  The study of topological magnetic excitations has attracted widespread
attention in the past few years. In this thesis, I have studied some examples
of novel topological magnonic phases/phenomena in low-dimensional quantum
magnets. The first chapter motivates the research based on the research gap in
this field of study. The second chapter is written to make the thesis
self-sufficient and the concepts are explained through examples. In the second
chapter, the following formalisms and physical observables are described:
Holstein-Primakoff, bond operator, Schwinger boson, Bogoliubov-Valatin, Group
theory, Berry-phase, Berry-curvature, Chern number, thermal Hall conductance,
Nernst conductivity, dynamical spin structure factor, edge-current. The main
results of the thesis are shown in the third, fourth, and fifth chapters. In
the third chapter, I have shown that anti-chiral edge states (co-propagating
edge states) arise in the ferromagnetic Heisenberg model on the honeycomb
lattice with Dzyaloshinskii-Moriya (DM) interactions. My results suggest that
such anti-chiral edge states may be induced in certain realistic models of
quantum magnets. In the fourth chapter, I have found the emergence of many
magnon band-topological phases in the flux state of the Shastry-Sutherland
model. I have derived a simple analytical form of the temperature dependence of
derivative of thermal Hall conductivity near the band topological transition
point which I propose to be experimentally useful. In the fifth chapter, I have
investigated the emergence of Weyl triplons due to inter-layer DM-interaction
in a microscopic model of SrCu2(BO3)2, a widely studied frustrated quantum
magnet. I have shown that the thermal magnon Hall conductivity has a
quasi-linear dependence as a function of the magnetic field in a Weyl-triplon
region.


  We consider first the standard model Lagrangian with $\mu_h^2$ Higgs
potential term set to zero. We point out that this clasically scale invariant
theory potentially exhibits radiative electroweak/scale symmetry breaking with
very high vacuum expectation value (VEV) for the Higgs field, $< \phi > \approx
10^{17-18}$ GeV. Furthermore, if such a vacuum were realized then cancellation
of vacuum energy automatically implies that this nontrivial vacuum is
degenerate with the trivial unbroken vacuum. Such a theory would therefore be
critical with the Higgs self-coupling and its beta function nearly vanishing at
the symmetry breaking minimum, $\lambda (\mu=< \phi >)\approx \beta_{\lambda}
(\mu=< \phi >)\approx 0$. A phenomenologically viable model that predicts this
criticality property arises if we consider two copies of the standard model
Lagrangian, with exact $Z_2$ symmetry swapping each ordinary particle with a
partner. The spontaneously broken vacuum can then arise where one sector gains
the high scale VEV, while the other gains the electroweak scale VEV. The low
scale VEV is perturbed away from zero due to a Higgs portal coupling, or via
the usual small Higgs mass terms $\mu_h^2$, which softly break the scale
invariance. In either case, the cancellation of vacuum energy requires $M_t =
(171.53 \pm 0.42)$ GeV, which is close to its measured value of $(173.34 \pm
0.76)$ GeV.


  The s-wave scattering length for elastic collisions between 87Rb atoms in the
state |f,m_f>=|1,1> is measured in the vicinity of a Feshbach resonance near
1007 G. Experimentally, the scattering length is determined from the mean-field
driven expansion of a Bose-Einstein condensate in a homogeneous magnetic field.
The scattering length is measured as a function of the magnetic field and
agrees with the theoretical expectation. The position and the width of the
resonance are determined to be 1007.40 G and 0.20 G, respectively.


  Perhaps the most classical diffusion model for chemotaxis is the Keller-Segel
system \begin{equation}\tag{$\ast$} \label{ks0} \left\{ \begin{aligned} u_t
=&\; \Delta u - \nabla \cdot(u \nabla v) \quad in {\mathbb
R}^2\times(0,\infty),\\ v =&\; (-\Delta_{\R^2})^{-1} u := \frac 1{2\pi}
\int_{R^2} \, \log \frac 1{|x-z|}\,u(z,t)\, dz, \\ & \qquad\ u(\cdot ,0) = u_0
\geq 0\quad\hbox{in } R^2. \end{aligned} \right. \end{equation} We consider the
{\em critical mass case} $\int_{R^2} u_0(x)\, dx = 8\pi$ which corresponds to
the exact threshold between finite-time blow-up and self-similar diffusion
towards zero. We find a radial function $u_0^*$ with mass $8\pi$ such that for
any initial condition $u_0$ sufficiently close to $u_0^*$ the solution $u(x,t)$
of \equ{ks0} is globally defined and blows-up in infinite time. As $t\to+\infty
$ it has the approximate profile $$ u(x,t) \approx \frac 1{\lambda^2}
\ch{U}\left (\frac {x-\xi(t)}{\lambda(t)} \right ), \quad \ch{U}(y)=
\frac{8}{(1+|y|^2)^2}, $$ where $\lambda(t) \approx \frac c{\sqrt{\log t}}, \
\xi(t)\to q $ for some $c>0$ and $q\in \R^2$. This result answers affirmatively
the nonradial stability conjecture raised in \cite{g}.


  Non-spreading wave packets and matter-wave packets in ring traps both have
attracted great research interests due to their miraculous physical properties
and tempting applications for quite a long time. Here, we proved that there
exists only one set of non-spreading matter-wave packets in a free ring, and
this set of wave packets have been found analytically. These non-spreading
matter-wave packets can be realized in a toroidal trapped Bose-Einstein
condensate system with the help of Feshbach resonance to eliminate contact
interaction between atoms. Since experimentally residual interaction noise will
always exist, its effect on the stability of these non-spreading wave packets
is also examined. Qualitatively, under weak residual interaction noise, these
non-spreading wave packets can preserve their shape for quite a long time,
while a stronger interaction noise will induce shape breathing of the wave
packets. Shape-keeping abilities of these wave packets are further studied
quantitatively. We found that this set of wave packets have the same
shape-keeping ability against interaction noise. And, the shape-keeping ability
is linearly related to the interaction noise strength.


  We briefly review helicity dynamics, inverse and bi-directional cascades in
fluid and magnetohydrodynamic (MHD) turbulence, with an emphasis on the latter.
The energy of a turbulent system, an invariant in the non-dissipative case, is
transferred to small scales through nonlinear mode coupling. Fifty years ago,
it was realized that, for a two-dimensional fluid, energy cascades instead to
larger scales, and so does magnetic helicity in three-dimensional MHD. However,
evidence obtained recently indicates that in fact, for a range of governing
parameters, there are systems for which their ideal invariants can be
transferred, with constant fluxes, to both the large scales and the small
scales, as for MHD or rotating stratified flows, in the latter case including
with quasi-geostrophic forcing. Such bi-directional, split, cascades directly
affect the rate at which mixing and dissipation occur in these flows in which
nonlinear eddies interact with fast waves with anisotropic dispersion laws, due
for example to imposed rotation, stratification or uniform magnetic fields. The
directions of cascades can be obtained in some cases through the use of
phenomenological arguments, one of which we derive here following classical
lines in the case of the inverse magnetic helicity cascade in electron MHD.
With more highly-resolved data sets stemming from large laboratory experiments,
high-performance computing and in-situ satellite observations, machine-learning
tools are bringing novel perspectives to turbulence research, e.g. in helping
devise new explicit sub-grid scale parameterizations, which may lead to
enhanced physical insight, including in the future in the case of these new
bi-directional cascades.


  We use a generic formalism designed to search for relations in
high-dimensional spaces to determine if the total mass of a subhalo can be
predicted from other internal properties such as velocity dispersion, radius,
or star-formation rate. We train neural networks using data from the Cosmology
and Astrophysics with MachinE Learning Simulations (CAMELS) project and show
that the model can predict the total mass of a subhalo with high accuracy: more
than 99% of the subhalos have a predicted mass within 0.2 dex of their true
value. The networks exhibit surprising extrapolation properties, being able to
accurately predict the total mass of any type of subhalo containing any kind of
galaxy at any redshift from simulations with different cosmologies,
astrophysics models, subgrid physics, volumes, and resolutions, indicating that
the network may have found a universal relation. We then use different methods
to find equations that approximate the relation found by the networks and
derive new analytic expressions that predict the total mass of a subhalo from
its radius, velocity dispersion, and maximum circular velocity. We show that in
some regimes, the analytic expressions are more accurate than the neural
networks. We interpret the relation found by the neural network and
approximated by the analytic equation as being connected to the virial theorem.


  We report on our early photometric and spectroscopic observations of the
extremely luminous Type II supernova (SN) 2008es. With an observed peak optical
magnitude of m_V = 17.8 and at a redshift z = 0.213, SN 2008es had a peak
absolute magnitude of M_V = -22.3, making it the second most luminous SN ever
observed. The photometric evolution of SN 2008es exhibits a fast decline rate
(~0.042 mag d^-1), similar to the extremely luminous Type II-L SN 2005ap. We
show that SN 2008es spectroscopically resembles the luminous Type II-L SN
1979C. Although the spectra of SN 2008es lack the narrow and intermediate-width
line emission typically associated with the interaction of a SN with the
circumstellar medium of its progenitor star, we argue that the extreme
luminosity of SN 2008es is powered via strong interaction with a dense,
optically thick circumstellar medium. The integrated bolometric luminosity of
SN 2008es yields a total radiated energy at ultraviolet and optical wavelengths
of >10^51 ergs. Finally, we examine the apparently anomalous rate at which the
Texas Supernova Search has discovered rare kinds of supernovae, including the
five most luminous supernovae observed to date, and find that their results are
consistent with those of other modern SN searches.


  Compact formulas for trapped-particle and passing-particle guiding-center
orbits in axisymmetric tokamak geometry are given in terms of the Jacobi
elliptic functions and complete elliptic integrals. These formulas can find
applications in bounce-center kinetic theory as well as neoclassical transport
theory.


  In strong line-of-sight millimeter-wave (mmWave) wireless systems, the
rank-deficient channel severely hampers spatial multiplexing. To address this
inherent deficiency, multiple reconfigurable-intelligent-surfaces (RISs) are
introduced in this study to customize the wireless channel. Utilizing the RIS
to reshape electromagnetic waves, we theoretically show that a favorable
channel with an arbitrary tunable rank and a minimized truncated condition
number can be established by elaborately designing the placement and reflection
matrix of RISs. Different from existing works on multi-RISs, the number of
elements needed for each RIS to combat the path loss and the limited phase
control is also considered. On the basis of the proposed channel customization,
a joint transmitter-RISs-receiver (Tx-RISs-Rx) design under a hybrid mmWave
system is investigated to maximize the spectral efficiency. Using the proposed
scheme, the optimal singular value decomposition-based hybrid beamforming at
the Tx and Rx can be obtained without matrix decomposition for the digital and
analog beamforming. The bottoms of the sub-channel mode in the water-filling
algorithm, which are conventionally uncontrollable, are proven to be
independently adjustable by RISs. Moreover, the transmit power required for
realizing multi-stream transmission is derived. Numerical results are presented
to verify our theoretical analysis and exhibit substantial gains over systems
without RISs.


