  Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to
extend the in-domain model to the distinctive target domains where the data
distributions differ. Most prior works focus on capturing the inter-domain
transferability but largely overlook rich intra-domain structures, which
empirically results in even worse discriminability. In this work, we introduce
a novel graph SPectral Alignment (SPA) framework to tackle the tradeoff. The
core of our method is briefly condensed as follows: (i)-by casting the DA
problem to graph primitives, SPA composes a coarse graph alignment mechanism
with a novel spectral regularizer towards aligning the domain graphs in
eigenspaces; (ii)-we further develop a fine-grained message propagation module
-- upon a novel neighbor-aware self-training mechanism -- in order for enhanced
discriminability in the target domain. On standardized benchmarks, the
extensive experiments of SPA demonstrate that its performance has surpassed the
existing cutting-edge DA methods. Coupled with dense model analysis, we
conclude that our approach indeed possesses superior efficacy, robustness,
discriminability, and transferability. Code and data are available at:
https://github.com/CrownX/SPA.


  Threshold methods are popular for ordinal regression problems, which are
classification problems for data with a natural ordinal relation. They learn a
one-dimensional transformation (1DT) of observations of the explanatory
variable, and then assign label predictions to the observations by thresholding
their 1DT values. In this paper, we study the influence of the underlying data
distribution and of the learning procedure of the 1DT on the classification
performance of the threshold method via theoretical considerations and
numerical experiments. Consequently, for example, we found that threshold
methods based on typical learning procedures may perform poorly when the
probability distribution of the target variable conditioned on an observation
of the explanatory variable tends to be non-unimodal. Another instance of our
findings is that learned 1DT values are concentrated at a few points under the
learning procedure based on a piecewise-linear loss function, which can make
difficult to classify data well.


  The formation of singularities on a free surface of a conducting ideal fluid
in a strong electric field is considered. It is found that the nonlinear
equations of two-dimensional fluid motion can be solved in the small-angle
approximation. This enables us to show that for almost arbitrary initial
conditions the surface curvature becomes infinite in a finite time.


  We examine normal form solutions of decision trees under typical choice
functions induced by lower previsions. For large trees, finding such solutions
is hard as very many strategies must be considered. In an earlier paper, we
extended backward induction to arbitrary choice functions, yielding far more
efficient solutions, and we identified simple necessary and sufficient
conditions for this to work. In this paper, we show that backward induction
works for maximality and E-admissibility, but not for interval dominance and
Gamma-maximin. We also show that, in some situations, a computationally cheap
approximation of a choice function can be used, even if the approximation
violates the conditions for backward induction; for instance, interval
dominance with backward induction will yield at least all maximal normal form
solutions.


  This paper introduces the offline meta-reinforcement learning (offline
meta-RL) problem setting and proposes an algorithm that performs well in this
setting. Offline meta-RL is analogous to the widely successful supervised
learning strategy of pre-training a model on a large batch of fixed,
pre-collected data (possibly from various tasks) and fine-tuning the model to a
new task with relatively little data. That is, in offline meta-RL, we
meta-train on fixed, pre-collected data from several tasks in order to adapt to
a new task with a very small amount (less than 5 trajectories) of data from the
new task. By nature of being offline, algorithms for offline meta-RL can
utilize the largest possible pool of training data available and eliminate
potentially unsafe or costly data collection during meta-training. This setting
inherits the challenges of offline RL, but it differs significantly because
offline RL does not generally consider a) transfer to new tasks or b) limited
data from the test task, both of which we face in offline meta-RL. Targeting
the offline meta-RL setting, we propose Meta-Actor Critic with Advantage
Weighting (MACAW), an optimization-based meta-learning algorithm that uses
simple, supervised regression objectives for both the inner and outer loop of
meta-training. On offline variants of common meta-RL benchmarks, we empirically
find that this approach enables fully offline meta-reinforcement learning and
achieves notable gains over prior methods.


  We have carried out a wide-field imaging survey for [OII]3727 emitting
galaxies at z~1.2 in the HST COSMOS 2 square degree field using the Suprime-Cam
on the Subaru Telescope. The survey covers a sky area of 6700 arcmin^2 in the
COSMOS field, and a redshift range between 1.17 and 1.20 (Delta_z = 0.03),
corresponding to a survey volume of 5.56*10^5 Mpc^3. We obtain a sample of 3176
[OII] emitting galaxies with observed emission-line equivalent widths greater
than 26 AA. Since our survey tends to sample brighter [OII]3727 emitting
galaxies, we also analyze a sample of fainter [OII]3727 emitting galaxies found
in the Subaru Deep Field (SDF). We find an extinction-corrected [OII]
luminosity density of 10^{40.35^+0.08_-0.06} ergs s^-1 Mpc-3, corresponding to
star formation rate density of 0.32^+0.06_-0.04 M_sun yr-1 Mpc^-3 in the COSMOS
field at z~1.2. This is the largest survey for [OII]3727 emitters beyond z=1
currently available.


  We are preparing a telescope system to carry out a survey of detection and
analysis of lunar impact flashes. In the framework of this project, here we
present all necessary methods to automatically identify these luminous events,
their lunar impact coordinates, the origin of the impacting meteoroids, as well
as the estimation of their physical properties such as mass and size. We tested
our methods against confirmed impact events and constructed the meteoroid size
frequency distribution of impactors using literature data of the last 20 years.
In addition, we present the first lunar impact event observed from the
Observatoire de la Cote d'Azur that was detected during the testing phase of
our project.


  Irreducible symplectic varieties are higher-dimensional analogues of K3
surfaces. In this paper, we prove the Shafarevich conjecture for irreducible
symplectic varieties of fixed deformation class. We also observe that the
second cohomological generalization of the Shafarevich conjecture does not hold
in general, and discuss another formulation of a cohomological generalization.


  In this paper, we prove the structural stability of the transonic shocks for
three dimensional axisymmetric Euler system with swirl velocity under the
perturbations for the incoming supersonic flow, the nozzle boundary, and the
exit pressure. Compared with the known results on the stability of transonic
shocks, one of the major difficulties for the axisymmetric flows with swirls is
that corner singularities near the intersection point of the shock surface and
nozzle boundary and the artificial singularity near the axis appear
simultaneously. One of the key points in the analysis for this paper is the
introduction of an invertible Lagrangian transformation which can straighten
the streamlines in the whole nozzle and help to represent the solutions of
transport equations explicitly.


  This report introduces the Qwen2 series, the latest addition to our large
language models and large multimodal models. We release a comprehensive suite
of foundational and instruction-tuned language models, encompassing a parameter
range from 0.5 to 72 billion, featuring dense models and a Mixture-of-Experts
model. Qwen2 surpasses most prior open-weight models, including its predecessor
Qwen1.5, and exhibits competitive performance relative to proprietary models
across diverse benchmarks on language understanding, generation, multilingual
proficiency, coding, mathematics, and reasoning.
  The flagship model, Qwen2-72B, showcases remarkable performance: 84.2 on
MMLU, 37.9 on GPQA, 64.6 on HumanEval, 89.5 on GSM8K, and 82.4 on BBH as a base
language model. The instruction-tuned variant, Qwen2-72B-Instruct, attains 9.1
on MT-Bench, 48.1 on Arena-Hard, and 35.7 on LiveCodeBench. Moreover, Qwen2
demonstrates robust multilingual capabilities, proficient in approximately 30
languages, spanning English, Chinese, Spanish, French, German, Arabic, Russian,
Korean, Japanese, Thai, Vietnamese, and more, underscoring its versatility and
global reach.
  To foster community innovation and accessibility, we have made the Qwen2
model weights openly available on Hugging Face and ModelScope, and the
supplementary materials including example code on GitHub. These platforms also
include resources for quantization, fine-tuning, and deployment, facilitating a
wide range of applications and research endeavors.


  We discuss transport properties of fully spin-polarized triplet
superconductors, where only electrons of one spin component (along a certain
axis) are paired. Due to the structure of the order parameter space, wherein
phase and spin rotations are intertwined, a configuration where the
superconducting phase winds by $4\pi$ in space is topologically equivalent to a
configuration with no phase winding. This opens the possibility of supercurrent
relaxation by a smooth deformation of the order parameter, where the order
parameter remains non-zero at any point in space throughout the entire process.
During the process, a spin texture is formed. We discuss the conditions for
such processes to occur and their physical consequences. In particular, we show
that when a voltage is applied, they lead to an unusual alternating-current
Josephson effect whose period is an integer multiple of the usual Josephson
period. These conclusions are substantiated in a simple time-dependent
Ginzburg-Landau model for the dynamics of the order parameter. One of the
potential applications of our analysis is for moir\'e systems, such as twisted
bilayer and double bilayer graphene, where superconductivity is found in the
vicinity of ferromagnetism.


  We investigate the localization of metastable four-dimensional gravity around
a bent dilatonic brane, embedded into a five-dimensional space, that exists
only up to distances sufficiently small compared to a crossover scale. Far from
such scale, five-dimensional effects strongly deviate the Newtonian potential.
We study this effect by considering localization of massive gravity on a
non-BPS bent dilatonic 3-brane solution of a five-dimensional supergravity. Our
results show that the cosmological constant on the bent brane controls the size
of the crossover scale. For sufficiently small positive cosmological constant,
that is in accord with the present observations, the crossover scale becomes
very large.


  We introduce a new method for studying large scale properties of random
walks. The new concepts of transience and recurrence on the average are
compared with the ones introduced by R.Burioni, D.Cassi and A.Vezzani and with
the usual ones; their relationships are analyzed and various examples are
provided.


  The coordination sequence of a lattice $\L$ encodes the word-length function
with respect to $M$, a set that generates $\L$ as a monoid. We investigate the
coordination sequence of the cyclotomic lattice $\L = \Z[\zeta_m]$, where
$\zeta_m$ is a primitive $m\th$ root of unity and where $M$ is the set of all
$m\th$ roots of unity. We prove several conjectures by Parker regarding the
structure of the rational generating function of the coordination sequence;
this structure depends on the prime factorization of $m$. Our methods are based
on unimodular triangulations of the $m\th$ cyclotomic polytope, the convex hull
of the $m$ roots of unity in $\R^{\phi(m)}$, with respect to a canonically
chosen basis of $\L$.


