  The electric conductivity, $\sigma_{\rm el}$, is a fundamental transport
coefficient of QCD matter that can be related to the zero-energy limit of the
electromagnetic (EM) spectral function at vanishing 3-momentum in the medium.
The EM spectral function is also the central quantity to describe the thermal
emission rates and pertinent spectra of photon and dilepton radiation in
heavy-ion collisions. Employing a model for dilepton rates that combines
hadronic many-body theory with nonperturbative QGP emission constrained by
lattice-QCD which describes existing dilepton measurements in heavy-ion
collisions, we investigate the sensitivity of low-mass dilepton spectra in
Pb-Pb collisions at the LHC to $\sigma_{\rm el}$. In particular, we disentangle
the contributions from QGP and hadronic emission, and identify signatures that
can help to extract $\sigma_{\rm el}$ from high-precision experimental data
expected to be attainable with future detector systems at the LHC.


  It has been pointed out in [1] that the familiar reciprocity relation between
the conductivity $\sigma$ and resistivity $\rho$ should not be expected to hold
in all possible settings, but, as argued in [2], is rather a property that may
(or may not) emerge as a consequence of specific features, or in certain limits
of interest, of a given theory. In this work, I prove the conventional
statement: $\rho= \sigma^{-1}$, which I refer to as $\textit{Ohm's reciprocity
relation}$, for a large class of holographic models with $d=3+1$ boundary
dimensions. The proof involves a generalisation of the electric-magnetic
duality in the $D=4+1$ bulk, the validity of which is discussed in detail. I
then confirm these findings by considering one specific model and run numerical
simulations in different settings.


  We demonstrate a method for probing interaction effects in a thermal beam of
strontium atoms using simultaneous measurements of Rydberg EIT and
spontaneously created ions or electrons. We present a Doppler-averaged optical
Bloch equation model that reproduces the optical signals and allows us to
connect the optical coherences and the populations. We use this to determine
that the spontaneous ionization process in our system occurs due to collisions
between Rydberg and ground state atoms in the EIT regime. We measure the cross
section of this process to be 0.6 $\pm$ 0.2 $\sigma_{\rm{geo}}$, where
$\sigma_{\rm{geo}}$ is the geometrical cross section of the Rydberg atom. This
results adds complementary insight to a range of recent studies of interacting
thermal Rydberg ensembles.


  We consider the radial buoyancy of vertical magnetic field lines in radiation
and gas pressure dominated accretion disks. We find that in addition to radial
drift driven by turbulent diffusion and biased by the global field geometry,
there are buoyancy effects which tend to move magnetic flux outward. In gas
pressure dominated disks the poloidal magnetic field will move outward at a
rate comparable to its inward advection. On the other hand, in a radiation
pressure dominated disk the poloidal magnetic field will usually move outward
faster than it is advected inward. This implies that the fields in disks in
active galactic nuclei are generated at small radii by an internal disk dynamo.
This conclusion can be avoided if the external field imposes a supersonic
Alfv\'en speed within the disk without giving rise to interchange
instabilities. In any case we note that variations in the mass transfer rate
will lead directly to a modulation of the nonthermal emission from the disk
system.


  A study of the 7Li(9Be,4He 10Be)2H reaction at E{beam}=70 MeV has been
performed using resonant particle spectroscopy techniques and provides the
first measurements of alpha-decaying states in 14C. Excited states are observed
at 14.7, 15.5, 16.4, 18.5, 19.8, 20.6, 21.4, 22.4 and 24.0 MeV. The
experimental technique was able to resolve decays to the various particle bound
states in 10Be, and provides evidence for the preferential decay of the high
energy excited states into states in 10Be at ~6 MeV. The decay processes are
used to indicate the possible cluster structure of the 14C excited states.


  We investigate homogeneous and isotropic cosmological models in scalar-tensor
theories of gravity where two scalar fields are nonminimally coupled to the
geometry. Exact solutions are found, by Noether symmetries, depending on the
form of couplings and self-interaction potentials. An interesting feature is
that we deal with the Brans-Dicke field and the inflaton on the same ground
since both are nonminimally coupled and not distinguished {\it a priori} as in
earlier models. This fact allows to improve dynamics to get successful extended
inflationary scenarios. Double inflationary solutions are also discussed.


  We study by comparison the structure of singlet type states in Q-bit space in
the light of quantum and classical paradigms. It is shown that only the
classical paradigm implies a variation in the visibility of correlation
coefficients, that has been observed in fact in experiments. We conclude that
Q-bit space in not a appropriate venue for an EPR test of quantum completeness.


  The capacity of multiuser networks has been a long-standing problem in
information theory. Recently, Avestimehr et al. have proposed a deterministic
network model to approximate multiuser wireless networks. This model, known as
the ADT network model, takes into account the broadcast nature of wireless
medium and interference.
  We show that the ADT network model can be described within the algebraic
network coding framework introduced by Koetter and Medard. We prove that the
ADT network problem can be captured by a single matrix, and show that the
min-cut of an ADT network is the rank of this matrix; thus, eliminating the
need to optimize over exponential number of cuts between two nodes to compute
the min-cut of an ADT network. We extend the capacity characterization for ADT
networks to a more general set of connections, including single
unicast/multicast connection and non-multicast connections such as multiple
multicast, disjoint multicast, and two-level multicast. We also provide
sufficiency conditions for achievability in ADT networks for any general
connection set. In addition, we show that random linear network coding, a
randomized distributed algorithm for network code construction, achieves the
capacity for the connections listed above. Furthermore, we extend the ADT
networks to those with random erasures and cycles (thus, allowing
bi-directional links).
  In addition, we propose an efficient linear code construction for the
deterministic wireless multicast relay network model. Avestimehr et al.'s
proposed code construction is not guaranteed to be efficient and may
potentially involve an infinite block length. Unlike several previous coding
schemes, we do not attempt to find flows in the network. Instead, for a layered
network, we maintain an invariant where it is required that at each stage of
the code construction, certain sets of codewords are linearly independent.


  Standard arguments state that Bose Einstein condensation (BEC) cannot occur
in dimensions lower than three in the thermodynamic limit as the expressions
for the number of bosons in the excited states are unbounded. These arguments
imply that the number density is infinite, which is an extraordinary condition.
As an alternative to this pedagogy, we explore the use of regularization
techniques to show that the number density of bosons in the excited state is
finite in two and one dimensions at low temperatures, making it possible to
have a BEC transition in the thermodynamic limit. We suggest creating a
two-dimensional optical traps of increasing sizes to test this hypothesis as
the thermodynamic limit is approached.


  We provide a novel approach to achieving a desired outcome in a coordination
game: the original 2x2 game is embedded in a 2x3 game where one of the players
may use a third action. For a large set of payoff values only one of the Nash
equilibria of the original 2x2 game is stable under replicator dynamics. We
show that this Nash equilibrium is the {\omega}-limit of all initial conditions
in the interior of the state space for the modified 2x3 game. Thus, the
existence of a third action for one of the players, although not used, allows
both players to coordinate on one Nash equilibrium. This Nash equilibrium is
the one preferred by, at least, the player with access to the new action. This
approach deals with both coordination failure (players choose the
payoff-dominant Nash equilibrium, if such a Nash equilibrium exists) and
miscoordination (players do not use mixed strategies).


  Autonomous cyber-physical systems (CPS) rely on the correct operation of
numerous components, with state-of-the-art methods relying on machine learning
(ML) and artificial intelligence (AI) components in various stages of sensing
and control. This paper develops methods for estimating the reachable set and
verifying safety properties of dynamical systems under control of neural
network-based controllers that may be implemented in embedded software. The
neural network controllers we consider are feedforward neural networks called
multilayer perceptrons (MLP) with general activation functions. As such
feedforward networks are memoryless, they may be abstractly represented as
mathematical functions, and the reachability analysis of the network amounts to
range (image) estimation of this function provided a set of inputs. By
discretizing the input set of the MLP into a finite number of hyper-rectangular
cells, our approach develops a linear programming (LP) based algorithm for
over-approximating the output set of the MLP with its input set as a union of
hyper-rectangular cells. Combining the over-approximation for the output set of
an MLP based controller and reachable set computation routines for ordinary
difference/differential equation (ODE) models, an algorithm is developed to
estimate the reachable set of the closed-loop system. Finally, safety
verification for neural network control systems can be performed by checking
the existence of intersections between the estimated reachable set and unsafe
regions. The approach is implemented in a computational software prototype and
evaluated on numerical examples.


  Star formation is slow, in the sense that the gas consumption time is much
longer than the dynamical time. It is also inefficient; essentially all star
formation in local galaxies takes place in giant molecular clouds (GMCs), but
the fraction of a GMC converted to stars is very small, ~5%. In the most
luminous starbursts, the GMC lifetime is shorter than the main sequence
lifetime of even the most massive stars, so that supernovae can play no role in
GMC disruption. We investigate the disruption of GMCs across a wide range of
galaxies, from normal spirals to the densest starbursts; we take into account
the effects of HII gas pressure, shocked stellar winds, protostellar jets, and
radiation pressure produced by the absorption and scattering of starlight on
dust grains. In the Milky Way, we find that a combination of three mechanisms,
jets, HII gas pressure, and radiation pressure, disrupts the clouds. In more
rapidly star forming galaxies such as ``clump'' galaxies at high-redshift,
ultra-luminous infrared galaxies (ULIRGs) and submillimeter galaxies, radiation
pressure dominates natal cloud distribution. We predict the presence of 10-20
clusters with masses ~10^7 Msun in local ULIRGs such as Arp 220 and a similar
number of clusters with M_* ~ 10^8 Msun in high redshift clump galaxies;
submillimeter galaxies will have even more massive clusters. We find that the
mass fraction of a GMC that ends up in stars is an increasing function of the
gas surface density of a galaxy, reaching ~35% in the most luminous starbursts.
Furthermore, the disruption of bubbles by radiation pressure stirs the
interstellar medium to velocities of ~10 km/s in normal galaxies and to ~100
km/s in ULIRGs like Arp 220, consistent with observations. Thus, radiation
pressure may play a dominant role in the ISM of star-forming galaxies.


