  Most existing data-driven power system short-term voltage stability
assessment (STVSA) approaches presume class-balanced input data. However, in
practical applications, the occurrence of short-term voltage instability
following a disturbance is minimal, leading to a significant class imbalance
problem and a consequent decline in classifier performance. This work proposes
a Transformer-based STVSA method to address this challenge. By utilizing the
basic Transformer architecture, a stability assessment Transformer (StaaT) is
developed {as a classification model to reflect the correlation between the
operational states of the system and the resulting stability outcomes}. To
combat the negative impact of imbalanced datasets, this work employs a
conditional Wasserstein generative adversarial network with gradient penalty
(CWGAN-GP) for synthetic data generation, aiding in the creation of a balanced,
representative training set for the classifier. Semi-supervised clustering
learning is implemented to enhance clustering quality, addressing the lack of a
unified quantitative criterion for short-term voltage stability. {Numerical
tests on the IEEE 39-bus test system extensively demonstrate that the proposed
method exhibits robust performance under class imbalances up to 100:1 and noisy
environments, and maintains consistent effectiveness even with an increased
penetration of renewable energy}. Comparative results reveal that the CWGAN-GP
generates more balanced datasets than traditional oversampling methods and that
the StaaT outperforms other deep learning algorithms. This study presents a
compelling solution for real-world STVSA applications that often face class
imbalance and data noise challenges.


  Energy storage is a basic physical process with many applications. When
considering this task at the quantum scale, it becomes important to optimise
the non-equilibrium dynamics of energy transfer to the storage device or
battery. Here, we tackle this problem using the methods of quantum feedback
control. Specifically, we study the deposition of energy into a quantum battery
via an auxiliary charger. The latter is a driven-dissipative two-level system
subjected to a homodyne measurement whose output signal is fed back linearly
into the driving field amplitude. We explore two different control strategies,
aiming to stabilise either populations or quantum coherences in the state of
the charger. In both cases, linear feedback is shown to counteract the
randomising influence of environmental noise and allow for stable and effective
battery charging. We analyse the effect of realistic control imprecisions,
demonstrating that this good performance survives inefficient measurements and
small feedback delays. Our results highlight the potential of continuous
feedback for the control of energetic quantities in the quantum regime.


  Given any positive integer n, we prove the existence of infinitely many right
triangles with area n and side lengths in certain number fields. This
generalizes the famous congruent number problem. The proof allows the explicit
construction of these triangles; for this purpose we find for any positive
integer n an explicit cubic number field Q(\lambda) (depending on n) and an
explicit point P_\lambda of infinite order in the Mordell-Weil group of the
elliptic curve Y^2=X^3-n^2*X over Q(\lambda).


  We prove that the m-generated Grassmann algebra can be embedded into a
2^{m-1}x2^{m-1} matrix algebra over a factor of a commutative polynomial
algebra in m indeterminates. Cayley-Hamilton and standard identities for nxn
matrices over the m-generated Grassmann algebra are derived from this
embedding. Other related embedding results are also presented.


  We present a mechanism for the creation of gaps in the spectra of
self-adjoint operators defined over a Hilbert space of functions on a graph,
which is based on the process of graph decoration. The resulting Hamiltonians
can be viewed as associated with discrete models exhibiting a repeated local
structure and a certain bottleneck in the hopping amplitudes.


  The existence of eV-mass sterile neutrinos is not ruled out because of
persistent experimental anomalies. Upcoming multi-messenger detections of
neutron-star merger remnants could provide indirect constraints on the
existence of these particles. We explore the active-sterile flavor conversion
phenomenology in a two-flavor scenario (1 active + 1 sterile species) as a
function of the sterile neutrino mixing parameters, neutrino emission angle
from the accretion torus, and temporal evolution of the merger remnant. The
torus geometry and the neutron richness of the remnant are responsible for the
occurrence of multiple resonant active-sterile conversions. The number of
resonances strongly depends on the neutrino emission direction above or inside
the remnant torus and leads to large production of sterile neutrinos (and no
antineutrinos) in the proximity of the polar axis as well as more sterile
antineutrinos than neutrinos in the equatorial region. As the black hole torus
evolves in time, the shallower baryon density is responsible for more adiabatic
flavor conversion, leading to larger regions of the mass-mixing parameter space
being affected by flavor mixing. Our findings imply that the production of
sterile states could have indirect implications on the disk cooling rate, its
outflows, and related electromagnetic observables which remain to be assessed.


  The aim of this thesis is to analyze part of the results presented by R.E.
Borcherds in his article on renormalization and quantum field theories. To
facilitate this, the first chapter presents the basic facts on category theory,
sheaf theory and the theory of distributions on a manifold. In the particular
case of distributions on manifolds we differentiate between the H-distribution
(introduced by H\"ormander) and the D-distributions (described in the book of
Dieudonn{\'e}) and extend the product of distributions defined by H\"ormander
only for H-distibution, to an action from H-distributions over D-distributions.
  Each one of these facts will be used along the thesis in order to exhibit
more detailed descriptions and explanations for the results. In Chapter 2 the
concepts of spacetime, fields, Lagrangians, propagators and Feynman measure are
defined. The notion of Feynman measure is introduced with a more detailed
explanation of the Gaussian condition and its relation with the cut propagator.
In the last chapter we define the renormalization group and immediately
characterize it using the results from Chapter 1. The algebraic structure of
this group is described in detail. We present a descending filtration of the
renormalization group by normal subgroups, compute the corresponding quotients
and commutators, and show that any element of the renormalization group can be
written as an infinite product of elements each one living in one of the
aforementioned subgroups. Lastly we show that an action from the
renormalization group over the set of Feynman measures is well defined and
prove that this action is transitive over the Feynman measures associated with
a given cut local propagator.


  Water quality has a direct impact on industry, agriculture, and public
health. Algae species are common indicators of water quality. It is because
algal communities are sensitive to changes in their habitats, giving valuable
knowledge on variations in water quality. However, water quality analysis
requires professional inspection of algal detection and classification under
microscopes, which is very time-consuming and tedious. In this paper, we
propose a novel multi-target deep learning framework for algal detection and
classification. Extensive experiments were carried out on a large-scale colored
microscopic algal dataset. Experimental results demonstrate that the proposed
method leads to the promising performance on algal detection, class
identification and genus identification.


  We propose a characterization and a quantification of general quantum
correlation which is exhibited even by a separable (unentangled) mixed
bipartite state in terms of the nonclassical values of the associated
Kirkwood-Dirac (KD) quasiprobability. Such a general quantum correlation,
wherein entanglement is a subset, is not only intriguing from a fundamental
point of view, but it has also been recognized as a resource in a variety of
schemes of quantum information processing and quantum technology. Given a
bipartite state, we construct a quantity based on the imaginary part the
associated KD quasiprobability defined over a pair of orthonormal product bases
and an optimization procedure over all pairs of such bases. We show that it
satisfies certain requirements expected for a quantifier of general quantum
correlations. It gives a lower bound to the total sum of the quantum standard
deviation of all the elements of the product (local) basis, minimized over all
such bases. It suggests an interpretation as the minimum genuine quantum share
of uncertainty in all possible local von-Neumann projective measurement.
Moreover, it is a faithful witness for entanglement and measurement-induced
nonlocality of pure bipartite states. We then discuss a variational scheme for
its estimation, and based on this, we offer information theoretical meanings of
the general quantum correlation. Our results suggest a deep connection between
the general quantum correlation and the nonclassical values of the KD
quasiprobability and the associated strange weak values.


  We define a family of string equations with perturbative expansions that
admit an interpretation as an unoriented minimal string theory with background
D-branes and R-R fluxes. The theory also has a well-defined non-perturbative
sector and we expect it to have a continuum interpretation as an orientifold
projection of the non-critical type~0A string for \hat{c}=0, the (2,4) model.
There is a second perturbative region which is consistent with an
interpretation in terms of background R-R fluxes. We identify a natural
parameter in the formulation that we speculate may have an interpretation as
characterizing the contribution of a new type of background D-brane. There is a
non-perturbative map to a family of string equations which we expect to be the
\hat{c}=0 type 0B string. The map exchanges D-branes and R-R fluxes. We present
the general structure of the string equations for the (2,4k) type 0A models.


  Mass currents in astrophysics generate gravitomagnetic fields of enormous
complexity. Gravitomagnetic helicity, in direct analogy with magnetic helicity,
is a measure of entwining of the gravitomagnetic field lines. We discuss
gravitomagnetic helicity within the gravitoelectromagnetic (GEM) framework of
linearized general relativity. Furthermore, we employ the spacetime curvature
approach to GEM in order to determine the gravitomagnetic helicity for static
observers in Kerr spacetime.


  Spontaneous and stimulated resonant inelastic X-ray Raman scattering signals
are calculated using the Keldysh-Schwinger closed-time path loop and expressed
as overlaps of Doorway and Window electron-hole wavepackets. These are recast
in terms of the one-particle Green's functions and expansion coefficients of
configuration interaction singles for valence excitations, which can be
obtained from standard electronic structure codes. Calculation for many-body
states of ground and core-excited system is avoided.


  We revisit the theoretical properties of Hamiltonian stochastic differential
equations (SDES) for Bayesian posterior sampling, and we study the two types of
errors that arise from numerical SDE simulation: the discretization error and
the error due to noisy gradient estimates in the context of data subsampling.
Our main result is a novel analysis for the effect of mini-batches through the
lens of differential operator splitting, revising previous literature results.
The stochastic component of a Hamiltonian SDE is decoupled from the gradient
noise, for which we make no normality assumptions. This leads to the
identification of a convergence bottleneck: when considering mini-batches, the
best achievable error rate is $\mathcal{O}(\eta^2)$, with $\eta$ being the
integrator step size. Our theoretical results are supported by an empirical
study on a variety of regression and classification tasks for Bayesian neural
networks.


  Human moving path is an important feature in architecture design. By studying
the path, architects know where to arrange the basic elements (e.g. structures,
glasses, furniture, etc.) in the space. This paper presents SimArch, a
multi-agent system for human moving path simulation. It involves a behavior
model built by using a Markov Decision Process. The model simulates human
mental states, target range detection, and collision prediction when agents are
on the floor, in a particular small gallery, looking at an exhibit, or leaving
the floor. It also models different kinds of human characteristics by assigning
different transition probabilities. A modified weighted A* search algorithm
quickly plans the sub-optimal path of the agents. In an experiment, SimArch
takes a series of preprocessed floorplans as inputs, simulates the moving path,
and outputs a density map for evaluation. The density map provides the
prediction that how likely a person will occur in a location. A following
discussion illustrates how architects can use the density map to improve their
floorplan design.


