  Current advancements in waterborne autonomous systems, together with the
development of cloud-based service-oriented architectures and the recent
availability of low-cost underwater acoustic modems and long-range above water
wireless devices, enabled the development of new applications to support ships
and port activities. Unmanned Surface Vehicle (USV) can, for instance, be used
to perform bathymetry and environmental data collection tasks to ensure
under-keel clearance and to monitor the quality of the water. Similarly,
Remotely Operated Vehicles (ROVs) can be deployed to inspect ship hulls and
typical port infrastructure elements, such as quay and sheet pilling walls. In
this paper we present the complete system deployed for the small-scale
demonstrations of the Robotic Vessels as-a-Service (RoboVaaS) project, which
introduces an on-demand service-based cloud system that dispatches Unmanned
Vehicles (UVs) capable of performing the required service either autonomously
or piloted. These vessels are able to interact with sensors deployed in the
port and with the shore station through an integrated underwater and above
water network. The developed system has been validated through sea trials and
showcased through an underwater sensor data collection service. The results of
the test presented in this paper provide a proof-of-concept of the system
design and indicate its technical feasibility. It also shows the need for
further developments for a mature technology allowing on-demand robotic
maritime assistance services in real operational scenarios.


  We present a Dirichlet process mixture model over discrete incomplete
rankings and study two Gibbs sampling inference techniques for estimating
posterior clusterings. The first approach uses a slice sampling subcomponent
for estimating cluster parameters. The second approach marginalizes out several
cluster parameters by taking advantage of approximations to the conditional
posteriors. We empirically demonstrate (1) the effectiveness of this
approximation for improving convergence, (2) the benefits of the Dirichlet
process model over alternative clustering techniques for ranked data, and (3)
the applicability of the approach to exploring large realworld ranking
datasets.


  In this brief article we discuss the corrections to the photon orbits of a
non-rotating black hole due to semiclassical fluctuations of the metric. It is
found that the photon orbit impact parameter differences with the critical
impact parameter become of the order of the semiclassical fluctuations. We
calculate the effect of the semi-classical fluctuations on the photon orbits
and show that instead of circulating the black hole infinite number of times at
the critical orbit, the photons bounce off the semiclassical geometry.


  The purpose of this paper is to find an equation with respect to the
principal congruence subgroup of level eight without considering modular
functions.


  Stokes inversion techniques are very powerful methods for obtaining
information on the thermodynamic and magnetic properties of solar and stellar
atmospheres. In recent years, very sophisticated inversion codes have been
developed that are now routinely applied to spectro-polarimetric observations.
Most of these inversion codes are designed for finding an optimum solution to
the nonlinear inverse problem. However, to obtain the location of potentially
multimodal cases (ambiguities), the degeneracies, and the uncertainties of each
parameter inferred from the inversions, algorithms such as Markov chain Monte
Carlo (MCMC), require to evaluate the likelihood of the model thousand of times
and are computationally costly. Variational methods are a quick alternative to
Monte Carlo methods by approximating the posterior distribution by a
parametrized distribution. In this study, we introduce a highly flexible
variational inference method to perform fast Bayesian inference, known as
normalizing flows. Normalizing flows are a set of invertible, differentiable,
and parametric transformations that convert a simple distribution into an
approximation of any other complex distribution. If the transformations are
conditioned on observations, the normalizing flows can be trained to return
Bayesian posterior probability estimates for any observation. We illustrate the
ability of the method using a simple Milne-Eddington model and a complex
non-LTE inversion. However, the method is extremely general and other more
complex forward models can be applied. The training procedure need only be
performed once for a given prior parameter space and the resulting network can
then generate samples describing the posterior distribution several orders of
magnitude faster than existing techniques.


  The MobilityCoin is a new, all-encompassing currency for the management of
the multimodal urban transportation system. MobilityCoins includes and replaces
various existing transport policy instruments while also incentivizing a shift
to more sustainable modes as well as empowering the public to vote for
infrastructure measures.


  We develop a general equality-constrained nonlinear optimization algorithm
based on a smooth penalty function proposed by Fletcher (1970). Although it was
historically considered to be computationally prohibitive in practice, we
demonstrate that the computational kernels required are no more expensive than
other widely accepted methods for nonlinear optimization. The main kernel
required to evaluate the penalty function and its derivatives is solving a
structured linear system. We show how to solve this system efficiently by
storing a single factorization each iteration when the matrices are available
explicitly. We further show how to adapt the penalty function to the class of
factorization-free algorithms by solving the linear system iteratively. The
penalty function therefore has promise when the linear system can be solved
efficiently, e.g., for PDE-constrained optimization problems where efficient
preconditioners exist. We discuss extensions including handling simple
constraints explicitly, regularizing the penalty function, and inexact
evaluation of the penalty function and its gradients. We demonstrate the merits
of the approach and its various features on some nonlinear programs from a
standard test set, and some PDE-constrained optimization problems.


  Interlayer magnetic interactions play a pivotal role in determining the
magnetic arrangement within van der Waals (vdW) magnets, and the remarkable
tunability of these interactions through applied pressure further enhances
their significance. Here, we investigate NiI2 flakes, a representative vdW
magnet, under hydrostatic pressures up to 11 GPa. We reveal a notable increase
in magnetic transition temperatures for both helimagnetic and antiferromagnetic
states, and find that a reversible transition from helimagnetic to
antiferromagnetic (AFM) phases at approximately 7 GPa challenges established
theoretical and experimental expectations. While the increase in transition
temperature aligns with pressure-enhanced overall exchange interaction
strengths, we identify the significant role of the second-nearest neighbor
interlayer interaction, which competes with intra-layer frustration and favors
the AFM state as demonstrated in the Monte Carlo simulations. Experimental and
simulated results converge on the existence of an intermediate helimagnetic
ordered state in NiI2 before transitioning to the AFM state. These findings
underscore the pivotal role of interlayer interactions in shaping the magnetic
ground state, providing fresh perspectives for innovative applications in
nanoscale magnetic device design.


  We study four-dimensional gauge theories coupled to fermions in the
fundamental and meson-like scalars. All requisite beta functions are provided
for general gauge group and fermion representation. In the regime where
asymptotic freedom is absent, we determine all interacting fixed points using
perturbation theory up to three loop in the gauge and two loop in the Yukawa
and quartic couplings. We find that the conformal window of ultraviolet fixed
points is narrowed-down by finite-$N$ corrections beyond the Veneziano limit.
We also find a new infrared fixed point whose main features such as scaling
exponents, UV-IR connecting trajectories, and phase diagram are provided. Both
fixed points collide upon varying the number of fermion flavours $N_{\rm f}$,
and conformality is lost through a saddle-node bifurcation. We further revisit
the prospect for ultraviolet fixed points in the large $N_{\rm f}$ limit where
matter field fluctuations dominate. Unlike at weak coupling, we do not find
clear evidence for new scaling solutions even in the presence of scalar and
Yukawa couplings.


  This paper investigates the geometry of smooth canonically polarized surfaces
defined over a field of positive characteristic which have a nontrivial global
vector field, and the implications that the existence of such surfaces has in
the moduli problem of canonically polarized surfaces.
  In particular, an explicit real valued function f(x) is obtained such that if
$X$ is a smooth canonically polarized surface defined over an algebraically
closed field of characteristic p>0 such that $K_X^2 <f(p)$, then $X$ is
unirational and the order of its algebraic fundamental group is at most two. As
a consequence of this result, large classes of canonically polarized surfaces
are identified whose moduli stack is Deligne-Mumford, a property that does not
hold in general in positive characteristic.


  Online scheduling in identical machines with makespan minimization has been a
well studied research problem in the literature. In online scheduling, the
scheduler receives a list of jobs one by one and assigns each incoming job on
the fly irrevocably to one of the machines before the arrival of the next job.
In this paper, we study a variant of the online scheduling problem, where jobs
are requested on the fly from k distinct sources. On simultaneous arrival of
jobs from multiple sources, it is a non-trivial research challenge to ensure
fairness in scheduling with respect to distinct sources, while optimizing the
overall makespan of the schedule. We develop a novel Multi Lists Scheduling
Model(MLS) and propose a 2-competitive deterministic online algorithm namely
Largest Job on Least Loaded Machine(LJLLM) based on the MLS model to address
the above mentioned research challenge.


  Let X be a compact complex surface with a real foliation. If all leaves are
compact complex curves, the foliation must be holomorphic.


  The main objective of this article is to develop a matrix pencil approach for
the study of the controllability and reachability of a class of linear singular
discrete time systems. The description equation of a practical system may be
established through selection of the proper state variables. Time domain
analysis is the method of analyzing the system based on this description
equation, through which we may gain a fair understanding of the system's
structural features as well as its internal properties. Using time domain
analysis, this article studies the fundamentals in system theory such as
reachability and controllability.


  Motivated by the notion of Lagrangian multiforms, which provide a Lagrangian
formulation of integrability, and by results of the authors on the role of
covariant Hamiltonian formalism for integrable field theories, we propose the
notion of Hamiltonian multiforms for integrable $1+1$-dimensional field
theories. They provide the Hamiltonian counterpart of Lagrangian multiforms and
encapsulate in a single object an arbitrary number of flows within an
integrable hierarchy. For a given hierarchy, taking a Lagrangian multiform as
starting point, we provide a systematic construction of a Hamiltonian multiform
based on a generalisation of techniques of covariant Hamiltonian field theory.
This also produces two other important objects: a symplectic multiform and the
related multi-time Poisson bracket. They reduce to a multisymplectic form and
the related covariant Poisson bracket if we restrict our attention to a single
flow in the hierarchy. Our framework offers an alternative approach to define
and derive conservation laws for a hierarchy. We illustrate our results on
three examples: the potential Korteweg-de Vries hierarchy, the sine-Gordon
hierarchy (in light cone coordinates) and the Ablowitz-Kaup-Newell-Segur
hierarchy.


