  We study Flipped $SU(5)\times U(1)$ Grand Unified Theories (GUTs) with
$\Gamma_3\simeq A_4$ modular symmetry. We propose two models with different
modular weights assignments, where the fermion mass hierarchy can arise from
weighton fields. In order to relax the constraint on the Dirac neutrino Yukawa
matrix we appeal to mechanisms which allow incomplete GUT representations,
allowing a good fit to quark and charged lepton masses and quark mixing for a
single modulus field $\tau$, with the neutrino masses and lepton mixing well
determined by the type I seesaw mechanism, at the expense of some tuning. We
also discuss the double seesaw possibility allowed by the extra singlets
generically predicted in such string inspired theories.


  This paper develops models describing asexual and sexual replication in
sporulating organisms. Replication via sporulation is the replication strategy
for all multicellular life, and may even be observed in unicellular life (such
as with budding yeast). We consider diploid populations replicating via one of
two possible sporulation mechanisms: (1) Asexual sporulation, whereby adult
organisms produce single-celled diploid spores that grow into adults
themselves. (2) Sexual sporulation, whereby adult organisms produce
single-celled diploid spores that divide into haploid gametes. The haploid
gametes enter a haploid "pool", where they may recombine with other haploids to
form a diploid spore that then grows into an adult. We consider a haploid
fusion rate given by second-order reaction kinetics. We work with a simplified
model where the diploid genome consists of only two chromosomes, each of which
may be rendered defective with a single point mutation of the wild-type. We
find that the asexual strategy is favored when the rate of spore production is
high compared to the characteristic growth rate from a spore to a reproducing
adult. Conversely, the sexual strategy is favored when the rate of spore
production is low compared to the characteristic growth rate from a spore to a
reproducing adult. As the characteristic growth time increases, or as the
population density increases, the critical ratio of spore production rate to
organism growth rate at which the asexual strategy overtakes the sexual one is
pushed to higher values. Therefore, the results of this model suggest that, for
complex multicellular organisms, sexual replication is favored at high
population densities, and low growth and sporulation rates.


  Nuclear magnetic resonance (NMR) is sensitive to dynamics on a wide range of
correlation times. Recently, we have shown that analysis of relaxation rates
via fitting to a correlation function with a small number of exponential terms
could yield a biased characterization of molecular motion in solid-state NMR,
due to limited sensitivity of experimental data to certain ranges of
correlation times. We introduced an alternative approach based on 'detectors'
in solid-state NMR, for which detector responses characterize motion for a
range of correlation times, and reduce potential bias resulting from the use of
simple models for the motional correlation functions. Here, we show that
similar bias can occur in the analysis of solution-state NMR relaxation data.
We have thus adapted the detector approach to solution-state NMR, specifically
separating overall tumbling motion from internal motions and accounting for
contributions of chemical exchange to transverse relaxation. We demonstrate
that internal protein motions can be described with detectors when the overall
motion and the internal motions are statistically independent. We illustrate
the detector analysis on ubiquitin with typical relaxation data sets recorded
at a single or at multiple high magnetic fields, and compare with results of
model-free analysis. We also compare our methodology to LeMaster's method of
dynamics analysis.


  We determine the probability distribution for the field inside a random
uniform distribution of electric or magnetic dipoles.
  For parallel dipoles, simulations and an analytical derivation show that
although the average contribution from any spherical shell around the probe
position vanishes, the Levy stable distribution of the field is symmetric
around a non-vanishing field amplitude.
  In addition we show how omission of contributions from a small volume around
the probe leads to a field distribution with a vanishing mean, which, in the
limit of vanishing excluded volume, converges to the shifted distribution.


  Let ${\bf A}$ be the ring of adeles of a number field $F$. Given a self-dual
irreducible, automorphic, cuspidal representation $\tau$ of $\GL_n(\BA)$, with
trivial central characters, we construct its full inverse image under the weak
Langlands functorial lift from the appropriate split classical group $G$. We do
this by a new automorphic descent method, namely the double descent. This
method is derived from the recent generalized doubling integrals of Cai,
Friedberg, Ginzburg and Kaplan \cite{CFGK17}, which represent the standard
$L$-functions for $G\times \GL_n$. Our results are valid also for double covers
of symplectic groups.


  We explicitly describe cycle-class maps c_H from motivic cohomology to
absolute Hodge cohomology, for smooth quasi-projective and (some) proper
singular varieties, and compute special cases of the latter. For smooth
projective varieties, we also study Hodge-theoretically defined "higher
Abel-Jacobi maps" on the kernel of c_H; this leads to new results on nontrivial
indecomposable higher Chow cycles in the regulator kernel.


  We propose a robust and efficient method for multiview triangulation and
uncertainty estimation. Our contribution is threefold: First, we propose an
outlier rejection scheme using two-view RANSAC with the midpoint method. By
prescreening the two-view samples prior to triangulation, we achieve the
state-of-the-art efficiency. Second, we compare different local optimization
methods for refining the initial solution and the inlier set. With an iterative
update of the inlier set, we show that the optimization provides significant
improvement in accuracy and robustness. Third, we model the uncertainty of a
triangulated point as a function of three factors: the number of cameras, the
mean reprojection error and the maximum parallax angle. Learning this model
allows us to quickly interpolate the uncertainty at test time. We validate our
method through an extensive evaluation.


  Let $X$ a probability measure space and $\psi_1....\psi_N$ measurable, real
valued functions on $X$. Consider all possible partitions of $X$ into $N$
disjoint subdomains $X_i$ on which $\int_{X_i}\psi_i$ are prescribed. We
address the question of characterizing the set $(m_1,,,m_N) \in R^N$ for which
there exists a partition $X_1, ...X_N$ of $X$ satisfying $\int_{X_i}\psi_i=
m_i$ and discuss some optimization problems on this set of partitions. The
relation of this problem to semi-discrete version of optimal mass
transportation is discussed as well.


  The physics case for studying top-quark physics at the International Linear
Collider is well established. This summary places in context the top-quark
physics goals, examines the current state-of-the art in understanding of the
top-quark mass, and identifies some areas in which the study of the top-quark
mass enhances our understanding of new techniques.


  In audio-visual navigation, an agent intelligently travels through a complex,
unmapped 3D environment using both sights and sounds to find a sound source
(e.g., a phone ringing in another room). Existing models learn to act at a
fixed granularity of agent motion and rely on simple recurrent aggregations of
the audio observations. We introduce a reinforcement learning approach to
audio-visual navigation with two key novel elements: 1) waypoints that are
dynamically set and learned end-to-end within the navigation policy, and 2) an
acoustic memory that provides a structured, spatially grounded record of what
the agent has heard as it moves. Both new ideas capitalize on the synergy of
audio and visual data for revealing the geometry of an unmapped space. We
demonstrate our approach on two challenging datasets of real-world 3D scenes,
Replica and Matterport3D. Our model improves the state of the art by a
substantial margin, and our experiments reveal that learning the links between
sights, sounds, and space is essential for audio-visual navigation. Project:
http://vision.cs.utexas.edu/projects/audio_visual_waypoints.


  Background The epidermal growth factor receptor (EGFR) is frequently
overexpressed in many cancers, including non-small cell lung cancer (NSCLC). In
silcio modeling is considered to be an increasingly promising tool to add
useful insights into the dynamics of the EGFR signal transduction pathway.
However, most of the previous modeling work focused on the molecular or the
cellular level only, neglecting the crucial feedback between these scales as
well as the interaction with the heterogeneous biochemical microenvironment.
  Results We developed a multiscale model for investigating expansion dynamics
of NSCLC within a two-dimensional in silico microenvironment. At the molecular
level, a specific EGFR-ERK intracellular signal transduction pathway was
implemented. Dynamical alterations of these molecules were used to trigger
phenotypic changes at the cellular level. Examining the relationship between
extrinsic ligand concentrations, intrinsic molecular profiles and microscopic
patterns, the results confirmed that increasing the amount of available growth
factor leads to a spatially more aggressive cancer system. Moreover, for the
cell closest to nutrient abundance, a phase-transition emerges where a minimal
increase in extrinsic ligand abolishes the proliferative phenotype altogether.
  Conclusions Our in silico results indicate that, in NSCLC, in the presence of
a strong extrinsic chemotactic stimulus, and depending on the cell's location,
downstream EGFR-ERK signaling may be processed more efficiently, thereby
yielding a migration-dominant cell phenotype and overall, an accelerated
spatio-temporal expansion rate.


  Using thermal quantum field theory we derive an expression for the exchange
constant that resembles Fukuyama's formula for the orbital magnetic
susceptibility (OMS). Guided by this formal analogy between the exchange
constant and OMS we identify a contribution to the exchange constant that
arises from the geometrical properties of the band structure in mixed phase
space. We compute the exchange constants for free electrons and show that the
geometrical contribution is generally important. Our formalism allows us to
study the exchange constants in the presence of spin-orbit interaction (SOI).
Thereby, we find sizable differences between the exchange constants of helical
and cycloidal spin spirals. Furthermore, we discuss how to calculate the
exchange constants based on a gauge-field approach in the case of the Rashba
model with an additional exchange splitting and show that the exchange
constants obtained from this gauge-field approach are in perfect agreement with
those obtained from the quantum field theoretical method.


  We study fundamental groups of clique complexes associated to random graphs.
We establish thresholds for their cohomological and geometric dimension and
torsion. We also show that in certain regime any aspherical subcomplex of a
random clique complex satisfies the Whitehead conjecture, i.e. all irs
subcomplexes are also aspherical.


  We consider the complete system of equations governing the motion of a
general compressible, viscous, electrically and heat conductive fluid driven by
non-conservative boundary conditions. We show the existence of a bounded
absorbing set in the energy space and asymptotic compactness of trajectories.
As a corollary, the set of all entire globally bounded solutions is identified
as a natural attractor. Examples of boundary conditions giving rise to
unbounded solutions are also discussed.