  WebAssembly (abbreviated WASM) has emerged as a promising language of the Web
and also been used for a wide spectrum of software applications such as mobile
applications and desktop applications. These applications, named as WASM
applications, commonly run in WASM runtimes. Bugs in WASM runtimes are
frequently reported by developers and cause the crash of WASM applications.
However, these bugs have not been well studied. To fill in the knowledge gap,
we present a systematic study to characterize and detect bugs in WASM runtimes.
We first harvest a dataset of 311 real-world bugs from hundreds of related
posts on GitHub. Based on the collected high-quality bug reports, we distill 31
bug categories of WASM runtimes and summarize their common fix strategies.
Furthermore, we develop a pattern-based bug detection framework to
automatically detect bugs in WASM runtimes. We apply the detection framework to
five popular WASM runtimes and successfully uncover 53 bugs that have never
been reported previously, among which 14 have been confirmed and 6 have been
fixed by runtime developers.


  This paper has the purpose to study the rate capability of the Resistive
Plate Chamber, RPC, starting from the basic physics of this detector. The
effect of different working parameters determining the rate capability is
analysed in detail, in order to optimize a new family of RPCs for applications
to heavy irradiation environments and in particular to the LHC phase 2. A
special emphasis is given to the improvement achievable by minimizing the
avalanche charge delivered in the gas. The paper shows experimental results of
Cosmic Ray tests, performed to study the avalanche features for different gas
gap sizes, with particular attention to the overall delivered charge. For this
purpose, the paper studies, in parallel to the prompt electronic signal, also
the ionic signal which gives the main contribution to the delivered charge.
Whenever possible the test results are interpreted on the base of the RPC
detector physics and are intended to extend and reinforce our physical
understanding of this detector.


  We consider the self organizing process of merging and regeneration of
vertices in complex networks and demonstrate that a scale-free degree
distribution emerges in a steady state of such a dynamics. The merging of
neighbor vertices in a network may be viewed as an optimization of efficiency
by minimizing redundancy. It is also a mechanism to shorten the distance and
thus decrease signaling times between vertices in a complex network. Thus the
merging process will in particular be relevant for networks where these issues
related to global signaling are of concern.


  The $\Lambda$CDM prediction of $S_8\equiv\sigma_8(\Omega_m/0.3)^{0.5}$ --
where $\sigma_8$ is the root mean square of matter fluctuations on a 8
$h^{-1}$Mpc scale -- once calibrated on Planck CMB data is $2-3\sigma$ lower
than its direct estimate by a number of weak lensing surveys. In this paper, we
explore the possibility that the '$S_8$-tension' is due to a non-thermal hot
dark matter (HDM) fractional contribution to the universe energy density
leading to a power suppression at small-scales in the matter power spectrum.
Any HDM models can be characterized by its effective mass $ m_{sp}^{\rm eff}$
and its contribution to the relativistic degrees of freedom at CMB decoupling
$\Delta N_{\rm eff}$. Taking the specific example of a sterile particle
produced from the decay of the inflaton during a matter dominated era, we find
that from Planck only the tension can be reduced below $2\sigma$, but Planck
does not favor a non-zero ${m_{sp}^{\rm eff},\Delta N_{\rm eff}}$. In
combination with a measurement of $S_8$ from KIDS1000+BOSS+2dfLenS, the
$S_8$-tension would hint at the existence of a particle of mass $ m_{sp}^{\rm
eff} \simeq 0.67_{-0.48}^{+0.26}$ ${\rm eV}$ with a contribution to $\Delta
N_{\rm eff} \simeq0.06\pm0.05$. However, Pantheon and BOSS BAO/$f\sigma_8$ data
restricts the particle mass to $m_{sp}^{\rm eff} \simeq 0.48_{-0.36}^{+0.17}$
and contribution to $\Delta N_{\rm eff} \simeq 0.046_{-0.031}^{+0.004}$. We
discuss implications of our results for other canonical non-thermal HDM models
-- the Dodelson-Widrow model and a thermal sterile particle with a different
temperature in the hidden sector. We report competitive results on such hidden
sector temperature which might have interesting implications for particle
physics model building, in particular connecting the $S_8$-tension to the
longstanding short baseline oscillation anomaly.


  The emergence of correlated insulating phases in magic-angle twisted bilayer
graphene exhibits strong sample dependence. Here, we derive an Anderson theorem
governing the robustness against disorder of the Kramers intervalley coherent
(K-IVC) state, a prime candidate for describing the correlated insulators at
even fillings of the moir\'e flat bands. We find that the K-IVC gap is robust
against local perturbations, which are odd under $\mathcal{PT}$, where
$\mathcal{P}$ and $\mathcal{T}$ denote particle-hole conjugation and time
reversal, respectively. In contrast, $\mathcal{PT}$-even perturbations will in
general induce subgap states and reduce or even eliminate the gap. We use this
result to classify the stability of the K-IVC state against various
experimentally relevant perturbations. The existence of an Anderson theorem
singles out the K-IVC state from other possible insulating ground states.


  Recent work by Serfaty and Serra give a formula for the velocity of the free
boundary of the obstacle problem at regular points [Serfaty-Serra 2018], and
much older work by King, Lacey, and Vazquez gives an example of a singular free
boundary point (in the Hele-Shaw flow) that remains stationary for a positive
amount of time [King-Lacey-Vazquez 1995]. The authors show how singular free
boundaries in the obstacle problem in some settings move immediately in
response to varying data. Three applications of this result are given, and in
particular, the authors show a uniqueness result: For sufficiently smooth
elliptic divergence form operators on domains in $\mathrm{I \! R}^n$ and for
the Laplace-Beltrami operator on a smooth manifold, the boundaries of distinct
mean value sets (of the type found in [Blank-Hao 2015] and
[Benson-Blank-LeCrone 2018]) which are centered at the same point do not
intersect.


  We investigate equilibration and generalized thermalization of the quantum
Harmonic chain under local quantum quench. The quench action we consider is
connecting two disjoint harmonic chains of different sizes and the system jumps
between two integrable settings. We verify the validity of the Generalized
Gibbs Ensemble description for this infinite dimensional Hilbert space system
and also identify equilibration between the subsystems as in classical systems.
Using Bogoliubov transformations, we show that the eigenstates of the system
prior to the quench evolve towards the Gibbs Generalized Ensemble description.
Eigenstates that are more delocalized (in the sense of inverse participation
ratio) prior to the quench, tend to equilibrate more rapidly. Further, through
the phase space properties of a Generalized Gibbs Ensemble and the strength of
stimulated emission, we identify the necessary criterion on the initial states
for such relaxation at late times and also find out the states which would
potentially not be described by the Gibbs Generalized Ensemble description.


  High-performance two-qubit gates have been reported with superconducting
qubits coupled via a single-transmon coupler (STC). Most of them are
implemented for qubits with a small detuning since reducing residual $ZZ$
coupling for highly detuned qubits by an STC is challenging. In terms of the
frequency crowding and crosstalk, however, highly detuned qubits are desirable.
Here, we numerically demonstrate a high-performance parametric gate for highly
detuned fixed-frequency qubits using a recently proposed tunable coupler called
a double-transmon coupler (DTC). Applying an ac flux pulse, we can perform a
maximally entangling universal gate ($\sqrt{\rm iSWAP}$) with an average
fidelity over 99.99$\%$ and a short gate time of about 24 ns. This speed is
comparable to resonance-based gates for slightly detuned tunable qubits.
Moreover, using a dc flux pulse alternatively, we can achieve another kind of
entangling gate called a CZ gate with an average fidelity over 99.99$\%$ and a
gate time of about 18 ns. Given the frexibility and feasible settings, we can
expect that the DTC will contribute to realizing a high-performance quantum
computer in the near future.


  This paper considers improving wireless communication and computation
efficiency in federated learning (FL) via model quantization. In the proposed
bitwidth FL scheme, edge devices train and transmit quantized versions of their
local FL model parameters to a coordinating server, which aggregates them into
a quantized global model and synchronizes the devices. The goal is to jointly
determine the bitwidths employed for local FL model quantization and the set of
devices participating in FL training at each iteration. We pose this as an
optimization problem that aims to minimize the training loss of quantized FL
under a per-iteration device sampling budget and delay requirement. However,
the formulated problem is difficult to solve without (i) a concrete
understanding of how quantization impacts global ML performance and (ii) the
ability of the server to construct estimates of this process efficiently. To
address the first challenge, we analytically characterize how limited wireless
resources and induced quantization errors affect the performance of the
proposed FL method. Our results quantify how the improvement of FL training
loss between two consecutive iterations depends on the device selection and
quantization scheme as well as on several parameters inherent to the model
being learned. Then, we show that the FL training process can be described as a
Markov decision process and propose a model-based reinforcement learning (RL)
method to optimize action selection over iterations. Compared to model-free RL,
this model-based RL approach leverages the derived mathematical
characterization of the FL training process to discover an effective device
selection and quantization scheme without imposing additional device
communication overhead. Simulation results show that the proposed FL algorithm
can reduce the convergence time.


  Four-fermion interaction models are considered to be prototype models for
dynamical symmetry breaking.The present review deals with recent developments
in the studies of dynamical symmetry breaking in the four-fermion interaction
models and their extension in curved spacetime. Starting with the Minkowski
spacetime in dimension $D$ ($2 \leq D \leq 4$) the effective potential in the
leading order of $1/N$-expansion is calculated and the phase structure of the
theory is investigated. In curved spacetime curvature-induced phase transitions
are discussed in the circumstances where fermion masses are dynamically
generated. Subsequently a possibility of curvature- and temperature-induced or
curvature- and topology-induced phase transitions is discussed. It is also
argued that the chiral symmetry broken by a weak magnetic field may be restored
due to the presence of gravitational field. Finally some applications of
four-fermion models in quantum gravity are briefly described.


