  Network-level privacy is the Achilles heel of financial privacy in
cryptocurrencies. Financial privacy amounts to achieving and maintaining
blockchain- and network-level privacy. Blockchain-level privacy recently
received substantial attention. Specifically, several privacy-enhancing
technologies were proposed and deployed to enhance blockchain-level privacy. On
the other hand, network-level privacy, i.e., privacy on the peer-to-peer layer,
has seen far less attention and development. In this work, we aim to provide a
peer-to-peer network simulator, ethp2psim, that allows researchers to evaluate
the privacy guarantees of privacy-enhanced broadcast and message routing
algorithms. Our goal is two-fold. First, we want to enable researchers to
implement their proposed protocols in our modular simulator framework. Second,
our simulator allows researchers to evaluate the privacy guarantees of
privacy-enhanced routing algorithms. Finally, ethp2psim can help choose the
right protocol parameters for efficient, robust, and private deployment.


  We examine the demographics of radio-emitting active galactic nuclei (AGN) in
the local universe as a function of host galaxy properties, most notably both
stellar mass and star formation rate. Radio AGN activity is theoretically
implicated in helping reduce star formation rates of galaxies, and therefore it
is natural to investigate the relationship between these two galaxy properties.
We use a sample of around 10, 000 galaxies from the Mapping Nearby Galaxies at
APO (MaNGA) survey, part of the Sloan Digital Sky Survey IV (SDSS-IV), along
with the Faint Images of the Radio Sky at Twenty centimeters (FIRST) radio
survey and the National Radio Astronomy Observatory (NRAO) Very Large Array
(VLA) Sky Survey (NVSS). There are 1,126 galaxies in MaNGA with radio
detections. Using star formation rate and stellar mass estimates based on
Pipe3D, inferred from the high signal-to-noise ratio measurements from MaNGA,
we show that star formation rates are strongly correlated with 20 cm radio
emission, as expected. We identify as radio AGN those radio emitters that are
much stronger than expected from the star formation rate. Using this sample of
AGN, the well-measured stellar velocity dispersions from MaNGA, and the black
hole M-sigma relationship, we examine the Eddington ratio distribution and its
dependence on stellar mass and star formation rate. We find that the Eddington
ratio distribution depends strongly on stellar mass, with more massive galaxies
having larger Eddington ratios. As found in previous studies, the AGN fraction
increases rapidly with stellar mass. We do not find any dependence on star
formation rate, specific star formation rate, or velocity dispersion when
controlling for stellar mass. We conclude that galaxy star formation rates
appear to be unrelated to the presence or absence of a radio AGN, which may be
useful in constraining theoretical models of AGN feedback.


  In previous work we studied the spin-boson model in the multiphoton regime,
using a rotation that provides a separation between terms that contribute most
of the level energies away from resonance, and terms responsible for the level
splittings at the anticrossing. Here, we consider a generalization of the
spin-boson model consisting of a three-level system coupled to an oscillator.
We construct a similar rotation and apply it to the more complicated model. We
find that the rotation provides a useful approximation to the energy levels in
the multiphoton region of the new problem. We find that good results can be
obtained for the level splittings at the anticrossings for resonances involving
the lower two levels in regions away from accidental or low-order resonances of
the upper two levels.


  Global rice production systems face two opposing challenges: the need to
increase production to accommodate the world's growing population while
simultaneously reducing greenhouse gas (GHG) emissions. Adaptations to drainage
regimes are one of the most promising options for methane mitigation in rice
production. Whereas several studies have focused on mid-season drainage (MD) to
mitigate GHG emissions, early-season drainage (ED) varying in timing and
duration has not been extensively studied. However, such ED periods could
potentially be very effective since initial available Carbon levels (and
thereby the potential for methanogenesis) can be very high in paddy systems
with rice straw incorporation. This study tested the effectiveness of seven
drainage regimes varying in their timing and duration (combinations of ED and
MD) to mitigate methane (CH4) and nitrous oxide (N2O) emissions in a 101-day
growth chamber experiment. Emissions were considerably reduced by early-season
drainage compared to both conventional continuous flooding (CF) and the MD
drainage regime. The results suggest that ED + MD drainage may have the
potential to reduce methane emissions and yield-scaled global warming potential
by 85 to 90% compared to CF and by 75 to 77% compared to MD only. A combination
of (short or long) ED drainage and one MD drainage episode was found to be the
most effective in mitigating methane emissions without negatively affecting
yield. In particular, compared with CF, the long early-season drainage
treatments LE + SM and LE + LM significantly decreased yield scaled global
warming potential by 85% and 87% respectively. This was associated with carbon
being stabilised early in the season, thereby reducing available carbon for
methanogenesis. Overall nitrous oxide emissions were small and not
significantly affected by ED.


  In this short paper, we consider a quadruple $(\Omega, \AA, \theta,
\mu)$,where $\AA$ is a $\sigma$-algebra of subsets of $\Omega$, and $\theta$ is
a measurable bijection from $\Omega$ into itself that preserves the measure
$\mu$. For each $B \in \AA$, we consider the measure $\mu_B$ obtained by taking
cycles (excursions) of iterates of $\theta$ from $B$. We then derive a relation
for $\mu_B$ that involves the forward and backward hitting times of $B$ by the
trajectory $(\theta^n \omega, n \in \Z)$ at a point $\omega \in \Omega$.
Although classical in appearance, its use in obtaining uniqueness of invariant
measures of various stochastic models seems to be new. We apply the concept to
countable Markov chains and Harris processes.


  A Parallel Self-Organizing Map (Parallel-SOM) is proposed to modify Kohonen's
SOM in parallel computing environment. In this model, two separate layers of
neurons are connected together. The number of neurons in both layers and
connections between them is the product of the number of all elements of input
signals and the number of possible classification of the data. With this
structure the conventional repeated learning procedure is modified to learn
just once. The once learning manner is more similar to human learning and
memorizing activities. During training, weight updating is managed through a
sequence of operations among some transformation and operation matrices. Every
connection between neurons of input/output layers is considered as a
independent processor. In this way, all elements of the Euclidean distance
matrix and weight matrix are calculated simultaneously. The minimum distance of
every line of distance matrix can be found by Grover's search algorithm. This
synchronization feature improves the weight updating sequence significantly.
With a typical classification example, the convergence result demonstrates
efficient performance of Parallel-SOM. Theoretic analysis and proofs also show
some important properties of proposed model. Especially, the paper proves that
Parallel-SOM has the same convergence property as Kohonen's SOM, but the
complexity of former is reduced obviously.


  The statistical modelling of ranking data has a long history and encompasses
various perspectives on how observed rankings arise. One of the most common
models, the Plackett-Luce model, is frequently used to aggregate rankings from
multiple rankers into a single ranking that corresponds to the underlying
quality of the ranked objects. Given that rankers frequently exhibit
heterogeneous preferences, mixture-type models have been developed to group
rankers with more or less homogeneous preferences together to reduce bias.
However, occasionally, these preference groups are known a-priori. Under these
circumstances, current practice consists of fitting Plackett-Luce models
separately for each group. Nevertheless, there might be some commonalities
between different groups of rankers, such that separate estimation implies a
loss of information. We propose an extension of the Plackett-Luce model, the
Sparse Fused Plackett-Luce model, that allows for joint learning of such
heterogeneous rank data, whereby information from different groups is utilised
to achieve better model performance. The observed rankings can be considered a
function of variables pertaining to the ranked objects. As such, we allow for
these types of variables, where information on the coefficients is shared
across groups. Moreover, as not all variables might be relevant for the ranking
of an object, we impose sparsity on the coefficients to improve
interpretability, estimation and prediction of the model. Simulations studies
indicate superior performance of the proposed method compared to existing
approaches. To illustrate the usage and interpretation of the method, an
application on data consisting of consumer preferences regarding various sweet
potato varieties is provided. An R package containing the proposed methodology
can be found on https://CRAN.R-project.org/package=SFPL.


  We study the structure of the international trade hypergraph consisting of
triangular hyperedges representing the exporter-importer-product relationship.
Measuring the mean hyperdegree of the adjacent vertices, we first find its
behaviors different from those in the pairwise networks and explain the origin
by tracing the relation between the hyperdegree and the pairwise degree. To
interpret the observed hyperdegree correlation properties in the context of
trade strategies, we decompose the correlation into two components by
identifying one with the background correlation remnant even in the exponential
random hypergraphs preserving the given empirical hyperdegree sequence. The
other component characterizes the net correlation and reveals the bias of the
exporters of low hyperdegree towards the importers of high hyperdegree and the
products of low hyperdegree, which information is not readily accessible in the
pairwise networks. Our study demonstrates the power of the hypergraph approach
in the study of real-world complex systems and offers a theoretical framework.


  Inspired by Zermelo's quasi-categoricity result characterizing the models of
second-order Zermelo-Fraenkel set theory $\text{ZFC}_2$, we investigate when
those models are fully categorical, characterized by the addition to
$\text{ZFC}_2$ either of a first-order sentence, a first-order theory, a
second-order sentence or a second-order theory. Thus we mount an analysis of
the categorical large cardinals. This mathematical analysis leads naturally to
philosophical issues concerning structuralism and realism, including especially
the tension between categoricity and reflection. Ultimately we identify grounds
for the preference of noncategoricity in one's foundations.


  Suppose that $T$ is an acyclic $r$-uniform hypergraph, with $r\ge 2$. We
define the ($t$-color) chromatic Ramsey number $\chi(T,t)$ as the smallest $m$
with the following property: if the edges of any $m$-chromatic $r$-uniform
hypergraph are colored with $t$ colors in any manner, there is a monochromatic
copy of $T$. We observe that $\chi(T,t)$ is well defined and $$\left\lceil
{R^r(T,t)-1\over r-1}\right \rceil +1 \le \chi(T,t)\le |E(T)|^t+1$$ where
$R^r(T,t)$ is the $t$-color Ramsey number of $H$. We give linear upper bounds
for $\chi(T,t)$ when T is a matching or star, proving that for $r\ge 2, k\ge 1,
t\ge 1$, $\chi(M_k^r,t)\le (t-1)(k-1)+2k$ and $\chi(S_k^r,t)\le t(k-1)+2$ where
$M_k^r$ and $S_k^r$ are, respectively, the $r$-uniform matching and star with
$k$ edges.
  The general bounds are improved for $3$-uniform hypergraphs. We prove that
$\chi(M_k^3,2)=2k$, extending a special case of Alon-Frankl-Lov\'asz' theorem.
We also prove that $\chi(S_2^3,t)\le t+1$, which is sharp for $t=2,3$. This is
a corollary of a more general result. We define $H^{[1]}$ as the 1-intersection
graph of $H$, whose vertices represent hyperedges and whose edges represent
intersections of hyperedges in exactly one vertex. We prove that $\chi(H)\le
\chi(H^{[1]})$ for any $3$-uniform hypergraph $H$ (assuming $\chi(H^{[1]})\ge
2$). The proof uses the list coloring version of Brooks' theorem.


