  The availability of big data has opened up big opportunities for individuals,
businesses and academics to view big into what is happening in their world.
Previous works of text representation mostly focused on informativeness from
massive words' frequency or cooccurrence. However, big data is a double-edged
sword which is big in volume but unstructured in format. The unstructured edge
requires specific techniques to transform 'big' into meaningful instead of
informative alone.
  This study presents KeypartX, a graph-based approach to represent perception
(text in general) by key parts of speech. Different from
bag-of-words/vector-based machine learning, this technique is human-like
learning that could extracts meanings from linguistic (semantic, syntactic and
pragmatic) information. Moreover, KeypartX is big-data capable but not hungry,
which is even applicable to the minimum unit of text:sentence.


  The exponential trapezoidal rule is proposed and analyzed for the numerical
integration of semilinear integro-differential equations. Although the method
is implicit, the numerical solution is easily obtained by standard fixed-point
iteration, making its implementation straightforward. Second-order convergence
in time is shown in an abstract Hilbert space framework under reasonable
assumptions on the problem. Numerical experiments illustrate the proven order
of convergence.


  A new approach to maximum likelihood learning of discrete graphical models
and RBM in particular is introduced. Our method, Perturb and Descend (PD) is
inspired by two ideas (I) perturb and MAP method for sampling (II) learning by
Contrastive Divergence minimization. In contrast to perturb and MAP, PD
leverages training data to learn the models that do not allow efficient MAP
estimation. During the learning, to produce a sample from the current model, we
start from a training data and descend in the energy landscape of the
"perturbed model", for a fixed number of steps, or until a local optima is
reached. For RBM, this involves linear calculations and thresholding which can
be very fast. Furthermore we show that the amount of perturbation is closely
related to the temperature parameter and it can regularize the model by
producing robust features resulting in sparse hidden layer activation.


  We consider the leading and subleading UV divergences for the four-point
on-shell scattering amplitudes in D=6,8,10 supersymmetric Yang-Mills theories
in the planar limit. These theories belong to the class of maximally
supersymmetric gauge theories and presumably possess distinguished properties
beyond perturbation theory. In the previous works, we obtained the recursive
relations that allow one to get the leading and subleading divergences in all
loops in a pure algebraic way. The all loop summation of the leading
divergences is performed with the help of the differential equations which are
the generalization of the RG equations for non-renormalizable theories. Here we
mainly focus on solving and analyzing these equations. We discuss the
properties of the obtained solutions and interpretation of the results.


  We report a measurement of the branching fraction of B^+ -> tau^+ nu_tau
decays using a data sample of 772 x 10^6 BBbar pairs, collected at the Y(4S)
resonance with the Belle detector at the KEKB asymmetric-energy e^+e^-
collider. We reconstruct the accompanying B meson in a semileptonic decay and
detect the recoiling B candidate in the decay channel B^+ -> tau^+ nu_tau. We
obtain a branching fraction of BR(B^+ -> tau^+ nu_tau) = [1.25 +- 0.28 (stat.)
+- 0.27 (syst.)] x 10^-4. This result is in good agreement with previous
measurements and the expectation from the calculation based on the Standard
Model.


  Pre-trained language models have achieved noticeable performance on the
intent detection task. However, due to assigning an identical weight to each
sample, they suffer from the overfitting of simple samples and the failure to
learn complex samples well. To handle this problem, we propose a density-based
dynamic curriculum learning model. Our model defines the sample's difficulty
level according to their eigenvectors' density. In this way, we exploit the
overall distribution of all samples' eigenvectors simultaneously. Then we apply
a dynamic curriculum learning strategy, which pays distinct attention to
samples of various difficulty levels and alters the proportion of samples
during the training process. Through the above operation, simple samples are
well-trained, and complex samples are enhanced. Experiments on three open
datasets verify that the proposed density-based algorithm can distinguish
simple and complex samples significantly. Besides, our model obtains obvious
improvement over the strong baselines.


  The sparse optimization problems arise in many areas of science and
engineering, such as compressed sensing, image processing, statistical and
machine learning. The $\ell_{0}$-minimization problem is one of such
optimization problems, which is typically used to deal with signal recovery.
The $\ell_{1}$-minimization method is one of the plausible approaches for
solving the $\ell_{0}$-minimization problems, and thus the stability of such a
numerical method is vital for signal recovery. In this paper, we establish a
stability result for the $\ell_{1}$-minimization problems associated with a
general class of $\ell_{0}$-minimization problems. To this goal, we introduce
the concept of restricted weak range space property (RSP) of a transposed
sensing matrix, which is a generalized version of the weak RSP of the
transposed sensing matrix introduced in [Zhao et al., Math. Oper. Res.,
44(2019), 175-193]. The stability result established in this paper includes
several existing ones as special cases.


  Optical diffraction tomography is an indispensable tool for studying objects
in three-dimensions due to its ability to accurately reconstruct scattering
objects. Until now this technique has been limited to coherent light because
spatial phase information is required to solve the inverse scattering problem.
We introduce a method that extends optical diffraction tomography to imaging
spatially incoherent contrast mechanisms such as fluorescent emission. Our
strategy mimics the coherent scattering process with two spatially coherent
illumination beams. The interferometric illumination pattern encodes spatial
phase in temporal variations of the fluorescent emission, thereby allowing
incoherent fluorescent emission to mimic the behavior of coherent illumination.
The temporal variations permit recovery of the propagation phase, and thus the
spatial distribution of incoherent fluorescent emission can be recovered with
an inverse scattering model.


  Development of future sensor, memory, and computing nanodevices based on
novel physical concepts is one of the significant research endeavors in
solid-state research. The field of spintronics is one such promising area of
nanoelectronics which utilizes both the charge and spin of an electron for
device operations. The advantage offered by spin systems is in their
non-volatility and low-power functionality. This paper reviews emerging
spintronic phenomena and the research advancements in diverse spin based
applications. Spin devices and systems for logic, memories, emerging computing
schemes, flexible electronics and terahertz emitters are discussed in this
report.


  Cells move differently on substrates with different elasticities. In
particular, the persistence time of their motion is higher on stiffer
substrates. We show that this behavior will result in a net transport of cells
directed up a soft-to-stiff gradient. Using simple random walk models with
controlled persistence and stochastic simulations, we characterize this
propensity to move in terms of the durotactic index measured in experiments. A
one-dimensional model captures the essential features of this motion and
highlights the competition between diffusive spreading and linear, wavelike
propagation. Since the directed motion is rooted in a non-directional change in
the behavior of individual cells, the motility is a kinesis rather than a
taxis. Persistence-driven durokinesis is generic and may be of use in the
design of instructive environments for cells and other motile, mechanosensitive
objects.


  We develop a systematic cluster expansion for dilute systems in the highly
dilute phase. We first apply it to the calculation of the entropy of the
K-satisfiability problem in the satisfiable phase. We derive a series expansion
in the control parameter, the average connectivity, that is identical to the
one obtained by using the replica approach with a replica symmetric ({\sc rs})
{\it Ansatz}, when the order parameter is calculated via a perturbative
expansion in the control parameter. As a second application we compute the
free-energy of the Viana-Bray model in the paramagnetic phase. The cluster
expansion allows one to compute finite-size corrections in a simple manner and
these are particularly important in optimization problems. Importantly enough,
these calculations prove the exactness of the {\sc rs} {\it Ansatz} below the
percolation threshold and might require its revision between this and the
easy-to-hard transition.


  We consider a Nonlinear Schr\"odinger Equation with a very general non linear
term and with a trapping $\delta $ potential on the line. We then discuss the
asymptotic behavior of all its small solutions, generalizing a recent result by
Masaki et al. We give also a result of dispersion in the case of defocusing
equations with a non--trapping delta potential.


  These notes aim at a pedagogical introduction to recent work on deformation
of spaces and deformation of vector bundles over them, which are relevant both
in mathematics and in physics, notably monopole and instanton bundles.
  We first decribe toric noncommutative manifolds (also known as isospectral
deformations) and give a detailed introduction to gauge theories on a toric
four-sphere. This includes a Yang-Mills action functional with associated
equations of motion and self-duality equations. We construct a particular class
of instanton solutions on a SU(2) bundle with a suitable use of twisted
conformal symmetries.
  In the second part, we describe a different deformation of an instanton
bundle over the classical four-sphere by constructing a quantum group SU_q(2)
bundle on a sphere which is different from the toric one.


  We consider an operational model of suicide bombing attacks -- an
increasingly prevalent form of terrorism -- against specific targets, and the
use of protective countermeasures based on the deployment of detectors over the
area under threat. These detectors have to be carefully located in order to
minimize the expected number of casualties or the economic damage suffered,
resulting in a hard optimization problem for which different metaheuristics
have been proposed. Rather than assuming random decisions by the attacker, the
problem is approached by considering different models of the latter, whereby he
takes informed decisions on which objective must be targeted and through which
path it has to be reached based on knowledge on the importance or value of the
objectives or on the defensive strategy of the defender (a scenario that can be
regarded as an adversarial game). We consider four different algorithms, namely
a greedy heuristic, a hill climber, tabu search and an evolutionary algorithm,
and study their performance on a broad collection of problem instances trying
to resemble different realistic settings such as a coastal area, a modern urban
area, and the historic core of an old town. It is shown that the adversarial
scenario is harder for all techniques, and that the evolutionary algorithm
seems to adapt better to the complexity of the resulting search landscape.


  I review the processes that shape the evolution of protoplanetary discs
around young, solar-mass stars. I first discuss observations of protoplanetary
discs, and note in particular the constraints these observations place on
models of disc evolution. The processes that affect the evolution of gas discs
are then discussed, with the focus in particular on viscous accretion and
photoevaporation, and recent models which combine the two. I then discuss the
dynamics and growth of dust grains in discs, considering models of grain
growth, the gas-grain interaction and planetesimal formation, and review recent
research in this area. Lastly, I consider the so-called "transitional" discs,
which are thought to be observed during disc dispersal. Recent observations and
models of these systems are reviewed, and prospects for using statistical
surveys to distinguish between the various proposed models are discussed.


