  Trajectory prediction forecasts nearby agents' moves based on their
historical trajectories. Accurate trajectory prediction is crucial for
autonomous vehicles. Existing attacks compromise the prediction model of a
victim AV by directly manipulating the historical trajectory of an attacker AV,
which has limited real-world applicability. This paper, for the first time,
explores an indirect attack approach that induces prediction errors via attacks
against the perception module of a victim AV. Although it has been shown that
physically realizable attacks against LiDAR-based perception are possible by
placing a few objects at strategic locations, it is still an open challenge to
find an object location from the vast search space in order to launch effective
attacks against prediction under varying victim AV velocities.
  Through analysis, we observe that a prediction model is prone to an attack
focusing on a single point in the scene. Consequently, we propose a novel
two-stage attack framework to realize the single-point attack. The first stage
of prediction-side attack efficiently identifies, guided by the distribution of
detection results under object-based attacks against perception, the state
perturbations for the prediction model that are effective and
velocity-insensitive. In the second stage of location matching, we match the
feasible object locations with the found state perturbations. Our evaluation
using a public autonomous driving dataset shows that our attack causes a
collision rate of up to 63% and various hazardous responses of the victim AV.
The effectiveness of our attack is also demonstrated on a real testbed car. To
the best of our knowledge, this study is the first security analysis spanning
from LiDAR-based perception to prediction in autonomous driving, leading to a
realistic attack on prediction. To counteract the proposed attack, potential
defenses are discussed.


  Offline reinforcement learning (RL) tries to learn the near-optimal policy
with recorded offline experience without online exploration. Current offline RL
research includes: 1) generative modeling, i.e., approximating a policy using
fixed data; and 2) learning the state-action value function. While most
research focuses on the state-action function part through reducing the
bootstrapping error in value function approximation induced by the distribution
shift of training data, the effects of error propagation in generative modeling
have been neglected. In this paper, we analyze the error in generative
modeling. We propose AQL (action-conditioned Q-learning), a residual generative
model to reduce policy approximation error for offline RL. We show that our
method can learn more accurate policy approximations in different benchmark
datasets. In addition, we show that the proposed offline RL method can learn
more competitive AI agents in complex control tasks under the multiplayer
online battle arena (MOBA) game Honor of Kings.


  We previously reported on a recursive method to generate the expansion of the
lattice Green function of the $d$-dimensional face-centred cubic lattice (fcc).
The method was used to generate many coefficients for d=7 and the corresponding
linear differential equation has been obtained. In this paper, we show the
strength and the limit of the method by producing the series and the
corresponding linear differential equations for d=8, 9, 10, 11, 12. The
differential Galois groups of these linear differential equations are shown to
be symplectic for d=8, 10, 12 and orthogonal for d= 9, 11. The recursion
relation naturally provides a 2-dimensional array $ T_d(n,j)$ where only the
coefficients $ t_d(n,0)$ correspond to the coefficients of the lattice Green
function of the d-dimensional fcc. The coefficients $ t_d(n,j)$ are associated
to D-finite bivariate series annihilated by linear partial differential
equations that we analyze.


  The thermal conductivity and stability of nanofluids have posed the biggest
challenges to their adoption as coolants in thermal applications in industries
such as electronic equipment, heat exchangers, and solar technologies. In this
paper, the thermal conductivity coefficient of an Al$_{12}$Mg$_{17}$ nanofluid
is measured by a novel beam displacement method. Besides, the stability,
particle size distribution (PSD), TEM micrograph, and electrical conductivity
of Al$_{12}$Mg$_{17}$ nanofluids are investigated. For the preparation of
nanofluids, three different surfactants are used to disperse Al$_{12}$Mg$_{17}$
nanoparticles in DI water using two-step method. Then, dispersion stability is
monitored visually and quantified using a zeta potential test. The thermal
conductivity coefficient and particle size distribution are measured using two
optical setups. For the purpose of evaluating the outcomes, the thermal
conductivity coefficients estimated using the beam displacement method are
compared with the KD2 Pro apparatus results, and the PSD findings are examined
using TEM micrographs. Results demonstrate that a 1:1 ratio of CTAB and
Al$_{12}$Mg$_{17}$ nanoparticles is proper for stabilizing Al$_{12}$Mg$_{17}$
nanofluid. Also, the optimum ultrasonication period is determined to be 2
hours, and the peak of particle size distribution is measured to be 154
nanometers at this time. Thermal conductivity measurements show that the
thermal conductivity coefficients improve as the concentration of
Al$_{12}$Mg$_{17}$ nanoparticles increases, reaching a maximum enhancement of
40% in comparison to the base fluid at a concentration of 0.05 vol.%.


  Galaxies evolve via a complex interaction of numerous different physical
processes, scales and components. In spite of this, overall trends often
appear. Simplified models for galaxy histories can be used to search for and
capture such emergent trends, and thus to interpret and compare results of
galaxy formation models to each other and to nature. Here, two approximations
are applied to galaxy integrated star formation rate histories, drawn from a
semi-analytic model grafted onto a dark matter simulation. Both a lognormal
functional form and principal component analysis (PCA) approximate the
integrated star formation rate histories fairly well. Machine learning, based
upon simplified galaxy halo histories, is somewhat successful at recovering
both fits. The fits to the histories give fixed time star formation rates which
have notable scatter from their true fixed time rates at final time, especially
for quiescent and "green valley" galaxies, and more so for the PCA fit. For
classifying galaxies into subfamilies sharing similar integrated histories,
both approximations are better than using final stellar mass or specific star
formation rate. Several subsamples from the simulation illustrate how these
simple parameterizations can provide points of contact for comparisons between
different galaxy formation samples, or more generally, models. As a side
result, the halo masses of simulated galaxies with early peak star formation
rate (according to the lognormal fit) lie on one of two tracks. The small
fraction of galaxies with a lower halo mass at peak star formation rate appear
to stall in their halo growth, even though they are central in their host
halos.


  Statistical learning evolves quickly with more and more sophisticated models
proposed to incorporate the complicated data structure from modern scientific
and business problems. Varying index coefficient models extend varying
coefficient models and single index models, becoming the latest
state-of-the-art for semiparametric regression. This new class of models offers
greater flexibility to characterize complicated nonlinear interaction effects
in regression analysis. To safeguard against outliers and extreme observations,
we consider a robust quantile regression approach to estimate the model
parameters in this paper. High-dimensional loading parameters are allowed in
our development under reasonable theoretical conditions. In addition, we
propose a regularized estimation procedure to choose between linear and
non-linear forms for interaction terms. We can simultaneously select
significant non-zero loading parameters and identify linear functions in
varying index coefficient models, in addition to estimate all the parametric
and nonparametric components consistently. Under technical assumptions, we show
that the proposed procedure is consistent in variable selection as well as in
linear function identification, and the proposed parameter estimation enjoys
the oracle property. Extensive simulation studies are carried out to assess the
finite sample performance of the proposed method. We illustrate our methods
with an environmental health data example.


  Seeing, the angular size of stellar images blurred by atmospheric turbulence,
is a critical parameter used to assess the quality of astronomical sites.
Median values at the best mid-latitude sites are generally in the range of
0.6--0.8\,arcsec. Sites on the Antarctic plateau are characterized by
comparatively-weak turbulence in the free-atmosphere above a strong but thin
boundary layer. The median seeing at Dome C is estimated to be 0.23--0.36
arcsec above a boundary layer that has a typical height of 30\,m. At Dome A and
F, the only previous seeing measurements were made during daytime. Here we
report the first direct measurements of night-time seeing at Dome A, using a
Differential Image Motion Monitor. Located at a height of just 8\,m, it
recorded seeing as low as 0.13\,arcsec, and provided seeing statistics that are
comparable to those for a 20\,m height at Dome C. It indicates that the
boundary layer was below 8\,m 31\% of the time. At such times the median seeing
was 0.31\,arcsec, consistent with free-atmosphere seeing. The seeing and
boundary layer thickness are found to be strongly correlated with the
near-surface temperature gradient. The correlation confirms a median thickness
of approximately 14\,m for the boundary layer at Dome A, as found from a sonic
radar. The thinner boundary layer makes it less challenging to locate a
telescope above it, thereby giving greater access to the free-atmosphere.


  We use weak gravitational lensing to measure the masses of five galaxy
clusters selected from the South Pole Telescope (SPT) survey, with the primary
goal of comparing these with the SPT Sunyaev--Zel'dovich (SZ) and X-ray based
mass estimates. The clusters span redshifts 0.28 < z < 0.43 and have masses
M_500 > 2 x 10^14 h^-1 M_sun, and three of the five clusters were discovered by
the SPT survey. We observed the clusters in the g'r'i' passbands with the
Megacam imager on the Magellan Clay 6.5m telescope. We measure a mean ratio of
weak lensing (WL) aperture masses to inferred aperture masses from the SZ data,
both within an aperture of R_500,SZ derived from the SZ mass, of 1.04 +/- 0.18.
We measure a mean ratio of spherical WL masses evaluated at R_500,SZ to
spherical SZ masses of 1.07 +/- 0.18, and a mean ratio of spherical WL masses
evaluated at R_500,WL to spherical SZ masses of 1.10 +/- 0.24. We explore
potential sources of systematic error in the mass comparisons and conclude that
all are subdominant to the statistical uncertainty, with dominant terms being
cluster concentration uncertainty and N-body simulation calibration bias.
Expanding the sample of SPT clusters with WL observations has the potential to
significantly improve the SPT cluster mass calibration and the resulting
cosmological constraints from the SPT cluster survey. These are the first WL
detections using Megacam on the Magellan Clay telescope.


  We consider linear inverse problems in a nonparametric statistical framework.
Both the signal and the operator are unknown and subject to error measurements.
We establish minimax rates of convergence under squared error loss when the
operator admits a blockwise singular value decomposition (blockwise SVD) and
the smoothness of the signal is measured in a Sobolev sense. We construct a
nonlinear procedure adapting simultaneously to the unknown smoothness of both
the signal and the operator and achieving the optimal rate of convergence to
within logarithmic terms. When the noise level in the operator is dominant, by
taking full advantage of the blockwise SVD property, we demonstrate that the
block SVD procedure overperforms classical methods based on Galerkin projection
or nonlinear wavelet thresholding. We subsequently apply our abstract framework
to the specific case of blind deconvolution on the torus and on the sphere.


