  We report on the effect of sp3 hybridized carbon atoms in acene derivatives
adsorbed on metal surfaces, namely decoupling the molecules from the supporting
substrates. In particular, we have used a Ag(100) substrate and hydrogenated
heptacene molecules, in which the longest conjugated segment determining its
frontier molecular orbitals amounts to five consecutive rings. The
non-planarity that the sp3 atoms impose on the carbon backbone results in
electronically decoupled molecules, as demonstrated by the presence of charging
resonances in dI/dV tunneling spectra and the associated double tunneling
barriers, or in the Kondo peak that is due to a net spin S=1/2 of the molecule
as its LUMO becomes singly charged. The spatially dependent appearance of the
charging resonances as peaks or dips in the differential conductance spectra is
further understood in terms of the tunneling barrier variation upon molecular
charging, as well as of the different orbitals involved in the tunneling
process.


  We study the effect of nonmagnetic impurities on the local density of states
(LDOS) in s-wave superconductors. The quasiclassical equations of
superconductivity are solved selfconsistently to show how LDOS evolves with
impurity concentration. The spatially averaged zero-energy LDOS is a linear
function of magnetic induction in low fields, N(E=0)=cB/H_{c2}, for all
impurity concentration. The constant of proportionality "c" depends weakly on
the electron mean free path. We present numerical data for differential
conductance and spatial profile of zero-energy LDOS which can help in
estimating the mean free path through the LDOS measurement.


  The phenomenological Landau-Lifshitz-Gilbert (LLG) equation of motion remains
as the cornerstone of contemporary magnetisation dynamics studies, wherein the
Gilbert damping parameter has been attributed to first-order relativistic
effects. To include magnetic inertial effects the LLG equation has previously
been extended with a supplemental inertia term and the arising inertial
dynamics has been related to second-order relativistic effects. Here we start
from the relativistic Dirac equation and, performing a Foldy-Wouthuysen
transformation, derive a generalised Pauli spin Hamiltonian that contains
relativistic correction terms to any higher order. Using the Heisenberg
equation of spin motion we derive general relativistic expressions for the
tensorial Gilbert damping and magnetic inertia parameters, and show that these
tensors can be expressed as series of higher-order relativistic correction
terms. We further show that, in the case of a harmonic external driving field,
these series can be summed and we provide closed analytical expressions for the
Gilbert and inertial parameters that are functions of the frequency of the
driving field.


  Interstellar magnetic fields exist over a broad range of spatial scales,
extending from the large Galactic scales ($\sim 10$ kpc) down to the very small
dissipative scales ($\ll 1$ pc). In this paper, we use a set of 490 pulsars
distributed over roughly one third of the Galactic disk out to a radius $R
\simeq 10$ kpc (assuming $R_\odot = 8.5$ kpc) and combine their observed
rotation and dispersion measures with their estimated distances to derive the
spatial energy spectrum of the Galactic interstellar magnetic field over the
scale range $0.5 - 15$ kpc. We obtain a nearly flat spectrum, with a 1D
power-law index $\alpha=-0.37\pm0.10$ for $E_{\rm B}(k)=C k^{\alpha}$ and an
rms field strength of approximately $6 \mu$G over the relevant scales. Our
study complements the derivation of the magnetic energy spectrum over the scale
range $0.03 - 100$ pc by \citet{ms96b}, showing that the magnetic spectrum
becomes flatter at larger scales. This observational result is discussed in the
framework of current theoretical and numerical models.


  We explain a method to find internal quark configurations of exotic hadron
candidates by using the constituent counting rule. The counting rule was
theoretically predicted in perturbative QCD for hard exclusive hadron
reactions, and it has been tested in experiments for stable hadrons including
compound systems of hadrons such as the deuteron, $^3$H, and $^3$He. It
indicates that the cross section scales as $d\sigma /dt \sim 1/s^{n-2}$, where
$s$ is the center-of-mass energy squared and $n$ is the total number of
constituents. We apply this method for finding internal configurations of
exotic hadron candidates, especially $\Lambda (1405)$. There is a possibility
that $\Lambda (1405)$ could be five-quark state or a $\bar K N$ molecule, and
scaling properties should be different between the ordinary three-quark state
or five-quark one. We predict such a difference in $\pi^- + p \to K^0 + \Lambda
(1405)$, and it could be experimentally tested, for example, at J-PARC. On the
other hand, there are already measurements for $\gamma + p \to K^+ + \Lambda
(1405)$ as well as the ground $\Lambda$ in photoproduction reactions. Analyzing
such data, we found an interesting indication that $\Lambda (1405)$ looks like
a five-quark state at medium energies and a three-quark one at high energies.
However, accurate higher-energy measurements are necessary for drawing a solid
conclusion, and it should be done at JLab by using the updated 12 GeV electron
beam. Furthermore, we discuss studies of exotic hadron candidates, such as $f_0
(980)$ and $a_0 (980)$, in electron-positron annihilation by using generalized
distribution amplitudes and the counting rule. These studies should be possible
as a KEKB experiment.


  Motivated by the noisy and fluctuating behavior of current quantum computing
devices, this paper presents a data-driven characterization approach for
estimating transition frequencies and decay times in a Lindbladian dynamical
model of a superconducting quantum device. The data includes parity events in
the transition frequency between the first and second excited states. A simple
but effective mathematical model, based upon averaging solutions of two
Lindbladian models, is demonstrated to accurately capture the experimental
observations. A deterministic point estimate of the device parameters is first
performed to minimize the misfit between data and Lindbladian simulations.
These estimates are used to make an informed choice of prior distributions for
the subsequent Bayesian inference. An additive Gaussian noise model is
developed for the likelihood function, which includes two hyper-parameters to
capture the noise structure of the data. The outcome of the Bayesian inference
are posterior probability distributions of the transition frequencies, which
for example can be utilized to design risk neutral optimal control pulses. The
applicability of our approach is demonstrated on experimental data from the
Quantum Device and Integration Testbed (QuDIT) at Lawrence Livermore National
Laboratory, using a tantalum-based superconducting transmon device.


  Poisson-Lie duality provides an algebraic extension of conventional Abelian
and non-Abelian target space dualities of string theory and has seen recent
applications in constructing quantum group deformations of holography. Here we
demonstrate a natural upgrading of Poisson-Lie to the context of M-theory using
the tools of exceptional field theory. In particular, we propose how the
underlying idea of a Drinfeld double can be generalised to an algebra we call
an exceptional Drinfeld algebra. These admit a notion of "maximally isotropic
subalgebras" and we show how to define a generalised Scherk-Schwarz truncation
on the associated group manifold to such a subalgebra. This allows us to define
a notion of Poisson-Lie U-duality. Moreover, the closure conditions of the
exceptional Drinfeld algebra define natural analogues of the cocycle and
co-Jacobi conditions arising in Drinfeld double. We show that upon making a
further coboundary restriction to the cocycle that an M-theoretic extension of
Yang-Baxter deformations arise. We remark on the application of this
construction as a solution-generating technique within supergravity.


  In this paper we define and explore the analytic spread $\ell(\mathcal I)$ of
a filtration in a local ring. We show that, especially for divisorial and
symbolic filtrations, some basic properties of the analytic spread of an ideal
extend to filtrations, even when the filtration is non Noetherian. We also
illustrate some significant differences between the analytic spread of a
filtration and the analytic spread of an ideal with examples.
  In the case of an ideal $I$, we have the classical bounds
$\mbox{ht}(I)\le\ell(I)\le \dim R$. The upper bound $\ell(\mathcal I)\le \dim
R$ is true for filtrations $\mathcal I$, but the lower bound is not true for
all filtrations. We show that for the filtration $\mathcal I$ of symbolic
powers of a height two prime ideal $\mathfrak p$ in a regular local ring of
dimension three (a space curve singularity), so that $\mbox{ht}(\mathcal I) =2$
and $\dim R=3$, we have that $0\le \ell(\mathcal I)\le 2$ and all values of 0,1
and 2 can occur. In the cases of analytic spread 0 and 1 the symbolic algebra
is necessarily non-Noetherian. The symbolic algebra is non-Noetherian if and
only if $\ell(\mathfrak p^{(n)})=3$ for all symbolic powers of $\mathfrak p$
and if and only if $\ell(\mathcal I_a)=3$ for all truncations $\mathcal I_a$ of
$\mathcal I$.


  The stack of iterated integrals of a path is embedded in a larger algebraic
structure where iterated integrals are indexed by decorated rooted trees and
where an extended Chen's multiplicative property involves the
D\"urr-Connes-Kreimer coproduct on rooted trees. This turns out to be the
natural setting for a non-geometric theory of rough paths.


  We investigate the use of quantum computers for building a portfolio out of a
universe of U.S. listed, liquid equities that contains an optimal set of
stocks. Starting from historical market data, we look at various problem
formulations on the D-Wave Systems Inc. D-Wave 2000Q(TM) System (hereafter
called DWave) to find the optimal risk vs return portfolio; an optimized
portfolio based on the Markowitz formulation and the Sharpe ratio, a simplified
Chicago Quantum Ratio (CQR), then a new Chicago Quantum Net Score (CQNS). We
approach this first classically, then by our new method on DWave. Our results
show that practitioners can use a DWave to select attractive portfolios out of
40 U.S. liquid equities.


  A completely relativistic quark model in the Bethe-Salpter framework is
employed to calculate the exclusive production ratio of the neutral over
charged Kaon pair in $e^+e^-$ annihilation continuum region for center of mass
energies smaller than the $J/\Psi$ mass. The valence quark charge plays the key
r\^{o}le. The cancellation of the diagrams for the same charge case (in $K_S +
K_L$) and the non-cancellation of the diagrams for the different charge case
(in $K^-+K^+$) lead to the ratio as $(m_s-m_d)^2/M_{Kaon}^2 \sim 1/10$.


  We develop a first-principles electron-transport simulator based on the
Lippmann--Schwinger (LS) equation within the framework of the real-space
finite-difference scheme. In our fully real-space based LS (grid LS) method,
the ratio expression technique for the scattering wave functions and the
Green's function elements of the reference system is employed to avoid
numerical collapse. Furthermore, we present analytical expressions and/or
prominent calculation procedures for the retarded Green's function, which are
utilized in the grid LS approach. In order to demonstrate the performance of
the grid LS method, we simulate the electron-transport properties of the
semiconductor/oxide interfaces sandwiched between semi-infinite metal
electrodes. The results confirm that the leakage current through the
(001)Si/SiO$_2$ model becomes much larger when the dangling-bond (DB) state is
induced by a defect in the oxygen layer while that through the (001)Ge/GeO$_2$
model is insensitive to the DB state.


