  Recently Biskup et al. [Europhys. Lett. 60 (2002) 21] studied the behaviour
of d-dimensional finite-volume liquid-vapour systems at a fixed excess $\delta
N$ of particles above the ambient gas density. They identify a dimensionless
parameter $\Delta (\delta N)$ and a universal constant $\Delta_\mathrm{c}(d)$
and show that for $\Delta < \Delta_c$ a droplet of the dense phase occurs while
for $\Delta > \Delta_c$ the excess is absorbed in the background. The fraction
$\lambda_\Delta$ of excess particles forming the droplet is given explicitly.
Furthermore, they state, that the same is true for solid-gas systems.
  To verify these results, we have simulated the spin-1/2 Ising model on a
square lattice at constant magnetisation equivalent to a fixed particle excess
in the lattice-gas picture. We measured the largest minority droplet,
corresponding to the solid phase, at various system sizes ($L=40, ..., 640$).
Using analytic values for the spontaneous magnetisation $m_0$, the
susceptibility $\chi$ and interfacial free energy $\tau_\mathrm{W}$ for the
infinite system, we were able to determine $\lambda_\Delta$ in very good
agreement with the theoretical prediction.


  In this paper we discuss two major conjectures in Mirror Symmetry:
Strominger-Yau-Zaslow conjecture about torus fibrations, and the homological
mirror conjecture (about an equivalence of the Fukaya category of a Calabi-Yau
manifold and the derived category of coherent sheaves on the dual Calabi-Yau
manifold). Our point of view on the origin of torus fibrations is based on the
standard differential-geometric picture of collapsing Riemannian manifolds as
well as analogous considerations for Conformal Field Theories. It seems to give
a description of mirror manifolds much more transparent than the one in terms
of D-branes.
  Also we make an attempt to prove the homological mirror conjecture using the
torus fibrations. In the case of abelian varieties, and for a large class of
Lagrangian submanifolds, we obtain an identification of Massey products on the
symplectic and holomorphic sides. Tools used in the proof are of a mixed
origin: not so classical Morse theory, homological perturbation theory and
non-archimedean analysis.


  We consider an initial boundary problem for the planar MHD system under the
general condition on the heat conductivity $\kappa$ that may depend on both the
density $\rho$ and the temperature $\theta$ satisfying
$\kappa(\rho,\theta)\geq\kappa_1 \theta^{q}$ for some constants $\kappa_1>0$
and $q>0.$ Firstly, the global existence of strong solution for large initial
data is obtained, and then the limit of the vanishing shear viscosity is
justified. In addition, the $L^2$ convergence rate is obtained together with
the estimation on the thickness of the boundary layer.


  We propose a novel TACLE (TAsk and CLass-awarE) framework to address the
relatively unexplored and challenging problem of exemplar-free semi-supervised
class incremental learning. In this scenario, at each new task, the model has
to learn new classes from both (few) labeled and unlabeled data without access
to exemplars from previous classes. In addition to leveraging the capabilities
of pre-trained models, TACLE proposes a novel task-adaptive threshold, thereby
maximizing the utilization of the available unlabeled data as incremental
learning progresses. Additionally, to enhance the performance of the
under-represented classes within each task, we propose a class-aware weighted
cross-entropy loss. We also exploit the unlabeled data for classifier
alignment, which further enhances the model performance. Extensive experiments
on benchmark datasets, namely CIFAR10, CIFAR100, and ImageNet-Subset100
demonstrate the effectiveness of the proposed TACLE framework. We further
showcase its effectiveness when the unlabeled data is imbalanced and also for
the extreme case of one labeled example per class.


  We study a quotient Conformal Field Theory, which describes a 3+1 dimensional
cosmological spacetime. Part of this spacetime is the Nappi-Witten (NW)
universe, which starts at a ``big bang'' singularity, expands and then
contracts to a ``big crunch'' singularity at a finite time. The gauged WZW
model contains a number of copies of the NW spacetime, with each copy connected
to the preceeding one and to the next one at the respective big bang/big crunch
singularities. The sequence of NW spacetimes is further connected at the
singularities to a series of non-compact static regions with closed timelike
curves. These regions contain boundaries, on which the observables of the
theory live. This suggests a holographic interpretation of the physics.


  Information geometry promotes an investigation of the geometric structure of
statistical manifolds, providing a series of elucidations in various areas of
scientific knowledge. In the physical sciences, especially in quantum theory,
this geometric method has an incredible parallel with the distinguishability of
states, an ability of great value for determining the effectiveness in
implementing physical processes. This gives us the context for this work. Here
we will approach a problem of uniparametric statistical inference from an
information-geometric perspective. We will obtain the generalised Bhattacharyya
higher-order corrections for the Cram\'{e}r-Rao bound, where the statistics is
given by a mixed quantum state. Using an unbiased estimator $T$, canonically
conjugated to the Hamiltonian $H$ that generates the dynamics, we find these
corrections independent of the specific choice of estimator. This procedure is
performed using information-geometric techniques, establishing connections with
corrections to the pure states case.


  Multi-object tracking (MOT) is a vital component of intelligent video
analytics applications such as surveillance and autonomous driving. The time
and storage complexity required to execute deep learning models for visual
object tracking hinder their adoption on embedded devices with limited
computing power. In this paper, we aim to accelerate MOT by transferring the
knowledge from high-level features of a complex network (teacher) to a
lightweight network (student) at both training and inference times. The
proposed AttTrack framework has three key components: 1) cross-model feature
learning to align intermediate representations from the teacher and student
models, 2) interleaving the execution of the two models at inference time, and
3) incorporating the updated predictions from the teacher model as prior
knowledge to assist the student model. Experiments on pedestrian tracking tasks
are conducted on the MOT17 and MOT15 datasets using two different object
detection backbones YOLOv5 and DLA34 show that AttTrack can significantly
improve student model tracking performance while sacrificing only minor
degradation of tracking speed.


  Consider time-harmonic acoustic scattering from a bounded penetrable obstacle
$D\subset \mathbb R^N$ embedded in a homogeneous background medium. The index
of refraction characterizing the material inside $D$ is supposed to be H\"older
continuous near the corners. If $D\subset \mathbb R^2$ is a convex polygon, we
prove that its shape and location can be uniquely determined by the far-field
pattern incited by a single incident wave at a fixed frequency. In dimensions
$N \geq 3$, the uniqueness applies to penetrable scatterers of rectangular type
with additional assumptions on the smoothness of the contrast. Our arguments
are motivated by recent studies on the absence of non-scattering wavenumbers in
domains with corners. As a byproduct, we show that the smoothness conditions in
previous corner scattering results are only required near the corners.


  We prove that the topological cycles of an arbitrary infinite graph induce a
matroid. This matroid in general is neither finitary nor cofinitary.


  We propose that spontaneous particle--anti-particle pair creations from the
discharged vacuum caused by the strong interactions in dense matter are major
sources of $\gamma$-ray bursts. Two neutron star collisions or black
hole-neutron star mergers at cosmological distance could produce a compact
object with its density exceeding the critical density for pair creations. The
emitted anti-particles annihilate with corresponding particles at the ambient
medium. This releases a large amount of energy. We discuss the spontaneous
$p\bar{p}$ pair creations within two neutron star collision and estimate the
exploded energy from $p\bar{p}$ annihilation processes. The total energy could
be around $10^{51} - 10^{53}$ erg depending on the impact parameter of
colliding neutron stars. This value fits well into the range of the initial
energy of the most energetic $\gamma$-ray bursts.


  We consider a U(2) Yang-Mills theory on M x S_F^2 where M is an arbitrary
noncommutative manifold and S_F^2 is a fuzzy sphere spontaneously generated
from a noncommutative U(N) Yang-Mills theory on M, coupled to a triplet of
scalars in the adjoint of U(N). Employing the SU(2)-equivariant gauge field
constructed in arXiv:0905.2338, we perform the dimensional reduction of the
theory over the fuzzy sphere. The emergent model is a noncommutative U(1) gauge
theory coupled adjointly to a set of scalar fields. We study this model on the
Groenewald-Moyal plane and find that, in certain limits, it admits
noncommutative, non-BPS vortex as well as flux-tube (fluxon) solutions and
discuss some of their properties.


  A U(1) BF-Yang-Mills theory on noncommutative ${\mathbb{R}}^4$ is presented
and in this formulation the U(1) Yang-Mills theory on noncommutative
${\mathbb{R}}^4$ is seen as a deformation of the pure BF theory. Quantization
using BRST symmetry formalism is discussed and Feynman rules are given.
Computations at one-loop order have been performed and their renormalization
studied. It is shown that the U(1) BFYM on noncommutative ${\mathbb{R}}^4$ is
asymptotically free and its UV-behaviour in the computation of the
$\beta$-function is like the usual SU(N) commutative BFYM and Yang Mills
theories.


  Light scattering in inhomogeneous media induces wavefront distortions which
pose an inherent limitation in many optical applications. Examples range from
microscopy and nanosurgery to astronomy. In recent years, ongoing efforts have
made the correction of spatial distortions possible by wavefront shaping
techniques. However, when ultrashort pulses are employed scattering induces
temporal distortions which hinder their use in nonlinear processes such as in
multiphoton microscopy and quantum control experiments. Here we show that
correction of both spatial and temporal distortions can be attained by
manipulating only the spatial degrees of freedom of the incident wavefront.
Moreover, by optimizing a nonlinear signal the refocused pulse can be shorter
than the input pulse. We demonstrate focusing of 100fs pulses through a 1mm
thick brain tissue, and 1000-fold enhancement of a localized two-photon
fluorescence signal. Our results open up new possibilities for optical
manipulation and nonlinear imaging in scattering media.


