  Despite the detection of numerous interstellar complex organic molecules
(iCOMs) for decades, it is still a matter of debate whether they are
synthesized in the gas-phase or on the icy surface of interstellar grains. In
the past, molecular deuteration has been used to constrain the formation paths
of small and abundant hydrogenated interstellar species. More recently, the
deuteration degree of formamide, one of the most interesting iCOM, has also
been explained in the hypothesis that it is formed by the gas-phase reaction
NH$_2$ + H$_2$CO. In this article, we aim at using molecular deuteration to
constrain the formation of another iCOM, glycolaldehyde, which is an important
prebiotic species. More specifically, we have performed dedicated electronic
structure and kinetic calculations to establish the glycolaldehyde deuteration
degree in relation to that of ethanol, which is its possible parent species
according to the suggestion of Skouteris et al. (2018). We found that the
abundance ratio of the species containing one D-atom over the all-protium
counterpart depends on the produced D isotopomer and varies from 0.9 to 0.5.
These theoretical predictions compare extremely well with the monodeuterated
isotopomers of glycolaldehyde and that of ethanol measured towards the
Solar-like protostar IRAS 16293-2422, supporting the hypothesis that
glycolaldehyde could be produced in the gas-phase for this source. In addition,
the present work confirms that the deuterium fractionation of iCOMs cannot be
simply anticipated based on the deuterium fractionation of the parent species
but necessitates a specific study, as already shown for the case of formamide.


  Experiments on the oscillatory motion of a suspended bar magnet throws light
on the damping effects acting on the pendulum. The viscous drag offered by air
was found the be the main contributor for slowing the pendulum down. The nature
and magnitude of the damping effects were shown to be strongly dependent on the
amplitude.


  We construct an exactly soluble Hamiltonian on the D=3 cubic lattice, whose
ground state is a topological phase of bosons protected by time reversal
symmetry, i.e a symmetry protected topological (SPT) phase. In this model
anyonic excitations are shown to exist at the surface but not in the bulk. The
statistics of these surface anyons is explicitly computed and shown to be
identical to the 3-fermion Z2 model, a variant of Z2 topological order which
cannot be realized in a purely D=2 system with time reversal symmetry. Thus the
model realizes a novel surface termination for SPT phases, which only becomes
available in D=3: that of a fully symmetric gapped surface with topological
order. The 3D phase found here is also outside the group cohomology
classification that appears to capture all SPT phases in lower dimensions. It
is identified with a phase previously predicted from a field theoretic
analysis, whose surface is roughly `half' of a Kitaev E8 phase. Our
construction utilizes the Walker-Wang prescription to create a 3D confined
phase with surface anyons,which can be extended to other symmetry classes and
topological orders.


  In a recent publication [B. Schuette, S. Bauch et al., accepted for
publication in Phys. Rev. Lett. 2012] it was demonstrated both, experimentally
and theoretically, that Auger electrons are subject to an energetic chirp if a
three-body-interaction dubbed post-collision interaction (PCI) is involved.
Here, we extend previous theoretical work and give a detailed analysis of
field-assisted PCI based on numerical solutions of the time-dependent
Schr\"odinger equation, extensive Monte-Carlo averaged molecular dynamics
simulations and analytical theory. The dependence on various streaking and
excitation conditions is investigated, and we discuss, how these findings may
help to improve XUV pulse characterization as well as understanding of
ultrafast atomic processes.


  We consider four subsets of the complexified spacetime algebra, namely the
real even part, the real odd part, the imaginary even part and the imaginary
odd part. This naturally leads to the four connected components of the Lorentz
group, supplemented each time by an additional symmetry. We then examine how
these four parts impact the Dirac equation and show that four types of matter
arise with positive and negative masses as well as positive and negative
charges.


  In this paper, we study model-checking of linear-time properties in
multi-valued systems. Safety property, invariant property, liveness property,
persistence and dual-persistence properties in multi-valued logic systems are
introduced. Some algorithms related to the above multi-valued linear-time
properties are discussed. The verification of multi-valued regular safety
properties and multi-valued $\omega$-regular properties using lattice-valued
automata are thoroughly studied. Since the law of non-contradiction (i.e.,
$a\wedge \neg a=0$) and the law of excluded-middle (i.e., $a\vee \neg a=1$) do
not hold in multi-valued logic, the linear-time properties introduced in this
paper have the new forms compared to those in classical logic. Compared to
those classical model checking methods, our methods to multi-valued model
checking are more directly accordingly. A new form of multi-valued model
checking with membership degree is also introduced. In particular, we show that
multi-valued model-checking can be reduced to the classical model checking. The
related verification algorithms are also presented. Some illustrative examples
and case study are also provided.


  Independent component analysis (ICA) has proven useful for modeling brain and
electroencephalographic (EEG) data. Here, we present a new, generalized method
to better capture the dynamics of brain signals than previous ICA algorithms.
We regard EEG sources as eliciting spatio-temporal activity patterns,
corresponding to, e.g., trajectories of activation propagating across cortex.
This leads to a model of convolutive signal superposition, in contrast with the
commonly used instantaneous mixing model. In the frequency-domain, convolutive
mixing is equivalent to multiplicative mixing of complex signal sources within
distinct spectral bands. We decompose the recorded spectral-domain signals into
independent components by a complex infomax ICA algorithm. First results from a
visual attention EEG experiment exhibit (1) sources of spatio-temporal dynamics
in the data, (2) links to subject behavior, (3) sources with a limited spectral
extent, and (4) a higher degree of independence compared to sources derived by
standard ICA.


  We investigate the quantum transport through a quantum dot coupled with a
superconducting (SC) nanowire. The distinguishing characteristics between the
Majorana bound states (MBSs) and Andreev bound states (ABSs) hosted in SC wire
are demonstrated in the differential conductance and finite-frequency current
noise spectrum. The former shows the well-known zero-bias peak (ZBP), while the
latter displays the degenerate Rabi dip (DRD) for MBSs with topological quality
factor q = 1. However, for ABSs with q < 1, the ZBP and DRD split. We confirm
that the ABSs with high quality factors greater than critical values can mimic
the signatures of topological MBSs. Especially, the corresponding critical
quality factors for the occurrence of ZBP and DRD are explicitly obtained and
their underlying mechanisms are addressed.


  In the existing expositions of the Karolyhazy model, quantum mechanical
uncertainties are mimicked by classical spreads. It is shown how to express
those uncertainties through entities of the future unified theory of general
relativity and quantum theory.


  The pion-nucleon and kaon-nucleon scattering lengths are studied in the QCD
sum rule. We show that the leading and next-to-leading order terms of the OPE
give rise to the Tomozawa-Weinberg and sigma terms, respectively. We also show
that in the kaon-nucleon system the $\Lambda(1405)$ contribution has to be
subtracted from the OPE side in order to obtain the scattering length. The odd
components of the $T$-matrices are in agreement with the experimental values
not only in the pion-nucleon channel but also in the kaon-nucleon channel after
the $\Lambda(1405)$ contribution subtracted. The even components disagree with
the experimental values in the pion-nucleon channel, which is similar to the
situation in the PCAC-plus-current-algebra approach at the Weinberg point. We
speculate that this discrepancy should be explained by the continuum
contribution in the spectral function above the pion-nucleon threshold.


  Given a semisimple linear algebraic $k$-group $G$, one has a spherical
building $\Delta_G$, and one can interpret the geometric realisation
$\Delta_G(\mathbb R)$ of $\Delta_G$ in terms of cocharacters of $G$. The aim of
this paper is to extend this construction to the case when $G$ is an arbitrary
connected linear algebraic group; we call the resulting object
$\Delta_G(\mathbb R)$ the spherical edifice of $G$. We also define an object
$V_G(\mathbb R)$ which is an analogue of the vector building for a semisimple
group; we call $V_G(\mathbb R)$ the vector edifice. The notions of a linear map
and an isomorphism between edifices are introduced; we construct some linear
maps arising from natural group-theoretic operations. We also devise a family
of metrics on $V_G(\mathbb R)$ and show they are all bi-Lipschitz equivalent to
each other; with this extra structure, $V_G(\mathbb R)$ becomes a complete
metric space. Finally, we present some motivation in terms of geometric
invariant theory and variations on the Tits Centre Conjecture.


  The Assignment problem is a fundamental and well-studied problem in the
intersection of Social Choice, Computational Economics and Discrete Allocation.
In the Assignment problem, a group of agents expresses preferences over a set
of items, and the task is to find a pareto optimal allocation of items to
agents. We introduce a generalized version of this problem, where each agent is
equipped with multiple incomplete preference lists: each list (called a layer)
is a ranking of items in a possibly different way according to a different
criterion. We introduce the concept of global optimality, which extends the
notion of pareto optimality to the multi-layered setting, and we focus on the
problem of deciding whether a globally optimal assignment exists. We study this
problem from the perspective of Parameterized Complexity: we consider several
natural parameters such as the number of layers, the number of agents, the
number of items, and the maximum length of a preference list. We present a
comprehensive picture of the parameterized complexity of the problem with
respect to these parameters.


  One of the biggest problems in current distributed systems is that presented
by one machine attempting to determine the liveness of another in a timely
manner. Unfortunately, the symptoms exhibited by a failed machine can also be
the result of other causes, e.g., an overloaded machine or network which drops
messages, making it impossible to detect a machine failure with cetainty until
that machine recovers. This is a well understood problem and one which has led
to a large body of research into failure suspectors: since it is not possible
to detect a failure, the best one can do is suspect a failure and program
accordingly. However, one machine's suspicions may not be the same as
another's; therefore, these algorithms spend a considerable effort in ensuring
a consistent view among all available machines of who is suspects of being
failed. This paper describes a thought experiment on how quantum mechanics may
be used to provide a failure detector that is guaranteed to give both accurate
and instantaneous information about the liveness of machines, no matter the
distances involved.


  Changepoint detection is an important problem with applications across many
application domains. There are many different types of changes that one may
wish to detect, and a wide-range of algorithms and software for detecting them.
However there are relatively few approaches for detecting changes-in-slope in
the mean of a signal plus noise model. We describe the R package, cpop,
available on the Comprehensive R Archive Network (CRAN). This package
implements CPOP, a dynamic programming algorithm, to find the optimal set of
changes that minimises an L_0 penalised cost, with the cost being a weighted
residual sum of squares. The package has extended the CPOP algorithm so it can
analyse data that is unevenly spaced, allow for heterogeneous noise variance,
and allows for a grid of potential change locations to be different from the
locations of the data points. There is also an implementation that uses the
CROPS algorithm to detect all segmentations that are optimal as you vary the
L_0 penalty for adding a change across a continuous range of values.


