  This paper investigates an Internet of Things (IoT) system in which multiple
devices are observing some object's physical parameters and then offloading
their observations back to the BS in time with opportunistic channel access.
Specifically, each device accesses the common channel through contention with a
certain probability firstly and then the winner evaluates the instant channel
condition and decides to accept the right of channel access or not. We analyze
this system through the perspective of Age of Information (AoI), which
describes the freshness of observed information. The target is to minimize
average AoI by optimizing the probability of device participation in contention
and the transmission rate threshold. The problem is hard to solve since the AoI
expression in fractional form is complex. We first decompose the original
problem into two single-variable optimization sub-problems through Dinkelbach
method and Block Coordinate Descent (BCD) method. And then we transform them to
Monotonic optimization problems by proving the monotonicity of the objective
functions, whose global optimal solution is able to be found through Polyblock
algorithm. Numerical results verify the validity of our proposed method.


  The recent paper "Linear Connectivity Forces Large Complete Bipartite Minors"
by Boehme et al. relies on a structure theorem for graphs with no H-minor. The
sketch provided of how to deduce this theorem from the work of Robertson and
Seymour appears to be incomplete. To fill this gap, we modify the main proof of
that paper to work with a mere restatement of Robertson and Seymour's original
results instead.


  Classical ghost imaging is a correlation-imaging technique in which the image
of the object is found through intensity correlations of light. We analyze
three different quality parameters, namely the visibility, the signal-to-noise
ratio (SNR), and the contrast-to-noise ratio (CNR), to assess the performance
of double- and triple-intensity correlation-imaging setups. The source is a
random partially polarized beam of light obeying Gaussian statistics and the
image quality is evaluated as a function of the degree of polarization (DoP).
We show that the visibility improves when the DoP and the order of imaging
increase, while the SNR behaves oppositely. The CNR is for the most part
independent of DoP and the imaging order. The results are important for the
development of new imaging devices using partially polarized light.


  We examine what happens if we replace ZFC with a localistic/relativistic
system, LZFC, whose central new axiom, denoted by $Loc({\rm ZFC})$, says that
every set belongs to a transitive model of ZFC. LZFC consists of $Loc({\rm
ZFC})$ plus some elementary axioms forming Basic Set Theory (BST). Some
theoretical reasons for this shift of view are given. All $\Pi_2$ consequences
of ZFC are provable in ${\rm LZFC}$. LZFC strongly extends Kripke-Platek (KP)
set theory minus $\Delta_0$-Collection and minus $\in$-induction scheme.
ZFC+``there is an inaccessible cardinal'' proves the consistency of LZFC. In
LZFC we focus on models rather than cardinals, a transitive model being
considered as the analogue of an inaccessible cardinal. Pushing this analogy
further we define $\alpha$-Mahlo models and $\Pi_1^1$-indescribable models, the
latter being the analogues of weakly compact cardinals. Also localization
axioms of the form $Loc({\rm ZFC}+\phi)$ are considered and their global
consequences are examined. Finally we introduce the concept of standard compact
cardinal (in ZFC) and some standard compactness results are proved.


  The existence and search for thermodynamic phase transitions is of unfading
interest. In this paper, we present numerical evidence of dynamical phase
transitions occurring in boundary driven systems with a constrained integrated
current. It is shown that certain models exhibit a discontinuous transition
between two different density profiles and a continuous transition between a
time-independent and a time-dependent profile. We also checked that the KMP
model does not exhibit phase transition in a range much larger than previously
explored.


  Modeling multi-agent systems on networks is a fundamental challenge in a wide
variety of disciplines. We jointly infer the weight matrix of the network and
the interaction kernel, which determine respectively which agents interact with
which others and the rules of such interactions from data consisting of
multiple trajectories. The estimator we propose leads naturally to a non-convex
optimization problem, and we investigate two approaches for its solution: one
is based on the alternating least squares (ALS) algorithm; another is based on
a new algorithm named operator regression with alternating least squares
(ORALS). Both algorithms are scalable to large ensembles of data trajectories.
We establish coercivity conditions guaranteeing identifiability and
well-posedness. The ALS algorithm appears statistically efficient and robust
even in the small data regime but lacks performance and convergence guarantees.
The ORALS estimator is consistent and asymptotically normal under a coercivity
condition. We conduct several numerical experiments ranging from Kuramoto
particle systems on networks to opinion dynamics in leader-follower models.


  The interband time lags between the flux variations of the Q2237+0305 quasar
have been determined from light curves in the Johnson-Cousins V, R, and I
spectral bands. The values of the time lags for filter pairs R-V, I-R, and I-V
are significantly higher than those predicted by the standard accretion disk
model by Shakura and Sunyaev. To explain the discrepancy, the idea of a
supercritical accretion regime in quasars considered in 1973 by Shakura and
Sunyaev is applied. This regime has been shown by them to cause an extended
scattering envelope around the accretion disk. The envelope efficiently
scatters and re-emits the radiation from the accretion disk and thus increases
the apparent disk size. We made use of analytical expressions for the envelope
radius and temperature derived by Shakura and Sunyaev in their analysis of
super-Eddington accretion and show that our results are consistent with the
existence of such an envelope. The corresponding parameters of the accretion
regime were calculated. They provide the radii of the envelope in the V, R, and
I spectral bands consistent with the inter-band time lags determined in our
work.


  Anderson localization is a famous wave phenomenon that describes the absence
of diffusion of waves in a disordered medium. Here we generalize the landscape
theory of Anderson localization to general elliptic operators and complex
boundary conditions using a probabilistic approach, and further investigate
some mathematical aspects of Anderson localization that are rarely discussed
before. First, we observe that under the Neumann boundary condition, the low
energy quantum states are localized on the boundary of the domain with high
probability. We provide a detailed explanation of this phenomenon using the
concept of extended subregions and obtain an analytical expression of this
probability in the one-dimensional case. Second, we find that the quantum
states may be localized in multiple different subregions with high probability
in the one-dimensional case and we derive an explicit expression of this
probability for various boundary conditions. Finally, we examine a bifurcation
phenomenon of the localization subregion as the strength of disorder varies.
The critical threshold of bifurcation is analytically computed based on a toy
model and the dependence of the critical threshold on model parameters is
analyzed.


  We demonstrate resonant excitation of a plasma wave by a train of short laser
pulses guided in a pre-formed plasma channel, for parameters relevant to a
plasma-modulated plasma accelerator (P-MoPA). We show experimentally that a
train of $N \approx 10$ short pulses, of total energy $\sim 1$ J, can be guided
through $110$ mm long plasma channels with on-axis densities in the range
$10^{17} - 10^{18}$ cm$^{-3}$. The spectrum of the transmitted train is found
to be strongly red-shifted when the plasma period is tuned to the intra-train
pulse spacing. Numerical simulations are found to be in excellent agreement
with the measurements and indicate that the resonantly excited plasma waves
have an amplitude in the range $3$ - $10$ GV m$^{-1}$, corresponding to an
accelerator stage energy gain of order $1$ GeV.


  We present and analyze a simple numerical method that diagonalizes a complex
normal matrix A by diagonalizing the Hermitian matrix obtained from a random
linear combination of the Hermitian and skew-Hermitian parts of A.


  The optical resonance problem is similar to but different from time-steady
Schr\"{o}dinger equation. One big challenge is that the eigenfunctions in
resonance problem is exponentially growing. We give physical explanation to
this boundary condition and introduce perfectly matched layer (PML) method to
transform eigenfunctions from exponential-growth to exponential-decay. Based on
the complex stretching technique, we construct a non-Hermitian Hamiltonian for
the optical resonance problem. We successfully validate the effectiveness of
the Hamiltonian by calculate its eigenvalues in the circular cavity and compare
with the analytical results. We also use the proposed Hamiltonian to
investigate the mode evolution around exceptional points in the quad-cosine
cavity.


  We extend the notion of matroid representations by matrices over fields and
consider new representations of matroids by matrices over finite semirings,
more precisely over the boolean and the superboolean semirings. This idea of
representations is generalized naturally to include also hereditary
collections. We show that a matroid that can be directly decomposed as
matroids, each of which is representable over a field, has a boolean
representation, and more generally that any arbitrary hereditary collection is
superboolean-representable.


  A search for new long-lived particles decaying to leptons is presented using
proton-proton collisions produced by the LHC at sqrt(s) = 8 TeV. Data used for
the analysis were collected by the CMS detector and correspond to an integrated
luminosity of 19.7 inverse femtobarns. Events are selected with an electron and
a muon that have transverse impact parameter values between 0.02 cm and 2 cm.
The search has been designed to be sensitive to a wide range of models with
nonprompt e-mu final states. Limits are set on the "displaced supersymmetry"
model, with pair production of top squarks decaying into an e-mu final state
via R-parity-violating interactions. The results are the most restrictive to
date on this model, with the most stringent limit being obtained for a top
squark lifetime corresponding to c tau = 2 cm, excluding masses below 790 GeV
at 95% confidence level.


  The use of a nonhomogeneous aerogel radiator, i.e. one consisting of layers
with different refractive indices, has been shown to improve the resolution of
the Cherenkov angle measured with a proximity focusing RICH detector. In order
to obtain further information on the performance of such a detector, a simple
model has been used to calculate the resolution and search for optimal radiator
parameters.


  Spherical convolutional networks have been introduced recently as tools to
learn powerful feature representations of 3D shapes. Spherical CNNs are
equivariant to 3D rotations making them ideally suited to applications where 3D
data may be observed in arbitrary orientations. In this paper we learn 2D image
embeddings with a similar equivariant structure: embedding the image of a 3D
object should commute with rotations of the object. We introduce a cross-domain
embedding from 2D images into a spherical CNN latent space. This embedding
encodes images with 3D shape properties and is equivariant to 3D rotations of
the observed object. The model is supervised only by target embeddings obtained
from a spherical CNN pretrained for 3D shape classification. We show that
learning a rich embedding for images with appropriate geometric structure is
sufficient for tackling varied applications, such as relative pose estimation
and novel view synthesis, without requiring additional task-specific
supervision.


  In this paper, we propose new algorithms for evacuation problems defined on
dynamic flow networks. A dynamic flow network is a directed graph in which
source nodes are given supplies (i.e., the number of evacuees) and a single
sink node is given a demand (i.e., the maximum number of acceptable evacuees).
The evacuation problem seeks a dynamic flow that sends all supplies from
sources to the sink such that its demand is satisfied in the minimum feasible
time horizon. For this problem, the current best algorithms are developed by
Schl\"oter (2018) and Kamiyama (2019), which run in strongly polynomial time
but with highorder polynomial time complexity because they use submodular
function minimization as a subroutine. In this paper, we propose new algorithms
that do not explicitly execute submodular function minimization, and we prove
that they are faster than those by Schl\"oter (2018) and Kamiyama (2019) when
an input network is restricted such that the sink has a small in-degree and
every edge has the same capacity.


