  A horizontal flow of two immiscible fluid layers with different densities,
viscosities and thicknesses, subject to vertical gravitational forces and with
an insoluble surfactant present at the interface, is investigated. The base
Couette flow is driven by the horizontal motion of the channel walls. Linear
and nonlinear stages of the (inertialess) surfactant and gravity dependent
long-wave instability are studied using the lubrication approximation, which
leads to a system of coupled nonlinear evolution equations for the interface
and surfactant disturbances. The linear stability is determined by an
eigenvalue problem for the normal modes. The growth rates and the amplitudes of
disturbances of the interface, surfactant, velocities, and pressures are found
analytically. For each wavenumber, there are two active normal modes. For each
mode, the instability threshold conditions in terms of the system parameters
are determined. In particular, it transpires that for certain parametric
ranges, even arbitrarily strong gravity cannot completely stabilize the flow.
The correlations of vorticity-thickness phase differences with instability,
present when the gravitational effects are neglected, are found to break down
when gravity is important. The physical mechanisms of instability for the two
modes are explained with vorticity playing no role in them. Unlike the
semi-infinite case that we previously studied, a small-amplitude nonlinear
saturation of the surfactant instability is possible in the absence of gravity.
For certain parametric ranges, the interface deflection is governed by a
decoupled Kuramoto-Sivashinsky equation, which provides a source term for a
linear convection-diffusion equation governing the surfactant concentration.
The full numerics confirm the prediction that, along with the interface, the
surfactant wave is chaotic, but the ratio of the two chaotic waves is constant.


  In recent years, the throughput requirements of e-commerce fulfillment
warehouses have seen a steep increase. This has resulted in various automation
solutions being developed for item picking and movement. In this paper, we
address the problem of manipulators picking heterogeneous items placed randomly
in a bin. Traditional solutions require that the items be picked to be placed
in an orderly manner in the bin and that the exact dimensions of the items be
known beforehand. Such solutions do not perform well in the real world since
the items in a bin are seldom placed in an orderly manner and new products are
added almost every day by e-commerce suppliers. We propose a cost-effective
solution that handles both the aforementioned challenges. Our solution
comprises of a dual sensor system comprising of a regular RGB camera and a 3D
ToF depth sensor. We propose a novel algorithm that fuses data from both these
sensors to improve object segmentation while maintaining the accuracy of pose
estimation, especially in occluded environments and tightly packed bins. We
experimentally verify the performance of our system by picking boxes using an
ABB IRB 1200 robot. We also show that our system maintains a high level of
accuracy in pose estimation that is independent of the dimensions of the box,
texture, occlusion or orientation. We further show that our system is
computationally less expensive and maintains a consistent detection time of 1
second. We also discuss how this approach can be easily extended to objects of
all shapes.


  As the world is still trying to rebuild from the destruction caused by the
widespread reach of the COVID-19 virus, and the recent alarming surge of human
monkeypox disease outbreaks in numerous countries threatens to become a new
global pandemic too. Human monkeypox disease syndromes are quite similar to
chickenpox, and measles classic symptoms, with very intricate differences such
as skin blisters, which come in diverse forms. Various deep-learning methods
have shown promising performances in the image-based diagnosis of COVID-19,
tumor cell, and skin disease classification tasks. In this paper, we try to
integrate deep transfer-learning-based methods, along with a convolutional
block attention module (CBAM), to focus on the relevant portion of the feature
maps to conduct an image-based classification of human monkeypox disease. We
implement five deep-learning models, VGG19, Xception, DenseNet121,
EfficientNetB3, and MobileNetV2, along with integrated channel and spatial
attention mechanisms, and perform a comparative analysis among them. An
architecture consisting of Xception-CBAM-Dense layers performed better than the
other models at classifying human monkeypox and other diseases with a
validation accuracy of 83.89%.


  An analysis of Deeply Virtual Compton Scattering (DVCS) is made within the
colour dipole model. We compare and contrast two models for the dipole
cross-section which have been successful in describing structure function data.
Both models agree with the available cross section data on DVCS from HERA. We
give predictions for various azimuthal angle asymmetries in HERA kinematics and
for the DVCS cross section in the THERA region.


  The experimental status of the pentaquark searches is briefly reviewed.
Recent null results by the CLAS collaboration are commented, and new strong
evidence of a very narrow Theta+ resonance by the DIANA collaboration is
presented. On the theory side, I revisit the argument against the existence of
the pentaquark -- that of Callan and Klebanov -- and show that actually a
strong resonance is predicted in that approach, however its width is grossly
overestimated. A recent calculation gives 2 MeV for the pentaquark width, and
this number is probably still an upper bound.


  In this paper I define the notion of a non-degenerate finitely semi-simple
semi-strict spherical 2-category of non-zero dimension. Given such a 2-category
I define a state-sum for any triangulated compact closed oriented 4-manifold
and show that this state-sum does not depend on the chosen triangulation by
proving invariance under the 4D Pachner moves. This invariant of piece-wise
linear closed compact oriented 4-manifolds generalizes the Crane-Yetter
invariant and probably it is also a generalization of the Crane-Frenkel
invariant. As an example we show how to obtain a 2-category of the right kind
from a finite group and a 4-cocycle on this group. The invariant we obtain from
this example looks like a four dimensional version of the Dijkgraaf-Witten
invariant.


  The graviton exchange diagram for the correlation function of arbitrary
scalar operators is evaluated in anti-de Sitter space, AdS(d+1). This enables
us to complete the computation of the 4-point amplitudes of dilaton and axion
fields in IIB supergravity on AdS5 X S5. By the AdS/CFT correspondence, we
obtain the 4-point functions of the marginal operators Tr (F^2+...) and Tr(F F~
+...) in N=4, d=4 SU(N) SYM at large N, large g^2 N. The short distance
asymptotics of the amplitudes are studied. We find that in the direct channel
the leading power singularity agrees with the expected contribution of the
stress-energy tensor in a double OPE expansion. Logarithmic singularities occur
in the complete 4-point functions at subleading orders.


  Owing to the growing population density of urban areas, many people are being
increasingly exposed to criminal activity. Increasing crime rates raise the
risk of both physical and psychological injury to law-abiding citizens,
creating anxiety. From the viewpoint of complex systems, crime prevention
through data science can be a solution to such issues. However, previous
studies have focused only on a single aspect of crime, ignoring the complex
interplay between the various characteristics, which must be considered in an
analysis to understand the dynamics underlying criminal activities. In this
study, we examined 12 features that have been identified as correlates of crime
rates using state-level statistics from the USA. We found that the correlates
of misdemeanors and felonies differ. The number of misdemeanors is strongly
associated with the police precinct, whereas felony rates are strongly
correlated with gun possession and happiness. Our findings suggest that the
countermeasures for misdemeanors should be treated differently from those for
felonies.


  We explore connections between our earlier work, in which we constructed
spectra that interpolate between bu and HZ, and earlier work of Kuhn and Priddy
on the Whitehead conjecture and of Rognes on the stable rank filtration in
algebraic K-theory. We construct a "chain complex of spectra" that is a
bu-analogue of an auxiliary complex used by Kuhn-Priddy; we conjecture that
this chain complex is "exact"; and we give some supporting evidence. We tie
this to work of Rognes by showing that our auxiliary complex can be constructed
in terms of the stable rank filtration. As a by-product, we verify for the case
of topological complex K-theory a conjecture made by Rognes about the
connectivity (for certain rings) of the filtration subquotients of the stable
rank filtration of algebraic K-theory.


  In recent years, colorectal cancer has become one of the most significant
diseases that endanger human health. Deep learning methods are increasingly
important for the classification of colorectal histopathology images. However,
existing approaches focus more on end-to-end automatic classification using
computers rather than human-computer interaction. In this paper, we propose an
IL-MCAM framework. It is based on attention mechanisms and interactive
learning. The proposed IL-MCAM framework includes two stages: automatic
learning (AL) and interactivity learning (IL). In the AL stage, a multi-channel
attention mechanism model containing three different attention mechanism
channels and convolutional neural networks is used to extract multi-channel
features for classification. In the IL stage, the proposed IL-MCAM framework
continuously adds misclassified images to the training set in an interactive
approach, which improves the classification ability of the MCAM model. We
carried out a comparison experiment on our dataset and an extended experiment
on the HE-NCT-CRC-100K dataset to verify the performance of the proposed
IL-MCAM framework, achieving classification accuracies of 98.98% and 99.77%,
respectively. In addition, we conducted an ablation experiment and an
interchangeability experiment to verify the ability and interchangeability of
the three channels. The experimental results show that the proposed IL-MCAM
framework has excellent performance in the colorectal histopathological image
classification tasks.


  Quantized neural networks (NN) are the common standard to efficiently deploy
deep learning models on tiny hardware platforms. However, we notice that
quantized NNs are as vulnerable to adversarial attacks as the full-precision
models. With the proliferation of neural networks on small devices that we
carry or surround us, there is a need for efficient models without sacrificing
trust in the prediction in presence of malign perturbations. Current mitigation
approaches often need adversarial training or are bypassed when the strength of
adversarial examples is increased.
  In this work, we investigate how a probabilistic framework would assist in
overcoming the aforementioned limitations for quantized deep learning models.
We explore Stochastic-Shield: a flexible defense mechanism that leverages input
filtering and a probabilistic deep learning approach materialized via Monte
Carlo Dropout. We show that it is possible to jointly achieve efficiency and
robustness by accurately enabling each module without the burden of
re-retraining or ad hoc fine-tuning.


  The COVID-19 pandemic has a devastating impact globally, claiming millions of
lives and causing significant social and economic disruptions. In order to
optimize decision-making and allocate limited resources, it is essential to
identify COVID-19 symptoms and determine the severity of each case. Machine
learning algorithms offer a potent tool in the medical field, particularly in
mining clinical datasets for useful information and guiding scientific
decisions. Association rule mining is a machine learning technique for
extracting hidden patterns from data. This paper presents an application of
association rule mining based Apriori algorithm to discover symptom patterns
from COVID-19 patients. The study, using 2875 patient's records, identified the
most common signs and symptoms as apnea (72%), cough (64%), fever (59%),
weakness (18%), myalgia (14.5%), and sore throat (12%). The proposed method
provides clinicians with valuable insight into disease that can assist them in
managing and treating it effectively.