  The propagation of plasma waves in a new non-linear, logarithmic
electrodynamics model is performed. A cold, uniform, collisionless fluid plasma
model is applied. Electrostatic waves in magnetized plasma are shown to
correspond to modified Trivelpiece-Gould modes, together with changes of the
plasma and upper-hybrid frequencies, driven by the logarithmic electrodynamics
effects. Electromagnetic waves are described by a generalized Appleton-Hartree
dispersion relation. The cases of propagation parallel or perpendicular to the
equilibrium magnetic field are analyzed in detail. In particular, generalized
ordinary and extraordinary modes are obtained. We determine the changes, due to
logarithmic electrodynamics, in the allowable and forbidden frequency bands of
the new extraordinary mode. Estimates are provided about the strength of the
ambient magnetic field, so that the non-linear electrodynamics effects become
decisive.


  Modeling of real-world systems with Petri nets allows to benefit from their
generic concepts of parallelism, synchronisation and conflict, and obtain a
concise yet expressive system representation. Algorithms for synthesis of a net
from a sequential specification enable the well-developed theory of Petri nets
to be applied for the system analysis through a net model. The problem of
$\tau$-synthesis consists in deciding whether a given directed labeled graph
$A$ is isomorphic to the reachability graph of a Boolean Petri net $N$ of type
$\tau$. In case of a positive decision, $N$ should be constructed. For many
Boolean types of nets, the problem is NP-complete. This paper deals with a
special variant of $\tau$-synthesis that imposes restrictions for the target
net $N$: we investigate dependency $d$-restricted tau-synthesis (DR$\tau$S)
where each place of $N$ can influence and be influenced by at most d
transitions. For a type $\tau$, if tau-synthesis is NP-complete then DR$\tau$S
is also NP-complete. In this paper, we show that DR$\tau$S parameterized by $d$
is in XP. Furthermore, we prove that it is W[2]-hard, for many Boolean types
that allow unconditional interactions set and reset.


  We present a dual formulation of the construction of N=2 nonlinear
sigma-model type conformal field theories with c=9 which are mainly used as
internal sectors of Calabi-Yau heterotic string compactifications. The
supercurrents and the higher components of the spectral flow superfields turn
out in each case to be tensor products of two simple currents of a c=1 and a
non-supersymmetric parafermionic c=8 CFT. The characters of the latter model
can be regarded as string functions of the coset sigma/U(1). In particular, for
the (1)^9 Gepner model we discuss these string functions in detail, realizing
this model contains a broken E_8 gauge symmetry of which only the abelian
subalgebra remains a symmetry of the spectrum. As an example, we construct a
simple model leading to a string theory with a massless spectrum of 36 E_6
generations and an extended gauge symmetry E_6 x SU(3)^4.


  Finite-time Lyapunov exponents of generic chaotic dynamical systems fluctuate
in time. These fluctuations are due to the different degree of stability across
the accessible phase-space. A recent numerical study of spatially-extended
systems has revealed that the diffusion coefficient D of the Lyapunov exponents
(LEs) exhibits a non-trivial scaling behavior, D(L) ~ L^{-\gamma}, with the
system size L. Here, we show that the wandering exponent \gamma can be
expressed in terms of the roughening exponents associated with the
corresponding "Lyapunov-surface". Our theoretical predictions are supported by
the numerical analysis of several spatially-extended systems. In particular, we
find that the wandering exponent of the first LE is universal: in view of the
known relationship with the Kardar-Parisi-Zhang equation, \gamma can be
expressed in terms of known critical exponents. Furthermore, our simulations
reveal that the bulk of the spectrum exhibits a clearly different behavior and
suggest that it belongs to a possibly unique universality class, which has,
however, yet to be identified.


  We present the first Green's function Monte Carlo calculations of light
nuclei with nuclear interactions derived from chiral effective field theory up
to next-to-next-to-leading order. Up to this order, the interactions can be
constructed in a local form and are therefore amenable to quantum Monte Carlo
calculations. We demonstrate a systematic improvement with each order for the
binding energies of $A=3$ and $A=4$ systems. We also carry out the first
few-body tests to study perturbative expansions of chiral potentials at
different orders, finding that higher-order corrections are more perturbative
for softer interactions. Our results confirm the necessity of a three-body
force for correct reproduction of experimental binding energies and radii, and
pave the way for studying few- and many-nucleon systems using quantum Monte
Carlo methods with chiral interactions.


  Recent research advances achieve human-level accuracy for de-identifying
free-text clinical notes on research datasets, but gaps remain in reproducing
this in large real-world settings. This paper summarizes lessons learned from
building a system used to de-identify over one billion real clinical notes, in
a fully automated way, that was independently certified by multiple
organizations for production use. A fully automated solution requires a very
high level of accuracy that does not require manual review. A hybrid
context-based model architecture is described, which outperforms a Named Entity
Recogniton (NER) - only model by 10% on the i2b2-2014 benchmark. The proposed
system makes 50%, 475%, and 575% fewer errors than the comparable AWS, Azure,
and GCP services respectively while also outperforming ChatGPT by 33%. It
exceeds 98% coverage of sensitive data across 7 European languages, without a
need for fine tuning. A second set of described models enable data obfuscation
-- replacing sensitive data with random surrogates -- while retaining name,
date, gender, clinical, and format consistency. Both the practical need and the
solution architecture that provides for reliable & linked anonymized documents
are described.


  We consider the problem of predicting human players' actions in repeated
strategic interactions. Our goal is to predict the dynamic step-by-step
behavior of individual players in previously unseen games. We study the ability
of neural networks to perform such predictions and the information that they
require. We show on a dataset of normal-form games from experiments with human
participants that standard neural networks are able to learn functions that
provide more accurate predictions of the players' actions than established
models from behavioral economics. The networks outperform the other models in
terms of prediction accuracy and cross-entropy, and yield higher economic
value. We show that if the available input is only of a short sequence of play,
economic information about the game is important for predicting behavior of
human agents. However, interestingly, we find that when the networks are
trained with long enough sequences of history of play, action-based networks do
well and additional economic details about the game do not improve their
performance, indicating that the sequence of actions encode sufficient
information for the success in the prediction task.


  We find by Wilson numerical renormalization group and conformal field theory
that a three-orbital Anderson impurity model for a C$_{60}^{n-}$ molecule has a
very rich phase diagram which includes non-Fermi-liquid stable and unstable
fixed points with interesting properties, most notably high sensitivity to
doping $n$. We discuss the implications of our results to the conductance
behavior of C$_{60}$-based single-molecule transistor devices.


  Let $Q(n)$ denote the count of the primitive subsets of the integers
$\{1,2\ldots n\}$. We give a new proof that $Q(n) = \alpha^{(1+o(1))n}$ which
allows us to give a good error term and to improve upon the lower bound for the
value of this constant $\alpha$. We also show that the method developed can be
applied to many similar problems that can be stated in terms of the divisor
graph, including other questions about primitive sets,
geometric-progression-free sets, and the divisor graph path-cover problem.


  In today's tech-savvy world every industry is trying to formulate methods for
recommending products by combining several techniques and algorithms to form a
pool that would bring forward the most enhanced models for making the
predictions. Building on these lines is our paper focused on the application of
sentiment analysis for recommendation in the insurance domain. We tried
building the following Machine Learning models namely, Logistic Regression,
Multinomial Naive Bayes, and the mighty Random Forest for analyzing the
polarity of a given feedback line given by a customer. Then we used this
polarity along with other attributes like Age, Gender, Locality, Income, and
the list of other products already purchased by our existing customers as input
for our recommendation model. Then we matched the polarity score along with the
user's profiles and generated the list of insurance products to be recommended
in descending order. Despite our model's simplicity and the lack of the key
data sets, the results seemed very logical and realistic. So, by developing the
model with more enhanced methods and with access to better and true data
gathered from an insurance industry may be the sector could be very well
benefitted from the amalgamation of sentiment analysis with a recommendation.


  We obtain new estimates for the existence time of the maximal solutions to
the nonlinear heat equation $\partial_tu-\Delta u=|u|^\alpha u,\;\alpha>0$ with
initial values in Lebesgue, weighted Lebesgue spaces or measures. Non-regular,
sign-changing, as well as non polynomial decaying initial data are considered.
The proofs of the lower-bound estimates of life-span are based on the local
construction of solutions. The proofs of the upper-bounds exploit a well-known
necessary condition for the existence of nonnegative solutions. In addition, we
establish new results for life-span using dilation methods and we give new
life-span estimates for Hardy-H\'enon parabolic equations.


  The presumed data owners' right to explanations brought about by the General
Data Protection Regulation in Europe has shed light on the social challenges of
explainable artificial intelligence (XAI). In this paper, we present a case
study with Deep Learning (DL) experts from a research and development
laboratory focused on the delivery of industrial-strength AI technologies. Our
aim was to investigate the social meaning (i.e. meaning to others) that DL
experts assign to what they do, given a richly contextualized and familiar
domain of application. Using qualitative research techniques to collect and
analyze empirical data, our study has shown that participating DL experts did
not spontaneously engage into considerations about the social meaning of
machine learning models that they build. Moreover, when explicitly stimulated
to do so, these experts expressed expectations that, with real-world DL
application, there will be available mediators to bridge the gap between
technical meanings that drive DL work, and social meanings that AI technology
users assign to it. We concluded that current research incentives and values
guiding the participants' scientific interests and conduct are at odds with
those required to face some of the scientific challenges involved in advancing
XAI, and thus responding to the alleged data owners' right to explanations or
similar societal demands emerging from current debates. As a concrete
contribution to mitigate what seems to be a more general problem, we propose
three preliminary XAI Mediation Challenges with the potential to bring together
technical and social meanings of DL applications, as well as to foster much
needed interdisciplinary collaboration among AI and the Social Sciences
researchers.


