  This paper considers the learning of Boolean rules in either disjunctive
normal form (DNF, OR-of-ANDs, equivalent to decision rule sets) or conjunctive
normal form (CNF, AND-of-ORs) as an interpretable model for classification. An
integer program is formulated to optimally trade classification accuracy for
rule simplicity. Column generation (CG) is used to efficiently search over an
exponential number of candidate clauses (conjunctions or disjunctions) without
the need for heuristic rule mining. This approach also bounds the gap between
the selected rule set and the best possible rule set on the training data. To
handle large datasets, we propose an approximate CG algorithm using
randomization. Compared to three recently proposed alternatives, the CG
algorithm dominates the accuracy-simplicity trade-off in 7 out of 15 datasets.
When maximized for accuracy, CG is competitive with rule learners designed for
this purpose, sometimes finding significantly simpler solutions that are no
less accurate.


  We give a gentle introduction to solar imaging data, focusing on the
challenges and opportunities of data-driven approaches for solar eruptions. The
various solar phenomenon prediction problems that might benefit from
statistical methods are presented. Available data products and software are
described. State-of-the-art solar eruption forecasting models with data-driven
approaches are summarized and discussed. Based on the characteristics of the
datasets and state-of-the-art approaches, we point out several promising
directions to explore from statistical modeling and computational perspectives.


  We derive an alternative representation for the relativistic non--local
kinetic energy operator and we apply it to solve the relativistic Salpeter
equation using the variational sinc collocation method. Our representation is
analytical and does not depend on an expansion in terms of local operators. We
have used the relativistic harmonic oscillator problem to test our formula and
we have found that arbitrarily precise results are obtained, simply increasing
the number of grid points. More difficult problems have also been considered,
observing in all cases the convergence of the numerical results. Using these
results we have also derived a new representation for the quantum mechanical
Green's function and for the corresponding path integral. We have tested this
representation for a free particle in a box, recovering the exact result after
taking the proper limits, and we have also found that the application of the
Feynman--Kac formula to our Green's function yields the correct ground state
energy. Our path integral representation allows to treat hamiltonians
containing non--local operators and it could provide to the community a new
tool to deal with such class of problems.


  As font is one of the core design concepts, automatic font identification and
similar font suggestion from an image or photo has been on the wish list of
many designers. We study the Visual Font Recognition (VFR) problem, and advance
the state-of-the-art remarkably by developing the DeepFont system. First of
all, we build up the first available large-scale VFR dataset, named AdobeVFR,
consisting of both labeled synthetic data and partially labeled real-world
data. Next, to combat the domain mismatch between available training and
testing data, we introduce a Convolutional Neural Network (CNN) decomposition
approach, using a domain adaptation technique based on a Stacked Convolutional
Auto-Encoder (SCAE) that exploits a large corpus of unlabeled real-world text
images combined with synthetic data preprocessed in a specific way. Moreover,
we study a novel learning-based model compression approach, in order to reduce
the DeepFont model size without sacrificing its performance. The DeepFont
system achieves an accuracy of higher than 80% (top-5) on our collected
dataset, and also produces a good font similarity measure for font selection
and suggestion. We also achieve around 6 times compression of the model without
any visible loss of recognition accuracy.


  There is growing experimental and theoretical evidence that very clean two
dimensional electron systems form unidirectional charge density waves (UCDW) or
``striped'' states at low temperatures and at Landau level filling fractions of
the form nu = M + x with 4 < M < 10 an integer and 0.4 < x < 0.6. Following
previous work, we model the striped state using a Hartree-Fock approach. We
construct the low energy excitations of the system by making smooth
deformations of the stripe edges analogous to the construction of edge state
excitations of quantum Hall droplets. These low energy excitations are
described as a coupled Luttinger liquid theory, as discussed previously by
MacDonald and Fisher (PRB61 5724 2000). Here, we extend that work and
explicitly derive all of the parameters of this low energy theory using a
Hartree Fock approach. We also make contact with the equivalent low energy
hydrodynamic approach of Fogler and Vinokur (PRL84 5828 2000) and similarly
derive the parameters of this theory. As examples of the use of these results,
we explicitly calculate the low-energy excitation spectrum and study tunneling
into the striped state.


  We investigate the existence of possible stable strange matter and related
stability windows at finite temperature for different models that are generally
applied to describe quark stars, namely, the quark-mass density dependent
model, the MIT bag model and the Nambu-Jona-Lasinio model. We emphasize that,
although the limits for stable strange matter depend on a comparison with the
ground state of 56Fe, which is a zero temperature state, the quantity that has
to be used in the search for strange matter in proto-quark stars is the free
energy and we analyze stability windows up to temperatures of the order of 40
MeV. The effects of strong magnetic fields on stability windows are computed
and the resulting mass-radius relations for different stages of the proto-quark
star are analyzed.


  The relative stability among the multiple spin density wave (MSDW) states in
fcc transition metals has been investigated on the basis of a Ginzburg-Landau
type of free energy with terms up to the fourth order in magnetic moments.
Obtained magnetic phase diagrams in the space of expansion coefficients
indicate the possibility of various 3Q MSDW states in fcc transition metals:
the commensurate 3Q state, the incommensurate linear 3Q state, and the
incommensurate helical 3Q state. It is shown that these 3Q states are always
stabilized, when they are compared with the corresponding 2Q and 1Q states, and
their magnetic moment amplitudes are the largest among those of the three
states. The results are compared with previous results of the ground-state
calculations, and possible scenarios to explain the experimental data of fcc-Fe
are proposed.


  Using the KEDR detector at the VEPP-4M $e^+e^-$ collider, we have measured
the values of $R_{\text{uds}}$ and $R$ at seven points of the center-of-mass
energy between 3.12 and 3.72 GeV. The total achieved accuracy is about or
better than $3.3\%$ at most of energy points with a systematic uncertainty of
about $2.1\%$. At the moment it is the most accurate measurement of $R(s)$ in
this energy range.


  Domain generalizable (DG) person re-identification (ReID) is a challenging
problem because we cannot access any unseen target domain data during training.
Almost all the existing DG ReID methods follow the same pipeline where they use
a hybrid dataset from multiple source domains for training, and then directly
apply the trained model to the unseen target domains for testing. These methods
often neglect individual source domains' discriminative characteristics and
their relevances w.r.t. the unseen target domains, though both of which can be
leveraged to help the model's generalization. To handle the above two issues,
we propose a novel method called the relevance-aware mixture of experts
(RaMoE), using an effective voting-based mixture mechanism to dynamically
leverage source domains' diverse characteristics to improve the model's
generalization. Specifically, we propose a decorrelation loss to make the
source domain networks (experts) keep the diversity and discriminability of
individual domains' characteristics. Besides, we design a voting network to
adaptively integrate all the experts' features into the more generalizable
aggregated features with domain relevance. Considering the target domains'
invisibility during training, we propose a novel learning-to-learn algorithm
combined with our relation alignment loss to update the voting network.
Extensive experiments demonstrate that our proposed RaMoE outperforms the
state-of-the-art methods.


  Transport at microscopic length scales is essential in biological systems and
various technologies, including microfluidics. Recent experiments achieved
self-organized transport phenomena in microtubule active matter using light to
modulate motor-protein activity in time and space. Here, we introduce a novel
phenomenological model to explain such experiments. Our model, based on
spatially modulated particle interactions, reveals a possible mechanism for
emergent transport phenomena in light-controlled active matter, including
motility and contraction. In particular, the model's analytic treatment
elucidates the conservation of the center of mass of activated particles as a
fundamental mechanism of material transport and demonstrates the necessity of
memory for sustained motility. Furthermore, we generalize the model to explain
other phenomena, like microtubule aster-aster interactions induced by more
complicated activation geometries. Our results demonstrate that the model
provides a possible foundation for the phenomenological understanding of
light-controlled active matter, and it will enable the design and optimization
of transport protocols for active matter devices.


  Using a two-dimensional galaxy image decomposition technique, we extract
global bulge and disk parameters for a complete sample of early type disk
galaxies in the near infrared K band. We find significant correlation of the
bulge parameter n with the central bulge surface brightness $\mu_b(0)$ and with
effective radius r_e. Using bivar iate analysis techniques, we find that $\log
n$, $\log r_e$ and $\mu_b(0)$ are distributed in a plane with small scatter. We
do not find a strong correlation of n with bulge-to-disk luminosity ratio,
contrary to earlier reports. r_e and the disk scale length r_d are well
correlated for these early type disk galaxies, but with large scatter. We
examine the implications of our results to various bulge formation scenarios in
disk galaxies.


  We have observed a sample of 64 small diameter sources towards the central -6
degree < l< 6 degree, -2 degree < b < 2 degree of the Galaxy with the aim of
studying the Faraday rotation measure near the Galactic Centre (GC) region. All
the sources were observed at 6 and 3.6 cm wavelengths using the ATCA and the
VLA. Fifty nine of these sources are inferred to be extragalactic. The
observations presented here constitute the first systematic study of the radio
polarisation properties of the background sources towards this direction and
increases the number of known extragalactic radio sources in this part of the
sky by almost an order of magnitude. Based on the morphology, spectral indices
and lack of polarised emission, we identify four Galactic HII regions in the
sample.


  The Riemannian geometry of covariance matrices has been essential to several
successful applications, in computer vision, biomedical signal and image
processing, and radar data processing. For these applications, an important
ongoing challenge is to develop Riemannian-geometric tools which are adapted to
structured covariance matrices. The present paper proposes to meet this
challenge by introducing a new class of probability distributions, Gaussian
distributions of structured covariance matrices. These are Riemannian analogs
of Gaussian distributions, which only sample from covariance matrices having a
preassigned structure, such as complex, Toeplitz, or block-Toeplitz. The
usefulness of these distributions stems from three features: (1) they are
completely tractable, analytically or numerically, when dealing with large
covariance matrices, (2) they provide a statistical foundation to the concept
of structured Riemannian barycentre (i.e. Fr\'echet or geometric mean), (3)
they lead to efficient statistical learning algorithms, which realise, among
others, density estimation and classification of structured covariance
matrices. The paper starts from the observation that several spaces of
structured covariance matrices, considered from a geometric point of view, are
Riemannian symmetric spaces. Accordingly, it develops an original theory of
Gaussian distributions on Riemannian symmetric spaces, of their statistical
inference, and of their relationship to the concept of Riemannian barycentre.
Then, it uses this original theory to give a detailed description of Gaussian
distributions of three kinds of structured covariance matrices, complex,
Toeplitz, and block-Toeplitz. Finally, it describes algorithms for density
estimation and classification of structured covariance matrices, based on
Gaussian distribution mixture models.