  Software engineers regularly use JavaScript and Python for both front-end and
back-end automation tasks. On top of JavaScript and Python, there are several
frameworks to facilitate automation tasks further. Some of these frameworks are
Node Manager Package (npm) and Python Package Index (PyPi), which are open
source (OS) package libraries. The public registries npm and PyPi use to host
packages allow any user with a verified email to publish code. The lack of a
comprehensive scanning tool when publishing to the registry creates security
concerns. Users can report malicious code on the registry; however, attackers
can still cause damage until they remove their tool from the platform.
Furthermore, several packages depend on each other, making them more vulnerable
to a bad package in the dependency tree. The heavy code reuse creates security
artifacts developers have to consider, such as the package reach. This project
will illustrate a high-level overview of common risks associated with OS
registries and the package dependency structure. There are several attack
types, such as typosquatting and combosquatting, in the OS package registries.
Outdated packages pose a security risk, and we will examine the extent of
technical lag present in the npm environment. In this paper, our main
contribution consists of a survey of common threats in OS registries.
Afterward, we will offer countermeasures to mitigate the risks presented. These
remedies will heavily focus on the applications of Machine Learning (ML) to
detect suspicious activities. To the best of our knowledge, the ML-focused
countermeasures are the first proposed possible solutions to the security
problems listed. In addition, this project is the first survey of threats in
npm and PyPi, although several studies focus on a subset of threats.


  In this paper we prove the twin prime conjecture by showing that
\begin{align} \sum \limits_{\substack{p\leq x\\p,p+2\in \mathbb{P}}}1\geq
(1+o(1))\frac{x}{2\mathcal{C}\log^2 x}\nonumber \end{align}where
$\mathcal{C}:=\mathcal{C}(2)>0$ fixed and $\mathbb{P}$ is the set of all prime
numbers. In particular it follows that \begin{align}
  \sum \limits_{p,p+2\in \mathbb{P}}1=\infty\nonumber \end{align}by taking
$x\longrightarrow \infty$ on both sides of the inequality. We start by
developing a general method for estimating correlations of the form
\begin{align} \sum \limits_{n\leq x}G(n)G(n+l)\nonumber \end{align}for a fixed
$1\leq l\leq x$ and where $G:\mathbb{N}\longrightarrow \mathbb{R}^{+}$.


  Despite its enormous empirical success, the formalism of quantum theory still
raises fundamental questions: why is nature described in terms of complex
Hilbert spaces, and what modifications of it could we reasonably expect to find
in some regimes of physics? Here we address these questions by studying how
compatibility with thermodynamics constrains the structure of quantum theory.
We employ two postulates that any probabilistic theory with reasonable
thermodynamic behavior should arguably satisfy. In the framework of generalized
probabilistic theories, we show that these postulates already imply important
aspects of quantum theory, like self-duality and analogues of projective
measurements, subspaces and eigenvalues. However, they may still admit a class
of theories beyond quantum mechanics. Using a thought experiment by von
Neumann, we show that these theories admit a consistent thermodynamic notion of
entropy, and prove that the second law holds for projective measurements and
mixing procedures. Furthermore, we study additional entropy-like quantities
based on measurement probabilities and convex decomposition probabilities, and
uncover a relation between one of these quantities and Sorkin's notion of
higher-order interference.


  Recently, our group has demonstrated an ultrafast, low-loss, fiber-loop
switch based on a nonlinear Sagnac-interferometer design, using which entangled
photons were shown to be routed without any measurable degradation in their
entanglement fidelity [Hall {\it et al.}, Phys. Rev. Lett. {\bf 106}, 053901
(2011)]. Such a device represents an enabling technology for a rich variety of
networked quantum applications. In this paper we develop a comprehensive
quantum theory for such switches in general, i.e., those based on nonlinear
Sagnac interferometers, where the in-coupling of quantum noise is carefully
modeled. Applying to the fiber-loop switch, the theory shows good agreement
with the experimental results without using any fitting parameter. This theory
can serve as an important guiding tool for configuring switches of this kind
for future quantum networking applications.


  Wind power uncertainty poses significant challenges for automatic generation
control (AGC) systems. It can enhance control performances to explicitly
consider wind power uncertainty distributions within controller design.
However, widely accepted wind uncertainties usually follow non-Gaussian
distributions which may lead to complicated stochastic AGC modeling and high
computational burdens. To overcome the issue, this paper presents a novel
Ito-theory-based model for the stochastic control problem (SCP) of AGC systems,
which reduces the computational burden of optimization considering non-Gaussian
wind power uncertainty to the same scale as that for deterministic control
problems. We present an Ito process model to exactly describe non-Gaussian wind
power uncertainty, and then propose an SCP based on the concept of stochastic
assessment functions (SAFs). Based on a convergent series expansion of the SAF,
the SCP is reformulated as a certain deterministic control problem without
sacrificing performance under non-Gaussian wind power uncertainty. The
reformulated control problem is proven as a convex optimization which can be
solved efficiently. A case study demonstrates the efficiency and accuracy of
the proposed approach compared with several conventional approaches.


  In this report, correlation of the pixels comprising a microarray spot is
investigated. Subsequently, correlation statistics namely: Pearson correlation
and Spearman rank correlation are used to segment the foreground and background
intensity of microarray spots. The performance of correlation-based
segmentation is compared to clustering-based (PAM, k-means) and seeded-region
growing techniques (SPOT). It is shown that correlation-based segmentation is
useful in flagging poorly hybridized spots, thus minimizes false-positives. The
present study also raises the intriguing question of whether a change in
correlation can be an indicator of differential gene expression.


  The ALICE Collaboration measures the production of low-mass dielectrons in
pp, p-Pb and Pb-Pb collisions at the LHC. The main detectors used in the
analyses are the Inner Tracking System, Time Projection Chamber and
Time-Of-Flight detector, all located at mid-rapidity. The dielectron yield in
p-Pb collisions shows an overall agreement with the hadronic cocktail. The pair
transverse momentum distributions are sensitive to the contributions from open
heavy-flavours. In Pb-Pb collisions, uncorrected background-subtracted yields
have been extracted in two centrality classes. In pp collisions the production
of virtual photons relative to the inclusive yield is determined by analyzing
the dielectron excess with respect to the expected hadronic sources. The direct
photon cross section is then calculated and found to be in agreement with NLO
pQCD calculations. A feasibility study for LHC Run 3 after the ALICE upgrade
indicates the possibility for a future measurement of the early effective
temperature.


  Here, we report a novel AuSb 2D superstructure on Au(111) that shows
agreements and discrepancies to the expected electronic features of the ideal
2D surface alloys with $\sqrt{3}\times\sqrt{3}$ periodicity. Using spin- and
angle-resolved photoemission spectroscopy, we find a spin splitting of the
alloy bands with antiparallel spin polarization, stemming from Rashba
spin-orbit coupling. However, the observed Rashba bands are significantly
broadened. Taking advantage of the good agreement between the experimental
results and DFT calculations, we determine that the broadening of the Rashba
band is due to the perturbations from the 3-pointed-star-shaped defects acting
as nonresonant impurities in the Au2Sb superstructure. These periodic defects
can shift the energy position of the Rashba band without breaking the in-plane
rotational symmetry and mirror symmetry, which suggests that introducing
periodic defects into a Rashba SOC system possesses a great potential in
engineering the spin-dependent properties of spintronic devices.


  We discuss a neutrino mass matrix $\mathcal{M}_\nu$ originally found by Babu,
Ma, and Valle (BMV) and show that this mass matrix can be characterized by a
simple algebraic relation. From this relation it follows that atmospheric
neutrino mixing is exactly maximal while at the same time an arbitrary mixing
angle $\theta_{13}$ of the lepton mixing matrix $U$ is allowed and--in the
usual phase convention--CP violation in mixing is maximal; moreover, neither
the neutrino mass spectrum nor the solar mixing angle are restricted. We put
forward a simple seesaw extension of the Standard Model where the family lepton
numbers are softly broken by the Majorana mass terms of the right-handed
neutrino singlets and the BMV mass matrix results from a non-standard $CP$
symmetry.


  Active surgical robots lack acceptance in clinical practice, because they do
not offer the flexibility and usability required for a versatile usage: the
systems require a large installation space or a complicated registration step,
where the preoperative plan is aligned to the patient and transformed to the
base frame of the robot. In this paper, a navigation system for robotic
osteotomies is designed, which uses the raw depth images from a camera mounted
on the flange of a lightweight robot arm. Consequently, the system does not
require any rigid attachment of the robot or fiducials to the bone and the
time-consuming registration step is eliminated. Instead, only a coarse
initialization is required which improves the usability in surgery. The full
six dimensional pose of the iliac crest bone is estimated with a particle
filter at a maximum rate of 90 Hz. The presented method is robust against
changing lighting conditions, blood or tissue on the bone surface and partial
occlusions caused by the surgeons. Proof of the usability in a clinical
environment is successfully provided in a corpse study, where surgeons used an
augmented reality osteotomy template, which was aligned to bone via the
particle filters pose estimates for the resection of transplants from the iliac
crest.


  The Frank-Wolfe algorithm, a very first optimization method and also known as
the conditional gradient method, was introduced by Frank and Wolfe in 1956. Due
to its simple linear subproblems, the Frank-Wolfe algorithm has recently been
received much attention for solving large-scale structured optimization
problems arising from many applied areas such as signal processing and machine
learning. In this paper we will discuss in detail the convergence analysis of
the Frank-Wolfe algorithm in Banach spaces. Two ways of the selections of the
stepsizes are discussed: the line minimization search method and the open loop
rule. In both cases, we prove the convergence of the Frank-Wolfe algorithm in
the case where the objective function $f$ has uniformly continuous (on bounded
sets) Fr\'echet derivative $f'$. We introduce the notion of the curvature
constant of order $\sigma\in (1,2]$ and obtain the rate
$O(\frac{1}{k^{\sigma-1}})$ of convergence of the Frank-Wolfe algorithm. In
particular, this rate reduces to $O(\frac{1}{k^{\nu}})$ if $f'$ is
$\nu$-H\"older continuous for $\nu\in (0,1]$, and to $O(\frac{1}{k})$ if $f'$
is Lipschitz continuous. A generalized Frank-Wolfe algorithm is also introduced
to address the problem of minimizing a composite objective function.
Convergence of iterates of both Frank-Wolfe and generalized Frank-Wolfe
algorithms are investigated.


