  In this paper we establish several recurrence relations about Euler-Ap\'ery
type multiple zeta star values and a parametric variant of it by using the
method of iterated integrals. Then using the formulas obtained, we find the
explicit evaluations for some specific Euler-Ap\'ery type multiple zeta star
values and one of its parametric variant, and Euler-Ap\'ery type multiple
$t$-star values.


  This paper discusses modelling, controllability and gait design for a
spherical flexible swimmer. We first present a kinematic model of a low
Reynolds number spherical flexible swimming mechanism with periodic surface
deformations in the radial and azimuthal directions. The model is then
converted to a finite dimensional driftless, affine-in-control principal
kinematic form by representing the surface deformations as a linear combination
of finitely many Legendre polynomials. A controllability analysis is then done
for this swimmer to conclude that the swimmer is locally controllable on
$\mathbb{R}^3$ for certain combinations of the Legendre polynomials. The rates
of the coefficients of the polynomials are considered as the control inputs for
surface deformation. Finally, the Abelian nature of the structure group of the
swimmer's configuration space is exploited to synthesize a curvature based gait
for the spherical flexile swimmer and a rigid-link swimmer.


  It is well know that for globally contractive autonomous systems, there
exists a unique equilibrium and the distance to the equilibrium evaluated along
any trajectory decreases exponentially with time. We show that, additionally,
the magnitude of the velocity evaluated along any trajectory decreases
exponentially, thus giving an alternative choice of Lyapunov function.


  Exchanging mutable data objects with untrusted code is a delicate matter
because of the risk of creating a data space that is accessible by an attacker.
Consequently, secure programming guidelines for Java stress the importance of
using defensive copying before accepting or handing out references to an
internal mutable object. However, implementation of a copy method (like
clone()) is entirely left to the programmer. It may not provide a sufficiently
deep copy of an object and is subject to overriding by a malicious sub-class.
Currently no language-based mechanism supports secure object cloning. This
paper proposes a type-based annotation system for defining modular copy
policies for class-based object-oriented programs. A copy policy specifies the
maximally allowed sharing between an object and its clone. We present a static
enforcement mechanism that will guarantee that all classes fulfil their copy
policy, even in the presence of overriding of copy methods, and establish the
semantic correctness of the overall approach in Coq. The mechanism has been
implemented and experimentally evaluated on clone methods from several Java
libraries.


  Let $X_1,X_2$ be independent random walks on $\mathbf{Z}_n^d$, $d\geq3$, each
starting from the uniform distribution. Initially, each site of
$\mathbf{Z}_n^d$ is unmarked, and, whenever $X_i$ visits such a site, it is set
irreversibly to $i$. The mean of $|\mathcal{A}_i|$, the cardinality of the set
$\mathcal{A}_i$ of sites painted by $i$, once all of $\mathbf{Z}_n^d$ has been
visited, is $\frac{1}{2}n^d$ by symmetry. We prove the following conjecture due
to Pemantle and Peres: for each $d\geq3$ there exists a constant $\alpha_d$
such that $\lim_{n\to\infty}\operatorname{Var}(|\mathcal
{A}_i|)/h_d(n)=\frac{1}{4}\alpha_d$ where $h_3(n)=n^4$, $h_4(n)=n^4(\log n)$
and $h_d(n)=n^d$ for $d\geq5$. We will also identify $\alpha_d$ explicitly and
show that $\alpha_d\to1$ as $d\to\infty$. This is a special case of a more
general theorem which gives the asymptotics of
$\operatorname{Var}(|\mathcal{A}_i|)$ for a large class of transient, vertex
transitive graphs; other examples include the hypercube and the Caley graph of
the symmetric group generated by transpositions.


  The no-pumping theorem states that seemingly natural driving cycles of
stochastic machines fail to generate directed motion. Initially derived for
single particle systems, the no-pumping theorem was recently extended to
many-particle systems with zero-range interactions. Interestingly, it is known
that the theorem is violated by systems with exclusion interactions. These two
paradigmatic interactions differ by two qualitative aspects: the range of
interactions, and the dependence of branching fractions on the state of the
system. In this work two different models are studied in order to identify the
qualitative property of the interaction that leads to breakdown of no-pumping.
A model with finite-range interaction is shown analytically to satisfy
no-pumping. In contrast, a model in which the interaction affects the
probabilities of reaching different sites, given that a particle is making a
transition, is shown numerically to violate the no-pumping theorem. The results
suggest that systems with interactions that lead to state-dependent branching
fractions do not satisfy the no-pumping theorem.


  Context. Thanks to more than 20 years of monitoring, the radial velocity (RV)
method has detected long-period companions (P > 10yr) around several dozens of
stars. Yet, the true nature of these companions remains unclear because of the
uncertainty as to the inclination of the companion orbital plane. Aims. We wish
to constrain the orbital inclination and the true mass of long-period single
companions. Methods. We used a Markov Chain Monte Carlo (MCMC) fitting
algorithm to combine RV measurements with absolute astrometry and, when
available, relative astrometry data. Results. We have lifted the sin(i)
indetermination for 7 seven long-period companions. We find true masses in the
planetary mass range for the candidate planets detected in the following
systems: Epsilon Indi A, HD 13931, HD 115954, and HD 222155. The mass of HD
219077 b is close to the deuterium-burning limit and its nature is uncertain
because of the imprecise mass of the host star. Using additional RV
measurements, we refine the orbital parameters of HIP 70849 b and find a mass
in the planetary range. By combining RV data with absolute and relative
astrometry, we significantly improve the characterization of HD 211847 B and
properly determine its mass, which appears to be in the low-mass star range.
This work illustrates how Gaia and Hipparcos allow for the orbital properties
and masses of long-period RV companions to be further constrained.


  The multi planet system HR8799 is the first target observed with MIRI's
coronagraphs as part of the MIRI-EC Guaranteed Time Observations exoplanets
programme in Nov. 2022. We obtained deep observations in three coronagraphic
filters from 10 to 15mic (F1065C, F1140C, F1550C), and one standard imaging
filter at 20 mic (F2100W), with the goal to extract the photometry of the four
planets, as well as to detect and investigate the distribution of circumstellar
dust. Using dedicated observations of a reference star, we tested several
algorithms to subtract the stellar diffraction pattern while preserving the
fluxes of planets, which can be significantly affected by over-subtraction.
Measuring correctly the planet's flux values requires accounting for the
attenuation by the coronagraphs as a function of their position, and to
estimate the normalisation with respect to the central star. We tested several
procedures to derive averaged photometric values and error bars. These
observations have enabled us to obtain two main results. First of all, the four
planets in the system are well recovered, and their mid-IR fluxes, combined
with near-IR flux values from the literature, are compared to two exoplanet
atmosphere models, ATMO and Exo-REM. As a main outcome, the MIRI photometric
data points imply larger radii (0.86 or 1.07 RJ for planet b) and cooler
temperatures (950 or 1100 K for planet b), especially for planet b, in better
agreement with evolutionary models. Second of all, these JWST/MIRI
coronagraphic data also deliver the first spatially resolved detection of the
inner warm debris disk, the radius of which is constrained to about 15 au, with
flux densities comparable, but lower than former unresolved spectroscopic
measurements with Spitzer. abridged...


  Strong representations of target speakers can help extract important
information about speakers and detect corresponding temporal regions in
multi-speaker conversations. In this study, we propose a neural architecture
that simultaneously extracts speaker representations consistent with the
speaker diarization objective and detects the presence of each speaker on a
frame-by-frame basis regardless of the number of speakers in a conversation. A
speaker representation (called z-vector) extractor and a time-speaker
contextualizer, implemented by a residual network and processing data in both
temporal and speaker dimensions, are integrated into a unified framework. Tests
on the CALLHOME corpus show that our model outperforms most of the methods
proposed so far. Evaluations in a more challenging case with simultaneous
speakers ranging from 2 to 7 show that our model achieves 6.4% to 30.9%
relative diarization error rate reductions over several typical baselines.


  Muon Spin Rotation/Relaxation ({\mu}SR) is a powerful probe to study
diffusion of hydrogen-like particles. One of the most common model to analyze
{\mu}SR signals from materials where diffusion occurs, is by using functions
derived from the stochastic theory of Kubo and Toyabe along with the strong
collision model. Unfortunately the current formalism can not be used to analyze
data from systems where not only muons diffuse, but also the magnetic moments
of the material are dynamic. In this article we introduce a new model which
accounts for both dynamical effects, and use this model to analyze {\mu}SR
signals from Dy0.4Y1.6Ti2O7.


  The new axiom in set theory, axiom of the empty sets, allows another way to
formulate Bell inequality in quantum mechanics. The new axiom emerges from the
historical and philosophical analysis of set theory.


  We describe a method to hyperpolarize 3He nuclear spins at high magnetic
fields (4.7 Tesla) solely by a magnetized plasma. The conditions for such a
magnetized plasma are fulfilled when the mean free path of the free electrons
is much larger than their gyration radius in the rf gas discharge.
Investigations are carried out in the 1-15 mbar pressure range with rf
excitation at ~100 MHz. Quantitative NMR measurements show that for different
cell sizes and 3He densities nuclear polarizations in the range 1% to 9% are
observed. We explain this phenomenon by an alignment-to-orientation conversion
mechanism in the excited 2 3P state of 3He which is most efficient when the
Zeeman and the spin-orbit energies are comparable. The method appears as a very
attractive alternative to established laser polarization techniques (spin
exchange or metastability exchange optical pumping). Application to 3He nuclear
magnetometry with a relative precision of 10-12 is demonstrated.


  Heavy goods vehicles (HGVs) are involved in 4.5% of police-reported road
crashes in Europe and 14.2% of fatal road crashes. Active and passive safety
systems can help to prevent crashes or mitigate the consequences but need
detailed scenarios to be designed effectively. The aim of this paper is to give
a comprehensive and up-to-date analysis of HGV crashes in Europe. The analysis
is based on general statistics from CARE, results about trucks weighing 16 tons
or more from national crash databases and a detailed study of in-depth crash
data from GIDAS. Three scenarios are identified that should be addressed by
future safety systems: (1) rear-end crashes with other vehicles in which the
truck is the striking partner, (2) conflicts during right turn maneuvers of the
truck and a cyclist and (3) pedestrians crossing the road perpendicular to the
direction of travel of the truck.


