  According to the entropy accumulation theorem, proving the unconditional
security of a device-independent quantum key distribution protocol reduces to
deriving tradeoff functions, i.e., bounds on the single-round von Neumann
entropy of the raw key as a function of Bell linear functionals, conditioned on
an eavesdropper's quantum side information. In this work, we describe how the
conditional entropy can be bounded in the 2-input/2-output setting, where the
analysis can be reduced to qubit systems, by combining entropy bounds for
variants of the well-known BB84 protocol with quantum constraints on qubit
operators on the bipartite system shared by Alice and Bob. The approach gives
analytic bounds on the entropy, or semi-analytic ones in reasonable computation
time, which are typically close to optimal. We illustrate the approach on a
variant of the device-independent CHSH QKD protocol where both bases are used
to generate the key as well as on a more refined analysis of the original
single-basis variant with respect to losses. We obtain in particular a
detection efficiency threshold slightly below 80.26%, within reach of current
experimental capabilities.


  The expansion of a Lie algebra entails finding a new, bigger algebra G,
through a series of well-defined steps, from an original Lie algebra g. One
incarnation of the method, the so-called S-expansion, involves the use of a
finite abelian semigroup S to accomplish this task. In this paper we put
forward a dual formulation of the S-expansion method which is based on the dual
picture of a Lie algebra given by the Maurer-Cartan forms. The dual version of
the method is useful in finding a generalization to the case of a gauge free
differential algebra, which in turn is relevant for physical applications in,
e.g., Supergravity. It also sheds new light on the puzzling relation between
two Chern-Simons Lagrangians for gravity in 2+1 dimensions, namely the
Einstein-Hilbert Lagrangian and the one for the so-called "exotic gravity".


  We study the finite dimensional partition properties of the countable
homogeneous dense local order. Some of our results use ideas borrowed from the
partition calculus of the rationals and are obtained thanks to a strengthening
of Milliken's theorem on trees.


  This paper examines the stochastic maximum principle (SMP) for a
forward-backward stochastic control system where the backward state equation is
characterized by the backward stochastic differential equation (BSDE) with
quadratic growth and the forward state at the terminal time is constrained in a
convex set with probability one. With the help of the theory of BSDEs with
quadratic growth and the bounded mean oscillation (BMO) martingales, we employ
the terminal perturbation approach and Ekeland's variational principle to
obtain a dynamic stochastic maximum principle. The main result has a wide range
of applications in mathematical finance and we investigate a robust recursive
utility maximization problem with bankruptcy prohibition as an example.


  This article discusses the security of McEliece-like encryption schemes using
subspace subcodes of Reed-Solomon codes, i.e. subcodes of Reed-Solomon codes
over $\mathbb{F}_{q^m}$ whose entries lie in a fixed collection of
$\mathbb{F}_q$-subspaces of $\mathbb{F}_{q^m}$. These codes appear to be a
natural generalisation of Goppa and alternant codes and provide a broader
flexibility in designing code based encryption schemes. For the security
analysis, we introduce a new operation on codes called the twisted product
which yields a polynomial time distinguisher on such subspace subcodes as soon
as the chosen $\mathbb{F}_q$-subspaces have dimension larger than $m/2$. From
this distinguisher, we build an efficient attack which in particular breaks
some parameters of a recent proposal due to Khathuria, Rosenthal and Weger.


  Let G=(V,E) be a graph. A set S is independent if no two vertices from S are
adjacent. The independence number alpha(G) is the cardinality of a maximum
independent set, and mu(G) is the size of a maximum matching. The number
id_{c}(G)=max{|I|-|N(I)|:I is an independent set} is called the critical
independence difference of G, and A is critical if |A|-|N(A)|=id_{c}(G). We
define core(G) as the intersection of all maximum independent sets, and
ker(G)as the intersection of all critical independent sets. In this paper we
prove that if a graph G is non-quasi-regularizable (i.e., there exists some
independent set A, such that |A|>|N(A)|), then: ker(G) is a subset of core(G),
and |ker(G)|> id_{c}(G) >= alpha(G)-mu(G) > 0.


  Macroscopic fields like electromagnetic, MHD, acoustic or gravitational waves
are usually described by classical wave equations with possible additional
damping terms and coherent sources. The aim of this paper is to develop a
complete macroscopic formalism including random/thermal sources, dissipation
and random scattering of waves by environment. The proposed reduced state of
field (RSF) combines averaged field with the two-point correlation function
called single-particle density matrix. The evolution equations for RSF is
obtained by reduction of the generalized quasi-free dynamical semigroups
describing irreversible evolution of bosonic quantum field and the definition
of RSF's entropy follows from the von Neumann entropy of quantum field states.
The presented formalism can be applied, for example, to superradiance phenomena
and allows to unify the Mueller and Jones calculi in polarization optics.


  In this work we present a fully atomistic reactive (ReaxFF force field)
molecular dynamics study of the structural and dynamical aspects of the
one-side hydrogenation of graphene membranes, leading to the formation of the
so-called graphone structure. We have considered different substrates:
graphene, few-layers graphene, graphite and platinum at different temperatures.
Our results showed that the hydrogenation rates are very dependent on the
substrate and thermal effects. Our results also showed that, similarly to
graphane, large hydrogenated domains are unlikely to be formed. These
hydrogenation processes occur through the formation of uncorrelated cluster
domains.


  Model calibration, which is concerned with how frequently the model predicts
correctly, not only plays a vital part in statistical model design, but also
has substantial practical applications, such as optimal decision-making in the
real world. However, it has been discovered that modern deep neural networks
are generally poorly calibrated due to the overestimation (or underestimation)
of predictive confidence, which is closely related to overfitting. In this
paper, we propose Annealing Double-Head, a simple-to-implement but highly
effective architecture for calibrating the DNN during training. To be precise,
we construct an additional calibration head-a shallow neural network that
typically has one latent layer-on top of the last latent layer in the normal
model to map the logits to the aligned confidence. Furthermore, a simple
Annealing technique that dynamically scales the logits by calibration head in
training procedure is developed to improve its performance. Under both the
in-distribution and distributional shift circumstances, we exhaustively
evaluate our Annealing Double-Head architecture on multiple pairs of
contemporary DNN architectures and vision and speech datasets. We demonstrate
that our method achieves state-of-the-art model calibration performance without
post-processing while simultaneously providing comparable predictive accuracy
in comparison to other recently proposed calibration methods on a range of
learning tasks.


  In this paper, by applying Greene-Shapere-Vafa-Yau semi-flat metric, we give
a new proof of closed formula of Weil-Petersson metric on moduli space of
Calabi-Yau varieties.


  Big data is data that exceeds the processing capacity of traditional
databases. The data is too big to be processed by a single machine. New and
innovative methods are required to process and store such large volumes of
data. This paper provides an overview on big data, its importance in our live
and some technologies to handle big data.


  The subgroup induction property is a property of self-similar groups acting
on rooted trees introduced by Grigorchuk and Wilson in 2003 that appears to
have strong implications on the structure of the groups possessing it. It was
for example used in the proof that the first Grigorchuk group as well as the
Gupta-Sidki 3-group are subgroup separable (locally extended residually finite)
or to describe their finitely generated subgroups as well as their weakly
maximal subgroups. However, until now, there were only two known examples of
groups with this property, namely the first Grigorchuk group and the
Gupta-Sidki 3-group.
  The aim of this article is twofold. First, we investigate various
consequences of the subgroup induction property for branch groups, a
particularly interesting class of self-similar groups. Notably, we show that
finitely generated branch groups with the subgroup induction property must be
torsion, just infinite and subgroup separable, and we establish conditions
under which all their maximal subgroups are of finite index and all their
weakly maximal subgroups are closed in the profinite topology. Then, we show
that every torsion GGS group has the subgroup induction property, hence
providing the first infinite family of examples of groups with this property.


  We report the experimental observation of Ising superconductivity in
3-dimensional NbSe2 stacked with single-layer MoS2. The angular dependence of
the upper critical magnetic field and the temperature dependence of the upper
parallel critical field confirm the appearance of two-dimensional Ising
superconductivity in the 3-dimensional NbSe2 with single-layer MoS2 overlay. We
show that the superconducting phase has strong Ising spin-orbit correlations
which make the holes spin non-degenerate. Our observation of Ising
superconductivity in heterostructures of few-layer NbSe2 of thickness ~ 15 nm
with single-layer MoS2 raises the interesting prospect of observing topological
chiral superconductors with nontrivial Chern numbers in a momentum-space
spin-split fermionic system.


  We present elemental abundances of 13 microlensed dwarf and subgiant stars in
the Galactic bulge, which constitute the largest sample to date. We show that
these stars span the full range of metallicity from Fe/H=-0.8 to +0.4, and that
they follow well-defined abundance trends, coincident with those of the
Galactic thick disc.


  Let $K/k$ be a finite extension of a global field. Such an extension can be
generated over $k$ by a single element. The aim of this article is to prove the
existence of a "small" generator in the function field case. This answers the
function field version of a question of Ruppert on small generators of number
fields.


  Giant radio halos are the most relevant examples of diffuse synchrotron
emission from galaxy clusters. A number of these sources have very steep
spectrum, with spectral index $\alpha \geq 1.5-1.6$ ($F(\nu) \propto
\nu^{-\alpha}$), and are ideal targets to test current models for the origin of
the relativistic particles. A2256 hosts the nearest radio halo with very steep
spectrum, with $\alpha =1.61$, and a very large population of relativistic
protons in the cluster would be necessary to explain the halo as due to
synchrotron emission from secondary particles. In this case the 0.1-1 GeV
$\gamma$--ray luminosity is expected 10-20 times larger than that of clusters
hosting radio halos with similar radio power at GHz frequencies but with
spectra more typical of the presently observed halo population, $\alpha \sim
1.2$. Under these assumptions incoming FERMI/GLAST observations are expected to
detect A2256, provided that the magnetic field in the central cluster region is
$\leq$10-15 $\mu$G. We show that this will allow for a prompt test of hadronic
models for the origin of radio halos, and for complementary constraints on both
the cluster magnetic field and the physics of particle acceleration mechanisms.


  Neutrino fluxes at telescopes depend on both initial fluxes out of
astronomical bursts and flavor mixing during their travel to the earth.
However, since the information on the initial composition requires better
precision in mixing angles and vice versa, the neutrino detection at telescopes
for itself cannot provide solutions to the both problems. Thus, a probability
to be measured at long baseline oscillation is considered as a complement to
the telescope, and problems like source identification and parameter degeneracy
are examined under a few assumptions.