  We investigate the redshift (z) evolution of dust properties, its dependences
on initial conditions of galaxy formation, and physical correlations between
dust, gas, and stellar contents at different z based on our original
chemodynamical simulations of galaxy formation with dust growth and
destruction. In this preliminary investigation, we first determine the
reasonable ranges of the most important two parameters for dust evolution,
i.e., the timescales of dust growth and destruction, by comparing the observed
and simulated dust properties and molecular hydrogen H2 content of the Galaxy.
We then investigate the z-evolution of dust-to-gas-ratios (D) and, H2 gas
fraction (f_H2), and gas-phase chemical abundances (e.g., A_O=12+log(O/H)) in
the simulated disk and dwarf galaxies. The principal results are as follows.
Both D and f_H2 can rapidly increase during the early dissipative formation of
galactic disks (z ~ 2-3) and the z-evolution of these depends on initial mass
densities, spin parameters, and masses of galaxies. The observed A_O-D relation
can be qualitatively reproduced, but the simulated dispersion of D at a given
A_O is smaller. The simulated galaxies with larger total dust masses show
larger H2 and stellar masses and higher f_H2. Disk galaxies show negative
radial gradients of D and the gradients are steeper for more massive galaxies.
Both dust-to-metal ratios and gas-phase [S/Fe] can be significantly different
within a single galaxy and between different galaxies at different z, which
means that fixed dust-to-metal ratios should not be used in investigating H2
contents and spectral energy distributions of galaxies.


  Future surveys will access large volumes of space and hence very long
wavelength fluctuations of the matter density and gravitational field. It has
been argued that the set of secondary effects that affect the galaxy
distribution, relativistic in nature, will bring new, complementary
cosmological constraints. We study this claim in detail by focusing on a subset
of wide-area future surveys: Stage-4 cosmic microwave background experiments
and photometric redshift surveys. In particular, we look at the magnification
lensing contribution to galaxy clustering and general relativistic corrections
to all observables. We quantify the amount of information encoded in these
effects in terms of the tightening of the final cosmological constraints as
well as the potential bias in inferred parameters associated with neglecting
them. We do so for a wide range of cosmological parameters, covering neutrino
masses, standard dark-energy parametrizations and scalar-tensor gravity
theories. Our results show that, while the effect of lensing magnification to
number counts does not contain a significant amount of information when galaxy
clustering is combined with cosmic shear measurements, this contribution does
play a significant role in biasing estimates on a host of parameter families if
unaccounted for. Since the amplitude of the magnification term is controlled by
the slope of the source number counts with apparent magnitude, $s(z)$, we also
estimate the accuracy to which this quantity must be known to avoid systematic
parameter biases, finding that future surveys will need to determine $s(z)$ to
the $\sim$5-10\% level. On the contrary, large-scale general-relativistic
corrections are irrelevant both in terms of information content and parameter
bias for most cosmological parameters, but significant for the level of
primordial non-Gaussianity.


  The precise form of the foregrounds for sky-averaged measurements of the
21-cm line during and before the epoch of reionization is unknown. We suggest
that the level of complexity in the foreground models used to fit global 21-cm
data should be driven by the data, under a Bayesian model selection
methodology. A first test of this approach is carried out by applying nested
sampling to simplified models of global 21-cm data to compute the Bayesian
evidence for the models. If the foregrounds are assumed to be polynomials of
order n in log-log space, we can infer the necessity to use n=4 rather than n=3
with <2h of integration with limited frequency coverage, for reasonable values
of the n=4 coefficient.
  Using a higher-order polynomial does not necessarily prevent a significant
detection of the 21-cm signal. Even for n=8, we can obtain very strong evidence
distinguishing a reasonable model for the signal from a null model with 128h of
integration. More subtle features of the signal may, however, be lost if the
foregrounds are this complex. This is demonstrated using a simpler model for
the signal that only includes absorption.
  The results highlight some pitfalls in trying to quantify the significance of
a detection from errors on the parameters of the signal alone.


  Damage spreading for 2D Ising cluster dynamics is investigated numerically by
using random numbers in a way that conforms with the notion of submitting the
two evolving replicas to the same thermal noise. Two damage spreading
transitions are found; damage does not spread either at low or high
temperatures. We determine the critical exponents at the high-temperature
transition point, which seem consistent with directed percolation.


  Reliability analysis of mechatronic systems is a recent field and a dynamic
branch of research. It is addressed whenever there is a need for reliable,
available, and safe systems. The studies of reliability must be conducted
earlier during the design phase, in order to reduce costs and the number of
prototypes required in the validation of the system. The process of reliability
is then deployed throughout the full cycle of development. This process is
broken down into three major phases: the predictive reliability, the
experimental reliability and operational reliability. The main objective of
this article is a kind of portrayal of the various studies enabling a
noteworthy mastery of the predictive reliability. The weak points are
highlighted. Presenting an overview of all the quantitative and qualitative
approaches concerned with modelling and evaluating the prediction of
reliability is so important for future reliability studies and for academic
research to come up with new methods and tools. The mechatronic system is a
hybrid system, it is dynamic, reconfigurable, and interactive. The modeling
carried out of reliability prediction must take into account these criteria.
Several methodologies have been developed in this track of research. In this
regard, the aforementioned methodologies will be analytically sketched in this
paper.


  Recent successes and advances in Deep Neural Networks (DNN) in machine vision
and Natural Language Processing (NLP) have motivated their use in traditional
signal processing and communications systems. In this paper, we present results
of such applications to the problem of automatic modulation recognition.
Variations in wireless communication channels are represented by statistical
channel models and their parameterization will increase with the advent of 5G.
In this paper, we report effect of simple two path channel model on our naive
deep neural network based implementation. We also report impact of adversarial
perturbation to the input signal.


  This paper investigates the capability of plain Vision Transformers (ViTs)
for semantic segmentation using the encoder-decoder framework and introduces
\textbf{SegViTv2}. In this study, we introduce a novel Attention-to-Mask (\atm)
module to design a lightweight decoder effective for plain ViT. The proposed
ATM converts the global attention map into semantic masks for high-quality
segmentation results. Our decoder outperforms the popular decoder UPerNet using
various ViT backbones while consuming only about $5\%$ of the computational
cost. For the encoder, we address the concern of the relatively high
computational cost in the ViT-based encoders and propose a \emph{Shrunk++}
structure that incorporates edge-aware query-based down-sampling (EQD) and
query-based upsampling (QU) modules. The Shrunk++ structure reduces the
computational cost of the encoder by up to $50\%$ while maintaining competitive
performance. Furthermore, we propose to adapt SegViT for continual semantic
segmentation, demonstrating nearly zero forgetting of previously learned
knowledge. Experiments show that our proposed SegViTv2 surpasses recent
segmentation methods on three popular benchmarks including ADE20k,
COCO-Stuff-10k and PASCAL-Context datasets. The code is available through the
following link: \url{https://github.com/zbwxp/SegVit}.


  Antithetic sampling, which goes back to the classical work by Hammersley and
Morton (1956), is one of the well-known variance reduction techniques for Monte
Carlo integration. In this paper we investigate its application to digital nets
over $\mathbb{Z}_b$ for quasi-Monte Carlo (QMC) integration, a deterministic
counterpart of Monte Carlo, of functions defined over the $s$-dimensional unit
cube. By looking at antithetic sampling as a geometric technique in a compact
totally disconnected abelian group, we first generalize the notion of
antithetic sampling from base $2$ to an arbitrary base $b\ge 2$. Then we
analyze the QMC integration error of digital nets over $\mathbb{Z}_b$ with
$b$-adic antithetics. Moreover, for a prime $b$, we prove the existence of good
higher order polynomial lattice point sets with $b$-adic antithetics for QMC
integration of smooth functions in weighted Sobolev spaces. Numerical
experiments based on Sobol' point sets up to $s=100$ show that the rate of
convergence can be improved for smooth integrands by using antithetic sampling
technique, which is quite encouraging beyond the reach of our theoretical
result and motivates future work to address.


  Context: Edge graphs are graphs whose edges are labelled with identifiers,
and nodes can have multiple edges between them. They are used to model a wide
range of systems, including networks with distances or degrees of connection
and complex relational data.
  Inquiry: Unfortunately, the homogeneity of this graph structure prevents an
effective representation in (functional) programs. Either their interface is
riddled with partial functions, or the representations are computationally
inefficient to process.
  Approach: We present a novel data type for edge graphs, based on total and
recursive definitions, that prevents usage errors from partial APIs and
promotes structurally recursive computations. We follow an algebraic approach
and provide a set of primitive constructors and combinators, along with
equational laws that identify semantically equivalent constructions.
  Knowledge: This algebra translates directly into an implementation using
algebraic data types, and its homomorphisms give rise to functions for
manipulating and transforming these edge graphs.
  Grounding: We exploit the fact that many common graph algorithms are such
homomorphisms to implement them in our framework.
  Importance: In giving a theoretical grounding for the edge graph data type,
we can formalise properties such as soundness and completeness of the
representation while also minimising usage errors and maximising re-usability.


  In-context learning (ICL) suffers from oversensitivity to the prompt, making
it unreliable in real-world scenarios. We study the sensitivity of ICL with
respect to multiple perturbation types. First, we find that label bias obscures
the true sensitivity, and therefore prior work may have significantly
underestimated ICL sensitivity. Second, we observe a strong negative
correlation between ICL sensitivity and accuracy: predictions sensitive to
perturbations are less likely to be correct. Motivated by these findings, we
propose \textsc{SenSel}, a few-shot selective prediction method that abstains
from sensitive predictions. Experiments on ten classification datasets show
that \textsc{SenSel} consistently outperforms two commonly used
confidence-based and entropy-based baselines on abstention decisions.


  We study the Terahertz (THz) emission mechanism in BSCCO with an external
magnetic field theoretically. The total power is proportional to the square of
the number of layers and the frequency. Under the small size limitation, the
angular distribution is similar to that in the dipole emission. The present
theoretical results for THz power output are in the same order of magnitude as
the experimental ones, in contrast with previous simulations. A series of
size-dependent intrinsic frequencies are also observed. When the external
frequency induced by the external current resonates with intrinsic ones, the
distinct THz emission is observed.


  In this work, a fast method of vertex reconstructing for incident ions in GEM
detectors was proposed. As inspired by the Time Projection Chamber (TPC), the
time information of the consecutive signal samples from Front End Electronics
(FEE) was employed. To demonstrate the method, an experiment involving a
2D-readout THGEM detector with an APV-25 FEE was taken. With one experiment
dependent parameter in the analysis, the proposed method led to a spatial
resolution of 0.45 mm, compared with the same number of 9.10 mm from the
traditional center of gravity method.