  If supersymmetry is discovered at the LHC, the extraction of the fundamental
parameters will be a formidable task. In such a system where measurements
depend on different combinations of the parameters in a highly correlated
system, the identification of the true parameter set in an efficient way
necessitates the development and use of sophisticated methods. A rigorous
treatment of experimental and theoretical errors is necessary to determine the
precision of the measurement of the fundamental parameters. The techniques
developed for this endeavor can also be applied to similar problems such as the
determination of the Higgs boson couplings at the LHC.


  In this paper, we introduce the Phoenix drone: the first completely
open-source tail-sitter micro aerial vehicle (MAV) platform. The vehicle has a
highly versatile, dual-rotor design and is engineered to be low-cost and easily
extensible/modifiable. Our open-source release includes all of the design
documents, software resources, and simulation tools needed to build and fly a
high-performance tail-sitter for research and educational purposes. The drone
has been developed for precision flight with a high degree of control
authority. Our design methodology included extensive testing and
characterization of the aerodynamic properties of the vehicle. The platform
incorporates many off-the-shelf components and 3D-printed parts, in order to
keep the cost down. Nonetheless, the paper includes results from flight trials
which demonstrate that the vehicle is capable of very stable hovering and
accurate trajectory tracking. Our hope is that the open-source Phoenix
reference design will be useful to both researchers and educators. In
particular, the details in this paper and the available open-source materials
should enable learners to gain an understanding of aerodynamics, flight
control, state estimation, software design, and simulation, while experimenting
with a unique aerial robot.


  In this paper, we propose a new deep neural network classifier that
simultaneously maximizes the inter-class separation and minimizes the
intra-class variation by using the polyhedral conic classification function.
The proposed method has one loss term that allows the margin maximization to
maximize the inter-class separation and another loss term that controls the
compactness of the class acceptance regions. Our proposed method has a nice
geometric interpretation using polyhedral conic function geometry. We tested
the proposed method on various visual classification problems including
closed/open set recognition and anomaly detection. The experimental results
show that the proposed method typically outperforms other state-of-the art
methods, and becomes a better choice compared to other tested methods
especially for open set recognition type problems.


  We report torsional oscillator supersolid studies of highly disordered
samples of solid $^4$He. In an attempt to approach the amorphous or glassy
state of the solid, we prepare our samples by rapid freezing from the normal
phase of liquid $^4$He. Less than two minutes is required for the entire
process of freezing and the subsequent cooling of the sample to below 1 K. The
supersolid signals observed for such samples are remarkably large, exceeding 20
% of the entire solid helium moment of inertia. These results, taken with the
finding that the magnitude of the small supersolid signals observed in our
earlier experiments can be reduced to an unobservable level by annealing,
strongly suggest that the supersolid state exists for the disordered or glassy
state of helium and is absent in high quality crystals of solid $^4$He.


  We revisit the concepts of acyclic orderings and number of acyclic orderings
of acyclic digraphs in terms of dispositions and counters for arbitrary
multidigraphs. We prove that when we add a sequence of nested directed paths to
a directed graph there is a unique polynomial such that the generatrix function
of the family of counters is the product of the polynomial and the exponential
function. We give an application, by considering a kind of digraphs arranged in
rows introduced by the authors in a previous paper, called dispositional
digraphs, in the particular case in which the digraph has two rows, to obtain
new families of linear differential equations of small order whose coefficients
are polynomials of small degree which admit polynomial solutions. In
particular, we obtain a new differential equation associated to Catalan
numbers, and the corresponding associated polynomials, which are solution of
this differential equation; we term them Catalan differencial equation and
Catalan polynomials, respectively. We prove that the Catalan polynomials
obtained when we connect the directed path to the second vertex of the lower
row of the digraph are orthogonal polynomials for an appropriate weight
function. We characterize the digraphs that maximize the counter of connected
dispositional digraphs and we find a new differential equation associated to
these digraphs. We introduce also dispositions and counters in any multidigraph
with non-strict inequalities in the dispositions, and we find new differential
equations associated to some of them.


  Observing a stationary time series, we propose a two-step procedure for the
prediction of the next value of the time series. The first step follows machine
learning theory paradigm and consists in determining a set of possible
predictors as randomized estimators in (possibly numerous) different predictive
models. The second step follows the model selection paradigm and consists in
choosing one predictor with good properties among all the predictors of the
first steps. We study our procedure for two different types of bservations:
causal Bernoulli shifts and bounded weakly dependent processes. In both cases,
we give oracle inequalities: the risk of the chosen predictor is close to the
best prediction risk in all predictive models that we consider. We apply our
procedure for predictive models such as linear predictors, neural networks
predictors and non-parametric autoregressive.


  We demonstrate strong coupling of single photons emitted by individual
molecules at cryogenic and ambient conditions to individual nanoparticles. We
provide images obtained both in transmission and reflection, where an
efficiency greater than 55% was achieved in converting incident narrow-band
photons to plasmon-polaritons (plasmons) of a silver nanoparticle. Our work
paves the way to spectroscopy and microscopy of nano-objects with sub-shot
noise beams of light and to triggered generation of single plasmons and
electrons in a well-controlled manner.


  This paper reports on computational evidence for the formation of cloud-like
dust structures around C-rich AGB stars. This spatio-temporal structure
formation process is caused by a radiative/thermal instability of dust forming
gases as identified by Woitke et al.(2000). Our 2D (axisymmetric) models
combine a time-dependent description of the dust formation process according to
Gail & Sedlmayr (1988) with detailed, frequency-dependent continuum radiative
transfer by means of a Monte Carlo method (Niccolini et al.2003) in an
otherwise static medium (v=0). These models show that the formation of dust
behind already condensed regions, which shield the stellar radiation field, is
strongly favoured. In the shadow of these clouds, the temperature decreases by
several hundred Kelvin which triggers the subsequent formation of dust and
ensures its thermal stability. Considering an initially dust-free gas with
small density inhomogeneities, we find that finger-like dust structures develop
which are cooler than the surroundings and point towards the centre of the
radiant emission, similar to the cometary knots observed in planetary nebulae
and star formation regions. Compared to a spherical symmetric reference model,
the clumpy dust distribution has little effect on the spectral energy
distribution, but dominates the optical appearance in near IR monochromatic
images.


  While scalable cell-free massive MIMO (CF-mMIMO) shows advantages in static
conditions, the impact of its changing serving access point (AP) set in a
mobile network is not yet addressed. In this paper we first derive the CPU
cluster and AP handover rates of scalable CF-mMIMO as exact numerical results
and tight closed form approximations. We then use our closed form handover rate
result to analyse the mobility-aware throughput. We compare the mobility-aware
spectral efficiency (SE) of scalable CF-mMIMO against distributed MIMO with
pure network- and UE-centric AP selection, for different AP densities and
handover delays. Our results reveal an important trade-off for future dense
networks with low control delay: under moderate to high mobility, scalable
CF-mMIMO maintains its advantage for the 95th-percentile users but at the cost
of degraded median SE.


  From the industrial standpoint on integrated sensing and communication
(ISAC), the preference lies in augmenting existing infrastructure with sensing
services while minimizing network changes and leveraging available resources.
This paper investigates the potential of utilizing the existing infrastructure
of fifth-generation (5G) new radio (NR) signals as defined by the 3rd
generation partnership project (3GPP), particularly focusing on pilot signals
for sensing within the ISAC framework. We propose to take advantage of the
existing positioning reference signal (PRS) for sensing and the physical
downlink shared channel (PDSCH) for communication, both readily available in 5G
NR. However, the use of PRS for sensing poses challenges, leading to the
appearance of ghost targets.
  To overcome this obstacle, we propose two innovative approaches for different
PRS comb sizes within the ISAC framework, leveraging the demodulation reference
signal (DMRS) within PDSCH to eliminate ghost targets. Subsequently, we
formulate a resource allocation problem between PRS and PDSCH and determine the
Pareto optimal point between communication and sensing without ghost targets.
Through comprehensive simulation and analysis, we demonstrate that the joint
exploitation of DMRS and PRS offers a promising solution for ghost target
removal, while effective time and frequency resource allocation enables the
achievement of Pareto optimality in ISAC.


  We introduce a construction of multiscale tight frames on general domains.
The frame elements are obtained by spectral filtering of the integral operator
associated with a reproducing kernel. Our construction extends classical
wavelets as well as generalized wavelets on both continuous and discrete
non-Euclidean structures such as Riemannian manifolds and weighted graphs.
Moreover, it allows to study the relation between continuous and discrete
frames in a random sampling regime, where discrete frames can be seen as Monte
Carlo estimates of the continuous ones. Pairing spectral regularization with
learning theory, we show that a sample frame tends to its population
counterpart, and derive explicit finite-sample rates on spaces of Sobolev and
Besov regularity. Our results prove the stability of frames constructed on
empirical data, in the sense that all stochastic discretizations have the same
underlying limit regardless of the set of initial training samples.


  Axion miniclusters are dense bound structures of dark matter axions that are
predicted to form in the post-inflationary Peccei-Quinn symmetry breaking
scenario. Although dense, miniclusters can easily be perturbed or even become
unbound by interactions with baryonic objects such as stars. Here, we
characterize the spatial distribution and properties of miniclusters in the
Milky Way (MW) today after undergoing these stellar interactions throughout
their lifetime. We do this by performing a suite of Monte Carlo simulations
which track the miniclusters' structure and, in particular, accounts for
partial disruption and mass loss through successive interactions. We consider
two density profiles - Navarro-Frenk-White (NFW) and Power-law (PL) - for the
individual miniclusters in order to bracket the uncertainties on the
minicluster population today due to their uncertain formation history. For our
fiducial analysis at the Solar position, we find a survival probability of 99%
for miniclusters with PL profiles and 46% for those with NFW profiles. Our work
extends previous estimates of this local survival probability to the entire MW.
We find that towards the Galactic center, the survival probabilities drop
drastically. Although we present results for a particular initial halo mass
function, our simulations can be easily recast to different models using the
provided data and code. Finally, we comment on the impact of our results on
lensing, direct, and indirect detection.


