  PSR B1821$-$24 is a solitary millisecond pulsar (MSP) which radiates
multi-wavelength pulsed photons. It has complex radio, X-ray and $\gamma$-ray
pulse profiles with distinct peak phase-separations that challenge the
traditional caustic emission models. Using the single-pole annular gap model
with suitable magnetic inclination angle ($\alpha=40^\circ$) and viewing angle
($\zeta=75^\circ$), we managed to reproduce its pulse profiles of three
wavebands. It is found that the middle radio peak is originated from the core
gap region at high altitudes, and the other two radio peaks are originated from
the annular gap region at relatively low altitudes. Two peaks of both X-ray and
$\gamma$-ray wavebands are fundamentally originated from annular gap region,
while the $\gamma$-ray emission generated from the core gap region contributes
somewhat to the first $\gamma$-ray peak. Precisely reproducing the
multi-wavelength pulse profiles of PSR B1821$-$24 enables us to understand
emission regions of distinct wavebands and justify pulsar emission models.


  How to obtain good value estimation is one of the key problems in
Reinforcement Learning (RL). Current value estimation methods, such as DDPG and
TD3, suffer from unnecessary over- or underestimation bias. In this paper, we
explore the potential of double actors, which has been neglected for a long
time, for better value function estimation in continuous setting. First, we
uncover and demonstrate the bias alleviation property of double actors by
building double actors upon single critic and double critics to handle
overestimation bias in DDPG and underestimation bias in TD3 respectively. Next,
we interestingly find that double actors help improve the exploration ability
of the agent. Finally, to mitigate the uncertainty of value estimate from
double critics, we further propose to regularize the critic networks under
double actors architecture, which gives rise to Double Actors Regularized
Critics (DARC) algorithm. Extensive experimental results on challenging
continuous control tasks show that DARC significantly outperforms
state-of-the-art methods with higher sample efficiency.


  Evaluating open-ended written examination responses from students is an
essential yet time-intensive task for educators, requiring a high degree of
effort, consistency, and precision. Recent developments in Large Language
Models (LLMs) present a promising opportunity to balance the need for thorough
evaluation with efficient use of educators' time. In our study, we explore the
effectiveness of LLMs ChatGPT-3.5, ChatGPT-4, Claude-3, and Mistral-Large in
assessing university students' open-ended answers to questions made about
reference material they have studied. Each model was instructed to evaluate 54
answers repeatedly under two conditions: 10 times (10-shot) with a temperature
setting of 0.0 and 10 times with a temperature of 0.5, expecting a total of
1,080 evaluations per model and 4,320 evaluations across all models. The RAG
(Retrieval Augmented Generation) framework was used as the framework to make
the LLMs to process the evaluation of the answers. As of spring 2024, our
analysis revealed notable variations in consistency and the grading outcomes
provided by studied LLMs. There is a need to comprehend strengths and
weaknesses of LLMs in educational settings for evaluating open-ended written
responses. Further comparative research is essential to determine the accuracy
and cost-effectiveness of using LLMs for educational assessments.


  We present three-dimensional numerical magnetohydrodynamic simulations of
radiatively inefficient spherical accretion onto a black hole. The simulations
are initialized with a Bondi flow, and with a weak, dynamically unimportant,
large-scale magnetic field. The magnetic field is amplified as the gas flows
in. When the magnetic pressure approaches equipartition with the gas pressure,
the field begins to reconnect and the gas is heated up. The heated gas is
buoyant and moves outward, causing line stretching of the frozen-in magnetic
field. This leads to further reconnection, and more heating and
buoyancy-induced motions, so that the flow makes a transition to a state of
self-sustained convection. The radial structure of the flow changes
dramatically from its initial Bondi profile, and the mass accretion rate onto
the black hole decreases significantly. Motivated by the numerical results, we
develop a simplified analytical model of a radiatively inefficient spherical
flow in which convective transport of energy to large radii plays an important
role. In this "convection-dominated Bondi flow" the accretion velocity is
highly subsonic and the density varies with radius as ~R^{-1/2} rather than the
standard Bondi scaling ~R^{-3/2}. We estimate that the mass accretion rate onto
the black hole is significantly less than the Bondi accretion rate.
Convection-dominated Bondi flows may be relevant for understanding many
astrophysical phenomena, e.g. post-supernova fallback and radiatively
inefficient accretion onto supermassive black holes, stellar-mass black holes
and neutron stars.


  We elaborate on the role of higher-derivative curvature invariants as a
quantum selection mechanism of regular spacetimes in the framework of the
Lorentzian path integral approach to quantum gravity. We show that for a large
class of black hole metrics prominently regular there are higher-derivative
curvature invariants which are singular. If such terms are included in the
action, according to the finite action principle applied to a higher-derivative
gravity model, not only singular spacetimes but also some of the regular ones
do not seem to contribute to the path integral.


  I discuss the key features of Smoothed Particle Hydrodynamics (SPH) as a
numerical method - in particular the key differences between SPH and more
standard grid based approaches - that are important to the practitioner. These
include the exact treatment of advection, the absence of intrinsic dissipation,
exact conservation and more subtle properties that arise from its Hamiltonian
formulation such as the existence of a minimum energy state for the particles.
The implications of each of these are discussed, showing how they can be both
advantages and disadvantages.


  The recent surge of versatile large language models (LLMs) largely depends on
aligning increasingly capable foundation models with human intentions by
preference learning, enhancing LLMs with excellent applicability and
effectiveness in a wide range of contexts. Despite the numerous related studies
conducted, a perspective on how human preferences are introduced into LLMs
remains limited, which may prevent a deeper comprehension of the relationships
between human preferences and LLMs as well as the realization of their
limitations. In this survey, we review the progress in exploring human
preference learning for LLMs from a preference-centered perspective, covering
the sources and formats of preference feedback, the modeling and usage of
preference signals, as well as the evaluation of the aligned LLMs. We first
categorize the human feedback according to data sources and formats. We then
summarize techniques for human preferences modeling and compare the advantages
and disadvantages of different schools of models. Moreover, we present various
preference usage methods sorted by the objectives to utilize human preference
signals. Finally, we summarize some prevailing approaches to evaluate LLMs in
terms of alignment with human intentions and discuss our outlooks on the human
intention alignment for LLMs.


  We introduce the model of inelastic hard spheres with random restitution
coefficient $\alpha$, in order to account for the fact that, in a vertically
shaken granular system interacting elastically with the vibrating boundary, the
energy injected vertically is transferred to the horizontal degrees of freedom
through collisions only, which leads to heating through collisions, i.e. to
inelastic horizontal collisions with an effective restitution coefficient that
can be larger than 1. This allows the system to reach a non-equilibrium steady
state, where we focus in particular on the single particle velocity
distribution $f(v)$ in the horizontal plane, and on its deviation from a
Maxwellian. Molecular Dynamics simulations and Direct Simulation Monte Carlo
(DSMC) show that, depending on the distribution of $\alpha$, different shapes
of $f(v)$ can be obtained, with very different high energy tails. Moreover, the
fourth cumulant of the velocity distribution quantifying the deviations from
Gaussian statistics is obtained analytically from the Boltzmann equation and
successfully tested against the simulations.


  In this paper, we study the generalized volume conjecture for the colored
Jones polynomials of links with complements containing more than one hyperbolic
piece. First of all, we construct an infinite family of prime links by
considering the cabling on the figure eight knot by the Whitehead chains. The
complement of these links consist of two hyperbolic pieces in their JSJ
decompositions. We show that at the $(N+\frac{1}{2})$-th root of unity, the
exponential growth rates for the $\vec{N}$-th colored Jones polynomials for
these links capture the simplicial volume of the link complements. As an
application, we prove the volume conjecture for the Turaev-Viro invariants for
these links complements.
  We also generalize the volume conjecture for links whose complement have more
than one hyperbolic piece in another direction. By considering the iterated
Whitehead double on the figure eight knot and the Hopf link, we construct two
infinite families of prime links. Furthermore, we assign certain "natural"
incomplete hyperbolic structures on the hyperbolic pieces of the complements of
these links and prove that the sum of their volume coincides with the
exponential growth rate of certain sequences of values of colored Jones
polynomials of the links.


  We study the one-dimensional active Ising model in which aligning particles
undergo diffusion biased by the signs of their spins. The phase diagram
obtained varying the density of particles, their hopping rate and the
temperature controlling the alignment shows a homogeneous disordered phase but
no homogeneous ordered one, as well as two phases with localized dense
structures. In the flocking phase, large ordered aggregates move ballistically
and stochastically reverse their direction of motion. In what we termed the
"aster" phase, dense immobile aggregates of opposite magnetization face each
other, exchanging particles, without any net motion of the aggregates. Using a
combination of numerical simulations and mean-field theory, we study the
evolution of the shapes of the flocks, the statistics of their reversal times,
and their coarsening dynamics. Solving exactly for the zero-temperature
dynamics of an aster allows us to understand their coarsening, which shows
extremal dynamics, while mean-field equations account for their shape.


  Longitudinal assessment of brain atrophy, particularly in the hippocampus, is
a well-studied biomarker for neurodegenerative diseases, such as Alzheimer's
disease (AD). In clinical trials, estimation of brain progressive rates can be
applied to track therapeutic efficacy of disease modifying treatments. However,
most state-of-the-art measurements calculate changes directly by segmentation
and/or deformable registration of MRI images, and may misreport head motion or
MRI artifacts as neurodegeneration, impacting their accuracy. In our previous
study, we developed a deep learning method DeepAtrophy that uses a
convolutional neural network to quantify differences between longitudinal MRI
scan pairs that are associated with time. DeepAtrophy has high accuracy in
inferring temporal information from longitudinal MRI scans, such as temporal
order or relative inter-scan interval. DeepAtrophy also provides an overall
atrophy score that was shown to perform well as a potential biomarker of
disease progression and treatment efficacy. However, DeepAtrophy is not
interpretable, and it is unclear what changes in the MRI contribute to
progression measurements. In this paper, we propose Regional Deep Atrophy
(RDA), which combines the temporal inference approach from DeepAtrophy with a
deformable registration neural network and attention mechanism that highlights
regions in the MRI image where longitudinal changes are contributing to
temporal inference. RDA has similar prediction accuracy as DeepAtrophy, but its
additional interpretability makes it more acceptable for use in clinical
settings, and may lead to more sensitive biomarkers for disease monitoring in
clinical trials of early AD.


