  Traditional segmentation methods for colonic polyps are mainly designed based
on low-level features. They could not accurately extract the location of small
colonic polyps. Although the existing deep learning methods can improve the
segmentation accuracy, their effects are still unsatisfied. To meet the above
challenges, we propose a hybrid network called Fusion-Transformer-HardNetMSEG
(i.e., Fu-TransHNet) in this study. Fu-TransHNet uses deep learning of
different mechanisms to fuse each other and is enhanced with multi-view
collaborative learning techniques. Firstly, the Fu-TransHNet utilizes the
Transformer branch and the CNN branch to realize the global feature learning
and local feature learning, respectively. Secondly, a fusion module is designed
to integrate the features from two branches. The fusion module consists of two
parts: 1) the Global-Local Feature Fusion (GLFF) part and 2) the Dense Fusion
of Multi-scale features (DFM) part. The former is built to compensate the
feature information mission from two branches at the same scale; the latter is
constructed to enhance the feature representation. Thirdly, the above two
branches and fusion modules utilize multi-view cooperative learning techniques
to obtain their respective weights that denote their importance and then make a
final decision comprehensively. Experimental results showed that the
Fu-TransHNet network was superior to the existing methods on five widely used
benchmark datasets. In particular, on the ETIS-LaribPolypDB dataset containing
many small-target colonic polyps, the mDice obtained by Fu-TransHNet were 12.4%
and 6.2% higher than the state-of-the-art methods HardNet-MSEG and TransFuse-s,
respectively.


  In 2014, Paul Yiu constructed two equilateral triangles inscribed in a
Kiepert hyperbola associated with a reference triangle. It was asserted that
each of the equilateral triangles is triply perspective to the reference
triangle, and in each case, the corresponding three perspectors are collinear.
In this note, we give a proof of his assertions. Furthermore as an analogue of
Lemoine's problem, we formulated and answered the question about how to recover
the reference triangle given a Kiepert hyperbola, one of the two Fermat points
and one vertex of the reference triangle.


  In this letter, I have considered one-dimensional quantum system with
different masses $m$ and $M$, which does not appear integrable in general.
However I have found an exact two-body wave function and due to the extension
of the integrable system to more general system, it was concluded that the
rapidity or quasi-momentum in the integrable system should be regarded as a
modification of velocity rather than that of momentum. I have also considered
the three-body wave function and argued its integrable condition.


  Deep learning for Information Retrieval (IR) requires a large amount of
high-quality query-document relevance labels, but such labels are inherently
sparse. Label smoothing redistributes some observed probability mass over
unobserved instances, often uniformly, uninformed of the true distribution. In
contrast, we propose knowledge distillation for informed labeling, without
incurring high computation overheads at evaluation time. Our contribution is
designing a simple but efficient teacher model which utilizes collective
knowledge, to outperform state-of-the-arts distilled from a more complex
teacher model. Specifically, we train up to x8 faster than the state-of-the-art
teacher, while distilling the rankings better. Our code is publicly available
at https://github.com/jihyukkim-nlp/CollectiveKD


  Classic and recent results for gauge effects on the properties of the
normal-to-superconducting phase transition in bulk and thin film
superconductors are reviewed. Similar problems in the description of other
natural systems (liquid crystals, quantum field theory, early universe) are
also discussed. The relatively strong gauge effects on the fluctuations of the
ordering field at low spatial dimensionality D and, in particular, in thin
(quasi-2D) films are considered in details. A special attention is paid to the
fluctuations of the gauge field. It is shown that the mechanism in which these
gauge fluctuations affect on the order of the phase transition and other phase
transition properties varies with the variation of the spatial dimensionality
D. The problem for the experimental confirmation of the theoretical predictions
about the order of the phase transitions in gauge systems is discussed.


  The main contribution of this MS Thesis is centered around taking steps
towards successful multi-modal demonstrations using Northeastern's
legged-aerial robot, Husky Carbon. This work discusses the challenges involved
in achieving multi-modal locomotion such as trotting-hovering and
thruster-assisted incline walking and reports progress made towards overcoming
these challenges. Animals like birds use a combination of legged and aerial
mobility, as seen in Chukars' wing-assisted incline running (WAIR), to achieve
multi-modal locomotion. Chukars use forces generated by their flapping wings to
manipulate ground contact forces and traverse steep slopes and overhangs.
Husky's design takes inspiration from birds such as Chukars. This MS thesis
presentation outlines the mechanical and electrical details of Husky's legged
and aerial units. The thesis presents simulated incline walking using a
high-fidelity model of the Husky Carbon over steep slopes of up to 45 degrees.


  Computing derivatives of noisy measurement data is ubiquitous in the
physical, engineering, and biological sciences, and it is often a critical step
in developing dynamic models or designing control. Unfortunately, the
mathematical formulation of numerical differentiation is typically ill-posed,
and researchers often resort to an \textit{ad hoc} process for choosing one of
many computational methods and its parameters. In this work, we take a
principled approach and propose a multi-objective optimization framework for
choosing parameters that minimize a loss function to balance the faithfulness
and smoothness of the derivative estimate. Our framework has three significant
advantages. First, the task of selecting multiple parameters is reduced to
choosing a single hyper-parameter. Second, where ground-truth data is unknown,
we provide a heuristic for automatically selecting this hyper-parameter based
on the power spectrum and temporal resolution of the data. Third, the optimal
value of the hyper-parameter is consistent across different differentiation
methods, thus our approach unifies vastly different numerical differentiation
methods and facilitates unbiased comparison of their results. Finally, we
provide an extensive open-source Python library \texttt{pynumdiff} to
facilitate easy application to diverse datasets
(https://github.com/florisvb/PyNumDiff).


  Considerable progress has been made in recent years in observations of
atmospheric signatures of giant exoplanets, but processes in rocky exoplanets
remain largely unknown due to major challenges in observing small planets.
Numerous efforts to observe spectra of super-Earths, exoplanets with masses of
1-10 Earth masses, have thus far revealed only featureless spectra. In this
paper we report a 4-$\sigma$ detection of variability in the dayside thermal
emission from the transiting super-Earth 55 Cancri e. Dedicated space-based
monitoring of the planet in the mid-infrared over eight eclipses revealed the
thermal emission from its dayside atmosphere varying by a factor 3.7 between
2012 and 2013. The amplitude and trend of the variability are not explained by
potential influence of star spots or by local thermal or compositional changes
in the atmosphere over the short span of the observations. The possibility of
large scale surface activity due to strong tidal interactions possibly similar
to Io, or the presence of circumstellar/circumplanetary material appear
plausible and motivate future long-term monitoring of the planet.


  We report here the realization and commissioning of an experiment dedicated
to the study of the optical properties of light matter hybrids constituted of
crystalline samples embedded in an optical cavity. The experimental assembly
developed offers the unique opportunity to study the weak and strong coupling
regime between a tunable optical cavity in cryogenic environment and low energy
degrees of freedom such as phonons, magnons or charge fluctuations. We describe
here the setup developed which allows the positioning of crystalline samples in
an optical cavity of different quality factor, the tuning of the cavity length
at cryogenic temperatures and its optical characterization with a broadband
time domain THz spectrometer (0.2-6 THz). We demonstrate the versatility of the
setup by studying the vibrational strong coupling in CuGeO3 single crystal at
cryogenic temperatures.


  We present an efficient raycasting algorithm for rendering Volumetric Depth
Images (VDIs), and we show how it can be used in a remote visualization setting
with VDIs generated and streamed from a remote server. VDIs are compact
view-dependent volume representations that enable interactive visualization of
large volumes at high frame rates by decoupling viewpoint changes from
expensive rendering calculations. However, current rendering approaches for
VDIs struggle with achieving interactive frame rates at high image resolutions.
Here, we exploit the properties of perspective projection to simplify
intersections of rays with the view-dependent frustums in a VDI and leverage
spatial smoothness in the volume data to minimize memory accesses. Benchmarks
show that responsive frame rates can be achieved close to the viewpoint of
generation for HD display resolutions, providing high-fidelity approximate
renderings of Gigabyte-sized volumes. We also propose a method to subsample the
VDI for preview rendering, maintaining high frame rates even for large
viewpoint deviations. We provide our implementation as an extension of an
established open-source visualization library.


  The foreseen Phase 2 pixel upgrades at the LHC have very challenging
requirements for the design of hybrid pixel readout chips. A versatile pixel
simulation platform is as an essential development tool for the design,
verification and optimization of both the system architecture and the pixel
chip building blocks (Intellectual Properties, IPs). This work is focused on
the implemented simulation and verification environment named VEPIX53, built
using the SystemVerilog language and the Universal Verification Methodology
(UVM) class library in the framework of the RD53 Collaboration. The environment
supports pixel chips at different levels of description: its reusable
components feature the generation of different classes of parameterized input
hits to the pixel matrix, monitoring of pixel chip inputs and outputs,
conformity checks between predicted and actual outputs and collection of
statistics on system performance. The environment has been tested performing a
study of shared architectures of the trigger latency buffering section of pixel
chips. A fully shared architecture and a distributed one have been described at
behavioral level and simulated; the resulting memory occupancy statistics and
hit loss rates have subsequently been compared.


  The experimental program of the Seventh International Workshop on Charm
Physics (CHARM 2015) is summarized. Highlights of the workshop include results
from heavy flavor production, quarkonium and exotic states, hadronic decays and
Dalitz analyses, semileptonic and leptonic decays, rare and radiative decays,
charm mixing, and CP and T violation.


  We design generative neural networks that generate Monte Carlo configurations
with complete absence of autocorrelation from which only short Markov chains
are needed before making measurements for physical observables, irrespective of
the system locating at the classical critical point, fermionic Mott insulator,
Dirac semimetal, or quantum critical point. We further propose a
network-initialized Monte Carlo scheme based on such neural networks, which
provides independent samplings and can accelerate the Monte Carlo simulations
by significantly reducing the thermalization process. We demonstrate the
performance of our approach on the two-dimensional Ising and fermion Hubbard
models, and expect it can systematically speed up the Monte Carlo simulations
especially for the very challenging many-electron problems.


  We theoretically investigate the electro-mechanics of a Suspended Carbon
Nanotube with a Deflection Sensitive Resistance subjected to a homogeneous
Magnetic Field and a constant Voltage Bias. We show that, (with the exception
of a singular case), for a sufficiently high magnetic field the
time-independent state of charge transport through the nanotube becomes
unstable to selfexcitations of the mechanical vibration accompanied by
oscialltions in the voltage drop and current across the nanotube.


