  This work is devoted to dissipative extension theory for dissipative linear
relations. We give a self-consistent theory of extensions by generalizing the
theory on symmetric extensions of symmetric operators. Several results on the
properties of dissipative relations are proven. Finally, we deal with the
spectral properties of dissipative extensions of dissipative relations and
provide results concerning particular realizations of this general setting.


  In the last decade the detection of individual massive dark matter sub-halos
has been possible using potential correction formalism in strong gravitational
lens imaging. Here we propose a statistical formalism to relate strong
gravitational lens surface brightness anomalies to the lens potential
fluctuations arising from dark matter distribution in the lens galaxy. We
consider these fluctuations as a Gaussian random field in addition to the
unperturbed smooth lens model. This is very similar to weak lensing formalism
and we show that in this way we can measure the power spectrum of these
perturbations to the potential. We test the method by applying it to simulated
mock lenses of different geometries and by performing an MCMC analysis of the
theoretical power spectra. This method can measure density fluctuations in
early type galaxies on scales of 1-10 kpc at typical rms-levels of a percent,
using a single lens system observed with the Hubble Space Telescope with
typical signal-to-noise ratios obtained in a single orbit.


  Wide classes of nonlinear mathematical physics equations are described that
admit order reduction through the use of the von Mises transformation, with the
unknown function taken as the new independent variable and an appropriate
partial derivative taken as the new dependent variable. RF-pairs and associated
B\"acklund transformations are constructed for evolution equations of general
form (special cases of which are Burgers, Korteweg--de Vries, and Harry Dym
type equations as well as many other nonlinear equations of mathematical
physics). The results obtained are used for order reduction and constructing
exact solutions of hydrodynamics equations. A generalized Calogero equation and
a number of other new integrable nonlinear equations are considered.


  When 0<p<1, it is known that the p-Bloch and (1-p)-Lipschitz spaces of the
unit ball in n-dimensional complex Eucllidean space are equal as sets. We prove
that these spaces are additionally norm-equivalent, thus extending known
results for n=1 and the polydisk. As an application, we generalize work by
Madigan on the disk by investigating boundedness of composition operators
between p- and q-Lipschitz spaces of the ball.


  We study the stellar cluster population in two adjacent fields in the nearby,
face-on spiral galaxy, M83, using WFC3/HST imaging. The clusters are selected
through visual inspection to be centrally concentrated, symmetric, and resolved
on the images, which allows us to differentiate between clusters and likely
unbound associations. We compare our sample with previous studies and show that
the differences between the catalogues are largely due to the inclusion of
large numbers of diffuse associations within previous catalogues. The
luminosity function of the clusters is well approximated by a power-law with
index, -2, over most of the observed range, however a steepening is seen at M_V
= -9.3 and -8.8 in the inner and outer fields, respectively. Additionally, we
show that the cluster population is inconsistent with a pure power-law mass
distribution, but instead exhibits a truncation at the high mass end. If
described as a Schechter function, the characteristic mass is 1.6 and 0.5 *
10^5 Msun, for the inner and outer fields, respectively, in agreement with
previous estimates of other cluster populations in spiral galaxies. Comparing
the predictions of the mass independent disruption (MID) and mass dependent
disruption (MDD) scenarios with the observed distributions, we find that both
models can accurately fit the data. However, for the MID case, the fraction of
clusters destroyed (or mass lost) per decade in age is dependent on the
environment, hence, the age/mass distributions of clusters are not universal.
In the MDD case, the disruption timescale scales with galactocentric distance
(being longer in the outer regions of the galaxy) in agreement with analytic
and numerical predictions. Finally, we discuss the implications of our results
on other extragalactic surveys, focussing on the fraction of stars that form in
clusters and the need (or lack thereof) for infant mortality.


  Multi-view learning leverages correlations between different sources of data
to make predictions in one view based on observations in another view. A
popular approach is to assume that, both, the correlations between the views
and the view-specific covariances have a low-rank structure, leading to
inter-battery factor analysis, a model closely related to canonical correlation
analysis. We propose a convex relaxation of this model using structured norm
regularization. Further, we extend the convex formulation to a robust version
by adding an l1-penalized matrix to our estimator, similarly to convex robust
PCA. We develop and compare scalable algorithms for several convex multi-view
models. We show experimentally that the view-specific correlations are
improving data imputation performances, as well as labeling accuracy in
real-world multi-label prediction tasks.


  In this paper we study the structure of manifolds that contain a quasi-line
and give some evidence towards the fact that the irreducible components of
degenerations of the quasi-line should determine the Mori cone. We show that
the minimality with respect to a quasi-line yields strong restrictions on fibre
space structures of the manifold.


  In this study, we present the first multidimensional core-collapse supernovae
(CCSNe) simulations including QCD axions in order to assess the impact on the
CCSN explosion mechanism. We include axions in our simulations through the
nucleon-nucleon bremsstrahlung emission channel and as a pure energy-sink term
under the assumption that the axions free-stream after being emitted. We
perform both spherically symmetric (1D) and axisymmetric (2D) simulations. In
1D, we utilize a parameterized heating scheme to achieve explosions, whereas in
2D we self-consistently realize explosions through the neutrino heating
mechanism. Our 2D results for a $20 M_\odot$ progenitor show an impact of the
axion emission on the shock behavior and the explosion time when considering
values of the Peccei-Quinn energy scale $f_a \leq 2 \times 10^8$ GeV. The
strong cooling due to the axion emission accelerates the contraction of the
core and leads to more efficient neutrino heating and earlier explosions. For
the axion emission formalism utilized, the values of $f_a$ that impact the
explosion are close to, but in tension with current limits based on the
neutrinos detected from SN1987A. However, given the non-linear behavior of the
emission and the multidimensional nature of CCSNe, we suggest that a
self-consistent, multidimensional approach to simulating CCSNe, including any
late time accretion and cooling, is needed to fully explore the axion bounds
from supernovae and the impact on the CCSN explosion mechanism.


  The origin and nature of ultrahigh energy cosmic rays with energies above
$10^{20}\,$eV is a puzzle for the physics and astrophysics of cosmic rays which
is still unresolved. In this paper, I report on an extensive study on the
propagation of extragalactic nucleons, $\gamma$-rays, and electrons in the
energy range between $10^8\,$eV and $10^{23}\,$eV. I have devised an efficient
numerical method to solve the transport equations for cosmic ray spectral
evolution. The universal radiation background spectrum in the energy range
between $\simeq 10^{9}\,$eV and $\simeq 1\,$eV is considered in the numerical
code, including the diffuse radio background, the cosmic microwave background,
and the infrared/optical background, as well as a possible extragalactic
magnetic field. I apply the code to compute the particle spectra predicted by
various models of ultrahigh energy cosmic ray origin. A comparison with the
observed fluxes, especially the diffuse $\gamma$-ray background in several
energy ranges, allows one to constrain certain classes of models. I conclude
that scenarios which attribute the highest energy cosmic rays to Grand
Unification Scale physics or to cosmological Gamma Ray Bursts are viable at the
present time.


  We prove Nikol'skii type inequalities which for polynomials on the
$n$-dimensional torus $\mathbb{T}^n$ relate the $L^p$-with the $L^q$-norm (with
respect to the normalized Lebesgue measure and $0 <p <q < \infty$). Among other
things we show that $C=\sqrt{q/p}$ is the best constant such that
$\|P\|_{L^q}\leq C^{\text{deg}(P)} \|P\|_{L^p}$ for all homogeneous polynomials
$P$ on $\mathbb{T}^n$. We also prove an exact inequality between the $L^p$-norm
of a polynomial $P$ on $\mathbb{T}^n$ and its Mahler measure $M(P)$, which is
the geometric mean of $|P|$ with respect to the normalized Lebesgue measure on
$\mathbb{T}^n$. Using extrapolation we transfer this estimate into a
Khintchine-Kahane type inequality, which, for polynomials on $\mathbb{T}^n$,
relates a certain exponential Orlicz norm and Mahler's measure. Applications
are given, including some interpolation estimates.


  This article is devoted to the construction of new numerical methods for the
semiclassical Schr\"odinger equation. A phase-amplitude reformulation of the
equation is described where the Planck constant epsilon is not a singular
parameter. This allows to build splitting schemes whose accuracy is spectral in
space, of up to fourth order in time, and independent of epsilon before the
caustics. The second-order method additionally preserves the L^2-norm of the
solution just as the exact flow does. In this first part of the paper, we
introduce the basic splitting scheme in the nonlinear case, reveal our strategy
for constructing higher-order methods, and illustrate their properties with
simulations. In the second part, we shall prove a uniform convergence result
for the first-order splitting scheme applied to the linear Schr\"odinger
equation with a potential.


  We present a lattice analysis of a confining Yang-Mills theory without
Goldstone boson. We have analytically investigated the model by a strong
coupling expansion and by an intensive lattice Monte Carlo simulation using
standard lattice QCD methods. We show that this theory is an interesting
candidate for describing weak bosons as composite particles.


  We suggest that the DNA molecules could form the cholesteric phase owing to
an interaction mediated by the network of the hydrogen bonds (H-network) in the
solvent. The model admits of the dependence of the optical activity of the
solution on the concentration of the PEG, and the change in the sense of the
cholesteric twist due to the intercalation by the daunomicyn. Using the
experimental data for the cholesteric phase of the DNA dispersion, we obtain a
rough estimate for the energy given by our model, and show that it should be
taken into account as well as the energy due to the steric repulsion, van der
Waals, and electrostatic forces, generally used for studying the DNA molecules.
The elastic constant of the H-network generating the interaction between the
DNA molecules is determined by the energy due to the proton's vibration in the
hydrogen bonds.


