  The Information Retrieval research has used semantics to provide accurate
search results, but the analysis of conceptual abstraction has mainly focused
on information integration. We consider session-based query expansion in
Geographical Information Retrieval, and investigate the impact of semantic
granularity (i.e., specificity of concepts representation) on the suggestion of
relevant types of information to search for. We study how different levels of
detail in knowledge representation influence the capability of guiding the user
in the exploration of a complex information space. A comparative analysis of
the performance of a query expansion model, using three spatial ontologies
defined at different semantic granularity levels, reveals that a fine-grained
representation enhances recall. However, precision depends on how closely the
ontologies match the way people conceptualize and verbally describe the
geographic space.


  Thermodynamics provides a unified perspective of thermodynamic properties of
various substances. To formulate thermodynamics in the language of
sophisticated mathematics, thermodynamics is described by a variety of
differential geometries, including contact and symplectic geometries. Meanwhile
affine geometry is a branch of differential geometry and is compatible with
information geometry, where information geometry is known to be compatible with
thermodynamics. By combining above, it is expected that thermodynamics is
compatible with affine geometry, and is expected that several affine geometric
tools can be introduced in the analysis of thermodynamic systems. In this paper
affine geometric descriptions of equilibrium and nonequilibrium thermodynamics
are proposed. For equilibrium systems, it is shown that several thermodynamic
quantities can be identified with geometric objects in affine geometry, and
that several geometric objects can be introduced in thermodynamics. Examples of
these include: specific heat is identified with the affine fundamental form, a
flat connection is introduced in thermodynamic phase space. For nonequilibrium
systems, two classes of relaxation processes are shown to be described in the
language of an extension of affine geometry. Finally this affine geometric
description of thermodynamics for equilibrium and nonequilibrium systems is
compared with a contact geometric description.


  For minimal unique ergodic diffeomorphisms $\alpha_n$ of $S^{2n+1} (n>0)$ and
$\alpha_m$ of $S^{2m+1}(m>0)$, the $C^*$-crossed product algebra
$C(S^{2n+1})\rtimes_{\alpha_n} \mathbb{Z}$ is isomorphic to
$C(S^{2m+1})\rtimes_{\alpha_m} \mathbb{Z}$ even though $n\neq m$ . However, by
cyclic cohomology, we show that smooth crossed product algebra
$C^\infty(S^{2n+1})\rtimes_{\alpha_n} \mathbb{Z}$ is not isomorphic to
$C^\infty(S^{2m+1})\rtimes_{\alpha_m} \mathbb{Z}$ if $n\neq m$.


  We present two deterministic dynamic algorithms for the maximum matching
problem. (1) An algorithm that maintains a $(2+\epsilon)$-approximate maximum
matching in general graphs with $O(\text{poly}(\log n, 1/\epsilon))$ update
time. (2) An algorithm that maintains an $\alpha_K$ approximation of the {\em
value} of the maximum matching with $O(n^{2/K})$ update time in bipartite
graphs, for every sufficiently large constant positive integer $K$. Here,
$1\leq \alpha_K < 2$ is a constant determined by the value of $K$. Result (1)
is the first deterministic algorithm that can maintain an $o(\log
n)$-approximate maximum matching with polylogarithmic update time, improving
the seminal result of Onak et al. [STOC 2010]. Its approximation guarantee
almost matches the guarantee of the best {\em randomized} polylogarithmic
update time algorithm [Baswana et al. FOCS 2011]. Result (2) achieves a
better-than-two approximation with {\em arbitrarily small polynomial} update
time on bipartite graphs. Previously the best update time for this problem was
$O(m^{1/4})$ [Bernstein et al. ICALP 2015], where $m$ is the current number of
edges in the graph.


  We compute the \emph{surface energy~(${\cal E}_{s}^{\pm}$), the rotational
energy~(${\cal E}_{r}^{\pm}$) and the electromagnetic energy~(${\cal
E}_{em}^{\pm}$)} for Newman-Unti-Tamburino~(NUT) class of black hole having the
event horizon~(${\cal H}^{+}$) and the Cauchy horizon~(${\cal H}^{-}$).
Remarkably, we find that the \emph{mass parameter can be expressed as sum of
three energies i. e. $M={\cal E}_{s}^{\pm}+{\cal E}_{r}^{\pm}+{\cal
E}_{em}^{\pm}$}. It has been \emph{tested} for Taub-NUT black hole,
Reissner-Nordstr\"{o}m-Taub-NUT black hole, Kerr-Taub-NUT black hole and
Kerr-Newman-Taub-NUT black hole. In each case of black hole, we find that
\emph{the sum of these energies is equal to the Komar mass}. It is plausible
only due to the introduction of new conserved charges i.e. $J_{N}=M\,N$~(where
$M=m$ is the Komar mass and $N=n$ is the gravitomagnetic charge), which is
closely analogue to the Kerr-like angular momentum parameter $J=a\,M$.


  We introduce novel information-entropic variables -- a Point Divergence Gain
(${\Omega}^{(l \rightarrow m)}_\alpha$), a Point Divergence Gain Entropy
($I_\alpha$), and a Point Divergence Gain Entropy Density ($P_\alpha$) -- which
are derived from the R\'{e}nyi entropy and describe spatio-temporal changes
between two consecutive discrete multidimensional distributions. The behavior
of ${\Omega}^{(l \rightarrow m)}_\alpha$ is simulated for typical distributions
and, together with $I_\alpha$ and $P_\alpha$, applied in analysis and
characterization of series of multidimensional datasets of computer-based and
real images.


  Collective Perception will play a crucial role for ensuring vehicular safety
in the near future, enabling the sharing of local perceived objects with other
Intelligent Transport System Stations (ITS-Ss). However, at the beginning of
the roll-out, low market penetration rates are expected. This paper proposes
and evaluates an application layer multi-hop Collective Perception Service
(CPS) for vehicular ad-hoc networks. The goal is to improve the environmental
awareness ratio in scenarios with low CPS market penetration. In such
scenarios, the CPS service without forwarding enabled struggles to achieve
complete awareness. A decentralized application layer forwarding algorithm is
presented that shares perceived object information across multiple hops while
maintaining a low age of information. The proposed approach is compared against
standard CPS with no forwarding and CPS with geographically-scoped (GBC)
multi-hop forwarding. Simulations according to standards of the European
Telecommunications Standards Institute (ETSI) demonstrate that the application
layer forwarding achieves near 100% awareness at 10% penetration rate versus
92% for standard CPS. The awareness improvement comes with moderate channel
load, unlike GBC forwarding which quickly saturates the channel. The median age
of information remains below 80 ms for the proposed scheme, enabling real-time
CPS operation. Our application layer multi-hop approach effectively improves
environmental awareness during initial CPS deployment while aligning with
latency and channel load requirements.


  We present a ground-based optical transmission spectrum for the warm
Saturn-mass exoplanet WASP-110b from two transit observations made with the
FOcal Reducer and Spectrograph (FORS2) on the Very Large Telescope (VLT). The
spectrum covers the wavelength range from 4000 to 8333\AA, which is binned in
46 transit depths measured to an averaged precision of 220 parts per million
(ppm) over an averaged 80\AA~bin for a Vmag=12.8 star. The measured transit
depths are unaffected by a dilution from a close A-type field dwarf, which was
fully resolved. The overall main characteristic of the transmission spectrum is
an increasing radius with wavelength and a lack of the theoretically predicted
pressure-broadened sodium and potassium absorption features for a cloud-free
atmosphere. We analyze archival high-resolution optical spectroscopy and find
evidence for low to moderate activity of the host star, which we take into
account in the atmospheric retrieval analysis. Using the AURA retrieval code,
we find that the observed transmission spectrum can be best explained by a
combination of unocculted stellar faculae and a cloud deck. Transmission
spectra of cloud-free and hazy atmospheres are rejected at a high confidence.
With a possible cloud deck at its terminator, WASP-110b joins the increasing
population of irradiated hot-Jupiter exoplanets with cloudy atmospheres
observed in transmission.


  We present a cross-domain inference technique that learns from synthetic data
to estimate depth and normals for in-the-wild omnidirectional 3D scenes
encountered in real-world uncontrolled settings. To this end, we introduce
UBotNet, an architecture that combines UNet and Bottleneck Transformer elements
to predict consistent scene normals and depth. We also introduce the
OmniHorizon synthetic dataset containing 24,335 omnidirectional images that
represent a wide variety of outdoor environments, including buildings, streets,
and diverse vegetation. This dataset is generated from expansive, lifelike
virtual spaces and encompasses dynamic scene elements, such as changing
lighting conditions, different times of day, pedestrians, and vehicles. Our
experiments show that UBotNet achieves significantly improved accuracy in depth
estimation and normal estimation compared to existing models. Lastly, we
validate cross-domain synthetic-to-real depth and normal estimation on real
outdoor images using UBotNet trained solely on our synthetic OmniHorizon
dataset, demonstrating the potential of both the synthetic dataset and the
proposed network for real-world scene understanding applications.


  We numerically investigate stellar and gaseous dynamical evolution of mergers
between five identical late-type disk galaxies with the special emphasis on
star formation history and chemical evolution of multiple galaxy mergers. We
found that multiple encounter and merging can trigger repetitive massive
starbursts (typically $\sim$ 100 $M_{\odot}$ ${\rm yr}^{-1}$) owing to the
strong tidal disturbance and the resultant gaseous dissipation during merging.
The magnitude of the starburst is found to depend on initial virial ratio
(i.e., the ratio of total kinematical energy to total potential energy) such
that the maximum star formation rate is larger for the merger with smaller
virial ratio. Furthermore we found that the time interval between the epochs of
the triggered starbursts is longer for the merger with the larger virial ratio.
The remnant of a multiple galaxy merger with massive starbursts is found to
have metal-poor gaseous halo that is formed by tidal stripping during the
merging. We accordingly suggest that metal-poor gaseous halo in a field
elliptical galaxy is a fossil record of the past multiple merging events for
the galaxy.


  For each pair of positive integers r,s, there is a so-called Kac
representation (r,s) associated with a Yang-Baxter integrable boundary
condition in the lattice approach to the logarithmic minimal model LM(1,p). We
propose a classification of these representations as finitely-generated
submodules of Feigin-Fuchs modules, and present a conjecture for their fusion
algebra which we call the Kac fusion algebra. The proposals are tested using a
combination of the lattice approach and applications of the
Nahm-Gaberdiel-Kausch algorithm. We also discuss how the fusion algebra may be
extended by inclusion of the modules contragredient to the Kac representations,
and determine polynomial fusion rings isomorphic to the conjectured Kac fusion
algebra and its contragredient extension.