  L'accent est plac\'e dans cet article sur la structure hi\'erarchique,
l'aspect parcimonieux de la repr\'esentation de l'information sonore, la tr\`es
grande dimension des caract\'eristiques ainsi que sur l'ind\'ependance des
caract\'eristiques permettant de d\'efinir les composantes des objets sonores.
Les notions d'objet sonore et de repr\'esentation neuronale sont d'abord
introduites, puis illustr\'ees avec une application en analyse de signaux
sonores vari\'es: parole, musique et environnements naturels ext\'erieurs.
Finalement, un nouveau syst\`eme de reconnaissance automatique de parole est
propos\'e. Celui-ci est compar\'e \`a un syst\`eme statistique conventionnel.
Il montre tr\`es clairement que l'analyse par objets sonores introduit une
grande polyvalence et robustesse en reconnaissance de parole. Cette
int\'egration des connaissances en neurosciences et traitement des signaux
acoustiques ouvre de nouvelles perspectives dans le domaine de la
reconnaissance de signaux acoustiques.
  The emphasis is put on the hierarchical structure, independence and
sparseness aspects of auditory signal representations in high-dimensional
spaces, so as to define the components of auditory objects. The concept of an
auditory object and its neural representation is introduced. An illustrative
application then follows, consisting in the analysis of various auditory
signals: speech, music and natural outdoor environments. A new automatic speech
recognition (ASR) system is then proposed and compared to a conventional
statistical system. The proposed system clearly shows that an object-based
analysis introduces a great flexibility and robustness for the task of speech
recognition. The integration of knowledge from neuroscience and acoustic signal
processing brings new ways of thinking to the field of classification of
acoustic signals.


  Coal power plants will need to be phased out and face stranded asset risks
under the net-zero energy system transition. Repurposing coal power plants
could recoup profits and reduce carbon emissions using the existing
infrastructure and grid connections. This paper investigates a retrofitting
strategy that turns coal power plants into thermal energy storage (TES) and
zero-carbon data centers (DCs). The proposed capacity expansion model considers
the co-locations of DCs, local renewablewith the system-generation, andlevel
coal retir energy storage ement and retrofitting. We optimize the DC system
configurations under the hourly-matching carbon policy and flexible operations.
Results show that under hourly-matching carbon constraints, the retrofitted TES
could complement the operations of lithium-ion batteries (LIBs) to reduce
system costs. This could render DCs with optimal co-located renewable
generations and energy storage more cost-effective than unconstrained DCs.


  The formulation of the Einstein field equations admitting two Killing vectors
in terms of harmonic mappings of Riemannian manifolds is a subject in which
Charlie Misner has played a pioneering role. We shall consider the hyperbolic
case of the Einstein-Maxwell equations admitting two hypersurface orthogonal
Killing vectors which physically describes the interaction of two electrovac
plane waves. Following Penrose's discussion of the Cauchy problem we shall
present the initial data appropriate to this collision problem. We shall also
present three different ways in which the Einstein-Maxwell equations for
colliding plane wave spacetimes can be recognized as a harmonic map. The goal
is to cast the Einstein-Maxwell equations into a form adopted to the initial
data for colliding impulsive gravitational and electromagnetic shock waves in
such a way that a simple harmonic map will directly yield the metric and the
Maxwell potential 1-form of physical interest.
  For Charles W. Misner on his 60th birthday


  The deposition of mechanical feedback from a supermassive black hole (SMBH)
in an active galactic nucleus (AGN) into the surrounding galaxy occurs via
broad-line winds which must carry mass and radial momentum as well as energy.
The effect can be summarized by the dimensionless parameter
$\eta=dot{M_outflow}/dot{M_accretion}= (2 \epsilon_w c^2)/v_w^2$ where
($\epslion_w \equiv dot{E}_w/(dot{M_accretion} c^2)$) is the efficiency by
which accreted matter is turned into wind energy in the disc surrounding the
central SMBH. The outflowing mass and omentum are proportional to $\eta$, and
many prior treatments have essentially assumed that $\eta=0$. We perform one-
and two-dimensional simulations and find that the growth of the central SMBH is
very sensitive to the inclusion of the mass and momentum driving but is
insensitive to the assumed mechanical efficiency. For example in representative
calculations, the omission of momentum and mass feedback leads to an hundred
fold increase in the mass of the SMBH to over $10^{10} \Msun$. When allowance
is made for momentum driving, the final SMBH mass is much lower and the wind
efficiencies which lead to the most observationally acceptable results are
relatively low with $\epsilon_w \lesssim 10^{-4}$.


  We study the swelling and shrinking behavior of core-shell microgels adsorbed
on silicon wafers. In these systems, the core is made of cross-linked poly(N
isopropylmethacrylamide) and the shell consists of cross linked poly(N-n
propylacrylamide). In suspension, these particles exhibit an extended linear
swelling behavior in the temperature interval between the lower critical
solution temperatures of the two polymers. Using ellipsometry and AFM, we show
that this linear response is also observed in the adsorbed state.


  We prove estimates for the level of distribution of the M\"obius function,
von Mangoldt function, and divisor functions in squarefree progressions in the
ring of polynomials over a finite field. Each level of distribution converges
to $1$ as $q$ goes to $\infty$, and the power savings converges to square-root
cancellation as $q$ goes to $\infty$. These results in fact apply to a more
general class of functions, the factorization functions, that includes these
three. The divisor estimates have applications to the moments of $L$-functions,
and the von Mangoldt estimate to one-level densities.


  A novel miniaturized printed folded dipole antenna has been designed for
narrowband sensing applications. It is shown that the antenna may be
systematically engineered to achieve matching to a practical source impedance
at frequencies well below its half-wavelength resonance using series-LC loading
and asymmetrical arm widths. This approach is used to design an inherently
well-matched antenna that does not require an external matching network and
demonstrates approximately 75% miniaturization, excellent co- to
cross-polarization separation, over 24 dB higher realized gain, and strongly
improved radiation efficiency relative to an unloaded folded dipole of the same
electrically small size. Experimental validation of a fabricated prototype
demonstrates excellent agreement with simulations.


  Maybe active discussions about entanglement in quantum information science
demonstrate some immaturity of this rather young area. So recent tries to look
for more accurate ways of classification devote rather encouragement than
criticism.


  In classical hydrodynamics with uniform density, vortices move with the local
fluid velocity. This description is rewritten in terms of forces arising from
the interaction with other vortices. Two such positive straight vortices
experience a repulsive interaction and precess in a positive (anticlockwise)
sense around their common centroid. A similar picture applies to vortices in a
two-component two-dimensional uniform Bose-Einstein condensate (BEC) coherently
coupled through rf Rabi fields. Unlike the classical case, however, the rf Rabi
coupling induces an attractive interaction and two such vortices with positive
signs now rotate in the negative (clockwise) sense. Pairs of counter-rotating
vortices are instead found to translate with uniform velocity perpendicular to
the line joining their cores. This picture is extended to a single vortex in a
two-component trapped BEC. Although two uniform vortex-free components
experience familiar Rabi oscillations of particle-number difference, such
behavior is absent for a vortex in one component because of the nonuniform
vortex phase. Instead the coherent Rabi coupling induces a periodic vorticity
transfer between the two components.


  An exactly solvable model of a quantum spin interacting with a spin
environment is considered. The interaction is chosen to be such that the state
of the environment is conserved. The reduced density matrix of the spin is
calculated for arbitrary coupling strength and arbitrary time. The stationary
state of the spin is obtained explicitely in the $t \to \infty$ limit.


  We consider a system that is composed of an energy constrained sensor node
and a sink node, and devise optimal data compression and transmission policies
with an objective to prolong the lifetime of the sensor node. While applying
compression before transmission reduces the energy consumption of transmitting
the sensed data, blindly applying too much compression may even exceed the cost
of transmitting raw data, thereby losing its purpose. Hence, it is important to
investigate the trade-off between data compression and transmission energy
costs. In this paper, we study the joint optimal compression-transmission
design in three scenarios which differ in terms of the available channel
information at the sensor node, and cover a wide range of practical situations.
We formulate and solve joint optimization problems aiming to maximize the
lifetime of the sensor node whilst satisfying specific delay and bit error rate
(BER) constraints. Our results show that a jointly optimized
compression-transmission policy achieves significantly longer lifetime (90% to
2000%) as compared to optimizing transmission only without compression.
Importantly, this performance advantage is most profound when the delay
constraint is stringent, which demonstrates its suitability for low latency
communication in future wireless networks.


  In this letter, acoustic interaction between cascade sub-chambers is
investigated by modelling the sound field in a silencer with cascade-connected
sub-chambers using a sub-structuring technique. The contribution of the
acoustic coupling to the net energy flow through each individual sub-chamber is
derived quantitatively and the mechanism by which evanescence contributes to
the sound transmission loss of the silencer is revealed.


  We study tools of the conformal bootstrap in simplifying limits, primarily a
limit of large operator dimensions and small cross-ratios corresponding to
non-relativistic physics in AdS. We show that T-channel conformal blocks give
the classical limit of correlation functions to linear order in an interaction
potential. We use the Lorentzian inversion formula to compute anomalous
dimensions due to T-channel exchanges and compare with time-independent
perturbation theory in AdS. These calculations include new results not
restricted to any limit, including anomalous dimensions from stress-tensor
exchange in general dimension. We also study a classical limit of large
operator dimension and spin, in which the Lorentzian inversion formula is
evaluated by saddle-point. Finally, we obtain a new `Lorentzian' inversion
formula for non-relativistic AdS by taking a limit of the CFT inversion
formula.


  We analyze charging effects in graphene quantum dots. Using a simple model,
we show that, when the Fermi level is far from the neutrality point, charging
effects lead to a shift in the electrostatic potential and the dot shows
standard Coulomb blockade features. Near the neutrality point, surface states
are partially occupied and the Coulomb interaction leads to a strongly
correlated ground state which can be approximated by either a Wigner crystal or
a Laughlin like wave function. The existence of strong correlations modify the
transport properties which show non equilibrium effects, similar to those
predicted for tunneling into other strongly correlated systems.