  (ABRIDGED) Studies of debris disks have shown that most systems are analogous
to the Edgeworth-Kuiper Belt. However a rare subset of sun-like stars possess
dust which lies in the terrestrial planet region. In this study we aim to
determine how many sources with apparent mid-IR excess are truly hosts of warm
dust, and investigate where the dust must lie. We observed using mid-IR imaging
with TIMMI2, VISIR and MICHELLE a sample of FGK main sequence stars reported to
have hot dust. A new modelling approach was developed to determine the
constraints that can be set on the radial extent of excess emission. We confirm
the presence of warm dust around 3 of the candidates (eta Corvi, HD145263 and
HD202406), and present constraints on the emitting dust regions. Of 2
alternative models for the eta Corvi excess emission, we find that a model with
1 hot dust component at <3 AU (combined with the known submm dust population)
fits the data better at the 2.6sigma level than an alternative model with 2
populations of dust in the mid-IR. We identify several systems which have a
companion (HD65277 and HD79873) or background object (HD53246, HD123356 and
HD128400) responsible for their mid-infrared excess, and for 3 other systems we
were able to rule out a point-like source near the star at the level of excess
observed in lower resolution observations (HD12039, HD69830 and HD191089). Hot
dust sources are either young and possibly primordial or transitional, or have
relatively small radius steady-state planetesimal belts, or they are old and
luminous with transient emission. High resolution imaging can be used to
constrain the location of the disk and help to discriminate between different
models of disk emission. For some small disks, interferometry is needed to
resolve the disk location.


  We consider integer quantum Hall states and calculate bulk entanglement
spectrum by formulating the correlation matrix in guiding center
representation. Our analytical approach is based on the projection operator
with redefining the inner product of states in Hilbert space to take care of
the restriction imposed by the (rectangle-tiled) checkerboard partition. The
resultant correlation matrix contains the coupling constants between states of
different guiding centers parameterized by magnetic length and the period of
partition. We find various band-crossings by tuning the flux $\Phi$ threading
each chekerborad pixel and by changing filling factor $\nu$. When $\nu=1$ and
$\Phi=2\pi$, or $\nu=2$ and $\Phi=\pi$, one Dirac band crossing is found. For
$\nu=1$ and $\Phi=\pi$, the band crossings are in the form of nodal line,
enclosing the Brillouin zone. As for $\nu=2$ and $\Phi=2\pi$, the doubled Dirac
point, or the quadratic point, is seen. Besides, we infer that the quadratic
point is protected by C$_4$ symmetry of the checkerboard partition since it
evolves into two separate Dirac points when the symmetry is lowered to C$_2$.
In addition, we also identify the emerging symmetries responsible for the
symmetric bulk entanglement spectra, which are absent in the underlying quantum
Hall states.


  A massive relativistic spinning point particle in any number of dimensions
has in a previous article been shown to be described by first class
constraints, which define a gauge theory. In the present paper we find the
corresponding finite gauge transformations. By comparing the integrated gauge
transformations to transformation equations found by Pryce, we conclude that
the selection of gauge corresponds to selection of the relativistic center of
mass frame in the model of Pryce, where a spinning particle is considered a
composite object. The Lorentz group is identified as the gauge group, and as
gauge field we identify the relativistic angular velocity. We also show that an
analogous physical interpretation is possible for the relativistic spherical
top of Hanson and Regge.


  Recent experiments on Bernal bilayer graphene (BLG) deposited on monolayer
WSe$_2$ revealed robust, ultra-clean superconductivity coexisting with sizable
induced spin-orbit coupling. Here we propose BLG/WSe$_2$ as a platform to
engineer gate-defined planar topological Josephson junctions, where the normal
and superconducting regions descend from a common material. More precisely, we
show that if superconductivity in BLG/WSe$_2$ is gapped and emerges from a
parent state with inter-valley coherence, then Majorana zero modes can form in
the barrier region upon applying weak in-plane magnetic fields. Our results
spotlight a potential pathway for `internally engineered' topological
superconductivity that minimizes detrimental disorder and
orbital-magnetic-field effects.


  We propose a renormalization prescription for the Wheeler-DeWitt equation of
(3+1)-dimensional Einstein gravity and also propose a strong coupling expansion
as an approximation scheme to probe quantum geometry at length scales much
smaller than the Planck length. We solve the Wheeler-DeWitt equation to the
second order in the expansion in a class of local solutions and discuss
problems arising in our approach.


  Let $c(H)$ be the smallest value for which $e(G)/|G|\geq c(H)$ implies $H$ is
a minor of $G$. We show a new upper bound on $c(H)$, which improves previous
bounds for graphs with a vertex partition where some pairs of parts have many
more edges than others -- for instance a complete bipartite graph with a small
number of edges placed inside one class. We also show a tight matching lower
bound for almost all such graphs. We apply these results to show $c(K_{ft/\log
t,t}) = (0.638\dotsc+o_{f}(1))t\sqrt{f}$, for $f = o(\log t) = \omega(1)$.


  The annotation of disease severity for medical image datasets often relies on
collaborative decisions from multiple human graders. The intra-observer
variability derived from individual differences always persists in this
process, yet the influence is often underestimated. In this paper, we cast the
intra-observer variability as an uncertainty problem and incorporate the label
uncertainty information as guidance into the disease screening model to improve
the final decision. The main idea is dividing the images into simple and hard
cases by uncertainty information, and then developing a multi-stream network to
deal with different cases separately. Particularly, for hard cases, we
strengthen the network's capacity in capturing the correct disease features and
resisting the interference of uncertainty. Experiments on a fundus image-based
glaucoma screening case study show that the proposed model outperforms several
baselines, especially in screening hard cases.


  We consider a computation of one-loop AdS_5 x S^5 superstring correction to
the energy radiated by the end-point of a string which moves along a wavy line
at the boundary of AdS_5 with a small transverse acceleration (the
corresponding classical solution was described by Mikhailov in hep-th/0305196).
We also compute the one-loop effective action for an arbitrary small transverse
string fluctuation background. It is related by an analytic continuation to the
Euclidean effective action describing one-loop correction to the expectation
value of a wavy Wilson line. We show that both the one-loop contribution to the
energy and to the Wilson line are controlled by the subleading term in the
strong-coupling expansion of the function B(\lambda) as suggested by Correa,
Henn, Maldacena and Sever in arXiv:1202.4455.


  In this paper, a randomized algorithm for deciding the irreducibility of an
irreducible polynomial and factoring a reducible polynomial over the field of
rational numbers is presented. The main idea underlying the algorithm is based
on conversion of a given polynomial into a polynomial with integer coefficients
and reduction to mod p, for several large prime numbers p, without applying a
lifting method.


  This work was firstly published in 1986 \cite{we}. No real two-dimensional
object with the zero-gap quasi-relativistic spectrum was known in that time.
Such an object is well known now: this is graphene. That is why we decided to
present it again as a e-print in a slightly modified form. A density of the
two-dimensional zero-gap electronic states at the quantizing magnetic field in
the presence the Gaussian random potential has been calculated. The problem is
reduced to zero-dimensional spinor field theory using the holomorphic
supersymmetric representation. The calculated density of states in the case of
the mass perturbation has a delta function peak in the Dirac point.This peak
smears due to the potential perturbation.


  In this paper, we study the effects of incorporating timestamps, such as
document creation dates, into generation systems. Two types of time-aware
prompts are investigated: (1) textual prompts that encode document timestamps
in natural language sentences; and (2) linear prompts that convert timestamps
into continuous vectors. To explore extrapolation to future data points, we
further introduce a new data-to-text generation dataset, TempWikiBio,
containing more than 4 millions of chronologically ordered revisions of
biographical articles from English Wikipedia, each paired with structured
personal profiles. Through data-to-text generation on TempWikiBio, text-to-text
generation on the content transfer dataset, and summarization on XSum, we show
that linear prompts on encoder and textual prompts improve the generation
quality on all datasets. Despite having less performance drop when testing on
data drawn from a later time, linear prompts focus more on non-temporal
information and are less sensitive to the given timestamps, according to human
evaluations and sensitivity analyses. Meanwhile, textual prompts establish the
association between the given timestamps and the output dates, yielding more
factual temporal information in the output.


  Neural Text-to-speech (TTS) synthesis is a powerful technology that can
generate speech using neural networks. One of the most remarkable features of
TTS synthesis is its capability to produce speech in the voice of different
speakers. This paper introduces voice cloning and speech synthesis
https://pypi.org/project/voice-cloning/ an open-source python package for
helping speech disorders to communicate more effectively as well as for
professionals seeking to integrate voice cloning or speech synthesis
capabilities into their projects. This package aims to generate synthetic
speech that sounds like the natural voice of an individual, but it does not
replace the natural human voice. The architecture of the system comprises a
speaker verification system, a synthesizer, a vocoder, and noise reduction.
Speaker verification system trained on a varied set of speakers to achieve
optimal generalization performance without relying on transcriptions.
Synthesizer is trained using both audio and transcriptions that generate Mel
spectrogram from a text and vocoder which converts the generated Mel
Spectrogram into corresponding audio signal. Then the audio signal is processed
by a noise reduction algorithm to eliminate unwanted noise and enhance speech
clarity. The performance of synthesized speech from seen and unseen speakers
are then evaluated using subjective and objective evaluation such as Mean
Opinion Score (MOS), Gross Pitch Error (GPE), and Spectral distortion (SD). The
model can create speech in distinct voices by including speaker characteristics
that are chosen randomly.


  The renormalization theory of the quantum Hall effect relies primarily on the
non-perturbative concept of theta renormalization by instantons. Within the
generalized non-linear sigma model approach initiated by Finkelstein we obtain
the physical observables of the interacting electron gas, formulate the general
(topological) principles by which the Hall conductance is robustly quantized
and derive - for the first time - explicit expressions for the non-perturbative
(instanton) contributions to the renormalization group beta- and gamma-
functions. Our results are in complete agreement with the recently proposed
idea of super universality which says that the fundamental aspects of the
quantum Hall effect are all generic features the instanton vacuum concept in
asymptotically free field theory.


  We solve $\square_b$ on a class of non-compact 3-dimensional strongly
pseudoconvex CR manifolds via a certain conformal equivalence. The idea is to
make use of a related $\square_b$ operator on a compact 3-dimensional strongly
pseudoconvex CR manifold, which we solve using a pseudodifferential calculus.
The way we solve $\square_b$ works whenever $\overline{\partial}_b$ on the
compact CR manifold has closed range in $L^2$; in particular, as in Beals and
Greiner, it does not require the CR manifold to be the boundary of a strongly
pseudoconvex domain in $\mathbb{C}^2$. Our result provides in turn a key step
in the proof of a positive mass theorem in 3-dimensional CR geometry, by Cheng,
Malchiodi and Yang, which they then applied to study the CR Yamabe problem in 3
dimensions.


