  We study the typical height of the (2+1)-dimensional solid-on-solid surface
with pinning interacting with an impenetrable wall in the delocalization phase.
More precisely, let $\Lambda_N$ be a $N \times N$ box of $\mathbb{Z}^2$, and we
consider a nonnegative integer-valued field $(\phi(x))_{x \in \Lambda_N}$ with
zero boundary conditions (i.e. $\phi|_{\Lambda_N^{\complement}}=0 $) associated
with the energy functional $$ \mathcal{V} (\phi)= \beta \sum_{x \sim y} \vert
\phi(x)-\phi(y) \vert- \sum_{x} h \mathbf{1}_{\{ \phi(x)=0\}},$$ where
$\beta>0$ is the inverse temperature and $h\ge 0$ is the pinning parameter.
Lacoin has shown that for sufficiently large $\beta$, there is a phase
transition between delocalization and localization at the critical point
$$h_w(\beta)= \log \left( \frac{e^{4 \beta}}{e^{4 \beta}-1}\right).$$ In this
paper we show that for $\beta\ge 1$ and $h \in (0, h_w)$, the values of $\phi$
concentrate at the height $H= \lfloor (4 \beta)^{-1} \log N \rfloor$ with
constant order fluctuations. Moreover, at criticality $h=h_w$, we provide
evidence for the conjectured typical height $H_w= \lfloor (6 \beta)^{-1} \log N
\rfloor$.


  Large systems are commonly internetworked. A security policy describes the
communication relationship between the networked entities. The security policy
defines rules, for example that A can connect to B, which results in a directed
graph. However, this policy is often implemented in the network, for example by
firewalls, such that A can establish a connection to B and all packets
belonging to established connections are allowed. This stateful implementation
is usually required for the network's functionality, but it introduces the
backflow from B to A, which might contradict the security policy. We derive
compliance criteria for a policy and its stateful implementation. In
particular, we provide a criterion to verify the lack of side effects in linear
time. Algorithms to automatically construct a stateful implementation of
security policy rules are presented, which narrows the gap between
formalization and real-world implementation. The solution scales to large
networks, which is confirmed by a large real-world case study. Its correctness
is guaranteed by the Isabelle/HOL theorem prover.


  This paper presents a novel recovery technique for a fixed-wing UAV (Unmanned
Aerial Vehicle) based on constrained optimization: i) we propose a trajectory
generation for landing the UAV where it first reduces its altitude by deep
stalling, then perches on a recovery net, ii) we design an NMPC (Nonlinear
Model Predictive Control) tracking controller with terminal constraints for the
optimal generated trajectory under disturbances. Compared to nominal net
recovery procedures, this technique greatly reduces the landing time and the
final airspeed of the UAV. Simulation results for various wind conditions
demonstrate the feasibility of the idea.


  Motivated by applications to poaching and burglary prevention, we define a
class of weighted Traveling Salesman Problems on metric spaces. The goal is to
output an infinite (though typically periodic) tour that visits the n points
repeatedly, such that no point goes unvisited for "too long." More
specifically, we consider two objective functions for each point x. The maximum
objective is simply the maximum duration of any absence from x, while the
quadratic objective is the normalized sum of squares of absence lengths from x.
For periodic tours, the quadratic objective captures the expected duration of
absence from x at a uniformly random point in time during the tour. The overall
objective is then the weighted maximum of the individual points' objectives.
When a point has weight w_x, the absences under an optimal tour should be
roughly a 1/w_x fraction of the absences from points of weight 1. Thus, the
objective naturally encourages visiting high-weight points more frequently, and
at roughly evenly spaced intervals.
  We give a polynomial-time combinatorial algorithm whose output is
simultaneously an O(log n) approximation under both objectives. We prove that
up to constant factors, approximation guarantees for the quadratic objective
directly imply the same guarantees for a natural security patrol game defined
in recent work.


  We explore an ensembled $\Sigma$-net for fast parallel MR imaging, including
parallel coil networks, which perform implicit coil weighting, and sensitivity
networks, involving explicit sensitivity maps. The networks in $\Sigma$-net are
trained in a supervised way, including content and GAN losses, and with various
ways of data consistency, i.e., proximal mappings, gradient descent and
variable splitting. A semi-supervised finetuning scheme allows us to adapt to
the k-space data at test time, which, however, decreases the quantitative
metrics, although generating the visually most textured and sharp images. For
this challenge, we focused on robust and high SSIM scores, which we achieved by
ensembling all models to a $\Sigma$-net.


  For any smooth projective variety with a C* action, we reduce the problem of
computing its Gromov-Witten invariants to the similar problem for its fixed
locus. Starting from the stacky version of variation of GIT for our variety, we
construct the building blocks for the fixed loci of the moduli space of stable
maps. We use this construction to compute their contribution to the virtual
fundamental class.


  We show that various known algorithms for finite-domain constraint
satisfaction problems (CSP), which are based on solving systems of linear
equations over the integers, fail to solve all tractable CSPs correctly. The
algorithms include $\mathbb{Z}$-affine $k$-consistency, BLP+AIP, every fixed
level of the BA$^{k}$-hierarchy, and the CLAP algorithm. In particular, we
refute the conjecture by Dalmau and Opr\v{s}al that there is a fixed constant
$k$ such that the $\mathbb{Z}$-affine $k$-consistency algorithm solves all
tractable finite domain CSPs.


  Proactive dialogues serve as a practical yet challenging dialogue problem in
the era of large language models (LLMs), where the dialogue policy planning is
the key to improving the proactivity of LLMs. Most existing studies enable the
dialogue policy planning of LLMs using various prompting schemes or iteratively
enhance this capability in handling the given case with verbal AI feedback.
However, these approaches are either bounded by the policy planning capability
of the frozen LLMs or hard to be transferred to new cases. In this work, we
introduce a new dialogue policy planning paradigm to strategize LLMs for
proactive dialogue problems with a tunable language model plug-in as a
plug-and-play dialogue policy planner, named PPDPP. Specifically, we develop a
novel training framework to facilitate supervised fine-tuning over available
human-annotated data as well as reinforcement learning from goal-oriented AI
feedback with dynamic interaction data collected by the LLM-based self-play
simulation. In this manner, the LLM-powered dialogue agent can not only be
generalized to different cases after the training, but also be applicable to
different applications by just substituting the learned plug-in. In addition,
we propose to evaluate the policy planning capability of dialogue systems under
the interactive setting. Experimental results demonstrate that PPDPP
consistently and substantially outperforms existing approaches on three
different proactive dialogue applications, including negotiation, emotional
support, and tutoring dialogues.


  Probability density functions (pdfs) of $^{13}CO$ emission line centroid
(line-of-sight, intensity-weighted average) velocities are presented for
several densely sampled molecular clouds as quantitative descriptors of their
underlying dynamics. Although some are approximately Gaussian in form, most of
the pdfs exhibit relatively broader, often nearly exponential, tails, similar
to the pdfs of velocity {\em differences} and {\em derivatives} (but not the
velocity field itself) found in experiments and numerical simulations of
incompressible turbulence. The broad pdf tails found in the present work are
also similar to those found in decades-old measurements of interstellar
velocity pdfs using atomic line centroids, and to the excess wing emission
recently found in individual molecular line profiles. Some possible
interpretations of the observed deviations are briefly discussed, although none
of these account for the nearly exponential tails.


  4D modeling of human-object interactions is critical for numerous
applications. However, efficient volumetric capture and rendering of complex
interaction scenarios, especially from sparse inputs, remain challenging. In
this paper, we propose NeuralHOFusion, a neural approach for volumetric
human-object capture and rendering using sparse consumer RGBD sensors. It
marries traditional non-rigid fusion with recent neural implicit modeling and
blending advances, where the captured humans and objects are layerwise
disentangled. For geometry modeling, we propose a neural implicit inference
scheme with non-rigid key-volume fusion, as well as a template-aid robust
object tracking pipeline. Our scheme enables detailed and complete geometry
generation under complex interactions and occlusions. Moreover, we introduce a
layer-wise human-object texture rendering scheme, which combines volumetric and
image-based rendering in both spatial and temporal domains to obtain
photo-realistic results. Extensive experiments demonstrate the effectiveness
and efficiency of our approach in synthesizing photo-realistic free-view
results under complex human-object interactions.


  With a simple power-law approximation of high-redshift ($\gtrsim3.5$) star
formation history, i.e., $\dot{\rho}_*(z)\propto [(1+z)/4.5]^{-\alpha}$, we
investigate the reionization of intergalactic medium (IGM) and the consequent
Thomson scattering optical depth for cosmic microwave background (CMB) photons.
A constraint on the evolution index $\alpha$ is derived from the CMB optical
depth measured by the {\it Wilkinson Microwave Anisotropy Probe} (WMAP)
experiment, which reads $\alpha\approx2.18\lg{\mathscr{N}_{\gamma}}-3.89$,
where the free parameter $\mathscr{N}_\gamma$ is the number of the escaped
ionizing ultraviolet photons per baryon. Moreover, the redshift for full
reionization, $z_f$, can also be expressed as a function of $\alpha$ as well as
$\mathscr{N}_{\gamma}$. By further taking into account the implication of the
Gunn-Peterson trough observations to quasars for the full reionization
redshift, i.e., $6\lesssim z_f \lesssim7$, we obtain
$0.3\lesssim\alpha\lesssim1.3$ and $80\lesssim\mathscr{N}_{\gamma}\lesssim230$.
For a typical number of $\sim4000$ of ionizing photons released per baryon of
normal stars, the fraction of these photons escaping from the stars, $f_{\rm
esc}$, can be constrained to within the range of $(2.0-5.8)%$.


  We study Weyl-loop semi-metals with short range interactions, focusing on the
possible interaction driven instabilities. We introduce an $\epsilon$ expansion
regularization scheme by means of which the possible instabilities may be
investigated in an unbiased manner through a controlled weak coupling
renormalization group calculation. The problem has enough structure that a
`functional' renormalization group calculation (necessary for an extended Fermi
surface) can be carried out analytically. The leading instabilities are
identified, and when there are competing degenerate instabilities a
Landau-Ginzburg calculation is performed to determine the most likely phase. In
the particle-particle channel, the leading instability is found to be to a
fully gapped chiral superconducting phase which spontaneously breaks time
reversal symmetry, in agreement with general symmetry arguments suggesting that
Weyl loops should provide natural platforms for such exotic forms of
superconductivity. In the particle hole channel, there are two potential
instabilities - to a gapless Pomeranchuk phase which spontaneously breaks
rotation symmetry, or to a fully gapped insulating phase which spontaneously
breaks mirror symmetry. The dominant instability in the particle hole channel
depends on the specific values of microscopic interaction parameters.


