  The emerging field of silicon photonics seeks to unify the high bandwidth of
optical communications with CMOS microelectronic circuits. Many components have
been demonstrated for on-chip optical communications, including those that
utilize the nonlinear optical properties of silicon[1, 2], silicon dioxide[3,
4] and silicon nitride[5, 6]. Processes such as second harmonic generation,
which are enabled by the second-order susceptibility, have not been developed
since the bulk $\chi^{(2)}$ vanishes in these centrosymmetric CMOS materials.
Generating the lowest-order nonlinearity would open the window to a new array
of CMOS-compatible optical devices capable of nonlinear functionalities not
achievable with the?$\chi^{(3)}$ response such as electro-optic modulation, sum
frequency up-conversion, and difference frequency generation. Here we
demonstrate second harmonic (SH) generation in CMOS compatible integrated
silicon nitride (Si3N4) waveguides. The $\chi^{(2)}$ response is induced in the
centrosymmetric material by using the nanoscale structure to break the bulk
symmetry. We use a high quality factor Q ring resonator cavity to enhance the
efficiency of the nonlinear optical process and detect SH output with milliwatt
input powers.


  Coulomb dissociation is an especially simple and important reaction
mechanism. Since the perturbation due to the electric field of the nucleus is
exactly known, firm conclusions can be drawn from such measurements.
Electromagnetic matrix elements and astrophysical S-factors for radiative
capture processes can be extracted from experiments. We describe the basic
theory, new results concerning higher order effects in the dissociation of
neutron halo nuclei, and briefly review the experimental results obtained up to
now. Some new applications of Coulomb dissociation for nuclear astrophysics and
nuclear structure physics are discussed.


  Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces
(BCIs) have shown its robustness in facilitating high-efficiency communication.
State-of-the-art training-based SSVEP decoding methods such as extended
Canonical Correlation Analysis (CCA) and Task-Related Component Analysis (TRCA)
are the major players that elevate the efficiency of the SSVEP-based BCIs
through a calibration process. However, due to notable human variability across
individuals and within individuals over time, calibration (training) data
collection is non-negligible and often laborious and time-consuming,
deteriorating the practicality of SSVEP BCIs in a real-world context. This
study aims to develop a cross-subject transferring approach to reduce the need
for collecting training data from a test user with a newly proposed
least-squares transformation (LST) method. Study results show the capability of
the LST in reducing the number of training templates required for a 40-class
SSVEP BCI. The LST method may lead to numerous real-world applications using
near-zero-training/plug-and-play high-speed SSVEP BCIs.


  We derive, by means of Gamma-convergence, the equations of homogenized
bending rod starting from $3D$ nonlinear elasticity equations. The main
assumption is that the energy behaves like h^2 (after dividing by the order h^2
of vanishing volume) where h is the thickness of the body. We do not presuppose
any kind of periodicity and work in the general framework. The result shows
that, on a subsequence, we always obtain the equations of bending-torsion rod
and identifies, in an abstract formulation, the limiting quadratic form
connected with that model. This is a generalization from periodic to
non-periodic homogenization of bending-torsion rod theory already present in
the literature.


  We investigate in this paper a Bickel-Rosenblatt test of goodness-of-fit for
the density of the noise in an autoregressive model. Since the seminal work of
Bickel and Rosenblatt, it is well-known that the integrated squared error of
the Parzen-Rosenblatt density estimator, once correctly renormalized, is
asymptotically Gaussian for independent and identically distributed (i.i.d.)
sequences. We show that the result still holds when the statistic is built from
the residuals of general stable and explosive autoregressive processes. In the
univariate unstable case, we prove that the result holds when the unit root is
located at $-1$ whereas we give further results when the unit root is located
at $1$. In particular, we establish that except for some particular asymmetric
kernels leading to a non-Gaussian limiting distribution and a slower
convergence, the statistic has the same order of magnitude. We also study some
common unstable cases, like the integrated seasonal process. Finally we build a
goodness-of-fit Bickel-Rosenblatt test for the true density of the noise
together with its empirical properties on the basis of a simulation study.


  We present a constructive method to compute the cellularization with respect
to K(Z/p, m) for any integer m > 0 of a large class of H-spaces, namely all
those which have a finite number of non-trivial K(Z/p, m)-homotopy groups (the
pointed mapping space map(K(Z/p, m), X) is a Postnikov piece). We prove in
particular that the K(Z/p, m)-cellularization of an H-space having a finite
number of K(Z/p, m)-homotopy groups is a p-torsion Postnikov piece. Along the
way we characterize the BZ/p^r-cellular classifying spaces of nilpotent groups.


  Biological locomotion in nature is often achieved by the interaction between
a flexible body and its surrounding medium. The interaction of a flexible body
with granular media is less understood compared with viscous fluids partially
due to its complex rheological properties. In this work we explore the effect
of flexibility on granular propulsion by considering a simple mechanical model
where a rigid rod is connected to a torsional spring that is under a
displacement actuation using a granular resistive force theory. Through a
combined numerical and asymptotic investigation, we characterize the propulsive
dynamics of such a flexible flapper in relation to the actuation amplitude and
spring stiffness and compare these dynamics with those observed in a viscous
fluid. In addition, we demonstrate that the maximum possible propulsive force
can be obtained in the steady propulsion limit with a finite spring stiffness
and large actuation amplitude. These results may apply to the development of
synthetic locomotive systems that exploit flexibility to move through complex
terrestrial media.


  The BVS database (Health Virtual Library) is a centralized source of
biomedical information for Latin America and Carib, created in 1998 and
coordinated by BIREME (Biblioteca Regional de Medicina) in agreement with the
Pan American Health Organization (OPAS). Abstracts are available in English,
Spanish, and Portuguese, with a subset in more than one language, thus being a
possible source of parallel corpora. In this article, we present the
development of parallel corpora from BVS in three languages: English,
Portuguese, and Spanish. Sentences were automatically aligned using the
Hunalign algorithm for EN/ES and EN/PT language pairs, and for a subset of
trilingual articles also. We demonstrate the capabilities of our corpus by
training a Neural Machine Translation (OpenNMT) system for each language pair,
which outperformed related works on scientific biomedical articles. Sentence
alignment was also manually evaluated, presenting an average 96% of correctly
aligned sentences across all languages. Our parallel corpus is freely
available, with complementary information regarding article metadata.


  In this paper we study the convexity properties of geodesics and balls in
Outer space equipped with the Lipschitz metric. We introduce a class of
geodesics called balanced folding paths and show that, for every loop $\alpha$,
the length of $\alpha$ along a balanced folding path is not larger than the
maximum of its lengths at the end points. This implies that out-going balls are
weakly convex. We then show that these results are sharp by providing several
counterexamples.


  A recently proposed parametrization for the deep inelastic diffractive cross
section is used to describe the H1 94 data. We find two possible solutions, and
we discuss them is some detail.


  We produce the first concrete evidence that violation of the weak cosmic
censorship conjecture can occur in asymptotically flat spaces of five
dimensions by numerically evolving perturbed black rings. For certain thin
rings, we identify a new, elastic-type instability dominating the evolution,
causing the system to settle to a spherical black hole. However, for
sufficiently thin rings the Gregory-Laflamme mode is dominant, and the
instability unfolds similarly to that of black strings, where the horizon
develops a structure of bulges connected by necks which become ever thinner
over time.


  [Background:] The band structure of the negative-parity states of $^{24}$Mg
has not yet been clarified. The $K^\pi=0^-$, $K^\pi=1^-$, and $K^\pi=3^-$ bands
have been suggested, but the assignments have been inconsistent between
experiments and theories. [Purpose:] Negative-parity states of $^{24}$Mg are
investigated by microscopic structure and reaction calculations via proton and
alpha inelastic scattering to clarify the band assignment for the observed
negative-parity spectra. [Method:] The structure of $^{24}$Mg was calculated
using the antisymmetrized molecular dynamics~(AMD). Proton and alpha inelastic
reactions were calculated using microscopic coupled-channel (MCC) calculations
by folding the Melbourne $g$-matrix $NN$ interaction with the AMD densities of
$^{24}$Mg. [Results:] The member states of the $K^\pi=0^+$, $K^\pi=2^+$,
$K^\pi=0^-$, $K^\pi=1^-$, and $K^\pi=3^-$ bands of $^{24}$Mg were obtained
through the AMD result. In the MCC+AMD results for proton and alpha elastic and
inelastic cross sections, reasonable agreements were obtained with existing
data, except in the case of the $4^+_1$ state. [Conclusions:] The $3^-$ state
of the $K^\pi=3^-$ band and the $1^-$ and $3^-$ states of the $K^\pi=0^-$ bands
were assigned to the $3^-_1$(7.62 MeV), $1^-_1$(7.56 MeV), and $3^-_2$(8.36
MeV) states, respectively. The present AMD calculation is the first microscopic
structure calculation to reproduce the energy ordering of the $K^\pi=0^-$,
$K^\pi=1^-$, and $K^\pi=3^-$ bands of $^{24}$Mg.


  Inspired by recent successes in neural machine translation and image caption
generation, we present an attention based encoder decoder model (AED) to
recognize Vietnamese Handwritten Text. The model composes of two parts: a
DenseNet for extracting invariant features, and a Long Short-Term Memory
network (LSTM) with an attention model incorporated for generating output text
(LSTM decoder), which are connected from the CNN part to the attention model.
The input of the CNN part is a handwritten text image and the target of the
LSTM decoder is the corresponding text of the input image. Our model is trained
end-to-end to predict the text from a given input image since all the parts are
differential components. In the experiment section, we evaluate our proposed
AED model on the VNOnDB-Word and VNOnDB-Line datasets to verify its efficiency.
The experiential results show that our model achieves 12.30% of word error rate
without using any language model. This result is competitive with the
handwriting recognition system provided by Google in the Vietnamese Online
Handwritten Text Recognition competition.


  Topological defects play a key role in a variety of physical systems, ranging
from high-energy to solid state physics. They yield fascinating emergent
phenomena and serve as a bridge between the microspic and macroscopic world. A
skyrmion is a unique type of topological defect, showing great promise for
applications in the fields of magnetic storage and spintronics. Here, we
discover and observe optical skyrmion lattices that can be easily created and
controlled, while illustrating their robustness to imperfections. Optical
skyrmions are experimentally demonstrated by interfering surface plasmon
polaritons and are measured via phase-resolved near-field optical microscopy.
This discovery could give rise to new physical phenomena involving skyrmions
and exclusive to photonic systems; open up new possibilities for inducing
skyrmions in material systems through light-matter interactions; and enable
applications in optical information processing, transfer and storage.