  Since signet ring cells (SRCs) are associated with high peripheral metastasis
rate and dismal survival, they play an important role in determining surgical
approaches and prognosis, while they are easily missed by even experienced
pathologists. Although automatic diagnosis SRCs based on deep learning has
received increasing attention to assist pathologists in improving the
diagnostic efficiency and accuracy, the existing works have not been
systematically overviewed, which hindered the evaluation of the gap between
algorithms and clinical applications. In this paper, we provide a survey on SRC
analysis driven by deep learning from 2008 to August 2023. Specifically, the
biological characteristics of SRCs and the challenges of automatic
identification are systemically summarized. Then, the representative algorithms
are analyzed and compared via dividing them into classification, detection, and
segmentation. Finally, for comprehensive consideration to the performance of
existing methods and the requirements for clinical assistance, we discuss the
open issues and future trends of SRC analysis. The retrospect research will
help researchers in the related fields, particularly for who without medical
science background not only to clearly find the outline of SRC analysis, but
also gain the prospect of intelligent diagnosis, resulting in accelerating the
practice and application of intelligent algorithms.


  This study aims to evaluate the functional status of bilateral neurovascular
bundles (NVBs) using pulsed wave Doppler ultrasound in patients undergoing
prostate radiotherapy (RT). Sixty-two patients (mean age: 66.1 +/- 7.2 years)
underwent transrectal ultrasound scan using a conventional ultrasound scanner,
a 7.5 MHz bi-plane probe and a mechanical stepper. The ultrasound protocol
comprised 3 steps: 1) 3D B-mode scans of the entire prostate, 2) localization
of NVBs using color flow Doppler imaging, and 3) measurement of NVB function
using pulsed wave Doppler. Five pulsed Doppler waveform features were
extracted: peak systolic velocity (PSV), end-diastolic velocity (EDV), mean
velocity (Vm), resistive index (RI), and pulsatile index (PI). In summary, this
study presents a Doppler evaluation of NVBs in patients undergoing prostate RT.
It highlights substantial differences in Doppler ultrasound waveform features
between bilateral NVBs. The proposed ultrasound method may prove valuable as
clinicians strive to deliver NVB-sparing RT to preserve sexual function
effectively and enhance patients' overall well-being.


  We consider the nonlinear Choquard equation $$\begin{cases} & - \Delta u =
(I_\alpha \ast F(u))F'(u) -\mu u \ \text{in}\ \mathbb{R}^N, & u \in \
H^1(\mathbb{R}^N), \ \int_{\mathbb{R}^N} |u|^2 dx=m, \end{cases} $$ where
$\alpha\in(0,N)$, $m>0$ is prescribed, $\mu \in \mathbb{R}$ is a Lagarange
multiplier, and $I_\alpha$ is the Riesz potential.
  Under general assumptions on the nonlinearity $F,$ we prove the existence and
multiplicity of normalized solutions.


  Zaslavsky conjectured the following: The minimum number of vertices to be
deleted to restore balance in a subcubic signed graph is the same as the
minimum number of edges to be deleted to restore balance. We prove this
conjecture. Also, we obtain a bound for these invariants in a special class of
signed graphs.


  We introduce a general class of symmetric polynomials that have saturated
Newton polytope and their Newton polytope has integer decomposition property.
The class covers numerous previously studied symmetric polynomials.


  Recently, B\'{e}nyi and the second author introduced two combinatorial
interpretations for symmetrized poly-Bernoulli polynomials. In the present
study, we construct bijections between these combinatorial objects. We also
define various combinatorial polynomials and prove that all of these
polynomials coincide with symmetrized poly-Bernoulli polynomials.


  Stanley in his paper [Stanley, Richard P.: Acyclic orientations of graphs In:
Discrete Mathematics 5 (1973), Nr. 2, S. 171-178.] provided interpretations of
the chromatic polynomial when it is substituted with negative integers. Greene
and Zaslavsky interpreted the coefficients of the chromatic polynomial in
[Greene, Curtis ; Zaslavsky, Thomas: On the interpretation of Whitney numbers
through arrangements of hyperplanes, zonotopes, non-Radon partitions, and
orientations of graphs. In: Transactions of the American Mathematical Society
280 (1983), jan, Nr. 1, S. 97-97.]. We shall develop an involution on
factorisations of heaps of pieces and using this involution, we shall provide
bijective proofs to results from both the papers.


  This paper proves the Hasse principle and weak approximation for varieties
defined by the smooth intersection of three quadratics in at least 19
variables, over arbitrary number fields.


  We present further development and the first public release of our multimodal
nested sampling algorithm, called MultiNest. This Bayesian inference tool
calculates the evidence, with an associated error estimate, and produces
posterior samples from distributions that may contain multiple modes and
pronounced (curving) degeneracies in high dimensions. The developments
presented here lead to further substantial improvements in sampling efficiency
and robustness, as compared to the original algorithm presented in Feroz &
Hobson (2008), which itself significantly outperformed existing MCMC techniques
in a wide range of astrophysical inference problems. The accuracy and economy
of the MultiNest algorithm is demonstrated by application to two toy problems
and to a cosmological inference problem focussing on the extension of the
vanilla $\Lambda$CDM model to include spatial curvature and a varying equation
of state for dark energy. The MultiNest software, which is fully parallelized
using MPI and includes an interface to CosmoMC, is available at
http://www.mrao.cam.ac.uk/software/multinest/. It will also be released as part
of the SuperBayeS package, for the analysis of supersymmetric theories of
particle physics, at http://www.superbayes.org


  The theory of quantum thermodynamics investigates how the concepts of heat,
work, and temperature can be carried over to the quantum realm, where
fluctuations and randomness are fundamentally unavoidable. Of particular
practical relevance is the investigation of quantum thermal machines: Machines
that use the flow of heat in order to perform some useful task. In this
lectures series, we give a brief introduction into how the laws of
thermodynamics arise from quantum theory and how thermal machines can be
described with Markovian quantum master equations. Recent results are
illustrated with examples such as a quantum dot heat engine and a qubit
entangler.


  The translation of medical diagnosis to clinical coding has wide range of
applications in billing, aetiology analysis, and auditing. Currently, coding is
a manual effort while the automation of such task is not straight forward.
Among the challenges are the messy and noisy clinical records, case
complexities, along with the huge ICD10 code space. Previous work mainly relied
on discharge notes for prediction and was applied to a very limited data scale.
We propose an ensemble model incorporating multiple clinical data sources for
accurate code predictions. We further propose an assessment mechanism to
provide confidence rates in predicted outcomes. Extensive experiments were
performed on two new real-world clinical datasets (inpatient & outpatient) with
unaltered case-mix distributions from Maharaj Nakorn Chiang Mai Hospital. We
obtain multi-label classification accuracies of 0.73 and 0.58 for average
precision, 0.56 and 0.35 for F1-scores and 0.71 and 0.4 accuracy in predicting
principal diagnosis for inpatient and outpatient datasets respectively.


  The implications of the two-pocket Fermi surface for macroscopic quantum
phenomena are considered. We demonstrate that in the case of the two-pocket
Fermi surface the g-wave pairing is closely related to the d-wave one. As a
result two macroscopic condensates arise. The Josephson tunneling for such
two-component system has very special properties. We prove that the presence of
the g-wave does not contradict the existing experimental data on tunneling. We
also discuss the possible ways to experimentally reveal the g-wave component.


  We calculate the exceptional points of the eigenvalues of several
parameter-dependent Hamiltonian operators of mathematical and physical
interest. We show that the calculation is greatly facilitated by the
application of the discriminant to the secular determinant. In this way the
problem reduces to finding the roots of a polynomial function of just one
variable, the parameter in the Hamiltonian operator. As illustrative examples
we consider a particle in a one-dimensional box with a polynomial potential,
the periodic Mathieu equation, the Stark effect in a polar rigid rotor and in a
polar symmetric top.


  The detection of primordial polarization $B$ modes of the Cosmic Microwave
Background (CMB) requires exquisite control of Galactic foreground
contamination. The Needlet Internal Linear Combination (NILC) method has proven
effective in reconstructing CMB $B$ modes without suffering from mis-modeling
errors of Galactic emission. However, with the most complex foreground models,
residual Galactic contamination from NILC is proved to bias, especially at
large angular scales, the recovered CMB $B$ modes from simulated data of future
CMB experiments. We therefore present two new extensions of NILC,
Multi-Clustering NILC (MC-NILC) and optimized constrained Moment ILC (ocMILC),
which allow to enhance foreground subtraction in the reconstructed CMB signal.


  Ultra-wideband (UWB) electromagnetic pulses of nanosecond duration, or
nanopulses, have been recently approved by the Federal Communications
Commission for a number of various applications. They are also being explored
for applications in biotechnology and medicine. The simulation of the
propagation of a nanopulse through biological matter, previously performed
using a two-dimensional finite difference-time domain method (FDTD), has been
extended here into a full three-dimensional computation. To account for the UWB
frequency range, a geometrical resolution of the exposed sample was $0.25 mm$,
and the dielectric properties of biological matter were accurately described in
terms of the Debye model. The results obtained from three-dimensional
computation support the previously obtained results: the electromagnetic field
inside a biological tissue depends on the incident pulse rise time and width,
with increased importance of the rise time as the conductivity increases; no
thermal effects are possible for the low pulse repetition rates, supported by
recent experiments. New results show that the dielectric sample exposed to
nanopulses behaves as a dielectric resonator. For a sample in a cuvette, we
obtained the dominant resonant frequency and the $Q$-factor of the resonator.


  Magnetic Resonance Fingerprinting (MRF) enables simultaneous mapping of
multiple tissue parameters such as T1 and T2 relaxation times. The working
principle of MRF relies on varying acquisition parameters pseudo-randomly, so
that each tissue generates its unique signal evolution during scanning. Even
though MRF provides faster scanning, it has disadvantages such as erroneous and
slow generation of the corresponding parametric maps, which needs to be
improved. Moreover, there is a need for explainable architectures for
understanding the guiding signals to generate accurate parametric maps. In this
paper, we addressed both of these shortcomings by proposing a novel neural
network architecture consisting of a channel-wise attention module and a fully
convolutional network. The proposed approach, evaluated over 3 simulated MRF
signals, reduces error in the reconstruction of tissue parameters by 8.88% for
T1 and 75.44% for T2 with respect to state-of-the-art methods. Another
contribution of this study is a new channel selection method: attention-based
channel selection. Furthermore, the effect of patch size and temporal frames of
MRF signal on channel reduction are analyzed by employing a channel-wise
attention.