  Micro-expressions (MEs) are spontaneous, unconscious facial expressions that
have promising applications in various fields such as psychotherapy and
national security. Thus, micro-expression recognition (MER) has attracted more
and more attention from researchers. Although various MER methods have emerged
especially with the development of deep learning techniques, the task still
faces several challenges, e.g. subtle motion and limited training data. To
address these problems, we propose a novel motion extraction strategy (MoExt)
for the MER task and use additional macro-expression data in the pre-training
process. We primarily pretrain the feature separator and motion extractor using
the contrastive loss, thus enabling them to extract representative motion
features. In MoExt, shape features and texture features are first extracted
separately from onset and apex frames, and then motion features related to MEs
are extracted based on the shape features of both frames. To enable the model
to more effectively separate features, we utilize the extracted motion features
and the texture features from the onset frame to reconstruct the apex frame.
Through pre-training, the module is enabled to extract inter-frame motion
features of facial expressions while excluding irrelevant information. The
feature separator and motion extractor are ultimately integrated into the MER
network, which is then fine-tuned using the target ME data. The effectiveness
of proposed method is validated on three commonly used datasets, i.e., CASME
II, SMIC, SAMM, and CAS(ME)3 dataset. The results show that our method performs
favorably against state-of-the-art methods.


  Visual object tracking, which is representing a major interest in image
processing field, has facilitated numerous real world applications. Among them,
equipping unmanned aerial vehicle (UAV) with real time robust visual trackers
for all day aerial maneuver, is currently attracting incremental attention and
has remarkably broadened the scope of applications of object tracking. However,
prior tracking methods have merely focused on robust tracking in the
well-illuminated scenes, while ignoring trackers' capabilities to be deployed
in the dark. In darkness, the conditions can be more complex and harsh, easily
posing inferior robust tracking or even tracking failure. To this end, this
work proposed a novel discriminative correlation filter based tracker with
illumination adaptive and anti dark capability, namely ADTrack. ADTrack firstly
exploits image illuminance information to enable adaptability of the model to
the given light condition. Then, by virtue of an efficient and effective image
enhancer, ADTrack carries out image pretreatment, where a target aware mask is
generated. Benefiting from the mask, ADTrack aims to solve a dual regression
problem where dual filters, i.e., the context filter and target focused filter,
are trained with mutual constraint. Thus ADTrack is able to maintain
continuously favorable performance in all-day conditions. Besides, this work
also constructed one UAV nighttime tracking benchmark UAVDark135, comprising of
more than 125k manually annotated frames, which is also very first UAV
nighttime tracking benchmark. Exhaustive experiments are extended on
authoritative daytime benchmarks, i.e., UAV123 10fps, DTB70, and the newly
built dark benchmark UAVDark135, which have validated the superiority of
ADTrack in both bright and dark conditions on a single CPU.


  We show that a smooth projective variety admits a Chow-Kunneth decomposition
if the cohomology has level at most one except for the middle degree. This can
be extended to the relative case in a weak sense if the morphism has only
isolated singularities, the base space is 1-dimensional, and the generic fiber
satisfies the above condition.


  Due to the extremely volatile nature of financial markets, it is commonly
accepted that stock price prediction is a task full of challenge. However in
order to make profits or understand the essence of equity market, numerous
market participants or researchers try to forecast stock price using various
statistical, econometric or even neural network models. In this work, we survey
and compare the predictive power of five neural network models, namely, back
propagation (BP) neural network, radial basis function (RBF) neural network,
general regression neural network (GRNN), support vector machine regression
(SVMR), least squares support vector machine regresssion (LS-SVMR). We apply
the five models to make price prediction of three individual stocks, namely,
Bank of China, Vanke A and Kweichou Moutai. Adopting mean square error and
average absolute percentage error as criteria, we find BP neural network
consistently and robustly outperforms the other four models.


  We consider the leading order perturbative renormalization of the
multicritical $\phi^{2n}$ models and some generalizations in curved space. We
pay particular attention to the nonminimal interaction with the scalar
curvature $\frac{1}{2}\xi \phi^2 R$ and discuss the emergence of the conformal
value of the coupling $\xi$ as the renormalization group fixed point of its
beta function at and below the upper critical dimension as a function of $n$.
We also examine our results in relation with Kawai and Ninomiya's formulation
of two dimensional gravity.


  We provide a simple and efficient algorithm for computing the Euclidean
projection of a point onto the capped simplex---a simplex with an additional
uniform bound on each coordinate---together with an elementary proof. Both the
MATLAB and C++ implementations of the proposed algorithm can be downloaded at
https://eng.ucmerced.edu/people/wwang5.


  While natural language processing systems often focus on a single language,
multilingual transfer learning has the potential to improve performance,
especially for low-resource languages. We introduce XLDA, cross-lingual data
augmentation, a method that replaces a segment of the input text with its
translation in another language. XLDA enhances performance of all 14 tested
languages of the cross-lingual natural language inference (XNLI) benchmark.
With improvements of up to $4.8\%$, training with XLDA achieves
state-of-the-art performance for Greek, Turkish, and Urdu. XLDA is in contrast
to, and performs markedly better than, a more naive approach that aggregates
examples in various languages in a way that each example is solely in one
language. On the SQuAD question answering task, we see that XLDA provides a
$1.0\%$ performance increase on the English evaluation set. Comprehensive
experiments suggest that most languages are effective as cross-lingual
augmentors, that XLDA is robust to a wide range of translation quality, and
that XLDA is even more effective for randomly initialized models than for
pretrained models.


  The paper researches the problem of representation learning for electronic
health records. We present the patient histories as temporal sequences of
diseases for which embeddings are learned in an unsupervised setup with a
transformer-based neural network model. Additionally the embedding space
includes demographic parameters which allow the creation of generalized patient
profiles and successful transfer of medical knowledge to other domains. The
training of such a medical profile model has been performed on a dataset of
more than one million patients. Detailed model analysis and its comparison with
the state-of-the-art method show its clear advantage in the diagnosis
prediction task. Further, we show two applications based on the developed
profile model. First, a novel Harbinger Disease Discovery method allowing to
reveal disease associated hypotheses and potentially are beneficial in the
design of epidemiological studies. Second, the patient embeddings extracted
from the profile model applied to the insurance scoring task allow significant
improvement in the performance metrics.


  In order to deal with the curse of dimensionality in reinforcement learning
(RL), it is common practice to make parametric assumptions where values or
policies are functions of some low dimensional feature space. This work focuses
on the representation learning question: how can we learn such features? Under
the assumption that the underlying (unknown) dynamics correspond to a low rank
transition matrix, we show how the representation learning question is related
to a particular non-linear matrix decomposition problem. Structurally, we make
precise connections between these low rank MDPs and latent variable models,
showing how they significantly generalize prior formulations for representation
learning in RL. Algorithmically, we develop FLAMBE, which engages in
exploration and representation learning for provably efficient RL in low rank
transition models.


  Given a quadratic two-parameter matrix polynomial Q, we develop a systematic
approach to generating a vector space of linear two-parameter matrix
polynomials. We identify a set of linearizations of Q that lie in the vector
space. Finally, we determine a class of linearizations for a quadratic two-
parameter eigenvalue problem.


  We investigate the performance of discrimination strategy in the comparison
task of known quantum states. In the discrimination strategy, one infers
whether or not two quantum systems are in the same state on the basis of the
outcomes of separate discrimination measurements on each system. In some cases
with more than two possible states, the optimal strategy in minimum-error
comparison is that one should infer the two systems are in different states
without any measurement, implying that the discrimination strategy performs
worse than the trivial "no-measurement" strategy. We present a sufficient
condition for this phenomenon to happen. For two pure states with equal prior
probabilities, we determine the optimal comparison success probability with an
error margin, which interpolates the minimum-error and unambiguous comparison.
We find that the discrimination strategy is not optimal except for the
minimum-error case.


  We demonstrate that by making satellite-based parallax measurements of Macho
events, it is possible to distinguish disk from bulge Machos and to determine
individual masses of disk Machos to an accuracy of $0.2$ in the log. This is at
least a 3-fold improvement over what can be done without parallaxes, i.e., just
from the observed time scale of the events. In addition, we show that the
physical distribution of disk Machos can be found from `reduced' velocities
determined by parallax measurements.


  The recent crash demonstrated (once again) that the description of the
financial market by present financial mathematics cannot be considered as
totally satisfactory. We remind that nowadays financial mathematics is heavily
based on the use of random variables and stochastic processes which are
described by Kolmogorov's measure-theoretic model for probability ("classical
probabilistic model"). I speculate that the present financial crises is a sign
(a kind of experiment to test validity of classical probability theory at the
financial market) that the use of this model in finances should be either
totally rejected or at least completed. One of the best candidates for a new
probabilistic financial model is quantum probability or its generalizations, so
to say quantum-like (QL) models. Speculations that the financial market may be
nonclassical have been present in scientific literature for many years. The aim
of this note is to move from the domain of speculation to rigorous statistical
arguments in favor of probabilistic nonclassicality of the financial market. I
design a corresponding statistical test which is based on violation of the
formula of total probability (FTP). The latter is the basic in classical
probability and its violation would be a strong sign in favor of QL behavior at
the market.


  We consider partitions of the edge set of a graph G into copies of a fixed
graph H and single edges. Let \phi_H(n) denote the minimum number p such that
any n-vertex G admits such a partition with at most p parts. We show that
\phi_H(n)=ex(n,K_r)+\Theta(biex(n,H)) for \chi(H)>2, where biex(n,H) is the
extremal number of the decomposition family of H. Since
biex(n,H)=O(n^{2-\gamma}) for some \gamma>0 this improves on the bound
\phi_H(n)=ex(n,H)+o(n^2) by Pikhurko and Sousa [J. Combin. Theory Ser. B 97
(2007), 1041-1055]. In addition it extends a result of \"Ozkahya and Person [J.
Combin. Theory Ser. B, to appear].