  Correlated scattering occurs naturally in frequency-selective fading channels
and its impact on the performance needs to be understood. In particular, we
answer the question whether the uncorrelated scattering model leads to an
optimistic or pessimistic estimation of the actual average capacity. In the
paper, we use majorization for functions to show that the average rate with
perfectly informed receiver is largest for uncorrelated scattering if the
transmitter is uninformed. If the transmitter knows the channel statistics, it
can exploit this knowledge. We show that for small SNR, the behavior is
opposite, uncorrelated scattering leads to a lower bound on the average
capacity. Finally, we provide an example of the theoretical results for an
attenuated Ornstein-Uhlenbeck process including illustrations.


  The tetrad-based equations for vacuum gravity published by Estabrook,
Robinson, and Wahlquist are simplified and adapted for numerical relativity. We
show that the evolution equations as partial differential equations for the
Ricci rotation coefficients constitute a rather simple first-order
symmetrizable hyperbolic system, not only for the Nester gauge condition on the
acceleration and angular velocity of the tetrad frames considered by Estabrook
et al., but also for the Lorentz gauge condition of van Putten and Eardley, and
for a fixed gauge condition. We introduce a lapse function and a shift vector
to allow general coordinate evolution relative to the timelike congruence
defined by the tetrad vector field.


  We study reinforcement learning for partially observed Markov decision
processes (POMDPs) with infinite observation and state spaces, which remains
less investigated theoretically. To this end, we make the first attempt at
bridging partial observability and function approximation for a class of POMDPs
with a linear structure. In detail, we propose a reinforcement learning
algorithm (Optimistic Exploration via Adversarial Integral Equation or
OP-TENET) that attains an $\epsilon$-optimal policy within $O(1/\epsilon^2)$
episodes. In particular, the sample complexity scales polynomially in the
intrinsic dimension of the linear structure and is independent of the size of
the observation and state spaces.
  The sample efficiency of OP-TENET is enabled by a sequence of ingredients:
(i) a Bellman operator with finite memory, which represents the value function
in a recursive manner, (ii) the identification and estimation of such an
operator via an adversarial integral equation, which features a smoothed
discriminator tailored to the linear structure, and (iii) the exploration of
the observation and state spaces via optimism, which is based on quantifying
the uncertainty in the adversarial integral equation.


  One of the main mechanisms that could drive mass outflows in AGNs is
radiation pressure due to spectral lines. Although straightforward to
understand, the actual magnitude of the radiation force is challenging to
compute because the force depends on the physical conditions in the gas, and
the strength, spectral energy distribution (SED), and geometry of the radiation
field. We present results from our photoionization and radiation transfer
calculations of the force multiplier, $M(\xi,t)$, using the same radiation
field to compute the gas photoionization and thermal balance. We assume low gas
density ($n = 10^4~\rm{cm^{-3}}$) and column density ($N \leq 10^{17}~\rm{
cm^{-2}}$), a Boltzmann distribution for the level populations, and the Sobolev
approximation. Here, we describe results for two SEDs corresponding to an
unobscured and obscured AGN in NGC 5548. Our main results are the following: 1)
although $M(\xi,t)$ starts to decrease with $\xi$ for $\xi \gtrsim 1$ as shown
by others, this decrease in our calculations is relatively gradual and could be
non-monotonic as $M(\xi,t)$ can increase by a factor of few for $\xi \approx
10-1000$; 2) at these same $\xi$ for which the multiplier is higher than in
previous calculations, the gas is thermally unstable by the isobaric criterion;
3) non-LTE effects reduce $M(t,\xi)$ by over two orders of magnitude for $\xi
\gtrsim 100$. The dynamical consequence of result (1) is that line driving can
be important for $\xi$ as high as $1000$ when the LTE approximation holds,
while result (2) provides a natural cloud formation mechanism that may account
for the existence of narrow line regions. Result (3) suggests that line driving
may not be important for $\xi\gtrsim100$ in tenuous plasma.


  We prove existence and uniqueness of the mild solution of an infinite
dimensional, operator valued, backward stochastic Riccati equation. We exploit
the regularizing properties of the semigroup generated by the unbounded
operator involved in the equation. Then the results will be applied to
characterize the value function and optimal feedback law for a infinite
dimensional, linear quadratic control problem with stochastic coefficients.


  The variable warm absorber seen with {\em ASCA} in the X-ray spectrum of
MCG$-$6-30-15 shows complex time behaviour in which the optical depth of OVIII
anticorrelates with the flux whereas that of OVII is unchanging. The
explanation in terms of a two zone absorber has since been challenged by {\em
BeppoSAX} observations. These present a more complicated behaviour for the
OVIII edge. We demonstrate here that the presence of a third, intermediate,
zone can explain all the observations. In practice, warm absorbers are likely
to be extended, multi-zone regions of which only part causes directly
observable absorption edges at any given time.


  Let $X$ be a hypersurface, of degree $d$, in an $n$--dimensional projective
space. The Hessian map is a rational map from $X$ to the projective space of
symmetric matrices that sends a point $p\in X$ to the Hessian matrix of the
defining polynomial of $X$ evaluated at $p$. The Hessian correspondence is the
map that sends a hypersurface to its Hessian variety; i.e. the Zariski closure
of its image via the Hessian map. In this paper, we study this correspondence
for hypersurfaces with Waring rank at most $n+1$ and for hypersurfaces of
degree $3$ and $4$. We prove that, for hypersurfaces with Waring rank $k\leq
n+1$, the map is birational onto its image for $d$ even, and it is generically
finite of degree $2^{k-1}$ for $d$ odd. We prove that, for degree $3$ and
$n=1$, the map is two to one, and that, for degree $3$ and $n\geq 2$, and for
degree $4$, the Hessian correspondence is birational. In this study, we
introduce the $k$--gradients varieties and analyze their main properties. We
provide effective algorithms for recovering a hypersurface from its Hessian
variety, for degree $3$ and $n\geq 1$, and for degree $4$ and $n$ even.


  The upper bound of polymer drag reduction is identified as a unique
transitional state between laminar and turbulent flow corresponding to the
onset of the nonlinear breakdown of flow instabilities.


  The importance of the color octet component in P-wave charmonia is discussed
in the context of exclusive decay processes. It is vital to include this
contribution already at the level of the two-body decay channels. The \pi \pi
and proton-antiproton channels are used as examples.


  We revisit the problem of characterizing band topology in dynamically-stable
quadratic bosonic Hamiltonians that do not conserve particle number. We show
this problem can be rigorously addressed by a smooth and local adiabatic
mapping procedure to a particle number conserving Hamiltonian. In contrast to a
generic fermionic pairing Hamiltonian, such a mapping can always be constructed
for bosons. Our approach shows that particle non-conserving bosonic
Hamiltonians can be classified using known approaches for fermionic models. It
also provides a simple means for identifying and calculating appropriate
topological invariants. We also explicitly study dynamically stable but
non-positive definite Hamiltonians (as arise frequently in driven photonic
systems). We show that in this case, each band gap is characterized by two
distinct invariants.


  We present BlockGAN, an image generative model that learns object-aware 3D
scene representations directly from unlabelled 2D images. Current work on scene
representation learning either ignores scene background or treats the whole
scene as one object. Meanwhile, work that considers scene compositionality
treats scene objects only as image patches or 2D layers with alpha maps.
Inspired by the computer graphics pipeline, we design BlockGAN to learn to
first generate 3D features of background and foreground objects, then combine
them into 3D features for the wholes cene, and finally render them into
realistic images. This allows BlockGAN to reason over occlusion and interaction
between objects' appearance, such as shadow and lighting, and provides control
over each object's 3D pose and identity, while maintaining image realism.
BlockGAN is trained end-to-end, using only unlabelled single images, without
the need for 3D geometry, pose labels, object masks, or multiple views of the
same scene. Our experiments show that using explicit 3D features to represent
objects allows BlockGAN to learn disentangled representations both in terms of
objects (foreground and background) and their properties (pose and identity).


  A commuting triple of operators $(A,B,P)$ on a Hilbert space $\mathcal{H}$ is
called a tetrablock contraction if the closure of the set $$ E =
\{\underline{x}=(x_1,x_2,x_3)\in \mathbb{C}^3:
  1-x_1z-x_2w+x_3zw \neq 0 \text{whenever}|z| \leq 1\text{and}|w| \leq 1 \} $$
is a spectral set. In this paper, we have constructed a functional model and
produced a complete unitary invariant for a pure tetrablock contraction. In
this construction, the fundamental operators, which are the unique solutions of
the operator equations $$ A-B^*P = D_PX_1D_P \text{and} B-A^*P=D_PX_2D_P,
\text{where $X_1,X_2 \in \mathcal{B}(\mathcal{D}_P)$}, $$ play a big role. As a
corollary to the functional model, we show that every pure tetrablock isometry
$(A,B,P)$ on a Hilbert space $\mathcal{H}$ is unitarily equivalent to
$(M_{G_1^*+G_2z}, M_{G_2^*+G_1z},M_z)$ on
$H^2_{\mathcal{D}_{P^*}}(\mathbb{D})$, where $G_1$ and $G_2$ are the
fundamental operators of $(A^*,B^*,P^*)$. We prove a Beurling-Lax-Halmos type
theorem for a triple of operators $(M_{F_1^*+F_2z},M_{F_2^*+F_1z},M_z)$, where
$\mathcal{E}$ is a Hilbert space and $F_1,F_2 \in \mathcal{B}(\mathcal{E})$. We
deal with a natural example of tetrablock contraction on functions space to
find out its fundamental operators.


  A significant challenge in object detection is accurate identification of an
object's position in image space, whereas one algorithm with one set of
parameters is usually not enough, and the fusion of multiple algorithms and/or
parameters can lead to more robust results. Herein, a new computational
intelligence fusion approach based on the dynamic analysis of agreement among
object detection outputs is proposed. Furthermore, we propose an online versus
just in training image augmentation strategy. Experiments comparing the results
both with and without fusion are presented. We demonstrate that the augmented
and fused combination results are the best, with respect to higher accuracy
rates and reduction of outlier influences. The approach is demonstrated in the
context of cone, pedestrian and box detection for Advanced Driver Assistance
Systems (ADAS) applications.


  We propose here a setup to generate and evaluate the entanglement between two
mechanical resonators in a cavity optomechanical setting. As in previous
proposals, our scheme includes two driving pumps allowing for the generation of
two-mode mechanical squeezing. In addition, we include here four additional
probing tones, which allow for the separate evaluation of the collective
mechanical quadratures required to estimate the Duan quantity, thus allowing us
to infer whether the mechanical resonators are entangled.


