  Connectivity matrices derived from diffusion MRI (dMRI) provide an
interpretable and generalizable way of understanding the human brain
connectome. However, dMRI suffers from inter-site and between-scanner
variation, which impedes analysis across datasets to improve robustness and
reproducibility of results. To evaluate different harmonization approaches on
connectivity matrices, we compared graph measures derived from these matrices
before and after applying three harmonization techniques: mean shift, ComBat,
and CycleGAN. The sample comprises 168 age-matched, sex-matched normal subjects
from two studies: the Vanderbilt Memory and Aging Project (VMAP) and the
Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD). First, we
plotted the graph measures and used coefficient of variation (CoV) and the
Mann-Whitney U test to evaluate different methods' effectiveness in removing
site effects on the matrices and the derived graph measures. ComBat effectively
eliminated site effects for global efficiency and modularity and outperformed
the other two methods. However, all methods exhibited poor performance when
harmonizing average betweenness centrality. Second, we tested whether our
harmonization methods preserved correlations between age and graph measures.
All methods except for CycleGAN in one direction improved correlations between
age and global efficiency and between age and modularity from insignificant to
significant with p-values less than 0.05.


  Anomaly-diffusing energy balance models (AD-EBM) are routinely employed to
analyze and emulate the warming response of both observed and simulated Earth
systems. We demonstrate a deficiency in common multi-layer as well as
continuous-diffusion AD-EBM variants: They are unable to, simultaneously,
properly represent surface warming and the vertical distribution of heat
uptake. We show that this inability is due to the diffusion approximation. On
the other hand, it is well understood that transport of water from the surface
mixed layer into the ocean interior is achieved, in large part, by the process
of ventilation---a process associated with outcropping isopycnals. We,
therefore, start from a configuration of outcropping isopycnals and demonstrate
how an AD-EBM can be modified to include the effect of ventilation on ocean
uptake of anomalous radiative forcing. The resulting EBM is able to
successfully represent both surface warming and the vertical distribution of
heat uptake, and indeed, a simple four layer model suffices. The simplicity of
the models notwithstanding, the analysis presented and the necessity of the
modification highlight the role played by processes related to the down-welling
branch of global ocean circulation in shaping the vertical distribution of
ocean heat uptake.


  We propose a new strategy for the determination of the step scaling function
$\sigma(u)$ in finite size scaling studies using the Gradient Flow. In this
approach the determination of $\sigma(u)$ is broken in two pieces: a change of
the flow time at fixed physical size, and a change of the size of the system at
fixed flow time. Using both perturbative arguments and a set of simulations in
the pure gauge theory we show that this approach leads to a better control over
the continuum extrapolations. Following this new proposal we determine the
running coupling at high energies in the pure gauge theory and re-examine the
determination of the $\Lambda$-parameter, with special care on the perturbative
truncation uncertainties.


  We constructed the three nonequivalent gradings in the algebra $D_4 \simeq
so(8)$. The first one is the standard one obtained with the Coxeter
automorphism $C_1=S_{\alpha_2} S_{\alpha_1}S_{\alpha_3}S_{\alpha_4}$ using its
dihedral realization. In the second one we use $C_2 = C_1R$ where $R$ is the
mirror automorphism. The third one is $C_3 = S_{\alpha_2}S_{\alpha_1}T$ where
$T$ is the external automorphism of order 3. For each of these gradings we
constructed the basis in the corresponding linear subspaces
$\mathfrak{g}^{(k)}$, the orbits of the Coxeter automorphisms and the related
Lax pairs generating the corresponding mKdV hierarchies. We found compact
expressions for each of the hierarchies in terms of the recursion operators. At
the end we wrote explicitly the first nontrivial mKdV equations and their
Hamiltonians. For $D_4^{(1)}$ these are in fact two mKdV systems, due to the
fact that in this case the exponent $3$ has multiplicity 2. Each of these mKdV
systems consist of 4 equations of third order with respect to $\partial_x$. For
$D_4^{(2)}$ this is a system of three equations of third order with respect to
$\partial_x$. Finally, for $D_4^{(3)}$ this is a system of two equations of
fifth order with respect to $\partial_x$.


  Distant supervision (DS) is a well established technique for creating
large-scale datasets for relation extraction (RE) without using human
annotations. However, research in DS-RE has been mostly limited to the English
language. Constraining RE to a single language inhibits utilization of large
amounts of data in other languages which could allow extraction of more diverse
facts. Very recently, a dataset for multilingual DS-RE has been released.
However, our analysis reveals that the proposed dataset exhibits unrealistic
characteristics such as 1) lack of sentences that do not express any relation,
and 2) all sentences for a given entity pair expressing exactly one relation.
We show that these characteristics lead to a gross overestimation of the model
performance. In response, we propose a new dataset, DiS-ReX, which alleviates
these issues. Our dataset has more than 1.5 million sentences, spanning across
4 languages with 36 relation classes + 1 no relation (NA) class. We also modify
the widely used bag attention models by encoding sentences using mBERT and
provide the first benchmark results on multilingual DS-RE. Unlike the competing
dataset, we show that our dataset is challenging and leaves enough room for
future research to take place in this field.


  Precise destination prediction of taxi trajectories can benefit many
intelligent location based services such as accurate ad for passengers.
Traditional prediction approaches, which treat trajectories as one-dimensional
sequences and process them in single scale, fail to capture the diverse
two-dimensional patterns of trajectories in different spatial scales. In this
paper, we propose T-CONV which models trajectories as two-dimensional images,
and adopts multi-layer convolutional neural networks to combine multi-scale
trajectory patterns to achieve precise prediction. Furthermore, we conduct
gradient analysis to visualize the multi-scale spatial patterns captured by
T-CONV and extract the areas with distinct influence on the ultimate
prediction. Finally, we integrate multiple local enhancement convolutional
fields to explore these important areas deeply for better prediction.
Comprehensive experiments based on real trajectory data show that T-CONV can
achieve higher accuracy than the state-of-the-art methods.


  This paper considers the behavior of discrete and continuous mathematical
models for gene expression in the presence of transcriptional/translational
bursting. We treat this problem in generality with respect to the distribution
of the burst size as well as the frequency of bursting, and our results are
applicable to both inducible and repressible expression patterns in prokaryotes
and eukaryotes. We have given numerous examples of the applicability of our
results, especially in the experimentally observed situation that burst size is
geometrically or exponentially distributed.


  RISC-V is a RISC based open and loyalty free instruction set architecture
which has been developed since 2010, and can be used for cost-effective soft
processors on FPGAs. The basic 32-bit integer instruction set in RISC-V is
defined as RV32I, which is sufficient to support the operating system
environment and suits for embedded systems. In this paper, we propose an
optimized RV32I soft processor named RVCoreP adopting five-stage pipelining.
The processor applies three effective optimization methods to improve the
operating frequency. These methods are instruction fetch unit optimization
including pipelined branch prediction mechanism, ALU optimization, and data
alignment and sign-extension optimization for data memory output. We implement
RVCoreP in Verilog HDL and verify the behavior using Verilog simulation and an
actual Xilinx Atrix-7 FPGA board. We evaluate IPC (instructions per cycle),
operating frequency, hardware resource utilization, and processor performance.
From the evaluation results, we show that RVCoreP achieves 30.0% performance
improvement compared with VexRiscv, which is a high-performance and open source
RV32I processor selected from some related works.


  First, the chiral phase transition at nonzero temperature and baryon chemical
potential is studied at mean field level in the sigma model that includes quark
degrees of freedom explicitly. For small bare quark masses the critical point
separating the first order phase transition line and the smooth crossover
region is determined, and the spinodal lines are drawn. Adiabatic lines are
computed showing that the critical point does not serve as focusing point in
the adiabatic expansion. Second, coarse-grained evolution equations for
inhomogeneous chiral condensate fields coupled to a heat bath are derived in
the linear sigma model. Multiple effects of the thermal background on the
Disoriented Chiral Condensate (DCC) are studied within linear response theory.
In-medium modification of the pion dispersion relation is examined. The
deviation from the speed of light of the velocity of soft Goldstone modes is
calculated. Decay widths of pions and sigma mesons are computed at nonzero
temperature at one- and two-loop order in perturbation theory. We show that
elastic scattering processes are significant for the dissipation of DCCs in the
phase transition region. Throughout this work we made sure that Goldstone's
Theorem is fulfilled when chiral symmetry is spontaneously broken.


  Background Large Language Models (LLMs), enhanced with Clinical Practice
Guidelines (CPGs), can significantly improve Clinical Decision Support (CDS).
However, methods for incorporating CPGs into LLMs are not well studied. Methods
We develop three distinct methods for incorporating CPGs into LLMs: Binary
Decision Tree (BDT), Program-Aided Graph Construction (PAGC), and
Chain-of-Thought-Few-Shot Prompting (CoT-FSP). To evaluate the effectiveness of
the proposed methods, we create a set of synthetic patient descriptions and
conduct both automatic and human evaluation of the responses generated by four
LLMs: GPT-4, GPT-3.5 Turbo, LLaMA, and PaLM 2. Zero-Shot Prompting (ZSP) was
used as the baseline method. We focus on CDS for COVID-19 outpatient treatment
as the case study. Results All four LLMs exhibit improved performance when
enhanced with CPGs compared to the baseline ZSP. BDT outperformed both CoT-FSP
and PAGC in automatic evaluation. All of the proposed methods demonstrated high
performance in human evaluation. Conclusion LLMs enhanced with CPGs demonstrate
superior performance, as compared to plain LLMs with ZSP, in providing accurate
recommendations for COVID-19 outpatient treatment, which also highlights the
potential for broader applications beyond the case study.


  Given a 3-cocycle $\psi$ in the cohomology of a finite group $G$, we can
define the Dijkgraaf-Witten invariant of closed 3-manifolds. In this paper, we
focus on the case where $\psi$ is a 3-cocycle canonically obtained from the
second Chern class of a representation of $G$, and show some procedures for
computing the invariant, and clarify its topological interpretation under some
conditions.


Wave refraction in negative-index media is always negative.


  An algebraic characterization of the contractions of the Poincar\'e group
permits a proper construction of a non-relativistic limit of its tachyonic
representation. We arrive at a consistent, nonstandard representation of the
Galilei group which was disregarded long ago by supposedly unphysical
properties. The corresponding quantum (and classical) theory shares with the
relativistic one their fundamentals, and serves as a toy model to better
comprehend the unusual behavior of the tachyonic representation. For instance,
we see that evolution takes place in a spatial coordinate rather than time, as
for relativistic tachyons, but the modulus of the three-momentum is the same
for all Galilean observers, leading to a new dispersion relation for a Galilean
system. Furthermore, the tachyonic objects described by the new representation
cannot be regarded as localizable in the standard sense.


