  The need for privacy-preserving analytics is higher than ever due to the
severity of privacy risks and to comply with new privacy regulations leading to
an amplified interest in privacy-preserving techniques that try to balance
between privacy and utility. In this work, we present an efficient method for
Text Classification while preserving the privacy of the content using Fully
Homomorphic Encryption (FHE). Our system (named \textbf{Priv}ate \textbf{F}ast
\textbf{T}ext (PrivFT)) performs two tasks: 1) making inference of encrypted
user inputs using a plaintext model and 2) training an effective model using an
encrypted dataset. For inference, we train a supervised model and outline a
system for homomorphic inference on encrypted user inputs with zero loss to
prediction accuracy. In the second part, we show how to train a model using
fully encrypted data to generate an encrypted model. We provide a GPU
implementation of the Cheon-Kim-Kim-Song (CKKS) FHE scheme and compare it with
existing CPU implementations to achieve 1 to 2 orders of magnitude speedup at
various parameter settings. We implement PrivFT in GPUs to achieve a run time
per inference of less than 0.66 seconds. Training on a relatively large
encrypted dataset is more computationally intensive requiring 5.04 days.


  In this paper, we consider a two-user multiple access fading channel under
quality-of-service (QoS) constraints. We initially formulate the transmission
rates for both transmitters, where the transmitters have arbitrarily
distributed input signals. We assume that the receiver performs successive
decoding with a certain order. Then, we establish the effective capacity region
that provides the maximum allowable sustainable arrival rate region at the
transmitters' buffers under QoS guarantees. Assuming limited transmission power
budgets at the transmitters, we attain the power allocation policies that
maximize the effective capacity region. As for the decoding order at the
receiver, we characterize the optimal decoding order regions in the plane of
channel fading parameters for given power allocation policies. In order to
accomplish the aforementioned objectives, we make use of the relationship
between the minimum mean square error and the first derivative of the mutual
information with respect to the power allocation policies. Through numerical
results, we display the impact of input signal distributions on the effective
capacity region performance of this two-user multiple access fading channel.


  The paper describes a MAS (multi-agent system) simulation approach for
controlling PM10 (Particulate Matter) crisis peaks. A dispersion model is used
with an Artificial Neural Network (ANN) to predict the PM10 concentration
level. The dispersion and ANN models are integrated into a MAS system. PM10
source controllers are modelled as software agents. The MAS is composed of
agents that cooperate with each other for reducing their emissions and control
the air pollution peaks. Different control strategies are simulated and
compared using data from Annaba (North-East Algeria). The simulator helps to
compare and assess the efficiency of policies to control peaks in PM10.


  We give an explicit quadratic Grobner basis for generalized Chow rings of
supersolvable built lattices, with the help of the operadic structure on
geometric lattices introduced in a previous article. This shows that the
generalized Chow rings associated to minimal building sets of supersolvable
lattices are Koszul. As another consequence, we get that the cohomology
algebras of the components of the extended modular operad in genus 0 are
Koszul.


  We present a method to estimate two-dimensional, time-invariant oceanic flow
fields based on data from both ensemble forecasts and online measurements. Our
method produces a realistic estimate in a computationally efficient manner
suitable for use in marine robotics for path planning and related applications.
We use kernel methods and singular value decomposition to find a compact model
of the ensemble data that is represented as a linear combination of basis flow
fields and that preserves the spatial correlations present in the data. Online
measurements of ocean current, taken for example by marine robots, can then be
incorporated using recursive Bayesian estimation. We provide computational
analysis, performance comparisons with related methods, and demonstration with
real-world ensemble data to show the computational efficiency and validity of
our method. Possible applications in addition to path planning include active
perception for model improvement through deliberate choice of measurement
locations.


  In this paper, we proposed a novel two-stage optimization method for network
community partition, which is based on inherent network structure information.
The introduced optimization approach utilizes the new network centrality
measure of both links and vertices to construct the key affinity description of
the given network, where the direct similarities between graph nodes or nodal
features are not available to obtain the classical affinity matrix. Indeed,
such calculated network centrality information presents the essential structure
of network, hence, the proper measure for detecting network communities, which
also introduces a `confidence' criterion for referencing new labeled benchmark
nodes. For the resulted challenging combinatorial optimization problem of graph
clustering, the proposed optimization method iteratively employs an efficient
convex optimization algorithm which is developed based under a new variational
perspective of primal and dual. Experiments over both artificial and real-world
network datasets demonstrate that the proposed optimization strategy of
community detection significantly improves result accuracy and outperforms the
state-of-the-art algorithms in terms of accuracy and reliability.


  We prove that the genus of the Turaev surface of a link diagram is determined
by a graph whose vertices correspond to the boundary components of the maximal
alternating regions of the link diagram. Furthermore, we use these graphs to
classify link diagrams whose Turaev surface has genus one or two, and we prove
that similar classification theorems exist for all genera.


  Deep learning has significantly advanced wireless sensing technology by
leveraging substantial amounts of high-quality training data. However,
collecting wireless sensing data encounters diverse challenges, including
unavoidable data noise, limited data scale due to significant collection
overhead, and the necessity to reacquire data in new environments. Taking
inspiration from the achievements of AI-generated content, this paper
introduces a signal generation method that achieves data denoising,
augmentation, and synthesis by disentangling distinct attributes within the
signal, such as individual and environment. The approach encompasses two
pivotal modules: structured signal selection and signal disentanglement
generation. Structured signal selection establishes a minimal signal set with
the target attributes for subsequent attribute disentanglement. Signal
disentanglement generation disentangles the target attributes and reassembles
them to generate novel signals. Extensive experimental results demonstrate that
the proposed method can generate data that closely resembles real-world data on
two wireless sensing datasets, exhibiting state-of-the-art performance. Our
approach presents a robust framework for comprehending and manipulating
attribute-specific information in wireless sensing.


  We discuss the two moments of human cognition, namely, apprehension (A),
whereby a coherent perception emerges from the recruitment of neuronal groups,
and judgment(B),that entails the comparison of two apprehensions acquired at
different times, coded in a suitable language and retrieved by memory. (B)
entails self-consciousness, in so far as the agent who expresses the judgment
must be aware that the two apprehensions are submitted to his/her own scrutiny
and that it is his/her task to extract a mutual relation. Since (B) lasts
around 3 seconds, the semantic value of the pieces under comparison must be
decided within that time. This implies a fast search of the memory contents. As
a fact, exploring human subjects with sequences of simple words, we find
evidence of a limited time window , corresponding to the memory retrieval of a
linguistic item in order to match it with the next one in a text flow (be it
literary, or musical,or figurative). While apprehension is globally explained
as a Bayes inference, judgment tresults from an inverse Bayes inference. As a
consequence, two hermeneutics emerge (called respectively circle and coil). The
first one acts in a pre-assigned space of features. The second one provides the
discovery of novel features, thus unveiling previously unknown aspects and
hence representing the road to reality.


  Recently a novel approach to find approximate exchange-correlation
functionals in density-functional theory (DFT) was presented (U. Mordovina et.
al., JCTC 15, 5209 (2019)), which relies on approximations to the interacting
wave function using density-matrix embedding theory (DMET). This approximate
interacting wave function is constructed by using a projection determined by an
iterative procedure that makes parts of the reduced density matrix of an
auxiliary system the same as the approximate interacting density matrix. If
only the diagonal of both systems are connected this leads to an approximation
of the interacting-to-non-interacting mapping of the Kohn-Sham approach to DFT.
Yet other choices are possible and allow to connect DMET with other DFTs such
as kinetic-energy DFT or reduced density-matrix functional theory. In this work
we give a detailed review of the basics of the DMET procedure from a DFT
perspective and show how both approaches can be used to supplement each other.
We do so explicitly for the case of a one-dimensional lattice system, as this
is the simplest setting where we can apply DMET and the one that was originally
presented. Among others we highlight how the mappings of DFTs can be used to
identify uniquely defined auxiliary systems and auxiliary projections in DMET
and how to construct approximations for different DFTs using DMET inspired
projections. Such alternative approximation strategies become especially
important for DFTs that are based on non-linearly coupled observables such as
kinetic-energy DFT, where the Kohn-Sham fields are no longer simply obtainable
by functional differentiation of an energy expression, or for reduced
density-matrix functional theories, where a straightforward Kohn-Sham
construction is not feasible.


  We conduct nonequilibrium molecular dynamics simulations to measure the shear
stress, the average inherent structure energy, and the effective temperature
$T_{eff}$ of a sheared model glass as a function of bath temperature $T$ and
shear strain rate. For $T$ above the glass transition temperature $T_0$, the
rheology approaches a Newtonian limit and $T_{eff}$ approaches $T$ as the
strain rate approaches zero, while for $T<T_0$, the shear stress approaches a
yield stress and $T_{eff}$ approaches a limiting value near $T_0$. In the
shear-dominated regime at high $T$, high strain rate or at low $T$, we find
that the shear stress and the average inherent structure energy each collapse
onto a single curve as a function of $T_{eff}$. This indicates that $T_{eff}$
is controlling behavior in this regime.


  We introduce the concept of 3D Dirac (Weyl) superconductors (SC), which have
protected bulk four(two)-fold nodal points and surface Andreev arcs at zero
energy. We provide a sufficient criterion for realizing them in centrosymmetric
SCs with odd-parity pairing and mirror symmetry, e.g., the nodal phases of
Cu$_x$Bi$_2$Se$_3$. Pairs of Dirac nodes appear in a mirror-invariant plane
when the mirror winding number is nontrivial. Breaking mirror symmetry may gap
Dirac nodes producing a topological SC. Each Dirac node evolves to a nodal ring
when inversion-gauge symmetry is broken. A Dirac node may split into a pair of
Weyl nodes, only when time-reversal symmetry is broken.


  We study the pairing symmetry of Sr$_2$RuO$_4$ through the group-theoretical
approach. We emphasize the role of pairing interaction between the
quasi-one-dimensional(Q1D) $d_{xz/yz}$ and quasi-two-dimensional(Q2D) $d_{xy}$
orbitals. It is found that two degenerate inter-orbital
time-reversal-invariant(TRI) p-wave pairings, one is spin-singlet and the other
spin-triplet with out-of-plane $\bm{d}$-vector, could be the most promising
candidates. Several important physical quantities are presented, including the
near-nodal gap structure, the unchanged out-of-plane Knight shift, and no split
transition under strain, which are consistent with the experiments. In
addition, these p-wave pairings shed light on resolving the contradiction
between the time-reversal breaking and reduced in-plane Knight shift
measurements. As the system reaches the Van Hove singularity under applied
strain, the pairing symmetry would become a d-wave pairing mainly consisting of
inter-orbital components, which could be responsible for the strained 3$K$
phase.