  Understanding the population structure and patterns of gene flow within
species is of fundamental importance to the study of evolution. In the fields
of population and evolutionary genetics, measures of genetic differentiation
are commonly used to gather this information. One potential caveat is that
these measures assume gene flow to be symmetric. However, asymmetric gene flow
is common in nature, especially in systems driven by physical processes such as
wind or water currents. Since information about levels of asymmetric gene flow
among populations is essential for the correct interpretation of the
distribution of contemporary genetic diversity within species, this should not
be overlooked. To obtain information on asymmetric migration patterns from
genetic data, complex models based on maximum likelihood or Bayesian approaches
generally need to be employed, often at great computational cost. Here, a new
simpler and more efficient approach for understanding gene flow patterns is
presented. This approach allows the estimation of directional components of
genetic divergence between pairs of populations at low computational effort,
using any of the classical or modern measures of genetic differentiation. These
directional measures of genetic differentiation can further be used to
calculate directional relative migration and to detect asymmetries in gene flow
patterns. This can be done in a user-friendly web application called
divMigrate-online introduced in this paper. Using simulated data sets with
known gene flow regimes, we demonstrate that the method is capable of resolving
complex migration patterns under a range of study designs.


  Deciding formulas mixing arithmetic and uninterpreted predicates is of
practical interest, notably for applications in verification. Some decision
procedures consist in building by structural induction an automaton that
recognizes the set of models of the formula under analysis, and then testing
whether this automaton accepts a non-empty language. A drawback is that
universal quantification is usually handled by a reduction to existential
quantification and complementation. For logical formalisms in which models are
encoded as infinite words, this hinders the practical use of this method due to
the difficulty of complementing infinite-word automata. The contribution of
this paper is to introduce an algorithm for directly computing the effect of
universal first-order quantifiers on automata recognizing sets of models, for
formulas involving natural numbers encoded in unary notation. This makes it
possible to apply the automata-based approach to obtain implementable decision
procedures for various arithmetic theories.


  A finite stack of thin superconducting tapes, all carrying a fixed current I,
can be approximated by an anisotropic superconducting bar with critical current
density Jc=Ic/2aD, where Ic is the critical current of each tape, 2a is the
tape width, and D is the tape-to-tape periodicity. The current density J must
obey the constraint \int J dx = I/D, where the tapes lie parallel to the x axis
and are stacked along the z axis. We suppose that Jc is independent of field
(Bean approximation) and look for a solution to the critical state for
arbitrary height 2b of the stack. For c<|x|<a we have J=Jc, and for |x|<c the
critical state requires that Bz=0. We show that this implies \partial
J/\partial x=0 in the central region. Setting c as a constant (independent of
z) results in field profiles remarkably close to the desired one (Bz=0 for
|x|<c) as long as the aspect ratio b/a is not too small. We evaluate various
criteria for choosing c, and we show that the calculated hysteretic losses
depend only weakly on how c is chosen. We argue that for small D/a the
anisotropic homogeneous-medium approximation gives a reasonably accurate
estimate of the ac losses in a finite Z stack. The results for a Z stack can be
used to calculate the transport losses in a pancake coil wound with
superconducting tape.


  Discourse structure is the hidden link between surface features and
document-level properties, such as sentiment polarity. We show that the
discourse analyses produced by Rhetorical Structure Theory (RST) parsers can
improve document-level sentiment analysis, via composition of local information
up the discourse tree. First, we show that reweighting discourse units
according to their position in a dependency representation of the rhetorical
structure can yield substantial improvements on lexicon-based sentiment
analysis. Next, we present a recursive neural network over the RST structure,
which offers significant improvements over classification-based methods.


  The recent measurement of $R_K$ at LHCb continues to support the hint of
violation of lepton flavor universality. We perform a global fit for new
physics in semileptonic $b\to s$ transitions using all the relevant data with a
Bayesian analysis technique. We include new measurements of $R_K$ at LHCb and
new determinations of $R_{K^*}$ and $R_{K^{*+}}$ at Belle. We perform the scan
for various NP scenarios and infer the 68\% and 95.4\% credibility regions of
the marginalized posterior probability density for all scenarios. We also
compare the models in pairs by calculating the Bayes factor given a common data
set. A few well-known BSM models are analyzed that can provide a high energy
framework for the EFT analysis. These include the exchange of a heavy $Z^{'}$
boson in models with heavy vector-like fermions and a scalar field, and a model
with scalar leptoquarks. We provide predictions for the BSM couplings and
expected mass values.


  We consider supersymmetric models in which sneutrinos are viable dark matter
candidates. These are either simple extensions of the Minimal Supersymmetric
Standard Model with additional singlet superfields, such as the inverse or
linear seesaw, or a model with an additional U(1) group. All of these models
can accomodate the observed small neutrino masses and large mixings. We
investigate the properties of sneutrinos as dark matter candidates in these
scenarios. We check for phenomenological bounds, such as correct relic
abundance, consistency with direct detection cross section limits and
laboratory constraints, among others lepton flavour violating (LFV) charged
lepton decays. While inverse and linear seesaw lead to different results for
LFV, both models have very similar dark matter phenomenology, consistent with
all experimental bounds. The extended gauge model shows some additional and
peculiar features due to the presence of an extra gauge boson Z' and an
additional light Higgs. Specifically, we point out that for sneutrino LSPs
there is a strong constraint on the mass of the Z' due to the experimental
bounds on the direct detecton scattering cross section.


  We derive a nice representation for point symmetry transformations of the
(1+1)-dimensional linear heat equation and properly interpret them. This allows
us to prove that the pseudogroup of these transformations has exactly two
connected components. That is, the heat equation admits a single independent
discrete symmetry, which can be chosen to be alternating the sign of the
dependent variable. We introduce the notion of pseudo-discrete elements of a
Lie group and show that alternating the sign of the space variable, which was
for a long time misinterpreted as a discrete symmetry of the heat equation, is
in fact a pseudo-discrete element of its essential point symmetry group. The
classification of subalgebras of the essential Lie invariance algebra of the
heat equation is enhanced and the description of generalized symmetries of this
equation is refined as well. We also consider the Burgers equation because of
its relation to the heat equation and prove that it admits no discrete point
symmetries. The developed approach to point-symmetry groups whose elements have
components that are linear fractional in some variables can directly be
extended to many other linear and nonlinear differential equations.


  In the context of the existence of primordial black holes (PBHs), they may
compose a fraction of the dark matter of the Universe. Assuming that PBHs fill
the dark content of the Milky Way Galaxy in the Galactic halo and dark disk, we
calculate the probability of collision of the PBHs with Earth. This collision
has different consequences as heating the interior of the earth through
dynamical friction and accretion processes. In this work, we calculate the rate
of collisions and a fraction of black holes that can be trapped inside the
earth. Finally, we compare the danger of PBH collision with the asteroid
impacts on Earth.


  The main quasi-particle characteristics of the one-dimensional polaron are
estimated within and beyond the most general Gaussian approximation at
arbitrary electron-phonon coupling. We have derived explicitly the ground-state
energy and the effective mass in the weak- and strong-coupling regimes. For
arbitrary coupling, the Gaussian leading-order term of the polaron self energy
improves the corresponding Feynman estimate and represents the lowest upper
bound available. We have calculated the next non-Gaussian corrections. Taking
into account systematically higher-order corrections does not perturb
considerably the obtained results.


  Spin-dependent transport through an endohedral fullerene Co@C$_{60}$ dimer
with gold electrodes is explored theoretically using density functional and
extended H\"{u}ckel theory. Density of states spin polarizations up to 95%, due
to spin-splitting of Co 3d orbitals, are found by varying the gate and/or bias
voltage. The current-voltage characteristics and strong (up to 100%) spin
polarization of the current indicate that the device can be utilized for highly
efficient spin injection into nonmagnetic conductors. This finding opens the
way to the realization of electrostatically tuned spintronic nano devices less
than 2 nanometers in size, without ferromagnetic electrodes.


  Deep learning architectures have an extremely high-capacity for modeling
complex data in a wide variety of domains. However, these architectures have
been limited in their ability to support complex prediction problems using
insurance claims data, such as readmission at 30 days, mainly due to data
sparsity issue. Consequently, classical machine learning methods, especially
those that embed domain knowledge in handcrafted features, are often on par
with, and sometimes outperform, deep learning approaches. In this paper, we
illustrate how the potential of deep learning can be achieved by blending
domain knowledge within deep learning architectures to predict adverse events
at hospital discharge, including readmissions. More specifically, we introduce
a learning architecture that fuses a representation of patient data computed by
a self-attention based recurrent neural network, with clinically relevant
features. We conduct extensive experiments on a large claims dataset and show
that the blended method outperforms the standard machine learning approaches.


  Asteroseismology of solar-type stars has an important part to play in the
exoplanet program of the NASA Kepler Mission. Precise and accurate inferences
on the stellar properties that are made possible by the seismic data allow very
tight constraints to be placed on the exoplanetary systems. Here, we outline
how to make an estimate of the detectability of solar-like oscillations in any
given Kepler target, using rough estimates of the temperature and radius, and
the Kepler apparent magnitude.


  Invoking Maxwell's classical electrodynamics in conjunction with expressions
for the electromagnetic (EM) energy, momentum, force, and torque, we use a few
simple examples to demonstrate the nature of linear and angular momentum
exchange between a wave-packet and a small spherical particle. The linear and
angular momenta of the EM field, when absorbed by the particle, will be seen to
elicit different responses from the particle.


  Processors with extensible instruction sets are often used today as
programmable hardware accelerators for various domains. When extending RISC-V
and other similar extensible processor architectures, the task of designing
specialized instructions arises. This task can be solved automatically by using
instruction synthesis algorithms. In this paper, we consider algorithms that
can be used in addition to the known approaches and improve the synthesized
instruction sets by recomputing common operations (the result of which is
consumed by multiple operations) of a program inside clustered synthesized
instructions (common operations clustering algorithm), and by identifying
redundant (which have equivalents among the other instructions) synthesized
instructions (subsuming functions algorithm).
  Experimental evaluations of the developed algorithms are presented for the
tests from the domains of cryptography and three-dimensional graphics. For
Magma cipher test, the common operations clustering algorithm allows reducing
the size of the compiled code by 9%, and the subsuming functions algorithm
allows reducing the synthesized instruction set extension size by 2 times. For
AES cipher test, the common operations clustering algorithm allows reducing the
size of the compiled code by 10%, and the subsuming functions algorithm allows
reducing the synthesized instruction set extension size by 2.5 times. Finally,
for the instruction set extension from Volume Ray-Casting test, the additional
use of subsuming functions algorithm allows reducing problem-specific
instruction extension set size from 5 to only 2 instructions without losing its
functionality.


