  For safely applying reinforcement learning algorithms on high-dimensional
nonlinear dynamical systems, a simplified system model is used to formulate a
safe reinforcement learning framework. Based on the simplified system model, a
low-dimensional representation of the safe region is identified and is used to
provide safety estimates for learning algorithms. However, finding a satisfying
simplified system model for complex dynamical systems usually requires a
considerable amount of effort. To overcome this limitation, we propose in this
work a general data-driven approach that is able to efficiently learn a
low-dimensional representation of the safe region. Through an online adaptation
method, the low-dimensional representation is updated by using the feedback
data such that more accurate safety estimates are obtained. The performance of
the proposed approach for identifying the low-dimensional representation of the
safe region is demonstrated with a quadcopter example. The results show that,
compared to previous work, a more reliable and representative low-dimensional
representation of the safe region is derived, which then extends the
applicability of the safe reinforcement learning framework.


  Many quantum key distribution (QKD) schemes are based on sending and
measuring qubits -- two-dimensional quantum systems. Yet, in practical
realizations and experiments, the measuring devices at the receiver's (Bob)
site commonly do not measure a two-dimensional system but rather a quantum
space of a larger dimension. Such an enlargement sometimes results from
imperfect devices. However, in various QKD protocols such enlargement exists
even in the ideal scenario when all devices are assumed to be perfect. This
issue is common, for instance, in QKD schemes implemented via photons, where
the parties' devices are based on Mach-Zehnder interferometers, as these
inherently enlarge the quantum space in use.
  We show how space enlargement at Bob's site exposes the implemented protocol
to new kinds of attacks, attacks that have not yet been explicitly pinpointed
nor rigorously analyzed. We name these the "reversed space attacks". A key
insight in formalizing our attacks, is the idea of taking all states defining
Bob's (large) measured space and reversing them in time in order to identify
precisely the space that an eavesdropper may attack. We employ such attacks on
two variants of intereferometric-based QKD recently experimented by several
groups, and show how to get full information on the qubit sent by Alice, while
inducing no errors at all. The technique we develop here has subsequently been
used in a closely related work (Boyer, Gelles, and Mor, Physical Review A,
2014) to demonstrate a (weaker variant of) reversed-space attack on both
interferometric-based and polarization-based QKD.


  We study an organic Hydrogen bonded material $\rm{H_2SQ}$ analytically and
map out the phase diagram as well as low energy excitations in the relevant
parameter space. At zeroth order the dynamics is governed by plaquette
interaction (product of $\sigma_z$ over a plaquette) which defines a $Z_2$
gauge theory and a deconfinement phase satisfying "ice rules". The system is
studied under additional interactions such as an external Zeeman field (with
strength $K$) in $x$-direction, a inter-molecular interaction (with strength
$J_1$) and a dipole-dipole interaction with strength $J_2$ such that
$K>J_1>J_2$. The effect of dipole-dipole interaction removes the local $Z_2$
symmetry and gives rise to four global degenerate states with Ferroeletric
order. Using meanfield analysis we chart out the phase diagram for classical
version of the model and find $K_c$ which defines the transition from
disordered phase to ordered phase in $J_1, K$ plane for various values of
$J_2$. We find that presence of $J_1$ and $J_2$ tends to stabilize the
deconfined phases. Over the classical ground states we perform spin-wave
analysis and surprisingly find that quantum fluctuations does not remove the
classical degeneracy at all at quadratic level. The spin-wave spectrum is found
for the four global degenerate ground states which shows both gapped spectrum
and gapless spectrum (near the vicinity of CDT transition) for $J_2=0$. However
for $J_2$ finite, the spectrum is always gapped. We perform PCUT analysis to
improve the results of spin-wave analysis and calculate ground state energy and
one particle dispersion and gap at high symmetry point. Using this we draw the
phase boundary between confined and deconfined phase in the $K-J_1$ plane. The
effect of $J_2$ is also discussed in the resulting phase boundary.


  We consider the problem of signal estimation (denoising) from a
statistical-mechanical perspective, in continuation to a recent work on the
analysis of mean-square error (MSE) estimation using a direct relationship
between optimum estimation and certain partition functions. The paper consists
of essentially two parts. In the first part, using the aforementioned
relationship, we derive single-letter expressions of the mismatched MSE of a
codeword (from a randomly selected code), corrupted by a Gaussian vector
channel. In the second part, we provide several examples to demonstrate phase
transitions in the behavior of the MSE. These examples enable us to understand
more deeply and to gather intuition regarding the roles of the real and the
mismatched probability measures in creating these phase transitions.


  In a recent work we proposed a new radio access technique based on filter
bank multi-carrier (FBMC) modulation using two orthogonal polarizations:
dual-polarization FBMC (DP-FBMC). We showed that with good cross-polarization
discrimination (XPD), DP-FBMC solves the intrinsic imaginary interference
shortcoming of FBMC without extra processing. DP-FBMC also has other
interesting advantages over cyclic prefix orthogonal frequency-division
multiplexing (CP-OFDM) and FBMC such as more robustness in dispersive channels,
and it is also more robust to receiver carrier frequency offset (CFO) and
timing offset (TO). In this paper we analyze the peak to average power ratio
(PAPR) of DP-FBMC and compare PAPR simulation results with that of conventional
FBMC, for different prototype filters and overlapping factors. According to the
analysis and results, with a proper choice of prototype filter, DP-FBMC has
comparable PAPR to FBMC.


  Wide-field and high-resolution (HR) imaging is essential for various
applications such as aviation reconnaissance, topographic mapping and safety
monitoring. The existing techniques require a large-scale detector array to
capture HR images of the whole field, resulting in high complexity and heavy
cost. In this work, we report an agile wide-field imaging framework with
selective high resolution that requires only two detectors. It builds on the
statistical sparsity prior of natural scenes that the important targets locate
only at small regions of interests (ROI), instead of the whole field. Under
this assumption, we use a short-focal camera to image wide field with a certain
low resolution, and use a long-focal camera to acquire the HR images of ROI. To
automatically locate ROI in the wide field in real time, we propose an
efficient deep-learning based multiscale registration method that is robust and
blind to the large setting differences (focal, white balance, etc) between the
two cameras. Using the registered location, the long-focal camera mounted on a
gimbal enables real-time tracking of the ROI for continuous HR imaging. We
demonstrated the novel imaging framework by building a proof-of-concept setup
with only 1181 gram weight, and assembled it on an unmanned aerial vehicle for
air-to-ground monitoring. Experiments show that the setup maintains
120$^{\circ}$ wide field-of-view (FOV) with selective 0.45$mrad$ instantaneous
FOV.


  The fireshell model for Gamma-Ray Bursts (GRBs) naturally leads to a
canonical GRB composed of a proper-GRB (P-GRB) and an afterglow. P-GRBs,
introduced by us in 2001, are sometimes considered "precursors" of the main GRB
event in the current literature. We show in this paper how the fireshell model
leads to the understanding of the structure of GRBs, with precise estimates of
the time sequence and intensities of the P-GRB and the of the afterglow. It
leads as well to a natural classification of the canonical GRBs which overcomes
the traditional one in short and long GRBs.


  A functional approach to the concept of pulse shape synthesis introduced
here. This paper reviews some of the mechanisms influencing the process of
signal induction, and the solution of electric field and charge carriers drift
phenoma are considered for abritrary crystal geometries. Then, preforming some
well known principles and a set of simple techniques provides an open way to
the implementation of pulse shape analysis algorithms.


  Radio interferometry probes astrophysical signals through incomplete and
noisy Fourier measurements. The theory of compressed sensing demonstrates that
such measurements may actually suffice for accurate reconstruction of sparse or
compressible signals. We propose new generic imaging techniques based on convex
optimization for global minimization problems defined in this context. The
versatility of the framework notably allows introduction of specific prior
information on the signals, which offers the possibility of significant
improvements of reconstruction relative to the standard local matching pursuit
algorithm CLEAN used in radio astronomy. We illustrate the potential of the
approach by studying reconstruction performances on simulations of two
different kinds of signals observed with very generic interferometric
configurations. The first kind is an intensity field of compact astrophysical
objects. The second kind is the imprint of cosmic strings in the temperature
field of the cosmic microwave background radiation, of particular interest for
cosmology.


  The problem of distributed maximal independent set (MIS) is investigated on
inhomogeneous random graphs with power-law weights by which the scale-free
networks can be produced. Such a particular problem has been solved on graphs
with $n$ vertices by state-of-the-art algorithms with the time complexity of
$O(\log{n})$. We prove that for a scale-free network with power-law exponent
$\beta > 3$, the induced subgraph is constructed by vertices with degrees
larger than $\log{n}\log^{*}{n}$ is a scale-free network with $\beta' = 2$,
almost surely (a.s.). Then, we propose a new algorithm that computes an MIS on
scale-free networks with the time complexity of
$O(\frac{\log{n}}{\log{\log{n}}})$ a.s., which is better than $O(\log{n})$.
Furthermore, we prove that on scale-free networks with $\beta \geq 3$, the
arboricity and degeneracy are less than $2^{log^{1/3}n}$ with high probability
(w.h.p.). Finally, we prove that the time complexity of finding an MIS on
scale-free networks with $\beta\geq 3$ is $O(log^{2/3}n)$ w.h.p.


  The possibility of quantum entanglement leading to a seemingly thermal
distribution of the initial partonic state that maps to the final hadronic
state in the evolution of the deconfined phase generated in relativistic heavy
ion collisions is discussed in the context of early thermalization and final
state particle distributions obtained in experiments at RHIC and the LHC.


  Recurrent neural networks are capable of learning the dynamics of an unknown
nonlinear system purely from input-output measurements. However, the resulting
models do not provide any stability guarantees on the input-output mapping. In
this work, we represent a recurrent neural network as a linear time-invariant
system with nonlinear disturbances. By introducing constraints on the
parameters, we can guarantee finite gain stability and incremental finite gain
stability. We apply this identification method to learn the motion of a
four-degrees-of-freedom ship that is moving in open water and compare it
against other purely learning-based approaches with unconstrained parameters.
Our analysis shows that the constrained recurrent neural network has a lower
prediction accuracy on the test set, but it achieves comparable results on an
out-of-distribution set and respects stability conditions.