  We present a new social animal inspired emotional swarm intelligence
technique. This technique is used to solve a variant of the popular collective
robots problem called foraging. We show with a simulation study how simple
interaction rules based on sensations like hunger and loneliness can lead to
globally coherent emergent behavior which allows sensor constrained robots to
solve the given problem


  Sterile massive neutrinos are a natural extension of the Standard Model of
elementary particles. The energy density of the extra sterile massive states
affects cosmological measurements in an analogous way to that of active
neutrino species. We perform here an analysis of current cosmological data and
derive bounds on the masses of the active and the sterile neutrino states as
well as on the number of sterile states. The so-called (3+2) models with three
sub-eV active massive neutrinos plus two sub-eV massive sterile species is well
within the 95% CL allowed regions when considering cosmological data only. If
the two extra sterile states have thermal abundances at decoupling, Big Bang
Nucleosynthesis bounds compromise the viability of (3+2) models. Forecasts from
future cosmological data on the active and sterile neutrino parameters are also
presented. Independent measurements of the neutrino mass from tritium beta
decay experiments and of the Hubble constant could shed light on sub-eV massive
sterile neutrino scenarios.


  We present and explore a model of stateless and self-stabilizing distributed
computation, inspired by real-world applications such as routing on today's
Internet. Processors in our model do not have an internal state, but rather
interact by repeatedly mapping incoming messages ("labels") to outgoing
messages and output values. While seemingly too restrictive to be of interest,
stateless computation encompasses both classical game-theoretic notions of
strategic interaction and a broad range of practical applications (e.g.,
Internet protocols, circuits, diffusion of technologies in social networks). We
embark on a holistic exploration of stateless computation. We tackle two
important questions: (1) Under what conditions is self-stabilization, i.e.,
guaranteed "convergence" to a "legitimate" global configuration, achievable for
stateless computation? and (2) What is the computational power of stateless
computation? Our results for self-stabilization include a general necessary
condition for self-stabilization and hardness results for verifying that a
stateless protocol is self-stabilizing. Our main results for the power of
stateless computation show that labels of logarithmic length in the number of
processors yield substantial computational power even on ring topologies. We
present a separation between unidirectional and bidirectional rings (L/poly vs.
P/poly), reflecting the sequential nature of computation on a unidirectional
ring, as opposed to the parallelism afforded by the bidirectional ring. We
leave the reader with many exciting directions for future research.


  Deep learning offers a promising solution to improve spectrum access
techniques by utilizing data-driven approaches to manage and share limited
spectrum resources for emerging applications. For several of these
applications, the sensitive wireless data (such as spectrograms) are stored in
a shared database or multistakeholder cloud environment and are therefore prone
to privacy leaks. This paper aims to address such privacy concerns by examining
the representative case study of shared database scenarios in 5G Open Radio
Access Network (O-RAN) networks where we have a shared database within the
near-real-time (near-RT) RAN intelligent controller. We focus on securing the
data that can be used by machine learning (ML) models for spectrum sharing and
interference mitigation applications without compromising the model and network
performances. The underlying idea is to leverage a (i) Shuffling-based
learnable encryption technique to encrypt the data, following which, (ii)
employ a custom Vision transformer (ViT) as the trained ML model that is
capable of performing accurate inferences on such encrypted data. The paper
offers a thorough analysis and comparisons with analogous convolutional neural
networks (CNN) as well as deeper architectures (such as ResNet-50) as
baselines. Our experiments showcase that the proposed approach significantly
outperforms the baseline CNN with an improvement of 24.5% and 23.9% for the
percent accuracy and F1-Score respectively when operated on encrypted data.
Though deeper ResNet-50 architecture is obtained as a slightly more accurate
model, with an increase of 4.4%, the proposed approach boasts a reduction of
parameters by 99.32%, and thus, offers a much-improved prediction time by
nearly 60%.


  Synthetic ferrimagnets are composite magnetic structures formed from two or
more anti- ferromagnetically coupled magnetic sublattices with different
magnetic moments. Here we report on atomistic spin simulations of the
laser-induced magnetization dynamics on such synthetic ferrimag- nets, and
demonstrate that the application of ultrashort laser pulses leads to
sub-picoscond magnetization dynamics and all-optical switching in a similar
manner as in ferrimagnetic alloys. Moreover, we present the essential material
properties for successful laser-induced switching, demonstrating the
feasibility of using a synthetic ferrimagnet as a high density magnetic storage
element without the need of a write field.


  We present a review of the spin and statistics of topological geons,
particles in 3+1 quantum gravity. They can have half-odd-integral spin and
fermionic statistics and since the underlying gravitational field is tensorial
and bosonic, this is an example of ``emergent'' non-trivial spin and statistics
as displayed by familiar non-gravitating objects such as skyrmions. We give the
topological background and show that in a ``canonical'' quantization of gravity
there is no spin-statistics correlation for topological geons. Allowing the
topology of space to change, for example in a sum-over-histories approach,
raises the possibility that a spin-statistics correlation can be recovered for
geons. We review a conjectured set of rules powerful enough to give such a
spin-statistics correlation for all topological geons. These would appear to
rule out the possibility of parastatistics and may rule out spinorial and
fermionic geons altogether.


  Autoencoders are commonly trained using element-wise loss. However,
element-wise loss disregards high-level structures in the image which can lead
to embeddings that disregard them as well. A recent improvement to autoencoders
that helps alleviate this problem is the use of perceptual loss. This work
investigates perceptual loss from the perspective of encoder embeddings
themselves. Autoencoders are trained to embed images from three different
computer vision datasets using perceptual loss based on a pretrained model as
well as pixel-wise loss. A host of different predictors are trained to perform
object positioning and classification on the datasets given the embedded images
as input. The two kinds of losses are evaluated by comparing how the predictors
performed with embeddings from the differently trained autoencoders. The
results show that, in the image domain, the embeddings generated by
autoencoders trained with perceptual loss enable more accurate predictions than
those trained with element-wise loss. Furthermore, the results show that, on
the task of object positioning of a small-scale feature, perceptual loss can
improve the results by a factor 10. The experimental setup is available online:
https://github.com/guspih/Perceptual-Autoencoders


  Upgrades to advanced scientific user facilities such as next-generation x-ray
light sources, nanoscience centers, and neutron facilities are revolutionizing
our understanding of materials across the spectrum of the physical sciences,
from life sciences to microelectronics. However, these facility and instrument
upgrades come with a significant increase in complexity. Driven by more
exacting scientific needs, instruments and experiments become more intricate
each year. This increased operational complexity makes it ever more challenging
for domain scientists to design experiments that effectively leverage the
capabilities of and operate on these advanced instruments. Large language
models (LLMs) can perform complex information retrieval, assist in
knowledge-intensive tasks across applications, and provide guidance on tool
usage. Using x-ray light sources, leadership computing, and nanoscience centers
as representative examples, we describe preliminary experiments with a
Context-Aware Language Model for Science (CALMS) to assist scientists with
instrument operations and complex experimentation. With the ability to retrieve
relevant information from facility documentation, CALMS can answer simple
questions on scientific capabilities and other operational procedures. With the
ability to interface with software tools and experimental hardware, CALMS can
conversationally operate scientific instruments. By making information more
accessible and acting on user needs, LLMs could expand and diversify scientific
facilities' users and accelerate scientific output.


  The space of Frobenius manifolds has a natural involutive symmetry on it:
there exists a map $I$ which send a Frobenius manifold to another Frobenius
manifold. Also, from a Frobenius manifold one may construct a so-called almost
dual Frobenius manifold which satisfies almost all of the axioms of a Frobenius
manifold. The action of $I$ on the almost dual manifolds is studied, and the
action of $I$ on objects such as periods, twisted periods and flows is studied.
A distinguished class of Frobenius manifolds sit at the fixed point of this
involutive symmetry, and this is made manifest in certain modular properties of
the various structures. In particular, up to a simple reciprocal
transformation, for this class of modular Frobenius manifolds, the flows are
invariant under the action of $I\,.$


  Let $H$ be a real algebraic group acting equivariantly with finitely many
orbits on a real algebraic manifold $X$ and a real algebraic bundle
$\mathcal{E}$ on $X$. Let $\mathfrak{h}$ be the Lie algebra of $H$. Let
$\mathcal{S}(X,\mathcal{E})$ be the space of Schwartz sections of
$\mathcal{E}$. We prove that $\mathfrak{h}\mathcal{S}(X,\mathcal{E})$ is a
closed subspace of $\mathcal{S}(X,\mathcal{E})$ of finite codimension.
  We give an application of this result in the case when $H$ is a real
spherical subgroup of a real reductive group $G$. We deduce an equivalence of
two old conjectures due to Casselman: the automatic continuity and the
comparison conjecture for zero homology. Namely, let $\pi$ be a
Casselman-Wallach representation of $G$ and $V$ be the corresponding
Harish-Chandra module. Then the natural morphism of coinvariants
$V_{\mathfrak{h}}\to \pi_{\mathfrak{h}}$ is an isomorphism if and only if any
linear $\mathfrak{h}$-invariant functional on $V$ is continuous in the topology
induced from $\pi$. The latter statement is known to hold in two important
special cases: if $H$ includes a symmetric subgroup, and if $H$ includes the
nilradical of a minimal parabolic subgroup of $G$.


  In a monotonic sequence game, two players alternately choose elements of a
sequence from some fixed ordered set. The game ends when the resulting sequence
contains either an ascending subsequence of length a or a descending one of
length d. We investigate the behaviour of this game when played on finite
linear orders or Q and provide some general observations for play on arbitrary
ordered sets.


  We consider the motion of a particle, taking into account its interaction
with environmental degrees of freedom. The dephasing time is determined by the
nature of the environment, and depends on the particle velocity. Our interest
is in the case where the environment consists of few chaotic degrees of
freedom. We obtain results for the dephasing time, and compare them with those
of the effective-bath approach. The latter approach is based on the conjecture
that the environment can be modelled as a collection of infinitely many
harmonic oscillators. The work is related to studies of driven systems, quantum
irreversibility, and fidelity. The specific model that we consider requires the
solution of the problem of a particle-in-a-box with moving wall, whose 1D
version is related to the Fermi acceleration problem.