  Let $\mathbf{k}$ be an algebraically closed field, let $\Lambda$ be a finite
dimensional $\mathbf{k}$-algebra, and let $\widehat{\Lambda}$ be the repetitive
algebra of $\Lambda$. For the stable category of finitely generated left
$\widehat{\Lambda}$-modules $\widehat{\Lambda}$-\underline{mod}, we show that
the irreducible morphisms fall into three canonical forms: (i) all the
component morphisms are split monomorphisms; (ii) all of them are split
epimorphisms; (iii) there is exactly one irreducible component. We next use
this fact in order to describe the shape of the Auslander-Reiten triangles in
$\widehat{\Lambda}$-\underline{mod}. We use the fact (and prove) that every
Auslander-Reiten triangle in $\widehat{\Lambda}$-\underline{mod} is induced
from an Auslander-Reiten sequence of finitely generated left
$\widehat{\Lambda}$-modules.


  We present a method for solving the coverage problem with the objective of
autonomously exploring an unknown environment under mission time constraints.
Here, the robot is tasked with planning a path over a horizon such that the
accumulated area swept out by its sensor footprint is maximized. Because this
problem exhibits a diminishing returns property known as submodularity, we
choose to formulate it as a tree-based sequential decision making process. This
formulation allows us to evaluate the effects of the robot's actions on future
world coverage states, while simultaneously accounting for traversability risk
and the dynamic constraints of the robot. To quickly find near-optimal
solutions, we propose an effective approximation to the coverage sensor model
which adapts to the local environment. Our method was extensively tested across
various complex environments and served as the local exploration algorithm for
a competing entry in the DARPA Subterranean Challenge.


  We present multi-color photometry and time resolved spectroscopy of OU Vir.
The analysis of the quiescent light curve shows that OU Vir is characterized by
i) strong cycle-to-cycle brightness variations, and ii) hot spot modulated
light curve with grazing eclipse of the impact region. Colors are derived both
in- and out- of eclipse. The time-resolved spectroscopy allows us to produce
the radial velocity curve from the H$\alpha$ accretion disk emission line which
possibly reveals only weak evidence for hot spot line emission. The hot spot is
believed to be a turbulent optically thick region, producing mostly continuum
emission.


  Practical reinforcement learning problems are often formulated as constrained
Markov decision process (CMDP) problems, in which the agent has to maximize the
expected return while satisfying a set of prescribed safety constraints. In
this study, we propose a novel simulator-based method to approximately solve a
CMDP problem without making any compromise on the safety constraints. We
achieve this by decomposing the CMDP into a pair of MDPs; reconnaissance MDP
and planning MDP. The purpose of reconnaissance MDP is to evaluate the set of
actions that are safe, and the purpose of planning MDP is to maximize the
return while using the actions authorized by reconnaissance MDP. RMDP can
define a set of safe policies for any given set of safety constraint, and this
set of safe policies can be used to solve another CMDP problem with different
reward. Our method is not only computationally less demanding than the previous
simulator-based approaches to CMDP, but also capable of finding a competitive
reward-seeking policy in a high dimensional environment, including those
involving multiple moving obstacles.


  There are two fundamental results in the classical theory of metric
Diophantine approximation: Khintchine's theorem and Jarnik's theorem. The
former relates the size of the set of well approximable numbers, expressed in
terms of Lebesgue measure, to the behavior of a certain volume sum. The latter
is a Hausdorff measure version of the former. We start by discussing these
theorems and show that they are both in fact a simple consequence of the notion
of `local ubiquity'. The local ubiquity framework introduced here is a much
simplified and more transparent version of that in \cite{memoirs}. Furthermore,
it leads to a single local ubiquity theorem that unifies the Lebesgue and
Hausdorff theories. As an application of our framework we consider the theory
of metric Diophantine approximation on limit sets of Kleinian groups. In
particular, we obtain a general Hausdorff measure version of Sullivan's
logarithm law for geodesics -- an aspect overlooked in \cite{memoirs}.


  We present the algorithms for three popular methods: F-expansion, modified
F-expansion, and first integral methods to automatically get closed-form
traveling-wave solutions of nonlinear partial differential equations (NLPDEs).
We generalize and improve the methods. The proposed algorithms are manageable,
straightforward, and powerful tools providing a high-performance evaluation of
the exact solutions of nonlinear ordinary differential equations (NLODEs) and
NLPDEs. For differential equations with parameters, the new algorithms
determine the conditions on the parameters to obtain exact solutions. The
algorithms show solutions to a wide variety of NLODEs and NLPDEs, both
integrable and non-integrable. It can solve NLODEs and NLPDEs that contain
complex functions. The algorithms are implemented in a C++ library named
GiNaCDE. The efficiency and effectiveness of the algorithms are demonstrated by
some examples with the help of GiNaCDE. The output results tally with the
previously known results, and in some cases, new exact traveling-wave solutions
are explicitly obtained. Use of the library, implementation issues, scope,
limitations, and future extensions of the software are addressed.


  We analyze the vacuum structure of spontaneously broken N=2 supersymmetric
gauge theory with the Fayet-Iliopoulos term. Our theory is based on the gauge
group SU(2) \times U(1) with N_f=2 massless quark hypermultiplets having the
same U(1) charges. In the classical potential, there are degenerate vacua even
in the absence of supersymmetry. It is shown that this vacuum degeneracy is
smoothed out, once quantum corrections are taken into account. While there is
the runaway direction in the effective potential, we found the promising
possibility that there appears the local minimum with broken supersymmetry at
the degenerate dyon point.


  Multi-valued logic gates, which can handle quaternary numbers as inputs, are
developed by exploiting the ballistic transport properties of quantum point
contacts in series. The principle of a logic gate that finds the minimum of two
quaternary number inputs is demonstrated. The device is scalable to allow
multiple inputs, which makes it possible to find the minimum of multiple inputs
in a single gate operation. Also, the principle of a half-adder for quaternary
number inputs is demonstrated. First, an adder that adds up two quaternary
numbers and outputs the sum of inputs is demonstrated. Second, a device to
express the sum of the adder into two quaternary digits [Carry (first digit)
and Sum (second digit)] is demonstrated. All the logic gates presented in this
paper can in principle be extended to allow decimal number inputs with high
quality QPCs.


  It is shown that the Constant Temperature (CT) model of nuclear level density
is a direct consequence of a symmetrized Poisson distribution of nuclear level
spacings. The standard CT model describing the total level density is shown to
be fatally flawed due to discontinuities at the Yrast energies, the onset of
new $J^{\pi}$ sequences, that disrupt the exponential formula and cause the
back shift parameter to become nonphysically negative. A new CT-JPI level
density model is proposed with a constant temperature and separate back shift
parameters for each $J^{\pi}$ sequence. The CT-JPI model is also constrained to
reproduce the spin distribution predicted by Ericson's spin distribution
function at the neutron separation energy. A fitting procedure is described for
determining the temperature $T$, back shifts $E_0(J^{\pi})$, and spin cutoff
parameters $\sigma_c$ from nuclear structure and resonance data. The CT-JPI
model is demonstrated to successfully predict the level densities for a wide
range of spins and parities for 46 nuclear with Z=7-92. In variance with
earlier predictions the spin cut-off parameters show no mass dependence and
instead substantial variation at all mass regions.


  In this note, an important extension to the recent construction of the
fluxtrap background is presented. The fluxtrap is a closed string background
based on the Melvin solution corresponding to the Omega deformation of flat
space. In this note, we introduce the mechanisms to extend it from
epsilon_1=-epsilon_2 in R to more general values of epsilon_1 and epsilon_2 in
C.


  The specific heat of an attractive (interaction $G<0$) non-local Hubbard
model is investigated. We use a two-pole approximation which leads to a set of
correlation functions. In particular, the correlation function $\
<\vec{S}_i\cdot\vec{S}_j\ >$ plays an important role as a source of anomalies
in the normal state of the model. Our results show that for a giving range of
$G$ and $\delta$ where $\delta=1-n_T$ ($n_T=n_{\uparrow}+n_{\downarrow}$), the
specific heat as a function of the temperature presents a two peak structure.
Nevertehelesss, the presence of a pseudogap on the anti-nodal points
$(0,\pm\pi)$ and $(\pm\pi,0)$ eliminates the two peak structure, the low
temperature peak remaining. The effects of the second nearest neighbor hopping
on the specific heat are also investigated.


  A new form of the Bohr-Sommerfeld quantization is given of the motion of
guiding center in strong magnetic field. This is obtained by the effective
action for the degree of guiding center which is deduced from the coherent
state path integral for the two types of motion under the mutual interaction;
the cyclotron motion and the motion of guiding center.


  We consider Diophantine equations of the kind $|F(x,y)|= m$, where $F(X,Y
)\in \bz [X,Y]$ is a homogeneous polynomial of degree $d\ge 3$ that has
non-zero discriminant and $m$ is a positive integer. We prove results that
simplify those of Stewart and provide heuristics for a conjecture of Stewart.


  Background: alpha-nucleus potentials play an essential role for the
calculation of alpha-induced reaction cross sections at low energies in the
statistical model...
  Purpose: The present work studies the total reaction cross section sigma_reac
of alpha-induced reactions at low energies which can be determined from the
elastic scattering angular distribution or from the sum over the cross sections
of all open non-elastic channels.
  Method: Elastic and inelastic 64Zn(a,a)64Zn angular distributions were
measured at two energies around the Coulomb barrier at 12.1 MeV and 16.1 MeV.
Reaction cross sections of the (a,g), (a,n), and (a,p) reactions were measured
at the same energies using the activation technique. The contributions of
missing non-elastic channels were estimated from statistical model
calculations.
  Results: The total reaction cross sections from elastic scattering and from
the sum of the cross sections over all open non-elastic channels agree well
within the uncertainties. This finding confirms the consistency of the
experimental data. At the higher energy of 16.1 MeV, the predicted significant
contribution of compound-inelastic scattering to the total reaction cross
section is confirmed experimentally. As a by-product it is found that most
recent global alpha-nucleus potentials are able to describe the reaction cross
sections for 64Zn around the Coulomb barrier.
  Conclusions: Total reaction cross sections of alpha-induced reactions can be
well determined from elastic scattering angular distributions. The present
study proves experimentally that the total cross section from elastic
scattering is identical to the sum of non-elastic reaction cross sections.
Thus, the statistical model can reliably be used to distribute the total
reaction cross section among the different open channels.