  Most of existing image denoising methods assume the corrupted noise to be
additive white Gaussian noise (AWGN). However, the realistic noise in
real-world noisy images is much more complex than AWGN, and is hard to be
modelled by simple analytical distributions. As a result, many state-of-the-art
denoising methods in literature become much less effective when applied to
real-world noisy images captured by CCD or CMOS cameras. In this paper, we
develop a trilateral weighted sparse coding (TWSC) scheme for robust real-world
image denoising. Specifically, we introduce three weight matrices into the data
and regularisation terms of the sparse coding framework to characterise the
statistics of realistic noise and image priors. TWSC can be reformulated as a
linear equality-constrained problem and can be solved by the alternating
direction method of multipliers. The existence and uniqueness of the solution
and convergence of the proposed algorithm are analysed. Extensive experiments
demonstrate that the proposed TWSC scheme outperforms state-of-the-art
denoising methods on removing realistic noise.


  In this study, we aim to better understand the cognitive-emotional experience
of visually impaired people when navigating in unfamiliar urban environments,
both outdoor and indoor. We propose a multimodal framework based on random
forest classifiers, which predict the actual environment among predefined
generic classes of urban settings, inferring on real-time, non-invasive,
ambulatory monitoring of brain and peripheral biosignals. Model performance
reached 93% for the outdoor and 87% for the indoor environments (expressed in
weighted AUROC), demonstrating the potential of the approach. Estimating the
density distributions of the most predictive biomarkers, we present a series of
geographic and temporal visualizations depicting the environmental contexts in
which the most intense affective and cognitive reactions take place. A linear
mixed model analysis revealed significant differences between categories of
vision impairment, but not between normal and impaired vision. Despite the
limited size of our cohort, these findings pave the way to emotionally
intelligent mobility-enhancing systems, capable of implicit adaptation not only
to changing environments but also to shifts in the affective state of the user
in relation to different environmental and situational factors.


  In this paper, we introduce FreSaDa, a French Satire Data Set, which is
composed of 11,570 articles from the news domain. In order to avoid reporting
unreasonably high accuracy rates due to the learning of characteristics
specific to publication sources, we divided our samples into training,
validation and test, such that the training publication sources are distinct
from the validation and test publication sources. This gives rise to a
cross-domain (cross-source) satire detection task. We employ two classification
methods as baselines for our new data set, one based on low-level features
(character n-grams) and one based on high-level features (average of CamemBERT
word embeddings). As an additional contribution, we present an unsupervised
domain adaptation method based on regarding the pairwise similarities (given by
the dot product) between the training samples and the validation samples as
features. By including these domain-specific features, we attain significant
improvements for both character n-grams and CamemBERT embeddings.


  We describe a detection system designed for precise measurements of angular
correlations in neutron $\beta$ decay. The system is based on thick, large
area, highly segmented silicon detectors developed in collaboration with Micron
Semiconductor, Ltd. The prototype system meets specifications for $\beta$
electron detection with energy thresholds below 10 keV, energy resolution of
$\sim$3 keV FWHM, and rise time of $\sim$50 ns with 19 of the 127 detector
pixels instrumented. Using ultracold neutrons at the Los Alamos Neutron Science
Center, we have demonstrated the coincident detection of $\beta$ particles and
recoil protons from neutron $\beta$ decay. The fully instrumented detection
system will be implemented in the UCNB and Nab experiments, to determine the
neutron $\beta$ decay parameters $B$, $a$, and $b$.


  We examine the space of allowed S-matrices on the Adler zeros' plane using
the recently resurrected (numerical) S-matrix bootstrap program for pion
scattering. Two physical quantities, an averaged total scattering
cross-section, and an averaged entanglement power for the boundary S-matrices,
are studied. Emerging linearity in the leading Regge trajectory is correlated
with a reduction in both these quantities. We identify two potentially viable
regions where the S-matrices give decent agreement with low energy S- and
P-wave scattering lengths and have leading Regge trajectory compatible with
experiments. We also study the line of minimum averaged total cross section in
the Adler zeros' plane. The Lovelace-Shapiro model, which was a precursor to
modern string theory, is given by a straight line in the Adler zeros' plane
and, quite remarkably, we find that this line intersects the space of allowed
S-matrices near both these regions.


  We demonstrate the magnetization reversal features in NiFe/IrMn/NiFe
thin-film structures with 40% and 75% relative content of Ni in Permalloy in
the temperature range from 80 K to 300 K. At the descending branches of the
hysteresis loops, the magnetization reversal sequence of the two ferromagnetic
layers is found to depend on the type of NiFe alloy. In the samples with 75%
relative content of Ni, the bottom ferromagnetic layer reverses prior to the
top one. On the contrary, in the samples with 40% of Ni, the top ferromagnetic
layer reverses prior to the bottom one. These tendencies of magnetization
reversal are preserved in the entire range of temperatures. These distinctions
can be explained by the morphological and structural differences of interfaces
in the samples based on two types of Permalloy.


  Machine learning (ML) methods already permeate environmental decision-making,
from processing high-dimensional data on earth systems to monitoring compliance
with environmental regulations. Of the ML techniques available to address
pressing environmental problems (e.g., climate change, biodiversity loss),
Reinforcement Learning (RL) may both hold the greatest promise and present the
most pressing perils. This paper explores how RL-driven policy refracts
existing power relations in the environmental domain while also creating unique
challenges to ensuring equitable and accountable environmental decision
processes. We leverage examples from RL applications to climate change
mitigation and fisheries management to explore how RL technologies shift the
distribution of power between resource users, governing bodies, and private
industry.


  Microarchitectural Data Sampling (MDS) is a set of hardware vulnerabilities
in Intel CPUs that allows an attacker to leak bytes of data from memory loads
and stores across various security boundaries. On affected CPUs, some of these
vulnerabilities were patched via microcode updates. Additionally, Intel
announced that the newest microarchitectures, namely Cascade Lake and Ice Lake,
were not affected by MDS. While Cascade Lake turned out to be vulnerable to the
ZombieLoad v2 MDS attack (also known as TAA), Ice Lake was not affected by this
attack.
  In this technical report, we show a variant of MSBDS (CVE2018-12126), an MDS
attack, also known as Fallout, that works on Ice Lake CPUs. This variant was
automatically synthesized using Transynther, a tool to find new variants of
Meltdown-type attacks. Based on the findings of Transynther, we analyze
different microcodes regarding this issue, showing that only microcode versions
after January 2020 prevent exploitation of the vulnerability. These results
show that Transynther is a valuable tool to find new variants, and also to test
for regressions possibly introduced with microcode updates.


  We have found an infinite dimensional manifold of exact solutions of the
Navier-Stokes loop equation for the Wilson loop in decaying Turbulence in
arbitrary dimension $d >2$. This solution family is equivalent to a fractal
curve in complex space $\mathbb C^d$ with random steps parametrized by $N$
Ising variables $\sigma_i=\pm 1$, in addition to a rational number
$\frac{p}{q}$ and an integer winding number $r$, related by $\sum \sigma_i = q
r$. This equivalence provides a dual theory describing a strong turbulent phase
of the Navier-Stokes flow in $\mathbb R_d$ space as a random geometry in a
different space, like ADS/CFT correspondence in gauge theory. From a
mathematical point of view, this theory implements a stochastic solution of the
unforced Navier-Stokes equations. For a theoretical physicist, this is a
quantum statistical system with integer-valued parameters, satisfying some
number theory constraints. Its long-range interaction leads to critical
phenomena when its size $N \rightarrow \infty$ or its chemical potential $\mu
\rightarrow 0$. The system with fixed $N$ has different asymptotics at odd and
even $N\rightarrow \infty$, but the limit $\mu \rightarrow 0$ is well defined.
The energy dissipation rate is analytically calculated as a function of $\mu$
using methods of number theory. It grows as $\nu/\mu^2$ in the continuum limit
$\mu \rightarrow 0$, leading to anomalous dissipation at $\mu \propto
\sqrt{\nu} \to 0$. The same method is used to compute all the local vorticity
distribution, which has no continuum limit but is renormalizable in the sense
that infinities can be absorbed into the redefinition of the parameters. The
small perturbation of the fixed manifold satisfies the linear equation we
solved in a general form. This perturbation decays as $t^{-\lambda}$, with a
continuous spectrum of indexes $\lambda$ in the local limit $\mu \to 0$.


  Using a metric perturbation method, we study gravitational waves from a test
particle scattered by a spherically symmetric relativistic star. We calculate
the energy spectrum and the waveform of gravitational waves for axial modes.
Since metric perturbations in axial modes do not couple to the matter fluid of
the star, emitted waves for a normal neutron star show only one peak in the
spectrum, which corresponds to the orbital frequency at the turning point,
where the gravitational field is strongest. However, for an ultracompact star
(the radius $R \lesssim 3M$), another type of resonant periodic peak appears in
the spectrum. This is just because of an excitation by a scattered particle of
axial quasinormal modes, which were found by Chandrasekhar and Ferrari. This
excitation comes from the existence of the potential minimum inside of a star.
We also find for an ultracompact star many small periodic peaks at the
frequency region beyond the maximum of the potential, which would be due to a
resonance of two waves reflected by two potential barriers (Regge-Wheeler type
and one at the center of the star). Such resonant peaks appear neither for a
normal neutron star nor for a Schwarzschild black hole. Consequently, even if
we analyze the energy spectrum of gravitational waves only for axial modes, it
would be possible to distinguish between an ultracompact star and a normal
neutron star (or a Schwarzschild black hole).