  A simple model of a quantum clock is applied to the old and controversial
problem of how long a particle takes to tunnel through a quantum barrier. The
model I employ has the advantage of yielding sensible results for energy
eigenstates, and does not require the use of time-dependant wave packets.
Although the treatment does not forbid superluminal tunneling velocities, there
is no implication of faster-than-light signaling because only the transit
duration is measurable, not the absolute time of transit. A comparison is given
with the weak-measurement post-selection calculations of Steinberg.


  Given the symmetric group $G = \operatorname{Sym}(n)$ and a multiplicity-free
subgroup $H\leq G$, the orbitals of the action of $G$ on $G/H$ by left
multiplication induce a commutative association scheme. The irreducible
constituents of the permutation character of $G$ acting on $G/H$ are indexed by
partitions of $n$ and if $\lambda \vdash n$ is the second largest partition in
dominance ordering among these, then the Young subgroup
$\operatorname{Sym}(\lambda)$ admits two orbits in its action on $G/H$, which
are $\mathcal{S}_\lambda$ and its complement.
  In their monograph [Erd\H{o}s-Ko-Rado theorems: Algebraic Approaches. {\it
Cambridge University Press}, 2016] (Problem~16.13.1), Godsil and Meagher asked
whether $\mathcal{S}_\lambda$ is a coclique of a graph in the commutative
association scheme arising from the action of $G$ on $G/H$. If such a graph
exists, then they also asked whether its smallest eigenvalue is afforded by the
$\lambda$-module.
  In this paper, we initiate the study of this question by taking $\lambda =
[n-1,1]$.
  We show that the answer to this question is affirmative for the pair of
groups $\left(G,H\right)$, where $G = \operatorname{Sym}(2k+1)$ and $H =
\operatorname{Sym}(2) \wr \operatorname{Sym}(k)$, or $G =
\operatorname{Sym}(n)$ and $H$ is one of $\operatorname{Alt}(k) \times
\operatorname{Sym}(n-k),\ \operatorname{Alt}(k) \times
\operatorname{Alt}(n-k)$, or $\left(\operatorname{Alt}(k)\times
\operatorname{Alt}(n-k)\right) \cap \operatorname{Alt}(n)$. For the pair $(G,H)
= \left(\operatorname{Sym}(2k),\operatorname{Sym}(k)\wr
\operatorname{Sym}(2)\right)$, we also prove that the answer to this question
of Godsil and Meagher is negative.


  Tensor decomposition is an effective tool for learning multi-way structures
and heterogeneous features from high-dimensional data, such as the multi-view
images and multichannel electroencephalography (EEG) signals, are often
represented by tensors. However, most of tensor decomposition methods are the
linear feature extraction techniques, which are unable to reveal the nonlinear
structure within high-dimensional data. To address such problem, a lot of
algorithms have been proposed for simultaneously performs linear and non-linear
feature extraction. A representative algorithm is the Graph Regularized
Non-negative Matrix Factorization (GNMF) for image clustering. However, the
normal 2-order graph can only models the pairwise similarity of objects, which
cannot sufficiently exploit the complex structures of samples. Thus, we propose
a novel method, named Hypergraph Regularized Non-negative Tensor Factorization
(HyperNTF), which utilizes hypergraph to encode the complex connections among
samples and employs the factor matrix corresponding with last mode of Canonical
Polyadic (CP) decomposition as low-dimensional representation. Extensive
experiments on synthetic manifolds, real-world image datasets, and EEG signals,
demonstrating that HyperNTF outperforms the state-of-the-art methods in terms
of dimensionality reduction, clustering, and classification.


  Our preferences depend on the circumstances in which we reveal them. We will
introduce a dependency which allows us to illustrate the relation between the
possibility of winning of particular candidates in a quantum election and the
type of preference. It occurs that if voters start to clearly prefer one of the
candidates, the significance of intransitive preferences in the quantum model
decreases. This dynamic change cannot be observed in the case of the classical
model.


  In this work, we model the zero-bias conductance for the four different DNA
strands that were used in conductance measurement experiment [A. K. Mahapatro,
K. J. Jeong, G. U. Lee, and D. B. Janes, Nanotechnology 18, 195202 (2007)]. Our
approach consists of three elements: (i) ab initio calculations of DNA, (ii)
Green's function approach for transport calculations, and (iii) the use of two
parameters to determine the decoherence rates. We first study the role of the
backbone. We find that the backbone can alter the coherent transmission
significantly at some energy points by interacting with the bases, though the
overall shape of the transmission stays similar for the two cases. More
importantly, we find that the coherent electrical conductance is tremendously
smaller than what the experiments measure. We consider DNA strands under a
variety of different experimental conditions and show that even in the most
ideal cases, the calculated coherent conductance is much smaller than the
experimental conductance. To understand the reasons for this, we carefully look
at the effect of decoherence. By including decoherence, we show that our model
can rationalize the measured conductance of the four strands, both
qualitatively and quantitatively. We find that the effect of decoherence on G:C
base pairs is crucial in getting agreement with the experiments. However, the
decoherence on G:C base pairs alone does not explain the experimental
conductance in strands containing a number of A:T base pairs. Including
decoherence on A:T base pairs is also essential. By fitting the experimental
trends and magnitudes in the conductance of the four different DNA molecules,
we estimate for the first time that the deocherence rate is 6 meV for G:C and
1.5 meV for A:T base pairs.


  The influence of uncorrelated, quenched disorder on the phase transition of
two dimensional Potts models will be reviewed. After an introduction where the
conditions of relevance of quenched randomness on phase transitions are
exemplified by experimental measurements, the results of perturbative and
numerical investigations in the case of the Potts model will be presented. The
Potts model is of particular interest, since it can have in the pure case a
second-order or a first-order transition, depending on the number of states per
spin. In 2D, transfer matrix calculations and Monte Carlo simulations were used
in order to check the validity of conformal invariance methods in disordered
systems. These techniques were then used to investigate the universality class
of the disordered Potts model, in both regimes of the pure model phase
transitions. A test of replica symmetry became possible through a study of
multiscaling properties and a detailed analysis of the probability distribution
of the correlation functions was also made possible.


  The CACTUS atmospheric Cherenkov telescope collaboration recently reported a
gamma-ray excess from the Draco dwarf spheroidal galaxy. Draco features a very
low gas content and a large mass-to-light ratio, suggesting as a possible
explanation annihilation of weakly interacting massive particles (WIMPs) in the
Draco dark-matter halo. We show that with improved angular resolution, future
measurements can determine whether the halo is cored or cuspy, as well as its
scale radius. We find the relevant WIMP masses and annihilation cross sections
and show that supersymmetric models can account for the required gamma-ray
flux. The annihilation cross section range is found to be not compatible with a
standard thermal relic dark-matter production. We compute for these
supersymmetric models the resulting Draco gamma-ray flux in the GLAST energy
range and the rates for direct neutralino detection and for the flux of
neutrinos from neutralino annihilation in the Sun. We also discuss the
possibility that the bulk of the signal detected by CACTUS comes from direct
WIMP annihilation to two photons and point out that a decaying-dark-matter
scenario for Draco is not compatible with the gamma-ray flux from the Galactic
center and in the diffuse gamma-ray background.


  We analyze tau lepton decay observables, namely moments of the hadronic
spectral density in the finite energy interval (0,M_\tau), within finite order
perturbation theory including \alpha_s^4 corrections. The start of asymptotic
growth of perturbation theory series is found at this order in a scheme
invariant manner. We establish the ultimate accuracy of finite order
perturbation theory predictions and discuss the construction of optimal
observables.


  Nonlinear Dirac equations in D+1 space-time are obtained by variation of the
spinor action whose Lagrangian components have the same conformal degree and
the coupling parameter of the self-interaction term is dimensionless. In 1+1
dimension, we show that these requirements result in the "conventional" quartic
form of the nonlinear interaction and present the general equation for various
coupling modes. These include, but not limited to, the Thirring and Gross-Neveu
models. We obtain a numerical solution for the special case of the spin and
pseudo-spin symmetric modes..


  In this paper, we present a detailed analysis of the coverage and spectral
efficiency of a downlink cellular network. Rather than relying on the first
order statistics of received signal-to-interference-ratio (SIR) such as
coverage probability, we focus on characterizing its meta-distribution. Our
analysis is based on the alpha-beta-gamma (ABG) path-loss model which provides
us with the flexibility to analyze urban macro (UMa) and urban micro (UMi)
deployments. With the help of an analytical framework, we demonstrate that
selection of underlying degrees-of-freedom such as BS height for optimization
of first order statistics such as coverage probability is not optimal in the
network-wide sense. Consequently, the SIR meta-distribution must be employed to
select appropriate operational points which will ensure consistent user
experiences across the network. Our design framework reveals that the
traditional results which advocate lowering of BS heights or even optimal
selection of BS height do not yield consistent service experience across users.
By employing the developed framework we also demonstrate how available spectral
resources in terms of time slots/channel partitions can be optimized by
considering the meta-distribution of the SIR.


  We investigate the metal-insulator transition (MIT) of the osmium pyrochlore
oxide Cd2Os2O7 through transport and magnetization measurements. The MIT and a
magnetic transition to the all-in/all-out (AIAO) order occur simultaneously at
227 K. We propose a mechanism based on a Lifshitz transition induced by the
AIAO magnetic order probably via strong spin-orbit couplings in the specific
semimetallic band structure. It is suggested, moreover, that two observed
puzzles, a finite conductivity near T = 0 and an emergence of weak
ferromagnetic moments, are not bulk properties but originate at magnetic domain
walls between two kinds of AIAO domains.


  An alcohol (pentanol) droplet exhibits spontaneous agitation on an aqueous
solution, driven by a solutal Marangoni effect. We found that the droplet's
mode of motion is controlled by its volume. A droplet with a volume of less
than $0.1 \mu\rm{l}$ shows irregular translational motion, whereas
intermediate-sized droplets of $0.1-200 \mu\rm{l}$ show vectorial motion. When
the volume is above $300 \mu\rm{l}$, the droplet splits into smaller drops.
These experimental results regarding mode selection are interpreted in terms of
the wave number selection depending on the droplet volume.