  In this paper, we find infinite hyperbolic 3-manifolds that admit no weakly
symplectically fillable contact structures, using tools in Heegaard Floer
theory. We also remark that part of these manifolds do admit tight contact
structures.


  Recent advances in transformer-based architectures which are pre-trained in
self-supervised manner have shown great promise in several machine learning
tasks. In the audio domain, such architectures have also been successfully
utilised in the field of speech emotion recognition (SER). However, existing
works have not evaluated the influence of model size and pre-training data on
downstream performance, and have shown limited attention to generalisation,
robustness, fairness, and efficiency. The present contribution conducts a
thorough analysis of these aspects on several pre-trained variants of wav2vec
2.0 and HuBERT that we fine-tuned on the dimensions arousal, dominance, and
valence of MSP-Podcast, while additionally using IEMOCAP and MOSI to test
cross-corpus generalisation. To the best of our knowledge, we obtain the top
performance for valence prediction without use of explicit linguistic
information, with a concordance correlation coefficient (CCC) of .638 on
MSP-Podcast. Furthermore, our investigations reveal that transformer-based
architectures are more robust to small perturbations compared to a CNN-based
baseline and fair with respect to biological sex groups, but not towards
individual speakers. Finally, we are the first to show that their extraordinary
success on valence is based on implicit linguistic information learnt during
fine-tuning of the transformer layers, which explains why they perform on-par
with recent multimodal approaches that explicitly utilise textual information.
Our findings collectively paint the following picture: transformer-based
architectures constitute the new state-of-the-art in SER, but further advances
are needed to mitigate remaining robustness and individual speaker issues. To
make our findings reproducible, we release the best performing model to the
community.


  The article focuses on study of clan model parameters and their target
dependence in light of void probability scaling for heavy (Ag and Br) and light
(C, N and O) groups of targets present in nuclear emulsion detector using 84 Kr
36 at around 1 A GeV. The variation of scaled rapidity gap (rap gap)
probability with single moment combination has been studied. We found that
experimental points are lying approximately on the negative binomial
distribution (NBD) curve, indicating a scaling behavior. The average clan
multiplicities for interactions, increases with the pseudo-rapidity interval
was also observed. The values of average multiplicity for AgBr targets are
larger than those for CNO target and also average number of particles per clan
increases with increase in pseudo-rapidity interval. We further observed that
for a particular target, average number of particles per clan increases with an
increase in the size of projectile nucleus.


  Inspired by the software industry's practice of offering different editions
or versions of a product tailored to specific user groups or use cases, we
propose a novel task, namely, training-free editioning, for text-to-image
models. Specifically, we aim to create variations of a base text-to-image model
without retraining, enabling the model to cater to the diverse needs of
different user groups or to offer distinct features and functionalities. To
achieve this, we propose that different editions of a given text-to-image model
can be formulated as concept subspaces in the latent space of its text encoder
(e.g., CLIP). In such a concept subspace, all points satisfy a specific user
need (e.g., generating images of a cat lying on the grass/ground/falling
leaves). Technically, we apply Principal Component Analysis (PCA) to obtain the
desired concept subspaces from representative text embedding that correspond to
a specific user need or requirement. Projecting the text embedding of a given
prompt into these low-dimensional subspaces enables efficient model editioning
without retraining. Intuitively, our proposed editioning paradigm enables a
service provider to customize the base model into its "cat edition" (or other
editions) that restricts image generation to cats, regardless of the user's
prompt (e.g., dogs, people, etc.). This introduces a new dimension for product
differentiation, targeted functionality, and pricing strategies, unlocking
novel business models for text-to-image generators. Extensive experimental
results demonstrate the validity of our approach and its potential to enable a
wide range of customized text-to-image model editions across various domains
and applications.


  Answering a question raised by S. Friedland, we show that the possible
eigenvalues of Hermitian matrices (or compact operators) A, B, and C with C <=
A + B are given by the same inequalities as in Klyachko's theorem for the case
where C = A + B, except that the equality corresponding to tr(C) = tr(A) +
tr(B) is replaced by the inequality corresponding to tr(C) <= tr(A) + tr(B).
The possible types of finitely generated torsion modules A, B, and C over a
discrete valuation ring such that there is an exact sequence B -> C -> A are
characterized by the same inequalities.


  In this report, we present our champion solutions to five tracks at Ego4D
challenge. We leverage our developed InternVideo, a video foundation model, for
five Ego4D tasks, including Moment Queries, Natural Language Queries, Future
Hand Prediction, State Change Object Detection, and Short-term Object
Interaction Anticipation. InternVideo-Ego4D is an effective paradigm to adapt
the strong foundation model to the downstream ego-centric video understanding
tasks with simple head designs. In these five tasks, the performance of
InternVideo-Ego4D comprehensively surpasses the baseline methods and the
champions of CVPR2022, demonstrating the powerful representation ability of
InternVideo as a video foundation model. Our code will be released at
https://github.com/OpenGVLab/ego4d-eccv2022-solutions


  Let $G$ be a connected reductive subgroup of a complex connected reductive
group $\hat{G}$. Fix maximal tori and Borel subgroups of $G$ and $\hat{G}$.
Consider the cone $LR^\circ(\hat{G},G)$ generated by the pairs
$(\nu,\hat{\nu})$ of strictly dominant characters such that $V_\nu$ is a
submodule of $V_{\hat\nu}$. The main result of this article is a bijective
parametrisation of the faces of $LR^\circ(\hat G,G)$. We also explain when such
a face is contained in another one. In way, we obtain results about the faces
of the Dolgachev-Hu's $G$-ample cone. We also apply our results to reprove
known results about the moment polytopes.


  Due to their capacity for non-classical light generation, high-efficiency
second-order nonlinear parametric processes play an important role in quantum
photonic technology, and chip-scale realization of these processes is
recognized as the key to building efficient light sources for integrated
quantum photonic circuits. To achieve ultra-high nonlinear conversion
efficiency, traditional method uses quasi-phase matching (QPM) technology.
However, QPM requires electric field poling, which is incompatible with the
CMOS fabrication process, and this hinders the wafer-scale production of
integrated photonic circuits. In this paper, we demonstrate efficient
spontaneous quasi-phase matched (SQPM) frequency conversion in a
micro-racetrack resonator. Our approach does not involve poling, but exploits
the anisotropy of the ferroelectric crystals to allow the phase-matching
condition to be fulfilled spontaneously as the TE-polarized light circulates in
a specifically designed racetrack resonator. SQPM second harmonic generation is
observed with a normalized intracavity conversion efficiency of 0.85%/W,
corresponding to the 111st-order QPM. This could theoretically reach 186,000%/W
by first-order QPM. In this case such high intracavity conversion efficiency
can be implemented in practice with an optimized outward coupling. Our
configurable SQPM approach will benefit the application of nonlinear frequency
conversion in chip-scale integrated photonics with CMOS-compatible fabrication
processes, and is applicable to other on-chip nonlinear processes such as
quantum frequency conversion or frequency-comb generation.


  Theories with more than one scalar field often exhibit phase transitions
producing potentially detectable gravitational wave (GW) signal. In this work
we study the semi-annihilating $\mathbb{Z}_3$ dark matter model, whose dark
sector comprises an inert doublet and a complex singlet, and assess its
prospects in future GW detectors. Without imposing limits from requirement of
providing a viable dark matter candidate, i.e. taking into account only other
experimental and theoretical constraints, we find that the first order phase
transition in this model can be strong enough to lead to a detectable signal.
However, direct detection and the dark matter thermal relic density constraint
calculated with the state-of-the-art method including the impact of early
kinetic decoupling, very strongly limit the parameter space of the model
explaining all of dark matter and providing observable GW peak amplitude.
Extending the analysis to underabundant dark matter thus reveals region with
detectable GWs from a single-step or multi-step phase transition.


  We propose a radiative seesaw model at one-loop level, introducing fields
with large multiplet of $SU(2)_L$. Thanks to large representations, any
additional symmetries are not needed. In this framework, we formulate lepton
and new fermion sector such as mass matrices, LFVs, and muon $g-2$.
Furthermore, we show our cut-off scale via RGEs of $SU(2)_L$, and numerical
analysis at a benchmark point included in dark matter candidate. Finally, we
brief discuss possibility to detect at colliders.


  We investigate the process of coarsening via annihilation of
vortex-antivortex pairs, following the quench to the condensate phase in a
nonresonantly pumped polariton system. We find that the late-time dynamics is
an example of universal phase ordering kinetics, characterized by scaling of
correlation functions in time. Depending on the parameters of the system, the
evolution of the characteristic length scale L(t) can be the same as for the
two-dimensional XY model, described by a power law with the dynamical exponent
z \aprox 2 and a logarithmic correction, or z \aprox 1 which agrees with
previous studies of conservative superfluids.


  In this article, we study rational matrix representations of VZ $p$-groups
($p$ is any prime). Utilizing our findings on VZ $p$-groups, we explicitly
obtain all inequivalent irreducible rational matrix representations of all
$p$-groups of order $\leq p^4$. Furthermore, we establish combinatorial
formulas to determine the Wedderburn decompositions of rational group algebras
for VZ $p$-groups and all $p$-groups of order $\leq p^4$, ensuring simplicity
in the process.


  We study semiclassical rotating strings in AdS/CFT backgrounds that exhibit
both confinement and finite-size effects. The energy versus spin dispersion
relation for short strings is the expected Regge trajectory behaviour, with the
same string tension as is measured by the Wilson loop. Long strings probe the
interplay between confinement and finite-size effects. In particular, the
dispersion relation for long strings shows a characteristic dependence on the
string tension and the finite-size scale.


  We study the derived Hecke action at $p$ on the ordinary $p$-adic cohomology
of arithmetic subgroups of semisimple groups $\mathrm G(\mathbb Q)$, i.e., we
study the derived version of Hida's theory for ordinary Hecke algebras. This is
the analog at $\ell=p$ of derived Hecke actions studied by Venkatesh in the
tame case. We show that properties of the derived Hecke action at $p$ are
related to deep conjectures in Galois cohomology which are higher analogs of
the classical Leopoldt conjecture.