  We introduce an ultrahigh-resolution (50\mu m\) robotic micro-CT design for
localized imaging of carotid plaques using robotic arms, cutting-edge detector,
and machine learning technologies. To combat geometric error-induced artifacts
in interior CT scans, we propose a data-driven geometry estimation method that
maximizes the consistency between projection data and the reprojection
counterparts of a reconstructed volume. Particularly, we use a normalized cross
correlation metric to overcome the projection truncation effect. Our approach
is validated on a robotic CT scan of a sacrificed mouse and a micro-CT phantom
scan, both producing sharper images with finer details than that prior
correction.


  We present a new construction of the entropy-maximizing, invariant
probability measure on a Smale space (the Bowen measure). Our construction is
based on points that are unstably equivalent to one given point, and stably
equivalent to another: heteroclinic points. The spirit of the construction is
similar to Bowen's construction from periodic points, though the techniques are
very different. We also prove results about the growth rate of certain sets of
heteroclinic points, and about the stable and unstable components of the Bowen
measure. The approach we take is to prove results through direct computation
for the case of a Shift of Finite type, and then use resolving factor maps to
extend the results to more general Smale spaces.


  The UNet architecture, based on Convolutional Neural Networks (CNN), has
demonstrated its remarkable performance in medical image analysis. However, it
faces challenges in capturing long-range dependencies due to the limited
receptive fields and inherent bias of convolutional operations. Recently,
numerous transformer-based techniques have been incorporated into the UNet
architecture to overcome this limitation by effectively capturing global
feature correlations. However, the integration of the Transformer modules may
result in the loss of local contextual information during the global feature
fusion process. To overcome these challenges, we propose a 2D medical image
segmentation model called Multi-scale Cross Perceptron Attention Network
(MCPA). The MCPA consists of three main components: an encoder, a decoder, and
a Cross Perceptron. The Cross Perceptron first captures the local correlations
using multiple Multi-scale Cross Perceptron modules, facilitating the fusion of
features across scales. The resulting multi-scale feature vectors are then
spatially unfolded, concatenated, and fed through a Global Perceptron module to
model global dependencies. Furthermore, we introduce a Progressive Dual-branch
Structure to address the semantic segmentation of the image involving finer
tissue structures. This structure gradually shifts the segmentation focus of
MCPA network training from large-scale structural features to more
sophisticated pixel-level features. We evaluate our proposed MCPA model on
several publicly available medical image datasets from different tasks and
devices, including the open large-scale dataset of CT (Synapse), MRI (ACDC),
fundus camera (DRIVE, CHASE_DB1, HRF), and OCTA (ROSE). The experimental
results show that our MCPA model achieves state-of-the-art performance. The
code is available at
https://github.com/simonustc/MCPA-for-2D-Medical-Image-Segmentation.


  This is a doctorate level lecture on the physics of accretion discs driving
magnetically self-confined jets, usually referred to in the literature as disc
winds. I will first review the governing magnetohydrodynamic equations and then
discuss their physical content. At that level, necessary conditions to drive
jets from keplerian accretion discs can already be derived. These conditions
are validated with self-similar calculations of accretion-ejection structures.
In a second part, I will critically discuss the biases introduced when using
self-similarity as well as some other questions such as: Are these systems
really unstable? Can a standard accretion disc provide the conditions to launch
jets in its innermost parts? What is the difference between X-winds and
disc-winds? Finally, the magnetic interaction between a protostar and its
circumstellar disc will be discussed with a focus on stellar spin down.


  We show that at 1PN all four-dimensional black hole solutions in
asymptotically flat spacetimes can be derived from leading singularities
involving minimally coupled three-particle amplitudes. Furthermore, we show
that the rotating solutions can be derived from their non-rotating counterparts
by a spin-factor deformation of the relevant minimally coupled amplitudes. To
show this, we compute the tree-level and one-loop leading singularities for a
heavy charged source with generic spin s. We compute the metrics both with and
without a spin factor and show that we get both the Kerr-Newman and
Reissner-Nordstr\"om solutions respectively. We then go on to compute the
impulse imparted to the probe particle in the infinite spin limit and show that
the spin factor induces a complex deformation of the impact parameter, as was
recently observed for Kerr black holes in a recent paper by Arkani-Hamed et al.
We interpret these observations as being the on-shell avatar of the
Janis-Newman algorithm for charged black holes.


  A group is known as `large' if some finite index subgroup admits a surjective
homomorphism onto a non-abelian free group. The main theorem of the paper is as
follows. Let G be a finitely generated, large group and let g_1,...,g_r be a
collection of elements of G. Then G/<<g_1^n,...,g_r^n>> is also large, for
infinitely many integers n. Furthermore, when G is free, this holds for all but
finitely many n. These results have the following application to Dehn surgery.
Let M be a compact orientable 3-manifold with boundary a torus. Suppose that
the 3-manifold obtained by Dehn filling some slope on the boundary has large
fundamental group. Then this is true for infinitely many filling slopes.


  We use optical tweezers based on time-multiplexed acousto-optic deflectors to
trap ultra-cold cesium atoms in one-dimensional arrays of atomic ensembles. For
temperatures between 2.5 $\mu$K and 50 nK we study the maximal time between
optical tweezer pulses that retains the number of atoms in a single trap. This
time provides an estimate on the maximal number of sites in an array of
time-multiplexed optical tweezers. We demonstrate evaporative cooling of atoms
in arrays of up to 25 optical tweezer traps and the preparation of atoms in a
box potential. Additionally, we demonstrate three different protocols for the
preparation of atomic-ensemble arrays by transfer from an expanding ultra-cold
atomic cloud. These result in the preparation of arrays of up to 74 atomic
ensembles consisting of $\sim$100 atoms on average.


  In this comment we point out some problems with the approach used in the
paper "Newtonian limit of String-Dilaton Gravity" in the attempt to explain the
properties of a weak field approximation for gravity theories.


  A Coxeter system is called two-dimensional if its associated Davis complex is
two-dimensional (equivalently, every spherical subgroup has rank less than or
equal to 2). We prove that given a two-dimensional system (W,S) and any other
system (W,S') which yields the same reflections, the diagrams corresponding to
these systems are isomorphic, up to the operation of diagram twisting defined
by N. Brady, J. McCammond, B. Muhlherr, and W. Neumann. As a step in the proof
of this result, certain two-dimensional groups are shown to be reflection rigid
in the sense defined by the above authors, and a result concerning the strong
rigidity of two-dimensional systems is given in the final section.


  Multi-modal Large Language Models (MLLMs) tuned on machine-generated
instruction-following data have demonstrated remarkable performance in various
multi-modal understanding and generation tasks. However, the hallucinations
inherent in machine-generated data, which could lead to hallucinatory outputs
in MLLMs, remain under-explored. This work aims to investigate various
hallucinations (i.e., object, relation, attribute hallucinations) and mitigate
those hallucinatory toxicities in large-scale machine-generated visual
instruction datasets. Drawing on the human ability to identify factual errors,
we present a novel hallucination detection and elimination framework,
HalluciDoctor, based on the cross-checking paradigm. We use our framework to
identify and eliminate hallucinations in the training data automatically.
Interestingly, HalluciDoctor also indicates that spurious correlations arising
from long-tail object co-occurrences contribute to hallucinations. Based on
that, we execute counterfactual visual instruction expansion to balance data
distribution, thereby enhancing MLLMs' resistance to hallucinations.
Comprehensive experiments on hallucination evaluation benchmarks show that our
method successfully mitigates 44.6% hallucinations relatively and maintains
competitive performance compared to LLaVA. The data and code for this paper are
publicly available. \url{https://github.com/Yuqifan1117/HalluciDoctor}.


  This report aims to investigate the effectiveness of using internal bar
strength (IBS) as a key indicator for trading country exchange-traded funds
(ETFs). The study uses a quantitative approach to analyze historical price data
for a bucket of country ETFs over a period of 10 years and uses the idea of
Mean Reversion to create a profitable trading strategy. Our findings suggest
that IBS can be a useful technical indicator for predicting short-term price
movements in this basket of ETFs.


  We report the discovery of two well-defined tidal tails emerging from the
Hyades star cluster. The tails were detected in Gaia DR2 data by selecting
cluster members in the three-dimensional galactocentric cylindrical velocity
space. The robustness of our member selection is reinforced by the fact that
the sources depict an almost noiseless, coeval stellar main sequence in the
observational Hertzsprung-Russel diagram. The spatial arrangement of the
selected members represents a highly flattened shape with respect to the
direction of movement along the clusters' orbit in the Galaxy. The size of the
entire structure, within the limits of the observations, measures about 200 pc
in its largest extent, while being only about 25 pc thick. This translates to
an on-sky extent of well beyond 100 deg. Intriguingly, a top-down view on the
spatial distribution reveals as distinct S-shape, reminiscent of tidal tails
both observed for globular clusters, as well as modelled for star clusters
bound to the Galactic disk. Even more remarkable, the spatial arrangement, as
well as the velocity dispersion of our source selection is in excellent
agreement with previously published theoretical predictions for the tidal tails
of the Hyades. An investigation into observed signatures of equipartition of
kinetic energy, i.e. mass segregation, remains unsuccessful, most likely due to
the sensitivity limit for radial velocity measurements with Gaia.


  Galactic cosmic rays (GCRs) entering the heliosphere are disturbed by the
magnetic field of the Sun, which varies the shape and intensity of their local
interstellar spectrum. The force-field approximation is a popular way of
dealing with solar modulation, especially for studies focused on galactic
transport of cosmic rays. The validity of this approach to reproduce the
modulated GCR fluxes at Earth is tested using monthly proton fluxes measured by
PAMELA between July 2006 and January 2014 and monthly proton and helium fluxes
measured by AMS-02 between May 2011 and May 2017. We show that the precision of
the new AMS-02 data requires a rigidity-dependent modification of the
force-field approximation.