  Generative AI has received substantial attention in recent years due to its
ability to synthesize data that closely resembles the original data source.
While Generative Adversarial Networks (GANs) have provided innovative
approaches for histopathological image analysis, they suffer from limitations
such as mode collapse and overfitting in discriminator. Recently, Denoising
Diffusion models have demonstrated promising results in computer vision. These
models exhibit superior stability during training, better distribution
coverage, and produce high-quality diverse images. Additionally, they display a
high degree of resilience to noise and perturbations, making them well-suited
for use in digital pathology, where images commonly contain artifacts and
exhibit significant variations in staining. In this paper, we present a novel
approach, namely ViT-DAE, which integrates vision transformers (ViT) and
diffusion autoencoders for high-quality histopathology image synthesis. This
marks the first time that ViT has been introduced to diffusion autoencoders in
computational pathology, allowing the model to better capture the complex and
intricate details of histopathology images. We demonstrate the effectiveness of
ViT-DAE on three publicly available datasets. Our approach outperforms recent
GAN-based and vanilla DAE methods in generating realistic images.


  We use baryon acoustic oscillation and redshift space distortion from the
completed Baryon Oscillation Spectroscopic Survey, corresponding to data
release 12 of the Sloan Digital Sky Survey, combined sample analysis in
combination with cosmic microwave background, supernova and redshift space
distortion measurements from additional spectroscopic surveys to test
deviations from general relativity. We present constraints on several
phenomenological models of modified gravity: First, we parametrise the growth
of structure using the growth index $\gamma$, finding $\gamma=0.566\pm0.058$
(68\% C.L.). Second, we modify the relation of the two Newtonian potentials by
introducing two additional parameters, $G_M$ and $G_L$. In this approach, $G_M$
refers to modifications of the growth of structure whereas $G_L$ to
modification of the lensing potential. We consider a power law to model the
redshift dependency of $G_M$ and $G_L$ as well as binning in redshift space,
introducing four additional degrees of freedom, $G_M(z<0.5)$, $G_M(z>0.5)$,
$G_L(z<0.5)$, $G_L(z>0.5)$. At 68\% C.L. we measure $G_M=0.980 \pm 0.096$ and
$G_L=1.082 \pm 0.060$ for a linear model, $G_M= 1.01\pm 0.36$ and $G_L=1.31 \pm
0.19$ for a cubic model as well as $G_M(z<0.5)=1.26 \pm 0.32$,
$G_M(z>0.5)=0.986\pm0.022$, $G_L(z<0.5)=1.067\pm0.058$ and
$G_L(z>0.5)=1.037\pm0.029$. Thirdly, we investigate general scalar tensor
theories of gravity, finding the model to be mostly unconstrained by current
data. Assuming a one-parameter $f(R)$ model we can constrain $B_0< 7.7 \times
10^{-5}$ (95\% C.L). For all models we considered we find good agreement with
general relativity.


  Using quasi-time dependent semi-classical transport theory in RTA, we
obtained coupled current equations in the presence of time varying field and
based on general scattering mechanism $\tau \propto \mathcal{E}^{\beta}$. We
find that close to the Dirac point, the characteristic exponent $\beta = +2$
corresponds to acoustic phonon scattering. $\beta = +1$ long-range Coulomb
scattering mechanism. $\beta = -1$ is short-range delta potential scattering in
which the conductivity is constant of temperature. The $\beta = 0$ case is
ballistic limit. In the low energy dynamics of Dirac electrons in graphene, the
effect of the time-dependent electric field is to alter just the electron
charge by $e \to e(1 + (\Omega \tau)^2)$ making electronic conductivity
non-linear. The effect of magnetic filed is also considered.


  Asbestos is the term for silicate minerals with a typical fibrous structure
and crystallise as separable fibres that can be released into the environment
as a result of natural processes and anthropogenic activities. There is a need
to intensify geo-environmental monitoring of the occurrence of natural asbestos
on a global scale. The study of this material is important to clarify the
impact of asbestos on public health and to have an accurate knowledge of the
requirements for asbestos replacement materials. At present, the technical as
well as the ecological reasons for switching to these fibres are difficult, as
asbestos replacement materials are subject to considerable technological and
economic demands, as well as demands for their biological safety. The aim of
this paper is to summarize the current knowledge regarding the vast issue of
asbestos occurrence, to seek to establish an appropriate methodology for
detecting the presence of asbestos in soil sediments, to identify the exact
types of asbestos from a series of samples, to highlight its impact on public
health, and to emphasize the need for deliberate mapping of the natural
occurrence of asbestos and its inclusion in the laws and decrees of the
Ministry of the Environment. Samples were analysed by electron microscopy and
X-ray diffraction and compared with standards or available literature. The
measurements demonstrated the presence of asbestos in the site sediments and
identified specific types of asbestos. The conclusion of this work is the
confirmation of the presence of asbestos in all samples, including its most
dangerous types, which can cause very serious diseases. In this context, the
mechanism of asbestos-related diseases will be further addressed, which is
linked to the size and shape of the individual fibres, the chemical composition
of the asbestos types and the links between their basic structural units.


  Policy gradient algorithms have been widely applied to Markov decision
processes and reinforcement learning problems in recent years. Regularization
with various entropy functions is often used to encourage exploration and
improve stability. This paper proposes an approximate Newton method for the
policy gradient algorithm with entropy regularization. In the case of Shannon
entropy, the resulting algorithm reproduces the natural policy gradient
algorithm. For other entropy functions, this method results in brand-new policy
gradient algorithms. We prove that all these algorithms enjoy Newton-type
quadratic convergence and that the corresponding gradient flow converges
globally to the optimal solution. We use synthetic and industrial-scale
examples to demonstrate that the proposed approximate Newton method typically
converges in single-digit iterations, often orders of magnitude faster than
other state-of-the-art algorithms.


  We propose a novel approach for loss reserving based on deep neural networks.
The approach allows for joint modeling of paid losses and claims outstanding,
and incorporation of heterogeneous inputs. We validate the models on loss
reserving data across lines of business, and show that they improve on the
predictive accuracy of existing stochastic methods. The models require minimal
feature engineering and expert input, and can be automated to produce forecasts
more frequently than manual workflows.


  We present spectroscopic confirmation of the "Pisces Overdensity", also known
as "Structure J", a photometric overdensity of RR Lyrae stars discovered by the
Sloan Digital Sky Survey (SDSS) at an estimated photometric distance of ~85kpc.
We measure radial velocities for 8 RR Lyrae stars within Pisces. We find that 5
of the 8 stars have heliocentric radial velocities within a narrow range of -87
km/s < v < -67 km/s, suggesting that the photometric overdensity is mainly due
to a physically associated system, probably a dwarf galaxy or a disrupted
galaxy. Two of the remaining 3 stars differ from one another by only 9 km/s,
but it would be premature to identify them as a second system.


  We present an extension of the spectral deconvolution method (Sparks & Ford
2002) to achieve very high contrast at small inner working radii. We apply the
method to the specific case of ground based adaptive optics fed integral field
spectroscopy (without a coronagraph). Utilising the wavelength dependence of
the Airy and speckle patterns, we make an accurate estimate of the PSF that can
be scaled and subtracted from the data cube. The residual noise in the
resulting spectra is very close to the photon noise from the starlight halo. We
utilise the technique to extract a very high SNR H & K band spectrum of AB Dor
C, the low mass companion to AB Dor A. By effectively eliminating all
contamination from AB Dor A, the extracted spectrum retains both continuum and
spectral features. The achieved 1 sigma contrast is 9 mag at 0.2", 11 mag at
0.5", in 20 mins exposure time, at an effective spectral bandwidth of 5.5 nm,
proving that the method is applicable even in low Strehl regimes.
  The spectral deconvolution method clearly demonstrates the efficacy of image
slicer based IFUs in achieving very high contrast imaging spectroscopy at small
angular separations, validating their use as high contrast
spectrographs/imagers for extreme adaptive optics systems.


  The correlations of multiple renewable power plants (RPPs) should be fully
considered in the power system with very high penetration renewable power
integration. This paper models the uncertainties, spatial correlation of
multiple RPPs based on Copula theory and actual probability historical
histograms by one-dimension distributions for economic dispatch (ED) problem.
An efficient dynamic renewable power scenario generation method based on Gibbs
sampling is proposed to generate renewable power scenarios considering the
uncertainties, spatial correlation and variability (temporal correlation) of
multiple RPPs, in which the sampling space complexity do not increase with the
number of RPPs. Distribution-based and scenario-based methods are proposed and
compared to solve the real-time ED problem with multiple RPPs. Results show
that the proposed dynamic scenario generation method is much more consist with
the actual renewable power. The proposed ED methods show better understanding
for the uncertainties, spatial and temporal correlations of renewable power and
more economical compared with the traditional ones.


  MatrixX is a solver for Abstract Argumentation Frameworks. Offensive and
defensive properties of an Argumentation Framework are notated in a matrix
style. Rows and columns of this matrix are systematically reduced by the
solver. This procedure is implemented through the use of hash maps in order to
accelerate calculation time. MatrixX works for stable and complete semantics
and was designed for the ICCMA 2021 competition.


  We characterize, up to a conjecture, probability distributions of all order
finite moments having ultraspherical type generating functions for orthogonal
polynomials.


  We present most recent PHENIX preliminary data on centrality dependence of
two-pion Bose-Einstein correlation functions measured in \sqrt{sNN}=200 GeV
Au+Au collisions at the Relativistic Heavy Ion Collider (RHIC). The data are
well described by assuming the source to be a L\'evy-stable distribution. The
L\'evy parameters, R, alpha, lambda are measured in 18 bins of transverse mass
(mT) for 4 centrality intervals. The L\'evy scale parameter R(mT) decreases
with mT and exhibits a clear centrality ordering which supports its geometrical
interpretation. The L\'evy exponent {\alpha}(mT) is independent of mT in every
centrality bin but shows some centrality dependence. At all centralities
{\alpha} is significantly different from that of a Gaussian ({\alpha} = 2) or
Cauchy ({\alpha} = 1) source distribution. The L\'evy strength parameter
{\lambda} was also investigated. We observed that {\lambda}(mT) decreases at
low mT and saturates around mT = 0.6 GeV/c2. In the already published 0-30%
centrality class, Monte Carlo simulations are found to be inconsistent with the
measurements, unless a significant reduction of the in-medium mass of the
etaprime meson is included.