  The STAR detector at RHIC, due to its large uniform acceptance and excellent
particle identification capabilities, has measured a variety of hadron species
($\pi^{\pm}$, $K^{\pm}$, $p$, $\bar{p}$, $K^{0}_{S}$, $\Lambda$,
$\bar{\Lambda}$, $\Xi^{-}$, $\bar{\Xi}^{+}$) produced in Au+Au collisions at
$\sqrt {s_{NN}}= 7.7$, 11.5, 27 and 39 GeV. These data are part of the Beam
Energy Scan (BES) program at RHIC and provide an opportunity to measure the
yields and transverse momentum spectra ($p_{T}$) of the particles produced in
the collisions. The corresponding measurements allow to study the freeze-out
properties and dynamics of heavy ion collisions. A statistical thermal model
analysis of particle production in BES energies in both grand canonical and
strangeness canonical ensembles, is used to extract the chemical freeze-out
parameters. The $p_{T}$ spectra, particle ratios, and the energy and centrality
dependence of freeze-out parameters determined from the thermal fit of particle
ratios are discussed.


  We prove denominator identities for the periplectic Lie superalgebra
$\mathfrak{p}(n)$, thereby completing the problem of finding denominator
identities for all simple classical finite-dimensional Lie superalgebras.


  Machine learning (ML) is becoming a critical tool for interrogation of large
complex data. Labeling, defined as the process of adding meaningful
annotations, is a crucial step of supervised ML. However, labeling datasets is
time consuming. Here we show that convolutional neural networks (CNNs), trained
on crudely labeled astronomical videos, can be leveraged to improve the quality
of data labeling and reduce the need for human intervention. We use videos of
the solar magnetic field, crudely labeled into two classes: emergence or
non-emergence of bipolar magnetic regions (BMRs), based on their first
detection on the solar disk. We train CNNs using crude labels, manually verify,
correct labeling vs. CNN disagreements, and repeat this process until
convergence. Traditionally, flux emergence labelling is done manually. We find
that a high-quality labeled dataset, derived through this iterative process,
reduces the necessary manual verification by 50%. Furthermore, by gradually
masking the videos and looking for maximum change in CNN inference, we locate
BMR emergence time without retraining the CNN. This demonstrates the
versatility of CNNs for simplifying the challenging task of labeling complex
dynamic events.


  We address the risk bounded trajectory optimization problem of stochastic
nonlinear robotic systems. More precisely, we consider the motion planning
problem in which the robot has stochastic nonlinear dynamics and uncertain
initial locations, and the environment contains multiple dynamic uncertain
obstacles with arbitrary probabilistic distributions. The goal is to plan a
sequence of control inputs for the robot to navigate to the target while
bounding the probability of colliding with obstacles. Existing approaches to
address risk bounded trajectory optimization problems are limited to particular
classes of models and uncertainties such as Gaussian linear problems. In this
paper, we deal with stochastic nonlinear models, nonlinear safety constraints,
and arbitrary probabilistic uncertainties, the most general setting ever
considered. To address the risk bounded trajectory optimization problem, we
first formulate the problem as an optimization problem with stochastic dynamics
equations and chance constraints. We then convert probabilistic constraints and
stochastic dynamics constraints on random variables into a set of deterministic
constraints on the moments of state probability distributions. Finally, we
solve the resulting deterministic optimization problem using nonlinear
optimization solvers and get a sequence of control inputs. To our best
knowledge, it is the first time that the motion planning problem to such a
general extent is considered and solved. To illustrate the performance of the
proposed method, we provide several robotics examples.


  We present a new Milky Way microlensing simulation code, dubbed PopSyCLE
(Population Synthesis for Compact object Lensing Events). PopSyCLE is the first
resolved microlensing simulation to include a compact object distribution
derived from numerical supernovae explosion models and both astrometric and
photometric microlensing effects. We demonstrate the capabilities of PopSyCLE
by investigating the optimal way to find black holes (BHs) with microlensing.
Candidate BHs have typically been selected from wide-field photometric
microlensing surveys, such as OGLE, by selecting events with long Einstein
crossing times ($t_E>120$ days). These events can be selected at closest
approach and monitored astrometrically in order to constrain the mass of each
lens; PopSyCLE predicts a BH detection rate of ~40% for such a program. We find
that the detection rate can be enhanced to ~85% by selecting events with both
$t_E>120$ days and a microlensing parallax of $\pi_E<0.08$. Unfortunately, such
a selection criterion cannot be applied during the event as $\pi_E$ requires
both pre- and post-peak photometry. However, historical microlensing events
from photometric surveys can be revisited using this new selection criteria in
order to statistically constrain the abundance of BHs in the Milky Way. The
future WFIRST microlensing survey provides both precise photometry and
astrometry and will yield individual masses of $\mathcal{O}(100-1000) black
holes, which is at least an order of magnitude more than is possible with
individual candidate follow-up with current facilities. The resulting sample of
BH masses from WFIRST will begin to constrain the shape of the black hole
present-day mass function, BH multiplicity, and BH kick velocity distributions.


  We provide a detailed exposition of our computational framework designed for
the accurate calculation of real-frequency dynamical correlation functions of
the single-impurity Anderson model (AM) in the regime of weak to intermediate
coupling. Using quantum field theory within the Keldysh formalism to directly
access the self-energy and dynamical susceptibilities in real frequencies, as
detailed in our recent publication
(https://doi.org/10.1103/PhysRevB.109.115128), the primary computational
challenge is the full three-dimensional real-frequency dependence of the
four-point vertex. Our codebase provides a fully MPI+OpenMP parallelized
implementation of the functional renormalization group (fRG) and the
self-consistent parquet equations within the parquet approximation. It
leverages vectorization to handle the additional complexity imposed by the
Keldysh formalism, using optimized data structures and highly performant
integration routines. Going beyond the results shown in the previous
publication, the code includes functionality to perform fRG calculations in the
multiloop framework, at arbitrary loop order, including self-consistent
self-energy iterations. Moreover, implementations of various regulators, such
as hybridization, interaction, frequency, and temperature are supplied.


  Discovering episodes, frequent sets of events from a sequence has been an
active field in pattern mining. Traditionally, a level-wise approach is used to
discover all frequent episodes. While this technique is computationally
feasible it may result in a vast number of patterns, especially when low
thresholds are used.
  In this paper we propose a new quality measure for episodes. We say that an
episode is significant if the average length of its minimal windows deviates
greatly when compared to the expected length according to the independence
model. We can apply this measure as a post-pruning step to test whether the
discovered frequent episodes are truly interesting and consequently to reduce
the number of output.
  As a main contribution we introduce a technique that allows us to compute the
distribution of lengths of minimal windows using the independence model. Such a
computation task is surpisingly complex and in order to solve it we compute the
distribution iteratively starting from simple episodes and progressively moving
towards the more complex ones. In our experiments we discover candidate
episodes that have a sufficient amount of minimal windows and test each
candidate for significance. The experimental results demonstrate that our
approach finds significant episodes while ignoring uninteresting ones.


  This manuscript presents an algorithm for individual Lithium-ion (Li-ion)
battery cell state of charge (SOC) estimation in a large-scale battery pack
under minimal sensing, where only pack-level voltage and current are measured.
For battery packs consisting of up to thousands of cells in electric vehicle or
stationary energy storage applications, it is desirable to estimate individual
cell SOCs without cell local measurements in order to reduce sensing costs.
Mathematically, pure series connected cells yield dynamics given by ordinary
differential equations under classical full voltage sensing. In contrast,
parallel--series connected battery packs are evidently more challenging because
the dynamics are governed by a nonlinear differential--algebraic equations
(DAE) system. The majority of the conventional studies on SOC estimation for
battery packs benefit from idealizing the pack as a lumped single cell which
ultimately lose track of cell-level conditions and are blind to potential risks
of cell-level over-charge and over-discharge. This work explicitly models a
battery pack with high fidelity cell-by-cell resolution based on the
interconnection of single cell models, and examines the observability of
cell-level state with only pack-level measurements. A DAE-based state observer
with linear output error injection is formulated, where the individual cell SOC
and current can be reconstructed from minimal number of pack sensing. The
mathematically guaranteed asymptotic convergence of differential and algebraic
state estimates is established by considering local Lipschitz continuity
property of system nonlinearities. Simulation results for Graphite/NMC cells
illustrate convergence for cell SOCs, currents, and voltages.


  Cross-Encoder (CE) and Dual-Encoder (DE) models are two fundamental
approaches for query-document relevance in information retrieval. To predict
relevance, CE models use joint query-document embeddings, while DE models
maintain factorized query and document embeddings; usually, the former has
higher quality while the latter benefits from lower latency. Recently,
late-interaction models have been proposed to realize more favorable
latency-quality tradeoffs, by using a DE structure followed by a lightweight
scorer based on query and document token embeddings. However, these lightweight
scorers are often hand-crafted, and there is no understanding of their
approximation power; further, such scorers require access to individual
document token embeddings, which imposes an increased latency and storage
burden. In this paper, we propose novel learnable late-interaction models
(LITE) that resolve these issues. Theoretically, we prove that LITE is a
universal approximator of continuous scoring functions, even for relatively
small embedding dimension. Empirically, LITE outperforms previous
late-interaction models such as ColBERT on both in-domain and zero-shot
re-ranking tasks. For instance, experiments on MS MARCO passage re-ranking show
that LITE not only yields a model with better generalization, but also lowers
latency and requires 0.25x storage compared to ColBERT.


  We investigated the possible feedback of type-I burst to the accretion
process during the spectral evolution of the atoll source 4U 1608--52. By
fitting the burst spectrum with a blackbody and an adjustable, persistent
spectral component, we found that the latter is significant state-dependent. In
the banana state the persistent flux increases along the burst evolution, while
in the island state this trend holds only when the bursts are less luminous and
starts to reverse at higher burst luminosities. We speculate that, by taking
into account both the Poynting-Robertson drag and radiation pressure, these
phenomena may arise from the interactions between the radiation field of the
type-I burst and the inner region of the accretion disc.


  A generalization of the Pistone-Sempi argument, demonstrating the utility of
non-commutative Orlicz spaces, is presented. The question of lifting positive
maps defined on von Neumann algebra to maps on corresponding noncommutative
Orlicz spaces is discussed. In particular, we describe those Jordan *-morphisms
on semifinite von Neumann algebras which in a canonical way induce quantum
composition operators on noncommutative Orlicz spaces. Consequently, it is
proved that the framework of noncommutative Orlicz spaces is well suited for an
analysis of large class of interesting noncommutative dynamical systems.