  We present exact results on the partition function of the $q$-state Potts
model on various families of graphs $G$ in a generalized external magnetic
field that favors or disfavors spin values in a subset $I_s = \{1,...,s\}$ of
the total set of possible spin values, $Z(G,q,s,v,w)$, where $v$ and $w$ are
temperature- and field-dependent Boltzmann variables. We remark on differences
in thermodynamic behavior between our model with a generalized external
magnetic field and the Potts model with a conventional magnetic field that
favors or disfavors a single spin value. Exact results are also given for the
interesting special case of the zero-temperature Potts antiferromagnet,
corresponding to a set-weighted chromatic polynomial $Ph(G,q,s,w)$ that counts
the number of colorings of the vertices of $G$ subject to the condition that
colors of adjacent vertices are different, with a weighting $w$ that favors or
disfavors colors in the interval $I_s$. We derive powerful new upper and lower
bounds on $Z(G,q,s,v,w)$ for the ferromagnetic case in terms of zero-field
Potts partition functions with certain transformed arguments. We also prove
general inequalities for $Z(G,q,s,v,w)$ on different families of tree graphs.
As part of our analysis, we elucidate how the field-dependent Potts partition
function and weighted-set chromatic polynomial distinguish, respectively,
between Tutte-equivalent and chromatically equivalent pairs of graphs.


  We present two sets of computable entanglement measures for multipartite
systems where each subsystem can have different degrees of freedom (so-called
qudits). One set, called 'separability' measure, reveals which of the
subsystems are separable/entangled. For that we have to extend the concept of
k-separability for multipartite systems to a novel unambiguous separability
concept which we call \gamma_k-separability. The second set of entanglement
measures reveals the 'kind' of entanglement, i.e. if it is bipartite,
tripartite, ..., n-partite entangled and is denoted as the 'physical' measure.
We show how lower bounds on both sets of measures can be obtained by the
observation that any entropy may be rewritten via operational expressions known
as m-concurrences. Moreover, for different classes of bipartite or multipartite
qudit systems we compute the bounds explicitly and discover that they are often
tight or equivalent to positive partial transposition (PPT).


  Community detection in networks is a key exploratory tool with applications
in a diverse set of areas, ranging from finding communities in social and
biological networks to identifying link farms in the World Wide Web. The
problem of finding communities or clusters in a network has received much
attention from statistics, physics and computer science. However, most
clustering algorithms assume knowledge of the number of clusters k. In this
paper we propose to automatically determine k in a graph generated from a
Stochastic Blockmodel. Our main contribution is twofold; first, we
theoretically establish the limiting distribution of the principal eigenvalue
of the suitably centered and scaled adjacency matrix, and use that distribution
for our hypothesis test. Secondly, we use this test to design a recursive
bipartitioning algorithm. Using quantifiable classification tasks on real world
networks with ground truth, we show that our algorithm outperforms existing
probabilistic models for learning overlapping clusters, and on unlabeled
networks, we show that we uncover nested community structure.


  Relativistic massless Dirac fermions can be probed with high-energy physics
experiments, but appear also as low-energy quasi-particle excitations in
electronic band structures. In condensed matter systems, their massless nature
can be protected by crystal symmetries. Classification of such
symmetry-protected relativistic band degeneracies has been fruitful, although
many of the predicted quasi-particles still await their experimental discovery.
Here we reveal, using angle-resolved photoemission spectroscopy, the existence
of two-dimensional type-II Dirac fermions in the high-temperature
superconductor La$_{1.77}$Sr$_{0.23}$CuO$_4$. The Dirac point, constituting the
crossing of $d_{x^2-y^2}$ and $d_{z^2}$ bands, is found approximately one
electronvolt below the Fermi level ($E_\mathrm{F}$) and is protected by mirror
symmetry. If spin-orbit coupling is considered, the Dirac point degeneracy is
lifted and the bands acquire a topologically non-trivial character. In certain
nickelate systems, band structure calculations suggest that the same type-II
Dirac fermions can be realised near $E_\mathrm{F}$.


  Consider the discrete maximal function acting on finitely supported functions
on the integers, \[ \mathcal{C}_\Lambda f(n) := \sup_{\lambda \in \Lambda} |
\sum_{p \in \pm \mathbb{P}} f(n-p) \log |p| \frac{e^{2\pi i \lambda p}}{p} |,\]
where $\pm \mathbb{P} := \{ \pm p : p \text{ is a prime} \}$, and $\Lambda
\subset [0,1]$. We give sufficient conditions on $\Lambda$, met by (finite
unions of) lacunary sets, for this to be a bounded sublinear operator on
$\ell^p(\mathbb{Z})$ for $\frac{3}{2} < p < 4$.


  Consider a system of n polynomial equations and r polynomial inequations in n
indeterminates of degree bounded by d with coefficients in a polynomial ring of
s parameters with rational coefficients of bit-size at most $\sigma$. From the
real viewpoint, solving such a system often means describing some
semi-algebraic sets in the parameter space over which the number of real
solutions of the considered parametric system is constant. Following the works
of Lazard and Rouillier, this can be done by the computation of a discriminant
variety. In this report we focus on the case where for a generic specialization
of the parameters the system of equations generates a radical zero-dimensional
ideal, which is usual in the applications. In this case, we provide a
deterministic method computing the minimal discriminant variety reducing the
problem to a problem of elimination. Moreover, we prove that the degree of the
computed minimal discriminant variety is bounded by $D:=(n+r)d^{(n+1)}$ and
that the complexity of our method is $\sigma^{\mathcal{O}(1)}
D^{\mathcal{O}(n+s)}$ bit-operations on a deterministic Turing machine.


  Intelligent Transportation Systems (ITSs) providing vehicle-related
statistical data are one of the key components for future smart cities. In this
context, knowledge about the current traffic flow is used for travel time
reduction and proactive jam avoidance by intelligent traffic control
mechanisms. In addition, the monitoring and classification of vehicles can be
used in the field of smart parking systems. The required data is measured using
networks with a wide range of sensors. Nevertheless, in the context of smart
cities no existing solution for traffic flow detection and vehicle
classification is able to guarantee high classification accuracy, low
deployment and maintenance costs, low power consumption and a
weather-independent operation while respecting privacy. In this paper, we
propose a radiobased approach for traffic flow detection and vehicle
classification using signal attenuation measurements and machine learning
algorithms. The results of comprehensive measurements in the field prove its
high classification success rate of about 99%.


  While machine learning approaches have shown remarkable performance in
biomedical image analysis, most of these methods rely on high-quality and
accurate imaging data. However, collecting such data requires intensive and
careful manual effort. One of the major challenges in imaging the Shoot Apical
Meristem (SAM) of Arabidopsis thaliana, is that the deeper slices in the
z-stack suffer from different perpetual quality-related problems like poor
contrast and blurring. These quality-related issues often lead to the disposal
of the painstakingly collected data with little to no control on quality while
collecting the data. Therefore, it becomes necessary to employ and design
techniques that can enhance the images to make them more suitable for further
analysis. In this paper, we propose a data-driven Deep Quantized Latent
Representation (DQLR) methodology for high-quality image reconstruction in the
Shoot Apical Meristem (SAM) of Arabidopsis thaliana. Our proposed framework
utilizes multiple consecutive slices in the z-stack to learn a low dimensional
latent space, quantize it and subsequently perform reconstruction using the
quantized representation to obtain sharper images. Experiments on a publicly
available dataset validate our methodology showing promising results.


  We propose bending energies for isotropic elastic plates and shells. For a
plate, we define and employ a surface tensor that symmetrically couples stretch
and curvature such that any elastic energy density constructed from its
invariants is invariant under spatial dilations. This kinematic measure and its
corresponding isotropic quadratic energy resolve outstanding issues in thin
structure elasticity, including the natural extension of primitive bending
strains for straight rods to plates, the assurance of a moment linear in the
bending measure, and the avoidance of induced mid-plane strains in response to
pure moments as found in some commonly used analytical plate models. Our
analysis also reveals that some other commonly used numerical models have the
right invariance properties, although they lack full generality at quadratic
order in stretch. We further extend our result to naturally-curved rods and
shells, for which the pure stretching of a curved rest configuration breaks
dilation invariance; the new shell bending measure we provide contrasts with
previous \emph{ad hoc} postulated forms. The concept that unifies these
theories is not dilation invariance, but rather through-thickness uniformity of
strain as a definition of pure stretching deformations. Our results provide a
clean basis for simple models of low-dimensional elastic systems, and should
enable more accurate analytical probing of the structure of singularities in
sheets and membranes.


  We demonstrate bending of light on the same side of the normal in a free
standing corrugated metal film under bi-directional illumination. Coherent
perfect absorption (CPA) is exploited to suppress the specular zeroth order
leading to effective back-bending of light into the `-1' order, while the `+1'
order is resonant with the surface mode. The effect is shown to be phase
sensitive yielding CPA and superscattering in the same geometry.


  We have obtained HST-NICMOS observations of five of M31's most metal rich
globular clusters. These data allow photometry of individual stars in the
clusters and their surrounding fields. However, to achieve our goals -- obtain
accurate luminosity functions to compare with their Galactic counterparts,
determine metallicities from the slope of the giant branch, identify long
period variables, and estimate ages from the AGB tip luminosity, we must be
able to disentangle the true properties of the population from the
observational effects associated with measurements made in very crowded fields.
  In this paper we present a careful analysis of photometry in crowded regions,
and show how image blending affects the results and interpretation of such
data. Although this analysis is specifically for our NICMOS observations in
M31, the techniques we develop can be applied to any imaging data taken in
crowded fields; we show how the effects of image blending will even limit NGST.
We use three different techniques to analyze the effects of crowding on our
data, including the insertion of artificial stars (traditional completeness
tests) and the creation of completely artificial clusters. They are used to
derive threshold- and critical-blending radii for each cluster, which determine
how close to the cluster center reliable photometry can be achieved. The
simulations also allow us to quantify and correct for the effects of blending
on the slope and width of the RGB at different surface brightness levels.