  We consider constant mean curvature surfaces of finite topology, properly
embedded in three-space in the sense of Alexandrov. Such surfaces with three
ends and genus zero were constructed and completely classified by the authors
in arXiv:math.DG/0102183. Here we extend the arguments to the case of an
arbitrary number of ends, under the assumption that the asymptotic axes of the
ends lie in a common plane: we construct and classify the entire family of
these genus-zero coplanar constant mean curvature surfaces.


  We present a comparison of the properties of galaxies in the most underdense
regions of the Universe, where the galaxy number density is less than 10% of
the mean density, with galaxies from more typical regions. We have compiled a
sample of galaxies in 46 large nearby voids that were identified using the
Sloan Digital Sky Survey DR4, which provides the largest coverage of the sky.
We study the u-r color distribution, morphology, specific star formation rate,
and radial number density profiles for a total of 495 galaxies fainter than
M_r=-20.4 +5logh located inside the voids and compare these properties with a
control sample of field galaxies. We show that there is an excess of blue
galaxies inside the voids. However, inspecting the properties of blue and red
galaxies separately, we find that galaxy properties such as color distribution,
bulge-to-total ratios, and concentrations are remarkably similar between the
void and overall sample. The void galaxies also show the same specific star
formation rate at fixed color as the control galaxies. We compare our results
with the predictions of cosmological simulations of galaxy formation using the
Millennium Run semi-analytic galaxy catalog. We show that the properties of the
simulated galaxies in large voids are in reasonably good agreement with those
found in similar environments in the real Universe. To summarize, in spite of
the fact that galaxies in voids live in the least dense large-scale
environment, this environment makes very little impact on properties of
galaxies.


  We consider an aggregation model with nonlinear diffusion in domains with
boundaries and investigate the zero diffusion limit of its solutions. We
establish the convergence of weak solutions for fixed times, as well as the
convergence of energy minimizers in this limit. Numerical simulations that
support the analytical results are presented. A second key scope of the
numerical studies is to demonstrate that adding small nonlinear diffusion
rectifies a flaw of the plain aggregation model in domains with boundaries,
which is to evolve into unstable equilibria (non-minimizers of the energy).


  We study left-invariant pseudo-K\"ahler and hypersymplectic structures on
semidirect products $G\rtimes H$; we work at the level of the Lie algebra
$\mathfrak{g}\rtimes\mathfrak{h}$. In particular we consider the structures
induced on $\mathfrak{g}\rtimes\mathfrak{h}$ by existing pseudo-K\"ahler
structures on $\mathfrak{g}$ and $\mathfrak{h}$; we classify all semidirect
products of this type with $\mathfrak{g}$ of dimension $4$ and
$\mathfrak{h}=\mathbb{R}^2$. In the hypersymplectic setting, we consider a more
general construction on semidirect products. We construct new $2$-step
nilpotent hypersymplectic Lie algebras; to our knowledge, these are the first
such examples whose underlying complex structure is not abelian


  Memorability measures how easily an image is to be memorized after glancing,
which may contribute to designing magazine covers, tourism publicity materials,
and so forth. Recent works have shed light on the visual features that make
generic images, object images or face photographs memorable. However, these
methods are not able to effectively predict the memorability of outdoor natural
scene images. To overcome this shortcoming of previous works, in this paper, we
provide an attempt to answer: "what exactly makes outdoor natural scenes
memorable". To this end, we first establish a large-scale outdoor natural scene
image memorability (LNSIM) database, containing 2,632 outdoor natural scene
images with their ground truth memorability scores and the multi-label scene
category annotations. Then, similar to previous works, we mine our database to
investigate how low-, middle- and high-level handcrafted features affect the
memorability of outdoor natural scenes. In particular, we find that the
high-level feature of scene category is rather correlated with outdoor natural
scene memorability, and the deep features learnt by deep neural network (DNN)
are also effective in predicting the memorability scores. Moreover, combining
the deep features with the category feature can further boost the performance
of memorability prediction. Therefore, we propose an end-to-end DNN based
outdoor natural scene memorability (DeepNSM) predictor, which takes advantage
of the learned category-related features. Then, the experimental results
validate the effectiveness of our DeepNSM model, exceeding the state-of-the-art
methods. Finally, we try to understand the reason of the good performance for
our DeepNSM model, and also study the cases that our DeepNSM model succeeds or
fails to accurately predict the memorability of outdoor natural scenes. Code:
github.com/JiaxinLu-home/Natural-Scene-Memorability-Dataset.


  We present kinematical analyses of 22 Galactic globular clusters using the
Hubble Space Telescope proper motion (HSTPROMO) catalogues recently presented
in Bellini et al. (2014). For most clusters, this is the first proper-motion
study ever performed, and, for many, this is the most detailed kinematic study
of any kind. We use cleaned samples of bright stars to determine binned
velocity-dispersion and velocity-anisotropy radial profiles and two-dimensional
velocity-dispersion spatial maps. Using these profiles, we search for
correlations between cluster kinematics and structural properties. We find
that: (1) more centrally-concentrated clusters have steeper radial
velocity-dispersion profiles; (2) on average, at 1\sigma confidence in two
dimensions, the photometric and kinematic centres of globular clusters agree to
within ~1", with a cluster-to-cluster rms of 4" (including observational
uncertainties); (3) on average, the cores of globular clusters have isotropic
velocity distributions to within 1% (\sigma_t/\sigma_r = 0.992 +/- 0.005), with
a cluster-to-cluster rms of 2% (including observational uncertainties); (4)
clusters generally have mildly radially anisotropic velocity distributions
(\sigma_t/\sigma_r ~ 0.8-1.0) near the half-mass radius, with bigger deviations
from isotropy for clusters with longer relaxation times; (5) there is a
relation between \sigma_minor/\sigma_major and ellipticity, such that the more
flattened clusters in the sample tend to be more anisotropic, with
\sigma_minor/\sigma_major ~ 0.9-1.0. Aside from these general results and
correlations, the profiles and maps presented here can provide a basis for
detailed dynamical modelling of individual globular clusters. Given the quality
of the data, this is likely to provide new insights into a range of topics
concerning globular cluster mass profiles, structure, and dynamics.


  High-spin yrast structure of 32S is investigated by means of the cranked
Skyrme-Hartree-Fock method in the three-dimensional Cartesian-mesh
representation without imposing restrictions on spatial symmetries. The result
suggests that 1) a crossover from the superdeformed to the hyperdeformed-like
configurations takes place on the yrast line at angular momentum $I \simeq 24$,
which corresponds to the ``band termination'' point in the cranked
harmonic-oscillator model, and 2) non-axial octupole deformations of the
$Y_{31}$ type play an important role in the yrast states in the range $5\leq I
\leq 13$.


  In this paper we develop techniques that eliminate the need of the
Generalized Riemann Hypothesis (GRH) from various (almost all) known results
about deterministic polynomial factoring over finite fields. Our main result
shows that given a polynomial f(x) of degree n over a finite field k, we can
find in deterministic poly(n^{\log n},\log |k|) time "either" a nontrivial
factor of f(x) "or" a nontrivial automorphism of k[x]/(f(x)) of order n. This
main tool leads to various new GRH-free results, most striking of which are:
  (1) Given a noncommutative algebra over a finite field, we can find a zero
divisor in deterministic subexponential time.
  (2) Given a positive integer r such that either 8|r or r has at least two
distinct odd prime factors. There is a deterministic polynomial time algorithm
to find a nontrivial factor of the r-th cyclotomic polynomial over a finite
field.
  In this paper, following the seminal work of Lenstra (1991) on constructing
isomorphisms between finite fields, we further generalize classical Galois
theory constructs like cyclotomic extensions, Kummer extensions, Teichmuller
subgroups, to the case of commutative semisimple algebras with automorphisms.
These generalized constructs help eliminate the dependence on GRH.


  The unusual structure and symmetry of low-energy states in twisted transition
metal dichalcogenides leads to large in-plane spin-exchange interactions
between spin-valley locked holes. We demonstrate that this exchange interaction
can stabilize a gapped spin-liquid phase with a quantized spin-Chern number of
three when the twist angle is sufficiently small and the system lies in a Mott
insulating phase. The gapped spin liquid may be understood as arising from
spinon pairing in the DIII Altland-Zirnbauer symmetry class. Applying an out of
plane electric field or increasing the twist angle is shown to drive a
transition respectively to an anomalous Hall insulator or an in-plane
antiferromagnet. Recent experiments indicate that a spin-Chern number three
phase occurs in twisted MoTe$_2$ at small twist angles with a transition to a
quantum anomalous Hall phase as the twist angle is increased above a critical
value of about $2.5^\circ$ in absence of applied electric field.


  In 1999, Molodtsov initiated the concept of Soft Sets Theory as a new
mathematical tool and a completely different approach for dealing with
uncertainties in many fields of applied sciences. In 2011, Shabir and Naz
introduced and studied the theory of soft topological spaces, also defining and
investigating many new soft properties as generalization of the classical ones.
In this paper, we introduce the notions of soft separation between soft points
and soft closed sets in order to obtain a generalization of the well-known
Embedding Lemma for soft topological spaces.


  Recently, it was observed that spatially-coupled LDPC code ensembles approach
the Shannon capacity for a class of binary-input memoryless symmetric (BMS)
channels. The fundamental reason for this was attributed to a "threshold
saturation" phenomena derived by Kudekar, Richardson and Urbanke. In
particular, it was shown that the belief propagation (BP) threshold of the
spatially coupled codes is equal to the maximum a posteriori (MAP) decoding
threshold of the underlying constituent codes. In this sense, the BP threshold
is saturated to its maximum value. Moreover, it has been empirically observed
that the same phenomena also occurs when transmitting over more general classes
of BMS channels. In this paper, we show that the effect of spatial coupling is
not restricted to the realm of channel coding. The effect of coupling also
manifests itself in compressed sensing. Specifically, we show that
spatially-coupled measurement matrices have an improved sparsity to sampling
threshold for reconstruction algorithms based on verification decoding. For
BP-based reconstruction algorithms, this phenomenon is also tested empirically
via simulation. At the block lengths accessible via simulation, the effect is
quite small and it seems that spatial coupling is not providing the gains one
might expect. Based on the threshold analysis, however, we believe this
warrants further study.


