest occupied states, the presence of a finite condensate fraction
requires the complete number basis for an exact representation of the many-body
ground state. In this work we present a novel truncation scheme to account for
contributions from higher number states. By simply adding a single
\textit{coherent-tail} state to this common truncation, we demonstrate
increased numerical accuracy and the possible increase in numerical efficiency
of this method for the Gutzwiller variational wave function and within
dynamical mean-field theory.


  This paper addresses the problem of distributed control for leader-follower
multi-agent systems under prescribed performance guarantees. Leader-follower is
meant in the sense that a group of agents with external inputs are selected as
leaders in order to drive the group of followers in a way that the entire
system can achieve consensus within certain prescribed performance transient
bounds. Under the assumption of tree graphs, a distributed control law is
proposed when the decay rate of the performance functions is within a
sufficient bound. Then, two classes of tree graphs that can have additional
followers are investigated. Finally, several simulation examples are given to
illustrate the results.


  With an increasing focus in STEM education on critical thinking skills,
science writing plays an ever more important role in curricula that stress
inquiry skills. A recently published dataset of two sets of college level lab
reports from an inquiry-based physics curriculum relies on analytic assessment
rubrics that utilize multiple dimensions, specifying subject matter knowledge
and general components of good explanations. Each analytic dimension is
assessed on a 6-point scale, to provide detailed feedback to students that can
help them improve their science writing skills. Manual assessment can be slow,
and difficult to calibrate for consistency across all students in large
classes. While much work exists on automated assessment of open-ended questions
in STEM subjects, there has been far less work on long-form writing such as lab
reports. We present an end-to-end neural architecture that has separate
verifier and assessment modules, inspired by approaches to Open Domain Question
Answering (OpenQA). VerAs first verifies whether a report contains any content
relevant to a given rubric dimension, and if so, assesses the relevant
sentences. On the lab reports, VerAs outperforms multiple baselines based on
OpenQA systems or Automated Essay Scoring (AES). VerAs also performs well on an
analytic rubric for middle school physics essays.


  In recent years, general matrix-matrix multiplication with non-regular-shaped
input matrices has been widely used in many applications like deep learning and
has drawn more and more attention. However, conventional implementations are
not suited for non-regular-shaped matrix-matrix multiplications, and few works
focus on optimizing tall-and-skinny matrix-matrix multiplication on CPUs. This
paper proposes an auto-tuning framework, AutoTSMM, to build high-performance
tall-and-skinny matrix-matrix multiplication. AutoTSMM selects the optimal
inner kernels in the install-time stage and generates an execution plan for the
pre-pack tall-and-skinny matrix-matrix multiplication in the runtime stage.
Experiments demonstrate that AutoTSMM achieves competitive performance
comparing to state-of-the-art tall-and-skinny matrix-matrix multiplication.
And, it outperforms all conventional matrix-matrix multiplication
implementations.


  Two \textit{NuSTAR} observations of the luminous X-ray pulsar LMC X-4 in
October and November 2015 captured several bright accretion flares from this
source, which has a long history of stable pulse and superorbital behavior. We
present a timing analysis of these data in which we detect a rapid pulse
turn-on in association with the accretion flares, during which the source
reaches super-Eddington luminosities. Pulsations, which are normally seen from
this source, are found to only occur for approximately one hour before and
during the bright flares. Beyond one hour before and after the flares, we find
pulsations to be weak or nonexistent, with fractional RMS amplitudes of less
than 0.05. At the onset of the flare, the pulse profiles exhibit a phase shift
of 0.25 cycles that could be associated with a change in the emission geometry.
This increase in pulse strength occurring well before the flare cannot be
explained by the propeller effect, and potentially offers a connection between
the magnetic properties of pulsars that accrete close to their Eddington limits
and ULX pulsars.


  Continual learning, also known as incremental learning or life-long learning,
stands at the forefront of deep learning and AI systems. It breaks through the
obstacle of one-way training on close sets and enables continuous adaptive
learning on open-set conditions. In the recent decade, continual learning has
been explored and applied in multiple fields especially in computer vision
covering classification, detection and segmentation tasks. Continual semantic
segmentation (CSS), of which the dense prediction peculiarity makes it a
challenging, intricate and burgeoning task. In this paper, we present a review
of CSS, committing to building a comprehensive survey on problem formulations,
primary challenges, universal datasets, neoteric theories and multifarious
applications. Concretely, we begin by elucidating the problem definitions and
primary challenges. Based on an in-depth investigation of relevant approaches,
we sort out and categorize current CSS models into two main branches including
data-replay and data-free sets. In each branch, the corresponding approaches
are similarity-based clustered and thoroughly analyzed, following qualitative
comparison and quantitative reproductions on relevant datasets. Besides, we
also introduce four CSS specialities with diverse application scenarios and
development tendencies. Furthermore, we develop a benchmark for CSS
encompassing representative references, evaluation results and reproductions,
which is available at~\url{https://github.com/YBIO/SurveyCSS}. We hope this
survey can serve as a reference-worthy and stimulating contribution to the
advancement of the life-long learning field, while also providing valuable
perspectives for related fields.


  The near-threshold 12C (0^+_2) resonance provides unique possibility for fast
helium burning in stars, as predicted by Hoyle to explain the observed
abundance of elements in the Universe. Properties of this resonance are
calculated within the framework of the alpha-cluster model whose two-body and
three-body effective potentials are tuned to describe the alpha - alpha
scattering data, the energies of the 0^+_1 and 0^+_2 states, and the
0^+_1-state root-mean-square radius. The extremely small width of the 0^+_2
state, the 0_2^+ to 0_1^+ monopole transition matrix element, and transition
radius are found in remarkable agreement with the experimental data. The
0^+_2-state structure is described as a system of three alpha-particles
oscillating between the ground-state-like configuration and the elongated chain
configuration whose probability exceeds 0.9.


  Cavity optomechanics has served as a platform for studying the interaction
between light and micromechanical motion via radiation pressure. Here we
observe such phenomena with a graphene mechanical resonator coupled to an
electromagnetic mode. We measure thermal motion and back-action cooling in a
bilayer graphene resonator coupled to a microwave on-chip cavity. We detect the
lowest flexural mode at 24 MHz down to 50 mK, corresponding to roughly
mechanical 40 quanta, representing nearly three orders of magnitude lower
phonon occupation than recorded to date with graphene resonators.


  We report on timing and spectral observations of the 2000 outburst of XTE
J1550-564 made by the Unconventional Stellar Aspect (USA) Experiment on board
the Advanced Research and Global Observation Satellite (ARGOS). We observe a
low-frequency quasi-periodic oscillation (LFQPO) with a centroid frequency that
tends to increase with increasing flux and a fractional rms amplitude which is
correlated with the hardness ratio. The evolution of the hardness ratio (4--16
keV/1--4 keV) with time and source flux is examined. The hardness-intensity
diagram (HID) shows a cyclical movement in the clockwise direction and possibly
indicates the presence of two independent accretion flows. We observe a
relationship between the USA 4--16 keV count rate and radio observations and
discuss this in the context of previously observed correlations between X-ray,
radio, optical and IR data. We examine our results in the context of models
invoking two accretion flows: a thin disk and a hot sub-Keplerian flow.


  This paper is devoted to the study of a semiclassical "black box" operator
$P$. We estimate the norm of its resolvent truncated near the trapped set by
the norm of its resolvent truncated on rings far away from the origin. For $z$
in the unphysical sheet with $- h |ln h| < Im z < 0$, we prove that this
estimate holds with a constant $h |Im z|^{-1} e^{C|Im z|/h}$. We also obtain
analogous bounds for the resonances states of $P$. These results hold without
any assumption on the trapped set neither any assumption on the multiplicity of
the resonances.


