  We propose a novel physically-based multi-phase thermo-mechanical model for
rock-ice avalanche. The model is built on a multi-phase mass flow model and
extends a two-phase rock-ice avalanche model. It considers rock, ice and fluid;
includes the mechanism of ice-melting and a rigorously derived dynamically
changing general temperature equation for avalanching bulk mass, the first of
its kind. It explains advection-diffusion of heat including the heat exchange
across the rock-ice avalanche body, basal heat conduction, production and loss
of heat due to frictional shearing and changing temperature, a general
formulation of the ice melting rate and enhancement of temperature due to basal
entrainment. The temperature equation includes a composite term containing
coupled dynamics: rate of change of thermal conductivity and temperature. Ice
melt intensity determines these rates as mixture conductivity evolves,
characterizing distinctive thermo-mechanical processes. The model highlights
essential aspects of rock-ice avalanches. Lateral heat productions play an
important role in temperature evolution. Fast moving avalanches produce higher
amount of heat. Fast ice melting results in substantial change in temperature.
We formally derive the melting efficiency dependent general fluid production
rate. The model includes internal mass and momentum exchanges between the
phases and mass and momentum productions due to entrainment. The latter
significantly changes the state of temperature; yet, the former exclusively
characterizes rock-ice avalanche. Temperature changes are rapid when heat
entrainment across the avalanche boundary is substantial. It also applies to
basal heat conduction. A strong coupling exists between phase mass and momentum
balances and the temperature equation. The new model offers the first-ever
complete dynamical solution for simulating rock-ice avalanche with changing
temperature.


  The electron-graviton interaction can be described in terms of the
gravitational form factors of the QED energy momentum tensor. Here we focus on
the form factor D(t), and we examine its properties and its interpretation in
terms of internal forces at one-loop accuracy in QED. We perform the
calculation by keeping separate the contributions due to the electron and the
photon parts of the energy momentum tensor. We also study the case of a nonzero
photon mass. Furthermore, we discuss similarities with and differences to the
form factor D(t) of hadronic bound states.


  With the explosive growth of mobile data demand, there has been an increasing
interest in deploying small cells of higher frequency bands underlying the
conventional homogeneous macrocell network, which is usually referred to as
heterogeneous cellular networks, to significantly boost the overall network
capacity. With vast amounts of spectrum available in the millimeter wave
(mmWave) band, small cells at mmWave frequencies are able to provide
multi-gigabit access data rates, while the wireless backhaul in the mmWave band
is emerging as a cost-effective solution to provide high backhaul capacity to
connect access points (APs) of the small cells. In order to operate the mobile
network optimally, it is necessary to jointly design the radio access and
backhaul networks. Meanwhile, direct transmissions between devices should also
be considered to improve system performance and enhance the user experience. In
this paper, we propose a joint transmission scheduling scheme for the radio
access and backhaul of small cells in the mmWave band, termed D2DMAC, where a
path selection criterion is designed to enable device-to-device transmissions
for performance improvement. In D2DMAC, a concurrent transmission scheduling
algorithm is proposed to fully exploit spatial reuse in mmWave networks.
Through extensive simulations under various traffic patterns and user
deployments, we demonstrate D2DMAC achieves near-optimal performance in some
cases, and outperforms other protocols significantly in terms of delay and
throughput. Furthermore, we also analyze the impact of path selection on the
performance improvement of D2DMAC under different selected parameters.


  This paper represents a model for microstructure formation in metallic foams
based on the multi-phase-field (MPF) approach. By the use of a no-coalescence
boundary condition within this MPF-framework, it is possible to completely
prevent coalescence of bubbles and thus focus on the formation of a closed
porous microstructure. A modification of this non-wetting criterion allows for
the controlled initiation of coalescence and the evolution of open structures.
The method is validated and used to simulate foam structure formation both in
two and three dimensions.


  Radiologists possess diverse training and clinical experiences, leading to
variations in the segmentation annotations of lung nodules and resulting in
segmentation uncertainty.Conventional methods typically select a single
annotation as the learning target or attempt to learn a latent space comprising
multiple annotations. However, these approaches fail to leverage the valuable
information inherent in the consensus and disagreements among the multiple
annotations. In this paper, we propose an Uncertainty-Aware Attention Mechanism
(UAAM) that utilizes consensus and disagreements among multiple annotations to
facilitate better segmentation. To this end, we introduce the Multi-Confidence
Mask (MCM), which combines a Low-Confidence (LC) Mask and a High-Confidence
(HC) Mask.The LC mask indicates regions with low segmentation confidence, where
radiologists may have different segmentation choices. Following UAAM, we
further design an Uncertainty-Guide Multi-Confidence Segmentation Network
(UGMCS-Net), which contains three modules: a Feature Extracting Module that
captures a general feature of a lung nodule, an Uncertainty-Aware Module that
produces three features for the the annotations' union, intersection, and
annotation set, and an Intersection-Union Constraining Module that uses
distances between the three features to balance the predictions of final
segmentation and MCM. To comprehensively demonstrate the performance of our
method, we propose a Complex Nodule Validation on LIDC-IDRI, which tests
UGMCS-Net's segmentation performance on lung nodules that are difficult to
segment using common methods. Experimental results demonstrate that our method
can significantly improve the segmentation performance on nodules that are
difficult to segment using conventional methods.


  When governed by underlying low-dimensional dynamics, the interdependence of
simultaneously recorded population of neurons can be explained by a small
number of shared factors, or a low-dimensional trajectory. Recovering these
latent trajectories, particularly from single-trial population recordings, may
help us understand the dynamics that drive neural computation. However, due to
the biophysical constraints and noise in the spike trains, inferring
trajectories from data is a challenging statistical problem in general. Here,
we propose a practical and efficient inference method, called the variational
latent Gaussian process (vLGP). The vLGP combines a generative model with a
history-dependent point process observation together with a smoothness prior on
the latent trajectories. The vLGP improves upon earlier methods for recovering
latent trajectories, which assume either observation models inappropriate for
point processes or linear dynamics. We compare and validate vLGP on both
simulated datasets and population recordings from the primary visual cortex. In
the V1 dataset, we find that vLGP achieves substantially higher performance
than previous methods for predicting omitted spike trains, as well as capturing
both the toroidal topology of visual stimuli space, and the noise-correlation.
These results show that vLGP is a robust method with a potential to reveal
hidden neural dynamics from large-scale neural recordings.


  The first stage of every knowledge base question answering approach is to
link entities in the input question. We investigate entity linking in the
context of a question answering task and present a jointly optimized neural
architecture for entity mention detection and entity disambiguation that models
the surrounding context on different levels of granularity. We use the Wikidata
knowledge base and available question answering datasets to create benchmarks
for entity linking on question answering data. Our approach outperforms the
previous state-of-the-art system on this data, resulting in an average 8%
improvement of the final score. We further demonstrate that our model delivers
a strong performance across different entity categories.


  A specific value for the cosmological constant, \Lambda, can account for
late-time cosmic acceleration. However, motivated by the so-called cosmological
constant problem(s), several alternative mechanisms have been explored. To
date, a host of well-studied dynamical dark energy and modified gravity models
exists. Going beyond \Lambda CDM often comes with additional degrees of freedom
(dofs). For these to pass existing observational tests, an efficient screening
mechanism must be in place. The linear and quasi-linear regimes of structure
formation are ideal probes of such dofs and can capture the onset of screening.
We propose here a semi-phenomenological treatment to account for screening
dynamics on LSS observables, with special emphasis on Vainshtein-type
screening.


  In this article, we introduce a framework for Hamiltonian tomography of
multi-qubit systems with random noise. We adopt the quantum quench protocol to
reconstruct a many-body Hamiltonian by local measurements that are distorted by
random unitary operators and time uncertainty. In particular, we consider a
transverse field Ising Hamiltonians describing interactions of two spins $1/2$
and three-qubit Hamiltonians of a heteronuclear system within the
radio-frequency field. For a sample of random Hamiltonians, we report the
fidelity of reconstruction versus the amount of noise quantified by two
parameters. Furthermore, we discuss the correlation between the accuracy of
Hamiltonian tomography and the number of pairs of quantum states involved in
the framework. The results provide valuable insight into the robustness of the
protocol against random noise.


  We use numerical methods to exhaustively study a novel family of hairy black
hole solutions in AdS$_5$. These solutions can be uplifted to solutions of type
IIB supergravity with AdS$_5\times S^5$ asymptotics and are thus expected to
play an important role in our understanding of AdS/CFT. We find an intricate
phase diagram, with the aforementioned family of hairy black hole solutions
branching from the Reissner-Nordstr\"om black hole at the onset of the
superradiance instability. We analyse black holes with spherical and planar
horizon topology and explain how they connect in the phase diagram. Finally, we
detail their global and local thermodynamic stability across several ensembles.


  Spurred by widening gap between data processing speed and data communication
speed in Von-Neumann computing architectures, some bioinformatic applications
have harnessed the computational power of Processing-in-Memory (PIM) platforms.
However, the performance of PIMs unavoidably diminishes when dealing with such
complex applications seeking bulk bit-wise comparison or addition operations.
In this work, we present an efficient Processing-in-MRAM Accelerated De Bruijn
Graph based DNA Assembly platform named PANDA based on an optimized and
hardware-friendly genome assembly algorithm. PANDA is able to assemble
large-scale DNA sequence data-set from all-pair overlaps. We first design PANDA
platform that exploits MRAM as a computational memory and converts it to a
potent processing unit for genome assembly. PANDA can execute not only
efficient bulk bit-wise X(N)OR-based comparison/addition operations heavily
required for the genome assembly task but a full-set of 2-/3-input logic
operations inside MRAM chip. We then develop a highly parallel and step-by-step
hardware-friendly DNA assembly algorithm for PANDA that only requires the
developed in-memory logic operations. The platform is then configured with a
novel data partitioning and mapping technique that provides local storage and
processing to fully utilize the algorithm-level's parallelism. The cross-layer
simulation results demonstrate that PANDA reduces the run time and power,
respectively, by a factor of 18 and 11 compared with CPU. Besides, speed-ups of
up-to 2-4x can be obtained over recent processing-in-MRAM platforms to perform
the same task.


