  Numerous decision-making tasks require estimating causal effects under
interventions on different parts of a system. As practitioners consider using
large language models (LLMs) to automate decisions, studying their causal
reasoning capabilities becomes crucial. A recent line of work evaluates LLMs
ability to retrieve commonsense causal facts, but these evaluations do not
sufficiently assess how LLMs reason about interventions. Motivated by the role
that interventions play in causal inference, in this paper, we conduct
empirical analyses to evaluate whether LLMs can accurately update their
knowledge of a data-generating process in response to an intervention. We
create benchmarks that span diverse causal graphs (e.g., confounding,
mediation) and variable types, and enable a study of intervention-based
reasoning. These benchmarks allow us to isolate the ability of LLMs to
accurately predict changes resulting from their ability to memorize facts or
find other shortcuts. Our analysis on four LLMs highlights that while GPT- 4
models show promising accuracy at predicting the intervention effects, they
remain sensitive to distracting factors in the prompts.


  We provide cross sections and Maxwell rate coefficients for reactive
collisions of slow electrons with BeH$^+$ ions on all the eighteen vibrational
levels ($X{^{1}\Sigma^{+}},v_{i}^{+}=0,1,2,\dots,17$) using a Multichannel
Quantum Defect Theory (MQDT) - type approach. These data on dissociative
recombination, vibrational excitation and vibrational de-excitation are
relevant for magnetic confinement fusion edge plasma modelling and
spectroscopy, in devices with beryllium based main chamber materials, such as
the International Thermonuclear Experimental Reactor (ITER) and the Joint
European Torus (JET). Our results are presented in graphical form and as fitted
analytical functions, the parameters of which are organized in tables.


  We derive and analyze monotone difference-quadrature schemes for Bellman
equations of controlled Levy (jump-diffusion) processes. These equations are
fully non-linear, degenerate parabolic integro-PDEs interpreted in the sense of
viscosity solutions. We propose new ``direct'' discretizations of the non-local
part of the equation that give rise to monotone schemes capable of handling
singular Levy measures. Furthermore, we develop a new general theory for
deriving error estimates for approximate solutions of integro-PDEs, which
thereafter is applied to the proposed difference-quadrature schemes.


  We review some aspects of the fermionic interpretation of the two-dimensional
Ising model. The use is made of the notion of the integral over the
anticommuting Grassmann variables. For simple and more complicated 2D Ising
lattices, the partition function can be expressed as a fermionic Gaussian
integral. Equivalently, the 2D Ising model can be reformulated as a
free-fermion theory on a lattice. For regular lattices, the analytic solution
then readily follows by passing to the momentum space for fermions. We also
comment on the effective field-theoretical (continuum-limit) fermionic
formulations for the 2D Ising models near the critical point.


  Learning goal conditioned control in the real world is a challenging open
problem in robotics. Reinforcement learning systems have the potential to learn
autonomously via trial-and-error, but in practice the costs of manual reward
design, ensuring safe exploration, and hyperparameter tuning are often enough
to preclude real world deployment. Imitation learning approaches, on the other
hand, offer a simple way to learn control in the real world, but typically
require costly curated demonstration data and lack a mechanism for continuous
improvement. Recently, iterative imitation techniques have been shown to learn
goal directed control from undirected demonstration data, and improve
continuously via self-supervised goal reaching, but results thus far have been
limited to simulated environments. In this work, we present evidence that
iterative imitation learning can scale to goal-directed behavior on a real
robot in a dynamic setting: high speed, precision table tennis (e.g. "land the
ball on this particular target"). We find that this approach offers a
straightforward way to do continuous on-robot learning, without complexities
such as reward design or sim-to-real transfer. It is also scalable -- sample
efficient enough to train on a physical robot in just a few hours. In real
world evaluations, we find that the resulting policy can perform on par or
better than amateur humans (with players sampled randomly from a robotics lab)
at the task of returning the ball to specific targets on the table. Finally, we
analyze the effect of an initial undirected bootstrap dataset size on
performance, finding that a modest amount of unstructured demonstration data
provided up-front drastically speeds up the convergence of a general purpose
goal-reaching policy. See https://sites.google.com/view/goals-eye for videos.


  We prove the existence of at least three solutions for a weighted
$p$-Laplacian operator involving Dirichlet boundary condition in a weighted
Sobolev space. The main tool we use here is a three solution theorem in
reflexive Banach spaces due to G. Bonanno and B. Ricceri.


  Blockchain technology is an interlinked systematic chain of blocks that
contains transaction history and other user data. It works under the principle
of decentralized distributed digital ledger. This technology enables
cryptographically secure and anonymous financial transactions among the user
nodes of the network enabling the transactions to be validated and approved by
all the users in a transparent environment. It is a revolutionary technology
that earned its emerging popularity through the usage of digital
cryptocurrencies. Even though Blockchain holds a promising scope of development
in the online transaction system, it is prone to several security and
vulnerability issues. In this paper, blockchain methodology, its applications,
and security issues are discussed which might shed some light on blockchain
enthusiasts and researchers.


  A special cubic fourfold is a smooth hypersurface of degree three and
dimension four that contains a surface not homologous to a complete
intersection. Special cubic fourfolds give rise to a countable family of
Noether-Lefschetz divisors C_d in the moduli space C of smooth cubic fourfolds.
These divisors are irreducible 19-dimensional varieties birational to certain
orthogonal modular varieties. We use the "low-weight cusp form trick" of
Gritsenko, Hulek, and Sankaran to obtain information about the Kodaira
dimension of C_d. For example, if d = 6n + 2, then we show that C_d is of
general type for n > 18, n not in {20,21,25}, it has nonnegative Kodaira
dimension if n > 13 and if n is not equal to 15. In combination with prior work
of Hassett, Lai, and Nuer, our investigation leaves only 20 values of d for
which no information on the Kodaira dimension of C_d is known. We discuss some
questions pertaining to the arithmetic of K3 surfaces raised by our results.


  We propose a mechanism of reheating after inflation in multi-throat scenarios
of warped extra dimensions. Validity of an effective field theory on the
standard model (SM) brane requires that the position of the SM brane during
inflation be different from the position after inflation. The latter is
supposed to be near the tip of the SM throat but the former is not. After
inflation, when the Hubble expansion rate becomes sufficiently low, the SM
brane starts moving towards the tip and eventually oscillates. The SM fields
are excited by the brane motion and the universe is reheated. Since interaction
between the brane position modulus and the SM fields is suppressed only by the
local string scale, the modulus effectively decays into the SM fields.


  In this paper we calculate Ozsv\'ath-Szab\'o Floer homology group HF+ for
Brieskorn spheres \Sigma(2,2n + 1,4n + 3).


  We consider an excitatory random network of leaky integrate-and-fire pulse
coupled neurons. The neurons are connected as in a directed Erd\"os-Renyi graph
with average connectivity $<k>$ scaling as a power law with the number of
neurons in the network. The scaling is controlled by a parameter $\gamma$,
which allows to pass from massively connected to sparse networks and therefore
to modify the topology of the system. At a macroscopic level we observe two
distinct dynamical phases: an Asynchronous State (AS) corresponding to a
desynchronized dynamics of the neurons and a Partial Synchronization (PS)
regime associated with a coherent periodic activity of the network. At low
connectivity the system is in an AS, while PS emerges above a certain critical
average connectivity $<k>_c$. For sufficiently large networks, $<k>_c$
saturates to a constant value suggesting that a minimal average connectivity is
sufficient to observe coherent activity in systems of any size irrespectively
of the kind of considered network: sparse or massively connected. However, this
value depends on the nature of the synapses: reliable or unreliable. For
unreliable synapses the critical value required to observe the onset of
macroscopic behaviors is noticeably smaller than for reliable synaptic
transmission. Due to the disorder present in the system, for finite number of
neurons we have inhomogeneities in the neuronal behaviors, inducing a weak form
of chaos, which vanishes in the thermodynamic limit. In such a limit the
disordered systems exhibit regular (non chaotic) dynamics and their properties
correspond to that of a homogeneous fully connected network for any
$\gamma$-value. Apart for the peculiar exception of sparse networks, which
remain intrinsically inhomogeneous at any system size.


  Exchanging hydrophobic alkyl-based side chains to hydrophilic glycol-based
side chains is a widely adopted method for improving mixed-transport device
performance, despite the impact on solid state packing and polymer-electrolyte
interactions being poorly understood. Presented here is a Molecular Dynamics
(MD) force field for modelling alkoxylated and glycolated polythiophenes. The
force field is validated against known packing motifs for their monomer
crystals. MD simulations, coupled with X-ray Diffraction (XRD), show that
alkoxylated polythiophenes will pack with a `tilted stack' and straight
interdigitating side chains, whilst their glycolated counterpart will pack with
a `deflected stack' and an s-bend side chain configuration. MD simulations
reveal water penetration pathways into the alkoxylated and glycolated crystals
- through the {\pi}-stack and through the lamellar stack respectively. Finally,
the two distinct ways tri-ethylene glycol polymers can bind to cations are
revealed, showing the formation of a meta-stable single bound state, or an
energetically deep double bound state, both with a strong side chain length
dependance. The minimum energy pathways for the formation of the chelates are
identified, showing the physical process through which cations can bind to one
or two side chains of a glycolated polythiophene, with consequences for ion
transport in bithiophene semiconductors.


  We discuss opportunities that may arise from subjecting high-multiplicity
events in relativistic heavy ion collisions to an analysis similar to the one
used in cosmology for the study of fluctuations of the Cosmic Microwave
Background (CMB). To this end, we discuss examples of how pertinent features of
heavy ion collisions including global characteristics, signatures of collective
flow and event-wise fluctuations are visually represented in a Mollweide
projection commonly used in CMB analysis, and how they are statistically
analyzed in an expansion over spherical harmonic functions. If applied to the
characterization of purely azimuthal dependent phenomena such as collective
flow, the expansion coefficients of spherical harmonics are seen to contain
redundancies compared to the set of harmonic flow coefficients commonly used in
heavy ion collisions. Our exploratory study indicates, however, that these
redundancies may offer novel opportunities for a detailed characterization of
those event-wise fluctuations that remain after subtraction of the dominant
collective flow signatures. By construction, the proposed approach allows also
for the characterization of more complex collective phenomena like higher-order
flow and other sources of fluctuations, and it may be extended to the
characterization of phenomena of non-collective origin such as jets.


