  Industrial activities carried out in confined spaces are characterized by a
very specific type of air pollution. The extended exposure to this kind of
pollution is often highly harmful, resulting in dramatic effects both on health
and safety aspects. The indoor industrial abatement systems, adopted to purify
the air, are typically applied to the emission points. The processed air is
subsequently emitted outside. In this study we present the experimental results
of three-stage wet scrubber systems installed in the industrial workplace of a
(i) fiberglass processing plant, where the highest exposure levels to volatile
compounds are nowadays today monitored,and of a (ii) waste-to-energy plant,
characterized by a very high particulate matter level. The adopted technology,
to be used as complementing strategy,does not require special disposal
procedures and the processed air is re-emitted in the same work environment for
the benefit of the work operators. The operation of the scrubbers network
during the working activities, in both the examined environments, has a)
reduced the level of volatile compounds by about 40% and b) maintained the
particulate matter concentration PM > 1 to a very low level. Industrial plants
are disseminated in mega-cities and represent dangerous diffuse pollution
emission sources. Remediation of highly polluted industrial plants inside our
towns, especially for the conventionally untreated emissions, is of extreme
benefit for the cities air quality.


  We consider the problem of online forecasting of sequences of length $n$ with
total-variation at most $C_n$ using observations contaminated by independent
$\sigma$-subgaussian noise. We design an $O(n\log n)$-time algorithm that
achieves a cumulative square error of $\tilde{O}(n^{1/3}C_n^{2/3}\sigma^{4/3} +
C_n^2)$ with high probability.We also prove a lower bound that matches the
upper bound in all parameters (up to a $\log(n)$ factor). To the best of our
knowledge, this is the first \emph{polynomial-time} algorithm that achieves the
optimal $O(n^{1/3})$ rate in forecasting total variation bounded sequences and
the first algorithm that \emph{adapts to unknown} $C_n$. Our proof techniques
leverage the special localized structure of Haar wavelet basis and the
adaptivity to unknown smoothness parameters in the classical wavelet smoothing
[Donoho et al., 1998]. We also compare our model to the rich literature of
dynamic regret minimization and nonstationary stochastic optimization, where
our problem can be treated as a special case. We show that the workhorse in
those settings --- online gradient descent and its variants with a fixed
restarting schedule --- are instances of a class of \emph{linear forecasters}
that require a suboptimal regret of $\tilde{\Omega}(\sqrt{n})$. This implies
that the use of more adaptive algorithms is necessary to obtain the optimal
rate.


  In the present article, a modified Cauchy problem (problem C) for the
hyperbolic equation of the third order with the data on the equation's
coefficients singularity plane is solved by Riemann method. The special class
in which the solution of the problem C has more simple appearance is introduced
and the area of values of the parameter p entering into the equation is
considerably expanded. In the special class the mixed problem, which decision
was been reduced to the two-dimensional Volterra's integral equations of the
first order with uncurtailed operators, is considered. Authors found the unique
solution of these equations at various values of the parameter p.


  Defining templates of galaxy spectra is useful to quickly characterise new
observations and organise databases from surveys. These templates are usually
built from a pre-defined classification based on other criteria. Aims. We
present an unsupervised classification of 702248 spectra of galaxies and
quasars with redshifts smaller than 0.25 that were retrieved from the Sloan
Digital Sky Survey (SDSS) database, release 7. The spectra were first corrected
for redshift, then wavelet-filtered to reduce the noise, and finally binned to
obtain about 1437 wavelengths per spectrum. The unsupervised clustering
algorithm Fisher-EM, relying on a discriminative latent mixture model, was
applied on these corrected spectra. The full set and several subsets of 100000
and 300000 spectra were analysed. The optimum number of classes given by a
penalised likelihood criterion is 86 classes, of which the 37 most populated
gather 99% of the sample. These classes are established from a subset of 302214
spectra. Using several cross-validation techniques we find that this
classification agrees with the results obtained on the other subsets with an
average misclassification error of about 15%. The large number of very small
classes tends to increase this error rate. In this paper, we do an initial
quick comparison of our classes with literature templates. This is the first
time that an automatic, objective and robust unsupervised classification is
established on such a large number of galaxy spectra. The mean spectra of the
classes can be used as templates for a large majority of galaxies in our
Universe.


  We use the martingale-theoretic approach of game-theoretic probability to
incorporate imprecision into the study of randomness. In particular, we define
a notion of computable randomness associated with interval, rather than
precise, forecasting systems, and study its properties. The richer mathematical
structure that thus arises lets us better understand and place existing results
for the precise limit. When we focus on constant interval forecasts, we find
that every infinite sequence of zeroes and ones has an associated filter of
intervals with respect to which it is computably random. It may happen that
none of these intervals is precise, which justifies the title of this paper. We
illustrate this by showing that computable randomness associated with
non-stationary precise forecasting systems can be captured by a stationary
interval forecast, which must then be less precise: a gain in model simplicity
is thus paid for by a loss in precision.


  We prove a purity property in telescopically localized algebraic $K$-theory
of ring spectra: For $n\geq 1$, the $T(n)$-localization of $K(R)$ only depends
on the $T(0)\oplus \dots \oplus T(n)$-localization of $R$. This complements a
classical result of Waldhausen in rational $K$-theory. Combining our result
with work of Clausen--Mathew--Naumann--Noel, one finds that $L_{T(n)}K(R)$ in
fact only depends on the $T(n-1)\oplus T(n)$-localization of $R$, again for $n
\geq 1$. As consequences, we deduce several vanishing results for
telescopically localized $K$-theory, as well as an equivalence between $K(R)$
and $\mathrm{TC}(\tau_{\geq 0} R)$ after $T(n)$-localization for $n\geq 2$.


  We consider the question of when points in tropical affine space uniquely
determine a tropical hypersurface. We introduce a notion of multiplicity of
points so that this question may be meaningful even if some of the points
coincide. We give a geometric/combinatorial way and a tropical linear-algebraic
way to approach this question. First, given a fixed hypersurface, we show how
one can determine whether points on the hypersurface determine it by looking at
a corresponding marking of the dual complex. With a regularity condition on the
dual complex and when the number of points is minimal, we show that our
condition is equivalent to the connectedness of an appropriate sub-complex.
Second, we introduce notions of non-singularity of tropical matrices and
solutions to tropical linear equations that take into account our notion of
multiplicity and prove a Cramer's Rule type theorem relating them.


  This work presents AEGIS, a novel mixed-signal framework for real-time
anomaly detection by examining sensor stream statistics. AEGIS utilizes Kernel
Density Estimation (KDE)-based non-parametric density estimation to generate a
real-time statistical model of the sensor data stream. The likelihood estimate
of the sensor data point can be obtained based on the generated statistical
model to detect outliers. We present CMOS Gilbert Gaussian cell-based design to
realize Gaussian kernels for KDE. For outlier detection, the decision boundary
is defined in terms of kernel standard deviation ($\sigma_{Kernel}$) and
likelihood threshold ($P_{Thres}$). We adopt a sliding window to update the
detection model in real-time. We use time-series dataset provided from Yahoo to
benchmark the performance of AEGIS. A f1-score higher than 0.87 is achieved by
optimizing parameters such as length of the sliding window and decision
thresholds which are programmable in AEGIS. Discussed architecture is designed
using 45nm technology node and our approach on average consumes $\sim$75 $\mu$W
power at a sampling rate of 2 MHz while using ten recent inlier samples for
density estimation. \textcolor{red}{Full-version of this research has been
published at IEEE TVLSI}


  Deploying a team of robots that can carefully coordinate their actions can
make the entire system robust to individual failures. In this report, we review
recent algorithmic development in making multi-robot systems robust to
environmental uncertainties, failures, and adversarial attacks.
  We find the following three trends in the recent research in the area of
multi-robot coordination: (1) resilient coordination to either withstand
failures and/or attack or recover from failures/attacks; (2) risk-aware
coordination to manage the trade-off risk and reward, where the risk stems due
to environmental uncertainty; (3) Graph Neural Networks based coordination to
learn decentralized multi-robot coordination policies. These algorithms have
been applied to tasks such as formation control, task assignment and
scheduling, search and planning, and informative data collection.
  In order for multi-robot systems to become practical, we need coordination
algorithms that can scale to large teams of robots dealing with dynamically
changing, failure-prone, contested, and uncertain environments. There has been
significant recent research on multi-robot coordination that has contributed
resilient and risk-aware algorithms to deal with these issues and reduce the
gap between theory and practice. Learning-based approaches have been seen to be
promising, especially since they can learn who, when, and how to communicate
for effective coordination. However, these algorithms have also been shown to
be vulnerable to adversarial attacks, and as such developing learning-based
coordination strategies that are resilient to such attacks and robust to
uncertainties is an important open area of research.


  This paper investigates the potential causes of the vulnerabilities of free
content websites to address risks and maliciousness. Assembling more than 1,500
websites with free and premium content, we identify their content management
system (CMS) and malicious attributes. We use frequency analysis at both the
aggregate and per category of content (books, games, movies, music, and
software), utilizing the unpatched vulnerabilities, total vulnerabilities,
malicious count, and percentiles to uncover trends and affinities of usage and
maliciousness of CMS{'s} and their contribution to those websites. Moreover, we
find that, despite the significant number of custom code websites, the use of
CMS{'s} is pervasive, with varying trends across types and categories. Finally,
we find that even a small number of unpatched vulnerabilities in popular
CMS{'s} could be a potential cause for significant maliciousness.


  We investigate the evolution of the Halpha equivalent width, EW(Halpha), with
redshift and its dependence on stellar mass, taking advantage of the first data
from the 3D-HST survey, a large spectroscopic Treasury program with the Hubble
Space Telescope WFC3. Combining our Halpha measurements of 854 galaxies at
0.8<z<1.5 with those of ground based surveys at lower and higher redshift, we
can consistently determine the evolution of the EW(Halpha) distribution from
z=0 to z=2.2. We find that at all masses the characteristic EW(Halpha) is
decreasing towards the present epoch, and that at each redshift the EW(Halpha)
is lower for high-mass galaxies. We measure a slope of EW(Halpha) ~ (1+z)^(1.8)
with little mass dependence. Qualitatively, this measurement is a
model-independent confirmation of the evolution of star forming galaxies with
redshift. A quantitative conversion of EW(Halpha) to sSFR is very model
dependent, because of differential reddening corrections between the continuum
SED and the Balmer lines. The observed EW(Halpha) can be reproduced with a
simple model in which the SFR for galaxies rises to the epoch of z~2.5 and then
decreases with time to z = 0. The model implies that the EW(Halpha) rises to
400 A at z=8. The sSFR evolves faster than EW(Halpha), as the mass-to-light
ratio also evolves with redshift. In this context, we find that the sSFR
evolves as (1+z)^(3.2), nearly independent of mass, consistent with previous
reddening insensitive estimates. We confirm previous results that the observed
slope of the sSFR-z relation is steeper than the one predicted by models, but
models and observations agree in finding little mass dependence.


