  In this work, we examine the list of 63 candidates to T Tauri star (TTS) in
the TAMC identified by their ultraviolet (UV) and infrared colours (IR)
measured from data obtained by the Galaxy Evolution Explorer all sky survey
(GALEX-AIS) and the Two Microns All Sky Survey (2MASS), respectively. The
objective of this work is twofold: evaluate whether they are pre-main sequence
(PMS) stars and evaluate the goodness of the UV-IR colour-colour diagram to
detect PMS stars in wide-fields.
  The astrometric properties of these sources have been retrieved from the Gaia
DR3 catalogue and used to evaluate their membership probability. Several
classification algorithms have been tested to search for the kinematical groups
but the final classification has been made with k-means++ algorithms.
Membership probability has been evaluated by applying Logistic Regression. In
addition, spectroscopic information available in the archive of the Large Sky
Area Multi Object Fiber Spectroscopic Telescope has been used to ascertain
their PMS nature when available.
  About 20% of the candidates share the kinematics of the TAMC members. Among
them, HD 281691 is a G8-type field star located in front of the cloud and HO
Aur is likely a halo star given the very low metallicity provided by Gaia. The
rest are three known PMS stars (HD 30171, V600 Aur and J04590305+3003004), two
previously unknown accreting M-type stars (J04510713+1708468 and
J05240794+2542438) and, five additional sources, which are very likely PMS
stars. Most of these new sources are concentrated at low galactic latitudes
over the Auriga-Perseus region.


  Gastric endoscopy is a common clinical practice that enables medical doctors
to diagnose the stomach inside a body. In order to identify a gastric lesion's
location such as early gastric cancer within the stomach, this work addressed
to reconstruct the 3D shape of a whole stomach with color texture information
generated from a standard monocular endoscope video. Previous works have tried
to reconstruct the 3D structures of various organs from endoscope images.
However, they are mainly focused on a partial surface. In this work, we
investigated how to enable structure-from-motion (SfM) to reconstruct the whole
shape of a stomach from a standard endoscope video. We specifically
investigated the combined effect of chromo-endoscopy and color channel
selection on SfM. Our study found that 3D reconstruction of the whole stomach
can be achieved by using red channel images captured under chromo-endoscopy by
spreading indigo carmine (IC) dye on the stomach surface.


  In this work we consider time reversal of the increasing geometrical
progression of the population of a simple biological species without any
enemies (predators) in the appropriate environment with unlimited resources
(food, territory, etc.). It is shown that such time reversal corresponds to
appearance of the cannibalism, i.e. self-predaciousness or self-damping
phenomena which can be described by a type of difference Verhulst equation.


  Re-speaking is a mechanism for obtaining high quality subtitles for use in
live broadcast and other public events. Because it relies on humans performing
the actual re-speaking, the task of estimating the quality of the results is
non-trivial. Most organisations rely on humans to perform the actual quality
assessment, but purely automatic methods have been developed for other similar
problems, like Machine Translation. This paper will try to compare several of
these methods: BLEU, EBLEU, NIST, METEOR, METEOR-PL, TER and RIBES. These will
then be matched to the human-derived NER metric, commonly used in re-speaking.


  The phase diagram of a frustrated S=1/2 antiferromagnetic spin ladder with
additional next-nearest neighbor exchanges, both diagonal and inchain, is
studied by a weak-coupling effective field theory approach combined with exact
diagonalization for finite systems. In addition to two known phases with
rung-singlet and Haldane-type ground states, we observe two new phases with
dimerization along the chains. Furthermore, the transitions between the
different phases are studied and shown to be either first order or to belong to
the universality class of the two-dimensional Ising model. The nature of
elementary excitations is discussed briefly.


  Standard NLP tasks do not incorporate several common real-world scenarios
such as seeking clarifications about the question, taking advantage of clues,
abstaining in order to avoid incorrect answers, etc. This difference in task
formulation hinders the adoption of NLP systems in real-world settings. In this
work, we take a step towards bridging this gap and present a multi-stage task
that simulates a typical human-human questioner-responder interaction such as
an interview. Specifically, the system is provided with question
simplifications, knowledge statements, examples, etc. at various stages to
improve its prediction when it is not sufficiently confident. We instantiate
the proposed task in Natural Language Inference setting where a system is
evaluated on both in-domain and out-of-domain (OOD) inputs. We conduct
comprehensive experiments and find that the multi-stage formulation of our task
leads to OOD generalization performance improvement up to 2.29% in Stage 1,
1.91% in Stage 2, 54.88% in Stage 3, and 72.02% in Stage 4 over the standard
unguided prediction. However, our task leaves a significant challenge for NLP
researchers to further improve OOD performance at each stage.


  We study competition of two spreading colors starting from single sources on
the configuration model with i.i.d. degrees following a power-law distribution
with exponent tau in (2,3). In this model two colors spread with a fixed but
not necessarily equal speed on the unweighted random graph. We show that if the
speeds are not equal, then the faster color paints almost all vertices, while
the slower color can paint only a random subpolynomial fraction of the
vertices. We investigate the case when the speeds are equal and typical
distances in a follow-up paper.


  The secondary structure of ribonucleic acid (RNA) is more stable and
accessible in the cell than its tertiary structure, making it essential for
functional prediction. Although deep learning has shown promising results in
this field, current methods suffer from poor generalization and high
complexity. In this work, we reformulate the RNA secondary structure prediction
as a K-Rook problem, thereby simplifying the prediction process into
probabilistic matching within a finite solution space. Building on this
innovative perspective, we introduce RFold, a simple yet effective method that
learns to predict the most matching K-Rook solution from the given sequence.
RFold employs a bi-dimensional optimization strategy that decomposes the
probabilistic matching problem into row-wise and column-wise components to
reduce the matching complexity, simplifying the solving process while
guaranteeing the validity of the output. Extensive experiments demonstrate that
RFold achieves competitive performance and about eight times faster inference
efficiency than the state-of-the-art approaches. The code and Colab demo are
available in (http://github.com/A4Bio/RFold).


  We first devise a branching algorithm that computes a minimum independent
dominating set on any graph with running time O*(2^0.424n) and polynomial
space. This improves the O*(2^0.441n) result by (S. Gaspers and M. Liedloff, A
branch-and-reduce algorithm for finding a minimum independent dominating set in
graphs, Proc. WG'06). We then show that, for every r>3, it is possible to
compute an r-((r-1)/r)log_2(r)-approximate solution for min independent
dominating set within time O*(2^(nlog_2(r)/r)).


  Matrix acidization is an important technique to enhance oil production at the
tertiary recovery stage, and its numerical simulation is never concluded. From
one of the earliest models, i.e. the two-scale model (Darcy framework), the
Darcy-Brinkman-Forchheimer (DBF) framework is developed by adding Brinkman term
and Forchheimer term to the momentum conservation equation. However, in the
momentum conservation equation of the DBF framework, porosity is put outside of
the time derivation term, which cannot describe the change of porosity well.
Thus, this work changes the expression so that the modified momentum
conservation equation can satisfy Newton's second law. The modified framework
is called improved DBF framework. Furthermore, based on the improved DBF
framework, the thermal DBF framework is given by introducing the energy balance
equation to the improved DBF framework. Both of the frameworks are verified by
the former works through numerical experiments and chemical experiments in
labs. Parallelization to the codes of the complicated frameworks is also
realized, and good scalability can be achieved.


  We define a potential-weighted connective constant that measures the
effective strength of a repulsive pair potential of a Gibbs point process
modulated by the geometry of the underlying space. We then show that this
definition leads to improved bounds for Gibbs uniqueness for all non-trivial
repulsive pair potentials on $\mathbb R^d$ and other metric measure spaces. We
do this by constructing a tree-branching collection of densities associated to
the point process that captures the interplay between the potential and the
geometry of the space. When the activity is small as a function of the
potential-weighted connective constant this object exhibits an infinite volume
uniqueness property. On the other hand, we show that our uniqueness bound can
be tight for certain spaces: the same infinite volume object exhibits
non-uniqueness for activities above our bound in the case when the underlying
space has the geometry of a tree.


  Let $X\subset{\mathbb R}^n$ be a (global) real analytic surface. Then every
positive semidefinite meromorphic function on $X$ is a sum of $10$ squares of
meromorphic functions on $X$. As a consequence, we provide a real
Nullstellensatz for (global) real analytic surfaces.


  It is stated in the main in essence new approach to mechanics of the stressed
state of the solid body from statistically isotropic material and the
homogeneous liquid dynamics. The approach essence is in the detected property
of the core-shell spontaneous structurization of internal energy of the solid
and liquid bodies in its natural state and under action of external forces. The
method elements of construction of physically adequate model of the stressed
state of the solid and liquid bodies, reproduced exactly its behavior on the
stages of elastic and plastic deformation, flow and fracture, are stated. It is
adduced a number of the examples of the stressed state construction of the
simple form bodies under action of its tension, compression, torsion and at its
contact interaction. For the first time it is adduced structure of the
principal - normal - stresses in cylindrical bar under action of the torsion
moment. The detected property and the developed method is one of necessary
bases for construction of physically adequate mathematical model of the
stressed state of the body and fluid in contrast to traditional approach.


  In this letter we study the low-energy phenomenology and cosmological
implications of supersymmetric grand unified theory with asymmetric flavor
structure, which is suggested by the recent observation of fermion masses and
mixing angles. The predictions of the scenario are rather dependent on a Yukawa
parameter fixed by group-theoretical argument. A reduced value of the parameter
gives a resolution to the sign problem of supersymmetric Higgs mass \mu, with
which the theory becomes simultaneously consistent with the experimental data
of bottom/tau mass ratio, flavor-changing rare decay of bottom quark, and the
muon anomalous magnetic moment. The relic abundance of the lightest
superparticle as cold dark matter of the universe is also investigated in light
of the three-years WMAP result. A new source of flavor violation is found in
the D-term induced scalar masses, that is a distinctive signature of the
generation asymmetry.