  The mathematical description of stable particle-like systems appearing in
relativistic quantum field theory at large, respectively small scales or
non-zero temperatures is discussed.


  The concept of FAIR Digital Objects (FDOs) aims to revolutionise the field of
digital preservation and accessibility in the next few years. Central to this
revolution is the alignment of FDOs with the FAIR (Findable, Accessible,
Interoperable, Reusable) Principles, particularly emphasizing
machine-actionability and interoperability across diverse data ecosystems. This
abstract introduces the "FDO Manager", a Minimum Viable Implementation,
designed to optimize the management of FDOs following these principles and the
FDO specifications. The FDO Manager is tailored to manage research artefacts
such as datasets, codes, and publications, to foster increased transparency and
reproducibility in research. The abstract presents the implementation details
of the FDO Manager, its underlying architecture, and the metadata schemas it
employs, thereby offering a clear and comprehensive understanding of its
functionalities and impact on the research domain.


  We introduce two new classes of measures of information for statistical
experiments which generalise and subsume $\phi$-divergences, integral
probability metrics, $\mathfrak{N}$-distances (MMD), and $(f,\Gamma)$
divergences between two or more distributions. This enables us to derive a
simple geometrical relationship between measures of information and the Bayes
risk of a statistical decision problem, thus extending the variational
$\phi$-divergence representation to multiple distributions in an entirely
symmetric manner. The new families of divergence are closed under the action of
Markov operators which yields an information processing equality which is a
refinement and generalisation of the classical data processing inequality. This
equality gives insight into the significance of the choice of the hypothesis
class in classical risk minimization.


  Recent neutron scattering experiments on CsNiCl_3 reveal some features which
are not well described by the nonlinear sigma model nor by numerical
simulations on isolated S=1 spin chains. In particular, in real systems the
intensity of the continuum of multiparticle excitations, at T=6K, is about 5
times greater than predicted. Also the gap is slightly higher and the
correlation length is smaller. We propose a theoretical scenario where the
interchain interaction is approximated by a staggered magnetic field, yielding
to a correct prediction of the observed quantities.


  A $(k,m)$-Furstenberg set $S \subset \mathbb{F}_q^n$ over a finite field is a
set that has at least $m$ points in common with a $k$-flat in every direction.
The question of determining the smallest size of such sets is a natural
generalization of the finite field Kakeya problem. The only previously known
bound for these sets is due to Ellenberg-Erman and requires sophisticated
machinery from algebraic geometry. In this work we give new, completely
elementary and simple, proofs which significantly improve the known bounds. Our
main result relies on an equivalent formulation of the problem using the notion
of min-entropy, which could be of independent interest.


  We consider the damped wave equation \alpha u_tt + u_t = u_xx - V'(u) on the
whole real line, where V is a bistable potential. This equation has travelling
front solutions of the form u(x,t) = h(x-st) which describe a moving interface
between two different steady states of the system, one of which being the
global minimum of V. We show that, if the initial data are sufficiently close
to the profile of a front for large |x|, the solution of the damped wave
equation converges uniformly on R to a travelling front as t goes to plus
infinity. The proof of this global stability result is inspired by a recent
work of E. Risler and relies on the fact that our system has a Lyapunov
function in any Galilean frame.


  In this paper we consider 0-th order pseudodifferential operators on the
circle. We show that inside any interval disjoint from critical values of the
principal symbol, the spectrum is absolutely continuous with possibly finitely
many embedded eigenvalues. We also give an example of embedded eigenvalues.


  Exoplanets of a few Earth masses can be now detected around nearby low-mass
stars using Doppler spectroscopy. In this paper, we investigate the radial
velocity variations of Kapteyn's star, which is both a sub-dwarf M-star and the
nearest halo object to the Sun. The observations comprise archival and new
HARPS, HIRES and PFS Doppler measurements. Two Doppler signals are detected at
periods of 48 and 120 days using likelihood periodograms and a Bayesian
analysis of the data. Using the same techniques, the activity indicies and
archival ASAS-3 photometry show evidence for low-level activity periodicities
of the order of several hundred days. However, there are no significant
correlations with the radial velocity variations on the same time-scales. The
inclusion of planetary Keplerian signals in the model results in levels of
correlated and excess white noise that are remarkably low compared to younger
G, K and M dwarfs. We conclude that Kapteyn's star is most probably orbited by
two super-Earth mass planets, one of which is orbiting in its circumstellar
habitable zone, becoming the oldest potentially habitable planet known to date.
The presence and long-term survival of a planetary system seems a remarkable
feat given the peculiar origin and kinematic history of Kapteyn's star. The
detection of super-Earth mass planets around halo stars provides important
insights into planet-formation processes in the early days of the Milky Way.


  In large-scale regression problems, random Fourier features (RFFs) have
significantly enhanced the computational scalability and flexibility of
Gaussian processes (GPs) by defining kernels through their spectral density,
from which a finite set of Monte Carlo samples can be used to form an
approximate low-rank GP. However, the efficacy of RFFs in kernel approximation
and Bayesian kernel learning depends on the ability to tractably sample the
kernel spectral measure and the quality of the generated samples. We introduce
Stein random features (SRF), leveraging Stein variational gradient descent,
which can be used to both generate high-quality RFF samples of known spectral
densities as well as flexibly and efficiently approximate traditionally
non-analytical spectral measure posteriors. SRFs require only the evaluation of
log-probability gradients to perform both kernel approximation and Bayesian
kernel learning that results in superior performance over traditional
approaches. We empirically validate the effectiveness of SRFs by comparing them
to baselines on kernel approximation and well-known GP regression problems.


  This report describes the use of a support vector machines with a novel
kernel, to determine the breathing rate and inhalation duration of a fire
fighter wearing a Self-Contained Breathing Apparatus. With this information, an
incident commander can monitor the firemen in his command for exhaustion and
ensure timely rotation of personnel to ensure overall fire fighter safety


  We present new Far Ultraviolet Spectroscopic Explorer (FUSE) observations of
Mira A's wind-accreting companion star, Mira B. We find that the strongest
lines in the FUSE spectrum are H2 lines fluoresced by H I Lyman-alpha. A
previously analyzed Hubble Space Telescope (HST) spectrum also shows numerous
Lyman-alpha fluoresced H2 lines. The HST lines are all Lyman band lines, while
the FUSE H2 lines are mostly Werner band lines, many of them never before
identified in an astrophysical spectrum. We combine the FUSE and HST data to
refine estimates of the physical properties of the emitting H2 gas. We find
that the emission can be reproduced by an H2 layer with a temperature and
column density of T=3900 K and log N(H2)=17.1, respectively. Another similarity
between the HST and FUSE data, besides the prevalence of H2 emission, is the
surprising weakness of the continuum and high temperature emission lines,
suggesting that accretion onto Mira B has weakened dramatically. The UV fluxes
observed by HST on 1999 August 2 were previously reported to be over an order
of magnitude lower than those observed by HST and the International Ultraviolet
Explorer (IUE) from 1979--1995. Analysis of the FUSE data reveals that Mira B
was still in a similarly low state on 2001 November 22.


  We consider the genus of $20$ classes of unimodular Hermitian lattices of
rank $12$ over the Eisenstein integers. This set is the domain for a certain
space of algebraic modular forms. We find a basis of Hecke eigenforms, and
guess global Arthur parameters for the associated automorphic representations,
which recover the computed Hecke eigenvalues. Congruences between Hecke
eigenspaces, combined with the assumed parameters, recover known congruences
for classical modular forms, and support new instances of conjectured
Eisenstein congruences for $\mathbb{U}(2,2)$ automorphic forms.


  We present spectral and timing analyses of simultaneous X-ray and UV
observations of the VY Scl system MV Lyr taken by XMM-Newton, containing the
longest continuous X-ray+UV light curve and highest signal-to-noise X-ray
(EPIC) spectrum to date. The RGS spectrum displays emission lines plus
continuum, confirming model approaches to be based on thermal plasma models. We
test the sandwiched model based on fast variability that predicts a
geometrically thick corona that surrounds an inner geometrically thin disc. The
EPIC spectra are consistent with either a cooling flow model or a 2-T
collisional plasma plus Fe emission lines in which the hotter component may be
partially absorbed which would then originate in a central corona or a
partially obscured boundary layer, respectively. The cooling flow model yields
a lower mass accretion rate than expected during the bright state, suggesting
an evaporated plasma with a low density, thus consistent with a corona. Timing
analysis confirms the presence of a dominant break frequency around log(f/Hz) =
-3 in the X-ray Power Density Spectrum (PDS) as in the optical PDS. The complex
soft/hard X-ray light curve behaviour is consistent with a region close to the
white dwarf where the hot component is generated. The soft component can be
connected to an extended region. We find another break frequency around
log(f/Hz) = -3.4 that is also detected by Kepler. We compared flares at
different wavelengths and found that the peaks are simultaneous but the rise to
maximum is delayed in X-rays with respect to UV.


  We propose a floating tunable coupler that does not rely on direct
qubit-qubit coupling capacitances to achieve the zero-coupling condition. We
show that the polarity of the qubit-coupler couplings can be engineered to
offset the otherwise constant qubit-qubit coupling and attain the zero-coupling
condition when the coupler frequency is above or below the qubit frequencies.
We experimentally demonstrate these two operating regimes of the tunable
coupler by implementing symmetric and asymmetric configurations of the
coupler's superconducting pads with respect to the qubits. Such a floating
tunable coupler provides flexibility in designing large-scale quantum
processors while reducing the always-on residual couplings.