  We have grown the ferromagnetic semiconductor GaMnAs containing up to 10% Mn
by migration enhanced epitaxy at a substrate temperature of 150^oC. The
alternate supply of As2 molecules and Ga and Mn atoms made it possible to grow
single crystalline GaMnAs layers at very low substrate temperature, at which
conventional molecular beam epitaxial growth under excess As supply is not
possible due to As condensation. Secondary ion mass spectroscopy and X-ray
diffraction measurements confirmed a higher Mn content in the films grown by
this method in comparison to the GaMnAs layers grown by low temperature
molecular beam epitaxy. The lattice constant of hypothetical zinc-blende
structure MnAs is determined to be 5.9 \AA, which deviates somewhat from
previously reported values. This deviation is ascribed to growth-condition
dependent density of point defects. It is stressed that this effect must be
taken into account for any assessment of Mn content from X-ray diffraction
data.
  Magnetization measurements showed an onset of ferromagnetic ordering around
75 K for the GaMnAs layer with 10% Mn. This means that the trend of falling
Curie temperatures with increasing Mn concentrations above 5.5% is broken.
  We tentatively assign this to the variation of the carrier concentration,
including contributions from donor and acceptor centers formed by antisite
defects and Mn doping, and increased density of magnetically active Mn ions.


  We consider the zero-error capacity of deletion channels. Specifically, we
consider the setting where we choose a codebook ${\cal C}$ consisting of
strings of $n$ bits, and our model of the channel corresponds to an adversary
who may delete up to $pn$ of these bits for a constant $p$. Our goal is to
decode correctly without error regardless of the actions of the adversary. We
consider what values of $p$ allow non-zero capacity in this setting. We suggest
multiple approaches, one of which makes use of the natural connection between
this problem and the problem of finding the expected length of the longest
common subsequence of two random sequences.


  Chest X-rays or chest radiography (CXR), commonly used for medical
diagnostics, typically enables limited imaging compared to computed tomography
(CT) scans, which offer more detailed and accurate three-dimensional data,
particularly contrast-enhanced scans like CT Pulmonary Angiography (CTPA).
However, CT scans entail higher costs, greater radiation exposure, and are less
accessible than CXRs. In this work we explore cross-modal translation from a 2D
low contrast-resolution X-ray input to a 3D high contrast and
spatial-resolution CTPA scan. Driven by recent advances in generative AI, we
introduce a novel diffusion-based approach to this task. We evaluate the models
performance using both quantitative metrics and qualitative feedback from
radiologists, ensuring diagnostic relevance of the generated images.
Furthermore, we employ the synthesized 3D images in a classification framework
and show improved AUC in a PE categorization task, using the initial CXR input.
The proposed method is generalizable and capable of performing additional
cross-modality translations in medical imaging. It may pave the way for more
accessible and cost-effective advanced diagnostic tools. The code for this
project is available: https://github.com/NoaCahan/X-ray2CTPA .


  Let $\Gamma$ be a finitely generated group and $N$ be a normal subgroup of
$\Gamma$. The fiber product of $\Gamma$ with respect to $N$ is the subgroup
$\Gamma \times_N \Gamma=\{(\gamma, \gamma w): \gamma \in \Gamma, w \in N\}$ of
the direct product $\Gamma \times \Gamma$. For every representation
$\rho:\Gamma \times_N \Gamma \rightarrow \mathsf{GL}_d(k)$, where $k$ is a
local field, we establish upper bounds for the norm of the Cartan projection of
$\rho$ in terms of a fixed word length function on $\Gamma$. As an application,
we exhibit examples of finitely generated and finitely presented fiber products
$P=\Gamma \times_N \Gamma$, where $\Gamma$ is linear and Gromov hyperbolic,
such that $P$ does not admit linear representations which are quasi-isometric
embeddings.


  A relativistic Hartree-Fock Lagrangian including a chiral potential and
nucleon polarisation is investigated in hopes of providing a better description
of dense nuclear matter. We fully consider the contribution of the exchange
Fock term to the energy and the self-energies, and in addition we investigate
the nucleon's compositeness and finite size effects (confinement and form
factors) and short range correlations modeled by a Jastrow ansatz. These
effects are added step by step, such that their impact on the dense matter
properties can be analysed in details. The parameters of the model are adjusted
to reproduce fundamental properties related to the QCD theory at low energy,
such as the chiral symmetry breaking, nucleon's quark substructure and
Lattice-QCD predictions, as well as two empirical properties at saturation: the
binding energy and the density. All other empirical parameters, e.g., symmetry
energy and its slope, incompressibility modulus, effective mass, as well as
spin-isospin Landau-Midgal parameter are predictions of the models and can be
used to evaluate the gain of the different approximation schemes in describing
nuclear properties. Bayesian statistics is employed in order to propagate
parameter uncertainties into predictions for the nuclear matter properties. We
show that the splitting of the effective Landau mass is largely influenced by
the value of the $\rho^T$ coupling, and we show that the fit to the symmetry
energy, which induces an increase of the coupling constant $g_\rho$ by about
20-25% compared to the case where it is fixed by the quark model, provides a
very good EoS compatible with the present nuclear physics knowledge.


  Progress on researches in the field of molecules at cold and ultracold
temperatures is reported in this review. It covers extensively the experimental
methods to produce, detect and characterize cold and ultracold molecules
including association of ultracold atoms, deceleration by external fields and
kinematic cooling. Confinement of molecules in different kinds of traps is also
discussed. The basic theoretical issues related to the knowledge of the
molecular structure, the atom-molecule and molecule-molecule mutual
interactions, and to their possible manipulation and control with external
fields, are reviewed. A short discussion on the broad area of applications
completes the review.


  We study spiky configurations of membranes in the SO(d)xSU(N) invariant
matrix models. A class of exact solutions (analogous to plane-waves) of the
corresponding Schroedinger equation for an arbitrary N is discussed. If the
large N limit is performed so that the energy scales like N^2, the N=infinity
wavefunctions reduce to the ground state of the d-dimensional harmonic
oscillator.


  A cyclic base ordering of a connected graph $G$, is a cyclic ordering of
$E(G)$ such that every cyclically consecutive $|V(G)|-1$ edges form a spanning
tree. In this project, we study cyclic base ordering of various families of
graphs, including square of cycles, wheel graphs, generalized wheel graphs and
broken wheel graphs, fan and broken fan graphs, prism graphs, and maximal
2-degenerate graphs. We also provide a polynomial time algorithm to verify any
giving edge ordering is a cyclic base ordering.


  Black hole (BH) spectroscopy has emerged as a powerful approach to extract
spacetime information from gravitational wave (GW) observed signals. Yet,
quasinormal mode (QNM) spectral instability under high wave-number
perturbations has been recently shown to be a common classical general
relativistic phenomenon [1]. This requires to assess its impact on the BH QNM
spectrum, in particular on BH QNM overtone frequencies. We conclude: i)
perturbed BH QNM overtones are indeed potentially observable in the GW
waveform, providing information on small-scale environment BH physics, and ii)
their detection poses a challenging data analysis problem of singular interest
for LISA astrophysics. We adopt a two-fold approach, combining theoretical
results from scattering theory with a fine-tuned data analysis on a
highly-accurate numerical GW ringdown signal. The former introduces a set of
effective parameters (partially lying on a BH Weyl law) to characterise QNM
instability physics. The latter provides a proof-of-principle demonstrating
that the QNM spectral instability is indeed accessible in the time-domain GW
waveform, though certainly requiring large signal-to-noise ratios. Particular
attention is devoted to discuss the patterns of isospectrality loss under QNM
instability, since the disentanglement between axial and polar GW parities may
already occur within the near-future detection range.


  In this paper we provide an extended formulation for the class of constraint
satisfaction problems and prove that its size is polynomial for instances whose
constraint graph has bounded treewidth. This implies new upper bounds on
extension complexity of several important NP-hard problems on graphs of bounded
treewidth.


  We explore the prompt magnetar progenitor scenario in the context of fast
radio burst (FRB) host galaxies demographics and offset distributions.
Magnetars are neutron stars with strong magnetic fields on the order of
$10^{15}$ G with a short decay lifetime of less than $10^4$ years. Due to their
extremely short lifetimes, magnetars should follow the demographics of galaxies
according to their current star-formation rate (SFR). Moreover, we hypothesize
that magnetars should follow the SFR profile within galaxies, which we assume
to follow an exponential profile. We construct a simple model for the host
galaxies of magnetars assuming these events track SFR in all galaxies and
compare it to observed properties from a sample of \nsecure\ secure FRB hosts.
We find the distribution of observed SFRs is inconsistent with the model at
$>95\%$ c.l. The offset distribution is consistent with this scenario; however,
this could be due to the limited sample size and the seeing limited estimates
for the effective radii of the FRB host galaxies. Despite the recent
association of an FRB with a magnetar in the Milky Way, magnetars may not be
the only source of FRBs in the universe, yet any other successful model must
account for the demographics of the FRB host in SFR and their observed
galactocentric offsets.


  We have computed phase diagrams for rotating spin-1 Bose-Einstein condensates
with long-range magnetic dipole-dipole interactions. Spin textures including
vortex sheets, staggered half-quantum- and skyrmion vortex lattices and higher
order topological defects have been found. These systems exhibit both
superfluidity and magnetic crystalline ordering and they could be realized
experimentally by imparting angular momentum in the condensate.


  Deep generative models provide a powerful set of tools to understand
real-world data. But as these models improve, they increase in size and
complexity, so their computational cost in memory and execution time grows.
Using binary weights in neural networks is one method which has shown promise
in reducing this cost. However, whether binary neural networks can be used in
generative models is an open problem. In this work we show, for the first time,
that we can successfully train generative models which utilize binary neural
networks. This reduces the computational cost of the models massively. We
develop a new class of binary weight normalization, and provide insights for
architecture designs of these binarized generative models. We demonstrate that
two state-of-the-art deep generative models, the ResNet VAE and Flow++ models,
can be binarized effectively using these techniques. We train binary models
that achieve loss values close to those of the regular models but are 90%-94%
smaller in size, and also allow significant speed-ups in execution time.