  Current knowledge distillation (KD) methods primarily focus on transferring
various structured knowledge and designing corresponding optimization goals to
encourage the student network to imitate the output of the teacher network.
However, introducing too many additional optimization objectives may lead to
unstable training, such as gradient conflicts. Moreover, these methods ignored
the guidelines of relative learning difficulty between the teacher and student
networks. Inspired by human cognitive science, in this paper, we redefine
knowledge from a new perspective -- the student and teacher networks' relative
difficulty of samples, and propose a pixel-level KD paradigm for semantic
segmentation named Relative Difficulty Distillation (RDD). We propose a
two-stage RDD framework: Teacher-Full Evaluated RDD (TFE-RDD) and
Teacher-Student Evaluated RDD (TSE-RDD). RDD allows the teacher network to
provide effective guidance on learning focus without additional optimization
goals, thus avoiding adjusting learning weights for multiple losses. Extensive
experimental evaluations using a general distillation loss function on popular
datasets such as Cityscapes, CamVid, Pascal VOC, and ADE20k demonstrate the
effectiveness of RDD against state-of-the-art KD methods. Additionally, our
research showcases that RDD can integrate with existing KD methods to improve
their upper performance bound.


  Neuronal activity arises from an interaction between ongoing firing generated
spontaneously by neural circuits and responses driven by external stimuli.
Using mean-field analysis, we ask how a neural network that intrinsically
generates chaotic patterns of activity can remain sensitive to extrinsic input.
We find that inputs not only drive network responses, they also actively
suppress ongoing activity, ultimately leading to a phase transition in which
chaos is completely eliminated. The critical input intensity at the phase
transition is a non-monotonic function of stimulus frequency, revealing a
"resonant" frequency at which the input is most effective at suppressing chaos
even though the power spectrum of the spontaneous activity peaks at zero and
falls exponentially. A prediction of our analysis is that the variance of
neural responses should be most strongly suppressed at frequencies matching the
range over which many sensory systems operate.


  A new complex network model, called q-snapback network, is introduced. Basic
topological characteristics of the network, such as degree distribution,
average path length, clustering coefficient and Pearson correlation
coefficient, are evaluated. The typical 4-motifs of the network are simulated.
The robustness of both state and structural controllabilities of the network
against targeted and random node- and edge-removal attacks, with comparisons to
the multiplex congruence network and the generic scale-free network, are
presented. It is shown that the q-snapback network has the strongest robustness
of controllabilities due to its advantageous inherent structure with many
chain- and loop-motifs.


  We present time-resolved scanning x-ray microscopy measurements with
picosecond photo-excitation via a tailored infrared pump laser at a scanning
transmission x-ray microscope. Specifically, we image the laser-induced
demagnetization and remagnetization of thin ferrimagnetic GdFe films proceeding
on a few nanoseconds time scale. Controlling the heat load on the sample via
additional reflector and heat-sink layers allows us to conduct destruction-free
measurements at a repetition rate of \SI{50}{\mega\hertz}. Near-field
enhancement of the photo-excitation and controlled annealing effects lead to
laterally heterogeneous magnetization dynamics which we trace with
\SI{30}{\nano\meter} spatial resolution. Our work opens new opportunities to
study photo-induced dynamics on the nanometer scale, with access to picosecond
to nanosecond timescales, which is of technological relevance, especially in
the field of magnetism.


  Ambient backscatter communications is an emerging paradigm and a key enabler
for pervasive connectivity of low-powered wireless devices. It is primarily
beneficial in the Internet of things (IoT) and the situations where computing
and connectivity capabilities expand to sensors and miniature devices that
exchange data on a low power budget. The premise of the ambient backscatter
communication is to build a network of devices capable of operating in a
battery-free manner by means of smart networking, radio frequency (RF) energy
harvesting and power management at the granularity of individual bits and
instructions. Due to this innovation in communication methods, it is essential
to investigate the performance of these devices under practical constraints. To
do so, this article formulates a model for wireless-powered ambient backscatter
devices and derives a closed-form expression of outage probability under
Rayleigh fading. Based on this expression, the article provides the
power-splitting factor that balances the tradeoff between energy harvesting and
achievable data rate. Our results also shed light on the complex interplay of a
power-splitting factor, amount of harvested energy, and the achievable data
rates.


  The $t$-onion star is the digraph obtained from a star with $2t$ leaves by
replacing every edge by a triple of arcs, where in $t$ triples we orient two
arcs away from the center, and in the remaining $t$ triples we orient two arcs
towards the center. Note that the $t$-onion star contains, as an immersion,
every digraph on $t$ vertices where each vertex has outdegree at most $2$ and
indegree at most $1$, or vice versa. We investigate the structure in digraphs
that exclude a fixed onion star as an immersion. The main discovery is that in
such digraphs, for some duality statements true in the undirected setting we
can prove their directed analogues. More specifically, we show the next two
statements.
  There is a function $f\colon \mathbb{N}\to \mathbb{N}$ satisfying the
following: If a digraph $D$ contains a set $X$ of $2t+1$ vertices such that for
any $x,y\in X$ there are $f(t)$ arc-disjoint paths from $x$ to $y$, then $D$
contains the $t$-onion star as an immersion.
  There is a function $g\colon \mathbb{N}\times \mathbb{N}\to \mathbb{N}$
satisfying the following: If $x$ and $y$ is a pair of vertices in a digraph $D$
such that there are at least $g(t,k)$ arc-disjoint paths from $x$ to $y$ and
there are at least $g(t,k)$ arc-disjoint paths from $y$ to $x$, then either $D$
contains the $t$-onion star as an immersion, or there is a family of $2k$
pairwise arc-disjoint paths with $k$ paths from $x$ to $y$ and $k$ paths from
$y$ to $x$.


  We consider surfaces immersed in three-dimensional pseudohermitian manifolds.
We define the notion of (p-)mean curvature and of the associated (p-)minimal
surfaces, extending some concepts previously given for the (flat) Heisenberg
group. We interpret the p-mean curvature not only as the tangential
sublaplacian of a defining function, but also as the curvature of a
characteristic curve, and as a quantity in terms of calibration geometry.
  As a differential equation, the p-minimal surface equation is degenerate
(hyperbolic and elliptic). To analyze the singular set, we formulate some {\em
extension} theorems, which describe how the characteristic curves meet the
singular set. This allows us to classify the entire solutions to this equation
and to solve a Bernstein-type problem (for graphs over the $xy$-plane) in the
Heisenberg group $H_1$. In $H_{1}$, identified with the Euclidean space
$R^{3}$, the p-minimal surfaces are classical ruled surfaces with the rulings
generated by Legendrian lines. We also prove a uniqueness theorem for the
Dirichlet problem under a condition on the size of the singular set in two
dimensions, and generalize to higher dimensions without any size control
condition.
  We also show that there are no closed, connected, $C^{2}$ smoothly immersed
constant p-mean curvature or p-minimal surfaces of genus greater than one in
the standard $S^{3}.$ This fact continues to hold when $S^{3}$ is replaced by a
general spherical pseudohermitian 3-manifold.


  The steady state behavior of thermodynamically supercritical natural
circulation loops (NCLs) is investigated in this work. Experimental steady
state results with supercritical carbon dioxide are presented for reduced
pressures in the range of 1.1-1.5, and temperatures in the range of 20-65
{\deg}C. Distinct thermodynamic states are reached by traversing a set of
isochors. A generalized equation for the prediction of the steady state is
presented, and its performance is assessed using empirical data. Changes of
mass flow rate as a result of changes of thermodynamic state, heating- and
driving height are shown to be accurately captured by the proposed predictive
equation. However, the enhanced viscous losses in the instrumentation of the
loop and in the proximity of heat transfer equipment are shown to significantly
limit the steady state flow rate. Subsequently, the findings are put forward in
aid of the development of safe, novel supercritical natural circulation
facilities.


  This article studies the synchronization problem of complex dynamical
networks. The impulsive control method is considered with a novel
event-triggered pinning algorithm. Sufficient conditions on the network
topology are obtained to ensure network synchronization. It is shown that
synchronization can be realized with a careful selection of the pinning nodes.
Furthermore, an adaptive coupling strength is incorporated into the network to
allow network synchronization with an arbitrary selection of the pinning nodes.
An example of a network with node dynamics described by the Chen system is
studied to demonstrate the theoretical results.


  In this paper, we introduce a new bivariate distribution we called it
bivariate expo- nentiated modified Weibull extension distribution (BEMWE). The
model introduced here is of Marshall-Olkin type. The marginals of the new
bivariate distribution have exponentiated modified Weibull extension
distribution which proposed by Sarhan et al.(2013). The joint probability
density function and the joint cumulative distribu- tion function are in closed
forms. Several properties of this distribution have been discussed.The maximum
likelihood estimators of the parameters are derived. One real data set are
analyzed using the new bivariate distribution, which show that the new
bivariate distribution can be used quite effectively in fitting and analyzing
real lifetime data


  Latent dynamical models are commonly used to learn the distribution of a
latent dynamical process that represents a sequence of noisy data samples.
However, producing samples from such models with high fidelity is challenging
due to the complexity and variability of latent and observation dynamics.
Recent advances in diffusion-based generative models, such as DDPM and NCSN,
have shown promising alternatives to state-of-the-art latent generative models,
such as Neural ODEs, RNNs, and Normalizing flow networks, for generating
high-quality sequential samples from a prior distribution. However, their
application in modeling sequential data with latent dynamical models is yet to
be explored. Here, we propose a novel latent variable model named latent
dynamical implicit diffusion processes (LDIDPs), which utilizes implicit
diffusion processes to sample from dynamical latent processes and generate
sequential observation samples accordingly. We tested LDIDPs on synthetic and
simulated neural decoding problems. We demonstrate that LDIDPs can accurately
learn the dynamics over latent dimensions. Furthermore, the implicit sampling
method allows for the computationally efficient generation of high-quality
sequential data samples from the latent and observation spaces.