  Using a volume-limited sample of 550 groups from the Galaxy And Mass Assembly
(GAMA) Galaxy Group Catalogue spanning the halo mass range $12.8 < \log
[M_{h}/M] < 14.2$, we investigate the merging potential of central Brightest
Group Galaxies (BGGs). We use spectroscopically-confirmed close-companion
galaxies as an indication of the potential stellar mass build-up of
low-redshift BGGs, $z \leq 0.2$. We identify 17 close-companion galaxies with
projected separations $r_{p} < 30$ kpc, relative velocities $\Delta v \leq 300$
km s$^{-1}$, and stellar-mass ratios $M_{BGG}/M_{CC} \leq 4$ relative to the
BGG. These close-companion galaxies yield a total pair fraction of $0.03 \pm
0.01$. Overall, we find that BGGs in our sample have the potential to grow in
stellar mass due to mergers by $2.2 \pm 1.5\%$ Gyr$^{-1}$. This is lower than
the stellar mass growth predicted by current galaxy evolution models.


  We investigate the interaction energy between two colloidal particles on or
immersed in nonadsorbing polymer brushes grafted onto the substrate as a
function of the separation of the particles by use of self-consistent field
theory calculation. Depending on the colloidal size and the penetration depth,
we demonstrate an existence of repulsive energy barrier of several $k_{B}T$,
which can be interpreted by separating the interaction energy into three parts:
colloids-polymer interfacial energy, entropic contribution due to ``depletion
zone" overlap of colloidal particles, and entropically elastic energy of
grafted chains by compression of particles. The existence of repulsive barrier
which is of entirely entropic origin, can lead to kinetic stabilization of the
mixture rather than depletion flocculation or phase separation. Therefore, the
present result may suggest an approach to control the self-assembling behavior
of colloids for the formation of target structures, by tuning the colloidal
interaction on the grafting substrate under appropriate selection of colloidal
size, effective gravity (influencing the penetration depth), and brush coverage
density.


  The hierarchical equations of motion (HEOM) provide a numerically exact
approach for computing the reduced dynamics of a quantum system linearly
coupled to a bath. We have found that HEOM contains temperature-dependent
instabilities that grow exponentially in time. In the case of continuous-bath
models, these instabilities may be delayed to later times by increasing the
hierarchy dimension; however, for systems coupled to discrete, non-dispersive
modes, increasing the hierarchy dimension does little to alleviate the problem.
We show that these instabilities can also be removed completely at a
potentially much lower cost via projection onto the space of stable eigenmodes;
furthermore, we find that for discrete-bath models at zero temperature, the
remaining projected dynamics computed with few hierarchy levels are essentially
identical to the exact dynamics that otherwise might require an intractably
large number of hierarchy levels for convergence. Recognizing that computation
of the eigenmodes might be prohibitive, e.g. for large or strongly-coupled
models, we present a Prony filtration algorithm that may be useful as an
alternative for accomplishing this projection when diagonalization is too
costly. We present results demonstrating the efficacy of HEOM projected via
diagonalization and Prony filtration. We also discuss issues associated with
the nonnormality of HEOM.


  Neural Architecture Search (NAS) has fostered the automatic discovery of
state-of-the-art neural architectures. Despite the progress achieved with NAS,
so far there is little attention to theoretical guarantees on NAS. In this
work, we study the generalization properties of NAS under a unifying framework
enabling (deep) layer skip connection search and activation function search. To
this end, we derive the lower (and upper) bounds of the minimum eigenvalue of
the Neural Tangent Kernel (NTK) under the (in)finite-width regime using a
certain search space including mixed activation functions, fully connected, and
residual neural networks. We use the minimum eigenvalue to establish
generalization error bounds of NAS in the stochastic gradient descent training.
Importantly, we theoretically and experimentally show how the derived results
can guide NAS to select the top-performing architectures, even in the case
without training, leading to a train-free algorithm based on our theory.
Accordingly, our numerical validation shed light on the design of
computationally efficient methods for NAS. Our analysis is non-trivial due to
the coupling of various architectures and activation functions under the
unifying framework and has its own interest in providing the lower bound of the
minimum eigenvalue of NTK in deep learning theory.


  We study extensions of the Standard Model (SM) in which copies of the SM
scalar SU(2) doublet are added to the Higgs sector. These scalar doublets
either acquire a Vacuum Expectation Value (VEV) and hence are active or do not
develop a VEV and are inert. We consider CP-violation (CPV) in both the active
and inert sector. As an example of a model with CPV in the active sector, we
present a Type-I 2-Higgs-Doublet Model (2HDM) with two active doublets and show
Large Hadron Collider (LHC) signals of such a scenario. The amount of CPV in
this case is very limited due to constraints coming from Electric Dipole Moment
experiments. Moreover, 2HDMs with only active doublets do not provide a Dark
Matter (DM) candidate. As a result, we turn to 3-Higgs-Doublet Models (3HDMs)
where unbounded CPV and viable DM candidates could be introduced simultaneously
in the inert sector. We investigate DM phenomenology of such models.


  In this paper, we investigate the existence of solutions for a class of
$p$-Laplacian fractional order Kirchhoff-type system with Riemann-Liouville
fractional derivatives and a parameter $\lambda$. By mountain pass theorem, we
obtain that system has at least one non-trivial weak solution $u_\lambda$ under
some local superquadratic conditions for each given large parameter $\lambda$.
We get a concrete lower bound of the parameter $\lambda$, and then obtain two
estimates of weak solutions $u_\lambda$. We also obtain that $u_\lambda\to 0$
if $\lambda$ tends to $\infty$. Finally, we present an example as an
application of our results.


  We consider a texture for the neutrino mass matrix characterized by one
vanishing $2\times2$ subtrace. We analyze phenomenologically and analytically
all the six possible patterns, and show that all non-singular ones are able to
accommodate the experimental bounds, whereas singular patterns allow only for
four inverted-hierarchy type textures. We then present some possible
realizations of this texture, within seesaw scenarios, either directly or
indirectly by relating it to zero-textures.


  We compute and compare even- and odd-parity superconducting order parameters
of strontium ruthenate ($\mathrm{Sr}_2\mathrm{RuO}_4$) in the limit of weak
interactions, resulting from a fully microscopic three-dimensional model
including spin-orbit coupling. We find that odd-parity helical and even-parity
$d$-wave order are favored for smaller and larger values of the Hund's coupling
parameter $J$, respectively. Both orders are found compatible with specific
heat data and the recently-reported nuclear magnetic resonance (NMR) Knight
shift drop [A. Pustogow et al. Nature 574, 72 (2019)]. The chiral $p$-wave
order, numerically very competitive with helical order, sharply conflicts with
the NMR experiment.


  We study public goods games played on networks with possibly non-reciprocal
relationships between players. Examples for this type of interactions include
one-sided relationships, mutual but unequal relationships, and parasitism. It
is well known that many simple learning processes converge to a Nash
equilibrium if interactions are reciprocal, but this is not true in general for
directed networks. However, by a simple tool of rescaling the strategy space,
we generalize the convergence result for a class of directed networks and show
that it is characterized by transitive weight matrices. Additionally, we show
convergence in a second class of networks; those rescalable into networks with
weak externalities. We characterize the latter class by the spectral properties
of the absolute value of the network's weight matrix and show that it includes
all directed acyclic networks.


  It has been shown that good structured codes over non-Abelian groups do
exist. Specifically, we construct codes over the smallest non-Abelian group
$\mathds{D}_6$ and show that the performance of these codes is superior to the
performance of Abelian group codes of the same alphabet size. This promises the
possibility of using non-Abelian codes for multi-terminal settings where the
structure of the code can be exploited to gain performance.


  A study of the decays $B^0\to \mu^+\mu^-$ and $B^0_s\to \mu^+\mu^-$ has been
performed using data corresponding to an integrated luminosity of $25$
fb$^{-1}$ of $7$ TeV and $8$ TeV proton--proton collisions collected with the
ATLAS detector during the LHC Run 1. For $B^0$, an upper limit on the branching
fraction is set at ${\cal B}(B^0 \to \mu^+\mu^-) < 4.2 \times 10^{-10}$ at
$95\%$ confidence level. For $B^0_s$, the branching fraction ${\cal B}(B^0_s
\to \mu^+\mu^-) = \left( 0.9^{+1.1}_{-0.8} \right) \times 10^{-9}$ is measured.
The results are consistent with the Standard Model expectation with a $p$-value
of $4.8\%$, corresponding to $2.0$ standard deviations.


  We analyze the formation and evolution of terrestrial-like planets around
solar-type stars in the absence of gaseous giants. In particular, we focus on
the physical and dynamical properties of those that survive in the system's
Habitable Zone (HZ). This study is based on a comparative study between N-body
simulations that include fragmentation and others that consider all collisions
as perfect mergers. We use an N-body code, presented in a previous paper, that
allows planetary fragmentation. We carry out three sets of 24 simulations for
400 Myr. Two sets are developed adopting a model that includes hit-and-run
collisions and planetary fragmentation, each one with different values of the
individual minimum mass allowed for the fragments. For the third set, we
considered that all collisions lead to perfect mergers. The systems produced in
N-body simulations with and without fragmentation are broadly similar, with
some differences. In runs with fragmentation, the formed planets have lower
masses since part it is distributed amongst collisional fragments.
Additionally, those planets presented lower eccentricities, presumably due to
dynamical friction with the generated fragments. Perfect mergers and
hit-and-run collisions are the most common outcome. Regardless of the
collisional treatment adopted, most of the planets that survive in the HZ start
the simulation beyond the snow line, having very high final water contents. The
fragments' contribution to their mass and water content is negligible. Finally,
the individual minimum mass for fragments may play an important role in the
planets' collisional history. Collisional models that incorporate fragmentation
and hit-and-run collisions lead to a more detailed description of the physical
properties of the terrestrial-like planets formed. We conclude that planetary
fragmentation is not a barrier to the formation of water worlds in the HZ.