  The paper constructs the analytic index for an elliptic pseudodifferential
family of $L^{m}_{\rho,\de}$-operators invariant under the proper action of a
continuous family groupoid on a $G$-compact, $C^{\infty,0}$ $G$-space.


  Predictions of the elastic proton-proton cross sections at energies of LHC
are calculate on the base of the high energy dynamical model. The growth of
$ds/dt$ at fixed transfer momenta are shown. The form of eikonal of elastic
hadron scattering at super high energies is discussed.


  Smart grid data can be evaluated for anomaly detection in numerous fields,
including cyber-security, fault detection, electricity theft, etc. The strange
anomalous behaviors may have been caused by various reasons, including peculiar
consumption patterns of the consumers, malfunctioning grid infrastructures,
outages, external cyber-attacks, or energy fraud. Recently, anomaly detection
of the smart grid has attracted a large amount of interest from researchers,
and it is widely applied in a number of high-impact fields. One of the most
significant challenges within the smart grid is the implementation of efficient
anomaly detection for multiple forms of aberrant behaviors. In this paper, we
provide a scoping review of research from the recent advancements in anomaly
detection in the context of smart grids. We categorize our study from numerous
aspects for deep understanding and inspection of the research challenges so
far. Finally, after analyzing the gap in the reviewed paper, the direction for
future research on anomaly detection in smart-grid systems has been provided
briefly.


  Given a sample $Y$ from an unknown manifold $X$ embedded in Euclidean space,
it is possible to recover the homology groups of $X$ by building a
Vietoris--Rips or \v{C}ech simplicial complex on top of the vertex set $Y$.
However, these simplicial complexes need not inherit the metric structure of
the manifold, in particular when $Y$ is infinite. Indeed, a simplicial complex
is not even metrizable if it is not locally finite. We instead consider metric
thickenings, called the \emph{Vietoris--Rips} and \emph{\v{C}ech thickenings},
which are equipped with the 1-Wasserstein metric in place of the simplicial
complex topology. We show that for Euclidean subsets $X$ with positive reach,
the thickenings satisfy metric analogues of Hausmann's theorem and the nerve
lemma (the metric Vietoris--Rips and \v{C}ech thickenings of $X$ are homotopy
equivalent to $X$ for scale parameters less than the reach). To our knowledge
this is the first version of Hausmann's theorem for Euclidean submanifolds (as
opposed to Riemannian manifolds), and our result also extends to non-manifold
shapes (as not all sets of positive reach are manifolds). In contrast to
Hausmann's original proof, our homotopy equivalence is a deformation
retraction, is realized by canonical maps in both directions, and furthermore
can be proven to be a homotopy equivalence via simple linear homotopies from
the map compositions to the corresponding identity maps.


  We prove that any Legendrian knot in $(S^3,\xi_{std})$ bounds an exact
Lagrangian surface in $\mathbb{R}^4\setminus B^4$ after a sufficient number of
stabilizations. In order to show this, we construct a family combinatorial
moves on knot projections with some additional data that correspond to
Lagrangian cobordisms between knots.


  Chirality and current-driven dynamics of topologically nontrivial
360$^{\circ}$ domain walls (360DWs) in magnetic heterostructures (MHs) are
systematically investigated. For MHs with normal substrates, the static 360DWs
are N\'{e}el-type with no chirality. While for those with heavy-metal
substrates, the interfacial Dzyaloshinskii-Moriya interaction (iDMI) therein
makes 360DWs prefer specific chirality. Under in-plane driving charge currents,
as the direct result of "full-circle" topology a certain 360DW does not undergo
the "Walker breakdown"-type process like a well-studied 180$^{\circ}$ domain
wall as the current density increases. Alternatively, it keeps a fixed
propagating mode (either steady-flow or precessional-flow, depending on the
effective damping constant of the MH) until it collapses or changes to other
types of solition when the current density becomes too high. Similarly, the
field-like spin-orbit torque (SOT) has no effects on the dynamics of 360DWs,
while the anti-damping SOT has. For both modes, modifications to the mobility
of 360DWs by iDMI and anti-damping SOT are provided.


  The size of the average fluctuations of net baryon number and electric charge
in a finite volume of hadronic matter differs widely between the confined and
deconfined phases. These differences may be exploited as indicators of the
formation of a quark-gluon plasma in relativistic heavy-ion collisions, because
fluctuations created in the initial state survive until freeze-out due to the
rapid expansion of the hot fireball.


  We introduce an operation of a kind of product which associates with a
partial Steiner triple system another partial Steiner triple system, the
starting one being a quotient of the result. We discuss relations of our
product to some other product-like constructions and prove some
preservation/non-preservation theorems. In particular, we show series of
anti-Pasch Steiner triple systems which are obtained that way.


  To improve the performance of Neural Machine Translation~(NMT) for
low-resource languages~(LRL), one effective strategy is to leverage parallel
data from a related high-resource language~(HRL). However, multilingual data
has been found more beneficial for NMT models that translate from the LRL to a
target language than the ones that translate into the LRLs. In this paper, we
aim to improve the effectiveness of multilingual transfer for NMT models that
translate \emph{into} the LRL, by designing a better decoder word embedding.
Extending upon a general-purpose multilingual encoding method Soft Decoupled
Encoding~\citep{SDE}, we propose DecSDE, an efficient character n-gram based
embedding specifically designed for the NMT decoder. Our experiments show that
DecSDE leads to consistent gains of up to 1.8 BLEU on translation from English
to four different languages.


  The observable outputs of many complex dynamical systems consist in time
series exhibiting autocorrelation functions of great diversity of behaviors,
including long-range power-law autocorrelation functions, as a signature of
interactions operating at many temporal or spatial scales. Often, algorithms
able to generate correlated noises reproducing the properties of real time
series produce \textsl{Gaussian} outputs, while real, experimentally observed
time series are often non-Gaussian, and may follow distributions with a
diversity of behaviors concerning the support, the symmetry or the tail
properties. Here, we study how the correlation of two Gaussian variables
changes when they are transformed to follow a different destination
distribution. Specifically, we consider bounded and unbounded distributions,
symmetric and non-symmetric distributions, and distributions with different
tail properties, from decays faster than exponential to heavy tail cases
including power-laws, and we find how these properties affect the correlation
of the final variables. We extend these results to Gaussian time series which
are transformed to have a different marginal distribution, and show how the
autocorrelation function of the final non-Gaussian time series depends on the
Gaussian correlations and on the final marginal distribution. As an application
of our results, we propose how to generalize standard algorithms producing
Gaussian power-law correlated time series in order to create synthetic time
series with arbitrary distribution and controlled power-law correlations.
Finally, we show a practical example of this algorithm by generating time
series mimicking the marginal distribution and the power-law tail of the
autocorrelation function of a real time series: the absolute returns of stock
prices.


  We propose a novel deep layer cascade (LC) method to improve the accuracy and
speed of semantic segmentation. Unlike the conventional model cascade (MC) that
is composed of multiple independent models, LC treats a single deep model as a
cascade of several sub-models. Earlier sub-models are trained to handle easy
and confident regions, and they progressively feed-forward harder regions to
the next sub-model for processing. Convolutions are only calculated on these
regions to reduce computations. The proposed method possesses several
advantages. First, LC classifies most of the easy regions in the shallow stage
and makes deeper stage focuses on a few hard regions. Such an adaptive and
'difficulty-aware' learning improves segmentation performance. Second, LC
accelerates both training and testing of deep network thanks to early decisions
in the shallow stage. Third, in comparison to MC, LC is an end-to-end trainable
framework, allowing joint learning of all sub-models. We evaluate our method on
PASCAL VOC and Cityscapes datasets, achieving state-of-the-art performance and
fast speed.


  We describe impulsive gravitational pp-waves entirely in the distributional
picture. Applying Colombeau's nonlinear framework of generalized functions we
handle the formally ill-defined products of distributions which enter the
geodesic as well as the geodesic deviation equation. Using a universal
regularization procedure we explicitly derive regularization independent
distributional limits. In the special case of impulsive plane waves we compare
our results with the particle motion derived from the continuous form of the
metric.


  In this manuscript we study channel-aware decision fusion (DF) in a wireless
sensor network (WSN) where: (i) the sensors transmit their decisions
simultaneously for spectral efficiency purposes and the DF center (DFC) is
equipped with multiple antennas; (ii) each sensor-DFC channel is described via
a Rician model. As opposed to the existing literature, in order to account for
stringent energy constraints in the WSN, only statistical channel information
is assumed for the non-line-of sight (scattered) fading terms. For such a
scenario, sub-optimal fusion rules are developed in order to deal with the
exponential complexity of the likelihood ratio test (LRT) and impractical
(complete) system knowledge. Furthermore, the considered model is extended to
the case of (partially unknown) jamming-originated interference. Then the
obtained fusion rules are modified with the use of composite hypothesis testing
framework and generalized LRT. Coincidence and statistical equivalence among
them are also investigated under some relevant simplified scenarios. Numerical
results compare the proposed rules and highlight their jammingsuppression
capability.


  We construct and analyze the Jacobi process - in mathematical biology
referred to as Wright-Fisher diffusion - using a Dirichlet form. The
corresponding Dirichlet space takes the form of a Sobolev space with different
weights for the function itself and its derivative. Depending on the parameters
we characterize the boundary behavior of the functions in the Dirichlet space,
show density results, derive Sobolev embeddings and verify functional
inequalities of Hardy type. Since the generator is a hypergeometric
differential operator, many of the proofs can be carried out by explicit
calculations involving hypergeometric functions. We deduce corresponding
properties for the associated semigroup and Markov process and show that the
latter is up to minor technical modifications a solution to the Jacobi SDE.