  We propose a new, discretized model for the study of 3+1-dimensional
canonical quantum gravity, based on the classical $SL(2,\C)$-connection
formulation. The discretization takes place on a topological $N^3$- lattice
with periodic boundary conditions. All operators and wave functions are
constructed from one-dimensional link variables, which are regarded as the
fundamental building blocks of the theory. The kinematical Hilbert space is
spanned by polynomials of certain Wilson loops on the lattice and is manifestly
gauge- and diffeomorphism- invariant. The discretized quantum Hamiltonian $\hat
H$ maps this space into itself. We find a large sector of solutions to the
discretized Wheeler-DeWitt equation $\hat H\psi=0$, which are labelled by
single and multiple Polyakov loops. These states have a finite norm with
respect to a natural scalar product on the space of holomorphic
$SL(2,\C)$-Wilson loops. We also investigate the existence of further solutions
for the case of the $1^3$-lattice. - Our results provide for the first time a
rigorous, regularized framework for studying non-perturbative quantum gravity.


  This paper presents expressions for gamma values at rational points with the
denominator dividing 24 or 60. These gamma values are expressed in terms of 10
distinct gamma values and rational powers of $\pi$ and a few real algebraic
numbers. Our elementary list of formulas can be conveniently used to evaluate,
for example, algebraic Gauss hypergeometric functions by the Gauss identity.
Also, algebraic independence of gamma values and their relation to the elliptic
K-function are briefly discussed.


  We study the inelastic light scattering response in two- (2D) and
three-dimensional (3D) Kitaev spin-liquid models with \ms band structures in
the symmetry classes BDI and D leading to protected gapless surface modes. We
present a detailed calculation of the resonant Raman/Brillouin scattering
vertex relevant to iridate and ruthenate compounds whose low-energy physics is
believed to be proximate to these spin-liquid phases. In the symmetry class
BDI, we find that while the resonant scattering on thin films can detect the
gapless boundary modes of spin liquids, the non-resonant processes do not
couple to them. For the symmetry class D, however, we find that the coupling
between both types of light-scattering processes and the low-energy surface
states is strongly suppressed. Additionally, we describe the effect of weak
time-reversal symmetry breaking perturbations on the bulk Raman response of
these systems.


  We present differentiable predictive control (DPC) as a deep learning-based
alternative to the explicit model predictive control (MPC) for unknown
nonlinear systems. In the DPC framework, a neural state-space model is learned
from time-series measurements of the system dynamics. The neural control policy
is then optimized via stochastic gradient descent approach by differentiating
the MPC loss function through the closed-loop system dynamics model. The
proposed DPC method learns model-based control policies with state and input
constraints, while supporting time-varying references and constraints. In
embedded implementation using a Raspberry-Pi platform, we experimentally
demonstrate that it is possible to train constrained control policies purely
based on the measurements of the unknown nonlinear system. We compare the
control performance of the DPC method against explicit MPC and report
efficiency gains in online computational demands, memory requirements, policy
complexity, and construction time. In particular, we show that our method
scales linearly compared to exponential scalability of the explicit MPC solved
via multiparametric programming.


  In a recent paper, Hawkins (1997) argues on the basis of statistical studies
of double-image gravitational lenses and lens candidates that a large
population of dark lenses exists and that these outnumber galaxies with more
normal mass-to-light ratios by a factor of 3:1. If correct, this is a very
important result for many areas of astronomy including galaxy formation and
cosmology. In this paper we discuss our new radio-selected gravitational lens
sample, JVAS/CLASS, in order to test and constrain this proposition. We have
obtained ground-based and HST images of all multiple-image lens systems in our
sample and in 12 cases out of 12 we find the lensing galaxies in the optical
and/or near infrared. Our success in finding lensing galaxies creates problems
for the dark lens hypothesis. If it is to survive, ad hoc modifications seem to
be necessary: only very massive galaxies (more than about one trillion solar
masses) can be dark, and the cutoff in mass must be sharp. Our finding of lens
galaxies in all the JVAS/CLASS systems is complementary evidence which supports
the conclusion of Kochanek et al. (1997) that many of the wide-separation
optically-selected pairs are physically distinct quasars rather than
gravitational lens systems.


  According to Bliokh et al., allowing free propagation along the direction of
a uniform magnetic field, the familiar Landau electron state can be regarded as
a non-diffracting version of the helical electron beam propagating along the
magnetic field. Based on this observation, they argued that, while propagating
along the magnetic field, the Landau electrons receive characteristic rotation
with three different angular velocities, depending on the eigen-value $m$ of
the canonical OAM operator, which is generally gauge-variant, and this
splitting was in fact experimentally confirmed. Through complete analyses of
highly mysterious $m$-dependent rotational dynamics of the quantum Landau
states, we try to make clear how and why their observation does not contradict
the widely-believed gauge principle.


  In this paper we present alternative approach for Future Circular
electron-positron Collider. Current 100 km circumference design with the top CM
energy of 365 GeV (182.5 GeV beam energy) is based on two storage rings to
circulate colliding beams. One of the ring-ring design shortcomings is enormous
power consumption needed to compensate for 100 MW of the beam energy losses for
synchrotron radiation. We propose to use energy recovery linac located in the
same tunnel to mitigate this drawback. We show in this paper that our approach
would allow a significant, up to an order of magnitude, reduction of the beam
energy losses while maintaining high luminosity in this collider at high
energies. Furthermore, our approach would allow to extend CM energy to 500 GeV
(or above), which is sufficient for double Higgs production.


  For the first time, we demonstrate the application of an inverse Compton
scattering X-ray Source, driven by a laser-plasma accelerator, to image an
additively manufactured component. X-rays with a mean energy of 380 keV were
produced and used to image an additively manufactured part made of an Inconel
(Nickel 718) alloy. Because inverse Compton scattering driven by laser-plasma
acceleration produces high-energy X-rays while maintaining a focal spot size on
the order of a micron, the source can provide several benefits over
conventional X-ray production methods, particularly when imaging superalloy
parts, with the potential to revolutionise what can be inspected.


  Shi and Xie [8] predicted the N-shape current-voltage characteristic $(CVC)$
for a 2DEG in a magnetic field where the zero-resistance state has been
observed in recent experiments. However it is known that in the absence of a
magnetic field the zero resistance state (zero differential resistance state)
is achieved in a system with the S-shape $CVC$. The difference in the behaviour
of systems with N- and S-shape $CVC$ was studied more than three decades ago
(see for example the review [9] and references therein) and is briefly
explained in this Comment. At present it is not clear whether the N-shape $CVC$
may lead to the zero resistance state in a 2DEG in a magnetic field.


  Guarded protocols were introduced in a seminal paper by Emerson and Kahlon
(2000), and describe systems of processes whose transitions are enabled or
disabled depending on the existence of other processes in certain local states.
We study parameterized model checking and synthesis of guarded protocols, both
aiming at formal correctness arguments for systems with any number of
processes. Cutoff results reduce reasoning about systems with an arbitrary
number of processes to systems of a determined, fixed size. Our work stems from
the observation that existing cutoff results for guarded protocols i) are
restricted to closed systems, and ii) are of limited use for liveness
properties because reductions do not preserve fairness. We close these gaps and
obtain new cutoff results for open systems with liveness properties under
fairness assumptions. Furthermore, we obtain cutoffs for the detection of
global and local deadlocks, which are of paramount importance in synthesis.
Finally, we prove tightness or asymptotic tightness for the new cutoffs.


  We present algorithms to numerically evaluate Daubechies wavelets and scaling
functions to high relative accuracy. These algorithms refine the suggestion of
Daubechies and Lagarias to evaluate functions defined by two-scale difference
equations using splines; carefully choosing amongst a family of rapidly
convergent interpolators which effectively capture all the smoothness present
in the function and whose error term admits a small asymptotic constant. We are
also able to efficiently compute derivatives, though with a smoothness-induced
reduction in accuracy. An implementation is provided in the Boost Software
Library.


  The data of J/psi suppression at large x_F in pA collisions are used to infer
the existence of gluon depletion as the projectile proton traverses the
nucleus. The modification of the gluon distribution is studied by use of a
convolution equation whose non-perturbative splitting function is determined
phenomenologically. The depletion factor at x_1=0.8 is found to be about 25% at
A=100.


  The ability to interact and understand the environment is a fundamental
prerequisite for a wide range of applications from robotics to augmented
reality. In particular, predicting how deformable objects will react to applied
forces in real time is a significant challenge. This is further confounded by
the fact that shape information about encountered objects in the real world is
often impaired by occlusions, noise and missing regions e.g. a robot
manipulating an object will only be able to observe a partial view of the
entire solid. In this work we present a framework, 3D-PhysNet, which is able to
predict how a three-dimensional solid will deform under an applied force using
intuitive physics modelling. In particular, we propose a new method to encode
the physical properties of the material and the applied force, enabling
generalisation over materials. The key is to combine deep variational
autoencoders with adversarial training, conditioned on the applied force and
the material properties. We further propose a cascaded architecture that takes
a single 2.5D depth view of the object and predicts its deformation. Training
data is provided by a physics simulator. The network is fast enough to be used
in real-time applications from partial views. Experimental results show the
viability and the generalisation properties of the proposed architecture.


  Gamma ray emission of 511 keV lines arising from electron-positron
annihilation has been detected from the Galaxy since the 70s. Spatially
resolved observations using the INTEGRAL satellite have shown its full sky
distribution to be strongly concentrated in the Galactic bulge, with a smaller
contribution from the disk, unlike the situation in any other wavelength. The
puzzling distribution of the positrons gave rise to various suggestions,
including stellar nucleosynthesis in core-collapse (CC) and type Ia
thermonuclear supernovae (SNe), accreting compact objects, and more "exotic"
explanations of annihilation of dark-matter particles. However, such models
encounter difficulties in reproducing the total Galactic 511 keV flux as well
as its peculiar bulge-centered distribution. Theoretical models of SNe from
thermonuclear Helium detonations on white dwarfs (WDs) were suggested as
potential additional sources of positrons, contributing to the Galactic
Gamma-ray emission. Here, we show that the recently discovered class of faint,
calcium-rich type Ib SNe (with the prototype SN 2005E), thought to arise from
such explosions, also called ".Ia" SNe, can explain both the 511 keV flux and
its distribution, and can eliminate the need for non-astrophysical (e.g., dark
matter annihilation) sources. Such SNe comprise a fraction of only ~2% of all
SNe, but they currently inject hundreds of times more positrons (from $^{44}$Ti
decay) to the ISM than injected by CC SNe, enough to reproduce the total
Galactic 511 keV line emission. They exclusively explode in old environments,
and their contribution to the 511 keV emission therefore follows the old (>10
Gyrs) Galactic stellar population, dominated by the bulge, thereby reproducing
the observed large bulge-to-disk ratio.