  The main physics goal of the NA62 experiment at CERN is to precisely measure
the branching ratio of the kaon rare decay $K^+\rightarrow \pi^+ \nu \bar\nu$.
This decay is strongly suppressed in the Standard Model and its branching ratio
is theoretically calculated with high accuracy. The NA62 experiment is designed
to measure this decay rate with an uncertainty better than 10\%. The
measurement can be a good probe of new physics phenomena, which can alter the
SM decay rate. The NA62 experiment has been successfully launched in October
2014. In this document, after an introduction to the theoretical framework, the
NA62 experimental setup is described and a first look at the pilot run data is
reported.


  We investigate the nonlinear dynamics of turbulent shear flows, with and
without rotation, in the context of a simple but physically motivated closure
of the equation governing the evolution of the Reynolds stress tensor. We show
that the model naturally accounts for some familiar phenomena in parallel shear
flows, such as the subcritical transition to turbulence at a finite Reynolds
number and the occurrence of a universal velocity profile close to a wall at
large Reynolds number. For rotating shear flows we find that, depending on the
Rayleigh discriminant of the system, the model predicts either linear
instability or nonlinear instability or complete stability as the Reynolds
number is increased to large values. We investigate the properties of
Couette-Taylor flows for varying inner and outer cylinder rotation rates and
identify the region of linear instability (similar to Taylor's), as well as
regions of finite-amplitude instability qualitatively compatible with recent
experiments. We also discuss quantitative predictions of the model in
comparison with a range of experimental torque measurements. Finally, we
consider the relevance of this work to the question of the hydrodynamic
stability of astrophysical accretion discs.


  Accurate parameters of the host stars of exoplanets are important for the
interpretation of the new planet systems that continue to emerge. The CoRoT
satellite recently discovered a transiting rocky planet with a density similar
to the inner planets in our solar system, a so-called Super Earth. This planet
is orbiting a relatively faint G9V star called CoRoT-7, and we wish to refine
its physical properties, which are important for the interpretation of the
properties of the planet system. We used spectra from<EMAIL_ADDRESS>and
<EMAIL_ADDRESS>From the analysis of Fe-1 and Fe-2 lines we determine Teff, log
g and microturbulence. We use the Balmer lines to constrain Teff and pressure
sensitive Mg-1b and Ca lines to constrain log g. From the analysis we find
Teff=5250+-60K, log g = 4.47+-0.05, [M/H]=+0.12+-0.06, and vsini = 1.1 km/s. We
compared the L/M ratio with isochrones to constrain the evolutionary status.
Using the age estimate of 1.2-2.3 Gyr based on stellar activity, we determine
the mass and radius 0.91+-0.03 Msun and 0.82+-0.04 Rsun. With these updated
constraints we fitted the CoRoT transit light curve for CoRoT-7b. We revise the
planet radius to be slightly smaller, R = 1.58+-0.10 Rearth, and the density
becomes higher, rho = 7.2+-1.8 g/cm3. The host star CoRoT-7 is a slowly
rotating, metal rich, unevolved type G9V star. The star is relatively faint
(V=11.7) and its fundamental parameters can only be determined through indirect
methods. Our methods rely on detailed spectral analyses that depend on the
adopted model atmospheres. From the analysis of spectra of stars with
well-known parameters with similar parameters to CoRoT-7 (the Sun and alpha Cen
B) we demonstrate that our methods are robust within the claimed uncertainties.
Therefore our methods can be reliably used in subsequent analyses of similar
exoplanet host stars.


  The hardware implementation of deep neural networks (DNNs) has recently
received tremendous attention: many applications in fact require high-speed
operations that suit a hardware implementation. However, numerous elements and
complex interconnections are usually required, leading to a large area
occupation and copious power consumption. Stochastic computing has shown
promising results for low-power area-efficient hardware implementations, even
though existing stochastic algorithms require long streams that cause long
latencies. In this paper, we propose an integer form of stochastic computation
and introduce some elementary circuits. We then propose an efficient
implementation of a DNN based on integral stochastic computing. The proposed
architecture has been implemented on a Virtex7 FPGA, resulting in 45% and 62%
average reductions in area and latency compared to the best reported
architecture in literature. We also synthesize the circuits in a 65 nm CMOS
technology and we show that the proposed integral stochastic architecture
results in up to 21% reduction in energy consumption compared to the binary
radix implementation at the same misclassification rate. Due to fault-tolerant
nature of stochastic architectures, we also consider a quasi-synchronous
implementation which yields 33% reduction in energy consumption w.r.t. the
binary radix implementation without any compromise on performance.


  Disordered systems under applied loading display slow creep flows at finite
temperature, which can lead to the material rupture. Renormalization group
arguments predicted that creep proceeds via thermal avalanches of activated
events. Recently, thermal avalanches were argued to control the dynamics of
liquids near their glass transition. Both theoretical approaches are markedly
different. Here we provide a scaling description that seeks to unify dynamical
heterogeneities in both phenomena, confirm it in simple models of pinned
elastic interfaces, and discuss its experimental implications.


  A frozen spin polarized deuteron target cooled by the 3He/4He dilution
refrigerator is described. Fully deuterated 1,2-propanediol was used as a
target material. Deuteron vector polarization about 40% was obtained for the
target in the shape of a cylinder of 2 cm diameter and 6 cm length. The target
is intended for a study of 3N interactions at the polarized neutron beam
generated by the Van de Graaff accelerator at the Charles University in Prague.


  At high temperature the infrared modes of a weakly coupled quantum field
theory can be treated nonperturbatively in real time using the classical field
approximation. We use this to introduce a nonperturbative approach to the
calculation of finite-temperature spectral functions, employing the classical
KMS condition in real time. The method is illustrated for the one-particle
spectral function in a scalar field theory in 2+1 dimensions. The result is
compared with resummed two-loop perturbation theory and both the plasmon mass
and width are found to agree with the analytical prediction.


  The effect of active galactic nuclei (AGN) feedback on the host galaxy, and
its role in quenching or enhancing star-formation, is still uncertain due to
the fact that usual star-formation rate (SFR) indicators -- emission-line
luminosities based on the assumption of photoionisation by young stars --
cannot be used for active galaxies as the ionising source is the AGN. We thus
investigate the use of SFR derived from the stellar population and its relation
with that derived from the gas for a sample of 170 AGN hosts and a matched
control sample of 291 galaxies. We compare the values of SFR densities obtained
via the Ha emission line (SFRg) for regions ionised by hot stars according to
diagnostic diagrams with those obtained from stellar population synthesis
(SFRstars) over the last 1 to 100~Myr. We find that the SFRstars over the last
20~Myrs closely reproduces the SFRg, although a better match is obtained via
the transformation: log(SFRstars) = (0.870+/-0.004) log(SFRg) +(0.007+/-0.006),
which is valid for both AGN hosts and non-active galaxies. We also compare the
reddening obtained via the gas Ha/Hb ratio with that derived via the full
spectral fitting in the stellar population synthesis. We find that the ratio
between the gas and stellar extinction is in the range 2.64 < A_Vgas/A_Vstar} <
2.85, in approximate agreement with previous results from the literature,
obtained for smaller samples. We interpret the difference as being due to the
fact that the reddening of the stars is dominated by that affecting the less
obscured underlying older population, while the reddening of the gas is larger
as it is associated to a younger stellar population buried deeper in the dust.


  Pseudoclassical supersymmetric model to describe massive particles with
higher spins (integer and half-integer) in $2+1$ dimensions is proposed. The
quantization of the model leads to the minimal (with only one polarization
state) quantum theory. In particular, the Bargmann-Wigner type equations for
higher spins arise in course of the canonical quantization. The cases of spin
one-half and one are considered in detail. Here one gets Dirac particles and
Chern-Simons particles respectively. A relation with the field theory is
discussed. On the basis of the model proposed, and using dimensional reduction
considerations, a model to describe Weyl particles with higher spins in $3+1$
dimensions is constructed.


  The branched pattern typically observed through the scanning gate microscopy
(SGM) of two dimensional electron gases in the presence of weak, smooth
disorder has recently been found to be robust against a very large shift in the
Fermi energy of the electron gas. We propose a toy model, where the potential
landscape reduces to a single localized feature, that makes it possible to
recast the understanding of branch formation through the effect of caustics in
an appropriate set of classical trajectories, and it is simple enough to allow
for a quantitative analysis of the energy and spatial dependence of the
branches. We find the energy stability to be extremely generic, as it rests
only upon the assumptions of weak disorder, weak scattering, and the
proportionality of the SGM response to the density of classical electron
trajectories. Therefore, the robustness against changes of the electron's Fermi
energy remains when adopting progressively realistic models of smooth disorder.


  (Abridged) This paper studies chaotic orbit ensembles evolved in triaxial
generalisations of the Dehnen potential which have been proposed to model
ellipticals with a strong density cusp that manifest significant deviations
from axisymmetry. Allowance is made for a possible supermassive black hole, as
well as low amplitude friction, noise, and periodic driving which can mimic
irregularities associated with discreteness effects and/or an external
environment. The degree of chaos is quantified by determining how (1) the
relative number of chaotic orbits and (2) the size of the largest Lyapunov
exponent depend on the steepness of the cusp and the black hole mass, and (3)
the extent to which Arnold webs significantly impede phase space transport,
both with and without perturbations. In the absence of irregularities, chaotic
orbits tend to be extremely `sticky,' so that different pieces of the same
chaotic orbit can behave very differently for 10000 dynamical times or longer,
but even very low amplitude perturbations can prove efficient in erasing many
-- albeit not all -- these differences. The implications thereof are discussed
both for the structure and evolution of real galaxies and for the possibility
of constructing approximate near-equilibrium models using Schwarzschild's
method. Much of the observed qualitative behaviour can be reproduced with a toy
potential given as the sum of an anisotropic harmonic oscillator and a
spherical Plummer potential, which suggests that the results may be generic.


  Hawley-Dolan and Winner had asked the art students to compare paintings by
abstract artists with paintings made by a child or by an animal. In 67% of the
cases, art students said that the painting by a renowned artist is better. I
compare this with the winning probability of the chessplayers of different
ratings. I conclude that the great artists score on the level of class D
amateurs.