  We review evidence supporting the role of early life programming in the
susceptibility for adult neurodegenerative diseases while highlighting
questions and proposing avenues for future research to advance our
understanding of this fundamental process. The key elements of this phenomenon
are chronic stress, neuroinflammation triggering microglial polarization,
microglial memory and their connection to neurodegeneration. We review the
mediating mechanisms which may function as early biomarkers of increased
susceptibility for neurodegeneration. Can we devise novel early life-modifying
interventions to steer developmental trajectories to their optimum?


  This paper considers the problem of information capacity of a random neural
network. The network is represented by matrices that are square and
symmetrical. The matrices have a weight which determines the highest and lowest
possible value found in the matrix. The examined matrices are randomly
generated and analyzed by a computer program. We find the surprising result
that the capacity of the network is a maximum for the binary random neural
network and it does not change as the number of quantization levels associated
with the weights increases.


  We give new applications of graded Lie algebras to: identities of standard
polynomials, deformation theory of quadratic Lie algebras, cyclic cohomology of
quadratic Lie algebras, $2k$-Lie algebras, generalized Poisson brackets and so
on.


  We give a construction of non-ordinary $p$-adic families of classes in the
cohomology of locally symmetric spaces associated to spherical pairs of
reductive groups. In the \'etale case, we show how to map these classes into
Galois cohomology. The methods developed in this paper can be used to give new
constructions of $p$-adic families of Euler systems and $p$-adic $L$-functions.
As an example, we show how the constructions of this paper can be used to
construct norm-compatible classes associated to non-ordinary Siegel modular
forms, generalising $p$-part of the Lemma--Flach Euler system constructed by
Loeffler--Skinner--Zerbes.


  Formulae for the matrix element squared of the process e gamma -> e gamma
with all four particles polarized are obtained using REDUCE.


  It has been noted at times that the rate of energy release in the most
violent explosive events in the Universe, such as supernovae and gamma-ray
bursts, rivals the stellar luminosity of the observable universe, Lstar. The
underlying reason turns out to be that both can be scaled to c^5/G = Lgrav,
albeit that for the explosions Lstar/Lgrav follows from first principles,
whereas for Lstar the scaling involves quantities too complex to derive from
elementary considerations at the present time. Under fairly general
circumstances, Lstar is dominated by stars whose age is similar to the Hubble
time.


  We apply E. Cartan's method of equivalence to classify 7-dimensional,
2-nondegenerate CR manifolds $M$ up to local CR equivalence in the case that
the cubic form of $M$ satisfies a certain symmetry property with respect to the
Levi form of $M$. The solution to the equivalence problem is given by a
parallelism on a principal bundle over $M$. When the nondegenerate part of the
Levi form has definite signature, the parallelism takes values in
$\mathfrak{su}(2,2)$. When this signature is split and an additional
"isotropy-switching" hypothesis is satisfied, the parallelism takes values in
$\mathfrak{su}(3,1)$. Differentiating the parallelism provides a complete set
of local invariants of $M$. We exhibit an explicit example of a real
hypersurface in $\mathbb{C}^4$ whose invariants are nontrivial.


  The cross section and decay angular distributions for the coherent \phi meson
photoproduction on the deuteron have been measured for the first time up to a
squared four-momentum transfer t =(p_{\gamma}-p_{\phi})^2 =-2 GeV^2/c^2, using
the CLAS detector at the Thomas Jefferson National Accelerator Facility. The
cross sections are compared with predictions from a re-scattering model. In a
framework of vector meson dominance, the data are consistent with the total
\phi-N cross section \sigma_{\phi N} at about 10 mb. If vector meson dominance
is violated, a larger \sigma_{\phi N} is possible by introducing larger t-slope
for the \phi N \to \phi N process than that for the \gamma N \to \phi N
process. The decay angular distributions of the \phi are consistent with
helicity conservation.


  In this paper, we extend some results of nonsmooth analysis from Euclidean
context to the Riemannian setting. In particular, we discuss the concept and
some properties of locally Lipschitz continuous vector fields on Riemannian
settings, such as Clarke generalized covariant derivative, upper semicontinuity
and Rademacher theorem. We also present a version of Newton method for finding
a singularity of a special class of locally Lipschitz continuous vector fields.
Under mild conditions, we establish the well-definedness and local convergence
of the sequence generated by the method in a neighborhood of a singularity. In
particular, a local convergence result for semismooth vector fields is
presented. Furthermore, under Kantorovich-type assumptions the convergence of
the sequence generated by the Newton method to a solution is established, and
its uniqueness in a suitable neighborhood of the starting point is verified.


  We study the effects of electron-electron interactions on the charge
excitation spectrum of the spinful Su-Schrieffer-Heeger (SSH) model, a
prototype of a 1D bulk obstructed topological insulator. In view of recent
progress in the fabrication of dopant-based quantum simulators we focus on
experimentally detectable signatures of interacting topology in finite
lattices. To this end we use Lanczos-based exact diagonalization to calculate
the single-particle spectral function in real space which generalizes the local
density of states to interacting systems. Its spatial and spectral resolution
allows for the direct investigation and identification of edge states. By
studying the non-interacting limit, we demonstrate that the topological in-gap
states on the boundary are robust against both finite-size effects as well as
random bond and onsite disorder which suggests the feasibility of simulating
the SSH model in engineered dopant arrays in silicon. While edge excitations
become zero-energy spin-like for any finite interaction strength, our analysis
of the spectral function shows that the single-particle charge excitations are
gapped out on the boundary. Despite the loss of topological protection we find
that these edge excitations are quasiparticle-like as long as they remain
within the bulk gap. Above a critical interaction strength of $U_c\approx 5 t$
these quasiparticles on the boundary loose their coherence which is explained
by the merging of edge and bulk states. This is in contrast to the many-body
edge excitations which survive the limit of strong coupling, as established in
the literature. Our findings show that for moderate repulsive interactions the
non-trivial phase of the interacting SSH model can be detected through remnant
signatures of topological single-particle states using single-particle local
measurement techniques such as scanning tunneling spectroscopy.


  We develop a code length principle which is invariant to the choice of
parameterization on the model distributions. An invariant approximation formula
for easy computation of the marginal distribution is provided for gaussian
likelihood models. We provide invariant estimators of the model parameters and
formulate conditions under which these estimators are essentially posteriori
unbiased for gaussian models. An upper bound on the coarseness of
discretization on the model parameters is deduced. We introduce a
discrimination measure between probability distributions and use it to
construct probability distributions on model classes. The total code length is
shown to equal the NML code length of Rissanen to within an additive constant
when choosing Jeffreys prior distribution on the model parameters together with
a particular choice of prior distribution on the model classes. Our model
selection principle is applied to a gaussian estimation problem for data in a
wavelet representation and its performance is tested and compared to
alternative wavelet-based estimation methods in numerical experiments


  We consider a Kepler problem in dimension two or three, with a time-dependent
$T$-periodic perturbation. We prove that for any prescribed positive integer
$N$, there exist at least $N$ periodic solutions (with period $T$) as long as
the perturbation is small enough. Here the solutions are understood in a
general sense as they can have collisions. The concept of generalized solutions
is defined intrinsically and it coincides with the notion obtained in Celestial
Mechanics via the theory of regularization of collisions.


  Recent improvements in neural audio codec (NAC) models have generated
interest in adopting pre-trained codecs for a variety of speech processing
applications to take advantage of the efficiencies gained from high
compression, but these have yet been applied to the speech separation (SS)
task. SS can benefit from high compression because the compute required for
traditional SS models makes them impractical for many edge computing use cases.
However, SS is a waveform-masking task where compression tends to introduce
distortions that severely impact performance. Here we propose a novel task of
Audio Codec-based SS, where SS is performed within the embedding space of a
NAC, and propose a new model, Codecformer, to address this task. At inference,
Codecformer achieves a 52x reduction in MAC while producing separation
performance comparable to a cloud deployment of Sepformer. This method charts a
new direction for performing efficient SS in practical scenarios.


  A formal definition of the graded algebra $\mathcal{R}$ of modular linear
differential operators is given and its properties are studied. An algebraic
structure of the solutions to modular linear differential equations (MLDEs) is
shown. It is also proved that any quasimodular form of weight $k$ and depth $s$
becomes a solution to a monic MLDE of weight $k-s$. By using the algebraic
properties of $\mathcal{R}$, linear differential operators which map the
solution space of a monic MLDE to that of another are determined for
sufficiently low weights and orders. Furthermore, a lower bound of the order of
monic MLDEs satisfied by ${E_4}^m{E_6}^n$ is found.


  Considering the physical conditions that are relevant for triggering star
formation in interactions involving SN shocks and neutral clouds, we have built
diagrams of the SNR radius versus the cloud density in which these conditions
constrain a shaded zone where star formation is allowed. The diagrams are also
tested with fully 3-D MHD radiative cooling simulations involving a SNR and a
self-gravitating cloud and we find that the numerical analysis is consistent
with the results predicted by the diagrams. While the inclusion of a
homogeneous magnetic field approximately perpendicular to the impact velocity
of the SNR with an intensity ~1 $mu$G results only a small shrinking of the
star formation zone in the diagrams, a larger magnetic field (~10 $\mu$G)
causes a significant shrinking, as expected. Applications of our results to
real star formation regions in our own galaxy have revealed that their
formation could have been triggered by a SN shock wave. Finally, we have
evaluated the effective global star formation efficiency of this sort of
interactions and found that it is smaller than the observed values in our
Galaxy (SFE ~0.01-0.3). This result is consistent with previous work in the
literature and also suggests that the mechanism presently investigated, though
very powerful to drive structure formation, supersonic turbulence and
eventually, local star formation, does not seem to be sufficient to drive
global star formation in normal star forming galaxies.