  We present a measurement of the Pi0 transverse momentum spectrum and of the
Eta/Pi0 ratio in proton-proton collisions at sqrt(s) = 7 TeV at the CERN LHC.
In this analysis the reconstruction of Pi0 and Eta mesons has been done via
photon conversions in the central tracking system of ALICE. Therefore, this
method is completely independent from the electromagnetic calorimeters. It
makes the Pi0 (Eta) measurement possible down to pt = 0.4 (0.6) GeV/c with a
very good resolution and a very small background. For 100 Mio. pp collisions
the pt reach is 7 GeV/c. The results are compared to NLO pQCD calculations.


  Amidst the COVID-19 pandemic, the authors of this paper organized a
Reinforcement Learning (RL) course for a graduate school in the field of data
science. We describe the strategy and materials for creating an exciting
learning experience despite the ubiquitous Zoom fatigue and evaluate the course
qualitatively. The key organizational features are a focus on a competitive
hands-on setting in teams, supported by a minimum of lectures providing the
essential background on RL. The practical part of the course revolved around
Hearts Gym, an RL environment for the card game Hearts that we developed as an
entry-level tutorial to RL. Participants were tasked with training agents to
explore reward shaping and other RL hyperparameters. For a final evaluation,
the agents of the participants competed against each other.


  The aim of this paper is to give a wavelet series representation of Linear
Multifractional Stable Motion (LMSM in brief), which is more explicit than that
introduced in (Ayache & Hamonier 2012). Instead of using Daubechies wavelet,
which is not given by a closed form, we use the Haar wavelet. In order to
obtain this new representation, we introduce a Haar expansion of the high and
low frequency parts of the $\mathcal{S}\alpha \mathcal{S}$ random field $X$
generating LMSM. Then, by using Abel transforms, we show that these series are
convergent, almost surely, in the space of continuous functions. Finally, we
determine their almost sure rates of convergence in the latter space. Note that
these representations of the high and low frequency parts of $X$, provide a new
method for simulating the high and low frequency parts of LMSM.


  Determination of the wave mode of short-wavelength electrostatic waves along
with their generation mechanism requires reliable measurement of the wave
electric field. We investigate the reliability of the electric field
measurement for short-wavelength waves observed by MMS. We develop a method,
based on spin-plane interferometry, to reliably determine the full 3D wave
vector of the observed waves. We test the method on synthetic data and then
apply it to ion acoustic wave bursts measured in situ in the solar wind. By
studying the statistical properties of ion acoustic waves in the solar wind we
retrieve the known results that the wave propagation is predominantly
field-aligned. We also determine the wavelength of the waves. We find that the
distribution peaks at around 100 m, which when normalized to the Debye length
corresponds to scales between 10 and 20 Debye lengths.


  Mechanical algesia is an important process for the preservation of living
organisms, allowing potentially life-saving reflexes or decisions when given
body parts are stressed. Yet, its various underlying mechanisms remain to be
fully unravelled. Here, we quantitatively discuss how the detection of painful
mechanical stimuli by the human central nervous system may, partly, rely on
thermal measurements. Indeed, most fractures in a body, including microscopic
ones, release some heat, which diffuses in the surrounding tissues. Through
this physical process, the thermo-sensitive TRP proteins, that translate
abnormal temperatures into action potentials, shall be sensitive to damaging
mechanical inputs. The implication of these polymodal receptors in mechanical
algesia has been regularly reported, and we here provide a physical explanation
for the coupling between thermal and mechanical pain. In particular, in the
human skin, we show how the neighbouring neurites of a broken collagen fiber
can undergo a sudden thermal elevation that ranges from a fraction to tens of
degrees. As this theoretical temperature anomaly lies in the sensibility range
of the TRPV3 and TRPV1 cation channels, known to trigger action potentials in
the neural system, a degree of mechanical pain can hence be generated.


  We use the nonperturbative linear \delta expansion method to evaluate
analytically the coefficients c_1 and c_2^{\prime \prime} which appear in the
expansion for the transition temperature for a dilute, homogeneous, three
dimensional Bose gas given by T_c= T_0 \{1 + c_1 a n^{1/3} + [ c_2^{\prime}
\ln(a n^{1/3}) +c_2^{\prime \prime} ] a^2 n^{2/3} + {\cal O} (a^3 n)\}, where
T_0 is the result for an ideal gas, a is the s-wave scattering length and n is
the number density. In a previous work the same method has been used to
evaluate c_1 to order-\delta^2 with the result c_1= 3.06. Here, we push the
calculation to the next two orders obtaining c_1=2.45 at order-\delta^3 and
c_1=1.48 at order-\delta^4. Analysing the topology of the graphs involved we
discuss how our results relate to other nonperturbative analytical methods such
as the self-consistent resummation and the 1/N approximations. At the same
orders we obtain c_2^{\prime\prime}=101.4, c_2^{\prime \prime}=98.2 and
c_2^{\prime \prime}=82.9. Our analytical results seem to support the recent
Monte Carlo estimates c_1=1.32 \pm 0.02 and c_2^{\prime \prime}= 75.7 \pm 0.4.


  With their constantly increasing peak performance and memory capacity, modern
supercomputers offer new perspectives on numerical studies of open many-body
quantum systems. These systems are often modeled by using Markovian quantum
master equations describing the evolution of the system density operators. In
this paper we address master equations of the Lindblad form, which are a
popular theoretical tool in quantum optics, cavity quantum electrodynamics, and
optomechanics. By using the generalized Gell-Mann matrices as a basis, any
Lindblad equation can be transformed into a system of ordinary differential
equations with real coefficients. This allows us to use standard
high-performance parallel algorithms to integrate the equations and thus to
emulate open quantum dynamics in a computationally efficient way. Recently we
presented an implementation of the transform with the computational complexity
scaling as $O(N^5 log N)$ for dense Lindbaldians and $O(N^3 log N)$ for sparse
ones. However, infeasible memory costs remain a serious obstacle on the way to
large models. Here we present a parallel cluster-based implementation of the
algorithm and demonstrate that it allows us to integrate a sparse Lindbladian
model of the dimension $N=2000$ and a dense random Lindbladian model of the
dimension $N=200$ by using $25$ nodes with $64$ GB RAM per node.


  This work is on vision-based planning strategies for legged robots that
separate locomotion planning into foothold selection and pose adaptation.
Current pose adaptation strategies optimize the robot's body pose relative to
given footholds. If these footholds are not reached, the robot may end up in a
state with no reachable safe footholds. Therefore, we present a Vision-Based
Terrain-Aware Locomotion (ViTAL) strategy that consists of novel pose
adaptation and foothold selection algorithms. ViTAL introduces a different
paradigm in pose adaptation that does not optimize the body pose relative to
given footholds, but the body pose that maximizes the chances of the legs in
reaching safe footholds. ViTAL plans footholds and poses based on skills that
characterize the robot's capabilities and its terrain-awareness. We use the 90
kg HyQ and 140 kg HyQReal quadruped robots to validate ViTAL, and show that
they are able to climb various obstacles including stairs, gaps, and rough
terrains at different speeds and gaits. We compare ViTAL with a baseline
strategy that selects the robot pose based on given selected footholds, and
show that ViTAL outperforms the baseline.


  We study conductivity of strongly disordered amorphous antimony films under
high bias voltages. We observe non-linear current-voltage characteristic, where
the conductivity value at zero bias is one of two distinct values, being
determined by the sign of previously applied voltage. Relaxation curves
demonstrate high stability of these conductivity values on a large timescale.
Investigations of the antimony film structure allows to determine the
percolation character of electron transport in strongly disordered films. We
connect the memory effect in conductivity with modification of the percolation
pattern due to recharging of some film regions at high bias voltages.


  The ideal uniform two-dimensional (2D) Fermi and Bose gases are considered
both in the thermodynamic limit and the finite case. We derive May's Theorem,
viz. the correspondence between the internal energies of the Fermi and Bose
gases in the thermodynamic limit. This results in both gases having the same
heat capacity. However, as we shall show, the thermodynamic limit is never
truly reached in two dimensions and so it is essential to consider finite-size
effects. We show in an elementary manner that for the finite 2D Bose gas, a
pseudo-Bose-Einstein condensate forms at low temperatures, incompatible with
May's Theorem. The two gases now have different heat capacities, dependent on
the system size and tending to the same expression in the thermodynamic limit.


  We present a method to calculate the One-loop mass correction to Kinks mass
in a (1+1)-dimensional field theoretical model in which the fluctuation
potential $V^{\prime\prime}(\phi_c)$ has shape invariance property. We use the
generalized zeta function regularization method to implement our setup for
describing quantum kink states.


  In a recent article [D. Suter, Phys. Rev. {\bf A 51}, 45 (1995)] Suter has
claimed to present an optical implementation of the quantum time-translation
machine which ``shows all the features that the general concept predicts and
also allows, besides the quantum mechanical, a classical description.'' It is
argued that the experiment proposed and performed by Suter does not have the
features of the quantum time-translation machine and that the latter has no
classical analog.


  As a model problem for the study of chaotic Hamiltonian systems, we look for
the effects of a long-tail distribution of recurrence times on a fixed
Hamiltonian dynamics. We follow Stanislavsky's approach of Hamiltonian
formalism for fractional systems. We prove that his formalism can be retrieved
from the fractional embedding theory. We deduce that the fractional Hamiltonian
systems of Stanislavsky stem from a particular least action principle, said
causal. In this case, the fractional embedding becomes coherent.


  The study of elastic membranes carrying topological defects has a
longstanding history, going back at least to the 1950s. When allowed to buckle
in three-dimensional space, membranes with defects can totally relieve their
in-plane strain, remaining with a bending energy, whose rigidity modulus is
small compared to the stretching modulus. In this paper, we study membranes
with a single edge-dislocation. We prove that the minimum bending energy
associated with strain-free configurations diverges logarithmically with the
size of the system.