  We report 850 micron Submillimeter Array (SMA) observations of four
gravitationally lensed submillimeter galaxies (SMGs), A370-2, A2390-1, A2390-3
and A2390-4, which were originally discovered with the Submillimeter
Common-User Bolometer Array (SCUBA). Our SMA detection of A370-2 with a
submillimeter flux of 7.95 +/- 0.60 mJy unambiguously identifies the
counterparts to this source at optical and radio wavelengths. A2390-1 is an
ultraluminous infrared galaxies with a submillimeter flux of 5.55 +/- 0.92 mJy
and a redshift of 1.8 +/- 0.2 computed from submillimeter/radio flux ratio
analysis. We resolve A2390-3 into two components, A2390-3a and A2390-3b, with
fluxes of 3.15 +/- 0.63 mJy and 1.92 +/- 0.60 mJy, respectively. The structure
of the system could be consistent with morphological distortion by
gravitational lensing. The lack of counterparts in the optical and infrared
indicates a heavily dust-enshrouded nature, and a non-detection in the radio
implies that these two sources probably lie at z > 4.7, which would make them
among the most distant SMGs known to date. Our non-detection of A2390-4
suggests either that there are multiple fainter submillimeter sources within
the SCUBA beam or that the SCUBA detection may have been false. Our precise
positions allow us to determine accurate amplifications and fluxes for all our
detected sources. Our new results give a shallower power-law fit (-1.10) to the
faint-end 850 micron cumulative number counts than previous work. We emphasize
the need for high-resolution observations of single-dish detected SMGs in order
to measure accurately the faint end of the 850 micron counts.


  We present an effective model for a subwavelength periodically patterned
metallic layer, its cavities being filled with a nonlinear dielectric material,
which accounts for both the linear and second order behavior. The effective non
linear susceptibility for the homogenized layer is driven by the nonlinearity
of the dielectric material and by the geometrical parameters, thus leading to
much higher susceptibility than existing materials. This leads to a huge
enhancement of non linear processes when used together with resonances.
Furthermore, multiple resonances are taking place in the metallic cavities, and
we investigate the mode matching situations for frequency conversion processes
and show how it enhances further their efficiency.


  We prove that the equilibrium fluctuations of the symmetric simple exclusion
process in contact with slow boundaries is given by an Ornstein-Uhlenbeck
process with Dirichlet, Robin or Neumann boundary conditions depending on the
range of the parameter that rules the slowness of the boundaries.


  A renowned information-theoretic formula by Shannon expresses the mutual
information rate of a white Gaussian channel with a stationary Gaussian input
as an integral of a simple function of the power spectral density of the
channel input. We give in this paper a rigorous yet elementary proof of this
classical formula. As opposed to all the conventional approaches, which either
rely on heavy mathematical machineries or have to resort to some "external"
results, our proof, which hinges on a recently proven sampling theorem, is
elementary and self-contained, only using some well-known facts from basic
calculus and matrix theory.


  In the present paper we consider a 3-dimensional differentiable manifold $M$
equipped with a Riemannian metric $g$ and an endomorphism $Q$, whose third
power is the identity and $Q$ acts as an isometry on $g$. Both structures $g$
and $Q$ determine an associated metric $f$ on $(M, g, Q)$. The metric $f$ is
necessary indefinite and it defines isotropic vectors in the tangent space
$T_{p}M$ at an arbitrary point $p$ on $M$.
  The physical forces are represented by vector fields. We investigate physical
forces whose vectors are in $T_{p}M$ on $(M, g, Q)$. Moreover, these vectors
are isotropic and they act along isotropic curves. We study the physical work
done by such forces.


  Conductivity mechanism in the regime of the intrinsic Josephson effect in
layered superconductors with singlet d-wave pairing is studied theoretically.
The cases of coherent and incoherent interlayer tunneling of electrons are
considered. The theory with coherent tunneling describes main qualitative
features of the effect observed in highly anisotropic high-T$_c$
superconductors at voltages smaller than the amplitude of the superconducting
gap, mechanism of the resistivity being related to excitations of the
quasiparticles via the d-wave gap. At voltages of the order of the gap
amplitude and larger, the I-V curves are better described by the incoherent
tunneling. Interaction of the Josephson junctions formed by the superconducting
layers due to charging effects is shown to be small.


  With the advancement of audio generation, generative models can produce
highly realistic audios. However, the proliferation of deepfake general audio
can pose negative consequences. Therefore, we propose a new task, deepfake
general audio detection, which aims to identify whether audio content is
manipulated and to locate deepfake regions. Leveraging an automated
manipulation pipeline, a dataset named FakeSound for deepfake general audio
detection is proposed, and samples can be viewed on website
https://FakeSoundData.github.io. The average binary accuracy of humans on all
test sets is consistently below 0.6, which indicates the difficulty humans face
in discerning deepfake audio and affirms the efficacy of the FakeSound dataset.
A deepfake detection model utilizing a general audio pre-trained model is
proposed as a benchmark system. Experimental results demonstrate that the
performance of the proposed model surpasses the state-of-the-art in deepfake
speech detection and human testers.


  We characterize Leavitt path algebras which are exchange rings in terms of
intrinsic properties of the graph and show that the values of the stable rank
for these algebras are 1, 2 or $\infty$. Concrete criteria in terms of
properties of the underlying graph are given for each case.


  A fluid droplet located on a super-hydrophobic surface makes contact with the
surface only at small isolated regions, and is mostly in contact with the
surrounding air. As a result, a fluid in motion near such a surface experiences
very low friction, and super-hydrophobic surfaces display strong drag-reduction
in the laminar regime. Here we consider theoretically a super-hydrophobic
surface composed of circular posts (so called fakir geometry) located on a
planar rectangular lattice. Using a superposition of point forces with suitably
spatially-dependent strength, we derive the effective surface slip length for a
planar shear flow on such a fakir surface as the solution to an infinite series
of linear equations. In the asymptotic limit of small surface coverage by the
posts, the series can be interpreted as Riemann sums, and the slip length can
be obtained analytically. For posts on a square lattice, our analytical results
are in excellent quantitative agreement with previous numerical computations.


  Peres algorithm applies the famous von Neumann trick recursively to produce
unbiased random bits from biased coin tosses. Its recursive nature makes the
algorithm simple and elegant, and yet its output rate approaches the
information-theoretic upper bound. However, it is relatively hard to explain
why it works, and it appears partly due to this difficulty that its
generalization to many-valued source was discovered only recently. Binarization
tree provides a new conceptual tool to understand the innerworkings of the
original Peres algorithm and the recently-found generalizations in both aspects
of the uniform random number generation and asymptotic optimality. Furthermore,
it facilitates finding many new Peres-style recursive algorithms that have been
arguably very hard to come by without this new tool.


  Large language models trained primarily in a monolingual setting have
demonstrated their ability to generalize to machine translation using zero- and
few-shot examples with in-context learning. However, even though zero-shot
translations are relatively good, there remains a discernible gap comparing
their performance with the few-shot setting. In this paper, we investigate the
factors contributing to this gap and find that this gap can largely be closed
(for about 70%) by matching the writing styles of the target corpus.
Additionally, we explore potential approaches to enhance zero-shot baselines
without the need for parallel demonstration examples, providing valuable
insights into how these methods contribute to improving translation metrics.


  This manuscript presents an algorithm for obtaining an approximation of
nonlinear high order control affine dynamical systems, that leverages the
controlled trajectories as the central unit of information. As the fundamental
basis elements leveraged in approximation, higher order control occupation
kernels represent iterated integration after multiplication by a given
controller in a vector valued reproducing kernel Hilbert space. In a
regularized regression setting, the unique optimizer for a particular
optimization problem is expressed as a linear combination of these occupation
kernels, which converts an infinite dimensional optimization problem to a
finite dimensional optimization problem through the representer theorem.
Interestingly, the vector valued structure of the Hilbert space allows for
simultaneous approximation of the drift and control effectiveness components of
the control affine system. Several experiments are performed to demonstrate the
effectiveness of the approach.


  Elastic constants of zero-temperature amorphous solids are given as the
difference between the Born term, which results from a hypothetical affine
deformation of an amorphous solid, and a correction term which originates from
the fact that the deformation of an amorphous solid due to an applied stress
is, at the microscopic level, non-affine. Both terms are non-negative and thus
it is a priori not obvious that the resulting elastic constants are
non-negative. In particular, theories that approximate the correction term may
spuriously predict negative elastic constants and thus an instability of an
amorphous solid. Here we derive alternative expressions for elastic constants
of zero-temperature amorphous solids that are explicitly non-negative. These
expressions provide a useful blueprint for approximate theories for elastic
constants and sound damping in zero temperature amorphous solids.


  In this paper we are concerned with a new type of backward equations with
anticipation which we call neutral backward stochastic functional differential
equations. We obtain the existence and uniqueness and prove a comparison
theorem. As an application, we discuss the optimal control of neutral
stochastic functional differential equations, establish a Pontryagin maximum
principle, and give an explicit optimal value for the linear optimal control.


  We have analyzed a nonsingular model with a variable cosmological term
following the Carvalho {\it et al}. ansatz. The model was shown to approximate
to the model of Freese {\it et al}. in one direction and to the \"{O}zer-Taha
in the other. We have then included the effect of viscosity in this cosmology,
as this effect has not been considered before. The analysis showed that this
viscous effect could be important with a present contribution to the cosmic
pressure, at most, of order of that of radiation. The model puts a stronger
upper bound on the baryonic matter than that required by the standard model. A
variable gravitational and cosmological constant were then introduced in a
scenario which conserves the energy and momentum in the presence of bulk
viscosity. The result of the analysis reveals that various models could be
viscous. A noteworthy result is that some nonsingular closed models evolve
asymptotically into a singular viscous one. The considered models solve for
many of the standard model problems. Though the introduction of bulk viscosity
results in the creation of particles, this scenario conserves energy and
momentum. As in the standard model the entropy remains constant. We have not
explained the generation of bulk viscosity but some workers attributes this to
neutrinos. Though the role of viscosity today is minute it could, nevertheless,
have had an important contribution at early times. We have shown that these
models encompass many of the old and recently proposed models, in particular,
Brans-Dicke, Dirac, Freese {\it et al}., Berman, Abdel Rahman and Kalligas {\it
et al}. models. Hence we claim that the introduction of bulk viscosity enriches
the adopted cosmology.


