  Self-adaptive software can assess and modify its behavior when the assessment
indicates that the program is not performing as intended or when improved
functionality or performance is available. Since the mid-1960s, the subject of
system adaptivity has been extensively researched, and during the last decade,
many application areas and technologies involving self-adaptation have gained
prominence. All of these efforts have in common the introduction of
self-adaptability through software. Thus, it is essential to investigate
systematic software engineering methods to create self-adaptive systems that
may be used across different domains. The primary objective of this research is
to summarize current advances in awareness requirements for adaptive strategies
based on an examination of state-of-the-art methods described in the
literature. This paper presents a review of self-adaptive systems in the
context of requirement awareness and summarizes the most common methodologies
applied. At first glance, it gives a review of the previous surveys and works
about self-adaptive systems. Afterward, it classifies the current self-adaptive
systems based on six criteria. Then, it presents and evaluates the most common
self-adaptive approaches. Lastly, an evaluation among the self-adaptive models
is conducted based on four concepts (requirements description, monitoring,
relationship, dependency/impact, and tools).


  The main purpose of this paper is to give fundamental properties of real
lightlike hypersurfaces of paraquaternionic manifolds and to prove the
non-existence of real lightlike hypersurfaces in paraquaternionic space forms
under some conditions.


  The single file diffusion in a circular channel of millimetric charged balls
is studied. The evolution in time of the mean square displacement is shown to
be subdiffusive, but slower than the power-like $t^{1/2}$ behavior observed in
circular colloidal systems or predicted in one-dimensional infinite systems.


  By using T. Oprea's optimization methods on submanifolds, we give another
proof of the inequalities relating the normalized $\delta-$Casorati curvature
$\hat{\delta}_c(n-1)$ for submanifolds in real space forms. Also, inequalities
relating the normalized $\delta-$Casorati curvature $\delta_C(n-1)$ for
submanifolds in real space forms are obtained. Besides, we characterize a kind
of Casorati ideal hypersurface of Euclidean 4-space. We also show that this
kind of Casorati ideal hypersurface is rigid.


  Given a graph G = (V,E) and an integer k, an edge modification problem for a
graph property P consists in deciding whether there exists a set of edges F of
size at most k such that the graph H = (V,E \vartriangle F) satisfies the
property P. In the P edge-completion problem, the set F of edges is constrained
to be disjoint from E; in the P edge-deletion problem, F is a subset of E; no
constraint is imposed on F in the P edge-edition problem. A number of
optimization problems can be expressed in terms of graph modification problems
which have been extensively studied in the context of parameterized complexity.
When parameterized by the size k of the edge set F, it has been proved that if
P is an hereditary property characterized by a finite set of forbidden induced
subgraphs, then the three P edge-modification problems are FPT. It was then
natural to ask whether these problems also admit a polynomial size kernel.
Using recent lower bound techniques, Kratsch and Wahlstrom answered this
question negatively. However, the problem remains open on many natural graph
classes characterized by forbidden induced subgraphs. Kratsch and Wahlstrom
asked whether the result holds when the forbidden subgraphs are paths or cycles
and pointed out that the problem is already open in the case of P4-free graphs
(i.e. cographs). This paper provides positive and negative results in that line
of research. We prove that parameterized cograph edge modification problems
have cubic vertex kernels whereas polynomial kernels are unlikely to exist for
the Pl-free and Cl-free edge-deletion problems for large enough l.


  Peer influence and social contagion are key denominators in the adoption and
participation of information spreading, such as news propagation, word-of-mouth
or viral marketing. In this study, we argue that it is biased to only focus on
the scale and coverage of information spreading, and propose that the level of
influence reinforcement, quantified by the re-exposure rate, i.e., the rate of
individuals who are repeatedly exposed to the same information, should be
considered together to measure the effectiveness of spreading. We show that
local network structural characteristics significantly affects the probability
of being exposed or re-exposed to the same information. After analyzing
trending news on the super large-scale online network of Sina Weibo (China's
Twitter) with 430 million connected users, we find a class of users with
extremely low exposure rate, even they are following tens of thousands of
others; and the re-exposure rate is substantially higher for news with more
transmission waves and stronger secondary forwarding. While exposure and
re-exposure rate typically grow together with the scale of spreading, we find
exceptional cases where it is possible to achieve a high exposure rate while
maintaining low re-exposure rate, or vice versa.


  We derive cooling rate and coefficient of performance as well as their
variances for a quantum Otto engine proceeding in finite-time cycle period.
This machine consists of two driven strokes, where the system isolated from the
heat reservoir undergoes finite-time unitary transformation, and two isochoric
steps, where the finite-time system-bath interaction durations take the system
away from the equilibrium even at the respective ends of the two stages. We
explicitly calculate the statistics of cooling rate and coefficient of
performance for the machine operating with an analytically solvable two-level
system. We clarify the role of finite-time durations of four processes on the
machine performance. We show that there is the trade-off between the
performance parameter and its corresponding variance, thereby indicating that
the cooling rate or coefficient of performance can be enhanced, but at the cost
of increasing the corresponding fluctuations.


  We revisit the theory of dissipative mechanics in RLC circuits, allowing for
circuit elements to have nonlinear constitutive relations, and for the circuit
to have arbitrary topology. We systematically generalize the dissipationless
Hamiltonian mechanics of an LC circuit to account for resistors and incorporate
the physical postulate that the resulting RLC circuit thermalizes with its
environment at a constant positive temperature. Our theory explains stochastic
fluctuations, or Johnson noise, which are mandated by the
fluctuation-dissipation theorem. Assuming Gaussian Markovian noise, we obtain
exact expressions for multiplicative Johnson noise through nonlinear resistors
in circuits with convenient (parasitic) capacitors and/or inductors. With
linear resistors, our formalism is describable using a
Kubo-Martin-Schwinger-invariant Lagrangian formalism for dissipative thermal
systems. Generalizing our technique to quantum circuits could lead to an
alternative way to study decoherence in nonlinear superconducting circuits
without the Caldeira-Leggett formalism.


  Self-supervised vision transformers (SSTs) have shown great potential to
yield rich localization maps that highlight different objects in an image.
However, these maps remain class-agnostic since the model is unsupervised. They
often tend to decompose the image into multiple maps containing different
objects while being unable to distinguish the object of interest from
background noise objects. In this paper, Discriminative Pseudo-label Sampling
(DiPS) is introduced to leverage these class-agnostic maps for
weakly-supervised object localization (WSOL), where only image-class labels are
available. Given multiple attention maps, DiPS relies on a pre-trained
classifier to identify the most discriminative regions of each attention map.
This ensures that the selected ROIs cover the correct image object while
discarding the background ones, and, as such, provides a rich pool of diverse
and discriminative proposals to cover different parts of the object.
Subsequently, these proposals are used as pseudo-labels to train our new
transformer-based WSOL model designed to perform classification and
localization tasks. Unlike standard WSOL methods, DiPS optimizes performance in
both tasks by using a transformer encoder and a dedicated output head for each
task, each trained using dedicated loss functions. To avoid overfitting a
single proposal and promote better object coverage, a single proposal is
randomly selected among the top ones for a training image at each training
step. Experimental results on the challenging CUB, ILSVRC, OpenImages, and
TelDrone datasets indicate that our architecture, in combination with our
transformer-based proposals, can yield better localization performance than
state-of-the-art methods.


  A graph $G(V,E)$ is $\Gamma$-harmonious when there is an injection $f$ from
$V$ to an Abelian group $\Gamma$ such that the induced edge labels defined as
$w(xy)=f(x)+f(y)$ form a bijection from $E$ to $\Gamma$. We study
$\Gamma$-harmonious labelings of several cycles-related classes of graphs,
including Dutch windmills, generalized prisms, generalized closed and open
webs, and superwheels.


  For endangered languages, data collection campaigns have to accommodate the
challenge that many of them are from oral tradition, and producing
transcriptions is costly. Therefore, it is fundamental to translate them into a
widely spoken language to ensure interpretability of the recordings. In this
paper we investigate how the choice of translation language affects the
posterior documentation work and potential automatic approaches which will work
on top of the produced bilingual corpus. For answering this question, we use
the MaSS multilingual speech corpus (Boito et al., 2020) for creating 56
bilingual pairs that we apply to the task of low-resource unsupervised word
segmentation and alignment. Our results highlight that the choice of language
for translation influences the word segmentation performance, and that
different lexicons are learned by using different aligned translations. Lastly,
this paper proposes a hybrid approach for bilingual word segmentation,
combining boundary clues extracted from a non-parametric Bayesian model
(Goldwater et al., 2009a) with the attentional word segmentation neural model
from Godard et al. (2018). Our results suggest that incorporating these clues
into the neural models' input representation increases their translation and
alignment quality, specially for challenging language pairs.


  The $^{242}$Pu neutron-induced fission cross section was evaluated from 100
keV to 200 MeV. The experimental $^{242}$Pu and $^{235}$U fission cross
sections and their ratios in the EXFOR library were reviewed and analysed by
the least-squares method. Additional simultaneous evaluation was performed by
including the experimental database of the $^{233,238}$U and $^{239,240,241}$Pu
fission cross sections and their ratios developed for JENDL-5 evaluation. The
$^{242}$Pu fission cross sections from our evaluation and JENDL-5 evaluation
are close to each other below 1 MeV while systematically differ from each other
above 10 MeV. The cross section from our evaluation is systematically lower
than the JENDL-4.0 cross section in the prompt fission neutron spectrum peak
region ($\sim$5% lower around 1 MeV). The newly evaluated $^{242}$Pu fission
cross section was verified against the cross section measured in the $^{252}$Cf
spontaneous fission neutron field and criticalities of small-sized LANL fast
systems, and demonstrated better performance than the JENDL-4.0 cross section
on the same level with the JENDL-5 cross section.


  In this paper we compute the decay constant of the pseudo-scalar heavy-light
mesons in the heavy quark effective theory framework of QCD sum rules. In our
analysis we include the recently evaluated three-loop result of order
$\alpha_s^2$ for the heavy-light current correlator. The value of the bottom
quark mass, which essentially limits the accuracy of the sum rules for $B$
meson, is extracted from the nonrelativistic sum rules for $\Upsilon$
resonances in the next-to-next-to-leading approximation. We find stability of
our result with respect to all types of corrections and the specific form of
the sum rule which reduces the uncertainty. Our results $f_B=206\pm 20$ MeV and
$f_D=195\pm 20$ MeV for the $B$ and $D$ meson decay constants are in impressive
agreement with recent lattice calculations.