  We study the preheating after inflation in the Starobinsky model with a
nonminimally coupled scalar field $\chi$. Using the lattice simulation, we
analyze the rescattering between the $\chi$ particles and the inflaton
condensate, and the backreaction effect of the scalar metric perturbations. We
find that the rescattering is an efficient mechanism promoting the growth of
the $\chi$ field variance. Meanwhile, copious inflaton particles can be knocked
out of the inflaton condensate by rescattering. As a result, the inflaton field
can become a nonnegligible gravitational wave source, even comparable with the
$\chi$ field in some parameter regions. For the scalar metric perturbations,
which are on the sub-Hubble scale in our analysis, our results show that they
have negligible effects on the evolution of scalar fields and the production of
gravitational waves in the model considered in present paper.


  Using a functional renormalization group approach, we study
interaction-driven instabilities in quadratic band crossing point two-orbital
models in two dimensions, extending a previous study of Sun et al. [1]. The
wavevector-dependence of the Bloch eigenvectors of the free Hamiltonian causes
interesting instabilities toward spin nematic, quantum anomalous Hall and
quantum spin Hall states. In contrast with other known examples of
interaction-driven topological insulators, in the system studied here, the QSH
state occurs at arbitrarily small interaction strength and for rather simple
intra- and inter-orbital repulsions.


  We consider magnetic flows on compact quotients of the 3-dimensional solvable
geometry Sol determined by the usual left-invariant metric and the
distinguished monopole. We show that these flows have positive Liouville
entropy and therefore are never completely integrable. This should be compared
with the known fact that the underlying geodesic flow is completely integrable
in spite of having positive topological entropy. We also show that for a large
class of twisted cotangent bundles of solvable manifolds every compact set is
displaceable.


  The characterisation of the physical properties of nanoparticles in their
native environment plays a central role in a wide range of fields, from
nanoparticle-enhanced drug delivery to environmental nanopollution assessment.
Standard optical approaches require long trajectories of nanoparticles
dispersed in a medium with known viscosity to characterise their diffusion
constant and, thus, their size. However, often only short trajectories are
available, while the medium viscosity is unknown, e.g., in most biomedical
applications. In this work, we demonstrate a label-free method to quantify size
and refractive index of individual subwavelength particles using two orders of
magnitude shorter trajectories than required by standard methods, and without
assumptions about the physicochemical properties of the medium. We achieve this
by developing a weighted average convolutional neural network to analyse the
holographic images of the particles. As a proof of principle, we distinguish
and quantify size and refractive index of silica and polystyrene particles
without prior knowledge of solute viscosity or refractive index. As an example
of an application beyond the state of the art, we demonstrate how this
technique can monitor the aggregation of polystyrene nanoparticles, revealing
the time-resolved dynamics of the monomer number and fractal dimension of
individual subwavelength aggregates. This technique opens new possibilities for
nanoparticle characterisation with a broad range of applications from
biomedicine to environmental monitoring.


  We build symmetry adapted maximally localized Wannier states, and construct
the low energy tight binding model for the four narrow bands of the twisted
bilayer graphene. We do so when the twist angle is commensurate, near the
`magic' value, and the narrow bands are separated from the rest of the bands by
energy gaps. On each layer and sublattice, every Wannier state has three peaks
near the triangular Moire lattice sites. However, each Wannier state is
localized and centered around a site of the honeycomb lattice that is dual to
the triangular Moire lattice. Space group and the time reversal symmetries are
realized locally. The corresponding tight binding model provides a starting
point for studying the correlated many-body phases.


  In this paper, the third in the series, we continue our study of
combinatorics in chaotic Newtonian dynamics. We study the chaotic four-body
problem in Newtonian gravity assuming finite-sized particles, and we focus on
interactions that produce direct collisions between any two stars. Our
long-term goal is to construct an equation that gives the probability of a
given collision event occurring over the course of the interaction, as a
function of the total encounter energy and angular momentum as well as the
numbers and properties of the particles. In previous papers, we varied the
number of interacting particles and the distribution of particle radii, for all
equal mass particles. Here, we focus on the effects of different combinations
of particle masses.
  We develop an analytic formalism for calculating the time-scales for
different collision scenarios to occur. Our analytic time-scales reproduce the
simulated time-scales when gravitational focusing is included. We present a
method for calculating the relative rates for different types of collisions to
occur, assuming two different limits for the particle orbits; radial and
tangential. These limits yield relative collision probabilities that bracket
the probabilities we obtain directly from numerical scattering experiments, and
are designed to reveal important information about the (time-averaged)
trajectories of the particles as a function of the interaction parameters.
Finally, we present a Collision Rate Diagram (CRD), which directly compares the
predictions of our analytic rates to the simulations and quantifies the quality
of the agreement. The CRD will facilitate refining our analytic collision rates
in future work, as we expand in to the remaining parameter space.


  Mat\'ern hard core processes of types I and II are the point processes of
choice to model concurrent transmitters in CSMA networks. We determine the mean
interference observed at a node of the process and compare it with the mean
interference in a Poisson point process of the same density. It turns out that
despite the similarity of the two models, they behave rather differently. For
type I, the excess interference (relative to the Poisson case) increases
exponentially in the hard-core distance, while for type II, the gap never
exceeds 1 dB.


  We demonstrate that an impulse action (`recoil') on a D-particle embedded in
a (four-dimensional) cosmological Robertson-Walker (RW) spacetime is described,
in a $\sigma$-model framework, by a suitably extended higher-order logarithmic
world-sheet algebra of relevant deformations. We study in some detail the
algebra of the appropriate two-point correlators, and give a careful discussion
as to how one can approach the world-sheet renormalization group infrared fixed
point, in the neighborhood of which the logarithmic algebra is valid. It is
found that, if the initial RW spacetime does not have cosmological horizons,
then there is no problem in approaching the fixed point. However, in the
presence of horizons, there are world-sheet divergences which imply the need
for Liouville dressing in order to approach the fixed point in the correct way.
A detailed analysis on the subtle subtraction of these divergences in the
latter case is given. In both cases, at the fixed point, the recoil-induced
spacetime is nothing other than a coordinate transformation of the initial
spacetime into the rest frame of the recoiling D-particle. However, in the
horizon case, if one identifies the Liouville mode with the target time, which
expresses physically the back reaction of the recoiling D-particle onto the
spacetime structure, it is found that the induced spacetime distortion results
in the removal of the initial cosmological horizon and the eventual stopping of
the acceleration of the Universe. In this latter sense, our model may be
thought of as a conformal field theory description of a (toy) Universe
characterized by a sort of `phase transition' at the moment of impulse,
implying a time-varying speed of light.


  This study evaluates the ability of Large Language Model (LLM)-based
Subpopulation Representative Models (SRMs) to generalize from empirical data,
utilizing in-context learning with data from the 2016 and 2020 American
National Election Studies. We explore generalization across response variables
and demographic subgroups. While conditioning with empirical data improves
performance on the whole, the benefit of in-context learning varies
considerably across demographics, sometimes hurting performance for one
demographic while helping performance for others. The inequitable benefits of
in-context learning for SRM present a challenge for practitioners implementing
SRMs, and for decision-makers who might come to rely on them. Our work
highlights a need for fine-grained benchmarks captured from diverse
subpopulations that test not only fidelity but generalization.


  A phenomenological model, proposed for the Kaonic Nuclear Cluster
  (KNC) K^-pp, is applied to the theoretical analysis of the KNC K^-pnn (or
S^0). The theoretical values of the binding energy and the width are equal to
epsilon_(S^0) = - 197 MeV and Gamma_(S^0) = 16 MeV. They agree well with the
experimental data epsilon^(exp)_(S^0) = - 194.0(+ 1.5/- 4.4) MeV and
Gamma^(exp)_(S^0) < 21 MeV} (PLB 597, 263 (2004)) and the theoretical values
epsilon_(S^0) = - 191 MeV and Gamma_(S^0) = 13 MeV, obtained by Akaishi and
Yamazaki within the potential model approach.


  We propose an SU(5)$\times $U(1)$_\text{X}\times$U(1)$_\text{PQ}$ model
without SUSY because the SUSY particles have not been observed yet. The
$\mathrm{U(1)_{X}}$ gauge symmetry is the generalization of the
$\mathrm{U(1)_{B-L}}$ gauge symmetry and $\mathrm{U(1)_{PQ}}$ symmetry is the
global Peccei-Quinn symmetry. We introduce three mirror families in order to
unify the SM gauge couplings and avoid the restriction of proton lifetime. In
order to obtain the difference of the masses for the down-type quarks and
charged leptons, we also introduce the $\mathbf{45}$ representation Higgs in
addition to the $\mathbf{5}$ representation one. In this paper, we discuss the
mass relations between the SM and mirror particles and identify the mass scales
of the mirror particles. Since the new particles exist in the intermediate
energy scale and contribute to the renormalization group equation, the SM gauge
couplings unify successfully at high energy. Our model can be tested by the
future proton decay search, e.g., the Hyper-Kamiokande experiment expected as
$\tau_p(p\to\pi^0{e^+})<1.0\times10^{35}$ years. Also, by using the mass
relations between the active and mirror neutrinos, we estimate the lower bound
of the heavy neutrino masses.


  The advent of Large Language Models (LLMs) has propelled dialogue generation
into new realms, particularly in the field of role-playing systems (RPSs).
While enhanced with ordinary role-relevant training dialogues, existing
LLM-based RPSs still struggle to align with roles when handling intricate and
trapped queries in boundary scenarios. In this paper, we design the Modular
ORchestrated Trap-setting Interaction SystEm (MORTISE) to benchmark and improve
the role-playing LLMs' performance. MORTISE can produce highly role-relevant
aggressive queries through the collaborative effort of multiple LLM-based
modules, and formulate corresponding responses to create an adversarial
training dataset via a consistent response generator. We select 190 Chinese and
English roles to construct aggressive queries to benchmark existing
role-playing LLMs. Through comprehensive evaluation, we find that existing
models exhibit a general deficiency in role alignment capabilities. We further
select 180 of the roles to collect an adversarial training dataset (named
RoleAD) and retain the other 10 roles for testing. Experiments on models
improved by RoleAD indicate that our adversarial dataset ameliorates this
deficiency, with the improvements demonstrating a degree of generalizability in
ordinary scenarios.