  Anisotropic core-shell model of a nano-grained polycrystal, proposed recently
for nanocrystalline copper, is applied to estimate elastic effective properties
for a set of crystals of cubic symmetry. Materials selected for analysis differ
in the lattice geometry (face-centered cubic vs. body-centered cubic) as well
as the value of a Zener factor: a ratio of two shear moduli defining elastic
anisotropy of a cubic crystal. The predictions are verified by means of the
atomistic simulations. The dependence of the overall bulk and shear moduli on
the average grain diameter is analysed. In the mean-field approach the
thickness of the shell is specified by the \emph{cutoff radius} of a
corresponding atomistic potential, while the grain shell is isotropic and its
properties are identified by molecular simulations performed for very small
grains with approximately all atoms belonging to the grain boundary zone. It is
shown that the core-shell model provides predictions of satisfactory
qualitative and quantitative agreement with atomistic simulations. Performed
study indicates that the variation of the bulk and shear moduli with the grain
size changes qualitatively when the Zener anisotropy factor is smaller or
greater than one.


  We explicitly construct the fermion propagator in a magnetic field background
B to take the lowest Landau-level approximation. We analyze the energy and
momentum dependence in the polarization tensor and discuss the collective
excitations. We find there appear two branches of collective modes in one of
two transverse gauge particles; one represents a massive and attenuated gauge
particle and the other behaves similar to the zero sound at finite density.


  All over the world the peak demand load is increasing and the load factor is
decreasing year-by-year. The fossil fuel is considered insufficient thus solar
energy systems are becoming more and more useful, not only in terms of
installation but monitoring of these systems is very crucial. Monitoring
becomes very important when there are a large number of solar panels.
Monitoring would allow early detection if the output falls below required level
or one of the solar panel out of 1000 goes down. In this study the target is to
monitor and control a developed solar panel by using available internet
foundation. This web-enabled software will provide more flexibility over the
system such as transmitting data from panel to the host computer and
disseminating information to relevant stake holders barring any geographical
barrier. The software would be built around web server with dynamic HTML and
JAVA, this paper presents the preliminary design of the proposed system.


  The family of log-concave density functions contains various kinds of common
probability distributions. Due to the shape restriction, it is possible to find
the nonparametric estimate of the density, for example, the nonparametric
maximum likelihood estimate (NPMLE). However, the associated uncertainty
quantification of the NPMLE is less well developed. The current techniques for
uncertainty quantification are Bayesian, using a Dirichlet process prior
combined with the use of Markov chain Monte Carlo (MCMC) to sample from the
posterior. In this paper, we start with the NPMLE and use a version of the
martingale posterior distribution to establish uncertainty about the NPMLE. The
algorithm can be implemented in parallel and hence is fast. We prove the
convergence of the algorithm by constructing suitable submartingales. We also
illustrate results with different models and settings and some real data, and
compare our method with that within the literature.


  In our previous works, we have studied quantum random walk search algorithm
on hypercube, with traversing coin constructed by using generalized Householder
reflection and a phase multiplier. When the same phases are used each
iteration, the algorithm is robust (stable against errors in the phases) if a
certain connection between the phases in the traversing coin is preserved,
otherwise small errors lead to poor algorithm performance. Here we investigate
how the robustness changes if different phases are used, depending on the
current iteration number. We numerically study six different examples with
different phase sequences. We show that usage of a particular sequence of
phases can make the algorithm more robust even if there is no preserved
connection between the phases in the traversing coin.


  I discuss the slow convergence of Discretized Light Cone Quantization (DLCQ)
in the small mass limit and suggest a solution.


  Positioning of nucleosomes along eukaryotic genomes plays an important role
in their organization and regulation. There are many different factors
affecting the location of nucleosomes. Some can be viewed as preferential
binding of a single nucleosome to different locations along the DNA and some as
interactions between neighboring nucleosomes. In this study we analyzed how
well nucleosomes are positioned along the DNA as a function of strength of the
preferential binding, correlation length of the binding energy landscape,
interactions between neighboring nucleosomes and others relevant system
properties. We analyze different scenarios: designed energy landscapes and
generically disordered ones and derive conditions for good positioning. Using
analytic and numerical approaches we find that, even if the binding preferences
are very weak, synergistic interplay between the interactions and the binding
preferences is essential for a good positioning of nucleosomes, especially on
correlated energy landscapes. Analyzing empirical energy landscape, we discuss
relevance of our theoretical results to positioning of nucleosomes on DNA
\emph{in vivo.}


  We report the first realization of molecular beam epitaxy grown strained GaN
quantum well field-effect transistors on single-crystal bulk AlN substrates.
The fabricated double heterostructure FETs exhibit a two- dimensional electron
gas (2DEG) density in excess of 2x10^13/cm2. Ohmic contacts to the 2DEG channel
were formed by n+ GaN MBE regrowth process, with a contact resistance of 0.13
Ohm-mm. Raman spectroscopy using the quantum well as an optical marker reveals
the strain in the quantum well, and strain relaxation in the regrown GaN
contacts. A 65-nm-long rectangular-gate device showed a record high DC drain
current drive of 2.0 A/mm and peak extrinsic transconductance of 250 mS/mm.
Small-signal RF performance of the device achieved current gain cutoff
frequency fT~120 GHz. The DC and RF performance demonstrate that bulk AlN
substrates offer an attractive alternative platform for strained quantum well
nitride transistors for future high-voltage and high-power microwave
applications.


  Modern smart glasses leverage advanced audio sensing and machine learning
technologies to offer real-time transcribing and captioning services,
considerably enriching human experiences in daily communications. However, such
systems frequently encounter challenges related to environmental noises,
resulting in degradation to speech recognition and speaker change detection. To
improve voice quality, this work investigates directional source separation
using the multi-microphone array. We first explore multiple beamformers to
assist source separation modeling by strengthening the directional properties
of speech signals. In addition to relying on predetermined beamformers, we
investigate neural beamforming in multi-channel source separation,
demonstrating that automatic learning directional characteristics effectively
improves separation quality. We further compare the ASR performance leveraging
separated outputs to noisy inputs. Our results show that directional source
separation benefits ASR for the wearer but not for the conversation partner.
Lastly, we perform the joint training of the directional source separation and
ASR model, achieving the best overall ASR performance.


  We extend our previous work on symplectic semiclassical Gaussian wave packet
dynamics to incorporate electromagnetic interactions by including a vector
potential. The main advantage of our formulation is that the equations of
motion derived are naturally Hamiltonian. We obtain an asymptotic expansion of
our equations in terms of $\hbar$ and show that our equations have
$\mathcal{O}(\hbar)$ corrections to those presented by Zhou, whereas ours also
recover the equations of Zhou in the case of a linear vector potential and
quadratic scalar potential. One and two dimensional examples of a particle in a
magnetic field are given and numerical solutions are presented and compared
with the classical solutions and the expectation values of the corresponding
observables as calculated by the Egorov or Initial Value Representation (IVR)
method. We numerically demonstrate that the $\mathcal{O}(\hbar)$ correction
terms improve the accuracy of the classical or Zhou's equations for short times
in the sense that our solutions converge to the expectation values calculated
using the Egorov/IVR method faster than the classical solutions or those of
Zhou as $\hbar \to 0$.


  A low-density parity-check (LDPC) code is a linear block code described by a
sparse parity-check matrix, which can be efficiently represented by a bipartite
Tanner graph. The standard iterative decoding algorithm, known as belief
propagation, passes messages along the edges of this Tanner graph. Density
evolution is an efficient method to analyze the performance of the belief
propagation decoding algorithm for a particular LDPC code ensemble, enabling
the determination of a decoding threshold. The basic problem addressed in this
work is how to optimize the Tanner graph so that the decoding threshold is as
large as possible. We introduce a new code optimization technique which
involves the search space range which can be thought of as minimizing
randomness in differential evolution or limiting the search range in exhaustive
search. This technique is applied to the design of good irregular LDPC codes
and multiedge type LDPC codes.


  A systematic study of phase separation effects in polycrystalline La_0.5
Ca_0.5 Mn O_3 obtained under different thermal treatments is reported. Samples
with average grain size ranging from 200 to 1300 nm were studied. Magnetic and
electrical measurements show quantitative differences among samples in their
low temperature behavior. indicating that the fraction of the ferromagnetic
(FM) phase gradually decreases as the grain size increase. Percolation of the
FM phase in samples with a small fraction of this phase suggests that grain
boundaries play a distinctive role in the spatial distribution of coexisting
phases. The defective structure at the grain surface could explain the local
inhibition of the antiferromagnetic charge ordered phase, an effect that is
gradually removed with increasing grain size. Qualitative agreement of the data
with this description is found. This effect is also found to be highly
dependent on the oxygen content of the samples and its spatial distribution.


  Community Question Answering (CQA) websites can be claimed as the most major
venues for knowledge sharing, and the most effective way of exchanging
knowledge at present. Considering that massive amount of users are
participating online and generating huge amount data, management of knowledge
here systematically can be challenging. Expert recommendation is one of the
major challenges, as it highlights users in CQA with potential expertise, which
may help match unresolved questions with existing high quality answers while at
the same time may help external services like human resource systems as another
reference to evaluate their candidates. In this paper, we in this work we
propose to exploring experts in CQA websites. We take advantage of recent
distributed word representation technology to help summarize text chunks, and
in a semantic view exploiting the relationships between natural language
phrases to extract latent knowledge domains. By domains, the users' expertise
is determined on their historical performance, and a rank can be compute to
given recommendation accordingly. In particular, Stack Overflow is chosen as
our dataset to test and evaluate our work, where inclusive experiment shows our
competence.


  Networked entanglement is an essential component for a plethora of quantum
computation and communication protocols. Direct transmission of quantum signals
over long distances is prevented by fibre attenuation and the no-cloning
theorem, motivating the development of quantum repeaters, designed to purify
entanglement, extending its range. Quantum repeaters have been demonstrated
over short distances, but error-corrected, global repeater networks with high
bandwidth require new technology. Here we show that error corrected quantum
memories installed in cargo containers and carried by ship can provide a
flexible connection between local networks, enabling low-latency, high-fidelity
quantum communication across global distances at higher bandwidths than
previously proposed. With demonstrations of technology with sufficient fidelity
to enable topological error-correction, implementation of the quantum memories
is within reach, and bandwidth increases with improvements in fabrication. Our
approach to quantum networking avoids technological restrictions of repeater
deployment, providing an alternate path to a worldwide Quantum Internet.