  The early Universe after inflation may have oscillations, kinations
(nonoscillatory evolution of a field), topological defects, relativistic and
non-relativistic particles at the same time. The Universe whose energy density
is a sum of those components can be called the multi-component Universe. The
components, which may have distinguishable density scalings, may decay
modulated. In this paper we study generation of the curvature perturbations
caused by the modulated decay in the multi-component Universe.


  Quantum error correcting code can diagnose potential errors and correct them
based on measured outcomes by leveraging syndrome measurement. However,
mid-circuit measurement has been technically challenging for early
fault-tolerant quantum computers and the readout-induced noise acts as a main
contributor to the logical infidelity. We present a different method for
syndrome extraction, namely Generalized Syndrome Measurement, that requires
only a single-shot measurement on a single ancilla, while the canonical
syndrome measurement requires multiple measurements to extract the eigenvalue
for each stabilizer generator. As such, we can detect the error in the logical
state with minimized readout-induced noise. By adopting our method as a
pre-check routine for quantum error correcting cycles, we can significantly
reduce the readout overhead, the idling time, and the logical error rate during
syndrome measurement. We numerically analyze the performance of our protocol
using Iceberg code and Steane code under realistic noise parameters based on
superconducting hardware and demonstrate the advantage of our protocol in the
near-term scenario. As mid-circuit measurements are still error-prone for
near-term quantum hardware, our method may boost the applications of early
fault-tolerant quantum computing.


  We show that a quantum phase transition from ergodic to many-body localized
(MBL) phases can be induced via periodic pulsed manipulation of spin systems.
Such a transition is enabled by the interplay between weak disorder and slow
heating rates. Specifically, we demonstrate that the Hamiltonian of a weakly
disordered ergodic spin system can be effectively engineered, by using
sufficiently fast coherent controls, to yield a stable MBL phase, which in turn
completely suppresses the energy absorption from external control field. Our
results imply that a broad class of existing many-body systems can be used to
probe non-equilibrium phases of matter for a long time, limited only by
coupling to external environment.


  In this paper, we propose a class of explicit positivity preserving numerical
methods for general stochastic differential equations which have positive
solutions. Namely, all the numerical solutions are positive. Under some
reasonable conditions, we obtain the convergence and the convergence rate
results for these methods. The main difficulty is to obtain the strong
convergence and the convergence rate for stochastic differential equations
whose coefficients are of exponential growth. Some numerical experiments are
provided to illustrate the theoretical results for our schemes.


  The van der Heijde modification of the Sharp (SvdH) score is a widely used
radiographic scoring method to quantify damage in Rheumatoid Arthritis (RA) in
clinical trials. However, its complexity with a necessity to score each
individual joint, and the expertise required limit its application in clinical
practice, especially in disease progression measurement. In this work, we
addressed this limitation by developing a bespoke, automated pipeline that is
capable of predicting the SvdH score and RA severity from hand radiographs
without the need to localise the joints first. Using hand radiographs from RA
and suspected RA patients, we first investigated the performance of the
state-of-the-art architectures in predicting the total SvdH score for hands and
wrists and its corresponding severity class. Secondly, we leveraged publicly
available data sets to perform transfer learning with different finetuning
schemes and ensemble learning, which resulted in substantial improvement in
model performance being on par with an experienced human reader. The best model
for RA scoring achieved a Pearson's correlation coefficient (PCC) of 0.925 and
root mean squared error (RMSE) of 18.02, while the best model for RA severity
classification achieved an accuracy of 0.358 and PCC of 0.859. Our score
prediction model attained almost comparable accuracy with experienced
radiologists (PCC = 0.97, RMSE = 18.75). Finally, using Grad-CAM, we showed
that our models could focus on the anatomical structures in hands and wrists
which clinicians deemed as relevant to RA progression in the majority of cases.


  We discuss some of the drawbacks of using event horizons to define black
holes and suggest ways in which black holes can be described without event
horizons, using trapping horizons. We show that these trapping horizons give
rise to thermodynamic behavior and possibly Hawking radiation too. This raises
the issue of whether the event horizon or the trapping horizon should be seen
as the true boundary of a black hole. This difference is important if we
believe that quantum gravity will resolve the central singularity of the black
hole and clarifies several of the issues associated with black hole
thermodynamics and information loss.


  In this note we describe how N=1 SQCD-like theories with a large number of
flavors can be given a dual description in terms of a string background
containing a large number of additional D5-branes. The dual geometries account
for the backreaction of the additional branes: they depend on the ratio between
the number of flavors and colors of the gauge theory.
  This note is based on hep-th/0602027. We present here also a new set of
solutions, which are not included in the original paper.


  Metadynamics (MTD) is a very powerful technique to sample high-dimensional
free energy landscapes, and due to its self-guiding property, the method has
been successful in studying complex reactions and conformational changes. MTD
sampling is based on filling the free energy basins by biasing potentials and
thus for cases with flat, broad and unbound free energy wells, the
computational time to sample them becomes very large. To alleviate this
problem, we combine the standard Umbrella Sampling (US) technique with MTD to
sample orthogonal collective variables (CVs) in a simultaneous way. Within this
scheme, we construct the equilibrium distribution of CVs from biased
distributions obtained from independent MTD simulations with umbrella
potentials. Reweighting is carried out by a procedure that combines US
reweighting and Tiwary-Parrinello MTD reweighting within the Weighted Histogram
Analysis Method (WHAM). The approach is ideal for a controlled sampling of a CV
in a MTD simulation, making it computationally efficient in sampling flat,
broad and unbound free energy surfaces. This technique also allows for a
distributed sampling of a high-dimensional free energy surface, further
increasing the computational efficiency in sampling. We demonstrate the
application of this technique in sampling high-dimensional surface for various
chemical reactions using ab initio and QM/MM hybrid molecular dynamics
simulations. Further, in order to carry out MTD bias reweighting for computing
forward reaction barriers in ab initio or QM/MM simulations, we propose a
computationally affordable approach that does not require recrossing
trajectories.


  Level structure of the $^{12}_{\Lambda}$C hypernucleus was precisely
determined by means of $\gamma$-ray spectroscopy. We identified four
$\gamma$-ray transitions via the $^{12}$C$(\pi^{+},K^{+}\gamma)$ reaction using
a germanium detector array, Hyperball2. The spacing of the ground-state doublet
$(2^{-}_{1},1^{-}_{1})$ was measured to be
$161.5\pm0.3\text{(stat)}\pm0.3\text{(syst)}$\,keV from the direct $M1$
transition. Excitation energies of the $1^{-}_{2}$ and $1^{-}_{3}$ states were
measured to be $2832\pm3\pm4$\,keV and $6050\pm8\pm7$\,keV, respectively. The
obtained level energies provide definitive references for the reaction
spectroscopy of $\Lambda$ hypernuclei.


  Risk-averse optimal control problems have gained a lot of attention in the
last decade, mostly due to their attractive mathematical properties and
practical importance. They can be seen as an interpolation between stochastic
and robust optimal control approaches, allowing the designer to trade-off
performance for robustness and vice-versa. Due to their stochastic nature,
risk-averse problems are of a very large scale, involving millions of decision
variables, which poses a challenge in terms of efficient computation. In this
work, we propose a splitting for general risk-averse problems and show how to
efficiently compute iterates on a GPU-enabled hardware. Moreover, we propose
Spock - a new algorithm that utilizes the proposed splitting and takes
advantage of the SuperMann scheme combined with fast directions from Anderson's
acceleration method for enhanced convergence speed. We implement Spock in Julia
as an open-source solver, which is amenable to warm-starting and massive
parallelization.


  We study various semiclassical observables in the duality proposed by
Guarino, Jafferis and Varela, between a warped $AdS_4\times$ squashed $S^6$
gravitational solution and a 3 dimensional ${\cal N}=2$ SYM-CS$_k$ conformal
gauge theory, deformed from the maximal $SU(N)$ ${\cal N}=8$ SYM. Baryon
vertices corresponding to particle-like branes have unusual behaviour with $N$
and $k$ and present strong evidence for a certain level-rank duality. Wilson
loops and the anomalous dimensions of operators of high spin scale like
$(N/k)^{3/2}$. The entanglement entropy behaves like in a usual CFT. Giant
magnon operators obey the same law as in 4 dimensional ${\cal N}=4$ SYM, and
giant gravitons are also sub-determinant operators.


  In this paper we study multi-task kernel ridge regression and try to
understand when the multi-task procedure performs better than the single-task
one, in terms of averaged quadratic risk. In order to do so, we compare the
risks of the estimators with perfect calibration, the \emph{oracle risk}. We
are able to give explicit settings, favorable to the multi-task procedure,
where the multi-task oracle performs better than the single-task one. In
situations where the multi-task procedure is conjectured to perform badly, we
also show the oracle does so. We then complete our study with simulated
examples, where we can compare both oracle risks in more natural situations. A
consequence of our result is that the multi-task ridge estimator has a lower
risk than any single-task estimator, in favorable situations.


  Here I explore a novel no-collapse interpretation of quantum mechanics which
combines aspects of two familiar and well-developed alternatives, Bohmian
mechanics and the many-worlds interpretation. Despite reproducing the empirical
predictions of quantum mechanics, the theory looks surprisingly classical. All
there is at the fundamental level are particles interacting via Newtonian
forces. There is no wave function. However, there are many worlds.