  The inner galaxy has hosted cosmic-ray burst events including those
responsible for the gamma-ray Fermi bubbles and the eROSITA bubbles in X-rays.
In this work, we study the AMS-02 positron fraction and find three features
around 12, 21 and 48 GeV of which the lowest energy has a 1.4 to 4.9-$\sigma$
significance, depending on astrophysical background assumptions. Using
background simulations that explain the cosmic-ray positron fraction, positron
flux and electron plus positron flux, by primary, secondary cosmic rays and
cosmic rays from local pulsars, we test these spectral features as originating
from electron/positron burst events from the inner galaxy. We find the 12 GeV
feature, to be explained by an event of age $\tau \simeq 3 - 10$ Myr; in
agreement with the proposed age of the Fermi bubbles. Furthermore, the energy
in cosmic-ray electrons and positrons propagating along the galactic disk and
not within the Fermi bubbles volume, is estimated to be $10^{51.5}-10^{57.5}$
ergs, or $O(10^{-4}) -O(1)$ the cosmic-ray energy causing the Fermi bubbles. We
advocate that these positron fraction features, are the counterpart signals of
the Fermi bubbles, or of substructures in them, or of the eROSITA bubbles.


  A set $V$ in a domain $U$ in $\mathbb{C}^n$ has the {\em norm-preserving
extension property} if every bounded holomorphic function on $V$ has a
holomorphic extension to $U$ with the same supremum norm. We prove that an
algebraic subset of the {\em symmetrized bidisc} \[ G := \{(z+w,zw):|z|<1, |w|
< 1 \} \] has the norm-preserving extension property if and only if it is
either a singleton, $G$ itself, a complex geodesic of $G$, or the union of the
set $\{(2z,z^2): |z|<1\}$ and a complex geodesic of degree $1$ in $G$. We also
prove that the complex geodesics in $G$ coincide with the nontrivial
holomorphic retracts in $G$. Thus, in contrast to the case of the ball or the
bidisc, there are sets in $G$ which have the norm-preserving extension property
but are not holomorphic retracts of $G$. In the course of the proof we obtain a
detailed classification of the complex geodesics in $G$ modulo automorphisms of
$G$. We give applications to von Neumann-type inequalities for
$\Gamma$-contractions (that is, commuting pairs of operators for which the
closure of $G$ is a spectral set) and for symmetric functions of commuting
pairs of contractive operators. We find three other domains that contain sets
with the norm-preserving extension property which are not retracts: they are
the spectral ball of $2\times 2$ matrices, the tetrablock and the pentablock.
We also identify the subsets of the bidisc which have the norm-preserving
extension property for symmetric functions.


  Computing strongly connected components (SCC) is a fundamental problems in
graph processing. As today's real-world graphs are getting larger and larger,
parallel SCC is increasingly important. SCC is challenging in the parallel
setting and is particularly hard on large-diameter graphs. Many existing
parallel SCC implementations can be even slower than Tarjan's sequential
algorithm on large-diameter graphs.
  To tackle this challenge, we propose an efficient parallel SCC implementation
using a new parallel reachability algorithm. Our solution is based on a novel
idea referred to as vertical granularity control (VGC). It breaks the
synchronization barriers to increase parallelism and hide scheduling overhead.
To use VGC in our SCC algorithm, we also design an efficient data structure
called the \emph{parallel hash bag}. It uses parallel dynamic resizing to avoid
redundant work in maintaining frontiers (vertices processed in a round).
  We implement the parallel SCC algorithm by Blelloch et al.\ (J.\ ACM, 2020)
using our new parallel reachability algorithm. We compare our implementation to
the state-of-the-art systems, including GBBS, iSpan, Multi-step, and our highly
optimized Tarjan's (sequential) algorithm, on 18 graphs, including social, web,
$k$-NN, and lattice graphs. On a machine with 96 cores, our implementation is
the fastest on 16 out of 18 graphs. On average (geometric means) over all
graphs, our SCC is 6.0$\times$ faster than the best previous parallel code
(GBBS), 12.8$\times$ faster than Tarjan's sequential algorithms, and
2.7$\times$ faster than the \emph{best existing implementation on each graph}.
  We believe that our techniques are of independent interest. We also apply our
parallel hash bag and VGC scheme to other graph problems, including
connectivity and least-element lists (LE-lists).


  A consistent Hartree-Fock study of the equation of state (EOS) of asymmetric
nuclear matter at finite temperature has been performed using realistic choices
of the effective, density dependent nucleon-nucleon (NN) interaction, which
were successfully used in different nuclear structure and reaction studies.
Given the importance of the nuclear symmetry energy in the neutron star
formation, EOS's associated with different behaviors of the symmetry energy
were used to study hot asymmetric nuclear matter. The slope of the symmetry
energy and nucleon effective mass with increasing baryon density was found to
affect the thermal properties of nuclear matter significantly. Different
density dependent NN interactions were further used to study the EOS of hot
protoneutron star (PNS) matter of the $npe\mu\nu$ composition in
$\beta$-equilibrium. The hydrostatic configurations of PNS in terms of the
maximal gravitational mass $M_{\rm max}$ and radius, central density, pressure
and temperature at the total entropy per baryon $S/A= 1,2$ and 4 have been
determined in both the neutrino-free and neutrino-trapped scenarios. The
obtained results show consistently a strong impact of the symmetry energy and
nucleon effective mass on thermal properties and composition of hot PNS matter.
$M_{\rm max}$ values obtained for the (neutrino-free) $\beta$-stable PNS at
$S/A=4$ were used to assess time $t_{\rm BH}$ of the collapse of 40 $M_\odot$
protoneutron progenitor to black hole, based on a correlation between $t_{\rm
BH}$ and $M_{\rm max}$ found from the hydrodynamic simulation by Hempel {\it et
al.}.


  We introduce and evaluate a new class of hypothesis testing procedures for
moment structures. The methods are valid under weak assumptions and includes
the well-known Satorra-Bentler adjustment as a special case. The proposed
procedures applies also to difference testing among nested models. We prove the
consistency of our approach. We introduce a bootstrap selection mechanism to
optimally choose a p-value approximation for a given sample. Also, we propose
bootstrap procedures for assessing the asymptotic robustness (AR) of the
normal-theory maximum likelihood test, and for the key assumption underlying
the Satorra-Bentler adjustment (Satorra-Bentler consistency). Simulation
studies indicate that our new p-value approximations performs well even under
severe nonnormality and realistic sample sizes, but that our tests for AR and
Satorra-Bentler consistency require very large sample sizes to work well.


  Existence of new gauge U(1) symmetry possessed by dark matter (DM) particles
implies the existence of a new Coulomb-like interaction, which leads to
Sommerfeld-Gamow-Sakharov enhancement of dark matter annihilation at low
relative velocities. We discuss a possibility to put constraints on the such
dark forces of dark matter from the observational data on the gamma radiation
in our Galaxy. Gamma-rays are supposed to originate from annihilation of DM
particles in the small scale clumps, in which annihilation rate is supposed to
be enhanced, besides higher density, due to smaller relative velocities $v$ of
DM particles. For possible cross sections, mass of annihilating particles,
masses of clumps and the contribution of annihilating particles in the total DM
density we constrain the strength of new dark long range forces from comparison
of predicted gamma ray signal with Fermi/LAT data on unidentified point-like
gamma-ray sources (PGS) as well as on diffuse $\gamma$-radiation.


  Light emitters in wide band gap semiconductors are of great fundamental
interest and have potential as optically addressable qubits. Here we describe
the discovery of a new color center in high-quality hexagonal boron nitride
(h-BN) with a sharp emission line at 435 nm. The emitters are activated and
deactivated by electron beam irradiation and have spectral and temporal
characteristics consistent with atomic color centers weakly coupled to lattice
vibrations. The emitters are conspicuously absent from commercially available
h-BN and are only present in ultra-high-quality h-BN grown using a
high-pressure, high-temperature Ba-B-N flux/solvent, suggesting that these
emitters originate from impurities or related defects specific to this unique
synthetic route. Our results imply that the light emission is activated and
deactivated by electron beam manipulation of the charge state of an
impurity-defect complex.


  We report on a series of chromophores that have been synthesized with a
modulated conjugation path between donor and acceptor. Hyper-Rayleigh
scattering measurements of the best molecule show an enhanced intrinsic
hyperpolarizability that breaches the apparent limit of all previously studied
molecules.


  A major bottleneck to scaling-up training of self-driving perception systems
are the human annotations required for supervision. A promising alternative is
to leverage "auto-labelling" offboard perception models that are trained to
automatically generate annotations from raw LiDAR point clouds at a fraction of
the cost. Auto-labels are most commonly generated via a two-stage approach --
first objects are detected and tracked over time, and then each object
trajectory is passed to a learned refinement model to improve accuracy. Since
existing refinement models are overly complex and lack advanced temporal
reasoning capabilities, in this work we propose LabelFormer, a simple,
efficient, and effective trajectory-level refinement approach. Our approach
first encodes each frame's observations separately, then exploits
self-attention to reason about the trajectory with full temporal context, and
finally decodes the refined object size and per-frame poses. Evaluation on both
urban and highway datasets demonstrates that LabelFormer outperforms existing
works by a large margin. Finally, we show that training on a dataset augmented
with auto-labels generated by our method leads to improved downstream detection
performance compared to existing methods. Please visit the project website for
details https://waabi.ai/labelformer


  The EUI instrument on the Solar Orbiter spacecraft has obtained the most
stable, high-resolution images of the solar corona from its orbit with a
perihelion near 0.4 AU. A sequence of 360 images obtained at 17.1 nm, between
25-Oct-2022 19:00 and 19:30 UT is scrutinized. One image pixel corresponds to
148 km at the solar surface. The widely-held belief that the outer atmosphere
of the Sun is in a continuous state of magnetic turmoil is pitted against the
EUI data. The observed plasma variations appear to fall into two classes. By
far the dominant behavior is a very low amplitude variation in brightness (1%)
in the coronal loops, with larger variations in some footpoint regions.
  No hints of observable changes in magnetic topology are associated with such
small variations. The larger amplitude, more rapid, rarer and less-well
organized changes are associated with flux emergence. It is suggested therefore
that while magnetic reconnection drives the latter, most of the active corona
is heated with no evidence of a role for large-scale (observable) reconnection.
Since most coronal emission line widths are subsonic, the bulk of coronal
heating, if driven by reconnection, can only be of tangentially discontinuous
magnetic fields, with angles below about $0.5c_S/c_A \sim 0.3\beta$, with
$\beta$ the plasma beta parameter ($\sim 0.01)$, and $c_S$ and $c_A$ sound and
Alfv\'en speeds. If heated by multiple small flare-like events, then these must
be $\lesssim 10^{21}$ erg, i.e. pico-flares. But processes other than
reconnection have yet to be ruled out, such as viscous dissipation, which may
contribute to the steady heating of coronal loops over active regions.