  According to the World Health Organization, dengue fever is the most
important mosquito-borne disease of humans, and it is currently estimated that
there may be 50 - 100 million yearly dengue infections worldwide. For the
purpose to provide new techniques to public health policies in course, we
introduce a predictive non-linear population dynamics model to describe the
population size of four stages of the development of \emph{Aedes aegypti},
having the coefficients set to be dependent on the rainfall index data. In
spite of the population dynamics of the \emph{Ae. aegypti} be mainly ruled by
the rainfall regime, most models are dedicated exclusively to effects of
temperature and only few models are dedicated to influence of rainfall. Vector
control actions are also implemented in many periods of the year in order to
compare relative efficiency of public health policies. The analysis of
equilibrium and stability was performed. Field rainfall time series data from
the City of Lavras (Minas Gerais, Brazil) was used for the model evaluation.
The model was validated in a comparison with experimental mosquito abundance
data acquired by field health agents. We evaluated and validated an
entomological conjecture that claims that control actions should be performed
during the dry season, instead of the common procedure adopted by vector
control programs, in which those are mainly applied in the rainy season.


  We have imaged a sample of 20 spiral galaxies in H$\alpha$ and in the
near-infrared K band (2.2 um), in order to determine the location and strength
of star formation in these objects with respect to perturbations in the old
stellar population. We have found that star formation rates are significantly
enhanced in the vicinity of K band arms. We have also found that this
enhancement in star formation rate in arm regions correlates well with a
quantity that measures the relative strengths of shocks in arms. Assuming that
the K band light is dominated by emission from the old stellar population, this
shows that density waves trigger star formation in the vicinity of spiral arms.


  We apply a variational wave function capable of describing qualitatively and
quantitatively the so called "resonating valence bond" in realistic materials,
by improving standard ab initio calculations by means of quantum Monte Carlo
methods. In this framework we clearly identify the Kekul\'e and Dewar
contributions to the chemical bond of the benzene molecule, and we establish
the corresponding resonating valence bond energy of these well known structures
($\simeq 0.01$eV/atom). We apply this method to unveil the nature of the
chemical bond in undoped graphene and show that this picture remains only
within a small "resonance length" of few atomic units.


  We define a class of ranked tree automata TABG generalizing both the tree
automata with local tests between brothers of Bogaert and Tison (1992) and with
global equality and disequality constraints (TAGED) of Filiot et al. (2007).
TABG can test for equality and disequality modulo a given flat equational
theory between brother subterms and between subterms whose positions are
defined by the states reached during a computation. In particular, TABG can
check that all the subterms reaching a given state are distinct. This
constraint is related to monadic key constraints for XML documents, meaning
that every two distinct positions of a given type have different values. We
prove decidability of the emptiness problem for TABG. This solves, in
particular, the open question of the decidability of emptiness for TAGED. We
further extend our result by allowing global arithmetic constraints for
counting the number of occurrences of some state or the number of different
equivalence classes of subterms (modulo a given flat equational theory)
reaching some state during a computation. We also adapt the model to unranked
ordered terms. As a consequence of our results for TABG, we prove the
decidability of a fragment of the monadic second order logic on trees extended
with predicates for equality and disequality between subtrees, and cardinality.


  Type Ia supernovae (SNe Ia) play a crucial role as standardizable
cosmological candles, though the nature of their progenitors is a subject of
active investigation. Recent observational and theoretical work has pointed to
merging white dwarf binaries, referred to as the double-degenerate channel, as
the possible progenitor systems for some SNe Ia. Additionally, recent
theoretical work suggests that mergers which fail to detonate may produce
magnetized, rapidly-rotating white dwarfs. In this paper, we present the first
multidimensional simulations of the post-merger evolution of white dwarf
binaries to include the effect of the magnetic field. In these systems, the two
white dwarfs complete a final merger on a dynamical timescale, and are tidally
disrupted, producing a rapidly-rotating white dwarf merger surrounded by a hot
corona and a thick, differentially-rotating disk. The disk is strongly
susceptible to the magnetorotational instability (MRI), and we demonstrate that
this leads to the rapid growth of an initially dynamically weak magnetic field
in the disk, the spin-down of the white dwarf merger, and to the subsequent
central ignition of the white dwarf merger. Additionally, these magnetized
models exhibit new features not present in prior hydrodynamic studies of white
dwarf mergers, including the development of MRI turbulence in the hot disk,
magnetized outflows carrying a significant fraction of the disk mass, and the
magnetization of the white dwarf merger to field strengths $\sim 2 \times 10^8$
G. We discuss the impact of our findings on the origins, circumstellar media,
and observed properties of SNe Ia and magnetized white dwarfs.


  We study valley-dependent spin transport theoretically in monolayer
transition-metal dichalcogenides in which a variety of spin and valley physics
are expected because of spin-valley coupling. The results show that the spins
are valley-selectively excited with appropriate carrier doping and valley
polarized spin current (VPSC) is generated. The VPSC leads to the spin-current
Hall effect, transverse spin accumulation originating from the Berry curvature
in momentum space. The results indicate that spin excitations with spin-valley
coupling lead to both valley and spin transport, which is promising for future
low-consumption nanodevice applications.


  Recently, diffusion-based methods, like InstructPix2Pix (IP2P), have achieved
effective instruction-based image editing, requiring only natural language
instructions from the user. However, these methods often inadvertently alter
unintended areas and struggle with multi-instruction editing, resulting in
compromised outcomes. To address these issues, we introduce the Focus on Your
Instruction (FoI), a method designed to ensure precise and harmonious editing
across multiple instructions without extra training or test-time optimization.
In the FoI, we primarily emphasize two aspects: (1) precisely extracting
regions of interest for each instruction and (2) guiding the denoising process
to concentrate within these regions of interest. For the first objective, we
identify the implicit grounding capability of IP2P from the cross-attention
between instruction and image, then develop an effective mask extraction
method. For the second objective, we introduce a cross attention modulation
module for rough isolation of target editing regions and unrelated regions.
Additionally, we introduce a mask-guided disentangle sampling strategy to
further ensure clear region isolation. Experimental results demonstrate that
FoI surpasses existing methods in both quantitative and qualitative
evaluations, especially excelling in multi-instruction editing task.


  Time stands still at a quantum critical point in the sense that correlation
functions near to the critical point are approximately independent of
frequency. In the case of a quantum liquid this would imply that classical
hydrodynamics breaks down near to the critical point, revealing the underlying
quantum degrees of freedom. An opportunity to see this effect for the first
time in the laboratory may be provided by relativistic heavy ion collisions
that are tuned so that the quark-gluon plasma passes through its critical point
forming a closed critical surface. In this note we point out that in certain
kinds of quantum fluids the temperature of a spherical critical surface will be
proportional to (radius)-1 and the entropy inside the surface will be close to
the Bekenstein bound. In these cases the breakdown in hydrodynamics near to the
critical point might serve as a model for the behavior of quantum gravity near
to an event horizon. Such a possibility is a fortiori notable because general
relativity predicts that nothing should happen at an event horizon.


  The interaction between the bacterial S-protein SbpA on different types of
lipid membranes has been studied using atomic force microscopy, transmission
electron microscopy, and quartz crystal microbalance with dissipation. On one
hand, It has been found that the bacterial forms two dimensional nanocrystals
on zwitterionic DOPC bilayers and negatively charged DMPG vesicles adsorbed on
mica, on zwitterionic DPPC and charged DPPC/DMPG (1:1) monolayers adsorbed on
carbon grids. On the other hand, SbpA protein adsorption took place on
zwitterionic DOPC bilayers and DOPC/DOPS (4:1) bilayers, previously adsorbed on
silicon supports. SbpA adsorption also took place on DPPC/DOPS (1:1) monolayers
adsorbed on carbon grids. Finally, neither SbpA adsorption, nor
recrystallization was observed on zwitterionic DMPC vesicles (previously
adsorbed on polyelectrolyte multilayers), and on DPPC vesicles supported on
silicon.


  This work presents the first study of using the popular Monte Carlo Tree
Search (MCTS) method combined with dedicated heuristics for solving the
Weighted Vertex Coloring Problem. Starting with the basic MCTS algorithm, we
gradually introduce a number of algorithmic variants where MCTS is extended by
various simulation strategies including greedy and local search heuristics. We
conduct experiments on well-known benchmark instances to assess the value of
each studied combination. We also provide empirical evidence to shed light on
the advantages and limits of each strategy.


  Moving beyond traditional 2D materials is now desirable to have switching
capabilities (e.g., transistors). Here we propose using borophene because, as
we will show in this letter, obtaining regions of the electronic bandstructure
which act as valence and conduction bands, with an apparent bandgap, may be
obtainable in the foreseeable future. Here for particular allotropes of
borophene, density of states (DOS) and electronic bandstructure diagrams with
E(k) vs k are found with much improved accuracy by ab initio quantum
calculations using hybrid functionals of several types. This procedure should
allow much better insight into how to obtain acceptable materials.


  The study presented hereafter shows a new methodology to reveal traces of
polyethylene (the most common microplastic particles, known as a structure of
C_2 H_4) in a sample of ocean water by the irradiation of a 50 keV, 1 uA
electron beam. This is performed by analyzing the photon (produced by the
electrons in water ) fluxes and spectra (i.e. fluxes as a function of photon
energy) at different types of contaminated water with an adequate device and in
particular looking at the peculiar interactions of electrons/photons with the
potential abnormal atomic hydrogen (H), oxygen (O), carbon (C), phosphorus (P)
compositions present in the water, as a function of living and not living
organic organisms with a PO4 group RNA/DNA strands in a cluster configuration
through a volumetric cells grid.


  The good performance of the GW approximation for band-structure calculations
in solids was long taken as a sign that the sum of self-energy diagrams is
converged and that all omitted terms are small. However, with modern
computational resources it has now become possible to evaluate self-consistency
and vertex corrections explicitly, and the numerical results show that they
are, in general, not individually negligible. In this review the available data
is examined, and the implications for practical calculations and the
theoretical foundation of the GW approximation are discussed.