  The one-sided P-value has a long history stretching at least as far back as
Laplace (1812) but has in recent times been mostly supplanted by the two-sided
P-value. We present justification for a bijective relationship between the
one-sided P-value and a likelihood ratio based on maximum likelihood, a
relationship that cannot be demonstrated for the two-sided P-value. A number of
criticisms of P-values are discussed and it is shown that many of these
criticisms are not justified when a likelihood ratio interpretation of a
one-sided P-value is employed. Converting a one-sided P-value to a likelihood
ratio provides the advantages of the likelihood evidential paradigm.


  The effect of isoelectronic substitutions on the microscopic properties of
LaFe1-xRuxAsO, for 0< x< 0.8, has been investigated by means of muSR and 139La
NMR. It was found that Ru substitution causes a progressive reduction of the
N\`eel temperature (T_N) and of the magnetic order parameter without leading to
the onset of superconductivity. The temperature dependence of 139La nuclear
spin-lattice relaxation rate 1/T_1 can be suitably described within a two-band
model. One band giving rise to the spin density wave ground-state, while the
other one is characterized by weakly correlated electrons. Fe for Ru
substitution yields to a progressive decrease of the density of states at the
Fermi level close to the one derived from band structure calculations. The
reduction of T_N with doping follows the predictions of the J_1-J_2 model on a
square lattice, which appears to be an effective framework to describe the
magnetic properties of the spin density wave ground-state.


  Stochastic Spatio-Temporal processes are prevalent across domains ranging
from modeling of plasma to the turbulence in fluids to the wave function of
quantum systems. This letter studies a measure-theoretic description of such
systems by describing them as evolutionary processes on Hilbert spaces, and in
doing so, derives a framework for spatio-temporal manipulation from fundamental
thermodynamic principles. This approach yields a variational optimization
framework for controlling stochastic fields. The resulting scheme is applicable
to a wide class of spatio-temporal processes and can be used for optimizing
parameterized control policies. Our simulated experiments explore the
application of two forms of this approach on four stochastic spatio-temporal
processes, with results that suggest new perspectives and directions for
studying stochastic control problems for spatio-temporal systems.


  We present a detailed study of SMM 02399-0136, a hyperluminous, active galaxy
selected from a sub-mm survey of the distant Universe. This galaxy is the
brightest source in the fields of seven rich, lensing clusters - total area
0.01 deg^2 - that we have mapped with a sensitivity of ~2 mJy/bm at 850um. We
identify a compact optical counterpart with B ~ 23 and a LSB companion 3" away.
Our spectroscopy shows that components have the same redshift; z = 2.803 +/-
0.003. The emission lines widths, FWHM ~ 1000-1500 km/s, and line ratios, along
with the compact morphology and high luminosity (M_B ~ -24.0) of the galaxy
indicate that SMM 02399-0136 contains a rare dust-embedded, narrow-line or
type-2 AGN. The source is lensed by the foreground cluster, amplifying its
apparent luminosity by a factor of 2.5. Taking this into account, we estimate
that SMM 02399-0136 is five times more luminous than F10214+4724. Its far-IR
and H-alpha luminosities and LSB radio emission are indicative of an extremely
high SFR - several thousand Mo/yr. This assumes that a starburst is the
dominant source of energy, but we cannot yet determine the relative
contributions of the starburst and the buried AGN. A dust mass of 5-7 x 10^8 Mo
is indicated by our data for T(dust) ~ 40-50K, independent of whether the
dominant energy source is an AGN or a starburst. We estimate the possible space
density of such luminous sub-mm sources and find that while a very large
population of these obscured sources could be detected in future wide-field
sub-mm surveys, they are unlikely to dominate the faint counts in this
waveband. Galaxies such as SMM 02399-0136 and F10214+4724 cannot be easily
detected in conventional AGN/QSO surveys, and so estimates of the prevalence of
AGN in the early Universe may require significant revision.


  An algorithm for a constraint satisfaction problem is called robust if it
outputs an assignment satisfying at least $(1-g(\varepsilon))$-fraction of the
constraints given a $(1-\varepsilon)$-satisfiable instance, where
$g(\varepsilon) \rightarrow 0$ as $\varepsilon \rightarrow 0$. Guruswami and
Zhou conjectured a characterization of constraint languages for which the
corresponding constraint satisfaction problem admits an efficient robust
algorithm. This paper confirms their conjecture.


  Additive manufacturing is advantageous for producing lightweight components
while addressing complex design requirements. This capability has been
bolstered by the introduction of unit lattice cells and the gradation of those
cells. In cases where loading varies throughout a part, it may be beneficial to
use multiple, distinct lattice cell types, resulting in multi-lattice
structures. In such structures, abrupt transitions between unit cell topologies
may cause stress concentrations, making the boundary between unit cell types a
primary failure point. Thus, these regions require careful design in order to
ensure the overall functionality of the part. Although computational design
approaches have been proposed, smooth transition regions are still difficult to
achieve, especially between lattices of drastically different topologies. This
work demonstrates and assesses a method for using variational autoencoders to
automate the creation of transitional lattice cells, examining the factors that
contribute to smooth transitions. Through computational experimentation, it was
found that the smoothness of transition regions was strongly predicted by how
closely the endpoints were in the latent space, whereas the number of
transition intervals was not a sole predictor.


  In this paper we prove new embedding results for compactly supported
deformations of $CR$ submanifolds of $\mathbb{C}^{n+d}$: We show that if $M$ is
a $2$-pseudoconcave $CR$ submanifold of type $(n,d)$ in $\mathbb{C}^{n+d}$,
then any compactly supported $CR$ deformation stays in the space of globally
$CR$ embeddable in $\mathbb{C}^{n+d}$ manifolds. This improves an earlier
result, where $M$ was assumed to be a quadratic $2$-pseudoconcave $CR$
submanifold of $\mathbb{C}^{n+d}$. We also give examples of weakly
$2$-pseudoconcave $CR$ manifolds admitting compactly supported $CR$
deformations that are not even locally $CR$ embeddable.


  Let $p, q \in (0, \infty]$ and $\ell_p^m(\ell_q^n)$ be the mixed-norm
sequence space of real matrices $x = (x_{i, j})_{i \leq m, j \leq n}$ endowed
with the (quasi-)norm $\Vert x \Vert_{p, q} := \big\Vert \big( \Vert (x_{i,
j})_{j \leq n} \Vert_q \big)_{i \leq m} \Vert_p$. We shall prove a
Poincar\'e-Maxwell-Borel lemma for suitably scaled matrices chosen uniformly at
random in the $\ell_p^m(\ell_q^n)$ unit balls $\mathbb{B}_{p, q}^{m, n}$, and
obtain both central and non-central limit theorems for their
$\ell_p(\ell_q)$-norms. We use those limit theorems to study the asymptotic
volume distribution in the intersection of two mixed-norm sequence balls. Our
approach is based on a new probabilistic representation of the uniform
distribution on $\mathbb{B}_{p, q}^{m, n}$.


  We present a precise measurement of double-polarization asymmetries in the
$^3\vec{\mathrm{He}}(\vec{\mathrm{e}},\mathrm{e}'\mathrm{d})$ reaction. This
particular process is a uniquely sensitive probe of hadron dynamics in
$^3\mathrm{He}$ and the structure of the underlying electromagnetic currents.
The measurements have been performed in and around quasi-elastic kinematics at
$Q^2 = 0.25\,(\mathrm{GeV}/c)^2$ for missing momenta up to
$270\,\mathrm{MeV}/c$. The asymmetries are in fair agreement with the
state-of-the-art calculations in terms of their functional dependencies on
$p_\mathrm{m}$ and $\omega$, but are systematically offset. Beyond the region
of the quasi-elastic peak, the discrepancies become even more pronounced. Thus,
our measurements have been able to reveal deficiencies in the most
sophisticated calculations of the three-body nuclear system, and indicate that
further refinement in the treatment of their two- and/or three-body dynamics is
required.


  In this paper, we investigate the topological photon sphere from two distinct
perspectives. In the first view, we examine the existence and characteristics
of topological photon(anti-photon)spheres for black holes with different
structures, such as Einstein-Young-Mills non-minimal, AdS black holes
surrounded by Chaplygin-like dark fluid, and Bardeen-like black holes in
Einstein-Gauss-Bonnet gravity. Furthermore, we delve into the deeper
perspective of the necessity of photon spheres for super-compact gravitational
structures such as black holes. By leveraging this necessity, we propose a
classification of the parameter space of black hole models based on the
existence and positioning of photon spheres. This approach enables the
determination of parameter ranges that delineate whether a solution represents
a black hole or a naked singularity. In essence, the paper illustrates the
utility of the photon sphere as a notable test for establishing the permissible
and non-permissible parameter ranges within specific theories of black hole
solutions.


  Background: Transposed elements (TEs) are known to affect transcriptomes,
because either new exons are generated from intronic transposed elements (this
is called exonization), or the element inserts into the exon, leading to a new
transcript. Several examples in the literature show that isoforms generated by
an exonization are specific to a certain tissue (for example the heart muscle)
or inflict a disease. Thus, exonizations can have negative effects for the
transcriptome of an organism. Results: As we aimed at detecting other tissue-
or tumor-specific isoforms in human and mouse genomes which were generated
through exonization of a transposed element, we designed the automated analysis
pipeline SERpredict (SER = Specific Exonized Retroelement) making use of
Bayesian Statistics. With this pipeline, we found several genes in which a
transposed element formed a tissue- or tumor-specific isoform. Conclusion: Our
results show that SERpredict produces relevant results, demonstrating the
importance of transposed elements in shaping both the human and the mouse
transcriptomes. The effect of transposed elements on the human transcriptome is
several times higher than the effect on the mouse transcriptome, due to the
contribution of the primate-specific Alu elements


  Using an effective field theory approach and the language of
SU(N)-structures, we study higher derivative corrections to the supersymmetry
constraints for compactifications of string or M-theory to Minkowski space. Our
analysis is done entirely in the target space and is thus very general, and
does not rely on theory-dependent details such as the amount of worldsheet
supersymmetry. For manifolds of real dimension n<4 we show that the internal
geometry remains flat and uncorrected. For n=4, 6, Kahler manifolds with
SU(N)-holonomy can become corrected to SU(N)-structure, while preserving
supersymmetry, once corrections are included.