  3D object detection is an indispensable component for scene understanding.
However, the annotation of large-scale 3D datasets requires significant human
effort. To tackle this problem, many methods adopt weakly supervised 3D object
detection that estimates 3D boxes by leveraging 2D boxes and
scene/class-specific priors. However, these approaches generally depend on
sophisticated manual priors, which is hard to generalize to novel categories
and scenes. In this paper, we are motivated to propose a general approach,
which can be easily adapted to new scenes and/or classes. A unified framework
is developed for learning 3D object detectors from RGB images and associated 2D
boxes. In specific, we propose three general components: prior injection module
to obtain general object geometric priors from LLM model, 2D space projection
constraint to minimize the discrepancy between the boundaries of projected 3D
boxes and their corresponding 2D boxes on the image plane, and 3D space
geometry constraint to build a Point-to-Box alignment loss to further refine
the pose of estimated 3D boxes. Experiments on KITTI and SUN-RGBD datasets
demonstrate that our method yields surprisingly high-quality 3D bounding boxes
with only 2D annotation. The source code is available at
https://github.com/gwenzhang/GGA.


  Given a graph $G$ and probability $p$, we form the random subgraph $G_p$ by
retaining each edge of $G$ independently with probability $p$. Given
$d\in\mathbb{N}$ and constants $0<c<1, \varepsilon>0$, we show that if every
subset $S\subseteq V(G)$ of size exactly $\frac{c|V(G)|}{d}$ satisfies
$|N(S)|\ge d|S|$ and $p=\frac{1+\varepsilon}{d}$, then the probability that
$G_p$ does not contain a cycle of length $\Omega(\varepsilon^2c^2|V(G)|)$ is
exponentially small in $|V(G)|$. As an intermediate step, we also show that
given $k,d\in \mathbb{N}$ and a constant $\varepsilon>0$, if every subset
$S\subseteq V(G)$ of size exactly $k$ satisfies $|N(S)|\ge kd$ and
$p=\frac{1+\varepsilon}{d}$, then the probability that $G_p$ does not contain a
path of length $\Omega(\varepsilon^2 kd)$ is exponentially small. We further
discuss applications of these results to $K_{s,t}$-free graphs of maximal
density.


  Let game B be Toral's cooperative Parrondo game with (one-dimensional)
spatial dependence, parameterized by N (3 or more) and p_0,p_1,p_2,p_3 in
[0,1], and let game A be the special case p_0=p_1=p_2=p_3=1/2. Let mu_B (resp.,
mu_(1/2,1/2)) denote the mean profit per turn to the ensemble of N players
always playing game B (resp., always playing the randomly mixed game
(1/2)(A+B)). In previous work we showed that, under certain conditions, both
sequences converge and the limits can be expressed in terms of a parameterized
spin system on the one-dimensional integer lattice. Of course one can get
similar results for mu_(gamma,1-gamma) corresponding to gamma A+(1-gamma)B for
0<gamma<1. In this paper we replace the random mixture with the nonrandom
periodic pattern A^r B^s, where r and s are positive integers. We show that,
under certain conditions, mu_[r,s], the mean profit per turn to the ensemble of
N players repeatedly playing the pattern A^r B^s, converges to the same limit
that mu_(gamma,1-gamma) converges to, where gamma:=r/(r+s). For a particular
choice of the probability parameters, namely p_0=1, p_1=p_2 in (1/2,1), and
p_3=0, we show that the Parrondo effect (i.e., mu_B is nonpositive and mu_[r,s]
is positive) is present if and only if N is even, at least when s=1.


  We present near infrared spectroscopy of the superluminal microquasar \grs\
obtained with the first unit of the VLT (Based on observations collected at the
European Southern Observatory, Chile (ESO No 63.H-0261).) and the ISAAC
spectro-imager. The emission features detected in the VLT data have been
identified as He I, Br $\gamma$, He II and Na I. The detection of Na I is
reported here for the first time, while our confirmation of weak He II emission
provides support to previous marginal detections of this feature. By comparing
the observed spectra with those of massive stars, we find that our results are
very consistent with \grs being a high mass X-ray system with an early type
primary, as previously proposed by Chaty et al. (1996) and Mirabel et al.
(1997). The VLT spectra also provide evidence of P Cygni profiles, that turn
into blue emission wings when the system is in outburst. This observed line
profile evolution implies that \grs must be surrounded by an expanding
envelope, that is partially blown out during the X-ray outbursts. The presence
of such circumstellar gaseous material around \grs is more naturally understood
in the context of a massive luminous star than if the system was a low-mass
X-ray binary.


  The purpose of the present paper is to establish moment estimates of
Rosenthal type for a rather general class of random variables satisfying
certain bounds on the cumulants. We consider sequences of random variables
which satisfy a central limit theorem and estimate the speed of convergence of
the corresponding moments to the moments of a standard normally distributed
variable. The examples of random objects we discuss include those where a
dependency graphs or a weighted dependency graph encodes the dependency
structure. We give applications to subgraph-counting statistics in
Erd\H{o}s-R\'enyi random graphs of type $G(n,p)$ and $G(n,m)$, crossings in
uniform random pair partitions and spins in the $d$-dimensional Ising model.
Moreover, we prove moment estimates for certain statistics appearing in random
matrix theory, namely characteristic polynomials of random unitary matrices as
well as the determinants of certain random matrix ensembles. We add estimates
for the $p(n)$-dimensional volume of the simplex with $p(n)+1$ points in
${\mathbb R}^n$ distributed according to special distributions, since it is
strongly connected to Gram matrix ensembles.


  Predicting future video frames is extremely challenging, as there are many
factors of variation that make up the dynamics of how frames change through
time. Previously proposed solutions require complex inductive biases inside
network architectures with highly specialized computation, including
segmentation masks, optical flow, and foreground and background separation. In
this work, we question if such handcrafted architectures are necessary and
instead propose a different approach: finding minimal inductive bias for video
prediction while maximizing network capacity. We investigate this question by
performing the first large-scale empirical study and demonstrate
state-of-the-art performance by learning large models on three different
datasets: one for modeling object interactions, one for modeling human motion,
and one for modeling car driving.


  We prove that the normalized standard generators of the free orthogonal
quantum group $O_N^+$ converge strongly to a free semicircular system as $N \to
\infty$. Analogous results are obtained for the free unitary quantum groups,
and some applications are given.


  As the quantum analog of the classical one-time pad, the private quantum
channel (PQC) plays a fundamental role in the construction of the maximally
mixed state (from any input quantum state), which is very useful for studying
secure quantum communications and quantum channel capacity problems. However,
the undoubted existence of a relation between the geometric shape of regular
polytopes and private quantum channels in the higher dimension has not yet been
reported. Recently, it was shown that a one-to-one correspondence exists
between single-qubit PQCs and three-dimensional regular polytopes (i.e.,
regular polyhedra). In this paper, we highlight these connections by exploiting
two strategies known as a generalized Gell-Mann matrix and modified quantum
Fourier transform. More precisely, we explore the explicit relationship between
PQCs over a qutrit system (i.e., a three-level quantum state) and regular
4-polytope. Finally, we attempt to devise a formula for connections on higher
dimensional cases.


  A signed magic rectangle $SMR(m,n;r, s)$ is an $m \times n$ array with
entries from $X$, where $X=\{0,\pm1,\pm2,\ldots, $ $\pm (ms-1)/2\}$ if $mr$ is
odd and $X = \{\pm1,\pm2,\ldots,\pm mr/2\}$ if $mr$ is even, such that
precisely $r$ cells in every row and $s$ cells in every column are filled,
every integer from set $X$ appears exactly once in the array and the sum of
each row and of each column is zero. In this paper we prove that a signed magic
rectangle $SMR(m,n;r, 2)$ exists if and only if either $m=2$ and $n=r\equiv 0,3
\pmod 4$ or $m,r\geq 3$ and $mr=2n$.


  Point-ion models have been extensively used to determine "hole numbers" at
copper and oxygen sites in high-temperature superconducting cuprate compounds
from measured nuclear quadrupole frequencies. The present study assesses the
reliability of point-ion models to predict electric field gradients accurately
and also the implicit assumption that the values can be calculated from the
"holes" and not the total electronic structure. First-principles cluster
calculations using basis sets centred on the nuclei have enabled the
determination of the charge and spin density distribution in the CuO2-plane.
The contributions to the electric field gradients and the magnetic hyperfine
couplings are analysed in detail. In particular they are partitioned into
regions in an attempt to find a correlation with the most commonly used
point-ion model, the Sternheimer equation which depends on the two parameters R
and gamma. Our most optimistic objective was to find expressions for these
parameters, which would improve our understanding of them, but although
estimates of the R parameter were encouraging the method used to obtain the
gamma parameter indicate that the two parameters may not be independent. The
problem seems to stem from the covalently bonded nature of the CuO2-planes in
these structures which severely questions using the Sternheimer equation for
such crystals, since its derivation is heavily reliant on the application of
perturbation theory to predominantly ionic structures. Furthermore it is shown
that the complementary contributions of electrons and holes in an isolated ion
cannot be applied to estimates of electric field gradients at copper and oxygen
nuclei in cuprates.


  Anderson localization, i.e., destructive quantum interference of
multiple-scattering paths, halts transport entirely. Contrarily, time-dependent
random forces expedite transport via Fermi acceleration, proposed as a
mechanism for high-energy cosmic rays. Their competition creates interesting
dynamics, but experimental observations are scarce. Here, we experimentally
study the expansion of an ultracold Fermi gas inside time-dependent disorder
and observe distinct regimes from sub- to superdiffusion. Unexpectedly, quantum
interference counteracts acceleration in strong disorder. Our system enables
the investigation of Fermi acceleration in the quantum-transport regime.


