  Temperature fluctuations in an atmospheric convective boundary layer are
investigated by means of Large Eddy Simulations (LES). A statistical
characterization for both weak temperature fluctuations and strong temperature
fluctuations has been found. Despite the nontriviality of the dynamics of
temperature fluctuations, our data support the idea that the most relevant
statistical properties can be captured solely in terms of two scaling
exponents, characterizing the entire mixed layer. Such exponents control
asymptotic (i.e. core and tails) rescaling properties of the probability
density functions of equal-time temperature differences, $\Delta_r \theta$,
between points separated by a distance ${\bm r}$. A link between statistical
properties of large temperature fluctuations and geometrical properties of the
set hosting such fluctuations is also provided. Finally, a possible application
of our new findings to the problem of subgrid-scale parameterizations for the
temperature field in a convective boundary layer is discussed.


  In this talk, I review the various measurements that have been made over the
years of CP-violating observables in $B$ decays. These include indirect CP
asymmetries, direct CP asymmetries, and triple products. All are discussed in
the context of the search for new physics (NP). The only hints of NP have
appeared in (i) $B \to \pi K$ decays and (ii) 3-body $B \to K\pi\pi$ and $B \to
K {\bar K}K$ decays, where the extracted (loop-level) value of $\gamma$ may
differ from the value found using (tree-level) $B^+ \to D^{(*)} K^{(*)+}$
decays. CP-violating observables may also be used to distinguish among the
models proposed to explain the current anomalies observed in $b \to s\mu^+
\mu^-$ and $b \to c \tau {\bar\nu}_\tau$ decays.


  We consider two-dimensional quantum gravity coupled to matter in the temporal
gauge, using the Polyakov path integral. We show that the integration over the
metric can be explicitly performed under some plausible assumptions. We also
discuss that the critical dimensions in string theory may not be determined in
the temporal gauge.


  Tokens or patches within Vision Transformers (ViT) lack essential semantic
information, unlike their counterparts in natural language processing (NLP).
Typically, ViT tokens are associated with rectangular image patches that lack
specific semantic context, making interpretation difficult and failing to
effectively encapsulate information. We introduce a novel transformer model,
Semantic Vision Transformers (sViT), which leverages recent progress on
segmentation models to design novel tokenizer strategies. sViT effectively
harnesses semantic information, creating an inductive bias reminiscent of
convolutional neural networks while capturing global dependencies and
contextual information within images that are characteristic of transformers.
Through validation using real datasets, sViT demonstrates superiority over ViT,
requiring less training data while maintaining similar or superior performance.
Furthermore, sViT demonstrates significant superiority in out-of-distribution
generalization and robustness to natural distribution shifts, attributed to its
scale invariance semantic characteristic. Notably, the use of semantic tokens
significantly enhances the model's interpretability. Lastly, the proposed
paradigm facilitates the introduction of new and powerful augmentation
techniques at the token (or segment) level, increasing training data diversity
and generalization capabilities. Just as sentences are made of words, images
are formed by semantic objects; our proposed methodology leverages recent
progress in object segmentation and takes an important and natural step toward
interpretable and robust vision transformers.


  We introduce a new class of sine-Gordon models, for which interaction term is
present in a region different from the domain over which quadratic part is
defined. We develop a novel non-perturbative approach for calculating partition
functions of such models, which relies on mapping them to statistical
properties of random surfaces. As a specific application of our method, we
consider the problem of calculating the amplitude of interference fringes in
experiments with two independent low dimensional Bose gases. We calculate full
distribution functions of interference amplitude for 1D and 2D gases with
nonzero temperatures.


  Filters approximately store a set of items while trading off accuracy for
space-efficiency and can address the limited memory on accelerators, such as
GPUs. However, there is a lack of high-performance and feature-rich GPU filters
as most advancements in filter research has focused on CPUs.
  In this paper, we explore the design space of filters with a goal to develop
massively parallel, high performance, and feature rich filters for GPUs. We
evaluate various filter designs in terms of performance, usability, and
supported features and identify two filter designs that offer the right trade
off in terms of performance, features, and usability.
  We present two new GPU-based filters, the TCF and GQF, that can be employed
in various high performance data analytics applications. The TCF is a set
membership filter and supports faster inserts and queries, whereas the GQF
supports counting which comes at an additional performance cost. Both the GQF
and TCF provide point and bulk insertion API and are designed to exploit the
massive parallelism in the GPU without sacrificing usability and necessary
features. The TCF and GQF are up to $4.4\times$ and $1.4\times$ faster than the
previous GPU filters in our benchmarks and at the same time overcome the
fundamental constraints in performance and usability in current GPU filters.


  A photonic wireless bridge operating at a carrier frequency of 250 GHz is
proposed and demonstrated. To mitigate the phase noise of the free-running
lasers present in such a link, the tone-assisted carrier recovery is used.
Compared to the blind phase noise compensation (PNC) algorithm, this technique
exhibited penalties of 0.15 dB and 0.46 dB when used with aggregated Lorentzian
linewidths of 28 kHz and 359 kHz, respectively, and 20 GBd 16-quadrature
amplitude modulation (QAM) signals. The wireless bridge is also demonstrated in
a wavelength division multiplexing (WDM) scenario, where 5 optical channels are
generated and sent to the Tx remote antenna unit (RAU). In this configuration,
the full band from 224 GHz to 294 GHz is used. Finally, a 50 Gbit/s
transmission is achieved with the proposed wireless bridge in single channel
configuration. The wireless transmission distance is limited to 10 cm due to
the low power emitted by the uni-travelling carrier photodiode used in the
experiments. However, link budget calculations based on state-of-the-art THz
technology show that distances >1000 m can be achieved with this approach.


  While artificial intelligence (AI) has made many successful applications in
various domains, its adoption in healthcare lags a little bit behind other
high-stakes settings. Several factors contribute to this slower uptake,
including regulatory frameworks, patient privacy concerns, and data
heterogeneity. However, one significant challenge that impedes the
implementation of AI in healthcare, particularly in orthopedics, is the lack of
explainability and interpretability around AI models. Addressing the challenge
of explainable AI (XAI) in orthopedics requires developing AI models and
algorithms that prioritize transparency and interpretability, allowing
clinicians, surgeons, and patients to understand the contributing factors
behind any AI-powered predictive or descriptive models. The current
contribution outlines several key challenges and opportunities that manifest in
XAI in orthopedic practice. This work emphasizes the need for interdisciplinary
collaborations between AI practitioners, orthopedic specialists, and regulatory
entities to establish standards and guidelines for the adoption of XAI in
orthopedics.


  We discuss the phase structure and thermodynamics of QCD by means of
dynamical chiral effective models. Quark and meson fluctuations are included
via the functional renormalization group. We study the influence of confinement
in addition to the impact of fluctuations by comparing the results of the
chiral models to their Polyakov-loop extended versions. Furthermore, we discuss
the mass sensitivity of the phase structure and thermodynamics and find
interesting modifications close to the chiral limit.


  Free energy landscapes decisively determine the progress of enzymatically
catalyzed reactions[1]. Time-resolved macromolecular crystallography unifies
transient-state kinetics with structure determination [2-4] because both can be
determined from the same set of X-ray data. We demonstrate here how barriers of
activation can be determined solely from five-dimensional crystallography [5].
Directly linking molecular structures with barriers of activation between them
allows for gaining insight into the structural nature of the barrier. We
analyze comprehensive time series of crystal-lographic data at 14 different
temperature settings and determine entropy and enthalpy contributions to the
barriers of activation. 100 years after the discovery of X-ray scattering, we
advance X-ray structure determination to a new frontier, the determination of
energy landscapes.


  We re-examine soft dilepton emission from a weakly coupled Quark-Gluon
Plasma. We show that Braaten, Pisarski, and Yuan's result that the dilepton
rate rises as E^-4 (and the spectral weight scales as 1/E) at small energy
E<<gT is correct, but that the coefficient they found for this behavior is not
correct, because their analysis was incomplete. At still smaller scales, the
behavior moderates to ~1/E^2 for E <= g^4 T, consistent with a finite
electrical conductivity. We evaluate the spectral weight in the E~g^4 T region
by kinetic theory techniques and show that it satisfies a sum rule, which makes
the determination of electrical conductivity from the Euclidean correlation
function very challenging.


  We investigate topological supersolidity of dipolar Fermi gases in a
spin-dependent 2D optical lattice. Numerical results show that the topological
supersolid states can be synthesized via the combination of topological
superfluid states with the stripe order, where the topological superfluid
states generated with dipolar interaction possess the $\Delta_{x}+i\Delta_{y}$
order, and it is of D class topological classification. By adjusting the ratio
between hopping amplitude $t_{x}/t_{y}$ and interaction strength $U$ with
dipole orientation $\phi \approx \frac{\pi}{4}$, the system will undergo phase
transitions among the $p_{x}+ip_{y}$-wave topological superfluid state, the
p-wave superfluid state, and the topological supersolid state. The topological
supersolid state is proved to be stable by the positive sign of the inverse
compressibility. We design an experimental protocol to realize the staggered
next-next-nearest-neighbour hopping via the laser assisted tunneling technique,
which is the key to synthesize topological supersolid states.


  Primordial magnetic fields seem to be a generic relic of phase transitions in
the early universe. We consider a primordial electromagnetic field formed as a
result of a second-order phase transition, and show that it is stable to
thermal fluctuations in the period immediately following. We also show how such
a field arises in first order phase transitions. In both cases there is a
transitive electric field produced during the transition.