  We introduce the first conceptual design of a free-space thin-sheet isolator
with unlimited aperture and the possibility of a broadband omnidirectional
rejection of the backward propagating light. The proposed design involves a
multilayered resonant cavity incorporating subwavelength magnetic layers,
dichroic nanolayers, and an optional metallic nanolayer. The cavity resonance
enhances the Faraday rotation produced by the subwavelength magnetic layers,
while providing nearly total absorption of the backward-propagating light by
the dichroic nanolayers. The latter is a necessary and the most challenging
condition for a thin-sheet isolator with unlimited aperture to function. The
(optional) metallic nanolayer provides rejection of the obliquely incident
light, which otherwise would be partially transmitted in either direction. Our
numerical simulations and quasi-optical measurements at millimeter-wave
frequencies illustrate how the key elements of the layered-sheet isolator work.
Our approach can be scaled down to long- and mid-infrared wavelengths.


  Batch normalization (BN) has become a critical component across diverse deep
neural networks. The network with BN is invariant to positively linear re-scale
transformation, which makes there exist infinite functionally equivalent
networks with different scales of weights. However, optimizing these equivalent
networks with the first-order method such as stochastic gradient descent will
obtain a series of iterates converging to different local optima owing to their
different gradients across training. To obviate this, we propose a quotient
manifold \emph{PSI manifold}, in which all the equivalent weights of the
network with BN are regarded as the same element. Next, we construct gradient
descent and stochastic gradient descent on the proposed PSI manifold to train
the network with BN. The two algorithms guarantee that every group of
equivalent weights (caused by positively re-scaling) converge to the equivalent
optima. Besides that, we give convergence rates of the proposed algorithms on
the PSI manifold. The results show that our methods accelerate training
compared with the algorithms on the Euclidean weight space. Finally, empirical
results verify that our algorithms consistently improve the existing methods in
both convergence rate and generalization ability under various experimental
settings.


  We propose an end-to-end variational generative model for scene layout
synthesis conditioned on scene graphs. Unlike unconditional scene layout
generation, we use scene graphs as an abstract but general representation to
guide the synthesis of diverse scene layouts that satisfy relationships
included in the scene graph. This gives rise to more flexible control over the
synthesis process, allowing various forms of inputs such as scene layouts
extracted from sentences or inferred from a single color image. Using our
conditional layout synthesizer, we can generate various layouts that share the
same structure of the input example. In addition to this conditional generation
design, we also integrate a differentiable rendering module that enables layout
refinement using only 2D projections of the scene. Given a depth and a
semantics map, the differentiable rendering module enables optimizing over the
synthesized layout to fit the given input in an analysis-by-synthesis fashion.
Experiments suggest that our model achieves higher accuracy and diversity in
conditional scene synthesis and allows exemplar-based scene generation from
various input forms.


  Autonomous agents powered by large language models (LLMs) show significant
potential for achieving high autonomy in various scenarios such as software
development. Recent research has shown that LLM agents can leverage past
experiences to reduce errors and enhance efficiency. However, the static
experience paradigm, reliant on a fixed collection of past experiences acquired
heuristically, lacks iterative refinement and thus hampers agents'
adaptability. In this paper, we introduce the Iterative Experience Refinement
framework, enabling LLM agents to refine experiences iteratively during task
execution. We propose two fundamental patterns: the successive pattern,
refining based on nearest experiences within a task batch, and the cumulative
pattern, acquiring experiences across all previous task batches. Augmented with
our heuristic experience elimination, the method prioritizes high-quality and
frequently-used experiences, effectively managing the experience space and
enhancing efficiency. Extensive experiments show that while the successive
pattern may yield superior results, the cumulative pattern provides more stable
performance. Moreover, experience elimination facilitates achieving better
performance using just 11.54% of a high-quality subset.


  Information encoding in the nervous system is supported through the precise
spike-timings of neurons; however, an understanding of the underlying processes
by which such representations are formed in the first place remains unclear.
Here we examine how networks of spiking neurons can learn to encode for input
patterns using a fully temporal coding scheme. To this end, we introduce a
learning rule for spiking networks containing hidden neurons which optimizes
the likelihood of generating desired output spiking patterns. We show the
proposed learning rule allows for a large number of accurate input-output spike
pattern mappings to be learnt, which outperforms other existing learning rules
for spiking neural networks: both in the number of mappings that can be learnt
as well as the complexity of spike train encodings that can be utilised. The
learning rule is successful even in the presence of input noise, is
demonstrated to solve the linearly non-separable XOR computation and
generalizes well on an example dataset. We further present a biologically
plausible implementation of backpropagated learning in multilayer spiking
networks, and discuss the neural mechanisms that might underlie its function.
Our approach contributes both to a systematic understanding of how pattern
encodings might take place in the nervous system, and a learning rule that
displays strong technical capability.


  This paper proposes a novel remote user authentication scheme using smart
cards which allows both the authentication server (AS) and the user to verify
authenticity of each other. Our scheme is efficient enough to resist the known
attacks that could be launched against remote user authentication process.


  Planetary-scale collisions are common during the last stages of formation of
solid planets, including the Solar system terrestrial planets. The problem of
growing planets has been divided into studying the gravitational interaction of
embryos relevant in million year timescales and treated with N-body codes and
the collision between objects with a timescale of hours to days and treated
with smoothed-particle hydrodynamics. These are now being coupled with simple
parameterized models. We set out to investigate if machine learning techniques
can offer a better solution by predicting the outcome of collisions which can
then be used in N-body simulations. We considered three different supervised
machine learning approaches: gradient boosting regression trees, nested models,
and gaussian processes. We found that the former produced the best results, and
that it was slightly surpassed by ensembling different algorithms. With
gaussian processes, we found the regions of parameter space that may yield the
most information to machine learning algorithms. Thus, we suggest SPH
calculations to focus first on mass ratios above 0.5.


  Active Queue Management (AQM) for mitigating Internet congestion has been
addressed via various feedback control syntheses, among which P, PI, and PID
regulators are quite popular and often associated to a Smith predictor. Here,
to better account for the many uncertainties, like the round trip time or the
number of TCP sessions, the above controllers are replaced by an intelligent
proportional controller associated to Model-Free Control (MFC) and by
forecasting techniques derived from a new approach to time series. Several
computer simulations via a well accepted linear modeling, where the delay is
assumed to be constant, are presented and discussed.


  Although large language models (LLMs) exhibit remarkable capacity to leverage
in-context demonstrations, it is still unclear to what extent they can learn
new concepts or facts from ground-truth labels. To address this question, we
examine the capacity of instruction-tuned LLMs to follow in-context concept
guidelines for sentence labeling tasks. We design guidelines that present
different types of factual and counterfactual concept definitions, which are
used as prompts for zero-shot sentence classification tasks. Our results show
that although concept definitions consistently help in task performance, only
the larger models (with 70B parameters or more) have limited ability to work
under counterfactual contexts. Importantly, only proprietary models such as
GPT-3.5 and GPT-4 can recognize nonsensical guidelines, which we hypothesize is
due to more sophisticated alignment methods. Finally, we find that
Falcon-180B-chat is outperformed by Llama-2-70B-chat is most cases, which
indicates that careful fine-tuning is more effective than increasing model
scale. Altogether, our simple evaluation method reveals significant gaps in
concept understanding between the most capable open-source language models and
the leading proprietary APIs.


  The behavior of stock market returns over a period of 1-60 days has been
investigated for S&P 500 and Nasdaq within the framework of nonextensive
Tsallis statistics. Even for such long terms, the distributions of the returns
are non-Gaussian. They have fat tails indicating that the stock returns do not
follow a random walk model. In this work, a good fit to a Tsallis q-Gaussian
distribution is obtained for the distributions of all the returns using the
method of Maximum Likelihood Estimate. For all the regions of data considered,
the values of the scaling parameter q, estimated from one day returns, lie in
the range 1.4 to 1.65. The estimated inverse mean square deviations (beta) show
a power law behavior in time with exponent values between -0.91 and -1.1
indicating normal to mildly subdiffusive behavior. Quite often, the dynamics of
market return distributions is modelled by a Fokker-Plank (FP) equation either
with a linear drift and a nonlinear diffusion term or with just a nonlinear
diffusion term. Both of these cases support a q-Gaussian distribution as a
solution. The distributions obtained from current estimated parameters are
compared with the solutions of the FP equations. For negligible drift term, the
inverse mean square deviations (betaFP) from the FP model follow a power law
with exponent values between -1.25 and -1.48 indicating superdiffusion. When
the drift term is non-negligible, the corresponding betaFP do not follow a
power law and become stationary after certain characteristic times that depend
on the values of the drift parameter and q. Neither of these behaviors is
supported by the results of the empirical fit.


  Swarm verification and parallel randomised depth-first search are very
effective parallel techniques to hunt bugs in large state spaces. In case bugs
are absent, however, scalability of the parallelisation is completely lost. In
recent work, we proposed a mechanism to inform the workers which parts of the
state space to explore. This mechanism is compatible with any action-based
formalism, where a state space can be represented by a labelled transition
system. With this extension, each worker can be strictly bounded to explore
only a small fraction of the state space at a time. In this paper, we present
the HIVE tool together with two search algorithms which were added to the
LTSmin tool suite to both perform a preprocessing step, and execute a bounded
worker search. The new tool is used to coordinate informed swarm explorations,
and the two new LTSmin algorithms are employed for preprocessing a model and
performing the individual searches.


  We demonstrate temporal group delays in coherently-coupled high-Q
multi-cavity photonic crystals, in an all-optical analogue to
electromagnetically induced transparency. We report deterministic control of
the group delay up to 4x the single cavity lifetime in our CMOS-fabricated
room-temperature chip. Supported by three-dimensional numerical simulations and
theoretical analyses, our multi-pump beam approach enables control of the
multi-cavity resonances and inter-cavity phase, in both single and double
transparency peaks. The standing-wave wavelength-scale photon localization
allows direct scalability for chip-scale optical pulse trapping and
coupled-cavity QED.


  In this report, we talked about a new quantitative strategy for choosing the
optimal(s) stock(s) to trade. The basic notions are generally very known by the
financial community. The key here is to understand 1) the standard score
applied to a sample and 2) the correlation factor applied to different time
series in real life. These notions are the core of our research. We are going
to begin with the introduction section. In this part, we talked about variance,
covariance, correlation factor, daily returns in stock trading and the
Shapiro-Wilk test to test the normality of a time serie. Next to that, I talked
about the core of my method (what do you do if you want to pick the optimal(s)
stock(s) to trade). At the end of this report, I talked about a new idea if you
want to analyze more than one stock at the time. All my work goes with a
primary reflexion : forecasting a stock direction is a random walk and nobody
can be 100 % sure where a stock is going. All we can do, is to pretend to have
a technic with a win/loss ratio greater than 51 %.