  We study the propagation of low frequency shallow water waves on a one
dimensional flow of varying depth. When taking into account dispersive effects,
the linear propagation of long wavelength modes on uneven bottoms excites new
solutions of the dispersion relation which possess a much shorter wavelength.
The peculiarity is that one of these new solutions has a negative energy. When
the flow becomes supercritical, this mode has been shown to be responsible for
the (classical) analog of the Hawking effect. For subcritical flows, the
production of this mode has been observed numerically and experimentally, but
the precise physics governing the scattering remained unclear. In this work, we
provide an analytic treatment of this effect in subcritical flows. We analyze
the scattering of low frequency waves using a new perturbative series, derived
from a generalization of the Bremmer series. We show that the production of
short wavelength modes is governed by a complex value of the position: a
complex turning point. Using this method, we investigate various flow profiles,
and derive the main characteristics of the induced spectrum.


  It is commonly assumed that a specific testing occasion (task, design,
procedure, etc.) provides insights that generalise beyond that occasion. This
assumption is infrequently carefully tested in data. We develop a statistically
principled method to directly estimate the correlation between latent
components of cognitive processing across tasks, contexts, and time. This
method simultaneously estimates individual-participant parameters of a
cognitive model at each testing occasion, group-level parameters representing
across-participant parameter averages and variances, and across-task
correlations. The approach provides a natural way to "borrow" strength across
testing occasions, which can increase the precision of parameter estimates
across all testing occasions. Two example applications demonstrate that the
method is practical in standard designs. The examples, and a simulation study,
also provide evidence about the reliability and validity of parameter estimates
from the linear ballistic accumulator model. We conclude by highlighting the
potential of the parameter-correlation method to provide an "assumption-light"
tool for estimating the relatedness of cognitive processes across tasks,
contexts, and time.


  We prove that two links related by a surgery along a connected, strict graph
clasper of degree n are C_n-equivalent, i.e, related by a sequence of surgeries
along strict tree claspers of degree n.


  By using the formalism of the light-cone wave function along with the colour
glass condensate effective theory, we consider next-to-leading order (NLO)
corrections to the production of a pair of hadrons in electron-proton, or
electron-nucleus, collisions at small Bjorken $x$. To the order of interest,
the process involves the fluctuation of a virtual photon into a quark-antiquark
pair, followed by the emission of a gluon from either the quark, or the
antiquark. For the case of a virtual photon with transverse polarization, we
compute the real NLO corrections, where the emitted gluon is present in the
final state. We first compute the tree-level cross-section for the production
of the quark-antiquark-gluon system and then deduce the real NLO corrections to
dihadron production by integrating out the kinematics of the gluon. We verify
in detail that, in the limit where the gluon is soft, our calculation
reproduces the (real piece of the) B-JIMWLK evolution of the leading-order
cross-section for quark-antiquark production. Similarly, in the limit where the
gluon is collinear with its emitter, we recover the real terms in the DGLAP
evolution of the fragmentation function. The virtual NLO corrections to
dihadron production will be presented by one of us in a subsequent publication.


  We prove a rigidity theorem for dominated H\"{o}lder cocycles with values on
diffeomorphism groups of a compact manifold over hyperbolic homeomorphisms.
More precisely, we show that if two such cocycles have equal periodic data,
then they are cohomologous.


  We present new bounds for the Berezin number inequalities which improve on
the existing bounds. We also obtain bounds for the Berezin norm of operators as
well as the sum of two operators.


  We tackle the problem of retrieving high-resolution (HR) texture maps of
objects that are captured from multiple view points. In the multi-view case,
model-based super-resolution (SR) methods have been recently proved to recover
high quality texture maps. On the other hand, the advent of deep learning-based
methods has already a significant impact on the problem of video and image SR.
Yet, a deep learning-based approach to super-resolve the appearance of 3D
objects is still missing. The main limitation of exploiting the power of deep
learning techniques in the multi-view case is the lack of data. We introduce a
3D appearance SR (3DASR) dataset based on the existing ETH3D [42], SyB3R [31],
MiddleBury, and our Collection of 3D scenes from TUM [21], Fountain [51] and
Relief [53]. We provide the high- and low-resolution texture maps, the 3D
geometric model, images and projection matrices. We exploit the power of 2D
learning-based SR methods and design networks suitable for the 3D multi-view
case. We incorporate the geometric information by introducing normal maps and
further improve the learning process. Experimental results demonstrate that our
proposed networks successfully incorporate the 3D geometric information and
super-resolve the texture maps.


  The excessive computational requirements of modern artificial neural networks
(ANNs) are posing limitations on the machines that can run them. Sparsification
of ANNs is often motivated by time, memory and energy savings only during model
inference, yielding no benefits during training. A growing body of work is now
focusing on providing the benefits of model sparsification also during
training. While these methods greatly improve the training efficiency, the
training algorithms yielding the most accurate models still materialize the
dense weights, or compute dense gradients during training. We propose an
efficient, always-sparse training algorithm with excellent scaling to larger
and sparser models, supported by its linear time complexity with respect to the
model width during training and inference. Moreover, our guided stochastic
exploration algorithm improves over the accuracy of previous sparse training
methods. We evaluate our method on CIFAR-10/100 and ImageNet using ResNet, VGG,
and ViT models, and compare it against a range of sparsification methods.


  In a recent letter (Phys. Rev. Lett. 84, 4629 (2000)), Lacks presents
evidence of a first order amorphous-amorphous transition in silica at T=0. He
calculates the free energy along a path of compression and successive
decompression of a sample of 108 SiO2 units. The free energy of the two
branches cross each other, and this is interpreted as evidence of a first order
transition. We show that this conclusion does not follow from the shown data,
since qualitatively the same phenomenology is obtained in a model where a first
order transition does not exist.


  We present the characteristics and performance of a demonstration experiment
devoted to the observation of ultra high- energy cosmic ray extensive air
showers using a radiodetection technique. In a first step, one antenna narrowed
band filtered acting as trigger, with a 4 $\sigma$ threshold above sky
background-level, was used to tag any radio transient in coincidence on the
antenna array. Recently, the addition of 4 particle detectors has allowed us to
observe cosmic ray events in coincidence with antennas.


  Modern GPUs synchronize threads grouped in a warp at every instruction. These
results in improving SIMD efficiency and makes sharing fetch and decode
resources possible. The number of threads included in each warp (or warp size)
affects divergence, synchronization overhead and the efficiency of memory
access coalescing. Small warps reduce the performance penalty associated with
branch and memory divergence at the expense of a reduction in memory
coalescing. Large warps enhance memory coalescing significantly but also
increase branch and memory divergence. Dynamic workload behavior, including
branch/memory divergence and coalescing, is an important factor in determining
the warp size returning best performance. Optimal warp size can vary from one
workload to another or from one program phase to the next. Based on this
observation, we propose Dynamic Warp Resizing (DWR). DWR takes innovative
microarchitectural steps to adjust warp size during runtime and according to
program characteristics. DWR outperforms static warp size decisions, up to 1.7X
to 2.28X, while imposing less than 1% area overhead. We investigate various
alternative configurations and show that DWR performs better for narrower SIMD
and larger caches.


  The generalized tight-binding model, based on the subenvelope functions of
distinct sublattices, is developed to investigate the magnetic quantization in
sliding bilayer graphenes. The relative shift of two graphene layers induces a
dramatic transformation between the Dirac-cone structure and the parabolic band
structure, and thus leads to drastic changes of Landau levels (LLs) in the
spatial symmetry, initial formation energy, intergroup anti-crossing, state
degeneracy and semiconductor-metal transition. There exist three kinds of LLs,
i.e., well-behaved, perturbed and undefined LLs, which are characterized by a
specific mode, a main mode plus side modes, and a disordered mode,
respectively. Such LLs are clearly revealed in diverse magneto-optical
selection rules. Specially, the undefined LLs frequently exhibit intergroup
anti-crossings in the field-dependent energy spectra, and show a large number
of absorption peaks without optical selection rules.


  To measure the strong coupling alpha_s from event shape observables two
ingredients are necessary. A perturbative prediction containing the dependence
of observables on alpha_s and a description of the hadronisation process to
match the perturbative prediction with the hadronic data.
  As perturbative prediction O(alpha_s^2), NLLA and combined calculations are
available. Beside the well known Monte-Carlo based models also analytical
predictions, so called power corrections, exist to describe the hadronisation.
Advantages and disadvantages of the different resulting methods for determining
the strong coupling and its energy dependence will be discussed, the newest
DELPHI results will be presented, and an overview of the LEP results will be
included.


  The optical excitations of elongated graphene nanoflakes of finite length are
investigated theoretically through quantum chemistry semi-empirical approaches.
The spectra and the resulting dipole fields are analyzed, accounting in full
atomistic details for quantum confinement effects, which are crucial in the
nanoscale regime. We find that the optical spectra of these nanostructures are
dominated at low energy by excitations with strong intensity, comprised of
characteristic coherent combinations of a few single-particle transitions with
comparable weight. They give rise to stationary collective oscillations of the
photoexcited carrier density extending throughout the flake, and to a strong
dipole and field enhancement. This behavior is robust with respect to width and
length variations, thus ensuring tunability in a large frequency range. The
implications for nanoantennas and other nanoplasmonic applications are
discussed for realistic geometries.


  We re-examine like-sign dilepton signals at the Fermilab Tevatron assuming
that the excess high-$Q^2$ events recently seen at HERA are due to scalar
resonances such as squarks of $R$-parity-violating supersymmetry. For gluinos
in the mass range of 200--350 GeV, the like-sign dilepton signal can help to
make the crucial distinction between the most favoured squark explanation and
other proposed solutions.


  I present a hybrid matrix factorisation model representing users and items as
linear combinations of their content features' latent factors. The model
outperforms both collaborative and content-based models in cold-start or sparse
interaction data scenarios (using both user and item metadata), and performs at
least as well as a pure collaborative matrix factorisation model where
interaction data is abundant. Additionally, feature embeddings produced by the
model encode semantic information in a way reminiscent of word embedding
approaches, making them useful for a range of related tasks such as tag
recommendations.