  Learning from data has led to paradigm shifts in a multitude of disciplines,
including web, text, and image search, speech recognition, as well as
bioinformatics. Can machine learning enable similar breakthroughs in
understanding quantum many-body systems? Here we develop an efficient deep
learning approach that enables spatially and chemically resolved insights into
quantum-mechanical observables of molecular systems. We unify concepts from
many-body Hamiltonians with purpose-designed deep tensor neural networks
(DTNN), which leads to size-extensive and uniformly accurate (1 kcal/mol)
predictions in compositional and configurational chemical space for molecules
of intermediate size. As an example of chemical relevance, the DTNN model
reveals a classification of aromatic rings with respect to their stability -- a
useful property that is not contained as such in the training dataset. Further
applications of DTNN for predicting atomic energies and local chemical
potentials in molecules, reliable isomer energies, and molecules with peculiar
electronic structure demonstrate the high potential of machine learning for
revealing novel insights into complex quantum-chemical systems.


  This thesis presents a measurement of the cross-section of the purely
electromagnetic production of $e^+e^-$ pairs accompanied by mutual nuclear
Coulomb excitation $AuAu\to Au^*Au^*+e^+e^-$, in ultra-peripheral gold-gold
collisions at RHIC at the center-of-mass collision energy of $\sqrt{s_{NN}} =
200$ GeV per nucleon. These reactions were selected by detecting neutron
emission by the excited gold ions in the Zero Degree Calorimeters. The charged
tracks in the $e^+e^-$ events were reconstructed with the STAR Time Projection
Chamber.
  The detector acceptance limits the kinematical range of the observed $e^+e^-$
pairs; therefore the measured cross-section is extrapolated to $4\pi$ with the
use of Monte Carlo simulations. We have developed a Monte Carlo simulation for
ultra-peripheral $e^+e^-$ production at RHIC based on the Equivalent Photon
Approximation, the lowest-order QED $e^+e^-$ production cross-section by two
real photons and the assumption that the mutual nuclear excitations and the \ee
production are independent (EPA model). %We present cross-section predictions
from this model in various kinematic regions.
  We compare our experimental results to two models: the EPA model and a model
based on full QED calculation of the \ee production, taking the photon
virtuality into account. The measured differential cross-section $d\sigma
/dM_{inv}$ ($M_{inv}$ -- $e^+e^-$ invariant mass) agrees well with both
theoretical models. The measured differential cross-section $d\sigma
/dp_{\perp}^{tot}$ ($p_{\perp}^{tot}$ -- $e^+e^-$ total transverse momentum)
favors the full QED calculation over the EPA model.


  A popular party trick is to fill a glass bottle with water and hit the top of
the bottle with an open hand, causing the bottom of the bottle to break open.
We investigate the source of the catastrophic cracking through the use of
high-speed video and an accelerometer. Upon closer inspection, it is obvious
that the acceleration caused by hitting the top of the bottle is followed by
the formation of bubbles near the bottom. The nearly instantaneous acceleration
creates an area of low pressure on the bottom of the bottle where cavitation
bubbles form. Moments later, the cavitation bubbles collapse at roughly 10
times the speed of formation, causing the bottle to break. The accelerometer
data shows that the bottle is broken after the bubbles collapse and that the
magnitude of the bubble collapse is greater than the initial impact. This fluid
dynamics video highlights that this trick will not work if the bottle is empty
nor if it is filled with a carbonated fluid because the vapor bubbles fill with
the CO2 dissolved in the liquid, preventing the bubbles from collapsing. A
modified cavitation number, including the acceleration of the fluid (a), vapor
pressure (Pv), and depth of the fluid column (h), is derived to determine when
cavity inception occurs. Through experimentation, visible cavitation bubbles
form when the cavitation number is less than 0.5. The experiments, based on the
modified cavitation number, reveal that the easiest way to break a glass bottle
with your bare hands is to fill it with a non-carbonated, high vapor pressure
fluid, and strike it hard.


  An effective Hamiltonian is derived to describe the conduction band of
monolayer black phosphorus (phosphorene) in the presence of spin-orbit coupling
and external electric field. Envelope function approximation along with
symmetry arguments and Lowdin partitioning are utilized to derive extrinsic
spin-orbit coupling. The resulting spin splitting appears in fourth order
perturbation terms and is shown to be linear in both the magnitude of the
external electric field and the strength of the atomic spin-orbit coupling,
similar to the Bychkov-Rashba expression but with an in-plane anisotropy. The
anisotropy depends on the coupling between conduction band and other bands both
close and distant in energy. The spin relaxation of conduction electrons is
then calculated within the Dyakonov-Perel mechanism where momentum scattering
randomizes the polarization of a spin ensemble. We show how the anisotropic
Fermi contour and the anisotropic extrinsic spin splitting contribute to the
anisotropy of spin-relaxation time. Scattering centers in the substrate are
considered to be charged impurities with screened Coulomb potential.


  Cooperative multi-agent reinforcement learning (MARL) has achieved
significant results, most notably by leveraging the representation-learning
abilities of deep neural networks. However, large centralized approaches
quickly become infeasible as the number of agents scale, and fully
decentralized approaches can miss important opportunities for information
sharing and coordination. Furthermore, not all agents are equal -- in some
cases, individual agents may not even have the ability to send communication to
other agents or explicitly model other agents. This paper considers the case
where there is a single, powerful, \emph{central agent} that can observe the
entire observation space, and there are multiple, low-powered \emph{local
agents} that can only receive local observations and are not able to
communicate with each other. The central agent's job is to learn what message
needs to be sent to different local agents based on the global observations,
not by centrally solving the entire problem and sending action commands, but by
determining what additional information an individual agent should receive so
that it can make a better decision. In this work we present our MARL algorithm
\algo, describe where it would be most applicable, and implement it in the
cooperative navigation and multi-agent walker domains. Empirical results show
that 1) learned communication does indeed improve system performance, 2)
results generalize to heterogeneous local agents, and 3) results generalize to
different reward structures.


  Distinguishing hadronically decaying boosted top quarks from massive QCD jets
is an important challenge at the Large Hadron Collider. In this paper we use
the power counting method to study jet substructure observables designed for
top tagging, and gain insight into their performance. We introduce a powerful
new family of discriminants formed from the energy correlation functions which
outperform the widely used N-subjettiness. These observables take a highly
non-trivial form, demonstrating the importance of a systematic approach to
their construction.


  This thesis develops the theory of bundle gerbes and examines a number of
useful constructions in this theory. These allow us to gain a greater insight
into the structure of bundle gerbes and related objects. Furthermore they
naturally lead to some interesting applications in physics.


  The connection between some features of the metallicity gradient in the
Galactic disk, best revealed by Open Clusters and Cepheids, and the spiral
structure, is explored. The step-like abrupt decrease in metallicity at 8.5 kpc
(with R_0= 7.5 kpc, or at 9.5 kpc if R_0 = 8.5 kpc is adopted) is well
explained by the corotation ring-shaped gap in the density of gas, which
isolates the internal and external regions of the disk one from the other. This
solves a long standing problem of understanding the different chemical
characteristics of the inner and outer parts of the disk. The time required to
build up the metallicity difference between the two sides of the step is a
measure of the minimal life-time of the present grand-design spiral pattern
structure, of the order of 3 Gyr. The plateaux observed on each side of the
step are interpreted in terms of the large scale radial motion of the stars and
of the gas flow induced by the spiral structure. The star-formation rate
revealed by the density of open clusters is maximum in the Galactic radial
range from 6 to 12 kpc (with an exception of a narrow gap at corotation),
coinciding with the region where the 4-arms mode is allowed to exist. We argue
that most of the old open clusters situated at large galactocentric radii were
born in this inner region where conditions more favorable to star-formation are
found. The ratio of $\alpha$-elements to Fe of the sample of Cepheids does not
vary appreciably with the Galactic radius, which reveals an homogeneous history
of star formation. Different arguments are given showing that usual
approximations of chemical evolution models, which assume fast mixing of
metallicity in the azimuthal direction and ignore the existence of the spiral
arms, are a poor ones.


  We present a simple experiment of creating an effective vector gauge
potential for Bose-Einstein condensed $^{87}$Rb in the F=2 hyperfine ground
state using two crossed 1064 $nm$ optical dipole trap lasers as the Raman
beams. Due to the far-detuning from the single-photon resonance with the
electronically excited state, the spontaneous emission is strongly reduced, at
the same time, the moderate strength of the Raman coupling still can be
achieved. The atoms at the far detuning of the Raman coupling are loaded
adiabatically into the dressed states by ramping the homogeneous bias magnetic
field to resonance and the different energy dressed states are studied. This
experiment is easily extended to produce synthetic magnetic or electric field
from a spatial or time dependence of the effective vector potential.


  We use OpenGGCM global MHD simulations to study the nightside magnetospheric,
magnetotail, and ionospheric responses to interplanetary (IP) fa st forward
shocks. Three cases are presented in this study: two inclined oblique shocks,
here after IOS-1 and IOS-2, where the latter has a Mach number twice stronger
than the former. Both shocks have impact angles of 30$^o$ in relation to the
Sun-Earth line. Lastly, we choose a frontal perpendicular shock, FPS, whose
shock normal is along the Sun-Earth line, with the same Mach number as IOS-1.
We find that, in the IOS-1 case, due to the north-south asymmetry, the
magnetotail is deflected southward, leading to a mild compression. The
geomagnetic activity observed in the nightside ionosphere is then weak. On the
other hand, in the head-on case, the FPS compresses the magnetotail from both
sides symmetrically. This compression triggers a substorm allowing a larger
amount of stored energy in the magnetotail to be released to the nightside
ionosphere, resulting in stronger geomagnetic activity. By comparing IOS-2 and
FPS, we find that, despite the IOS-2 having a larger Mach number, the FPS leads
to a larger geomagnetic response in the nightside ionosphere. As a result, we
conclude that IP shocks with similar upstream conditions, such as magnetic
field, speed, density, and Mach number, can have different geoeffectiveness,
depending on their shock normal orientation.


