  Understanding the properties of type III radio bursts in the solar corona and
interplanetary space is one of the best ways to remotely deduce the
characteristics of solar accelerated electron beams and the solar wind plasma.
One feature of all type III bursts is the lowest frequency they reach (or
stopping frequency). This feature reflects the distance from the Sun that an
electron beam can drive the observable plasma emission mechanism. The stopping
frequency has never been systematically studied before from a theoretical
perspective. Using numerical kinetic simulations, we explore the different
parameters that dictate how far an electron beam can travel before it stops
inducing a significant level of Langmuir waves, responsible for plasma radio
emission. We use the quasilinear approach to model self-consistently the
resonant interaction between electrons and Langmuir waves in inhomogeneous
plasma, and take into consideration the expansion of the guiding magnetic flux
tube and the turbulent density of the interplanetary medium. We find that the
rate of radial expansion has a significant effect on the distance an electron
beam travels before enhanced leves of Langmuir waves, and hence radio waves,
cease. Radial expansion of the guiding magnetic flux tube rarefies the electron
stream to the extent that the density of non-thermal electrons is too low to
drive Langmuir wave production. The initial conditions of the electron beam
have a significant effect, where decreasing the beam density or increasing the
spectral index of injected electrons would cause higher type III stopping
frequencies. We also demonstrate how the intensity of large-scale density
fluctuations increases the highest frequency that Langmuir waves can be driven
by the beam and how the magnetic field geometry can be the cause of type III
bursts only observed at high coronal frequencies.


  The half width rule provides a way to consider 1/Nc corrections to hadronic
models containing resonances. Consequences of such ideas for hadron form
factors and Regge trajectories are explored, with special emphasis on the
possibility to describe the spectrum of light and heavy unflavored vector
mesons in a universal way.


  We accurately identify and classify the variability of A-F stars in the
southern continuous viewing zone of the TESS satellite. The brightness limit
was set to 10 mag to ensure the utmost reliability of our results and allow for
spectroscopic follow-up observations using small telescopes. We aim to compare
our findings with existing catalogues of variable stars. The light curves from
TESS and their Fourier transform were used to manually classify stars in our
sample. Cross-matching with other catalogues was performed to identify
contaminants and false positives. We have identified 1171 variable stars (51 %
of the sample). Among these variable stars, 67 % have clear classifications,
which includes $\delta$ Sct and $\gamma$ Dor pulsating stars and their hybrids,
rotationally variables, and eclipsing binaries. We have provided examples of
the typical representatives of variable stars and discussed the ambiguous
cases. We found 20 pairs of stars with the same frequencies and identified the
correct source of the variations. Additionally, we found that the variations in
12 other stars are caused by the contamination with the light of faint nearby
large-amplitude variable stars. To compare our sample with other variable star
catalogues, we have defined two parameters reflecting the agreement in
identification of variable stars and their classification. This comparison
reveals intriguing disagreements in classification ranging from 52 % to 100 %.
However, if we assume that stars without specific types are only marked as
variable, then the agreement is relatively good, ranging from 57 % to 85 %
(disagreement 15-43 %). We have demonstrated that the TESS classification is
superior to the classification based on other photometric surveys. The
classification of stellar variability is complex and requires careful
consideration. Caution should be exercised when using catalogue
classifications.


  As Wireless Sensor Networks are penetrating into the industrial domain, many
research opportunities are emerging. One such essential and challenging
application is that of node localization. A feed-forward neural network based
methodology is adopted in this paper. The Received Signal Strength Indicator
(RSSI) values of the anchor node beacons are used. The number of anchor nodes
and their configurations has an impact on the accuracy of the localization
system, which is also addressed in this paper. Five different training
algorithms are evaluated to find the training algorithm that gives the best
result. The multi-layer Perceptron (MLP) neural network model was trained using
Matlab. In order to evaluate the performance of the proposed method in real
time, the model obtained was then implemented on the Arduino microcontroller.
With four anchor nodes, an average 2D localization error of 0.2953 m has been
achieved with a 12-12-2 neural network structure. The proposed method can also
be implemented on any other embedded microcontroller system.


  The size of the smallest detectable sample feature in an x-ray imaging system
is usually restricted by the spatial resolution of the system. This limitation
can now be overcome using the diffusive dark-field signal, which is generated
by unresolved phase effects or the ultra-small-angle x-ray scattering from
unresolved sample microstructures. A quantitative measure of this dark-field
signal can be useful in revealing the microstructure size or material for
medical diagnosis, security screening and materials science. Recently, we
derived a new method to quantify the diffusive dark-field signal in terms of a
scattering angle using a single-exposure grid-based approach. In this
manuscript, we look at the problem of quantifying the sample microstructure
size from this single-exposure dark-field signal. We do this by quantifying the
diffusive dark-field signal produced by 5 different sizes of polystyrene
microspheres, ranging from 1.0 $\mu$m to 10.8 $\mu$m, to investigate how the
strength of the dark-field signal changes with the sample microstructure size,
$S$. We also explore the feasibility of performing single-exposure dark-field
imaging with a simple equation for the optimal propagation distance given
microstructure with a specific size and thickness, and successfully verify this
equation with experimental data. Our theoretical model predicts that the
dark-field scattering angle is inversely proportional to $\sqrt{S}$, which is
consistent with our experimental data.


  We prove that if the initial hypersurface of the mean curvature flow in
spheres satisfies a sharp pinching condition, then the solution of the flow
converges to a round point or a totally geodesic sphere. Our result improves
the famous convergence theorem due to Huisken [9]. Moreover, we prove a
convergence theorem under the weakly pinching condition. In particular, we
obtain a classification theorem for weakly pinched hypersurfaces. It should be
emphasized that our pinching condition implies that the Ricci curvature of the
initial hypersurface is positive, but does not imply positivity of the
sectional curvature.


  Millimeter-wave (mmWave) bands will be used for the fifth generation
communication systems to support high data rates. For the proper
characterization of the mmWave propagation channel, it is essential to measure
the power angular-delay profile (PADP) of the channel which includes
angle-of-departure (AoD) and angle-of-arrival (AoA) of the multipath components
(MPCs). In this paper, we first describe in detail our 28 GHz channel sounder
where directional horn antennas are placed on rotating gimbals. Then, for this
specific sounder class, we describe and address the following two problems in
extracting the MPCs from the measurements: 1) For the channel measurements at
large distances between the transmitter (TX) and the receiver (RX), it is not
possible to generate the triggering signal for the TX and the RX using a single
clock (SICL). This necessitates the use of separate clocks (SECLs) which
introduces a random timing drift between the clocks. 2) As the positions of the
antennas change during the scanning process, total distance traveled by the
same MPC differs at each measurement. These two errors together cause missing
some of the MPCs and detecting MPCs that do not exist in reality. We propose an
algorithm to correct the clock drift and the errors in the MPC delays due to
the rotation of the antennas. We compare the MPCs from the SICL measurement and
the corrected SECL measurements using a Hungarian algorithm based MPC matching
method. We show that the percentage of the matched MPCs increases from 28.36%
to 74.13% after the correction process.


  In this paper, we study self-dual gravity in the Newman-Penrose formalism. We
specify the self-dual solution space from the Newman-Unti solutions. We show
that the asymptotic symmetries of the self-dual gravity are still the
(extended) BMS symmetries. We transform the self-dual Taub-NUT solution into
the Newman-Unti gauge in analytical form.


  We study a two-speed model with variant drift fields, which generalizes the
Goldstein-Taylor model but the two advection fields are not necessarily in
proportion. Due to the lack of a local equilibrium structure of the steady
state, prevailing hypocoercivity methods could not be directly applied to such
a system. To prove the exponential convergence to equilibrium for this model,
we use a recent generalization of the Gearhart-Pr\"uss theorem [Sci. China
Math. 64 (2021), no. 3, 507-518] to gain semigroup bounds, where the relative
entropy estimate plays a central role in gaining desired resolvent estimates
via a compactness argument.


  Quantum state preparation involves preparing a target state from an initial
system, a process integral to applications such as quantum machine learning and
solving systems of linear equations. Recently, there has been a growing
interest in qumodes due to advancements in the field and their potential
applications. However there is a notable gap in the literature specifically
addressing this area. This paper aims to bridge this gap by providing
performance benchmarks of various optimizers used in state preparation with
Variational Quantum Algorithms. We conducted extensive testing across multiple
scenarios, including different target states, both ideal and sampling
simulations, and varying numbers of basis gate layers. Our evaluations offer
insights into the complexity of learning each type of target state and
demonstrate that some optimizers perform better than others in this context.
Notably, the Powell optimizer was found to be exceptionally robust against
sampling errors, making it a preferred choice in scenarios prone to such
inaccuracies. Additionally, the Simultaneous Perturbation Stochastic
Approximation optimizer was distinguished for its efficiency and ability to
handle increased parameter dimensionality effectively.


  We discuss the discovery potential of a dark force carrier ($Z'$) of very
light mass, $m_{Z'} \lesssim {\cal O}(1-10)$ GeV, at hadron colliders via rare
top quark decays, especially when it decays invisibly in typical search
schemes. We emphasize that the top sector is promising for the discovery of new
particles because top quark pairs are copiously produced at the Large Hadron
Collider. The signal process is initiated by a rare top decay into a bottom
quark and a charged Higgs boson ($H^\pm$) decaying subsequently into a $W$ and
one or multiple $Z'$s. The light $Z'$ can be invisible in collider searches in
various scenarios, and it would be hard to distinguish the relevant collider
signature from the regular $t\bar{t}$ process in the Standard Model. We suggest
a search strategy using the recently proposed on-shell constrained $M_2$
variables. Our signal process is featured by an $\textit{asymmetric}$ event
topology, while the $t\bar{t}$ is $\textit{symmetric}$. The essence behind the
strategy is to evoke some contradiction in the relevant observables by applying
the kinematic variables designed under the assumption of the $t\bar{t}$ event
topology. To see the viability of the proposed technique, we perform Monte
Carlo simulations including realistic effects such as cuts, backgrounds,
detector resolution, and so on at the LHC of $\sqrt{s}=14$ TeV.


