  We present the results of an extensive spectroscopic survey of RX
J0152.7-1357, one of the most massive distant clusters of galaxies known.
Multi-object spectroscopy, carried out with FORS1 and FORS2 on the ESO Very
Large Telescope (VLT), has allowed us to measure more than 200 redshifts in the
cluster field and to confirm 102 galaxies as cluster members. The mean redshift
of the cluster is $z=0.837 \pm 0.001$ and we estimate the velocity dispersion
of the overall cluster galaxy distribution to be $\sim 1600 \mathrm{km \
s^{-1}}$. The distribution of cluster members is clearly irregular, with two
main clumps that follow the X-ray cluster emission mapped by Chandra. A third
clump of galaxies to the east of the central structure and at the cluster
redshift has also been identified. The two main clumps have velocity
dispersions of $\sim919$ and $\sim737 \mathrm{km s^{-1}}$ respectively, and the
peculiar velocity of the two clumps suggests that they will merge into a single
more massive cluster. A segregation in the star formation activity of the
member galaxies is observed. All star forming galaxies are located outside the
high-density peaks, which are populated only by passive galaxies. A population
of red galaxies (belonging to the cluster red sequence) with clear
post-starburst spectral features and [OII] ($\lambda$3727) emission lines is
observed in the outskirts of the cluster. Two AGNs, which were previously
confused with the diffuse X-ray emission from the intracluster medium in ROSAT
and BeppoSAX observations, are found to be cluster members.


  In this paper, the quantile based flattened logistic distribution introduced
by Gilchrist has been studied. Some classical and quantile based properties of
the distribution have been obtained. Closed form expression of L-moments and
L-ratios of the distribution have been obtained. A quantile based analysis
based on the methods of matching L-moments estimation is employed to estimate
the parameters of the proposed model. We further derive the asymptotic
variance-covariance matrix of the L-Moments estimator of the proposed model.
Finally, we apply the proposed model to a real data set and perform some
goodness of fit tests.


  We present data for the gluon and quark propagators computed in the standard
lattice Landau's gauge and for three values of the covariant gauge-fixing
parameter lambda=0,8,16. Our results are obtained using the SU(3) Wilson action
in the quenched approximation at beta=6.0 and volume=16^3x32.


  As text-conditioned diffusion models (DMs) achieve breakthroughs in image,
video, and 3D generation, the research community's focus has shifted to the
more challenging task of text-to-4D synthesis, which introduces a temporal
dimension to generate dynamic 3D objects. In this context, we identify Score
Distillation Sampling (SDS), a widely used technique for text-to-3D synthesis,
as a significant hindrance to text-to-4D performance due to its Janus-faced and
texture-unrealistic problems coupled with high computational costs. In this
paper, we propose \textbf{P}ixel-\textbf{L}evel \textbf{A}lignments for
Text-to-\textbf{4D} Gaussian Splatting (\textbf{PLA4D}), a novel method that
utilizes text-to-video frames as explicit pixel alignment targets to generate
static 3D objects and inject motion into them. Specifically, we introduce Focal
Alignment to calibrate camera poses for rendering and GS-Mesh Contrastive
Learning to distill geometry priors from rendered image contrasts at the pixel
level. Additionally, we develop Motion Alignment using a deformation network to
drive changes in Gaussians and implement Reference Refinement for smooth 4D
object surfaces. These techniques enable 4D Gaussian Splatting to align
geometry, texture, and motion with generated videos at the pixel level.
Compared to previous methods, PLA4D produces synthesized outputs with better
texture details in less time and effectively mitigates the Janus-faced problem.
PLA4D is fully implemented using open-source models, offering an accessible,
user-friendly, and promising direction for 4D digital content creation. Our
project page: https://miaoqiaowei.github.io/PLA4D/.


  Recently, deep neural network (DNN)-based drug-target interaction (DTI)
models were highlighted for their high accuracy with affordable computational
costs. Yet, the models' insufficient generalization remains a challenging
problem in the practice of in-silico drug discovery. We propose two key
strategies to enhance generalization in the DTI model. The first is to predict
the atom-atom pairwise interactions via physics-informed equations
parameterized with neural networks and provides the total binding affinity of a
protein-ligand complex as their sum. We further improved the model
generalization by augmenting a broader range of binding poses and ligands to
training data. We validated our model, PIGNet, in the comparative assessment of
scoring functions (CASF) 2016, demonstrating the outperforming docking and
screening powers than previous methods. Our physics-informing strategy also
enables the interpretation of predicted affinities by visualizing the
contribution of ligand substructures, providing insights for further ligand
optimization.


  We argue that for a smooth surface S, considered as a ramified cover over the
projective plane branched over a nodal-cuspidal curve B one could use the
structure of the fundamental group of the complement of the branch curve to
understand other properties of the surface and its degeneration and vice-versa.
  In this paper, we look at embedded-degeneratable surfaces - a class of
surfaces admitting a planar degeneration with a few combinatorial conditions
imposed on its degeneration. We close a conjecture of Teicher on the virtual
solvability of the mentioned fundamental group for these surfaces and present
two new conjectures on the structure of this group, regarding
non-embedded-degeneratable surfaces. We prove two theorems supporting our
conjectures, and show that for an empbedding of a product of a projective line
with a curve of genus g, the fundamental group of the complement of the branch
curve is a quotient of an Artin group associated to the degeneration.


  Magnon band structures in altermagnets are characterized by an energy
splitting of modes with opposite chirality, even in the absence of applied
external fields and relativistic effects, due to an anisotropy in the
Heisenberg exchange interactions. We perform quantitative atomistic spin
dynamics simulations based on ab initio electronic structure calculations on
rutile RuO$_2$, a prototypical "d-wave" altermagnet, to study magnon currents
generated by thermal gradients. We report substantial spin Seebeck and spin
Nernst effects, i.e., longitudinal or transverse spin currents, depending on
the propagation direction of the magnons with respect to the crystal, together
with a finite spin accumulation associated with non-linearities in the
temperature profile. Our findings are consistent with the altermagnetic
spin-group symmetry, as well as predictions from linear spin wave theory and
semiclassical Boltzmann transport theory.


  Context. Closed-loop Doppler data obtained by deep space tracking networks
(e.g., NASA's DSN and ESA's Estrack) are routinely used for navigation and
science applications. By "shadow tracking" the spacecraft signal, Earth-based
radio telescopes involved in Planetary Radio Interferometry and Doppler
Experiment (PRIDE) can provide open-loop Doppler tracking data when the
dedicated deep space tracking facilities are operating in closed-loop mode
only. Aims. We explain in detail the data processing pipeline, discuss the
capabilities of the technique and its potential applications in planetary
science. Methods. We provide the formulation of the observed and computed
values of the Doppler data in PRIDE tracking of spacecraft, and demonstrate the
quality of the results using as a test case an experiment with ESA's Mars
Express spacecraft. Results. We find that the Doppler residuals and the
corresponding noise budget of the open-loop Doppler detections obtained with
the PRIDE stations are comparable to the closed-loop Doppler detections
obtained with the dedicated deep space tracking facilities.


  The CuO-planes of high-T_c superconductors were found to consist of geometric
stripes with alternating superconducting and antiferromagnetic areas. Here we
will investigate the repulsive Hubbard model of striped clusters as a possible
microscopic description of the superconducting elements. The focus of our
attention lies on the superconducting properties. We report in agreement with
the square Hubbard model a signal in the d-wave channel and investigate its
dependence on system size, cluster shape and interaction strength.


  The stationary functional of the all-electron density functional plus
dynamical mean field theory (DFT+DMFT) formalism to perform free energy
calculations and structural relaxations is implemented for the first time.
Here, the first order error in the density leads to a much smaller, second
order error in the free energy. The method is applied to several well known
correlated materials; metallic SrVO$_3$, Mott insulating FeO, and elemental
Cerium, to show that it predicts the lattice constants with very high accuracy.
In Cerium, we show that our method predicts the iso-structural transition
between the $\alpha$ and $\gamma$ phases, and resolve the long standing
controversy in the driving mechanism of this transition.


  We have carried out a millimeter interferometric continuum survey toward 7
YSOs in the MBM 12 cloud. Thermal emissions associated with 2 YSOs were
detected above the 3-$\sigma$ level at 2.1 mm, and one also showed a 1.3 mm
thermal emission. Another object was marginally detected at 2.1 mm. Spectral
energy distributions of the YSOs are well fitted by a simple power-law disk
model. Masses of the circumstellar disks are estimated to be an order of $0.05
M_{\sun}$. The circumstellar disks in the MBM 12 cloud have properties in
common with the disks in nearby star-forming regions, in terms of disk
parameters such as a disk mass, as well as an infrared excess.


  For a compact homogeneous space $G/K$, we study the problem of existence of
$G$-invariant Riemannian metrics such that each eigenspace of the Laplacian is
a real irreducible representation of $G$. We prove that the normal metric of a
compact irreducible symmetric space has this property only in rank one.
Furthermore, we provide existence results for such metrics on certain isotropy
reducible spaces.


  Intelligent agents offer a new and exciting way of understanding the world of
work. In this paper we apply agent-based modeling and simulation to investigate
a set of problems in a retail context. Specifically, we are working to
understand the relationship between human resource management practices and
retail productivity. Despite the fact we are working within a relatively novel
and complex domain, it is clear that intelligent agents could offer potential
for fostering sustainable organizational capabilities in the future. The
project is still at an early stage. So far we have conducted a case study in a
UK department store to collect data and capture impressions about operations
and actors within departments. Furthermore, based on our case study we have
built and tested our first version of a retail branch simulator which we will
present in this paper.


  In this paper, we propose a method to obtain a compact and accurate 3D
wireframe representation from a single image by effectively exploiting global
structural regularities. Our method trains a convolutional neural network to
simultaneously detect salient junctions and straight lines, as well as predict
their 3D depth and vanishing points. Compared with the state-of-the-art
learning-based wireframe detection methods, our network is simpler and more
unified, leading to better 2D wireframe detection. With global structural
priors from parallelism, our method further reconstructs a full 3D wireframe
model, a compact vector representation suitable for a variety of high-level
vision tasks such as AR and CAD. We conduct extensive evaluations on a large
synthetic dataset of urban scenes as well as real images. Our code and datasets
have been made public at https://github.com/zhou13/shapeunity.