  The chemical abundances of planet-hosting stars offer a glimpse into the
composition of planet-forming environments. To further understand this
connection, we make the first ever measurement of the correlation between
planet occurrence and chemical abundances for ten different elements (C, Mg,
Al, Si, S, K, Ca, Mn, Fe, and Ni). Leveraging data from the Apache Point
Observatory Galactic Evolution Experiment (APOGEE) and Gaia to derive precise
stellar parameters ($\sigma_{R_\star}\approx2.3\%$,
$\sigma_{M_\star}\approx4.5\%$) for a sample of 1,018 Kepler Objects of
Interest, we construct a sample of well-vetted Kepler planets with precisely
measured radii ($\sigma_{R_p}\approx3.4\%$). After controlling for biases in
the Kepler detection pipeline and the selection function of the APOGEE survey,
we characterize the relationship between planet occurrence and chemical
abundance as the number density of nuclei of each element in a star's
photosphere raised to a power, $\beta$. $\beta$ varies by planet type, but is
consistent within our uncertainties across all ten elements. For hot planets
($P$ = 1-10 days), an enhancement in any element of 0.1 dex corresponds to an
increased occurrence of $\approx$20% for Super-Earths ($R_p=1-1.9R_\oplus$) and
$\approx$60% for Sub-Neptunes ($R_p=1.9-4R_\oplus$). Trends are weaker for warm
($P$ = 10-100 days) planets of all sizes and for all elements, with the
potential exception of Sub-Saturns ($R_p=4-8R_\oplus$). Finally, we conclude
this work with a caution to interpreting trends between planet occurrence and
stellar age due to degeneracies caused by Galactic chemical evolution and make
predictions for planet occurrence rates in nearby open clusters to facilitate
demographics studies of young planetary systems.


  We study the effect on the cosmic microwave background (CMB) anisotropy and
large scale structure (LSS) power spectrum of a scattering interaction between
cold dark matter and baryons. This scattering alters the CMB anisotropy and LSS
spectrum through momentum transfer between the cold dark matter particles and
the baryons. We find that current CMB observations can put an upper limit on
the scattering cross section which is comparable with or slightly stronger than
previous disk heating constraints at masses greater than 1 GeV, and much
stronger at smaller masses. When large-scale structure constraints are added to
the CMB limits, our constraint is more stringent than this previous limit at
all masses. In particular, a dark matter-baryon scattering cross section
comparable to the ``Spergel-Steinhardt'' cross section is ruled out for dark
matter mass greater than 1 GeV.


  Deep learning approaches have demonstrated remarkable progress in automatic
Chest X-ray analysis. The data-driven feature of deep models requires training
data to cover a large distribution. Therefore, it is substantial to integrate
knowledge from multiple datasets, especially for medical images. However,
learning a disease classification model with extra Chest X-ray (CXR) data is
yet challenging. Recent researches have demonstrated that performance
bottleneck exists in joint training on different CXR datasets, and few made
efforts to address the obstacle. In this paper, we argue that incorporating an
external CXR dataset leads to imperfect training data, which raises the
challenges. Specifically, the imperfect data is in two folds: domain
discrepancy, as the image appearances vary across datasets; and label
discrepancy, as different datasets are partially labeled. To this end, we
formulate the multi-label thoracic disease classification problem as weighted
independent binary tasks according to the categories. For common categories
shared across domains, we adopt task-specific adversarial training to alleviate
the feature differences. For categories existing in a single dataset, we
present uncertainty-aware temporal ensembling of model predictions to mine the
information from the missing labels further. In this way, our framework
simultaneously models and tackles the domain and label discrepancies, enabling
superior knowledge mining ability. We conduct extensive experiments on three
datasets with more than 360,000 Chest X-ray images. Our method outperforms
other competing models and sets state-of-the-art performance on the official
NIH test set with 0.8349 AUC, demonstrating its effectiveness of utilizing the
external dataset to improve the internal classification.


  This work considers the problem of decentralized online learning, where the
goal is to track the optimum of the sum of time-varying functions, distributed
across several nodes in a network. The local availability of the functions and
their gradients necessitates coordination and consensus among the nodes. We put
forth the Generalized Gradient Tracking (GGT) framework that unifies a number
of existing approaches, including the state-of-the-art ones. The performance of
the proposed GGT algorithm is theoretically analyzed using a novel semidefinite
programming-based analysis that yields the desired regret bounds under very
general conditions and without requiring the gradient boundedness assumption.
The results are applicable to the special cases of GGT, which include various
state-of-the-art algorithms as well as new dynamic versions of various
classical decentralized algorithms. To further minimize the regret, we consider
a condensed version of GGT with only four free parameters. A procedure for
offline tuning of these parameters using only the problem parameters is also
detailed. The resulting optimized GGT (oGGT) algorithm not only achieves
improved dynamic regret bounds, but also outperforms all state-of-the-art
algorithms on both synthetic and real-world datasets.


  We give an infinite family of knots that are not rationally concordant to
their reverses. More precisely, if R denotes the involution of the rational
knot concordance group QC induced by string reversal and Fix(R) denotes the
subgroup of knots fixed under R in QC, then QC/Fix(R) contains an infinite rank
subgroup. As a corollary, we show that there exists a knot K such that for
every pair of coprime integers p and q, the (p,q)-cable of K is not concordant
to the reverse of the (p,q)-cable of K.


  We present ten different characterizations of functions satisfying a weak
reverse H\"older inequality on an open subset of a metric measure space with a
doubling measure. Among others, we describe these functions as a class of weak
$A_\infty$ weights, which is a generalization of Muckenhoupt weights that
allows for nondoubling weights. Although our main results are modeled after
conditions that hold true for Muckenhoupt weights, we also discuss two
conditions for Muckenhoupt $A_\infty$ weights that fail to hold for weak
$A_\infty$ weights.


  The sensitivity parameter is widely used for quantifying fine tuning.
However, examples show it fails to give correct results under certain
circumstances. We argue that the problems of the sensitivity parameter are
almost identical to the consequences we have to solved if the time-varying fine
structure constant is proved to be true. The high sensitivity of the energy
scale parameter (\Lambda) to the dimensionless coupling constant plays an
important role in these problems. It affects the reliability of the sensitivity
parameter via mechanisms such as dynamical symmetry breaking, chiral symmetry
breaking etc. The reliability of the sensitivity parameter can be improved if
it is used properly.


  We develop a biparameter theory for matrix weights and provide various
biparameter matrix-weighted bounds for Journ\'e operators as well as other
central operators under the assumption of the product matrix Muckenhoupt
condition. In particular, we provide a complete theory for biparameter Journ\'e
operator bounds on matrix-weighted $L^2$ spaces. We also achieve bounds in the
general case of matrix-weighted $L^p$ spaces, for $1 < p < \infty$ for
paraproduct-free Journ\'e operators. Finally, we expose an open problem
involving a matrix-weighted Fefferman--Stein inequality, on which our methods
rely in the general setting of matrix-weighted bounds for arbitrary Journ\'e
operators and $p \neq 2.$


  We consider the feedback stabilization of Rabi oscillations in a
superconducting qubit which is coupled to a microwave readout cavity. The
signal is readout by homodyne detection of the in-phase quadrature amplitude of
the weak measurement output. By multiplying the time-delayed Rabi reference,
one can extract the signal, with maximum signal-to-noise ratio, from the noise.
We further track and stabilize the Rabi oscillations by using Lyapunov feedback
control to properly adjust the input Rabi drives. Theoretical and simulation
results illustrate the effectiveness of the proposed control law.


  In this paper, we investigate the impact of spatial, temporal and amplitude
resolution (STAR) on the perceptual quality of a compressed video. Subjective
quality tests were carried out on a mobile device. Seven source sequences are
included in the tests and for each source sequence we have 27 test
configurations generated by JSVM encoder (3 QP levels, 3 spatial resolutions,
and 3 temporal resolutions), resulting a total of 189 processed video sequences
(PVSs). Videos coded at different spatial resolutions are displayed at the full
screen size of the mobile platform. Subjective data reveal that the impact of
spatial resolution (SR), temporal resolution (TR) and quantization stepsize
(QS) can each be captured by a function with a single content-dependent
parameter. The joint impact of SR, TR and QS can be accurately modeled by the
product of these three functions with only three parameters. We further find
that the quality decay rates with SR and QS, respectively are independent of
TR, and likewise, the decay rate with TR is independent of SR and QS,
respectively. However, there is a significant interaction between the effects
of SR and QS. The overall quality model is further validated on five other
datasets with very high accuracy. The complete model correlates well with the
subjective ratings with a Pearson Correlation Coefficient (PCC) of 0.991.