  We report the doping evolution of magnetic susceptibility $\chi (T)$ and Hall
coefficient $R_H$ in high quality Ba$_{1-x}$K$_x$Fe$_2$As$_2$ ($0.13 \leq x
\leq 1$) single crystals. It is found that the normal-state magnetic
susceptibility of Ba$_{1-x}$K$_x$Fe$_2$As$_2$ compounds undergoes a crossover
from linear-$T$ dependence in the undoped and underdoped samples into
KFe$_2$As$_2$-type magnetic response in the overdoped samples with increasing K
content. Hall coefficient $R_H$ of underdoped sample $x$=0.22 shows a rapid
increase above spin-density-wave transition temperature $T_{SDW}$. Below
$T_{SDW}$, it increases slowly. $R_H$ of optimally doped and slightly overdoped
samples ($0.34 \leq x \leq 0.65$) shows relative weak temperature dependence
and saturation tendency below 150 K. However, $R_H$ of K heavily overdoped
samples ($0.80 \leq x \leq 1$) increases rapidly below 150 K. Meanwhile, Hall
angle $cot \theta _H$ displays a concave temperature dependence within the
doping range $0.22 \leq x \leq 0.55$, whereas it changes to a convex
temperature dependence within the doping range $0.65 \leq x \leq 1$. The
dramatic change coincides with Lifshitz transition occurred around the critical
doping $x$=0.80, where ARPES measurements had confirmed that electron pocket
disappears with excess hole doping in Ba$_{1-x}$K$_x$Fe$_2$As$_2$ system. It is
suggested that the characteristic temperature $T$* at around $120 \sim 150$ K
observed in susceptibility and Hall coefficient, as well as previously reported
resistivity data, may indicate an incoherence-coherence crossover in
Ba$_{1-x}$K$_x$Fe$_2$As$_2$ system.


  This paper deals with robust marginal estimation under a general regression
model when missing data occur in the response and also in some of covariates.
The target is a marginal location parameter which is given through an
$M-$functional. To obtain robust Fisher--consistent estimators, properly
defined marginal distribution function estimators are considered. These
estimators avoid the bias due to missing values by assuming a missing at random
condition. Three methods are considered to estimate the marginal distribution
function which allows to obtain the $M-$location of interest: the well-known
inverse probability weighting, a convolution--based method that makes use of
the regression model and an augmented inverse probability weighting procedure
that prevents against misspecification. The robust proposed estimators and the
classical ones are compared through a numerical study under different missing
models including clean and contaminated samples. We illustrate the estimators
behaviour under a nonlinear model. A real data set is also analysed.


  We study the ground state phases of the $S=1/2$ Heisenberg quantum
antiferromagnet on the spatially anisotropic triangular lattice and on the
square lattice with up to next-next-nearest neighbor coupling (the $J_1J_2J_3$
model), making use of Takahashi's modified spin-wave (MSW) theory supplemented
by ordering vector optimization. We compare the MSW results with exact
diagonalization and projected-entangled-pair-states calculations, demonstrating
their qualitative and quantitative reliability. We find that MSW theory
correctly accounts for strong quantum effects on the ordering vector of the
magnetic phases of the models under investigation: in particular collinear
magnetic order is promoted at the expenses of non-collinear (spiral) order, and
several spiral states which are stable at the classical level, disappear from
the quantum phase diagram. Moreover, collinear states and non-collinear ones
are never connected continuously, but they are separated by parameter regions
in which MSW breaks down, signaling the possible appearance of a non-magnetic
ground state. In the case of the spatially anisotropic triangular lattice, a
large breakdown region appears also for weak couplings between the chains
composing the lattice, suggesting the possible occurrence of a large
non-magnetic region continuously connected with the spin-liquid state of the
uncoupled chains.


  In this paper, we investigate a category of constrained fractional
optimization problems that emerge in various practical applications. The
objective function for this category is characterized by the ratio of a
numerator and denominator, both being convex, semi-algebraic, Lipschitz
continuous, and differentiable with Lipschitz continuous gradients over the
constraint sets. The constrained sets associated with these problems are
closed, convex, and semi-algebraic. We propose an efficient algorithm that is
inspired by the proximal gradient method, and we provide a thorough convergence
analysis. Our algorithm offers several benefits compared to existing methods.
It requires only a single proximal gradient operation per iteration, thus
avoiding the complicated inner-loop concave maximization usually required.
Additionally, our method converges to a critical point without the typical need
for a nonnegative numerator, and this critical point becomes a globally optimal
solution with an appropriate condition. Our approach is adaptable to unbounded
constraint sets as well. Therefore, our approach is viable for many more
practical models. Numerical experiments show that our method not only reliably
reaches ground-truth solutions in some model problems but also outperforms
several existing methods in maximizing the Sharpe ratio with real-world
financial data.


  This article provides a novel continuous-time state feedback control strategy
to stabilize an eigenstate of the Hermitian measurement operator of a two-level
quantum system. In open loop, such system converges stochastically to one of
the eigenstates of the measurement operator. Previous work has proposed state
feedback that destabilizes the undesired eigenstates and relies on a
probabilistic analysis to prove convergence. In contrast, we here associate the
state observer to an adaptive version of so-called Markovian feedback
(essentially, proportional control) and we show that this leads to a global
exponential convergence property with a strict Lyapunov function. Furthermore,
besides the instantaneous measurement output, our controller only depends on
the single coordinate along the measurement axis, which opens the way to
replacing the full state observer by lower-complexity filters in the future.


  The transition \gamma_{v}pN^* amplitudes (electrocouplings) for prominent
excited nucleon states obtained in a wide area of photon virtualities offer
valuable information for the exploration of the N^* structure at different
distances and allow us to access the complex dynamics of non-perturbative
strong interaction. The current status in the studies of \gamma_{v}pN^*
electrocouplings from the data on exclusive meson electroproduction off protons
measured with the CLAS detector at Jefferson Lab is presented. The impact of
these results on exploration of the N^* structure is discussed.


  In this paper we discuss some examples and general properties of holonomy
groups of $\nabla^\varphi$ introduced by Wylie and the author, the connection
corresponding to the $N=1$ Bakry-\'Emery Ricci curvature, and also Wylie's
$\bar{\mathrm{sec}}_f$. In particular we classify all possible holonomy groups
in dimension 2 and also provide two infinite families: $SL_n(\mathbb{R})$ and
$SO^+(p,q)$.


  An analysis with transferred hyperfine field has been performed on the
spectra of the $^{115}$In nuclear quadrupole resonance experiments of heavy
fermion superconductor Ce$_{3}$PtIn$_{11}$ exhibiting co-occurrence of two
successive antiferromagnetic orderings ($T_{\rm N1}$ = 2.2 K and $T_{\rm N2}$ =
2.0 K) followed by superconducting transition ($T_{\rm c}$ = 0.32 K). The
spectral changes at magnetic transition temperatures $T_{\rm N1}$ and $T_{\rm
N2}$ indicate that the Ce(2) site has the dominant magnetic contribution, with
a small magnetic moment, but not negligible, at the Ce(1) site. Our analysis
using transferred hyperfine field which overcomes the previous simple dipolar
model evinces that for $T_{\rm N2} < T < T_{\rm N1 } $, the propagation vectors
at the Ce(1) and Ce(2) sublattices are q1 = q2 = (1/2, 1/2, 0 or 1/2), whereas
for $T < T_{\rm N2 }$, the propagation vectors are q1 = (1/2,~1/2, 1/6 or 1/3)
and q2 = (1/2, 1/2, 1/2 or 0), respectively.


  Within the Langevin framework, we investigate the dynamics of the fusion
process for production of transfermium elements in reactions of $^{48}$Ca,
$^{50}$Ti, $^{54}$Cr, and $^{58}$Fe with $^{208}$Pb. After the reacting nuclei
have made contact, the early dynamical stage is dominated by the dissipation of
the initial radial kinetic energy, while the subsequent shape evolution is
diffusive. The probability for surmounting the inner barrier and forming a
compound system is obtained by simulating the evolution as a Metropolis random
walk in a five-dimensional potential-energy landscape. Good agreement with the
available data is obtained, especially for the maximal formation probability.


  The quark star in the quark mean field (QMF) extended to include the strange
mesons interaction is examined. The quark star properties in this model are
studied.


  Indications for the production of a neutral excited hyperon in the reaction
pp -> p K^+ Y^{0*} are observed in an experiment performed with the ANKE
spectrometer at COSY-J\"ulich at a beam momentum of 3.65 GeV/c. Two final
states were investigated simultaneously, viz. Y^{0*} -> pi^+X^- and pi^-X^+,
and consistent results were obtained in spite of the quite different
experimental conditions. The parameters of the hyperon state are M(Y^{0*})=
(1480 +/- 15) MeV/c^2 and Gamma(Y^{0*})= (60 +/- 15) MeV/c^2. The production
cross section is of the order of few hundred nanobarns. Since the isospin of
the Y^{0*} has not been determined here, it could either be an observation of
the Sigma(1480), a one-star resonance of the PDG tables, or alternatively a
Lambda hyperon. Relativistic quark models for the baryon spectrum do not
predict any excited hyperon in this mass range and so the Y^{0*} may be of
exotic nature.


  The $\mathrm{^{16}O}(\alpha, \alpha^{\prime})$ reaction was studied at
$\theta_{lab} = 0^\circ$ at an incident energy of $\textrm{E}_{lab}$ = 200 MeV
using the K600 magnetic spectrometer at iThemba LABS. Proton and $\alpha$-decay
from the natural parity states were observed in a large-acceptance
silicon-strip detector array at backward angles. The coincident charged
particle measurements were used to characterize the decay channels of the
$0_{6}^{+}$ state in $\mathrm{^{16}O}$ located at $E_{x} = 15.097(5)$ MeV. This
state is identified by several theoretical cluster calculations to be a good
candidate for the 4-$\alpha$ cluster state. The results of this work suggest
the presence of a previously unidentified resonance at $E_{x}\approx15$ MeV
that does not exhibit a $0^{+}$ character. This unresolved resonance may have
contaminated previous observations of the $0_{6}^{+}$ state.


  This dissertation is focused on the role of objectivity in peer review.
Through an examination of aspects of peer review including anonymity, trust,
expertise, and the question of who has standing to evaluate research, we find
that objectivity in peer review differs significantly from other uses of the
term objectivity in science. In peer review it is not required for this
objectivity to have correspondence to an outside world, instead it is enough
for it to operate inside the "rules" of the community. Neither is the
objectivity here empirical in the sense of using data about the scientific
problem in question. Rather, the objectivity is one of judgment, cleaving to
the epistemological standards of a community that are formed by background
assumptions and beliefs. As a consequence, we highlight the role of
subjectivity in what is usually taken as a practice of objectivity, and arrive
at the insight that objectivity is not defined by one core value, but a balance
of transparency, confidentiality, trust, representation, and living up to
community standards. As such, objectivity in peer review is a highly specific
sense of the term that is not reducible to that used in other aspects of
scientific practice.


  We review the computation of scattering amplitudes of planar maximally
super-symmetric Yang-Mills at strong coupling. By using the AdS/CFT duality the
problem boils down to the computation of the area of certain minimal surfaces
on AdS. The integrability of the model can then be efficiently used in order to
give an answer for the problem in terms of a set of integral equations.