  We consider charged spherically symmetric static solutions of the
Einstein-Maxwell equations with a positive cosmological constant $\Lambda$. If
$r$ denotes the area radius, $m_g$ and $q$ the gravitational mass and charge of
a sphere with area radius $r$ respectively, we find that for any solution which
satisfies the condition $p+2p_{\perp}\leq \rho,$ where $p\geq 0$ and
$p_{\perp}$ are the radial and tangential pressures respectively, $\rho\geq 0$
is the energy density, and for which $0\leq \frac{q^2}{r^2}+\Lambda r^2\leq 1,$
the inequality $\frac{m_g}{r} \leq 2/9+\frac{q^2}{3r^2}-\frac{\Lambda
r^2}{3}+2/9\sqrt{1+\frac{3q^2}{r^2}+3\Lambda r^2}$ holds. We also investigate
the issue of sharpness, and we show that the inequality is sharp in a few cases
but generally this question is open.


  An isolated, isothermal, and wide magnetic flux tube embedded either in the
solar chromosphere or in the lower solar corona is considered, and the
propagation of linear torsional Alfv'en waves is investigated. It is shown that
the wideness of the tube leads to a new cutoff frequency, which is a local
quantity that gives the conditions for the wave propagation at different
atmospheric heights. The cutoff is used to establish the ranges of frequencies
for the propagating and reflected waves in the solar chromosphere and lower
solar corona. The obtained results are compared to those previously obtained
for thin magnetic flux tubes and the differences are discussed. Moreover, the
results are also compared to some current observational data, and used to
establish the presence of propagating waves in the data at different
atmospheric heights; this has profound implications on the energy and momentum
transfer by the waves in the solar atmosphere, and the role of linear torsional
Alfv'en waves in the atmospheric heating and wind acceleration.


  We present a novel approach for generating high-quality, spatio-temporally
coherent human videos from a single image under arbitrary viewpoints. Our
framework combines the strengths of U-Nets for accurate condition injection and
diffusion transformers for capturing global correlations across viewpoints and
time. The core is a cascaded 4D transformer architecture that factorizes
attention across views, time, and spatial dimensions, enabling efficient
modeling of the 4D space. Precise conditioning is achieved by injecting human
identity, camera parameters, and temporal signals into the respective
transformers. To train this model, we curate a multi-dimensional dataset
spanning images, videos, multi-view data and 3D/4D scans, along with a
multi-dimensional training strategy. Our approach overcomes the limitations of
previous methods based on GAN or UNet-based diffusion models, which struggle
with complex motions and viewpoint changes. Through extensive experiments, we
demonstrate our method's ability to synthesize realistic, coherent and
free-view human videos, paving the way for advanced multimedia applications in
areas such as virtual reality and animation. Our project website is
https://human4dit.github.io.


  Epigenetic modifications to histones may promote either activation or
repression of the transcription of nearby genes. Recent experimental studies
show that the promoters of many lineage-control genes in stem cells have
"bivalent domains" in which the nucleosomes contain both active (H3K4me3) and
repressive (H3K27me3) marks. It is generally agreed that bivalent domains play
an important role in stem cell differentiation, but the underlying mechanisms
remain unclear. Here we formulate a mathematical model to investigate the
dynamic properties of histone modification patterns. We then illustrate that
our modeling framework can be used to capture key features of experimentally
observed combinatorial chromatin states.


  This paper addresses the design of input signals for the purpose of
discriminating among a finite set of models dynamic systems within a given
finite time interval. A motivating application is fault detection and
isolation. We propose several specific optimization problems, with objectives
or constraints based on signal power, signal amplitude, and probability of
successful model discrimination. Since these optimization problems are
nonconvex, we suggest a suboptimal solution via a random search algorithm
guided by the semidefinite relaxation (SDR) and analyze the accuracy of the
suboptimal solution. We conclude with a simple example taken from a benchmark
problem on fault detection for wind turbines.


  We study some aspects of linearized gravity as gauge theory, with massive
deformation. Recently it has been shown that there are two distinct solutions,
which represent physical massive gravity. The purpose of the present work is to
show that these two seemingly discrete solutions are equivalent at the level of
generating functional. The significance of this simple work lies in the fact
that one solution represent physical massive gravity at Fierz-Pauli (FP) point
and other outside the FP point.


  Despite the success of Quantum Neural Networks (QNNs) in decision-making
systems, their fairness remains unexplored, as the focus primarily lies on
accuracy. This work conducts a design space exploration, unveiling QNN
unfairness, and highlighting the significant influence of QNN deployment and
quantum noise on accuracy and fairness. To effectively navigate the vast QNN
deployment design space, we propose JustQ, a framework for deploying fair and
accurate QNNs on NISQ computers. It includes a complete NISQ error model,
reinforcement learning-based deployment, and a flexible optimization objective
incorporating both fairness and accuracy. Experimental results show JustQ
outperforms previous methods, achieving superior accuracy and fairness. This
work pioneers fair QNN design on NISQ computers, paving the way for future
investigations.


  We consider the gamma process perturbed by a Brownian motion (independent of
the gamma process) as a degradation model. Parameters estimation is studied
here. We assume that $n$ independent items are observed at irregular instants.
From these observations, we estimate the parameters using the moments method.
Then, we study the asymptotic properties of the estimators. Furthermore we
derive some particular cases of items observed at regular or non-regular
instants. Finally, some numerical simulations and two real data applications
are provided to illustrate our method.


  We explore the total cross section of ground state polar molecules in an
electric field at various energies, focusing on RbCs and RbK. An external
electric field polarizes the molecules and induces strong dipolar interactions
leading to non-zero partial waves contributing to the scattering even as the
collision energy goes to zero. This results in the need to compute scattering
problems with many different values of total M to converge the total cross
section. An accurate and efficient approximate total cross section is
introduced and used to study the low field temperature dependence. To
understand the scattering of the polar molecules we compare a semi-classical
cross section with quantum unitarity limit. This comparison leads to the
ability to characterize the scattering based on the value of the electric field
and the collision energy.


  Due to the time reversal invariance of the angular momentum operator J^2, the
average energies and variances at fixed J for random two-body Hamiltonians
exhibit odd-even-J staggering, that may be especially strong for J=0. It is
shown that upon ensemble averaging over random runs, this behaviour is
reflected in the yrast states. Displaced (attractive) random ensembles lead to
rotational spectra with strongly enhanced BE2 transitions for a certain class
of model spaces. It is explained how to generalize these results to other forms
of collectivity.


  We consider nonnegative integer matrices with specified row and column sums
and upper bounds on the entries. We show that the logarithm of the number of
such matrices is approximated by a concave function of the row and column sums.
We give efficiently computable estimators for this function, including one
suggested by a maximum-entropy random model; we show that these estimators are
asymptotically exact as the dimension of the matrices goes to infinity. We
finish by showing that, for kappa >= 2 and for sufficiently small row and
column sums, the number of matrices with these row and column sums and with
entries <= kappa is greater by an exponential factor than predicted by a
heuristic of independence.


  We developed a mathematical model to derive time scales and the presence of
BS stars. The model is based on the variation of mass through a circle into the
cluster defined by a radius, and at a time; this mass cross is translated into
a differential equation that it can be integrated for a given radius (r) and a
determined time (t). From this equation we can derive the different time scales
that allows us to reach conclusions like: clusters not containing blue
strugglers (BS) stars disappear younger than those clusters containing BS. In
clusters containing BS stars, the volume which takes up half of the cluster
mass is bigger than the one corresponding to clusters without BS stars but the
time to catch it up is shorter. We also studied, by means of this equation, the
core collapse of stars of the cluster and the region where this concentration
is stopped/retained; this region is identified by means of the relation $c/ch$,
being $c=\log(rt/rc)$ and $ch=\log(rc/rh)$. Where rt and rc are the tidal and
the core radius respectively, and rh is the radius where half of the cluster
mass is concentrated. The model also drove us to the conclusion that the number
of the blue straggler stars in a cluster follows a distribution function whose
components are the ratio between relaxation time and the age, labelled as $\it
f$, and a factor, named $\varpi$, which is an indicator of the origin of the
BS; $\varpi$ increases as the number of BS increase but it is limited
to$\sim$5.0. The mentioned distribution function is expressed as $\it NBS$
$\sim$ $\it f^3$($\frac{1}{e^{\frac{f}{\varpi}}-1}$). The validity of this
function was carried out by means of matching the number of observed blue
straggler (BS) stars to the number of predicted ones in the available sample of
OC.


  This thesis investigates the interactions of different degrees of freedom of
one joint system within the theory of stochastic thermodynamics. First, a
comprehensive introduction to the subjects of stochastic processes, information
theory and the theory of stochastic thermodynamics is given, thereby
highlighting the key results.
  In the second part, systems with interacting degrees of freedom are
considered. This allows investigation of thermalization properties of
collisional baths, i.e. particles at equilibrium interacting with a localized
system via collisions. It is shown that the interactions between system and
bath must be reversible to ensure thermalization of the system. Moreover, the
role of information in thermodynamics is presented and interpreted in the
context of interacting systems. Using the concept of causal conditioning, a
framework is developed for finding entropy productions that capture the mutual
influence of coupled subsystems. This framework is applied to diverse setups
which are usually studied separately in information thermodynamics.
  The third part covers the case of important system variables being hidden
from observation. The problem is motivated by presenting a microswimmer model
and showing that its movement can be approximated by active Brownian motion.
However, its energy dissipation is massively underestimated by this procedure.
It is shown that this is a consequence of the fact that the swimming mechanism
is a hidden variable. Subsequently, different methods of effective description
are discussed and applied to a simple model system with which the impact of
hidden slow degrees of freedom on fluctuation theorems is studied. Finally, a
setting is investigated in which it is possible to give bounds for the hidden
entropy production from only partial observation of the system dynamics by
fitting an underlying hidden Markov process to the observable data.


  A lambda-type divergent rise of Raman linewidth of liquid nitrogen near its
critical temperature has been a subject of many discussions in the past[1-5].
Here we explore the possibility of such an anomaly in infra-red and Raman
spectroscopy of super-critical water (SCW) by varying the density across the
Widom line just above its critical temperature. Vibrational phase relaxation is
expected to be a sensitive probe of fluid dynamics. We carry out computer
simulations of two different model potentials (SPC/E and TIP4P/2005) to obtain
the necessary time correlation functions. An additional feature of this work is
a quantum chemical calculation of the anharmonicity parameter that largely
controls frequency fluctuations. We find a sharp rise in the vibrational
relaxation rate (or the line widths) for both the models as we travel across
the Widom line. The rise is noticeably less sharp in water than in nitrogen. We
attribute this difference to the faster relaxation rate in water. We
demonstrate that the anomalous rise is due to sharp increase in the mean square
frequency fluctuation and not due to any dynamical critical slowing down
because the time constant of the normalized frequency-frequency time
correlation functions show lack of any noticeable change as we move across the
Widom line. We present an explanation of the observed results using the mode
coupling theory of liquid dynamics.