  We present a feasibility-driven teleoperation framework designed to generate
humanoid multi-contact maneuvers for use in unstructured environments. Our
framework is designed for motions with arbitrary contact modes and postures.
The operator configures a pre-execution preview robot through contact points
and kinematic tasks. A fast estimation of the preview robot's quasi-static
feasibility is performed by checking contact stability and collisions along an
interpolated trajectory. A visualization of Center of Mass (CoM) stability
margin, based on friction and actuation constraints, is displayed and can be
previewed if the operator chooses to add or remove contacts. Contact points can
be placed anywhere on a mesh approximation of the robot surface, enabling
motions with knee or forearm contacts. We demonstrate our approach in
simulation and hardware on a NASA Valkyrie humanoid, focusing on multi-contact
trajectories which are challenging to generate autonomously or through
alternative teleoperation approaches.


  We extend the formalism of integrable operators a' la
Its-Izergin-Korepin-Slavnov to matrix-valued convolution operators on a
semi-infinite interval and to matrix integral operators with a kernel of the
form E_1^T(x) E_2(y)/(x+y) thus proving that their resolvent operators can be
expressed in terms of solutions of some specific Riemann-Hilbert problems. We
also describe some applications, mainly to a noncommutative version of
Painleve' II (recently introduced by Retakh and Rubtsov), a related
noncommutative equation of Painleve' type. We construct a particular family of
solutions of the noncommutative Painleve' II that are pole-free (for real
values of the variables) and hence analogous to the Hastings-McLeod solution of
(commutative) Painleve' II. Such a solution plays the same role as its
commutative counterpart relative to the Tracy-Widom theorem, but for the
computation of the Fredholm determinant of a matrix version of the Airy kernel.


  The nature of X-shaped sources is a matter of considerable debate: it has
even been proposed that they provide evidence for black hole mergers$ / $spin
reorientation, and therefore constrain the rate of strong gravitational wave
events (Merritt & Ekers 2002). Based on morphological and spectral
characteristics of these sources, currently a strong contender to explain the
nature of these sources is the `alternative' model of Lal & Rao (2007), in
which these sources consist of two pairs of jets, which are associated with two
unresolved AGNs. Detailed morphological and spectral results on
milliarcsecond-scales (mas) provide a crucial test of this model, and hence
these sources are excellent candidates to study on mas; {\it i.e.}, to detect
he presence/absence of double nuclei/AGNs, signs of helical/disrupted jets,
thereby, to investigate spatially resolved/unresolved binary AGN systems and
providing clues to understanding the physics of merging of AGNs on mas. We
conducted a systematic study of a large sample of known X-shaped, comparison FR
II radio galaxies, and newly discovered X-shaped candidate sources using Giant
Metrewave Radio Telescope and Very Large Array at several radio frequencies. In
our new observations of `comparison' FR II radio galaxies we find that almost
all of our targets show standard spectral steepening as a function of distance
from the hotspot. However, one source, 3C 321, has a low-surface-brightness
extension that shows a flatter spectral index than the high-surface-brightness
hotspots$ / $lobes, as found in `known' X-shaped sources.


  In this paper, we study the Gauss-Newton method for a special class of
systems of nonlinear equation. Under the hypothesis that the derivative of the
function under consideration satisfies a majorant condition, semi-local
convergence analysis is presented. In this analysis the conditions and proof of
convergence are simplified by using a simple majorant condition to define
regions where the Gauss-Newton sequence is "well behaved". Moreover, special
cases of the general theory are presented as applications.


  Coupling-based normalizing flows (e.g. RealNVP) are a popular family of
normalizing flow architectures that work surprisingly well in practice. This
calls for theoretical understanding. Existing work shows that such flows weakly
converge to arbitrary data distributions. However, they make no statement about
the stricter convergence criterion used in practice, the maximum likelihood
loss. For the first time, we make a quantitative statement about this kind of
convergence: We prove that all coupling-based normalizing flows perform
whitening of the data distribution (i.e. diagonalize the covariance matrix) and
derive corresponding convergence bounds that show a linear convergence rate in
the depth of the flow. Numerical experiments demonstrate the implications of
our theory and point at open questions.


  We found a very likely counterpart to the recently discovered hard X-ray
source IGR J2018+4043 in the multi-wavelength observations of the source field.
The source, originally discovered in the 20-40 keV band, is now confidently
detected also in the 40-80 keV band, with a flux of (1.4 +/- 0.4) x 10(-11) erg
cm(-2) s(-1). A 5 ks Swift observation of the IGR J2018+4043 field revealed a
hard point-like source with the observed 0.5-10 keV flux of 3.4(+0.7)(-0.8) x
10(-12) erg cm(-2) s(-1) (90% confidence level) at alpha = 20h18m38.55s, delta
= +40d41m00.4s (with a 4.2" uncertainty). The combined Swift-INTEGRAL spectrum
can be described by an absorbed power-law model with photon index gamma = 1.3
+/- 0.2 and N_H = 6.1(+3.2)(-2.2) x 10(22) cm(-2). In archival optical and
infrared data we found a slightly extended and highly absorbed object at the
Swift source position. There is also an extended VLA 1.4 GHz source peaked at a
beam-width distance from the optical and X-ray positions. The observed
morphology and multiwavelength spectra of IGR J2018+4043 are consistent with
those expected for an obscured accreting object, i.e. an AGN or a Galactic
X-ray binary. The identification suggests possible connection of IGR J2018+4043
to the bright gamma-ray source GEV J2020+4023 (3EG J2020+4017) detected by COS
B and CGRO EGRET in the gamma-Cygni SNR field.


  The NP-complete Permutation Pattern Matching problem asks whether a
$k$-permutation $P$ is contained in a $n$-permutation $T$ as a pattern. This is
the case if there exists an order-preserving embedding of $P$ into $T$. In this
paper, we present a fixed-parameter algorithm solving this problem with a
worst-case runtime of $\mathcal{O}(1.79^{\mathsf{run}(T)}\cdot n\cdot k)$,
where $\mathsf{run}(T)$ denotes the number of alternating runs of $T$. This
algorithm is particularly well-suited for instances where $T$ has few runs,
i.e., few ups and downs. Moreover, since $\mathsf{run}(T)<n$, this can be seen
as a $\mathcal{O}(1.79^{n}\cdot n\cdot k)$ algorithm which is the first to beat
the exponential $2^n$ runtime of brute-force search. Furthermore, we prove that
under standard complexity theoretic assumptions such a fixed-parameter
tractability result is not possible for $\mathsf{run}(P)$.


  Motivated by the models of geometry with discrete spaces as additional
dimensions we investigate the stability of cosmological solutions in models
with two metrics of the Friedmann-Lema\^itre-Robertson-Walker type. We propose
an effective gravity action that couples the two metrics in a similar manner as
in the bimetric theory of gravity and analyse whether standard solutions with
identical metrics are stable under small perturbations.


  How did star formation begin in the universe? Some of the questions addressed
at this first meeting on "The First Stars" are summarized here from a
theoretical perspective, and some brief comments are made on what we may have
learned so far.


  Microarchitectural attacks have become more threatening the hardware security
than before with the increasing diversity of attacks such as Spectre and
Meltdown. Vendor patches cannot keep up with the pace of the new threats, which
makes the dynamic anomaly detection tools more evident than before.
Unfortunately, previous studies utilize hardware performance counters that lead
to high performance overhead and profile limited number of microarchitectural
attacks due to the small number of counters that can be profiled concurrently.
This yields those detection tools inefficient in real-world scenarios.
  In this study, we introduce MAD-EN dynamic detection tool that leverages
system-wide energy consumption traces collected from a generic Intel RAPL tool
to detect ongoing anomalies in a system. In our experiments, we show that
CNN-based MAD-EN can detect 10 different microarchitectural attacks with a
total of 15 variants with the highest F1 score of 0.999, which makes our tool
the most generic attack detection tool so far. Moreover, individual attacks can
be distinguished with a 98% accuracy after an anomaly is detected in a system.
We demonstrate that MAD-EN introduces 69.3% less performance overhead compared
to performance counter-based detection mechanisms.