  We introduce and analyze the properties of dynamical Franz-Keldysh effect,
i.e. the change of density-of-states, or absorption spectra, of semiconductors
under the influence of {\it time-dependent} electric fields. In the case of a
harmonic time-dependence, we predict the occurence of significant fine
structure, both below and above the zero-field band-gap, which should be
experimentally observable.


  In the paper of Uwano [Czech. J. of Phys., vol.56, pp.1311-1316 (2006)], a
gradient system is found on the space of density matrices endowed with the
quantum SLD Fisher metric (to be referred to as the quantum information space)
that realizes a generalization of a gradient system on the space of multinomial
distributions studied by Nakamura [Japan J. Indust. Appl. Math., vol.10,
pp.179-189 (1993)]. On motived by those papers, the present paper aims to
construct a gradient system on the quantum information space that realizes the
Karmarkar flow, the continuous limit of the Karmarkar projective scaling
algorithm for linear programming.


  The effects of the soft gluon emission in hard scattering processes at the
phase boundary are resummed in the soft-collinear effective theory (SCET). In
SCET, the soft gluon emission is decoupled from the energetic collinear part,
and is obtained by the vacuum expectation value of the soft Wilson-line
operator. The form of the soft Wilson lines is universal in deep inelastic
scattering, in the Drell-Yan process, in the jet production from e+e-
collisions, and in the gamma* gamma* -> pi0 process, but its analytic structure
is slightly different in each process. The anomalous dimensions of the soft
Wilson-line operators for these processes are computed along the light-like
path at leading order in SCET and to first order in alpha_s, and the
renormalization group behavior of the soft Wilson lines is discussed.


  We improve the error terms of some estimates related to counting lattices
from recent work of L. Fukshansky, P. Guerzhoy and F. Luca (2017). This
improvement is based on some analytic techniques, in particular on bounds of
exponential sums coupled with the use of Vaaler polynomials.


  The light-matter coupling between electromagnetic modes guided by a
semiconductor nanowire and excitonic states of molecules localized in its
surrounding media is studied from both classical and quantum perspectives, with
the aim of describing the strong coupling regime. Weakly guided modes (bare
photonic modes) are found through a classical analysis, identifying those
lowest-order modes presenting large electromagnetic fields spreading outside
the nanowire, while preserving their robust guided behavior. Experimental fits
of the dielectric permittivity of an organic dye that exhibits excitonic states
are used for realistic scenarios. A quantum model properly confirms through an
avoided mode crossing that the strong coupling regime can be achieved for this
configuration, leading to Rabi splitting values above 100 meV. In addition, it
is shown that the coupling strength depends on the fraction of energy spread
outside the nanowire, rather than on the mode field localization. These results
open up a new avenue towards strong coupling phenomenology involving
propagating modes in non-absorbing media.


  We review our recent Michelson-Morley (MM) and Kennedy-Thorndike (KT)
experiment, which tests Lorentz invariance in the photon sector, and report
first results of our ongoing atomic clock test of Lorentz invariance in the
matter sector. The MM-KT experiment compares a cryogenic microwave resonator to
a hydrogen maser, and has set the most stringent limit on a number of
parameters in alternative theories to special relativity. We also report first
results of a test of Lorentz invariance in the SME (Standard Model Extension)
matter sector, using Zeeman transitions in a laser cooled Cs atomic fountain
clock. We describe the experiment together with the theoretical model and
analysis. Recent experimental results are presented and we give a first
estimate of components of the $\tilde{c}^p$ parameters of the SME matter
sector. A full analysis of systematic effects is still in progress, and will be
the subject of a future publication together with our final results. If
confirmed, the present limits would correspond to first ever measurements of
some $\tilde{c}^p$ components, and improvements by 11 and 14 orders of
magnitude on others.


  We investigate a model for contact angle motion of quasi-static capillary
drops resting on a horizontal plane. We prove global in time existence and long
time behavior (convergence to equilibrium) in a class of star-shaped initial
data for which we show that topological changes of drops can be ruled out for
all times. Our result applies to any drop which is initially star-shaped with
respect to a a small ball inside the drop, given that the volume of the drop is
sufficiently large. For the analysis, we combine geometric arguments based on
the moving-plane type method with energy dissipation methods based on the
formal gradient flow structure of the problem.


  The flux of upward through-going muons of minimum (mean) threshold energy >
1.6 (3.0) GeV is measured, based on a total of 372 events observed by the
Kamiokande II+III detector during 2456 detector live days. The observed muon
flux was (1.94+-0.10(stat.)+0.07-0.06(sys.))*10^-13 /cm^2/s/sr, which is
compared to an expected value of (2.46+-0.54(theo.))*10^-13/cm^2/s/sr. The
observation is in agreement with the prediction within the errors. The zenith
angle dependence of the observed upward through-going muons supports the
previous indication of neutrino oscillations made by Kamiokande using sub- and
multi-GeV atmospheric neutrino events.


  This article answers a question that naturally arises from the articles by
Grabitz and Broussous "Pure elements and intertwining classes of simple strata
in local central simple algebras" and Broussous and Lemaire "Buildings of
GL(m,D) and Centralizers". For an Azumaya-Algebra A over a non-Archimedean
local field F, Grabitz and Broussous have introduced embedding invariants for
field embeddings, that is for pairs (E,a), where E is a field extension of F in
A, and $a$ is a hereditary order which is normalised by E^x. On the other hand
if we take such a field extension E and define B to be the centralizer of E in
A, then G:=A^x and G_E:=B^x are sets of rational points of reductive groups
defined over F and E respectively. Broussous and Lemaire have defined a map
j_E: I^{E^x}\to I_E, where $I$ is the the Euclidean building of $G$, and I_E is
the Euclidean building of G_E. The question which we address is to relate the
embedding invariants to the behavior of the map j_E with respect to the
simplicial structures of $I$ and I_E. I have to thank very much Prof. Zink from
Homboldt University Berlin for his helpful remarks, the revision of the work
and for giving my the interesting task.


  Over the previous decade, many approaches for the modelling of radio emission
from cosmic ray air showers have been developed. However, there remained
significant deviations between the models, reaching from important qualitative
differences such as unipolar versus bipolar pulses to large variations in the
predicted absolute amplitudes of up to factors of 20. Only recently, it has
been realized that in the many models predicting unipolar pulses, a radio
emission contribution due to the time-variation of the number of charged
particles or, equivalently, the acceleration of the particles at the beginning
and the end of their trajectories, had not been taken into account. We discuss
here the nature of the underlying problem and demonstrate that by including the
missing contribution in REAS3, the discrepancies are reconciled. Furthermore,
we show a direct comparison of REAS3 and MGMR simulations for a set of
prototype showers. The results of these two completely independent and very
different modelling approaches show a good level of agreement except for
regions of parameter space where differences in the underlying air shower model
become important. This is the first time that two radio emission models show
such close concordance, illustrating that the modelling of radio emission from
extensive air showers has indeed made a true breakthrough.


  This paper introduces a new two-parameter family of dwarf spheroidal (dSph)
galaxy models. The density distribution has a Plummer profile and falls like
the inverse fourth power of distance in projection, in agreement with the
star-count data. The first free parameter controls the velocity anisotropy, the
second controls the dark matter content. The dark matter distribution can be
varied from one extreme of mass-follows-light through a near-isothermal halo
with flat rotation curve to the other extreme of an extended dark halo with
harmonic core. This family of models is explored analytically in some detail --
the distribution functions, the intrinsic moments and the projected moments are
all calculated. For the nearby Galactic dSphs, samples of hundreds of discrete
radial velocities are becoming available. A technique is developed to extract
the anisotropy and dark matter content from such data sets by maximising the
likelihood function of the sample of radial velocities. This is constructed
from the distribution function and corrected for observational errors and the
effects of binaries. Tests on simulated data sets show that samples of 1000
discrete radial velocities are ample to break the degeneracy between mass and
anisotropy in the nearby dSphs. Interesting constraints can already be placed
on the distribution of the dark matter with samples of 160 radial velocities
(the size of the present-day data set for Draco).


  3D reconstruction from a single image is a key problem in multiple
applications ranging from robotic manipulation to augmented reality. Prior
methods have tackled this problem through generative models which predict 3D
reconstructions as voxels or point clouds. However, these methods can be
computationally expensive and miss fine details. We introduce a new
differentiable layer for 3D data deformation and use it in DeformNet to learn a
model for 3D reconstruction-through-deformation. DeformNet takes an image
input, searches the nearest shape template from a database, and deforms the
template to match the query image. We evaluate our approach on the ShapeNet
dataset and show that - (a) the Free-Form Deformation layer is a powerful new
building block for Deep Learning models that manipulate 3D data (b) DeformNet
uses this FFD layer combined with shape retrieval for smooth and
detail-preserving 3D reconstruction of qualitatively plausible point clouds
with respect to a single query image (c) compared to other state-of-the-art 3D
reconstruction methods, DeformNet quantitatively matches or outperforms their
benchmarks by significant margins. For more information, visit:
https://deformnet-site.github.io/DeformNet-website/ .


  We describe a method for programming the autonomous navigation of active
colloidal particles in response to spatial gradients in a scalar stimulus.
Functional behaviors such as positive or negative chemotaxis are encoded in the
particle shape, which responds to the local stimulus and directs self-propelled
particle motions. We demonstrate this approach using a physical model of
stimuli-responsive clusters of self-phoretic spheres. We show how multiple
autonomous behaviors can be achieved by designing the particle geometry and its
stimulus response.


  Two-component dipolar fermions in zigzag optical lattices allow for the
engineering of spin-orbital models. We show that dipolar lattice fermions
permit the exploration of a regime typically unavailable in solid-state
compounds that is characterized by a novel spin-liquid phase with a finite
magnetization and spontaneously broken SU(2) symmetry. This peculiar spin
liquid may be understood as a Luttinger liquid of composite particles
consisting of bound states of spin waves and orbital domain walls moving in an
unsaturated ferromagnetic background. In addition, we show that the system
exhibits a boundary phase transitions involving non-local entanglement of edge
spins.