  Motivated by the results in {\tt hep-th/0508228}, we perform a careful
analysis of the allowed linear constraints on $N=(2,2)$ scalar superfields. We
show that only chiral, twisted-chiral and semi-chiral superfields are possible.
Various subtleties are discussed.


  The flawless functioning of a protein is essentially linked to its own
three-dimensional structure. Therefore, the prediction of a protein structure
from its amino acid sequence is a fundamental problem in many fields that draws
researchers attention. This problem can be formulated as a combinatorial
optimization problem based on simplified lattice models such as the
hydrophobic-polar model. In this paper, we propose a new hybrid algorithm
combining three different well-known heuristic algorithms: genetic algorithm,
tabu search strategy and local search algorithm in order to solve the PSP
problem. Regarding the assessment of suggested algorithm, an experimental study
is included, where we considered the quality of the produced solution as the
main quality criterion. Furthermore, we compared the suggested algorithm with
state-of-the-art algorithms using a selection of well-studied benchmark
instances.


  We discuss a mechanism for the apparently universal scaling in the
high-multiplicity tail of charged particle distributions for high energy
nuclear collisions. We argue that this scaling behavior originates from rare
fluctuations of the nucleon density. We discuss a pair of simple models of
proton shape fluctuations. A "fat" proton with a size of 3 fm occurs with
observable frequency. In light of this result, collective flow behavior in the
ensuing nuclear interaction seems feasible. We discuss the influence of these
models on the large $x$ structure of the proton and the likely influences on
the distribution of initial state spatial eccentricities $\epsilon_{n}$.


  We consider the first-order theory of random variables with the probabilistic
independence relation, which concerns statements consisting of random
variables, the probabilistic independence symbol, logical operators, and
existential and universal quantifiers. Although probabilistic independence is
the only non-logical relation included, this theory is surprisingly expressive,
and is able to interpret the true first-order arithmetic over natural numbers
(and hence is undecidable). We also construct a single-letter characterization
of the capacity region for a general class of multiuser coding settings
(including broadcast channel, interference channel and relay channel), using a
first-order formula. We then introduce the linear entropy hierarchy to classify
single-letter characterizations according to their complexity.


  The silicon-vacancy (SiV) and nitrogen-vacancy (NV) centers in diamond are
commonly regarded as prototypical defects for solid-state quantum information
processing. Here we show that when silicon and nitrogen are simultaneously
introduced into the diamond lattice these defects can strongly interact and
form larger complexes. Nitrogen atoms strongly bind to Si and SiV centers and
complex formation can occur. Using a combination of hybrid density functional
theory (DFT) and group theory, we analyze the electronic structure and provide
various useful physical properties, such as hyperfine structure, quasi-local
vibrational modes, and zero-phonon line, to enable experimental identification
of these complexes. We demonstrate that the presence of substitutional silicon
adjacent to nitrogen significantly shifts the donor level toward the conduction
band, resulting in an activation energy for the SiN center that is comparable
to phosphorus. We also find that the neutral SiNV center is of particular
interest due to its photon emission at $\sim$1530 nm, which falls within the C
band of telecom wavelengths, and its paramagnetic nature. In addition, the
optical transition associated with the SiNV$^0$ color center exhibits very
small electron--phonon coupling (Huang--Rhys factor~=~0.78) resulting in high
quantum efficiency (Debye-Waller factor = 46\%) for single-photon emission.
These features render this new center very attractive for potential application
in scalable quantum telecommunication networks.


  In the absence of natural flavor conservation, multi-Higgs doublet models
generally contain new sources of CP violation and anomalous charged Higgs
Yukawa couplings. We present a charged-Higgs CP violation study of one such
two-Higgs doublet model (2HDM) which treats the top quark differently from the
other quarks. The phenomenological implications for the K-Kbar system and for B
decays differ significantly from those of the standard model (SM) and of the
2HDM's with natural flavor conservation. In particular, the SM phase in this
model could take a wide range of values, and the CP asymmetry in the
"gold-plated" decay mode B to J/psi K_S could be quite different from and even
of opposite sign relative to the SM prediction. A new mechanism is also noted
for generating a large neutron electric dipole moment which is close to the
present experimental limit.


  The purpose of this article is to initiate a study of a class of Lorentz
invariant, yet tractable, Lagrangian Field Theories which may be viewed as an
extension of the Klein-Gordon Lagrangian to many scalar fields in a novel
manner. These Lagrangians are quadratic in the Jacobians of the participating
fields with respect to the base space co-ordinates. In the case of two fields,
real valued solutions of the equations of motion are found and a phenomenon
reminiscent of instanton behaviour is uncovered; an ansatz for a subsidiary
equation which implies a solution of the full equations yields real solutions
in three-dimensional Euclidean space. Each of these is associated with a
spherical harmonic function.


  One of the open problems of the formation and evolution of the nanostructures
on solid surfaces that are driven by particle irradiation is whether all the
nano-patterns can be accounted for by the continuum theory, since the
Cuerno-Barabasi continuum theory explanations have thus far focused mainly on
ripple patterns and rough self-affine scaling. In this article, we extend
continuum theoretical calculations based on the nonlinear Cuerno-Barabasi
theory to regions yet unexplored in the continuum theory literature but
recently shown, in Monte Carlo simulations, to be devoid of ripple patterns. We
obtained results of weighted ion penetrations and for different impingement
angles. Our results show that the balance of sputtering coefficients required
for ripple patterns was never attained in this region, which confirmed that
ripple patterns are indeed absent and that the continuum theory does not fail
as previous studies indicated. In particular our results show a remarkable
agreement between the continuum and the discrete (solid-on-solid) models; by
calculations based on the continuum theory cascade parameters alone, we
obtained the same topographic phase diagram predicted in the recent Monte Carlo
simulation studies.


  Recently, statistical properties of the World-Wide Web have attracted
considerable attention when self-similar regimes have been observed in the
scaling of its link structure. Here we recall a classical model for general
scaling phenomena and argue that it offers an explanation for the World-Wide
Web's scaling exponent when combined with a recent measurement of internet
growth.


  Starting from certain rational varieties blown-up from (P^1)^N, we construct
a tropical, i.e., subtraction-free birational, representation of Weyl groups as
a group of pseudo isomorphisms of the varieties. Furthermore, we develop an
algebro-geometric framework of tau-functions as defining functions of
exceptional divisors on the varieties. In the case where the corresponding root
system is of affine type, our construction yields a class of (higher order)
q-difference Painleve equations and its algebraic degree grows quadratically.


  We give a proof of the Jardine-Tillmann generalized group completion theorem.
It is much in the spirit of the original homology fibration approach by McDuff
and Segal, but follows a modern treatment of homotopy colimits, using as little
simplicial technology as possible. We compare simplicial and topological
definitions of homology fibrations.


  Recently, transformer-based methods have demonstrated impressive results in
various vision tasks, including image super-resolution (SR), by exploiting the
self-attention (SA) for feature extraction. However, the computation of SA in
most existing transformer based models is very expensive, while some employed
operations may be redundant for the SR task. This limits the range of SA
computation and consequently the SR performance. In this work, we propose an
efficient long-range attention network (ELAN) for image SR. Specifically, we
first employ shift convolution (shift-conv) to effectively extract the image
local structural information while maintaining the same level of complexity as
1x1 convolution, then propose a group-wise multi-scale self-attention (GMSA)
module, which calculates SA on non-overlapped groups of features using
different window sizes to exploit the long-range image dependency. A highly
efficient long-range attention block (ELAB) is then built by simply cascading
two shift-conv with a GMSA module, which is further accelerated by using a
shared attention mechanism. Without bells and whistles, our ELAN follows a
fairly simple design by sequentially cascading the ELABs. Extensive experiments
demonstrate that ELAN obtains even better results against the transformer-based
SR models but with significantly less complexity. The source code can be found
at https://github.com/xindongzhang/ELAN.


  Visual object tracking is an important task that requires the tracker to find
the objects quickly and accurately. The existing state-ofthe-art object
trackers, i.e., Siamese based trackers, use DNNs to attain high accuracy.
However, the robustness of visual tracking models is seldom explored. In this
paper, we analyze the weakness of object trackers based on the Siamese network
and then extend adversarial examples to visual object tracking. We present an
end-to-end network FAN (Fast Attack Network) that uses a novel drift loss
combined with the embedded feature loss to attack the Siamese network based
trackers. Under a single GPU, FAN is efficient in the training speed and has a
strong attack performance. The FAN can generate an adversarial example at 10ms,
achieve effective targeted attack (at least 40% drop rate on OTB) and
untargeted attack (at least 70% drop rate on OTB).


  Automatic lesion analysis is critical in skin cancer diagnosis and ensures
effective treatment. The computer aided diagnosis of such skin cancer in
dermoscopic images can significantly reduce the clinicians workload and help
improve diagnostic accuracy. Although researchers are working extensively to
address this problem, early detection and accurate identification of skin
lesions remain challenging. This research focuses on a two step framework for
skin lesion segmentation followed by classification for lesion analysis. We
explored the effectiveness of deep convolutional neural network based
architectures by designing an encoder-decoder architecture for skin lesion
segmentation and CNN based classification network. The proposed approaches are
evaluated quantitatively in terms of the Accuracy, mean Intersection over Union
and Dice Similarity Coefficient. Our cascaded end to end deep learning based
approach is the first of its kind, where the classification accuracy of the
lesion is significantly improved because of prior segmentation.