  We introduce a machine learning (ML) supervised model function that is
inspired by the variational principle of physics. This ML hypothesis
evolutionary method, termed ML-Omega, allows us to go from data to differential
equation(s) underlying the physical (chemical, engineering, etc.) phenomena the
data are derived from. The fundamental equations of physics can be derived from
this ML-Omega evolutionary method when provided the proper training data. By
training the ML-Omega model function with only three hydrogen-like atom
energies, the method can find Schr\"odinger's exact functional and, from it,
Schr\"odinger's fundamental equation. Then, in the field of density functional
theory (DFT), when the model function is trained with the energies from the
known Thomas-Fermi (TF) formula E = -0.7687Z^7/3, it correctly finds the exact
TF functional. Finally, the method is applied to find a local orbital-free (OF)
functional expression of the independent electron kinetic energy functional Ts
based on the gamma-TF-lambda-vW model. By considering the theoretical energies
of only 5 atoms (He, Be, Ne, Mg, Ar) as the training set, the evolutionary
ML-Omega method finds an ML-Omega-OF-DFT local Ts functional
(gamma-TF-lambda-vW (0.964, 1/4)) that outperforms all the OF- DFT functionals
of a representative group. Moreover, our ML-Omega-OF functional overcomes the
LDA's and some local GGA-DFT's functionals' difficulty to describe the
stretched bond region at the correct spin configuration of diatomic molecules.
Although our evolutionary ML-Omega model function can work without an explicit
prior-form functional, by using the techniques of symbolic regression, in this
work we exploit prior-form functional expressions to make the training process
faster in the example problems presented here.


  A set $D$ of vertices in a graph $G$ is a dominating set if every vertex of
$G$, which is not in $D$, has a neighbor in $D$. A set of vertices $D$ in $G$
is convex (respectively, isometric), if all vertices in all shortest paths
(respectively, all vertices in one of the shortest paths) between any two
vertices in $D$ lie in $D$. The problem of finding a minimum convex dominating
(respectively, isometric dominating) set is considered in this paper from
algorithmic point of view. For the class of weak dominating pair graphs
(i.e.,~the graphs that contain a dominating pair, which is a pair of vertices
$x,y\in V(G)$ such that vertices of any path between $x$ and $y$ form a
dominating set), we present an efficient algorithm that finds a minimum
isometric dominating set of such a graph. On the other hand, we prove that even
if one restricts to weak dominating pair graphs that are also chordal graphs,
the problem of deciding whether there exists a convex dominating set bounded by
a given arbitrary positive integer is NP-complete. By further restricting the
class of graphs to chordal dominating pair graphs (i.e.,~the chordal graphs in
which every connected induced subgraph has a dominating pair) we are able to
find a polynomial time algorithm that determines the minimum size of a convex
dominating set of such a graph.


  Unfulfilled expectations from macro-economic initiatives during the Great
Recession and the massive shift into globalization echo today with political
upheaval, anti-establishment propaganda, and looming trade/currency wars that
threaten domestic and international value chains. Once stable entities like the
EU now look fragile and political instability in the US presents unprecedented
challenges to an International Monetary System (IMS) that predominantly relies
on the USD and EUR as reserve currencies. In this environment, it is critical
for an international organization mandated to ensure stability to plan and act
ahead. This paper argues that Decentralized Ledger-based technology (DLT) is
key for the International Monetary Fund (IMF) to mitigate some of those risks,
promote stability and safeguard world prosperity. Over the last two years, DLT
has made headline news globally and created a worldwide excitement not seen
since the internet entered the mainstream. The rapid adoption and open-to-all
philosophy of DLT has already redefined global socioeconomics, promises to
shake up the world of commerce/finance and challenges the workings of central
governments/regulators. This paper examines DLT core premises and proposes a
two-step approach for the IMF to expand Special Drawing Rights (SDR) into that
sphere so as to become the originally envisioned numeraire and reserve currency
for cross-border transactions in this new decentralized century.


  A number of studies showed association of mental status with heart rate
variability. This work discovered a feature of frequency structure of heart
rate variability that is associated with mental readiness. In three independent
groups of 64, 39, and 19 volunteers by the factor analysis of heart rate
periodograms, it has been discovered that there are at least two other heart
rate oscillation phenomena apart from the well known low frequency oscillations
and respiratory arrhythmia. They have periods of 3 and 4 heart beats.
Association of amplitude of 3-beats oscillation with level of mental readiness
was shown due to further observation in two independent groups of 12 and 7.
Moreover, possibility of assessment of mental readiness by the mathematical
model based on heart rate periodogram was suggested.


  As huge complex systems consisting of geographic regions, natural resources,
people and economic entities, countries follow the allometric scaling law which
is ubiquitous in ecological, urban systems. We systematically investigated the
allometric scaling relationships between a large number of macroscopic
properties and geographic (area), demographic (population) and economic (GDP,
gross domestic production) sizes of countries respectively. We found that most
of the economic, trade, energy consumption, communication related properties
have significant super-linear (the exponent is larger than 1) or nearly linear
allometric scaling relations with GDP. Meanwhile, the geographic (arable area,
natural resources, etc.), demographic(labor force, military age population,
etc.) and transportation-related properties (road length, airports) have
significant and sub-linear (the exponent is smaller than 1) allometric scaling
relations with area. Several differences of power law relations with respect to
population between countries and cities were pointed out. Firstly, population
increases sub-linearly with area in countries. Secondly, GDP increases linearly
in countries but not super-linearly as in cities. Finally, electricity or oil
consumptions per capita increases with population faster than cities.


  Sparsity of a learning solution is a desirable feature in machine learning.
Certain reproducing kernel Banach spaces (RKBSs) are appropriate hypothesis
spaces for sparse learning methods. The goal of this paper is to understand
what kind of RKBSs can promote sparsity for learning solutions. We consider two
typical learning models in an RKBS: the minimum norm interpolation (MNI)
problem and the regularization problem. We first establish an explicit
representer theorem for solutions of these problems, which represents the
extreme points of the solution set by a linear combination of the extreme
points of the subdifferential set, of the norm function, which is
data-dependent. We then propose sufficient conditions on the RKBS that can
transform the explicit representation of the solutions to a sparse kernel
representation having fewer terms than the number of the observed data. Under
the proposed sufficient conditions, we investigate the role of the
regularization parameter on sparsity of the regularized solutions. We further
show that two specific RKBSs: the sequence space $\ell_1(\mathbb{N})$ and the
measure space can have sparse representer theorems for both MNI and
regularization models.


  We present a model for one cycle of a classical nova outburst based on a
self-consistent wind mass loss accelerated by the gradient of radiation
pressure, i.e., the so-called optically thick winds. Evolution models are
calculated by a Henyey code for a 1.0 $M_\odot$ white dwarf (WD) with a mass
accretion rate of $5 \times 10^{-9}~M_\odot$ yr$^{-1}$. The outermost part of
hydrogen-rich envelope is connected to a steadily moving envelope when
optically thick winds occur. We confirm that no internal shock waves occur at
the thermonuclear runaway. The wind mass loss rate reaches a peak of $1.4
\times 10^{-4}~M_\odot$ yr$^{-1}$ at the epoch of the maximum photospheric
expansion, where the photospheric temperature decreases to $\log T_{\rm ph}$
(K)=3.90. Almost all of the accreted mass is lost in the wind. The nuclear
energy generated in hydrogen burning is lost in a form of photon emission (64
%), gravitational energy (lifting-up the wind matter against the gravity, 35
%), and kinetic energy of the wind (0.23 %). A classical nova should be very
bright in a far-UV (100 - 300 \AA) band, during a day just after the onset of
thermonuclear runaway ($\sim$25 d before the optical maximum). In the decay
phase of the nova outburst, the envelope structure is very close to that of a
steady state solution.


  We propose a new approach to represent nonparametrically the linear
dependence structure of a spatio-temporal process in terms of latent common
factors. Though it is formally similar to the existing reduced rank
approximation methods (Section 7.1.3 of Cressie and Wikle, 2011), the
fundamental difference is that the low-dimensional structure is completely
unknown in our setting, which is learned from the data collected irregularly
over space but regularly over time. Furthermore a graph Laplacian is
incorporated in the learning in order to take the advantage of the continuity
over space, and a new aggregation method via randomly partitioning space is
introduced to improve the efficiency. We do not impose any stationarity
conditions over space either, as the learning is facilitated by the
stationarity in time. Krigings over space and time are carried out based on the
learned low-dimensional structure, which is scalable to the cases when the data
are taken over a large number of locations and/or over a long time period.
Asymptotic properties of the proposed methods are established. Illustration
with both simulated and real data sets is also reported.


  {}From three quasar samples with a total of 1038 objects in the redshift
range $1.0 \div 2.2$ we measure the variance $\sigma^2$ of counts in cells of
volume $V_u$. By a maximum likelihood analysis applied separately on these
samples we obtain estimates of $\sigma^2(\ell)$, with $\ell \equiv V_u^{1/3}$.
The analysis from a single catalog for $\ell = ~40~h^{-1}$ Mpc and from a
suitable average over the three catalogs for $\ell = ~60,~80$ and $100~h^{-1}$
Mpc, gives $\sigma^2(\ell) = 0.46^{+0.27}_{-0.27}$, $0.18^{+0.14}_{-0.15}$,
$0.05^{+0.14}_{-0.05}$ and $0.12^{+0.13}_{-0.12}$, respectively, where the
$70\%$ confidence ranges account for both sampling errors and statistical
fluctuations in the counts. This allows a comparison of QSO clustering on large
scales with analogous data recently obtained both for optical and IRAS
galaxies: QSOs seem to be more clustered than these galaxies by a biasing
factor $b_{QSO}/b_{gal} \sim 1.4 - 2.3$.


  Quantum entanglement in the motion of macroscopic objects is of significance
to both fundamental studies and quantum technologies. Here we show how to
entangle the mechanical vibration modes of two massive ferrimagnets that are
placed in the same microwave cavity. Each ferrimagnet supports a magnon mode
and a low-frequency vibration mode coupled by the magnetostrictive force. The
two magnon modes are, respectively, coupled to the microwave cavity by the
magnetic dipole interaction. We first generate a stationary nonlocal entangled
state between the vibration mode of the ferrimagnet-1 and the magnon mode of
the ferrimagnet-2. This is realized by continuously driving the ferrimagnet-1
with a strong red-detuned microwave field and the entanglement is achieved by
exploiting the magnomechanical parametric down-conversion and the cavity-magnon
state-swap interaction. We then switch off the pump on the ferrimagnet-1 and,
simultaneously, turn on a red-detuned pulsed drive on the ferrimagnet-2. The
latter drive is used to activate the magnomechanical beamsplitter interaction,
which swaps the magnonic and mechanical states of the ferrimagnet-2.
Consequently, the previously generated phonon-magnon entanglement is
transferred to the mechanical modes of two ferrimagnets. The work provides a
scheme to prepare entangled states of mechanical motion of two massive objects,
which may find applications in various studies exploiting macroscopic entangled
states.