  Let $E/F$ be a quadratic extension of a non-Archimedian local field.
Splitting of the 2-fold metaplectic cover of ${\rm Sp}_{2n}(F)$ when restricted
to various subgroups of ${\rm Sp}_{2n}(F)$ plays an important role in
application of the Weil representation of the metaplectic group. In this paper
we prove the splitting of the metaplectic cover of ${\rm GL}_{2}(E)$ over the
subgroups ${\rm GL}_{2}(F)$ and $D_{F}^{\times}$, where $D_{F}$ is the
quaternion division algebra with center $F$, as a first step in our study of
the restriction of representations of metaplectic cover of ${\rm GL}_{2}(E)$ to
${\rm GL}_{2}(F)$ and $D_{F}^{\times}$. These results were suggested to the
author by Professor Dipendra Prasad.


  In this thesis we study sets of points in the plane and their Voronoi
diagrams, in particular when the points coincide. We bring together two ways of
studying point sets that have received a lot of attention in recent years:
Voronoi diagrams and compactifications of configuration spaces. We study moving
and colliding points and this enables us to introduce `limit Voronoi diagrams'.
We define several compactifications by considering geometric properties of
pairs and triples of points. In this way we are able to define a smooth, real
version of the Fulton-MacPherson compactification. We show how to define
Voronoi diagrams on elements of these compactifications and describe the
connection with the limit Voronoi diagrams.


  A search for supersymmetry in the context of general gauge-mediated (GGM)
breaking with the lightest neutralino as the next-to-lightest supersymmetric
particle and the gravitino as the lightest is presented. The data sample
corresponds to an integrated luminosity of 36 inverse picobarns recorded by the
CMS experiment at the LHC. The search is performed using events containing two
or more isolated photons, at least one hadronic jet, and significant missing
transverse energy. No excess of events at high missing transverse energy is
observed. Upper limits on the signal cross section for GGM supersymmetry
between 0.3 and 1.1 pb at the 95% confidence level are determined for a range
of squark, gluino, and neutralino masses, excluding supersymmetry parameter
space that was inaccessible to previous experiments.


  We solve the quaternionic Dirac equation ($\mathbbm H$DE) in the real Hilbert
space, and we ascertain that their free particle solutions set comprises eight
elements in the case of a massive particle, and a four elements solution set in
the case of a massless particle, a richer situation when compared to the four
elements solutions set of the usual complex Dirac equation ($\mathbbm C$DE).
These free particle solutions were unknown in the previous solutions of
anti-hermitian quaternionic quantum mechanics, and constitute an essential
element in order to build a quaternionic quantum field theory ($\mathbbm
H$QFT).


  More and more people rely on Web information and with the advance of Web 2.0
technologies they can increasingly easily participate to the creation of this
information. Country-level politicians could not ignore this trend and have
started to use the Web to promote them or to demote their opponents. This paper
presents how candidates to a French mayor local election and with less budget
have engineered their Web campaign and online reputation. After presenting the
settings of the local election, the Web tools used by the different candidates
and the local journalists are detailed. These tools are evaluated from a
security point of view and the legal issues that they have created are
underlined.


  Magnetic nanoparticle (MNP) has attracted interest in various research fields
due to its special superparamagnetic and strong magneto-optical effects,
especially as contrast agents to enhance the contrast of medical imaging. By
introducing the interaction coefficient, we propose a model of the nonlinear
Faraday rotation of MNP under the excitation of an external alternating
magnetic field. In our homemade device (which can detect the rotation angle as
low as about 2e-7 rad), it has been verified that the higher harmonics of the
Faraday rotation can avoid the interference of paramagnetic and diamagnetic
background at lower concentrations. What's more, the higher harmonics of the
Faraday rotation of MNP can be detected in real time and they have a linear
relationship with concentration. In the future, it is expected to use MNP as a
magneto-optical contrast agent to achieve high-resolution imaging in vivo.


  We present results of anisotropic thermal expansion and low temperature
magnetostriction measurements on YNi$_2$B$_2$C single crystals grown by high
temperature flux and floating zone techniques. Quantum oscillations of
magnetostriction were observed at low temperatures for $H \| c$ starting at
fields significantly below $H_{c2}$ ($H < 0.7 H_{c2}$). Large irreversible,
longitudinal magnetostriction was seen in both, in-plane and along the c-axis,
directions of the applied magnetic field in the intermediate superconducting
state. Anisotropic uniaxial pressure dependencies of $T_c$ were evaluated using
results of zero field, thermal expansion measurements.


  Simulating computationally intractable many-body problems on a quantum
simulator holds great potential to deliver insights into physical, chemical,
and biological systems. While the implementation of Hamiltonian dynamics within
a quantum simulator has already been demonstrated in many experiments, the
problem of initialization of quantum simulators to a suitable quantum state has
hitherto remained mostly unsolved. Here, we show that already a single
dissipatively driven auxiliary particle can efficiently prepare the quantum
simulator in a low-energy state of largely arbitrary Hamiltonians. We
demonstrate the scalability of our approach and show that it is robust against
unwanted sources of decoherence. While our initialization protocol is largely
independent of the physical realization of the simulation device, we provide an
implementation example for a trapped ion quantum simulator.


  During the spreading of a liquid over a solid substrate, the contact line can
stay pinned at sharp edges until the contact angle exceeds a critical value. At
(or sufficiently near) equilibrium, this is known as Gibbs' criterion. Here, we
show both experimentally and theoretically that for completely wetting volatile
liquids there also exists a dynamically-produced critical angle for depinning,
which increases with the evaporation rate. This suggests that one may introduce
a simple modification of the Gibbs' criterion for (de)pinning, that accounts
for the non-equilibrium effect of evaporation.


  We show that the definition and many useful properties of Soergel's functor
$\mathbb{V}$ extend to "universal" variants of the BGG category $\mathcal{O}$,
such as the category which drops the semisimplicity condition on the Cartan
action. We show that this functor naturally factors through a quotient known as
the nondegenerate quotient and show that this quotient admits a "Gelfand-Graev"
action of the Weyl group. Although these categories are not finite length in
any sense, we explicitly compute the kernel of this functor in a universal case
and use this to extend these results to an arbitrary DG category with an action
of a reductive group.
  Along the way, we prove a result which may be of independent interest, which
says that a functor of $G$-categories with a continuous adjoint is an
equivalence if and only if it is an equivalence at each field-valued point,
strengthening a result of Ben-Zvi--Gunningham--Orem.


  Time-dependent orbital-free DFT is an efficient method for calculating the
dynamic properties of large scale quantum systems due to the low computational
cost compared to standard time-dependent DFT. We formalize this method by
mapping the real system of interacting fermions onto a fictitious system of
non-interacting bosons. The dynamic Pauli potential and associated kernel
emerge as key ingredients of time-tependent orbital-free DFT. Using the uniform
electron gas as a model system, we derive an approximate frequency-dependent
Pauli kernel. Pilot calculations suggest that space nonlocality is a key
feature for this kernel. Nonlocal terms arise already in the second order
expansion with respect to unitless frequency and reciprocal space variable
($\frac{\omega}{q\, k_F}$ and $\frac{q}{2\, k_F}$, respectively). Given the
encouraging performance of the proposed kernel, we expect it will lead to more
accurate orbital-free DFT simulations of nanoscale systems out of equilibrium.
Additionally, the proposed path to formulate nonadiabatic Pauli kernels
presents several avenues for further improvements which can be exploited in
future works to improve the results.


  We introduce a class of $f(t)$-factorials, or $f(t)$-Pochhammer symbols, that
includes many, if not most, well-known factorial and multiple factorial
function variants as special cases. We consider the combinatorial properties of
the corresponding generalized classes of Stirling numbers of the first kind
which arise as the coefficients of the symbolic polynomial expansions of these
$f$-factorial functions. The combinatorial properties of these more general
parameterized Stirling number triangles we prove within the article include
analogs to known expansions of the ordinary Stirling numbers by $p$-order
harmonic number sequences through the definition of a corresponding class of
$p$-order $f$-harmonic numbers.


  We are proposing an extension of the recursive neural network that makes use
of a variant of the long short-term memory architecture. The extension allows
information low in parse trees to be stored in a memory register (the `memory
cell') and used much later higher up in the parse tree. This provides a
solution to the vanishing gradient problem and allows the network to capture
long range dependencies. Experimental results show that our composition
outperformed the traditional neural-network composition on the Stanford
Sentiment Treebank.


  Triangular meshes are widely used to represent three-dimensional objects. As
a result, many recent works have address the need for geometric deep learning
on 3D mesh. However, we observe that the complexities in many of these
architectures does not translate to practical performance, and simple deep
models for geometric graphs are competitive in practice. Motivated by this
observation, we minimally extend the update equations of E(n)-Equivariant Graph
Neural Networks (EGNNs) (Satorras et al., 2021) to incorporate mesh face
information, and further improve it to account for long-range interactions
through hierarchy. The resulting architecture, Equivariant Mesh Neural Network
(EMNN), outperforms other, more complicated equivariant methods on mesh tasks,
with a fast run-time and no expensive pre-processing. Our implementation is
available at https://github.com/HySonLab/EquiMesh


  Recommender systems utilizing explicit feedback have witnessed significant
advancements and widespread applications over the past years. However,
generating recommendations in few-shot scenarios remains a persistent
challenge. Recently, large language models (LLMs) have emerged as a promising
solution for addressing natural language processing (NLP) tasks, thereby
offering novel insights into tackling the few-shot scenarios encountered by
explicit feedback-based recommender systems. To bridge recommender systems and
LLMs, we devise a prompting template that generates user and item
representations based on explicit feedback. Subsequently, we integrate these
LLM-processed representations into various recommendation models to evaluate
their significance across diverse recommendation tasks. Our ablation
experiments and case study analysis collectively demonstrate the effectiveness
of LLMs in processing explicit feedback, highlighting that LLMs equipped with
generative and logical reasoning capabilities can effectively serve as a
component of recommender systems to enhance their performance in few-shot
scenarios. Furthermore, the broad adaptability of LLMs augments the
generalization potential of recommender models, despite certain inherent
constraints. We anticipate that our study can inspire researchers to delve
deeper into the multifaceted dimensions of LLMs's involvement in recommender
systems and contribute to the advancement of the explicit feedback-based
recommender systems field.