  In most supersymmetric models the stability of the proton is ensured by
invoking R-parity. A necessary ingredient to enforce R-parity is the
possibility of distinguishing the lepton superfields from the Higgs ones. This
is generally achieved either by assuming different charges under some matter
parity, or by assigning the superfields to different representations of a
unified gauge group. We want to put forward the idea that the replica of the
fermion generations, which constitute an intrinsic difference between the
fermions and the Higgs superfields, can give a clue to understand R-parity as
an accidental symmetry. More ambitiously, we suggest a possible relation
between proton stability and the actual number of fermion generations. We carry
out our investigation in the framework of non-Abelian horizontal gauge
symmetries. We identify $SU(4)_H$ as the only acceptable horizontal gauge group
whichcan naturally ensure the absence of R parity violating operators, without
conflicting with other theoretical and phenomenological constraints. We analyze
a version of the supersymmetric standard model equipped with a gauged
horizontal $SU(4)_H$, in which R-parity is accidental. The model predicts four
families of fermions, it allows for the dynamical generation of a realistic
hierarchy of fermion masseswithout any ad hoc choice of small Yukawa couplings,
it ensures in a natural way the heaviness of all the fourth family fermions
(including the neutrino) and it predicts a {\it lower} limit for the
$\tau$-neutrino mass of a few eV. The scale of the breaking of the horizontal
symmetry can be constrained rather precisely in a narrow window around $\sim
10^{11}$ GeV. Some interesting astrophysical and cosmological implications of
the model are addressed as well.


  The hint for BSM CP violation in B-> phi K_S is now more confused, but the
phi K^* polarization anomaly seems real. We present a picture based on a
flavor-mixed, right-handed "strange-beauty" squark \widetilde{sb}_{1R}, driven
light by the large \tilde s_R-\tilde b_R squark flavor mixing, which carries a
unique new CP phase. The \widetilde{sb}_{1R} could impact on S_{phi K_S} (or
alternatively S_{eta^\prime K_S}), B_s mixing, \sin2Phi_{B_s},
S_{K_S\pi^0\gamma} and other b-> s transitions, and can be searched for
directly at the Tevatron. Whether SM or BSM, a heuristic model is given where
transverse phi K^* polarization descends from the gluon helicity of on-shell
b-> sg.


  This paper presents a signal-free intersection control system for CAVs by
combination of a pixel reservation algorithm and a Deep Reinforcement Learning
(DRL) decision-making logic, followed by a corridor-level impact assessment of
the proposed model. The pixel reservation algorithm detects potential colliding
maneuvers and the DRL logic optimizes vehicles' movements to avoid collision
and minimize the overall delay at the intersection. The proposed control system
is called Decentralized Sparse Coordination System (DSCLS) since each vehicle
has its own control logic and interacts with other vehicles in coordinated
states only. Due to the chain impact of taking random actions in the DRL's
training course, the trained model can deal with unprecedented volume
conditions, which poses the main challenge in intersection management. The
performance of the developed model is compared with conventional and CAV-based
control systems, including fixed traffic lights, actuated traffic lights, and
the Longest Queue First (LQF) control system under three volume regimes in a
corridor of four intersections in VISSIM software. The simulation result
revealed that the proposed model reduces delay by 50%, 29%, and 23% in
moderate, high, and extreme volume regimes compared to the other CAV-based
control system. Improvements in travel time, fuel consumption, emission, and
Surrogate Safety Measures (SSM) are also noticeable.


  Given a finite group $G$ and a faithful irreducible $FG$-module $V$ where $F$
has prime order, does $G$ have a regular orbit on $V$? This problem is
equivalent to determining which primitive permutation groups of affine type
have a base of size 2. Let $G$ be a covering group of an almost simple group
whose socle $T$ is sporadic, and let $V$ be a faithful irreducible $FG$-module
where $F$ has prime order dividing $|G|$. We classify the pairs $(G,V)$ for
which $G$ has no regular orbit on $V$, and determine the minimal base size of
$G$ in its action on $V$. To obtain this classification, for each non-trivial
$g\in G/Z(G)$, we compute the minimal number of $T$-conjugates of $g$
generating $\langle T,g\rangle$.


  We present Finite Volume methods for diffusion equations on generic meshes,
that received important coverage in the last decade or so. After introducing
the main ideas and construction principles of the methods, we review some
literature results, focusing on two important properties of schemes (discrete
versions of well-known properties of the continuous equation): coercivity and
minimum-maximum principles. Coercivity ensures the stability of the method as
well as its convergence under assumptions compatible with real-world
applications, whereas minimum-maximum principles are crucial in case of strong
anisotropy to obtain physically meaningful approximate solutions.


  We develop a definition of rate-induced tipping (R-tipping) in discrete-time
dynamical systems (maps) and prove results giving conditions under which
R-tipping will or will not happen. Specifically, we study (possibly
non-invertible) maps with a time-varying parameter subject to a parameter
shift. We show that each stable path has a unique associated solution (a local
pullback attractor) which stays near the path for all negative time. When the
parameter changes slowly, this local pullback attractor stays near the path for
all time, but if the parameter changes quickly, the local pullback attractor
may move away from the path in positive time; this is the phenomenon of
R-tipping. We demonstrate that forward basin stability is an insufficient
condition to prevent R-tipping in maps of any dimension but that forward
inflowing stability is sufficient. Furthermore, we show that R-tipping will
happen when there is a certain kind of forward basin instability, and we prove
precisely what happens to the local pullback attractor as the rate of the
parameter change approaches infinity. We then highlight the differences between
discrete- and continuous-time systems by showing that when a map is obtained by
discretizing a flow, the pullback attractors for the map and flow can have
dramatically different behavior; there may be R-tipping in one system but not
in the other. We finish by applying our results to demonstrate R-tipping in the
2-dimensional Ikeda map.


  In supersymmetric gluodynamics (N=1 super-Yang-Mills theory) we show that the
spectral functions induced by the nonchiral operator Tr G_{\alpha\beta}
\bar\lambda^2 are fully degenerate in the J^{PC}=1^{\pm -} channels. The above
operator is related to N=1/2 generalization of SUSY. Using the planar
equivalence, this translates into the statement of degeneracy between the
mesons produced from the vacuum by the operators (\bar \Psi \vec E\Psi + i\bar
\Psi \vec B \gamma^5\Psi) and (\bar \Psi \vec B\Psi - i\bar \Psi \vec E
\gamma^5\Psi) in one-flavor QCD, up to 1/N corrections. Here \Psi is the quark
field, and \vec E ,\vec B are chromoelectric/chromomagnetic fields,
respectively.


  Mixed mode (I+III) loading induces segmented crack front \'echelon structures
connected by steps. We study this instability in a highly deformable,
strain-hardening material. We find that \'echelons develop beyond a finite,
size-independent mode mixity threshold, markedly growing with energy release
rate. They appear via nucleation of localized helical front distortions, and
their emergence is the continuation of the mode I cross-hatching instability of
gels and rubbers, shifted by the biasing effect of shear. This result, at odds
with the direct bifurcation predicted by linear elastic fracture mechanics, can
be assigned to the controlling role of elastic nonlinearity.


  In this paper, we give bounds on the dichromatic number $\vec{\chi}(\Sigma)$
of a surface $\Sigma$, which is the maximum dichromatic number of an oriented
graph embeddable on $\Sigma$. We determine the asymptotic behaviour of
$\vec{\chi}(\Sigma)$ by showing that there exist constants $a_1$ and $a_2$ such
that, $a_1\frac{\sqrt{-c}}{\log(-c)} \leq \vec{\chi}(\Sigma) \leq a_2
\frac{\sqrt{-c}}{\log(-c)} $ for every surface $\Sigma$ with Euler
characteristic $c\leq -2$. We then give more explicit bounds for some surfaces
with high Euler characteristic. In particular, we show that the dichromatic
numbers of the projective plane $\mathbb{N}_1$, the Klein bottle
$\mathbb{N}_2$, the torus $\mathbb{S}_1$, and Dyck's surface $\mathbb{N}_3$ are
all equal to $3$, and that the dichromatic numbers of the $5$-torus
$\mathbb{S}_5$ and the $10$-cross surface $\mathbb{N}_{10}$ are equal to $4$.
We also consider the complexity of deciding whether a given digraph or oriented
graph embeddable on a fixed surface is $k$-dicolourable. In particular, we show
that for any fixed surface, deciding whether a digraph embeddable on this
surface is $2$-dicolourable is NP-complete, and that deciding whether a planar
oriented graph is $2$-dicolourable is NP-complete unless all planar oriented
graphs are $2$-dicolourable (which was conjectured by Neumann-Lara).