  We study the problem of a mobile impurity of mass $M$ interacting {\sl via} a
s-wave broad or narrow Feshbach resonance with a Fermi sea of particles of mass
$m$. Truncating the Hilbert space to at most one pair of particle-hole
excitations of the Fermi sea, we determine ground state properties of the
polaronic branch other than its energy, namely the polaron quasiparticle
residue $Z$, and the impurity-to-fermion pair correlation function $G(x)$. We
show that $G(x)$ deviates from unity at large distances as $-(A\_4+B\_4 \cos 2
k\_F x)/(k\_F x)^4$, where $k\_F$ is the Fermi momentum; since $A\_4>0$ and
$B\_4>0$, the polaron has a diverging rms radius and exhibits Friedel-like
oscillations. In the weakly attractive limit, we obtain analytical results,
that in particular detect the failure of the Hilbert space truncation for a
diverging mass impurity, as expected from Anderson orthogonality catastrophe;
at distances between $\sim 1/k\_F$ and the asymptotic distance where the
$1/x^4$ law applies, they reveal that $G(x)$ exhibits an intriguing multiscale
structure.


  Mining electronic health records for patients who satisfy a set of predefined
criteria is known in medical informatics as phenotyping. Phenotyping has
numerous applications such as outcome prediction, clinical trial recruitment,
and retrospective studies. Supervised machine learning for phenotyping
typically relies on sparse patient representations such as bag-of-words. We
consider an alternative that involves learning patient representations. We
develop a neural network model for learning patient representations and show
that the learned representations are general enough to obtain state-of-the-art
performance on a standard comorbidity detection task.


  Visible Light Communication (VLC) using light emitting diodes (LEDs) has been
gaining increasing attention in recent years as it is appealing for a wide
range of applications such as indoor positioning. Orthogonal frequency division
multiplexing (OFDM) has been applied to indoor wireless optical communications
in order to mitigate the effect of multipath distortion of the optical channel
as well as increasing data rate. In this paper, a novel OFDM VLC system is
proposed which can be utilized for both communications and indoor positioning.
A positioning algorithm based on power attenuation is used to estimate the
receiver coordinates. We further calculate the positioning errors in all the
locations of a room and compare them with those using single carrier modulation
scheme, i.e., on-off keying (OOK) modulation. We demonstrate that OFDM
positioning system outperforms its conventional counterpart. Finally, we
investigate the impact of different system parameters on the positioning
accuracy of the proposed OFDM VLC system.


  Eight PbWO4 crystals produced for the electromagnetic calorimeter of the CMS
experiment at LHC have been irradiated in a 20 GeV/c proton beam up to fluences
of 5.4 E13 p/cm2. The damage recovery in these crystals, stored in the dark at
room temperature, has been followed for over a year. Comparative irradiations
with 60Co photons have been performed on seven other crystals using a dose rate
of 1 kGy/h. The issue whether hadrons cause a specific damage to the
scintillation mechanism has been studied through light output measurements on
the irradiated crystals using cosmic rays. The correlation between light output
changes and light transmission changes is measured to be the same for
proton-irradiated crystals and for gamma-irradiated crystals. Thus, within the
precision of the measurements and for the explored range of proton fluences, no
additional, hadron-specific damage to the scintillation mechanism is observed.


  Jing Zhang proved the consistency of
$\binom{\omega_2}{\omega_1}\rightarrow\binom{n}{\omega_1}_\omega$ for every
$n\in\omega$ with the negative relation
$\binom{\omega_2}{\omega_1}\nrightarrow\binom{\omega}{\omega_1}_\omega$. We
reduce the consistency strength of this statement to an $\omega_1$-Erdos
cardinal.


  New social and economic activities massively exploit big data and machine
learning algorithms to do inference on people's lives. Applications include
automatic curricula evaluation, wage determination, and risk assessment for
credits and loans. Recently, many governments and institutions have raised
concerns about the lack of fairness, equity and ethics in machine learning to
treat these problems. It has been shown that not including sensitive features
that bias fairness, such as gender or race, is not enough to mitigate the
discrimination when other related features are included. Instead, including
fairness in the objective function has been shown to be more efficient.
  We present novel fair regression and dimensionality reduction methods built
on a previously proposed fair classification framework. Both methods rely on
using the Hilbert Schmidt independence criterion as the fairness term. Unlike
previous approaches, this allows us to simplify the problem and to use multiple
sensitive variables simultaneously. Replacing the linear formulation by kernel
functions allows the methods to deal with nonlinear problems. For both linear
and nonlinear formulations the solution reduces to solving simple matrix
inversions or generalized eigenvalue problems. This simplifies the evaluation
of the solutions for different trade-off values between the predictive error
and fairness terms. We illustrate the usefulness of the proposed methods in toy
examples, and evaluate their performance on real world datasets to predict
income using gender and/or race discrimination as sensitive variables, and
contraceptive method prediction under demographic and socio-economic sensitive
descriptors.


  The discrete tomography of B-type and F-type icosahedral model sets is
investigated, with an emphasis on reconstruction and uniqueness problems. These
are motivated by the request of materials science for the unique reconstruction
of quasicrystalline structures from a small number of images produced by
quantitative high resolution transmission electron microscopy.


  A Mach-Zehnder interferometer with spatial and spectral resolution was used
to probe spontaneous coherence in cold exciton gases, which are implemented
experimentally in the ring of indirect excitons in coupled quantum wells. A
strong enhancement of the exciton coherence length is observed at temperatures
below a few Kelvin. The increase of the coherence length is correlated with the
macroscopic spatial ordering of excitons.


  Let $\pi: {\mathbb C}_g \to {\mathbb M}_g$ be the universal family of compact
Riemann surfaces of genus $g \geq 1$. We introduce a real-valued function on
the moduli space ${\mathbb M}_g$ and compute the first and the second
variations of the function. As a consequence we relate the Chern form of the
relative tangent bundle $T_{{\mathbb C}_g/{\mathbb M}_g}$ induced by the
Arakelov-Green function with differential forms on ${\mathbb C}_g$ induced by a
flat connection whose holonomy gives Johnson's homomorphisms on the mapping
class group.


  We consider the problem of clustering (or reconstruction) in the stochastic
block model, in the regime where the average degree is constant. For the case
of two clusters with equal sizes, recent results by Mossel, Neeman and Sly, and
by Massoulie, show that reconstructability undergoes a phase transition at the
Kesten-Stigum bound of $\lambda_2^2 d = 1$, where $\lambda_2$ is the second
largest eigenvalue of a related stochastic matrix and $d$ is the average
degree. In this paper, we address the general case of more than two clusters
and/or unbalanced cluster sizes. Our main result is a sufficient condition for
clustering to be impossible, which matches the existing result for two clusters
of equal sizes. A key ingredient in our result is a new connection between
non-reconstructability and non-distinguishability of the block model from an
Erd\H{o}s-R\'enyi model with the same average degree. We also show that it is
some times possible to reconstruct even when $\lambda_2^2 d < 1$. Our results
provide evidence supporting a series of conjectures made by Decelle, Krzkala,
Moore and Zdeborov\'a regarding reconstructability and distinguishability of
stochastic block models (but do not settle them).


  With the development of convolution neural network, more and more researchers
focus their attention on the advantage of CNN for face recognition task. In
this paper, we propose a deep convolution network for learning a robust face
representation. The deep convolution net is constructed by 4 convolution
layers, 4 max pooling layers and 2 fully connected layers, which totally
contains about 4M parameters. The Max-Feature-Map activation function is used
instead of ReLU because the ReLU might lead to the loss of information due to
the sparsity while the Max-Feature-Map can get the compact and discriminative
feature vectors. The model is trained on CASIA-WebFace dataset and evaluated on
LFW dataset. The result on LFW achieves 97.77% on unsupervised setting for
single net.


  Understanding and reducing the in-orbit instrumental backgrounds are
essential to achieving high sensitivity in hard X-ray astronomical
observations. The observational data of the Hard X-ray Imager (HXI) on board
the Hitomi satellite provides useful information on the background components,
owing to its multi-layer configuration with different atomic numbers: the HXI
consists of a stack of four layers of Si (Z = 14) detectors and one layer of
CdTe (Z = 48, 52) detector surrounded by well-type BGO (Bi4Ge3O12) active
shields. Based on the observational data, the backgrounds of top Si layer, the
three underlying Si layers, and the CdTe layer are inferred to be dominated by
different components, namely, low-energy electrons, albedo neutrons, and
proton-induced radioactivation, respectively. Monte Carlo simulations of the
in-orbit background of the HXI reproduce the observed background spectrum of
each layer well, thereby verifying the above hypothesis quantitatively. In
addition, we suggest the inclusion of an electron shield to reduce the
background.


  We experimentally compare the optical bandwidth of a conventional single-mode
fiber (SMF) with 3 different photonic crystal fibers (PCF) all optimized for
visible applications. The spectral attenuation, single-turn bend loss, and
mode-field diameters (MFD) are measured and the PCF is found to have a
significantly larger bandwidth than the SMF for an identical MFD. It is shown
how this advantage can be utilized for realizing a larger MFD for the PCF while
maintaining a bending resistant fiber.


  Deep learning methods have been shown to be effective for the automatic
segmentation of structures and pathologies in medical imaging. However, they
require large annotated datasets, whose manual segmentation is a tedious and
time-consuming task, especially for large structures. We present a new method
of partial annotations that uses a small set of consecutive annotated slices
from each scan with an annotation effort that is equal to that of only few
annotated cases. The training with partial annotations is performed by using
only annotated blocks, incorporating information about slices outside the
structure of interest and modifying a batch loss function to consider only the
annotated slices. To facilitate training in a low data regime, we use a
two-step optimization process. We tested the method with the popular soft Dice
loss for the fetal body segmentation task in two MRI sequences, TRUFI and
FIESTA, and compared full annotation regime to partial annotations with a
similar annotation effort. For TRUFI data, the use of partial annotations
yielded slightly better performance on average compared to full annotations
with an increase in Dice score from 0.936 to 0.942, and a substantial decrease
in Standard Deviations (STD) of Dice score by 22% and Average Symmetric Surface
Distance (ASSD) by 15%. For the FIESTA sequence, partial annotations also
yielded a decrease in STD of the Dice score and ASSD metrics by 27.5% and 33%
respectively for in-distribution data, and a substantial improvement also in
average performance on out-of-distribution data, increasing Dice score from
0.84 to 0.9 and decreasing ASSD from 7.46 to 4.01 mm. The two-step optimization
process was helpful for partial annotations for both in-distribution and
out-of-distribution data. The partial annotations method with the two-step
optimizer is therefore recommended to improve segmentation performance under
low data regime.