  Ab initio calculations of the magnon dispersion in ferromagnetic materials
typically rely on the adiabatic local density approximation (ALDA) in which the
effective exchange-correlation field is everywhere parallel to the
magnetization. These calculations, however, tend to overestimate the "magnon
stiffness", defined as the curvature of the magnon frequency vs. wave vector
relation evaluated at zero wave vector. Here we suggest a simple procedure to
improve the magnon dispersion by taking into account gradient corrections to
the ALDA at the exchange-only level. We find that this gradient correction
always reduces the magnon stiffness. The surprisingly large size of these
corrections ($\sim 30\%$) greatly improves the agreement between the calculated
and the observed magnon stiffness for cobalt and nickel, which are known to be
overestimated within the ALDA.


  After a brief introduction to the sigma model in QCD, we discuss how the
sigma model can be relevant in the Standard Model. It is shown to be useful in
the analysis of weak processes, such as the study of $\Delta {I} = 1/2$ rule,
and in the analysis of the Higgs sector as well. The sigma model description is
also shown to be quite useful in searching for the effects of New Physics via
gauge boson self-interactions.


  In this paper we consider a physiologically structured population model with
distributed states at birth, formulated on the space of non-negative Radon
measures. Using a characterisation of the pre-dual space of bounded Lipschitz
functions, we show how to apply the theory of strongly continuous positive
semigroups to such a model. In particular, we establish the exponential
convergence of solutions to a one-dimensional global attractor.


  The CRESST (Cryogenic Rare Event Search with Superconducting Thermometers)
and the EURECA (European Underground Rare Event Calorimeter Array) experiments
are direct dark matter search experiments where cryogenic detectors are used to
detect spin-independent, coherent WIMP (Weakly Interacting Massive
Particle)-nucleon scattering events by means of the recoil energy. The
cryogenic detectors use a massive single crystal as absorber which is equipped
with a TES (transition edge sensor) for signal read-out. They are operated at
mK-temperatures. In order to enable a mass production of these detectors, as
needed for the EURECA experiment, a so-called composite detector design (CDD)
that allows decoupling of the TES fabrication from the optimization procedure
of the absorber single-crystal was developed and studied. To further
investigate, understand and optimize the performance of composite detectors a
detailed thermal detector model which takes into account the CDD has been
developed.


  The G-normal distribution was introduced by Peng [2007] as the limiting
distribution in the central limit theorem for sublinear expectation spaces.
Equivalently, it can be interpreted as the solution to a stochastic control
problem where we have a sequence of random variables, whose variances can be
chosen based on all past information. In this note we study the tail behavior
of the G-normal distribution through analyzing a nonlinear heat equation.
Asymptotic results are provided so that the tail "probabilities" can be easily
evaluated with high accuracy. This study also has a significant impact on the
hypothesis testing theory for heteroscedastic data; we show that even if the
data are generated under the null hypothesis, it is possible to cheat and
attain statistical significance by sequentially manipulating the error
variances of the observations.


  Within no inertial frame can stationary charge exist. All charge, wherever it
exists, experiences perpetual interaction with charge elsewhere and so can only
exist as non-trivial current. It follows that the notion of the electrostatic
scalar potential is a pragmatic idealization - it is not fundamental and is
useful simply because, in many practical circumstances, the time scales of
interest are sufficiently small that charges can be considered stationary in
appropriately chosen inertial frames. In this paper, we take the view that the
subsuming of the electrostatic scalar potential within classical
electrodynamics raises issues of fundamental principle and consider the
question of whether it is possible to have a relativistic electrodynamics sans
the electrostatic scalar potential - that is, expressed purely in terms of some
form of relativistic magnetic potential. Surprisingly, we find that such a
thing can be realized in a remarkably simple way - and which is cost-free for
ordinary applications of electrodynamics. But the overall formalism has
fundamental ramifications for our deeper understanding of electrodynamics - for
example, a new relativistic symmetry arises which, naturally interpreted,
implies that the Lorentz force must be considered as a local-action contact
force in which energy-momentum is locally conserved through the medium of an
uncharged massive vector particle.


  Results on the existence of various types of spanning subgraphs of graphs are
milestones in structural graph theory and have been diversified in several
directions. In the present paper, we consider "local" versions of such
statements. In 1966, for instance, D. W. Barnette proved that a $3$-connected
planar graph contains a spanning tree of maximum degree at most $3$. A local
translation of this statement is that if $G$ is a planar graph, $X$ is a subset
of specified vertices of $G$ such that $X$ cannot be separated in $G$ by
removing $2$ or fewer vertices of $G$, then $G$ has a tree of maximum degree at
most $3$ containing all vertices of $X$.
  Our results constitute a general machinery for strengthening statements about
$k$-connected graphs (for $1 \leq k \leq 4$) to locally spanning versions, i.e.
subgraphs containing a set $X\subseteq V(G)$ of a (not necessarily planar)
graph $G$ in which only $X$ has high connectedness. Given a graph $G$ and
$X\subseteq V(G)$, we say $M$ is a minor of $G$ rooted at $X$, if $M$ is a
minor of $G$ such that each bag of $M$ contains at most one vertex of $X$ and
$X$ is a subset of the union of all bags. We show that $G$ has a highly
connected minor rooted at $X$ if $X\subseteq V(G)$ cannot be separated in $G$
by removing a few vertices of $G$.
  Combining these investigations and the theory of Tutte paths in the planar
case yields to locally spanning versions of six well-known results about
degree-bounded trees, hamiltonian paths and cycles, and $2$-connected subgraphs
of graphs.


  We consider 1-dimensional location estimation, where we estimate a parameter
$\lambda$ from $n$ samples $\lambda + \eta_i$, with each $\eta_i$ drawn i.i.d.
from a known distribution $f$. For fixed $f$ the maximum-likelihood estimate
(MLE) is well-known to be optimal in the limit as $n \to \infty$: it is
asymptotically normal with variance matching the Cram\'er-Rao lower bound of
$\frac{1}{n\mathcal{I}}$, where $\mathcal{I}$ is the Fisher information of $f$.
However, this bound does not hold for finite $n$, or when $f$ varies with $n$.
We show for arbitrary $f$ and $n$ that one can recover a similar theory based
on the Fisher information of a smoothed version of $f$, where the smoothing
radius decays with $n$.


  We give a new method for analysing the mixing time of a Markov chain using
path coupling with stopping times. We apply this approach to two hypergraph
problems. We show that the Glauber dynamics for independent sets in a
hypergraph mixes rapidly as long as the maximum degree Delta of a vertex and
the minimum size m of an edge satisfy m>= 2Delta+1. We also show that the
Glauber dynamics for proper q-colourings of a hypergraph mixes rapidly if m>= 4
and q > Delta, and if m=3 and q>=1.65Delta. We give related results on the
hardness of exact and approximate counting for both problems.


  Subgraph counting aims to count the occurrences of a subgraph template T in a
given network G. The basic problem of computing structural properties such as
counting triangles and other subgraphs has found applications in diverse
domains. Recent biological, social, cybersecurity and sensor network
applications have motivated solving such problems on massive networks with
billions of vertices. The larger subgraph problem is known to be memory bounded
and computationally challenging to scale; the complexity grows both as a
function of T and G. In this paper, we study the non-induced tree subgraph
counting problem, propose a novel layered softwarehardware co-design approach,
and implement a shared-memory multi-threaded algorithm: 1) reducing the
complexity of the parallel color-coding algorithm by identifying and pruning
redundant graph traversal; 2) achieving a fully-vectorized implementation upon
linear algebra kernels inspired by GraphBLAS, which significantly improves
cache usage and maximizes memory bandwidth utilization. Experiments show that
our implementation improves the overall performance over the state-of-the-art
work by orders of magnitude and up to 660x for subgraph templates with size
over 12 on a dual-socket Intel(R) Xeon(R) Platinum 8160 server. We believe our
approach using GraphBLAS with optimized sparse linear algebra can be applied to
other massive subgraph counting problems and emerging high-memory bandwidth
hardware architectures.


  It is commonly believed that millisecond radio pulsars have been spun up by
transfer of matter and angular momentum from a low-mass companion during an
X-ray active mass transfer phase. A subclass of low-mass X-ray binaries is that
of the accreting millisecond X-ray pulsars, transient systems that show periods
of X-ray quiescence during which radio emission could switch on. The aim of
this work is to search for millisecond pulsations from three accreting
millisecond X-ray pulsars, XTE J1751-305, XTE J1814-338, and SAX J1808.4-3658,
observed during their quiescent X-ray phases at high radio frequencies (5 - 8
GHz) in order to overcome the problem of the free-free absorption due to the
matter engulfing the system. A positive result would provide definite proof of
the recycling model, providing the direct link between the progenitors and
their evolutionary products. The data analysis methodology has been chosen on
the basis of the precise knowledge of orbital and spin parameters from X-ray
observations. It is subdivided in three steps: we corrected the time series for
the effects of (I) the dispersion due to interstellar medium and (II) of the
orbital motions, and finally (III) folded modulo the spin period to increase
the signal-to-noise ratio. No radio signal with spin and orbital
characteristics matching those of the X-ray sources has been found in our
search, down to very low flux density upper limits. We analysed several
mechanisms that could have prevented the detection of the signal, concluding
that the low luminosity of the sources and the geometric factor are the most
likely reasons for this negative result.


  Constant modulus sequence having lower side-lobe levels in its
auto-correlation function plays an important role in the applications like
SONAR, RADAR and digital communication systems. In this paper, we consider the
problem of minimizing the Integrated Sidelobe Level (ISL) metric, to design a
complex unimodular sequence of any length. The underlying optimization problem
is solved iteratively using the Block Majorization-Minimization(MM) technique,
which ensures that the resultant algorithm to be monotonic. We also show a
computationally efficient way to implement the algorithm using Fast Fourier
Transform (FFT) and Inverse Fast Fourier Transform (IFFT) operations. Numerical
experiments were conducted to compare the proposed algorithm with the
state-of-the art algorithms and was found that the proposed algorithm performs
better in terms of computational complexity and speed of convergence.


  We give a topological stability result for the action of the fundamental
group of a compact manifold of negative curvature on its boundary at infinity:
any nearby action of this group by homeomorphisms of the sphere is
semi-conjugate to the standard boundary action. Using similar techniques we
prove a global rigidity result for the "slithering actions" of 3-manifold
groups that come from skew-Anosov flows. As applications, we construct
hyperbolic 3-manifolds that admit arbitrarily many topologically inequivalent
Anosov flows, answering a question from Kirby's problem list, and also give a
more conceptual proof of a theorem of the second author on {\em global}
$C^0$--rigidity of geometric surface group actions on the circle.