  Cybercrime is increasing at a faster pace and sometimes causes billions of
dollars of business- losses so investigating attackers after commitment is of
utmost importance and become one of the main concerns of network managers.
Network forensics as the process of Collecting, identifying, extracting and
analyzing data and systematically monitoring traffic of network is one of the
main requirements in detection and tracking of criminals. In this paper, we
propose an architecture for network forensic system. Our proposed architecture
consists of five main components: collection and indexing, database management,
analysis component, SOC communication component and the database. The main
difference between our proposed architecture and other systems is in analysis
component. This component is composed of four parts: Analysis and investigation
subsystem, Reporting subsystem, Alert and visualization subsystem and the
malware analysis subsystem. The most important differentiating factors of the
proposed system with existing systems are: clustering and ranking of malware,
dynamic analysis of malware, collecting and analysis of network flows and
anomalous behaviour analysis.


  We consider the abstract relational reasoning task, which is commonly used as
an intelligence test. Since some patterns have spatial rationales, while others
are only semantic, we propose a multi-scale architecture that processes each
query in multiple resolutions. We show that indeed different rules are solved
by different resolutions and a combined multi-scale approach outperforms the
existing state of the art in this task on all benchmarks by 5-54%. The success
of our method is shown to arise from multiple novelties. First, it searches for
relational patterns in multiple resolutions, which allows it to readily detect
visual relations, such as location, in higher resolution, while allowing the
lower resolution module to focus on semantic relations, such as shape type.
Second, we optimize the reasoning network of each resolution proportionally to
its performance, hereby we motivate each resolution to specialize on the rules
for which it performs better than the others and ignore cases that are already
solved by the other resolutions. Third, we propose a new way to pool
information along the rows and the columns of the illustration-grid of the
query. Our work also analyses the existing benchmarks, demonstrating that the
RAVEN dataset selects the negative examples in a way that is easily exploited.
We, therefore, propose a modified version of the RAVEN dataset, named
RAVEN-FAIR. Our code and pretrained models are available at
https://github.com/yanivbenny/MRNet.


  We use deep reinforcement learning (RL) to optimize a weapons to target
assignment (WTA) policy for multi-vehicle hypersonic strike against multiple
targets. The objective is to maximize the total value of destroyed targets in
each episode. Each randomly generated episode varies the number and initial
conditions of the hypersonic strike weapons (HSW) and targets, the value
distribution of the targets, and the probability of a HSW being intercepted. We
compare the performance of this WTA policy to that of a benchmark WTA policy
derived using non-linear integer programming (NLIP), and find that the RL WTA
policy gives near optimal performance with a 1000X speedup in computation time,
allowing real time operation that facilitates autonomous decision making in the
mission end game.


  In decentralized decision-making problems, communicating agents choose their
actions based on locally available information and knowledge about decision
rules or strategies of other agents. In this work, we consider a strategic
communication game between an informed encoder and two decoders communicating
via a Gray-Wyner network. All three agents are assumed to be rational and
endowed with distinct objectives captured by non-aligned cost functions. The
encoder selects and announces beforehand the compression scheme to be
implemented. Then, it transmits three signals: a public signal, and a private
signal to each decoder inducing a Bayesian game among the decoders. Our goal is
to characterize the encoder's minimal long-run cost function subject to the
optimal compression scheme that satisfies both decoders incentives constraints.


  A recent LHCb measurement of the CP violating flavour specific asymmetry in
$B_s^0$ decays, \asls, is presented. This measurement is based on a data sample
corresponding to an integrated luminosity of 1 fb$^{-1}$ of $pp$ collisions at
$\sqrt{s}$ $=$ 7 TeV, collected during the 2011 run of the LHC. The result is
$\asls = (-0.24 \pm 0.54_{\rm stat} \pm 0.33_{\rm syst})\times 10^{-2}$, which
is the most precise measurement of this quantity to date and agrees with the
Standard Model prediction.}


  This paper investigates the benefits of Amplify-and-Forward (AF) relaying in
the setup of multi-antenna wireless networks. The concept of Random Sequential
(RS) relaying is previously introduced in the literature and showed that it
achieves the maximum diversity gain in a general multi-antenna network. Here,
we show that random unitary matrix multiplication at the relay nodes empowers
the RS scheme to achieve a better Diversity-Multiplexing Tradeoff (DMT) as
compared to the traditional AF relaying. First, we study the case of a
multi-antenna full-duplex single-relay two-hop network, for which we show that
the RS achieves the optimum DMT. Applying this result, we derive a new
achievable DMT for the case of multi-antenna half-duplex parallel relay
network. Interestingly, it turns out that the DMT of the RS scheme is optimum
for the case of multi-antenna two parallel non-interfering half-duplex relays.
Next, we show that random unitary matrix multiplication also improves the DMT
of the Non-Orthogonal AF relaying scheme in the case of a multi-antenna single
relay channel. Finally, we study the general case of multi-antenna full-duplex
relay networks and derive a new lower-bound on its DMT using the RS scheme.


  The generation of super-high energetic electrons influenced by pre-plasma at
relativistic intensity laser-matter interaction is studied in a one-dimensional
slab approximation with particle-in-cell simulations. Different pre-plasma
scale-lengths of $1\ \mu\text{m}$, $5\ \mu\text{m}$, $10\ \mu\text{m}$ and $15\
\mu\text{m}$ are considered, showing an increase in both particle number and
cut-off kinetic energy of electrons with the increase of pre-plasma
scale-length, and the cut-off kinetic energy greatly exceeding the
corresponding laser ponderomotive energy. A two-stage electron acceleration
model is proposed to explain the underlying physics. The first stage is
attributed to the synergetic acceleration by longitudinal electric field and
laser pulse, with its efficiency depending on the pre-plasma scale-length.
These electrons pre-accelerated in the first stage could build up an intense
electrostatic potential barrier with its maximal value several times as large
of the initial electron kinetic energy. Part of energetic electrons could be
further accelerated by the reflection off the electrostatic potential barrier,
with their finial kinetic energies significantly higher than the values
pre-accelerated in the first stage.


  Experimental detection of entanglement in superconducting qubits has been
mostly limited, for more than two qubits, to witness-based and related
approaches that can certify the presence of some entanglement, but not
rigorously quantify how much. Here we measure the entanglement of three- and
four-qubit GHZ and linear cluster states prepared on the 16-qubit IBM
Rueschlikon (ibmqx5) chip, by estimating their entanglement monotones. GHZ and
cluster states not only have wide application in quantum computing, but also
have the convenient property of having similar state preparation circuits and
fidelities, allowing for a meaningful comparison of their degree of
entanglement. We also measure the decay of the monotones with time, and find in
the GHZ case that they actually oscillate, which we interpret as a drift in the
relative phase between the $|0\rangle^{\otimes n}$ and $|1\rangle^{\otimes n}$
components, but not an oscillation in the actual entanglement. After
experimentally correcting for this drift with virtual Z rotations we find that
the GHZ states appear to be considerably more robust than cluster states,
exhibiting higher fidelity and entanglement at later times. Our results
contribute to the quantification and understanding of the strength and
robustness of multi-qubit entanglement in the noisy environment of a
superconducting quantum computer.


  We present a software package, IDA, which can easily handle two-dimensional
spectroscopy data. IDA has been written in IDL and offers a window-based
interface. The available tools can visualize a recovered image from spectra at
any desired wavelength interval, obtain velocity fields, velocity dispersion
distributions, etc.


  I discuss the effects of fermionic condensation in systems of constant
density of states. I show that the condensation leads to a correction of the
chemical potential and of the Fermi distribution in canonical Fermi systems at
low temperatures. This implies that the canonical and grandcanonical ensembles
are not equivalent even for Fermi systems.