  Using properties of skew-Hamiltonian matrices and classic connectedness
results, we prove that the moduli space $M_{ort}^0(r,n)$ of stable rank $r$
orthogonal vector bundles on $\mathbb{P}^2$, with Chern classes
$(c_1,c_2)=(0,n)$, and trivial splitting on the general line, is smooth
irreducible of dimension $(r-2)n-{r \choose 2}$ for specific values of $r$ and
$n$.


  The asymptotic Samuel function generalizes to arbitrary rings the usual order
function of a regular local ring. In this paper, we use this function to
introduce the notion of the Samuel slope of a Noetherian local ring, and we
study some of its properties. In particular, we focus on the case of a local
ring at singular point of a variety, and, among other results, we prove that
the Samuel slope of these rings is related to some invariants used in
algorithmic resolution of singularities.


  Communication is essential for successful interaction. In human-robot
interaction, implicit communication enhances robots' understanding of human
needs, emotions, and intentions. This paper introduces a method to foster
implicit communication in HRI without explicitly modeling human intentions or
relying on pre-existing knowledge. Leveraging Transfer Entropy, we modulate
influence between agents in social interactions in scenarios involving either
collaboration or competition. By integrating influence into agents' rewards
within a partially observable Markov decision process, we demonstrate that
boosting influence enhances collaboration or competition performance, while
resisting influence diminishes performance. Our findings are validated through
simulations and real-world experiments with human participants.


  Let $f:X\to Y$ be a finite ramified Galois covering of algebraic varieties
defined over the complex numbers. In this paper, we prove some structure
theorems for such coverings in the case that the non-abelian Galois group of
the cover is metabelian (in particular metacyclic). Our results extend the
previous ones, obtained by several authors, in the case of abelian and dihedral
Galois coverings. In analogy with the abelian (and generalizing the dihedral)
case, we find "building data" of metabelian covers from which the cover can be
reconstructed. In addition to analyzing several examples, we compute invariants
and eigensheaves of the group actions of the coverings under study with some
applications.


  Archimedes' hat-box theorem states that uniform measure on a sphere projects
to uniform measure on an interval. This fact can be used to derive Simpson's
rule. We present various constructions of, and lower bounds for, numerical
cubature formulas using moment maps as a generalization of Archimedes' theorem.
We realize some well-known cubature formulas on simplices as projections of
spherical designs. We combine cubature formulas on simplices and tori to make
new formulas on spheres. In particular $S^n$ admits a 7-cubature formula
(sometimes a 7-design) with $O(n^4)$ points. We establish a local lower bound
on the density of a PI cubature formula on a simplex using the moment map.
  Along the way we establish other quadrature and cubature results of
independent interest. For each $t$, we construct a lattice trigonometric
$(2t+1)$-cubature formula in $n$ dimensions with $O(n^t)$ points. We derive a
variant of the M\"oller lower bound using vector bundles. And we show that
Gaussian quadrature is very sharply locally optimal among positive quadrature
formulas.


  The primary contribution of this work is to examine the energy efficiency of
pulse coupled oscillation for time synchronization in a realistic wireless
network environment and to explore the impact of mobility on convergence rate.
Energy coupled oscillation is susceptible to interference; this approach uses
reception and decoding of short packet bursts to eliminate this problem. The
energy efficiency of a commonly used timestamp broadcast algorithm is compared
and contrasted with pulse-coupled oscillation. The emergent pulse coupled
oscillation technique shows greater energy efficiency as well as robustness
with mobility. A proportion of the sensors may be integrated with GPS receivers
in order to obtain a master clock time.


  Existing determinations show that n(C)/n(Fe) is more than a factor of 10
below solar in the atmospheres of three white dwarfs that appear to be
externally-polluted. These results are not easily explained if the stars have
accreted interstellar matter, and we re-interpret these measurements as
evidence that these stars have accreted asteroids of a chrondritic composition.


  Many theoretical approaches find d-wave superconductivity in the prototypical
one-band Hubbard model for high-temperature superconductors. At strong-coupling
(U > W, where U is the on-site repulsion and W=8t the bandwidth) pairing is
controlled by the exchange energy J=4t^2/U. One may then surmise, ignoring
retardation effects, that near-neighbor Coulomb repulsion V will destroy
superconductivity when it becomes larger than J, a condition that is easily
satisfied in cuprates for example. Using Cellular Dynamical Mean-Field theory
with an exact diagonalization solver for the extended Hubbard model, we show
that pairing, at strong coupling, is preserved even when V>>J, as long as
V<U/2. While at weak coupling V always reduces the spin fluctuations and hence
d-wave pairing, at strong coupling, in the underdoped regime, the increase of
J=4t^2/(U-V) caused by V increases binding at low frequency while the
pair-breaking effect of V is pushed to high frequency. These two effects
compensate in the underdoped regime, in the presence of a pseudogap. While the
pseudogap competes with superconductivity, the proximity to the Mott transition
that leads to the pseudogap, and retardation effects, protect d-wave
superconductivity from V.


  The Hubeny-Rangamani-Takayanagi surface $\gamma_{HRT}$ computing the entropy
$S(D)$ of a domain of dependence $D$ on an asymptotically AdS boundary is known
to be causally inaccessible from $D$. We generalize this gravitational result
to higher replica numbers $n >1$ by considering the replica-invariant surfaces
(aka `splitting surfaces') $\boldsymbol{\mathbf{\gamma}}$ of real-time
replica-wormhole saddle-points computing R\'enyi entropies $S_n(D)$ and showing
that there is a sense in which $D$ must again be causally inaccessible from
$\boldsymbol{\mathbf{\gamma}}$ when the saddle preserves both replica and
conjugation symmetry. This property turns out to imply the $S_n(D)$ to be
independent of any choice of any Cauchy surface $\Sigma_D$ for $D$, and also
that the $S_n(D)$ are independent of the choice of boundary sources within $D$.
This is a key hallmark of unitary evolution in any dual field theory.
Furthermore, from the bulk point of view it adds to the evidence that time
evolution of asymptotic observables in quantum gravity is implemented by a
unitary operator in each baby universe superselection sector. Though we focus
here on pure Einstein-Hilbert gravity and its Kaluza-Klein reductions, we
expect the argument to extend to any two-derivative theory who satisfies the
null convergence condition. We consider both classical saddles and the effect
of back-reaction from quantum corrections.


  Keyphrases are capable of providing semantic metadata characterizing
documents and producing an overview of the content of a document. Since
keyphrase extraction is able to facilitate the management, categorization, and
retrieval of information, it has received much attention in recent years. There
are three approaches to address keyphrase extraction: (i) traditional two-step
ranking method, (ii) sequence labeling and (iii) generation using neural
networks. Two-step ranking approach is based on feature engineering, which is
labor intensive and domain dependent. Sequence labeling is not able to tackle
overlapping phrases. Generation methods (i.e., Sequence-to-sequence neural
network models) overcome those shortcomings, so they have been widely studied
and gain state-of-the-art performance. However, generation methods can not
utilize context information effectively. In this paper, we propose a novelty
Span Keyphrase Extraction model that extracts span-based feature representation
of keyphrase directly from all the content tokens. In this way, our model
obtains representation for each keyphrase and further learns to capture the
interaction between keyphrases in one document to get better ranking results.
In addition, with the help of tokens, our model is able to extract overlapped
keyphrases. Experimental results on the benchmark datasets show that our
proposed model outperforms the existing methods by a large margin.


  Two different ways of trimming the sample path of a stochastic process in
D[0, 1]: global ("trim as you go") trimming and record time ("lookback")
trimming are analysed to find conditions for the corresponding operators to be
continuous with respect to the (strong) J1-topology. A key condition is that
there should be no ties among the largest ordered jumps of the limit process.
As an application of the theory, via the continuous mapping theorem we prove
limit theorems for trimmed Levy processes, using the functional convergence of
the underlying process to a stable process. The results are applied to a
reinsurance ruin time problem.


  We study the interpolation group whose elements are suitable pairs of formal
power series. This group has a faithful representation into infinite lower
triangular matrices and carries thus a natural structure as a Lie group. The
matrix exponential between its Lie algebra and its matrix representation gives
rise to a function with interesting properties extending the usual exponential
function to two variables (which are formal power series) We finish with an
application to enumerative combinatorics and the description of an algebra
which generalizes the interpolation group.


  A classification of models with three light neutrinos is given. This
classification includes virtually all of the three-neutrino models proposed in
the last few years, of which there are approximately one hundred. The essential
ideas, attractive features, and characteristic problems of the different
classes of model are discussed. The classification is based principally on how
the large \nu_{\mu} - \nu_{\tau} mixing is obtained. A general discussion of
the mixing parameter U_{e3} is then given, showing what values are to be
expected for it in each type of model.


  Faddeev-Yakubovski equations in configuration space are used to solve four
nucleon problem for bound and scattering states. Different realistic
interaction models are tested, elucidating open problems in nuclear interaction
description. On one hand, by example of nonlocal Doleschall potential, we
reveal possibility of reducing three-nucleon force. On the other hand we
disclose discrepancies in describing n+$^3$H resonance, which seems to be
hardly related with off-shell structure of nucleon-nucleon interaction.


  With the ever-increasing complexity of large-scale pre-trained models coupled
with a shortage of labeled data for downstream training, transfer learning has
become the primary approach in many fields, including natural language
processing, computer vision, and multi-modal learning. Despite recent progress,
the fine-tuning process for large-scale pre-trained models in vision still
mostly relies on trial and error. This work investigates the relationship
between neural collapse (NC) and transfer learning for classification problems.
NC is an intriguing while prevalent phenomenon that has been recently
discovered in terms of the final-layer features and linear classifiers of
trained neural networks. Specifically, during the terminal phase of training,
NC implies that the variability of the features within each class diminishes to
zero, while the means of features between classes are maximally and equally
distanced. In this work, we examine the NC attributes of pre-trained models on
both downstream and source data for transfer learning, and we find strong
correlation between feature collapse and downstream performance. In particular,
we discovered a systematic pattern that emerges when linear probing pre-trained
models on downstream training data: the more feature collapse of pre-trained
models on downstream training data, the higher the transfer accuracy.
Additionally, we also studied the relationship between NC and transfer accuracy
on the source data. Moreover, these findings allow us to develop a principled,
parameter-efficient fine-tuning method that employs skip-connection to induce
the last-layer feature collapse on downstream data. Our proposed fine-tuning
methods deliver good performances while reducing fine-tuning parameters by at
least 90% and mitigating overfitting in situations especially when the
downstream data is scarce.