  We develop generalization of the fixed-phase diffusion Monte Carlo method for
Hamiltonians which explicitly depend on particle spins such as for spin-orbit
interactions. The method is formulated in zero variance manner and is similar
to treatment of nonlocal operators in commonly used static- spin calculations.
Tests on atomic and molecular systems show that it is very accurate, on par
with the fixed-node method. This opens electronic structure quantum Monte Carlo
methods to a vast research area of quantum phenomena in which spin-related
interactions play an important role.


  We explore whether the growth dynamics paradigm of Causal Set Theory is
compatible with past-infinite causal sets. We modify the Classical Sequential
Growth dynamics of Rideout and Sorkin to accommodate growth "into the past" and
discuss what form physical constraints such as causality could take in this new
framework. We propose convex-suborders as the "observables" or "physical
properties" in a theory in which causal sets can be past-infinite and use this
proposal to construct a manifestly covariant framework for dynamical models of
growth for past-infinite causal sets.


  The bulk upper critical field H_{c2}(T) of superconducting MgB_2 and its
anisotropy are established by analyzing experimental data on the temperature
and magnetic field dependences of the ab-plane thermal conductivity of a
single-crystalline sample in external magnetic fields oriented both parallel
(H_{c2}^c) and perpendicular (H_{c2}^{ab}) to the c axis of the hexagonal
lattice. From numerical fits we deduce the anisotropy ratio \gamma_0 =
H_{c2}^{ab}(0) / H_{c2}^c(0)=4.2 at T=0 K. Both the values and the temperature
dependences of H_{c2}^c and H_{c2}^{ab} are distinctly different from previous
claims based on measurements of the electrical resistivity.


  Federated learning is a collaborative method that aims to preserve data
privacy while creating AI models. Current approaches to federated learning tend
to rely heavily on secure aggregation protocols to preserve data privacy.
However, to some degree, such protocols assume that the entity orchestrating
the federated learning process (i.e., the server) is not fully malicious or
dishonest. We investigate vulnerabilities to secure aggregation that could
arise if the server is fully malicious and attempts to obtain access to
private, potentially sensitive data. Furthermore, we provide a method to
further defend against such a malicious server, and demonstrate effectiveness
against known attacks that reconstruct data in a federated learning setting.


  The electroweak currents of quarks and leptons in the $SO(5) \times U(1)$
gauge-Higgs unification model in the Randall-Sundrum warped space are
determined. The 4D gauge couplings deviate from those in the standard model and
the weak universality is slightly violated. It is shown that the model is free
from 4D anomalies and the deviations of the gauge couplings are tiny, less than
1% except for the top quark. The $Zt_L \bar t_L$, $Zt_R \bar t_R$, $Zb_L \bar
b_L$ and $Zb_R \bar b_R$ couplings deviate from those in the standard model by
-7%, 18%, 0.3% and 0.9% with the warp factor $z_L = 10^{15}$, respectively. The
violation of the $\mu$-$e$, $\tau$-$e$ and $t$-$e$ universality in the charged
current interactions is ${\cal O}(10^{-8})$, ${\cal O}(10^{-6})$ and 2.3%,
respectively.


  File synchronization services such as Dropbox, Google Drive, Microsoft
OneDrive, Apple iCloud, etc., are becoming increasingly popular in today's
always-connected world. A popular alternative to the aforementioned services is
BitTorrent Sync. This is a decentralized/cloudless file synchronization service
and is gaining significant popularity among Internet users with privacy
concerns over where their data is stored and who has the ability to access it.
The focus of this paper is the remote recovery of digital evidence pertaining
to files identified as being accessed or stored on a suspect's computer or
mobile device. A methodology for the identification, investigation, recovery
and verification of such remote digital evidence is outlined. Finally, a
proof-of-concept remote evidence recovery from BitTorrent Sync shared folder
highlighting a number of potential scenarios for the recovery and verification
of such evidence.


  We present a phenomenological theory for coupled spin-charge dynamics in
magnetic van der Waals heterostructures. The system studied consists of a
layered antiferromagnet inserted into a capacitive vdW heterostructure. It has
been recently demonstrated that charge doping in such layered antiferromagnets
can modulate the strength, and even the sign, of exchange coupling between the
layer magnetizations. This provides a mechanism for electrically generating
magnetization dynamics. The central result we predict here is that the
magnetization dynamics reciprocally results in inducing charge dynamics. Such
dynamics makes magnetic van der Waals heterostructures interesting candidates
for spintronics applications. To this end, we also show that these systems can
be used to convert sub-THz radiation induced magnetization dynamics into
electrical signals.


  Most animal body plans have some degree of left-right asymmetry. This
chirality at the tissue and organ level is often assumed to originate from the
intrinsic handedness of biological molecules. How this handedness might be
transferred from molecules to tissues during development, however, is not well
understood. Here we explore an alternative paradigm where tissue chirality
results from spontaneous symmetry breaking at the cellular scale, with
molecular chirality acting only as a weak bias that ensures that one handedness
predominates over the other. Specifically, we show that systems capable of
generating planar polarity, found in many epithelial tissues, can also
generically break left-right symmetry, and we identify the key interaction
parameters that must be varied to access the chiral phase. In addition to a
chiral polar phase corresponding to one found in liquid crystal films, a
two-dimensional chiral nematic phase with no liquid crystal analog is also
possible. Our results have clear implications for the interpretation of mutant
phenotypes, especially in certain Drosophila epithelia.


  We point out that there are solutions to the scalar wave equation on 1+1
dimensional Minkowski space with finite energy tails which, if they reflect off
a uniformly accelerated mirror due to (say) Dirichlet boundary conditions on
it, develop an infinite stress-energy tensor on the mirror's Rindler horizon.
We also show that, in the presence of an image mirror in the opposite Rindler
wedge, suitable compactly supported arbitrarily small initial data on a
suitable initial surface will develop an arbitrarily large stress-energy scalar
near where the two horizons cross. Also, while there is a regular
Hartle-Hawking-Israel-like state for the quantum theory between these two
mirrors, there are coherent states built on it for which there are similar
singularities in the expectation value of the renormalized stress-energy
tensor. We conjecture that in other situations with analogous enclosed horizons
such as a (maximally extended) Schwarzschild black hole in equilibrium in a
(stationary spherical) box or the (maximally extended) Schwarzschild-AdS
spacetime, there will be similar stress-energy singularities and
almost-singularities -- leading to instability of the horizons when gravity is
switched on and matter and gravity perturbations are allowed for. All this
suggests it is incorrect to picture a black hole in equilibrium in a box or a
Schwarzschild-AdS black hole as extending beyond the past and future horizons
of a single Schwarzschild (/Schwarzschild-AdS) wedge. It would thus provide new
evidence for 't Hooft's brick wall model while seeming to invalidate the
picture in Maldacena's 'Eternal black holes in AdS'. It would thereby also
support the validity of the author's matter-gravity entanglement hypothesis and
of the paper 'Brick walls and AdS/CFT' by the author and Ort\'iz.


  Recently, temporal action localization (TAL) has garnered significant
interest in information retrieval community. However, existing
supervised/weakly supervised methods are heavily dependent on extensive labeled
temporal boundaries and action categories, which is labor-intensive and
time-consuming. Although some unsupervised methods have utilized the
``iteratively clustering and localization'' paradigm for TAL, they still suffer
from two pivotal impediments: 1) unsatisfactory video clustering confidence,
and 2) unreliable video pseudolabels for model training. To address these
limitations, we present a novel self-paced incremental learning model to
enhance clustering and localization training simultaneously, thereby
facilitating more effective unsupervised TAL. Concretely, we improve the
clustering confidence through exploring the contextual feature-robust visual
information. Thereafter, we design two (constant- and variable- speed)
incremental instance learning strategies for easy-to-hard model training, thus
ensuring the reliability of these video pseudolabels and further improving
overall localization performance. Extensive experiments on two public datasets
have substantiated the superiority of our model over several state-of-the-art
competitors.


  We propose and analyze an efficient and accurate numerical method for
computing ground states of spin-2 Bose-Einstein condensates (BECs) by using the
normalized gradient flow (NGF). In order to successfully extend the NGF to
spin-2 BECs which has five components in the vector wave function but with only
two physical constraints on total mass conservation and magnetization
conservation, two important techniques are introduced for designing the
proposed numerical method. The first one is to systematically investigate the
ground state structure and property of spin-2 BECs within a spatially uniform
system, which can be used on how to properly choose initial data in the NGF for
computing ground states of spin-2 BECs. The second one is to introduce three
additional projection conditions based on the relations between the chemical
potentials, together with the two existing physical constraints, such that the
five projection parameters used in the projection step of the NGF can be
uniquely determined. Then a backward-forward Euler finite difference method is
adapted to discretize the NGF. We prove rigorously that there exists a unique
solution of the nonlinear system for determining the five projection parameters
in the full discretization of the NGF under a mild condition on the time step
size. Extensive numerical results on ground states of spin-2 BECs with
ferromagnetic/nematic/cyclic phase and harmonic/optical lattice/box potential
in one/two dimensions are reported to show the efficiency and accuracy of the
proposed numerical method and to demonstrate several interesting physical
phenomena on ground states of spin-2 BECs.


  In the Byzantine Empire of 11-15 CE chess was played on the circular board.
Two versions were known - REGULAR and SYMMETRIC. The difference between them is
easy: the white queen is placed either on light (regular) or on dark square
(symmetric). However, the computer analysis reveals the results of this 'small
perturbation'.


  In this paper we provide sufficient conditions that ensure the existence of
the solution of some vector equilibrium problems in Hausdorff topological
vector spaces ordered by a cone. The conditions that we consider are imposed
not on the whole domain of the operators involved, but rather on a self
segment-dense subset of it, a special type of dense subset. We apply the
results obtained to vector optimization and vector variational inequalities.