  Protocells are supposed to have played a key role in the self-organizing
processes leading to the emergence of life. Existing models either (i) describe
protocell architecture and dynamics, given the existence of sets of
collectively self-replicating molecules for granted, or (ii) describe the
emergence of the aforementioned sets from an ensemble of random molecules in a
simple experimental setting (e.g. a closed system or a steady-state flow
reactor) that does not properly describe a protocell. In this paper we present
a model that goes beyond these limitations by describing the dynamics of sets
of replicating molecules within a lipid vesicle. We adopt the simplest possible
protocell architecture, by considering a semi-permeable membrane that selects
the molecular types that are allowed to enter or exit the protocell and by
assuming that the reactions take place in the aqueous phase in the internal
compartment. As a first approximation, we ignore the protocell growth and
division dynamics. The behavior of catalytic reaction networks is then
simulated by means of a stochastic model that accounts for the creation and the
extinction of species and reactions. While this is not yet an exhaustive
protocell model, it already provides clues regarding some processes that are
relevant for understanding the conditions that can enable a population of
protocells to undergo evolution and selection.


  This paper presents a method to generate high quality triangular or
quadrilateral meshes that uses direction fields and a frontal point insertion
strategy. Two types of direction fields are considered: asterisk fields and
cross fields. With asterisk fields we generate high quality triangulations,
while with cross fields we generate right-angled triangulations that are
optimal for transformation to quadrilateral meshes. The input of our algorithm
is an initial triangular mesh and a direction field calculated on it. The goal
is to compute the vertices of the final mesh by an advancing front strategy
along the direction field. We present an algorithm that enables to efficiently
generate the points using solely information from the base mesh. A
multi-threaded implementation of our algorithm is presented, allowing us to
achieve significant speedup of the point generation. Regarding the
quadrangulation process, we develop a quality criterion for right-angled
triangles with respect to the local cross field and an optimization process
based on it. Thus we are able to further improve the quality of the output
quadrilaterals. The algorithm is demonstrated on the sphere and examples of
high quality triangular and quadrilateral meshes of coastal domains are
presented.


  Unraveling the nature of the communication model that governs which two
individuals in a swarm interact with each other is an important line of inquiry
in the collective behavior sciences. A number of models have been proposed in
the biological swarm literature, with the leading models being the metric,
topological, and visual models. The hypothesis evaluated in this manuscript is
whether the choice of a communication model impacts the performance of a tasked
artificial swarm. The biological models are used to design coordination
algorithms for a simulated swarm, which are evaluated over a range of six swarm
robotics tasks. Each task has an associated set of performance metrics that are
used to evaluate how the communication models fare against each other. The
general findings demonstrate that the communication model significantly affects
the swarm's performance for individual tasks, and this result implies that the
communication model-task pairing is an important consideration when designing
artificial swarms. Further analysis of each tasks' performance metrics reveal
instances in which pairwise considerations of model and one of the various
experimental factors becomes relevant. The reported research demonstrates that
the artificial swarm's task performance can be increased through the careful
selection of a communications model.


  We consider one-dimensional discrete-time random walks (RWs) in the presence
of finite size traps of length $\ell$ over which the RWs can jump. We study the
survival probability of such RWs when the traps are periodically distributed
and separated by a distance $L$. We obtain exact results for the mean
first-passage time and the survival probability in the special case of a
double-sided exponential jump distribution. While such RWs typically survive
longer than if they could not leap over traps, their survival probability still
decreases exponentially with the number of steps. The decay rate of the
survival probability depends in a non-trivial way on the trap length $\ell$ and
exhibits an interesting regime when $\ell\rightarrow 0$ as it tends to the
ratio $\ell/L$, which is reminiscent of strongly chaotic deterministic systems.
We generalize our model to continuous-time RWs, where we introduce a power-law
distributed waiting time before each jump. In this case, we find that the
survival probability decays algebraically with an exponent that is independent
of the trap length. Finally, we derive the diffusive limit of our model and
show that, depending on the chosen scaling, we obtain either diffusion with
uniform absorption, or diffusion with periodically distributed point absorbers.


  Silicon's weak intrinsic spin-orbit coupling and centrosymmetric crystal
structure are a critical bottleneck to the development of Si spintronics,
because they lead to an insignificant spin-Hall effect (spin current
generation) and inverse spin-Hall effect (spin current detection). Here, we
undertake current, magnetic field, crystallography dependent magnetoresistance
and magneto thermal transport measurements to study the spin transport behavior
in freestanding Si thin films. We observe a large spin-Hall magnetoresistance
in both p-Si and n-Si at room temperature and it is an order of magnitude
larger than that of Pt. One explanation of the unexpectedly large and efficient
spin-Hall effect is spin-phonon coupling instead of spin-orbit coupling. The
macroscopic origin of the spin-phonon coupling can be large strain gradients
that can exist in the freestanding Si films. This discovery in a light, earth
abundant and centrosymmetric material opens a new path of strain engineering to
achieve spin dependent properties in technologically highly-developed
materials.


  Interactive visualization and simulation of astrophysical phenomena help
astronomers and enable digital planetariums and television documentaries to
take their spectators on a journey into deep space to explore the astronomical
wonders of our universe in 3D.


  We prove that the average size of the squares of differences between
consecutive primes less than $x$ is $O(x^{0.23+\varepsilon})$ for any fixed
$\varepsilon>0$. This improves on a result of Peck, who gave bound
$O(x^{0.25+\varepsilon})$ in the place of $O(x^{0.23+\varepsilon})$. Key
ingredients are Harman's sieve, Heath-Brown's mean value theorem for sparse
Dirichlet polynomials and Heath-Brown's $R^*$ bound.


  Calculating the capacity (with or without feedback) of channels with memory
and continuous alphabets is a challenging task. It requires optimizing the
directed information (DI) rate over all channel input distributions. The
objective is a multi-letter expression, whose analytic solution is only known
for a few specific cases. When no analytic solution is present or the channel
model is unknown, there is no unified framework for calculating or even
approximating capacity. This work proposes a novel capacity estimation
algorithm that treats the channel as a `black-box', both when feedback is or is
not present. The algorithm has two main ingredients: (i) a neural distribution
transformer (NDT) model that shapes a noise variable into the channel input
distribution, which we are able to sample, and (ii) the DI neural estimator
(DINE) that estimates the communication rate of the current NDT model. These
models are trained by an alternating maximization procedure to both estimate
the channel capacity and obtain an NDT for the optimal input distribution. The
method is demonstrated on the moving average additive Gaussian noise channel,
where it is shown that both the capacity and feedback capacity are estimated
without knowledge of the channel transition kernel. The proposed estimation
framework opens the door to a myriad of capacity approximation results for
continuous alphabet channels that were inaccessible until now.


  The anharmonic lattice dynamics of rock-salt thermoelectric compounds SnTe
and PbTe are investigated with inelastic neutron scattering (INS) and
first-principles calculations. The experiments show that, surprisingly,
although SnTe is closer to the ferroelectric instability, phonon spectra in
PbTe exhibit a more anharmonic character. This behavior is reproduced in
first-principles calculations of the temperature-dependent phonon self-energy.
Our simulations reveal how the nesting of phonon dispersions induces prominent
features in the self-energy, which account for the measured INS spectra and
their temperature dependence. We establish that the phase-space for
three-phonon scattering processes, rather than just the proximity to the
lattice instability, is the mechanism determining the complex spectrum of the
transverse-optical ferroelectric mode.


  A solvable current-current deformation of the worldsheet theory of strings on
$AdS_3$ has been recently conjectured to be dual to an irrelevant deformation
of the spacetime orbifold CFT, commonly referred to as single-trace $T\bar{T}$.
These deformations give rise to a family of bulk geometries which realize a
non-trivial flow towards the UV. For a particular sign of this deformation, the
corresponding three-dimensional geometry approaches $AdS_3$ in the interior,
but has a curvature singularity at finite radius, beyond which there are closed
timelike curves. It has been suggested that this singularity is due to the
presence of "negative branes," which are exotic objects that generically change
the metric signature. We propose an alternative UV-completion for these
geometries by cutting and gluing to a regular background which approaches a
linear dilaton vacuum in the UV. In the S-dual picture, a singularity
resolution mechanism known as the enhan\c{c}on induces this transition by the
formation of a shell of $D5$-branes at a fixed radial position near the
singularity. The solutions involving negative branes gain a new interpretation
in this context.


  The celebrated theorem of Feuerbach states that the nine-point circle of a
nonequilateral triangle is tangent to both its incircle and its three
excircles. In this note, we give a simple proof of Feuerbach's Theorem using
straightforward vector computations. All required preliminaries are proven here
for the sake of completeness.


  We investigate the statistics of single-mode delay times of waves reflected
from a disordered waveguide in the presence of wave localization. The
distribution of delay times is qualitatively different from the distribution in
the diffusive regime, and sensitive to coherent backscattering: The probability
to find small delay times is enhanced by a factor close to sqrt(2) for
reflection angles near the angle of incidence. This dynamic effect of coherent
backscattering disappears in the diffusive regime.