  We prove two splitting theorems, one topological, the other metric, for open
manifolds with nonnegative sectional curvature.


  We study the iteration complexity of stochastic gradient descent (SGD) for
minimizing the gradient norm of smooth, possibly nonconvex functions. We
provide several results, implying that the $\mathcal{O}(\epsilon^{-4})$ upper
bound of Ghadimi and Lan~\cite{ghadimi2013stochastic} (for making the average
gradient norm less than $\epsilon$) cannot be improved upon, unless a
combination of additional assumptions is made. Notably, this holds even if we
limit ourselves to convex quadratic functions. We also show that for nonconvex
functions, the feasibility of minimizing gradients with SGD is surprisingly
sensitive to the choice of optimality criteria.


  We have performed a detailed analysis of the XMM-Newton observation of IGR
J16318-4848, to study the properties of the matter responsible for the
obscuration and for the emission of Fe and Ni lines. Even if the line of sight
material has a column density of about 2x10^24 cm^-2, from the Fe Kalpha line
EW and Compton Shoulder we argue that the matter should have an average column
density of a few x10^23 cm^-2, along with a covering factor of about 0.1-0.2.
The iron Kalpha line varies on time scales as short as 1000 s, implying a size
of the emitting region smaller than about 3x10^13 cm. The flux of the line
roughly follows the variations of the continuum, but not exactly, suggesting a
variation of the geometrical properties of the emitting region on similar time
scales.


  Recently, there has been a great deal of attention in a class of controllers
based on time-varying gains, called prescribed-time controllers, that steer the
system's state to the origin in the desired time, a priori set by the user,
regardless of the initial condition. Furthermore, such a class of controllers
has been shown to maintain a prescribed-time convergence in the presence of
disturbances even if the disturbance bound is unknown. However, such properties
require a time-varying gain that becomes singular at the terminal time, which
limits its application to scenarios under quantization or measurement noise.
This chapter presents a methodology to design a broader class of controllers,
called predefined-time controllers, with a prescribed convergence-time bound.
Our approach allows designing robust predefined-time controllers based on
time-varying gains while maintaining uniformly bounded time-varying gains. We
analyze the condition for uniform Lyapunov stability under the proposed
time-varying controllers.


  Innovations in fluid mechanics are leading to better food since ancient
history, while creativity in cooking inspires applied and fundamental science.
Here, we review how recent advances in hydrodynamics are changing food science,
and we highlight how the surprising phenomena that arise in the kitchen lead to
discoveries and technologies across the disciplines, including rheology, soft
matter, biophysics and molecular gastronomy. This review is structured like a
menu, where each course highlights different aspects of culinary fluid
mechanics. Our main themes include multiphase flows, complex fluids, thermal
convection, hydrodynamic instabilities, viscous flows, granular matter, porous
media, percolation, chaotic advection, interfacial phenomena, and turbulence.
For every topic, we first provide an introduction accessible to food
professionals and scientists in neighbouring fields. We then assess the
state-of-the-art knowledge, the open problems, and likely directions for future
research. New gastronomic ideas grow rapidly as the scientific recipes keep
improving too.


  In this work we solve thermo-hydrodynamical equations considering a two
dimensional electron system in the integer quantum Hall regime, to calculate
the spatial distribution of the local electron temperature. We start from the
self-consistently calculated electrostatic and electrochemical potentials in
equilibrium. Next, by imposing an external current, we investigate the
variations of the electron temperature in the linear-response regime. Here a
local relation between the electron density and conductivity tensor elements is
assumed. Following the Ohm's law we obtain local current densities and by
implementing the results of the thermo-hydrodynamical theory, calculate the
local electron temperature. We observe that the local electron temperature
strongly depends on the formation of compressible and incompressible strips.


  The ongoing high precision E821 Brookhaven National Laboratory experiment on
muon g-2 is promising to probe a theory involving supersymmetry. We have
studied the constraints on the minimal Anomaly Mediated Supersymmetry Breaking
(AMSB) model using the current data of muon g-2 from Brookhaven. A scenario of
seeing no deviation from the Standard Model is also considered, within a
$2\sigma$ limit of the combined error from the Standard Model result and the
Brookhaven predicted uncertainty level. The resulting constraint is found to be
complementary to what one obtains from $b \to s+ \gamma$ bounds within the AMSB
scenario, since only a definite sign of $\mu$ is effectively probed via $b \to
s+ \gamma$. A few relevant generic features of the model are also described for
disallowed regions of parameter space.


  Given a diagonalizable matrix $A$, we study the stability of its invariant
subspaces when its matrix of eigenvectors is ill-conditioned. Let
$\mathcal{X}_1$ be some invariant subspace of $A$ and $X_1$ be the matrix
storing the right eigenvectors that spanned $\mathcal{X}_1$. It is generally
believed that when the condition number $\kappa_2(X_1)$ gets large, the
corresponding invariant subspace $\mathcal{X}_1$ will become unstable to
perturbation. This paper proves that this is not always the case. Specifically,
we show that the growth of $\kappa_2(X_1)$ alone is not enough to destroy the
stability. As a direct application, our result ensures that when $A$ gets
closer to a Jordan form, one may still estimate its invariant subspaces from
the noisy data stably.


  Partitions associated with mock theta functions have received a great deal of
attention in the literature. Recently, Choi and Kim derived several partition
identities from the third and sixth order mock theta functions. In addition,
three Ramanujan-type congruences were established by them. In this paper, we
present some new congruences for these partition functions.


  A 20-kiloton liquid scintillator detector is designed in the Jiangmen
Underground Neutrino Observatory (JUNO) for multiple physics purposes,
including the determination of the neutrino mass ordering through reactor
neutrinos, as well as measuring supernova neutrinos, solar neutrinos, and
atmosphere neutrinos to explore different physics topics. Efficient
reconstruction algorithms are needed to achieve these physics goals in a wide
energy range from MeV to GeV. In this paper, we present a novel method for
reconstructing the energy of events using hit information from 25600 3-inch
photomultiplier tubes (PMTs) and the OCCUPANCY method. Our algorithm exhibits
good performance in accurate energy reconstruction, validated with electron
Monte Carlo samples spanning kinetic energies from 10 MeV to 1 GeV.


  We derive an analytical expression of a Wigner function that approximately
describes the time evolution of the one-dimensional motion of a particle in a
nonharmonic potential. Our method involves two exact frame transformations,
accounting for both the classical dynamics of the centroid of the initial state
and the rotation and squeezing about that trajectory. Subsequently, we employ
two crucial approximations, namely the constant-angle and
linearized-decoherence approximations. These approximations are effective in
the regime of wide potentials and small fluctuations, namely potentials that
enable spatial expansions orders of magnitude larger than the one of the
initial state but that remain smaller compared to the relevant dynamical length
scale (e.g., distance between turning points). Our analytical result elucidates
the interplay between classical and quantum physics and the impact of
decoherence during nonlinear dynamics. This analytical result is instrumental
to design, optimize and understand proposals using nonlinear dynamics to
generate macroscopic quantum states of massive particles.


  This is a footnote to earlier joint work with Yu. Berest, which constructed a
bijection between the space of ideal classes of the Weyl algebra and a union of
Calogero-Moser varieties. A key property of this bijection is that it is
equivariant with respect to the action of the automorphism group of the Weyl
algebra: the main result of the present note is that it is uniquely determined
by that property.


  For any closed analytic set X in C^2 there exists a proper holomorphic
embedding of the unit disk into C^2 such that the image avoids X.


  Implicative algebras have been recently introduced by Miquel in order to
provide a unifying notion of model, encompassing the most relevant and used
ones, such as realizability (both classical and intuitionistic), and forcing.
In this work, we initially approach implicative algebras as a generalization of
locales, and we extend several topological-like concepts to the realm of
implicative algebras, accompanied by various concrete examples. Then, we shift
our focus to viewing implicative algebras as a generalization of partial
combinatory algebras. We abstract the notion of a category of assemblies,
partition assemblies, and modest sets to arbitrary implicative algebras, and
thoroughly investigate their categorical properties and interrelationships.


  Two flavour massless QCD has a second order chiral transition which has been
argued to belong to the universality class of the $3d$ O(4) spin model. The
arguments have been questioned recently, and the transition was claimed to be
mean field behaved. We discuss this issue at the example of the $3d$
Gross-Neveu model. A solution is obtained by applying various well established
analytical methods.


  For a pair of groups $G, H$ we study pairs of actions $G$ on $H$ and $H$ on
$G$ such that these pairs are compatible and non-abelian tensor products $G
\otimes H$ are defined.


  This paper illustrates the prospective need for structural fingerprinting
methods for nanocrystals. A review of the existing fingerprinting methods for
crystal structures by means of transmission electron microscopy which work for
a single setting of the specimen goniometer is given. Suggestions are made on
how some of these methods could be enhanced when nanocrystals and novel
instrumentation are involved, i.e. when either the kinematic or quasi-kinematic
scattering approximations are sufficiently well satisfied. A novel strategy for
lattice-fringe fingerprinting of nanocrystals from Fourier transforms of
high-resolution phase contrast transmission electron microscopy images is
briefly outlined. Nanocrystal structure specific limitations to the application
of this strategy are discussed. An appeal is made to share the structural data
of nanocrystals freely over the internet and infrastructure that would allow
this sharing is mentioned. A shorter version of this paper has been accepted
for publication in a special issue on the Structure of Nanocrystals of the
Zeitschrift fuer Kristallographie, International journal for structural,
physical, and chemical aspects of crystalline materials.


  In the previous decades, the theory of first passage percolation became a
highly important area of probability theory. In this work, we will observe what
can be said about the corresponding structure if we forget about the
probability measure defined on the product space of edges and simply consider
topology in the terms of residuality. We focus on interesting questions arising
in the probabilistic setup that make sense in this setting, too. We will see
that certain classical almost sure events, as the existence of geodesics have
residual counterparts, while the notion of the limit shape or time constants
gets as chaotic as possible.


  In this paper we estimate the Large Synoptic Survey Telescope (LSST) yield of
eclipsing binary stars, which will survey ~20,000 square degrees of the
southern sky during the period of 10 years in 6 photometric passbands to r ~
24.5. We generate a set of 10,000 eclipsing binary light curves sampled to the
LSST time cadence across the whole sky, with added noise as a function of
apparent magnitude. This set is passed to the Analysis of Variance (AoV) period
finder to assess the recoverability rate for the periods, and the successfully
phased light curves are passed to the artificial intelligence-based pipeline
EBAI to assess the recoverability rate in terms of the eclipsing binaries'
physical and geometric parameters. We find that, out of ~24 million eclipsing
binaries observed by LSST with S/N>10 in mission life-time, ~28% or 6.7 million
can be fully characterized by the pipeline. Of those, ~25% or 1.7 million will
be double-lined binaries, a true treasure trove for stellar astrophysics.