  This work is devoted to generating optimal guidance commands in real time for
attitude-constrained solar sailcrafts in coplanar circular-to-circular
interplanetary transfers. Firstly, a nonlinear optimal control problem is
established, and necessary conditions for optimality are derived by the
Pontryagin's Minimum Principle. Under some assumptions, the attitude
constraints are rewritten as control constraints, which are replaced by a
saturation function so that a parameterized system is formulated to generate an
optimal trajectory via solving an initial value problem. This approach allows
for the efficient generation of a dataset containing optimal samples, which are
essential for training Neural Networks (NNs) to achieve real-time
implementation. However, the optimal guidance command may suddenly change from
one extreme to another, resulting in discontinuous jumps that generally impair
the NN's approximation performance. To address this issue, we use two co-states
that the optimal guidance command depends on, to detect discontinuous jumps. A
procedure for preprocessing these jumps is then established, thereby ensuring
that the preprocessed guidance command remains smooth. Meanwhile, the sign of
one co-state is found to be sufficient to revert the preprocessed guidance
command back into the original optimal guidance command. Furthermore, three NNs
are built and trained offline, and they cooperate together to precisely
generate the optimal guidance command in real time. Finally, numerical
simulations are presented to demonstrate the developments of the paper.


  We show that supersymmetry breaking in a class of theories with SU(N) x
SU(N-2) gauge symmetry can be studied in a calculable sigma model. We use the
sigma model to show that the supersymmetry breaking vacuum in these theories
leaves a large subgroup of flavor symmetries intact, and to calculate the
masses of the low-lying states. By embedding the Standard Model gauge groups in
the unbroken flavor symmetry group we construct a class of models in which
supersymmetry breaking is communicated by both gravitational and gauge
interactions. One distinguishing feature of these models is that the messenger
fields, responsible for the gauge mediated communication of supersymmetry
breaking, are an integral part of the supersymmetry breaking sector. We also
show how, by lowering the scale that suppresses the nonrenormalizable
operators, a class of purely gauge mediated models with a combined
supersymmetry breaking-cum-messenger sector can be built. We briefly discuss
the phenomenological features of the models we construct.


  We study the Cauchy problem for the first order evolutive Hamilton-Jacobi
equation with a Lipschitz initial condition. The Hamiltonian is not necessarily
convex in the momentum variable and not a priori compactly supported. We build
and study an operator giving a variational solution of this problem, and get
local Lipschitz estimates on this operator. Iterating this variational operator
we obtain the viscosity operator and extend the estimates to the viscosity
framework. We also check that the construction of the variational operator
gives the Lax-Oleinik semigroup if the Hamiltonian is convex or concave in the
momentum variable.


  Coherence rephasing is an essential step in quantum storage protocols that
use echo-based strategies. We present a thorough analysis on how two adiabatic
rapid passages (ARP) are able to rephase atomic coherences in an
inhomogeneously broadened ensemble. We consider both the cases of optical and
spin coherences, rephased by optical or radio-frequency (rf) ARPs,
respectively. We show how a rephasing sequence consisting of two ARPs in a
double-echo scheme is equivalent to the identity operator (any state can be
recovered), as long as certain conditions are fulfilled. Our mathematical
treatment of the ARPs leads to a very simple geometrical interpretation within
the Bloch sphere that permits a visual comprehension of the rephasing process.
We also identify the conditions that ensure the rephasing, finding that the
phase of the optical or rf ARP fields plays a key role in the capability of the
sequence to preserve the phase of the superposition state. This settles a
difference between optical and rf ARPs, since field phase control is not
readily guaranteed in the former case. We also provide a quantitative
comparison between $\pi$-pulse and ARP rephasing efficiencies, showing the
superiority of the latter. We experimentally verify the conclusions of our
analysis through rf ARP rephasing sequencies performed on the rare-earth
ion-doped crystal Tm$^{3+}$:YAG, of interest in quantum memories.


  We extend the Deep Image Prior (DIP) framework to one-dimensional signals.
DIP is using a randomly initialized convolutional neural network (CNN) to solve
linear inverse problems by optimizing over weights to fit the observed
measurements. Our main finding is that properly tuned one-dimensional
convolutional architectures provide an excellent Deep Image Prior for various
types of temporal signals including audio, biological signals, and sensor
measurements. We show that our network can be used in a variety of recovery
tasks including missing value imputation, blind denoising, and compressed
sensing from random Gaussian projections. The key challenge is how to avoid
overfitting by carefully tuning early stopping, total variation, and weight
decay regularization. Our method requires up to 4 times fewer measurements than
Lasso and outperforms NLM-VAMP for random Gaussian measurements on audio
signals, has similar imputation performance to a Kalman state-space model on a
variety of data, and outperforms wavelet filtering in removing additive noise
from air-quality sensor readings.


  In this paper, we construct two kinds of exponential SAV approach with
relaxation (R-ESAV) for dissipative system. The constructed schemes are linear
and unconditionally energy stable. They can guarantee the positive property of
SAV without any assumption compared with R-SAV and R-GSAV approaches, preserve
all the advantages of the ESAV approach and satiesfy dissipation law with
respect to a modified energy which is directly related to the original free
energy. Moreover the second version of R-ESAV approach is easy to construct
high-order BDF$k$ schemes. Especially for Navier-Stokes equations, we construct
wo kinds of novel schemes based on the R-ESAV method. Finally, ample numerical
examples are presented to exhibit that the proposed approaches are accurate and
effective.


  Reasoning using negation is known to be difficult for transformer-based
language models. While previous studies have used the tools of
psycholinguistics to probe a transformer's ability to reason over negation,
none have focused on the types of negation studied in developmental psychology.
We explore how well transformers can process such categories of negation, by
framing the problem as a natural language inference (NLI) task. We curate a set
of diagnostic questions for our target categories from popular NLI datasets and
evaluate how well a suite of models reason over them. We find that models
perform consistently better only on certain categories, suggesting clear
distinctions in how they are processed.


  It is well known that any model for derived manifolds must form a higher
category. In this paper, we propose a universal property for this higher
category, classifying it up to equivalence. Namely, the $\infty$-category
$\mathbf{DMfd}$ of derived manifolds has finite limits, is idempotent complete,
and receives a functor from the category of manifolds which preserves
transverse pullbacks and the terminal object, and moreover is universal with
respect to these properties. We then show this universal property is equivalent
to another one, intimately linking the $\infty$-category of derived manifolds
to the theory of $C^\infty$-rings. More precisely, $\mathbb{R}$ is a
$C^\infty$-ring object in $\mathbf{DMfd}$, and the pair
$\left(\mathbf{DMfd},\mathbb{R}\right)$ is universal among idempotent complete
$\infty$-categories with finite limits and a $C^\infty$-ring object. We then
show that (a slight extension beyond the quasi-smooth setting of) Spivak's
original model satisfies our universal property.


  The CALICE collaboration is developing calorimeters for a future linear
collider, and has collected a large amount of physics data during test beam
efforts. For the analysis of these data, standard software available for linear
collider detector studies is applied. This software provides reconstruction of
raw data, simulation, digitization and data management, which is based on grid
tools. The data format for analysis is compatible with the general linear
collider software. Moreover, existing frameworks such as Marlin are employed
for the CALICE software needs. The structure and features of the software
framework are reported here as well as results from the application of this
software to test beam data.


  The connected sum of two flat virtual knots depends on the choice of diagrams
and basepoints. We show that any minimal crossing diagram of a composite flat
virtual knot is a connected sum diagram. We also show the crossing number of
flat virtual knots is super-additive under connected sum.


  On the basis of ab initio density functional theory (DFT) calculations, we
derive the underlying microscopic model Hamiltonian for TiOCl, a unique system
that shows two consecutive phase transitions from a Mott insulator to a
spin-Peierls insulator through a structurally incommensurate phase. We show
with our model that the presence of magnetic frustration in TiOCl leads to a
competition with the spin-Peierls distortion, which results in the novel
incommensurate phase. In addition, our calculations indicate that the
spin-Peierls state is triggered by adiabatic phonons, which is essential for
understanding the nature of the phase transition.


  We study the pre-Lie algebra of specified Feynman graphs $\wt{V}_{\Cal T}$
and we define a pre-Lie structure on its doubling space $\wt{\Cal F}_{\Cal T}$.
We prove that $\wt{\Cal F}_{\Cal T}$ is pre-Lie module on $\wt{V}_{\Cal T}$ and
we find some relations between the two pre-Lie structures. Also, we study the
enveloping algebras of two pre-Lie algebras denoted respectively by $(\wt {\Cal
D'}_{\Cal T}, \bigstar, \Phi)$ and $(\wt {\Cal H'}_{\Cal T}, \star, \Psi)$ and
we prove that $(\wt {\Cal D'}_{\Cal T}, \bigstar, \Phi)$ is a module-bialgebra
on $(\wt {\Cal H'}_{\Cal T}, \star, \Psi)$.


  We present precise photometric estimates of stellar parameters, including
effective temperature, metallicity, luminosity classification, distance, and
stellar age, for nearly 26 million stars using the methodology developed in the
first paper of this series, based on the stellar colors from the Stellar
Abundances and Galactic Evolution Survey (SAGES) DR1 and Gaia EDR3. The optimal
design of stellar-parameter sensitive $uv$ filters by SAGES has enabled us to
determine photometric-metallicity estimates down to $-3.5$, similar to our
previous results with the SkyMapper Southern Survey (SMSS), yielding a large
sample of over five million metal-poor (MP; [Fe/H]$\le -1.0$) stars and nearly
one million very metal-poor (VMP; [Fe/H]$\le -2.0$) stars. The typical
precision is around $0.1$ dex for both dwarf and giant stars with
[Fe/H]$>-1.0$, and 0.15-0.25/0.3-0.4 dex for dwarf/giant stars with
[Fe/H]$<-1.0$. Using the precise parallax measurements and stellar colors from
Gaia, effective temperature, luminosity classification, distance and stellar
age are further derived for our sample stars. This huge data set in the
Northern sky from SAGES, together with similar data in the Southern sky from
SMSS, will greatly advance our understanding of the Milky Way, in particular
its formation and evolution.