  In this paper we offer a unified approach to the problem of nonparametric
regression on the unit interval. It is based on a universal, honest and
non-asymptotic confidence region which is defined by a set of linear
inequalities involving the values of the functions at the design points.
Interest will typically centre on certain simplest functions in that region
where simplicity can be defined in terms of shape (number of local extremes,
intervals of convexity/concavity) or smoothness (bounds on derivatives) or a
combination of both. Once some form of regularization has been decided upon the
confidence region can be used to provide honest non-asymptotic confidence
bounds which are less informative but conceptually much simpler.


  Line systems passing through the origin of the $d$ dimensional Euclidean
space admitting exactly two distinct angles are called biangular. It is shown
that the maximum cardinality of biangular lines is at least $2(d-1)(d-2)$, and
this result is sharp for $d\in\{4,5,6\}$. Connections to binary codes,
few-distance sets, and association schemes are explored, along with their
multiangular generalization.


  The quark (gluon)-hadron duality constitutes a basis for the theoretical
treatment of a wide range of inclusive processes -- from hadronic \tau decays
and R_{e^+e^-}, to semileptonic and nonleptonic decay rates of heavy flavor
hadrons. A theoretical analysis of these processes is carried out by using the
operator product expansion in the Euclidean domain, with subsequent analytic
continuation to the Minkowski domain. We formulate the notion of the quark
(gluon)-hadron duality in quantitative terms, then classify various
contributions leading to violations of duality. A prominent role in the
violations of duality seems to belong to the so called exponential terms which,
conceptually, may represent the (truncated) tail of the power series. A
qualitative model, relying on an instanton background field, is developed,
allowing one to get an estimate of the exponential terms. We then discuss a
number of applications, mostly from heavy quark physics.


  An extended Bose-Hubbard (BH) model with number-dependent multi-site and
infinite-range hopping is proposed, which, similar to the original BH model,
describes a phase transition between the delocalized super-fluid (SF) phase and
localized Mott insulator (MI) phase. It is shown that this extended model with
local Euclidean E2 symmetry is exactly solvable when on-site local potential or
disorder is included, while the model without local potential or disorder is
quasi-exactly solvable, which means only a part of the excited states including
the ground state being exactly solvable. As applications of the exact solution
for the ground state, phase diagram of the model in 1D without local potential
and on-site disorder for filling factor rho = 1 with M = 6 sites and that with
M = 10 are obtained. The probabilities to detect n particles on a single site,
Pn, for n = 0, 1, 2 as functions of the control parameter U/t in these two
cases are also calculated. It is shown that the critical point in Pn and in the
entanglement measure is away from that of the SF-MI transition determined in
the phase analysis. It is also shown that the the model-independent
entanglement measure is related with Pn, which, therefore, may be practically
useful because Pn is measurable experimentally.


  Molecular dynamics simulations have been performed to understand the
variations in deformation mechanisms of Cu nanowires as a function of
orientation and loading mode (tension or compression). Cu nanowires of
different crystallographic orientations distributed uniformly on the standard
stereographic triangle have been considered under tensile and compressive
loading. The simulation results indicate that under compressive loading, the
orientations close to $<$100$>$ corner deform by twinning mechanism, while the
remaining orientations deform by dislocation slip. On the other hand, all the
nanowires deform by twinning mechanism under tensile loading. Further, the
orientations close to $<$110$>$ and $<$111$>$ corner exhibit
tension-compression asymmetry in deformation mechanisms. In addition to
deformation mechanisms, Cu nanowires also display tension-compression asymmetry
in yield stress. The orientations close to $<$001$>$ corner exhibits higher
yield stress in tension than in compression, while the opposite behaviour
(higher yield stress in compression than in tension) has been observed in
orientations close to $<$110$>$ and $<$111$>$ corners. For the specific
orientation of $<$102$>$, the yield stress asymmetry has not been observed. The
tension-compression asymmetry in deformation mechanisms has been explained
based on the parameter $\alpha_M$, defined as the ratio of Schmid factors for
leading and trailing partial dislocations. Similarly, the asymmetry in yield
stress values has been attributed to the different Schmid factor values for
leading partial dislocations under tensile and compressive loading.


  The problem of exact observability is analyzed for a wide class of neutral
type systems by an infinite dimensional approach. The duality with the exact
controllabil-ity problem is the main tool. It is based on an explicit
expression of a neutral type system which corresponding to the abstract adjoint
system. A nontrivial relation is obtained between the initial neutral system
and the system obtained via the adjoint abstract state operator. The
characterization of the duality between controllability and observability is
deduced, and then observability conditions are obtained.


  Click-Through Rate (CTR) prediction is a crucial component in the online
advertising industry. In order to produce a personalized CTR prediction, an
industry-level CTR prediction model commonly takes a high-dimensional (e.g.,
100 or 1000 billions of features) sparse vector (that is encoded from query
keywords, user portraits, etc.) as input. As a result, the model requires
Terabyte scale parameters to embed the high-dimensional input. Hierarchical
distributed GPU parameter server has been proposed to enable GPU with limited
memory to train the massive network by leveraging CPU main memory and SSDs as
secondary storage. We identify two major challenges in the existing GPU
training framework for massive-scale ad models and propose a collection of
optimizations to tackle these challenges: (a) the GPU, CPU, SSD rapidly
communicate with each other during the training. The connections between GPUs
and CPUs are non-uniform due to the hardware topology. The data communication
route should be optimized according to the hardware topology; (b) GPUs in
different computing nodes frequently communicates to synchronize parameters. We
are required to optimize the communications so that the distributed system can
become scalable.
  In this paper, we propose a hardware-aware training workflow that couples the
hardware topology into the algorithm design. To reduce the extensive
communication between computing nodes, we introduce a $k$-step model merging
algorithm for the popular Adam optimizer and provide its convergence rate in
non-convex optimization. To the best of our knowledge, this is the first
application of $k$-step adaptive optimization method in industrial-level CTR
model training. The numerical results on real-world data confirm that the
optimized system design considerably reduces the training time of the massive
model, with essentially no loss in accuracy.


  Tadpole Galaxies look like a star forming head with a tail structure to the
side. They are also named cometaries. In a series of recent works we have
discovered a number of issues that lead us to consider them extremely
interesting targets. First, from images, they are disks with a lopsided
starburst. This result is firmly established with long slit spectroscopy in a
nearby representative sample. They rotate with the head following the rotation
pattern but displaced from the rotation center. Moreover, in a search for
extremely metal poor (XMP) galaxies, we identified tadpoles as the dominant
shapes in the sample- nearly 80% of the local XMP galaxies have a tadpole
morphology. In addition, the spatially resolved analysis of the metallicity
shows the remarkable result that there is a metallicity drop right at the
position of the head. This is contrary to what intuition would say and
difficult to explain if star formation has happened from gas processed in the
disk. The result could however be understood if the star formation is driven by
pristine gas falling into the galaxy disk. If confirmed, we could be unveiling,
for the first time, cool flows in action in our nearby world. The tadpole class
is relatively frequent at high redshift - 10% of resolvable galaxies in the
Hubble UDF but less than 1% in the local Universe. They are systems that could
track cool flows and test models of galaxy formation.


  It is suggested that the decline with energy of the boron to carbon abundance
ratio in Galactic cosmic rays is due, in part, to a correlation between the
maximum energy attainable by shock acceleration in a given region of the
Galactic disk and the grammage traversed before escape. In this case the energy
dependence of the escape rate from the Galaxy may be less than previously
thought and the spectrum of antiprotons becomes easier to understand.


  The second largest eigenvalue of a transition matrix $P$ has connections with
many properties of the underlying Markov chain, and especially its convergence
rate towards the stationary distribution. In this paper, we give an asymptotic
upper bound for the second eigenvalue when $P$ is the transition matrix of the
simple random walk over a random directed graph with given degree sequence.
This is the first result concerning the asymptotic behavior of the spectral gap
for sparse non-reversible Markov chains with an unknown stationary
distribution. An immediate consequence of our result is a proof of the Alon
conjecture for directed regular graphs. Our result is based on a variation of
the trace method introduced by Bordenave (2015).


  By using self-consistent dynamic model atmospheres which simulate
pulsation-enhanced dust-driven winds of AGB stars we studied in detail the
influence of (i) pulsations of the stellar interiors, and (ii) the development
of dusty stellar winds on the spectral appearance of long period variables with
carbon-rich atmospheric chemistry. While the pulsations lead to large-amplitude
photometric variability, the dusty envelopes cause pronounced circumstellar
reddening. Based on one selected dynamical model which is representative of
C-type Mira variables with intermediate mass loss rates, we calculated
synthetic spectra and photometry for standard broad-band filters from the
visual to the near-infrared. Our modelling allows to investigate in detail the
substantial effect of circumstellar dust on the resultant photometry. The
pronounced absorption of amorphous carbon dust grains leads to colour indices
which are significantly redder than the corresponding ones based on hydrostatic
dust-free models. Only if we account for this circumstellar reddening we get
synthetic colours that are comparable to observations of evolved AGB stars. The
photometric variations of the dynamical model were compared to observed
lightcurves of the C-type Mira RU_Vir which appears to be quite similar to the
model. We found good agreement concerning the principal behaviour of the
BVRIJHKL lightcurves and also quantitatively fitting details. The analysed
model is able to reproduce the variations of RU_Vir and other Miras in (J-H)
vs. (H-K) diagrams throughout the light cycle. Contrasting the model photometry
with observational data for a variety of galactic C-rich giants in such
colour-colour diagrams proved that the chosen atmospheric model fits well into
a sequence of objects with increasing mass loss rates, i.e., redder colour
indices.