  The purely leptonic decays Ds -> tau nu and Ds -> mu nu are studied in a
sample of four million hadronic Z decays collected with the ALEPH detector at
the LEP e+e- collider from 1991 to 1995. The branching fractions are extracted
from a combination of two analyses, one optimized to select Ds -> tau nu decays
with tau -> e nu nubar or mu nu nubar, and the other optimized for Ds-> mu nu
decays. The results are used to evaluate the Ds decay constant, within the
Standard Model: fDs = [285 +- 19(stat) +- 40 (syst)] MeV


  Magnetic impurity effects in carbon nanotubes are studied theoretically. The
multi channel Kondo effect is investigated with the band structure of the
metallic nanotubes. The local non-Fermi liquid behavior is realized at
temperatures lower than the Kondo temperature T_K. The density of states of
localized electron has a singularity |omega|^1/2 which gives rise to a pseudo
gap at the Kondo resonance in low temperatures. The temperature dependence of
the electronic resistivity is predicted as T^1/2, and the imaginary part of
dynamical susceptibilities has the |omega|^1/2 dependence.


  Recent work has investigated the vulnerability of local surrogate methods to
adversarial perturbations on a machine learning (ML) model's inputs, where the
explanation is manipulated while the meaning and structure of the original
input remains similar under the complex model. While weaknesses across many
methods have been shown to exist, the reasons behind why still remain little
explored. Central to the concept of adversarial attacks on explainable AI (XAI)
is the similarity measure used to calculate how one explanation differs from
another A poor choice of similarity measure can result in erroneous conclusions
on the efficacy of an XAI method. Too sensitive a measure results in
exaggerated vulnerability, while too coarse understates its weakness. We
investigate a variety of similarity measures designed for text-based ranked
lists including Kendall's Tau, Spearman's Footrule and Rank-biased Overlap to
determine how substantial changes in the type of measure or threshold of
success affect the conclusions generated from common adversarial attack
processes. Certain measures are found to be overly sensitive, resulting in
erroneous estimates of stability.


  In a recent paper Moon and coworkers [C.R. Moon et al., Nature Nanotechnology
4, 167 (2009)] have shown that the single-atom limit for information storage
density can be overcome by using the coherence of electrons in a
two-dimensional electron gas to produce quantum holograms comprised of
individually manipulated molecules projecting an electronic pattern onto a
portion of a surface. We propose to further extend the concept by introducing
quantum spin holography - a version of quantum holographic encoding allowing to
store the information in two spin channels independently.


  We study graded symmetric algebras, which are the symmetric monoids in the
monoidal category of vector spaces graded by a group. We show that a finite
dimensional graded semisimple algebra is graded symmetric. The center of a
symmetric algebra is not necessarily symmetric, but we prove that the center of
a finite dimensional graded division algebra is symmetric, provided that the
order of the grading group is not divisible by the characteristic of the base
field.


  We solve the wavelet set existence problem. That is, we characterize the
full-rank lattices $\Gamma\subset \mathbb R^n$ and invertible $n \times n$
matrices $A$ for which there exists a measurable set $W$ such that $\{W +
\gamma: \gamma \in \Gamma\}$ and $\{A^j(W): j\in \mathbb Z\}$ are tilings of
$\mathbb R^n$. The characterization is a non-obvious generalization of the one
found by Ionascu and Wang, which solved the problem in the case $n = 2$. As an
application of our condition and a theorem of Margulis, we also strengthen a
result of Dai, Larson, and the second author on the existence of wavelet sets
by showing that wavelet sets exist for matrix dilations, all of whose
eigenvalues $\lambda$ satisfy $|\lambda| \ge 1$. As another application, we
show that the Ionascu-Wang characterization characterizes those dilations whose
product of two smallest eigenvalues in absolute value is $\ge 1$.


  I give a brief overview of the model-independent analysis of unstable baryon
resonances in the 1/N_c QCD expansion. This approach produces numerous
surprising semi-quantitative phenomenological predictions that are supported by
available observations.


  Low frequency electromagnetic turbulence is generated in relativistically
outflowing plasma that sweeps up particles from the surrounding environment.
Electrons are energized by stochastic gyroresonant acceleration with the
turbulence produced by the isotropization of captured protons and charged dust.
Protons are accelerated stochastically by the dust-induced turbulence.
Analytical solutions for the proton and electron energy distributions are
obtained and used to calculate broadband synchrotron emission. The solutions
are compared with generic spectral behavior of blazars and gamma-ray bursts.
Dust captured by a blast wave can generate turbulence that could accelerate
protons to very high energies.


  Semiconductor quantum dots are promising candidates for the generation of
nonclassical light. Coupling a quantum dot to a device capable of providing
polarization-selective enhancement of optical transitions is highly beneficial
for advanced functionalities such as efficient resonant driving schemes or
applications based on optical cyclicity. Here, we demonstrate broadband
polarization-selective enhancement by coupling a quantum dot emitting in the
telecom O-band to an elliptical bullseye resonator. We report bright
single-photon emission with a degree of linear polarization of 96%, Purcell
factor of 3.9, and count rates up to 3 MHz. Furthermore, we present a
measurement of two-photon interference without any external polarization
filtering and demonstrate compatibility with compact Stirling cryocoolers by
operating the device at temperatures up to 40 K. These results represent an
important step towards practical integration of optimal quantum dot photon
sources in deployment-ready setups.


  Experimental results describing random, uni-directional, long crested, water
waves over non-uniform bathymetry confirm the formation of stable coherent wave
packages traveling with almost uniform group velocity. The waves are generated
with JONSWAP spectrum for various steepness, height and constant period. A set
of statistical procedures were applied to the experimental data, including the
space and time variation of kurtosis, skewness, BFI, Fourier and moving Fourier
spectra, and probability distribution of wave heights. Stable wave packages
formed out of the random field and traveling over shoals, valleys and slopes
were compared with exact solutions of the NLS equation resulting in good
matches and demonstrating that these packages are very similar to deep water
breathers solutions, surviving over the non-uniform bathymetry. We also present
events of formation of rogue waves over those regions where the BFI, kurtosis
and skewness coefficients have maximal values.


  Assessing the impact of the individual actions performed by soccer players
during games is a crucial aspect of the player recruitment process.
Unfortunately, most traditional metrics fall short in addressing this task as
they either focus on rare actions like shots and goals alone or fail to account
for the context in which the actions occurred. This paper introduces (1) a new
language for describing individual player actions on the pitch and (2) a
framework for valuing any type of player action based on its impact on the game
outcome while accounting for the context in which the action happened. By
aggregating soccer players' action values, their total offensive and defensive
contributions to their team can be quantified. We show how our approach
considers relevant contextual information that traditional player evaluation
metrics ignore and present a number of use cases related to scouting and
playing style characterization in the 2016/2017 and 2017/2018 seasons in
Europe's top competitions.


  Kolmogorov-Arnold Networks (KANs) have very recently been introduced into the
world of machine learning, quickly capturing the attention of the entire
community. However, KANs have mostly been tested for approximating complex
functions or processing synthetic data, while a test on real-world tabular
datasets is currently lacking. In this paper, we present a benchmarking study
comparing KANs and Multi-Layer Perceptrons (MLPs) on tabular datasets. The
study evaluates task performance and training times. From the results obtained
on the various datasets, KANs demonstrate superior or comparable accuracy and
F1 scores, excelling particularly in datasets with numerous instances,
suggesting robust handling of complex data. We also highlight that this
performance improvement of KANs comes with a higher computational cost when
compared to MLPs of comparable sizes.


  Observations of magnetars and some of the high magnetic field pulsars have
shown that their thermal luminosity is systematically higher than that of
classical radio-pulsars, thus confirming the idea that magnetic fields are
involved in their X-ray emission. Here we present the results of 2D simulations
of the fully-coupled evolution of temperature and magnetic field in neutron
stars, including the state-of-the-art kinetic coefficients and, for the first
time, the important effect of the Hall term. After gathering and thoroughly
re-analysing in a consistent way all the best available data on isolated,
thermally emitting neutron stars, we compare our theoretical models to a data
sample of 40 sources. We find that our evolutionary models can explain the
phenomenological diversity of magnetars, high-B radio-pulsars, and isolated
nearby neutron stars by only varying their initial magnetic field, mass and
envelope composition. Nearly all sources appear to follow the expectations of
the standard theoretical models. Finally, we discuss the expected outburst
rates and the evolutionary links between different classes. Our results
constitute a major step towards the grand unification of the isolated neutron
star zoo.


  The exciton valley dynamics in van der Waals heterostructures with transition
metal dichalcogenides monolayers is driven by the long-range exchange
interaction between the electron and the hole in the exciton. It couples the
states active in the opposite circular polarizations resulting in the
longitudinal-transverse splitting of excitons propagating in the monolayer
plane. Here we study theoretically the effect of the dielectric environment on
the long-range exchange interaction and demonstrate how the encapsulation in
the hexagonal boron nitride modifies the exciton longitudinal-transverse
splitting. We calculate the exciton spin/valley polarization relaxation due to
the long-range exchange interaction and demonstrate that the variation of the
monolayer environment results in significant, up to five-fold, enhancement of
the exciton valley polarization lifetime.


  This study is devoted to solving the problem to determine the professional
adaptive capabilities of construction management staff using artificial
intelligence systems.It is proposed Fully Connected Feed-Forward Neural Network
architecture and performed empirical modeling to create a Data Set. Model of
artificial intelligence system allows evaluating the processes in an Fully
Connected Feed-Forward Neural Network during the execution of multi-value
classification of professional areas. A method has been developed for the
training process of a machine learning model, which reflects the internal
connections between the components of an artificial intelligence system that
allow it to learn from training data. To train the neural network, a data set
of 35 input parameters and 29 output parameters was used; the amount of data in
the set is 936 data lines. Neural network training occurred in the proportion
of 10% and 90%, respectively. Results of this study research can be used to
further improve the knowledge and skills necessary for successful professional
realization.


