  This paper presents a generalized treatment of Type I planetary migration in
the presence of stochastic perturbations. In many planet-forming disks, the
Type I migration mechanism, driven by asymmetric torques, acts on a short time
scale and compromises planet formation. If the disk also supports MHD
instabilities, however, the corresponding turbulent fluctuations produce
additional stochastic torques that modify the steady inward migration scenario.
This work studies the migration of planetary cores in the presence of
stochastic fluctuations using complementary methods, including a Fokker-Planck
approach and iterative maps. Stochastic torques have two main effects: [1]
Through outward diffusion, a small fraction of the planetary cores can survive
in the face of Type I inward migration. [2] For a given starting condition, the
result of any particular realization of migration is uncertain, so that results
must be described in terms of the distributions of outcomes. In addition to
exploring different regimes of parameter space, this paper considers the
effects of the outer disk boundary condition, varying initial conditions, and
time-dependence of the torque parameters. For disks with finite radii, the
fraction of surviving planets decreases exponentially with time. We find the
survival fractions and decay rates for a range of disk models, and find the
expected distribution of locations for surviving planets. For expected disk
properties, the survival fraction lies in the range $0.01 < p_S < 0.1$.


  Functional encryption introduces a new paradigm of public key encryption that
decryption only reveals the function value of encrypted data. To curb key
leakage issues and trace users in FE-IP, a new primitive called traceable
functional encryption for inner product (TFE-IP) has been proposed. However,
the privacy protection of user's identities has not been considered in the
existing TFE-IP schemes. In order to balance privacy and accountability, we
propose the concept of privacy-preserving traceable functional encryption for
inner product (PPTFE-IP) and give a concrete construction. Our scheme provides
the following features: (1) To prevent key sharing, a user's key is bound with
both his/her identity and a vector; (2) The key generation center (KGC) and a
user execute a two-party secure computing protocol to generate a key without
the former knowing anything about the latter's identity; (3) Each user can
verify the correctness of his/her key; (4) A user can calculate the inner
product of the two vectors embedded in his/her key and in a ciphertext; (5)
Only the tracer can trace the identity embedded in a key. The security of our
scheme is formally reduced to well-known complexity assumptions, and the
implementation is conducted to evaluate its efficiency. The novelty of our
scheme is to protect users' privacy and provide traceability if required.


  The advent of location-based services has led to the widespread adoption of
indoor localization systems, which enable location tracking of individuals
within enclosed spaces such as buildings. While these systems provide numerous
benefits such as improved security and personalized services, they also raise
concerns regarding privacy violations. As such, there is a growing need for
privacy-preserving solutions that can protect users' sensitive location
information while still enabling the functionality of indoor localization
systems. In recent years, Differentially Private Generative Adversarial
Networks (DPGANs) have emerged as a powerful methodology that aims to protect
the privacy of individual data points while generating realistic synthetic data
similar to original data. DPGANs combine the power of generative adversarial
networks (GANs) with the privacy-preserving technique of differential privacy
(DP). In this paper, we introduce an indoor localization framework employing
DPGANs in order to generate privacy-preserving indoor location data. We
evaluate the performance of our framework on a real-world indoor localization
dataset and demonstrate its effectiveness in preserving privacy while
maintaining the accuracy of the localization system.


  Point cloud 3D object detection has recently received major attention and
becomes an active research topic in 3D computer vision community. However,
recognizing 3D objects in LiDAR (Light Detection and Ranging) is still a
challenge due to the complexity of point clouds. Objects such as pedestrians,
cyclists, or traffic cones are usually represented by quite sparse points,
which makes the detection quite complex using only point cloud. In this
project, we propose a framework that uses both RGB and point cloud data to
perform multiclass object recognition. We use existing 2D detection models to
localize the region of interest (ROI) on the RGB image, followed by a pixel
mapping strategy in the point cloud, and finally, lift the initial 2D bounding
box to 3D space. We use the recently released nuScenes dataset---a large-scale
dataset contains many data formats---to training and evaluate our proposed
architecture.


  The ply elastic constants needed for classical lamination theory analysis of
multi-directional laminates may differ from those obtained from unidirectional
laminates because of three dimensional effects. In addition, the unidirectional
laminates may not be available for testing. In such cases, full-field
displacement measurements offer the potential of identifying several material
properties simultaneously. For that, it is desirable to create complex
displacement fields that are strongly influenced by all the elastic constants.
In this work, we explore the potential of using a laminated plate with an
open-hole under traction loading to achieve that and identify all four ply
elastic constants (E 1, E 2, 12, G 12) at once. However, the accuracy of the
identified properties may not be as good as properties measured from individual
tests due to the complexity of the experiment, the relative insensitivity of
the measured quantities to some of the properties and the various possible
sources of uncertainty. It is thus important to quantify the uncertainty (or
confidence) with which these properties are identified. Here, Bayesian
identification is used for this purpose, because it can readily model all the
uncertainties in the analysis and measurements, and because it provides the
full coupled probability distribution of the identified material properties. In
addition, it offers the potential to combine properties identified based on
substantially different experiments. The full-field measurement is obtained by
moir\'e interferometry. For computational efficiency the Bayesian approach was
applied to a proper orthogonal decomposition (POD) of the displacement fields.
The analysis showed that the four orthotropic elastic constants are determined
with quite different confidence levels as well as with significant correlation.
Comparison with manufacturing specifications showed substantial difference in
one constant, and this conclusion agreed with earlier measurement of that
constant by a traditional four-point bending test. It is possible that the POD
approach did not take full advantage of the copious data provided by the full
field measurements, and for that reason that data is provided for others to use
(as on line material attached to the article).


  A theoretical formulation for the two-point correlation function on a
light-cone is developed in the redshift space. On the basis of the previous
work by Yamamoto & Suto (1999), in which a theoretical formula for the
two-point correlation function on a light-cone has been developed in the real
space, we extend it to the formula in the redshift space by taking the peculiar
velocity of the sources into account. A simple expression for the two-point
correlation function is derived. We briefly discuss QSO correlation functions
on a light-cone adopting a simple model of the sources.


  The purpose of this paper is to study holomorphic approximation and
approximation of $\bar\partial$-closed forms in complex manifolds of complex
dimension $n\geq 1$. We consider extensions of the classical Runge theorem and
the Mergelyan property to domains in complex manifolds for the smooth and the
$L^2$ topology. We characterize the Runge or Mergelyan property in terms of
certain Dolbeault cohomology groups and some geometric sufficient conditions
are given.


  Let N be a (large positive integer, let b > 1 be an integer relatively prime
to N, and let r be the order of b modulo N. Finally, let QC be a quantum
computer whose input register has the size specified in Shor's original
description of his order-finding algorithm. We prove that when Shor's algorithm
is implemented on QC, then the probability P of obtaining a (nontrivial)
divisor of r exceeds 0.7 whenever N exceeds 2^{11}-1 and r exceeds 39, and we
establish that 0.7736 is an asymptotic lower bound for P. When N is not a power
of an odd prime, Gerjuoy has shown that P exceeds 90 percent for N and r
sufficiently large. We give easily checked conditions on N and r for this 90
percent threshold to hold, and we establish an asymptotic lower bound for P of
(2/Pi) Si(4Pi), about .9499, in this situation. More generally, for any
nonnegative integer q, we show that when QC(q) is a quantum computer whose
input register has q more qubits than does QC, and Shor's algorithm is run on
QC(q), then an asymptotic lower bound on P is (2/Pi) Si(2^(q+2) Pi) (if N is
not a power of an odd prime). Our arguments are elementary and our lower bounds
on P are carefully justified.


  Although the vast majority of high-$T_c$ cuprate superconductors are
hole-doped, a small family of electron-doped compounds exists. Under
investigated until recently, there has been tremendous recent progress in their
characterization. A consistent view is being reached on a number of formerly
contentious issues, such as their order parameter symmetry, phase diagram, and
normal state electronic structure. Many other aspects have been revealed
exhibiting both their similarities and differences with the hole-doped
compounds. This review summarizes the current experimental status of these
materials, with a goal to providing a snapshot of our current understanding of
electron-doped cuprates. When possible we put our results in the context of the
hole-doped compounds. We attempt to synthesize this information into a
consistent view on a number of topics important to both this material class as
well as the overall cuprate phenomenology including the phase diagram, the
superconducting order parameter symmetry, phase separation, pseudogap effects,
the role of competing orders, the spin-density wave mean-field description of
the normal state, and electron-phonon coupling.


  Corrections of orders alpha^5 and alpha^6 are calculated in the hyperfine
splittings of 1S and 2S - energy levels in the ion of muonic helium. The
electron vacuum polarization effects, the nuclear structure corrections and
recoil corrections are taken into account. The obtained numerical values of the
hyperfine splittings -1334.56 meV (1S state), -166.62 meV (2S state) can be
considered as a reliable estimate for the comparison with the future
experimental data. The hyperfine splitting interval Delta_{12}=(8 Delta
E^{hfs}(2S)- Delta E^{hfs}(1S)) = 1.64 meV can be used for the check of quantum
electrodynamics.


  In this note we study caustic-free regions for convex billiard tables in the
hyperbolic plane or the hemisphere. In particular, following a result by Gutkin
and Katok in the Euclidean case, we estimate the size of such regions in terms
of the geometry of the billiard table. Moreover, we extend to this setting a
theorem due to Hubacher which shows that no caustics exist near the boundary of
a convex billiard table whose curvature is discontinuous.


  The order-disorder layering transitions, of the Blume-Capel model, are
studied using the Monte Carlo (MC) simulations, in the presence of a variable
crystal field. For a very low temperature, the results are in good agreement
with the ground state study. The first order transition line, found for low
temperatures, is connected to the second order transition line, seen for higher
temperatures, by a tri-critical point, for each layer. The reentrant phenomena,
caused by a competition of thermal fluctuations and an inductor magnetic field
created by the deeper layers, is found for the first $k_0$ layers from the
surface, where $k_0$ is exactly the number of layering transitions allowed by
the ground state study. The layer magnetizations $m_k$, the magnetic
susceptibilities $\chi_{m,k}$ and the quadrupolar magnetic susceptibilities
$\chi_{q,k}$ and the corresponding critical exponent, for each layer $k$, are
also investigated.