  The decays of the $\beta$-delayed neutron emitters $^{137}$I and $^{95}$Rb
have been studied with the total absorption $\gamma$-ray spectroscopy
technique. The purity of the beams provided by the JYFLTRAP Penning trap at the
ion guide isotope separator on-line facility in Jyv\"askyl\"a allowed us to
carry out a campaign of isotopically pure measurements with the decay total
absorption $\gamma$-ray spectrometer, a segmented detector composed of eighteen
NaI(Tl) modules. The contamination coming from the interaction of neutrons with
the spectrometer has been carefully studied, and we have tested the use of time
differences between prompt $\gamma$-rays and delayed neutron interactions to
eliminate this source of contamination. Due to the sensitivity of our
spectrometer, we have found a significant amount of $\beta$-intensity to states
above the neutron separation energy that de-excite by $\gamma$-rays, comparable
to the neutron emission probability. The competition between $\gamma$
de-excitation and neutron emission has been compared with Hauser-Feshbach
calculations, and it can be understood as a nuclear structure effect. In
addition, we have studied the impact of the $\beta$-intensity distributions
determined in this work on reactor decay heat and reactor antineutrino spectrum
summation calculations. The robustness of our results is demonstrated by a
thorough study of uncertainties, and with the reproduction of the spectra of
the individual modules and the module-multiplicity gated spectra. This work
represents the state-of-the-art of our analysis methodology for segmented total
absorption spectrometers.


  The two-color above-threshold ionization (ATI) of atoms and ions is
investigated for a vortex Bessel beam in the presence of a strong near-infrared
(NIR) light field. While the photoionization is caused by the photons from the
weak but extreme ultra-violet (XUV) vortex Bessel beam, the energy and angular
distribution of the photoelectrons and their sideband structure are affected by
the plane-wave NIR field. We here explore the energy spectra and angular
emission of the photoelectrons in such two-color fields as a function of the
size and location of the target (atoms) with regard to the beam axis. In
addition, analogue to the circular dichroism in typical two-color ATI
experiments with circularly polarized light, we define and discuss seven
different dichroism signals for such vortex Bessel beams that arise from the
various combinations of the orbital and spin angular momenta of the two light
fields. For localized targets, it is found that these dichroism signals
strongly depend on the size and position of the atoms relative to the beam. For
macroscopically extended targets, in contrast, three of these dichroism signals
tend to zero, while the other four just coincide with the (standard) circular
dichroism, similar as for Bessel beams with small opening angle. Detailed
computations of the dichroism are performed and discussed for the $4s$
valence-shell photoionization of Ca$^+$ ions.


  The dynamics of dark-bright solitons beyond the mean-field approximation is
investigated. We first examine the case of a single dark-bright soliton and its
oscillations within a parabolic trap. Subsequently, we move to the setting of
collisions, comparing the mean-field approximation to that involving multiple
orbitals in both the dark and the bright component. Fragmentation is present
and significantly affects the dynamics, especially in the case of slower
solitons and in that of lower atom numbers. It is shown that the presence of
fragmentation allows for bipartite entanglement between the distinguishable
species. Most importantly the interplay between fragmentation and entanglement
leads to the splitting of each of the parent mean-field dark-bright solitons,
placed off-center within the parabolic trap, into a fast and a slow daughter
solitary wave. The latter process is in direct contrast to the predictions of
the mean-field approximation. A variety of excitations including dark-bright
solitons in multiple (concurrently populated) orbitals is observed.
Dark-antidark states and domain-wall-bright soliton complexes can also be
observed to arise spontaneously in the beyond mean-field dynamics.


  We investigate the effect of memory terms on the entropy decay of the
solutions to equations with Ornstein-Uhlenbeck operators. Our assumptions on
the memory kernels include Caputo-Fabrizio operators and, more generally, the
stretched exponential functions. We establish a sharp rate decay for the
entropy. Examples and numerical simulations are also given to illustrate the
results.


  Starting with the first-order singular Lagrangian containing the redundant
variables, the noncommutative quantum mechanics on a curved space is
investigated by the constraint star-product quantization formalism of the
projection operator method. Imposing the additional constraints to eliminate
the reduntant degrees of freedom, the noncommutative quantum system with
noncommutativity among the coordinates on the curved space is exactly
constructed. Then, it is shown that the resultant Hamiltonian contains the
quantum corrections in the exact form. We further discuss the additional
constraints to realize the noncommutativities both of coordinates and momenta
on the curved space.


  This talk summarises what is currently understood about the phenomenon that
has come to be known as {\it chaotic mixing}. The first part presents a concise
statement as to what chaotic mixing actually is, and then explains why it
should be important in galactic dynamics. The second discusses in detail what
is currently known, describing the manifestations of chaotic mixing both for
flows in time-independent Hamiltonian systems and for systems subjected to
comparatively weak time-dependent perturbations. The third part discusses what
one might expect if one were to allow for a strongly time-dependent potential,
including possible implications for violent relaxation.


  Radio frequency antennas based on Rydberg atom vapor cells can in principle
reach sensitivities beyond those of any wire antenna, especially at lower
frequencies where long wires are needed to accommodate a growing wavelength.
They also have other desirable features such as nonmetallic, lower profile,
elements. This paper presents a detailed theoretical investigation of Rydberg
antenna sensitivity, elucidating parameter regimes that could cumulatively lead
to 2--3 orders of magnitude sensitivity increase beyond that of currently
tested configurations. The key insight is to optimally combine the advantages
of two well-studied approaches: (i) three laser ``2D star configuration''
setups that, enhanced also with increased laser power, help compensate for atom
motion-induced Doppler broadening, and (ii) resonant coupling between a pair of
near-degenerate Rydberg levels, tuned via a local oscillator to the incident
signal. The advantage of the star setup is subtle because it only restores
overall sensitivity to the expected Doppler-limited value, compensating for
additional off-resonance reductions where differently moving atom populations
destructively interfere with each other in the net signal. The additional
unique advantage of the local oscillator tuning is that it leads to vastly
narrower line widths, as low as ~10 kHz set by the intrinsic Rydberg state
lifetimes, rather than the typical ~10 MHz set by the core state lifetimes.
Intuitively, with this setup the two Rydberg states may be tuned to act as an
independent high-q cavity, a point of view supported through a study of the
frequency-dependence of the antenna resonant response. There are a number of
practical experimental advances, especially larger ~1 cm laser beam widths,
required to suppress various extrinsic line broadening effects and to fully
exploit this cavity response.


  Extracting a common robust signal from data divided into heterogeneous groups
can be difficult when each group -- in addition to the signal -- can contain
large, unique variation components. Previously, maximin estimation has been
proposed as a robust estimation method in the presence of heterogeneous noise.
We propose soft maximin estimation as a computationally attractive alternative
aimed at striking a balance between pooled estimation and (hard) maximin
estimation.
  The soft maximin method provides a range of estimators, controlled by a
parameter $\zeta>0$, that interpolates pooled least squares estimation and
maximin estimation. By establishing relevant theoretical properties we argue
that the soft maximin method is both statistically sensibel and computationally
attractive.
  We also demonstrate, on real and simulated data, that the soft maximin
estimator can offer improvements over both pooled OLS and hard maximin in terms
of predictive performance and computational complexity.
  A time and memory efficient implementation is provided in the R package
\verb+SMME+ available on CRAN.


  Regularized M-estimators are used in diverse areas of science and engineering
to fit high-dimensional models with some low-dimensional structure. Usually the
low-dimensional structure is encoded by the presence of the (unknown)
parameters in some low-dimensional model subspace. In such settings, it is
desirable for estimates of the model parameters to be \emph{model selection
consistent}: the estimates also fall in the model subspace. We develop a
general framework for establishing consistency and model selection consistency
of regularized M-estimators and show how it applies to some special cases of
interest in statistical learning. Our analysis identifies two key properties of
regularized M-estimators, referred to as geometric decomposability and
irrepresentability, that ensure the estimators are consistent and model
selection consistent.


  We describe systems and methods for the deployment of global quantum key
distribution (QKD) networks covering transoceanic, long-haul, metro, and access
segments of the network. A comparative study of the state-of-the-art QKD
technologies is carried out, including both terrestrial QKD via optical fibers
and free-space optics, as well as spaceborne solutions via satellites. We
compare the pros and cons of various existing QKD technologies, including
channel loss, potential interference, distance, connection topology, deployment
cost and requirements, as well as application scenarios. Technical selection
criteria and deployment requirements are developed for various different QKD
solutions in each segment of networks. For example, optical fiber-based QKD is
suitable for access networks due to its limited distance and compatibility with
point-to-multipoint (P2MP) topology; with the help of trusted relays, it can be
extended to long-haul and metro networks. Spaceborne QKD on the other hand, has
much smaller channel loss and extended transmission distance, which can be used
for transoceanic and long-haul networks exploiting satellite-based trusted
relays.


  We deal with pointwise approximation of solutions of scalar stochastic
differential equations in the presence of informational noise about underlying
drift and diffusion coefficients. We define a randomized derivative-free
version of Milstein algorithm $\mathcal{\bar A}^{df-RM}_n$ and investigate its
error. We also study lower bounds on the error of an arbitrary algorithm. It
turns out that in some case the scheme $\mathcal{\bar A}^{df-RM}_n$ is the
optimal one. Finally, in order to test the algorithm $\mathcal{\bar
A}^{df-RM}_n$ in practice, we report performed numerical experiments.


  Mountaineering is a sport of contrary forces: teamwork plays a large role in
mental fortitude and skills, but the actual act of climbing, and indeed
survival, is largely individualistic. This work studies the effects of the
structure and topology of relationships within climbers on the level of
cooperation and success. It does so using simplicial complexes, where
relationships between climbers are captured through simplexes that correspond
to joint previous expeditions with dimension given by the number of climbers
minus one and weight given by the number of occurrences of the simplex. First,
this analysis establishes the importance of relationships and shows that
chances of failure to summit reduce drastically when climbing with repeat
partners. From a climber-centric perspective, climbers that belong to simplexes
with large dimension were more likely to be successful, across experience
levels. From an expedition-centric perspective, the distribution of
relationships within a group is explored to identify collective human behavior:
from polarized to cooperative. Expeditions containing simplices with large
dimension, and usually low weight, i.e., a large number of people had a small
number of previous joint expeditions, tended to be more cooperative, with more
homogeneity in success amongst climbers. On the other hand, the existence of
small, usually strong, subgroups lead to a polarized style where climbers that
were not a part of the subgroup were less likely to succeed. Lastly, this work
examines the effects of individual features and expedition-wide factors that
may play different roles in individualistic and cooperative expeditions.
Centrality indicates that individual traits of youth and oxygen use while
ascending are strong drivers of success. Of expedition-wide factors, the
expedition size and number of expedition days are found to be strongly
correlated with success rate.