  Images of realistic scenes often contain intra-class objects that are heavily
occluded from each other, making the amodal perception task that requires
parsing the occluded parts of the objects challenging. Although important for
downstream tasks such as robotic grasping systems, the lack of large-scale
amodal datasets with detailed annotations makes it difficult to model
intra-class occlusions explicitly. This paper introduces two new amodal
datasets for image amodal completion tasks, which contain a total of over 267K
images of intra-class occlusion scenarios, annotated with multiple masks,
amodal bounding boxes, dual order relations and full appearance for instances
and background. We also present a point-supervised scheme with layer priors for
amodal instance segmentation specifically designed for intra-class occlusion
scenarios. Experiments show that our weakly supervised approach outperforms the
SOTA fully supervised methods, while our layer priors design exhibits
remarkable performance improvements in the case of intra-class occlusion in
both synthetic and real images.


  We study dynamical behavior of local structures, such as sources and holes,
in traveling wave patterns in a very long (2 m) heated wire convection
experiment. The {\em sources} undergo a transition from stable coherent
behavior to erratic behavior when the driving parameter $\epsilon$ is {\em
decreased}. This transition, as well as the scaling of the average source width
in the erratic regime, are both qualitatively and quantitatively in accord with
earlier theoretical predictions. We also present new results for the {\em
holes} sent out by the erratic sources.


  We introduce an algorithm for the minimization of deterministic Kripke
structures with O(kn log2 n) time complexity. We prove the correctness and
complexity properties of this algorithm.


  The understanding of few-nucleon systems at low energies is essential, e.g.
for accurate predictions of element abundances in big-bang and stellar fusion.
Novel effective field theories, taking only nucleons, or nucleons and pions as
explicit degrees of freedom, provide a systematic approach, permitting an
estimate of theoretical uncertainties. Basic constants parameterising the short
range physics are derived from only a handful of experimental values. The
doublet neutron scattering length a_2 of the deuteron is particularly sensitive
to a three-nucleon contact interaction, but experimentally known with only 6%
accuracy. It can be deduced from the two experimentally accessible parameters
of the nd scattering length. We plan to measure the poorly known "incoherent"
nd scattering length a_{i,d} with 10^{-3} accuracy, using a Ramsey apparatus
for pseudomagnetic precession with a cold polarised neutron beam at PSI. A
polarised target containing both deuterons and protons will permit a
measurement relative to the incoherent np scattering length, which is know
experimentally with an accuracy of 2.4\times 10^{-4}.


  Unsupervised semantic segmentation aims to categorize each pixel in an image
into a corresponding class without the use of annotated data. It is a widely
researched area as obtaining labeled datasets is expensive. While previous
works in the field have demonstrated a gradual improvement in model accuracy,
most required neural network training. This made segmentation equally
expensive, especially when dealing with large-scale datasets. We thus propose a
lightweight clustering framework for unsupervised semantic segmentation. We
discovered that attention features of the self-supervised Vision Transformer
exhibit strong foreground-background differentiability. Therefore, clustering
can be employed to effectively separate foreground and background image
patches. In our framework, we first perform multilevel clustering across the
Dataset-level, Category-level, and Image-level, and maintain consistency
throughout. Then, the binary patch-level pseudo-masks extracted are upsampled,
refined and finally labeled. Furthermore, we provide a comprehensive analysis
of the self-supervised Vision Transformer features and a detailed comparison
between DINO and DINOv2 to justify our claims. Our framework demonstrates great
promise in unsupervised semantic segmentation and achieves state-of-the-art
results on PASCAL VOC and MS COCO datasets.


  We prove that the Bergman and the Teichmuller metrics are equivalent on
Teichmuller spaces.


  We consider the so-called length-interacting Arak-Surgailis polygonal Markov
fields with V-shaped nodes - a continuum and isometry invariant process in the
plane sharing a number of properties with the two-dimensional Ising model. For
these polygonal fields we establish a low-temperature phase separation theorem
in the spirit of the Dobrushin-Kotecky-Shlosman theory, with the corresponding
Wulff shape deteremined to be a disk due to the rotation invariant nature of
the considered model. As an important tool replacing the classical cluster
expansion techniques and very well suited for our geometric setting we use a
graphical construction built on contour birth and death process, following the
ideas of Fernandez, Ferrari and Garcia.


  The representation of binary relations has been intensively studied and many
different theoretical and practical representations have been proposed to
answer the usual queries in multiple domains. However, ternary relations have
not received as much attention, even though many real-world applications
require the processing of ternary relations. In this paper we present a new
compressed and self-indexed data structure that we call Interleaved $K^2$-tree
(I$K^2$-tree), designed to compactly represent and efficiently query general
ternary relations. The I$K^2$-tree is an evolution of an existing data
structure, the $K^2$-tree, initially designed to represent Web graphs and later
applied to other domains. The I$K^2$-tree is able to extend the $K^2$-tree to
represent a ternary relation, based on the idea of decomposing it into a
collection of binary relations but providing indexing capabilities in all the
three dimensions. We present different ways to use I$K^2$-tree to model
different types of ternary relations using as reference two typical domains:
RDF and Temporal Graphs. We also experimentally evaluate our representations
comparing them in space usage and performance with other solutions of the state
of the art.


  Orbital-Free Density Functional Theory (OF-DFT) promises to describe the
electronic structure of very large quantum systems, being its computational
cost linear with the system size. However, the OF-DFT accuracy strongly depends
on the approximation made for the kinetic energy (KE) functional. To date, the
most accurate KE functionals are non-local functionals based on the
linear-response kernel of the homogeneous electron gas, i.e. the jellium model.
Here, we use the linear-response kernel of the jellium-with-gap model, to
construct a simple non-local KE functional (named KGAP) which depends on the
band gap energy. In the limit of vanishing energy-gap (i.e. in the case of
metals), the KGAP is equivalent to the Smargiassi-Madden (SM) functional, which
is accurate for metals. For a series of semiconductors (with different
energy-gaps), the KGAP performs much better than SM, and results are close to
the state-of-the-art functionals with complicated density-dependent kernels.


  We present a study of the impact of photoionization feedback from young
massive stars on the turbulent statistics of star-forming molecular clouds.
This feedback is expected to alter the density structure of molecular clouds
and affect future star formation. Using the AMUN- Rad code, we first generate a
converged isothermal forced turbulent density structure inside a periodic box.
We then insert an ionizing source in this box and inject photoionization energy
using a two-temperature pseudo-isothermal equation of state. We study the
impact of sources at different locations in the box and of different source
luminosities. We find that photoionization has a minor impact on the 2D and 3D
statistics of turbulence when turbulence continues to be driven in the presence
of a photoionizing source. Photoionization is only able to disrupt the cloud if
the turbulence is allowed to decay. In the former scenario, the presence of an
HII region inside our model cloud does not lead to a significant impact on
observable quantities, independent of the source parameters.


  This paper is withdrawn due to a mistake. The revised version with a new
tiltle can be found in hep-ph/0502199.


  Let $\Lambda$ be a complete noetherian local ring with finite residue field
of characteristic $p$ and $K/\mathbb{Q}_p$ a $p$-adic field. We show that, by
deformation of the structure sheaf on the (transversal) prismatic site of a
bounded $p$-adic formal scheme $\mathfrak{X}$, the category of prismatic
$(\Lambda,F)$-crystals on $\mathfrak{X}$ is equivalent to $\Lambda$-\'etale
local systems on the generic adic fiber of $\mathfrak{X}$ and that the
cohomology of $(\Lambda,F)$-crystals recovers the pro-\'etale cohomology of the
corresponding local systems. The proof follows the strategy used in
\cite{bhatt2023prismatic} and \cite{marks2023prismatic}. From this we construct
an isomorphism between Iwasawa cohomology of a $p$-adic Lie extension of $K$
and prismatic cohomology. Following \cite{wu2021galois}, we then reprove Dee's
classical result \cite{article} on the equivalence between families of Galois
representations and \'etale $(\varphi,\Gamma)$-modules.


  We present a spike-based unsupervised regenerative learning scheme to train
Spiking Deep Networks (SpikeCNN) for object recognition problems using
biologically realistic leaky integrate-and-fire neurons. The training
methodology is based on the Auto-Encoder learning model wherein the
hierarchical network is trained layer wise using the encoder-decoder principle.
Regenerative learning uses spike-timing information and inherent latencies to
update the weights and learn representative levels for each convolutional layer
in an unsupervised manner. The features learnt from the final layer in the
hierarchy are then fed to an output layer. The output layer is trained with
supervision by showing a fraction of the labeled training dataset and performs
the overall classification of the input. Our proposed methodology yields
0.92%/29.84% classification error on MNIST/CIFAR10 datasets which is comparable
with state-of-the-art results. The proposed methodology also introduces
sparsity in the hierarchical feature representations on account of event-based
coding resulting in computationally efficient learning.


  The mapping between the fermion and spinon compositions of eigenstates in the
one-dimensional spin-1/2 XX model on a lattice with N sites is used to describe
the spinon interaction from two different perspectives: (i) For finite N the
energy of all eigenstates is expressed as a function of spinon momenta and
spinon spins, which, in turn, are solutions of a set of Bethe ansatz equations.
The latter are the basis of an exact thermodynamic analysis in the spinon
representation of the XX model. (ii) For N -> infinity the energy per site of
spinon configurations involving any number of spinon orbitals is expressed as a
function of reduced variables representing momentum, filling, and magnetization
of each orbital. The spins of spinons in a single orbital are found to be
coupled in a manner well described by an Ising-like equivalent-neighbor
interaction, switching from ferromagnetic to antiferromagnetic as the filling
exceeds a critical level. Comparisons are made with results for the
Haldane-Shastry model.


  Two familiar notions of correlation are rediscovered as extreme operating
points for simulating a discrete memoryless channel, in which a channel output
is generated based only on a description of the channel input. Wyner's "common
information" coincides with the minimum description rate needed. However, when
common randomness independent of the input is available, the necessary
description rate reduces to Shannon's mutual information. This work
characterizes the optimal tradeoff between the amount of common randomness used
and the required rate of description.