  Existing multi-turn context-response matching methods mainly concentrate on
obtaining multi-level and multi-dimension representations and better
interactions between context utterances and response. However, in real-place
conversation scenarios, whether a response candidate is suitable not only
counts on the given dialogue context but also other backgrounds, e.g., wording
habits, user-specific dialogue history content. To fill the gap between these
up-to-date methods and the real-world applications, we incorporate
user-specific dialogue history into the response selection and propose a
personalized hybrid matching network (PHMN). Our contributions are two-fold: 1)
our model extracts personalized wording behaviors from user-specific dialogue
history as extra matching information; 2) we perform hybrid representation
learning on context-response utterances and explicitly incorporate a customized
attention mechanism to extract vital information from context-response
interactions so as to improve the accuracy of matching. We evaluate our model
on two large datasets with user identification, i.e., personalized Ubuntu
dialogue Corpus (P-Ubuntu) and personalized Weibo dataset (P-Weibo).
Experimental results confirm that our method significantly outperforms several
strong models by combining personalized attention, wording behaviors, and
hybrid representation learning.


  In this paper, we study the problem of addressee and response selection in
multi-party conversations. Understanding multi-party conversations is
challenging because of complex speaker interactions: multiple speakers exchange
messages with each other, playing different roles (sender, addressee,
observer), and these roles vary across turns. To tackle this challenge, we
propose the Speaker Interaction Recurrent Neural Network (SI-RNN). Whereas the
previous state-of-the-art system updated speaker embeddings only for the
sender, SI-RNN uses a novel dialog encoder to update speaker embeddings in a
role-sensitive way. Additionally, unlike the previous work that selected the
addressee and response separately, SI-RNN selects them jointly by viewing the
task as a sequence prediction problem. Experimental results show that SI-RNN
significantly improves the accuracy of addressee and response selection,
particularly in complex conversations with many speakers and responses to
distant messages many turns in the past.


  Two-dimensional (2D) honeycomb lattices beyond graphene, such as germanene,
promise new physical properties such as quantum spin Hall effect. While there
have been many claims of growth of germanene, the lack of precise structural
characterization of the epitaxial layers synthesized hinders further research.
The striped layer formed by Ge deposition on Ag(111) has been recently ascribed
as a stretched germanene layer. Using surface X-ray diffraction and density
functional theory calculations, we demonstrate that it corresponds in fact to a
Ag$_2$Ge surface alloy with an atomic density 6.45% higher than the Ag(111)
atomic density. The overall structure is formed by stripes associated with a
face-centered cubic top-layer alignment, alternating with stripes associated
with an hexagonal-close-packed top-layer alignment, in great analogy with the
($22\times\sqrt3$) Au(111) reconstruction.


  Spin evolution opened a large field in quantum plasma research. The spin
waves in plasmas were considered among new phenomena considered in spin-1/2
quantum plasmas. The spin density evolution equation found by means of the
many-particle quantum hydrodynamics shows existence of the "thermal" part of
the spin current, which is an analog of the thermal pressure, or the Fermi
pressure for degenerate electron gas, existing in the Euler equation. However,
this term has been dropped, since there has not been found any equation of
state for the thermal part of the spin current (TPSC), like we have for the
pressure. In this paper we derive the equation of state for the TPSC and apply
it for study of spectrum of collective excitations in spin-1/2 quantum plasmas.
We focus our research on the spectrum of spin waves, since this spectrum is
affected by the thermal part of the spin current. We consider two kinds of
plasmas: electron-ion plasma with motionless ions and degenerate electrons, and
degenerate electron-positron plasmas. We also present the non-linear Pauli
equation with the spinor pressure term containing described effects. The
thermal part of the flux of spin current existing in the spin current evolution
equation is also derived. We also consider the contribution of the TPSC in the
grand generalized vorticity evolution.


  The nonlinear wave equation $u_{tt}-c(u)(c(u)u_x)_x=0$ determines a flow of
conservative solutions taking values in the space $H^1(\mathbb{R})$. However,
this flow is not continuous w.r.t. the natural $H^1$ distance. Aim of this
paper is to construct a new metric which renders the flow uniformly Lipschitz
continuous on bounded subsets of $H^1(\mathbb{R})$. For this purpose, $H^1$ is
given the structure of a Finsler manifold, where the norm of tangent vectors is
defined in terms of an optimal transportation problem. For paths of piecewise
smooth solutions, one can carefully estimate how the weighted length grows in
time. By the generic regularity result proved in [7], these piecewise regular
paths are dense and can be used to construct a geodesic distance with the
desired Lipschitz property.


  The Hyades open cluster presents a unique laboratory for planet formation and
stellar pollution studies because all of the stars have essentially the same
age and were born from the same cloud of gas. Furthermore, with an age of
roughly 650 Myr most of the intermediate and low mass stars are on the main
sequence. Given these assumptions, the accretion of metal rich material onto
the surface of a star during and shortly after the formation of planetary
systems should be evident via the enhanced metallicity of the star. Building on
previous work, stellar evolution models which include the effects of stellar
pollution are applied to the Hyades. The results of several Monte Carlo
simulations, in which the amount of accreted material is drawn at random from a
Gaussian distribution with standard deviation equal to half the mean, are
presented. An effective temperature-[Fe/H] relation is produced and compared to
recent observations. The theoretical predictions presented in this letter will
be useful in future searches for evidence of stellar pollution due to planet
formation. It is concluded that stellar pollution effects at the mean level of
>=2 Earth masses of iron are ruled out by current observational data.


  We extend previous results by Cumplido, Martin and Vaskou on parabolic
subgroups of large-type Artin groups to a broader family of two-dimensional
Artin groups. In particular, we prove that an arbitrary intersection of
parabolic subgroups of a $(2,2)$-free two-dimensional Artin group is itself a
parabolic subgroup. An Artin group is $(2,2)$-free if its defining graph does
not have two consecutive edges labeled by $2$. As a consequence of this result,
we solve the conjugacy stability problem for this family by applying an
algorithm introduced by Cumplido. All of this is accomplished by considering
systolic-by-function complexes, which generalize systolic complexes.
Systolic-by-function complexes have a more flexible structure than systolic
complexes since we allow the edges to have different lengths. At the same time,
their geometry is rigid enough to satisfy an analogue of the Cartan-Hadamard
theorem and other geometric properties similar to those of systolic complexes.


  The apparent universality of Bloch's T^{3/2}-law for the temperature
dependence of the spontaneous magnetization, and of generalizations thereof, is
considered. It is argued that in the derivation one should not only consider
the exchange interaction between the spins, but also the other interactions
between them, leading to elliptical spin precession and deviations from the
parabolic dispersion of magnons. Also interaction effects are important to
explain the apparent universality of generalized Bloch law exponents e_B,
defined by M_s(T)= M_s(0)-const. x T^{e_B}, valid in a wide temperature range
T_1 < T < T_2, and for dimensionalities d = 1, 2, and 3. The above-mentioned
temperature range, the 'Bloch range', lies above the quantum range, where
magnetic long-range order (e.g. in d=2 dimensions) is nontrivially enforced by
the additional interactions, but below the thermal critical region, where
universal 'anomalous scaling dimensions' apply. In contrast, for the Bloch
temperature region, the universality is only apparent, i.e. a
crossover-phenomenon, and simple scaling considerations with 'normal
dimensions' apply.
  However, due to interactions, the Bloch exponent e_B depends not only on the
dimensionality d of the system, but also on the spin quantum number s (mod
(1/2)) of the system, i.e. for given d the Bloch exponent e_B is different for
half-integer s and for integer s.


  Using an under-relaxed cooling algorithm we investigated the vacuum in the
$2d\ O(3)$ model and $4d$ pure gauge $SU(2)$. We calibrated the amount of
cooling performed to have similar physical effect at different lattice
spacings.


  Automated online and App-based cognitive assessment tasks are becoming
increasingly popular in large-scale cohorts and biobanks due to advantages in
affordability, scalability and repeatability. However, the summary scores that
such tasks generate typically conflate the cognitive processes that are the
intended focus of assessment with basic visuomotor speeds, testing device
latencies and speed-accuracy tradeoffs. This lack of precision presents a
fundamental limitation when studying brain-behaviour associations. Previously,
we developed a novel modelling approach that leverages continuous performance
recordings from large-cohort studies to achieve an iterative decomposition of
cognitive tasks (IDoCT), which outputs data-driven estimates of cognitive
abilities, and device and visuomotor latencies, whilst recalibrating
trial-difficulty scales. Here, we further validate the IDoCT approach with UK
BioBank imaging data. First, we examine whether IDoCT can improve ability
distributions and trial-difficulty scales from an adaptive picture-vocabulary
task (PVT). Then, we confirm that the resultant visuomotor and cognitive
estimates associate more robustly with age and education than the original PVT
scores. Finally, we conduct a multimodal brain-wide association study with
free-text analysis to test whether the brain regions that predict the IDoCT
estimates have the expected differential relationships with visuomotor vs.
language and memory labels within the broader imaging literature. Our results
support the view that the rich performance timecourses recorded during
computerised cognitive assessments can be leveraged with modelling frameworks
like IDoCT to provide estimates of human cognitive abilities that have superior
distributions, re-test reliabilities and brain-wide associations.


  Conditional language models are predominantly trained with maximum likelihood
estimation (MLE), giving probability mass to sparsely observed target
sequences. While MLE trained models assign high probability to plausible
sequences given the context, the model probabilities often do not accurately
rank-order generated sequences by quality. This has been empirically observed
in beam search decoding as output quality degrading with large beam sizes, and
decoding strategies benefiting from heuristics such as length normalization and
repetition-blocking. In this work, we introduce sequence likelihood calibration
(SLiC) where the likelihood of model generated sequences are calibrated to
better align with reference sequences in the model's latent space. With SLiC,
decoding heuristics become unnecessary and decoding candidates' quality
significantly improves regardless of the decoding method. Furthermore, SLiC
shows no sign of diminishing returns with model scale, and presents alternative
ways to improve quality with limited training and inference budgets. With SLiC,
we exceed or match SOTA results on a wide range of generation tasks spanning
abstractive summarization, question generation, abstractive question answering
and data-to-text generation, even with modest-sized models.


  We review the basic concepts of a non-equilibrium kinetic theory of a trapped
bosonic gas. By extending the successful mean-field concept of the
Gross-Pitaevskii equation with the effects of non-local, two particle quantum
correlations, one obtains a renormalized binary interaction and allows for the
dynamic establishment of non-classical many-particle quantum correlations.
These concepts are illustrated by self-consistent numerical calculations of the
first and second order ground state quantum correlations of a harmonically
trapped, quasi one-dimensional bosonic gas. We do find a strong suppression of
the density fluctuations or, in other words, an enhanced number squeezing with
decreasing particle density.