  We discuss the high energy scattering of hadrons with the multi - Regge
kinematics. The effective $2D$ model for the interaction between the produced
hard gluons appears as a result of the dimensional reduction, which is similar
to the dimensional reduction $3+1$ D $\to 3$ D of the high temperature gauge
theory. It is demonstrated that being supplemented by the exchange by virtual
$3+1$ D gluons this $2D$ model gives rise to the hermitian version of the
effective action of Lipatov for the interaction between the ordinary and the
reggeized gluons.


  Subsampling from a large data set is useful in many supervised learning
contexts to provide a global view of the data based on only a fraction of the
observations. Diverse (or space-filling) subsampling is an appealing
subsampling approach when no prior knowledge of the data is available. In this
paper, we propose a diversity subsampling approach that selects a subsample
from the original data such that the subsample is independently and uniformly
distributed over the support of distribution from which the data are drawn, to
the maximum extent possible. We give an asymptotic performance guarantee of the
proposed method and provide experimental results to show that the proposed
method performs well for typical finite-size data. We also compare the proposed
method with competing diversity subsampling algorithms and demonstrate
numerically that subsamples selected by the proposed method are closer to a
uniform sample than subsamples selected by other methods. The proposed DS
algorithm is shown to be more efficient than known methods and takes only a few
minutes to select tens of thousands of subsample points from a data set of size
one million. Our DS algorithm easily generalizes to select subsamples following
distributions other than uniform. We provide the FADS Python package to
implement the proposed methods.


  We introduce a scheme for fault tolerantly dealing with losses (or other
"leakage" errors) in cluster state computation that can tolerate up to 50%
qubit loss. This is achieved passively using an adaptive strategy of
measurement - no coherent measurements or coherent correction is required.
Since the scheme relies on inferring information about what would have been the
outcome of a measurement had one been able to carry it out, we call this
"counterfactual" error correction.


  We report on high time resolution observations of the SNR 3C 58 using the
High Resolution Camera on the Chandra X-ray Observatory. These data show a
point-like central source, from which we detect 65.68 ms pulsations at 6.7
sigma significance. We interpret these pulsations as corresponding to a young
rotation-powered pulsar (PSR J-205+6449) which is associated with, and powers,
3C 58. Analysis of archival RXTE data from three years earlier confirms these
pulsations, and allows us to determine a spin-down rate of P-dot=1.93x10^-13
s/s. Assuming a magnetic dipole model for PSR J0205+6449, we infer a surface
magnetic field of 3.6x10^12 G. The characteristic age for this pulsar is 5400
yr, indicating either that 3C 58 was not the supernova of 1181 CE, or that the
pulsar's initial spin period was ~60 ms.


  We study the question of realisability of iterated higher Whitehead products
with a given form of nested brackets by simplicial complexes, using the notion
of the moment-angle complex $Z_K$. Namely, we say that a simplicial complex $K$
realises an iterated higher Whitehead product $w$ if $w$ is a nontrivial
element of $\pi_*(Z_K)$. The combinatorial approach to the question of
realisability uses the operation of substitution of simplicial complexes: for
any iterated higher Whitehead product $w$ we describe a simplicial complex
$\partial\Delta_w$ that realises $w$. Furthermore, for a particular form of
brackets inside $w$, we prove that $\partial\Delta_w$ is the smallest complex
that realises $w$. We also give a combinatorial criterion for the nontriviality
of the product $w$. In the proof of nontriviality we use the Hurewicz image of
$w$ in the cellular chains of $Z_K$ and the description of the cohomology
product of $Z_K$. The second approach is algebraic: we use the coalgebraic
versions of the Koszul and Taylor complex for the face coalgebra of $K$ to
describe the canonical cycles corresponding to iterated higher Whitehead
products $w$. This gives another criterion for realisability of $w$.


  We study the structure constants defining two related rings: the spherical
Hecke algebra of a split connected reductive group over a non-Archimedean local
field, and the representation ring of the Langlands dual group.


  Assuming the existence of a strong cardinal $\kappa$, a weakly compact
cardinal $\lambda$ above it and $\gamma > \lambda,$ we force a generic
extension in which $\kappa$ is a singular strong limit cardinal of any given
cofinality $\delta$, $2^\kappa\geq \gamma$ and such that the tree property
holds at $\kappa^{++}$.


  The glaciological record of atmospheric composition suggests O2 has declined
over the last 800000 years at an average rate of 0.3 x 1012 mol (Tmol) O2 yr-1.
Because the geological carbon cycle regulates long term atmospheric oxygen
concentrations, fluctuations in atmospheric O2 are typically attributed to an
imbalance between the weathering of organic carbon (OC) and reduced sulfur on
land, a sink of atmospheric O2, and the burial of OC and reduced sulfur in
marine sediments, a source of O2. Here we compile and confront a database of C,
Fe, S and H exchanges between the fluid Earth (atmosphere, ocean, biosphere)
and lithosphere (crust and upper mantle) with the record of deoxygenation to
quantify organic carbon sources and sinks in the Cenozoic. We show that the
subduction of oxidized oceanic lithosphere and degassing of reduced gas from
the Earth's interior is a sink of oxygen, and that this sink significantly
exceeds the rate of atmospheric deoxygenation in the Pleistocene. A relative
constancy of atmospheric O2 in the Cenozoic requires that the organic carbon
cycle was a net source of O2 and sink of CO2, photosynthesis outpaced
respiration by an average of ca 40 MtC yr-1 over the last 50 million years. The
cost for the relatively invariant atmospheric oxygen concentration is the
coexistence of two photosynthetically-driven imbalances in the cycles of iron
and carbon that offset each other to near perfection. The weak escape of OC
from continents and oceans to the lithosphere is more intriguing. It
demonstrates that the organic carbon cycle remains under surprisingly strong
kinetic control, despite evolution/optimization of respiratory metabolisms and
rising atmospheric O2 for more than 2.4 billion years.


  Modelling of the spectra of magnetic A and B main sequence stars is generally
done assuming that all the lines are split by the magnetic field according to
the Zeeman effect. However, a number of prominent spectral lines are produced
by closely spaced doublets or triplets. Such lines should be treated using the
theory of the partial Paschen-Back (PPB) effect. Depending on the strength and
orientation of magnetic field, the PPB effect can result in the Stokes IV
profiles of a spectral line that differ significantly from those predicted by
the Zeeman effect theory. It is important to understand the size and types of
errors that are introduced into magnetic spectrum synthesis by treating such
lines with the usual Zeeman splitting theory rather than using the correct
theoretical treatment of line splitting. To estimate the error introduced by
the use of the Zeeman approximation, numerical simulations have been performed
for spectral lines of the element silicon, for which a number of important
lines are actually in the PPB regime, assuming an oblique rotator model, for
various silicon abundances and Vsini values. A comparative analysis of the
Stokes IV profiles calculated assuming the PPB and Zeeman splitting has been
carried out for a number of both strong and weak SiII and SiIII lines. The
analysis indicates that for high precision studies of some spectral lines the
PPB approach should be used if the field strength at the magnetic poles is
Bp>10kG. In the case of the SiII line 5041A, the difference between the two
simulated profiles is caused by a significant contribution from a so called
"ghost" line.


  In the present paper, we consider the following singularly perturbed problem:
\begin{equation*}
  \left\{
  \begin{array}{ll}
  -\varepsilon^2\triangle u+V(x)u=\varepsilon^{-\alpha}(I_{\alpha}*F(u))f(u), &
x\in \R^N;
  u\in H^1(\R^N),
  \end{array}
  \right. \end{equation*} where $\varepsilon>0$ is a parameter, $N\ge 3$,
$\alpha\in (0, N)$, $F(t)=\int_{0}^{t}f(s)\mathrm{d}s$ and $I_{\alpha}:
\R^N\rightarrow \R$ is the Riesz potential. By introducing some new tricks, we
prove that the above problem admits a semiclassical ground state solution
($\varepsilon\in (0,\varepsilon_0)$) and a ground state solution
($\varepsilon=1$) under the general "Berestycki-Lions assumptions" on the
nonlinearity $f$ which are almost necessary, as well as some weak assumptions
on the potential $V$. When $\varepsilon=1$, our results generalize and improve
the ones in [V. Moroz, J. Van Schaftingen, T. Am. Math. Soc. 367 (2015)
6557-6579] and [H. Berestycki, P.L. Lions, Arch. Rational Mech. Anal. 82 (1983)
313-345] and some other related literature. In particular, our approach is
useful for many similar problems.


  The random q-state quantum Potts model is studied on hypercubic lattices in
dimensions 2 and 3 using the numerical implementation of the Strong Disorder
Renormalization Group introduced by Kovacs and Igl{\'o}i [Phys. Rev. B 82,
054437 (2010)]. Critical exponents $\nu$, d f and $\psi$ at the Infinite
Disorder Fixed Point are estimated by Finite-Size Scaling for several numbers
of states q between 2 and 50. When scaling corrections are not taken into
account, the estimates of both d f and $\psi$ systematically increase with q.
It is shown however that q-dependent scaling corrections are present and that
the exponents are compatible within error bars, or close to each other, when
these corrections are taking into account. This provides evidence of the
existence of a super-universality of all 2D and 3D random Potts models.


  We prove three sharp bounds for solutions to the porous medium equation posed
on Riemannian manifolds, or for weighted versions of such equation. Firstly we
prove a smoothing effect for solutions which is valid on any Cartan-Hadamard
manifold whose sectional curvatures are bounded above by a strictly negative
constant. This bound includes as a special case the sharp smoothing effect
recently proved by V\'azquez on the hyperbolic space in \cite{V}, which is
similar to the absolute bound valid in the case of bounded Euclidean domains
but has a logarithmic correction. Secondly we prove a bound which interpolates
between such smoothing effect and the Euclidean one, supposing a suitable
quantitative Sobolev inequality holds, showing that it is sharp by means of
explicit examples. Finally, assuming a stronger functional inequality of
sub-Poincar\'e type, we prove that the above mentioned (sharp) absolute bound
holds, and provide examples of weighted porous media equations on manifolds of
infinite volume in which it holds, in contrast with the non-weighted Euclidean
situation. It is also shown that sub-Poincar\'e inequalities cannot hold on
Cartan-Hadamard manifolds.