  A novel method to fabricate large-area superconducting hybrid tunnel
junctions with a suspended central normal metal part is presented. The samples
are fabricated by combining photo-lithography and chemical etch of a
superconductor - insulator - normal metal multilayer. The process involves few
fabrication steps, is reliable and produces extremely high-quality tunnel
junctions. Under an appropriate voltage bias, a significant electronic cooling
is demonstrated.


  Large-amplitude Sgr A* near-infrared flares result from energy injection into
electrons near the black hole event horizon. Astrometry data show continuous
rotation of the emission region during bright flares, and corresponding
rotation of the linear polarization angle. One broad class of physical flare
models invokes magnetic reconnection. Here we show that such a scenario can
arise in a general relativistic magnetohydrodynamic simulation of a
magnetically arrested disc. Saturation of magnetic flux triggers eruption
events, where magnetically dominated plasma is expelled from near the horizon
and forms a rotating, spiral structure. Dissipation occurs via reconnection at
the interface of the magnetically dominated plasma and surrounding fluid. This
dissipation is associated with large increases in near-infrared emission in
models of Sgr A*, with durations and amplitudes consistent with the observed
flares. Such events occur at roughly the timescale to re-accumulate the
magnetic flux from the inner accretion disc, 10h for Sgr A*. We study
near-infrared observables from one sample event to show that the emission
morphology tracks the boundary of the magnetically dominated region. As the
region rotates around the black hole, the near-infrared centroid and linear
polarization angle both undergo continuous rotation, similar to the behavior
seen in Sgr A* flares.


  Axion-like particles (ALPs) are a class of hypothetical pseudoscalar
particles which feebly interact with ordinary matter. The hot plasma of
core-collapse supernovae is a possible laboratory to explore physics beyond the
standard model including ALPs. Once produced, some of the ALPs can be absorbed
by the supernova matter and affect energy transfer. In this study, we calculate
the ALP emission in core-collapse supernovae and the backreaction on supernova
dynamics consistently. It is found that the stalled bounce shock can be revived
if the coupling between ALPs and photons is as high as g_{ag}~10^{-9} GeV^{-1}
and the ALP mass is 40-400 MeV. Most of the models result in more energetic
explosions than the average observed supernova. While this can be used to place
constraints on those ALPs, long-term simulations across multiple progenitors
need to be further investigated to place robust limits.


  Gapless bilayer graphene is susceptible to a variety of spontaneously gapped
states. As predicted by theory and observed by experiment, the ground state is
however topologically trivial, because a valley-independent gap is
energetically favorable. Here, we show that under the application of interlayer
electric field and circularly polarized light, one valley can be selected to
exhibit the original interaction instability while the other is frozen out.
Tuning this Floquet system stabilizes multiple competing topological ordered
states, distinguishable by edge transport and circular dichroism. Notably,
quantized charge, spin, and valley Hall conductivities coexist in one
stabilized state.


  We prove the orthogonality of the Bethe Ansatz eigenfunctions for the
Laplacian on a hyperoctahedral Weyl alcove with repulsive homogeneous Robin
boundary conditions at the walls. To this end these eigenfunctions are
retrieved as the continuum limit of an orthogonal basis of algebraic Bethe
Ansatz eigenfunctions for a finite $q$-boson system endowed with diagonal
open-end boundary interactions.


  Sentiment classification is a fundamental task in natural language
processing, assigning one of the three classes, positive, negative, or neutral,
to free texts. However, sentiment classification models are highly domain
dependent; the classifier may perform classification with reasonable accuracy
in one domain but not in another due to the Semantic multiplicity of words
getting poor accuracy. This article presents a new Persian/Arabic multi-domain
sentiment analysis method using the cumulative weighted capsule networks
approach. Weighted capsule ensemble consists of training separate capsule
networks for each domain and a weighting measure called domain belonging degree
(DBD). This criterion consists of TF and IDF, which calculates the dependency
of each document for each domain separately; this value is multiplied by the
possible output that each capsule creates. In the end, the sum of these
multiplications is the title of the final output, and is used to determine the
polarity. And the most dependent domain is considered the final output for each
domain. The proposed method was evaluated using the Digikala dataset and
obtained acceptable accuracy compared to the existing approaches. It achieved
an accuracy of 0.89 on detecting the domain of belonging and 0.99 on detecting
the polarity. Also, for the problem of dealing with unbalanced classes, a
cost-sensitive function was used. This function was able to achieve 0.0162
improvements in accuracy for sentiment classification. This approach on Amazon
Arabic data can achieve 0.9695 accuracies in domain classification.


  Half-Heusler alloys have been one of the benchmark high temperature
thermoelectric materials owing to their thermal stability and promising figure
of merit ZT. Simonson et al. early showed that small amounts of vanadium doped
in Hf0.75Zr0.25NiSn enhanced the Seebeck coefficient and correlated the change
with the increased density of states near the Fermi level. We herein report a
systematic study on the role of vanadium (V), niobium (Nb), and tantalum (Ta)
as prospective resonant dopants in enhancing the ZT of n-type half-Heusler
alloys based on Hf0.6Zr0.4NiSn0.995Sb0.005. The V doping was found to increase
the Seebeck coefficient in the temperature range 300-1000 K, consistent with a
resonant doping scheme. In contrast, Nb and Ta act as normal n-type dopants, as
evident by the systematic decrease in electrical resistivity and Seebeck
coefficient. The combination of enhanced Seebeck coefficient due to the
presence of V resonant states and the reduced thermal conductivity has led to a
state-of-the-art ZT of 1.3 near 850 K in n-type
(Hf0.6Zr0.4)0.99V0.01NiSn0.995Sb0.005 alloys.


  In this paper, we investigate some properties of the Perron vector of
connected graphs. These results are used to characterize that all extremal
connected graphs with having the minimum (maximum) spectra radius among all
connected graphs of order $n=k\alpha$ with the independence number $\alpha$,
respectively.


  We study the dynamics of iterates at the transition to chaos in the logistic
map and find that it is constituted by an infinite family of Mori's $q$-phase
transitions. Starting from Feigenbaum's $\sigma $ function for the diameters
ratio, we determine the atypical weak sensitivity to initial conditions $\xi
_{t}$ associated to each $q$-phase transition and find that it obeys the form
suggested by the Tsallis statistics. The specific values of the variable $q$ at
which the $q$-phase transitions take place are identified with the specific
values for the Tsallis entropic index $q$ in the corresponding $\xi_{t}$. We
describe too the bifurcation gap induced by external noise and show that its
properties exhibit the characteristic elements of glassy dynamics close to
vitrification in supercooled liquids, e.g. two-step relaxation, aging and a
relationship between relaxation time and entropy.


  Some models of the 5/2 fractional quantum Hall state predict that the
quasi-particles, which carry the charge, have non-Abelian statistics: exchange
of two quasi-particles changes the wave function more dramatically than just
the usual change of phase factor. Such non-Abelian statistics would make the
system less sensitive to decoherence, making it a candidate for implementation
of topological quantum computation. We measure quasi-particle tunneling as a
function of temperature and DC bias between counter-propagating edge states.
Fits to theory give e*, the quasi-particle effective charge, close to the
expected value of e/4 and g, the strength of the interaction between
quasi-particles, close to 3/8. Fits corresponding to the various proposed wave
functions, along with qualitative features of the data, strongly favor the
Abelian 331 state.


  We provide an enhancement of Shipley's algebraicization theorem which behaves
better in the context of commutative algebras. This involves defining flat
model structures as in Shipley and Pavlov-Scholbach, and showing that the
functors still provide Quillen equivalences in this refined context. The use of
flat model structures allows one to identify the algebraic counterparts of
change of groups functors, as demonstrated in forthcoming work of the author.


  To address the non-negativity dropout problem of quaternion models, a novel
quasi non-negative quaternion matrix factorization (QNQMF) model is presented
for color image processing. To implement QNQMF, the quaternion projected
gradient algorithm and the quaternion alternating direction method of
multipliers are proposed via formulating QNQMF as the non-convex constraint
quaternion optimization problems. Some properties of the proposed algorithms
are studied. The numerical experiments on the color image reconstruction show
that these algorithms encoded on the quaternion perform better than these
algorithms encoded on the red, green and blue channels. Furthermore, we apply
the proposed algorithms to the color face recognition. Numerical results
indicate that the accuracy rate of face recognition on the quaternion model is
better than on the red, green and blue channels of color image as well as
single channel of gray level images for the same data, when large facial
expressions and shooting angle variations are presented.


  A new image encryption scheme using the advanced encryption standard (AES), a
chaotic map, a genetic operator, and a fuzzy inference system is proposed in
this paper. In this work, plain images were used as input, and the required
security level was achieved. Security criteria were computed after running a
proposed encryption process. Then an adaptive fuzzy system decided whether to
repeat the encryption process, terminate it, or run the next stage based on the
achieved results and user demand. The SHA-512 hash function was employed to
increase key sensitivity. Security analysis was conducted to evaluate the
security of the proposed scheme, which showed it had high security and all the
criteria necessary for a good and efficient encryption algorithm were met.
Simulation results and the comparison of similar works showed the proposed
encryptor had a pseudo-noise output and was strongly dependent upon the
changing key and plain image.


  A simple and interpretable way to learn a dynamical system from data is to
interpolate its vector-field with a kernel. In particular, this strategy is
highly efficient (both in terms of accuracy and complexity) when the kernel is
data-adapted using Kernel Flows (KF)~\cite{Owhadi19} (which uses gradient-based
optimization to learn a kernel based on the premise that a kernel is good if
there is no significant loss in accuracy if half of the data is used for
interpolation). Despite its previous successes, this strategy (based on
interpolating the vector field driving the dynamical system) breaks down when
the observed time series is not regularly sampled in time. In this work, we
propose to address this problem by directly approximating the vector field of
the dynamical system by incorporating time differences between observations in
the (KF) data-adapted kernels. We compare our approach with the classical one
over different benchmark dynamical systems and show that it significantly
improves the forecasting accuracy while remaining simple, fast, and robust.


  In this paper, we study the production of spin-one color-octet resonances
(colorons) at hadron colliders in a model independent way. We use dijets data
measured by CDF (at \sqrt{s}=1.96 TeV and L=1.13$ 1/pb) and ATLAS (at
\sqrt{s}=7$ TeV and L=315 1/nb) collaborations at the Tevatron and the LHC
respetively to impose limits on the coupling of colorons to fermions. We show
that CDF data still produce the more stringent limits on the coloron coupling
constant.