  For fixed positive integers m, we consider the product of m independent n by
n random matrices with iid entries as in the limit as n tends to infinity.
Under suitable assumptions on the entries of each matrix, it is known that the
limiting empirical distribution of the eigenvalues is described by the m-th
power of the circular law. Moreover, this same limiting distribution continues
to hold if each iid random matrix is additively perturbed by a bounded rank
deterministic error. However, the bounded rank perturbations may create one or
more outlier eigenvalues. We describe the asymptotic location of the outlier
eigenvalues, which extends a result of Terence Tao for the case of a single iid
matrix. Our methods also allow us to consider several other types of
perturbations, including multiplicative perturbations.


  We give an explicit grobner basis for the ideal of the tangent cone at any
T-fixed point of a Richardson variety in the Symplectic Grassmannian, thus
generalizing a result of Ghorpade and Raghavan.


  This paper is concerned with necessary and sufficient conditions for
near-optimal singular stochastic controls for systems driven by a nonlinear
stochastic differential equations (SDEs in short). The proof of our result is
based on Ekeland's variational principle and some delicate estimates of the
state and adjoint processes. This result is a generalization of Zhou's
stochastic maximum principle for near-optimality to singular control problem.


  Recently, massive architectures based on Convolutional Neural Network (CNN)
and self-attention mechanisms have become necessary for audio classification.
While these techniques are state-of-the-art, these works' effectiveness can
only be guaranteed with huge computational costs and parameters, large amounts
of data augmentation, transfer from large datasets and some other tricks. By
utilizing the lightweight nature of audio, we propose an efficient network
structure called Paired Inverse Pyramid Structure (PIP) and a network called
Paired Inverse Pyramid Structure MLP Network (PIPMN). The PIPMN reaches 96\% of
Environmental Sound Classification (ESC) accuracy on the UrbanSound8K dataset
and 93.2\% of Music Genre Classification (MGC) on the GTAZN dataset, with only
1 million parameters. Both of the results are achieved without data
augmentation or model transfer. Public code is available at:
https://github.com/JNAIC/PIPMN


  The surroundings of a cancerous tumor impact how it grows and develops in
humans. New data from early breast cancer patients contains information on the
collagen fibers surrounding the tumorous tissue -- offering hope of finding
additional biomarkers for diagnosis and prognosis -- but poses two challenges
for typical analysis. Each image section contains information on hundreds of
fibers, and each tissue has multiple image sections contributing to a single
prediction of tumor vs. non-tumor. This nested relationship of fibers within
image spots within tissue samples requires a specialized analysis approach.
  We devise a novel support vector machine (SVM)-based predictive algorithm for
this data structure. By treating the collection of fibers as a probability
distribution, we can measure similarities between the collections through a
flexible kernel approach. By assuming the relationship of tumor status between
image sections and tissue samples, the constructed SVM problem is non-convex
and traditional algorithms can not be applied. We propose two algorithms that
exchange computational accuracy and efficiency to manage data of all sizes. The
predictive performance of both algorithms is evaluated on the collagen fiber
data set and additional simulation scenarios. We offer reproducible
implementations of both algorithms of this approach in the R package mildsvm.


  Accretion disks around supermassive black holes (SMBH) are promising sites
for stellar mass black hole (BH) mergers due to mass segregation and merger
acceleration by disk gas torques. Here we show that a GW-kick at BH merger
causes ram-pressure stripping of gas within the BH Hill sphere. If $R_{H}\geq
H$, the disk height, an off-center UV flare at $a_{\rm BH} \sim 10^{3}r_{g}$
emerges within $t_{\rm UV} \sim \rm{O}(2 {\rm days})(a_{\rm
BH}/10^{3}r_{g})(M_{\rm SMBH}/10^{8}M_{\odot})(v_{\rm kick}/10^{2}\rm{km/s})$
post-merger and lasts O$(R_{H}/v_{\rm kick}) \sim \rm{O}(5 t_{\rm UV}$). The
flare emerges with luminosity O($10^{42}{\rm erg/s})(t_{\rm UV}/2{\rm
days})^{-1}(M_{\rm Hill}/1M_{\odot})(v_{\rm kick}/10^{2}{\rm km/s})^{2}$. AGN
optical/UV photometry alters and asymmetric broad emission line profiles can
develop after weeks. If $R_{H}<H$, detectability depends on disk optical depth.
Follow-up by large optical sky surveys is optimized for small GW error volumes
and for LIGO/Virgo triggers $>50M_{\odot}$.


  We demonstrate experimentally and numerically that in fiber tips as they are
used in NSOMs azimuthally polarized electrical fields
(|E$_{\text{azi}}$|$^2$/|E$_{\text{tot}}$|$^2$ $\approx$ 55% $\pm $ 5% for
1.4\mu m tip aperture diameter and \lambda$_0$ = 1550nm), respectively
subwavelength confined (FWHM $\approx$ 450nm $\approx$ \lambda$_0$/3.5)
magnetic fields, are generated for a certain tip aperture diameter (d = 1.4\mu
m). We attribute the generation of this field distribution in metal-coated
fiber tips to symmetry breaking in the bend and subsequent plasmonic mode
filtering in the truncated conical taper.


  For the second lowest-order Raviart--Thomas mixed method, we prove that the
canonical interpolant and finite element solution for the vector variable in
elliptic problems are superclose in the $H(\text{div})$-norm on mildly
structured meshes, where most pairs of adjacent triangles form approximate
parallelograms. We then develop a family of postprocessing operators for
Raviart--Thomas mixed elements on triangular grids by using the idea of local
least squares fittings. Super-approximation property of the postprocessing
operators for the lowest and second lowest order Raviart--Thomas elements is
proved under mild conditions. Combining the supercloseness and
super-approximation results, we prove that the postprocessed solution
superconverges to the exact solution in the $L^2$-norm on mildly structured
meshes.


  This PhD thesis has the following structure: Chapter 1 - General
introduction; Chapter 2 - Preliminaries; Chapter 3 - The Replicated Transfer
Matrix; Chapter 4 - Finite Size Corrections On Random Graphs; Chapter 5 - The
Random Field Ising Model; Chapter 6 - The Euclidean Assignment Problem; Chapter
7 - The Euclidean Matching Problem; Chapter 8 - The Large M Expansion.


  We investigate an optimal regime of negative-valued conductance, emerging in
a resistively and capacitively shunted Josephson junction, which is driven
simultaneously by both, a time-periodic (ac) and a constant (dc) current. We
analyze the current-voltage characteristics in the regime of absolute negative
conductance. We additionally explore the stability of the negative response
with respect to the ac-current frequency.


  We test the conjectured relationship between N=4 super Yang-Mills theory in
four dimensions and IIB supergravity compactified on $AdS_5\times S_5$ by
computing the two- and three-point functions of R-symmetry currents. We observe
that the integral expressions describing the general three-point correlator on
the supergravity side have a structure similar to one-loop triangle diagrams in
N=4 super Yang-Mills theory. This allows us to compare the expressions on both
sides of the AdS/CFT correspondence without the technical complications of the
loop integrations. We confirm that the two- and three-point correspondence
arises at only one-loop in the N=4 super Yang-Mills theory. Higher-point
functions as well as further three-point functions may be analyzed similarly.


  We study the zero temperature mean-field phase diagram of the Bose-Hubbard
model in the presence of local coupling between the bosons and an external
bath. We consider a coupling that conserves the on-site occupation number,
preserving the robustness of the Mott and superfluid phases. We show that the
coupling to the bath renormalizes the chemical potential and the interaction
between the bosons and reduces the size of the superfluid regions between the
insulating lobes. For strong enough coupling, a finite value of hopping is
required to obtain superfluidity around the degeneracy points where Mott phases
with different occupation numbers coexist. We discuss the role that such a bath
coupling may play in experiments that probe the formation of the
insulator-superfluid shell structure in systems of trapped atoms.


  Content-Based Image Retrieval (CBIR) systems have been widely used for a wide
range of applications such as Art collections, Crime prevention and
Intellectual property. In this paper, a novel CBIR system, which utilizes
visual contents (color, texture and shape) of an image to retrieve images, is
proposed. The proposed system builds three feature vectors and stores them into
MySQL database. The first feature vector uses descriptive statistics to
describe the distribution of data in each channel of RGB channels of the image.
The second feature vector describes the texture using eigenvalues of the 39
sub-bands that are generated after applying four levels 2D DWT in each channel
(red, green and blue channels) of the image. These wavelets sub-bands perfectly
describes the horizontal, vertical and diagonal edges that exist in the
multi-resolution analysis of the image. The third feature vector describes the
basic shapes that exist in the skeletonization version of the black and white
representation of the image. Experimental results on a private MYSQL database
that consists of 10000 images, using color, texture, shape and stored relevance
feedbacks, showed 96.4% average correct retrieval rate in an efficient recovery
time.


  We present the generalization of the CNC formalism, based on closed and
noncontextual sets of Pauli observables, to the setting of
odd-prime-dimensional qudits. By introducing new CNC-type phase space point
operators, we construct a quasiprobability representation for quantum
computation which is covariant with respect to the Clifford group and
positivity preserving under Pauli measurements, and whose nonnegative sector
strictly contains the subtheory of quantum theory described by nonnegative
Wigner functions. This allows for a broader class of magic state quantum
circuits to be efficiently classically simulated than those covered by the
stabilizer formalism and Wigner function methods.


  We introduce a renormalization model which explains how the behavior of a
discrete-time continuous dynamical system changes as the dimension of the
system varies. The model applies to some two-dimensional systems, including
H\'enon and Lozi maps. Here, we focus on the orientation preserving Lozi
family, a two-parameter family of continuous piecewise affine maps, and treat
the family as a perturbation of the tent family from one to two dimensions.
  First, we give a new prove that all periodic orbits can be classified by
using symbolic dynamics. For each coding, the associated periodic orbit depends
on the parameters analytically on the domain of existence. The creation or
annihilation of periodic orbits happens when there is a border collision
bifurcation. Next, we prove that the bifurcation parameters of some types of
periodic orbits form analytic curves in the parameter space. This improves a
theorem of Ishii (1997). Finally, we use the model and the analytic curves to
prove that, when the Lozi family is arbitrary close to the tent family, the
order of periodic orbit creation reverses. This shows that a forcing relation
(Guckenheimer 1979 and Collett and Eckmann 1980) on orbit creations breaks down
in two dimensions. In fact, the forcing relation does not have a continuation
to two dimensions even when the family is arbitrary close to one dimension.