  We propose an accelerated meta-algorithm, which allows to obtain accelerated
methods for convex unconstrained minimization in different settings. As an
application of the general scheme we propose nearly optimal methods for
minimizing smooth functions with Lipschitz derivatives of an arbitrary order,
as well as for smooth minimax optimization problems. The proposed
meta-algorithm is more general than the ones in the literature and allows to
obtain better convergence rates and practical performance in several settings.


  We introduce a new class of two dimensional conformal field theories by
extending Wess-Zumino-Witten (WZW) models to chiral algebras with matrix-valued
levels. The new CFTs are based on holomorphic currents with an operator product
expansion characterized by a positive integer-valued matrix $K_{AB}$. We use
the Sugawara construction to compute the energy-momentum tensor, the central
charge, and the spectrum of conformal dimensions of the CFTs based on this
algebra. We also construct a set of genus-$1$ characters and show that they
fulfil a representation of the modular group $\text{SL}(2,\mathbb{Z})$ up to a
modular anomaly.


  3D Gaussian Splatting-based techniques have recently advanced 3D scene
reconstruction and novel view synthesis, achieving high-quality real-time
rendering. However, these approaches are inherently limited by the underlying
pinhole camera assumption in modeling the images and hence only work for
All-in-Focus (AiF) sharp image inputs. This severely affects their
applicability in real-world scenarios where images often exhibit defocus blur
due to the limited depth-of-field (DOF) of imaging devices. Additionally,
existing 3D Gaussian Splatting (3DGS) methods also do not support rendering of
DOF effects.
  To address these challenges, we introduce DOF-GS that allows for rendering
adjustable DOF effects, removing defocus blur as well as refocusing of 3D
scenes, all from multi-view images degraded by defocus blur. To this end, we
re-imagine the traditional Gaussian Splatting pipeline by employing a finite
aperture camera model coupled with explicit, differentiable defocus rendering
guided by the Circle-of-Confusion (CoC). The proposed framework provides for
dynamic adjustment of DOF effects by changing the aperture and focal distance
of the underlying camera model on-demand. It also enables rendering varying DOF
effects of 3D scenes post-optimization, and generating AiF images from
defocused training images. Furthermore, we devise a joint optimization strategy
to further enhance details in the reconstructed scenes by jointly optimizing
rendered defocused and AiF images. Our experimental results indicate that
DOF-GS produces high-quality sharp all-in-focus renderings conditioned on
inputs compromised by defocus blur, with the training process incurring only a
modest increase in GPU memory consumption. We further demonstrate the
applications of the proposed method for adjustable defocus rendering and
refocusing of the 3D scene from input images degraded by defocus blur.


  BioScape is a concurrent language motivated by the biological landscapes
found at the interface of biology and biomaterials. It has been motivated by
the need to model antibacterial surfaces, biofilm formation, and the effect of
DNAse in treating and preventing biofilm infections. As its predecessor, SPiM,
BioScape has a sequential semantics based on Gillespie's algorithm, and its
implementation does not scale beyond 1000 agents. However, in order to model
larger and more realistic systems, a semantics that may take advantage of the
new multi-core and GPU architectures is needed. This motivates the introduction
of parallel semantics, which is the contribution of this paper: Parallel
BioScape, an extension with fully parallel semantics.


  We report on 7Li NMR studies of a spin S = 3/2 triangular lattice
antiferromagnet LiCrO2 (Neel temperature TN = 62 K) in the paramagnetic state
by using the free-induction decay of 7Li nuclear magnetization. We observed
critical divergence of the 7Li nuclear spin-lattice relaxation rate 1/T1 near
TN, a narrow critical region, and a critical exponent w = 0.45 from a fit of
1/T1 $\propto$ (T/TN - 1)$^{-w}$. Although spin frustration effects have been
explored for this system, the dynamical critical phenomena suggest that LiCrO2
in the critical region is a poor low dimensional antiferromagnetic system.


  We study a model based on the dihedral group D4 in which the dark matter is
stabilized by the interplay between a remnant Z2 symmetry, of the same
spontaneously broken non-abelian group, and an auxiliary Z2 introduced to
eliminate unwanted couplings in the scalar potential. In the lepton sector the
model is compatible with normal hierarchy only and predicts a vanishing reactor
mixing angle. Since m1=0, we also have a simple prediction for the effective
mass in terms of the solar angle. There also exists a large portion of the
model parameter space where the upper bounds on lepton flavor violating
processes are not violated. We incorporate quarks in the same scheme finding
that a description of the CKM mixing matrix is possible and that semileptonic K
and D decays mediated by flavour changing neutral currents are under control.


  This note re-visits the rolling-horizon control approach to the problem of a
Markov decision process (MDP) with infinite-horizon discounted expected reward
criterion. Distinguished from the classical value-iteration approach, we
develop an asynchronous on-line algorithm based on policy iteration integrated
with a multi-policy improvement method of policy switching. A sequence of
monotonically improving solutions to the forecast-horizon sub-MDP is generated
by updating the current solution only at the currently visited state, building
in effect a rolling-horizon control policy for the MDP over infinite horizon.
Feedbacks from "supervisors," if available, can be also incorporated while
updating. We focus on the convergence issue with a relation to the transition
structure of the MDP. Either a global convergence to an optimal
forecast-horizon policy or a local convergence to a "locally-optimal"
fixed-policy in a finite time is achieved by the algorithm depending on the
structure.


  Longitudinal imaging studies have moved to the forefront of medical research
due to their ability to characterize spatio-temporal features of biological
structures across the lifespan. Credible models of the correlations in
longitudinal imaging require two or more pattern components. Valid inference
requires enough flexibility of the correlation model to allow reasonable
fidelity to the true pattern. On the other hand, the existence of computable
estimates demands a parsimonious parameterization of the correlation structure.
For many one-dimensional spatial or temporal arrays, the linear exponent
autoregressive (LEAR) correlation structure meets these two opposing goals in
one model. The LEAR structure is a flexible two-parameter correlation model
that applies in situations in which the within-subject correlation decreases
exponentially in time or space. It allows for an attenuation or acceleration of
the exponential decay rate imposed by the commonly used continuous-time AR(1)
structure. Here we propose the Kronecker product LEAR correlation structure for
multivariate repeated measures data in which the correlation between
measurements for a given subject is induced by two factors. We also provide a
scientifically informed approach to assessing the adequacy of a Kronecker
product LEAR model and a general unstructured Kronecker product model. The
approach provides useful guidance for high dimension, low sample size data that
preclude using standard likelihood based tests. Longitudinal medical imaging
data of caudate morphology in schizophrenia illustrates the appeal of the
Kronecker product LEAR correlation structure.


  In this thesis, the main objects of study are probability measures on the
isomorphism classes of countable, connected rooted graphs. An important class
of such measures is formed by unimodular measures, which satisfy a certain
equation, sometimes referred to as the intrinsic mass transport principle. The
so-called law of a finite graph is an example of a unimodular measure. We say
that a measure is sustained by a countable graph if the set of rooted connected
components of the graph has full measure. We demonstrate several new results
involving sustained unimodular measures, and provide thorough arguments for
known ones. In particular, we give a criterion for unimodularity on connected
graphs, deduce that connected graphs sustain at most one unimodular measure,
and prove that unimodular measures sustained by disconnected graphs are convex
combinations. Furthermore, we discuss weak limits of laws of finite graphs, and
construct counterexamples to seemingly reasonable conjectures.


  We study model-based end-to-end learning in the context of integrated sensing
and communication (ISAC) under hardware impairments. A monostatic orthogonal
frequency-division multiplexing (OFDM) sensing and multiple-input single-output
(MISO) communication scenario is considered, incorporating hardware
imperfections at the ISAC transceiver antenna array. To enable end-to-end
learning of the ISAC transmitter and sensing receiver, we propose a novel
differentiable version of the orthogonal matching pursuit (OMP) algorithm that
is suitable for multi-target sensing. Based on the differentiable OMP, we
devise two model-based parameterization strategies to account for hardware
impairments: (i) learning a dictionary of steering vectors for different
angles, and (ii) learning the parameterized hardware impairments. For the
single-target case, we carry out a comprehensive performance analysis of the
proposed model-based learning approaches, a neural-network-based learning
approach and a strong baseline consisting of least-squares beamforming,
conventional OMP, and maximum-likelihood symbol detection for communication.
Results show that learning the parameterized hardware impairments offers higher
detection probability, better angle and range estimation accuracy, lower
communication symbol error rate (SER), and exhibits the lowest complexity among
all learning methods. Lastly, we demonstrate that learning the parameterized
hardware impairments is scalable also to multiple targets, revealing
significant improvements in terms of ISAC performance over the baseline.


  Deep learning techniques have been hugely successful for traditional
supervised and unsupervised machine learning problems. In large part, these
techniques solve continuous optimization problems. Recently however, discrete
generative deep learning models have been successfully used to efficiently
search high-dimensional discrete spaces. These methods work by representing
discrete objects as sequences, for which powerful sequence-based deep models
can be employed. Unfortunately, these techniques are significantly hindered by
the fact that these generative models often produce invalid sequences. As a
step towards solving this problem, we propose to learn a deep recurrent
validator model. Given a partial sequence, our model learns the probability of
that sequence occurring as the beginning of a full valid sequence. Thus this
identifies valid versus invalid sequences and crucially it also provides
insight about how individual sequence elements influence the validity of
discrete objects. To learn this model we propose an approach inspired by
seminal work in Bayesian active learning. On a synthetic dataset, we
demonstrate the ability of our model to distinguish valid and invalid
sequences. We believe this is a key step toward learning generative models that
faithfully produce valid discrete objects.


  To conduct a radiomics or deep learning research experiment, the radiologists
or physicians need to grasp the needed programming skills, which, however,
could be frustrating and costly when they have limited coding experience. In
this paper, we present DARWIN, a flexible research platform with a graphical
user interface for medical imaging research. Our platform is consists of a
radiomics module and a deep learning module. The radiomics module can extract
more than 1000 dimension features(first-, second-, and higher-order) and
provided many draggable supervised and unsupervised machine learning models.
Our deep learning module integrates state of the art architectures of
classification, detection, and segmentation tasks. It allows users to manually
select hyperparameters, or choose an algorithm to automatically search for the
best ones. DARWIN also offers the possibility for users to define a custom
pipeline for their experiment. These flexibilities enable radiologists to carry
out various experiments easily.