  We prove that a Coxeter group containing an element of finite order, which is
generated by two non-commuting involutions, can not be embedded into the
Riordan group.


  The relation between limit cycles of planar differential systems and the
inverse integrating factor was first shown in an article of Giacomini, Llibre
and Viano appeared in 1996. From that moment on, many research articles are
devoted to the study of the properties of the inverse integrating factor and
its relation with limit cycles and their bifurcations. This paper is a summary
of all the results about this topic. We include a list of references together
with the corresponding related results aiming at being as much exhaustive as
possible. The paper is, nonetheless, self-contained in such a way that all the
main results on the inverse integrating factor are stated and a complete
overview of the subject is given. Each section contains a different issue to
which the inverse integrating factor plays a role: the integrability problem,
relation with Lie symmetries, the center problem, vanishing set of an inverse
integrating factor, bifurcation of limit cycles from either a period annulus or
from a monodromic $\omega$-limit set and some generalizations.


  The goal of recommendation is to show users items that they will like. Though
usually framed as a prediction, the spirit of recommendation is to answer an
interventional question---for each user and movie, what would the rating be if
we "forced" the user to watch the movie? To this end, we develop a causal
approach to recommendation, one where watching a movie is a "treatment" and a
user's rating is an "outcome." The problem is there may be unobserved
confounders, variables that affect both which movies the users watch and how
they rate them; unobserved confounders impede causal predictions with
observational data. To solve this problem, we develop the deconfounded
recommender, a way to use classical recommendation models for causal
recommendation. Following Wang & Blei [23], the deconfounded recommender
involves two probabilistic models. The first models which movies the users
watch; it provides a substitute for the unobserved confounders. The second one
models how each user rates each movie; it employs the substitute to help
account for confounders. This two-stage approach removes bias due to
confounding. It improves recommendation and enjoys stable performance against
interventions on test sets.


  We use the Risaliti & Lusso (2015) compilation of 808 X-ray and UV flux
measurements of quasars (QSOs) in the redshift range $0.061 \leq z \leq 6.28$,
alone and in conjuction with baryon acoustic oscillation (BAO) and Hubble
parameter [$H(z)$] measurements, to constrain cosmological parameters in six
cosmological models. The QSO data constraints are significantly weaker than,
but consistent with, those from the $H(z)$ + BAO data. A joint analysis of the
QSO + $H(z)$ + BAO data is consistent with the current standard model,
spatially-flat $\Lambda$CDM, but mildly favors closed spatial hypersurfaces and
dynamical dark energy.


  In gamma-ray burst research it is quite common to fit the afterglow light
curves with a broken power law to interpret the data. We apply this method to a
computer simulated population of afterglows and find systematic differences
between the known model parameters of the population and the ones derived from
the power law fits. In general, the slope of the electron energy distribution
is overestimated from the pre-break light curve slope while being
underestimated from the post-break slope. We also find that the jet opening
angle derived from the fits is overestimated in narrow jets and underestimated
in wider ones. Results from fitting afterglow light curves with broken power
laws must therefore be interpreted with caution since the uncertainties in the
derived parameters might be larger than estimated from the fit. This may have
implications for Hubble diagrams constructed using gamma-ray burst data.


  A cone spherical metric is called irreducible if any developing map of the
metric does not have monodromy in ${\rm U(1)}$. By using the theory of
indigenous bundles, we construct on a compact Riemann surface $X$ of genus $g_X
\geq 1$ a canonical surjective map from the moduli space of stable extensions
of two line bundles to that of irreducible metrics with cone angles in $2 \pi
\mathbb{Z}_{>1}$, which is generically injective in the algebro-geometric sense
as $g_X \geq 2$. As an application, we prove the following two results about
irreducible metrics:
  $\bullet$ as $g_X \geq 2$ and $d$ is even and greater than $12g_X - 7$, the
effective divisors of degree $d$ which could be represented by irreducible
metrics form an arcwise connected Borel subset of Hausdorff dimension $\geq
2(d+3-3g_X)$ in ${\rm Sym}^d(X)$;
  $\bullet$ as $g_X \geq 1$, for almost every effective divisor $D$ of degree
odd and greater than $2g_X-2$ on $X$, there exist finitely many cone spherical
metrics representing $D$.


  ULTIMATE-Subaru (Ultra-wide Laser Tomographic Imager and MOS with AO for
Transcendent Exploration on Subaru) and WFIRST (Wide Field Infra-Red Survey
Telescope) are the next generation near-infrared instruments that have a large
field-of-view. They allow us to conduct deep and wide transient surveys in
near-infrared. Such a near-infrared transient survey enables us to find very
distant supernovae that are redshifted to the near-infrared wavelengths. We
have performed the mock transient surveys with ULTIMATE-Subaru and WFIRST to
investigate their ability to discover Population III pair-instability
supernovae. We found that a 5-year 1 deg2 K-band transient survey with the
point-source limiting magnitude of 26.5 mag with ULTIMATE-Subaru may find about
2 Population III pair-instability supernovae beyond the redshift of 6. A 5-year
10 deg2 survey with WFIRST reaching 26.5 mag in the F184 band may find about 7
Population III pair-instability supernovae beyond the redshift of 6. We also
find that the expected numbers of the Population III pair-instability supernova
detections increase about a factor of 2 if the near-infrared transient surveys
are performed towards clusters of galaxies. Other supernovae such as Population
II pair-instability supernovae would also be detected in the same survey. This
study demonstrates that the future wide-field near-infrared instruments allow
us to investigate the explosions of the first generation supernovae by
performing the deep and wide near-infrared transient surveys.


  Continued reliance on human operators for managing data centers is a major
impediment for them from ever reaching extreme dimensions. Large computer
systems in general, and data centers in particular, will ultimately be managed
using predictive computational and executable models obtained through
data-science tools, and at that point, the intervention of humans will be
limited to setting high-level goals and policies rather than performing
low-level operations. Data-driven autonomics, where management and control are
based on holistic predictive models that are built and updated using generated
data, opens one possible path towards limiting the role of operators in data
centers. In this paper, we present a data-science study of a public Google
dataset collected in a 12K-node cluster with the goal of building and
evaluating a predictive model for node failures. We use BigQuery, the big data
SQL platform from the Google Cloud suite, to process massive amounts of data
and generate a rich feature set characterizing machine state over time. We
describe how an ensemble classifier can be built out of many Random Forest
classifiers each trained on these features, to predict if machines will fail in
a future 24-hour window. Our evaluation reveals that if we limit false positive
rates to 5%, we can achieve true positive rates between 27% and 88% with
precision varying between 50% and 72%. We discuss the practicality of including
our predictive model as the central component of a data-driven autonomic
manager and operating it on-line with live data streams (rather than off-line
on data logs). All of the scripts used for BigQuery and classification analyses
are publicly available from the authors' website.


  Future advanced nuclear rocket propulsion, and the availability of new
nuclear power cycle designs, will benefit substantially from the large current
investment in alternative nuclear energy that is underway today. We propose a
new nuclear cycle which includes the primary fission of lithium-6, followed by
secondary fusion of deuterium and tritium, and a secondary fission of lithium-7
by tritium. This cycle does not produce nuclear waste from its nuclear fuel,
since all byproducts of these cascade reactions are stable, provided that the
triton production during the primary reaction is fully consumed in the
secondary reactions. This cycle may, however, activate surrounding technical
materials from its neutron flux. This light-element nuclear fuel is readily
obtained through the ongoing expansion of the lithium mining industry and
electric vehicle (EV) battery recycling industries.


  In an earlier paper the author defined N1L configurations, and stated a
conjecture concerning them which would lead to an improvement by a constant
factor to the sphere-packing bound for linear double error correcting codes.
Here a computer search is presented, in an effort to gather evidence on the
conjecture.


  In this paper, we give an RTT presentation of the twisted quantum affine
algebra of type $A_{2n-1}^{(2)}$ and show that it is isomorphic to the Drinfeld
new realization via the Gauss decomposition of the L-operators. This provides
the first such presentation for twisted quantum affine algebras with nontrivial
central element.


  It is shown that initiated by action of molecular viscosity impulse flow,
directed usually from the moving fluid to limiting it solid surface, can, under
certain conditions, turn to zero and get negative values in the case of
non-stationary flow caused by alternating in time longitudinal (along the pipe
axis) pressure gradient. It is noted that this non-equilibrium mechanism of
negative friction resistance in the similar case of pulsating blood flow in the
blood vessels, in addition to the stable to turbulent disturbances swirled
blood flow structure providing, can also constitute hydro-mechanical basis of
the observed but not explained yet paradoxically high energy effectiveness of
the normal functioning of the cardio-vascular system (CVS). We consider
respective mechanism of affecting on the stability of the normal work of CVS by
environmental variable factors using shifting of hydro-dynamic mode with
negative resistance realization range boundaries and variation of linear
hydro-dynamic instability leading to the structurally stable swirled blood flow
organization.


  We report a thorough investigation into the absorptive and dispersive
properties of hot caesium vapour, culminating in the development of a simple
analytical model for off-resonant Faraday rotation. The model, applicable to
all hot alkali metal vapours, is seen to predict the rotation observed in
caesium, at temperatures as high as 115 $^{\circ}$C, to within 1% accuracy for
probe light detuned by greater than 2 GHz from the $D_{2}$ lines. We also
demonstrate the existence of a weak probe intensity limit, below which the
effect of hyperfine pumping is negligible. Following the identification of this
regime we validate a more comprehensive model for the absorption and dispersion
in the vicinity of the $D_{2}$ lines, implemented in the form of a computer
code. We demonstrate the ability of this model to predict Doppler-broadened
spectra to within 0.5% rms deviation for temperatures up to 50 $^{\circ}$C.


  In brane-world models with low tension, massive branons are natural
candidates for dark matter. The phenomenology of these WIMP-like particles is
completely determined by their mass, the brane tension and, in the case of
effects due to radiative corrections, by the cutoff setting the scale of
validity of the branon effective theory. In this paper, we review the main
constraints on branon physics coming from colliders, astrophysics and
cosmological observations, and include more recent limits obtained from
electroweak precision measurements.