  We show that real tight frames that generate lattices must be rational, and
use this observation to describe a construction of lattices from vertex
transitive graphs. In the case of irreducible group frames, we show that the
corresponding lattice is always strongly eutactic. This is the case for the
more restrictive class of distance transitive graphs. We show that such
lattices exist in arbitrarily large dimensions and demonstrate examples arising
from some notable families of graphs. In particular, some well-known root
lattices and those related to them can be recovered this way. We discuss
various properties of this construction and also mention some potential
applications of lattices generated by incoherent systems of vectors.


  Recent progress in deep learning and natural language processing has given
rise to powerful models that are primarily trained on a cloze-like task and
show some evidence of having access to substantial linguistic information,
including some constructional knowledge. This groundbreaking discovery presents
an exciting opportunity for a synergistic relationship between computational
methods and Construction Grammar research. In this chapter, we explore three
distinct approaches to the interplay between computational methods and
Construction Grammar: (i) computational methods for text analysis, (ii)
computational Construction Grammar, and (iii) deep learning models, with a
particular focus on language models. We touch upon the first two approaches as
a contextual foundation for the use of computational methods before providing
an accessible, yet comprehensive overview of deep learning models, which also
addresses reservations construction grammarians may have. Additionally, we
delve into experiments that explore the emergence of constructionally relevant
information within these models while also examining the aspects of
Construction Grammar that may pose challenges for these models. This chapter
aims to foster collaboration between researchers in the fields of natural
language processing and Construction Grammar. By doing so, we hope to pave the
way for new insights and advancements in both these fields.


  We study the most central regions of AGN jets with an angular resolution of
tens of micro-arcseconds using VLBI at millimeter wavelengths (mm-VLBI). We
present and discuss a new 86 GHz VLBI survey of compact radio sources. We show
new high dynamic range images of two nearby radio galaxies (3C 120 and M87). In
M87 the size of the compact VLBI core (the jet base) is < 15 Schwarzschild
radii. Future mm-VLBI observations at 1 mm and shorter wavelengths should lead
to images of galactic and extragalactic radio sources with a spatial resolution
down to a few Schwarzschild radii of the central super massive black holes. To
achieve this, the participation of large and sensitive millimeter and
sub-millimeter telescopes in VLBI is essential. Owing to their high sensitivity
existing and planned phased interferometers like the IRAM Plateau de Bure
interferometer, CARMA, the SMA and ALMA could play an important role.


  Large Language Models (LLMs) are prone to generating content that exhibits
gender biases, raising significant ethical concerns. Alignment, the process of
fine-tuning LLMs to better align with desired behaviors, is recognized as an
effective approach to mitigate gender biases. Although proprietary LLMs have
made significant strides in mitigating gender bias, their alignment datasets
are not publicly available. The commonly used and publicly available alignment
dataset, HH-RLHF, still exhibits gender bias to some extent. There is a lack of
publicly available alignment datasets specifically designed to address gender
bias. Hence, we developed a new dataset named GenderAlign, aiming at mitigating
a comprehensive set of gender biases in LLMs. This dataset comprises 8k
single-turn dialogues, each paired with a "chosen" and a "rejected" response.
Compared to the "rejected" responses, the "chosen" responses demonstrate lower
levels of gender bias and higher quality. Furthermore, we categorized the
gender biases in the "rejected" responses of GenderAlign into 4 principal
categories. The experimental results show the effectiveness of GenderAlign in
reducing gender bias in LLMs.


  The scaling behaviour of the Lyapunov exponent near the transition to chaos
via type-III intermittency is determined for a generic map. A critical exponent
$\beta$ expressing the scaling of the Lyapunov exponent as a function of both,
the reinjection probability and the nonlinearity of the map is calculated. It
is found that the critical exponent varies on the interval $0 < \beta < 1$.
This contrasts with earlier predictions for the scaling behaviour of the
Lyapunov exponent in type-III intermittency.


  We study the problem of the escape and transport of Cosmic-Rays (CR) from a
source embedded in a fully ionised, hot phase of the interstellar medium (HIM).
In particular, we model the CR escape and their propagation in the source
vicinity taking into account excitation of Alfv\'enic turbulence by CR
streaming and mechanisms damping the self-excited turbulence itself. Our
estimates of escape radii and times result in large values (100 pc,
$2\times10^5$ yr) for particle energies $\lesssim20$ GeV and smaller values for
particles with increasing energies (35 pc and 14 kyr at 1 TeV). These escape
times and radii, when used as initial conditions for the CR propagation outside
the source, result in relevant suppression of the diffusion coefficient (by a
factor 5-10) on time-scales comparable with their (energy dependent) escape
time-scale. The damping mechanisms are fast enough that even on shorter time
scales the Alfv\'enic turbulence is efficiently damped, and the ratio between
random and ordered component of the magnetic field is $\delta B/B_0\ll 1$,
justifying the use of quasi-linear theory. In spite of the suppressed diffusion
coefficient, and then the increased residence time in the vicinity (<200 pc) of
their source, the grammage accumulated by CRs after their escape is found to be
negligible (at all energies) as compared to the one accumulated while diffusing
in the whole Galaxy, due to the low density of the HIM.


  We characterize strongly finitary monads on categories $\mathsf{Pos}$,
$\mathsf{CPO}$ and $\mathsf{DCPO}$ as precisely those preserving sifted
colimits. Or, equivalently, enriched finitary monads preserving reflexive
coinserters. We study sifted colimits in general enriched categories.
  For $\mathsf{CPO}$ and $\mathsf{DCPO}$ we characterize varieties of
continuous algebras as precisely the monadic categories for strongly finitary
monads.


  We show that there exists a finitely generated group of growth ~f for all
functions f:\mathbb{R}\rightarrow\mathbb{R} satisfying f(2R) \leq f(R)^{2} \leq
f(\eta R) for all R large enough and \eta\approx2.4675 the positive root of
X^{3}-X^{2}-2X-4. This covers all functions that grow uniformly faster than
\exp(R^{\log2/\log\eta}).
  We also give a family of self-similar branched groups of growth
~\exp(R^\alpha) for a dense set of \alpha\in(\log2/\log\eta,1).


  For the past four decades, cost and features have driven CMOS scaling. Severe
lithography and material limitations seen below the 20 nm node, however, are
challenging the fundamental premise of affordable CMOS scaling. Just continuing
to co-optimize leaf cell circuit and layout designs with process technology
does not enable us to exploit the challenges of a sub-20 nm CMOS. For
affordable scaling it is imperative to work past sub-20 nm technology
impediments while exploiting its features. To this end, we propose to broaden
the scope of design technology co-optimization (DTCO) to be more holistic by
including micro-architecture design and CAD, along with circuits, layout and
process technology. Applying such holistic DTCO to the most significant block
in a system-on-chip (SoC), embedded memory, we can synthesize smarter and
efficient embedded memory blocks that are customized to application needs.
  To evaluate the efficacy of the proposed holistic DTCO process, we designed,
fabricated and tested several design experiments in a state-of-the-art IBM
14SOI process. DTCOed leaf cells, standard cells and SRAM bitcells were robust
during testing, but failed to meet node to node area scaling requirements.
Holistic DTCO, when applied to a widely used parallel access SRAM sub-block,
consumed 25% less area with a 50% better performance per watt compared to a
traditional implementation using compiled SRAM blocks and standard cells. To
extend the benefits of holistic DTCO to other embedded memory intensive
sub-blocks in SoCs, we developed a readily customizable smart memory synthesis
framework (SMSF). We believe that such an approach is important to establish an
affordable path for sub-20 nm scaling.


  Many important properties of quantum channels are quantified by means of
entropic functionals. Characteristics of such a kind are closely related to
different representations of a quantum channel. In the Jamio{\l}kowski-Choi
representation, the given quantum channel is described by the so-called
dynamical matrix. Entropies of the rescaled dynamical matrix known as map
entropies describe a degree of introduced decoherence. Within the so-called
natural representation, the quantum channel is formally posed by another matrix
obtained as reshuffling of the dynamical matrix. The corresponding entropies
characterize an amount, with which the receiver a priori knows the channel
output. As was previously shown, the map and receiver entropies are mutually
complementary characteristics. Indeed, there exists a non-trivial lower bound
on their sum. First, we extend the concept of receiver entropy to the family of
unified entropies. Developing the previous results, we further derive
non-trivial lower bounds on the sum of the map and receiver $(q,s)$-entropies.
The derivation is essentially based on some inequalities with the Schatten
norms and anti-norms.


  This paper is devoted to show that the last quarter of the past century can
be considered as the golden age of the Mathematical Finance. In this period the
collaboration of great economists and the best generation of probabilists, most
of them from the Strasbourg's School led by Paul Andr\'e Meyer, gave rise to
the foundations of this discipline. They established the two fundamentals
theorems of arbitrage theory, close formulas for options, the main modelling a


  In this paper, we investigate output accuracy for a Discrete Event Simulation
(DES) model and Agent Based Simulation (ABS) model. The purpose of this
investigation is to find out which of these simulation techniques is the best
one for modelling human reactive behaviour in the retail sector. In order to
study the output accuracy in both models, we have carried out a validation
experiment in which we compared the results from our simulation models to the
performance of a real system. Our experiment was carried out using a large UK
department store as a case study. We had to determine an efficient
implementation of management policy in the store's fitting room using DES and
ABS. Overall, we have found that both simulation models were a good
representation of the real system when modelling human reactive behaviour.


  We construct consistent interacting gauge theories for M conformal massless
spin-2 fields ("Weyl gravitons") with the following properties: (i) in the free
limit, each field fulfills the equation ${\cal B}^{\mu \nu} = 0$, where ${\cal
B}^{\mu \nu}$ is the linearized Bach tensor, (ii) the interactions contain no
more than four derivatives, just as the free action and (iii) the internal
metric for the Weyl gravitons is not positive definite. The interacting
theories are obtained by gauging appropriate non-semi-simple extensions of the
conformal algebra $so(4,2)$ with commutative, associative algebras of dimension
M. By writing the action in terms of squares of supercurvatures,
supersymmetrization is immediate and leads to consistent conformal
supergravities with M interacting gravitons.