  We present chemical abundance measurements for 19 damped lya systems observed
with HIRES on the 10m W.M. Keck Telescope. Our principal goal is to investigate
the abundance patterns of the damped systems and thereby determine the
underlying physical processes which dominate their chemical evolution. We place
particular emphasis on gauging the relative importance of two complementary
effects often invoked to explain the damped lya abundances: (1) nucleosynthetic
enrichment from Type II supernovae and (2) an ISM-like dust depletion pattern.
  Similar to the principal results of Lu et al. (1996), our observations lend
support both for dust depletion and Type II SN enrichment. Specifically, the
observed overabundance of Zn/Fe and underabundance of Ni/Fe relative to solar
abundances suggest significant dust depletion within the damped lya systems.
Meanwhile, the relative abundances of Al, Si, and Cr vs. Fe are consistent with
both dust depletion and Type II supernova enrichment. Our measurements of Ti/Fe
and the Mn/Fe measurements from Lu et al. (1996), however, cannot be explained
by dust depletion and indicate an underlying Type II SN pattern. Finally, the
observed values of [S/Fe] are inconsistent with the combined effects of dust
depletion and the nucleosynthetic yields expected for Type II supernovae. This
last result emphasizes the need for another physical process to explain the
damped lya abundance patterns.
  We also examine the metallicity of the damped lya systems both with respect
to Zn/H and Fe/H. Our results confirm previous surveys by Pettini and
collaborators, i.e., [<Zn/H>] = -1.15 +/- 0.15 dex. [abridged]


  Let $\Omega $ be a smooth bounded domain in $\R^N, N>1$ and let $n\in \N^*$.
We are concerned here with the existence of nonnegative solutions $u\_n$ in
$BV(\Omega)$, to the problem $$(P\_n) \begin{cases} -{\rm div} \sigma +2n
(\int\_ \Omega u -1) {\rm sign}^+ (u)=0 \quad \text{in} \Omega, \sigma \cdot
\nabla u= |\nabla u| \quad \text{in} \Omega, u \text{\rm is not identically
zero}, -\sigma \cdot \overrightarrow {n} u=u \quad \text{on} \partial\Omega,
\end{cases}$$ where $\overrightarrow {n}$ denotes the unit outer normal to
$\partial\Omega$, and ${\rm sign}^+(u)$ denotes some $L^{\infty}(\Omega)$
function defined as: $${\rm sign}^+ (u). u =u^+, 0 \leq {\rm sign}^+(u) \leq
1.$$ Moreover, we prove the tight convergence of $u\_n$ towards one of the
first eingenfunctions for the first $1-$Laplacian Operator $-\Delta\_1$ on
$\Omega$ when $n$ goes to $+\infty$.


  The q-field theories are constructed by substituting quantum groups for the
usual Lie groups. In earlier papers this construction was carried out for the
quantum group SU_q(2). Here the investigation is extended to SL_q(3). The
resulting theory describes two sectors, one sector lying close to the standard
theory and accessible by perturbation theory, while the second sector describes
particles that should be difficult to detect and become invisible in the q=1
limit. In this note we discuss these hypothetical particles: three quark-like
spinor particles coupled to three gluon-like vector particles.


  In this paper, we study a new type of BSDE, where the distribution of the
Y-component of the solution is required to satisfy an additional constraint,
written in terms of the expectation of a loss function. This constraint is
imposed at any deterministic time t and is typically weaker than the classical
pointwise one associated to reflected BSDEs. Focusing on solutions (Y, Z, K)
with deterministic K, we obtain the well-posedness of such equation, in the
presence of a natural Skorokhod type condition. Such condition indeed ensures
the minimality of the enhanced solution, under an additional structural
condition on the driver. Our results extend to the more general framework where
the constraint is written in terms of a static risk measure on Y. In
particular, we provide an application to the super hedging of claims under
running risk management constraint.


  Felix de Roy (1883-1942), an internationality recognised amateur astronomer,
made significant contributions to variable star research. As an active
observer, he made some 91,000 visual estimates of a number of different
variable stars. A Belgian national, he took refuge in England during World War
1. While there, de Roy became well enough known to later serve as Director of
the BAA Variable Star Section for seventeen years. Through this office, and his
connections with other organisations around the world, he encouraged others to
pursue the observation of variable stars. Not merely content to accumulate
observational data, de Roy also analysed the data and published numerous
papers.


  We study electron-phonon interaction induced decoherence between two-electron
singlet and triplet states in a semiconductor double quantum dot using a
spin-boson model. We investigate the onset and time evolution of this
dephasing, and study its dependence on quantum dot parameters such as dot size
and double dot separations, as well as the host materials (GaAs and Si). At the
short time limit, electron-phonon interaction only causes an incomplete initial
Gaussian decay of the off-diagonal density matrix element in the
singlet-triplet Hilbert space. A complete long-time exponential decay due to
phonon relaxation would eventually dominate over two-spin decoherence. We
analyze two-spin decoherence in both symmetric and biased double quantum dots,
identifying their difference in electron-phonon coupling and the relevant
consequences.


  We study the exchange interactions and resulting magnetic phases in the
honeycomb cobaltates. For a broad range of trigonal crystal fields acting on
Co2+ ions, the low-energy pseudospin-1/2 Hamiltonian is dominated by
bond-dependent Ising couplings that constitute the Kitaev model. The non-Kitaev
terms nearly vanish at small values of trigonal field \Delta, resulting in spin
liquid ground state. Considering Na3Co2SbO6 as an example, we find that this
compound is proximate to a Kitaev spin liquid phase, and can be driven into it
by slightly reducing \Delta by \sim 20 meV, e.g., via strain or pressure
control. We argue that due to the more localized nature of the magnetic
electrons in 3d compounds, cobaltates offer the most promising search area for
Kitaev model physics.


  Radiation therapy is a critical component of cancer treatment. However, the
delivery of radiation poses inherent challenges, particularly in minimizing
radiation exposure to healthy organs surrounding the tumor site. One
significant contributing factor to this challenge is the patient's respiration,
which introduces uncertainties in the precise targeting of radiation. Managing
these uncertainties during radiotherapy is essential to ensure effective tumor
treatment while minimizing the adverse effects on healthy tissues. This
research addresses the crucial objective of achieving a balanced dose
distribution during radiation therapy under conditions of respiration
uncertainty. To tackle this issue, we begin by developing a motion uncertainty
model employing probability density functions that characterize breathing
motion patterns. This model forms the foundation for our efforts to optimize
radiation dose delivery. Next, we employ three bio-inspired optimization
techniques: Cuckoo search optimization (CSO), flower pollination algorithm
(FPA), and bat search Optimization (BSO). Our research evaluates the dose
distribution in Gy on both the tumor and healthy organs by applying these
bio-inspired optimization methods to identify the most effective approach. This
research ultimately aids in refining the strategies used in radiation therapy
planning under the challenging conditions posed by respiration uncertainty.
Through the application of bio-inspired optimization techniques and a
comprehensive evaluation of dose distribution, we seek to improve the precision
and safety of radiation therapy, thereby advancing cancer treatment outcomes.


  (Abridged) We report on new and archival X-ray and near-infrared observations
of the anomalous X-ray pulsar 1E 1048.1-5937 performed between 2001-2007 with
RXTE, CXO, Swift, HST, and VLT. During its ~2001-2004 active period, 1E
1048.-5937 exhibited two large, long-term X-ray pulsed-flux flares as well as
short bursts, and large (>10x) torque changes. Monitoring with RXTE revealed
that the source entered a phase of timing stability in 2004; at the same time,
a series of four simultaneous observations with CXO and HST in 2006 showed that
its X-ray flux and spectrum and near-IR flux, all variable prior to 2005,
stabilized. The near-IR flux, when detected by HST (H~22.7 mag) and VLT
(K_S~21.0 mag), was considerably fainter than previously measured. Recently, in
2007 March, this newfound quiescence was interrupted by a sudden flux
enhancement, X-ray spectral changes and a pulse morphology change, simultaneous
with a large spin-up glitch and near-IR enhancement. Our RXTE observations
revealed a sudden pulsed flux increase by a factor of ~3 in the 2-10 keV band.
In observations with CXO and Swift, we found that the total X-ray flux
increased much more than the pulsed flux, reaching a peak value of >7 times the
quiescent value (2-10 keV). With these recent data, we find a strong
anti-correlation between X-ray flux and pulsed fraction, and a correlation
between X-ray spectral hardness and flux. Simultaneously with the radiative and
timing changes, we observed a significant X-ray pulse morphology change such
that the profile went from nearly sinusoidal to having multiple peaks. We
compare these remarkable events with other AXP outbursts and discuss
implications in the context of the magnetar model and other models of AXP
emission.


  For a simple connected graph $ G $ of order $ n $, the normalized Laplacian
is a square matrix of order $ n $, defined as $\mathcal{L}(G)=
D(G)^{-\frac{1}{2}}L(G)D(G)^{-\frac{1}{2}}$, where $ D(G)^{-\frac{1}{2}} $ is
the diagonal matrix whose $ i$-th diagonal entry is $ \frac{1}{\sqrt{d_{i}}} $.
In this article, we find the normalized Laplacian eigenvalues of the joined
union of regular graphs in terms of the adjacency eigenvalues and the
eigenvalues of quotient matrix associated with graph $ G $. For a finite group
$\mathcal{G}$, the power graph $\mathcal{P}(\mathcal{G})$ of a group $
\mathcal{G} $ is defined as the simple graph in which two distinct vertices are
joined by an edge if and only if one is the power of other. As a consequence of
the joined union of graphs, we investigate the normalized Laplacian eigenvalues
of power graphs of finite cyclic group $ \mathbb{Z}_{n}. $


  Nanodiamonds containing nitrogen vacancy (NV-) centers show promise for a
number of emerging applications including targeted in vivo imaging and
generating nuclear spin hyperpolarization for enhanced NMR spectroscopy and
imaging. Here, we develop a detailed understanding of the magnetic resonance
behavior of NV- centers in an ensemble of nanodiamonds with random crystal
orientations. Two-dimensional optically detected magnetic resonance
spectroscopy reveals the distribution of energy levels, spin populations, and
transition probabilities that give rise to a complex spectrum. We identify
overtone transitions that are inherently insensitive to crystal orientation and
give well-defined transition frequencies that access the entire nanodiamond
ensemble. These transitions may be harnessed for high-resolution imaging and
generation of nuclear spin hyperpolarization. The data are well described by
numerical simulations from the zero- to high-field regimes, including the
intermediate regime of maximum complexity. We evaluate the prospects of
nanodiamond ensembles specifically for nuclear hyperpolarization and show that
frequency-swept dynamic nuclear polarization may transfer a large amount of the
NV- center's hyperpolarization to nuclear spins by sweeping over a small region
of its spectrum.