  We study a Gibbs measure over Brownian motion with a pair potential which
depends only on the increments. Assuming a particular form of this pair
potential, we establish that in the infinite volume limit the Gibbs measure can
be viewed as Brownian motion moving in a dynamic random environment. Thereby we
are in a position to use the technique of Kipnis and Varadhan and to prove a
functional central limit theorem.


  The Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics (TIMED)
satellite has been making observations of the mesosphere and lower thermosphere
(MLT) region for two decades. The TIMED Doppler Interferometer (TIDI) measures
the neutral winds using four orthogonal telescopes. In this study, the line of
sight (LOS) winds from individual telescopes are compared to the measurements
from the Ionospheric Connection Explorer's (ICON's) Michelson Interferometer
for Global High-resolution Thermospheric Imaging (MIGHTI) instrument from
90-100 km altitude during 2020. With the MIGHTI vector winds projected onto the
LOS direction of each TIDI telescope, coincidences of the two datasets are
found. The four telescopes perform differently and the performance depends on
the satellite configuration and local solar zenith angle. Measurements from the
coldside telescopes, Telescope 1 (Tel1) and Telescope 2 (Tel2), are better
correlated with the MIGHTI winds in general with Tel2 having higher correlation
coefficients across all conditions. The performance of Tel1 is comparable to
that of Tel2 during backward flight while showing systematic errors larger than
the average wind speeds during forward flight. The warmside LOS winds from
Telescope 3 (Tel3) and Telescope 4 (Tel4) vary widely in magnitude, especially
on the nightside. Compared with MIGHTI winds, the Tel4 measurements have the
weakest correlation, while the Tel3 performance is comparable to that of the
coldside telescopes during the ascending phase but deteriorates during the
descending phase. Based on the TIDI/MIGHTI comparisons, figures of merit are
generated to quantify the quality of measurements from individual telescopes in
different configurations.


  A generic synchrotron external shock model is the widely preferred paradigm
used to interpret the broad-band afterglow data of gamma-ray bursts (GRBs),
including predicted observable signatures from a reverse shock which have been
confirmed by observations. Investigations of the nature of the reverse shock
emission can provide valuable insights into the intrinsic properties of the GRB
ejecta. Here we briefly review the standard and the extended models of the
reverse shock emission, discussing the connection between the theory and
observations, including the implications of the latest observational advances.


  In this work, we introduce a novel local pairwise descriptor and then develop
a simple, effective iterative method to solve the resulting quadratic
assignment through sparsity control for shape correspondence between two
approximate isometric surfaces. Our pairwise descriptor is based on the
stiffness and mass matrix of finite element approximation of the
Laplace-Beltrami differential operator, which is local in space, sparse to
represent, and extremely easy to compute while containing global information.
It allows us to deal with open surfaces, partial matching, and topological
perturbations robustly. To solve the resulting quadratic assignment problem
efficiently, the two key ideas of our iterative algorithm are: 1) select pairs
with good (approximate) correspondence as anchor points, 2) solve a regularized
quadratic assignment problem only in the neighborhood of selected anchor points
through sparsity control. These two ingredients can improve and increase the
number of anchor points quickly while reducing the computation cost in each
quadratic assignment iteration significantly. With enough high-quality anchor
points, one may use various pointwise global features with reference to these
anchor points to further improve the dense shape correspondence. We use various
experiments to show the efficiency, quality, and versatility of our method on
large data sets, patches, and point clouds (without global meshes).


  The aim of this paper is the reconstruction of a smooth surface from an
unorganized point cloud sampled by a closed surface, with the preservation of
geometric shapes, without any further information other than the point cloud.
Implicit neural representations (INRs) have recently emerged as a promising
approach to surface reconstruction. However, the reconstruction quality of
existing methods relies on ground truth implicit function values or surface
normal vectors. In this paper, we show that proper supervision of partial
differential equations and fundamental properties of differential vector fields
are sufficient to robustly reconstruct high-quality surfaces. We cast the
$p$-Poisson equation to learn a signed distance function (SDF) and the
reconstructed surface is implicitly represented by the zero-level set of the
SDF. For efficient training, we develop a variable splitting structure by
introducing a gradient of the SDF as an auxiliary variable and impose the
$p$-Poisson equation directly on the auxiliary variable as a hard constraint.
Based on the curl-free property of the gradient field, we impose a curl-free
constraint on the auxiliary variable, which leads to a more faithful
reconstruction. Experiments on standard benchmark datasets show that the
proposed INR provides a superior and robust reconstruction. The code is
available at \url{https://github.com/Yebbi/PINC}.


  The aim of this paper is to provide improvments to Pontryagin principles in
infinite-horizon discrete-time framework when the space of states and of space
of controls are infinite-dimensional. We use the method of reduction to finite
horizon and several functional-analytic lemmas to realize our aim.


  The Heisenberg uncertainty relation is known to be obtainable by a purely
mathematical argument. Based on that fact, here it is shown that the Heisenberg
uncertainty relation remains valid when Quantum Mechanics is re-formulated
within far wider frameworks of {\it scalars}, namely, within one or the other
of the infinitely many {\it reduced power algebras} which can replace the usual
real numbers $\mathbb{R}$, or complex numbers $\mathbb{C}$. A major advantage
of such a re-formulation is, among others, the disappearance of the well known
and hard to deal with problem of the so called "infinities in Physics". The use
of reduced power algebras also opens up a foundational question about the role,
and in fact, about the very meaning and existence, of fundamental constants in
Physics, such as Planck's constant $h$. A role, meaning, and existence which
may, or on the contrary, may not be so objective as to be independent of the
scalars used, be they the usual real numbers $\mathbb{R}$, complex numbers
$\mathbb{C}$, or scalars given by any of the infinitely many reduced power
algebras, algebras which can so easily be constructed and used.


  Two-particle azimuthal correlations have been shown to be a powerful probe
for extracting novel features of the interaction between hard scattered partons
and the medium produced in Au+Au collisions at RHIC. At intermediate $p_T$,
2-5GeV/c, jets have been shown to be significantly modified in both particle
composition and angular distribution compared to p+pcollisions. We present
recent PHENIX results from Au+Au collisions for a variety of $p_T$ and particle
combinations.


  We study the phase structure of QCD in the $T-\mu$ plane using a histogram
method and the reweighting technique by performing phase quenched simulations
of two-flavor QCD with RG-improved gauge action and O($a$) improved Wilson
quark action. Taking the effects of the complex phase of the quark determinant
using the cumulant expansion method, we calculate the probability distribution
function of plaquette and phase-quenched determinant as a function of $T$ and
$\mu$. We discuss the order of the QCD phase transition consulting the shape of
the probability distribution function.


  In the literature of Round-Robin scheduling scheme, each job is processed,
one after the another after giving a fix quantum. In case of First-come
first-served, each process is executed, if the previously arrived processed is
completed. Both these scheduling schemes are used in this paper as its special
cases. A Markov chain model is used to compare several scheduling schemes of
the class. An index measure is defined to compare the model based efficiency of
different scheduling schemes. One scheduling scheme which is the mixture of
FIFO and round robin is found efficient in terms of model based study. The
system simulation procedure is used to derive the conclusion of the content


  We present a model which provides a plausible explanation of the effect of
zero-resistance and zero-conductance states in two-dimensional electron systems
subjected to a magnetic field and irradiated with microwaves observed in a
number of experiments and of the effect main features. The model is based on
the concept of absolute negative conductivity associated with photon-assisted
scattering of electrons on impurities. It is shown that the main features of
the effect can be attributed to the interplay of different electron scattering
mechanisms.


  This work identifies geometric effects on dynamics due to nonadiabatic
couplings in Born Oppenheimer systems and provides a systematic method for
deriving corrections to mixed quantum-classical methods. Specifically, an exact
path integral formulation of the quantum nonadiabatic dynamics of Born
Oppenheimer systems is described. Stationary phase approximations to the
propagator for full quantum dynamics are derived. It is shown that quantum
corrections to mixed quantum classical methods can be obtained through
stationary phase approximations to the full quantum dynamics. A rigorous
description of the quantum corrections due to electronic nonadiabatic coupling
on the nuclear dynamics within the Ehrenfest framework is obtained. The fewest
switches surface hopping method is shown to be obtained as a quasiclassical
approximation to the dynamics and natural semiclassical extensions to include
classically forbidden nonadiabatic transitions are suggested.


  We study deformations of free boundary constant mean curvature (CMC)
hypersurfaces whose Jacobi operator is degenerate due to symmetries of the
ambient space. The value of the mean curvature and the ambient metric are
allowed to vary simultaneously, provided that the infinitesimal ambient
symmetries change smoothly. We discuss applications to free boundary CMC disks
and Delaunay annuli in the unit ball of a space form.


  We show that, consistently, for some regular cardinals theta<lambda, there
exists a Boolean algebra B such that B=lambda^+ and for every subalgebra B' of
B of size lambda^+ we have Depth(B')=theta.


  We provide the localization procedure for monoidal categories by a real
commuting family of braiders. For an element $w$ of the Weyl group,
$\mathscr{C}_w$ is a subcategory of modules over quiver Hecke algebra which
categorifies the quantum unipotent coordinate algebra $A_q[\mathfrak{n}(w)]$.
We construct the localization $\widetilde{\mathscr{C}_w}$ of $\mathscr{C}_w$ by
adding the inverses of simple modules which correspond to the frozen variables
in the quantum cluster algebra $A_q[\mathfrak{n}(w)]$. The localization
$\widetilde{\mathscr{C}_w}$ is left rigid and we expect that it is rigid.