  As online retail services proliferate and are pervasive in modern lives,
applications for classifying fashion apparel features from image data are
becoming more indispensable. Online retailers, from leading companies to
start-ups, can leverage such applications in order to increase profit margin
and enhance the consumer experience. Many notable schemes have been proposed to
classify fashion items, however, the majority of which focused upon classifying
basic-level categories, such as T-shirts, pants, skirts, shoes, bags, and so
forth. In contrast to most prior efforts, this paper aims to enable an in-depth
classification of fashion item attributes within the same category. Beginning
with a single dress, we seek to classify the type of dress hem, the hem length,
and the sleeve length. The proposed scheme is comprised of three major stages:
(a) localization of a target item from an input image using semantic
segmentation, (b) detection of human key points (e.g., point of shoulder) using
a pre-trained CNN and a bounding box, and (c) three phases to classify the
attributes using a combination of algorithmic approaches and deep neural
networks. The experimental results demonstrate that the proposed scheme is
highly effective, with all categories having average precision of above 93.02%,
and outperforms existing Convolutional Neural Networks (CNNs)-based schemes.


  Spiking neural networks (SNNs) that enable low-power design on edge devices
have recently attracted significant research. However, the temporal
characteristic of SNNs causes high latency, high bandwidth and high energy
consumption for the hardware. In this work, we propose a binary weight spiking
model with IF-based Batch Normalization for small time steps and low hardware
cost when direct training with input encoding layer and spatio-temporal back
propagation (STBP). In addition, we propose a vectorwise hardware accelerator
that is reconfigurable for different models, inference time steps and even
supports the encoding layer to receive multi-bit input. The required memory
bandwidth is further reduced by two-layer fusion mechanism. The implementation
result shows competitive accuracy on the MNIST and CIFAR-10 datasets with only
8 time steps, and achieves power efficiency of 25.9 TOPS/W.


  This article shows a form of measuring semantic informativity of deductions.
Dynamic concepts of complexity and relevancy are presented according to
explicit definitions of insertion and deletion on databases. Hence, with
respect to finite databases, it solves Bar-Hillel-Carnap paradox and Hintikka'
scandal of deduction.


  An explicit description of the virtualization map for the (modified) Nakajima
monomial model for crystals is given. We give an explicit description of the
Lusztig data for modified Nakajima monomials in type $A_n$.


  With more than 5500 detected exoplanets, the search for life is entering a
new era. Using life on Earth as our guide, we look beyond green landscapes to
expand our ability to detect signs of surface life on other worlds. While
oxygenic photosynthesis gives rise to modern green landscapes,
bacteriochlorophyll-based anoxygenic phototrophs can also colour their habitats
and could dominate a much wider range of environments on Earth-like exoplanets.
Here, we characterize the reflectance spectra of a collection of purple sulfur
and purple non-sulfur bacteria from a variety of anoxic and oxic environments.
We present models for Earth-like planets where purple bacteria dominate the
surface and show the impact of their signatures on the reflectance spectra of
terrestrial exoplanets. Our research provides a new resource to guide the
detection of purple bacteria and improves our chances of detecting life on
exoplanets with upcoming telescopes. Our biological pigment data base for
purple bacteria and the high-resolution spectra of Earth-like planets,
including ocean worlds, snowball planets, frozen worlds, and Earth analogues,
are available online, providing a tool for modellers and observers to train
retrieval algorithms, optimize search strategies, and inform models of
Earth-like planets, where purple is the new green.


  We present a minimal left-right dark matter framework that can simultaneously
explain the recently observed 3.55 keV X-ray line from several galaxy clusters
and gauge coupling unification at high energy scale. Adopting a minimal dark
matter strategy, we consider both left and right handed triplet fermionic dark
matter candidates which are stable by virtue of a remnant $\mathcal{Z}_2\simeq
(-1)^{B-L}$ symmetry arising after the spontaneous symmetry breaking of
left-right gauge symmetry to that of the standard model. A scalar bitriplet
field is incorporated whose first role is to allow radiative decay of right
handed triplet dark matter into the left handed one and a photon with energy
3.55 keV. The other role this bitriplet field at TeV scale plays is to assist
in achieving gauge coupling unification at a high energy scale within a
non-supersymmetric $SO(10)$ model while keeping the scale of left-right gauge
symmetry around the TeV corner. Apart from solving the neutrino mass problem
and giving verifiable new contributions to neutrinoless double beta decay and
charged lepton flavour violation, the model with TeV scale gauge bosons can
also give rise to interesting collider signatures like diboson excess, dilepton
plus two jets excess reported recently in the large hadron collider data.


  The increasing demands of various high data rate wireless applications have
been seen in the recent years and it will continue in the future. To fulfill
these demands, the limited existing wireless resources should be utilized
properly or new wireless technology should be developed. Therefore, we propose
some novel idea to manage the wireless resources and deployment of
femtocellular network technology. The study was mainly divided into two parts:
(a) femtocellular network deployment and resource allocation and (b) resource
management for macrocellular networks. The femtocellular network deployment
scenarios, integrated femtocell/macrocell network architectures, cost-effective
frequency planning, and mobility management schemes are presented in first
part. In the second part, we provide a CAC based on adaptive bandwidth
allocation for the wireless network in. The proposed CAC relies on adaptive
multi-level bandwidth-allocation scheme for non-real-time calls. We propose
video service provisioning over wireless networks. We provide a QoS adaptive
radio resource allocation as well as popularity based bandwidth allocation
schemes for scalable videos over wireless cellular networks. All the proposed
schemes are verified through several numerical and simulation results. The
research results presented in this dissertation clearly imply the advantages of
our proposed schemes.


  We report on the detection of the secondary eclipse of the very-hot Jupiter
OGLE-TR-56b from combined z-band time series photometry obtained with the VLT
and Magellan telescopes. We measure a flux decrement of 0.0363+/-0.0091 percent
from the combined Magellan and VLT datasets, which indicates a blackbody
brightness temperature of 2718 (+127/-107) K, a very low albedo, and a small
incident radiation redistribution factor, indicating a lack of strong winds in
the planet's atmosphere. The measured secondary depth is consistent with
thermal emission, but our precision is not sufficient to distinguish between a
black-body emitting planet, or emission as predicted by models with strong
optical absorbers such as TiO/VO. This is the first time that thermal emission
from an extrasolar planet is detected at optical wavelengths and with
ground-based telescopes.


  We examine the impact of helium reionization on the structure of the
intergalactic medium (IGM). We model the reionization using a radiative
transfer (RT) code coupled to the combined gravity hydrodynamics code Enzo.
Neutral hydrogen and helium are initially ionized by a starburst spectrum,
which is allowed to gradually evolve into a power law spectrum over the
redshift interval 3 < z < 4. The temperature-density relation of the gas is
found to fan out and flatten following HeII reionization, with an inversion for
overdensities above 5. Peculiar velocities of up to 10 km/s are induced by the
increased pressure, with the gas density field distorted over large coherent
regions by 10-20%, and the dark matter by levels of 1%. The
photoionization-induced flows may thus distort the matter power spectrum at
comoving wavenumbers k > 0.5 h/Mpc by a few percent by z = 2. Absorption
spectra for HI and HeII are drawn from the simulations, and absorption lines
are fit to the spectra. A median Doppler parameter of 35 km/s is obtained for
the HI absorption systems at z = 3. Dividing into subsamples optically thick
and optically thin at line centre reveals that the optically thick systems
undergo only mild evolution while the optically thin systems evolve rapidly
following HeII reionization. A comparison between HeII and HI absorption
features shows a broad distribution in the HeII and HI column density ratio,
peaking near the measured value and only slightly narrower than measured. A
comparison with approximate simulation methods shows moderately good agreement
in the absorption line properties, but not to the precision to which they may
be measured.


  Electron hydrodynamics gives rise to surprising correlated behaviors in which
electrons "cooperate" to quench dissipation and reduce the electric fields
needed to sustain the flow. Such collective "free" flows are usually expected
at the hydrodynamic lengthscales exceeding the electron-electron scattering
mean free path $\ell_{\rm ee}$. Here we predict that in two-dimensional
electron gases the collective free flows actually occur at the distances much
smaller than $\ell_{\rm ee}$, in a nominally ballistic regime. The
sub-$\ell_{\rm ee}$ free flows arise due to retroreflected holes originating
from head-on electron electron collisions, which retrace the paths of impinging
electrons and cancel out their potential. An exact solution, obtained in
Corbino geometry, predicts potential strongly screened by the hole backflow.
Screened potential is described by a fractional power law $r^{-5/3}$ over a
wide range of $r$ values, from macroscales down to deep sub-$\ell_{\rm ee}$
scales, and a distinct non-Fermi-liquid temperature dependence.


  Stock price prediction is important for value investments in the stock
market. In particular, short-term prediction that exploits financial news
articles is promising in recent years. In this paper, we propose a novel deep
neural network DP-LSTM for stock price prediction, which incorporates the news
articles as hidden information and integrates difference news sources through
the differential privacy mechanism. First, based on the autoregressive moving
average model (ARMA), a sentiment-ARMA is formulated by taking into
consideration the information of financial news articles in the model. Then, an
LSTM-based deep neural network is designed, which consists of three components:
LSTM, VADER model and differential privacy (DP) mechanism. The proposed DP-LSTM
scheme can reduce prediction errors and increase the robustness. Extensive
experiments on S&P 500 stocks show that (i) the proposed DP-LSTM achieves 0.32%
improvement in mean MPA of prediction result, and (ii) for the prediction of
the market index S&P 500, we achieve up to 65.79% improvement in MSE.


  Supernovae connected with long-duration gamma-ray bursts (GRBs) are
hyper-energetic explosion resulting from the collapse of very massive stars
(about 40Mo, where Mo is the mass of the Sun) stripped of their outher hydrogen
and helium envelopes. A very massive progenitor, collapsing to a black hole,
was thought to be a requirement for the launch of a GRB. Here we report the
results of modelling the spectra and light curve of SN 2006aj, which
demonstrate that the supernova had a much smaller explosion energy and ejected
much less mass than the other GRB-sueprnovae, suggesting that it was produced
by a star whose initial mass was only about 20Mo. A star of this mass is
expected to form a neutron star rather than a black hole when its core
collapses. The smaller explosion energy of SN 2006aj is matched by the weakness
and softness of GRB 060218 (an X-ray flash), and the weakness of the radio flux
of the supernova. Our results indicate that the supernova-GRB connection
extends to a much broader range of stellar masses than previously thought,
possibly involving different physical mechanisms: a `collapsar' for the more
massive stars collapsing to a black hole, and magnetic activity of the nascent
neutron star for the less massive stars.