  We propose a new gradient projection algorithm that compares favorably with
the fastest algorithms available to date for $\ell_1$-constrained sparse
recovery from noisy data, both in the compressed sensing and inverse problem
frameworks. The method exploits a line-search along the feasible direction and
an adaptive steplength selection based on recent strategies for the alternation
of the well-known Barzilai-Borwein rules. The convergence of the proposed
approach is discussed and a computational study on both well-conditioned and
ill-conditioned problems is carried out for performance evaluations in
comparison with five other algorithms proposed in the literature.


  The aim of this paper is to compare numerically the performance of two
estimators based on Hyv\"arinen's local homogeneous scoring rule with that of
the full and the pairwise maximum likelihood estimators. In particular, two
different model settings, for which both full and pairwise maximum likelihood
estimators can be obtained, have been considered: the first order
autoregressive model (AR(1)) and the moving average model (MA(1)). Simulation
studies highlight very different behaviours for the Hyv\"arinen scoring rule
estimators relative to the pairwise likelihood estimators in these two
settings.


  Let X be a connected open set in n-dimensional Euclidean space, partially
ordered by a closed convex cone K with nonempty interior: y > x if and only if
y-x is nonzero and in K.
  Theorem: If F is a monotone local flow in X whose periodic points are dense
in X, then F is globally periodic.


  The Natural Language to Visualization (NL2Vis) task aims to transform
natural-language descriptions into visual representations for a grounded table,
enabling users to gain insights from vast amounts of data. Recently, many deep
learning-based approaches have been developed for NL2Vis. Despite the
considerable efforts made by these approaches, challenges persist in
visualizing data sourced from unseen databases or spanning multiple tables.
Taking inspiration from the remarkable generation capabilities of Large
Language Models (LLMs), this paper conducts an empirical study to evaluate
their potential in generating visualizations, and explore the effectiveness of
in-context learning prompts for enhancing this task. In particular, we first
explore the ways of transforming structured tabular data into sequential text
prompts, as to feed them into LLMs and analyze which table content contributes
most to the NL2Vis. Our findings suggest that transforming structured tabular
data into programs is effective, and it is essential to consider the table
schema when formulating prompts. Furthermore, we evaluate two types of LLMs:
finetuned models (e.g., T5-Small) and inference-only models (e.g., GPT-3.5),
against state-of-the-art methods, using the NL2Vis benchmarks (i.e., nvBench).
The experimental results reveal that LLMs outperform baselines, with
inference-only models consistently exhibiting performance improvements, at
times even surpassing fine-tuned models when provided with certain few-shot
demonstrations through in-context learning. Finally, we analyze when the LLMs
fail in NL2Vis, and propose to iteratively update the results using strategies
such as chain-of-thought, role-playing, and code-interpreter. The experimental
results confirm the efficacy of iterative updates and hold great potential for
future study.


  Graph based semi-supervised learning (GSSL) has intuitive representation and
can be improved by exploiting the matrix calculation. However, it has to
perform iterative optimization to achieve a preset objective, which usually
leads to low efficiency. Another inconvenience lying in GSSL is that when new
data come, the graph construction and the optimization have to be conducted all
over again. We propose a sound assumption, arguing that: the neighboring data
points are not in peer-to-peer relation, but in a partial-ordered relation
induced by the local density and distance between the data; and the label of a
center can be regarded as the contribution of its followers. Starting from the
assumption, we develop a highly efficient non-iterative label propagation
algorithm based on a novel data structure named as optimal leading forest
(LaPOLeaF). The major weaknesses of the traditional GSSL are addressed by this
study. We further scale LaPOLeaF to accommodate big data by utilizing block
distance matrix technique, parallel computing, and Locality-Sensitive Hashing
(LSH). Experiments on large datasets have shown the promising results of the
proposed methods.


  The arithmetic regularity lemma due to Green [GAFA 2005] is an analogue of
the famous Szemer{\'e}di regularity lemma in graph theory. It shows that for
any abelian group $G$ and any bounded function $f:G \to [0,1]$, there exists a
subgroup $H \le G$ of bounded index such that, when restricted to most cosets
of $H$, the function $f$ is pseudorandom in the sense that all its nontrivial
Fourier coefficients are small. Quantitatively, if one wishes to obtain that
for $1-\epsilon$ fraction of the cosets, the nontrivial Fourier coefficients
are bounded by $\epsilon$, then Green shows that $|G/H|$ is bounded by a tower
of twos of height $1/\epsilon^3$. He also gives an example showing that a tower
of height $\Omega(\log 1/\epsilon)$ is necessary. Here, we give an improved
example, showing that a tower of height $\Omega(1/\epsilon)$ is necessary.


  We study the motions inside 28 OB-associations with the use of Gaia DR2
proper motions. The average velocity dispersion calculated for 28
OB-associations including more than 20 stars with Gaia DR2 proper motion is
sigma_v =4.5 km s-1. The median virial and stellar masses of OB-associations
are M_vir=8.9 x 10^5 and M_st=8.1 x 10^3 Ms, respectively. The median
star-formation efficiency in parent giant molecular clouds appears to be
epsilon=1.2 per cent. Gaia DR2 proper motions confirm the expansion in the Per
OB1, Car OB1 and Sgr OB1 associations found earlier with Gaia DR1 data. We also
detect the expansion in Gem OB1, Ori OB1 and Sco OB1 associations which became
possible for the first time now when analyzed with Gaia DR2 proper motions. The
analysis of the distribution of OB-stars in the Per OB1 association shows the
presence of a shell-like structure with the radius of 40 pc. Probably, the
expansion of the Per OB1 association started with the velocity greater than the
present-day expansion velocity equal to 5.0 +\- 1.7 km s-1.


  Lax-Wendroff Flux Reconstruction (LWFR) is a single-stage, high order,
quadrature free method for solving hyperbolic conservation laws. We develop a
subcell based limiter by blending LWFR with a lower order scheme, either first
order finite volume or MUSCL-Hancock scheme. While the blending with a lower
order scheme helps to control oscillations, it may not guarantee admissibility
of discrete solution, e.g., positivity property of quantities like density and
pressure. By exploiting the subcell structure and admissibility of lower order
schemes, we devise a strategy to ensure that the blended scheme is
admissibility preserving for the mean values and then use a scaling limiter to
obtain admissibility of the polynomial solution. For MUSCL-Hancock scheme on
non-cell-centered subcells, we develop a slope limiter, time step restrictions
and suitable blending of higher order fluxes, that ensures admissibility of
lower order updates and hence that of the cell averages. By using the
MUSCL-Hancock scheme on subcells and Gauss-Legendre points in flux
reconstruction, we improve small-scale resolution compared to the subcell-based
RKDG blending scheme with first order finite volume method and
Gauss-Legendre-Lobatto points. We demonstrate the performance of our scheme on
compressible Euler's equations, showcasing its ability to handle shocks and
preserve small-scale structures.


  We investigate an interacting particle system inspired by the gypsy moth,
whose populations grow until they become sufficiently dense so that an epidemic
reduces them to a low level. We consider this process on a random 3-regular
graph and on the $d$-dimensional lattice and torus, with $d\geq2$. On the
finite graphs with global dispersal or with a dispersal radius that grows with
the number of sites, we prove convergence to a dynamical system that is chaotic
for some parameter values. We conjecture that on the infinite lattice with a
fixed finite dispersal distance, distant parts of the lattice oscillate out of
phase so there is a unique nontrivial stationary distribution.


  We consider the structure of $H$-free subgraphs of graphs with high minimal
degree. We prove that for every $k>m$ there exists an
$\epsilon:=\epsilon(k,m)>0$ so that the following holds. For every graph $H$
with chromatic number $k$ from which one can delete an edge and reduce the
chromatic number, and for every graph $G$ on $n>n_0(H)$ vertices in which all
degrees are at least $(1-\epsilon)n$, any subgraph of $G$ which is $H$-free and
contains the maximum number of copies of the complete graph $K_m$ is
$(k-1)$-colorable.
  We also consider several extensions for the case of a general forbidden graph
$H$ of a given chromatic number, and for subgraphs maximizing the number of
copies of balanced blowups of complete graphs.


  In this paper, we study the spin excitation properties of the frustrated
triangular-lattice antiferromagnet Yb(BaBO$_3$)$_3$ with nuclear magnetic
resonance. From the spectral analysis, neither magnetic ordering nor spin
freezing is observed with temperature down to $T=0.26$ K, far below its
Curie-Weiss temperature $|\theta_w|\sim2.3$ K. From the nuclear relaxation
measurement, precise temperature-independent spin-lattice relaxation rates are
observed at low temperatures under a weak magnetic field, indicating the
gapless spin excitations. Further increasing the field intensity, we observe a
spin excitation gap with the gap size proportional to the field intensity.
These phenomena suggest a very unusual strongly correlated quantum disordered
phase, and the implications for the quantum spin liquid state are further
discussed.


  Gravitational waves (GWs) provide a revolutionary tool to investigate yet
unobserved astrophysical objects. Especially the first stars, which are
believed to be more massive than present-day stars, might be indirectly
observable via the merger of their compact remnants. We develop a
self-consistent, cosmologically representative, semi-analytical model to
simulate the formation of the first stars. By extrapolating binary
stellar-evolution models at 10% solar metallicity to metal-free stars, we track
the individual systems until the coalescence of the compact remnants. We
estimate the contribution of primordial stars to the merger rate density and to
the detection rate of the Advanced Laser Interferometer Gravitational-Wave
Observatory (aLIGO). Owing to their higher masses, the remnants of primordial
stars produce strong GW signals, even if their contribution in number is
relatively small. We find a probability of $\gtrsim1\%$ that the current
detection GW150914 is of primordial origin. We estimate that aLIGO will detect
roughly 1 primordial BH-BH merger per year for the final design sensitivity,
although this rate depends sensitively on the primordial initial mass function
(IMF). Turning this around, the detection of black hole mergers with a total
binary mass of $\sim 300\,\mathrm{M}_\odot$ would enable us to constrain the
primordial IMF.