  We present the new ARTEMIS emulator suite of high resolution (baryon mass of
$2.23 \times 10^{4}$ $h^{-1}$M$_{\odot}$) zoom-in simulations of Milky Way mass
systems. Here, three haloes from the original ARTEMIS sample have been rerun
multiple times, systematically varying parameters for the stellar feedback
model, the density threshold for star formation, the reionisation redshift and
the assumed warm dark matter (WDM) particle mass (assuming a thermal relic).
From these simulations emulators are trained for a wide range of statistics
that allow for fast predictions at combinations of parameters not originally
sampled, running in $\sim 1$ms (a factor of $\sim 10^{11}$ faster than the
simulations). In this paper we explore the dependence of the central haloes'
stellar mass on the varied parameters, finding the stellar feedback parameters
to be the most important. When constraining the parameters to match the
present-day stellar mass halo mass relation inferred from abundance matching we
find that there is a strong degeneracy in the stellar feedback parameters,
corresponding to a freedom in formation time of the stellar component for a
fixed halo assembly history. We additionally explore the dependence of the
satellite stellar mass function, where it is found that variations in stellar
feedback, the reionisation redshift and the WDM mass all have a significant
effect. The presented emulators are a powerful tool which allows for
fundamentally new ways of analysing and interpreting cosmological hydrodynamic
simulations. Crucially, allowing their free (subgrid) parameters to be varied
and marginalised, leading to more robust constraints and predictions.


  Galaxies in the early universe appear to have grown too big too fast,
assembling into massive, monolithic objects more rapidly than anticipated in
the hierarchical $\Lambda$CDM structure formation paradigm. The available data
are consistent with there being a population of massive galaxies that form
early ($z \gtrsim 10$) and follow an approximately exponential star formation
history with a short ($\lesssim 1$ Gyr) e-folding timescale on the way to
becoming massive ($M_* \approx 10^{11}\;\mathrm{M}_{\odot}$) galaxies by $z =
0$, consistent with the traditional picture for the evolution of giant
elliptical galaxies. Observations of the kinematics of spiral galaxies as a
function of redshift similarly show that massive disks and their scaling
relations were in place at early times, indicating a genuine effect in mass
that cannot be explained as a quirk of luminosity evolution. That massive
galaxies could form by $z = 10$ was explicitly predicted in advance by MOND. We
discuss some further predictions of MOND, such as the early emergence of
clusters of galaxies and the cosmic web.


  We consider a class of semilinear elliptic system of the form $-\Delta
u(x,y)+\nabla W(u(x,y))=0,\quad (x,y)\in\R^{2}$ where $W:\R^{2}\to\R$ is a
double well non negative symmetric potential. We show, via variational methods,
that if the set of solutions to the one dimensional system $-\ddot q(x)+\nabla
W(q(x))=0,\ x\in\R$, which connect the two minima of $W$ as $x\to\pm\infty$ has
a discrete structure, then the given system has infinitely many layered
solutions.


  The main aim of this paper is to investigate (H_{p},L_{p})-type inequalities
for maximal operators of logarithmic means of one-dimensional Vilenkin-Fourier
series.


  Inspired by protein folding, we introduce funnels into the complex cost
function landscapes of two processes, the tuning of networks, and the jamming
of ideal spheres. In both processes, geometrical frustration plays a role --
tuning pressure differences between pairs of target nodes far from the source
in a flow network impedes tuning of nearby pairs more than the reverse process,
while unjamming the system in one region can make it more difficult to unjam
elsewhere. By modifying the cost functions to control the order in which
functions are tuned or regions unjam, we smooth out local minima while leaving
global minima unaffected, significantly increasing the success rate of reaching
global minima.


  Mean field games model equilibria in games with a continuum of players as
limiting systems of symmetric $n$-player games with weak interaction between
the players. We consider a finite-state, infinite-horizon problem with two cost
criteria: discounted and ergodic. Under the Lasry--Lions monotonicity condition
we characterize the stationary ergodic mean field game equilibrium by a mean
field game system of two coupled equations: one for the value and the other for
the stationary measure. This system is linked with the ergodic master equation.
Several discounted mean field game systems are utilized in order to set up the
relevant discounted master equations. We show that the discounted master
equations are smooth, uniformly in the discount factor. Taking the discount
factor to zero, we achieve the smoothness of the ergodic master equation.


  The effects of ion temperature are discussed in a two-ion electron plasma and
for a model applicable to the oscillating sheath theory that has recently been
much in the focus of researchers. The differences between the fluid and kinetic
models have been pointed out, as well as the differences between the
approximative kinetic description (which involves the expansion of the plasma
dispersion function), and the exact kinetic description. It is shown that the
approximative kinetic description, first, can not describe the additional
acoustic mode which naturally exists in the plasma with an additional ion
population with a finite temperature, and, second, it yields an inaccurate
Landau damping of the bulk ion acoustic mode. The reasons for these two
failures are described. In addition to this, a fluid model is presented that is
capable of capturing both of these features that are missing in the
approximative kinetic description, i.e., two (fast and slow) ion acoustic
modes, and the corresponding Landau damping of both modes.


  We report first-principles results on the electronic structure of various
silicene structures. For planar and simply buckled silicenes, we confirm their
zero-gap nature and show a significant renormalization of their Fermi velocity
by including many-electron effects. However, the other two recently proposed
silicene structures exhibit a finite band gap, indicating that they are gapped
semiconductors instead of previously expected Dirac-fermion semimetals.
Moreover, our calculated quasiparticle gap quantitatively explains the recent
angle-resolved photoemission spectroscopy measurements. In particular, the band
gap of the latter two structures can be tuned in a wide range by applying
strain, giving hope to bipolar-devices applications.


  In recent years, neural networks have shown impressive performance gains on
long-standing AI problems, and in particular, answering queries from natural
language text. These advances raise the question of whether they can be
extended to a point where we can relax the fundamental assumption of database
management, namely, that our data is represented as fields of a pre-defined
schema.
  This paper presents a first step in answering that question. We describe
NeuralDB, a database system with no pre-defined schema, in which updates and
queries are given in natural language. We develop query processing techniques
that build on the primitives offered by the state of the art Natural Language
Processing methods.
  We begin by demonstrating that at the core, recent NLP transformers, powered
by pre-trained language models, can answer select-project-join queries if they
are given the exact set of relevant facts. However, they cannot scale to
non-trivial databases and cannot perform aggregation queries. Based on these
findings, we describe a NeuralDB architecture that runs multiple Neural SPJ
operators in parallel, each with a set of database sentences that can produce
one of the answers to the query. The result of these operators is fed to an
aggregation operator if needed. We describe an algorithm that learns how to
create the appropriate sets of facts to be fed into each of the Neural SPJ
operators. Importantly, this algorithm can be trained by the Neural SPJ
operator itself. We experimentally validate the accuracy of NeuralDB and its
components, showing that we can answer queries over thousands of sentences with
very high accuracy.


  We show that puzzling recent experimental results on spin diffusion in a
strongly interacting atomic gas may be understood in terms of the predicted
spin diffusion coefficient for a generic strongly interacting system. Three
important features play a central role: a) Fick's law for diffusion must be
modified to allow for the trapping potential, b) the diffusion coefficient is
inhomogeneous, due to the density variations in the cloud and c) the diffusion
approximation fails in the outer parts of the cloud, where the mean free path
is long.


  Let Y be a normal crossing divisor in the smooth projective algebraic variety
X (defined over ${\mathbb C}$) and let U be a tubular neighbourhood of Y in X.
We construct homological cycles generating $H_*(A,B)$, where (A,B) is one of
the following pairs $(Y,\emptyset)$, (X,Y), (X,X-Y), $(X-Y,\emptyset)$ and
$(\partial U,\emptyset)$. The construction is compatible with the weights in
$H_*(A,B,{\mathbb Q})$ of Deligne's mixed Hodge structure.


  It is (or should be) well-known that the Hawking flux that reaches spatial
infinity is extremely sparse, and extremely thin, with the Hawking quanta,
one-by-one, slowly dribbling out of the black hole. The typical time between
quanta reaching infinity is much larger than the timescale set by the energy of
the quanta. Among other things, this means that the Hawking evaporation of a
black hole should be viewed as a sequential cascade of 2-body decays.


  Logarithmic potentials and many other potentials satisfy maximum principle.
The dyadic version of logarithmic potential can be easily introduced, it lives
on dyadic tree and also satisfies maximum principle. But its analog on bi-tree
does not have this property. We prove here that "on average" we can still have
something like maximum principle on bi-tree. We use the surrogate maximum
principle to prove embedding theorems of Carleson type on bi-disc.


  These lecture notes give an introduction to a number of ideas and methods
that have been useful in the study of complex systems ranging from spin glasses
to D-branes on Calabi-Yau manifolds. Topics include the replica formalism,
Parisi's solution of the Sherrington-Kirkpatrick model, overlap order
parameters, supersymmetric quantum mechanics, D-brane landscapes and their
black hole duals.


  In our setting enumeration amounts to generate all solutions of a problem
instance without duplicates. We address the problem of enumerating the models
of B-formulae. A B-formula is a propositional formula whose connectives are
taken from a fixed set B of Boolean connectives. Without imposing any specific
order to output the solutions, this task is solved. We completely classify the
complexity of this enumeration task for all possible sets of connectives B
imposing the orders of (1) non-decreasing weight, (2) non-increasing weight;
the weight of a model being the number of variables assigned to 1. We consider
also the weighted variants where a non-negative integer weight is assigned to
each variable and show that this add-on leads to more sophisticated enumeration
algorithms and even renders previously tractable cases intractable, contrarily
to the constraint setting. As a by-product we obtain complete complexity
classifications for the optimization problems known as Min-Ones and Max-Ones
which are in the B-formula setting two different tasks.


  The idea that dark matter could be made of stable relics of microscopic black
holes is not new. In this article, we revisit this hypothesis, focusing on the
creation of black holes by the scattering of trans-Planckian particles in the
early Universe. The only new physics required in this approach is an unusually
high-energy scale for inflation. We show that dark matter emerges naturally and
we study the question of fine-tuning. We finally give some lines of thoughts
for a possible detection.