  Opening talk presented at the Adriatico Research Conference -- Trends in
Collider Spin Physics and at the RIKEN Symposium -- Spin Structure of the
Nucleon.
  We present an overview of the opportunities to explore QCD at a very high
energy polarized hadron collider. We review connections to low energy flavor
physics, transverse spin in QCD and higher twist. It is clear that polarized
colliders will provide a wealth of new information about the behavior of quarks
and gluons inside hadrons complementing that available from lepton scattering
and other more familiar probes of hadron structure.


  We consider digital expansions to the base of $\tau$, where $\tau$ is an
algebraic integer. For a $w \geq 2$, the set of admissible digits consists of 0
and one representative of every residue class modulo $\tau^w$ which is not
divisible by $\tau$. The resulting redundancy is avoided by imposing the width
$w$-NAF condition, i.e., in an expansion every block of $w$ consecutive digits
contains at most one non-zero digit. Such constructs can be efficiently used in
elliptic curve cryptography in conjunction with Koblitz curves.
  The present work deals with analysing the number of occurrences of a fixed
non-zero digit. In the general setting, we study all $w$-NAFs of given length
of the expansion. We give an explicit expression for the expectation and the
variance of the occurrence of such a digit in all expansions. Further a central
limit theorem is proved.
  In the case of an imaginary quadratic $\tau$ and the digit set of minimal
norm representatives, the analysis is much more refined: We give an asymptotic
formula for the number of occurrence of a digit in the $w$-NAFs of all elements
of $\Z[\tau]$ in some region (e.g. a disc). The main term coincides with the
full block length analysis, but a periodic fluctuation in the second order term
is also exhibited. The proof follows Delange's method.
  We also show that in the case of imaginary quadratic $\tau$ and $w \geq 2$,
the digit set of minimal norm representatives leads to $w$-NAFs for \emph{all}
elements of $\Z[\tau]$. Additionally some properties of the fundamental domain
are stated.


  We consider the quasi-one dimensional system realized by an array of weakly
coupled parallel one-dimensional "tubes" in a two-dimensional lattice which
permits free motion of atoms in an axial direction in the presence of a Zeeman
field, Rashba type spin orbit coupling (SOC), and an s-wave attractive
interaction, while the radial motion is tightly confined. We solve the
zero-temperature (T=0) Bogoliubov-de Gennes (BdG) equations for the quasi-1D
Fermi gas with the dispersion modified by tunneling between the tubes, and show
that the T=0 phase diagram hosts the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO)
phase with non-zero center of mass momentum Cooper pairs for small values of
the SOC while for larger values of the SOC and high Zeeman fields the uniform
superfluid phase with zero center of mass momentum Cooper pairs has an
instability towards the topological uniform superfluid phase with Majorana
fermions at the tube ends. Also, we show that tuning the two-dimensional
optical lattice strength in this model allows one to explore the crossover
behaviors of the phases during the transition between the 3D and the 1D system
and in general the FFLO (for small SOC) and the topological uniform superfluid
phase (for large SOC) are favored as the system becomes more one-dimensional.
We also find evidence of the existence of a Zeeman tuned topological quantum
phase transition (TQPT) within the FFLO phase itself and for large values of
the Zeeman field and small SOC the TQPT gives rise to a topologically distinct
FFLO phase.


  Lepton flavor violating Higgs boson decays are studied within the context of
seesaw models with Majorana massive neutrinos. Two models are considered: The
SM-seesaw, with the Standard Model Particle content plus three right handed
neutrinos, and the MSSM-seesaw, with the Minimal Supersymmetric Standard Model
particle content plus three right handed neutrinos and their supersymmetric
partners. The widths for these decays are derived from a full one-loop
diagrammatic computation in both models, and they are analyzed numerically in
terms of the seesaw parameters, namely, the Dirac and Majorana mass matrices.
Several possible scenarios for these mass matrices that are compatible with
neutrino data are considered. In the SM-seesaw case, very small branching
ratios are found for all studied scenarios. These ratios are explained as a
consequence of the decoupling behaviour of the heavy right handed neutrinos. In
contrast, in the MSSM-seesaw case, sizeable branching ratios are found for some
of the leptonic flavor violating decays of the MSSM neutral Higgs bosons and
for some choices of the seesaw matrices and MSSM parameters. The relevance of
the two competing sources of lepton flavor changing interactions in the
MSSM-seesaw case is also discussed. The non-decoupling behaviour of the
supersymmetric particles contributing in the loop-diagrams is finally shown.


  I review the status of the issue of the k-string tension in Yang-Mills
theory. After a summary of known facts I discuss a weakly coupled
four-dimensional Yang-Mills theory that supports non-Abelian strings and can,
in certain aspects, serve as a toy model for QCD strings. In the second part of
the talk I present original results obtained in a two-dimensional toy model
which provides some evidence for the sine formula.


  Species coexistence is one of the central themes in modern ecology.
Coexistence is a prerequisite of biological diversity. However, the question
arises how biodiversity can be reconciled with the statement of competition
theory, which asserts that competing species cannot coexist. To solve this
problem natural selection theory is rejected because it contradicts kinetic
models of interacting populations. Biological evolution is presented as a
process equivalent to a chemical reaction. The main point is that interactions
occur between self-replicating units. Under these assumptions biodiversity is
possible if and only if species are identical with respect to the patterns of
energy flow in which individuals are involved.


  Digital forensics in smart environments is an emerging field that deals with
the investigation and analysis of digital evidence in smart devices and
environments. As smart environments continue to evolve, digital forensic
investigators face new challenges in retrieving, preserving, and analyzing
digital evidence. At the same time, recent advancements in digital forensic
tools and techniques offer promising solutions to overcome these challenges. In
this survey, we examine recent advancements and challenges in digital forensics
within smart environments. Specifically, we review the current state-of-the-art
techniques and tools for digital forensics in smart environments and discuss
their strengths and limitations. We also identify the major challenges that
digital forensic investigators face in smart environments and propose potential
solutions to overcome these challenges. Our survey provides a comprehensive
overview of recent advancements and challenges in digital forensics in the age
of smart environments, and aims to inform future research in this area.


  We study the null controllability of three parabolic equations. The control
is acting only on one of the three equations. The three equations are coupled
by means of two cubic nonlinearities. The linearized control system around 0 is
not null controllable. However, using the cubic nonlinearities, we prove the
(global) null controllability of the control system. The proof relies on the
return method, an algebraic solvability and smoothing properties of the
parabolic equations.


  Photochemistry is a fundamental process of planetary atmospheres that
regulates the atmospheric composition and stability. However, no unambiguous
photochemical products have been detected in exoplanet atmospheres to date.
Recent observations from the JWST Transiting Exoplanet Early Release Science
Program found a spectral absorption feature at 4.05 $\mu$m arising from SO$_2$
in the atmosphere of WASP-39b. WASP-39b is a 1.27-Jupiter-radii, Saturn-mass
(0.28 M$_J$) gas giant exoplanet orbiting a Sun-like star with an equilibrium
temperature of $\sim$1100 K. The most plausible way of generating SO$_2$ in
such an atmosphere is through photochemical processes. Here we show that the
SO$_2$ distribution computed by a suite of photochemical models robustly
explains the 4.05 $\mu$m spectral feature identified by JWST transmission
observations with NIRSpec PRISM (2.7$\sigma$) and G395H (4.5$\sigma$). SO$_2$
is produced by successive oxidation of sulphur radicals freed when hydrogen
sulphide (H$_2$S) is destroyed. The sensitivity of the SO$_2$ feature to the
enrichment of the atmosphere by heavy elements (metallicity) suggests that it
can be used as a tracer of atmospheric properties, with WASP-39b exhibiting an
inferred metallicity of $\sim$10$\times$ solar. We further point out that
SO$_2$ also shows observable features at ultraviolet and thermal infrared
wavelengths not available from the existing observations.


  We present an investigation of how topological data analysis (TDA) can be
applied to condition-based monitoring (CBM) of wind turbines for energy
generation. TDA is a branch of data analysis focusing on extracting meaningful
information from complex datasets by analyzing their structure in state space
and computing their underlying topological features. By representing data in a
high-dimensional state space, TDA enables the identification of patterns,
anomalies, and trends in the data that may not be apparent through traditional
signal processing methods. For this study, wind turbine data was acquired from
a wind park in Norway via standard vibration sensors at different locations of
the turbine's gearbox. Both the vibration acceleration data and its frequency
spectra were recorded at infrequent intervals for a few seconds at high
frequency and failure events were labelled as either gear-tooth or ball-bearing
failures. The data processing and analysis are based on a pipeline where the
time series data is first split into intervals and then transformed into
multi-dimensional point clouds via a time-delay embedding. The shape of the
point cloud is analyzed with topological methods such as persistent homology to
generate topology-based key health indicators based on Betti numbers,
information entropy and signal persistence. Such indicators are tested for CBM
and diagnosis (fault detection) to identify faults in wind turbines and
classify them accordingly. Topological indicators are shown to be an
interesting alternative for failure identification and diagnosis of operational
failures in wind turbines.


  Agricultural systems experience land-use changes that are driven by
population growth and intensification of technological inputs. This results in
land-use and cover change (LUCC) dynamics representing a complex landscape
transformation process. In order to study the LUCC process we developed a
spatially explicit agent-based model in the form of a Cellular Automata
implemented with the Cell-DEVS formalism. The resulting model called AgroDEVS
is used for predicting LUCC dynamics along with their associated economic and
environmental changes. AgroDEVS is structured using behavioral rules and
functions representing a) crop yields, b) weather conditions, c) economic
profit, d) farmer preferences, e) technology level adoption and f) natural
resources consumption based on embodied energy accounting. Using data from a
typical location of the Pampa region (Argentina) for the 1988-2015 period,
simulation exercises showed that the economic goals were achieved, on average,
each 6 out of 10 years, but the environmental thresholds were only achieved in
1.9 out of 10 years. In a set of 50-years simulations, LUCC patterns quickly
converge towards the most profitable crop sequences, with no noticeable
tradeoff between the economic and environmental conditions.