  Sequence of numbers generated by the recurrence relation based on the Collatz
conjecture is investigated. An arithmetic operation on the Collatz conjecture
is called descending operation, and ascending operation is carried out
reversely to the descending operation. Study on the 3x+1 problem against every
integer is relevant to that of descending operation against every odd. Any odds
can be generated by ascending operations against odds not multiple of 3, and
vise versa. Ascending operations against an odd not a multiple of 3 can
generate infinite number of odds in the next generation. The odds multiple of 3
are terminal numbers of sequences that no further ascending operations are
possible. Descending operations against the odds 1,5,21,.. reach 1, and
otherwise reach the odds greater than or equal to 5. No two odds except 1 in
each sequence are the same, and the each descending sequence is unique. Every
descending sequence can start from terminal numbers, and reaches ultimately the
origin of 1 as indicated by the Collatz conjecture. Sequences of odds in the
same pattern ignoring the sizes of the odds are generated by ascending
operations with the same number of doubling operations against the odds in
modular arithmetic.


  Variable density incompressible flows are governed by parabolic equations.
The artificial compressibility method makes these equations hyperbolic-type,
which means that they can be solved using techniques developed for compressible
flows, such as Godunov-type schemes. While the artificial compressibility
method is well-established, its application to variable density flows has been
largely neglected in the literature. This paper harnesses recent advances in
the wider field by applying a more robust Riemann solver and a more easily
parallelisable time discretisation to the variable density equations than
previously. We also develop a new method for calculating the pressure gradient
as part of the second-order reconstruction step. Based on a rearrangement of
the momentum equation and an exploitation of the other gradients and source
terms, the new pressure gradient calculation automatically captures the
pressure gradient discontinuity at the free surface. Benchmark tests
demonstrate the improvements gained by this robust Riemann solver and new
pressure gradient calculation.


  Motivated by the triumph and limitation of graphene for electronic
applications, atomically thin layers of group VI transition metal
dichalcogenides are attracting extensive interest as a class of graphene-like
semiconductors with a desired band-gap in the visible frequency range. The
monolayers feature a valence band spin splitting with opposite sign in the two
valleys located at corners of 1st Brillouin zone. This spin-valley coupling,
particularly pronounced in tungsten dichalcogenides, can benefit potential
spintronics and valleytronics with the important consequences of spin-valley
interplay and the suppression of spin and valley relaxations. Here we report
the first optical studies of WS2 and WSe2 monolayers and multilayers. The
efficiency of second harmonic generation shows a dramatic even-odd oscillation
with the number of layers, consistent with the presence (absence) of inversion
symmetry in even-layer (odd-layer). Photoluminescence (PL) measurements show
the crossover from an indirect band gap semiconductor at mutilayers to a
direct-gap one at monolayers. The PL spectra and first-principle calculations
consistently reveal a spin-valley coupling of 0.4 eV which suppresses
interlayer hopping and manifests as a thickness independent splitting pattern
at valence band edge near K points. This giant spin-valley coupling, together
with the valley dependent physical properties, may lead to rich possibilities
for manipulating spin and valley degrees of freedom in these atomically thin 2D
materials.


  We discuss some fundamental properties of the XXZ spin chain, which are
important in the algebraic Bethe-ansatz derivation for the multiple-integral
representations of the spin-s XXZ correlation function with an arbitrary
product of elementary matrices. For instance, we construct Hermitian conjugate
vectors in the massless regime and introduce the spin-s Hermitian elementary
matrices.


  The DArk Matter Particle Explorer (DAMPE) can measure $\gamma$-rays in the
energy range from a few GeV to about 10 TeV. The direction of each $\gamma$-ray
is reconstructed with respect to the reference system of the DAMPE payload. In
this paper, we adopt a maximum likelihood method and use the $\gamma$-ray data
centered around several bright point-like sources to measure and correct the
angular deviation from the real celestial coordinate system, the so called
``boresight alignment'' of the DAMPE payload. As a check, we also estimate the
boresight alignment for some sets of simulation data with artificial
orientation and obtain consistent results. The time-dependent boresight
alignment analysis does not show evidence for significant variation of the
parameters.


  Cross sections for elastic production of J/Psi mesons in photoproduction and
electroproduction are measured in electron proton collisions at HERA using an
integrated luminosity of 55 pb^{-1}. Results are presented for photon
virtualities Q^2 up to 80 GeV^2. The dependence on the photon-proton centre of
mass energy W_{gamma p} is analysed in the range 40 < \Wgp < 305 GeV in
photoproduction and 40 < \Wgp < 160 GeV in electroproduction. The \Wgp
dependences of the cross sections do not change significantly with Q^2 and can
be described by models based on perturbative QCD. Within such models, the data
show a high sensitivity to the gluon density of the proton in the domain of low
Bjorken x and low Q^2. Differential cross sections d\sigma/dt, where t is the
squared four-momentum transfer at the proton vertex, are measured in the range
|t|<1.2 GeV^2 as functions of \Wgp and Q^2. Effective Pomeron trajectories are
determined for photoproduction and electroproduction. The J/Psi production and
decay angular distributions are consistent with s-channel helicity
conservation. The ratio of the cross sections for longitudinally and
transversely polarised photons is measured as a function of Q^2 and is found to
be described by perturbative QCD based models.


  The purpose of this paper is to contribute to the challenge of transferring
know-how, theories and methods from design research to the design processes in
information science and technologies. More specifically, we shall consider a
domain, namely data-science, that is becoming rapidly a globally invested
research and development axis with strong imperatives for innovation given the
data deluge we are currently facing. We argue that, in order to rise to the
data-related challenges that the society is facing, data-science initiatives
should ensure a renewal of traditional research methodologies that are still
largely based on trial-error processes depending on the talent and insights of
a single (or a restricted group of) researchers. It is our claim that design
theories and methods can provide, at least to some extent, the much-needed
framework. We will use a worldwide data-science challenge organized to study a
technical problem in physics, namely the detection of Higgs boson, as a use
case to demonstrate some of the ways in which design theory and methods can
help in analyzing and shaping the innovation dynamics in such projects.


  Transformer-based foundation models have become crucial for various domains,
most notably natural language processing (NLP) or computer vision (CV). These
models are predominantly deployed on high-performance GPUs or hardwired
accelerators with highly customized, proprietary instruction sets. Until now,
limited attention has been given to RISC-V-based general-purpose platforms. In
our work, we present the first end-to-end inference results of transformer
models on an open-source many-tiny-core RISC-V platform implementing
distributed Softmax primitives and leveraging ISA extensions for SIMD
floating-point operand streaming and instruction repetition, as well as
specialized DMA engines to minimize costly main memory accesses and to tolerate
their latency. We focus on two foundational transformer topologies,
encoder-only and decoder-only models. For encoder-only models, we demonstrate a
speedup of up to 12.8x between the most optimized implementation and the
baseline version. We reach over 79% FPU utilization and 294 GFLOPS/W,
outperforming State-of-the-Art (SoA) accelerators by more than 2x utilizing the
HW platform while achieving comparable throughput per computational unit. For
decoder-only topologies, we achieve 16.1x speedup in the Non-Autoregressive
(NAR) mode and up to 35.6x speedup in the Autoregressive (AR) mode compared to
the baseline implementation. Compared to the best SoA dedicated accelerator, we
achieve 2.04x higher FPU utilization.


  Low resolution spectra of 23 stars towards the bulge globular clusters NGC
6528 and NGC 6553 are analysed. Radial velocities and atmospheric parameters
are derived in order to check their membership in the clusters. Effective
temperatures were obtained from photometric data for stars with Teff > 3800 K,
whereas for cooler stars, they were derived from equivalent widths of TiO
bands. Calibrations of W(TiO) as a function of stellar parameters based on a
grid of synthetic spectra are presented. Metallicities were derived from a
comparison of the observed spectra to a grid of synthetic spectra. The sample
comprises evolutionary stages from the Red Giant Branch to the Horizontal
Branch, with parameters in the range 3200 <= Teff <= 5000 K and -0.5 <= log g
<= 2.4. The mean metallicities obtained for NGC 6528 and NGC 6553 are [Fe/H] ~
-0.5 and -0.7, in both cases with [Mg/Fe] = +0.3; assuming the same
overabundance for the $\alpha$ elements O, Mg, Si, S, Ca and Ti, this gives
[Z/Zsolar] = -0.25 and -0.45. Membership verification by means of low
resolution spectra is a crucial step in preparing targets for high resolution
spectroscopy with 8m class telescopes.


  Perturbation Theory (PT) applied to a cosmological density field with
Gaussian initial fluctuations suggests a specific hierarchy for the correlation
functions when the variance is small. In particular quantitative predictions
have been made for the moments and the shape of the one-point probability
distribution function (PDF) of the top-hat smoothed density. In this paper we
perform a series of systematic checks of these predictions against N-body
computations both in 2D and 3D with a wide range of featureless power spectra.
In agreement with previous studies, we found that the reconstructed PDF-s work
remarkably well down to very low probabilities, even when the variance
approaches unity. Our results for 2D reproduce the features for the 3D
dynamics. In particular we found that the PT predictions are more accurate for
spectra with less power on small scales. The nonlinear regime has been explored
with various tools, PDF-s, moments and Void Probability Function (VPF). These
studies have been done with unprecedented dynamical range, especially for the
2D case, allowing in particular more robust determinations of the asymptotic
behaviour of the VPF. We have also introduced a new method to determine the
moments based on the factorial moments. Results using this method and taking
into account the finite volume effects are presented.


  We present a new algorithm to decide finiteness of matrix groups defined over
a field of positive characteristic. Together with previous work for groups in
zero characteristic, this provides the first complete solution of the
finiteness problem for finitely generated matrix groups over an arbitrary
field. We also give an algorithm to compute the order of a finite matrix group
over a function field of positive characteristic. Our MAGMA implementations of
these algorithms are publicly available.


  Situation awareness (SA) is an important constituent in human information
processing and essential in pilots' decision-making processes. Acquiring and
maintaining appropriate levels of SA is critical in aviation environments as it
affects all decisions and actions taking place in flights and air traffic
control. This paper provides an overview of recent measurement models and
approaches to establishing and enhancing SA in aviation environments. Many
aspects of SA are examined including the classification of SA techniques into
six categories, and different theoretical SA models from individual, to shared
or team, and to distributed or system levels. Quantitative and qualitative
perspectives pertaining to SA methods and issues of SA for unmanned vehicles
are also addressed. Furthermore, future research directions regarding SA
assessment approaches are raised to deal with shortcomings of the existing
state-of-the-art methods in the literature.