  General Relativity is a hugely successful description of gravitation.
However, both theory and observations suggest that General Relativity might
have significant classical and quantum corrections in the Strong Gravity
regime. Testing the strong field limit of gravity is one of the main objectives
of the future gravitational wave detectors. One way to detect strong gravity is
through the polarization of gravitational waves. For quasi-normal modes of
black-holes in General Relativity, the two polarisation states of gravitational
waves have the same amplitude and frequency spectrum. Using the principle of
energy conservation, we show that, the polarisations differ for modified
gravity theories. We obtain a diagnostic parameter for polarization mismatch
that provides a unique way to distinguish General Relativity and modified
gravity theories in gravitational wave detectors.


  Using recently developed techniques based on scattering theory, we find the
electromagnetic Casimir energy for geometries involving semi-infinite planes, a
case that is of particular interest in the design of microelectromechanical
devices. We obtain both approximate analytic formulae and exact results
requiring only modest numerical computation. Using these results, we analyze
the effects of edges and orientation on the Casimir energy. We also demonstrate
the accuracy, simplicity, and utility of our approximation scheme, which is
based on a multiple reflection expansion.


  We consider the simplest example of a time-dependent first order
Hamilton-Jacobi equation, in one space dimension and with a bounded and
Lipschitz continuous Hamiltonian which only depends on the spatial derivative.
We show that if the initial function has a finite number of jump
discontinuities, the corresponding discontinuous viscosity solution of the
corresponding Cauchy problem on the real line is unique. Uniqueness follows
from a comparison theorem for semicontinuous viscosity sub- and supersolutions,
using the barrier effect of spatial discontinuities of a solution. We also
prove an existence theorem, as well as a comparison theorem for viscosity
solutions with different initial data. In addition, we describe several
properties of the evolution of the jump discontinuities.


  Compact K\"{a}hler manifolds satisfy several nice Hodge-theoretic properties
such as the Hodge symmetry, the Hard Lefschetz property and the Hodge-Riemann
bilinear relations, etc. In this note, we investigate when such nice properties
hold on compact complex manifolds with semistable degenerations. For compact
complex manifolds which can be obtained as smoothings of SNC varieties without
triple intersection locus, we show the Hodge symmetry when the monodromy
logarithm induces isomorphisms on the associated graded. We also show the
Hodge-Riemann relations on H^3 of compact complex 3-folds with such semistable
degenerations under some conditions.


  We propose an electro-hydrodynamics model to describe the dynamic evolution
of a slender drop containing a dilute ionic surfactant on a naturally wettable
surface, with a varying external electric field. This unified model reproduces
fundamental microfluidic operations controlled by electrical signals, including
dewetting, rewetting, and droplet shifting. In this paper, lubrication theory
analysis and numerical simulations illustrate how to electrically control the
wettability of surface via the charged surfactant. Our numerical results show
that electric field promotes dewetting by attracting ionic surfactants onto the
transition thin-film region and promotes rewetting by attracting them away from
the region.


  We have calculated the low-field magnetic susceptibility $\chi$ of a system
consisting of non-interacting mono-dispersed nanoparticles using a classical
statistical approach. The model makes use of the assumption that the axes of
symmetry of all nanoparticles are aligned and oriented at a certain angle
$\psi$ with respect to the external magnetic field. An analytical expression
for the temperature dependence of the susceptibility $\chi(T)$ above the
blocking temperature is obtained. The derived expression is a generalization of
the Curie law for the case of anisotropic magnetic particles. We show that the
normalized susceptibility is a universal function of the ratio of the
temperature over the anisotropy constant for each angle $\psi$. In the case
that the easy-axis is perpendicular to the magnetic field the susceptibility
has a maximum. The temperature of the maximum allows one to determine the
anisotropy energy.


  The ability to distinguish between stochastic systems based on their
trajectories is crucial in thermodynamics, chemistry, and biophysics. The
Kullback-Leibler (KL) divergence, $D_{\text{KL}}^{AB}(0,\tau)$, quantifies the
distinguishability between the two ensembles of length-$\tau$ trajectories from
Markov processes A and B. However, evaluating $D_{\text{KL}}^{AB}(0,\tau)$ from
histograms of trajectories faces sufficient sampling difficulties, and no
theory explicitly reveals what dynamical features contribute to the
distinguishability. This letter provides a general formula that decomposes
$D_{\text{KL}}^{AB}(0,\tau)$ in space and time for any Markov processes,
arbitrarily far from equilibrium or steady state. It circumvents the sampling
difficulty of evaluating $D_{\text{KL}}^{AB}(0,\tau)$. Furthermore, it
explicitly connects trajectory KL divergence with individual transition events
and their waiting time statistics. The results provide insights into
understanding distinguishability between Markov processes, leading to new
theoretical frameworks for designing biological sensors and optimizing signal
transduction.


  The new data release (DR5) of the RAdial Velocity Experiment (RAVE) includes
radial velocities of 520,781 spectra of 457,588 individual stars, of which
215,590 individual stars are released in the Tycho-Gaia astrometric solution
(TGAS) in Gaia DR1. Therefore, RAVE contains the largest TGAS overlap of the
recent and ongoing Milky Way spectroscopic surveys. Most of the RAVE stars also
contain stellar parameters (effective temperature, surface gravity, overall
metallicity), as well as individual abundances for Mg, Al, Si, Ca, Ti, Fe, and
Ni. Combining RAVE with TGAS brings the uncertainties in space velocities down
by a factor of 2 for stars in the RAVE volume -- 10 km/s uncertainties in space
velocities are now able to be derived for the majority (70%) of the RAVE-TGAS
sample, providing a powerful platform for chemo-dynamic analyses of the Milky
Way. Here we discuss the RAVE-TGAS impact on Galactic archaeology as well as
how the Gaia parallaxes can be used to break degeneracies within the RAVE
spectral regime for an even better return in the derivation of stellar
parameters and abundances.


  Supply chain risk assessment (SCRA) has witnessed a profound evolution
through the integration of artificial intelligence (AI) and machine learning
(ML) techniques, revolutionizing predictive capabilities and risk mitigation
strategies. The significance of this evolution stems from the critical role of
robust risk management strategies in ensuring operational resilience and
continuity within modern supply chains. Previous reviews have outlined
established methodologies but have overlooked emerging AI/ML techniques,
leaving a notable research gap in understanding their practical implications
within SCRA. This paper conducts a systematic literature review combined with a
comprehensive bibliometric analysis. We meticulously examined 1,717 papers and
derived key insights from a select group of 48 articles published between 2014
and 2023. The review fills this research gap by addressing pivotal research
questions, and exploring existing AI/ML techniques, methodologies, findings,
and future trajectories, thereby providing a more encompassing view of the
evolving landscape of SCRA. Our study unveils the transformative impact of
AI/ML models, such as Random Forest, XGBoost, and hybrids, in substantially
enhancing precision within SCRA. It underscores adaptable post-COVID
strategies, advocating for resilient contingency plans and aligning with
evolving risk landscapes. Significantly, this review surpasses previous
examinations by accentuating emerging AI/ML techniques and their practical
implications within SCRA. Furthermore, it highlights the contributions through
a comprehensive bibliometric analysis, revealing publication trends,
influential authors, and highly cited articles.


  The proton diffractive structure function $F_2^{D(3)}$ measured in the H1 and
ZEUS experiments at HERA are analysed in terms of perturbative QCD in the
perspective of the QCD extrapolation to the Tevatron and the LHC. It is shown
that both data sets can be well described by a QCD analysis in which point-like
parton distributions, evolving according to the next-leading DGLAP equations,
are assigned to the leading and sub-leading Regge exchanges. For present data
from H1 and ZEUS the gluon distributions are found to be quite different and we
give the corresponding sets of quark and gluon parton distributions for the
Pomeron, extracted from the two experiments. An extrapolation to the Tevatron
range is compared with CDF data on single diffraction. Conclusions on
factorization breaking between HERA and Tevatron critically depend on whether
H1 (strong violation) or ZEUS (compatibility at low $\beta$) fits are taken
into account. Using the double Pomeron formulation in central diffractive dijet
production we show that the Tevatron mass fraction is much sensitive to the
high $\beta$ tail of the gluon in the Pomeron, suggesting a new way of handling
the otherwise badly known gluon distribution in the Pomeron. Extrapolation of
the fits to very high $Q^2$ are given since they will be relevant for QCD and
diffraction studies at the LHC.


  We show that data collected from corpuses of documents violate the
Clauser-Horne-Shimony-Holt version of Bell's inequality (CHSH inequality) and
therefore indicate the presence of quantum entanglement in their structure. We
obtain this result by considering two concepts and their combination and
coincidence operations consisting of searches of co-occurrences of exemplars of
these concepts in specific corpuses of documents. Measuring the frequencies of
these co-occurrences and calculating the relative frequencies as approximate
probabilities entering in the CHSH inequality, we obtain manifest violations of
the latter for all considered corpuses of documents. In comparing these
violations with those analogously obtained in an earlier work for the same
combined concepts in psychological coincidence experiments with human
participants, also violating the CHSH inequality, we identify the entanglement
as being carried by the meaning connection between the two considered concepts
within the combination they form. We explain the stronger violation for the
corpuses of documents, as compared to the violation in the psychology
experiments, as being due to the superior meaning domain of the human mind and,
on the other side, to the latter reaching a broader domain of meaning and being
possibly also actively influenced during the experimentation. We mention some
of the issues to be analyzed in future work such as the violations of the CHSH
inequality being larger than the `Cirel'son bound' for all of the considered
corpuses of documents.


  We determine the degeneracy factor and the average particle mass of particles
that produce the Lattice QCD pressure and specific entropy at zero baryon
chemical potential. The number of states of the gluons and the quarks are found
to converge above $T=$230 MeV to almost constant values, close to the number of
states of an ideal Quark-Gluon Phase, while their assigned masses retain high
values. The number of states and the average mass of a system containing quarks
in interaction with gluons are found to decrease steeply with increase of
temperature between $T \sim 150$ and 160 MeV, a region contained within the
region of the chiral transition. The minimum value of the number of states
within this temperature interval indicates that the states are of hadronic
nature.