  There is an embedding of affine vertex algebras $V^k(\mathfrak{gl}_n)
\hookrightarrow V^k(\mathfrak{sl}_{n+1})$, and the coset $\mathcal{C}^k(n) =
\text{Com}(V^k(\mathfrak{gl}_n), V^k(\mathfrak{sl}_{n+1}))$ is a natural
generalization of the parafermion algebra of $\mathfrak{sl}_2$. It was called
the algebra of generalized parafermions by the third author and was shown to
arise as a one-parameter quotient of the universal two-parameter
$\mathcal{W}_{\infty}$-algebra of type $\mathcal{W}(2,3,\dots)$. In this paper,
we consider an analogous structure of orthogonal type, namely $\mathcal{D}^k(n)
= \text{Com}(V^k(\mathfrak{so}_{2n}),
V^k(\mathfrak{so}_{2n+1}))^{\mathbb{Z}_2}$. We realize this algebra as a
one-parameter quotient of the two-parameter even spin
$\mathcal{W}_{\infty}$-algebra of type $\mathcal{W}(2,4,\dots)$, and we
classify all coincidences between its simple quotient $\mathcal{D}_k(n)$ and
the algebras $\mathcal{W}_{\ell}(\mathfrak{so}_{2m+1})$ and
$\mathcal{W}_{\ell}(\mathfrak{so}_{2m})^{\mathbb{Z}_2}$. As a corollary, we
show that for the admissible levels $k = -(2n-2) + \frac{1}{2} (2 n + 2 m -1)$
for $\widehat{\mathfrak{so}}_{2n}$ the simple affine algebra
$L_k(\mathfrak{so}_{2n})$ embeds in $L_k(\mathfrak{so}_{2n+1})$, and the coset
is strongly rational. As a consequence, the category of ordinary modules of
$L_k(\mathfrak{so}_{2n+1})$ at such a level is a braided fusion category.


  This thesis is entirely devoted to black hole physics as a natural arena to
study quantum gravity related questions. The five main Chapters present the
results already published in arXiv:1512.04566 [gr-qc], arXiv:1604.07222
[gr-qc], arXiv:1707.00479 [gr-qc], arXiv:1807.02041 [gr-qc], arXiv:1811.03667
[gr-qc]. A main introductory Section, together with the Sections introducing
each of the two parts of the manuscript compose the \emph{fil rouge} of the
thesis, and are not published elsewhere. They contain reviews (together with my
personal view) on the information paradox (Part 1) and the thermodynamics of
spacetime (Part 2).


  Consider two free measure preserving group actions $\Gamma \actson (X, \mu),
\Delta \actson (X, \mu)$, and a measure preserving action $\Delta \actson^a (Z,
\nu)$ where $(X, \mu), (Z, \nu)$ are standard probability spaces. We show how
to construct free measure preserving actions $\Gamma \actson^c (Y, m)$, $\Delta
\actson^d (Y, m)$ on a standard probability space such that $E_{\Delta}^d
\subset E_{\Gamma}^c$ and $d$ has $a$ as a factor. This generalizes the
standard notion of co-induction of actions of groups from actions of subgroups.
We then use this construction to show that if $\Gamma$ is a countable
non-amenable group, then $\Gamma$ admits continuum many orbit inequivalent
free, measure preserving, ergodic actions on a standard probability space.


  We calculated functions for the potential, Vch, in the channel of a field
effect transistor with various densities of the localized states (DOS) by using
a device simulator. In the saturation condition, Vch is found to fit
satisfactorily to the analytical function, Vch(y)=Vg(1-(1-y/L) k), where Vg is
the gate bias, y is the position along the channel, and L is the channel
length. The power k, which depends on the DOS, ranges from 0 to 0.5 even with
an extensive variation of DOS. As a result, the total induced charge density in
the saturation condition can be expressed a way similar to the linear condition
by introducing a correction factor, alpha, ranging between 0.65 and 1.0.


  We report the first detection of a planetary-mass companion to a star using
the High Resolution Spectrograph (HRS) of the Hobby-Eberly Telescope (HET). The
HET-HRS now gives routine radial velocity precision of 2-3 m/s for high SNR
observations of quiescent stars. The planetary-mass companion to the metal-rich
K0V star HD37605 has an orbital period of 54.23 days, an orbital eccentricity
of 0.737, and a minimum mass of 2.84 Jupiter masses. The queue-scheduled
operation of the Hobby-Eberly Telescope enabled us to discovery of this
relatively short-period planet with a total observation time span of just two
orbital periods. The ability of queue-scheduled large-aperture telescopes to
respond quickly to interesting and important results demonstrates the power of
this new approach in searching for extra-solar planets as well as in other ares
of research requiring rapid response time critical observations.


  We report the results of a Green Bank Telescope survey for water masers,
ammonia (1,1) and (2,2), and the H66-alpha recombination line toward 506
luminous compact 24 micron-emitting regions in the Andromeda Galaxy (M31). We
include the 206 sources observed in the Darling (2011) water maser survey for
completeness. The survey was sensitive enough to detect any maser useful for
~10 microarcsecond/yr astrometry. No new water masers, ammonia lines, or
H66-alpha recombination lines were detected individually or in spectral stacks
reaching rms noise levels of ~3 mJy and ~0.2 mJy, respectively, in 3.1-3.3 km/s
channels. The lack of detections in individual spectra and in the spectral
stacks is consistent with Galactic extrapolations. Contrary to previous
assertions, there do not seem to be additional bright water masers to be found
in M31. The strong variability of water masers may enable new maser detections
in the future, but variability may also limit the astrometric utility of known
(or future) masers since flaring masers must also fade.


  Separatism is examined in a long-run perspective. Accordingly, many political
or economic factors which may be crucial in dealing with short-term episodes
can be safely disregarded. Extending an approach pioneered by J. Jenkins, the
paper assesses the role of spatial and historical factors. It shows that the
means used to stage a separatist struggle are to a notable extent borrowed from
former historical episodes, an analysis which supports and illustrates Charles
Tilly's thesis of restricted repertoires of action. The purpose of the present
paper is to introduce the model, to make it plausible and to demonstrate its
potential usefulness by examining a number of critical examples. A more
systematic analysis is carried out in a follow-up paper by using a data set
that includes about 40 cases of separatist struggles.


  Mobility analysis is a crucial element in the research area of transportation
systems. Forecasting traffic information offers a viable solution to address
the conflict between increasing transportation demands and the limitations of
transportation infrastructure. Predicting human travel is significant in aiding
various transportation and urban management tasks, such as taxi dispatch and
urban planning. Machine learning and deep learning methods are favored for
their flexibility and accuracy. Nowadays, with the advent of large language
models (LLMs), many researchers have combined these models with previous
techniques or applied LLMs to directly predict future traffic information and
human travel behaviors. However, there is a lack of comprehensive studies on
how LLMs can contribute to this field. This survey explores existing approaches
using LLMs for mobility forecasting problems. We provide a literature review
concerning the forecasting applications within transportation systems,
elucidating how researchers utilize LLMs, showcasing recent state-of-the-art
advancements, and identifying the challenges that must be overcome to fully
leverage LLMs in this domain.


  We present techniques for obtaining presision astrometry using old
photographic plates from assorted large-aperture reflectors in combination with
recent CCD Mosaic Imager frames. At the core of this approach is a
transformation of plate/CCD coordinates into a previously constructed
astrometric reference frame around the open cluster NGC 188. Our primary result
is a new catalog of proper motions and positions for 7812 objects down to V=21
in the 0.75 square degree area around NGC 188. The precision for well-measured
stars is 0.15 mas/yr for proper motions and 2 mas for positions on the system
of the Tycho-2 catalog. The sum of membership probabilities indicates that NGC
188 contains 1050 stars down to V=21. Comprehensive lists of the candidate blue
stragglers and red giants substantially enlarge the number of such stars known
in NGC 188. We have also obtained a small correction to the proper motions from
the mean `motion' of background galaxies. (Abridged)


  Deep neural networks have made tremendous gains in emulating human-like
intelligence, and have been used increasingly as ways of understanding how the
brain may solve the complex computational problems on which this relies.
However, these still fall short of, and therefore fail to provide insight into
how the brain supports strong forms of generalization of which humans are
capable. One such case is out-of-distribution (OOD) generalization-successful
performance on test examples that lie outside the distribution of the training
set. Here, we identify properties of processing in the brain that may
contribute to this ability. We describe a two-part algorithm that draws on
specific features of neural computation to achieve OOD generalization, and
provide a proof of concept by evaluating performance on two challenging
cognitive tasks. First we draw on the fact that the mammalian brain represents
metric spaces using grid cell code (e.g., in the entorhinal cortex): abstract
representations of relational structure, organized in recurring motifs that
cover the representational space. Second, we propose an attentional mechanism
that operates over the grid cell code using Determinantal Point Process (DPP),
that we call DPP attention (DPP-A) -- a transformation that ensures maximum
sparseness in the coverage of that space. We show that a loss function that
combines standard task-optimized error with DPP-A can exploit the recurring
motifs in the grid cell code, and can be integrated with common architectures
to achieve strong OOD generalization performance on analogy and arithmetic
tasks. This provides both an interpretation of how the grid cell code in the
mammalian brain may contribute to generalization performance, and at the same
time a potential means for improving such capabilities in artificial neural
networks.


  Analytic solutions of the Regge-Wheeler equation are presented in the form of
series of hypergeometric functions and Coulomb wave functions which have
different regions of convergence. Relations between these solutions are
established. The series solutions are given as the Post-Minkowskian expansion
with respect to a parameter $\epsilon \equiv 2M\omega$, $M$ being the mass of
black hole. This expansion corresponds to the post-Newtonian expansion when
they are applied to the gravitational radiation from a particle in circular
orbit around a black hole. These solutions can also be useful for numerical
computations.


  An $F$-manifold is complex manifold with a multiplication on the holomorphic
tangent bundle with a certain integrability condition. Important examples are
Frobenius manifolds and especially base spaces of universal unfoldings of
isolated hypersurface singularities. This paper reviews the construction of
hermitian metrics on $F$-manifolds from $tt^*$ geometry. It clarifies the logic
between several notions. It also introduces a new {\it canonical} hermitian
metric. Near irreducible points it makes the manifold almost hyperbolic. This
holds for the singularity case and will hopefully lead to applications there.


  Lal and Chaturvedi proposed two authentication schemes based on the
difficulty of the Root Problem in the braid group. We point out that the first
scheme is not really as secure as the Root Problem, and describe an efficient
way to crack it. The attack works for any group.