  The outburst and proliferation of the COVID-19 pandemic, together with the
subsequent social distancing measures, have raised massive challenges in almost
all domains of public and private life around the globe. The stay-at-home
movement has pushed the news audiences into social networks, which, in turn,
has become the most prolific field for receiving and sharing news updates, as
well as for public expression of opinions, concerns and feelings about the
pandemic. Public opinion is a critical aspect in analysing how the information
and events impact peoples lives, and research has shown that social media data
may be promising in understanding how people respond to health risks and social
crisis, which are the feelings they tend to share and how they are adapting to
unforeseen circumstances that threaten almost all societal spheres. This paper
presents results from a social media analysis of 61532 news headlines posted by
the major daily news outlet in Portugal, Sic Noticias, on Facebook, from
January to December 2020, focusing on the issues attention cycle and audiences
emotional response to the COVID news outburst. This work adds to the emergent
body of studies examining public response to the coronavirus pandemic on social
media data.


  Previous work has shown that the macroscopic structure of the theory of
quantum gravity defined by causal dynamical triangulations (CDT) is compatible
with that of a de Sitter universe. After emphasizing the strictly
nonperturbative nature of this semiclassical limit we present a detailed study
of the three-volume data, which allows us to re-confirm the de Sitter
structure, exhibit short-distance discretization effects, and make a first
detailed investigation of the presence of higher-order curvature terms in the
effective action for the scale factor. Technically, we make use of a novel way
of fixing the total four-volume in the simulations.


  Human attention mechanisms often work in a top-down manner, yet it is not
well explored in vision research. Here, we propose the Top-Down Attention
Framework (TDAF) to capture top-down attentions, which can be easily adopted in
most existing models. The designed Recursive Dual-Directional Nested Structure
in it forms two sets of orthogonal paths, recursive and structural ones, where
bottom-up spatial features and top-down attention features are extracted
respectively. Such spatial and attention features are nested deeply, therefore,
the proposed framework works in a mixed top-down and bottom-up manner.
Empirical evidence shows that our TDAF can capture effective stratified
attention information and boost performance. ResNet with TDAF achieves 2.0%
improvements on ImageNet. For object detection, the performance is improved by
2.7% AP over FCOS. For pose estimation, TDAF improves the baseline by 1.6%. And
for action recognition, the 3D-ResNet adopting TDAF achieves improvements of
1.7% accuracy.


  A search is performed for Higgs bosons decaying into invisible final states,
produced in association with a Zo boson in e+e- collisions at energies between
183 and 209 GeV. The search is based on data samples collected by the OPAL
detector at LEP corresponding to an integrated luminosity of about 660 pb-1.
The analysis aims to select events containing the hadronic decay products of
the Zo boson and large missing momentum, as expected from Higgs boson decay
into a pair of stable weakly interacting neutral particles, such as the
lightest neutralino in the Minimal Supersymmetric Standard Model. The same
analysis is applied to a search for nearly invisible Higgs boson cascade decays
into stable weakly interacting neutral particles. No excess over the expected
background from Standard Model processes is observed. Limits on the production
of invisibly decaying Higgs bosons produced in association with a Zo boson are
derived. Assuming a branching ratio BR(ho->invisible)=1, a lower limit of 108.2
GeV is placed on the Higgs boson mass at the 95% confidence level. Limits on
the production of nearly invisibly decaying Higgs bosons are also obtained.


  The paper is devoted to the integral functionals $\int_0^\infty
f(X_t)\,{\mathrm{d}t}$ of Markov processes in $\X$ in the case $d\ge 3$. It is
established that such functionals can be presented as the integrals $\int_{\X}
f(y) \G(x, \mathrm{d}y, \omega)$ with vector valued random measure $\G(x,
\mathrm{d}y, \omega)$. Some examples such as compound Poisson processes,
Brownian motion and diffusions are considered.


  Several scenarios explaining how filaments are formed can be found in
literature. In this paper, we analyzed the observations of an active region
filament and critically evaluated the observed properties in the context of
current filament formation models. This study is based on multi-height
spectropolarimetric observations. The inferred vector magnetic field has been
extrapolated starting either from the photosphere or from the chromosphere. The
line-of-sight motions of the filament, which was located near disk center, have
been analyzed inferring the Doppler velocities. We conclude that a part of the
magnetic structure emerged from below the photosphere.


  Effects of an incoming spin-polarized current on a magnetic moment are
explored. We found that the spin torque occurs only when the incoming spin
changes as a function of time inside of the magnetic film. This implies that
some modifications are necessary in a phenomenological model where the
coefficient of the spin torque term is a constant, and the coefficient is
determined by dynamics instead of geometrical details. The precession of the
magnetization reversal depends on the incoming energy of electrons in the
spin-polarized current. If the incoming energy is smaller than the interaction
energy, the magnetization does not precess while reversing its direction. We
also found that the relaxation time associated with the reversal depends on the
incoming energy. The coupling between an incoming spin and a magnetic moment
can be estimated by measuring the relaxation time.


  Since the discovery of Hawking radiation, its consistency with quantum theory
has been widely questioned. In the widely described picture, irrespective of
what initial state a black hole starts with before collapsing, it eventually
evolves into a thermal state of Hawking radiations after the black hole is
exhausted. This scenario violates the principle of unitarity as required for
quantum mechanics and leads to the acclaimed "information loss paradox". This
paradox has become an obstacle or a reversed touchstone for any possible theory
to unify the gravity and quantum mechanics. Based on the results from Hawking
radiation as tunneling, we recently show that Hawking radiations can carry off
all information about the collapsed matter in a black hole. After discovering
the existence of information-carrying correlation, we show in great detail that
entropy is conserved for Hawking radiation based on standard probability theory
and statistics. We claim that information previously considered lost remains
hidden inside Hawking radiation. More specifically, it is encoded into
correlations between Hawking radiations. Our study thus establishes harmony
between Harking radiation and the unitarity of quantum mechanics, which
establishes the basis for a significant milestone towards resolving the
long-standing information loss paradox. The paper provides a brief review of
the exciting development on Hawking raidation. In addition to summarize our own
work on this subject, we compare and address other related studies.


  To describe the interaction of molecular vibrations with electrons at a
quantum dot contacted to metallic leads, we extend an analytical approach that
we previously developed for the many-polaron problem. Our scheme is based on an
incomplete variational Lang-Firsov transformation, combined with a perturbative
calculation of the electron-phonon self-energy in the framework of generalised
Matsubara functions. This allows us to describe the system at weak to strong
coupling and intermediate to large phonon frequencies. We present results for
the quantum dot spectral function and for the kinetic coefficient that
characterises the electron transport through the dot. With these results we
critically examine the strengths and limitations of our approach, and discuss
the properties of the molecular quantum dot in the context of polaron physics.
We place particular emphasis on the importance of corrections to the concept of
an antiadiabatic dot polaron suggested by the complete Lang-Firsov
transformation.


  A common challenge in estimating parameters of probability density functions
is the intractability of the normalizing constant. While in such cases maximum
likelihood estimation may be implemented using numerical integration, the
approach becomes computationally intensive. The score matching method of
Hyv\"arinen [2005] avoids direct calculation of the normalizing constant and
yields closed-form estimates for exponential families of continuous
distributions over $\mathbb{R}^m$. Hyv\"arinen [2007] extended the approach to
distributions supported on the non-negative orthant, $\mathbb{R}_+^m$. In this
paper, we give a generalized form of score matching for non-negative data that
improves estimation efficiency. As an example, we consider a general class of
pairwise interaction models. Addressing an overlooked inexistence problem, we
generalize the regularized score matching method of Lin et al. [2016] and
improve its theoretical guarantees for non-negative Gaussian graphical models.


  The k-point correlation functions of the Gaussian Random Matrix Ensembles are
certain determinants of functions which depend on only two arguments. They are
referred to as kernels, since they are the building blocks of all correlations.
We show that the kernels are obtained, for arbitrary level number, directly
from supermatrix models for one-point functions. More precisely, the generating
functions of the one-point functions are equivalent to the kernels. This is
surprising, because it implies that already the one-point generating function
holds essential information about the k-point correlations. This also
establishes a link to the averaged ratios of spectral determinants, i.e. of
characteristic polynomials.


  Classic optimal transport theory is built on minimizing the expected cost
between two given distributions. We propose the framework of distorted optimal
transport by minimizing a distorted expected cost. This new formulation is
motivated by concrete problems in decision theory, robust optimization, and
risk management, and it has many distinct features compared to the classic
theory. We choose simple cost functions and study different distortion
functions and their implications on the optimal transport plan. We show that on
the real line, the comonotonic coupling is optimal for the distorted optimal
transport problem when the distortion function is convex and the cost function
is submodular and monotone. Some forms of duality and uniqueness results are
provided. For inverse-S-shaped distortion functions and linear cost, we obtain
the unique form of optimal coupling for all marginal distributions, which turns
out to have an interesting ``first comonotonic, then counter-monotonic"
dependence structure; for S-shaped distortion functions a similar structure is
obtained. Our results highlight several challenges and features in distorted
optimal transport, offering a new mathematical bridge between the fields of
probability, decision theory, and risk management.


  The 5-year survival rate of Head and Neck Cancer (HNC) has not improved over
the past decade and one common cause of treatment failure is recurrence. In
this paper, we built Cox proportional hazard (CoxPH) models that predict the
recurrence free survival (RFS) of oropharyngeal HNC patients. Our models
utilise both clinical information and multimodal radiomics features extracted
from tumour regions in Computed Tomography (CT) and Positron Emission
Tomography (PET). Furthermore, we were one of the first studies to explore the
impact of segmentation accuracy on the predictive power of the extracted
radiomics features, through under- and over-segmentation study. Our models were
trained using the HEad and neCK TumOR (HECKTOR) challenge data, and the best
performing model achieved a concordance index (C-index) of 0.74 for the model
utilising clinical information and multimodal CT and PET radiomics features,
which compares favourably with the model that only used clinical information
(C-index of 0.67). Our under- and over-segmentation study confirms that
segmentation accuracy affects radiomics extraction, however, it affects PET and
CT differently.