  We consider the classical problem of determining the largest possible
cardinality of a minimal presentation of a numerical monoid with given
embedding dimension and multiplicity. Very few values of this cardinality are
known. In addressing this problem, we apply tools from Hilbert functions and
free resolutions of artinian standard graded algebras. This approach allows us
to solve the problem in many cases and, at the same time, identify subtle
difficulties in the remaining cases. As a by-product of our analysis, we deduce
results for the corresponding problem for the type of a numerical monoid.


  The cloud-based solutions are becoming inefficient due to considerably large
time delays, high power consumption, security and privacy concerns caused by
billions of connected wireless devices and typically zillions bytes of data
they produce at the network edge. A blend of edge computing and Artificial
Intelligence (AI) techniques could optimally shift the resourceful computation
servers closer to the network edge, which provides the support for advanced AI
applications (e.g., video/audio surveillance and personal recommendation
system) by enabling intelligent decision making on computing at the point of
data generation as and when it is needed, and distributed Machine Learning (ML)
with its potential to avoid the transmission of large dataset and possible
compromise of privacy that may exist in cloud-based centralized learning.
Therefore, AI is envisioned to become native and ubiquitous in future
communication and networking systems. In this paper, we conduct a comprehensive
overview of recent advances in distributed intelligence in wireless networks
under the umbrella of native-AI wireless networks, with a focus on the basic
concepts of native-AI wireless networks, on the AI-enabled edge computing, on
the design of distributed learning architectures for heterogeneous networks, on
the communication-efficient technologies to support distributed learning, and
on the AI-empowered end-to-end communications. We highlight the advantages of
hybrid distributed learning architectures compared to the state-of-art
distributed learning techniques. We summarize the challenges of existing
research contributions in distributed intelligence in wireless networks and
identify the potential future opportunities.


  In this paper, we propose a framework of building knowledgeable robot control
in the scope of smart human-robot interaction, by empowering a basic
uncalibrated visual servoing controller with contextual knowledge through the
joint usage of event knowledge graphs (EKGs) and large-scale pretrained
vision-language models (VLMs). The framework is expanded in twofold: first, we
interpret low-level image geometry as high-level concepts, allowing us to
prompt VLMs and to select geometric features of points and lines for motor
control skills; then, we create an event knowledge graph (EKG) to conceptualize
a robot manipulation task of interest, where the main body of the EKG is
characterized by an executable behavior tree, and the leaves by semantic
concepts relevant to the manipulation context. We demonstrate, in an
uncalibrated environment with real robot trials, that our method lowers the
reliance of human annotation during task interfacing, allows the robot to
perform activities of daily living more easily by treating low-level
geometric-based motor control skills as high-level concepts, and is beneficial
in building cognitive thinking for smart robot applications.


  Vibrational energy harvesters capture mechanical energy from ambient
vibrations and convert the mechanical energy into electrical energy to power
wireless electronic systems. Challenges exist in the process of capturing
mechanical energy from ambient vibrations. For example, resonant harvesters may
be used to improve power output near their resonance, but their narrow
bandwidth makes them less suitable for applications with varying vibrational
frequencies. Higher operating frequencies can increase harvesters power output,
but many vibrational sources are characterized by lower frequencies, such as
human motions. This paper provides a thorough review of state of the art energy
harvesters based on various energy sources such as solar, thermal,
electromagnetic and mechanical energy, as well as smart materials including
piezoelectric materials and carbon nanotubes. The paper will then focus on
vibrational energy harvesters to review harvesters using typical transduction
mechanisms and various techniques to address the challenges in capturing
mechanical energy and delivering it to the transducers.


  A method to reconstruct sleptons in cascade-decays at the FLC is presented.
It is shown that experimental mass-resolutions as low as 8.7 MeV/c^2 are
attainable.


  We introduce and investigate a new notion of resilience in graph spanners.
Let $S$ be a spanner of a graph $G$. Roughly speaking, we say that a spanner
$S$ is resilient if all its point-to-point distances are resilient to edge
failures. Namely, whenever any edge in $G$ fails, then as a consequence of this
failure all distances do not degrade in $S$ substantially more than in $G$
(i.e., the relative distance increases in $S$ are very close to those in the
underlying graph $G$). In this paper we show that sparse resilient spanners
exist, and that they can be computed efficiently.


  Humans make extensive use of vision and touch as complementary senses, with
vision providing global information about the scene and touch measuring local
information during manipulation without suffering from occlusions. While prior
work demonstrates the efficacy of tactile sensing for precise manipulation of
deformables, they typically rely on supervised, human-labeled datasets. We
propose Self-Supervised Visuo-Tactile Pretraining (SSVTP), a framework for
learning multi-task visuo-tactile representations in a self-supervised manner
through cross-modal supervision. We design a mechanism that enables a robot to
autonomously collect precisely spatially-aligned visual and tactile image
pairs, then train visual and tactile encoders to embed these pairs into a
shared latent space using cross-modal contrastive loss. We apply this latent
space to downstream perception and control of deformable garments on flat
surfaces, and evaluate the flexibility of the learned representations without
fine-tuning on 5 tasks: feature classification, contact localization, anomaly
detection, feature search from a visual query (e.g., garment feature
localization under occlusion), and edge following along cloth edges. The
pretrained representations achieve a 73-100% success rate on these 5 tasks.


  We study the dynamical evolution of idealised stellar systems by averaging
results from many $N$-body simulations, each having modest numbers of stars.
For isolated systems with stars of uniform mass, we discuss aspects of
evolution up to the point of core collapse: relaxation and its $N$-dependence,
the evolution of the density profile, the development of the velocity
dispersion and anisotropy, and the rate of stellar escape.


  We discuss the calculation of soft supersymmetry breaking terms in type IIB
string theoretic models in the Large Volume Scenario (LVS). The suppression of
FCNC gives a lower bound on the size of the compactification volume. This leads
to soft terms which are strongly suppressed relative to the gravitino mass so
that the dominant contribution to the gaugino masses comes from the Weyl
anomaly. The other soft terms are essentially generated by the renormalization
group running from the string scale to the TeV scale.


  Ranking has always been one of the top concerns in information retrieval
researches. For decades, the lexical matching signal has dominated the ad-hoc
retrieval process, but solely using this signal in retrieval may cause the
vocabulary mismatch problem. In recent years, with the development of
representation learning techniques, many researchers turn to Dense Retrieval
(DR) models for better ranking performance. Although several existing DR models
have already obtained promising results, their performance improvement heavily
relies on the sampling of training examples. Many effective sampling strategies
are not efficient enough for practical usage, and for most of them, there still
lacks theoretical analysis in how and why performance improvement happens. To
shed light on these research questions, we theoretically investigate different
training strategies for DR models and try to explain why hard negative sampling
performs better than random sampling. Through the analysis, we also find that
there are many potential risks in static hard negative sampling, which is
employed by many existing training methods. Therefore, we propose two training
strategies named a Stable Training Algorithm for dense Retrieval (STAR) and a
query-side training Algorithm for Directly Optimizing Ranking pErformance
(ADORE), respectively. STAR improves the stability of DR training process by
introducing random negatives. ADORE replaces the widely-adopted static hard
negative sampling method with a dynamic one to directly optimize the ranking
performance. Experimental results on two publicly available retrieval benchmark
datasets show that either strategy gains significant improvements over existing
competitive baselines and a combination of them leads to the best performance.


  We consider information update systems on a gossip network, which consists of
a single source and $n$ receiver nodes. The source encrypts the information
into $n$ distinct keys with version stamps, sending a unique key to each node.
For decryption in a $(k, n)$-Threshold Signature Scheme, each receiver node
requires at least $k+1$ different keys with the same version, shared over
peer-to-peer connections. We consider two different schemes: a memory scheme
(in which the nodes keep the source's current and previous encrypted messages)
and a memoryless scheme (in which the nodes are allowed to only keep the
source's current message). We measure the ''timeliness'' of information updates
by using the version age of information. Our work focuses on determining
closed-form expressions for the time average age of information in a
heterogeneous random graph. Our work not only allows to verify the expected
outcome that a memory scheme results in a lower average age compared to a
memoryless scheme, but also provides the quantitative difference between the
two. In our numerical results, we quantify the value of memory and demonstrate
that the advantages of memory diminish with infrequent source updates, frequent
gossipping between nodes, or a decrease in $k$ for a fixed number of nodes.