  We present the results of a new spectroscopic study of Fe K-band absorption
in Active Galactic Nuclei (AGN). Using data obtained from the Suzaku public
archive we have performed a statistically driven blind search for Fe XXV Hea
and/or Fe XXVI Lyb absorption lines in a large sample of 51 type 1.0-1.9 AGN.
Through extensive Monte Carlo simulations we find statistically significant
absorption is detected at E>6.7 keV in 20/51 sources at the P(MC)>95% level,
which corresponds to ~40% of the total sample. In all cases, individual
absorption lines are detected independently and simultaneously amongst the two
(or three) available XIS detectors which confirms the robustness of the line
detections. The most frequently observed outflow phenomenology consists of two
discrete absorption troughs corresponding to Fe XXV Hea and Fe XXVI Lyb at a
common velocity shift. From xstar fitting the mean column density and
ionisation parameter for the Fe K absorption components are log(NH/cm^{-2})~23
and log(xi/erg cm s^{-1})~4.5, respectively. Measured outflow velocities span a
continuous range from <1,500 km/s up to ~100,000 km/s, with mean and median
values of ~0.1c and ~0.056c, respectively. The results of this work are
consistent with those recently obtained using XMM-Newton and independently
provides strong evidence for the existence of very highly-ionised circumnuclear
material in a significant fraction of both radio-quiet and radio-loud AGN in
the local universe.


  We consider an underlay cognitive radio network where the secondary user (SU)
harvests energy from the environment. We consider a slotted-mode of operation
where each slot of SU is used for either energy harvesting or data
transmission. Considering block fading with memory, we model the energy arrival
and fading processes as a stationary Markov process of first order. We propose
a harvest-or-transmit policy for the SU along with optimal transmit powers that
maximize its expected throughput under three different settings. First, we
consider a learning-theoretic approach where we do not assume any apriori
knowledge about the underlying Markov processes. In this case, we obtain an
online policy using Q-learning. Then, we assume that the full statistical
knowledge of the governing Markov process is known apriori. Under this
assumption, we obtain an optimal online policy using infinite horizon
stochastic dynamic programming. Finally, we obtain an optimal offline policy
using the generalized Benders decomposition algorithm. The offline policy
assumes that for a given time deadline, the energy arrivals and channel states
are known in advance at all the transmitters. Finally, we compare all policies
and study the effects of various system parameters on the system performance.


  A compact complex manifold $X$ is called elliptically connected if any pair
of points in $X$ can be connected by a chain of elliptic or rational curves. We
prove that the fundamental group of an elliptically connected compact complex
surface is almost abelian. This confirms a conjecture which states that the
fundamental group of an elliptically connected K\"ahler manifold must be almost
abelian.


  Strategies for extracting CKM phases from non-leptonic $B$ decays are
reviewed briefly. Both general aspects and some recent developments including
CP-violating asymmetries, the $B_s$ system in light of a possible width
difference $\Delta\Gamma_s$, and triangle relations among $B$ decay amplitudes
are discussed. The role of electroweak penguins in these strategies is
illustrated briefly.


  Term weighting schemes are widely used in Natural Language Processing and
Information Retrieval. In particular, term weighting is the basis for keyword
extraction. However, there are relatively few evaluation studies that shed
light about the strengths and shortcomings of each weighting scheme. In fact,
in most cases researchers and practitioners resort to the well-known tf-idf as
default, despite the existence of other suitable alternatives, including
graph-based models. In this paper, we perform an exhaustive and large-scale
empirical comparison of both statistical and graph-based term weighting methods
in the context of keyword extraction. Our analysis reveals some interesting
findings such as the advantages of the less-known lexical specificity with
respect to tf-idf, or the qualitative differences between statistical and
graph-based methods. Finally, based on our findings we discuss and devise some
suggestions for practitioners. Source code to reproduce our experimental
results, including a keyword extraction library, are available in the following
repository: https://github.com/asahi417/kex


  Modern problems in magnetization dynamics require more and more the numerical
determination of the spin-wave spectra and -dispersion in magnetic systems
where analytic theories are not yet available. Micromagnetic simulations can be
used to compute the spatial profiles and oscillation frequencies of spin-waves
in magnetic system with almost arbitrary geometry and different magnetic
interactions. Although numerical approaches are very versatile, they often do
not give the same insight and physical understanding as analytical theories.
For example, it is not always possible to decide whether a certain feature
(such as dispersion asymmetry, for example) is governed by one magnetic
interaction or the other. Moreover, since numerical approaches typically yield
the normal modes of the system, it is not always feasible to disentangle
hybridized modes. In this manuscript, we build a bridge between numerics and
analytics by presenting a methodology to calculate the individual contributions
to general spin-wave dispersions in a fully numerical manner. We discuss the
general form of any spin-wave dispersion in terms of the effective (stiffness)
fields produced by the modes. Based on a special type of micromagnetic
simulations, the numerical dynamic-matrix approach, we show how to calculate
each stiffness field in the respective dispersion law, separately for each
magnetic interaction. In particular, it becomes possible to disentangle
contributions of different magnetic interactions to the dispersion asymmetry in
systems where non-reciprocity is present. At the same time, dipolar-hybridized
modes can be easily disentangled. Since this method is independent of the
geometry or the involved magnetic interactions at hand, we believe it is
attractive for experimental and theoretical studies of magnetic systems where
there are no analytics yet, but also to aid the development of new analytical
theories.


  We compute the equivariant $K$-theory $K_G^*(G)$ for a simply connected Lie
group $G$ (acting on itself by conjugation). We prove that $K_G^*(G)$ is
isomorphic to the algebra of Grothendieck differentials on the representation
ring. We also study a special example of a non-simply connected Lie group $G$,
namely PSU(3), and compute the corresponding equivariant $K$-theory.


  Despite the remarkable performance of deep learning methods on various tasks,
most cutting-edge models rely heavily on large-scale annotated training
examples, which are often unavailable for clinical and health care tasks. The
labeling costs for medical images are very high, especially in medical image
segmentation, which typically requires intensive pixel/voxel-wise labeling.
Therefore, the strong capability of learning and generalizing from limited
supervision, including a limited amount of annotations, sparse annotations, and
inaccurate annotations, is crucial for the successful application of deep
learning models in medical image segmentation. However, due to its intrinsic
difficulty, segmentation with limited supervision is challenging and specific
model design and/or learning strategies are needed. In this paper, we provide a
systematic and up-to-date review of the solutions above, with summaries and
comments about the methodologies. We also highlight several problems in this
field, discussed future directions observing further investigations.


  We present an interstellar medium and stellar population analysis of three
spectroscopically confirmed $z>7$ galaxies in the ERO JWST NIRCam and JWST
NIRSpec data of the SMACS J0723.3-7327 cluster. We use the Bayesian spectral
energy distribution (SED) fitting code \texttt{Prospector} with a flexible
star-formation history (SFH), a variable dust attenuation law, and a
self-consistent model of nebular emission (continuum and emission lines).
Importantly, we self-consistently fit both the emission line fluxes from JWST
NIRSpec and the broad-band photometry from JWST NIRCam, taking into account
slit-loss effects. We find that these three $z=7.6-8.5$ galaxies
($M_{\star}\approx10^{8}~M_{\odot}$) are young with rising SFHs and
mass-weighted ages of $3-4$ Myr, though we find indications for underlying
older stellar populations. The inferred gas-phase metallicities broadly agree
with the direct metallicity estimates from the auroral lines. The galaxy with
the lowest gas-phase metallicity ($\mathrm{Z}_{\rm
gas}=0.06~\mathrm{Z}_{\odot}$) has a steeply rising SFH, is very compact
($<0.2~\mathrm{kpc}$) and has a high star-formation rate surface density
($\Sigma_{\rm
SFR}\approx22~\mathrm{M}_{\odot}~\mathrm{yr}^{-1}~\mathrm{kpc}^{-2}$),
consistent with rapid gas accretion. The two other objects with higher
gas-phase metallicity show more complex multi-component morphologies on kpc
scales, indicating that their recent increase in star-formation rate is driven
by mergers or internal, gravitational instabilities. We discuss effects of
assuming different SFH priors or only fitting the photometric data. Our
analysis highlights the strength and importance of combining JWST imaging and
spectroscopy for fully assessing the nature of galaxies at the earliest epochs.


  Motivated by a problem in additive Ramsey theory, we extend Todorcevic's
partitions of three-dimensional combinatorial cubes to handle additional
three-dimensional objects. As a corollary, we get that if the continuum
hypothesis fails, then for every Abelian group $G$ of size $\aleph_2$, there
exists a coloring $c:G\rightarrow\mathbb Z$ such that for every uncountable
$X\subseteq G$ and every integer $k$, there are three distinct elements $x,y,z$
of $X$ such that $c(x+y+z)=k$.


  Generally, convolutional neural networks (CNNs) process data on a regular
grid, e.g. data generated by ordinary cameras. Designing CNNs for sparse and
irregularly spaced input data is still an open research problem with numerous
applications in autonomous driving, robotics, and surveillance. In this paper,
we propose an algebraically-constrained normalized convolution layer for CNNs
with highly sparse input that has a smaller number of network parameters
compared to related work. We propose novel strategies for determining the
confidence from the convolution operation and propagating it to consecutive
layers. We also propose an objective function that simultaneously minimizes the
data error while maximizing the output confidence. To integrate structural
information, we also investigate fusion strategies to combine depth and RGB
information in our normalized convolution network framework. In addition, we
introduce the use of output confidence as an auxiliary information to improve
the results. The capabilities of our normalized convolution network framework
are demonstrated for the problem of scene depth completion. Comprehensive
experiments are performed on the KITTI-Depth and the NYU-Depth-v2 datasets. The
results clearly demonstrate that the proposed approach achieves superior
performance while requiring only about 1-5% of the number of parameters
compared to the state-of-the-art methods.


  Molecular dynamics simulations frequently employ periodic boundary conditions
where the positions of the periodic images are manipulated in order to apply
deformation to the material sample. For example, Lees-Edwards conditions use
moving periodic images to apply simple shear. Here, we examine the problem of
precisely comparing this type of simulation to continuum solid mechanics. We
employ a hypoelastoplastic mechanical model, and develop a projection method to
enforce quasistatic equilibrium. We introduce a simulation framework that uses
a fixed Cartesian computational grid on a reference domain, and which imposes
deformation via a time-dependent coordinate transformation to the physical
domain. As a test case for our method, we consider the evolution of shear bands
in a bulk metallic glass using the shear transformation zone theory of
amorphous plasticity. We examine the growth of shear bands in simple shear and
pure shear conditions as a function of the initial preparation of the bulk
metallic glass.