  Multifrequency polarimetry with the VLBA confirms the previously reported
time-varying Faraday rotation measure (RM) in the quasar 3C-279. Variability in
the RM and electric vector position angle (EVPA) of the jet component (C4) is
seen making it an unreliable absolute EVPA calibrator. 3C-273 is also shown to
vary its RM structure on 1.5 year time-scales. Variation in the RM properties
of quasars may result from a Faraday screen which changes on time-scales of a
few years, or from the motion of jet components which sample spatial variations
in the screen. A new component emerging from the core of 3C-279 appears to be
starting to sample such a spatial variation. Future monitoring of this
component and its RM properties is suggested as a diagnostic of the narrow line
region in 3C-279. We also present a new method of EVPA calibration using the
VLA Monitoring Program.


  This paper presents an algorithm for searching for the minimum number of
neurons in fully connected layers of an arbitrary network solving given
problem, which does not require multiple training of the network with different
number of neurons. The algorithm is based at training the initial wide network
using the cross-validation method over at least two folds. Then by using
truncated singular value decomposition autoencoder inserted after the studied
layer of trained network we search the minimum number of neurons in inference
only mode of the network.
  It is shown that the minimum number of neurons in a fully connected layer
could be interpreted not as network hyperparameter associated with the other
hyperparameters of the network, but as internal (latent) property of the
solution, determined by the network architecture, the training dataset, layer
position, and the quality metric used. So the minimum number of neurons can be
estimated for each hidden fully connected layer independently. The proposed
algorithm is the first approximation for estimating the minimum number of
neurons in the layer, since, on the one hand, the algorithm does not guarantee
that a neural network with the found number of neurons can be trained to the
required quality, and on the other hand, it searches for the minimum number of
neurons in a limited class of possible solutions.
  The solution was tested on several datasets in classification and regression
problems.


  NGC6217 is a nearby spiral galaxy with a starburst region near its center.
Evidence for a low luminosity Active Galactic Nucleus (AGN) in its core has
also been found in optical spectra. Intriguingly, X-ray observations by ROSAT
revealed three knots aligned with the galaxy center, resembling a jet
structure. This paper presents a study of XMM-Newton observations made to
assess the hypothesis of a jet emitted from the center of NGC6217. The XMM data
confirm the knots found with ROSAT and our spectral analysis shows that they
have similar spectral properties with a hard photon index Gamma~1.7. The core
of NGC6217 is well fitted by a model with an AGN and a starburst component,
where the AGN contributes at most 46% of the total flux. The candidate jet has
an apparent length ~15 kpc and a luminosity of ~5 X 10^38 erg/s. It stands out
by being hosted by a spiral galaxy, since jets are more widely associated with
ellipticals. To explain the jet launching mechanism we consider the hypothesis
of an advection dominated accretion flow with a low accretion rate. The
candidate jet emitted from NGC6217 is intriguing since it represents a
challenge to the current knowledge of the connection between AGN, jets and host
galaxies.


  We demonstrate direct transport between two opposing sets of Yu-Shiba-Rusinov
(YSR) subgap states realized in a double quantum dot. This sub-gap transport
relies on intrinsic quasiparticle relaxation, but the tunability of the device
allows us to explore also an additional relaxation mechanism based on charge
transferring Andreev reflections. The transition between these two relaxation
regimes is identified in the experiment as a marked gate-induced stepwise
change in conductance. We present a transport calculation, including YSR bound
states and multiple Andreev reflections alongside with quasiparticle
relaxation, due to a weak tunnel coupling to a nearby normal metal, and obtain
excellent agreement with the data.


  Galaxy shapes are not randomly oriented, rather they are statistically
aligned in a way that can depend on formation environment, history and galaxy
type. Studying the alignment of galaxies can therefore deliver important
information about the physics of galaxy formation and evolution as well as the
growth of structure in the Universe. In this review paper we summarise key
measurements of galaxy alignments, divided by galaxy type, scale and
environment. We also cover the statistics and formalism necessary to understand
the observations in the literature. With the emergence of weak gravitational
lensing as a precision probe of cosmology, galaxy alignments have taken on an
added importance because they can mimic cosmic shear, the effect of
gravitational lensing by large-scale structure on observed galaxy shapes. This
makes galaxy alignments, commonly referred to as intrinsic alignments, an
important systematic effect in weak lensing studies. We quantify the impact of
intrinsic alignments on cosmic shear surveys and finish by reviewing practical
mitigation techniques which attempt to remove contamination by intrinsic
alignments.


  The semiclassical theory of Bloch wave packet dynamics predicts a
self-rotation angular momentum in asymmetric periodic potentials, which has
never been observed. We show how this is manifested in
  Bose-Einstein condensed atoms in optical lattices. Displacing the wave packet
to a corner of the Brillouin zone we obtain a current distribution with a
non-quantized angular momentum, independent of the size of the distribution. A
weak interatomic interaction does not modify the results, affecting only the
rate of spreading in the lattice. A strong repulsive interaction results in a
collapse of the wavefunction into matter-wave lattice solitons.


  We show the relative bicentralizer flow and the relative flow of weights are
isomorphic for an inclusion of injective factors of type III$_1$ with finite
index, or an irreducible discrete inclusion whose small algebra is an injective
factor of type III$_1$.


  This paper considers the information bottleneck (IB) problem of a Rayleigh
fading multiple-input multiple-out (MIMO) channel. Due to the bottleneck
constraint, it is impossible for the oblivious relay to inform the destination
node of the perfect channel state information (CSI) in each channel
realization. To evaluate the bottleneck rate, we provide an upper bound by
assuming that the destination node can get the perfect CSI at no cost and two
achievable schemes with simple symbol-by-symbol relay processing and
compression. Numerical results show that the lower bounds obtained by the
proposed achievable schemes can come close to the upper bound on a wide range
of relevant system parameters.


  A detailed study of the electric field gradient (EFG) across the Pr1-xCaxMnO3
phase diagram and its temperature dependence is given. Clearly, distinct EFG
behaviours for samples outside/inside the charge order (CO) region are
observed. The EFG temperature dependence evidences a new phase transition
occurring over the broad CO region of the phase diagram. This transition is
discontinuous and occurs at temperatures between the charge ordering and the
Neel temperatures. The features observed in the EFG are associated with polar
atomic vibrations which eventually lead to a spontaneous local electric
polarization below CO transition.


  We consider a question: Can a given AD-family be ADR for two orthogonal
uncountable towers? If $b > \omega_1$, then we rebuilt any AD-family of the
cardinality $\omega_1$ onto a Hausdorff pre-gap. Moreover, if a such AD-family
is a Luzin gap, then we obtain a Hausdorff gap. Under $b = \omega_1$, a similar
rebuilding is impossible.


  We analyze the time-dependent free energy functionals of the semiclassical
one-dimensional Bose-Hubbard chain. We first review the weakly chaotic dynamics
and the consequent early-time anomalous diffusion in the system. The anomalous
diffusion is robust, appears with strictly quantized coefficients, and persists
even for very long chains (more than hundred sites), crossing over to normal
diffusion at late times. We identify fast (angle) and slow (action) variables
and thus consider annealed and quenched partition functions, corresponding to
fixing the actions and integrating over the actions, respectively. We observe
the leading quantum effects in the annealed free energy, whereas the quenched
energy is undefined in the thermodynamic limit, signaling the absence of
thermodynamic equilibrium in the quenched regime. But already the leading
correction away from the quenched regime reproduces the annealed partition
function exactly. This encapsulates the fact that in both slow- and fast-chaos
regime both the anomalous and the normal diffusion can be seen (though at
different times).


  The Stern-Brocot tree and Minkowki's question mark function $?(x)$ (or
Conway's box function) are related to the continued fraction expansion of
numbers from Q with unary encoding of the partial denominators. We first define
binary encodings $C_I, C_{II}$ of the natural numbers, adapted to the
Gau\ss-Kuz'min measure for the distribution of partial denominators. We then
define the V$_1$ tree as analogue to the Stern-Brocot tree, using the binary
encondings $C_I, C_{II}$. We shall see that all numbers with denominator $q$
are present in the first $3.44\log_2(q)$ levels, instead of $1/q$ appearing in
level $q$ in the Stern-Brocot tree. The extension of the V$_1$ tree, the V
tree, covers all numbers from Q exactly once. We also define the binary version
of Minkowski's question mark function, $?_V$, and conjecture that it has no
derivative at rational points (for the original, $?'(x)=0, x\in Q$).


  Our investigation focuses on the rapidity-dependent directed flow, $v_1(y)$,
of identified hadrons in Au+Au collisions across a broad range of $\sqrt{s_{\rm
NN}}$ from 7.7 to 200 GeV. Employing a (3+1)-dimensional hybrid framework, our
study successfully reproduces the characteristic features of the measured
$v_1(y)$ for both mesons and baryons across the considered beam energies.
Notably, our analysis reveals the constraining power of baryonic $v_1(y)$ on
the initial baryon stopping mechanism. Together with mesonic $v_1(y)$, the
directed flow serves as a crucial tool for probing the equation of state
governing dense nuclear matter at finite chemical potentials.


  Survivability is mission-critical for elastic optical networks (EONs) as they
are expected to carry an enormous amount of data. In this paper, we consider
the problem of designing shared backup path protection (SBPP) based EON that
facilitates the minimum quality-of-transmission (QoT) assured allocation
against physical layer impairments (PLIs) under any single link/shared risk
link group (SRLG) failure for static and dynamic traffic scenarios. In general,
the effect of PLIs on lightpath varies based on the location of failure of a
link as it introduces different active working and backup paths. To address
these issues in the design of SBPP EON, we formulate a mixed integer linear
programming (MILP) based robust optimization framework for static traffic with
the objective of minimizing overall fragmentation. In this process, we use the
efficient bitloading technique for spectrum allocation for the first time in
survivable EONs. In addition, we propose a novel SBPP-impairment aware
(SBPP-IA) algorithm considering the limitations of MILP for larger networks.
For this purpose, we introduce a novel sorting technique named most congested
working-least congested backup first (MCW-LCBF) to sort the given set of static
requests. Next, we employ our SBPP-IA algorithm for dynamic traffic scenario
and compare it with existing algorithms in terms of different QoT parameters.
We demonstrated through simulations that our study provides around 40% more QoT
guaranteed requests compared to existing ones.