  In previous work a relation between a large class of Kac-Moody algebras and
meromorphic connections on global curves was established---notably the Weyl
group gives isomorphisms between different moduli spaces of connections, and
the root system is also seen to play a role. This involved a modular
interpretation of many Nakajima quiver varieties, as moduli spaces of
connections, whenever the underlying graph was a complete k-partite graph (or
more generally a supernova graph). However in the isomonodromy story, or wild
nonabelian Hodge theory, slightly larger moduli spaces of connections are
considered. This raises the question of whether the full moduli spaces admit
Weyl group isomorphisms, rather than just the open parts isomorphic to quiver
varieties. This question will be solved here, by developing a "multiplicative
version" of the previous approach. This amounts to constructing many algebraic
symplectic isomorphisms between wild character varieties. This approach also
enables us to state a conjecture for certain irregular Deligne--Simpson
problems and introduce some noncommutative algebras, generalising the
"generalised double affine Hecke algebras".


  The usage of mathematical formulas as concise representations of a document's
key ideas is common practice. Correctly interpreting these formulas, by
identifying mathematical symbols and extracting their descriptions, is an
important task in document understanding. This paper makes the following
contributions to the mathematical identifier description reading (MIDR) task:
  (i) introduces the Math Formula Question Answering Dataset (MFQuAD) with
$7508$ annotated identifier occurrences;
  (ii) describes novel variations of the noun phrase ranking approach for the
MIDR task;
  (iii) reports experimental results for the SOTA noun phrase ranking approach
and our novel variations of the approach, providing problem insights and a
performance baseline;
  (iv) provides a position on the features that make an effective dataset for
the MIDR task.


  For type I seesaw and in the basis where the charged lepton and heavy
right-handed neutrino mass matrices are real and diagonal, four has been shown
to be the maximum number of zeros allowed in the neutrino Yukawa coupling
matrix $Y_\nu$. These four zero textures have been classified into two distinct
categories. We investigate certain phenomenological consequences of these
textures within a supersymmetric framework. This is done by using conditions
implied on elements of the neutrino Majorana mass matrix for textures of each
category in $Y_\nu$. These conditions turn out to be stable under radiative
corrections. Including the effective mass, which appears in neutrinoless double
beta decay, along with the usual neutrino masses, mixing angles and phases, it
is shown analytically and through scatter plots how restricted regions in the
seesaw parameter space are selected by these conditions. We also make
consequential statements on the yet unobserved radiative lepton flavor
violating decays such as $\mu \to e \gamma$. All these decay amplitudes are
proportional to the moduli of entries of the neutrino Majorana mass matrix. We
also show under which conditions the low energy CP violation, showing up in
neutrino oscillations, is directly linked to the CP violation required for
producing successful flavor dependent and flavor independent lepton asymmetries
during leptogenesis.


  Environmental monitoring of lakeside green areas is crucial for environmental
protection. Compared to manual inspections, computer vision technologies offer
a more efficient solution when deployed on-site. Multispectral imaging provides
diverse information about objects under different spectrums, aiding in the
differentiation between waste and lakeside lawn environments. This study
introduces WasteMS, the first multispectral dataset established for the
semantic segmentation of lakeside waste. WasteMS includes a diverse range of
waste types in lawn environments, captured under various lighting conditions.
We implemented a rigorous annotation process to label waste in images.
Representative semantic segmentation frameworks were used to evaluate
segmentation accuracy using WasteMS. Challenges encountered when using WasteMS
for segmenting waste on lakeside lawns were discussed. The WasteMS dataset is
available at https://github.com/zhuqinfeng1999/WasteMS.


  We report the discovery of a type IIn supernova in the low-metallicity dwarf
galaxy J1320+2155, with an oxygen abundance 12+logO/H = 8.0+/-0.2. This finding
is based on SDSS (February 2008) and 3.5m Apache Point Observatory (February
2009) spectra taken one year apart, and on the observations that: the Hbeta and
Halpha emission lines show broad components corresponding to gas expansion
velocities of ~1600 km/s; the Balmer decrement is exceeedingly high: the
Halpha/Hbeta flux ratio, being more than 30, implies a very dense environment
(>10^7 cm^-3); and the Halpha broad luminosity decreases slowly, by only a
factor of ~1.8 over the course of a year, typical of the slow luminosity
evolution of a type IIn supernova. Several weak coronal lines of [Fe VII] and
[Fe X] are also seen in the SDSS spectrum, implying ionization of the pre-shock
circumstellar medium by shock-induced X-ray emission. The galaxy J1320+2155 is
the first dwarf system ever to be discovered with a type IIn supernova
exhibiting coronal lines in its spectrum.


  Surface Plasmon Resonance (SPR) is an important bio-sensing technique for
real-time label-free detection. However, it is pivotal to optimize various
parameters of the sensor configuration for efficient and highly sensitive
sensing. To that effect, we focus on optimizing two different SPR structures --
the basic Kretschmann configuration and narrow groove grating. Our analysis
aims to detect two different types of lipids known as phospholipid and eggyolk,
which are used as analyte (sensing layer) and two different types of proteins
namely tryptophan and bovine serum albumin (BSA) are used as ligand (binding
site). For both the configurations, we investigate all possible lipid-protein
combinations to understand the effect of various parameters on sensitivity,
minimum reflectivity and full width half maximum (FWHM). Lipids are the
structural building block of cell membranes and mutation of these layers by
virus and bacteria is one the prime reasons of many diseases in our body.
Hence, improving the performance of a SPR sensor to detect very small change in
lipid holds immense significance. We use finite-difference time-domain (FDTD)
technique to perform quantitative analysis to get an optimized structure. We
find that sensitivity increases when lipid concentration is increased and it is
the highest (21.95 degree/RIU) for phospholipid and tryptophan combination when
metal and lipid layer thickness are 45 nm and 30 nm respectively. However,
metal layer thickness does not cause any significant variation in sensitivity,
but as it increases to 50 nm, minimum reflectivity and full width half maximum
(FWHM) decreases to the lowest. In case of narrow groove grating structure,
broad range of wavelengths can generate SPR and the sensitivity is highest
(900nm/RIU) for a configuration of 10 nm groove width and 70 nm groove height
at a resonance wavelength of 1411 nm.


  The $\sqrt{13}\times\sqrt{13}$ charge density wave state of the T polytype of
MX$_2$ (M=Nb,Ta, X=S, Se) is known to host a half-filled flat band, which
electronic correlations drive into a Mott insulating state. When T polytypes
are coupled to strongly metallic H polytypes, such as in T/H bilayer
heterostructures or the bulk 4H$_b$ polytype, charge transfer can destabilize
the Mott state, but quantifying its magnitude has been a source of controversy.
In this work, we perform a systematic ab-initio study of charge transfer for
all experimentally relevant T/H bilayers and bulk 4H$_b$ structures. In all
cases we find charge transfer from T to H layers which depends strongly on the
interlayer distance but weakly on the Hubbard interaction. Additionally, Se
compounds display smaller charge transfer than S compounds, and 4H$_b$ bulk
polytypes display more charge transfer than isolated bilayers. We rationalize
these findings in terms of band structure properties, and argue they might
explain differences between compounds observed experimentally. Our work reveals
the tendency to Mott insulation and the origin of superconductivity may vary
significantly across the family of T/H heterostructures.


  We present existence, uniqueness, and sharp regularity results of solution
  to the stochastic partial differential equation (SPDE)
  \begin{align}
  \label{abs eqn}
  du=(a^{ij}(\omega,t)u_{x^ix^j}+f)dt +
(\sigma^{ik}(\omega,t)u_{x^i}+g^k)dw^k_t, \quad u(0,x)=u_0,
  \end{align}
  where $\{w^k_t:k=1,2,\cdots\}$ is a sequence of independent Brownian motions.
The coefficients are merely measurable in $(\omega,t)$ and can be unbounded and
fully degenerate, that is, coefficients $a^{ij}$, $\sigma^{ik}$ merely satisfy
  \begin{align}
  \label{abs only} \left(\alpha^{ij}(\omega,t)\right)_{d\times d}:=
\left(a^{ij}(\omega,t)-\frac{1}{2}\sum_{k=1}^{\infty}
\sigma^{ik}(\omega,t)\sigma^{jk}(\omega,t)\right) \geq 0.
  \end{align} In this article, we prove that there exists a unique solution $u$
to \eqref{abs eqn}, and \begin{align}
  \notag \|u_{xx}\|_{\mathbb{H}^\gamma_p(\tau,\delta)} &\leq N(d,p) \bigg(
\|u_0\|_{\mathbb{B}_p^{\gamma+2 \left(1-1/ p \right)}} + \|
f\|_{\mathbb{H}^\gamma_p( \tau,\delta^{1-p} )}
  \label{abs est} &\qquad \qquad+\|g_x\|^p_{\mathbb{H}^\gamma_p( \tau,
|\sigma|^p \delta^{1-p},l_2)}+ \| g_x\|_{\mathbb{H}^\gamma_p(
\tau,\delta^{1-p/2},l_2)} \bigg), \end{align} where $p\geq 2$, $\gamma\in
\mathbf{R}$, $\tau$ is an arbitrary stopping time, $\delta(\omega, t)$ is the
smallest eigenvalue of $\alpha^{ij}(\omega, t)$, $\mathbb{H}_p^\gamma(\tau,
\delta)$ is a weighted stochastic Sobolev space, and $\mathbb{B}_p^{\gamma+2
\left(1-1/ p \right)}$ is a stochastic Besov space.


  Motivated by recent developments in the realm of matter waves, we explore the
potential of creating solitary waves on the surface of a torus. This is an
intriguing perspective due to the role of curvature in the shape and dynamics
of the coherent structures. We find different families of bright solitary waves
for attractive nonlinearities including ones localized in both angular
directions, as well as waves localized in one direction and homogeneous in the
other. The waves localized in both angular directions have also been
partitioned into two types: those whose magnitude decays to zero and those who
do not. The stability properties of the waves are examined and one family is
found to be spectrally stable while most are spectrally unstable, a feature
that we comment on. Finally, the nature of the ensuing nonlinear dynamics is
touched upon.