  In this two-parts paper we propose a decentralized strategy, based on a
game-theoretic formulation, to find out the optimal precoding/multiplexing
matrices for a multipoint-to-multipoint communication system composed of a set
of wideband links sharing the same physical resources, i.e., time and
bandwidth. We assume, as optimality criterion, the achievement of a Nash
equilibrium and consider two alternative optimization problems: 1) the
competitive maximization of mutual information on each link, given constraints
on the transmit power and on the spectral mask imposed by the radio spectrum
regulatory bodies; and 2) the competitive maximization of the transmission
rate, using finite order constellations, under the same constraints as above,
plus a constraint on the average error probability. In Part I of the paper, we
start by showing that the solution set of both noncooperative games is always
nonempty and contains only pure strategies. Then, we prove that the optimal
precoding/multiplexing scheme for both games leads to a channel diagonalizing
structure, so that both matrix-valued problems can be recast in a simpler
unified vector power control game, with no performance penalty. Thus, we study
this simpler game and derive sufficient conditions ensuring the uniqueness of
the Nash equilibrium. Interestingly, although derived under stronger
constraints, incorporating for example spectral mask constraints, our
uniqueness conditions have broader validity than previously known conditions.
Finally, we assess the goodness of the proposed decentralized strategy by
comparing its performance with the performance of a Pareto-optimal centralized
scheme. To reach the Nash equilibria of the game, in Part II, we propose
alternative distributed algorithms, along with their convergence conditions.


  I present a numerical algorithm for direct evaluation of multiple Grassmann
integrals. The approach is exact and suffers no Fermion sign problems. Memory
requirements grow exponentially with the interaction range and the transverse
size of the system. Low dimensional systems of order a thousand Grassmann
variables can be evaluated on a workstation.


  We consider the problem of learning predictive models from longitudinal data,
consisting of irregularly repeated, sparse observations from a set of
individuals over time. Such data often exhibit {\em longitudinal correlation}
(LC) (correlations among observations for each individual over time), {\em
cluster correlation} (CC) (correlations among individuals that have similar
characteristics), or both. These correlations are often accounted for using
{\em mixed effects models} that include {\em fixed effects} and {\em random
effects}, where the fixed effects capture the regression parameters that are
shared by all individuals, whereas random effects capture those parameters that
vary across individuals. However, the current state-of-the-art methods are
unable to select the most predictive fixed effects and random effects from a
large number of variables, while accounting for complex correlation structure
in the data and non-linear interactions among the variables. We propose
Longitudinal Multi-Level Factorization Machine (LMLFM), to the best of our
knowledge, the first model to address these challenges in learning predictive
models from longitudinal data. We establish the convergence properties, and
analyze the computational complexity, of LMLFM. We present results of
experiments with both simulated and real-world longitudinal data which show
that LMLFM outperforms the state-of-the-art methods in terms of predictive
accuracy, variable selection ability, and scalability to data with large number
of variables. The code and supplemental material is available at
\url{https://github.com/junjieliang672/LMLFM}.


  This paper describes a novel text-to-speech (TTS) technique based on deep
convolutional neural networks (CNN), without use of any recurrent units.
Recurrent neural networks (RNN) have become a standard technique to model
sequential data recently, and this technique has been used in some cutting-edge
neural TTS techniques. However, training RNN components often requires a very
powerful computer, or a very long time, typically several days or weeks. Recent
other studies, on the other hand, have shown that CNN-based sequence synthesis
can be much faster than RNN-based techniques, because of high
parallelizability. The objective of this paper is to show that an alternative
neural TTS based only on CNN alleviate these economic costs of training. In our
experiment, the proposed Deep Convolutional TTS was sufficiently trained
overnight (15 hours), using an ordinary gaming PC equipped with two GPUs, while
the quality of the synthesized speech was almost acceptable.


  This is a review of the observational and theoretical evidence for nanoflare
heating of the magnetically-closed corona.


  We recently identified a population of low surface brightness objects in the
field of the z=0.023 Coma cluster, using the Dragonfly Telephoto Array. Here we
present Keck spectroscopy of one of the largest of these "ultra-diffuse
galaxies" (UDGs), confirming that it is a member of the cluster. The galaxy has
prominent absorption features, including the Ca II H+K lines and the G-band,
and no detected emission lines. Its radial velocity of cz=6280 +- 120 km/s is
within the 1 sigma velocity dispersion of the Coma cluster. The galaxy has an
effective radius of 4.3 +- 0.3 kpc and a Sersic index of 0.89 +- 0.06, as
measured from Keck imaging. We find no indications of tidal tails or other
distortions, at least out to a radius of ~2 r_e. We show that UDGs are located
in a previously sparsely populated region of the size - magnitude plane of
quiescent stellar systems, as they are ~6 magnitudes fainter than normal
early-type galaxies of the same size. It appears that the luminosity
distribution of large quiescent galaxies is not continuous, although this could
largely be due to selection effects. Dynamical measurements are needed to
determine whether the dark matter halos of UDGs are similar to those of
galaxies with the same luminosity or to those of galaxies with the same size.


  Hong-Ou-Mandel (HOM) interference, the bunching of indistinguishable photons
at a beam splitter, is a staple of quantum optics and lies at the heart of many
quantum sensing approaches and recent optical quantum computers. Here, we
report a full-field, scan-free, quantum imaging technique that exploits HOM
interference to reconstruct the surface depth profile of transparent samples.
We demonstrate the ability to retrieve images with micrometre-scale depth
features with a photon flux as small as 7 photon pairs per frame. Using a
single photon avalanche diode camera we measure both the bunched and
anti-bunched photon-pair distributions at the HOM interferometer output which
are combined to provide a lower-noise image of the sample. This approach
demonstrates the possibility of HOM microscopy as a tool for label-free imaging
of transparent samples in the very low photon regime.


  Sluice resolution is a problem where a system needs to output the
corresponding antecedents of wh-ellipses. The antecedents are elided contents
behind the wh-words but are implicitly referred to using contexts. Previous
work frames sluice resolution as question answering where this setting
outperforms all its preceding works by large margins. Ellipsis and questions
are referentially dependent expressions (anaphoras) and retrieving the
corresponding antecedents are like answering questions to output pieces of
clarifying information. However, the task is not fully solved. Therefore, we
want to further investigate what makes sluice resolution differ to question
answering and fill in the error gaps. We also present some results using recent
state-of-the-art question answering systems which improve the previous work
(86.01 to 90.39 F1).


  In order to shed light on how galactic properties depend on the intergalactic
medium (IGM) environment traced by the Ly$\alpha$ forest, we observationally
investigate the IGM-galaxy connection using the publicly available 3D IGM
tomography data (CLAMATO) and several galaxy catalogs in the COSMOS field. We
measure the cross-correlation function (CCF) for $570$ galaxies with spec-$z$
measurements and detect a correlation with the IGM up to $50$ $h^{-1}$ comoving
Mpc. We show that galaxies with stellar masses of $10^9-10^{10}$ M$_\odot$ are
the dominant contributor to the total CCF signal. We also investigate CCFs for
several galaxy populations: Ly$\alpha$ emitters (LAEs), H$\alpha$ emitters
(HAEs), [OIII] emitters (O3Es), active galactic nuclei (AGNs), and
submillimeter galaxies (SMGs), and we detect the highest signal in AGNs and
SMGs at large scales ($r\geq5$ $h^{-1}$ Mpc), but in LAEs at small scales
($r<5$ $h^{-1}$ Mpc). We find that they live in various IGM environments --
HAEs trace the IGM in a similar manner to the continuum-selected galaxies, but
LAEs and O3Es tend to reside in higher-density regions. Additionally, LAEs' CCF
is flat up to $r\sim3$ $h^{-1}$ Mpc, indicating that they tend to avoid the
highest-density regions. For AGNs and SMGs, the CCF peak at $r=5-6$ $h^{-1}$
Mpc implies that they tend to be in locally lower-density regions. We suspect
that it is due to the photoionization of IGM HI by AGNs, i.e., the proximity
effect.


  We have carried out a thorough benchmark of the FDE-ET method for calculating
hole transfer couplings. We have considered 10 exchange-correlation
functionals, 3 non-additive kinetic energy functionals and 3 basis sets.
Overall, we conclude that with a 7% mean relative unsigned error, the PBE
functional coupled with the PW91k non-additive Kinetic energy functional and a
TZP basis set constitutes the most stable, and accurate level of theory for
hole-transfer coupling calculations. The FDE-ET method is found to be an
excellent tool for computing diabatic couplings for hole transfer reactions.


  The increasing data privacy concerns in recommendation systems have made
federated recommendations (FedRecs) attract more and more attention. Existing
FedRecs mainly focus on how to effectively and securely learn personal
interests and preferences from their on-device interaction data. Still, none of
them considers how to efficiently erase a user's contribution to the federated
training process. We argue that such a dual setting is necessary. First, from
the privacy protection perspective, ``the right to be forgotten'' requires that
users have the right to withdraw their data contributions. Without the
reversible ability, FedRecs risk breaking data protection regulations. On the
other hand, enabling a FedRec to forget specific users can improve its
robustness and resistance to malicious clients' attacks. To support user
unlearning in FedRecs, we propose an efficient unlearning method FRU (Federated
Recommendation Unlearning), inspired by the log-based rollback mechanism of
transactions in database management systems. It removes a user's contribution
by rolling back and calibrating the historical parameter updates and then uses
these updates to speed up federated recommender reconstruction. However,
storing all historical parameter updates on resource-constrained personal
devices is challenging and even infeasible. In light of this challenge, we
propose a small-sized negative sampling method to reduce the number of item
embedding updates and an importance-based update selection mechanism to store
only important model updates. To evaluate the effectiveness of FRU, we propose
an attack method to disturb FedRecs via a group of compromised users and use
FRU to recover recommenders by eliminating these users' influence. Finally, we
conduct experiments on two real-world recommendation datasets with two widely
used FedRecs to show the efficiency and effectiveness of our proposed
approaches.


  The time of protostellar wind breakout may be determined by the evolution of
the infalling flow, rather than any sudden change in the central engine. I
examine the transition from pure infall to outflow, in the context of the
inside-out collapse of a rotating molecular cloud core. I have followed
numerically the motion of the shocked shell created by the impact of a stellar
wind and infalling gas. These fully time-dependent calculations include cases
both where the shell falls back to the stellar surface, and where it breaks out
as a true outflow. Assuming a wind launched from the protostellar surface, the
breakout time is determined in terms of the parameters describing the wind
(Mdot_w, V_w) and collapsing cloud core (a_0, Omega). The trapped wind phase
consists of a wind sufficiently strong to push material back from the stellar
surface, but too weak to carry the heavy, shocked infall out of the star's
gravitational potential. To produce a large-scale outflow, the shocked material
must be able to climb out of the star's gravitational potential well, carrying
with it the dense, swept-up infall.