  We expect quantum field theories for matter to acquire intricate corrections
due to their coupling to quantum fluctuations of the gravitational field. This
can be precisely worked out in 3d quantum gravity: after integrating out
quantum gravity, matter fields are effectively described as noncommutative
quantum field theories, with quantum-deformed Lorentz symmetries. An open
question remains: Are such theories unitary or not? On the one hand, since
these are effective field theories obtained after integrating out high energy
degrees of freedom, we may expect the loss of unitarity. On the other hand, as
rigorously defined field theories built with Lorentz symmetries and standing on
their own, we naturally expect the conservation of unitarity. In an effort to
settle this issue, we explicitly check unitarity for a scalar field at one-loop
level in both Euclidean and Lorentzian space-time signatures. We find that
unitarity requires adding an extra-term to the propagator of the noncommutative
theory, corresponding to a massless mode and given by a representation with
vanishing Plancherel measure, thus usually ignored in spinfoam path integrals
for quantum gravity. This indicates that the inclusion of matter in spinfoam
models, and more generally in quantum gravity, might be more subtle than
previously thought.


  Analysis of the transit light curve deformed by the stellar gravity darkening
allows us to photometrically measure both components of the spin-orbit angle
$\psi$, its sky projection $\lambda$ and inclination of the stellar spin axis
$i_\star$. In this paper, we apply the method to two transiting hot Jupiter
systems monitored with the Kepler spacecraft, Kepler-13A and HAT-P-7. For
Kepler-13A, we find $i_\star=81^\circ\pm5^\circ$ and $\psi=60^\circ\pm2^\circ$
adopting the spectroscopic constraint $\lambda=58.6^\circ\pm2.0^\circ$ by
Johnson et al. (2014). In our solution, the discrepancy between the above
$\lambda$ and that previously reported by Barnes et al. (2011) is solved by
fitting both of the two parameters in the quadratic limb-darkening law. We also
report the temporal variation in the orbital inclination of Kepler-13Ab,
$\mathrm{d} |\cos i_{\rm
orb}|/\mathrm{d}t=(-7.0\pm0.4)\times10^{-6}\,\mathrm{day}^{-1}$, providing
further evidence for the spin-orbit precession in this system. By fitting the
precession model to the time series of $i_{\rm orb}$, $\lambda$, and $i_\star$
obtained with the gravity-darkened model, we constrain the stellar quadrupole
moment $J_2=(6.1\pm0.3)\times10^{-5}$ for our new solution, which is several
times smaller than $J_2=(1.66\pm0.08)\times10^{-4}$ obtained for the previous
one. We show that the difference can be observable in the future evolution of
$\lambda$, thus providing a possibility to test our solution with follow-up
observations. The second target, HAT-P-7, is the first F-dwarf star analyzed
with the gravity-darkening method. Our analysis points to a nearly pole-on
configuration with $\psi=101^\circ\pm2^\circ$ or $87^\circ\pm2^\circ$ and the
gravity-darkening exponent $\beta$ consistent with $0.25$. Such an
observational constraint on $\beta$ can be useful for testing the theory of
gravity darkening.


  We show in this paper that the set of irreducible components of the family of
Galois coverings of P^1_C with Galois group isomorphic to D_n is in bijection
with the set of possible numerical types. In this special case the numerical
type is the equivalence class (for automorphisms of D_n) of the function which
to each conjugacy class \mathcal{C} in D_n associates the number of branch
points whose local monodromy lies in the class \mathcal{C}.


  Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations
of Gaussian processes (GPs) and are formally equivalent to neural networks with
multiple, infinitely wide hidden layers. DGPs are nonparametric probabilistic
models and as such are arguably more flexible, have a greater capacity to
generalise, and provide better calibrated uncertainty estimates than
alternative deep models. This paper develops a new approximate Bayesian
learning scheme that enables DGPs to be applied to a range of medium to large
scale regression problems for the first time. The new method uses an
approximate Expectation Propagation procedure and a novel and efficient
extension of the probabilistic backpropagation algorithm for learning. We
evaluate the new method for non-linear regression on eleven real-world
datasets, showing that it always outperforms GP regression and is almost always
better than state-of-the-art deterministic and sampling-based approximate
inference methods for Bayesian neural networks. As a by-product, this work
provides a comprehensive analysis of six approximate Bayesian methods for
training neural networks.


  As an application of the Cartan invariants obtained using the Karlhede
algorithm, we study a simple subclass of the PP-wave spacetimes, the
gravitational plane waves. We provide an invariant classification of these
spacetimes and then study a few notable subcases: the linearly polarized plane
waves, the weak-field circularly polarized waves, and another class of plane
waves found by imposing conditions on the set of invariants. As we study these
spacetimes we relate the invariant structure (i.e., Cartan scalars) to the
physical description of these spacetimes using the geodesic deviation equations
relative to timelike geodesic observers.


  In this paper, we introduce two new model-based versions of the widely-used
standardized precipitation index (SPI) for detecting and quantifying the
magnitude of extreme hydro-climatic events. Our analytical approach is based on
generalized additive models for location, scale and shape (GAMLSS), which helps
as to overcome some limitations of the SPI. We compare our model-based
standardised indices (MBSIs) with the SPI using precipitation data collected
between January 2004 - December 2013 (522 weeks) in Caapiranga, a road-less
municipality of Amazonas State. As a result, it is shown that the MBSI-1 is an
index with similar properties to the SPI, but with improved methodology. In
comparison to the SPI, our MBSI-1 index allows for the use of different
zero-augmented distributions, it works with more flexible time-scales, can be
applied to shorter records of data and also takes into account temporal
dependencies in known seasonal behaviours. Our approach is implemented in an R
package, mbsi, available from Github.


  In this paper, we propose a new method to overcome catastrophic forgetting by
adding generative regularization to Bayesian inference framework. Bayesian
method provides a general framework for continual learning. We could further
construct a generative regularization term for all given classification models
by leveraging energy-based models and Langevin-dynamic sampling to enrich the
features learned in each task. By combining discriminative and generative loss
together, we empirically show that the proposed method outperforms
state-of-the-art methods on a variety of tasks, avoiding catastrophic
forgetting in continual learning. In particular, the proposed method
outperforms baseline methods over 15% on the Fashion-MNIST dataset and 10% on
the CUB dataset


  Generalized $t$-designs, which form a common generalization of objects such
as $t$-designs, resolvable designs and orthogonal arrays, were defined by
Cameron [P.J. Cameron, A generalisation of $t$-designs, \emph{Discrete Math.}\
{\bf 309} (2009), 4835--4842]. In this paper, we define a related class of
combinatorial designs which simultaneously generalize packing designs and
packing arrays. We describe the sometimes surprising connections which these
generalized designs have with various known classes of combinatorial designs,
including Howell designs, partial Latin squares and several classes of triple
systems, and also concepts such as resolvability and block colouring of
ordinary designs and packings, and orthogonal resolutions and colourings.
Moreover, we derive bounds on the size of a generalized packing design and
construct optimal generalized packings in certain cases. In particular, we
provide methods for constructing maximum generalized packings with $t=2$ and
block size $k=3$ or 4.


  Copper-doped zinc sulfide (ZnS:Cu) exhibits down-conversion luminescence in
the UV, visible, and IR regions of the electromagnetic spectrum; the visible
red, green, and blue emission is referred to as R-Cu, G-Cu, and B-Cu,
respectively. The sub-bandgap emission arises from optical transitions between
localized electronic states created by point defects, making ZnS:Cu a prolific
phosphor material and an intriguing candidate material for quantum information
science, where point defects excel as single-photon sources and spin qubits.
Colloidal nanocrystals (NCs) of ZnS:Cu are particularly interesting as hosts
for the creation, isolation, and measurement of quantum defects, since their
size, composition, and surface chemistry can be precisely tailored for
bio-sensing and opto-electronic applications. Here, we present a method for
synthesizing colloidal ZnS:Cu NCs that emit primarily R-Cu, which has been
proposed to arise from the Cu$_{Zn}$-V$_S$ complex, an impurity-vacancy point
defect structure analogous to well-known quantum defects in other materials
that produce favorable optical and spin dynamics. First principles calculations
confirm the thermodynamic stability and electronic structure of
Cu$_{Zn}$-V$_S$. Temperature- and time-dependent optical properties of ZnS:Cu
NCs show blueshifting luminescence and an anomalous plateau in the intensity
dependence as temperature is increased from 19 K to 290 K, for which we propose
an empirical dynamical model based on thermally-activated coupling between two
manifolds of states inside the ZnS bandgap. Understanding of R-Cu emission
dynamics, combined with a controlled synthesis method for obtaining R-Cu
centers in colloidal NC hosts, will greatly facilitate the development of
Cu$_{Zn}$-V$_S$ and related complexes as quantum point defects in ZnS.


  Cold-start problem is a fundamental challenge for recommendation tasks. The
recent self-supervised learning (SSL) on Graph Neural Networks (GNNs) model,
PT-GNN, pre-trains the GNN model to reconstruct the cold-start embeddings and
has shown great potential for cold-start recommendation. However, due to the
over-smoothing problem, PT-GNN can only capture up to 3-order relation, which
can not provide much useful auxiliary information to depict the target
cold-start user or item. Besides, the embedding reconstruction task only
considers the intra-correlations within the subgraph of users and items, while
ignoring the inter-correlations across different subgraphs. To solve the above
challenges, we propose a multi-strategy based pre-training method for
cold-start recommendation (MPT), which extends PT-GNN from the perspective of
model architecture and pretext tasks to improve the cold-start recommendation
performance. Specifically, in terms of the model architecture, in addition to
the short-range dependencies of users and items captured by the GNN encoder, we
introduce a Transformer encoder to capture long-range dependencies. In terms of
the pretext task, in addition to considering the intra-correlations of users
and items by the embedding reconstruction task, we add embedding contrastive
learning task to capture inter-correlations of users and items. We train the
GNN and Transformer encoders on these pretext tasks under the meta-learning
setting to simulate the real cold-start scenario, making the model easily and
rapidly being adapted to new cold-start users and items. Experiments on three
public recommendation datasets show the superiority of the proposed MPT model
against the vanilla GNN models, the pre-training GNN model on user/item
embedding inference and the recommendation task.