  The evaluation of infectious disease processes on radiologic images is an
important and challenging task in medical image analysis. Pulmonary infections
can often be best imaged and evaluated through computed tomography (CT) scans,
which are often not available in low-resource environments and difficult to
obtain for critically ill patients. On the other hand, X-ray, a different type
of imaging procedure, is inexpensive, often available at the bedside and more
widely available, but offers a simpler, two dimensional image. We show that by
relying on a model that learns to generate CT images from X-rays synthetically,
we can improve the automatic disease classification accuracy and provide
clinicians with a different look at the pulmonary disease process.
Specifically, we investigate Tuberculosis (TB), a deadly bacterial infectious
disease that predominantly affects the lungs, but also other organ systems. We
show that relying on synthetically generated CT improves TB identification by
7.50% and distinguishes TB properties up to 12.16% better than the X-ray
baseline.


  The FloatSD technology has been shown to have excellent performance on
low-complexity convolutional neural networks (CNNs) training and inference. In
this paper, we applied FloatSD to recurrent neural networks (RNNs),
specifically long short-term memory (LSTM). In addition to FloatSD weight
representation, we quantized the gradients and activations in model training to
8 bits. Moreover, the arithmetic precision for accumulations and the master
copy of weights were reduced from 32 bits to 16 bits. We demonstrated that the
proposed training scheme can successfully train several LSTM models from
scratch, while fully preserving model accuracy. Finally, to verify the proposed
method's advantage in implementation, we designed an LSTM neuron circuit and
showed that it achieved significantly reduced die area and power consumption.


  Uniformly controlled one-qubit gates are quantum gates which can be
represented as direct sums of two-dimensional unitary operators acting on a
single qubit. We present a quantum gate array which implements any n-qubit gate
of this type using at most 2^{n-1} - 1 controlled-NOT gates, 2^{n-1} one-qubit
gates and a single diagonal n-qubit gate. The circuit is based on the so-called
quantum multiplexor, for which we provide a modified construction. We
illustrate the versatility of these gates by applying them to the decomposition
of a general n-qubit gate and a local state preparation procedure. Moreover, we
study their implementation using only nearest-neighbor gates. We give upper
bounds for the one-qubit and controlled-NOT gate counts for all the
aforementioned applications. In all four cases, the proposed circuit topologies
either improve on or achieve the previously reported upper bounds for the gate
counts. Thus, they provide the most efficient method for general gate
decompositions currently known.


  Electrochemical energy systems rely on particulate porous electrodes to store
or convert energies. While the three-dimensional porous structures were
introduced to maximize the interfacial area for better overall performance of
the system, spatiotemporal heterogeneities arose from materials thermodynamics
localize the charge transfer processes onto a limited portion of the available
interfaces. Here, we demonstrate a simple but precision method that can
directly track and analyze the operando (i.e. local and reacting) interfaces at
the mesoscale in a practical graphite porous electrode to obtain the true local
current density, which turned out to be two orders of magnitude higher than the
globally averaged current density adopted by existing studies. Our results
resolve the long-standing discrepancies between kinetics parameters derived
from electroanalytical measurements and from first principles predictions.
Contradictory to prevailing beliefs, the electrochemical dynamics is not
controlled by the solid-state diffusion process once the spatiotemporal
reaction heterogeneities emerge in porous electrodes.


  A general theoretical framework based on group-subgroup and group-supergroup
relations is proposed to describe and to derive interpenetrating nets.


  This paper introduces AQA-Bench, a novel benchmark to assess the sequential
reasoning capabilities of large language models (LLMs) in algorithmic contexts,
such as depth-first search (DFS). The key feature of our evaluation benchmark
lies in its interactive evaluation protocol -- for example, in DFS, the
availability of each node's connected edge is contingent upon the model's
traversal to that node, thereby necessitating the LLM's ability to effectively
remember visited nodes and strategize subsequent moves. We comprehensively
build AQA-Bench with three different algorithms, namely binary search,
depth-first search, and breadth-first search, and to evaluate the sequential
reasoning ability of 12 different LLMs. Our investigations reveal several
interesting findings: (1) Closed-source models like GPT-4 and Gemini generally
show strong sequential reasoning ability, significantly outperforming
open-source LLMs. (2) Naively providing interactive examples may inadvertently
hurt few-shot performance. (3) A very limited number of predecessor steps
following the optimal policy can substantially boost small models' performance.
(4) The scaling correlation between performance and model size is not always
significant, sometimes even showcasing an inverse trend. We hope our study can
catalyze future work on advancing the understanding and enhancement of LLMs'
capabilities in sequential reasoning. The code is available at
https://github.com/UCSC-VLAA/AQA-Bench.


  The use of Artificial Neural Networks (ANNs) as a classifier of digital
spectra is investigated. Using both simulated and real data, it is shown that
neural networks can be trained to discriminate between the spectra of different
classes of active galactic nucleus (AGN) with realistic sample sizes and
signal-to-noise ratios. By working in the Fourier domain, neural nets can
classify objects without knowledge of their redshifts.


  Numerous attempts have been made so far to explore the quantum anomalous Hall
effect (QAHE), but the ultralow observed temperature strongly hinders its
practical applications. Hence, it is of great interest to go beyond the
existing paradigm of QAHE. Here, we propose that Floquet engineering offers a
strategy to realize the QAHE via hybridization of Floquet-Bloch bands. Based on
first-principles calculations and Floquet theorem, we unveil that
nonequilibrium valley-polarized QAHE (VP-QAHE), independent of magnetic orders,
is widely present in ferromagnetic and nonmagnetic members of two-dimensional
family materials $M$Si$_2$$Z_4$ ($M$ = Mo, W, V; $Z$ = N, P, As) by irradiating
circularly polarized light (CPL). Remarkably, by tuning the frequency,
intensity, and handedness of incident CPL, the Chern number of VP-QAHE is
highly tunable and up to $\mathcal{C}=\pm 4$. We reveal that such Chern number
tunable VP-QAHE attributes to light-induced trigonal warping and multiple band
inversion at different valleys. The valley-resolved chiral edge states and
quantized plateau of Hall conductance, which facilitates the experimental
measurement, are visible inside the global band gap. Our work not only
establishes Floquet Engineering of nonequilibrium VP-QAHE with tunable Chern
number in realistic materials, but also provides a promising avenue to explore
emergent topological phases under light irradiation.


  We estimate the heat kernel of the smooth open set for the isotropic unimodal
pure-jump L\'evy process with infinite L\'evy measure and weakly scaling
L\'evy-Kchintchine exponent.


  Trypanosoma cruzi and Giardia intestinalis are two human pathogens and
protozoan parasites responsible for the diseases Chagas disease and giardiasis,
respectively. Both diseases cause suffering and illness in several million
individuals. The former disease occurs primarily in South America and Central
America, and the latter disease occurs worldwide. Current therapeutics are
toxic and lack efficacy, and potential vaccines are far from the market.
Increased knowledge about the biology of these parasites is essential for drug
and vaccine development, and new diagnostic tests. In this thesis,
high-throughput sequencing was applied together with extensive bioinformatic
analyses to yield insights into the biology and evolution of Trypanosoma cruzi
and Giardia intestinalis. Bioinformatics analysis of DNA and RNA sequences was
performed to identify features that may be of importance for parasite biology
and functional characterization. This thesis is based on five papers (i-v).
Paper i and ii describe comparative genome studies of three distinct genotypes
of Giardia intestinalis (A, B and E). Paper iii describes a genome comparison
of the human infecting Trypanosoma cruzi with the bat-restricted subspecies
Trypanosoma cruzi marinkellei. Paper iv describes the repertoire of small
non-coding RNAs in Trypanosoma cruzi epimastigotes. Paper v describes
transcriptome analysis using paired-end RNA-Seq of three distinct genotypes of
Giardia intestinalis (A, B and E).


  This paper considers the MapReduce-like coded distributed computing framework
originally proposed by Li et al., which uses coding techniques when distributed
computing servers exchange their computed intermediate values, in order to
reduce the overall traffic load. Their original model servers are connected via
an error-free common communication bus allowing broadcast transmissions.
However, this assumption is one of the major limitations in practice since the
practical cloud computing network topologies are far more involved than a
simple single bus. We formulate a topological coded distributed computing
problem, where the distributed servers communicate with each other through some
switch network. By using a special instance of fat-tree topologies, referred to
as t-ary fat-tree proposed by Al-Fares et al. which can be built by some cheap
switches, we propose a coded distributed computing scheme to achieve the
minimum max-link communication load defined as the maximum load over all links.


  We prove a Harnack inequality for distributional solutions to a type of
degenerate elliptic PDEs in $N$ dimensions. The differential operators in
question are related to the Kolmogorov operator, made up of the Laplacian in
the last $N-1$ variables, a first-order term corresponding to a shear flow in
the direction of the first variable, and a bounded measurable potential term.
The first-order coefficient is a smooth function of the last $N-1$ variables
and its derivatives up to certain order do not vanish simultaneously at any
point, making the operators in question hypoelliptic.


  We study gravitational pivoting, a constrained version of in-hand
manipulation, where we aim to control the rotation of an object around the grip
point of a parallel gripper. To achieve this, instead of controlling the
gripper to avoid slip, we embrace slip to allow the object to rotate in-hand.
We collect two real-world datasets, a static tracking dataset and a
controller-in-the loop dataset, both annotated with object angle and angular
velocity labels. Both datasets contain force-based tactile information on ten
different household objects. We train an LSTM model to predict the angular
position and velocity of the held object from purely tactile data. We integrate
this model with a controller that opens and closes the gripper allowing the
object to rotate to desired relative angles. We conduct real-world experiments
where the robot is tasked to achieve a relative target angle. We show that our
approach outperforms a sliding-window based MLP in a zero-shot generalization
setting with unseen objects. Furthermore, we show a 16.6% improvement in
performance when the LSTM model is fine-tuned on a small set of data collected
with both the LSTM model and the controller in-the-loop. Code and videos are
available at https://rhys-newbury.github.io/projects/pivoting/


  We investigate the Barkhausen noise in ferromagnetic thin films with zigzag
domain walls. We use a cellular automaton model that describes the motion of a
zigzag domain wall in an impure ferromagnetic quasi-two dimensional sample with
in-plane uniaxial magnetization at zero temperature, driven by an external
magnetic field. The main ingredients of this model are the dipolar spin-spin
interactions and the anisotropy energy. A power law behavior with a cutoff is
found for the probability distributions of size, duration and correlation
length of the Barkhausen avalanches, and the critical exponents are in
agreement with the available experiments. The link between the size and the
duration of the avalanches is analyzed too, and a power law behavior is found
for the average size of an avalanche as a function of its duration.