  The objective of this exploratory study was to examine the interpersonal
dynamics of an afterschool math club for middle schoolers. Using social network
analysis, two networks were identified and analyzed: (a) a network of
friendship relationships and (b) a network of working relationships. The
interconnections and correlations between friendship relationships, working
relationships, and a student opinion survey were studied. We identified a core
working group of students from within the network of working relations. This
group acted as a central go-between for other members in the club. This core
working group also expanded into the largest friendship group in the friendship
network. A second group was formed from popular but aloof students who reported
less impact from the club. Although there were working isolates, they were not
found to be socially isolated. Students who were less popular tended to report
a greater favorable impact from club participation than those who were more
popular.


  In this paper, we generalize the construction of Deligne-Hitchin twistor
space by gluing two certain Hodge moduli spaces. We investigate such
generalized Deligne-Hitchin twistor space as a complex analytic manifold. More
precisely, we show it admits holomorphic sections with weight-one property and
semi-negative energy, and it carries a balanced metric, and its holomorphic
tangent bundle (for the case of rank one) is stable. Moreover, we also study
the automorphism groups of the Hodge moduli spaces and the generalized
Deligne-Hitchin twistor space.


  This paper addresses the problem of autonomous robotic inspection in complex
and unknown environments. This capability is crucial for efficient and precise
inspections in various real-world scenarios, even when faced with perceptual
uncertainty and lack of prior knowledge of the environment. Existing methods
for real-world autonomous inspections typically rely on predefined targets and
waypoints and often fail to adapt to dynamic or unknown settings. In this work,
we introduce the Semantic Belief Behavior Graph (SB2G) framework as a novel
approach to semantic-aware autonomous robot inspection. SB2G generates a
control policy for the robot, featuring behavior nodes that encapsulate various
semantic-based policies designed for inspecting different classes of objects.
We design an active semantic search behavior to guide the robot in locating
objects for inspection while reducing semantic information uncertainty. The
edges in the SB2G encode transitions between these behaviors. We validate our
approach through simulation and real-world urban inspections using a legged
robotic platform. Our results show that SB2G enables a more efficient
inspection policy, exhibiting performance comparable to human-operated
inspections.


  Simple form scalar differential equation with delay and nonlinear negative
periodic feedback is considered. The existence of several types of slowly
oscillating periodic solutions is shown with the same and double periods of the
feedback coefficient. The periodic solutions are built explicitly in the case
of piecewise constant nonlinearities involved. The periodic dynamics are shown
to persist under small perturbations of the equation which make it smooth. The
theoretical results are verified by extensive numerical simulations.


  The most recent Ebola outbreak in West Africa highlighted critical weaknesses
in the medical infrastructure of the affected countries, including effective
diagnostics tools, sufficient isolation wards, and enough medical personnel.
Here, we develop and analyze a mathematical model to assess the impact of early
diagnosis of pre-symptomatic individuals on the transmission dynamics of Ebola
virus disease in West Africa in scenarios where Ebola may remain at low levels
in the population. Our findings highlight the importance of implementing
integrated control measures of early diagnosis and isolation. The mathematical
analysis shows a threshold where early diagnosis of pre-symptomatic
individuals, combined with a sufficient level of effective isolation, can lead
to an epidemic control of Ebola virus disease. That is, the local erradication
of the disease or the effective management of the disease at low levels of
endemicity.


  Estimation of the operational topology of the power grid is necessary for
optimal market settlement and reliable dynamic operation of the grid. This
paper presents a novel framework for topology estimation for general power
grids (loopy or radial) using time-series measurements of nodal voltage phase
angles that arise from the swing dynamics. Our learning framework utilizes
multivariate Wiener filtering to unravel the interaction between fluctuations
in voltage angles at different nodes and identifies operational edges by
considering the phase response of the elements of the multivariate Wiener
filter. The performance of our learning framework is demonstrated through
simulations on standard IEEE test cases.


  Background: MR-based subchondral bone effectively predicts knee
osteoarthritis. However, its clinical application is limited by the cost and
time of MR. Purpose: We aim to develop a novel distillation-learning-based
method named SRRD for subchondral bone microstructural analysis using
easily-acquired CT images, which leverages paired MR images to enhance the
CT-based analysis model during training. Materials and Methods: Knee joint
images of both CT and MR modalities were collected from October 2020 to May
2021. Firstly, we developed a GAN-based generative model to transform MR images
into CT images, which was used to establish the anatomical correspondence
between the two modalities. Next, we obtained numerous patches of subchondral
bone regions of MR images, together with their trabecular parameters (BV / TV,
Tb. Th, Tb. Sp, Tb. N) from the corresponding CT image patches via regression.
The distillation-learning technique was used to train the regression model and
transfer MR structural information to the CT-based model. The regressed
trabecular parameters were further used for knee osteoarthritis classification.
Results: A total of 80 participants were evaluated. CT-based regression results
of trabecular parameters achieved intra-class correlation coefficients (ICCs)
of 0.804, 0.773, 0.711, and 0.622 for BV / TV, Tb. Th, Tb. Sp, and Tb. N,
respectively. The use of distillation learning significantly improved the
performance of the CT-based knee osteoarthritis classification method using the
CNN approach, yielding an AUC score of 0.767 (95% CI, 0.681-0.853) instead of
0.658 (95% CI, 0.574-0.742) (p<.001). Conclusions: The proposed SRRD method
showed high reliability and validity in MR-CT registration, regression, and
knee osteoarthritis classification, indicating the feasibility of subchondral
bone microstructural analysis based on CT images.


  Previous claims of significant evidence for mirror-parity in the large-scale
cosmic microwave background (CMB) data from the Wilkinson Microwave Anisotropy
Probe (WMAP) experiment have been recently echoed in the first study of
isotropy and statistics of CMB data from Planck. We revisit these claims with a
careful analysis of the latest data available. We construct statistical
estimators in both harmonic and pixel space, test them on simulated data with
and without mirror-parity symmetry, apply different Galactic masks, and study
the dependence of the results on arbitrary choices of free parameters. We
confirm that the data exhibit evidence for odd mirror-parity at a significance
which reaches as high as ~ 99 per cent C.L., under some circumstances. However,
given the inherent biases in the pixel-based statistic and the dependence of
both pixel and harmonic space statistics on the particular form of Galactic
masking and other a-posteriori choices, we conclude that these results are not
in significant tension with the predictions of the concordance cosmological
model.


  The flattening of a crystal cone below its roughening transition is studied
by means of a step flow model. Numerical and analytical analyses show that the
height profile, h(r,t), obeys the scaling scenario dh/dr = F(r t^{-1/4}). The
scaling function is flat at radii r<R(t) \sim t^{1/4}. We find a one parameter
family of solutions for the scaling function, and propose a selection criterion
for the unique solution the system reaches.


  We obtain explicit inversion formulas for the Radon-like transform that
assigns to a function on the unit sphere the integrals of that function over
hemispheres lying in lower dimensional central cross-sections. The results are
applied to determination of star bodies from the volumes of their central
half-sections.


  We review selected topics in the field of nonleptonic and rare B meson
decays. We concentrate in particular on exclusive channels, discussing recent
developments based on the concepts of factorization in QCD and the heavy-quark
limit.


  While coreference resolution typically involves various linguistic
challenges, recent models are based on a single pairwise scorer for all types
of pairs. We present LingMess, a new coreference model that defines different
categories of coreference cases and optimize multiple pairwise scorers, where
each scorer learns a specific set of linguistic challenges. Our model
substantially improves pairwise scores for most categories and outperforms
cluster-level performance on Ontonotes and 5 additional datasets. Our model is
available in https://github.com/shon-otmazgin/lingmess-coref


  Compression of Neural Networks (NN) has become a highly studied topic in
recent years. The main reason for this is the demand for industrial scale usage
of NNs such as deploying them on mobile devices, storing them efficiently,
transmitting them via band-limited channels and most importantly doing
inference at scale. In this work, we propose to join the Soft-Weight Sharing
and Variational Dropout approaches that show strong results to define a new
state-of-the-art in terms of model compression.


  We investigate the finite-temperature quantum chromodynamics (QCD) on a
rotating lattice with $N_f=2+1$ staggered fermions and the projective plane
boundary condition. We observe a negative rotational rigidity (defined in the
main text) and a negative quark spin susceptibility associated with the chiral
vortical effect. In contrast to most of the effective model predictions, we
find that the chiral condensate decreases and the Polyakov loop increases with
imaginary rotation, implying a rotational catalysis of chiral symmetry breaking
and confinement by real rotation. We determine the phase boundaries for both
chiral and confinement-deconfinement phase transitions on the $\Omega_I$-$T$
plane, where $\Omega_I$ is the imaginary angular velocity.


  We study (relative) $\mathcal K$-Mittag-Leffler modules as was done in the
author's habilitation thesis, rephrase old, unpublished results in terms of
definable subcategories, and present newer ones, culminating in a
characterization of countably generated $\cal K$-Mittag-Leffler modules.


  Recently the explicit applicability of bound entanglement in quantum
cryptography has been shown. In this paper some of recent results respecting
this topic are reviewed. In particular relevant notions and definitions are
reminded. The new construction of bound entangled states containing secure
correlations is presented. It provides low dimensional 6\otimes6 bound
entangled states with nonzero distillable key.


  We identify, in spacetimes satisfying the null convergence condition, a
certain natural class of null hypersurfaces that admit null sections with
constant surface gravity. Our work is meant to offer complementary results to
previous work on null hypersurfaces of zero expansion arising from the study of
Cauchy and black hole horizons in general relativity.


  Recently, much attention has been focused on the replicability of scientific
results, causing scientists, statisticians, and journal editors to examine
closely their methodologies and publishing criteria. Experimental particle
physicists have been aware of the precursors of non-replicable research for
many decades and have many safeguards to ensure that the published results are
as reliable as possible. The experiments require large investments of time and
effort to design, construct, and operate. Large collaborations produce and
check the results, and many papers are signed by more than three thousand
authors. This paper gives an introduction to what experimental particle physics
is and to some of the tools that are used to analyze the data. It describes the
procedures used to ensure that results can be computationally reproduced, both
by collaborators and by non-collaborators. It describes the status of publicly
available data sets and analysis tools that aid in reproduction and recasting
of experimental results. It also describes methods particle physicists use to
maximize the reliability of the results, which increases the probability that
they can be replicated by other collaborations or even the same collaborations
with more data and new personnel. Examples of results that were later found to
be false are given, both with failed replication attempts and one with
alarmingly successful replications. While some of the characteristics of
particle physics experiments are unique, many of the procedures and techniques
can be and are used in other fields.