  We prove a sharp stability estimate for Schur iterates of contractive
analytic functions in the open unit disk. We then apply this result in the
setting of the inverse scattering approach and obtain a fast algorithm for
solving the discrete integrable nonlinear Schr\"odinger equation
(Ablowitz-Ladik equation) on the integer lattice, $\mathbb{Z}$. We also give a
self-contained introduction to the theory of the nonlinear Fourier transform
from the perspective of Schur functions and orthogonal polynomials on the unit
circle.


  Non-stationary online learning has drawn much attention in recent years. In
particular, dynamic regret and adaptive regret are proposed as two principled
performance measures for online convex optimization in non-stationary
environments. To optimize them, a two-layer online ensemble is usually deployed
due to the inherent uncertainty of the non-stationarity, in which a group of
base-learners are maintained and a meta-algorithm is employed to track the best
one on the fly. However, the two-layer structure raises the concern about the
computational complexity -- those methods typically maintain $\mathcal{O}(\log
T)$ base-learners simultaneously for a $T$-round online game and thus perform
multiple projections onto the feasible domain per round, which becomes the
computational bottleneck when the domain is complicated. In this paper, we
present efficient methods for optimizing dynamic regret and adaptive regret,
which reduce the number of projections per round from $\mathcal{O}(\log T)$ to
$1$. Moreover, our obtained algorithms require only one gradient query and one
function evaluation at each round. Our technique hinges on the reduction
mechanism developed in parameter-free online learning and requires non-trivial
twists on non-stationary online methods. Empirical studies verify our
theoretical findings.


  We report the behaviour of mixtures of fine bronze and glass spheres under
sinusoidal vertical vibration. Depending upon the ratio of their diameters and
the amplitude and frequency of the vibration, we observe the formation of sharp
separation boundaries between glass-rich and bronze-rich phases, the absence of
gross convection which would mix these phases, and a number of oscillatory and
non-periodic behaviours. These phenomena are related to the differential air
damping of the glass and bronze grains, disappearing completely in the absence
of air.


  This paper introduces AIM, a collection of vision models pre-trained with an
autoregressive objective. These models are inspired by their textual
counterparts, i.e., Large Language Models (LLMs), and exhibit similar scaling
properties. Specifically, we highlight two key findings: (1) the performance of
the visual features scale with both the model capacity and the quantity of
data, (2) the value of the objective function correlates with the performance
of the model on downstream tasks. We illustrate the practical implication of
these findings by pre-training a 7 billion parameter AIM on 2 billion images,
that achieves 84.0% on ImageNet-1k with a frozen trunk. Interestingly, even at
this scale, we observe no sign of saturation in performance, suggesting that
AIM potentially represents a new frontier for training large-scale vision
models. The pre-training of AIM is similar to the pre-training of LLMs, and
does not require any image-specific strategy to stabilize the training at
scale.


  The structure of chiral symmetry restorations at finite temperature is
thoroughly investigated in the supersymmetric Nambu-Jona-Lasinio model with a
soft supersymmetry breaking term. It is found that the broken chiral symmetry
at vanishing temperature is restored at sufficiently high temperature in two
patterns, i. e., the first order and second order phase transition depending on
the choice of the coupling constant $G$ and the supersymmetry breaking
parameter $\Delta$. The critical curves expressing the phase boundaries in the
$G-\Delta$ plane are completely determined and the dynamically generated
fermion mass is calculated as a function of temperature.


  We discuss the intergalactic propagation of ultra-high-energy cosmic rays
(UHECRs) with energies E \geq 10^{18.5} eV. We consider the propagation of
UHECRs under the influence of the energy-dependent deflection by a weak random
magnetic field in the intergalactic medium and energy losses by photo-pion and
pair production. We calculate arrival spectra taking full account of the
kinematics of photo-pion production and the Poisson statistics of the
photo-pion interaction rate.
  We give estimates for the deflection of UHECRs from the line of sight to the
source, time delays with respect to photons from the same source, arrival
spectra and source statistics. These estimates are confirmed by numerical
simulations of the propagation in energy evolution of UHECRs. These simulations
demonstrate that the often-used continuous approximation in the treatment of
energy losses due to photo-pion production on the cosmic microwave background
(CMWB) cannot be justified for UHECRs.
  We discuss the implications of these results for the observed flux of
particles above the Greisen-Zatsepin-Kuz'min cut-off in two of the scenarios
that have been proposed for the production of these particles: continuous
production in the large shock waves associated with powerful radio galaxies, or
possibly large-scale structure formation, and the impulsive production at
relativistic blast waves associated with cosmological gamma-ray bursts.


  The chemical-potential dependence of pi and sigma meson masses is analyzed at
both real and imaginary chemical potentials, $\mu_\mathrm{R}$ and
$\mu_\mathrm{I}$, by using the Polyakov-loop extended Nambu--Jona-Lasinio
(PNJL) model that possesses both the extended ${\mathbb Z}_3$ symmetry and the
chiral symmetry. In the $\mu_\mathrm{I}$ region, the meson masses have the
Roberge-Weiss periodicity. Assuming that the meson masses will be measured at
finite $\mu_\mathrm{I}$ by lattice QCD in future, we simulate how meson masses
at finite $\mu_\mathrm{R}$ are extracted from those at finite $\mu_\mathrm{I}$,
and propose a reliable extraction method.


  We have studied the effects of nonlinear scale evolution of the parton
distribution functions to charm production in $pp$ collisions at center-of-mass
energies of 5.5, 8.8 and 14 TeV. We find that the differential charm cross
section can be enhanced up to a factor of 4-5 at low $p_T$. The enhancement is
quite sensitive to the charm quark mass and the renormalization/factorization
scales.


  We prove that episturmian words and Arnoux-Rauzy sequences can be
characterized using a local balance property. We also give a new
characterization of epistandard words and show that the set of finite words
that are not factors of an episturmian word is not context-free.


  Large Language Models (LLMs) demonstrate strong machine translation
capabilities on languages they are trained on. However, the impact of factors
beyond training data size on translation performance remains a topic of debate,
especially concerning languages not directly encountered during training. Our
study delves into Llama2's translation capabilities. By modeling a linear
relationship between linguistic feature distances and machine translation
scores, we ask ourselves if there are potentially better central languages for
LLMs other than English. Our experiments show that the 7B Llama2 model yields
above 10 BLEU when translating into all languages it has seen, which rarely
happens for languages it has not seen. Most translation improvements into
unseen languages come from scaling up the model size rather than instruction
tuning or increasing shot count. Furthermore, our correlation analysis reveals
that syntactic similarity is not the only linguistic factor that strongly
correlates with machine translation scores. Interestingly, we discovered that
under specific circumstances, some languages (e.g. Swedish, Catalan), despite
having significantly less training data, exhibit comparable correlation levels
to English. These insights challenge the prevailing landscape of LLMs,
suggesting that models centered around languages other than English could
provide a more efficient foundation for multilingual applications.


  Conic programming has well-documented merits in a gamut of signal processing
and machine learning tasks. This contribution revisits a recently developed
first-order conic descent (CD) solver, and advances it in three aspects:
intuition, theory, and algorithmic implementation. It is found that CD can
afford an intuitive geometric derivation that originates from the dual problem.
This opens the door to novel algorithmic designs, with a momentum variant of
CD, momentum conic descent (MOCO) exemplified. Diving deeper into the dual
behavior CD and MOCO reveals: i) an analytically justified stopping criterion;
and, ii) the potential to design preconditioners to speed up dual convergence.
Lastly, to scale semidefinite programming (SDP) especially for low-rank
solutions, a memory efficient MOCO variant is developed and numerically
validated.


  In this paper, we explore the use of multi-agent deep learning as well as
learning to cooperate principles to meet stringent service level agreements, in
terms of throughput and end-to-end delay, for a set of classified network
flows. We consider agents built on top of a weighted fair queuing algorithm
that continuously set weights for three flow groups: gold, silver, and bronze.
We rely on a novel graph-convolution based, multi-agent reinforcement learning
approach known as DGN. As benchmarks, we propose centralized and distributed
deep Q-network approaches and evaluate their performances in different network,
traffic, and routing scenarios, highlighting the effectiveness of our proposals
and the importance of agent cooperation. We show that our DGN-based approach
meets stringent throughput and delay requirements across all scenarios.


  When developing general purpose robots, the overarching software architecture
can greatly affect the ease of accomplishing various tasks. Initial efforts to
create unified robot systems in the 1990s led to hybrid architectures,
emphasizing a hierarchy in which deliberative plans direct the use of reactive
skills. However, since that time there has been significant progress in the
low-level skills available to robots, including manipulation and perception,
making it newly feasible to accomplish many more tasks in real-world domains.
There is thus renewed optimism that robots will be able to perform a wide array
of tasks while maintaining responsiveness to human operators. However, the top
layer in traditional hybrid architectures, designed to achieve long-term goals,
can make it difficult to react quickly to human interactions during goal-driven
execution. To mitigate this difficulty, we propose a novel architecture that
supports such transitions by adding a top-level reactive module which has
flexible access to both reactive skills and a deliberative control module. To
validate this architecture, we present a case study of its application on a
domestic service robot platform.


  We present an analysis of the classical contact process on scale-free
networks. A mean-field study, both for finite and infinite network sizes,
yields an absorbing-state phase transition at a finite critical value of the
control parameter, characterized by a set of exponents depending on the network
structure. Since finite size effects are large and the infinite network limit
cannot be reached in practice, a numerical study of the transition requires the
application of finite size scaling theory. Contrary to other critical phenomena
studied previously, the contact process in scale-free networks exhibits a
non-trivial critical behavior that cannot be quantitatively accounted for by
mean-field theory.


  Regularity theory in generalized function algebras of Colombeau type is
largely based on the notion of ${\mathcal G}^\infty$-regularity, which reduces
to $C^\infty$-regularity when restricted to Schwartz distributions.
Surprisingly, in the nonstandard version of the Colombeau algebras, this basic
property of ${\mathcal G}^\infty$-regularity does not hold.