  Animals have evolved to rapidly detect and recognise brief and intermittent
encounters with odour packages, exhibiting recognition capabilities within
milliseconds. Artificial olfaction has faced challenges in achieving comparable
results -- existing solutions are either slow; or bulky, expensive, and
power-intensive -- limiting applicability in real-world scenarios for mobile
robotics. Here we introduce a miniaturised high-speed electronic nose;
characterised by high-bandwidth sensor readouts, tightly controlled sensing
parameters and powerful algorithms. The system is evaluated on a high-fidelity
odour delivery benchmark. We showcase successful classification of
tens-of-millisecond odour pulses, and demonstrate temporal pattern encoding of
stimuli switching with up to 60 Hz. Those timescales are unprecedented in
miniaturised low-power settings, and demonstrably exceed the performance
observed in mice. For the first time, it is possible to match the temporal
resolution of animal olfaction in robotic systems. This will allow for
addressing challenges in environmental and industrial monitoring, security,
neuroscience, and beyond.


  We illustrate in a simple setting the instantaneous shock tracking approach
to stability of viscous conservation laws introduced by Howard, Mascia, and
Zumbrun. This involves a choice of the definition of instanteous location of a
viscous shock-- we show that this choice is time-asymptotically equivalent both
to the natural choice of least-squares fit pointed out by Goodman and to a
simple phase condition used by Gu\`es, M\'etivier, Williams, and Zumbrun in
other contexts. More generally, we show that it is asymptotically equivalent to
any location defined by a localized projection


  We report magnetic susceptibility $\chi(T)$ measurements on oxygen-isotope
exchanged La$_{1-x}$Ca$_{x}$MnO$_{3+y}$ up to 700 K. The $1/\chi(T)$ data show
that the ferromagnetic exchange-energy $J$ depends strongly on the
oxygen-isotope mass. The isotope effect on $J$ decreases with temperature up to
400 K and then increases again with temperature above 400 K. This unusual
temperature dependence of the isotope effect cannot be explained by existing
theories of the colossal magnetoresistance effect for doped manganites. The
present results thus provide essential constraints on the physics of
manganites.


  This paper investigates the capability of LLMs in storytelling, focusing on
narrative development and plot progression. We introduce a novel computational
framework to analyze narratives through three discourse-level aspects: i) story
arcs, ii) turning points, and iii) affective dimensions, including arousal and
valence. By leveraging expert and automatic annotations, we uncover significant
discrepancies between the LLM- and human- written stories. While human-written
stories are suspenseful, arousing, and diverse in narrative structures, LLM
stories are homogeneously positive and lack tension. Next, we measure narrative
reasoning skills as a precursor to generative capacities, concluding that most
LLMs fall short of human abilities in discourse understanding. Finally, we show
that explicit integration of aforementioned discourse features can enhance
storytelling, as is demonstrated by over 40% improvement in neural storytelling
in terms of diversity, suspense, and arousal.


  The key to device-edge co-inference paradigm is to partition models into
computation-friendly and computation-intensive parts across the device and the
edge, respectively. However, for Graph Neural Networks (GNNs), we find that
simply partitioning without altering their structures can hardly achieve the
full potential of the co-inference paradigm due to various
computational-communication overheads of GNN operations over heterogeneous
devices. We present GCoDE, the first automatic framework for GNN that
innovatively Co-designs the architecture search and the mapping of each
operation on Device-Edge hierarchies. GCoDE abstracts the device communication
process into an explicit operation and fuses the search of architecture and the
operations mapping in a unified space for joint-optimization. Also, the
performance-awareness approach, utilized in the constraint-based search process
of GCoDE, enables effective evaluation of architecture efficiency in diverse
heterogeneous systems. We implement the co-inference engine and runtime
dispatcher in GCoDE to enhance the deployment efficiency. Experimental results
show that GCoDE can achieve up to $44.9\times$ speedup and $98.2\%$ energy
reduction compared to existing approaches across various applications and
system configurations.


  The detection of offensive, hateful and profane language has become a
critical challenge since many users in social networks are exposed to
cyberbullying activities on a daily basis. In this paper, we present an
analysis of combining different textual features for the detection of hateful
or offensive posts on Twitter. We provide a detailed experimental evaluation to
understand the impact of each building block in a neural network architecture.
The proposed architecture is evaluated on the English Subtask 1A: Identifying
Hate, offensive and profane content from the post datasets of HASOC-2021
dataset under the team name TIB-VA. We compared different variants of the
contextual word embeddings combined with the character level embeddings and the
encoding of collected hate terms.


  We examine the effect of the decoherence-induced reduction of correlation
length on a one-dimensional scattering problem by solving numerically the
evolution equation for the Wigner function with decoherence proposed in [L.
Barletti, G. Frosali and E. Giovannini, Journal of Computational and
Theoretical Transport 47, 209 (2018)]. The numerical solution is achieved by
the Splitting-Scheme algorithm. Three cases are examined, corresponding to a
reflection-dominated regime, a transmission-dominated regime and an
intermediate one. The dynamic evolution of the Wigner function is followed
until the separation process of the reflected and of the transmitted packets is
complete and it is observed for three different values of the correlation
length. The outcomes show a broadening and flattening of the Wigner function
which becomes progressively more pronounced as the correlation length is
decreased. This results in a reduced reflection at low energies and in a
reduced transmission at high energies.


  We extend the so-called singlet doublet dark matter model, where the dark
matter is an admixture of a Standard Model singlet and a pair of electroweak
doublet fermions, by a singlet scalar field. The new portal coupling of it with
the dark sector not only contributes to the dark matter phenomenology
(involving relic density and direct detection limits), but also becomes
important for generation of dark matter mass through its vacuum expectation
value. While the presence of dark sector fermions affects the stability of the
electroweak vacuum adversely, we find this additional singlet is capable of
making the electroweak vacuum absolutely stable upto the Planck scale. A
combined study of dark matter phenomenology and Higgs vacuum stability issue
reflects that the scalar sector mixing angle can be significantly constrained
in this scenario.


  The present day gravitational wave (GW) detectors strive to detect the length
variation $\delta L = h L$, which, owing to the smallness of the metric
perturbation $\sim h$, is an extremely small length $\mathcal{O} \sim 10^{-18}
- 10^{-21}$ meter. The recently proposed noncommutative structure of space has
a characteristic length-scale $\sqrt{\theta}$ which has an estimated
upper-bound in similar length-scale range. We therefore propose that GW data
can be used as an effective probe of noncommutative structure of space and
demonstrate how spatial noncommutativity modifies the responding frequency of
the resonant mass detectors of GW and also the corresponding probabilities of
GW induced transitions that the phonon modes of the resonant mass detectors
undergo. In this paper we present the complete perturbative calculation
involving both time-independent and time-dependent perturbation terms in the
Hamiltonian.


  Using sum rules, the dipolar terms can be eliminated from the commonly-used
sum-over-states (SOS) expression for nonlinear susceptibilities. This new
dipole-free expression is more compact, converges to the same results as the
common SOS equation, and is more appropriate for analyzing certain systems such
as octupolar molecules. The dipole-free theory can be used as a tool for
analyzing the uncertainties in quantum calculations of susceptibilities, can be
applied to a broader set of quantum systems in the three-level model where the
standard SOS expression fails, and more naturally leads to fundamental limits
of the nonlinear susceptibilities.


  We present proper motion measurements of 37 jets and HH objects in the Carina
Nebula measured in two epochs of H$\alpha$ images obtained $\sim 10$ yrs apart
with HST/ACS. Transverse velocities in all but one jet are faster than $\gtrsim
25$ km s$^{-1}$, confirming that the jet-like H$\alpha$ features identified by
Smith et al. (2010) trace outflowing gas. Proper motions constrain the location
of the jet-driving source and provide kinematic confirmation of the
intermediate-mass protostars that we identify for 20/37 jets. Jet velocities do
not correlate with the estimated protostar mass and embedded driving sources do
not have slower jets. Instead, transverse velocities (median $\sim 75$ km
s$^{-1}$) are similar to those in jets from low-mass stars. Assuming a constant
velocity since launch, we compute jet dynamical ages (median $\sim 10^4$ yr).
If continuous emission from inner jets traces the duration of the most recent
accretion bursts, then these episodes are sustained longer (median $\sim 700$
yr) than the typical decay time of an FU Orionis outburst. These jets can carry
appreciable momentum that may be injected into the surrounding environment. The
resulting outflow force, $dP/dt$, lies between that measured in low- and
high-mass sources, despite the very different observational tracers used.
Smooth scaling of the outflow force argues for a common physical process
underlying outflows from protostars of all masses. This latest kinematic result
adds to a growing body of evidence that intermediate-mass star formation
proceeds like a scaled-up version of the formation of low-mass stars.


  The limits of applicability of the Lang-Kobayashi (LK) model for a
semiconductor laser with optical feedback are analyzed. The model equations,
equipped with realistic values of the parameters, are investigated below
solitary laser threshold where Low Frequency Fluctuations (LFF) are usually
observed. The numerical findings are compared with experimental data obtained
for the selected polarization mode from a Vertical Cavity Surface Laser (VCSEL)
subject to polarization selective external feedback. The comparison reveals the
bounds within which the dynamics of the LK can be considered as realistic. In
particular, it clearly demonstrates that the deterministic LK, for realistic
values of the linewidth enhancement factor $\alpha$, reproduces the LFF only as
a transient dynamics towards one of the stationary modes with maximal gain. A
reasonable reproduction of real data from VCSEL can be obtained only by
considering noisy LK or alternatively deterministic LK for extremely high
$\alpha$-values.


  Let $V\rightarrow X$ be a surjective quasi-projective morphism between
irreducible quasi-projective varieties over a number field, where $X$ is
smooth. We prove that there exists a non-empty Zariski open subset of $X$ such
that the $S$-integral points in it are covered by subpolynomially many
geometrically irreducible subvarieties, each lying in a fiber of the mixed
period mapping. We also prove analogous theorems for semi-simplicial varieties
and abstract variations of mixed Hodge structures. They are based on recent
works by Brunebarbe-Maculan and Ellenberg-Lawrence-Venkatesh. As an
application, we prove that there are subpolynomially many $S$-integral Laurent
polynomials with fixed reflexive Newton polyhedron $\Delta$ and fixed non-zero
principal $\Delta$-determinant. Our results answer a question in
Ellenberg-Lawrence-Venkatesh.


  Coordination of distributed energy resources is critical for electricity grid
management. Although nodal pricing schemes can mitigate congestion and voltage
deviations, the resulting prices are not necessarily equitable. In this work,
we leverage market mechanisms for DER coordination and propose a daily dynamic
nodal pricing scheme that incorporates equity. We introduce a pricing "oracle,"
which we call the Power Distribution Authority, that sets equitable prices to
manage the grid. We present two algorithms for executing this scheme and show
that both methods are able to set prices that satisfy both voltage and equity
constraints. Both proposed algorithms also outperform the common utility
time-of-use pricing schemes by at least 45%. New market mechanisms are needed
as the grid is transforming, and power system operators may leverage these
methods for pricing electricity in a grid-aware, equitable fashion.