  Line intensity mapping (LIM) experiments probing the nearby universe can
expect a considerable amount of cosmic infrared background (CIB) contiuum
emission coming from near and far-infrared galaxies. For the purpose of using
the LIM data to constrain the star formation rate (SFR), we argue that the CIB
continuum - traditionally treated as contamination - can be combined with the
LIM signal to enhance the SFR constraints achievable. We first present a power
spectrum model that is capable of joining continuum and line emissions that
assume the same prior SFR model. We subsequently analyze the effectiveness of
the joint model in the context of the EXperiment for Cryogenic Large-Aperture
Intensity Mapping (EXCLAIM), which utilizes the [CII] molecular line to study
the SFR. We numerically compute the theoretical power spectra according to our
model and the EXCLAIM survey specifics, and perform Fisher analysis to obtain
SFR parameter constraints. We find that although the joint model has no
considerable advantage over LIM alone assuming the current survey level of
EXCLAIM, its effects become significant when we consider more optimistic values
of survey resolution and angular span that are expected of future LIM
experiments. By manipulating the Fisher formalism, we show that the CIB is not
only an additional SFR sensitive signal, but also serves to break the SFR
parameter degeneracy that naturally emerges from the [CII] Fisher matrix. For
this reason, addition of the CIB will allow improvements in the survey
parameters to be better reflected in the SFR constraints, and can be
effectively utilized by future LIM experiments.


  It is often argued that inflation erases all the information about what took
place before it started. Quantum gravity, relevant in the Planck era, seems
therefore mostly impossible to probe with cosmological observations. In
general, only very ad hoc scenarios or hyper fine-tuned initial conditions can
lead to observationally testable theories. Here we consider a well-defined and
well motivated candidate quantum cosmology model that predicts inflation. Using
the most recent observational constraints on the cosmic microwave background B
modes, we show that the model is excluded for all its parameter space, without
any tuning. Some important consequences are drawn for the deformed algebra
approach to loop quantum cosmology. We emphasize that neither loop quantum
cosmology in general nor loop quantum gravity are disfavored by this study but
their falsifiability is established.


  Hierarchical beta process has found interesting applications in recent years.
In this paper we present a modified hierarchical beta process prior with
applications to hierarchical modeling of multiple data sources. The novel use
of the prior over a hierarchical factor model allows factors to be shared
across different sources. We derive a slice sampler for this model, enabling
tractable inference even when the likelihood and the prior over parameters are
non-conjugate. This allows the application of the model in much wider contexts
without restrictions. We present two different data generative models a linear
GaussianGaussian model for real valued data and a linear Poisson-gamma model
for count data. Encouraging transfer learning results are shown for two real
world applications text modeling and content based image retrieval.


  We present an asteroseismic analysis of 33 solar-type stars observed in short
cadence during Campaigns (C) 1-3 of the NASA K2 mission. We were able to
extract both average seismic parameters and individual mode frequencies for
stars with dominant frequencies up to ~3300{\mu}Hz, and we find that data for
some targets are good enough to allow for a measurement of the rotational
splitting. Modelling of the extracted parameters is performed by using
grid-based methods using average parameters and individual frequencies together
with spectroscopic parameters. For the target selection in C3, stars were
chosen as in C1 and C2 to cover a wide range in parameter space to better
understand the performance and noise characteristics. For C3 we still detected
oscillations in 73% of the observed stars that we proposed. Future K2 campaigns
hold great promise for the study of nearby clusters and the chemical evolution
and age-metallicity relation of nearby field stars in the solar neighbourhood.
We expect oscillations to be detected in ~388 short-cadence targets if the K2
mission continues until C18, which will greatly complement the ~500 detections
of solar-like oscillations made for short-cadence targets during the nominal
Kepler mission. For ~30-40 of these, including several members of the Hyades
open cluster, we furthermore expect that inference from interferometry should
be possible.


  We propose an approach on model checking information flow for imperative
language with procedures. We characterize our model with pushdown system, which
has a stack of unbounded length that naturally models the execution of
procedural programs. Because the type-based static analysis is sometimes too
conservative and rejects safe program as ill-typed, we take a semantic-based
approach by self-composing symbolic pushdown system and specifying
noninterference with LTL formula. Then we verify this LTL-expressed property
via model checker Moped. Except for overcoming the conservative characteristic
of type-based approach, our motivation also includes the insufficient state of
arts on precise information flow analysis under inter-procedural setting. To
remedy the inefficiency of model checking compared with type system, we propose
both compact form and contracted form of self-composition. According to our
experimental results, they can greatly increase the efficiency of realistic
verification. Our method provides flexibility on separating program abstraction
from noninterference verification, thus could be expected to use on different
programming languages.


  In this chapter we discuss aspects of the quantum critical behavior that
occurs at a quantum phase transition separating a topological phase from a
conventionally ordered one. We concentrate on a family of quantum lattice
models, namely certain deformations of the toric code model, that exhibit
continuous quantum phase transitions. One such deformation leads to a
Lorentz-invariant transition in the 3D Ising universality class. An alternative
deformation gives rise to a so-called conformal quantum critical point where
equal-time correlations become conformally invariant and can be related to
those of the 2D Ising model. We study the behavior of several physical
observables, such as non-local operators and entanglement entropies, that can
be used to characterize these quantum phase transitions. Finally, we briefly
consider the role of thermal fluctuations and related phase transitions, before
closing with a short overview of field theoretical descriptions of these
quantum critical points.


  For an ample Hausdorff groupoid $G$, and the Steinberg algebra $A_R(G)$ with
coefficients in the commutative ring $R$ with unit, we describe the centraliser
of subalgebra $A_R(U)$ with $U$ an open closed invariant subset of unit space
of $G$. In particular, we obtain that the algebra of the interior of the
isotropy is indeed the centraliser of the diagonal subalgebra of Steinberg
algebra. This will unify several results in the literature and the
corresponding results for Leavitt path algebras follow.


  Little inquiry has explicitly addressed the role of action spaces in
language-guided visual navigation -- either in terms of its effect on
navigation success or the efficiency with which a robotic agent could execute
the resulting trajectory. Building on the recently released VLN-CE setting for
instruction following in continuous environments, we develop a class of
language-conditioned waypoint prediction networks to examine this question. We
vary the expressivity of these models to explore a spectrum between low-level
actions and continuous waypoint prediction. We measure task performance and
estimated execution time on a profiled LoCoBot robot. We find more expressive
models result in simpler, faster to execute trajectories, but lower-level
actions can achieve better navigation metrics by approximating shortest paths
better. Further, our models outperform prior work in VLN-CE and set a new
state-of-the-art on the public leaderboard -- increasing success rate by 4%
with our best model on this challenging task.


  Unsupervised 3D object detection methods have emerged to leverage vast
amounts of data efficiently without requiring manual labels for training.
Recent approaches rely on dynamic objects for learning to detect objects but
penalize the detections of static instances during training. Multiple rounds of
(self) training are used in which detected static instances are added to the
set of training targets; this procedure to improve performance is
computationally expensive. To address this, we propose the method UNION. We use
spatial clustering and self-supervised scene flow to obtain a set of static and
dynamic object proposals from LiDAR. Subsequently, object proposals' visual
appearances are encoded to distinguish static objects in the foreground and
background by selecting static instances that are visually similar to dynamic
objects. As a result, static and dynamic foreground objects are obtained
together, and existing detectors can be trained with a single training. In
addition, we extend 3D object discovery to detection by using object
appearance-based cluster labels as pseudo-class labels for training object
classification. We conduct extensive experiments on the nuScenes dataset and
increase the state-of-the-art performance for unsupervised object discovery,
i.e. UNION more than doubles the average precision to 33.9. The code will be
made publicly available.


  When assessing a software-based system, the results of Bayesian statistical
inference on operational testing data can provide strong support for software
reliability claims. For inference, this data (i.e. software successes and
failures) is often assumed to arise in an independent, identically distributed
(i.i.d.) manner. In this paper we show how conservative Bayesian approaches
make this assumption unnecessary, by incorporating one's doubts about the
assumption into the assessment. We derive conservative confidence bounds on a
system's probability of failure on demand (pfd), when operational testing
reveals no failures. The generality and utility of the confidence bounds are
illustrated in the assessment of a nuclear power-plant safety-protection
system, under varying levels of skepticism about the i.i.d. assumption. The
analysis suggests that the i.i.d. assumption can make Bayesian reliability
assessments extremely optimistic - such assessments do not explicitly account
for how software can be very likely to exhibit no failures during extensive
operational testing despite the software's pfd being undesirably large.


  In this paper we present new measurements of the gas kinematics in M33 using
the CO J=1-0 line. The resulting rotational velocities complement previous
21-cm line data for a very accurate and extended rotation curve. The implied
dark matter mass, within the total gaseous extent, is a factor 5 higher than
the visible baryonic mass. Dark matter density profiles with an inner cusp as
steep as R^{-1}, are compatible with M33 data. The dark matter concentrations
required for fitting the M33 rotation curve are very low but marginally
consistent with halos forming in a standard Cold Dark Matter cosmology. The M33
virialized dark halo is at least 50 times more massive than the visible baryons
and its size is comparable with the M33-M31 separation. Inner cusps as steep as
R^{-1.5} are ruled out, while halo models with a large size core of constant
density are consistent with the M33 data. A central spheroid of stars is needed
and we evaluate its dynamical mass range. Using accurate rotational velocity
gradients and the azimuthally averaged baryonic surface densities, we show that
a disk instability can regulate the star formation activity in M33. Considering
the gaseous surface density alone, the predicted outer star formation threshold
radius is consistent with the observed drop of the H-alpha surface brightness
if a shear rate criterion is used. The classical Toomre criterion predicts
correctly the size of the unstable region only when the stellar or dark halo
gravity, derived in this paper, is added to that of the gaseous disk.


  A comprehensive review of the equations of general relativity in the
quasi-Maxwellian (QM) formalism introduced by Jordan, Ehlers and Kundt is made.
Our main interest concerns its applications to the analysis of the perturbation
of standard cosmology in the Friedman-Lema\^itre-Robertson-Walker framework.
The major achievement of the QM scheme is its use of completely gauge
independent quantities. We shall see that in the QM-scheme we deal directly
with observable quantities. This reveals its advantage over the old method
introduced by Lifshitz et al that deals with perturbation in the standard
Einstein framework. For completeness, we compare the QM-scheme to the
gauge-independent method of Bardeen, a procedure consisting on particular
choices of the perturbed variables as a combination of gauge dependent
quantities.