  This paper presents an analysis of near-resonant, ro-vibrational two-photon
spectroscopy and the use of cavity ring-down spectroscopy for its detection.
Expressions are derived for the photon absorption rate of a three-level system,
correct to all orders and the simpler expressions that result from various
approximations. The analysis includes the angular momentum projection
degeneracies and linear or circular polarization of the exciting field.
Expressions are derived for the rate of two-photon power loss for light inside
a resonant cavity. Explicit calculations are made for excitation of the $\nu_3$
mode of $^{12}\textrm{C}^{16}\textrm{O}_2$ for which the two-photon excitation
spectrum is dominated by a single $v_3 = 0 \rightarrow 2, Q(16)$ line at
$\tilde{\nu} = 2335.826$\,cm$^{-1}$. This transition has an intermediate $v_3 =
0 \rightarrow 1,P(16)$ one-photon transition that is off resonance by 0.093
cm$^{-1}$ (2.8 GHz). At 1\,torr total pressure, the Q(16) two-photon transition
has a calculated cross-section of $2.99 \cdot 10^{-38}$\,cm$^4$s per CO$_2$
molecule in the $J = 16$ state or $2.24 \cdot 10^{-39}$\,cm$^4$s per CO$_2$
molecule at 300\,K is calculated. Analysis of the sensitivity limits for
2-photon cavity ring-down spectroscopy predicts a theoretical detection limit
of 32\,ppq ($10^{-15}$) Hz$^{-1/2}$ for $^{12}\textrm{C}^{16}\textrm{O}_2$,
higher sensitivity than has been realized using one-photon absorption. The
analysis predicts that most polyatomic molecules will have sparse, Doppler-Free
two-photon absorption spectra, which will dramatically increase the selectivity
of trace gas detection of samples with multiple components with overlapping
absorption bands. This is demonstrated by the predicted mid-IR two-photon
absorption spectrum of butadiene using theoretical spectroscopic constants.


  We propose novel time-domain dynamic integral quadratic constraints with a
terminal cost for exponentially weighted slope-restricted gradients of not
necessarily convex functions. This extends recent results for subdifferentials
of convex function and their link to so-called O'Shea-Zames-Falb multipliers.
The benefit of merging time-domain and frequency-domain techniques is
demonstrated for linear saturated systems.


  Rastall's theory is based on the non-conservation of the energy-momentum
tensor. We show that, in this theory, if we introduce a two-fluid model, one
component representing vacuum energy whereas the other pressureless matter
(e.g. baryons plus cold dark matter), the cosmological scenario is the same as
for the \Lambda CDM model, both at background and linear perturbative levels,
except for one aspect: now dark energy may cluster. We speculate that this can
lead to a possibility of distinguishing the models at the non-linear
perturbative level.


  Professor Dr. Karl Dragutin Rakos passed away on October 31, 2011 one day
before his 86th birthday. With that the Vienna astronomical community lost a
valued researcher, university teacher and co-founder of modern astrophysical
research at the Institut f\"ur Astronomie of the University of Vienna.


  In this paper, we study the maximum likelihood estimate of the probability
mass function (pmf) of $n$ independent and identically distributed (i.i.d.)
random variables, in the non-asymptotic regime. We are interested in
characterizing the Neyman--Pearson criterion, i.e., the log-likelihood ratio
for testing a true hypothesis within a larger hypothesis. Wilks' theorem states
that this ratio behaves like a $\chi^2$ random variable in the asymptotic case;
however, less is known about the precise behavior of the ratio when the number
of samples is finite. In this work, we find an explicit bound for the
difference between the cumulative distribution function (cdf) of the
log-likelihood ratio and the cdf of a $\chi^2$ random variable. Furthermore, we
show that this difference vanishes with a rate of order $1/\sqrt{n}$ in
accordance with Wilks' theorem.


  We investigated the correlation between intensities of the $^{12}$CO and
$^{13}$CO ($J=1$-0) lines toward the Galactic giant molecular clouds (GMCs)
W51A, W33, N35-N36 complex, W49A, M17SW, G12.02-00.03, W43, and M16 using the
FUGIN (FOREST Unbiased Galactic plane Imaging survey with the Nobeyama 45-m
telescope) CO line data. All the GMCs show intensity saturation in the
$^{12}$CO line when the brightness temperature of $^{13}$CO is higher than a
threshold temperature of about $\sim 5$ K. We obtained high-resolution ($\sim
20"$) distribution maps of the $X_{\rm CO}$ factor ($X_{\rm CO, iso}$) in
individual GMCs using correlation diagrams of the CO isotopologues. It is shown
that $X_{\rm CO, iso}$ is variable in each GMC within the range of $X_{\rm CO,
iso} \sim (0.9 {\rm -} 5) \times 10^{20}$ cm$^{-2}$ (K km s$^{-1})^{-1}$.
Despite the variability in the GMCs, the average value among the GMCs is found
to be nearly constant at $X_{\rm CO, iso} = (2.17 \pm 0.27) \times 10^{20}$
cm$^{-2}$ (K km s$^{-1})^{-1}$, which is consistent with that from previous
studies in the Milky Way.


  We investigate a tropical analogue of the Hessian group $G_{216}$, the group
of linear automorphisms acting on the Hesse pencil. Through the procedure of
ultradiscretization, the group law on the Hesse pencil reduces to that on the
tropical Hesse pencil. We then show that the dihedral group $\mathcal{D}_3$ of
degree three is the group of linear automorphisms acting on the tropical Hesse
pencil.


  It is shown using Schur complement techniques that a non-negative operator
valued trigonometric polynomial in two variables with degree (d_1,d_2) can be
written as a finite sum of hermitian squares of at most 2d_2 analytic
polynomials with degrees at most (d_1, 2d_2-1).


  We extend the theory of spectral submanifolds (SSMs) to general
non-autonomous dynamical systems that are either weakly forced or slowly
varying. Examples of such systems arise in structural dynamics, fluid-structure
interactions and control problems. The time-dependent SSMs we construct under
these assumptions are normally hyperbolic and hence will persist for larger
forcing and faster time dependence that are beyond the reach of our precise
existence theory. For this reason, we also derive formal asymptotic expansions
that, under explicitly verifiable nonresonance conditions, approximate SSMs and
their aperiodic anchor trajectories accurately for stronger, faster or even
temporally discontinuous forcing. Reducing the dynamical system to these
persisting SSMs provides a mathematically justified model reduction technique
for non-autonomous physical systems whose time dependance is moderate either in
magnitude or speed. We illustrate the existence, persistence and computation of
temporally aperiodic SSMs in mechanical examples under chaotic forcing.


  Maximum spanning tree (MST) is a popular tool in market network analysis.
Large number of publications are devoted to the MST calculation and it's
interpretation for particular stock markets. However, much less attention is
payed in the literature to the analysis of uncertainty of obtained results. In
the present paper we suggest a general framework to measure uncertainty of MST
identification. We study uncertainty in the framework of the concept of random
variable network (RVN). We consider different correlation based networks in the
large class of elliptical distributions. We show that true MST is the same in
three networks: Pearson correlation network, Fechner correlation network, and
Kendall correlation network. We argue that among different measures of
uncertainty the FDR (False Discovery Rate) is the most appropriated for MST
identification. We investigate FDR of Kruskal algorithm for MST identification
and show that reliability of MST identification is different in these three
networks. In particular, for Pearson correlation network the FDR essentially
depends on distribution of stock returns. We prove that for market network with
Fechner correlation the FDR is non sensitive to the assumption on stock's
return distribution. Some interesting phenomena are discovered for Kendall
correlation network. Our experiments show that FDR of Kruskal algorithm for MST
identification in Kendall correlation network weakly depend on distribution and
at the same time the value of FDR is almost the best in comparison with MST
identification in other networks. These facts are important in practical
applications.


  The dynamics of cavitation bubbles in the vicinity of a solid cylinder or
fibre are seen in water treatment, demolition and/or cleaning of composite
materials, as well as bio-medical scenarios such as ultrasound-induced bubbles
near the tubular structures in the body. When the bubble collapses near the
surface, violent fluid jets may be generated. Understanding whether these jets
occur and predicting their directions -- departing or approaching the solid
surface -- is crucial for assessing their potential impact on the solid phase.
However, the criteria for classifying the onset and directions of the jets
created by cavitation near a curved surface of a cylinder have not been
established. In this research, we present models to predict the occurrence and
directions of the jet in such scenarios. The onset criteria and the
direction(s) of the jets are dictated by the bubble stand-off distance and the
cylinder diameter. Our models are validated by comprehensive experiments. The
results not only predict the jetting behaviour but can serve as guidelines for
designing and controlling the jets when a cavitation bubble collapses near a
cylinder, whether for protective or destructive purposes.


  We present a halo-model-based approach to calculate the cross-correlation
between $\rm{21\,cm}$ HI intensity fluctuations and $\rm{Ly}\alpha$ emitters
(LAE) during the epoch of reionization (EoR). Ionizing radiation around dark
matter halos are modeled as bubbles with the size and growth determined based
on the reionization photon production, among other physical parameters. The
cross-correlation shows a clear negative-to-positive transition, associated
with transition from ionized to neutral hydrogen in the intergalactic medium
during EoR. The cross-correlation is subject to several foreground
contaminants, including foreground radio point sources important for
$\rm{21\,cm}$ experiments and low-$z$ interloper emission lines, such as
$\rm{H}\alpha$, OIII, and OII, for $\rm{Ly}\alpha$ experiments. Our
calculations show that by masking out high fluxes in the $\rm{Ly}\alpha$
measurement, the correlated foreground contamination on the
$\rm{21\,cm}$-$\rm{Ly}\alpha$ cross-correlation can be dramatically reduced. We
forecast the detectability of $\rm{21\,cm}$-$\rm{Ly}\alpha$ cross-correlation
at different redshifts and adopt a Fisher matrix approach to estimate
uncertainties on the key EoR parameters that have not been well constrained by
other observations of reionization. This halo-model-based approach enables us
to explore the EoR parameter space rapidly for different $\rm{21\,cm}$ and
$\rm{Ly}\alpha$ experiments.