  Motivated by the famous Waddington's epigenetic landscape metaphor in
developmental biology, biophysicists and applied mathematicians made different
proposals to realize this metaphor in a rationalized way. We adopt
comprehensive perspectives to systematically investigate three different but
closely related realizations in recent literature: namely the potential
landscape theory from the steady state distribution of stochastic differential
equations (SDEs), the quasi-potential from the large deviation theory, and the
construction through SDE decomposition and A-type integral.The connections
among these theories are established in this paper. We demonstrate that the
quasi-potential is the zero noise limit of the potential landscape. We also
show that the potential function in the third proposal coincides with the
quasi-potential. The most probable transition path by minimizing the
Onsager-Machlup or Freidlin-Wentzell action functional is discussed as well.
Furthermore, we compare the difference between local and global quasi-potential
through the exchange of limit order for time and noise amplitude. As a
consequence of such explorations, we arrive at the existence result for the SDE
decomposition while deny its uniqueness in general cases. It is also clarified
that the A-type integral is more appropriate to be applied to the decomposed
SDEs rather than the original one. Our results contribute to a better
understanding of existing landscape theories for biological systems.


  We consider a mechanism for area preserving Hamiltonian systems which leads
to the enhanced probability, $P(\lambda, t)$, to find small values of the
finite time Lyapunov exponent, $\lambda$. In our investigation of chaotic
dynamical systems we go beyond the linearized stability analysis of nearby
divergent trajectories and consider folding of the phase space in the course of
chaotic evolution. We show that the spectrum of the Lyapunov exponents
$F(\lambda)= \lim_{t\to\infty} t^{-1}\ln P(\lambda, t)$ at the origin has a
finite value $F(0)=-\tilde{\lambda}$ and a slope $F'(0)\le 1$. This means that
all negative moments of the distribution $<e^{-m\lambda t}>$ are saturated by
rare events with $\lambda\to 0$. Extensive numerical simulations confirm our
findings.


  The causal discovery of Bayesian networks is an active and important research
area, and it is based upon searching the space of causal models for those which
can best explain a pattern of probabilistic dependencies shown in the data.
However, some of those dependencies are generated by causal structures
involving variables which have not been measured, i.e., latent variables. Some
such patterns of dependency "reveal" themselves, in that no model based solely
upon the observed variables can explain them as well as a model using a latent
variable. That is what latent variable discovery is based upon. Here we did a
search for finding them systematically, so that they may be applied in latent
variable discovery in a more rigorous fashion.


  This paper reviews current experimental and theoretical progress in the study
of dipolar quantum gases of ground and meta-stable atoms with a large magnetic
moment. We emphasize the anisotropic nature of Feshbach resonances due to
coupling to fast-rotating resonant molecular states in ultracold s-wave
collisions between magnetic atoms in external magnetic fields. The dramatic
differences in the distribution of resonances of magnetic $^7$S$_3$ chromium
and magnetic lanthanide atoms with a submerged 4f shell and non-zero electron
angular momentum is analyzed. We focus on Dysprosium and Erbium as important
experimental advances have been recently made to cool and create
quantum-degenerate gases for these atoms. Finally, we describe progress in
locating resonances in collisions of meta-stable magnetic atoms in electronic P
states with ground-state atoms, where an interplay between collisional
anisotropies and spin-orbit coupling exists.


  In this paper, we consider a network of agents that has to self-assign a set
of tasks while respecting resource constraints. One possible formulation is the
Generalized Assignment Problem, where the goal is to find a maximum payoff
while satisfying capability constraints. We propose a purely distributed
branch-and-price algorithm to solve this problem in a cooperative fashion.
Inspired by classical (centralized) branch-and-price schemes, in the proposed
algorithm each agent locally solves small linear programs, generates columns by
solving simple knapsack problems, and communicates to its neighbors a fixed
number of basic columns. We prove finite-time convergence of the algorithm to
an optimal solution of the problem. Then, we apply the proposed scheme to a
generalized assignment scenario in which a team of robots has to serve a set of
tasks. We implement the proposed algorithm in a ROS testbed and provide
experiments for a team of heterogeneous robots solving the assignment problem.


  We report an inelastic neutron scattering study of the spin fluctuations in
the nearly-ferromagnetic element palladium. Dispersive over-damped collective
magnetic excitations or ``paramagnons'' are observed up to 128 meV. We analyze
our results in terms of a Moriya-Lonzarich-type spin fluctuation model and
estimate the contribution of the spin fluctuations to the low temperature heat
capacity. In spite of the paramagnon excitations being relatively strong, their
relaxation rates are large. This leads to a small contribution to the
low-temperature electronic specific heat.


  Stick graphs are defined as follows. Let A (respectively B) be a set of
vertical (respectively horizontal) segments in the plane such that the bottom
endpoints of the segments in A and the left endpoints of the segments in B lie
on the same ground straight line with slope -1. The Stick graph defined by A
and B, which is necessarily bipartite, is the intersection graph of the
segments in A with the segments in B. We answer an open problem by showing that
recognizing Stick graphs is NP-complete. This result allows us to easily solve
two other open problems, namely the recognition of BipHook graphs and of max
point-tolerance graphs. We show that both of them are NP-complete problems.


  This paper presents a robust and secure framework for achieving accurate and
reliable mutual localization in multiple unmanned aerial vehicle (UAV) systems.
Challenges of accurate localization and security threats are addressed and
corresponding solutions are brought forth and accessed in our paper with
numerical simulations. The proposed solution incorporates two key components:
the Mobility Adaptive Gradient Descent (MAGD) and Time-evolving Anomaly
Detectio (TAD). The MAGD adapts the gradient descent algorithm to handle the
configuration changes in the mutual localization system, ensuring accurate
localization in dynamic scenarios. The TAD cooperates with reputation
propagation (RP) scheme to detect and mitigate potential attacks by identifying
UAVs with malicious data, enhancing the security and resilience of the mutual
localization


  This research study was conducted to illustrate how it is easily possible to
get data access to disabled or blocked repositories in Github using GraphQL.
There are situations in which you can lose access to your Github repositories;
When you use the paid version of Github services and do not pay the monthly
payment or another situation is that when you use Github from the countries in
the United States sanction list. Having an insecure repository with malicious
usages can also put your repository in Github blacklist. In all of these
situations, Github will block and disable your repository and you will lose
access to your files, codes and project assets. Here, we will discuss the
procedure of how an Ethical Hacker can gain access to all those blocked data
with GraphQL functionality.


  Isolated stellar-mass BH accrete gas, often at supersonic speeds, and can
form outflows that can influence the accreted gas. The latter process, known as
mechanical feedback, can significantly affect the accretion rate. We use
hydrodynamical simulations to assess the impact of mechanical feedback on the
accretion rate when the BH moves supersonically through a uniform medium. We
carry out 3D hydrodynamical simulations of outflows fueled by accretion that
interact with a uniform medium, probing scales similar to and larger than the
accretor gravitational sphere of influence. In the simulations the accretor is
at rest and the medium moves at supersonic speeds. The outflow power is assumed
to be proportional to the accretion rate. The simulations are run for different
outflow-medium motion angles and velocity ratios. The impact of different
degrees of outflow collimation, accretor size, and resolution is also
investigated. In general, the accretion rate is significantly affected by
mechanical feedback. The effect is small for outflows perpendicular to the
medium motion, and quickly grows for smaller angles. Moreover, the smaller the
medium-to-outflow velocity ratio is, the more accretion decreases. On the other
hand, the impact of outflow collimation seems moderate. The effect is enhanced
when the accretor size is reduced. For a population of BH with random outflow
orientations, the average accretion rate drops by (high-low resolution)
~0.2-0.4 and ~0.1-0.2 for medium-to-outflow velocity ratios of 1/20 and 1/100,
respectively, when compared to the corresponding cases without outflow. Our
results strongly indicate that, on the considered scales, mechanical feedback
can easily reduce the energy available from supersonic accretion by a factor of
several. This should be taken into account when studying the mechanical,
thermal and non-thermal output of isolated BH.


  Let $\mathbb{K}=\mathbb{R}$ or $\mathbb{C}$, and $T_n(\mathbb{K})$ be the set
of $n\times n$ lower triangular matrices with entries in $\mathbb{K}$. We show
that $T_n(\mathbb{K})$ has dense subsemigroups that are generated by $n+1$
matrices.


  An incremental-stencil WENO reconstruction method, which uses low-order
candidate stencils with incrementally increasing width, is proposed for
finite-volume simulation of compressible two-phase flow with the
quasi-conservative interface model. While recovering the original 5th-order
WENO reconstruction in smooth region of the solution, due to the presence of
2-point candidate stencils, the present method is able to handle closely
located discontinuities, which is a typical scenario of shock-interface
interaction. Furthermore, a MOOD-type positivity preserving approach is applied
to ensure physical meaningful reconstruction. Compared with the hybrid method
which switches between with the 5th-order WENO and 2nd-order reconstructions,
the present method is free of problem-dependent tunable parameters. A number of
numerical examples show that the present method achieves small numerical
dissipation and good robustness for simulating two-phase flow problems with
strong shock-interface interaction and large density ratio.


  Hyperbolic propagation offers exciting opportunities in nanophotonics, from
sub-diffraction imaging to enhanced local density of states. This transport
regime is typically induced by strong modulation of conductivity, i.e., with
alternating metallic and dielectric material properties. Here, we analyze a
moving impedance surface, showing that suitably tailored homogeneous
metasurfaces can support one-way hyperbolic propagation when in motion, adding
non-reciprocity to hyperbolic propagation phenomena, and without suffering from
nonlocal effects stemming from discretization or finite granularity of the
surface.


  We investigate the laws of thermodynamics and weak cosmic censorship
conjecture in the normal and extended phase space of a Kerr-AdS black hole by
analyzing the energy-momentum relation of the absorbed fermion dropping into
the horizon. In the normal phase space, the first law, second law as well as
the weak cosmic censorship conjecture are found to be valid in all the initial
states of the black hole. However, in the extended phase space, although the
first law and weak cosmic censorship conjecture are still valid, the second law
becomes more subtle. We find that the validity or violation of the second law
depends on the spin parameter, the radius of the AdS spacetime, and their
variations. In addition, in the extended phase space, the configurations of the
extremal and near-extremal Kerr-AdS black holes are unchanged as the fermions
are absorbed since the final and initial states are the same.