  Contrary to previous studies that identified the ground state crystal
structure of the entire R_3Co series (R is a rare earth) as orthorhombic Pnma,
we show that Y_3Co undergoes a structural phase transition at T_t=160K. Single
crystal neutron diffraction data reveal that at T_t the trigonal prisms formed
by a cobalt atom and its six nearest-neighbor yttrium atoms experience
distortions accompanied by notable changes of the Y-Co distances. The formation
of the low-temperature phase is accompanied by a pronounced lattice distortion
and anomalies seen in heat capacity and resistivity measurements. Density
functional theory calculations reveal a dynamical instability of the Pnma
structure of Y_3Co. In particular, a transversal acoustic phonon mode along the
(00z) direction has imaginary frequencies at z<1/4. Employing inelastic neutron
scattering measurements we find a strong damping of the (00z) phonon mode below
a critical temperature T_t. The observed structural transformation causes the
reduction of dimensionality of electronic bands and decreases the electronic
density of states at the Fermi level that identifies Y_3Co as a system with the
charge density wave instability.


  The frequency dependent conductivity $\sigma(\omega)$ of the strongly coupled
Quark-Gluon Plasma (QGP) is estimated using a bottom up holographic model that
can adequately describe recent lattice data for QCD thermodynamics at zero
chemical potential. Different choices for the coupling between the bulk gauge
field and the other bulk fields that define the background (the metric and a
scalar field) are used in order to fit the lattice data for the electric charge
susceptibility $\chi_2^Q/T^2$. The ratio $\sigma_{DC}/T$ is found to vary near
the deconfinement transition in a way that is similar to recent lattice
results. This model is used to compute the charge diffusion coefficient $D$ of
the strongly coupled plasma. We find that the dimensionless combination $DT$
has the same type of temperature dependence displayed by $\sigma_{DC}/T$ and,
thus, charge diffusion is suppressed at low temperatures. The frequency
dependent conductivity $\sigma(\omega)$ reveals some nontrivial structure for
values of the temperature near the phase transition. None of these structures
appear in the associated Euclidean correlator, which we also compute. Our
results suggest that the conformal invariance violation near the QCD
deconfinement phase transition may be seen in the Euclidean correlator through
a downward shift of its value at the minimum, which gives a rough estimate of
the temperature dependence of the DC conductivity in the plasma.


  We will present the benefits of using methods of non-standard analysis in
dynamic projective geometry. One major application will be the
desingulariazation of geometric constructions.


  Shubnikov de Haas resistance oscillations of highly mobile two dimensional
helical electrons propagating on a conducting surface of strained HgTe 3D
topological insulator are studied in magnetic fields B tilted by angle $\theta$
from the normal to the conducting layer. Strong decrease of oscillation
amplitude A is observed with the tilt: $A \sim \exp(-\xi/cos(\theta))$, where
$\xi$ is a constant. Evolution of the oscillations with temperature T shows
that the parameter $\xi$ contains two terms: $\xi=\xi_1+\xi_2 T$. The
temperature independent term, $\xi_1$, describes reduction of electron mean
free path in magnetic field B pointing toward suppression of the topological
protection of the electron states against impurity scattering. The temperature
dependent term, $\xi_2 T$, indicates increase of the reciprocal velocity of 2D
helical electrons suggesting modification of the electron spectrum in magnetic
fields.


  In the past decades, spectral clustering (SC) has become one of the most
effective clustering algorithms. However, most previous studies focus on
spectral clustering tasks with a fixed task set, which cannot incorporate with
a new spectral clustering task without accessing to previously learned tasks.
In this paper, we aim to explore the problem of spectral clustering in a
lifelong machine learning framework, i.e., Lifelong Spectral Clustering (L2SC).
Its goal is to efficiently learn a model for a new spectral clustering task by
selectively transferring previously accumulated experience from knowledge
library. Specifically, the knowledge library of L2SC contains two components:
1) orthogonal basis library: capturing latent cluster centers among the
clusters in each pair of tasks; 2) feature embedding library: embedding the
feature manifold information shared among multiple related tasks. As a new
spectral clustering task arrives, L2SC firstly transfers knowledge from both
basis library and feature library to obtain encoding matrix, and further
redefines the library base over time to maximize performance across all the
clustering tasks. Meanwhile, a general online update formulation is derived to
alternatively update the basis library and feature library. Finally, the
empirical experiments on several real-world benchmark datasets demonstrate that
our L2SC model can effectively improve the clustering performance when
comparing with other state-of-the-art spectral clustering algorithms.


  We consider the model of self-interacting complex scalar fields with a rigid
gauge invariance under an arbitrary gauge group $G$. In order to analyze the
phenomenon of Bose-Einstein condensation finite temperature and the possibility
of a finite background charge is included. Different approaches to derive the
relevant high-temperature behaviour of the theory are presented.


  In this paper, two sufficient conditions are provided for given two
K-equivalent map-germs to be bi-Lipschitz A-equivalent. These are Lipschitz
analogues of the known results on C^r-A-equivalence $(0 \leq r \leq \infty)$
for given two K-equivalent map-germs. As a corollary of one of our results, a
Lipschitz version of the well-known Fukuda-Fukuda theorem is provided.


  In this work, we investigate the thermodynamic properties of black holes
(BHs) that have non-trivial topological features in their phase diagrams. We
consider three different models of BHs: (1) a class of BHs in dRGT massive
gravity, which adds a mass term to general relativity; (2) a class of BHs in 5D
Yang-Mills massive gravity, which combines dRGT massive gravity with a
non-Abelian gauge field; and (3) a D-dimensional RN-AdS BH surrounded by
Quintessence and a cloud of strings, which are strange forms of matter that
change the thermodynamics of the BH. Our goal is to find the critical points of
these BHs, which provide the location of first-order phase transitions and
figure out their corresponding topological charges. Topological charges are
numbers that show how complicated the BH topology is. Then, we look at these
BHs as topological defects in the thermodynamic domain, which is the space of
thermodynamic variables like pressure and temperature. We calculate winding
numbers to analyze topology on a global and local scale at these defects, which
are integers that indicate how many times a curve encircling the defect wraps
around the origin. Our analysis reveals that the total topological charge is
either equal to 0 or 1 for all models, meaning that the BHs have either a
trivial or simple topology. In some cases, we see that the BH's topology
belongs to a different thermodynamic topological class. This means that the BHs
can go through topological phase transitions.


  Characteristic curves of a Hamilton-Jacobi equation can be seen as action
minimizing trajectories of fluid particles. However this description is valid
only for smooth solutions. For nonsmooth "viscosity" solutions, which give rise
to discontinuous velocity fields, this picture holds only up to the moment when
trajectories hit a shock and cease to minimize the Lagrangian action. In this
paper we discuss two physically meaningful regularisation procedures, one
corresponding to vanishing viscosity and another to weak noise limit. We show
that for any convex Hamiltonian, a viscous regularization allows to construct a
nonsmooth flow that extends particle trajectories and determines dynamics
inside the shock manifolds. This flow consists of integral curves of a
particular "effective" velocity field, which is uniquely defined everywhere in
the flow domain and is discontinuous on shock manifolds. The effective velocity
field arising in the weak noise limit is generally non-unique and different
from the viscous one, but in both cases there is a fundamental self-consistency
condition constraining the dynamics.


  Database replication is an important component of reliable, disaster tolerant
and highly available distributed systems. However, data replication also causes
communication and processing overhead. Quantification of these overheads is
crucial in choosing a suitable DBMS form several available options and capacity
planning. In this paper, we present results from a comparative empirical
analysis of replication activities of three commonly used DBMSs - MySQL,
PostgreSQL and Cassandra under text as well as image traffic. In our
experiments, the total traffic with two replicas (which is the norm) was as
much as $300$\% higher than the total traffic with no replica. Furthermore,
activation of the compression option for replication traffic, built into MySQL,
reduced the total network traffic by as much as $20$\%. We also found that
average CPU utilization and memory utilization were not impacted by the number
of replicas or the dataset.