  We present observations of redshifted CO(1-0) and CO(2-1) in a field
containing an overdensity of Lyman break galaxies (LBGs) at z=5.12. Our
Australia Telescope Compact Array observations were centered between two
spectroscopically-confirmed z=5.12 galaxies. We place upper limits on the
molecular gas masses in these two galaxies of M(H_2) <1.7 x 10^10 M_sun and
<2.9 x 10^9 M_sun (2 sigma), comparable to their stellar masses. We detect an
optically-faint line emitter situated between the two LBGs which we identify as
warm molecular gas at z=5.1245 +/- 0.0001. This source, detected in the CO(2-1)
transition but undetected in CO(1-0), has an integrated line flux of 0.106 +/-
0.012 Jy km/s, yielding an inferred gas mass M(H_2)=(1.9 +/- 0.2) x 10^10
M_sun. Molecular line emitters without detectable counterparts at optical and
infrared wavelengths may be crucial tracers of structure and mass at high
redshift.


  This paper presents the first implementation of a coupling between advanced
wave function theories and molecular density functional theory (MDFT). This
method enables the modeling of solvent effect into quantum mechanical (QM)
calculations by incorporating an electrostatic potential generated by solvent
charges into the electronic Hamiltonian. Solvent charges are deduced from the
spatially and angularly dependent solvent particle density. Such density is
obtained through the minimization of the functional associated to the molecular
mechanics (MM) Hamiltonian describing the interaction between the fluid
particles. The introduced QM/MDFT framework belongs to QM/MM family of methods
but its originality lies in the use of MDFT as the MM solver, offering two main
advantages. Firstly, its functional formulation makes it competitive with
respect to sampling-based molecular mechanics. Secondly, it preserves a
molecular-level description lost in macroscopic continuum approaches. Excited
states properties of water and formaldehyde molecules solvated into water have
been computed at the selected configuration interaction (SCI) level. Excitation
energies and dipole moment have been compared with experimental data and
previous theoretical work. A key finding is that using the Hartree-Fock method
to describe the solute allows for predicting the solvent charge around the
ground-state with sufficient precision for the subsequent SCI calculations of
excited-states. This significantly reduces the computational cost of the
described procedure, paving the way for the study of more complex molecules.


  This paper is concerned with the study of solutions to discrete parabolic
equations in divergence form with random coefficients, and their convergence to
solutions of a homogenized equation. It has previously been shown that if the
random environment is translational invariant and ergodic, then solutions of
the random equation converge under diffusive scaling to solutions of a
homogenized parabolic PDE. In this paper point-wise estimates are obtained on
the difference between the averaged solution to the random equation and the
solution to the homogenized equation for certain random environments which are
strongly mixing.


  The analytical exact iteration method (AEIM) have been used widely to
calculate N-dimensional radial Schrodinger equation with medium modified form
of Cornell potential and is generalized to the finite value of magnetic field
(eB) with quasi-particle approach in hot quantum chromodynamics (QCD) medium.
In N-dimensional space the energy eigen values have been calculated for any
states (n,l). These results have been used to study the properties of
quarkonium states (i.e, the binding energy and mass spectra, dissociation
temperature and thermodynamical properties in the N-dimensional space). We have
determined the binding energy of the ground states of quarkonium with magnetic
field and dimensionality number. We have also determined the effects of
magnetic field and dimensionality number on mass spectra for ground states of
quarkonia. But main result is quite noticeable for the values of dissociation
temperature in terms of magnetic field and dimensionality number for ground
states of quarkonia after using the criteria of dissociation energy. At last,
we have also calculated the thermodynamical properties of QGP (i.e., pressure,
energy density and speed of sound) using the parameter eB with ideal equation
of states (EoS).


  Temporal data mining algorithms are becoming increasingly important in many
application domains including computational neuroscience, especially the
analysis of spike train data. While application scientists have been able to
readily gather multi-neuronal datasets, analysis capabilities have lagged
behind, due to both lack of powerful algorithms and inaccessibility to powerful
hardware platforms. The advent of GPU architectures such as Nvidia's GTX 280
offers a cost-effective option to bring these capabilities to the
neuroscientist's desktop. Rather than port existing algorithms onto this
architecture, we advocate the need for algorithm transformation, i.e.,
rethinking the design of the algorithm in a way that need not necessarily
mirror its serial implementation strictly. We present a novel implementation of
a frequent episode discovery algorithm by revisiting "in-the-large" issues such
as problem decomposition as well as "in-the-small" issues such as data layouts
and memory access patterns. This is non-trivial because frequent episode
discovery does not lend itself to GPU-friendly data-parallel mapping
strategies. Applications to many datasets and comparisons to CPU as well as
prior GPU implementations showcase the advantages of our approach.


  A novel diagnostic of cosmic-ray modified shocks by polarimetry of H $\alpha$
emissions is suggested. In a cosmic-ray modified shock, the pressure of cosmic
rays is sufficiently high compared to the upstream ram pressure to force the
background plasma to decelerate (measured in the shock rest frame).
Simultaneously, a fraction of the hydrogen atoms co-existing in the upstream
plasma collide with the decelerated protons and undergo charge-exchange
reactions. As a result, hydrogen atoms with the same bulk velocity of the
decelerated protons are generated. We show that when the shock is observed from
edge-on, the H $\alpha$ radiated by these upstream hydrogen atoms is linearly
polarized with a sizable degree of a few per cent as a result of resonant
scattering of Ly $\beta$. The polarization direction depends strongly on the
velocity modification; the direction is parallel to the shock surface for the
case of no modification, while the direction is parallel to the shock velocity
for the case of a modified shock.


  This paper investigates the size performance of Wald tests for CAViaR models
(Engle and Manganelli, 2004). We find that the usual estimation strategy on
test statistics yields inaccuracies. Indeed, we show that existing density
estimation methods cannot adapt to the time-variation in the conditional
probability densities of CAViaR models. Consequently, we develop a method
called adaptive random bandwidth which can approximate time-varying conditional
probability densities robustly for inference testing on CAViaR models based on
the asymptotic normality of the model parameter estimator. This proposed method
also avoids the problem of choosing an optimal bandwidth in estimating
probability densities, and can be extended to multivariate quantile regressions
straightforward.


  We update our recently published model for GAlaxy Evolution and Assembly
(GAEA), to include a self-consistent treatment of the partition of cold gas in
atomic and molecular hydrogen. Our model provides significant improvements with
respect to previous ones used for similar studies. In particular, GAEA (i)
includes a sophisticated chemical enrichment scheme accounting for
non-instantaneous recycling of gas, metals, and energy; (ii) reproduces the
measured evolution of the galaxy stellar mass function; (iii) reasonably
reproduces the observed correlation between galaxy stellar mass and gas
metallicity at different redshifts. These are important prerequisites for
models considering a metallicity dependent efficiency of molecular gas
formation. We also update our model for disk sizes and show that model
predictions are in nice agreement with observational estimates for the gas,
stellar and star forming disks at different cosmic epochs. We analyse the
influence of different star formation laws including empirical relations based
on the hydro-static pressure of the disk, analytic models, and prescriptions
derived from detailed hydro-dynamical simulations. We find that modifying the
star formation law does not affect significantly the global properties of model
galaxies, neither their distributions. The only quantity showing significant
deviations in different models is the cosmic molecular-to-atomic hydrogen
ratio, particularly at high redshift. Unfortunately, however, this quantity
also depends strongly on the modelling adopted for additional physical
processes. Useful constraints on the physical processes regulating star
formation can be obtained focusing on low mass galaxies and/or at higher
redshift. In this case, self-regulation has not yet washed out differences
imprinted at early time.


  We present the full one-loop radiative corrections to pair production of
neutralinos in e+e- collisions within the Minimal Supersymmetric Standard
Model. Particular attention is paid to the definition of weak and QED
corrections. The non-universal QED corrections are extracted by subtracting the
initial state radiation. We give numerical results for two different SUSY
scenarios for e+e- -> neu_1 neu_2 and e+e- -> neu_2 neu_2. The weak and QED
corrections are up to several percent or even higher and need to be taken into
account at future linear collider experiments.


  In this paper, we study pattern avoidance in weak ascent sequences, giving
some results for patterns of length 3. This is an analogous study to one given
by Duncan and Steingr\'imsson (2011) for ascent sequences. More precisely, we
provide systematically the generating functions for the number of weak ascent
sequences avoiding the patterns $001, 011, 012, 021$, and $102$. Additionally,
we establish bijective connections between pattern-avoiding weak ascent
sequences and other combinatorial objects, such as compositions, upper
triangular 01-matrices, and plane trees.


  In this Letter, we prove the unconditional security of single-photon
differential phase shift quantum key distribution (DPS-QKD) protocol, based on
the conversion to an equivalent entanglement-based protocol. We estimate the
upper bound of the phase error rate from the bit error rate, and show that
DPS-QKD can generate unconditionally secure key when the bit error rate is not
greater than 4.12%. This proof is the first step to the unconditional security
proof of coherent state DPS-QKD.


  Monero is a popular crypto-currency which focuses on privacy. The blockchain
uses cryptographic techniques to obscure transaction values as well as a `ring
confidential transaction' which seeks to hide a real transaction among a
variable number of spoofed transactions. We have developed training sets of
simulated blockchains of 10 and 50 agents, for which we have control over the
ground truth and keys, in order to test these claims. We featurize Monero
transactions by characterizing the local structure of the public-facing
blockchains and use labels obtained from the simulations to perform machine
learning. Machine Learning of our features on the simulated blockchain shows
that the technique can be used to aide in identifying individuals and groups,
although it did not successfully reveal the hidden transaction values. We apply
the technique on the real Monero blockchain to identify ShapeShift
transactions, a cryptocurrency exchange that has leaked information through
their API providing labels for themselves and their users.


  The goal of the article is to prove that four explicitly given
transformations, two Heisenberg translations, a rotation and an involution
generate the Picard modular group with Gaussian integers acting on the two
dimensional complex hyperbolic space. The result answers positively a question
raised by A. Kleinschmidt and D. Persson.