  This paper generalizes the piggybacking constructions for distributed storage
systems by considering various protected instances and piggybacked instances.
Analysis demonstrates that the proportion of protected instances determines the
average repair bandwidth for a systematic node. By optimizing the proportion of
protected instances, the repair ratio of generalized piggybacking codes
approaches zero instead of 50% as the number of parity check nodes tends to
infinity. Furthermore, the computational complexity for repairing a single
systematic node cost by generalized piggybacking codes is less than that of the
existing piggybacking designs.


  In order to robustly deploy object detectors across a wide range of
scenarios, they should be adaptable to shifts in the input distribution without
the need to constantly annotate new data. This has motivated research in
Unsupervised Domain Adaptation (UDA) algorithms for detection. UDA methods
learn to adapt from labeled source domains to unlabeled target domains, by
inducing alignment between detector features from source and target domains.
Yet, there is no consensus on what features to align and how to do the
alignment. In our work, we propose a framework that generalizes the different
components commonly used by UDA methods laying the ground for an in-depth
analysis of the UDA design space. Specifically, we propose a novel UDA
algorithm, ViSGA, a direct implementation of our framework, that leverages the
best design choices and introduces a simple but effective method to aggregate
features at instance-level based on visual similarity before inducing group
alignment via adversarial training. We show that both similarity-based grouping
and adversarial training allows our model to focus on coarsely aligning feature
groups, without being forced to match all instances across loosely aligned
domains. Finally, we examine the applicability of ViSGA to the setting where
labeled data are gathered from different sources. Experiments show that not
only our method outperforms previous single-source approaches on Sim2Real and
Adverse Weather, but also generalizes well to the multi-source setting.


  Coupling constants of $\rho$- and $\omega$- meson-nucleon are related with
each other by isospin relation. In present work, it is aimed at studying the
violation (if it exists) of isospin symmetry of the $\omega$-meson and to study
the temperature dependence of $\omega$-meson-nucleon-delta and
$\omega$-meson-delta coupling constants using the soft-wall model of
holographic QCD. Firstly, the coupling constants are obtained by applying the
profile functions of the vector and fermion fields to the model at a finite
temperature. The interaction Lagrangian is written, including the minimum and
magnetic interactions of vector and fermion fields which is defined in the bulk
of the 5-dimensional AdS space-time. Secondly, the temperature dependence of
the coupling constants $g_{\omega N N}(T)$, $g_{\omega \Delta \Delta}(T)$ and
$g_{\omega N \Delta}(T)$ has been plotted. Comparing $g_{\omega N N}(T)$ with
the coupling constant $g_{\rho NN}(T)$, it obtained that the isospin symmetry
of the omega meson is not violated at the finite temperature. It is also
observed that the coupling constants of the $\omega$ vector meson with baryons
decreases with increasing temperature, and this coupling constant becomes zero
near the Hawking phase transition temperature.


  We investigate fluid-to-particle conversion using the usual Cooper-Frye
approach but with more general local equilibrium distributions than the
Boltzmann or Bose/Fermi distributions typically used. Even though we study
ideal fluids (i.e., shear stress and bulk pressure are zero everywhere), we
find a suppression of elliptic flow (v2) at high transverse momenta (pT>1.5
GeV/c), relative to results obtained with the traditional Boltzmann
distributions. The non-viscous suppression shows qualitatively similar features
to the well-known shear viscous suppression of v2; for example, it increases
with pT, and it is smaller for heavier species as seen in self-consistent
kinetic theory calculations. Our results question whether all of the v2
suppression seen in the data can be attributed to viscous effects, and indicate
that shear viscosities extracted from RHIC and LHC elliptic flow data might be
overestimated.


  We present new radial-velocity measurements of HAT-P-13, a star with two
previously known companions: a transiting giant planet "b" with an orbital
period of 3 days, and a more massive object "c" on a 1.2 yr, highly eccentric
orbit. For this system, dynamical considerations would lead to constraints on
planet b's interior structure, if it could be shown that the orbits are
coplanar and apsidally locked. By modeling the Rossiter-McLaughlin effect, we
show that planet b's orbital angular momentum vector and the stellar spin
vector are well-aligned on the sky (lambda = -0.9 +/- 8.5 deg), suggesting that
the planetary orbits are also well-aligned. The refined orbital solution favors
a slightly eccentric orbit for planet b (e = 0.0142_{-0.0044}^{+0.0052}),
although it is not clear whether it is apsidally locked with c's orbit
(\Delta\omega = 48_{-38}^{+25} deg). We find a long-term trend in the star's
radial velocity and interpret it as evidence for an additional body "d", which
may be another planet or a low-mass star. The next inferior conjunction of c,
when a transit may happen, is expected on JD 2,455,315.2 +/- 1.9 (centered on
UT 17h 2010 April 28).


  We investigate the potential of low-redshift Lyman alpha (Ly$\alpha$) forest
for constraining active galactic nuclei (AGN) feedback models by analyzing the
Illustris and IllustrisTNG simulation at z=0.1. These simulations are ideal for
studying the impact of AGN feedback on the intergalactic medium (IGM) as they
share initial conditions with significant differences in the feedback
prescriptions. Both simulations reveal that the IGM is significantly impacted
by AGN feedback. Specifically, feedback is stronger in Illustris and results in
reducing cool baryon fraction to 23% relative to 39% in IllustrisTNG. However,
when comparing various statistics of Ly$\alpha$ forest such as 2D and
marginalized distributions of Doppler widths and H I column density, line
density, and flux power spectrum with real data, we find that most of these
statistics are largely insensitive to the differences in feedback models. This
lack of sensitivity arises because of the fundamental degeneracy between the
fraction of cool baryons and the H I photoionization rate ($\Gamma_{\rm HI}$)
as their product determines the optical depth of the Ly$\alpha$ forest. Since
the $\Gamma_{\rm HI}$ cannot be precisely predicted from first principles, it
needs to be treated as a nuisance parameter adjusted to match the observed
Ly$\alpha$ line density. After adjusting $\Gamma_{\rm HI}$, the distinctions in
the considered statistics essentially fade away. Only the Ly$\alpha$ flux power
spectrum at small spatial scales exhibits potentially observable differences,
although this may be specific to the relatively extreme feedback model employed
in Illustris. Without independent constraints on either $\Gamma_{\rm HI}$ or
cool baryon fraction, constraining AGN feedback with low-redshift Ly$\alpha$
forest will be very challenging.


  We study the $m$-th Gauss map in the sense of F.~L.~Zak of a projective
variety $X \subset \mathbb{P}^N$ over an algebraically closed field in any
characteristic. For all integer $m$ with $n:=\dim(X) \leq m < N$, we show that
the contact locus on $X$ of a general tangent $m$-plane is a linear variety if
the $m$-th Gauss map is separable. We also show that for smooth $X$ with $n <
N-2$, the $(n+1)$-th Gauss map is birational if it is separable, unless $X$ is
the Segre embedding $\mathbb{P}^1 \times \mathbb{P}^n \subset
\mathbb{P}^{2n-1}$. This is related to L. Ein's classification of varieties
with small dual varieties in characteristic zero.


  A new set of three-dimensional visualisations of a large-scale direct
numerical simulations (DNS) of a turbulent boundary layer is presented. The
Reynolds number ranges from $Re_\theta=180$ to 4300, based on the momentum-loss
thickness $\theta$ and the free-stream velocity $U_\infty$. The focus of the
present fluid dynamics video is on analysing the coherent vortical structures
in the boundary layer: It is clearly shown that the initial phases are
dominated by coherent so-called hairpin vortices which are characteristic
remainders of the laminar-turbulent transition at lower Reynolds numbers. At
higher $Re$ (say $Re_\theta>2000$), these structures are no longer seen as
being dominant; the coherence is clearly lost, both in the near-wall region as
well as in the outer layer of the boundary layer. Note, however, that
large-scale streaks in the streamwise velocity, which have their peak energy at
about half the boundary-layer thickness, are unambiguously observed.
  In addition to visualisation with classical three-dimensional isosurfaces,
the video is also rendered using stereoscopic views using red-cyan anaglyphs.


  The reduction of carbon emissions in the manufacturing industry holds
significant importance in achieving the national "double carbon" target.
Ensuring energy efficiency is a crucial factor to be incorporated into future
generation manufacturing systems. In this study, energy consumption is
considered in the distributed homogeneous flow shop scheduling problem
(DHFSSP). A knowledge-driven memetic algorithm (KDMA) is proposed to address
the energy-efficient DHFSSP (EEDHFSSP). KDMA incorporates a collaborative
initialization strategy to generate high-quality initial populations.
Furthermore, several algorithmic improvements including update strategy, local
search strategy, and carbon reduction strategy are employed to improve the
search performance of the algorithm. The effectiveness of KDMA in solving
EEDHFSSP is verified through extensive simulation experiments. It is evident
that KDMA outperforms many state-of-the-art algorithms across various
evaluation aspects.


  We perform a detailed analysis of Galilean field theories, starting with free
theories and then interacting theories. We consider non-relativistic versions
of massless scalar and Dirac field theories before we go on to review our
previous construction of Galilean Electrodynamics and Galilean Yang-Mills
theory. We show that in all these cases, the field theories exhibit
non-relativistic conformal structure (in appropriate dimensions). The
surprising aspect of the analysis is that the non-relativistic conformal
structure exhibited by these theories, unlike relativistic conformal
invariance, becomes infinite dimensional even in spacetime dimensions greater
than two. We then couple matter with Galilean gauge theories and show that
there is a myriad of different sectors that arise in the non-relativistic limit
from the parent relativistic theories. In every case, if the parent
relativistic theory exhibited conformal invariance, we find an infinitely
enhanced Galilean conformal invariance in the non-relativistic case. This leads
us to suggest that infinite enhancement of symmetries in the non-relativistic
limit is a generic feature of conformal field theories in any dimension.


  We apply Ohi's criterion for faithfully flatness of extensions of commutative
rings to prove that any \'etale extension $k[Y_1, \ldots, Y_n]\subseteq k[X_1,
\ldots, X_n]$ of polynomial rings (each in $n$ indeterminates) over a
commutative ring $k$ is faithfully flat. In particular, if $k$ is an
algebraically closed field then any \'etale polynomial map $k^{n} \to k^{n}$ is
surjective.