  The temperature targets in the Paris Agreement cannot be met without very
rapid reduction of greenhouse gas emissions and removal of carbon dioxide from
the atmosphere. The latter requires large, perhaps prohibitively large
subsidies. The central estimate of the costs of climate policy, unrealistically
assuming least-cost implementation, is 3.8-5.6\% of GDP in 2100. The central
estimate of the benefits of climate policy, unrealistically assuming constant
vulnerability, is 2.8-3.2\% of GDP. The uncertainty about the benefits is
larger than the uncertainty about the costs. The Paris targets do not pass the
cost-benefit test unless risk aversion is high and discount rate low.


  In network embedding, random walks play a fundamental role in preserving
network structures. However, random walk based embedding methods have two
limitations. First, random walk methods are fragile when the sampling frequency
or the number of node sequences changes. Second, in disequilibrium networks
such as highly biases networks, random walk methods often perform poorly due to
the lack of global network information. In order to solve the limitations, we
propose in this paper a network diffusion based embedding method. To solve the
first limitation, our method employs a diffusion driven process to capture both
depth information and breadth information. The time dimension is also attached
to node sequences that can strengthen information preserving. To solve the
second limitation, our method uses the network inference technique based on
cascades to capture the global network information. To verify the performance,
we conduct experiments on node classification tasks using the learned
representations. Results show that compared with random walk based methods,
diffusion based models are more robust when samplings under each node is rare.
We also conduct experiments on a highly imbalanced network. Results shows that
the proposed model are more robust under the biased network structure.


  With the rise of GPS-enabled smartphones and other similar mobile devices,
massive amounts of location data are available. However, no scalable solutions
for soft real-time spatial queries on large sets of moving objects have yet
emerged. In this paper we explore and measure the limits of actual algorithms
and implementations regarding different application scenarios. And finally we
propose a novel distributed architecture to solve the scalability issues.


  We study a concrete lattice regularization of a U(1) chiral gauge theory. We
use Wilson fermions, and include a Lorentz gauge-fixing term and a gauge-boson
mass counterterm. For a reduced version of the model, in which the gauge fields
are constrained to the trivial orbit, we show that there are no species
doublers, and that the fermion spectrum contains only the desired states in the
continuum limit, namely charged left-handed (LH) fermions and neutral
right-handed (RH) fermions.


  We prove the existence of an unbounded connected branch of nontrivial
homoclinic trajectories of a family of discrete nonautonomous asymptotically
hyperbolic systems parametrized by a circle under assumptions involving the
topological properties of the asymptotic stable bundles.


  We show that in single scalar field models of the dark energy with equations
of state satisfying $w \equiv p / \rho < -1$, the effective Lagrangian for
fluctuations about the homogeneous background has a wrong sign spatial kinetic
term. In most cases, spatial gradients are ruled out by microwave background
observations. The sign of $w+1$ is not connected to the sign of the time
derivative kinetic term in the effective Lagrangian.


  A theoretical study on the dynamics of an antiferromagnetic (AFM) skyrmion is
indispensable for revealing the underlying physics and understanding the
numerical and experimental observations. In this work, we present a reliable
theoretical treatment of the spin current induced motion of an AFM skyrmion in
the absence and presence of pinning defect. For an ideal AFM system free of
defect, the skyrmion motion velocity as a function of the intrinsic parameters
can be derived, based on the concept that the skyrmion profile agrees well with
the 360 domain wall formula, leading to an explicit description of the skyrmion
dynamics. However, for an AFM lattice containing a defect, the skyrmion can be
pinned and the depinning field as a function of damping constant and pinning
strength can be described by the Thiele approach. It is revealed that the
depinning behavior can be remarkably influenced by the time dependent
oscillation of the skyrmion trajectory. The present theory provides a
comprehensive scenario for manipulating the dynamics of AFM skyrmion,
informative for future spintronic applications based on antiferromagnets.


  In natural resource management, or more generally in the study of
sustainability issues, often the objective is to maintain the state of a given
system within a desirable configuration, typically established in terms of
standards or thresholds. For instance, in fisheries management, the procedure
of designing policies may include keeping the spawning stock biomass over a
critical threshold and also ensuring minimal catches. Given a controlled
dynamical system in discrete-time, representing the evolution of some natural
resources under the action of controls and uncertainties, and an initial
endowment of the resources, the aim of this paper is to characterize the set of
robust sustainable thresholds, that is, the thresholds for which there exists
some control path, along with its corresponding state trajectory, satisfying
for all possible uncertainty scenarios, prescribed mixed constraints
parametrized by such thresholds. This set provides useful information to users
and decision-makers, illustrating the trade-offs between constraints and it is
strongly related to the robust viability, one of the key concepts in viability
theory, discipline that study the consistency between a controlled dynamical
system and given constraints. Specifically, we are concerned with
characterizing the weak and strong Pareto fronts of the set of robust
sustainable thresholds, providing a practical method for computing such objects
based on optimal control theory and a level-set approach. A numerical example,
relying on renewable resource management, is shown to demonstrate the proposed
method.


  Our nearest large cosmological neighbour, the Andromeda galaxy (M31), is a
dynamical system, and an accurate measurement of its total mass is central to
our understanding of its assembly history, the life-cycles of its satellite
galaxies, and its role in shaping the Local Group environment. Here, we apply a
novel approach to determine the dynamical mass of M31 using high velocity
Planetary Nebulae (PNe), establishing a hierarchical Bayesian model united with
a scheme to capture potential outliers and marginalize over tracers unknown
distances. With this, we derive the escape velocity run of M31 as a function of
galacto-centric distance, with both parametric and non-parametric approaches.
We determine the escape velocity of M31 to be $470\pm{40}$ km s$^{-1}$ at a
galacto-centric distance of 15 kpc, and also, derive the total potential of
M31, estimating the virial mass and radius of the galaxy to be
$0.8\pm{0.1}\times10^{12}\,M_\odot$ and $240\pm{10}$ kpc, respectively. Our M31
mass is on the low-side of the measured range, this supports the lower expected
mass of the M31-Milky Way system from the timing and momentum arguments,
satisfying the HI constraint on circular velocity between $10\lesssim
R/\textrm{kpc}<35$, and agreeing with the stellar mass Tully-Fisher relation.
To place these results in a broader context, we compare them to the key
predictions of the $\Lambda{\rm CDM}$ cosmological paradigm, including the
stellar-mass-halo-mass and the dark matter halo concentration-virial mass
correlation, and finding it to be an outlier to this relation.


  In this paper, we present a novel non-parametric clustering technique. Our
technique is based on the notion that each latent cluster is comprised of
layers that surround its core, where the external layers, or border points,
implicitly separate the clusters. Unlike previous techniques, such as DBSCAN,
where the cores of the clusters are defined directly by their densities, here
the latent cores are revealed by a progressive peeling of the border points.
Analyzing the density of the local neighborhoods allows identifying the border
points and associating them with points of inner layers. We show that the
peeling process adapts to the local densities and characteristics to
successfully separate adjacent clusters (of possibly different densities). We
extensively tested our technique on large sets of labeled data, including
high-dimensional datasets of deep features that were trained by a convolutional
neural network. We show that our technique is competitive to other
state-of-the-art non-parametric methods using a fixed set of parameters
throughout the experiments.


  We provide new, mild conditions for strict stationarity and ergodicity of a
class of BEKK processes. By exploiting that the processes can be represented as
multivariate stochastic recurrence equations, we characterize the tail behavior
of the associated stationary laws. Specifically, we show that the each
component of the BEKK processes is regularly varying with some tail index. In
general, the tail index differs along the components, which contrasts most of
the existing literature on the tail behavior of multivariate GARCH processes.


  The watershed is one of the most used tools in image segmentation. We present
how its concept is born and developed over time. Its implementation as an
algorithm or a hardwired device evolved together with the technology which
allowed it. We present also how it is used in practice, first together with
markers, and later introduced in a multiscale framework, in order to produce
not a unique partition but a complete hierarchy.


  Compressed sensing theory indicates that selecting a few measurements
independently at random is a near optimal strategy to sense sparse or
compressible signals. This is infeasible in practice for many acquisition
devices that acquire sam- ples along continuous trajectories. Examples include
magnetic resonance imaging (MRI), radio-interferometry, mobile-robot sampling,
... In this paper, we propose to generate continuous sampling trajectories by
drawing a small set of measurements independently and joining them using a
travelling salesman problem solver. Our contribution lies in the theoretical
derivation of the appropriate probability density of the initial drawings.
Preliminary simulation results show that this strategy is as efficient as
independent drawings while being implementable on real acquisition systems.


  Jones proposed the study of two subfactors of a $II_1$ factor as a
quantization of two closed subspaces in a Hilbert space. The Pimsner-Popa
probabilistic constant, Sano-Watatani angle, interior and exterior angle, and
Connes-St{\o}rmer relative entropy (along with a slight variant of it) are a
few key invariants for pair of subfactors that analyze their relative position.
In practice, however, the explicit computation of these invariants is often
difficult. In this article, we provide an in-depth analysis of a special class
of two subfactors, namely a pair of spin model subfactors of the hyperfinite
type $II_1$ factor $R$. We first characterize when two distinct $n\times n$
complex Hadamard matrices give rise to distinct spin model subfactors. Then, a
detailed investigation has been carried out for pairs of (Hadamard equivalent)
complex Hadamard matrices of order $2\times 2$ as well as Hadamard inequivalent
complex Hadamard matrices of order $4\times 4$. To the best of our knowledge,
this article is the first instance in the literature where the exact value of
the Pimsner-Popa probabilistic constant and the noncommutative relative entropy
for pairs of (non-trivial) subfactors have been obtained. Furthermore, we prove
the factoriality of the intersection of the corresponding pair of subfactors
using the `commuting square technique'. En route, we construct an infinite
family of potentially new subfactors of $R$. All these subfactors are
irreducible with Jones index $4n,n\geq 2$. As a consequence, the rigidity of
the interior angle between the spin model subfactors is established. Last but
not least, we explicitly compute the Sano-Watatani angle between the spin model
subfactors.