  Thorne-Zytkow objects (TZOs) are a theoretical class of star in which a
compact neutron star is surrounded by a large, diffuse envelope. Supergiant
TZOs are predicted to be almost identical in appearance to red supergiants
(RSGs). The best features that can be used at present to distinguish TZOs from
the general RSG population are the unusually strong heavy-element and Li lines
present in their spectra, products of the star's fully convective envelope
linking the photosphere with the extraordinarily hot burning region in the
vicinity of the neutron star core. Here we present our discovery of a TZO
candidate in the Small Magellanic Cloud. It is the first star to display the
distinctive chemical profile of anomalous element enhancements thought to be
unique to TZOs. The positive detection of a TZO will provide the first direct
evidence for a completely new model of stellar interiors, a theoretically
predicted fate for massive binary systems, and never-before-seen
nucleosynthesis processes that would offer a new channel for Li and
heavy-element production in our universe.


  The investment risk minimization problem with budget and return constraints
has been the subject of research using replica analysis but there are
shortcomings in the extant literature. With respect to Tobin's separation
theorem and the capital asset pricing model, it is necessary to investigate the
implications of a risk-free asset and examine its influence on the optimal
portfolio. Accordingly, in this work, we explore the investment risk
minimization problem in the presence of a risk-free asset with budget and
return constraints. Moreover, we discuss opportunity loss, the Pythagorean
theorem of the Sharpe ratio, and Tobin's separation theorem.


  We present a new approach to doing Eulerian computational fluid dynamics that
is designed to work at high Mach numbers encountered in hydrodynamical
simulations of the IGM. In conventional Eulerian CFD, the thermal energy is
poorly tracked in supersonic bulk flows where local fluid variables cannot be
accurately separated from the much larger bulk flow components. We described a
method in which local fluid quantities can be directly tracked and the Eulerian
fluid equations solved in a local frame moving with the flow. The new algorithm
has been used to run large hydrodynamical simulations on a 1024^3 grid to study
the kinetic SZ effect. The KSZ power spectrum is broadly peaked at l~10^4 with
temperature fluctuations on micro Kelvin levels.


  Using the toroidal compactification of string theory on n-dimensional tori,
Tn, we investigate dyonic objects in arbitrary dimensions. First, we present a
class of dyonic black solutions formed by two different D-branes using a
correspondence between toroidal cycles and objects possessing both magnetic and
electric charges, belonging to %%%% dyonic gauge symmetry. This symmetry could
be associated with electrically charged magnetic monopole solutions in stringy
model buildings of the standard model extensions. Then, we consider in some
details such black hole classes obtained from even dimensional toroidal
compactifications, and we find that they are linked to Cl(n) Clifford algebras
using the vee product. It is believed that this analysis could be extended to
dyonic objects which can be obtained from local Calabi-Yau manifold
compactifications.


  We consider theories with time-dependent Hamiltonians which alternate between
being bounded and unbounded from below. For appropriate frequencies dynamical
stabilization can occur rendering the effective potential of the system stable.
We first study a free field theory on a torus with a time-dependent mass term,
finding that the stability regions are described in terms of the phase diagram
of the Mathieu equation. Using number theory we have found a compactification
scheme such as to avoid resonances for all momentum modes in the theory. We
further consider the gravity dual of a conformal field theory on a sphere in
three spacetime dimensions, deformed by a doubletrace operator. The gravity
dual of the theory with a constant unbounded potential develops big crunch
singularities; we study when such singularities can be cured by dynamical
stabilization. We numerically solve the Einstein-scalar equations of motion in
the case of a time-dependent doubletrace deformation and find that for
sufficiently high frequencies the theory is dynamically stabilized and big
crunches get screened by black hole horizons.


  Motivated by spectral asymptotics for orbital integrals in a relative trace
formula, we generalize a number of geometric properties of geodesics in the
hyperbolic plane, to maximal flat submanifolds of symmetric spaces of
non-compact type.


  A detailed standard model (SM) calculation of the O(alpha_s) virtual
corrections to the decay width for b -> s g is presented (g denotes a gluon).
Also the complete expressions for the corresponding bremsstrahlung corrections
are given. Taking into account the existing next-to-leading logarithmic (NLL)
result for the Wilson coefficient C_8^eff, a complete NLL result for the
branching ratio BR(b->sg) is derived. Numerically, we obtain BR(b->sg)=(5.0 +/-
1.0) * 10^(-3), which is more than a factor of two larger than the leading
logarithmic result. The impact of these corrections on the inclusive charmless
hadronic branching ratio of B-mesons, which can be used to extract
|V_(ub)/V_(cb)|, is shown to be of similar importance as NLL corrections to
b-quark decay modes with three quarks in the final state. Finally, the impact
of the NLL corrections to b->sg on the charmless hadronic branching ratio is
investigated in scenarios, where the Wilson coefficient C_8 is enhanced by new
physics.


  Earth observation (EO) satellite missions have been providing detailed images
about the state of the Earth and its land cover for over 50 years. Long term
missions, such as NASA's Landsat, Terra, and Aqua satellites, and more
recently, the ESA's Sentinel missions, record images of the entire world every
few days. Although single images provide point-in-time data, repeated images of
the same area, or satellite image time series (SITS) provide information about
the changing state of vegetation and land use. These SITS are useful for
modeling dynamic processes and seasonal changes such as plant phenology. They
have potential benefits for many aspects of land and natural resource
management, including applications in agricultural, forest, water, and disaster
management, urban planning, and mining. However, the resulting satellite image
time series (SITS) are complex, incorporating information from the temporal,
spatial, and spectral dimensions. Therefore, deep learning methods are often
deployed as they can analyze these complex relationships. This review presents
a summary of the state-of-the-art methods of modelling environmental,
agricultural, and other Earth observation variables from SITS data using deep
learning methods. We aim to provide a resource for remote sensing experts
interested in using deep learning techniques to enhance Earth observation
models with temporal information.


  The performance of standard learning procedures has been observed to differ
widely across groups. Recent studies usually attribute this loss discrepancy to
an information deficiency for one group (e.g., one group has less data). In
this work, we point to a more subtle source of loss discrepancy---feature
noise. Our main result is that even when there is no information deficiency
specific to one group (e.g., both groups have infinite data), adding the same
amount of feature noise to all individuals leads to loss discrepancy. For
linear regression, we thoroughly characterize the effect of feature noise on
loss discrepancy in terms of the amount of noise, the difference between
moments of the two groups, and whether group information is used or not. We
then show this loss discrepancy does not vanish immediately if a shift in
distribution causes the groups to have similar moments. On three real-world
datasets, we show feature noise increases the loss discrepancy if groups have
different distributions, while it does not affect the loss discrepancy on
datasets where groups have similar distributions.


  We study entropy of chaotic oscillation of electrical currents in the Chua's
circuit controlled by triggering a pulse that brings the orbit that goes onto
an unstable branch back to a stable branch. A numerical simulation of the
voltage of the two capacitors and the current that flows on an inductor of the
Chua's circuit reveals various oscillation patterns as the conductance that is
connected between the two capacitors and directly connected to an inductor is
varied. At small conductance, the Lissajous graph of the voltage of the two
capacitors shows a spiral, while at high conductance a double scroll pattern
appears. The entropy of the current that flows on the inductor is alocal
minimum in the spiral state which is in the steady state, while it is maximum
in the stable double scroll state. The stable double scroll samples are
analyzed by using the Hidden Markov Model (HMM) and the eigenvectors of the
transition matrix of long time series are found to be strictly positive but
those of unstable short time series have negative components. We thus confirm
maximum entropy production in the double scroll of the longest time series
around the right fixed point, while the local minimum entropy production occurs
in the spiral around the left fixed point.


  The ability to ask questions is a powerful tool to gather information in
order to learn about the world and resolve ambiguities. In this paper, we
explore a novel problem of generating discriminative questions to help
disambiguate visual instances. Our work can be seen as a complement and new
extension to the rich research studies on image captioning and question
answering. We introduce the first large-scale dataset with over 10,000
carefully annotated images-question tuples to facilitate benchmarking. In
particular, each tuple consists of a pair of images and 4.6 discriminative
questions (as positive samples) and 5.9 non-discriminative questions (as
negative samples) on average. In addition, we present an effective method for
visual discriminative question generation. The method can be trained in a
weakly supervised manner without discriminative images-question tuples but just
existing visual question answering datasets. Promising results are shown
against representative baselines through quantitative evaluations and user
studies.


  We investigate the quantum effects of the non-minimal matter-gravity
couplings derived by Cangemi and Jackiw in the realm of a specific fermionic
theory, namely the abelian Thirring model on a Riemann surface of genus zero
and one. The structure and the strength of the new interactions are seen to be
highly constrained, when the topology of the underlying manifold is taken into
account. As a matter of fact, by requiring to have a well-defined action, we
are led both to quantization rules for the coupling constants and to selection
rules for the correlation functions. Explicit quantum computations are carried
out in genus one (torus). In particular the two-point function and the chiral
condensate are carefully derived for this case. Finally the effective
gravitational action, coming from integrating out the fermionic degrees of
freedoom, is presented. It is different from the standard Liouville one: a new
non-local functional of the conformal factor arises and the central charge is
improved, depending also on the Thirring coupling constant. This last feature
opens the possibility of giving a new explicit representation of the minimal
series in terms of a fermionic interacting model.


  We discuss the role of weakly normal formulas in the theory of thorn forking,
as part of a commentary on the paper "Thorn forking and stable forking" by Ealy
and Onshuus (Rev. acad. colomb. cienc. exact. fis. nat. vol.40 no.157 Bogot\'a
Oct./Dec. 2016). We also give a counterexample to Corollary 4.2 from that
paper, and in the process discuss "theories with selectors".


  We review the motivations and some results on leptogenesis in seesaw models
with an almost conserved lepton number. The paper is based on a talk given at
the 5th International Symposium on Symmetries in Subatomic Physics, SSP2012.