  We used the WINERED spectrograph to perform near-infrared high-resolution
spectroscopy (resolving power $R = 28$,000) of 13 young intermediate-mass stars
in the Taurus star-forming region. Based on the presence of near- and
mid-infrared continuum emission, young intermediate-mass stars can be
classified into three different evolutionary stages: Phases I, II, and III in
the order of evolution. Our obtained spectra ($\lambda = 0.91$--1.35\,$\mu$m)
depict \HeI $\lambda$10830 and P$\beta$ lines that are sensitive to
magnetospheric accretion and winds. We also investigate five sources each for
P$\beta$ and \ion{He}{1} lines that were obtained from previous studies along
with our targets. We observe that the P$\beta$ profile morphologies in Phases I
and II corresponded to an extensive variety of emission features; however,
these features are not detected in Phase III. We also observe that the He I
profile morphologies are mostly broad subcontinuum absorption lines in Phase I,
narrow subcontinuum absorption lines in Phase II, and centered subcontinuum
absorption features in Phase III. Our results indicate that the profile
morphologies exhibit a progression of the dominant mass flow processes: stellar
wind and probably magnetospheric accretion in the very early stage,
magnetospheric accretion and disk wind in the subsequent stage, and no
activities in the final stage. These interpretations further suggest that
opacity in protoplanetary disks plays an important role in mass flow processes.
Results also indicate that \ion{He}{1} absorption features in Phase III
sources, associated with chromospheric activities even in such young phases,
are characteristics of intermediate-mass stars.


  There exist many bivariate parametric copulas to model bivariate data with
different dependence features. We propose a new bivariate parametric copula
family that cannot only handle various dependence patterns that appear in the
existing parametric bivariate copula families, but also provides a more
enriched dependence structure. The proposed copula construction exploits finite
mixtures of bivariate normal distributions. The mixing operation, the distinct
correlation and mean parameters at each mixture component introduce quite a
flexible dependence. The new parametric copula is theoretically investigated,
compared with a set of classical bivariate parametric copulas and illustrated
on two empirical examples from astrophysics and agriculture where some of the
variables have peculiar and asymmetric dependence, respectively.


  In order to rigorously evaluate the energy and dipole moment of a certain
configuration of molecules one needs to solve the Schr\"odinger equation.
Repeating this for many different configurations allows one to determine the
potential energy surface (PES) and the dipole moment surface (DMS). Since the
early days of computer simulation it has been implicitly accepted that for
empirical potentials the charges used to fit the PES should also be used to
describe the DMS. This is a mistake. Partial charges are not observable
magnitudes. They should be regarded as adjustable fitting parameters. Optimal
values used to describe the PES are not necessarily the best to describe the
DMS. One could use two fits: one for the PES, and another for the DMS. This is
a common practice in the quantum chemistry community, but not used so often by
the community performing computer simulations. This idea affects all types of
modelling of water (with the exception of ab-initio calculations) from coarse
grained to non-polarizable and polarizable models. We anticipate that an area
that will benefit dramatically from having both, a good PES and a good DMS, is
the modelling of water in the presence of electric fields.


  We construct non-linear sigma model plus Skyrme term (Skyrme model) with a
twist in the gravitational field. To simplify the solution, first we examine
non-linear sigma model without Skyrme term, in particular with a twist, which
comprises a vortex solution with an added dependence on a twist term $mkz$,
where $z$ is the vertical coordinate. We find that vortex solution for
non-linear sigma model with a twist is similar with vortex solution without a
twist. The work is still progress.


  We review the experimental searches on those interactions where the
fundamental particles could exhibit a non point-like behavior. In particular we
focus on the QED reaction measuring the differential cross sections for the
process $ \EEGG $ at energies from sqrt{s} =55 GeV to 207 GeV using the data
collected with the VENUS, TOPAZ, ALEPH, DELPHI L3 and OPAL from 1989 to 2003.
The global fit to the data is 5 standard deviations away from the standard
model expectation for the hypothesis of an excited state of the electron,
corresponding to the cut-off scale Lambda =12.5 TeV. Assuming that this cut-off
scale restricts the characteristic size of QED interaction to 15.7x10^{-18} cm,
we perform an effort to assign in a semi-mechanical way all available
properties of fundamental particles to a hypothetical classical object. Such
object can be modeled as a classical gyroscope consisted of a non rotating
inner massive kernel surrounded by an outer rotating massive layer equipped
with charged sorted in a way to match the charge contents for different
interactions. The model size of an electron agrees with 1.86x10^{-17} cm with
the experiment. The introduction of a particle like structure related to
gravity allows to estimate the inner mass kernel of an electron to 1.7x10^{-19}
cm and the mass of a scaler to 154 GeV. The extension of the model to
electrical charged particle-like structure in nonlinear electrodynamics coupled
to General Relativity confirms the model in the global geometrical structure of
mass and field distribution.


  Machine learning algorithms frequently require careful tuning of model
hyperparameters, regularization terms, and optimization parameters.
Unfortunately, this tuning is often a "black art" that requires expert
experience, unwritten rules of thumb, or sometimes brute-force search. Much
more appealing is the idea of developing automatic approaches which can
optimize the performance of a given learning algorithm to the task at hand. In
this work, we consider the automatic tuning problem within the framework of
Bayesian optimization, in which a learning algorithm's generalization
performance is modeled as a sample from a Gaussian process (GP). The tractable
posterior distribution induced by the GP leads to efficient use of the
information gathered by previous experiments, enabling optimal choices about
what parameters to try next. Here we show how the effects of the Gaussian
process prior and the associated inference procedure can have a large impact on
the success or failure of Bayesian optimization. We show that thoughtful
choices can lead to results that exceed expert-level performance in tuning
machine learning algorithms. We also describe new algorithms that take into
account the variable cost (duration) of learning experiments and that can
leverage the presence of multiple cores for parallel experimentation. We show
that these proposed algorithms improve on previous automatic procedures and can
reach or surpass human expert-level optimization on a diverse set of
contemporary algorithms including latent Dirichlet allocation, structured SVMs
and convolutional neural networks.


  In network analysis, the betweenness centrality of a node informally captures
the fraction of shortest paths visiting that node. The computation of the
betweenness centrality measure is a fundamental task in the analysis of modern
networks, enabling the identification of the most central nodes in such
networks. Additionally to being massive, modern networks also contain
information about the time at which their events occur. Such networks are often
called temporal networks. The temporal information makes the study of the
betweenness centrality in temporal networks (i.e., temporal betweenness
centrality) much more challenging than in static networks (i.e., networks
without temporal information). Moreover, the exact computation of the temporal
betweenness centrality is often impractical on even moderately-sized networks,
given its extremely high computational cost. A natural approach to reduce such
computational cost is to obtain high-quality estimates of the exact values of
the temporal betweenness centrality. In this work we present ONBRA, the first
sampling-based approximation algorithm for estimating the temporal betweenness
centrality values of the nodes in a temporal network, providing rigorous
probabilistic guarantees on the quality of its output. ONBRA is able to compute
the estimates of the temporal betweenness centrality values under two different
optimality criteria for the shortest paths of the temporal network. In
addition, ONBRA outputs high-quality estimates with sharp theoretical
guarantees leveraging on the \emph{empirical Bernstein bound}, an advanced
concentration inequality. Finally, our experimental evaluation shows that ONBRA
significantly reduces the computational resources required by the exact
computation of the temporal betweenness centrality on several real world
networks, while reporting high-quality estimates with rigorous guarantees.


  Social engineering (SE) attacks remain a significant threat to both
individuals and organizations. The advancement of Artificial Intelligence (AI),
including diffusion models and large language models (LLMs), has potentially
intensified these threats by enabling more personalized and convincing attacks.
This survey paper categorizes SE attack mechanisms, analyzes their evolution,
and explores methods for measuring these threats. It highlights the challenges
in raising awareness about the risks of AI-enhanced SE attacks and offers
insights into developing proactive and adaptable defense strategies.
Additionally, we introduce a categorization of the evolving nature of
AI-powered social engineering attacks into "3E phases": Enlarging, wherein the
magnitude of attacks expands through the leverage of digital media; Enriching,
introducing novel attack vectors and techniques; and Emerging, signifying the
advent of novel threats and methods. Moreover, we emphasize the necessity for a
robust framework to assess the risk of AI-powered SE attacks. By identifying
and addressing gaps in existing research, we aim to guide future studies and
encourage the development of more effective defenses against the growing threat
of AI-powered social engineering.


  In this article, I show how periodically-tapered waveguides can be employed
as efficient quasi-phase-matching schemes for four-wave mixing parametric
processes in third-order nonlinear materials. As an example, a thorough study
of enhancing third-harmonic generation in sinusoidally-tapered fibres has been
conducted. The quasi-phase-matching condition has been obtained for nonlinear
parametric interactions in these structures using Fourier-series analysis. The
dependencies of the conversion efficiency of the third harmonic on the
modulation amplitude, tapering period, longitudinal-propagation direction, and
pump wavelength have been studied. In comparison to uniform waveguides, the
conversion efficiency has been enhanced by orders of magnitudes. I envisage
that this work can reshape the field of guided nonlinear optics using
centrosymmetric materials.


  In [Hamaker-Pechenik-Speyer-Weigandt, Nenashev, Pechenik-Weigandt] are
studied certain operators on polynomials and power series that commute with all
divided difference operators $\partial_i$. We introduce a second set of
"martial" operators {\martial_i} that generate the full commutant, and show how
a Hopf-algebraic approach naturally reproduces the operators $\xi^\nu$ from
[Nenashev]. We then pause to study Klyachko's homomorphism $H^*(Fl(n)) \to
H^*($the permutahedral toric variety$)$, and extract the part of it relevant to
Schubert calculus, the "affine-linear genus''. This genus is then re-obtained
using Leibniz combinations of the {\martial_i}. We use Nadeau-Tewari's
$q$-analogue of Klyachko's genus to study the equidistribution of $\ell$ and
comaj on $[n]\choose k$, generalizing known results on $S_n$.


  Hamiltonian simulation is a promising application for quantum computers to
achieve a quantum advantage. We present classical algorithms based on tensor
network methods to optimize quantum circuits for this task. We show that,
compared to Trotter product formulas, the classically optimized circuits can be
orders of magnitude more accurate and significantly extend the total simulation
time.