  We demonstrate a 1-D velocity selection technique which relies on combining
magnetic and optical potentials. We have selected atom clouds with temperatures
as low as 2.9% of the initial temperature, with an efficiency of 1%. The
efficiency (percentage of atoms selected) of the technique can vary as slowly
as the square root of the final temperature. In addition to selecting the
coldest atoms from a cloud, this technique imparts a sharp cut-off in the
velocity distribution. The cold selected atoms are confined in a small well,
spatially separated from higher energy atoms. Such a non-thermal distribution
may be useful for atom optics experiments, such as studies of atom tunneling.


  We introduce matrix-valued weight functions of arbitrary size, which are
analogues of the weight function for the Gegenbauer or ultraspherical
polynomials for the parameter $\nu>0$. The LDU-decomposition of the weight is
explicitly given in terms of Gegenbauer polynomials. We establish a
matrix-valued Pearson equation for these matrix weights leading to explicit
shift operators relating the weights for parameter $\nu$ and $\nu+1$. The
matrix coefficients of the Pearson equation are obtained using a special
matrix-valued differential operator in a commutative algebra of symmetric
differential operators.
  The corresponding orthogonal polynomials are the matrix-valued Gegenbauer
polynomials which are eigenfunctions for the symmetric matrix-valued
differential operators. Using the shift operators we find the squared norm and
we establish a simple Rodrigues formula. The three-term recurrence relation is
obtained explicitly using the shift operators as well. We give an explicit
non-trivial expression for the matrix entries of the matrix-valued Gegenbauer
polynomials in terms of scalar-valued Gegenbauer and Racah polynomials using
the LDU-decomposition and differential operators. The case $\nu=1$ reduces to
the case of matrix-valued Chebyshev polynomials previously obtained using group
theoretic considerations.


  From a Chandra sample of active galactic nuclei (AGNs) in nearby galaxies, we
find that for low-luminosity AGNs (LLAGNs), either the intrinsic absorption
column density, or the fraction of absorbed AGNs, positively scales with the
Eddington ratio for $L_{\rm bol}/L_{\rm Edd} \lesssim 10^{-2}$. Such a
behavior, along with the softness of the X-ray spectrum at low luminosities, is
in good agreement with the picture that they are powered by hot accretion flows
surrounding supermassive black holes. Numerical simulations find that outflows
are inevitable with hot accretion flows, and the outflow rate is correlated
with the innermost accretion rate in the low-luminosity regime. This agrees
well with our results, suggesting that the X-ray absorption originates from or
is associated with the outflow material. Gas and dust on larger scales may also
produce the observed correlation. Future correlation analysis may help
differentiate the two scenarios.


  We study the activity, i.e., the number of transactions per unit time, of
financial markets. Using the diffusion entropy technique we show that the
autocorrelation of the activity is caused by the presence of peaks whose time
distances are distributed following an asymptotic power law which ultimately
recovers the Poissonian behavior. We discuss these results in comparison with
ARCH models, stochastic volatility models and multi-agent models showing that
ARCH and stochastic volatility models better describe the observed experimental
evidences.


  The curse of outlier measurements in estimation problems is a well known
issue in a variety of fields. Therefore, outlier removal procedures, which
enables the identification of spurious measurements within a set, have been
developed for many different scenarios and applications. In this paper, we
propose a statistically motivated outlier removal algorithm for time
differences of arrival (TDOAs), or equivalently range differences (RD),
acquired at sensor arrays. The method exploits the TDOA-space formalism and
works by only knowing relative sensor positions. As the proposed method is
completely independent from the application for which measurements are used, it
can be reliably used to identify outliers within a set of TDOA/RD measurements
in different fields (e.g. acoustic source localization, sensor synchronization,
radar, remote sensing, etc.). The proposed outlier removal algorithm is
validated by means of synthetic simulations and real experiments.


  We present preliminary results from two observations of the radio galaxy
Centaurus A performed by the BeppoSAX satellite. We did not detect any spectral
variation of the nuclear continuum in spite of the long-term flux change (by a
factor 1.3) between the two observations. At both epochs, the nuclear
point-like emission was well fitted with a strongly absorbed power law with an
exponential cutoff at high energies (E_cutoff>200 keV). We also observed a
significant flux variation of the iron line between the two observations. The
flux of the line and of the continuum changed in the opposite sense. The line
is more intense at the first epoch, when the nuclear source was at the lower
intensity level. The implied delay between the continuum and line variations
strongly suggests that the cold material responsible for the iron line
production is not located very near to the primary X-ray source. There is also
evidence that the line profile changed between the two epochs, being broader
and slightly blueshifted when the source was fainter. It is possible that the
emission feature is a blend of cold and ionized iron lines produced in separate
regions surrounding the nuclear source.


  We describe how we have used simultaneously ${\cal O}(10^3)$ nodes of the
EGEE Grid, accumulating ca. 300 CPU-years in 2-3 months, to determine an
important property of Quantum Chromodynamics. We explain how Grid resources
were exploited efficiently and with ease, using user-level overlay based on
Ganga and DIANE tools above standard Grid software stack. Application-specific
scheduling and resource selection based on simple but powerful heuristics
allowed to improve efficiency of the processing to obtain desired scientific
results by a specified deadline. This is also a demonstration of combined use
of supercomputers, to calculate the initial state of the QCD system, and Grids,
to perform the subsequent massively distributed simulations. The QCD simulation
was performed on a $16^3\times 4$ lattice. Keeping the strange quark mass at
its physical value, we reduced the masses of the up and down quarks until,
under an increase of temperature, the system underwent a second-order phase
transition to a quark-gluon plasma. Then we measured the response of this
system to an increase in the quark density. We find that the transition is
smoothened rather than sharpened. If confirmed on a finer lattice, this finding
makes it unlikely for ongoing experimental searches to find a QCD critical
point at small chemical potential.


  We study torsion generators for the (extended) mapping class group or the
extended mapping class group of a closed connected orientable surface of genus
g. We show that for every g is grater than or equal to 14, mapping class group
can be generated by two torsion elements of order g+1 if g is even, and of
orders g+1 and g+1 if g is odd. Also for g grater than or equal to 16, mapping
class group can be generated by two torsion elements of orders g+1 if g+1 is
not divisible by 3, and of orders g+1 and g+1 if g+1 is divisible by 3.
Similarly, we obtain two torsion elements generating extended mapping class
groups.


  In this article, we study exponents which preserve complete monotonicity of
functions on lattices. We prove that for any completely monotone function $f$
on a finite lattice, $f^\alpha$ is completely monotone for all $\alpha\geq c$,
where $c$ is explicitly described. For finite distributive lattices we show
that the bound $c$ is sharp. Important examples of completely monotone
functions are void functionals of random closed sets. We prove that if
$V_{\mathcal{X}}$ is the void functional of a random subset $\mathcal{X}$ of
$[n]$, then $V_{\mathcal{X}}^\alpha$ is void functional of some random closed
set for $\alpha\geq n-1$. The results are analogous to the result of FitzGerald
and Horn on Hadamard powers of positive semi-definite matrices. Also, we study
the question of approximating an $m$-divisible random set by infinitely
divisible random sets, and its generalization to lattices.


  Drug discovery is vitally important for protecting human against disease.
Target-based screening is one of the most popular methods to develop new drugs
in the past several decades. This method efficiently screens candidate drugs
inhibiting target protein in vitro, but it often fails due to inadequate
activity of the selected drugs in vivo. Accurate computational methods are
needed to bridge this gap. Here, we propose a novel graph multi task deep
learning model to identify compounds carrying both target inhibitory and cell
active (MATIC) properties. On a carefully curated SARS-CoV-2 dataset, the
proposed MATIC model shows advantages comparing with traditional method in
screening effective compounds in vivo. Next, we explored the model
interpretability and found that the learned features for target inhibition (in
vitro) or cell active (in vivo) tasks are different with molecular property
correlations and atom functional attentions. Based on these findings, we
utilized a monte carlo based reinforcement learning generative model to
generate novel multi-property compounds with both in vitro and in vivo
efficacy, thus bridging the gap between target-based and cell-based drug
discovery.


  The Bayesian decision-theoretic approach to design of experiments involves
specifying a design (values of all controllable variables) to maximise the
expected utility function (expectation with respect to the distribution of
responses and parameters). For most common utility functions, the expected
utility is rarely available in closed form and requires a computationally
expensive approximation which then needs to be maximised over the space of all
possible designs. This hinders practical use of the Bayesian approach to find
experimental designs. However, recently, a new utility called Fisher
information gain has been proposed. The resulting expected Fisher information
gain reduces to the prior expectation of the trace of the Fisher information
matrix. Since the Fisher information is often available in closed form, this
significantly simplifies approximation and subsequent identification of optimal
designs. In this paper, it is shown that for exponential family models,
maximising the expected Fisher information gain is equivalent to maximising an
alternative objective function over a reduced-dimension space, simplifying even
further the identification of optimal designs. However, if this function does
not have enough global maxima, then designs that maximise the expected Fisher
information gain lead to non-identifiablility.


  Pulsar timing arrays have found evidence for a low-frequency gravitational
wave background (GWB). Assuming the GWB is produced by supermassive black hole
binaries (SMBHBs), the next gravitational wave (GW) signals astronomers
anticipate are Continuous Waves (CWs) from single SMBHBs and their associated
GWB anisotropy. The prospects for detecting CWs and anisotropy are highly
dependent on the astrophysics of SMBHB populations. Thus, information from
single sources can break degeneracies in astrophysical models and place much
more stringent constraints than the GWB alone. We simulate and evolve SMBHB
populations, model their GWs, and calculate their anisotropy and detectability.
We investigate how varying components of our semi-analytic model, including the
galaxy stellar mass function, the SMBH--host galaxy relation
($M_\mathrm{BH}$--$M_\mathrm{bulge}$), and the binary evolution prescription
impact the expected detections. The CW occurrence rate is greatest for few
total binaries, high SMBHB masses, large scatter in
$M_\mathrm{BH}$--$M_\mathrm{bulge}$, and long hardening times. The occurrence
rate depends most on the binary evolution parameters, implying that CWs offer a
novel avenue to probe binary evolution. The most detectable CW sources are in
the lowest frequency bin for a 16.03-year PTA, have masses from
$\sim\!\!10^9-10^{10}\mathrm{M}_\odot$, and are $\sim\!\!1$ Gpc away. The level
of anisotropy increases with frequency, with the angular power spectrum over
multipole modes $\ell$ varying in low-frequency $C_{\ell>0}/C_0$ from
$\sim\!\!5\times 10^{-3}$ to $\sim\!\!2\times10^{-1}$, depending on the model;
typical values are near current upper limits. Observing this anisotropy would
support SMBHB models for the GWB over cosmological models, which tend to be
isotropic.