  Guruswami and Xing introduced subspace designs in 2013 to give the first
construction of positive rate rank metric codes list-decodable beyond half the
distance. In this paper we provide bounds involving the parameters of a
subspace design, showing they are tight via explicit constructions. We point
out a connection with sum-rank metric codes, dealing with optimal codes and
minimal codes with respect to this metric. Applications to two-intersection
sets with respect to hyperplanes, two-weight codes, cutting blocking sets and
lossless dimension expanders are also provided.


  Hypergraphs as an expressive and general structure have attracted
considerable attention from various research domains. Most existing hypergraph
node representation learning techniques are based on graph neural networks, and
thus adopt the two-stage message passing paradigm (i.e. node -> hyperedge ->
node). This paradigm only focuses on local information propagation and does not
effectively take into account global information, resulting in less optimal
representations. Our theoretical analysis of representative two-stage message
passing methods shows that, mathematically, they model different ways of local
message passing through hyperedges, and can be unified into one-stage message
passing (i.e. node -> node). However, they still only model local information.
Motivated by this theoretical analysis, we propose a novel one-stage message
passing paradigm to model both global and local information propagation for
hypergraphs. We integrate this paradigm into HGraphormer, a Transformer-based
framework for hypergraph node representation learning. HGraphormer injects the
hypergraph structure information (local information) into Transformers (global
information) by combining the attention matrix and hypergraph Laplacian.
Extensive experiments demonstrate that HGraphormer outperforms recent
hypergraph learning methods on five representative benchmark datasets on the
semi-supervised hypernode classification task, setting new state-of-the-art
performance, with accuracy improvements between 2.52% and 6.70%. Our code and
datasets are available.


  In this paper we propose a framework towards achieving two intertwined
objectives: (i) equipping reinforcement learning with active exploration and
deliberate information gathering, such that it regulates state and parameter
uncertainties resulting from modeling mismatches and noisy sensory; and (ii)
overcoming the huge computational cost of stochastic optimal control. We
approach both objectives by using reinforcement learning to attain the
stochastic optimal control law. On one hand, we avoid the curse of
dimensionality prohibiting the direct solution of the stochastic dynamic
programming equation. On the other hand, the resulting stochastic control
inspired reinforcement learning agent admits the behavior of a dual control,
namely, caution and probing, that is, regulating the state estimate together
with its estimation quality. Unlike exploration and exploitation, caution and
probing are employed automatically by the controller in real-time, even after
the learning process is concluded. We use the proposed approach on a numerical
example of a model that belongs to an emerging class in system identification.
We show how, for the dimensionality of the stochastic version of this model,
Dynamic Programming is prohibitive, Model Predictive Control requires an
expensive nonlinear optimization, and a Linear Quadratic Regulator with the
certainty equivalence assumption leads to poor performance and filter
divergence, all contrasting our approach which is shown to be both:
computationally convenient, stabilizing and of an acceptable performance.


  Being inspired by child's learning experience - taught first and followed by
observation and questioning, we investigate a critically supervised learning
methodology for object detection in this work. Specifically, we propose a
taught-observe-ask (TOA) method that consists of several novel components such
as negative object proposal, critical example mining, and machine-guided
question-answer (QA) labeling. To consider labeling time and performance
jointly, new evaluation methods are developed to compare the performance of the
TOA method, with the fully and weakly supervised learning methods. Extensive
experiments are conducted on the PASCAL VOC and the Caltech benchmark datasets.
The TOA method provides significantly improved performance of weakly
supervision yet demands only about 3-6% of labeling time of full supervision.
The effectiveness of each novel component is also analyzed.


  In past works, various schemes for adaptive synchronization of chaotic
systems have been proposed. The stability of such schemes is central to their
utilization. As an example addressing this issue, we consider a recently
proposed adaptive scheme for maintaining the synchronized state of identical
coupled chaotic systems in the presence of a priori unknown slow temporal drift
in the couplings. For this illustrative example, we develop an extension of the
master stability function technique to study synchronization stability with
adaptive coupling. Using this formulation, we examine local stability of
synchronization for typical chaotic orbits and for unstable periodic orbits
within the synchronized chaotic attractor (bubbling). Numerical experiments
illustrating the results are presented. We observe that the stable range of
synchronism can be sensitively dependent on the adaption parameters, and we
discuss the strong implication of bubbling for practically achievable adaptive
synchronization.


  We present preliminary results of an experiment performed at the 1-GeV
electron synchrotron of the Lebedev Physical Institute. Using the
bremsstrahlung photon beam with the end-point energy of 650-850 MeV and the
carbon target, correlated pi+/n pairs with opening angle 180 deg and energies
E_pi=300 MeV, E_n=100 MeV have been observed. They arise from the process gamma
+ 12C -> N + eta + (A-1) -> N + pi+ + n + (A-2) and provide evidence for the
existence of eta-11B and eta-11C eta-mesic nuclei.


  Misinterpretation in the preceding Comment of my recent analysis of the past
of a photon is corrected. There is nothing in this analysis which is "contrary
to the usual quantum expectations" but, nevertheless, it does provide "further
understanding and interpretation of the system considered". In particular, it
teaches us that the naive common sense argument frequently used in the
literature including the Comment has to be abandoned.


  We provide a unified treatment of conformally soft Goldstone modes which
arise when spin-one or spin-two conformal primary wavefunctions become pure
gauge for certain integer values of the conformal dimension $\Delta$. This
effort lands us at the crossroads of two ongoing debates about what the
appropriate conformal basis for celestial CFT is and what the asymptotic
symmetry group of Einstein gravity at null infinity should be. Finite energy
wavefunctions are captured by the principal continuous series $\Delta\in
1+i\mathbb{R}$ and form a complete basis. We show that conformal primaries with
analytically continued conformal dimension can be understood as certain contour
integrals on the principal series. This clarifies how conformally soft
Goldstone modes fit in but do not augment this basis. Conformally soft
gravitons of dimension two and zero which are related by a shadow transform are
shown to generate superrotations and non-meromorphic diffeomorphisms of the
celestial sphere which we refer to as shadow superrotations. This dovetails the
Virasoro and Diff(S$^2$) asymptotic symmetry proposals and puts on equal
footing the discussion of their associated soft charges, which correspond to
the stress tensor and its shadow in the two-dimensional celestial CFT.


  Anisotropy in the mechanical response of materials with microstructure is
common and yet is difficult to assess and model. To construct accurate response
models given only stress-strain data, we employ classical representation
theory, novel neural network layers, and L1 regularization. The proposed
tensor-basis neural network can discover both the type and orientation of the
anisotropy and provide an accurate model of the stress response. The method is
demonstrated with data from hyperelastic materials with off-axis transverse
isotropy and orthotropy, as well as materials with less well-defined symmetries
induced by fibers or spherical inclusions. Both plain feed-forward neural
networks and input-convex neural network formulations are developed and tested.
Using the latter, a polyconvex potential can be established, which, by
satisfying the growth condition can guarantee the existence of boundary value
problem solutions.


  We performed an observational program with the X-ray satellite BeppoSAX to
study objects with extreme synchrotron peak frequencies (nu_peak > 1 keV). Of
the seven sources observed, four showed peak frequencies in the range 1-5 keV,
while one (1ES 1426+428) displayed a flat power law spectrum (alpha= 0.92),
locating its synchrotron peak at or above 100 keV. This is the third source of
this type ever found, after Mkn 501 and 1ES 2344+514. Our data confirm the
large nu_peak variability of this class of sources, compared with lower peaked
objects. The high synchrotron peak energies, flagging the presence of high
energy electrons, make the extreme BL Lacs also good candidates for TeV
emission, and therefore good probes for the IR background.


  We define the Euler number of a bipartite graph on $n$ vertices to be the
number of labelings of the vertices with $1,2,...,n$ such that the vertices
alternate in being local maxima and local minima. We reformulate the problem of
computing the Euler number of certain subgraphs of the Cartesian product of a
graph $G$ with the path $P_m$ in terms of self adjoint operators. The
asymptotic expansion of the Euler number is given in terms of the eigenvalues
of the associated operator. For two classes of graphs, the comb graphs and the
Cartesian product $P_2 \Box P_m$, we numerically solve the eigenvalue problem.


  Supersymmetric CP violating phases are examined within the framework of
gravity mediated supergravity grand unified models with R parity invariance for
models with a light ($\stackrel{<}{\sim} 1$ TeV) particle spectrum. In the
minimal model, the nearness of the t quark Landau pole naturally suppresses the
t-quark cubic soft breaking parameter at the electroweak scale allowing the
electron and neutron experimental electric dipole moment (EDM) constraints to
be satisfied with a large GUT scale phase. However, the EDM constraints require
that $\theta_B$, the quadratic soft breaking parameter phase be small at the
electroweak scale unless tan$\beta\stackrel{<}{\sim}3$, which then implies that
at the GUT scale this phase must be large and highly fine tuned to satisfy
radiative breaking of $SU(2)\times U(1)$. Similar results hold for non minimal
models, and a possible GUT model is discussed where all GUT scale CP violating
phases are naturally small (i.e. O($10^{-2})$). An interesting D-brane model is
examined which enhances the size of the phases over much of the parameter space
at the electroweak sector for tan$\beta\stackrel{<}{\sim} 5$, but still
possesses the fine tuning problem at the GUT scale.


  We study when the property that a field is dense in its real and p-adic
closures is elementary in the language of rings and deduce that all models of
the theory of algebraic fields have this property.


  New high resolution, very high signal-to-noise spectra of ultra-metal-poor
(UMP) giant stars HD 115444 and HD 122563 have been gathered with the
High-Resolution Echelle Spectrometer of the McDonald Observatory 2.7m
Telescope. With these spectra, line identification and model atmosphere
analyses have been conducted, emphasizing the neutron-capture elements. Twenty
elements with Z > 30 have been identified in the spectrum of HD 115444. This
star is known to have overabundances of the neutron-capture elements, but it
has lacked a detailed analysis necessary to compare with nucleosynthesis
predictions. The new study features a line-by-line differential abundance
comparison of HD 115444 with the bright, well-studied halo giant HD 122563. For
HD 115444, the overall metallicity is [Fe/H]~ -3.0. The abundances of the light
and iron-peak elements generally show the same pattern as other UMP stars (e.g.
overdeficiencies of manganese and chromium, overabundances of cobalt), but the
differential analysis indicates several nucleosynthesis signatures that are
unique to each star.