  Network meta-analysis (NMA) is a technique used in medical statistics to
combine evidence from multiple medical trials. NMA defines an inference and
information processing problem on a network of treatment options and trials
connecting the treatments. We believe that statistical physics can offer useful
ideas and tools for this area, including from the theory of complex networks,
stochastic modelling and simulation techniques. The lack of a unique source
that would allow physicists to learn about NMA effectively is a barrier to
this. In this article we aim to present the `NMA problem' and existing
approaches to it coherently and in a language accessible to statistical
physicists. We also summarise existing points of contact between statistical
physics and NMA, and describe our ideas of how physics might make a difference
for NMA in the future. The overall goal of the article is to attract physicists
to this interesting, timely and worthwhile field of research.


  We report the discovery of COOL J1241+2219, a strongly-lensed galaxy at
redshift $z$=5.043$\pm$0.002 with observed magnitude $z_{AB}=20.47$, lensed by
a moderate-mass galaxy cluster at $z$=1.001$\pm$0.001. COOL J1241+2219 is the
brightest lensed galaxy currently known at optical and near-infrared
wavelengths at $z$ $\gtrsim$ 5; it is $\sim$5 times brighter than the prior
record-holder lensed galaxy, and several magnitudes brighter than the brightest
unlensed galaxies known at these redshifts. It was discovered as part of
COOL-LAMPS, a collaboration initiated to find strongly lensed systems in recent
public optical imaging data. We characterise the lensed galaxy, as well as the
central galaxy of the lensing cluster using ground-based $griz$JH imaging and
optical spectroscopy. We report model-based magnitudes, and derive stellar
masses, dust content, metallicity and star-formation rates via
stellar-population synthesis modeling. Our lens mass modeling, based on
ground-based imaging, implies a median source magnification of $\sim$30, which
puts the stellar mass and star formation rate (in the youngest age bin, closest
to the epoch of observation) at logM$_{*}$ = $10.11^{+0.21}_{-0.26}$ and SFR =
$27^{+13}_{-9}$ M$_{\odot}$/yr, respectively. We constrain a star formation
history for COOL J1241+2219 consistent with constant star formation across
$\sim$1 Gyr of cosmic time, and that places this galaxy on the high-mass end of
the star-forming main sequence. COOL J1241+2219 is 2-4 times more luminous than
a galaxy with the characteristic UV luminosity at these redshifts. The UV
continuum slope $\beta$= -2.2$\pm$0.2 places this galaxy on the blue side of
the observed distribution of galaxies at $z$=5, although the lack of Ly$\alpha$
emission indicates dust sufficient to suppress this emission.


  Effort estimation models are a fundamental tool in software management, and
used as a forecast for resources, constraints and costs associated to software
development. For Free/Open Source Software (FOSS) projects, effort estimation
is especially complex: professional developers work alongside occasional,
volunteer developers, so the overall effort (in person-months) becomes
non-trivial to determine.
  The objective of this work it to develop a simple effort estimation model for
FOSS projects, based on the historic data of developers' effort. The model is
fed with direct developer feedback to ensure its accuracy.
  After extracting the personal development profiles of several thousands of
developers from 6 large FOSS projects, we asked them to fill in a questionnaire
to determine if they should be considered as full-time developers in the
project that they work in. Their feedback was used to fine-tune the value of an
effort threshold, above which developers can be considered as full-time.
  With the help of the over 1,000 questionnaires received, we were able to
determine, for every project in our sample, the threshold of commits that
separates full-time from non-full-time developers.%, and that minimizes the
type I and type II errors. We finally offer guidelines and a tool to apply our
model to FOSS projects that use a version control system.


  We suggest an experiment to investigate the linear superposition of two
spatially separated Bose-Einstein condensates. Due to the coherent combination
of the two wave functions, the dynamic structure factor, measurable through
inelastic photon scattering at high momentum transfer $q$, is predicted to
exhibit interference fringes with frequency period $\Delta\nu = q/md$ where $d$
is the distance between the condensates. We show that the coherent
configuration corresponds to an eigenstate of the physical observable measured
in the experiment and that the relative phase of the condensates is hence
created through the measurement process.


  Sex classification of children's voices allows for an investigation of the
development of secondary sex characteristics which has been a key interest in
the field of speech analysis. This research investigated a broad range of
acoustic features from scripted and spontaneous speech and applied a
hierarchical clustering-based machine learning model to distinguish the sex of
children aged between 5 and 15 years. We proposed an optimal feature set and
our modelling achieved an average F1 score (the harmonic mean of the precision
and recall) of 0.84 across all ages. Our results suggest that the sex
classification is generally more accurate when a model is developed for each
year group rather than for children in 4-year age bands, with classification
accuracy being better for older age groups. We found that spontaneous speech
could provide more helpful cues in sex classification than scripted speech,
especially for children younger than 7 years. For younger age groups, a broad
range of acoustic factors contributed evenly to sex classification, while for
older age groups, F0-related acoustic factors were found to be the most
critical predictors generally. Other important acoustic factors for older age
groups include vocal tract length estimators, spectral flux, loudness and
unvoiced features.


  Let $g$, $h$ be a random pair of generators of $G=Sym(n)$ or $G=Alt(n)$. We
show that, with probability tending to $1$ as $n\to \infty$, (a) the diameter
of $G$ with respect to $S = \{g,h,g^{-1},h^{-1}\}$ is at most $O(n^2 (\log
n)^c)$, and (b) the mixing time of $G$ with respect to $S$ is at most $O(n^3
(\log n)^c)$. (Both $c$ and the implied constants are absolute.)
  These bounds are far lower than the strongest worst-case bounds known (in
Helfgott--Seress, 2013); they roughly match the worst known examples. We also
give an improved, though still non-constant, bound on the spectral gap.
  Our results rest on a combination of the algorithm in (Babai--Beals--Seress,
2004) and the fact that the action of a pair of random permutations is almost
certain to act as an expander on $\ell$-tuples, where $\ell$ is an arbitrary
constant (Friedman et al., 1998).


  Bragg diffraction of atoms by light waves has been used to create high
momentum components in a Bose-Einstein condensate. Collisions between atoms
from two distinct momentum wavepackets cause elastic scattering that can remove
a significant fraction of atoms from the wavepackets and cause the formation of
a spherical shell of scattered atoms. We develop a slowly varying envelope
technique that includes the effects of this loss on the condensate dynamics
described by the Gross-Pitaevski equation. Three-dimensional numerical
calculations are presented for two experimental situations: passage of a moving
daughter condensate through a non-moving parent condensate, and four-wave
mixing of matter waves.


  The extraction of $k$-mers is a fundamental component in many complex
analyses of large next-generation sequencing datasets, including reads
classification in genomics and the characterization of RNA-seq datasets. The
extraction of all $k$-mers and their frequencies is extremely demanding in
terms of running time and memory, owing to the size of the data and to the
exponential number of $k$-mers to be considered. However, in several
applications, only frequent $k$-mers, which are $k$-mers appearing in a
relatively high proportion of the data, are required by the analysis. In this
work we present SPRISS, a new efficient algorithm to approximate frequent
$k$-mers and their frequencies in next-generation sequencing data. SPRISS
employs a simple yet powerful reads sampling scheme, which allows to extract a
representative subset of the dataset that can be used, in combination with any
$k$-mer counting algorithm, to perform downstream analyses in a fraction of the
time required by the analysis of the whole data, while obtaining comparable
answers. Our extensive experimental evaluation demonstrates the efficiency and
accuracy of SPRISS in approximating frequent $k$-mers, and shows that it can be
used in various scenarios, such as the comparison of metagenomic datasets and
the identification of discriminative $k$-mers, to extract insights in a
fraction of the time required by the analysis of the whole dataset.


  The dynamics of Bose-Einstein condensation in a three-dimensional harmonic
trap is studied explicitly including the Bogoliubov approximation for
temperatures below the critical one. To model the evolution towards equilibrium
at each cooling step, we derive quantum kinetic equations that describe the
dynamics of the gas for temperatures above and below the transition
temperature. These equations, valid in the Born and Markov approximations,
consider the essential role of the chemical potential as the main parameter
that signals the transition. The kinetic equation that describes the growth of
the condensate below the transition temperature is derived within the
Bogoliubov approximation. To illustrate our results we propose an energetic
cooling protocol and simulate the whole sequence of the formation of a
condensate.


  This paper introduces a new method for change detection in psychometric
studies based on the recently introduced pseudo Score statistic, for which the
sampling distribution under the alternative hypothesis has been determined. Our
approach has the advantage of simplicity in its computation, eliminating the
need for resampling or simulations to obtain critical values. Additionally, it
comes with a known null/alternative distribution, facilitating easy
calculations for power levels and sample size planning. The paper indeed also
discusses the topic of power analysis in segmented regression, namely the
estimation of sample size or power level when the study data being collected
focuses on a covariate expected to affect the mean response via a piecewise
relationship with an unknown breakpoint. We run simulation results showing that
our method outperforms other Tests for a Change Point (TFCP) with both normally
distributed and binary data and carry out a real SAT Critical reading data
analysis. The proposed test contributes to the framework of psychometric
research, and it is available on the Comprehensive R Archive Network (CRAN) and
in a more user-friendly Shiny App, both illustrated at the end of the paper.


  We present the results of a search for X--ray luminous distant clusters of
galaxies. We found extended X--ray emission characteristic of a cluster towards
two of our candidate clusters of galaxies. They both have a luminosity in the
ROSAT bandpass of $\simeq10^{44}{\rm \,erg\,s^{-1}}$ and a redshift of $>0.5$;
thus making them two of the most distant X--ray clusters ever observed.
Furthermore, we show that both clusters are optically rich and have a known
radio source associated with them. We compare our result with other recent
searches for distant X--ray luminous clusters and present a lower limit of
$1.2\times10^{-7}\,{\rm Mpc^{-3}}$ for the number density of such high redshift
clusters. This limit is consistent with the expected abundance of such clusters
in a standard (b=2) Cold Dark Matter Universe. Finally, our clusters provide
important high redshift targets for further study into the origin and evolution
of massive clusters of galaxies. Accepted for publication in the 10th September
1994 issue of ApJ.


  We study the realization problem which asks if a given oriented link in an
open 3-manifold can be realized as a fiber of a submersion to the Euclidean
plane. We correct the results obtained before by the author which contains an
error and certain imperfection, and investigate a necessary and sufficient
condition for the realization in the words of well-known invariants. We obtain
the condition expressed by the first homology group with mod 2 coefficient.