  The main goal of the present article is the computation of the Heegaard Floer
homology introduced by Ozsvath and Szabo for a family of plumbed rational
homology 3-spheres. The main motivation is the study of the Seiberg-Witten type
invariants of links of normal surface singularities.


  Simple cellular automata models are able to reproduce the basic properties of
highway traffic. The comparison with empirical data for microscopic quantities
requires a more detailed description of the elementary dynamics. Based on
existing cellular automata models we propose an improved discrete model
incorporating anticipation effects, reduced acceleration capabilities and an
enhanced interaction horizon for braking. The modified model is able to
reproduce the three phases (free-flow, synchronized, and stop-and-go) observed
in real traffic. Furthermore we find a good agreement with detailed empirical
single-vehicle data in all phases.


  In this paper, we study maximal subextension of $m$-subharmonic functions
with given boundary values. We also study the relationship between convergence
in $m$-capacity of $m$-subharmonic functions with given boundary values and the
convergence in $m$-capacity of subsequence of the corresponding maximal
subextensions with given boundary values.


  We investigate two approaches to increase the efficiency of phenotypic
prediction from genome-wide markers, which is a key step for genomic selection
(GS) in plant and animal breeding. The first approach is feature selection
based on Markov blankets, which provide a theoretically-sound framework for
identifying non-informative markers. Fitting GS models using only the
informative markers results in simpler models, which may allow cost savings
from reduced genotyping. We show that this is accompanied by no loss, and
possibly a small gain, in predictive power for four GS models: partial least
squares (PLS), ridge regression, LASSO and elastic net. The second approach is
the choice of kinship coefficients for genomic best linear unbiased prediction
(GBLUP). We compare kinships based on different combinations of centring and
scaling of marker genotypes, and a newly proposed kinship measure that adjusts
for linkage disequilibrium (LD).
  We illustrate the use of both approaches and examine their performances using
three real-world data sets from plant and animal genetics. We find that elastic
net with feature selection and GBLUP using LD-adjusted kinships performed
similarly well, and were the best-performing methods in our study.


  A new particle identification device for Jlab 12 GeV program is proposed. It
is based on the measurement of time information obtained by means of a new
photon detector and time measuring concept. The expected time measurement
precision for the Cherenkov time-of-flight detector is about or less than 10
picosecond for Cherenkov radiators with lengths less than 50 cm.


  Ground-state OH masers identified in the Southern Parkes Large-Area Survey in
Hydroxyl were observed with the Australia Telescope Compact Array to obtain
positions with high accuracy ($\sim$1\,arcsec). We classified these OH masers
into evolved star OH maser sites, star formation OH maser sites, supernova
remnant OH maser sites, planetary nebula OH maser sites and unknown maser sites
using their accurate positions. Evolved star and star formation OH maser sites
in the Galactic Centre region (between Galactic longitudes of $-5^{\circ}$ to
$+5^{\circ}$ and Galactic latitudes of $-2^{\circ}$ and $+2^{\circ}$) were
studied in detail to understand their distributions.


  Neural codes allow the brain to represent, process, and store information
about the world. Combinatorial codes, comprised of binary patterns of neural
activity, encode information via the collective behavior of populations of
neurons. A code is called convex if its codewords correspond to regions defined
by an arrangement of convex open sets in Euclidean space. Convex codes have
been observed experimentally in many brain areas, including sensory cortices
and the hippocampus, where neurons exhibit convex receptive fields. What makes
a neural code convex? That is, how can we tell from the intrinsic structure of
a code if there exists a corresponding arrangement of convex open sets? In this
work, we provide a complete characterization of local obstructions to
convexity. This motivates us to define max intersection-complete codes, a
family guaranteed to have no local obstructions. We then show how our
characterization enables one to use free resolutions of Stanley-Reisner ideals
in order to detect violations of convexity. Taken together, these results
provide a significant advance in understanding the intrinsic combinatorial
properties of convex codes.


  We investigate systems of nature where the common physical processes
diffusion and fragmentation compete. We derive a rate equation for the size
distribution of fragments. The equation leads to a third order differential
equation which we solve exactly in terms of Bessel functions. The stationary
state is a universal Bessel distribution described by one parameter, which fits
perfectly experimental data from two very different system of nature, namely,
the distribution of ice crystal sizes from the Greenland ice sheet and the
length distribution of alpha-helices in proteins.


  With smart devices being an essential part of our everyday lives,
unsupervised access to the mobile sensors' data can result in a multitude of
side-channel attacks. In this paper, we study potential data leaks from Apple
Pencil (2nd generation) supported by the Apple iPad Pro, the latest stylus pen
which attaches to the iPad body magnetically for charging. We observe that the
Pencil's body affects the magnetic readings sensed by the iPad's magnetometer
when a user is using the Pencil. Therefore, we ask: Can we infer what a user is
writing on the iPad screen with the Apple Pencil, given access to only the
iPad's motion sensors' data? To answer this question, we present Side-channel
attack on Stylus pencil through Sensors (S3), a system that identifies what a
user is writing from motion sensor readings. We first use the sharp
fluctuations in the motion sensors' data to determine when a user is writing on
the iPad. We then introduce a high-dimensional particle filter to track the
location and orientation of the Pencil during usage. Lastly, to guide
particles, we build the Pencil's magnetic map serving as a bridge between the
measured magnetic data and the Pencil location and orientation. We evaluate S3
with 10 subjects and demonstrate that we correctly identify 93.9%, 96%, 97.9%,
and 93.33% of the letters, numbers, shapes, and words by only having access to
the motion sensors' data.


  In this paper we study the long-term dynamical evolution of
multiple-population clusters, focusing on the evolution of the spatial
distributions of the first- (FG) and second-generation (SG) stars.In previous
studies we have suggested that SG stars formed from the ejecta of FG AGB stars
are expected initially to be concentrated in the cluster inner regions. Here,
by means of N-body simulations, we explore the time scales and the dynamics of
the spatial mixing of the FG and the SG populations and their dependence on the
SG initial concentration.Our simulations show that, as the evolution proceeds,
the radial profile of the SG/FG number ratio, NSG/NFG, is characterized by
three regions: 1) a flat inner part; 2) a declining part in which FG stars are
increasingly dominant; and 3) an outer region where the NSG/NFG profile
flattens again (the NSG/NFG profile may rise slightly again in the outermost
cluster regions). The radial variation of NSG/NFG implies that the fraction of
SG stars determined by observations covering a limited range of radial
distances is not, in general, equal to the SG global fraction, (NSG/NFG)glob.
The distance at which NSG/NFG equals (NSG/NFG)glob is approximately between 1
and 2 cluster half-mass radii. The results of our simulations suggest that in
many Galactic globular clusters the SG should still be more spatially
concentrated than the FG.[abridged]


  The absolute values of the conductivity in the normal state sigma_n and of
the low temperature penetration depths lambda(0) were measured for a number of
different samples of the YBaCuO family. We found a striking correlation between
sigma_n and 1/lambda^2, regardless of doping, oxygen reduction or defects, thus
providing a simple method to predict the superconducting penetration depth and
to have an estimate of the sample quality by measuring the normal-state
conductivity.


  Four large/medium size photometric surveys are being conducted by Warsaw
astronomers: Warsaw-LCO survey of globular clusters, OGLE, DIRECT and ASAS. In
this contribution we provide short description of these projects and discuses
briefly some results obtained for pulsating variables.


  Neutron stars, with their strong surface gravity, have interestingly short
timescales for the sedimentation of heavy elements. Recent observations of
unstable thermonuclear burning (observed as X-ray bursts) on the surfaces of
slowly accreting neutron stars ($< 0.01$ of the Eddington rate) motivate us to
examine how sedimentation of CNO isotopes affects the ignition of these bursts.
We further estimate the burst development using a simple one-zone model with a
full reaction network. We report a region of mass accretion rates for weak H
flashes. Such flashes can lead to a large reservoir of He, the unstable burning
of which may explain some observed long bursts (duration $\sim 1000$ s).