  In order to elucidate the emission properties of ultraluminous X-ray sources
(ULXs) during their power-law (PL) state, we examined long-term X-ray spectral
data of IC342 X-1 during its PL state by using our own Suzaku data and the
archival data by XMM-Newton, Chandra, and Swift observations. The PL state of
this source seems to be classified into two sub-states in terms of the X-ray
luminosities in 0.5-10 keV: the low luminosity PL state with 4-6*10^{39} erg/s
and the high luminosity one with 1.1-1.4*10^{40} erg/s. During the Suzaku
observations which were made in 2010 August and 2011 March, X-1 stayed in the
low luminosity PL state. The observed X-ray luminosity (4.9-5.6*10^{39} erg/s)
and the spectral shape (photon index = 1.67-1.83) slightly changed between the
two observations. Using the Suzaku PIN detector, we for the first time
confirmed a PL tail extending up to at least 20 keV with no signatures of a
high-energy turnover in both of the Suzaku observations. In contrast, a
turnover at about 6 keV was observed during the high luminosity PL state in
2004 and 2005 with XMM-Newton. Importantly, photon indices are similar between
the two PL states and so is the Compton y-parameters of y ~ 1, which indicates
a similar energy balance (between the corona and the accretion disk) holding in
the two PL states despite different electron temperatures. From spectral
similarities with recent studies about other ULXs and the Galactic black hole
binary GRS1915+105, IC342 X-1 is also likely to be in a state with a
supercritical accretion rate, although more sensitive higher energy
observations would be necessary to conclude.


  Split-plot designs find wide applicability in multifactor experiments with
randomization restrictions. Practical considerations often warrant the use of
unbalanced designs. This paper investigates randomization based causal
inference in split-plot designs that are possibly unbalanced. Extension of
ideas from the recently studied balanced case yields an expression for the
sampling variance of a treatment contrast estimator as well as a conservative
estimator of the sampling variance. However, the bias of this variance
estimator does not vanish even when the treatment effects are strictly
additive. A careful and involved matrix analysis is employed to overcome this
difficulty, resulting in a new variance estimator, which becomes unbiased under
milder conditions. A construction procedure that generates such an estimator
with minimax bias is proposed.


  Let $ R=k[x_1...x_r]$ and $M$ a multigraded $R-$module. In this work we
interpret $M$ as a multipersistent homology module and give a multigraded
resolution of it. The construction involves cellular resolutions of monomial
ideals and reflects the combinatorial structure of multipersistence homology
modules. In the one critical case, a multifiltration is represented by a
labelled cellular complex. A multipersistence homology module measures the
defect of acyclicity of the associated multigraded cellular chain complex.


  Machine learning (ML) is increasingly being used to support high-stakes
decisions. However, there is frequently a construct gap: a gap between the
construct of interest to the decision-making task and what is captured in
proxies used as labels to train ML models. As a result, ML models may fail to
capture important dimensions of decision criteria, hampering their utility for
decision support. Thus, an essential step in the design of ML systems for
decision support is selecting a target label among available proxies. In this
work, we explore the use of historical expert decisions as a rich -- yet also
imperfect -- source of information that can be combined with observed outcomes
to narrow the construct gap. We argue that managers and system designers may be
interested in learning from experts in instances where they exhibit consistency
with each other, while learning from observed outcomes otherwise. We develop a
methodology to enable this goal using information that is commonly available in
organizational information systems. This involves two core steps. First, we
propose an influence function-based methodology to estimate expert consistency
indirectly when each case in the data is assessed by a single expert. Second,
we introduce a label amalgamation approach that allows ML models to
simultaneously learn from expert decisions and observed outcomes. Our empirical
evaluation, using simulations in a clinical setting and real-world data from
the child welfare domain, indicates that the proposed approach successfully
narrows the construct gap, yielding better predictive performance than learning
from either observed outcomes or expert decisions alone.


  In this paper we prove a new representation formula for slice regular
functions, which shows that the value of a slice regular function $f$ at a
point $q=x+yI$ can be recovered by the values of $f$ at the points $q+yJ$ and
$q+yK$ for any choice of imaginary units $I, J, K.$ This result allows us to
extend the known properties of slice regular functions defined on balls
centered on the real axis to a much larger class of domains, called axially
symmetric domains. We show, in particular, that axially symmetric domains play,
for slice regular functions, the role played by domains of holomorphy for
holomorphic functions.


  Audiovisual segmentation (AVS) is a challenging task that aims to segment
visual objects in videos according to their associated acoustic cues. With
multiple sound sources and background disturbances involved, establishing
robust correspondences between audio and visual contents poses unique
challenges due to (1) complex entanglement across sound sources and (2)
frequent changes in the occurrence of distinct sound events. Assuming sound
events occur independently, the multi-source semantic space can be represented
as the Cartesian product of single-source sub-spaces. We are motivated to
decompose the multi-source audio semantics into single-source semantics for
more effective interactions with visual content. We propose a semantic
decomposition method based on product quantization, where the multi-source
semantics can be decomposed and represented by several disentangled and
noise-suppressed single-source semantics. Furthermore, we introduce a
global-to-local quantization mechanism, which distills knowledge from stable
global (clip-level) features into local (frame-level) ones, to handle frequent
changes in audio semantics. Extensive experiments demonstrate that our
semantically decomposed audio representation significantly improves AVS
performance, e.g., +21.2% mIoU on the challenging AVS-Semantic benchmark with
ResNet50 backbone. https://github.com/lxa9867/QSD.


  We present experimental evidence of ultra-high energy density plasma states
with the keV bulk electron temperatures and near-solid electron densities
generated during the interaction of high contrast, relativistically intense
laser pulses with planar metallic foils. The bulk electron temperature and
density have been measured using x-ray spectroscopy tools; the temperature of
supra-thermal electrons traversing the target was determined from measured
bremsstrahlung spectra; run-away electrons were detected using magnet
spectrometers. The measured electron energy distribution was in a good
agreement with results of Particle-in-Cell (PIC) simulations. Analysis of the
bremsstrahlung spectra and results on measurements of the run-away electrons
showed a suppression of the hot electrons production in the case of the high
laser contrast. By application of Ti-foils covered with nm-thin Fe-layers we
demonstrated that the thickness of the created keV hot dense plasma does not
exceed 150 nm. Results of the pilot hydro-dynamic simulations that are based on
a wide-range two-temperature EOS, wide-range description of all transport and
optical properties, ionization, electron and radiative heating, plasma
expansion, and Maxwell equations (with a wide-range permittivity) for
description of the laser absorption are in excellent agreement with
experimental results. According to these simulations, the generation of keV-hot
bulk electrons is caused by the collisional mechanism of the laser pulse
absorption in plasmas with a near solid step-like electron density profile. The
laser energy firstly deposited into the nm-thin skin-layer is then transported
into the target depth by the electron heat conductivity. This scenario is
opposite to the volumetric character of the energy deposition produced by
supra-thermal electrons.


  We consider the second-order cone function (SOCF) $f: {\mathbb R}^n \to
\mathbb R$ defined by $f(x)= c^T x + d -\|A x + b \|$. Every SOCF is concave.
We give necessary and sufficient conditions for strict concavity of $f$. The
parameters $A \in {\mathbb R}^{m \times n}$ and $b \in {\mathbb R}^m$ are not
uniquely determined. We show that every SOCF can be written in the form $f(x) =
c^T x + d -\sqrt{\delta^2 + (x-x_*)^TM(x-x_*)}$. We give necessary and
sufficient conditions for the parameters $c$, $d$, $\delta$, $M = A^T A$, and
$x_*$ to be uniquely determined. We also give necessary and sufficient
conditions for $f$ to be bounded above.


  Quantum resonant tunneling behaviors of double-barrier structures on graphene
are investigated under the tight-binding approximation. The Klein tunneling and
resonant tunneling are demonstrated for the quasiparticles with energy close to
the Dirac points. The Klein tunneling vanishes by increasing the height of the
potential barriers to more than 300 meV. The Dirac transport properties
continuously change to the Schrodinger ones. It is found that the peaks of
resonant tunneling approximate to the eigen-levels of graphene nanoribbons
under appropriate boundary conditions. A comparison between the zigzag- and
armchair-edge barriers is given.


  In practice, there are processes where the in-control mean and standard
deviation of a quality characteristic is not stable. In such cases, the
coefficient of variation (CV) is a more appropriate measure for assessing
process stability. In this paper, we consider the statistical design of Run
Rules based control charts for monitoring the CV of multivariate data. A Markov
chain approach is used to evaluate the statistical performance of the proposed
charts. The computational results show that the Run Rules based charts
outperform significantly the standard Shewhart control chart. Moreover, by
choosing an appropriate scheme, the Run Rules based charts perform better than
the Rum Sum control chart for monitoring the multivariate CV. An example in a
spring manufacturing process is given to illustrate the implementation of the
proposed charts.


  We introduce genetic algorithms as a means to analyze supernovae type Ia data
and extract model-independent constraints on the evolution of the Dark Energy
equation of state. Specifically, we will give a brief introduction to the
genetic algorithms along with some simple examples to illustrate their
advantages and finally we will apply them to the supernovae type Ia data. We
find that genetic algorithms can lead to results in line with already
established parametric and non-parametric reconstruction methods and could be
used as a complementary way of treating SnIa data. As a non-parametric method,
genetic algorithms provide a model-independent way to analyze data and can
minimize bias due to premature choice of a dark energy model.


  A symplectic form is called hyperbolic if its pull-back to the universal
cover is a differential of a bounded one-form. The present paper is concerned
with the properties and constructions of manifolds admitting hyperbolic
symplectic forms. The main results are:
  * If a symplectic form represents a bounded cohomology class then it is
hyperbolic.
  * The symplectic hyperbolicity is equivalent to a certain isoperimetric
inequality.
  * The fundamental group of symplectically hyperbolic manifold is
non-amenable.
  We also construct hyperbolic symplectic forms on certain bundles and
Lefschetz fibrations, discuss the dependenc of the symplectic hyperbolicity on
the fundamental group and discuss some properties of the group of symplectic
diffeomorphisms of a symplectically hyperbolic manifold.


  Quantum reactive scattering calculations are reported for the ultracold
hydrogen-exchange reaction and its non-reactive atom-exchange isotopic
counterparts, proceeding from excited rotational states. It is shown that while
the geometric phase (GP) does not necessarily control the reaction to all final
states one can always find final states where it does. For the isotopic
counterpart reactions these states can be used to make a measurement of the GP
effect by separately measuring the even and odd symmetry contributions, which
experimentally requires nuclear-spin final-state resolution. This follows from
symmetry considerations that make the even and odd identical-particle exchange
symmetry wavefunctions which include the GP locally equivalent to the opposite
symmetry wavefunctions which do not. This equivalence reflects the important
role discrete symmetries play in ultracold chemistry generally and highlights
the key role ultracold reactions can play in understanding fundamental aspects
of chemical reactivity.