  Large Language Models (LLMs) have demonstrated efficacy in various linguistic
applications, including text summarization and controlled text generation.
However, studies into their capacity of switching between styles via
fine-tuning remain underexplored. This study concentrates on textual
professionalism and introduces a novel methodology, named ProSwitch, which
equips a language model with the ability to produce both professional and
non-professional responses through knowledge-guided instruction tuning.
ProSwitch unfolds across three phases: data preparation for gathering domain
knowledge and training corpus; instruction tuning for optimizing language
models with multiple levels of instruction formats; and comprehensive
evaluation for assessing the professionalism discrimination and reference-based
quality of generated text. Comparative analysis of ProSwitch against both
general and specialized language models reveals that our approach outperforms
baselines in switching between professional and non-professional text
generation.


  We prove some results on the fibers and images of rational maps from a
hyper-K\"ahler manifold. We study in particular the minimal genus of fibers of
a fibration into curves. The last section of this paper is devoted to the study
of the rational map defined by a linear system on a hyper-K\"ahler fourfold
satisfying numerical conditions similar to those considered by O'Grady in his
study of fourfolds numerically equivalent to $K3^{[2]}$. We extend his results
to this more general context.


  We report 31 GHz CARMA observations of IDCS J1426.5+3508, an
infrared-selected galaxy cluster at z = 1.75. A Sunyaev-Zel'dovich decrement is
detected towards this cluster, indicating a total mass of M200 = (4.3 +/- 1.1)
x 10^{14} Msun in agreement with the approximate X-ray mass of ~5 x 10^{14}
Msun. IDCS J1426.5+3508 is by far the most distant cluster yet detected via the
Sunyaev-Zel'dovich effect, and the most massive z >= 1.4 galaxy cluster found
to date. Despite the mere ~1% probability of finding it in the 8.82 deg^2 IRAC
Distant Cluster Survey, IDCS J1426.5+3508 is not completely unexpected in LCDM
once the area of large, existing surveys is considered. IDCS J1426.5+3508 is,
however, among the rarest, most extreme clusters ever discovered, and indeed is
an evolutionary precursor to the most massive known clusters at all redshifts.
We discuss how imminent, highly sensitive Sunyaev-Zel'dovich experiments will
complement infrared techniques for statistical studies of the formation of the
most massive galaxy clusters in the z > 1.5 Universe, including potential
precursors to IDCS J1426.5+3508.


  An enviromental-random effect over a deterministic population mo\-del, a
resource ({\it e.g.}, a fish stock) is introduced. It is assumed that the
harvest activity is concentrated at a non predetermined sequence of instants,
at which the abundance reaches a certain predetermined level, for then to fall
abruptly a constant capture quota (pulse harvesting). So that, the abundance is
modeled by a stochastic impulsive type differential equation, incorporating an
standard Brownian motion in the per capita rate of growth. With this random
effect, the pulse times are images of a random variable, more precisely, they
are "stopping-times" of the stochastic process. The proof of the finite
expectation of the next access time, {\it i.e.}, the feasibility of the
regulation, is the main result.


  We investigate different ways of generating approximate solutions to the
pairwise Markov random field (MRF) selection problem. We focus mainly on the
inverse Ising problem, but discuss also the somewhat related inverse Gaussian
problem because both types of MRF are suitable for inference tasks with the
belief propagation algorithm (BP) under certain conditions. Our approach
consists in to take a Bethe mean-field solution obtained with a maximum
spanning tree (MST) of pairwise mutual information, referred to as the
\emph{Bethe reference point}, for further perturbation procedures. We consider
three different ways following this idea: in the first one, we select and
calibrate iteratively the optimal links to be added starting from the Bethe
reference point; the second one is based on the observation that the natural
gradient can be computed analytically at the Bethe point; in the third one,
assuming no local field and using low temperature expansion we develop a dual
loop joint model based on a well chosen fundamental cycle basis. We indeed
identify a subclass of planar models, which we refer to as \emph{Bethe-dual
graph models}, having possibly many loops, but characterized by a singly
connected dual factor graph, for which the partition function and the linear
response can be computed exactly in respectively O(N) and $O(N^2)$ operations,
thanks to a dual weight propagation (DWP) message passing procedure that we set
up. When restricted to this subclass of models, the inverse Ising problem being
convex, becomes tractable at any temperature. Experimental tests on various
datasets with refined $L_0$ or $L_1$ regularization procedures indicate that
these approaches may be competitive and useful alternatives to existing ones.


  Person re-identification (re-ID) plays an important role in applications such
as public security and video surveillance. Recently, learning from synthetic
data, which benefits from the popularity of synthetic data engine, has
attracted great attention from the public eyes. However, existing datasets are
limited in quantity, diversity and realisticity, and cannot be efficiently used
for re-ID problem. To address this challenge, we manually construct a
large-scale person dataset named FineGPR with fine-grained attribute
annotations. Moreover, aiming to fully exploit the potential of FineGPR and
promote the efficient training from millions of synthetic data, we propose an
attribute analysis pipeline called AOST, which dynamically learns attribute
distribution in real domain, then eliminates the gap between synthetic and
real-world data and thus is freely deployed to new scenarios. Experiments
conducted on benchmarks demonstrate that FineGPR with AOST outperforms (or is
on par with) existing real and synthetic datasets, which suggests its
feasibility for re-ID task and proves the proverbial less-is-more principle.
Our synthetic FineGPR dataset is publicly available at
https://github.com/JeremyXSC/FineGPR.


  Portfolio selection in the periodic investment of securities modeled by a
multivariate Merton model with dependent jumps is considered. The optimization
framework is designed to maximize expected terminal wealth when portfolio risk
is measured by the Condition-Value-at-Risk ($CVaR$). Solving the portfolio
optimization problem by Monte Carlo simulation often requires intensive and
time-consuming computation; hence a faster and more efficient portfolio
optimization method based on closed-form comonotonic bounds for the risk
measure $CVaR$ of the terminal wealth is proposed.


  The field of condensed matter physics had its genesis this century and it has
had a remarkable evolution. A closer look at its growth reveals a hidden aim in
the collective consciousness of the field - a part of the development this
century is a kind of warm up exercise to understand the nature of living
condensed matter, namely the field of biology, by a growing new breed of
scientists in the coming century. Through some examples the vitality of this
interaction will be pointed out.


  The set of pure terms which are typable in the $\lambda$$\Pi$-calculus in a
given context is not recursive. So there is no general type inference algorithm
for the programming language Elf and, in some cases, some type information has
to be mentioned by the programmer.


  We study the luminescence dynamics of telecom wavelength InAs quantum dots
grown on InP(111)A by droplet epitaxy. The use of the ternary alloy InAlGaAs as
a barrier material leads to photon emission in the 1.55 $\mu$m telecom C-band.
The luminescence decay is well described in terms of the theoretical interband
transition strength without the impact of nonradiative recombination. The
intensity autocorrelation function shows clear anti-bunching photon statistics.
The results suggest that our quantum dots are useful for constructing a
practical source of single photons and quantum entangled photon pairs.


  There are many "minimax" complexity functions in mathematics: width of a tree
or a link, Heegaard genus of a 3-manifold, the Cheeger constant of a Riemannian
manifold. We define such a function w, "width", on countable (or finite) groups
and show w(Z^k) = k-1.


  The motion of a bead on a rotating circular hoop is investigated using
elementary calculus and simple symmetry arguments. The peculiar trajectories of
the bead at different speeds of rotation of the hoop are presented. Phase
portraits and nature of fixed points are studied. Bifurcation is observed with
change in the rotational speed of the hoop. At a critical speed of rotation of
the hoop, there appears an interesting relation between the time period and
amplitude of oscillation of the bead. The study introduces several important
aspects of nonlinear dynamics. It is suitable for students having basic
understanding in elementary calculus and classical mechanics.


  Manifest N=2 supersymmetric hypermultiplet mass terms can be introduced in
the projective N=2 superspace formalism. In the case of complex
hypermultiplets, where the gauge covariantized spinor derivatives have an
explicit representation in terms of gauge prepotentials, it is possible to
interpret such masses as vacuum expectation values of an Abelian vector
multiplet. The duality transformation that relates the N=2 off-shell projective
description of the hypermultiplet to the on-shell description involving two N=1
chiral superfields allows us to obtain the massive propagators of the N=1
complex linear fields in the projective hypermultiplet. The N=1 massive
propagators of the component superfields in the projective hypermultiplet
suggest a possible ansatz for the N=2 massive propagator, which agrees with an
explicit calculation in N=2 superspace.


  The AdS/CFT understanding of CFT entanglement is based on HRT surfaces in the
dual bulk spacetime. While such surfaces need not exist in sufficiently general
spacetimes, the maximin construction demonstrates that they can be found in any
smooth asymptotically locally AdS spacetime without horizons or with only
Kasner-like singularities. In this work, we introduce restricted maximin
surfaces anchored to a particular boundary Cauchy slice $C_\partial$. We show
that the result agrees with the original unrestricted maximin prescription when
the restricted maximin surface lies in a smooth region of spacetime. We then
use this construction to extend the existence theorem for HRT surfaces to
generic charged or spinning AdS black holes whose mass-inflation singularities
are not Kasner-like. We also discuss related issues in time-independent charged
wormholes.


  Hom-algebras are generalizations of algebras obtained using a twisting by a
linear map. But there is a priori a freedom on where to twist. We enumerate
here all the possible choices in the Lie and associative categories and study
the relations between the obtained algebras. The associative case is richer
since it admits the notion of unit element. We use this fact to find sufficient
conditions for hom-associative algebras to be associative and classify the
implications between the hom-associative types of unital algebras.


  Full polarimetric radio interferometric calibration is performed by
estimating 2 by 2 Jones matrices representing instrumental and propagation
effects. The solutions obtained in this way differ from the true solutions by a
2 by 2 unitary matrix ambiguity. This ambiguity is common to all stations for
which a solution is obtained but it is different for solutions obtained at
different time and frequency intervals. Therefore, straightforward
interpolation of solutions obtained at different time and frequency intervals
is not possible. In this paper, we propose to use the theory of quotient
manifolds for obtaining correct interpolants that are immune to unitary matrix
ambiguities.