  Induced coherence in parametric down-conversion between two coherently pumped
nonlinear crystals that share a common idler mode can be used as an imaging
technique. Based on the interference between the two signal modes of the
crystals, an image can be reconstructed. By obtaining an expression for the
interference pattern that is valid in both the low- and the high-gain regimes
of parametric down-conversion, we show how the coherence of the light emitted
by the two crystals can be controlled. With our comprehensive analysis we
provide deeper insight into recent discussions about the application of induced
coherence to imaging in different regimes. Moreover, we propose a scheme for
optimizing the visibility of the interference pattern so that it directly
corresponds to the degree of coherence of the light generated in the two
crystals. We find that this scheme leads in the high-gain regime to a
visibility arbitrarily close to unity.


  Functions with low $c$-differential uniformity were proposed in $2020$ and
attracted lots of attention, especially the P$c$N and AP$c$N functions, due to
their applications in cryptography. The objective of this paper is to study
P$c$N and AP$c$N functions. As a consequence, we propose a class of P$c$N
functions and four classes of AP$c$N functions by using the cyclotomic
technique and the switch method. In addition, four classes of P$c$N or AP$c$N
functions are presented by virtue of (generalized) AGW criterion.


  In this note I revisit the calculation of partition function of simple one
dimensional systems solvable by Bethe Ansatz. Particularly I show that by the
precise definition and treatment of the partition function the nontrivial
normalization factor proposed in a recent work to give the correct O(1)
corrections to the free energy can be derived in a straightforward manner.


  With the rapid growth of the machine learning applications, the workloads of
future HPC systems are anticipated to be a mix of scientific simulation, big
data analytics, and machine learning applications. Simulation is a great
research vehicle to understand the performance implications of co-running
scientific applications with big data and machine learning workloads on
large-scale systems. In this paper, we present Union, a workload manager that
provides an automatic framework to facilitate hybrid workload simulation in
CODES. Furthermore, we use Union, along with CODES, to investigate various
hybrid workloads composed of traditional simulation applications and emerging
learning applications on two dragonfly systems. The experiment results show
that both message latency and communication time are important performance
metrics to evaluate network interference. Network interference on HPC
applications is more reflected by the message latency variation, whereas ML
application performance depends more on the communication time.


  It is studied that `no-scale' model makes hierarchy between scalar top mass
and Z boson mass naturally. The supersymmetry breaking parameters are
constrained by flavor changing neutral currents in minimal supersymmetric
standard model. One of the solution of the problem is that gaugino mass is the
only source of the supersymmetry breaking parameters at Planck scale. However,
in such scenario, we need a cancellation between Higgs mass parameters in
minimization condition of the Higgs potential. We insist that there is no such
cancellation in no-scale model, and that the no-scale model gives us the
prediction of scalar top mass and lightest Higgs mass. The lightest Higgs mass
is predicted as $m_H = 110 \pm 5 $ GeV.


  Various constraints of Static Random Access Memory (SRAM) are leading to
consider new memory technologies as candidates for building on-chip shared
last-level caches (SLLCs). Spin-Transfer Torque RAM (STT-RAM) is currently
postulated as the prime contender due to its better energy efficiency, smaller
die footprint and higher scalability. However, STT-RAM also exhibits some
drawbacks, like slow and energy-hungry write operations, that need to be
mitigated. In this work we address these shortcomings by leveraging a new
management mechanism for STT-RAM SLLCs. This approach is based on the previous
observation that the stream of references arriving at the SLLC of a Chip
MultiProcessor (CMP) exhibits reuse locality, i.e., those blocks referenced
several times manifest high probability of forthcoming reuse. In this paper, we
employ a cache management mechanism that selects the contents of the SLLC aimed
to exploit reuse locality instead of temporal locality. Specifically, our
proposal consists in the inclusion of a Reuse Detector between private cache
levels and the STT-RAM SLLC to detect blocks that do not exhibit reuse, in
order to avoid their insertion in the SLLC, hence reducing the number of write
operations and the energy consumption in the STT-RAM. Our evaluation reveals
that our scheme reports on average, energy reductions in the SLLC in the range
of 37-30\%, additional energy savings in the main memory in the range of 6-8\%
and performance improvements of 3\% up to 14\% (16-core) compared to an STT-RAM
SLLC baseline where no reuse detector is employed. More importantly, our
approach outperforms DASCA, the state-of-the-art STT-RAM SLLC management,
reporting SLLC energy savings in the range of 4-11\% higher than those of
DASCA, delivering higher performance in the range of 1.5-14\%, and additional
improvements in DRAM energy consumption in the range of 2-9\% higher than
DASCA.


  This paper deals with diffusive limit of the p-system with damping and its
approximation by an Asymptotic Preserving (AP) Finite Volume scheme. Provided
the system is endowed with an entropy-entropy flux pair, we give the
convergence rate of classical solutions of the p-system with damping towards
the smooth solutions of the porous media equation using a relative entropy
method. Adopting a semi-discrete scheme, we establish that the convergence rate
is preserved by the approximated solutions. Several numerical experiments
illustrate the relevance of this result.


  The unprecedented precision of Gaia has led to a paradigm shift in membership
determination of open clusters where a variety of machine learning (ML) models
can be employed. In this paper, we apply the unsupervised Gaussian Mixture
Model (GMM) to a sample of thirteen clusters with varying ages ($log \ t
\approx$ 6.38-9.64) and distances (441-5183 pc) from Gaia DR3 data to determine
membership. We use ASteca to determine parameters for the clusters from our
revised membership data. We define a quantifiable metric Modified Silhouette
Score (MSS) to evaluate its performance. We study the dependence of MSS on age,
distance, extinction, galactic latitude and longitude, and other parameters to
find the particular cases when GMM seems to be more efficient than other
methods. We compared GMM for nine clusters with varying ages but we did not
find any significant differences between GMM performance for younger and older
clusters. But we found a moderate correlation between GMM performance and the
cluster distance, where GMM works better for closer clusters. We find that GMM
does not work very well for clusters at distances larger than 3~kpc.


  We consider weighted graphs satisfying sub-Gaussian estimate for the natural
random walk. On such graphs, we study symmetric Markov chains with heavy tailed
jumps. We establish a threshold behavior of such Markov chains when the index
governing the tail heaviness (or jump index) equals the escape time exponent
(or walk dimension) of the sub-Gaussian estimate. In a certain sense, this
generalizes the classical threshold corresponding to the second moment
condition.


  Isotropic diffusion is a key assumption in many models of cosmic-ray
electrons and positrons. We find that simulation results imply a critical
energy of ~10-1000~GeV above which electrons and positrons can spend their
entire lives in streams threading magnetic fields, due to energy losses. This
would restrict the number of electron/positron sources contributing at Earth,
likely leading to smooth electron/positron spectra, as is observed. For
positrons, this could be as few as one, with an enhanced flux that would ease
energetics concerns of a pulsar origin of the positron excess, or even zero,
bringing dark matter to the fore. We conclude that ideas about
electron/positron propagation must be revised and discuss implications for
recent AMS-02 data.


  The main result of this paper shows that "test configurations" give new lower
bounds on the $L^{2}$ norm of the scalar curvature on a Kahler manifold. This
is closely analogous to the analysis of the Yang-Mills functional over Riemann
surfaces by Atiyah and Bott. The proof uses asymptotic approximation by
finite-dimensional problems: the essential ingredient being the
Tian-Zelditch-Lu expansion of the "density of states" function.


  The wave nature of matter remains one of the most striking aspects of quantum
mechanics. Since its inception, a wealth of experiments has demonstrated the
interference, diffraction or scattering of massive particles. More recently,
experiments with ever increasing control and resolution have allowed imaging
the wavefunction of individual atoms. Here, we use quantum gas microscopy to
image the in-situ spatial distribution of deterministically prepared
single-atom wave packets as they expand in a plane. We achieve this by
controllably projecting the expanding wavefunction onto the sites of a deep
optical lattice and subsequently performing single-atom imaging. The protocol
established here for imaging extended wave packets via quantum gas microscopy
is readily applicable to the wavefunction of interacting many-body systems in
continuous space, promising a direct access to their microscopic properties,
including spatial correlation functions up to high order and large distances.


  Point clouds obtained with 3D scanners or by image-based reconstruction
techniques are often corrupted with significant amount of noise and outliers.
Traditional methods for point cloud denoising largely rely on local surface
fitting (e.g., jets or MLS surfaces), local or non-local averaging, or on
statistical assumptions about the underlying noise model. In contrast, we
develop a simple data-driven method for removing outliers and reducing noise in
unordered point clouds. We base our approach on a deep learning architecture
adapted from PCPNet, which was recently proposed for estimating local 3D shape
properties in point clouds. Our method first classifies and discards outlier
samples, and then estimates correction vectors that project noisy points onto
the original clean surfaces. The approach is efficient and robust to varying
amounts of noise and outliers, while being able to handle large densely-sampled
point clouds. In our extensive evaluation, both on synthesic and real data, we
show an increased robustness to strong noise levels compared to various
state-of-the-art methods, enabling accurate surface reconstruction from
extremely noisy real data obtained by range scans. Finally, the simplicity and
universality of our approach makes it very easy to integrate in any existing
geometry processing pipeline.


  This paper analyzes the robustness of state-of-the-art AI-based models for
power grid operations under the $N-1$ security criterion. While these models
perform well in regular grid settings, our results highlight a significant loss
in accuracy following the disconnection of a line.%under this security
criterion. Using graph theory-based analysis, we demonstrate the impact of node
connectivity on this loss. Our findings emphasize the need for practical
scenario considerations in developing AI methodologies for critical
infrastructure.


  We uncover and characterize different chaotic transport scenarios on perfect
periodic surfaces by controlling the chaotic dynamics of particles subjected to
periodic external forces in the absence of a ratchet effect. After identifying
relevant {\it symmetries} of chaotic solutions, analytical estimates in
parameter space for the occurrence of different transport scenarios are
provided and confirmed by numerical simulations. These scenarios are highly
sensitive to variations of the system's asymmetry parameters, including the
eccentricity of the periodic surface and the direction of dc and ac forces,
which could be useful for particle sorting purposes in those cases where chaos
is unavoidable.