  Neutron star mergers are the canonical multimessenger events: they have been
observed through photons for half a century, gravitational waves since 2017,
and are likely to be sources of neutrinos and cosmic rays. Studies of these
events enable unique insights into astrophysics, particles in the
ultrarelativistic regime, the heavy element enrichment history through cosmic
time, cosmology, dense matter, and fundamental physics. Uncovering this science
requires vast observational resources, unparalleled coordination, and
advancements in theory and simulation, which are constrained by our current
understanding of nuclear, atomic, and astroparticle physics. This review begins
with a summary of our current knowledge of these events, the expected
observational signatures, and estimated detection rates for the next decade. I
then present the key observations necessary to advance our understanding of
these sources, followed by the broad science this enables. I close with a
discussion on the necessary future capabilities to fully utilize these
enigmatic sources to understand our universe.


  Possible formation mechanisms of massive close binary black holes that can
merge in the Hubble time to produce powerful gravitational wave bursts detected
during advanced LIGO O1 science run are briefly discussed. The pathways include
the evolution from field low-metallicity massive binaries, the dynamical
formation in globular clusters and primordial black holes. Low effective black
hole spins inferred for LIGO GW150914 and LTV151012 events are discussed.
Population synthesis calculations of the expected spin and chirp mass
distributions from the standard field massive binary formation channel are
presented for different metallicities (from zero-metal Population III stars up
to solar metal abundance). We conclude that that merging binary black holes can
contain systems from different formation channels, discrimination between which
can be made with increasing statistics of mass and spin measurements from
ongoing and future gravitational wave observations.


  Let $f(n)=\sum_k \binom nk^{-1}$. In a previous paper, we defined for a
p-adic integer x that f(x) is p-definable if lim $f(x_j)$ exists in $Q_p$,
where $x_j$ denotes the mod $p^j$ reduction of $x$. We proved that if p is odd,
then -1 is the only element of $Z_p-N$ for which f(x) is p-definable. For p=2,
we proved that if the 1's in the binary expansion of x are eventually
extraordinarily sparse, then f(x) is 2-definable. Here we present some
conjectures that f(x) is 2-definable for many more 2-adic integers. We discuss
the extent to which we can prove these conjectures.


  We prove the homogenization to the Brinkman equations for the incompressible
Stokes equations in a bounded domain which is perforated by a random collection
of small spherical holes. The fluid satisfies a no-slip boundary condition at
the holes. The balls generating the holes have centres distributed according to
a Poisson point process and i.i.d. unbounded radii satisfying a suitable moment
condition. We stress that our assumption on the distribution of the radii does
not exclude that, with overwhelming probability, the holes contain clusters
made by many overlapping balls. We show that the formation of these clusters
has no effect on the limit Brinkman equations. Due to the incompressiblility
condition and the lack of a maximum principle for the Stokes equations, our
proof requires a very careful study of the geometry of the random holes
generated by the class of probability measures considered.


  This paper proposes a probabilistic Bayesian formulation for system
identification (ID) and estimation of nonseparable Hamiltonian systems using
stochastic dynamic models. Nonseparable Hamiltonian systems arise in models
from diverse science and engineering applications such as astrophysics,
robotics, vortex dynamics, charged particle dynamics, and quantum mechanics.
The numerical experiments demonstrate that the proposed method recovers
dynamical systems with higher accuracy and reduced predictive uncertainty
compared to state-of-the-art approaches. The results further show that accurate
predictions far outside the training time interval in the presence of sparse
and noisy measurements are possible, which lends robustness and
generalizability to the proposed approach. A quantitative benefit is prediction
accuracy with less than 10% relative error for more than 12 times longer than a
comparable least-squares-based method on a benchmark problem.


  Non-degenerate real hypersurfaces of almost Hermite-like manifolds are
examined. Tangential real hypersurfaces are introduced and the main identities
of such hypersurfaces are obtained. With the help of these identities, contact
metric structures of certain kinds, namely K-contact and cosymplectic cases,
are discussed.


  We examine the coefficients in front of Chern numbers for complex genera, and
pay special attention to the $\text{Td}^{\frac{1}{2}}$-genus, the
$\Gamma$-genus as well as the Todd genus. Some related geometric applications
to hyper-K\"{a}hler and Calabi-Yau manifolds are discussed. Along this line and
building on the work of Doubilet in 1970s, various Hoffman-type formulas for
multiple-(star) zeta values and transition matrices among canonical bases of
the ring of symmetric functions can be uniformly treated in a more general
framework.


  Estimating causal queries, such as changes in protein abundance in response
to a perturbation, is a fundamental task in the analysis of biomolecular
pathways. The estimation requires experimental measurements on the pathway
components. However, in practice many pathway components are left unobserved
(latent) because they are either unknown, or difficult to measure. Latent
variable models (LVMs) are well-suited for such estimation. Unfortunately,
LVM-based estimation of causal queries can be inaccurate when parameters of the
latent variables are not uniquely identified, or when the number of latent
variables is misspecified. This has limited the use of LVMs for causal
inference in biomolecular pathways. In this manuscript, we propose a general
and practical approach for LVM-based estimation of causal queries. We prove
that, despite the challenges above, LVM-based estimators of causal queries are
accurate if the queries are identifiable according to Pearl's do-calculus, and
describe an algorithm for its estimation. We illustrate the breadth and the
practical utility of this approach for estimating causal queries in four
synthetic and two experimental case studies, where structures of biomolecular
pathways challenge the existing methods for causal query estimation. The code
and the data documenting all the case studies are available at
\url{https://github.com/srtaheri/LVMwithDoCalculus}


  In the framework of the kinetic approach based on the Boltzmann equation for
the phonon distribution function, we analyze phonon heat transfer in a
heterostructure containing a layer of a normal metal ($ N $) and a layer of a
ferromagnetic insulator ($ F $). Two realistic methods for creating a
temperature gradient in such a heterostructure are considered: by heating of
the $N$-layer by an electric current and by placing the $N/F$-bilayer between
massive dielectrics with different temperatures. The electron temperature $ T_e
$ in the $ N $-layer and the magnon temperature $ T_m $ in the $ F $-layer are
calculated. The difference in these temperatures determines the voltage $
V_{ISHE} $ on the $ N $-layer in the Seebeck spin effect regime. The dependence
of $ V_{ISHE} $ on the bath temperature and on the thickness of the $ N $ and $
F $ layers is compared with the available experimental data.


  We consider the design of two-dimensional electrostatic traps for dipolar
indirect excitons. We show that the excitons dipole-dipole interaction,
combined with the in-plane electric fields that arise due to the trap geometry,
constrain the maximal density and lifetime of trapped excitons. We derive an
analytic estimate of these values and determine their dependence on the trap
geometry, thus suggesting the optimal design for high density trapping as a
route for observing excitonic Bose-Einstein condensation.


  The c=1 Liouville theory has received some attention recently as the
Euclidean version of an exact rolling tachyon background. In an earlier paper
it was shown that the bulk theory can be identified with the interacting c=1
limit of unitary minimal models. Here we extend the analysis of the c=1-limit
to the boundary problem. Most importantly, we show that the FZZT branes of
Liouville theory give rise to a new 1-parameter family of boundary theories at
c=1. These models share many features with the boundary Sine-Gordon theory, in
particular they possess an open string spectrum with band-gaps of finite width.
We propose explicit formulas for the boundary 2-point function and for the
bulk-boundary operator product expansion in the c=1 boundary Liouville model.
As a by-product of our analysis we also provide a nice geometric interpretation
for ZZ branes and their relation with FZZT branes in the c=1 theory.


  We provide a framework for studying the expansion rate of the image of a
bounded set under a flow in Euclidean space and apply it to stochastic
differential equations (SDEs for short) with singular coefficients. If the
singular drift of the SDE can be split into two terms, one of which is singular
and the radial component of the other term has a radial component of sufficient
strength in the direction of the origin, then the random dynamical system
generated by the SDE admits a pullback attractor.


  In this paper we derive a relation for a class of Racah polynomials that
appear in a conjecture of Kresch and Tamvakis. The relation follows from an
inversion formula for a transformation of a discrete sequence of complex
numbers $\{ x_n \}_{n=0}^{\infty}$. As a result of our inversion formula, we
also obtain other combinatorial identities.


  For $\varphi$ a normalized positive definite function on a locally compact
abelian group $G$, we consider on the one hand the unitary representation
$\pi_\varphi$ associated to $\varphi$ by the GNS construction, on the other
hand the probability measure $\mu_\varphi$ on the Pontryagin dual $\hat{G}$
provided by Bochner's theorem. We give necessary and sufficient conditions for
the vanishing of 1-cohomology $H^1(G,\pi_\varphi)$ and reduced 1-cohomology
$\bar{H}^1(G,\pi_\varphi)$. For example, $\bar{H}^1(G,\pi_\varphi)=0$ if and
only if either $Hom(G,\mathbb{C})=0$ or $\mu_\varphi(1_G)=0$, where $1_G$ is
the trivial character of $G$.


  I would like to thank Solar Physics colleagues for asking me to write this
chapter on my professional life. My main interest has always been focused on
the Sun, our star, from the heating of the corona, to the dynamics of
prominences and their eruptions, ares and coronal mass ejections until their
impact on the Earth. I built a new group in solar physics and gave to them my
enthusiasm. They brought to me a lot of satisfaction. We have made important
advances in solar physics with a step forward to understand the triggers of
solar activity and their terrestrial effects. Our avant-garde research and
discovery has opened new topics for the solar community. Mixing observations
obtained on the ground and in space with theory and numerical simulations
brings a new perspective in research.


  In this publication, uncertainties in and differences between the MC@NLO and
POWHEG methods for matching next-to-leading order QCD calculations with parton
showers are discussed. Implementations of both algorithms within the event
generator Sherpa and based on Catani-Seymour subtraction are employed to assess
the impact on a representative selection of observables. In the case of MC@NLO
a substantial simplification is achieved by using dipole subtraction terms to
generate the first emission. A phase space restriction is employed, which
allows to vary in a transparent way the amount of non-singular radiative
corrections that are exponentiated. Effects on various observables are
investigated, using the production of a Higgs boson in gluon fusion, with or
without an associated jet, as a benchmark process. The case of H+jet production
is presented for the first time in an NLO+PS matched simulation. Uncertainties
due to scale choices and non-perturbative effects are explored in the
production of W and Z bosons in association with a jet. Corresponding results
are compared to data from the Tevatron and LHC experiments.