  A 20-band sp3d5s* spin-orbit-coupled, semi-empirical, atomistic tight-binding
model is used with a semi-classical, ballistic, field-effect-transistor (FET)
model, to examine the ON-current variations to size variations of [110]
oriented PMOS nanowire devices. Infinitely long, uniform, rectangular nanowires
of side dimensions from 3nm to 12nm are examined and significantly different
behavior in width vs. height variations are identified and explained. Design
regions are identified, which show minor ON-current variations to significant
width variations that might occur due to lack of line width control. Regions
which show large ON-current variations to small height variations are also
identified. The considerations of the full band model here show that ON-current
doubling can be observed in the ON-state at the onset of volume inversion to
surface inversion transport caused by structural side size variations. Strain
engineering can smooth out or tune such sensitivities to size variations. The
cause of variations described is the structural quantization behavior of the
nanowires, which provide an additional variation mechanism to any other
ON-current variations such as surface roughness, phonon scattering etc.


  A Lorentz and gauge symmetry preserving regularization method is proposed in
4 dimension based on momentum cutoff. We use the conditions of gauge invariance
or freedom of shift of the loop-momentum to define the evaluation of the terms
carrying Lorentz indices, e.g. proportional to k_{\mu}k_{\nu}. The remaining
scalar integrals are calculated with a four dimensional momentum cutoff. The
finite terms (independent of the cutoff) are unambiguous and agree with the
result of dimensional regularization.


  In [arXiv:1006.4939] the enumeration order reducibility is defined on natural
numbers. For a c.e. set A, [A] denoted the class of all subsets of natural
numbers which are co-order with A. In definition 5 we redefine co-ordering for
rational numbers. One of the main questions there, was: "For a specific c.e.
set A, consider set of all enumerations of it which is generated by some Turing
machine {TM_A} what are the associated order types in [A]?" Here, we propose
the same question for rational numbers, and we try to investigate the varieties
of c.e. sets on Q. The theories here are hold for R_c and we could repeat the
same theories in this domain, in a parallel way.


  We present a density functional theory based method for calculating NMR
shielding tensors for 3d transition metal nuclei using periodic boundary
conditions. Calculations employ the gauge-including projector augmented-wave
pseudopotentials method. The effects of ultrasoft pseudopotential and induced
approximations on the second-order magnetic response are intensively examined.
The reliability and the strength of the approach for 49Ti and 51V nuclei is
shown by comparison with traditional quantum chemical methods, using benchmarks
of finite organometallic systems. Application to infinite systems is validated
through comparison to experimental data for the 51V nucleus in various vanadium
oxide based compounds. The successful agreement obtained for isotropic chemical
shifts contrasts with full estimation of the shielding tensor eigenvalues,
revealing the limitation of pure exchange-correlation functionals compared to
their exact-exchange corrected analogues.


  The operator level proof of factorization theorem exhibited in [1] is
extended to the semi-inclusive deep inelastic scattering process(SIDIS).
Factorization theorem can be proved at operator level if there are not detected
soft hadrons.


  We discuss a GC formation scenario in which the first generation (1G) of
single asymptotic giant branch (AGB) stars and intermediate-mass close binaries
(IMCBs) eject gas, from which the second generation (2G) of stars can be
formed. The two key parameters in the scenario are the fractions of binary
stars (f_b) and the slopes (alpha) of the stellar initial mass functions (IMFs)
for 1G stars. Principle results derived by analytic and one-zone models of GC
formation are as follows. The mass fraction of 2G stars (f_2g) can be higher
than ~0.4 for alpha < 1.8 and is not so dependent on f_b. The ratio of the
initial mass of a GC to the present-day mass (M_gc) ranges from 2 to 7
depending on alpha for 0.5 < f_b <0.9. The differences in [Na/Fe] between 1G
and 2G stars can be as large as 0.7 for a wide range of model parameters. The
Li abundances of 2G stars can be as high as those of 1G even if the pristine
gas from IMCBs is assumed to be Li-free. Formation histories of 2G stars show
at least two peaks owing to two peaks in the total ejection rate of gas from
IMCB populations. The observed correlation between f_2g and M_gc can be due to
alpha depending on M_gc. The hypothetical long duration of 2G formation (~ 10^8
yr) is possible, because massive star formation can be suppressed through
frequent dynamical interaction between 1G stars and gas clouds.


  All 4D gauge and gravitational theories in asymptotically flat spacetimes
contain an infinite number of non-trivial symmetries. They can be succinctly
characterized by generalized 2D currents acting on the celestial sphere. A
complete classification of these symmetries and their algebras is an open
problem. Here we construct two towers of such 2D currents from
positive-helicity photons, gluons, or gravitons with integer conformal weights.
These generate the symmetries associated to an infinite tower of conformally
soft theorems. The current algebra commutators are explicitly derived from the
poles in the OPE coefficients, and found to comprise a rich closed subalgebra
of the complete symmetry algebra.


  We investigate the effect of a waveguide on an emitter spontaneous emission
in its vicinity. The impact of various possible orientations of an emitter with
respect to the waveguide surface is studied through simulations and compared
with experimental demonstration. Quantum emitters are dip coated on waveguides
and Purcell enhancement and a decrease in the lifetime of the emitter are
observed. This study serves as a proof of concept for the Purcell effect
offered by waveguides and helps ineffectively estimate the efficiency of
waveguide-based evanescent sensors and quantum photonic applications


  Neutron scattering measurements have been performed on the S=1/2
quasi-one-dimensional system Sr_{14-x}Y_{x}Cu_{24}O_{41}, which has both simple
chains and two-leg ladders of copper ions. We observed that when a small amount
of yttrium is substituted for strontium, which is expected to reduce the number
of holes, the dimerized state and the structure in the chain are changed
drastically. The inelastic peaks originating from the dimerized state of the
chain becomes broader in energy but not in momentum space. This implies that
the dimerized state becomes unstable but the spin correlations are unchanged
with yttrium substitution. Furthermore, it was observed that nuclear Bragg peak
intensities originating from the chain show strong temperature and x
dependence, which suggests that the chains slide along the c axis as
temperature and x are varied.


  In an increasingly complex, mobile and interconnected world, we face growing
threats of disasters, whether by chance or deliberately. Disruption of
coordinated response and recovery efforts due to organizational, technical,
procedural, random or deliberate attack could result in the risk of massive
loss of life. This requires urgent action to explore the development of optimal
information-sharing environments for promoting collective disaster response and
preparedness using multijurisdictional hierarchical networks. Innovative
approaches to information flow modeling and analysis for dealing with
challenges of coordinating across multi layered agency structures as well as
development of early warnings through social systems using social media
analytics may be pivotal to timely responses to dealing with large scale
disasters where response strategies need to be viewed as a shared
responsibility. How do facilitate the development of collective disaster
response in a multijurisdictional setting? How do we develop and test the level
and effectiveness of shared multijurisdictional hierarchical networks for
improved preparedness and response? What is the role of multi layered training
and exercises in building the shared learning space for collective disaster
preparedness and response? The aim of this is therefore to determine factors
that may be responsible for affecting disaster response.


  We study the evolution of networks through `triplets' - three-node graphlets.
We develop a method to compute a transition matrix to describe the evolution of
triplets in temporal networks. To identify the importance of higher-order
interactions in the evolution of networks, we compare both artificial and
real-world data to a model based on pairwise interactions only. The significant
differences between the computed matrix and the calculated matrix from the
fitted parameters demonstrate that non-pairwise interactions exist for various
real-world systems in space and time, such as our data sets. Furthermore, this
also reveals that different patterns of higher-order interaction are involved
in different real-world situations.
  To test our approach, we then use these transition matrices as the basis of a
link prediction algorithm. We investigate our algorithm's performance on four
temporal networks, comparing our approach against ten other link prediction
methods. Our results show that higher-order interactions in both space and time
play a crucial role in the evolution of networks as we find our method, along
with two other methods based on non-local interactions, give the best overall
performance. The results also confirm the concept that the higher-order
interaction patterns, i.e., triplet dynamics, can help us understand and
predict the evolution of different real-world systems.


  In standard Walrasian auctions, the price of a good is defined as the point
where the supply and demand curves intersect. Since both curves are generically
regular, the response to small perturbations is linearly small. However, a
crucial ingredient is absent of the theory, namely transactions themselves.
What happens after they occur? To answer the question, we develop a dynamic
theory for supply and demand based on agents with heterogeneous beliefs. When
the inter-auction time is infinitely long, the Walrasian mechanism is
recovered. When transactions are allowed to happen in continuous time, a
peculiar property emerges: close to the price, supply and demand vanish
quadratically, which we empirically confirm on the Bitcoin. This explains why
price impact in financial markets is universally observed to behave as the
square root of the excess volume. The consequences are important, as they imply
that the very fact of clearing the market makes prices hypersensitive to small
fluctuations.


  Originally inspired by categorical quantum mechanics (Abramsky and Coecke,
LiCS'04), the categorical compositional distributional model of natural
language meaning of Coecke, Sadrzadeh and Clark provides a conceptually
motivated procedure to compute the meaning of a sentence, given its grammatical
structure within a Lambek pregroup and a vectorial representation of the
meaning of its parts. The predictions of this first model have outperformed
that of other models in mainstream empirical language processing tasks on large
scale data. Moreover, just like CQM allows for varying the model in which we
interpret quantum axioms, one can also vary the model in which we interpret
word meaning.
  In this paper we show that further developments in categorical quantum
mechanics are relevant to natural language processing too. Firstly, Selinger's
CPM-construction allows for explicitly taking into account lexical ambiguity
and distinguishing between the two inherently different notions of homonymy and
polysemy. In terms of the model in which we interpret word meaning, this means
a passage from the vector space model to density matrices. Despite this change
of model, standard empirical methods for comparing meanings can be easily
adopted, which we demonstrate by a small-scale experiment on real-world data.
This experiment moreover provides preliminary evidence of the validity of our
proposed new model for word meaning.
  Secondly, commutative classical structures as well as their non-commutative
counterparts that arise in the image of the CPM-construction allow for encoding
relative pronouns, verbs and adjectives, and finally, iteration of the
CPM-construction, something that has no counterpart in the quantum realm,
enables one to accommodate both entailment and ambiguity.