  Spelling error detection serves as a crucial preprocessing in many natural
language processing applications. Due to the characteristics of Chinese
Language, Chinese spelling error detection is more challenging than error
detection in English. Existing methods are mainly under a pipeline framework,
which artificially divides error detection process into two steps. Thus, these
methods bring error propagation and cannot always work well due to the
complexity of the language environment. Besides existing methods only adopt
character or word information, and ignore the positive effect of fusing
character, word, pinyin1 information together. We propose an LF-LSTM-CRF model,
which is an extension of the LSTMCRF with word lattices and
character-pinyin-fusion inputs. Our model takes advantage of the end-to-end
framework to detect errors as a whole process, and dynamically integrates
character, word and pinyin information. Experiments on the SIGHAN data show
that our LF-LSTM-CRF outperforms existing methods with similar external
resources consistently, and confirm the feasibility of adopting the end-to-end
framework and the availability of integrating of character, word and pinyin
information.


  Quantile regression (QR) is a statistical tool for distribution-free
estimation of conditional quantiles of a target variable given explanatory
features. QR is limited by the assumption that the target distribution is
univariate and defined on an Euclidean domain. Although the notion of quantiles
was recently extended to multi-variate distributions, QR for multi-variate
distributions on manifolds remains underexplored, even though many important
applications inherently involve data distributed on, e.g., spheres (climate and
geological phenomena), and tori (dihedral angles in proteins). By leveraging
optimal transport theory and c-concave functions, we meaningfully define
conditional vector quantile functions of high-dimensional variables on
manifolds (M-CVQFs). Our approach allows for quantile estimation, regression,
and computation of conditional confidence sets and likelihoods. We demonstrate
the approach's efficacy and provide insights regarding the meaning of
non-Euclidean quantiles through synthetic and real data experiments.


  Bilayer transition metal dichalcogenides (TMDs) belong to a class of
materials with two unique features, the coupled spin-valley-layer degrees of
freedom and the crystal structure that is globally centrosymmetric but locally
non-centrosymmetric. In this work, we will show that the combination of these
two features can lead to a rich phase diagram for unconventional
superconductivity, including intra-layer and inter-layer singlet pairings and
inter-layer triplet pairings, in bilayer superconducting TMDs. In particular,
we predict that the inhomogeneous Fulde-Ferrell-Larkin-Ovchinnikov state can
exist in bilayer TMDs under an in-plane magnetic field. We also discuss the
experimental relevance of our results and possible experimental signatures.


  We have measured the rotational velocities (v.sini) of the mass donors in a
number of D'--type symbiotic stars, using the cross-correlation function
method. Four from five D' symbiotic stars with known v.sini, appeared to be
very fast rotators compared with the catalogues of v.sini for the corresponding
spectral types. At least three of these stars rotate at a substantial fraction
(>=0.5) of the critical velocity. This means that at least in D'-type
symbiotics the cool components rotate faster than isolated giants. If these
binary stars are synchronized, their orbital periods should be relatively short
(4-60 days). We also briefly discuss the possible origin of the rapid rotation
and its connection with mass loss and dust formation.


  We give a necessary condition for a Riemannian manifold to admit limiting
Carleman weights in terms of the Weyl tensor (in dimensions 4 and higher) and
the Cotton-York tensor in dimension 3. As an application we provide explicit
examples of manifolds without limiting Carleman weights and show that the set
of such metrics on a given manifold contains an open and dense set.


  We obtain new effective results in best approximation theory, specifically
moduli of uniqueness and constants of strong unicity, for the problem of best
uniform approximation with bounded coefficients, as first considered by Roulier
and Taylor. We make use of techniques from the field of proof mining, as
introduced by Kohlenbach in the 1990s. In addition, some bounds are obtained
via the Lagrangian interpolation formula as extended through the use of Schur
polynomials to cover the case when certain coefficients are restricted to be
zero.


  The present Editorial introduces the Special Issue dedicated by the journal
Universe to the General Theory of Relativity, the beautiful theory of
gravitation of Einstein, a century after its birth. It reviews some of its key
features in a historical perspective, and, in welcoming distinguished
researchers from all over the world to contribute it, some of the main topics
at the forefront of the current research are outlined.


  Parikh's theorem states that every Context Free Language (CFL) has the same
Parikh image as that of a regular language. A finite state automaton accepting
such a regular language is called a Parikh-equivalent automaton. In the worst
case, the number of states in any non-deterministic Parikh-equivalent automaton
is exponentially large in the size of the Context Free Grammar (CFG). We
associate a regularity width d with a CFG that measures the closeness of the
CFL with regular languages. The degree m of a CFG is one less than the maximum
number of variable occurrences in the right hand side of any production. Given
a CFG with n variables, we construct a Parikh-equivalent non-deterministic
automaton whose number of states is upper bounded by a polynomial in $n
(d^{2d(m+1)}), the degree of the polynomial being a small fixed constant. Our
procedure is constructive and runs in time polynomial in the size of the
automaton. In the terminology of parameterized complexity, we prove that
constructing a Parikh-equivalent automaton for a given CFG is Fixed Parameter
Tractable (FPT) when the degree m and regularity width d are parameters. We
also give an example from program verification domain where the degree and
regularity are small compared to the size of the grammar.


  We obtain a mathematically simple characterization of all functionals
coinciding with the von Neumann reduced entropy on pure states based on the
Khinchin-Faddeev axiomatization of Shannon entropy and give a physical
interpretation of the axioms in terms of entanglement.


  We introduced here the study of a QCD based on a complex group. Our aim is to
show that a gauge theory with a complex symmetry develops some of the features
required for the description of a confined phase. This theory leads to gluons
with propagations characteristic of i-particles, fundamental constituents of
propagators in the Gribov scenario, together with a gluon propagating with the
mode predicted by 't Hooft for the quark confinement. In this way, we present
in this same theory a possible gluon condensate, together with a confining
interquark potential after the coupling to fermions. This is a novelty, as, up
to now, all theories of the GZ kind do not generate quark confinement.


  Within this thesis we propose a platform for combining Augmented Reality (AR)
hardware with machine learning in a user-oriented pipeline, offering to the
medical staff an intuitive 3D visualization of volumetric Computed Tomography
(CT) and Magnetic Resonance Imaging (MRI) medical image segmentations inside
the AR headset, that does not need human intervention for loading, processing
and segmentation of medical images. The AR visualization, based on Microsoft
HoloLens, employs a modular and thus scalable frontend-backend architecture for
real-time visualizations on multiple AR headsets. As Convolutional Neural
Networks (CNNs) have lastly demonstrated superior performance for the machine
learning task of image semantic segmentation, the pipeline also includes a
fully automated CNN algorithm for the segmentation of the liver from CT scans.
The model is based on the Deep Retinal Image Understanding (DRIU) model which
is a Fully Convolutional Network with side outputs from feature maps with
different resolution, extracted at different stages of the network. The
algorithm is 2.5D which means that the input is a set of consecutive scan
slices. The experiments have been performed on the Liver Tumor Segmentation
Challenge (LiTS) dataset for liver segmentation and demonstrated good results
and flexibility. While multiple approaches exist in the domain, only few of
them have focused on overcoming the practical aspects which still largely hold
this technology away from the operating rooms. In line with this, we also are
next planning an evaluation from medical doctors and radiologists in a
real-world environment.


  Isostatic frames are mechanical networks that are simultaneously rigid and
free of self-stress states, and is a powerful concept in understanding phase
transitions in soft matter and designing of mechanical metamaterials. Here we
analyze substructures of isostatic frames, by generalizing ``Assur graphs'' to
the torus and examine them in physical systems. We show that the contact
network of marginally jammed packings approach torus Assur graphs in the
thermodynamic limit, and demostrate how Assur graphs offer a new design
principle for mechanical metamaterials in which motion and stress can propagate
in reconfigurable pathways, while rigidity of the entire structure is
maintained.


  We investigate fast data aggregation via over-the-air computation (AirComp)
over wireless networks. In this scenario, an access point (AP) with multiple
antennas aims to recover the arithmetic mean of sensory data from multiple
wireless devices. To minimize estimation distortion, we formulate a
mean-squared-error (MSE) minimization problem that considers joint optimization
of transmit scalars at wireless devices, denoising factor, and receive
beamforming vector at the AP. We derive closed-form expressions for the
transmit scalars and denoising factor, resulting in a non-convex quadratic
constrained quadratic programming (QCQP) problem concerning the receive
beamforming vector. To tackle the computational complexity of the beamforming
design, particularly relevant in massive multiple-input multiple-output (MIMO)
AirComp systems, we explore the optimal structure of receive beamforming using
successive convex approximation (SCA) and Lagrange duality. By leveraging the
proposed optimal beamforming structure, we develop two efficient algorithms
based on SCA and semi-definite relaxation (SDR). These algorithms enable fast
wireless aggregation with low computational complexity and yield almost
identical mean square error (MSE) performance compared to baseline algorithms.
Simulation results validate the effectiveness of our proposed methods.


  Recent experiments and simulations of amorphous solids plastically deformed
by oscillatory drive have foundsurprising behavior - for small strain
amplitudes the dynamics can be reversible, which is contrary to the usual
notion of plasticity as an irreversible form of deformation. This reversibility
allows the system to reach limit-cycles in which plastic events repeat
indefinitely under the oscillatory drive. Reaching reversible limit-cycles, can
take a large number of driving cycles and it was surmised that the plastic
events encountered during the transient period are not encountered again and
are thus irreversible. Using a graph representation of the stable
configurations of the system and the plastic events connecting them, we show
that the notion of reversibility is more subtle. We find that reversible
plastic events are abundant, and that a large portion of the plastic events
encountered during the transient period are actually reversible, in the sense
that they can be part of a reversible deformation path. We observe that the
transition graph can be decomposed into clusters of configurations that are
connected by reversible transitions. These clusters are the strongly connected
components of the graph and their sizes turn out to be power-law distributed.
The largest of these are grouped in regions of reversibility, which in turn are
confined by regions of irreversibility whose number proliferates at larger
strains. Our results provide an explanation for the irreversibility transition
- the divergence of the transient period at a critical forcing amplitude. Long
transients result from transition between clusters of reversibility in a search
for a cluster large enough to contain a limit-cycle of a specific amplitude.
For large enough amplitudes, the search time becomes very large, since sizes of
the limit cycles become incompatible with the sizes of the regions of
reversibility.