  The cumulative residual extropy (CRJ) is a measure of uncertainty that serves
as an alternative to extropy. It replaces the probability density function with
the survival function in the expression of extropy. This work introduces a new
concept called normalized dynamic survival extropy (NDSE), a dynamic variation
of CRJ. We observe that NDSE is equivalent to CRJ of the random variable of
interest $X_{[t]}$ in the age replacement model at a fixed time $t$.
Additionally, we have demonstrated that NDSE remains constant exclusively for
exponential distribution at any time. We categorize two classes, INDSE and
DNDSE, based on their increasing and decreasing NDSE values. Next, we present a
non-parametric test to assess whether a distribution follows an exponential
pattern against INDSE. We derive the exact and asymptotic distribution for the
test statistic $\widehat{\Delta}^*$. Additionally, a test for asymptotic
behavior is presented in the paper for right censoring data. Finally, we
determine the critical values and power of our exact test through simulation.
The simulation demonstrates that the suggested test is easy to compute and has
significant statistical power, even with small sample sizes. We also conduct a
power comparison analysis among other tests, which shows better power for the
proposed test against other alternatives mentioned in this paper. Some
numerical real-life examples validating the test are also included.


  We give complete and exact descriptions of spaces of ultradifferentiable
functions that are closed under composition with either holomorphic or
ultradifferentiable functions -- which are two distinct cases. The proof works
by considering formal power series, and stability under differentiation is not
required. As an application of the power series approach we reprove regularity
results for solutions of ode's and pde's without employing tedious estimates
imploying the Fa\`a di Bruno formula for higher derivatives of composite maps.


  Quantile is an important risk measure quantifying the stochastic system
random behaviors. This paper studies a pooled quantile estimator, which is the
sample quantile of detailed simulation outputs after directly pooling
independent sample paths together. We derive the asymptotic representation of
the pooled quantile estimator and further prove its normality. By comparing
with the classical quantile estimator used in stochastic simulation, both
theoretical and empirical studies demonstrate the advantages of the proposal
under the context of parallel simulation.


  Let $(Z_n)$ be a supercritical branching process in a random environment $\xi
= (\xi_n)$. We establish a Berry-Esseen bound and a Cram\'er's type large
deviation expansion for $\log Z_n$ under the annealed law $\mathbb P$. We also
improve some earlier results about the harmonic moments of the limit variable
$W=lim_{n\to \infty} W_n$, where $W_n =Z_n/ \mathbb{E}_{\xi} Z_n$ is the
normalized population size.


  We consider the combined effects of large spin-orbit couplings and a
perpendicular magnetic field in a 2D honeycomb fermionic lattice. This system
provides an elegant setup to generate versatile spin textures propagating along
the edge of a sample. The spin-orbit coupling is shown to induce topological
phase transitions between a helical quantum spin Hall phase and a chiral
spin-imbalanced quantum Hall state. Besides, we find that the spin orientation
of a single topological edge state can be tuned by a Rashba spin-orbit
coupling, opening an interesting route towards quantum spin manipulation. We
discuss the possible realization of our results using cold atoms trapped in
optical lattices, where large synthetic magnetic fields and spin-orbit
couplings can be engineered and finely tuned. In particular, this system would
lead to the observation of a time-reversal-symmetry-broken quantum spin Hall
phase.


  We determine the computational difficulty of finding ground states of
one-dimensional (1D) Hamiltonians which are known to be Matrix Product States
(MPS). To this end, we construct a class of 1D frustration free Hamiltonians
with unique MPS ground states and a polynomial gap above, for which finding the
ground state is at least as hard as factoring. By lifting the requirement of a
unique ground state, we obtain a class for which finding the ground state
solves an NP-complete problem. Therefore, for these Hamiltonians it is not even
possible to certify that the ground state has been found. Our results thus
imply that in order to prove convergence of variational methods over MPS, as
the Density Matrix Renormalization Group, one has to put more requirements than
just MPS ground states and a polynomial spectral gap.


  Post-training dropout based approaches achieve high sparsity and are well
established means of deciphering problems relating to computational cost and
overfitting in Neural Network architectures. Contrastingly, pruning at
initialization is still far behind. Initialization pruning is more efficacious
when it comes to scaling computation cost of the network. Furthermore, it
handles overfitting just as well as post training dropout.
  In approbation of the above reasons, the paper presents two approaches to
prune at initialization. The goal is to achieve higher sparsity while
preserving performance. 1) K-starts, begins with k random p-sparse matrices at
initialization. In the first couple of epochs the network then determines the
"fittest" of these p-sparse matrices in an attempt to find the "lottery ticket"
p-sparse network. The approach is adopted from how evolutionary algorithms find
the best individual. Depending on the Neural Network architecture, fitness
criteria can be based on magnitude of network weights, magnitude of gradient
accumulation over an epoch or a combination of both. 2) Dissipating gradients
approach, aims at eliminating weights that remain within a fraction of their
initial value during the first couple of epochs. Removing weights in this
manner despite their magnitude best preserves performance of the network.
Contrarily, the approach also takes the most epochs to achieve higher sparsity.
3) Combination of dissipating gradients and kstarts outperforms either methods
and random dropout consistently.
  The benefits of using the provided pertaining approaches are: 1) They do not
require specific knowledge of the classification task, fixing of dropout
threshold or regularization parameters 2) Retraining of the model is neither
necessary nor affects the performance of the p-sparse network.


  A criterion for elements of free Zinbiel algebras to be Lie or Jordan is
established. This criterion is used in studying speciality problems of Tortkara
algebras. We construct a base of free special Tortkara algebras. Furthermore,
we prove analogues of classical Cohn's and Shirshov's theorems for Tortkara
algebras.


  A backdoor in a finite-domain CSP instance is a set of variables where each
possible instantiation moves the instance into a polynomial-time solvable
class. Backdoors have found many applications in artificial intelligence and
elsewhere, and the algorithmic problem of finding such backdoors has
consequently been intensively studied. Sioutis and Janhunen (Proc. 42nd German
Conference on AI (KI-2019)) have proposed a generalised backdoor concept
suitable for infinite-domain CSP instances over binary constraints. We
generalise their concept into a large class of CSPs that allow for higher-arity
constraints. We show that this kind of infinite-domain backdoors have many of
the positive computational properties that finite-domain backdoors have: the
associated computational problems are fixed-parameter tractable whenever the
underlying constraint language is finite. On the other hand, we show that
infinite languages make the problems considerably harder: the general backdoor
detection problem is W[2]-hard and fixed-parameter tractability is ruled out
under standard complexity-theoretic assumptions. We demonstrate that backdoors
may have suboptimal behaviour on binary constraints -- this is detrimental from
an AI perspective where binary constraints are predominant in, for instance,
spatiotemporal applications. In response to this, we introduce sidedoors as an
alternative to backdoors. The fundamental computational problems for sidedoors
remain fixed-parameter tractable for finite constraint language (possibly also
containing non-binary relations). Moreover, the sidedoor approach has appealing
computational properties that sometimes leads to faster algorithms than the
backdoor approach.


  The temperature-dependent electron spin relaxation of positively charged
excitons in a single InAs quantum dot (QD) was measured by time-resolved
photoluminescence spectroscopy at zero applied magnetic fields. The
experimental results show that the electron-spin relaxation is clearly divided
into two different temperature regimes: (i) T < 50 K, spin relaxation depends
on the dynamical nuclear spin polarization (DNSP) and is approximately
temperature-independent, as predicted by Merkulov et al. (ii) T > about 50 K,
spin relaxation speeds up with increasing temperature. A model of two LO phonon
scattering process coupled with hyperfine interaction is proposed to account
for the accelerated electron spin relaxation at higher temperatures.


  Over the past two decades, photonics have been developed as technological
solutions for astronomical instrumentation for, e.g., near-infrared
spectroscopy and long baseline interferometry. With increasing instrument
capabilities, large quantities of high precision optical components are
required to guide, manipulate, and analyze the light from astronomical sources.
Photonic integrated circuits (PICs) and fiber-based devices offer enormous
potential for astronomical instrumentation, as they can reduce the amount of
bulky free-space optics and pave the way for innovative solutions.
Astrophotonic devices are particularly interesting for interferometry due to
their compact design on the centimeter scale, even for a large number of
telescope inputs. Already, astrophotonic components are integrated in high-end
instruments at the VLTI and at the CHARA Array. Photonic beam combiners at
wavelengths from visible to mid-infrared have been fabricated using
lithographic techniques or ultrafast-laser inscription, with several components
tested on-sky. This paper will provide a glimpse into the growing field of
astrophotonics, its current status and potential for new technologies.


  Renormalization Group (RG) and optimized Pade-approximant methods are used to
estimate the three-loop perturbative contributions to the inclusive
semileptonic b \to u and b \to c decay rates. It is noted that the \bar{MS}
scheme works favorably in the b \to u case whereas the pole mass scheme shows
better convergence in the b \to c case. Upon the inclusion of the estimated
three-loop contribution, we find the full perturbative decay rate to be
192\pi^3\Gamma(b\to u\bar\nu_\ell\ell^-)/(G_F^2| V_{ub}|^2) = 2065 \pm 290{\rm
GeV^5} and 192\pi^3\Gamma(b\to c\ell^-\bar\nu_\ell)/(G_F^2|V_{cb}|^2)= 992 \pm
198 {\rm GeV^5}, respectively. The errors are inclusive of theoretical
uncertainties and non-perturbative effects. Ultimately, these perturbative
contributions reduce the theoretical uncertainty in the extraction of the CKM
matrix elements |V_{ub}| and |V_{cb}| from their respective measured inclusive
semileptonic branching ratio(s).