  We show that recently proposed linear sigma models with torsion can be
obtained from unconventional branches of conventional gauge theories. This
observation puts models with log interactions on firm footing. If non-anomalous
multiplets are integrated out, the resulting low-energy theory involves log
interactions of neutral fields. For these cases, we find a sigma model geometry
which is both non-toric and includes brane sources. These are heterotic sigma
models with branes. Surprisingly, there are massive models with compact complex
non-Kahler target spaces, which include brane/anti-brane sources. The simplest
conformal models describe wrapped heterotic NS5-branes. We present examples of
both types.


  While the one-Cooper pair problem is now a textbook exercise, the energy of
two pairs of electrons with opposite spins and zero total momentum has not been
derived yet, the exact handling of Pauli blocking between bound pairs being not
that easy for N=2 already. The two-Cooper pair problem however is quite
enlightening to understand the very peculiar role played by the Pauli exclusion
principle in superconductivity. Pauli blocking is known to drive the change
from 1 to $N$ pairs, but no precise description of this continuous change has
been given so far. Using Richardson procedure, we here show that Pauli blocking
increases the free part of the two-pair ground state energy, but decreases the
binding part when compared to two isolated pairs - the excitation gap to break
a pair however increasing from one to two pairs. When extrapolated to the dense
BCS regime, the decrease of the pair binding while the gap increases strongly
indicates that, at odd with common belief, the average pair binding energy
cannot be of the order of the gap.


  We propose a novel class of Edge-Based Interface-Tracking (EBIT) methods in
the field of multiphase flows for advecting the interface. The position of the
interface is tracked by marker points located on the edges of the underlying
grid, making the method flexible with respect to the choice of spatial
discretization and suitable for parallel computation. In this paper we present
a simple EBIT method based on two-dimensional Cartesian grids and on a linear
interface representation.


  Within the framework of six-dimensional ${\cal N}=(1,0)$ conformal
supergravity, we introduce new off-shell multiplets ${\cal O}{}^{*}(n)$, where
$n=3,4,\dots,$ and use them to construct higher-rank extensions of the linear
multiplet action. The ${\cal O}{}^{*}(n)$ multiplets may be viewed as being
dual to well-known superconformal ${\cal O}(n)$ multiplets. We provide
prepotential formulations for the ${\cal O}(n)$ and ${\cal O}{}^{*}(n)$
multiplets coupled to conformal supergravity. For every ${\cal O}{}^{*}(n)$
multiplet, we construct a higher derivative invariant which is superconformal
on arbitrary superconformally flat backgrounds. We also show how our results
can be used to construct new higher derivative actions in supergravity.


  We constrain the amplitude of primordial non-Gaussianity in the CMB data
taking into account the presence of foreground residuals in the maps. We
generalise the needlet bispectrum estimator marginalizing over the amplitudes
of thermal dust, free-free and synchrotron templates. We apply our procedure to
WMAP 5 year data, finding fNL= 38\pm 47 (1 \sigma), while the analysis without
marginalization provides fNL= 35\pm 42. Splitting the marginalization over each
foreground separately, we found that the estimates of fNL are positively cross
correlated of 17%, 12% with the dust and synchrotron respectively, while a
negative cross correlation of about -10% is found for the free-free component.


  A fundamental problem in quantum information is to explore the roles of
different quantum correlations in a quantum information procedure. Recent work
[Phys. Rev. Lett., 107 (2011) 080401] shows that the protocol for assisted
optimal state discrimination (AOSD) may be implemented successfully without
entanglement, but with another correlation, quantum dissonance. However, both
the original work and the extension to discrimination of $d$ states [Phys. Rev.
A, 85 (2012) 022328] have only proved that entanglement can be absent in the
case with equal a \emph{priori} probabilities. By improving the protocol in
[Sci. Rep., 3 (2013) 2134], we investigate this topic in a simple case to
discriminate three nonorthogonal states of a qutrit, with positive real
overlaps. In our procedure, the entanglement between the qutrit and an
auxiliary qubit is found to be completely unnecessary. This result shows that
the quantum dissonance may play as a key role in optimal state discrimination
assisted by a qubit for more general cases.


  The unresolved transition array (UTA) of iron M-shell ions is a prominent
absorption feature in the X-ray spectrum of many active galactic nuclei (AGNs).
Modeling photoionized plasmas in attempt to match the observed silicon and
oxygen lines fail to predict the level of ionization of iron as inferred by
this feature. It is suggested that the discrepancy is due to underestimation of
the low-temperature dielectronic recombination rates for iron M-shell ions.
Modified ionization balance calculations, based on new (guessed) atomic data,
support this idea. The results are shown and compared to the global properties
of several observed UTAs. Implications for AGN absorbing gas are discussed
including an analysis of the ionization parameter distribution in such sources.
The need for real calculations of such atomic data is stressed.


  We establish Schauder a priori estimates and regularity for solutions to a
class of boundary-degenerate elliptic linear second-order partial differential
equations. Furthermore, given a smooth source function, we prove regularity of
solutions up to the portion of the boundary where the operator is degenerate.
Degenerate-elliptic operators of the kind described in our article appear in a
diverse range of applications, including as generators of affine diffusion
processes employed in stochastic volatility models in mathematical finance,
generators of diffusion processes arising in mathematical biology, and the
study of porous media.


  Drift instabilities contribute to the formation of edge turbulence and zonal
flows, and thus the anomalous transport in tokamaks. Experiments often found
micro-scale tur- bulence strongly coupled with large-scale magnetohydrodynamic
(MHD) processes, whereas a general framework has been lacking that can cover
both regimes, in partic- ular, their coupling. In this paper, the linear
resistive drift wave instability (DWI) is investigated using a full 2-fluid MHD
model, as well as its numerical implementation in NIMROD code. Both analytical
and numerical analyses reveal a macro-scale global drift wave eigenmode coupled
with MHD dynamics and illustrate a non-monotonic dispersion relation with
respect to both perpendicular and parallel wavenumbers. NIMROD results also
reveal an edge-localized behavior in the radial mode structure as the azimuthal
mode number increases, implying the dependence of the 2-fluid ef- fects due to
the inhomogeneous density profile. The edge-localization introduces a
non-trivial dependence of the effective perpendicular wavenumber to the
perpendicular mode number, which may explain the quantitative difference
between the global dispersion relation and its local approximation from the
conventional local theory.


  Let $G$ be a group with a non-elementary action on a proper CAT(0) space $X$,
and let $\mu$ be a measure on $G$ such that the random walk $(Z_n)_n$ generated
by $\mu$ has finite second moment on $X$. Let $o$ be a basepoint in $X$, and
assume that there exists a rank one isometry in $G$. We prove that in this
context, $(Z_n o )_n$ satisfies a Central Limit Theorem, namely that the random
variables $\frac{1}{\sqrt{n}}(d(Z_n o, o) - n \lambda) $ converge in law to a
non-degenerate Gaussian distribution $N_\mu$, for $\lambda$ the (positive)
drift of the random walk. The strategy relies on the use of hyperbolic models
introduced by H. Petyt, A. Zalloum and D. Spriano, which are analogues of curve
graphs and cubical hyperplanes for the class of CAT(0) spaces. As a side
result, we prove that the probability that the nth-step $Z_n$ acts on $X$ as a
contracting isometry goes to 1 as $n$ goes to infinity.


  To improve the purity of coal and prevent damage to the coal mining machine,
it is necessary to identify coal and rock in underground coal mines. At the
same time, the mined coal needs to be purified to remove rock and gangue. These
two procedures are manually operated by workers in most coal mines. The
realization of automatic identification and purification is not only conducive
to the automation of coal mines, but also ensures the safety of workers. We
discuss the possibility of using image-based methods to distinguish them. In
order to find a solution that can be used in both scenarios, a model that
forwards image feature extracted by DenseNet to Gaussian process is proposed,
which is trained on images taken on surface and achieves high accuracy on
images taken underground. This indicates our method is powerful in few-shot
learning such as identification of coal and rock/gangue and might be beneficial
for realizing automation in coal mines.


  A stochastic model of a continuous nondemolition observation of a free
quantum Brownian motion is presented. The nonlinear stochastic wave equation
describing the posterior dynamics of the observed quantum system is solved in a
Gaussian case for a free particle of mass m. It is shown that the dispersion of
the wave packet does not increase to infinity like for the free unobserved
particle but tends to the finite limit.


  Chondrules are millimeter-sized silicate spherules ubiquitous in primitive
meteorites, but whose origin remains mysterious. One of the main proposed
mechanisms for producing them is melting of solids in shock waves in the
gaseous protoplanetary disk. However, evidence is mounting that
chondrule-forming regions were enriched in solids well above solar abundances.
Given the high velocities involved in shock models destructive collisions would
be expected between differently sized grains after passage of the shock front
as a result of differential drag. We investigate the probability and outcome of
collisions of particles behind a 1D shock using analytic methods as well as a
full integration of the coupled mass, momentum, energy and radiation equations.
Destruction of protochondrules seems unavoidable for solid/gas ratios $\epsilon
\gtrsim 0.1$, and possibly even for solar abundances because of "sandblasting"
by finer dust. A flow with $\epsilon \gtrsim 10$ requires much smaller shock
velocities ($\sim 2$ vs 8 km s$^{-1}$) in order to achieve chondrule-melting
temperatures, and radiation trapping allows slow cooling of the shocked
fragments. Initial destruction would still be extensive; although re-assembly
of mm-sized particles would naturally occur by grain sticking afterward, the
compositional heterogeneity of chondrules may be difficult to reproduce. We
finally note that solids passing through small-scale bow shocks around
few-km-sized planetesimals might experience partial melting and yet escape
fragmentation.


  : In this paper a method to make inputting electrical model upon factors that
affect melting process of high ultra power(UHP) electric furnace by using fuzzy
rule and regression model is suggested and its effectiveness is verified with
simulation experiment.