  Working with any gradient-based machine learning algorithm involves the
tedious task of tuning the optimizer's hyperparameters, such as its step size.
Recent work has shown how the step size can itself be optimized alongside the
model parameters by manually deriving expressions for "hypergradients" ahead of
time.
  We show how to automatically compute hypergradients with a simple and elegant
modification to backpropagation. This allows us to easily apply the method to
other optimizers and hyperparameters (e.g. momentum coefficients). We can even
recursively apply the method to its own hyper-hyperparameters, and so on ad
infinitum. As these towers of optimizers grow taller, they become less
sensitive to the initial choice of hyperparameters. We present experiments
validating this for MLPs, CNNs, and RNNs. Finally, we provide a simple PyTorch
implementation of this algorithm (see
people.csail.mit.edu/kach/gradient-descent-the-ultimate-optimizer).


  Spin correlations in the pyrochlore antiferromagnet Y_2Ru_2O_7 with
Curie-Weiss temperature $\Theta_{CW}=-1100$ K and critical temperature T_N=77 K
were examined through neutron scattering. For $T_N<T<\Theta_{CW}/3$ the data
show spin relaxation with a rate $\hbar\Gamma= 1.17(9)k_BT$. For T<T_N spectral
weight moves to higher energies with substantial changes up to $4\times
k_BT_N$. For T<<T_N there is a $\Delta=11(1)$ meV energy gap and a pronounced
spectral maximum at 19.7 meV. Throughout the temperature range examined the
wave vector dependence of inelastic scattering exhibits a broad peak for
$Qd\approx 3.8$ (d is Ru-Ru spacing) consistent with dipolar spin correlations.


  Self-supervised cross-modal super-resolution (SR) can overcome the difficulty
of acquiring paired training data, but is challenging because only
low-resolution (LR) source and high-resolution (HR) guide images from different
modalities are available. Existing methods utilize pseudo or weak supervision
in LR space and thus deliver results that are blurry or not faithful to the
source modality. To address this issue, we present a mutual modulation SR
(MMSR) model, which tackles the task by a mutual modulation strategy, including
a source-to-guide modulation and a guide-to-source modulation. In these
modulations, we develop cross-domain adaptive filters to fully exploit
cross-modal spatial dependency and help induce the source to emulate the
resolution of the guide and induce the guide to mimic the modality
characteristics of the source. Moreover, we adopt a cycle consistency
constraint to train MMSR in a fully self-supervised manner. Experiments on
various tasks demonstrate the state-of-the-art performance of our MMSR.


  The design and fabrication of phononic crystals (PnCs) hold the key to
control the propagation of heat and sound at the nanoscale. However, there is a
lack of experimental studies addressing the impact of order/disorder on the
phononic properties of PnCs. Here, we present a comparative investigation of
the influence of disorder on the hypersonic and thermal properties of
two-dimensional PnCs. PnCs of ordered and disordered lattices are fabricated of
circular holes with equal filling fractions in free-standing Si membranes.
Ultrafast pump and probe spectroscopy (asynchronous optical sampling) and Raman
thermometry based on a novel two-laser approach are used to study the phononic
properties in the gigahertz (GHz) and terahertz (THz) regime, respectively.
Finite element method simulations of the phonon dispersion relation and
three-dimensional displacement fields furthermore enable the unique
identification of the different hypersonic vibrations. The increase of surface
roughness and the introduction of short-range disorder are shown to modify the
phonon dispersion and phonon coherence in the hypersonic (GHz) range without
affecting the room-temperature thermal conductivity. On the basis of these
findings, we suggest a criteria for predicting phonon coherence as a function
of roughness and disorder.


  We argue that the topological charge of nonabelian gauge theory is
unphysical. To show this statement, we use the Adler-Bardeen theorem and the
Becchi-Rouet-Stora-Tyutin symmetry which are warranted by the perturbative
finiteness of the chiral anomaly, thus being free of Gribov ambiguity. In
addition to the original argument using the unphysical gauge field component
collinear to the spatial derivative of the gauge function, we show the
unobservability of the topological charge using the Ward-Takahashi identity. We
then present the consequences of this finding and show the consistency with
many physical pictures and ideas that have been developed around the topology
of nonabelian gauge theory. The most important ones are the resolution of the
Strong CP problem, the unobservability of topological instantons, the physical
relevance of the axial $U(1)$ symmetry, the independence of the vacuum energy
on the vacuum angle, and the impossibility to realize the sphaleron induced
baryogenesis and chiral magnetic effects. The axial $U(1)$ symmetry and the
unphysical $\theta$-term imply that the physical complex phase of the
Cabibbo-Kobayashi-Maskawa matrix is the sole source of CP violation in the
standard model. The unphysical sphaleron also means that the lepton number is
phenomenologically free from the baryon number, and their violations may be
modeled separately. We also comment on the consistency with the results of
lattice calculations.


  Let $G=(V,E)$ be a finite, connected graph. We consider a greedy selection of
vertices: given a list of vertices $x_1, \dots, x_k$, take $x_{k+1}$ to be any
vertex maximizing the sum of distances to the existing vertices and iterate: we
keep adding the `most remote' vertex. The frequency with which the vertices of
the graph appear in this sequence converges to a set of probability measures
with nice properties. The support of these measures is, generically, given by a
rather small number of vertices $m \ll |V|$. We prove that this suggests that
the graph $G$ is at most '$m$-dimensional' by exhibiting an explicit
$1-$Lipschitz embedding $\phi: G \rightarrow \ell^1(\mathbb{R}^m)$ with good
properties.


  We obtain a generalized Schwarzschild (GS-) and a generalized
Reissner-Nordstrom (GRN-) black hole geometries in (3+1)-dimensions, in a
noncommutative string theory. In particular, we consider an effective theory of
gravity on a curved $D_3$-brane in presence of an electromagnetic (EM-) field.
Two different length scales, inherent in its noncommutative counter-part, are
exploited to obtain a theory of effective gravity coupled to an U(1)
noncommutative gauge theory to all orders in $\Theta$. It is shown that the
GRN-black hole geometry, in the Planckian regime, reduces to the GS-black hole.
However in the classical regime it may be seen to govern both
Reissner-Nordstrom and Schwarzschild geometries independently. The emerging
notion of 2D black holes evident in the frame-work are analyzed. It is argued
that the $D$-string in the theory may be described by the near horizon 2D black
hole geometry, in the gravity decoupling limit. Finally, our analysis explains
the nature of the effective force derived from the nonlinear EM-field and
accounts for the Hawking radiation phenomenon in the formalism.


  Symbiotic stars are a heterogeneous class of interacting binaries. Among
them, RT Cru has been classified as prototype of a subclass that is
characterised by hard X-ray spectra extending past ~20 keV. We analyse ~8.6 Ms
of archival INTEGRAL data collected in the period 2003-2014, ~140 ks of
Swift/XRT data, and a Suzaku observation of 39 ks, to study the spectral X-ray
emission and investigate the nature of the compact object. Based on the 2MASS
photometry, we estimate the distance to the source of 1.2-2.4 kpc. The X-ray
spectrum obtained with Swift/XRT, JEM-X, IBIS/ISGRI, and Suzaku data is well
fitted by a cooling flow model modified by an absorber that fully covers the
source and two partial covering absorbers. Assuming that the hard X-ray
emission of RT Cru originates from an optically thin boundary layer around a
non-magnetic white dwarf, we estimated a mass of the WD of about 1.2 M_Sun. The
mass accretion rate obtained for this source might be too high for the
optically thin boundary layer scenario. Therefore we investigate other
plausible scenarios to model its hard X-ray emission. We show that,
alternatively, the observed X-ray spectrum can be explained with the X-ray
emission from the post-shock region above the polar caps of a magnetised white
dwarf with mass ~0.9-1.1 M_Sun.


  Nodes in a multiplex network are connected by multiple types of relations.
However, most existing network embedding methods assume that only a single type
of relation exists between nodes. Even for those that consider the multiplexity
of a network, they overlook node attributes, resort to node labels for
training, and fail to model the global properties of a graph. We present a
simple yet effective unsupervised network embedding method for attributed
multiplex network called DMGI, inspired by Deep Graph Infomax (DGI) that
maximizes the mutual information between local patches of a graph, and the
global representation of the entire graph. We devise a systematic way to
jointly integrate the node embeddings from multiple graphs by introducing 1)
the consensus regularization framework that minimizes the disagreements among
the relation-type specific node embeddings, and 2) the universal discriminator
that discriminates true samples regardless of the relation types. We also show
that the attention mechanism infers the importance of each relation type, and
thus can be useful for filtering unnecessary relation types as a preprocessing
step. Extensive experiments on various downstream tasks demonstrate that DMGI
outperforms the state-of-the-art methods, even though DMGI is fully
unsupervised.


  The growing connectivity of industrial devices as a result of the Internet of
Things is increasing the risks to Industrial Control Systems. Since attacks on
such devices can also cause damage to people and machines, they must be
properly secured. Therefore, a threat analysis is required in order to identify
weaknesses and thus mitigate the risk. In this paper, we present a systematic
and holistic procedure for analyzing the attack surface and threats of
Industrial Internet of Things devices. Our approach is to consider all
components including hardware, software and data, assets, threats and attacks
throughout the entire product life cycle.


  Polarized and unpolarized neutron scattering has been used to determine the
effect of superconductivity on the magnetic excitation spectra of YBa_2 Cu_3
O_{6.5} (T_c = 52K) and YBa_2 Cu_3 O_{6.7} (T_c = 67K). Pronounced enhancements
of the spectral weight centered around 25 meV and 33 meV, respectively, are
observed below T_c in both crystals, compensated predominantly by a loss of
spectral weight at higher energies. The data provide important clues to the
origin of the 40 meV magnetic resonance peak in YBa_2 Cu_3 O_7.


  We present an exact solution of a 1D model: a particle of incident energy $E$
colliding with a target which is a 1D harmonic ``solid slab'' with $N$ atoms in
its ground state; the Hilbert space of the target is restricted to the ($N+1$)
states with zero or one phonon present. For the case of a short range
interaction, $V(z)$, between the particle and the surface atom supporting a
bound state, an explicit non-perturbative solution of the collision problem is
presented. For finite and large $N$, there is no true sticking but only
so-called Feshbach resonances. A finite sticking coefficient ${\sl s}(E)$ is
obtained by introducing a small phonon decay rate $\eta$ and letting
$N\to\infty$. Our main interest is in the behavior of ${\sl s}(E)$ as $E\to 0$.
For a short range $V(z)$, we find ${\sl s}(E)\sim E^{1/2}$, regardless of the
strength of the particle-phonon coupling. However, if $V(z)$ has a Coulomb
$z^{-1}$ tail, we find ${\sl s}(E)\to\alpha$, where $0 < \alpha < 1$. [A fully
classical calculation gives ${\sl s}(E)\to 1$ in both cases.] We conclude that
the same threshold laws apply to 3D systems of neutral and charged particles
respectively.