  One of the goals of NASA's Fermi Gamma-ray Space Telescope (formerly GLAST)
will be detection of gamma rays from dark-matter annihilation in the Galactic
halo. Theoretical arguments suggest that dark matter may be bound into subhalos
with masses as small as 10^{-4}--10^2 Earth mass. If so, it may be possible to
detect individual subhalos as point sources in the Fermi Telescope. It has
further been argued that some of these point sources may exhibit proper
motions. Here we show that upper limits to the diffuse gamma-ray background
constrain the number of subhalos close enough to exhibit proper motions to be
less than one.


  We report the Terahertz optical properties of the Vanadium doped [100]
\b{eta}-Ga2O3 using Terahertz Time-Domain Spectroscopy (THz-TDS). The V-doped
\b{eta}-Ga2O3 crystal shows strong birefringence in the 0.2-2.4 THz range.
Further, phase retardation by the V-doped \b{eta}-Ga2O3 has been measured over
the whole THz range by Terahertz Time-Domain Polarimetry (THz-TDP). It is
observed that the V-doped \b{eta}-Ga2O3 crystal behaves both as a quarter
waveplate (QWP) at 0.38, 1.08, 1.71, 2.28 THz, and a half waveplate (HWP) at
0.74 and 1.94 THz, respectively.


  We examine the problem of the evaluation of both the propagator and of the
partition function of a spinning particle in an external field at the classical
as well as the quantum level, in connection with the asserted exactness of the
saddle point approximation (SPA) for this problem. At the classical level we
argue that exactness of the SPA stems from the fact that the dynamics (on the
two--sphere $S^2$) of a classical spinning particle in a magnetic field is the
reduction from $\br^4$ to $S^2$ of a linear dynamical system on $\br^4$. At the
quantum level, however, and within the path integral approach, the restriction,
inherent to the use of the SPA, to regular paths clashes with the fact that no
regulators are present in the action that enters the path integral. This is
shown to lead to a prefactor for the path integral that is strictly divergent
except in the classical limit. A critical comparison is made with the various
approaches to the same problem that have been presented in the literature. The
validity of a formula given in literature for the spin propagator is extended
to the case of motion in an arbitrary magnetic field.


  Spatial understanding is a fundamental problem with wide-reaching real-world
applications. The representation of spatial knowledge is often modeled with
spatial templates, i.e., regions of acceptability of two objects under an
explicit spatial relationship (e.g., "on", "below", etc.). In contrast with
prior work that restricts spatial templates to explicit spatial prepositions
(e.g., "glass on table"), here we extend this concept to implicit spatial
language, i.e., those relationships (generally actions) for which the spatial
arrangement of the objects is only implicitly implied (e.g., "man riding
horse"). In contrast with explicit relationships, predicting spatial
arrangements from implicit spatial language requires significant common sense
spatial understanding. Here, we introduce the task of predicting spatial
templates for two objects under a relationship, which can be seen as a spatial
question-answering task with a (2D) continuous output ("where is the man w.r.t.
a horse when the man is walking the horse?"). We present two simple
neural-based models that leverage annotated images and structured text to learn
this task. The good performance of these models reveals that spatial locations
are to a large extent predictable from implicit spatial language. Crucially,
the models attain similar performance in a challenging generalized setting,
where the object-relation-object combinations (e.g.,"man walking dog") have
never been seen before. Next, we go one step further by presenting the models
with unseen objects (e.g., "dog"). In this scenario, we show that leveraging
word embeddings enables the models to output accurate spatial predictions,
proving that the models acquire solid common sense spatial knowledge allowing
for such generalization.


  We investigate the complex conductivity of a highly disordered MoC
superconducting film with $k_Fl\approx 1$, where $k_F$ is the Fermi wavenumber
and $l$ is the mean free path, derived from experimental transmission
characteristics of coplanar waveguide resonators in a wide temperature range
below the superconducting transition temperature $T_c$. We find that the
original Mattis-Bardeen model with a finite quasiparticle lifetime, $\tau$,
offers a perfect description of the experimentally observed complex
conductivity. We show that $\tau$ is appreciably reduced by scattering effects.
Characteristics of the scattering centers are independently found by the
scanning tunneling spectroscopy and agree with those determined from the
complex conductivity.


  We present two models of continuous opinion dynamics under bounded confidence
which are representable as nonnegative discrete dynamical systems, namely the
Hegselmann-Krause model (Hegselmann and Krause, Journal of Artificial Societies
and Social Simulation 5(3), 2002) and the Deffuant-Weisbuch model (Deffuant et
al, Advances in Complex Systems, 3, 2000). We fully characterize the set of
fixed points for both models. They are identical. Further on, we present
reformulations of both models on the more general level of densities of agents
in the opinion space as interactive Markov chains. We also characterize the
sets of fixed points as identical in both models.


  Several first order stochastic optimization methods commonly used in the
Euclidean domain such as stochastic gradient descent (SGD), accelerated
gradient descent or variance reduced methods have already been adapted to
certain Riemannian settings. However, some of the most popular of these
optimization tools - namely Adam , Adagrad and the more recent Amsgrad - remain
to be generalized to Riemannian manifolds. We discuss the difficulty of
generalizing such adaptive schemes to the most agnostic Riemannian setting, and
then provide algorithms and convergence proofs for geodesically convex
objectives in the particular case of a product of Riemannian manifolds, in
which adaptivity is implemented across manifolds in the cartesian product. Our
generalization is tight in the sense that choosing the Euclidean space as
Riemannian manifold yields the same algorithms and regret bounds as those that
were already known for the standard algorithms. Experimentally, we show faster
convergence and to a lower train loss value for Riemannian adaptive methods
over their corresponding baselines on the realistic task of embedding the
WordNet taxonomy in the Poincare ball.


  We prove that closed connected contact manifolds of dimension $\geq 5$
related by an h-cobordism with a flexible Weinstein structure become
contactomorphic after some kind of stabilization. We also provide examples of
non-conjugate contact structures on a closed manifold with exact
symplectomorphic symplectizations.


  Let $(M^m,g)$ be a m-dimensional complete Riemannian manifold which satisfies
the n-Sobolev inequality and on which the volume growth is comparable to the
one of $\R^n$ for big balls; if the Hodge Laplacian on 1-forms is strongly
positive and the Ricci tensor is in $L^{\frac{n}{2}\pm \epsilon}$ for an
$\epsilon>0$, then we prove a Gaussian estimate on the heat kernel of the Hodge
Laplacian on 1-forms. This allows us to prove that, under the same hypotheses,
the Riesz transform $d\Delta^{-1/2}$ is bounded on $L^p$ for all $1


  We explore the consequences of heavy flavour, heavy quark spin and heavy
antiquark-diquark symmetries for hadronic molecules within an effective field
theory framework.. Owing to heavy antiquark-diquark symmetry, the doubly heavy
baryons have approximately the same light-quark structure as the heavy
antimesons. As a consequence, the existence of a heavy meson-antimeson molecule
implies the possibility of a partner composed of a heavy meson and a
doubly-heavy baryon. In this regard, the D\bar D* molecular nature of the
X(3872) will hint at the existence of several baryonic partners with isospin
I=0 and J^P = 5/2^- or 3/2^-. Moreover, if the Zb(10650) turns out to be a
B*\bar B* bound state, we can be confident of the existence of \Xibb* \bar B*
hadronic molecules with quantum numbers I(J^P) = 1(1/2^-) and I(J^P) =
1(3/2^-). These states are of special interest since they can be considered to
be triply-heavy pentaquarks.


  We consider the 1d CFT defined by the half-BPS Wilson line in planar
$\mathcal{N}=4$ super Yang-Mills. Using analytic bootstrap methods we derive
the four-point function of the super-displacement operator at fourth order in a
strong coupling expansion. Via AdS/CFT, this corresponds to the first
three-loop correlator in AdS ever computed. To do so we address the operator
mixing problem by considering a family of auxiliary correlators. We further
extract the anomalous dimension of the lightest non-protected operator and find
agreement with the integrability-based numerical result of Grabner, Gromov and
Julius.


  In this contribution it is shown how closed formulas for anomalous dimensions
of two classes of operators in N=4 SYM can be derived, either by investigating
the numerics or on the basis of QCD-inspired assumptions. We discuss the case
of twist three "gauge" operators in which a complete proof of reciprocity can
be carried out.


  This paper presents a useful method to achieve classification in satellite
imagery. The approach is based on pixel level study employing various features
such as correlation, homogeneity, energy and contrast. In this study gray-scale
images are used for training the classification model. For supervised
classification, two classification techniques are employed namely the Support
Vector Machine (SVM) and the Naive Bayes. With textural features used for
gray-scale images, Naive Bayes performs better with an overall accuracy of 76%
compared to 68% achieved by SVM. The computational time is evaluated while
performing the experiment with two different window sizes i.e., 50x50 and
70x70. The required computational time on a single image is found to be 27
seconds for a window size of 70x70 and 45 seconds for a window size of 50x50.


  This article presents the results of a quantitative analysis of Ukrainian
Arts and Humanities (A&H) research from 2012 to 2021, as observed in Scopus.
The overall publication activity and the relative share of A&H publications in
relation to Ukraine's total research output, comparing them with other
countries. The study analyzes the diversity and total number of sources, as
well as the geographic distribution of authors and citing authors, to provide
insights into the internationalization level of Ukrainian A&H research.
Additionally, the topical spectrum and language usage are considered to
complete the overall picture. According to our results, the publication
patterns for Ukrainian A&H research exhibit dynamics comparable to those of
other countries, with a gradual increase in the total number of papers and
sources. However, the citedness is lower than expected, and the share of
publications in top-quartile sources is lower for 2020-2021 period compared to
the previous years. The impact of internationally collaborative papers,
especially those in English, is higher. Nevertheless, over half of all works
remain uncited, probably due to the limited readership of the journals selected
for publication.


  We report an experimental demonstration of resonance fluorescence in a
two-level superconducting artificial atom under two driving fields coupled to a
detuned cavity. One of the fields is classical and the other is varied from
quantum (vacuum fluctuations) to classical one by controlling the photon number
inside the cavity. The device consists of a transmon qubit strongly coupled to
a one-dimensional transmission line and a coplanar waveguide resonator. We
observe a sideband anti-crossing and asymmetry in the emission spectra of the
system through a one-dimensional transmission line, which is fundamentally
different from the weak coupling case. By changing the photon number inside the
cavity, the emission spectrum of our doubly driven system approaches to the
case when the atom is driven by two classical bichromatic fields. We also
measure the dynamical evolution of the system through the transmission line and
study the properties of the first-order correlation function, Rabi oscillations
and energy relaxation in the system. The study of resonance fluorescence from
an atom driven by two fields promotes understanding decoherence in
superconducting quantum circuits and may find applications in superconducting
quantum computing and quantum networks.