  Let $n$ be a primitive non-deficient number where $n=p_1^{a_1}p_2^{a_2}
\cdots p_k^{a_k}$ where $p_1, p_2 \cdots p_k$ are distinct primes. We prove
that there exists an $i$ such that $$p_i^{a_i+1} < 2k(p_1p_2p_3\cdots p_k).$$
We conjecture that in fact one can always find an $i$ such that ${p_i}^{a_i+1}
< p_1p_2p_3\cdots p_k$.


  We propose a simple drop-in noise-tolerant replacement for the standard
finite difference procedure used ubiquitously in blackbox optimization. In our
approach, parameter perturbation directions are defined by a family of
structured orthogonal matrices. We show that at the small cost of computing a
Fast Walsh-Hadamard/Fourier Transform (FWHT/FFT), such structured finite
differences consistently give higher quality approximation of gradients and
Jacobians in comparison to vanilla approaches that use coordinate directions or
random Gaussian perturbations. We find that trajectory optimizers like
Iterative LQR and Differential Dynamic Programming require fewer iterations to
solve several classic continuous control tasks when our methods are used to
linearize noisy, blackbox dynamics instead of standard finite differences. By
embedding structured exploration in a quasi-Newton optimizer (LBFGS), we are
able to learn agile walking and turning policies for quadruped locomotion, that
successfully transfer from simulation to actual hardware.We theoretically
justify our methods via bounds on the quality of gradient reconstruction and
provide a basis for applying them also to nonsmooth problems.


  We present quantitative probing as a model-agnostic framework for validating
causal models in the presence of quantitative domain knowledge. The method is
constructed as an analogue of the train/test split in correlation-based machine
learning and as an enhancement of current causal validation strategies that are
consistent with the logic of scientific discovery. The effectiveness of the
method is illustrated using Pearl's sprinkler example, before a thorough
simulation-based investigation is conducted. Limits of the technique are
identified by studying exemplary failing scenarios, which are furthermore used
to propose a list of topics for future research and improvements of the
presented version of quantitative probing. The code for integrating
quantitative probing into causal analysis, as well as the code for the
presented simulation-based studies of the effectiveness of quantitative probing
is provided in two separate open-source Python packages.


  The compact mm-wavelength signal in the central cavity of the PDS70 disc,
revealed by deep ALMA observations, is aligned with unresolved Halpha emission,
and is thought to stem from a circum-planetary disc (CPD) around PDS70c. We
revisit the available ALMA data on PDS70c with alternative imaging strategies,
and with special attention to uncertainties and to the impact of the so-called
"JvM correction", which is thought to improve the dynamic range of restored
images. We also propose a procedure for the alignment and joint imaging of
multi-epoch visibility data. We find that the JvM correction exaggerates the
peak signal-to-noise of the data, by up to a factor of 10. In the case of
PDS70, we recover the detection of PDS70c from the July 2019 data, but only at
8sigma. However, its non-detection in Dec. 2017 suggests that PDS70c is
variable by at least 42%+-13% over a 1.75yr time-span, so similar to models of
the Halpha variability. We also pick up fine structure in the inner disc, such
that its peak is offset by ~ 0.04" from the disc centre. The inner disc is
variable too, which we tentatively ascribe to Keplerian rotation as well as
intrinsic morphological changes.


  Radiation field and channel of energy method have become important tools in
the study of nonlinear wave equations in recent years. In this work we give
basic theory of radiation fields of free waves in the energy sub-critical case.
We also show that the asymptotic behaviours of non-radiative solutions to a
wide range of non-linear wave equations resemble those of non-radiative free
waves. Our theory is completely given in the critical Sobolev spaces of the
corresponding nonlinear wave equation and avoids any assumption on the energy
of the solutions.


  The main purpose of this paper is providing a systematic study and
classification of non-scalar kernels for Reproducing Kernel Hilbert Spaces
(RKHS), to be used in the analysis of deformation in shape spaces endowed with
metrics induced by the action of groups of diffeomorphisms. After providing an
introduction to matrix-valued kernels and their relevant differential
properties, we explore extensively those, that we call TRI kernels, that induce
a metric on the corresponding Hilbert spaces of vector fields that is both
translation- and rotation-invariant. These are analyzed in an effective manner
in the Fourier domain, where the characterization of RKHS of curl-free and
divergence-free vector fields is particularly natural. A simple technique for
constructing generic matrix-valued kernels from scalar kernels is also
developed. We accompany the exposition of the theory with several examples, and
provide numerical results that show the dynamics induced by different choices
of TRI kernels on the manifold of labeled landmark points.


  Hydrogen-electrical (HE) microgrids are increasingly assuming an important
role on the pathway toward decarbonization of energy and transportation
systems. This paper studies networked HE microgrids planning (NHEMP),
considering a critical but often-overlooked issue, i.e., the demand-inducing
effect (DIE) associated with infrastructure development decisions.
Specifically, higher refueling capacities will attract more refueling demand of
hydrogen-powered vehicles (HVs). To capture such interactions between
investment decisions and induced refueling demand, we introduce a
decision-dependent uncertainty (DDU) set and build a trilevel stochastic-robust
formulation. The upper-level determines optimal investment strategies for HE
microgrids, the lower-level optimizes the risk-aware operation schedules across
a series of stochastic scenarios, and, for each scenario, the middle-level
identifies the "worst" situation of refueling demand within an individual DDU
set to ensure economic feasibility. Then, an adaptive and exact decomposition
algorithm, based on Parametric Column-and-Constraint Generation (PC&CG), is
customized and developed to address the computational challenge and to
quantitatively analyze the impact of DIE. Case studies on an IEEE exemplary
system validate the effectiveness of the proposed NHEMP model and the PC&CG
algorithm. It is worth highlighting that DIE can make an important contribution
to the economic benefits of NHEMP, yet its significance will gradually decrease
when the main bottleneck transits to other system restrictions.


  We present specific-heat data on the type-II superconductors V3Si, LuNi2B2C
and NbSe2 which were acquired with a low-temperature thermal analysis (DTA)
technique. We compare our data with available literature data on these
superconductors. In the first part we show that the DTA technique allows for
fast measurements while providing a very high resolution on the temperature
scale. Sharp features in the specific heat such as at the one at the transition
to superconductivity are resolved virtually without instrumental broadening. In
the second part we investigate the magnetic-field dependence of the specific
heats of V3Si and LuNi2B2C at a fixed temperature T=7.5K to demonstrate that
DTA techniques also allow for sufficiently precise measurements of absolute
values of cp even in the absence of a sharp phase transition. The corresponding
data for V3Si and LuNi2B2C are briefly discussed.


  We put forward a method for achieving fast and robust for magnetization
reversal in a nanomagnet, by combining the inverse engineering and composite
pulses. The magnetic fields, generated by microwave with time-dependent
frequency, are first designed inversely within short operation time, and
composite pulses are further incorporated to improve the fidelity through
reducing the effect of magnetic anisotropy. The high-fidelity magnetization
reversals are illustrated with numerical examples, and visualized on Bloch
sphere. The influence of damping parameters, relevant to the pulse sequence, is
finally discussed based on Landau-Lifshitz-Gilber equation. These results pave
the way for precise but fast magnetization reversal or switching, with the
applications in high density information storage and processing.


  We propose InDEX, an Indonesian Idiom and Expression dataset for cloze test.
The dataset contains 10438 unique sentences for 289 idioms and expressions for
which we generate 15 different types of distractors, resulting in a large
cloze-style corpus. Many baseline models of cloze test reading comprehension
apply BERT with random initialization to learn embedding representation. But
idioms and fixed expressions are different such that the literal meaning of the
phrases may or may not be consistent with their contextual meaning. Therefore,
we explore different ways to combine static and contextual representations for
a stronger baseline model. Experimentations show that combining definition and
random initialization will better support cloze test model performance for
idioms whether independently or mixed with fixed expressions. While for fixed
expressions with no special meaning, static embedding with random
initialization is sufficient for cloze test model.


  One fundamental assumption in statistical physics is that generic closed
quantum many-body systems thermalize under their own dynamics. Recently, the
emergence of many-body localized systems has questioned this concept,
challenging our understanding of the connection between statistical physics and
quantum mechanics. Here we report on the observation of a many-body
localization transition between thermal and localized phases for bosons in a
two-dimensional disordered optical lattice. With our single site resolved
measurements we track the relaxation dynamics of an initially prepared
out-of-equilibrium density pattern and find strong evidence for a diverging
length scale when approaching the localization transition. Our experiments mark
the first demonstration and in-depth characterization of many-body localization
in a regime not accessible with state-of-the-art simulations on classical
computers.


  Functional depth is the functional data analysis technique that orders a
functional data set. Unlike the case of data on the real line, defining this
order is non-trivial, and particularly, with functional data, there are a
number of properties that any depth should satisfy. We propose a new depth
which both satisfies the properties required of a functional depth but also one
which can be used in the case where there are a very large number of functional
observations or in the case where the observations are functions of several
continuous variables (such as images, for example). We give theoretical
justification for our choice, and evaluate our proposed depth through
simulation. We finally apply the proposed depth to the problem of yielding a
completely non-parametric deconvolution of Positron Emission Tomography (PET)
data for a very large number of curves across the image, as well as to the
problem of finding a representative subject from a set of PET scans.


  Traveling wave solutions of reaction-diffusion equations are well-studied for
Lipschitz continuous monostable and bistable reaction functions. These special
solutions play a key role in mathematical biology and in particular in the
study of ecological invasions. However, if there are more than two stable
steady states, the invasion phenomenon may become more intricate and involve
intermediate steps, which leads one to consider not a single but a collection
of traveling waves with ordered speeds. In this paper we show that, if the
reaction function is discontinuous at the stable steady states, then such a
collection of traveling waves exists and even provides a special solution which
we call a terrace solution. More precisely, we will address both the existence
and uniqueness of the terrace solution.