  Cloud computing has brought a fundamental transformation in how organizations
operate their applications, enabling them to achieve affordable high
availability of services. Kubernetes has emerged as the preferred choice for
container orchestration and service management across many Cloud computing
platforms. The scheduler in Kubernetes plays a crucial role in determining the
placement of newly deployed service containers. However, the default scheduler,
while fast, often lacks optimization, leading to inefficient service placement
or even deployment failures.
  This paper introduces SAGE, a tool for optimal solutions in Kubernetes
clusters that can also assist the Kubernetes default scheduler and any other
custom scheduler in application deployment. SAGE computes an optimal deployment
plan based on the constraints of the application to be deployed and the
available Cloud resources. We show the potential benefits of using SAGE by
considering test cases with various characteristics. It turns out that SAGE
surpasses other schedulers by comprehensively analyzing the application demand
and cluster image. This ability allows it to better understand the needs of the
pods, resulting in consistently optimal solutions across all scenarios. The
accompanying material of this paper is publicly available at
https://github.com/SAGE-Project/SAGE-Predeployer.


  We present a multiwavelength analysis of the long duration flare observed on
15 April 2002 (soft X-ray peak time at 03:55 UT, SOL2002-04-15T03:55). This
flare occurred on the disk (S15W01) in NOAA 9906 and was observed by a number
of space instruments including the Extreme-Ultraviolet Imaging Telescope on the
Solar and Heliospheric Observatory (SOHO/EIT), the RESIK spectrometer onboard
the Coronas-F spacecraft, and the Ramaty High Energy Solar Spectroscopic Imager
(RHESSI). We have performed a complex analysis of these measurements and
studied the morphology and physical parameters characterizing the conditions in
flaring plasmas. The 195 A SOHO/EIT images have been used to study evolution of
flaring loops. Analysis of RHESSI data provided the opportunity for a detailed
analysis of hard X-ray emission with 1 keV energy resolution. We have used
Geostationary Operational Environmental Satellite (GOES) observations for
isothermal interpretation of the X-ray measurements. Temperature diagnostics of
the flaring plasma have been carried out by means of a differential emission
measure (DEM) analysis based on RESIK X-ray spectra.


  We study spectral properties of sub-Riemannian Laplacians, which are
hypoelliptic operators. The main objective is to obtain quantum ergodicity
results, what we have achieved in the 3D contact case. In the general case we
study the small-time asymptotics of sub-Riemannian heat kernels. We prove that
they are given by the nilpotentized heat kernel. In the equiregular case, we
infer the local and microlocal Weyl law, putting in light the Weyl measure in
sR geometry. This measure coincides with the Popp measure in low dimension but
differs from it in general. We prove that spectral concentration occurs on the
shief generated by Lie brackets of length r-1, where r is the degree of
nonholonomy. In the singular case, like Martinet or Grushin, the situation is
more involved but we obtain small-time asymptotic expansions of the heat kernel
and the Weyl law in some cases. Finally, we give the Weyl law in the general
singular case, under the assumption that the singular set is stratifiable.


  We predict a dynamical resonant effect, which is driven by externally applied
linear and quadratic Zeeman fields, in a spin-orbit-coupled spin-1
Bose-Einstein condensate. The Bose-Einstein condensate is assumed to be
initialized in some superposed state of Zeeman sublevels and subject to a
sudden shift of the trapping potential. It is shown that the time-averaged
center-of-mass oscillation and the spin polarizations of the Bose-Einstein
condensate exhibit remarkable resonant peaks when the Zeeman fields are tuned
to certain strengths. The underlying physics behind this resonance can be
traced back to the out-of-phase interference of the dynamical phases carried by
different spinorbit states. By analyzing the single particle spectrum, the
resonant condition is summarized as a simple algebraic relation, connecting the
strengths of the linear and quadratic Zeeman fields. This property is
potentially applicable in quantum information and quantum precision
measurement.


  We consider a free boundary problem for the axially symmetric incompressible
ideal magnetohydrodynamic equations that describes the motion of the plasma in
vacuum. Both the plasma magnetic field and vacuum magnetic field are tangent
along the plasma-vacuum interface. Moreover, the vacuum magnetic field is
composed in a non-simply connected domain and hence is non-trivial. Under the
Rayleigh-Taylor sign condition on the free surface, we prove the local
well-posedness of the problem in Sobolev spaces. Furthermore, we also prove the
local well-posdeness under a more general "stability" assumption for the
initial data, which provided that the Rayleigh-Taylor sign condition is
satisfied at all those points of the initial interface where the
non-collinearity condition fails.


  We prove that the frieze sequences of cluster variables associated with the
vertices of an affine quiver satisfy linear recurrence relations. In
particular, we obtain a proof of a recent conjecture by Assem-Reutenauer-Smith.


  A room-temperature infrared spectroscopy study of elemental tellurium at
pressures up to 8.44 GPa in the energy range 0.015-2 eV is reported. Optical
signatures of the high-pressure polymorphs are investigated and compared to the
results of density-functional band-structure calculations. A Drude peak is
first seen in the optical conductivity around 3.5 GPa indicating a
semiconductor-to-metal transition within trigonal Te-I. A sharp increase in the
Drude spectral weight and dc-conductivity around 4.3 GPa signals the
transformation toward the triclinic Te-II polymorph. An absorption peak around
0.15 eV appears above 5 GPa concomitant with the gradual transformation of
Te-II into the structurally similar but incommensurately modulated Te-III.
Microscopically, this peak can only be reproduced within a sufficiently large
commensurate approximant, suggesting the low-energy optical response as a
fingerprint of the structural modulation.


  We discuss the effects of spatial interference between two infectious
hotspots as a function of the mobility of individuals (wind speed) between the
two and their relative degree of infectivity. As long as the upstream hotspot
is less contagious than the downstream one, increasing the wind speed leads to
a monotonic decrease of the infection peak in the downstream hotspot. Once the
upstream hotspot becomes about between twice and five times more infectious
than the downstream one, an optimal wind speed emerges, whereby a local minimum
peak intensity is attained in the downstream hotspot, along with a local
maximum beyond which the beneficial effect of the wind is restored. Since this
non-monotonic trend is reminiscent of the equation of state of non-ideal f
luids, we dub the above phenomena epidemic condensation. When the relative
infectivity of the upstream hotspot exceeds about a factor five, the beneficial
effect of the wind above the optimal speed is completely lost: any wind speed
above the optimal one leads to a higher infection peak. It is also found that
spatial correlation between the two hotspots decay much more slowly than their
inverse distance. It is hoped that the above findings may offer a qualitative
clue for optimal confinement policies between different cities and urban
agglomerates.


  We propose a real time deep learning framework for video-based facial
expression capture. Our process uses a high-end facial capture pipeline based
on FACEGOOD to capture facial expression. We train a convolutional neural
network to produce high-quality continuous blendshape weight output from video
training. Since this facial capture is fully automated, our system can
drastically reduce the amount of labor involved in the development of modern
narrative-driven video games or films involving realistic digital doubles of
actors and potentially hours of animated dialogue per character. We demonstrate
compelling animation inference in challenging areas such as eyes and lips.


  Based on the fundamental momentum-transfer theorem [Phys. Rev. Lett. 15, 11
(1965)] a novel contribution to the retarding force of metallic systems for
slow intruders is derived. This contribution is associated with sudden
charge-changing cycles during the path of projectiles. The sum of the novel and
the well-known conventional contributions, both expressed in terms of
scattering phase shifts, are used to discuss experimental data obtained for
different targets. It is found that our two-channel modeling, with two
nonlinear channels, improves the agreement between several data and theory and
thus, as predictive modeling, can contribute to the desired convergence between
experimental and theoretical attempts on the retarding force.


  We discuss the geometrical optics of coincidence imaging for two kinds of
spatial correlations which are related to a classical thermal light source and
a two-photon quantum entangled state.


  Data sharing for medical research has been difficult as open-sourcing
clinical data may violate patient privacy. Traditional methods for face
de-identification wipe out facial information entirely, making it impossible to
analyze facial behavior. Recent advancements on whole-body keypoints detection
also rely on facial input to estimate body keypoints. Both facial and body
keypoints are critical in some medical diagnoses, and keypoints invariability
after de-identification is of great importance. Here, we propose a solution
using deepfake technology, the face swapping technique. While this swapping
method has been criticized for invading privacy and portraiture right, it could
conversely protect privacy in medical video: patients' faces could be swapped
to a proper target face and become unrecognizable. However, it remained an open
question that to what extent the swapping de-identification method could affect
the automatic detection of body keypoints. In this study, we apply deepfake
technology to Parkinson's disease examination videos to de-identify subjects,
and quantitatively show that: face-swapping as a de-identification approach is
reliable, and it keeps the keypoints almost invariant, significantly better
than traditional methods. This study proposes a pipeline for video
de-identification and keypoint preservation, clearing up some ethical
restrictions for medical data sharing. This work could make open-source high
quality medical video datasets more feasible and promote future medical
research that benefits our society.


  A parameterization that is a modified version of a previous work is proposed
for the returns and correlation matrix of financial time series and its
properties are studied. This parameterization allows easy introduction of
non-stationarity and it shows several of the characteristics of the true,
observed realizations, such as fat tails, volatility clustering, and a spectrum
of eigenvalues of the correlation matrix that can be understood as an extension
of Random Matrix Theory results. The predicted behavior of this
parameterization for the eigenvalues is compared with the eigenvalues of
Brazilian assets and it is shown that those predictions fit the data better
than Random Matrix Theory.


  This paper follows cognitive studies to investigate a graph representation
for sketches, where the information of strokes, i.e., parts of a sketch, are
encoded on vertices and information of inter-stroke on edges. The resultant
graph representation facilitates the training of a Graph Neural Networks for
classification tasks, and achieves accuracy and robustness comparable to the
state-of-the-art against translation and rotation attacks, as well as stronger
attacks on graph vertices and topologies, i.e., modifications and addition of
strokes, all without resorting to adversarial training. Prior studies on
sketches, e.g., graph transformers, encode control points of stroke on
vertices, which are not invariant to spatial transformations. In contrary, we
encode vertices and edges using pairwise distances among control points to
achieve invariance. Compared with existing generative sketch model for one-shot
classification, our method does not rely on run-time statistical inference.
Lastly, the proposed representation enables generation of novel sketches that
are structurally similar to while separable from the existing dataset.