  We compute the NLO QCD corrections to the loop-induced gluon fusion
contribution in $W^+W^-$ production at the LHC. We consider the full leptonic
process $pp\to \ell^+\ell^{\prime\, -}\nu_{\ell}{\bar\nu}_{\ell^\prime}+X$, by
including resonant and non-resonant diagrams, spin correlations and off-shell
effects. Quark-gluon partonic channels are included for the first time in the
calculation, and our results are combined with NNLO predictions to the quark
annihilation channel at the fully differential level. The computed corrections,
which are formally of ${\cal O}(\alpha_{\mathrm{S}}^3)$, increase the NNLO
cross section by only about 2%, but have an impact on the shapes of kinematical
distributions, in part due to the jet veto, which is usually applied to reduce
the top-quark background. Our results, supplemented with NLO EW effects,
provide the most advanced fixed-order predictions available to date for this
process, and are compared with differential ATLAS data at $\sqrt{s}=$ 13 TeV.


  In a recent Rapid Communication [A. Stan, Phys. Rev. B \textbf{93}, 041103(R)
(2016)], the reliability of the Keldysh--Kadanoff--Baym equations (KBE) using
correlated selfenergy approximations applied to linear and nonlinear response
has been questioned. In particular, the existence of a universal attractor has
been predicted that would drive the dynamics of any correlated system towards
an unphysical homogeneous density distribution regardless of the system type,
the interaction and the many-body approximation. Moreover, it was conjectured
that even the mean-field dynamics would be damped. Here, by performing accurate
solutions of the KBE for situations studied in that paper, we prove these
claims wrong being caused by numerical inaccuracies.


  The ability to identify influential training examples enables us to debug
training data and explain model behavior. Existing techniques to do so are
based on the flow of training data influence through the model parameters. For
large models in NLP applications, it is often computationally infeasible to
study this flow through all model parameters, therefore techniques usually pick
the last layer of weights. However, we observe that since the activation
connected to the last layer of weights contains "shared logic", the data
influenced calculated via the last layer weights prone to a ``cancellation
effect'', where the data influence of different examples have large magnitude
that contradicts each other. The cancellation effect lowers the discriminative
power of the influence score, and deleting influential examples according to
this measure often does not change the model's behavior by much. To mitigate
this, we propose a technique called TracIn-WE that modifies a method called
TracIn to operate on the word embedding layer instead of the last layer, where
the cancellation effect is less severe. One potential concern is that influence
based on the word embedding layer may not encode sufficient high level
information. However, we find that gradients (unlike embeddings) do not suffer
from this, possibly because they chain through higher layers. We show that
TracIn-WE significantly outperforms other data influence methods applied on the
last layer significantly on the case deletion evaluation on three language
classification tasks for different models. In addition, TracIn-WE can produce
scores not just at the level of the overall training input, but also at the
level of words within the training input, a further aid in debugging.


  We consider a rational map $\phi: \mathbb{P}_k^{m} \dashrightarrow
\mathbb{P}_k^n$ that is a parameterization of an $m$-dimensional variety. Our
main goal is to study the $(m-1)$-dimensional fibers of $\phi$ in relation to
the $m$-th local cohomology modules of the Rees algebra of its base ideal.


  In recent years, machine learning (ML) techniques have created numerous
opportunities for intelligent mobile networks and have accelerated the
automation of network operations. However, complex network tasks may involve
variables and considerations even beyond the capacity of traditional ML
algorithms. On the other hand, large language models (LLMs) have recently
emerged, demonstrating near-human-level performance in cognitive tasks across
various fields. However, they remain prone to hallucinations and often lack
common sense in basic tasks. Therefore, they are regarded as assistive tools
for humans. In this work, we propose the concept of "generative AI-in-the-loop"
and utilize the semantic understanding, context awareness, and reasoning
abilities of LLMs to assist humans in handling complex or unforeseen situations
in mobile communication networks. We believe that combining LLMs and ML models
allows both to leverage their respective capabilities and achieve better
results than either model alone. To support this idea, we begin by analyzing
the capabilities of LLMs and compare them with traditional ML algorithms. We
then explore potential LLM-based applications in line with the requirements of
next-generation networks. We further examine the integration of ML and LLMs,
discussing how they can be used together in mobile networks. Unlike existing
studies, our research emphasizes the fusion of LLMs with traditional ML-driven
next-generation networks and serves as a comprehensive refinement of existing
surveys. Finally, we provide a case study to enhance ML-based network intrusion
detection with synthesized data generated by LLMs. Our case study further
demonstrates the advantages of our proposed idea.


  We present our proposals for generating total hyperfine spin zero state for
two f=1 or two f=2 particles, starting from initial unentangled states. We show
that our goal can be achieved by exploiting spin changing dynamics and
quadratic Zeeman shifts with realistic choices of external magnetic fields and
evolution time intervals.


  In this work, we present the f(R)-Einstein-Palatini formalism in arbitrary
dimensions and the study of consistency applied to brane models, the so-called
braneworld sum rules. We show that it is possible a scenario of thick branes in
five dimensions with compact extra dimension in the framework of the
f(R)-Einstein-Palatini theory by the accomplishment of an assertive criteria.


  We investigate the tunneling of an electron with momentum p in the direction
of V potential and under an angle {\theta} to the normal potential. Using the
boundary conditions, the conditions of continuity and Snell's law, we obtain
tunneling for various angles.


  Deep reinforcement learning (DRL) has a great potential for solving complex
decision-making problems in autonomous driving, especially in mixed-traffic
scenarios where autonomous vehicles and human-driven vehicles (HDVs) drive
together. Safety is a key during both the learning and deploying reinforcement
learning (RL) algorithms process. In this paper, we formulate the on-ramp
merging as a Markov Decision Process (MDP) problem and solve it with an
off-policy RL algorithm, i.e., Soft Actor-Critic for Discrete Action Settings
(SAC-Discrete). In addition, a motion predictive safety controller including a
motion predictor and an action substitution module, is proposed to ensure
driving safety during both training and testing. The motion predictor estimates
the trajectories of the ego vehicle and surrounding vehicles from kinematic
models, and predicts potential collisions. The action substitution module
updates the actions based on safety distance and replaces risky actions, before
sending them to the low-level controller. We train, evaluate and test our
approach on a gym-like highway simulation with three different levels of
traffic modes. The simulation results show that even in harder traffic
densities, our proposed method still significantly reduces collision rate while
maintaining high efficiency, outperforming several state-of-the-art baselines
in the considered on-ramp merging scenarios. The video demo of the evaluation
process can be found at: https://www.youtube.com/watch?v=7FvjbAM4oFw


  We have seen much recent progress in rigid object manipulation, but
interaction with deformable objects has notably lagged behind. Due to the large
configuration space of deformable objects, solutions using traditional
modelling approaches require significant engineering work. Perhaps then,
bypassing the need for explicit modelling and instead learning the control in
an end-to-end manner serves as a better approach? Despite the growing interest
in the use of end-to-end robot learning approaches, only a small amount of work
has focused on their applicability to deformable object manipulation. Moreover,
due to the large amount of data needed to learn these end-to-end solutions, an
emerging trend is to learn control policies in simulation and then transfer
them over to the real world. To-date, no work has explored whether it is
possible to learn and transfer deformable object policies. We believe that if
sim-to-real methods are to be employed further, then it should be possible to
learn to interact with a wide variety of objects, and not only rigid objects.
In this work, we use a combination of state-of-the-art deep reinforcement
learning algorithms to solve the problem of manipulating deformable objects
(specifically cloth). We evaluate our approach on three tasks --- folding a
towel up to a mark, folding a face towel diagonally, and draping a piece of
cloth over a hanger. Our agents are fully trained in simulation with domain
randomisation, and then successfully deployed in the real world without having
seen any real deformable objects.


  We prove that the polynomials counting locally free, absolutely
indecomposable, rank 1 representations of quivers over rings of truncated power
series have non-negative coefficients. This is a generalisation to higher depth
of positivity for toric Kac polynomials. The proof goes by inductively
contracting/deleting arrows of the quiver and is inspired from a previous work
of Abdelgadir, Mellit and Rodriguez-Villegas on toric Kac polynomials. We also
relate counts of absolutely indecomposable quiver representations in higher
depth and counts of jets over fibres of quiver moment maps. This is expressed
in a plethystic identity involving generating series of these counts. In rank
1, we prove a cohomological upgrade of this identity, by computing the
compactly supported cohomology of jet spaces over preprojective stacks. This is
reminiscent of PBW isomorphisms for preprojective cohomological Hall algebras.
Finally, our plethystic identity allows us to prove two conjectures by Wyss on
the asymptotic behaviour of both counts, when depth goes to infinity.


  Galaxies co-evolve with their host dark matter halos. Models of the
galaxy-halo connection, calibrated using cosmological hydrodynamic simulations,
can be used to populate dark matter halo catalogs with galaxies. We present a
new method for inferring baryonic properties from dark matter subhalo
properties using message-passing graph neural networks (GNNs). After training
on subhalo catalog data from the Illustris TNG300-1 hydrodynamic simulation,
our GNN can infer stellar mass from the host and neighboring subhalo positions,
kinematics, masses, and maximum circular velocities. We find that GNNs can also
robustly estimate stellar mass from subhalo properties in 2d projection. While
other methods typically model the galaxy-halo connection in isolation, our GNN
incorporates information from galaxy environments, leading to more accurate
stellar mass inference.


  We describe a glueing construction for a certain self-dual reduction of the
Yang-Mills equations in dimension 8.


  Interfacial charge transfer plays an essential role in establishing the
relative alignment of the metal Fermi level and the energy bands of organic
semiconductors. While the details remain elusive in many systems, this charge
transfer has been inferred in a number of photoemission experiments. We present
electronic transport measurements in very short channel ($L < 100$ nm)
transistors made from poly(3-hexylthiophene) (P3HT). As channel length is
reduced, the evolution of the contact resistance and the zero-gate-voltage
conductance are consistent with such charge transfer. Short channel conduction
in devices with Pt contacts is greatly enhanced compared to analogous devices
with Au contacts, consistent with charge transfer expectations. Alternating
current scanning tunneling microscopy (ACSTM) provides further evidence that
holes are transferred from Pt into P3HT, while much less charge transfer takes
place at the Au/P3HT interface.