  Analyzing large sets of visual media remains a challenging task, particularly
in mixed-method studies dealing with problematic information and human
subjects. Using AI tools in such analyses risks reifying and exacerbating
biases, as well as untenable computational and cost limitations. As such, we
turn to adopting geometric computer graphics and vision methods towards
analyzing a large set of images from a problematic information campaign, in
conjunction with human-in-the-loop qualitative analysis. We illustrate an
effective case of this approach with the implementation of color quantization
towards analyzing online hate image at the US-Mexico border, along with a
historicist trace of the history of color quantization and skin tone scales, to
inform our usage and reclamation of these methodologies from their racist
origins. To that end, we scaffold motivations and the need for more researchers
to consider the advantages and risks of reclaiming such methodologies in their
own work, situated in our case study.


  In this paper, we empirically analyze a simple, non-learnable, and
nonparametric Nadaraya-Watson (NW) prediction head that can be used with any
neural network architecture. In the NW head, the prediction is a weighted
average of labels from a support set. The weights are computed from distances
between the query feature and support features. This is in contrast to the
dominant approach of using a learnable classification head (e.g., a
fully-connected layer) on the features, which can be challenging to interpret
and can yield poorly calibrated predictions. Our empirical results on an array
of computer vision tasks demonstrate that the NW head can yield better
calibration with comparable accuracy compared to its parametric counterpart,
particularly in data-limited settings. To further increase inference-time
efficiency, we propose a simple approach that involves a clustering step run on
the training set to create a relatively small distilled support set.
Furthermore, we explore two means of interpretability/explainability that fall
naturally from the NW head. The first is the label weights, and the second is
our novel concept of the ``support influence function,'' which is an
easy-to-compute metric that quantifies the influence of a support element on
the prediction for a given query. As we demonstrate in our experiments, the
influence function can allow the user to debug a trained model. We believe that
the NW head is a flexible, interpretable, and highly useful building block that
can be used in a range of applications.


  The electron gas inside a neutron star is highly degenerate and relativistic.
Due to the electron-electron magnetic interaction, the differential
susceptibility can equal or exceed 1, which causes the magnetic system of the
neutron star to become metastable or unstable. The Fermi liquid of nucleons
under the crust can be in a metastable state, while the crust is unstable to
the formation of layers of alternating magnetization. The change of the
magnetic stress acting on adjacent domains can result in a series of shifts or
fractures in the crust. The releasing of magnetic free energy and elastic
energy in the crust can cause the bursts observed in magnetars. Simultaneously,
a series of shifts or fractures in the deep crust which is closed to the Fermi
liquid of nucleons can trigger the phase transition of the Fermi liquid of
nucleons from a metastable state to a stable state. The released magnetic free
energy in the Fermi liquid of nucleons corresponds to the giant flares observed
in some magnetars.


  In this work we develop, through a governing field, genus theory for a number
field $\K$ with tame ramification in $T$ and splitting in $S$, where $T$ and
$S$ are finite disjoint sets of primes of $\K$. This approach extends that
initiated by the second author in the case of the class group. It allows
expressing the $S$-$T$ genus number of a cyclic extension $\L/\K$ of degree $p$
in terms of the rank of a matrix constructed from the Frobenius elements of the
primes ramified in $\L/\K$, in the Galois group of the underlying governing
extension. For quadratic extensions $\L/\Q$, the matrices in question are
constructed from the Legendre symbols between the primes ramified in $\L/\Q$
and the primes in $S$.


  We report high resolution (2.5 arcsec) CO J=1-0 line imaging which confirms
the presence of a molecular disk around the origin of the spectacular, 14
arcmin times 4 arcmin, episodic jets in the planetary nebula KjPn 8. The disk
is 30 arcsec in diameter with an expansion velocity of approximately 7 km/s.
The axis of the disk is aligned with the youngest and fastest (approximately
300 km/s) of the bipolar jets, and there is evidence for interaction between
the jets and the disk material. The inner 4 arcsec of the disk are
photo-ionized by the central star. The disk-jet system dominates the
environment of this young nebula, and should govern the morphology of KjPn 8 as
it evolves to become fully ionized.


  It has been proposed recently that the small size and long lifetime of
ultra-compact HII regions (UCHIIs) could be due to pressure confinement if the
thermal pressure of the ambient gas is higher than previous estimates. We point
out that confinement by thermal pressure alone implies emission measures in
excess of observed values. We show that turbulent pressure, inferred from
observed non-thermal velocities, is sufficient to confine UC HIIs and explain
their longevity. We predict an anti-correlation between the size of UCHIIs and
the velocity dispersion of the ambient neutral gas, and show that it is
consistent with existing observations.


  The electron states in a size-quantaized coated semiconductor wire in a
uniform magnetic field applied parallel to the wire axis is considered. The
wave functions are found and the equation for determination of energy
eigenvalues depending both on magnetic field and parameters of the wire and the
coating is obtained. The binding energy of a hydrogen-like impurity located on
the wire axis is computed by the use of the variational method, and it is shown
that this energy increases with the increase of the magnetic field.


  In this work, we obtain the Raychaudhuri equations for various types of
Finsler spaces as the Finsler-Randers (FR) space-time and in a more general
geometrical structure of the space-time manifold which contains two fibres that
are two scalars which represent inflaton fields $\phi^{(1)}, \phi^{(2)}.$In
addition, the energy-conditions are studied in a FR cosmology and are
correlated with FRW model. Finally an application of Raychaudhuri equation for
the model $M \times \{ \phi^{(1)} \} \times \{ \phi^{(2)} \}$ with M a FRW
space is given.


  Harnessing chaos or intrinsic nonlinear behaviours from dynamical systems is
a promising avenue for the development of unconventional information processing
technologies. However, the exploitation of such features in spintronic devices
has not been attempted despite the many theoretical and experimental evidence
of nonlinear behaviour of the magnetization dynamics in nanomagnetic systems.
Here, we propose a first step in that direction by unveiling and characterizing
the patterns and symbolic dynamics originating from the nonlinear chaotic
time-resolved electrical signals generated experimentally by a nanocontact
vortex oscillator (NCVO). We use advanced filtering methods to dissociate
nonlinear deterministic patterns from thermal fluctuations and show that the
emergence of chaos results in the unpredictable alternation of simple
oscillatory patterns controlled by the NCVO's core-polarity switching. With
phase-space reconstruction techniques, we perform a symbolic analysis of the
time series to assess the level of complexity and entropy generated in the
chaotic regime. We find that at the centre of its incommensurate region, it can
exhibit maximal entropy and complexity. This suggests that NCVOs are promising
nonlinear nanoscale source of entropy that could be harnessed for information
processing.


  Voronoi and Delaunay (Delone) cells of the root and weight lattices of the
Coxeter-Weyl groups W(an) and W(dn) are constructed. The face centered cubic
(fcc) and body centered cubic (bcc)lattices are obtained in this context. Basic
definitions are introduced such as parallelotope, fundamental simplex, contact
polytope, root polytope, Voronoi cell, Delone cell, n-simplex, n-octahedron
(cross polytope), n-cube and n-hemicube and their volumes are calculated.
Voronoi cell of the root lattice is constructed as the dual of the root
polytope which turns out to be the union of Delone cells. It is shown that the
Delone cells centered at the origin of the root lattice An are the polytopes of
the fundamental weights w1, w2, ..., wn and the Delone cells of the root
lattice Dn are the polytopes obtained from the weights w1, wn-1, wn. A simple
mechanism explains the tessellation of the root lattice by Delone cells. We
prove that the (n-1)-facet of the Voronoi cell of the root lattice An is
(n-1)-dimensional rhombohedron and similarly the (n-1) -facet of the Voronoi
cell of the root lattice Dn is a dipyramid with a base of (n-2)-cube. Volume of
the Voronoi cell is calculated via its (n-1) -facet which in turn can be
obtained from the fundamental simplex. Tessellations of the root lattice with
the Voronoi and Delone cells are explained by giving examples from lower
dimensions. Similar considerations are also worked out for the weight lattices
An* and Dn*.


  Can agents be trained to answer difficult mathematical questions by playing a
game? We consider the integer feasibility problem, a challenge of deciding
whether a system of linear equations and inequalities has a solution with
integer values. This is a famous NP-complete problem with applications in many
areas of Mathematics and Computer Science. Our paper describes a novel
algebraic reinforcement learning framework that allows an agent to play a game
equivalent to the integer feasibility problem. We explain how to transform the
integer feasibility problem into a game over a set of arrays with fixed margin
sums. The game starts with an initial state (an array), and by applying a legal
move that leaves the margins unchanged, we aim to eventually reach a winning
state with zeros in specific positions. To win the game the player must find a
path between the initial state and a final terminal winning state if one
exists. Finding such a winning state is equivalent to solving the integer
feasibility problem. The key algebraic ingredient is a Gr\"obner basis of the
toric ideal for the underlying axial transportation polyhedron. The Gr\"obner
basis can be seen as a set of connecting moves (actions) of the game. We then
propose a novel RL approach that trains an agent to predict moves in continuous
space to cope with the large size of action space. The continuous move is then
projected onto the set of legal moves so that the path always leads to valid
states. As a proof of concept we demonstrate in experiments that our agent can
play well the simplest version of our game for 2-way tables. Our work
highlights the potential to train agents to solve non-trivial mathematical
queries through contemporary machine learning methods used to train agents to
play games.


  Recent development of the universal seesaw mass matrix model is reviewed. The
model was proposed in order to explain why quark and lepton masses are so small
compared with the electroweak scale \Lambda_L =<\phi_L^0>=174 GeV. However, the
recently observed top-quark mass m_t\simeq 180 GeV seems to make an objection
against the seesaw mass picture. For this problem, it has recently pointed out
that the seesaw mass matrix model is rather favorable to the fact m_t\sim
\Lambda_L if we consider the model with det M_F=0 for up-quark sector, where
M_F is a 3 x 3 mass matrix of hypothetical heavy fermions F. The model can give
a natural explanation why only top-quark acquire the mass of the order of
\Lambda_L. The model with det M_U=0 offers abundant new physics to us (e.g.,
the fourth up-quark t', FCNC, and so on).