  Knowledge graphs (KGs) play a vital role in enhancing search results and
recommendation systems. With the rapid increase in the size of the KGs, they
are becoming inaccuracy and incomplete. This problem can be solved by the
knowledge graph completion methods, of which graph attention network
(GAT)-based methods stand out since their superior performance. However,
existing GAT-based knowledge graph completion methods often suffer from
overfitting issues when dealing with heterogeneous knowledge graphs, primarily
due to the unbalanced number of samples. Additionally, these methods
demonstrate poor performance in predicting the tail (head) entity that shares
the same relation and head (tail) entity with others. To solve these problems,
we propose GATH, a novel GAT-based method designed for Heterogeneous KGs. GATH
incorporates two separate attention network modules that work synergistically
to predict the missing entities. We also introduce novel encoding and feature
transformation approaches, enabling the robust performance of GATH in scenarios
with imbalanced samples. Comprehensive experiments are conducted to evaluate
the GATH's performance. Compared with the existing SOTA GAT-based model on
Hits@10 and MRR metrics, our model improves performance by 5.2% and 5.2% on the
FB15K-237 dataset, and by 4.5% and 14.6% on the WN18RR dataset, respectively.


  We have used spectral fits to SSP-based atmosphere models to derive an
estimate of the average stellar age for an almost complete sample of 15
Early-Type Galaxies (ETG) at 0.88<z<1.3. The results are in only partial
agreement with the age estimates previously obtained for the same objects from
an analysis of the M/L_B ratio, derived from the Fundamental Plane (FP)
parameters. In particular spectral fits seem to underestimate the age of the
most luminous ETG, and therefore do not reproduce the downsizing effect, which
is clear for the FP ages. We also analyse the relationship between the
spectral-fit ages and various near-UV spectral indices.


  In this short manuscript, we will put some light on the different outcomes
when two non-constant meromorphic functions share a value with prescribed
weight two.


  In this article we discuss the Dirac equation in the presence of an
attractive cylindrical \delta-shell potential V(\rho)=-a\delta(\rho-\rho_0),
where \rho is the radial coordinate and a>0. We present a detailed discussion
on the boundary conditions the wave function has to satisfy when crossing the
support of the potential, proceeding then to explore the dependence of the
ground state on the parameter a, analyzing the occurrence of supercritical
effects. We also apply the Foldy-Wouthuysen transformation, discussing the
non-relativistic limit of this problem.


  The Gateway will represent a primary space system useful for the Artemis
program, Earth-Moon transportation, and deep space exploration. It is expected
to serve as a staging location on the way to the lunar surface. This study
focuses on low-thrust transfer dynamics, from the Near-Rectilinear Halo Orbit
traveled by Gateway to a specified Low-altitude Lunar Orbit (LLO). This
research addresses: (i) determination of the minimum-time low-thrust trajectory
and (ii) design, implementation, and testing of a guidance and control
architecture, for a space vehicle that travels from Gateway to LLO. Orbit
dynamics is described in terms of modified equinoctial elements, in the context
of a high-fidelity ephemeris model. The minimum-time trajectory from Gateway to
a specified lunar orbit is detected through an indirect heuristic approach,
which uses the analytical conditions arising in optimal control theory in
conjunction with a heuristic technique. However, future missions will pursue a
growing level of autonomy, and this circumstance implies the mandatory design
of an efficient feedback guidance scheme, capable of compensating for
nonnominal flight conditions. This research proposes nonlinear orbit control as
a viable option for autonomous explicit guidance of low-thrust transfers from
Gateway to LLO. This approach allows defining a feedback law that enjoys
quasi-global stability properties without requiring any offline reference
trajectory. The overall spacecraft dynamics is modeled including attitude
control and actuation. The latter is demanded to an array of reaction wheels,
arranged in a pyramidal configuration. Guidance, attitude control, and
actuation are implemented in an iterative scheme. Monte Carlo simulations
demonstrate that the guidance and control architecture is effective with random
starting points from Gateway and the temporary unavailability of the propulsion
system.


  We describe the possible scenarios for the evolution of a thin spherically
symmetric self-gravitating phantom shell around the Schwarzschild black hole.
The general equations describing the motion of the shell with a general form of
equation of state are derived and analyzed. The different types of space-time
R- and T-regions and shell motion are classified depending on the parameters of
the problem. It is shown that in the case of a positive shell mass there exist
three scenarios for the shell evolution with an infinite motion and two
distinctive types of collapse. Analogous scenarios were classified for the case
of a negative shell mass. In particular this classification shows that it is
impossible for the physical observer to detect the fantom energy flow. We
shortly discuss the importance of our results for astrophysical applications.


  In a recent paper we claimed that both the group algebra of a finite Coxeter
group $W$ as well as the Orlik-Solomon algebra of $W$ can be decomposed into a
sum of induced one-dimensional representations of centralizers, one for each
conjugacy class of elements of $W$, and gave a uniform proof of this claim for
symmetric groups. In this note we outline an inductive approach to our
conjecture. As an application of this method, we prove the inductive version of
the conjecture for finite Coxeter groups of rank up to 2.


  We present exact expressions for the Sagnac effect of Goedel's Universe. For
this purpose we first derive a formula for the Sagnac time delay along a
circular path in the presence of an arbitrary stationary metric in cylindrical
coordinates. We then apply this result to Goedel's metric for two different
experimental situations: First, the light source and the detector are at rest
relative to the matter generating the gravitational field. In this case we find
an expression that is formally equivalent to the familiar nonrelativistic
Sagnac time delay. Second, the light source and the detector are rotating
relative to the matter. Here we show that for a special rotation rate of the
detector the Sagnac time delay vanishes. Finally we propose a formulation of
the Sagnac time delay in terms of invariant physical quantities. We show that
this result is very close to the analogous formula of the Sagnac time delay of
a rotating coordinate system in Minkowski spacetime.


  The paper presents the results of the study of international and national
cooperation of Ukrainian scientists from different scientific fields using
citation analysis data from Scopus in the period of 2011-2015. The results show
that during the period under study, the number of documents of highly cited
Ukrainian scientists that have received enough citations to be included to the
top 1%, 5% and 10% most cited documents in the world, evidenced an increase and
were significantly different in different subjects areas. Papers written by a
group of co-authors predominate among highly cited documents of Ukrainian
scientists. Consequently, international cooperation plays an important role in
Ukrainian scientists research results visibility and impact. At the same time,
up to 16%-27% highly cited articles of Ukrainian scientists have been written
without partnering with foreign colleagues, that means a significant part of
important scientific results in Ukraine are carried out by oneself. Therefore,
for generating an optimal policy of science development in Ukraine it is
important to provide a balanced view of the expectations of the results of the
international cooperation of Ukrainian scientists and achieving a required
balance between the inter-country and international collaboration.


  We propose a framework for aligning and fusing multiple images into a single
view using neural image representations (NIRs), also known as implicit or
coordinate-based neural representations. Our framework targets burst images
that exhibit camera ego motion and potential changes in the scene. We describe
different strategies for alignment depending on the nature of the scene motion
-- namely, perspective planar (i.e., homography), optical flow with minimal
scene change, and optical flow with notable occlusion and disocclusion. With
the neural image representation, our framework effectively combines multiple
inputs into a single canonical view without the need for selecting one of the
images as a reference frame. We demonstrate how to use this multi-frame fusion
framework for various layer separation tasks. The code and results are
available at https://shnnam.github.io/research/nir.


  In a recent study, based on homogeneous barium abundance measurements in open
clusters, a trend of increasing [Ba/Fe] ratios for decreasing cluster age was
reported. We present here further abundance determinations, relative to four
other elements hav- ing important s-process contributions, with the aim of
investigating whether the growth found for [Ba/Fe] is or not indicative of a
general property, shared also by the other heavy elements formed by slow
neutron captures. In particular, we derived abundances for yttrium, zirconium,
lanthanum and cerium, using equivalent widths measurements and the MOOG code.
Our sample includes 19 open clusters of different ages, for which the spectra
were obtained at the ESO VLT telescope, using the UVES spectrometer. The growth
previously suggested for Ba is confirmed for all the elements analyzed in our
study. This fact implies significant changes in our views of the Galactic
chemical evolution for elements beyond iron. Our results necessarily require
that very low-mass AGB stars (M < 1.5M\odot) produce larger amounts of
s-process elements (hence acti- vate the 13 C-neutron source more effectively)
than previously expected. Their role in producing neutron-rich elements in the
Galactic disk has been so far underestimated and their evolution and
neutron-capture nucleosynthesis should now be reconsidered.


  We introduce a new distributed policy gradient algorithm and show that it
outperforms existing reward-aware training procedures such as REINFORCE,
minimum risk training (MRT) and proximal policy optimization (PPO) in terms of
training stability and generalization performance when optimizing machine
translation models. Our algorithm, which we call MAD (on account of using the
mean absolute deviation in the importance weighting calculation), has
distributed data generators sampling multiple candidates per source sentence on
worker nodes, while a central learner updates the policy. MAD depends crucially
on two variance reduction strategies: (1) a conditional reward normalization
method that ensures each source sentence has both positive and negative reward
translation examples and (2) a new robust importance weighting scheme that acts
as a conditional entropy regularizer. Experiments on a variety of translation
tasks show that policies learned using the MAD algorithm perform very well when
using both greedy decoding and beam search, and that the learned policies are
sensitive to the specific reward used during training.


  The problem of background independent quantum gravity is the problem of
defining a quantum field theory of matter and gravity in the absence of an
underlying background geometry. Loop quantum gravity (LQG) is a promising
proposal for addressing this difficult task. Despite the steady progress of the
field, dynamics remains to a large extend an open issue in LQG. Here we present
the main ideas behind a series of proposals for addressing the issue of
dynamics. We refer to these constructions as the {\em spin foam representation}
of LQG. This set of ideas can be viewed as a systematic attempt at the
construction of the path integral representation of LQG.
  The {\em spin foam representation} is mathematically precise in 2+1
dimensions, so we will start this chapter by showing how it arises in the
canonical quantization of this simple theory. This toy model will be used to
precisely describe the true geometric meaning of the histories that are summed
over in the path integral of generally covariant theories.
  In four dimensions similar structures appear. We call these constructions
{\em spin foam models} as their definition is incomplete in the sence that at
least one of the following issues remains unclear: 1) the connection to a
canonical formulation, and 2) regularization independence (renormalizability).
In the second part of this chapter we will describe the definition of these
models emphasizing the importance of these open issues. We also discuss the non
standard picture of quantum spacetime that follows from background
independence.