  Let X be a general smooth projective algebraic curve of genus g>1. We prove
that the moduli space G(\alpha:n,d,k) of $\alpha $-stable coherent systems of
type (n,d,k) over X is empty if k>n and the Brill-Noether number is negative.
Moreover, if the Brill-Noether number is positive and <g and for some $\alpha
>0$, G(\alpha:n,d,k) is non-empty G(\alpha :n,d,k) is non-empty for all $\alpha
>0$ and G(\alpha:n,d,k)= G(\alpha ':n,d,k) for all $\alpha ,\alpha '>0$ and the
generic element is generated.


  An \emph{induced packing} of cycles in a graph is a set of vertex-disjoint
cycles with no edges between them. We generalise the classic Erd\H{o}s-P\'osa
theorem to induced packings of cycles. More specifically, we show that there
exists a function ${f(k) = \mathcal{O}(k \log k)}$ such that for every positive
integer ${k}$, every graph $G$ contains either an induced packing of $k$ cycles
or a set $X$ of at most~${f(k)}$ vertices such that the closed neighbourhood of
$X$ intersects all cycles in $G$. Our proof is constructive and yields a
polynomial-time algorithm finding either the induced packing of cycles or the
set $X$. Furthermore, we show that for every positive integer ${d}$, if a graph
$G$ does not contain two cycles at distance more than $d$, then $G$ contains
sets $X_1,X_2\subseteq V(G)$ with $\lvert {X_1} \rvert \leq12(d+1)$ and $\lvert
{X_2} \rvert \leq12$ such that after removing the ball of radius $2d$ around
$X_1$ or the ball of radius $3d$ around $X_2$, the resulting graphs are
forests.
  As a corollary, we prove that every graph with no $K_{1,t}$ induced subgraph
and no induced packing of $k$ cycles has tree-independence number at most
$\mathcal{O}(tk\log k)$, and one can construct a corresponding
tree-decomposition in polynomial time. This resolves a special case of a
conjecture of Dallard et al. (arXiv:2402.11222), and implies that on such
graphs, many NP-hard problems, such as Maximum Weight Independent Set, Maximum
Weight Induced Matching, Graph Homomorphism, and Minimum Weight Feedback Vertex
Set, are solvable in polynomial time. On the other hand, we show that the class
of all graphs with no $K_{1,3}$ induced subgraph and no two cycles at distance
more than $2$ has unbounded tree-independence number.


  Representing a text as a graph for obtaining automatic text summarization has
been investigated for over ten years. With the development of attention or
Transformer on natural language processing (NLP), it is possible to make a
connection between the graph and attention structure for a text. In this paper,
an attention matrix between the sentences of the whole text is adopted as a
weighted adjacent matrix of a fully connected graph of the text, which can be
produced through the pre-training language model. The GCN is further applied to
the text graph model for classifying each node and finding out the salient
sentences from the text. It is demonstrated by the experimental results on two
typical datasets that our proposed model can achieve a competitive result in
comparison with sate-of-the-art models.


  Spatiotemporal evolution in the real Ginzburg-Landau equation is studied with
space-time noise and a slowly increasing critical parameter. Analytical
estimates for the characteristic size of the domains formed in a slow sweep
through the critical point agree with the results of finite difference solution
of the stochastic PDEs.


  While linear mixed modeling methods are foundational concepts introduced in
any statistical education, adequate general methods for interval estimation
involving models with more than a few variance components are lacking,
especially in the unbalanced setting. Generalized fiducial inference provides a
possible framework that accommodates this absence of methodology. Under the
fabric of generalized fiducial inference along with sequential Monte Carlo
methods, we present an approach for interval estimation for both balanced and
unbalanced Gaussian linear mixed models. We compare the proposed method to
classical and Bayesian results in the literature in a simulation study of
two-fold nested models and two-factor crossed designs with an interaction term.
The proposed method is found to be competitive or better when evaluated based
on frequentist criteria of empirical coverage and average length of confidence
intervals for small sample sizes. A MATLAB implementation of the proposed
algorithm is available from the authors.


  Recently, a class of theories of massive gravity has been shown to be
ghost-free. We study the spherically symmetric solutions in the bigravity
formulation of such theories. In general, the solutions admit both a Lorentz
invariant and a Lorentz breaking asymptotically flat behaviour and also fall in
two branches. In the first branch, all solutions can be found analitycally and
are Schwarzschild-like, with no modification as is found for other classes of
theories. In the second branch, exact solutions are hard to find, and relying
on perturbation theory, Yukawa-like modifications of the static potential are
found. The general structure of the solutions suggests that the bigravity
formulation of massive gravity is crucial and more than a tool.


  Deep clustering algorithms combine representation learning and clustering by
jointly optimizing a clustering loss and a non-clustering loss. In such
methods, a deep neural network is used for representation learning together
with a clustering network. Instead of following this framework to improve
clustering performance, we propose a simpler approach of optimizing the
entanglement of the learned latent code representation of an autoencoder. We
define entanglement as how close pairs of points from the same class or
structure are, relative to pairs of points from different classes or
structures. To measure the entanglement of data points, we use the soft nearest
neighbor loss, and expand it by introducing an annealing temperature factor.
Using our proposed approach, the test clustering accuracy was 96.2% on the
MNIST dataset, 85.6% on the Fashion-MNIST dataset, and 79.2% on the EMNIST
Balanced dataset, outperforming our baseline models.


  Generative AI has exhibited considerable potential to transform various
industries and public life. The role of news media coverage of generative AI is
pivotal in shaping public perceptions and judgments about this significant
technological innovation. This paper provides in-depth analysis and rich
insights into the temporal and spatial distribution of topics, sentiment, and
substantive themes within global news coverage focusing on the latest emerging
technology --generative AI. We collected a comprehensive dataset of news
articles (January 2018 to November 2023, N = 24,827). For topic modeling, we
employed the BERTopic technique and combined it with qualitative coding to
identify semantic themes. Subsequently, sentiment analysis was conducted using
the RoBERTa-base model. Analysis of temporal patterns in the data reveals
notable variability in coverage across key topics--business, corporate
technological development, regulation and security, and education--with spikes
in articles coinciding with major AI developments and policy discussions.
Sentiment analysis shows a predominantly neutral to positive media stance, with
the business-related articles exhibiting more positive sentiment, while
regulation and security articles receive a reserved, neutral to negative
sentiment. Our study offers a valuable framework to investigate global news
discourse and evaluate news attitudes and themes related to emerging
technologies.


  Changing someone's opinion is arguably one of the most important challenges
of social interaction. The underlying process proves difficult to study: it is
hard to know how someone's opinions are formed and whether and how someone's
views shift. Fortunately, ChangeMyView, an active community on Reddit, provides
a platform where users present their own opinions and reasoning, invite others
to contest them, and acknowledge when the ensuing discussions change their
original views. In this work, we study these interactions to understand the
mechanisms behind persuasion.
  We find that persuasive arguments are characterized by interesting patterns
of interaction dynamics, such as participant entry-order and degree of
back-and-forth exchange. Furthermore, by comparing similar counterarguments to
the same opinion, we show that language factors play an essential role. In
particular, the interplay between the language of the opinion holder and that
of the counterargument provides highly predictive cues of persuasiveness.
Finally, since even in this favorable setting people may not be persuaded, we
investigate the problem of determining whether someone's opinion is susceptible
to being changed at all. For this more difficult task, we show that stylistic
choices in how the opinion is expressed carry predictive power.


  Interest has grown around the classification of stance that users assume
within online debates in recent years. Stance has been usually addressed by
considering users posts in isolation, while social studies highlight that
social communities may contribute to influence users' opinion. Furthermore,
stance should be studied in a diachronic perspective, since it could help to
shed light on users' opinion shift dynamics that can be recorded during the
debate. We analyzed the political discussion in UK about the BREXIT referendum
on Twitter, proposing a novel approach and annotation schema for stance
detection, with the main aim of investigating the role of features related to
social network community and diachronic stance evolution. Classification
experiments show that such features provide very useful clues for detecting
stance.


  Adding noise to a sensory signal generally decreases human performance.
However noise can improve performance too, due to a process called stochastic
resonance (SR). This paradoxical effect may be exploited in psychophysical
experiments, to provide additional insights into how the sensory system deals
with noise. Here, I develop a model for stochastic resonance to study the
influence of noise on human perception, in which the biological parameter of
`lapse rate' was included. I show that the inclusion of lapse rate allows for
the occurrence of stochastic resonance in terms of the performance metric d'.
At the same time, I show that high levels of lapse rate cause stochastic
resonance to disappear. It is also shown that noise generated in the brain
(i.e., internal noise) may obscure any effect of stochastic resonance in
experimental settings. I further relate the model to a standard equivalent
noise model, the linear amplifier model, and show that the lapse rate can
function to scale the threshold versus noise (TvN) curve, similar to the
efficiency parameter in equivalent noise (EN) models. Therefore, lapse rate
provides a psychophysical explanation for reduced efficiency in EN paradigms.
Furthermore, I note that ignoring lapse rate may lead to an overestimation of
internal noise in equivalent noise paradigms. Overall, describing stochastic
resonance in terms of signal detection theory, with the inclusion of lapse
rate, may provide valuable new insights into how human performance depends on
internal and external noise.


  We present a fit of the virtual-photon scattering asymmetry of polarized Deep
Inelastic Scattering which combines a Monte Carlo technique with the use of a
redundant parametrization based on Neural Networks. We apply the result to the
analysis of CLAS data on a polarized proton target.


  Transformers achieve unrivalled performance in modelling language, but remain
inefficient in terms of memory and time complexity. A possible remedy is to
reduce the sequence length in the intermediate layers by pooling fixed-length
segments of tokens. Nevertheless, natural units of meaning, such as words or
phrases, display varying sizes. To address this mismatch, we equip language
models with a dynamic-pooling mechanism, which predicts segment boundaries in
an autoregressive fashion. We compare several methods to infer boundaries,
including end-to-end learning through stochastic re-parameterisation,
supervised learning (based on segmentations from subword tokenizers or spikes
in conditional entropy), as well as linguistically motivated boundaries. We
perform character-level evaluation on texts from multiple datasets and
morphologically diverse languages. The results demonstrate that dynamic
pooling, which jointly segments and models language, is both faster and more
accurate than vanilla Transformers and fixed-length pooling within the same
computational budget.