  After discussing the localization of Abelian and non-Abelian gauge fields and
Higgs fields on a thick brane, we introduce a procedure of dimensional
reduction and its consequences to the rescaled parameters of the boson sector
of the Standard Model. The parameters encodes some power dependence on the
extra dimension, usually narrow, warp factor and hence it also depend on the
position related with the extra dimension inside the thick brane. In this vein,
the observable parameters may be used to refine the braneworld models via the
brane thickness.


  We formulate and prove an analog of Poonen's finite-field Bertini theorem
with Taylor conditions that holds in the Grothendieck ring of varieties. This
gives a broad generalization of the work of Vakil-Wood, who treated the case of
smooth hypersurface sections. In fact, our techniques give analogs in motivic
statistics of all known results in arithmetic statistics that have been proven
using Poonen's sieve, including work of Bucur-Kedlaya on complete intersections
and Erman-Wood on semi-ample Bertini theorems. A key ingredient is the use of
motivic Euler products, as introduced by the first author, to write down
candidate motivic probabilities. We also formulate a conjecture on the uniform
convergence of zeta functions that unifies motivic and arithmetic statistics
for varieties over finite fields.


  In the present paper, several properties concerning generalized derivatives
of multifunctions implicitly defined by set-valued inclusions are studied by
techniques of variational analysis. Set-valued inclusions are problems
formalizing the robust fulfilment of cone constraint systems, whose data are
affected by a "crude knowledge" of uncertain elements, so they can not be
casted in traditional generalized equations.
  The focus of this study in on the first-order behaviour of the solution
mapping associated with a parameterized set-valued inclusion, starting with
Lipschitzian properties and then considering its graphical derivative. In
particular, a condition for the Aubin continuity of the solution mapping is
established in terms of outer prederivative of the set-valued mapping defining
the inclusion. A large class of parameterized set-valued inculsions is singled
out, whose solution mapping turns out to be convex. Some relevant consequences
on the graphical derivative are explored. In the absence of that, formulae for
the inner and outer approximation of the graphical derivative are provided by
means of prederivatives of the problem data. A representation useful to
calculate the coderivative of the solution mapping is also obtained via the
subdifferential of a merit function.


  Pathwise predictability and predictors for discrete time processes are
studied in deterministic setting. It is suggested to approximate convolution
sums over future times by convolution sums over past time. It is shown that all
band-limited processes are predictable in this sense, as well as high-frequency
processes with zero energy at low frequencies. In addition, a process of mixed
type still can be predicted if an ideal low-pass filter exists for this
process.


  Deep learning networks generally use non-biological learning methods. By
contrast, networks based on more biologically plausible learning, such as
Hebbian learning, show comparatively poor performance and difficulties of
implementation. Here we show that Hebbian learning in hierarchical,
convolutional neural networks can be implemented almost trivially with modern
deep learning frameworks, by using specific losses whose gradients produce
exactly the desired Hebbian updates. We provide expressions whose gradients
exactly implement a plain Hebbian rule (dw ~= xy), Grossberg's instar rule (dw
~= y(x-w)), and Oja's rule (dw ~= y(x-yw)). As an application, we build Hebbian
convolutional multi-layer networks for object recognition. We observe that
higher layers of such networks tend to learn large, simple features (Gabor-like
filters and blobs), explaining the previously reported decrease in decoding
performance over successive layers. To combat this tendency, we introduce
interventions (denser activations with sparse plasticity, pruning of
connections between layers) which result in sparser learned features, massively
increase performance, and allow information to increase over successive layers.
We hypothesize that more advanced techniques (dynamic stimuli, trace learning,
feedback connections, etc.), together with the massive computational boost
offered by modern deep learning frameworks, could greatly improve the
performance and biological relevance of multi-layer Hebbian networks.


  Unmanned vehicle navigation concerns estimating attitude, position, and
linear velocity of the vehicle the six degrees of freedom (6 DoF). It has been
known that the true navigation dynamics are highly nonlinear modeled on the Lie
Group of $\mathbb{SE}_{2}(3)$. In this paper, a nonlinear filter for inertial
navigation is proposed. The filter ensures systematic convergence of the error
components starting from almost any initial condition. Also, the errors
converge asymptotically to the origin. Experimental results validates the
robustness of the proposed filter.


  Optomechanics, nano-electromechanics, and integrated photonics have brought
about a renaissance in phononic device physics and technology. Central to this
advance are devices and materials that support ultra long-lived photonic and
phononic excitations, providing access to novel regimes of classical and
quantum dynamics based on tailorable photon-phonon coupling. Silica-based
devices have been at the forefront of such innovations for their ability to
support optical excitations persisting for nearly 1 billion cycles, and for
their low optical nonlinearity. Remarkably, acoustic phonon modes can persist
for a comparable number of cycles in crystalline solids at cryogenic
temperatures, permitting radical enhancement of photon-phonon coupling.
However, it has not been possible to achieve similar phononic coherence times
in silica, as silica becomes acoustically opaque at low temperatures. In this
paper, we demonstrate that intrinsic forms of phonon dissipation are greatly
reduced (by > 90%) using nonlinear saturation with continuous driving fields of
disparate frequencies. We demonstrate steady-state phononic spectral hole
burning for the first time, and show that this technique for controlling
dissipation in glass produces a wide-band transparency window. These studies
were carried out in a micro-scale fiber waveguide where the acoustic
intensities necessary to manipulate phonon dissipation can be achieved with
optically generated phonon fields of modest (nW) powers. We developed a simple
model that explains both dissipative and dispersive changes produced by
phononic saturation. In showing how the dissipative and dispersive properties
of glasses can be manipulated using external fields, we open the door to
dynamical phononic switching and the use of glasses as low loss phononic media
which may enable new forms of controllable laser dynamics, information
processing, and precision metrology.


  We consider special subclasses of the class of Lindel\"of Sigma-spaces
obtained by imposing restrictions on the weight of the elements of compact
covers that admit countable networks: A space $X$ is in the class
$L\Sigma(\leq\kappa)$ if it admits a cover by compact subspaces of weight
$\kappa$ and a countable network for the cover. We restrict our attention to
$\kappa\leq\omega$. In the case $\kappa=\omega$, the class includes the class
of metrizably fibered spaces considered by Tkachuk, and the $P$-approximable
spaces considered by Tkacenko. The case $\kappa=1$ corresponds to the spaces of
countable network weight, but even the case $\kappa=2$ gives rise to a
nontrivial class of spaces. The relation of known classes of compact spaces to
these classes is considered. It is shown that not every Corson compact of
weight $\aleph_1$ is in the class $L\Sigma(\leq \omega)$, answering a question
of Tkachuk. As well, we study whether certain compact spaces in
$L\Sigma(\leq\omega)$ have dense metrizable subspaces, partially answering a
question of Tkacenko. Other interesting results and examples are obtained, and
we conclude the paper with a number of open questions.


  We explicitly determine the spectrum of transfer operators (acting on spaces
of holomorphic functions) associated to analytic expanding circle maps arising
from finite Blaschke products. This is achieved by deriving a convenient
natural representation of the respective adjoint operators.


  Robots assist in many areas that are considered unsafe for humans to operate.
For instance, in handling pandemic diseases such as the recent Covid-19
outbreak and other outbreaks like Ebola, robots can assist in reaching areas
dangerous for humans and do simple tasks such as pick up the correct medicine
(among a set of bottles prescribed) and deliver to patients. In such cases, it
might not be good to rely on the fully autonomous operation of robots. Since
many mobile robots are fully functional with low-level tasks such as grabbing
and moving, we consider the mixed-initiative control where the user can guide
the robot remotely to finish such tasks. For this mixed-initiative control, the
user controlling the robot needs to visualize a 3D scene as seen by the robot
and guide it. Mixed reality can virtualize reality and immerse users in the 3D
scene that is reconstructed from the real-world environment. This technique
provides the user more freedom such as choosing viewpoints at view time. In
recent years, benefiting from the high-quality data from Light Detection and
Ranging (LIDAR) and RGBD cameras, mixed reality is widely used to build
networked platforms to improve the performance of robot teleoperations and
robot-human collaboration, and enhanced feedback for mixed-initiative control.
In this paper, we proposed a novel haptic-enabled mixed reality system, that
provides haptic interfaces to interact with the virtualized environments and
give remote guidance for mobile robots towards high-level tasks. The
experimental results show the effectiveness and flexibility of the proposed
haptic enabled mixed reality system.


  The purpose of this study is to identify the characteristics of Agile
development processes that impact user satisfaction. We used user reviews of
OSS smartphone apps and various data from version control systems to examine
the relationships, especially time-series correlations, between user
satisfaction and development metrics that are expected to be related to user
satisfaction. Although no metrics conclusively indicate an improved user
satisfaction, motivation of the development team, the ability to set
appropriate work units, the appropriateness of work rules, and the improvement
of code maintainability should be considered as they are correlated with
improved user satisfaction. In contrast, changes in the release frequency and
workload are not correlated.


  Euclid and the Large Synoptic Survey Telescope (LSST) are poised to
dramatically change the astronomy landscape early in the next decade. The
combination of high cadence, deep, wide-field optical photometry from LSST with
high resolution, wide-field optical photometry and near-infrared photometry and
spectroscopy from Euclid will be powerful for addressing a wide range of
astrophysical questions. We explore Euclid/LSST synergy, ignoring the political
issues associated with data access to focus on the scientific, technical, and
financial benefits of coordination. We focus primarily on dark energy
cosmology, but also discuss galaxy evolution, transient objects, solar system
science, and galaxy cluster studies. We concentrate on synergies that require
coordination in cadence or survey overlap, or would benefit from pixel-level
co-processing that is beyond the scope of what is currently planned, rather
than scientific programs that could be accomplished only at the catalog level
without coordination in data processing or survey strategies. We provide two
quantitative examples of scientific synergies: the decrease in photo-z errors
(benefitting many science cases) when high resolution Euclid data are used for
LSST photo-z determination, and the resulting increase in weak lensing
signal-to-noise ratio from smaller photo-z errors. We briefly discuss other
areas of coordination, including high performance computing resources and
calibration data. Finally, we address concerns about the loss of independence
and potential cross-checks between the two missions and potential consequences
of not collaborating.