  Large language models (LLMs) are typically prompted to follow a single
instruction per inference call. In this work, we analyze whether LLMs also hold
the capability to handle multiple instructions simultaneously, denoted as
Multi-Task Inference. For this purpose, we introduce the MTI Bench(Multi-Task
Inference Benchmark), a comprehensive evaluation benchmark encompassing 5,000
instances across 25 tasks. Each task in the MTI Bench involves 2 to 3
sub-tasks. As expected, we first demonstrate that Multi-Task Inference reduces
the total inference time by 1.46 times in average since it does not require
multiple inference calls. Interestingly, contrary to the expectation that LLMs
would perform better when tasks are divided, we find that state-of-the-art
LLMs, such as Llama-2-Chat-70B and GPT-4, show up to 7.3% and 12.4% improved
performance with Multi-Task Inference compared to Single-Task Inference on the
MTI Bench. We release the MTI Bench dataset and our code at this link
https://github.com/guijinSON/MTI-Bench.


  We investigate the life time distribution in one and two dimensional
coarsening processes modelled by Ising - Glauber dynamics at zero temperature.
We find that the life time distribution obeys a scaling ansatz, asymptotically.
An independent life time model where the life times are sampled from a
distribution with power law tail is presented, which predicts analytically the
qualitative features of the scaling function.


  The nilpotent Dirac formalism has been shown, in previous publications, to
generate new physical explanations for aspects of particle physics, with the
additional possibility of calculating some of the parameters involved in the
Standard Model. The applications so far obtained are summarised, with an
outline of some more recent developments.


  The AGILE satellite detected transient high-energy gamma-ray emission from
the X-ray binary V404 Cygni, during the June 2015 outburst observed in radio,
optical, X-ray and soft gamma-ray frequencies. The activity was observed by
AGILE in the 50-400 MeV energy band, between 2015-06-24 UT 06:00:00 and
2015-06-26 UT 06:00:00 (MJD 57197.25 - 57199.25), with a detection significance
of ${\sim}4.3{\sigma}$. The gamma-ray detection, consistent with a
contemporaneous observation by Fermi-LAT, is correlated with a bright flare
observed at radio and hard X-ray frequencies, and with a strong enhancement of
the 511 keV line emission, possibly indicating plasmoid ejections in a
lepton-dominated transient jet. The AGILE observations of this binary system
are compatible with a microquasar scenario in which transient jets are
responsible for the high-energy gamma-ray emission.


  Probabilistic context-free grammars have a long-term record of use as
generative models in machine learning and symbolic regression. When used for
symbolic regression, they generate algebraic expressions. We define the latter
as equivalence classes of strings derived by grammar and address the problem of
calculating the probability of deriving a given expression with a given
grammar. We show that the problem is undecidable in general. We then present
specific grammars for generating linear, polynomial, and rational expressions,
where algorithms for calculating the probability of a given expression exist.
For those grammars, we design algorithms for calculating the exact probability
and efficient approximation with arbitrary precision.


  We consider the problem of when a smooth Ricci flow, for positive time, that
attains smooth initial data in a weak sense must be smooth down to the initial
time. We obtain curvature estimates for an example where this fails. We prove a
positive result in the case that the flow satisfies a lower IC1 curvature
bound, equivalent to a lower Ricci bound in three dimensions. As an
application, we prove that Gromov-Hausdorff limits of WPIC1 manifolds are
WPIC1.


  Let $\mathcal{N}$ be a non-Archimedean ordered field extension of the real
numbers that is real closed and Cauchy complete in the topology induced by the
order, and whose Hahn group is Archimedean. In this paper, we first review the
properties of weakly locally uniformly differentiable (WLUD) functions, $k$
times weakly locally uniformly differentiable (WLUD$^k$) functions, and
WLUD$^\infty$ functions at a point or on an open subset of $\mathcal{N}$. Then
we show under what conditions a WLUD$^\infty$ function at a point
$x_0\in\mathcal{N}$ is analytic in an interval around $x_0$, that is, it has a
convergent Taylor series at any point in that interval.
  We generalize the concepts of WLUD$^k$ and WLUD$^\infty$ to functions from
$\mathcal{N}^n$ to $\mathcal{N}$, for any $n\in\mathbb{N}$. Then we formulate
conditions under which a WLUD$^\infty$ function at a point $\boldsymbol{x_0}
\in \mathcal{N}^n$ is analytic at that point.


  We show that equivariant elliptic genera of toric Calabi-Yau 3-folds are
generalized weak Jacobi forms. We also introduce a notion of averaged
equivariant elliptic genera of toric Calabi-Yau 3-folds, and show that they are
ordinary weak Jacobi forms given by an explicit formula predicted by Eguchi and
Sugawara.


  Let $\mathcal T_{s,p,n}$ be the canonical blow-up of the Grassmann manifold
$G(p,n)$ constructed by blowing up the Pl\"ucker coordinate subspaces
associated with the parameter $s$. We prove that the higher cohomology groups
of the tangent bundle of $\mathcal T_{s,p,n}$ vanish. As an application,
$\mathcal T_{s,p,n}$ is locally rigid in the sense of Kodaira-Spencer.


  The thin-liquid film drainage between two curved surfaces is a fundamental
process for many hydrodynamic measurements, for which Vinogradova's formula has
played a central role when flow slip occurs at fluid-solid interfaces. By
performing a rigorous order-of-magnitude analysis, we reveal the importance of
the curvature contribution to boundary flow, neglected sofar. Vinogradova's
result is found to considerably underestimate the slip-induced reduction of the
hydrodynamic drainage force. Our theory can play a crucial role in
distinguishing finite-slip from no-slip and quantifying the degree of flow slip
at fluid-solid surfaces, which is a fundamental but controversial issue in
fluid dynamics. Moreover, qualitatively different from previous theories, our
theory predicts a finite hydrodynamic repulsive force for two hydrophobic
particles in touch, thus allowing particle collision to occur in a finite time
without any additional attractive surface forces. This finding has deep and
immediate implications on particle coagulation, adsorption and sedimentation
processes relevant for numerous industrial technologies as well as natural
phenomena on the earth.


  $G_\infty$-structure is shown to exist on the deformation complex of a
morphism of associative algebras. The main step of the construction is
extension of a $B_\infty$-algebra by an associative algebra. Actions of
$B_\infty$-algebras on associative and $B_\infty$-algebras are analyzed,
extensions of $B_\infty$-algebras by associative and $B_\infty$-algebras, that
they act upon, are constructed. The resulting $G_\infty$-algebra on the
deformation complex of a morphism is shown to be quasi-isomorphic to the
$G_\infty$-algebra on deformation complex of the corresponding diagram algebra.


  For two matrices in $\mathbb R^{n_1\times n_2}$, the von Neumann inequality
says that their scalar product is less than or equal to the scalar product of
their singular spectrum. In this short note, we extend this result to real
tensors and provide a complete study of the equality case.


  Inspired by the photosynthetic energy transfer process, we theoretically
propose a method to realize non-reciprocal optical transmission in an array of
coupled resonators. The optical non-reciprocity of the coupled resonators
arises from the frequency gradient between adjacent cavities and the
interaction with the environment, which is similar to photosynthetic energy
transfer. An increase in the frequency gradient or the number of the cavities
can lead to better non-reciprocity. However, although a higher environment
temperature will increase the total photon number in the coupled cavities,
non-reciprocity will be weakened. All these findings can be well described by
the detailed balance. Our discovery reveals the similarity between the
noise-induced optical non-reciprocity and exciton energy transfer in natural
photosynthesis.


  A stellar mass black hole can grow its mass noticeably through Bondi
accretion, if it is embedded in an extremely dense and massive molecular cloud
with slow motion with respect to the ambient medium for an extended period of
time. This provides a novel, yet challenging channel for the formation of
massive stellar-mass black holes. We discuss how this channel may account for
the massive binary black hole merger system GW190521 as observed by LIGO/Virgo
gravitational wave detectors as well as the claimed massive black hole
candidate LB-1.


  This article reviews recent developments in the non-Hermitian skin effect
(NHSE), particularly on its rich interplay with topology. The review starts off
with a pedagogical introduction on the modified bulk-boundary correspondence,
the synergy and hybridization of NHSE and band topology in higher dimensions,
as well as, the associated topology on the complex energy plane such as
spectral winding topology and spectral graph topology. Following which,
emerging topics are introduced such as non-Hermitian criticality, dynamical
NHSE phenomena, and the manifestation of NHSE beyond the traditional linear
non-interacting crystal lattices, particularly its interplay with quantum
many-body interactions. Finally, we survey the recent demonstrations and
experimental proposals of NHSE.


  An inverse iterative algorithm for microwave imaging based on moment method
solution is presented here. The iterative scheme has been developed on
constrained optimization technique and is certain to converge. Different mesh
size for the model has been used here to overcome the Inverse Crime. The
synthetic data at the receivers is contaminated with different percentage of
noise. The ill-posedness of the problem is solved by Levenberg-Marquardt
method. The algorithm is applied to synthetic data and the reconstructed image
is then further enhanced through the Image enhancement technique


  Quantum thermodynamics studies quantum effects in thermal machines. But when
is a heat engine, which cyclically interacts with external reservoirs that
unavoidably destroy its quantum coherence, really quantum? We here use the
Leggett-Garg inequality to assess the nonclassical properties of a single
two-level Otto engine. We provide the complete phase diagram characterizing the
quantumness of the engine as a function of its parameters and identify three
distinct phases. We further derive an explicit expression for the transition
temperature.


  Exploiting high-energy electron beams colliding into high-intensity laser
pulses brings an opportunity to reach high values of the dimensionless
rest-frame acceleration $\chi$ and thereby invoke processes described by
strong-field quantum electrodynamics (SFQED). Measuring deviations from the
results of perturbative SFQED at high $\chi$ can be valuable for testing the
existing predictions, as well as for guiding further theoretical developments.
Nevertheless such experimental measurements are challenging due to the
probabilistic nature of the interaction processes, a strong background produced
by low-$\chi$ interactions and limited capabilities to control and measure the
alignment and synchronization in such collision experiments. Here we elaborate
a methodology of using approximate Bayesian computations (ABC) for retrieving
statistically justified inferences based on the results of many repeated
experiments even in case of partially unknown collision parameters that vary
from experiment to experiment. As a proof of principles, we consider the
problem of inferring the effective mass change due to coupling with
strong-field environment.