  We study the x-ray edge problem for a chaotic quantum dot or nanoparticle
displaying mesoscopic fluctuations. In the bulk, x-ray physics is known to
produce deviations from the naively expected photoabsorption cross section in
the form of a peaked or rounded edge. For a coherent system with chaotic
dynamics, we find substantial changes and in particular that a photoabsorption
cross section showing a rounded edge in the bulk will change to a slightly
peaked edge on average as the system size is reduced to a mesoscopic (coherent)
scale.


  Complex organic molecules (COMs) have been detected in the gas-phase in cold
and lightless molecular cores. Recent solid-state laboratory experiments have
provided strong evidence that COMs can be formed on icy grains through
'non-energetic' processes. In this contribution, we show that propanal and
1-propanol can be formed in this way at the low temperature of 10 K. Propanal
has already been detected in space. 1-propanol is an astrobiologically relevant
molecule, as it is a primary alcohol, and has not been astronomically detected.
Propanal is the major product formed in the C2H2 + CO + H experiment, and
1-propanol is detected in the subsequent propanal + H experiment. The results
are published in Qasim et al. (2019c). ALMA observations towards IRAS
16293-2422B are discussed and provide a 1-propanol:propanal upper limit of <
0.35 - 0.55, which are complemented by computationally-derived activation
barriers in addition to the performed laboratory experiments.


  In June 2016, Apple announced that it will deploy differential privacy for
some user data collection in order to ensure privacy of user data, even from
Apple. The details of Apple's approach remained sparse. Although several
patents have since appeared hinting at the algorithms that may be used to
achieve differential privacy, they did not include a precise explanation of the
approach taken to privacy parameter choice. Such choice and the overall
approach to privacy budget use and management are key questions for
understanding the privacy protections provided by any deployment of
differential privacy.
  In this work, through a combination of experiments, static and dynamic code
analysis of macOS Sierra (Version 10.12) implementation, we shed light on the
choices Apple made for privacy budget management. We discover and describe
Apple's set-up for differentially private data processing, including the
overall data pipeline, the parameters used for differentially private
perturbation of each piece of data, and the frequency with which such data is
sent to Apple's servers.
  We find that although Apple's deployment ensures that the (differential)
privacy loss per each datum submitted to its servers is $1$ or $2$, the overall
privacy loss permitted by the system is significantly higher, as high as $16$
per day for the four initially announced applications of Emojis, New words,
Deeplinks and Lookup Hints. Furthermore, Apple renews the privacy budget
available every day, which leads to a possible privacy loss of 16 times the
number of days since user opt-in to differentially private data collection for
those four applications.
  We advocate that in order to claim the full benefits of differentially
private data collection, Apple must give full transparency of its
implementation, enable user choice in areas related to privacy loss, and set
meaningful defaults on the privacy loss permitted.


  In this paper we tackle distributed detection of a non-cooperative target
with a Wireless Sensor Network (WSN). When the target is present, sensors
observe an unknown random signal with amplitude attenuation depending on the
distance between the sensor and the target (unknown) positions, embedded in
white Gaussian noise. The Fusion Center (FC) receives sensors decisions through
error-prone Binary Symmetric Channels (BSCs) and is in charge of performing a
(potentially) more-accurate global decision. The resulting problem is a
one-sided testing with nuisance parameters present only under the
target-present hypothesis. We first focus on fusion rules based on Generalized
Likelihood Ratio Test (GLRT), Bayesian and hybrid approaches. Then, aimed at
reducing the computational complexity, we develop fusion rules based on
generalizations of the well-known Locally-Optimum Detection (LOD) framework.
Finally, all the proposed rules are compared in terms of performance and
complexity.


  Slave-spin mean-field theory, a light and accurate technique to model
electronic correlations in Fermi-liquid phases of multi-orbital materials, is
pedagogically exposed in this chapter, with a focus on its recent successful
application to the physics of Iron-based superconductors. Beside introducing
electronic correlations and recalling the Fermi-liquid phenomenology, the
manuscript accompanies the step-by-step explanation of the slave-spin technique
with a set of useful complements providing analytical insight into Mott and
Hund's physics, which are at the heart of the physics of strongly correlated
materials. Some original research material is also exposed, such as the
Hund-induced shrinking of the Hubbard bands flanking the gap of the half-filled
Mott insulator, and the low-energy description of the "orbital-decoupling"
mechanism.


  The feedback exponential stabilization to trajectories for semilinear
parabolic equations in a given bounded domain is addressed. The controls take
values in a finite-dimensional space and are supported in a small region. Both
internal and boundary controls are considered.


  For two differentiable maps between two manifolds of possibly different
dimensions, the local and global coincidence homology classes are introduced
and studied by Bisi- Bracci-Izawa-Suwa (2016) in the framework of Cech-de Rham
cohomology. We take up the problem from the combinatorial viewpoint and give
some finer results, in particular for the local classes. As to the global
class, we clarify the relation with the cohomology coincidence class as studied
by Biasi-Libardi-Monis (2015). In fact they introduced such a class in the
context of several maps and we also consider this case. In particular we define
the local homology class and give some explicit expressions. These all together
lead to a generalization of the classical Lefschetz coincidence point formula.


  The universal enveloping algebra $U(\mathfrak{g} )$ of a current
(super)algebra or loop (super)algebra $\mathfrak{g} $ is considered over an
algebraically closed field $\mathbb{K} $ with characteristic $p\ge 0$. This
paper focuses on the structure of the center $Z(\mathfrak{g} )$ of
$U(\mathfrak{g} )$. In the case of zero characteristic, $Z(\mathfrak{g} )$ is
generated by the centers of $\mathfrak{g} $. In the case of prime
characteristic, $Z(\mathfrak{g} )$ is generated by the centers of $\mathfrak{g}
$ and the $p$-centers of $U(\mathfrak{g} )$. We also study the structure of
$Z(\mathfrak{g} )$ in the semisimple Lie (super)algebra.


  The purely algebraic technique associated with the creation and annihilation
operators to resolve the radial equation of Hydrogen-like atoms (HLA) for
generating the bound energy spectrum and the corresponding wave functions is
suitable for many calculations in quantum physics. However, the analytical
approach with series is extensively used based on wave mechanics theory in most
of quantum textbooks. Indeed, much More complete than the old solution of
Schr\"odinger's time-independent differential equation (TISE), one can simply
earn all quantum information of a system by using the operational method. In
addition to earlier two models, including the quantum harmonic oscillator and
the total angular momentum, it can undoubtedly be a third fundamental model to
solve Schr\"odinger's eigenvalue equation of the HLA systems in Hilbert space
similar to the harmonic oscillator. We will illustrate how systematically
making an appropriate groundwork to discover the coherent states can lead to
providing the energy quantization and normalized radial wave functions attached
to the matrix representation without additional assumptions.


  Nematicity plays an important role in the physics of iron-based
superconductors (IBS). Its microscopic origin and in particular its importance
for the mechanism of high-temperature superconductivity itself are highly
debated. A crucial knowledge in this regard is the degree to which the nematic
order influences the electronic structure of these materials. Earlier
angle-resolved photoemission spectroscopy (ARPES) studies found that the effect
is dramatic in three families of IBS including 11, 111 and 122 compounds:
energy splitting reaches 70 meV and Fermi surface becomes noticeably distorted.
More recent experiments, however, reported significantly lower energy scale in
11 and 111 families, thus questioning the degree and universality of the impact
of nematicity on the electronic structure of IBS. Here we revisit the
electronic structure of undoped parent BaFe2As2 (122 family). Our systematic
ARPES study including the detailed temperature and photon energy dependencies
points to the significantly smaller energy scale also in this family of
materials, thus establishing the universal scale of this phenomenon in IBS. Our
results form a necessary quantitative basis for theories of high-temperature
superconductivity focused on the nematicity.


  We explain in detail how to estimate mean values and assess statistical
errors for arbitrary functions of elementary observables in Monte Carlo
simulations. The method is to estimate and sum the relevant autocorrelation
functions, which is argued to produce more certain error estimates than binning
techniques and hence to help toward a better exploitation of expensive
simulations. An effective integrated autocorrelation time is computed which is
suitable to benchmark efficiencies of simulation algorithms with regard to
specific observables of interest. A Matlab code is offered for download that
implements the method. It can also combine independent runs (replica) allowing
to judge their consistency.


  We study the existence of minimal supersolutions of BSDEs under a family of
mutually singular probability measures. We consider generators that are jointly
lower semicontinuous, positive, and either convex in the control variable and
monotone in the value variable, or that fulfill a specific normalization
property.


  Surface chemistry on cosmic dust grains plays an important role in the
formation of molecules at low temperatures in the interstellar and
circumstellar environments. For the first time, we experimentally put in
evidence the catalytic role of dust surfaces using the thermal reaction CO2 +
2NH3 = NH4+NH2COO-, which is also a proxy of radical-radical reactions.
Nanometre-sized amorphous silicate and carbon grains produced in our laboratory
were used as grain analogues. Surface catalysis on grains accelerates the
kinetics of the reaction studied at a temperature of 80 K by a factor of up to
3 compared to the reaction occurring in the molecular solid. The evidence of
the catalytic effect of grain surfaces opens a door for experiments and
calculations on the surface formation of interstellar and circumstellar
molecules on dust. Ammonium carbamate on the surface of grains or released
intact into protostellar or protoplanetary disk phases can give start to a
network of prebiotic reactions. Therefore, there should be a great interest to
search for ammonium carbamate and its daughter molecule, carbamic acid, in
interstellar clouds, protostellar envelopes, and protoplanetary disks.


  A large number of coronagraphs have been proposed to overcome the ratio that
exists between the star and its planet. The planet finder of the Extremely
Large Telescope, which is called EPICS, will certainly need a more efficient
coronagraph than the ones that have been developed so far. We propose to use a
combination of chromatic Four Quadrant Phase Mask coronagraph to achromatize
the dephasing of the device while maintaining a high rejection performance.
After describing this multi-stage FQPM coronagraph, we show preliminary results
of a study on its capabilities in the framework of the EPICS instrument, the
planet finder of the European Extremely Large Telescope. Eventually, we present
laboratory tests of a rough prototype of a multi-stage four-quadrant phase
mask. On one hand, we deduce from our laboratory data that a detection at the
10^-10 level is feasible in monochromatic light. On the other hand, we show the
detection of a laboratory companion fainter than 10^-8 with a spectral
bandwidth larger than 20%.