  In $\mathcal{N}=4$ SYM with $U(N)$ gauge symmetry, the multiplicity of
half-BPS states with fixed dimension can be labelled by Young diagrams and can
be distinguished using conserved charges corresponding to Casimirs of $U(N)$.
The information theoretic study of LLM geometries and superstars in the dual
$AdS_5 \times S^5$ background has raised a number of questions about the
distinguishability of Young diagrams when a finite set of Casimirs are known.
Using Schur-Weyl duality relations between unitary groups and symmetric groups,
these questions translate into structural questions about the centres of
symmetric group algebras. We obtain analytic and computational results about
these structural properties and related Shannon entropies, and generate
associated number sequences. A characterization of Young diagrams in terms of
content distribution functions relates these number sequences to diophantine
equations. These content distribution functions can be visualized as connected,
segmented, open strings in content space.


  Automatic International Classification of Diseases (ICD) coding plays a
crucial role in the extraction of relevant information from clinical notes for
proper recording and billing. One of the most important directions for boosting
the performance of automatic ICD coding is modeling ICD code relations.
However, current methods insufficiently model the intricate relationships among
ICD codes and often overlook the importance of context in clinical notes. In
this paper, we propose a novel approach, a contextualized and flexible
framework, to enhance the learning of ICD code representations. Our approach,
unlike existing methods, employs a dependent learning paradigm that considers
the context of clinical notes in modeling all possible code relations. We
evaluate our approach on six public ICD coding datasets and the experimental
results demonstrate the effectiveness of our approach compared to
state-of-the-art baselines.


  Fermi balls (FBs) and primordial black holes (PBHs) can be copiously produced
during a dark first-order phase transition (FOPT). The radius distribution of
false vacuum bubbles causes FBs and PBHs to have extended mass distributions.
We show how gravitational wave (GW), microlensing and Hawking evaporation
signals deviate from the case of monochromatic distributions. The peak of the
GW spectrum is shifted to lower frequencies and the spectrum is broadened at
frequencies below the peak frequency. Thus, the radius distribution of true
vacuum bubbles introduces another uncertainty in the evaluation of the GW
spectrum from a FOPT. The extragalactic gamma-ray signal at AMEGO-X/e-ASTROGAM
from PBH evaporation may evince a break in the power-law spectrum between 5~MeV
and 10~MeV for an extended PBH mass distribution. Optical microlensing surveys
may observe PBHs lighter than $10^{-10}M_\odot$, which is not possible for
monochromatic mass distributions. This expands the FOPT parameter space that
can be explored with microlensing.


  We derive uncertainty relation inequalities according to the mutually
unbiased measurements. Based on the calculation of the index of coincidence of
probability distribution given by $d+1$ MUMs on any density operator $\rho$ in
$\mathbb{C}^{d}$, both state-dependent and state-independent forms of lower
entropic bounds are given. Furthermore, we formulate uncertainty relations for
MUMs in terms of R\'{e}nyi and Tsallis entropies.


  The resonant state of the open quantum system is studied from the viewpoint
of the outgoing momentum flux. We show that the number of particles is
conserved for a resonant state, if we use an expanding volume of integration in
order to take account of the outgoing momentum flux; the number of particles
would decay exponentially in a fixed volume of integration. Moreover, we
introduce new numerical methods of treating the resonant state with the use of
the effective potential. We first give a numerical method of finding a
resonance pole in the complex energy plane. The method seeks an energy
eigenvalue iteratively. We found that our method leads to a super-convergence,
the convergence exponential with respect to the iteration step. The present
method is completely independent of commonly used complex scaling. We also give
a numerical trick for computing the time evolution of the resonant state in a
limited spatial area. Since the wave function of the resonant state is
diverging away from the scattering potential, it has been previously difficult
to follow its time evolution numerically in a finite area.


  How do you bring your assets as close as possible to your target allocation
by only investing a fixed amount of additional funds, and not selling any
assets? We look at two versions of this problem which have simple, closed form
solutions revealed by basic calculus and algebra.


  In the 1977 paper \cite{MTW} of B. McCoy, C. Tracy and T. Wu it was shown
that the limiting two-point correlation function in the two-dimensional Ising
model is related to a second order nonlinear Painlev\'e function. This result
identified the scaling function as a tau-function and the corresponding
connection problem was solved by C. Tracy in 1991 \cite{T}, see also the works
by C. Tracy and H. Widom in 1998 \cite{TW}. Here we present the solution to a
certain generalized version of the above connection problem which is obtained
through a refinement of the techniques in \cite{B}.


  Early B-type stars are invaluable indicators for elemental abundances of
their birth environments. In contrast to the surrounding neutral interstellar
matter (ISM) and HII regions their chemical composition is unaffected by
depletion onto dust grains and by the derivation of different abundances from
recombination and collisional lines. In combination with ISM or nebular
gas-phase abundances they facilitate the dust-phase composition to be
constrained. Precise abundances of C, N, Mg, Ne, Fe in early B-type stars in
the Orion star-forming region are determined in order to: a) review previous
determinations using a self-consistent quantitative spectral analysis based on
modern stellar atmospheres and recently updated model atoms, b) complement
results found in Paper I for oxygen and silicon, c) establish an accurate and
reliable set of stellar metal abundances to constrain the dust-phase
composition of the Orion HII region in Paper II of the series. A detailed,
self-consistent spectroscopic study of a sample of 13 narrow-lined B0V-B2V
stars in Ori OB1 is performed. High-quality spectra obtained with FIES@NOT are
analysed using a non-LTE method and line-profile fitting techniques, validating
the approach by comparison with results obtained in Paper I using
line-blanketed non-LTE model atmospheres and a curve-of-growth analysis. The
two independent analysis strategies give consistent results for basic stellar
parameters and abundances of oxygen and silicon. The extended analysis to C, N,
Mg, Ne, and Fe finds a high degree of chemical homogeneity, with the
1sigma-scatter adopting values of 0.03--0.07 dex around the mean for the
various elements. Present-day abundances from B-type stars in Ori OB1 are
compatible at similar precision with cosmic abundance standard values as
recently established from early-type stars in the solar neighbourhood and also
with the Sun. (abridged)


  The implementation of governmental Non-Pharmaceutical Interventions (NPIs)
has been the primary means of controlling the spread of the COVID-19 disease.
The intended effect of these NPIs has been to reduce mobility. A strong
reduction in mobility is believed to have a positive effect on the reduction of
COVID-19 transmission by limiting the opportunity for the virus to spread in
the population. Due to the huge costs of implementing these NPIs, it is
essential to have a good understanding of their efficacy. Using global mobility
data, released by Apple and Google, and ACAPS NPI data, we investigate the
proportional contribution of NPIs on i) size of the change (magnitude) of
transition between pre- and post-lockdown mobility levels and ii) rate
(gradient) of this transition. Using generalized linear models to find the best
fit model we found similar results using Apple or Google data. NPIs found to
impact the magnitude of the change in mobility were: Lockdown measures (Apple,
Google Retail and Recreation (RAR) and Google Transit and Stations (TS)),
declaring a state of emergency (Apple, Google RAR and Google TS), closure of
businesses and public services (Google RAR) and school closures (Apple). Using
cluster analysis and chi square tests we found that closure of businesses and
public services, school closures and limiting public gatherings as well as
border closures and international flight suspensions were closely related. The
implementation of lockdown measures and limiting public gatherings had the
greatest effect on the rate of mobility change. In conclusion, we were able to
quantitatively assess the efficacy of NPIs in reducing mobility, which enables
us to understand their fine grained effects in a timely manner and therefore
facilitate well-informed and cost-effective interventions.


  The paper introduces the Hidden Tree Markov Network (HTN), a
neuro-probabilistic hybrid fusing the representation power of generative models
for trees with the incremental and discriminative learning capabilities of
neural networks. We put forward a modular architecture in which multiple
generative models of limited complexity are trained to learn structural feature
detectors whose outputs are then combined and integrated by neural layers at a
later stage. In this respect, the model is both deep, thanks to the unfolding
of the generative models on the input structures, as well as wide, given the
potentially large number of generative modules that can be trained in parallel.
Experimental results show that the proposed approach can outperform
state-of-the-art syntactic kernels as well as generative kernels built on the
same probabilistic model as the HTN.


  The paper introduces the novel Modular Pipe Climber III with a Three-Output
Open Differential (3-OOD) mechanism to eliminate slipping of the tracks due to
the changing cross-sections of the pipe. This will be achieved in any
orientation of the robot. Previous pipe climbers use three-wheel/track modules,
each with an individual driving mechanism to achieve stable traversing.
Slipping of tracks is prevalent in such robots when it encounters the pipe
turns. Thus, active control of each module's speed is employed to mitigate the
slip, thereby requiring substantial control effort. The proposed pipe climber
implements the 3-OOD to address this issue by allowing the robot to
mechanically modulate the track speeds as it encounters a turn. The proposed
3-OOD is the first three-output differential to realize the functional
abilities of a traditional two-output differential.


  This paper investigates approaches to parallelizing Bounded Model Checking
(BMC) for shared memory environments as well as for clusters of workstations.
We present a generic framework for parallelized BMC named Tarmo. Our framework
can be used with any incremental SAT encoding for BMC but for the results in
this paper we use only the current state-of-the-art encoding for full PLTL.
Using this encoding allows us to check both safety and liveness properties,
contrary to an earlier work on distributing BMC that is limited to safety
properties only.
  Despite our focus on BMC after it has been translated to SAT, existing
distributed SAT solvers are not well suited for our application. This is
because solving a BMC problem is not solving a set of independent SAT instances
but rather involves solving multiple related SAT instances, encoded
incrementally, where the satisfiability of each instance corresponds to the
existence of a counterexample of a specific length. Our framework includes a
generic architecture for a shared clause database that allows easy clause
sharing between SAT solver threads solving various such instances.
  We present extensive experimental results obtained with multiple variants of
our Tarmo implementation. Our shared memory variants have a significantly
better performance than conventional single threaded approaches, which is a
result that many users can benefit from as multi-core and multi-processor
technology is widely available. Furthermore we demonstrate that our framework
can be deployed in a typical cluster of workstations, where several multi-core
machines are connected by a network.