  We present a model of center vortices, represented by closed random lines in
continuous 2+1- dimensional space- time. These random lines are modeled as
being piece-wise linear and an ensemble is generated by Monte Carlo methods.
The physical space in which the vortex lines are defined is a cuboid with
periodic boundary conditions. Besides moving, growing and shrinking of the
vortex configuration, also reconnections are allowed. Our ensemble therefore
contains not a fixed, but a variable number of closed vortex lines. This is
expected to be important for realizing the deconfining phase transition. Using
the model, we study both vortex percolation and the potential V (R) between
quark and anti-quark as a function of distance R at different vortex densities,
vortex segment lengths, reconnection conditions and at different temperatures.
We have found three deconfinement phase transitions, as a function of density,
as a function of vortex segment length, and as a function of temperature. The
model reproduces the qualitative features of confinement physics seen in SU(2)
Yang-Mills theory.


  In 1980 Rostislav Grigorchuk constructed a group $G$ of intermediate growth,
and later obtained the following estimates on its growth function:
$$e^{\sqrt{n}}\precsim\gamma(n)\precsim e^{n^\beta},$$ where
$\beta=\log_{32}(31)\approx0.991$. Using elementary methods we bring the upper
bound down to $\log(2)/\log(2/\eta)\approx0.767$, where $\eta\approx0.811$ is
the real root of the polynomial $X^3+X^2+X-2$.


  We present a comprehensive investigation of the evolution of helimagnetic
correlations in Mn$_{1-x}$Fe$_x$Si with increasing doping. By combining
polarised neutron scattering and high resolution Neutron Spin Echo spectroscopy
we investigate three samples with $x$=0.09, 0.11 and 0.14, i.e. with
compositions on both sides of the concentration $x^* \sim 0.11$ where the
helimagnetic Bragg peaks disappear and between $x^*$ and the quantum critical
concentration $x_C \sim 0.17$, where $T_C$ vanishes. We find that the abrupt
disappearance of the long range helical periodicity at $x^*$, does not affect
the precursor fluctuating correlations. These build up with decreasing
temperature in a similar way as for the parent compound MnSi. Also the dynamics
bears strong similarities to MnSi. The analysis of our results indicates that
frustration, possibly due to achiral RKKY interactions, increases with
increasing Fe doping. We argue that this effect explains both the expansion of
the precursor phase with increasing $x$ and the abrupt disappearance of long
range helimagnetic periodicity at $x^*$.


  We consider a model of Non-Brownian self-propelled particles with
anti-alignment interactions where particles try to avoid each other by
attempting to turn into opposite directions. The particles undergo apparent
Brownian motion, even though the particle's equations are fully deterministic.
We show that the deterministic interactions lead to internal, dynamical noise.
Starting from the N-particle Liouville equation, a kinetic equation for the
one-particle distribution function is obtained. We show that the usual
mean-field assumption of molecular chaos leads to qualitatively wrong
predictions such as an infinite coefficient of self-diffusion. Going beyond
mean-field and applying the refined assumption of ``one-sided molecular
chaos'', we analytically calculate the scattering of particles in the limit of
low density and obtain explicit expressions for the dynamical noise of an
effective one-particle Langevin-equation and the corresponding self-diffusion.
In this calculation, the so-called superposition principle of traditional
kinetic theory was modified to handle a system with non-Hamiltonian dynamics
involving phase-space compression. The predicted theoretical expressions for
the relaxation of hydrodynamic modes and the self-diffusion coefficient are in
excellent, quantitative agreement with agent-based simulations at small density
and small anti-alignment strength. At large particle densities, a given
particle is constantly approached and abandoned by different collision
partners. Modelling this switching of partners by a random telegraph process
and exactly solving a self-consistent integral equation, we obtain explicit
expressions for the noise correlations of the effective one-particle
Langevin-equation. Comparison with agent-based simulation show very good
agreement.


  Motion prediction and planning are vital tasks in autonomous driving, and
recent efforts have shifted to machine learning-based approaches. The
challenges include understanding diverse road topologies, reasoning traffic
dynamics over a long time horizon, interpreting heterogeneous behaviors, and
generating policies in a large continuous state space. Inspired by the success
of large language models in addressing similar complexities through model
scaling, we introduce a scalable trajectory model called State Transformer
(STR). STR reformulates the motion prediction and motion planning problems by
arranging observations, states, and actions into one unified sequence modeling
task. Our approach unites trajectory generation problems with other sequence
modeling problems, powering rapid iterations with breakthroughs in neighbor
domains such as language modeling. Remarkably, experimental results reveal that
large trajectory models (LTMs), such as STR, adhere to the scaling laws by
presenting outstanding adaptability and learning efficiency. Qualitative
results further demonstrate that LTMs are capable of making plausible
predictions in scenarios that diverge significantly from the training data
distribution. LTMs also learn to make complex reasonings for long-term
planning, without explicit loss designs or costly high-level annotations.


  We compute the effects of generic short-range interactions on gapless
electrons residing at the quantum critical point separating a two-dimensional
Dirac semimetal (DSM) and a symmetry-preserving band insulator (BI). The
electronic dispersion at this critical point is anisotropic ($E_{\mathbf k}=\pm
\sqrt{v^2 k^2_x + b^2 k^{2n}_y}$ with $n=2$), which results in unconventional
scaling of physical observables. Due to the vanishing density of states
($\varrho(E) \sim |E|^{1/n}$), this anisotropic semimetal (ASM) is stable
against weak short-range interactions. However, for stronger interactions the
direct DSM-BI transition can either $(i)$ become a first-order transition, or
$(ii)$ get avoided by an intervening broken-symmetry phase (BSP). We perform a
renormalization group analysis by perturbing away from the one-dimensional
limit with the small parameter $\epsilon = 1/n$, augmented with a $1/n$
expansion (parametrically suppressing quantum fluctuations in higher
dimension). We identify charge density wave (CDW), antiferromagnet (AFM) and
singlet s-wave superconductor as the three dominant candidates for the BSP. The
onset of any such order at strong coupling $(\sim \epsilon)$ takes place
through a continuous quantum phase transition across multicritical point. We
also present the phase diagram of an extended Hubbard model for the ASM,
obtained via the controlled deformation of its counterpart in one dimension.
The latter displays spin-charge separation and instabilities to CDW, spin
density wave, and Luther-Emery liquid phases at arbitrarily weak coupling. The
spin density wave and Luther-Emery liquid phases deform into pseudospin
SU(2)-symmetric quantum critical points separating the ASM from the AFM and
superconducting orders, respectively. Our results can be germane for a
uniaxially strained honeycomb lattice or organic compound
$\alpha$-(BEDT-TTF)$_2\text{I}_3$.


  We theoretically investigate the inhomogeneous Fulde-Ferrell (FF)
superfluidity in a three dimensional atomic Fermi gas with Rashba spin-orbit
coupling near a broad Feshbach resonance. We show that within mean-field theory
the FF superfluid state is always more favorable than the standard
Bardeen-Cooper-Schrieffer (BCS) superfluid state when an in-plane Zeeman field
is applied. We present a qualitative finite-temperature phase diagram near
resonance and argue that the predicted FF superfluid is observable with
experimentally attainable temperatures (i.e., $T\sim0.2T_{F}$, where $T_{F}$ is
the characteristic Fermi degenerate temperature).


  We have obtained high dynamic range, good natural seeing i' images of BL
Lacertae objects (BL Lacs) to search for the AGN host and thus constrain the
source redshift. These objects are drawn from a sample of bright flat-spectrum
radio sources that are either known (via recent Fermi LAT observations)
gamma-ray emitters or similar sources that might be detected in continuing
gamma-ray observations. All had spectroscopic confirmation as BL Lac sources,
but no redshift solution. We detected hosts for 25/49 objects. As these
galaxies have been argued to be standard candles, our measured host magnitudes
provide redshift estimates (ranging from 0.2--1.0). Lower bounds are
established on the redshifts of non-detections. The mean of the fit redshifts
(and lower limits) is higher than those of spectroscopic solutions in the
radio- and gamma-ray- loud parent samples, suggesting corrections may be needed
for the luminosity function and evolution of these sources.


  Integer Linear Programming (ILP) has been proposed as a formalism for
encoding precise structural and semantic constraints for Natural Language
Inference (NLI). However, traditional ILP frameworks are non-differentiable,
posing critical challenges for the integration of continuous language
representations based on deep learning. In this paper, we introduce a novel
approach, named Diff-Comb Explainer, a neuro-symbolic architecture for
explanation-based NLI based on Differentiable BlackBox Combinatorial Solvers
(DBCS). Differently from existing neuro-symbolic solvers, Diff-Comb Explainer
does not necessitate a continuous relaxation of the semantic constraints,
enabling a direct, more precise, and efficient incorporation of neural
representations into the ILP formulation. Our experiments demonstrate that
Diff-Comb Explainer achieves superior performance when compared to conventional
ILP solvers, neuro-symbolic black-box solvers, and Transformer-based encoders.
Moreover, a deeper analysis reveals that Diff-Comb Explainer can significantly
improve the precision, consistency, and faithfulness of the constructed
explanations, opening new opportunities for research on neuro-symbolic
architectures for explainable and transparent NLI in complex domains.


  Pump-probe time-resolved x-ray diffraction of allowed and nearly forbidden
reflections in InSb is used to follow the propagation of a coherent acoustic
pulse generated by ultrafast laser-excitation. The surface and bulk components
of the strain could be simultaneously measured due to the large x-ray
penetration depth. Comparison of the experimental data with dynamical
diffraction simulations suggests that the conventional model for impulsively
generated strain underestimates the partitioning of energy into coherent modes.


  We present a method to compute the many-body real-time Green's function using
an adaptive variational quantum dynamics simulation approach. The real-time
Green's function involves the time evolution of a quantum state with one
additional electron with respect to the ground state wavefunction that is first
expressed as a linear combination of state vectors. The real-time evolution and
Green's function is obtained by combining the dynamics of the individual
statevectors in the linear combination. The use of the adaptive protocol
enables us to generate compact ans\"atze on-the-fly while running the
simulation.
  In order to improve the convergence of spectral features
  Pad\'e approximants are applied to obtain the Fourier transform of Green's
function. We demonstrate the evaluation of Green's function on an IBM Q quantum
computer. As a part of our error mitigation strategy, we develop a
resolution-enhancing method that we successfully apply on the noisy data from
the real quantum hardware.


  The influence of Unruh effect on the quantum and classical correlation of a
quantum entangled state is investigated, when one or two of the observers are
accelerated. It is found that the quantum and classical correlation would
approach to a finite value with the increase of the acceleration when only one
observer is accelerated, but they would decrease to zero quickly when two
observers are accelerated. The latter result implies that the gravitational
field would break the correlation obviously when the acceleration is caused by
the gravitational field. Thus, when the two observers stay in the gravitational
field, the quantum or classical correlation would not easy to be preserved for
a long time.