  We motivate the use of dressed charges by arguing that such objects are
needed to describe, e.g., constituent quarks and, in general, physical charged
states in gauge theories. We give a short introduction to dressings in both QED
and QCD. We put special emphasis on the infra-red properties of a moving
dressed charge. To be more precise, we demonstrate that the one loop propagator
of a relativistic dressed charge can be renormalized in the mass shell scheme
with no infra-red divergence showing up.


  We analyse the $L^2$ Hilbert complexes naturally associated to a non-compact
complex manifold, namely the ones which originate from the Dolbeault and the
Aeppli-Bott-Chern complexes. In particular we define the $L^2$
Aeppli-Bott-Chern Hilbert complex and examine its main properties on general
Hermitian manifolds, on complete K\"ahler manifolds and on Galois coverings of
compact complex manifolds. The main results are achieved through the study of
self-adjoint extensions of various differential operators whose kernels, on
compact Hermitian manifolds, are isomorphic to either Aeppli or Bott-Chern
cohomology.


  We study the coherent state and two-mode squeezed state in the q-deformed
Pegg-Barnett(PB) formalism. We show that when the truncation of the Fock space
S is large enough, the phase properties of the q-deformed PB coherent state
approach that of the undeformed PB coherent state. We also investigate the
entanglement properties of the two-mode squeezed states in both the q-deformed
and undeformed PB Fock space with the real squeezing parameter r. We see that
if S is sufficiently large, the conventional two-mode squeezed states can be
approximated with the PB (deformed and undeformed) states for arbitrary r.
However, the value of S required increases more rapidly with the q-deformed PB
states than the PB states as a function of r.


  Starting from a Langevin formulation of a thermally perturbed nonlinear
elastic model of the ferroelectric smectic-C$^*$ (SmC${*}$) liquid crystals in
the presence of an electric field, this article characterizes the hitherto
unexplored dynamical phase transition from a thermo-electrically forced
ferroelectric SmC${}^{*}$ phase to a chiral nematic liquid crystalline phase
and vice versa. The theoretical analysis is based on a combination of dynamic
renormalization (DRG) and numerical simulation of the emergent model. While the
DRG architecture predicts a generic transition to the Kardar-Parisi-Zhang (KPZ)
universality class at dynamic equilibrium, in agreement with recent
experiments, the numerical simulations of the model show simultaneous existence
of two phases, one a "subdiffusive" (SD) phase characterized by a dynamical
exponent value of 1, and the other a KPZ phase, characterized by a dynamical
exponent value of 1.5. The SD phase flows over to the KPZ phase with increased
external forcing, offering a new universality paradigm, hitherto unexplored in
the context of ferroelectric liquid crystals.


  Second-order coding rate of channel coding is discussed for general sequence
of channels. The optimum second-order transmission rate with a constant error
constraint $\epsilon$ is obtained by using the information spectrum method. We
apply this result to the discrete memoryless case, the discrete memoryless case
with a cost constraint, the additive Markovian case, and the Gaussian channel
case with an energy constraint. We also clarify that the Gallager bound does
not give the optimum evaluation in the second-order coding rate.


  The paper analyzes a new public key cryptosystem whose security is based on a
matrix version of the discrete logarithm problem over an elliptic curve. It is
shown that the complexity of solving the underlying problem for the proposed
system is dominated by the complexity of solving a fixed number of discrete
logarithm problems in the group of an elliptic curve. Using an adapted Pollard
rho algorithm it is shown that this problem is essentially as hard as solving
one discrete logarithm problem in the group of an elliptic curve.


  A graph is 1-planar if it can be drawn in the plane so that each edge is
crossed by at most one another edge. In this work we prove that each 1-planar
graph of minimum degree at least $3$ contains an edge with degrees of its
endvertices of type $(3,\leq23)$ or $(4,\leq11)$ or $(5,\leq9)$ or $(6,\leq8)$
or $(7,7)$. Moreover, the upper bounds $9,8$ and $7$ here are sharp and the
upper bounds $23$ and $11$ are very close to the possible sharp ones, which may
be 20 and 10, respectively. This generalizes a result of Fabrici and Madaras
[Discrete Math., 307 (2007) 854--865] which says that each 3-connected 1-planar
graph contains a light edge, and improves a result of Hud\'ak and \v{S}ugerek
[Discuss. Math. Graph Theory, 32(3) (2012) 545--556], which states that each
1-planar graph of minimum degree at least $4$ contains an edge with degrees of
its endvertices of type $(4,\leq 13)$ or $(5,\leq 9)$ or $(6,\leq 8)$ or $(7,
7)$.


  For any given Salem number, we construct an automorphism on a simple abelian
variety whose first dynamical degree is the square of the Salem number. Our
construction works for both simple abelian varieties with totally indefinite
quaternion multiplication and for simple abelian varieties of the second kind.
We then give a complete classification of the dynamical degree sequences for
abelian varieties of dimension at most four and obtain an ergodic result for
sequences of pullbacks of forms.


  We consider the problem of learning a manifold from a teacher's
demonstration. Extending existing approaches of learning from randomly sampled
data points, we consider contexts where data may be chosen by a teacher. We
analyze learning from teachers who can provide structured data such as
individual examples (isolated data points) and demonstrations (sequences of
points). Our analysis shows that for the purpose of teaching the topology of a
manifold, demonstrations can yield remarkable decreases in the amount of data
points required in comparison to teaching with randomly sampled points. We also
discuss the implications of our analysis for learning in humans and machines.


  A characterization of the foliation by spacelike slices of an
$(n+1)$-dimensional spatially closed Generalized Robertson-Walker spacetime is
given by means of studying a natural mean curvature type equation on spacelike
graphs. Under some natural assumptions, of physical or geometric nature, all
the entire solutions of such an equation are obtained. In particular, the case
of entire spacelike graphs in de Sitter spacetime is faced and completely
solved by means of a new application of a known integral formula.


  This paper proposes an efficient adaptive variant of a quadratic penalty
accelerated inexact proximal point (QP-AIPP) method proposed earlier by the
authors. Both the QP-AIPP method and its variant solve linearly set constrained
nonconvex composite optimization problems using a quadratic penalty approach
where the generated penalized subproblems are solved by a variant of the
underlying AIPP method. The variant, in turn, solves a given penalized
subproblem by generating a sequence of proximal subproblems which are then
solved by an accelerated composite gradient algorithm. The main difference
between AIPP and its variant is that the proximal subproblems in the former are
always convex while the ones in the latter are not necessarily convex due to
the fact that their prox parameters are chosen as aggressively as possible so
as to improve efficiency. The possibly nonconvex proximal subproblems generated
by the AIPP variant are also tentatively solved by a novel adaptive accelerated
composite gradient algorithm based on the validity of some key convergence
inequalities. As a result, the variant generates a sequence of proximal
subproblems where the stepsizes are adaptively changed according to the
responses obtained from the calls to the accelerated composite gradient
algorithm. Finally, numerical results are given to demonstrate the efficiency
of the proposed AIPP and QP-AIPP variants.


  We show how the scheme- and scale-dependence of the short-distance operator
product expansion with four-quark operators can be correctly accounted for in
the framework of the $1/N_c$-expansion once the hadronization of two-quark
currents has been fixed. We show formulas explicitly in the case of the
$B_K$-parameter. We then use them with our earlier estimates of the
long-distance effects. We compare Chiral Perturbation Theory at Leading- and
Next-to-Leading-Order with the ENJL model results in the chiral limit. Good
matching between the long- and short-distance regimes is obtained and our final
value for the physical scheme independent $\hat B_K$ is $0.77\pm0.05\pm0.05$.


  Environmental effects are crucial to the understanding of the evolution of
galaxies in dense environments, such as galaxy clusters. Using the large
field-of-view of SITELLE, the unique imaging fourier transform spectrograph at
CFHT, we are able to obtain 2D spectral information for a large and complete
sample of cluster galaxies out to the infall region. We describe a pipeline
developed to identify emission line galaxies (ELGs) from the datacube using
cross-correlation techniques. We present results based on the spatial offsets
between the emission-line regions and stellar continua in ELGs from two
z$\sim$0.25 galaxy clusters, Abell 2390 and Abell 2465. We find a preference in
the offsets being pointed away from the cluster center. Combining the two
clusters, there is a 3$\sigma$ excess for high-velocity galaxies within the
virial radius having the offsets to be pointed away from the cluster center.
Assuming the offset being a proxy for the velocity vector of a galaxy, as
expected from ram pressure stripping, this excess indicates that ram pressure
stripping occurs most effectively during the first passage of an infalling
galaxy, leading to the quenching of its star formation. We also find that,
outside the virial region, the continuum-normalized H$\alpha$ line flux for
infalling galaxies with large offsets are on average lower than those with
small or no measurable offset, further supporting ram pressure as a dominant
quenching mechanism during the initial infall stages.


  We report an experimental study of the plasmon-assisted spontaneous emission
and the F\"orster resonance energy transfer between organic molecules and
semiconductor colloidal quantum dots. The localized plasmonic field in the
nanogap between a gold nano-popcorn's tips and a 5-nm separated gold film
supports high photonic density of states and provides pathways for the
light-matter interaction mechanisms. We demonstrate that, besides the total
spontaneous emission rate enhancement factor up to 66 for quantum dots and
molecules, the F\"orster resonance energy transfer efficiency and rate constant
are simultaneously modified. While the energy transfer efficiency is reduced
from 84 to 35 per cent due to the non-radiative quenching effect and fast donor
spontaneous emission rate, the energy transfer rate constant is significantly
increased from 4 to 20 ns-1. Our results have quantitatively elucidated decay
mechanisms that are important toward understanding and controlling of the
light-matter interaction at the nanoscale.