  Theory of spin noise in low dimensional systems and bulk semiconductors is
reviewed. Spin noise is usually detected by optical means, continuously
measuring the rotation angle of the polarization plane of the probe beam
passing through the sample. Spin noise spectra yield rich information about the
spin properties of the system including, for example, $g$-factors of the charge
carriers, spin relaxation times, parameters of the hyperfine interaction,
spin-orbit interaction constants, frequencies and widths of the optical
resonances. The review describes basic models of spin noise, methods of its
theoretical description, and their relation with the experimental results. We
also discuss the relation between the spin noise spectroscopy, the strong and
weak quantum measurements and the spin flip Raman scattering, and analyze
similar effects including manifestations of the charge, current and valley
polarization fluctuations in the optical response. Possible directions for
further development of the spin noise spectroscopy are outlined.


  We study the covariant derivatives of an eigenfunction for the
Laplace-Beltrami operator on a complete, connected Riemannian manifold with
nonzero constant sectional curvature. We show that along every parallel tensor,
the covariant derivative is a scalar multiple of the eigenfunction. We also
show that the scalar is a polynomial depending on the eigenvalue and prove some
properties. A conjecture motivated by the study of vertex algebraic structure
on space forms is also announced, suggesting the existence of interesting
structures in these polynomials that awaits further exploration.


  Absorption of a photon by an electron moving parallel to a rough surface is
studied.In the weak scattering regime we have evaluated the absorption
probability of absorption of a single photon. It is shown the absorption
probability with diffusional contribution becomes large by a lin/l factor
compared to the analogous result with the single scattering contribution. The
maximum of probability takes place for the infrared wavelengths and strongly
depends on the particle energy. We also discuss the case of two-dimensional
periodical surface profile and indicate optimal conditions for maximal
absorption probability. The results can be used in electron energy gain
spectroscopy and in laser-driven acceleration.


  We study a deflation method to reduce and to solve linear
dfferential-algebraic equations (DAEs). It consists to define a sequence of
DAEs with index reduction of one unit by step. This is simultaneously performed
by substitution and differentiation. At the end of process, we obtain at most
an ODE and a list of algebraic constraints which solve the initial DAE. We show
on classical examples how works the method. Moreover, we explain how this
method extends in the case of linear time-varying DAEs.


  We show that in the absence of a Ramond-Ramond sector both the type IIA and
type IIB free string gases have a thermal instability due to low temperature
tachyon modes. The gas of free IIA strings undergoes a thermal duality
transition into a gas of free IIB strings at the self-dual temperature. The
free heterotic string gas is a tachyon-free ensemble with gauge symmetry
SO(16)$\times$SO(16) in the presence of a timelike Wilson line background. It
exhibits a holographic duality relation undergoing a self-dual phase transition
with positive free energy and positive specific heat. The type IB open and
closed string ensemble is related by thermal duality to the type I' string
ensemble. We identify the order parameter for the Kosterlitz-Thouless phase
transition from a low temperature gas of short open strings to a high
temperature long string phase at or below T_C. Note Added (Sep 2005).


  We study a robust utility maximization problem in a general discrete-time
frictionless market. The investor is assumed to have a random, nonconcave and
nondecreasing utility function, which may or may not be finite on the whole
real-line. She also faces model ambiguity on her beliefs about the market,
which is modeled through a set of priors. We prove, using only primal methods,
the existence of an optimal investment strategy when the utility function is
also upper-semicontinuous. For that, we introduce the new notion of
projectively measurable functions. We show basic properties of these functions
as stability under sums, differences, products, suprema, infima and
compositions but also assuming the set-theoretical axiom of Projective
Determinacy (PD) stability under integration and existence of
$\epsilon$-optimal selectors. We consider projectively measurable random
utility function and price process and assume that the graphs of the sets of
local priors are projective sets. Our other assumptions are stated on a
prior-by-prior basis and correspond to generally accepted assumptions in the
literature on markets without ambiguity.


  We study the homogenization problem for matrix strongly elliptic operators on
$L_2(\mathbb R^d)^n$ of the form $\mathcal
A^\varepsilon=-\operatorname{div}A(x,x/\varepsilon)\nabla$. The function $A$ is
Lipschitz in the first variable and periodic in the second. We do not require
that $A^*=A$, so $\mathcal A^\varepsilon$ need not be self-adjoint. In this
paper, we provide, for small $\varepsilon$, two terms in the uniform
approximation for $(\mathcal A^\varepsilon-\mu)^{-1}$ and a first term in the
uniform approximation for $\nabla(\mathcal A^\varepsilon-\mu)^{-1}$. Primary
attention is paid to proving sharp-order bounds on the errors of the
approximations.


  In this article, for holomorphic foliations of codimension one at
$(\mathbb{C}^{3},0)$, we define the family of second type foliations. This is
formed by foliations having, in the reduction process by blow-up maps, only
well oriented singularities, meaning that the reduction divisor does not
contain weak separatrices of saddle-node singularities. We prove that the
reduction of singularities of a non-dicritical foliation of second type
coincides with the desingularization of its set of separatrices.


  The estimation of time-varying quantities is a fundamental component of
decision making in fields such as healthcare and finance. However, the
practical utility of such estimates is limited by how accurately they quantify
predictive uncertainty. In this work, we address the problem of estimating the
joint predictive distribution of high-dimensional multivariate time series. We
propose a versatile method, based on the transformer architecture, that
estimates joint distributions using an attention-based decoder that provably
learns to mimic the properties of non-parametric copulas. The resulting model
has several desirable properties: it can scale to hundreds of time series,
supports both forecasting and interpolation, can handle unaligned and
non-uniformly sampled data, and can seamlessly adapt to missing data during
training. We demonstrate these properties empirically and show that our model
produces state-of-the-art predictions on multiple real-world datasets.


  The generalised random graph contains $n$ vertices with positive i.i.d.
weights. The probability of adding an edge between two vertices is increasing
in their weights. We require the weight distribution to have finite second
moments and study the point process $\mathcal{C}_n$ on $\{3,4,\dots\}$, which
counts how many cycles of the respective length are present in the graph. We
establish convergence of $\mathcal{C}_n$ to a Poisson point process. Under the
stronger assumption of the weights having finite fourth moments we provide the
following results. When $\mathcal{C}_n$ is evaluated on a bounded set $A$, we
provide a rate of convergence. If the graph is additionally subcritical, we
extend this to unbounded sets $A$ at the cost of a slower rate of convergence.
From this we deduce the limiting distribution of the length of the shortest and
the longest cycle when the graph is subcritical, including rates of
convergence. All mentioned results also apply to the Chung-Lu model and the
Norros-Reittu model.


  In this paper, we propose a distributed version of the Hungarian Method to
solve the well known assignment problem. In the context of multi-robot
applications, all robots cooperatively compute a common assignment that
optimizes a given global criterion (e.g. the total distance traveled) within a
finite set of local computations and communications over a peer-to-peer
network. As a motivating application, we consider a class of multi-robot
routing problems with "spatio-temporal" constraints, i.e. spatial targets that
require servicing at particular time instants. As a means of demonstrating the
theory developed in this paper, the robots cooperatively find online,
suboptimal routes by applying an iterative version of the proposed algorithm,
in a distributed and dynamic setting. As a concrete experimental test-bed, we
provide an interactive "multi-robot orchestral" framework in which a team of
robots cooperatively plays a piece of music on a so-called orchestral floor.


  We study the question of how to compute a point in the convex hull of an
input set $S$ of $n$ points in ${\mathbb R}^d$ in a differentially private
manner. This question, which is trivial non-privately, turns out to be quite
deep when imposing differential privacy. In particular, it is known that the
input points must reside on a fixed finite subset $G\subseteq{\mathbb R}^d$,
and furthermore, the size of $S$ must grow with the size of $G$. Previous works
focused on understanding how $n$ needs to grow with $|G|$, and showed that
$n=O\left(d^{2.5}\cdot8^{\log^*|G|}\right)$ suffices (so $n$ does not have to
grow significantly with $|G|$). However, the available constructions exhibit
running time at least $|G|^{d^2}$, where typically $|G|=X^d$ for some (large)
discretization parameter $X$, so the running time is in fact $\Omega(X^{d^3})$.
  In this paper we give a differentially private algorithm that runs in
$O(n^d)$ time, assuming that $n=\Omega(d^4\log X)$. To get this result we study
and exploit some structural properties of the Tukey levels (the regions $D_{\ge
k}$ consisting of points whose Tukey depth is at least $k$, for $k=0,1,...$).
In particular, we derive lower bounds on their volumes for point sets $S$ in
general position, and develop a rather subtle mechanism for handling point sets
$S$ in degenerate position (where the deep Tukey regions have zero volume). A
naive approach to the construction of the Tukey regions requires $n^{O(d^2)}$
time. To reduce the cost to $O(n^d)$, we use an approximation scheme for
estimating the volumes of the Tukey regions (within their affine spans in case
of degeneracy), and for sampling a point from such a region, a scheme that is
based on the volume estimation framework of Lov\'asz and Vempala (FOCS 2003)
and of Cousins and Vempala (STOC 2015). Making this framework differentially
private raises a set of technical challenges that we address.


  We identify the need for a gamified self-driving simulator where game
mechanics encourage high-quality data capture, and design and apply such a
simulator to collecting lane-following training data. The resulting synthetic
data enables a Convolutional Neural Network (CNN) to drive an in-game vehicle.
We simultaneously develop a physical test platform based on a radio-controlled
vehicle and the Robotic Operating System (ROS) and successfully transfer the
simulation-trained model to the physical domain without modification. The
cross-platform simulator facilitates unsupervised crowdsourcing, helping to
collect diverse data emulating complex, dynamic environment data, infrequent
events, and edge cases. The physical platform provides a low-cost solution for
validating simulation-trained models or enabling rapid transfer learning,
thereby improving the safety and resilience of self-driving algorithms.