  The benefits of investments in sustainability are often public goods that
accrue at broad scales and to many people. Urban forests exemplify this; trees
supply ecosystem service benefits from local (in the case of shade) to global
(in the case of carbon sequestration) scales. The complex mosaic of public and
private ownership that typically defines an urban forest makes the public goods
problem of investing in forest sustainability especially acute. This results in
incentives for private tree owners to invest in tree care that typically fall
short of those of a public forest manager aiming for the social optimum. The
management of a forest pest, such as emerald ash borer, provides a salient
focus area because pests threaten the provision of public goods from urban
forests and pest management generates feedback that alters pest spread and
shapes future risks. We study how managers can design policies to address
forest pest outbreaks and achieve uniform management across a mosaic of
ownership types. We develop a game theoretic model to derive optimal subsidies
for the treatment of forests pests and evaluate the efficacy of these policies
in mitigating the spread of forest pests with a dynamic epidemiological model.
Our results suggest that a combination of optimal treatment subsidies for
privately owned trees and targeted treatment of public trees can be far more
effective at reducing pest-induced tree mortality than either approach in
isolation. While we focus on the management of urban forests, designing
programs that align private and public incentives for investment in public
goods could advance sustainability in a wide range of systems.


  This paper concerns the analysis of the Shapley value in matching games.
Matching games constitute a fundamental class of cooperative games which help
understand and model auctions and assignments. In a matching game, the value of
a coalition of vertices is the weight of the maximum size matching in the
subgraph induced by the coalition. The Shapley value is one of the most
important solution concepts in cooperative game theory.
  After establishing some general insights, we show that the Shapley value of
matching games can be computed in polynomial time for some special cases:
graphs with maximum degree two, and graphs that have a small modular
decomposition into cliques or cocliques (complete k-partite graphs are a
notable special case of this). The latter result extends to various other
well-known classes of graph-based cooperative games.
  We continue by showing that computing the Shapley value of unweighted
matching games is #P-complete in general. Finally, a fully polynomial-time
randomized approximation scheme (FPRAS) is presented. This FPRAS can be
considered the best positive result conceivable, in view of the #P-completeness
result.


  In the standard hot cosmological model, the black-body temperature of the
Cosmic Microwave Background (CMB), $T_{\rm CMB}$, increases linearly with
redshift. Across the line of sight CMB photons interact with the hot
($\sim10^{7-8}$ K) and diffuse gas of electrons from galaxy clusters. This
interaction leads to the well known thermal Sunyaev-Zel'dovich effect (tSZ),
which produces a distortion of the black-body emission law, depending on
$T_{\rm CMB}$. Using tSZ data from the ${\it Planck}$ satellite it is possible
to constrain $T_{\rm CMB}$ below z=1. Focusing on the redshift dependance of
$T_{\rm CMB}$ we obtain $T_{\rm CMB}(z)=(2.726\pm0.001)\times (1+z)^{1-\beta}$
K with $\beta=0.009\pm0.017$, improving previous constraints. Combined with
measurements of molecular species absorptions, we derive $\beta=0.006\pm0.013$.
These constraints are consistent with the standard (i.e. adiabatic, $\beta=0$)
Big-Bang model.


  Two predictions about finite-N non-supersymmetric "orientifold field
theories" are made by using the dual type 0' string theory on C^3 / Z_2 x Z_2
orbifold singularity. First, the mass ratio between the lowest pseudoscalar and
scalar color-singlets is estimated to be equal to the ratio between the axial
anomaly and the scale anomaly at strong coupling, M_- / M_+ ~ C_- / C_+.
Second, the ratio between the domain wall tension and the value of the quark
condensate is computed.


  We present a simple proof of Christer Borell's general inequality in the
Brunn-Minkowski theory. We then discuss applications of Borell's inequality to
the log-Brunn-Minkowski inequality of B\"or\"oczky, Lutwak, Yang and Zhang.


  Although pathway-specific kinetic theories are fundamentally important to
describe and understand reversible polymerisation kinetics, they come in
principle at a cost of having a large number of system-specific parameters.
Here, we construct a dynamical Landau theory to describe the kinetics of
activated linear supramolecular self-assembly, which drastically reduces the
number of parameters and still describes most of the interesting and generic
behavior of the system in hand. This phenomenological approach hinges on the
fact that if nucleated, the polymerisation transition resembles a phase
transition. We are able to describe hysteresis, overshooting, undershooting and
the existence of a lag time before polymerisation takes off, and pinpoint the
conditions required for observing these types of phenomenon in the assembly and
disassembly kinetics. We argue that the phenomenological kinetic parameter in
our theory is a pathway controller, i.e., it controls the relative weights of
the molecular pathways through which self-assembly takes place.


  We examine the recent COMPASS data on the spin structure function g_1
singlet. We show that it is rather difficult to use the data in the present
form in order to draw conclusions on the initial parton densities. However, our
tentative estimate is that the data better agree with positive rather than
negative initial gluon densities.


  As an integral part of source code files, code comments help improve program
readability and comprehension. However, developers sometimes do not comment on
their program code adequately due to the incurred extra efforts, lack of
relevant knowledge, unawareness of the importance of code commenting or some
other factors. As a result, code comments can be inadequate, absent or even
mismatched with source code, which affects the understanding, reusing and the
maintenance of software. To solve these problems of code comments, researchers
have been concerned with generating code comments automatically. In this work,
we aim at conducting a survey of automatic code commenting researches. First,
we generally analyze the challenges and research framework of automatic
generation of program comments. Second, we present the classification of
representative algorithms, the design principles, strengths and weaknesses of
each category of algorithms. Meanwhile, we also provide an overview of the
quality assessment of the generated comments. Finally, we summarize some future
directions for advancing the techniques of automatic generation of code
comments and the quality assessment of comments.


  We examine the performance of a recently developed nonlocal density
functional in predicting a model noncovalent interaction, the weak bond between
an aromatic $\pi$ system and an aliphatic C-H group. The new functional is a
significant improvement over traditional density functionals, providing results
which compare favorably to high-level quantum-chemistry techniques but at
considerably lower computational cost. Interaction energies in several model
C-H/$\pi$ systems are in generally good agreement with coupled-cluster
calculations, though equilibrium distances are consistently overpredicted when
using the revPBE functional for exchange. The new functional correctly predicts
changes in energy upon addition of halogen substituents.


  The latent world model provides a promising way to learn policies in a
compact latent space for tasks with high-dimensional observations, however, its
generalization across diverse environments with unseen dynamics remains
challenging. Although the recurrent structure utilized in current advances
helps to capture local dynamics, modeling only state transitions without an
explicit understanding of environmental context limits the generalization
ability of the dynamics model. To address this issue, we propose a Prototypical
Context-Aware Dynamics (ProtoCAD) model, which captures the local dynamics by
time consistent latent context and enables dynamics generalization in
high-dimensional control tasks. ProtoCAD extracts useful contextual information
with the help of the prototypes clustered over batch and benefits model-based
RL in two folds: 1) It utilizes a temporally consistent prototypical
regularizer that encourages the prototype assignments produced for different
time parts of the same latent trajectory to be temporally consistent instead of
comparing the features; 2) A context representation is designed which combines
both the projection embedding of latent states and aggregated prototypes and
can significantly improve the dynamics generalization ability. Extensive
experiments show that ProtoCAD surpasses existing methods in terms of dynamics
generalization. Compared with the recurrent-based model RSSM, ProtoCAD delivers
13.2% and 26.7% better mean and median performance across all dynamics
generalization tasks.


  An efficient monotone data augmentation (MDA) algorithm is proposed for
missing data imputation for incomplete multivariate nonnormal data that may
contain variables of different types, and are modeled by a sequence of
regression models including the linear, binary logistic, multinomial logistic,
proportional odds, Poisson, negative binomial, skew-normal, skew-t regressions
or a mixture of these models. The MDA algorithm is applied to the sensitivity
analyses of longitudinal trials with nonignorable dropout using the controlled
pattern imputations that assume the treatment effect reduces or disappears
after subjects in the experimental arm discontinue the treatment. We also
describe a heuristic approach to implement the controlled imputation, in which
the fully conditional specification method is used to impute the intermediate
missing data to create a monotone missing pattern, and the missing data after
dropout are then imputed according to the assumed nonignorable mechanisms. The
proposed methods are illustrated by simulation and real data analyses.


  We clarify the origin of anomalous tunneling [Yu. Kagan et al. Phys. Rev.
Lett. 90 (2003) 130402] i.e. the perfect transmission at low energy limit of
tunneling of phonon excitations across the potential barrier separating two
Bose condensates. The perfect transmission is a consequence of the coincidence
of the wave function of the excited state at low energy limit and the
macroscopic wave function of the condensate. We show that the perfect
transmission at low energy occurs even at finite temperatures within the scheme
of Popov approximation.


  A magnetite (Fe3O4) single crystal (111) surface has been studied at various
oxygen-iron surface stoichiometries. The stoichiometry was modified by
controlling the in-situ sample anneal conditions. We have found the conditions
that lead to the formation of an oxygen-rich surface that forms a
quasi-hexagonal superstructure with a 42A periodicity. The superstructure is
highly regular and was observed by both LEED and STM. The superstructure
consists of three regions, two of which have identical atomic scale structures
with a periodicity of 2.8A, and a third having a periodicity that is about 10%
larger (3.1A). The subtle difference in the atomic periodicities between the
three areas results from the modulation of intrinsic strain developed along the
surface. The superstructure results from electronic effects rather than being a
mosaic of different iron oxide terminations. The onset of the superstructure is
sensitive to the surface stoichiometry. From our results we could estimate the
critical density of defects leading to the disappearance of the superstructure.
We have modelled the experimental results and calculated the electron density
using a DFT algorithm. The model clearly shows the development of strain along
the surface.