  Finding the best configuration of algorithms' hyperparameters for a given
optimization problem is an important task in evolutionary computation. We
compare in this work the results of four different hyperparameter tuning
approaches for a family of genetic algorithms on 25 diverse pseudo-Boolean
optimization problems. More precisely, we compare previously obtained results
from a grid search with those obtained from three automated configuration
techniques: iterated racing, mixed-integer parallel efficient global
optimization, and mixed-integer evolutionary strategies.
  Using two different cost metrics, expected running time and the area under
the empirical cumulative distribution function curve, we find that in several
cases the best configurations with respect to expected running time are
obtained when using the area under the empirical cumulative distribution
function curve as the cost metric during the configuration process. Our results
suggest that even when interested in expected running time performance, it
might be preferable to use anytime performance measures for the configuration
task. We also observe that tuning for expected running time is much more
sensitive with respect to the budget that is allocated to the target
algorithms.


  The Baryon Oscillation Spectroscopic Survey (BOSS), part of the Sloan Digital
Sky Survey (SDSS) III project, has provided the largest survey of galaxy
redshifts available to date, in terms of both the number of galaxy redshifts
measured by a single survey, and the effective cosmological volume covered. Key
to analysing the clustering of these data to provide cosmological measurements
is understanding the detailed properties of this sample. Potential issues
include variations in the target catalogue caused by changes either in the
targeting algorithm or properties of the data used, the pattern of
spectroscopic observations, the spatial distribution of targets for which
redshifts were not obtained, and variations in the target sky density due to
observational systematics. We document here the target selection algorithms
used to create the galaxy samples that comprise BOSS. We also present the
algorithms used to create large scale structure catalogues for the final Data
Release (DR12) samples and the associated random catalogues that quantify the
survey mask. The algorithms are an evolution of those used by the BOSS team to
construct catalogues from earlier data, and have been designed to accurately
quantify the galaxy sample. The code used, designated MKSAMPLE, is released
with this paper.


  The symmetrization postulate and the associated Bose/Fermi (anti)-commutators
for field mode operators are among the pillars on which local quantum field
theory lays its foundations. They ultimately determine the structure of Fock
space and are closely connected with the local properties of the fields and
with the action of symmetry generators on observables and states. We here show
that the quantum field theory describing relativistic particles coupled to
three dimensional Einstein gravity as a topological defect must be constructed
using a deformed algebra of creation and annihilation operators. This reflects
a non-trivial group manifold structure of the classical momentum space and a
modification of the Leibniz rule for the action of symmetry generators governed
by Newton's constant. We outline various arguments suggesting that, at least at
the qualitative level, these three-dimensional results could also apply to real
four-dimensional world thus forcing us to re-think the ordinary multiparticle
structure of quantum field theory and many of the fundamental aspects connected
to it.


We discuss the first three particle colliders : AdA, VEP-1 and CBX


  Inducing semantic representations directly from speech signals is a highly
challenging task but has many useful applications in speech mining and spoken
language understanding. This study tackles the unsupervised learning of
semantic representations for spoken utterances. Through converting speech
signals into hidden units generated from acoustic unit discovery, we propose
WavEmbed, a multimodal sequential autoencoder that predicts hidden units from a
dense representation of speech. Secondly, we also propose S-HuBERT to induce
meaning through knowledge distillation, in which a sentence embedding model is
first trained on hidden units and passes its knowledge to a speech encoder
through contrastive learning. The best performing model achieves a moderate
correlation (0.5~0.6) with human judgments, without relying on any labels or
transcriptions. Furthermore, these models can also be easily extended to
leverage textual transcriptions of speech to learn much better speech
embeddings that are strongly correlated with human annotations. Our proposed
methods are applicable to the development of purely data-driven systems for
speech mining, indexing and search.


  This thesis concerns the observation of a new particle and the measurements
of its properties, from the search of the Higgs boson through its decay into
two photons at the CMS experiment at CERN's Large Hadron Collider (LHC), on the
full LHC "Run I" data collected by the CMS detector during 2011 and 2012,
consisting of proton-proton collision events at $\sqrt{s}$ $=$ $7~\mathrm{TeV}$
with $L$ $=$ $5.1~\mathrm{fb^{-1}}$ and at $\sqrt{s}$ $=$ $8~\mathrm{TeV}$ with
$L$ $=$ $19.7~\mathrm{fb^{-1}}$, with the final calibration. In particular, an
excess of events above the background expectation is observed, with a local
significance of 5.7 standard deviations at a mass of 124.7 GeV, which
constitutes the observation of a new particle through the two photon decay
channel. A further measurement provides the precise mass of this new particle
as $124.72_{-0.36}^{+0.35}$ GeV =
124.72$_{-0.32}^{+0.31}$(stat)$_{-0.16}^{+0.16}$(syst) GeV. Its total
production cross section times two photon decay branching ratio relative to
that of the Standard Model Higgs boson is determined as $1.12_{-0.23}^{+0.26}$
= 1.12$_{-0.21}^{+0.21}$(stat)$_{-0.09}^{+0.15}$(syst), compatible with the
Higgs boson expectation. Further extractions of its properties relative to the
Higgs boson, including the production cross section times decay branching
ratios for separate Higgs production processes, couplings to bosons and to
fermions, and effective couplings to the photon and to the gluon, are all
compatible with the expectations for the Standard Model Higgs boson.


  GUT scale threshold corrections in minimal SU(5) supergravity grand
unification are discussed. It is shown that predictions may be made despite
uncertainties associated with the high energy scale. A bound relating the
strong coupling constant to the mass scales associated with proton decay and
supersymmetry is derived, and a sensitive probe of the underlying theory is
outlined. In particular, low energy measurements can in principle determine the
presence of Planck scale ($ 1 / {{\rm M}_{\rm Pl}} $) terms.


  Electronic charge delocalization on the molecular backbones of ionic
liquid-forming ions substantially impacts their molecular polarizabilities.
Density functional theory calculations of polarizabilities and volumes of many
cations and anions are reported and applied to yield refractive indices of 1216
ionic liquids. A novel expression for the precise estimation of the molecular
volumes of the ionic liquids from simulation data is also introduced, adding
quadratic corrections to the usual sum of atomic volumes. Our significant
findings include i) that the usual assumption of uniform, additive atomic
polarizabilities is challenged when highly mobile electrons in conjugated
systems are present, and ii) that cations with conjugated large carbon chains
can be used together with anions for the design of ionic liquids with very high
refractive indices. A novel relation for the polarizability volume is reported
together with a refractive index map made up of the studied ionic liquids


  Machine learning models can reach high performance on benchmark natural
language processing (NLP) datasets but fail in more challenging settings. We
study this issue when a pre-trained model learns dataset artifacts in natural
language inference (NLI), the topic of studying the logical relationship
between a pair of text sequences. We provide a variety of techniques for
analyzing and locating dataset artifacts inside the crowdsourced Stanford
Natural Language Inference (SNLI) corpus. We study the stylistic pattern of
dataset artifacts in the SNLI. To mitigate dataset artifacts, we employ a
unique multi-scale data augmentation technique with two distinct frameworks: a
behavioral testing checklist at the sentence level and lexical synonym criteria
at the word level. Specifically, our combination method enhances our model's
resistance to perturbation testing, enabling it to continuously outperform the
pre-trained baseline.


  It has been recently suggested in hep-ph/9911535 and hep-ph/0006324 that the
fourth generation of leptons and quarks is not excluded by the precision
Z-boson measurements, provided that the mass of the fourth neutrino is around
50 GeV and that its mixing with the neutrinos of the first three generations is
negligibly small. We would like to note that in this case the decay rate of the
Standard Model Higgs boson into the pair of such neutrinos would be about two
orders of magnitude higher than than the Higgs to b-bbar rate. Thus, the
predominant decay mode of the Higgs would be invisible, which would require a
special strategy for its searches.


  We provide an explicit example of a higher-dimensional model describing a
non-supersymmetric spectrum of 4D particles of mass M, whose 4D geometry --
{\em including loop effects} -- has a curvature that is of order R ~
m_KK^4/M_p^2, where m_KK is the extra-dimensional Kaluza-Klein scale and M_p is
the 4D Planck constant. m_KK is stabilized and can in particular satisfy m_KK
<< M. The system consists of a (5+1)-dimensional model with a flux-stabilized
supersymmetric bulk coupled to non-supersymmetric matter localized on a
(3+1)-dimensional positive-tension brane. We use recent techniques for
calculating how extra dimensions respond to changes in brane properties to show
(at the classical level) that the low-energy 4D geometry is exactly flat,
independent of the value of the brane tensions. Its mechanism for doing so is
the transfer of stabilizing flux between the bulk and the branes. The UV
completion of the model can arise at scales much larger than M, allowing the
calculation of quantum effects like the zero-point energy of very massive
particles in the vacuum. We find that brane-localized loops do not affect the
4D curvature at all, but bulk loops can. These can be estimated on general
grounds and we show that supersymmetry dictates that they generate curvatures
that are generically of order m_KK^4/M_p^2. For realistic applications this
points to a world with two supersymmetric extra dimensions, with supersymmetry
in the bulk broken at the sub-eV KK scale - as proposed in hep-th/0304256 -
requiring a 6D gravity scale somewhat higher than 10 TeV. Ordinary Standard
Model particles are brane-localized and not at all supersymmetric (implying in
particular no superpartners or the MSSM). We discuss how the model evades
various no-go theorems that would naively exclude it, and briefly outline
several striking observational implications for tests of gravity and at the
LHC.


  The essential decorrelation rate of a hyperbolic dynamical system is the
decay rate of time-correlations one expects to see stably for typical
observables once resonances are projected out. We define and illustrate these
notions and study the conjecture that for observables in $C^1$, the essential
decorrelation rate is never faster than what is dictated by the {\em smallest}
unstable Liapunov multiplier.


  Homing preset and adaptive experiments with Finite State Machines (FSMs) are
widely used when a non-initialized discrete event system is given for testing
and thus, has to be set to the known state at the first step. The length of a
shortest homing sequence is known to be exponential with respect to the number
of states for a complete observable nondeterministic FSM while the problem of
checking the existence of such sequence (Homing problem) is PSPACE-complete. In
order to decrease the complexity of related problems, one can consider adaptive
experiments when a next input to be applied to a system under experiment
depends on the output responses to the previous inputs. In this paper, we study
the problem of the existence of an adaptive homing experiment for complete
observable nondeterministic machines. We show that if such experiment exists
then it can be constructed with the use of a polynomial-time algorithm with
respect to the number of FSM states.