  We propose a heterostructure device comprised of magnets and piezoelectrics
that significantly improves the delay and the energy dissipation of an all-spin
logic (ASL) device. This paper studies and models the physics of the device,
illustrates its operation, and benchmarks its performance using SPICE
simulations. We show that the proposed device maintains low voltage operation,
non-reciprocity, non-volatility, cascadability, and thermal reliability of the
original ASL device. Moreover, by utilizing the deterministic switching of a
magnet from the saddle point of the energy profile, the device is more
efficient in terms of energy and delay and is robust to thermal fluctuations.
The results of simulations show that compared to ASL devices, the proposed
device achieves 21x shorter delay and 27x lower energy dissipation per bit for
a 32-bit arithmetic-logic unit (ALU).


  We consider and extend the adversarial agent-based learning approach of
Gy{\"o}rfi {\it et al} to the situation of zero-cost portfolio selection
implemented with a quadratic approximation derived from the mutual fund
separation theorems. The algorithm is applied to daily sampled sequential
Open-High-Low-Close data and sequential intraday 5-minute bar-data from the
Johannesburg Stock Exchange (JSE). Statistical tests of the algorithms are
considered. The algorithms are directly compared to standard NYSE test cases
from prior literature. The learning algorithm is used to select parameters for
agents (or experts) generated by pattern matching past dynamics using a simple
nearest-neighbour search algorithm. It is shown that there is a speed advantage
associated with using an analytic solution of the mutual fund separation
theorems. It is argued that the expected loss in performance does not undermine
the potential application to intraday quantitative trading and that when
transactions costs and slippage are considered the strategies can still remain
profitable when unleveraged. The paper demonstrates that patterns in financial
time-series on the JSE can be systematically exploited in collective but that
this does not imply predictability of the individual asset time-series
themselves.


  We are concerned with the following time-fractional semilinear heat equation
in the $N$-dimensional whole space ${\bf R}^N$ with $N \geq 1$.
  \[
  {\rm (P)}_\alpha \qquad
  \partial_t^\alpha u -\Delta u = u^p,\quad t>0,\,\,\, x\in{\bf R}^N, \qquad
  u(0) = \mu \quad \mbox{in}\quad {\bf R}^N,
  \]
  where $\partial_t^\alpha$ denotes the Caputo derivative of order $\alpha \in
(0,1)$, $p>1$, and $\mu$ is a nonnegative Radon measure on ${\bf R}^N$. The
case $\alpha=1$ formally gives the Fujita-type equation (P)$_1$ \
$\partial_tu-\Delta u=u^p$. In particular, we mainly focus on the Fujita
critical case where $p=p_F:=1+2/N$. It is well known that the Fujita exponent
$p_F$ separates the ranges of $p$ for the global-in-time solvability of
(P)$_1$. In particular, (P)$_1$ with $p=p_F$ possesses no global-in-time
solutions, and does not locally-in-time solvable in its scale critical space
$L^1(\mathbf{R}^N)$. It is also known that the exponent $p_F$ plays the same
role for the global-in-time solvability for (P)$_\alpha$. However, the problem
(P)$_\alpha$ with $p=p_F$ is globally-in-time solvable, and exhibites
local-in-time solvability in its scale critical space $L^1(\mathbf{R}^N)$. The
purpose of this paper is to clarify the collapse of the global and
local-in-time solvability of (P)$_\alpha$ as $\alpha$ approaches $1-0$.


  This paper deals with the physics of monolayer graphite, with a particular
focus on the electronics and structural properties. In contrast to the previous
electronic band structure of doped single-walled carbon nanotube calculation,
where just a ballistic graphite plate is considered, here for the carbon bonds
length alteration is calculated in terms of hole doping. It is found that doped
holes play crucial roles on the bond structure compared to that obtained no
doping configurations and it changes as hole doping increasing.


  An update is presented to the software package SeBa for simulating single
star and binary evolution in which new stellar evolution tracks have been
implemented. SeBa is applied to study the population of close double white
dwarf and the delay time distribution of double white dwarf mergers that may
lead to Supernovae Type Ia.


  Adversarial attack has inspired great interest in computer vision, by showing
that classification-based solutions are prone to imperceptible attack in many
tasks. In this paper, we propose a method, SMART, to attack action recognizers
which rely on 3D skeletal motions. Our method involves an innovative perceptual
loss which ensures the imperceptibility of the attack. Empirical studies
demonstrate that SMART is effective in both white-box and black-box scenarios.
Its generalizability is evidenced on a variety of action recognizers and
datasets. Its versatility is shown in different attacking strategies. Its
deceitfulness is proven in extensive perceptual studies. Finally, SMART shows
that adversarial attack on 3D skeletal motion, one type of time-series data, is
significantly different from traditional adversarial attack problems.


  We use Monte Carlo simulations and free-energy techniques to show that binary
solutions of penta- and hexavalent two-dimensional patchy particles can form
thermodynamically stable quasicrystals even at very narrow patch widths,
provided their patch interactions are chosen in an appropriate way. Such patchy
particles can be thought of as a coarse-grained representation of DNA multi-arm
`star' motifs, which can be chosen to bond with one another very specifically
by tuning the DNA sequences of the protruding arms. We explore several possible
design strategies and conclude that DNA star tiles that are designed to
interact with one another in a specific but not overly constrained way could
potentially be used to construct soft quasicrystals in experiment. We verify
that such star tiles can form stable dodecagonal motifs using oxDNA, a
realistic coarse-grained model of DNA.


  We study random perturbations of multidimensional piecewise expanding maps.
We characterize absolutely continuous stationary measures (acsm) of randomly
perturbed dynamical systems in terms of pseudo-orbits linking the ergodic
components of absolutely invariant measures (acim) of the unperturbed system.
We focus on those components, called least-elements, which attract
pseudo-orbits. We show that each least element admits a neighbourhood which
supports exactly one ergodic acsm of the random system. We use this result to
identify random perturbations that exhibit a metastable behavior.


  We provide certain compatibility conditions for m-accretive operators such
that the adjoint of the sum is given by the closure of the sum of the
respective adjoint. We revisit the proof of well-posedness of the abstract
class of partial differential-algebraic equations known as evolutionary
equations. We show that the general mechanism provided here can be applied to
establish well-posedness for non-autonomous evolutionary equations with
$L_{\infty}$-coefficients thus not only generalising known results but opening
up new directions other methods such as evolution families have a hard time to
come by.


  We show that a collinear Heisenberg antiferromagnet, whose sublattice
symmetry is broken at the Hamiltonian level, becomes a fluctuation-induced
ferrimagnet at any finite temperature $T$ below the N\'eel temperature $T_{\rm
N}$. We demonstrate this using a layered variant of a square-lattice
$J_1$-$J_2$ model. Linear spin-wave theory is used to determine the
low-temperature behavior of the uniform magnetization, and non-linear
corrections are argued to yield a temperature-induced qualitative change of the
magnon spectrum. We then consider a layered Shastry-Sutherland model,
describing a frustrated arrangement of orthogonal dimers. This model displays
an antiferromagnetic phase for large intra-dimer couplings. A lattice
distortion which breaks the glide symmetry between the two types of dimers
corresponds to broken sublattice symmetry and hence gives rise to
ferrimagnetism. Given indications that such a distortion is present in the
material SrCu$_2$(BO$_3$)$_2$ under hydrostatic pressure, we suggest the
existence of a fluctuation-induced ferrimagnetic phase in pressurized
SrCu$_2$(BO$_3$)$_2$. We predict a non-monotonic behavior of the uniform
magnetization as function of temperature.


  A Lie-Rinehart algebra consists of a commutative algebra and a Lie algebra
with additional structure which generalizes the mutual structure of interaction
between the algebra of functions and the Lie algebra of smooth vector fields on
a smooth manifold. Lie-Rinehart algebras provide the correct categorical
language to solve the problem whether Kaehler quantization commutes with
reduction which, in turn, may be seen as a descent problem.


  Deep learning techniques with neural networks have been used effectively in
computational fluid dynamics (CFD) to obtain solutions to nonlinear
differential equations. This paper presents a physics-informed neural network
(PINN) approach to solve the Blasius function. This method eliminates the
process of changing the non-linear differential equation to an initial value
problem. Also, it tackles the convergence issue arising in the conventional
series solution. It is seen that this method produces results that are at par
with the numerical and conventional methods. The solution is extended to the
negative axis to show that PINNs capture the singularity of the function at
$\eta=-5.69$


  Recent observations of the growth of protein crystals have identified two
different growth regimes. At low supersaturation, the surface of the crystal is
smooth and increasing in size due to the nucleation of steps at defects and the
subsequent growth of the steps. At high supersaturation, nucleation occurs at
many places simultaneously, the crystal surface becomes rough and the growth
velocity increases more rapidly with increasing supersaturation than in the
smooth regime. Kinetic roughening transitions are typically assumed to be due
to the vanishing of the barrier for two-dimension nucleation on the surface of
the crystal. We show here, by means of both analytic mean field models and
kinetic Monte Carlo simulations that a transition between different growth
modes reminiscent of kinetic roughening can also arise as a kinetic effect
occurring at finite nucleation barriers.


  We study the propagation of electromagnetic waves in one-dimensional chirped
periodic structures composed of alternating layers of negative-index (or
left-handed) metamaterial and conventional dielectric, under the condition of
the zero average refractive index. We consider the case when the periodic
structure has a linear chirp, and the chirp is introduced by varying linearly
the thickness of the layers across the structure. We apply an asymptotic
analytical method for the analysis of the Bloch oscillations and find that, in
a sharp contrast to ordinary periodic dielectric structures, the energy flow in
multi-layered stacks with metamaterials may have the opposite direction at the
band edges, thus providing novel possibilities for the beam steering in the
transmission band.


  Recent work by the authors equips Petri occurrence nets (PN) with probability
distributions which fully replace nondeterminism. To avoid the so-called
confusion problem, the construction imposes additional causal dependencies
which restrict choices within certain subnets called structural branching cells
(s-cells). Bayesian nets (BN) are usually structured as partial orders where
nodes define conditional probability distributions. In the paper, we unify the
two structures in terms of Symmetric Monoidal Categories (SMC), so that we can
apply to PN ordinary analysis techniques developed for BN. Interestingly, it
turns out that PN which cannot be SMC-decomposed are exactly s-cells. This
result confirms the importance for Petri nets of both SMC and s-cells.