  Learning multiple domains/tasks with a single model is important for
improving data efficiency and lowering inference cost for numerous vision
tasks, especially on resource-constrained mobile devices. However,
hand-crafting a multi-domain/task model can be both tedious and challenging.
This paper proposes a novel approach to automatically learn a multi-path
network for multi-domain visual classification on mobile devices. The proposed
multi-path network is learned from neural architecture search by applying one
reinforcement learning controller for each domain to select the best path in
the super-network created from a MobileNetV3-like search space. An adaptive
balanced domain prioritization algorithm is proposed to balance optimizing the
joint model on multiple domains simultaneously. The determined multi-path model
selectively shares parameters across domains in shared nodes while keeping
domain-specific parameters within non-shared nodes in individual domain paths.
This approach effectively reduces the total number of parameters and FLOPS,
encouraging positive knowledge transfer while mitigating negative interference
across domains. Extensive evaluations on the Visual Decathlon dataset
demonstrate that the proposed multi-path model achieves state-of-the-art
performance in terms of accuracy, model size, and FLOPS against other
approaches using MobileNetV3-like architectures. Furthermore, the proposed
method improves average accuracy over learning single-domain models
individually, and reduces the total number of parameters and FLOPS by 78% and
32% respectively, compared to the approach that simply bundles single-domain
models for multi-domain learning.


  Many of the recent advances in the field of artificial intelligence have been
fueled by the highly successful backpropagation of error (BP) algorithm, which
efficiently solves the credit assignment problem in artificial neural networks.
However, it is unlikely that BP is implemented in its usual form within
biological neural networks, because of its reliance on non-local information in
propagating error gradients. Since biological neural networks are capable of
highly efficient learning and responses from BP trained models can be related
to neural responses, it seems reasonable that a biologically viable
approximation of BP underlies synaptic plasticity in the brain.
Gradient-adjusted incremental target propagation (GAIT-prop or GP for short)
has recently been derived directly from BP and has been shown to successfully
train networks in a more biologically plausible manner. However, so far, GP has
only been shown to work on relatively low-dimensional problems, such as
handwritten-digit recognition. This work addresses some of the scaling issues
in GP and shows it to perform effective multi-layer credit assignment in deeper
networks and on the much more challenging ImageNet dataset.


  In this paper we study the properties of diquarks (composed of $u$ and/or $d$
quarks) in the Bethe-Salpeter formalism under the covariant instantaneous
approximation. We calculate their BS wave functions and study their effective
interaction with the pion. Using the effective coupling constant among the
diquarks and the pion, in the heavy quark limit $m_Q\to\infty$, we calculate
the decay widths of $\Sigma_Q^{(*)}$ ($Q=c,b$) in the BS formalism under the
covariant instantaneous approximation and then give predictions of the decay
widths $\Gamma(\Sigma_b^{(*)}\to\Lambda_b+\pi)$.


  We survey the highly ionized circumgalactic media (CGM) of 29 blindly
selected galaxies at 0.49 < z_(gal) < 1.44 based on high-S/N ultraviolet
spectra of z > 1 QSOs and the galaxy database from the COS Absorption Survey of
Baryon Harbors (CASBaH). We detect the Ne VIII doublet in nine of the galaxies,
and for gas with N(Ne VIII) > 10^13.3 cm^-2 (> 10^13.5 cm^-2), we derive a Ne
VIII covering fraction f_c = 75 +15/-25% (44 +22/-20%) within impact parameter
(rho) < 200 kpc of M_* = 10^(9.5-11.5) Msol galaxies and f_c = 70 +16/-22% (f_c
= 42 +20/-17%) within rho < 1.5 virial radii. We estimate the mass in Ne
VIII-traced gas to be M_gas(Ne VIII) > 10^9.5 Msol (Z/Zsol)^-1, or 6-20% of the
expected baryonic mass if the Ne VIII absorbers have solar metallicity.
Ionizing Ne VII to Ne VIII requires 207 eV, and photons with this energy are
scarce in the CGM. However, for the median halo mass and redshift of our
sample, the virial temperature is close to the peak temperature for the Ne VIII
ion, and the Ne VIII-bearing gas is plausibly collisionally ionized near this
temperature. Moreover, we find that photoionized Ne VIII requires cool and
low-density clouds that would be highly underpressured (by approximately two
orders of magnitude) relative to the putative, ambient virialized medium,
complicating scenarios where such clouds could survive. Thus, more complex
(e.g., non-equilibrium) models may be required; this first statistical sample
of Ne VIII absorber/galaxy systems will provide stringent constraints for
future CGM studies.


  This is a short summary of main results of our paper arXiv:0811.2435 where
the concept of motivic Donaldson-Thomas invariant was introduced. It also
contains a discussion of some open questions from the loc.cit., in particular,
the geometry related to the split attractor flow.


  In this paper we highlight the scopes of engineering bio-inspired political
systems, which are political systems based on the properties of life that
self-organize the increasing complexity of human social systems. We describe
bio-inspired political systems and conjecture about various ways to get to
them, most notably, metaheuristics, modeling and simulation and complexified
topologies. Bio-inspired political systems operate with nature-based dynamics,
inspired on the knowledge that has been acquired about complexity from natural
social systems and life. Bio-inspired political systems are presented as the
best alternative for organizing human sociopolitical interactions as
computation and microelectronics-based technology profoundly modify the ways in
which humans decide. Therefore, weakening classical political systems. For
instance, dwindling top-down power structures, modifying the notion of
geographical spatiality and augmenting the political granularity. We also argue
that, more than a new theoretical proposal, bio-inspired political systems are
coming to be the political systems of the future.


  This paper proposes a cross wavelet transform based method for
Electrocardiogram signal analysis where parameters are identified from wavelet
cross spectrum and wavelet cross coherence of ECG patterns. Most of the ECG
analysing systems use explicit time plane features for cardiac pattern
classification. Application of this proposed technique for classification
eliminates the need for extraction of various explicit time plane features and
hence reduces the complexity of the system. The cross-correlation is the
measure of similarity between two waveforms or two time series and the cross
examination reveals localized similarities in time and scale. Parameters
extracted from Wavelet Cross Spectrum (WCS) and Wavelet Coherence (WCOH) is
used for classification. A pathologically varying pattern in QT zone of
inferior lead III shows the presence of Inferior Myocardial Infarction (IMI).
The Cross Wavelet Transform and Wavelet Coherence is used for the cross
examination of single normal and abnormal (IMI) beats. A normal template beat
is selected as the absolute normal pattern. Computation of the WCS and WCOH of
the selected normal template and various other normal and abnormal beats
reveals the existence of variation among patterns under study. The Wavelet
cross spectrum and Wavelet coherence of various ECG patterns shows
distinguishing characteristics over two specific regions R1 and R2, where R1 is
the QRS complex location and R2 is the T wave region. Parameters are identified
for classification of Type 1 IMI (non Q type, with ST elevation and attenuated
QRS complex) and Type 2 IMI (Q type MI with deep Q and inverted T) and normal
subjects. Accuracy of the proposed classification method is obtained as 99.43%
for normal and abnormal class and 88.5% and 87.02% for Type I and Type II
respectively.


  The study of high pressure (2 GPa) synthesized MgB2-based materials allows us
to conclude that higher borides (with near MgB12 stoichiometry) and
oxygen-enriched Mg-B-O inclusions can be pinning centers in nanostructural
magnesium diboride matrix (with average grain sizes of 15-37 nm). It has been
established that additions of Ti or SiC as well as manufacturing temperature
can affect the size, amount and distribution of these inclusions in the
material structure and thus, influence critical current density. The
superconducting behavior of materials with near MgB12 stoichiometry of matrix
is discussed.


  In this paper we present a necessary conditions, that simple close geodesics
on regular tetrahedra in the 3-dimensional hyperbolic space must satisfy.
Furthermore, we explicitly describe three classes of simple closed geodesics on
regular tetrahedra in the hyperbolic 3-space. These are so-called
2-homogeneous, 3-homogeneous and (3,2)-homogeneous geodesics. Up to a rigid
motion of a tetrahedron there exists a unique geodesic in each class.


  Surface assays such as ELISA are pervasive in clinics and research and
predominantly standardized in microtiter plates (MTP). MTPs provide many
advantages but are often detrimental to surface assay efficiency due to
inherent mass transport limitations. Microscale flows can overcome these and
largely improve assay kinetics. However, the disruptive nature of microfluidics
with existing labware and protocols has narrowed its transformative potential.
We present WellProbe, a novel microfluidic concept compatible with MTPs. With
it, we show that immunoassays become more sensitive at low concentrations (up
to 9-fold signal improvement in 12-fold less time), richer in information with
3-4 different kinetic conditions, and can be used to estimate kinetic
parameters, minimize washing steps and non-specific binding, and identify
compromised results. We further multiplex single-well assays combining
WellProbes kinetic regions with tailored microarrays. Finally, we demonstrate
our system in a context of immunoglobulin subclass evaluation, increasingly
regarded as clinically relevant.


  The following is a response to the US Department of Commerce's Request for
Information (RFI) regarding AI and Open Government Data Assets. First, we
commend the Department for its initiative in seeking public insights on the
organization and sharing of data. To facilitate scientific discovery and
advance AI development, it is crucial for all data producers, including the
Department of Commerce and other governmental entities, to prioritize the
quality of their data corpora. Ensuring data is accessible, scalable, and
secure is essential for harnessing its full potential. In our response, we
outline best practices and key considerations for AI and the Department of
Commerce's Open Government Data Assets.


  Capturing more information, e.g. geometry and material, using optical cameras
can greatly help the perception and understanding of complex scenes. This paper
proposes a novel method to capture the spectral and light field information
simultaneously. By using a delicately designed chromatic aberration enlarged
camera, the spectral-varying slices at different depths of the scene can be
easily captured. Afterwards, the multispectral focal stack, which is composed
of a stack of multispectral slice images focusing on different depths, can be
recovered from the spectral-varying slices by using a Local Linear
Transformation (LLT) based algorithm. The experiments verify the effectiveness
of the proposed method.