  To make the historical radio telescope of A.\"{U}. Kreiken Observatory that
includes full-wavelength VHF and UHF dipole arrays operational, the antenna
rotator mechanism is renovated and the antenna matching structure is designed
in a simulation environment. The matching structure is designed by using
short-circuited parallel transmission line stubs. The use of high-gain
full-wavelength elements, as opposed to the conventional half-wavelength
elements, increases the importance of the structure and the matching method.
  --
  A.\"{U}. Kreiken Rasathanesinde bulunan, tam dalgaboyunda dipol elemanlara
sahip VHF ve UHF anten dizilerinden olu\c{s}an tarihi radyo teleskobun faal
hale getirilmesi i\c{c}in anten y\"{o}nlendirme mekanizmasi incelenip tamir
edilmi\c{s}, teleskop anteninin uyumlama tasarimi benzetim ortaminda
ger\c{c}ekle\c{s}tirilmi\c{s}tir. Kisa devre ile sonlandirilmi\c{s} paralel
iletim hatti saplamalari kullanarak uyumlama tasarlanmi\c{s}tir. Dipol anten
elemanlarinin boyunun yaygin tercih edilen yarim dalgaboyu yerine y\"{u}ksek
kazan\c{c}li tam dalgaboyu olmasi, antenin ve uyumlama y\"{o}nteminin
\"{o}nemini artirmaktadir.


  In this paper, we generalize our apriori estimates on cscK(constant scalar
curvature K\"ahler) metric equation to more general scalar curvature type
equations (e.g., twisted cscK metric equation). As applications, under the
assumption that the automorphism group is discrete, we prove the celebrated
Donaldson's conjecture that the non-existence of cscK metric is equivalent to
the existence of a destabilized geodesic ray where the $K$-energy is
non-increasing. Moreover, we prove that the properness of $K$-energy in terms
of $L^1$ geodesic distance $d_1$ in the space of K\"ahler potentials implies
the existence of cscK metric. Finally, we prove that weak minimizers of the
$K$-energy in $(\mathcal{E}^1, d_1)$ are smooth.


  In this study, we consider one-dimension (1D) quantum spin systems with the
translation and discrete symmetries (spin reversal, space inversion and time
reversal symmetries). By combining the continuous U(1) symmetry with the
discrete symmetries and using the extended Lieb-Schultz-Mattis theorem
\cite{Lieb-Schultz-Mattis-1961}\cite{Nomura-Morishige-Isoyama-2015}, we
investigate the relation between the ground states, energy spectra and
symmetries. For half-integer spin cases, we generalize the dimer and N\'eel
concepts using the discrete symmetries, and we can reconcile the LSM theorem
with the dimer or N\'eel states, since there was a subtle dilemma. Furthermore,
a part of discrete symmetries is enough to classify possible phases. Thus we
can deepen our understanding of the relation between the LSM theorem and the
discrete symmetries.


  In response to the worldwide outbreak of the coronavirus disease COVID-19, a
variety of nonpharmaceutical interventions such as face masks and social
distancing have been implemented. A careful assessment of the effects of such
containment strategies is required to avoid exceeding social and economical
costs as well as a dangerous "second wave" of the pandemic. In this work, we
combine a recently developed dynamical density functional theory model and an
extended SIRD model with hysteresis to study effects of various measures and
strategies using realistic parameters. Depending on intervention thresholds, a
variety of phases with different numbers of shutdowns and deaths are found.
Spatiotemporal simulations provide further insights into the dynamics of a
second wave. Our results are of crucial importance for public health policy.


  In this paper, we present a real-time successive convexification algorithm
for a generalized free-final-time 6-degree-of-freedom powered descent guidance
problem. We build on our previous work by introducing the following
contributions: (i) a free-ignition-time modification that allows the algorithm
to determine the optimal engine ignition time, (ii) a tractable aerodynamics
formulation that models both lift and drag, and (iii) a continuous
state-triggered constraint formulation that emulates conditionally enforced
constraints. In particular, contribution (iii) effectively allows constraints
to be enabled or disabled by if-statements conditioned on the solution
variables of the parent continuous optimization problem. To the best of our
knowledge, this represents a novel formulation in the optimal control
literature, and enables a number of interesting applications, including
velocity-triggered angle of attack constraints and range-triggered line of
sight constraints. Our algorithm converts the resulting generalized powered
descent guidance problem from a non-convex free-final-time optimal control
problem into a sequence of tractable convex second-order cone programming
subproblems. With the aid of virtual control and trust region modifications,
these subproblems are solved in succession until convergence is attained.
Simulations using a third-party solver demonstrate the real-time capabilities
of the proposed algorithm, with a maximum execution time of less than 0.7
seconds over a multitude of problem feature combinations.


  Although there have been extensive studies on transmit beamforming in
multi-input single-output (MISO) multicell networks, achieving optimal sum-rate
with limited channel state information (CSI) is still a challenge even with a
single user per cell. A novel cooperative downlink multicell MISO beamforming
scheme is proposed with highly limited information exchange among the base
stations (BSs) to maximize the sum-rate. In the proposed scheme, each BS can
design its beamforming vector with only local CSI based on limited information
exchange on CSI. Unlike previous studies, the proposed beamforming design is
non-iterative and does not require any vector or matrix feedback but requires
only quantized scalar information. The proposed scheme closely achieves the
optimal sum-rate bound in almost all signal-to-noise ratio regime based on
non-iterative optimization with lower amount of information exchange than
existing schemes, which is justified by numerical simulations.


  We provide new results concerning label efficient, polynomial time, passive
and active learning of linear separators. We prove that active learning
provides an exponential improvement over PAC (passive) learning of homogeneous
linear separators under nearly log-concave distributions. Building on this, we
provide a computationally efficient PAC algorithm with optimal (up to a
constant factor) sample complexity for such problems. This resolves an open
question concerning the sample complexity of efficient PAC algorithms under the
uniform distribution in the unit ball. Moreover, it provides the first bound
for a polynomial-time PAC algorithm that is tight for an interesting infinite
class of hypothesis functions under a general and natural class of
data-distributions, providing significant progress towards a longstanding open
question.
  We also provide new bounds for active and passive learning in the case that
the data might not be linearly separable, both in the agnostic case and and
under the Tsybakov low-noise condition. To derive our results, we provide new
structural results for (nearly) log-concave distributions, which might be of
independent interest as well.


  We consider a class of global quantum quenches in the Heisenberg XXZ spin
chain, where the initial states are given by products of local two-site states.
The two main examples are the N\'eel state and the dimer state. We derive an
exact analytic result for the "Loschmidt echo per site" at imaginary times and
also consider the analytic continuation back to real times. As a by-product we
obtain an exact result for the "overlap per site" between the N\'eel state and
the ground state of the XXZ Hamiltonian in the massive regime.


  Exoplanet transmission spectra provide rich information about the chemical
composition, clouds and temperature structure of exoplanet atmospheres. Most
exoplanet transmission spectra only span infrared wavelengths ($\gtrsim$ 1
$\rm{\mu m}$), which can preclude crucial atmospheric information from shorter
wavelengths. Here, we explore how retrieved atmospheric parameters from
exoplanet transmission spectra change with the addition of optical data. From a
sample of 14 giant planets with transit spectra from 0.3-4.5 $\rm{\mu m}$,
primarily from the Hubble and Spitzer space telescopes, we apply a free
chemistry retrieval to planetary spectra for wavelength ranges of 0.3-4.5
$\rm{\mu m}$, 0.6-4.5 $\rm{\mu m}$, and 1.1-4.5 $\rm{\mu m}$. We analyse the
posterior distributions of these retrievals and perform an information content
analysis, finding wavelengths below 0.6 $\rm{\mu m}$ are necessary to constrain
cloud scattering slope parameters ($\log{a}$ and $\gamma$) and alkali species
Na and K. There is limited improvement in the constraints on the remaining
atmospheric parameters. Across the population, we find limb temperatures are
retrieved colder than planetary equilibrium temperatures but have an overall
good agreement with Global Circulation Models. As JWST extends to a minimum
wavelength of 0.6 $\rm{\mu m}$, we demonstrate that exploration into
complementing JWST observations with optical HST data is important to further
our understanding of aerosol properties and alkali abundances in exoplanet
atmospheres.


  Gray-Vanhecke conjectured that the volumes of small geodesic balls could
determine if the manifold is a space form, and provided a proof for the compact
4-dimensional manifold, and some cases. In this paper, similar results for the
4-dimensional case are obtained, based upon tensor calculus and classical
theorems rather than the topological characterizations in [6].


  We review here the physics of purely leptonic decays of pi-, K-, D+, Ds+, and
B- pseudoscalar mesons. The measured decay rates are related to the product of
the relevant weak interaction based CKM matrix element of the constituent
quarks and a strong interaction parameter related to the overlap of the quark
and anti-quark wave-functions in the meson, called the decay constant fP. The
interplay between theory and experiment is different for each particle.
Theoretical predictions that are necessary in the B sector can be tested, for
example, in the charm sector. One such measurement, that of fDs, differs from
the most precise unquenched lattice calculation and may indicate the presence
of new intermediate particles, or the theoretical prediction could be
misleading. The lighter pi and K mesons provide stringent comparisons due to
the accuracy of both the measurements and the theoretical predictions. This
review was prepared for the Particle Data Group's 2008 edition.


  We report a comprehensive study of the wake of a porous disc, the design of
which has been modified to incorporate a swirling motion at an inexpensive
cost. The swirl intensity is passively controlled by varying the internal disc
geometry, i.e. the pitch angle of the blades. A swirl number is introduced to
characterise the competition between the linear (drag) and the azimuthal
(swirl) momentums on the wake recovery. Assuming that swirl dominates the near
wake and non-equilibrium turbulence theory applies, new scaling laws of the
mean wake properties are derived. To assess these theoretical predictions, an
in-depth analysis of the aerodynamics of these original porous discs has been
conducted experimentally. It is found that at the early stage of wake recovery,
the swirling motion induces a low-pressure core, which controls the mean
velocity deficit properties. The measurements collected in the swirling wake of
the porous discs support the new scaling laws proposed in this work. Finally,
it is shown that, as far as swirl is injected in the wake, the characteristics
of the mean velocity deficit profiles match very well those of both lab-scale
and real-scale wind turbine data extracted from the literature. Overall, our
results emphasise that by setting the initial conditions of the wake recovery,
swirl is a key ingredient to be taken into account in order to faithfully
replicate the mean wake of wind turbines.


