  Wigner rotations and Iwasawa decompositions are manifestations of the
internal space-time symmetries of massive and massless particles, respectively.
It is shown to be possible to produce combinations of optical filters which
exhibit transformations corresponding to Wigner rotations and Iwasawa
decompositions. This is possible because the combined effects of rotation,
phase-shift, and attenuation filters lead to transformation matrices of the
six-parameter Lorentz group applicable to Jones vectors and Stokes parameters
for polarized light waves. The symmetry transformations in special relativity
lead to a set of experiments which can be performed in optics laboratories.


  The growing popularity of Machine Learning (ML) has led to its deployment in
various sensitive domains, which has resulted in significant research focused
on ML security and privacy. However, in some applications, such as
Augmented/Virtual Reality, integrity verification of the outsourced ML tasks is
more critical--a facet that has not received much attention. Existing
solutions, such as multi-party computation and proof-based systems, impose
significant computation overhead, which makes them unfit for real-time
applications. We propose Fides, a novel framework for real-time integrity
validation of ML-as-a-Service (MLaaS) inference. Fides features a novel and
efficient distillation technique--Greedy Distillation Transfer Learning--that
dynamically distills and fine-tunes a space and compute-efficient verification
model for verifying the corresponding service model while running inside a
trusted execution environment. Fides features a client-side attack detection
model that uses statistical analysis and divergence measurements to identify,
with a high likelihood, if the service model is under attack. Fides also offers
a re-classification functionality that predicts the original class whenever an
attack is identified. We devised a generative adversarial network framework for
training the attack detection and re-classification models. The evaluation
shows that Fides achieves an accuracy of up to 98% for attack detection and 94%
for re-classification.


  Softmax classifiers with a very large number of classes naturally occur in
many applications such as natural language processing and information
retrieval. The calculation of full softmax is costly from the computational and
energy perspective. There have been various sampling approaches to overcome
this challenge, popularly known as negative sampling (NS). Ideally, NS should
sample negative classes from a distribution that is dependent on the input
data, the current parameters, and the correct positive class. Unfortunately,
due to the dynamically updated parameters and data samples, there is no
sampling scheme that is provably adaptive and samples the negative classes
efficiently. Therefore, alternative heuristics like random sampling, static
frequency-based sampling, or learning-based biased sampling, which primarily
trade either the sampling cost or the adaptivity of samples per iteration are
adopted. In this paper, we show two classes of distributions where the sampling
scheme is truly adaptive and provably generates negative samples in
near-constant time. Our implementation in C++ on CPU is significantly superior,
both in terms of wall-clock time and accuracy, compared to the most optimized
TensorFlow implementations of other popular negative sampling approaches on
powerful NVIDIA V100 GPU.


  We consider free and self-interacting quantum scalar fields satisfying
modified dispersion relations in the framework of Einstein-Aether theory. Using
adiabatic regularization, we study the renormalization of the equation for the
mean value of the field in the self-interacting case, and the renormalization
of the semiclassical Einstein-Aether equations for free fields. In both cases
we consider Bianchi type I background spacetimes. Contrary to what happens for
{\it free} fields in {\it flat} Robertson-Walker spacetimes, the
self-interaction and/or the anisotropy produce non-purely geometric terms in
the adiabatic expansion, i.e terms that involve both the metric $g_{\mu\nu}$
and the aether field $u_{\mu}$. We argue that, in a general spacetime, the
renormalization of the theory would involve new counterterms constructed with
$g_{\mu\nu}$ and $u_{\mu}$, generating a fine-tuning problem for the
Einstein-Aether theory.


  The critical behaviour of d-dimensional n-vector models at m-axial Lifshitz
points is considered for general values of m in the large-n limit. It is proven
that the recently obtained large-N expansions [J. Phys.: Condens. Matter 17,
S1947 (2005)] of the correlation exponents \eta_{L2}, \eta_{L4} and the related
anisotropy exponent \theta are fully consistent with the dimensionality
expansions to second order in \epsilon=4+m/2-d [Phys. Rev. B 62, 12338 (2000);
Nucl. Phys. B 612, 340 (2001)] inasmuch as both expansions yield the same
contributions of order \epsilon^2/n.