  We examine an extension of the Standard Model which results from a $10D$,
$\mathcal{N}=1$, $E_8$ gauge theory. The theory is dimensionally reduced over a
$M_4 \times B_0/ \mathbf{Z}_3 $ space, where $B_0$ is the nearly-K\"ahler
manifold $SU(3)/U(1) \times U(1)$ and $\mathbf{Z}_3$ is a freely acting
discrete group on $B_0$ that triggers a Wilson flux breaking, leading to an
$\mathcal{N}=1$, $SU(3)^3\times U(1)^2$ effective theory in $4D$. At lower
energies we are left with the Split NMSSM. Its 2-loop analysis yields third
generation quark and light Higgs masses within the experimental limits and
predicts a neutralino LSP mass $<800$ GeV.


  In his 1918 paper 'A General Form of Integral', Percy John Daniell developed
a theory of integration capable of dealing with functions on arbitrary sets.
Daniell's method differs from the measure-theoretic notion of integration.
Linear functionals over vector lattices were considered as the fundamental
objects on which he built the theory, rather than measures over sets. In this
document, we explore Daniell's concept of integration and how his theory
relates to the measure-theoretic notion of integration. We paint a picture of
the historical context surrounding Daniell's ideas. Furthermore, we present
examples due to Norbert Wiener, where the Daniell integral was employed on
spaces too general for the standard integration techniques of the time.


  A promising method for real time early warning of gravity driven rupture that
considers both the heterogeneity of natural media and characteristics of
acoustic emissions attenuation is proposed. The method capitalizes on
co-detection of elastic waves emanating from micro-cracks by multiple and
spatially separated sensors. Event co-detection is considered as surrogate for
large event size with more frequent co-detected events marking imminence of
catastrophic failure. Using a spatially explicit fiber bundle numerical model
with spatially correlated mechanical strength and two load redistribution
rules, we constructed a range of mechanical failure scenarios and associated
failure events (mapped into AE) in space and time. Analysis considering
hypothetical arrays of sensors and consideration of signal attenuation
demonstrate the potential of the co-detection principles even for insensitive
sensors to provide early warning for imminent global failure.


  We analyze the dependence of galaxy evolution on cluster dynamical state and
galaxy luminosities for a sample of 146 galaxy clusters from the Yang SDSS
catalog. Clusters were split according to their velocity distribution in
Gaussians (G) and Non-Gaussians (NG), and further divided by luminosity regime.
We performed a classification in the Age-SSFR plane providing three classes:
star-forming (SF), passive (PAS), and intermediate (GV -- green valley). We
show that galaxies evolve in the same way in G and NG systems, but also suggest
that their formation histories leads to different mixtures of galactic types
and infall patterns. Separating the GV into star-forming and passive
components, we find more bright galaxies in the passive mode of NG than in G
systems. We also find more intermediate faint galaxies in the star-forming
component of NG than in G systems. Our results suggest the GV as the stage
where the transition from types Sab and Scd to S0 must be taking place, but the
conversion between morphological types is independent of the dynamical stage of
the clusters. Analyzing the velocity dispersion profiles, we find that objects
recently infalling in clusters have a different composition between G and NG
systems. While all galaxy types infall onto G systems, Sab and Scd dominate the
infall onto NG systems. Finally, we find that faint Scd in the outskirts of NG
systems present higher asymmetries relative to the mean asymmetry of field
galaxies, suggesting environmental effects acting on these objects.


  A description of the short time behavior of solutions of the Allen-Cahn
equation with a smoothened additive noise is presented. The key result is that
in the sharp interface limit solutions move according to motion by mean
curvature with an additional stochastic forcing. This extends a similar result
of Funaki in spatial dimension $n=2$ to arbitrary dimensions.


  Social Virtual Reality (VR) is growing for remote socialization and
collaboration. However, current social VR applications are not accessible to
people with visual impairments (PVI) due to their focus on visual experiences.
We aim to facilitate social VR accessibility by enhancing PVI's peripheral
awareness of surrounding avatar dynamics. We designed VRBubble, an audio-based
VR technique that provides surrounding avatar information based on social
distances. Based on Hall's proxemic theory, VRBubble divides the social space
with three Bubbles -- Intimate, Conversation, and Social Bubble -- generating
spatial audio feedback to distinguish avatars in different bubbles and provide
suitable avatar information. We provide three audio alternatives: earcons,
verbal notifications, and real-world sound effects. PVI can select and combine
their preferred feedback alternatives for different avatars, bubbles, and
social contexts. We evaluated VRBubble and an audio beacon baseline with 12 PVI
in a navigation and a conversation context. We found that VRBubble
significantly enhanced participants' avatar awareness during navigation and
enabled avatar identification in both contexts. However, VRBubble was shown to
be more distracting in crowded environments.


  We study the set of two-qubit pure states with real amplitudes and their
geometrical representation in the three-dimensional sphere. In this
representation, we show that the maximally entangled states --those locally
equivalent to the Bell States --form two disjoint circles perpendicular to each
other. We also show that taking the natural Riemannian metric on the sphere,
the set of states connected by local gates are equidistant to this pair of
circles. Moreover, the unentangled, or so-called product states, are $\pi/4$
units away to the maximally entangled states. This is, the unentangled states
are the farthest away to the maximally entangled states. In this way, if we
define two states to be equivalent if they are connected by local gates, we
have that there are as many equivalent classes as points in the interval
$[0,\pi/4]$ with the point $0$ corresponding to the maximally entangled states.
The point $\pi/4$ corresponds to the unentangled states which geometrically are
described by a torus. Finally, for every $0< d < \pi/4$ the point $d$
corresponds to a disjoint pair of torus. We also show that if a state is $d$
units away from the maximally entangled states, then its entanglement entropy
is $S(d) = 1- \log_2 \sqrt{\frac{(1+\sin 2 d)^{1+\sin 2 d}}{(1-\sin 2
d)^{-1+\sin 2 d}}}$. Finally, we also show how this geometrical interpretation
allows us to clearly see that any pair of two-qubit states with real amplitudes
can be connected with a circuit that only has single-qubit gates and one
controlled-Z gate.


  We investigate the impact of spin anisotropic interactions, promoted by
spin-orbit coupling, on the magnetic phase diagram of the iron-based
superconductors. Three distinct magnetic phases with Bragg peaks at $(\pi,0)$
and $(0,\pi)$ are possible in these systems: one $C_2$ (i.e. orthorhombic)
symmetric stripe magnetic phase and two $C_4$ (i.e. tetragonal) symmetric
magnetic phases. While the spin anisotropic interactions allow the magnetic
moments to point in any direction in the $C_2$ phase, they restrict the
possible moment orientations in the $C_4$ phases. As a result, an interesting
scenario arises in which the spin anisotropic interactions favor a $C_2$ phase,
but the other spin isotropic interactions favor a $C_4$ phase. We study this
frustration via both mean-field and renormalization-group approaches. We find
that, to lift this frustration, a rich magnetic landscape emerges well below
the magnetic transition temperature, with novel $C_2$, $C_4$, and mixed
$C_2$-$C_4$ phases. Near the putative magnetic quantum critical point, spin
anisotropies promote a stable Gaussian fixed point in the renormalization-group
flow, which is absent in the spin isotropic case, and is associated with a
near-degeneracy between $C_2$ and $C_4$ phases. We argue that this frustration
is the reason why most $C_4$ phases in the iron pnictides only appear inside
the $C_2$ phase, and discuss additional manifestations of this frustration in
the phase diagrams of these materials.


  In this paper we suggest an analytic model, and derive simple formulas, for
the beam dynamics in a wakefield structure of arbitrary cross-section. The
results could be applied to estimate an upper limit of the projected beam size
in devices such as the dechirper and the wakefield streaking device. The
suggested formalism is also applicable to the case when slices of the beam are
distributed along an arbitrary line in 3D.


  We consider a generalisation of the specialisation functor on Soergel
bimodules and show that this generalised version still takes Soergel bimodules
to Soergel modules.


  Chart visualizations are essential for data interpretation and communication;
however, most charts are only accessible in image format and lack the
corresponding data tables and supplementary information, making it difficult to
alter their appearance for different application scenarios. To eliminate the
need for original underlying data and information to perform chart editing, we
propose ChartReformer, a natural language-driven chart image editing solution
that directly edits the charts from the input images with the given instruction
prompts. The key in this method is that we allow the model to comprehend the
chart and reason over the prompt to generate the corresponding underlying data
table and visual attributes for new charts, enabling precise edits.
Additionally, to generalize ChartReformer, we define and standardize various
types of chart editing, covering style, layout, format, and data-centric edits.
The experiments show promising results for the natural language-driven chart
image editing.


  We study the structure of jets in proton-proton collisions at LHC energies
using \textsc{Pythia} 8 Monte Carlo simulations. We demonstrate that the radial
jet profiles exhibit scaling properties with charged-hadron event multiplicity
over a broad transverse-momentum range. We also provide parametrizations of the
jet profiles based on different statistically-motivated analytical
distributions. Based on this we propose that the scaling behavior stems from
fundamental statistical properties of jet fragmentation.


  Near-surface perennial water ice on Mars has been previously inferred down to
latitudes of about 45{\deg} and could result from either water vapor diffusion
through the regolith under current conditions or previous ice ages
precipitations. In this paper we show that at latitudes as low as 25{\deg} in
the southern hemisphere buried water ice in the shallow (< 1 m) subsurface is
required to explain the observed surface distribution of seasonal CO2 frost on
pole facing slopes. This result shows that possible remnants of the last ice
age, as well as water that will be needed for the future exploration of Mars,
are accessible significantly closer to the equator than previously thought,
where mild conditions for both robotic and human exploration lie.


  The Third Law of Thermodynamics states that the entropy of any system in
equilibrium has to vanish at absolute zero temperature. At nonzero
temperatures, on the other hand, matter is expected to accumulate entropy near
a quantum critical point (QCP), where it undergoes a continuous transition from
one ground state to another. Here, we determine, based on general thermodynamic
principles, the spatial-dimensional profile of the entropy S near a QCP and its
steepest descent in the corresponding multidimensional stress space. We
demonstrate this approach for the canonical quantum critical compound
CeCu6-xAux near its onset of antiferromagnetic order. We are able to link the
directional stress dependence of S to the previously determined geometry of
quantum critical fluctuations. Our demonstration of the multidimensional
entropy landscape provides the foundation to understand how quantum criticality
nucleates novel phases such as high-temperature superconductivity.