  A perfect number is a positive integer $N$ such that the sum of all the
positive divisors of $N$ equals $2N$, denoted by $\sigma(N) = 2N$. The question
of the existence of odd perfect numbers (OPNs) is one of the longest unsolved
problems of number theory. This thesis presents some of the old as well as new
approaches to solving the OPN Problem. In particular, a conjecture predicting
an injective and surjective mapping $X = \sigma(p^k)/p^k, Y = \sigma(m^2)/m^2$
between OPNs $N = {p^k}{m^2}$ (with Euler factor $p^k$) and rational points on
the hyperbolic arc $XY = 2$ with $1 < X < 1.25 < 1.6 < Y < 2$ and $2.85 < X + Y
< 3$, is disproved. Various results on the abundancy index and solitary numbers
are used in the disproof. Numerical evidence against the said conjecture will
likewise be discussed. We will show that if an OPN $N$ has the form above, then
$p^k < (2/3){m^2}$ follows from \cite{D10}. We will also attempt to prove a
conjectured improvement of this last result to $p^k < m$ by observing that
$\sigma(p^k)/m \neq 1$ and $\sigma(p^k)/m \neq \sigma(m)/p^k$ in all cases.
Lastly, we also prove the following generalization: If $N =
\displaystyle\prod_{i=1}^r {{p_i}^{{\alpha}_i}}$ is the canonical factorization
of an OPN $N$, then $$\sigma({p_i}^{{\alpha}_i}) \leq
(2/3){\frac{N}{{p_i}^{{\alpha}_i}}}$$ for all $i$. This gives rise to the
inequality $$N^{2 - r} \leq (1/3)(2/3)^{r - 1}$$ which is true for all $r$,
where $r = \omega(N)$ is the number of distinct prime factors of $N$.


  The non-interacting and non-Hermitian, parity-time ($\mathcal{PT}$)-symmetric
Anderson model exhibits an exceptional point (EP) at a non-Hermitian coupling
$g=1$, which remains unrenormalized in the presence of interactions (Lourenco
et al, arXiv:1806.03116), where the EP was shown to coincide with the quantum
critical point (QCP) for Kondo destruction. In this work, we consider a quantum
dot hybridizing with metallic leads having Rashba spin-orbit coupling
($\lambda$). We show that for a non-Hermitian hybridization, $\lambda$ can
renormalize the exceptional point even in the non-interacting case, stabilizing
$\mathcal{PT}$-symmetry beyond $g=1$. Through exact diagonalization of a
zero-bandwidth, three-site model, we show that the quantum critical point and
the exceptional point bifurcate, with the critical point for Kondo destruction
at $g_c=1$, and the exceptional coupling being $g_{\scriptscriptstyle{EP}} > 1$
for all $U\neq 0$ and $\lambda\geq 0; \lambda\neq U/2$. On the line
$\lambda=U/2$, the critical point and the EP again coincide at
$g_c=g_{\scriptscriptstyle{EP}}=1$. The full model with finite bandwidth leads
is investigated through the slave-boson approach, using which we show that, in
the strong coupling regime, $\lambda$ and interactions co-operate in strongly
reducing the critical point associated with Kondo destruction, below the
$\lambda=0$ value.


  In this paper, we consider the challenge of reconstructing jointly sparse
vectors from linear measurements. Firstly, we show that by utilizing the rank
of the output data matrix we can reduce the problem to a full column rank case.
This result reveals a reduction in the computational complexity of the original
problem and enables a simple implementation of joint sparse recovery algorithms
for full-rank setting. Secondly, we propose a new method for joint sparse
recovery in the form of a non-convex optimization problem on a non-compact
Stiefel manifold. In our numerical experiments our method outperforms the
commonly used $\ell_{2,1}$ minimization in the sense that much fewer
measurements are required for accurate sparse reconstructions. We postulate
this approach possesses the desirable rank aware property, that is, being able
to take advantage of the rank of the unknown matrix to improve the recovery.


  A new tuning mechanism is demonstrated for single-mode metal-clad plasmonic
lasers, in which refractive-index of the laser's surrounding medium affects the
resonant-cavity mode in the same vein as refractive-index of gain medium inside
the cavity. Reversible, continuous, and mode-hop-free tuning of ~57GHz is
realized for single-mode narrow-beam terahertz plasmonic quantum-cascade lasers
(QCLs), which is demonstrated at much more practical temperature of 78K. The
tuning is based on post-process deposition/etching of a dielectric
(Silicon-dioxide) on a QCL chip that has already been soldered and wire-bonded
onto a copper mount. This is a considerably larger tuning range compared to
previously reported results for terahertz QCLs with directional far-field
radiation patterns. The key enabling mechanism for tuning is a recently
developed antenna-feedback scheme for plasmonic lasers, which leads to
generation of hybrid surface-plasmon-polaritons propagating outside the cavity
of the laser with a large spatial extent. The effect of dielectric deposition
on QCL's characteristics is investigated in detail including that on maximum
operating temperature, peak output power and far-field radiation patterns.
Single-lobed beam with low divergence (<7deg) is maintained through the tuning
range. The antenna-feedback scheme is ideally suited for modulation of
plasmonic lasers and their sensing applications due to the sensitive dependence
of spectral and radiative properties of the laser on its surrounding medium.


  We demonstrate that in the mass independent renormalization scheme. the
renormalization group equations associated with the unphysical parameters that
characterize the renormalization scheme and the mass scale leads to summation
that results in a cancellation between the implicit and explicit dependence on
these parameters. The resulting perturbative expansion is consequently
independent of these arbitrary parameters. We illustrate this by considering R,
the cross section for e+e- -> hadrons.


  Topological materials featuring exotic band structures, unconventional
current flow patterns, and emergent organizing principles offer attractive
platforms for the development of next-generation transformative quantum
electronic technologies. The family of MnBi2Te4 (Bi2Te3)n materials is
naturally relevant in this context due to their nontrivial band topology,
tunable magnetism, and recently discovered extraordinary quantum transport
behaviors. Despite numerous pioneering studies, to date, the local magnetic
properties of MnBi2Te4 (Bi2Te3)n remain an open question, hindering a
comprehensive understanding of their fundamental material properties.
Exploiting nitrogen-vacancy (NV) centers in diamond, we report nanoscale
quantum imaging of magnetic phase transitions and spin fluctuations in
exfoliated MnBi2Te4 (Bi2Te3)n flakes, revealing the underlying spin transport
physics and magnetic domains at the nanoscale. Our results highlight the unique
advantage of NV centers in exploring the magnetic properties of emergent
quantum materials, opening new opportunities for investigating the interplay
between topology and magnetism.


  Previous work on action representation learning focused on global
representations for short video clips. In contrast, many practical
applications, such as video alignment, strongly demand learning the intensive
representation of long videos. In this paper, we introduce a new framework of
contrastive action representation learning (CARL) to learn frame-wise action
representation in a self-supervised or weakly-supervised manner, especially for
long videos. Specifically, we introduce a simple but effective video encoder
that considers both spatial and temporal context by combining convolution and
transformer. Inspired by the recent massive progress in self-supervised
learning, we propose a new sequence contrast loss (SCL) applied to two related
views obtained by expanding a series of spatio-temporal data in two versions.
One is the self-supervised version that optimizes embedding space by minimizing
KL-divergence between sequence similarity of two augmented views and prior
Gaussian distribution of timestamp distance. The other is the weakly-supervised
version that builds more sample pairs among videos using video-level labels by
dynamic time wrapping (DTW). Experiments on FineGym, PennAction, and Pouring
datasets show that our method outperforms previous state-of-the-art by a large
margin for downstream fine-grained action classification and even faster
inference. Surprisingly, although without training on paired videos like in
previous works, our self-supervised version also shows outstanding performance
in video alignment and fine-grained frame retrieval tasks.


  The phenomenon of spontaneous scalarization of Reissner-Nordstr\"{o}m (RN)
black holes has recently been found in an Einstein-Maxwell-scalar (EMS) model
due to a non-minimal coupling between the scalar and Maxwell fields. Non-linear
electrodynamics, e.g., Born-Infeld (BI) electrodynamics, generalizes Maxwell's
theory in the strong field regime. Non-minimally coupling the BI field to the
scalar field, we study spontaneous scalarization of an
Einstein-Born-Infeld-scalar (EBIS) model in this paper. It shows that there are
two types of scalarized black hole solutions, i.e., scalarized RN-like and
Schwarzschild-like solutions. Although the behavior of scalarized RN-like
solutions in the EBIS model is quite similar to that of scalarize solutions in
the EMS model, we find that there exist significant differences between
scalarized Schwarzschild-like solutions in the EBIS model and scalarized
solutions in the EMS model. In particular, the domain of existence of
scalarized Schwarzschild-like solutions possesses a certain region, which is
composed of two branches. The branch of larger horizon area is a family of
disconnected scalarized solutions, which do not bifurcate from scalar-free
black holes. However, the branch of smaller horizon area may or may not
bifurcate from scalar-free black holes depending on the parameters.
Additionally, these two branches of scalarized solutions can be both
entropically disfavored over comparable scalar-free black holes in some
parameter region.


  A conformal structure on a manifold $M^n$ induces natural second order
conformally invariant operators, called M\"obius and Laplace structures, acting
on specific weight bundles of $M$, provided that $n\ge 3$. By extending the
notions of M\"obius and Laplace structures to the case of surfaces and curves,
we develop here the theory of extrinsic conformal geometry for submanifolds,
find tensorial invariants of a conformal embedding, and use these invariants to
characterize various forms of geodesic submanifolds.


  In this work, we investigate the following question. Given a Pythagorean
triangle BCA, with the right angle at C, let P be a point on the hupotenuse BA;
and let D and E be the perpendicular projections of the point P onto the sides
BC and CA respectively.When is either of the right triangles BDP and PEA
Pythagorean? As it turns out, according to Theorem1, they are either both
Pythagorean, or neither of them is.When they are both Pythagorean,a complete
parametric description of these two triangles is given; in terms of the
parameters that describe the Pythagorean triangle BCA. Later in the paper, we
offer a complete analysis of three special cases:the case wherein the point P
is the midpoint M of the hypotenuse BA; the case when P is the foot I of the 90
degree angle bisector;and the case in ehich the point P is the foot F of the
perpendicular drawn from the vertex C to the hypotenuse. After that, some other
cases are investigated as well.Specifically, we consider the case in which not
only the triangles BDP and PEA are Pythagorean;but the four congruent right
triangles DCP,DEP,DCE,and CEP,are Pythagorean as well.Precise conditions in
order for this to occur, are given in Theorem7. Also,in Theorem 2, part(i), we
show that if the triangle BCA is a primitive Pythagorean triangle, there exists
no point P along the hypotenuse BA, for which both right triangles BDP and PEA
are Pythagorean.