  Measuring and analyzing the flow of customers in retail stores is essential
for a retailer to better comprehend customers' behavior and support
decision-making. Nevertheless, not much attention has been given to the
development of novel technologies for automatic people counting. We introduce
LRCN-RetailNet: a recurrent neural network architecture capable of learning a
non-linear regression model and accurately predicting the people count from
videos captured by low-cost surveillance cameras. The input video format
follows the recently proposed RGBP image format, which is comprised of color
and people (foreground) information. Our architecture is capable of considering
two relevant aspects: spatial features extracted through convolutional layers
from the RGBP images; and the temporal coherence of the problem, which is
exploited by recurrent layers. We show that, through a supervised learning
approach, the trained models are capable of predicting the people count with
high accuracy. Additionally, we present and demonstrate that a straightforward
modification of the methodology is effective to exclude salespeople from the
people count. Comprehensive experiments were conducted to validate, evaluate
and compare the proposed architecture. Results corroborated that LRCN-RetailNet
remarkably outperforms both the previous RetailNet architecture, which was
limited to evaluating a single image per iteration; and a state-of-the-art
neural network for object detection. Finally, computational performance
experiments confirmed that the entire methodology is effective to estimate
people count in real-time.


  In recent years, a growing interest on the equilibrium of compact
astrophysical objects like white dwarf and neutron stars has been manifested.
In particular, various modifications due to Planck scale energy effects have
been considered. In this paper we analyze the modification induced by Gravity's
Rainbow on the equilibrium configurations described by the
Tolman-Oppenheimer-Volkoff (TOV) equation. Our purpose is to explore the
possibility that the Rainbow Planck-scale deformation of space-time could
support the existence of different compact stars.


  The muzzle blast caused by the discharge of a firearm generates a loud,
impulsive sound that propagates away from the shooter in all directions. The
location of the source can be computed from time-of-arrival measurements of the
muzzle blast on multiple acoustic sensors at known locations, a technique known
as multilateration. The multilateration problem is considerably simplified by
assuming straight-line propagation in a homogeneous medium, a model for which
there are multiple published solutions. Live-fire tests of the ShotSpotter
gunshot location system in Pittsburgh, PA were analyzed off-line under several
algorithms and geometric constraints to evaluate the accuracy of acoustic
multilateration in a forensic context. Best results were obtained using the
algorithm due to Mathias, Leonari and Galati under a two-dimensional geometric
constraint. Multilateration on random subsets of the participating sensor array
show that 96% of shots can be located to an accuracy of 15 m or better when six
or more sensors participate in the solution.


  The deleterious effects of the background and radiation environment produced
by the decaying muon beam are studied in the Muon Collider Interaction Region,
detector and Machine-Detector Interface designs.


  It is shown, that the radiation of the charge, moving with uniform
acceleration or uniformly moving round a circle and also freely moving in a
gravitational field, contradicts the principle of equivalence. It is also
shown, that the interaction of the charges, moving with uniform acceleration or
uniformly circling, which has been calculated within the framework of classical
electrodynamics, leads to the violation of laws of conservation of energy,
impulse and angular momentum. We have offered a method in which way to conform
electrodynamics to the principle of equivalence. So in the electrodynamics,
which has been conformed in such a way, all the mentioned violations of the
laws of conservation are automatically removed and the stability of
Rutherford's atom is explained. It is shown that the changes, which we have
brought into the electrodynamics, do not contradict the results of experiments.


  We give an analysis of the spin-weighted Green's functions well-defined in a
conical space. We apply these results in the case of a straight cosmic string
and in the Rindler space in order to determine generally the Euclidean Green's
functions for the massless spin 1/2 field and for the electromagnetic field. We
give also the corresponding Green's functions at zero temperature. However,
except for the scalar field, it seems that these Euclidean Green's functions do
not correspond to the thermal Feynman Green's functions.


  We investigate the problem of searching for a lexeme-set in speech by
searching for its inflectional variants. Experimental results indicate how
lexeme-set search performance changes with the number of hypothesized
inflections, while ablation experiments highlight the relative importance of
different components in the lexeme-set search pipeline and the value of using
curated inflectional paradigms. We provide a recipe and evaluation set for the
community to use as an extrinsic measure of the performance of inflection
generation approaches.


  Deep learning recommendation models (DLRM) rely on large embedding tables to
manage categorical sparse features. Expanding such embedding tables can
significantly enhance model performance, but at the cost of increased
GPU/CPU/memory usage. Meanwhile, tech companies have built extensive
cloud-based services to accelerate training DLRM models at scale. In this
paper, we conduct a deep investigation of the DLRM training platforms at
AntGroup and reveal two critical challenges: low resource utilization due to
suboptimal configurations by users and the tendency to encounter abnormalities
due to an unstable cloud environment. To overcome them, we introduce
DLRover-RM, an elastic training framework for DLRMs designed to increase
resource utilization and handle the instability of a cloud environment.
DLRover-RM develops a resource-performance model by considering the unique
characteristics of DLRMs and a three-stage heuristic strategy to automatically
allocate and dynamically adjust resources for DLRM training jobs for higher
resource utilization. Further, DLRover-RM develops multiple mechanisms to
ensure efficient and reliable execution of DLRM training jobs. Our extensive
evaluation shows that DLRover-RM reduces job completion times by 31%, increases
the job completion rate by 6%, enhances CPU usage by 15%, and improves memory
utilization by 20%, compared to state-of-the-art resource scheduling
frameworks. DLRover-RM has been widely deployed at AntGroup and processes
thousands of DLRM training jobs on a daily basis. DLRover-RM is open-sourced
and has been adopted by 10+ companies.


  To handle different types of Many-Objective Optimization Problems (MaOPs),
Many-Objective Evolutionary Algorithms (MaOEAs) need to simultaneously maintain
convergence and population diversity in the high-dimensional objective space.
In order to balance the relationship between diversity and convergence, we
introduce a Kernel Matrix and probability model called Determinantal Point
Processes (DPPs). Our Many-Objective Evolutionary Algorithm with Determinantal
Point Processes (MaOEADPPs) is presented and compared with several
state-of-the-art algorithms on various types of MaOPs \textcolor{blue}{with
different numbers of objectives}. The experimental results demonstrate that
MaOEADPPs is competitive.


  There are around 5.3 billion Internet users, amounting to 65.7% of the global
population, and web technology is the backbone of the services delivered via
the Internet. To ensure web applications are free from security-related bugs,
web developers test the server-side web applications before deploying them to
production. The tests are commonly conducted through the interfaces (i.e., Web
API) that the applications expose since they are the entry points to the
application. Fuzzing is one of the most promising automated software testing
techniques suitable for this task; however, the research on (server-side) web
application fuzzing has been rather limited compared to binary fuzzing which is
researched extensively. This study reviews the state-of-the-art fuzzing
frameworks for testing web applications through web API, identifies open
challenges, and gives potential future research. We collect papers from seven
online repositories of peer-reviewed articles over the last ten years. Compared
to other similar studies, our review focuses more deeply on revealing prior
work strategies in generating valid HTTP requests, utilising feedback from the
Web Under Tests (WUTs), and expanding input spaces. The findings of this survey
indicate that several crucial challenges need to be solved, such as the
ineffectiveness of web instrumentation and the complexity of handling
microservice applications. Furthermore, some potential research directions are
also provided, such as fuzzing for web client programming. Ultimately, this
paper aims to give a good starting point for developing a better web fuzzing
framework.


  Multi-crystalline silicon is widely used for producing low-cost and
high-efficiency solar cells. During crystal growth and device fabrication,
silicon solar cells contain grain boundaries (GBs) which are preferential
segregation sites for atomic impurities such as oxygen atoms. GBs can induce
charge carriers recombination significantly reducing carrier lifetimes and
therefore they can be detrimental for Si device performance. We studied the
correlation between structural, energetic and electronic properties of
{\Sigma}3{111} Si GB in the presence of vacancies, strain and multiple O
segregation. The study of the structural and energetic properties of GBs in the
presence of strain and vacancies gives an accurate description of the complex
mechanisms that control the segregation of oxygen atoms. We analysed tensile
and compressive strain and we obtained that local tensile strain around O
impurities is very effective for segregation. We also studied the role of
multiple O impurities in the presence of Si vacancies finding that the
segregation is favorite for those structures which have restored tetrahedral
covalent bonds. The presence of vacancies attract atomic impurities in order to
restore the electronic stability: the interstitial impurity becomes
substitutional. This analysis was the starting point to correlate the change of
the electronic properties in {\Sigma}3{111}Si GBs with O impurities in the
presence of strain and vacancies. For each structure we analysed the density of
states and its projection on atoms and states, the band gaps, the segregation
energy and their correlation in order to characterise the nature of new energy
levels. Actually, knowing the origin of defined electronic states would allow
the optimization of materials in order to reduce non-radiative electron-hole
recombination avoiding charge and energy losses and therefore improving solar
cell efficiency.


  This paper is a review of recent mathematical and computational advances in
optical tomography. We discuss the physical foundations of forward models for
light propagation on microscopic, mesoscopic and macroscopic scales. We also
consider direct and numerical approaches to the inverse problems that arise at
each of these scales. Finally, we outline future directions and open problems
in the field.


  Coulomb interactions that occur in electronic structure calculations are
correlated by allowing basis function components of the interacting densities
to polarize, thereby reducing the magnitude of the interaction. Exchange
integrals of molecular orbitals are not correlated. The modified Coulomb
interactions are used in single-determinant or configuration interaction
calculations. The objective is to account for dynamical correlation effects
without explicitly introducing higher spherical harmonic functions into the
molecular orbital basis. Molecular orbital densities are decomposed into a
distribution of spherical components that conserve the charge and each of the
interacting components is considered as a two-electron wavefunction embedded in
the system acted on by an average field Hamiltonian plus . A method of avoiding
redundancy is described. Applications to atoms, negative ions and molecules
representing different types of bonding and spin states are discussed.


  We propose a means for constructing highly accurate equations of state (EOS)
for elemental solids and liquids essentially from first principles, based upon
a particular decomposition of the underlying condensed matter Hamiltonian for
the nuclei and electrons. We also point out that at low pressures the neglect
of anharmonic and electron-phonon terms, both contained in this formalism,
results in errors of less than 5% in the thermal parts of the thermodynamic
functions. Then we explicitly display the forms of the remaining terms in the
EOS, commenting on the use of experiment and electronic structure theory to
evaluate them. We also construct an EOS for Aluminum and compare the resulting
Hugoniot with data up to 5 Mbar, both to illustrate our method and to see
whether the approximation of neglecting anharmonicity et al. remains viable to
such high pressures. We find a level of agreement with experiment that is
consistent with the low-pressure results.