  We construct efficient or query efficient quantum property testers for two
existential group properties which have exponential query complexity both for
their decision problem in the quantum and for their testing problem in the
classical model of computing. These are periodicity in groups and the common
coset range property of two functions having identical ranges within each coset
of some normal subgroup. Our periodicity tester is efficient in Abelian groups
and generalizes, in several aspects, previous periodicity testers. This is
achieved by introducing a technique refining the majority correction process
widely used for proving robustness of algebraic properties. The periodicity
tester in non-Abelian groups and the common coset range tester are query
efficient.


  This paper presents a novel adaptive control methodology for uncertain
systems with time-varying unknown parameters and time-varying bounded
disturbance. The adaptive controller ensures uniformly bounded transient and
asymptotic tracking for system's both signals, input and output,
simultaneously. The performance bounds can be systematically improved by
increasing the adaptation gain. Simulations of a robotic arm with time-varying
friction verify the theoretical findings.


  The main object of the present paper is a numerical criterion determining
when a Weil divisor of a $\Q$--factorial complete toric variety admits a
positive multiple Cartier divisor which is either numerically effective (nef)
or ample. It is a consequence of $\Z$--linear interpretation of Gale duality
and se\-con\-dary fan as developed in several previous papers of us. As a
byproduct we get a computation of the Cartier index of a Weil divisor and a
numerical characterization of weak $\Q$--Fano, $\Q$--Fano, Gorenstein, weak
Fano and Fano toric varieties. Several examples are then given and studied.
\keywords{$\Q$--factorial complete toric variety \and ample divisor \and nef
divisor \and $\Z$-liner Gale duality \and secondary fan \and ampleness
criterion \and Cartier index \and $\Q$-Fano toric variety.


  Secret image sharing deals with splitting confidential images into several
shares and the original image can be reconstructed from the qualified subset of
the shares. Secret sharing schemes are used in transmission and storage of
private medical images and military secrets. Increased confidentiality and
availability are the major achievements. We propose an efficient (2, 2) scheme
and (2, 3) scheme for secret image sharing. The scheme is lossless and also the
share size is same as the secret size. The sharing and revealing phase uses
simple modular arithmetic which can be very easily implemented. Experimental
results on Binary and Gray scale images show that the proposed scheme is secure
and efficient.


  We show how the Schr\"{o}dinger equation for the hydrogen atom in two
dimensions gives rise to an algebraic family of Harish-Chandra pairs that
codifies hidden symmetries. The hidden symmetries vary continuously between
$SO(3)$, $SO(2,1)$ and the Euclidean group $O(2)\ltimes \mathbb{R}^2$. We show
that solutions of the Schr\"{o}dinger equation may be organized into an
algebraic family of Harish-Chandra modules. Furthermore, we use Jantzen
filtration techniques to algebraically recover the spectrum of the
Schr\"{o}dinger operator. This is a first application to physics of the
algebraic families of Harish-Chandra pairs and modules developed in the work of
Bernstein et al. [Int. Math. Res. Notices, rny147 (2018); rny146 (2018)].


  Quantum information-theoretic approach has been identified as a way to
understand the foundations of quantum mechanics as early as 1950 due to
Shannon. However there hasn't been enough advancement or rigorous development
of the subject. In the following paper we try to find relationship between a
general quantum mechanical observable and it von Neumann entropy. We find that
the expectation values and the uncertainties of the observables have bounds
which depend on the entropy. The results also show that von Neumann entropy is
not just the uncertainty of the state but also encompasses the information
about expectation values and uncertainties of any observable which depends on
the observers choice for a particular measurement. Also a reverse uncertainty
relation is derived for n quantum mechanical observables.


  In a joint experimental and theoretical endeavour, photoionization of
metastable C4+(1s 2s 3S1) ions via intermediate levels with hollow,
double-K-vacancy configurations 2s2p, 2s3p, 2p3s, 2p3d, 2s4p, 2p4s and 2p4d has
been investigated. High-resolution photon-ion merged-beams measurements were
carried out with the resolving power reaching up to 25,000 which is sufficient
to separate the leading fine-structure components of the 2s2p 3P term.
Many-body perturbation theory was employed to determine level-to-level cross
sections for K-shell excitation with subsequent autoionization. The resonance
energies were calculated with inclusion of electron correlation and radiative
contributions. Their uncertainties are estimated to be below $\pm$1 meV.
Detailed balance confirms the present photoionization cross-section results by
comparison with previous dielectronic-recombination measurements. The high
accuracy of the theoretical transition energies together with the present
experimental results qualify photoabsorption resonances in heliumlike ions as
new, greatly improved energy-reference standards at synchrotron radiation
facilities.


  Existing 3D human pose estimators face challenges in adapting to new datasets
due to the lack of 2D-3D pose pairs in training sets. To overcome this issue,
we propose \textit{Multi-Hypothesis \textbf{P}ose \textbf{Syn}thesis
\textbf{D}omain \textbf{A}daptation} (\textbf{PoSynDA}) framework to bridge
this data disparity gap in target domain. Typically, PoSynDA uses a
diffusion-inspired structure to simulate 3D pose distribution in the target
domain. By incorporating a multi-hypothesis network, PoSynDA generates diverse
pose hypotheses and aligns them with the target domain. To do this, it first
utilizes target-specific source augmentation to obtain the target domain
distribution data from the source domain by decoupling the scale and position
parameters. The process is then further refined through the teacher-student
paradigm and low-rank adaptation. With extensive comparison of benchmarks such
as Human3.6M and MPI-INF-3DHP, PoSynDA demonstrates competitive performance,
even comparable to the target-trained MixSTE model\cite{zhang2022mixste}. This
work paves the way for the practical application of 3D human pose estimation in
unseen domains. The code is available at https://github.com/hbing-l/PoSynDA.


  In a generic switching problem, a switching pattern consists of a one-to-one
mapping from a set of inputs to a set of outputs (i.e., a permutation). We
propose and investigate a wireless switching framework in which a multi-antenna
relay is responsible for switching traffic among a set of $N$ stations. We
refer to such a relay as a MIMO switch. With beamforming and linear detection,
the MIMO switch controls which stations are connected to which stations. Each
beamforming matrix realizes a permutation pattern among the stations. We refer
to the corresponding permutation matrix as a switch matrix. By scheduling a set
of different switch matrices, full connectivity among the stations can be
established. In this paper, we focus on "fair switching" in which equal amounts
of traffic are to be delivered for all $N(N-1)$ ordered pairs of stations. In
particular, we investigate how the system throughput can be maximized. In
general, for large $N$ the number of possible switch matrices (i.e.,
permutations) is huge, making the scheduling problem combinatorially
challenging. We show that for N=4 and 5, only a subset of $N-1$ switch matrices
need to be considered in the scheduling problem to achieve good throughput. We
conjecture that this will be the case for large $N$ as well. This conjecture,
if valid, implies that for practical purposes, fair-switching scheduling is not
an intractable problem.


  The minimal supersymmetric extension of the standard model (MSSM) is extended
to the $U(1)_X$SSM, whose local gauge group is $SU(3)_C \times SU(2)_L \times
U(1)_Y \times U(1)_X$. To obtain the $U(1)_X$SSM, we add the new superfields to
the MSSM, namely: three Higgs singlets $\hat{\eta},~\hat{\bar{\eta}},~\hat{S}$
and right-handed neutrinos $\hat{\nu}_i$. The CP violating effects are
considered to study the lepton electric dipole moment(EDM) in $U(1)_X$SSM. The
CP violating phases in $U(1)_X$SSM are more than those in the standard
model(SM). In this model, some new parameters $(\theta_S, \theta_{BB^{\prime}},
\theta_{BL})$ as CP violating phases are considered, so there are new
contributions to lepton EDMs. It is conducive to exploring the source of CP
violation and probing new physical beyond SM.


  In close eclipsing binaries, measurements of the variations in binary's
eclipse timing may be used to infer information about the existence of
circumbinary objects. To determine the possibility of the detection of such
variations with CoRoT and Kepler space telescopes, we have carried out an
extensive study of the dynamics of a binary star system with a circumbinary
planet, and calculated its eclipse timing variations (ETV) for different values
of the mass-ratio and orbital elements of the binary and the perturbing body.
Here, we present the results of our study and assess the detectability of the
planet by comparing the resulting values of ETVs with the temporal sensitivity
of CoRoT and Kepler. Results point to extended regions in the parameter-space
where the perturbation of a planet may become large enough to create measurable
variations in the eclipse timing of the secondary star. Many of these
variations point to potentially detectable ETVs and the possible existence of
Jovian-type planets.


  This paper reports a novel method for supervised machine learning based on
the mathematical formalism that supports quantum mechanics. The method uses
projective quantum measurement as a way of building a prediction function.
Specifically, the relationship between input and output variables is
represented as the state of a bipartite quantum system. The state is estimated
from training samples through an averaging process that produces a density
matrix. Prediction of the label for a new sample is made by performing a
projective measurement on the bipartite system with an operator, prepared from
the new input sample, and applying a partial trace to obtain the state of the
subsystem representing the output. The method can be seen as a generalization
of Bayesian inference classification and as a type of kernel-based learning
method. One remarkable characteristic of the method is that it does not require
learning any parameters through optimization. We illustrate the method with
different 2-D classification benchmark problems and different quantum
information encodings.


  As a by-product of our work on super Pl\"{u}cker embedding, we came to the
notion of a weighted projective superspace $P_{+1,-1}(V\oplus W)$ with weights
$+1,-1$. The construction is not in itself super and makes sense in ordinary
(purely even) framework. Unlike the familiar weighted projective spaces with
positive weights, the (super)space $P_{+1,-1}(V\oplus W)$ is a smooth
(super)manifold. We describe its structure and show that it possesses an analog
of the Fubini--Study form.


  Chinese word segmentation (CWS) is an important task for Chinese NLP.
Recently, many neural network based methods have been proposed for CWS.
However, these methods require a large number of labeled sentences for model
training, and usually cannot utilize the useful information in Chinese
dictionary. In this paper, we propose two methods to exploit the dictionary
information for CWS. The first one is based on pseudo labeled data generation,
and the second one is based on multi-task learning. The experimental results on
two benchmark datasets validate that our approach can effectively improve the
performance of Chinese word segmentation, especially when training data is
insufficient.