  We study edge asymptotics of poissonized Plancherel-type measures on skew
Young diagrams (integer partitions). These measures can be seen as
generalizations of those studied by Baik--Deift--Johansson and Baik--Rains in
resolving Ulam's problem on longest increasing subsequences of random
permutations and the last passage percolation (corner growth) discrete versions
thereof. Moreover they interpolate between said measures and the uniform
measure on partitions. In the new KPZ-like 1/3 exponent edge scaling limit with
logarithmic corrections, we find new probability distributions generalizing the
classical Tracy--Widom GUE, GOE and GSE distributions from the theory of random
matrices.


  A large amount of research has been devoted to the detection and
investigation of epistatic interactions in genome-wide association studies
(GWASs). Most of the literature focuses on low-order interactions between
single-nucleotide polymorphisms (SNPs) with significant main effects.In this
paper we propose an original approach for detecting epistasis at the gene
level, without systematically filtering on significant genes. We first compute
interaction variables for each gene pair by finding its Eigen-Epistasis
component, defined as the linear combination of Gene SNPs having the highest
correlation with the phenotype. The selection of significant effects is done
using a penalized regression method based on Group Lasso controlling the False
Discovery Rate.The method is tested against two recent alternative proposals
from the literature using synthetic data, and shows good performances in
different settings. We demonstrate the power of our approach by detecting new
gene-gene interactions on three genome-wide association studies.


  We present the study of a fuzzy clustering algorithm for the Belle II
electromagnetic calorimeter using Graph Neural Networks. We use a realistic
detector simulation including simulated beam backgrounds and focus on the
reconstruction of both isolated and overlapping photons. We find significant
improvements of the energy resolution compared to the currently used
reconstruction algorithm for both isolated and overlapping photons of more than
30% for photons with energies E < 0.5 GeV and high levels of beam backgrounds.
Overall, the GNN reconstruction improves the resolution and reduces the tails
of the reconstructed energy distribution and therefore is a promising option
for the upcoming high luminosity running of Belle II.


  In order to examine the pulse profile from a pulsar, we derive the formula
for describing the flux from antipodal hot spots with any static, spherically
symmetric spacetime. We find that the pulse profiles are almost independent of
the gravitational geometry outside the star when the compactness of neutron
stars is low enough, e.g., the stellar mass and radius are $1.4M_\odot$ and 14
km, respectively. On the other hand, the pulse profiles depend strongly on the
gravitational geometry when the compactness of neutron stars is so high, e.g.,
the stellar mass and radius are $1.8M_\odot$ and 10 km, respectively. Thus, one
may probe the spacetime geometry outside the star and even distinguish
gravitational theories via the observation of pulse profile with the help of
another observations for the stellar compactness, if the compactness of central
object is high enough. We also derive the 1st and 2nd order approximation of
the flux with respect to a parameter defined by the radio of the gravitational
radius of considered spacetime to the stellar radius. Then, we find that the
relative error from full order numerical results in the bending angle becomes
$\sim 20-30\%$ with the 1st order and $\sim 5-10\%$ with the 2nd order
approximations for a typical neutron star, whose mass and radius are
$1.4M_\odot$ and 12 km, respectively. Our results with the 1st order
approximation for the Schwarzschild spacetime are different from those obtained
in the literature, which suggests that the 1st order approximation has been
misunderstood to yield highly accurate prediction.


  This article is an exposition of an elementary constructive proof of
canonical resolution of singularities in characteristic zero, presented in
detail in Invent. Math. 128 (1997), 207-302. We define a new local invariant
and get an algorithm for canonical desingularization by successively blowing up
its maximum loci. The invariant can be described by local computations that
provide equations for the centres of blowing up. We describe the origin of our
approach and present the proof (in the hypersurface case) in parallel with a
worked example.


  This paper presents a framework of imitating the principal investor's
behavior for optimal pricing and hedging options. We construct a
non-deterministic Markov decision process for modeling stock price change
driven by the principal investor's decision making. However, low
signal-to-noise ratio and instability that are inherent in equity markets pose
challenges to determine the state transition (stock price change) after
executing an action (the principal investor's decision) as well as decide an
action based on current state (spot price). In order to conquer these
challenges, we resort to a Bayesian deep neural network for computing the
predictive distribution of the state transition led by an action. Additionally,
instead of exploring a state-action relationship to formulate a policy, we seek
for an episode based visible-hidden state-action relationship to
probabilistically imitate the principal investor's successive decision making.
Unlike conventional option pricing that employs analytical stochastic processes
or utilizes time series analysis to model and sample underlying stock price
movements, our algorithm simulates stock price paths by imitating the principal
investor's behavior which requires no preset probability distribution and fewer
predetermined parameters. Eventually the optimal option price is learned by
reinforcement learning to maximize the cumulative risk-adjusted return of a
dynamically hedged portfolio over simulated price paths.


  Online communities can serve as meaningful sources of social support,
particularly for marginalized and vulnerable groups. Disclosure of personal
information facilitates integration into online communities but may also expose
individuals to harm, including cyberbullying and manipulation. To better
understand negative user experiences resulting from self-disclosure in online
conversations, we interviewed 25 participants from target populations on
Reddit. Through thematic analysis, we outline the harm they experience,
including damage to self- and group identities. We find that encountering
online harm can worsen offline adversity. We discuss how users protect
themselves and recover from harm in the context of current platform
affordances, highlighting ongoing challenges. Finally, we explore design
implications for a community-driven, bottom-up approach to enhance user
well-being and safety.


  In this paper we assume that a perfect fluid is the source of the
gravitational field while analyzing the solutions to the Einstein field
equations.


  Geodesics for a 5D magnetized Schwarzschild-like solution are analyzed by
reducing the problem to the motion of a test particle in an effective
potential. In absence of magnetic field comparison is established with
Schwarzschild's geometry. Embedding diagrams are constructed in order to
visualize the geometry of the metric. The study performed here is also valid,
when the electromagnetic interactions are neglected, for the low energy
superstring theory and the Brans-Dicke theory.


  We consider a model of an electron in a crystal moving under the influence of
an external electric field: Schroedinger's equation in one spatial dimension
with a potential which is the sum of a periodic function $V$ and a smooth
function $W$. We assume that the period of $V$ is much shorter than the scale
of variation of $W$ and denote the ratio of these scales by $\epsilon$. We
consider the dynamics of $\textit{semiclassical wavepacket}$ asymptotic (in the
limit $\epsilon \downarrow 0$) solutions which are spectrally localized near to
a $\textit{crossing}$ of two Bloch band dispersion functions of the periodic
operator $- \frac{1}{2} \partial_z^2 + V(z)$. We show that the dynamics is
qualitatively different from the case where bands are well-separated: at the
time the wavepacket is incident on the band crossing, a second wavepacket is
`excited' which has $\textit{opposite}$ group velocity to the incident
wavepacket. We then show that our result is consistent with the solution of a
`Landau-Zener'-type model.


  We seek to improve crowd counting as we perceive limits of currently
prevalent density map estimation approach on both prediction accuracy and time
efficiency. We leverage multilevel pixelation of density map as it helps
improve SNR of training data and therefore, reduce prediction error. To achieve
a better model, we introduce multilayer gradient fusion for training a
density-aware global count regressor. More specifically, on training stage, a
backbone network receives gradients from multiple branches to learn the density
information, whereas those branches are to be detached to accelerate inference.
By taking advantages of such method, our model improves benchmark results on
public datasets and exhibits itself to be a new solution to crowd counting
problems in practice.


  We present a multiwavelength study of the cometary \hii region G12.21--0.10
using the VLA and OVRO. Both radio continuum (0.3, 0.7, 2 and 3.6~cm) and
spectral lines of H41$\alpha$, $^{13}$CS(2$-$1) \& (1$-$0), and NH$_{\rm
3}$(2,2) \& (4,4) observations are included. We find two 3~mm continuum peaks
toward G12.21--0.10, one of them is spatially coincident with the UC~H~II
region, while the other coincides spatially with a molecular clump. We also
find that the 0.7, 2 and 3.6~cm continuum and H41$\alpha$ line are only
detected toward the UC~H~II region, while the $^{13}$CS, and NH$_{\rm 3}$ is
spatially associated with the molecular clump. Based on the morphology, kinetic
temperature ($\sim$86~K), volumetric density
($\sim$1.5$\times$10$^6$~cm$^{-3}$) and linear size ($\sim$0.22~pc) of the
molecular clump, we suggest this source is consistent with a hot molecular
core.


  Neutrinos produced in the atmosphere traverse a column density of air before
being detected at neutrino observatories like IceCube or KM3NeT. In this work,
we extend the neutrino flavor evolution in the {nuSQuIDS} code accounting for
the varying height of neutrino production and the variable air density in the
atmosphere. These effects can lead to sizeable spectral distortions in standard
neutrino oscillations and are crucial to accurately describe some new physics
scenarios. As an example, we study a model of quasi-sterile neutrinos that
induce resonant flavor conversions at neutrino energies of ${O}(300)\text{
MeV}$ in matter densities of $1 \text{ g/cm}^3$. In atmospheric air densities,
the same resonance is then realized at neutrino energies of ${O}(300-
700)$~GeV. We find that the new resonance can deplete the $\nu_\mu +
\overline{\nu}_\mu$ flux at the IceCube Neutrino Observatory by as much as
$10\%$ in the direction of the horizon.


  Solutions with scaling-invariant bounds such as self-similar solutions, play
an important role in the understanding of the regularity and asymptotic
structures of solutions to the Navier-Stokes equations. In this paper, we prove
that any steady solution satisfying $|\Bu(x)|\leq C/|x|$ for any constant $C$
in $\mathbb{R}^n\setminus \{0\}$ with $ n \geq 4$, must be zero. Our main idea
is to analyze the velocity field and the total head pressure via weighted
energy estimates with suitable multipliers so that the proof is pretty
elementary and short. These results not only give the Liouville-type theorem
for steady solutions in higher dimensions with neither smallness nor
self-similarity type assumptions, but also help to remove a class of
singularities of solutions and give the optimal asymptotic behaviors of
solutions at infinity in the exterior domains.