  We study a collection of concepts and theorems that laid the foundation of
matchgate computation. This includes the signature theory of planar matchgates,
and the parallel theory of characters of not necessarily planar matchgates. Our
aim is to present a unified and, whenever possible, simplified account of this
challenging theory. Our results include: (1) A direct proof that Matchgate
Identities (MGI) are necessary and sufficient conditions for matchgate
signatures. This proof is self-contained and does not go through the character
theory. More importantly it rectifies a gap in the existing proof. (2) A proof
that Matchgate Identities already imply the Parity Condition. (3) A simplified
construction of a crossover gadget. This is used in the proof of sufficiency of
MGI for matchgate signatures. This is also used to give a proof of equivalence
between the signature theory and the character theory which permits omittable
nodes. (4) A direct construction of matchgates realizing all
matchgate-realizable symmetric signatures.


  A detailed study of the Counter-Rotating Model (CRM) for generic
electrostatic (magnetostatic) axially symmetric thin disks without radial
pressure is presented. We find a general constraint over the counter-rotating
tangential velocities needed to cast the surface energy-momentum tensor of the
disk as the superposition of two counter-rotating charged dust fluids. We then
show that this constraint is satisfied if we take the two counter-rotating
streams as circulating along electrogeodesics with equal and opposite
tangential velocities. We also find explicit expressions for the energy
densities, electrostatic (magnetostatic) charge densities and velocities of the
counter-rotating fluids. Three specific examples are considered where we obtain
some CRM well behaved based in simple solutions to the Einstein-Maxwell
equations. The considered solutions are Reissner-Nordstrom in the electrostatic
case, its magnetostatic counterpart and two solutions obtained from Taub-NUT
and Kerr solutions.


  The self-similar energy spectrum of a particle in a periodic potential under
a magnetic field, known as the Hofstadter butterfly, is determined by the
lattice geometry as well as the external field. Recent realizations of
artificial gauge fields and adjustable optical lattices in cold atom
experiments necessitate the consideration of these self-similar spectra for the
most general two-dimensional lattice. In a previous work, we investigated the
evolution of the spectrum for an experimentally realized lattice which was
tuned by changing the unit cell structure but keeping the square Bravais
lattice fixed. We now consider all possible Bravais lattices in two dimensions
and investigate the structure of the Hofstadter butterfly as the lattice is
deformed between lattices with different point symmetry groups. We model the
optical lattice by a sinusoidal real space potential and obtain the tight
binding model for any lattice geometry by calculating the Wannier functions. We
introduce the magnetic field via Peierls substitution and numerically calculate
the energy spectrum. The transition between the two most symmetric lattices,
i.e. the triangular and the square lattice displays the importance of bipartite
symmetry featuring deformation as well as closing of some of the major energy
gaps. The transition from the square to rectangular and from the triangular to
centered rectangular lattices are analyzed in terms of coupling of
one-dimensional chains. We calculate the Chern numbers of the major gaps and
Chern number transfer between bands during the transitions. We use gap Chern
numbers to identify distinct topological regions in the space of Bravais
lattices.


  In this paper we present a grammar and control synthesis framework for online
modification of Event-based Signal Temporal Logic (STL) specifications, during
execution. These modifications allow a user to change the robots' task in
response to potential future violations, changes to the environment, or
user-defined task design changes. In cases where a modification is not
possible, we provide feedback to the user and suggest alternative
modifications. We demonstrate our task modification process using a Hello Robot
Stretch satisfying an Event-based STL specification.


  The critical current density $J_{c0}$ required for switching the
magnetization of the free layer (FL) in a spin-transfer torque magnetic random
access memory (STT-MRAM) cell is proportional to the product of the damping
parameter, saturation magnetization and thickness of the free layer, $\alpha
M_S t_F$. Conventional FLs have the structure CoFeB/nonmagnetic spacer/CoFeB.
By reducing the spacer thickness, W in our case, and also splitting the single
W layer into two layers of sub-monolayer thickness, we have reduced $t_F$ while
minimizing $\alpha$ and maximizing $M_S$, ultimately leading to lower $J_{c0}$
while maintaining high thermal stability. Bottom-pinned MRAM cells with device
diameter in the range of 55-130 nm were fabricated, and $J_{c0}$ is lowest for
the thinnest (1.2 nm) FLs, down to 4 MA/cm$^2$ for 65 nm devices, $\sim$30%
lower than 1.7 nm FLs. The thermal stability factor $\Delta_{\mathrm{dw}}$, as
high as 150 for the smallest device size, was determined using a domain wall
reversal model from field switching probability measurements. With high
$\Delta_{\mathrm{dw}}$ and lowest $J_{c0}$, the thinnest FLs have the highest
spin-transfer torque efficiency.


  Convolutional Neural Networks (CNNs) are known to be significantly
over-parametrized, and difficult to interpret, train and adapt. In this paper,
we introduce a structural regularization across convolutional kernels in a CNN.
In our approach, each convolution kernel is first decomposed as 2D dictionary
atoms linearly combined by coefficients. The widely observed correlation and
redundancy in a CNN hint a common low-rank structure among the decomposed
coefficients, which is here further supported by our empirical observations. We
then explicitly regularize CNN kernels by enforcing decomposed coefficients to
be shared across sub-structures, while leaving each sub-structure only its own
dictionary atoms, a few hundreds of parameters typically, which leads to
dramatic model reductions. We explore models with sharing across different
sub-structures to cover a wide range of trade-offs between parameter reduction
and expressiveness. Our proposed regularized network structures open the door
to better interpreting, training and adapting deep models. We validate the
flexibility and compatibility of our method by image classification experiments
on multiple datasets and underlying network structures, and show that CNNs now
maintain performance with dramatic reduction in parameters and computations,
e.g., only 5\% parameters are used in a ResNet-18 to achieve comparable
performance. Further experiments on few-shot classification show that faster
and more robust task adaptation is obtained in comparison with models with
standard convolutions.


  Conversational Information Retrieval (CIR) is an emerging field of
Information Retrieval (IR) at the intersection of interactive IR and dialogue
systems for open domain information needs. In order to optimize these
interactions and enhance the user experience, it is necessary to improve IR
models by taking into account sequential heterogeneous user-system
interactions. Reinforcement learning has emerged as a paradigm particularly
suited to optimize sequential decision making in many domains and has recently
appeared in IR. However, training these systems by reinforcement learning on
users is not feasible. One solution is to train IR systems on user simulations
that model the behavior of real users. Our contribution is twofold: 1)reviewing
the literature on user modeling and user simulation for information access, and
2) discussing the different research perspectives for user simulations in the
context of CIR


  We use the steepest descents method to study the integral kernel of a family
of normal random matrix ensembles with eigenvalue distribution
P_{N}(z_{1},...,z_{N}) = Z_{N}^{-1} e^{-N\Sigma_{i=1}^{N}V_{\alpha}(z_{i})}
\Pi_{1\leqi<j\leqN}|z_{i}-z_{j}|^{2} where V_{\alpha}(z)=|z|^{\alpha}, z \in C
and \alpha \in ]0,\infty[. Asymptotic analysis with error estimates are
obtained. A corollary of this expansion is a scaling limit for the n-point
function in terms of the integral kernel for the classical Segal--Bargmann
space.


  This article reports on tunable electron backscattering investigated with the
biased tip of a scanning force microscope. Using a channel defined by a pair of
Schottky gates, the branched electron flow of ballistic electrons injected from
a quantum point contact is guided by potentials of a tunable height well below
the Fermi energy. The transition from injection into an open two-dimensional
electron gas to a strongly confined channel exhibits three experimentally
distinct regimes: one in which branches spread unrestrictedly, one in which
branches are confined but the background conductance is affected very little,
and one where the branches have disappeared and the conductance is strongly
modified. Classical trajectory-based simulations explain these regimes at the
microscopic level. These experiments allow us to understand under which
conditions branches observed in scanning gate experiments do or do not reflect
the flow of electrons.


  The two main advantages of exoplanet imaging are the discovery of objects in
the outer part of stellar systems -- constraining models of planet formation
--, and its ability to spectrally characterize the planets -- information on
their atmosphere. It is however challenging because exoplanets are up to 1e10
times fainter than their star and separated by a fraction of arcsecond. Current
instruments like SPHERE/VLT or GPI/Gemini detect young and massive planets
because they are limited by non-common path aberrations (NCPA) that are not
corrected by the adaptive optics system. To probe fainter exoplanets, new
instruments capable of minimizing the NCPA is needed. One solution is the
self-coherent camera (SCC) focal plane wavefront sensor, whose performance was
demonstrated in laboratory attenuating the starlight by factors up to several
1e8 in space-like conditions at angular separations down to 2L/D. In this
paper, we demonstrate the SCC on the sky for the first time. We installed an
SCC on the stellar double coronagraph (SDC) instrument at the Hale telescope.
We used an internal source to minimize the NCPA that limited the vortex
coronagraph performance. We then compared to the standard procedure used at
Palomar. On internal source, we demonstrated that the SCC improves the
coronagraphic detection limit by a factor between 4 and 20 between 1.5 and
5L/D. Using this SCC calibration, the on-sky contrast is improved by a factor
of 5 between 2 and 4L/D. These results prove the ability of the SCC to be
implemented in an existing instrument. This paper highlights two interests of
the self-coherent camera. First, the SCC can minimize the speckle intensity in
the field of view especially the ones that are very close to the star where
many exoplanets are to be discovered. Then, the SCC has a 100% efficiency with
science time as each image can be used for both science and NCPA minimization.


  Grad-div stabilization, adding a term -gamma grad div u, has proven to be a
useful tool in the simulation of incompressible flows. Such a term requires a
choice of the coefficient gamma and studies have begun appearing with various
suggestions for its value. We give an analysis herein that provides a
restricted range of possible values for the coefficient in 3d turbulent flows
away from walls.


  SCFTs in six dimensions are interrelated by networks of RG flows.
Compactifying such models on a Riemann surface with flux for the $6d$ global
symmetry, one can obtain a wide variety of theories in four dimensions. These
four dimensional models are also related by a network of RG flows. In this
paper we study some examples of four dimensional flows relating theories that
can be obtained from six dimensions starting with different SCFTs connected by
$6d$ RG flows. We compile a dictionary between different orders of such flows,
$6d\to 6d\to 4d$ and $6d\to 4d\to 4d$, in the particular case when the six
dimensional models are the ones residing on M5 branes probing different
$A$-type singularities. The flows we study are triggered by vacuum expectation
values (vevs) to certain operators charged under the six dimensional symmetry.
We find that for generic choices of parameters the different orders of flows,
$6d\to 6d\to 4d$ and $6d\to 4d\to 4d$, involve compactifications on different
Riemann surfaces with the difference being in the number of punctures the
surface has.