  In this paper, after a short introduction to the physics of neutrino
telescopes, we will report on first performances of the IceCube detector and a
selection of preliminary results obtained from data taken while IceCube
operated in a partially completed configuration (22 strings and 40 strings). We
will emphasize new analysis methods recently developed for the study of the
Southern Hemisphere as well as for extended regions. Based on the long term
experience of AMANDA and IceCube, the South Pole ice has proven to be an ideal
site for astroparticle physics. New ideas and projects about the future beyond
IceCube will conclude this presentation.


  We have studied spin relaxation in the spin ice compound Dy2Ti2O7 through
measurements of the a.c. magnetic susceptibility. While the characteristic spin
relaxation time is thermally activated at high temperatures, it becomes almost
temperature independent below Tcross ~ 13 K, suggesting that quantum tunneling
dominates the relaxation process below that temperature. As the low-entropy
spin ice state develops below Tice ~ 4 K, the spin relaxation time increases
sharply with decreasing temperature, suggesting the emergence of a collective
degree of freedom for which thermal relaxation processes again become important
as the spins become highly correlated.


  The discontinuous dependence of the properties of a quantum game on its
entanglement has been shown up to be very much like phase transitions viewed in
the entanglement-payoff diagram [J. Du et al., Phys. Rev. Lett, 88, 137902
(2002)]. In this paper we investigate such phase-transition-like behavior of
quantum games, by suggesting a method which would help to illuminate the origin
of such kind of behavior. For the particular case of the generalized Prisoners'
Dilemma, we find that, for different settings of the numerical values in the
payoff table, even though the classical game behaves the same, the quantum game
exhibits different and interesting phase-transition-like behavior.


  We show that, for each symmetry class based on the tenfold way
classification, the effective Dirac operator obtained by integrating out the
additional bulk direction takes a value in the corresponding classifying space,
from which we obtain the flat band Hamiltonian. We then obtain the overlap
Dirac operator for each symmetry class and establish the Ginsparg--Wilson
relation associated with $\mathcal{C}$ and $\mathcal{T}$ symmetries, and also
the mod-two index theorem.


  Along with the proliferation of electric vehicles (EVs), optimizing the use
of EV charging space can significantly alleviate the growing load on
intelligent transportation systems. As the foundation to achieve such an
optimization, a spatiotemporal method for EV charging demand prediction in
urban areas is required. Although several solutions have been proposed by using
data-driven deep learning methods, it can be found that these
performance-oriented methods may suffer from misinterpretations to correctly
handle the reverse relationship between charging demands and prices. To tackle
the emerging challenges of training an accurate and interpretable prediction
model, this paper proposes a novel approach that enables the integration of
graph and temporal attention mechanisms for feature extraction and the usage of
physic-informed meta-learning in the model pre-training step for knowledge
transfer. Evaluation results on a dataset of 18,013 EV charging piles in
Shenzhen, China, show that the proposed approach, named PAG, can achieve
state-of-the-art forecasting performance and the ability in understanding the
adaptive changes in charging demands caused by price fluctuations.


  An unorthodox unified theory is developed to describe external and internal
attributes and symmetries of fundamental fermions, quarks and leptons. Basic
ingredients of the theory are an algebra which consists of all the
triple-direct-products of Dirac gamma-matrices and a triple-spinor-field,
called a triplet field, defined on the algebra. The algebra possesses three
commutative sub-algebras which describe, respectively, the external space-time
symmetry, the family structure and the internal color symmetry of quarks and
leptons. The triplet field includes threefold (fourfold) repetitionary modes of
spin 1/2 component fields with SU(3) (SU(4)) color symmetry. It is possible to
qualify the Yukawa interaction and to make a new interpretation of its coupling
constants naturally in an intrinsic mechanism of the triplet field formalism.
The Dirac mass matrices with quasi-democratic structure are derived as an
illustration.


  We present new results from our survey of diffuse O VI-emitting gas in the
interstellar medium with the Far Ultraviolet Spectroscopic Explorer (FUSE).
Background observations obtained since 2005 have yielded eleven new O VI
detections of 3-sigma significance, and archival searches have revealed two
more. An additional 15 sight lines yield interesting upper limits. Combined
with previous results, these observations reveal the large-scale structure of
the O VI-bearing gas in the quadrant of the sky centered on the Magellanic
Clouds. The most prominent feature is a layer of low-velocity O VI emission
extending more than 70 degrees from the Galactic plane. At low latitudes (|b| <
30 degrees), the emission comes from narrow, high-density conductive interfaces
in the local ISM. At high latitudes, the emission is from extended, low-density
regions in the Galactic halo. We also detect O VI emission from the interface
region of the Magellanic System, a structure recently identified from H I
observations. These are the first detections of emission from high-ionization
species in the Magellanic System outside of the Clouds themselves.


  In this paper, we propose a distortion-aware loop filtering model to improve
the performance of intra coding for 360$^o$ videos projected via
equirectangular projection (ERP) format. To enable the awareness of distortion,
our proposed module analyzes content characteristics based on a coding unit
(CU) partition mask and processes them through partial convolution to activate
the specified area. The feature recalibration module, which leverages cascaded
residual channel-wise attention blocks (RCABs) to adjust the inter-channel and
intra-channel features automatically, is capable of adapting with different
quality levels. The perceptual geometry optimization combining with weighted
mean squared error (WMSE) and the perceptual loss guarantees both the local
field of view (FoV) and global image reconstruction with high quality.
Extensive experimental results show that our proposed scheme achieves
significant bitrate savings compared with the anchor (HM + 360Lib), leading to
8.9%, 9.0%, 7.1% and 7.4% on average bit rate reductions in terms of PSNR,
WPSNR, and PSNR of two viewports for luminance component of 360^o videos,
respectively.


  Hybrid superconductor-semiconductor nanowires with Rashba spin-orbit coupling
are arguably becoming the leading platform for the search of Majorana bound
states (MBSs) in engineered topological superconductors. We perform a
systematic numerical study of the low-energy Andreev spectrum and supercurrents
in short and long superconductor-normal-superconductor junctions made of
nanowires with strong Rashba spin-orbit coupling, where an external Zeeman
field is applied perpendicular to the spin-orbit axis. In particular, we
investigate the detailed evolution of the Andreev bound states from the trivial
into the topological phase and their relation with the emergence of MBSs. Due
to finite length, the system hosts four MBSs, two at the inner part of the
junction and two at the outer one. They hybridize and give rise to a finite
energy splitting at superconducting phase difference $\phi=\pi$, a well-visible
effect that can be traced back to the evolution of the energy spectrum with the
Zeeman field: from the trivial phase with Andreev bound states into the
topological phase with MBSs. Similarly, we carry out a detailed study of
supercurrents for short and long junctions from the trivial to the topological
phases. The supercurrent, calculated from the Andreev spectrum, is
$2\pi$-periodic in the trivial and topological phases. In the latter it
exhibits a clear \emph{sawtooth} profile at phase difference of $\pi$ when the
energy splitting is negligible, signalling a strong dependence of current-phase
curves on the length of the superconducting regions. Effects of temperature,
scalar disorder and reduction of normal transmission on supercurrents are also
discussed. Further, we identify the individual contribution of MBSs. In short
junctions the MBSs determine the current-phase curves, while in long junctions
the spectrum above the gap (quasi-continuum) introduces an important
contribution.


  We show explicitly in two dimensional spacetime (d = 2) that the N = 2
Volkov-Akulov model is equivalent to the spontaneously broken linear
supersymmetry (LSUSY) interacting gauge theory for N = 2 vector and N = 2
scalar supermultiplets. The local gauge interaction of LSUSY is induced by the
specific composite structure of the auxiliary fields and the consequent
transformations.


  Let $K$ be a field, let $\sigma$ be an automorphism of $K$, and let $\delta$
be a derivation of $K$. We show that if $D$ is one of $K(x;\sigma)$ or
$K(x;\delta)$, then $D$ either contains a free algebra over its center on two
generators, or every finitely generated subalgebra of $D$ satisfies a
polynomial identity. As a corollary, we are able to show that the quotient
division ring of any iterated Ore extension of an affine domain satisfying a
polynomial identity either again satisfies a polynomial identity or it contains
a free algebra over its center on two variables.


  In the UK, approximately 400,000 people with type 1 diabetes (T1D) rely on
insulin delivery due to insufficient pancreatic insulin production. Managing
blood glucose (BG) levels is crucial, with continuous glucose monitoring (CGM)
playing a key role. CGM, tracking BG every 5 minutes, enables effective blood
glucose level prediction (BGLP) by considering factors like carbohydrate intake
and insulin delivery.
  Recent research has focused on developing sequential models for BGLP using
historical BG data, incorporating additional attributes such as carbohydrate
intake, insulin delivery, and time. These methods have shown notable success in
BGLP, with some providing temporal explanations. However, they often lack clear
correlations between attributes and their impact on BGLP. Additionally, some
methods raise privacy concerns by aggregating participant data to learn
population patterns.
  Addressing these limitations, we introduced a graph attentive memory (GAM)
model, combining a graph attention network (GAT) with a gated recurrent unit
(GRU). GAT applies graph attention to model attribute correlations, offering
transparent, dynamic attribute relationships. Attention weights dynamically
gauge attribute significance over time. To ensure privacy, we employed
federated learning (FL), facilitating secure population pattern analysis.
  Our method was validated using the OhioT1DM'18 and OhioT1DM'20 datasets from
12 participants, focusing on 6 key attributes. We demonstrated our model's
stability and effectiveness through hyperparameter impact analysis.