  With the development of dialog techniques, conversational search has
attracted more and more attention as it enables users to interact with the
search engine in a natural and efficient manner. However, comparing with the
natural language understanding in traditional task-oriented dialog which
focuses on slot filling and tracking, the query understanding in E-commerce
conversational search is quite different and more challenging due to more
diverse user expressions and complex intentions. In this work, we define the
real-world problem of query tracking in E-commerce conversational search, in
which the goal is to update the internal query after each round of interaction.
We also propose a self attention based neural network to handle the task in a
machine comprehension perspective. Further more we build a novel E-commerce
query tracking dataset from an operational E-commerce Search Engine, and
experimental results on this dataset suggest that our proposed model
outperforms several baseline methods by a substantial gain for Exact Match
accuracy and F1 score, showing the potential of machine comprehension like
model for this task.


  Monero is a privacy-centric cryptocurrency that makes payments untraceable by
adding decoys to every real input spent in a transaction. Two studies from 2017
found methods to distinguish decoys from real inputs, which enabled
traceability for a majority of transactions. Since then, a number protocol
changes have been introduced, but their effectiveness has not yet been
reassessed. Furthermore, little is known about traceability of Monero
transactions across hard fork chains. We formalize a new method for tracing
Monero transactions, which is based on analyzing currency hard forks. We use
that method to perform a (passive) traceability analysis on data from the
Monero, MoneroV and Monero Original blockchains and find that only a small
amount of inputs are traceable. We then use the results to estimate the
effectiveness of known heuristics for recent transactions and find that they do
not significantly outperform random guessing. Our findings suggest that Monero
is currently mostly immune to known passive attack vectors and resistant to
tracking and tracing methods applied to other cryptocurrencies.


  In this paper we discuss the BFK type gluing formula for zeta-determinants of
Laplacians with respect to the Robin boundary condition on a compact Riemannian
manifold. As a special case, we discuss the gluing formula with respect to the
Neumann boundary condition. We also compute the difference of two
zeta-determinants with respect to the Robin and Dirichlet boundary conditions.
We use this result to compute the zeta-determinant of a Laplacian on a cylinder
when the Robin boundary condition is imposed, which extends a result in [25].
We also discuss the gluing formula more precisely when the product structure is
given near a cutting hypersurface.


  Understanding stories is a challenging reading comprehension problem for
machines as it requires reading a large volume of text and following long-range
dependencies. In this paper, we introduce the Shmoop Corpus: a dataset of 231
stories that are paired with detailed multi-paragraph summaries for each
individual chapter (7,234 chapters), where the summary is chronologically
aligned with respect to the story chapter. From the corpus, we construct a set
of common NLP tasks, including Cloze-form question answering and a simplified
form of abstractive summarization, as benchmarks for reading comprehension on
stories. We then show that the chronological alignment provides a strong
supervisory signal that learning-based methods can exploit leading to
significant improvements on these tasks. We believe that the unique structure
of this corpus provides an important foothold towards making machine story
comprehension more approachable.


  Use of online platforms for education is a vibrant and growing arena,
incorporating a variety of software platforms and technologies, including
various modalities of extended reality. We present our Enhanced Reality
Teaching Concierge, an open networking hub architected to enable efficient and
easy connectivity between a wide variety of services or applications to a wide
variety of clients, designed to showcase 3D for academic purposes across web
technologies, virtual reality, and even virtual worlds. The agnostic nature of
the system, paired with efficient architecture, and simple and open protocols
furnishes an ecosystem that can easily be tailored to maximize the innate
characteristics of each 3D display environment while sharing common data and
control systems with the ultimate goal of a seamless, expandable, nimble
education metaverse.


  We prove that an integrated version of the Gurau colored tensor model
supplemented with the usual Bosonic propagator on $U(1)^4$ is renormalizable to
all orders in perturbation theory. The model is of the type expected for
quantization of space-time in 4D Euclidean gravity and is the first example of
a renormalizable model of this kind. Its vertex and propagator are
four-stranded like in 4D group field theories, but without gauge averaging on
the strands. Surprisingly perhaps, the model is of the $\phi^6$ rather than of
the $\phi^4$ type, since two different $\phi^6$-type interactions are
log-divergent, i.e. marginal in the renormalization group sense. The
renormalization proof relies on a multiscale analysis. It identifies all
divergent graphs through a power counting theorem. These divergent graphs have
internal and external structure of a particular kind called melonic. Melonic
graphs dominate the 1/N expansion of colored tensor models and generalize the
planar ribbon graphs of matrix models. A new locality principle is established
for this category of graphs which allows to renormalize their divergences
through counterterms of the form of the bare Lagrangian interactions. The model
also has an unexpected anomalous log-divergent $(\int \phi^2)^2$ term, which
can be interpreted as the generation of a scalar matter field out of pure
gravity.


  When a spin glass is cooled down, a memory of the cooling process is
imprinted in the spin structure. This memory can be disclosed in a continuous
heating measurement of the ac-susceptibility. E.g., if a continuous cooling
process is intermittently halted during a certain aging time at one or two
intermediate temperatures, the trace of the previous stop(s) is recovered when
the sample is continuously re-heated [1]. However, heating the sample above the
aging temperature, but keeping it below Tg, erases the memory of the thermal
history at lower temperatures. We also show that a memory imprinted at a higher
temperature can be erased by waiting a long enough time at a lower temperature.
Predictions from two complementary spin glass descriptions, a hierarchical
phase space model and a real space droplet picture are contested with these
memory phenomena and interference effects.
  [1] K. Jonason, E. Vincent, J. Hammann, J. P. Bouchaud and P. Nordblad, Phys.
Rev. Lett. 31, 3243 (1998).


  We have performed a wide-area ultraviolet (UV) imaging survey using the
GALaxy Evolution eXplorer (GALEX) to search for bright, point-like UV sources
behind M31's extended halo. Our survey consisted of 46 pointings covering an
effective area of ~50 deg^2, in both the far-UV and near-UV channels. We
combined these data with optical R-band observations acquired with the WIYN
Mosaic-1 imager on the Kitt Peak National Observatory 0.9m WIYN telescope. An
analysis of the brightness and colors of sources matched between our
photometric catalogs yielded ~100 UV-bright quasar candidates. We have obtained
discovery spectra for 76 of these targets with the Kast spectrometer on the
Lick 3m telescope and confirm 30 active galactic nuclei and quasars, 29
galaxies at z > 0.02 including several early-type systems, 16 Galactic stars
(hot main-sequence stars), and one featureless source previously identified as
a BL Lac object. Future UV spectroscopy of the brightest targets with the
Cosmic Origins Spectrograph on the Hubble Space Telescope will enable a
systematic search for diffuse gas in the extended halo of M31.


  The diagnostics of stellar flaring coronal loops have been so far largely
based on the analysis of the decay phase. We derive new diagnostics from the
analysis of the rise and peak phase of stellar flares. We release the
assumption of full equilibrium of the flaring loop at the flare peak, according
to the frequently observed delay between the temperature and the density
maximum. From scaling laws and hydrodynamic simulations we derive diagnostic
formulas as a function of observable quantities and times. We obtain a
diagnostic toolset related to the rise phase, including the loop length,
density and aspect ratio. We discuss the limitations of this approach and find
that the assumption of loop equilibrium in the analysis of the decay leads to a
moderate overestimate of the loop length. A few relevant applications to
previously analyzed stellar flares are shown. The analysis of the flare rise
and peak phase complements and completes the analysis of the decay phase.


  Terrestrial laser interferometers for gravitational-wave detection made the
landmark first detection of gravitational waves in 2015. We provide an overview
of the history of how these laser interferometers prevailed as the most
promising technology in the search for gravitational waves. We describe their
working principles and their limitations, and provide examples of some of the
most important technologies that enabled their construction. We introduce each
of the four large-scale laser interferometer gravitational-wave detectors in
operation around the world today and provide a brief outlook for the future of
ground-based detectors.


  We generalise previous studies on the extension of Goldstone's theorem from
Hermitian to non-Hermitian quantum field theories with Abelian symmetries to
theories possessing a glocal non-Abelian symmetry. We present a detailed
analysis for a non-Hermitian field theory with two complex two component scalar
fields possessing a SU(2)-symmetry and indicate how our finding extend to the
general case. In the PT-symmetric regime and at the standard exceptional point
the Goldstone theorem is shown to apply, although different identification
procedures need to be employed. At the zero exceptional points the Goldstone
boson can not be identified. Comparing our approach, based on the
pseudo-Hermiticity of the model, to an alternative approach that utilises
surface terms to achieve compatibility for the non-Hermitian system, we find
that the explicit forms of the Goldstone boson fields are different.


  One considers Hilbert space valued measures on the Borel sets of a compact
metric space. A natural numerical valued integral of vector valued continuous
functions with respect to vector valued functions is defined. Using this
integral, different norms (we called them Monge-Kantorovich norm, modified
Monge-Kantorovich norm and Hanin norm) on the space of measures are introduced,
generalizing the theory of (weak) convergence for probability measures on
metric spaces. These norms introduce new (equivalent) metrics on the initial
compact metric space.