  The recently developed tensor renormalization-group (TRG) method provides a
highly precise technique for deriving thermodynamic and critical properties of
lattice Hamiltonians. The TRG is a local coarse-graining transformation, with
the elements of the tensor at each lattice site playing the part of the
interactions that undergo the renormalization-group flows. These tensor flows
are directly related to the phase diagram structure of the infinite system,
with each phase flowing to a distinct surface of fixed points. Fixed-point
analysis and summation along the flows give the critical exponents, as well as
thermodynamic functions along the entire temperature range. Thus, for the
ferromagnetic triangular lattice Ising model, the free energy is calculated to
better than 10^-5 along the entire temperature range. Unlike previous
position-space renormalization-group methods, the truncation (of the tensor
index range D) in this general method converges under straightforward and
systematic improvements. Our best results are easily obtained with D = 24,
corresponding to 4624-dimensional renormalization-group flows.


  It has been suggested that non-relativistic outflows from quasars can
naturally account for the missing component of the extragalactic $\gamma$-ray
background and explain the cumulative neutrino background through pion decay in
collisions between protons accelerated by the outflow shock and interstellar
protons. Here we show that the same quasar outflows are capable of accelerating
protons to energies of $\sim 10^{20}$ eV during the early phase of their
propagation. The overall quasar population is expected to produce a cumulative
ultra high energy cosmic ray flux of $\sim10^{-7}\,\rm
GeV\,cm^{-2}s^{-1}sr^{-1}$ at $E_{\rm CR}\gtrsim10^{18}$ eV. The spectral shape
and amplitude is consistent with recent observations for outflow parameters
constrained to fit secondary $\gamma$-rays and neutrinos without any additional
parameter tuning. This indicates that quasar outflows simultaneously account
for all three messengers at their observed levels.


  Research in deep learning for multi-speaker source separation has received a
boost in the last years. However, most studies are restricted to mixtures of a
specific number of speakers, called a specific scenario. While some works
included experiments for different scenarios, research towards combining data
of different scenarios or creating a single model for multiple scenarios have
been very rare. In this work it is shown that data of a specific scenario is
relevant for solving another scenario. Furthermore, it is concluded that a
single model, trained on different scenarios is capable of matching performance
of scenario specific models.


  In this paper, we evaluate a semi-autonomous brain-computer interface (BCI)
for manipulation tasks. In such system, the user controls a robotic arm through
motor imagery commands. In traditional process-control BCI systems, the user
has to provide those commands continuously in order manipulate the effector of
the robot step-by-step, which results in a tiresome process for simple tasks
such as pick and replace an item from a surface. Here, we take a
semi-autonomous approach based on a conformal geometric algebra model that
solves the inverse kinematics of the robot on the fly, then the user only has
to decide on the start of the movement and the final position of the effector
(goal-selection approach). Under these conditions, we implemented
pick-and-place tasks with a disk as an item and two target areas placed on the
table at arbitrary positions. An artificial vision (AV) algorithm was used to
obtain the positions of the items expressed in the robot frame through images
captured with a webcam. Then, the AV algorithm is integrated to the inverse
kinematics model to perform the manipulation tasks. As proof-of-concept,
different users were trained to control the pick-and-place tasks through the
process-control and semi-autonomous goal-selection approaches, so that the
performance of both schemes could be compared. Our results show the superiority
in performance of the semi-autonomous approach, as well as evidence of less
mental fatigue with it.


  We provide a description of the space of continuous and translation invariant
Minkowski valuations $\Phi:\mathcal{K}^n\to\mathcal{K}^n$ for which there is an
upper and a lower bound for the volume of $\Phi(K)$ in terms of the volume of
the convex body $K$ itself. Although no invariance with respect to a group
acting on the space of convex bodies is imposed, we prove that only two types
of operators appear: a family of operators having only cylinders over
$(n-1)$-dimensional convex bodies as images, and a second family consisting
essentially of 1-homogeneous operators. Using this description, we give
improvements of some known characterization results for the difference body.


  Using different experimental techniques we examine the dynamical scaling of
the quantum Hall plateau transition in a frequency range f = 0.1-55 GHz. We
present a scheme that allows for a simultaneous scaling analysis of these
experiments and all other data in literature. We observe a universal scaling
function with an exponent kappa = 0.5 +/- 0.1, yielding a dynamical exponent z
= 0.9 +/- 0.2.


  In this paper we suggest a realization for the SU(N) Kondo effect, using
quantum dots at strong magnetic field. We purpose using edge states of the
quantum Hall effect as pseudo spin that interact with multiple quantum dots
structures. In the suggested realization one can access each pseudo spin
separately and hence may perform a set of experiments that were impossible
until now. We focus on the realization of SU(2) and SU(3) Kondo and find a
conductivity of 3/4 quantum conductance in the SU(3) case.


  We investigate hydrodynamic evolution of the quark gluon plasma for the
colour glass condensate type initial conditions. We solve full second-order
viscous hydrodynamic equations in the longitudinal direction to find that
non-boost invariant expansion leads to visible deformation on the initial
rapidity distribution. The results indicate that hydrodynamic evolution with
viscosity plays an important role in determining parameters for the initial
distributions.


  The radiative decay of $f_1(1285)$ to vector mesons $\rho$ and $\phi$ is
calculated in a non-relativistic approach. The results obtained are compared to
experimental data to estimate the glue content of $f_1$. We find a small or
nonexistent glue content when our results are compared to the PDG data. However
a large glue content for $f_1(1285)$ was found when we compared our results to
the CLAS data.


  Suppose that a three-manifold M contains infinitely many distinct strongly
irreducible Heegaard splittings H + nK, obtained by Haken summing the surface H
with n copies of the surface K. We show that K is incompressible. All known
examples, of manifolds containing infinitely many irreducible Heegaard
splittings, are of this form. We also give new examples of such manifolds.


  The seesaw mechanism can play a key role in the generation of the leptonic
mixing in unified models. We consider an unified model with a family symmetry
and extended seesaw, and obtain viable fermion masses and mixing (leptonic
mixing is close to tri-bi-maximal).


  The results of field work on drift ice during wave propagation are analyzed
and presented. The field work was performed in the Barents Sea, and the main
focus of the paper is on wave processes in the MIZ. A model of wave damping in
broken ice is formulated and applied to interpret the field work results. It is
confirmed that waves of higher frequencies are subjected to stronger damping
when they propagate below the ice. This reduces the frequency of most energetic
wave with increasing distance from the ice edge. Difference of wave spectra
measured in two relatively close locations within the MIZ is discussed. The
complicated geometry and dynamics of the MIZ in the North-West Barents Sea
allow waves from the Atlantic Ocean and south regions of the Barents Sea to
penetrate into different locations of the MIZ.


  The extensive control of spin makes spintronics a promising candidate for
future scalable quantum devices. For the generation of spin-superfluid systems,
a detailed understanding of the build-up of coherence and relaxation is
necessary. However, to determine the relevant parameters for robust coherence
properties and faithfully witnessing thermalization, the direct access to
space- and time-resolved spin observables is needed. Here, we study the
thermalization of an easy-plane ferromagnet employing a homogeneous
one-dimensional spinor Bose gas. Building on the pristine control of
preparation and readout we demonstrate the dynamic emergence of long-range
coherence for the spin field and verify spin-superfluidity by experimentally
testing Landau's criterion. We reveal the structure of the emergent
quasi-particles: one 'massive'(Higgs) mode, and two 'massless' (Goldstone)
modes - a consequence of explicit and spontaneous symmetry breaking,
respectively. Our experiments allow for the first time to observe the
thermalization of an easy-plane ferromagnetic Bose gas; we find agreement for
the relevant momentum-resolved observables with a thermal prediction obtained
from an underlying microscopic model within the Bogoliubov approximation. Our
methods and results pave the way towards a quantitative understanding of
condensation dynamics in large magnetic spin systems and the study of the role
of entanglement and topological excitations for its thermalization.


  Analog neuromorphic computing systems emulate the parallelism and
connectivity of the human brain, promising greater expressivity and energy
efficiency compared to digital systems. Though many devices have emerged as
candidates for artificial neurons and artificial synapses, there have been few
device candidates for artificial dendrites. In this work, we report on
biocompatible graphene-based artificial dendrites (GrADs) that can implement
dendritic processing. By using a dual side-gate configuration, current applied
through a Nafion membrane can be used to control device conductance across a
trilayer graphene channel, showing spatiotemporal responses of leaky recurrent,
alpha, and gaussian dendritic potentials. The devices can be variably connected
to enable higher order neuronal responses, and we show through data-driven
spiking neural network classification simulations that overall spiking activity
is reduced by up to 15% without accuracy loss while low frequency operation is
stabilized. This positions the GrADs as strong candidates for energy efficient
bio-interfaced spiking neural networks.


  The NA57 experiment has been designed to study the production of strange and
multi-strange particles in Pb-Pb and p-Be collisions at the CERN SPS. The
predecessor experiment WA97 has measured an enhanced abundance of strange
particles in Pb-Pb collisions relative to p-A reactions at 160 GeV/c per
nucleon beam momentum. NA57 has extended the WA97 measurements to investigate
the evolution of the strangeness enhancement pattern as a function of the beam
energy and over a wider centrality range. In this paper, we report results on
cascade production for about the 60% most central collisions at 160 GeV/c per
nucleon.


  A shadow of a numerical solution to a chaotic system is an_exact_ solution to
the equations of motion that remains close to the numerical solution for a long
time. In a collisionless n-body system, we know that particle motion is
governed by the global potential rather than by inter-particle interactions. As
a result, the trajectory of each individual particle in the system is
independently shadowable. It is thus meaningful to measure the number of
particles that have shadowable trajectories as a function of time. We find that
the number of shadowable particles decays exponentially with time as exp(-mu
t), and that for eps in [~0.2,1] (in units of the local mean inter-particle
separation $\bar n$), there is an explicit relationship between the decay
constant mu, the timestep h of the leapfrog integrator, the softening eps, and
the number of particles N in the simulation. Thus, given N and eps, it is
possible to pre-compute the timestep h necessary to acheive a desired fraction
of shadowable particles after a given length of simulation time. We demonstrate
that a large fraction of particles remain shadowable over ~100 crossing times
even if particles travel up to about 1/3 of the softening length per timestep.
However, a sharp decrease in the number of shadowable particles occurs if the
timestep increases to allow particles to travel further than 1/3 the softening
length in one timestep, or if the softening is decreased below ~0.2$\bar n$.