  Friction between ordered, atomically smooth surfaces at the nanoscale
(nanofriction) is often governed by stick-slip processes. To test long-standing
atomistic models of such processes, we implement a synthetic nanofriction
interface between a laser-cooled Coulomb crystal of individually addressable
ions as the moving object, and a periodic light-field potential as the
substrate. We show that stick-slip friction can be tuned from maximal to nearly
frictionless via arrangement of the ions relative to the substrate. By varying
the ion number, we also show that this strong dependence of friction on the
structural mismatch, as predicted by many-particle models, already emerges at
the level of two or three atoms. This model system enables a microscopic and
systematic investigation of friction, potentially even into the quantum
many-body regime.


  We present a theoretical and exact analysis of the bispectrum of projected
galaxy catalogues. The result can be generalized to evaluate the projection in
spherical harmonics of any 3D bispectrum and therefore has applications to
cosmic microwave background and gravitational lensing studies. By expanding the
2D distribution of galaxies on the sky in spherical harmonics, we show how the
3-point function of the coefficients can be used in principle to determine the
bias parameter of the galaxy sample. If this can be achieved, it would allow a
lifting of the degeneracy between the bias and the matter density parameter of
the Universe which occurs in linear analysis of 3D galaxy catalogues. In
previous papers we have shown how a similar analysis can be done in three
dimensions, and we show here, through an error analysis and by implementing the
method on a simulated projected catalogue, that ongoing three-dimensional
galaxy redshift surveys (even with all the additional uncertainties introduced
by partial sky coverage, redshift-space distortions and smaller numbers) will
do far better than all-sky projected catalogues with similar selection
function.


  It is shown that the light scattered by a finite 2D crystal strip in the
region of an exciton resonance should display strong backscattering.
  This phenomenon is related to the exciton reflection from the edge of the
strip.


  The analysis of high-intensity runs (or sprints) in soccer has long been a
topic of interest for sports science researchers and practitioners. In
particular, recent studies suggested contextualizing sprints based on their
tactical purposes to better understand the physical-tactical requirements of
modern match-play. However, they have a limitation in scalability, as human
experts have to manually classify hundreds of sprints for every match. To
address this challenge, this paper proposes a deep learning framework for
automatically classifying sprints in soccer into contextual categories. The
proposed model covers the permutation-invariant and sequential nature of
multi-agent trajectories in soccer by deploying Set Transformers and a
bidirectional GRU. We train the model with category labels made through the
collaboration of human annotators and a rule-based classifier. Experimental
results show that our model classifies sprints in the test dataset into 15
categories with the accuracy of 77.65%, implying the potential of the proposed
framework for facilitating the integrated analysis of soccer sprints at scale.


  We extend a previous statistical solar flare study of 155 GOES M- and X-class
flares observed with AIA/SDO (Aschwanden 2012) to all 7 coronal wavelengths
(94, 131, 171, 193, 211, 304, 335 \ang) to test the wavelength-dependence of
scaling laws and statistical distributions. Except for the 171 and 193 \ang\
wavelengths, which are affected by EUV dimming caused by coronal mass ejections
(CMEs), we find near-identical size distributions of geometric (lengths $L$,
flare areas $A$, volumes $V$, fractal dimension $D_2$), temporal (flare
durations $T$), and spatio-temporal parameters (diffusion coefficient $\kappa$,
spreading exponent $\beta$, and maximum expansion velocities $v_{max}$) in
different wavelengths, which are consistent with the universal predictions of
the fractal-diffusive avalanche model of a slowly-driven self-organized
criticality (FD-SOC) system, i.e., $N(L) \propto L^{-3}$, $N(A) \propto
A^{-2}$, $N(V) \propto V^{-5/3}$, $N(T) \propto T^{-2}$, $D_2=3/2$, for a
Euclidean dimension $d=3$. Empirically we find also a new strong correlation
$\kappa \propto L^{0.94\pm0.01}$ and the 3-parameter scaling law $L \propto
\kappa\ T^{0.1}$, which is more consistent with the logistic-growth model than
with classical diffusion. The findings suggest long-range correlation lengths
in the FD-SOC system that operate in the vicinity of a critical state, which
could be used for predictions of individual extreme events. We find also that
eruptive flares (with accompanying CMEs), have larger volumes $V$, longer flare
durations $T$, higher EUV and soft X-ray fluxes, and somewhat larger diffusion
coefficients $\kappa$ than confined flares (without CMEs).


  We generalize Lewis's result about a central series associated with the
vanishing off subgroup. We write $V_{1}=V(G)$ for the vanishing off subgroup of
$G$, and $V_{i}=[V_{i-1},G]$ for the terms in this central series. Lewis proved
that there exists a positive integer $n$ such that if $V_{3} < G_{3}$, then
$|G:V_{1}|=|G':V_{2}|^{2}=p^{2n}$. Let $D_{3}/V_{3} = C_{G/V_{3}}(G'/V_{3})$.
He also showed that if $V_{3} < G_{3}$, then either $|G:D_{3}|=p^{n}$ or
$D_{3}=V_{1}$. We show that if $V_{i} <G_{i}$ for $i\ge 4,$ where $G_{i}$ is
the $i$-th term in the lower central series of $G$, then
$|G_{i-1}:V_{i-1}|=|G:D_{3}|.$


  For a probability measure $\mu$ and for square integrable fields
$(\mathscr{A}_t)$ and $(\mathscr{B}_t)$ ($t\in\Omega$) of commuting normal
operators we prove Landau type inequality
\llu\int_\Omega\mathscr{A}_tX\mathscr{B}_td\mu(t)-
  \int_\Omega\mathscr{A}_t\,d\mu(t)X \int_\Omega\mathscr{B}_t\,d\mu(t) \rru \le
\llu \sqrt{\,\int_\Omega|\mathscr{A}_t|^2\dt-|\int_\Omega\mathscr{A}_t\dt|^2}X
\sqrt{\,\int_\Omega|\mathscr{B}_t|^2 \dt-|\int_\Omega\mathscr{B}_t\dt|^2} \rru
for all $X\in\mathcalb{B}(\mathcal{H})$ and for all unitarily invariant norms
$\lluo\cdot\rruo$.
  For Schatten $p$-norms similar inequalities are given for arbitrary double
square integrable fields. Also, for all bounded self-adjoint fields satisfying
$C\le\mathscr{A}_t\le D$ and $E\le\mathscr{B}_t\le F$ for all $t\in\Omega $ and
some bounded self-adjoint operators $C,D,E$ and $F$, then for all $X\in\ccu$ we
prove Gr\"uss type inequality \llu\int_\Omega\mathscr{A}_tX\mathscr{B}_t \dt-
\int_\Omega \mathscr{A}_t\,d\mu(t)X \int_\Omega\mathscr{B}_t\,d\mu(t) \rru\leq
\frac{\|D-C\|\cdot\|F-E\|}4\cdot\lluo X\rruo. More general results for
arbitrary bounded fields are also given.


  Given a set of detections, detected at each time instant independently, we
investigate how to associate them across time. This is done by propagating
labels on a set of graphs, each graph capturing how either the spatio-temporal
or the appearance cues promote the assignment of identical or distinct labels
to a pair of detections. The graph construction is motivated by a locally
linear embedding of the detection features. Interestingly, the neighborhood of
a node in appearance graph is defined to include all the nodes for which the
appearance feature is available (even if they are temporally distant). This
gives our framework the uncommon ability to exploit the appearance features
that are available only sporadically. Once the graphs have been defined,
multi-object tracking is formulated as the problem of finding a label
assignment that is consistent with the constraints captured each graph, which
results into a difference of convex (DC) program. We propose to decompose the
global objective function into node-wise sub-problems. This not only allows a
computationally efficient solution, but also supports an incremental and
scalable construction of the graph, thereby making the framework applicable to
large graphs and practical tracking scenarios. Moreover, it opens the
possibility of parallel implementation.


  In mobile database environments, multiple users may access similar data items
irrespective of their physical location leading to concurrent access anomalies.
As disconnections and mobility are the common characteristics in mobile
environment, performing concurrent access to a particular data item leads to
inconsistency. Most of the approaches use locking mechanisms to achieve
concurrency control. However this leads to increase in blocking and abort rate.
In this paper an optimistic concurrency control strategy using on-demand
multicasting is proposed for mobile database environments which guarantees
consistency and introduces application-specific conflict detection and
resolution strategies. The simulation results specify increase in system
throughput by reducing the transaction abort rates as compared to the other
optimistic strategies proposed in literature.


  Vortices in protoplanetary disks can capture solid particles and form
planetary cores within shorter timescales than those involved in the standard
core-accretion model. We investigate vortex generation in thin unmagnetized
protoplanetary disks with an embedded giant planet with planet to star mass
ratio $10^{-4}$ and $10^{-3}$. Two-dimensional hydrodynamical simulations of a
protoplanetary disk with a planet are performed using two different numerical
methods. The results of the non-linear simulations are compared with a
time-resolved modal analysis of the azimuthally averaged surface density
profiles using linear perturbation theory. Finite-difference methods
implemented in polar coordinates generate vortices moving along the gap created
by Neptune-mass to Jupiter-mass planets. The modal analysis shows that unstable
modes are generated with growth rate of order $0.3 \Omega_K$ for azimuthal
numbers m=4,5,6, where $\Omega_K$ is the local Keplerian frequency.
Shock-capturing Cartesian-grid codes do not generate very much vorticity around
a giant planet in a standard protoplanetary disk. Modal calculations confirm
that the obtained radial profiles of density are less susceptible to the growth
of linear modes on timescales of several hundreds of orbital periods.
Navier-Stokes viscosity of the order $\nu=10^{-5}$ (in units of $a^2 \Omega_p$)
is found to have a stabilizing effect and prevents the formation of vortices.
This result holds at high resolution runs and using different types of boundary
conditions. Giant protoplanets of Neptune-mass to Jupiter-mass can excite the
Rossby wave instability and generate vortices in thin disks. The presence of
vortices in protoplanetary disks has implications for planet formation, orbital
migration, and angular momentum transport in disks.


  The role of Coulomb interaction between the mobile particles in ionic
conductors is still under debate. To clarify this aspect we perform Monte Carlo
simulations on two simple lattice models (Counter Ion Model and Random Energy
Model) which contain Coulomb interaction between the positively charged mobile
particles, moving on a static disordered energy landscape. We find that the
nature of static disorder plays an important role if one wishes to explore the
impact of Coulomb interaction on the microscopic dynamics. This Coulomb type
interaction impedes the dynamics in the Random Energy Model, but enhances
dynamics in the Counter Ion Model in the relevant parameter range.