  We report studies of charge-independent (CI) and charge-dependent (CD)
two-particle differential number correlation functions, $R_{2} \left( \Delta
\eta, \Delta \varphi \right)$, and transverse momentum correlation functions,
$P_2 \left( \Delta \eta, \Delta \varphi \right)$ of charged particles produced
in \PbPb\ collisions at the LHC centre-of-mass energy $\sqrt{s_{\rm NN}} =$
2.76 TeV with the UrQMD, AMPT and EPOS models. Model predictions for $R_2$ and
$P_2$ correlation functions are presented for inclusive charged hadrons
($h^\pm$) in selected transverse momentum ranges and with full azimuthal
coverage in the pseudorapidity range $|\eta|< 1.0$. We compare these
predictions for the strength, shape, and particularly the width of the
correlation functions with recent measurements of these observables by the
ALICE collaboration. Our analysis indicate that comparative studies of $R_2$
and $P_2$ correlation functions provide valuable insight towards the
understanding of particle production in Pb--Pb collisions. We find, in
particular, that these models have quantitatively different predictions for
these three observables but none reproduce the measured correlation functions
reported by the ALICE collaboration. Accounting for quantum number conservation
in models, particularly charge conservation, is mandatory to reproduce the
detailed measurements of number and transverse momentum correlation functions.


  Saddle points of a vector logarithmic energy with a vector polynomial
external field on the plane constitute the vector critical measures, a notion
that finds a natural motivation in several branches of analysis. We study in
depth the case of measures $\vec \mu=(\mu_1, \mu_2,\mu_3)$ when the mutual
interaction comprises both attracting and repelling forces.
  For arbitrary vector polynomial external fields we establish general
structural results about critical measures, such as their characterization in
terms of an algebraic equation solved by an appropriate combination of their
Cauchy transforms, and the symmetry properties (or the S-properties) exhibited
by such measures. In consequence, we conclude that vector critical measures are
supported on a finite number of analytic arcs, that are trajectories of a
quadratic differential globally defined on a three-sheeted Riemann surface. The
complete description of the so-called critical graph for such a differential is
the key to the construction of the critical measures.
  We illustrate these connections studying in depth for a one-parameter family
of critical measures under the action of a cubic external field. This choice is
motivated by the asymptotic analysis of a family of (non-hermitian) multiple
orthogonal polynomials, that is subject of a forthcoming paper. Here we compute
explicitly the Riemann surface and the corresponding quadratic differential,
and analyze the dynamics of its critical graph as a function of the parameter,
giving a detailed description of the occurring phase transitions. When
projected back to the complex plane, this construction gives us the complete
family of vector critical measures, that in this context turn out to be vector
equilibrium measures.


  The rotationally-covariant renormalization group equations of motion for the
density wave amplitudes in the phase field crystal model are shown to follow
from a dynamical equation driven by an effective free energy density that we
derive. We show that this free energy can be written purely as a function of
the strain tensor and thence derive the corresponding equations governing the
nonlinear elastic response.


  We conduct a theoretical study of SU(N) fermions confined by a
one-dimensional harmonic potential. Firstly, we introduce a new numerical
approach for solving the trapped interacting few-body problem, by which one may
obtain accurate energy spectra across the full range of interaction strengths.
In the strong-coupling limit, we map the SU(N) Hamiltonian to a spin-chain
model. We then show that an existing, extremely accurate ansatz - derived for a
Heisenberg SU(2) spin chain - is extendable to these N-component systems.
Lastly, we consider balanced SU(N) Fermi gases that have an equal number of
particles in each spin state for N=2, 3, 4. In the weak- and strong-coupling
regimes, we find that the ground-state energies rapidly converge to their
expected values in the thermodynamic limit with increasing atom number. This
suggests that the many-body energetics of N-component fermions may be
accurately inferred from the corresponding few-body systems of N
distinguishable particles.


  We present preliminary results of VLA HI imaging of selected Virgo cluster
galaxies. The goal is to study environmental effects on galaxy evolution. Our
sample of 41 galaxies is spread throughout the cluster and spans a wide range
in star formation properties. Here we present the total HI maps of 13 galaxies.
We find a number of galaxies with extended HI tails, almost all pointing away
from the cluster center. Truncated HI disks are found close to the center but
also in the outer region. Some galaxies near the cluster center show
compression of the gas on one side. Multiwavelength data of NGC 4569 and
kinematics on NGC 4396 indicate that some of the HI is extra-planar. These
preliminary results on the HI morphology already suggest that a variety of
environmental effects such as ICM-ISM interactions, harassment, tidal
interactions or mergers may be at work to affect the evolution of galaxies.


  The influences of quantum effects on the structure of the phase-space of
generalized Gauss-Bonnet theory, introduced by the Lagrangian F(R,G), have been
studied. G is the Gauss-Bonnet invariant, and the quantum effects are described
via the account of conformal anomaly. It has been shown that the quantum
effects change many aspects of the attractors of F(R,G) gravity models in the
R-H plane, including the location of the attractors, the number of them and
their stability properties. These variations are not, in general, from the type
of small perturbations, but instead, it can induce the great, so not ignorable,
variations which have root in the "singular perturbation" nature of this
effect. In other words, one can not ignore the quantum corrections and must be
always considered. The influences of the perfect barotropic fluids on this
problem have been studied, and it has been shown that this kind of matters do
not alter the quantum effects. It has been shown that the classical
contribution of the coupled-quintessence model, which is responsible for
inducing the quantum effects, is of this type, that is a perfect barotropic
fluid, and therefore can not change our results.


  Natural memories are associative, declarative and distributed. Symbolic
computing memories resemble natural memories in their declarative character,
and information can be stored and recovered explicitly; however, they lack the
associative and distributed properties of natural memories. Sub-symbolic
memories developed within the connectionist or artificial neural networks
paradigm are associative and distributed, but are unable to express symbolic
structure and information cannot be stored and retrieved explicitly; hence,
they lack the declarative property. To address this dilemma, we use
Relational-Indeterminate Computing to model associative memory registers that
hold distributed representations of individual objects. This mode of computing
has an intrinsic computing entropy which measures the indeterminacy of
representations. This parameter determines the operational characteristics of
the memory. Associative registers are embedded in an architecture that maps
concrete images expressed in modality-specific buffers into abstract
representations, and vice versa, and the memory system as a whole fulfills the
three properties of natural memories. The system has been used to model a
visual memory holding the representations of hand-written digits, and
recognition and recall experiments show that there is a range of entropy
values, not too low and not too high, in which associative memory registers
have a satisfactory performance. The similarity between the cue and the object
recovered in memory retrieve operations depends on the entropy of the memory
register holding the representation of the corresponding object. The
experiments were implemented in a simulation using a standard computer, but a
parallel architecture may be built where the memory operations would take a
very reduced number of computing steps.


  We study saturated packings produced according to random sequential
adsorption (RSA) protocol built of identical rectangles deposited on a flat,
continuous plane. An aspect ratio of rectangles is defined as the
length-to-width ratio, $f=l/w$. The rectangles have a fixed unit area (i.e., $l
\times w=1$), and therefore, their shape is defined by the value of $f$
($l=\sqrt{f}$ and $w=1/\sqrt{f}$). The rectangles are allowed to align either
vertically or horizontally with equal probability. The particles are deposited
on a flat square substrate of side length $L$ (measured in units of particle
length, $L \in [20, 1000]$) and periodic boundary conditions are applied along
both directions. The finite-size scaling effects are characterized by a scaled
anisotropy defined as $\alpha = l/L = \sqrt{f}/L$. We showed that the
properties of such packings strongly depend on the value of aspect ratio $f$
and the most significant scaling effects are observed for relatively long
rectangles when $l\ge L/2$ (i.e. $\alpha \ge 0.5$). It is especially visible
for the mean packing fraction as a function of the scaled anisotropy $\alpha$.
The kinetics of packing growth for low to moderate rectangle anisotropy is to
be governed by $\ln t/t$ law, where $t$ is proportional to the number of RSA
iterations, which is the same as in the case of RSA of parallel squares. We
also analyzed global orientational ordering in such packings and properties of
domains consisting of a set of neighboring rectangles of the same orientation,
and the probability that such domain forms a percolation.


  The Vera Rubin Observatory will provide an unprecedented set of
time-dependent observations of the sky. The planned Legacy Survey of Space and
Time (LSST) operating for 10 years will provide dense lightcurves for thousands
of active galactic nuclei (AGN) in Deep Drilling Fields (DDFs) and less dense
lightcurves for millions of AGN. We model the prospects for measuring time
delays for emission lines with respect to the continuum, using these data. We
model the artificial lightcurves using Timmer-Koenig algorithm, we use the
exemplary cadence to sample them, we supplement lightcurves with the expected
contamination by the strong emission lines (Hbeta, Mg II and CIV as well as
with Fe II pseudo-continuum and the starlight). We choose the suitable
photometric bands appropriate for the redshift and compare the assumed line
time delay with the recovered time delay for 100 statistical realizations of
the light curves. We show that time delays for emission lines can be well
measured from the Main Survey for the bright tail of the quasar distribution
(about 15% of all sources) with the accuracy within 1 sigma error, for DDFs
results for fainter quasars are also reliable when all 10 years of data are
used. There are also some prospects to measure the time delays for the faintest
quasars at the smallest redshifts from the first two years of data, and
eventually even from the first season. The entire quasar population will allow
obtaining results of apparently high accuracy but in our simulations, we see a
systematic offset between the assumed and recovered time delay depending on the
redshift and source luminosity which will not disappear even in the case of
large statistics. Such a problem might affect the slope of the
radius-luminosity relation and cosmological applications of quasars if
simulations correcting for such effects are not performed.


  Two recent articles of the Munster University experimental team led by S.O.
Demokritov displayed several important facts related to the Bose-Einstein
condensation of magnons (BECM) under permanent pumping first discovered in
2006. They contradicted existing theories of this phenomenon, which predict the
attractive interaction between magnons and strong spontaneous violation of the
reflection symmetry. In this article, we show that these theories implicitly
assumed all relaxation processes to be fast compared with the lifetime of the
magnons, whereas one of them -- relaxation between two minima of energy -- is
slow. We classify processes responsible for the inter-minima relaxation and
present their analytic theory. We analyze how the slow inter-minima relaxation
modifies the anticipated properties of a ferromagnet with the magnon
condensate.