  We study the problem of compressing a weighted graph $G$ on $n$ vertices,
building a "sketch" $H$ of $G$, so that given any vector $x \in \mathbb{R}^n$,
the value $x^T L_G x$ can be approximated up to a multiplicative $1+\epsilon$
factor from only $H$ and $x$, where $L_G$ denotes the Laplacian of $G$. One
solution to this problem is to build a spectral sparsifier $H$ of $G$, which,
using the result of Batson, Spielman, and Srivastava, consists of $O(n
\epsilon^{-2})$ reweighted edges of $G$ and has the property that
simultaneously for all $x \in \mathbb{R}^n$, $x^T L_H x = (1 \pm \epsilon) x^T
L_G x$. The $O(n \epsilon^{-2})$ bound is optimal for spectral sparsifiers. We
show that if one is interested in only preserving the value of $x^T L_G x$ for
a {\it fixed} $x \in \mathbb{R}^n$ (specified at query time) with high
probability, then there is a sketch $H$ using only $\tilde{O}(n
\epsilon^{-1.6})$ bits of space. This is the first data structure achieving a
sub-quadratic dependence on $\epsilon$. Our work builds upon recent work of
Andoni, Krauthgamer, and Woodruff who showed that $\tilde{O}(n \epsilon^{-1})$
bits of space is possible for preserving a fixed {\it cut query} (i.e., $x\in
\{0,1\}^n$) with high probability; here we show that even for a general query
vector $x \in \mathbb{R}^n$, a sub-quadratic dependence on $\epsilon$ is
possible. Our result for Laplacians is in sharp contrast to sketches for
general $n \times n$ positive semidefinite matrices $A$ with $O(\log n)$ bit
entries, for which even to preserve the value of $x^T A x$ for a fixed $x \in
\mathbb{R}^n$ (specified at query time) up to a $1+\epsilon$ factor with
constant probability, we show an $\Omega(n \epsilon^{-2})$ lower bound.


  Incorporating high-level knowledge is an effective way to expedite
reinforcement learning (RL), especially for complex tasks with sparse rewards.
We investigate an RL problem where the high-level knowledge is in the form of
reward machines, i.e., a type of Mealy machine that encodes the reward
functions. We focus on a setting in which this knowledge is a priori not
available to the learning agent. We develop an iterative algorithm that
performs joint inference of reward machines and policies for RL (more
specifically, q-learning). In each iteration, the algorithm maintains a
hypothesis reward machine and a sample of RL episodes. It derives q-functions
from the current hypothesis reward machine, and performs RL to update the
q-functions. While performing RL, the algorithm updates the sample by adding RL
episodes along which the obtained rewards are inconsistent with the rewards
based on the current hypothesis reward machine. In the next iteration, the
algorithm infers a new hypothesis reward machine from the updated sample. Based
on an equivalence relationship we defined between states of reward machines, we
transfer the q-functions between the hypothesis reward machines in consecutive
iterations. We prove that the proposed algorithm converges almost surely to an
optimal policy in the limit if a minimal reward machine can be inferred and the
maximal length of each RL episode is sufficiently long. The experiments show
that learning high-level knowledge in the form of reward machines can lead to
fast convergence to optimal policies in RL, while standard RL methods such as
q-learning and hierarchical RL methods fail to converge to optimal policies
after a substantial number of training steps in many tasks.


  Space charge formation in chromium-compensated GaAs sensors is investigated
by the laser-induced transient current technique applying pulsed and DC bias.
Formation of non-standard space charge manifested by an appearance of both
negatively and positively charged regions in DC biased sensors was revealed
during 5 ms after switching bias. Using Monte Carlo simulations of current
transients we determined enhanced electron lifetime {\tau} = 150 ns and
electron drift mobility {\mu}d = 3650 cm2/Vs. We developed and successfully
applied theoretical model based on fast hole trapping in the system with
spatially variable hole conductivity.


  Faithful communication is a necessary precondition for large scale
all-optical networking and quantum information processing. Related theoretical
investigations in different areas of physics have led to various proposals in
which finite discrete lattices are used as channels for short-distance
communication tasks. Here, in the framework of femtosecond-laser-written
waveguide arrays, we present the first experimental realization of such a
channel with judiciously engineered couplings.


  The transition from mild cognitive impairment (MCI) to Alzheimer's disease
(AD) is of great interest to clinical researchers. This phenomenon also serves
as a valuable data source for quantitative methodological researchers
developing new approaches for classification. However, the growth of machine
learning (ML) approaches for classification may falsely lead many clinical
researchers to underestimate the value of logistic regression (LR), yielding
equivalent or superior classification accuracy over other ML methods. Further,
in applications with many features that could be used for classifying the
transition, clinical researchers are often unaware of the relative value of
different selection procedures. In the present study, we sought to investigate
the use of automated and theoretically-guided feature selection techniques, and
as well as the L-1 norm when applying different classification techniques for
predicting conversion from MCI to AD in a highly characterized and studied
sample from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We propose
an alternative pre-selection technique that utilizes an efficient feature
selection based on clinical knowledge of brain regions involved in AD. The
present findings demonstrate how similar performance can be achieved using
user-guided pre-selection versus algorithmic feature selection techniques.
Finally, we compare the performance of a support vector machine (SVM) with that
of logistic regression on multi-modal data from ADNI. The present findings show
that although SVM and other ML techniques are capable of relatively accurate
classification, similar or higher accuracy can often be achieved by LR,
mitigating SVM's necessity or value for many clinical researchers.


  Hyperbolic Dehn surgery and the bending procedure provide two ways which can
be used to describe hyperbolic deformations of a complete hyperbolic structure
on a 3-manifold. Moreover, one can obtain examples of non-Haken manifolds
without the use of Thurston's Uniformization Theorem. We review these gluing
techniques and present a logical continuity between these ideas and gluing
methods for Higgs bundles. We demonstrate how one can construct certain model
objects in representation varieties $\text{Hom} \left( \pi_{1} \left( \Sigma
\right), G \right) $ for a topological surface $\Sigma$ and a semisimple Lie
group $G$. Explicit examples are produced in the case of $\Theta$-positive
representations lying in the smooth connected components of the $\text{SO}
\left(p,p+1 \right)$-representation variety.


  In this survey article we discuss a framework of noncommutative geometry with
differential graded categories as models for spaces. We outline a construction
of the category of noncommutative spaces and also include a discussion on
noncommutative motives. We propose a motivic measure with values in a motivic
ring. This enables us to introduce certain zeta functions of noncommutative
spaces.


  We show that a necessary and sufficient condition for a smooth function on
the tangent bundle of a manifold to be a Lagrangian density whose action can be
minimized is, roughly speaking, that it be the sum of a constant, a nonnegative
function vanishing on the support of the minimizers, and an exact form.
  We show that this exact form corresponds to the differential of a Lipschitz
function on the manifold that is differentiable on the projection of the
support of the minimizers, and its derivative there is Lipschitz. This function
generalizes the notion of subsolution of the Hamilton-Jacobi equation that
appears in weak KAM theory, and the Lipschitzity result allows for the recovery
of Mather's celebrated 1991 result as a special case. We also show that our
result is sharp with several examples.
  Finally, we apply the same type of reasoning to an example of a finite
horizon Legendre problem in optimal control, and together with the Lipschitzity
result we obtain the Hamilton-Jacobi-Bellman equation and the Maximum
Principle.
  This version contains errata correcting an issue in the published version.


  We study ultrafast ionization dynamics using orthogonally polarized two-color
(OTC) laser pulses involving the resonant "first plus second"
($\omega+2\omega$) scheme. The scheme is illustrated by numerical simulations
of the time-dependent Schr\"odinger equation and recording the photoelectron
momentum distribution. On the basis of the simulations of this resonant
ionization, we identify signatures of the dynamic Autler-Townes effect and
dynamic interference, in which their characterization is not possible in
spectral domain. Taking advantage of the OTC scheme we show that these
dynamical effects, which occur at the same time scale, can be characterized in
momentum space by controlling the spatial quantum interference. In particular,
we show that with the use of this control scheme, one can tailor the properties
of the control pulse to lead to enhancement of the ionization rate through the
Autler-Townes effect without affecting the dynamic interference. This
enhancement is shown to result from constructive interferences between partial
photoelectron waves having opposite-parity, and found to manifest by
symmetry-breaking of the momentum distribution. The scenario is investigated
for a prototype of a hydrogen atom and is broadly applicable to other systems.
Our findings may have applications for photoelectron interferometers to control
the electron dynamics in time and space, and for accurate temporal
characterization of attosecond pulses.


  Given a cusped hyperbolic 3-manifold with finite volume, we define two types
of complex parameters which capture geometric information about the preimages
of geodesic arcs traveling between cusp cross-sections. We prove that these
parameters are elements of the invariant trace field of the manifold, providing
a connection between the intrinsic geometry of a 3-manifold and its
number-theoretic invariants. Further, we explore the question of choosing a
minimal collection of arcs and associated parameters to generate the field. We
prove that for a tunnel number $k$ manifold it is enough to choose $3k$
specific parameters. For many hyperbolic link complements, this approach allows
one to compute the field from a link diagram. We also give examples of infinite
families of links where a single parameter can be chosen to generate the field,
and the polynomial for it can be constructed from the link diagram as well.


  By means of the nonequilibrium Green function technique, the effect of
spin-flip scatterings on the spin-dependent electrical transport in
ferromagnet-insulator-ferromagnet (FM-I-FM) tunnel junctions is investigated.
It is shown that Julliere's formula for the tunnel conductance must be modified
when including the contribution from the spin-flip scatterings. It is found
that the spin-flip scatterings could lead to an angular shift of the tunnel
conductance, giving rise to the junction resistance not being the largest when
the orientations of magnetizations in the two FM electrodes are antiparallel,
which may offer an alternative explanation for such a phenomenon observed
previously in experiments in some FM-I-FM junctions. The spin-flip assisted
tunneling is also observed.