  Dense Self-Supervised Learning (SSL) methods address the limitations of using
image-level feature representations when handling images with multiple objects.
Although the dense features extracted by employing segmentation maps and
bounding boxes allow networks to perform SSL for each object, we show that they
suffer from coupling and positional bias, which arise from the receptive field
increasing with layer depth and zero-padding. We address this by introducing
three data augmentation strategies, and leveraging them in (i) a decoupling
module that aims to robustify the network to variations in the object's
surroundings, and (ii) a de-positioning module that encourages the network to
discard positional object information. We demonstrate the benefits of our
method on COCO and on a new challenging benchmark, OpenImage-MINI, for object
classification, semantic segmentation, and object detection. Our extensive
experiments evidence the better generalization of our method compared to the
SOTA dense SSL methods


  Text-based person search (TBPS) is a problem that gained significant interest
within the research community. The task is that of retrieving one or more
images of a specific individual based on a textual description. The multi-modal
nature of the task requires learning representations that bridge text and image
data within a shared latent space. Existing TBPS systems face two major
challenges. One is defined as inter-identity noise that is due to the inherent
vagueness and imprecision of text descriptions and it indicates how
descriptions of visual attributes can be generally associated to different
people; the other is the intra-identity variations, which are all those
nuisances e.g. pose, illumination, that can alter the visual appearance of the
same textual attributes for a given subject. To address these issues, this
paper presents a novel TBPS architecture named MARS
(Mae-Attribute-Relation-Sensitive), which enhances current state-of-the-art
models by introducing two key components: a Visual Reconstruction Loss and an
Attribute Loss. The former employs a Masked AutoEncoder trained to reconstruct
randomly masked image patches with the aid of the textual description. In doing
so the model is encouraged to learn more expressive representations and
textual-visual relations in the latent space. The Attribute Loss, instead,
balances the contribution of different types of attributes, defined as
adjective-noun chunks of text. This loss ensures that every attribute is taken
into consideration in the person retrieval process. Extensive experiments on
three commonly used datasets, namely CUHK-PEDES, ICFG-PEDES, and RSTPReid,
report performance improvements, with significant gains in the mean Average
Precision (mAP) metric w.r.t. the current state of the art.


  We study the problem of identification of a proper state-space for the
stochastic dynamics of free particles in continuum, with their possible birth
and death. In this dynamics, the motion of each separate particle is described
by a fixed Markov process $M$ on a Riemannian manifold $X$. The main problem
arising here is a possible collapse of the system, in the sense that, though
the initial configuration of particles is locally finite, there could exist a
compact set in $X$ such that, with probability one, infinitely many particles
will arrive at this set at some time $t>0$. We assume that $X$ has infinite
volume and, for each $\alpha\ge1$, we consider the set $\Theta_\alpha$ of all
infinite configurations in $X$ for which the number of particles in a compact
set is bounded by a constant times the $\alpha$-th power of the volume of the
set. We find quite general conditions on the process $M$ which guarantee that
the corresponding infinite particle process can start at each configuration
from $\Theta_\alpha$, will never leave $\Theta_\alpha$, and has cadlag (or,
even, continuous) sample paths in the vague topology. We consider the following
examples of applications of our results: Brownian motion on the configuration
space, free Glauber dynamics on the configuration space (or a birth-and-death
process in $X$), and free Kawasaki dynamics on the configuration space. We also
show that if $X=\mathbb R^d$, then for a wide class of starting distributions,
the (non-equilibrium) free Glauber dynamics is a scaling limit of
(non-equilibrium) free Kawasaki dynamics.


  We have found evidence for fluxes of energetic electrons in interplanetary
space on board the ISEE-3 spacecraft which we interpret as the decay products
of neutrons generated in a solar flare on 1980 June 21. The decay electrons
arrived at the s/c shortly before the electrons from the flare and can be
distinguished from the latter by their distinctive energy spectrum. The time
profile of the decay electrons is in good agreement with the results from a
simulation based on a scattering mean free path derived from a fit to the flare
electron data. The comparison with simultaneously observed decay protons and a
published direct measurement of high-energy neutrons places important
constraints on the parent neutron spectrum.


  We determine the full post-Newtonian limit of theories of gravity that extend
general relativity by replacing the Ricci scalar, R, in the generating
Lagrangian by some analytic function, f(R). We restrict ourselves to theories
that admit Minkowski space as a suitable background, and perform a perturbative
expansion in the manner prescribed by the parameterised post-Newtonian
formalism. Extra potentials are found to be present that are not accounted for
in the usual treatment, and a discussion is provided on how they may be used to
observationally distinguished these theories from general relativity at the
post-Newtonian level.


  We propose a method to analytically solve the bootstrap equation for two
point functions in boundary CFT. We consider the analytic structure of the
correlator in Lorentzian signature and in particular the discontinuity of bulk
and boundary conformal blocks to extract CFT data. As an application, the
correlator $\langle \phi \phi \rangle$ in $\phi^4$ theory at the Wilson-Fisher
fixed point is computed to order $\epsilon^2$ in the $\epsilon$ expansion.


  Recent observations uncover various phenomena around the protostar such as
misalignment between the outflow and magnetic field, precession of the jet, and
time variability of the ejected clumps, whose origins are under debate. We
perform a three-dimensional resistive magnetohydrodynamics simulation of the
protostar formation in a star-forming core whose rotation axis is tilted at an
angle $45^\circ$ with respect to the initial magnetic field, in which the
protostar is resolved with a spatial resolution of $0.01\,$au. In low-dense
outer region, the prestellar core contracts along the magnetic field lines due
to the flux freezing. In high-dense inner region, on the other hand, the
magnetic dissipation becomes efficient and weakens the magnetic effects when
the gas number density exceeds about $10^{11}\,{\rm cm^{-3}}$. Then, the normal
direction of the flattened disc is aligned with the angular momentum vector.
The outflow, jet, and protostellar ejection are driven from different scales of
the circumstellar disc and spout in different directions normal to the warped
disc. These axes do not coincide with the global magnetic field direction and
vary with time. This study demonstrates that a couple of misalignment natures
reported by observations can be simultaneously reproduced only by assuming the
star-forming core rotating around a different direction from the magnetic
field.


  Augmented reality (AR) has great potential for use in healthcare
applications, especially remote medical training and supervision. In this
paper, we analyze the usage of an AR communication system to teach a medical
procedure, the placement of a central venous catheter (CVC) under ultrasound
guidance. We examine various AR communication and collaboration components,
including gestural communication, volumetric information, annotations,
augmented objects, and augmented screens. We compare how teaching in AR differs
from teaching through videoconferencing-based communication. Our results
include a detailed medical training steps analysis in which we compare how
verbal and visual communication differs between video and AR training. We
identify procedural steps in which medical experts give visual instructions
utilizing AR components. We examine the change in AR usage and interaction over
time and recognize patterns between users. Moreover, AR design recommendations
are given based on post-training interviews.


  In order to discover new X-ray transients, the data taken by XMM-Newton as it
slews between targets are being processed and cross-correlated with other X-ray
observations.
  A bright source, XMMSL1 J060636.2-694933, was detected on 18 July 2006 at a
position where no previous X-ray source had been seen. The XMM-Newton slew
data, plus follow-up dedicated XMM-Newton and Swift observations, plus optical
data acquired with the Magellan Clay telescope, and archival All-Sky Automated
Survey (ASAS) data were used to classify the new object, and to investigate its
properties.
  No XMM-Newton slew X-ray counts are detected above 1 keV and the source is
seen to be over five hundred times brighter than the ROSAT All-Sky Survey upper
limit at that position. The line-rich optical spectrum acquired with the
Magellan telescope allows the object to be classified as an Ao auroral phase
nova, and the soft X-ray spectrum indicates that the nova was in a super-soft
source state in the X-ray decline seen in the follow-up X-ray observations. The
archival ASAS data suggests that the nova at onset (Oct 2005) was a 'very fast'
nova, and an estimate of its distance is consistent with the nova being
situated within the LMC.
  With the discovery presented here of a new classical nova in the LMC, it is
clear that XMM-Newton slew data are continuing to offer a powerful opportunity
to find new X-ray transient objects.


  I review recent progress in lattice QCD at non-zero temperature whith
emphasis on the calculations of equation and the deconfinement as well as the
chiral aspects of the QCD transition at non-zero temperature. I also briefly
discuss meson correlation functions at high temperatures.


  This article presents industrial experience of validating large data sets
against specification written using the B / Event-B mathematical language and
the ProB model checker.


  The aim of this paper is to underline conditions necessary for the emergence
and development of life. They are placed at local planetary scale, at Galactic
scale and within the cosmological evolution, as pointed out by the Anthropic
Cosmological Principle. We will consider the circumstellar habitable zone (CHZ)
for planetary systems and a Galactic Habitable Zone (GHZ) including also a set
of strong cosmological constraints to allow life (cosmological habitability
(COSH)). Some requirements are specific of a single scale and its related
physical phenomena, while others are due to the conspired effects occurring at
more than one scale. The scenario emerging from this analysis is that all the
habitability conditions here detailed must at least be met. Then life asks
something which may appear as "a monstrous sequence of accidents" as Hoyle
(1959) thought, or as providential collaborations leading to realize how finely
tuned is the architecture within which Life, as precious stone, is embedded.


  Symplectic integrators are a foundation to the study of dynamical $N$-body
phenomena, at scales ranging from from planetary to cosmological. These
integrators preserve the Poincar\'e invariants of Hamiltonian dynamics. The
$N$-body Hamiltonian has another, perhaps overlooked, symmetry: it is smooth,
or, in other words, it has infinite differentiability class order (DCO) for
particle separations greater than $0$. Popular symplectic integrators, such as
hybrid methods or block adaptive stepping methods do not come from smooth
Hamiltonians and it is perhaps unclear whether they should. We investigate the
importance of this symmetry by considering hybrid integrators, whose DCO can be
tuned easily. Hybrid methods are smooth, except at a finite number of phase
space points. We study chaotic planetary orbits in a test considered by Wisdom.
We find that increasing smoothness, at negligible extra computational cost in
particular tests, improves the Jacobi constant error of the orbits by about $5$
orders of magnitude in long-term simulations. The results from this work
suggest that smoothness of the $N$-body Hamiltonian is a property worth
preserving in simulations.


  Variational quantum algorithms (VQAs) have shown strong evidences to gain
provable computational advantages for diverse fields such as finance, machine
learning, and chemistry. However, the heuristic ansatz exploited in modern VQAs
is incapable of balancing the tradeoff between expressivity and trainability,
which may lead to the degraded performance when executed on the noisy
intermediate-scale quantum (NISQ) machines. To address this issue, here we
demonstrate the first proof-of-principle experiment of applying an efficient
automatic ansatz design technique, i.e., quantum architecture search (QAS), to
enhance VQAs on an 8-qubit superconducting quantum processor. In particular, we
apply QAS to tailor the hardware-efficient ansatz towards classification tasks.
Compared with the heuristic ansatze, the ansatz designed by QAS improves test
accuracy from 31% to 98%. We further explain this superior performance by
visualizing the loss landscape and analyzing effective parameters of all
ansatze. Our work provides concrete guidance for developing variable ansatze to
tackle various large-scale quantum learning problems with advantages.