  Self-consistent proton-neutron quasiparticle random phase approximation based
on the spherical nonlinear point-coupling relativistic Hartree-Bogoliubov
theory is established and used to investigate the $\beta^+$/EC-decay half-lives
of neutron-deficient Ar, Ca, Ti, Fe, Ni, Zn, Cd, and Sn isotopes. The isoscalar
proton-neutron pairing is found to play an important role in reducing the decay
half-lives, which is consistent with the same mechanism in the $\beta$ decays
of neutron-rich nuclei. The experimental $\beta^+$/EC-decay half-lives can be
well reproduced by a universal isoscalar proton-neutron pairing strength.


  Predictive uncertainty quantification is crucial in decision-making problems.
We investigate how to adequately quantify predictive uncertainty with missing
covariates. A bottleneck is that missing values induce heteroskedasticity on
the response's predictive distribution given the observed covariates. Thus, we
focus on building predictive sets for the response that are valid conditionally
to the missing values pattern. We show that this goal is impossible to achieve
informatively in a distribution-free fashion, and we propose useful
restrictions on the distribution class. Motivated by these hardness results, we
characterize how missing values and predictive uncertainty intertwine.
Particularly, we rigorously formalize the idea that the more missing values,
the higher the predictive uncertainty. Then, we introduce a generalized
framework, coined CP-MDA-Nested*, outputting predictive sets in both regression
and classification. Under independence between the missing value pattern and
both the features and the response (an assumption justified by our hardness
results), these predictive sets are valid conditionally to any pattern of
missing values. Moreover, it provides great flexibility in the trade-off
between statistical variability and efficiency. Finally, we experimentally
assess the performances of CP-MDA-Nested* beyond its scope of theoretical
validity, demonstrating promising outcomes in more challenging configurations
than independence.


  We question physical existence of shape invariant solitons in three
dimensional nematic liquid crystals. Using modified Petviashvili's method for
finding eigenvalues and eigenfunctions, we determine shape invariant solitons
in a realistic physical model that includes the highly nonlocal nature of the
liquid crystal system. We check the stability of such solutions by propagating
them for long distances. We establish that any noise added to the medium or to
the fundamental solitons induces them to breathe, rendering them practically
unobservable.


  In 2022 the BL Lac object S4 0954+65 underwent a major variability phase,
reaching its historical maximum brightness in the optical and $\gamma$-ray
bands. We present optical photometric and polarimetric data acquired by the
Whole Earth Blazar Telescope (WEBT) Collaboration from 2022 April 6 to July 6.
Many episodes of unprecedented fast variability were detected, implying an
upper limit to the size of the emitting region as low as $10^{-4}$ parsec. The
WEBT data show rapid variability in both the degree and angle of polarization.
We analyse different models to explain the polarization behaviour in the
framework of a twisting jet model, which assumes that the long-term trend of
the flux is produced by variations in the emitting region viewing angle. All
the models can reproduce the average trend of the polarization degree, and can
account for its general anticorrelation with the flux, but the dispersion of
the data requires the presence of intrinsic mechanisms, such as turbulence,
shocks, or magnetic reconnection. The WEBT optical data are compared to
$\gamma$-ray data from the Fermi satellite. These are analysed with both fixed
and adaptive binning procedures. We show that the strong correlation between
optical and $\gamma$-ray data without measurable delay assumes different slopes
in faint and high brightness states, and this is compatible with a scenario
where in faint states we mainly see the imprint of the geometrical effects,
while in bright states the synchrotron self-Compton process dominates.


  We study the production of a heavy CP-odd $A^0$ boson in association with a
photon $e^+e^-\to A^0\gamma$ and a Z boson $e^+e^-\to A^0 Z$ as well as the
single production of $A^0$ via $e^+e^- \to \nu_e\bar{\nu}_e A^0$ in the MSSM
with CP violating phases. In the case of $e^+e^-\to A^0\gamma / A^0 Z$, we show
that the squark contribution, which vanishes in the MSSM with real parameters,
turns out to be sizeable in presence of CP violating phases in the soft SUSY
parameters. For $e^+e^- \to \nu_e \bar{\nu}_e A^0$ in both the 2HDM and MSSM
with real parameters, the cross section does not reach observable rates at a
NLC. It is found that with a large CP violating phase for $A_t$, cross sections
of the order 0.1 fb are attainable for all the processes $e^+e^-\to A^0\gamma$,
$e^+e^-\to A^0 Z$ and $e^+e^- \to \nu_e \bar{\nu}_e A^0$.


  By means of exact diagonalization, we investigate the onset of 'eigenstate
thermalization' and the crossover to ergodicity in a system of 1D fermions with
increasing interaction. We show that the fluctuations in the expectation values
of the momentum distribution from eigenstate to eigenstate decrease with
increasing coupling strength and system size. It turns out that these
fluctuations are proportional to the inverse participation ratio of eigenstates
represented in the Fock basis. We demonstrate that eigenstate thermalization
should set in even for vanishingly small perturbations in the thermodynamic
limit.


  We show a counterexample to a conjecture of de Bobadilla, Luengo,
Melle-Hern\'{a}ndez and N\'{e}methi on rational cuspidal projective plane
curves. The counterexample is a tricuspidal curve of degree 8. On the other
hand, we show that if the number of cusps is at most 2, then the original
conjecture can be deduced from the recent results of Borodzik and Livingston
and (lattice cohomology) computations of N\'emethi and Rom\'an.
  Then we formulate a `simplified' (slightly weaker) version, more in the
spirit of the motivation of the original conjecture (comparing index type
numerical invariants), and we prove it for all currently known rational
cuspidal curves. We make all these identities and inequalities more transparent
in the language of lattice cohomology of surgery 3-manifolds $S^3_{-d}(K)$,
where $K$ is a connected sum of algebraic knots $\{K_i\}_i$.
  Finally, we prove that the zeroth lattice cohomology of this surgery manifold
depends only on the multiset of multiplicities occurring in the multiplicity
sequences describing the algebraic knots $K_i$. This result is closely related
to the lattice-cohomological reformulation of the above mentioned theorems and
conjectures, and provides new computational and comparison procedures.


  In this paper, we present a robust and low complexity deep learning model for
Remote Sensing Image Classification (RSIC), the task of identifying the scene
of a remote sensing image. In particular, we firstly evaluate different low
complexity and benchmark deep neural networks: MobileNetV1, MobileNetV2,
NASNetMobile, and EfficientNetB0, which present the number of trainable
parameters lower than 5 Million (M). After indicating best network
architecture, we further improve the network performance by applying attention
schemes to multiple feature maps extracted from middle layers of the network.
To deal with the issue of increasing the model footprint as using attention
schemes, we apply the quantization technique to satisfy the maximum of 20 MB
memory occupation. By conducting extensive experiments on the benchmark
datasets NWPU-RESISC45, we achieve a robust and low-complexity model, which is
very competitive to the state-of-the-art systems and potential for real-life
applications on edge devices.


  In wireless sensor networks (WSNs), simulation practices, system models,
algorithms, and protocols have been published worldwide based on the assumption
of randomness. The applied statistics used for randomness in WSNs are broad in
nature, e.g., random deployment, activity tracking, packet generation, etc.
Even though with adequate formal and informal information provided and pledge
by authors, validation of the proposal became a challenging issue. The
minuscule information alteration in implementation and validation can reflect
the enormous effect on eventual results. In this proposal, we show how the
results are affected by the generalized assumption made on randomness. In
sensor node deployment, ambiguity arises due to node error-value ($\epsilon$),
and it's upper bound in the relative position is estimated to understand the
delicacy of diminutives changes. Moreover, the effect of uniformity in the
traffic and contribution of scheduling position of nodes also generalized. We
propose an algorithm to generate the unified dataset for the general and some
specific applications system models in WSNs. The results produced by our
algorithm reflects the pseudo-randomness and can efficiently regenerate through
seed value for validation.


  Sparse subspace clustering methods with sparsity induced by $\ell^{0}$-norm,
such as $\ell^{0}$-Sparse Subspace Clustering
($\ell^{0}$-SSC)~\citep{YangFJYH16-L0SSC-ijcv}, are demonstrated to be more
effective than its $\ell^{1}$ counterpart such as Sparse Subspace Clustering
(SSC)~\citep{ElhamifarV13}. However, the theoretical analysis of $\ell^{0}$-SSC
is restricted to clean data that lie exactly in subspaces. Real data often
suffer from noise and they may lie close to subspaces. In this paper, we show
that an optimal solution to the optimization problem of noisy $\ell^{0}$-SSC
achieves subspace detection property (SDP), a key element with which data from
different subspaces are separated, under deterministic and semi-random model.
Our results provide theoretical guarantee on the correctness of noisy
$\ell^{0}$-SSC in terms of SDP on noisy data for the first time, which reveals
the advantage of noisy $\ell^{0}$-SSC in terms of much less restrictive
condition on subspace affinity. In order to improve the efficiency of noisy
$\ell^{0}$-SSC, we propose Noisy-DR-$\ell^{0}$-SSC which provably recovers the
subspaces on dimensionality reduced data. Noisy-DR-$\ell^{0}$-SSC first
projects the data onto a lower dimensional space by random projection, then
performs noisy $\ell^{0}$-SSC on the projected data for improved efficiency.
Experimental results demonstrate the effectiveness of Noisy-DR-$\ell^{0}$-SSC.


  The concept of counterfactual explanations (CE) has emerged as one of the
important concepts to understand the inner workings of complex AI systems. In
this paper, we translate the idea of CEs to linear optimization and propose,
motivate, and analyze three different types of CEs: strong, weak, and relative.
While deriving strong and weak CEs appears to be computationally intractable,
we show that calculating relative CEs can be done efficiently. By detecting and
exploiting the hidden convex structure of the optimization problem that arises
in the latter case, we show that obtaining relative CEs can be done in the same
magnitude of time as solving the original linear optimization problem. This is
confirmed by an extensive numerical experiment study on the NETLIB library.