  The advent of fifth generation (5G) networks has opened new avenues for
enhancing connectivity, particularly in challenging environments like remote
areas or disaster-struck regions. Unmanned aerial vehicles (UAVs) have been
identified as a versatile tool in this context, particularly for improving
network performance through the Integrated access and backhaul (IAB) feature of
5G. However, existing approaches to UAV-assisted network enhancement face
limitations in dynamically adapting to varying user locations and network
demands. This paper introduces a novel approach leveraging deep reinforcement
learning (DRL) to optimize UAV placement in real-time, dynamically adjusting to
changing network conditions and user requirements. Our method focuses on the
intricate balance between fronthaul and backhaul links, a critical aspect often
overlooked in current solutions. The unique contribution of this work lies in
its ability to autonomously position UAVs in a way that not only ensures robust
connectivity to ground users but also maintains seamless integration with
central network infrastructure. Through various simulated scenarios, we
demonstrate how our approach effectively addresses these challenges, enhancing
coverage and network performance in critical areas. This research fills a
significant gap in UAV-assisted 5G networks, providing a scalable and adaptive
solution for future mobile networks.


  The random sequential adsorption of various particle shapes is studied in
order to determine the influence of particle anisotropy on the saturated random
packing. For all tested particles there is an optimal level of anisotropy which
maximizes the saturated packing fraction. It is found that a concave shape
derived from a dimer of disks gives a packing fraction of 0.5833, which is
comparable to the maximum packing fraction of ellipsoids and spherocylinders
and higher than any other studied shape. Discussion why this shape is so
beneficial for random sequential adsorption is given.


  A fundamental roadblock to the exact numerical solution of many-fermion
problems is the exponential growth of the Hilbert space with system size. It
manifests as extreme dynamical memory and computation-time requirements for
simulating many-fermion processes. Here we construct a novel reorganization of
the Hilbert space to establish that the exponential growth of dynamical-memory
requirement is suppressed inversely with system size in our approach.
Consequently, the state-of-the-art resolvent computation can be performed with
substantially less memory. The memory-efficiency does not rely on Hamiltonian
symmetries, sparseness, or boundary conditions and requires no additional
memory to handle long-range density-density interaction and hopping. We provide
examples calculations of interacting fermion ground state energy, the
many-fermion density of states and few-body excitations in interacting ground
states in one and two dimensions.


  This letter proposes a novel and highly efficient distribution system state
estimation (DSSE) algorithm with nonlinear measurements from supervisory
control and data acquisition (SCADA) systems. Conventional DSSE, i.e., a
weighted least square (WLS)-based method, requires multiple Monte Carlo
simulations for overall accuracy evaluation and has high calculation cost due
to the nonlinear iterative process. The proposed method uses the Taylor series
of voltages for constructing a linear DSSE model in the interval form and then
solve this model by interval arithmetic. Compared with the nonlinear WLS-based
method, the proposed method obtains accurate and robust estimates via a single
random sampling of measurements and is computationally efficient. The
comparative analysis of the IEEE 34-bus distribution system points to improved
estimation results relative to the WLS-based method.


  Let $K$ be a totally real number field of degree $n \geq 2$. The inverse
different of $K$ gives rise to a lattice in $\mathbb{R}^n$. We prove that the
space of Schwartz Fourier eigenfunctions on $\mathbb{R}^n$ which vanish on the
"component-wise square root" of this lattice, is infinite dimensional. The
Fourier non-uniqueness set thus obtained is a discrete subset of the union of
all spheres $\sqrt{m}S^{n-1}$ for integers $m \geq 0$ and, as $m \rightarrow
\infty$, there are $\sim c_{K} m^{n-1}$ many points on the $m$-th sphere for
some explicit constant $c_{K}$, proportional to the square root of the
discriminant of $K$. This contrasts a recent Fourier uniqueness result by
Stoller. Using a different construction involving the codifferent of $K$, we
prove an analogue of our results for discrete subsets of ellipsoids. In special
cases, these sets also lie on spheres with more densely spaced radii, but with
fewer points on each.
  We also study a related question about existence of Fourier interpolation
formulas with nodes "$\sqrt{\Lambda}$" for general lattices $\Lambda \subset
\mathbb{R}^n$. Using results about lattices in Lie groups of higher rank, we
prove that, if $n \geq 2$ and if a certain group $\Gamma_{\Lambda} \leq
\operatorname{PSL}_2(\mathbb{R})^n$ is discrete, then such interpolation
formulas cannot exist. Motivated by these more general considerations, we
revisit the case of one radial variable and prove, for all $n \geq 5$ and all
real $\lambda > 2$, Fourier interpolation results for sequences of spheres
$\sqrt{2 m/ \lambda}S^{n-1}$, where $m$ ranges over any fixed cofinite set of
non-negative integers. The proof relies on a series of Poincar\'e type for
Hecke groups of infinite covolume, similarly to the construction previously
used by Stoller.


  We propose new parameters to describe the geometry of a warped disc when
viewed edge-on, a global warp parameter $w$, and a family of three parameters
$A$, $B$ and $C$ that describe, independently, the shape of the warp. These
parameters are useful to detect some key effects in the understanding of warps.
We have also developed software (WIG) which is able to calculate these
parameters for a given image of a galaxy in any wavelength and which is
described here with some examples, namely ESO 235-53 in the optical, NGC 4013
in 21 cm and NGC 4565 in 1.2 mm continuum.


  Privacy-preserving price e-negotiation (3PEN) is an important topic of secure
multi-party computation (SMC) in the electronic commerce field, and the key
point of its security is to guarantee the privacy of seller's and buyer's
prices. In this study, a novel and efficient quantum solution to the 3PEN
problem is proposed, where the oracle operation and the qubit comparator are
utilized to obtain the comparative results of buyer's and seller's prices, and
then quantum counting is executed to summarize the total number of products
which meets the trading conditions. Analysis shows that our solution not only
guarantees the correctness and the privacy of 3PEN, but also has lower
communication complexity than those classical ones.


  We consider the nonequilibrium quantum vibrations of a molecule clamped
between two macroscopic leads in a current-carrying state at finite voltages.
Our approach is based on the nonequilibrium Green function technique and the
self-consistent Born approximation. Kinetic equations for the average
populations of electrons and vibrons are formulated in the weak electron-vibron
coupling case and self-consistent solutions are obtained. The effects of vibron
emission and vibronic instability are demonstrated using few-orbital models.
The importance of the electron-vibron resonance is shown.


  The non-commutative five-term relation $T_{1,0} T_{0,1} = T_{0,1} T_{1,1}
T_{1,0}$ is shown to hold for certain operators acting on symmetric functions.
The "generalized recursion" conjecture of Bergeron and Haiman is a corollary of
this result.


  A popular approach to solving a decision process with non-Markovian rewards
(NMRDP) is to exploit a compact representation of the reward function to
automatically translate the NMRDP into an equivalent Markov decision process
(MDP) amenable to our favorite MDP solution method. The contribution of this
paper is a representation of non-Markovian reward functions and a translation
into MDP aimed at making the best possible use of state-based anytime
algorithms as the solution method. By explicitly constructing and exploring
only parts of the state space, these algorithms are able to trade computation
time for policy quality, and have proven quite effective in dealing with large
MDPs. Our representation extends future linear temporal logic (FLTL) to express
rewards. Our translation has the effect of embedding model-checking in the
solution method. It results in an MDP of the minimal size achievable without
stepping outside the anytime framework, and consequently in better policies by
the deadline.


  We show exact dimensionality of harmonic measures associated with random
walks on groups acting on a hyperbolic space under finite first moment
condition, and establish the dimension formula by the entropy over the drift.
We also treat the case when a group acts on a non-proper hyperbolic space
acylindrically. Applications of this formula include continuity of the
Hausdorff dimension with respect to driving measures and Brownian motions on
regular coverings of a finite volume Riemannian manifold.


  In the category of abelian groups, Pareigis constructed a Hopf ring whose
comodules are differential graded abelian groups. We show that this Hopf ring
can be obtained by combining grading and differential Hopf rings using
semidirect product in fairly general symmetric monoidal additive categories.


  Magnetohydrodynamic simulations of core-collapse supernovae have become
increasingly mature and important in recent years. Magnetic fields take center
stage in scenarios for explaining hypernova explosions, but are now also
considered in supernova theory more broadly as an important factor even in
neutrino-driven explosions, especially in the context of neutron star birth
properties. Here we present an overview of simulation approaches currently used
for magnetohydrodynamic supernova simulations and sketch essential physical
concepts for understanding the role of magnetic fields in supernovae of slowly
or rapidly rotating massive stars. We review progress on simulations of
neutrino-driven supernovae, magnetorotational supernovae, and the relevant
field amplification processes. Recent results on the nucleosynthesis and
gravitational wave emission from magnetorotational supernovae are also
discussed. We highlight efforts to provide better initial conditions for
magnetohydrodynamic supernova models by simulating short phases of the
progenitor evolution in 3D to address uncertainties in the treatment of
rotation and magnetic fields in current stellar evolution models.


  For the next generation of spectroscopic galaxy surveys, it is important to
forecast their performances and to accurately interpret their large data sets.
For this purpose, it is necessary to consistently simulate different
populations of galaxies, in particular Emission Line Galaxies (ELGs), less used
in the past for cosmological purposes. In this work, we further the forward
modeling approach presented in Fagioli et al. 2018, by extending the spectra
simulator Uspec to model galaxies of different kinds with improved parameters
from Tortorelli et al. 2020. Furthermore, we improve the modeling of the
selection function by using the image simulator Ufig. We apply this to the
Sloan Digital Sky Survey (SDSS), and simulate $\sim157,000$ multi-band images.
We pre-process and analyse them to apply cuts for target selection, and finally
simulate SDSS/BOSS DR14 galaxy spectra. We compute photometric, astrometric and
spectroscopic properties for red and blue, real and simulated galaxies, finding
very good agreement. We compare the statistical properties of the samples by
decomposing them with Principal Component Analysis (PCA). We find very good
agreement for red galaxies and a good, but less pronounced one, for blue
galaxies, as expected given the known difficulty of simulating those. Finally,
we derive stellar population properties, mass-to-light ratios, ages and
metallicities, for all samples, finding again very good agreement. This shows
how this method can be used not only to forecast cosmology surveys, but it is
also able to provide insights into studies of galaxy formation and evolution.