  We study harmonic functions on general weighted graphs which allow for a
compatible intrinsic metric. We prove an $L^{p}$ Liouville type theorem which
is a quantitative integral $L^{p}$ estimate of harmonic functions analogous to
Karp's theorem for Riemannian manifolds. As corollaries we obtain Yau's
$L^{p}$-Liouville type theorem on graphs, identify the domain of the generator
of the semigroup on $L^{p}$ and get a criterion for recurrence. As a side
product, we show an analogue of Yau's $L^{p}$ Caccioppoli inequality.
Furthermore, we derive various Liouville type results for harmonic functions on
graphs and harmonic maps from graphs into Hadamard spaces.


  To maintain the accuracy of supervised learning models in the presence of
evolving data streams, we provide temporally-biased sampling schemes that
weight recent data most heavily, with inclusion probabilities for a given data
item decaying exponentially over time. We then periodically retrain the models
on the current sample. This approach speeds up the training process relative to
training on all of the data. Moreover, time-biasing lets the models adapt to
recent changes in the data while -- unlike in a sliding-window approach --
still keeping some old data to ensure robustness in the face of temporary
fluctuations and periodicities in the data values. In addition, the
sampling-based approach allows existing analytic algorithms for static data to
be applied to dynamic streaming data essentially without change. We provide and
analyze both a simple sampling scheme (T-TBS) that probabilistically maintains
a target sample size and a novel reservoir-based scheme (R-TBS) that is the
first to provide both complete control over the decay rate and a guaranteed
upper bound on the sample size, while maximizing both expected sample size and
sample-size stability. The latter scheme rests on the notion of a "fractional
sample" and, unlike T-TBS, allows for data arrival rates that are unknown and
time varying. R-TBS and T-TBS are of independent interest, extending the known
set of unequal-probability sampling schemes. We discuss distributed
implementation strategies; experiments in Spark illuminate the performance and
scalability of the algorithms, and show that our approach can increase machine
learning robustness in the face of evolving data.


  We show that the study of critical properties of the Blume-Capel model at two
dimensions can be deduced from Monte Carlo simulations with good accuracy even
for small system sizes when one analyses the behaviour of the zeros of the
partition function. The phase diagram of the model displays a line of
second-order phase transitions ending at a tricritical point, then a line of
first-order transitions. We concentrate on critical and tricritical properties
and compare the accuracy achieved via standard finite-size scaling of
thermodynamic quantities with that from the zeros analysis. This latter
analysis showcases spectacular precision, even for systems as small as 64
spins! We also show that the zeros are very sensitive to subtle crossover
effects.


  We discuss several methods of calculating the DIS structure functions
F_2(x,Q^2) based on BFKL-type small x resummations. Taking into account new
HERA data ranging down to small x and low Q^2, the pure leading order
BFKL-based approach is excluded. Other methods based on high energy
factorization are closer to conventional renormalization group equations.
Despite several difficulties and ambiguities in combining the renormalization
group equations with small x resummed terms, we find that a fit to the current
data is hardly feasible, since the data in the low Q^2 region are not as steep
as the BFKL formalism predicts. Thus we conclude that deviations from the
(successful) renormalization group approach towards summing up logarithms in
1/x are disfavoured by experiment.


  We investigate the possibility of enhancement of effective axion decay
constant in well controlled constructions in string theory. To this end, we
study the dynamics of axions arising in the compactifications of type IIA
string theory on Calabi-Yau orientifolds with background fluxes (with
non-perturbative effects included to ensure stabilization of all moduli). In
this setup, we attempt to obtain large effective axion decay constant in two
different ways: by searching for a direction in field space in which the
potential is sufficiently flat and by arriving at a very explicit stringy
embedding of the Kim-Nilles-Peloso (KNP) alignment mechanism. We do not find
super-Planckian effective decay constants by either of the approaches.
Furthermore, we find that the alignment angle of KNP mechanism can not be made
arbitrarily small by adjusting the fluxes.