  Theoretical study of dynamical phenomena induced by a linearly polarized
plane wave incident perpendicularly on a planar aligned nematic layer with the
light intensity as the control parameter is reported. We find the threshold of
the Optically Induced Freedericksz Transition for the planar state as a
function of the problem parameters. It occurs that the threshold is
substantially lower than that expected before. Above the primary instability
the director settles either to a stationary or to an oscillatory states
depending on a thickness of the layer. These states become unstable at a
secondary threshold through a heteroclinic bifurcation and the director settles
to a new stationary distorted state.


  The proper handling of out-of-distribution (OOD) samples in deep classifiers
is a critical concern for ensuring the suitability of deep neural networks in
safety-critical systems. Existing approaches developed for robust OOD detection
in the presence of adversarial attacks lose their performance by increasing the
perturbation levels. This study proposes a method for robust classification in
the presence of OOD samples and adversarial attacks with high perturbation
levels. The proposed approach utilizes a fully connected neural network that is
trained to use training samples as its attractors, enhancing its robustness.
This network has the ability to classify inputs and identify OOD samples as
well. To evaluate this method, the network is trained on the MNIST dataset, and
its performance is tested on adversarial examples. The results indicate that
the network maintains its performance even when classifying adversarial
examples, achieving 87.13% accuracy when dealing with highly perturbed MNIST
test data. Furthermore, by using fashion-MNIST and CIFAR-10-bw as OOD samples,
the network can distinguish these samples from MNIST samples with an accuracy
of 98.84% and 99.28%, respectively. In the presence of severe adversarial
attacks, these measures decrease slightly to 98.48% and 98.88%, indicating the
robustness of the proposed method.


  We first characterize all faithful arc-transitive covers of complete graphs
and we give a general construction of such covers. For a counterpart of cover,
we say a graph $\Gamma$ is a pseudocover of its quotient $\Sigma$ if they have
the same valency and $\Gamma$ is not a cover of $\Sigma$.As the second result
of this paper, we prove that the complete graph $\K_n$ has a connected
arc-transitive pseudocover if and only if $n-1$ is not a prime.


  We construct, in an SU(2) gauge theory with two adjoint scalars, flux strings
with monopoles attached at the ends. One scalar breaks SU(2) to U(1) and
produces monopoles, the other then breaks the U(1) and produces strings.
Dualizing, we write the theory in terms of effective string variables and show
that the flux in the string is exactly saturated by the monopoles at the ends.


  We consider planar systems of predator-prey models with small predator death
rate $\epsilon>0$. Using geometric singular perturbation theory and Floquet
theory, we derive characteristic functions that determines the location and the
stability of relaxation oscillations as $\epsilon\to 0$. When the prey-isocline
has a single interior local extremum, we prove that the system has a unique
nontrivial periodic orbit, which forms a relaxation oscillation. For some
systems with prey-isocline possessing two interior local extrema, we show that
either the positive equilibrium is globally stable, or the system has exact two
periodic orbits. In particular, for a predator-prey model with the Holling type
IV functional response we derive a threshold value of the carrying capacity
that separates these two outcomes. This result supports the so-called paradox
of enrichment.


  [Abridged] In recent years, conflicting results have provided an uncertain
view of the dust-attenuated properties of $z>4$ star-forming galaxies (SFGs).
To solve this, we used the deepest data publicly available in COSMOS to build a
mass-complete ($>10^{9.5}\,M_{\odot}$) sample of SFGs at $4<z<5$ and measured
their dust-attenuated properties by stacking all archival ALMA band 6 and 7
observations available. Combining this information with their rest-frame
ultraviolet emission from the COSMOS2020 catalog, we constrained the IRX
($\equiv L_{\rm IR}/L_{\rm UV}$)--$\beta_{\rm UV}$, IRX--$M_\ast$, and
SFR--$M_\ast$ relations at $z\sim4.5$. Finally, using these relations and the
stellar mass function of SFGs at $z\sim4.5$, we inferred the unattenuated and
dust-attenuated SFRD at this epoch. SFGs at $z\sim4.5$ follow an
IRX--$\beta_{\rm UV}$ relation that is consistent with that of local
starbursts, while they follow a steeper IRX--$M_\ast$ relation than observed
locally. The grain properties of dust in these SFGs seems thus similar to those
in local starbursts but its mass and geometry result in lower attenuation in
low-mass SFGs. SFGs at $z\sim4.5$ lie on a linear SFR--$M_\ast$ relation, whose
normalization varies by 0.3 dex, when we exclude or include from our stacks the
ALMA primary targets. The cosmic SFRD$(>M_\ast)$ converges at
$M_\ast<10^{9}\,M_\odot$ and is dominated by SFGs with
$M_\ast\sim10^{9.5-10.5}\,M_\odot$. The fraction of the cosmic SFRD that is
attenuated by dust, ${\rm SFRD}_{\rm IR}(>M_\ast)/ {\rm SFRD}(>M_\ast)$, is
$90\pm4\%$ for $M_\ast\,=\,10^{10}\,M_\odot$, $68\pm10\%$ for
$M_\ast=10^{8.9}\,M_\odot$ (i.e., $0.03\times M^\star$; $M^\star$ being the
characteristic stellar mass of SFGs) and this value converges to $60\pm10\%$
for $M_\ast=10^{8}\,M_\odot$. Even at this early epoch, the fraction of the
cosmic SFRD that is attenuated by dust remains thus significant.


  We summarize a search for the top quark with the Collider Detector at
Fermilab (CDF) in a sample of $\bar{p}p$ collisions at $\sqrt{s}$= 1.8 TeV with
an integrated luminosity of 19.3~pb$^{-1}$. We find 12 events consistent with
either two $W$ bosons, or a $W$ boson and at least one $b$ jet. The probability
that the measured yield is consistent with the background is 0.26%. Though the
statistics are too limited to establish firmly the existence of the top quark,
a natural interpretation of the excess is that it is due to $t\bar{t}$
production. Under this assumption, constrained fits to individual events yield
a top quark mass of $174 \pm 10^{+13}_{-12}$ GeV/c$^2$. The $t\bar{t}$
production cross section is measured to be $13.9^{+6.1}_{-4.8}$~pb. (Submitted
to Physical Review Letters on May 16, 1994).


  In the present work a new method for studying porous media by nuclear
magnetic resonance of liquid 3He has been proposed. This method has been
demonstrated in an example of a clay mineral sample. For the first time the
integral porosity of clay sample has been measured. For investigated samples
the value of integral porosity is in the range of 10-30%. Inverse Laplace
transform of 3He longitudinal magnetization recovery curve has been carried
out, thus distribution of relaxation times T1 has been obtained.


  Using coupling by change of measure and an approximation technique, Wang's
Harnack inequalities are established for a class of functional SDEs driven by
subordinate Brownian motions. The results cover the corresponding ones in the
case without delay.


  When superconducting pairing occurs between Fermi surfaces with different
Chern numbers, the Cooper pairs possess nontrivial pair Berry phase, which
enforces pairing gap nodes. The resulting pairing order is further
distinguished from the familiar $s$-, $p$-, and $d$-wave pairing orders by a
nonzero pair monopole charge and is described by monopole harmonics. To date,
this exotic monopole pairing order is yet to be achieved experimentally. We
therefore study the magnetically doped Dirac semimetal Cd$_3$As$_2$ as a
candidate material for realizing monopole superconductivity with pair monopole
charges $q_p=1$ or $2$, depending on the chemical potential. For each case of
pair monopole charge, we explore representatives of uniform pairing orders in
all allowed irreducible representations of the $C_{4h}$ symmetry of
magnetically doped Cd$_3$As$_2$. We demonstrate the distinctions in the
monopole analogs of different higher partial wave superconducting orders that
result from the combination of topological Fermi surfaces of higher Chern
number and crystalline symmetry. In all cases, the patterns of the
superconducting phase winding around a Fermi surface are constrained by the
topologically invariant total winding number, which depends only on the pair
monopole charge and requires nodes in the $q_p\neq 0$ superconducting order.
Our work can guide further experimental investigation of monopole orders in the
topological semimetal Cd$_3$As$_2$ and can be generalized to other materials
with Fermi surfaces of higher Chern number, which enforce monopole pairing
order in higher partial waves, such as the monopole analog of $f$-wave pairing.


  Miss-ratio curve (MRC), or equivalently hit-ratio curve (HRC), construction
techniques have recently gathered the attention of many researchers. Recent
advancements have allowed for approximating these curves in constant time,
allowing for online working-set-size (WSS) measurement. Techniques span the
algorithmic design paradigm from classic dynamic programming to artificial
intelligence inspired techniques. Our survey produces broad classification of
the current techniques primarily based on \emph{what} locality metric is being
recorded and \emph{how} that metric is stored for processing.
  Applications of theses curves span from dynamic cache partitioning in the
processor, to improving block allocation at the operating system level. Our
survey will give an overview of the historical, exact MRC construction methods,
and compare them with the state-of-the-art methods present in today's
literature. In addition, we will show where there are still open areas of
research and remain excited to see what this domain can produce with a strong
theoretical background.


  The electronic structure of the nanolaminated transition metal carbide Ti2AlC
has been investigated by bulk-sensitive soft x-ray emission spectroscopy. The
measured Ti L, C K and Al L emission spectra are compared with calculated
spectra using ab initio density-functional theory including dipole matrix
elements. The detailed investigation of the electronic structure and chemical
bonding provides increased understanding of the physical properties of this
type of nanolaminates. Three different types of bond regions are identified;
the relatively weak Ti 3d - Al 3p hybridization 1 eV below the Fermi level, and
the Ti 3d - C 2p and Ti 3d - C 2s hybridizations which are stronger and deeper
in energy are observed around 2.5 eV and 10 eV below the Fermi level,
respectively. A strongly modified spectral shape of the 3s final states in
comparison to pure Al is detected for the buried Al monolayers indirectly
reflecting the Ti 3d - Al 3p hybridization. The differences between the
electronic and crystal structures of Ti2AlC, Ti3AlC2 and TiC are discussed in
relation to the number of Al layers per Ti layer in the two former systems and
the corresponding change of the unusual materials properties.