  This paper is concerned with the derivation of first- and second-order
sufficient optimality conditions for optimistic bilevel optimization problems
involving smooth functions. First-order sufficient optimality conditions are
obtained by estimating the tangent cone to the feasible set of the bilevel
program in terms of initial problem data. This is done by exploiting several
different reformulations of the hierarchical model as a single-level problem.
To obtain second-order sufficient optimality conditions, we exploit the
so-called value function reformulation of the bilevel optimization problem,
which is then tackled with the aid of second-order directional derivatives. The
resulting conditions can be stated in terms of initial problem data in several
interesting situations comprising the settings where the lower level is linear
or possesses strongly stable solutions.


  Recent investigations into coalescence dynamics of complex fluid droplets
revealed the existence of sub-Newtonian behaviour for polymeric fluids (elastic
and shear thinning). We hypothesize that such delayed coalescence or
sub-Newtonian coalescence dynamics may be extended to the general class of
shear thickening fluids. To investigate this droplets of aqueous corn-starch
suspensions were chosen and its coalescence in sessile pendant configuration
was probed by high-speed real time imaging. Temporal evolution of the neck
(growth) during coalescence was quantified as a function of suspended particle
weight fraction \phi_w. The necking behaviour was found to evolve as the
power-law relation $R=at^b$ where R is neck radius with exponent $\b\le0.5$
implying it is a subset of the generic sub-Newtonian coalescence. Second
significant delay in the coalescence dynamics is observed for particle
fractions beyond the jamming fraction {\ \phi}_w>\ \phi_J\geq0.35}. Our
proposed theoretical model captures this delay implicitly through altered
suspension viscosity stemming from increased particle content.


  Aim of this work is a broad-band study with INTEGRAL, Swift and XMM-Newton
satellites of a sample of 9 blazars (7 FSRQ and 2 BL Lac) with redshift up to
about 4. The spectral analysis has shown clear evidence of a flattening of the
continuum towards the low energies ($E<3$ keV observer frame). This behaviour
is well reproduced both with an absorbed power-law model
($N_H\sim10^{20}$-$10^{23}$ cm$^{-2}$ in the rest-frame of the sources) or a
broken power-law continuum model (with an energy break below 3 keV in the
observer-frame). No Compton reflection features, Fe $K\alpha$ line and hump at
high energies, have been detected, with the exception of the source IGR
J22517+2218 that shows the presence of a weak iron line. In this work we also
investigate a possible correlation between the absorption column density $N_H$
and the red-shift. We confirm the existence of a $N_H$-z trend, with the higher
absorption at z$>$2 for a larger sample compared to previous results. The
distribution of the $N_H$ and the photon index $\Gamma$ is also presented. The
hard X-ray data allow us to detect highly absorbed sources (with
$N_H\ge10^{23}$cm$^{-2}$ in rest-frame of the source) characterized by photon
index distribution peaked at harder values ($\Gamma\sim1.4$) with respect to
that obtained with XMM data only ($\Gamma\sim2$).


  I review heavy quarkonium physics in view of recent experimental results. In
particular, I discuss new results on spin singlet states, photon and hadronic
transitions, D-states and discovery of yet unexplained narrow X(3872) state.


  We construct various kinds of gauged noncommutative WZW models. In
particular, axial gauged noncommutative U(2)/U(1) WZW model is studied and by
integrating out the gauge fields, we obtain a noncommutative non-linear
$\sigma$-model.


  Entropic dynamics is a framework in which quantum theory is derived as an
application of entropic methods of inference. Entropic dynamics on flat spaces
has been extensively studied. The objective of this paper is to extend the
entropic dynamics of $N$ particles to curved spaces. The important new feature
is that the displacement of a particle does not transform like a vector because
fluctuations can be large enough to feel the effects of curvature. The final
result is a modified Schr\"odinger equation in which the usual Laplacian is
replaced by the Laplace-Beltrami operator.


  Choice functions accept a set of alternatives as input and produce a
preferred subset of these alternatives as output. We study the problem of
learning such functions under conditions of context-dependence of preferences,
which means that the preference in favor of a certain choice alternative may
depend on what other options are also available. In spite of its practical
relevance, this kind of context-dependence has received little attention in
preference learning so far. We propose a suitable model based on
context-dependent (latent) utility functions, thereby reducing the problem to
the task of learning such utility functions. Practically, this comes with a
number of challenges. For example, the set of alternatives provided as input to
a choice function can be of any size, and the output of the function should not
depend on the order in which the alternatives are presented. To meet these
requirements, we propose two general approaches based on two representations of
context-dependent utility functions, as well as instantiations in the form of
appropriate end-to-end trainable neural network architectures. Moreover, to
demonstrate the performance of both networks, we present extensive empirical
evaluations on both synthetic and real-world datasets.


  Existing multi-agent reinforcement learning methods are limited typically to
a small number of agents. When the agent number increases largely, the learning
becomes intractable due to the curse of the dimensionality and the exponential
growth of agent interactions. In this paper, we present \emph{Mean Field
Reinforcement Learning} where the interactions within the population of agents
are approximated by those between a single agent and the average effect from
the overall population or neighboring agents; the interplay between the two
entities is mutually reinforced: the learning of the individual agent's optimal
policy depends on the dynamics of the population, while the dynamics of the
population change according to the collective patterns of the individual
policies. We develop practical mean field Q-learning and mean field
Actor-Critic algorithms and analyze the convergence of the solution to Nash
equilibrium. Experiments on Gaussian squeeze, Ising model, and battle games
justify the learning effectiveness of our mean field approaches. In addition,
we report the first result to solve the Ising model via model-free
reinforcement learning methods.


  A cosmological analysis considering the inclusion of dark energy and dark
matter cosmic components in the context of some particular cases for
intersecting S-brane solutions is presented and discussed. Direct solution of
$d$-dim field equations and dimensional reduction processes, are implemented
for the pure dark energy and dark energy with dark matter cosmological
scenarios, respectively. In the first case, explicit expressions and evolution
for $d$-dim scale factors are founded and studied. Secondly, a low energy
effective $4$-dim model is obtained analytically and their resulting field
equations are solved numerically. The role of internal space geometry is always
a central element of our analysis. In some intersecting cases and under certain
considerations, a suitable late-time cosmic acceleration description is founded
and an adequate behavior for scale factor $a(t)$ and realistic values for dark
energy $\Omega_{DE}$ and dark matter $\Omega_{DM}$ relative energy densities
are obtained as well. As a result, we do specifically obtain late-time cosmic
acceleration in one scenario where our universe makes part either of an
SM2$\bot$SM2 or an SM2$\bot$SM5 intersection. This could give rise to further
possible configurations where late-time cosmic acceleration is present.


  Conventional Learning-to-Rank (LTR) methods optimize the utility of the
rankings to the users, but they are oblivious to their impact on the ranked
items. However, there has been a growing understanding that the latter is
important to consider for a wide range of ranking applications (e.g. online
marketplaces, job placement, admissions). To address this need, we propose a
general LTR framework that can optimize a wide range of utility metrics (e.g.
NDCG) while satisfying fairness of exposure constraints with respect to the
items. This framework expands the class of learnable ranking functions to
stochastic ranking policies, which provides a language for rigorously
expressing fairness specifications. Furthermore, we provide a new LTR algorithm
called Fair-PG-Rank for directly searching the space of fair ranking policies
via a policy-gradient approach. Beyond the theoretical evidence in deriving the
framework and the algorithm, we provide empirical results on simulated and
real-world datasets verifying the effectiveness of the approach in individual
and group-fairness settings.


  In this paper, we propose a new graph-based transform and illustrate its
potential application to signal compression. Our approach relies on the careful
design of a graph that optimizes the overall rate-distortion performance
through an effective graph-based transform. We introduce a novel graph
estimation algorithm, which uncovers the connectivities between the graph
signal values by taking into consideration the coding of both the signal and
the graph topology in rate-distortion terms. In particular, we introduce a
novel coding solution for the graph by treating the edge weights as another
graph signal that lies on the dual graph. Then, the cost of the graph
description is introduced in the optimization problem by minimizing the
sparsity of the coefficients of its graph Fourier transform (GFT) on the dual
graph. In this way, we obtain a convex optimization problem whose solution
defines an efficient transform coding strategy. The proposed technique is a
general framework that can be applied to different types of signals, and we
show two possible application fields, namely natural image coding and piecewise
smooth image coding. The experimental results show that the proposed
graph-based transform outperforms classical fixed transforms such as DCT for
both natural and piecewise smooth images. In the case of depth map coding, the
obtained results are even comparable to the state-of-the-art graph-based coding
method, that are specifically designed for depth map images.


  We present our theoretical results on the ground states in layered
triangular-lattice compounds ANiO2 (A=Na, Li, Ag). To describe the interplay
between charge, spin, orbital, and lattice degrees of freedom in these
materials, we study a doubly-degenerate Hubbard model with electron-phonon
couplings by the Hartree-Fock approximation combined with the adiabatic
approximation. In a weakly-correlated region, we find a metallic state
accompanied by \sqroot3x\sqroot3 charge ordering. On the other hand, we obtain
an insulating phase with spin-ferro and orbital-ferro ordering in a wide range
from intermediate to strong correlation. These phases share many
characteristics with the low-temperature states of AgNiO2 and NaNiO2,
respectively. The charge-ordered metallic phase is stabilized by a compromise
between Coulomb repulsions and effective attractive interactions originating
from the breathing-type electronphonon coupling as well as the Hund's-rule
coupling. The spin-orbital-ordered insulating phase is stabilized by the
cooperative effect of electron correlations and the Jahn-Teller coupling, while
the Hund's-rule coupling also plays a role in the competition with other
orbital-ordered phases. The results suggest a unified way of understanding a
variety of low-temperature phases in ANiO2. We also discuss a keen competition
among different spin-orbital-ordered phases in relation to a puzzling behavior
observed in LiNiO2.


  Recent measurements of the scattering of He and Ne atoms at Rh(110) suggest
that these two rare-gas atoms measure a qualitatively different surface
corrugation: While Ne atom scattering seemingly reflects the electron-density
undulation of the substrate surface, the scattering potential of He atoms
appears to be anticorrugated. An understanding of this perplexing result is
lacking. In this paper we present density functional theory calculations of the
interaction potentials of He and Ne with Rh(110). We find that, and explain
why, the nature of the interaction of the two probe particles is qualitatively
different, which implies that the topographies of their scattering potentials
are indeed anticorrugated.