  We consider trapped atomic Fermi gases with Feshbach-resonance enhanced
interactions in pseudogap and superfluid temperatures. We calculate the
spectrum of RF(or laser)-excitations for transitions that transfer atoms out of
the superfluid state. The spectrum displays the pairing gap and also the
contribution of unpaired atoms, i.e. in-gap excitations. The results support
the conclusion that a superfluid, where pairing is a many-body effect, was
observed in recent experiments on RF spectroscopy of the pairing gap.


  The book "N-ary Groups" (in Russian) consists of two Parts. It is intended on
the one hand as an initial introduction to the theory of n-ary groups, and on
the other hand it contains the published results by the author on this subject.
At present, the theory of n-ary groups developing but slowly from group theory.
Nonetheless, ternary and n-ary structures have recently been applied to modern
models of elementary particle physics. One of the author's goals in this book
is to draw the attention of mathematicians and theoretical physicists to the
theory of n-ary groups, to some of its distinguishing features, and to details
relevant to its further development and application. Part I: Theorems of Post
and Gluskin-Hosszu. 1.1. Classical definitions of n-ary groups. Examples. 1.2.
Analogies of identity and inverse elements. 1.3. Equivalent sequences. 1.4.
Post's coset theorem. 1.5. Theorem of Gluskin-Hosszu. 1.6. Connection between
the Post's coset theorem and theorem of Gluskin-Hosszu. Addition and comments.
Part II: n-ary Analogies of Some Binary Notations. 2.1. n-ary subgroups.
Cosets. 2.2 Connection between n-ary subgroups of n-ary groups. 2.3. Invariant
and semiinvariant n-ary subgroups. 2.4. Conjugate and semiconjugate n-ary
subgroups. 2.5. Cyclic and semicyclic n-ary groups. 2.6. Abelian n-ary groups
and their generalizations. 2.7. Products of n-ary groups. 2.8. Semiabelian
n-ary groups with idempotents. Addition and comments. Notations. Index.
References.


  It is well known that formal solutions to the Associativity Equations are the
same as cyclic algebras over the homology operad $(H_*(\bar{M}_{0,n+1}))$ of
the moduli spaces of $n$--pointed stable curves of genus zero. In this paper we
establish a similar relationship between the pencils of formal flat connections
(or solutions to the Commutativity Equations) and homology of a new series
$\bar{L}_n$ of pointed stable curves of genus zero. Whereas $\bar{M}_{0,n+1}$
parametrizes trees of $\bold{P}^1$'s with pairwise distinct nonsingular marked
points, $\bar{L}_n$ parametrizes strings of $\bold{P}^1$'s stabilized by marked
points of two types. The union of all $\bar{L}_n$'s forms a semigroup rather
than operad, and the role of operadic algebras is taken over by the
representations of the appropriately twisted homology algebra of this union.


  In this paper the codimension of the complement to the set of factorial
hypersurfaces of degree $d$ in ${\mathbb P}^N$ is estimated for $d\geqslant 4$,
$N\geqslant 7$.


  We prove a lower bound on the number of the convex components of a compact
set with non-empty interior in $\mathbb{R}^n$ for all $n\ge2$. Our result
generalizes and improves the inequalities previously obtained in M. Carozza, F.
Giannetti, F. Leonetti and A. Passarelli di Napoli, "Convex components", in
Communications in Contemporary Mathematics, Vol. 21, No. 06, 1850036 (2019) and
in M. La Civita and F. Leonetti, "Convex components of a set and the measure of
its boundary", Atti. Sem. Mat. Fis. Univ. Modena Reggio Emilia 56 (2008-2009)
71-78.


  Magnetic skyrmions offer promising prospects for constructing future
energy-efficient and high-density information technology, leading to extensive
explorations of new skyrmionic materials recently. The topological Hall effect
has been widely adopted as a distinctive marker of skyrmion emergence.
Alternately, here we propose a novel signature of skyrmion state by
quantitatively investigating the magnetoresistance (MR) induced by skyrmionic
bubbles in CeMn2Ge2. An intriguing finding was revealed: the anomalous MR
measured at different temperatures can be normalized into a single curve,
regardless of sample thickness. This behavior can be accurately reproduced by
the recent chiral spin textures MR model. Further analysis of the MR anomaly
allowed us to quantitatively examine the effective magnetic fields of various
scattering channels. Remarkably, the analyses, combined with the Lorentz
transmission electronic microscopy results, indicate that the in-plane
scattering channel with triplet exchange interactions predominantly governs the
magnetotransport in the Bloch-type skyrmionic bubble state. Our results not
only provide insights into the quantum correction on MR induced by skyrmionic
bubble phase, but also present an electrical probing method for studying chiral
spin texture formation, evolution and their topological properties, which opens
up exciting possibilities for identifying new skyrmionic materials and
advancing the methodology for studying chiral spin textures.


  The space missions TESS and PLATO plan to double the number of 4000
exoplanets already discovered and will measure the size of thousands of
exoplanets around the brightest stars in the sky, allowing ground-based radial
velocity spectroscopy follow-up to determine the orbit and mass of the detected
planets. The new facility we are developing, MARVEL (Raskin et al. this
conference), will enable the ground-based follow-up of large numbers of
exoplanet detections, expected from TESS and PLATO, which cannot be carried out
only by the current facilities that achieve the necessary radial velocity
accuracy of 1 m/s or less. This paper presents the MARVEL observation strategy
and performance analysis based on predicted PLATO transit detection yield
simulations. The resulting observation scenario baseline will help in the
instrument design choices and demonstrate the effectiveness of MARVEL as a TESS
and PLATO science enabling facility.


  We focused on the transverse AC magneto-conductance of a high mobility
$p$-GaAs/AlGaAs quantum well ($p=1.2\times 10^{11}$~cm$^{-2}$) in the vicinity
of two values of the Landau level filling factor $\nu$: $\nu =1$ (integer
quantum Hall effect) and $\nu =1/3$ (fractional quantum Hall effect). The
complex transverse AC conductance, $\sigma_{xx}^{AC} (\omega)$, was found from
simultaneous measurements of attenuation and velocity of surface acoustic waves
(SAWs) propagating along the interface between a piezoelectric crystal and the
two-dimensional hole system under investigation. We analyzed both the real and
imaginary parts of the hole conductance and compared the similarities and
differences between the results for filling factor 1 and filling factor 1/3.
Both to the left and to the right of these values maxima of a specific shape,
"wings", arose in the $\sigma (\nu)$ dependences at those two $\nu$. Analysis
of the results of our acoustic measurements at different temperatures and
surface acoustic wave frequencies allowed us to attribute these wings to the
formation of collective localized states, namely the domains of a pinned Wigner
crystal, i.e., a Wigner solid. While the Wigner solid has been observed in 2D
hole systems previously, we were able to detect 20 it at the highest hole
density and, therefore, the lowest hole-hole interaction reported.


  Let $(F_n)_{n\geq 0}$ be the Fibonacci sequence given by $F_0 = 0, F_1 = 1$
and $F_{n+2} = F_{n+1}+F_n$ for $n \geq 0$. In this paper, we have determined
all the powers of 2 which are sums of five Fibonacci numbers with few
exceptions that we characterize. We have also stated an open problem relating
to the number of solutions of equations like those studied in this paper.


  We present the status of our calculation of hadronic matrix elements for $D$-
and $B$-meson mixing. We use a large set of the MILC collaboration's $N_f=2+1$
asqtad ensembles, which includes lattice spacings in the range
$a\approx0.12$-0.045 fm, and up/down to strange quark mass ratios as low as
0.05. The asqtad action is also employed for the light valence quarks. For the
heavy quarks we use the Sheikholeslami-Wohlert action with the Fermilab
interpretation. Our calculation covers the complete set of five local operators
needed to describe $B$-meson mixing in the Standard Model and Beyond. In the
charm sector, our calculation of local mixing matrix elements may be used to
constrain new physics models. We present final correlator fit results on the
full data set for the $B$-meson mixing project and preliminary fit results for
the $D$-meson mixing project.


  The Nobel Prize in Physics 2020 has been divided, one half awarded to Roger
Penrose for the discovery that black hole formation is a robust prediction of
the general theory of relativity. and the other half jointly to Reinhard Genzel
and Andrea Ghez for the discovery of a supermassive compact object at the
centre of our galaxy. Here we describe their work and put it in historical
context and discuss specific advances that have been rewarded.


  We suggest baryon-baryon correlations as an experimentally accessible
signature for a first-order phase transition between a baryon-rich phase, like
quarkyonic, and a baryon-suppressed hadronic phase in the QCD phase diagram. We
examine the consequences of baryon-rich bubble formation in an expanding medium
and show how the two-particle correlations vary in the transverse and
longitudinal direction depending on the strength of the radial flow, the bubble
temperature, and the time when the baryons are emitted.


  3D model retrieval techniques can be classified as histogram-based,
view-based and graph-based approaches. We propose a hybrid shape descriptor
which combines the global and local radial distance features by utilizing the
histogram-based and view-based approaches respectively. We define an
area-weighted global radial distance with respect to the center of the bounding
sphere of the model and encode its distribution into a 2D histogram as the
global radial distance shape descriptor. We then uniformly divide the bounding
cube of a 3D model into a set of small cubes and define their centers as local
centers. Then, we compute the local radial distance of a point based on the
nearest local center. By sparsely sampling a set of views and encoding the
local radial distance feature on the rendered views by color coding, we extract
the local radial distance shape descriptor. Based on these two shape
descriptors, we develop a hybrid radial distance shape descriptor for 3D model
retrieval. Experiment results show that our hybrid shape descriptor outperforms
several typical histogram-based and view-based approaches.