  For a continuous-time Bienaym\'e-Galton-Watson process, $X$, with immigration
and culling, $0$ as an absorbing state, call $X^q$ the process that results
from killing $X$ at rate $q\in (0,\infty)$, followed by stopping it on
extinction or explosion. Then an explicit identification of the relevant
harmonic functions of $X^q$ allows to determine the Laplace transforms (at
argument $q$) of the first passage times downwards and of the explosion time
for $X$. Strictly speaking, this is accomplished only when the killing rate $q$
is sufficiently large (but always when the branching mechanism is not
supercritical or if there is no culling). In particular, taking the limit
$q\downarrow 0$ (whenever possible) yields the passage downwards and explosion
probabilities for $X$. A number of other consequences of these results are
presented.


  In this paper, we inspect well-known population genetics and social dynamics
models. In these models, interacting individuals, while participating in a
self-organizing process, give rise to the emergence of complex behaviors and
patterns. While one main focus in population genetics is on the adaptive
behavior of a population, social dynamics is more often concerned with the
splitting of a connected array of individuals into a state of global
polarization, that is, the emergence of speciation. Applying computational and
mathematical tools we show that the way the mechanisms of selection,
interaction and replacement are constrained and combined in the modeling have
an important bearing on both adaptation and the emergence of speciation.
Differently (un)constraining the mechanism of individual replacement provides
the conditions required for either speciation or adaptation, since these
features appear as two opposing phenomena, not achieved by one and the same
model. Even though natural selection, operating as an external, environmental
mechanism, is neither necessary nor sufficient for the creation of speciation,
our modeling exercises highlight the important role played by natural selection
in the interplay of the evolutionary and the self-organization modeling
methodologies.


  Energy consumption imposes a significant cost for data centers; yet much of
that energy is used to maintain excess service capacity during periods of
predictably low load. Resultantly, there has recently been interest in
developing designs that allow the service capacity to be dynamically resized to
match the current workload. However, there is still much debate about the value
of such approaches in real settings. In this paper, we show that the value of
dynamic resizing is highly dependent on statistics of the workload process. In
particular, both slow time-scale non-stationarities of the workload (e.g., the
peak-to-mean ratio) and the fast time-scale stochasticity (e.g., the burstiness
of arrivals) play key roles. To illustrate the impact of these factors, we
combine optimization-based modeling of the slow time-scale with stochastic
modeling of the fast time scale. Within this framework, we provide both
analytic and numerical results characterizing when dynamic resizing does (and
does not) provide benefits.


  We report $^{123}$Sb nuclear quadrupole resonance (NQR) measurements of the
filled skutterudite heavy-fermion superconductor PrOs$_4$Sb$_{12}$ under high
pressure. The temperature dependence of NQR frequency and the spin-lattice
relaxation rate $1/T_1$ indicate that the crystal-electric-field splitting
$\Delta_{\rm CEF}$ between the ground state $\Gamma_1$ singlet and the first
excited state $\Gamma_4^{(2)}$ triplet decreases with increasing pressure.
Ac-susceptibility measurements indicate that the superconducting transition
temperature ($T_{\rm c}$) also decreases with increasing pressure. However,
above $P$ $\sim$ 2 GPa, both $\Delta_{\rm CEF}$ and $T_{\rm c}$ do not depend
on external pressure up to $P$ = 3.82 GPa. These pressure dependences of
$\Delta_{\rm CEF}$ and $T_{\rm c}$ suggest an intimate relationship between
quadrupole excitations associated with the $\Gamma_4^{(2)}$ level and
unconventional superconductivity in PrOs$_4$Sb$_{12}$. In the superconducting
state, 1/$T_1$ below $T_{\rm c}$ = 1.55 and 1.57 K at $P$ = 1.91 and 2.63 GPa
shows a power-law temperature variations and are proportional to $T^5$ at
temperatures considerably below $T_{\rm c}$. These data can be well fitted by
the gap model $\Delta(\theta) = \Delta_0\sin\theta$ with $\Delta_0$ = 3.08
$k_{\rm B}T_{\rm c}$ and 3.04 $k_{\rm B}T_{\rm c}$ for $P$ = 1.91 and 2.63 GPa,
respectively. The results indicate there exists point nodes in the gap
function.


  Data from the SDSS-IV / Apache Point Observatory Galactic Evolution
Experiment (APOGEE-2) have been released as part of SDSS Data Releases 13
(DR13) and 14 (DR14). These include high resolution H-band spectra, radial
velocities, and derived stellar parameters and abundances. DR13, released in
August 2016, contained APOGEE data for roughly 150,000 stars, and DR14,
released in August 2017, added about 110,000 more. Stellar parameters and
abundances have been derived with an automated pipeline, the APOGEE Stellar
Parameter and Chemical Abundance Pipeline (ASPCAP). We evaluate the performance
of this pipeline by comparing the derived stellar parameters and abundances to
those inferred from optical spectra and analysis for several hundred stars. For
most elements -- C, Na, Mg, Al, Si, S, Ca, Cr, Mn, Ni -- the DR14 ASPCAP
analysis have systematic differences with the comparisons samples of less than
0.05 dex (median), and random differences of less than 0.15 dex (standard
deviation). These differences are a combination of the uncertainties in both
the comparison samples as well as the ASPCAP-analysis. Compared to the
references, magnesium is the most accurate alpha-element derived by ASPCAP, and
shows a very clear thin/thick disk separation, while nickel is the most
accurate iron-peak element (besides iron).


  In 1916, Schur introduced the Ramsey number $r(3;m)$, which is the minimum
integer $n$ such that for any $m$-coloring of the edges of the complete graph
$K_n$, there is a monochromatic copy of $K_3$. He showed that $r(3;m) \leq
O(m!)$, and a simple construction demonstrates that $r(3;m) \geq
2^{\Omega(m)}$. An old conjecture of Erd\H os states that $r(3;m) =
2^{\Theta(m)}$. In this note, we prove the conjecture for $m$-colorings with
bounded VC-dimension, that is, for $m$-colorings with the property that the set
system $\mathcal{F}$ induced by the neighborhoods of the vertices with respect
to each color class has bounded VC-dimension.


  The collisional pumping of H$_2$O and CH$_3$OH masers in magnetohydrodynamic
nondissociative C-type shocks is considered. A grid of C-type shock models with
speeds in the range $5-70$ km s$^{-1}$ and preshock gas densities $n_{\rm
H_2,0} = 10^4-10^7$ cm$^{-3}$ is constructed. The large velocity gradient
approximation is used to solve the radiative transfer equation in molecular
lines. The para-H$_2$O 183.3 GHz and ortho-H$_2$O 380.1 and 448.0 GHz
transitions are shown to be inverted and to have an optical depth along the
shock velocity $\vert \tau \vert \sim 1$ at relatively low gas densities in the
maser zone, $n_{\rm H_2} \gtrsim 10^5-10^6$ cm$^{-3}$. Higher gas densities,
$n_{\rm H_2} \gtrsim 10^7$ cm$^{-3}$, are needed for efficient pumping of the
remaining H$_2$O masers. Simultaneous generation of H$_2$O and class I CH$_3$OH
maser emission in a shock is possible at preshock gas densities $n_{\rm H_2,0}
\approx 10^5$ cm$^{-3}$ and shock speeds in the range $u_{\rm s} \approx
17.5-22.5$ km s$^{-1}$. The possibility of detecting class I CH$_3$OH and
para-H$_2$O 183.3 GHz masers in star-forming regions and near supernova
remnants is investigated.


  The Wigner-Yanase skew information was proposed to quantify the information
contained in quantum states with respect to a conserved additive quantity, and
it was later extended to the Wigner-Yanase-Dyson skew informations. Recently,
the Wigner-Yanase-Dyson skew informations have been recognized as valid
resource measures for the resource theory of asymmetry, and their properties
have been investigated from a resource-theoretic perspective. The
Wigner-Yanse-Dyson skew informations have been further generalized to a class
called metric-adjusted skew informations, and this general family of skew
informations have also been found to be valid asymmetry monotones. Here, we
analyze this general family of the skew informations from an operational point
of view by utilizing the fact that they are valid asymmetry resource monotones.
We show that such an approach allows for clear physical meanings as well as
simple proofs of some of the basic properties of the skew informations.
Notably, we constructively prove that any type of skew information cannot be
superadditive, where the violation of the superadditivity had been only known
for a specific class of skew informations with numerical counterexamples. We
further show a weaker version of superadditivity relation applicable to the
general class of the skew informations, which proves a conjecture made for the
Wigner-Yanase skew information as a special case. We finally discuss an
application of our results for a situation where quantum clocks are distributed
to multiple parties.


  In this paper, we propose an automated evaluation metric for text entry. We
also consider possible improvements to existing text entry evaluation metrics,
such as the minimum string distance error rate, keystrokes per character, cost
per correction, and a unified approach proposed by MacKenzie, so they can
accommodate the special characteristics of Chinese text. Current methods lack
an integrated concern about both typing speed and accuracy for Chinese text
entry evaluation. Our goal is to remove the bias that arises due to human
factors. First, we propose a new metric, called the correction penalty (P),
based on Fitts' law and Hick's law. Next, we transform it into the approximate
amortized cost (AAC) of information theory. An analysis of the AAC of Chinese
text input methods with different context lengths is also presented.


  Motivated by applications to mathematical biology, we study the averaging
problem for slow-fast systems, {\em in the case in which the fast dynamics is a
stochastic process with multiple invariant measures}. We consider both the case
in which the fast process is decoupled from the slow process and the case in
which the two components are fully coupled. We work in the setting in which the
slow process evolves according to an Ordinary Differential Equation (ODE) and
the fast process is a continuous time Markov Process with finite state space
and show that, in this setting, the limiting (averaged) dynamics can be
described as a random ODE (that is, an ODE with random coefficients.)
  Keywords. Multiscale methods, Processes with multiple equilibria, Averaging,
Collective Navigation, Interacting Piecewise Deterministic Markov Processes.