  Lawson's iteration is a classical and effective method for solving the linear
(polynomial) minimax approximation in the complex plane. Extension of Lawson's
iteration for the rational minimax approximation with both computationally high
efficiency and theoretical guarantee is challenging. A recent work [L.-H.
Zhang, L. Yang, W. H. Yang and Y.-N. Zhang, A convex dual programming for the
rational minimax approximation and Lawson's iteration, 2023,
arXiv:2308.06991v1] reveals that Lawson's iteration can be viewed as a method
for solving the dual problem of the original rational minimax approximation,
and a new type of Lawson's iteration was proposed. Such a dual problem is
guaranteed to obtain the original minimax solution under Ruttan's sufficient
condition, and numerically, the proposed Lawson's iteration was observed to
converge monotonically with respect to the dual objective function. In this
paper, we perform theoretical convergence analysis for Lawson's iteration for
both the linear and rational minimax approximations. In particular, we show
that (i) for the linear minimax approximation, the near-optimal Lawson exponent
$\beta$ in Lawson's iteration is $\beta=1$, and (ii) for the rational minimax
approximation, the proposed Lawson's iteration converges monotonically with
respect to the dual objective function for any sufficiently small $\beta>0$,
and the limit approximant fulfills the complementary slackness: any node
associated with positive weight either is an interpolation point or has a
constant error.


  For the asymmetric spherical dimer of an endohedrally confined atom and a
host fullerene, an innershell vacancy of either system can decay through the
continuum of an outer electron hybridized between the systems. Such decays,
viewed as coherent superpositions of the single-center Auger and two-center
inter-Coulombic (ICD) amplitudes, are found to govern leading decay mechanisms
in noble-gas endofullerenes, and are likely omnipresent in this class of
nanomolecules. A comparison between resulting autoionizing resonances
calculated in the photoionization of Ar@C60 and Kr@C60 exhibits details of the
underlying processes.


  Based on a simple site-independent approach, we attempt to reproduce the
solar $r$-process abundance with four nuclear mass models, and investigate the
impact of the nuclear mass uncertainties on the $r$ process. In this paper, we
first analyze the reliability of an adopted empirical formula for $\beta$-decay
half-lives which is a key ingredient for the $r$ process. Then we apply four
different mass tables to study the $r$-process nucleosynthesis together with
the calculated $\beta$-decay half-lives, and the existing $\beta$-decay data
from FRDM+QRPA is also considered for comparison. The numerical results show
that the main features of the solar $r$-process pattern and the locations of
the abundance peaks can be reproduced well via the $r$-process simulations.
Moreover, we also find that the mass uncertainties can significantly affect the
derived astrophysical conditions for the $r$-process site, and resulting in a
remarkable impact on the $r$ process.


  We present a theoretical study of the director fields and energetics of
nematic liquid crystal shells with two pairs of surface defects. The pairs of
defects can undergo abrupt transitions between a configuration of maximum
separation to at state in which the defects are confined to the thinnest
hemisphere. We construct a phase diagram that maps out the stability and
coexistence of these two configurations as a function of shell thickness and
thickness inhomogeneity. Our results compare favorably with the experimentally
observed transitions in nematic double emulsion droplets and explain their
hysteretic character.


  We describe the supersingular locus of a GU(2,2) Shimura variety at a prime
inert in the corresponding quadratic imaginary field.


  The generation of solar non-axisymmetric magnetic fields is studied based on
a linear alpha2-Omega dynamo model in a rotating spherical frame. The model
consists of a solar-like differential rotation, a magnetic diffusivity varied
with depth, and three types of alpha-effects with different locations, i.e. the
tachocline, the whole convective zone and the sub-surface. Some comparisons of
the critical alpha-values of axisymmetric m=0 and longitude-dependent modes
m=1,2,3 are presented to show the roles of the magnetic diffusivity in the
problem of modes selection. With the changing of diffusivity intensity for the
given solar differential rotation system, the dominant mode possibly changes
likewise and the stronger the diffusivity is, the easier the non-axisymmetric
modes are excited. The influence of the diffusivity and differential rotation
on the configurations of the dominant modes are also presented.


  The key observation about the quantum reality is that it looks as if there is
a moment when a probability of a quantum event or events becomes reality for
us. However, after careful analysis, it looks plausible that what we believe is
a definite state in our reality, observed as an outcome of a quantum experiment
or experiments, is actually not a definite state. From there, we conclude that
quantum world is an active world whose influence lies beyond statistical and
permanent determination of reality as we know it.


  The notion of actual causation, as formalized by Halpern and Pearl, has been
recently applied to relational databases, to characterize and compute actual
causes for possibly unexpected answers to monotone queries. Causes take the
form of database tuples, and can be ranked according to their causal
responsibility, a numerical measure of their relevance as a cause to the query
answer. In this work we revisit this notion, introducing and making a case for
an alternative measure of causal contribution, that of causal effect. The
measure generalizes actual causes, and can be applied beyond monotone queries.
We show that causal effect provides intuitive and intended results.


  The effects of space charges on hysteresis loops and field distributions in
ferroelectrics have been investigated numerically using the phenomenological
Landau-Ginzburg-Devonshire theory. Cases with the ferroelectric fully and
partially depleted have been considered. In general, increasing the number of
charged impurities results in a lowering of the polarization and coercive field
values. Squarer loops were observed in the partially depleted cases and a
method was proposed to identify fully depleted samples experimentally from
dielectric and polarization measurements alone. Unusual field distributions
found for higher dopant concentrations have some interesting implications for
leakage mechanisms and limit the range of validity of usual semiconductor
equations for carrier transport.


  We discuss properties of the specific strongly interacting transient
collective state of matter in hadron and nuclei reactions and emphasize
similarity in their dynamics. We consider elliptic flow introduced for
description of nucleus collisions and discuss its possible behavior in hadronic
reactions.


  The possibility that nuclear matter at a density relevant to the interior of
massive neutron stars may be a quarkynoic matter has attracted considerable
recent interest. In this work, we construct a field theoretical model to
describe the quarkyonic matter, that would allow quantitative and systematic
calculations of its various properties. This is implemented by synthesizing the
Walecka model together with the quark-meson model, where both quark and nucleon
degrees of freedom are present based on the quarkyonic scenario. With this
model we compute at mean-field level the thermodynamic properties of the
symmetric nuclear matter and calibrate model parameters through well-known
nuclear physics measurements. We find this model gives a very good description
of the symmetric nuclear matter from moderate to high baryon density and
demonstrates a continuous transition from nucleon-dominance to quark-dominance
for the system.


  It is well established in the compressive sensing (CS) literature that
sensing matrices whose elements are drawn from independent random distributions
exhibit enhanced reconstruction capabilities. In many CS applications, such as
electromagnetic imaging, practical limitations on the measurement system
prevent one from generating sensing matrices in this fashion. Although one can
usually randomized the measurements to some degree, these sensing matrices do
not achieve the same reconstruction performance as the truly randomized sensing
matrices. In this paper, we present a novel method, based upon capacity
maximization, for designing sensing matrices with enhanced block-sparse signal
reconstruction capabilities. Through several numerical examples, we demonstrate
how our method significantly enhances reconstruction performance.


  In recent years, the level of the extragalactic radio background has become a
point of considerable interest, with some lines of argument pointing to an
entirely new cosmological synchrotron background. The contribution of the known
discrete source population to the sky temperature is key to this discussion.
Because of the steep spectral index of the excess over the cosmic microwave
background, it is best studied at low frequencies where the signal is
strongest. The Low-Frequency Array (LOFAR) wide and deep sky surveys give us
the best constraints yet on the contribution of discrete extragalactic sources
at 144 MHz, and in particular allow us to include contributions from diffuse,
low-surface-brightness emission that could not be fully accounted for in
previous work. We show that, even with these new data, known sources can still
only account for around a quarter of the estimated extragalactic sky
temperature at LOFAR frequencies.


  This paper presents a constructive proof of the existence of a regular
non-atomic strictly-positive measure on any second-countable non-atomic locally
compact Hausdorff space. This construction involves a sequence of
finitely-additive set functions defined recursively on an ascending sequence of
rings of subsets with a premeasure limit that is extendable to a measure with
the desired properties. Non-atomicity of the space provides a non-trivial way
to ensure that the limit is a premeasure.


  A claim has been made by van den Bosch et al. (2012) that NGC1277 hosts an
over-massive BH with a mass larger than half its spheroid mass. We revisit this
claim by examining the predictions from dynamical realisations based on new MGE
models of NGC1277. We present realisations which fit the observed photometry.
M/L is fixed following scaling relations which predict a Salpeter-like IMF. A
model without a BH provides a surprisingly good fit of the observed kinematics
outside the unresolved central region, but not, as expected, of the central
dispersion and h4 values. A model with a MBH of 5 10^9 Msun allows to fit the
dispersion profile, consistently with models of the same mass and M/L in
vdB+12. It departs from the central h4 values by only about twice the given
uncertainty. A slightly varying M/L or the addition of high velocity stars
would further lower the need for a very massive BH. These results do not rule
out the presence of an over-massive BH at the centre of NGC1277. However, they
lead us to advocate the use of 3-sigma confidence intervals for derived MBH as
better, more conservative, guidelines for such studies. We caution for the use
of ill-defined spheroidal components as an input for scaling relations, and
emphasise the fact that a MBH in the range 2-5 10^9 Msun would represent less
than 5% of the spheroid mass of our models. This would make the BH in NGC1277
consistent or just twice as large as what a recent version of the MBH-sigma
relation predicts. We examine the impact of the presence of a bar by running
simulations from the same MGE model but with extreme anisotropies. An inner
small bar forms, and an end-on view gets closer to fitting the central
dispersion profile without the need for a central BH, while adding a black hole
of 2.5 10^9 Msun, in line with the prediction from scaling relations, allows to
fit the dispersion peak and h3 profiles. EDITED and ABRIDGED.


  Deviations from the Standard Model (SM) can be parameterized in terms of the
SM effective field theory (SMEFT), which is typically truncated at dimension-6.
Including higher dimension operators -- as well as considering simultaneous
insertions of multiple dimension-6 operators -- may be necessary in some
processes, in order to correctly capture the properties of the underlying UV
theory. As a step towards clarifying this in the Higgs boson production in
gluon fusion process, we study double insertions of dimension-6 operators in
the 1-loop virtual amplitude. We present needed Feynman rules up to
$\mathcal{O}(1/\Lambda^4)$ and we numerically study the impact of various
approximations to the $\mathcal{O}(1/\Lambda^4)$ expansion.