  This paper presents a nonlinear mixing model for joint hyperspectral image
unmixing and nonlinearity detection. The proposed model assumes that the pixel
reflectances are linear combinations of known pure spectral components
corrupted by an additional nonlinear term, affecting the endmembers and
contaminated by an additive Gaussian noise. A Markov random field is considered
for nonlinearity detection based on the spatial structure of the nonlinear
terms. The observed image is segmented into regions where nonlinear terms, if
present, share similar statistical properties. A Bayesian algorithm is proposed
to estimate the parameters involved in the model yielding a joint nonlinear
unmixing and nonlinearity detection algorithm. The performance of the proposed
strategy is first evaluated on synthetic data. Simulations conducted with real
data show the accuracy of the proposed unmixing and nonlinearity detection
strategy for the analysis of hyperspectral images.


  Commercial Cloud services have been increasingly supplied to customers in
industry. To facilitate customers' decision makings like cost-benefit analysis
or Cloud provider selection, evaluation of those Cloud services are becoming
more and more crucial. However, compared with evaluation of traditional
computing systems, more challenges will inevitably appear when evaluating
rapidly-changing and user-uncontrollable commercial Cloud services. This paper
proposes an expert system for Cloud evaluation that addresses emerging
evaluation challenges in the context of Cloud Computing. Based on the knowledge
and data accumulated by exploring the existing evaluation work, this expert
system has been conceptually validated to be able to give suggestions and
guidelines for implementing new evaluation experiments. As such, users can
conveniently obtain evaluation experiences by using this expert system, which
is essentially able to make existing efforts in Cloud services evaluation
reusable and sustainable.


  A novel algebra underlying integrable systems is shown to generate and unify
a large class of quantum integrable models with given $R$-matrix, through
reductions of an ancestor Lax operator and its different realizations. Along
with known discrete and field models a new class of inhomogeneous and impurity
models are obtained.


  Gas detector are very light instrument used in high energy physics to measure
the particle properties: position and momentum. Through high electric field is
possible to use the Gas Electron Multiplier (GEM) technology to detect the
charged particles and to exploit their properties to construct a large area
detector, such as the new IT for BESIII. The state of the art in the GEM
production allows to create very large area GEM foils (up to 50x100
$\mathrm{cm}^2$) and thanks to the small thickness of these foils is it
possible to shape it to the desired form: a Cylindrical Gas Electron Multiplier
(CGEM) is then proposed. The innovative construction technique based on
Rohacell, a PMI foam, will give solidity to cathode and anode with a very low
impact on material budget. The entire detector is sustained by Permaglass rings
glued at the edges. These rings are used to assembly the CGEM, together with a
dedicated Vertical Insertion System and moreover they host the On-Detector
electronic. The anode has been improved w.r.t. the state of the art through a
jagged readout that minimize the inter-strip capacitance. The mechanical
challenge of this detector requires a precision of the entire geometry within
few hundreds of microns in the whole area. In this contribution an overview of
the construction technique, the validation of this technique through the
realization of a CGEM, and its first tests will be presented. These activities
are performed within the framework of the BESIIICGEM Project (645664), funded
by the European Commission in the action H2020-RISE-MSCA-2014.


  An approximate analytical solution is presented, along with numerical
calculations, for a system of two single-photon wavepackets interacting via an
ideal, localized Kerr medium. It is shown that, because of spontaneous emission
into the initially unoccupied temporal modes, the cross-phase modulation in the
Schrodinger picture is very small as long as the spectral width of the
single-photon pulses is well within the medium's bandwidth. In this limit, the
Hamiltonian used can be derived from the "giant Kerr effect" for a four-level
atom, under conditions of electromagnetically-induced transparency; it is shown
explicitly that the linear absorption in this system increases as the pulse's
spectral width approaches the medium's transparency bandwidth, and hence, as
long as the absorption probability remains small, the maximum cross-phase
modulation is limited to essentially useless values. These results are in
agreement with the general, causality- and unitarity-based arguments of Shapiro
and co-workers.


  We study the dynamical process of equilibration of topological properties in
quantum many-body systems undergoing a parameter quench between two
topologically inequivalent Hamiltonians. This scenario is motivated by recent
experiments on ultracold atomic gases, where a trivial initial state is
prepared before the Hamiltonian is ramped into a topological insulator phase.
While the many-body wave function must stay topologically trivial in the
coherent post-quench dynamics, here we show how the topological properties of
the single particle density matrix dynamically change and equilibrate in the
presence of interactions. In this process, the single particle density matrix
goes through a characteristic level crossing as a function of time, which plays
an analogous role to the gap closing of a Hamiltonian in an equilibrium
topological quantum phase transition. As an exact case study exemplifying this
mechanism, we numerically solve the quench dynamics of an interacting
one-dimensional topological insulator.


  We perform molecular dynamics simulations of model granular systems
undergoing boundary-driven planar shear flow in two spatial dimensions with the
goal of developing a more complete understanding of how dense particulate
systems respond to applied shear. In particular, we are interested in
determining when these systems will possess linear velocity profiles and when
they will develop highly localized velocity profiles in response to shear. In
previous work on similar systems we showed that nonlinear velocity profiles
form when the speed of the shearing boundary exceeds the speed of shear waves
in the material. However, we find that nonlinear velocity profiles in these
systems are unstable at very long times. The degree of nonlinearity slowly
decreases in time; the velocity profiles become linear when the granular
temperature and density profiles are uniform across the system at long times.
We measure the time $t_l$ required for the velocity profiles to become linear
and find that $t_l$ increases as a power-law with the speed of the shearing
boundary and increases rapidly as the packing fraction approaches random close
packing. We also performed simulations in which differences in the granular
temperature across the system were maintained by vertically vibrating one of
the boundaries during shear flow. We find that nonlinear velocity profiles form
and are stable at long times if the difference in the granular temperature
across the system exceeds a threshold value that is comparable to the glass
transition temperature in an equilibrium system at the same average density.
Finally, the sheared and vibrated systems form stable shear bands, or highly
localized velocity profiles, when the applied shear stress is lowered below the
yield stress of the static part of the system.


  The main goal of this research is to develop the concepts of a revolutionary
processor system called Functional Processor System. The fairly novel work
carried out in this proposal concentrates on decoding of function pipelines and
distributing it in FPUs as a part of scheduling approach. As the functional
programs are super-level programs that entails requirements only at functional
level, decoding of functions and distribution of functions in the heterogeneous
functional processor units are a challenge. We explored the possibilities of
segregation of the functions from the application program and distributing the
functions on the relevant FPUs by using address mapping techniques. Here we
pursue the perception of feeding the functions into the processor farm rather
than the processor fetching the instructions or functions and executing it.
This work is carried out at theoretical levels and it requires a long way to go
in the realization of this work in hardware perhaps with a large industrial
team with a pragmatic time frame.


  We construct a Schroedinger field theory invariant under local spatial
scaling. It is shown to provide an effective theory of superfluid turbulence by
deriving, analytically, the observed Kolmogorov 5/3 law and to lead to a
Biot-Savart interaction between the observed filament excitations of the system
as well.


  Tidal disruption events (TDEs) can potentially probe low-mass black holes in
host galaxies that might not adhere to bulge or stellar-dispersion
relationships. At least initially, TDEs can also reveal super-Eddington
accretion. X-ray spectroscopy can potentially constrain black hole masses, and
reveal ionized outflows associated with super-Eddington accretion. Our analysis
of XMM-Newton X-ray observations of the TDE AT2021ehb, around 300 days
post-disruption, reveals a soft spectrum and can be fit with a combination of
multi-color disk blackbody and power-law components. Using two independent disk
models with properties suited to TDEs, we estimate a black hole mass at $M
\simeq 10^{5.5}~M_{\odot}$, indicating AT2021ehb may expose the elusive
low-mass end of the nuclear black hole population. These models offer simple
yet robust characterization; more complicated models are not required, but
provide important context and caveats in the limit of moderately sensitive
data. If disk reflection is included, the disk flux is lower and inferred black
hole masses are $\sim$ 0.35 dex higher. Simple wind formulations imply an
extremely fast $v_{\mathrm{out}} = -0.2~c$ outflow and obviate a disk continuum
component. Assuming a unity filling factor, such a wind implies an
instantaneous mass outflow rate of $\dot{M} \simeq 5~M_{\odot}~{\rm yr}^{-1}$.
Such a high rate suggests that the filling factor for the Ultra Fast Outflow
(UFO) must be extremely low, and/or the UFO phase is ephemeral. We discuss the
strengths and limitations of our analysis and avenues for future observations
of TDEs.


  We introduce and study a complexity function on words $c_x(n),$ called
\emph{cyclic complexity}, which counts the number of conjugacy classes of
factors of length $n$ of an infinite word $x.$ We extend the well-known
Morse-Hedlund theorem to the setting of cyclic complexity by showing that a
word is ultimately periodic if and only if it has bounded cyclic complexity.
Unlike most complexity functions, cyclic complexity distinguishes between
Sturmian words of different slopes. We prove that if $x$ is a Sturmian word and
$y$ is a word having the same cyclic complexity of $x,$ then up to renaming
letters, $x$ and $y$ have the same set of factors. In particular, $y$ is also
Sturmian of slope equal to that of $x.$ Since $c_x(n)=1$ for some $n\geq 1$
implies $x$ is periodic, it is natural to consider the quantity
$\liminf_{n\rightarrow \infty} c_x(n).$ We show that if $x$ is a Sturmian word,
then $\liminf_{n\rightarrow \infty} c_x(n)=2.$ We prove however that this is
not a characterization of Sturmian words by exhibiting a restricted class of
Toeplitz words, including the period-doubling word, which also verify this same
condition on the limit infimum. In contrast we show that, for the Thue-Morse
word $t$, $\liminf_{n\rightarrow \infty} c_t(n)=+\infty.$


  In this paper, we studied the federated bilevel optimization problem, which
has widespread applications in machine learning. In particular, we developed
two momentum-based algorithms for optimizing this kind of problem and
established the convergence rate of our two algorithms, providing the sample
and communication complexities. Importantly, to the best of our knowledge, our
convergence rate is the first one achieving the linear speedup with respect to
the number of devices for federated bilevel optimization algorithms. At last,
our extensive experimental results confirm the effectiveness of our two
algorithms.