  We show that the implementation of chiral symmetry in recent studies of the
hadron spectrum in the context of the constituent quark model is inconsistent
with chiral perturbation theory. In particular, we show that the leading
nonanalytic (LNA) contributions to the hadron masses are incorrect in such
approaches. The failure to implement the correct chiral behaviour of QCD
results in incorrect systematics for the corrections to the masses.


  We study the Mackey-Glass type monostable delayed reaction-diffusion equation
with a unimodal birth function $g(u)$. This model, designed to describe
evolution of single species populations, is considered here in the presence of
the weak Allee effect ($g(u_0)>g'(0)u_0$ for some $u_0>0$). We focus our
attention on the existence of slow monotonic traveling fronts to the equation:
under given assumptions, this problem seems to be rather difficult since the
usual positivity and monotonicity arguments are not effective. First, we solve
the front existence problem for small delays, $h \in [0,h_p]$, where $h_p$
(given by an explicit formula) is optimal in a certain sense. Then we take a
representative piece-wise linear unimodal birth function making possible
explicit computation of traveling fronts. In this case, we find out that a)
increase of delay can destroy asymptotically stable pushed fronts; b) the set
of all admissible wavefront speeds has usual structure of a semi-infinite
interval $[c_*, +\infty)$; c) for each $h\geq 0$, the pushed wavefront is
unique (if it exists); d) pushed wave can oscillate slowly around the positive
equilibrium for sufficiently large delays.


  A grand unification model with a single $E_8$ gauge superfield in 10
dimensions is presented. It is shown that, through the orbifold
$T^6/(\mathbb{Z}_6\times \mathbb{Z}_2)$ and its corresponding Wilson lines, the
symmetry is broken to the Standard Model one. Furthermore it is shown that the
model is anomaly free at all levels without the need to add any extra field
content. Thus a complete unification of the Standard Model into a single gauge
superfield is achieved consistently.


  We study the convergence of the predictive surface of regression trees and
forests. To support our analysis we introduce a notion of adaptive
concentration for regression trees. This approach breaks tree training into a
model selection phase in which we pick the tree splits, followed by a model
fitting phase where we find the best regression model consistent with these
splits. We then show that the fitted regression tree concentrates around the
optimal predictor with the same splits: as d and n get large, the discrepancy
is with high probability bounded on the order of sqrt(log(d) log(n)/k)
uniformly over the whole regression surface, where d is the dimension of the
feature space, n is the number of training examples, and k is the minimum leaf
size for each tree. We also provide rate-matching lower bounds for this
adaptive concentration statement. From a practical perspective, our result
enables us to prove consistency results for adaptively grown forests in high
dimensions, and to carry out valid post-selection inference in the sense of
Berk et al. [2013] for subgroups defined by tree leaves.


  Nonlinear evolution equation at small x with impact parameter dependence is
analyzed numerically. Saturation scales and the radius of expansion in impact
parameter are extracted as functions of rapidity. Running coupling is included
in this evolution, and it is found that the solution is sensitive to the
infrared regularization. Kinematical effects beyond leading logarithmic
approximation are taken partially into account by modifying the kernel which
includes the rapidity dependent cuts. While the local nonlinear evolution is
not very sensitive to these effects, the kinematical constraints cannot be
neglected in the evolution with impact parameter.


  We study the equational theory of Parigot's second-order
λμ-calculus in connection with a call-by-name continuation-passing
style (CPS) translation into a fragment of the second-order λ-calculus.
It is observed that the relational parametricity on the target calculus induces
a natural notion of equivalence on the λμ-terms. On the other hand,
the unconstrained relational parametricity on the λμ-calculus turns
out to be inconsistent with this CPS semantics. Following these facts, we
propose to formulate the relational parametricity on the λμ-calculus
in a constrained way, which might be called ``focal parametricity''.


  Causal models of agents have been used to analyse the safety aspects of
machine learning systems. But identifying agents is non-trivial -- often the
causal model is just assumed by the modeler without much justification -- and
modelling failures can lead to mistakes in the safety analysis. This paper
proposes the first formal causal definition of agents -- roughly that agents
are systems that would adapt their policy if their actions influenced the world
in a different way. From this we derive the first causal discovery algorithm
for discovering agents from empirical data, and give algorithms for translating
between causal models and game-theoretic influence diagrams. We demonstrate our
approach by resolving some previous confusions caused by incorrect causal
modelling of agents.


  We first study the problem of the one-loop partition function for a free
massive quantum field theory living on a fixed background hyperbolic space on
the field of real numbers, $\mathbb{H}^n(\mathbb{R}), \,\, n\geq 2$. Earlier
attempts were limited to $n=3$ dimensions due to the computational complexity.
We have developed a new method to determine the fundamental solution of the
heat equation and techniques to specify its asymptotics in the small time
limit. These enable us to determine the regular part and the ultra violet
divergences of the one-loop effective action in the scalar case. The
contribution of the Abelian gauge excitations to the one-loop partition
function were treated separately using Fourier analysis and bi-tensor
techniques on $\mathbb{H}^n(\mathbb{R})$. Finally, by employing the DeWitt's
method, we confirmed the correctness and extended our results to any dimension
regarding the first three heat kernel coefficients.


  We investigate the spin transport and ferromagnetic resonance properties of
giant magnetoresistive (GMR) Co/Cu-Ni multilayers with variable levels of Ni
doping in the Cu spacer. We present an experimental evidence for a
magnetic-to-diamagnetic transition in the atomic magnetic moment of Ni in the
Cu matrix for concentrations below 15 at. % Ni. As its concentration is
increased, Ni atoms turn into spin scattering centers, which is manifested
experimentally as a step-like change in the GMR of the multilayers. This
behavior is observed in multilayers with gradient-doped Cu spacers, where only
the inner region was doped with Ni. In the uniformly doped spacers the GMR
decreases monotonously with increasing Ni content, indicating that Ni atoms are
magnetic and act as spin relaxation centers in the entire dopant-concentration
range studied. We explain the difference in the observed GMR behavior as due to
a strong magnetic proximity effect in the uniform spacers, which is efficiently
suppressed in the gradient spacers. The observed magnetic phase transition is
fully supported by our detailed ab-initio calculations, taking into
consideration structural relaxation in the system as well as potential Ni
clustering. Controlling the loss or gain of the atomic magnetism for a specific
dopant can be a tool in probing and controlling spin relaxation in materials
and devices for spin-valve and spin-torque based applications.


  We consider an ensemble of atoms with $\Lambda$-type level structure trapped
in a single-mode cavity, and propose a geometric scheme of coherent
manipulation of quantum states on the subspace of zero-energy states within the
quantum Zeno subspace of the system. We find that the particular subspace
inherits the decoherence-free nature of the quantum Zeno subspace and features
a symmetry-protected degeneracy, fulfilling all the conditions for a universal
scheme of arbitrary unitary operations on it.


  Almost every biological, economic and social system is a complex adaptive
system (CAS). Mathematical and computer models are relevant to CAS. Some
approaches to modeling CAS are given. Applications in vaccination and the
immune system are studied. Mathematical topics motivated by CAS are discussed.


  Benchmarking numerical methods in quantum chemistry is one of the key
opportunities that quantum simulators can offer. Here, we propose an analog
simulator for discrete 2D quantum chemistry models based on cold atoms in
optical lattices. We first analyze how to simulate simple models, like the
discrete versions of H and H$_2^+$, using a single fermionic atom. We then show
that a single bosonic atom can mediate an effective Coulomb repulsion between
two fermions, leading to the analog of molecular Hydrogen in two dimensions. We
extend this approach to larger systems by introducing as many mediating atoms
as fermions, and derive the effective repulsion law. In all cases, we analyze
how the continuous limit is approached for increasing optical lattice sizes.


  Recent data on the (inelastic) Nachtmann moments of the unpolarized proton
structure function F2p, obtained at low momentum transfer with the CLAS
detector at Jefferson Lab, have been interpreted in terms of the dominance of
the elastic coupling of the virtual photon with extended substructures inside
the proton. Adopting the same constituent form factors and the same light-front
wave function describing the motion of the constituents in the proton, the
(inelastic) Nachtmann moments of the longitudinal proton structure function FLp
are calculated (without further parameters) for values of the squared
four-momentum transfer 0.2 < Q**2 (GeV/c)**2 < 2. The different role played by
the Pauli form factor of the constituents in the transverse and longitudinal
channels is illustrated. Our predictions, including an estimate of the
theoretical uncertainties, may be checked against the forthcoming results of
the experiment E94110 at Jefferson Lab. A positive comparison with the new data
may provide compelling evidence that constituent quarks are intermediate
substructures between the hadrons and the current quarks and gluons of QCD.


  We construct the fundamental solution of second order parabolic equations in
non-divergence form under the assumption that the coefficients are of Dini mean
oscillation in the spatial variables. We also prove that the fundamental
solution satisfies a sub-Gaussian estimate. In the case when the coefficients
are Dini continuous in the spatial variables and measurable in the time
variable, we establish the Gaussian bounds for the fundamental solutions. We
present a method that works equally for second order parabolic systems in
non-divergence form.


  The weak form factors of the nucleon, including the induced pseudoscalar form
factor and second class terms, are constrained using a microscopic calculation
of the weak capture process $^3He(\mu^{-},\nu_\mu)^3{H}$. The calculation is
parameter free, and yields a rate of 1499(16) Hz, in agreement with the
remarkable experimental measurement 1496(4) Hz. The nuclear wave functions are
obtained using the EIHH method with the Argonne V18 nucleon--nucleon potential
and the Urbana-IX three nucleon force. The weak currents in the nuclei are
described using HB$\chi$PT formalism. The induced pseudoscalar form factor is
found to agree with HB$\chi$PT prediction. The result is compatible with
vanishing second class currents, with the tightest constraint to date on the
conservation of vector current (CVC) hypothesis.


  Neutrino-photon processes, forbidden in vacuum, can take place in the
presence of a thermal medium and/or an external electro-magnetic field,
mediated by the corresponding charged leptons (real or virtual). Such
interactions affect the propagation of neutrinos through a magnetized plasma.
We investigate the neutrino-photon absorptive processes, at the one-loop level,
for massless neutrinos in a weakly magnetized plasma. We find that there is no
correction to the absorptive part of the axial-vector--vector amplitude due to
the presence of a magnetic field to the linear order in the field strength.