  I present the sketch of a "physicist's" derivation of the AdS/CFT
correspondence for the original pair, string theory in $AdS_5\times S^5$ vs.
${\cal N}=4$ SYM, based on the pp wave limit, and deviations from it. I show
that one can reverse the logic, and derive the action of ${\cal N}=4$ SYM, as
kinetic terms plus vertices, as well as the elements of its path integral, from
string theory on the pp wave. One can also treat consistently deviations from
the "strings on the pp wave" limit, starting from the SYM side, and a priori
reconstruct the full $AdS_5\times S^5$ geometry perturbatively, as well as full
perturbative string theory on it.


  We study penetrative convection of a fluid confined between two horizontal
plates, the temperatures of which are such that a temperature of maximum
density lies between them. The range of Rayleigh numbers studied is $Ra =
\left[10^6, 10^8 \right]$ and the Prandtl numbers are $Pr = 1$ and $11.6$. An
evolution equation for the growth of the convecting region is obtained through
an integral energy balance. We identify a new non-dimensional parameter,
$\Lambda$, which is the ratio of temperature difference between the stable and
unstable regions of the flow; larger values of $\Lambda$ denote increased
stability of the upper stable layer. We study the effects of $\Lambda$ on the
flow field using well-resolved lattice Boltzmann simulations, and show that the
characteristics of the flow depend sensitively upon it. For the range $\Lambda
= \left[0.01, 4\right]$, we find that for a fixed $Ra$ the Nusselt number,
$Nu$, increases with decreasing $\Lambda$. We also investigate the effects of
$\Lambda$ on the vertical variation of convective heat flux and the
Brunt-V\"{a}is\"{a}l\"{a} frequency. Our results clearly indicate that in the
limit $\Lambda \rightarrow 0$ the problem reduces to that of the classical
Rayleigh-B\'enard convection.


  We apply path integration techniques to obtain option pricing with stochastic
volatility using a generalized Black-Scholes equation known as the Merton and
Garman equation. We numerically simulate the option prices using the technique
of path integration. Using market data, we determine the parameters of the
model. It is found that the market chooses a special class of models for which
a more efficient algorithm, called the bisection method, is applicable. Using
our simulated data, we generate some implied volatility curves. We also analyze
and study in detail some of the characteristics of the volatility curves within
the model.


  The Lieb-Robinson bound sets a theoretical upper limit on the speed at which
information can propagate in non-relativistic quantum spin networks. In its
original version, it results in an exponentially exploding function of the
evolution time, which is partially mitigated by an exponentially decreasing
term that instead depends upon the distance covered by the signal (the ratio
between the two exponents effectively defining an upper bound on the
propagation speed). In the present paper, by properly accounting for the free
parameters of the model, we show how to turn this construction into a stronger
inequality where the upper limit only scales polynomially with respect to the
evolution time. Our analysis applies to any chosen topology of the network, as
long as the range of the associated interaction is explicitly finite. For the
special case of linear spin networks we present also an alternative derivation
based on a perturbative expansion approach which improves the previous
inequality. In the same context we also establish a lower bound to the speed of
the information spread which yields a non trivial result at least in the limit
of small propagation times.


  The discovery of a non-thermal radio ring of low surface brightness about one
degree in diameter has been recently reported around the location in the sky of
the Calvera pulsar, at a high Galactic latitude. The radio properties point to
it likely being a new supernova remnant (SNR), G118.4+37.0. We report an
analysis of almost 14 years of observations of this region by the gamma-ray
Large Area Telescope onboard the Fermi satellite. We detect extended GeV
emission consistent with the size and location of the radio source, which
confirms the presence of relativistic particles. The spectrum of the
high-energy emission is fully compatible with an origin in the same
relativistic particles producing the radio emission. These features and its
similarities to other isolated SNRs establish this source as the remnant of a
supernova. A simple model of the non-thermal emission from radio to GeV
energies resulting from leptonic emission from electrons produced by the SNR is
presented. G118.4+37.0 and other similar isolated remnants could be part of a
radio-dim SNR population evolving in low density environments showing hard GeV
emission of leptonic origin. Future deeper surveys in radio and gamma-rays
could discover new members of the group.


  We prove a homological stability theorem for congruence subgroups of
symplectic groups. From this theorem, we deduce a generalization of a theorem
of Borel showing that certain homology groups of a congruence subgroup do not
depend on the level of the congruence subgroup.


  Over the last 15 years, Software Carpentry has evolved from a week-long
training course at the US national laboratories into a worldwide volunteer
effort to raise standards in scientific computing. This article explains what
we have learned along the way the challenges we now face, and our plans for the
future.


  Let f be analytic in D={z:|z|<1} with f(0)=0 and f'(0)=1. We give sharp
bounds for the second Hankel determinant of f, when f is starlike of order
alpha in D.


  We describe the design and FPGA implementation of a 3D torus network (TNW) to
provide nearest-neighbor communications between commodity multi-core
processors. The aim of this project is to build up tightly interconnected and
scalable parallel systems for scientific computing. The design includes the
VHDL code to implement on latest FPGA devices a network processor, which can be
accessed by the CPU through a PCIe interface and which controls the external
PHYs of the physical links. Moreover, a Linux driver and a library implementing
custom communication APIs are provided. The TNW has been successfully
integrated in two recent parallel machine projects, QPACE and AuroraScience. We
describe some details of the porting of the TNW for the AuroraScience system
and report performance results.


  We report on an investigation into stellar evolution and nucleosynthesis in
the low and extremely low metallicity regime, including models of stars with a
pure Big Bang composition (i.e. Z=0). The metallicity range of the extremely
metal poor (EMP) models we have calculated is -6.5 < [Fe/H] < -3.0, whilst our
models are in the mass range 0.85 < M < 3.0 Msun. Many of the EMP and Z=0
models experience violent evolutionary episodes not seen at higher
metallicities. We refer to these events as `Dual Flashes' since they are
characterised by peaks in the hydrogen and helium burning luminosities
occurring at roughly the same time. Some of the material processed by these
events is later dredged up by the convective envelope, causing signifcant
surface pollution. These events have been reported by previous studies, so our
results reaffirm their occurrence -- at least in 1D stellar models. The novelty
of this study is that we have calculated the entire evolution of the Z=0 and
EMP models, from the ZAMS to the end of the TPAGB, including detailed
nucleosynthesis. We have also calculated the nucleosynthetic yields, which will
soon be available in electronic format. Although subject to many uncertainties
these are, as far as we are aware, the only yields available in this mass and
metallicity range. In this paper we briefy describe some of the results in the
context of abundance observations of EMP halo stars. This work formed part of
SWC's PhD thesis (completed in March 2007) and a series of subsequent papers
will describe the results of the study in more detail.


  Recently Liu et al. [PRL 87, 030404 (2001)] examined the lowest state of a
weakly-interacting Bose-Einstein condensate. In addition to other interesting
results, using the method of the pair correlation function, they questioned the
validity of the mean-field picture of the formation of vortices and stated that
the vortices are generated at the center of the cloud. This is in apparent
contradiction to the Gross-Pitaevskii approach, which predicts that the
vortices successively enter the cloud from its outer parts as L/N (where N is
the number of atoms in the trap and hbar(L) is the angular momentum of the
system) increases. We have managed to reproduce the results of Liu et al.
however a more careful analysis presented below confirms the validity of the
mean-field approach.


  A multidimensional nonnegative matrix is called polystochastic if the sum of
its entries at each line is equal to $1$. The set of all polystochastic
matrices of order $n$ and dimension $d$ is a convex polytope $\Omega_n^d$. In
the present paper, we compare known bounds on the number $V(n,d)$ of vertices
of the polytope $\Omega_n^d$, propose two constructions of vertices of
$\Omega_n^d$ based on multidimensional matrix multiplication, and list all
vertices of the polytope $\Omega_3^4$.


  We study the role of the second language in bilingual word embeddings in
monolingual semantic evaluation tasks. We find strongly and weakly positive
correlations between down-stream task performance and second language
similarity to the target language. Additionally, we show how bilingual word
embeddings can be employed for the task of semantic language classification and
that joint semantic spaces vary in meaningful ways across second languages. Our
results support the hypothesis that semantic language similarity is influenced
by both structural similarity as well as geography/contact.


  The evolution of the biosphere unfolds as a luxuriant generative process of
new living forms and functions. Organisms adapt to their environment, exploit
novel opportunities that are created in this continuous blooming dynamics.
Affordances play a fundamental role in the evolution of the biosphere, for
organisms can exploit them for new morphological and behavioral adaptations
achieved by heritable variations and selection. This way, the opportunities
offered by affordances are then actualized as ever novel adaptations. In this
paper we maintain that affordances elude a formalization that relies on set
theory: we argue that it is not possible to apply set theory to affordances,
therefore we cannot devise a set-based mathematical theory of the diachronic
evolution of the biosphere.


  The valley degree of freedom is one of the most intriguing properties of
atomically thin transition metal dichalcogenides. Together with the possibility
to address this degree of freedom by valley-contrasting optical selection
rules, it has the potential to enable a completely new class of future
electronic and optoelectronic devices. Resonant optical nanostructures emerge
as promising tools for interacting with and controlling the valley degree of
freedom at the nanoscale. However, a critical understanding gap remains in how
nanostructures and their nearfields affect the circular polarization properties
of valley-selective emission hindering further developments in this field. In
order to address this issue, our study delves into the experimental
investigation of a hybrid model system where valley-specific emission from a
monolayer of molybdenum disulfide is interacting with a resonant plasmonic
nanosphere. Contrary to the simple intuition suggesting that a centrosymmetric
nanoresonator preserves the degree of circular polarization in the forward
scattered farfield by angular momentum conservation, our cryogenic
photoluminescence microscopy reveals that the light emitted from the
nanoparticle position is largely unpolarized, i.e. we observe depolarization.
We rigorously study the nature of this phenomenon numerically considering the
monolayer-nanoparticle interaction at different levels including excitation and
emission. In doing so, we find that the farfield degree of polarization
strongly reduces in the hybrid system when including excitons emitting from
outside of the system's symmetry point, which in combination with
depolarisation at the excitation level causes the observed effect. Our results
highlight the importance of considering spatially distributed emitters for
precise predictions of polarization responses in these hybrid systems.