  Statistical methods are developed for analysis of clinical and virus genetics
data from phase 3 randomized, placebo-controlled trials of vaccines against
novel coronavirus COVID-19. Vaccine efficacy (VE) of a vaccine to prevent
COVID-19 caused by one of finitely many genetic strains of SARS-CoV-2 may vary
by strain. The problem of assessing differential VE by viral genetics can be
formulated under a competing risks model where the endpoint is virologically
confirmed COVID-19 and the cause-of-failure is the infecting SARS-CoV-2
genotype. Strain-specific VE is defined as one minus the cause-specific hazard
ratio (vaccine/placebo). For the COVID-19 VE trials, the time to COVID-19 is
right-censored, and a substantial percentage of failure cases are missing the
infecting virus genotype. We develop estimation and hypothesis testing
procedures for strain-specific VE when the failure time is subject to right
censoring and the cause-of-failure is subject to missingness, focusing on $J
\ge 2$ discrete categorical unordered or ordered virus genotypes. The
stratified Cox proportional hazards model is used to relate the cause-specific
outcomes to explanatory variables. The inverse probability weighted
complete-case (IPW) estimator and the augmented inverse probability weighted
complete-case (AIPW) estimator are investigated. Hypothesis tests are developed
to assess whether the vaccine provides at least a specified level of efficacy
against some viral genotypes and whether VE varies across genotypes, adjusting
for covariates. The finite-sample properties of the proposed tests are studied
through simulations and are shown to have good performances. In preparation for
the real data analyses, the developed methods are applied to a pseudo dataset
mimicking the Moderna COVE trial.


  This paper proposes a new topology of a non-isolated two-phase interleaved
boost DC-DC converters with modified Greinacher voltage multiplier cells (VMCs)
in continuous conduction mode (CCM). The proposed topology can quickly achieve
a high gain with low stress on components and proper duty cycle. Moreover, this
topology requires a small inductance size in CCM while maintaining the
continuity of the input current due to the interleaved technique used in this
converter. Because of these features, this topology can be used in many
renewable applications such as PV, DC microgrids, etc. The design procedures
and component selection are presented in this paper. Furthermore, the proposed
converter was simulated to convert an input voltage of 20 V to output voltage
of 400 V with an output power of 200 W. Finally, experimental results have been
provided to confirm the analysis and simulation results.


  In this paper, we carry out tests on the Monte Carlo Markov Chain (MCMC)
technique with the aim of determining: 1) its ability to retrieve isothermal
plasmas from a set of spectral line intensities, with and without random noise;
2) to what extent can it discriminate between an isothermal solution and a
narrow multithermal distribution; and 3) how well it can detect multiple
isothermal components along the line of sight. We also test the effects of 4)
atomic data uncertainties on the results, and 5) the number of ions whose lines
are available for the DEM reconstruction. We find that the MCMC technique is
unable to retrieve isothermal plasmas to better than Delta log T = 0.05. Also,
the DEM curves obtained using lines calculated with an isothermal plasma and
with a Gaussian distribution with FWHM of log T = 0.05 are very similar. Two
near-isothermal components can be resolved if their temperature separation is
Delta log T = 0.2 or larger. Thus, DEM diagnostics has an intrinsic resolving
power of log T = 0.05. Atomic data uncertainties may significantly affect both
temperature and peak DEM values, but do not alter our conclusions. The
availability of small sets of lines also does not worsen the performance of the
MCMC technique, provided these lines are formed in a wide temperature range.
Our analysis shows the present limitations in our ability to identify the
presence of strictly isothermal plasmas in stellar and solar coronal spectra.


  A quantum thermal machine is an open quantum system that enables the
conversion between heat and work at the micro or nano-scale. Optimally
controlling such out-of-equilibrium systems is a crucial yet challenging task
with applications to quantum technologies and devices. We introduce a general
model-free framework based on Reinforcement Learning to identify
out-of-equilibrium thermodynamic cycles that are Pareto optimal trade-offs
between power and efficiency for quantum heat engines and refrigerators. The
method does not require any knowledge of the quantum thermal machine, nor of
the system model, nor of the quantum state. Instead, it only observes the heat
fluxes, so it is both applicable to simulations and experimental devices. We
test our method on a model of an experimentally realistic refrigerator based on
a superconducting qubit, and on a heat engine based on a quantum harmonic
oscillator. In both cases, we identify the Pareto-front representing optimal
power-efficiency tradeoffs, and the corresponding cycles. Such solutions
outperform previous proposals made in the literature, such as optimized Otto
cycles, reducing quantum friction.


  Language models (LMs) often generate incoherent outputs: they refer to events
and entity states that are incompatible with the state of the world described
in their inputs. We introduce SituationSupervision, a family of approaches for
improving coherence in LMs by training them to construct and condition on
explicit representations of entities and their states. SituationSupervision has
two components: an auxiliary situation modeling task that trains models to
predict state representations in context, and a latent state inference
procedure that imputes these states from partially annotated training data.
SituationSupervision can be applied to both fine-tuning (by supervising LMs to
encode state variables in their hidden representations) and prompting (by
inducing LMs to interleave textual descriptions of entity states with output
text). In both cases, SituationSupervision requires only a small number of
state annotations to produce major coherence improvements (between 4-11%),
showing that standard LMs can be sample-efficiently trained to model not just
language but the situations it describes.


  We examine transient circular dichroism spectroscopy (TRCD) as a technique to
investigate signatures of exciton coherence dynamics under the influence of
structured vibrational environments. We consider a pump-probe configuration
with a linearly polarized pump and a circularly polarized probe, with a
variable angle $\theta$ between the two directions of propagation. In our
theoretical formalism the signal is decomposed in chiral and achiral doorway
and window functions. Using this formalism, we show that the chiral doorway
component, which beats during the population time, can be isolated by comparing
signals with different values of $\theta$. As in the majority of time-resolved
pump-probe spectroscopy, the overall TRCD response shows signatures of both
excited and ground state dynamics. However, we demonstrate that the chiral
doorway function has only a weak ground state contribution, which can generally
be neglected if an impulsive pump pulse is used. These findings suggest that
the pump-probe configuration of optical TRCD in the impulsive limit has the
potential to unambiguously probe quantum coherence beating in the excited
state. We present numerical results for theoretical signals in an example dimer
system.


  A finite group $G$ is called a DCI-group if two Cayley digraphs over $G$ are
isomorphic if and only if their connection sets are conjugate by a group
automorphism. We prove that the group $C_4\times C_p^2$, where $p$ is a prime,
is a DCI-group if and only if $p\neq 2$.


  The CDF collaboration has recently reported an updated result on the
$W$-boson mass measurement, showing a $7\sigma$ deviation from the standard
model prediction. The discrepancy may indicate new contributions to the Fermi
coupling constant. We study simple extensions of the standard model by
introducing an extra scalar, fermion or vector field. It is found that the
tension implies the new physics existing in multi-TeV scales if the new
coupling to the electron and/or muon is of order unity.


  The use of continuous probability distributions has been widespread in
problems with purely discrete nature. In general, such distributions are not
appropriate in this scenario. In this paper, we introduce a class of discrete
and asymmetric distributions based on the family of continuous log-symmetric
distributions. Some properties are discussed as well as estimation by the
maximum likelihood method. A Monte Carlo simulation study is carried out to
evaluate the performance of the estimators, and censored and uncensored data
sets are used to illustrate the proposed methodology.


  In this paper, a new method for handwritten signature identification based on
rotated complex wavelet filters is proposed. We have proposed to use the
rotated complex wavelet filters (RCWF) and dual tree complex wavelet
transform(DTCWT) together to derive signature feature extraction, which
captures information in twelve different directions. In identification phase,
Canberra distance measure is used. The proposed method is compared with
discrete wavelet transform (DWT). From experimental results it is found that
signature identification rate of proposed method is superior over DWT


  For any root system and any commutative ring we give a relatively simple
presentation of a group related to its Steinberg group St. This includes the
case of infinite root systems used in Kac-Moody theory, for which the Steinberg
group was defined by Tits and Morita-Rehmann. In most cases our group equals
St, giving a presentation with many advantages over the usual presentation of
St. This equality holds for all spherical root systems, all irreducible affine
root systems of rank>2, and all 3-spherical root systems. When the coefficient
ring satisfies a minor condition, the last condition can be relaxed to
2-sphericity.
  Our presentation is defined in terms of the Dynkin diagram rather than the
full root system. It is concrete, with no implicit coefficients or signs. It
makes manifest the exceptional diagram automorphisms in characteristics 2 and
3, and their generalizations to Kac-Moody groups. And it is a Curtis-Tits style
presentation: it is the direct limit of the groups coming from 1- and 2-node
subdiagrams of the Dynkin diagram. Over non-fields this description as a direct
limit is new and surprising. Our main application is that many Steinberg and
Kac-Moody groups over finitely-generated rings are finitely presented.


  Using projective construction, a generalized parton construction, we
construct many non-Abelian quantum Hall (QH) states, which include the Pfaffian
state at filling fraction $\nu=1/2$. The projective construction allows us to
calculate the bulk and the edge effective theory for the constructed QH state.
We illustrate how to use the bulk effective theory to calculate the ground
state degeneracy of non-Abelian QH liquids on torus. We point out that the full
description of the effective theory requires both the effective Lagrangian and
the definition of electron operators. The latter generates all physical states
and defines the gauge structure of the theory.


  There has been always an ambiguity in division when zero is present in the
denominator. So far this ambiguity has been neglected by assuming that division
by zero as a non-allowed operation. In this paper, I have derived the new set
of numbers by considering that a number divided by zero gives rise to "a beyond
complex number". This is very similar to the way imaginary numbers are defined.
It was only after considering i = sqrt(-1), we have been able to deduce all the
laws for imaginary numbers. Similarly here, I have considered "a number when
divided by zero gives rise to a beyond complex number". This is the
introduction paper to this "beyond complex numbers" containing the algebra of
it.


  correlation, assuming that the spin of the top quark is either correlated
with the spin of the anti-top quark as predicted by the standard model or is
uncorrelated. For the first time we use a matrix-element-based approach to
study ttbar spin correlation. We use {ttbar -> W+bW-bbar ->l+nubl-nub} final
states produced in ppbar collisions at a center of mass energy sqrt(s)=1.96
TeV, where l denotes an electron or a muon. The data correspond to an
integrated luminosity of 5.4 fb-1 and were collected with the dzero detector at
the Fermilab Tevatron collider. The result agrees with the standard model
prediction. We exclude the hypothesis that the spins of the ttbar are
uncorrelated at the 97.7% C.L.