  Let $\mathfrak{g}$ be a symmetrizable Kac-Moody algebra, $U_q(\mathfrak{g})$
its quantum group, and $U_q(\mathfrak{k}) \subset U_q(\mathfrak{g})$ a quantum
symmetric pair subalgebra determined by a distinguished Lie algebra
automorphism $\theta$. We introduce a category $W_\theta$ of \emph{weight}
$U_q(\mathfrak{k})$-modules, which is acted upon by the category of weight
$U_q(\mathfrak{g})$-modules via tensor products. We construct a universal
tensor K-matrix $\mathbb{K}$ (that is, a solution of a reflection equation) in
a completion of $U_q(\mathfrak{k}) \otimes U_q(\mathfrak{g})$. This yields a
natural operator on any tensor product $M \otimes V$, where $M\in W_\theta$ and
$V\in O_\theta$, \ie $V$ is a $U_q(\mathfrak{g})$-module in category $O$
satisfying an integrability property determined by $\theta$. $W_\theta$ is
equipped with a canonical structure of a bimodule category over $O_\theta$ and
the action of $\mathbb{K}$ is encoded by a new categorical structure, which we
refer to as a \emph{boundary} structure on the bimodule category $W_\theta$.
This provides the most comprehensive algebraic framework to date for quantum
integrability in the presence of boundaries. In particular, it generalizes a
theorem of Kolb which describes a braided module structure on
finite-dimensional $U_q(\mathfrak{k})$-modules when $\mathfrak{g}$ is
finite-dimensional.
  We apply our construction to the case of an affine Lie algebra, where it
yields a formal tensor K-matrix valued in the endomorphisms of the tensor
product of any module in $W_\theta$ and any finite-dimensional module over the
corresponding quantum affine algebra. We prove that this formal series can be
normalized to a trigonometric K-matrix if the factors in the tensor product are
both finite-dimensional irreducible modules over the quantum affine algebra.


  We show that if A is a d-dimensional abelian variety in a smooth quadric of
dimension 2d then d=1 and A is an elliptic curve of bidegree (2,2) on a
quadric. This extends a result of Van de Ven which says that A only can be
embedded in P^{2d} when d=1 or 2.


  In this lecture we review some of the recent developments in string theory on
an introductory and qualitative level. In particular we focus on S-T-U
dualities of toroidally compactified ten-dimensional string theories and
outline the connection to M-theory. Dualities among string vacua with less
supersymmetries in six and four space-time dimensions is discussed and the
concept of F-theory is briefly presented. (Lecture given by J. Louis at the
Workshop on Gauge Theories, Applied Supersymmetry and Quantum Gravity, Imperial
College, London, UK, July 5--10, 1996.)


  We are proud to introduce this special issue of the Journal of Theory and
Practice of Logic Programming (TPLP), dedicated to the full papers accepted for
the 28th International Conference on Logic Programming (ICLP). The ICLP
meetings started in Marseille in 1982 and since then constitute the main venue
for presenting and discussing work in the area of logic programming.


  Using the chromo-electric dipole coupling Hamiltonian from QCD multipole
expansion, we derive the dissociation cross sections of heavy quarkonia by
thermal gluons at next-to-leading order (NLO, also known as inelastic parton
scattering dissociation) in the Quark-Gluon Plasma (QGP) in the framework of
second order quantum mechanical perturbation theory. While suffering divergence
(infrared and soft-collinear divergences) in vacuum, the cross sections thus
derived become finite in the QGP as rendered by the finite thermal gluon
masses. In contrast to the leading order (LO, also known as gluo-dissociation)
counterparts rapidly dropping off with increasing incident gluon energy, the
NLO cross sections exhibits finite value toward high energies because of new
phase space being opened up. We then carry out a full calculation of the
dissociation rates for various charmonia and bottomonia within a
non-relativistic in-medium potential model. The NLO process is shown to
dominate the dissociation rate toward high temperatures when the binding
energies of heavy quarkonia become smaller relative to the Debye screening
mass.


  We have investigated the growth by molecular-beam epitaxy of the II-VI
diluted magnetic semiconductor (Zn,Mn)Se on As-passivated Si(100) substrates.
The growth start has been optimized by using low-temperature epitaxy. Surface
properties were assessed by Nomarski and scanning electron microscopy. Optical
properties of (Zn,Mn)Se have been studied by photoluminescence and a giant
Zeeman splitting of up to 30 meV has been observed. Our observations indicate a
high crystalline quality of the epitaxial films.


  A pure Dirac's method of Yang-Mills expressed as a constrained BF-like theory
is performed. In this paper we study an action principle composed by the
coupling of two topological BF-like theories, which at the Lagrangian level
reproduces Yang-Mills equations. By a pure Dirac's method we mean that we
consider all the variables that occur in the Lagrangian density as dynamical
variables and not only those ones that involve temporal derivatives. The
analysis in the complete phase space enable us to calculate the extended
Hamiltonian, the extended action, the constraint algebra, the gauge
transformations and then we carry out the counting of degrees of freedom. We
show that the constrained BF-like theory correspond at classical level to
Yang-Mills theory. From the results obtained, we discuss briefly the
quantization of the theory. In addition we compare our results with
alternatives models that have been reported in the literature


  We realize the integral Specht modules for the symmetric group $S_n$ as
induced modules from the subalgebra of the group algebra generated by the
Jucys-Murphy elements. We deduce from this that the simple modules for $FS_n$
are generated by reductions modulo $p$ of the corresponding Jucys-Murphy
idempotents.


  We give labeled natural deduction systems for a family of tense logics
extending the basic linear tense logic Kl. We prove that our systems are sound
and complete with respect to the usual Kripke semantics, and that they possess
a number of useful normalization properties (in particular, derivations reduce
to a normal form that enjoys a subformula property). We also discuss how to
extend our systems to capture richer logics like (fragments of) LTL.


  Let $K$ be a complete discretely valued field with residue field $k$ with
$\mathrm{char}(k)\neq 2$. Assuming that the norm principle holds for extended
Clifford groups $\Omega(q)$ for every even dimensional non-degenerate quadratic
form $q$ defined over any finite extension of $k$, we show that it holds for
extended Clifford groups $\Omega(Q)$ for every even dimensional non-degenerate
quadratic form $Q$ defined over $K$.


  In the present paper we consider $F_k(x)=x^{k}-\sum_{t=0}^{k-1}x^t,$ the
characteristic polynomial of the $k$-th order Fibonacci sequence, the latter
denoted $G(k,l).$ We determine the limits of the real roots of certain odd and
even degree polynomials related to the derivatives and integrals of $F_k(x),$
that form infinite sequences of polynomials, of increasing degree. In
particular, as $k \to \infty,$ the limiting values of the zeros are determined,
for both odd and even cases. It is also shown, in both cases, that the
convergence is monotone for sufficiently large degree. We give an upper bound
for the modulus of the complex zeros of the polynomials for each sequence. This
gives a general solution related to problems considered by Dubeau 1989, 1993,
Miles 1960, Flores 1967, Miller 1971 and later by the second author in the
present paper, and Narayan 1997.


  A graph G is a multi-interval PCG if there exist an edge weighted tree T with
non-negative real values and disjoint intervals of the non-negative real
half-line such that each node of G is uniquely associated to a leaf of T and
there is an edge between two nodes in G if and only if the weighted distance
between their corresponding leaves in T lies within any such intervals. If the
number of intervals is k, then we call the graph a k-interval-PCG; in symbols,
G = k-interval-PCG (T, I1, . . . , Ik). It is known that 2-interval-PCGs do not
contain all graphs and the smallest known graph outside this class has 135
nodes. Here we prove that all graphs with at most 8 nodes are 2-interval-PCGs,
so doing one step towards the determination of the smallest value of n such
that there exists an n node graph that is not a 2-interval-PCG.


  Among the bent tail radio galaxies common in galaxy clusters are some with
long, collimated tails (so-called head-tail galaxies) shaped by their
interactions with the intracluster medium (ICM). Here we report the discovery
of intricate filamentary structure in and beyond the ~28' (570 kpc) long,
helical radio tail of the Corkscrew Galaxy (1610-60.5, ESO137-G007), which
resides in the X-ray bright cluster Abell 3627 (D = 70 Mpc). Deep radio
continuum data were obtained with wide-field Phased Array Feeds on the
Australian Square Kilometer Array Pathfinder (ASKAP) at 944 MHz and 1.4 GHz.
The Corkscrew Galaxy is located 15' north of the prominent wide-angle tail
(WAT) radio galaxy 1610-60.8 (ESO137-G006) near the cluster centre. While the
bright (young) part of its radio tail is highly collimated, the faint (old)
part shows increasing oscillation amplitudes, break-ups, and filaments. We find
a stunning set of arc-shaped radio filaments beyond and mostly orthogonal to
the collimated Corkscrew tail end, forming a partial bubble. This may be the
first detection of a "proto-lobe" seen in 3D MHD simulations by Nolting et al.
(2019), formed by the face-on impact of the Corkscrew Galaxy with a shock front
in the cluster outskirts. Interactions of the radio galaxy tail with the ICM
are likely responsible for the tail collimation and shear forces within the ICM
for its increasingly filamentary structure. We also report the discovery of
small (~20-30 kpc) ram-pressure stripped radio tails in four Abell 3627 cluster
galaxies.


  We consider a thought experiment, in which a neutrino is produced by an
electron on a nucleus in a crystal. The wave function of the oscillating
neutrino is calculated assuming that the electron is described by a wave
packet. If the electron is relativistic and the spatial size of its wave packet
is much larger than the size of the crystal cell, then the wave packet of the
produced neutrino has essentially the same size as the wave packet of the
electron. We investigate the suppression of neutrino oscillations at large
distances caused by two mechanisms: 1) spatial separation of wave packets
corresponding to different neutrino masses; 2) neutrino energy dispersion for
given neutrino mass eigenstates. We resolve contributions of these two
mechanisms.


  Dialog is an effective way to exchange information, but subtle details and
nuances are extremely important. While significant progress has paved a path to
address visual dialog with algorithms, details and nuances remain a challenge.
Attention mechanisms have demonstrated compelling results to extract details in
visual question answering and also provide a convincing framework for visual
dialog due to their interpretability and effectiveness. However, the many data
utilities that accompany visual dialog challenge existing attention techniques.
We address this issue and develop a general attention mechanism for visual
dialog which operates on any number of data utilities. To this end, we design a
factor graph based attention mechanism which combines any number of utility
representations. We illustrate the applicability of the proposed approach on
the challenging and recently introduced VisDial datasets, outperforming recent
state-of-the-art methods by 1.1% for VisDial0.9 and by 2% for VisDial1.0 on
MRR. Our ensemble model improved the MRR score on VisDial1.0 by more than 6%.