  We give finite presentations for the fundamental group of moduli stacks of
smooth Weierstrass curves over complex projective space P^n which extend the
classical result for elliptic curves to positive dimensional base. We thus get
natural generalisations of SL_2(Z) and pave the way to understanding the
fundamental group of moduli stacks of elliptic surfaces in general.
  Our approach exploits the natural involution on Weierstrass curves and the
identification of its fixed loci with smooth hypersurfaces in an appropriate
linear system on a projective line bundle over P^n. The fundamental group of
the corresponding discriminant complement can be presented in terms of finitely
many generators and relations using methods in the Zariski tradition, which
were sucessfully elaborated in mathAG/0602371.


  We present a generalization of Krylov-Rozovskii's result on the existence and
uniqueness of solutions to monotone stochastic differential equations. As an
application, the stochastic generalized porous media and fast diffusion
equations are studied for $\sigma$-finite reference measures, where the drift
term is given by a negative definite operator acting on a time-dependent
function, which belongs to a large class of functions comparable with the
so-called $N$-functions in the theory of Orlicz spaces.


  Video summarization attracts attention for efficient video representation,
retrieval, and browsing to ease volume and traffic surge problems. Although
video summarization mostly uses the visual channel for compaction, the benefits
of audio-visual modeling appeared in recent literature. The information coming
from the audio channel can be a result of audio-visual correlation in the video
content. In this study, we propose a new audio-visual video summarization
framework integrating four ways of audio-visual information fusion with
GRU-based and attention-based networks. Furthermore, we investigate a new
explainability methodology using audio-visual canonical correlation analysis
(CCA) to better understand and explain the role of audio in the video
summarization task. Experimental evaluations on the TVSum dataset attain F1
score and Kendall-tau score improvements for the audio-visual video
summarization. Furthermore, splitting video content on TVSum and COGNIMUSE
datasets based on audio-visual CCA as positively and negatively correlated
videos yields a strong performance improvement over the positively correlated
videos for audio-only and audio-visual video summarization.


  We use the Space Telescope Imaging Spectrograph on board the Hubble Space
Telescope to obtain new high-quality spectra covering the 1900 to 2360 Angstrom
wavelength range for two metal-poor stars, HD 108317 and HD 128279. We derive
abundances of Cu II, Zn II, As I, Se I, Mo II, and Cd II, which have not been
detected previously in either star. Abundances derived for Ge I, Te I, Os II,
and Pt I confirm those derived from lines at longer wavelengths. We also derive
upper limits from the non-detection of W II, Hg II, Pb II, and Bi I. The mean
[As/Fe] ratio derived from these two stars and five others in the literature is
unchanged over the metallicity range -2.8 < [Fe/H] < -0.6, <[As/Fe]> = +0.28
+/- 0.14 (std. dev. = 0.36 dex). The mean [Se/Fe] ratio derived from these two
stars and six others in the literature is also constant, <[Se/Fe]> = +0.16 +/-
0.09 (std. dev. = 0.26 dex). The As and Se abundances are enhanced relative to
a simple extrapolation of the iron-peak abundances to higher masses, suggesting
that this mass region (75 < A < 82) may be the point at which a different
nucleosynthetic mechanism begins to dominate the quasi-equilibrium alpha-rich
freezeout of the iron peak. <[CuII/CuI]> = +0.56 +/- 0.23 in HD 108317 and HD
128279, and we infer that lines of Cu I may not be formed in local
thermodynamic equilibrium in these stars. The [Zn/Fe], [Mo/Fe], [Cd/Fe], and
[Os/Fe] ratios are also derived from neutral and ionized species, and each
ratio pair agrees within the mutual uncertainties, which range from 0.15 to
0.52 dex.


  The light-induced femtosecond dynamics of the sublattice magnetizations in
the antiferromagnetically ordered phase of the semiconductor $\alpha$-MnTe is
investigated theoretically as function of an external driving field. The
electromagnetic field is coupled to optical modes and the concomitant atomic
displacements modulate the Heisenberg exchange couplings. We derive the
equations of motion for the time-dependent sublattice magnetization in spin
wave theory and analyze the contributions from the driven magnon modes. The
antiferromagnetic order parameter exhibits coherent longitudinal oscillations
determined by the external driving frequency which decay due to dephasing.
Including a phenomenological dissipative term to mimic spin-lattice relaxation
processes leads to relaxation back to thermal equilibrium. We provide
approximate analytic solutions of the resulting differential equations which
allow us to understand the effect of the driving light pulse on the amplitude,
frequency, and lifetime of the coherent spin dynamics.


  Using the Calderbank-Shor-Steane (CSS) construction, pure $q$-ary asymmetric
quantum error-correcting codes attaining the quantum Singleton bound are
constructed. Such codes are called pure CSS asymmetric quantum maximum distance
separable (AQMDS) codes. Assuming the validity of the classical MDS Conjecture,
pure CSS AQMDS codes of all possible parameters are accounted for.


  We report progress on applying technologies developed for LHC-era experiments
to cosmic ray detection, using scintillating tiles with embedded
wavelength-shifting fibers and avalanche photodiode readouts as parts of a
robust, inexpensive cosmic air shower detector. We are planning to deploy such
detectors in high schools as part of an outreach effort able to search for
long-distance correlations between airshowers.


  We prove complete controllability for rotational states of an asymmetric top
molecule belonging to degenerate values of the orientational quantum number M.
Based on this insight, we construct a pulse sequence that energetically
separates population initially distributed over degenerate M-states, as a
precursor for orientational purification.
  Introducing the concept of enantio-selective controllability, we determine
the conditions for complete enantiomer-specific population transfer in chiral
molecules and construct pulse sequences realizing this transfer for population
initially distributed over degenerate M-states. This degeneracy presently
limits enantiomer-selectivity for any initial state except the rotational
ground state. Our work thus shows how to overcome an important obstacle towards
separating, with electric fields only, left-handed from right-handed molecules
in a racemic mixture.


  In this work, we consider the multi-wall braneworld arisen from multi-scalar
fields, and investigate the localization and resonances of spin-1/2 fermion on
the multi-walls. We build two analytic multi-wall solutions with a polynomial
superpotential and a modified sine-Gordon superpotential respectively. The
massless fermion is the only bound state and localized between the two
outermost sub-branes. The factors affecting the number of massive resonant
fermions are analyzed. What interesting is that all the fermion resonant states
are non-degenerate for the cases of single- and two-walls, however,
doubly-degenerate fermion resonant states emerge for the cases of three- and
four-walls. This novel phenomenon could be potentially interesting in
phenomenology.


  The advent of Large Language Models (LLMs) has revolutionized various
applications by providing advanced natural language processing capabilities.
However, this innovation introduces new cybersecurity challenges. This paper
explores the threat modeling and risk analysis specifically tailored for
LLM-powered applications. Focusing on potential attacks like data poisoning,
prompt injection, SQL injection, jailbreaking, and compositional injection, we
assess their impact on security and propose mitigation strategies. We introduce
a framework combining STRIDE and DREAD methodologies for proactive threat
identification and risk assessment. Furthermore, we examine the feasibility of
an end-to-end threat model through a case study of a custom-built LLM-powered
application. This model follows Shostack's Four Question Framework, adjusted
for the unique threats LLMs present. Our goal is to propose measures that
enhance the security of these powerful AI tools, thwarting attacks, and
ensuring the reliability and integrity of LLM-integrated systems.


  We study a joint facility location and cost planning problem in a competitive
market under random utility maximization (RUM) models. The objective is to
locate new facilities and make decisions on the costs (or budgets) to spend on
the new facilities, aiming to maximize an expected captured customer demand,
assuming that customers choose a facility among all available facilities
according to a RUM model. We examine two RUM frameworks in the discrete choice
literature, namely, the additive and multiplicative RUM. While the former has
been widely used in facility location problems, we are the first to explore the
latter in the context. We numerically show that the two RUM frameworks can well
approximate each other in the context of the cost optimization problem. In
addition, we show that, under the additive RUM framework, the resultant cost
optimization problem becomes highly non-convex and may have several local
optima. In contrast, the use of the multiplicative RUM brings several
advantages to the competitive facility location problem. For instance, the cost
optimization problem under the multiplicative RUM can be solved efficiently by
a general convex optimization solver or can be reformulated as a conic
quadratic program and handled by a conic solver available in some off-the-shelf
solvers such as CPLEX or GUROBI. Furthermore, we consider a joint location and
cost optimization problem under the multiplicative RUM and propose three
approaches to solve the problem, namely, an equivalent conic reformulation, a
multi-cut outer-approximation algorithm, and a local search heuristic. We
provide numerical experiments based on synthetic instances of various sizes to
evaluate the performances of the proposed algorithms in solving the cost
optimization, and the joint location and cost optimization problems.


  Correlated quantum many-body phenomena in lattice models have been identified
as a set of physically interesting problems that cannot be solved classically.
Analog quantum simulators, in photonics and microwave superconducting circuits,
have emerged as near-term platforms to address these problems. An important
ingredient in practical quantum simulation experiments is the tomography of the
implemented Hamiltonians -- while this can easily be performed if we have
individual measurement access to each qubit in the simulator, this could be
challenging to implement in many hardware platforms. In this paper, we present
a scheme for tomography of quantum simulators which can be described by a
Bose-Hubbard Hamiltonian while having measurement access to only some sites on
the boundary of the lattice. We present an algorithm that uses the
experimentally routine transmission and two-photon correlation functions,
measured at the boundary, to extract the Hamiltonian parameters at the standard
quantum limit. Furthermore, by building on quantum enhanced spectroscopy
protocols that, we show that with the additional ability to switch on and off
the on-site repulsion in the simulator, we can sense the Hamiltonian parameters
beyond the standard quantum limit.


  Kubo formula gives a linear response of a quantum system to external fields,
which are classical and weak with respect to the energy of the system. In this
work, we take the quantum nature of the external field into account, and define
a Hall conductance to characterize the linear response of a two-band system to
the quantized field. The theory is then applied to topological insulators.
Comparisons with the traditional Hall conductance are presented and discussed.