  There is an abundance of prior research on the optimization of production
systems, but there is a research gap when it comes to optimizing which
components should be included in a design, and how they should be connected. To
overcome this gap, a novel approach is presented for topology optimization of
production systems using a genetic algorithm (GA). This GA employs
similarity-based mutation and recombination for the creation of offspring, and
discrete-event simulation for fitness evaluation. To reduce computational cost,
an extension to the GA is presented in which a neural network functions as a
surrogate model for simulation. Three types of neural networks are compared,
and the type most effective as a surrogate model is chosen based on its
optimization performance and computational cost.
  Both the unassisted GA and neural network-assisted GA are applied to an
industrial case study and a scalability case study. These show that both
approaches are effective at finding the optimal solution in industrial
settings, and both scale well as the number of potential solutions increases,
with the neural network-assisted GA having the better scalability of the two.


  Human-Robot Interaction, in which a robot with some level of autonomy
interacts with a human to achieve a specific goal has seen much recent
progress. With the introduction of autonomous robots and the possibility of
widespread use of those in near future, it is critical that humans understand
the robot's intention while interacting with them as this will foster the
development of human-robot trust. The new conceptualization of trust which had
been introduced by researchers in recent years considers trust in Human-Robot
Interaction to be a multidimensional nature. Two main aspects which are
attributed to trust are performance trust and moral trust. We aim to design an
experiment to investigate the consequences of performance-trust violation and
moral-trust violation in a search and rescue scenario. We want to see if two
similar robot failures, one caused by a performance-trust violation and the
other by a moral-trust violation have distinct effects on human trust. In
addition to this, we plan to develop an interface that allows us to investigate
whether altering the interface's modality from grid-world scenario (2D
environment) to realistic simulation (3D environment) affects human perception
of the task and the effects of the robot's failure on human trust.


  Sperner theory is one of the most important branches in extremal set theory.
It has many applications in the field of operation research, computer science,
hypergraph theory and so on. The LYM property has become an important tool for
studying Sperner property. In this paper, we provide a general relationship for
LYM inequalities between Boolean lattices and linear lattices. As applications,
we use this relationship to derive generalizations of some well-known theorems
on maximum sizes of families containing no copy of certain poset or certain
configuration from Boolean lattices to linear lattices, including
generalizations of the well-known Kleitman theorem on families containing no
$s$ pairwise disjoint members (a non-uniform variant of the famous Erd\H{o}s
matching conjecture) and Johnston-Lu-Milans theorem and Polymath theorem on
families containing no $d$-dimensional Boolean algebras.


  Droplet microfluidics has emerged as a powerful platform allowing a large
number of individual reactions to be carried out in spatially distinct
microcompartments. Due to their small size, however, the spectroscopic
characterisation of species encapsulated in such systems remains challenging.
In this paper, we demonstrate the acquisition of infrared spectra from single
microdroplets containing aggregation-prone proteins. To this effect, droplets
are generated in a microfluidic flow-focussing device and subsequently
deposited in a square array onto a ZnSe prism using a micro stamp. After
drying, the solutes present in the droplets are illuminated locally by an
infrared laser through the prism, and their thermal expansion upon absorption
of infrared radiation is measured with an atomic force microscopy tip, granting
nanoscale resolution. Using this approach, we resolve structural differences in
the amide bands of the spectra of monomeric and aggregated lysozyme from single
microdroplets with picolitre volume.


  The nonlinear interaction between one graviton and two scalars is enhanced in
specific inflationary models, potentially leading to distinguishable signatures
in the bispectrum of the cosmic microwave background (CMB) anisotropies. We
develop the tools to examine such bispectrum signatures, and show a first
application using WMAP temperature data. We consider several $\ell$-ranges,
estimating the $g_{tss}$ amplitude parameter, by means of the so-called
separable modal methodology. We do not find any evidence of a
tensor-scalar-scalar signal at any scale. Our tightest bound on the size of the
tensor-scalar-scalar correlator is derived from our measurement including all
the multipoles in the range $ 2 \leq \ell \leq 500$ and it reads $g_{tss} = -48
\pm 28$ ($68\%$CL). This is the first direct observational constraint on the
primordial tensor-scalar-scalar correlation, and it will be cross-checked and
improved by applying the same pipeline to high-resolution temperature and
polarization data from $Planck$ and forthcoming CMB experiments.


  We consider a fermion in the presence of a rotating black hole immersed in a
universe with positive cosmological constant. After deriving new formulae for
the event, Cauchy and cosmological horizons we adopt the Carter tetrad to
separate the aforementioned equation into a radial and angular equation. We
show how the Chandrasekhar ansatz leads to the construction of a symmetry
operator that can be interpreted as the square root of the squared total
angular momentum operator. Furthermore, we prove that the the spectrum of the
angular operator is discrete and consists of simple eigenvalues and by means of
the functional Bethe ansatz method we also derive a set of necessary and
sufficient conditions for the angular operator to have polynomial solutions.
Finally, we show that there exist no bound states for the Dirac equation in the
non-extreme case.


  Particle accelerators require constant tuning during operation to meet beam
quality, total charge and particle energy requirements for use in a wide
variety of physics, chemistry and biology experiments. Maximizing the
performance of an accelerator facility often necessitates multi-objective
optimization, where operators must balance trade-offs between multiple
objectives simultaneously, often using limited, temporally expensive beam
observations. Usually, accelerator optimization problems are solved offline,
prior to actual operation, with advanced beamline simulations and parallelized
optimization methods (NSGA-II, Swarm Optimization). Unfortunately, it is not
feasible to use these methods for online multi-objective optimization, since
beam measurements can only be done in a serial fashion, and these optimization
methods require a large number of measurements to converge to a useful
solution.Here, we introduce a multi-objective Bayesian optimization scheme,
which finds the full Pareto front of an accelerator optimization problem
efficiently in a serialized manner and is thus a critical step towards
practical online multi-objective optimization in accelerators.This method uses
a set of Gaussian process surrogate models, along with a multi-objective
acquisition function, which reduces the number of observations needed to
converge by at least an order of magnitude over current methods.We demonstrate
how this method can be modified to specifically solve optimization challenges
posed by the tuning of accelerators.This includes the addition of optimization
constraints, objective preferences and costs related to changing accelerator
parameters.


  The interaction of electrical fields and liquids can lead to phenomena that
defies intuition. Some famous examples can be found in Electrohydrodynamics as
Taylor cones, whipping jets or non-coalescing drops. A less famous example is
the Floating Water Bridge: a slender thread of water held between two glass
beakers in which a high voltage difference is applied. Surprisingly, the water
bridge defies gravity even when the beakers are separated at distances up to 2
cm. In the presentation, experimental measurements and simple models are
proposed and discussed for the stability of the bridge and the source of the
flow, revealing an important role of polarization forces on the stability of
the water bridge. On the other hand, the observed flow can only be explained
due to the non negligible free charge present in the surface. In this sense,
the Floating Water Bridge can be considered as an extreme case of a leaky
dielectric liquid (J. R. Melcher and G. I. Taylor, Annu. Rev. Fluid Mech.,
1:111, 1969).


  In the study of QCD dynamics, C* boundary conditions are physically relevant
in certain cases. In this paper we study the implementation of these boundary
conditions in the lattice formulation of full QCD with staggered fermions. In
particular, we show that the usual even-odd partition trick to avoid the
redoubling of the fermion matrix is still valid in this case. We give an
explicit implementation of these boundary conditions for the Hybrid Monte Carlo
algorithm.


  We perform a linear stability analysis of extended domains in
phase-separating fluids of equal viscosity, in two dimensions. Using the
coupled Cahn-Hilliard and Stokes equations, we derive analytically the
stability eigenvalues for long wavelength fluctuations. In the quiescent state
we find an unstable varicose mode which corresponds to an instability towards
coarsening. This mode is stabilized when an external shear flow is imposed on
the fluid. The effect of the shear is seen to be qualitatively similar to that
found in experiments.


  We describe Bott towers as sequences of toric manifolds M^k, and identify the
omniorientations which correspond to their original construction as toric
varieties. We show that the suspension of M^k is homotopy equivalent to a wedge
of Thom complexes, and display its complex K-theory as an algebra over the
coefficient ring. We extend the results to KO-theory for several families of
examples, and compute the effects of the realification homomorphism; these
calculations breathe geometric life into Bahri and Bendersky's recent analysis
of the Adams Spectral Sequence. By way of application we investigate stably
complex structures on M^k, identifying those which arise from omniorientations
and those which are almost complex. We conclude with observations on the role
of Bott towers in complex cobordism theory.


  The global log canonical threshold of each non-singular complex del Pezzo
surface was computed by Cheltsov. The proof used Koll\'ar-Shokurov's
connectedness principle and other results relying on vanishing theorems of
Kodaira type, not known to be true in finite characteristic.
  We compute the global log canonical threshold of non-singular del Pezzo
surfaces over an algebraically closed field. We give algebraic proofs of
results previously known only in characteristic $0$. Instead of using of the
connectedness principle we introduce a new technique based on a classification
of curves of low degree. As an application we conclude that non-singular del
Pezzo surfaces in finite characteristic of degree lower or equal than $4$ are
K-semistable.


  Femtosecond time-resolved optical spectroscopy was used to systematically
study photoexcited carrier relaxation dynamics in the intermediate-valence
heavy fermion system Yb1-xLuxAl3 (0 < x < 1). Given the demonstrated
sensitivity of this experimental technique to the presence of the low energy
gaps in the charge excitation spectrum, the aim of this work was to study the
effect of dilution of the Kondo lattice on its low energy electronic structure.
The results imply that in Yb1-xLuxAl3 the hybridization gap, resulting from
hybridization of local moments and conduction electrons, persists up to 30%
doping. Interestingly, below some characteristic, doping dependent temperature
T*(x) the relaxation time divergence, governed by the relaxation bottleneck due
to the presence of the indirect hybridization gap, is truncated. This
observation is attributed to the competing ballistic transport of hot electrons
out of the probed volume at low temperatures. The derived theoretical model
accounts for both the functional form of relaxation dynamics below T*(x), as
well as the doping dependence of the low temperature relaxation rate in
Yb1-xLuxAl3.