  Learning scene flow from a monocular camera still remains a challenging task
due to its ill-posedness as well as lack of annotated data. Self-supervised
methods demonstrate learning scene flow estimation from unlabeled data, yet
their accuracy lags behind (semi-)supervised methods. In this paper, we
introduce a self-supervised monocular scene flow method that substantially
improves the accuracy over the previous approaches. Based on RAFT, a
state-of-the-art optical flow model, we design a new decoder to iteratively
update 3D motion fields and disparity maps simultaneously. Furthermore, we
propose an enhanced upsampling layer and a disparity initialization technique,
which overall further improves accuracy up to 7.2%. Our method achieves
state-of-the-art accuracy among all self-supervised monocular scene flow
methods, improving accuracy by 34.2%. Our fine-tuned model outperforms the best
previous semi-supervised method with 228 times faster runtime. Code will be
publicly available.


  Over the eight months since its release, ChatGPT and its underlying model,
GPT3.5, have garnered massive attention, due to their potent mix of capability
and accessibility. While a niche-industry of papers have emerged examining the
scope of capabilities these models possess, the information fed to and
extracted from these networks has been either natural language text or
stylized, code-like language. Drawing inspiration from the prowess we expect a
truly human-level intelligent agent to have across multiple signal modalities,
in this work we examine GPT3.5's aptitude for visual tasks, where the inputs
feature content provided as ASCII-art without overt distillation into a lingual
summary. We conduct experiments analyzing the model's performance on image
recognition tasks after various transforms typical in visual settings, trials
investigating knowledge of image parts, and tasks covering image generation.


  For any integers $p\geq 2$ and $q\geq 1$, let $\mathbb{H}^{p,q}$ be the
pseudo-Riemannian hyperbolic space of signature $(p,q)$. We prove that if
$\Gamma$ is the fundamental group of a closed aspherical $p$-manifold, then the
set of representations of $\Gamma$ to $\mathrm{PO}(p,q+1)$ which are convex
cocompact in $\mathbb{H}^{p,q}$ is a union of connected components of
$\mathrm{Hom}(\Gamma,\mathrm{PO}(p,q+1))$. This gives new examples of
higher-dimensional higher-rank Teichm\"uller spaces.


  In this paper we present a fast scalable heuristic for bin packing that
partitions the given problem into identical sub-problems of constant size and
solves these constant size sub-problems by considering only a constant number
of bin configurations with bounded unused space. We present some empirical
evidence to support the scalability of our heuristic and its tighter empirical
analysis of hard instances due to improved lower bound on the necessary wastage
in an optimal solution.


  A brief review is given of the Continuous Spontaneous Localization (CSL)
model in which a classical field interacts with quantized particles to cause
dynamical wavefunction collapse. One of the model's predictions is that
particles "spontaneously" gain energy at a slow rate. When applied to the
excitation of a nucleon in a Ge nucleus, it is shown how a limit on the
relative collapse rates of neutron and proton could be obtained, and a rough
estimate is made from data. When applied to the spontaneous excitation of 1s
electrons in Ge, by a more detailed analysis of more accurate data than
previously given, an updated limit is obtained on the relative collapse rates
of the electron and proton, suggesting that the coupling of the field to
electrons and nucleons is mass proportional.


  We compute the circular Wilson loop of N=4 SYM theory at large N in the rank
k symmetric and antisymmetric tensor representations. Using a quadratic
Hermitian matrix model we obtain expressions for all values of the 't Hooft
coupling. At large and small couplings we give explicit formulae and reproduce
supergravity results from both D3 and D5 branes within a systematic framework.


  Visual relationship detection aims to locate objects in images and recognize
the relationships between objects. Traditional methods treat all observed
relationships in an image equally, which causes a relatively poor performance
in the detection tasks on complex images with abundant visual objects and
various relationships. To address this problem, we propose an attention based
model, namely AVR, to achieve salient visual relationships based on both local
and global context of the relationships. Specifically, AVR recognizes
relationships and measures the attention on the relationships in the local
context of an input image by fusing the visual features, semantic and spatial
information of the relationships. AVR then applies the attention to assign
important relationships with larger salient weights for effective information
filtering. Furthermore, AVR is integrated with the priori knowledge in the
global context of image datasets to improve the precision of relationship
prediction, where the context is modeled as a heterogeneous graph to measure
the priori probability of relationships based on the random walk algorithm.
Comprehensive experiments are conducted to demonstrate the effectiveness of AVR
in several real-world image datasets, and the results show that AVR outperforms
state-of-the-art visual relationship detection methods significantly by up to
$87.5\%$ in terms of recall.


  In this paper, we present the Cantor Intersection Theorem and a formulation
of Baire Theorem in complete PM spaces. In addition, the Heine-Borel property
for PM spaces is considered in detail.


  A major open question in the theory of Gorenstein liaison is whether or not
every arithmetically Cohen-Macaulay subscheme of $\mathbb{P}^n$ can be G-linked
to a complete intersection. Migliore and Nagel showed that, if such a scheme is
generically Gorenstein (e.g., reduced), then, after re-embedding so that it is
viewed as a subscheme of $\mathbb{P}^{n+1}$, indeed it can be G-linked to a
complete intersection. Motivated by this result, we consider techniques for
constructing G-links on a scheme from G-links on a closely related reduced
scheme.
  Polarization is a tool for producing a squarefree monomial ideal from an
arbitrary monomial ideal. Basic double G-links on squarefree monomial ideals
can be induced from vertex decompositions of their Stanley-Reisner complexes.
Given a monomial ideal $I$ and a vertex decomposition of the Stanley-Reisner
complex of its polarization $\mathcal{P}(I)$, we give conditions that allow for
the lifting of an associated basic double G-link of $\mathcal{P}(I)$ to a basic
double G-link of $I$ itself. We use the relationship we develop in the process
to show that the Stanley-Reisner complexes of polarizations of artinian
monomial ideals and of stable Cohen-Macaulay monomial ideals are vertex
decomposable, recovering and strengthening the recent result of Fl{\o}ystad and
Mafi that these complexes are shellable.
  We then introduce and study polarization of a Gr\"obner basis of an arbitrary
homogeneous ideal and give a relationship between geometric vertex
decomposition of a polarization and elementary G-biliaison that is analogous to
our result on vertex decomposition and basic double G-linkage.


  Carath\'eodory's well-known conjecture states that every sufficiently smooth,
closed convex surface in three dimensional Euclidean space admits at least two
umbilic points. It has been established that the conjecture is true for all
rotationally symmetric surfaces; in this paper, we investigate the umbilic
points of two families of surfaces without rotational symmetry, and compute
their indices. In particular, we find that the family of surfaces of the form
$ax^{2k}+by^{2k}+cz^{2k}=1$ with $a,b,c>0$, $k\in\mathbb{Z}_{>1}$ admit 14
umbilic points: six of one known form and eight of another. For many tested
values of $a,b,c,k$, such umbilic points have indices $-1/2$ and $1$,
respectively. We also explore the dependence of the umbilic points on the
parameter $\epsilon$ of the surface $ax^2+\epsilon x^4+ay^2+\epsilon
y^4+bz^2=1$. In particular, for both $a<b$ and $a>b,$ there exist exactly two
umbilic points with index 1 for $\epsilon$ smaller than certain critical
values. For larger $\epsilon,$ surfaces with $a>b$ admit exactly ten umbilic
points; for many tested values of $a,b,\epsilon,$ these points have indices 1/2
and -1. For larger $\epsilon,$ surfaces with $a<b$ admit eighteen umbilic
points; for many tested values of $a,b,\epsilon,$ these points have indices
-1/2 and 1.


  Let $A$ be an $n \times n$ positive definite Hermitian matrix with all
eigenvalues between 1 and 2. We represent the permanent of $A$ as the integral
of some explicit log-concave function on ${\Bbb R}^{2n}$. Consequently, there
is a fully polynomial randomized approximation scheme (FPRAS) for the permanent
of $A$.


  For a positive integer $n$, let $X_n \to X_{n-1} \to \ldots \to X_2 \to X_1
\to X_0$ be a Bott tower of height $n$, and let $L$ be a nef line bundle on
$X_n$. We compute Seshadri constants $\varepsilon(X_n,L,x)$ of $L$ at any point
$x \in X_n$ under some conditions.


  Event-based moving object detection is a challenging task, where static
background and moving object are mixed together. Typically, existing methods
mainly align the background events to the same spatial coordinate system via
motion compensation to distinguish the moving object. However, they neglect the
potential spatial tailing effect of moving object events caused by excessive
motion, which may affect the structure integrity of the extracted moving
object. We discover that the moving object has a complete columnar structure in
the point cloud composed of motion-compensated events along the timestamp.
Motivated by this, we propose a novel joint spatio-temporal reasoning method
for event-based moving object detection. Specifically, we first compensate the
motion of background events using inertial measurement unit. In spatial
reasoning stage, we project the compensated events into the same image
coordinate, discretize the timestamp of events to obtain a time image that can
reflect the motion confidence, and further segment the moving object through
adaptive threshold on the time image. In temporal reasoning stage, we construct
the events into a point cloud along timestamp, and use RANSAC algorithm to
extract the columnar shape in the cloud for peeling off the background.
Finally, we fuse the results from the two reasoning stages to extract the final
moving object region. This joint spatio-temporal reasoning framework can
effectively detect the moving object from motion confidence and geometric
structure. Moreover, we conduct extensive experiments on various datasets to
verify that the proposed method can improve the moving object detection
accuracy by 13\%.


  In this article, it is shown how the extended conformal Einstein field
equations and a gauge based on the properties of conformal geodesics can be
used to analyse the non-linear stability of de Sitter-like spacetimes with
spatial sections of negative scalar curvature. This class of spacetimes admits
a smooth conformal extension with a space-like conformal boundary. Central to
the analysis is the use of conformal Gaussian systems to obtain a hyperbolic
reduction of the conformal Einstein field equations for which standard Cauchy
stability results for symmetric hyperbolic systems can be employed. The use of
conformal methods allows us to rephrase the question of the global existence of
solutions to the Einstein field equations into considerations of finite
existence time for the conformal evolution system.