  We study the expressive power of successor-invariant first-order logic, which
is an extension of first-order logic where the usage of an additional successor
relation on the structure is allowed, as long as the validity of formulas is
independent of the choice of a particular successor on finite structures.
  We show that when the degree is bounded, successor-invariant first-order
logic is no more expressive than first-order logic.


  We derive the gravitational energy-momentum pseudotensor $ \tau^{\sigma}_
{\phantom {\sigma} \lambda} $ in metric $ f\left (R \right) $ gravity and in
teleparallel $ f\left (T\right) $ gravity. In the first case, $R$ is the Ricci
curvature scalar for a torsionless Levi-Civita connection; in the second case,
$T$ is the curvature-free torsion scalar derived by tetrads and Weitzenb\"ock
connection. For both classes of theories the continuity equations are obtained
in presence of matter. $ f \left (R \right) $ and $ f \left (T \right) $ are
non-equivalent but differ for a quantity $ \omega \left (T, B \right) $
containing the torsion scalar $T$ and a boundary term $ B $. It is possible to
obtain the field equations for $ \omega \left (T, B \right) $ and the related
gravitational energy-momentum pseudotensor $ \tau^{\sigma}_{\phantom
{\sigma}\lambda} \vert \omega $. Finally we show that, thanks to this further
pseudotensor, it is possible to pass from $ f \left (R \right) $ to $ f \left (
T \right) $ and viceversa through a simple relation between gravitational
pseudotensors.


  Employing machine learning models in the real world requires collecting large
amounts of data, which is both time consuming and costly to collect. A common
approach to circumvent this is to leverage existing, similar data-sets with
large amounts of labelled data. However, models trained on these canonical
distributions do not readily transfer to real-world ones. Domain adaptation and
transfer learning are often used to breach this "reality gap", though both
require a substantial amount of real-world data. In this paper we discuss a
more general approach: we propose learning a general transformation to bring
arbitrary images towards a canonical distribution where we can naively apply
the trained machine learning models. This transformation is trained in an
unsupervised regime, leveraging data augmentation to generate off-canonical
examples of images and training a Deep Learning model to recover their original
counterpart. We quantify the performance of this transformation using
pre-trained ImageNet classifiers, demonstrating that this procedure can recover
half of the loss in performance on the distorted data-set. We then validate the
effectiveness of this approach on a series of pre-trained ImageNet models on a
real world data set collected by printing and photographing images in different
lighting conditions.


  The prospect of mini black holes, either primordial or in planned experiments
at the Large Hadron Collider, interacting with the earth motivate us to examine
how they may be detected and the scope of their impact on the earth. We propose
that the more massive of these objects may gravitationally bind matter without
significant absorption. Since the wave functions of gravitationally bound atoms
orbiting a black hole are analogous to those of electrons around a nucleus, we
call such an object the Gravitationally Equivalent of an Atom (GEA). Mini black
holes are expected to lose mass through quantum evaporation, which has become
well accepted on purely theoretical grounds. Since all attempts to directly
observe x-rays from an evaporating black hole have failed, we examine the
possibility of the inverse test: search for extant mini black holes by looking
for emissions from matter bound in a GEA. If quantum evaporation does not
occur, then miniature black holes left over from the early universe may be
stable, contribute to dark matter, and in principle be detectable through
emissions associated with the bound matter. We show that small black holes-with
masses below \sim10^12 kg-can bind matter without readily absorbing it into the
black hole but the emissions are too weak to be detected from earth.


  Many cyber network defense tools rely on the National Vulnerability Database
(NVD) to provide timely information on known vulnerabilities that exist within
systems on a given network. However, recent studies have indicated that the NVD
is not always up to date, with known vulnerabilities being discussed publicly
on social media platforms, like Twitter and Reddit, months before they are
published to the NVD. To that end, we present a framework for unsupervised
classification to filter tweets for relevance to cyber security. We consider
and evaluate two unsupervised machine learning techniques for inclusion in our
framework, and show that zero-shot classification using a Bidirectional and
Auto-Regressive Transformers (BART) model outperforms the other technique with
83.52% accuracy and a F1 score of 83.88, allowing for accurate filtering of
tweets without human intervention or labelled data for training. Additionally,
we discuss different insights that can be derived from these cyber-relevant
tweets, such as trending topics of tweets and the counts of Twitter mentions
for Common Vulnerabilities and Exposures (CVEs), that can be used in an alert
or report to augment current NVD-based risk assessment tools.


  We investigate the binding surface along the Bain path and phonon dispersion
relations for the cubic phase of the ferromagnetic binary alloys Fe3X (X = Ni,
Pd, Pt) for L12 and DO22 ordered phases from first principles by means of
density functional theory. The phonon dispersion relations exhibit a softening
of the transverse acoustic mode at the M-point in the L12-phase in accordance
with experiments for ordered Fe3Pt. This instability can be associated with a
rotational movement of the Fe-atoms around the Ni-group element in the
neighboring layers and is accompanied by an extensive reconstruction of the
Fermi surface. In addition, we find an incomplete softening in [111] direction
which is strongest for Fe3 Ni. We conclude that besides the valence electron
density also the specific Fe-content and the masses of the alloying partners
should be considered as parameters for the design of Fe-based functional
magnetic materials.


  The proliferation of misleading information in everyday access media outlets
such as social media feeds, news blogs, and online newspapers have made it
challenging to identify trustworthy news sources, thus increasing the need for
computational tools able to provide insights into the reliability of online
content. In this paper, we focus on the automatic identification of fake
content in online news. Our contribution is twofold. First, we introduce two
novel datasets for the task of fake news detection, covering seven different
news domains. We describe the collection, annotation, and validation process in
detail and present several exploratory analysis on the identification of
linguistic differences in fake and legitimate news content. Second, we conduct
a set of learning experiments to build accurate fake news detectors. In
addition, we provide comparative analyses of the automatic and manual
identification of fake news.


  In the current paradigm, red sequence galaxies are believed to have formed as
blue disk galaxies that subsequently had their star formation quenched. Since
red-sequence galaxies typically have an early-type morphology, the transition
from the blue to the red sequence also involves a morphological transformation.
In this paper we study the impact of transformation mechanisms that operate
only on satellite galaxies, such as strangulation, ram-pressure stripping and
galaxy harassment. Using a large galaxy group catalogue constructed from the
SDSS, we compare the colors and concentrations of satellites galaxies to those
of central galaxies of the same stellar mass, adopting the hypothesis that the
latter are the progenitors of the former. On average, satellites are redder and
more concentrated than central galaxies of the same stellar mass.
Central-satellite pairs that are matched in both stellar mass and color,
however, show no average concentration difference, indicating that the
transformation mechanisms affect color more than morphology. The color and
concentration differences of matched central-satellite pairs are completely
independent of the halo mass of the satellite galaxy, indicating that
satellite-specific transformation mechanisms are equally efficient in haloes of
all masses. This strongly favors strangulation as the main quenching mechanism
for satellite galaxies. Finally, we determine the relative importance of
satellite quenching for the build-up of the red sequence. We find that roughly
70 percent of red sequence satellite galaxies with a stellar mass of 10^9 Msun
had their star formation quenched as satellites. This drops rapidly to zero
with increasing stellar mass, indicating that a significant fraction of red
satellites were already quenched before they became a satellite.


  We present a revised distance to the Cygnus Loop supernova remnant of
$725\pm15$ pc based on Gaia Early Data Release 3 parallax measurements (EDR3)
for several stars previously found to be located either inside or behind the
supernova based on the presence of high-velocity absorption lines in their
spectra. This revised distance estimate and error means the Cygnus Loop remnant
now has an estimated distance uncertainty comparable to that of its $\simeq$18
pc radius.


  Here we report on a novel chemical route to synthesize homogenous cobalt
doped titanate nanotubes (CoTNT), using an amorphous Co-doped precursor. The
influence of the synthesis temperature, autoclave dwell time and metal doping
on the structural and microstructural as well as on the optical properties of
the synthesized titanate nanotubes is studied and discussed. The optical band
gaps of the CoTNT samples are red shifted in comparison with the values
determined for the undoped samples, such red shifts bringing the absorption
edge of the CoTNT samples into the visible region. CoTNT materials also
demonstrate particular high adsorption ability for methylene blue, the amount
of the adsorbed dye being higher than the one predictable for a monolayer
formation. This suggests the possibility of intercalation of the dye molecule
between the TiO6 layers of the TNT structure. It is also shown that the
methylene blue sensitized Co-doped nanostructures are highly stable under UV
radiation and present a strong and broad absorption in the visible region.


  Specific data compression techniques, formalized by the concept of coresets,
proved to be powerful for many optimization problems. In fact, while tightly
controlling the approximation error, coresets may lead to significant speed up
of the computations and hence allow to extend algorithms to much larger problem
sizes. The present paper deals with a weight-balanced clustering problem from
imaging in materials science. Here, the class of desired coresets is naturally
confined to those which can be viewed as lowering the resolution of the data.
Hence one would expect that such resolution coresets are inferior to
unrestricted coreset. We show, however, that the restrictions are more than
compensated by utilizing the underlying structure of the data. In particular,
we prove bounds for resolution coresets which improve known bounds in the
relevant dimensions and also lead to significantly faster algorithms practice.


  Combinig the harmonic balance method (HBM) and a continuation method is a
well-known technique to follow the periodic solutions of dynamical systems when
a control parameter is varied. However, since deriving the algebraic system
containing the Fourier coefficients can be a highly cumbersome procedure, the
classical HBM is often limited to polynomial (quadratic and cubic)
nonlinearities and/or a few harmonics. Several variations on the classical HBM,
such as the incremental HBM or the alternating frequency/time domain HBM, have
been presented in the literature to overcome this shortcoming. Here, we present
an alternative approach that can be applied to a very large class of dynamical
systems (autonomous or forced) with smooth equations. The main idea is to
systematically recast the dynamical system in quadratic polynomial form before
applying the HBM. Once the equations have been rendered quadratic, it becomes
obvious to derive the algebraic system and solve it by the so-called ANM
continuation technique. Several classical examples are presented to illustrate
the use of this numerical approach.