  In this paper the author studies the isoperimetric problem in $\re^n$ with
perimeter density $|x|^p$ and volume density $1.$ We settle completely the case
$n=2,$ completing a previous work by the author: we characterize the case of
equality if $0\leq p\leq 1$ and deal with the case $-\infty<p<-1$ (with the
additional assumption $0\in\Omega$). In the case $n\geq 3$ we deal mainly with
the case $-\infty<p<0,$ showing among others that the results in $2$ dimensions
do not generalize for the range $-n+1<p<0.$


  This paper presents a proof-of-concept method for classifying chemical
compounds directly from NMR data without doing structure elucidation. This can
help to reduce time in finding good structure candidates, as in most cases
matching must be done by a human engineer, or at the very least a process for
matching must be meaningfully interpreted by one. Therefore, for a long time
automation in the area of NMR has been actively sought. The method identified
as suitable for the classification is a convolutional neural network (CNN).
Other methods, including clustering and image registration, have not been found
suitable for the task in a comparative analysis. The result shows that deep
learning can offer solutions to automation problems in cheminformatics.


  By using Fubini theorem or Tonelli theorem, we find that the zeta function
value at 2 is equal to a special integral. Furthermore, We find that this
special integral is two times of another special integral. By using this fact
we obtain the relationship between Genocchi numbers and Bernoulli numbers. And
get some results about Bernoulli polynomials.


  Titanium is the base material for a number of technologically important
alloys for energy conversion and structural applications. Atomic-scale studies
of Ti-based metals employing first-principles methods, such as density
functional theory, are limited to ensembles of a few hundred atoms. To perform
large-scale and/or finite temperature simulations, computationally more
efficient interatomic potentials are required. In this work, we coarse grain
the tight-binding (TB) approximation to the electronic structure and develop an
analytic bond-order potential (BOP) for Ti by fitting to the energies and
forces of elementary deformations of simple structures. The BOP predicts the
structural properties of the stable and defective phases of Ti with a quality
comparable to previous TB parametrizations at a much lower computational cost.
The predictive power of the model is demonstrated for simulations of
martensitic transformations.


  A central question in arrangement theory is to determine whether the
characteristic polynomial $\Delta_q$ of the algebraic monodromy acting on the
homology group $H_q(F(\mathcal{A}),\mathbb{C})$ of the Milnor fiber of a
complex hyperplane arrangement $\mathcal{A}$ is determined by the intersection
lattice $L(\mathcal{A})$. Under simple combinatorial conditions, we show that
the multiplicities of the factors of $\Delta_1$ corresponding to certain
eigenvalues of order a power of a prime $p$ are equal to the Aomoto--Betti
numbers $\beta_p(\mathcal{A})$, which in turn are extracted from
$L(\mathcal{A})$. When $\mathcal{A}$ defines an arrangement of projective lines
with only double and triple points, this leads to a combinatorial formula for
the algebraic monodromy. To obtain these results, we relate nets on the
underlying matroid of $\mathcal{A}$ to resonance varieties in positive
characteristic. Using modular invariants of nets, we find a new realizability
obstruction (over $\mathbb{C}$) for matroids, and we estimate the number of
essential components in the first complex resonance variety of $\mathcal{A}$.
Our approach also reveals a rather unexpected connection of modular resonance
with the geometry of ${\rm SL}_2(\mathbb{C})$-representation varieties, which
are governed by the Maurer--Cartan equation.


  Uncertainty relations take a crucial and fundamental part in the frame of
quantum theory, and are bringing on many marvelous applications in the emerging
field of quantum information sciences. Especially, as entropy is imposed into
the uncertainty principle, entropy-based uncertainty relations lead to a number
of applications including quantum key distribution, entanglement witness,
quantum steering, quantum metrology, and quantum teleportation. Herein, the
history of the development of the uncertainty relations is discussed,
especially focusing on the recent progress with regard to
quantum-memory-assisted entropic uncertainty relations and dynamical
characteristics of the measured uncertainty in some explicit physical systems.
The aims are to help deepen the understanding of entropic uncertainty relations
and prompt further explorations for versatile applications of the relations on
achieving practical quantum tasks.


  We develop an ansatz for expressing the free energy of the two dimensional
$q$-states Potts model for $q > 4$ near its first order phase transition point.
We notice that for the moderate values of $ q \lesssim 15 $, the energy profile
at the phase transition is not expressible as a sum of gaussians. We discuss
how this affects the traditional finite size analysis of this phase transition.
In particular, the dominant length scale governing the finite size corrections
turns out to be much (${} \sim 6$ ~times) larger than the largest correlation
length in the problem.


  When a white dwarf (WD) is weakly magnetized and its accretion disk is thin,
accreted material first reaches the WD's surface at its equator. This matter
slows its orbit as it comes into co-rotation with the WD, dissipating kinetic
energy into thermal energy and creating a hot band of freshly accreted material
around the equator. Radiating in the extreme ultraviolet and soft X-rays, this
material moves toward the pole as new material piles behind it, eventually
becoming part of the WD once it has a temperature and rotational velocity
comparable with the surface. We present a set of solutions which describe the
properties of this ``spreading layer'' in the steady state limit based on the
conservation equations derived by Inogamov & Sunyaev (1999) for accreting
neutron stars. Our analysis and subsequent solutions show that the case of WDs
is qualitatively different. We investigate example solutions of the spreading
layer for a WD of mass $M=0.6M_\odot$ and radius $R=9\times10^{8}{\rm cm}$.
These solutions show that the spreading layer typically extends to an angle of
$\theta_{\rm SL}\approx0.01-0.1$ (with respect to the equator), depending on
accretion rate and the magnitude of the viscosity. At low accretion rates,
$\dot{M}\lesssim10^{18}{\rm g s}^{-1}$, the amount of spreading is negligible
and most of the dissipated energy is radiated back into the accretion disk.
When the accretion rate is high, such as in dwarf novae, symbiotic binaries,
and supersoft sources, the material may spread to latitudes high enough to be
directly visible above the accretion disk.


  The Giant Stellar Stream (GSS) is a prominent tidal feature in the halo of
the Andromeda Galaxy (M31), representing the ongoing destruction of a satellite
galaxy. In this paper, we investigate the formation of the GSS through detailed
numerical simulations of the tidal disruption of a progenitor system. Assuming
that the stream was created in a single merger event between M31 and a dwarf
spheroidal galaxy with stellar mass of $10^{9}M_{\odot}$, we successfully
reproduce the dynamical properties of the GSS. As the metallicity distribution
along the stream has been well determined from the observations (PAndAS and
AMIGA data sets), we use Monte Carlo simulations to reconstruct the original
metallicity distribution of the dwarf progenitor. We find that a progenitor
dwarf galaxy with a negative radial metallicity gradient, $\Delta$FeH = -$0.3
\pm 0.2$, reproduces the observed GSS properties at a time between 2.4 and 2.9
Gyrs into the merger. We also show that the observed double peak metallicity
distribution along the stream is a transitory structure caused by unique merger
circumstances where two groups of streaming stars are moving in opposite
directions, intersecting to produce the peaks.


  The gadolinium pyrochlores, Gd$_2B_2$O$_7$, are amongst the best realizations
of antiferromagnetically coupled Heisenberg spins on a pyrochlore lattice. We
present a magnetic characterization of Gd$_2$Pt$_2$O$_7$, a unique member of
this family. Magnetic susceptibility, heat capacity, and muon spin relaxation
measurements show that Gd$_2$Pt$_2$O$_7$ undergoes an antiferromagnetic
ordering transition at $T_N = 1.6$ K. This transition is strongly first order,
as indicated by the sharpness of the heat capacity anomaly, thermal hysteresis
in the magnetic susceptibility, and a non-divergent relaxation rate in $\mu$SR.
The form of the heat capacity below $T_N$ suggests that the ground state is an
anisotropic collinear antiferromagnet with an excitation spectrum that is
gapped by 0.245(1) meV. The ordering temperature in Gd$_2$Pt$_2$O$_7$, $T_N =
1.6$ K, is a substantial 160% increase from other gadolinium pyrochlores, which
have been found to order at 1 K or lower. We attribute this enhancement in
$T_N$ to the $B$-site cation, platinum, which, despite being non-magnetic, has
a filled $5d$ $t_{2g}$ orbital and an empty $5d$ $e_g$ orbital that can
facilitate superexchange. Thus, the magnetic frustration in Gd$_2$Pt$_2$O$_7$
is partially "relieved", thereby promoting magnetic order.


  The warping matrix has been defined for knot projections and knot diagrams by
using warping degrees. In particular, the warping matrix of a knot diagram
represents the knot diagram uniquely. In this paper we show that the rank of
the warping matrix is one greater than the crossing number. We also discuss the
linearly independence of knot diagrams by considering the warping incidence
matrix.


  We propose a solution to the novel task of rendering sharp videos from new
viewpoints from a single motion-blurred image of a face. Our method handles the
complexity of face blur by implicitly learning the geometry and motion of faces
through the joint training on three large datasets: FFHQ and 300VW, which are
publicly available, and a new Bern Multi-View Face Dataset (BMFD) that we
built. The first two datasets provide a large variety of faces and allow our
model to generalize better. BMFD instead allows us to introduce multi-view
constraints, which are crucial to synthesizing sharp videos from a new camera
view. It consists of high frame rate synchronized videos from multiple views of
several subjects displaying a wide range of facial expressions. We use the high
frame rate videos to simulate realistic motion blur through averaging. Thanks
to this dataset, we train a neural network to reconstruct a 3D video
representation from a single image and the corresponding face gaze. We then
provide a camera viewpoint relative to the estimated gaze and the blurry image
as input to an encoder-decoder network to generate a video of sharp frames with
a novel camera viewpoint. We demonstrate our approach on test subjects of our
multi-view dataset and VIDTIMIT.