  Learning fine-grained distinctions between vocabulary items is a key
challenge in learning a new language. For example, the noun "wall" has
different lexical manifestations in Spanish -- "pared" refers to an indoor wall
while "muro" refers to an outside wall. However, this variety of lexical
distinction may not be obvious to non-native learners unless the distinction is
explained in such a way. In this work, we present a method for automatically
identifying fine-grained lexical distinctions, and extracting concise
descriptions explaining these distinctions in a human- and machine-readable
format. We confirm the quality of these extracted descriptions in a language
learning setup for two languages, Spanish and Greek, where we use them to teach
non-native speakers when to translate a given ambiguous word into its different
possible translations. Code and data are publicly released here
(https://github.com/Aditi138/LexSelection)


  In the last few decades we have witnessed how the pheromone of social insect
has become a rich inspiration source of swarm robotics. By utilising the
virtual pheromone in physical swarm robot system to coordinate individuals and
realise direct/indirect inter-robot communications like the social insect,
stigmergic behaviour has emerged. However, many studies only take one single
pheromone into account in solving swarm problems, which is not the case in real
insects. In the real social insect world, diverse behaviours, complex
collective performances and flexible transition from one state to another are
guided by different kinds of pheromones and their interactions. Therefore,
whether multiple pheromone based strategy can inspire swarm robotics research,
and inversely how the performances of swarm robots controlled by multiple
pheromones bring inspirations to explain the social insects' behaviours will
become an interesting question. Thus, to provide a reliable system to undertake
the multiple pheromone study, in this paper, we specifically proposed and
realised a multiple pheromone communication system called ColCOS$\Phi$. This
system consists of a virtual pheromone sub-system wherein the multiple
pheromone is represented by a colour image displayed on a screen, and the
micro-robots platform designed for swarm robotics applications. Two case
studies are undertaken to verify the effectiveness of this system: one is the
multiple pheromone based on an ant's forage and another is the interactions of
aggregation and alarm pheromones. The experimental results demonstrate the
feasibility of ColCOS$\Phi$ and its great potential in directing swarm robotics
and social insects research.


  Recent indications of a neutrino mass raise the possibility that the dark
matter may be hot, which would be a severe challenge for structure formation
theory. We argue that generic defect theories fare very well in hot dark matter
cosmologies, and also work in the presence of curvature or a cosmological
constant. If this model is correct, then the MAP and PLANCK missions will not
measure what people expect them to (oscillations); rather, they will measure a
broad hump.


  This article proves the uniqueness for two kinds of inverse problems of
identifying fractional orders in diffusion equations with multiple
time-fractional derivatives by pointwise observation. By means of eigenfunction
expansion and Laplace transform, we reduce the uniqueness for our inverse
problems to the uniqueness of expansions of some special function and complete
the proof.


  Atmospheric parameters for hot DB (helium atmosphere) white dwarfs near
effective temperatures of 25000K are extremely difficult to determine from
optical spectroscopy. This is particularly unfortunate, because this is the
range of variable DBV or V777 Her stars. Accurate atmospheric parameters are
needed to help or confirm the asteroseismic analysis of these objects. Another
important aspect is the new class of white dwarfs - the hot DQ - detected by
Dufour et al. (2007), with spectra dominated by carbon lines. The analysis
shows that their atmospheres are pure carbon. The origin of these stars is not
yet understood, but they may have an evolutionary link with the hotter DBs as
studied here. Our aim is to determine accurate atmospheric parameters and
element abundances and study the implications for the evolution white dwarfs of
spectral classes DB and hot DQ. High resolution UV spectra of five DBs are
studied with model atmospheres. We determine stellar parameters and abundances
or upper limits of C and Si. These objects are compared with cooler DBs below
20000K. We find photospheric C and no other heavy elements - with extremely
high limits on the C/Si ratio - in two of the five hot DBs. We compare various
explanations for this unusual composition, which have been proposed in the
literature: accretion of interstellar or circumstellar matter, radiative
levitation, carbon dredge-up from deeper interior below the helium layer, and a
residual stellar wind. None of these explanations is completely satisfactory,
and the problem of the origin of the hot DQ remains an open question.


  The maximum entanglement between two coupled qubits in the steady state under
two independent incoherent sources of excitation is reported. Asymmetric
configurations where one qubit is excited while the other one dissipates the
excitation are optimal for entanglement, reaching values three times larger
than with thermal sources. The reason is the purification of the steady state
mixture (that includes a Bell state) thanks to the saturation of the pumped
qubit. Photon antibunching between the cross emission of the qubits can be used
to experimentally evidence the large degrees of entanglement.


  We expand on an idea of Vinberg to take a tensor space and the natural Lie
algebra that acts on it and embed their direct sum into an auxiliary algebra.
Viewed as endomorphisms of this algebra, we associate adjoint operators to
tensors. We show that the group actions on the tensor space and on the adjoint
operators are consistent, which means that the invariants of the adjoint
operator of a tensor, such as the Jordan decomposition, are invariants of the
tensor. We show that there is an essentially unique algebra structure that
preserves the tensor structure and has a meaningful Jordan decomposition. We
utilize aspects of these adjoint operators to study orbit separation and
classification in examples relevant to tensor decomposition and quantum
information.


  Different non-classicality criteria expressed in the form of inequalities
among intensity moments and elements of photon-number distributions are applied
to noisy twin beams and other two-mode states obtained from a twin beam by
using a beam splitter. Their performance in revealing the non-classicality is
judged in comparison with the exact results provided by suitable entanglement
and local non-classicality quantifiers. Whereas the non-classicality of noisy
twin beams is always revealed by these criteria, not all the nonclassical
states obtained at the output of the beam splitter can be identified by these
experimentally easily reachable criteria.


  Searches for known waveforms in gravitational wave detector data are often
done using matched filtering. When used on real instrumental data, matched
filtering often does not perform as well as might be expected, because
non-stationary and non-Gaussian detector noise produces large spurious filter
outputs (events). This paper describes a chi-squared time-frequency test which
is one way to discriminate such spurious events from the events that would be
produced by genuine signals. The method works well only for broad-band signals.
The case where the filter template does not exactly match the signal waveform
is also considered, and upper bounds are found for the expected value of
chi-squared.


  We have successfully synthesized the fluoride-arsenide compounds
Ca$_{1-x}$RE$_x$FeAsF (RE=Nd, Pr; x=0, 0.6). The x-ray powder diffraction
confirmed that the main phases of our samples are Ca$_{1-x}$RE$_x$FeAsF with
the ZrCuSiAs structure. By measuring resistivity, superconductivity was
observed at 57.4 K in Nd-doped and 52.8 K in Pr-doped samples with x=0.6. Bulk
superconductivity was also proved by the DC magnetization measurements in both
samples. Hall effect measurements revealed hole-like charge carriers in the
parent compound CaFeAsF with a clear resistivity anomaly below 118 K, while the
Hall coefficient $R_H$ in the normal state is negative for the superconducting
samples Ca$_{0.4}$Nd$_{0.6}$FeAsF and Ca$_{0.4}$Pr$_{0.6}$FeAsF. This indicates
that the rare earth element doping introduces electrons into CaFeAsF which
induces the high temperature superconductivity.


  Recently, stripe phases in spin-orbit coupled Bose-Einstein condensates
(BECs) have attracted much attention since they are identified as supersolid
phases. In this paper, we exploit experimentally reachable parameters and show
theoretically that annular stripe phases with large stripe spacing and high
stripe contrast can be achieved in spin-orbital-angular-momentum coupled
(SOAMC) BECs. In addition to using Gross-Pitaevskii numerical simulations, we
develop a variational ansatz that captures the essential interaction effects to
first order, which are not present in the ansatz employed in previous
literature. Our work should open the possibility toward directly observing
stripe phases in SOAMC BECs in experiments.


  By means of a Floquet analysis, we study the quantum dynamics of a fully
connected Lipkin-Ising ferromagnet in a periodically driven transverse field
showing that thermalization in the steady state is intimately connected to
properties of the $N\to \infty$ classical Hamiltonian dynamics. When the
dynamics is ergodic, the Floquet spectrum obeys a Wigner-Dyson statistics and
the system satisfies the eigenstate thermalization hypothesis (ETH):
Independently of the initial state, local observables relax to the $T=\infty$
thermal value, and Floquet states are delocalized in the Hilbert space. On the
contrary, if the classical dynamics is regular no thermalization occurs. We
further discuss the relationship between ergodicity and dynamical phase
transitions, and the relevance of our results to other fully-connected
periodically driven models (like the Bose-Hubbard), and possibilities of
experimental realization in the case of two coupled BEC.


  We reexamine, within the nonrelativistic constituent quark model (NRCQM), a
recent claim that the current data on the strangeness form factors indicates
that the uuds\bar{s} component in the proton is such that the uuds subsystem
has the mixed spatial symmetry [31]_X and flavor spin symmetry
[4]_{FS}[22]_F[22]_S, with \bar{s} in S state (configuration I). We find this
claim to be invalid if corrected expressions for the contributions of the
transition current to G_A^s and G_E^s are used. We show that, instead, it is
the lowest-lying uuds\bar{s} configuration with uuds subsystem of completely
symmetric spatial symmetry [4]_X and flavor spin symmetry [4]_{FS}[22]_F[22]_S,
with \bar{s} in P state (configuration II), which could account for the
empirical signs of all form factors G_E^s, G_M^s, and G_A^s. Further, we find
that removing the center-of-mass motion of the clusters will considerably
enhance the contributions of the transition current. We also demonstrate that
it is possible to give a reasonable description of the existing form factors
data with a tiny probability P_{s\bar{s}}=0.025% for the uuds\bar{s} component.
We further see that with a small admixture of configuration I, the agreement of
our prediction with data for G_A^s at low-q^2 region can be markedly improved.
We find that without removing CM motion, P_{s\bar{s}} would be overestimated by
about a factor of four in the case when transition current dominates. We also
explore the consequence of a recent estimate reached from analyzing existing
data on \bar{d} -\bar{u}, s +\bar{s}, and \bar{u} + \bar{d} - s -\bar{s}, that
P_{s\bar{s}} lies between 2.4-2.9%. It would lead to a large size for the
five-quark system and a small bump in both G_E^s+\eta G_M^s and G_E^s in the
region of q^2<=0.1 GeV^2 within the considered model.