  We consider second-order uniformly elliptic operators subject to Dirichlet
boundary conditions. Such operators are considered on a bounded domain $\Omega$
and on the domain $\phi(\Omega)$ resulting from $\Omega$ by means of a
bi-Lipschitz map $\phi$. We consider the solutions $u$ and $\tilde u$ of the
corresponding elliptic equations with the same right-hand side $f\in
L^2(\Omega\cup\phi(\Omega))$. Under certain assumptions we estimate the
difference $\|\nabla\tilde u-\nabla u\|_{L^2(\Omega\cup\phi(\Omega))}$ in terms
of certain measure of vicinity of $\phi$ to the identity map. For domains
within a certain class this provides estimates in terms of the Lebesgue measure
of the symmetric difference of $\phi(\Omega)$ and $\Omega$, that is
$|\phi(\Omega)\triangle \Omega|$. We provide an example which shows that the
estimates obtained are in a certain sense sharp.


  We report $^{77}$Se and $^{63}$Cu nuclear magnetic resonance (NMR)
investigation on the charge-density-wave (CDW) superconductor Cu$_x$TiSe$_2$
($x=0.05$ and 0.07). At high magnetic fields where superconductivity is
suppressed, the temperature dependence of $^{77}$Se and $^{63}$Cu spin-lattice
relaxation rates 1/T_{1}$ follow a linear relation. The slope of $^{77}1/T_{1}$
vs \emph{T} increases with the Cu doping. This can be described by a modified
Korringa relation which suggests the significance of electronic correlations
and the Se 4\emph{p}- and Ti 3\emph{d}-band contribution to the density of
states at the Fermi level in the studied compounds.


  Trading on the day-ahead electricity markets requires accurate information
about the realization of electricity prices and the uncertainty attached to the
predictions. Deriving accurate forecasting models presents a difficult task due
to the day-ahead price's non-stationarity resulting from changing market
conditions, e.g., due to changes resulting from the energy crisis in 2021. We
present a probabilistic forecasting approach for day-ahead electricity prices
using the fully data-driven deep generative model called normalizing flow. Our
modeling approach generates full-day scenarios of day-ahead electricity prices
based on conditional features such as residual load forecasts. Furthermore, we
propose extended feature sets of prior realizations and a periodic retraining
scheme that allows the normalizing flow to adapt to the changing conditions of
modern electricity markets. Our results highlight that the normalizing flow
generates high-quality scenarios that reproduce the true price distribution and
yield accurate forecasts. Additionally, our analysis highlights how our
improvements towards adaptations in changing regimes allow the normalizing flow
to adapt to changing market conditions and enable continued sampling of
high-quality day-ahead price scenarios.


  This article includes the description of the geometric parameter gage device
prototype for synchrotron radiation of HERA collider (DESY). The system
construction which capable to measure photo current, caused by such a radiation
in a refractory metal, described here. The system component parts are:
measuring heads and photo current measuring electronics designed by IHEP,
stepper motor by Vacuum Generators with HEDS-550X encoder by Hewlett Packard,
PCI-STEP-4CX 4-Axis Closed Loop Step controller by National Instruments. The
device is controlled by means of Microsoft Visual Basic program using Value
Motion Windows Libraries. The device prototype was tested in the beam of the
DORIS storage ring.


  Keyword extraction is the task of identifying words (or multi-word
expressions) that best describe a given document and serve in news portals to
link articles of similar topics. In this work we develop and evaluate our
methods on four novel data sets covering less represented, morphologically-rich
languages in European news media industry (Croatian, Estonian, Latvian and
Russian). First, we perform evaluation of two supervised neural
transformer-based methods (TNT-KID and BERT+BiLSTM CRF) and compare them to a
baseline TF-IDF based unsupervised approach. Next, we show that by combining
the keywords retrieved by both neural transformer based methods and extending
the final set of keywords with an unsupervised TF-IDF based technique, we can
drastically improve the recall of the system, making it appropriate to be used
as a recommendation system in the media house environment.


  We present PSiLON Net, an MLP architecture that uses $L_1$ weight
normalization for each weight vector and shares the length parameter across the
layer. The 1-path-norm provides a bound for the Lipschitz constant of a neural
network and reflects on its generalizability, and we show how PSiLON Net's
design drastically simplifies the 1-path-norm, while providing an inductive
bias towards efficient learning and near-sparse parameters. We propose a
pruning method to achieve exact sparsity in the final stages of training, if
desired. To exploit the inductive bias of residual networks, we present a
simplified residual block, leveraging concatenated ReLU activations. For
networks constructed with such blocks, we prove that considering only a subset
of possible paths in the 1-path-norm is sufficient to bound the Lipschitz
constant. Using the 1-path-norm and this improved bound as regularizers, we
conduct experiments in the small data regime using overparameterized PSiLON
Nets and PSiLON ResNets, demonstrating reliable optimization and strong
performance.


  We approach the problem of finding generalized states for matter fields in a
de Sitter universe, moving from a group theoretical point of view. This has
profound consequences for cosmological perturbations during inflation, and for
other CMB observables. In a systematic derivation, we find all the allowed
generalities that such a state may have, within the requirements of coherent
states. Furthermore, we show their relation to the more familiar excited
initial states, often used in inflation.


  Context. The analysis of luminosity and mass distributions of young stellar
clusters is essential to understanding the star-formation process. However, the
gas and dust left over by this process extinct the light of the newborn stars
and can severely bias both the census of cluster members and its luminosity
distribution. Aims. We aim to develop a Bayesian methodology to infer, with
minimal biases due to photometric extinction, the candidate members and
magnitude distributions of embedded young stellar clusters. Methods. We improve
a previously published methodology and extend its application to embedded
stellar clusters. We validate the method using synthetically extincted data
sets of the Pleiades cluster with varying degrees of extinction. Results. Our
methodology can recover members from data sets extincted up to Av ~ 6 mag with
accuracies, true positive, and contamination rates that are better than 99%,
80%, and 9%, respectively. Missing values hamper our methodology by introducing
contaminants and artifacts into the magnitude distributions. Nonetheless, these
artifacts vanish through the use of informative priors in the distribution of
the proper motions. Conclusions. The methodology presented here recovers, with
minimal biases, the members and distributions of embedded stellar clusters from
data sets with a high percentage of sources with missing values (>96%).


  At high red shift, the temperature of cosmic microwave background is
sufficiently high for the ultrahigh-energy photons to pair-produce muons and
pions through interactions with the background photons. At the same time, the
radio background and magnetic fields are too weak to drain energy out of the
electromagnetic cascade before the muons and pions are produced. Decays of the
energetic muons and pions yield neutrinos with some distinctive spectral
properties that can be detected and can indicate the presence of
ultrahigh-energy photons at high red shift. The neutrino signature can help
identify the origin of cosmic rays beyond the Greisen-Zatsepin-Kuzmin cutoff.


  D$_2$ molecules, excited by linearly cross-polarized femtosecond extreme
ultraviolet (XUV) and near-infrared (NIR) light pulses, reveal highly
structured D$^+$ ion fragment momenta and angular distributions that originate
from two different 4-step dissociative ionization pathways after four photon
absorption (1 XUV + 3 NIR). We show that, even for very low dissociation
kinetic energy release $\le$~240~meV, specific electronic excitation pathways
can be identified and isolated in the final ion momentum distributions. With
the aid of {\it ab initio} electronic structure and time-dependent
Schr\"odinger equation calculations, angular momentum, energy, and parity
conservation are used to identify the excited neutral molecular states and
molecular orientations relative to the polarization vectors in these different
photoexcitation and dissociation sequences of the neutral D$_2$ molecule and
its D$_2^+$ cation. In one sequential photodissociation pathway, molecules
aligned along either of the two light polarization vectors are excluded, while
another pathway selects molecules aligned parallel to the light propagation
direction. The evolution of the nuclear wave packet on the intermediate \Bstate
electronic state of the neutral D$_2$ molecule is also probed in real time.


  We consider a semantics-aware communication system, where timeliness is the
semantic measure, with a source which maintains the most current version of a
file, and a network of $n$ user nodes with the goal to acquire the latest
version of the file. The source gets updated with newer file versions as a
point process, and forwards them to the user nodes, which further forward them
to their neighbors using a memoryless gossip protocol. We study the average
version age of the network in the presence of $\tilde{n}$ jammers that disrupt
inter-node communications, for the connectivity-constrained ring topology and
the connectivity-rich fully connected topology. For the ring topology, we
construct an alternate system model of mini-rings and prove that the version
age of the original model can be sandwiched between constant multiples of the
version age of the alternate model. We show in a ring network that when the
number of jammers scales as a fractional power of the network size, i.e.,
$\tilde n= cn^\alpha$, the version age scales as $\sqrt{n}$ when $\alpha <
\frac{1}{2}$, and as $n^{\alpha}$ when $\alpha \geq \frac{1}{2}$. As version
age of a ring network without any jammers scales as $\sqrt{n}$, our result
implies that version age with gossiping is robust against upto $\sqrt{n}$
jammers in a ring network. We then study the connectivity-rich fully connected
topology, where we derive a greedy approach to place $\tilde{n}$ jammers to
maximize age of the resultant network, which uses jammers to isolate as many
nodes as possible, thereby consolidating all links into a single mini-fully
connected network. We show in this network that version age scales as $\log{n}$
when $\tilde{n}=cn\log{n}$ and as $n^{\alpha-1}$, $1<\alpha\leq2$ when
$\tilde{n}=cn^{\alpha}$, implying the network is robust against $n\log{n}$
jammers, since the age in a fully connected network without jammers scales as
$\log{n}$.


  We investigate one-halo galactic conformity (the tendency for satellite
galaxies to mirror the properties of their central) in both star-formation and
morphology using a sample of 8230 galaxies in 1266 groups with photometry and
spectroscopy from the Sloan Digital Sky Survey, morphologies from Galaxy Zoo
and group memberships as determined by Yang et al. This is the first paper to
investigate galactic conformity in both star-formation and visual morphology
properties separately. We find that the signal of galactic conformity is
present at low significance in both star-formation and visual morphological
properties, however it is stronger in star-formation properties. Over the
entire halo mass range we find that groups with star-forming (spiral) centrals
have, on average, a fraction 0.18+-0.08 (0.08+-0.06) more star-forming (spiral)
satellites than groups with passive (early-type) centrals at a similar halo
mass. We also consider conformity in groups with four types of central: passive
early-types, star-forming spirals, passive spirals and star-forming early-types
(which are very rarely centrals), finding that the signal of morphological
conformity is strongest around passive centrals regardless of morphology;
although blue spiral centrals are also more likely than average to have blue
spiral satellites. We interpret these observations of the relative size of the
conformity signal as supporting a scenario where star-formation properties are
relatively easily changed, while morphology changes less often/more slowly for
galaxies in the group environment.