  The main purpose of this article is to make Astronomers aware that Searches
for Extraterrestrial Intelligence can be carried out by analyzing standard
astronomical spectra, including those they already have taken. Simplicity is
the outstanding advantage of a search in spectra. The spectra can be analyzed
by simple eye inspection or a few lines of code that uses Fourier transform
software. Theory, confirmed by published experiments, shows that periodic
signals in spectra can be easily generated by sending light pulses separated by
constant time intervals. While part of this article, like all articles on
searches for ETI, is highly speculative the basic physics is sound. In
particular, technology now available on Earth could be used to send signals
having the required energy to be detected at a target located 1000 light years
away. Extraterrestrial Intelligence (ETI) could use these signals to make us
aware of their existence. For an ETI, the technique would also have the
advantage that the signals could be detected both in spectra and searches for
intensity pulses like those currently carried out on Earth.


  This paper proposes a method to design an optimal dynamic contract between a
principal and an agent, who has the authority to control both the principal's
revenue and an engineered system. The key characteristic of our problem setting
is that the principal has very limited information: the principal has no
capability to monitor the agent's control or the state of the engineered
system. The agent has perfect observations. With this asymmetry of information,
we show that the principal can induce the agent to control both the revenue and
the system processes in a way that maximizes the principal's utility, if the
principal offers appropriate real-time and end-time compensation. We
reformulate the dynamic contract design problem as a stochastic optimal control
of both the engineered system and the agent's future expected payoff, which can
be numerically solved using an associated Hamilton-Jacobi-Bellman equation. The
performance and usefulness of the proposed contract are demonstrated with an
indirect load control problem.


  Optimizer plays an important role in neural network training with high
efficiency and performance. Weight update based on its gradient is the central
part of the optimizer. It has been shown that normalization and standardization
operation on weight and gradient can accelerate the training process and
improve performance such as Weight Standardization (WS), weight normalization
(WN) and gradient normalization (GN); there is also gradient centralization
(GC). In this work, we introduce a new optimization technique based on the
gradient magnitude in a gradient vector named adaptive gradient regularization
(AGR), which normalizes the gradient vector in all dimensions as a coefficient
vector and subtracts the product of the gradient and its coefficient vector by
the vanilla gradient. It can be viewed as an adaptive gradient clipping method.
We show that the AGR can improve the loss function Lipschitzness with a more
stable training process and better generalization performance. AGR is very
simple to be embedded into vanilla optimizers such as Adan and AdamW with only
three lines of code. Our experiments are conducted in image generation, image
classification and language representation, which shows that our AGR improves
the training result.


  Safe reinforcement learning (RL) trains a policy to maximize the task reward
while satisfying safety constraints. While prior works focus on the performance
optimality, we find that the optimal solutions of many safe RL problems are not
robust and safe against carefully designed observational perturbations. We
formally analyze the unique properties of designing effective observational
adversarial attackers in the safe RL setting. We show that baseline adversarial
attack techniques for standard RL tasks are not always effective for safe RL
and propose two new approaches - one maximizes the cost and the other maximizes
the reward. One interesting and counter-intuitive finding is that the maximum
reward attack is strong, as it can both induce unsafe behaviors and make the
attack stealthy by maintaining the reward. We further propose a robust training
framework for safe RL and evaluate it via comprehensive experiments. This paper
provides a pioneer work to investigate the safety and robustness of RL under
observational attacks for future safe RL studies. Code is available at:
\url{https://github.com/liuzuxin/safe-rl-robustness}


  The reflection equation of Cherednik is a counterpart to the celebrated
Yang-Baxter equation, with importance in the theory of integrable systems. We
obtain several new solutions of the reflection equation using braces building
on the work of Smoktunowicz, Vendramin and Weston. In particular, we show that
every brace yields several simple solutions and that a natural class of
solutions is a near-ring. We find more solutions for factorizable rings. Some
of our solutions apply to the original parameter-dependent equation.


  Conventional machine learning methods are predominantly designed to predict
outcomes based on a single data type. However, practical applications may
encompass data of diverse types, such as text, images, and audio. We introduce
interpretable tensor fusion (InTense), a multimodal learning method for
training neural networks to simultaneously learn multimodal data
representations and their interpretable fusion. InTense can separately capture
both linear combinations and multiplicative interactions of diverse data types,
thereby disentangling higher-order interactions from the individual effects of
each modality. InTense provides interpretability out of the box by assigning
relevance scores to modalities and their associations. The approach is
theoretically grounded and yields meaningful relevance scores on multiple
synthetic and real-world datasets. Experiments on six real-world datasets show
that InTense outperforms existing state-of-the-art multimodal interpretable
approaches in terms of accuracy and interpretability.


  The main purpose of the present paper is analyzing magnetic brane solutions
of cubic quasi-topological gravity in the presence of a linear electromagnetic
Maxwell field and a nonlinear electromagnetic Born-Infeld field. We show that
the mentioned magnetic solutions have no curvature singularity and also no
horizons, but we observe that there is a conic geometry with a related deficit
angle. We obtain the metric function and deficit angle and consider their
behavior. We show that the attributes of our solution are dependent on cubic
quasi-topological coefficient and the Gauss-Bonnet parameter.


  In some domestic professional sports leagues, the home stadiums are located
in cities connected by a common train line running in one direction. For these
instances, we can incorporate this geographical information to determine
optimal or nearly-optimal solutions to the n-team Traveling Tournament Problem
(TTP), an NP-hard sports scheduling problem whose solution is a double
round-robin tournament schedule that minimizes the sum total of distances
traveled by all n teams. We introduce the Linear Distance Traveling Tournament
Problem (LD-TTP), and solve it for n=4 and n=6, generating the complete set of
possible solutions through elementary combinatorial techniques. For larger n,
we propose a novel "expander construction" that generates an approximate
solution to the LD-TTP. For n congruent to 4 modulo 6, we show that our
expander construction produces a feasible double round-robin tournament
schedule whose total distance is guaranteed to be no worse than 4/3 times the
optimal solution, regardless of where the n teams are located. This
4/3-approximation for the LD-TTP is stronger than the currently best-known
ratio of 5/3 + epsilon for the general TTP. We conclude the paper by applying
this linear distance relaxation to general (non-linear) n-team TTP instances,
where we develop fast approximate solutions by simply "assuming" the n teams
lie on a straight line and solving the modified problem. We show that this
technique surprisingly generates the distance-optimal tournament on all
benchmark sets on 6 teams, as well as close-to-optimal schedules for larger n,
even when the teams are located around a circle or positioned in
three-dimensional space.


  While LiDAR data acquisition is easy, labeling for semantic segmentation
remains highly time consuming and must therefore be done selectively. Active
learning (AL) provides a solution that can iteratively and intelligently label
a dataset while retaining high performance and a low budget. In this work we
explore AL for LiDAR semantic segmentation. As a human expert is a component of
the pipeline, a practical framework must consider common labeling techniques
such as sequential labeling that drastically improve annotation times. We
therefore propose a discwise approach (DiAL), where in each iteration, we query
the region a single frame covers on global coordinates, labeling all frames
simultaneously. We then tackle the two major challenges that emerge with
discwise AL. Firstly we devise a new acquisition function that takes 3D point
density changes into consideration which arise due to location changes or
ego-vehicle motion. Next we solve a mixed-integer linear program that provides
a general solution to the selection of multiple frames while taking into
consideration the possibilities of disc intersections. Finally we propose a
semi-supervised learning approach to utilize all frames within our dataset and
improve performance.


  Mossbauer spectroscopy and neutron scattering measurements on proteins
embedded in solvents including water and aqueous mixtures have emphasized the
observation of the distinctive temperature dependence of the atomic mean square
displacements, <u2>, commonly referred to as the dynamic transition at some
temperature Td. At low temperatures, <u2> increases slowly, but it assume
stronger temperature dependence after crossing Td, which depends on the
time/frequency resolution of the spectrometer. Various authors have made
connection of the dynamics of solvated proteins including the dynamic
transition to that of glass-forming substances. Notwithstanding, no connection
is made to the similar change of temperature dependence of <u2> obtained by
quasielastic neutron scattering when crossing the glass transition temperature
Tg, generally observed in inorganic, organic and polymeric glass-formers.
Evidences are presented to show that such change of the temperature dependence
of <u2> from neutron scattering at Tg is present in hydrated or solvated
proteins, as well as in the solvents used unsurprisingly since the latter is
just another organic glass-formers. The <u2> obtained by neutron scattering at
not so low temperatures has contributions from the dissipation of molecules
while caged by the anharmonic intermolecular potential at times before
dissolution of cages by the onset of the Johari-Goldstein beta-relaxation. The
universal change of <u2> at Tg of glass-formers had been rationalized by
sensitivity to change in volume and entropy of the beta-relaxation, which is
passed onto the dissipation of the caged molecules and its contribution to
<u2>. The same rationalization applies to hydrated and solvated proteins for
the observed change of <u2> at Tg.


  We report the results of a Sloan Digital Sky Survey-IV eBOSS program to
target X-ray sources and mid-infrared-selected WISE AGN candidates in a 36.8
deg$^2$ region of Stripe 82. About half this survey (15.6 deg$^2$) covers the
largest contiguous portion of the Stripe 82 X-ray survey. This program
represents the largest spectroscopic survey of AGN candidates selected solely
by their WISE colors. We combine this sample with X-ray and WISE AGN in the
field identified via other sources of spectroscopy, producing a catalog of 4847
sources that is 82% complete to $r\sim22$. Based on X-ray luminosities or WISE
colors, 4730 of these sources are AGN, with a median sample redshift of
$z\sim1$. About 30% of the AGN are optically obscured (i.e., lack broad lines
in their optical spectra). BPT analysis, however, indicates that 50% of the
WISE AGN at $z<0.5$ have emission line ratios consistent with star-forming
galaxies, so whether they are buried AGN or star-forming galaxy contaminants is
currently unclear. We find that 61% of X-ray AGN are not selected as MIR AGN,
with 22% of X-ray AGN undetected by WISE. Most of these latter AGN have high
X-ray luminosities ($L_{\rm x} > 10^{44}$ erg s$^{-1}$), indicating that MIR
selection misses a sizable fraction of the highest luminosity AGN, as well as
lower luminosity sources where AGN heated dust is not dominating the MIR
emission. Conversely, $\sim$58% of WISE AGN are undetected by X-rays, though we
do not find that they are preferentially redder than the X-ray detected WISE
AGN.