  The uncertainty relation and the probability interpretation of quantum
mechanics are intrinsically connected, as is evidenced by the evaluation of
standard deviations. It is thus natural to ask if one can associate a very
small uncertainty product of suitably sampled events with a very small
probability. We have shown elsewhere that some examples of the evasion of the
uncertainty relation noted in the past are in fact understood in this way. We
here numerically illustrate that a very small uncertainty product is realized
if one performs a suitable sampling of measured data which occur with a very
small probability. It is also shown that our analysis is consistent with the
Landau-Pollak type uncertainty relation. It is suggested that the present
analysis may help reconcile the contradicting views about the "standard quantum
limit" in the detection of gravitational waves.


  One of the remarkable feats of intelligent life is that it restructures the
world it lives in for its own benefit. This extended abstract outlines how the
information-theoretic principle of empowerment, as an intrinsic motivation, can
be used to restructure the environment an agent lives in. We present a first
qualitative evaluation of how an agent in a 3d-gridworld builds a
staircase-like structure, which reflects the agent's embodiment.


  Observations of local star forming galaxies have revealed a correlation
between the rate at which galaxies form stars and their X-Ray luminosity. We
combine this correlation with the most recent observational constraints on the
integrated star formation rate density, and find that star forming galaxies
account for 5-20% of the total soft and hard X-ray backgrounds, where the
precise number depends on the energy band and the assumed average X-ray
spectral energy distribution of the galaxies below ~20 keV. If we combine the
L_X-SFR relation with recently derived star formation rate function, then we
find that star forming galaxies whose X-ray flux falls well (more than a factor
of 10) below the detection thresholds of the Chandra Deep Fields, can fully
account for the unresolved soft X-ray background, which corresponds to ~6% of
its total. Motivated by this result, we put limits on the allowed redshift
evolution of the parameter c_X \equiv L_X/SFR, and/or its evolution towards
lower and higher star formation rates. If we parametrize the redshift evolution
of c_X ~ (1+z)^b, then we find that b \leq 1.3 (95% CL). On the other hand, the
observed X-ray luminosity functions (XLFs) of star forming galaxies indicate
that c_X may be increasing towards higher redshifts and/or higher star
formation rates at levels that are consistent with the X-ray background, but
possibly at odds with the locally observed L_X-SFR relation.


  Spectral and scattering theory at low energy for the relativistic
Schroedinger operator are investigated. Some striking properties at thresholds
of this operator are exhibited, as for example the absence of 0-energy
resonance. Low energy behavior of the wave operators and of the scattering
operator are studied, and stationary expressions in terms of generalized
eigenfunctions are proved for the former operators. Under slightly stronger
conditions on the perturbation the absolute continuity of the spectrum on the
positive semi axis is demonstrated. Finally, an explicit formula for the action
of the free evolution group is derived. Such a formula, which is well known in
the usual Schroedinger case, was apparently not available in the relativistic
setting.


  Li-Vogelius and Li-Nirenberg gave a gradient estimate for solutions of
strongly elliptic equations and systems of divergence forms with piecewise
smooth coefficients, respectively. The discontinuities of the coefficients are
assumed to be given by manifolds of codimension 1, which we called them
manifolds of discontinuities. Their gradient estimate is independent of the
distances between manifolds of discontinuities. In this paper, we gave a
parabolic version of their results. That is, we gave a gradient estimate for
parabolic equations of divergence forms with piecewise smooth coefficients. The
coefficients are assumed to be independent of time and their discontinuities
are likewise the previous elliptic equations. As an application of this
estimate, we also gave a pointwise gradient estimate for the fundamental
solution of a parabolic operator with piecewise smooth coefficients. The both
gradient estimates are independent of the distances between manifolds of
discontinuities.


  In recent years, deep learning-based approaches have significantly improved
the performance of single-channel speech enhancement. However, due to the
limitation of training data and computational complexity, real-time enhancement
of full-band (48 kHz) speech signals is still very challenging. Because of the
low energy of spectral information in the high-frequency part, it is more
difficult to directly model and enhance the full-band spectrum using neural
networks. To solve this problem, this paper proposes a two-stage real-time
speech enhancement model with extraction-interpolation mechanism for a
full-band signal. The 48 kHz full-band time-domain signal is divided into three
sub-channels by extracting, and a two-stage processing scheme of `masking +
compensation' is proposed to enhance the signal in the complex domain. After
the two-stage enhancement, the enhanced full-band speech signal is restored by
interval interpolation. In the subjective listening and word accuracy test, our
proposed model achieves superior performance and outperforms the baseline model
overall by 0.59 MOS and 4.0% WAcc for the non-personalized speech denoising
task.


  A comparative analysis is performed between high-quality integrated spectra
of 30 globular clusters in M31, 20 Milky Way clusters, and a sample of field
and cluster elliptical galaxies. We find that the Lick CN indices in the M31
and Galactic clusters are enhanced relative to the bulges of the Milky Way,
M31, and elliptical spheroids. Although not seen in the Lick CN indices, the
near-UV cyanogen feature (3883 A) is strongly enhanced in M31 clustesr with
respect to the Galactic globulars at metallicities, --1.5<[Fe/H]<--0.3. Carbon
shows signs of varying amongst these two groups. For [Fe/H]>--0.8, we observe
no siginificant differences in the Hdelta, Hgamma, or Hbeta indices between the
M31 and Galactic globulars. The sample of ellipticals lies offset from the loci
of all the globulars in the Cyanogen--[MgFe], and Balmer--[MgFe] planes. Six of
the M31 cluster spectra appear young, and are projected onto the M31 disk.
Population synthesis models suggest that these are metal-rich clusters with
ages 100--800 Myr, metallicities --0.20 < [Fe/H] <0.35, and masses 0.7
-7.0x10^4 Msun. Two other young clusters are Hubble V in NGC 205, and an older
(~3 Gyr) cluster ~7 kpc away from the plane of the disk. The six clusters
projected onto the disk rotate in a similar fashion to the HI gas in M31, and
three clusters exhibit thin disk kinematics (Morrison et al.). Dynamical masses
and structural parameters are required for these objects to determine whether
they are massive open clusters or globular clusters. If the latter, our
findings suggest globular clusters may trace the build up of galaxy disks. In
either case, we conclude that these clusters are part of a young, metal-rich
disk cluster system in M31, possibly as young as 1 Gyr old.


  Let $f:X\rightarrow Y$ be a smooth fibration between two complex manifolds
$X$ and $Y$, and let $L$ be a pseudo-effective line bundle on $X$. We obtain a
sufficient condition for $R^{q}f_{\ast}(K_{X/Y}\otimes L)$ to be reflexive and
hence derive a Koll\'{a}r-type vanishing theorem.


  This paper was published in the special issue of the Journal of Inequalities
and Special Functions dedicated to Professor Ivan Dimovski's contributions to
different fields of mathematics: transmutation theory, special functions,
integral transforms, function theory etc.
  In this paper we study fractional powers of the Bessel differential operator.
The fractional powers are defined explicitly in the integral form without use
of integral transforms in its definitions. Some general properties of the
fractional powers of the Bessel differential operator are proved and some are
listed. Among them are different variations of definitions, relations with the
Mellin and Hankel transforms, group property, generalized Taylor formula with
Bessel operators, evaluation of resolvent integral operator in terms of the
Wright or generalized Mittag--Leffler functions. At the end, some topics are
indicated for further study and possible generalizations. Also the aim of the
paper is to attract attention and give references to not widely known results
on fractional powers of the Bessel differential operator.


  We study the impurity effects on the Caroli-de Gennes-Matricon (CdGM) states,
particularly on the level spacings in a vortex core in topological s-wave
superconductor (SC) by two means, numerically and analytically. The topological
s-wave SC belongs to the same class as a chiral p-wave SC and thus there are
two inequivalent vortices in terms of any symmetry operation. We take into
account this inequivalence and numerically calculate the scattering rates based
on an improved version of Kopnin-Kravtsov (iKK) scheme, which enables us to
treat the discrete levels in the presence of white-noise disorder. We also
construct the Andreev equation for the topological s-wave SC and obtain the
Andreev bound states analytically. We use a correspondence between the wave
functions for the Bogoliubov-de Gennes equation and the Andreev equation in the
iKK scheme and deduce the formula of scattering rates described by the wave
function for the Andreev equation. With this formula, we discuss the origin of
impurity scattering rates for CdGM states of topological s-wave SC and the
dependence on the types of vortices related to the inequivalence.


  Deep learning, particularly convolutional neural networks for image
recognition, has been recently used in meteorology. One of the promising
applications is developing a statistical surrogate model that converts the
output images of low-resolution dynamic models to high-resolution images. Our
study exhibits a preliminary experiment that evaluates the performance of a
model that downscales synoptic temperature fields to mesoscale temperature
fields every 6 hours. The deep learning model was trained with operational
22-km gridded global analysis surface winds and temperatures as the input,
operational 5-km gridded regional analysis surface temperatures as the desired
output, and a target domain covering central Japan. The results confirm that
our deep convolutional neural network (DCNN) is capable of estimating the
locations of coastlines and mountain ridges in great detail, which are not
retained in the inputs, and providing high-resolution surface temperature
distributions. For instance, while the average root-mean-square error (RMSE) is
2.7 K between the global and regional analyses at altitudes greater than 1000
m, the RMSE is reduced to 1.0 K, and the correlation coefficient is improved
from 0.6 to 0.9 by the surrogate model. Although this study evaluates a
surrogate model only for surface temperature, it probably can be improved by
augmenting the downscaling variables and vertical profiles. Surrogate models of
DCNNs require only a small amount of computational power once their training is
finished. Therefore, if the surrogate models are implemented at short time
intervals, they will provide high-resolution weather forecast guidance or
environment emergency alerts at low cost.