  It is shown that classical nonsupersymmetric Yang-Mills theory in 4
dimensions is symmetric under a generalized dual transform which reduces to the
usual dual *-operation for electromagnetism. The parallel phase transport
$\tilde{A}_\mu(x)$ constructed earlier for monopoles is seen to function also
as a potential in giving full description of the gauge field, playing thus an
entirely dual symmetric role to the usual potential $A_\mu(x)$. Sources of $A$
are monopoles of $\tilde{A}$ and vice versa, and the Wu-Yang criterion for
monopoles is found to yield as equations of motion the standard Wong and
Yang-Mills equations for respectively the classical and Dirac point charge;
this applies whether the charge is electric or magnetic, the two cases being
related just by a dual transform. The dual transformation itself is explicit,
though somewhat complicated, being given in terms of loop space variables of
the Polyakov type.


  The Berezinskii-Kosterlitz-Thouless (BKT) phase transition drives the
unbinding of topological defects in many two-dimensional systems. In the
two-dimensional Coulomb gas, it corresponds to an insulator-conductor
transition driven by charge deconfinement. We investigate the global
topological properties of this transition, both analytically and by numerical
simulation, using a lattice-field description of the two-dimensional Coulomb
gas on a torus. The BKT transition is shown to be an ergodicity breaking
between the topological sectors of the electric field, which implies a
definition of topological order in terms of broken ergodicity. The breakdown of
local topological order at the BKT transition leads to the excitation of global
topological defects in the electric field, corresponding to different
topological sectors. The quantized nature of these classical excitations, and
their strict suppression by ergodicity breaking in the low-temperature phase,
afford striking global signatures of topological-sector fluctuations at the BKT
transition. We discuss how these signatures could be detected in experiments
on, for example, magnetic films and cold-atom systems.


  For the zero-range interaction providing a given mass M_2 of the two-body
bound state, the mass M_3 of the relativistic three-boson state is calculated.
We have found that the three-body system exists only when M_2 is greater than a
critical value M_c approximately 1.43 m (m is the constituent mass). For
M_2=M_c the mass M_3 turns into zero and for M_2<M_c there is no solution with
real value of M_3.


  Using a patching module constructed in recent work of Caraiani, Emerton, Gee,
Geraghty, Pa{\v{s}}k{\=u}nas and Shin we construct some kind of analogue of an
eigenvariety. We can show that this patched eigenvariety agrees with a union of
irreducible components of a space of trianguline Galois representations.
Building on this we discuss the relation with the modularity conjectures for
the crystalline case, a conjecture of Breuil on the locally analytic socle of
representations occurring in completed cohomology and with a conjecture of
Bella\"iche and Chenevier on the complete local ring at certain points of
eigenvarieties.


  Causal discovery, i.e., learning the causal graph from data, is often the
first step toward the identification and estimation of causal effects, a key
requirement in numerous scientific domains. Causal discovery is hampered by two
main challenges: limited data results in errors in statistical testing and the
computational complexity of the learning task is daunting. This paper builds
upon and extends four of our prior publications (Mokhtarian et al., 2021;
Akbari et al., 2021; Mokhtarian et al., 2022, 2023a). These works introduced
the concept of removable variables, which are the only variables that can be
removed recursively for the purpose of causal discovery. Presence and
identification of removable variables allow recursive approaches for causal
discovery, a promising solution that helps to address the aforementioned
challenges by reducing the problem size successively. This reduction not only
minimizes conditioning sets in each conditional independence (CI) test, leading
to fewer errors but also significantly decreases the number of required CI
tests. The worst-case performances of these methods nearly match the lower
bound. In this paper, we present a unified framework for the proposed
algorithms, refined with additional details and enhancements for a coherent
presentation. A comprehensive literature review is also included, comparing the
computational complexity of our methods with existing approaches, showcasing
their state-of-the-art efficiency. Another contribution of this paper is the
release of RCD, a Python package that efficiently implements these algorithms.
This package is designed for practitioners and researchers interested in
applying these methods in practical scenarios. The package is available at
github.com/ban-epfl/rcd, with comprehensive documentation provided at
rcdpackage.com.


  `Superthins' are a subset of edge-on spiral galaxies exhibiting disks with
large axial ratios (a/b>10), extraordinarily small stellar scale heights, and
no bulge component. These are thus examples of dynamically cold, pure disk
galaxies. We have recently obtained multiwavelength (HI, optical, NIR)
observations for a large sample of superthin spirals. Our data lead to a
picture of superthins as `underevolved' disk galaxies in both a dynamical and a
star formation sense. Studies of these relatively simple disk systems can
therefore provide unique constraints on galaxy disk formation and evolution
without looking beyond the local universe. We present the results of a detailed
analysis of the nearby superthin UGC 7321 as an illustration of these ideas.


  Distributed energy resources (DER) as non-wires alternatives, regardless of
owner, have the potential to reduce system operating costs and delay system
upgrades. However, it is difficult to determine the appropriate economic signal
to incentivize DER investors to install capacity that will benefit both the DER
investors and the system operator. In an attempt to determine this co-optimal
price signal, we present a bilevel optimization framework for determining the
least cost solution to distribution system over-loads. A key output of the
framework is a spatiotemporal price signal to DER owners that simultaneously
guarantees the DER owners' required rate of return and minimizes the system
operation costs. The framework is demonstrated with a case by which the system
operator considers utility owned battery energy storage systems, traditional
system upgrades, and energy purchase from DER owners. The results show that by
valuing DER for non-wires alternatives the utility owned storage system sizes
can be reduced, less hardware upgrades are necessary, and upfront capital costs
as well as operating costs are reduced.


  Looking for associations among multiple variables is a topical issue in
statistics due to the increasing amount of data encountered in biology,
medicine and many other domains involving statistical applications. Graphical
models have recently gained popularity for this purpose in the statistical
literature. Following the ideas of the LASSO procedure designed for the linear
regression framework, recent developments dealing with graphical model
selection have been based on $\ell_1$-penalization. In the binary case,
however, exact inference is generally very slow or even intractable because of
the form of the so-called log-partition function. Various approximate methods
have recently been proposed in the literature and the main objective of this
paper is to compare them. Through an extensive simulation study, we show that a
simple modification of a method relying on a Gaussian approximation achieves
good performance and is very fast. We present a real application in which we
search for associations among causes of death recorded on French death
certificates.


  In general, the orbit-averaged radial magnetic drift of trapped particles in
stellarators is non-zero due to the three-dimensional nature of the magnetic
field. Stellarators in which the orbit-averaged radial magnetic drift vanishes
are called omnigeneous, and they exhibit neoclassical transport levels
comparable to those of axisymmetric tokamaks. However, the effect of deviations
from omnigeneity cannot be neglected in practice. For sufficiently low
collision frequencies (below the values that define the $1/\nu$ regime), the
components of the drifts tangential to the flux surface become relevant. This
article focuses on the study of such collisionality regimes in stellarators
close to omnigeneity when the gradient of the non-omnigeneous perturbation is
small. First, it is proven that closeness to omnigeneity is required to
preserve radial locality in the drift-kinetic equation for collisionalities
below the $1/\nu$ regime. Then, it is shown that neoclassical transport is
determined by two layers in phase space. One of the layers corresponds to the
$\sqrt{\nu}$ regime and the other to the superbanana-plateau regime. The
importance of the superbanana-plateau layer for the calculation of the
tangential electric field is emphasized, as well as the relevance of the latter
for neoclassical transport in the collisionality regimes considered in this
paper. In particular, the tangential electric field is essential for the
emergence of a new subregime of superbanana-plateau transport when the radial
electric field is small. A formula for the ion energy flux that includes the
$\sqrt{\nu}$ regime and the superbanana-plateau regime is given. The energy
flux scales with the square of the size of the deviation from omnigeneity.
Finally, it is explained why below a certain collisionality value the
formulation presented in this article ceases to be valid.


  We present a near-infrared extinction study of the dark globule L694-2, a
starless core that shows strong evidence for inward motions in molecular line
profiles. The J,H, and K band data were taken using the European Southern
Observatory New Technology Telescope. The best fit simple spherical power law
model has index p=2.6 +/- 0.2, over the 0.036--0.1 pc range in radius sampled
in extinction. This power law slope is steeper than the value of p=2 for a
singular isothermal sphere, the initial condition of the inside-out model for
protostellar collapse. Including an additional extinction component along the
line of sight further steepens the inferred profile. Fitting a Bonnor-Ebert
sphere results in a super-critical value of the dimensionless radius xi_max=25
+/- 3. The unstable configuration of material may be related to the observed
inward motions. The Bonnor-Ebert model matches the shape of the observed
profile, but significantly underestimates the amount of extinction (by a factor
of ~4). This discrepancy in normalization has also been found for the nearby
protostellar core B335 (Harvey et al. 2001). A cylindrical density model with
scale height H=0.0164+/- 0.002 pc viewed at a small inclination to the cylinder
axis provides an equally good radial profile as a power law model, and
reproduces the asymmetry of the core remarkably well. In addition, this model
provides a basis for understanding the discrepancy in the normalization of the
Bonnor-Ebert model, namely that L694-2 has prolate structure, with the full
extent (mass) of the core being missed by assuming symmetry between the
profiles in the plane of the sky and along the line-of-sight. If the core is
sufficiently magnetized then fragmentation may be avoided, and later evolution
might produce a protostar similar to B335.


  We contribute the Habitat Synthetic Scene Dataset, a dataset of 211
high-quality 3D scenes, and use it to test navigation agent generalization to
realistic 3D environments. Our dataset represents real interiors and contains a
diverse set of 18,656 models of real-world objects. We investigate the impact
of synthetic 3D scene dataset scale and realism on the task of training
embodied agents to find and navigate to objects (ObjectGoal navigation). By
comparing to synthetic 3D scene datasets from prior work, we find that scale
helps in generalization, but the benefits quickly saturate, making visual
fidelity and correlation to real-world scenes more important. Our experiments
show that agents trained on our smaller-scale dataset can match or outperform
agents trained on much larger datasets. Surprisingly, we observe that agents
trained on just 122 scenes from our dataset outperform agents trained on 10,000
scenes from the ProcTHOR-10K dataset in terms of zero-shot generalization in
real-world scanned environments.