  We analyze the one-dimensional extended Hubbard model with a single static
impurity by using a computational technique based on the functional
renormalization group. This extends previous work for spinless fermions to
spin-1/2 fermions. The underlying approximations are devised for weak
interactions and arbitrary impurity strengths, and have been checked by
comparing with density-matrix renormalization-group data. We present results
for the density of states, the density profile and the linear conductance.
Two-particle backscattering leads to striking effects, which are not captured
if the bulk system is approximated by its low-energy fixed point, the Luttinger
model. In particular, the expected decrease of spectral weight near the
impurity and of the conductance at low energy scales is often preceded by a
pronounced increase, and the asymptotic power laws are modified by logarithmic
corrections.


  We study some issues related to the effective theory of Calabi-Yau
compactifications with fluxes in Type II theories. At first the scalar
potential for a generic electric abelian gauging of the Heisenberg algebra,
underlying all possible gaugings of RR isometries, is presented and shown to
exhibit, in some circumstances, a "dual'' no-scale structure under the
interchange of hypermultiplets and vector multiplets. Subsequently a new
setting of such theories, when all RR scalars are dualized into antisymmetric
tensors, is discussed. This formulation falls in the class of non-polynomial
tensor theories considered long ago by Freedman and Townsend and it may be
relevant for the introduction of both electric and magnetic charges.


  We propose a unified neural network architecture and learning algorithm that
can be applied to various natural language processing tasks including:
part-of-speech tagging, chunking, named entity recognition, and semantic role
labeling. This versatility is achieved by trying to avoid task-specific
engineering and therefore disregarding a lot of prior knowledge. Instead of
exploiting man-made input features carefully optimized for each task, our
system learns internal representations on the basis of vast amounts of mostly
unlabeled training data. This work is then used as a basis for building a
freely available tagging system with good performance and minimal computational
requirements.


  In this talk, I review the possibility that CPT and Lorentz symmetry might be
spontaneously broken in nature by effects originating in a theory beyond the
standard model, and I discuss some existing and future experimental tests.


  There are circular planar null geodesics at $r=3M$ around a Schwarzschild
black hole of mass $M$. These geodesics form a photon sphere. Null geodesics of
the Schwarzschild space-time which do not form the photon sphere are either
escape to null infinity or get captured by the black hole. Thus, from the
dynamical point of view, the photon sphere represents a smooth basin boundary
that separates the basins of escape and capture of the dynamical system
governing the null geodesics. Here we consider a Schwarzschild black hole
distorted by an external, static, and axisymmetric quadrupolar gravitational
field. We study null geodesics around such a black hole and show that the
photon sphere transforms into a fractal basin boundary that indicates chaotic
behavior of the null geodesics. We calculate the box-counting fractal dimension
of the basin boundary and the related uncertainty exponent, which depend on the
value of the quadrupole moment.


  Underlying relationships among multiagent systems (MAS) in hazardous
scenarios can be represented as game-theoretic models. In adversarial
environments, the adversaries can be intentional or unintentional based on
their needs and motivations. Agents will adopt suitable decision-making
strategies to maximize their current needs and minimize their expected costs.
This paper proposes and extends the new hierarchical network-based model,
termed Game-theoretic Utility Tree (GUT), to arrive at a cooperative pursuit
strategy to catch an evader in the Pursuit-Evasion game domain. We verify and
demonstrate the performance of the proposed method using the Robotarium
platform compared to the conventional constant bearing (CB) and pure pursuit
(PP) strategies. The experiments demonstrated the effectiveness of the GUT, and
the performances validated that the GUT could effectively organize cooperation
strategies, helping the group with fewer advantages achieve higher performance.


  The nature of its intrinsic ripples is the key factor for understanding the
stability of suspended graphene, and for unraveling the long-standing
theoretical debate of the existence of low-dimensional crystalline state. The
rippling morphology of graphene, discovered also in other 2D materials, has a
profound impact on its electronic, mechanical and chemical properties.
Actually, before the discovery of graphene, rippling phenomena are widely
observed: for example, the roughing transition of crystalline interface, the
rippled phase in biomembrane, and crumpling of flexible sheet polymers modeled
by tethered surfaces. The fascinating truth that ripples exist in so many
different membrane-like materials implies possible existence of a universal
physical mechanism which was unclear. We consider the ripples in suspended
graphene as two parts, characterizing the first part by the spontaneous
curvature k which stabilizes the possible soft ZA modes, and the second part by
the thermal curvature kt which is caused directly by height fluctuation. By
choosing k as the order parameter of the system, we establish the Landau theory
modified by thermal fluctuation for wrinkling transition of large sized
graphene. We find that as temperature rises from 0K, a second order phase
transition occurs at a size dependent critical temperature Tc, which
corresponds to a change of equilibrium configuration from a flat state to a
rippling state. Interestingly, the order parameter is stablized as temperature
increases, and the phase transition is associated with a jump of equilibrium
bond length as well as a vanishing intrinsic bending rigidity. The results
obtained suggest that the interplay between the rippling morphology and the
elementary excitations is vital for understanding the behavior of 2D materials.
The concepts and theory developed here is of general significance at least for
tethered membranes.


  This paper addresses the problem of learning binary hash codes for large
scale image search by proposing a novel hashing method based on deep neural
network. The advantage of our deep model over previous deep model used in
hashing is that our model contains necessary criteria for producing good codes
such as similarity preserving, balance and independence. Another advantage of
our method is that instead of relaxing the binary constraint of codes during
the learning process as most previous works, in this paper, by introducing the
auxiliary variable, we reformulate the optimization into two sub-optimization
steps allowing us to efficiently solve binary constraints without any
relaxation.
  The proposed method is also extended to the supervised hashing by leveraging
the label information such that the learned binary codes preserve the pairwise
label of inputs.
  The experimental results on three benchmark datasets show the proposed
methods outperform state-of-the-art hashing methods.


  One of the main applications of semidefinite programming lies in linear
systems and control theory. Many problems in this subject, certainly the
textbook classics, have matrices as variables, and the formulas naturally
contain non-commutative polynomials in matrices. These polynomials depend only
on the system layout and do not change with the size of the matrices involved,
hence such problems are called "dimension-free". Analyzing dimension-free
problems has led to the development recently of a non-commutative (nc) real
algebraic geometry (RAG) which, when combined with convexity, produces
dimension-free Semidefinite Programming. This article surveys what is known
about convexity in the non-commutative setting and nc SDP and includes a brief
survey of nc RAG. Typically, the qualitative properties of the non-commutative
case are much cleaner than those of their scalar counterparts - variables in
R^g. Indeed we describe how relaxation of scalar variables by matrix variables
in several natural situations results in a beautiful structure.


  Current methods of blended targets domain adaptation (BTDA) usually infer or
consider domain label information but underemphasize hybrid categorical feature
structures of targets, which yields limited performance, especially under the
label distribution shift. We demonstrate that domain labels are not directly
necessary for BTDA if categorical distributions of various domains are
sufficiently aligned even facing the imbalance of domains and the label
distribution shift of classes. However, we observe that the cluster assumption
in BTDA does not comprehensively hold. The hybrid categorical feature space
hinders the modeling of categorical distributions and the generation of
reliable pseudo labels for categorical alignment. To address these, we propose
a categorical domain discriminator guided by uncertainty to explicitly model
and directly align categorical distributions $P(Z|Y)$. Simultaneously, we
utilize the low-level features to augment the single source features with
diverse target styles to rectify the biased classifier $P(Y|Z)$ among diverse
targets. Such a mutual conditional alignment of $P(Z|Y)$ and $P(Y|Z)$ forms a
mutual reinforced mechanism. Our approach outperforms the state-of-the-art in
BTDA even compared with methods utilizing domain labels, especially under the
label distribution shift, and in single target DA on DomainNet. Source codes
are available at
\url{https://github.com/Pengchengpcx/Class-overwhelms-Mutual-Conditional-Blended-Target-Domain-Adaptation}.


  The aim of this paper is to study dynamical and topological properties of a
flow in the region of influence of an isolated non-saddle set. We see, in
particular, that some topological conditions are sufficient to guarantee that
these sets are attractors or repellers. We study in detail the existence of
dissonant points of the flow, which play a key role in the description of the
region of influence of a non-saddle set. These points are responsible for much
of the dynamical and topological complexity of the system. We also study
non-saddle sets from the point of view of the Conley index theory and consider,
among other things, the case of flows on manifolds with trivial first
cohomology group. For flows on these manifolds, dynamical robustness is
equivalent to topological robustness. We carry out a particular study of
2-dimensional flows and give a topological condition which detects the
existence of dissonant points for flows on surfaces. We also prove that
isolated invariant continua of planar flows with global region of influence are
necessarily attractors or repellers.


  The steady-state distributions and dynamical behaviour of Zero Range
Processes with hopping rates which are non-monotonic functions of the site
occupation are studied. We consider two classes of non-monotonic hopping rates.
The first results in a condensed phase containing a large (but subextensive)
number of mesocondensates each containing a subextensive number of particles.
The second results in a condensed phase containing a finite number of extensive
condensates. We study the scaling behaviour of the peak in the distribution
function corresponding to the condensates in both cases. In studying the
dynamics of the condensate we identify two timescales: one for creation, the
other for evaporation of condensates at a given site. The scaling behaviour of
these timescales is studied within the Arrhenius law approach and by numerical
simulations.


  Non-Hermitian skin effect(NHSE) describes a unique non-Hermitian phenomenon
that all eigen-modes are localized near the boundary, and has profound impact
on a wide range of bulk properties. In particular, topological systems with
NHSE have stimulated extensive research interests recently, given the fresh
theoretical and experimental challenges therein. Here we propose a readily
implementable scheme for achieving NHSE with band topology in ultracold gases.
Specifically, the scheme realizes the one-dimensional optical Raman lattice
with two types of spin-orbit coupling (SOC) and an additional laser-induced
dissipation. By tuning the dissipation and the SOC strengths, NHSE and band
topology can be individually controlled such that they can coexist in a
considerable parameter regime. To identify the topological phase in the
presence of NHSE, we have restored the bulk-boundary correspondence by invoking
the non-Bloch band theory, and discussed the dynamic signals for detection. Our
work serves as a guideline for engineering topological lattices with NHSE in
the highly tunable environment of cold atoms, paving the way for future studies
of exotic non-Hermitian physics in a genuine quantum many-body setting.