  In this letter, we outline a methodology to calculate microscopically mass
and charge distributions of spontaneous fission yields. We combine the
multi-dimensional minimization of collective action for fission with stochastic
Langevin dynamics to track the relevant fission paths from the ground-state
configuration up to scission. The nuclear potential energy and collective
inertia governing the tunneling motion are obtained with nuclear density
functional theory in the collective space of shape deformations and pairing. We
obtain a quantitative agreement with experimental data and find that both the
charge and mass distributions in the spontaneous fission of 240Pu are sensitive
both to the dissipation in collective motion and to adiabatic characteristics.


  We provide evidence of an extremely slow thermalization occurring in the
Discrete NonLinear Schr\"odinger (DNLS) model. At variance with many similar
processes encountered in statistical mechanics - typically ascribed to the
presence of (free) energy barriers - here the slowness has a purely dynamical
origin: it is due to the presence of an adiabatic invariant, which freezes the
dynamics of a tall breather. Consequently, relaxation proceeds via rare events,
where energy is suddenly released towards the background. We conjecture that
this exponentially slow relaxation is a key ingredient contributing to the
non-ergodic behavior recently observed in the negative temperature region of
the DNLS equation.


  Student learning in undergraduate physics laboratory courses has garnered
increased attention within the PER community. Considerable work has been done
to develop curricular materials and pedagogical techniques designed to enhance
student learning within laboratory learning environments. Examples of these
transformation efforts include the Investigative Science Learning Environment
(ISLE), Modeling Instruction, and integrated lab/lecture environments (e.g.,
studio physics). In addition to improving students' understanding of the
physics content, lab courses often have an implicit or explicit goal of
increasing students' understanding and appreciation of the nature of
experimental physics. We examine students' responses to a laboratory-focused
epistemological assessment -- the Colorado Learning Attitudes about Science
Survey for Experimental Physics (E-CLASS) -- to explore whether courses using
transformed curricula or pedagogy show more expert-like student epistemologies
relative to courses using traditional guided labs, as well as how this trend
varies based on student major or gender. Data for this study are drawn from an
existing data set of responses to the E-CLASS from multiple courses and
institutions.


  Lifting theorems are theorems that relate the query complexity of a function
$f:\{0,1\}^{n}\to\{0,1\}$ to the communication complexity of the composed
function $f \circ g^{n}$, for some "gadget"
$g:\{0,1\}^{b}\times\{0,1\}^{b}\to\{0,1\}$. Such theorems allow transferring
lower bounds from query complexity to the communication complexity, and have
seen numerous applications in the recent years. In addition, such theorems can
be viewed as a strong generalization of a direct-sum theorem for the gadget
$g$.
  We prove a new lifting theorem that works for all gadgets $g$ that have
logarithmic length and exponentially-small discrepancy, for both deterministic
and randomized communication complexity. Thus, we significantly increase the
range of gadgets for which such lifting theorems hold.
  Our result has two main motivations: First, allowing a larger variety of
gadgets may support more applications. In particular, our work is the first to
prove a randomized lifting theorem for logarithmic-size gadgets, thus improving
some applications of the theorem. Second, our result can be seen as a strong
generalization of a direct-sum theorem for functions with low discrepancy.


  We succeed to generalize spun knots of classical 1-knots to the virtual
1-knot case by using the `spinning construction'. That, is, we prove the
following: Let $Q$ be a spun knot of a virtual 1-knot $K$ by our method. The
embedding type $Q$ in $S^4$ depends only on $K$. Furthermore we prove the
following: The submanifolds, $Q$ and the embedded torus made from $K,$ defined
by Satoh's method, in $S^4$ are isotopic.
  We succeed to generalize the above construction to the virtual 2-knot case.
Note that Satoh's method says nothing about the virtual 2-knot case. Rourke's
interpretation of Satoh's method is that one puts `fiber-circles' on each point
of each virtual 1-knot diagram. If there is no virtual branch point in a
virtual 2-knot diagram, our way gives such fiber-circles to each point of the
virtual 2-knot diagram. We prove the following: If a virtual 2-knot diagram
$\alpha$ has a virtual branch point, $\alpha$ cannot be covered by such
fiber-circles.
  We obtain a new equivalence relation, the $\mathcal E$-equivalence relation
of the set of virtual 2-knot diagrams, by using our spinning construction. We
prove that there are virtual 2-knot diagrams that are virtually nonequivalent
but are $\mathcal E$-equivalent.
  Although Rourke claimed that two virtual 1-knot diagrams $\alpha$ and $\beta$
are fiberwise equivalent if and only if $\alpha$ and $\beta$ are welded
equivalent, we state that this claim is wrong. We prove that two virtual 1-knot
diagrams $\alpha$ and $\beta$ are fiberwise equivalent if and only if $\alpha$
and $\beta$ are rotational welded equivalent.


  Type IIP supernovae are recognized as independent extragalactic distance
indicators, however, keeping in view of the diverse nature of their observed
properties as well as the availability of good quality data, more and newer
events need to be tested for their applicability as a reliable distance
indicators. We use early photometric and spectroscopic data of eight type-IIP
SNe to derive distances to their host galaxies using the expanding photosphere
method (EPM). For five of these, EPM is applied for the first time. In this
work, we improved EPM application by using SYNOW estimated velocities and by
semi-deconvolving the broadband filter responses while deriving color
temperatures and black-body angular radii. We find that the derived EPM
distances are consistent with that derived using other redshift independent
methods.


  In a competitive sport, every little thing matters. Yet, many sports leave
some large levers out of the reach of the teams, and in the hands of fate. In
cricket, world's second most popular sport by some measures, one such
lever---the toss---has been subject to much recent attention. Using a large
novel dataset of 44,224 cricket matches, we estimate the impact of winning the
toss on the probability of winning. The data suggest that winning the toss
increases the chance of winning by a small ($\sim$ 2.8\%) but significant
margin. The advantage varies heftily and systematically, by how closely matched
the competing teams are, and by playing conditions---tautologically, winning
the toss in conditions where the toss grants a greater advantage, for e.g., in
day and night matches, has a larger impact on the probability of winning.


  We study the scale and redshift dependence of the power spectra for density
perturbations and peculiar velocities, and the evolution of a coarse grained
phase space density for (WDM) particles that decoupled during the radiation
dominated stage. The (WDM) corrections are obtained in a perturbative expansion
valid in the range of redshifts at which N-body simulations set up initial
conditions, and for a wide range of scales. The redshift dependence is
determined by the kurtosis $\beta_2$ of the distribution function at
decoupling. At large redshift there is an enhancement of peculiar velocities
for $\beta_2 > 1$ that contributes to free streaming and leads to further
suppression of the matter power spectrum and an enhancement of the peculiar
velocity autocorrelation function at scales smaller than the free streaming
scale. Statistical fluctuations of peculiar velocities are also suppressed on
these scales by the same effect. In the linearized approximation, the coarse
grained phase space density features redshift dependent (WDM) corrections from
gravitational perturbations determined by the power spectrum of density
perturbations and $\beta_2$. For $\beta_2 > 25/21$ it \emph{grows
logarithmically} with the scale factor as a consequence of the suppression of
statistical fluctuations. Two specific models for WDM are studied in detail.
The (WDM) corrections relax the bounds on the mass.


  A liquid volume containing dissolved solutes moves through a charged
nanofluidic channel under the influence of the concentration gradient of the
solutes, non-trivially modulated by the electrostatic interaction between ionic
liquid and charged surface. The available studies in this paradigm primarily
focus on either of diffusioosmosis or electrodiffusioosmosis modulated
physicochemical hydrodynamical phenomenon, essentially to obtain a net
throughput at the overlapping scales. Here, we develop a theoretical model that
accounts for the induced pressure gradient stemming from the concentration
gradient of the solutes alongside the axially varying electrical double layer
effect in tandem and characterizes the chemiosmotic flow in a
reservoir-connected nanofluidic system. Starting from the potential
distribution developed due to the solute gradient modulated electrical double
layer effect, we look at the effect of pertinent physicochemical parameters and
their eventual manifestations onto the purely chemiosmotic transport, aptly
described in this endeavor. We analytically establish a chemiosmotic velocity
scale from a macroscopic viewpoint, relating flow velocity with the relevant
parameters, and uniquely measuring the magnitude of chemiosmotic velocity. A
closer as well as consistent agreement on theoretical predictions with the
corresponding full-scale simulated results, both in the limit and beyond the
Debye-Huckel approximation, substantiates the efficacy of our theory.


  The multitudes of inverter-based distributed energy resources (DERs) can be
envisioned as distributed reactive power (var) devices (\textit{mini-SVCs})
that can offer var flexibility at TSO-DSO interface. To facilitate this vision,
a systematic methodology is proposed to derive an aggregated var capability
curve of a distribution system with DERs at the substation level, analogous to
a conventional bulk generator. Since such capability curve will be contingent
to the operating conditions and network constraints, an optimal power flow
(OPF) based approach is proposed that takes inverter headroom flexibility,
unbalanced nature of system and coupling with grid side voltage into account
along with changing operating conditions. Further, the influence of several
factors such as compliance to IEEE 1547 on the capability curve is thoroughly
investigated on an IEEE 37 bus and 123 bus distribution test system along with
unbalanced DER proliferation. Validation with nonlinear analysis is presented
along with demonstration of a scenario with T-D co-simulation.


  The narrow-line region (NLR) consists of gas clouds ionized by the strong
radiation from the active galactic nucleus (AGN), distributed in the spatial
scale of AGN host galaxies. The strong emission lines from the NLR are useful
to diagnose physical and chemical properties of the interstellar medium in AGN
host galaxies. However, the origin of the NLR is unclear; the gas clouds in
NLRs may be originally in the host and photoionized by the AGN radiation, or
they may be transferred from the nucleus with AGN-driven outflows. For studying
the origin of the NLR, we systematically investigate the gas density and
velocity dispersion of NLR gas clouds using a large spectroscopic data set
taken from the Sloan Digital Sky Survey. The [S II] emission-line flux ratio
and [O III] velocity width of 9,571 type-2 Seyfert galaxies and 110,041
star-forming galaxies suggest that the gas density and velocity dispersion of
NLR clouds in Seyfert galaxies (ne ~ 194 cm-3 and sigma([O III]) ~ 147 km s-1)
are systematically larger than those of clouds in H II regions of star-forming
galaxies (ne ~ 29 cm-3 and sigma([O III]) ~ 58 km s-1). Interestingly, the
electron density and velocity dispersion of NLR gas clouds are larger for
Seyfert galaxies with a higher [O III]/Hbeta flux ratio, i.e., with a more
active AGN. We also investigate the spatially-resolved kinematics of ionized
gas clouds using the Mapping Nearby Galaxies at the Apache Point Observatory
(MaNGA) survey data for 90 Seyfert galaxies and 801 star-forming galaxies. We
find that the velocity dispersion of NLR gas clouds in Seyfert galaxies is
larger than that in star-forming galaxies at a fixed stellar mass, at both
central and off-central regions. These results suggest that gas clouds in NLRs
come from the nucleus, probably through AGN outflows.