  The large scale structure bispectrum in the squeezed limit couples large with
small scales. Since relativity is important at large scales and non-linear loop
corrections are important at small scales, the proper calculation of the
observed bispectrum in this limit requires a non-linear relativistic
calculation. We compute the matter bispectrum in general relativity in the weak
field approximation. The calculation is as involved as existing second-order
results. We find several differences with the Newtonian calculation such as the
non-cancellation of IR divergences, the need to renormalize the background, and
the fact that initial conditions must be set at second order in perturbation
theory. For the bispectrum, we find relativistic corrections to be as large as
the newtonian result in the squeezed limit. In that limit relativistic one-loop
contributions, which we compute for the first time, can be as large as tree
level results and have the same $1/k^2$ dependence as a primordial local
non-Gaussianity signal where $k$ is the momentum approaching zero. Moreover, we
find the time dependence of the relavistic corrections to the bispectrum to be
the same as that of a primordial non-Gaussianity signal.


  Unless the scale of electroweak symmetry breaking is stabilized dynamically,
most of the universes in a multiverse theory will lack an observable weak
nuclear interaction. Such "weakless universes" could support intelligent life
based on organic chemistry, as long as other parameters are properly adjusted.
By taking into account the seemingly-unrelated flavor dynamics that address the
hierarchy of quark masses and mixings, we show that such weakless (but
hospitable) universes can be far more common than universes like ours. The
gauge hierarchy problem therefore calls for a dynamical (rather than anthropic)
solution.


  Random sequential adsorption is an irreversible surface deposition of
extended objects. In systems with continuous degrees of freedom coverage
follows a power law, theta(t) = theta_J - c t^{-alpha}, where the exponent
alpha depends on the geometric shape (symmetry) of the objects. Lattice models
give typically exponential saturation to jamming coverage. We discuss how such
function theta(t) can be computed by series expansions and analyzed with Pade
approximations. We consider the applications of efficient Monte Carlo computer
simulation method (event-driven method) to random sequential adsorptions with
high precision and at very long-time scale.


  The conjecture of Serre referred in the title is the one about modularity of
odd Galois representations into GL(2,F) where F is a finite field of
characteristic p. We present an analogous conjecture where GL(2) is replaced by
GL(n). We explain the analogue of "oddness." We then present some theoretical
and experimental evidence for the conjecture, primarily when n = 3. Our
conjecture does not require the Galois representation to be irreducible. Our
most interesting examples involve the sum of an irreducible even 2-dimensional
representation and a 1-dimensional representation.


  Kopitzki et al (preceeding comment) claim that the relationship between
Renormalized and Kullback-Leibler entropies has already been given in their
previous papers. Moreover, they argue that the first can give more useful
information for e.g. localizing the seizure-generating area in epilepsy
patients.
  In our reply we stress that if the relationship between both entropies would
have been known by them, they should have noticed that the condition on the
effective temperature is unnecessary. Indeed, this condition led them to choose
different reference segments for different channels, even if this was
physiologically unplausible. Therefore, we still argue that it is very unlikely
that renormalized entropy will give more information than the conventional
Kullback-Leibler entropy.


  Randomly generated programs are popular for testing compilers and program
analysis tools, with hundreds of bugs in real-world C compilers found by random
testing. However, existing random program generators may generate large amounts
of dead code (computations whose result is never used). This leaves relatively
little code to exercise a target compiler's more complex optimizations.
  To address this shortcoming, we introduce liveness-driven random program
generation. In this approach the random program is constructed bottom-up,
guided by a simultaneous structural data-flow analysis to ensure that the
generator never generates dead code.
  The algorithm is implemented as a plugin for the Frama-C framework. We
evaluate it in comparison to Csmith, the standard random C program generator.
Our tool generates programs that compile to more machine code with a more
complex instruction mix.


  Within the past ten years many new hadrons states were observed
experimentally, some of which do not fit into the conventional quark model. I
will talk about the few-body systems composed of heavy quarks, including the
charmonium-like states and some loosely bound states.


  The LIGO and Virgo Collaborations currently conduct searches for
gravitational waves from compact binary coalescences in real-time. For
promising candidate events, a sky map and distance estimation are released in
low-latency, to facilitate their electromagnetic follow-up. Currently, no
information is released about the masses of the compact objects. Recently,
Margalit and Metzger (2019) have suggested that knowledge of the chirp mass of
the detected binary neutron stars could be useful to prioritize the
electromagnetic follow-up effort, and have urged the LIGO-Virgo collaboration
to release chirp mass information in low-latency. One might worry that
low-latency searches for compact binaries make simplifying assumptions that
could introduce biases in the mass parameters: neutron stars are treated as
point particles with dimensionless spins below $0.05$ and perfectly aligned
with the orbital angular momentum. Furthermore, the template bank used to
search for them has a finite resolution. In this paper we show that none of
these limitations can introduce chirp mass biases larger than $\sim
10^{-3}~M_\odot$. Even the total mass is usually accurately estimated, with
biases smaller than 6%. The mass ratio and effective inspiral spins, on the
other hand, can suffer from more severe biases.


  As part of our radial velocity planet-search survey performed with SARG at
TNG, we monitored the components of HD 132563 for ten years. It is a binary
system formed by two rather similar solar type stars with a projected
separation of 4.1 arcsec, which corresponds to 400 AU at the distance of 96 pc.
The two components are moderately metal-poor and the age of the system is about
5 Gyr. We detected RV variations of HD 132563B with period of 1544 days and
semi-amplitude of 26 m/s. From the star characteristics and line profile
measurements, we infer their Keplerian origin. Therefore HD 132563B turns out
to host a planet with a projected mass msini=1.49 MJup at 2.6 AU with a
moderately eccentric orbit (e=0.22). The planet around HD 132563B is one of the
few that are known in triple stellar systems, as we found that the primary HD
132563A is itself a spectroscopic binary with a period longer than 15 years and
an eccentricity higher than 0.65. The spectroscopic component was not detected
in adaptive-optics images taken with AdOpt@TNG, since it expected at a
projected separation that was smaller than 0.2 arcsec at the time of our
observations. A small excess in K band difference between the components with
respect to the difference in V band is compatible with a companion of about
0.55 Msun. A preliminary statistical analysis of the occurrence of planets in
triple systems indicate a similar frequency of planets around the isolated
component in a triple system, components of wide binaries and single stars.
There is no significant iron abundance difference between the components. The
lack of stars in binary systems and open clusters showing strong enhancements
of iron abundance, comparable to the typical metallicity difference between
stars with and without giant planets, agrees with the idea that accretion of
planetary material producing iron abundance anomalies larger than 0.1 dex is
rare.


  The transmission of fermions of mass m and energy E through an electrostatic
potential barrier of rectangular shape (i.e. supporting an infinite electric
field), of height U> E + m - due to the many-body nature of the Dirac equation
evidentiated by the Klein paradox - has been widely studied. We exploit here
the analytical solution, given by Sauter for the linearly rising potential
step, to show that the tunnelling rate through a more realistic trapezoidal
barrier is exponentially depressed, as soon as the length of the regions
supporting a finite electric field exceeds the Compton wavelenght of the
particle - the latter circumstance being hardly escapable in most realistic
cases.


  This paper is devoted to study the electrochemical behavior of Pt catalyst in
a polymer electrolyte fuel cell at various operating conditions and at
different electric potential difference (also known as voltage) cycling applied
in accelerated stress tests. The degradation of platinum is considered with
respect to the Pt ion dissolution and the Pt oxide coverage of catalyst
described by a one-dimensional model. In the model, degradation rate increases
with temperature and decreasing particle diameter of Pt nano-particles. The
theoretical study of the underlying diffusion system with the nonlinear
reactions is presented by analytical methods and gives explicit solutions
through a first integral of the ODE system. Numerical tests are obtained using
a second order implicit-explicit scheme. The computer simulation shows that the
lifetime of the catalyst depends on the voltage profile and the upper potential
level. By this Pt mass loss is more significant at the membrane surface than at
the gas diffusion layer.


  In this thesis, we investigate the implications of the LHC Higgs data on
different BSM scenarios. Since the data seem to agree with the SM expectations,
any nonstandard couplings will be strongly constrained. First we investigate,
in a model independent way, the constraints on the nonstandard Higgs couplings
with the fermions and the vector bosons in view of high energy unitarity and
the measured value of the Higgs to diphoton signal strength. Then we
concentrate on a particular BSM scenario, namely, the two Higgs-doublet models
(2HDMs). Consistency of the Higgs data with the corresponding SM predictions
strongly motivates us to work in the {\em alignment limit}. In this limit,
including the informations of Higgs mass and its SM-like nature, we find many
new constraints on the nonstandard masses and $\tan\beta$. We also study the
constraints on the charged scalar mass arising from the $h\to \gamma\gamma$
signal strength measurements and observe that the charged scalar does not
necessarily decouple from the diphoton decay width. We then move on to some
particular variants of 2HDMs, known as BGL models, and study the flavor
constraints on these models. Here we find that lighter than conventionally
allowed nonstandard scalars can successfully negotiate the stringent bounds
coming from flavor physics data and can leave unconventional decay signatures
that can be used as distinctive features of these models. We also analyze the
stability and unitarity constraints in a three Higgs-doublet model (3HDM) with
$S_3$ symmetry and find that there must be many more nonstandard particles
below 1~TeV. We also observe that the nondecoupling feature of the charged
scalar in the context of $h\to \gamma\gamma$ is not unique to the 2HDMs only,
instead it is a general property of the multi doublet extensions of the SM with
an exact discrete symmetry.


  Thin film alloys with perpendicular anisotropy were studied using Lorentz
Transmission Electron Microscopy (LTEM). This work focuses on the configuration
of domain walls and demonstrates the suitability and accuracy of LTEM for the
magnetic characterisation of perpendicular magnetic anisotropy materials. Thin
films of chemically ordered ($\unit{L1_0}$) FePd alloys were investigated by
micro-magnetic modelling and LTEM phase retrieval approach. The different
components of magnetization described by the modeling were studied on
experimental images and confirmed by LTEM contrast simulation. Furthermore,
quantitative measurements of magnetic induction inside the domain walls were
made by using an original method to separate the electrical and magnetical
contributions to the phase information. Irregularities were also observed along
the domain walls which could play a major role during the magnetization
processes.