  Observations by recent space missions reported the detection of Rossby waves
(r-modes) in light curves of many stars (mostly A, B, and F spectral types)
with outer radiative envelope. This paper aims to study the theoretical
dynamics of Rossby-type waves in such stars. Hydrodynamic equations in a
rotating frame were split into horizontal and vertical parts connected by a
separation constant (or an equivalent depth). Vertical equations were solved
analytically for a linear temperature profile and the equivalent depth was
derived through free surface boundary condition. It is found that the vertical
modes are concentrated in the near-surface layer with a thickness of several
tens of surface density scale height. Then with the equivalent width,
horizontal structure equations were solved, and the corresponding dispersion
relation for Rossby, Rossby-gravity, and inertia-gravity waves was obtained.
The solutions were found to be confined around the equator leading to the
equatorially trapped waves. It was shown that the wave frequency depends on the
vertical temperature gradient as well as on stellar rotation. Therefore,
observations of wave frequency in light curves of stars with known parameters
(radius, surface gravity, rotation period) could be used to estimate the
temperature gradient in stellar outer layers. Consequently, the Rossby mode may
be considered as an additional tool in asteroseismology apart from acoustic and
gravity modes.


  New rotating dyonic dipole black ring solutions are derived in 5D
Einstein-dilaton gravity with antisymmetric forms. The black rings are analyzed
and their thermodynamics is discussed. New dyonic black string solutions are
also presented.


  Generative models are known to be difficult to assess. Recent works,
especially on generative adversarial networks (GANs), produce good visual
samples of varied categories of images. However, the validation of their
quality is still difficult to define and there is no existing agreement on the
best evaluation process. This paper aims at making a step toward an objective
evaluation process for generative models. It presents a new method to assess a
trained generative model by evaluating the test accuracy of a classifier
trained with generated data. The test set is composed of real images.
Therefore, The classifier accuracy is used as a proxy to evaluate if the
generative model fit the true data distribution. By comparing results with
different generated datasets we are able to classify and compare generative
models. The motivation of this approach is also to evaluate if generative
models can help discriminative neural networks to learn, i.e., measure if
training on generated data is able to make a model successful at testing on
real settings. Our experiments compare different generators from the
Variational Auto-Encoders (VAE) and Generative Adversarial Network (GAN)
frameworks on MNIST and fashion MNIST datasets. Our results show that none of
the generative models is able to replace completely true data to train a
discriminative model. But they also show that the initial GAN and WGAN are the
best choices to generate on MNIST database (Modified National Institute of
Standards and Technology database) and fashion MNIST database.


  A tunable optical multistability scheme based on a single cavity mode coupled
with two separate atomic transitions in an atom-cavity system is proposed and
demonstrated. Under the collective strong coupling condition, multiple
polariton eigenstates of the atom-cavity system are produced. The threshold and
optical multistability curve can be tuned freely by system parameters in a
broadband range. Moreover, a certain bistability region of the system is split
to two bistability regions due to destructive quantum interference induced by
an extra weak control field. Compared to traditional optical multistabilities
created by two or more light fields, the proposed optical multistability scheme
has compactness and is easy to be miniaturized. The proposed scheme is useful
for manufacturing integrated application of multi-state all-optical logic
devices and constructing basic elements of all-optical communication networks.


  In linear wireless networked control systems whose control is based on the
system state's noisy and delayed observations, an accurate functional
relationship is derived between the estimation error and the observations'
freshness and precision. The proposed functional relationship is then applied
to formulate and solve the problem of scheduling among different wireless links
from multiple noisy sensors, where a sliding window algorithm is further
proposed. The algorithm's simulation results show significant performance gain
over existing policies, even in scenarios that require high freshness or
precision of observations.


  An update profile of the CKM matrix is given, providing numerical and
graphical constraints on the CKM parameters in the Standard Model. Constraints
on additional parameters accounting for possible new physics contribution in a
model-independent analysis are also reported with emphasis on the leptonic
decay of the Bd and on the Bs mixing phase.


  We propose a kernel-based partial permutation test for checking the equality
of functional relationship between response and covariates among different
groups. The main idea, which is intuitive and easy to implement, is to keep the
projections of the response vector $\boldsymbol{Y}$ on leading principle
components of a kernel matrix fixed and permute $\boldsymbol{Y}$'s projections
on the remaining principle components. The proposed test allows for different
choices of kernels, corresponding to different classes of functions under the
null hypothesis. First, using linear or polynomial kernels, our partial
permutation tests are exactly valid in finite samples for linear or polynomial
regression models with Gaussian noise; similar results straightforwardly extend
to kernels with finite feature spaces. Second, by allowing the kernel feature
space to diverge with the sample size, the test can be large-sample valid for a
wider class of functions. Third, for general kernels with possibly
infinite-dimensional feature space, the partial permutation test is exactly
valid when the covariates are exactly balanced across all groups, or
asymptotically valid when the underlying function follows certain regularized
Gaussian processes. We further suggest test statistics using likelihood ratio
between two (nested) GPR models, and propose computationally efficient
algorithms utilizing the EM algorithm and Newton's method, where the latter
also involves Fisher scoring and quadratic programming and is particularly
useful when EM suffers from slow convergence. Extensions to correlated and
non-Gaussian noises have also been investigated theoretically or numerically.
Furthermore, the test can be extended to use multiple kernels together and can
thus enjoy properties from each kernel. Both simulation study and application
illustrate the properties of the proposed test.


  For a pair of real or complex scattering potentials
$v_j:\mathbb{R}\to\mathbb{C}$ ($j=1,2$) with support $I_j$ and transfer matrix
$M_j$, the transfer matrix of $v_1+v_2$ is given by the product $M_2 M_1$
provided that $I_1$ lies to the left of $I_2$. We explore the prospects of
generalizing this composition rule for the cases that $I_1$ and $I_2$ have a
small intersection. In particular, we show that if $I_1$ and $I_2$ intersect in
a finite closed interval of length $\ell$ in which both the potentials are
analytic, then the lowest order correction to the above composition rule is
proportional to $\ell^5$. This correction is of the order of $\ell^3$, if $v_1$
and $v_2$ are respectively analytic throughout this interval except at $x=\ell$
and $x=0$. We use these results to explore the superposition of a pair of
unidirectionally invisible potentials with overlapping support.


  We present high resolution FLAMES/VLT spectroscopy of intracluster planetary
nebula (ICPN) candidates, targeting three new fields in the Virgo cluster core
with surface brightness down to mu_B = 28.5. Based on the projected phase space
information we separate the old and 12 newly-confirmed PNs into galaxy and
intracluster components. The M87 PNs are confined to the extended stellar
envelope of M87, within a projected radius of ~ 160 kpc, while the ICPNs are
scattered across the whole surveyed region between M87 and M86. The velocity
dispersions determined from the M87 PNs at projected radii of 60 kpc and 144
kpc show that the galaxy's velocity dispersion profile decreases in the outer
halo, down to 78 +/- 25 km/s. A Jeans model for the M87 halo stars in the
gravitational potential traced by the X-ray emission fits the observed velocity
dispersion profile only if the stellar orbits are strongly radially anisotropic
(beta ~= 0.4 at r ~= 10 kpc increasing to 0.8 at the outer edge), and if
additionally the stellar halo is truncated at ~= 150 kpc average elliptical
radius. From the spatial and velocity distribution of the ICPNs we infer that
M87 and M86 are falling towards each other and that we may be observing them
just before the first close pass. The inferred luminosity-specific PN numbers
for the M87 halo and the ICL are in the range of values observed for old (> 10
Gyr) stellar populations (abridged).


  We extend the concept of Anderson localization, the confinement of quantum
information in a spatially irregular potential, to quantum circuits.
Considering matchgate circuits, generated by time-dependent spin-1/2 XY
Hamiltonians, we give an analytic formula for the out-of-time-ordered
correlator of a local observable, and show that it can be efficiently evaluated
by a classical computer even when the explicit Heisenberg time evolution
cannot. Because this quantity bounds the average error incurred by truncating
the evolution to a spatially limited region, we demonstrate dynamical
localization as a means for classically simulating quantum computation and give
examples of localized phases under certain spatio-temporal disordered
Hamiltonians.


  The experimental and theoretical challenges posed by the study of dynamically
inhomogeneous systems are outlined in the context of cuprates and other oxides.
Considering the pitfalls in the single-component approach to the analysis of
inhomogeneous systems, the effect of either temporal or spatial averaging by
different experiments is discussed. A group-theoretical symmetry analysis of
the observed inhomogeneities in real space and k-space observed in the cuprates
is shown to lead to a quantitatively verifiable description of the
inhomogeneous state, comprising of bound singlet pairs in the ground state and
unbound fermions in the excited state. The predicted symmetry breaking
associated with pairing is shown to be verifiable experimentally.


  We present the Multiple Criteria Decision Analysis Methods Selection Software
(MCDA-MSS). This decision support system helps analysts answering a recurring
question in decision science: Which is the most suitable Multiple Criteria
Decision Analysis method (or a subset of MCDA methods) that should be used for
a given Decision-Making Problem (DMP)?. The MCDA-MSS includes guidance to lead
decision-making processes and choose among an extensive collection (over 200)
of MCDA methods. These are assessed according to an original comprehensive set
of problem characteristics. The accounted features concern problem formulation,
preference elicitation and types of preference information, desired features of
a preference model, and construction of the decision recommendation. The
applicability of the MCDA-MSS has been tested on several case studies. The
MCDA-MSS includes the capabilities of (i) covering from very simple to very
complex DMPs, (ii) offering recommendations for DMPs that do not match any
method from the collection, (iii) helping analysts prioritize efforts for
reducing gaps in the description of the DMPs, and (iv) unveiling methodological
mistakes that occur in the selection of the methods. A community-wide
initiative involving experts in MCDA methodology, analysts using these methods,
and decision-makers receiving decision recommendations will contribute to
expansion of the MCDA-MSS.