  State-of-the-art instance-aware semantic segmentation algorithms use
axis-aligned bounding boxes as an intermediate processing step to infer the
final instance mask output. This often leads to coarse and inaccurate mask
proposals due to the following reasons: Axis-aligned boxes have a high
background to foreground pixel-ratio, there is a strong variation of mask
targets with respect to the underlying box, and neighboring instances
frequently reach into the axis-aligned bounding box of the instance mask of
interest. In this work, we overcome these problems by proposing to use oriented
boxes as the basis to infer instance masks. We show that oriented instance
segmentation improves the mask predictions, especially when objects are
diagonally aligned, touching, or overlapping each other. We evaluate our model
on the D2S and Screws datasets and show that we can significantly improve the
mask accuracy by 10% and 12% mAP compared to instance segmentation using
axis-aligned bounding boxes, respectively. On the newly introduced Pill Bags
dataset we outperform the baseline using only 10% of the mask annotations.


  The aim of the paper is to formulate a discrete analogue of the claim made by
Alvarez-Gaume et al., realizing the partition function of the free fermion on a
closed Riemann surface of genus g as a linear combination of 2^{2g} Pfaffians
of Dirac operators. Let G=(V,E) be a finite graph embedded in a closed Riemann
surface X of genus g, x_e the collection of independent variables associated
with each edge e of G (collected in one vector variable x) and S the set of all
2^{2g} Spin-structures on X. We introduce 2^{2g} rotations rot_s and (2|E|
times 2|E|) matrices D(s)(x), s in S, of the transitions between the oriented
edges of G determined by rotations rot_s. We show that the generating function
for the even subsets of edges of G, i.e., the Ising partition function, is a
linear combination of the square roots of 2^{2g} Ihara-Selberg functions
I(D(s)(x)) also called Feynman functions. By a result of Foata--Zeilberger
holds I(D(s)(x))= det(I-D'(s)(x)), where D'(s)(x) is obtained from D(s)(x) by
replacing some entries by 0. Thus each Feynman function is computable in
polynomial time. We suggest that in the case of critical embedding of a
bipartite graph G, the Feynman functions provide suitable discrete analogues
for the Pfaffians of discrete Dirac operators.


  For models of concurrent and distributed systems, it is important and also
challenging to establish correctness in terms of safety and/or liveness
properties. Theories of distributed systems consider equivalences fundamental,
since they (1) preserve desirable correctness characteristics and (2) often
allow for component substitution making compositional reasoning feasible.
Modeling distributed systems often requires abstraction utilizing
nondeterminism which induces unintended behaviors in terms of infinite
executions with one nondeterministic choice being recurrently resolved, each
time neglecting a single alternative. These situations are considered
unrealistic or highly improbable. Fairness assumptions are commonly used to
filter system behaviors, thereby distinguishing between realistic and
unrealistic executions. This allows for key arguments in correctness proofs of
distributed systems, which would not be possible otherwise. Our contribution is
an equivalence spectrum in which fairness assumptions are preserved. The
identified equivalences allow for (compositional) reasoning about correctness
incorporating fairness assumptions.


  In 1998 the physicist Andre Trautman conjectured that a three-dimensional CR
manifold is locally realizable if and only if its canonical bundle admits a
closed nowhere zero section. We review the relevant definitions, give the
physical context, and outline an earlier result which established a weak
version of the Conjecture.


  We prove the existence and uniqueness of positive periodic solution for a
model describing the dynamics of cell volume flux, introduced in Julio A.
Hernandez \cite{H}. We also show that the periodic solution is a global
attractor. Our results confirm the conjectures made in an interesting recent
book of P.J. Torres \cite{T}.


  We present high statistics Monte Carlo results for the Drossel-Schwabl forest
fire model in 2 dimensions. They extend to much larger lattices (up to
$65536\times 65536$) than previous simulations and reach much closer to the
critical point (up to $\theta \equiv p/f = 256000$). They are incompatible with
all previous conjectures for the (extrapolated) critical behaviour, although
they in general agree well with previous simulations wherever they can be
directly compared. Instead, they suggest that scaling laws observed in previous
simulations are spurious, and that the density $\rho$ of trees in the critical
state was grossly underestimated. While previous simulations gave $\rho\approx
0.408$, we conjecture that $\rho$ actually is equal to the critical threshold
$p_c = 0.592...$ for site percolation in $d=2$. This is however still far from
the densities reachable with present day computers, and we estimate that we
would need many orders of magnitude higher CPU times and storage capacities to
reach the true critical behaviour -- which might or might not be that of
ordinary percolation.


  The values of the so-called {\em Dedekind--Rademacher cocycle} at certain
real quadratic arguments are shown to be global $p$-units in the narrow Hilbert
class field of the associated real quadratic field, as predicted by conjectures
of Darmon, Dasgupta, and Vonk. The strategy for proving this result combines an
approach of Darmon-Pozzi-Vonk with one crucial extra ingredient: the study of
infinitesimal deformations of irregular Hilbert Eisenstein series of weight one
in the anti-parallel direction, building on the techniques in earlier work of
Betina, Dimitrov, and Pozzi.


  Humans tend to form quick subjective first impressions of non-physical
attributes when seeing someone's face, such as perceived trustworthiness or
attractiveness. To understand what variations in a face lead to different
subjective impressions, this work uses generative models to find semantically
meaningful edits to a face image that change perceived attributes. Unlike prior
work that relied on statistical manipulation in feature space, our end-to-end
framework considers trade-offs between preserving identity and changing
perceptual attributes. It maps identity-preserving latent space directions to
changes in attribute scores, enabling transformation of any input face along an
attribute axis according to a target change. We train on real and synthetic
faces, evaluate for in-domain and out-of-domain images using predictive models
and human ratings, demonstrating the generalizability of our approach.
Ultimately, such a framework can be used to understand and explain biases in
subjective interpretation of faces that are not dependent on the identity.


  In the typical analysis of a data set, a single method is selected for
statistical reporting even when equally applicable methods yield very different
results. Examples of equally applicable methods can correspond to those of
different ancillary statistics in frequentist inference and of different prior
distributions in Bayesian inference. More broadly, choices are made between
parametric and nonparametric methods and between frequentist and Bayesian
methods.
  Rather than choosing a single method, it can be safer, in a game-theoretic
sense, to combine those that are equally appropriate in light of the available
information. Since methods of combining subjectively assessed probability
distributions are not objective enough for that purpose, this paper introduces
a method of distribution combination that does not require any assignment of
distribution weights. It does so by formalizing a hedging strategy in terms of
a game between three players: nature, a statistician combining distributions,
and a statistician refusing to combine distributions. The optimal move of the
first statistician reduces to the solution of a simpler problem of selecting an
estimating distribution that minimizes the Kullback-Leibler loss maximized over
the plausible distributions to be combined. The resulting combined distribution
is a linear combination of the most extreme of the distributions to be combined
that are scientifically plausible. The optimal weights are close enough to each
other that no extreme distribution dominates the others.
  The new methodology is illustrated by combining conflicting empirical Bayes
methodologies in the context of gene expression data analysis.


  A mixed quantum state shared between two parties is said to be distillable
if, by means of a protocol involving only local quantum operations and
classical communication, the two parties can transform some number of copies of
that state into a single shared pair of qubits having high fidelity with a
maximally entangled state state. In this paper it is proved that there exist
states that are distillable, but for which an arbitrarily large number of
copies is required before any distillation procedure can produce a shared pair
of qubits with even a small amount of entanglement. Specifically, for every
positive integer n there exists a state that is distillable, but given n or
fewer copies of that state every distillation procedure outputting a single
shared pair of qubits will output those qubits in a separable state.
Essentially all previous examples of states proved to be distillable were such
that some distillation procedure could output an entangled pair of qubits given
a single copy of the state in question.


  We investigate the minimum conductivity of graphene within a quasiclassical
approach taking into account electron-hole coherence effects which stem from
the chiral nature of low energy excitations. Relying on an analytical solution
of the kinetic equation in the electron-hole coherent and incoherent cases we
study both the electrical and thermal conductivity whose relation fullfills
Wiedemann-Franz law. We found that the most of the previous findings based on
the Boltzmann equation are restricted to only high mobility samples where
electron-hole coherence effects are not sufficient.


  Understanding how and when galaxies formed stars over the history of the
Universe is fundamental to the study of galaxy evolution. The star formation
histories (SFHs) of galaxies in the local Universe can be measured with high
precision using deep imaging with space telescopes. Such resolved SFHs are
based on modelling the observed color-magnitude diagram (CMD) with stellar
evolution models and rely on age-sensitive features like the main sequence
turn-off to measure a galaxy's star formation rate as a function of time. There
are many other population-level parameters that factor into these measurements,
such as the stellar initial mass function (IMF), binary fraction, and
metallicity, to name a few. We present and release StarFormationHistories.jl, a
modular, open-source Julia package for measuring resolved SFHs with a focus on
model flexibility for these types of population parameters. The code can model
unresolved photometric binaries and supports arbitrary IMFs. Random
uncertainties in the SFH measurements can be quantified with Monte Carlo
posterior sampling methods. We illustrate the performance of the package on
JWST/NIRCAM data of the Local Group dwarf irregular galaxy WLM
$\left(M_v\approx-14.2\right)$, which exhibits a complex, well-sampled CMD, and
HST/ACS data of the ultra-faint Milky Way satellite dwarf galaxy Horologium I
$\left(M_v\approx-3.7\right)$, which has a much simpler but sparser CMD.


  We consider spin transport in a two-component ultracold Fermi gas with
attractive interspecies interactions close to the BCS pairing transition. In
particular, we consider the spin-transport relaxation rate and the
spin-diffusion constant. Upon approaching the transition, the scattering
amplitude is enhanced by pairing fluctuations. However, as the system
approaches the transition, the spectral weight for excitations close to the
Fermi level is decreased by the formation of a pseudogap. To study the
consequence of these two competing effects, we determine the spin-transport
relaxation rate and the spin-diffusion constant using both a Boltzmann approach
and a diagrammatic approach. The former ignores pseudogap physics and finite
lifetime effects. In the latter, we incorporate the full pseudogap physics and
lifetime effects, but we ignore vertex corrections, so that we effectively
calculate single-particle relaxation rates instead of transport relaxation
rates. We find that there is qualitative agreement between these two approaches
although the results for the transport coefficients differ quantitatively.