  Although surveys of infall motions in dense cores have been carried out for
years, few surveys have focused on mapping infall across cores using multiple
spectral line observations. To fill this gap, we present IRAM 30-m Telescope
maps of N2H+(1-0), DCO+(2-1), DCO+(3-2), and HCO+(3-2) emission towards two
prestellar cores (L492 and L694-2) and one protostellar core (L1521F). We find
that the measured infall velocity varies with position across each core and
choice of molecular line, likely as a result of radial variations in core
chemistry and dynamics. Line-of-sight infall speeds estimated from DCO+(2-1)
line profiles can decrease by 40-50 m/s when observing at a radial offset >=
0.04 pc from the core's dust continuum emission peak. Median infall speeds
calculated from all observed positions across a core can also vary by as much
as 65 m/s depending on the transition. These results show that while
single-pointing, single-transition surveys of core infall velocities may be
good indicators of whether a core is either contracting or expanding, the
magnitude of the velocities they measure are significantly impacted by the
choice of molecular line, proximity to the core center, and core evolutionary
state.


  We show that the recently developed soldering formalism in the Lagrangian
approach and canonical transformations in the Hamiltonian approach are
complementary. The examples of gauged chiral bosons in two dimensions and
self-dual models in three dimensions are discussed in details.


  This paper studies function theory on periodic and aperiodic inflationary
tessellations.


  Motivated by the optical-bar scheme of Braginsky, Gorodetsky and Khalili, we
propose to add to a high power detuned signal-recycling interferometer a local
readout scheme which measures the motion of the arm-cavity front mirror. At low
frequencies this mirror moves together with the arm-cavity end mirror, under
the influence of gravitational waves. This scheme improves the low-frequency
quantum-noise-limited sensitivity of optical-spring interferometers
significantly and can be considered as a incorporation of the optical-bar
scheme into currently planned second-generation interferometers. On the other
hand it can be regarded as an extension of the optical bar scheme. Taking
compact-binary inspiral signals as an example, we illustrate how this scheme
can be used to improve the sensitivity of the planned Advanced LIGO
interferometer, in various scenarios, using a realistic classical-noise budget.
We also discuss how this scheme can be implemented in Advanced LIGO with
relative ease.


  Deformable object manipulation remains a challenging task in robotics
research. Conventional techniques for parameter inference and state estimation
typically rely on a precise definition of the state space and its dynamics.
While this is appropriate for rigid objects and robot states, it is challenging
to define the state space of a deformable object and how it evolves in time. In
this work, we pose the problem of inferring physical parameters of deformable
objects as a probabilistic inference task defined with a simulator. We propose
a novel methodology for extracting state information from image sequences via a
technique to represent the state of a deformable object as a distribution
embedding. This allows to incorporate noisy state observations directly into
modern Bayesian simulation-based inference tools in a principled manner. Our
experiments confirm that we can estimate posterior distributions of physical
properties, such as elasticity, friction and scale of highly deformable
objects, such as cloth and ropes. Overall, our method addresses the real-to-sim
problem probabilistically and helps to better represent the evolution of the
state of deformable objects.


  Ranking institutions such as medical centers or universities is based on an
indicator accompanied with an uncertainty measure such as a standard deviation,
and confidence intervals should be calculated to assess the quality of these
ranks. We consider the problem of constructing simultaneous confidence
intervals for the ranks of centers based on an observed sample. We present in
this paper a novel method based on multiple testing which uses the partitioning
principle and employs the likelihood ratio (LR) test on the partitions. The
complexity of the algorithm is super exponential. We present several ways and
shortcuts to reduce this complexity. We provide also a polynomial algorithm
which produces a very good bracketing for the multiple testing by linearizing
the critical value of the LR test. We show that Tukey's Honest Significant
Difference (HSD) test can be written as a partitioning procedure. The new
methodology has promising properties in the sens that it opens the door in a
simple and easy way to construct new methods which may trade the exponential
complexity with power of the test or vice versa. In comparison to Tukey's HSD
test, the LR test seems to give better results when the centers are close to
each others or the uncertainty in the data is high which is confirmed during a
simulation study.


  We review the salient features of spacetime and worldvolume supersymmetric
super p-brane actions. These are sigma models for maps from a worldvolume
superspace to the target superspace. For p-branes, the symmetries of the model
depend crucially on the existence of closed super (p+1)-forms on a worldvolume
superspace, built out of the pull-backs of the Kalb-Ramond super (p+1)-form in
target superspace and its curvature. This formulation of super p-branes is
usually referred to as the twistor-like formulation.


  VERITAS is one of the world's most sensitive detectors of astrophysical very
high energy (VHE; E > 100 GeV) gamma rays. This observatory has operated for
~14 years, and nearly 7,000 hours of its observations have been targeted on
active galactic nuclei (AGN). Approximately 300 AGN were observed with VERITAS,
and 40 are detected. These studies are generally accompanied by
contemporaneous, broadband observations, which enable detailed probes of the
underlying jet-powered processes. Recent scientific results from VERITAS AGN
observations are presented.


  We propose a new method for obtaining complete asymptotic expansions in a
systematic manner, which is suitable for counting sequences of various graph
families in dense regime. The core idea is to encode the two-dimensional array
of expansion coefficients into a special bivariate generating function, which
we call a coefficient generating function. We show that coefficient generating
functions possess certain general properties that make it possible to express
asymptotics in a short closed form. Also, in most scenarios, we indicate a
combinatorial meaning of the involved coefficients. Applications of our method
include asymptotics of connected graphs, irreducible tournaments, strongly
connected digraphs, satisfiable 2-SAT formulae and contradictory strongly
connected implication digraphs. Moreover, due to its flexibility, the method
allows to treat a wide range of structural variations, including fixing the
numbers of connected, irreducible, strongly connected and contradictory
components, having the variations on source-like, sink-like and isolated
components, or adding weights and marking variables.


  Moire lattices consist of two identical periodic structures overlaid with a
relative rotation angle. Present even in everyday life, moire lattices have
been also produced, e.g., with coupled graphene-hexagonal boron nitride
monolayers, graphene-graphene layers, and layers on a silicon carbide surface.A
fundamental question that remains unexplored is the evolution of waves in the
potentials defined by the moire lattices. Here we experimentally create
two-dimensional photonic moire lattices, which, unlike their material
predecessors, have readily controllable parameters and symmetry allowing to
explore transitions between structures with fundamentally different geometries:
periodic, general aperiodic and quasi-crystal ones. Equipped with such
realization, we observe localization of light in deterministic linear lattices.
Such localization is based on at band physics, in contrast to previous schemes
based on light difusion in optical quasicrystals,where disorder is required for
the onset of Anderson localization. Using commensurable and incommensurable
moire patterns, we report the first experimental demonstration of
two-dimensional localization-delocalization-transition (LDT) of light. Moire
lattices may feature almost arbitrary geometry that is consistent with the
crystallographic symmetry groups of the sublattices, and therefore afford a
powerful tool to control the properties of light patterns, to explore the
physics of transitions between periodic and aperiodic phases, and
two-dimensional wavepacket phenomena relevant to several areas of science.


  The selective visual attention mechanism in the human visual system (HVS)
restricts the amount of information to reach visual awareness for perceiving
natural scenes, allowing near real-time information processing with limited
computational capacity [Koch and Ullman, 1987]. This kind of selectivity acts
as an 'Information Bottleneck (IB)', which seeks a trade-off between
information compression and predictive accuracy. However, such information
constraints are rarely explored in the attention mechanism for deep neural
networks (DNNs). In this paper, we propose an IB-inspired spatial attention
module for DNN structures built for visual recognition. The module takes as
input an intermediate representation of the input image, and outputs a
variational 2D attention map that minimizes the mutual information (MI) between
the attention-modulated representation and the input, while maximizing the MI
between the attention-modulated representation and the task label. To further
restrict the information bypassed by the attention map, we quantize the
continuous attention scores to a set of learnable anchor values during
training. Extensive experiments show that the proposed IB-inspired spatial
attention mechanism can yield attention maps that neatly highlight the regions
of interest while suppressing backgrounds, and bootstrap standard DNN
structures for visual recognition tasks (e.g., image classification,
fine-grained recognition, cross-domain classification). The attention maps are
interpretable for the decision making of the DNNs as verified in the
experiments. Our code is available at https://github.com/ashleylqx/AIB.git.


  It shown that the quantum-classical dynamical bracket recently proposed in J.
Chem. Phys. 124, 201104 (2006) fails to satisfy the Jacobi identity.


  For a simple $n$-polytope $P$, a quasitoric manifold over $P$ is a
$2n$-dimensional smooth manifold with a locally standard action of the
$n$-dimensional torus for which the orbit space is identified with $P$. This
paper shows the topological classification of quasitoric manifolds over the
dual cyclic polytope $C^n(m)^*$, when $n>3$ or $m-n=3$. Besides, we classify
small covers, the "real version" of quasitoric manifolds, over all dual cyclic
polytopes.


  Minimal models are developed to examine the origin of large negative thermal
expansion (NTE) in under-constrained systems. The dynamics of these models
reveals how underconstraint can organize a thermodynamically extensive manifold
of low-energy modes which not only drives NTE but extends across the Brillioun
zone. Mixing of twist and translation in the eigenvectors of these modes, for
which in ZrW2O8 there is evidence from infrared and neutron scattering
measurements, emerges naturally in our model as a signature of the dynamics of
underconstraint.