  This paper presents a part of our ground-breaking work on evaluation of
buildings in terms of wireless friendliness in the building-design stage. The
main goal is to devise construction practices that provide for a good
performance of wireless networks deployed in buildings. In this paper, the
interference gain (IG) and power gain (PG) are defined as two figures of merit
(FoM) of the wireless performance of buildings. The FoMs bridge the gap between
building design and wireless communications industries. An approach to derive
exact closed-form equations for these FoMs is proposed for the first time. The
derived analytic expressions facilitate straightforward and more
computationally efficient numerical evaluation of the proposed FoMs as compared
to Monte Carlo simulations for well-known indoor propagation models. It is
shown that the derived closed-form expression can be readily employed to
evaluate the impact of building properties, such as the sizes and the aspect
ratios (ARs) of rooms, on the wireless performance. The proposed approach sheds
light to architects on evaluation and design of wireless-friendly building
layouts.


  In this study, we use research publication data from the field of social
science to identify collaboration networks among social science research
communities of India and Pakistan. We have used Scopus database to extract
information of social science journals for both countries India and Pakistan.
Study of this data is significant as both countries have common social issues
and many of common social values. Keywords analysis has been done to see common
research areas in both communities like poverty, education, the issue of gender
etc. Despite having many of the common social issues, collaboration among
social science research communities of both countries is not strong.


  We present results of a study of 12 dust-reddened quasars with 0.4 < z < 2.65
and reddenings in the range 0.15 < E(B-V) < 1.7. We obtained ACIS-S X-ray
spectra of these quasars, estimated the column densities towards them, and
hence obtained the gas:dust ratios in the material obscuring the quasar. We
detect all but one of the red quasars in the X-rays. Even though there is no
obvious correlation between the X-ray determined column densities of our
sources and their optical color or reddening, all of the sources show absorbed
X-ray spectra. When we correct the luminosity for absorption, they can be
placed among luminous quasars; therefore our objects belong to the group of
high luminosity analogues of the sources contributing to the X-ray background
seen in deep X-ray observations. Such sources are also found in serendipitous
shallow X-ray surveys. There is a hint that the mean spectral slope of the red
quasar is higher than that of normal, unobscured quasars, which could be an
indication for higher accretion rates and/or an evolutionary effect. We
investigate the number density of these sources compared to type 2 AGN based on
the X-ray background and estimate how many moderate luminosity red quasars may
be found in deep X-ray fields.


  Let $A(G)$ and $D(G)$ be the adjacency matrix and the degree diagonal matrix
of a graph $G$, respectively. Then $L(G)=D(G)-A(G)$ is called Laplacian matrix
of the graph $G$. Let $G$ be a graph with $n$ vertices and $m$ edges. Then the
$LI$-matrix of $G$ are defined as $LI(G)=L(G)-\frac{2m}{n}I_n$, where $I_n$ is
the identity matrix. In this paper, we are interested in extremal properties of
the Ky Fan $k$-norm of the $LI$-matrix of graphs, which is closely related to
the well known problems and results in spectral graph theory, such as the
Laplacian spectral radius, the Laplacian spread, the sum of the $k$ largest
Laplacian eigenvalues, the Laplacian energy, and other parameters. Some bounds
on the Ky Fan $k$-norm of the $LI$-matrix of graphs are given, and the extremal
graphs are partly characterized. In addition, upper and lower bounds on the Ky
Fan $k$-norm of $LI$-matrix of trees, unicyclic graphs and bicyclic graphs are
determined, and the corresponding extremal graphs are characterized.


  Developing mobile applications remains difficult, time consuming, and
error-prone, in spite of the number of existing platforms and tools. In this
paper, we define MoDroid, a high-level modeling language to ease the
development of Android applications. MoDroid allows developing models
representing the core of applications. MoDroid provides Android programmers
with the following advantages: (1) Models are built using high-level primitives
that abstract away several implementation details; (2) It allows the definition
of interfaces between models to automatically compose them; (3) Java native
android can be automatically generated along with the required permissions; (4)
It supports efficient model-based testing that operates on models. MoDroid is
fully implemented and was used to develop several non-trivial Android
applications.


  The Gaussian-radial-basis function neural network (GRBFNN) has been a popular
choice for interpolation and classification. However, it is computationally
intensive when the dimension of the input vector is high. To address this
issue, we propose a new feedforward network - Separable Gaussian Neural Network
(SGNN) by taking advantage of the separable property of Gaussian functions,
which splits input data into multiple columns and sequentially feeds them into
parallel layers formed by uni-variate Gaussian functions. This structure
reduces the number of neurons from O(N^d) of GRBFNN to O(dN), which
exponentially improves the computational speed of SGNN and makes it scale
linearly as the input dimension increases. In addition, SGNN can preserve the
dominant subspace of the Hessian matrix of GRBFNN in gradient descent training,
leading to a similar level of accuracy to GRBFNN. It is experimentally
demonstrated that SGNN can achieve 100 times speedup with a similar level of
accuracy over GRBFNN on tri-variate function approximations. The SGNN also has
better trainability and is more tuning-friendly than DNNs with RuLU and Sigmoid
functions. For approximating functions with complex geometry, SGNN can lead to
three orders of magnitude more accurate results than a RuLU-DNN with twice the
number of layers and the number of neurons per layer.


  This paper concerns a class of uncertain linear quantum systems subject to
quadratic perturbations in the system Hamiltonian. A small gain approach is
used to evaluate the performance of the given quantum system. In order to get
improved control performance, we propose two methods to design a coherent
controller for the system. One is to formulate a static quantum controller by
adding a controller Hamiltonian to the given system, and the other is to build
a dynamic quantum controller which is directly coupled to the given system.
Both controller design methods are given in terms of LMIs and a non-convex
equality. Hence, a rank constrained LMI method is used as a numerical
procedure. An illustrative example is given to demonstrate the proposed methods
and also to make a performance comparison with different controller design
methods. Results show that for the same uncertain quantum system, the dynamic
quantum controller can offer an improvement in performance over the static
quantum controller.


  We construct a set of positive integers A in {1,..., n} with |A|>> n^{2/3}
that does not contain Hilbert cubes of dimension 3. As a consequence we prove
that ex(n; K^(3)(2,2,2))>> n^{8/3} where K^(3)(2,2,2) is the simplest complete
3-partite hypergraph. This is the first case of an improvement on the trivial
lower bound for ex(n; L) when L is a complete r-partite hypergraph.


  The cross section of the reaction $pp \to pp \gamma \gamma$ is estimated and
the exploration possibilities of this reaction to probe for a possible
excitation of subnuclear degrees of freedom in two-nucleon systems, in
particular, the production and decay of the NN-decoupled dibaryon resonances,
are briefly discussed.Some arguments for and implications of the intermediate
dibaryon resonance excitation are inferred from preliminary data on the
two-photon yield and energy distribution observed by the DIBAR2$\gamma$ -
Collaboration at the kinetic energy of the initial proton $T_{lab} \simeq 200
MeV$.


  This work describes the use of on-board vehicle data from cars with advanced
driver assistance features as a trip summary, with the goal of helping drivers
contextualize their driving habits in terms of sustainability. The approach is
similar to recent advancements in fitness tracking apps, which leverage
smartwatches and other wearable devices to characterize activities during a
workout or as part of daily fitness monitoring. Instead of adding new vehicle
sensors, the data used for this work is from on-board driving data, namely,
signals decoded from the vehicle's Controller Area Network (CAN) bus. With the
deepening research of automatic driving technologies, Autonomous Vehicles (AVs)
have gradually entered the consumer field, and more users are benefiting from
the convenience and safety assistance provided by driving assistance and
autonomous driving. However, various technical obstacles persist due to the
complex environment, the non-communication of technologies, and users' trust.
We propose indicators for evaluating the key characteristics of each drive, to
facilitate drivers' familiarity with advanced driver assistance systems and to
allow them to consider how different driving styles affect sustainability
metrics. Further extensions will allow users to add feedback as part of the
driving summary, laying a data foundation for future controller iterations
based on real driving data and the attitude of drivers towards it.


  In these proceedings, we report on the possible signatures of a light scalar
WIMP, a dark matter candidate with M_DM ~ few GeV, which is supposed to
interact with the Standard Model particles through the Higgs, and which might
be related to the annual modulation observed by DAMA.


  Dark matter (DM) self-interactions have been proposed to solve problems on
small length scales within the standard cold DM cosmology. Here, we investigate
the effects of DM self-interactions in merging systems of galaxies and galaxy
clusters with equal and unequal mass ratios. We perform N-body DM-only
simulations of idealized setups to study the effects of DM self-interactions
that are elastic and velocity-independent. We go beyond the commonly adopted
assumption of large-angle (rare) DM scatterings, paying attention to the impact
of small-angle (frequent) scatterings on astrophysical observables and related
quantities. Specifically, we focus on DM-galaxy offsets, galaxy--galaxy
distances, halo shapes, morphology, and the phase--space distribution.
Moreover, we compare two methods to identify peaks: one based on the
gravitational potential and one based on isodensity contours. We find that the
results are sensitive to the peak finding method, which poses a challenge for
the analysis of merging systems in simulations and observations, especially for
minor mergers. Large DM-galaxy offsets can occur in minor mergers, especially
with frequent self-interactions. The subhalo tends to dissolve quickly for
these cases. While clusters in late merger phases lead to potentially large
differences between rare and frequent scatterings, we believe that these
differences are non-trivial to extract from observations. We therefore study
the galaxy/star populations which remain distinct even after the DM haloes have
coalesced. We find that these collisionless tracers behave differently for rare
and frequent scatterings, potentially giving a handle to learn about the
micro-physics of DM.


  We generalize the leading twist theory of nuclear shadowing and calculate
quark and gluon generalized parton distributions (GPDs) of spinless nuclei. We
predict very large nuclear shadowing for nuclear GPDs. In the limit of the
purely transverse momentum transfer, our nuclear GPDs become
impact-parameter-dependent nuclear parton distributions (PDFs). Nuclear
shadowing induces nontrivial correlations between the impact parameter b and
the light-cone fraction x. We make predictions for the deeply virtual Compton
scattering (DVCS) amplitude and the DVCS cross section on Pb-208 at high
energies. We calculate the cross section of the Bethe-Heitler (BH) process and
address the issue of the extraction of the DVCS signal from the e A \to e
\gamma A cross section. We find that the e A \to e \gamma A differential cross
section is dominated by DVCS at the momentum transfer t near the minima of the
nuclear form factor. We also find that nuclear shadowing leads to dramatic
oscillations of the DVCS beam-spin asymmetry, A_{LU}, as a function of t. The
position of the points where A_{LU} changes sign is directly related to the
magnitude of nuclear shadowing.