  In this study, we investigate the optimal transmission policies within an
energy harvesting status update system, where the demand for status updates
depends on the state of the source. The system monitors a two-state Markovian
source that characterizes a stochastic process, which can be in either a normal
state or an alarm state, with a higher demand for fresh updates when the source
is in the alarm state. We propose a metric to capture the freshness of status
updates for each state of the stochastic process by introducing two Age of
Information (AoI) variables, extending the definition of AoI to account for the
state changes of the stochastic process. We formulate the problem as a Markov
Decision Process (MDP), utilizing a transition cost function that applies
linear and non-linear penalties based on AoI and the state of the stochastic
process. Through analytical investigation, we delve into the structure of the
optimal transmission policy for the resulting MDP problem. Furthermore, we
evaluate the derived policies via numerical results and demonstrate their
effectiveness in reserving energy in anticipation of forthcoming alarm states.


  In this paper, a measurement of tau polarization in W->taunu decays is
presented. It is measured from the energies of the decay products in hadronic
tau decays with a single final state charged particle. The data, corresponding
to an integrated luminosity of 24 pb^-1, were collected by the ATLAS experiment
at the Large Hadron Collider in 2010. The measured value of the tau
polarization is Ptau = -1.06 +/- 0.04 (stat) + 0.05 (syst) - 0.07 (syst), in
agreement with the Standard Model prediction, and is consistent with a
physically allowed 95% CL interval [-1,-0.91]. Measurements of tau polarization
have not previously been made at hadron colliders.


  A fundamental task in science is to determine the underlying causal relations
because it is the knowledge of this functional structure what leads to the
correct interpretation of an effect given the apparent associations in the
observed data. In this sense, Causal Discovery is a technique that tackles this
challenge by analyzing the statistical properties of the constituent variables.
In this work, we target the generalizability of the discovery method by
following a reductionist approach that only involves two variables, i.e., the
pairwise or bi-variate setting. We question the current (possibly misleading)
baseline results on the basis that they were obtained through supervised
learning, which is arguably contrary to this genuinely exploratory endeavor. In
consequence, we approach this problem in an unsupervised way, using robust
Mutual Information measures, and observing the impact of the different variable
types, which is oftentimes ignored in the design of solutions. Thus, we provide
a novel set of standard unbiased results that can serve as a reference to guide
future discovery tasks in completely unknown environments.


  By comparing the Laplace spectrum of the sphere $\mathbb{S}^n$ to its Weyl
function $w(x) = \frac{\omega_n}{(2\pi)^n}|\mathbb{S}^n|x^{n/2}$, we show that
no analogue of P\'olya's eigenvalue conjecture holds in general for Riemannian
manifolds with positive sectional curvature.


  We examine the dark matter properties of nearby early-type galaxies using
planetary nebulae (PNe) as mass probes. We have designed a specialised
instrument, the Planetary Nebula Spectrograph (PN.S) operating at the William
Herschel telescope, with the purpose of measuring PN velocities with best
efficiency. The primary scientific objective of this custom-built instrument is
the study of the PN kinematics in 12 ordinary round galaxies. Preliminary
results showing a dearth of dark matter in ordinary galaxies (Romanowsky et al.
2003) are now confirmed by the first complete PN.S datasets. On the other hand
early-type galaxies with a "regular" dark matter content are starting to be
observed among the brighter PN.S target sample, thus confirming a correlation
between the global dark-to-luminous mass virial ratio (f_DM=M_DM/M_star) and
the galaxy luminosity and mass.


  Policy gradient methods have become one of the most popular classes of
algorithms for multi-agent reinforcement learning. A key challenge, however,
that is not addressed by many of these methods is multi-agent credit
assignment: assessing an agent's contribution to the overall performance, which
is crucial for learning good policies. We propose a novel algorithm called
Dr.Reinforce that explicitly tackles this by combining difference rewards with
policy gradients to allow for learning decentralized policies when the reward
function is known. By differencing the reward function directly, Dr.Reinforce
avoids difficulties associated with learning the Q-function as done by
Counterfactual Multiagent Policy Gradients (COMA), a state-of-the-art
difference rewards method. For applications where the reward function is
unknown, we show the effectiveness of a version of Dr.Reinforce that learns an
additional reward network that is used to estimate the difference rewards.


  It is a well-known fact that every group $G$ has a presentation of the form
$G = F/R$, where $F$ is a free group and $R$ the kernel of the natural
epimorphism from $F$ onto $G$. Driven by the desire to obtain a similar
presentation of the group of automorphisms $Aut(G)$, we can consider the
subgroup $Stab(R) \subseteq Aut(F)$ of those automorphisms of $F$ that
stabilize $R$, and try to figure out if the natural homomorphism $Stab(R) \to
Aut(G)$ is onto, and if it is, to determine its kernel. Both parts of this task
are usually quite hard. The former part received considerable attention in the
past, whereas the latter, more difficult, part (determining the kernel) seemed
unapproachable. Here we approach this problem for a class of one-relator groups
with a special kind of small cancellation condition. Then, we address a
somewhat easier case of 2-generator (not necessarily one-relator) groups, and
determine the kernel of the above mentioned homomorphism for a rather general
class of those groups.


  Young planets are expected to cause perturbations in protostellar disks that
may be used to infer their presence. Clear detection of still-forming planets
embedded within gas-rich disks is rare. HD 169142 is a very young Herbig Ae-Be
star surrounded by a pre-transitional disk, composed of at least three rings.
While claims of sub-stellar objects around this star have been made previously,
follow-up studies remain inconclusive. We used SPHERE at ESO VLT to obtain a
sequence of high-contrast images of the immediate surroundings of this star
over about three years. This enables a photometric and astrometric analysis of
the structures in the disk. While we were unable to definitively confirm the
previous claims of a massive sub-stellar object at 0.1-0.15 arcsec from the
star, we found both spirals and blobs within the disk. The spiral pattern may
be explained as due to the presence of a primary, a secondary, and a tertiary
arm excited by a planet of a few Jupiter masses lying along the primary arm,
likely in the cavities between the rings. The blobs orbit the star consistently
with Keplerian motion, allowing a dynamical determination of the mass of the
star. While most of these blobs are located within the rings, we found that one
of them lies in the cavity between the rings, along the primary arm of the
spiral design. This blob might be due to a planet that might also be
responsible for the spiral pattern observed within the rings and for the cavity
between the two rings. The planet itself is not detected at short wavelengths,
where we only see a dust cloud illuminated by stellar light, but the planetary
photosphere might be responsible for the emission observed in the K band. The
mass of this putative planet may be constrained using photometric and dynamical
arguments; it should be between 1 and 4 Jupiter masses. The brightest blobs are
found at the 1:2 resonance with this putative planet


  The theory of inflation provides a mechanism to explain the structures we
observe today in the Universe, starting from quantum-mechanically generated
fluctuations. However, this leaves the question of: how did the
quantum-to-classical transition, occur? During inflation, tensor perturbations
interact (at least gravitationally) with other fields, meaning that we need to
view these perturbations as an open system that interacts with an environment.
In this paper, the evolution of the system is described using a Lindblad
equation, which describes the quantum decoherence of the system. This is a
possible mechanism for explaining the quantum-to-classical transition. We show
that this quantum decoherence leads to a scale-dependent increase of the
gravitational wave power spectrum, depending on the strength and time
dependence of the interaction between the system and the environment. By using
current upper bounds on the gravitational wave power spectrum from inflation,
obtained from CMB and the LIGO-Virgo-KAGRA constraints, we find an upper bound
on the interaction strength. Furthermore, we compute the decoherence criterion,
which indicates the minimal interaction strength needed for a specific scale to
have successfully decohered by the end of inflation. Assuming that the CMB
modes have completely decohered, we indicate a lower bound on the interaction
strength. In addition, this decoherence criterion allows us to look at which
scales might not have fully decohered and could still show some relic quantum
signatures. Lastly, we use sensitivity forecasts to study how future
gravitational-wave detectors, such as LISA and ET, could constrain the
decoherence parameter space. Due to the scale-dependence of the power spectrum,
LISA could only have a very small impact. However, ET will be able to
significantly improve our current constraints for specific decoherence
scenarios.


  The Gaia satellite, to be launched in 2012, will offer an unprecedented
survey of the whole sky down to magnitude 20. The multi-epoch nature of the
mission provides a unique opportunity to study variable sources with their
astrometric, photometric, spectro-photometric and radial velocity measurements.
Many tens of millions of classical variable objects are expected to be
detected, mostly stars but also QSOs and asteroids. The high number of objects
observed by Gaia will enable statistical studies of populations of variable
sources and of their properties. But Gaia will also allow the study of
individual objects to some depth depending on their variability types, and the
identification of potentially interesting candidates that would benefit from
further ground based observations by the scientific community. Within the Gaia
Data Processing and Analysis Consortium (DPAC), which is subdivided into 9
Coordination Units (CU), one (CU7) is dedicated to the variability analysis.
Its goal is to provide information on variable sources for the Gaia
intermediate and final catalogue releases.


  The experimental advances in cold atomic and molecular gases stimulate the
investigation of lattice correlated systems beyond the conventional on-site
Hubbard approximation, by possibly including multi-particle processes. We study
fermionic extended Hubbard models in a one dimensional lattice with different
types of particle couplings, including also three- and four-body interaction up
to nearest neighboring sites. By using the Bosonization technique, we
investigate the low-energy regime and determine the conditions for the
appearance of ordered phases, for arbitrary particle filling. We find that
three- and four-body couplings may significantly modify the phase diagram. In
particular, diagonal three-body terms that directly couple the local particle
densities have qualitatively different effects from off-diagonal three-body
couplings originating from correlated hopping, and favor the appearance of a
Luther-Emery phase even when two-body terms are repulsive. Furthermore, the
four-body coupling gives rise to a rich phase diagram and may lead to the
realization of the Haldane insulator phase at half-filling.


  A deep learning approach has been widely applied in sequence modeling
problems. In terms of automatic speech recognition (ASR), its performance has
significantly been improved by increasing large speech corpus and deeper neural
network. Especially, recurrent neural network and deep convolutional neural
network have been applied in ASR successfully. Given the arising problem of
training speed, we build a novel deep recurrent convolutional network for
acoustic modeling and then apply deep residual learning to it. Our experiments
show that it has not only faster convergence speed but better recognition
accuracy over traditional deep convolutional recurrent network. In the
experiments, we compare the convergence speed of our novel deep recurrent
convolutional networks and traditional deep convolutional recurrent networks.
With faster convergence speed, our novel deep recurrent convolutional networks
can reach the comparable performance. We further show that applying deep
residual learning can boost the convergence speed of our novel deep recurret
convolutional networks. Finally, we evaluate all our experimental networks by
phoneme error rate (PER) with our proposed bidirectional statistical n-gram
language model. Our evaluation results show that our newly proposed deep
recurrent convolutional network applied with deep residual learning can reach
the best PER of 17.33\% with the fastest convergence speed on TIMIT database.
The outstanding performance of our novel deep recurrent convolutional neural
network with deep residual learning indicates that it can be potentially
adopted in other sequential problems.