  Using the bottom-up approach in a holographic setting, we attempt to study
both the transport and thermodynamic properties of a generic system in 3+1
dimensional bulk spacetime. We show the exact 1/T and $T^2$ dependence of the
longitudinal conductivity and Hall angle, as seen experimentally in most
copper-oxide systems, which are believed to be close to quantum critical point.
This particular temperature dependence to conductivities are possible in two
different cases: (1) Background solutions with scale invariant and broken
rotational symmetry, (2) solutions with pseudo-scaling and unbroken rotational
symmetry but only at low density limit. Generically, the study of the transport
properties in a scale invariant background solution, using the probe brane
approach, at high density and at low temperature limit suggests us to consider
only metrics with two exponents. More precisely, the spatial part of the metric
components should not be same i.e., $g_{xx}\neq g_{yy}$. In doing so, we have
generated the above mentioned behavior to conductivity with a very special
behavior to specific heat which at low temperature goes as: $C_V\sim T^3$.
However, if we break the scaling symmetry of the background solution by
including a nontrivial dilaton, axion or both and keep the rotational symmetry
then also we can generate such a behavior to conductivity but only in the low
density regime. As far as we are aware, this particular temperature dependence
to both the conductivity and Hall angle is being shown for the first time using
holography.


  Conditional matrix variate student $t$ distribution was introduced by
Battulga (2024a). In this paper, we propose a new version of the conditional
matrix variate student $t$ distribution. The paper provides EM algorithms,
which estimate parameters of the conditional matrix variate student $t$
distributions, including general cases and special cases with Minnesota prior.


  Information-theoretic definitions for the noise associated with a quantum
measurement and the corresponding disturbance to the state of the system have
recently been introduced [F. Buscemi et al., Phys. Rev. Lett. 112, 050401
(2014)]. These definitions are invariant under relabelling of measurement
outcomes, and lend themselves readily to the formulation of state-independent
uncertainty relations both for the joint estimate of observables (noise-noise
relations) and the noise-disturbance tradeoff. Here we derive such relations
for incompatible qubit observables, which we prove to be tight in the case of
joint estimates, and present progress towards fully characterising the
noise-disturbance tradeoff. In doing so, we show that the set of obtainable
noise-noise values for such observables is convex, whereas the conjectured form
for the set of obtainable noise-disturbance values is not. Furthermore,
projective measurements are not optimal with respect to the joint-measurement
noise or noise-disturbance tradeoffs. Interestingly, it seems that four-outcome
measurements are needed in the former case, whereas three-outcome measurements
are optimal in the latter.


  We present specific heat data on three samples of the dilute Ising magnet
$\HoYLF$ with $x = 0.018$, 0.045 and 0.080. Previous measurements of the ac
susceptibility of an $x = 0.045$ sample showed the Ho$^{3+}$ moments to remain
dynamic down to very low temperatures and the specific heat was found to have
unusually sharp features. In contrast, our measurements do not exhibit these
sharp features in the specific heat and instead show a broad feature, for all
three samples studied, which is qualitatively consistent with a spin glass
state. Integrating $C/T$, however, reveals an increase in residual entropy with
lower Ho concentration, consistent with recent Monte Carlo simulations showing
a lack of spin glass transition for low x.


  Large language models (LLMs) are known to generate biased responses where the
opinions of certain groups and populations are underrepresented. Here, we
present a novel approach to achieve controllable generation of specific
viewpoints using LLMs, that can be leveraged to produce multiple perspectives
and to reflect the diverse opinions. Moving beyond the traditional reliance on
demographics like age, gender, or party affiliation, we introduce a data-driven
notion of persona grounded in collaborative filtering, which is defined as
either a single individual or a cohort of individuals manifesting similar views
across specific inquiries. As individuals in the same demographic group may
have different personas, our data-driven persona definition allows for a more
nuanced understanding of different (latent) social groups present in the
population. In addition to this, we also explore an efficient method to steer
LLMs toward the personas that we define. We show that our data-driven personas
significantly enhance model steerability, with improvements of between
$57\%-77\%$ over our best performing baselines.


  The misalignment mechanism allows for the efficient, and usually very cold,
production of light scalar bosons, such as axion-like particles (ALPs), making
them an appealing dark matter candidate. However, in certain cases, such as in
the presence of a monodromy, the self-interactions of ALPs can be sufficiently
strong such that the homogeneous field fragments soon after the onset of
oscillations. The resulting large inhomogeneities can lead to the production of
gravitational waves (GWs). We investigate the nonlinear dynamics of
fragmentation, as well as of the subsequent turbulent regime, and calculate the
stochastic GW background that is produced from this process. The GW background
can be enhanced if the time evolution features an extended intermediate phase
of ultrarelativistic dynamics due to a small mass at the bottom of the
potential. Yet, this enhancement is limited by the requirement that the dark
matter remains sufficiently cold. In some cases the resulting GWs may be within
reach of future GW detectors, allowing a complementary probe of this type of
dark matter.


  For the past three years we have been conducting a survey for WR stars in the
Large and Small Magellanic Clouds (LMC, SMC). Our previous work has resulted in
the discovery of a new type of WR star in the LMC, which we are calling WN3/O3.
These stars have the emission-line properties of a WN3 star (strong N V but no
N IV), plus the absorption-line properties of an O3 star (Balmer hydrogen plus
Pickering He II but no He I). Yet these stars are 15x fainter than an O3 V star
would be by itself, ruling out these being WN3+O3 binaries. Here we report the
discovery of two more members of this class, bringing the total number of these
objects to 10, 6.5% of the LMC's total WR population. The optical spectra of
nine of these WN3/O3s are virtually indistinguishable from each other, but one
of the newly found stars is significantly different, showing a lower excitation
emission and absorption spectrum (WN4/O4-ish). In addition, we have newly
classified three unusual Of-type stars, including one with a strong C III 4650
line, and two rapidly rotating "Oef" stars. We also "rediscovered" a low mass
x-ray binary, RX J0513.9-6951, and demonstrate its spectral variability.
Finally, we discuss the spectra of ten low priority WR candidates that turned
out not to have He II emission. These include both a Be star and a B[e] star.


  We consider the problem of classification of an object given multiple
observations that possibly include different transformations. The possible
transformations of the object generally span a low-dimensional manifold in the
original signal space. We propose to take advantage of this manifold structure
for the effective classification of the object represented by the observation
set. In particular, we design a low complexity solution that is able to exploit
the properties of the data manifolds with a graph-based algorithm. Hence, we
formulate the computation of the unknown label matrix as a smoothing process on
the manifold under the constraint that all observations represent an object of
one single class. It results into a discrete optimization problem, which can be
solved by an efficient and low complexity algorithm. We demonstrate the
performance of the proposed graph-based algorithm in the classification of sets
of multiple images. Moreover, we show its high potential in video-based face
recognition, where it outperforms state-of-the-art solutions that fall short of
exploiting the manifold structure of the face image data sets.


  This paper is concerned with minimizing the average of $n$ cost functions
over a network in which agents may communicate and exchange information with
each other. We consider the setting where only noisy gradient information is
available. To solve the problem, we study the distributed stochastic gradient
descent (DSGD) method and perform a non-asymptotic convergence analysis. For
strongly convex and smooth objective functions, DSGD asymptotically achieves
the optimal network independent convergence rate compared to centralized
stochastic gradient descent (SGD). Our main contribution is to characterize the
transient time needed for DSGD to approach the asymptotic convergence rate,
which we show behaves as $K_T=\mathcal{O}\left(\frac{n}{(1-\rho_w)^2}\right)$,
where $1-\rho_w$ denotes the spectral gap of the mixing matrix. Moreover, we
construct a "hard" optimization problem for which we show the transient time
needed for DSGD to approach the asymptotic convergence rate is lower bounded by
$\Omega \left(\frac{n}{(1-\rho_w)^2} \right)$, implying the sharpness of the
obtained result. Numerical experiments demonstrate the tightness of the
theoretical results.


  In this note we prove the following theorem in any Carnot group of step two
$\mathbb{G}$: \[ \underset{s\nearrow 1/2}{\lim} (1 - 2s) \mathfrak P_{H,s}(E) =
\frac{4}{\sqrt \pi}\ \mathfrak P_H(E). \] Here, $\mathfrak P_H(E)$ represents
the horizontal perimeter of a measurable set $E\subset \mathbb{G}$, whereas the
nonlocal horizontal perimeter $\mathfrak P_{H,s}(E)$ is a heat based Besov
seminorm. This result represents a dimensionless sub-Riemannian counterpart of
a famous characterisation of Bourgain-Brezis-Mironescu and D\'avila.


  Given a tensor-triangulated category $T$, we prove that every flat
tensor-idempotent in the module category over $T^c$ (the compacts) comes from a
unique smashing ideal in $T$. We deduce that the lattice of smashing ideals
forms a frame.


  In this work, we propose an axiomatic approach for measuring the
performance/welfare of a system consisting of concurrent agents in a
resource-driven system. Our approach provides a unifying view on popular system
optimality principles, such as the maximal average/total utilities and the
max-min fairness. Moreover, it gives rise to other system optimality notions
that have not been fully exploited yet, such as the maximal lowest total
subgroup utilities. For the axiomatically defined welfare measures, we provide
a generic gradient-based method to find an optimal resource allocation and
present a theoretical guarantee for its success. Lastly, we demonstrate the
power of our approach through the power control application in wireless
networks.


  We present a simple way to calculate non-Gaussianity in inflation using fully
non-linear equations on long wavelengths with stochastic sources to take into
account the short-wavelength quantum fluctuations. Our formalism includes both
scalar metric and matter perturbations, combining them into variables which are
invariant under changes of time slicing in the long-wavelength limit. We
illustrate this method with a perturbative calculation in the single-field
slow-roll case. We also introduce a convenient choice of variables to
graphically present the full momentum dependence of the three-point correlator.


  Let $\Gamma$ be a chart, and we denote by $\Gamma_m$ the union of all the
edges of label $m$. A chart $\Gamma$ is of type $(3,2,2)$ if there exists a
label $m$ such that $w(\Gamma)=7$, $w(\Gamma_m\cap\Gamma_{m+1})=3$,
$w(\Gamma_{m+1}\cap\Gamma_{m+2})=2$, and $w(\Gamma_{m+2}\cap\Gamma_{m+3})=2$
where $w(G)$ is the number of white vertices in $G$. In this paper, we prove
that there is no minimal chart of type $(3,2,2)$.


  We consider the barotropic Navier-Stokes system in three space dimensions
with periodic boundary condition in the transversal direction. We show the
long-time behavior of the 3D barotropic Navier-Stokes flow perturbed from a
composition of two shock waves with suitably small amplitudes. We prove that
the perturbed Navier-Stokes flow converges, uniformly in space, towards a
composition of two planar viscous shock waves as time goes to infinity, up to
dynamical shifts. This is the first result on time-asymptotic stability of
composite wave of two shocks for multi-D Navier-Stokes system. The main part of
proof is based on the method of a-contraction with shifts.