  Subsurface datasets inherently possess big data characteristics such as vast
volume, diverse features, and high sampling speeds, further compounded by the
curse of dimensionality from various physical, engineering, and geological
inputs. Among the existing dimensionality reduction (DR) methods, nonlinear
dimensionality reduction (NDR) methods, especially Metric-multidimensional
scaling (MDS), are preferred for subsurface datasets due to their inherent
complexity. While MDS retains intrinsic data structure and quantifies
uncertainty, its limitations include unstabilized unique solutions invariant to
Euclidean transformations and an absence of out-of-sample points (OOSP)
extension. To enhance subsurface inferential and machine learning workflows,
datasets must be transformed into stable, reduced-dimension representations
that accommodate OOSP.
  Our solution employs rigid transformations for a stabilized Euclidean
invariant representation for LDS. By computing an MDS input dissimilarity
matrix, and applying rigid transformations on multiple realizations, we ensure
transformation invariance and integrate OOSP. This process leverages a convex
hull algorithm and incorporates loss function and normalized stress for
distortion quantification. We validate our approach with synthetic data,
varying distance metrics, and real-world wells from the Duvernay Formation.
Results confirm our method's efficacy in achieving consistent LDS
representations. Furthermore, our proposed "stress ratio" (SR) metric provides
insight into uncertainty, beneficial for model adjustments and inferential
analysis. Consequently, our workflow promises enhanced repeatability and
comparability in NDR for subsurface energy resource engineering and associated
big data workflows.


  We present SMA interferometric observations of the 12CO J=2-1, 13CO (J=2-1),
and C18O (J=2-1) lines and 225 GHz continuum emission and SMT single-dish
observations of C18O (J=2-1) toward L43, a protostellar object in transition
from Class I to II. The 225 GHz continuum emission shows a weak (~23.6 mJy),
compact (<1000 AU) component associated with the central protostar. Our
simulated observations show that it can be explained by dust thermal emission
arising from an envelope which has a hole or a constant intensity region within
a few hundred AU of the protostar. This suggests the disappearance or a lower
concentration distribution of the envelope on a small scale. The 12CO and 13CO
emission exhibit molecular outflows to the south and north. The C18O emission
shows two molecular blobs, which correspond to the reflection nebulosity seen
in near-infrared images, while there is no C18O emission associated with the
protostar. The near-infrared features are likely due to the scattering at the
positions of the blobs. The visible scattering features should result from the
optical thinness of the envelope material, which is consistent with the
less-concentrated distribution in the continuum emission. From single-dish
observations, we found that the mass of the envelope (~1.5 Msun) + protostar
(~0.5 Msun) is comparable with the virial mass of M_vir=1.0 Msun within 40
arcsec. This suggests that the envelope is likely gravitationally bound. We
suggest that the protostellar envelope of L43 has been disappearing by
consumption through accretion, at least in the close vicinity of the protostar.


  We examine the propagation and interaction properties of three successive
coronal mass ejections (CMEs) from 2001 November 21-22, with a focus on their
connection with the behaviors of the associated long-duration complex type II
radio burst. In combination with coronagraph and multi-point in situ
observations, the long-duration type II burst provides key features for
resolving the propagation and interaction complexities of the three CMEs. The
two CMEs from November 22 interacted first and then overtook the November 21
CME at a distance of about 0.85 AU from the Sun. The time scale for the shock
originally driven by the last CME to propagate through the preceding two CMEs
is estimated to be about 14 and 6 hr, respectively. We present a simple
analytical model without any free parameters to characterize the whole
Sun-to-Earth propagation of the shock, which shows a remarkable consistency
with all the available data and MHD simulations even out to the distance of
Ulysses (2.34 AU). The coordination of in situ measurements at the Earth and
Ulysses, which were separated by about $71.4^{\circ}$ in latitude, gives
important clues for the understanding of shock structure and the interpretation
of in situ signatures. The results also indicate means to increase
geo-effectiveness with multiple CMEs, which can be considered as another
manifestation of the "perfect storm" scenario proposed by [liu14a] although the
current case is not "super" in the same sense as the 2012 July 23 event.


  We present the serendipitous discovery of (1) a large double radio relic
associated with the galaxy cluster PSZ2 G277.93+12.34 and (2) a new odd radio
circle, ORC J1027-4422, both found in the same deep MeerKAT 1.3 GHz wide-band
radio continuum image. The angular separation of the two arc-shaped cluster
relics is ~16 arcmin or ~2.6 Mpc for a cluster redshift of z ~ 0.158. The thin
southern relic, which shows several ridges/shocks including one possibly moving
inwards, has a linear extent of ~1.64 Mpc. In contrast, the northern relic is
about twice as wide, twice as bright, but only has a largest linear size of
~0.66 Mpc. Complementary SRG/eROSITA X-ray images reveal extended emission from
hot intracluster gas between the two relics and around the narrow-angle tail
(NAT) radio galaxy PMN J1033-4335 (z ~ 0.153) located just east of the northern
relic. The radio morphologies of the NAT galaxy and the northern relic, which
are also detected with the Australian Square Kilometer Array Pathfinder (ASKAP)
at 888 MHz, suggest both are moving in the same outward direction. The
discovery of ORC J1027-4422 in a different part of the same MeerKAT image makes
it the 4th known single ORC. It has a diameter of ~90 arcsec corresponding to
400 kpc at a tentative redshift of z ~ 0.3 and remains undetected in X-ray
emission. Supported by simulations, we discuss similarities between outward
moving galaxy and cluster merger shocks as the formation mechanisms for ORCs
and radio relics, respectively.


  We study a model for the motion of a tracer particle inside an active gel,
exposing the properties of the van Hove distribution of the particle
displacements. Active events of a typical force magnitude give rise to
non-Gaussian distributions, having exponential tails or side-peaks. The
side-peaks appear when the local bulk elasticity of the gel is large enough and
few active sources are dominant. We explain the regimes of the different
distributions, and study the structure of the peaks for active sources that are
susceptible to the elastic stress that they cause inside the gel. We show how
the van Hove distribution is altered by both the duty cycle of the active
sources and their susceptibility, and suggest it as a sensitive probe to
analyze microrheology data in active systems with restoring elastic forces.


  We prove a formula expressing a general n by n Toeplitz determinant as a
Fredholm determinant of an operator 1-K acting on l_2({n,n+1,...}), where the
kernel K admits an integral representation in terms of the symbol of the
original Toeplitz matrix.
  The proof is based on the results of one of the authors, see math.RT/9907127,
and a formula due to Gessel which expands any Toeplitz determinant into a
series of Schur functions.
  We also consider 3 examples where the kernel involves the Gauss
hypergeometric function and its degenerations.


  Light curve observations of a recently discovered interstellar object
1I/`Oumuamua suggest that this object has an extremely elongated shape with the
axis ratio 0.3 or smaller. Planetesimal collisions can produce irregular shapes
including elongated shapes. In this paper, we suggest that the extremely
elongated shape of 1I/`Oumuamua may be the result of such an impact. To find
detailed impact conditions to form the extremely elongated objects, we conduct
numerical simulations of planetesimal collisions using Smoothed Particle
Hydrodynamics method for elastic dynamics with self-gravity and interparticle
friction. Impacts into strengthless target planetesimals with radius 50 m are
conducted with various ratios of impactor mass to target mass q, friction
angles phi_d, impact velocities v_imp, and impact angles theta_imp. We find
that impacts with q \geq 0.5, phi_d \geq 40 degrees, v_imp \leq 40 degrees, and
theta_imp \leq 30 degrees produce remnants with the ratio of intermediate to
major axis length less than 0.3. This impact condition suggests that the parent
protoplanetary disk in the planetesimal collision stage was weakly turbulent
(alpha < 10^{-4} for the inner disk) and composed of planetesimals smaller than
~ 7 km to ensure small impact velocity.


  Understanding seasonal climatic conditions is critical for better management
of resources such as water, energy and agriculture. Recently, there has been a
great interest in utilizing the power of artificial intelligence methods in
climate studies. This paper presents a cutting-edge deep learning model
(UNet++) trained by state-of-the-art global CMIP6 models to forecast global
temperatures a month ahead using the ERA5 reanalysis dataset. ERA5 dataset was
also used for finetuning as well performance analysis in the validation
dataset. Three different setups (CMIP6; CMIP6 + elevation; CMIP6 + elevation +
ERA5 finetuning) were used with both UNet and UNet++ algorithms resulting in
six different models. For each model 14 different sequential and non-sequential
temporal settings were used. The Mean Absolute Error (MAE) analysis revealed
that UNet++ with CMIP6 with elevation and ERA5 finetuning model with "Year 3
Month 2" temporal case provided the best outcome with an MAE of 0.7. Regression
analysis over the validation dataset between the ERA5 data values and the
corresponding AI model predictions revealed slope and $R^2$ values close to 1
suggesting a very good agreement. The AI model predicts significantly better
than the mean CMIP6 ensemble between 2016 and 2021. Both models predict the
summer months more accurately than the winter months.


  We analysed ASAS-3 photometry of bright early-type stars with the goal of
finding new Beta Cephei stars. We were particularly interested in stars that
would be good for seismic analysis, i.e., stars that (i) have a large number of
excited modes, (ii) show rotationally split modes, (iii) are components of
eclipsing binary systems, (iv) have low-frequency modes, that is, are hybrid
Beta Cephei/SPB stars. Our study was made with a homogeneous sample of over
4100 stars having MK spectral type B5 or earlier. For these stars, the ASAS-3
photometry was analysed by means of a Fourier periodogram. We have discovered
103 Beta Cephei stars, nearly doubling the number of previously known stars of
this type. Among these stars, four are components of eclipsing binaries, seven
have modes equidistant or nearly equidistant in frequency. In addition, we
found five Beta Cephei stars that show low-frequency periodic variations, very
likely due to pulsations. We therefore regard them as candidate hybrid Beta
Cephei/SPB pulsators. All these stars are potentially very useful for seismic
modeling. Moreover, we found Beta Cephei-type pulsations in three late O-type
stars and fast period changes in one, HD 168050.