  We consider the problem of approximation of a continuous function $f$ defined
on a compact metric space $X$ by elements from a sum of two algebras. We prove
a de la Vall\'{e}e Poussin type theorem, which estimates the approximation
error $E(f)$ from below. We also obtain a duality formula for the precise
computation of $E(f)$.


  Quantum machine learning has gained considerable attention as quantum
technology advances, presenting a promising approach for efficiently learning
complex data patterns. Despite this promise, most contemporary quantum methods
require significant resources for variational parameter optimization and face
issues with vanishing gradients, leading to experiments that are either limited
in scale or lack potential for quantum advantage. To address this, we develop a
general-purpose, gradient-free, and scalable quantum reservoir learning
algorithm that harnesses the quantum dynamics of neutral-atom analog quantum
computers to process data. We experimentally implement the algorithm, achieving
competitive performance across various categories of machine learning tasks,
including binary and multi-class classification, as well as timeseries
prediction. Effective and improving learning is observed with increasing system
sizes of up to 108 qubits, demonstrating the largest quantum machine learning
experiment to date. We further observe comparative quantum kernel advantage in
learning tasks by constructing synthetic datasets based on the geometric
differences between generated quantum and classical data kernels. Our findings
demonstrate the potential of utilizing classically intractable quantum
correlations for effective machine learning. We expect these results to
stimulate further extensions to different quantum hardware and machine learning
paradigms, including early fault-tolerant hardware and generative machine
learning tasks.


  We present measurements of the rates for an electron to tunnel on and off a
quantum dot, obtained using a quantum point contact charge sensor. The tunnel
rates show exponential dependence on drain-source bias and plunger gate
voltages. The tunneling process is shown to be elastic, and a model describing
tunneling in terms of the dot energy relative to the height of the tunnel
barrier quantitatively describes the measurements.


  The two-dimensional O(3) model at a vacuum angle theta=pi is investigated.
This model has a severe sign problem. By a Wolff cluster algorithm an integer
or half-integer topological charge is assigned to each cluster. The meron
clusters (clusters with half-integer topological charge) are used to construct
an improved estimator for the correlation function of two spins at theta=pi.
Only configurations with 0 and 2 merons contribute to this correlation
function.
  An algorithm, that generates configurations with only 0 and 2 merons, is
constructed and numerical simulations at theta=pi are performed.
  The numerical results indicate the presence of long range correlations at
theta=pi.


  Stromgren indices v and b are combined with broad-band index I, and a new
index p, the short wavelength half of the v band, to estimate CN 4215A
molecular absorption in a sample of stars in M22. The results have been used to
estimate carbon and nitrogen abundances and suggest groups of stars within this
cluster, each with a characteristic nitrogen abundance, but with a range of
carbon abundances. The results suggest the possibility of stars consisting of
material which has undergone CNO recycling two or three times. The method can
be subsequently used for other globular clusters.


  Using the framework of non-relativistic effective field theory, the
finite-volume ground-state energy shift is calculated up-to-and-including
$O(L^{-6})$ for the system of three pions in the channel with the total isospin
$I=1$. The relativistic corrections are included perturbatively, up to the same
order in the inverse of the box size $L$. The obtained explicit expression,
together with the known result for the system with maximal isospin $I=3$, can
be used for the extraction of two independent effective three-body couplings
from the measured ground-state spectrum of three pions.


  %auto-ignore This paper has been withdrawn by the author(s) since somebody do
not agree with my idea.


  For the first time, we compute non-perturbatively, i.e. without lattice
perturbation theory, the renormalization constants of two-fermion operators in
the quenched approximation at $\beta=6.0$, 6.2 and 6.4 using the Wilson and the
tree-level improved SW-Clover actions. We apply these renormalization constants
to fully non-perturbatively estimate quark masses in the $\bar{MS}$ scheme from
lattice simulations of both the hadron spectrum and the Axial Ward Identity in
the quenched approximation. Some very preliminary unquenched Wilson results
obtained from the gluon configurations generated by the T$\chi$L Collaboration
at $\beta=5.6$ and $N_{f}=2$ are also discussed.


  In this work we establish universal ensemble independent bounds on the mean
and variance of the mutual information and channel capacity for imaging through
a complex medium. Both upper and lower bounds are derived and are solely
dependent on the mean transmittance of the medium and the number of degrees of
freedom $N$. In the asymptotic limit of large $N$, upper bounds on the channel
capacity are shown to be well approximated by that of a bimodal channel with
independent identically Bernoulli distributed transmission eigenvalues.
Reflection based imaging modalities are also considered and permitted regions
in the transmission-reflection information plane defined. Numerical examples
drawn from the circular and DMPK random matrix ensembles are used to illustrate
the validity of the derived bounds. Finally, although the mutual information
and channel capacity are shown to be non-linear statistics of the transmission
eigenvalues, the existence of central limit theorems is demonstrated and
discussed.


  We introduce the concept of general gauge theory which includes Yang-Mills
models. In the framework of the causal approach and show that the anomalies can
appear only in the vacuum sector of the identities obtained from the gauge
invariance condition by applying derivatives with respect to the basic fields.
Then we provide a general result about the absence of anomalies in higher
orders of perturbation theory. This result reduces the renormalizability proof
to the study of lower orders of perturbation theory. For the Yang-Mills model
one can perform this computation explicitly and obtains its renormalizability
in all orders.


  It is shown that geodesics in the space of K\"ahler potentials can be
uniformly approximated by geodesics in the spaces of Bergman metrics. Two
important tools in the proof are the Tian-Yau-Zelditch approximation theorem
for K\"ahler potentials and the pluripotential theory of Bedford-Taylor,
suitably adapted to K\"ahler manifolds.


  Three-dimensional hydrodynamic simulations of large scale structure in the
Universe have shown that accretion shocks form during the gravitational
collapse of one-dimensional caustics, and that clusters of galaxies formed at
intersections of the caustics are surrounded by these accretion shocks.
Estimated speed and curvature radius of the shocks are 1000-3000 \kms and about
5 Mpc, respectively, in the $\Omega=1$ CDM universe. Assuming that energetic
protons are accelerated by these accretion shocks via the first-order Fermi
process and modeling particle transport around the shocks through Bohm
diffusion, we suggest that protons can be accelerated up to the {\it Greisen
cutoff energy} near $6\times 10^{19}$ eV, provided the mean magnetic field
strength in the region around the shocks is at least of order a microgauss. We
have also estimated the proton flux at earth from the Virgo cluster. Assuming a
few (1-10) \% of the ram pressure of the infalling matter would be transferred
to the cosmic-rays, the estimated flux for $ E \sim 10^{19}$eV is consistent
with observations, so that such clusters could be plausible sources of the UHE
CRs.


  The aim of this short note is to present an option for a source of ultracold
neutrons (UCNs), which could profit from the pulse time-structure of the future
ESS spallation neutron source in Lund, and thus which could produce a very high
UCN density and a rather high UCN flux simultaneously. In order to realize this
idea one has to install a relatively thin solid-deuterium UCN source in a close
vicinity to the spallation target and to couple it with an extraction UCN guide
with an entrance membrane window, which is moving periodically and
synchronously with the operation cycle of the spallation source, as explained
in the text below. This proposal profits from the fact that all characteristic
parameters of the problem, such as the pulse duration of the ESS spallation
source, the typical thickness of solid deuterium source that could be easily
realized, the typical time of generation of UCNs in solid deuterium, the length
and diameter of the extraction neutron guide and the time diagram of the
membrane motion that is still realistic, they all fit nicely to optimum desired
parameters. The UCN density produced in such a way could approach 10^6 UCN/cm3.


  We review some of the main physical and statistical properties of the X-ray
absorber in AGNs. In particular, we review the distribution of the absorbing
column density inferred from X-ray observations of various AGN samples. We
discuss the location of the X-ray absorber and the relation with the dust
absorption at optical and infrared wavelengths. Finally, we shortly review the
recent findings on X-ray absorption at high luminosities and at high redshift.


  We survey a few minimal scalar extensions of the standard electroweak model
that provide a simple setup for massive neutrinos in connection with an
invisible axion. The presence of a chiral $U(1)$ \`a la Peccei-Quinn drives the
pattern of Majorana neutrino masses while providing a dynamical solution to the
strong CP problem and an axion as a dark matter candidate. We paradigmatically
apply such a renormalizable framework to type-II seesaw and to two viable
models for neutrino oscillations where the neutrino masses arise at one and two
loops, respectively. We comment on the naturalness of the effective setups as
well as on their implications for vacuum stability and electroweak
baryogenesis.


  The spontaneous CP-violation in three kinds of extension of the standard
model are investigated. We find that if no additional symmetry is introduced to
reduce the number of coefficients, then all three extended models possess the
spontaneous CP violation (SCPV) in the Higgs potential. When symmetry under
which the doublets transform as $\Phi_1 \to \Phi_1$ and $\Phi_2 \to
e^{i\gamma}\Phi_2$ is introduced, the two-Higgs doublet model will lose its
SCPV terms. In the other two extended models, the SCPV terms will be preserved
since both models contain at least one Higgs singlet which can appear in odd
powers. The extended model with two doublets and one singlet is reviewed and
the general conditions which make the CP-violating $V_{min}$ the true minimum
are given. The generally called natural flavour-conserving models are also
discussed briefly.


  Ab initio calculations show that the Dzyaloshinskii-Moriya
interaction(DMI)and net magnetization per unit cell in BiFeO3 are reduced when
U is increasing from 0 to 2.9 eV, and independent of $J$. Interestingly, the
DMI is even destroyed as $U$ exceeds a critical value of 2.9 eV. We propose a
simple model to explain this phenomenon and present the nature of the rotation
of the magnetization corresponding to altered antiferrodistortive distortions
under DMI in BiFeO3.