  Let g be a complex, semi-simple Lie algebra, h a Cartan subalgebra of g and D
a subdiagram of the Dynkin diagram of g. Let g_D and l_D be the corresponding
semi-simple and Levi subalgebras and consider two invariant solutions Phi,
Phi_D of the pentagon equation for g and g_D respectively. Motivated by the
theory of quasi-Coxeter quasitriangular quasibialgebras \cite{TL3}, we study in
this paper the existence of a relative twist, that is an element F invariant
under l_D such that the twist of Phi by F is Phi_D. Adapting the method of
Donin and Shnider, who treated the case of an empty D, so that l_D=h and
Phi_D=1, we give a cohomological construction of such an F under the assumption
that Phi_D is the image of Phi under the generalised Harish-Chandra
homomorphism. We also show that F is unique up to a gauge transformation if l_D
is of corank 1 or F satisfies F^\Theta= F^{21} where \Theta is an involution of
g acting as -1 on h.


  The ATCA, MERLIN and VLA interferometers were used to measure the absolute
positions of 35 6.7 GHz methanol masers to subarcsecond or higher accuracy. Our
measurements represent essential preparatory data for Very Long Baseline
Interferometry, which can provide accurate parallax and proper motion
determinations of the star-forming regions harboring the masers. Our data also
allow associations to be established with infrared sources at different
wavelengths. Our findings support the view that the 6.7 GHz masers are
associated with the earliest phases of high-mass star formation.


  We study the scaling of quantum discord (a measure of quantum correlation
beyond entanglement) in spin models analytically and systematically. We find
that at finite temperature the block scaling of quantum discord satisfies an
area law for any two-local Hamiltonian. We show that generically and
heuristically the two-site scaling of quantum discord is similar to that of
correlation functions. In particular, at zero temperature it decays
exponentially and polynomially in gapped and gapless (critical) systems,
respectively; at finite temperature it decays exponentially in both gapped and
gapless systems. We compute the two-site scaling of quantum discord in the XXZ
chain, the XY chain (in a magnetic field), and the transverse field Ising chain
at zero temperature.


  A next-generation liquid-scintillator detector will be able to perform
high-statistics measurements of the solar neutrino flux. In LENA, solar Be-7
neutrinos are expected to cause 1.7x10^4 electron recoil events per day in a
fiducial volume of 35 kilotons. Based on this signal, a search for periodic
modulations on sub-percent level can be conducted, surpassing the sensitivity
of current detectors by at least a factor of 20. The range of accessible
periods reaches from several minutes, corresponding to modulations induced by
helioseismic g-modes, to tens of years, allowing to study long-term changes in
solar fusion rates.


  This paper aims to enhance the physical layer security against potential
internal eavesdroppings by exploiting the maneuverability of an unmanned aerial
vehicle (UAV). We consider a scenario where two receivers with different
security clearance levels require to be served by a legitimate transmitter with
the aid of the UAV. We jointly design the trajectory and resource allocation to
maximize the accumulated system confidential data rate. The design is
formulated as a mixed-integer non-convex optimization problem which takes into
account the partial position information of a potential eavesdropper. To
circumvent the problem non-convexity, a series of transformations and
approximations are proposed which facilitates the design of a computationally
efficient suboptimal solution. Simulation results are presented to provide
important system design insights and demonstrate the advantages brought by the
robust joint design for enhancing the physical layer security.


  Let $M$ be a compact subset of a superreflexive Banach space. We prove that
the Lipschitz-free space $\mathcal{F}(M)$, the predual of the Banach space of
Lipschitz functions on $M$, has the Pe{\l}czy\'nski's property ($V^\ast$). As a
consequence, the Lipschitz-free space $\mathcal{F}(M)$ is weakly sequentially
complete.


  Patch-based physical attacks have increasingly aroused concerns.
  However, most existing methods focus on obscuring targets captured on the
ground, and some of these methods are simply extended to deceive aerial
detectors.
  They smear the targeted objects in the physical world with the elaborated
adversarial patches, which can only slightly sway the aerial detectors'
prediction and with weak attack transferability.
  To address the above issues, we propose to perform Contextual Background
Attack (CBA), a novel physical attack framework against aerial detection, which
can achieve strong attack efficacy and transferability in the physical world
even without smudging the interested objects at all.
  Specifically, the targets of interest, i.e. the aircraft in aerial images,
are adopted to mask adversarial patches.
  The pixels outside the mask area are optimized to make the generated
adversarial patches closely cover the critical contextual background area for
detection, which contributes to gifting adversarial patches with more robust
and transferable attack potency in the real world.
  To further strengthen the attack performance, the adversarial patches are
forced to be outside targets during training, by which the detected objects of
interest, both on and outside patches, benefit the accumulation of attack
efficacy.
  Consequently, the sophisticatedly designed patches are gifted with solid
fooling efficacy against objects both on and outside the adversarial patches
simultaneously.
  Extensive proportionally scaled experiments are performed in physical
scenarios, demonstrating the superiority and potential of the proposed
framework for physical attacks.
  We expect that the proposed physical attack method will serve as a benchmark
for assessing the adversarial robustness of diverse aerial detectors and
defense methods.


  The non-equilibrium structural and dynamical properties of semiflexible
polymers confined to two dimensions are investigated by molecular dynamics
simulations. Three different scenarios are considered: The force-extension
relation of tethered polymers, the relaxation of an initially stretched
semiflexible polymer, and semiflexible polymers under shear flow. We find
quantitative agreement with theoretical predictions for the force-extension
relation and the time dependence of the entropically contracting polymer. The
semiflexible polymers under shear flow exhibit significant conformational
changes at large shear rates, where less stiff polymers are extended by the
flow, whereas rather stiff polymers are contracted. In addition, the polymers
are aligned by the flow, thereby the two-dimensional semiflexible polymers
behave similarly to flexible polymers in three dimensions. The tumbling times
display a power-law dependence at high shear rate rates with an exponent
comparable to the one of flexible polymers in three-dimensional systems.


  In this paper two ways to compute singular values are presented which use
Cholesky decomposition as their basic operation.


  By subtracting from a recent lattice measurement of the thermal
vector-current correlator the known 5-loop vacuum contribution, we demonstrate
that the remainder is small and shows no visible short-distance divergence. It
can therefore in principle be subjected to model-independent analytic
continuation. Testing a particular implementation, we obtain estimates for the
flavour-diffusion coefficient (2 pi T D \gsim 0.8) and electrical conductivity
which are significantly smaller than previous results. Although systematic
errors remain beyond control at present, some aspects of our approach could be
of a wider applicability.


  We report an optimized chemical vapor transport method, which allows growing
FeP single crystals up to 500 mg in mass and 80 $mm^{3}$ in volume. The high
quality of the crystals obtained by this method was confirmed by means of EDX,
high-resolution TEM, low-temperature single crystal XRD and neutron diffraction
experiments. We investigated the transport and magnetic properties of the
single crystals and calculated the electronic band structure of FeP. We show
both theoretically and experimentally, that the ground state of FeP is
metallic. The examination of the magnetic data reveals antiferromagnetic order
below T$_{N}$ =119 K while transport remains metallic in both the paramagnetic
and the antiferromagnetic phase. The analysis of the neutron diffraction data
shows an incommensurate magnetic structure with the propagation vector Q=(0, 0,
$\pm{\delta}$), where ${\delta}$ $\sim$ 0.2. For the full understanding of the
magnetic state, further experiments are needed. The successful growth of large
high-quality single crystals opens the opportunity for further investigations
of itinerant magnets with incommensurate spin structures using a wide range of
experimental tools.


  In this paper, we aim to redesign the vision Transformer (ViT) as a new
backbone to realize semantic image transmission, termed wireless image
transmission transformer (WITT). Previous works build upon convolutional neural
networks (CNNs), which are inefficient in capturing global dependencies,
resulting in degraded end-to-end transmission performance especially for
high-resolution images. To tackle this, the proposed WITT employs Swin
Transformers as a more capable backbone to extract long-range information.
Different from ViTs in image classification tasks, WITT is highly optimized for
image transmission while considering the effect of the wireless channel.
Specifically, we propose a spatial modulation module to scale the latent
representations according to channel state information, which enhances the
ability of a single model to deal with various channel conditions. As a result,
extensive experiments verify that our WITT attains better performance for
different image resolutions, distortion metrics, and channel conditions. The
code is available at https://github.com/KeYang8/WITT.


  Data augmentation has been actively studied for robust neural networks. Most
of the recent data augmentation methods focus on augmenting datasets during the
training phase. At the testing phase, simple transformations are still widely
used for test-time augmentation. This paper proposes a novel instance-level
test-time augmentation that efficiently selects suitable transformations for a
test input. Our proposed method involves an auxiliary module to predict the
loss of each possible transformation given the input. Then, the transformations
having lower predicted losses are applied to the input. The network obtains the
results by averaging the prediction results of augmented inputs. Experimental
results on several image classification benchmarks show that the proposed
instance-aware test-time augmentation improves the model's robustness against
various corruptions.


  This study investigates the temperature-dependent quasi-static
magnetoelectric (ME) response of electrically poled lead-free
Na$_{0.4}$K$_{0.1}$Bi$_{0.5}$TiO$_3$-NiFe$_2$O$_4$ (NKBT-NFO) laminated
composites. The aim is to understand the temperature stability of ME-based
sensors and devices. The relaxor ferroelectric nature of NKBT is confirmed
through impedance and polarization-electric (PE) hysteresis loop studies, with
a depolarization temperature (Td) of approximately 110$^\circ$C. Heating causes
a decrease and disappearance of planar electromechanical coupling, charge
coefficient, and remnant polarization above Td. The temperature rise also leads
to a reduction in magnetostriction and magnetostriction coefficient of NFO by
approximately 33% and 25%, respectively, up to approximately 125$^\circ$C. At
room temperature, the bilayer and trilayer configurations exhibit maximum ME
responses of approximately 33 mV/cm.Oe and 80 mV/cm.Oe, respectively, under low
magnetic field conditions (300-450 Oe). The ME response of NKBT/NFO is highly
sensitive to temperature, decreasing with heating in accordance with the
individual temperature-dependent properties of NKBT and NFO. This study
demonstrates a temperature window for the effective utilization of
NKBT-NFO-based laminated composite ME devices.