  An invariant is introduced for negative definite plumbed $3$-manifolds
equipped with a spin$^c$-structure. It unifies and extends two theories with
rather different origins and structures. One theory is lattice cohomology,
motivated by the study of normal surface singularities, known to be isomorphic
to the Heegaard Floer homology for certain classes of plumbed 3-manifolds.
Another specialization gives BPS $q$-series which satisfy some remarkable
modularity properties and recover ${\rm SU}(2)$ quantum invariants of
$3$-manifolds at roots of unity. In particular, our work gives rise to a
$2$-variable refinement of the $\widehat Z$-invariant.


  We studied the optical conductivity of electron doped Pr{1-x)Ce(x)CuO(4) from
the underdoped to the overdoped regime. The observation of low to high
frequency spectral weight transfer reveals the presence of a gap, except in the
overdoped regime. A Drude peak at all temperatures shows the partial nature of
this gap. The close proximity of the doping at which the gap vanishes to the
antiferromagnetic phase boundary leads us to assign this partial gap to a spin
density wave.


  Surface plasmon polaritons are electromagnetic waves propagating on the
surface of a metal. Thanks to subwavelength confinement to the surface, they
can concentrate optical energy on the micrometer or even nanometer scale,
enabling new applications in bio-sensing, optical interconnects, and nonlinear
optics, where small footprint and strong field concentration is of the essence.
The major obstacle in developing plasmonic applications is dissipative loss,
which limits the propagation length of surface plasmons and broadens the
bandwidth of surface-plasmon resonances. Here we present a new analysis of
plasmonic materials and geometries that fully considers the trade-off between
the propagation length and the degree of confinement of surface plasmon
polaritons and allows a fair comparison between different substrates.


  We study localization and delocalization in a class of non-hermitean
Hamiltonians inspired by the problem of vortex pinning in superconductors. We
show how to take into account multiple scattering. We also obtain some bounds
on the complex energy spectrum.


  Magnetic properties of a quasi one-dimensional magnet CuV_2O_6 have been
studied by means of susceptibility measurement, NMR and powder neutron
diffraction. Cu^{2+} spins (S=1/2) order antiferromagnetically below 22.6 K,
and the saturation moment is about 0.70 \mu_{B} at 0 K. The magnetic structure
determined in the present study indicates that in CuV_2O_6 one-dimensional
antiferromagnetic spin chains are along [110], suggesting a dominant
antiferromagnetic exchange interaction between the next-nearest-neighbor spins
rather than the nearest-neighbor ones.


  We describe an alternating current method to measure the Nernst effect in
superconducting thin films at low temperatures. The Nernst effect is an
important tool in the understanding superconducting fluctuations and, in
particular, vortex motion near critical points. However, in most materials, the
Nernst signal in a typical experimental setup rarely exceeds a few $\mu$V, in
some cases being as low as a few nV. DC measurements of such small signals
require extensive signal processing and protection against stray pickups and
offsets, limiting the sensitivity of such measurements to $>$5nV. Here we
describe a method utilizing a one-heater-two-thermometer setup with the heating
element and thermometers fabricated on-chip with the sample, which helped to
reduce thermal load and temperature lag between the substrate and thermometer.
Using AC heating power and 2$\omega$ measurement, we are able to achieve
sub-nanovolt sensitivity in 20-30nm MoGe thin films on glass substrate,
compared to a sensitivity of $\sim$9nV using DC techniques on the same setup.


  We examine the so-called micropolar equations in three dimensional
cylindrical domains under Navier boundary conditions. These equations form a
generalization of the ordinary incompressible Navier-Stokes model, taking the
structure of the fluid into account. We prove that under certain smallness
assumption on the rate of change of the initial data and the external data
there exists a unique and strong solution for any finite time $T$.


  Super B factories that will further probe the flavor sector of the Standard
Model and physics beyond will demand excellent charged particle identification
(PID), particularly K/pi separation, for momenta up to 4 GeV/c, as well as the
ability to operate under beam backgrounds significantly higher than current B
factory experiments. We describe an Imaging Time-of-Propagation (iTOP) detector
which shows significant potential to meet these requirements. Photons emitted
from charged particle interactions in a Cerenkov radiator bar are internally
reflected to the end of the bar, where they are collected on a compact image
plane using photodetectors with fine spatial segmentation in two dimensions.
Precision measurements of photon arrival time are used to enhance the two
dimensional imaging, allowing the system to provide excellent PID capabilities
within a reduced detector envelope. Results of the ongoing optimization of the
geometric and physical properties of such a detector are presented, as well as
simulated PID performance. Validation of simulations is being performed using a
prototype in a cosmic ray test stand at the University of Hawaii.


  We investigate the Knizhnik-Zamolodchikov linear differential system. The
coefficients of this system are rational functions. We prove that the solution
of the KZ system is rational when $k$ is equal to two and $n$ is equal to
three. While doing so, we found the coefficients of expansion in a neighborhood
of a singular point.


  We consider $k$ mobile agents of limited energy that are initially located at
vertices of an edge-weighted graph $G$ and have to collectively deliver data
from a source vertex $s$ to a target vertex $t$. The data are to be collected
by an agent reaching $s$, who can carry and then hand them over another agent
etc., until some agent with the data reaches $t$. The data can be carried only
over a fixed $s-t$ path of $G$; each agent has an initial energy budget and
each time it passes an edge, it consumes the edge's weights in energy units and
stalls if its energy is not anymore sufficient to move. The main result of this
paper is a 3-approximation polynomial time algorithm for the data delivery
problem over a fixed $s-t$ path in the graph, for identical initial energy
budgets and at most one allowed data hand-over per agent.


  There is an unprecedented increase in cybercrime globally observed over the
last years. One of the regions driving this increase is Africa, where
significant financial losses are reported. Yet, citizens of African countries
are not aware of the risks present in cyberspace. The design and implementation
of national awareness campaigns by African countries to address this problem
are in their infancy state, mainly due to the absence of capacity building
efforts. As part of the Global Cybersecurity Capacity Centre (GCSCC) programme,
we conducted a series of focus groups in six African countries, in order to
assess their cybersecurity posture, a critical component of which is user and
executive awareness of cyber risks. This paper is an extended version of
previous work where an initial analysis of awareness for cyber risk in African
countries was presented. In this extended version, we reflect on best practice
approaches for developing national awareness campaigns and use these as a
framework to analyse qualitative data from the focus groups. We discuss the
current state of African countries with regards to the implementation of
national cybersecurity awareness campaigns for. users and executives, the main
obstacles in combating cybercrime and conclude with recommendations on how
African countries can identify and prioritise activities to increase their
capacity regarding cybersecurity awareness.


  It is shown that the differential cross sections of the reactions $dd\to ^3He
n$ and $dd\to ^3H p$ measured at c.m.s.scattering angle$\theta_{cm}=60^\circ$
in the interval of the deuteron beam energy 0.5 - 1.2 GeV demonstrate the
scaling behaviour,$d\sigma/d t\sim s^{-22}$, which follows from constituent
quark counting rules. It is found also that the differential cross section of
the elastic $dp\to dp$ scattering at $\theta_{cm}=125^\circ-135^\circ$ follows
the scaling regime $\sim s^{-16}$ at beam energies 0.5 - 5 GeV. These data are
parameterized here using the Reggeon exchange.


  Stripping extraction of hydrogen molecular ions has gained interest in the
cyclotron industry due to its high extraction efficiency. However, the magnetic
field could result in undesired Lorentz dissociation of the hydrogen
anion/molecular ions during acceleration. Studies of dissociation under
electric fields comparable to that of a Lorentz-transformed magnetic field in a
typical cyclotron (a few MV/cm) are sparse. Hence, in order to fill in the
missing yet crucial information when designing a cyclotron, this work compiles
and summarizes the study of Lorentz dissociation of H$^-$, H$_2^+$ and H$_3^+$
for stripping extraction in a cyclotron.


  The group focused on a model problem of idealised moist air convection in a
single column of atmosphere. Height, temperature and moisture variables were
chosen to simplify the mathematical representation (along the lines of the
Boussinesq approximation in a height variable defined in terms of pressure).
This allowed exact simple solutions of the numerical and partial differential
equation problems to be found. By examining these, we identify column
behaviour, stability issues and explore the feasibility of a more general
solution process.


  We consider various homotopy algebras related to Yang-Mills theory and
two-dimensional conformal field theory (CFT). Our main objects of study are
Yang-Mills $L_{\infty}$ and $C_{\infty}$ algebras and their relation to the
certain algebraic structures of Lian-Zuckerman type in CFT. We also consider
several examples of algebras related to gauge theory, involving first order
formulations and gauge theories with matter fields.


  We present numerical investigations of the dynamics on the energy landscape
of a realistic model of the high-temperature ceramic a-Si3B3N7. Below a
critical temperature Tc = 2000 K, the system is no longer in equilibrium, and
we predict that the material has a glass transition in this temperature range
at high pressure. Analyzing the two-time energy correlation function shows
aging in this system, which is linked to the geometrical properties of the
energy landscape.


  Gradient descent is one of the most basic algorithms for solving continuous
optimization problems. In [Jordan, PRL, 95(5):050501, 2005], Jordan proposed
the first quantum algorithm for estimating gradients of functions close to
linear, with exponential speedup in the black-box model. This quantum algorithm
was greatly enhanced and developed by [Gily\'en, Arunachalam, and Wiebe, SODA,
pp. 1425-1444, 2019], providing a quantum algorithm with optimal query
complexity $\widetilde{\Theta}(\sqrt{d}/\varepsilon)$ for a class of smooth
functions of $d$ variables, where $\varepsilon$ is the accuracy. This is
quadratically faster than classical algorithms for the same problem.
  In this work, we continue this research by proposing a new quantum algorithm
for another class of functions, namely, analytic functions $f(\boldsymbol{x})$
which are well-defined over the complex field. Given phase oracles to query the
real and imaginary parts of $f(\boldsymbol{x})$ respectively, we propose a
quantum algorithm that returns an $\varepsilon$-approximation of its gradient
with query complexity $\widetilde{O}(1/\varepsilon)$. This achieves exponential
speedup over classical algorithms in terms of the dimension $d$. As an
extension, we also propose two quantum algorithms for Hessian estimation,
aiming to improve quantum analogs of Newton's method. The two algorithms have
query complexity $\widetilde{O}(d/\varepsilon)$ and
$\widetilde{O}(d^{1.5}/\varepsilon)$, respectively, under different
assumptions. Moreover, if the Hessian is promised to be $s$-sparse, we then
have two new quantum algorithms with query complexity
$\widetilde{O}(s/\varepsilon)$ and $\widetilde{O}(sd/\varepsilon)$,
respectively. The former achieves exponential speedup over classical
algorithms. We also prove a lower bound of $\widetilde{\Omega}(d)$ for Hessian
estimation in the general case.