  We study the effects of pressure on the electron-phonon interaction in MgB_2
using density-functional-based methods. Our results show that the
superconductivity in MgB_2 vanishes by 100 GPa, and then reappears at higher
pressures. In particular, we find a superconducting transition temperature T_c
\~ 2 K for mu*=0.1 at a pressure of 137 GPa.


  This paper presents FastFit, a novel neural vocoder architecture that
replaces the U-Net encoder with multiple short-time Fourier transforms (STFTs)
to achieve faster generation rates without sacrificing sample quality. We
replaced each encoder block with an STFT, with parameters equal to the temporal
resolution of each decoder block, leading to the skip connection. FastFit
reduces the number of parameters and the generation time of the model by almost
half while maintaining high fidelity. Through objective and subjective
evaluations, we demonstrated that the proposed model achieves nearly twice the
generation speed of baseline iteration-based vocoders while maintaining high
sound quality. We further showed that FastFit produces sound qualities similar
to those of other baselines in text-to-speech evaluation scenarios, including
multi-speaker and zero-shot text-to-speech.


  Inclusive production via gluon fusion will be the most important production
channel for Higgs boson discovery at the LHC. I report on a calculation of the
inclusive Higgs boson production cross section at $pp$ and $p\bar p$ colliders
at next-to-next-to-leading order in QCD.


  The primitive elements of the supersymmetry algebra cohomology as defined in
a previous paper are computed for standard supersymmetry algebras in four and
five dimensions, for all signatures of the metric and any number of
supersymmetries.


  The exact 2-point function of certain physically motivated operators in
SYK-like spin glass models is computed, bypassing the Schwinger-Dyson
equations. The models possess an IR low energy conformal window, but our
results are exact at all time scales. The main tool developed is a new approach
to the combinatorics of chord diagrams, allowing to rewrite the spin glass
system using an auxiliary Hilbert space, and Hamiltonian, built on the space of
open chord diagrams. We argue the latter can be interpreted as the bulk
description and that it reduces to the Schwarzian action in the low energy
limit.


  We present the electrical characterization of N-polar AlN layers grown by
metal-organic vapor phase epitaxy and the demonstration of N-polar AlN-channel
metal-semiconductor field-effect transistors (MESFETs). A high concentration of
silicon is unintentionally incorporated during the high-temperature growth of
N-polar AlN, causing high buffer leak current. The silicon concentration
reduces from 2x10^18 /cm^3 to 9x10^15 /cm^3 with decreasing growth temperature,
reducing the buffer leak current to 5.6 nA/mm at a 100 V bias. The N-polar AlN
MESFET exhibits an off-state drain current of 0.27 nA/mm and a transistor
on/off ratio of 4.6x10^4 due to the high-resistivity AlN buffer layers.


  Fluid and solute transfer in X-junctions between straight channels is shown
to depend critically on the junction angle in the Stokes flow regime.
Experimentally, water and a water-dye solution are injected at equal flow rates
in two facing channels of the junction: Planar Laser Induced fluorescence
(PLIF) measurements show that the largest part of each injected fluid "bounces
back" preferentially into the outlet channel at the lowest angle to the
injection; this is opposite to the inertial case and requires a high curvature
of the corresponding streamlines. The proportion of this fluid in the other
channel decreases from 50% at 90\degree to zero at a threshold angle. These
counterintuitive features reflect the minimization of energy dissipation for
Stokes flows. Finite elements numerical simulations of a 2D Stokes flow of
equivalent geometry con rm these results and show that, below the threshold
angle 33.8\degree recirculation cells are present in the center part of the
junction and separate the two injected flows of the two solutions. Reducing
further leads to the appearance of new recirculation cells with lower flow
velocities.


  Massive MIMO systems can achieve high spectrum and energy efficiency in
downlink (DL) based on accurate estimate of channel state information (CSI).
Existing works have developed learning-based DL CSI estimation that lowers
uplink feedback overhead. One often overlooked problem is the limited number of
DL pilots available for CSI estimation. One proposed solution leverages
temporal CSI coherence by utilizing past CSI estimates and only sending
CSI-reference symbols (CSI-RS) for partial arrays to preserve CSI recovery
performance. Exploiting CSI correlations, FDD channel reciprocity is helpful to
base stations with direct access to uplink CSI. In this work, we propose a new
learning-based feedback architecture and a reconfigurable CSI-RS placement
scheme to reduce DL CSI training overhead and to improve encoding efficiency of
CSI feedback. Our results demonstrate superior performance in both indoor and
outdoor scenarios by the proposed framework for CSI recovery at substantial
reduction of computation power and storage requirements at UEs.


  In this paper we study the truncated power moment problem with an odd number
of prescribed moments. A Nevanlinna-type formula is derived for this moment
problem in the case when the moment problem has more than one solution (the
indeterminate case). The coefficients of the corresponding linear fractional
transformation are expressed explicitly in terms of the given moments. In the
determinate case the solution to the moment problem is constructed, as well.
Those solutions of the moment problem, which satisfy the following condition:
$M(\Delta)=0$, where $\Delta$ is a prescribed open subset of $\mathbb{R}$, and
$M$ is the matrix measure generated by a solution of the moment problem, are
described.


  In online communities, antisocial behavior such as trolling disrupts
constructive discussion. While prior work suggests that trolling behavior is
confined to a vocal and antisocial minority, we demonstrate that ordinary
people can engage in such behavior as well. We propose two primary trigger
mechanisms: the individual's mood, and the surrounding context of a discussion
(e.g., exposure to prior trolling behavior). Through an experiment simulating
an online discussion, we find that both negative mood and seeing troll posts by
others significantly increases the probability of a user trolling, and together
double this probability. To support and extend these results, we study how
these same mechanisms play out in the wild via a data-driven, longitudinal
analysis of a large online news discussion community. This analysis reveals
temporal mood effects, and explores long range patterns of repeated exposure to
trolling. A predictive model of trolling behavior shows that mood and
discussion context together can explain trolling behavior better than an
individual's history of trolling. These results combine to suggest that
ordinary people can, under the right circumstances, behave like trolls.


  This work is aimed at exploiting Second-Order Circular Suprasegmental Hidden
Markov Models (CSPHMM2s) as classifiers to enhance talking condition
recognition in stressful and emotional talking environments (completely two
separate environments). The stressful talking environment that has been used in
this work uses Speech Under Simulated and Actual Stress (SUSAS) database, while
the emotional talking environment uses Emotional Prosody Speech and Transcripts
(EPST) database. The achieved results of this work using Mel-Frequency Cepstral
Coefficients (MFCCs) demonstrate that CSPHMM2s outperform each of Hidden Markov
Models (HMMs), Second-Order Circular Hidden Markov Models (CHMM2s), and
Suprasegmental Hidden Markov Models (SPHMMs) in enhancing talking condition
recognition in the stressful and emotional talking environments. The results
also show that the performance of talking condition recognition in stressful
talking environments leads that in emotional talking environments by 3.67%
based on CSPHMM2s. Our results obtained in subjective evaluation by human
judges fall within 2.14% and 3.08% of those obtained, respectively, in
stressful and emotional talking environments based on CSPHMM2s.


  Understanding the low-frequency radio sky in depth is necessary to subtract
foregrounds in order to detect the redshifted 21 cm signal of neutral hydrogen
from the Cosmic Dawn, Epoch of Reionization (EoR) and post-reionization era. In
this second paper of the series, we present the upgraded Giant Metrewave Radio
Telescope (uGMRT) observation of the ELAIS N1 field made at 300-500 MHz. The
image covers an area of $\sim 1.8$ $\mathrm{deg}^{2}$ and has a central
background rms noise of $\sim$ 15 $\mu \mathrm{Jy}$ $\mathrm{beam}^{-1}$. We
present a radio source catalog containing 2528 sources (with flux densities >
100 $\mu$Jy) and normalized source counts derived from that. The detailed
comparison of detected sources with previous radio observations is shown. We
discuss flux scale accuracy, positional offsets, spectral index distribution
and correction factors in source counts. The normalized source counts are in
agreement with previous observations of the same field, as well as model source
counts from the Square Kilometre Array Design Study (SKADS) simulation. It
shows a flattening below $\sim$1 mJy which corresponds to rise in population of
star forming galaxies and radio-quiet AGN. For the first time, we estimated the
spectral characteristics of the angular power spectrum or Multi-Frequency
Angular Power Spectrum (MFAPS) of diffuse Galactic synchrotron emission (DGSE)
over the wide frequency bandwidth of $300-500$~MHz from radio interferometric
observations. This work demonstrates the improved capabilities of the uGMRT.


  We study an ocean related system with a small viscosity parameter, which is
the linearized version of the modified Primitive Equations. As the parameter
goes to zero, a $L^\infty$ convergence result is obtained together with the
estimation on the thickness of the boundary layer.


  We use continuous wavelet transform techniques to construct the global and
environment-dependent wavelet statistics, such as energy spectrum and kurtosis,
to study the fluctuation and intermittency of the turbulent motion in the
cosmic fluid velocity field with the IllustrisTNG simulation data. We find that
the peak scales of the energy spectrum and the spectral ratio define two
characteristic scales, which can be regarded as the integral scale and the
dissipation scale of turbulence, respectively, so that the energy spectrum can
be divided into the energy-containing range, the inertial range and the
dissipation range of turbulence. The wavelet kurtosis is an increasing function
of the wavenumber $k$, first grows rapidly then slowly with $k$, indicating
that the cosmic fluid becomes increasingly intermittent with $k$. In the
energy-containing range, the energy spectrum increases significantly from $z =
2$ to $1$, but remains almost unchanged from $z = 1$ to $0$. We find that both
the environment-dependent spectrum and kurtosis are similar to the global ones,
and the magnitude of the spectrum is smallest in the lowest-density and largest
in the highest-density environment, suggesting that the cosmic fluid is more
turbulent in a high-density than in a low-density environment. In the inertial
range, the energy spectrum's exponent is steeper than both the Kolmogorov and
Burgers exponents, indicating more efficient energy transfer compared to
Kolmogorov or Burgers turbulence.