  We find an upper bound for the Gromov width of coadjoint orbits of U(n) with
respect to the Kirillov-Kostant-Souriau symplectic form by computing certain
Gromov-Witten invariants. The approach presented here is closely related to the
one used by Gromov in his celebrated non-squeezing theorem.


  We determined the frequency dependent effective permittivity of a large
ternary network of randomly positioned resistors, capacitors, and diodes. A
linear circuit analysis of such systems is shown to match the experimental
dielectric response of single-walled carbon nanotube (SWCNT) filled polymers.
This modeling method is able to reproduce the two most important features of
SWCNT filled composites, i.e. the low frequency dispersion and dipolar
relaxation. As a result of the modeling important physical conclusion proved by
the experimental data was done: the low frequency behavior of SWCNT-filled
polymer composites is mostly caused by the fraction of semiconducting SWCNTs.


  We propose a dynamic product adoption persuasion model involving an impatient
partially informed sender who gradually learns the state. In this model, the
sender gathers information over time, and hence her posteriors' sequence forms
a discrete-time martingale. The sender commits to a dynamic revelation policy
to persuade the agent to adopt a product. We demonstrate that under the
assumption that the sender's martingale possesses Blackwell-preserving kernels,
the family of optimal strategies for the sender takes an interval form; namely,
in every period the set of martingale realizations in which adoption occurs is
an interval. Utilizing this, we prove that if the sender is sufficiently
impatient, then under a random walk martingale, the optimal policy is fully
transparent up to the moment of adoption; namely, the sender reveals the entire
information she privately holds in every period.


  We provide an analytic solution to the coupled-mode equations describing the
steady-state of a single periodically-modulated optical resonator driven by a
monochromatic input. The phenomenology of this system was qualitatively
understood only in the adiabatic limit, i.e. for low modulation speed. However,
both in and out of this regime, we find highly non-trivial effects for specific
parameters of the modulation. For example, we show complete suppression of the
transmission even with zero detuning between the input and the static resonator
frequency. We also demonstrate the possibility for complete, lossless frequency
conversion of the input into the side-band frequencies, as well as for
optimizing the transmitted signal towards a given target temporal waveform. The
analytic results are validated by first-principle simulations.


  We study large deviations for random walks on stratified (Carnot) Lie groups.
For such groups, there is a natural collection of vectors which generates their
Lie algebra, and we consider random walks with increments in only these
directions. Under certain constraints on the distribution of the increments, we
prove a large deviation principle for these random walks with a natural rate
function adapted to the sub-Riemannian geometry of these spaces.


  Thul et al. (2020) called attention to problems that arise when chronometric
experiments implementing specific factorial designs are analysed with the
generalized additive mixed model (GAMM), using factor smooths to capture
trial-to-trial dependencies. From a series of simulations incorporating such
dependencies, they conclude that GAMMs are inappropriate for between-subject
designs. They argue that in addition GAMMs come with too many modeling
possibilities, and advise using the linear mixed model (LMM) instead. We
address the questions raised by Thul et al. (2020), who clearly demonstrated
that problems can indeed arise when using factor smooths in combination with
factorial designs. We show that the problem does not arise when using
by-smooths. Furthermore, we have traced a bug in the implementation of factor
smooths in the mgcv package, which will have been removed from version 1.8-36
onwards. To illustrate that GAMMs now produce correct estimates, we report
simulation studies implementing different by-subject longitudinal effects. The
maximal LMM emerges as slightly conservative compared to GAMMs, and GAMMs
provide estimated coefficients that can be less variable across simulation
runs. We also discuss two datasets where time-varying effects interact with
numerical predictors in a theoretically informative way. Furthermore, we argue
that the wide range of tools that GAMMs make available to researcher across all
domains of scientific inquiry do not come with uncontrolled researcher degrees
of freedom once confronted with a specific psycholinguistic datasets. We also
introduce a distinction between replicable and non-replicable non-linear
effects. We conclude that GAMMs are an excellent and reliable tool for
understanding experimental data, including chronometric data with time-varying
effects.


  Community detection in graphs is the problem of finding groups of vertices
which are more densely connected than they are to the rest of the graph. This
problem has a long history, but it is undergoing a resurgence of interest due
to the need to analyze social and biological networks. While there are many
ways to formalize it, one of the most popular is as an inference problem, where
there is a "ground truth" community structure built into the graph somehow. The
task is then to recover the ground truth knowing only the graph.
  Recently it was discovered, first heuristically in physics and then
rigorously in probability and computer science, that this problem has a phase
transition at which it suddenly becomes impossible. Namely, if the graph is too
sparse, or the probabilistic process that generates it is too noisy, then no
algorithm can find a partition that is correlated with the planted one---or
even tell if there are communities, i.e., distinguish the graph from a purely
random one with high probability. Above this information-theoretic threshold,
there is a second threshold beyond which polynomial-time algorithms are known
to succeed; in between, there is a regime in which community detection is
possible, but conjectured to require exponential time.
  For computer scientists, this field offers a wealth of new ideas and open
questions, with connections to probability and combinatorics, message-passing
algorithms, and random matrix theory. Perhaps more importantly, it provides a
window into the cultures of statistical physics and statistical inference, and
how those cultures think about distributions of instances, landscapes of
solutions, and hardness.


  Initial-boundary value problem for the generalized Zakharov-Kuznetsov
equation posed on a bounded rectangle is considered. Critical and subcritical
powers in nonlinearity are studied.


  In recent years, with the rapid development of graph neural networks (GNN),
more and more graph datasets have been published for GNN tasks. However, when
an upstream data owner publishes graph data, there are often many privacy
concerns, because many real-world graph data contain sensitive information like
person's friend list. Differential privacy (DP) is a common method to protect
privacy, but due to the complex topological structure of graph data, applying
DP on graphs often affects the message passing and aggregation of GNN models,
leading to a decrease in model accuracy. In this paper, we propose a novel
graph edge protection framework, graph publisher (GraphPub), which can protect
graph topology while ensuring that the availability of data is basically
unchanged. Through reverse learning and the encoder-decoder mechanism, we
search for some false edges that do not have a large negative impact on the
aggregation of node features, and use them to replace some real edges. The
modified graph will be published, which is difficult to distinguish between
real and false data. Sufficient experiments prove that our framework achieves
model accuracy close to the original graph with an extremely low privacy
budget.


  Electron screening changes appreciably the magnitude of astrophysical nuclear
reactions within stars. This effect is also observed in laboratory experiments
on Earth, where atomic electrons are present in the nuclear targets.
Theoretical models were developed over the past 30 years and experimental
measurements have been carried out to study electron screening in thermonuclear
reactions. None of the theoretical models were able to explain the high values
of the experimentally determined screening potentials. We explore the
possibility that the "electron screening puzzle" is due to nuclear
clusterization and polarization effects in the fusion reactions. We will
discuss the supporting arguments for this scenario.


  Vehicle velocity and inter-vehicle distance estimation are essential for ADAS
(Advanced driver-assistance systems) and autonomous vehicles. To save the cost
of expensive ranging sensors, recent studies focus on using a low-cost
monocular camera to perceive the environment around the vehicle in a
data-driven fashion. Existing approaches treat each vehicle independently for
perception and cause inconsistent estimation. Furthermore, important
information like context and spatial relation in 2D object detection is often
neglected in the velocity estimation pipeline. In this paper, we explore the
relationship between vehicles of the same frame with a
global-relative-constraint (GLC) loss to encourage consistent estimation. A
novel multi-stream attention network (MSANet) is proposed to extract different
aspects of features, e.g., spatial and contextual features, for joint vehicle
velocity and inter-vehicle distance estimation. Experiments show the
effectiveness and robustness of our proposed approach. MSANet outperforms
state-of-the-art algorithms on both the KITTI dataset and TuSimple velocity
dataset.


  Ptychography is a technique widely used in microscopy for achieving
high-resolution imaging. This method relies on computational processing of
images gathered from diffraction patterns produced by several partial
illuminations of a sample. In this work, we numerically studied the effect of
using different shapes for illuminating the aforementioned sample: convex
shapes, such as circles and regular polygons, and unconnected shapes that
resemble a QR code. Our results suggest that the use of unconnected shapes
seems to outperform convex shapes in terms of convergence and, in some cases,
accuracy.


  The quantum Hall effect in graphene is regarded to be involving half-integer
topological numbers associated with the massless Dirac particle, this is
usually not apparent due to the doubling of the Dirac cones. Here we
theoretically consider two classes of lattice models in which we manipulate the
Dirac cones with either (a) two Dirac points that have mutually different
energies, or (b) multiple Dirac cones having different Fermi velocities. We
have shown, with an explicit calculation of the topological (Chern) number for
case (a) and with an adiabatic argument for case (b) that the results are
consistent with the picture that a single Dirac fermion contributes the
half-odd integer series (... -3/2, -1/2, 1/2, 3/2, ...) to the Hall
conductivity when the Fermi energy traverses the Landau levels.


  Despite the close connection between exploration and sample efficiency, most
state of the art reinforcement learning algorithms include no considerations
for exploration beyond maximizing the entropy of the policy. In this work we
address this seeming missed opportunity. We observe that the most common
formulation of directed exploration in deep RL, known as bonus-based
exploration (BBE), suffers from bias and slow coverage in the few-sample
regime. This causes BBE to be actively detrimental to policy learning in many
control tasks. We show that by decoupling the task policy from the exploration
policy, directed exploration can be highly effective for sample-efficient
continuous control. Our method, Decoupled Exploration and Exploitation Policies
(DEEP), can be combined with any off-policy RL algorithm without modification.
When used in conjunction with soft actor-critic, DEEP incurs no performance
penalty in densely-rewarding environments. On sparse environments, DEEP gives a
several-fold improvement in data efficiency due to better exploration.