  The non-perturbative effects of QED-vacuum reconstruction under influence of
a supercritical EM-source ($Z>Z_{cr,1}$) are considered in view of lepton
number conservation. The question is here that if the vacuum shells formation
and spontaneous positron emission, associated with discrete levels diving into
the lower continuum, exist in reality, then it by no means should be a signal
of new physics. The reason is that the emitted positrons carry away the lepton
number equal to $(-1)\times$their total number, and so the corresponding amount
of positive lepton number should be shared by VP-density, concentrated in
vacuum shells. In this case instead of integer lepton number of real particles
there should appear the lepton number VP-density. Otherwise either the lepton
number conservation in such processes must be broken, or the spontaneous
positron emission prohibited.
  The conditions, under which the emission of vacuum positrons can be
unambiguously detected on the nuclear conversion pairs background, are also
discussed.


  Stochastic differential equations can describe a wide range of dynamical
systems, and obtaining the governing equations of these systems is the premise
of studying the nonlinear dynamic behavior of the system. Neural networks are
currently the most popular approach in the inverse problem of dynamical
systems. In order to obtain accurate dynamical equations, neural networks need
a large amount of trajectory data as a training set. To address this
shortcoming, we propose a residual-based multi-peaks sampling algorithm.
Evaluate the training results of each epoch of neural network, calculate the
probability density function $P(x)$ of the residual, perform sampling where the
$P(x)$ is large, and add samples to the training set to retrain the neural
network. In order to prevent the neural network from falling into the trap of
overfitting, we discretize the sampling points. We conduct case studies using
two classical nonlinear dynamical systems and perform bifurcation and first
escape probability analyzes of the fitted equations. Results show that our
proposed sampling strategy requires only 20$\sim $30\% of the sample points of
the original method to reconstruct the stochastic dynamical behavior of the
system. Finally, the algorithm is tested by adding interference noise to the
data, and the results show that the sampling strategy has better numerical
robustness and stability.


  This paper deals with the problem of estimating second-order parameter
sensitivities for stochastic reaction networks, where the reaction dynamics is
modeled as a continuous time Markov chain over a discrete state space.
Estimation of such second-order sensitivities (the Hessian) is necessary for
implementing the Newton-Raphson scheme for optimization over the parameter
space. To perform this estimation, Wolf and Anderson have proposed an efficient
finite-difference method, that uses a coupling of perturbed processes to reduce
the estimator variance. The aim of this paper is to illustrate that the same
coupling can be exploited to derive an exact representation for second-order
parameter sensitivity. Furthermore with this representation one can construct
an unbiased estimator which is easy to implement. The ideas contained in this
paper are extensions of the ideas presented in our recent papers on first-order
parameter sensitivity estimation.


  Auction theories are believed to provide a better selling opportunity for the
resources to be allocated. Various organizations have taken measures to
increase trust among participants towards their auction system, but trust alone
cannot ensure a high level of participation. We propose a new type of auction
system which takes advantage of lucky draw and gambling addictions to increase
the engagement level of candidates in an auction. Our system makes use of
security features present in existing auction systems for ensuring fairness and
maintaining trust among participants.


  Riemann's non-differentiable function is a classic example of a continuous
function which is almost nowhere differentiable, and many results concerning
its analytic regularity have been shown so far. However, it can also be given a
geometric interpretation, so questions on its geometric regularity arise. This
point of view is developed in the context of the evolution of vortex filaments,
modelled by the Vortex Filament Equation or the binormal flow, in which a
generalisation of Riemann's function to the complex plane can be regarded as
the trajectory of a particle. The objective of this document is to show that
the trajectory represented by its image does not have a tangent anywhere. For
that, we discuss several concepts of tangent vectors in view of the set's
irregularity.


  We perform and extend real-time numerical simulation of a low-dimensional
scalar field theory or a quantum mechanical system using stochastic
quantization. After a brief review of the quantization method and the complex
Langevin dynamics, we calculate the propagator and make a comparison with
analytical results. This is a first step toward general applications, and we
focus only on the vacuum properties of the theory; this enables us to handle
the boundary condition with the $i\epsilon$ prescription in frequency space.
While we can control stability of the numerical simulation for any coupling
strength, our results turn out to flow into an unphysical fixed-point, which is
qualitatively understood from the corresponding Fokker-Planck equation. We
propose a simple truncation scheme, "restricted phase-space approximation," to
avoid the unphysical fixed-point. With this method, we obtain stable results at
reasonably good accuracy. Finally we give a short discussion on the closed-time
path formalism and demonstrate the direct computation of the vacuum expectation
value not with the $i\epsilon$ prescription but from an explicit construction
of the Feynman kernel.


  This dissertation presents an investigation into the electrical properties of
two types of G4-DNA and several DNA-based molecules, targeting them as
candidates for molecular wires and devices. Atomic force microscopy (AFM) and
electrostatic force microscopy (EFM) comparison of co-deposited tetra- and
intra-molecular G4-DNA reveals variations in morphology and different
sensitivity to the applied electric field, suggesting that the folding
orientation of the strands affects the molecular structure, i.e. either the
tetrad unit or the tetrad-tetrad stacking or both, and therefore the charge
mobility. Tetra-molecular G4-DNA is found to be twice as polarizable as
intra-molecular G4-DNA, suggesting it has greater charge mobility. These
promising results motivated direct electrical transport measurements on
tetra-molecular G4-DNA using a special conductive AFM setup, profiling the
conductance along the molecule. Reproducible currents of tens to over 100 pA
were measured in many tetra-molecular G4-DNA molecules over distances ranging
from tens to over 100 nm. The measured charge transport is compatible with
long-range thermally activated hopping between multi-tetrad segments. To form
the stationary electrical contact, a new variant of stencil lithography was
developed, based on reversible electrostatic clamping, overcoming indeterminate
blurring effects associated with the problem of metal penetration in standard
mask patterning techniques. This method enabled to demonstrate full mask
compliance in scanning electron microscopy (SEM) and AFM measurements. The
pull-in instability was demonstrated inside an SEM chamber and was confirmed by
a non-linear transient response computation. New mechanisms were proposed for
the replica formation and the blurring effect based on cluster evaporation and
mobile source decay, explaining the ultimate resolution of the masking
technique and its limitations.


  Absorbing boundaries are frequently employed in real-time propagation of the
Schr\"odinger equation to remove spurious reflections and efficiently emulate
outgoing boundary conditions. These conditions are a fundamental ingredient for
an implicit description of observables involving infinitely extended continuum
states. In the literature, several boundary absorbers have been proposed. They
mostly fall into three main families: mask function absorbers, complex
absorbing potentials, and exterior complex-scaled potentials. To date none of
the proposed absorbers is perfect, and all present a certain degree of
reflections. Characterization of such reflections is thus a critical task with
strong implications for time-dependent simulations of atoms and molecules. We
introduce a method to evaluate the reflection properties of a given absorber
and present a comparison of selected samples for each family of absorbers.
Further, we discuss the connections between members of each family and show how
the same reflection curves can be obtained with very different absorption
schemes.


  We study the statistics of a system of N random levels with integer values,
in the presence of a logarithmic repulsive potential of Dyson type. This
probleme arises in sums over representations (Young tableaux) of GL(N) in
various matrix problems and in the study of statistics of partitions for the
permutation group. The model is generalized to include an external source and
its correlators are found in closed form for any N. We reproduce the density of
levels in the large N and double scaling limits and the universal correlation
functions in Dyson's short-distance scaling limit. We also study the statistics
of small levels.


  In this paper, we have considered that the universe is filled with tachyon,
hessence (or hantom) dark energies. Subsequently we have investigated the
interactions between tachyon and hessence (hantom) dark energies and calculated
the potentials considering the power law form of the scale factor. It has been
revealed that the tachyonic potential always decreases and hessence (or hantom)
potential increases with corresponding fields. Furthermore, we have considered
a correspondence between the hessence (or hantom) dark energy density and new
variable modified Chaplygin gas energy density. From this, we have found the
expressions of the arbitrary positive constants B0 and C of new variable
modified Chaplygin gas.


  We extend the framework of combinatorial model categories, so that the
category of small presheaves over large indexing categories and ind-categories
would be embraced by the new machinery called class-combinatorial model
categories.
  The definition of the new class of model categories is based on the
corresponding extension of the theory of locally presentable and accessible
categories developed in the companion paper [arXiv:1110.0605], where we
introduced the concepts of locally class-presentable and class-accessible
categories.
  In this work we prove that the category of weak equivalences of a nice
class-combinatorial model category is class-accessible. Our extension of J.
Smith localization theorem depends on the verification of a cosolution-set
condition. The deepest result is that the (left Bousfield) localization of a
class-combinatorial model category with respect to a strongly class-accessible
localization functor is class-combinatorial again.


  Since the invention of chirped pulse amplification, which was recognized by a
Nobel prize in physics in 2018, there has been a continuing increase in
available laser intensity. Combined with advances in our understanding of the
kinetics of relativistic plasma, studies of laser-plasma interactions are
entering a new regime where the physics of relativistic plasmas is strongly
affected by strong-field quantum electrodynamics (QED) processes, including
hard photon emission and electron-positron ($e^+$-$e^-$) pair production. This
coupling of quantum emission processes and relativistic collective particle
dynamics can result in dramatically new plasma physics phenomena, such as the
generation of dense $e^+$-$e^-$ pair plasma from near vacuum, complete laser
energy absorption by QED processes or the stopping of an ultrarelativistic
electron beam, which could penetrate a cm of lead, by a hair's breadth of laser
light. In addition to being of fundamental interest, it is crucial to study
this new regime to understand the next generation of ultra-high intensity
laser-matter experiments and their resulting applications, such as high energy
ion, electron, positron, and photon sources for fundamental physics studies,
medical radiotherapy, and next generation radiography for homeland security and
industry.


  The first in a long series of papers by John T. Lewis,
  G. W. Ford and the present author, considered the problem of the most general
coupling of a quantum particle to a linear passive heat bath, in the course of
which they derived an exact formula for the free energy of an oscillator
coupled to a heat bath in thermal equilibrium at temperature T. This formula,
and its later extension to three dimensions to incorporate a magnetic field,
has proved to be invaluable in analyzing problems in quantum thermodynamics.
Here, we address the question raised in our title viz. Nernst's third law of
thermodynamics.