  A countable semigroup is $\aleph_0$-categorical if it can be characterised,
up to isomorphism, by its first-order properties. In this paper we continue our
investigation into the $\aleph_0$-categoricity of semigroups. Our main results
are a complete classification of $\aleph_0$-categorical orthodox completely
0-simple semigroups, and descriptions of the $\aleph_0$-categorical members of
certain classes of strong semilattices of semigroups.


  Near-wall blood flow and wall shear stress (WSS) regulate major forms of
cardiovascular disease, yet they are challenging to quantify with high
fidelity. Patient-specific computational and experimental measurement of WSS
suffers from uncertainty, low resolution, and noise issues. Physics-informed
neural networks (PINN) provide a flexible deep learning framework to integrate
mathematical equations governing blood flow with measurement data. By
leveraging knowledge about the governing equations (herein, Navier-Stokes),
PINN overcomes the large data requirement in deep learning. In this study, it
was shown how PINN could be used to improve WSS quantification in diseased
arterial flows. Specifically, blood flow problems where the inlet and outlet
boundary conditions were not known were solved by assimilating very few
measurement points. Uncertainty in boundary conditions is a common feature in
patient-specific computational fluid dynamics models. It was shown that PINN
could use sparse velocity measurements away from the wall to quantify WSS with
very high accuracy even without full knowledge of the boundary conditions.
Examples in idealized stenosis and aneurysm models were considered
demonstrating how partial knowledge about the flow physics could be combined
with partial measurements to obtain accurate near-wall blood flow data. The
proposed hybrid data-driven and physics-based deep learning framework has high
potential in transforming high-fidelity near-wall hemodynamics modeling in
cardiovascular disease.


  Tourism has been experiencing very relevant changes since when Information
and Communication Technologies (ICTs), in all their forms, have started to
pervade the industry and the market. In the last decade, a new concept gained
the attention of both researchers and practitioners, that of Digital Business
Ecosystem (DBE). It can be considered as a technological infrastructure aimed
at creating a digital environment to support and enhance networking between
enterprises and stakeholders operating within a sector. Aim of this paper is to
assess the extent to which the technological connection has affected the
structural configuration of the tourism system and, specifically, of tourism
destinations. The present study argues that two components can be considered
when assessing the relationships among stakeholders within a tourism
destination: a real and a virtual one. Further it shows how these two
components are structurally strongly coupled and co-evolve forming a single
system.


  Accurate measurement of efficiency for thermoelectric generator (TEG) is of
great importance for materials research and development. Approximately all the
parameters of a material are temperature dependent, so we can't directly apply
the $\eta_\text{max}$ formula for efficiency calculation in the large
temperature range. To overcome that problem, we tried to calculate the
efficiency of TEG by dividing large working temperature range into a number of
small temperature difference. The aim is to make temperature dependent
parameter to be constant for that small temperature range. Using maximum
individual efficiency of each segment obtained by $\eta_\text{max}$ in the
equation of $\eta_\text{overall}$, which gives overall efficiency. The
$\eta_\text{overall}$ of TEG using $Bi_2Te_3$ and $TAGS$ as thermoelectric
materials come out to be $7.1\%$ and $8.94\%$, respectively, which is close to
experimental results. For the high-temperature region, we have used $SiGe$
material in TEG and found out $\eta_\text{overall}=3.5\%$. The cumulative
efficiency obtained by keeping one end temperature fixed with another end
varying can be applied in real life application, i.e. automobile sector. The
present work provides a simple way for the design engineers to calculate the
efficiency of TEG by using the temperature dependent materials parameters like
thermal conductivity, electrical conductivity, and Seebeck coefficient on which
$z\bar{T}$ depends.


  It is argued taking into account the observed mass spectra of the leptons and
quarks that the phenomenon of flavor mixing is intimately related to the mass
spectra. We discuss a particularly interesting way to describe the flavor
mixing which is particularly suited for models of quark mass matrices based on
flavor symmetries. CP violated is maximal or class to being maximal in our
approach.


  The time evolution of Mach-like structure (the splitting of the away side
peak in di-hadron $\Delta\phi$ correlation) is presented in the framework of a
dynamical partonic transport model. With the increasing of the lifetime of
partonic matter, Mach-like structure can be produced and developed by strong
parton cascade process. Not only the splitting parameter but also the number of
associated hadrons ($N_{h}^{assoc}$) increases with the lifetime of partonic
matter and partonic interaction cross section. Both the explosion of
$N_{h}^{assoc}$ following the formation of Mach-like structure and the
corresponding results of three-particle correlation support that a partonic
Mach-like shock wave can be formed by strong parton cascade mechanism.
Therefore, the studies about Mach-like structure may give us some critical
information, such as the lifetime of partonic matter and hadronization time.


  Recent neural headline generation models have shown great results, but are
generally trained on very large datasets. We focus our efforts on improving
headline quality on smaller datasets by the means of pretraining. We propose
new methods that enable pre-training all the parameters of the model and
utilize all available text, resulting in improvements by up to 32.4% relative
in perplexity and 2.84 points in ROUGE.


  Polydisperse systems are commonly encountered when dealing with soft matter
in general or any non simple fluid. Yet their treatment within the framework of
statistical thermodynamics is a delicate task as the latter has been
essentially devised for simple --- non fully polydisperse --- systems. In this
paper, we address the issue of defining a non ambiguous combinatorial entropy
for these systems. We do so by focusing on the general property of extensivity
of the thermodynamic potentials and discussing a specific mixing experiment.
This leads us to introduce the new concept of composition entropy for single
phase systems that we do not assimilate to a mixing entropy. We then show that
they do not contribute to the thermodynamics of the system at fixed composition
and prescribe to substract $\ln N!$ from the free energy characterizing a
system however polydisperse it can be. We then re-derive general expressions
for the mixing entropy between any two polydisperse systems and interpret them
in term of distances between probability distributions and show that one of
these metrics relates naturally to a recent extension of Landauer's principle.
We then propose limiting expressions for the mixing entropy in the case of
mixing with equal proportion in the original compositions and finally address
the challenging problem of chemical reactions.


  Fruit recognition using Deep Convolutional Neural Network (CNN) is one of the
most promising applications in computer vision. In recent times, deep learning
based classifications are making it possible to recognize fruits from images.
However, fruit recognition is still a problem for the stacked fruits on
weighing scale because of the complexity and similarity. In this paper, a fruit
recognition system using CNN is proposed. The proposed method uses deep
learning techniques for the classification. We have used Fruits-360 dataset for
the evaluation purpose. From the dataset, we have established a dataset which
contains 17,823 images from 25 different categories. The images are divided
into training and test dataset. Moreover, for the classification accuracies, we
have used various combinations of hidden layer and epochs for different cases
and made a comparison between them. The overall performance losses of the
network for different cases also observed. Finally, we have achieved the best
test accuracy of 100% and a training accuracy of 99.79%.


  In order for autonomous vehicles to become a part of the Intelligent
Transportation Ecosystem, they are required to guarantee a particular level of
safety. For that to happen a safe vehicle control algorithms need to be
developed, which include assessing the probability of a collision while driving
along a given trajectory and selecting control signals that minimize this
probability. In this paper, we propose a speed control system that estimates a
collision probability taking into account static and dynamic obstacles as well
as ego-pose uncertainty and chooses the maximum safe speed. For that, the
planned trajectory is converted by the control system into control signals that
form input for the dynamic vehicle model. The model predicts a real vehicle
path. The predicted trajectory is generated for each particle -- a weighted by
a probability hypothesis of the localization system about the vehicle pose.
Based on the predicted particles' trajectories, the probability of collision is
calculated, and a decision is made on the maximum safe speed. The proposed
algorithm was validated on the real autonomous vehicle. The experimental
results demonstrate that the proposed speed control system reduces the vehicle
speed to a safe value when performing maneuvers and driving through narrow
openings. Therefore the observed behavior of the system is mimicking a human
driver behavior when driving in difficult and ambiguous traffic situations.


  We investigate the signature of the low-energy electronic excitations in the
Raman spectrum of monolayer and bilayer graphenes. The dominant contribution to
the Raman spectra is due to the interband electron-hole pairs, which belong to
the irreducible representation A$_2$ of the point group C$_{6v}$ of the
graphene lattice, and are characterised by crossed polarisation of incoming and
outgoing photons. At high magnetic fields, this is manifested by the excitation
of electron-hole (e-h) inter-Landau-level transitions with selection rule
$n^-\to n^+$. Weaker Raman-active inter-Landau-level modes also exist. One of
those has a selection rule similar to the infrared absorption process,
$n^-\to(n\pm1)^+$, but the created e-h excitation belongs to the irreducible
representation E$_2$ (rather than E$_1$) and couples to the optical phonon
mode, thus undergoing an anticrossing with the optical phonon G-line in Raman
in strong magnetic field. The fine structure of acquired by the G-line due to
such anticrossing depends on the carrier density, inhomogeneity of doping, and
presence of inhomogeneous strain in the sample.


  Let ${\mathcal H}$ be a multiplicative subgroup of $\mathbb{F}_p^*$ of order
$H>p^{1/4}$. We show that $$ \max_{(a,p)=1}\left|\sum_{x\in {\mathcal H}}
{\mathbf{\,e}}_p(ax)\right| \le H^{1-31/2880+o(1)}, $$ where
${\mathbf{\,e}}_p(z) = \exp(2 \pi i z/p)$, which improves a result of Bourgain
and Garaev (2009). We also obtain new estimates for double exponential sums
with product $nx$ with $x \in {\mathcal H}$ and $n \in {\mathcal N}$ for a
short interval ${\mathcal N}$ of consecutive integers.