  We construct solar models with the newly calculated radiative opacities from
the Opacity Project (OP) and recently determined (lower) heavy element
abundances. We compare results from the new models with predictions of a series
of models that use OPAL radiative opacities, older determinations of the
surface heavy element abundances, and refinements of nuclear reaction rates.
For all the variations we consider, solar models that are constructed with the
newer and lower heavy element abundances advocated by Asplund et al. (2005)
disagree by much more than the estimated measuring errors with
helioseismological determinations of the depth of the solar convective zone,
the surface helium composition, the internal sound speeds, and the density
profile. Using the new OP radiative opacities, the ratio of the 8B neutrino
flux calculated with the older and larger heavy element abundances (or with the
newer and lower heavy element abundances) to the total neutrino flux measured
by the Sudbury Neutrino Observatory is 1.09 (0.87) with a 9% experimental
uncertainty and a 16% theoretical uncertainty, 1 sigma errors.


  HTTP/3, the latest evolution of the Hypertext Transfer Protocol, utilizes
QUIC, a new transport protocol leveraging UDP to overcome limitations such as
connection time and head-of-line blocking prevalent in HTTP/2. This advancement
is enhanced by the Extensible Prioritization Scheme (EPS), which introduces a
flexible prioritization framework for improving website resource delivery. This
paper proposes a mixed scheduling mechanism that delivers using mixed
incremental and non-incremental resource delivery and adheres to EPS urgency
levels to improve the QoE. Additionally, we propose an EPS priority mapping to
enhance the QoE further. This mapping is based on the priority indicated by the
Chromium browser and the resource type. The result of the experimental
evaluation indicates that the proposed mechanism and mapping improve
commonly-used website performance measures for sites featuring a comparatively
large number and size of resources.


  We report the influence of Ni/Mn ordering on the spin-phonon coupling in
multifunctional La2NiMnO6. Three types of films with different levels of
structural order, including long-range Ni/Mn cation order, cation disorder, and
an admixture of the ordered and disordered phases, are compared by polarized
micro-Raman spectroscopy and magnetometry. Each film displays a strong
dependence on the polarization configuration and a unique set of Raman active
phonon excitations. Long-range cation ordering results in the splitting of
Raman active phonon peaks because of Brillouin zone folding and lowering
symmetry. Phonon mode softening begins clearly at a distinct temperature for
each sample revealing a strong spin-lattice interaction. It follows closely the
magnetization curve in ordered films. Unlike the admixture and the ordered
films, softening behavior is strongly suppressed in the cation-disordered
films. These differences may be understood based on the variation in amplitude
of the spin-spin correlation functions due to the local Ni/Mn cation ordering.


  Bayes' rule describes how to infer posterior beliefs about latent variables
given observations, and inference is a critical step in learning algorithms for
latent variable models (LVMs). Although there are exact algorithms for
inference and learning for certain LVMs such as linear Gaussian models and
mixture models, researchers must typically develop approximate inference and
learning algorithms when applying novel LVMs. In this paper we study the line
that separates LVMs that rely on approximation schemes from those that do not,
and develop a general theory of exponential family, latent variable models for
which inference and learning may be implemented exactly. Firstly, under mild
assumptions about the exponential family form of a given LVM, we derive
necessary and sufficient conditions under which the LVM prior is in the same
exponential family as its posterior, such that the prior is conjugate to the
posterior. We show that all models that satisfy these conditions are
constrained forms of a particular class of exponential family graphical model.
We then derive general inference and learning algorithms, and demonstrate them
on a variety of example models. Finally, we show how to compose our models into
graphical models that retain tractable inference and learning. In addition to
our theoretical work, we have implemented our algorithms in a collection of
libraries with which we provide numerous demonstrations of our theory, and with
which researchers may apply our theory in novel statistical settings.


  The Dzyaloshinskii-Moriya interaction (DMI) has explained successfully the
weak ferromagnetism in some centrosymmetric antiferromagnets. However, in the
last years, it was generally claimed that the DMI is not effective in
centrosymmetric systems. We reconciled these views by separating the
conventional antiferromagnets and altermagnets. Altermagnets represent
collinear antiferromagnetic compounds with spin-up and spin-down sublattices
connected only by mirror and roto-translational symmetries. Consequently, the
system shows even-parity wave spin order in the k-space lifting the Kramer's
degeneracy in the non-relativistic band structure. We emphasize that the DMI
can create weak ferromagnetism in centrosymmetric altermagnets while it is not
effective in centrosymmetric conventional antiferromagnets. Additionally, DMI
can create weak ferromagnetism or weak ferrimagnetism in noncentrosymmetric
altermagnets. Once the spin-orbit coupling is included in an altermagnetic
system without time-reversal symmetry, the components of spin moments of the
two sublattices along the Neel vector are antiparallel but the other two spin
components orthogonal to the N\'eel vector can be either parallel or
antiparallel for centrosymetric systems. For noncentrosymmetric systems, we can
have different bands showing parallel or antiparallel spin components resulting
in weak ferrimagnetism. We can divide the altermagnetic compounds into classes
based on the weak ferromagnetism or weak ferrimagnetism properties. The weak
ferromagnetism and weak ferrimagnetism induced by DMI is a property exclusively
of the altermagnets, not present in either ferromagnets or conventional
antiferromagnets so we propose that altermagnets should be classified based on
this property.


  Process mining is a methodology for the derivation and analysis of process
models based on the event log. When process mining is employed to analyze
business processes, the process discovery step, the conformance checking step,
and the enhancements step are repeated. If a user wants to analyze a process
from multiple perspectives (such as activity perspectives, originator
perspectives, and time perspectives), the above procedure, inconveniently, has
to be repeated over and over again. Although past studies involving process
mining have applied detailed stepwise methodologies, no attempt has been made
to incorporate and optimize multi-perspective process mining procedures. This
paper contributes to developing a solution approach to this problem. First, we
propose an automatic discovery framework of a multi-perspective process model
based on deep Q-Learning. Our Dual Experience Replay with Experience
Distribution (DERED) approach can automatically perform process model discovery
steps, conformance check steps, and enhancements steps. Second, we propose a
new method that further optimizes the experience replay (ER) method, one of the
key algorithms of deep Q-learning, to improve the learning performance of
reinforcement learning agents. Finally, we validate our approach using six
real-world event datasets collected in port logistics, steel manufacturing,
finance, IT, and government administration. We show that our DERED approach can
provide users with multi-perspective, high-quality process models that can be
employed more conveniently for multi-perspective process mining.


  The Galactic center of the Milky-Way harbors a massive black hole (BH)
orbited by a diverse population of young and old stars. A significant fraction
of the youngest stars ($\sim4-7$ Myr) reside in a thin stellar disk with
puzzling properties; the disk appears to be warped, shows asymmetries, and
contains one or more clumpy structures (e.g. IRS 13). Models explaining the
clumping invoked the existence of an intermediate-mass BH of $10^{3}-10^{4}$
M$_{\odot}$, but no kinematic evidence for such a BH has been found. Here we
use extended $N$-body simulations and hybrid self-consistent field method
models to show that naturally formed residual temporal asphericity of the
hosting nuclear star cluster gives rise to torques on the disk, which lead to
changes in its orientation over time, and to recurrent formation and
dissolution of single spiral arm ($m=1$ modes) structures. The changing
orientation leads to a flapping-like behavior of the disk and to the formation
of a warped disk structure. The spiral arms may explain the over-densities in
the disk (clumping) and its observed asymmetry, without invoking the existence
of an intermediate-mass BH. The spiral arms are also important for the overall
disk evolution and can be used to constrain the structure and composition of
the nuclear stellar cluster.


  In this paper we propose a new algorithm for streaming principal component
analysis. With limited memory, small devices cannot store all the samples in
the high-dimensional regime. Streaming principal component analysis aims to
find the $k$-dimensional subspace which can explain the most variation of the
$d$-dimensional data points that come into memory sequentially. In order to
deal with large $d$ and large $N$ (number of samples), most streaming PCA
algorithms update the current model using only the incoming sample and then
dump the information right away to save memory. However the information
contained in previously streamed data could be useful. Motivated by this idea,
we develop a new streaming PCA algorithm called History PCA that achieves this
goal. By using $O(Bd)$ memory with $B\approx 10$ being the block size, our
algorithm converges much faster than existing streaming PCA algorithms. By
changing the number of inner iterations, the memory usage can be further
reduced to $O(d)$ while maintaining a comparable convergence speed. We provide
theoretical guarantees for the convergence of our algorithm along with the rate
of convergence. We also demonstrate on synthetic and real world data sets that
our algorithm compares favorably with other state-of-the-art streaming PCA
methods in terms of the convergence speed and performance.


  Natural language processing (NLP) models often require a massive number of
parameters for word embeddings, resulting in a large storage or memory
footprint. Deploying neural NLP models to mobile devices requires compressing
the word embeddings without any significant sacrifices in performance. For this
purpose, we propose to construct the embeddings with few basis vectors. For
each word, the composition of basis vectors is determined by a hash code. To
maximize the compression rate, we adopt the multi-codebook quantization
approach instead of binary coding scheme. Each code is composed of multiple
discrete numbers, such as (3, 2, 1, 8), where the value of each component is
limited to a fixed range. We propose to directly learn the discrete codes in an
end-to-end neural network by applying the Gumbel-softmax trick. Experiments
show the compression rate achieves 98% in a sentiment analysis task and 94% ~
99% in machine translation tasks without performance loss. In both tasks, the
proposed method can improve the model performance by slightly lowering the
compression rate. Compared to other approaches such as character-level
segmentation, the proposed method is language-independent and does not require
modifications to the network architecture.


  We investigate the scaling properties of zero temperature conductances at
integer quantum Hall plateau transitions in the lowest Landau band of a
two-dimensional tight-binding model. Scaling is obeyed for all energy and
system sizes with critical exponent nu =7/3 . The arithmetic average of the
conductance at the localization-delocalization critical point is found to be
<G>_c = 0.506 e^2 / h, in agreement with the universal longitudinal conductance
predicted by an analytical theory. The probability distribution of the
conductance at the critical point is broad with a dip at small G.


  Within the framework of modified Newtonian gravity it is shown that the
corrected Newtonian scheme can't explain both Mercury's perihelion advance and
the bending of light at the same time.


  Here we prove some special cases of the following conjecture: that the sum of
the Betti numbers of a 1-connected elliptic space is greater than the total
rank of its homotopy groups. Our main tool is Sullivan's minimal model.