  Bayesian approaches are one of the primary methodologies to tackle an inverse
problem in high dimensions. Such an inverse problem arises in hydrology to
infer the permeability field given flow data in a porous media. It is common
practice to decompose the unknown field into some basis and infer the
decomposition parameters instead of directly inferring the unknown. Given the
multiscale nature of permeability fields, wavelets are a natural choice for
parameterizing them. This study uses a Bayesian approach to incorporate the
statistical sparsity that characterizes discrete wavelet coefficients. First,
we impose a prior distribution incorporating the hierarchical structure of the
wavelet coefficient and smoothness of reconstruction via scale-dependent
hyperparameters. Then, Sequential Monte Carlo (SMC) method adaptively explores
the posterior density on different scales, followed by model selection based on
Bayes Factors. Finally, the permeability field is reconstructed from the
coefficients using a multiresolution approach based on second-generation
wavelets. Here, observations from the pressure sensor grid network are computed
via Multilevel Adaptive Wavelet Collocation Method (AWCM). Results highlight
the importance of prior modeling on parameter estimation in the inverse
problem.


  We discuss a natural game of competition and solve the corresponding mean
field game with \emph{common noise} when agents' rewards are \emph{rank
dependent}. We use this solution to provide an approximate Nash equilibrium for
the finite player game and obtain the rate of convergence.


  Magnetic Resonance Imaging (MRI) has excellent soft tissue contrast but is
hindered by an inherently slow data acquisition process. Compressed sensing,
which reconstructs sparse signals from incoherently sampled data, has been
widely applied to accelerate MRI acquisitions. Compressed sensing MRI requires
one or more model parameters to be tuned, which is usually done by hand, giving
sub-optimal tuning in general. To address this issue, we build on previous work
by the authors on the single-coil Variable Density Approximate Message Passing
(VDAMP) algorithm, extending the framework to multiple receiver coils to
propose the Parallel VDAMP (P-VDAMP) algorithm. For Bernoulli random variable
density sampling, P-VDAMP obeys a "state evolution", where the intermediate
per-iteration image estimate is distributed according to the ground truth
corrupted by a zero-mean Gaussian vector with approximately known covariance.
To our knowledge, P-VDAMP is the first algorithm for multi-coil MRI data that
obeys a state evolution with accurately tracked parameters. We leverage state
evolution to automatically tune sparse parameters on-the-fly with Stein's
Unbiased Risk Estimate (SURE). P-VDAMP is evaluated on brain, knee and
angiogram datasets and compared with four variants of the Fast Iterative
Shrinkage-Thresholding algorithm (FISTA), including two tuning-free variants
from the literature. The proposed method is found to have a similar
reconstruction quality and time to convergence as FISTA with an optimally tuned
sparse weighting and offers substantial robustness and reconstruction quality
improvements over competing tuning-free methods.


  We list multiplier and convolutor spaces of the spaces occurring in L.
Schwartz' "Th\'eorie des distributions". Furthermore we clarify whether the
multiplications and convolutions are continuous or not.


  We study the diffusion annihilation process which occurs when spin ice is
quenched from a high temperature paramagnetic phase deep into the spin ice
regime, where the excitations -- magnetic monopoles -- are sparse. We find that
due to the Coulomb interaction between the monopoles, a dynamical arrest
occurs, in which `non-universal' lattice-scale constraints impede the complete
decay of charge fluctuations. This phenomenon is outside the reach of universal
mean-field theory for a two-component Coulomb liquid. We identify the relevant
timescales for the dynamical arrest and propose an experiment for detecting
monopoles and their dynamics in spin ice based on this non-equilibrium
phenomenon.


  The equivalent external noise temperature of time-varying antennas is studied
using the concept of cross-frequency effective aperture, which quantifies the
intermodulation conversion of external noise across the frequency spectrum into
a receiver's operational bandwidth. The theoretical tools for this approach are
laid out following the classical method for describing external noise
temperature of linear time-invariant antennas, with generalizations made along
the way to capture the effects of time-varying components or materials. The
results demonstrate the specific ways that a time-varying system's noise
characteristics are dependent on its cross-frequency effective aperture and the
broadband noise environment. The general theory is applied to several examples,
including abstract models of hypothetical systems, antennas integrated with
parametric amplification, and time-modulated arrays.


  There are a number of studies in the literature on search for Charge-Parity
(CP) violating signals in top quark productions at the LHC. In most of these
studies, ChromoMagnetic Dipole Moments (CMDM) and ChromoElectric Dipole Moments
(CEDM) of top quarks is bounded either by deviations from the Standard Model
(SM) cross sections or by T-odd asymmetries in di-muon channels. However, the
required precision on these cross section values are far beyond from that of
ATLAS or CMS experiments can reach. In this letter, the investigation of CEDM
based asymmetries in the semileptonic top pair decays are presented as T-odd
asymmetries in CMS experiment. Expected asymmetry values are determined at the
detector level using MadGraph5, Pythia8 and Delphes softwares along with the
discrimination of the signal and the background with Deep Neural Networks
(DNN).


  A non-perturbative expansion method which gives a well-defined analytic
continuation of the running coupling constant from the spacelike to the
timelike region is applied to the inclusive semileptonic decay of the
$\tau$--lepton. The method allows us to evaluate $R_{\tau}$ by integration over
the non-perturbative physical region, rather than by using Cauchy's theorem,
and hence to incorporate threshold effects in a very direct way. Within our
framework the difference between the effective coupling constants in the
timelike and spacelike domains can be substantial and is not simply a matter of
the standard $\pi^2$ terms.


  With the success of down streaming task using English pre-trained language
model, the pre-trained Chinese language model is also necessary to get a better
performance of Chinese NLP task. Unlike the English language, Chinese has its
special characters such as glyph information. So in this article, we propose
the Chinese pre-trained language model StyleBERT which incorporate the
following embedding information to enhance the savvy of language model, such as
word, pinyin, five stroke and chaizi. The experiments show that the model
achieves well performances on a wide range of Chinese NLP tasks.


  No-hair theorems for scalar-tensor theories imply that the trivial scalar
field configuration is the unique configuration around stationary black hole
spacetimes. The most basic assumption in these theorems is that a constant
scalar configuration is actually admissible. In this paper, we classify
shift-symmetric Horndeski theories according to whether or not they admit the
trivial scalar configuration as a solution and under which conditions. Local
Lorentz symmetry and the presence of a linear coupling between the scalar field
and Gauss-Bonnet invariant plays feature prominently in this classification. We
then use the classification to show that any theory without linear Gauss-Bonnet
coupling that respects Local Lorentz symmetry admits all GR solutions. We also
study the scalar hair configuration around black hole spacetimes in theories
where the linear Gauss-Bonnet coupling is present. We show that the scalar hair
of the configuration is secondary, fixed by the regularity of the horizon, and
is determined by the black hole horizon properties.


  The Clausius inequality for closed systems was deduced from the Riemann
integration of closed Carnot cycle loops for irreversible transitions. The
corresponding inequalities for the recently developed open system Carnot cycles
are derived here and their properties indicate that no new nonequilibrium
entropy results from them as has been proposed over the decades. A simulation
using a unique dimer molecule with a hysteresis potential - which fulfills all
the dynamical laws - is used to show breakdown of the local equilibrium
hypothesis. The cardinal Curie principle is shown inadequate as a paradigm to
select force-flux relationships


  The article overviews the algorithm of identification of spectrum lines of
gravitational emission of electron with expected emission intensity close to
electromagnetic emission.


  The empirical mass discrepancy-acceleration (MDA) relation of disk galaxies
provides a key test for models of galactic dynamics. In terms of modified laws
of gravity and/or inertia, the MDA relation quantifies the transition from
Newtonian to modified dynamics at low centripetal accelerations a_c < 10^-10
m/s^2. As yet, neither dynamical models based on dark matter nor proposed
modifications of the laws of gravity/inertia have predicted the functional form
of the MDA relation. In this work, I revisit the MDA data and compare them to
four different theoretical scaling laws. Three of these scaling laws are
entirely empirical, the fourth one - the "simple mu" function of Modified
Newtonian Dynamics - derives from a toy model of gravity based on massive
gravitons (the "graviton picture"). All theoretical MDA relations comprise one
free parameter of the dimension of an acceleration, Milgrom's constant a_M. I
find that the "simple mu" function provides a good fit to the data free of
notable systematic residuals and provides the best fit among the four scaling
laws tested. The best-fit value of Milgrom's constant is a_M = (1.06 +/-
0.05)*10^-10 m/s^2. Given the successful prediction of the functional form of
the MDA relation, plus an overall agreement with the observed kinematics of
stellar systems spanning eight orders of magnitude in size and 14 orders of
magnitude in mass, I conclude that the "graviton picture" is sufficient (albeit
probably not a necessary or unique approach) to describe galactic dynamics on
all scales well beyond the scale of the solar system. This suggests that, at
least on galactic scales, gravity behaves as if it was mediated by massive
particles.


  We investigate regularity and a priori estimates for Fokker-Planck and
Hamilton-Jacobi equations with unbounded ingredients driven by the fractional
Laplacian of order $s\in(1/2,1)$. As for Fokker-Planck equations, we establish
integrability estimates under a fractional version of the Aronson-Serrin
interpolated condition on the velocity field and Bessel regularity when the
drift has low Lebesgue integrability with respect to the solution itself. Using
these estimates, through the Evans' nonlinear adjoint method we prove new
integral, sup-norm and H\"older estimates for weak and strong solutions to
fractional Hamilton-Jacobi equations with unbounded right-hand side and
polynomial growth in the gradient. Finally, by means of these latter results,
exploiting Calder\'on-Zygmund-type regularity for linear nonlocal PDEs and
fractional Gagliardo-Nirenberg inequalities, we deduce optimal $L^q$-regularity
for fractional Hamilton-Jacobi equations.


  We revisit empirical Bayes in the absence of a tractable likelihood function,
as is typical in scientific domains relying on computer simulations. We
investigate how the empirical Bayesian can make use of neural density
estimators first to use all noise-corrupted observations to estimate a prior or
source distribution over uncorrupted samples, and then to perform
single-observation posterior inference using the fitted source distribution. We
propose an approach based on the direct maximization of the log-marginal
likelihood of the observations, examining both biased and de-biased estimators,
and comparing to variational approaches. We find that, up to symmetries, a
neural empirical Bayes approach recovers ground truth source distributions.
With the learned source distribution in hand, we show the applicability to
likelihood-free inference and examine the quality of the resulting posterior
estimates. Finally, we demonstrate the applicability of Neural Empirical Bayes
on an inverse problem from collider physics.