  We prove that an $F$-crystal $(M,\vph)$ over an algebraically closed field
$k$ of characteristic $p>0$ is determined by $(M,\vph)$ mod $p^n$, where $n\ge
1$ depends only on the rank of $M$ and on the greatest Hodge slope of
$(M,\vph)$. We also extend this result to triples $(M,\vph,G)$, where $G$ is a
flat, closed subgroup scheme of ${\bf GL}_M$ whose generic fibre is connected
and has a Lie algebra normalized by $\vph$. We get two purity results. If
${\got C}$ is an $F$-crystal over a reduced ${\bf F}_p$-scheme $S$, then each
stratum of the Newton polygon stratification of $S$ defined by ${\got C}$, is
an affine $S$-scheme (a weaker result was known before for $S$ noetherian). The
locally closed subscheme of the Mumford scheme ${\Ma_{d,1,N}}_k$ defined by the
isomorphism class of a principally quasi-polarized $p$-divisible group over $k$
of height 2d, is an affine ${\Ma_{d,1,N}}_k$-scheme.


  A theorem of Katanaga, Saeki, Teragaito, and Yamada relates Gluck and Price
twists of 4-manifolds. Using trisection diagrams, we give a purely diagrammatic
proof of this theorem, and answer a question of Kim and Miller.


  The methodology of single crystal growth of metastable $\beta$-Ti alloy
TIMETAL LCB in an optical floating zone furnace is presented in this paper.
Chemical compositions of both precursor material and single crystals were
checked. It was found that the concentration of base alloying elements did not
change significantly during the growth process, while the concentrations of
interstitial elements O and N increased. DSC measurement determined that this
concentration shift has a slight impact on ongoing phase transformations, as in
the single-crystalline material peak associated with $\alpha$ phase
precipitation moves by a few degrees to a lower temperature and peak attributed
to diffusion controlled growth of $\omega$ particles shifts to a higher
temperature. X-ray reciprocal space maps were measured and their simulation
showed that the single crystal has a mosaic structure with mean size of mosaic
blocks of approximately 60 nm.


  We study the homogenization of obstacle problems in Orlicz-Sobolev spaces for
a wide class of monotone operators (possibly degenerate or singular) of the
$p(\cdot)$-Laplacian type. Our approach is based on the Lewy-Stampacchia
inequalities, which then give access to a compactness argument. We also prove
the convergence of the coincidence sets under non-degeneracy conditions.


  A uniform $k$-{\sc dag} generalizes the uniform random recursive tree by
picking $k$ parents uniformly at random from the existing nodes. It starts with
$k$ ''roots''. Each of the $k$ roots is assigned a bit. These bits are
propagated by a noisy channel. The parents' bits are flipped with probability
$p$, and a majority vote is taken. When all nodes have received their bits, the
$k$-{\sc dag} is shown without identifying the roots. The goal is to estimate
the majority bit among the roots. We identify the threshold for $p$ as a
function of $k$ below which the majority rule among all nodes yields an error
$c+o(1)$ with $c<1/2$. Above the threshold the majority rule errs with
probability $1/2+o(1)$.


  Electronic many-body correlation effects in one-dimensional (1D) systems such
as carbon nanotubes have been predicted to modify strongly the nature of
photoexcited states. Here we directly probe this effect using broadband elastic
light scattering from individual suspended carbon nanotubes under electrostatic
gating conditions. We observe significant shifts in optical transition
energies, as well as line broadening, as the carrier density is increased. The
results demonstrate the differing role of screening of many-body electronic
interactions on the macroscopic and microscopic length scales, a feature
inherent to quasi-1D systems. Our findings further demonstrate the possibility
of electrical tuning of optical transitions and provide a basis for
understanding of various optical phenomena in carbon nanotubes and other
quasi-1D systems in the presence of charge carrier doping.


  We investigate the propagation of Gaussian beams through optical waveguide
lattices characterized by correlated non-Hermitian disorder. In the framework
of coupled mode theory, we demonstrate how the imaginary part of the refractive
index needs to be adjusted to achieve perfect beam transmission, despite the
presence of disorder. Remarkably, the effects of both diagonal and off-diagonal
disorder in the waveguides and their couplings can be efficiently eliminated by
our non-Hermitian design. Waveguide arrays thus provide an ideal platform for
the experimental realization of non-Hermitian phenomena in the context of
discrete photonics.


  We consider extended scalar sectors of the Standard Model as
ultraviolet-complete motivations for studying the effective Higgs
self-interaction operators of the Standard Model effective field theory. We
investigate all motivated heavy scalar models which generate the dimension-6
effective operator, $|H|^6$, at tree level and proceed to identify the full set
of tree-level dimension-six operators by integrating out the heavy scalars. Of
seven models which generate $|H|^6$ at tree level only two, quadruplets of
hypercharge $Y=3Y_H$ and $Y=Y_H$, generate only this operator. Next we perform
global fits to constrain relevant Wilson coefficients from the LHC single Higgs
measurements as well as the electroweak oblique parameters $S$ and $T$. We find
that the $T$ parameter puts very strong constraints on the Wilson coefficient
of the $|H|^6$ operator in the triplet and quadruplet models, while the singlet
and doublet models could still have Higgs self-couplings which deviate
significantly from the standard model prediction. To determine the extent to
which the $|H|^6$ operator could be constrained, we study the dihiggs
signatures at the future 100 TeV collider and explore future sensitivity of
this operator. Projected onto the Higgs potential parameters of the extended
scalar sectors, with $3$ ab$^{-1}$ luminosity data we will be able to explore
the Higgs potential parameters in all seven models.


  We consider a nonlinear parametric Dirichlet problem driven by the
$p$-Laplace differential operator and a reaction which has the competing
effects of a parametric singular term and of a Carath\'eodory perturbation
which is ($p-1$)-linear near $+\infty$. The problem is uniformly nonresonant
with respect to the principal eigenvalue of $(-\Delta_p,W^{1,p}_0(\Omega))$. We
look for positive solutions and prove a bifurcation-type theorem describing in
an exact way the dependence of the set of positive solutions on the parameter
$\lambda>0$.


  Since its launch in April 1990, the Hubble Space Telescope (HST) has produced
an increasing flow of scientific results. The large number of refereed
publications based on HST data allows a detailed evaluation of the
effectiveness of this observatory and of its scientific programs. This paper
presents the results of selected science metrics related to paper counts,
citation counts, citation history, high-impact papers, and the most productive
programs and most cited papers, through the end of 2003. All these indicators
point towards the high-quality scientific impact of HST.


  Let S(n) be the integer sequence which is the coefficient of x^{n(n+1)/4} in
the expansion of (1+x)(1+x^2), ..., (1+x^n) for positive integers n congruent
to 0 or 3 mod 4. We prove a conjecture of Andrica and Tomescu that S(n) is
asymptotic to \sqrt{6/\pi} 2^n n^{-3/2} as n approaches infinity.


  First some definite integrals of W. H. L. Russell, almost all with
trigonometric function integrands, are derived, and many generalized. Then a
list is given in Russell-style of generalizations of integral identities of
Amdeberhan and Moll. We conclude with a brief and noncomprehensive description
of directions for further investigation, including the significant
generalization to elliptic functions.


  We prove there is only one involution (up to conjugacy) on the n-torus which
acts as $-\mathrm{Id}$ on the first homology group when $n$ is of the form
$4k$, is of the form $4k+1$, or is less than $4$. In all other cases we prove
there are infinitely many such involutions up to conjugacy, but each of them
has exactly $2^n$ fixed points and is conjugate to a smooth involution. The key
technical point is that we completely compute the equivariant structure set for
the corresponding crystallographic group action on $\mathbb{R}^n$ in terms of
the Cappell $\mathrm{UNil}$-groups arising from its infinite dihedral
subgroups. We give a complete analysis of equivariant topological rigidity for
this family of groups.


  The distribution of prime numbers is here considered. We show a formula for
$li^{-1}$ and we study the $\pi(x)$ function and Riemann's hypothesis.


  We investigate the astrometric effects of stellar surface structures as a
practical limitation to ultra-high-precision astrometry, e.g. in the context of
exoplanet searches, and to quantify the expected effects in different regions
of the HR-diagram. Stellar surface structures are likely to produce
fluctuations in the integrated flux and radial velocity of the star, as well as
a variation of the observed photocentre, i.e. astrometric jitter, and closure
phase. We use theoretical considerations supported by Monte Carlo simulations
to derive statistical relations between the corresponding astrometric,
photometric, and radial-velocity effects. For most stellar types the
astrometric jitter due to stellar surface structures is expected to be of order
10 micro-AU or greater. This is more than the astrometric displacement
typically caused by an Earth-size exoplanet in the habitable zone, which is
about 1-4 micro-AU for long-lived main-sequence stars. Only for stars with
extremely low photometric variability (<0.5 mmag) and low magnetic activity,
comparable to that of the Sun, will the astrometric jitter be of order 1
micro-AU, suffcient to allow the astrometric detection of an Earth-sized planet
in the habitable zone. While stellar surface structure may thus seriously
impair the astrometric detection of small exoplanets, it has in general
negligible impact on the detection of large (Jupiter-size) planets and on the
determination of stellar parallax and proper motion. From the starspot model we
also conclude that the commonly used spot filling factor is not the most
relevant parameter for quantifying the spottiness in terms of the resulting
astrometric, photometric and radial-velocity variations.


  Deep learning constitutes a recent, modern technique for image processing and
data analysis, with promising results and large potential. As deep learning has
been successfully applied in various domains, it has recently entered also the
domain of agriculture. In this paper, we perform a survey of 40 research
efforts that employ deep learning techniques, applied to various agricultural
and food production challenges. We examine the particular agricultural problems
under study, the specific models and frameworks employed, the sources, nature
and pre-processing of data used, and the overall performance achieved according
to the metrics used at each work under study. Moreover, we study comparisons of
deep learning with other existing popular techniques, in respect to differences
in classification or regression performance. Our findings indicate that deep
learning provides high accuracy, outperforming existing commonly used image
processing techniques.


  This paper considers the asymptotic limit of small aspect ratio between
vertical and horizontal spatial scales for viscous isothermal compressible
flows. In particular, it is observed that fast vertical acoustic waves arise
and induce an averaging mechanism of the density in the vertical variable,
which at the limit leads to the hydrostatic approximation of compressible
flows, i.e., the compressible primitive equations of atmospheric dynamics. We
justify the hydrostatic approximation for general as well as ``well-prepared''
initial data. The initial data is called well-prepared when it is close to the
hydrostatic balance in a strong topology. Moreover, the convergence rate is
calculated in the well-prepared initial data case in terms of the aspect ratio,
as the latter goes to zero.