  Graphite, with many industrial applications, is one of the widely
sought-after allotropes of carbon. The sp2 hybridized and thermodynamically
stable form of carbon forms a layered structure with strong in-plane carbon
bonds and weak inter-layer van der Waals bonding. Graphite is also a
high-temperature ceramic, and shaping them into complex geometries is
challenging, given its limited sintering behavior even at high temperatures.
Although the geometric design of the graphite structure in many of the
applications could dictate its precision performance, conventional synthesis
methods for formulating complex geometric graphite shapes are limited due to
the intrinsic brittleness and difficulties of high-temperature processing.
Here, we report the development of colloidal graphite ink from commercial
graphite powders with reproducible rheological behavior that allows the
fabrication of any complex architectures with tunable geometry and
directionality via 3D printing at room temperature. The method is enabled via
using small amounts of clay, another layered material, as an additive, allowing
the proper design of the graphene ink and subsequent binding of graphite
platelets during printing. Sheared layers of clay are easily able to flow,
adapt, and interface with graphite layers forming strong binding between the
layers and between particles that make the larger structures. The direct ink
printing of complex 3D architectures of graphite without further heat
treatments could lead to easy shape engineering and related applications of
graphite at various length scales, including complex graphite molds or
crucibles. The 3D printed complex graphitic structures exhibit excellent
thermal, electrical, and mechanical properties, and the clay additive does not
seem to alter these properties due to the excellent inter-layer dispersion and
mixing within the graphite material.


  Inspired by the seminal work of Hyland, Plotkin, and Power on the combination
of algebraic computational effects via sum and tensor, we develop an analogous
theory for the combination of quantitative algebraic effects. Quantitative
algebraic effects are monadic computational effects on categories of metric
spaces, which, moreover, have an algebraic presentation in the form of
quantitative equational theories, a logical framework introduced by Mardare,
Panangaden, and Plotkin that generalises equational logic to account for a
concept of approximate equality. As our main result, we show that the sum and
tensor of two quantitative equational theories correspond to the categorical
sum (i.e., coproduct) and tensor, respectively, of their effects qua monads. We
further give a theory of quantitative effect transformers based on these two
operations, essentially providing quantitative analogues to the following monad
transformers due to Moggi: exception, resumption, reader, and writer
transformers. Finally, as an application, we provide the first quantitative
algebraic axiomatizations to the following coalgebraic structures: Markov
processes, labelled Markov processes, Mealy machines, and Markov decision
processes, each endowed with their respective bisimilarity metrics. Apart from
the intrinsic interest in these axiomatizations, it is pleasing they have been
obtained as the composition, via sum and tensor, of simpler quantitative
equational theories.


  Gradient-based temporal difference (GTD) algorithms are widely used in
off-policy learning scenarios. Among them, the two time-scale TD with gradient
correction (TDC) algorithm has been shown to have superior performance. In
contrast to previous studies that characterized the non-asymptotic convergence
rate of TDC only under identical and independently distributed (i.i.d.) data
samples, we provide the first non-asymptotic convergence analysis for two
time-scale TDC under a non-i.i.d.\ Markovian sample path and linear function
approximation. We show that the two time-scale TDC can converge as fast as
O(log t/(t^(2/3))) under diminishing stepsize, and can converge exponentially
fast under constant stepsize, but at the cost of a non-vanishing error. We
further propose a TDC algorithm with blockwisely diminishing stepsize, and show
that it asymptotically converges with an arbitrarily small error at a
blockwisely linear convergence rate. Our experiments demonstrate that such an
algorithm converges as fast as TDC under constant stepsize, and still enjoys
comparable accuracy as TDC under diminishing stepsize.


  A brief review is given of the status of nucleon spin structure functions as
determined from polarized deep inelastic lepton-nucleon scatttering, including
current outstanding problems. The characteristics of a polarized HERA collider,
some of the particle physics topics it could address, and the accelerator
physics challenges it must meet are discussed.


  Recent analysis of scientific data from Cassini and earth-based observations
gave evidence for a global ocean under a surrounding solid ice shell on
Saturn's moon Enceladus. Images of Enceladus' South Pole showed several
fissures in the ice shell with plumes constantly exhausting frozen water
particles, building up the E-Ring, one of the outer rings of Saturn. In this
southern region of Enceladus, the ice shell is considered to be as thin as 2
km, about an order of magnitude thinner than on the rest of the moon. Under the
ice shell, there is a global ocean consisting of liquid water. Scientists are
discussing different approaches the possibilities of taking samples of water,
i.e. by melting through the ice using a melting probe. FH Aachen UAS developed
a prototype of maneuverable melting probe which can navigate through the ice
that has already been tested successfully in a terrestrial environment. This
means no atmosphere and or ambient pressure, low ice temperatures of around 100
to 150 K (near the South Pole) and a very low gravity of 0.114 m/s$^2$ or 1100
{\mu}g. Two of these influencing measures are about to be investigated at FH
Aachen UAS in 2017, low ice temperature and low ambient pressure below the
triple point of water. Low gravity cannot be easily simulated inside a large
experiment chamber, though. Numerical simulations of the melting process at
RWTH Aachen however are showing a gravity dependence of melting behavior.
Considering this aspect, VIPER provides a link between large-scale experimental
simulations at FH Aachen UAS and numerical simulations at RWTH Aachen. To
analyze the melting process, about 90 seconds of experiment time in reduced
gravity and low ambient pressure is provided by the REXUS rocket.


  We prove that the class of convex-cocompact Kleinian groups is
quasi-isometrically rigid. We also establish that a word hyperbolic group with
a planar boundary different from the sphere is virtually a convex-cocompact
Kleinian group provided that its boundary has Ahlfors regular conformal
dimension strictly less than $2$ or if it acts geometrically on a CAT(0) cube
complex.


  Electronic spectra of typical single FeSe layer superconductors obtained from
ARPES data reveal several puzzles: what is the origin of shallow and the so
called "replica" bands near M-point and why the hole-like Fermi surfaces near
$\Gamma$-point are absent. Our extensive LDA+DMFT calculations show that
correlation effects on Fe-3d states can almost quantitatively reproduce rather
complicated band structure, which is observed in ARPES, in close vicinity of
the Fermi level for FeSe/STO and K$_x$Fe$_{2-y}$Se$_{2}$. Rather unusual
shallow electron-like bands around the M(X)-point in the Brillouin zone are
well reproduced. However, in FeSe/STO correlation effects are apparently
insufficient to eliminate the hole-like Fermi surfaces around the
$\Gamma$-point, which are not observed in most ARPES experiments. Detailed
analysis of the theoretical and experimental quasiparticle bands with respect
to their origin and orbital composition is performed. It is shown that for
FeSe/STO system the LDA calculated Fe-3d$_{xy}$ band, renormalized by
electronic correlations within DMFT gives the quasiparticle band almost exactly
in the energy region of the experimentally observed "replica" quasiparticle
band at the M-point. For the case of K$_x$Fe$_{2-y}$Se$_{2}$ most bands
observed in ARPES can also be understood as correlation renormalized Fe-3d LDA
calculated bands, with overall semi-quantitative agreement with our LDA+DMFT
calculations. Thus the shallow bands near the M-point are common feature for
FeSe-based systems, not just FeSe/STO. We also present some simple estimates of
"forward scattering" electron-optical phonon interaction at FeSe/STO interface,
showing that it is apparently irrelevant for the formation of "replica" band in
this system and significant increase of superconducting $T_c$.


  Dirichlet Process Mixture Models (DPMMs) are widely used to address
clustering problems. Their main advantage lies in their ability to
automatically estimate the number of clusters during the inference process
through the Bayesian non-parametric framework. However, the inference becomes
considerably slow as the dataset size increases. This paper proposes a new
distributed Markov Chain Monte Carlo (MCMC) inference method for DPMMs (DisCGS)
using sufficient statistics. Our approach uses the collapsed Gibbs sampler and
is specifically designed to work on distributed data across independent and
heterogeneous machines, which habilitates its use in horizontal federated
learning. Our method achieves highly promising results and notable scalability.
For instance, with a dataset of 100K data points, the centralized algorithm
requires approximately 12 hours to complete 100 iterations while our approach
achieves the same number of iterations in just 3 minutes, reducing the
execution time by a factor of 200 without compromising clustering performance.
The code source is publicly available at
https://github.com/redakhoufache/DisCGS.


  Intent discovery is the task of inferring latent intents from a set of
unlabeled utterances, and is a useful step towards the efficient creation of
new conversational agents. We show that recent competitive methods in intent
discovery can be outperformed by clustering utterances based on abstractive
summaries, i.e., "labels", that retain the core elements while removing
non-essential information. We contribute the IDAS approach, which collects a
set of descriptive utterance labels by prompting a Large Language Model,
starting from a well-chosen seed set of prototypical utterances, to bootstrap
an In-Context Learning procedure to generate labels for non-prototypical
utterances. The utterances and their resulting noisy labels are then encoded by
a frozen pre-trained encoder, and subsequently clustered to recover the latent
intents. For the unsupervised task (without any intent labels) IDAS outperforms
the state-of-the-art by up to +7.42% in standard cluster metrics for the
Banking, StackOverflow, and Transport datasets. For the semi-supervised task
(with labels for a subset of intents) IDAS surpasses 2 recent methods on the
CLINC benchmark without even using labeled data.


  We report the results obtained from XMM-Newton observations of the
TeV-detected supernova remnant (SNR) HESS J1534-571. We focus on the nature of
the cosmic-ray particle content in the SNR, which is revealed by its
$\gamma$-ray emission. No signatures of X-ray synchrotron emission were
detected from the SNR. This is consistent with earlier results obtained with
Suzaku from other regions of the object. A joint modeling of the XMM-Newton and
Suzaku spectra yields an upper limit for the total X-ray flux from the SNR area
of $\sim$ 5.62$ \times 10^{-13} \ \mathrm{erg\ cm^{-2}\ s^{-1}}$ (95% c.l.) in
the energy band of 2-10 keV, for an assumed photon index of 2.0. On the other
hand, we do find evidence in the XMM-Newton data for a line-like emission
feature at 6.4 keV from localized regions, again confirming earlier Suzaku
measurements. We discuss the findings in the context of the origin of the
observed $\gamma$-ray emission. Although neither hadronic nor leptonic
scenarios can be fully ruled out, the observed line emission can be interpreted
as the result of interactions between lower energy ($\sim$ MeV) cosmic-ray
protons with high gas density regions in and around HESS J1534-571, and thus
potentially be associated with particles accelerated in the SNR.


  We investigate the propagation of electromagnetic waves through a static
wormhole. It is shown that the problem can be reduced to a one-dimensional
Schr\"odinger-like equation with a barrier-type potential. Using numerical
methods, we calculate the transmission coefficient as a function of the energy.
We also discuss the polarization of the outgoing radiation due to this
gravitational scattering.