  Humans interact and cooperate in structured societies, which are often
represented by complex networks. Previous explorations mainly focus on pairwise
and static network structures, where each link connects two nodes permanently
and never changes. However, empirical human collective interactions go beyond
static time-invariant one-to-one relationships. Recently, researchers have made
vital progress in modelling higher-order interactions using hypernetworks,
where a link may connect more than two individuals. Here, we study collective
cooperation through temporal hypernetworks, capturing the time-varying
higher-order interactions in human societies. We find that temporal
hypernetworks may promote cooperation compared with their static counterparts,
and the traditional static pairwise network may underestimate the positive
effect of local interactions on fostering cooperators. Moreover, temporal
hypernetworks with sparse components and higher-order interactions can
facilitate cooperation. Surprisingly, we report that when the scale of
interactions is of the same order as population size, moderately small
hyperlink sizes best facilitate cooperation. Our findings underscore the
significant impact of real-world temporal higher-order interactions on the
evolution of cooperation.


  Given a prime $p$, a group is called residually $p$ if the intersection of
its $p$-power index normal subgroups is trivial. A group is called virtually
residually $p$ if it has a finite index subgroup which is residually $p$. It is
well-known that finitely generated linear groups over fields of characteristic
zero are virtually residually $p$ for all but finitely many $p$. In particular,
fundamental groups of hyperbolic 3-manifolds are virtually residually $p$. It
is also well-known that fundamental groups of 3-manifolds are residually
finite. In this paper we prove a common generalization of these results: every
3-manifold group is virtually residually $p$ for all but finitely many $p$.
This gives evidence for the conjecture (Thurston) that fundamental groups of
3-manifolds are linear groups.


  New data was obtained for a frequency band that had not been so well-studied
for sea surface probing applications before. During the described 2-weeks sea
experiment 1-3 kHz tonal pulses were emitted from a platform, located on the
northern Black Sea shelf, and Doppler spectrum of reverberation was studied. We
believe that this band is worth further studying due the sound propagation
range is large enough to meet practical needs in coastal zone while the
angle-distance resolution is quite moderate. However it is quite difficult to
interpret the obtained data since backscattering spectrum shape is influenced
by a series of effects and has a complicated link to wind waves and currents
parameters. Backscattering of acoustical signals was received for distances
around 2 nautical miles. Significant wave height, dominant wave frequency were
estimated as the result of such signals processing with the use of machine
learning tools. A decision-tree-based mathematical regression model was trained
to solve the inverse problem. Wind waves prediction is in a good agreement with
direct measurements, made on the platform, and machine learning results allow
physical interpretation.


  Photo Response Non-Uniformity (PRNU) based source camera attribution is an
effective method to determine the origin camera of visual media (an image or a
video). However, given that modern devices, especially smartphones, capture
images, and videos at different resolutions using the same sensor array, PRNU
attribution can become ineffective as the camera fingerprint and query visual
media can be misaligned. We examine different resizing techniques such as
binning, line-skipping, cropping and scaling that cameras use to downsize the
raw sensor image to different media. Taking such techniques into account, this
paper studies the problem of source camera attribution. We define the notion of
Ratio of Alignment, which is a measure of shared sensor elements among
spatially corresponding pixels within two media objects resized with different
techniques. We then compute the Ratio of Alignment between the different
combinations of three common resizing methods under simplified conditions and
experimentally validate our analysis. Based on the insights drawn from the
different techniques used by cameras and the RoA analysis, the paper proposes
an algorithm for matching the source of a video with an image and vice versa.
We also present an efficient search method resulting in significantly improved
performance in matching as well as computation time.


  In the current era, image manipulation is becoming increasingly easier,
yielding more natural looking images, owing to the modern tools in image
processing and computer vision techniques. The task of the segregation of
forged images has become very challenging. To tackle such problems, publicly
available datasets are insufficient. In this paper, we propose to create a
synthetic forged dataset using deep semantic image inpainting algorithm.
Furthermore, we use an unsupervised domain adaptation network to detect
copy-move forgery in images. Our approach can be helpful in those cases, where
the classification of data is unavailable.


  It is now a well established fact that galaxies undergo significant
morphological transformation during their lifetimes, manifesting as an
evolution along the Hubble sequence from the late to the early Hubble types.
The physical processes commonly believed to be responsible for this observed
evolution trend, i.e. the major and minor mergers, as well as gas accretion
under a barred potential, though demonstrated applicability to selected types
of galaxies, on the whole have failed to reproduce the most important
statistical and internal properties of galaxies. The secular evolution
mechanism reviewed in this paper has the potential to overcome most of the
known difficulties of the existing theories to provide a natural and coherent
explanation of the properties of present day as well as high-redshift galaxies.


  We define integral odd Khovanov homology of principally unimodular bipartite
graph-links.


  We consider the spin-$J$ XXZ-Hamiltonian on general graphs $\mathcal{G}$ and
show its equivalence to a direct sum of discrete many-particle Schr\"odinger
type operators on what we call "$N$-particle graphs with maximal local
occupation number $M$", where the kinetic term is described by a weighted
Laplacian. Generalizing previous results for the spin-$1/2$ case, we give
sufficient conditions for the existence of spectral gaps above the low-lying
droplet band when the underlying graph $\mathcal{G}$ is (i) the chain and (ii)
a strip of width $L$.


  Context. The CARMENES spectrograph is surveying ~300 M dwarf stars in search
for exoplanets. Among the target stars, spectroscopic binary systems have been
discovered, which can be used to measure fundamental properties of stars. Aims.
Using spectroscopic observations, we determine the orbital and physical
properties of nine new double-line spectroscopic binary systems by analysing
their radial velocity curves. Methods. We use two-dimensional cross-correlation
techniques to derive the radial velocities of the targets, which are then
employed to determine the orbital properties. Photometric data from the
literature are also analysed to search for possible eclipses and to measure
stellar variability, which can yield rotation periods. Results. Out of the 342
stars selected for the CARMENES survey, 9 have been found to be double-line
spectroscopic binaries, with periods ranging from 1.13 to ~8000 days and orbits
with eccentricities up to 0.54. We provide empirical orbital properties and
minimum masses for the sample of spectroscopic binaries. Absolute masses are
also estimated from mass-luminosity calibrations, ranging between ~0.1 and ~0.6
Msol . Conclusions. These new binary systems increase the number of double-line
M dwarf binary systems with known orbital parameters by 15%, and they have
lower mass ratios on average.


  Recently, Active Galactic Nuclei (AGNs) have been proposed as standardizable
candles, thanks to an observed non-linear relation between their X-ray and
optical-ultraviolet (UV) luminosities, which provides an independent
measurement of their distances. In this paper, we use these observables for the
first time to estimate the parameters of f(R) gravity models (specifically the
Hu-Sawicki and the exponential models) together with the cosmological
parameters. The importance of this type of modified gravity theories lies in
the fact that they can explain the late time accelerated expansion of the
universe without the inclusion of a dark energy component. We have also
included other observable data to the analyses such as estimates of the Hubble
parameter H(z) from Cosmic Chronometers, the Pantheon Type Ia supernovae
compilation, and Baryon Acoustic Oscillations measurements. Our results show
that the allowed space parameter is restricted when both AGN and BAO data are
added to CC and SnIa data, being the BAO data set the most restrictive one. We
can also conclude that even though our results are consistent with the ones
from the LCDM model, small deviations from General Relativity, than can be
successfully described by the f(R) models studied in this paper, are also
allowed by the considered data sets.


  In slow collisions of two bare nuclei with the total charge number larger
than the critical value, $Z_{\rm cr} \approx 173$, the initially neutral vacuum
can spontaneously decay into the charged vacuum and two positrons. Detection of
the spontaneous emission of positrons would be the direct evidence of this
fundamental phenomenon. However, the spontaneous emission is generally masked
by the dynamical positron emission, which is induced by a strong time-dependent
electric field created by the colliding nuclei. In our recent paper [I.A.
Maltsev et al., Phys. Rev. Lett. 123, 113401 (2019)] it has been shown that the
spontaneous pair production can be observed via measurements of the
pair-production probabilities for a given set of nuclear trajectories. In the
present paper, we have significantly advanced this study by exploring
additional aspects of the process we are interested in. We calculate the
positron energy spectra and find that these spectra can give a clear signature
of the transition from the subcritical to the supercritical regime. It is found
that focusing on a part of the positron spectrum, which accounts for the energy
region where the spontaneously created positrons can contribute, allows to get
a much stronger evidence of the transition to the supercritical mode, making it
very well pronounced in collisions, for example, of two uranium nuclei. The
possibility of extending this study to collisions of bare nuclei with neutral
atoms is also considered. The probability of a vacancy in the lowest-energy
state of a quasimolecule which is formed in collisions of a bare U nucleus with
neutral U and Cm atoms has been calculated. The relatively large values of this
probability make such collisions suitable for observing the vacuum decay.


  Flux-rope-based magnetohydrodynamic modeling of coronal mass ejections (CMEs)
is a promising tool for the prediction of the CME arrival time and magnetic
field at Earth. In this work, we introduce a constant-turn flux rope model and
use it to simulate the 12-July-2012 16:48 CME in the inner heliosphere. We
constrain the initial parameters of this CME using the graduated cylindrical
shell (GCS) model and the reconnected flux in post-eruption arcades. We
correctly reproduce all the magnetic field components of the CME at Earth, with
an arrival time error of approximately 1 hour. We further estimate the average
subjective uncertainties in the GCS fittings, by comparing the GCS parameters
of 56 CMEs reported in multiple studies and catalogs. We determined that the
GCS estimates of the CME latitude, longitude, tilt, and speed have average
uncertainties of 5.74 degrees, 11.23 degrees, 24.71 degrees, and 11.4%
respectively. Using these, we have created 77 ensemble members for the
12-July-2012 CME. We found that 55% of our ensemble members correctly reproduce
the sign of the magnetic field components at Earth. We also determined that the
uncertainties in GCS fitting can widen the CME arrival time prediction window
to about 12 hours for the 12-July-2012 CME. On investigating the forecast
accuracy introduced by the uncertainties in individual GCS parameters, we
conclude that the half-angle and aspect ratio have little impact on the
predicted magnetic field of the 12-July-2012 CME, whereas the uncertainties in
longitude and tilt can introduce a relatively large spread in the magnetic
field predicted at Earth.