  Software systems are complex, and behavioral comprehension with the
increasing amount of AI components challenges traditional testing and
maintenance strategies.The lack of tools and methodologies for behavioral
software comprehension leaves developers to testing and debugging that work in
the boundaries of known scenarios. We present Probabilistic Software Modeling
(PSM), a data-driven modeling paradigm for predictive and generative methods in
software engineering. PSM analyzes a program and synthesizes a network of
probabilistic models that can simulate and quantify the original program's
behavior. The approach extracts the type, executable, and property structure of
a program and copies its topology. Each model is then optimized towards the
observed runtime leading to a network that reflects the system's structure and
behavior. The resulting network allows for the full spectrum of statistical
inferential analysis with which rich predictive and generative applications can
be built. Applications range from the visualization of states, inferential
queries, test case generation, and anomaly detection up to the stochastic
execution of the modeled system. In this work, we present the modeling
methodologies, an empirical study of the runtime behavior of software systems,
and a comprehensive study on PSM modeled systems. Results indicate that PSM is
a solid foundation for structural and behavioral software comprehension
applications.


  We describe a numerical scheme for magnetohydrodynamics simulations of
dust-gas mixture by extending smoothed particle magnetohydrodynamics. We employ
the single-species particle approach to describe dust-gas mixture with several
modifications from the previous studies. We assume that the charged and neutral
dusts can be treated as single-fluid and the electro-magnetic force acts on the
gas and that on the charged dust is negligible. The validity of these
assumption in the context of protostar formation is not obvious and is
extensively evaluated. By investigating the electromagnetic force and electric
current with terminal velocity approximation, it is found that as the dust size
increases, the contribution of dust to them becomes smaller and negligible. We
conclude that our assumptions of the electro-magnetic force on the dusts is
negligible are valid for the dust size with a d & 10{\mu}m. On the other hand,
they do not produce the numerical artifact for the dust a d . 10{\mu}m in
envelope and disk where the perfect coupling between gas and dusts realizes.
However, we also found that our assumptions may break down in outflow (or under
environment with very strong magnetic field and low density) for the dust a d .
10{\mu}m. We conclude that our assumptions are valid in almost all cases where
macroscopic dust dynamics is important in the context of protostar formation.
We conduct numerical tests of dusty wave, dusty magnetohydrodynamics shock, and
gravitational collapse of magnetized cloud core with our simulation code. The
results show that our numerical scheme well reproduces the dust dynamics in the
magnetized medium.


  We discuss recent work showing that in certain cases the membrane paradigm
equations governing the dynamics of black hole horizons can be recast as
relativistic conservation law equations. In the context of gauge/gravity
dualities, these equations are interpreted as defining the viscous
hydrodynamics of a holographically dual relativistic field theory. Using this
approach, one can derive the viscous transport coefficients and the form of the
entropy current for field theories dual to gravity plus matter fields.


  We calculate the radial profiles of galaxies where the nuclear region is
self-gravitating, consisting of self-interacting dark matter (SIDM) with $F$
degrees of freedom. For sufficiently high density this dark matter becomes
collisional, regardless of its behaviour on galaxy scales. Our calculations
show a spike in the central density profile, with properties determined by the
dark matter microphysics, and the densities can reach the `mean density' of a
black hole (from dividing the black-hole mass by the volume enclosed by the
Schwarzschild radius). For a galaxy halo of given compactness
($\chi=2GM/Rc^2$), certain values for the dark matter entropy yield a dense
central object lacking an event horizon. For some soft equations of state of
the SIDM (e.g. $F\ge6$), there are multiple horizonless solutions at given
compactness. Although light propagates around and through a sphere composed of
dark matter, it is gravitationally lensed and redshifted. While some
calculations give non-singular solutions, others yield solutions with a central
singularity. In all cases the density transitions smoothly from the central
body to the dark-matter envelope around it, and to the galaxy's dark matter
halo. We propose that pulsar timing observations will be able to distinguish
between systems with a centrally dense dark matter sphere (for different
equations of state) and conventional galactic nuclei that harbour a
supermassive black hole.


  In high-energy particle collisions, charged track finding is a complex yet
crucial endeavour. We propose a quantum algorithm, specifically quantum
template matching, to enhance the accuracy and efficiency of track finding.
Abstracting the Quantum Amplitude Amplification routine by introducing a data
register, and utilising a novel oracle construction, allows data to be parsed
to the circuit and matched with a hit-pattern template, without prior knowledge
of the input data. Furthermore, we address the challenges posed by missing hit
data, demonstrating the ability of the quantum template matching algorithm to
successfully identify charged-particle tracks from hit patterns with missing
hits. Our findings therefore propose quantum methodologies tailored for
real-world applications and underline the potential of quantum computing in
collider physics.


  We discuss how triple product asymmetries can be used to discover and
constrain new physics in $B \to VV$ decays.


  This technical report deals with the concept of an artificial DNA which
contains a blueprint of the structure and organization of an embedded system.
This blueprint can be used to build up the embedded system in a self-organizing
manner at run-time. The report describes in detail the basic principles of the
the artificial DNA and its relationship to standard design methods for embedded
systems. A prototypic implementation is presented and evaluated. Additionally,
future work is described and a conclusion is given.


  We provide an exact analytical solution of the collapse dynamics of
self-gravitating Brownian particles and bacterial populations at zero
temperature. These systems are described by the Smoluchowski-Poisson system or
Keller-Segel model in which the diffusion term is neglected. As a result, the
dynamics is purely deterministic. A cold system undergoes a gravitational
collapse leading to a finite time singularity: the central density increases
and becomes infinite in a finite time t_coll. The evolution continues in the
post collapse regime. A Dirac peak emerges, grows and finally captures all the
mass in a finite time t_end, while the central density excluding the Dirac peak
progressively decreases. Close to the collapse time, the pre and post collapse
evolution is self-similar. Interestingly, if one starts from a parabolic
density profile, one obtains an exact analytical solution that describes the
whole collapse dynamics, from the initial time to the end, and accounts for non
self-similar corrections that were neglected in previous works. Our results
have possible application in different areas including astrophysics,
chemotaxis, colloids and nanoscience.


  (Note. The results of this manuscript has been merged and published with
another paper of the same authors: A new approach to nonrepetitve sequences.)
  A repetition of size $h$ ($h\geqslant1$) in a given sequence is a subsequence
of consecutive terms of the form: $xx=x_1... x_hx_1... x_h$. A sequence is
nonrepetitive if it does not contain a repetition of any size. The remarkable
construction of Thue asserts that 3 different symbols are enough to build an
arbitrarily long nonrepetitive sequence. We consider game-theoretic versions of
results on nonrepetitive sequences. A nonrepetitive game is played by two
players who pick, one by one, consecutive terms of a sequence over a given set
of symbols. The first player tries to avoid repetitions, while the second
player, in contrast, wants to create them. Of course, by simple imitation, the
second player can force lots of repetitions of size 1. However, as proved by
Pegden, there is a strategy for the first player to build an arbitrarily long
sequence over 37 symbols with no repetitions of size $>1$. Our techniques allow
to reduce 37 to 6. Another game we consider is an erase-repetition game. Here,
whenever a repetition occurs, the repeated block is immediately erased and the
next player to move continues the play. We prove that there is a strategy for
the first player to build an arbitrarily long nonrepetitive sequence over 8
symbols. Our approach is inspired by a new algorithmic proof of the Lov\'asz
Local Lemma due to Moser and Tardos and previous work of Moser (his so called
entropy compression argument).


  We prove a lower bound for the number of negative eigenvalues for a
Schr\"{o}dinger operator on a Riemannian manifold via the integral of the
potential.


  Different formalisms for unintegrated parton densities are discussed, and
some results and applications are presented.


  {Although there is considerable evidence supporting an ubiquitous magnetic
field in solar/stellar photospheres, its impact in the determination of
abundances has never been quantified. In this work we investigate whether the
magnetic field plays a measurable role for this kind of studies. To that end,
we carry out simulations of spectral line formation in the presence of a
magnetic field, and then use those profiles to derive the abundance of several
atomic species (Fe, Si, C and O) neglecting the magnetic field. In this way, we
find that the derived iron abundance can be significantly biased, with
systematic errors up to 0.1 dex. In the case of silicon, carbon and oxygen
their role is very marginal (errors smaller than 0.02 dex). We also find that
the effect of the magnetic field strongly depends on its inclination with
respect to the observer. We show that fields that are aligned with the observer
lead to an underestimation of the real abundance, whereas more inclined ones
overestimate it. In the case of a mixture of fields with different inclinations
these effects are likely to partly cancel each other out, making the role of
the magnetic field even less important. Finally, we derive a simple model that
can be used to determine the suitability of a spectral line when we wish to
avoid the bias introduced by the neglect of the magnetic field.


  We investigate the coupled formation and evolution of galaxies and their
embedded supermassive black holes using state-of-the-art hydrodynamic
simulations of cosmological structure formation. For the first time, we
self-consistently follow the dark matter dynamics, radiative gas cooling, star
formation, as well as black hole growth and associated feedback processes,
starting directly from initial conditions appropriate for the LambdaCDM
cosmology. Our modeling of the black hole physics is based on an approach we
have developed in simulations of isolated galaxy mergers. Here we examine: (i)
the predicted global history of black hole mass assembly (ii) the evolution of
the local black hole-host mass correlations and (iii) the conditions that allow
rapid growth of the first quasars, and the properties of their hosts and
descendants today. We find a total black hole mass density in good agreement
with observational estimates. The black hole accretion rate density peaks at
lower redshift and evolves more strongly at high redshift than the star
formation rate density, but the ratio of black hole to stellar mass densities
shows only a moderate evolution at low redshifts. We find strong correlations
between black hole masses and properties of the stellar systems, agreeing well
with the measured local M_BH-sigma and M_BH -M_* relationships, but also
suggesting (dependent on the mass range) a weak evolution with redshift in the
normalization and the slope. Our simulations also produce massive black holes
at high redshift, due to extended periods of exponential growth in regions that
collapse early and exhibit strong gas inflows. These first supermassive BH
systems however are not necessarily the most massive ones today, since they are
often overtaken in growth by quasars that form later. (abridged)


  Let $X$ be a complete toric variety equipped with the action of a torus $T$
and $G$ a reductive algebraic group, defined over an algebraically closed field
$K$. We introduce the notion of a compatible $\Sigma$--filtered algebra
associated to $X$, generalizing the notion of a compatible $\Sigma$--filtered
vector space due to Klyachko, where $\Sigma$ denotes the fan of $X$. We combine
Klyachko's classification of $T$--equivariant vector bundles on $X$ with Nori's
Tannakian approach to principal $G$--bundles, to give an equivalence of
categories between $T$--equivariant principal $G$--bundles on $X$ and certain
compatible $\Sigma$--filtered algebras associated to $X$, when the
characteristic of $K$ is $0$.