  This paper proposes a class of ratio type estimators of finite population
variance, when the population variance of an auxiliary character is known.
Asymptotic expression for mean square error (MSE) is derived and compared with
the mean square errors of some existing estimators. An empirical study is
carried out to illustrate the performance of the constructed estimator over
others.


  We study the wave breaking mechanism for the weakly dispersive defocusing
nonlinear Schroedinger (NLS) equation with a constant phase dark initial datum
that contains a vacuum point at the origin. We prove by means of the exact
solution to the initial value problem that, in the dispersionless limit, the
vacuum point is preserved by the dynamics until breaking occurs at a finite
critical time. In particular, both Riemann invariants experience a simultaneous
breaking at the origin. Although the initial vacuum point is no longer
preserved in the presence of a finite dispersion, the critical behaviour
manifests itself through an abrupt transition occurring around the breaking
time.


  We discuss low-lying collective excitations of $\Lambda$ hypernuclei using
the self-consistent mean-field approaches. We first discuss the deformation
properties of $\Lambda$ hypernuclei in the $sd$-shell region. Based on the
relativistic mean-field (RMF) approach, we show that the oblate deformation for
$^{28}$Si nucleus may disappear when a $\Lambda$ particle is added to this
nucleus. We then discuss the rotational excitations of $^{25}_{\Lambda}$Mg
nucleus using the three-dimensional potential energy surface in the deformation
plane obtained with the Skyrme-Hartree-Fock method. The deformation of
$^{25}_{\Lambda}$Mg nucleus is predicted to be slightly reduced due to an
addition of $\Lambda$ particle. We demonstrate that this leads to a reduction
of electromagnetic transition probability, $B(E2)$, in the ground state
rotational band. We also present an application of random phase approximation
(RPA) to hypernuclei, and show that a new dipole mode, which we call a soft
dipole $\Lambda$ mode, appears in hypernuclei, which can be interpreted as an
oscillation of $\Lambda$ particle against the core nucleus.


  We present the probabilistic stellar mass function (pSMF) of galaxies in the
DESI Bright Galaxy Survey (BGS), observed during the One-Percent Survey. The
One-Percent Survey was one of DESI's survey validation programs conducted from
April to May 2021, before the start of the main survey. It used the same target
selection and similar observing strategy as the main survey and successfully
observed the spectra and redshifts of 143,017 galaxies in the $r < 19.5$
magnitude-limited BGS Bright sample and 95,499 galaxies in the fainter surface
brightness and color selected BGS Faint sample over $z < 0.6$. We derive pSMFs
from posteriors of stellar mass, $M_*$, inferred from DESI photometry and
spectroscopy using the Hahn et al. (2022a; arXiv:2202.01809) PRObabilistic
Value-Added BGS (PROVABGS) Bayesian SED modeling framework. We use a
hierarchical population inference framework that statistically and rigorously
propagates the $M_*$ uncertainties. Furthermore, we include correction weights
that account for the selection effects and incompleteness of the BGS
observations. We present the redshift evolution of the pSMF in BGS as well as
the pSMFs of star-forming and quiescent galaxies classified using average
specific star formation rates from PROVABGS. Overall, the pSMFs show good
agreement with previous stellar mass function measurements in the literature.
Our pSMFs showcase the potential and statistical power of BGS, which in its
main survey will observe >100$\times$ more galaxies. Moreover, we present the
statistical framework for subsequent population statistics measurements using
BGS, which will characterize the global galaxy population and scaling relations
at low redshifts with unprecedented precision.


  To create early warning capabilities for upcoming Space Weather disturbances,
we have selected a dataset of 61 emerging active regions, which allows us to
identify characteristic features in the evolution of acoustic power density to
predict continuum intensity emergence. For our study, we have utilized Doppler
shift and continuum intensity observations from the Helioseismic and Magnetic
Imager (HMI) onboard the Solar Dynamics Observatory (SDO). The local tracking
of 30.66 x 30.66-degree patches in the vicinity of active regions allowed us to
trace the evolution of active regions starting from the pre-emergence state. We
have developed a machine learning model to capture the acoustic power flux
density variations associated with upcoming magnetic flux emergence. The
trained Long Short-Term Memory (LSTM) model is able to predict 5 hours ahead
whether, in a given area of the solar surface, continuum intensity values will
decrease. The performed study allows us to investigate the potential of the
machine learning approach to predict the emergence of active regions using
acoustic power maps as input.


  In this paper, we consider the global well-posedness of the defocusing,
$L^{2}$ - critical nonlinear Schr{\"o}dinger equation in dimensions $n \geq 3$.
Using the I-method, we show the problem is globally well-posed in $n = 3$ when
$s > {2/5}$, and when $n \geq 4$, for $s > \frac{n - 2}{n}$. We combine energy
increments for the I-method, interaction Morawetz estimates, and almost
Morawetz estimates to prove the result.


  We introduce a new type of random walk where the definition of edge
reinforcement is very different from the one in the reinforced random walk
models studied so far, and investigate its basic properties, such as
null/positive recurrence, transience, and speed. Two basic cases will be dubbed
"impatient" and"ageing" random walks.


  High-dimensional always-changing environments constitute a hard challenge for
current reinforcement learning techniques. Artificial agents, nowadays, are
often trained off-line in very static and controlled conditions in simulation
such that training observations can be thought as sampled i.i.d. from the
entire observations space. However, in real world settings, the environment is
often non-stationary and subject to unpredictable, frequent changes. In this
paper we propose and openly release CRLMaze, a new benchmark for learning
continually through reinforcement in a complex 3D non-stationary task based on
ViZDoom and subject to several environmental changes. Then, we introduce an
end-to-end model-free continual reinforcement learning strategy showing
competitive results with respect to four different baselines and not requiring
any access to additional supervised signals, previously encountered
environmental conditions or observations.


  It is well known that without randomization, Byzantine agreement (BA)
requires a linear number of rounds in the synchronous setting, while it is flat
out impossible in the asynchronous setting. The primitive which allows to
bypass the above limitation is known as oblivious common coin (OCC). It allows
parties to agree with constant probability on a random coin, where agreement is
oblivious, i.e., players are not aware whether or not agreement has been
achieved.
  The starting point of our work is the observation that no known protocol
exists for information-theoretic multi-valued OCC with optimal resiliency in
the asynchronous setting (with eventual message delivery). This apparent hole
in the literature is particularly problematic, as multi-valued OCC is
implicitly or explicitly used in several constructions.
  In this paper, we present the first information-theoretic multi-valued OCC
protocol in the asynchronous setting with optimal resiliency, i.e., tolerating
$t < n/3$ corruptions, thereby filling this important gap. Further, our
protocol efficiently implements OCC with an exponential-size domain, a property
which is not even achieved by known constructions in the simpler, synchronous
setting.
  We then turn to the problem of round-preserving parallel composition of
asynchronous BA. A protocol for this task was proposed by Ben-Or and El-Yaniv
[Distributed Computing '03]. Their construction, however, is flawed in several
ways. Thus, as a second contribution, we provide a simpler, more modular
protocol for the above task. Finally, and as a contribution of independent
interest, we provide proofs in Canetti's Universal Composability framework;
this makes our work the first one offering composability guarantees, which are
important as BA is a core building block of secure multi-party computation
protocols.


  In this paper, visible light positioning (VLP) where the receiver adopts a
commercial image sensor is considered. We firstly analyze the theoretical
limits and error source of the VLP system using image sensor. And then, we
develop a VLP positioning model on the receiver movement and further propose
two novel error calibration algorithms, namely Rotation Calibration Method and
dispersion circle calibration method. The rotation algorithm estimates the
rotation center in the image instead of treating the image center as the
rotation center, leading to reduced positioning error. For the dispersion
circle, it can offset the shift error created by the conversation between
different coordinate during the positioning calculation. According to the
experimental results, the average positioning error of the proposed methods can
be reduced to 0.82cm, which achieve state-of-the-art in the VLP field.


  We describe key elements of the perturbative similarity renormalization group
procedure for Hamiltonians using two, third-order examples: phi^3 interaction
term in the Hamiltonian of scalar field theory in 6 dimensions and triple-gluon
vertex counterterm in the Hamiltonian of QCD in 4 dimensions. These examples
provide insight into asymptotic freedom in Hamiltonian approach to quantum
field theory. The renormalization group procedure also suggests how one may
obtain ultraviolet-finite effective Schr\"odinger equations that correspond to
the asymptotically free theories, including transition from quark and gluon to
hadronic degrees of freedom in case of strong interactions. The dynamics is
invariant under boosts and allows simultaneous analysis of bound state
structure in the rest and infinite momentum frames.


  We derive the usual Oberbeck--Boussinesq approximation as a constitutive
limit of the full system describing the motion of an compressible linearly
viscous fluid. To this end the starting system is written, using the Gibbs free
energy, in the variables $\mathbf v, \theta$ and $p$. The Oberbeck--Boussinesq
system is then obtained as the thermal expansion coefficient $\alpha$ and the
isothermal compressibility coefficient $\beta$ tend to zero.


  Incremental and parallel builds are crucial features of modern build systems.
Parallelism enables fast builds by running independent tasks simultaneously,
while incrementality saves time and computing resources by processing the build
operations that were affected by a particular code change. Writing build
definitions that lead to error-free incremental and parallel builds is a
challenging task. This is mainly because developers are often unable to predict
the effects of build operations on the file system and how different build
operations interact with each other. Faulty build scripts may seriously degrade
the reliability of automated builds, as they cause build failures, and
non-deterministic and incorrect build results.
  To reason about arbitrary build executions, we present buildfs, a
generally-applicable model that takes into account the specification (as
declared in build scripts) and the actual behavior (low-level file system
operation) of build operations. We then formally define different types of
faults related to incremental and parallel builds in terms of the conditions
under which a file system operation violates the specification of a build
operation. Our testing approach, which relies on the proposed model, analyzes
the execution of single full build, translates it into buildfs, and uncovers
faults by checking for corresponding violations.
  We evaluate the effectiveness, efficiency, and applicability of our approach
by examining hundreds of Make and Gradle projects. Notably, our method is the
first to handle Java-oriented build systems. The results indicate that our
approach is (1) able to uncover several important issues (245 issues found in
45 open-source projects have been confirmed and fixed by the upstream
developers), and (2) orders of magnitude faster than a state-of-the-art tool
for Make builds.