  Interest in semi-autonomous systems (SAS) is growing rapidly as a paradigm to
deploy autonomous systems in domains that require occasional reliance on
humans. This paradigm allows service robots or autonomous vehicles to operate
at varying levels of autonomy and offer safety in situations that require human
judgment. We propose an introspective model of autonomy that is learned and
updated online through experience and dictates the extent to which the agent
can act autonomously in any given situation. We define a competence-aware
system (CAS) that explicitly models its own proficiency at different levels of
autonomy and the available human feedback. A CAS learns to adjust its level of
autonomy based on experience to maximize overall efficiency, factoring in the
cost of human assistance. We analyze the convergence properties of CAS and
provide experimental results for robot delivery and autonomous driving domains
that demonstrate the benefits of the approach.


  Ultrasensitive detection of methane, isotopic carbon dioxide, carbon
monoxide, formaldehyde, acetylene and ethylene is performed in the spectral
range 2.5 - 5 \mu m using intracavity spectroscopy in broadband optical
parametric oscillators (OPOs). The OPOs were operated near degeneracy and
synchronously pumped either by a mode-locked erbium (1560 nm) or thulium (2050
nm) fiber laser. A large instantaneous bandwidth of up to 800 cm$^{-1}$ allows
for simultaneous detection of several gases. We observe an effective path
length enhancement due to coherent interaction inside the OPO cavity and
achieve part-per-billion sensitivity levels. The measured spectral shapes are
in good agreement with a model that takes into account group delay dispersion
across the broad OPO frequency band.


  Fishes are integral to both ecological systems and economic sectors, and
studying fish traits is crucial for understanding biodiversity patterns and
macro-evolution trends. To enable the analysis of visual traits from fish
images, we introduce the Fish-Visual Trait Analysis (Fish-Vista) dataset - a
large, annotated collection of about 60K fish images spanning 1900 different
species, supporting several challenging and biologically relevant tasks
including species classification, trait identification, and trait segmentation.
These images have been curated through a sophisticated data processing pipeline
applied to a cumulative set of images obtained from various museum collections.
Fish-Vista provides fine-grained labels of various visual traits present in
each image. It also offers pixel-level annotations of 9 different traits for
2427 fish images, facilitating additional trait segmentation and localization
tasks. The ultimate goal of Fish-Vista is to provide a clean, carefully
curated, high-resolution dataset that can serve as a foundation for
accelerating biological discoveries using advances in AI. Finally, we provide a
comprehensive analysis of state-of-the-art deep learning techniques on
Fish-Vista.


  A search for a standard-model-like Higgs boson in the H to WW and H to ZZ
decay channels is reported, for Higgs boson masses in the range 145 < m[H] <
1000 GeV. The search is based upon proton-proton collision data samples
corresponding to an integrated luminosity of up to 5.1 inverse femtobarns at
sqrt(s) = 7 TeV and up to 5.3 inverse femtobarns at sqrt(s) = 8 TeV, recorded
by the CMS experiment at the LHC. The combined upper limits at 95% confidence
level on products of the cross section and branching fractions exclude a
standard-model-like Higgs boson in the range 145 < m[H] < 710 GeV, thus
extending the mass region excluded by CMS from 127-600 GeV up to 710 GeV.


  Near-Eddington radiation from active galactic nuclei (AGNs) has significant
dynamical influence on the surrounding dusty gas, plausibly furnishing AGNs
with geometrically thick obscuration. We investigate this paradigm with
radiative magnetohydrodynamics simulations. The simulations solve the
magnetohydrodynamics equations simultaneously with the infrared (IR) and
ultraviolet (UV) radiative transfer (RT) equations; no approximate closure is
used for RT. We find that our torus, when given a suitable sub-Keplerian
angular momentum profile, spontaneously evolves toward a state in which its
opening angle, density distribution, and flow pattern change only slowly. This
"steady" state lasts for as long as there is gas resupply toward the inner
edge. The torus is best described as a mid-plane inflow and a high-latitude
outflow. The outflow is launched from the torus inner edge by UV radiation and
expands in solid angle as it ascends; IR radiation continues to drive the
wide-angle outflow outside the central hole. The dusty outflow obscures the
central source in soft X-rays, the IR, and the UV over three quarters of solid
angle, and each decade in column density covers roughly equal solid angle
around the central source; these obscuration properties are similar to what
observations imply.


  Golay complementary set (GCS) plays a vital role in reducing peak-to-mean
envelope power ratio (PMEPR) in orthogonal frequency division multiplexing
(OFDM). A more general version of GCS is a multiple shift complementary set
(MSCS), where by relaxing the condition of zero auto-correlation sum throughout
all the non-zero time shifts to the integer multiples of some fixed time shift,
more sequence sets can be made available. In this paper, we propose direct
constructions of MSCSs with flexible and arbitrary lengths and flexible set
sizes, by using multivariable functions, which have not been reported before.


  Models with spontaneously broken parity symmetry can solve the strong $CP$
problem in a natural way. We construct such a model in the context of $\SU3^3$
unification. Parity has the conventional meaning in this model, and the gauge
group is unified.


  We study numerically a monodisperse model of interacting classical particles
predicted to exhibit a static liquid-glass transition. Using a dynamical Monte
Carlo method we show that the model does not freeze into a glassy phase at low
temperatures. Instead, depending on the choice of the hard-core radius for the
particles the system either collapses trivially or a polycrystalline hexagonal
structure emerges.


  Massive amounts of satellite data have been gathered over time, holding the
potential to unveil a spatiotemporal chronicle of the surface of Earth. These
data allow scientists to investigate various important issues, such as land use
changes, on a global scale. However, not all land-use phenomena are equally
visible on satellite imagery. In particular, the creation of an inventory of
the planet's road infrastructure remains a challenge, despite being crucial to
analyze urbanization patterns and their impact. Towards this end, this work
advances data-driven approaches for the automatic identification of roads based
on open satellite data. Given the typical resolutions of these historical
satellite data, we observe that there is inherent variation in the visibility
of different road types. Based on this observation, we propose two deep
learning frameworks that extend state-of-the-art deep learning methods by
formalizing road detection as an ordinal classification task. In contrast to
related schemes, one of the two models also resorts to satellite time series
data that are potentially affected by missing data and cloud occlusion. Taking
these time series data into account eliminates the need to manually curate
datasets of high-quality image tiles, substantially simplifying the application
of such models on a global scale. We evaluate our approaches on a dataset that
is based on Sentinel~2 satellite imagery and OpenStreetMap vector data. Our
results indicate that the proposed models can successfully identify large and
medium-sized roads. We also discuss opportunities and challenges related to the
detection of roads and other infrastructure on a global scale.


  Topological Deep Learning seeks to enhance the predictive performance of
neural network models by harnessing topological structures in input data.
Topological neural networks operate on spaces such as cell complexes and
hypergraphs, that can be seen as generalizations of graphs. In this work, we
introduce the Cellular Transformer (CT), a novel architecture that generalizes
graph-based transformers to cell complexes. First, we propose a new formulation
of the usual self- and cross-attention mechanisms, tailored to leverage
incidence relations in cell complexes, e.g., edge-face and node-edge relations.
Additionally, we propose a set of topological positional encodings specifically
designed for cell complexes. By transforming three graph datasets into cell
complex datasets, our experiments reveal that CT not only achieves
state-of-the-art performance, but it does so without the need for more complex
enhancements such as virtual nodes, in-domain structural encodings, or graph
rewiring.


  Extensive games are tools largely used in economics to describe decision
processes ofa community of agents. In this paper we propose a formal
presentation based on theproof assistant COQ which focuses mostly on infinite
extensive games and theircharacteristics. COQ proposes a feature called
"dependent types", which meansthat the type of an object may depend on the type
of its components. For instance,the set of choices or the set of utilities of
an agent may depend on the agentherself. Using dependent types, we describe
formally a very general class of gamesand strategy profiles, which corresponds
somewhat to what game theorists are used to.We also discuss the notions of
infiniteness in game theory and how this can beprecisely described.


  Complex networks can be used to represent complex systems which originate in
the real world. Here we study a transformation of these complex networks into
simplicial complexes, where cliques represent the simplices of the complex. We
extend the concept of node centrality to that of simplicial centrality and
study several mathematical properties of degree, closeness, betweenness,
eigenvector, Katz, and subgraph centrality for simplicial complexes. We study
the degree distributions of these centralities at the different levels. We also
compare and describe the differences between the centralities at the different
levels. Using these centralities we study a method for detecting essential
proteins in PPI networks of cells and explain the varying abilities of the
centrality measures at the different levels in identifying these essential
proteins. The paper is written in a self-contained way, such that it can be
used by practitioners of network theory as a basis for further developments.


  QCD in a five-dimensional sliced bulk with chiral extra-quarks on the
boundaries is generically free from the strong CP problem. Accidental axial
symmetry is naturally present except for suppressed breaking interactions,
which plays a role of the Peccei-Quinn symmetry to make the strong CP phase
sufficiently small.


  Large vision-language models (LVLMs) have significantly improved multimodal
reasoning tasks, such as visual question answering and image captioning. These
models embed multimodal facts within their parameters, rather than relying on
external knowledge bases to store factual information explicitly. However, the
content discerned by LVLMs may deviate from actual facts due to inherent bias
or incorrect inference. To address this issue, we introduce MFC-Bench, a
rigorous and comprehensive benchmark designed to evaluate the factual accuracy
of LVLMs across three tasks: Manipulation, Out-of-Context, and Veracity
Classification. Through our evaluation on MFC-Bench, we benchmarked 12 diverse
and representative LVLMs, uncovering that current models still fall short in
multimodal fact-checking and demonstrate insensitivity to various forms of
manipulated content. We hope that MFC-Bench could raise attention to the
trustworthy artificial intelligence potentially assisted by LVLMs in the
future. The MFC-Bench and accompanying resources are publicly accessible at
https://github.com/wskbest/MFC-Bench, contributing to ongoing research in the
multimodal fact-checking field.