  The problem of finding clusters in complex networks has been extensively
studied by mathematicians, computer scientists and, more recently, by
physicists. Many of the existing algorithms partition a network into clear
clusters, without overlap. We here introduce a method to identify the nodes
lying ``between clusters'' and that allows for a general measure of the
stability of the clusters. This is done by adding noise over the weights of the
edges of the network. Our method can in principle be applied with any
clustering algorithm, provided that it works on weighted networks. We present
several applications on real-world networks using the Markov Clustering
Algorithm (MCL).


  Phyllotaxis, the search for the most homogeneous and dense organizations of
small disks inside a large circular domain, was first developed to analyze
arrangements of leaves or florets in plants. Then it has become an object of
study not only in botany, but also in mathematics, computer simulations and
physics. Although the mathematical solution is now well known, an algorithm
setting out the centers of the small disks on a Fermat spiral, the very nature
of this organization and its properties of symmetry remain to be examined. The
purpose of this paper is to describe a phyllotactic organization of points
through its Voronoi cells and Delaunay triangulation and to refer to the
concept of defects developed in condensed matter physics. The topological
constraint of circular symmetry introduces an original inflation-deflation
symmetry taking the place of the translational and rotational symmetries of
classical crystallography.


  We report on experiments of spin filtering through ultra-thin single-crystal
layers of the insulating and ferromagnetic oxide BiMnO3 (BMO). The spin
polarization of the electrons tunneling from a gold electrode through BMO is
analyzed with a counter-electrode of the half-metallic oxide La2/3Sr1/3MnO3
(LSMO). At 3 K we find a 50% change of the tunnel resistances according to
whether the magnetizations of BMO and LSMO are parallel or opposite. This
effect corresponds to a spin filtering effciency of up to 22%. Our results thus
show the potential of complex ferromagnetic insulating oxides for spin
filtering and injection.


  This paper learns multi-modal embeddings from text, audio, and video
views/modes of data in order to improve upon down-stream sentiment
classification. The experimental framework also allows investigation of the
relative contributions of the individual views in the final multi-modal
embedding. Individual features derived from the three views are combined into a
multi-modal embedding using Deep Canonical Correlation Analysis (DCCA) in two
ways i) One-Step DCCA and ii) Two-Step DCCA. This paper learns text embeddings
using BERT, the current state-of-the-art in text encoders. We posit that this
highly optimized algorithm dominates over the contribution of other views,
though each view does contribute to the final result. Classification tasks are
carried out on two benchmark datasets and on a new Debate Emotion data set, and
together these demonstrate that the one-Step DCCA outperforms the current
state-of-the-art in learning multi-modal embeddings.


  This paper presents an application of the TwO-Dimensional CORrelation
(TODCOR) algorithm to multi-order spectra. The combination of many orders
enables the detection and measurement of the radial velocities of very faint
companions. The technique is first applied here to the case of HD41004, where
the secondary is 3.68 magnitudes fainter than the primary in the V band. When
applied to CORALIE spectra of this system, the technique measures the secondary
velocities with a precision of 0.6 km/s and facilitates an orbital solution of
the HD41004B subsystem. The orbit of HD41004B is nearly circular, with a
companion of a 19 Jupiter masses (minimum mass). The precision achieved for the
primary is 10 m/s, allowing the measurement of a long-term trend in the
velocities of HD41004A.


  The graphene/MoS2 heterojunction formed by joining the two components
laterally in a single plane promises to exhibit a low-resistance contact
according to the Schottky-Mott rule. Here we provide an atomic-scale
description of the structural, electronic, and magnetic properties of this type
of junction. We first identify the energetically favorable structures in which
the preference of forming C-S or C-Mo bonds at the boundary depends on the
chemical conditions. We find that significant charge transfer between graphene
and MoS2 is localized at the boundary. We show that the abundant 1D boundary
states substantially pin the Fermi level in the lateral contact between
graphene and MoS2, in close analogy to the effect of 2D interfacial states in
the contacts between 3D materials. Furthermore, we propose specific ways in
which these effects can be exploited to achieve spin-polarized currents.


  We summarize the consequences of the twin assumptions of (discrete)
self-similarity and spherical symmetry for the global structure of a spacetime.
All such spacetimes can be constructed from two building blocks, the "fan" and
"splash". Each building block contains one radial null geodesic that is
invariant under the self-similarity (self-similarity horizon).


  For a system randomly prepared in a number of quantum states, we present a
lower bound for the distinguishability of the quantum states, that is, the
success probability of determining the states in the form of entropy. When the
states are all pure, acquiring the entropic lower bound requires only the
density operator and the number of the possible states. This entropic bound
shows a relation between the von Neumann entropy and the distinguishability.


  We investigate the equivariant intersection cohomology of a toric variety.
Considering the defining fan of the variety as a finite topological space with
the subfans being the open sets (that corresponds to the "toric" topology given
by the invariant open subsets), equivariant intersection cohomology provides a
sheaf (of graded modules over a sheaf of graded rings) on that "fan space". We
prove that this sheaf is a "minimal extension sheaf", i.e., that it satisfies
three relatively simple axioms which are known to characterize such a sheaf up
to isomorphism. In the verification of the second of these axioms, a key role
is played by "equivariantly formal" toric varieties, where equivariant and
"usual" (non-equivariant) intersection cohomology determine each other by
Kunneth type formulae. Minimal extension sheaves can be constructed in a purely
formal way and thus also exist for non-rational fans. As a consequence, we can
extend the notion of an equivariantly formal fan even to this general setup. In
this way, it will be possible to introduce "virtual" intersection cohomology
for equivariantly formal non-rational fans.


  Sequential and temporal data arise in many fields of research, such as
quantitative finance, medicine, or computer vision. A novel approach for
sequential learning, called the signature method and rooted in rough path
theory, is considered. Its basic principle is to represent multidimensional
paths by a graded feature set of their iterated integrals, called the
signature. This approach relies critically on an embedding principle, which
consists in representing discretely sampled data as paths, i.e., functions from
$[0,1]$ to $\mathbb{R}^d$. After a survey of machine learning methodologies for
signatures, the influence of embeddings on prediction accuracy is investigated
with an in-depth study of three recent and challenging datasets. It is shown
that a specific embedding, called lead-lag, is systematically the strongest
performer across all datasets and algorithms considered. Moreover, an empirical
study reveals that computing signatures over the whole path domain does not
lead to a loss of local information. It is concluded that, with a good
embedding, combining signatures with other simple algorithms achieves results
competitive with state-of-the-art, domain-specific approaches.


  The anomalous mean square fluctuations are shown to arise naturally from the
ordinary diffusion equation interpreted scale invariantly in a formalism
endowing real numbers with a nonarchimedean multiplicative structure. A
variable $t$ approaching 0 linearly in the ordinary analysis is shown to enjoy
instead a sublinear $t\log t^{-1}$ flow in the presence of this scale invariant
structure. Diffusion on an ultrametric Cantor set is also generically
subdiffusive with the above sublinear mean square deviation. The present study
seems to offer a new interpretation of a possible emergence of complex patterns
from an apparently simple system.


  This paper addressed the issue of estimating the damage caused by a computer
virus. First, an individual-level delayed SIR model capturing the spreading
process of a digital virus is derived. Second, the damage inflicted by the
virus is modeled as the sum of the economic losses and the cost for developing
the antivirus. Next, the impact of different factors, including the delay and
the network structure, on the damage is explored by means of computer
simulations. Thereby some measures of reducing the damage of a virus are
recommended. To our knowledge, this is the first time the antivirus-developing
cost is taken into account when estimating the damage of a virus.


  We have conducted a high-dispersion ($R \sim$ 34,000) optical spectroscopic
study of 10 young stars near the cometary gloule CG 30 in the Gum Nebula, a
diffuse H \textsc{ii} region home to at least 32 cometary globules. All 10
spectroscopically observed stars at the nebula's northern edge are of low mass
(spectral types M4.5 - K5), have broad H$\alpha$ emission, and show spectral
veiling. Eight of the 10 are classical T Tauri stars. We spectroscopically
measure the photospheric properties of CG 30 IRS 4 inside CG 30. Though
embedded, CG 30 IRS 4 is T Tauri-like, with relatively slow projected rotation
and moderate veiling. Undepleted Li absorptions, strong H$\alpha$ emissions,
and positions well above the main sequence on an HR diagram suggest the 10
stars are $\lesssim$1 Myr old. Using our measurements, previous spectroscopy,
and previous photometry of 11 other young stars in the area, we determine
stellar, kinematic, and accretion properties of a total of 21 young stars.
Shared radial velocities, proper motions, distances, and ages suggest 14 of the
young stars (including CG 30 IRS 4) are kinematically related to CG 30. From
\textit{Gaia} DR2 distances to 6 of these stars, we derive a distance of
$358.1\pm2.2$ pc to the cometary globule complex CG 30/31/38. The CG 30
association has an accretor fraction of $29\pm14$\%, low for quiescent clusters
of similar age but consistent with other irradiated clusters. The Gum Nebula's
moderate radiation environment ($G_0=6.6^{+3.2}_{-2.7}$ at CG 30) may be strong
enough to shorten disk lifetimes.


  For a Lie group G and a smooth manifold W, we study the difference between
smooth actions of G on W and bundles over the classifying space of G with fiber
W and structure group Diff(W). In particular, we exhibit smooth manifold
bundles over BSU(2) that are not induced by an action. The main tool for
reaching this goal is a technical result that gives a constraint for the values
of tautological classes pulled back to the cohomology of BSU(2) along a map
induced by an action.


  A model for homogeneous anisotropic incompressible turbulence is proposed.
The model generalizes the GISS model of homogeneous isotropic turbulence; the
generalization involves the solution of the GISS equations along a set of
integration paths in wavenumber (k-) space. In order to make the problem
tractable, these integration paths ("cascade lines") must be chosen in such a
way that the behaviour of the energy spectral function along different cascade
lines should be reasonably similar. In practice this is realized by defining
the cascade lines as the streamlines of a cascade flow; in the simplest case
the source of this flow may be identified with the source function of the
turbulence. Owing to the different approximations involved, the resulting
energy spectral function is not exact but is expected to give good
approximative values for the bulk quantities characterising the turbulent
medium, and for the measure of the anisotropy itself in particular.
  The model is then applied to the case of low Prandtl number thermal
convection. The energy spectral function and the bulk quantities characterizing
the flow are derived for different values of the parameter $S=Ra \sigma$. The
most important new finding is that unlike the anisotropy of the most unstable
mode in linear stability analysis the anisotropy of the turbulence does not
grow indefinitely with increasing S but it rather saturates to a relatively
moderate finite asymptotic value.