  Videos are a commonly-used type of content in learning during Web search.
Many e-learning platforms provide quality content, but sometimes educational
videos are long and cover many topics. Humans are good in extracting important
sections from videos, but it remains a significant challenge for computers. In
this paper, we address the problem of assigning importance scores to video
segments, that is how much information they contain with respect to the overall
topic of an educational video. We present an annotation tool and a new dataset
of annotated educational videos collected from popular online learning
platforms. Moreover, we propose a multimodal neural architecture that utilizes
state-of-the-art audio, visual and textual features. Our experiments
investigate the impact of visual and temporal information, as well as the
combination of multimodal features on importance prediction.


  Surfaces sputtered by ion beam bombardment have been known to exhibit
patterns whose behavior is modeled with stochastic partial differential
equations. A widely accepted model is the Cuerno-Barabasi model which is robust
in its predictions of sputtered surface morphologies. An understanding of the
factors responsible for such surface topographies can be achieved by using
scaling arguments on the stochastic model. For such explanations, knowledge of
the coefficients is crucial. The more so since these vary with different
materials, the sputtering process itself generates non-equilibrium surfaces
within some finite timescale, and the implication of recent results of surface
topographies unexplained by the continuum theory. We calculate and study these
coefficients as functions of the sputtering parameters for yet unreported cases
of anisotropic ion energy distribution within the sputtered material.
Consequently, we present phase diagrams for the significant case of anisotropic
ion straggle. We observe shifts in the phase boundaries when the collision
cascade geometry rotates, and we also found saturation behavior in the
diagrams; in which case the boundaries become independent of the penetration
depths. Our results indicate a possible origin of yet unexplained nanodot
topographies arising from oblique incidence ion etching of amorphized surfaces.


  We study perpetual American option pricing problems in an extension of the
Black-Merton-Scholes model in which the dividend and volatility rates of the
underlying risky asset depend on the running values of its maximum and maximum
drawdown. The optimal exercise times are shown to be the first times at which
the underlying asset hits certain boundaries depending on the running values of
the associated maximum and maximum drawdown processes. We obtain closed-form
solutions to the equivalent free-boundary problems for the value functions with
smooth fit at the optimal stopping boundaries and normal reflection at the
edges of the state space of the resulting three-dimensional Markov process. The
optimal exercise boundaries for the perpetual American options on the maximum
of the market depth with fixed and floating strikes are determined as the
minimal solutions of certain first-order nonlinear ordinary differential
equations.


  Bj\"orner and Wachs introduced CL-shellability as a technique for studying
the topological structure of order complexes of partially ordered sets
(posets). They also introduced the notion of recursive atom ordering, and they
proved that a finite bounded poset is CL-shellable if and only if it admits a
recursive atom ordering.
  In this paper, a generalization of the notion of recursive atom ordering is
introduced. A finite bounded poset is proven to admit such a generalized
recursive atom ordering if and only if it admits a traditional recursive atom
ordering. This is also proven equivalent to admitting a CC-shelling (a type of
shelling introduced by Kozlov) with a further property called self-consistency.
Thus, CL-shellability is proven equivalent to self-consistent CC-shellability.
As an application, the uncrossing posets, namely the face posets for stratified
spaces of planar electrical networks, are proven to be dual CL-shellable.


  We establish some nonlinear integral inequalities for functions defined on a
time scale. The results extend some previous Gronwall and Bihari type
inequalities on time scales. Some examples of time scales for which our results
can be applied are provided. An application to the qualitative analysis of a
nonlinear dynamic equation is discussed.


  We give necessary and sufficient existence criteria, and methods for finding,
continuous solutions of linear equations whose coefficients are polynomials.


  The Wigner function is known to evolve classically under the exclusive action
of a quadratic hamiltonian. If the system does interact with the environment
through Lindblad operators that are linear functions of position and momentum,
we show that the general evolution is the convolution of the classically
evolving Wigner function with a phase space gaussian that broadens in time. We
analyze the three generic cases of elliptic, hyperbolic and parabolic
Hamiltonians. The Wigner function always becomes positive in a definite time,
which is shortest in the hyperbolic case. We also derive an exact formula for
the evolving linear entropy as the average of a narrowing gaussian taken over a
probability distribution that depends only on the initial state. This leads to
a long time asymptotic formula for the growth of linear entropy.


  IC 10 X-1 is a bright (Lx=10^38 ergs/s) variable X-ray source in the local
group starburst galaxy IC 10. The most plausible optical counterpart is a
luminous Wolf-Rayet star, making IC 10 X-1 a rare example of a Wolf-Rayet X-ray
binary. In this paper, we report on the detection of an X-ray orbital period
for IC 10 X-1of 34.4 hours. This result, combined with a re-examination of
optical spectra, allow us to determine a mass function for the system f(m)=7.8
Msun and a probable mass for the compact object of 24-36 Msun. If this analysis
is correct, the compact object is the most massive known stellar black black
hole. We further show that the observed period is inconsistent with Roche lobe
overflow, suggesting that the binary is detached and the black hole is
accreting the wind of the Wolf-Rayet star. The observed mass loss rate of
[MAC92] 17-A is sufficient to power the X-ray luminosity of IC 10 X-1.


  Gradients in temperature, concentration or electrostatic potential cannot
exert forces on a bulk fluid; they can, however, exert forces on a fluid in a
microscopic boundary layer surrounding a (nano)colloidal solute, resulting in
so-called phoretic flow. Here we present a simulation study of phoretic flow
around a spherical colloid held fixed in a concentration gradient. We show that
the resulting flow velocity depends non-monotonically on the strength of the
colloid-fluid interaction. The reason for this non-monotonic dependence is that
solute particles are effectively trapped in a shell around the colloid and
cannot contribute to diffusio-phoresis. We also observe that the flow depends
sensitively on the anisotropy of solute-colloid interaction.


  Following multiple instructions is a crucial ability for large language
models (LLMs). Evaluating this ability comes with significant challenges: (i)
limited coherence between multiple instructions, (ii) positional bias where the
order of instructions affects model performance, and (iii) a lack of
objectively verifiable tasks. To address these issues, we introduce a benchmark
designed to evaluate models' abilities to follow multiple instructions through
sequential instruction following (SIFo) tasks. In SIFo, the successful
completion of multiple instructions is verifiable by examining only the final
instruction. Our benchmark evaluates instruction following using four tasks
(text modification, question answering, mathematics, and security rule
following), each assessing different aspects of sequential instruction
following. Our evaluation of popular LLMs, both closed-source and open-source,
shows that more recent and larger models significantly outperform their older
and smaller counterparts on the SIFo tasks, validating the benchmark's
effectiveness. All models struggle with following sequences of instructions,
hinting at an important lack of robustness of today's language models.


  The existing results on controllability of multi-agents networks are mostly
based on homogeneous nodes. This paper focuses on controllability of
heterogeneous multi-agent networks, where the agents are modeled as two types.
One type is that the agents are of the same high-order dynamics, and the
interconnection topologies of the information flow in different orders are
supposed to be different. It is proved that a heterogeneous-topology network is
controllable if and only if the first-order information topology is
leader-follower connected, and there exists a Laplacian matrix, which is a
linear combination of the Laplacian matrices of each order information, whose
corresponding topology is controllable. The other type is that the agents are
of generic linear dynamics, and the dynamics are supposed to be heterogeneous.
A necessary and sufficient condition for controllability of
heterogeneous-dynamic networks is that each agent contains a controllable
dynamic part, and the interconnection topology of the network is
leader-follower connected. If some dynamics of the agents are not controllable,
the controllability between the agents and the whole network is also studied by
introducing the concept of eigenvector-uncontrollable. Different illustrative
examples are provided to demonstrate the effectiveness of the theoretical
results in this paper.


  The security of patient information is important during the transfer of
medical data. A hybrid spatial domain watermarking algorithm that includes
encryption, integrity protection and steganography is proposed to strengthen
the information originality based on the authentication. The proposed algorithm
checks whether the information of patients has been deliberately changed or
not. The created code is distributed at every pixel of the medical image and
not only in the regions of noninterest pixels, while the image details are
still preserved. To enhance the security of the watermarking code, "SHA-1" is
used to get the initial key for the Symmetric Encryption Algorithm. The target
of this approach is to preserve the content of the image and the watermark
simultaneously, this is achieved by synthesizing an encrypted watermark from
one of the components of the original image and not by embedding a watermark in
the image. To evaluate the proposed code the Least Significant Bit (LSB),
Bit2SB, and Bit3SB were used. The evaluation of the proposed code showed that
the LSB is of better quality but overall the Bit2SB is better in its ability
against the active attacks up to a size of 2*2 pixels, and it preserves the
high image quality.


  Trajectory planning for autonomous cars can be addressed by primitive-based
methods, which encode nonlinear dynamical system behavior into automata. In
this paper, we focus on optimal trajectory planning. Since, typically, multiple
criteria have to be taken into account, multiobjective optimization problems
have to be solved. For the resulting Pareto-optimal motion primitives, we
introduce a universal automaton, which can be reduced or reconfigured according
to prioritized criteria during planning. We evaluate a corresponding
multi-vehicle planning scenario with both simulations and laboratory
experiments.


  It is well-known that decision-making problems from stochastic control can be
formulated by means of a forward-backward stochastic differential equation
(FBSDE). Recently, the authors of Ji et al. 2022 proposed an efficient deep
learning algorithm based on the stochastic maximum principle (SMP). In this
paper, we provide a convergence result for this deep SMP-BSDE algorithm and
compare its performance with other existing methods. In particular, by adopting
a strategy as in Han and Long 2020, we derive a-posteriori estimate, and show
that the total approximation error can be bounded by the value of the loss
functional and the discretization error. We present numerical examples for
high-dimensional stochastic control problems, both in case of drift- and
diffusion control, which showcase superior performance compared to existing
algorithms.


  Necessary and sufficient conditions for large Nc equivalence between parent
and daughter theories, for a wide class of orbifold projections of U(Nc) gauge
theories, are just the natural requirements that the discrete symmetry used to
define the projection not be spontaneously broken in the parent theory, and the
discrete symmetry permuting equivalent gauge group factors not be spontaneously
broken in the daughter theory. In this paper, we discuss the application of
this result to Z_k projections of N=1 supersymmetric Yang-Mills theory in four
dimensions, as well as various multi-flavor generalizations. Z_k projections
with k > 2 yielding chiral gauge theories violate the symmetry realization
conditions needed for large Nc equivalence, due to the spontaneous symmetry
breaking of discrete chiral symmetry in the parent super-Yang-Mills theory. But
for Z_2 projections, we show that previous assertions of large Nc
inequivalence, in infinite volume, between the parent and daughter theories
were based on incorrect mappings of vacuum energies, theta angles, or connected
correlators between the two theories. With the correct identifications, there
is no sign of any inconsistency. A subtle but essential feature of the
connection between parent and daughter theories involves multi-valuedness in
the mapping of theta parameters from parent to daughter.