  The Weyl and Ricci tensors can be algebraically classified in a Lorentzian
spacetime of arbitrary dimensions using alignment theory. Used in tandem with
the boost weight decomposition and curvature operators, the algebraic
classification of the Weyl tensor and the Ricci tensor in higher dimensions can
then be refined utilizing their eigenbivector and eigenvalue structure,
respectively. In particular, for a tensor of a particular algebraic type, the
associated operator will have a restricted eigenvector structure, and this can
then be used to determine necessary conditions for a particular algebraic type.
We shall present an analysis of the discriminants of the associated
characteristic equation for the eigenvalues of an operator to determine the
conditions on (the associated) curvature tensor for a given algebraic type. We
will describe an algorithm which enables us to completely determine the
eigenvalue structure of the curvature operator, up to degeneracies, in terms of
a set of discriminants. We then express these conditions (discriminants) in
terms of these polynomial curvature invariants. In particular, we can use the
techniques described to study the necessary conditions in arbitrary dimensions
for the Weyl and Ricci curvature operators (and hence the higher dimensional
Weyl and Ricci tensors) to be of algebraic type II or D, and create syzygies
which are necessary for the special algebraic type to be fulfilled. We are
consequently able to determine the necessary conditions in terms of simple
scalar polynomial curvature invariants in order for the higher dimensional Weyl
and Ricci tensors to be of type II or D. We explicitly determine the scalar
polynomial curvature invariants for a Weyl or Ricci tensor to be of type II or
D in 5D. A number of simple examples are presented and, in particular, we
present a detailed analysis of the important example of a 5D rotating black
ring.


  Transferability of adversarial samples became a serious concern due to their
impact on the reliability of machine learning system deployments, as they find
their way into many critical applications. Knowing factors that influence
transferability of adversarial samples can assist experts to make informed
decisions on how to build robust and reliable machine learning systems. The
goal of this study is to provide insights on the mechanisms behind the
transferability of adversarial samples through an attack-centric approach. This
attack-centric perspective interprets how adversarial samples would transfer by
assessing the impact of machine learning attacks (that generated them) on a
given input dataset. To achieve this goal, we generated adversarial samples
using attacker models and transferred these samples to victim models. We
analyzed the behavior of adversarial samples on victim models and outlined four
factors that can influence the transferability of adversarial samples. Although
these factors are not necessarily exhaustive, they provide useful insights to
researchers and practitioners of machine learning systems.


  The relation between Geisteswissenschaft and Naturwissenschaft has been
discussed by Munster in hep-th/9305104. The plan of this paper is to begin with
the empty set; use it to form sets and quivers (sets of points plus sets of
arrows between pairs of points); and then use them to make complex vector
spaces and to get the A-D-E Coxeter-Dynkin diagrams. The Dn Spin(2n) Lie
algebras have spinor representations to describe fermions. D4 Spin(8) triality
gives automorphisms among its vector and two half-spinor representations. D5
Spin(10) contains both Spin(8) and the complexification of the vector
representation of Spin(8). E6 contains both Spin(10) and the two half-spinor
representations of Spin(10), and therefore contains the adjoint representation
of Spin(8) and the complexifications of the vector and the two half-spinor
representations of Spin(8). E6 is the basis for construction of a fundamental
model of physics that is consistent with experiment (see hep-th/9302030,
hep-ph/9301210).


  We study the long-range effective drift and diffusivity of a particle in a
random medium moving subject to a given molecular diffusivity and a local
drift. The local drift models the effect of a random electrostatic field on a
neutral but polarizable molecule. Although the electrostatic field is assumed
to obey Gaussian statistics the induced statistics of the drift velocity field
are non-Gaussian.
  We show that a four-loop perturbation theory calculation of the effective
diffusivity is in rather good agreement with the outcome of a numerical
simulation for a reasonable range of the disorder parameter. We also measure
the effective drift in our simulation and confirm the validity of the
``Einstein relation'' that expresses the equality of the renormalization
factors, induced by the random medium, for the effective drift and effective
diffusivity, relative to their molecular values. The Einstein relation has
previously only been confirmed for Gaussian random drift fields. The simulation
result, for our non-Gaussian drift model, is consistent with a previous
theoretical analysis showing the Einstein relation should remain true,
independently of the precise character of the statistics of the drift velocity
field.


  Multipartite entanglement is one of the crucial resources in quantum
information processing tasks such as quantum metrology, quantum computing and
quantum communications. It is essential to verify not only the multipartite
entanglement, but also the entanglement structure in both fundamental theories
and the applications of quantum information technologies. However, it is proved
to be challenging to detect the entanglement structures, including entanglement
depth, entanglement intactness and entanglement stretchability, especially for
general states and large-scale quantum systems. By using the partitions of the
tensor product space we propose a systematic method to construct powerful
entanglement witnesses which identify better the multipartite entanglement
structures. Besides, an efficient algorithm using semi-definite programming and
a gradient descent algorithm are designed to detect entanglement structure from
the inner polytope of the convex set containing all the states with the same
entanglement structure. We demonstrate by detailed examples that our criteria
perform better than other known ones. Our results may be applied to many
quantum information processing tasks.


  Based on previous results of digital topology, this paper focuses on
algorithms of topological invariants of objects in 2D and 3D Digital Spaces. We
specifically interest in solving hole counting of 2D objects and genus of
closed surface in 3D. We first prove a new formula for hole counting in 2D. The
number of of holes is $h=1 + (|C_4|-|C_2|)/4$ where $C_4$ and $C_2$ are sets of
inward and outward corner points, respectively.
  This paper mainly deals with algorithm design and implementation of practical
computation of topological invariants in digital space. The algorithms relating
to data structures, and pathological case detection and original data
modification are main issues.
  This paper designed fast algorithms for topological invariants such as
connected components, hole counting in 2D and boundary surface genus for 3D.
For 2D images, we designed a linear time algorithm to solve hole counting
problem. In 3D, we designed also O(n) time algorithm to get genus of the closed
surface. These two algorithms are both in $O(\log n)$ space complexity.


  This paper discusses the transmission line pulse (TLP) analysis, generally
used for electrostatic discharge (ESD) device characterization, as high
potential usable tool also for non-ESD structures. TLP technique, combined with
DC and pulsed I-V characterization, is performed to study the contribution of
trap states on current conduction in metal-insulator-metal (MIM) capacitors
with an HfAlO stack. The importance of the above mentioned methods is
demonstrated by comparing two generations of samples with slightly different
charge trapping mechanisms; their impact on the current conduction is
furthermore studied by decreasing the pulse width down to 50nsec. TLP analysis
is finally discussed as interesting method to investigate the influence of trap
states on the device robustness. The evaluation of breakdown voltage for
different time pulses allows to discriminate whether different failure
mechanisms occur or not and to establish the impact of trap states on
short-term reliability.


  In homotopy category away from $2$, we decompose a simply connected closed
$7$-manifold into indecomposable pieces after suspension. As an immediate
consequence, this shows certain rigidity property of the manifold. Interesting
examples include the respective suspension decompositions of string
$7$-manifolds and generalized Aloff-Wallach manifolds.


  We prove that all harmonic maps from $\mathbb R^2$ to $\mathbb S^2$ with
finite energy are nondegenerate. That is, for any harmonic map $u$ from
$\mathbb R^2$ to $\mathbb S^2$ of degree $m$ (in $\mathbb Z$), all bounded
kernel maps of the linearized operator $L_u$ at $u$ are generated by these
harmonic maps near $u$ and hence the real dimension of bounded kernel space of
$L_u$ is $4|m|+2$.


  The atmospheres of small, potentially rocky exoplanets are expected to cover
a diverse range in composition and mass. Studying such objects therefore
requires flexible and wide-ranging modeling capabilities. We present in this
work the essential development steps that lead to our flexible radiative
transfer module, REDFOX, and validate REDFOX for the Solar system planets
Earth, Venus and Mars, as well as for steam atmospheres. REDFOX is a
k-distribution model using the correlated-k approach with random overlap method
for the calculation of opacities used in the $\delta$-two-stream approximation
for radiative transfer. Opacity contributions from Rayleigh scattering, UV /
visible cross sections and continua can be added selectively. With the improved
capabilities of our new model, we calculate various atmospheric scenarios for
K2-18b, a super-Earth / sub-Neptune with $\sim$8 M$_\oplus$ orbiting in the
temperate zone around an M-star, with recently observed H$_2$O spectral
features in the infrared. We model Earth-like, Venus-like, as well as H$_2$-He
primary atmospheres of different Solar metallicity and show resulting climates
and spectral characteristics, compared to observed data. Our results suggest
that K2-18b has an H$_2$-He atmosphere with limited amounts of H$_2$O and
CH$_4$. Results do not support the possibility of K2-18b having a water
reservoir directly exposed to the atmosphere, which would reduce atmospheric
scale heights, hence too the amplitudes of spectral features inconsistent with
the observations. We also performed tests for H$_2$-He atmospheres up to 50
times Solar metallicity, all compatible with the observations.


  Activity of modern scholarship creates online footprints galore. Along with
traditional metrics of research quality, such as citation counts, online images
of researchers and institutions increasingly matter in evaluating academic
impact, decisions about grant allocation, and promotion. We examined 400
biographical Wikipedia articles on academics from four scientific fields to
test if being featured in the world's largest online encyclopedia is correlated
with higher academic notability (assessed through citation counts). We found no
statistically significant correlation between Wikipedia articles metrics
(length, number of edits, number of incoming links from other articles, etc.)
and academic notability of the mentioned researchers. We also did not find any
evidence that the scientists with better WP representation are necessarily more
prominent in their fields. In addition, we inspected the Wikipedia coverage of
notable scientists sampled from Thomson Reuters list of "highly cited
researchers". In each of the examined fields, Wikipedia failed in covering
notable scholars properly. Both findings imply that Wikipedia might be
producing an inaccurate image of academics on the front end of science. By
shedding light on how public perception of academic progress is formed, this
study alerts that a subjective element might have been introduced into the
hitherto structured system of academic evaluation.