  Motivated by the problem of learning a linear regression model whose
parameter is a large fixed-rank non-symmetric matrix, we consider the
optimization of a smooth cost function defined on the set of fixed-rank
matrices. We adopt the geometric framework of optimization on Riemannian
quotient manifolds. We study the underlying geometries of several well-known
fixed-rank matrix factorizations and then exploit the Riemannian quotient
geometry of the search space in the design of a class of gradient descent and
trust-region algorithms. The proposed algorithms generalize our previous
results on fixed-rank symmetric positive semidefinite matrices, apply to a
broad range of applications, scale to high-dimensional problems and confer a
geometric basis to recent contributions on the learning of fixed-rank
non-symmetric matrices. We make connections with existing algorithms in the
context of low-rank matrix completion and discuss relative usefulness of the
proposed framework. Numerical experiments suggest that the proposed algorithms
compete with the state-of-the-art and that manifold optimization offers an
effective and versatile framework for the design of machine learning algorithms
that learn a fixed-rank matrix.


  We present LCDM cosmological parameter constraints obtained from delensed
microwave background power spectra. Lensing maps from a subset of DR4 data from
the Atacama Cosmology Telescope (ACT) are used to undo the lensing effect in
ACT spectra observed at 150 and 98 GHz. At 150 GHz, we remove the lensing
distortion with an effective efficiency of 30% (TT), 30% (EE), 26% (TE) and 20%
(BB); this results in detections of the delensing effect at 8.7 sigma (TT), 5.1
sigma (EE), 2.6 sigma (TE), and 2.4 sigma (BB) significance. The combination of
150 and 98 GHz TT, EE, and TE delensed spectra is well fit by a standard LCDM
model. We also measure the shift in best-fit parameters when fitting delensed
versus lensed spectra; while this shift does not inform our ability to measure
cosmological parameters, it does provide a three-way consistency check among
the lensing inferred from the best-fit parameters, the lensing in the CMB power
spectrum, and the reconstructed lensing map. This shift is predicted to be zero
when fitting with the correct model since both lensed and delensed spectra
originate from the same region of sky. Fitting with a LCDM model and
marginalizing over foregrounds, we find that the shift in cosmological
parameters is consistent with zero. Our results show that gravitational lensing
of the microwave background is internally consistent within the framework of
the standard cosmological model.


  In a high-dimensional regression framework, we study consequences of the
naive two-step procedure where first the dimension of the input variables is
reduced and second, the reduced input variables are used to predict the output
variable with kernel regression. In order to analyze the resulting regression
errors, a novel stability result for kernel regression with respect to the
Wasserstein distance is derived. This allows us to bound errors that occur when
perturbed input data is used to fit the regression function. We apply the
general stability result to principal component analysis (PCA). Exploiting
known estimates from the literature on both principal component analysis and
kernel regression, we deduce convergence rates for the two-step procedure. The
latter turns out to be particularly useful in a semi-supervised setting.


  We prove that the maximal development of any spherically symmetric spacetime
with collisionless matter (obeying the Vlasov equation) or a massless scalar
field (obeying the massless wave equation) and possessing a constant mean
curvature $S^1 \times S^2$ Cauchy surface also contains a maximal Cauchy
surface. Combining this with previous results establishes that the spacetime
can be foliated by constant mean curvature Cauchy surfaces with the mean
curvature taking on all real values, thereby showing that these spacetimes
satisfy the closed-universe recollapse conjecture. A key element of the proof,
of interest in itself, is a bound for the volume of any Cauchy surface $\Sigma$
in any spacetime satisfying the timelike convergence condition in terms of the
volume and mean curvature of a fixed Cauchy surface $\Sigma_0$ and the maximal
distance between $\Sigma$ and $\Sigma_0$. In particular, this shows that any
globally hyperbolic spacetime having a finite lifetime and obeying the
timelike-convergence condition cannot attain an arbitrarily large spatial
volume.


  We present a comprehensive strategy and its practical implementation using
the commercial ScanImage software platform to perform hyperspectral point
scanning microscopy when a fast time dependent signal varies at each pixel
level. In the proposed acquisition scheme the scan along the X axis is slowed
down while the data acquisition is maintained at high pace to enable the rapid
acquisition of the time dependent signal at each pixel level. The ScanImage
generated raw 2D images have a very asymmetric aspect ratio between X and Y,
the X axis encoding both for space and time acquisition. The results are X axis
macro-pixel where the associated time depend signal is sampled therefore
providing an hyperspectral information. We exemplified the proposed
hyperspectral scheme in the context of time domain coherent Raman imaging where
a pump pulse impulsively excites molecular vibrations that are subsequently
probed by a time delayed probe pulse. In this case the time dependent signal is
a fast acousto-optics delay line that can scan a delay of 4.5ps in 25$\mu$s, at
each pixel level. We this acquisition scheme we demonstrate ultra-fast
hyperspectral vibrational imaging in the low frequency range [10$cm^{-1}$, 150
$cm^{-1}$] over a 500 $\mu m$ field of view in 14ms (7 frames/s). The proposed
acquisition scheme can be readily extended to other applications requiring to
acquired a fast evolving signal at each pixel level.


  Laser excitation of nanometer-sized atomic and molecular clusters offers
various opportunities to explore and control ultrafast many-particle dynamics.
Whereas weak laser fields allow the analysis of photoionization, excited-state
relaxation, and structural modifications on these finite quantum systems,
large-amplitude collective electron motion and Coulomb explosion can be induced
with intense laser pulses. This review provides an overview of key phenomena
arising from laser-cluster interactions with focus on nonlinear optical
excitations and discusses the underlying processes according to the current
understanding. A brief general survey covers basic cluster properties and
excitation mechanisms relevant for laser-driven cluster dynamics. Then, after
an excursion in theoretical and experimental methods, results for single- and
multiphoton excitations are reviewed with emphasis on signatures from time- and
angular resolved photoemission. A key issue of this review is the broad
spectrum of phenomena arising from clusters exposed to strong fields, where the
interaction with the laser pulse creates short-lived and dense nanoplasmas. The
implications for technical developments include the controlled generation of
ion, electron, and radiation pulses, as will be addressed along with
corresponding examples. Finally, future prospects of laser-cluster research as
well as experimental and theoretical challenges are discussed.


  The free energy is shown to decrease along Wilson renormalization group
trajectories, in a dimension-independent fashion, for $d>2.$ The argument
assumes the monotonicity of the cutoff function, and positivity of a spectral
representation of the two point function. The argument is valid to all orders
in perturbation theory.


  Index modulation (IM) significantly enhances the spectral efficiency of fluid
antennas (FAs) enabled multiple-input multiple-output (MIMO) systems, which is
named FA-IM. However, due to the dense distribution of ports on the FA, the
wireless channel exhibits a high spatial correlation, leading to severe
performance degradation in the existing FA-IM-assisted MIMO systems. To tackle
this issue, this paper proposes a novel fluid antenna grouping index modulation
(FA-GIM) scheme to mitigate the high correlation between the activated ports.
Specifically, considering the characteristics of the FA two-dimensional (2D)
surface structure and the spatially correlated channel model in FA-assisted
MIMO systems, a block grouping method is adopted, where adjacent ports are
assigned to the same group. Subsequently, different groups independently
perform port index selection and constellation symbol mapping, with only one
port being activated within each group during each transmission interval.
Numerical results show that, compared to state-of-the-art schemes, the proposed
FA-GIM scheme consistently achieves significant bit error rate (BER)
performance gains under various conditions. It has also been confirmed that the
proposed scheme is both efficient and robust, enhancing the performance of
FA-assisted MIMO systems.


  We review first how properties of curved surfaces can be studied using Hill's
thermodynamics, also called nano-thermodynamics. We proceed to show for the
first time that Hill's analysis is equivalent to Gibbs for curved surfaces.
This simplifies the study of surfaces that are curved on the nano-scale, and
opens up a possibility to study non-equilibrium systems in a systematic manner.


  Directed evolution as a widely-used engineering strategy faces obstacles in
finding desired mutants from the massive size of candidate modifications. While
deep learning methods learn protein contexts to establish feasible searching
space, many existing models are computationally demanding and fail to predict
how specific mutational tests will affect a protein's sequence or function.
This research introduces a lightweight graph representation learning scheme
that efficiently analyzes the microenvironment of wild-type proteins and
recommends practical higher-order mutations exclusive to the user-specified
protein and function of interest. Our method enables continuous improvement of
the inference model by limited computational resources and a few hundred
mutational training samples, resulting in accurate prediction of variant
effects that exhibit near-perfect correlation with the ground truth across deep
mutational scanning assays of 19 proteins. With its affordability and
applicability to both computer scientists and biochemical laboratories, our
solution offers a wide range of benefits that make it an ideal choice for the
community.


  We have carried out studies on the rheological properties of regenerated silk
fibroin (RSF) solution using video microscopy. The degummed silk from the
Bombyx mori silkworm was used to prepare RSF solution by dissolving it in
calcium nitrate tetrahydrate-methanol solvent. Measurements were carried out by
tracking the position of an embedded micron-sized polystyrene bead within the
RSF solution through video imaging. The time dependent mean squared
displacement (MSD) of the bead in solution and hence, the complex shear modulus
of this solution was calculated from the bead's position information. An
optical tweezer was used to transport and locate the bead at any desired site
within the micro-volume of the sample, to facilitate the subsequent free-bead
video analysis. We present here the results of rheological measurements of the
silk polymer network in solution over a frequency range, whose upper limit is
the frame capture rate of our camera, at full resolution. By examining the
distribution of MSD of beads at different locations within the sample volume,
we demonstrate that this probe technique enables us to detect local
inhomogeneties at micrometer length scales, not detectable either by a
rheometer or from diffusing wave spectroscopy.