  Contextual Markov decision processes (CMDPs) describe a class of
reinforcement learning problems in which the transition kernels and reward
functions can change over time with different MDPs indexed by a context
variable. While CMDPs serve as an important framework to model many real-world
applications with time-varying environments, they are largely unexplored from
theoretical perspective. In this paper, we study CMDPs under two linear
function approximation models: Model I with context-varying representations and
common linear weights for all contexts; and Model II with common
representations for all contexts and context-varying linear weights. For both
models, we propose novel model-based algorithms and show that they enjoy
guaranteed $\epsilon$-suboptimality gap with desired polynomial sample
complexity. In particular, instantiating our result for the first model to the
tabular CMDP improves the existing result by removing the reachability
assumption. Our result for the second model is the first-known result for such
a type of function approximation models. Comparison between our results for the
two models further indicates that having context-varying features leads to much
better sample efficiency than having common representations for all contexts
under linear CMDPs.


  By the geometric Satake correspondence, the number of components of certain
fibres of the affine Grassmannian convolution morphism equals the tensor
product multiplicity for representations of the Langlands dual group. On the
other hand, in the case of GL_n, combinatorial objects called hives also count
tensor product multiplicities. The purpose of this paper is to give a simple
bijection between hives and the components of these fibres. In particular, we
give a description of the individual components. We also describe a conjectural
generalization involving the octahedron recurrence.


  In 2017, LHCb collaboration reported their first observation of the rare
decays $B_s \to \phi (f_0(980)$ $/f_2(1270) \to ) \pi^+\pi^-$ and the evidence
of $B^0 \to \phi(f_0(980)/f_2(1270)\to)\pi^+\pi^-$. Motivated by this, we study
these quasi-two-body decays in the perturbative QCD approach. The branching
fractions, $CP$ asymmetries and the polarization fractions are calculated. We
find that within the appropriate two-meson wave functions, the calculated
branching fractions are in agreement with the measurements of LHCb. Based on
the narrow-width approximation, We also calculate the branching fractions of
the quasi-two-body $B_{d,s}\to \phi(f_0(980)/f_2(1270)\to) \pi^0\pi^0$ and
$B_{d,s}\to \phi(f_2(1270)\to) K^+K^-$, and hope the predictions to be tested
in the ongoing LHCb and Belle II experiments. Moreover, the processes
$B_{d,s}\to \phi f_2(1270)$ are also analyzed under this approximation. We note
that the $CP$ asymmetries of these decays are very small, because these decays
are either penguin dominant or pure penguin processes.


  Nonlinear interactions are recognized as potential resources for quantum
metrology, facilitating parameter estimation precisions that scale as the
exponential Heisenberg limit of $2^{-N}$. We explore such nonlinearity and
propose an associated quantum measurement scenario based on the nonlinear
interaction of $N$-probe entanglement generating form. This scenario provides
an enhanced precision scaling of $D^{-N}/(N-1)!$ with $D > 2$ a tunable
parameter. In addition, it can be readily implemented in a variety of
experimental platforms and applied to measurements of a wide range of
quantities, including local gravitational acceleration $g$, magnetic field, and
its higher-order gradients.


  The source of the gravitational-wave signal GW170817, very likely a binary
neutron star merger, was also observed electromagnetically, providing the first
multi-messenger observations of this type. The two week long electromagnetic
counterpart had a signature indicative of an r-process-induced optical
transient known as a kilonova. This Letter examines how the mass of the
dynamical ejecta can be estimated without a direct electromagnetic observation
of the kilonova, using gravitational-wave measurements and a phenomenological
model calibrated to numerical simulations of mergers with dynamical ejecta.
Specifically, we apply the model to the binary masses inferred from the
gravitational-wave measurements, and use the resulting mass of the dynamical
ejecta to estimate its contribution (without the effects of wind ejecta) to the
corresponding kilonova light curves from various models. The distributions of
dynamical ejecta mass range between $M_{ej} = 10^{-3} - 10^{-2} M_{\odot}$ for
various equations of state, assuming the neutron stars are rotating slowly. In
addition, we use our estimates of the dynamical ejecta mass and the neutron
star merger rates inferred from GW170817 to constrain the contribution of
events like this to the r-process element abundance in the Galaxy when ejecta
mass from post-merger winds is neglected. We find that if $\gtrsim10\%$ of the
matter dynamically ejected from BNS mergers is converted to r-process elements,
GW170817-like BNS mergers could fully account for the amount of r-process
material observed in the Milky Way.


  Small scale characteristics of turbulence such as velocity gradients and
vorticity fluctuate rapidly in magnitude and oscillate in sign. Much work
exists on the characterization of magnitude variations, but far less on sign
oscillations. While averages performed on large scales tend to zero because of
the oscillatory character, those performed on increasingly smaller scales will
vary with the averaging scale in some characteristic way. This characteristic
variation at high Reynolds numbers is captured by the so-called cancellation
exponent, which measures how local averages tend to cancel out as the averaging
scale increases, in space or time. Past experimental work suggests that the
exponents in turbulence depend on whether one considers quantities in full
three-dimensional space or uses their one- or two-dimensional cuts. We compute
cancellation exponents of vorticity and longitudinal as well as transverse
velocity gradients in isotropic turbulence at Taylor-scale Reynolds number up
to 1300 on $8192^3$ grids. The 2D cuts yield the same exponents as those for
full 3D, while the 1D cuts yield smaller numbers, suggesting that the results
in higher dimensions are more reliable. We make the case that the presence of
vortical filaments in isotropic turbulence leads to this conclusion. This
effect is particularly conspicuous in magnetohydrodynamic turbulence, where an
increased degree of spatial coherence develops along the imposed magnetic
field.


  Context. The properties of dust grains, in particular their size
distribution, are expected to differ from the interstellar medium to the
high-density regions within molecular clouds. Aims. We measure the mid-infrared
extinction law produced by dense material in molecular cloud cores. Since the
extinction at these wavelengths is caused by dust, the extinction law in cores
should depart from that found in low-density environments if the dust grains
have different properties. Methods. We use the unbiased LINES method to measure
the slope of the reddening vectors in color-color diagrams. We derive the
mid-infrared extinction law toward the dense cores B59 and FeSt 1-457 in the
Pipe Nebula over a range of visual extinction between 10 and 50 magnitudes,
using a combination of Spitzer/IRAC, and ESO NTT/VLT data. Results. The
mid-infrared extinction law in both cores departs significantly from a
power-law between 3.6 and 8 micron, suggesting that these cores contain dust
with a considerable fraction of large dust grains. We find no evidence for a
dependence of the extinction law with column density up to 50 magnitudes of
visual extinction in these cores, and no evidence for a variation between our
result and those for other clouds at lower column densities reported elsewhere
in the literature. This suggests that either large grains are present even in
low column density regions, or that the existing dust models need to be revised
at mid-infrared wavelengths. We find a small but significant difference in the
extinction law of the two cores, that we tentatively associate with the onset
of star formation in B59.


  Evolutionary algorithms (EAs) have found many successful real-world
applications, where the optimization problems are often subject to a wide range
of uncertainties. To understand the practical behaviors of EAs theoretically,
there are a series of efforts devoted to analyzing the running time of EAs for
optimization under uncertainties. Existing studies mainly focus on noisy and
dynamic optimization, while another common type of uncertain optimization,
i.e., robust optimization, has been rarely touched. In this paper, we analyze
the expected running time of the (1+1)-EA solving robust linear optimization
problems (i.e., linear problems under robust scenarios) with a cardinality
constraint $k$. Two common robust scenarios, i.e., deletion-robust and
worst-case, are considered. Particularly, we derive tight ranges of the robust
parameter $d$ or budget $k$ allowing the (1+1)-EA to find an optimal solution
in polynomial running time, which disclose the potential of EAs for robust
optimization.


  Using data samples collected by the BESIII detector operating at the BEPCII
storage ring, we measure the $e^+e^- \to K_{S}^{0}K^{\pm}\pi^{\mp}$ Born cross
sections at center-of-mass energies between 3.8 and 4.6\,GeV, corresponding to
a luminosity of about 5.0~fb$^{-1}$. The results are compatible with the BABAR
measurements, but with the precision significantly improved. A simple $1/s^n$
dependence for the continuum process can describe the measured cross sections,
but a better fit is obtained by an additional resonance near 4.2\,GeV, which
could be an excited charmonium or a charmonium-like state.


  We discuss level splitting and sideband transitions induced by a modulated
coupling between a superconducting quantum circuit and a nanomechanical
resonator. First, we show how to achieve an unconventional time-dependent
longitudinal coupling between a flux (transmon) qubit and the resonator.
Considering a sinusoidal modulation of the coupling strength, we find that a
first-order sideband transition can be split into two. Moreover, under the
driving of a red-detuned field, we discuss the optical response of the qubit
for a resonant probe field. We show that level splitting induced by modulating
this longitudinal coupling can enable two-color electromagnetically induced
transparency (EIT), in addition to single-color EIT. In contrast to standard
predictions of two-color EIT in atomic systems, we apply here only a single
drive (control) field. The monochromatic modulation of the coupling strength is
equivalent to employing two eigenfrequency-tunable mechanical resonators. Both
drive-probe detuning for single-color EIT and the distance between transparent
windows for two-color EIT, can be adjusted by tuning the modulation frequency
of the coupling.


  A high-precision relativistic calculations of Cd-like Nd$^{12+}$, Sm$^{14+}$
and Sn-like Pr$^{9+}$, Nd$^{10+}$ atomic properties is carried out using an
approach that combines configuration interaction and a linearized
coupled-cluster method. These ions have long-lived metastable states with
transitions accessible by laser excitations, relatively simple electronic
structure, high sensitivity to $\alpha$ variation, and stable isotopes. Breit
and QED corrections were included into the calculations. Energies, transition
wavelengths, electric- and magnetic-multipole reduced matrix elements,
lifetimes, and sensitivity coefficients $q$ and $K$ to the variation of the
fine-structure constant $\alpha$ were obtained. A detailed study of
uncertainties was performed. Energies for similar Cd-like Ba$^{8+}$, La$^{9+}$,
Ce$^{10+}$, Pr$^{11+}$ and Sn-like Ba$^{6+}$ ions were calculated and compared
with experiment for further tests of the accuracy.


  We present an all-dielectric metadevice platform realized by combining an
inverse electromagnetic design computational method with additive
manufacturing. As opposed to conventional flat metasurface based devices that
are composed of resonant building blocks resulting in narrow band operation,
our design approach creates non-resonant, extremely broadband metadevices. We
propose and demonstrate high-efficiency (transmission > 60%), using thin
all-dielectric metadevices that are capable of polarization splitting, beam
bending, and focusing. Experimental demonstrations are performed at
millimeter-wave frequencies using 3D-printed metadevices, however our proposed
platform can be readily applied to the design and fabrication of
electromagnetic and photonic metadevices spanning microwave to optical
frequencies.