  We construct world-volume effective actions of exotic $5^2_2$-branes in type
IIA and IIB string theories. The effective actions are given in fully
space-time covariant forms with two Killing vectors associated with background
isometries. The effective theories are governed by the six-dimensional
$\mathcal{N}=(2,0)$ tensor multiplet and $\mathcal{N}=(1,1)$ vector multiplet,
respectively. Performing the S-duality transformation to the $5^2_2$-brane
effective action in type IIB string theory, we also work out the world-volume
action of the $5^2_3$-brane. We discuss some additional issues relevant to the
exotic five-branes in type I and heterotic string theories.


  This paper studies a strategy for data-driven algorithm design for
large-scale combinatorial optimization problems that can leverage existing
state-of-the-art solvers in general purpose ways. The goal is to arrive at new
approaches that can reliably outperform existing solvers in wall-clock time. We
focus on solving integer programs, and ground our approach in the large
neighborhood search (LNS) paradigm, which iteratively chooses a subset of
variables to optimize while leaving the remainder fixed. The appeal of LNS is
that it can easily use any existing solver as a subroutine, and thus can
inherit the benefits of carefully engineered heuristic or complete approaches
and their software implementations. We show that one can learn a good
neighborhood selector using imitation and reinforcement learning techniques.
Through an extensive empirical validation in bounded-time optimization, we
demonstrate that our LNS framework can significantly outperform compared to
state-of-the-art commercial solvers such as Gurobi.


  India is expected to witness rapid growth in electricity use over the next
two decades. Here, we introduce a custom regression model to project
electricity consumption in India over the coming decades, which includes a
bottom-up estimate of electricity consumption for two major growth drivers, air
conditioning, and vehicle electrification. The model projections are available
at a customizable level of spatial aggregation at an hourly temporal
resolution, which makes them useful as inputs to long-term electricity
infrastructure planning studies. The approach is used to develop electricity
consumption data sets spanning various technology adoption and growth scenarios
up to the year 2050 in five-year increments. The aim of the data is to provide
a range of scenarios for India's demand growth given new technology adoption.
With long-term hourly demand projections serving as an essential input for
electricity infrastructure modeling, this data publication enables further work
on energy efficiency, generation, and transmission expansion planning for a
fast-growing and increasingly important region from a global climate mitigation
perspective.


  The search for equilibrium in a two-stage traffic flow model reduces to the
solution of a special nonsmooth convex optimization problem with two groups of
different variables. For numerical solution of this problem, the paper proposes
to use the accelerated block-coordinate Nesterov-Stich method with a special
choice of block probabilities at each iteration. Theoretical estimates of the
complexity of this approach can markedly improve the estimates of previously
used approaches. However, in the general case they do not guarantee faster
convergence. Numerical experiments with the proposed algorithms are carried out
in the paper.


  Laser frequency can be upconverted in a plasma undergoing ionization. For
finite ionization rates, the laser pulse energy is partitioned into a pair of
counter-propagating waves and static transverse currents. The wave amplitudes
are determined by the ionization rates and the input pulse duration. The
strongest output waves can be obtained when the plasma is fully ionized in a
time that is shorter than the pulse duration. The static transverse current can
induce a static magnetic field with instant ionization, but it dissipates as
heat if the ionization time is longer than a few laser periods. This picture
comports with experimental data, providing a description of both laser
frequency upconverters as well as other laser-plasma interaction with evolving
plasma densities.


  This work applies a quantitative metric well-known to the data assimilation
community to a new context in order to capture the relative representativeness
of non-simultaneous or non-co-located observations and quantify how these
observations decorrelate in both space and time. This methodology allows for
the effective determination of thresholding decisions for representative
matchup conditions, and is especially useful for informing future network
designs and architectures. Future weather and climate satellite missions must
consider a range of architectural trades to meet observing requirements.
Frequently, fundamental decisions such as the number of observatories, the
instruments manifested, and orbit parameters are determined based upon
assumptions about the characteristic temporal and spatial scales of variability
of the target observation. With the introduced methodology, representativity
errors due to separations in space and time can be quantified without prior
knowledge of instrument performance, and errors driven by constellation design
can be estimated without model ingest or analysis.


  An influence of high magnetic fields on hyperfine interaction in the
rare-earth ions with non-magnetic ground state (Van Vleck ions) is
theoretically investigated for the case of $Tm^{3+}$ ion in axial symmetrical
crystal electric field (ethylsulphate crystal). It is shown that magnetic-field
induced distortions of $4f$-electron shell lead to essential changes in
hyperfine magnetic field at the nucleus. The proposed theoretical model is in
agreement with recent experimental data.


  Photoinduced polarons in solid films of polymer-fullerene blends were studied
by photoluminescence (PL), photoinduced absorption (PIA) and electron spin
resonance (ESR). The donor materials used were P3HT and MEH-PPV. As acceptors
we employed PC60BM as reference and various soluble C70-derivates: PC70BM, two
different diphenylmethano-[70]fullerene oligoether (C70-DPM-OE) and two dimers,
C70-C70 and C60-C70. Blend films containing C70 revealed characteristic
spectroscopic signatures not seen with C60. Light-induced ESR showed signals at
g\geq2.005, assigned to an electron localized on the C70 cage. The formation of
C70 radical anions also leads to a subgap PIA band at 0.92 eV, hidden in the
spectra of C70-based P3HT and MEH-PPV blends, which allows for more exact
studies of charge separated states in conjugated polymer:C70 blends.


  Consensus protocols are currently the bottlenecks that prevent blockchain
systems from scaling. However, we argue that transaction execution is also
important to the performance and security of blockchains. In other words, there
are ample opportunities to speed up and further secure blockchains by reducing
the cost of transaction execution.
  Our goal is to understand how much we can speed up blockchains by exploiting
transaction concurrency available in blockchain workloads. To this end, we
first analyze historical data of seven major public blockchains, namely
Bitcoin, Bitcoin Cash, Litecoin, Dogecoin, Ethereum, Ethereum Classic, and
Zilliqa. We consider two metrics for concurrency, namely the single-transaction
conflict rate per block, and the group conflict rate per block. We find that
there is more concurrency in UTXO-based blockchains than in account-based ones,
although the amount of concurrency in the former is lower than expected.
Another interesting finding is that some blockchains with larger blocks have
more concurrency than blockchains with smaller blocks. Next, we propose an
analytical model for estimating the transaction execution speed-up given an
amount of concurrency. Using results from our empirical analysis, the model
estimates that 6x speed-ups in Ethereum can be achieved if all available
concurrency is exploited.


  Most of the previous methods for table recognition rely on training datasets
containing many richly annotated table images. Detailed table image annotation,
e.g., cell or text bounding box annotation, however, is costly and often
subjective. In this paper, we propose a weakly supervised model named WSTabNet
for table recognition that relies only on HTML (or LaTeX) code-level
annotations of table images. The proposed model consists of three main parts:
an encoder for feature extraction, a structure decoder for generating table
structure, and a cell decoder for predicting the content of each cell in the
table. Our system is trained end-to-end by stochastic gradient descent
algorithms, requiring only table images and their ground-truth HTML (or LaTeX)
representations. To facilitate table recognition with deep learning, we create
and release WikiTableSet, the largest publicly available image-based table
recognition dataset built from Wikipedia. WikiTableSet contains nearly 4
million English table images, 590K Japanese table images, and 640k French table
images with corresponding HTML representation and cell bounding boxes. The
extensive experiments on WikiTableSet and two large-scale datasets: FinTabNet
and PubTabNet demonstrate that the proposed weakly supervised model achieves
better, or similar accuracies compared to the state-of-the-art models on all
benchmark datasets.


  This paper characterizes the potential behaviors of higher commutators in a
simple algebra.


  The use of the existing proton storage rings combined with electron linear
accelerator as a ring-linac type electron-proton or photon-proton collider is
investigated. The total CM-energy of Proton-ring and Electron-linac Collider
(PRELC) is in the range of 1.0 TeV. The most important physical issues are
listed and the most critical machine aspects of the PRELC are studied. It is
shown that the luminosities in the range of $10^{31}$ to $10^{32}$ could be
achieved. The PRELC could be used simultaneously with the $e^+e^-$-collider if
the refocused electron beam could be used as the electron beam for the PRELC.


  Monolayers of group 6 transition metal dichalcogenides are promising
candidates for future spin-, valley-, and charge-based applications. Quantum
transport in these materials reflects a complex interplay between real spin and
pseudo-spin (valley) relaxation processes, which leads to either positive or
negative quantum correction to the classical conductivity. Here we report
experimental observation of a crossover from weak localization to weak
anti-localization in highly n-doped monolayer MoS2. We show that the crossover
can be explained by a single parameter associated with electron spin lifetime
of the system. We find that the spin lifetime is inversely proportional to
momentum relaxation time, indicating that spin relaxation occurs via
Dyakonov-Perel mechanism.


  In this work a numerical model for two-dimensional axisymmetric continuous
freezing by impingement of processed meat or similar products in food industry
moving along a conveyor belt is presented. The model represents a more
computationally efficient alternative to solve conjugate heat transfer between
a fluid and a solid, accompanied by phase change in some constituents of the
solid phase. In the model presented here it is assumed that the solid can be
represented as an homogeneous medium, with its thermophysical properties
depending on the temperature. The impingement freezing model is conceived to be
valid for highly processed vegetarians products or meat such as sausages, mince
or ham freezing. Furthermore, this approach is much simpler in terms of
computational cost whilst it still captures the complexity of continuous
freezing under industrial setting. The methodology is implemented as a new
solver in the widely used open-source Computational Fluid Dynamics (CFD)
library OpenFOAM. Overall, highly non-linear freezing behaviour was found due
to the phase change inside the solid and the associated heat of fusion. We
studied the effect of high fluid Reynolds numbers as well as investigating the
optimal distance between the jet and the solid surface for different speeds of
the conveyor. We found that the maximum freezing is obtained positioning the
jet at a distance H = 7.2D (where D is the diameter of the impinging jet) and
setting the speed of the conveyor such that the P\'eclet number of the solid is
$\text{Pe}_{\text{s}}=8244$. The methodology developed allows to obtain
detailed insight on the freezing process for various impingement configurations
at a minimum computational cost using a freely available open-source tool.