  The current paradigm to explain the presence of Jupiters with small orbital
periods (P $<$ 10 days; hot Jupiters) that involves their formation beyond the
snow line following inward migration, has been challenged by recent works that
explored the possibility of in situ formation. We aim to test whether stars
harbouring hot Jupiters and stars with more distant gas-giant planets show any
chemical peculiarity that could be related to different formation processes.
Our results show that stars with hot Jupiters have higher metallicities than
stars with cool distant gas-giant planets in the metallicity range +0.00/+0.20
dex. The data also shows a tendency of stars with cool Jupiters to show larger
abundances of $\alpha$ elements. No abundance differences between stars with
cool and hot Jupiters are found when considering iron peak, volatile elements
or the C/O, and Mg/Si ratios. The corresponding $p$-values from the statistical
tests comparing the cumulative distributions of cool and hot planet hosts are
0.20, $<$ 0.01, 0.81, and 0.16 for metallicity, $\alpha$, iron-peak, and
volatile elements, respectively. We confirm previous works suggesting that more
distant planets show higher planetary masses as well as larger eccentricities.
We note differences in age and spectral type between the hot and cool planet
hosts samples that might affect the abundance comparison. The differences in
the distribution of planetary mass, period, eccentricity, and stellar host
metallicity suggest a different formation mechanism for hot and cool Jupiters.
The slightly larger $\alpha$ abundances found in stars harbouring cool Jupiters
might compensate their lower metallicities allowing the formation of gas-giant
planets.


  Automatic reading diagnosis systems can benefit both teachers for more
efficient scoring of reading exercises and students for accessing reading
exercises with feedback more easily. However, there are limited studies on
Automatic Speech Recognition (ASR) for child speech in languages other than
English, and limited research on ASR-based reading diagnosis systems. This
study investigates how efficiently state-of-the-art (SOTA) pretrained ASR
models recognize Dutch native children speech and manage to detect reading
miscues. We found that Hubert Large finetuned on Dutch speech achieves SOTA
phoneme-level child speech recognition (PER at 23.1\%), while Whisper (Faster
Whisper Large-v2) achieves SOTA word-level performance (WER at 9.8\%). Our
findings suggest that Wav2Vec2 Large and Whisper are the two best ASR models
for reading miscue detection. Specifically, Wav2Vec2 Large shows the highest
recall at 0.83, whereas Whisper exhibits the highest precision at 0.52 and an
F1 score of 0.52.


  We propose to use Fermi-Dirac distributions for quark and antiquark partons.
It allows a fair description of the $x$-dependence of the very recent NMC data
on the proton and neutron structure functions $F_2^p(x)$ and $F_2^n(x)$ at
$Q^2=4$ GeV$^2$, as well as the CCFR antiquark distribution $x\overline q(x)$.
We show that one can also use a corresponding Bose-Einstein expression to
describe consistently the gluon distribution. The Pauli exclusion principle,
which has been identified to explain the flavor asymmetry of the light-quark
sea of the proton, is advocated to guide us for making a simple construction of
the polarized parton distributions. We predict the spin dependent structure
functions $g_1^p(x)$ and $g_1^n(x)$ in good agreement with EMC and SLAC data.
The quark distributions involve some parameters whose values support well the
hypothesis that the violation of the quark parton model sum rules is a
consequence of the Pauli principle.


  Using an approach based on the heat kernel we prove an Atiyah-Bott-Lefschetz
theorem for the $L^2-$Lefschetz numbers associated to an elliptic complex of
cone differential operators over a compact manifold with conical singularities.
We then apply our results to the case of the de Rham complex.


  Determining the associated metrics we get a local classification of contact
metric three manifolds.


  The paper presents our design of a next generation information retrieval
system based on tag co-occurrences and subsequent clustering. We help users
getting access to digital data through information visualization in the form of
tag clusters. Current problems like the absence of interactivity and semantics
between tags or the difficulty of adding additional search arguments are
solved. In the evaluation, based upon SERVQUAL and IT systems quality
indicators, we found out that tag clusters are perceived as more useful than
tag clouds, are much more trustworthy, and are more enjoyable to use.


  A palindrome is a string that reads the same forward and backward. A
palindromic substring $P$ of a string $S$ is called a shortest unique
palindromic substring ($\mathit{SUPS}$) for an interval $[x, y]$ in $S$, if $P$
occurs exactly once in $S$, this occurrence of $P$ contains interval $[x, y]$,
and every palindromic substring of $S$ which contains interval $[x, y]$ and is
shorter than $P$ occurs at least twice in $S$. The $\mathit{SUPS}$ problem is,
given a string $S$, to preprocess $S$ so that for any subsequent query interval
$[x, y]$ all the $\mathit{SUPS}\mbox{s}$ for interval $[x, y]$ can be answered
quickly. We present an optimal solution to this problem. Namely, we show how to
preprocess a given string $S$ of length $n$ in $O(n)$ time and space so that
all $\mathit{SUPS}\mbox{s}$ for any subsequent query interval can be answered
in $O(k+1)$ time, where $k$ is the number of outputs.


  One of the main scientific drivers of the Space InfraRed Telescope Facility
(SIRTF) is the search for brown dwarfs and extrasolar superplanets. We discuss
observational strategies for identification of these objects, and conclude that
an optimal strategy is a wide IRAC survey (18 deg$^2$) with a 5 $\sigma$
sensitivity of 3.9 $\mu$Jy in channel 2 (M$\sim19.1^m$). For this sensitivity,
we provide estimates of the number of low mass brown dwarfs and isolated
planets detected per square degree for power-law mass functions with
$\alpha$=1.5, and 1.0. Shallower surveys covering a larger area are inefficient
because of large overheads and detector noise. Deeper surveys covering a
smaller area become more and more affected by crowding with galaxies. A survey
like the one that we propose would determine the field mass function down to a
few Jupiter masses through the identification of a large sample of brown dwarfs
and isolated planets. The proposed SIRTF survey would also allow the first
detection of ultracool substellar objects with temperatures between 700 K and
200 K. The cooling curves of substellar objects with masses less than 20
Jupiters imply that they should spend most of their lifetimes at temperatures
below 700 K. Preliminary models indicate that their atmospheres could be
dominated by water clouds, which would diminish their optical and near-infrared
fluxes. The properties of those objects are still completely unexplored.


  We propose some new simplifying ingredients for Feynman diagrams that seem
necessary for random lattice formulations of superstrings. In particular, half
the fermionic variables appear only in particle loops (similarly to loop
momenta), reducing the supersymmetry of the constituents of the Type IIB
superstring to N=1, as expected from their interpretation in the 1/N expansion
as super Yang-Mills.


  Model calibration aims to align confidence with prediction correctness. The
Cross-Entropy (CE) loss is widely used for calibrator training, which enforces
the model to increase confidence on the ground truth class. However, we find
the CE loss has intrinsic limitations. For example, for a narrow
misclassification, a calibrator trained by the CE loss often produces high
confidence on the wrongly predicted class (e.g., a test sample is wrongly
classified and its softmax score on the ground truth class is around 0.4),
which is undesirable. In this paper, we propose a new post-hoc calibration
objective derived from the aim of calibration. Intuitively, the proposed
objective function asks that the calibrator decrease model confidence on
wrongly predicted samples and increase confidence on correctly predicted
samples. Because a sample itself has insufficient ability to indicate
correctness, we use its transformed versions (e.g., rotated, greyscaled and
color-jittered) during calibrator training. Trained on an in-distribution
validation set and tested with isolated, individual test samples, our method
achieves competitive calibration performance on both in-distribution and
out-of-distribution test sets compared with the state of the art. Further, our
analysis points out the difference between our method and commonly used
objectives such as CE loss and mean square error loss, where the latters
sometimes deviates from the calibration aim.


  The physics objectives of SuperB, an asymmetric electron-positron collider
with a luminosity above 10^36/cm^2/s are described, together with the
conceptual design of a novel low emittance design that achieves this
performance with wallplug power comparable to that of the current B Factories,
and an upgraded detector capable of doing the physics in the SuperB
environment.


  We argue that there are currently two major bottlenecks to the commercial use
of statistical machine learning approaches for natural language generation
(NLG): (a) The lack of reliable automatic evaluation metrics for NLG, and (b)
The scarcity of high quality in-domain corpora. We address the first problem by
thoroughly analysing current evaluation metrics and motivating the need for a
new, more reliable metric. The second problem is addressed by presenting a
novel framework for developing and evaluating a high quality corpus for NLG
training.


  Transport from nutrient-rich coastal upwellings is a key factor influencing
biological activity in surrounding waters and even in the open ocean. The rich
upwelling in the North-Western African coast is known to interact strongly with
the wake of the Canary islands, giving rise to filaments and other mesoscale
structures of increased productivity. Motivated by this scenario, we introduce
a simplified two-dimensional kinematic flow describing the wake of an island in
a stream, and study the conditions under which there is a net transport of
substances across the wake. For small vorticity values in the wake, it acts as
a barrier, but there is a transition when increasing vorticity so that for
values appropriate to the Canary area, it entrains fluid and enhances
cross-wake transport.


  Motivated by the Invariant Subspace Problem, we describe explicitly the
closed subspace $H^2$ generated by the limit points in the $H^2$ norm of the
orbit of a thin Blaschke product $B$ under composition operators $C_\phi$
induced by non-elliptic automorphisms. This description exhibits a surprising
connection to model spaces. Finally, we give a constructive characterization of
the $C_\phi$-eigenfunctions in $H^p$ for $1\le p\le \infty$.


  The vector and pseudoscalar decay constants are calculated in the framework
of the Field Correlator Method. Di-electron widths: $\Gamma_{ee}(J/\psi)=5.41$
keV, $\Gamma_{ee}(\psi'(3686))=2.47$ keV, $\Gamma_{ee}(\psi''(3770))=0.248$
keV, in good agreement with experiment, are obtained with the same coupling,
$\alpha_s=0.165$, in QCD radiative corrections. We show that the larger
$\alpha_s=0.191\pm 0.004$ is needed to reach agreement with experiment for
$\Gamma_{\gamma\gamma}(\eta_c)=7.22$ keV, $\Gamma_{\gamma\gamma}
(\chi(^3P_0))=3.3$ keV, $\Gamma_{\gamma\gamma}(\chi(^3P_2))= 0.54$ keV, and
also for $\Gamma(J/\psi\to 3g)=59.5$ keV, $\Gamma(J/\psi\to \gamma 2g)=5.7$
keV. Meanwhile even larger $\alpha_s=0.238$ gives rise to good description of
$\Gamma(\psi'\to 3g)=52.7$ keV, $\Gamma(\psi'\to \gamma 2g)= 3.5$ keV, and
provides correct ratio of the branching fractions: $\frac{\mathcal{B}(J/\psi\to
light hadrons)}{\mathcal{B}(\psi'\to light hadrons)}=0.24.$


  In earlier work we developed the theory of signatures of hermitian forms over
algebras with involution with respect to orderings on the base field of the
algebra and obtained in particular that the total signature of a hermitian form
is a continuous function from the space of orderings of that field to
$\mathbb{Z}$. In this note we give another presentation of signatures and also
introduce and study the stability index of algebras with involution.