  We consider a phenomenological extension of the minimal supersymmetric
standard model which incorporates chaotic inflation and a radiatively generated
intermediate mass scale. Initially a period of chaotic inflation is driven by a
quartic potential associated with the right-handed electron sneutrino.
Supersymmetry relates the quartic coupling of the inflationary potential to the
electron Majorana neutrino Yukawa coupling, $h_1$. The microwave background
temperature anisotropy determines this coupling to be $h_1\simeq 10^{-7}$,
which is similar in magnitude to the electron Dirac Yukawa coupling. A U$(1)$
Peccei-Quinn (PQ) symmetry is broken by radiative corrections at an
intermediate scale $\simeq 10^{12}$GeV when the universe cools to a temperature
$T\lesssim 10^3$GeV. This leads to an invisible axion, a weak scale $\mu$-term
and an electron Majorana neutrino mass $M_{N_1}\simeq 10^5$GeV. A second
inflationary period can also occur via a flat-direction field. In this case the
universe can be reheated to a temperature $T_{RH}\simeq 10^6$GeV, without
restoring PQ symmetry. Baryogenesis will then occur via out-of-equilibrium
neutrino decay.


  This thesis aims at concluding the classification results for topological
phases with symmetry in 2+1 dimensions. The main result is that topological
phases are classified by a triple of unitary braided fusion categories
$\mathcal E\subset\mathcal C\subset\mathcal M$ plus the chiral central charge
$c$. Here $\mathcal E$ is a symmetric fusion category, $\mathcal
E=\mathrm{Rep}(G)$ for boson systems or $\mathcal E=\mathrm{sRep}(G^f)$ for
fermion systems, consisting of the representations of the symmetry group and
describing the local excitations with symmetry; $\mathcal C$ is the category of
all the quasiparticle excitations in the bulk, containing $\mathcal E$ as its
M\"uger center; $\mathcal M$ is a minimal modular extension of $\mathcal C$,
that also includes the gauged symmetry defects. We also study the stacking of
topological phases with symmetry and two types of anyon condensations based on
such classification.


  Multitype branching processes (MTBP) model branching structures, where the
nodes of the resulting tree are objects of different types. One field of
application of such models in biology is in studies of cell proliferation. A
sampling scheme that appears frequently is observing the cell count in several
independent colonies at discrete time points (sometimes only one). Thus, the
process is not observable in the sense of the whole tree, but only as the
"generation" at given moment in time, which consist of the number of cells of
every type. This requires an EM-type algorithm to obtain a maximum likelihood
(ML) estimation of the parameters of the branching process. A computational
approach for obtaining such estimation of the offspring distribution is
presented in the class of Markov branching processes with terminal types.


  A specific class of stochastic heat engines driven cyclically by
time-dependent potential, which is defined in the half-line ($0<x<+\infty$), is
analysed. For such engines, most of their physical quantities can be obtained
explicitly, including the entropy and internal energy of the heat engine, as
well as output work (power) and heat exchange with the environment during a
finite time interval. The optimisation method based on the external potential
to reduce {\it irreversible} work and increase energy efficiency is presented.
With this optimised potential, efficiency $\eta^*$ and its particular value at
maximum power $\eta^*_{\rm EMP}$ are calculated and discussed briefly.


  In this paper, we study the bandits with knapsacks (BwK) problem and develop
a primal-dual based algorithm that achieves a problem-dependent logarithmic
regret bound. The BwK problem extends the multi-arm bandit (MAB) problem to
model the resource consumption associated with playing each arm, and the
existing BwK literature has been mainly focused on deriving asymptotically
optimal distribution-free regret bounds. We first study the primal and dual
linear programs underlying the BwK problem. From this primal-dual perspective,
we discover symmetry between arms and knapsacks, and then propose a new notion
of sub-optimality measure for the BwK problem. The sub-optimality measure
highlights the important role of knapsacks in determining algorithm regret and
inspires the design of our two-phase algorithm. In the first phase, the
algorithm identifies the optimal arms and the binding knapsacks, and in the
second phase, it exhausts the binding knapsacks via playing the optimal arms
through an adaptive procedure. Our regret upper bound involves the proposed
sub-optimality measure and it has a logarithmic dependence on length of horizon
$T$ and a polynomial dependence on $m$ (the numbers of arms) and $d$ (the
number of knapsacks). To the best of our knowledge, this is the first
problem-dependent logarithmic regret bound for solving the general BwK problem.


  This paper proposes and studies a detection technique for adversarial
scenarios (dubbed deterministic detection). This technique provides an
alternative detection methodology in case the usual stochastic methods are not
applicable: this can be because the studied phenomenon does not follow a
stochastic sampling scheme, samples are high-dimensional and subsequent
multiple-testing corrections render results overly conservative, sample sizes
are too low for asymptotic results (as e.g. the central limit theorem) to kick
in, or one cannot allow for the small probability of failure inherent to
stochastic approaches. This paper instead designs a method based on insights
from machine learning and online learning theory: this detection algorithm -
named Online FAult Detection (FADO) - comes with theoretical guarantees of its
detection capabilities. A version of the margin is found to regulate the
detection performance of FADO. A precise expression is derived for bounding the
performance, and experimental results are presented assessing the influence of
involved quantities. A case study of scene detection is used to illustrate the
approach. The technology is closely related to the linear perceptron rule,
inherits its computational attractiveness and flexibility towards various
extensions.


  A new method for measuring the quantities influenced by bin-to-bin and inside
bin correlations is presented. This method is essential for large multiplisity
and/or high density of particles in phase spase. The method was applied to the
two particle correation functions of e+e- -> W+W- events.


  Two elements $g$ and $h$ of a permutation group $G$ acting on a set $V$ are
said to be intersecting if $g(v) = h(v)$ for some $v \in V$. More generally, a
subset ${\cal F}$ of $G$ is an intersecting set if every pair of elements of
${\cal F}$ is intersecting. The intersection density $\rho(G)$ of a transitive
permutation group $G$ is the maximum value of the quotient $|{\cal F}|/|G_v|$
where ${\cal F}$ runs over all intersecting sets in $G$ and $G_v$ is a
stabilizer of $v\in V$. In this paper the intersection density of transitive
groups of degree twice a prime is determined, and proved to be either $1$ or
$2$. In addition, it is proved that the intersection density of transitive
groups of prime power degree is $1$.


  We study the steady-state dynamics of the Hubbard model driven
out-of-equilibrium by a constant electric field and coupled to a dissipative
heat bath. For very strong field, we find a dimensional reduction: the system
behaves as an equilibrium Hubbard model in lower dimensions. We derive
steady-state equations for the dynamical mean field theory in the presence of
dissipation. We discuss how the electric field induced dimensional crossover
affects the momentum resolved and integrated spectral functions, the energy
distribution function, as well as the steady current in the non-linear regime.


  There is growing evidence that relativistic jets in active galactic nuclei
undergo extended (parsec-scale) acceleration. We argue that, contrary to some
suggestions in the literature, this acceleration cannot be purely hydrodynamic.
Using exact semianalytic solutions of the relativistic MHD equations, we
demonstrate that the parsec-scale acceleration to relativistic speeds inferred
in sources like the radio galaxy NGC 6251 and the quasar 3C 345 can be
attributed to magnetic driving. Additional observational implications of this
model will be explored in future papers in this series.


  A quantized message passing decoding algorithm for low-density parity-check
codes is presented. The algorithm relies on the min approximation at the check
nodes, and on modelling the variable node inbound messages as observations of
an extrinsic discrete memoryless channel. The performance of the algorithm is
analyzed and compared to quantized min-sum decoding by means of density
evolution, and almost closes the gap with the performance of the sum-product
algorithm. A stability analysis is derived, which highlights the role played by
degree-$3$ variable nodes in the stability condition. Finite-length simulation
results confirm large gains predicted by the asymptotic analysis.


  Strong hyperfine coupling in a 0.2% Holmium doped LiYF_4 single crystal
induces staircaselike hysteresis loops of the magnetization at very low
temperatures. The field sweep rate dependence of hysteresis loops allows the
study of two different regimes in the magnetic relaxation of these weakly
coupled magnetic moments. At slow field sweep rates, quantum tunneling of the
magnetization occurs at avoided level crossings in the low-energy scheme of a
single ion Ho^3+. At faster sweep rates, nonequilibrated spin-phonon and
spin-spin transitions, mediated by weak dipolar interactions, lead to
magnetization oscillations and additional steps.


  Below we establish the conditions guaranteeing the reality of all the zeros
of polynomials $P_n(z)$ in the polynomial sequence $\{P_n(z)\}_{n=1}^{\infty}$
satisfying a five-term recurrence relation
  $$P_{n}(z)= zP_{n-1}(z) + \alpha P_{n-2}(z)+\beta P_{n-3}(z)+\gamma
P_{n-4}(z),$$ with the standard initial conditions $$P_0(z) = 1, P_{-1}(z) =
P_{-2}(z) =P_{-3}(z) = 0,$$ where $\alpha, \beta, \gamma$ are real
coefficients, $\gamma\neq 0$ and $z$ is a complex variable. We interprete this
sequence of polynomials as principal minors of an appropriate banded Teoplitz
matrix whose associated Laurent polynomial $b(z)$ is holomorphic in
$\mathbb{C}\setminus \{0\}$. We show that when either the critical points of
$b(z)$ are all real; or when they are two real and one pair of complex
conjugate critical points with some extra conditions on the parameters, the set
$b^{-1}(\mathbb{R})$ contains a Jordan curve with $0$ in its interior and in
some cases a non-simple curve enclosing $0$. The presence of the said curves is
necessary and sufficient for every polynomial in the sequence
$\{P_n(z)\}_{n=1}^{\infty}$ to be hyperbolic (real-rooted).


  The half antivortex, a fundamental topological structure which determines
magnetization reversal of submicron magnetic devices with domain walls, has
been suggested also to play a crucial role in spin torque induced vortex core
reversal in circular disks. Here we report on magnetization reversal in
circular disks with nanoholes through consecutive metastable states with half
antivortices. In-plane anisotropic magnetoresistance and broadband
susceptibility measurements accompanied by micromagnetic simulations reveal
that cobalt disks with two and three linearly arranged nanoholes directed at 45
and 135 degrees with respect to the external magnetic field show reproducible
step-like changes in the anisotropic magnetoresistance and magnetic
permeability due to transitions between different intermediate states mediated
by vortices and half antivortices confined to the dot nanoholes and edges,
respectively. Our findings are relevant for the development of multi-hole based
spintronic and magnetic memory devices.