  We present a language $L_n$ which is recognizable by a probabilistic finite
automaton (PFA) with probability $1 - \epsilon$ for all $\epsilon > 0$ with
$O(log^2n)$ states, with a deterministic finite automaton (DFA) with O(n)
states, but a quantum finite automaton (QFA) needs at least $2^{\Omega(n/ \log
n)}$ states.


  The role of hadronic matter in the suppression of open heavy flavored mesons
has been studied. The heavy-quarks (HQs) suppression factors have been
calculated and contrasted with the experimental data obtained from nuclear
collisions at Relativistic Heavy Ion Collider (RHIC) and Large Hadronic
Collider (LHC) experiments. It is found that the suppression in the hadronic
phase at RHIC energy is around $20%-25%$ whereas at the LHC it is around
$10%-12%$ for the D meson. In case of B meson the hadronic suppression is
around $10%-12%$ and $5%-6%$ at RHIC and LHC energies respectively. Present
study suggests that the suppression of heavy flavor in the hadronic phase is
significant at RHIC. However, the effect of hadronic suppression at LHC is
marginal, this makes the characterization of QGP at LHC less complicated.


  Many machine learning problems, especially multi-modal learning problems,
have two sets of distinct features (e.g., image and text features in news story
classification, or neuroimaging data and neurocognitive data in cognitive
science research). This paper addresses the joint dimensionality reduction of
two feature vectors in supervised learning problems. In particular, we assume a
discriminative model where low-dimensional linear embeddings of the two feature
vectors are sufficient statistics for predicting a dependent variable. We show
that a simple algorithm involving singular value decomposition can accurately
estimate the embeddings provided that certain sample complexities are
satisfied, without specifying the nonlinear link function (regressor or
classifier). The main results establish sample complexities under multiple
settings. Sample complexities for different link functions only differ by
constant factors.


  In this Letter, we study the evolution of the autocovariance function (ACF)
of density field fluctuations in star-forming clouds and thus of the
correlation length $l_c(\rho)$ of these fluctuations, which can be identified
as the average size of the most correlated structures within the cloud.
Generalizing the transport equation derived by Chandrasekhar (1951) for static,
homogeneous turbulence, we show that the mass contained within these structures
is an invariant, i.e. that the average mass contained in the most correlated
structures remains constant during the evolution of the cloud, whatever
dominates the global dynamics (gravity or turbulence).
  We show that the growing impact of gravity on the turbulent flow yields an
increase of the variance of the density fluctuations and thus a drastic
decrease of the correlation length. Theoretical relations are successfully
compared to numerical simulations. This picture brings a robust support to star
formation paradigms where the mass concentration in turbulent star-forming
clouds evolves from initially large, weakly correlated filamentary structures
to smaller, denser more correlated ones, and eventually to small, tightly
correlated prestellar cores. We stress that the present results rely on a pure
statistical approach of density fluctuations and do not involve any specific
condition for the formation of prestellar cores. Interestingly enough, we show
that, under average conditions typical of Milky Way molecular clouds, this
invariant average mass is about a solar mass, providing an appealing
explanation for the apparent universality of the IMF under such environments.


  Properties of four infinite families of special functions of two real
variables, based on the compact simple Lie group G2, are compared and
described. Two of the four families (called here C- and S-functions) are well
known, whereas the other two (S^L- and S^S-functions) are not found elsewhere
in the literature. It is shown explicitly that all four families have similar
properties. In particular, they are orthogonal when integrated over a finite
region F of the Euclidean space, and they are discretely orthogonal when their
values, sampled at the lattice points F_M \subset F, are added up with a weight
function appropriate for each family. Products of ten types among the four
families of functions, namely CC, CS, SS, SS^L, CS^S, SS^L, SS^S, S^SS^S,
S^LS^S and S^LS^L, are completely decomposable into the finite sum of the
functions. Uncommon arithmetic properties of the functions are pointed out and
questions about numerous other properties are brought forward.


  Domain Adaptation (DA), the process of effectively adapting task models
learned on one domain, the source, to other related but distinct domains, the
targets, with no or minimal retraining, is typically accomplished using the
process of source-to-target manifold alignment. However, this process often
leads to unsatisfactory adaptation performance, in part because it ignores the
task-specific structure of the data. In this paper, we improve the performance
of DA by introducing a discriminative discrepancy measure which takes advantage
of auxiliary information available in the source and the target domains to
better align the source and target distributions. Specifically, we leverage the
cohesive clustering structure within individual data manifolds, associated with
different tasks, to improve the alignment. This structure is explicit in the
source, where the task labels are available, but is implicit in the target,
making the problem challenging. We address the challenge by devising a deep DA
framework, which combines a new task-driven domain alignment discriminator with
domain regularizers that encourage the shared features as task-specific and
domain invariant, and prompt the task model to be data structure preserving,
guiding its decision boundaries through the low density data regions. We
validate our framework on standard benchmarks, including Digits (MNIST, USPS,
SVHN, MNIST-M), PACS, and VisDA. Our results show that our proposed model
consistently outperforms the state-of-the-art in unsupervised domain
adaptation.


  We present an ab initio full-potential linearized augmented plane-wave
(FLAPW) study of the structural and electronic properties of the two bulk
unstable compounds FeSi (CsCl structure) and FeSi$_2$ (CaF$_2$ structure) which
have recently been grown by molecular beam epitaxy on Si(111). We obtain
equilibrium bulk lattice constants of 2.72 \AA\ and 5.32 \AA\ for FeSi and
FeSi$_2$, respectively. The density of states (DOS) of FeSi agrees well with
experiment, and shows metallic behavior. In agreement with a previous
calculation the DOS of FeSi$_2$ shows a large density of $d$-states at the
Fermi level, explaining the instability of the bulk phase. The electron charge
distributions reveal a small charge transfer from Si to Fe atomic spheres in
both compounds. While in FeSi the Fe-Si bond is indeed partially ionic, we show
that in FeSi$_2$ the electron distribution corresponds to a covalent charge
accumulation in the Fe-Si bond region. The reversed order of $d$-bands in FeSi
with respect to FeSi$_2$ is understood in terms of crystal field splitting and
Fe-Fe nearest neighbor $dd$-interactions in the CsCl structure, and a strong Si
$p$/Fe $d$ bonding in the fluorite structure, respectively.


  In this paper, the integral $\pmatrix{\lambda_1 &\lambda_2 &\lambda_3\cr 0 &0
&0\cr}\, \int_0^\infty \, r^{\lambda_3+2}\, \exp{(-\alpha r^2)}\,
j_{\lambda_1}(k_1r) \,j_{\lambda_2}(k_2r) \,dr$, where $k_1$, $k_2$ and
$\alpha$ are positive, is evaluated analytically. The result is a finite sum
over the modified spherical Bessel function of the first kind. This result will
be useful for nuclear scattering calculations, where harmonic oscillator
nuclear wavefunctions are used or when evaluating momentum space matrix
elements for a Gaussian potential.


  In recent years, several gauge-symmetric particle-in-cell (PIC) methods have
been developed whose simulations of particles and electromagnetic fields
exactly conserve charge. While it is rightly observed that these methods' gauge
symmetry gives rise to their charge conservation, this causal relationship has
generally been asserted via ad hoc derivations of the associated conservation
laws. In this work, we develop a comprehensive theoretical grounding for charge
conservation in gauge-symmetric Lagrangian and Hamiltonian PIC algorithms. For
Lagrangian variational PIC methods, we apply Noether's second theorem to
demonstrate that gauge symmetry gives rise to a local charge conservation law
as an off-shell identity. For Hamiltonian splitting methods, we show that the
momentum map establishes their charge conservation laws. We define a new class
of algorithms -- gauge-compatible splitting methods -- that exactly preserve
the momentum map associated with a Hamiltonian system's gauge symmetry -- even
after time discretization. This class of algorithms affords splitting schemes a
decided advantage over alternative Hamiltonian integrators. We apply this
general technique to design a novel, explicit, symplectic, gauge-compatible
splitting PIC method, whose momentum map yields an exact local charge
conservation law. Our study clarifies the appropriate initial conditions for
such schemes and examines their symplectic reduction.


  In instruction conditioned navigation, agents interpret natural language and
their surroundings to navigate through an environment. Datasets for studying
this task typically contain pairs of these instructions and reference
trajectories. Yet, most evaluation metrics used thus far fail to properly
account for the latter, relying instead on insufficient similarity comparisons.
We address fundamental flaws in previously used metrics and show how Dynamic
Time Warping (DTW), a long known method of measuring similarity between two
time series, can be used for evaluation of navigation agents. For such, we
define the normalized Dynamic Time Warping (nDTW) metric, that softly penalizes
deviations from the reference path, is naturally sensitive to the order of the
nodes composing each path, is suited for both continuous and graph-based
evaluations, and can be efficiently calculated. Further, we define SDTW, which
constrains nDTW to only successful paths. We collect human similarity judgments
for simulated paths and find nDTW correlates better with human rankings than
all other metrics. We also demonstrate that using nDTW as a reward signal for
Reinforcement Learning navigation agents improves their performance on both the
Room-to-Room (R2R) and Room-for-Room (R4R) datasets. The R4R results in
particular highlight the superiority of SDTW over previous success-constrained
metrics.


  Recent neural text-to-speech (TTS) models with fine-grained latent features
enable precise control of the prosody of synthesized speech. Such models
typically incorporate a fine-grained variational autoencoder (VAE) structure,
extracting latent features at each input token (e.g., phonemes). However,
generating samples with the standard VAE prior often results in unnatural and
discontinuous speech, with dramatic prosodic variation between tokens. This
paper proposes a sequential prior in a discrete latent space which can generate
more naturally sounding samples. This is accomplished by discretizing the
latent features using vector quantization (VQ), and separately training an
autoregressive (AR) prior model over the result. We evaluate the approach using
listening tests, objective metrics of automatic speech recognition (ASR)
performance, and measurements of prosody attributes. Experimental results show
that the proposed model significantly improves the naturalness in random sample
generation. Furthermore, initial experiments demonstrate that randomly sampling
from the proposed model can be used as data augmentation to improve the ASR
performance.