  Using direct numerical simulations we study dynamo action under the
Taylor-Green forcing with Prandtl number less than one. We observe bistability
with a weak magnetic field branch and a strong magnetic field branch. Both the
dynamo branches undergo subcritical dynamo transition. We also observe host of
dynamo states including constant, periodic, quasiperiodic, and chaotic magnetic
fields. One of the chaotic state originates through a quasiperiodic route with
phase locking, while another chaotic attractor appears to follow
Newhouse-Ruelle-Takens route to chaos. We also observe intermittent transitions
among quasiperiodic and chaotic states for a given Taylor-Green forcing.


  We investigate the roles of stochastic grain heating in the formation of
complex organic molecules (COMs) in cold cores, where COMs have been detected.
Two different types of grain-size distributions are used in the chemical
models. The first one is the MRN distribution, and the second one considers
grain coagulation to study its effects on the chemical evolution in these
environments. The macroscopic Monte Carlo method is used to perform the
two-phase chemical model simulations. We find that (1) grain coagulation can
affect certain gas-phase species, such as CO$_2$ and N$_2$H$^+$, in the cold
core environments, which can be attributed to the volatile precursors
originating from the small grains with temperature fluctuations; (2) grains
with radii around 4.6 $\times$ 10$^{-3}$ $\mu$m contribute most to the
production of COMs on dust grains under cold core conditions, while few species
can be formed on even smaller grains with radii less than 2 $\times$ 10$^{-3}$
$\mu$m; (3) COMs formed on stochastically heated grains could help explain the
observed abundances of gas-phase COMs in cold cores.


  We prove that if $\mathbb{F}$ is an algebraically closed field of zero
characteristic which has infinite transcendence degree over $\mathbb{Q}$, then
there exists a field automorphism $\varphi$ of ${\rm SL}_n(\mathbb{F})$ and
${\rm GL}_n(\mathbb{F})$ such that $R(\varphi)=1$. This fact implies that ${\rm
SL}_n(\mathbb{F})$ and ${\rm GL}_n(\mathbb{F})$ do not possess the
$R_{\infty}$-property. However, if the transcendece degree of $\mathbb{F}$ over
$\mathbb{Q}$ is finite, then ${\rm SL}_n(\mathbb{F})$ and ${\rm
GL}_n(\mathbb{F})$ are known to possess the $R_{\infty}$-property.


  The Solar Eclipse Coronal Imaging System (SECIS) is a simple and extremely
fast, high-resolution imaging instrument designed for studies of the solar
corona. Light from the corona (during, for example, a total solar eclipse) is
reflected off a heliostat and passes via a Schmidt-Cassegrain telescope and
beam splitter to two CCD cameras capable of imaging at 60 frames a second. The
cameras are attached via SCSI connections to a purpose-built PC that acts as
the data acquisition and storage system. Each optical channel has a different
filter allowing observations of the same events in both white light and in the
green line (Fe XIV at 5303 A). Wavelet analysis of the stabilized images has
revealed high frequency oscillations which may make a significant contribution
on the coronal heating process. In this presentation we give an outline of the
instrument and its future development.


  Current communication networks use design methodologies that prevent the
realization of maximum network efficiency. In the first place, while users'
perception of satisfactory service diverges widely, current networks are
designed to be a "universal fit," where they are generally over-engineered to
deliver services appealing to all types of users. Also, current networks lack
user-level data cognitive intelligence that would enable fast personalized
network decisions and actions through automation. Thus, in this article, we
propose the utilization of AI, big data analytics, and real-time non-intrusive
user feedback in order to enable the personalization of wireless networks.
Based on each user's actual QoS requirements and context, a multi-objective
formulation enables the network to micro-manage and optimize the provided QoS
and user satisfaction levels simultaneously. Moreover, in order to enable user
feedback tracking and measurement, we propose a user satisfaction model based
on the zone of tolerance concept. Furthermore, we propose a big-data-driven and
AI-based personalization framework to integrate personalization into wireless
networks. Finally, we implement a personalized network prototype to demonstrate
the proposed personalization concept and its potential benefits through a case
study. The case study shows how personalization can be realized to enable the
efficient optimization of network resources such that certain requirement
levels of user satisfaction and revenue in the form of saved resources are
achieved.


  The last few years have seen an explosion of interest in hydrodynamic effects
in interacting electron systems in ultra-pure materials. In this paper we
briefly review the recent advances, both theoretical and experimental, in the
hydrodynamic approach to electronic transport in graphene, focusing on viscous
phenomena, Coulomb drag, non-local transport measurements, and possibilities
for observing nonlinear effects.


  We model the spectral quantum-mechanical purity of heralded single photons
from a photon-pair source based on nondegenerate spontaneous four-wave mixing
taking the impact of distributed dispersion fluctuations into account. The
considered photon-pair-generation scheme utilizes pump-pulse walk-off to
produce pure heralded photons and phase matching is achieved through the
dispersion properties of distinct spatial modes in a few-mode silica step-index
fiber. We show that fiber-core-radius fluctuations in general severely impact
the single-photon purity. Furthermore, by optimizing the fiber design we show
that generation of single photons with very high spectral purity is feasible
even in the presence of large core-radius fluctuations. At the same time,
contamination from spontaneous Raman scattering is greatly mitigated by
separating the single-photon frequency by more than 32 THz from the pump
frequency.


  In most experiments on the Casimir force the comparison between measurement
data and theory was done using the concept of the root-mean-square deviation, a
procedure that has been criticized in literature. Here we propose a special
statistical analysis which should be performed separately for the experimental
data and for the results of the theoretical computations. In so doing, the
random, systematic, and total experimental errors are found as functions of
separation, taking into account the distribution laws for each error at 95%
confidence. Independently, all theoretical errors are combined to obtain the
total theoretical error at the same confidence. Finally, the confidence
interval for the differences between theoretical and experimental values is
obtained as a function of separation. This rigorous approach is applied to two
recent experiments on the Casimir effect.


  We derive exact finite-size corrections for the free energy $F$ of the Ising
model on the ${\cal M} \times 2 {\cal N}$ square lattice with Brascamp-Kunz
boundary conditions. We calculate ratios $r_p(\rho)$ of $p$th coefficients of F
for the infinitely long cylinder (${\cal M} \to \infty$) and the infinitely
long Brascamp-Kunz strip (${\cal N} \to \infty$) at varying values of the
aspect ratio $\rho={(\cal M}+1) / 2{\cal N}$. Like previous studies have shown
for the two-dimensional dimer model, the limiting values $p \to \infty$ of
$r_p(\rho)$ exhibit abrupt anomalous behaviour at certain values of $\rho$.
These critical values of $\rho$ and the limiting values of the
finite-size-expansion-coefficient ratios differ, however, between the two
models.


  This paper presents AdaChain, a learning-based blockchain framework that
adaptively chooses the best permissioned blockchain architecture in order to
optimize effective throughput for dynamic transaction workloads. AdaChain
addresses the challenge in the Blockchain-as-a-Service (BaaS) environments,
where a large variety of possible smart contracts are deployed with different
workload characteristics. AdaChain supports automatically adapting to an
underlying, dynamically changing workload through the use of reinforcement
learning. When a promising architecture is identified, AdaChain switches from
the current architecture to the promising one at runtime in a way that respects
correctness and security concerns. Experimentally, we show that AdaChain can
converge quickly to optimal architectures under changing workloads,
significantly outperform fixed architectures in terms of the number of
successfully committed transactions, all while incurring low additional
overhead.


  Second magnetization peak (SMP) in hole-doped cuprates and iron pnictide
superconductors has been widely explored. However, similar feature in the
family of electron-doped cuprates is not common. Here, we report the vortex
dynamics study in the single crystal of an electron-doped cuprate
Pr$_{0.87}$LaCe$_{0.13}$CuO$_4$ superconductor using dc magnetization
measurements. A SMP feature in the isothermal $M(H)$ was observed for
$H$$\parallel$$ab$-planes. On the other hand, no such feature was observed for
$H$$\parallel$$c$-axis in the crystal. Using magnetic relaxation data, a
detailed analysis of activation pinning energy via collective creep theory
suggests an elastic to plastic creep crossover across the SMP. Moreover, for
$H$$\parallel$$ab$, a peak in the temperature dependence of critical current
density is also observed near 7 K, which is likely be related to a dimensional
crossover (3D-2D) associated to the emergence of Josephson vortices at low
temperatures. The anisotropy parameter obtained $\gamma$ $\approx$ 8-11
indicates the 3D nature of vortex lattice mainly for $H$$\parallel$$c$-axis.
The $H$-$T$ phase diagrams for $H$$\parallel$$c$ and $H$$\parallel$$ab$ are
presented.


  We consider parametrized systems of generalized polynomial equations (with
real exponents) in $n$ positive variables and involving $m$ monomials with
positive parameters; that is, $x \in \mathbb{R}^n_>$ such that $A \, (c \circ
x^B) = 0$ with coefficient matrix $A \in \mathbb{R}^{l \times m}$, exponent
matrix $B \in \mathbb{R}^{n \times m}$, parameter vector $c \in
\mathbb{R}^m_>$, and componentwise product $\circ$. We instantiate a general
result for polynomial inequalities obtained in parallel work. In the setting of
equations, the relevant geometric objects of the problem are a polytope arising
from the coefficients and two subspaces representing monomial differences and
dependencies. The dimension of the latter subspace (the monomial dependency
$d$) is crucial. Indeed, we rewrite the problem in terms of $d$ binomial
conditions on the "coefficient polytope", involving $d$ monomials in the
parameters. Thereby, we obtain a classification of polynomial equation systems.
If $d=0$, solutions exist (for all $c$) and can be parametrized explicitly. If
$d>0$, the analysis requires additional objects and methods such as
sign-characteristic functions, Descartes' rule of signs for functions, and
Wronskians. Our framework allows a unified treatment of multivariate polynomial
equations. We demonstrate its novelty and relevance in three examples and two
classes of examples from real fewnomial theory. In particular, we study (i) $n$
trinomials involving ${n+2}$ monomials in $n$ variables and (ii) one trinomial
and one $t$-nomial in two variables. For (ii), we improve previous upper bounds
for the number of positive solutions.