  Explicit solution for the 2-point correlation function in a non-equilibrium
steady state of a nearly isotropic boundary-driven open XY spin 1/2 chain in
the Lindblad formulation is provided. A non-equilibrium quantum phase
transition from exponentially decaying correlations to long-range order is
discussed analytically. In the regime of long-range order a new phenomenon of
correlation resonances is reported, where the correlation response of the
system is unusually high for certain discrete values of the external bulk
parameter, e.g. the magnetic field.


  Recent studies have demonstrated that In-Context Learning (ICL), through the
use of specific demonstrations, can align Large Language Models (LLMs) with
human preferences known as In-Context Alignment (ICA), indicating that models
can comprehend human instructions without requiring parameter adjustments.
However, the exploration of the mechanism and applicability of ICA remains
limited. In this paper, we begin by dividing the context text used in ICA into
three categories: format, system prompt, and example. Through ablation
experiments, we investigate the effectiveness of each part in enabling ICA to
function effectively. We then examine how variants in these parts impact the
model's alignment performance. Our findings indicate that the example part is
crucial for enhancing the model's alignment capabilities, with changes in
examples significantly affecting alignment performance. We also conduct a
comprehensive evaluation of ICA's zero-shot capabilities in various alignment
tasks. The results indicate that compared to parameter fine-tuning methods, ICA
demonstrates superior performance in knowledge-based tasks and tool-use tasks.
However, it still exhibits certain limitations in areas such as multi-turn
dialogues and instruction following.


  The ferromagnetic phase in pure neutron matter as well as charge neutral,
beta-equilibrated nuclear matter is considered. We employ Quantum
Hadrodynamics, a relativistic field theory description of nuclear matter with
meson degrees of freedom, and include couplings between the baryon (proton and
neutron) magnetic dipole moment as well as between their charge and the
magnetic field in the Lagrangian density describing such a system. We vary the
strength of the baryon magnetic dipole moment till a non-zero value of the
magnetic field, for which the total energy density of the magnetised system is
at a minimum, is found. The system is then assumed to be in the ferromagnetic
state.
  The ferromagnetic equation of state is employed to study matter in the
neutron star interior. We find that as the density increases the ferromagnetic
field does not increase continuously, but exhibit sudden rapid increases. These
sudden increases in the magnetic field correspond to shifts between different
configurations of the charged particle's Landau levels and can have significant
observational consequences for neutron stars. We also found that although the
ferromagnetic phase softens the neutron star equation of state it does not
significantly alter the star's mass-radius relationship.
  The properties of magnetised symmetric nuclear matter were also studied. We
confirm that magnetised matter tends to be more proton-rich but become more
weakly bound for stronger magnetic fields. We show that the behaviour of the
compressibility of nuclear matter is influenced by the Landau quantisation and
tends to have an oscillatory character as it increases with the magnetic field.
The symmetry energy also exhibits similar behaviour.


  In this paper we consider the spectral and nonlinear stability of periodic
traveling wave solutions of a generalized Kuramoto-Sivashinsky equation. In
particular, we resolve the long-standing question of nonlinear modulational
stability by demonstrating that spectrally stable waves are nonlinearly stable
when subject to small localized (integrable) perturbations. Our analysis is
based upon detailed estimates of the linearized solution operator, which are
complicated by the fact that the (necessarily essential) spectrum of the
associated linearization intersects the imaginary axis at the origin. We carry
out a numerical Evans function study of the spectral problem and find bands of
spectrally stable periodic traveling waves, in close agreement with previous
numerical studies of Frisch-She-Thual, Bar-Nepomnyashchy,
Chang-Demekhin-Kopelevich, and others carried out by other techniques. We also
compare predictions of the associated Whitham modulation equations, which
formally describe the dynamics of weak large scale perturbations of a periodic
wave train, with numerical time evolution studies, demonstrating their
effectiveness at a practical level. For the reader's convenience, we include in
an appendix the corresponding treatment of the Swift-Hohenberg equation, a
nonconservative counterpart of the generalized Kuramoto-Sivashinsky equation
for which the nonlinear stability analysis is considerably simpler, together
with numerical Evans function analyses extending spectral stability analyses of
Mielke and Schneider.


  We investigate the existence problem of group invariant matrices using
algebraic approaches. We extend the usual concept of multipliers to group rings
with cyclotomic integers as coefficients. This concept is combined with the
field descent method and rational idempotents to develop new non-existence
results.


  Quantitative investigation on the current-induced torque in antiferromagnets
represents a great challenge, due to the lack of an independent method for
controlling N\'eel vectors. Here by utilizing an antiferromagnetic insulator
with Dzyaloshinskii-Moriya interaction, {\alpha}-Fe2O3, we show that the N\'eel
vector can be controlled with a moderate external field, which is further
utilized to calibrate the current-induced magnetic dynamics. We find that the
current-induced magnetoresistance change in antiferromagnets can be complicated
by resistive switching that does not have a magnetic origin. By excluding
non-magnetic switching and comparing the current-induced dynamics with the
field-induced one, we determine the nature and magnitude of current-induced
effects in Pt/{\alpha}-Fe2O3 bilayer films.


  We propose a practical and scalable Gaussian process model for large-scale
nonlinear probabilistic regression. Our mixture-of-experts model is
conceptually simple and hierarchically recombines computations for an overall
approximation of a full Gaussian process. Closed-form and distributed
computations allow for efficient and massive parallelisation while keeping the
memory consumption small. Given sufficient computing resources, our model can
handle arbitrarily large data sets, without explicit sparse approximations. We
provide strong experimental evidence that our model can be applied to large
data sets of sizes far beyond millions. Hence, our model has the potential to
lay the foundation for general large-scale Gaussian process research.


  We present new SO(4)-invariant and non-supersymmetric instanton solutions for
the conformally coupled m^2=-2 and massive m^2=+4 (pseudo)scalars arising from
a consistent truncation of 11-dimensional supergravity over AdS_4 x S^7/Z_k
when the internal space is a S^1 Hopf fibration on CP^3, and we consider
backreaction. In fact, the bulk configurations associate with (anti)membranes
wrapped around mixed internal (and external) directions, which in turn probe
the Wick-rotated or skew-whiffed background, break all supersymmetries as well
as parity invariance. From near the boundary behavior of the closed solution
for the coupled bulk (pseudo)scalar, we get a marginal triple-trace deformation
with mixed boundary condition (valid also for the bulk massless m^2=0
(pseudo)scalar, raised when considering the external space backreaction, with
Dirichlet boundary condition) and as a result, the corresponding boundary
effective potential is unbounded from below and causes an instability because
of the Fubini-like instanton. Presenting dual effective actions, we see that
the boundary solutions and counterparts realize in singlet sectors of
three-dimensional U(N) and O(N) Chern--Simons-matter field theories. In
particular, we use versions of massless and mass-deformed regular and critical
boson and fermion models, find instantons and confirm state-operator
AdS_4/CFT_3 correspondence and also Bose-Fermi duality at the level of the
solutions. In addition, we discuss on relations of our setups with Vasiliev's
Higher-Spin theories, deformations of the Aharony-Bergman-Jafferis-Maldacena
model and other related studies.


  Much research has studied foundations for correct and reliable
communication-centric systems. A salient approach to correctness uses session
types to enforce structured communications; a recent approach to reliability
uses reversible actions as a way of reacting to unanticipated events or
failures. This note develops a simple observation: the machinery required to
define asynchronous semantics and monitoring can also support reversible
protocols. We propose a process framework of session communication in which
monitors support reversibility. A key novelty in our approach are session types
with present and past, which allow us to streamline the semantics of reversible
actions.


  We propose a formulation of adaptive computation of free energy differences,
in the ABF or nonequilibrium metadynamics spirit, using conditional
distributions of samples of configurations which evolve in time. This allows to
present a truly unifying framework for these methods, and to prove convergence
results for certain classes of algorithms. From a numerical viewpoint, a
parallel implementation of these methods is very natural, the replicas
interacting through the reconstructed free energy. We show how to improve this
parallel implementation by resorting to some selection mechanism on the
replicas. This is illustrated by computations on a model system of
conformational changes.


  In this study, the modulation of symbols on OFDM subcarriers is classified
for transmissions following Wi-Fi~6 and 5G downlink specifications. First, our
approach estimates the OFDM symbol duration and cyclic prefix length based on
the cyclic autocorrelation function. We propose a feature extraction algorithm
characterizing the modulation of OFDM signals, which includes removing the
effects of a synchronization error. The obtained feature is converted into a 2D
histogram of phase and amplitude and this histogram is taken as input to a
convolutional neural network (CNN)-based classifier. The classifier does not
require prior knowledge of protocol-specific information such as Wi-Fi preamble
or resource allocation of 5G physical channels. The classifier's performance,
evaluated using synthetic and real-world measured over-the-air (OTA) datasets,
achieves a minimum accuracy of 97\% accuracy with OTA data when SNR is above
the value required for data transmission.


  For an eigenvalue $\lambda_0$ of a Hermitian matrix $A$, the formula of
Thompson and McEnteggert gives an explicit expression of the adjoint of
$\lambda_0 I-A$, $\mathrm{adj}(\lambda_0 I-A)$, in terms of eigenvectors of $A$
for $\lambda_0$ and all its eigenvalues. In this paper Thompson-McEnteggert's
formula is generalized to include any matrix with entries in an arbitrary
field. In addition, for any nonsingular matrix $A$, a formula for the
elementary divisors of $\mathrm{adj}(A)$ is provided in terms of those of $A$.
Finally, a generalization of the eigenvalue-eigenvector identity and two
applications of the Thompson-McEnteggert's formula are presented.


  By refining the method proposed in arXiv:2010.07660, entropy current and
entropy density for a relativistic hydrostatic equilibrium system with
spherical symmetry are constructed as a non-Noether conserved charge in the
Einstein gravity with cosmological constant. It is shown that the constructed
entropy density satisfies both the local Euler relation and the first law of
thermodynamics non-perturbatively with respect to the Newton constant. Finally
the established relativistic thermodynamics is applied to one of the systems
with uniform energy density as a crude model of a degenerate star and its local
thermodynamic observables are determined analytically.