  This paper sets out to examine the skills gaps between the industrial
application of Information Technology and university academic programmes
(curriculum). It looks at some of the causes, and considers the probable
solutions for bridging the gap between them and suggests the possibilities of
exploring a new role for our universities and employers of labor. It also
highlights strategies to abolish the misalignment between university and
industry. The main concept is to blend the academic rigidity with the
industrial relevance.


  We show that the Iwahori-Hecke algebras H_n of type A_{n-1} satisfy
homological stability, where homology is interpreted as an appropriate Tor
group. Our result precisely recovers Nakaoka's homological stability result for
the symmetric groups in the case that the defining parameter is equal to 1. We
believe that this paper, and our joint work with Boyd on Temperley-Lieb
algebras, are the first time that the techniques of homological stability have
been applied to algebras that are not group algebras.


  Speech-to-speech translation (S2ST) enables spoken communication between
people talking in different languages. Despite a few studies on multilingual
S2ST, their focus is the multilinguality on the source side, i.e., the
translation from multiple source languages to one target language. We present
the first work on multilingual S2ST supporting multiple target languages.
Leveraging recent advance in direct S2ST with speech-to-unit and vocoder, we
equip these key components with multilingual capability. Speech-to-masked-unit
(S2MU) is the multilingual extension of S2U, which applies masking to units
which don't belong to the given target language to reduce the language
interference. We also propose multilingual vocoder which is trained with
language embedding and the auxiliary loss of language identification. On
benchmark translation testsets, our proposed multilingual model shows superior
performance than bilingual models in the translation from English into $16$
target languages.


  Background:
  Scatterometry is a fast, indirect and non-destructive optical method for
quality control in the production of lithography masks. To solve the inverse
problem in compliance with the upcoming need for improved accuracy, a
computationally expensive forward model has to be defined which maps geometry
parameters to diffracted light intensities.
  Aim:
  To quantify the uncertainties in the reconstruction of the geometry
parameters, a fast to evaluate surrogate for the forward model has to be
introduced.
  Approach:
  We use a non-intrusive polynomial chaos based approximation of the forward
model which increases speed and thus enables the exploration of the posterior
through direct Bayesian inference. Additionally, this surrogate allows for a
global sensitivity analysis at no additional computational overhead.
  Results:
  This approach yields information about the complete distribution of the
geometry parameters of a silicon line grating, which in return allows to
quantify the reconstruction uncertainties in the form of means, variances and
higher order moments of the parameters.
  Conclusion:
  The use of a polynomial chaos surrogate allows to quantify both parameter
influences and reconstruction uncertainties. This approach is easy to use since
no adaptation of the expensive forward model is required.


  In this paper, we propose a GAN-based approach for gap filling in borehole
images created by wireline microresistivity imaging tools. The proposed method
utilizes a generator, global discriminator, and local discriminator to inpaint
the missing regions of the image. The generator is based on an auto-encoder
architecture with skip-connections, and the loss function used is the
Wasserstein GAN loss. Our experiments on a dataset of borehole images
demonstrate that the proposed model can effectively deal with large-scale
missing pixels and generate realistic completion results. This approach can
improve the quantitative evaluation of reservoirs and provide an essential
basis for interpreting geological phenomena and reservoir parameters.


  The observed value $\Lambda_{\rm obs}$ of the cosmological constant $\Lambda$
is extremely smaller than theoretical expectations, and the anthropic argument
has been proposed as a solution to this problem because galaxies do not form
when $\Lambda \gg \Lambda_{\rm obs}$. However, the contemporary galaxy
formation theory predicts that stars form even with a high value of $\Lambda /
\Lambda_{\rm obs} \sim$ 50, which makes the anthropic argument less persuasive.
Here we calculate the probability distribution of $\Lambda$ using a model of
cosmological galaxy formation, considering extinction of observers caused by
radiation from nearby supernovae. The life survival probability decreases in a
large $\Lambda$ universe because of higher stellar density. Using a reasonable
rate of lethal supernovae, we find that the mean expectation value of $\Lambda$
can be close to $\Lambda_{\rm obs}$, and hence this effect may be essential to
understand the small but nonzero value of $\Lambda$. It is predicted that we
are located on the edge of habitable regions about stellar density in the
Galaxy, which may be tested by future exoplanet studies.


  Observations of YZ LMi show enhanced emission along the stream trajectory
beyond impact at disk rim during outbursts as well as when the quiescent disk
is large. We investigated whether these features can be explained in terms of
either gas stream overflow or penetration within the frameworks of the
disk-instability (DIM) and the mass-transfer instability (MTIM) models of
outbursting disks. Gas stream overflow is not possible because the vertical
scaleheight of the stream is significantly lower than that of the outer disk
and because there is no combination of parameters which enables stream overflow
on a larger disk while preventing it on a smaller disk. Stream penetration
requires the gas stream to be denser than the outer disk regions. This
requirement cannot be met by a low-viscosity DIM disk because its density is
significantly larger than that of the gas stream over the whole range of mass
transfer rates where the thermal-viscous instability occurs. On the other hand,
the high-viscosity MTIM disk has much lower densities which decrease with
increasing radius, easily allowing for gas stream penetration during outbursts
(when mass transfer rate and stream density increase) as well as in large
quiescent disks. The observed features are not consistent with DIM, but can be
plausibly explained by MTIM. These results suggest that the outbursts of YZ LMi
are the response of a high-viscosity disk to bursts of enhanced mass transfer
rate. In this case, the outburst decline timescale of (2-3) d implies a
viscosity parameter in the range alpha=3-4.


  New MMT/MIRAC (9-11 {\mu}m), SOFIA/FORCAST (11-37 {\mu}m), and Herschel/PACS
(70 and 160 {\mu}m) infrared (IR) imaging and photometry is presented for three
famous OH/IR red supergiants (NML Cyg, VX Sgr, and S Per) and two normal red
supergiants (RS Per and T Per). We model the observed spectral energy
distributions (SEDs) using radiative transfer code DUSTY. Azimuthal average
profiles from the SOFIA/FORCAST imaging, in addition to dust mass distribution
profiles from DUSTY, constrain the mass-loss histories of these supergiants.
For all of our observed supergiants, the DUSTY models suggest that constant
mass-loss rates do not produce enough dust to explain the observed infrared
emission in the stars' SEDs. Combining our results with Shenoy et al. (2016)
(Paper I) we find mixed results with some red supergiants showing evidence for
variable and high mass-loss events while others have constant mass loss over
the past few thousand years.


  The coupling of phonons with collective modes and single-particle gap
excitations associated with one (1d) and two-directional (2d)
electronically-driven charge-density wave (CDW) ordering in metallic RTe_3 is
investigated as a function of rare-earth ion chemical pressure (R=Tb, Dy, Ho)
using femtosecond pump-probe spectroscopy. From the T-dependence of the CDW gap
\Delta_{CDW} and the amplitude mode (AM) we find that while the transition to a
1d-CDW ordered state at T_{c1} initially proceeds in an exemplary mean-field
(MF) -like fashion, below T_{c1}, \Delta_{CDW} is depressed and departs from
the MF behavior. The effect is apparently triggered by resonant mode-mixing of
the amplitude mode (AM) with a totally symmetric phonon at 1.75 THz. At low
temperatures, when the state evolves into a 2d-CDW ordered state at T_{c2} in
the DyTe_3 and HoTe_3, additional much weaker mode mixing is evident but no
soft mode is observed.


  In this paper we extend our recently proposed Deadzone-Adapted Disturbance
Suppression (DADS) Control approach from systems with matched uncertainties to
general systems in parametric strict feedback form. The DADS approach prevents
gain and state drift regardless of the size of the disturbance and unknown
parameter and achieves an attenuation of the plant output to an assignable
small level, despite the presence of persistent disturbances and unknown
parameters of arbitrary and unknown bounds. The controller is designed by means
of a step-by-step backstepping procedure which can be applied in an algorithmic
fashion. Examples are provided which illustrate the efficiency of the DADS
controller compared to existing adaptive control schemes.


  A version of the secretary problem is considered. The ranks of items, whose
values are independent, identically distributed random variables
$X_1,X_2,...,X_n$ from a uniform distribution on $[0; 1]$, are observed
sequentially by the grader. He has to select exactly one item, when it appears,
and receives a payoff which is a function of the unobserved realization of
random variable assigned to the item diminished by some cost. The methods of
analysis are based on the existence of an embedded Markov chain and use the
technique of backward induction. The result is a generalization of the
selection model considered by Bearden(2006). The asymptotic behaviour of the
solution is also investigated.


  In this paper, we present a novel explicit analytical solution for the
normalized state equations of mutually-coupled simple chaotic systems. A
generalized analytical solution is obtained for a class of simple nonlinear
electronic circuits with two different nonlinear elements. The synchronization
dynamics of the circuit systems were studied using the analytical solutions.
the analytical results thus obtained have been validated through numerical
simulation results. Further, we provide a sufficient condition for
synchronization in mutually-coupled, second-order simple chaotic systems
through an analysis on the eigenvalues of the difference system. The
bifurcation of the eigenvalues of the difference system as functions of the
coupling parameter in each of the piecewise-linear regions, revealing the
existence of stable synchronized states is presented. The stability of
synchronized states are studied using the {\emph{Master Stability Function}}.
Finally, the electronic circuit experimental results confirming the phenomenon
of complete synchronization in each of the circuit system is presented.


  We analyze the classical problem of the stochastic dynamics of a particle
confined in a periodic potential, through the so called Il'in and Khasminskii
model, with a novel semi-analytical approach. Our approach gives access to the
transient and the asymptotic dynamics in all damping regimes, which are
difficult to investigate in the usual Brownian model. We show that the
crossover from the overdamped to the underdamped regime is associated with the
loss of a typical time scale and of a typical length scale, as signaled by the
divergence of the probability distribution of a certain dynamical event. In the
underdamped regime, normal diffusion coexists with a non Gaussian displacement
probability distribution for a long transient, as recently observed in a
variety of different systems. We rationalize the microscopic physical processes
leading to the non-Gaussian behavior, as well as the timescale to recover the
Gaussian statistics. The theoretical results are supported by numerical
calculations and are compared to those obtained for the Brownian model.