  Exploiting the inner-shot and inter-shot dependencies is essential for
key-shot based video summarization. Current approaches mainly devote to
modeling the video as a frame sequence by recurrent neural networks. However,
one potential limitation of the sequence models is that they focus on capturing
local neighborhood dependencies while the high-order dependencies in long
distance are not fully exploited. In general, the frames in each shot record a
certain activity and vary smoothly over time, but the multi-hop relationships
occur frequently among shots. In this case, both the local and global
dependencies are important for understanding the video content. Motivated by
this point, we propose a Reconstructive Sequence-Graph Network (RSGN) to encode
the frames and shots as sequence and graph hierarchically, where the
frame-level dependencies are encoded by Long Short-Term Memory (LSTM), and the
shot-level dependencies are captured by the Graph Convolutional Network (GCN).
Then, the videos are summarized by exploiting both the local and global
dependencies among shots. Besides, a reconstructor is developed to reward the
summary generator, so that the generator can be optimized in an unsupervised
manner, which can avert the lack of annotated data in video summarization.
Furthermore, under the guidance of reconstruction loss, the predicted summary
can better preserve the main video content and shot-level dependencies.
Practically, the experimental results on three popular datasets i.e., SumMe,
TVsum and VTW) have demonstrated the superiority of our proposed approach to
the summarization task.


  We examine the possibility that the lepton mixing matrix may exhibit a strong
form of mu-tau universality, in which corresponding elements of the mu- and
tau-neutrino flavour eigenstates have equal moduli, and find that it is
consistent with present data on neutrino oscillations. We point out that in the
Dirac case, this is equivalent to symmetry under mu-tau reflection, ie. the
combined operation of mu-tau flavour exchange in the MNS matrix with a CP
transformation on the whole leptonic sector. We give the most general form for
such a mixing matrix, examine the lepton mass matrices under such a symmetry,
and explore the observable manifestations of the symmetry in neutrino
oscillations.


  We design a circuit structure with linear depth to implement an $n$-qubit
Toffoli gate. The proposed construction uses a quadratic-size circuit consists
of elementary 2-qubit controlled-rotation gates around the x axis and uses no
ancilla qubit. Circuit depth remains linear in quantum technologies with
finite-distance interactions between qubits. The suggested construction is
related to the long-standing construction by Barenco et al. (Phys. Rev. A, 52:
3457-3467, 1995, arXiv:quant-ph/9503016), which uses a quadratic-size,
quadratic-depth quantum circuit for an $n$-qubit Toffoli gate.


  We demonstrate the high accuracy of the density splitting method to compute
the gravitational potential and field in the plane of razor-thin, axially
symmetric discs, as preliminarily outlined in Pierens & Hure (2004).
  Because residual kernels in Poisson integrals are not C^infinity-class
functions, we use a dynamical space mapping in order to increase the efficiency
of advanced quadrature schemes. In terms of accuracy, results are better by
orders of magnitude than for the classical FFT-methods.


  The effects of nonperturbatively generated ``quark sea'' have been
investigated to determine the flavor structure of the octet baryons. The chiral
constituent quark model ($\chi$CQM), which is known to provide a satisfactory
explanation of the proton spin and related issues in the nonperturbative
regime, is able to explain the qualitative generation of the requisite amount
of quark sea. The importance of quark sea has been studied at different values
of the Bjorken scaling variable $x$ by including it phenomenologically in the
sea quark distribution functions. The results for the quark sea asymmetries
like $\bar d(x)-\bar u(x)$, $\bar d(x)/\bar u(x)$ and Gottfried integral for
the octet baryons strengthen the significance of quark sea at lower values of
$x$.


  Particle flows in spatial networks are susceptible to congestion. In this
paper, we analyze the phase transitions of these networks to a state of
congested transport and the influence of both topology and spatial dynamics on
its emergence. We systematically show that the value of the critical loading
rate at which congestion emerges is affected by the addition of spatial
dynamics, changing the nature of this transition from a continuous to a
discontinuous one. Our numerical results are confirmed by introducing an
analytical solvable framework. As a case of study, we explore the implications
of our findings in the San Francisco road network where we can locate the roads
that originate the congested phase. These roads are the spatially constrained,
and not necessarily those with high betweenness as predicted by models without
spatial dynamics.


  In the regime of change-point detection, a nonparametric framework based on
scan statistics utilizing graphs representing similarities among observations
is gaining attention due to its flexibility and good performances for
high-dimensional and non-Euclidean data sequences, which are ubiquitous in this
big data era. However, this graph-based framework encounters problems when
there are repeated observations in the sequence, which often happens for
discrete data, such as network data. In this work, we extend the graph-based
framework to solve this problem by averaging or taking union of all possible
optimal graphs resulted from repeated observations. We consider both the single
change-point alternative and the changed-interval alternative, and derive
analytic formulas to control the type I error for the new methods, making them
fast applicable to large datasets. The extended methods are illustrated on an
application in detecting changes in a sequence of dynamic networks over time.
All proposed methods are implemented in an R package gSeg available on CRAN.


  We present preliminary results of a non-perturbative study of the
scale-dependent renormalization constants of a complete basis of Delta F=2
parity-odd four-fermion operators that enter the computation of hadronic
B-parameters within the Standard Model (SM) and beyond. We consider
non-perturbatively O(a) improved Wilson fermions and our gauge configurations
contain two flavors of massless sea quarks. The mixing pattern of these
operators is the same as for a regularization that preserves chiral symmetry,
in particular there is a "physical" mixing between some of the operators. The
renormalization group running matrix is computed in the continuum limit for a
family of Schrodinger Functional (SF) schemes through finite volume recursive
techniques. We compute non-perturbatively the relation between the
renormalization group invariant operators and their counterparts renormalized
in the SF at a low energy scale, together with the non-perturbative matching
matrix between the lattice regularized theory and the various SF schemes.


  Many real networks have been found to have a rich degree of symmetry, which
is a very important structural property of complex network, yet has been rarely
studied so far. And where does symmetry comes from has not been explained. To
explore the mechanism underlying symmetry of the networks, we studied
statistics of certain local symmetric motifs, such as symmetric bicliques and
generalized symmetric bicliques, which contribute to local symmetry of
networks. We found that symmetry of complex networks is a consequence of
similar linkage pattern, which means that nodes with similar degree tend to
share similar linkage targets. A improved version of BA model integrating
similar linkage pattern successfully reproduces the symmetry of real networks,
indicating that similar linkage pattern is the underlying ingredient that
responsible for the emergence of the symmetry in complex networks.


  Causal inference methods are widely applied in the fields of medicine,
policy, and economics. Central to these applications is the estimation of
treatment effects to make decisions. Current methods make binary yes-or-no
decisions based on the treatment effect of a single outcome dimension. These
methods are unable to capture continuous space treatment policies with a
measure of intensity. They also lack the capacity to consider the complexity of
treatment such as matching candidate treatments with the subject. We propose to
formulate the effectiveness of treatment as a parametrizable model, expanding
to a multitude of treatment intensities and complexities through the continuous
policy treatment function, and the likelihood of matching. Our proposal to
decompose treatment effect functions into effectiveness factors presents a
framework to model a rich space of actions using causal inference. We utilize
deep learning to optimize the desired holistic metric space instead of
predicting single-dimensional treatment counterfactual. This approach employs a
population-wide effectiveness measure and significantly improves the overall
effectiveness of the model. The performance of our algorithms is. demonstrated
with experiments. When using generic continuous space treatments and matching
architecture, we observe a 41% improvement upon prior art with
cost-effectiveness and 68% improvement upon a similar method in the average
treatment effect. The algorithms capture subtle variations in treatment space,
structures the efficient optimizations techniques, and opens up the arena for
many applications.


  Motion of an object near a soft wall with intervening fluid is a canonical
problem in elastohydrodynamics, finding presence in subjects spanning biology
to tribology. Particularly, motion of a sphere towards a soft substrate with
intervening fluid is often encountered in the context of scanning probe
microscopy. While there have been fundamental theoretical studies on this
setup, they have focussed on specific applications and thus enforced suitable
simplifications. Here we present a versatile semi-analytical framework for
studying the elastohydrodynamics of axisymmetric loading of a rigid sphere near
a soft elastic substrate coated on a rigid platform mediated by an aqueous
electrolytic solution. Three loading modes are considered - approach, recession
and oscillatory. The framework incorporates - large oscillation frequency and
amplitude, two-way coupling between pressure and substrate deformation, and
presence of DLVO forces. From computations using the framework, we gain
insights on the effects of DLVO forces, substrate thickness, substrate material
compressibility (quantified by Poisson's ratio) and high oscillation frequency
for different physical setups encountered in SPM and the likes. We list some
key observations. A substrate that is thicker and more compressible allows for
larger deformation, i.e. is effectively softer. Presence of DLVO forces lead to
magnification in force of upto two orders of magnitude and in substrate
deformation of upto an order of magnitude for oscillatory loading at low
frequencies and approach/recession loading at low speed. For oscillatory
loading at high frequencies, DLVO forces do not appreciably affect the force
and deflection behaviour of the system. Having demonstrated the versatility and
utility of our framework, we expect it to evolve into a diverse and useful tool
for solving problems of soft-lubrication.


  The dispersion of the star-formation main sequence (SFMS) reflects the
diversity of star formation histories and variation in star formation rates
(SFRs) in star-forming galaxies (SFGs) with similar stellar masses ($M^\ast$).
We examine the dispersion of local SFMS using a complete sample of Sloan
Digital Sky Survey galaxies at 0.01$<z<$0.03 with $\log(M^\ast/M_\odot)>$8.8.
The SFRs are estimated from H$\alpha$ in combination with 22$\mu m$ observation
from WISE. The catalog of bulge+disk decomposition from Simard et al. (2011) is
available for the sample galaxies. We measure the dispersion of specific SFR
(SSFR) as a function of $M^*$. We confirm that the dispersion increases with
$M^*$ from 0.37$\pm0.01$dex at $\log(M^\ast/M_\odot)<$9.6 to 0.51$\pm0.02$dex
at $\log(M^\ast/M_\odot)>$10.2. Despite star formation is mostly associated
with disks, the dispersion of disk SSFR still increases with $M^*$. We conclude
that the presence of bulges/bars is likely responsible for the large dispersion
of SSFR in massive SFGs while low-mass SFGs are mostly disk-dominated and thus
with small dispersion. Our results suggest that star formation on galactic
scales is dramatically affected by central dense structures through both
enhancing and/or quenching processes; while lower-mass SFGs tend to have less
bursty star formation histories. However, the dispersion of SSFR becomes
significantly smaller and remains constant when only disk-dominated SFGs are
counted. This finding implies that the impact of stochastic stellar feedback on
star formation is likely to follow the same pattern in all disk galaxies,
showing no correlation with halo potential.