  We calculate the rapidity dependence of the transverse double-spin asymmetry
for the Drell-Yan process to next-to-leading order in the strong coupling.
Input transversity distributions are obtained by saturating the Soffer
inequality at a low hadronic mass scale. Results for the polarized BNL-RHIC
proton-proton collider and the proposed HERA-N fixed-target experiment are
presented, and the influence of the limited muon acceptance of the detectors on
measurements of the asymmetry is studied in detail.


  Separate spin evolution quantum hydrodynamics is generalized to include the
Coulomb exchange interaction. The Coulomb exchange interaction is considered as
the interaction between the spin-down electrons being in the quantum states
occupied by one electron, giving main contribution in the equilibrium. The
generalized model is applied to study the non-linear spin-electron acoustic
waves. Existence of the spin-electron acoustic soliton is demonstrated.
Contributions of the concentration, spin polarization, and exchange interaction
in the properties of the spin electron acoustic soliton are studied.


  Despite the popularization of deep neural networks (DNNs) in many fields, it
is still challenging to deploy state-of-the-art models to resource-constrained
devices due to high computational overhead. Model pruning provides a feasible
solution to the aforementioned challenges. However, the interpretation of
existing pruning criteria is always overlooked. To counter this issue, we
propose a novel layer pruning method by exploring the Stochastic
Re-initialization. Our SR-init method is inspired by the discovery that the
accuracy drop due to stochastic re-initialization of layer parameters differs
in various layers. On the basis of this observation, we come up with a layer
pruning criterion, i.e., those layers that are not sensitive to stochastic
re-initialization (low accuracy drop) produce less contribution to the model
and could be pruned with acceptable loss. Afterward, we experimentally verify
the interpretability of SR-init via feature visualization. The visual
explanation demonstrates that SR-init is theoretically feasible, thus we
compare it with state-of-the-art methods to further evaluate its
practicability. As for ResNet56 on CIFAR-10 and CIFAR-100, SR-init achieves a
great reduction in parameters (63.98% and 37.71%) with an ignorable drop in
top-1 accuracy (-0.56% and 0.8%). With ResNet50 on ImageNet, we achieve a
15.59% FLOPs reduction by removing 39.29% of the parameters, with only a drop
of 0.6% in top-1 accuracy. Our code is available at
https://github.com/huitang-zjut/SR-init.


  Graphene is a nonmagnetic semimetal and cannot be directly used as electronic
or spintronic devices. We demonstrate that graphene quantum dots (GQDs) can
exhibit strong edge magnetism and tunable energy gaps due to the presence of
localized edge states. By using large-scale first principle density functional
theory (DFT) calculations and detailed analysis based on model Hamiltonians, we
can show that the zigzag edge states in GQDs become much stronger and more
localized as the system size increases. The enhanced edge states induce strong
electron-electron interactions along the edges of GQDs, ultimately resulting in
a magnetic phase transition from nonmagnetic to intra-edge ferromagnetic and
inter-edge antiferromagnetic, when the diameter is larger than 4.5 nm. Our
analysis shows that the inter-edge superexchange interaction of
antiferromagnetic states between two nearest-neighbor zigzag edges in GQDs is
much stronger than that exists between two parallel zigzag edges in GQDs and
graphene nanoribbons. Furthermore, such strong and localized edge states also
induce GQDs semiconducting with tunable energy gaps, mainly controlled by
adjusting the system size. Our results show that the quantum confinement
effect, inter-edge superexchange (antiferromagnetic), and intra-edge direct
exchange (ferromagnetic) interactions are crucial for the electronic and
magnetic properties of zigzag GQDs at the nanoscale.


  Inherent symmetry of a quantum system may protect its otherwise fragile
states. Leveraging such protection requires testing its robustness against
uncontrolled environmental interactions. Using 47 superconducting qubits, we
implement the one-dimensional kicked Ising model which exhibits non-local
Majorana edge modes (MEMs) with $\mathbb{Z}_2$ parity symmetry. Remarkably, we
find that any multi-qubit Pauli operator overlapping with the MEMs exhibits a
uniform late-time decay rate comparable to single-qubit relaxation rates,
irrespective of its size or composition. This characteristic allows us to
accurately reconstruct the exponentially localized spatial profiles of the
MEMs. Furthermore, the MEMs are found to be resilient against certain
symmetry-breaking noise owing to a prethermalization mechanism. Our work
elucidates the complex interplay between noise and symmetry-protected edge
modes in a solid-state environment.


  Over the last decades, realistic 3D radiative-MHD simulations have become the
dominant theoretical tool for understanding the complex interactions between
the plasma and the magnetic field on the Sun. Most of such simulations are
based on approximations of magnetohydrodynamics, without directly considering
the consequences of the very low degree of ionization of the solar plasma in
the photosphere and bottom chromosphere. The presence of large amount of
neutrals leads to a partial decoupling of the plasma and the magnetic field. As
a consequence of that, a series of non-ideal effects (ambipolar diffusion, Hall
effect and battery effect) arises. The ambipolar effect is the dominant one in
the solar chromosphere. Here we report on the first three-dimensional realistic
simulations of magneto-convection including ambipolar diffusion and battery
effects. The simulations are done using the newly developed Mancha3D code. Our
results reveal that ambipolar diffusion causes measurable effects on the
amplitudes of waves excited by convection in the simulations, on the absorption
of Poynting flux and heating and on the formation of chromospheric structures.
We provide a low limit on the chromospheric temperature increase due to the
ambipolar effect using the simulations with battery-excited dynamo fields.


See hep-th/0005054.


  We review the production function and the hypothesis of equilibrium in the
neoclassical framework. We notify that in a soup of sectors in economy, while
capital and labor resemble extensive variables, wage and rate of return on
capital act as intensive variables. As a result, Baumol and Bowen's statement
of equal wages is inevitable from the thermodynamics point of view. We try to
see how aggregation can be performed concerning the extensive variables in a
soup of firms. We provide a toy model to perform aggregation for production and
the labor income as extensive quantities in a neoclassical framework.


  The study of almost surely discrete random probability measures is an active
line of research in Bayesian nonparametrics. The idea of assuming interaction
across the atoms of the random probability measure has recently spurred
significant interest in the context of Bayesian mixture models. This allows the
definition of priors that encourage well separated and interpretable clusters.
In this work, we provide a unified framework for the construction and the
Bayesian analysis of random probability measures with interacting atoms,
encompassing both repulsive and attractive behaviors. Specifically we derive
closed-form expressions for the posterior distribution, the marginal and
predictive distributions, which were not previously available except for the
case of measures with i.i.d. atoms. We show how these quantities are
fundamental both for prior elicitation and to develop new posterior simulation
algorithms for hierarchical mixture models. Our results are obtained without
any assumption on the finite point process that governs the atoms of the random
measure. Their proofs rely on new analytical tools borrowed from the theory of
Palm calculus and that might be of independent interest. We specialize our
treatment to the classes of Poisson, Gibbs, and Determinantal point processes,
as well as to the case of shot-noise Cox processes.


  We investigate the production and decays of doubly-charged Higgs bosons for
the Type-II seesaw mechanism at an $e^{+} e^{-}$ collider with two center of
mass energies, $\sqrt{s}=380$ GeV and 3 TeV, and analyze the fully hadronic
final states in detail. Lower mass ranges can be probed during the 380 GeV run
of the collider, while high mass ranges, which are beyond the 13 TeV Large
Hadron Collider discovery reach, can be probed with $\sqrt{s}=3$ TeV. For such
a heavy Higgs boson, the final decay products are collimated, resulting in
fat-jets. We perform a substructure analysis to reduce the background and find
that a doubly-charged Higgs boson in the mass range 800-1120 GeV can be
discovered during the 3 TeV run, with integrated luminosity $\mathcal{L} \sim
95\, \rm{fb}^{-1}$ of data. For 380 GeV center of mass energy, we find that for
the doubly-charged Higgs boson in the range 160-172 GeV, a $5\sigma$
significance can be achieved with only integrated luminosity $\mathcal{L} \sim
24 \, \rm{fb}^{-1}$. Therefore, a light Higgs boson can be discovered
immediately during the run of a future $e^{+} e^{-}$ collider.


  We report on a new search for continuous gravitational waves from NS 1987A,
the neutron star born in SN 1987A, using open data from Advanced LIGO and
Virgo's third observing run (O3). The search covered frequencies from 35-1050
Hz, more than five times the band of the only previous gravitational wave
search to constrain NS 1987A [B. J. Owen et al., ApJL 935, L7 (2022)]. It used
an improved code and coherently integrated from 5.10 days to 14.85 days
depending on frequency. No astrophysical signals were detected. By expanding
the frequency range and using O3 data, this search improved on strain upper
limits from the previous search and was sensitive at the highest frequencies to
ellipticities of 1.6e-5 and r-mode amplitudes of 4.4e-4, both an order of
magnitude improvement over the previous search and both well within the range
of theoretical predictions.


  This paper presents two new deterministic algorithms for constructing
consensus trees. Given an input of k phylogenetic trees with identical leaf
label sets and n leaves each, the first algorithm constructs the majority rule
(+) consensus tree in O(kn) time, which is optimal since the input size is
Omega(kn), and the second one constructs the frequency difference consensus
tree in min(O(kn^2), O(kn (k+log^2 n))) time.


  We constrain the energy scale of noncommutativity of spacetime using CMB data
from PLANCK. We find that PLANCK data puts the lower bound on the
noncommutativity energy scale to about 20 TeV, which is about a factor of two
larger than a previous constraint that was obtained using data from WMAP, ACBAR
and CBI. We further show that inclusion of data of $E$ mode of CMB polarization
will not significantly change the constraint.


  In this note we relate the Hamiltonian of the integrable non-compact spin $s$
XXZ chain to the Markov generator of a stochastic particle process. The hopping
rates of the continuous-time process are identified with the ones of a q-Hahn
asymmetric zero range model. The main difference with the asymmetric simple
exclusion process (ASEP), which can be mapped to the ordinary XXZ spin chain,
is that multiple particles can occupy one and the same site. For the
non-compact spin $\frac{1}{2}$ XXZ chain the associated stochastic process
reduces to the multiparticle asymmetric diffusion model introduced by
Sasamoto-Wadati.