  If people with high risk of suicide can be identified through social media
like microblog, it is possible to implement an active intervention system to
save their lives. Based on this motivation, the current study administered the
Suicide Probability Scale(SPS) to 1041 weibo users at Sina Weibo, which is a
leading microblog service provider in China. Two NLP (Natural Language
Processing) methods, the Chinese edition of Linguistic Inquiry and Word Count
(LIWC) lexicon and Latent Dirichlet Allocation (LDA), are used to extract
linguistic features from the Sina Weibo data. We trained predicting models by
machine learning algorithm based on these two types of features, to estimate
suicide probability based on linguistic features. The experiment results
indicate that LDA can find topics that relate to suicide probability, and
improve the performance of prediction. Our study adds value in prediction of
suicidal probability of social network users with their behaviors.


  We present a full analysis of galaxy major merger pair fractions, merger
rates, and mass accretion rates, thus uncovering the role of mergers in galaxy
formation at the earliest previously unexplored epoch of $4.5<z<11.5$. We
target galaxies with masses $\log_{10}(\mathrm{M}_*/\mathrm{M}_\odot) = 8.0 -
10.0$, utilizing data from eight JWST Cycle-1 fields (CEERS, JADES GOODS-S,
NEP-TDF, NGDEEP, GLASS, El-Gordo, SMACS-0723, MACS-0416), covering an unmasked
area of 189.36 $\mathrm{arcmin}^2$. We develop a new probabilistic
pair-counting methodology that integrates full photometric redshift posteriors
and corrects for detection incompleteness to quantify close pairs with physical
projected separations between 20 and 50 kpc. Our analysis reveals an increase
in pair fractions up to $z = 8$, reaching $0.211 \pm 0.065$, followed by a
statistically flat evolution to $z = 11.5$. We find that the galaxy merger rate
increases from the local Universe up to $z = 6$ and then stabilizes at a value
of $\sim 6$ Gyr$^{-1}$ up to $z = 11.5$. We fit both a power-law and a
power-law + exponential model to our pair fraction and merger rate redshift
evolution, finding that the latter model describes the trends more accurately,
particularly at $z = 8.0 - 11.5$. In addition, we measure that the average
galaxy increases its stellar mass due to mergers by a factor of $2.77 \pm 0.99$
from redshift $z = 10.5$ to $z = 5.0$. Lastly, we investigate the impact of
mergers on galaxy stellar mass growth, revealing that mergers contribute $71
\pm 25\%$ as much to galaxy stellar mass increases as star formation from gas.
This indicates that mergers drive about half of galaxy assembly at high
redshift.


  We summarize the work done in the ``Beyond the Standard Model'' group of the
1995-96 HERA Workshop. Of the various proposed HERA upgrades, the luminosity
improvement is the most important for this physics. With an integrated
luminosity of 1 $fb^{-1}$, collected by 2005, HERA will remain a competitive
and potentially fruitful facility for new physics searches. The full
proceedings of the group with the individual reports can be found at:
http://ucosun.desy.de/~heraws96/proceedings/beyondSM/ The topics covered
include:
  1. Higgs Physics
  2. Contact Interactions and Compositeness
  3. Lepton Flavor Violation
  4. Heavy Neutral Leptons
  5. Supersymmetry
  5.1 MSSM
  5.2 R-Parity Violation
  5.3 Light Gluinos
  6. Leptoquarks


  In this paper, we obtain almost sure invariance principles with rate of order
$n^{1/p}\log^\beta n$, $2< p\le 4$, for sums associated to a sequence of
reverse martingale differences. Then, we apply those results to obtain similar
conclusions in the context of some non-invertible dynamical systems. For
instance we treat several classes of uniformly expanding maps of the interval
(for possibly unbounded functions). A general result for $\phi$-dependent
sequences is obtained in the course.