  Cosmic microwave background (CMB) temperature anisotropies follow a Gaussian
statistical distribution in the standard inflationary model, but there are
non-Gaussian contributions due to astrophysical foregrounds. The detection of
the non-Gaussian signal due to extragalactic point sources and its distinction
from the possible intrinsic non-Gaussianity is an issue of great importance in
CMB analysis. The Mexican Hat Wavelet Family (MHWF), which has been proved very
useful for the detection of extragalactic point sources, is applied here to the
study of non-Gaussianity due to point sources in CMB maps. We carry out
simulations of CMB maps with the characteristics of the forthcoming Planck
mission at 70 and 100 GHz and filter them with the MHWF. By comparing the
skewness and kurtosis of simulated maps with and without point sources, we are
able to detect clearly the non-Gaussian signal due to point sources for flux
limits as low as 0.4 Jy (70 GHz) and 0.3 Jy (100 GHz). The second and third
members of the MHWF perform better in this respect than the Mexican Hat Wavelet
(MHW1) and much better than the Daubechies 4 wavelet. We have also estimated
the third order, $K_3$, and fourth order, $K_4$, cumulants produced by point
sources at these Planck channels by means of a fit with the MHWF. The average
relative errors with respect to the real values are below 12% for fluxes down
to 0.6 Jy (70 GHz) and 0.4 Jy (100 GHz). The values of these cumulants allow us
to distinguish between different source counts models.


  We demonstrate an interface-sensitive NMR in a semiconducting nanostructure,
where an NMR signal from the minute heterojunction region of a model
heterojunction structure (In$_{0.48}$Ga$_{0.52}$P/GaAs) is detected by using
nuclear hyperpolarization created by optical pumping. The key to the detection
is the use of minute lattice distortions occurring at the heterojunction due to
the lattice mismatch, which enables us to create and localize hyperpolarization
at the heterojunction and distinguish it from the other parts. In particular,
the suppression of nuclear spin diffusion by the spatial variation in the
strain and the resultant unexpectedly stable hyperpolarization at the
heterojunction are the keys to successful detection.


  The response of low-dimensional solid objects combines geometry and physics
in unusual ways, exemplified in structures of great utility such as a
thin-walled tube that is ubiquitous in nature and technology. Here we provide a
particularly surprising consequence of this confluence of geometry and physics
in tubular structures: the anomalously large persistence of a localized pinch
in an elastic pipe whose effect decays very slowly as an oscillatory
exponential with a persistence length that diverges as the thickness of the
tube vanishes, which we confirm experimentally. The result is more a
consequence of geometry than material properties, and is thus equally
applicable to carbon nanotubes as it is to oil pipelines.


  Humans have the remarkable ability to use held objects as tools to interact
with their environment. For this to occur, humans internally estimate how hand
movements affect the object's movement. We wish to endow robots with this
capability. We contribute methodology to jointly estimate the geometry and pose
of objects grasped by a robot, from RGB images captured by an external camera.
Notably, our method transforms the estimated geometry into the robot's
coordinate frame, while not requiring the extrinsic parameters of the external
camera to be calibrated. Our approach leverages 3D foundation models, large
models pre-trained on huge datasets for 3D vision tasks, to produce initial
estimates of the in-hand object. These initial estimations do not have
physically correct scales and are in the camera's frame. Then, we formulate,
and efficiently solve, a coordinate-alignment problem to recover accurate
scales, along with a transformation of the objects to the coordinate frame of
the robot. Forward kinematics mappings can subsequently be defined from the
manipulator's joint angles to specified points on the object. These mappings
enable the estimation of points on the held object at arbitrary configurations,
enabling robot motion to be designed with respect to coordinates on the grasped
objects. We empirically evaluate our approach on a robot manipulator holding a
diverse set of real-world objects.


  Recently, variational autoencoder (VAE), a deep representation learning (DRL)
model, has been used to perform speech enhancement (SE). However, to the best
of our knowledge, current VAE-based SE methods only apply VAE to the model
speech signal, while noise is modeled using the traditional non-negative matrix
factorization (NMF) model. One of the most important reasons for using NMF is
that these VAE-based methods cannot disentangle the speech and noise latent
variables from the observed signal. Based on Bayesian theory, this paper
derives a novel variational lower bound for VAE, which ensures that VAE can be
trained in supervision, and can disentangle speech and noise latent variables
from the observed signal. This means that the proposed method can apply the VAE
to model both speech and noise signals, which is totally different from the
previous VAE-based SE works. More specifically, the proposed DRL method can
learn to impose speech and noise signal priors to different sets of latent
variables for SE. The experimental results show that the proposed method can
not only disentangle speech and noise latent variables from the observed signal
but also obtain a higher scale-invariant signal-to-distortion ratio and speech
quality score than the similar deep neural network-based (DNN) SE method.


  We present predictions for the pseudo-rapidity density of charged particles
produced in central Pb-Pb collisions at the LHC. Particle production in such
collisions is calculated in the framework of k_t-factorization. The nuclear
unintegrated gluon distributions at LHC energies are determined from numerical
solutions of the Balitsky-Kovchegov equation including recently calculated
running coupling corrections. The initial conditions for the evolution are
fixed by fitting RHIC data at collision energies \sqrt{s}}=130 and 200 GeV per
nucleon. We obtain dN^{Pb-Pb}_{ch}/d\eta (\sqrt{s}=5.5 TeV, \eta=0)\approx
1290\div 1480.


  It is shown that the Coulomb interaction can lead to delocalization of two
electron states in two-dimensional (2D) disordered potential in a way similar
to the Anderson transition in three dimensions (3D). At fixed disorder strength
the localized phase corresponds to low electron density and large value of
parameter r_s.


  We show (modulo a parity condition) that, a generalized complex brane in a
generalized complex manifold is locally equivalent to a holomorphic coisotropic
submanifold of a holomorphic Poisson structure, with higher-rank branes
corresponding to holomorphic Poisson modules. We describe (but do not prove
here) the global version of this holomorphicity result. Finally, we use the
"local holomorphic gauges" to give examples, in the Hopf surface with
nonstandard generalized complex structure, of branes which are neither
Lagrangian nor complex.


  With the broad application of deep neural networks (DNNs), backdoor attacks
have gradually attracted attention. Backdoor attacks are insidious, and
poisoned models perform well on benign samples and are only triggered when
given specific inputs, which cause the neural network to produce incorrect
outputs. The state-of-the-art backdoor attack work is implemented by data
poisoning, i.e., the attacker injects poisoned samples into the dataset, and
the models trained with that dataset are infected with the backdoor. However,
most of the triggers used in the current study are fixed patterns patched on a
small fraction of an image and are often clearly mislabeled, which is easily
detected by humans or defense methods such as Neural Cleanse and SentiNet.
Also, it's difficult to be learned by DNNs without mislabeling, as they may
ignore small patterns. In this paper, we propose a generalized backdoor attack
method based on the frequency domain, which can implement backdoor implantation
without mislabeling and accessing the training process. It is invisible to
human beings and able to evade the commonly used defense methods. We evaluate
our approach in the no-label and clean-label cases on three datasets (CIFAR-10,
STL-10, and GTSRB) with two popular scenarios (self-supervised learning and
supervised learning). The results show our approach can achieve a high attack
success rate (above 90%) on all the tasks without significant performance
degradation on main tasks. Also, we evaluate the bypass performance of our
approach for different kinds of defenses, including the detection of training
data (i.e., Activation Clustering), the preprocessing of inputs (i.e.,
Filtering), the detection of inputs (i.e., SentiNet), and the detection of
models (i.e., Neural Cleanse). The experimental results demonstrate that our
approach shows excellent robustness to such defenses.


  Federated learning (FL) has evolved as a prominent method for edge devices to
cooperatively create a unified prediction model while securing their sensitive
training data local to the device. Despite the existence of numerous research
frameworks for simulating FL algorithms, they do not facilitate comprehensive
deployment for automatic speech recognition tasks on heterogeneous edge
devices. This is where Ed-Fed, a comprehensive and generic FL framework, comes
in as a foundation for future practical FL system research. We also propose a
novel resource-aware client selection algorithm to optimise the waiting time in
the FL settings. We show that our approach can handle the straggler devices and
dynamically set the training time for the selected devices in a round. Our
evaluation has shown that the proposed approach significantly optimises waiting
time in FL compared to conventional random client selection methods.


  Recent theory and experiments have shown how the buildup of a
high-concentration polymer layer at a one-dimensional solvent-air interface can
lead to an evaporation rate that scales with time as $t^{-1/2}$ and that is
insensitive to the ambient humidity. Using phase field modelling we show that
this scaling law constitutes a naturally emerging robust regime,
Diffusion-Limited Evaporation (DLE). This regime dominates the dynamical state
diagram of the system, which also contains regions of constant and arrested
evaporation, confirming and extending understanding of recent experimental
observations and theoretical predictions. We provide a theoretical argument to
show that the scaling observed in the DLE regime occurs for a wide range of
parameters, and our simulations predict that it can occur in two-dimensional
geometries as well. Finally, we discuss possible extensions to more complex
systems.


  The redshifts and luminosities of Type 1A supernovae are conventionally
fitted with the current paradigm, which holds that the galaxies are locally
stationary in an expanding metric. The fit fails unless the expansion is
accelerating; driven perhaps by "dark energy". Is the recession of the galaxies
slowed down by gravity or speeded up by some repulsive force? To shed light on
this question the redshifts and apparent magnitudes of type 1A supernovae are
re-analysed in a cartesian frame of reference omitting gravitational effects.
The redshift is ascribed to the relativistic Doppler effect which gives the
recession velocity when the light was emitted; if this has not changed, the
distance reached and the luminosity follow immediately. This simple concept
fits the observations surprisingly well. It appears that the galaxies recede at
unchanging velocities, so on the largest scale there is no significant
intergalactic force. Reasons for the apparent absence of an intergalactic force
are discussed. There is no gravity out there and no dark energy.