  The high cost of pixel-level annotations makes it appealing to train saliency
detection models with weak supervision. However, a single weak supervision
source usually does not contain enough information to train a well-performing
model. To this end, we propose a unified framework to train saliency detection
models with diverse weak supervision sources. In this paper, we use category
labels, captions, and unlabelled data for training, yet other supervision
sources can also be plugged into this flexible framework. We design a
classification network (CNet) and a caption generation network (PNet), which
learn to predict object categories and generate captions, respectively,
meanwhile highlight the most important regions for corresponding tasks. An
attention transfer loss is designed to transmit supervision signal between
networks, such that the network designed to be trained with one supervision
source can benefit from another. An attention coherence loss is defined on
unlabelled data to encourage the networks to detect generally salient regions
instead of task-specific regions. We use CNet and PNet to generate pixel-level
pseudo labels to train a saliency prediction network (SNet). During the testing
phases, we only need SNet to predict saliency maps. Experiments demonstrate the
performance of our method compares favourably against unsupervised and weakly
supervised methods and even some supervised methods.


  The helical edge state of a quantum spin-Hall insulator can carry a
supercurrent in equilibrium between two superconducting electrodes (separation
L, coherence length xi). We calculate the maximum (critical) current I_c that
can flow without dissipation along a single edge, going beyond the
short-junction restriction L<<xi of earlier work, and find a dependence on the
fermion parity of the ground state when L becomes larger than xi.
Fermion-parity conservation doubles the critical current in the
low-temperature, long-junction limit, while for a short junction I_c is the
same with or without parity constraints. This provides a phase-insensitive, DC
signature of the 4pi-periodic Josephson effect.


  To enable remote Virtual Reality (VR) and Augmented Reality (AR) clients to
collaborate as if they were in the same space during Mixed Reality (MR)
telepresence, it is essential to overcome spatial heterogeneity and generate a
unified shared collaborative environment by integrating remote spaces into a
target host space. Especially when multiple remote users connect, a large
shared space is necessary for people to maintain their personal space while
collaborating, but the existing simple intersection method leads to the
creation of narrow shared spaces as the number of remote spaces increases. To
robustly align to the host space even as the number of remote spaces increases,
we propose a spatial affordance-aware interactable subspace allocation
algorithm. The key concept of our approach is to consider the perceivable and
interactable areas separately, where every user views the same mutual space,
but each remote user has a different interactable subspace, considering their
location and spatial affordance. We conducted an evaluation with 900 space
combinations, varying the number of remote spaces as two, four, and six, and
results show our method outperformed in securing wide interactable mutual space
and instantiating users compared to the other spatial matching methods. Our
work enables multiple clients from diverse remote locations to access the AR
host's space, allowing them to interact directly with the table, wall, or floor
by aligning their physical subspaces within a connected mutual space.


  A search for chargino$-$neutralino pair production in three-lepton final
states with missing transverse momentum is presented. The study is based on a
dataset of $\sqrt{s} = 13$ TeV $pp$ collisions recorded with the ATLAS detector
at the LHC, corresponding to an integrated luminosity of 139 fb$^{-1}$. No
significant excess relative to the Standard Model predictions is found in data.
The results are interpreted in simplified models of supersymmetry, and
statistically combined with results from a previous ATLAS search for compressed
spectra in two-lepton final states. Various scenarios for the production and
decay of charginos ($\tilde\chi^\pm_1$) and neutralinos ($\tilde\chi^0_2$) are
considered. For pure higgsino $\tilde\chi^\pm_1\tilde\chi^0_2$ pair-production
scenarios, exclusion limits at 95% confidence level are set on $\tilde\chi^0_2$
masses up to 210 GeV. Limits are also set for pure wino
$\tilde\chi^\pm_1\tilde\chi^0_2$ pair production, on $\tilde\chi^0_2$ masses up
to 640 GeV for decays via on-shell $W$ and $Z$ bosons, up to 300 GeV for decays
via off-shell $W$ and $Z$ bosons, and up to 190 GeV for decays via $W$ and
Standard Model Higgs bosons.


  We consider the Kuramoto-Sivashinsky (KS) equation in finite domains of the
form $[-L,L]^d$. Our main result provides refined Gevrey estimates for the
solutions of the one dimensional differentiated KS, which in turn imply
effective new estimates for higher Sobolev norms of the solutions in terms of
powers of $L$. We illustrate our method on a simpler model, namely the
regularized Burger's equation. We also show local well-posedness for the two
dimensional KS equation and provide an explicit criteria for (eventual) blow-up
in terms of its $L^2$ norm. The common underlying idea in both results is that
{\it a priori} control of the $L^2$ norm is enough in order to conclude higher
order regularity and allows one to get good estimates on the high-frequency
tails of the solutions.


  In this paper, we propose a simple yet effective method to deal with the
violation of the Closed-World Assumption for a classifier. Previous works tend
to apply a threshold either on the classification scores or the loss function
to reject the inputs that violate the assumption. However, these methods cannot
achieve the low False Positive Ratio (FPR) required in safety applications. The
proposed method is a rejection option based on hypothesis testing with
probabilistic networks. With probabilistic networks, it is possible to estimate
the distribution of outcomes instead of a single output. By utilizing Z-test
over the mean and standard deviation for each class, the proposed method can
estimate the statistical significance of the network certainty and reject
uncertain outputs. The proposed method was experimented on with different
configurations of the COCO and CIFAR datasets. The performance of the proposed
method is compared with the Softmax Response, which is a known top-performing
method. It is shown that the proposed method can achieve a broader range of
operation and cover a lower FPR than the alternative.


  We consider the Hele-Shaw problem in a randomly perforated domain with zero
Neumann boundary conditions. A homogenization limit is obtained as the
characteristic scale of the domain goes to zero. Specifically, we prove that
the solutions as well as their free boundaries converge uniformly to those
corresponding to a homogeneous and anisotropic Hele-Shaw problem set in
$\mathbb{R}^d$. The main challenge when deriving a limit lies in controlling
the oscillations of the free boundary, this is overcome first by extending De
Giorgi-Nash-Moser type estimates to perforated domains and second by proving
the almost surely non-degenerate growth of the solution near its free boundary.


  Informal language is a style of spoken or written language frequently used in
casual conversations, social media, weblogs, emails and text messages. In
informal writing, the language faces some lexical and/or syntactic changes
varying among different languages. Persian is one of the languages with many
differences between its formal and informal styles of writing, thus developing
informal language processing tools for this language seems necessary. Such a
converter needs a large aligned parallel corpus of colloquial-formal sentences
which can be useful for linguists to extract a regulated grammar and
orthography for colloquial Persian as is done for the formal language. In this
paper we explain our methodology in building a parallel corpus of 50,000
sentence pairs with alignments in the word/phrase level. The sentences were
attempted to cover almost all kinds of lexical and syntactic changes between
informal and formal Persian, therefore both methods of exploring and collecting
from the different resources of informal scripts and following the phonological
and morphological patterns of changes were applied to find as much instances as
possible. The resulting corpus has about 530,000 alignments and a dictionary
containing 49,397 word and phrase pairs.


  We report the development of numerical tools for the topological analysis of
sub--degree resolution, all--sky maps. Software to be released in the HEALFAST
(V0.9) package defines neighbour relationships for the HEALPIX tessellation of
the sphere. We apply this routine to a fast extrema search which scales
strictly linearly in the number of pixels, $N_{p}$. We also present a highly
efficient algorithm for simulating the gradient vector and curvature tensor
fields ``on--the--fly'' with the temperature map, needing only of order $N_{p}
\log_2 N_{p}$ more operations.


  Motivated by the concepts of the inverse Kazhdan-Lusztig polynomial and the
equivariant Kazhdan-Lusztig polynomial, Proudfoot defined the equivariant
inverse Kazhdan-Lusztig polynomial for a matroid. In this paper, we show that
the equivariant inverse Kazhdan-Lusztig polynomial of a matroid is very useful
for determining its equivariant Kazhdan-Lusztig polynomials, and we determine
the equivariant inverse Kazhdan-Lusztig polynomials for Boolean matroids and
uniform matroids. As an application, we give a new proof of Gedeon, Proudfoot
and Young's formula for the equivariant Kazhdan-Lusztig polynomials of uniform
matroids. Inspired by Lee, Nasr and Radcliffe's combinatorial interpretation
for the ordinary Kazhdan-Lusztig polynomials of uniform matroids, we further
present a new formula for the corresponding equivariant Kazhdan-Lusztig
polynomials.


  We state a unified geometrical version of the variational principles for
second-order classical field theories. The standard Lagrangian and Hamiltonian
variational principles and the corresponding field equations are recovered from
this unified framework.


  We undertake a detailed analysis of the non-local properties of the
fundamental problem of two trapped, distinguishable neutral atoms which
interact with a short range potential characterised by an s-wave scattering
length. We show that this interaction generates continuous variable (CV)
entanglement between the external degrees of freedom of the atoms and consider
its behaviour as a function of both, the distance between the traps and the
magnitude of the inter-particle scattering length. We first quantify the
entanglement in the ground state of the system at zero temperature and then,
adopting a phase-space approach, test the violation of the
Clauser-Horn-Shimony-Holt inequality at zero and non-zero temperature and under
the effects of general dissipative local environments.