  Pebble games are a powerful tool in the study of finite model theory,
constraint satisfaction and database theory. Monads and comonads are basic
notions of category theory which are widely used in semantics of computation
and in modern functional programming. We show that existential k-pebble games
have a natural comonadic formulation. Winning strategies for Duplicator in the
k-pebble game for structures A and B are equivalent to morphisms from A to B in
the coKleisli category for this comonad. This leads on to comonadic
characterisations of a number of central concepts in Finite Model Theory: -
Isomorphism in the co-Kleisli category characterises elementary equivalence in
the k-variable logic with counting quantifiers. - Symmetric games corresponding
to equivalence in full k-variable logic are also characterized. - The treewidth
of a structure A is characterised in terms of its coalgebra number: the least k
for which there is a coalgebra structure on A for the k-pebbling comonad. -
Co-Kleisli morphisms are used to characterize strong consistency, and to give
an account of a Cai-F\"urer-Immerman construction. - The k-pebbling comonad is
also used to give semantics to a novel modal operator. These results lay the
basis for some new and promising connections between two areas within logic in
computer science which have largely been disjoint: (1) finite and algorithmic
model theory, and (2) semantics and categorical structures of computation.


  Recently, many breakthroughs are made in the field of Video Object Detection
(VOD), but the performance is still limited due to the imaging limitations of
RGB sensors in adverse illumination conditions. To alleviate this issue, this
work introduces a new computer vision task called RGB-thermal (RGBT) VOD by
introducing the thermal modality that is insensitive to adverse illumination
conditions. To promote the research and development of RGBT VOD, we design a
novel Erasure-based Interaction Network (EINet) and establish a comprehensive
benchmark dataset (VT-VOD50) for this task. Traditional VOD methods often
leverage temporal information by using many auxiliary frames, and thus have
large computational burden. Considering that thermal images exhibit less noise
than RGB ones, we develop a negative activation function that is used to erase
the noise of RGB features with the help of thermal image features. Furthermore,
with the benefits from thermal images, we rely only on a small temporal window
to model the spatio-temporal information to greatly improve efficiency while
maintaining detection accuracy.
  VT-VOD50 dataset consists of 50 pairs of challenging RGBT video sequences
with complex backgrounds, various objects and different illuminations, which
are collected in real traffic scenarios. Extensive experiments on VT-VOD50
dataset demonstrate the effectiveness and efficiency of our proposed method
against existing mainstream VOD methods. The code of EINet and the dataset will
be released to the public for free academic usage.


  We propose Flock; a simple and scalable protocol that enables live migration
of Virtual Machines (VMs) across heterogeneous edge and conventional cloud
platforms to improve the responsiveness of cloud services. Flock is designed
with properties that are suitable for the use cases of the Internet of Things
(IoT). We describe the properties of regularized latency measurements that
Flock can use for asynchronous and autonomous migration decisions. Such
decisions allow communicating VMs to follow a flocking-like behavior that
consists of three simple rules: separation, alignment, and cohesion. Using game
theory, we derive analytical bounds on Flock's Price of Anarchy (PoA), and
prove that flocking VMs converge to a Nash Equilibrium while settling in the
best possible cloud platforms. We verify the effectiveness of Flock through
simulations and discuss how its generic objective can simply be tweaked to
achieve other objectives, such as cloud load balancing and energy consumption
minimization.


  A computation of the inflationary observables n_{s} and r is made for
`logamediate' inflation where the cosmological scale factor expands as $a=\exp
(A(\ln t)^{\lambda})$, and is compared to their predicted values in the
intermediate inflationary theory, where $a=\exp (Bt^{f})$. Both versions prove
to be consistent with observational measurements of the cosmic background
radiation. It is shown that the dynamics of a single inflaton field can be
mimicked by a system of several fields in an analogous manner to that created
by the joint evolution of the fields in assisted power-law inflation.


  The paper reviews several topics associated with the homogenization of
transport processed in historical masonry structures. Since these often
experience an irregular or random pattern, we open the subject by summarizing
essential steps in the formulation of a suitable computational model in the
form of Statistically Equivalent Periodic Unit Cell (SEPUC). Accepting SEPUC as
a reliable representative volume element is supported by application of the
Fast Fourier Transform to both the SEPUC and large binary sample of real
masonry in search for effective thermal conductivities limited here to a steady
state heat conduction problem. Fully coupled non-stationary heat and moisture
transport is addressed next in the framework of two-scale first-order
homogenization approach with emphases on the application of boundary and
initial conditions on the meso-scale.


  We investigate the galaxy overdensity around proto-cluster scale quasar pairs
at high (z>3) and low (z~1) redshift based on the unprecedentedly wide and deep
optical survey of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP).
Using the first-year survey data covering effectively ~121 deg^2 with the
5sigma depth of i~26.4 and the SDSS DR12Q catalog, we find two luminous pairs
at z~3.3 and 3.6 which reside in >5sigma overdense regions of g-dropout
galaxies at i<25. The projected separations of the two pairs are R_perp=1.75
and 1.04 proper Mpc, and their velocity offsets are Delta V=692 and 1448 km
s^{-1}, respectively. This result is in clear contrast to the average z~4
quasar environments as discussed in Uchiyama et al. (2017) and implies that the
quasar activities of the pair members are triggered via major mergers in
proto-clusters, unlike the vast majority of isolated quasars in general fields
that may turn on via non-merger events such as bar and disk instabilities. At
z~1, we find 37 pairs with R_perp<2 pMpc and Delta V<2300 km s^{-1} in the
current HSC-Wide coverage, including four from Hennawi et al. (2006). The
distribution of the peak overdensity significance within two arcminutes around
the pairs has a long tail toward high density (>4sigma) regions. Thanks to the
large sample size, we find a statistical evidence that this excess is unique to
the pair environments when compared to single quasar and randomly selected
galaxy environments at the same redshift range. Moreover, there are nine
small-scale (R_perp<1 pMpc) pairs, two of which are found to reside in cluster
fields. Our results demonstrate that <2 pMpc-scale quasar pairs at both
redshift range tend to occur in massive haloes, although perhaps not the most
massive ones, and that they are useful to search for rare density peaks.


  This paper describes a study to deduce fundamental parameters and magnetic
field characteristics for all magnetic Ap/Bp stars with in a 100 parsec
heliocentric radius volume. This study has allowed for the first time the
determination of an effectively unbiased magnetic field distribution of a
sample of intermediate mass stars. From published catalogues and other
literature sources, we have identified 57 bone fide magnetic A and B stars in
the volume, corresponding to 1.7% of all intermediate mass stars within 100
parsec of the Sun. The masses of Ap stars range from 1.5 to 6 solar masses,
with the peak of the mass incidence distribution between 3.3 and 3.6 solar
masses. Observations of 30 of the Ap/Bp stars were obtained using the MuSiCoS
spectropolarimeter at the Telescope Bernard Lyot at Pic du Midi Observatory.
These observations will be used to refine periods, and determine magnetic field
strength and geometry.


  We describe algorithms for drawing media, systems of states, tokens and
actions that have state transition graphs in the form of partial cubes. Our
algorithms are based on two principles: embedding the state transition graph in
a low-dimensional integer lattice and projecting the lattice onto the plane, or
drawing the medium as a planar graph with centrally symmetric faces.


  We present a fully automated framework based on the FeynRules and MadGraph5
aMC@NLO programs that allows for accurate simulations of supersymmetric QCD
processes at the LHC. Starting directly from a model Lagrangian that features
squark and gluino interactions, event generation is achieved at the
next-to-leading order in QCD, matching short-distance events to parton showers
and including the subsequent decay of the produced supersymmetric particles. As
an application, we study the impact of higher-order corrections in gluino
pair-production in a simplified benchmark scenario inspired by current gluino
LHC searches.


  We report the results from the temporal and spectral analysis of an
XMM-Newton observation of Nova Centauri 1986 (V842 Cen). We detect a period at
3.51$\pm$0.4 h in the EPIC data and at 4.0$\pm$0.8 h in the OM data. The X-ray
spectrum is consistent with the emission from an absorbed thin thermal plasma
with a temperature distribution given by an isobaric cooling flow. The maximum
temperature of the cooling flow model is $kT_{max}=43_{-12}^{+23}$ keV. Such a
high temperature can be reached in a shocked region and, given the periodicity
detected, most likely arises in a magnetically-channelled accretion flow
characteristic of intermediate polars. The pulsed fraction of the 3.51 h
modulation decreases with energy as observed in the X-ray light curves of
magnetic CVs, possibly due either to occultation of the accretion column by the
white dwarf body or phase-dependent to absorption. We do not find the 57 s
white dwarf spin period, with a pulse amplitude of 4 mmag, reported by Woudt et
al. (2009) either in the Optical Monitor (OM) data, which are sensitive to
pulse amplitudes $\gtrsim$ 0.03 magnitudes, or the EPIC data, sensitive to
pulse fractions $p \gtrsim$ 14 $\pm$2%.


  We examine the Xeon Phi, which is based on Intel's Many Integrated Cores
architecture, for its suitability to run the FDK algorithm--the most commonly
used algorithm to perform the 3D image reconstruction in cone-beam computed
tomography. We study the challenges of efficiently parallelizing the
application and means to enable sensible data sharing between threads despite
the lack of a shared last level cache. Apart from parallelization, SIMD
vectorization is critical for good performance on the Xeon Phi; we perform
various micro-benchmarks to investigate the platform's new set of vector
instructions and put a special emphasis on the newly introduced vector gather
capability. We refine a previous performance model for the application and
adapt it for the Xeon Phi to validate the performance of our optimized
hand-written assembly implementation, as well as the performance of several
different auto-vectorization approaches.