  We discuss the Casimir effect between two identical, parallel slabs,
emphasizing the role of dissipation and temperature. Starting from quite
general assumptions, we analyze the behavior of the Casimir entropy in the
limit T->0 and link it to the behavior of the slab's reflection coefficients at
low frequencies. We also derive a formula in terms of a sum over modes, valid
for dissipative slabs that can be interpreted in terms of a damped quantum
oscillator.


  In a deformation quantization of $\Real^n$, the Jacobi identity is
automatically satisfied. This article poses the contrary question: Given a set
of commutators which satisfies the Jacobi identity, is the resulting
associative algebra a deformation quantization of $\Real^n$? It is shown that
the result is true. However care must taken when stating precisely how and in
which algebra the Jacobi identity is satisfied.


  We introduce two-dimensional (2D) linear and nonlinear Talbot effects. They
are produced by propagating periodic 2D diffraction patterns and can be
visualized as 3D stacks of Talbot carpets. The nonlinear Talbot effect
originates from 2D rogue waves and forms in a bulk 3D nonlinear medium. The
recurrences of an input rogue wave are observed at the Talbot length and at the
half-Talbot length, with a \pi phase shift; no other recurrences are observed.
Different from the nonlinear Talbot effect, the linear effect displays the
usual fractional Talbot images as well. We also find that the smaller the
period of incident rogue waves, the shorter the Talbot length. Increasing the
beam intensity increases the Talbot length, but above a threshold this leads to
a catastrophic self-focusing phenomenon which destroys the effect. We also find
that the Talbot recurrence can be viewed as a self-Fourier transform of the
initial periodic beam that is automatically performed during propagation. In
particular, linear Talbot effect can be viewed as a fractional self-Fourier
transform, whereas the nonlinear Talbot effect can be viewed as the regular
self-Fourier transform. Numerical simulations demonstrate that the rogue wave
initial condition is sufficient but not necessary for the observation of the
effect. It may also be observed from other periodic inputs, provided they are
set on a finite background. The 2D effect may find utility in the production of
3D photonic crystals.


  We consider a pair of quantum dot-based spin qubits that interact via
microwave photons in a superconducting cavity, and that are also parametrically
driven by separate external electric fields. For this system, we formulate a
model for spin qubit entanglement in the presence of mutually off-resonant
qubit and cavity frequencies. We show that the sidebands generated via the
driving fields enable highly tunable qubit-qubit entanglement using only ac
control and without requiring the qubit and cavity frequencies to be tuned into
simultaneous resonance. The model we derive can be mapped to a variety of qubit
types, including detuning-driven one-electron spin qubits in double quantum
dots and three-electron resonant exchange qubits in triple quantum dots. The
high degree of nonlinearity inherent in spin qubits renders these systems
particularly favorable for parametric drive-activated entanglement. We
determine multiple common resonance conditions for the two driven qubits and
the cavity and identify experimentally relevant parameter regimes that enable
the implementation of entangling gates with suppressed sensitivity to cavity
photon occupation and decay. The parametrically driven sideband resonance
approach we describe provides a promising route toward scalability and
modularity in spin-based quantum information processing through drive-enabled
tunability that can also be implemented in micromagnet-free electron and hole
systems for spin-photon coupling.


  The cellular automaton model of Piegari, Di Maio, Scandone and Milano, J.
Volc. Geoth. Res., 202, 22-28 (2011) is extended to include magma-induced
stress (i.e. a local magma-related augmentation of the stress field). This
constitutes a nonlinear coupling between the magma and stress fields considered
by this model, which affects the statistical distributions of eruptions
obtained. The extended model retains a power law relation between eruption size
and frequency for most events, as expected from the self organised criticality
inspiring this model; but the power law now applies for a reduced range of
size, and there are new peaks of relatively more frequent eruptions of
intermediate and large size. The cumulative frequency of repose time between
events remains well modelled by a stretched exponential function of repose time
(approaching a pure exponential distribution only for the longest repose
times), but the time scales of this behaviour are slightly longer, reflecting
the increased preference for larger events. The eruptions are relatively more
likely to have high volatile (water) content, so would generally be more
explosive. The new model also naturally favours a central `axial' transport
conduit, as found in many volcano systems, but which otherwise must be
artificially imposed within such models.


  Dislocation model of the supersolid state of $^4$He was proposed in 1987 by
one of the authors of the review. The model obtained strong support by numerous
experimental and theoretical investigations from 2007 to date. In these
investigations the validity of the idea put forward in 1987 was confirmed, and
new conceptions of superclimb of dislocations and of the giant isochoric
compressibility or the syringe effect were proposed. In this paper we review
main achievements of theoretical and experimental studies of
dislocation-induced supersolid and present current understanding of this
phenomenon.


  We present the Global Rapid Advanced Network Devoted to the Multi-messenger
Addicts (GRANDMA). The network consists of 21 telescopes with both photometric
and spectroscopic facilities. They are connected together thanks to a dedicated
infrastructure. The network aims at coordinating the observations of large sky
position estimates of transient events to enhance their follow-up and reduce
the delay between the initial detection and the optical confirmation. The
GRANDMA program mainly focuses on follow-up of gravitational-wave alerts to
find and characterise the electromagnetic counterpart during the third
observational campaign of the Advanced LIGO and Advanced Virgo detectors. But
it allows for any follow-up of transient alerts involving neutrinos or
gamma-ray bursts, even with poor spatial localisation. We present the different
facilities, tools, and methods we developed for this network, and show its
efficiency using observations of LIGO/Virgo S190425z, a binary neutron star
merger candidate. We furthermore report on all GRANDMA follow-up observations
performed during the first six months of the LIGO-Virgo observational campaign,
and we derive constraints on the kilonova properties assuming that the events'
locations were imaged by our telescopes.


  Two maxima in transverse relaxation rate of Cu(2) nuclei in
YBa$_2$Cu$_3$O$_{7-y}$ are observed, at T = 35 K and T = 47 K. Comparison of
the $^{63}$Cu(2) and $^{65}$Cu(2) rates at T = 47 K indicates the magnetic
character of relaxation. The enhancement at T = 47 K of fluctuating local
magnetic fields perpendicular to the CuO$_2$ planes is connected with the
critical fluctuations of orbital currents. Maximum at T = 35 K is connected
with the appearance of inhomogeneous supeconducting phase. Together with data
published to date, our experimental results allow to suggest a qualitatively
new phase diagram of the superconducting phase.


  We identify the operational conditions for maximum power of a
nanothermoelectric engine consisting of a single quantum level embedded between
two leads at different temperatures and chemical potentials. The corresponding
thermodynamic efficiency agrees with the Curzon-Ahlborn expression up to
quadratic terms in the gradients, supporting the thesis of universality beyond
linear response.


  Recommendation algorithms have been pivotal in handling the overwhelming
volume of online content. However, these algorithms seldom consider direct user
input, resulting in superficial interaction between them. Efforts have been
made to include the user directly in the recommendation process through
conversation, but these systems too have had limited interactivity. Recently,
Large Language Models (LLMs) like ChatGPT have gained popularity due to their
ease of use and their ability to adapt dynamically to various tasks while
responding to feedback. In this paper, we investigate the effectiveness of
ChatGPT as a top-n conversational recommendation system. We build a rigorous
pipeline around ChatGPT to simulate how a user might realistically probe the
model for recommendations: by first instructing and then reprompting with
feedback to refine a set of recommendations. We further explore the effect of
popularity bias in ChatGPT's recommendations, and compare its performance to
baseline models. We find that reprompting ChatGPT with feedback is an effective
strategy to improve recommendation relevancy, and that popularity bias can be
mitigated through prompt engineering.


  We prove normal distribution laws for primes of bad semistable reduction in
families of curves. As a consequence, we deduce that when ordered by height,
$100\%$ of curves in these families have, in a precise sense, many such primes.


  We discuss the nuclear anapole moment, a new P odd, T even electromagnetic
multipole. Its discovery in the cesium experiment gives a first-rate
information on parity-nonconserving nuclear forces. New prospects in the field
are considered. Upper limits on the electric dipole moments, which are P odd, T
odd multipoles, of elementary particles and atoms are presented, and their
physical implications discussed. The atomic experiments are demonstrated to be
as informative in this respect as the neutron ones. Tremendous progress in the
field can be expected from experiments at ion storage rings and linear
electrostatic traps.


  The capture rates of non-relativistic neutrinos on beta decaying nuclei
depends on whether their mass is Dirac or Majorana. It is known that for relic
neutrinos from the big-bang, and within minimal assumptions, the rate is a
factor two larger in the Majorana case. We show that this difference also
depends on the value of the lightest neutrino mass and on the type of mass
hierarchy. If the lightest neutrino has a mass below the meV, so that it is
still relativistic today, its capture rate for the case of Dirac masses becomes
equal to that for Majorana masses. As a consequence, for the case of normal
neutrino mass hierarchy, for which the total capture rate is dominated by the
contribution from the lightest neutrino, if this one is below the meV the
distinction between the Dirac and Majorana scenarios can only rely on the
detection of the two heavier neutrinos, which is something very challenging.