  We introduce a new approach to the maximum flow problem in undirected,
capacitated graphs using $\alpha$-\emph{congestion-approximators}:
easy-to-compute functions that approximate the congestion required to route
single-commodity demands in a graph to within a factor of $\alpha$. Our
algorithm maintains an arbitrary flow that may have some residual excess and
deficits, while taking steps to minimize a potential function measuring the
congestion of the current flow plus an over-estimate of the congestion required
to route the residual demand. Since the residual term over-estimates, the
descent process gradually moves the contribution to our potential function from
the residual term to the congestion term, eventually achieving a flow routing
the desired demands with nearly minimal congestion after
$\tilde{O}(\alpha\eps^{-2}\log^2 n)$ iterations. Our approach is similar in
spirit to that used by Spielman and Teng (STOC 2004) for solving Laplacian
systems, and we summarize our approach as trying to do for $\ell_\infty$-flows
what they do for $\ell_2$-flows.
  Together with a nearly linear time construction of a
$n^{o(1)}$-congestion-approximator, we obtain $1+\eps$-optimal single-commodity
flows undirected graphs in time $m^{1+o(1)}\eps^{-2}$, yielding the fastest
known algorithm for that problem. Our requirements of a congestion-approximator
are quite low, suggesting even faster and simpler algorithms for certain
classes of graphs. For example, an $\alpha$-competitive oblivious routing tree
meets our definition, \emph{even without knowing how to route the tree back in
the graph}. For graphs of conductance $\phi$, a trivial
$\phi^{-1}$-congestion-approximator gives an extremely simple algorithm for
finding $1+\eps$-optimal-flows in time $\tilde{O}(m\phi^{-1})$.


  We present a many-body expansion (MBE) formulation and implementation for
efficient computation of analytical energy gradients from OSV-MP2 theory based
on our earlier work (Zhou et al. J. Chem. Theory Comput. 2020, 16, 196-210).
The third-order MBE(3) expansion of OSV-MP2 wavefunction was developed to adopt
the orbital-specific clustering and long-range termination schemes, which
avoids term-by-term differentiations of the MBE energy bodies. We achieve
better efficiency by exploiting the algorithmic sparsity that allows to prune
out insignificant fitting integrals and OSV relaxations. With these
approximations, the present implementation is benchmarked on a range of
molecules that show an economic scaling in the linear and quadratic regimes for
computing MBE(3)-OSV-MP2 amplitude and gradient equations, respectively, and
yields normal accuracy comparable to the original OSV-MP2 results. The
MPI-3-based parallelism through shared memory one-sided communication is
further developed for improving parallel scalability and memory accessibility
by sorting the MBE(3) orbital clusters into independent tasks that are
distributed on multiple processes across many nodes, supporting both global and
local data locations in which selected MBE(3)-OSV-MP2 intermediates of
different sizes are distinguished and accordingly placed. The accuracy and
efficiency level of our MBE(3)-OSV-MP2 analytical gradient implementation is
finally illustrated in two applications: we show that the subtle coordination
structure differences of mechanically interlocked Cu-catenane complexes can be
distinguished when tuning ligand lengths; and the porphycene molecular dynamics
reveals the emergence of the vibrational signature arising from softened N-H
stretching associated with hydrogen transfer, using an MP2 level of electron
correlation and classical nuclei for the first time.


  We study diamond-alpha integrals on time scales. A diamond-alpha version of
Fermat's theorem for stationary points is also proved, as well as Rolle's,
Lagrange's, and Cauchy's mean value theorems on time scales.


  Research on nearest-neighbor methods tends to focus somewhat dichotomously
either on the statistical or the computational aspects -- either on, say, Bayes
consistency and rates of convergence or on techniques for speeding up the
proximity search. This paper aims at bridging these realms: to reap the
advantages of fast evaluation time while maintaining Bayes consistency, and
further without sacrificing too much in the risk decay rate. We combine the
locality-sensitive hashing (LSH) technique with a novel missing-mass argument
to obtain a fast and Bayes-consistent classifier. Our algorithm's prediction
runtime compares favorably against state of the art approximate NN methods,
while maintaining Bayes-consistency and attaining rates comparable to minimax.
On samples of size $n$ in $\R^d$, our pre-processing phase has runtime $O(d n
\log n)$, while the evaluation phase has runtime $O(d\log n)$ per query point.


  Reducing feature redundancy has shown beneficial effects for improving the
accuracy of deep learning models, thus it is also indispensable for the models
of unsupervised domain adaptation (UDA). Nevertheless, most recent efforts in
the field of UDA ignores this point. Moreover, main schemes realizing this in
general independent of UDA purely involve a single domain, thus might not be
effective for cross-domain tasks. In this paper, we emphasize the significance
of reducing feature redundancy for improving UDA in a bi-level way. For the
first level, we try to ensure compact domain-specific features with a
transferable decorrelated normalization module, which preserves specific domain
information whilst easing the side effect of feature redundancy on the sequel
domain-invariance. In the second level, domain-invariant feature redundancy
caused by domain-shared representation is further mitigated via an alternative
brand orthogonality for better generalization. These two novel aspects can be
easily plugged into any BN-based backbone neural networks. Specifically, simply
applying them to ResNet50 has achieved competitive performance to the
state-of-the-arts on five popular benchmarks. Our code will be available at
https://github.com/dreamkily/gUDA.


  Recently, guiding electromagnetic surface waves without sacrificing
scattering losses through paths that have arbitrary shape bumps has gained a
lot of interest due to its wealth of advantages in modern photonics and
plasmonics devices. In this study, based on transformation optics (TO)
methodology, a feasible approach to control the flow of surface plasmon
plariton (SPPs) at metal-dielectric interfaces with arbitrary curvature is
proposed. The obtained material becomes homogeneous and independent of the
bump's geometry. That is, one constant material is required to route SPP waves
without scattering the energy into the far-field region, which overcome the
bottlenecks encountered in the previous works. Several numerical simulations
are carried out to illustrate the capability of the propounded cloak to control
the SPP flows at metal/dielectric interfaces. The unique designing approach
introduced here may open a new horizon to nano-optics and downscaling of
photonic circuits.


  By studying the optical brightness surface density of the globular cluster
NGC 6388, it has been recently proposed that it harbors a central
intermediate-mass black hole with mass $\simeq 5.7\times 10^3$ M$_{\odot}$. We
expect that the compact object in the center of NGC 6388 emits radiation in the
$X$-ray band as a consequence of the accretion from the surrounding matter. We
searched for $XMM$-Newton and $Chandra$ observations towards NGC 6388 to test
this hypothesis. The $Chandra$ satellite disentangles several point-like
$X$-ray sources, probably low mass $X$-ray binaries, well within the core
radius of the globular cluster. However, three of them, coinciding with the
cluster center of gravity, remain unresolved. Their total luminosity is
$L_X^{Obs}\simeq 2.7\times 10^{33}$ erg s$^{-1}$. If one of these sources is
the $X$-ray counterpart of the intermediate-mass black hole in NGC 6388, the
corresponding upper limit on the accretion efficiency, with respect to the
Eddington luminosity, is $3\times 10^{-9}$. This measurement could be tightened
if moderately deep radio observations of the field were performed.


  In this paper, we propose a Text-Degradation Invariant Auto Encoder
(Text-DIAE), a self-supervised model designed to tackle two tasks, text
recognition (handwritten or scene-text) and document image enhancement. We
start by employing a transformer-based architecture that incorporates three
pretext tasks as learning objectives to be optimized during pre-training
without the usage of labeled data. Each of the pretext objectives is
specifically tailored for the final downstream tasks. We conduct several
ablation experiments that confirm the design choice of the selected pretext
tasks. Importantly, the proposed model does not exhibit limitations of previous
state-of-the-art methods based on contrastive losses, while at the same time
requiring substantially fewer data samples to converge. Finally, we demonstrate
that our method surpasses the state-of-the-art in existing supervised and
self-supervised settings in handwritten and scene text recognition and document
image enhancement. Our code and trained models will be made publicly available
at~\url{ http://Upon_Acceptance}.


  We estimate the experimental accuracies to which the Ke4 parameters will be
measured at DAPHNE. We consider the accuracies obtainable using asymmetries,
and the maximum likelihood method. We find the current determinations of the
relevant parameters will be improved by a factor of five to ten after a year of
running at the anticipated luminosity of DAPHNE.


  We say a turnstile streaming algorithm is "non-adaptive" if, during updates,
the memory cells written and read depend only on the index being updated and
random coins tossed at the beginning of the stream (and not on the memory
contents of the algorithm). Memory cells read during queries may be decided
upon adaptively. All known turnstile streaming algorithms in the literature are
non-adaptive.
  We prove the first non-trivial update time lower bounds for both randomized
and deterministic turnstile streaming algorithms, which hold when the
algorithms are non-adaptive. While there has been abundant success in proving
space lower bounds, there have been no non-trivial update time lower bounds in
the turnstile model. Our lower bounds hold against classically studied problems
such as heavy hitters, point query, entropy estimation, and moment estimation.
In some cases of deterministic algorithms, our lower bounds nearly match known
upper bounds.


  The negative thermal expansion (NTE) mechanism in Cu2O has been characterised
via mapping of different Cu2O structural flexibility models onto phonons
obtained using ab-initio lattice dynamics. Low frequency acoustic modes that
are responsible for the NTE in this material correspond to vibrations of rigid
O-Cu-O rods. There is also some small contribution from higher frequency optic
modes that correspond to rotations of rigid and near-rigid OCu4 tetrahedra as
well as of near-rigid O-Cu-O rods. The primary NTE mode also drives a
ferroelastic phase transition at high pressure; our calculations predict this
to be to an orthorhombic structure with space group Pnnn.


  The aim of this paper is to present an explicit reduction algorithm for
Hilbert modular groups over arbitrary totally real number fields. An
implementation of the algorithm is available to download from [19]. The
exposition is self-contained and sufficient details are given for the reader to
understand how it works and implement their own version if desired.


  The remarkable Cooper-like pairing phenomenon in the Aharonov-Bohm
interference of a Fabry-Perot interferometer (FPI)$\rm{-}$operating in the
integer quantum Hall regime$\rm{-}$remains baffling. Here, we report the
interference of paired electrons employing 'interface edge modes'. These modes
are born at the interface between the bulk of the FPI and an outer gated region
tuned to a lower filling factor. Such configuration allows toggling the spin
and the orbital of the Landau level (LL) of the edge modes at the interface. We
find that electron pairing occurs only when the two modes (the interfering
outer and the first inner) belong to the same spinless LL.