  Nearly every massive galaxy harbors a supermassive black hole (SMBH) in its
nucleus. SMBH masses are millions to billions $M_{\odot}$, and they correlate
with properties of spheroids of their host galaxies. While the SMBH growth
channels, mergers and gas accretion, are well established, their origin remains
uncertain: they could have either emerged from massive "seeds" ($10^5-10^6
M_{\odot}$) formed by direct collapse of gas clouds in the early Universe or
from smaller ($100 M_{\odot}$) black holes, end-products of first stars. The
latter channel would leave behind numerous intermediate mass black holes
(IMBHs, $10^2-10^5 M_{\odot}$). Although many IMBH candidates have been
identified, none is accepted as definitive, thus their very existence is still
debated. Using data mining in wide-field sky surveys and applying dedicated
analysis to archival and follow-up optical spectra, we identified a sample of
305 IMBH candidates having masses $3\times10^4<M_{\mathrm{BH}}<2\times10^5
M_{\odot}$, which reside in galaxy centers and are accreting gas that creates
characteristic signatures of a type-I active galactic nucleus (AGN). We
confirmed the AGN nature of ten sources (including five previously known
objects which validate our method) by detecting the X-ray emission from their
accretion discs, thus defining the first bona fide sample of IMBHs in galactic
nuclei. All IMBH host galaxies possess small bulges and sit on the low-mass
extension of the $M_{\mathrm{BH}}-M_{\mathrm{bulge}}$ scaling relation
suggesting that they must have experienced very few if any major mergers over
their lifetime. The very existence of nuclear IMBHs supports the stellar mass
seed scenario of the massive black hole formation.


  Various new phenomena emerge in quantum materials under elastic deformations,
such as hydrostatic or uniaxial stresses. In particular, using uniaxial strain
or stress can help to tune or uncover specific structural or electronic orders
in materials with multiple coexisting phases. Those phases may be associated
with a quantum phase transition requiring a millikelvin environment combined
with multiple experimental probes. Here, we describe our unique apparatus,
which allows in situ tuning of strain in large samples inside a dilution
refrigerator while the samples are monitored via an optical microscope. We
describe the engineering details and show some typical results of
characterizing superconducting strontium titanate under stress. This letter
should serve as a practical reference for experts in ultra-low temperature
experimental physics involving uniaxial stresses or strains.


  Most of the available advanced misfit functions for full waveform inversion
(FWI) are hand-crafted, and the performance of those misfit functions is
data-dependent. Thus, we propose to learn a misfit function for FWI, entitled
ML-misfit, based on machine learning. Inspired by the optimal transport of the
matching filter misfit, we design a neural network (NN) architecture for the
misfit function in a form similar to comparing the mean and variance for two
distributions. To guarantee the resulting learned misfit is a metric, we
accommodate the symmetry of the misfit with respect to its input and a Hinge
loss regularization term in a meta-loss function to satisfy the "triangle
inequality" rule. In the framework of meta-learning, we train the network by
running FWI to invert for randomly generated velocity models and update the
parameters of the NN by minimizing the meta-loss, which is defined as
accumulated difference between the true and inverted models. We first
illustrate the basic principle of the ML-misfit for learning a convex misfit
function for travel-time shifted signals. Further, we train the NN on 2D
horizontally layered models, and we demonstrate the effectiveness and
robustness of the learned ML-misfit by applying it to the well-known Marmousi
model.


  We investigate the role of the environment in a quantum erasure setup in the
cavity quantum electrodynamics domain. Two slightly different schemes are
analyzed. We show that the effects of the environment vary when a scheme is
exchanged for another. This can be used to estimate the macroscopic parameters
related to the system-environment microscopic correlations.


  Systematic variations of average observational characteristics and
correlation properties between different parameters of gamma-ray bursts (GRBs)
with time from 1991 April to 1994 September are revealed. It is hard to explain
the observed long-term variability by variations of experimental conditions.
The variability of GRB properties with the time scale of months to years,
together with the similarity between GRBs, solar hard X-ray flares and
terrestrial gamma-ray flashes, may indicate an origin of GRBs, at least partly,
within the heliosphere. Large-voltage and high-temperature pinch plasma columns
produced by disruptive electrical discharges in the outer heliosphere can
generate hard X-ray and gamma-ray flashes with energy spectra and spectral
evolution characters consistent with that observed in GRBs.


  We analyse the effective potential of the scalar wave equation near generic
space-time singularities of power-law type (Szekeres-Iyer metrics) and show
that the effective potential exhibits a universal and scale invariant leading
x^{-2} inverse square behaviour in the ``tortoise coordinate'' x provided that
the metrics satisfy the strict Dominant Energy Condition (DEC). This result
parallels that obtained in hep-th/0403252 for probes consisting of families of
massless particles (null geodesic deviation, a.k.a. the Penrose Limit). The
detailed properties of the scalar wave operator depend sensitively on the
numerical coefficient of the x^{-2}-term, and as one application we show that
timelike singularities satisfying the DEC are quantum mechanically singular in
the sense of the Horowitz-Marolf (essential self-adjointness) criterion. We
also comment on some related issues like the near-singularity behaviour of the
scalar fields permitted by the Friedrichs extension.


  Typical flows in stellar interiors are much slower than the speed of sound.
To follow the slow evolution of subsonic motions, various sound-proof equations
are in wide use, particularly in stellar astrophysical fluid dynamics. These
low-Mach number equations include the anelastic equations. Generally, these
equations are valid in nearly adiabatically stratified regions like stellar
convection zones, but may not be valid in the sub-adiabatic, stably stratified
stellar radiative interiors. Understanding the coupling between the convection
zone and the radiative interior is a problem of crucial interest and may have
strong implications for solar and stellar dynamo theories as the interface
between the two, called the tachocline in the Sun, plays a crucial role in many
solar dynamo theories. Here we study the properties of gravity waves in
stably-stratified atmospheres. In particular, we explore how gravity waves are
handled in various sound-proof equations. We find that some anelastic
treatments fail to conserve energy in stably-stratified atmospheres, instead
conserving pseudo-energies that depend on the stratification, and we
demonstrate this numerically. One anelastic equation set does conserve energy
in all atmospheres and we provide recommendations for converting low-Mach
number anelastic codes to this set of equations.


  To help bridge the gap between internet vision-style problems and the goal of
vision for embodied perception we instantiate a large-scale navigation task --
Embodied Question Answering [1] in photo-realistic environments (Matterport
3D). We thoroughly study navigation policies that utilize 3D point clouds, RGB
images, or their combination. Our analysis of these models reveals several key
findings. We find that two seemingly naive navigation baselines, forward-only
and random, are strong navigators and challenging to outperform, due to the
specific choice of the evaluation setting presented by [1]. We find a novel
loss-weighting scheme we call Inflection Weighting to be important when
training recurrent models for navigation with behavior cloning and are able to
out perform the baselines with this technique. We find that point clouds
provide a richer signal than RGB images for learning obstacle avoidance,
motivating the use (and continued study) of 3D deep learning models for
embodied navigation.


  Aligning large language models (LLMs) with human preferences is critical for
their deployment. Recently, decoding-time alignment has emerged as an effective
plug-and-play technique that requires no fine-tuning of model parameters.
However, generating text that achieves both high reward and high likelihood
remains a significant challenge. Existing methods often fail to generate
high-reward text or incur substantial computational costs. In this paper, we
propose Cascade Reward Sampling (CARDS) to address both issues, guaranteeing
the generation of high-reward and high-likelihood text with significantly low
costs. Based on our analysis of reward models (RMs) on incomplete text and our
observation that high-reward prefixes induce high-reward complete text, we use
rejection sampling to iteratively generate small semantic segments to form such
prefixes. The segment length is dynamically determined by the predictive
uncertainty of LLMs. This strategy guarantees desirable prefixes for subsequent
generations and significantly reduces wasteful token re-generations and the
number of reward model scoring. Our experiments demonstrate substantial gains
in both generation efficiency and alignment ratings compared to the baselines,
achieving five times faster text generation and 99\% win-ties in GPT-4/Claude-3
helpfulness evaluation.


  The severe constraints imposed on the parameter space of the minimal split
supersymmetry model by the infrared fixed point solution of the top Yukawa
coupling $Y_t$ are studied in detail in terms of the value of the top quark
mass measured at the Tevatron together with the lower bound on the lightest
Higgs mass established by LEP. The sensitive dependence of the higgsino mass
parameter $\mu$ and of the gaugino coupling strengths ${\tilde g}_{u,d}$,
${\tilde g}^\prime_{u,d}$ on the value of $Y_t$ in the vicinity of the Landau
pole is a remarkable consequence. A few other interesting features emerge,
though the model is found to be disfavored within the infrared fixed point
scenario because of the need to have several fine tunings at work on account of
the requirement of a low upper bound on $\tan\beta$.


  We introduce a new type of positional games, played on a vertex set of a
graph. Given a graph $G$, two players claim vertices of $G$, where the outcome
of the game is determined by the subgraphs of $G$ induced by the vertices
claimed by each player (or by one of them). We study classical positional games
such as Maker-Breaker, Avoider-Enforcer, Waiter-Client and Client-Waiter games,
where the board of the game is the vertex set of the binomial random graph
$G\sim G(n,p)$. Under these settings, we consider those games where the target
sets are the vertex sets of all graphs containing a copy of a fixed graph $H$,
called $H$-games, and focus on those cases where $H$ is a clique or a cycle. We
show that, similarly to the edge version of $H$-games, there is a strong
connection between the threshold probability for these games and the one for
the corresponding vertex Ramsey property (that is, the property that every
$r$-vertex-coloring of $G(n,p)$ spans a monochromatic copy of $H$). Another
similarity to the edge version of these games we demonstrate, is that the games
in which $H$ is a triangle or a forest present a different behavior compared to
the general case.


  The target mass corrections are calculated for all structure functions of
neutral and charged current deep inelastic scattering in lowest order in the
coupling constant. Representations of the correction to the twist--2 and
twist--3 contributions are derived both in Mellin-$n$ and $x$-space. The impact
of the target mass corrections on the general relations between the twist--2
and twist--3 parts of the structure functions is studied and three new
relations between the twist--3 contributions are derived.