  The rapid proliferation of non-cooperative spacecraft and space debris in
orbit has precipitated a surging demand for on-orbit servicing and space debris
removal at a scale that only autonomous missions can address, but the
prerequisite autonomous navigation and flightpath planning to safely capture an
unknown, non-cooperative, tumbling space object is an open problem. This
requires algorithms for real-time, automated spacecraft feature recognition to
pinpoint the locations of collision hazards (e.g. solar panels or antennas) and
safe docking features (e.g. satellite bodies or thrusters) so safe, effective
flightpaths can be planned. Prior work in this area reveals the performance of
computer vision models are highly dependent on the training dataset and its
coverage of scenarios visually similar to the real scenarios that occur in
deployment. Hence, the algorithm may have degraded performance under certain
lighting conditions even when the rendezvous maneuver conditions of the chaser
to the target spacecraft are the same. This work delves into how humans perform
these tasks through a survey of how aerospace engineering students experienced
with spacecraft shapes and components recognize features of the three
spacecraft: Landsat, Envisat, Anik, and the orbiter Mir. The survey reveals
that the most common patterns in the human detection process were to consider
the shape and texture of the features: antennas, solar panels, thrusters, and
satellite bodies. This work introduces a novel algorithm SpaceYOLO, which fuses
a state-of-the-art object detector YOLOv5 with a separate neural network based
on these human-inspired decision processes exploiting shape and texture.
Performance in autonomous spacecraft detection of SpaceYOLO is compared to
ordinary YOLOv5 in hardware-in-the-loop experiments under different lighting
and chaser maneuver conditions at the ORION Laboratory at Florida Tech.


  Active Galactic Nuclei (AGN) are axisymmetric systems to first order; their
observed properties are likely strong functions of inclination with respect to
our line of sight, yet the specific inclinations of all but a few AGN are
generally unknown. By determining the inclinations and geometries of nearby
Seyfert galaxies using the kinematics of their narrow-line regions (NLRs), and
comparing them with observed properties, we find strong correlations between
inclination and total hydrogen column density, infrared color, and H-beta
full-width at half maximum (FWHM). These correlations provide evidence that the
orientation of AGN with respect to our line of sight affects how we perceive
them, beyond the Seyfert type dichotomy. They can also be used to constrain 3D
models of AGN components such as the broad-line region and torus. Additionally,
we find weak correlations between AGN luminosity and several modeled NLR
parameters, which suggests that the NLR geometry and kinematics are dependent
to some degree on the AGN's radiation field.


  Over the last two decades, $B \to \pi K$ modes have received a lot of
attention. They are particularly interesting since the main contribution to
these decays comes from QCD penguin topologies. Furthermore, electroweak
penguin amplitudes enter at a level comparable to the tree topologies. In the
past, a discrepancy was found in the correlation between the CP asymmetries of
$B_d^0 \to \pi^0 K_{\rm S}$. We provide the state-of-the-art picture of this
situation and consider new constraints, finding that the effect has become
larger. An attractive explanation is offered by a modified electroweak penguin
sector. We propose a new method to determine the relevant parameters. It
employs an isospin relation between the amplitudes of the $B \to \pi K$ decays,
and makes only minimal use of the $SU(3)$ flavour symmetry. Using current data
as well as future scenarios, we demonstrate how the mixing-induced CP asymmetry
of $B_d^0 \to \pi^0 K_{\rm S}$ plays a key role in this analysis. The
application of our strategy at the next generation of $B$-physics experiments
may establish New Physics and reveal new sources of CP violation in the
electroweak penguin sector.


  This paper presents a methodology for the construction of simple Control
Lyapunov Functionals (CLFs) for boundary controlled parabolic Partial
Differential Equations (PDEs). The proposed methodology provides functionals
that contain only simple (and not double or triple) integrals of the state.
Moreover, the constructed CLF is "almost diagonal" in the sense that it
contains only a finite number of cross-products of the (generalized) Fourier
coefficients of the state. The methodology for the construction of a CLF is
combined with a novel methodology for boundary feedback design in parabolic
PDEs. The proposed feedback design methodology is Lyapunov-based and the
feedback controller is an "integral" controller with internal dynamics. It is
also shown that the obtained simple CLFs can provide nonlinear boundary
feedback laws which achieve global exponential stabilization of semilinear
parabolic PDEs with nonlinearities that satisfy a linear growth condition.


  Missing values are prevalent across various fields, posing challenges for
training and deploying predictive models. In this context, imputation is a
common practice, driven by the hope that accurate imputations will enhance
predictions. However, recent theoretical and empirical studies indicate that
simple constant imputation can be consistent and competitive. This empirical
study aims at clarifying if and when investing in advanced imputation methods
yields significantly better predictions. Relating imputation and predictive
accuracies across combinations of imputation and predictive models on 20
datasets, we show that imputation accuracy matters less i) when using
expressive models, ii) when incorporating missingness indicators as
complementary inputs, iii) matters much more for generated linear outcomes than
for real-data outcomes. Interestingly, we also show that the use of the
missingness indicator is beneficial to the prediction performance, even in MCAR
scenarios. Overall, on real-data with powerful models, improving imputation
only has a minor effect on prediction performance. Thus, investing in better
imputations for improved predictions often offers limited benefits.


  We propose and experimentally demonstrate a photonic crystal nanocavity with
multiple resonances that can be tuned nearly independently. The design is
composed of two orthogonal intersecting nanobeam cavities. Experimentally, we
measure cavity quality factors of 6,600 and 1000 for resonances separated by
382 nm; we measure a maximum separation between resonances of 506 nm. These
structures are promising for enhancing efficiency in nonlinear optical
processes such as sum/difference frequency and stimulated Raman scattering.


  In this paper we consider charge current generated by maintaining a
temperature difference over a nanowire at zero voltage bias. For topological
insulator nanowires in a perpendicular magnetic field the current can change
sign as the temperature of one end is increased. Here we study how this
thermoelectric current sign reversal depends on magnetic field and how
impurities affect the size of the thermoelectric current. We consider both
scalar and magnetic impurities and show that their influence on the current are
quite similar, although the magnetic impurities seem to be more effective in
reducing the effect. For moderate impurity concentration the sign reversal
persists.


  We show that separating metallic from semiconducting carbon nanotubes by
dielectrophoresis is developing towards a bulk separation method, which allows
for the first time to produce thin films of only metallic single-walled carbon
nanotubes and to measure their optical absorption spectra. The data proofs that
the selectivity of the separation scheme is independent from the nanotube
diameter.


  In this article, we investigate $2$-$(v,k,\lambda)$ designs with
$\gcd(r,\lambda)=1$ admitting flag-transitive automorphism groups $G$. We prove
that if $G$ is an almost simple group, then such a design belongs to one of the
seven infinite families of $2$-designs or it is one of the eleven well-known
examples. We describe all these examples of designs. We, in particular, prove
that if $\mathcal{D}$ is a symmetric $(v,k,\lambda)$ design with
$\gcd(k,\lambda)=1$ admitting a flag-transitive automorphism group $G$, then
either $G\leq A\Gamma L_{1}(q)$ for some odd prime power $q$, or $\mathcal{D}$
is a projective space or the unique Hadamard design with parameters $(11,5,2)$.


  We study the time-evolution of localized wavepackets in graphene quantum dots
under a perpendicular magnetic field, focusing on the quasiclassical and
revival periodicities, for different values of the magnetic field intensities
in a theoretical framework. We have considered contributions of the two
inequivalent points in the Brillouin zone. The revival time has been found as
an observable that shows the break valley degeneracy.


  New capacity upper bounds are presented for the discrete-time Poisson channel
with no dark current and an average-power constraint. These bounds are a simple
consequence of techniques developed for the seemingly unrelated problem of
upper bounding the capacity of binary deletion and repetition channels.
Previously, the best known capacity upper bound in the regime where the
average-power constraint does not approach zero was due to Martinez (JOSA B,
2007), which is re-derived as a special case of the framework developed in this
paper. Furthermore, this framework is carefully instantiated in order to obtain
a closed-form bound that noticeably improves the result of Martinez everywhere.
Finally, capacity-achieving distributions for the discrete-time Poisson channel
are studied under an average-power constraint and/or a peak-power constraint
and arbitrary dark current. In particular, it is shown that the support of the
capacity-achieving distribution under an average-power constraint only must be
countably infinite. This settles a conjecture of Shamai (IEE Proceedings I,
1990) in the affirmative. Previously, it was only known that the support must
be unbounded.


  We investigate environmental effects on evolution of bright cluster galaxies
in a $\Lambda$ dominated cold dark matter universe using a combination of
dissipationless N-body simulations and a semi-analytic galaxy formation model.
The N-body simulations enable us to calculate orbits of galaxies in simulated
clusters. Therefore we can incorporate stripping of cold gas from galactic
disks by ram pressure from intracluster medium into our model. In this paper we
study how ram pressure stripping (RPS) and small starburst induced by a minor
merger affect colors, star formation rates, and morphologies of cluster
galaxies. We find that the RPS is not important for colors and SFRs of galaxies
in the cluster core if star formation time-scale is properly chosen, because
the star formation is sufficiently suppressed by consumption of the cold gas in
the disks. Then observed color and SFR gradients can be reproduced without the
RPS. The small starburst triggered by a minor merger hardly affects the SFRs
and colors of the galaxies as well. We also examine whether these two processes
can resolve the known problem that the major merger-driven bulge formation
scenario predict too few galaxies of intermediate bulge-to-total luminosity
ratio (B/T) in clusters. When the minor burst is taken into account, the
intermediate B/T population is increased and the observed morphology gradients
in clusters are successfully reproduced. When the minor burst is considered,
the RPS also plays an important role in formation of the intermediate B/T
galaxies. We present redshift evolution of morphological fractions predicted by
our models. The predicted number ratios of the intermediate B/T galaxies to the
bulge-dominated galaxies show nearly flat or slightly increasing trends with
increasing redshift.


  Predicting traits from images lacking visual cues is challenging, as
algorithms are designed to capture visually correlated ground truth. This
problem is critical in biomedical sciences, and their solution can improve the
efficacy of non-invasive methods. For example, a recent challenge of predicting
MGMT methylation status from MRI images is critical for treatment decisions of
glioma patients. Using less robust models poses a significant risk in these
critical scenarios and underscores the urgency of addressing this issue.
Despite numerous efforts, contemporary models exhibit suboptimal performance,
and underlying reasons for this limitation remain elusive. In this study, we
demystify the complexity of MGMT status prediction through a comprehensive
exploration by performing benchmarks of existing models adjoining transfer
learning. Their architectures were further dissected by observing gradient flow
across layers. Additionally, a feature selection strategy was applied to
improve model interpretability. Our finding highlighted that current models are
unlearnable and may require new architectures to explore applications in the
real world. We believe our study will draw immediate attention and catalyse
advancements in predictive modelling with non-visible cues.