  This PhD thesis is organized as follows. In the first two chapters I will
review some basic notions of statistical physics of disordered systems, such as
random graph theory, the mean-field approximation, spin glasses and
combinatorial optimization. The replica method will also be introduced and
applied to the Sherrington-Kirkpatrick model, one of the simplest mean-field
models of spin-glasses. The second part of the thesis deals with mean-field
combinatorial optimization problems. The attention will be focused on the study
of finite-size corrections of random integer matching problems (chapter 3) and
fractional ones (chapter 4). In chapter 5 I will discuss a very general
relation connecting multi-overlaps and the moments of the cavity magnetization
distribution. In the third part we consider random Euclidean optimization
problems. I will start solving the traveling-salesman-problem (TSP) in one
dimension both in its bipartite and monopartite version (chapter 6). In chapter
7 I will discuss the possible optimal solutions of the 2-factor problem. In
chapter 8 I will solve the bipartite TSP in two dimensions, in the limit of
large number of points. Chapter 9 contains some conclusions.


  We study the problem of distance-based formation control in autonomous
multi-agent systems in which only distance measurements are available. This
means that the target formations as well as the sensed variables are both
determined by distances. We propose a fully distributed distance-only control
law, which requires neither a time synchronization of the agents nor storage of
measured data. The approach is applicable to point agents in the Euclidean
space of arbitrary dimension. Under the assumption of infinitesimal rigidity of
the target formations, we show that the proposed control law induces local
uniform asymptotic stability. Our approach involves sinusoidal perturbations in
order to extract information about the negative gradient direction of each
agent's local potential function. An averaging analysis reveals that the
gradient information originates from an approximation of Lie brackets of
certain vector fields. The method is based on a recently introduced approach to
the problem of extremum seeking control. We discuss the relation in the paper.


  Although latent factor models (e.g., matrix factorization) obtain good
performance in predictions, they suffer from several problems including
cold-start, non-transparency, and suboptimal recommendations. In this paper, we
employ text with side data to tackle these limitations. We introduce a hybrid
generative probabilistic model that combines a neural network with a latent
topic model, which is a four-level hierarchical Bayesian model. In the model,
each document is modeled as a finite mixture over an underlying set of topics
and each topic is modeled as an infinite mixture over an underlying set of
topic probabilities. Furthermore, each topic probability is modeled as a finite
mixture over side data. In the context of text, the neural network provides an
overview distribution about side data for the corresponding text, which is the
prior distribution in LDA to help perform topic grouping. The approach is
evaluated on several different datasets, where the model is shown to outperform
standard LDA and Dirichlet-multinomial regression (DMR) in terms of topic
grouping, model perplexity, classification and comment generation.


  Thyroid cancer is the most common endocrine malignancy, and accurately
distinguishing between benign and malignant thyroid tumors is crucial for
developing effective treatment plans in clinical practice. Pathologically,
thyroid tumors pose diagnostic challenges due to improper specimen sampling. In
this study, we have designed a three-stage model using representation learning
to integrate pixel-level and slice-level annotations for distinguishing thyroid
tumors. This structure includes a pathology structure recognition method to
predict structures related to thyroid tumors, an encoder-decoder network to
extract pixel-level annotation information by learning the feature
representations of image blocks, and an attention-based learning mechanism for
the final classification task. This mechanism learns the importance of
different image blocks in a pathological region, globally considering the
information from each block. In the third stage, all information from the image
blocks in a region is aggregated using attention mechanisms, followed by
classification to determine the category of the region. Experimental results
demonstrate that our proposed method can predict microscopic structures more
accurately. After color-coding, the method achieves results on unstained
pathology slides that approximate the quality of Hematoxylin and eosin
staining, reducing the need for stained pathology slides. Furthermore, by
leveraging the concept of indirect measurement and extracting polarized
features from structures correlated with lesions, the proposed method can also
classify samples where membrane structures cannot be obtained through sampling,
providing a potential objective and highly accurate indirect diagnostic
technique for thyroid tumors.


  In this work, we consider the problem of jointly minimizing the average cost
of sampling and transmitting status updates by users over a wireless channel
subject to average Age of Information (AoI) constraints. Errors in the
transmission may occur and a scheduling policy has to decide if the users
sample a new packet or attempt for retransmission of the packet sampled
previously. The cost consists of both sampling and transmission costs. The
sampling of a new packet after a failure imposes an additional cost on the
system. We formulate a stochastic optimization problem with the average cost in
the objective under average AoI constraints. To solve this problem, we propose
three scheduling policies; a) a dynamic policy, that is centralized and
requires full knowledge of the state of the system, b) two stationary
randomized policies that require no knowledge of the state of the system. We
utilize tools from Lyapunov optimization theory in order to provide the dynamic
policy, and we prove that its solution is arbitrary close to the optimal one.
In order to provide the randomized policies, we model the system by utilizing
Discrete Time Markov Chain (DTMC). We provide closed-form and approximated
expressions for the average AoI and its distribution, for each randomized
policy. Simulation results show the importance of providing the option to
transmit an old packet in order to minimize the total average cost.


  We use the techniques of supersymmetric localization to compute the BPS black
hole entropy in N=2 supergravity. We focus on the n_v+1 vector multiplets on
the black hole near horizon background which is AdS_2 x S^2 space. We find the
localizing saddle point of the vector multiplets by solving the localization
equations, and compute the exact one loop partition function on the saddle
point. Furthermore, we propose the appropriate functional integration measure.
Through this measure, the one loop determinant is written in terms of the
radius of the physical metric, which depends on the localizing saddle point
value of the vector multiplets. The result for the one loop determinant is
consistent with the logarithmic corrections to the BPS black hole entropy from
vector multiplets.


  The growing system complexity from microservice architectures and the
bilateral enhancement of artificial intelligence (AI) for both attackers and
defenders presents increasing security challenges for cloud-native operations.
In particular, cloud-native operators require a holistic view of the dynamic
security posture for the cloud-native environment from a defense aspect.
Additionally, both attackers and defenders can adopt advanced AI technologies.
This makes the dynamic interaction and benchmark among different intelligent
offense and defense strategies more crucial. Hence, following the multi-agent
deep reinforcement learning (RL) paradigm, this research develops an
agent-based intelligent security service framework (ISSF) for cloud-native
operation. It includes a dynamic access graph model to represent the
cloud-native environment and an action model to represent offense and defense
actions. Then we develop an approach to enable the training, publishing, and
evaluating of intelligent security services using diverse deep RL algorithms
and training strategies, facilitating their systematic development and
benchmark. The experiments demonstrate that our framework can sufficiently
model the security posture of a cloud-native system for defenders, effectively
develop and quantitatively benchmark different services for both attackers and
defenders and guide further service optimization.


  It is argued that quantum and relativistic correlations can be described in a
unified way, in that both assume free will as an axiom, and happen without any
continuous connection in space-time. This description may contribute to a
coherent definition of "space-time quantization" and highlights the importance
of solving the "measurement problem".


  One Majorana doublet can be realized at each end of the
time-reversal-invariant Majorana nanowires. We investigate the Josephson effect
in the Majorana-doublet-presented junction modified by different inter-doublet
coupling manners. It is found that when the Majorana doublets couple indirectly
via a non-magnetic quantum dot, only the normal Josephson effects occur, and
the fermion parity in the system just affects the current direction and
amplitude. However, in the odd-parity case, applying finite magnetic field on
the quantum dot can induce the appearance of the fractional Josephson effect.
Next, when the direct and indirect couplings between the Majorana doublets
coexist, no fractional Josephson effect takes place, regardless of finite
magnetic field on the quantum dot. Instead, the $\pi$-period current has an
opportunity to appear in some special cases. All the results are clarified by
analyzing the influence of the fermion occupation in the quantum dot on the
parity conservation in the whole system. We ascertain that this work will be
helpful for describing the dot-assisted Josephson effect between the Majorana
doublets.


  We derive exact equations that determine the spectra of undirected and
directed sparsely connected regular graphs containing loops of arbitrary
length. The implications of our results to the structural and dynamical
properties of networks are discussed by showing how loops influence the size of
the spectral gap and the propensity for synchronization. Analytical formulas
for the spectrum are obtained for specific length of the loops.


  Millimeter-wave (mm-wave) communication is a key technology for future
wireless networks. To combat significant path loss and exploit the abundant
mm-wave spectrum, effective beamforming is crucial. Nevertheless, conventional
fully digital beamforming techniques are inapplicable, as they demand a
separate radio frequency (RF) chain for each antenna element, which is costly
and consumes too much energy. Hybrid beamforming is a cost-effective
alternative, which can significantly reduce the hardware cost and power
consumption by employing a small number of RF chains. This paper presents a
holistic view on hybrid beamforming for 5G and beyond mm-wave systems, based on
a new taxonomy for different hardware structures. We take a pragmatic approach
and compare different proposals from three key aspects: 1) hardware efficiency,
i.e., the required hardware components; 2) computational efficiency of the
associated beamforming algorithm; and 3) achievable spectral efficiency, a main
performance indicator. Through systematic comparisons, the interplay and
trade-off among these three design aspects are demonstrated, and promising
candidates for hybrid beamforming in future wireless networks are identified.


  Extreme-mass-ratio-inspiral (EMRI) is one of the most important sources for
the future space-borne gravitational wave detectors. In such kind of systems,
the compact objects usually orbit around the central supermassive black holes
with complicated trajectories. Usually, the trajectory is approximated as
geodesic of a test-particle in Kerr space-time, and the orbital evolution are
simulated with the help of adiabatic approximation. However, this omits the
influence of the compact object on the back ground. In the present paper,
employing effective-one-body formalism, we analytically calculate out the
trajectories of a compact object around a massive Kerr black hole in
equatorial-eccentric orbit, and express the fundamental orbital frequencies in
explicit forms. Our formalism include the first-order corrections of mass-ratio
in the conservative orbital motion. Furthermore, we insert the mass-ratio
related terms in the first post-Newtonian energy fluxes. By calculating the
gravitational waves from the Teukolsky equations, we quantitatively reveal the
influence of the mass of the compact object on the data analysis. We find that
the shrinking of geodesic motion by taking the small objects as test particles
may be not appropriate for the detection of EMRIs.