  We study reductions of the Hamiltonian flows restricted to their invariant
submanifolds. As examples, we consider partial Lagrange-Routh reductions of the
natural mechanical systems such as geodesic flows on compact Lie groups and
$n$-dimensional variants of the classical Hess-Appel'rot case of a heavy rigid
body motion about a fixed point.


  An analytical model for the evolution of the boundary of the new phase in
transformations ruled by nucleation and growth is presented. Both homogeneous
and heterogeneous nucleation have been considered: The former includes
transformations in 2D and 3D space and the latter nucleation and growth on flat
solid substrate. The theory is formulated for the general case of spatially
correlated nuclei, arbitrary nucleation rate and power growth law of nuclei. In
the case of heterogeneous nucleation, spheroidal nuclei have been assumed and
the dependence of the kinetics on contact angle investigated. The validity of
the present approach is deemed through comparison with experimental data from
literature which also comprise oxide growth by ALD (Atomic Layer Deposition)
metal electrodeposition at solid substrate and alloy recrystallization.


  The Florence branch of an Italian supermarket chain recently implemented a
strategy that permanently lowered the price of numerous store brands in several
product categories. To quantify the impact of such a policy change, researchers
often use synthetic control methods for estimating causal effects when a subset
of units receive a single persistent treatment, and the rest are unaffected by
the change. In our applications, however, competitor brands not assigned to
treatment are likely impacted by the intervention because of substitution
effects; more broadly, this type of interference occurs whenever the treatment
assignment of one unit affects the outcome of another. This paper extends the
synthetic control methods to accommodate partial interference, allowing
interference within predefined groups but not between them. Focusing on a class
of causal estimands that capture the effect both on the treated and control
units, we develop a multivariate Bayesian structural time series model for
generating synthetic controls that would have occurred in the absence of an
intervention enabling us to estimate our novel effects. In a simulation study,
we explore our Bayesian procedure's empirical properties and show that it
achieves good frequentists coverage even when the model is misspecified. We use
our new methodology to make causal statements about the impact on sales of the
affected store brands and their direct competitors. Our proposed approach is
implemented in the CausalMBSTS R package.


  The rate for complete two-photon annihilation of molecular positronium
Ps$_{2}$ is reported. This decay channel involves a four-body collision among
the fermions forming Ps$_{2}$, and two photons of 1.022 MeV, each, as the final
state. The quantum electrodynamics result for the rate of this process is found
to be $\Gamma_{Ps_{2} \rightarrow \gamma\gamma}$ = 9.0 $\times 10^{-12}$
s$^{-1}$. This decay channel completes the most comprehensive decay chart for
Ps$_{2}$ up to date.


  Graph neural networks (GNNs) have extended the success of deep neural
networks (DNNs) to non-Euclidean graph data, achieving ground-breaking
performance on various tasks such as node classification and graph property
prediction. Nonetheless, existing systems are inefficient to train large graphs
with billions of nodes and edges with GPUs. The main bottlenecks are the
process of preparing data for GPUs - subgraph sampling and feature retrieving.
This paper proposes BGL, a distributed GNN training system designed to address
the bottlenecks with a few key ideas. First, we propose a dynamic cache engine
to minimize feature retrieving traffic. By a co-design of caching policy and
the order of sampling, we find a sweet spot of low overhead and high cache hit
ratio. Second, we improve the graph partition algorithm to reduce
cross-partition communication during subgraph sampling. Finally, careful
resource isolation reduces contention between different data preprocessing
stages. Extensive experiments on various GNN models and large graph datasets
show that BGL significantly outperforms existing GNN training systems by 20.68x
on average.


  The present paper proposes a comparison between the extinction theorem and
the Sturm-Liouville theory approaches for calculating the e.m. field inside an
optical cavity. We discuss for the first time to the best of our knowledge, in
the framework of classical electrodynamics, a simple link between the Quasi
Normal Modes (QNMs) and the Natural Modes (NMs) for one-dimensional (1D),
two-sided, open cavities. The QNM eigenfrequencies and eigenfunctions are
calculated for a linear Fabry-Perot (FP) cavity. The first-order Born
approximation is applied to the same cavity in order to compare the first-order
Born approximated and the actual QNM eigenfunctions of the cavity. We
demonstrate that the first-order Born approximation for an FP cavity introduces
symmetry breaking: in fact, each Born approximated QNM eigenfunction produces
values below or above the actual QNM eigenfunction value on the terminal
surfaces of the same cavity. Consequently, the two error-functions for an
approximated QNM are not equal in proximity to the two terminal surfaces of the
cavity.


  We present the results of a large-scale proper motion study of the central
~36'x16' of the Milky Way, based on our high angular resolution GALACTICNUCLEUS
survey (epoch 2015) combined with the HST Paschen-alpha survey (epoch 2008).
Our catalogue contains roughly 80,000 stars, an unprecedented kinematic data
set for this region. We describe the data analysis and the preparation of the
proper motion catalogue. We verify the catalogue by comparing our results with
measurements from previous work and data. We provide a preliminary analysis of
the kinematics of the studied region. Foreground stars in the Galactic Disc can
be easily identified via their small reddening. Consistent with previous work
and with our expectations, we find that stars in the nuclear stellar disc have
a smaller velocity dispersion than Bulge stars, in particular in the direction
perpendicular to the Galactic Plane. The rotation of the nuclear stellar disc
can be clearly seen in the proper motions parallel to the Galactic Plane. Stars
on the near side of the nuclear stellar disc are less reddened than stars on
its far side. Proper motions enable us to detect co-moving groups of stars that
may be associated with young clusters dissolving in the Galactic Centre that
are difficult to detect by other means. We demonstrate a technique based on a
density clustering algorithm that can be used to find such groups of stars.


  Logs are semi-structured text generated by logging statements in software
source code. In recent decades, software logs have become imperative in the
reliability assurance mechanism of many software systems because they are often
the only data available that record software runtime information. As modern
software is evolving into a large scale, the volume of logs has increased
rapidly. To enable effective and efficient usage of modern software logs in
reliability engineering, a number of studies have been conducted on automated
log analysis. This survey presents a detailed overview of automated log
analysis research, including how to automate and assist the writing of logging
statements, how to compress logs, how to parse logs into structured event
templates, and how to employ logs to detect anomalies, predict failures, and
facilitate diagnosis. Additionally, we survey work that releases open-source
toolkits and datasets. Based on the discussion of the recent advances, we
present several promising future directions toward real-world and
next-generation automated log analysis.


  We study the essential dimension of representations of a fixed quiver with
given dimension vector. We also consider the question of when the genericity
property holds, i.e., when essential dimension and generic essential dimension
agree. We classify the quivers satisfying the genericity property for every
dimension vector and show that for every wild quiver the genericity property
holds for infinitely many of its Schur roots. We also construct a large class
of examples, where the genericity property fails. Our results are particularly
detailed in the case of Kronecker quivers.


  The analog signals generated in the read-out electronics of radiation
detectors are shaped prior to the digitization in order to improve the signal
to noise ratio (SNR). The real amplitude of the analog signal is then obtained
using digital filters, which provides information about the energy deposited in
the detector. The classical digital filters have a good performance in ideal
situations with Gaussian electronic noise and no pulse shape distortion.
However, high-energy particle colliders, such as the Large Hadron Collider
(LHC) at CERN, can produce multiple simultaneous radiation events, which
produce signal pileup. The performance of classical digital filters
deteriorates in these conditions since the signal pulse shape gets distorted.
In addition, this type of experiments produces a high rate of collisions, which
requires high throughput data acquisitions systems. In order to cope with these
harsh requirements, new read-out electronics systems are based on
high-performance FPGAs, which permit the utilization of more advanced real-time
signal reconstruction algorithms. In this paper, a deep learning method is
proposed for real-time signal reconstruction in high pileup radiation
detectors. The performance of the new method has been studied using simulated
data and the results are compared with a classical FIR filter method. In
particular, the signals and FIR filter used in the ATLAS Tile Calorimeter are
used as benchmark. The implementation, resources usage and performance of the
proposed Neural Network algorithm in FPGA are also presented.


  We summarize our recent studies on the origin of solar wind kinetic scale
turbulence and electron halo in the electron velocity distribution function.
Increasing observations of nanoflares and microscopic type III radio bursts
strongly suggest that nanoflares and accelerated electron beams are common in
the corona. Based on particle-in-cell simulations, we show that both the
core-halo feature and kinetic scale turbulence observed in the solar wind can
be produced by the nonlinear evolution of electron two-stream instability
driven by nanoflare accelerated electron beams. The energy exchange between
waves and particles reaches equilibrium in the inner corona and the key
features of the turbulence and velocity distribution are preserved as the solar
wind escapes into interplanetary space along open magnetic field lines.
Observational tests of the model and future theoretical work are discussed.


  The nucleon-deuteron analyzing power $A_y$ in elastic nucleon-deuteron
scattering poses a longstanding puzzle. At energies $E_{lab}$ below
approximately 30 MeV $A_y$ cannot be described by any realistic NN force. The
inclusion of existing three-nucleon forces does not improve the situation.
Because of recent questions about the $^3P_J$ NN phases, we examine whether
reasonable changes in the NN force can resolve the puzzle. In order to do this
we investigate the effect on the $^3P_J$ waves produced by changes in different
parts of the potential (viz., the central force, tensor force, etc.), as well
as on the 2-body observables and on $A_y$. We find that it is not possible with
reasonable changes in the NN potential to increase the 3-body $A_y$ and at the
same time to keep the 2-body observables unchanged. We therefore conclude that
the $A_y$ puzzle is likely to be solved by new three-nucleon forces, such as
those of spin-orbit type, which have not yet been taken into account.