  Branching processes are a class of continuous-time Markov chains (CTMCs) with
ubiquitous applications. A general difficulty in statistical inference under
partially observed CTMC models arises in computing transition probabilities
when the discrete state space is large or uncountable. Classical methods such
as matrix exponentiation are infeasible for large or countably infinite state
spaces, and sampling-based alternatives are computationally intensive,
requiring a large integration step to impute over all possible hidden events.
Recent work has successfully applied generating function techniques to
computing transition probabilities for linear multitype branching processes.
While these techniques often require significantly fewer computations than
matrix exponentiation, they also become prohibitive in applications with large
populations. We propose a compressed sensing framework that significantly
accelerates the generating function method, decreasing computational cost up to
a logarithmic factor by only assuming the probability mass of transitions is
sparse. We demonstrate accurate and efficient transition probability
computations in branching process models for hematopoiesis and transposable
element evolution.


  Diffractive dissociation of particles can be used to study their light-cone
wave functions. Results from Fermilab experiment E791 for diffractive
dissociation of 500 GeV/c pi- mesons into di-jets show that the |q qbar>
light-cone asymptotic wave function describes the data well for Q^2 ~ 10
(GeV/c)^2 or more.


  Named entity recognition (NER) is a critical step in modern search query
understanding. In the domain of eCommerce, identifying the key entities, such
as brand and product type, can help a search engine retrieve relevant products
and therefore offer an engaging shopping experience. Recent research shows
promising results on shared benchmark NER tasks using deep learning methods,
but there are still unique challenges in the industry regarding domain
knowledge, training data, and model production. This paper demonstrates an
end-to-end solution to address these challenges. The core of our solution is a
novel model training framework "TripleLearn" which iteratively learns from
three separate training datasets, instead of one training set as is
traditionally done. Using this approach, the best model lifts the F1 score from
69.5 to 93.3 on the holdout test data. In our offline experiments, TripleLearn
improved the model performance compared to traditional training approaches
which use a single set of training data. Moreover, in the online A/B test, we
see significant improvements in user engagement and revenue conversion. The
model has been live on homedepot.com for more than 9 months, boosting search
conversions and revenue. Beyond our application, this TripleLearn framework, as
well as the end-to-end process, is model-independent and problem-independent,
so it can be generalized to more industrial applications, especially to the
eCommerce industry which has similar data foundations and problems.


  We present an extended model for the lattice-induced light shifts of the
clock frequency in optical lattice clocks, applicable to a wide range of
operating conditions. The model extensions cover radial motional states with
sufficient energies to invalidate the harmonic approximation of the confining
potential. We reevaluate lattice-induced light shifts in our Yb optical lattice
clock with an uncertainty of 6.1E-18 under typical clock operating conditions.


  The Sunyaev-Zeldovich (SZ) effect is a spectral distortion in the Cosmic
Microwave Background (CMB), caused due to up-scattering of CMB photons by high
energy electron distributions. The largest SZ distortion in the CMB is caused
by the hot electrons present in the intra-cluster medium (ICM). However,
several other small scale astrophysical processes can also contribute to the SZ
distortion in the CMB.
  Analytic studies have shown that the interstellar (ISM) electron gas of the
host galaxy heated by quasar feedback can also cause substantial SZ effect. For
successful detection of the quasar feedback signal, the SZ signal from the
virialized gas in the host halos of quasars needs to be properly quantified. In
this dissertation work, I have estimated the SZ signal from quasar hosts using
analytic models of the virialized gas in the ICM/ISM. As a new extension to
existing work I have used the measured Halo Occupation Distribution properties
of quasar hosts. The results show that the average SZ signal from quasar hosts
decreases with redshift. This result is consist what what has been observed by
the Planck team.
  I have compared by calculations with the experimental results of Ruan et al.
(2015). While Ruan et al. (2015) claim their detection to be from quasar
feedback, I find that within the errors of my model, their detection can be
explained with halo signal alone, without introducing feedback.


  We study methods for improving fairness to subgroups in settings with
overlapping populations and sequential predictions. Classical notions of
fairness focus on the balance of some property across different populations.
However, in many applications the goal of the different groups is not to be
predicted equally but rather to be predicted well. We demonstrate that the task
of satisfying this guarantee for multiple overlapping groups is not
straightforward and show that for the simple objective of unweighted average of
false negative and false positive rate, satisfying this for overlapping
populations can be statistically impossible even when we are provided
predictors that perform well separately on each subgroup. On the positive side,
we show that when individuals are equally important to the different groups
they belong to, this goal is achievable; to do so, we draw a connection to the
sleeping experts literature in online learning. Motivated by the one-sided
feedback in natural settings of interest, we extend our results to such a
feedback model. We also provide a game-theoretic interpretation of our results,
examining the incentives of participants to join the system and to provide the
system full information about predictors they may possess. We end with several
interesting open problems concerning the strength of guarantees that can be
achieved in a computationally efficient manner.


  Secure ranging is poised to play a critical role in several emerging
applications such as self-driving cars, unmanned aerial systems, wireless IoT
devices, and augmented reality. In this paper, we propose a design of a secure
broadcast ranging systems with unique features and techniques. Its
spectral-flexibility, and low-power short ranging bursts enable co-existence
with existing systems such as in the 2.4GHz ISM band. We exploit a set of RF
techniques such as upsampling and successive interference cancellation to
achieve high accuracy and scalability to tens of reflectors even when operating
over narrow bands of spectrum. We demonstrate that it can be implemented on
popular SDR platforms FPGA and/or hosts (with minimal FPGA modifications). The
protocol design, and cryptographically generated/detected signals, and
randomized timing of transmissions, provide stealth and security against denial
of service, sniffing, and distance manipulation attacks. Through extensive
experimental evaluations (and simulations for scalability to over 100
reflectors) we demonstrate an accuracy below 20cm on a wide range of SNR (as
low as 0dB), spectrum 25MHz-100MHz, with bursts as short as 5us.


  In settings where only unlabelled speech data is available, speech technology
needs to be developed without transcriptions, pronunciation dictionaries, or
language modelling text. A similar problem is faced when modelling infant
language acquisition. In these cases, categorical linguistic structure needs to
be discovered directly from speech audio. We present a novel unsupervised
Bayesian model that segments unlabelled speech and clusters the segments into
hypothesized word groupings. The result is a complete unsupervised tokenization
of the input speech in terms of discovered word types. In our approach, a
potential word segment (of arbitrary length) is embedded in a fixed-dimensional
acoustic vector space. The model, implemented as a Gibbs sampler, then builds a
whole-word acoustic model in this space while jointly performing segmentation.
We report word error rates in a small-vocabulary connected digit recognition
task by mapping the unsupervised decoded output to ground truth transcriptions.
The model achieves around 20% error rate, outperforming a previous HMM-based
system by about 10% absolute. Moreover, in contrast to the baseline, our model
does not require a pre-specified vocabulary size.


  Miniature concave hollows, made by wet etching silicon through a circular
mask, can be used as mirror substrates for building optical micro-cavities on a
chip. In this paper we investigate how ICP polishing improves both shape and
roughness of the mirror substrates. We characterise the evolution of the
surfaces during the ICP polishing using white-light optical profilometry and
atomic force microscopy. A surface roughness of 1 nm is reached, which reduces
to 0.5 nm after coating with a high reflectivity dielectric. With such smooth
mirrors, the optical cavity finesse is now limited by the shape of the
underlying mirror.


  SU(N) Yang-Mills integrals form a new class of matrix models which, in their
maximally supersymmetric version, are relevant to recent non-perturbative
definitions of 10-dimensional IIB superstring theory and 11-dimensional
M-theory. We demonstrate how Monte Carlo methods may be used to establish
important properties of these models. In particular we consider the partition
functions as well as the matrix eigenvalue distributions. For the latter we
derive a number of new exact results for SU(2). We also report preliminary
computations of Wilson loops.
  (Based on talk presented by M. Staudacher at Strings '99, Potsdam, July 19-24
1999)


  In Multi-Goal Reinforcement Learning, an agent learns to achieve multiple
goals with a goal-conditioned policy. During learning, the agent first collects
the trajectories into a replay buffer, and later these trajectories are
selected randomly for replay. However, the achieved goals in the replay buffer
are often biased towards the behavior policies. From a Bayesian perspective,
when there is no prior knowledge about the target goal distribution, the agent
should learn uniformly from diverse achieved goals. Therefore, we first propose
a novel multi-goal RL objective based on weighted entropy. This objective
encourages the agent to maximize the expected return, as well as to achieve
more diverse goals. Secondly, we developed a maximum entropy-based
prioritization framework to optimize the proposed objective. For evaluation of
this framework, we combine it with Deep Deterministic Policy Gradient, both
with or without Hindsight Experience Replay. On a set of multi-goal robotic
tasks of OpenAI Gym, we compare our method with other baselines and show
promising improvements in both performance and sample-efficiency.


  A spin model which exhibits glassy dynamics has been proposed by Newman and
Moore. This model possesses no randomness for exchange interactions. We propose
partial trace methods for obtaining static exact solutions of this model under
free boundary conditions, and show some static exact solutions. We obtain that,
from the exact solutions of the specific heat and the correlation functions,
the transition temperature is zero, although the thermal average of a spin is
zero when the temperature is zero. We investigate the ground states. In the
ground states, a part of the present results especially disagrees with the part
of the previous results. We discuss the disagreements.


  We study the extensively monitored Seyfert galaxy NGC 5548 and compare its
nuclear emission with models of accretion disk with accreting corona. To obtain
the intrinsic nuclear spectra from the observed spectra we had to estimate and
subtract the contribution from circumnuclear components such as stars, the
Balmer continuum and blended FeII lines, and the FC2 extended, featureless
continuum. The true nuclear spectra were compared with a two parameter model of
the accreting disk with an accreting corona, described by the mass of the
central black hole and viscosity. The model that best fitted the data was for
M_{BH}=1.4e8 solar masses and the viscosity parameter alpha=0.033. Such a low
viscosity parameter was necessary to produce the sufficient amount of X-rays.
The vertical outflow of mass from corona in the form of wind had to be
neglected in our model in order to fit into high and low states that NGC 5548
underwent. The model also predicts the behavior of the overall opt/UV/X
continuum of NGC 5548 during the whole five year campaign.


  In this paper we present some symmetric nonnegative 5x5 matrix families that
realize a previously unknown region. We also prove that these and other
symmetric nonnegative 5x5 matrix families are closed under perturbations first
presented in W. Guo. Eigenvalues of nonnegative matrices. Linear Algebra and
its Applications, 266:261-270, 1997.