  We map the neutral atomic gas content of M33 using high resolution VLA and
GBT observations and fit a tilted ring model to determine the orientation of
the extended gaseous disk and its rotation curve. The disk of M33 warps from 8
kpc outwards without substantial change of its inclination with respect to the
line of sight. Rotational velocities rise steeply with radius in the inner
disk, reaching 100 km/s in 4 kpc, then the rotation curve becomes more
perturbed and flatter with velocities as high as 120-130 km/s out to 23 kpc. We
derive the stellar mass surface density map of M33's optical disk, via pixel
-SED fitting methods based on population synthesis models, which highlights
variations in the mass-to-light ratio. The stellar mass surface further out is
estimated from deep images of outer disk fields. Stellar and gas maps are then
used in the dynamical analysis of the rotation curve to constrain the dark
matter distribution which is relevant at all radii. A dark matter halo with a
Navarro-Frenk-White density profile in a LCDM cosmology, provides the best fit
to the rotation curve for a dark halo concentration C=10 and a total halo mass
of 4.3 10^{11}Msun. This imples a baryonic fraction of order 0.02 and the
evolutionary history of this galaxy should account for loss of a large fraction
of its original baryonic content.


  A sharp version of the Central Limit Theorem for linear combinations of
iterates of an inner function is proved. The authors previously showed this
result assuming a suboptimal condition on the coefficients of the linear
combination. Here we explain a variation of the original argument which leads
to the sharp result. We also review the steps of the proof as well as the main
technical tool, which is Aleksandrov Desintegration Theorem for
Aleksandrov-Clark measures.


  We attempt to build a model that describes the {\it Fermi} galactic gamma-ray
excess (FGCE) within a UV-complete Supersymmetric framework; we find this to be
highly non-trivial. At the very least a successful Supersymmetric explanation
must have several important ingredients in order to fit the data and satisfy
other theoretical and experimental constraints. Under the assumption that a
{\it single} annihilation mediator is responsible for both the observed relic
density as well as the FGCE, we show that the requirements are not easily
satisfied in many TeV-scale SUSY models, but can be met with some model
building effort in the general NMSSM with $\sim 10$ parameters beyond the MSSM.
We find that the data selects a particular region of the parameter space with a
mostly singlino lightest Supersymmetric particle and a relatively light CP-odd
Higgs boson that acts as the mediator for dark matter annihilation. We study
the predictions for various observables within this parameter space, and find
that searches for this light CP-odd state at the LHC, as well as searches for
the direct detection of dark matter, are likely to be quite challenging. It is
possible that a signature could be observed in the flavor sector; however,
indirect detection remains the best probe of this scenario.


  Aims: Stars twinkle because their light propagates through the atmosphere.
The same phenomenon is expected at a longer time scale when the light of remote
stars crosses an interstellar molecular cloud, but it has never been observed
at optical wavelength. In a favorable case, the light of a background star can
be subject to stochastic fluctuations on the order of a few percent at a
characteristic time scale of a few minutes. Our ultimate aim is to discover or
exclude these scintillation effects to estimate the contribution of molecular
hydrogen to the Galactic baryonic hidden mass. This feasibility study is a
pathfinder toward an observational strategy to search for scintillation,
probing the sensitivity of future surveys and estimating the background level.
Methods: We searched for scintillation induced by molecular gas in visible dark
nebulae as well as by hypothetical halo clumpuscules of cool molecular hydrogen
($\mathrm{H_2-He}$) during two nights. We took long series of 10s infrared
exposures with the ESO-NTT telescope toward stellar populations located behind
visible nebulae and toward the Small Magellanic Cloud (SMC). We therefore
searched for stars exhibiting stochastic flux variations similar to what is
expected from the scintillation effect. According to our simulations of the
scintillation process, this search should allow one to detect (stochastic)
transverse gradients of column density in cool Galactic molecular clouds of
order of $\sim 3\times 10^{-5}\,\mathrm{g/cm^2/10\,000\,km}$. Results: We found
one light-curve that is compatible with a strong scintillation effect through a
turbulent structure characterized by a diffusion radius $R_{diff}<100\, km$ in
the B68 nebula. Complementary observations are needed to clarify the status of
this candidate, and no firm conclusion can be established from this single
observation. We can also infer limits on the existence of turbulent dense cores
(of number density $n>10^9\, cm^{-3}$) within the dark nebulae. Because no
candidate is found toward the SMC, we are also able to establish upper limits
on the contribution of gas clumpuscules to the Galactic halo mass. Conclusions:
The limits set by this test do not seriously constrain the known models, but we
show that the short time-scale monitoring for a few $10^6 star\times hour$ in
the visible band with a $>4$ meter telescope and a fast readout camera should
allow one to quantify the contribution of turbulent molecular gas to the
Galactic halo. The LSST (Large Synoptic Survey Telescope) is perfectly suited
for this search.


  Research on human-robot collaboration or human-robot teaming, has focused
predominantly on understanding and enabling collaboration between a single
robot and a single human. Extending human-robot collaboration research beyond
the dyad, raises novel questions about how a robot should distribute resources
among group members and about what the social and task related consequences of
the distribution are. Methodological advances are needed to allow researchers
to collect data about human robot collaboration that involves multiple people.
This paper presents Tower Construction, a novel resource distribution task that
allows researchers to examine collaboration between a robot and groups of
people. By focusing on the question of whether and how a robot's distribution
of resources (wooden blocks required for a building task) affects collaboration
dynamics and outcomes, we provide a case of how this task can be applied in a
laboratory study with 124 participants to collect data about human robot
collaboration that involves multiple humans. We highlight the kinds of insights
the task can yield. In particular we find that the distribution of resources
affects perceptions of performance, and interpersonal dynamics between human
team-members.


  The success of open source software (OSS) projects relies on voluntary
contributions from various community roles.Being a committer signifies gaining
trust and higher privileges. Substantial studies have focused on the
requirements of becoming a committer, but most of them are based on interviews
or several hypotheses, lacking a comprehensive understanding of committers'
qualifications.We explore both the policies and practical implementations of
committer qualifications in modern top OSS communities. Through a thematic
analysis of these policies, we construct a taxonomy of committer
qualifications, consisting of 26 codes categorized into nine themes, including
Personnel-related to Project, Communication, and Long-term Participation. We
also highlight the variations in committer qualifications emphasized in
different OSS community governance models. For example, projects following the
core maintainer model value project comprehension, while projects following the
company-backed model place significant emphasis on user issue resolution. Then,
we propose eight sets of metrics and perform survival analysis on two
representative OSS projects to understand how these qualifications are
implemented in practice. We find that the probability of gaining commit rights
decreases as participation time passes.The selection criteria in practice are
generally consistent with the community policies. Developers who submit
high-quality code, actively engage in code review, and make extensive
contributions to related projects are more likely to be granted commit rights.
However, there are some qualifications that do not align precisely, and some
are not adequately evaluated. This study contributes to the understanding of
trust establishment in modern top OSS communities, assists communities in
better allocating commit rights, and supports developers in achieving
self-actualization through OSS participation.


  We consider disordered models of pinning of directed polymers on a defect
line, including (1+1)-dimensional interface wetting models, disordered
Poland--Scheraga models of DNA denaturation and other (1+d)-dimensional
polymers in interaction with columnar defects. We consider also random
copolymers at a selective interface. These models are known to have a
(de)pinning transition at some critical line in the phase diagram. In this work
we prove that, as soon as disorder is present, the transition is at least of
second order: the free energy is differentiable at the critical line, and the
order parameter (contact fraction) vanishes continuously at the transition. On
the other hand, it is known that the corresponding non-disordered models can
have a first order (de)pinning transition, with a jump in the order parameter.
Our results confirm predictions based on the Harris criterion.


  There has been an emergence of various models for long-term time series
forecasting. Recent studies have demonstrated that a single linear layer, using
Channel Dependent (CD) or Channel Independent (CI) modeling, can even
outperform a large number of sophisticated models. However, current research
primarily considers CD and CI as two complementary yet mutually exclusive
approaches, unable to harness these two extremes simultaneously. And it is also
a challenging issue that both CD and CI are static strategies that cannot be
determined to be optimal for a specific dataset without extensive experiments.
In this paper, we reconsider whether the current CI strategy is the best
solution for time series forecasting. First, we propose a simple yet effective
strategy called CSC, which stands for $\mathbf{C}$hannel
$\mathbf{S}$elf-$\mathbf{C}$lustering strategy, for linear models. Our Channel
Self-Clustering (CSC) enhances CI strategy's performance improvements while
reducing parameter size, for exmpale by over 10 times on electricity dataset,
and significantly cutting training time. Second, we further propose Channel
Rearrangement (CR), a method for deep models inspired by the self-clustering.
CR attains competitive performance against baselines. Finally, we also discuss
whether it is best to forecast the future values using the historical values of
the same channel as inputs. We hope our findings and methods could inspire new
solutions beyond CD/CI.


  We provide a status report on the calculation of the Equation of State (EoS)
of QCD at finite temperature using lattice QCD. Most of the discussion will
focus on comparison of recent results obtained by the HotQCD and
Wuppertal-Budapest (W-B) collaborations. We will show that very significant
progress has been made towards obtaining high precision results over the
temperature range of T=150-700 MeV. The various sources of systematic
uncertainties will be discussed and the differences between the two
calculations highlighted. Our final conclusion is that the lattice results of
EoS are getting precise enough to justify being used in the phenomenological
analysis of heavy ion experiments at RHIC and LHC.


  We consider the temporal evolution of a zero energy edge Majorana of a
spinless $p$-wave superconducting chain following a sudden change of a
parameter of the Hamiltonian. Starting from one of the topological phases that
has an edge Majorana, the system is suddenly driven to the other topological
phase or to the (topologically) trivial phases and also to the quantum critical
points (QCPs) separating these phases. The survival probability of the initial
edge Majorana as a function of time is studied following the quench.
Interestingly when the chain is quenched to the QCP, we find a nearly perfect
oscillations of the survival probability, indicating that the Majorana travels
back and forth between two ends, with a time period that scales with the system
size. We also generalize to the situation when there is a next-nearest-neighbor
hopping in superconducting chain and there resulting in a pair of edge Majorana
at the each end of the chain in the topological phase. We show that the
frequency of oscillation of the survival probability gets doubled in this case.
We also perform an instantaneous quenching the Hamiltonian (with two Majorana
modes at each end of the chain) to an another Hamiltonian which has only one
Majorana mode in equilibrium; the MSP shows oscillations as a function of time
with a noticeable decay in the amplitude. On the other hand for a quenching
which is reverse to the previous one, the MSP decays rapidly and stays close to
zero with fluctuations in amplitude.