  In 1981, the Be/X-ray binary (Be/XRB) A0538-66 showed outbursts characterized
by high peak luminosities in the X-ray and optical bands. The optical outbursts
were qualitatively explained as X-ray reprocessing in a gas cloud surrounding
the binary system. Since then, further important information about A0538-66
have been obtained, and sophisticated photoionization codes have been developed
to calculate the radiation emerging from a gas nebula illuminated by a central
X-ray source. In the light of the new information and tools available, we
studied again the enhanced optical emission displayed by A0538-66 to understand
the mechanisms responsible for these unique events among the class of Be/XRBs.
We performed about 10^5 simulations of a gas envelope photoionized by an X-ray
source. We assumed for the shape of the gas cloud either a sphere or a
circumstellar disc observed edge-on. We studied the effects of varying the main
properties of the envelope and the influence of different input X-ray spectra
on the optical/UV emission emerging from the photoionized cloud. We compared
the computed spectra with the IUE spectrum and photometric UBV measurements
obtained during the outburst of 29 April 1981. We also explored the role played
by the X-ray heating of the surface of the donor star irradiated by the X-ray
emission of the neutron star (NS). We found that reprocessing in a spherical
cloud with a shallow radial density distribution can reproduce the optical/UV
emission. To our knowledge, this configuration has never been observed either
in A0538-66 during other epochs or in other Be/XRBs. We found, contrary to the
case of most other Be/XRBs, that the optical/UV radiation produced by the X-ray
heating of the surface of the donor star irradiated by the NS is
non-negligible, due to the particular orbital parameters of this system that
bring the NS very close to its companion.


  Mining the distribution of features and sorting items by combined attributes
are two common tasks in exploring and understanding multi-attribute (or
multivariate) data. Up to now, few have pointed out the possibility of merging
these two tasks into a united exploration context and the potential benefits of
doing so. In this paper, we present SemanticAxis, a technique that achieves
this goal by enabling analysts to build a semantic vector in two-dimensional
space interactively. Essentially, the semantic vector is a linear combination
of the original attributes. It can be used to represent and explain abstract
concepts implied in local (outliers, clusters) or global (general pattern)
features of reduced space, as well as serving as a ranking metric for its
defined concepts. In order to validate the significance of combining the above
two tasks in multi-attribute data analysis, we design and implement a visual
analysis system, in which several interactive components cooperate with
SemanticAxis seamlessly and expand its capacity to handle complex scenarios. We
prove the effectiveness of our system and the SemanticAxis technique via two
practical cases.


  This paper explores the long journey from projective tensor products of a
pair of Banach spaces, passing through the definition of nuclear operators
still on the realm of projective tensor products, to the of notion of
trace-class operators on a Hilbert space, and shows how and why these concepts
(nuclear and trace-class operators, that is) agree in the end.


  We propose an algorithm for a family of optimization problems where the
objective can be decomposed as a sum of functions with monotonicity properties.
The motivating problem is optimization of hyperparameters of machine learning
algorithms, where we argue that the objective, validation error, can be
decomposed as monotonic functions of the hyperparameters. Our proposed
algorithm adapts Bayesian optimization methods to incorporate the monotonicity
constraints. We illustrate the advantages of exploiting monotonicity using
illustrative examples and demonstrate the improvements in optimization
efficiency for some machine learning hyperparameter tuning applications.


  In this paper we give explicit closed forms for the semi-reproducing kernels
associated with thinplate spline interpolation on the sphere. Polyharmonic or
thinplate splines for ${\mathbb R}^d$ were introduced by Duchon and have become
a widely used tool in myriad applications. The analogues for ${\mathbb
S}^{d-1}$ are the thin plate splines for the sphere. The topic was first
discussed by Wahba in the early 1980's, for the ${\mathbb S}^2$ case. Wahba
presented the associated semi-reproducing kernels as infinite series. These
semi-reproducing kernels play a central role in expressions for the solution of
the associated spline interpolation and smoothing problems. The main aims of
the current paper are to give a recurrence for the semi-reproducing kernels,
and also to use the recurrence to obtain explicit closed form expressions for
many of these kernels. The closed form expressions will in many cases be
significantly faster to evaluate than the series expansions. This will enhance
the practicality of using these thinplate splines for the sphere in
computations.


  Chip-based optical frequency combs address the demand for compact, bright,
coherent light sources of equidistant phase-locked lines. Traditionally, the
Fourier Transform Spectroscopy (FTS) technique has been considered a suboptimal
choice for resolving comb lines in chip-based sensing applications due to the
requirement of long optical delays, and spectral distortion from the
instrumental line shape. Here, we develop a sub-nominal resolution FTS
technique that precisely extracts the comb's offset frequency in any spectral
region directly from the measured interferogram without resorting to nonlinear
$f$-to-$2f$ interferometry. This in turn enables MHz-resolution spectrometry
with millimeter optical retardations. Low-pressure MHz-wide absorption lines
probed by widely-tunable chip-scale mid-infrared OFCs with electrical pumping
are fully resolved over a span of tens of nanometers. This versatile technique
paves the way for compact, electrostatically-actuated, or even all-on-chip
high-fidelity FTS, and can be readily applied to boost the resolution of
existing commercial instruments several hundred times.


  We consider the quantum magnet $\rm LiHo_xY_{1-x}F_4$ at $x = 0.167$.
Experimentally the spin glass to paramagnet transition in this system was
studied as a function of the transverse magnetic field and temperature, showing
peculiar features: for example (i) the spin glass order is destroyed much
faster by thermal fluctuations than by the transverse field; and (ii) the cusp
in the nonlinear susceptibility signaling the glass state {\it decreases} in
size at lower temperature. Here we show that the hyperfine interactions of the
Ho atom must dominate in this system, and that along with the transverse
inter-Ho dipolar interactions they dictate the structure of the phase diagram.
The experimental observations are shown to be natural consequences of this.


  To mine large digital libraries in humanistically meaningful ways, scholars
need to divide them by genre. This is a task that classification algorithms are
well suited to assist, but they need adjustment to address the specific
challenges of this domain. Digital libraries pose two problems of scale not
usually found in the article datasets used to test these algorithms. 1) Because
libraries span several centuries, the genres being identified may change
gradually across the time axis. 2) Because volumes are much longer than
articles, they tend to be internally heterogeneous, and the classification task
needs to begin with segmentation. We describe a multi-layered solution that
trains hidden Markov models to segment volumes, and uses ensembles of
overlapping classifiers to address historical change. We test this approach on
a collection of 469,200 volumes drawn from HathiTrust Digital Library. To
demonstrate the humanistic value of these methods, we extract 32,209 volumes of
fiction from the digital library, and trace the changing proportions of first-
and third-person narration in the corpus. We note that narrative points of view
seem to have strong associations with particular themes and genres.


  The Generalized Relativistic Effective Core Potential (GRECP) method is
described which allows one to simulate Breit interaction and finite nuclear
models by an economic way and with high accuracy. The corresponding GRECPs for
the uranium, plutonium, eka-mercury (E112), eka-thallium (E113) and eka-lead
(E114) atoms are generated. The accuracy of these GRECPs and of the RECPs of
other groups is estimated in atomic numerical SCF calculations with Coulomb
two-electron interactions and point nucleus as compared to the corresponding
all-electron Hartree-Fock-Dirac-Breit calculations with the Fermi nuclear
charge distribution. Different nuclear models and contributions of the Breit
interaction between different shells are studied employing all-electron
four-component methods.


  This paper studies the air-to-ground (AG) ultra-wideband (UWB) propagation
channel through measurements between 3.1 GHz to 4.8 GHz using
unmanned-aerial-vehicles (UAVs). Different line-of-sight (LOS) and obstructed-
LOS scenarios and two antenna orientations were used in the experiments.
Channel statistics for different propagation scenarios were obtained, and the
Saleh-Valenzuela (SV) model was found to provide a good fit for the statistical
channel model. An analytical path loss model based on antenna gains in the
elevation plane is provided for unobstructed UAV hovering and moving (in a
circular path) propagation scenarios.


  A growing body of research has been dedicated to DL model testing. However,
there is still limited work on testing DL libraries, which serve as the
foundations for building, training, and running DL models. Prior work on
fuzzing DL libraries can only generate tests for APIs which have been invoked
by documentation examples, developer tests, or DL models, leaving a large
number of APIs untested. In this paper, we propose DeepREL, the first approach
to automatically inferring relational APIs for more effective DL library
fuzzing. Our basic hypothesis is that for a DL library under test, there may
exist a number of APIs sharing similar input parameters and outputs; in this
way, we can easily "borrow" test inputs from invoked APIs to test other
relational APIs. Furthermore, we formalize the notion of value equivalence and
status equivalence for relational APIs to serve as the oracle for effective bug
finding. We have implemented DeepREL as a fully automated end-to-end relational
API inference and fuzzing technique for DL libraries, which 1) automatically
infers potential API relations based on API syntactic or semantic information,
2) synthesizes concrete test programs for invoking relational APIs, 3)
validates the inferred relational APIs via representative test inputs, and
finally 4) performs fuzzing on the verified relational APIs to find potential
inconsistencies. Our evaluation on two of the most popular DL libraries,
PyTorch and TensorFlow, demonstrates that DeepREL can cover 157% more APIs than
state-of-the-art FreeFuzz. To date, DeepREL has detected 162 bugs in total,
with 106 already confirmed by the developers as previously unknown bugs.
Surprisingly, DeepREL has detected 13.5% of the high-priority bugs for the
entire PyTorch issue-tracking system in a three-month period. Also, besides the
162 code bugs, we have also detected 14 documentation bugs (all confirmed).


  As the number of unauthorized operations of Unmanned Aerial Vehicles (UAVs)
is rising, the implementation of a versatile counter-drone system is becoming a
necessity. In this work, we develop a drone-based counter-drone system, that
employs algorithms for detecting and tracking a rogue drone, in conjunction
with wireless interception capabilities to jointly jam the rogue drone while
achieving self positioning for the pursuer drone. In the proposed system a
software-defined-radio (SDR) is used for switching between jamming
transmissions and spectrum sweeping functionalities to achieve the desired GPS
disruption and self-localization, respectively. Extensive field experiments
demonstrate the effectiveness of the proposed solution in a realworld
environment under various parameter settings.


  The postulate of universal Weyl conformal symmetry for all elementary
physical fields introduces nonclassical gravitational effects in both conformal
gravitation(CG) and the conformal Higgs model (CHM). The resulting theory is
found to explain major observed phenomena including excessive galactic rotation
velocities and accelerating Hubble expansion, without invoking dark matter
(DM). The recent history of this development is surveyed here. Implications of
the theory include galactic baryonic Tully-Fisher relations and dark galactic
haloes of definite large radius. Cosmological CHM parameters exclude a massive
Higgs boson but are consistent with a novel alternative particle of the
observed mass.