  Investigations of the resistivity and the Hall effect as a function of
electric field and temperature in the normal state and upper part of the
superconducting transition of an optimally doped, very thin film of
YBa$_{2}$Cu$_{3}$O$_{7-\delta}$ are reported. Using a fast pulsed-current
technique allowed to reduce the self-heating of the sample and to reach
electric fields up to 1 kV/cm. An intrinsic non-Ohmic behavior of the Hall
conductivity above the critical temperature that appears to originate from two
different, partially counteracting effects is revealed. The major contribution
stems from the suppression of Aslamazov-Larkin superconducting fluctuations in
high electric fields.


  This work proposes a Momentum-Enabled Kronecker-Factor-Based Optimizer Using
Rank-1 updates, called MKOR, that improves the training time and convergence
properties of deep neural networks (DNNs). Second-order techniques, while
enjoying higher convergence rates vs first-order counterparts, have cubic
complexity with respect to either the model size and/or the training batch
size. Hence they exhibit poor scalability and performance in transformer
models, e.g. large language models (LLMs), because the batch sizes in these
models scale by the attention mechanism sequence length, leading to large model
size and batch sizes. MKOR's complexity is quadratic with respect to the model
size, alleviating the computation bottlenecks in second-order methods. Because
of their high computation complexity, state-of-the-art implementations of
second-order methods can only afford to update the second order information
infrequently, and thus do not fully exploit the promise of better convergence
from these updates. By reducing the communication complexity of the
second-order updates as well as achieving a linear communication complexity,
MKOR increases the frequency of second order updates. We also propose a hybrid
version of MKOR (called MKOR-H) that mid-training falls backs to a first order
optimizer if the second order updates no longer accelerate convergence. Our
experiments show that MKOR outperforms state -of-the-art first order methods,
e.g. the LAMB optimizer, and best implementations of second-order methods, i.e.
KAISA/KFAC, up to 2.57x and 1.85x respectively on BERT-Large-Uncased on 64
GPUs.


  We present a theoretical framework that accounts for the new $D_J$ and
$D_{sJ}$ mesons measured in the open-charm sector. These resonances are
properly described if considered as a mixture of conventional $P-$wave
quark-antiquark states and four-quark components. The narrowest states are
basically $P-$wave quark-antiquark mesons, while the dominantly four-quark
states are shifted above the corresponding two-meson threshold, being broad
resonances. We study the electromagnetic decay widths as basic tools to
scrutiny their nature. The proposed explanation incorporates in a natural way
the most recently discovered mesons in charmonium spectroscopy.


  A triangulation of a polytope into simplices is refined recursively. In every
refinement round, some simplices which have been marked by an external
algorithm are bisected and some others around also must be bisected to retain
regularity of the triangulation. The ratio of the total number of marked
simplices and the total number of bisected simplices is bounded from above.
Binev, Dahmen and DeVore proved under a certain initial condition a bound that
depends only on the initial triangulation. This thesis proposes a new way to
obtain a better bound in any dimension. Furthermore, the result is proven for a
weaker initial condition, invented by Alk\"amper, Gaspoz and Kl\"ofkorn, who
also found an algorithm to realise this condition for any regular initial
triangulation. Supposably, it is the first proof for a Binev-Dahmen-DeVore
theorem in any dimension with always practically realiseable initial conditions
without an initial refinement. Additionally, the initialisation refinement
proposed by Kossaczk\'y and Stevenson is generalised, and the number of
recursive bisections of one single simplex in one refinement round is bounded
from above by twice the dimension, sharpening a result of Gallistl, Schedensack
and Stevenson.


  Patterns and waves are basic and important phenomena that govern the dynamics
of physical and biological systems. A common theme in investigating such
systems is to identify the intrinsic factors responsible for such
self-organization. The $\Gamma$-convergence is a well-known technique
applicable to variational formulation in studying the concentration phenomena
of stable patterns. A geometric variational functional associated with the
$\Gamma$-limit of standing waves of FitzHugh-Nagumo system has recently been
built. This article studies the $\Gamma$-limit of traveling waves. To the best
of our knowledge, this is the first attempt to expand the scope of
applicability of $\Gamma$-convergence to cover non-stationary problems.


  The cyclotomic Birman-Murakami-Wenzl (BMW) algebras B_n^k, introduced by R.
H\"aring-Oldenburg, are a generalisation of the BMW algebras associated with
the cyclotomic Hecke algebras of type G(k,1,n) (aka Ariki-Koike algebras) and
type B knot theory.
  In this paper, we prove the algebra is free and of rank k^n (2n-1)!! over
ground rings with parameters satisfying so-called "admissibility conditions".
These conditions are necessary in order for these results to hold and
originally arise from the representation theory of B_2^k, which is analysed by
the authors in a previous paper. Furthermore, we obtain a geometric realisation
of B_n^k as a cyclotomic version of the Kauffman tangle algebra, in terms of
affine n-tangles in the solid torus, and produce explicit bases that may be
described both algebraically and diagrammatically.
  The admissibility conditions are the most general offered in the literature
for which these results hold; they are necessary and sufficient for all results
for general n.


  Photon-added Gazeau-Klauder and Klauder-Perelomov coherent states are
investigated. Application to P\"oschl-Teller potentials is given.


  Accurate understanding of electromagnetic propagation properties in real
environments is necessary for efficient design and deployment of cellular
systems. In this paper, we show a method to estimate high-resolution channel
parameters with a massive antenna array in real network deployments. An antenna
array mounted on a vehicle is used to receive downlink long-term evolution
(LTE) reference signals from neighboring base stations (BS) with mutual
interference. Delay and angular information of multipath components is
estimated with a novel inter-cell interference cancellation algorithm and an
extension of the RIMAX algorithm. The estimated high-resolution channel
parameters are consistent with the movement pattern of the vehicle and the
geometry of the environment and allow for refined channel modeling and precise
cellular positioning.


  We theoretically investigate the effects of backscattering and
superconducting proximity terms between the edges of two multi-layer fractional
quantum Hall (FQH) systems. While the different layers are strongly
interacting, we assume that tunneling between them is absent. Studying the
boundaries between regions gapped by the two mechanisms in an $N$-layer system,
we find $N$ localized zero-mode operators realizing a generalized parafermionic
algebra. We further propose an experiment capable of probing imprints of the
generalized parafermionic bound states. This is done by coupling different
superconducting contacts to different layers, and examining the periodicity of
the Josephson effect as a function of the various relative superconducting
phases. Remarkably, even if we apply a phase difference between the
superconductors in one layer, we induce a Josephson current at the other layers
due to inter-layer interactions. Furthermore, while the Josephson effect is
commonly used to probe only charged degrees of freedom, the possibility of
independently controlling the superconducting phase differences between the
layers allows us to find imprints of the neutral modes of the underlying
multi-layer system. In particular, we propose two configurations, one of which
is capable of isolating the signal associated with the charge modes, while the
other probes the neutral modes.


  Context. The properties of the X-ray emission lines are a fundamental tool
for studying the nature of the matter surrounding the neutron star and the
phenomena that produce these lines. Aims. The aim of this work is to analyze
the X-ray spectrum of 4U 1538-52 obtained by the XMM-Newton observatory and to
look for the presence of diagnostic lines in the energy range 0.3-11.5 keV.
Methods. We used a 54 ks PN & MOS/XMM-Newton observation of the high mass X-ray
binary 4U 1538-52 covering the orbital phase between 0.75 to 1.00 (the
eclipse-ingress). We have modelled the 0.3-11.5 keV continuum emission with
three absorbed power laws and looked for the emission lines. Results. We found
previously unreported recombination lines, in this system, at 2.4 keV, 1.9 keV
and 1.3 keV, consistent with the presence of highly ionized states of S XV He?,
Si XIII He? and Mg K? or Mg XI He?. On the other hand, both out of eclipse and
in eclipse we detect a fluorescence iron emission line at 6.4 keV which is
resolved into two components: a narrow ($\sigma \leq$ 10 eV) fluorescence Fe K?
line plus one hot line from highly photoionized Fe XXV. Conclusions. The
detection of new recombination lines during eclipse-ingress in 4U 1538-52
indicates that there is an extended ionized region surrounding the neutron
star.


  Reducing energy consumption is crucial to reduce the human debt's with regard
to our planet. Therefore most companies try to reduce their energetic
consumption while taking care to preserve the service delivered to their
customers. To do so, a service provider (SP) typically downscale or shutdown
part of its infrastructure in periods of low-activity where only few customers
need the service. However an SP still needs to maintain part of its
infrastructure "on", which still requires significant energy. For example a
mobile national operator (MNO) needs to maintain most of its radio access
network (RAN) active. Could an SP do better by cooperating with other SPs who
would temporarily support its users, thus allowing it to temporarily shut down
its infrastructure, and then reciprocate during another low-activity period? To
answer this question, we investigated a novel collaboration framework based on
multi-agent reinforcement learning (MARL) allowing negotiations between SPs as
well as trustful reports from a distributed ledger technology (DLT) to evaluate
the amount of energy being saved. We leveraged it to experiment three different
sets of rules (free, recommended, or imposed) regulating the negotiation
between multiple SPs (3, 4, 8, or 10). With respect to four cooperation metrics
(efficiency, safety, incentive-compatibility, and fairness), the simulations
showed that the imposed set of rules proved to be the best mode.


  If moduli, or other long-lived heavy states, decay in the early universe in
part into light and feebly interacting particles (such as axions), these decay
products could account for the additional energy density in radiation that is
suggested by recent measurements of the CMB. These moduli decays will also,
however, alter the expansion history of the early universe, potentially
diluting the thermal relic abundance of dark matter. If this is the case, then
dark matter particles must annihilate with an even lower cross section than
required in the standard thermal scenario (sigma v < 3x10^-26 cm^3/s) if they
are to make up the observed density of dark matter. This possibility has
significant implications for direct and indirect searches for dark matter.