  It is shown that high-energy features of the cosmic ray spectrum, in
particular the kink around 4 PeV and the corresponding change in spectral
index, may be explained from interactions between highly energetic cosmic
protons and relic Big Bang antineutrinos, if the latter have a rest mass of
about 0.4 eV/$c^2$. This explanation is supported by experimental data from
extensive air-shower experiments, and in particular by the observation (Fly's
Eye) of a second kink around 300 PeV, and by the abrupt change in the chemical
composition of the cosmic ray spectrum that occurs at that energy. Both facts
follow naturally from our theory, which predicts additional verifiable features
of the cosmic ray spectrum in the few-PeV region, e.g. an abrupt decrease in
the $p/\alpha$ ratio.


  In this paper, using the standard truncated Painleve analysis, the
Schwartzian equation of (2+1)-dimensional generalised variable coefficient
shallow water wave (SWW)equation is obtained. With the help of lax pairs,
nonlocal symmetries of the SWW equation are constructed which be localized by a
complicated calculation process. Furthermore, using the Lie point symmetries of
the closed system and Schwartzian equation, some exact interaction solutions
are obtained, such as soliton-cnoidal wave solutions. Corresponding 2D and 3D
figures are placed on illustrate dynamic behavior of the generalised variable
coefficient SWW equation.


  We conjecture that a non-flat $D$-real-dimensional compact Calabi-Yau
manifold, such as a quintic hypersurface with D=6, or a K3 manifold with D=4,
has locally length minimizing closed geodesics, and that the number of these
with length less than L grows asymptotically as L^{D}. We also outline the
physical arguments behind this conjecture, which involve the claim that all
states in a nonlinear sigma model can be identified as "momentum" and "winding"
states in the large volume limit.


  Two neutrino double beta decay of $^{76}$Ge to excited states of $^{76}$Se
has been studied using data from Phase I of the GERDA experiment. An array
composed of up to 14 germanium detectors including detectors that have been
isotopically enriched in $^{76}$Ge was deployed in liquid argon. The analysis
of various possible transitions to excited final states is based on coincidence
events between pairs of detectors where a de-excitation $\gamma$ ray is
detected in one detector and the two electrons in the other.
  No signal has been observed and an event counting profile likelihood analysis
has been used to determine Frequentist 90\,\% C.L. bounds for three
transitions: ${0^+_{\rm g.s.}-2^+_1}$: $T^{2\nu}_{1/2}>$1.6$\cdot10^{23}$ yr,
${0^+_{\rm g.s.}-0^+_1}$: $T^{2\nu}_{1/2}>$3.7$\cdot10^{23}$ yr and ${0^+_{\rm
g.s.}-2^+_2}$: $T^{2\nu}_{1/2}>$2.3$\cdot10^{23}$ yr. These bounds are more
than two orders of magnitude larger than those reported previously. Bayesian
90\,\% credibility bounds were extracted and used to exclude several models for
the ${0^+_{\rm g.s.}-0^+_1}$ transition.


  This paper handles the problem of converting real pictures into traditional
Chinese ink-wash paintings, i.e., Chinese ink-wash painting style transfer.
Though this problem could be realized by a wide range of image-to-image
translation models, a notable issue with all these methods is that the original
image content details could be easily erased or corrupted due to transfer of
ink-wash style elements. To solve or ameliorate this issue, we propose to
incorporate saliency detection into the unpaired image-to-image translation
framework to regularize content information of the generated paintings. The
saliency map is utilized for content regularization from two aspects, both
explicitly and implicitly: (\romannumeral1) we propose saliency IOU (SIOU) loss
to explicitly regularize saliency consistency before and after stylization;
(\romannumeral2) we propose saliency adaptive normalization (SANorm) which
implicitly enhances content integrity of the generated paintings by injecting
saliency information to the generator network to guide painting generation.
Besides, we also propose saliency attended discriminator network which
harnesses saliency mask to focus generative adversarial attention onto salient
image regions, it contributes to producing finer ink-wash stylization effect
for salient objects of images. Qualitative and quantitative experiments
consistently demonstrate superiority of our model over related advanced methods
for Chinese ink-wash painting style transfer.


  Superconductor Electronics (SCE) is a fast and power-efficient technology
with great potential for overcoming conventional CMOS electronics' scaling
limits. Nevertheless, the primary challenge confronting SCE today pertains to
its integration level, which lags several orders of magnitude behind CMOS
circuits. In this study, we have innovated and simulated a novel logic family
grounded in the principles of phase shifts occurring in 0 and $\pi$ Josephson
junctions. The fast phase logic (FPL) eliminates the need for large inductor
loops and shunt resistances by combining the half-flux and phase logic.
Therefore, the Josephson junction (JJ) area only limits the integration
density. The cells designed with this paradigm are fast, and the clock-to-Q
delay is about 4ps while maintaining over 50% parameter margins. This logic is
power efficient and can increase the integration by at least 100$\times$ in the
SCE chips.


  The non-linear evolution of the energy density of the radiation is shown to
induce spectral distortions of the cosmic microwave background both at
recombination and during the reionization era. This distortion has the same
spectral signature as the one produced by the re-scattering of photons by
non-relativistic hot electrons, the thermal Sunyaev-Zeldovich effect, whose
amplitude is quantified by a Compton y parameter. A diffuse y-sky is then
expected to emerge from mode couplings in the non-linear evolution of the
cosmological perturbations and to superimpose to the point source contributions
of galaxy clusters. The equations describing the evolution of the y field and a
hierarchy governing its angular multipoles are derived from the second order
Boltzmann equation. These equations are then integrated numerically to obtain
the first predicted power spectrum of the diffuse y-sky. It is found to be a
remarkable tracer of the reionization history of the Universe.


  Kinetic rate constants fundamentally characterize the dynamics of the
chemical interaction of macromolecules, and thus their study sets a major
direction in experimental biochemistry. The estimation of such constants is
often challenging, partly due to the noisiness of data, and partly due to the
theoretical framework. Novel and qualitatively reasonable methods are presented
for the estimation of the rate constants of complex formation and dissociation
in Kinetic Capillary Electrophoresis (KCE). This also serves the broader effort
to resolve the inverse problem of KCE, where these estimates pose as initial
starting points in the non-linear optimization space, along with the asymmetric
Gaussian parameters describing the injected plug concentration profiles, which
is also hereby estimated. This rate constant estimation method is also compared
to an earlier one.


  We propose a novel mechanism for the origin of non-Gaussian tails in the
probability distribution functions (PDFs) of local variables in nonlinear,
diffusive, dynamical systems including passive scalars advected by chaotic
velocity fields. Intermittent fluctuations on appropriate time scales in the
amplitude of the (chaotic) noise can lead to exponential tails. We provide
numerical evidence for such behavior in deterministic, discrete-time passive
scalar models. Different possibilities for PDFs are also outlined.


  To constrain cosmological parameters, one often makes a joint analysis with
different combinations of observational data sets. In this paper we take the
figure of merit (FoM) for Dark Energy Task Force fiducial model (CPL model) to
estimate goodness of different combinations of data sets, which include 11
widely-used observational data sets (Type Ia Supernovae, Observational Hubble
Parameter, Baryon Acoustic Oscillation, Cosmic Microwave Background, X-ray
Cluster Baryon Mass Fraction, and Gamma-Ray Bursts). We analyze different
combinations and make a comparison for two types of combination based on two
types of basic combinations, which are often adopted in the literatures. We
find two sets of combinations, which have strong ability to constrain the dark
energy parameters, one has the largest FoM, the other contains less
observational data with a relative large FoM and a simple fitting procedure.


  We conduct X-ray spectral fits on 184 likely counterparts to Fermi-LAT 3FGL
unassociated sources. Characterization and classification of these sources
allows for more complete population studies of the high-energy sky. Most of
these X-ray spectra are well fit by an absorbed power law model, as expected
for a population dominated by blazars and pulsars. A small subset of 7 X-ray
sources have spectra unlike the power law expected from a blazar or pulsar and
may be linked to coincident stars or background emission. We develop a
multiwavelength machine learning classifier to categorize unassociated sources
into pulsars and blazars using gamma- and X-ray observations. Training a random
forest procedure with known pulsars and blazars, we achieve a cross-validated
classification accuracy of 98.6%. Applying the random forest routine to the
unassociated sources returned 126 likely blazar candidates (defined as $
P_{bzr} > 90 \% $) and 5 likely pulsar candidates ($ P_{bzr} < 10 \% $). Our
new X-ray spectral analysis does not drastically alter the random forest
classifications of these sources compared to previous works, but it builds a
more robust classification scheme and highlights the importance of X-ray
spectral fitting. Our procedure can be further expanded with UV, visual, or
radio spectral parameters or by measuring flux variability.


  We report on mid-infrared imaging observations of the anomalous X-ray pulsars
(AXPs) 1E 1048.1-5937, 1RXS J170849-400910, and XTE J1810-197. The observations
were carried out at 4.5 and 8.0 microns with the Infrared Array Camera and at
24 microns with the Multiband Imaging Photometer on the Spitzer Space
Telescope. No mid-infrared counterparts were detected. As infrared emission
from AXPs may be related to their X-ray emission either via the magnetosphere
or via a dust disk, we compare the derived upper limits on the infrared/X-ray
flux ratios of the AXPs to the same ratio for 4U 0142+61, an AXP previously
detected in the mid-infrared range. The comparison indicates that our three
non-detections are consistent with their relatively low X-ray fluxes. For XTE
J1810-197, our upper limits set a constraint on its rising radio/millimeter
energy spectrum, suggesting a spectral break between 1.5$\times
10^{11}$--6$\times10^{13}$ Hz.


  There is a huge interest in developing super-repellent surfaces for
anti-fouling and heat transfer applications. To characterize the wetting
properties of such surfaces, the most common approach is to place a
millimetric-sized droplet and measure its contact angles. The adhesion and
friction forces can then be indirectly inferred from the Furmidge's relation.
While easy to implement, contact angle measurements are semi-quantitative and
cannot resolve wetting variations on a surface. Here, we attach a
micrometric-sized droplet to an Atomic Force Microscope cantilever to directly
measure adhesion and friction forces with nanonewton force resolutions. We
spatially map the micron-scale wetting properties of superhydrophobic surfaces
and observe the time-resolved pinning-depinning dynamics as a droplet detaches
from or moves across the surface.