  Understanding how entanglements affect the behaviour of polymeric complex
fluids is an open challenge in many fields. To elucidate the nature and
consequence of entanglements in dense polymer solutions, we propose a novel
method: a "dynamical entanglement analysis" (DEA) to extract spatio-temporal
entanglement structures from the pair-wise displacement correlation of
entangled chains. By applying this method to large-scale Molecular Dynamics
simulations of linear and unknotted, nonconcatenated ring polymers, we find a
strong and unexpected cooperative dynamics: the footprint of mutual entrainment
between entangled chains. We show that DEA is a powerful and sensitive probe
that reveals previously unnoticed, and architecture-dependent, spatio-temporal
structures of dynamical entanglement in polymeric solutions. We also propose a
mean-field approximation of our analysis which provides previously
under-appreciated physical insights into the dynamics of generic entangled
polymers. We envisage DEA will be useful to analyse the dynamical evolution of
entanglements in generic polymeric systems such as blends and composites.


  Prompting language models (LMs) is the main interface for applying them to
new tasks. However, for smaller LMs, prompting provides low accuracy compared
to gradient-based finetuning. Tree Prompting is an approach to prompting which
builds a decision tree of prompts, linking multiple LM calls together to solve
a task. At inference time, each call to the LM is determined by efficiently
routing the outcome of the previous call using the tree. Experiments on
classification datasets show that Tree Prompting improves accuracy over
competing methods and is competitive with fine-tuning. We also show that
variants of Tree Prompting allow inspection of a model's decision-making
process.


  Superconductors play a crucial role in the advancement of high-field
electromagnets. Unfortunately, their performance can be compromised by
thermomagnetic instabilities, wherein the interplay of rapid magnetic and slow
heat diffusion can result in catastrophic flux jumps eventually leading to
irreversible damage. This issue has long plagued high-$J_c$ Nb$_3$Sn wires at
the core of high-field magnets. In this study, we introduce a groundbreaking
large-scale GPU-optimized algorithm aimed at tackling the complex intertwined
effects of electromagnetism, heating, and strain acting concomitantly during
the quenching process of superconducting coils. We validate our model by
conducting comparisons with magnetization measurements obtained from short
multifilamentary Nb$_3$Sn wires and further experimental tests conducted on
solenoid coils while subject to ramping transport currents. Furthermore,
leveraging our developed numerical algorithm, we unveil the dynamic propagation
mechanisms underlying thermomagnetic instabilities (including flux jumps and
quenches) within the coils. Remarkably, our findings reveal that the velocity
field of flux jumps and quenches within the coil is correlated with the amount
of Joule heating experienced by each wire over a specific time interval, rather
than solely being dependent on instantaneous Joule heating or maximum
temperature. These insights have the potential to pave the way for optimizing
the design of next-generation superconducting magnets, thereby directly
influencing a wide array of technologically relevant and multidisciplinary
applications.


  We use the coupled cluster method implemented to high orders of approximation
to investigate the frustrated spin-$\frac{1}{2}$ $J_{1}$--$J_{2}$--$J_{3}$
antiferromagnet on the honeycomb lattice with isotropic Heisenberg interactions
of strength $J_{1} > 0$ between nearest-neighbor pairs, $J_{2}>0$ between
next-nearest-neighbor pairs, and $J_{3}>0$ between next-next-neareast-neighbor
pairs of spins. In particular, we study both the ground-state (GS) and
lowest-lying triplet excited-state properties in the case $J_{3}=J_{2} \equiv
\kappa J_{1}$, in the window $0 \leq \kappa \leq 1$ of the frustration
parameter, which includes the (tricritical) point of maximum classical
frustration at $\kappa_{{\rm cl}} = \frac{1}{2}$. We present GS results for the
spin stiffness, $\rho_{s}$, and the zero-field uniform magnetic susceptibility,
$\chi$, which complement our earlier results for the GS energy per spin, $E/N$,
and staggered magnetization, $M$, to yield a complete set of accurate
low-energy parameters for the model. Our results all point towards a phase
diagram containing two quasiclassical antiferromagnetic phases, one with N\'eel
order for $\kappa < \kappa_{c_{1}}$, and the other with collinear striped order
for $\kappa > \kappa_{c_{2}}$. The results for both $\chi$ and the spin gap
$\Delta$ provide compelling evidence for a quantum paramagnetic phase that is
gapped over a considerable portion of the intermediate region $\kappa_{c_{1}} <
\kappa < \kappa_{c_{2}}$, especially close to the two quantum critical points
at $\kappa_{c_{1}}$ and $\kappa_{c_{2}}$. Each of our fully independent sets of
results for the low-energy parameters is consistent with the values
$\kappa_{c_{1}} = 0.45 \pm 0.02$ and $\kappa_{c_{2}} = 0.60 \pm 0.02$, and with
the transition at $\kappa_{c_{1}}$ being of continuous (and probably of the
deconfined) type and that at $\kappa_{c_{2}}$ being of first-order type.


  We present a novel algorithm for (i) detecting approximate symmetries
inherently present among spatially localized molecular orbitals and (ii)
enforcing these in numerically exact manners by means of unitary optimization
techniques. The vast potential of our algorithm to compress a full set of
molecular orbitals into only a minimal set of symmetry-unique orbitals is
demonstrated, starting from localized bases of either Pipek-Mezey or
Foster-Boys orbitals. Comparisons of results based on either of these two
localization procedures indicate that Foster-Boys molecular orbitals can be
spanned by a smaller number of symmetry-unique orbitals on average, making
these outstanding candidates for the exploitation of general, (non-)Abelian
point-group symmetries in a range of local correlation methods. As an
illustration of said compressibility, our algorithm is capable of identifying a
mere 14 symmetry-unique orbitals for the buckminsterfullerene in the highly
symmetric $I_h$ molecular point group, corresponding to only $1.7\%$ of its
total 840 molecular orbitals in a standard double-$\zeta$ basis set. The
present work thus marks an important advancement in the exploitation of
point-group symmetry within local correlation methods, for which the
appropriate adaption of symmetry uniqueness among orbitals has the potential to
yield unprecedented speed-ups.


  We present an analysis of the polymorphic energy ordering and properties of
the rock salt and zincblende structures of manganese oxide using fixed node
diffusion Monte Carlo (DMC). Manganese oxide is a correlated, antiferromagnetic
material that has proven to be challenging to model from first principles
across a variety of approaches. Unlike conventional density functional theory
and some hybrid functionals, fixed node diffusion Monte Carlo finds the rock
salt structure to be more stable than the zincblende structure, and thus
recovers the correct energy ordering. Analysis of the site-resolved charge
fluctuations of the wave functions according to DMC and other electronic
structure descriptions give insights into elements that are missing in other
theories. While the calculated band gaps within DMC are in agreement with
predictions that the zincblende polymorph has a lower band gap, the gaps
themselves overestimate reported experimental values.


  We offer our two cents to the ongoing discussion on whether profile
likelihoods are "true" likelihood functions, by showing that the profile
likelihood function can in fact be identical to a marginal likelihood in the
special case of normal models. Thus, profile likelihoods can be "true"
likelihoods insofar as marginal likelihoods are "true" likelihoods. The prior
distribution that achieves this equivalence turns out to be the Jeffreys prior.
We suspect, however, that normal models are the only class of models for which
such an equivalence between maximization and marginalization is exact.


  Traditionally, in supervised machine learning, (a significant) part of the
available data (usually 50% to 80%) is used for training and the rest for
validation. In many problems, however, the data is highly imbalanced in regard
to different classes or does not have good coverage of the feasible data space
which, in turn, creates problems in validation and usage phase. In this paper,
we propose a technique for synthesising feasible and likely data to help
balance the classes as well as to boost the performance in terms of confusion
matrix as well as overall. The idea, in a nutshell, is to synthesise data
samples in close vicinity to the actual data samples specifically for the less
represented (minority) classes. This has also implications to the so-called
fairness of machine learning. In this paper, we propose a specific method for
synthesising data in a way to balance the classes and boost the performance,
especially of the minority classes. It is generic and can be applied to
different base algorithms, e.g. support vector machine, k-nearest neighbour,
deep networks, rule-based classifiers, decision trees, etc. The results
demonstrated that: i) a significantly more balanced (and fair) classification
results can be achieved; ii) that the overall performance as well as the
performance per class measured by confusion matrix can be boosted. In addition,
this approach can be very valuable for the cases when the number of actual
available labelled data is small which itself is one of the problems of the
contemporary machine learning.


  The high sensitivity of lithium detector to pep- and Be7-neutrinos makes a
lithium radiochemical detector a powerful tool for the search of the
oscillations of solar neutrinos. The first phase of lithium experiment on the
installation with 10 tons of metallic lithium will enable to collect data
within 1 year of measurements to provide very definite information about SMA
MSW solution. the second phase with 10 modules 10 tons each will measure the
semiannual variations of the signal, thus the contributions of pep- and
Be7-lines will be weighted, what will give a "smoking-gun" evidence about
"just-so" solution for large mixing angles and delta-m2 about 10-10 - 10-9 eV2.
If both regions are not confirmed the results of lithium detector can be
interpreted in favor of LMA MSW solution.


  BVR photometry of a recently identified RS CVn binary star HD61396, carried
out during 2001, is presented. The new photometry reveal significant evolution
in the shape and amplitude of light curve when compared with those reported
earlier by Padmakar etal (2000). The traditional two-starspot model has been
used to obtain the spot parameters from the observed light curve. Changes in
the spot area and their location on the stellar surface are discernible from
the extracted parameters from the new photometry.


  We examine the relationship between Dependence Logic and game logics. A
variant of Dynamic Game Logic, called Transition Logic, is developed, and we
show that its relationship with Dependence Logic is comparable to the one
between First-Order Logic and Dynamic Game Logic discussed by van Benthem. This
suggests a new perspective on the interpretation of Dependence Logic formulas,
in terms of assertions about reachability in games of im- perfect information
against Nature. We then capitalize on this intuition by developing expressively
equivalent variants of Dependence Logic in which this interpretation is taken
to the foreground.