  The role of planetary nebulae as probes for the galactic chemical evolution
is reviewed. Their abundances throughout the Galaxy are discussed for key
elements, in particular oxygen and other alpha elements. The abundance
distribution derived from planetary nebulae leads to the establishment of
radial abundance gradients in the galactic disk that are important constraints
to model the chemical evolution of the Galaxy. The radial gradient, well
determined for the solar neighborhood, is examined for distinct regions. For
the galactic anticenter in particular, the observational data confirm results
from galactic evolution models that point to a decreasing in the gradient slope
at large galactocentric distances. The possible time evolution of the radial
gradient is also examined comparing samples of planetary nebulae of different
ages, and the results indicate that a flattening in the gradient occurred,
which is confirmed by some galactic evolution models. The galactic bulge is
another important region whose modeling can be constrained by observational
results obtained from planetary nebulae. Results derived in the last few years
indicate that bulge nebulae have an abundance distribution similar to that of
disk objects, however with a larger dispersion.


  We study families of algebraic varieties parametrized by topological spaces
and generalize some classical results such as Hilbert Nullstellensatz and
primary decomposition of commutative rings. We show that there is an
equivalence between the category of bivariant coherent sheaves and the category
of sheaves of finitely generated modules.


  Recently, spiking neural networks (SNNs) have demonstrated substantial
potential in computer vision tasks. In this paper, we present an Efficient
Spiking Deraining Network, called ESDNet. Our work is motivated by the
observation that rain pixel values will lead to a more pronounced intensity of
spike signals in SNNs. However, directly applying deep SNNs to image deraining
task still remains a significant challenge. This is attributed to the
information loss and training difficulties that arise from discrete binary
activation and complex spatio-temporal dynamics. To this end, we develop a
spiking residual block to convert the input into spike signals, then adaptively
optimize the membrane potential by introducing attention weights to adjust
spike responses in a data-driven manner, alleviating information loss caused by
discrete binary activation. By this way, our ESDNet can effectively detect and
analyze the characteristics of rain streaks by learning their fluctuations.
This also enables better guidance for the deraining process and facilitates
high-quality image reconstruction. Instead of relying on the ANN-SNN conversion
strategy, we introduce a gradient proxy strategy to directly train the model
for overcoming the challenge of training. Experimental results show that our
approach gains comparable performance against ANN-based methods while reducing
energy consumption by 54%. The code source is available at
https://github.com/MingTian99/ESDNet.


  Fisher Discriminant Analysis (FDA) is a subspace learning method which
minimizes and maximizes the intra- and inter-class scatters of data,
respectively. Although, in FDA, all the pairs of classes are treated the same
way, some classes are closer than the others. Weighted FDA assigns weights to
the pairs of classes to address this shortcoming of FDA. In this paper, we
propose a cosine-weighted FDA as well as an automatically weighted FDA in which
weights are found automatically. We also propose a weighted FDA in the feature
space to establish a weighted kernel FDA for both existing and newly proposed
weights. Our experiments on the ORL face recognition dataset show the
effectiveness of the proposed weighting schemes.


  In this paper, we develop an ultra-weak discontinuous Galerkin (DG) method to
solve the one-dimensional nonlinear Schr\"odinger equation. Stability
conditions and error estimates are derived for the scheme with a general class
of numerical fluxes. The error estimates are based on detailed analysis of the
projection operator associated with each individual flux choice. Depending on
the parameters, we find out that in some cases, the projection can be defined
element-wise, facilitating analysis. In most cases, the projection is global,
and its analysis depends on the resulting $2\times2$ block-circulant matrix
structures. For a large class of parameter choices, optimal $\textit{a priori}$
$L^2$ error estimates can be obtained. Numerical examples are provided
verifying theoretical results.


  Current language understanding approaches focus on small documents, such as
newswire articles, blog posts, product reviews and discussion forum entries.
Understanding and extracting information from large documents like legal
briefs, proposals, technical manuals and research articles is still a
challenging task. We describe a framework that can analyze a large document and
help people to know where a particular information is in that document. We aim
to automatically identify and classify semantic sections of documents and
assign consistent and human-understandable labels to similar sections across
documents. A key contribution of our research is modeling the logical and
semantic structure of an electronic document. We apply machine learning
techniques, including deep learning, in our prototype system. We also make
available a dataset of information about a collection of scholarly articles
from the arXiv eprints collection that includes a wide range of metadata for
each article, including a table of contents, section labels, section
summarizations and more. We hope that this dataset will be a useful resource
for the machine learning and NLP communities in information retrieval,
content-based question answering and language modeling.


  Information delivery via molecular signals is abundant in nature and
potentially useful for industry sensing. Many propagation channels (e.g.,
tissue membranes and catalyst beds) contain porous medium materials and the
impact this has on communication performance is not well understood. Here,
communication through realistic porous channels is investigated for the first
time via statistical breakthrough curves. Assuming that the number of arrived
molecules can be approximated as a Gaussian random variable and using fully
resolved computational fluid dynamics results for the breakthrough curves, the
numerical results for the throughput, mutual information, error probability,
and information diversity gain are presented. Using these numerical results,
the unique characteristics of the porous medium channel are revealed.


  We propose a WIMP baryogensis achieved by the annihilation of non-thermally
produced WIMPs from decay of heavy particles, which can result in low reheating
temerature. Dark matter (DM) can be produced non-thermally during a reheating
period created by the decay of long-lived heavy particle, and subsequently
re-annihilate to lighter particles even after the thermal freeze-out. The
re-annihilation of DM provides the observed baryon asymmetry as well as the
correct relic density of DM. We investigate how wahout effects can affect the
generation of the baryon asymmetry and study a model suppressing them. In this
scenario, we find that DM can be heavy enough and its annihilation cross
section can also be larger than that adopted in the usual thermal WIMP
baryogenesis.


  In this note we compare the a-invariant of a homogeneous algebra B to the
a-invariant of a subalgebra A. In particular we show that if $A \subset B$ is a
finite homogeneous inclusion of standard graded domains over an algebraically
closed field with A normal and B of minimal multiplicity then A has minimal
multiplicity. In some sense these results are algebraic generalizations of
Hurwitz type theorems.


  We prove an intermediate value theorem of an arithmetical flavor, involving
the consecutive averages of sequences with terms in a given finite set A. For
every such set we completely characterize the numbers x ("intermediate values")
with the property that the consecutive averages of every sequence with terms in
A cannot increase from a value less than x to a value greater than x without
taking the value x somewhere in between.


  We analyze the role played by temperature in QCD_3 by means of a dimensional
interpolating approach. Pure gauge QCD_3 is defined on a strip of finite width
L, which acts as an interpolating parameter between two and three dimensions. A
two-dimensional effective theory can be constructed for small enough widths
giving the same longitudinal physics as QCD_3. Explicit calculations of
T-dependent QCD_3 observables can thus be performed. The generation of a
deconfinig phase transition, absent in QCD_2, is proven through an exact
calculation of the electric or Debye mass at high T. Low and high T behaviors
of relevant thermodynamic functions are also worked out. An accurate estimate
of the critical temperature is given and its evolution with L is studied in
detail.


  Copyright detection is an effective method to prevent piracy. However,
untrustworthy detection parties may lead to falsified detection results. Due to
its credibility and tamper resistance, blockchain has been applied to copyright
protection. Previous works mainly utilized blockchain for reliable copyright
information storage or copyrighted digital media trading. As far as we know,
the problem of credible copyright detection has not been addressed. In this
paper, we propose a credible copyright detection architecture based on the
blockchain, called DCDChain. In this architecture, the detection agency first
detects copyrights off the chain, then uploads the detection records to the
blockchain. Since data on the blockchain are publicly accessible, media
providers can verify the correctness of the copyright detection, and appeal to
a smart contract if there is any dissent. The smart contract then arbitrates
the disputes by verifying the correctness of detection on the chain. The
detect-verify-and-arbitrate mechanism guarantees the credibility of copyright
detection. Security analysis and experimental simulations show that the digital
copyright detection architecture is credible, secure and efficient. The
proposed credible copyright detection scheme is highly important for copyright
protection. The future work is to improve the scheme by designing more
effective locality sensitive hash algorithms for various digital media.


  Let $K$ be an infinite field of characteristic $\neq 2$. In this article we
study the $*$-space $C(R,*)$ of central polynomials with involution of the
$K$-algebra $R= M_{1,1}(E)$, with an involution ($*$) obtanied from a
superinvolution on $M_{1,1}(F)$ (i.e. $M_2(F)$ with its canonical
$\mathbb{Z}_2$-grading).


  We study the effect of transversal electric-field (E-field) on the electronic
properties of multilayer armchair-graphene-nanoribbon (AGNR). The bandgap in
multilayer-AGNRs can be reversibly modulated with the application of E-field.
At optimized widths, we obtain a semiconductor (SC) to metallic (M), as well as
M-SC transition. The AGNR electronic bands undergo vivid transformations due to
the E-field, leading to phenomena such as increase in electron velocity, change
in the sign of the electron effective mass, and the formation of linear
dispersion with massless Dirac fermions similar to 2D-graphene. These effects
are very useful and can be utilized for device applications.


  Within unitary transformed Hamiltonian of Fr\"ohlich type, using the Green's
functions method, exact renormalized energy spectrum of quasiparticle strongly
interacting with two-mode polarization phonons is obtained at $T=0$ K in a
model of the system with limited number of its initial states. Exact analytical
expressions for the average number of phonons in ground state and in all
satellite states of the system are presented. Their dependences on a magnitude
of interaction between quasiparticle and both phonon modes are analyzed.


  We consider a human-assisted autonomy sensor fusion for dynamic target
localization in a Bayesian framework. To compensate for the shortcomings of an
autonomous tracking system, we propose to collect spatial sensing information
from human operators who visually monitor the target and can provide target
localization information in the form of free sketches encircling the area where
the target is located. Our focus in this paper is to construct an adaptive
probabilistic model for human-provided inputs where the adaption terms capture
the level of reliability of the human inputs. The next contribution of this
paper is a novel joint Bayesian learning method to fuse human and autonomous
sensor inputs in a manner that the dynamic changes in human detection
reliability are also captured and accounted for. A unique aspect of this
Bayesian modeling framework is its analytical closed-form update equations,
endowing our method with significant computational efficiency. Simulations
demonstrate our results.


  Granular damping devices constitute an emerging technology for the
attenuation of vibrations based on the dissipative nature of particle
collisions. We show that the performance of such devices is independent of the
material properties of the particles for working conditions where damping is
optimal. Even the suppression of a dissipation mode (collisional or frictional)
is unable to alter the response. We explain this phenomenon in terms of the
inelastic collapse of granular materials. These findings provide a crucial
standpoint for the design of such devices in order to achieve the desired low
maintenance feature that makes particle dampers particularly suitable to harsh
environments.