  The standard model of cosmology assumes that the Universe can be described to
hover around a homogeneous-isotropic solution of Einstein's general theory of
relativity. This description needs (sometimes hidden) hypotheses that restrict
the generality, and relaxing these restrictions is the headline of a new
physical approach to cosmology that refurnishes the cosmological framework.
Considering a homogeneous geometry as a template geometry for the in reality
highly inhomogeneous Universe must be considered a strong idealization.
Unveiling the limitations of the standard model opens the door to rich
consequences of general relativity, giving rise to effective (i.e. spatially
averaged) cosmological models that may even explain the longstanding problems
of dark energy and dark matter. We explore in this talk the influence of
structure formation on average properties of the Universe by discussing: (i)
general thoughts on why considering average properties, on the key-issue of
non-conserved curvature, and on the global gravitational instability of the
standard model of cosmology; (ii) the general set of cosmological equations
arising from averaging the scalar parts of Einstein's equations, the generic
property of structure formation interacting with the average properties of the
Universe in a scale-dependent way, and the description of cosmological
backreaction in terms of an effective scalar field.


  Recently 3D-aware GAN methods with neural radiance field have developed
rapidly. However, current methods model the whole image as an overall neural
radiance field, which limits the partial semantic editability of synthetic
results. Since NeRF renders an image pixel by pixel, it is possible to split
NeRF in the spatial dimension. We propose a Compositional Neural Radiance Field
(CNeRF) for semantic 3D-aware portrait synthesis and manipulation. CNeRF
divides the image by semantic regions and learns an independent neural radiance
field for each region, and finally fuses them and renders the complete image.
Thus we can manipulate the synthesized semantic regions independently, while
fixing the other parts unchanged. Furthermore, CNeRF is also designed to
decouple shape and texture within each semantic region. Compared to
state-of-the-art 3D-aware GAN methods, our approach enables fine-grained
semantic region manipulation, while maintaining high-quality 3D-consistent
synthesis. The ablation studies show the effectiveness of the structure and
loss function used by our method. In addition real image inversion and cartoon
portrait 3D editing experiments demonstrate the application potential of our
method.


  To encourage and guide decarbonization efforts, better tools are needed to
monitor real-time CO2 and criteria air pollutant emissions from electricity
consumption, production, imports, and exports. Using real-time data from the
electricity system is especially challenging for quantitative applications
requiring high quality and physically consistent data. Until now,
time-intensive, ad-hoc and manual data verification and cleaning strategies
have been used to prepare the data for quantitative analysis. As an alternative
to existing techniques, here we provide a physics-informed framework to greatly
accelerate and automate data processing to enable internally consistent
electric system consumption, production, import, and export data in near
real-time. A key component of this framework is an optimization program to
minimize the data adjustments required to satisfy energy conservation
equations. The effectiveness of this method is demonstrated by applying it to
the continental United States electricity network. The resulting
publicly-available data set, which provides in near-real time, hourly updates
on electricity generation, consumption, imports, exports and associated
emissions, is the first of this nature.


  We study the dynamics of a two-level quantum system interacting with an
external electromagnetic field periodic and quasiperiodic in time. The quantum
evolution is described exactly by the classical equations of motion of a
gyromagnet in a time-dependent magnetic field. We prove that this classical
system is integrable as a consequence of the underlying unitary quantum
dynamics. As a consequence, for the periodic case: i) rigorous assessment of
the validity of the rotating-wave approximation (RWA) becomes possible even
beyond the assumptions of resonance and weak coupling (the latter conditions
are also shown to follow from the method of averaging); ii) we determine
conditions for the realization of the quantum NOT operation beyond the RWA, by
means of classical stroboscopic maps. The results bear upon areas as diverse as
quantum optics, nuclear magnetic resonance, and quantum computation.


  This paper presents a new C++ framework, DELPHES, performing a fast
multipurpose detector response simulation. The simulation includes a tracking
system, embedded into a magnetic field, calorimeters and a muon system, and
possible very forward detectors arranged along the beamline. The framework is
interfaced to standard file formats (e.g. Les Houches Event File or HepMC) and
outputs observables such as isolated leptons, missing transverse energy and
collection of jets which can be used for dedicated analyses. The simulation of
the detector response takes into account the effect of magnetic field, the
granularity of the calorimeters and subdetector resolutions. A simplified
preselection can also be applied on processed events for trigger emulation.
Detection of very forward scattered particles relies on the transport in
beamlines with the HECTOR software. Finally, the FROG 2D/3D event display is
used for visualisation of the collision final states.


  Let $D$ be an integral domain with quotient field $K$ and let $X$ be an
indeterminate over $D$. Also, let $\boldsymbol{\mathcal{T}}:=\{T_{\lambda}\mid
\lambda
  \in \Lambda \}$ be a defining family of quotient rings of $D$ and suppose
that $\ast $ is a finite type star operation on $D$ induced by
$\boldsymbol{\mathcal{T}}$. We show that $D$ is a P$ \ast $MD (resp., P$v$MD)
if and only if $(\co_D(fg))^{\ast}=(\co_D(f)\co_D(g))^{\ast}$ (resp.,
$(\co_D(fg))^{w}=(\co_D(f)\co_D(g))^{w}$) for all $0 \ne f,g \in K[X]$. A more
general version of this result is given in the semistar operation setting. We
give a method for recognizing P$v$MD's which are not P$\ast $MD's for a certain
finite type star operation $\ast $. We study domains $D$ for which the $\ast
$--class group $\Cl^{\ast}(D)$ equals the $t$--class group $\Cl^{t}(D)$ for any
finite type star operation $\ast $, and we indicate examples of P$v$MD's $D$
such that $\Cl^{\ast}(D)\subsetneq \Cl^{t}(D)$. We also compute $\Cl^v(D)$ for
certain valuation domains $D$.


  This paper considers no-regret learning for repeated continuous-kernel games
with lossy bandit feedback. Since it is difficult to give the explicit model of
the utility functions in dynamic environments, the players' action can only be
learned with bandit feedback. Moreover, because of unreliable communication
channels or privacy protection, the bandit feedback may be lost or dropped at
random. Therefore, we study the asynchronous online learning strategy of the
players to adaptively adjust the next actions for minimizing the long-term
regret loss. The paper provides a novel no-regret learning algorithm, called
Online Gradient Descent with lossy bandits (OGD-lb). We first give the regret
analysis for concave games with differentiable and Lipschitz utilities. Then we
show that the action profile converges to a Nash equilibrium with probability 1
when the game is also strictly monotone. We further provide the mean square
convergence rate $\mathcal{O}\left(k^{-2\min\{\beta, 1/6\}}\right)$ when the
game is $\beta-$ strongly monotone. In addition, we extend the algorithm to the
case when the loss probability of the bandit feedback is unknown, and prove its
almost sure convergence to Nash equilibrium for strictly monotone games.
Finally, we take the resource management in fog computing as an application
example, and carry out numerical experiments to empirically demonstrate the
algorithm performance.


  Berends-Giele currents are fundamental building blocks for on-shell
amplitudes in non-abelian gauge theory. We present a novel procedure to
construct them using the Bern-Kosower formalism for one-loop gluon amplitudes.
Applying the pinch procedure of that formalism to a suitable special case the
currents are naturally obtained in terms of multi-particle fields and obeying
colour-kinematics duality. As a feedback to the Bern-Kosower formalism we
outline how the multi-particle polarisations and field-strength tensors can be
used to significantly streamline the pinch procedure.


  We take the Gauss' linking integral of two curves as a starting point to
discuss the connection between the equation of continuity and the inhomogeneous
Maxwell equations. Gauss' formula has been discussed before, as being derivable
from the line integral of a magnetic field generated by a steady current
flowing through a loop. We argue that a purely geometrical result - such as
Gauss' formula - cannot be claimed to be derivable from a law of Nature, i.e.,
from one of Maxwell's equations, which is the departing point for the
calculation of the magnetic field. We thus discuss anew the derivation of
Gauss' formula, this time resting on Helmholtz's theorem for vector fields.
Such a derivation, in turn, serves to shed light into the connection existing
between a conservation law like charge conservation and the Maxwell equations.
The key role played by the constitutive equations in the construction of
Maxwell's electromagnetism is briefly discussed, as well.


  We propose a new transport mechanism through tunnel-coupled quantum dots
based on the coherent population trapping effect. Coupling to an excited level
by the coherent radiation of two microwaves can lead to an extremely narrow
current antiresonance. The effect can be used to determine interdot dephasing
rates and is a mechanism for a very sensitive, optically controlled current
switch.


  Large language models (LLMs) have been applied in many fields with rapid
development in recent years. As a classic machine learning task, time series
forecasting has recently received a boost from LLMs. However, there is a
research gap in the LLMs' preferences in this field. In this paper, by
comparing LLMs with traditional models, many properties of LLMs in time series
prediction are found. For example, our study shows that LLMs excel in
predicting time series with clear patterns and trends but face challenges with
datasets lacking periodicity. We explain our findings through designing prompts
to require LLMs to tell the period of the datasets. In addition, the input
strategy is investigated, and it is found that incorporating external knowledge
and adopting natural language paraphrases positively affects the predictive
performance of LLMs for time series. Overall, this study contributes to insight
into the advantages and limitations of LLMs in time series forecasting under
different conditions.


  Variations in chemical abundances with evolutionary phase have been
identified among stars in globular and open clusters with a wide range of
metallicities. In the metal-poor clusters, these variations compare well with
predictions from stellar structure and evolution models considering the
internal diffusive motions of atoms and ions, collectively known as atomic
diffusion, when moderated by an additional mixing process with a fine-tuned
efficiency. We present here an investigation of these effects in the Galactic
globular cluster NGC 6121 (M4) ([Fe/H] = -1.13) through a detailed chemical
abundance analysis of 86 stars using high-resolution ESO/VLT FLAMES
spectroscopy. The stars range from the main-sequence turnoff point (TOP) to the
red giant branch (RGB) just above the bump. We identify C-N-O and Mg-Al-Si
abundance anti-correlations, and confirm the presence of a bimodal population
differing by 1 dex in nitrogen abundance. The composition of the
second-generation stars imply pollution from both massive (20-40 Msol) and
asymptotic giant branch stars. We find evolutionary variations in chemical
abundances between the TOP and RGB, which are robust to uncertainties in
stellar parameters and modelling assumptions. The variations are weak, but
match predictions well when employing efficient additional mixing. Without
correcting for Galactic production of lithium, we derive an initial lithium
abundance 2.63+-0.10, which is marginally lower than the predicted primordial
BBN value.