  This work presents an innovative application of the well-known concept of
cortico-muscular coherence for the classification of various motor tasks, i.e.,
grasps of different kinds of objects. Our approach can classify objects with
different weights (motor-related features) and different surface frictions
(haptics-related features) with high accuracy (over 0:8). The outcomes
presented here provide information about the synchronization existing between
the brain and the muscles during specific activities; thus, this may represent
a new effective way to perform activity recognition.


  This paper studies the problem of selecting a minimum-size set of input nodes
to guarantee stability of a networked system in the presence of uncertainties
and time delays. Current approaches to input selection in networked dynamical
systems focus on nominal systems with known parameter values in the absence of
delays. We derive sufficient conditions for existence of a stabilizing
controller for an uncertain system that are based on a subset of system modes
lying within the controllability subspace induced by the set of inputs. We then
formulate the minimum input selection problem and prove that it is equivalent
to a discrete optimization problem with bounded submodularity ratio, leading to
polynomial-time algorithms with provable optimality bounds. We show that our
approach is applicable to different types of uncertainties, including additive
and multiplicative uncertainties in the system matrices as well as uncertain
time delays. We demonstrate our approach in a numerical case study on the IEEE
39-bus test power system.


  Although the Hubble constant $H_0$ and spatial curvature $\Omega_{K}$ have
been measured with very high precision, they still suffer from some tensions.
In this paper, we propose an improved method to combine the observations of
ultra-compact structure in radio quasars and strong gravitational lensing with
quasars acting as background sources to determine $H_0$ and $\Omega_{K}$
simultaneously. By applying the distance sum rule to the time-delay
measurements of 7 strong lensing systems and 120 intermediate-luminosity
quasars calibrated as standard rulers, we obtain stringent constraints on the
Hubble constant ($H_0=78.3\pm2.9 \mathrm{~km~s^{-1}~Mpc^{-1}}$) and the cosmic
curvature ($\Omega_K=0.49\pm0.24$). On the one hand, in the framework of a flat
universe, the measured Hubble constant ($H_0=73.6^{+1.8}_{-1.6}
\mathrm{~km~s^{-1}~Mpc^{-1}}$) is strongly consistent with that derived from
the local distance ladder, with a precision of 2\%. On the other hand, if we
use the local $H_0$ measurement as a prior, our results are marginally
compatible with zero spatial curvature ($\Omega_K=0.23^{+0.15}_{-0.17}$) and
there is no significant deviation from a flat universe. Finally, we also
evaluate whether strongly lensed quasars would produce robust constraints on
$H_0$ and $\Omega_{K}$ in the non-flat and flat $\Lambda$CDM model if the
compact radio structure measurements are available from VLBI observations.


  The atomic model that Niels Bohr suggested in 1913 celebrated its greatest
victories in connection with one-electron atoms. Among them were the isotopic
spectral effect and what became known as Rydberg atoms, insights that were
fully recognized only many years later. He considered the original ring model a
first step towards an understanding of atomic structure, and during the
following years he developed it into more ambitious models that, he hoped,
would also describe many-electron atoms. His theory of the periodic system
marked the culmination of the orbital atom within the framework of the old
quantum theory. However, the theory would soon be replaced by more symbolic
models that heralded the coming of the quantum-mechanical atom.


  In the setting of symplectic manifolds which are convex at infinity, we use a
version of the Aleksandrov maximum principle to derive uniform estimates for
Floer solutions that are valid for a wider class of Hamiltonians and almost
complex structures than is usually considered. This allows us to extend the
class of Hamiltonians which one can use in the direct limit when constructing
symplectic homology. As an application, we detect elements of infinite order in
the symplectic mapping class group of a Liouville domain, and obtain existence
results for translated points.


  Motivated by the geometry of spins in the material CaCu$_2$O$_3$, we study a
two-layer, spin-half Heisenberg model, with nearest-neighbor exchange couplings
J and \alpha*J along the two axes in the plane and a coupling J_\perp
perpendicular to the planes. We study these class of models using the
Stochastic Series Expansion (SSE) Quantum Monte Carlo simulations at finite
temperatures and series expansion methods at T=0. The critical value of the
interlayer coupling, J_\perp^c, separating the N{\'e}el ordered and disordered
ground states, is found to follow very closely a square root dependence on
$\alpha$. Both T=0 and finite-temperature properties of the model are
presented.


  We study the smoothness and preserving orientation properties of a global and
nonautonomous version of the Hartman--Grobman Theorem when the linear system
has a nonuniform contraction on the half line. The nonuniform contraction
implies the existence of a density function (\emph{i.e} a dual type of Lyapunov
function) for the linear system which combined with the above diffeomorphism
allow us to construct a density function for the nonlinear system.


  We study reheating processes and its cosmological consequences in the
Starobinsky model embedded in the old-minimal supergravity. First, we consider
minimal coupling between the gravity and matter sectors in the higher curvature
theory, and transform it to the equivalent standard supergravity coupled to
additional matter superfields. We then discuss characteristic decay modes of
the inflaton and the reheating temperature $T_{\rm R}$. Considering a simple
model of supersymmetry breaking sector, we estimate gravitino abundance from
inflaton decay, and obtain limits on the masses of gravitino and supersymmetry
breaking field. We find $T_{\rm R}\simeq 1.0\times10^9$ GeV and the allowed
range of gravitino mass as $10^4$ GeV $\lesssim m_{3/2} \lesssim 10^5$ GeV,
assuming anomaly-induced decay into the gauge sector as the dominant decay
channel.


  The simplest decomposition of a Toffoli gate acting on three qubits requires
{\em five} 2-qubit gates. If we restrict ourselves to controlled-sign (or
controlled-NOT) gates this number climbs to six. We show that the number of
controlled-sign gates required to implement a Toffoli gate can be reduced to
just {\em three} if one of the three quantum systems has a third state that is
accessible during the computation, i.e. is actually a qutrit. Such a
requirement is not unreasonable or even atypical since we often artificially
enforce a qubit structure on multilevel quantums systems (eg. atoms, photonic
polarization and spatial modes). We explore the implementation of these
techniques in optical quantum processing and show that linear optical circuits
could operate with much higher probabilities of success.


  The fixed point construction is a method for designing tile sets and cellular
automata with highly nontrivial dynamical and computational properties. It
produces an infinite hierarchy of systems where each layer simulates the next
one. The simulations are implemented entirely by computations of Turing
machines embedded in the tilings or spacetime diagrams. We present an overview
of the construction and list its applications in the literature.


  This paper is devoted to global existence and optimal decay rate of weak
solutions to some inviscid Oldroyd-B models with center diffusion. By virtue of
the properties of Calderon-Zygmund operator and the Littlewood-Paley
decomposition theory, we firstly prove that the 2-D co-rotation inviscid
Oldroyd-B model admits global weak solutions with some large data under
different integrability conditions. Furthermore, we prove the energy
conservation of weak solutions for the co-rotation case. These obtained results
generalize and cover the classical results for the Euler equation. Moreover, we
establish global weak solutions with small data for the 2-D noncorotation
inviscid Oldroyd-B model without damping. Finally, we prove optimal decay rate
of global weak solutions for the noncorotation case by the improved Fourier
splitting method.


  Using the quantum covariant Poisson bracket (QCPB) theory, we can accomplish
much more compatible explanations of the quantum mechanics supported by the
G-dynamics. We further study the generalized quantum harmonic oscillator
equipped with the G-dynamics of type I, such as geometric creation and
annihilation operators, and the geometric number operator as an extension of
the number operator is well given for the deep discussions, the geometric
Hamiltonian operator is expressed as another form. Especially, the geometric
quantization rules based on the QCPB theory is then calculated.


  Poplar is one of the best fast-growing trees in the world, widely used for
windbreak and wood product. Although architecture of poplar has direct impact
on its applications, it has not been descried in previous poplar models,
probably because of the difficulties raised by measurement, data processing and
parameterization. In this paper, the functional-structural model GreenLab is
calibrated by using poplar data of 3, 4, 5, 6 years old. The data was acquired
by simplifying measurement. The architecture was also simplified by classifying
the branches into several types (physiological age) using clustering analysis,
which decrease the number of parameters. By multi-fitting the sampled data of
each tree, the model parameters were identified and the plant architectures at
different tree ages were simulated.


  We investigate the complexity of {\sc{Constructive Control by Adding/Deleting
Votes}} (CCAV/CCDV) for $r$-approval, Condorcet, Maximin and
Copeland$^{\alpha}$ in $k$-axes and $k$-candidates partition single-peaked
elections. In general, we prove that CCAV and CCDV for most of the voting
correspondences mentioned above are NP-hard even when~$k$ is a very small
constant. Exceptions are CCAV and CCDV for Condorcet and CCAV for $r$-approval
in $k$-axes single-peaked elections, which we show to be fixed-parameter
tractable with respect to~$k$. In addition, we give a polynomial-time algorithm
for recognizing $2$-axes elections, resolving an open problem. Our work leads
to a number of dichotomy results. To establish an NP-hardness result, we also
study a property of $3$-regular bipartite graphs which may be of independent
interest. In particular, we prove that for every $3$-regular bipartite graph,
there are two linear orders of its vertices such that the two endpoints of
every edge are consecutive in at least one of the two orders.


  The conventional approach of moving data to the CPU for computation has
become a significant performance bottleneck for emerging scale-out
data-intensive applications due to their limited data reuse. At the same time,
the advancement in 3D integration technologies has made the decade-old concept
of coupling compute units close to the memory --- called near-memory computing
(NMC) --- more viable. Processing right at the "home" of data can significantly
diminish the data movement problem of data-intensive applications.
  In this paper, we survey the prior art on NMC across various dimensions
(architecture, applications, tools, etc.) and identify the key challenges and
open issues with future research directions. We also provide a glimpse of our
approach to near-memory computing that includes i) NMC specific
microarchitecture independent application characterization ii) a compiler
framework to offload the NMC kernels on our target NMC platform and iii) an
analytical model to evaluate the potential of NMC.