  YbInCu$_4$ undergoes a first order structural phase transition near $T_v$=40
K associated with an abrupt change of Yb valence state. We perform ultrafast
pump-probe measurement on YbInCu$_4$ and find that the expected heavy fermion
properties arising from the \emph{c-f} hybridization exist only in a limited
temperature range above $T_v$. Below $T_v$, the compound behaves like a normal
metal though a prominent hybridization energy gap is still present in infrared
measurement. We elaborate that those seemingly controversial phenomena could be
well explained by assuming that the Fermi level suddenly shifts up and becomes
far away from the flat \emph{f}-electron band as well as the indirect
hybridization energy gap in the mixed valence state below $T_v$.


  An ensemble of cosmological models based on generalized BF-theory is
constructed where the role of vacuum (zero-level) coupling constants is played
by topologically invariant rational intersection forms (cosmological-constant
matrices) of 4-dimensional plumbed V-cobordisms which are interpreted as
Euclidean spacetime regions. For these regions describing topology changes, the
rational and integer intersection matrices are calculated. A relation is found
between the hierarchy of certain elements of these matrices and the hierarchy
of coupling constants of the universal (low-energy) interactions.
  PACS numbers: 0420G, 0240, 0460


  Time-of-flight (TOF) cameras are based on a new technology that delivers
distance maps by the use of a modulated light source. In this paper we first
describe a set of experiments that we performed with TOF cameras. We then
propose a noise model which is able to explain some of the phenomena observed
in the experiments. The model is based on assuming a noise source that is
correlated with the light source (shot noise) and an additional additive noise
source (dark current noise). The model predicts well the dependency of the
distance errors on the image intensity and the true distance at an individual
pixel.


  A Herglotz function is a holomorphic map from the open complex unit disk into
the closed complex right halfplane. A classical Herglotz function has an
integral representation against a positive measure on the unit circle. We prove
a free analytic analogue of the Herglotz representation and describe how our
representations specialize to the free probabilistic case. We also show that
the set of representable Herglotz functions arising from noncommutative
conditional expectations must be closed in a natural topology.


  A distributed semantic graph processing system that provides locality
control, indexing, graph query, and parallel processing capabilities is
presented.


  We consider a 2D ballistic and quasi-ballistic structures with
spin-orbit-related splitting of the electron spectrum. The ballistic region is
attached to the leads with a voltage applied between them. We calculate the
edge spin density which arises in the presence of a charge current through the
structure. We solve the problem with the use of the method of scattering states
and clarify the important role of the unitarity of scattering. In the case of a
straight boundary it leads to exact cancellation of long-wavelength
oscillations of the spin density. In general, however, the smooth spin
oscillations with the spin precession length may arise, as it happens, e.g.,
for the wiggly boundary. Moreover, we show that the result crucially depends on
the form of the spin-orbit Hamiltonian.


  The production of heavy quarks in pp collisions at the LHC energies provides
a reliable test of perturbative QCD calculations. Comparisons of pp and $\pPb$
measurements of their hadronization products allow us to investigate how cold
nuclear matter effects affect the heavy-quark production. We present ALICE
measurements of azimuthal correlations of prompt D mesons with charged hadrons
in pp collisions at $\s$ = 7 TeV and $\pPb$ collisions at $\sNN$ = 5.02 TeV. We
also show the per-event D-meson yields as a function of the charged-particle
multiplicity in pp collisions at $\s$ = 7 TeV.


  I show that the structure of the firm is not neutral in respect to regulatory
capital budgeted under rules which are based on the Value-at-Risk.


  We report results on an elastic cross section measurement in proton-proton
collisions at a center-of-mass energy $\sqrt{s}=510$ GeV, obtained with the
Roman Pot setup of the STAR experiment at the Relativistic Heavy Ion Collider
(RHIC). The elastic differential cross section is measured in the four-momentum
transfer squared range $0.23 \leq -t \leq 0.67$ GeV$^2$. We find that a
constant slope $B$ does not fit the data in the aforementioned $t$ range, and
we obtain a much better fit using a second-order polynomial for $B(t)$. The $t$
dependence of $B$ is determined using six subintervals of $t$ in the STAR
measured $t$ range, and is in good agreement with the phenomenological models.
The measured elastic differential cross section $\mathrm{d}\sigma/\mathrm{dt}$
agrees well with the results obtained at $\sqrt{s} = 546$ GeV for
proton--antiproton collisions by the UA4 experiment. We also determine that the
integrated elastic cross section within the STAR $t$-range is
$\sigma^\mathrm{fid}_\mathrm{el} = 462.1 \pm 0.9 (\mathrm{stat.}) \pm 1.1
(\mathrm {syst.}) \pm 11.6 (\mathrm {scale})$~$\mu\mathrm{b}$.


  Autonomous terrain classification is an important problem in planetary
navigation, whether the goal is to identify scientific sites of interest or to
traverse treacherous areas safely. Past Martian rovers have relied on human
operators to manually identify a navigable path from transmitted imagery. Our
goals on Mars in the next few decades will eventually require rovers that can
autonomously move farther, faster, and through more dangerous
landscapes--demonstrating a need for improved terrain classification for
traversability. Autonomous navigation through extreme environments will enable
the search for water on the Moon and Mars as well as preparations for human
habitats. Advancements in machine learning techniques have demonstrated
potential to improve terrain classification capabilities for ground vehicles on
Earth. However, classification results for space applications are limited by
the availability of training data suitable for supervised learning methods.
This paper contributes an open source automatic data processing pipeline that
uses camera geometry to co-locate Curiosity and Perseverance Mastcam image
products with Mars overhead maps via ray projection over a terrain model. In
future work, this automated data processing pipeline will be leveraged for
development of machine learning methods for terrain classification.


  Graphene oxide (GO) flakes have been deposited to bridge the gap between two
epitaxial graphene electrodes to produce all-graphene devices. Electrical
measurements indicate the presence of Schottky barriers (SB) at the
graphene/graphene oxide junctions, as a consequence of the band-gap in GO. The
barrier height is found to be about 0.7 eV, and is reduced after annealing at
180 $^\circ$C, implying that the gap can be tuned by changing the degree of
oxidation. A lower limit of the GO mobility was found to be 850 cm$^2$/Vs,
rivaling silicon. {\it In situ} local oxidation of patterned epitaxial graphene
has been achieved.


  We present uniformly measured star formation histories (SFHs) of 40 Local
Group dwarf galaxies based on color-magnitude diagram (CMD) analysis from
archival Hubble Space Telescope imaging. We demonstrate that accurate SFHs can
be recovered from CMDs that do not reach the oldest main sequence turn-off
(MSTO), but emphasize that the oldest MSTO is critical for precisely
constraining the earliest epochs of star formation. We find that: (1) the
average lifetime SFHs of dwarf spheroidals (dSphs) can be approximated by an
exponentially declining SFH with $\tau$ $\sim$ 5 Gyr; (2) lower luminosity
dSphs are less likely to have extended SFHs than more luminous dSphs; (3) the
average SFHs of dwarf irregulars (dIrrs), transition dwarfs (dTrans), and dwarf
ellipticals (dEs) can be approximated by the combination of an exponentially
declining SFH ($\tau$ $\sim$ 3-4 Gyr) for lookback ages $>$ 10-12 Gyr ago and a
constant SFH thereafter; (4) the observed fraction of stellar mass formed prior
to z=2 ranges considerably (80\% for galaxies with M $<$ 10$^5$ M$_{\odot}$ to
30\% for galaxies with M$>$10$^7$ M$_{\odot}$) and is largely explained by
environment; (5) the distinction between "ultra-faint" and "classical" dSphs is
arbitrary; (6) LG dIrrs formed a significantly higher fraction of stellar mass
prior to z=2 than the SDSS galaxies from Leiter 2012 and the SFHs from the
abundance matching models of Behroozi et al. 2013. This may indicate higher
than expected star-formation efficiencies at early times in low mass galaxies.
Finally, we provide all the SFHs in tabulated electronic format for use by the
community.


  Boson sampling can simulate physical problems for which classical simulations
are inefficient. However, not all problems simulated by boson sampling are
classically intractable. We consider a situation in which it is known that the
outcome from boson sampling is sparse. It can be determined from a few marginal
distributions which are classically calculable. Still, recovering of the joint
distribution can be of high complexity. We show classically efficient methods
of the recovery assuming high sparsity of the joint distribution. Various
extensions are discussed including a version involving quantum annealing.


  In this paper we introduce and study a new feature-preserving nonlinear
anisotropic diffusion for denoising signals. The proposed partial differential
equation is based on a novel diffusivity coefficient that uses a nonlocal
automatically detected parameter related to the local bounded variation and the
local oscillating pattern of the noisy input signal. We provide a mathematical
analysis of the existence of the solution of our nonlinear and nonlocal
diffusion equation in the two dimensional case (images processing). Finally, we
propose a numerical scheme with some numerical experiments which demonstrate
the effectiveness of the new method.


  This paper considers the problem of representative selection: choosing a
subset of data points from a dataset that best represents its overall set of
elements. This subset needs to inherently reflect the type of information
contained in the entire set, while minimizing redundancy. For such purposes,
clustering may seem like a natural approach. However, existing clustering
methods are not ideally suited for representative selection, especially when
dealing with non-metric data, where only a pairwise similarity measure exists.
In this paper we propose $\delta$-medoids, a novel approach that can be viewed
as an extension to the $k$-medoids algorithm and is specifically suited for
sample representative selection from non-metric data. We empirically validate
$\delta$-medoids in two domains, namely music analysis and motion analysis. We
also show some theoretical bounds on the performance of $\delta$-medoids and
the hardness of representative selection in general.