  In the context of stationary $\mathbb{Z}^d$ nearest-neighbour Gibbs measures
$\mu$ satisfying strong spatial mixing, we present a new combinatorial
condition (the topological strong spatial mixing property (TSSM)) on the
support of $\mu$ sufficient for having an efficient approximation algorithm for
topological pressure. We establish many useful properties of TSSM for studying
strong spatial mixing on systems with hard constraints. We also show that TSSM
is, in fact, necessary for strong spatial mixing to hold at high rate. Part of
this work is an extension of results obtained by D. Gamarnik and D. Katz
(2009), and B. Marcus and R. Pavlov (2013), who gave a special representation
of topological pressure in terms of conditional probabilities.


  The non-uniform photoelectric response of infrared imaging systems results in
fixed-pattern stripe noise being superimposed on infrared images, which
severely reduces image quality. As the applications of degraded infrared images
are limited, it is crucial to effectively preserve original details. Existing
image destriping methods struggle to concurrently remove all stripe noise
artifacts, preserve image details and structures, and balance real-time
performance. In this paper we propose a novel algorithm for destriping degraded
images, which takes advantage of neighbouring column signal correlation to
remove independent column stripe noise. This is achieved through an iterative
deep unfolding algorithm where the estimated noise of one network iteration is
used as input to the next iteration. This progression substantially reduces the
search space of possible function approximations, allowing for efficient
training on larger datasets. The proposed method allows for a more precise
estimation of stripe noise to preserve scene details more accurately. Extensive
experimental results demonstrate that the proposed model outperforms existing
destriping methods on artificially corrupted images on both quantitative and
qualitative assessments.


  Generalist segmentation models are increasingly favored for diverse tasks
involving various objects from different image sources. Task-Incremental
Learning (TIL) offers a privacy-preserving training paradigm using tasks
arriving sequentially, instead of gathering them due to strict data sharing
policies. However, the task evolution can span a wide scope that involves
shifts in both image appearance and segmentation semantics with intricate
correlation, causing concurrent appearance and semantic forgetting. To solve
this issue, we propose a Comprehensive Generative Replay (CGR) framework that
restores appearance and semantic knowledge by synthesizing image-mask pairs to
mimic past task data, which focuses on two aspects: modeling image-mask
correspondence and promoting scalability for diverse tasks. Specifically, we
introduce a novel Bayesian Joint Diffusion (BJD) model for high-quality
synthesis of image-mask pairs with their correspondence explicitly preserved by
conditional denoising. Furthermore, we develop a Task-Oriented Adapter (TOA)
that recalibrates prompt embeddings to modulate the diffusion model, making the
data synthesis compatible with different tasks. Experiments on incremental
tasks (cardiac, fundus and prostate segmentation) show its clear advantage for
alleviating concurrent appearance and semantic forgetting. Code is available at
https://github.com/jingyzhang/CGR.


  We formalise the undecidability of solvability of Diophantine equations, i.e.
polynomial equations over natural numbers, in Coq's constructive type theory.
To do so, we give the first full mechanisation of the
Davis-Putnam-Robinson-Matiyasevich theorem, stating that every recursively
enumerable problem -- in our case by a Minsky machine -- is Diophantine. We
obtain an elegant and comprehensible proof by using a synthetic approach to
computability and by introducing Conway's FRACTRAN language as intermediate
layer. Additionally, we prove the reverse direction and show that every
Diophantine relation is recognisable by $\mu$-recursive functions and give a
certified compiler from $\mu$-recursive functions to Minsky machines.


  The observed classicality of primordial perturbations, despite their quantum
origin during inflation, calls for a mechanism for quantum-to-classical
transition of these initial fluctuations. As literature suggests a number of
plausible mechanisms which try to address this issue, it is of importance to
seek for concrete observational signatures of these several approaches in order
to have a better understanding of the early universe dynamics. Among these
several approaches, it is the spontaneous collapse dynamics of Quantum
Mechanics which is most viable of leaving discrete observational signatures as
collapse mechanism inherently changes the generic Quantum dynamics. We observe
in this study that the observables from the scalar sector, i.e. scalar tilt
$n_s$, running of scalar tilt $\alpha_s$ and running of running of scalar tilt
$\beta_s$, can not potentially distinguish a collapse modified inflationary
dynamics in the realm of canonical scalar field and $k-$inflationary scenarios.
The only distinguishable imprint of collapse mechanism lies in the observables
of tensor sector in the form of modified consistency relation and a blue-tilted
tensor spectrum only when the collapse parameter $\delta$ is non-zero and
positive.


  The origin of the erraticity behaviour observed recently in the experiment is
studied in some detail. The negative-binomial distribution is used to fit the
experimental multiplicity distribution. It is shown that, with the multiplicity
distribution taken into account, the experimentally observed erraticity
behaviour can be well reproduced using a flat probability distribution. The
dependence of erraticity behaviour on the width of multiplicity distribution is
studied.


  In this paper we consider the numerical upscaling of the Brinkman equation in
the presence of high-contrast permeability fields. We develop and analyze a
robust and efficient Generalized Multiscale Finite Element Method (GMsFEM) for
the Brinkman model. In the fine grid, we use mixed finite element method with
the velocity and pressure being continuous piecewise quadratic and piecewise
constant finite element spaces, respectively. Using the GMsFEM framework we
construct suitable coarse-scale spaces for the velocity and pressure that yield
a robust mixed GMsFEM. We develop a novel approach to construct a coarse
approximation for the velocity snapshot space and a robust small offline space
for the velocity space. The stability of the mixed GMsFEM and a priori error
estimates are derived. A variety of two-dimensional numerical examples are
presented to illustrate the effectiveness of the algorithm.


  In the nuclear and particle astrophysics session of CIPANP 2003 we heard
talks on a number of topics, focused for the most part into four broad areas.
Here we outline the discussions of the standard cosmological model, dark matter
searches, cosmic rays, and neutrino astrophysics. The robustness of theoretical
and experimental programs in all of these areas is very encouraging, and we
expect to have many questions answered, and new ones asked, in time for CIPANP
2006.


  The accessible information and the informational power quantify the amount of
information extractable from a quantum ensemble and by a quantum measurement,
respectively. So-called spherical quantum 2-designs constitute a class of
ensembles and measurements relevant in testing entropic uncertainty relations,
quantum cryptography, and quantum tomography. We provide lower bounds on the
entropy of 2-design ensembles and measurements, from which upper bounds on
their accessible information and informational power follow, as a function of
the dimension only. We show that the statistics generated by 2-designs,
although optimal for the abovementioned protocols, never contains more than one
bit of information. Finally, we specialize our results to the relevant cases of
symmetric informationally complete (SIC) sets and maximal sets of mutually
unbiased bases (MUBs), and we generalize them to the arbitrary-rank case.


  Learned image compression has achieved great success due to its excellent
modeling capacity, but seldom further considers the Rate-Distortion
Optimization (RDO) of each input image. To explore this potential in the
learned codec, we make the first attempt to build a neural data-dependent
transform and introduce a continuous online mode decision mechanism to jointly
optimize the coding efficiency for each individual image. Specifically, apart
from the image content stream, we employ an additional model stream to generate
the transform parameters at the decoder side. The presence of a model stream
enables our model to learn more abstract neural-syntax, which helps cluster the
latent representations of images more compactly. Beyond the transform stage, we
also adopt neural-syntax based post-processing for the scenarios that require
higher quality reconstructions regardless of extra decoding overhead. Moreover,
the involvement of the model stream further makes it possible to optimize both
the representation and the decoder in an online way, i.e. RDO at the testing
time. It is equivalent to a continuous online mode decision, like coding modes
in the traditional codecs, to improve the coding efficiency based on the
individual input image. The experimental results show the effectiveness of the
proposed neural-syntax design and the continuous online mode decision
mechanism, demonstrating the superiority of our method in coding efficiency
compared to the latest conventional standard Versatile Video Coding (VVC) and
other state-of-the-art learning-based methods.


  Finite domains of a Langmuir monolayer in a phase with tilted molecules can
be modeled by a simple elastic free energy of an XY order parameter with
isotropic and anisotropic line tension terms. The domains can and often do
contain nontrivial textures, which in turn influence the shape of the domains.
Herein we investigate the properties of a simplified isotropic model with a
single elastic constant. For circular domains a first-order phase transition is
found between two distinct textures: an exterior defect (or ``virtual boojum'')
texture, and an interior defect texture. Starting with a circular domain and
either of these two textures as a ground state, we find shape instabilities
develop that depend on the elastic constants and line tensions in the
simplified model. In both cases a necessary but not sufficient condition for
the onset of shape instabilities is the possibility for a local negative
effective line tension to develop from the anisotropic line tension term.


  The Ehrenfest time dependence of the suppression of the weak localization
correction to the conductance of a {\em clean} chaotic cavity is calculated.
Unlike in earlier work, no impurity scattering is invoked to imitate
diffraction effects. The calculation extends the semiclassical theory of K.
Richter and M. Sieber [Phys. Rev. Lett. {\bf 89}, 206801 (2002)] to include the
effect of a finite Ehrenfest time.


  The experiment that the high spin selectivity and the length-dependent spin
polarization are observed in double-stranded DNA [Science ${\bf 331}$, 894
(2011)], is elucidated by considering the combination of the spin-orbit
coupling, the environment-induced dephasing, and the helical symmetry. We show
that the spin polarization in double-stranded DNA is significant even in the
case of weak spin-orbit coupling, while no spin polarization appears in
single-stranded DNA. Furthermore, the underlying physical mechanism and the
parameters-dependence of the spin polarization are studied.


  In this paper, we present a novel Deep Triphone Embedding (DTE)
representation derived from Deep Neural Network (DNN) to encapsulate the
discriminative information present in the adjoining speech frames. DTEs are
generated using a four hidden layer DNN with 3000 nodes in each hidden layer at
the first-stage. This DNN is trained with the tied-triphone classification
accuracy as an optimization criterion. Thereafter, we retain the activation
vectors (3000) of the last hidden layer, for each speech MFCC frame, and
perform dimension reduction to further obtain a 300 dimensional representation,
which we termed as DTE. DTEs along with MFCC features are fed into a
second-stage four hidden layer DNN, which is subsequently trained for the task
of tied-triphone classification. Both DNNs are trained using tri-phone labels
generated from a tied-state triphone HMM-GMM system, by performing a
forced-alignment between the transcriptions and MFCC feature frames. We conduct
the experiments on publicly available TED-LIUM speech corpus. The results show
that the proposed DTE method provides an improvement of absolute 2.11% in
phoneme recognition, when compared with a competitive hybrid tied-state
triphone HMM-DNN system.