  Recently the space of tree level color structures for gluon scattering was
determined in arXiv:1403.6837 together with its transformation properties under
permutations. Here we generalize the discussion to loops, demonstrating a
reduction of an arbitrary color diagram to its vacuum skeleton plus rays. For
1-loop there are no residual relations and we determine the space of color
structures both diagrammatically and algebraically in terms of certain sunny
diagrams. We present the generating function for the characteristic polynomials
and a list of irreducible representations for $3 \le n \le 9$ external legs.
Finally we present a new proof for the 1-loop shuffle relations based on the
cyclic shuffle and split operations.


  Extensions of monotone operators into compact sets are provided under a
general context of dual systems using nonlinear analysis tools.


  In this paper, which is a sequel to \cite{part1}, we proceed with our study
of covers and decomposition laws for geometries related to generalized
quadrangles. In particular, we obtain a higher decomposition law for all
Kantor-Knuth generalized quadrangles which generalizes one of the main results
in \cite{part1}. In a second part of the paper, we study the set of all
Kantor-Knuth ovoids (with given parameter) in a fixed finite parabolic
quadrangle, and relate this set to embeddings of parabolic quadrangles into
Kantor-Knuth quadrangles. This point of view gives rise to an answer of a
question posed in \cite{JATSEP}.


  The present study aim is to know the information professionals-library
professionals knowledge sharing behaviours and attitudes among the institutes.
This study investigated six countries' library professionals: Bangladesh,
Bhutan, India, Nepal, Pakistan, and Sri Lanka. The study discussed knowledge
sharing behaviour, technological equipment used for knowledge management and
disseminating the sources of knowledge; academic social networking sites used
for sharing the information and knowledge as well as challenges in knowledge
management faced by the librarians examined in detail. The implication of the
study highlighted the various areas of knowledge management such as training,
budget, lack of staff and reward.


  We extend our earlier study of the electroweak interactions of quantum knots
to their gravitational and strong interactions. The knots are defined by
appropriate quantum groups and are intended to describe all knotted field
structures that conserve mass and spin, charge and hypercharge, as well as
color charge and color hypercharge. As sources of the gravitational fields the
knots are described as representations of the quantum group $SL_q(2)$ and as
sources of the electroweak and strong fields they are described by $SU_q(2)$.
When the point sources of the standard theory are replaced by the quantum
knots, the interaction terms of the new Lagrangian density acquire knot form
factors and the standard local gauge invariance is supplemented by an
additional global $U(1)\times U(1)$ invariance of the $SU_q(2)$ algebra.


  In this paper we study the dimension reduction limits of the compressible
Navier--Stokes equations over product Riemannian manifolds
$\mathcal{O}_\epsilon \cong \mathcal{M} \times \epsilon\mathcal{F}$, such that
$\dim\,(\mathcal{M})=n$ and $\dim\,(\mathcal{F})=d$ are arbitrary. Using the
method of relative entropies, we establish the convergence of the suitable weak
solutions of the Navier--Stokes equations on $\mathcal{O}_\epsilon$ to the
classical solution of the limiting equations on $\mathcal{M}$ as $\epsilon
\rightarrow 0^+$, provided the latter exists. In addition, we also deduce the
vanishing viscosity limit. The limiting equations identified through our
analysis contain the weight function $A:\mathcal{M} \rightarrow \mathbb{R}^+$
as a parameter, where $A(x)$ = area of fibre $\mathcal{F}_x$. Our work is based
on and generalises the results in P. Bella, E. Feireisl, M. Lewicka and A.
Novotn\'{y}, A rigorous justification of the Euler and Navier--Stokes equations
with geometric effects, \textit{SIAM J. Math. Anal.}, \textbf{48} (2016),
3907--3930, and it contains as special cases the physical models of circular
nozzles, thin plate limits and finite-length longitudinal nozzles.