  Observations of TeV emission from early gamma-ray burst (GRB) afterglows
revealed the long sought for inverse Compton (IC) upscattering of the lower
energy synchrotron. However, it turned out that the long hoped for ability to
easily interpret the synchrotron-self-Compton (SSC) spectra didn't materialize.
The TeV emission is in the Klein-Nishina (KN) regime and the simple Thomson
regime SSC spectrum is modified, complicating the scene. We describe here a
methodology to obtain an analytic approximation to an observed spectrum and
infer the conditions at the emitting region. The methodology is general and can
be used in any such source. As a test case, we apply it to the observations of
GRB 190114C. We find that the procedure of fitting the model parameters using
the analytic SSC spectrum suffers from some generic problems. However, at the
same time, it conveniently gives a useful insight into the conditions that
shape the spectrum. Once we introduce a correction to the standard KN
approximation, the best fit solution is consistent with the one found in
detailed numerical simulations. As in the numerical analysis, we find a family
of solutions that provide a good approximation to the data and satisfy roughly
$B\propto \Gamma^{-3}$ between the magnetic field and the bulk Lorentz factor,
and we provide a tentative explanation why such a family arises.


  Let (W, S) be a Coxeter system. A W-graph is an encoding of a representation
of the corresponding Iwahori-Hecke algebra. Especially important examples
include the W-graph corresponding to the action of the Iwahori-Hecke algebra on
the Kazhdan-Lusztig basis, as well as this graph's strongly connected
components (cells). In 2008, Stembridge identified some common features of the
Kazhdan-Lusztig graphs and gave a combinatorial characterization of all
W-graphs that have these features. He conjectured, and checked up to n=9, that
all such A_n-cells are Kazhdan-Lusztig cells. The current paper provides a
first step toward a potential proof of the conjecture. More concretely, we
prove that the connected subgraphs of A_n-cells consisting of simple (i.e.
directed both ways) edges are the same as in the Kazhdan-Lusztig cells.


  We study the collective motion of Janus particles in a temperature or
concentration gradient. Because of the torque exerted by an external or
self-generated field, the particles align their axis on this gradient. In a
swarm of self-driven particles, this polarization enhances the
interactiondriven confinement. Self-polarization in a non-uniform laser beam
could be used for guiding hot particles along a given trajectory.


  Multiple-view visualizations (MVs) have been widely used for visual analysis.
Each view shows some part of the data in a usable way, and together multiple
views enable a holistic understanding of the data under investigation. For
example, an analyst may check a social network graph, a map of sensitive
locations, a table of transaction records, and a collection of reports to
identify suspicious activities. While each view is designed to preserve its own
visual context with visible borders or perceivable spatial distance from
others, the key to solving real-world analysis problems often requires
"breaking" such boundaries, and further integrating and synthesizing the data
scattered across multiple views. This calls for blending the boundaries in MVs,
instead of simply breaking them, which brings key questions: what are possible
boundaries in MVs, and what are design options that can support the boundary
blending in MVs? To answer these questions, we present three boundaries in MVs:
1) data boundary, 2) representation boundary, and 3) semantic boundary,
corresponding to three major aspects regarding the usage of MVs: encoded
information, visual representation, and interpretation. Then, we discuss four
design strategies (highlighting, linking, embedding, and extending) and their
pros and cons for supporting boundary blending in MVs. We conclude our
discussion with future research opportunities.


  This work reviews and examines two particular issues related with the new
technique of electrical detection of ferromagnetic resonance (FMR). This
powerful technique has been broadly applied for studying magnetization and spin
dynamics over the past few years. The first issue is the relation and
distinction between different mechanisms that give rise to a photovoltage via
FMR in composite magnetic structures, and the second is the proper analysis of
the FMR line shape, which remains the "Achilles heel" in interpreting
experimental results, especially for either studying the spin pumping effect or
quantifying the spin Hall angles via the electrically detected FMR.


  In this paper, we deal with the Cauchy problem of the quasilinear
Schr\"{o}dinger equation \begin{equation*} \left\{ \begin{array}{lll}
iu_t=\Delta u+2uh'(|u|^2)\Delta h(|u|^2)+F(|u|^2)u \quad {\rm for} \ x\in
\mathbb{R}^N, \ t>0\\ u(x,0)=u_0(x),\quad x\in \mathbb{R}^N. \end{array}\right.
\end{equation*} Here $h(s)$ and $F(s)$ are some real-valued functions, with
various choices for models from mathematical physics. We examine the interplay
between the quasilinear effect of $h$ and nonlinear effect of $F$ for the
global existence and blowup phenomena. We provide sufficient conditions on the
blowup in finite time and global existence of the solution. In some cases, we
can deduce the watershed from these conditions. In the focusing case, we
construct the sharp threshold for the blowup in finite time and global
existence of the solution and lower bound for blowup rate of the blowup
solution. Moreover, we establish the pseudo-conformal conservation law and some
asymptotic behavior results on the global solution.


  We present a preconditioning method for the multi-dimensional Helmholtz
equation with smoothly varying coefficient. The method is based on a frame of
functions, that approximately separates components associated with different
singular values of the operator. For the small singular values, corresponding
to propagating waves, the frame functions are constructed using ray theory. A
series of 2-D numerical experiments demonstrates that the number of iterations
required for convergence is small and independent of the frequency. In this
sense the method is optimal.


  We have used the ASCA satellite to make the first X-ray spectra of Markarian
6, a bright Seyfert 1.5 galaxy with complex and variable permitted lines, an
ionization cone, and remarkable radio structures. Our 0.6--9.5 keV spectra
penetrate to the black hole core of this Seyfert and reveal heavy and complex
intrinsic X-ray absorption. Both total covering and single partial covering
models fail to acceptably fit the observed absorption, and double partial
covering or partial covering plus warm absorption appears to be required. The
double partial covering model provides the best statistical fit to the data,
and we measure large column densities of ~ (3-20) * 10^{22} cm^{-2}
irrespective of the particular spectral model under consideration. These X-ray
columns are over an order of magnitude larger than expected based on
observations at longer wavelengths. Our data suggest that most of the X-ray
absorption occurs either in gas that has a relatively small amount of dust or
in gas that is located within the Broad Line Region. The X-ray absorber may
well be the putative `atmosphere' above the torus that collimates the
ionization cone. We also detect an apparently broad 6.4 keV iron K-alpha line,
and we present optical spectra demonstrating that the optical emission lines
were in a representative state during our ASCA observation.


  Finding simple, non-recursive, base noun phrases is an important subtask for
many natural language processing applications. While previous empirical methods
for base NP identification have been rather complex, this paper instead
proposes a very simple algorithm that is tailored to the relative simplicity of
the task. In particular, we present a corpus-based approach for finding base
NPs by matching part-of-speech tag sequences. The training phase of the
algorithm is based on two successful techniques: first the base NP grammar is
read from a ``treebank'' corpus; then the grammar is improved by selecting
rules with high ``benefit'' scores. Using this simple algorithm with a naive
heuristic for matching rules, we achieve surprising accuracy in an evaluation
on the Penn Treebank Wall Street Journal.


  Positrons bind to molecules leading to vibrational excitation and
spectacularly enhanced annihilation. Whilst positron binding energies have been
measured via resonant annihilation spectra for $\sim$90 molecules in the past
two decades, an accurate \emph{ab initio} theoretical description has remained
elusive. Of the molecules studied experimentally, calculations exist for only
6, and for these, standard quantum chemistry approaches have proved severely
deficient, agreeing with experiment to at best 25% accuracy for polar
molecules, and failing to predict binding in nonpolar molecules. The mechanisms
of binding are not understood. Here, we develop a many-body theory of
positron-molecule interactions and uncover the role of strong many-body
correlations including polarization of the electron cloud, screening of the
positron-electron Coulomb interaction by molecular electrons, and crucially,
the unique non-perturbative process of virtual-positronium formation (where a
molecular electron temporarily tunnels to the positron): they dramatically
enhance binding in polar molecules and enable binding in nonpolars. We also
elucidate the role of individual molecular orbitals, highlighting the
importance of electronic $\pi$ bonds. Overall, we calculate binding energies in
agreement with experiment (to within 1% in cases), and we predict binding in
formamide and nucleobases. As well as supporting resonant annihilation
experiments and positron-based molecular spectroscopy, the approach can be
extended to positron scattering and annihilation $\gamma$ spectra in molecules
and condensed matter, to provide fundamental insight and predictive capability
required to properly interpret materials science diagnostics, develop
antimatter-based technologies (including positron traps, beams and positron
emission tomography), and understand positrons in the galaxy.


  We have carried out the $^{11}$B NMR measurement on the itinerant
paramagnetic systems U$M_{3}$B$_{2}$ ($M =$ Co, Ir) to investigate the
low-dimensional characteristics of the $5f$-electrons due to the structural
anisotropy. The recent X-ray analysis suggests that UIr$_3$B$_2$ has a
different structure modulated from the ever-known superlattice. The azimuth
angle variation of NMR spectrum within the $ab$-plane clarified that B atoms
occupy the single site, and a certain ligands arrangement surrounding B atom
turns to the same orientation as the another one through the three- or six-fold
rotation around the c-axis. These results have been consistent with the X-ray
proposition. To evaluate the temperature ($T$) development of general
susceptibility ($\chi_{q,\omega}$), Knight shift and nuclear spin-lattice
relaxation rates measurements were performed and the similar variations of
$\chi_{q,\omega}$ were identified in both UCo$_{3}$B$_{2}$ and
UIr$_{3}$B$_{2}$. Above a crossover point defined as $T^{*}\simeq50$ K, the
evolution of $\chi_{q,\omega}$ is dominant at $q=0$, suggesting that
ferromagnetic correlations develop in high-$T$ regimes; meanwhile, below
$T^{*}$, the $q=0$ part in $\chi_{q,\omega}$ shows the saturation tendency, and
a different class of dispersion at finite-$q$ suddenly emerges. This particular
magnetic correlations are interpreted as the antiferromagnetic correlations,
and notable feature of the magnetic state dynamics in low-$T$ regimes is that
the antiferromagnetic correlations arise together with the ferromagnetic
component at the same time. The unique magnetic correlations obtained from NMR
experiment will be discussed by the possible low-dimensionality of
U$M_{3}$B$_{2}$ lattice.