  Reciprocal best matches play an important role in numerous applications in
computational biology, in particular as the basis of many widely used tools for
orthology assessment. Nevertheless, very little is known about their
mathematical structure. Here, we investigate the structure of reciprocal best
match graphs (RBMGs). In order to abstract from the details of measuring
distances, we define reciprocal best matches here as pairwise most closely
related leaves in a gene tree, arguing that conceptually this is the notion
that is pragmatically approximated by distance- or similarity-based heuristics.
We start by showing that a graph $G$ is an RBMG if and only if its quotient
graph w.r.t.\ a certain thinness relation is an RBMG. Furthermore, it is
necessary and sufficient that all connected components of $G$ are RBMGs. The
main result of this contribution is a complete characterization of RBMGs with 3
colors/species that can be checked in polynomial time. For 3 colors, there are
three distinct classes of trees that are related to the structure of the
phylogenetic trees explaining them. We derive an approach to recognize RBMGs
with an arbitrary number of colors; it remains open however, whether a
polynomial-time for RBMG recognition exists. In addition, we show that RBMGs
that at the same time are cographs (co-RBMGs) can be recognized in polynomial
time. Co-RBMGs are characterized in terms of hierarchically colored cographs, a
particular class of vertex colored cographs that is introduced here. The (least
resolved) trees that explain co-RBMGs can be constructed in polynomial time.


  We report the development of laser systems -- a "Voigt laser" -- using a
Voigt anomalous dispersion optical filter as the frequency-selective element,
working at the wavelength of 780 nm of $^{87}$Rb-D2 resonance line. Compared
with Faraday anomalous dispersion optical filter, the Voigt anomalous
dispersion optical filter can generate a stronger and more uniform magnetic
field with a compact size of magnet, and obtains a transmission spectrum with
narrower linewidth and more stable lineprofile. In this case, the frequency
stability of the Voigt laser reaches 5$\times$10$^{-9}$ at the averaging time
of 200 s, and the wavelength fluctuation of 8-hours free operation is $\pm$0.1
pm. Besides, the Voigt laser has greater immunity to diode current than the
Faraday laser, with a wavelength fluctuation of $\pm$0.5 pm in the current
range from 73 mA to 150 mA. Finally, the Voigt laser frequency can be
controlled by the cell temperature of the Voigt optical filter, which is
expected to achieve a frequency detuning of 20 GHz. Consequently, the Voigt
laser, whose frequency could correspond to the atomic transition frequency by
tuning the cell temperature, obtains good robustness to the current and
temperature fluctuation of laser diode, and could realize a compact optical
standard for precise measurement once stabilized by modulation transfer
spectroscopy.


  Current quantum devices execute specific tasks that are hard for classical
computers and have the potential to solve problems such as quantum simulation
of material science and chemistry, even without error correction. For practical
applications it is highly desirable to reconfigure the connectivity of the
device, which for superconducting quantum processors is determined at
fabrication. In addition, we require a careful design of control lines and
couplings to resonators for measurements. Therefore, it is a cumbersome and
slow undertaking to fabricate a new device for each problem we want to solve.
Here we periodically drive a one-dimensional chain to engineer effective
Hamiltonians that simulate arbitrary connectivities. We demonstrate the
capability of our method by engineering driving sequences to simulate star,
all-to-all, and ring connectivities. We also simulate a minimal example of the
3-SAT problem including three-body interactions, which are difficult to realize
experimentally. Our results open a new paradigm to perform quantum simulation
in near term quantum devices by enabling us to stroboscopically simulate
arbitrary Hamiltonians with a single device and optimized driving sequences


  In the framework of pionless nucleon-nucleon effective field theory we study
different approximation schemes for the nuclear many body problem. We consider,
in particular, ladder diagrams constructed from particle-particle, hole-hole,
and particle-hole pairs. We focus on the problem of finding a suitable starting
point for perturbative calculations near the unitary limit (k_Fa)->infinity and
(k_Fr)-> 0, where k_F is the Fermi momentum, a is the scattering length and r
is the effective range. We try to clarify the relationship between different
classes of diagrams and the large g and large D approximations, where g is the
fermion degeneracy and D is the number of space time dimensions. In the large D
limit we find that the energy per particle in the strongly interacting system
is 1/2 the result for free fermions.


  Unsupervised methods for learning distributed representations of words are
ubiquitous in today's NLP research, but far less is known about the best ways
to learn distributed phrase or sentence representations from unlabelled data.
This paper is a systematic comparison of models that learn such
representations. We find that the optimal approach depends critically on the
intended application. Deeper, more complex models are preferable for
representations to be used in supervised systems, but shallow log-linear models
work best for building representation spaces that can be decoded with simple
spatial distance metrics. We also propose two new unsupervised
representation-learning objectives designed to optimise the trade-off between
training time, domain portability and performance.


  Observations reveal nearly power-law spectra of magnetic and density plasma
fluctuations at subproton scales in the solar wind, which indicates the
presence of a turbulent cascade. We discuss the three-field and two-field
models for micro-scale plasma fluctuations, and then present the results of
numerical simulations of a two-field model of kinetic-Alfven turbulence, which
models plasma motion at sub-proton scales.


  A novel framework is proposed to integrate communication, control and
computing (3C) into the fifth-generation and beyond (5GB) wireless networks for
satisfying the ultra-reliable low-latency connectivity requirements of
remote-e-Health systems. Non-orthogonal multiple access (NOMA) enabled 5GB
network architecture is envisioned, while the benefits of bringing to the
remote-e-Health systems are demonstrated. Firstly, the application of NOMA into
e-Health systems is presented. To elaborate further, a unified NOMA framework
for e-Health is proposed. As a further advance, NOMA-enabled autonomous
robotics (NOMA-ARs) systems and NOMA-enabled edge intelligence (NOMA-EI)
towards remote-e-Health are discussed, respectively. Furthermore, a pair of
case studies are provided to show the great performance enhancement with the
use of NOMA technique in typical application scenarios of 5GB in
remote-e-Health systems. Finally, potential research challenges and
opportunities are discussed.


  The paper analyzes dynamic epistemic logic from a topological perspective.
The main contribution consists of a framework in which dynamic epistemic logic
satisfies the requirements for being a topological dynamical system thus
interfacing discrete dynamic logics with continuous mappings of dynamical
systems. The setting is based on a notion of logical convergence,
demonstratively equivalent with convergence in Stone topology. Presented is a
flexible, parametrized family of metrics inducing the latter, used as an
analytical aid. We show maps induced by action model transformations continuous
with respect to the Stone topology and present results on the recurrent
behavior of said maps.


  Our aim is to find the irreducibility criteria for the Koopman
representation, when the group acts on some space with a measure (Conjecture
1.5). Some general necessary conditions of the irreducibility of this
representation are established. In the particular case of the group ${\rm
GL}_0(2\infty,{\mathbb R})$ $= \varinjlim_{n}{\rm GL}(2n-1,{\mathbb R})$, the
inductive limit of the general linear groups we prove that these conditions are
also the necessary ones. The corresponding measure is infinite tensor products
of one-dimensional arbitrary Gaussian non-centered measures. The corresponding
$G$-space $X_m$ is a subspace of the space ${\rm Mat}(2\infty,{\mathbb R})$ of
infinite in both directions real matrices. In fact, $X_m$ is a collection of
$m$ infinite in both directions rows. This result was announced in [20]. We
give the proof only for $m\leq 2$. The general case will be studied later.


  In geophysics, volcanoes are particularly difficult to image because of the
multi-scale heterogeneities of fluids and rocks that compose them and their
complex non-linear dynamics. By exploiting seismic noise recorded by a sparse
array of geophones, we are able to reveal the magmatic and hydrothermal
plumbing system of La Soufri\`ere volcano in Guadeloupe. Spatio-temporal
cross-correlation of seismic noise actually provides the impulse responses
between virtual geophones located inside the volcano. The resulting reflection
matrix can be exploited to numerically perform an auto-focus of seismic waves
on any reflector of the underground. An unprecedented view on the volcano's
inner structure is obtained at a half-wavelength resolution. This innovative
observable provides fundamental information for the conceptual modeling and
high-resolution monitoring of volcanoes.


  We classify the solutions to a system of equations, introduced by Bondal,
which encode numerical constraints on full exceptional collections of length 4
on surfaces. The corresponding result for length 3 is well-known and states
that there is essentially one solution, namely the one corresponding to the
standard exceptional collection on the surface $\mathbb{P}^2$. This was
essentially proven by Markov in 1879. It turns out that in the length 4 case,
there is one special solution which corresponds to
$\mathbb{P}^1\times\mathbb{P}^1$ whereas the other solutions are obtained from
$\mathbb{P}^2$ by a procedure we call numerical blowup. Among these solutions,
three are of geometric origin ($\mathbb{P}^2\cup \{\bullet\}$,
$\mathbb{P}^1\times\mathbb{P}^1$ and the ordinary blowup of $\mathbb{P}^2$ at a
point). The other solutions are parametrized by $\mathbb{N}$ and very likely do
not correspond to commutative surfaces. However they can be realized as
noncommutative surfaces, as was recently shown by Dennis Presotto and the first
author.


  The Biot-Savart law is used in aerodynamic theory to calculate the velocity
induced by curved vortex lines. Explicit formulas are developed, using
multivariate Appell hypergeometric functions, for the velocity induced by a
general parabolic vortex segment. The formulas are derived by constructing a
particular pencil of elliptic curves whose period integrals provide the
solution to the induced velocity. We use numerical integration and a
perturbation expansion to evaluate the validity of our formulas.


  The lattice fermion determinants, in a given background gauge field, are
evaluated for two different kinds of random lattices and compared to those of
naive and wilson fermions in the continuum limit. While the fermion doubling is
confirmed on one kind of lattices, there is positive evidence that it may be
absent for the other, at least for vector interactions in two dimensions.
Combined with previous studies, arbitrary randomness by itself is shown to be
not a sufficient condition to remove the fermion doublers.