  Mutual sticking of dust aggregates is the first step toward planetesimal
formation in protoplanetary disks. In spite that the electric charging of dust
particles is well recognized in some contexts, it has been largely ignored in
the current modeling of dust coagulation. In this study, we present a general
analysis of the dust charge state in protoplanetary disks, and then demonstrate
how the electric charging could dramatically change the currently accepted
scenario of dust coagulation. First, we describe a new semianalytical method to
calculate the dust charge state and gas ionization state self-consistently.
This method is far more efficient than previous numerical methods, and provides
a general and clear description of the charge state of gas-dust mixture.
Second, we apply this analysis to early evolutionary stages where the dust has
been thought to grow into fractal ($D \sim 2$) aggregates with a
quasi-monodisperse (i.e., narrow) size distribution. We find that, for a wide
range of model parameters, the fractal growth is strongly inhibited by the
electric repulsion between colliding aggregates and eventually "freezes out" on
its way to the subsequent growth stage involving collisional compression.
Strong disk turbulence would help the aggregates to overcome this growth
barrier, but then it would cause catastrophic collisional fragmentation in
later growth stages. These facts suggest that the combination of electric
repulsion and collisional fragmentation would impose a serious limitation on
dust growth in protoplanetary disks. We propose a possible scenario of dust
evolution after the freeze-out. Finally, we point out that the fractal growth
of dust aggregates tends to maintain a low ionization degree and, as a result,
a large magnetorotationally stable region in the disk.


  Writing an ad text that attracts people and persuades them to click or act is
essential for the success of search engine advertising. Therefore, ad creators
must consider various aspects of advertising appeals (A$^3$) such as the price,
product features, and quality. However, products and services exhibit unique
effective A$^3$ for different industries. In this work, we focus on exploring
the effective A$^3$ for different industries with the aim of assisting the ad
creation process. To this end, we created a dataset of advertising appeals and
used an existing model that detects various aspects for ad texts. Our
experiments demonstrated that different industries have their own effective
A$^3$ and that the identification of the A$^3$ contributes to the estimation of
advertising performance.


  Cancer segmentation in whole-slide images is a fundamental step for viable
tumour burden estimation, which is of great value for cancer assessment.
However, factors like vague boundaries or small regions dissociated from viable
tumour areas make it a challenging task. Considering the usefulness of
multi-scale features in various vision-related tasks, we present a
structure-aware scale-adaptive feature selection method for efficient and
accurate cancer segmentation. Based on a segmentation network with a popular
encoder-decoder architecture, a scale-adaptive module is proposed for selecting
more robust features to represent the vague, non-rigid boundaries. Furthermore,
a structural similarity metric is proposed for better tissue structure
awareness to deal with small region segmentation. In addition, advanced designs
including several attention mechanisms and the selective-kernel convolutions
are applied to the baseline network for comparative study purposes. Extensive
experimental results show that the proposed structure-aware scale-adaptive
networks achieve outstanding performance on liver cancer segmentation when
compared to top ten submitted results in the challenge of PAIP 2019. Further
evaluation on colorectal cancer segmentation shows that the scale-adaptive
module improves the baseline network or outperforms the other excellent designs
of attention mechanisms when considering the tradeoff between efficiency and
accuracy.


  We apply a functional implementation of the field-theoretical renormalization
group (RG) method up to two loops to the single-impurity Anderson model. To
achieve this, we follow a RG strategy similar to that proposed by Vojta
\emph{et al.} [Phys. Rev. Lett. \textbf{85}, 4940 (2000)], which consists of
defining a soft ultraviolet regulator in the space of Matsubara frequencies for
the renormalized Green's function. Then we proceed to derive analytically and
solve numerically integro-differential flow equations for the effective
couplings and the quasiparticle weight of the present model, which fully treat
the interplay of particle-particle and particle-hole parquet diagrams and the
effect of the two-loop self-energy feedback into them. We show that our results
correctly reproduce accurate numerical renormalization group data for weak to
slightly moderate interactions. These results are in excellent agreement with
other functional Wilsonian RG works available in the literature. Since the
field-theoretical RG method turns out to be easier to implement at higher loops
than the Wilsonian approach, higher-order calculations within the present
approach could improve further the results for this model at stronger
couplings. We argue that the present RG scheme could thus offer a possible
alternative to other functional RG methods to describe electronic correlations
within this model.


  In this review, the formation, evolution, and decay of the large-scale
structure of the Universe is discussed in the context of observational data,
numerical simulations, and the Cosmological Standard Model (CSM). Problems
concerning measuring and interpreting cosmological parameters, determining the
composition of matter, and normalizing density perturbation spectra are
especially highlighted.


  Statistical graphics and data visualization have long histories, but their
modern forms began only in the early 1800s. Between roughly 1850 and 1900
($\pm10$), an explosive growth occurred in both the general use of graphic
methods and the range of topics to which they were applied. Innovations were
prodigious and some of the most exquisite graphics ever produced appeared,
resulting in what may be called the ``Golden Age of Statistical Graphics.'' In
this article I trace the origins of this period in terms of the infrastructure
required to produce this explosive growth: recognition of the importance of
systematic data collection by the state; the rise of statistical theory and
statistical thinking; enabling developments of technology; and inventions of
novel methods to portray statistical data. To illustrate, I describe some
specific contributions that give rise to the appellation ``Golden Age.''


  To resolve the controversial issue of the topological nature of the
electronic structure of YbB$_{6}$, we have made a combined study using density
functional theory (DFT) and angle resolved photoemission spectroscopy (ARPES).
Accurate determination of the low energy band topology in DFT requires the use
of modified Becke-Johnson exchange potential incorporating the spin-orbit
coupling and the on-site Coulomb interaction $U$ of Yb $4f$ electrons as large
as 7 eV. We have double-checked the DFT result with the more precise GW band
calculation. ARPES is done with the non-polar (110) surface termination to
avoid band bending and quantum well confinement that have confused ARPES
spectra taken on the polar (001) surface termination. Thereby we show
definitively that YbB$_{6}$ has a topologically trivial B 2$p$-Yb 5$d$
semiconductor band gap, and hence is a non-Kondo non-topological insulator
(TI). In agreement with theory, ARPES shows pure divalency for Yb and a $p$-$d$
band gap of 0.3 eV, which clearly rules out both of the previous scenarios of
$f$-$d$ band inversion Kondo TI and $p$-$d$ band inversion non-Kondo TI. We
have also examined the pressure-dependent electronic structure of YbB$_{6}$,
and found that the high pressure phase is not a Kondo TI but a
\emph{p}-\emph{d} overlap semimetal.


  Studying the driving modes of turbulence is important for characterizing the
impact of turbulence in various astrophysical environments. The driving mode of
turbulence is parameterized by $b$, which relates the width of the gas density
PDF to the turbulent Mach number; $b\approx 1/3$, $1$, and $0.4$ correspond to
driving that is solenoidal, compressive, and a natural mixture of the two,
respectively. In this work, we use high-resolution (sub-pc) ALMA $^{12}$CO ($J$
= $2-1$), $^{13}$CO ($J$ = $2-1$), and C$^{18}$O ($J$ = $2-1$) observations of
filamentary molecular clouds in the star-forming region N159E (the Papillon
Nebula) in the Large Magellanic Cloud (LMC) to provide the first measurement of
turbulence driving parameter in an extragalactic region. We use a non-local
thermodynamic equilibrium (NLTE) analysis of the CO isotopologues to construct
a gas density PDF, which we find to be largely log-normal in shape with some
intermittent features indicating deviations from lognormality. We find that the
width of the log-normal part of the density PDF is comparable to the supersonic
turbulent Mach number, resulting in $b \approx 0.9$. This implies that the
driving mode of turbulence in N159E is primarily compressive. We speculate that
the compressive turbulence could have been powered by gravo-turbulent
fragmentation of the molecular gas, or due to compression powered by H I flows
that led to the development of the molecular filaments observed by ALMA in the
region. Our analysis can be easily applied to study the nature of turbulence
driving in resolved star-forming regions in the local as well as the
high-redshift Universe.


  We present a new formulation of the correlated electron-ion dynamics (CEID)
by using equations of motion for nonequilibrium Green's functions, which
generalizes CEID to a general nonequilibrium statistical ensemble that allows
for a variable total number of electrons. We make a rigorous connection between
CEID and diagrammatic perturbation theory, which furthermore allows the key
approximations in CEID to be quantified in diagrammatic terms, and, in
principle, improved. We compare analytically the limiting behavior of CEID and
the self-consistent Born approximation (SCBA) for a general dynamical
nonequilibrium state. This comparison shows that CEID and SCBA coincide in the
weak electron-phonon coupling limit, while they differ in the large ionic mass
limit where we can readily quantify their difference. In particular, we
illustrate the relation between CEID and SCBA by perturbation theory at the
fourth-order in the coupling strength.


  The spatial distribution of the railway crossings on the river Danube in
Hungary is very uneven. There is only one electrified and double-tracked bridge
in the country, the Southern Railway Bridge in Budapest. The \'Ujpest bridge in
Budapest only provides connection through line 4 which is not electrified and
the Baja bridge is not electrified, too, and both of them are single-tracked.
The long-planned V0 railway line that is to be cross the Danube approximately
halfway between Budapest and Baja would not only help to redistribute the total
network flow which currently passes through almost exclusively the Southern
bridge but would also provide redundancy for the existing bridges in the case
of their disruption. Four of the five proposed V0 path alternatives are
analyzed on the basis of these two network properties.


  The recent advent of noisy intermediate-scale quantum devices, especially
near-term quantum computers, has sparked extensive research efforts concerned
with their possible applications. At the forefront of the considered approaches
are variational methods that use parametrized quantum circuits. The classical
and quantum Fisher information are firmly rooted in the field of quantum
sensing and have proven to be versatile tools to study such parametrized
quantum systems. Their utility in the study of other applications of noisy
intermediate-scale quantum devices, however, has only been discovered recently.
Hoping to stimulate more such applications, this article aims to further
popularize classical and quantum Fisher information as useful tools for
near-term applications beyond quantum sensing. We start with a tutorial that
builds an intuitive understanding of classical and quantum Fisher information
and outlines how both quantities can be calculated on near-term devices. We
also elucidate their relationship and how they are influenced by noise
processes. Next, we give an overview of the core results of the quantum sensing
literature and proceed to a comprehensive review of recent applications in
variational quantum algorithms and quantum machine learning.