  Due to its simplicity and outstanding ability to generalize, stochastic
gradient descent (SGD) is still the most widely used optimization method
despite its slow convergence. Meanwhile, adaptive methods have attracted rising
attention of optimization and machine learning communities, both for the
leverage of life-long information and for the profound and fundamental
mathematical theory. Taking the best of both worlds is the most exciting and
challenging question in the field of optimization for machine learning. Along
this line, we revisited existing adaptive gradient methods from a novel
perspective, refreshing understanding of second moments. Our new perspective
empowers us to attach the properties of second moments to the first moment
iteration, and to propose a novel first moment optimizer,
\emph{Angle-Calibrated Moment method} (\method). Our theoretical results show
that \method is able to achieve the same convergence rate as mainstream
adaptive methods. Furthermore, extensive experiments on CV and NLP tasks
demonstrate that \method has a comparable convergence to SOTA Adam-type
optimizers, and gains a better generalization performance in most cases.


  Contrary to the finite-dimensional case, the Moebius group admits interesting
self-representations when infinite-dimensional. We construct and classify all
these self-representations.
  The proofs are obtained in the equivalent setting of isometries of
Lobachevsky spaces and use kernels of hyperbolic type, in analogy to the
classical concepts of kernels of positive and negative type.


  The Network-on-Chips is a promising candidate for addressing communication
bottlenecks in many-core processors and neural network processors. In this
work, we consider the generalized fault-tolerance topology generation problem,
where the link or switch failures can happen, for application-specific
network-on-chips (ASNoC). With a user-defined number, K, we propose an integer
linear programming (ILP) based method to generate ASNoC topologies, which can
tolerate at most K faults in switches or links. Given the communication
requirements between cores and their floorplan, we first propose a
convex-cost-flow based method to solve a core mapping problem for building
connections between the cores and switches. Second, an ILP based method is
proposed to allocate K+1 switch-disjoint routing paths for every communication
flow between the cores. Finally, to reduce switch sizes, we propose sharing the
switch ports for the connections between the cores and switches and formulate
the port sharing problem as a clique-partitioning problem Additionally, we
propose an ILP-based method to simultaneously solve the core mapping and
routing path allocation problems when considering physical link failures only.
Experimental results show that the power consumptions of fault-tolerance
topologies increase almost linearly with K because of the routing path
redundancy. When both switch faults and link faults are considered, port
sharing can reduce the average power consumption of fault-tolerance topologies
with K = 1, K = 2 and K = 3 by 18.08%, 28.88%, and 34.20%, respectively. When
considering only the physical link faults, the experimental results show that
compared to the FTTG algorithm, the proposed method reduces power consumption
and hop count by 10.58% and 6.25%, respectively; compared to the DBG based
method, the proposed method reduces power consumption and hop count by 21.72%
and 9.35%, respectively.


  We present a new construction of a Skorohod embedding, namely, given a
probability measure mu with zero expectation and finite variance, we construct
an integrable stopping time T adapted to a filtration F_t, such that W_t has
the law mu, where W_t is a standard Wiener process adapted to the same
filtration. We find several sufficient conditions for the stopping time T to be
bounded or to have a sub-exponential tail. In particular, our embedding seems
rather natural for the case that mu is a log-concave measure and the tail
behaviour of $T$ admits some tight bounds in that case. Our embedding admits
the property that the stochastic measure-valued process {mu_t} (0<t<T), where
mu_t is as the law of W_T conditioned on F_t, is a Markov process.


  Unorganized heaps of analysis code are a growing liability as data analysis
pipelines are getting longer and more complicated. This is worrying, as
neuroscience papers are getting retracted due to programmer error. In this
paper, some guidelines are presented that help keep analysis code well
organized, easy to understand and convenient to work with:
  1. Each analysis step is one script
  2. A script either processes a single recording, or aggregates across
recordings, never both
  3. One master script to run the entire analysis
  4. Save all intermediate results
  5. Visualize all intermediate results
  6. Each parameter and filename is defined only once
  7. Distinguish files that are part of the official pipeline from other
scripts
  In addition to discussing the reasoning behind each guideline, an example
analysis pipeline is presented as a case study to see how each guideline
translates into code.


  We demonstrate that there exists a large class of action functionals of the
scalar curvature and of the Gauss-Bonnet invariant which are able to relax
dynamically a large cosmological constant (CC), whatever it be its starting
value in the early universe. Hence, it is possible to understand, without
fine-tuning, the very small current value of the CC as compared to its
theoretically expected large value in quantum field theory and string theory.
In our framework, this relaxation appears as a pure gravitational effect, where
no ad hoc scalar fields are needed. The action involves a positive power of a
characteristic mass parameter, M, whose value can be, interestingly enough, of
the order of a typical particle physics mass of the Standard Model of the
strong and electroweak interactions or extensions thereof, including the
neutrino mass. The model universe emerging from this scenario (the "Relaxed
Universe") falls within the class of the so-called LXCDM models of the cosmic
evolution. Therefore, there is a "cosmon" entity X (represented by an effective
object, not a field), which in this case is generated by the effective
functional and is responsible for the dynamical adjustment of the cosmological
constant. This model universe successfully mimics the essential past epochs of
the standard (or "concordance") cosmological model (LCDM). Furthermore, it
provides interesting clues to the coincidence problem and it may even connect
naturally with primordial inflation.


  The presence of R-parity violating interactions may relieve the tension
between existing LHC constraints and natural supersymmetry. In this paper we
lay down the theoretical framework and explore models of dynamical R-parity
violation in which the breaking of R-parity is communicated to the visible
sector by heavy messenger fields. We find that R-parity violation is often
dominated by non-holomorphic operators that have so far been largely ignored,
and might require a modification of the existing searches at the LHC. The
dynamical origin implies that the effects of such operators are suppressed by
the ratio of either the light fermion masses or the supersymmetry breaking
scale to the mediation scale, thereby providing a natural explanation for the
smallness of R-parity violation. We consider various scenarios, classified by
whether R-parity violation, flavor breaking and/or supersymmetry breaking are
mediated by the same messenger fields. The most compact case, corresponding to
a deformation of the so called flavor mediation scenario, allows for the
mediation of supersymmetry breaking, R-parity breaking, and flavor symmetry
breaking in a unified manner.


  We present new classical generalized Jacobi elliptic one monopole -
antimonopole pair (MAP) solutions of the SU(2) Yang-Mills-Higgs theory with the
Higgs field in the adjoint representation. These generalized 1-MAP solutions
are solved with $\theta$-winding number $m$=1 and $\phi$-winding number $n$=1,
2, 3, ... 6. Similar to the generalized Jacobi elliptic one monopole solutions,
these generalized 1-MAP solutions are solved by generalizing the large distance
behaviour of the solutions to the Jacobi elliptic functions and solving the
second order equations of motion numerically when the Higgs potential is
vanishing ($\lambda$=0) and non vanishing ($\lambda$=1). These generalized
1-MAP solutions possess total energies that are comparable to the total energy
of the standard 1-MAP solution with winding number $m$=1. However these total
energies are significantly lower than the total energy of the standard 1-MAP
solution with winding number $m$=2. All these new generalized solutions are
regular numerical finite energy non-BPS solutions of the Yang-Mills-Higgs field
theory.


  We describe a system to transport and identify barium ions produced in liquid
xenon, as part of R&D towards the second phase of a double beta decay
experiment, nEXO. The goal is to identify the Ba ion resulting from an
extremely rare nuclear decay of the isotope $^{136}$Xe, hence providing a
confirmation of the occurrence of the decay. This is achieved through Resonance
Ionization Spectroscopy (RIS). In the test setup described here, Ba ions can be
produced in liquid xenon or vacuum and collected on a clean substrate. This
substrate is then removed to an analysis chamber under vacuum, where
laser-induced thermal desorption and RIS are used with time-of-flight (TOF)
mass spectroscopy for positive identification of the barium decay product.


  We report the observation of large exchange bias in Ni50-xCoxMn38Sb12 Heusler
alloys with x=0, 2, 3, 4, 5, which is attributed to the coexistence of
ferromagnetic and antiferromagnetic phases in the martensitic phase. The phase
coexistence is possibly due to the supercooling of the high temperature
ferromagnetic phase and the predominant antiferromagnetic component in the
martensitic phase. The presence of exchange bias is well supported by the
observation of training effect. The exchange bias field increases with Co
concentration. The maximum value of 480 Oe at T=3K is observed in x=5 after
field cooling in 50 kOe, which is almost double the highest value reported so
far in any Heusler alloy system. Increase in the antiferromagnetic coupling
after Co substitution is found to be responsible for the increase in the
exchange bias.


  We report on the realization and characterization of a magnetic microtrap for
ultra cold atoms near a straight superconducting Nb wire with circular cross
section. The trapped atoms are used to probe the magnetic field outside the
superconducting wire. The Meissner effect shortens the distance between the
trap and the wire, reduces the radial magnetic-field gradients and lowers the
trap depth. Measurements of the trap position reveal a complete exclusion of
the magnetic field from the superconducting wire for temperatures lower than
6K. As the temperature is further increased, the magnetic field partially
penetrates the superconducting wire; hence the microtrap position is shifted
towards the position expected for a normal-conducting wire.


  We apply a basis function expansion method to create a time-evolving
density/potential approximation of the late growth of simulated N-body dark
matter haloes. We demonstrate how the potential of a halo from the Aquarius
Project can be accurately represented by a small number of basis functions, and
show that the halo expansion (HEX) method provides a way to replay simulations.
We explore the level of accuracy of the technique as well as some of its
limitations. We find that the number of terms included in the expansion must be
large enough to resolve the large-scale distribution and shape of the halo but,
beyond this, additional terms result in little further improvement. Particle
and subhalo orbits can be integrated in this realistic, time-varying halo
potential approximation, at much lower cost than the original simulation, with
high fidelity for many individual orbits, and a good match to the distributions
of orbital energy and angular momentum. Statistically, the evolution of
structural subhalo properties, such as mass, half-mass radius and
characteristic circular velocity, are very well reproduced in the halo
expansion approximation over several gigayears. We demonstrate an application
of the technique by following the evolution of an orbiting subhalo at much
higher resolution than can be achieved in the original simulation. Our method
represents a significant improvement over commonly used techniques based on
static analytical descriptions of the halo potential.