  Semantic segmentation with fine-grained pixel-level accuracy is a fundamental
component of a variety of computer vision applications. However, despite the
large improvements provided by recent advances in the architectures of
convolutional neural networks, segmentations provided by modern
state-of-the-art methods still show limited boundary adherence. We introduce a
fully unsupervised post-processing algorithm that exploits Monte Carlo sampling
and pixel similarities to propagate high-confidence pixel labels into regions
of low-confidence classification. Our algorithm, which we call probabilistic
Region Growing Refinement (pRGR), is based on a rigorous mathematical
foundation in which clusters are modelled as multivariate normally distributed
sets of pixels. Exploiting concepts of Bayesian estimation and variance
reduction techniques, pRGR performs multiple refinement iterations at varied
receptive fields sizes, while updating cluster statistics to adapt to local
image features. Experiments using multiple modern semantic segmentation
networks and benchmark datasets demonstrate the effectiveness of our approach
for the refinement of segmentation predictions at different levels of
coarseness, as well as the suitability of the variance estimates obtained in
the Monte Carlo iterations as uncertainty measures that are highly correlated
with segmentation accuracy.


  Single-electron transistors would represent an approach for less power
consuming microelectronic devices if room-temperature operation and
industry-compatible fabrication were possible. We present a concept based on
stripes of small, self-assembled, colloidal, metal nanoparticles on a back-gate
device architecture which leads to well-defined and well-controllable
transistor characteristics. This Coulomb transistor has three main advantages:
By employing the scalable Langmuir-Blodgett method we combine high-quality
chemically synthesized metal nanoparticles with standard lithography
techniques. The resulting transistors show on/off ratios above 90 %, reliable
and sinusoidal Coulomb oscillations and room-temperature operation.
Furthermore, this concept allows for versatile tuning of the device properties
like Coulomb-energy gap, threshold voltage, as well as period, position and
strength of the oscillations.


  The asymptotic behavior of a cellular automaton iterated on a random
configuration is well described by its limit probability measure(s). In this
paper, we characterize measures and sets of measures that can be reached as
limit points after iterating a cellular automaton on a simple initial measure,
in the same spirit as SRB measures. In addition to classical topological
constraints, we exhibit necessary computational obstructions. With an
additional hypothesis of connectivity, we show these computability conditions
are sufficient by constructing a cellular automaton realising these sets, using
auxiliary states in order to perform computations. Adapting this construction,
we obtain a similar characterization for the Ces\`aro mean convergence, a Rice
theorem on the sets of limit points, and we are able to perform computation on
the set of measures, i.e. the cellular automaton converges towards a set of
limit points that depends on the initial measure. Last, under non-surjective
hypotheses, it is possible to remove auxiliary states from the construction.


  A theory of Rydberg atom interactions is used to derive analytical forms for
the spin wave pair correlation function in laser-excited cold-atom vapors. This
function controls the quantum statistics of light emission from dense,
inhomogeneous clouds of cold atoms of various spatial dimensionalities. The
results yield distinctive scaling behaviors on the microsecond timescale,
including generalized exponential decay. A detailed comparison is presented
with a recent experiment on a cigar-shaped atomic ensemble [Y. Dudin and A.
Kuzmich, Science 336, 887 (2012)], in which Rb atoms are excited to a set of
Rydberg levels.


  In the past few years systems with slow dynamics have attracted considerable
theoretical and experimental interest. Ageing phenomena are observed during
this ever-lasting non-equilibrium evolution. A simple instance of such a
behaviour is provided by the dynamics that takes place when a system is
quenched from its high-temperature phase to the critical point. The aim of this
review is to summarize the various numerical and analytical results that have
been recently obtained for this case. Particular emphasis is put to the
field-theoretical methods that can be used to provide analytical predictions
for the relevant dynamical quantities. Fluctuation-dissipation relations are
discussed and in particular the concept of fluctuation-dissipation ratio (FDR)
is reviewed, emphasizing its connection with the definition of a possible
effective temperature. The Renormalization-Group approach to critical dynamics
is summarized and the scaling forms of the time-dependent non-equilibrium
correlation and response functions of a generic observable are discussed. From
them the universality of the associated FDR follows as an amplitude ratio. It
is then possible to provide predictions for ageing quantities in a variety of
different models. In particular the results for Model A, B, and C dynamics of
the O(N) Ginzburg-Landau Hamiltonian, and Model A dynamics of the weakly dilute
Ising magnet and of a \phi^3 theory, are reviewed and compared with the
available numerical results and exact solutions. The effect of a planar surface
on the ageing behaviour of Model A dynamics is also addressed within the
mean-field approximation.


  Functional polymers, such as poly(ethylene glycol) (PEG) terminated with a
single phosphonic acid, hereafter PEGik-Ph are often applied to coat metal
oxide surfaces during post synthesis steps, but are not sufficient to stabilize
sub-10 nm particles in protein-rich biofluids. The instability is attributed to
the weak binding affinity of post-grafted phosphonic acid groups, resulting in
a gradual detachment of the polymers the surface. Here, we assess these
polymers as coating agents using an alternative route, namely the one-step
wet-chemical synthesis, where PEGik-Ph is introduced with cerium precursors
during the synthesis. Characterization of the coated cerium oxide nanoparticles
indicates a core-shell structure, where the cores are 3 nm cerium oxide and the
shell consists of functionalized PEG polymers in a brush configuration. Results
show that cerium oxide nanoparticles coated with PEG1k-Ph and PEG2k-Ph are of
potential interest for applications as nanomedicine due to their high Ce(III)
content and increased colloidal stability in cell culture media. We further
demonstrate that the cerium oxide nanoparticles in the presence of hydrogen
peroxide show an additional absorbance band in the UV-vis spectrum, which is
attributed to Ce-O22- peroxo-complexes and could be used in the evaluation of
their catalytic activity for scavenging reactive oxygen species.


  A theoretical and experimental description of the threshold, amplitude, and
stability of a self-oscillating nanowire in a field emission configuration is
presented. Two thresholds for the onset of self-oscillation are identified, one
induced by fluctuations of the electromagnetic environment and a second
revealed by these fluctuations by measuring the probability density function of
the current. The ac and dc components of the current and the phase stability
are quantified. An ac to dc ratio above 100% and an Allan deviation of 1.3x10-5
at room temperature can be attained. Finally, it is shown that a simple
nonlinear model cannot describe the equilibrium effective potential in the
self-oscillating regime due to the high amplitude of oscillations.


  We prove that the geodesic flow for the Weil-Petersson metric on the moduli
space of Riemann surfaces is ergodic (in fact Bernoulli) and has finite,
positive metric entropy.


  In this paper, we study the question of quantization of quantum field
theories in a general light-front frame. We quantize scalar, fermion as well as
gauge field theories in a systematic manner carrying out the Hamiltonian
analysis carefully. The decomposition of the fields into positive and negative
frequency terms needs to be done carefully after which we show that the (anti)
commutation relations for the quantum operators become frame independent. The
frame dependence is completely contained in the functions multiplying these
operators in the field decomposition. We derive the propagators from the vacuum
expectation values of the time ordered products of the fields.


  In models with universal extra dimensions, the isosinglet Kaluza-Klein (KK)
quarks, q^1, have very narrow widths, of O(5-10) MeV, and will thus hadronize.
Studies of KK-quarkonia, \bar{q}^1 q^1, show very sharp resonances and dramatic
signatures at the Linear Collider. In this Brief Report, we consider the
possibility of detecting KK-mesons, \bar{q}^1 q, and show that detection at a
Linear Collider is unlikely.


  In this introductory chapter of the Special Issue entitled `The Structure and
Evolution of Stars', we highlight the recent major progress made in our
understanding in the physics that governs stellar interiors. In so doing, we
combine insight from observations, 1D evolutionary modelling and 2+3D rotating
(magneto)hydrodynamical simulations. Therefore, a complete and compelling
picture of the necessary ingredients in state-of-the-art stellar structure
theory and areas in which improvements still need to be made are
contextualised. Additionally, the over-arching perspective that links all the
themes of subsequent chapters is presented.


  3D teeth reconstruction from X-ray is important for dental diagnosis and many
clinical operations. However, no existing work has explored the reconstruction
of teeth for a whole cavity from a single panoramic radiograph. Different from
single object reconstruction from photos, this task has the unique challenge of
constructing multiple objects at high resolutions. To conquer this task, we
develop a novel ConvNet X2Teeth that decomposes the task into teeth
localization and single-shape estimation. We also introduce a patch-based
training strategy, such that X2Teeth can be end-to-end trained for optimal
performance. Extensive experiments show that our method can successfully
estimate the 3D structure of the cavity and reflect the details for each tooth.
Moreover, X2Teeth achieves a reconstruction IoU of 0.681, which significantly
outperforms the encoder-decoder method by $1.71X and the retrieval-based method
by $1.52X. Our method can also be promising for other multi-anatomy 3D
reconstruction tasks.


  Quantum fluctuations in curved space-time cause the emission of particles. In
order to understand how they may be detected in a laboratory experiment, we
consider a moving refractive index perturbation in an optical medium, which
exhibits optical event horizons. Based on the field theory in curved space-time
we formulate an analytical method to calculate the scattering matrix that
completely describes mode coupling leading to the emission of photon pairs in
various configurations. We then quantify the spectrally resolved photon number
correlations. Moreover, we apply our method in a case study, in which we
consider a moving refractive index step in bulk fused silica. We calculate key
observables in the moving frame as well as in the laboratory frame, such as the
emission spectrum and the spectrally resolved quantum correlations of the
photon number. We observe significant spectral correlations between modes of
opposite norm, evidence of their vacuum origin. We find that emission from
horizons is characterised by an increased photon flux, a signature spectral
shape as well as a correlation with the partner photon mode approaching unity.
These methods and findings pave the way to the observation of particles from
the event horizon in dispersive systems.


  The results of difference sequences theory are applied to analytic function
theory and Diophantine equations. As a result we have the equation which
connects the $n$-th derivative of a function with the difference sequence for
the values of this function. Also the results of difference sequences theory
helps to discover some features of the whole kind of Diophantine equations. The
method presented allows to find limits where Diophantine equation does not have
integer solutions. The higher power of Diophantine equation the better this
method works.