  Special role in computer simulations of supercooled liquid and glasses is
played by few general models representing certain classes of real glass-forming
systems. Recently, it was shown that one of the most widely used model
glassformers -- Kob-Andersen binary Lennard-Jones mixture -- crystalizes in
quite lengthy molecular dynamics simulations and, moreover, it is in fact a
very poor glassformer at large system sizes. Thus, our understanding of
crystallization stability of model glassformers is far from complete due to the
fact that relatively small system sizes and short timescales have been
considered so far. Here we address this issue for two embedded atom models
intensively used last years in numerical studies of Cu-Zr-(Al) bulk metallic
glasses. We consider ${\rm Cu_{64.5}Zr_{35.5}}$ and ${\rm
Cu_{46}Zr_{46}Al_{8}}$ alloys as those having high glass-forming ability.
Exploring their structural evolution at continuous cooling and isothermal
annealing, we observe that both systems nucleate in sufficiently lengthy
simulations, though ${\rm Cu_{46}Zr_{46}Al_{8}}$ demonstrate order of magnitude
higher critical nucleation time. Moreover, ${\rm Cu_{64.5}Zr_{35.5}}$ is
actually unstable to crystallization for large system sizes ($N > 20,000$).
Both systems crystallize with the formation of tetrahedrally close packed Laves
phases of different types. We reveal that structure of both systems in liquid
and glassy state contains comparable amount of polytetrahedral clusters. We
argue that nucleation instability of simulated ${\rm Cu_{64.5}Zr_{35.5}}$ alloy
is due to the fact that its composition is very close to that for stable ${\rm
Cu_2 Zr}$ compound with C15 Laves phase structure.


  In this article, we give an exposition on the Holmes-Thompson theory
developed by Alvarez. The space of geodesics in Minkowski space has a
symplectic structure which is induced by the projection from the sphere-bundle.
we show that it can be also obtained from the symplectic structure on the
tangent bundle of the Riemannian manifold, the tangent bundle of the Minkowski
unit sphere. We give detailed descriptions and expositions on Holmes-Thompson
volumes in Minkowski space by the symplectic structure and the Crofton measures
for them. For the Minkowski plane, a normed two dimensional space, we express
the area explicitly in an integral geometry way, by putting a measure on the
plane, which gives an extension of Alvarez's result for higher dimensional
cases.


  Neural networks and particularly Deep learning have been comparatively little
studied from the theoretical point of view. Conversely, Mathematical Morphology
is a discipline with solid theoretical foundations. We combine these domains to
propose a new type of neural architecture that is theoretically more
explainable. We introduce a Binary Morphological Neural Network (BiMoNN) built
upon the convolutional neural network. We design it for learning morphological
networks with binary inputs and outputs. We demonstrate an equivalence between
BiMoNNs and morphological operators that we can use to binarize entire
networks. These can learn classical morphological operators and show promising
results on a medical imaging application.


  Systems of spins with strong dipolar interactions and controlled
dimensionality enable new explorations in quantum sensing and simulation. In
this work, we investigate the creation of strong dipolar interactions in a
two-dimensional ensemble of nitrogen-vacancy (NV) centers generated via
plasma-enhanced chemical vapor deposition (PECVD) on (111)-oriented diamond
substrates. We find that diamond growth on the (111) plane yields high
incorporation of spins, both nitrogen and NV centers, where the density of the
latter is tunable via the miscut of the diamond substrate. Our process allows
us to form dense, preferentially aligned, 2D NV ensembles with
volume-normalized AC sensitivity down to $\eta_{AC}$ = 810 pT um$^{3/2}$
Hz$^{-1/2}$. Furthermore, we show that (111) affords maximally positive dipolar
interactions amongst a 2D NV ensemble, which is crucial for leveraging
dipolar-driven entanglement schemes and exploring new interacting spin physics.


  It is studied a 3-dimensional Riemannian manifold equipped with a tensor
structure of type (1,1), whose third power is the identity. This structure has
a circulant matrix with respect to some basis, i.e. the structure is circulant.
On such a manifold a fundamental tensor by the metric and by the covariant
derivative of the circulant structure is defined. An important characteristic
identity for this tensor is obtained. It is established that the image of the
fundamental tensor with respect to the usual conformal transformation satisfies
the same identity. A Lie group as a manifold of the considered type is
constructed and some of its geometrical characteristics are found.


  We present AlloyInEcore, a tool for specifying metamodels with their static
semantics to facilitate automated, formal reasoning on models. Software
development projects require that software systems be specified in various
models (e.g., requirements models, architecture models, test models, and source
code). It is crucial to reason about those models to ensure the correct and
complete system specifications. AlloyInEcore allows the user to specify
metamodels with their static semantics, while, using the semantics, it
automatically detects inconsistent models, and completes partial models. It has
been evaluated on three industrial case studies in the automotive domain
(https://modelwriter.github.io/AlloyInEcore/).


  This paper accompanies with our recent work on quantum error correction (QEC)
and entanglement spectrum (ES) in tensor networks (arXiv:1806.05007). We
propose a general framework for planar tensor network state with tensor
constraints as a model for $AdS_3/CFT_2$ correspondence, which could be viewed
as a generalization of hyperinvariant tensor networks recently proposed by
Evenbly. We elaborate our proposal on tensor chains in a tensor network by
tiling $H^2$ space and provide a diagrammatical description for general
multi-tensor constraints in terms of tensor chains, which forms a generalized
greedy algorithm. The behavior of tensor chains under the action of greedy
algorithm is investigated in detail. In particular, for a given set of tensor
constraints, a critically protected (CP) tensor chain can be figured out and
evaluated by its average reduced interior angle. We classify tensor networks
according to their ability of QEC and the flatness of ES. The corresponding
geometric description of critical protection over the hyperbolic space is also
given.


  We present a formalism to study quantum networks made up by single-channel
quantum wires in the presence of Rashba spin-orbit coupling and magnetic field.
In particular, linear transport through one-dimensional and two-dimensional
finite-size networks is studied by means of the scattering formalism. In some
particular quantum networks, the action of the magnetic field or of the Rashba
spin-orbit coupling induces localization of the electron wave function. This
phenomenon, which relies on both the quantum-mechanical interference and the
geometry of the network, is manifested through the suppression of the
conductance for specific values of the spin-orbit-coupling strength or of the
magnetic field. Furthermore, the interplay of the Aharonov-Bohm phases and of
the non-Abelian phases introduced by spin-orbit coupling, is discussed in a
number of cases.


  The physics behind the acceleration of the cosmic expansion can be elucidated
through comparison of the predictions of dark energy equations of state to
observational data. In seeking to optimize this, we investigate the advantages
and disadvantages of using principal component analysis, uncorrelated
bandpowers, and the equation of state within redshift bins. We demonstrate that
no one technique is a panacea, with tension between clear physical
interpretation from localization and from decorrelated errors, as well as model
dependence and form dependence. Specific lessons include the critical role of
proper treatment of the high redshift expansion history and the lack of a
unique, well defined signal-to-noise or figure of merit.


  Linear processes are defined as a discrete-time convolution between a kernel
and an infinite sequence of i.i.d. random variables. We modify this convolution
by introducing decimation, that is, by stretching time accordingly. We then
establish central limit theorems for arrays of squares of such decimated
processes. These theorems are used to obtain the asymptotic behavior of
estimators of the spectral density at specific frequencies. Another
application, treated elsewhere, concerns the estimation of the long-memory
parameter in time-series, using wavelets.


  A convection-driven MHD dynamo in a rotating spherical shell, with clearly
defined structural elements in the flow and magnetic field, is simulated
numerically. Such dynamos can be called deterministic, in contrast to those
explicitly dependent on the assumed properties of turbulence. The cases most
interesting from the standpoint of studying the nature of stellar magnetism
demonstrate the following features. On a global scale, the convective flows can
maintain a ``general'' magnetic field with a sign-alternating dipolar
component. Local (in many cases, bipolar) magnetic structures are associated
with convection cells. Disintegrating local structures change into background
fields, which drift toward the poles. From time to time, reversals of the
magnetic fields in the polar regions occur, as ``new'' background fields expel
the ``old'' fields.


  Obtaining text datasets with semantic annotations is an effortful process,
yet crucial for supervised training in natural language processsing (NLP). In
general, developing and applying new NLP pipelines in domain-specific contexts
for tasks often requires custom designed datasets to address NLP tasks in
supervised machine learning fashion. When operating in non-English languages
for medical data processing, this exposes several minor and major,
interconnected problems such as lack of task-matching datasets as well as
task-specific pre-trained models. In our work we suggest to leverage pretrained
language models for training data acquisition in order to retrieve sufficiently
large datasets for training smaller and more efficient models for use-case
specific tasks. To demonstrate the effectiveness of your approach, we create a
custom dataset which we use to train a medical NER model for German texts,
GPTNERMED, yet our method remains language-independent in principle. Our
obtained dataset as well as our pre-trained models are publicly available at:
https://github.com/frankkramer-lab/GPTNERMED


  Deep learning is still not a very common tool in speaker verification field.
We study deep convolutional neural network performance in the text-prompted
speaker verification task. The prompted passphrase is segmented into word
states - i.e. digits -to test each digit utterance separately. We train a
single high-level feature extractor for all states and use cosine similarity
metric for scoring. The key feature of our network is the Max-Feature-Map
activation function, which acts as an embedded feature selector. By using
multitask learning scheme to train the high-level feature extractor we were
able to surpass the classic baseline systems in terms of quality and achieved
impressive results for such a novice approach, getting 2.85% EER on the RSR2015
evaluation set. Fusion of the proposed and the baseline systems improves this
result.


  Discovering causal relations from observational time series without making
the stationary assumption is a significant challenge. In practice, this
challenge is common in many areas, such as retail sales, transportation
systems, and medical science. Here, we consider this problem for a class of
non-stationary time series. The structural causal model (SCM) of this type of
time series, called the semi-stationary time series, exhibits that a finite
number of different causal mechanisms occur sequentially and periodically
across time. This model holds considerable practical utility because it can
represent periodicity, including common occurrences such as seasonality and
diurnal variation. We propose a constraint-based, non-parametric algorithm for
discovering causal relations in this setting. The resulting algorithm,
PCMCI$_{\Omega}$, can capture the alternating and recurring changes in the
causal mechanisms and then identify the underlying causal graph with
conditional independence (CI) tests. We show that this algorithm is sound in
identifying causal relations on discrete time series. We validate the algorithm
with extensive experiments on continuous and discrete simulated data. We also
apply our algorithm to a real-world climate dataset.