  The availability of larger and larger graph datasets, growing exponentially
over the years, has created several new algorithmic challenges to be addressed.
Sequential approaches have become unfeasible, while interest on parallel and
distributed algorithms has greatly increased.
  Appropriately partitioning the graph as a preprocessing step can improve the
degree of parallelism of its analysis. A number of heuristic algorithms have
been developed to solve this problem, but many of them subdivide the graph on
its vertex set, thus obtaining a vertex-partitioned graph.
  Aim of this paper is to explore a completely different approach based on edge
partitioning, in which edges, rather than vertices, are partitioned into
disjoint subsets. Contribution of this paper is twofold: first, we introduce a
graph processing framework based on edge partitioning, that is flexible enough
to be applied to several different graph problems. Second, we show the
feasibility of these ideas by presenting a distributed edge partitioning
algorithm called d-fep.
  Our framework is thoroughly evaluated, using both simulations and an Hadoop
implementation running on the Amazon EC2 cloud. The experiments show that d-fep
is efficient, scalable and obtains consistently good partitions. The resulting
edge-partitioned graph can be exploited to obtain more efficient
implementations of graph analysis algorithms.


  We prove Riemann hypothesis, Generalized Riemann hypothesis, and Ramanujan
$\tau$-Dirichlet series hypothesis. Method is to show the convexity of function
which has zeros critical strip the same as zeta function.


  Most of the existing diffusion models use Gaussian noise for training and
sampling across all time steps, which may not optimally account for the
frequency contents reconstructed by the denoising network. Despite the diverse
applications of correlated noise in computer graphics, its potential for
improving the training process has been underexplored. In this paper, we
introduce a novel and general class of diffusion models taking correlated noise
within and across images into account. More specifically, we propose a
time-varying noise model to incorporate correlated noise into the training
process, as well as a method for fast generation of correlated noise mask. Our
model is built upon deterministic diffusion models and utilizes blue noise to
help improve the generation quality compared to using Gaussian white (random)
noise only. Further, our framework allows introducing correlation across images
within a single mini-batch to improve gradient flow. We perform both
qualitative and quantitative evaluations on a variety of datasets using our
method, achieving improvements on different tasks over existing deterministic
diffusion models in terms of FID metric.


  The dark matter enclosed in a density perturbation with a large initial
amplitude (delta-rho/rho > 1e-3) collapses shortly after recombination and
forms an ultracompact minihalo (UCMH). Their high central densities make UCMHs
especially suitable for detection via astrometric microlensing: as the UCMH
moves, it changes the apparent position of background stars. A UCMH with a mass
larger than a few solar masses can produce a distinctive astrometric
microlensing signal that is detectable by the space astrometry mission Gaia. If
Gaia does not detect gravitational lensing by any UCMHs, then it establishes an
upper limit on their abundance and constrains the amplitude of the primordial
power spectrum for k~2700 Mpc^{-1}. These constraints complement the upper
bound on the amplitude of the primordial power spectrum derived from limits on
gamma-ray emission from UCMHs because the astrometric microlensing signal
produced by an UCMH is maximized if the dark-matter annihilation rate is too
low to affect the UCMH's density profile. If dark matter annihilation within
UCMHs is not detectable, a search for UCMHs by Gaia could constrain the
amplitude of the primordial power spectrum to be less than 1e-5; this bound is
three orders of magnitude stronger than the bound derived from the absence of
primordial black holes.


  We propose, BanditRepair, a system that systematically explores and assesses
a set of possible runtime patches. The system is grounded on so-called bandit
algorithms, that are online machine learning algorithms, designed for
constantly balancing exploitation and exploration. BanditRepair's runtime
patches are based on modifying the execution state for repairing null
dereferences. BanditRepair constantly trades the ratio of automatically handled
failures for searching for new runtime patches and vice versa. We evaluate the
system with 16 null dereference field bugs, where BanditRepair identifies a
total of 8460 different runtime patches, which are composed of 1 up to 8
decisions (execution modifications) taken in a row. We are the first to finely
characterize the search space and the outcomes of runtime repair based on
execution modification.


  The meson spectrum of QCD is studied in the framework of nonperturbative QCD
as a function of varying quark masses $m_q$. It is shown, that the total
spectrum consists of two branches: 1) the standard one, which may be called the
flux-tube spectrum, depending approximately linearly on $m_q$ and 2) the chiral
symmetry breaking (CSB) spectrum for pseudoscalar flavor nonsinglet (PS)
mesons, with mass dependence $\sqrt{m_q}$. The formalism for PS mesons is
derived from the QCD Lagrangian with $m_q$ corrections, and a unified form of
the PS propagator was derived. It is shown, that the CSB branch of PS mesons
joins to the flux-tube branch at around $m_q=200$ MeV. All these results are in
close correspondence with recent numerical data on large lattices.


  We study the optical properties of tetravalent vanadium impurities in 4H
silicon carbide (4H SiC). Emission from two crystalline sites is observed at
wavelengths of 1.28 \mum and 1.33 \mum, with optical lifetimes of 163 ns and 43
ns. Group theory and ab initio density functional supercell calculations enable
unequivocal site assignment and shed light on the spectral features of the
defects. We conclude with a brief outlook on applications in quantum photonics.


  Previous studies on automatic berthing systems based on artificial neural
network (ANN) showed great berthing performance by training the ANN with ship
berthing data as training data. However, because the ANN requires a large
amount of training data to yield robust performance, the ANN-based automatic
berthing system is somewhat limited due to the difficulty in obtaining the
berthing data. In this study, to overcome this difficulty, the automatic
berthing system based on one of the reinforcement learning (RL) algorithms,
proximal policy optimization (PPO), is proposed because the RL algorithms can
learn an optimal control policy through trial-and-error by interacting with a
given environment and does not require any pre-obtained training data, where
the control policy in the proposed PPO-based automatic berthing system controls
revolutions per second (RPS) and rudder angle of a ship. Finally, it is shown
that the proposed PPO-based automatic berthing system eliminates the need for
obtaining the training dataset and shows great potential for the actual
berthing application.


  In this paper, two Intelligent reflecting surfaces (double IRS) assisted
single-user single input single output (SISO) communication system is
considered. The cascaded channels (mobile user
(MU)$\rightarrow$IRS-1$\rightarrow$base station (BS),
MU$\rightarrow$IRS-2$\rightarrow$BS and
MU$\rightarrow$IRS-1$\rightarrow$IRS-2$\rightarrow$BS channels) are estimated
under Bayesian setting. Here, the goal is to evaluate the performance of the
estimator in case of MU$\rightarrow$IRS-1$\rightarrow$BS and
MU$\rightarrow$IRS-2$\rightarrow$BS channel links using Bayesian Cramer-Rao
lower bound (CRLB). Without the knowledge of closed form pdf of inner product
of circularly symmetric complex Gaussian (CSCG) random vectors, we cannot
obtain the fisher information. Hence, by numerical computation we obtain the
Bayesian CRLB. In the simulation results, we show that we can approximate the
pdf of the inner product of CSCG random vectors by a Rayleigh distribution by
increasing the number of elements on the IRS, which is analogous to Central
Limit Theorem (CLT). Also, the results convey that the mean squared error (MSE)
almost matches with the Bayesian CRLB.


  In the present article we point out a conceptual issue of the Third
Quantization formalism for Canonical Quantum Gravity. Based on earlier results
on interuniversal entanglement, the picture of noninteracting
universe-antiuniverse pairs is incomplete. In particular, the variation of the
entanglement entropy with the scale factor, understood as a measure of time,
implies either an interaction between pairs of universes or universes with a
heat bath, i.e. an external field. We propose a simple solution to the problem
by adding a new quantum field to the formalism. Correspondingly, particles of
this field mediate interactions between universes -- depending on the model at
tree level or one loop -- and may further provide a heat bath in the
background.


  The increasing volumes of data produced by high-throughput instruments
coupled with advanced computational infrastructures for scientific computing
have enabled what is often called a {\em Fourth Paradigm} for scientific
research based on the exploration of large datasets. Current scientific
research is often interdisciplinary, making data integration a critical
technique for combining data from different scientific domains. Research data
management is a critical part of this paradigm, through the proposition and
development of methods, techniques, and practices for managing scientific data
through their life cycle. Research on microbial communities follows the same
pattern of production of large amounts of data obtained, for instance, from
sequencing organisms present in environmental samples. Data on microbial
communities can come from a multitude of sources and can be stored in different
formats. For example, data from metagenomics, metatranscriptomics,
metabolomics, and biological imaging are often combined in studies. In this
article, we describe the design and current state of implementation of an
integrative research data management framework for the Cluster of Excellence
Balance of the Microverse aiming to allow for data on microbial communities to
be more easily discovered, accessed, combined, and reused. This framework is
based on research data repositories and best practices for managing workflows
used in the analysis of microbial communities, which includes recording
provenance information for tracking data derivation.


  We present the detector performance and early science results from GRBAlpha,
a 1U CubeSat mission, which is a technological pathfinder to a future
constellation of nanosatellites monitoring gamma-ray bursts (GRBs). GRBAlpha
was launched in March 2021 and operates on a 550 km altitude sun-synchronous
orbit. The gamma-ray burst detector onboard GRBAlpha consists of a 75x75x5 mm
CsI(Tl) scintillator, read out by a dual-channel multi-pixel photon counter
(MPPC) setup. It is sensitive in the ~30-900 keV range. The main goal of
GRBAlpha is the in-orbit demonstration of the detector concept, verification of
the detector's lifetime, and measurement of the background level on low-Earth
orbit, including regions inside the outer Van Allen radiation belt and in the
South Atlantic Anomaly. GRBAlpha has already detected five, both long and
short, GRBs and two bursts were detected within a time-span of only 8 hours,
proving that nanosatellites can be used for routine detection of gamma-ray
transients. For one GRB, we were able to obtain a high resolution spectrum and
compare it with measurements from the Swift satellite. We find that, due to the
variable background, the time fraction of about 67 % of the low-Earth polar
orbit is suitable for gamma-ray burst detection. One year after launch, the
detector performance is good and the degradation of the MPPC photon counters
remains at an acceptable level. The same detector system, but double in size,
was launched in January 2022 on VZLUSAT-2 (3U CubeSat). It performs well and
already detected three GRBs and two solar flares. Here, we present early
results from this mission as well.