  A new satellite based test of Special and General Relativity is proposed. For
the Michelson-Morley experiment we expect an improvement of at least three
orders of magnitude, and for the Kennedy-Thorndike experiment an improvement of
more than one order of magnitude. Furthermore, an improvement by two orders of
the test of the universality of the gravitational red shift by comparison of an
atomic clock with an optical clock is projected.
  The tests are based on ultrastable optical cavities, an atomic clock and a
comb generator.


  We show that there exist uncountably many (tall and nontall) pairwise
nonisomorphic density-like ideals on $\omega$ which are not generalized density
ideals. In addition, they are nonpathological. This answers a question posed by
Borodulin-Nadzieja, Farkas, and Plebanek in [J. Symb. Log. \textbf{80} (2015),
1268--1289]. Lastly, we provide sufficient conditions for a density-like ideal
to be necessarily a generalized density ideal.


  We will introduce a cyclic derivative for fully (stably) matricial functions
and study its basic properties. In particular, we will show the Poincar\'{e}
lemma for stably matricial functions of certain classes. We will also position
Voiculescu's framework of fully matricial functions in the context of nc
functions due to Kaliuzhnyi-Verbovetskyi and Vinnikov in order to clarify the
relation between the present work and previous related works.


  Kitaev spin liquid (KSL) system has attracted tremendous attention in past
years because of its fundamental significance in condensed matter physics and
promising applications in fault-tolerant topological quantum computation.
Material realization of such a system remains a major challenge in the field
due to the unusual configuration of anisotropic spin interactions, though great
effort has been made before. Here we reveal that rare-earth chalcohalides REChX
(RE=rare earth, Ch=O, S, Se, Te, X=F, Cl, Br, I) can serve as a family of KSL
candidates. Most family members have the typical SmSI-type structure with a
high symmetry of R-3m and rare-earth magnetic ions form an undistorted
honeycomb lattice. The strong spin-orbit coupling of 4f electrons intrinsically
offers anisotropic spin interactions as required by Kitaev model. We have grown
the crystals of YbOCl and synthesized the polycrystals of SmSI, ErOF, HoOF and
DyOF, and made careful structural characterizations. We carry out magnetic and
heat capacity measurements down to 1.8 K and find no obvious magnetic
transition in all the samples but DyOF. The van der Waals interlayer coupling
highlights the true two-dimensionality of the family which is vital for the
exact realization of Abelian/non-Abelian anyons, and the graphene-like feature
will be a prominent advantage for developing miniaturized devices. The family
is expected to act as an inspiring material platform for the exploration of KSL
physics.


  Chirped radio pulses known as perytons are consistent with plasma
propagation, but an astronomical origin has been excluded. TE and TM waveguide
modes have dispersion relations formally similar to those of plasmas, and
emission at the plasma frequency of a plasma recombining by two-particle
processes also produces a chirp $\delta t \propto \omega^{-2}$, but these
hypotheses fail quantitatively. The chirp might be approximately fitted to
three-body recombination, cyclotron radiation in exponentially decaying fields,
or to the resonance of a distributed LC circuit whose component conductors
move, but no specific source has been identified.


  Density waves are characteristic for fluidized beds and affect measurements
on liquid-like dynamics in fluidized granular media. Here, the intensity
autocorrelation function as obtainable with diffusing-wave spectroscopy is
derived in the presence of density waves. The predictions by the derived form
of the IACF match experimental observations from a gas-fluidized bed. The model
suggests separability of the contribution from density waves from the
contribution by microscopic scatterer displacement to the decay of correlation,
thus paves the way for characterizing microscopic particle motions using
diffusing-wave spectroscopy as well as heterogeneities in fluidized granular
media.


  The normal and the inverted pendulum continue to be one of the main physical
models and metaphors in science. The inverted pendulum is also a classic study
case in control theory. In this paper we consider a special demonstration
version of the inverted pendulum which is controlled via a spring. If the
spring constant is below a critical level the springy control will be unstable
and the pendulum will be kept from falling only by exercising a dynamically
varying control. This situation resembles the case of human bipedal quiet
standing with the Achilles tendon serving as the spring. The paper is written
with physics students in mind.


  TensorFlow has been the most widely adopted Machine/Deep Learning framework.
However, little exists in the literature that provides a thorough understanding
of the capabilities which TensorFlow offers for the distributed training of
large ML/DL models that need computation and communication at scale. Most
commonly used distributed training approaches for TF can be categorized as
follows: 1) Google Remote Procedure Call (gRPC), 2) gRPC+X: X=(InfiniBand
Verbs, Message Passing Interface, and GPUDirect RDMA), and 3) No-gRPC: Baidu
Allreduce with MPI, Horovod with MPI, and Horovod with NVIDIA NCCL. In this
paper, we provide an in-depth performance characterization and analysis of
these distributed training approaches on various GPU clusters including the Piz
Daint system (6 on Top500). We perform experiments to gain novel insights along
the following vectors: 1) Application-level scalability of DNN training, 2)
Effect of Batch Size on scaling efficiency, 3) Impact of the MPI library used
for no-gRPC approaches, and 4) Type and size of DNN architectures. Based on
these experiments, we present two key insights: 1) Overall, No-gRPC designs
achieve better performance compared to gRPC-based approaches for most
configurations, and 2) The performance of No-gRPC is heavily influenced by the
gradient aggregation using Allreduce. Finally, we propose a truly CUDA-Aware
MPI Allreduce design that exploits CUDA kernels and pointer caching to perform
large reductions efficiently. Our proposed designs offer 5-17X better
performance than NCCL2 for small and medium messages, and reduces latency by
29% for large messages. The proposed optimizations help Horovod-MPI to achieve
approximately 90% scaling efficiency for ResNet-50 training on 64 GPUs.
Further, Horovod-MPI achieves 1.8X and 3.2X higher throughput than the native
gRPC method for ResNet-50 and MobileNet, respectively, on the Piz Daint
cluster.


  Because of the limits input/output systems currently impose on
high-performance computing systems, a new generation of workflows that include
online data reduction and analysis is emerging. Diagnosing their performance
requires sophisticated performance analysis capabilities due to the complexity
of execution patterns and underlying hardware, and no tool could handle the
voluminous performance trace data needed to detect potential problems. This
work introduces Chimbuko, a performance analysis framework that provides
real-time, distributed, in situ anomaly detection. Data volumes are reduced for
human-level processing without losing necessary details. Chimbuko supports
online performance monitoring via a visualization module that presents the
overall workflow anomaly distribution, call stacks, and timelines. Chimbuko
also supports the capture and reduction of performance provenance. To the best
of our knowledge, Chimbuko is the first online, distributed, and scalable
workflow-level performance trace analysis framework, and we demonstrate the
tool's usefulness on Oak Ridge National Laboratory's Summit system.


  Multivariate relations are general in various types of networks, such as
biological networks, social networks, transportation networks, and academic
networks. Due to the principle of ternary closures and the trend of group
formation, the multivariate relationships in social networks are complex and
rich. Therefore, in graph learning tasks of social networks, the identification
and utilization of multivariate relationship information are more important.
Existing graph learning methods are based on the neighborhood information
diffusion mechanism, which often leads to partial omission or even lack of
multivariate relationship information, and ultimately affects the accuracy and
execution efficiency of the task. To address these challenges, this paper
proposes the multivariate relationship aggregation learning (MORE) method,
which can effectively capture the multivariate relationship information in the
network environment. By aggregating node attribute features and structural
features, MORE achieves higher accuracy and faster convergence speed. We
conducted experiments on one citation network and five social networks. The
experimental results show that the MORE model has higher accuracy than the GCN
(Graph Convolutional Network) model in node classification tasks, and can
significantly reduce time cost.


  In this paper we study the variational problem associated to support vector
regression in Banach function spaces. Using the Fenchel-Rockafellar duality
theory, we give explicit formulation of the dual problem as well as of the
related optimality conditions. Moreover, we provide a new computational
framework for solving the problem which relies on a tensor-kernel
representation. This analysis overcomes the typical difficulties connected to
learning in Banach spaces. We finally present a large class of tensor-kernels
to which our theory fully applies: power series tensor kernels. This type of
kernels describe Banach spaces of analytic functions and include
generalizations of the exponential and polynomial kernels as well as, in the
complex case, generalizations of the Szeg\"o and Bergman kernels.


  In this paper we present an efficient algorithm for bivariate interpolation,
which is based on the use of the partition of unity method for constructing a
global interpolant. It is obtained by combining local radial basis function
interpolants with locally supported weight functions. In particular, this
interpolation scheme is characterized by the construction of a suitable
partition of the domain in cells so that the cell structure strictly depends on
the dimension of its subdomains. This fact allows us to construct an efficient
cell-based searching procedure, which provides a significant reduction of CPU
times. Complexity analysis and numerical results show such improvements on the
algorithm performances.


  Mitochondrial genetic material is widely used for phylogenetic reconstruction
and as a barcode for species identification. Here we study how mito-nuclear
interactions affect the accuracy of species identification by mtDNA, as well as
the speciation process itself. We simulate the evolution of a population of
individuals who carry a recombining nuclear genome and a mitochondrial genome
inherited maternally. We compare a null model fitness landscape that lacks any
mito-nuclear interaction against a scenario in which interactions influence
fitness. Fitness is assigned to individuals according to their mito-nuclear
compatibility, which drives the coevolution of the nuclear and mitochondrial
genomes. When the population breaks into distinct species we analyze the
accuracy of mtDNA barcode for species identification. Remarkably, we find that
species identification by mtDNA is equally accurate in the presence or absence
of mito-nuclear coupling and that the success of the DNA barcode derives mainly
from population geographical isolation during speciation. Nevertheless,
selection imposed by mito-nuclear compatibility influences the diversification
process and leaves signatures in the genetic content and spatial distribution
of the populations, in three ways: phylogenetic trees are more balanced; clades
correlate strongly with the spatial distribution and; there is a substantial
increase in the intraspecies mtDNA similarity. We compare the evolutionary
patterns observed in our model to empirical data from copepods (\textit{T.
californicus}). We find good qualitative agreement in the geographic patterns
and the topology of the phylogenetic tree, provided the model includes
selection based on mito-nuclear interactions. These results highlight the role
of mito-nuclear compatibility in the speciation process and its reconstruction
from genetic data.