  We establish a family of uncertainty principles for finite linear
combinations of Hermite functions. More precisely, we give a geometric
criterion on a subset $S\subset \RR^d$ ensuring that the $L^2$-seminorm
associated to $S$ is equivalent to the full $L^2$-norm on $\RR^d$ when
restricted to the space of Hermite functions up to a given degree. We give
precise estimates how the equivalence constant depends on this degree and on
geometric parameters of $S$. From these estimates we deduce that the parabolic
equation whose generator is the harmonic oscillator is null-controllable from
$S$.
  In all our results, the set $S$ may have sub-exponentially decaying density
and, in particular, finite volume. We also show that bounded sets are not
efficient in this context.


  We present MCDA, a modification of the CoRoT detrend algorithm (CDA) suitable
to detrend chromatic light curves. By means of robust statistics and better
handling of short term variability, the implementation decreases the systematic
light curve variations and improves the detection of exoplanets when compared
with the original algorithm. All CoRoT chromatic light curves (a total of
65,655) were analysed with our algorithm. Dozens of new transit candidates and
all previously known CoRoT exoplanets were rediscovered in those light curves
using a box-fitting algorithm. For three of the new cases spectroscopic
measurements of the candidates' host stars were retrieved from the ESO Science
Archive Facility and used to calculate stellar parameters and, in the best
cases, radial velocities. In addition to our improved detrend technique we
announce the discovery of a planet that orbits a
$0.79_{-0.09}^{+0.08}\,R_\odot$ star with a period of $6.71837\pm0.00001$ days
and has $0.57_{-0.05}^{+0.06}\,R_{\rm J}$ and $0.15\pm0.10\,M_{\rm J}$. We also
present the analysis of two cases in which parameters found suggest the
existence of possible planetary companions.


  We perform a Bayesian grid-based analysis of the solar l=0,1,2 and 3 p modes
obtained via BiSON in order to deliver the first Bayesian asteroseismic
analysis of the solar composition problem. We do not find decisive evidence to
prefer either of the contending chemical compositions, although the revised
solar abundances (AGSS09) are more probable in general. We do find indications
for systematic problems in standard stellar evolution models, unrelated to the
consequences of inadequate modelling of the outer layers on the higher-order
modes. The seismic observables are best fit by solar models that are several
hundred million years older than the meteoritic age of the Sun. Similarly,
meteoritic age calibrated models do not adequately reproduce the observed
seismic observables. Our results suggest that these problems will affect any
asteroseismic inference that relies on a calibration to the Sun.


  Planets are typically thought to form in protoplanetary disks left over from
protostellar disk of their newly formed host star. However, an additional
planetary formation route may exist in old evolved binary systems. In such
systems stellar evolution could lead to the formation of symbiotic stars, where
mass transferred from the expanding evolved star to its binary companion could
form an accretion disk around it. Such a disk could provide the necessary
environment for the formation of a second generation of planets in both
circumstellar or circumbinary configurations. Pre-existing first generation
planets surviving the post-MS evolution of such systems may serve as seeds for,
and/or interact with, the second generation planets, possibly forming atypical
planetary systems. Second generation planetary systems should be typically
found in white dwarf binary systems, and may show various observational
signatures. Most notably, second generation planets could form in environment
which are inaccessible, or less favorable, for first generation planets. The
orbital phase space available for the second generation planets could be
forbidden (in terms of the system stability) to first generation planets in the
pre-evolved progenitor binaries. In addition planets could form in metal poor
environments such as globular clusters and/or in double compact object
binaries. Observations of planets in such forbidden or unfavorable regions may
serve to uniquely identify their second generation character. Finally, we point
out a few observed candidate second generation planetary systems, including PSR
B1620-26 (in a globular cluster), Gl 86, HD 27442 and all of the currently
observed circumbinary planet candidates. A second generation origin for these
systems could naturally explain their unique configurations.


  Wireless mobile sensor networks (WMSNs) are groups of mobile sensing agents
with multi-modal sensing capabilities that communicate over wireless networks.
WMSNs have more flexibility in terms of deployment and exploration abilities
over static sensor networks. Sensor networks have a wide range of applications
in security and surveillance systems, environmental monitoring, data gathering
for network-centric healthcare systems, monitoring seismic activities and
atmospheric events, tracking traffic congestion and air pollution levels,
localization of autonomous vehicles in intelligent transportation systems, and
detecting failures of sensing, storage, and switching components of smart
grids. The above applications require target tracking for processes and events
of interest occurring in an environment. Various methods and approaches have
been proposed in order to track one or more targets in a pre-defined area.
Usually, this turns out to be a complicated job involving higher order
mathematics coupled with artificial intelligence due to the dynamic nature of
the targets. To optimize the resources we need to have an approach that works
in a more straightforward manner while resulting in fairly satisfactory data.
In this paper we have discussed the various cases that might arise while
flocking a group of sensors to track targets in a given environment. The
approach has been developed from scratch although some basic assumptions have
been made keeping in mind some previous theories. This paper outlines a
customized approach for feasibly tracking swarms of targets in a specific area
so as to minimize the resources and optimize tracking efficiency.


  Given a conditionally completely positive map $\mathcal L$ on a unital
$\ast$-algebra $\A$, we find an interesting connection between the second
Hochschild cohomology of $\A$ with coefficients in the bimodule $E_{\mathcal
L}=\B^a(\A \oplus M)$ of adjointable maps, where $M$ is the GNS bimodule of
$\mathcal L$, and the possibility of constructing a quantum random walk (in the
sense of \cite{AP,LP,L,KBS}) corresponding to $\mathcal L$.


  We present the SIMUB package developed at Dubna for MC generation of B meson
production and decays.
  The starting version of the package includes lepton modes of B decays, in
particular, semileptonic decays B->D^*l\nu and "golden" mode
B->J/\psi(\mu\mu)\phi(KK) with taking into account all theoretical refinements
including B^0-Bbar^0 oscillations and angular correlations.


  We consider the Schwinger's method of angular momentum addition using the
SU(2) algebra with both a fermionic and a bosonic oscillator. We show that the
total spin states obtained are: one boson singlet state and an arbitrary number
of spin-1/2 states, the later ones are energy degenerate. It means that we have
in this case supersymmetric quantum mechanics and also the addition of angular
momentum for massless particles. We review too the cases of two bosonic and
fermionic oscillators.


  This paper presents some applications of using recently developed algorithms
for smooth-continuous data reconstruction based on the digital-discrete method.
The classical discrete method for data reconstruction is based on domain
decomposition according to guiding (or sample) points. Then uses Splines (for
polynomial) or finite elements method (for PDE) to fit the data. Our method is
based on the gradually varied function that does not assume the property of the
linearly separable among guiding points, i.e. no domain decomposition methods
are needed. We also demonstrate the flexibility of the new method and the
potential to solve variety of problems. The examples include some real data
from water well logs and harmonic functions on closed 2D manifolds. This paper
presented the results from six different algorithms. This method can be easily
extended to higher multi-dimensions.


  We examine the physical parameters that affect the accumulation of gas in
molecular clouds to high column densities where the formation of stars takes
place. In particular, we analyze the dense gas mass fraction (DGMF) in a set of
self-gravitating, isothermal, magnetohydrodynamic turbulence simulations
including sink particles to model star formation. We find that the simulations
predict close to exponential DGMFs over the column density range N(H2) = 3-25 x
10^{21} cm^{-2} that can be easily probed via, e.g., dust extinction
measurements. The exponential slopes correlate with the type of turbulence
driving and also with the star formation efficiency. They are almost
uncorrelated with the sonic Mach number and magnetic-field strength. The slopes
at early stages of cloud evolution are steeper than at the later stages. A
comparison of these predictions with observations shows that only simulations
with relatively non-compressive driving (b ~< 0.4) agree with the DGMFs of
nearby molecular clouds. Massive infrared dark clouds can show DGMFs that are
in agreement with more compressive driving. The DGMFs of molecular clouds can
be significantly affected by how compressive the turbulence is on average.
Variations in the level of compression can cause scatter to the DGMF slopes,
and some variation is indeed necessary to explain the spread of the observed
DGMF slopes. The observed DGMF slopes can also be affected by the clouds' star
formation activities and statistical cloud-to-cloud variations.


  We give an $n^{O(\log\log n)}$-time membership query algorithm for properly
and agnostically learning decision trees under the uniform distribution over
$\{\pm 1\}^n$. Even in the realizable setting, the previous fastest runtime was
$n^{O(\log n)}$, a consequence of a classic algorithm of Ehrenfeucht and
Haussler. Our algorithm shares similarities with practical heuristics for
learning decision trees, which we augment with additional ideas to circumvent
known lower bounds against these heuristics. To analyze our algorithm, we prove
a new structural result for decision trees that strengthens a theorem of
O'Donnell, Saks, Schramm, and Servedio. While the OSSS theorem says that every
decision tree has an influential variable, we show how every decision tree can
be "pruned" so that every variable in the resulting tree is influential.


  Blessing, Insko, Johnson and Mauretour gave a generalization of the
domination number of a graph $G=(V,E)$ called the $(t,r)$ broadcast domination
number which depends on the positive integer parameters $t$ and $r$. In this
setting, a vertex $v \in V$ is a broadcast vertex of transmission strength $t$
if it transmits a signal of strength $t-d(u,v)$ to every vertex $u \in V$,
where $d(u,v)$ denotes the distance between vertices $u$ and $v$ and $d(u,v)
<t$. Given a set of broadcast vertices $S\subseteq V$, the reception at vertex
$u$ is the sum of the transmissions from the broadcast vertices in $S$. The set
$S \subseteq V$ is called a $(t,r)$ broadcast dominating set if every vertex $u
\in V$ has a reception strength $r(u) \geq r$ and for a finite graph $G$ the
cardinality of a smallest broadcast dominating set is called the $(t,r)$
broadcast domination number of $G$. In this paper, we consider the infinite
triangular grid graph and define efficient $(t,r)$ broadcast dominating sets as
those broadcasts that minimize signal waste. Our main result constructs
efficient $(t,r)$ broadcasts on the infinite triangular lattice for all $t\geq
r\geq 1$. Using these broadcasts, we then provide upper bounds for the $(t,r)$
broadcast domination numbers for triangular matchstick graphs when
$(t,r)\in\{(2,1),(3,1),(3,2),(4,1),(4,2),(4,3),(t,t)\}$.