  Small Fermi energy effects are induced in MgB$_2$ by the low hole doping in
the $\sigma$ bands which are characterized by a Fermi energy $E_{\rm F}^\sigma
\sim 0.5$ eV. We show that, due to the particularly strong deformation
potential relative to the $E_{2g}$ phonon mode, lattice fluctuations are
reflected in strong fluctuations in the electronic band structure. Quantum
fluctuations associated to the zero-point lattice motion are responsible for an
uncertainty of the Fermi energy of the order of the Fermi energy itself,
leading to the breakdown of the adiabatic principle underlying the
Born-Oppenheimer approximation in MgB$_2$ even if $\omega_{\rm ph}/E_{\rm F}
\sim 0.1-0.2$, where $\omega_{\rm ph}$ are the characteristic phonon
frequencies. This amounts to a new nonadiabatic regime, which could be relevant
to other unconventional superconductors.


  A study of the particle multiplicity between jets with large rapidity
separation has been performed using the D{\O}detector at the Fermilab Tevatron
$p\bar{p}$ Collider operating at $\sqrt{s}=1.8$ TeV. A significant excess of
low-multiplicity events is observed above the expectation for color-exchange
processes. The measured fractional excess is $1.07 \pm 0.10({\rm stat})^{+
0.25}_{- 0.13}({\rm syst})%$, which is consistent with a strongly-interacting
color-singlet (colorless) exchange process and cannot be explained by
electroweak exchange alone. A lower limit of 0.80% (95% C.L.) is obtained on
the fraction of dijet events with color-singlet exchange, independent of the
rapidity gap survival probability.


  The numerical results for the computed moduli of the irreducible three-loop
contributions to the thermodynamical pressure of an SU(2) Yang-Mills theory in
the effective theory for the deconfinning phase are explained in detail.
Irreducible three-loop integrations are compared with two-loop integrations and
the different nature of their integrations is scrutinized and illustrated
numerically. The numerical results show a rapid convergence in the loop
expansion of Yang-Mills thermodynamics. The statistical method used for
irreducible three-loop integrations is explained and checked for two-loop
integrations. The statistical results for two-loop integrations are compatible
with the former computed analytical results showing the reliability of the
statistical method.This is a companion paper to [1].


  A recent reanalysis of world proton and deuteron structure function
measurements showed that a significant amount of the apparent model dependence
in the extraction of the neutron structure function was related to
inconsistencies between the kinematics of the data and those assumed for the
calculation, suggesting that the true model dependence is smaller than commonly
believed. We present a detailed comparison of the neutron structure function as
extracted using different models, with care taken to ensure that all other
aspects of the comparison are done consistently. The neutron structure function
is extracted using a fit to these data evaluated at fixed Q^2_0=16 GeV^2. We
compare the results obtained using a variety of N--N potentials and deuteron
binding models to determine the model dependence of the extraction. As in the
recent extraction, F_{2n}/F_{2p} falls with x with no sign of plateau and
follows the low edge of the wide range of earlier F_{2n} extractions. The
model-dependent uncertainty in F_{2n}/F_{2p} is shown to be considerably
smaller than previously believed, particularly at large-x.


  Labeling explicit discourse relations is one of the most challenging
sub-tasks of the shallow discourse parsing where the goal is to identify the
discourse connectives and the boundaries of their arguments. The
state-of-the-art models achieve slightly above 45% of F-score by using
hand-crafted features. The current paper investigates the efficacy of the
pre-trained language models in this task. We find that the pre-trained language
models, when finetuned, are powerful enough to replace the linguistic features.
We evaluate our model on PDTB 2.0 and report the state-of-the-art results in
the extraction of the full relation. This is the first time when a model
outperforms the knowledge intensive models without employing any linguistic
features.


  We study the generalized diffusion-limited aggregates (DLA), with two seeds
placed at distance d lattice units and investigate the probability p(d) that
the patterns generated from those seeds get connected. In this model, one can
vary the parameter $\alpha$, and get a range of patterns from fractal-DLA to
compact one. For a fractal-DLA, p(d) decays rapidly with d, the decay is slower
for compact pattern in which case p(d)>>1 for all practical distances. We
demonstrate a similar phenomenon experimentally in viscous fingering and
electrochemical deposition with two injection points/cathodes.


  After reexamining the above barrier diffusion problem where we notice that
the wave packet collision implies the existence of {\em multiple} reflected and
transmitted wave packets, we analyze the way of obtaining phase times for
tunneling/reflecting particles in a particular colliding configuration where
the idea of multiple peak decomposition is recovered. To partially overcome the
analytical incongruities which frequently rise up when the stationary phase
method is adopted for computing the (tunneling) phase time expressions, we
present a theoretical exercise involving a symmetrical collision between two
identical wave packets and a unidimensional squared potential barrier where the
scattered wave packets can be recomposed by summing the amplitudes of
simultaneously reflected and transmitted wave components so that the conditions
for applying the stationary phase principle are totally recovered. Lessons
concerning the use of the stationary phase method are drawn.


  Recent advances of deep learning have brought exceptional performance on many
computer vision tasks such as semantic segmentation and depth estimation.
However, the vulnerability of deep neural networks towards adversarial examples
have caused grave concerns for real-world deployment. In this paper, we present
to the best of our knowledge the first systematic study of adversarial attacks
on monocular depth estimation, an important task of 3D scene understanding in
scenarios such as autonomous driving and robot navigation. In order to
understand the impact of adversarial attacks on depth estimation, we first
define a taxonomy of different attack scenarios for depth estimation, including
non-targeted attacks, targeted attacks and universal attacks. We then adapt
several state-of-the-art attack methods for classification on the field of
depth estimation. Besides, multi-task attacks are introduced to further improve
the attack performance for universal attacks. Experimental results show that it
is possible to generate significant errors on depth estimation. In particular,
we demonstrate that our methods can conduct targeted attacks on given objects
(such as a car), resulting in depth estimation 3-4x away from the ground truth
(e.g., from 20m to 80m).


  ChatGPT, as a recently launched large language model (LLM), has shown
superior performance in various natural language processing (NLP) tasks.
However, two major limitations hinder its potential applications: (1) the
inflexibility of finetuning on downstream tasks and (2) the lack of
interpretability in the decision-making process. To tackle these limitations,
we propose a novel framework that leverages the power of ChatGPT for specific
tasks, such as text classification, while improving its interpretability. The
proposed framework conducts a knowledge graph extraction task to extract
refined and structural knowledge from the raw data using ChatGPT. The rich
knowledge is then converted into a graph, which is further used to train an
interpretable linear classifier to make predictions. To evaluate the
effectiveness of our proposed method, we conduct experiments on four datasets.
The result shows that our method can significantly improve the performance
compared to directly utilizing ChatGPT for text classification tasks. And our
method provides a more transparent decision-making process compared with
previous text classification methods.


  We denote by $\mathcal{H}_{d,g,r}$ the Hilbert scheme of smooth curves, which
is the union of components whose general point corresponds to a smooth
irreducible and non-degenerate curve of degree $d$ and genus $g$ in $\PP^r$. In
this note, we show that any non-empty $\mathcal{H}_{g+2,g,4}$ is irreducible
without any restriction on the genus $g$. Our result augments the
irreducibility result obtained earlier by Hristo Iliev(2006), in which several
low genus $g\le 10$ cases have been left untreated.


  There are two types of edge states in graphene with/without magnetic field.
One is a quantum Hall edge state, which is topologically protected against
small perturbation. The other is a chiral zero mode that is localized near the
boundary with/without magnetic field. The latter is also topological but is
guaranteed to be zero energy by the chiral symmetry, which is also responsible
for massless Dirac like dispersion. Conceptual roles of the edge states are
stressed and reviewed from a view point of the bulk-edge correspondence and the
topological order.


  CUPID is a proposed future tonne-scale bolometric neutrinoless double beta
decay ($0\nu\beta\beta$) experiment to probe the Majorana nature of neutrinos
and discover Lepton Number Violation in the so-called inverted hierarchy region
of the neutrino mass. CUPID will be built on experience, expertise and lessons
learned in CUORE, and will exploit the current CUORE infrastructure as much as
possible. In order to achieve its ambitious science goals, CUPID aims to
dramatically reduce the backgrounds in the region of interest introducing a
high efficiently $\alpha$/$\beta$ discrimination techniques, also demonstrated
by the CUPID-0 and CUPID-Mo experiments, and using a high transition energy
double beta decay nucleus, $^{100}$Mo. This document describe the main concepts
related with the design of the CUPID experiment and indicates the projected
sensitivities and the global scientific goal of the experiment.


  The SiliconMicro-strip Tracker (SMT) at the D{\O} experiment in the Fermilab
Tevatron collider has been operating since 2001. In 2006, an additional layer,
referred to as 'Layer 0', was installed to improve impact parameter resolution
and compensate for detector degradation due to radiation damage to the original
innermost SMT layer. The SMT detector provides valuable tracking and vertexing
information for the experiment. This contribution will highlight aspects of the
long term operation of the SMT, including the impact of the silicon readout
test-stand. Due to the full integration of the test-stand into the D{\O}
trigger framework, this test-stand provides an advantageous tool for training
of new experts and studying subtle effects in the SMT while minimizing impact
on the global data acquisition.


  We present a deterministic approximation algorithm to compute logarithm of
the number of `good' truth assignments for a random k-satisfiability (k-SAT)
formula in polynomial time (by `good' we mean that violate a small fraction of
clauses). The relative error is bounded above by an arbitrarily small constant
epsilon with high probability as long as the clause density (ratio of clauses
to variables) alpha<alpha_{u}(k) = 2k^{-1}\log k(1+o(1)). The algorithm is
based on computation of marginal distribution via belief propagation and use of
an interpolation procedure. This scheme substitutes the traditional one based
on approximation of marginal probabilities via MCMC, in conjunction with
self-reduction, which is not easy to extend to the present problem.
  We derive 2k^{-1}\log k (1+o(1)) as threshold for uniqueness of the Gibbs
distribution on satisfying assignment of random infinite tree k-SAT formulae to
establish our results, which is of interest in its own right.