  COVID-19 has challenged health systems to learn how to learn. This paper
describes the context, methods and challenges for learning to improve COVID-19
care at one academic health center. Challenges to learning include: (1)
choosing a right clinical target; (2) designing methods for accurate
predictions by borrowing strength from prior patients' experiences; (3)
communicating the methodology to clinicians so they understand and trust it;
(4) communicating the predictions to the patient at the moment of clinical
decision; and (5) continuously evaluating and revising the methods so they
adapt to changing patients and clinical demands. To illustrate these
challenges, this paper contrasts two statistical modeling approaches -
prospective longitudinal models in common use and retrospective analogues
complementary in the COVID-19 context - for predicting future biomarker
trajectories and major clinical events. The methods are applied to and
validated on a cohort of 1,678 patients who were hospitalized with COVID-19
during the early months of the pandemic. We emphasize graphical tools to
promote physician learning and inform clinical decision making.


  We study isotropic antiferromagnetic Heisenberg chains coupled by
antiferromagnetic Ising interaction as an effective spin model for the
ytterbium aluminum perovskite YbAlO$_3$. Using the density-matrix
renormalization group (DMRG) method we calculate the magnetization curve, local
spin, central charge, and dynamical spin structure factors in the presence of
magnetic field. From the fitting of the experimental magnetization curve, the
effective intrachain and interchain couplings are estimated as $J=2.3$K and
$J_{\rm ic}=0.8$K, respectively. We can quantitatively explain the experimental
observations: (i) phase transition from antiferromagnetic to incommensurate
order at field 0.35T, and (ii) quantum critical behaviors at the saturation
field of 1.21T. Furthermore, the low-energy excitations in the experimental
inelastic neutron scattering spectra can be well described by our DMRG results
of the dynamical structure factors.


  Traditional thermal evolution models of giant planets employ arbitrary
initial conditions selected more for computational expediency than physical
accuracy. Since the initial conditions are eventually forgotten by the evolving
planet, this approach is valid for mature planets, if not young ones. To
explore the evolution at young ages of jovian mass planets we have employed
model planets created by one implementation of the core accretion mechanism as
initial conditions for evolutionary calculations. We find that young jovian
planets are smaller, cooler, and several to 100 times less luminous than
predicted by earlier models. Furthermore the time interval during which the
young jupiters are fainter than expected depends on the mass of planet. Jupiter
mass planets (1 M_J) align with the conventional model luminosity in as little
at 20 million years, but 10 M_J planets can take up to 1 billion years to match
commonly cited luminosities, given our implementation of the core accretion
mechanism. If our assumptions, especially including our treatment of the
accretion shock, are correct, then young jovian planets are substantially
fainter at young ages than currently believed. These results have important
consequences both for detection strategies and for assigning masses to young
jovian planets based on observed luminosities.


  A new ontological view of the quantum measurement processes is given, which
has bearings on many broader issues in the foundations of quantum mechanics as
well. In this scenario a quantum measurement is a non-equilibrium phase
transition in a ``resonant cavity'' formed by the entire physical universe
including all of its material and energy content. A quantum measurement
involves the energy and matter exchange among not only the system being
measured and the measuring apparatus but also the global environment of the
universe resonant cavity, which together constrain the nature of the phase
transition. Strict realism, including strict energy and angular momentum
conservation, is recovered in this view of the quantum measurement process
beyond the limit set by the uncertainty relations, which are themselves derived
from the exact commutation relations for quantum conjugate variables. Both the
amplitude and the phase of the quantum mechanical wavefunction acquire
substantial meanings in the new ontology, and the probabilistic element is
removed from the foundations of quantum mechanics, its apparent presence in the
quantum measurement being solely a result of the sensitive dependence on
initial/boundary conditions of the phase transitions of a many
degree-of-freedom system which is effectively the whole universe. Vacuum
fluctuations are viewed as the ``left over'' fluctuations after forming the
whole numbers of nonequilibrium resonant modes in the universe cavity. This new
view on the quantum processes helps to clarify many puzzles in the foundations
of quantum mechanics.


  We present an analysis of the kinematics of 14 satellites of the Milky Way
(MW). We use proper motions (PMs) from the $Gaia$ Early Data Release 3 (EDR3)
and line-of-sight velocities ($v_{\mathrm{los}}$) available in the literature
to derive the systemic 3D motion of these systems. For six of them, namely the
Carina, Draco, Fornax, Sculptor, Sextans, and Ursa Minor dwarf spheroidal
galaxies (dSph), we study the internal kinematics projecting the stellar PMs
into radial, $V_R$ (expansion/contraction), and tangential, $V_T$ (rotation),
velocity components with respect to the centre of mass. We find significant
rotation in the Carina ($|V_T| = 9.6 \pm 4.5 \ {\rm{km \ s^{-1}}}\>$), Fornax
($|V_T| = 2.8 \pm 1.3 \ {\rm{km \ s^{-1}}}\>$), and Sculptor ($|V_T| = 3.0 \pm
1.0 \ {\rm{km \ s^{-1}}}\>$) dSphs. Besides the Sagittarius dSph, these are the
first measurements of internal rotation in the plane of the sky in the MW's
classical dSphs. All galaxies except Carina show $|V_T| / \sigma_v < 1$. We
find that slower rotators tend to show, on average, larger sky-projected
ellipticity (as expected for a sample with random viewing angles) and are
located at smaller Galactocentric distances (as expected for tidal stirring
scenarios in which rotation is transformed into random motions as satellites
sink into the parent halo). However, these trends are small and not
statistically significant, indicating that rotation has not played a dominant
role in shaping the 3D structure of these galaxies. Either tidal stirring had a
weak impact on the evolution of these systems or it perturbed them with similar
efficiency regardless of their current Galactocentric distance.


  The $\Phi^4_3$ measure is one of the easiest non-trivial examples of a
Euclidean quantum field theory (EQFT) whose rigorous construction in the 1970's
has been one of the celebrated achievements of constructive quantum field
theory. In recent years, progress in the field of singular stochastic PDEs,
initiated by the theory of regularity structures, has allowed for a new
construction of the $\Phi^4_3$ EQFT as the invariant measure of a previously
ill-posed Langevin dynamics, a strategy originally proposed by Parisi and Wu
('81) under the name stochastic quantisation. We apply the same methodology to
obtain a large deviation principle (LDP) for the family of periodic $\Phi^4_3$
measures at varying temperature. In addition, we show that the rate functional
of the LDP and the $\Phi^4_3$ action functional coincide up to a constant.


  We discuss the effects of double octupole and quadrupole phonon excitations
in $^{144}$Sm on fusion reactions between $^{16}$O and $^{144}$Sm at subbarrier
energies. The effects of anharmonicities of the vibrational states are taken
into account by using the $sdf$-interacting boson model. We compare the results
with those in the harmonic limit to show that anharmonicities play an essential
role in reproducing the experimental fusion barrier distribution. From the
analysis of the high quality fusion data available for this system, we deduce
negative static quadrupole moments for both the first 2$^{+}$ and 3$^{-}$
states in $^{144}$Sm. This is the first time that the sign of static quadrupole
moments of phonon states in a spherical nucleus is determined from the data of
subbarrier fusion reactions.


  Immune interventions consisting in repeated injection are broadly used as
they are thought to improve the quantity and the quality of the immune
response. However, they also raised several questions that remains unanswered,
in particular the number of injections to make or the delay to respect between
different injections to acheive this goal. Practical and financial
considerations add constraints to these questions, especially in the framework
of human studies. We specifically focus here on the use of interleukine-7
(IL-7) injections in HIV-infected patients under antiretroviral treatment, but
still unable to restore normal levels of CD4+ T lymphocytes. Clinical trials
have already shown that repeated cycles of injections of IL-7 could help
maintaining CD4+ T lymphocytes levels over the limit of 500 cells per microL,
by affecting proliferation and survival of CD4+ T cells. We then aim at
answering the question : how to maintain a patient's level of CD4+ T
lymphocytes by using a minimum number of injections (ie optimizing the strategy
of injections) ? Based on mechanistic models that were previously developed for
the dynamics of CD4+ T lymphocytes in this context, we model the process by a
piecewise deterministic Markov model. We then address the question by using
some recently established theory on impulse control problem in order to develop
a numerical tool determining the optimal strategy. Results are obtained on a
reduced model, as a proof of concept : the method allows to defined an optimal
strategy for a given patient. This method could applied to optimize injections
schedules in clinical trials.


  Numerical results for particle acceleration at multiple oblique shocks are
presented. We calculate the steady state spectral slope of test particles
accelerated by the first order Fermi process. The results are compared to
analytical treatments, for parameters, where the diffusion approximation does
apply. Effects of injection and finite shock extend are included
phenomenologically. We find the spectrum of accelerated particles to harden
substantially at multiple oblique shocks and discuss the influence of the
number of shocks compared to the obliquity itself.


  Arrays of Fe-nanoparticles grown by STM-assited CVD have been placed on top
of a narrow stripe of Py. The magnetic coupling between the nanoparticles and
the underlying Py film results in distinct negative jumps of the Py
magnetoresistance. The switching of the magnetization orientation of individual
particles is clearly reflected in the Py magnetoresistance as a consequence of
AMR and DWMR, with a homogeneous particle magnetization orientation yielding
the highest resistances.


  Deep, high resolution spectroscopic observations have been obtained for six
compact, strongly star-forming galaxies at redshift z~0.1-0.3, most of them
also known as Green Peas. Remarkably, these galaxies show complex emission-line
profiles in the spectral region including H\alpha, [NII]$\lambda\lambda 6548,
6584$ and [SII]$\lambda\lambda 6717, 6731$, consisting of the superposition of
different kinematical components on a spatial extent of few kpc: a very broad
line emission underlying more than one narrower component. For at least two of
the observed galaxies some of these multiple components are resolved spatially
in their 2D-spectra, whereas for another one a faint detached H\alpha\ blob
lacking stellar continuum is detected at the same recessional velocity ~7 kpc
away from the galaxy. The individual narrower H\alpha\ components show high
intrinsic velocity dispersion (\sigma ~30-80 km s$^{-1}$), suggesting together
with unsharped masking HST images that star formation proceeds in an ensemble
of several compact and turbulent clumps, with relative velocities of up to ~500
km s$^{-1}$. The broad underlying H\alpha\ components indicate in all cases
large expansion velocities (full width zero intensity FWZI $\ge$ 1000 km
s$^{-1}$) and very high luminosities (up to ~10$^{42}$ erg s$^{-1}$), probably
showing the imprint of energetic outflows from SNe. These intriguing results
underline the importance of Green Peas for studying the assembly of low-mass
galaxies near and far.