  We describe the results obtained from an improved model for protein folding.
We find that a good agreement with the native structure of a 46 residue long,
five-letter protein segment is obtained by carefully tuning the parameters of
the self-avoiding energy. In particular we find an improved free-energy
profile. We also compare the efficiency of the multidimensional replica
exchange method with the widely used parallel tempering.


  This work provides a thorough introduction to the field of gammoids and
presents new results that are considered helpful for solving the problems of
recognizing and coloring gammoids.


  For the reliable fabrication of the current and next generation of
nanostructures it is essential to be able to determine their material
composition and dimensional parameters. Using the grazing incidence X-ray
fluoresence technique, which is taking advantage of the X-ray standing wave
field effect, nanostructures can be investigated with a high sensitivity with
respect to the structural and elemental composition. This is demonstrated using
lamellar gratings made of Si$_3$N$_4$. Rigorous field simulations obtained from
a Maxwell solver based on the finite element method allow to determine the
spatial distribution of elemental species and the geometrical shape with sub-nm
resolution. The increasing complexity of nanostructures and demanded
sensitivity for small changes quickly turn the curse of dimensionality for
numerical simulation into a problem which can no longer be solved rationally
even with massive parallelisation. New optimization schemes, e.g. machine
learning, are required to satisfy the metrological requirements. We present
reconstruction results obtained with a Bayesian optimization approach to reduce
the computational effort.


  This paper describes an R package named flare, which implements a family of
new high dimensional regression methods (LAD Lasso, SQRT Lasso, $\ell_q$ Lasso,
and Dantzig selector) and their extensions to sparse precision matrix
estimation (TIGER and CLIME). These methods exploit different nonsmooth loss
functions to gain modeling flexibility, estimation robustness, and tuning
insensitiveness. The developed solver is based on the alternating direction
method of multipliers (ADMM). The package flare is coded in double precision C,
and called from R by a user-friendly interface. The memory usage is optimized
by using the sparse matrix output. The experiments show that flare is efficient
and can scale up to large problems.


  With JWST, new opportunities to study the evolution of galaxies in the early
Universe are emerging. Spitzer constraints on rest-optical properties of
$z\gtrsim7$ galaxies demonstrated the power of using galaxy stellar masses and
star formation histories (SFHs) to indirectly infer the cosmic star formation
history. However, only the brightest individual $z\gtrsim8$ objects could be
detected with Spitzer, making it difficult to robustly constrain activity at
$z\gtrsim10$. Here, we leverage the greatly improved rest-optical sensitivity
of JWST at $z\gtrsim8$ to constrain the ages of seven UV-bright
($M_{UV}\lesssim-19.5$) galaxies selected to lie at $z\sim8.5-11$, then
investigate implications for $z\gtrsim15$ star formation. We infer the
properties of individual objects with two spectral energy distribution
modelling codes, then infer a distribution of ages for bright $z\sim8.5-11$
galaxies. We find a median age of $\sim20$ Myr, younger than that inferred at
$z\sim7$ with a similar analysis, consistent with an evolution towards larger
specific star formation rates at early times. The age distribution suggests
that only $\sim3$ percent of bright $z\sim8.5-11$ galaxies would be similarly
luminous at $z\gtrsim15$, implying that the number density of bright galaxies
declines by at least an order of magnitude between $z\sim8.5-11$ and $z\sim15$.
This evolution is challenging to reconcile with some early JWST results
suggesting the abundance of bright galaxies does not significantly decrease
towards very early times, but we suggest this tension may be eased if young
stellar populations form on top of older stellar components, or if bright
$z\sim15$ galaxies are observed during a burst of star formation.


  We study the problem of solving linear program in the streaming model. Given
a constraint matrix $A\in \mathbb{R}^{m\times n}$ and vectors $b\in
\mathbb{R}^m, c\in \mathbb{R}^n$, we develop a space-efficient interior point
method that optimizes solely on the dual program. To this end, we obtain
efficient algorithms for various different problems:
  * For general linear programs, we can solve them in $\widetilde O(\sqrt
n\log(1/\epsilon))$ passes and $\widetilde O(n^2)$ space for an
$\epsilon$-approximate solution. To the best of our knowledge, this is the most
efficient LP solver in streaming with no polynomial dependence on $m$ for both
space and passes.
  * For bipartite graphs, we can solve the minimum vertex cover and maximum
weight matching problem in $\widetilde O(\sqrt{m})$ passes and $\widetilde
O(n)$ space.
  In addition to our space-efficient IPM, we also give algorithms for solving
SDD systems and isolation lemma in $\widetilde O(n)$ spaces, which are the
cornerstones for our graph results.


  The ability to identify reliably a positive or negative partial correlation
between the expression levels of two genes is influenced by the number $p$ of
genes, the number $n$ of analyzed samples, and the statistical properties of
the measurements. Classical statistical theory teaches that the product of the
root sample size multiplied by the size of the partial correlation is the
crucial quantity. But this has to be combined with some adjustment for
multiplicity depending on $p$, which makes the classical analysis somewhat
arbitrary. We investigate this problem through the lens of the Kullback-Leibler
divergence, which is a measure of the average information for detecting an
effect. We conclude that commonly sized studies in genetical epidemiology are
not able to reliably detect moderately strong links.


  Several experimental techniques are used for phase identification and
microstructure characterization of austempered vermicular cast irons (XRD, SEM,
TEM and Mossbauer spectroscopy). Acicular structures were found to be composed
by ferrite and austenite with average sizes compatible with those reported for
bainitic ferrite in steels and Austempered Ductile Iron. An assessment of the
free energy change involved in the austenite to bainite transformation assuming
a plate-like nucleation shape for bainite gave an average characteristic length
close to the observed from statistical length distributions. The kinetics of
the transformation was modelled in the Avrami framework; both the diffusion
controlled and the diffusionless growth hypothesis were considered in order to
elucidate the mechanism underlying the austempering phase transformation.
Results indicated that diffusion of C is the responsible of the nucleation
process of the bainite sheaves, that appear as a consequence of a localized
displacive transformation when the C concentration is adequate, but further
growth of the bainite plates is almost suppressed.


  This paper addresses the sensitivity analysis for hybrid systems with
discontinuous (jumping) state trajectories. We consider state-triggered jumps
in the state evolution, potentially accompanied by mode switching in the
control vector field as well. For a given trajectory with state jumps, we show
how to construct an approximation of a nearby perturbed trajectory
corresponding to a small variation of the initial condition and input. A major
complication in the construction of such an approximation is that, in general,
the jump times corresponding to a nearby perturbed trajectory are not equal to
those of the nominal one. The main contribution of this work is the development
of a notion of error to clarify in which sense the approximate trajectory is,
at each instant of time, a firstorder approximation of the perturbed
trajectory. This notion of error naturally finds application in the (local)
tracking problem of a time-varying reference trajectory of a hybrid system. To
illustrate the possible use of this new error definition in the context of
trajectory tracking, we outline how the standard linear trajectory tracking
control for nonlinear systems -based on linear quadratic regulator (LQR) theory
to compute the optimal feedback gain- could be generalized for hybrid systems.


  While manufacturers have been generating highly distributed data from various
systems, devices and applications, a number of challenges in both data
management and data analysis require new approaches to support the big data
era. These challenges for industrial big data analytics is real-time analysis
and decision-making from massive heterogeneous data sources in manufacturing
space. This survey presents new concepts, methodologies, and applications
scenarios of industrial big data analytics, which can provide dramatic
improvements in velocity and veracity problem solving. We focus on five
important methodologies of industrial big data analytics: 1) Highly distributed
industrial data ingestion: access and integrate to highly distributed data
sources from various systems, devices and applications; 2) Industrial big data
repository: cope with sampling biases and heterogeneity, and store different
data formats and structures; 3) Large-scale industrial data management:
organizes massive heterogeneous data and share large-scale data; 4) Industrial
data analytics: track data provenance, from data generation through data
preparation; 5) Industrial data governance: ensures data trust, integrity and
security. For each phase, we introduce to current research in industries and
academia, and discusses challenges and potential solutions. We also examine the
typical applications of industrial big data, including smart factory
visibility, machine fleet, energy management, proactive maintenance, and just
in time supply chain. These discussions aim to understand the value of
industrial big data. Lastly, this survey is concluded with a discussion of open
problems and future directions.


  Stasheff showed that if a map between H-spaces is an H-map, then the
suspension of the map is extendable to a map between cprojective planes of the
H-spaces. Stahseff also proved the converse under the assumption that the
multiplication of the target space of the map is homotopy associative. We show
by giving an example that the assumption of homotopy associativity of the
multiplication of the target space is necessary to show the converse. We also
show an analogous fact for maps between higher homotopy associative H-spaces.


  The advent of the era of machines has limited human interaction and this has
increased their presence in the last decade. The requirement to increase the
effectiveness, durability and reliability in the robots has also risen quite
drastically too. Present paper covers the various embedded system and computer
vision methodologies, techniques and innovations used in the field of spray
painting robots. There have been many advancements in the sphere of painting
robots utilized for high rise buildings, wall painting, road marking paintings,
etc. Review focuses on image processing, computational and computer vision
techniques that can be applied in the product to increase efficiency of the
performance drastically. Image analysis, filtering, enhancement, object
detection, edge detection methods, path and localization methods and fine
tuning of parameters are being discussed in depth to use while developing such
products. Dynamic system design is being deliberated by using which results in
reduction of human interaction, environment sustainability and better quality
of work in detail. Embedded systems involving the micro-controllers,
processors, communicating devices, sensors and actuators, soft-ware to use
them; is being explained for end-to-end development and enhancement of accuracy
and precision in Spray Painting Robots.


  An algebraic geometry method is used to calculate the moments of the electron
density of states as a function of the energy for lattices in the tight binding
approximation. Interpreting the moments as the Mellin transform of the density
allows writing down a formula for the density as an inverse Mellin transform.
The method is illustrated by working out the density function for the
two-dimensional square and honeycomb lattices.


  The correlations between three arbitrarily far-apart regions of the vacuum
state of the free Klein-Gordon field are investigated by means of its finite
duration coupling to three localized detectors. It is shown that these
correlations cannot be reproduced in terms of a hybrid local-nonlocal
hidden-variable model, i.e., the correlations between three arbitrarily
separated regions of the vacuum are fully nonlocal.


  In this paper exact confidence intervals for the Orey index of Gaussian
processes are obtained using concentration inequalities for Gaussian quadratic
forms and discrete observations of the underlying process. The obtained result
is applied to Gaussian processes with the Orey index which not necessarily have
stationary increments.