  We report a measurement of the mass of the top quark in lepton+jets final
states of ppbar->ttbar data corresponding to 2.6 fb^{-1} of integrated
luminosity collected by the D0 experiment at the Fermilab Tevatron Collider.
Using a matrix element method, we combine an insitu jet energy calibration with
our standard jet energy scale derived in studies of {\gamma}+jet and dijet
events and employ a novel flavor-dependent jet response correction to measure a
top-quark mass of mt = 176.01 +/- 1.64 GeV. Combining this result with our
previous result obtained on an independent data set, we measure a top-quark
mass of mt = 174.94 +/- 1.49 GeV for a total integrated luminosity of 3.6
fb^{-1}.


  Visualizing high-dimensional data is an essential task in Data Science and
Machine Learning. The Centroid-Encoder (CE) method is similar to the
autoencoder but incorporates label information to keep objects of a class close
together in the reduced visualization space. CE exploits nonlinearity and
labels to encode high variance in low dimensions while capturing the global
structure of the data. We present a detailed analysis of the method using a
wide variety of data sets and compare it with other supervised dimension
reduction techniques, including NCA, nonlinear NCA, t-distributed NCA,
t-distributed MCML, supervised UMAP, supervised PCA, Colored Maximum Variance
Unfolding, supervised Isomap, Parametric Embedding, supervised Neighbor
Retrieval Visualizer, and Multiple Relational Embedding. We empirically show
that centroid-encoder outperforms most of these techniques. We also show that
when the data variance is spread across multiple modalities, centroid-encoder
extracts a significant amount of information from the data in low dimensional
space. This key feature establishes its value to use it as a tool for data
visualization.


  In this review we examine the dynamics and gravitational wave detectability
of rotating strained neutron stars. The discussion is divided into two halves:
triaxial stars, and precessing stars. We summarise recent work on how crustal
strains and magnetic fields can sustain triaxiality, and suggest that Magnus
forces connected with pinned superfluid vortices might contribute to
deformation also. The conclusions that could be drawn following the successful
gravitational wave detection of a triaxial star are discussed, and areas
requiring further study identified. The latest ideas regarding free precession
are then outlined, and the recent suggestion of Middleditch et al (2000a,b)
that the remnant of SN1987A contains a freely precessing star, spinning-down by
gravitational wave energy loss, is examined critically. We describe what we
would learn about neutron stars should the gravitational wave detectors prove
this hypothesis to be correct.


  This paper describes a method for asking statistical questions about a large
text corpus. We exemplify the method by addressing the question, "What
percentage of Federal Register documents are real documents, of possible
interest to a text researcher or analyst?" We estimate an answer to this
question by evaluating 200 documents selected from a corpus of 45,820 Federal
Register documents. Stratified sampling is used to reduce the sampling
uncertainty of the estimate from over 3100 documents to fewer than 1000. The
stratification is based on observed characteristics of real documents, while
the sampling procedure incorporates a Bayesian version of Neyman allocation. A
possible application of the method is to establish baseline statistics used to
estimate recall rates for information retrieval systems.


  The goal of this work is studying the evolution of thermoelectric transport
across the members of the Ruddlesden-Popper series iridates Srn+1IrnO3n+1,
where a metal-insulator transition driven by bandwidth change occurs, from the
strongly insulating Sr2IrO4 to the metallic non Fermi liquid behavior of
SrIrO3. Sr2IrO4 (n=1), Sr3Ir2O7 (n=2) and SrIrO3 (n=inf.) polycrystals are
synthesized at high pressure and characterized by structural, magnetic,
electric and thermoelectric transport analyses. We find a complex
thermoelectric phenomenology in the three compounds. Thermal diffusion of
charge carriers accounts for the Seebeck behavior of Sr2IrO4, whereas
additional drag mechanisms come into play in determining the Seebeck
temperature dependence of Sr3Ir2O7 and SrIrO3. These findings reveal close
relationship between magnetic, electronic and thermoelectric properties, strong
coupling of charge carriers with phonons and spin fluctuations as well as
relevance of multiband description in these compounds.


  The Talbot effect, i.e. the self-imaging property of a periodic wave in
near-field diffraction, is a remarkable interference phenomenon in paraxial
systems with continuous translational invariance. In crystals, i.e. systems
with discrete translational invariance, self-imaging has been regarded so far
as a seldom effect, restricted to special sets of initial field distributions.
Here it is shown that in a class of gapless $\mathcal{PT}$-symmetric complex
crystals at the symmetry breaking threshold Talbot revivals can arise for
almost any initial periodic wave distribution which is commensurate with the
lattice period. A possible experimental realization of commensurate Talbot
self-imaging for light pulses in complex 'temporal' crystals, realized in an
optical dispersive fiber loop with amplitude and phase modulators, is briefly
discussed.


  In recent years, deep learning based object detection methods have achieved
promising performance in controlled environments. However, these methods lack
sufficient capabilities to handle underwater object detection due to these
challenges: (1) images in the underwater datasets and real applications are
blurry whilst accompanying severe noise that confuses the detectors and (2)
objects in real applications are usually small. In this paper, we propose a
novel Sample-WeIghted hyPEr Network (SWIPENET), and a robust training paradigm
named Curriculum Multi-Class Adaboost (CMA), to address these two problems at
the same time. Firstly, the backbone of SWIPENET produces multiple high
resolution and semantic-rich Hyper Feature Maps, which significantly improve
small object detection. Secondly, a novel sample-weighted detection loss
function is designed for SWIPENET, which focuses on learning high weight
samples and ignore learning low weight samples. Moreover, inspired by the human
education process that drives the learning from easy to hard concepts, we here
propose the CMA training paradigm that first trains a clean detector which is
free from the influence of noisy data. Then, based on the clean detector,
multiple detectors focusing on learning diverse noisy data are trained and
incorporated into a unified deep ensemble of strong noise immunity. Experiments
on two underwater robot picking contest datasets (URPC2017 and URPC2018) show
that the proposed SWIPENET+CMA framework achieves better accuracy in object
detection against several state-of-the-art approaches.


  In this paper we conduct a phenomenological study for the search of evidence
for the intrinsic charm mechanism in double $J/\psi$ production at the COMPASS
experiment using the CERN $\pi^-$ beam at 190 GeV/c. We also re-review the
double $J/\psi$ production data provided by the NA3 experiment using the CERN
$\pi^-$ beam at 150 and 280 GeV/c with incident on a platinum target.


  In this paper we find extremal one-sided approximations of exponential type
for a class of truncated and odd functions with a certain exponential
subordination. These approximations optimize the $L^1(\mathbb{R},
|E(x)|^{-2}dx)$-error, where $E$ is an arbitrary Hermite-Biehler entire
function of bounded type in the upper half-plane. This extends the work of Holt
and Vaaler [Duke Math. Journal 83 (1996), 203-247] for the signum function. We
also provide periodic analogues of these results, finding optimal one-sided
approximations by trigonometric polynomials of a given degree to a class of
periodic functions with exponential subordination. These extremal trigonometric
polynomials optimize the $L^1(\mathbb{R}/\mathbb{Z}, d\vartheta)$-error, where
$\vartheta$ is an arbitrary nontrivial measure on $\mathbb{R}/\mathbb{Z}$. The
periodic results extend the work of Li and Vaaler [Indiana Univ. Math. J. 48,
No. 1 (1999), 183-236], who considered this problem for the sawtooth function
with respect to Jacobi measures. Our techniques are based on the theory of
reproducing kernel Hilbert spaces (of entire functions and of polynomials) and
on the construction of suitable interpolations with nodes at the zeros of
Laguerre-P\'{o}lya functions.


  In contrast to typical laboratory experiments, the everyday use of online
educational resources by large populations and the prevalence of software
infrastructure for A/B testing leads us to consider how platforms can embed in
vivo experiments that do not merely support research, but ensure practical
improvements to their educational components. Examples are presented of
randomized experimental comparisons conducted by subsets of the authors in
three widely used online educational platforms Khan Academy, edX, and
ASSISTments. We suggest design principles for platform technology to support
randomized experiments that lead to practical improvements enabling Iterative
Improvement and Collaborative Work and explain the benefit of their
implementation by WPI co-authors in the ASSISTments platform.


  In this paper we study the frequentist convergence rate for the Latent
Dirichlet Allocation (Blei et al., 2003) topic models. We show that the maximum
likelihood estimator converges to one of the finitely many equivalent
parameters in Wasserstein's distance metric at a rate of $n^{-1/4}$ without
assuming separability or non-degeneracy of the underlying topics and/or the
existence of more than three words per document, thus generalizing the previous
works of Anandkumar et al. (2012, 2014) from an information-theoretical
perspective. We also show that the $n^{-1/4}$ convergence rate is optimal in
the worst case.


  Nitrogen doped carbon nanotubes have been synthesized using pyrolysis and
characterized by Scanning Tunneling Spectroscopy and transmission electron
microscopy. The doped nanotubes are all metallic and exhibit strong electron
donor states near the Fermi level. Using tight-binding and ab initio
calculations, we observe that pyridine-like N structures are responsible for
the metallic behavior and the prominent features near the Fermi level. These
electron rich structures are the first example of n-type nanotubes, which could
pave the way to real molecular hetero-junction devices.


  A dynamic decision-making system that includes a mass of indistinguishable
agents could manifest impressive heterogeneity. This kind of nonhomogeneity is
postulated to result from macroscopic behavioral tactics employed by almost all
involved agents. A State-Space Based (SSB) mass event-history model is
developed here to explore the potential existence of such macroscopic
behaviors. By imposing an unobserved internal state-space variable into the
system, each individual's event-history is made into a composition of a common
state duration and an individual specific time to action. With the common state
modeling of the macroscopic behavior, parametric statistical inferences are
derived under the current-status data structure and conditional independence
assumptions. Identifiability and computation related problems are also
addressed. From the dynamic perspectives of system-wise heterogeneity, this SSB
mass event-history model is shown to be very distinct from a random effect
model via the Principle Component Analysis (PCA) in a numerical experiment.
Real data showing the mass invasion by two species of parasitic nematode into
two species of host larvae are also analyzed. The analysis results not only are
found coherent in the context of the biology of the nematode as a parasite, but
also include new quantitative interpretations.