  We propose a novel framework to learn 3D point cloud semantics from 2D
multi-view image observations containing pose error. On the one hand, directly
learning from the massive, unstructured and unordered 3D point cloud is
computationally and algorithmically more difficult than learning from
compactly-organized and context-rich 2D RGB images. On the other hand, both
LiDAR point cloud and RGB images are captured in standard automated-driving
datasets. This motivates us to conduct a "task transfer" paradigm so that 3D
semantic segmentation benefits from aggregating 2D semantic cues, albeit pose
noises are contained in 2D image observations. Among all difficulties, pose
noise and erroneous prediction from 2D semantic segmentation approaches are the
main challenges for the task transfer. To alleviate the influence of those
factor, we perceive each 3D point using multi-view images and for each single
image a patch observation is associated. Moreover, the semantic labels of a
block of neighboring 3D points are predicted simultaneously, enabling us to
exploit the point structure prior to further improve the performance. A
hierarchical full attention network~(HiFANet) is designed to sequentially
aggregates patch, bag-of-frames and inter-point semantic cues, with
hierarchical attention mechanism tailored for different level of semantic cues.
Also, each preceding attention block largely reduces the feature size before
feeding to the next attention block, making our framework slim. Experiment
results on Semantic-KITTI show that the proposed framework outperforms existing
3D point cloud based methods significantly, it requires much less training data
and exhibits tolerance to pose noise. The code is available at
https://github.com/yuhanghe01/HiFANet.


  Vibrational spectra of proteins and topologically disordered solids display a
common anomaly at low frequencies, known as Boson peak. We show that such
feature in globular proteins can be deciphered in terms of an energy landscape
picture, as it is for glassy systems. Exploiting the tools of Euclidean random
matrix theory, we clarify the physical origin of such anomaly in terms of a
mechanical instability of the system. As a natural explanation, we argue that
such instability is relevant for proteins in order for their molecular
functions to be optimally rooted in their structures.


  A kombucha is a tea and sugar fermented by over sixty kinds of yeasts and
bacteria. This symbiotic community produces kombucha mats, which are
cellulose-based hydrogels. The kombucha mats can be used as an alternative to
animal leather in industry and fashion once they have been dried and cured.
Prior to this study, we demonstrated that living kombucha mats display dynamic
electrical activity and distinct stimulating responses. For use in organic
textiles, cured mats of kombucha are inert. To make kombucha wearables
functional, it is necessary to incorporate electrical circuits. We demonstrate
that creating electrical conductors on kombucha mats is possible. After
repeated bending and stretching, the circuits maintain their functionality. In
addition, the abilities and electronic properties of the proposed kombucha,
such as being lighter, less expensive, and more flexible than conventional
electronic systems, pave the way for their use in a diverse range of
applications.


  We introduce a second quantization scheme based on quasinormal modes, which
are the dissipative modes of leaky optical cavities and plasmonic resonators
with complex eigenfrequencies. The theory enables the construction of
multi-plasmon/photon Fock states for arbitrary three-dimensional dissipative
resonators and gives a solid understanding to the limits of phenomenological
dissipative Jaynes-Cummings models. In the general case, we show how different
quasinormal modes interfere through an off-diagonal mode coupling and
demonstrate how these results affect cavity-modified spontaneous emission. To
illustrate the practical application of the theory, we show examples using a
gold nanorod dimer and a hybrid dielectric-metal cavity structure.


  Many elusive quantum phenomena emerge from the interaction of a quantum
system with its classical environment. Quantum simulators enable us to program
this interaction by using measurement operations. Measurements generally remove
part of the quantum entanglement built between the qubits inside a simulator.
While in simple cases entanglement may disappear at a constant rate as we
measure qubits one by one, the evolution of entanglement under measurements for
a given class of quantum states is generally unknown. Here, we show that
consecutive measurements of the qubits in a quantum simulator can lead to
criticality, separating two phases of entanglement. We prepare an entangled
superposition of ground states to a classical spin model and show that
progressively measuring the qubits drives the simulator into a spin glass phase
of entanglement. By entangling and measuring up to 48 qubits in this fashion,
we determine the vitrification point and its critical exponent, which obey spin
glass theory exactly. Our findings show that measurements alone can trigger
entanglement criticality and suggest that coupling to a classical environment
can drive critical phenomena in more general quantum states.


  The response of an infinite, periodic, insulating, solid to an
infinitesimally small electric field is investigated in the framework of
Density Functional Theory. We find that the applied perturbing potential is not
a unique functional of the periodic density change~: it depends also on the
change in the macroscopic {\em polarization}. Moreover, the dependence of the
exchange-correlation energy on polarization induces an exchange-correlation
electric field. These effects are exhibited for a model semiconductor. We also
show that the scissor-operator technique is an approximate way of bypassing
this polarization dependence.


  Learning to follow human instructions is a long-pursued goal in artificial
intelligence. The task becomes particularly challenging if no prior knowledge
of the employed language is assumed while relying only on a handful of examples
to learn from. Work in the past has relied on hand-coded components or manually
engineered features to provide strong inductive biases that make learning in
such situations possible. In contrast, here we seek to establish whether this
knowledge can be acquired automatically by a neural network system through a
two phase training procedure: A (slow) offline learning stage where the network
learns about the general structure of the task and a (fast) online adaptation
phase where the network learns the language of a new given speaker. Controlled
experiments show that when the network is exposed to familiar instructions but
containing novel words, the model adapts very efficiently to the new
vocabulary. Moreover, even for human speakers whose language usage can depart
significantly from our artificial training language, our network can still make
use of its automatically acquired inductive bias to learn to follow
instructions more effectively.


  A solid slender beam of length $L$, made from a material of Young's modulus
$Y$ and subject to a gentle compressive force $F$, requires a volume of
material proportional to $L^{3}f^{1/2}$ [where $f\equiv F/(YL^{2})\ll 1$] in
order to be stable against Euler buckling. By constructing a hierarchical space
frame, we are able to systematically change the scaling of required material
with $f$ so that it is proportional to $L^{3}f^{(G+1)/(G+2)}$, through changing
the number of hierarchical levels $G$ present in the structure. Based on simple
choices for the geometry of the space frames, we provide expressions specifying
in detail the optimal structures (in this class) for different values of the
loading parameter $f$. These structures may then be used to create effective
materials which are elastically isotropic and have the combination of low
density and high crush strength. Such a material could be used to make
light-weight components of arbitrary shape.


  We study role of the Hall diffusion in the magnetic star-disc interaction. In
a simplified steady state configuration, the total torque is calculated in
terms of the fastness parameter and a new term because of the Hall diffusion.
We show the total torque reduces as the Hall term becomes more significant.
Also, the critical fastness parameter (at which the total torque is zero)
reduces because of the Hall diffusion.


  A fundamental problem in science and engineering is designing optimal control
policies that steer a given system towards a desired outcome. This work
proposes Control Physics-Informed Neural Networks (Control PINNs) that
simultaneously solve for a given system state, and for the optimal control
signal, in a one-stage framework that conforms to the underlying physical laws.
Prior approaches use a two-stage framework that first models and then controls
a system in sequential order. In contrast, a Control PINN incorporates the
required optimality conditions in its architecture and in its loss function.
The success of Control PINNs is demonstrated by solving the following open-loop
optimal control problems: (i) an analytical problem, (ii) a one-dimensional
heat equation, and (iii) a two-dimensional predator-prey problem.


  We have obtained polarimetric measurements of asteroid (101955) Bennu, a
presumably primitive near-Earth object (NEO) that is the target of NASA's
sample return mission OSIRIS-REx. During our observing campaign, Bennu was
visible from Earth under a wide range of illumination conditions, with
phase-angle in the range 16 to 57 degrees. Together with (3200) Phaethon and
(152679) 1998 KU2, observed very recently, Bennu is the only existing example
of a primitive NEO observed in polarimetric mode over a wide interval of phase
angles. Based on our polarimetric data, we propose that Bennu belongs to the
unusual F taxonomic class defined in the 80s. According to previous works, the
F-class includes objects with cometary features. This fact can be of great
importance for the interpretation of the results of the exploration of this
object by OSIRIS-REx. From polarimetry we also derive an estimate of the
geometric albedo of Bennu: 0.059 +/- 0.003.


  $f$-divergences are a general class of divergences between probability
measures which include as special cases many commonly used divergences in
probability, mathematical statistics and information theory such as
Kullback-Leibler divergence, chi-squared divergence, squared Hellinger
distance, total variation distance etc. In this paper, we study the problem of
maximizing or minimizing an $f$-divergence between two probability measures
subject to a finite number of constraints on other $f$-divergences. We show
that these infinite-dimensional optimization problems can all be reduced to
optimization problems over small finite dimensional spaces which are tractable.
Our results lead to a comprehensive and unified treatment of the problem of
obtaining sharp inequalities between $f$-divergences. We demonstrate that many
of the existing results on inequalities between $f$-divergences can be obtained
as special cases of our results and we also improve on some existing non-sharp
inequalities.


  CRESST-II, standing for Cryogenic Rare Events Search with Superconducting
Thermometers phase II, is an experiment searching for Dark Matter. In the LNGS
facility in Gran Sasso, Italy, a cryogenic detector setup is operated in order
to detect WIMPs by elastic scattering off nuclei, generating phononic lattice
excitations and scintillation light. The thermometers used in the experiment
consist of a tungsten thin-film structure evaporated onto the CaWO4 absorber
crystal. The process of evaporation causes a decrease in the scintillation
light output. This, together with the need of a big-scale detector production
for the upcoming EURECA experiment lead to investigations for producing
thermometers on smaller crystals which are glued onto the absorber crystal. In
our Run 31 we tested composite detectors for the first time in the Gran Sasso
setup. They seem to produce higher light yields as hoped and could provide an
additional time based discrimination mechanism for low light yield clamp
events.