  As the world's largest professional network, LinkedIn wants to create
economic opportunity for everyone in the global workforce. One of its most
critical missions is matching jobs with processionals. Improving job targeting
accuracy and hire efficiency align with LinkedIn's Member First Motto. To
achieve those goals, we need to understand unstructured job postings with noisy
information. We applied deep transfer learning to create domain-specific job
understanding models. After this, jobs are represented by professional
entities, including titles, skills, companies, and assessment questions. To
continuously improve LinkedIn's job understanding ability, we designed an
expert feedback loop where we integrated job understanding models into
LinkedIn's products to collect job posters' feedback. In this demonstration, we
present LinkedIn's job posting flow and demonstrate how the integrated deep job
understanding work improves job posters' satisfaction and provides significant
metric lifts in LinkedIn's job recommendation system.


  A simple spin coating approach is introduced to fabricate Yttria-Stabilized
Zirconia (YSZ) electrolyte layer on NiO-YSZ anode substrate for solid oxide
fuel cell applications. Heating the substrate during the slurry spin coating
process enhances the affected area through the layer and leads to optimize the
compacting amount of coated electrolyte particles before sintering procedure.
On top of this bilayer substrate, LCFN is deposited as an intermediate
temperature solid oxide fuel cell cathode, in order to have suitable output
performances at intermediate temperature ranges. Applying humidified hydrogen
as fuel and ambient air as the oxidant, cell performance characterizations
exhibit higher power outputs for prepared sample. Electrochemical Impedance
Spectroscopy measurements on single cells also demonstrate that both
interfacial and Ohmic resistances are reduced. These results along with
Scanning Electron Microscope micrographs reveal that the density of the YSZ
layer which is coated by the suggested approach is better than the usual slurry
spin coating method.


  We present a variational augmentation procedure to optimize the exponents of
Gaussian continuum basis sets for simulating strong-field laser ionization
phenomena such as higher harmonic generation (HHG) in atoms and ions using the
time-dependent configuration interaction (TDCI) method. We report the
distribution of the optimized exponents and discuss how efficiently the
resulting basis functions span the variational space to describe the
near-continuum states involved in HHG. Further, we calculated the higher
harmonic spectra of three two-electron systems -- H$^{-}$, He and Li$^{+}$ --
generated by 800nm driving laser-pulses with pulse-width of 54fs and peak
intensities in the tunnel ionization regime of each system. We analyze the
performance of these basis sets with an increasing number of higher angular
momentum functions and show that up to $g$-type functions are required to
obtain qualitatively accurate harmonic spectra. Additionally, we also comment
on the impact of electron correlation on the HHG spectra. Finally, we show that
by systematically augmenting additional shells we model the strong-field
dynamics at higher laser peak intensities.


  A phenomenon analogous to the conical refraction well-known in the
crystalooptics and crystaloacoustics is considered for the
magnetohydrodynamical waves in a collisionless plasma with anisotropic thermal
pressure. Imposing the most general (generalization of Tsiklauri, 1996, Phys.
Plasmas, 3, 800) condition for the existence of the phenomenon, angle of the
conical refraction is calculated which appeared to be dependent on the ratio of
the Alfven velocity and sound speed measured in the perpendicular direction in
respect to the external magnetic field. Feasible ways of experimental
demonstration of the phenomenon are discussed and a novelty brought by the
general consideration is outlined.


  This article explores the critical role of statistical analysis in precision
medicine. It discusses how personalized healthcare is enhanced by statistical
methods that interpret complex, multidimensional datasets, focusing on
predictive modeling, machine learning algorithms, and data visualization
techniques. The paper addresses challenges in data integration and
interpretation, particularly with diverse data sources like electronic health
records (EHRs) and genomic data. It also delves into ethical considerations
such as patient privacy and data security. In addition, the paper highlights
the evolution of statistical analysis in medicine, core statistical
methodologies in precision medicine, and future directions in the field,
emphasizing the integration of artificial intelligence (AI) and machine
learning (ML).


  We investigate a class of dark energy models in which the equation of state
undergoes a rapid transition and for which the Hubble SN Ia diagram is known to
be poorly discriminant. Interestingly enough, we find that transitions at high
redshift can lead to distortion in the correlation function of dark matter at
lower redshift. We therefore use a combination of the SN Ia Hubble diagram,
Cosmic Microwave Background data and power spectrum from the Sloan Digital Sky
Survey Luminous Red Galaxies (SDSS LRG) to constrain the redshift of a possible
transition. We find that the fundamental cosmological parameters are well
constrained independently of the presence of a transition. Acceptable
transitions from an equation of state close to $w = 0$ to a value close to -1
are strongly rejected at redshifts much higher than those for which Large Scale
Structure and SN Ia data are available: the transition redshift can be rejected
up to a value as high as 10. We conclude that no preference for a transition
appears from present-day data.


  In diffusion MRI (dMRI), a good sampling scheme is important for efficient
acquisition and robust reconstruction. Diffusion weighted signal is normally
acquired on single or multiple shells in q-space. Signal samples are typically
distributed uniformly on different shells to make them invariant to the
orientation of structures within tissue, or the laboratory coordinate frame.
The Electrostatic Energy Minimization (EEM) method, originally proposed for
single shell sampling scheme in dMRI, was recently generalized to multi-shell
schemes, called Generalized EEM (GEEM). GEEM has been successfully used in the
Human Connectome Project (HCP). However, EEM does not directly address the goal
of optimal sampling, i.e., achieving large angular separation between sampling
points. In this paper, we propose a more natural formulation, called Spherical
Code (SC), to directly maximize the minimal angle between different samples in
single or multiple shells. We consider not only continuous problems to design
single or multiple shell sampling schemes, but also discrete problems to
uniformly extract sub-sampled schemes from an existing single or multiple shell
scheme, and to order samples in an existing scheme. We propose five algorithms
to solve the above problems, including an incremental SC (ISC), a sophisticated
greedy algorithm called Iterative Maximum Overlap Construction (IMOC), an 1-Opt
greedy method, a Mixed Integer Linear Programming (MILP) method, and a
Constrained Non-Linear Optimization (CNLO) method. To our knowledge, this is
the first work to use the SC formulation for single or multiple shell sampling
schemes in dMRI. Experimental results indicate that SC methods obtain larger
angular separation and better rotational invariance than the state-of-the-art
EEM and GEEM. The related codes and a tutorial have been released in DMRITool.


  Using the O(4) linear $\sigma$ model, we address the topic of non-equilibrium
relaxation of an inhomogeneous initial configuration due to quantum and thermal
fluctuations. The space-time evolution of an inhomogeneous fluctuation of the
condensate in the isoscalar channel decaying via the emission of pions in the
medium is studied within the context of disoriented chiral condensates. We use
out of equilibrium closed time path methods in field theory combined with the
amplitude expansion. We give explicit expressions for the asymptotic space-time
evolution of an initial inhomogeneous configuration including the contribution
of thresholds at zero and non-zero temperature. At non-zero temperature we find
new relaxational processes due to thermal cuts that have no counterpart in the
homogeneous case. Within the one-loop approximation, we find that the space
time evolution of such inhomogeneous configuration out of equilibrium is
effectively described in terms of a rapidity dependent temperature
$T(\vartheta)=T/\cosh[\vartheta]$ as well as a rapidity dependent decay rate
$\Gamma(\vartheta, T(\vartheta))$. This rate is to be interpreted as the
production minus absorption rate of pions in the medium and approaches the zero
temperature value at large rapidities. An initial configuration localized on a
bounded region spreads and decays in spherical waves with slower relaxational
dynamics at large rapidity.


  Let $H:=-\Delta+V$ be a nonnegative Schr\"odinger operator on $L^2({\bf
R}^N)$, where $N\ge 2$ and $V$ is a radially symmetric function decaying
quadratically at the space infinity. In this paper we consider the
Schr\"odinger heat semigroup $e^{-tH}$, and make a complete table of the decay
rates of the operator norms of $e^{-tH}$ in the Lorentz spaces as $t\to\infty$.


  Although the categorical arithmetic is not effectively axiomatizable, the
belief that the incompleteness Theorems can be apply to it is fairly common.
Furthermore, the so-called "essential" (or "inherent") semantic incompleteness
of the second-order Logic that can be deduced by these same Theorems does not
imply the standard semantic incompleteness that can be derived using the
Loewenheim-Skolem or the compactness Theorem. This state of affairs has its
origins in an incorrect and misinterpreted Goedel's comment at the Koenigsberg
congress of 1930 and has consolidated due to different circumstances. This
paper aims to clear up these questions and proposes an alternative
interpretation for the Goedel's statement.


  Automated machine learning (AutoML) has received increasing attention in the
recent past. While the main tools for AutoML, such as Auto-WEKA, TPOT, and
auto-sklearn, mainly deal with single-label classification and regression,
there is very little work on other types of machine learning tasks. In
particular, there is almost no work on automating the engineering of machine
learning applications for multi-label classification. This paper makes two
contributions. First, it discusses the usefulness and feasibility of an AutoML
approach for multi-label classification. Second, we show how the scope of
ML-Plan, an AutoML-tool for multi-class classification, can be extended towards
multi-label classification using MEKA, which is a multi-label extension of the
well-known Java library WEKA. The resulting approach recursively refines MEKA's
multi-label classifiers, which sometimes nest another multi-label classifier,
up to the selection of a single-label base learner provided by WEKA. In our
evaluation, we find that the proposed approach yields superb results and
performs significantly better than a set of baselines.


  We present a heuristic policy and performance bound for risk-sensitive convex
stochastic control that generalizes linear-exponential-quadratic regulator
(LEQR) theory. Our heuristic policy extends standard, risk-neutral model
predictive control (MPC); however, instead of ignoring uncertain noise terms,
our policy assumes these noise terms turns out either favorably or unfavorably,
depending on a risk aversion parameter. In the risk-seeking case, this modified
planning problem is convex. In the risk-averse case, it requires minimizing a
difference of convex functions, which is done (approximately) using the
convex-concave procedure. In both cases, we obtain a lower bound on the optimal
cost as a by-product of solving the planning problem. We give a numerical
example of controlling a battery to power an uncertain load, and show that our
policy reduces the risk of a very bad outcome (as compared with standard
certainty equivalent control) with negligible impact on the the average
performance.