  Biconical-type antennas featuring high directivity have been designed,
created, and tested in anechoic chamber. Results in the range between 1 and 5
GHz are presented in this article. In particular, two different configurations
have been tested, with and without dielectric lenses, both involving rapid
prototyping tools (3D printing) for the dielectric and the antenna support. A
very high directivity is nowadays demanded by efficient and sustainable
point-to-point communications or energy transfer protocols, to avoid releasing
energy in neighboring areas and preserve data transfer security. As
demonstrated here, special biconical type antennas featuring a 3D printed
polylactic acid (PLA) dielectric lens can achieve a good directivity, with a
corresponding emission lobe centered around 8.4 degrees, featuring a FWHM of
6.4 degrees. Dielectric lens-free antennas, featuring an unconventional shape,
can also achieve a good directivity, with a corresponding emission lobe
centered around 10.0 degrees, featuring a FWHM of 14.2 degrees. The preliminary
results shown here explore some of the aspects of the vast configuration space
(which include fabrication techniques, dielectric materials, conductive
supports, etc.) and open the route for further optimization studies. The aim
would be to adjust the various degrees of freedom in order to achieve what can
be defined as "infinite" directivity.


  We consider the problem of estimating the trace of a matrix function $f(A)$.
In certain situations, in particular if $f(A)$ cannot be well approximated by a
low-rank matrix, combining probing methods based on graph colorings with
stochastic trace estimation techniques can yield accurate approximations at
moderate cost. So far, such methods have not been thoroughly analyzed, though,
but were rather used as efficient heuristics by practitioners. In this
manuscript, we perform a detailed analysis of stochastic probing methods and,
in particular, expose conditions under which the expected approximation error
in the stochastic probing method scales more favorably with the dimension of
the matrix than the error in non-stochastic probing. Extending results from [E.
Aune, D. P. Simpson, J. Eidsvik, Parameter estimation in high dimensional
Gaussian distributions, Stat. Comput., 24, pp. 247--263, 2014], we also
characterize situations in which using just one stochastic vector is always --
not only in expectation -- better than the deterministic probing method.
Several numerical experiments illustrate our theory and compare with existing
methods.


  An efficient way to calculate one-loop counterterms within the Feynman
diagrammatic approach and dimensional regularization is to expand the
propagators in the integrands of the Feynman integrals around vanishing
external momentum. In this way, a generic one-loop diagram is reduced to a sum
of vacuum diagrams. The logarithmically divergent part can be extracted by
power counting arguments. In case of higher derivative theories, the standard
implementation of this procedure on a computer algebra system can become
quickly inefficient due to a high proliferation of terms coming from the
intermediate replacement of high-rank tensor-integrals with symmetrized product
of metric tensors. In this note we present a simple combinatorial solution to
this problem which makes the implementation much more efficient. This method is
especially relevant in the renormalization of higher derivative theories, but
might as well be integrated as a standard routine in existing computer algebra
programs designed to automatize Feynman diagrammatic calculations.


  The charmed-strange meson spectrum is calculated with the overlap valence
fermions on 2+1 flavor domain wall dynamical configurations for $32^3\times 64$
lattices with a spatial size of 2.7 fm. Both charm and strange quark
propagators are calculated with the overlap fermion action. The calculated
scalar meson at 2304(22) MeV and axial-vector meson at 2546(27) MeV are in good
agreement with the experimental masses of $D{s0}^*$(2317) and $D_{s1}$(2536).


  We investigate the effect of self-interacting dark matter (SIDM) on the
density profiles of $V_{\rm max} \simeq 40~km~s^{-1}$ isolated dwarf dark
matter halos -- the scale of relevance for the too big to fail problem (TBTF)
-- using very high-resolution cosmological zoom simulations. Each halo has
millions of particles within its virial radius. We find that SIDM models with
cross sections per unit mass spanning the range \sigma/m = $0.5 - 50$
$cm^2~g^{-1}$ alleviate TBTF and produce constant density cores of size
300-1000 pc, comparable to the half-light radii of $M_\star$ ~ $10^{5-7}$
$M_\odot$ dwarfs. The largest, lowest density cores develop for cross sections
in the middle of this range, \sigma/m ~ $5-10~cm^2~g^{-1}$. Our largest SIDM
cross section run (\sigma/m = $50~cm^2~g^{-1}$) develops a slightly denser core
owing to mild core-collapse behavior, but it remains less dense than the CDM
case and retains a constant density core profile. Our work suggests that SIDM
cross sections as large or larger than $50~cm^2~g^{-1}$ remain viable on
velocity scales of dwarf galaxies ($v_{\rm rms}$ ~ $40~km~s^{-1}$). The range
of SIDM cross sections that alleviate TBTF and the cusp/core problem spans at
least two orders of magnitude and therefore need not be particularly
fine-tuned.


  We have investigated the role of a few prominent excitation mechanisms viz.
collisional excitation, recombination, continuum fluorescence and Lyman beta
fluorescence on the OI line spectra in Herbig Ae/Be stars. The aim is to
understand which of them is the central mechanism that explains the observed OI
line strengths. The study is based on an analysis of the observed optical
spectra of 62 Herbig Ae/Be stars and near-infrared spectra of 17 Herbig Ae/Be
stars. The strong correlation observed between the line fluxes of OI
$\lambda$8446 and OI $\lambda$11287, as well as a high positive correlation
between the line strengths of OI $\lambda$8446 and H$\alpha$ suggest that Lyman
beta fluorescence is the dominant excitation mechanism for the formation of OI
emission lines in Herbig Ae/Be stars. Further, from an analysis of the emission
line fluxes of OI $\lambda\lambda$7774, 8446, and comparing the line ratios
with those predicted by theoretical models, we assessed the contribution of
collisional excitation in the formation of OI emission lines.


  The Steklov function $\mu_f(\cdot,t)$ is defined to average a continuous
function $f$ at each point of its domain by using a window of size given by
$t>0$. It has traditionally been used to approximate $f$ smoothly with small
values of $t$. In this paper, we first find a concise and useful expression for
$\mu_f$ for the case when $f$ is a multivariate quartic polynomial. Then we
show that, for large enough $t$, $\mu_f(\cdot,t)$ is convex; in other words,
$\mu_f(\cdot,t)$ convexifies $f$. We provide an easy-to-compute formula for $t$
with which $\mu_f$ convexifies certain classes of polynomials. We present an
algorithm which constructs, via an ODE involving $\mu_f$, a trajectory $x(t)$
emanating from the minimizer of the convexified $f$ and ending at $x(0)$, an
estimate of the global minimizer of $f$. For a family of quartic polynomials,
we provide an estimate for the size of a ball that contains all its global
minimizers. Finally, we illustrate the working of our method by means of
numerous computational examples.


  We construct the conserved charge of generic gravity theories built on
arbitrary contractions of the Riemann tensor (but not on its derivatives) for
asymptotically (anti)-de Sitter spacetimes. Our construction is a
generalization of the ADT charges of linear and quadratic gravity theories in
cosmological backgrounds. As an explicit example we find the energy and angular
momentum of the BTZ black hole in the 2+1 dimensional Born-Infeld gravity.


  In online social networks people often express attitudes towards others,
which forms massive sentiment links among users. Predicting the sign of
sentiment links is a fundamental task in many areas such as personal
advertising and public opinion analysis. Previous works mainly focus on textual
sentiment classification, however, text information can only disclose the "tip
of the iceberg" about users' true opinions, of which the most are unobserved
but implied by other sources of information such as social relation and users'
profile. To address this problem, in this paper we investigate how to predict
possibly existing sentiment links in the presence of heterogeneous information.
First, due to the lack of explicit sentiment links in mainstream social
networks, we establish a labeled heterogeneous sentiment dataset which consists
of users' sentiment relation, social relation and profile knowledge by
entity-level sentiment extraction method. Then we propose a novel and flexible
end-to-end Signed Heterogeneous Information Network Embedding (SHINE) framework
to extract users' latent representations from heterogeneous networks and
predict the sign of unobserved sentiment links. SHINE utilizes multiple deep
autoencoders to map each user into a low-dimension feature space while
preserving the network structure. We demonstrate the superiority of SHINE over
state-of-the-art baselines on link prediction and node recommendation in two
real-world datasets. The experimental results also prove the efficacy of SHINE
in cold start scenario.


  A partial wave analysis on the process
$e^{+}e^{-}\rightarrow\omega\pi^{+}\pi^{-}$ is performed using 647 pb$^{-1}$ of
data sample collected by using the BESIII detector operating at the BEPCII
storage ring at center-of-mass (c.m.) energies from 2.000 GeV to 3.080 GeV. The
Born cross section of the $e^{+}e^{-}\rightarrow\omega\pi^{+}\pi^{-}$ process
is measured, with precision improved by a factor of 3 compared to that of
previous studies. A structure near 2.25 GeV is observed in the energy-dependent
cross sections of $e^{+}e^{-}\to\omega\pi^{+}\pi^{-}$ and
$\omega\pi^{0}\pi^{0}$ with a statistical significance of 7.6$\sigma$, and its
determined mass and width are 2232 $\pm$ 19 $\pm$ 27 MeV$/c^{2}$ and 93 $\pm$
53 $\pm$ 20 MeV, respectively, where the first and second uncertainties are
statistical and systematic, respectively. By analyzing the cross sections of
subprocesses $e^{+}e^{-}\rightarrow \omega f_{0}(500)$, $\omega f_{0}(980)$,
$\omega f_{0}(1370)$, $\omega f_{2}(1270)$, and $b_{1}(1235)\pi$, a structure,
with mass M = 2200 $\pm$ 11 $\pm$ 17 MeV/$c^2$ and width $\Gamma$ = 74 $\pm$ 20
$\pm$ 24 MeV, is observed with a combined statistical significance of
7.9$\sigma$. The measured resonance parameters will help to reveal the nature
of vector states around 2.25 GeV.


  We study the universal non-stationary evolution of wave turbulence (WT) in
Bose-Einstein condensates (BECs). Their temporal evolution can exhibit
different kinds of self-similar behavior corresponding to a large-time
asymptotic of the system or to a finite-time blowup. We identify self-similar
regimes in BECs by numerically simulating the forced and unforced
Gross-Pitaevskii equation (GPE) and the associated wave kinetic equation (WKE)
for the direct and inverse cascades, respectively. In both the GPE and the WKE
simulations for the direct cascade, we observe the first-kind self-similarity
that is fully determined by energy conservation. For the inverse cascade
evolution, we verify the existence of a self-similar evolution of the second
kind describing self-accelerating dynamics of the spectrum leading to blowup at
the zero mode (condensate) at a finite time. We believe that the universal
self-similar spectra found in the present paper are as important and relevant
for understanding the BEC turbulence in past and future experiments as the
commonly studied stationary Kolmogorov-Zakharov (KZ) spectra.