  The conventional solutions for fault-detection, identification, and
reconstruction (FDIR) require centralized decision-making mechanisms which are
typically combinatorial in their nature, necessitating the design of an
efficient distributed FDIR mechanism that is suitable for multi-agent
applications. To this end, we develop a general framework for efficiently
reconstructing a sparse vector being observed over a sensor network via
nonlinear measurements. The proposed framework is used to design a distributed
multi-agent FDIR algorithm based on a combination of the sequential convex
programming (SCP) and the alternating direction method of multipliers (ADMM)
optimization approaches. The proposed distributed FDIR algorithm can process a
variety of inter-agent measurements (including distances, bearings, relative
velocities, and subtended angles between agents) to identify the faulty agents
and recover their true states. The effectiveness of the proposed distributed
multi-agent FDIR approach is demonstrated by considering a numerical example in
which the inter-agent distances are used to identify the faulty agents in a
multi-agent configuration, as well as reconstruct their error vectors.


  We performed spectroscopic and photometric analyses on the early B-type LP
Ori young stellar object located in the Orion Nebula. The high-resolution
spectra of LP Ori was obtained at the Ankara University Kreiken Observatory in
2023, while all spectra recorded over the past 19 years were extracted from the
ESO and ESPaDOnS archives. In these spectra of LP Ori, there is typically an
emission observed in the core of the Balmer profile. This structure is
accompanied by a $\sim$14-year interval inverse P-Cygni repetition superimposed
on the Balmer profile. Additionally, an emission in the $\lambda$5875 He~I line
is visible in the spectra in the year 2023. When this emission is considered
together with the inverse P-Cygni structures, these observations suggest that
LP Ori is a Herbig Ae/Be star. The abundance pattern of LP Ori is close to
solar with the exception of a slightly rich helium and slightly poor Al
abundance. Additionally, the spectral energy distribution of LP Ori was
constructed to confirm the infrared excess caused by its circumstellar disk.
Furthermore, the photometric analysis performed on the TESS observations of LP
Ori shows significant photometric variability and the frequency analysis
reveals a $\beta$~Cephei star in its forthcoming evolution.


  This brief paper presents simple simulation-based algorithms for obtaining an
approximately optimal policy in a given finite set in large finite constrained
Markov decision processes. The algorithms are adapted from playing strategies
for "sleeping experts and bandits" problem and their computational complexities
are independent of state and action space sizes if the given policy set is
relatively small. We establish convergence of their expected performances to
the value of an optimal policy and convergence rates, and also almost-sure
convergence to an optimal policy with an exponential rate for the algorithm
adapted within the context of sleeping experts.


  We discuss phenomenology of extra time dimensions in a scenario where the
standard model particles are localized in ``our'' time, whereas gravity can
propagate in all time dimensions. For an odd number of extra times, at small
distances, the (real part of the) Newtonian potential is ``screened'' by
tachyonic Kaluza-Klein gravitons. In general, the gravitational self-energy of
objects acquires an imaginary part. This complexity may either be interpreted
as an amplitude for the disappearance into ``nothing'', leading to the
causality and probability violation in low-energy processes, or as an artifact
of the fictitious decay into the unphysical negative energy tachyons. The
former case would put the most severe phenomenological restrictions on such
theories. In the latter case the size of extra times may be within the reach of
the proposed gravitational experiments. In such a case these experiments should
observe that the strength of the Newtonian gravity diminishes at short
distances.


  We give an algorithm, based on the $\phi$-expansion of Parry, in order to
compute the topological entropy of a class of shift spaces. The idea is the
solve an inverse problem for the dynamical systems $\beta x+\alpha \mod1$.The
first part is an exposition of the $\phi$-expansion applied to piecewise
monotone dynamical systems. We formulate for the validity of the
$\phi$-expansion, necessary and sufficient conditions, which are different from
those in Parry's paper.


  [Background] Abundance anomalies in some globular clusters, such as the
enhancement of potassium and the depletion of magnesium, can be explained in
terms of an earlier generation of stars polluting the presently observed ones.
It was shown that the potential range of temperatures and densities of the
polluting sites depends on the strength of a few number of critical reaction
rates. The reaction has been identified as one of these important reactions.
[Purpose] The key ingredient for evaluating the thermonuclear reaction rate is
the strength of the resonances which, at low energy, are proportional to their
proton width. Therefore the goal of this work is to determine the proton widths
of unbound 31P states. [Method] States in 31P were studied at the
Maier-Leibnitz-Laboratorium using the one-proton transfer reaction. Deuterons
were detected with the Q3D magnetic spectrometer. Angular distribution and
spectroscopic factors were extracted for 27 states, and proton widths and
resonance strengths were calculated for the unbound states. [Results] Several
unbound states have been observed for the first time in a one-proton transfer
reaction. Above 20 MK, the reaction rate is now entirely estimated from the
observed properties of states. The reaction rate uncertainty from all
resonances other than the resonance has been reduced down to less than a factor
of two above that temperature. The unknown spin and parity of the resonance
dominates the uncertainty in the rate in the relevant temperature range.
[Conclusion] The remaining source of uncertainty on the reaction rate comes
from the unknown spin and parity of the resonance which can change the reaction
rate by a factor of ten in the temperature range of interest.


  Learning domain-invariant representations has become a popular approach to
unsupervised domain adaptation and is often justified by invoking a particular
suite of theoretical results. We argue that there are two significant flaws in
such arguments. First, the results in question hold only for a fixed
representation and do not account for information lost in non-invertible
transformations. Second, domain invariance is often a far too strict
requirement and does not always lead to consistent estimation, even under
strong and favorable assumptions. In this work, we give generalization bounds
for unsupervised domain adaptation that hold for any representation function by
acknowledging the cost of non-invertibility. In addition, we show that
penalizing distance between densities is often wasteful and propose a bound
based on measuring the extent to which the support of the source domain covers
the target domain. We perform experiments on well-known benchmarks that
illustrate the short-comings of current standard practice.


  We examine the impact of a new tool for suppressing the expression of
dissent---a daily tax on social media use. Using a synthetic control framework,
we estimate that the tax reduced the number of georeferenced Twitter users in
Uganda by 13 percent. The estimated treatment effects are larger for poorer and
less frequent users. Despite the overall decline in Twitter use, tweets
referencing collective action increased by 31 percent and observed protests
increased by 47 percent. These results suggest that taxing social media use may
not be an effective tool for reducing political dissent.


  The effects of confinement on colloidal self-assembly in the case of fixed
number of confined particles are studied in the one dimensional lattice model
solved exactly in the Grand Canonical Ensemble (GCE) in [J. P\k{e}kalski et al.
J. Chem. Phys. 142, 014903 (2015)]. The model considers a pair interaction
defined by a short-range attraction plus a longer-range repulsion. We consider
thermodynamic states corresponding to self-assembly into clusters. Both, fixed
and adaptive boundaries are studied. For fixed boundaries, there are particular
states in which, for equal average densities, the number of clusters in the GCE
is larger than in the Canonical Ensemble. The dependence of pressure on density
has a different form when the system size changes with fixed number of
particles and when the number of particles changes with fixed size of the
system. In the former case the pressure has a nonmonotonic dependence on the
system size. The anomalous increase of pressure for expanding system is
accompanied by formation of a larger number of smaller clusters. In the case of
elastic confining surfaces we observe a bistability, i.e. two significantly
different system sizes occur with almost the same probability. The mechanism of
the bistability in the closed system is different to that of the case of
permeable walls, where the two equilibrium system sizes correspond to a
different number of particles.


  The article describes new model of the Sun having a hollow core as introduced
in [1]. The model helps to explain a number of experimental facts of kinetics,
energetics, and Sun spectroscopy based on classic physics. The origin of the
Sun's energy is not the thermonuclear process taking place in its core, but the
coherent, anisotropic, gravitational compression of the atomic hydrogen in the
solar shell at the temperature of 6289 K.


  Privacy concerns have long been expressed around smart devices, and the
concerns around Android apps have been studied by many past works. Over the
past 10 years, we have crawled and scraped data for almost 1.9 million apps,
and also stored the APKs for 135,536 of them. In this paper, we examine the
trends in how Android apps have changed over time with respect to privacy and
look at it from two perspectives: (1) how privacy behavior in apps have changed
as they are updated over time, (2) how these changes can be accounted for when
comparing third-party libraries and the app's own internals. To study this, we
examine the adoption of HTTPS, whether apps scan the device for other installed
apps, the use of permissions for privacy-sensitive data, and the use of unique
identifiers. We find that privacy-related behavior has improved with time as
apps continue to receive updates, and that the third-party libraries used by
apps are responsible for more issues with privacy. However, we observe that in
the current state of Android apps, there has not been enough of an improvement
in terms of privacy and many issues still need to be addressed.


  Distributed storage plays an essential role in realizing robust and secure
data storage in a network over long periods of time. A distributed storage
system consists of a data owner machine, multiple storage servers and channels
to link them. In such a system, secret sharing scheme is widely adopted, in
which secret data are split into multiple pieces and stored in each server. To
reconstruct them, the data owner should gather plural pieces. Shamir's (k,
n)-threshold scheme, in which the data are split into n pieces (shares) for
storage and at least k pieces of them must be gathered for reconstruction,
furnishes information theoretic security, that is, even if attackers could
collect shares of less than the threshold k, they cannot get any information
about the data, even with unlimited computing power. Behind this scenario,
however, assumed is that data transmission and authentication must be perfectly
secure, which is not trivial in practice. Here we propose a totally information
theoretically secure distributed storage system based on a user-friendly
single-password-authenticated secret sharing scheme and secure transmission
using quantum key distribution, and demonstrate it in the Tokyo metropolitan
area.