  The hybrid photon-atom integrated circuits, which include photonic
microcavities and trapped single neutral atom in their evanescent field, are of
great potential for quantum information processing. In this platform, the atoms
provide the single-photon nonlinearity and long-lived memory, which are
complementary to the excellent passive photonics devices in conventional
quantum photonic circuits. In this work, we propose a stable platform for
realizing the hybrid photon-atom circuits based on an unsuspended photonic
chip. By introducing high-order modes in the microring, a feasible
evanescent-field trap potential well $\sim0.3\,\mathrm{mK}$ could be obtained
by only $10\,\mathrm{mW}$-level power in the cavity, compared with
$100\,\mathrm{mW}$-level power required in the scheme based on fundamental
modes. Based on our scheme, stable single atom trapping with relatively low
laser power is feasible for future studies on high-fidelity quantum gates,
single-photon sources, as well as many-body quantum physics based on a
controllable atom array in a microcavity.


  Model distillation has been a popular method for producing interpretable
machine learning. It uses an interpretable "student" model to mimic the
predictions made by the black box "teacher" model. However, when the student
model is sensitive to the variability of the data sets used for training even
when keeping the teacher fixed, the corresponded interpretation is not
reliable. Existing strategies stabilize model distillation by checking whether
a large enough corpus of pseudo-data is generated to reliably reproduce student
models, but methods to do so have so far been developed for a specific student
model. In this paper, we develop a generic approach for stable model
distillation based on central limit theorem for the average loss. We start with
a collection of candidate student models and search for candidates that
reasonably agree with the teacher. Then we construct a multiple testing
framework to select a corpus size such that the consistent student model would
be selected under different pseudo samples. We demonstrate the application of
our proposed approach on three commonly used intelligible models: decision
trees, falling rule lists and symbolic regression. Finally, we conduct
simulation experiments on Mammographic Mass and Breast Cancer datasets and
illustrate the testing procedure throughout a theoretical analysis with Markov
process. The code is publicly available at
https://github.com/yunzhe-zhou/GenericDistillation.


  We investigate stabilizer codes with carrier qudits of equal dimension $D$,
an arbitrary integer greater than 1. We prove that there is a direct relation
between the dimension of a qudit stabilizer code and the size of its
corresponding stabilizer, and this implies that the code and its stabilizer are
dual to each other. We also show that any qudit stabilizer can be put in a
standard, or canonical, form using a series of Clifford gates, and we provide
an explicit efficient algorithm for doing this. Our work generalizes known
results that were valid only for prime dimensional systems and may be useful in
constructing efficient encoding/decoding quantum circuits for qudit stabilizer
codes and better qudit quantum error correcting codes.


  We describe the first convergent numerical method to determine static black
hole solutions (with S^3 horizon) in 5d compactified spacetime. We obtain a
family of solutions parametrized by the ratio of the black hole size and the
size of the compact extra dimension. The solutions satisfy the demanding
integrated first law. For small black holes our solutions approach the 5d
Schwarzschild solution and agree very well with new theoretical predictions for
the small corrections to thermodynamics and geometry. The existence of such
black holes is thus established. We report on thermodynamical (temperature,
entropy, mass and tension along the compact dimension) and geometrical
measurements. Most interestingly, for large masses (close to the
Gregory-Laflamme critical mass) the scheme destabilizes. We interpret this as
evidence for an approach to a physical tachyonic instability. Using
extrapolation we speculate that the system undergoes a first order phase
transition.


  In this paper, we present the algorithm that alters the subset of marginals
of multivariate standard distributed data into such modelled by an Archimedean
copula. Proposed algorithm leaves a correlation matrix almost unchanged, but
introduces a higher order correlation into a subset of marginals. Our data
transformation algorithm can be used to analyse whether particular machine
learning algorithm, especially a dimensionality reduction one, utilises higher
order correlations or not. We present an exemplary application on two features
selection algorithms, mention that features selection is one of the approaches
to dimensionality reduction. To measure higher order correlation, we use
multivariate higher order cumulants, hence to utilises higher order
correlations be to use the Joint Skewness Band Selection (JSBS) algorithm that
uses third-order multivariate cumulant. We show the robust performance of the
JSBS in contrary to the poor performance of the Maximum Ellipsoid Volume (MEV)
algorithm that does not utilise such higher order correlations. With this
result, we confirm the potential application of our data generation algorithm
to analyse a performance of various dimensionality reduction algorithms.


  Massive binaries that merge as compact objects are the progenitors of
gravitational-wave sources. Most of these binaries experience one or more
phases of mass transfer, during which one of the stars loses part or all of its
outer envelope and becomes a stripped-envelope star. The evolution of the size
of these stripped stars is crucial in determining whether they experience
further interactions and their final fate. We present new calculations of
stripped-envelope stars based on binary evolution models computed with MESA. We
use these to investigate their radius evolution as a function of mass and
metallicity. We further discuss their pre-supernova observable characteristics
and potential consequences of their evolution on the properties of supernovae
from stripped stars. At high metallicity we find that practically all of the
hydrogen-rich envelope is removed, in agreement with earlier findings. Only
progenitors with initial masses below 10\Msun expand to large radii (up to
100\Rsun), while more massive progenitors stay compact. At low metallicity, a
substantial amount of hydrogen remains and the progenitors can, in principle,
expand to giant sizes (> 400\Rsun), for all masses we consider. This implies
that they can fill their Roche lobe anew. We show that the prescriptions
commonly used in population synthesis models underestimate the stellar radii by
up to two orders of magnitude. We expect that this has consequences for the
predictions for gravitational-wave sources from double neutron star mergers, in
particular for their metallicity dependence.


  High-T_c superconductors in small magnetic fields directed away from the
crystal symmetry axes have been found to exhibit inhomogeneous chains of flux
lines (vortices), in contrast to the usual regular triangular flux-line
lattice. We review the experimental observations of these chains, and summarize
the theoretical background that explains their appearance. We treat separately
two classes of chains: those that appear in superconductors with moderate
anisotropy due to an attractive part of the interaction between tilted flux
lines, and those with high anisotropy where the tilted magnetic flux is created
by two independent and perpendicular crossing lattices. In the second case it
is the indirect attraction between a flux line along the layers (Josephson
vortex) and a flux line perpendicular to the layers (pancake vortex stack) that
leads to the formation of chains of the pancake vortex stacks. This complex
system contains a rich variety of phenomena, with several different equilibrium
phases, and an extraordinary dynamic interplay between the two sets of crossing
vortices. We compare the theoretical predictions of these phenomena with the
experimental observations made to date. We also contrast the different
techniques used to make these observations. While it is clear that this system
forms a wonderful playground for probing the formation of structures with
competing interactions, we conclude that there are important practical
implications of the vortex chains that appear in highly anisotropic
superconductors.


  We provide a new proof of Maurer, Renard, and Pietzak's result that the sum
of the nCPA advantages of random permutations $P$ and $Q$ bound the CCA
advantage of $P^{-1} \circ Q$. Our proof uses probability directly, as opposed
to information theory, and has the advantage of providing an alternate
sufficient condition of low CCA advantage. Namely, the CCA advantage of a
random permutation can be bounded by its separation distance from the uniform
distribution. We use this alternate condition to tighten the best known bound
on the security of the swap-or-not shuffle in the special case of having fewer
queries than the square root of the number of cards.


  We consider the problem of minimizing a sum of several convex non-smooth
functions. We introduce a new algorithm called the selective linearization
method, which iteratively linearizes all but one of the functions and employs
simple proximal steps. The algorithm is a form of multiple operator splitting
in which the order of processing partial functions is not fixed, but rather
determined in the course of calculations. Global convergence is proved and
estimates of the convergence rate are derived. Specifically, the number of
iterations needed to achieve solution accuracy $\varepsilon$ is of order
$\mathcal{O}\big(\ln(1/\varepsilon)/\varepsilon\big)$. We also illustrate the
operation of the algorithm on structured regularization problems.


  The BayesMortalityPlus package provides a framework for modelling and
predicting mortality data. The package includes tools for the construction of
life tables based on Heligman-Pollard laws, and also on dynamic linear
smoothers. Flexibility is available in terms of modelling so that the response
variable may be modeled as Poisson, Binomial or Gaussian. If temporal data is
available, the package provides a Bayesian implementation for the well-known
Lee-Carter model that allows for estimation, projection of mortality over time,
and assessment of uncertainty of any linear or nonlinear function of parameters
such as life expectancy. Illustrations are considered to show the capability of
the proposed package to model mortality data.


  Graphene hosts a unique electron system in which electron-phonon scattering
is extremely weak but electron-electron collisions are sufficiently frequent to
provide local equilibrium above liquid nitrogen temperature. Under these
conditions, electrons can behave as a viscous liquid and exhibit hydrodynamic
phenomena similar to classical liquids. Here we report strong evidence for this
transport regime. We find that doped graphene exhibits an anomalous (negative)
voltage drop near current injection contacts, which is attributed to the
formation of submicrometer-size whirlpools in the electron flow. The viscosity
of graphene's electron liquid is found to be ~0.1 m$^2$ /s, an order of
magnitude larger than that of honey, in agreement with many-body theory. Our
work shows a possibility to study electron hydrodynamics using high quality
graphene.


  We investigate the phase diagram of phase separation for the hole-doped two
dimensional system of antiferromagnetically correlated electrons based on the
U(1) slave-boson functional integral approach to the t-J model. We show that
the phase separation occurs for all values of J/t, that is, whether $0 < J/t <
1$ or $J/t \geq 1$ with J, the Heisenberg coupling constant and t, the hopping
strength. This is consistent with other numerical studies of hole-doped two
dimensional antiferromagnets. The phase separation in the physically
interesting J region, $0 < J/t \lesssim 0.4$ is examined by introducing
hole-hole (holon-holon) repulsive interaction. We find from this study that
with high repulsive interaction between holes the phase separation boundary
tends to remain robust in this low $J$ region, while in the high J region, J/t
> 0.4, the phase separation boundary tends to disappear.


  We study two quantization schemes for compact symplectic manifolds with
almost complex structures. The first of these is the Spin-c quantization. We
prove the analog of Kodaira vanishing for the Spin-c Dirac operator, which
shows that the index space of this operator provides an honest (not virtual)
vector space semiclassically. We also introduce a new quantization scheme,
based on a rescaled Laplacian, for which we are able to prove strong
semiclassical properties. The two quantizations are shown to be close
semiclassically.