  We construct a two-channel scattering model for the triangular network of
valley Hall states in interlayer-biased minimally twisted bilayer graphene from
symmetry arguments and investigate electronic transport in a four-terminal
setup. In the absence of forward scattering, a single phenomenological
parameter tunes the network between a triplet of chiral zigzag modes and
pseudo-Landau levels. Moreover, the chiral zigzag modes give rise to robust
Aharonov-Bohm resonances in the longitudinal conductance in the presence of a
perpendicular magnetic field or an in-plane electric field. Interestingly, we
find that when both a magnetic field and an in-plane electric field are
applied, the resonances of different zigzag branches split depending on their
propagation direction relative to the in-plane electric field. We further
demonstrate that while the Hall response vanishes in the chiral zigzag regime,
a finite Hall response is obtained without destroying the Aharonov-Bohm
resonances in the longitudinal response, by weakly coupling different zigzag
branches, which also gives rise to Hofstadter physics at accessible magnetic
fields.


  Reinforcement learning in multiagent systems has been studied in the fields
of economic game theory, artificial intelligence and statistical physics by
developing an analytical understanding of the learning dynamics (often in
relation to the replicator dynamics of evolutionary game theory). However, the
majority of these analytical studies focuses on repeated normal form games,
which only have a single environmental state. Environmental dynamics, i.e.,
changes in the state of an environment affecting the agents' payoffs has
received less attention, lacking a universal method to obtain deterministic
equations from established multistate reinforcement learning algorithms.
  In this work we present a novel methodological extension, separating the
interaction from the adaptation time scale, to derive the deterministic limit
of a general class of reinforcement learning algorithms, called temporal
difference learning. This form of learning is equipped to function in more
realistic multistate environments by using the estimated value of future
environmental states to adapt the agent's behavior. We demonstrate the
potential of our method with the three well established learning algorithms Q
learning, SARSA learning and Actor-Critic learning. Illustrations of their
dynamics on two multiagent, multistate environments reveal a wide range of
different dynamical regimes, such as convergence to fixed points, limit cycles,
and even deterministic chaos.


  We calculate the correction to the conductivity of a disordered ferromagnetic
metal due to spin-wave-mediated electron--electron interactions. This
correction is the generalization of the Altshuler-Aronov correction to
spin-wave-mediated interactions. We derive a general expression for the
conductivity correction to lowest order in the spin-wave-mediated interaction
and for the limit that the exchange splitting $\Delta$ is much smaller than the
Fermi energy. For a "clean" ferromagnet with $\Delta\tau_{\rm el}/\hbar \gg 1$,
with $\tau_{\rm el}$ the mean time for impurity scattering, we find a
correction $\delta \sigma \propto -T^{5/2}$ at temperatures $T$ above the spin
wave gap. In the opposite, "dirty" limit, $\Delta\tau_{\rm el}/\hbar \ll 1$,
the correction is a non-monotonous function of temperature.


  Quickly localizing the identified white dwarf (WD) binaries is the basic
requirement for the space-based gravitational wave (GW) detection. In fact, the
amplitude of GW signals are modulated by the periodic motion of GW detectors on
the solar orbit. The intensity of the observed signals is enhanced according to
the observation time beyond a year to enhance a high signal to noise ratio
(SNR). As data gap exists, the completeness of the data observed for a long
time depends on filling gaps in the data. Actually, in a year period, the GW
sources have a best observation orbit position of GW detectors, where the
detector response intensity of GW is maximum. Thus, the best positions, where
the direction of GW source is perpendicular to the detection arms, can be
searched for the verified GW sources of the sky map to enhance SNR too. For the
three arms response intensity of the GW signals changing more clearly with the
location of the GW sources relative to the detector, the noises and the
suppression of noise by time delay interferometer are ignored. In the four
chosen sources, the two verification WD binaries: J0806 and V407 Vul are
observed at the best orbit positions by TAIJI for the short time of 2 and 3
days respectively. The intensities of those GWs are above the values of the
TAIJI sensitivity curve, significantly. Compared with a single detector, the
network of two detectors does not significantly improve the accuracy of
location of the verification binaries. These results imply that the searching
of GW signals and parameter estimation of GW sources from the experimental data
of the space-based mission do not ignore the orbit positions relevant to GW
sources.


  As a few large-scale pre-trained models become the major choices of various
applications, new challenges arise for model pruning, e.g., can we avoid
pruning the same model from scratch for every downstream task? How to reuse the
pruning results of previous tasks to accelerate the pruning for a new task? To
address these challenges, we create a small model for a new task from the
pruned models of similar tasks. We show that a few fine-tuning steps on this
model suffice to produce a promising pruned-model for the new task. We study
this ''meta-pruning'' from nearest tasks on two major classes of pre-trained
models, convolutional neural network (CNN) and vision transformer (ViT), under
a limited budget of pruning iterations. Our study begins by investigating the
overlap of pruned models for similar tasks and how the overlap changes over
different layers and blocks. Inspired by these discoveries, we develop a simple
but effective ''Meta-Vote Pruning (MVP)'' method that significantly reduces the
pruning iterations for a new task by initializing a sub-network from the pruned
models of its nearest tasks. In experiments, we demonstrate MVP's advantages in
accuracy, efficiency, and generalization through extensive empirical studies
and comparisons with popular pruning methods over several datasets.


  In this paper, by making use of one of Chen's theorems and the method of
mathematical analysis, we refine Edwards-Child's inequality and solve a
conjecture posed by Liu.


  It has been proposed that photon-exchange effects in atom-photon interactions
could lead to greatly enhanced optical nonlinearities. These might have
widespread application (e.g. quantum information). Here we demonstrate
experimentally that such exchange effects can indeed enhance the probability of
real absorption of photon pairs. Using nonclassical pairs of photons with
variable time separation, we observe a maximum suppression of pair transmission
by at least 5% with respect to the result for uncorrelated photons.


  Self-similar sets with open set condition, the linear objects of fractal
geometry, have been considered mainly for crystallographic data. Here we
introduce new symmetry classes in the plane, based on rotation by irrational
angles. Examples without characteristic directions, with strong connectedness
and small complexity were found in a computer-assisted search. They are
surprising since the rotations are given by rational matrices, and the proof of
the open set condition usually requires integer data. We develop a
classification of self-similar sets by symmetry class and algebraic numbers.
Examples are given for various quadratic number fields. .


  Self-supervised video transformer pre-training has recently benefited from
the mask-and-predict pipeline. They have demonstrated outstanding effectiveness
on downstream video tasks and superior data efficiency on small datasets.
However, temporal relation is not fully exploited by these methods. In this
work, we explicitly investigate motion cues in videos as extra prediction
target and propose our Masked Appearance-Motion Modeling (MAM2) framework.
Specifically, we design an encoder-regressor-decoder pipeline for this task.
The regressor separates feature encoding and pretext tasks completion, such
that the feature extraction process is completed adequately by the encoder. In
order to guide the encoder to fully excavate spatial-temporal features, two
separate decoders are used for two pretext tasks of disentangled appearance and
motion prediction. We explore various motion prediction targets and figure out
RGB-difference is simple yet effective. As for appearance prediction, VQGAN
codes are leveraged as prediction target. With our pre-training pipeline,
convergence can be remarkably speed up, e.g., we only require half of epochs
than state-of-the-art VideoMAE (400 v.s. 800) to achieve the competitive
performance. Extensive experimental results prove that our method learns
generalized video representations. Notably, our MAM2 with ViT-B achieves 82.3%
on Kinects-400, 71.3% on Something-Something V2, 91.5% on UCF101, and 62.5% on
HMDB51.


  In the study of homology cobordisms, knot concordance and link concordance,
the following technical problem arises frequently: let $\pi$ be a group and let
$M \to N$ be a homomorphism between projective $\Z[\pi]$-modules such that
$\Z_p \otimes_{\Z[\pi]} M\to \Z_p \otimes_{\Z[\pi]} N$ is injective; for which
other right $\Z[\pi]$-modules $V$ is the induced map $V \otimes_{\Z[\pi]} M\to
V\otimes_{\Z[\pi]}N$ also injective? Our main theorem gives a new criterion
which combines and generalizes many previous results.


  We consider the $L$-$R$ symmetric composite model for quarks and leptons
where constituent preons possessing some local $SU(N)_{MF}$ metaflavor symmetry
are bound by the chiral $SO(n)_{L}\times SO(n)_{R}$ gauge metacolor forces. The
strengthening of the 't Hooft's anomaly matching condition, when the massless
fermion composites are required to complete a single representation of the
$SU(N)_{MF}$ rather than some set of its representations, allows to fix the
number of basic metaflavors $N$. Particularly, just eight left-handed and eight
right-handed preons and their composites preserving the global chiral symmetry
$SU(8)_{L}\times SU(8)_{R}$ are turned out to underlie the physical world at
small distances that uniquely identifies the local metaflavor symmetry
$SU(8)_{MF}$ as its effective unified symmetry. We next show that the
spontaneous $L$-$R$ symmetry violation caused by composite scalars reduces this
initially vectorlike $SU(8)_{MF}$ theory down to the conventional $SU(5)$ GUT
with an extra local family symmetry $SU(3)_{F}$ and three standard families of
quarks and leptons. Though the tiny confinement scale for universal preons
composing both quarks and leptons makes it impossible to directly confirm their
composite nature, simultaneous emergence of several extra $SU(5)\times
SU(3)_{F}$ multiplets of heavy composite fermions may help with a model
verification. Generally, they may be located at scales from $O(100)$ $TeV$ up
to the Planck mass scale depending on an interplay between the compositeness
scale and scale of the family symmetry $SU(3)_{F}$. Some of them through a
natural see-saw mechanism provide neutrino masses which, in contrast to
conventional picture, appear to follow an inverted family hierarchy. Others mix
with ordinary quark-lepton families in a way that there may arise a marked
violation of unitarity in the CKM matrix for leptons.


  The aim of this paper is to prove new uncertainty principles for an integral
operator $\tt$ with a bounded kernel for which there is a Plancherel theorem.
The first of these results is an extension of Faris's local uncertainty
principle which states that if a nonzero function $f\in L^2(\R^d,\mu)$ is
highly localized near a single point then $\tt (f)$ cannot be concentrated in a
set of finite measure. The second result extends the Benedicks-Amrein-Berthier
uncertainty principle and states that a nonzero function $f\in L^2(\R^d,\mu)$
and its integral transform $\tt (f)$ cannot both have support of finite
measure. From these two results we deduce a global uncertainty principle of
Heisenberg type for the transformation $\tt$. We apply our results to obtain a
new uncertainty principles for the Dunkl and Clifford Fourier transforms.