  The commuting graph of a non-abelian group is a simple graph in which the
vertices are the non-central elements of the group, and two distinct vertices
are adjacent if and only if they commute. In this paper, we classify (up to
isomorphism) all finite non-abelian groups whose commuting graphs are acyclic,
planar or toroidal. We also derive explicit formulas for the genus of the
commuting graphs of some well-known class of finite non-abelian groups, and
show that, every collection of finite non-abelian groups whose commuting graphs
have the same genus is finite.


  Magnetic order of the manganese and rare-earth lattices according to
different symmetry representations is observed in multiferroic hexagonal (h-)
DyMnO$_3$ by optical second harmonic generation and neutron diffraction. The
incompatibility reveals that the 3d-4f coupling in the h-$R$MnO$_3$ system ($R$
= Sc, Y, In, Dy - Lu) is substantially less developed than commonly expected.
As a consequence, magnetoelectric coupling effects in this type of split-order
parameter multiferroic that were previously assigned to a pronounced 3d-4f
coupling have now to be scrutinized with respect to their origin.


  We present a search for the decays B+ -> mu+ nu_mu and B+ -> e+ nu_e in a 253
fb-1 data sample collected at the \Upsilon(4S) resonance with the Belle
detector at the KEKB asymmetric-energy B factory. We find no significant
evidence for a signal and set 90% confidence level upper limits of B(B+ -> mu+
nu_mu) < 1.7 x 10^{-6} and B(B+ -> e+ nu_e) < 9.8 x 10^{-7}.


  Tissue growth kinetics and interface dynamics depend on the properties of the
tissue environment and cell-cell interactions. In cellular environments,
substrate heterogeneity and geometry arise from a variety factors, such as the
structure of the extracellular matrix and nutrient concentration. We used the
CellSim3D model, a kinetic division simulator, to investigate the growth
kinetics and interface roughness dynamics of epithelial tissue growth on
heterogeneous substrates with varying topologies. The results show that the
presence of quenched disorder has a clear effect on the colony morphology and
the roughness scaling of the interface in the moving interface regime. In a
medium with quenched disorder, the tissue interface has a smaller interface
roughness exponent, $\alpha$, and a larger growth exponent, $\beta$. The
scaling exponents also depend on the topology of the substrate and cannot be
categorized by well-known universality classes.


  A new way of solving the descent equations corresponding to the Wess-Zumino
consistency conditions is presented. The method relies on the introduction of
an operator $\delta$ which allows to decompose the exterior space-time
derivative $d$ as a $BRS$ commutator. The case of the Yang-Mills theories is
treated in detail.


  The observation of a strong nonlinear diamagnetic signal well above T_c in
several cuprate superconductors constitutes clear evidence of the persistence
of local superconducting correlations.


  This paper presents a federated learning (FL) approach to train an AI model
for SARS-Cov-2 variant classification. We analyze the SARS-CoV-2 spike
sequences in a distributed way, without data sharing, to detect different
variants of this rapidly mutating coronavirus. Our method maintains the
confidentiality of local data (that could be stored in different locations) yet
allows us to reliably detect and identify different known and unknown variants
of the novel coronavirus SARS-CoV-2. Using the proposed approach, we achieve an
overall accuracy of $93\%$ on the coronavirus variant identification task. We
also provide details regarding how the proposed model follows the main laws of
federated learning, such as Laws of data ownership, data privacy, model
aggregation, and model heterogeneity. Since the proposed model is distributed,
it could scale on ``Big Data'' easily. We plan to use this proof-of-concept to
implement a privacy-preserving pandemic response strategy.


  Near a bifurcation point, the response time of a system is expected to
diverge due to the phenomenon of critical slowing down. We investigate critical
slowing down in well-mixed stochastic models of biochemical feedback by
exploiting a mapping to the mean-field Ising universality class. This mapping
allows us to quantify critical slowing down in experiments where we measure the
response of T cells to drugs. Specifically, the addition of a drug is
equivalent to a sudden quench in parameter space, and we find that quenches
that take the cell closer to its critical point result in slower responses. We
further demonstrate that our class of biochemical feedback models exhibits the
Kibble-Zurek collapse for continuously driven systems, which predicts the
scaling of hysteresis in cellular responses to more gradual perturbations. We
discuss the implications of our results in terms of the tradeoff between a
precise and a fast response.


  Various control strategies and field experiments have been designed for
connected and automated vehicles (CAVs) to stabilize mixed traffic that
contains both CAVs and Human-driven Vehicles (HVs). The effect of these
stabilizing CAV control strategies on traffic safety is still under
investigation. In an effort to prioritize safety over stability, a
safety-critical filter via control barrier functions (CBFs) can be designed by
modifying the stabilizing nominal control input in a minimal fashion and
imparting collision-free driving behaviors for CAVs and HVs. However, such
formal safety guarantees can be violated if there are delays in the actuation
and communication channels of the CAV. Considering both actuator and sensor
delays, and disturbances, we propose robust safety-critical traffic control
(RSTC) design to ensure ``robust safety'' of the mixed traffic. While
predictor-based CBF has been developed to compensate for the actuator delay,
uncertain speed disturbances from the head vehicle cause prediction error and
require novel robust CBF design. Besides, safety-critical control with sensor
delay also remains an open question. In RSTC, a state predictor with bounded
error is designed, and robust CBF constraints are constructed to guarantee
safety under actuator delay and disturbances. When there is a sensor delay, a
state observer is designed and integrated with a predictor-based CBF to ensure
robust safety. Numerical simulations demonstrate that the proposed RSTC avoids
rear-end collisions for two unsafe traffic scenarios in the presence of
actuator, sensor delays and disturbances.


  We show that in codimension at least 3, spaces of locally flat topological
embeddings of manifolds are correctly modelled by derived spaces of maps
between their configuration categories (under mild smoothability conditions,
and perhaps with one notable exception: source dimension 1 and target dimension
4). That general claim was reduced in an earlier paper to the special cases
where the manifolds in question are euclidean spaces. We deal with these
special cases by comparing to other special cases where the manifolds have the
form "torus" and "torus times euclidean space" respectively, and by setting up
a torus trick for configuration categories.


  We study an electronic structure of CeRhSb$_{1-x}$Sn$_x$ system, which
displays quantum critical transition from a Kondo insulator to a non-Fermi
liquid at $x=0.13$. We provide ultraviolet photoelectron spectra of valence
band obtained at 12.5 K. Acoherent peak at the Fermi level is not present in
the data, but a signal related to 4f$^1$$_{7/2}$ final state is detected.
Spectral intensity at the Fermi edge has a general tendency to grow with Sn
content. Theoretical calculations of band structure are realized with
full-potential local-orbital minimum-basis code using scalar relativistic and
full relativistic approach. The calculations reveal a depletion of density of
states at the Fermi level for CeRhSb. This gap is shifted above the Fermi
energy with increasing Sn content and thus a rise of density of states at the
Fermi level is reflected in the calculations. It agrees with metallic
properties of compounds with larger $x$. The calculations also yield another
important effects of Sn substitution. Band structure is displaced in a
direction corresponding to hole doping, although with deviations from a rigid
band shift scenario. Lifshitz transitions modify a topology of the Fermi
surface a few times and a number of bands crossing the Fermi level increases.


  We consider bound states of fermions with an anomalous magnetic
moments(neutrinos,neutrons) in radial electric and magnetic field of
monopole.In case of radial magnetic field the interaction $\vec{\Sigma}\vec{H}$
violates P-parity and for this reason we must use the method of
(hep-ph/9901248) where both components of spinor considererd as a linear
combination of spheric spinors with different P-parity. Also we apply
pseudoscalar-like interaction (2) obtained in \cite{DF} to monopole case and
add it in Dirac equation.We obtain the system of differential equations for
radial functions which define energy levels of fermions with anomalous magnetic
moments in the presence of monopole.


  We prove a general Levinson's theorem for Schr\"odinger operators in two
dimensions with threshold obstructions at zero energy. Our results confirm and
simplify earlier seminal results of Boll\'e, Gesztesy et al., while providing
an explicit topological interpretation. We also derive explicit formulas for
the wave operators, and so show that they are elements of a $C^*$-algebra
introduced by Cordes. As a consequence of our approach, we provide an
evaluation of the spectral shift function at zero in the presence of
$p$-resonances.


  Light field imaging is limited in its computational processing demands of
high sampling for both spatial and angular dimensions. Single-shot light field
cameras sacrifice spatial resolution to sample angular viewpoints, typically by
multiplexing incoming rays onto a 2D sensor array. While this resolution can be
recovered using compressive sensing, these iterative solutions are slow in
processing a light field. We present a deep learning approach using a new, two
branch network architecture, consisting jointly of an autoencoder and a 4D CNN,
to recover a high resolution 4D light field from a single coded 2D image. This
network decreases reconstruction time significantly while achieving average
PSNR values of 26-32 dB on a variety of light fields. In particular,
reconstruction time is decreased from 35 minutes to 6.7 minutes as compared to
the dictionary method for equivalent visual quality. These reconstructions are
performed at small sampling/compression ratios as low as 8%, allowing for
cheaper coded light field cameras. We test our network reconstructions on
synthetic light fields, simulated coded measurements of real light fields
captured from a Lytro Illum camera, and real coded images from a custom CMOS
diffractive light field camera. The combination of compressive light field
capture with deep learning allows the potential for real-time light field video
acquisition systems in the future.


  In his works [R1,R2] David Robbins proposed several interrelated conjectures
on the area of the polygons inscribed in a circle as an algebraic function of
its sides. Most recently, these conjectures have been established in the course
of several independent investigations. In this note we give an informal outline
of these developments.