  We study the effect of a DC magnetic field on the phase sensitivity of a
double-lambda system coupled by two laser fields, a probe and a pump. It is
demonstrated that the gain and the refractive index of the probe can be
controlled by either the magnetic field or the relative phase between the two
laser fields. More interestingly, when the system reduces to a single-lambda
system, turning on the magnetic field transforms the system from a
phase-insensitive process to a phase-sensitive one. In the pulsed-probe regime,
we observed switching between slow and fast light when the magnetic field or
the relative phase was adjusted. Experiments using a coated 87Rb vapor cell
produced results in good agreement with our numerical simulation. This work
provides a novel and simple means to manipulate phase sensitive
electromagnetically-induced-transparency or four-wave mixing, and could be
useful for applications in quantum optics, nonlinear optics and magnetometery
based on such systems.


  Visual Place Recognition aims at recognizing previously visited places by
relying on visual clues, and it is used in robotics applications for SLAM and
localization. Since typically a mobile robot has access to a continuous stream
of frames, this task is naturally cast as a sequence-to-sequence localization
problem. Nevertheless, obtaining sequences of labelled data is much more
expensive than collecting isolated images, which can be done in an automated
way with little supervision. As a mitigation to this problem, we propose a
novel Joint Image and Sequence Training protocol (JIST) that leverages large
uncurated sets of images through a multi-task learning framework. With JIST we
also introduce SeqGeM, an aggregation layer that revisits the popular GeM
pooling to produce a single robust and compact embedding from a sequence of
single-frame embeddings. We show that our model is able to outperform previous
state of the art while being faster, using 8 times smaller descriptors, having
a lighter architecture and allowing to process sequences of various lengths.
Code is available at https://github.com/ga1i13o/JIST


  This study presents an innovative solution for powering electric vehicles,
named Charging-as-a-Service (CaaS), that concerns the potential large-scale
adoption of light-duty electric vehicles (LDEV) in the Mobility-as-a-Service
(MaaS) industry. Analogous to the MaaS, the core idea of the CaaS is to
dispatch service vehicles (SVs) that carry modular battery units (MBUs) to
provide LDEVs for mobility service with on-demand battery delivery. The CaaS
system is expected to tackle major bottlenecks of a large-scale LDEV adoption
in the MaaS industry due to the lack of charging infrastructure and excess
waiting and charging time. A hybrid agent-based simulation model (HABM) is
developed to model the dynamics of the CaaS system with SV agents, and a
trip-based stationary charging probability distribution is introduced to
simulate the generation of charging demand for LDEVs. Two dispatching
algorithms are further developed to support the optimal operation of the CaaS.
The model is validated by assuming electrifying all 13,000 yellow taxis in New
York City (NYC) that follow the same daily trip patterns. Multiple scenarios
are analyzed under various SV fleet sizes and dispatching strategies. The
results suggest that optimal deployment of 250 SVs may serve the LDEV fleet in
NYC with an average waiting time of 5 minutes, save the travel distance at over
50 miles per minute, and gain considerable profits of up to $50 per minute.
This study offers significant insights into the feasibility, service
efficiency, and financial sustainability for deploying city-wide CaaS systems
to power the electric MaaS industry.


  A four-dimensional integrable rigid-body system is considered and it is shown
that it represents two twisted three-dimensional Lagrange tops. A polynomial
Lax representation, which doesn't fit neither in Dubrovin's nor in Adler - van
Moerbeke's picture is presented. The algebro-geometric integration procedure is
based on deep facts from the geometry of the Prym varietiesof double coverings
of hypeelliptic curves. The correspondence between all such coverings with Prym
varieties splitted as a sum of two varieties of the same dimension and the
integrable hierarchy associated to the initial system is established.


  ProtoDUNE-DP is a 6x6x6 m3 liquid argon time-projection-chamber (LArTPC)
operated at the Neutrino Platform at CERN in 2019-2020 as a prototype of the
DUNE Far Detector. DUNE is a dual-site experiment for long-baseline neutrino
oscillation studies, neutrino astrophysics and nucleon decay searches. The
light signal in these detectors is crucial to provide precise timing
capabilities. In ProtoDUNE-DP, scintillation light produced by cosmic muons
inthe LArTPC is collected by the photomultiplier tubes (PMTs) placed up to 7 m
away from the point of interaction. The scintillation light production and
propagation processes are analyzed and compared to simulations, improving the
understanding of some liquid argon properties.


  Cycle expansions are an efficient scheme for computing the properties of
chaotic systems. When enumerating the orbits for a cycle expansion not all
orbits that one would expect at first are present --- some are pruned. This
pruning leads to convergence difficulties when computing properties of chaotic
systems. In numerical schemes, I show that pruning reduces the number of
reliable eigenvalues when diagonalizing quantum mechanical operators, and that
pruning slows down the convergence rate of cycle expansion calculations. I then
exactly solve a diffusion model that displays chaos and show that its cycle
expansion develops a branch point.


  I calculate characters of certain representations of loop groups based on non
simply connected Lie groups. This gives a generalization of the Kac-Weyl
character formula.


  The purpose of this study is to show that the exact solutions to Yang-Mills
theory can occur in two-dimensional space-time. We show that the instability of
the stationary solutions for the nonabelian gauge field theories questioned by
the dimensions of space-time. We show that instability of the stationary
solution for the nonabelian gauge field theory questioned by the dimension of
space-time. Another intention of this work is research of the source-free
solutions to Maxwell's equations in a two-dimensional noncommutative temporal
state space-time. We will show that there is comprehensively similarity between
the existence of the bound states of nonabelian gauge theory and $u_\star(1)$.


  Self-reciprocal and self-conjugate-reciprocal polynomials over finite fields
have been of interest due to their rich algebraic structures and wide
applications. Self-reciprocal irreducible monic factors of $x^n-1$ over finite
fields and their applications have been quite well studied. In this paper,
self-conjugate-reciprocal irreducible monic (SCRIM) factors of $x^n-1$ over
finite fields of square order have been focused on. The characterization of
such factors is given together the enumeration formula. In many cases,
recursive formulas for the number of SCRIM factors of $x^n-1$ have been given
as well. As applications, Hermitian complementary dual codes over finite fields
and Hermitian self-dual cyclic codes over finite chain rings of prime
characteristic have been discussed.


  Plasmons in nanostructured metals are widely utilized to trigger strong
light--matter interactions with quantum light sources. While the nonclassical
behavior of such quantum emitters (QEs) is well-understood in this context, the
role of quantum and surface effects in the plasmonic resonator is usually
neglected. Here, we combine the Green's tensor approach with the Feibelman
$d$-parameter formalism to theoretically explore the influence of quantum
surface effects in metal-dielectric layered nanostructures on the relaxation
dynamics of a proximal two-level QE. Having identified electron spill-out as
the dominant source of quantum effects in jellium-like metals, we focus our
study on sodium. Our results reveal a clear splitting in the emission spectrum,
indicative of having reached the strong-coupling regime, and, more importantly,
non-Markovian relaxation dynamics of the emitter. Our findings establish that
strong light--matter coupling is not suppressed by the emergence of
nonclassical surface effects in the optical response of the metal.


  We present a scheme for three-party simultaneous quantum secure direct
communication by using EPR pairs. In the scheme, three legitimate parties can
simultaneously exchange their secret messages. It is also proved to be secure
against the intercept-and-resend attack, the disturbance attack and the
entangled-and-measure attack.


  The balance between exploration and exploitation is a key problem for
reinforcement learning methods, especially for Q-learning. In this paper, a
fidelity-based probabilistic Q-learning (FPQL) approach is presented to
naturally solve this problem and applied for learning control of quantum
systems. In this approach, fidelity is adopted to help direct the learning
process and the probability of each action to be selected at a certain state is
updated iteratively along with the learning process, which leads to a natural
exploration strategy instead of a pointed one with configured parameters. A
probabilistic Q-learning (PQL) algorithm is first presented to demonstrate the
basic idea of probabilistic action selection. Then the FPQL algorithm is
presented for learning control of quantum systems. Two examples (a spin- 1/2
system and a lamda-type atomic system) are demonstrated to test the performance
of the FPQL algorithm. The results show that FPQL algorithms attain a better
balance between exploration and exploitation, and can also avoid local optimal
policies and accelerate the learning process.


  In the present paper, we introduce and study oriented Getzler-Kapranov
complexes. These complexes are generalizations of S. Merkulov's oriented graph
complex. We investigate their relation to the cohomology of moduli spaces of
complex and tropical curves, ribbon graph complexes, and motivic structures in
string topology.


  A geometric approach to time-dependent optimal control problems is proposed.
This formulation is based on the Skinner and Rusk formalism for Lagrangian and
Hamiltonian systems. The corresponding unified formalism developed for optimal
control systems allows us to formulate geometrically the necessary conditions
given by Pontryagin's Maximum Principle, provided that the differentiability
with respect to controls is assumed and the space of controls is open.
Furthermore, our method is also valid for implicit optimal control systems and,
in particular, for the so-called descriptor systems (optimal control problems
including both differential and algebraic equations).


  For a compact, connected metric graphs with a boundary that consists of $k$
vertices, we prove that an arbitrary symmetric $k\times k$ matrix with real
entries can be realized as the Dirichlet-to-Neumann operator for the Laplacian
plus a constant.


  Major mergers or/and the repeated minor mergers lead to dynamical heating of
disks of galaxies. We analyze the available data on the velocity dispersion of
stellar disks of S-S0 galaxies, including the new observational data obtained
at 6m telescope of SAO RAS. As a measure of dynamical (over)heating, we use the
ratio of the observed velocity dispersion to the minimal dispersion which
provides the local stability of the stellar disks with respect to gravitational
perturbations. We came to conclusion that stellar disks in a significant part
of galaxies (including LSB and some S0 galaxies) are close to the marginal
stability condition (or are slightly overheated) -- at least at radial
distances $r\sim$ 2-3 radial scalelenghts. It enables to constrain the role of
merging in the heating of stellar disks: in many cases it seems to be
non-efficient. Marginal stability condition may also be successfully used to
estimate the mass of a disk and the midplane volume gas (stars) densities on
the basis of kinematic measurements.