  We present a simple but novel hybrid approach to hyperspectral data cube
reconstruction from computed tomography imaging spectrometry (CTIS) images that
sequentially combines neural networks and the iterative Expectation
Maximization (EM) algorithm. We train and test the ability of the method to
reconstruct data cubes of $100\times100\times25$ and $100\times100\times100$
voxels, corresponding to 25 and 100 spectral channels, from simulated CTIS
images generated by our CTIS simulator. The hybrid approach utilizes the
inherent strength of the Convolutional Neural Network (CNN) with regard to
noise and its ability to yield consistent reconstructions and make use of the
EM algorithm's ability to generalize to spectral images of any object without
training. The hybrid approach achieves better performance than both the CNNs
and EM alone for seen (included in CNN training) and unseen (excluded from CNN
training) cubes for both the 25- and 100-channel cases. For the 25 spectral
channels, the improvements from CNN to the hybrid model (CNN + EM) in terms of
the mean-squared errors are between 14-26%. For 100 spectral channels, the
improvements between 19-40% are attained with the largest improvement of 40%
for the unseen data, to which the CNNs are not exposed during the training.


  For general connections, the problem of finding network codes and optimizing
resources for those codes is intrinsically difficult and little is known about
its complexity. Most of the existing solutions rely on very restricted classes
of network codes in terms of the number of flows allowed to be coded together,
and are not entirely distributed. In this paper, we consider a new method for
constructing linear network codes for general connections of continuous flows
to minimize the total cost of edge use based on mixing. We first formulate the
minimumcost network coding design problem. To solve the optimization problem,
we propose two equivalent alternative formulations with discrete mixing and
continuous mixing, respectively, and develop distributed algorithms to solve
them. Our approach allows fairly general coding across flows and guarantees no
greater cost than any solution without network coding.


  Valencia et al. recently claimed that the mass of a Super-Earth (SE) is a
sole factor in determining whether a SE is tectonically active or not. However,
mass resolving astrometry is unable to discern between a SE and its moons if
any. The fact that no exomoons have been discovered yet is rather a matter of
instrumentation imperfection at the present, not of physical absence of
exomoons. This, with recently discovered relationships between geometric and
physical properties in astronomical bodies (Transiting planets; the Earth)
makes it impossible to know yet if the Wageners (here constraining) supposition
on somehow-tidally caused tectonics holds universally or not also.


  This article introduces a new transcription, change point localization, and
mesh refinement scheme for direct optimization-based solutions and for uniform
approximation of optimal control trajectories associated with a class of
nonlinear constrained optimal control problems (OCPs). The base transcription
algorithm for which we establish the refinement algorithm is a $\textit{direct
multiple shooting technique}$ -- $\mathsf{QuITO}$ $\textsf{v.2}$
(Quasi-Interpolation based Trajectory Optimization). The mesh refinement
technique consists of two steps -- localization of certain irregular regions in
an optimal control trajectory via wavelets, followed by a targeted
$h$-refinement approach around such regions of irregularity. Theoretical
approximation guarantees on uniform grids are presented for optimal controls
with certain regularity properties along with guarantees of localization of
change points by wavelet transform. Numerical illustrations are provided for
control profiles involving discontinuities to show the effectiveness of the
localization and refinement strategy. We also announce and make freely
available a new software package developed based on $\mathsf{QuITO}$
$\textsf{v.2}$ along with its functionalities to make the article complete.


  Starting from a quantum description of multiple Lambda-type 3-level atoms
driven with a coherent microwave field and incoherent optical pumping, we
derive a microscopic model of lasing from which we move towards a consistent
macroscopic picture. Our analysis applies across the range of system sizes from
nanolasers to the thermodynamic limit of conventional lasing. We explore the
necessary conditions to achieve lasing without inversion by calculating the
non-equilibrium steady state solutions of the model at, and between, its
microscopic and macroscopic limits. For the macroscopic picture, we use
mean-field theory to present a thorough analysis of the lasing phase
transition. In the microscopic case, we exploit the underlying permutation
symmetry of the density matrix to calculate exact solutions for N 3-level
systems. This allows us to show that the steady state solutions approach the
thermodynamic limit as N increases, restoring the sharp non-equilibrium phase
transition in this limit. We demonstrate how the lasing phase transition and
degree of population inversion can be adjusted by simply varying the phase of
the coherent driving field. The high level of quantum control presented by this
microscopic model and the framework outlined here have applications to further
understanding and developing nanophotonic technology.


  In classical field theory, radiation does not reflect off of reflectionless
kinks. In quantum field theory, radiation quanta, called mesons, can be
reflected. We provide a general analytical formula for the leading order
amplitude and probability for the elastic scattering of mesons off of
reflectionless quantum kinks. In the case of the Sine-Gordon model we verify
that, due to a cancellation of six contributing processes, our general formula
yields an amplitude of zero, as is required by integrability.


  Comparing survival experiences of different groups of data is an important
issue in several applied problems. A typical example is where one wishes to
investigate treatment effects. Here we propose a new Bayesian approach based on
restricted mean survival times (RMST). A nonparametric prior is specified for
the underlying survival functions: this extends the standard univariate neutral
to the right processes to a multivariate setting and induces a prior for the
RMST's. We rely on a representation as exponential functionals of compound
subordinators to determine closed form expressions of prior and posterior mixed
moments of RMST's. These results are used to approximate functionals of the
posterior distribution of RMST's and are essential for comparing
time--to--event data arising from different samples.


  We propose a new method, Patch-CNN, for diffusion tensor (DT) estimation from
only six-direction diffusion weighted images (DWI). Deep learning-based methods
have been recently proposed for dMRI parameter estimation, using either
voxel-wise fully-connected neural networks (FCN) or image-wise convolutional
neural networks (CNN). In the acute clinical context -- where pressure of time
limits the number of imaged directions to a minimum -- existing approaches
either require an infeasible number of training images volumes (image-wise
CNNs), or do not estimate the fibre orientations (voxel-wise FCNs) required for
tractogram estimation. To overcome these limitations, we propose Patch-CNN, a
neural network with a minimal (non-voxel-wise) convolutional kernel
(3$\times$3$\times$3). Compared with voxel-wise FCNs, this has the advantage of
allowing the network to leverage local anatomical information. Compared with
image-wise CNNs, the minimal kernel vastly reduces training data demand.
Evaluated against both conventional model fitting and a voxel-wise FCN,
Patch-CNN, trained with a single subject is shown to improve the estimation of
both scalar dMRI parameters and fibre orientation from six-direction DWIs. The
improved fibre orientation estimation is shown to produce improved tractogram.


  Nonlinear conductivity effects are studied experimentally and theoretically
for thin samples of disordered ionic conductors. Following previous work in
this field the {\it experimental nonlinear conductivity} of sodium ion
conducting glasses is analyzed in terms of apparent hopping distances. Values
up to 43 \AA are obtained. Due to higher-order harmonic current density
detection, any undesired effects arising from Joule heating can be excluded.
Additionally, the influence of temperature and sample thickness on the
nonlinearity is explored. From the {\it theoretical side} the nonlinear
conductivity in a disordered hopping model is analyzed numerically. For the 1D
case the nonlinearity can be even handled analytically. Surprisingly, for this
model the apparent hopping distance scales with the system size. This result
shows that in general the nonlinear conductivity cannot be interpreted in terms
of apparent hopping distances. Possible extensions of the model are discussed.


  Let $R$ be an Artin algebra and $e$ an idempotent of $R$. Assume that ${\rm
Tor}_i^{eRe}(Re,G)=0$ for any $G\in{\rm GProj} eRe$ and $i$ sufficiently large.
Necessary and sufficient conditions are given for the Schur functor $S_e$ to
induce a triangle-equivalence $\mathbb{D}_{def}(R)\simeq\mathbb{D}_{def}(eRe)$.
Combine this with a result of Psaroudakis-Skartsaterhagen-Solberg [29], we
provide necessary and sufficient conditions for the singular equivalence
$\mathbb{D}_{sg}(R)\simeq\mathbb{D}_{sg}(eRe)$ to restrict to a
triangle-equivalence $\underline{{\rm GProj} R}\simeq\underline{{\rm GProj}
eRe}$. Applying these to the triangular matrix algebra $T=\left(
  \begin{array}{cc}
  A & M \quad
  0 & B
  \end{array}
  \right)$, corresponding results between candidate categories of $T$ and $A$
(resp. $B$) are obtained. As a consequence, we infer Gorensteinness and
CM-freeness of $T$ from those of $A$ (resp. $B$). Some concrete examples are
given to indicate one can realise the Gorenstein defect category of a
triangular matrix algebra as the singularity category of one of its corner
algabras.


  A major challenge in performing quantum error correction (QEC) is
implementing reliable measurements and conditional feed-forward operations. In
quantum computing platforms supporting unconditional qubit resets, or a
constant supply of fresh qubits, alternative schemes which do not require
measurements are possible. In such schemes, the error correction is realized
via crafted coherent quantum feedback. We propose implementations of small
measurement-free QEC schemes, which are fault-tolerant to circuit-level noise.
These implementations are guided by several heuristics to achieve
fault-tolerance: redundant syndrome information is extracted, and additional
single-shot flag qubits are used. By carefully designing the circuit, the
additional overhead of these measurement-free schemes is moderate compared to
their conventional measurement-and-feed-forward counterparts. We highlight how
this alternative approach paves the way towards implementing resource-efficient
measurement-free QEC on neutral-atom arrays.


  We present theoretical study of relative stability as well as of the magnetic
and electronic properties of AgF2 and CuF2, in two related structural forms:
orthorhombic (ambient pressure form of AgF2) and monoclinic (ambient pressure
form of CuF2), using Density Functional Theory. We show, that at P21/c --> Pbca
structural transition is associated with weakening of intra-sheet magnetic
superexchange. This finding is consistent with the flattening of 2D layers,
smaller charge-transfer energy and stronger admixing of Agd/Cud-Fp states in
monoclinic structure, comparing to orthorhombic form. Consequently, monoclinic
AgF2 should be targeted in experiment as it should show stronger magnetic
coupling than its orthorhombic sibling. The dynamically stable P21/c form of
AgF2 could be achieved via two alternative paths: by applied negative strain,
or by rapid quenching silver(II) difluoride from temperatures higher than 480 K
to low temperatures.