  For a symmetric bounded measurable function W on [0,1]^2, "moments" of W can
be defined as values t(F,W) indexed by simple graphs. We prove that every such
function is determined by its moments up to a measure preserving transformation
of the variables. This implies that the limit of a convergent dense graph
sequence is unique up to measure preserving transformation.


  The cosine-based softmax losses and their variants achieve great success in
deep learning based face recognition. However, hyperparameter settings in these
losses have significant influences on the optimization path as well as the
final recognition performance. Manually tuning those hyperparameters heavily
relies on user experience and requires many training tricks. In this paper, we
investigate in depth the effects of two important hyperparameters of
cosine-based softmax losses, the scale parameter and angular margin parameter,
by analyzing how they modulate the predicted classification probability. Based
on these analysis, we propose a novel cosine-based softmax loss, AdaCos, which
is hyperparameter-free and leverages an adaptive scale parameter to
automatically strengthen the training supervisions during the training process.
We apply the proposed AdaCos loss to large-scale face verification and
identification datasets, including LFW, MegaFace, and IJB-C 1:1 Verification.
Our results show that training deep neural networks with the AdaCos loss is
stable and able to achieve high face recognition accuracy. Our method
outperforms state-of-the-art softmax losses on all the three datasets.


  Pre-trained Transformer-based models are achieving state-of-the-art results
on a variety of Natural Language Processing data sets. However, the size of
these models is often a drawback for their deployment in real production
applications. In the case of multilingual models, most of the parameters are
located in the embeddings layer. Therefore, reducing the vocabulary size should
have an important impact on the total number of parameters. In this paper, we
propose to generate smaller models that handle fewer number of languages
according to the targeted corpora. We present an evaluation of smaller versions
of multilingual BERT on the XNLI data set, but we believe that this method may
be applied to other multilingual transformers. The obtained results confirm
that we can generate smaller models that keep comparable results, while
reducing up to 45% of the total number of parameters. We compared our models
with DistilmBERT (a distilled version of multilingual BERT) and showed that
unlike language reduction, distillation induced a 1.7% to 6% drop in the
overall accuracy on the XNLI data set. The presented models and code are
publicly available.


  The Committee on the Status of Women in Astronomy (CSWA) is calling on
federal science funding agencies, in their role as the largest sources of
funding for astronomy in the United States, to take actions that will advance
career development and improve workplace conditions for women and minorities in
astronomy. Funding agencies can and should lead the charge to produce excellent
diversity and inclusion outcomes in our field by the 2030 Astrophysics Decadal
Survey. Anecdotal and quantitative evidence, gathered both by the CSWA and
other groups, shows that many structural barriers to success remain in our
community. We acknowledge the success of programs like NSF-INCLUDES and
NSF-ADVANCE and endorse their continued work. We also recommend further action
to remove barriers to success for women and minority astronomers.
  Key recommendations are:
  - Federal agencies should push academic institutions to reconsider their
support systems for scientists by requiring a high standard of pay and
benefits.
  - Federal agencies should fund research and programs that explore and
implement strategies for improving the experiences of scientists.
  - Federal agencies should require Principal Investigators (PIs) to provide
plans for creating inclusive work environments and mentoring young scientists.


  Recent advances in crowd counting have achieved promising results with
increasingly complex convolutional neural network designs. However, due to the
unpredictable domain shift, generalizing trained model to unseen scenarios is
often suboptimal. Inspired by the observation that density maps of different
scenarios share similar local structures, we propose a novel adversarial
learning approach in this paper, i.e., CODA (\emph{Counting Objects via
scale-aware adversarial Density Adaption}). To deal with different object
scales and density distributions, we perform adversarial training with pyramid
patches of multi-scales from both source- and target-domain. Along with a
ranking constraint across levels of the pyramid input, consistent object counts
can be produced for different scales. Extensive experiments demonstrate that
our network produces much better results on unseen datasets compared with
existing counting adaption models. Notably, the performance of our CODA is
comparable with the state-of-the-art fully-supervised models that are trained
on the target dataset. Further analysis indicates that our density adaption
framework can effortlessly extend to scenarios with different objects.
\emph{The code is available at https://github.com/Willy0919/CODA.}


  We report a molecular dynamics study of transport coefficients and infinite
frequency shear mod- ulus of liquid iron at high temperatures and high
pressures. We observe a simultaneous rise of both shear viscosity and diffusion
coefficient along the melting line and estimate if liquid iron can vitrify
under Earth-core conditions. We show that in frames of the model studied in our
work iron demonstrates a moderate increase of viscosity along the melting line.
It is also demonstrated that in the limit of high temperatures and high
pressures the liquid iron behaves similar to the soft spheres system with
exponent n=4.6.


  High current bright sources are needed to power the next generation of
compact rf and microwave systems. A major requirement is that such a source
could be sustainably operated at high frequencies, well above 1 GHz, and high
gradients, well above 100 MV/m. Field emission sources offer simplicity and
scalability in a high frequency era of the injector design, but the output rf
cycle charge and high gradient operation remain a great and largely unaddressed
challenge. Here, a field emission cathode based on ultra-nano-crystalline
diamond or UNCD, an efficient planar field emission material, was tested at 100
MV/m in an L-band injector. A very high charge of 38 pC per rf cycle (300 nC
per rf pulse corresponding to rf pulse current of 120 mA) was demonstrated.
This operating condition revealed a space charge dominated emission from the
cathode and revealed a condition under which the 1D Child Langmuir limit was
surpassed. Finally, a beam brightness of $\sim 10^{14}-10^{15}$
A/m$^2\times$rad$^2$ was estimated.


  The ordinary generating function of the number of complete subgraphs of $G$
is called a clique polynomial of $G$ and is denoted by $C(G,x)$. A real root of
$C(G,x)$ is called a clique root of the graph $G$. Hajiabolhasan and Mehrabadi
showed that the clique polynomial has always a real root in the interval
$[-1,0)$. Moreover, they showed that the class of triangle-free graphs has only
clique roots. Here, we generalize their result by showing that the class of
$K_4$-free chordal graphs has also only clique roots. Moreover, we show that
this class has always a clique root $-1$. We finally conclude the paper with
several important questions and conjectures.


  The Galactic black hole transient GRS1915+105 is famous for its markedly
variable X-ray and radio behaviour, and for being the archetypal galactic
source of relativistic jets. It entered an X-ray outburst in 1992 and has been
active ever since. Since 2018 GRS1915+105 has declined into an extended
low-flux X-ray plateau, occasionally interrupted by multi-wavelength flares.
Here we report the radio and X-ray properties of GRS1915+105 collected in this
new phase, and compare the recent data to historic observations. We find that
while the X-ray emission remained unprecedentedly low for most of the time
following the decline in 2018, the radio emission shows a clear mode change
half way through the extended X-ray plateau in 2019 June: from low flux (~3mJy)
and limited variability, to marked flaring with fluxes two orders of magnitude
larger. GRS1915+105 appears to have entered a low-luminosity canonical hard
state, and then transitioned to an unusual accretion phase, characterised by
heavy X-ray absorption/obscuration. Hence, we argue that a local absorber hides
from the observer the accretion processes feeding the variable jet responsible
for the radio flaring. The radio-X-ray correlation suggests that the current
low X-ray flux state may be a signature of a super-Eddington state akin to the
X-ray binaries SS433 or V404 Cyg.


  We investigate a minisuperspace model of Einstein gravity plus dilaton that
describes a static spherically symmetric configuration or a Kantowski - Sachs
like universe. We develop the canonical formalism and identify canonical
quantities that generate rigid symmetries of the Hamiltonian. Quantization is
performed by the Dirac and the reduced methods. Both approaches lead to the
same positive definite Hilbert space.


  Image processing on quantum platform is a hot topic for researchers now a
day. Inspired from the idea of quantum physics, researchers are trying to shift
their focus from classical image processing towards quantum image
processing.This paper starts with a brief review of the principles which
underlie quantum computing, and also deals with some of the basics of qubits
and quantum computing.Then this paper starts to deal with some different
methods of image storage, representation and retrieval in a quantum system.This
paper also describes the advantages of using those methods in quantum systems
compare to classical systems. In the next section, a short discussion on some
of the important aspects, comparison among them and applications of quantum
image processing is presented. A few other hot topics and open problems in
quantum image processing are mentioned in this paper. This review article will
provide the readership an overview of progress witnessed in the area of Quantum
Image processing while also simulating further interest to pursue more advanced
research in it.


  Representation learning on a knowledge graph (KG) is to embed entities and
relations of a KG into low-dimensional continuous vector spaces. Early KG
embedding methods only pay attention to structured information encoded in
triples, which would cause limited performance due to the structure sparseness
of KGs. Some recent attempts consider paths information to expand the structure
of KGs but lack explainability in the process of obtaining the path
representations. In this paper, we propose a novel Rule and Path-based Joint
Embedding (RPJE) scheme, which takes full advantage of the explainability and
accuracy of logic rules, the generalization of KG embedding as well as the
supplementary semantic structure of paths. Specifically, logic rules of
different lengths (the number of relations in rule body) in the form of Horn
clauses are first mined from the KG and elaborately encoded for representation
learning. Then, the rules of length 2 are applied to compose paths accurately
while the rules of length 1 are explicitly employed to create semantic
associations among relations and constrain relation embeddings. Besides, the
confidence level of each rule is also considered in optimization to guarantee
the availability of applying the rule to representation learning. Extensive
experimental results illustrate that RPJE outperforms other state-of-the-art
baselines on KG completion task, which also demonstrate the superiority of
utilizing logic rules as well as paths for improving the accuracy and
explainability of representation learning.


  In this paper, we give a general definition of Kahler angle. There are many
results about Kahler angle one can try to generalize to the general case.


  A general formalism for computing the full counting statistics of energy
exchanged between 'N' squeezed thermal photon reservoirs weakly coupled to a
cavity with 'M' photon modes is presented. The formalism is based on the
two-point measurement scheme and is applied to two simple special cases, the
relaxation dynamics of a single mode cavity in contact with a single squeezed
thermal photon reservoir and the steady-state energy transport between two
squeezed thermal photon reservoirs coupled to a single cavity mode. Using
analytical results, it is found that the short time statistics is significantly
affected by noncommutivity of the initial energy measurements with the
reservoirs squeezed states, and may lead to negative probabilities if not
accounted properly. Furthermore, it is found that for the single reservoir
setup, generically there is no transient or steady-state fluctuation theorems
for energy transport. In contrast, for the two reservoir case, although there
is no generic transient fluctuation theorem, steady-state fluctuation theorem
with a non-universal affinity is found to be valid. Statistics of energy
currents are further discussed.