  Non-deterministic Constraint Logic is a family of graph games introduced by
Demaine and Hearn that facilitates the construction of complexity proofs. It is
convenient for the analysis of games, providing a uniform view. We focus on the
acyclic version, apply this to Klondike, Mahjong Solitaire and Nonogram (that
requires planarity), and discuss the more complicated game of Dou Shou Qi.
While for the first three games we reobtain known characterizations in a simple
and uniform manner, the result for Dou Shou Qi is new.


  We consider the problem of incompressible, forced, nonhelical, homogeneous,
isotropic MHD turbulence with no mean magnetic field. This problem is
essentially different from the case with externally imposed uniform mean field.
There is no scale-by-scale equipartition between magnetic and kinetic energies
as would be the case for the Alfven-wave turbulence. The isotropic MHD
turbulence is the end state of the turbulent dynamo which generates folded
fields with small-scale direction reversals. We propose that the statistics
seen in numerical simulations of isotropic MHD turbulence could be explained as
a superposition of these folded fields and Alfven-like waves that propagate
along the folds.


  We consider general symmetric systems of first order linear partial
differential operators on domains $\Omega \subset \mathbb{R}^d$, and we seek
sufficient conditions on the coefficients which ensure essential
self-adjointness. The coefficients of the first order terms are only required
to belong to $C^1(\Omega)$ and there is no ellipticity condition. Our criterion
writes as the completeness of an associated Riemannian structure which encodes
the propagation velocities of the system. As an application we obtain
sufficient conditions for confinement of energy for some wave propagation
problems of classical physics.


  Mindfulness is the state of paying attention to the present moment on purpose
and meditation is the technique to obtain this state. This study aims to
develop a robot assistant that facilitates mindfulness training by means of a
Brain Computer Interface (BCI) system. To achieve this goal, we collected EEG
signals from two groups of subjects engaging in a meditative vs. nonmeditative
human robot interaction (HRI) and evaluated cerebral hemispheric asymmetry,
which is recognized as a well defined indicator of emotional states. Moreover,
using self reported affective states, we strived to explain asymmetry changes
based on pre and post experiment mood alterations. We found that unlike earlier
meditation studies, the frontocentral activations in alpha and theta frequency
bands were not influenced by robot guided mindfulness practice, however there
was a significantly greater right sided activity in the occipital gamma band of
Meditation group, which is attributed to increased sensory awareness and open
monitoring. In addition, there was a significant main effect of Time on
participants self reported affect, indicating an improved mood after
interaction with the robot regardless of the interaction type. Our results
suggest that EEG responses during robot-guided meditation hold promise in
realtime detection and neurofeedback of mindful state to the user, however the
experienced neurophysiological changes may differ based on the meditation
practice and recruited tools. This study is the first to report EEG changes
during mindfulness practice with a robot. We believe that our findings driven
from an ecologically valid setting, can be used in development of future BCI
systems that are integrated with social robots for health applications.


  The onset of macroscopic phase coherence in superconducting cuprates is
considered to be determined by random percolation between mesoscopic
Jahn-Teller pairs, stripes or clusters. The model is found to predict the onset
of superconductivity near 6% doping, maximum Tc near 15% doping and Tc= T* at
optimum doping, and accounts for the destruction of superconductivity by Zn
doping near 7%. The model also predicts a relation between the pairing
(pseudogap) energy and Tc in terms of experimentally measurable quantities.


  One primary difficulty in understanding the nature of the putative accretion
disk in the central engine of AGNs is that some of its key intrinsic spectral
signatures are buried under the emissions from the surrounding regions, i.e.
the broad line region (BLR) and the obscuring torus. We argue here that these
signatures can be revealed by using optical and near-IR polarization. At least
in some quasars, the polarization is seen only in the continuum and is not
shared by emission lines. In this case, the polarized flux is considered to
show the intrinsic spectrum interior to the BLR, removing off the emissions
from the BLR and torus. We have used this polarization to reveal the
Balmer-edge feature and near-IR spectral shape of the central engine, both of
which are important for testing the fundamental aspects of the models.


  In the rapidly evolving global business landscape, the demand for software
has intensified competition among organizations, leading to challenges in
retaining highly qualified IT members in software organizations. One of the
problems faced by IT organizations is the retention of these strategic
professionals, also known as talent. This work presents an actionable framework
for Talent Retention (TR) used in IT organizations. It is based on our findings
from interviews performed with 21 IT managers. The TR Framework is our main
research outcome. Our framework encompasses a set of factors, contextual
characteristics, barriers, strategies, and coping mechanisms.
  Our findings indicated that software engineers can be differentiated from
other professional groups, and beyond competitive salaries, other elements for
retaining talent in IT organizations should be considered, such as
psychological safety, work-life balance, a positive work environment,
innovative and challenging projects, and flexible work. A better understanding
of factors could guide IT managers in improving talent management processes by
addressing Software Engineering challenges, identifying important elements, and
exploring strategies at the individual, team, and organizational levels.


  An individual defect in a nematic liquid crystal moves not only in response
to its interaction with other defects but also in response to an external
field. We analyze the motion of a wedge disclination in the presence of an
applied field of strength $H$. We neglect backflow and seek steadily travelling
patterns. The stationary picture yields a semi-infinite wall of strength $\pi$,
bounded by the defect line. We find that the disclination advances into the
region containing the wall at velocity $v(H)$, where $v$ scales as $H/|\log H|$
as long as the magnetic coherence length is greater than the core radius. When
the external field is applied in the presence of a pair of disclinations, their
dynamics is strongly influenced. We compute the expected relative velocity of
the disclinations as a function of distance and field. The natural tendency for
the disclinations to annihilate each other can be overcome by a sufficiently
strong field suitably directed.


  We present a measurement of the W+W- production cross section using 184/pb of
ppbar collisions at a center-of-mass energy of 1.96 TeV collected with the
Collider Detector at Fermilab. Using the dilepton decay channel W+W- ->
l+l-vvbar, where the charged leptons can be either electrons or muons, we find
17 candidate events compared to an expected background of 5.0+2.2-0.8 events.
The resulting W+W- production cross section measurement of sigma(ppbar -> W+W-)
= 14.6 +5.8 -5.1 (stat) +1.8 -3.0 (syst) +-0.9 (lum) pb agrees well with the
Standard Model expectation.


  Adversarial attacks against deep learning-based object detectors (ODs) have
been studied extensively in the past few years. These attacks cause the model
to make incorrect predictions by placing a patch containing an adversarial
pattern on the target object or anywhere within the frame. However, none of
prior research proposed a misclassification attack on ODs, in which the patch
is applied on the target object. In this study, we propose a novel, universal,
targeted, label-switch attack against the state-of-the-art object detector,
YOLO. In our attack, we use (i) a tailored projection function to enable the
placement of the adversarial patch on multiple target objects in the image
(e.g., cars), each of which may be located a different distance away from the
camera or have a different view angle relative to the camera, and (ii) a unique
loss function capable of changing the label of the attacked objects. The
proposed universal patch, which is trained in the digital domain, is
transferable to the physical domain. We performed an extensive evaluation using
different types of object detectors, different video streams captured by
different cameras, and various target classes, and evaluated different
configurations of the adversarial patch in the physical domain.


  Recent diffractive structure function measurements in deep inelastic
scattering (DIS) by the H1 and ZEUS experiments at HERA are reviewed. In
neutral current (NC) DIS, precise measurements have been made of diffraction,
$ep \to eXY$, the signature of which is a large rapidity gap (LRG) between the
hadronic system $X$ and the proton or one of its low-mass excited states $Y$.
New results on charged current (CC) DIS are also presented. They are identified
in a CC DIS sample by a large rapidity gap, corresponding to a diffractive
process $ep \to \nu XY$ with a $W$ boson exchange. Moreover, within the
framework of QCD hard scattering factorisation in DIS, all these data provide
constraints on the diffractive parton distribution functions of the proton and
predict a large gluon content. For example, the resulting gluon distribution,
extracted from the H1 measurements, carries an integrated fraction of about 70%
of the exchanged momentum. Note that these functions are crucial inputs for the
calculations of diffractive dijet or charm production in DIS. These parton
distribution functions may also be applicable to hadron-hadron scattering
provided that an additional rapidity gap survival probability is taken into
account.


  We present absolute frequency measurements of the ${}^{1}\text{P}_1
\leftarrow {}^{1}\text{S}_0$ ($229$nm) and ${}^{3}\text{P}_1 \leftarrow
{}^{1}\text{S}_0$ ($326$nm) transitions for all naturally occurring isotopes of
cadmium. The isotope shifts and hyperfine intervals of the fermionic isotopes
are determined with an accuracy of 3.3MHz. We find that quantum interference in
the laser-induced fluorescence spectra of the ${}^{1}\text{P}_1 \leftarrow
{}^{1}\text{S}_0$ transition causes an error of up to 29(5)MHz in determining
the hyperfine splitting, when not accounted for with an appropriate model.
Using a King-plot analysis, we extract the field- and mass-shift parameters and
determine nuclear charge radius differences for the fermions. The lifetime of
the $^1\text{P}_1$ state is determined to be 1.60(5)ns by measuring the natural
linewidth of the ${}^{1}\text{P}_1 \leftarrow {}^{1}\text{S}_0$ transition.
These results resolve significant discrepancies among previous measurements.


  In modern machine learning problems we deal with datasets that are either
distributed by nature or potentially large for which distributing the
computations is usually a standard way to proceed, since centralized algorithms
are in general ineffective. We propose a distributed learning approach for
mixtures of experts (MoE) models with an aggregation strategy to construct a
reduction estimator from local estimators fitted parallelly to distributed
subsets of the data. The aggregation is based on an optimal minimization of an
expected transportation divergence between the large MoE composed of local
estimators and the unknown desired MoE model. We show that the provided
reduction estimator is consistent as soon as the local estimators to be
aggregated are consistent, and its construction is performed by a proposed
majorization-minimization (MM) algorithm that is computationally effective. We
study the statistical and numerical properties for the proposed reduction
estimator on experiments that demonstrate its performance compared to namely
the global estimator constructed in a centralized way from the full dataset.
For some situations, the computation time is more than ten times faster, for a
comparable performance. Our source codes are publicly available on Github.