  Inspired by Barany's colourful Caratheodory theorem, we introduce a colourful
generalization of Liu's simplicial depth. We prove a parity property and
conjecture that the minimum colourful simplicial depth of any core point in any
d-dimensional configuration is d^2+1 and that the maximum is d^(d+1)+1. We
exhibit configurations attaining each of these depths and apply our results to
the problem of bounding monochrome (non-colourful) simplicial depth.


  We study the discrete memoryless Z-interference channel (ZIC) where the
transmitter of the pair that suffers from interference is cognitive. We first
provide upper and lower bounds on the capacity of this channel. We then show
that, when the channel of the transmitter-receiver pair that does not face
interference is noiseless, the two bounds coincide and therefore yield the
capacity region. The obtained results imply that, unlike in the Gaussian
cognitive ZIC, in the considered channel superposition encoding at the
non-cognitive transmitter as well as Gel'fand-Pinsker encoding at the cognitive
transmitter are needed in order to minimize the impact of interference. As a
byproduct of the obtained capacity region, we obtain the capacity result for a
generalized Gel'fand-Pinsker problem.


  We propose a finite element discretisation approach for the incompressible
Euler equations which mimics their geometric structure and their variational
derivation. In particular, we derive a finite element method that arises from a
nonholonomic variational principle and an appropriately defined Lagrangian,
where finite element H(div) vector fields are identified with advection
operators; this is the first successful extension of the structure-preserving
discretisation of Pavlov et al. (2009) to the finite element setting. The
resulting algorithm coincides with the energy-conserving scheme presented in
Guzm\'an et al. (2016). Through the variational derivation, we discover that it
also satisfies a discrete analogous of Kelvin's circulation theorem. Further,
we propose an upwind-stabilised version of the scheme which dissipates
enstrophy whilst preserving energy conservation and the discrete Kelvin's
theorem. We prove error estimates for this version of the scheme, and we study
its behaviour through numerical tests.


  Existing theoretical studies on offline reinforcement learning (RL) mostly
consider a dataset sampled directly from the target task. In practice, however,
data often come from several heterogeneous but related sources. Motivated by
this gap, this work aims at rigorously understanding offline RL with multiple
datasets that are collected from randomly perturbed versions of the target task
instead of from itself. An information-theoretic lower bound is derived, which
reveals a necessary requirement on the number of involved sources in addition
to that on the number of data samples. Then, a novel HetPEVI algorithm is
proposed, which simultaneously considers the sample uncertainties from a finite
number of data samples per data source and the source uncertainties due to a
finite number of available data sources. Theoretical analyses demonstrate that
HetPEVI can solve the target task as long as the data sources collectively
provide a good data coverage. Moreover, HetPEVI is demonstrated to be optimal
up to a polynomial factor of the horizon length. Finally, the study is extended
to offline Markov games and offline robust RL, which demonstrates the
generality of the proposed designs and theoretical analyses.


  One goal in the quantum-walk research is the exploitation of the intrinsic
quantum nature of multiple walkers, in order to achieve the full computational
power of the model. Here we study the behaviour of two non-interacting
particles performing a quantum walk on the line when the possibility of lattice
imperfections, in the form of missing links, is considered. We investigate two
regimes, statical and dynamical percolation, that correspond to different time
scales for the imperfections evolution with respect to the quantum-walk one. By
studying the qualitative behaviour of three two-particle quantities for
different probabilities of having missing bonds, we argue that the chosen
symmetry under particle-exchange of the input state strongly affects the output
of the walk, even in noisy and highly non-ideal regimes. We provide evidence
against the possibility of gathering information about the walkers
indistinguishability from the observation of bunching phenomena in the output
distribution, in all those situations that require a comparison between
averaged quantities. Although the spread of the walk is not substantially
changed by the addition of a second particle, we show that the presence of
multiple walkers can be beneficial for a procedure to estimate the probability
of having a broken link.


  The quantum kinetic equation for charged particles in strong electric fields
is derived and analyzed with respect to the particle flux. It is found that the
applied electric field is screened nonlinearly. The relaxation field is
calculated completely quantum mechanically and up to any order in the applied
field. The classical limit is given in analytical form. In the range of weak
fields the deformation of the screening cloud is responsible for the
Debye-Onsager relaxation effect. The result beyond linear response presented
here allows to investigate a field regime where no screening cloud is present.
The descreening field is determined as a function of thermal energy density of
the plasma. For stronger fields the moving charge is accelerated by accumulated
opposite charges in front of the particle. This can be understood in analogue
to the accoustic Doppler effect. A critical field strength is presented up to
which value a thermalized plasma is possible. The range of applicability of the
treatment is discussed with respect to applied field strength and space
gradients.


  Investigations of dense neutrino cloud evolution through quantum kinetic
equations led to the possibility of ``fast flavor" (FF) processes. It is shown
here that the usual quantum kinetic equations, while signaling the
instabilities that make some instances of FF possible, are being erroneously
interpreted. Approaching the subject directly from the quantum field theory
that defines the standard model shows the computational structures in most of
the recent FF literature to be completely invalid. Our revisions also underlie
what promise to be new early universe applications and a general result for
relic supernova neutrinos that could be tested as a feature of the diffuse
neutrino spectrum.


  Inspired by the squared eigenfunction symmetry constraint, we introduce a new
$\ta_k$-flow by ``extending'' a specific $t_n$-flow of discrete KP hierarchy
(DKPH). We construct extended discrete KPH (exDKPH), which consists of
$t_n$-flow, $\ta_k$-flow and $t_n$ evolution of eigenfunction and adjoin
eigenfunctions, and its Lax representation. The exDKPH contains two types of
discrete KP equation with self-consistent sources (DKPESCS). Two reductions of
exDKPH are obtained. The generalized dressing approach for solving the exDKPH
is proposed and the N-soliton solutions of two types of the DKPESCS are
presented.


  We present a tunable phononic crystal which undergoes a phase transition from
mechanically insulating to mechanically transmissive (metallic). Specifically,
in our simulations for a phononic lattice under biaxial tension ($\sigma_{xx}
=\sigma_{yy}$ = 0.01 N$/$m), we find a bandgap for out-of-plane phonons in the
range of 48.8 - 56.4 MHz, which we can close by increasing the degree of
tension uniaxiality ($\sigma_{xx} / \sigma_{yy}$) to 1.7. To manipulate the
tension distribution, we design a realistic device of finite size, where
$\sigma_{xx} / \sigma_{yy}$ is tuned by applying a gate voltage to a phononic
crystal made from suspended graphene. We show that the phase transition can be
probed via acoustic transmission measurements and that the phononic bandgap
persists even after the inclusion surface contaminants and random tension
variations present in realistic devices. The proposed system acts as a
transistor for phonons with an on/off ratio of $10^5$ (100 dB suppression) and
is thus a valuable extension for phonon logic applications. In addition, this
mechanical analogue to a metal-insulator transition (mMIT) allows tunable
coupling between mechanical entities (e.g. mechanical qubits).


  Cloud computing has garnered attention as a platform of query processing
systems. However, data privacy leakage is a critical problem. Chowdhury et al.
proposed Crypt(epsilon), which executes differential privacy (DP) over
encrypted data on two non-colluding semi-honest servers. Further, the DP index
proposed by these authors summarizes a dataset to prevent information leakage
while improving the performance. However, two problems persist: 1) the original
data are decrypted to apply sorting via a garbled circuit, and 2) the added
noise becomes large because the sorted data are partitioned with equal width,
regardless of the data distribution. To solve these problems, we propose a new
method called DP-summary that summarizes a dataset into differentially private
data over a homomorphic encryption without decryption, thereby enhancing data
security. Furthermore, our scheme adopts Li et al.'s data-aware and
workload-aware (DAWA) algorithm for the encrypted data, thereby minimizing the
noise caused by DP and reducing the errors of query responses. An experimental
evaluation using torus fully homomorphic encryption (TFHE), a bit-wise fully
homomorphic encryption library, confirms the applicability of the proposed
method, which summarized eight 16-bit data in 12.5 h. We also confirmed that
there was no accuracy degradation even after adopting TFHE along with the DAWA
algorithm.


  This paper uses the Continued Fraction Expansion (CFE) method for analog
realization of fractional order differ-integrator and few special classes of
fractional order (FO) controllers viz. Fractional Order
Proportional-Integral-Derivative (FOPID) controller, FO[PD] controller and FO
lead-lag compensator. Contemporary researchers have given several formulations
for rational approximation of fractional order elements. However, approximation
of the controllers studied in this paper, due to having fractional power of a
rational transfer function, is not available in analog domain; although its
digital realization already exists. This motivates us for applying CFE based
analog realization technique for complicated FO controller structures to get
equivalent rational transfer functions in terms of the controller tuning
parameters. The symbolic expressions for rationalized transfer function in
terms of the controller tuning parameters are especially important as ready
references, without the need of running CFE algorithm every time and also helps
in the synthesis of analog circuits for such FO controllers.


  In 1993, Y.-G. Oh proposed a problem whether standard Lagrangian tori in C^n
are volume minimizing under Hamiltonian isotopies of C^n. In this article, we
prove that most of them do not have such property if the dimension n is greater
than two. We also discuss the existence of Hamiltonian non-volume minimizing
Lagrangian torus orbits of compact toric Kahler manifolds.


  We estimate the frequency spectrum of the diffuse anomalous microwave
emission (AME) on the North Celestial Pole (NCP) region of the sky with the
Correlated Component Analysis (CCA) component separation method applied to WMAP
7-yr data. The NCP is a suitable region for this analysis because the AME is
weakly contaminated by synchrotron and free-free emission. By modeling the AME
component as a peaked spectrum we estimate the peak frequency to be
$21.7\pm0.8$\,GHz, in agreement with previous analyses which favored $\nu_{\rm
p}<23$\,GHz. The ability of our method to correctly recover the position of the
peak is verified through simulations. We compare the estimated AME spectrum
with theoretical spinning dust models to constrain the hydrogen density $n_{\rm
H}$. The best results are obtained with densities around 0.2--0.3\,cm$^{-3}$,
typical of warm ionised medium (WIM) to warm neutral medium (WNM) conditions.
The degeneracy with the gas temperature prevents an accurate determination of
$n_{\rm H}$, especially for low hydrogen ionization fractions, where densities
of a few cm$^{-3}$ are also allowed.