  The formation of a charge density wave state is characterized by an order
parameter. The way it is established provides unique information on both the
role that correlation plays in driving the charge density wave formation and
the mechanism behind its formation. Here we use time and angle resolved
photoelectron spectroscopy to optically perturb the charge-density phase in
1T-TiSe$_2$ and follow the recovery of its order parameter as a function of
energy, momentum and excitation density. Our results reveal that two distinct
orders contribute to the gap formation, a CDW order and pseudogap-like order,
manifested by an overall robustness to optical excitation. A detailed analysis
of the magnitude of the the gap as a function of excitation density and delay
time reveals the excitonic long-range nature of the CDW gap and the short-range
Jahn-Teller character of the pseudogap order. In contrast to the gap, the
intensity of the folded Se$_{4p}$* band can only give access to the excitonic
order. These results provide new information into the the long standing debate
on the origin of the gap in TiSe$_2$ and place it in the same context of other
quantum materials where a pseudogap phase appears to be a precursor of
long-range order.


  Recently fabricated InSe monolayers exhibit remarkable characteristics that
indicate the potential of this material to host a number of many-body
phenomena. Here, we consistently describe collective electronic effects in
hole-doped InSe monolayers using advanced many-body techniques. To this end, we
derive a realistic electronic-structure model from first principles that takes
into account the most important characteristics of this material, including a
flat band with prominent van Hove singularities in the electronic spectrum,
strong electron-phonon coupling, and weakly-screened long-ranged Coulomb
interactions. We calculate the temperature-dependent phase diagram as a
function of band filling and observe that this system is in a regime with
coexisting charge density wave and ferromagnetic instabilities that are driven
by strong electronic Coulomb correlations. This regime can be achieved at
realistic doping levels and high enough temperatures, and can be verified
experimentally. We find that the electron-phonon interaction does not play a
crucial role in these effects, effectively suppressing the local Coulomb
interaction without changing the qualitative physical picture.


  Two theorems of elmentary arithmetic, one stating that the sum of the
reciprocals of any number of consecutive positive integers is never an integer,
and a generalization thereof by Trygve Nagell, are shown to be provable inside
a very weak arithmetic, Richard Kaye's $PA^-$, in which there is no induction
axiom whatsoever.


  Online change detection involves monitoring a stream of data for changes in
the statistical properties of incoming observations. A good change detector
will detect any changes shortly after they occur, while raising few false
alarms. Although there are algorithms with confirmed optimality properties for
this task, they rely on the exact specifications of the relevant probability
distributions and this limits their practicality. In this work we describe a
kernel-based variant of the Cumulative Sum (CUSUM) change detection algorithm
that can detect changes under less restrictive assumptions. Instead of using
the likelihood ratio, which is a parametric quantity, the Kernel CUSUM (KCUSUM)
algorithm compares incoming data with samples from a reference distribution
using a statistic based on the Maximum Mean Discrepancy (MMD) non-parametric
testing framework. The KCUSUM algorithm is applicable in settings where there
is a large amount of background data available and it is desirable to detect a
change away from this background setting. Exploiting the random-walk structure
of the test statistic, we derive bounds on the performance of the algorithm,
including the expected delay and the average time to false alarm.


  Our goal is to build robust optimization problems for making decisions based
on complex data from the past. In robust optimization (RO) generally, the goal
is to create a policy for decision-making that is robust to our uncertainty
about the future. In particular, we want our policy to best handle the the
worst possible situation that could arise, out of an uncertainty set of
possible situations. Classically, the uncertainty set is simply chosen by the
user, or it might be estimated in overly simplistic ways with strong
assumptions; whereas in this work, we learn the uncertainty set from data
collected in the past. The past data are drawn randomly from an (unknown)
possibly complicated high-dimensional distribution. We propose a new
uncertainty set design and show how tools from statistical learning theory can
be employed to provide probabilistic guarantees on the robustness of the
policy.


  We present a detailed analysis of the neutral hydrogen kinematics of 12
nearby dwarf irregular galaxies observed as part of the Local Volume HI Survey
(LVHIS) conducted at the Australia Telescope Compact Array. For each galaxy we
measure the disk parameters (inclination, position angle) and the HI rotation
curve. Six galaxies in our sample (AM0605-341, Argo Dwarf, ESO059-G001,
ESO137-G018, ESO174-G?001, ESO308-G022) have their atomic hydrogen distribution
studied for the first time. AM0605-341 was found to have an extension of
redshifted HI which we propose is due to a tidal interaction with NGC2188.
There is evidence that ESO215-G?009 has extraplanar HI gas. We also compare the
global galaxy properties, in particular the integrated HI flux density and
velocity widths of the observed HI spectra with the results from the low
angular resolution HI Parkes All Sky Survey (HIPASS). We discuss under what
circumstances the 21cm emission line profile can accurately predict the
galaxy's rotation velocity, an observational parameter crucial to study the
classical and baryonic Tully-Fisher relations.


  A supersaturated design is a design whose run size is not large enough for
estimating all the main effects. The goodness of multi-level supersaturated
designs can be judged by the generalized minimum aberration criterion proposed
by Xu and Wu [Ann. Statist. 29 (2001) 1066--1077]. A new lower bound is derived
and general construction methods are proposed for multi-level supersaturated
designs. Inspired by the Addelman--Kempthorne construction of orthogonal
arrays, several classes of optimal multi-level supersaturated designs are given
in explicit form: Columns are labeled with linear or quadratic polynomials and
rows are points over a finite field. Additive characters are used to study the
properties of resulting designs. Some small optimal supersaturated designs of
3, 4 and 5 levels are listed with their properties.


  Topic models are a popular tool for understanding text collections, but their
evaluation has been a point of contention. Automated evaluation metrics such as
coherence are often used, however, their validity has been questioned for
neural topic models (NTMs) and can overlook a models benefits in real world
applications. To this end, we conduct the first evaluation of neural,
supervised and classical topic models in an interactive task based setting. We
combine topic models with a classifier and test their ability to help humans
conduct content analysis and document annotation. From simulated, real user and
expert pilot studies, the Contextual Neural Topic Model does the best on
cluster evaluation metrics and human evaluations; however, LDA is competitive
with two other NTMs under our simulated experiment and user study results,
contrary to what coherence scores suggest. We show that current automated
metrics do not provide a complete picture of topic modeling capabilities, but
the right choice of NTMs can be better than classical models on practical task.


  Ultra-Reliable and Low-Latency Communications (URLLC) services in vehicular
networks on millimeter-wave bands present a significant challenge, considering
the necessity of constantly adjusting the beam directions. Conventional methods
are mostly based on classical control theory, e.g., Kalman filter and its
variations, which mainly deal with stationary scenarios. Therefore, severe
application limitations exist, especially with complicated, dynamic
Vehicle-to-Everything (V2X) channels. This paper gives a thorough study of this
subject, by first modifying the classical approaches, e.g., Extended Kalman
Filter (EKF) and Particle Filter (PF), for non-stationary scenarios, and then
proposing a Reinforcement Learning (RL)-based approach that can achieve the
URLLC requirements in a typical intersection scenario. Simulation results based
on a commercial ray-tracing simulator show that enhanced EKF and PF methods
achieve packet delay more than $10$ ms, whereas the proposed deep RL-based
method can reduce the latency to about $6$ ms, by extracting context
information from the training data.


  A characterization of the proximal normal cone is obtained and a separation
theorem for convex subsets of Riemannian manifolds is established. Moreover,
the convexity of the distance function $d_S$ for a convex subset $S$ in the
cases where the boundary of $S$ contains a geodesic segment, the boundary of
$S$ is $C^2$ or the boundary of $S$ is not regular is discussed. Furthermore, a
nonsmooth version of positive semi-definiteness of Hessian of convex functions
on Riemannian manifolds is established.


  The L1544 pre-stellar core has been observed as part of the ASAI IRAM 30m
Large Program as well as follow-up programs. These observations have revealed
the chemical richness of the earliest phases of low-mass star-forming regions.
In this paper we focus on the twenty-one sulphur bearing species (ions,
isotopomers and deuteration) that have been detected in this spectral-survey
through fifty one transitions: CS, CCS, C3S, SO, SO2, H2CS, OCS, HSCN, NS,
HCS+, NS+ and H2S. We also report the tentative detection (4 {\sigma} level)
for methyl mercaptan (CH3SH). LTE and non-LTE radiative transfer modelling have
been performed and we used the nautilus chemical code updated with the most
recent chemical network for sulphur to explain our observations. From the
chemical modelling we expect a strong radial variation for the abundances of
these species, which mostly are emitted in the external layer where non thermal
desorption of other species has previously been observed. We show that the
chemical study cannot be compared to what has been done for the TMC-1 dark
cloud, where the abundance is supposed constant along the line of sight, and
conclude that a strong sulphur depletion is necessary to fully reproduce our
observations of the prototypical pre-stellar core L1544.


  The study assesses the impact of cloud-based microservices architectures on
application performance. Several aspects of performance evaluation are
discussed, including response time, throughput, scalability, and reliability.
This article examines the advantages and challenges of adopting a cloud-based
approach. It explores potential bottlenecks and issues in a microservices
architecture and presents optimization techniques. With the help of case
studies and empirical studies, it compares cloud-based microservices
architectures with traditional monolithic architectures. Furthermore, the paper
examines the challenges of monitoring and troubleshooting distributed
microservices. In conclusion, it emphasizes the importance of planning,
designing, and testing during the adoption of cloud-based microservices.


  In presence of string solitons, index theorems for the generalised Dirac
operators have to be revisited. We show that in supersymmetric configurations
the fermionic operators decouple, so that there are no mixing effects between
different fermions in the index theorems. We extend the index theorems in
presence of torsion to the generic case of manifolds with boundary, which
naturally appear in string solutions and apply this result to the soliton
solution by Callan, Harvey and Strominger.


  In this paper we calculate the ring of unstable (possibly non-additive)
operations from algebraic Morava K-theory K(n) to Chow groups with
$\mathbb{Z}_{(p)}$-coefficients. More precisely, we prove that it is a formal
power series ring on generators $c_i:K(n)\rightarrow CH^i \otimes
\mathbb{Z}_{(p)}$, which satisfy a Cartan-type formula.