  Complex systems are characterized by specific time-dependent interactions
among their many constituents. As a consequence they often manifest rich,
non-trivial and unexpected behavior. Examples arise both in the physical and
non-physical world. The study of complex systems forms a new interdisciplinary
research area that cuts across physics, biology, ecology, economics, sociology,
and the humanities. In this paper we review the essence of complex systems from
a physicist's point of view, and try to clarify what makes them conceptually
different from systems that are traditionally studied in physics. Our goal is
to demonstrate how the dynamics of such systems may be conceptualized in
quantitative and predictive terms by extending notions from statistical physics
and how they can often be captured in a framework of co-evolving multiplex
network structures. We mention three areas of complex-systems science that are
currently studied extensively, the science of cities, dynamics of societies,
and the representation of texts as evolutionary objects. We discuss why these
areas form complex systems in the above sense. We argue that there exists
plenty of new land for physicists to explore and that methodical and conceptual
progress is needed most.


  NGC 3783 was observed for approximately 210ks by Suzaku and in this time
showed significant spectral and flux variability at both short (20ks) and long
(100ks) time scales. The full observation is found to consist of approximately
six "spectral periods" where the behaviour of the soft (0.3-1.0keV) and hard
(2-10keV) bands are somewhat distinct. Using a variety of methods we find that
the strong warm absorber present in this source does not change on these time
scales, confirming that the broad-band variability is intrinsic to the central
source. The time resolved difference-spectra are well modelled with an absorbed
powerlaw below 10keV, but show an additional hard excess at ~20keV in the
latter stages of the observation. This suggests that, in addition to the
variable powerlaw, there is a further variable component that varies with time
but not monotonically with flux. We show that a likely interpretation is that
this further component is associated with variations in the reflection fraction
or possibly ionization state of the accretion disk a few gravitational radii
from the black hole.


  The electroweak (EW) symmetry breaking in the simplest supersymmetric (SUSY)
extensions of the standard model (SM), i.e. minimal and next-to-minimal
supersymmetric standard models (MSSM and NMSSM), is considered. The spectrum of
the Higgs particles, upper bound on the mass of the lightest Higgs boson and
little hierarchy problem are discussed. The breakdown of gauge symmetry and
Higgs phenomenology within the E6 inspired SUSY models with extra U(1)' factor
are briefly reviewed.


  Generalized Zero-shot Semantic Segmentation aims to segment both seen and
unseen categories only under the supervision of the seen ones. To tackle this,
existing methods adopt the large-scale Vision Language Models (VLMs) which
obtain outstanding zero-shot performance. However, as the VLMs are designed for
classification tasks, directly adapting the VLMs may lead to sub-optimal
performance. Consequently, we propose CLIP-ZSS (Zero-shot Semantic
Segmentation), a simple but effective training framework that enables any image
encoder designed for closed-set segmentation applied in zero-shot and
open-vocabulary tasks in testing without combining with VLMs or inserting new
modules. CLIP-ZSS consists of two key modules: Global Learning Module (GLM) and
Pixel Learning Module (PLM). GLM is proposed to probe the knowledge from the
CLIP visual encoder by pulling the CLS token and the dense features from the
image encoder of the same image and pushing others apart. Moreover, to enhance
the ability to discriminate unseen categories, PLM consisting of pseudo labels
and weight generation is designed. To generate semantically discriminated
pseudo labels, a multi-scale K-Means with mask fusion working on the dense
tokens is proposed. In pseudo weight generation, a synthesizer generating
pseudo semantic features for the unannotated area is introduced. Experiments on
three benchmarks show large performance gains compared with SOTA methods.


  The linewidth of ions has been observed to be systematically narrower than
that of the coexisting neutrals in molecular clouds (Houde et al. 2000) and
been interpreted as the signature of the decoupling of the neutral turbulence
from magnetic fields in partially ionized medium (Li & Houde 2008; Paper I,
hereafter). As a sequel of Paper I, here we present further observational
evidence that lend support to these earlier proposals with the velocity
coordinate spectrum analysis (Lazarian & Pogosyan 2006). We recover the
turbulent energy spectra of HCN and HCO+(4-3) in a starless molecular filament
in NGC 6334 where magnetic fields play a dynamically important role (Li et al.
2015). Our analysis showed that the neutral spectrum is consistent with
Kolmogorov-type (k^-5/3, where k is the wave number), while that of the ions is
the same on the large scale but steeper (k^-2) for scales smaller than 0.404pc.
We carefully ruled out the possibilities that the spectrum difference can stem
from the differences of ion and neutral optical depth and hyper-fine
structures.


  Radio-loud quasars (RLQs) are known to produce excess X-ray emission,
compared to radio-quiet quasars (RQQs) of the same luminosity, commonly
attributed to jet-related emission. Recently, we found that the HeII EW and
$\alpha_{\rm{ox}}$ in RQQs are strongly correlated, which suggests that their
extreme-ultraviolet (EUV) and X-ray emission mechanisms are tightly related.
Using 48 RLQs, we show that steep-spectrum radio quasars (SSRQs) and low
radio-luminosity ($L_{\rm R}$) flat-spectrum radio quasars (FSRQs) follow the
$\alpha_{\rm ox}$--HeII EW relation of RQQs. This suggests that the X-ray and
EUV emission mechanisms in these types of RLQs is the same as in RQQs, and is
not jet related. High-$L_{\rm R}$ FSRQs show excess X-ray emission given their
HeII EW by a factor of $\approx$ 3.5, which suggests that only in this type of
RLQ is the X-ray production likely jet related.


  Coordination in a large number of networked robots is a challenging task,
especially when robots are constantly moving around the environment and there
are malicious attacks within the network. Various approaches in the literature
exist for detecting malicious robots, such as message sampling or suspicious
behavior analysis. However, these approaches require every robot to sample
every other robot in the network, leading to a slow detection process that
degrades team performance. This paper introduces a method that significantly
decreases the detection time for legitimate robots to identify malicious robots
in a scenario where legitimate robots are randomly moving around the
environment. Our method leverages the concept of ``Dynamic Crowd Vetting" by
utilizing observations from random encounters and trusted neighboring robots'
opinions to quickly improve the accuracy of detecting malicious robots. The key
intuition is that as long as each legitimate robot accurately estimates the
legitimacy of at least some fixed subset of the team, the second-hand
information they receive from trusted neighbors is enough to correct any
misclassifications and provide accurate trust estimations of the rest of the
team. We show that the size of this fixed subset can be characterized as a
function of fundamental graph and random walk properties. Furthermore, we
formally show that as the number of robots in the team increases the detection
time remains constant. We develop a closed form expression for the critical
number of time-steps required for our algorithm to successfully identify the
true legitimacy of each robot to within a specified failure probability. Our
theoretical results are validated through simulations demonstrating significant
reductions in detection time when compared to previous works that do not
leverage trusted neighbor information.


  We measure the time-energy correlation of broadband, spontaneously generated
four wave mixing (FWM), and demonstrate novel time-frequency coupling effects;
specifically, we observe a power-dependent splitting of the correlation in both
energy and time. By pumping a photonic crystal fiber with narrowband picosecond
pulses we generate FWM in a unique regime, where broadband (>100nm), sidebands
are generated that are incoherent, yet time-energy correlated. Although the
observed time-energy correlation in FWM is conceptually similar to parametric
down conversion, its unique dependence on pump intensity due to self and cross
phase modulation effects, yields spectral and temporal structure in the
correlations. While these effects are minute compared to the time duration and
bandwidth of the FWM sidebands, they are well observed using sum frequency
generation as a precise, ultrafast, wide-bandwidth correlation detector.


  This study presents advanced neural network architectures including
Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long
Short-Term Memory Networks (LSTMs), and Deep Belief Networks (DBNs) for
enhanced ECG signal analysis using Field Programmable Gate Arrays (FPGAs). We
utilize the MIT-BIH Arrhythmia Database for training and validation,
introducing Gaussian noise to improve algorithm robustness. The implemented
models feature various layers for distinct processing and classification tasks
and techniques like EarlyStopping callback and Dropout layer are used to
mitigate overfitting. Our work also explores the development of a custom Tensor
Compute Unit (TCU) accelerator for the PYNQ Z1 board, offering comprehensive
steps for FPGA-based machine learning, including setting up the Tensil
toolchain in Docker, selecting architecture, configuring PS-PL, and compiling
and executing models. Performance metrics such as latency and throughput are
calculated for practical insights, demonstrating the potential of FPGAs in
high-performance biomedical computing. The study ultimately offers a guide for
optimizing neural network performance on FPGAs for various applications.


  Employing the high-resolution observations from the Solar Dynamics
Observatory (SDO) and the Interface Region Imaging Spectrograph (IRIS), we
statistically investigate flux rope proxies in NOAA AR 11897 from 14-Nov-2013
to 19-Nov-2013 and display two fan-spine structures in this AR. For the first
time, we detect flux rope proxies of NOAA 11897 for total 30 times in 4
different locations. These flux rope proxies were either tracked in both lower
and higher temperature wavelengths or only detected in hot channels. Specially,
none of these flux rope proxies was observed to erupt, but just faded away
gradually. In addition to these flux rope proxies, we firstly detect a
secondary fan-spine structure. It was covered by dome-shaped magnetic fields
which belong to a larger fan-spine topology. These new observations imply that
considerable amounts of flux ropes can exist in an AR and the complexity of AR
magnetic configuration is far beyond our imagination.


  As a canonical model for wetting far from thermal equilibrium we study a
Kardar-Parisi-Zhang interface growing on top of a hard-core substrate.
Depending on the average growth velocity the model exhibits a non-equilibrium
wetting transition which is characterized by an additional surface critical
exponent theta. Simulating the single-step model in one spatial dimension we
provide accurate numerical estimates for theta and investigate the distribution
of contact points between the substrate and the interface as a function of
time. Moreover, we study the influence of finite-size effects, in particular
the time needed until a finite substrate is completely covered by the wetting
layer for the first time.


  In this paper, we consider the replicator-mutator dynamics for pairwise
social dilemmas where the payoff entries are random variables. The randomness
is incorporated to take into account the uncertainty, which is inevitable in
practical applications and may arise from different sources such as lack of
data for measuring the outcomes, noisy and rapidly changing environments, as
well as unavoidable human estimate errors. We analytically and numerically
compute the probability that the replicator-mutator dynamics has a given number
of equilibria for four classes of pairwise social dilemmas (Prisoner's Dilemma,
Snow-Drift Game, Stag-Hunt Game and Harmony Game). As a result, we characterise
the qualitative behaviour of such probabilities as a function of the mutation
rate. Our results clearly show the influence of the mutation rate and the
uncertainty in the payoff matrix definition on the number of equilibria in
these games. Overall, our analysis has provided novel theoretical contributions
to the understanding of the impact of uncertainty on the behavioural diversity
in a complex dynamical system.