  Microwave, submillimetre-wave, and far-infrared phased arrays are of
considerable importance for astronomy. We consider the behaviour imaging phased
arrays and interferometric phased arrays from a functional perspective. It is
shown that the average powers, field correlations, power fluctuations, and
correlations between power fluctuations at the output ports of an imaging or
interferometric phased array can be found once the synthesised reception
patterns are known. The reception patterns do not have to be orthogonal or even
linearly independent. It is shown that the operation of phased arrays is
intimately related to the mathematical theory of frames, and that the theory of
frames can be used to determine the degree to which any class of intensity or
field distribution can be reconstructed unambiguously from the complex
amplitudes of the travelling waves at the output ports. The theory can be used
to set up a likelihood function that can, through Fisher information, be used
to determine the degree to which a phased array can be used to recover the
parameters of a parameterised source. For example, it would be possible to
explore the way in which a system, perhaps interferometric, might observe two
widely separated regions of the sky simultaneously.


  Stanley proved that, in characteristic zero, all artinian monomial complete
intersections have the strong Lefschetz property. We provide a positive
characteristic complement to Stanley's result in the case of artinian monomial
complete intersections generated by monomials all of the same degree, and also
for arbitrary artinian monomial complete intersections in characteristic two.
  To establish these results, we first prove an a priori lower bound on the
characteristics that guarantee the Lefschetz properties. We then use a variety
of techniques to complete the classifications.


  Speech representations learned from Self-supervised learning (SSL) models can
benefit various speech processing tasks. However, utilizing SSL representations
usually requires fine-tuning the pre-trained models or designing task-specific
downstream models and loss functions, causing much memory usage and human
labor. Recently, prompting in Natural Language Processing (NLP) has been found
to be an efficient technique to leverage pre-trained language models (LMs).
Specifically, prompt tuning optimizes a limited number of task-specific
parameters with a fixed pre-trained model; as a result, only a small set of
parameters is needed to be stored for each task. Prompt tuning improves
computation and memory efficiency by leveraging the pre-trained LM's prediction
ability. Nevertheless, such a paradigm is little studied in the speech
community. We report in this paper the first exploration of the prompt tuning
paradigm for speech processing tasks based on Generative Spoken Language Model
(GSLM). Experiment results show that the prompt tuning technique achieves
competitive performance in speech classification tasks with fewer trainable
parameters than fine-tuning specialized downstream models. We further study the
technique in challenging sequence generation tasks. Prompt tuning also
demonstrates its potential, while the limitation and possible research
directions are discussed in this paper. The source code is available on
https://github.com/ga642381/SpeechPrompt.


  Automatic photo adjustment is to mimic the photo retouching style of
professional photographers and automatically adjust photos to the learned
style. There have been many attempts to model the tone and the color adjustment
globally with low-level color statistics. Also, spatially varying photo
adjustment methods have been studied by exploiting high-level features and
semantic label maps. Those methods are semantics-aware since the color mapping
is dependent on the high-level semantic context. However, their performance is
limited to the pre-computed hand-crafted features and it is hard to reflect
user's preference to the adjustment. In this paper, we propose a deep neural
network that models the semantics-aware photo adjustment. The proposed network
exploits bilinear models that are the multiplicative interaction of the color
and the contexual features. As the contextual features we propose the semantic
adjustment map, which discovers the inherent photo retouching presets that are
applied according to the scene context. The proposed method is trained using a
robust loss with a scene parsing task. The experimental results show that the
proposed method outperforms the existing method both quantitatively and
qualitatively. The proposed method also provides users a way to retouch the
photo by their own likings by giving customized adjustment maps.


  We point out that a model with scalar fields with a large nonminimal coupling
to the Ricci scalar, such as Higgs inflation, can be regarded as a nonlinear
sigma model (NLSM). With the inclusion of not only the scalar fields but also
the conformal mode of the metric, our definition of the target space of the
NLSM is invariant under the frame transformation. We show that the
$\sigma$-meson that linearizes this NLSM to be a linear sigma model (LSM)
corresponds to the scalaron, the degree of freedom associated to the $R^2$ term
in the Jordan frame. We demonstrate that quantum corrections inevitably induce
this $\sigma$-meson in the large-$N$ limit, thus providing a frame independent
picture for the emergence of the scalaron. The resultant LSM only involves
renormalizable interactions and hence its perturbative unitarity holds up to
the Planck scale unless it hits a Landau pole, which is in agreement with the
renormalizability of quadratic gravity.


  Douady and Hubbard introduced the operation of mating of polynomials. This
identifies two filled Julia sets and the dynamics on them via external rays. In
many cases one obtains a rational map. Here the opposite question is tackled.
Namely we ask when a given (postcritically finite) rational map $f$ arises as a
mating. A sufficient condition when this is possible is given. If this
condition is satisfied, we present a simple explicit algorithm to unmate the
rational map. This means we decompose $f$ into polynomials, that when mated
yield $f$. Several examples of unmatings are presented.


  We study inverse problems for the Einstein-Maxwell equations. We prove that
it is possible to generate gravitational waves from the nonlinear interactions
of electromagnetic waves. By sending electromagnetic waves from a neighborhood
of a freely falling observer and taking measurements of the gravitational
perturbations in the same neighborhood, one can determine the vacuum space-time
structure up to diffeomorphisms in the largest region where these waves can
travel to from the observer and return.


  This work concentrates on Extremal Regions of Extremum Level (EREL)
selection. EREL is a recently proposed feature detector aiming at detecting
regions from a set of extremal regions. This is a branching problem derived
from segmentation of arterial wall boundaries from Intravascular Ultrasound
(IVUS) images. For each IVUS frame, a set of EREL regions is generated to
describe the luminal area of human coronary. Each EREL is then fitted by an
ellipse to represent the luminal border. The goal is to assign the most
appropriate EREL as the lumen. In this work, EREL selection carries out in two
rounds. In the first round, the pattern in a set of EREL regions is analyzed
and used to generate an approximate luminal region. Then, the two-dimensional
(2D) correlation coefficients are computed between this approximate region and
each EREL to keep the ones with tightest relevance. In the second round, a
compactness measure is calculated for each EREL and its fitted ellipse to
guarantee that the resulting EREL has not affected by the common artifacts such
as bifurcations, shadows, and side branches. We evaluated the selected ERELs in
terms of Hausdorff Distance (HD) and Jaccard Measure (JM) on the train and test
set of a publicly available dataset. The results show that our selection
strategy outperforms the current state-of-the-art.


  Systems driven out of equilibrium experience large fluctuations of the
dissipated work. The same is true for wave function amplitudes in disordered
systems close to the Anderson localization transition.\cite{Mirlin-review} In
both cases the probability distribution function (PDF) is given by the large
deviation ansatz. Here we exploit the analogy between the PDF of work
dissipated in a driven single-electron box (SEB) and that of random
multifractal wave function amplitudes and uncover new relations which
generalize the Jarzynski equality. We checked the new relations experimentally
by measuring the dissipated work in a driven SEB and found a remarkable
correspondence. The results represent an important universal feature of the
work statistics in systems out of equilibrium and help to understand the nature
of the symmetry of multifractal exponents in the theory of Anderson
localization.


  We study the dynamics of a self-gravitating cooling filamentary cloud using a
simplified model. We concentrate on the radial distribution and restrict
ourselves to quasi-hydrostatic, cylindrically symmetric cooling flows. For a
power-law dependence of cooling function on the temperature, self-similar
solutions which describe quasi-hydrostatic cooling flows are derived. We
consider obtically thin filaments with a constant mass per unit length and the
solutions are parameterized by their line masses. There is no polytropic
relation between the density and the pressure. The filament experiences
radiative condensation, irrespective of the \gamma, the gas specific heat
ratio. So, the filament becomes denser due to the quasi-hydrostatic flows and
the density at the center increases in proportion to (t_{0}-t)^{-1}, where t
denotes the time. The term, t_{0}, denotes an epoch at which the central
density increases infinitely. We also found that the radius of the filament
decreases in proportion to (t_{0}-t)^{0.5}.


  A divide and conquer strategy for enhancement of noisy speeches in adverse
environments involving lower levels of SNR is presented in this paper, where
the total system of speech enhancement is divided into two separate steps. The
first step is based on noise compensation on short time magnitude and the
second step is based on phase compensation. The magnitude spectrum is
compensated based on a modified spectral subtraction method where the
cross-terms containing spectra of noise and clean speech are taken into
consideration, which are neglected in the traditional spectral subtraction
methods. By employing the modified magnitude and unchanged phase, a procedure
is formulated to compensate the overestimation or underestimation of noise by
phase compensation method based on the probability of speech presence. A
modified complex spectrum based on these two steps are obtained to synthesize a
musical noise free enhanced speech. Extensive simulations are carried out using
the speech files available in the NOIZEUS database in order to evaluate the
performance of the proposed method. It is shown in terms of the objective
measures, spectrogram analysis and formal subjective listening tests that the
proposed method consistently outperforms some of the state-of-the-art methods
of speech enhancement for noisy speech corrupted by street or babble noise at
very low as well as medium levels of SNR.


  An ideal network troubleshooting system would be an almost fully automated
system, monitoring the whole network at once, feeding the results to a
knowledge-based decision making system that suggests actions to the operator or
corrects the failure automatically. Reality is quite the contrary: operators
separated in their offices try to track down complex networking failures in
their own way, which is generally a long sequence of manually edited parallel
shell commands (mostly ping, traceroute, route, iperf, ofctl etc.). This
process requires operators to be "masters of complexity" (which they often are)
and continuous interaction. In this paper we aim at narrowing this huge gap
between vision and reality by introducing a modular framework capable of (i)
formalizing troubleshooting processes as the concatenation of executable
functions [called Troubleshooting Graphs (TSGs)], (ii) executing these graphs
via an interpreter, (iii) evaluating and navigating between the outputs of the
functions and (iv) sharing troubleshooting know-hows in a formalized manner.