  Linking classical microwave electrical circuits to the optical
telecommunication band is at the core of modern communication. Future quantum
information networks will require coherent microwave-to-optical conversion to
link electronic quantum processors and memories via low-loss optical
telecommunication networks. Efficient conversion can be achieved with
electro-optical modulators operating at the single microwave photon level. In
the standard electro-optic modulation scheme this is impossible because both,
up- and downconverted, sidebands are necessarily present. Here we demonstrate
true single sideband up- or downconversion in a triply resonant whispering
gallery mode resonator by explicitly addressing modes with asymmetric free
spectral range. Compared to previous experiments, we show a three orders of
magnitude improvement of the electro-optical conversion efficiency reaching
0.1% photon number conversion for a 10GHz microwave tone at 0.42mW of optical
pump power. The presented scheme is fully compatible with existing
superconducting 3D circuit quantum electrodynamics technology and can be used
for non-classical state conversion and communication. Our conversion bandwidth
is larger than 1MHz and not fundamentally limited.


  Bitcoin is a peer-to-peer payment system proposed by Nakamoto in 2008. Based
on the Nakamoto consensus, Bagaria, Kannan, Tse, Fanti, and Viswanath proposed
the Prism protocol in 2018 and showed that it achieves near-optimal blockchain
throughput while maintaining a similar level of security as bitcoin. Previous
probabilistic security guarantees for the bitcoin and Prism backbone protocols
were either established under a simplified discrete-time model or expressed in
terms of exponential order results. This paper presents a streamlined and
strengthened analysis under a more realistic continuous-time model. A fully
rigorous model for blockchains is developed with no restrictions on adversarial
miners except for an upper bound on their aggregate mining rate. The only
assumption on the peer-to-peer network is that all block propagation delays are
upper bounded by a constant. A new notion of "t-credible blockchains" is
introduced, which, together with some carefully defined "typical" events
concerning block production over time intervals, is crucial to establish
probabilisitic security guarantees in continuous time. A blockchain growth
theorem, a blockchain quality theorem, and a common prefix theorem are
established with explicit probability bounds. Moreover, under a certain typical
event which occurs with probability close to $1$, a valid transaction that is
deep enough in one credible blockchain is shown to be permanent in the sense
that it must be found in} in all future credible blockchains.


  We show that every spherical 2-Dupin submanifold that is not a hypersurface
is conformally congruent to the standard embedding of the real, complex,
quaternionic or octonionic projective plane. We also classify 2-CPC,
2-umbilical and weakly 2-umbilical submanifolds in space forms.


  The induced electric field in a tokamak drives a parallel electron current
flow. In an inhomogeneous, finite beta plasma, when this electron flow is
comparable to the ion thermal speed, the Alfven mode wave solutions of the
electromagnetic gyrokinetic equation can become nearly purely growing kink
modes. Using the new "low-flow" version of the gyrokinetic code GS2 developed
for momentum transport studies [Barnes et al 2013 Phys. Rev. Lett. 111,
055005], we are able to model the effect of the induced parallel electric field
on the electron distribution to study the destabilizing influence of current on
stability. We identify high mode number kink modes in GS2 simulations and make
comparisons to analytical theory in sheared magnetic geometry. We demonstrate
reassuring agreement with analytical results both in terms of parametric
dependences of mode frequencies and growth rates, and regarding the radial mode
structure.


  Stochastic gradient descent (SGD) with stochastic momentum is popular in
nonconvex stochastic optimization and particularly for the training of deep
neural networks. In standard SGD, parameters are updated by improving along the
path of the gradient at the current iterate on a batch of examples, where the
addition of a ``momentum'' term biases the update in the direction of the
previous change in parameters. In non-stochastic convex optimization one can
show that a momentum adjustment provably reduces convergence time in many
settings, yet such results have been elusive in the stochastic and non-convex
settings. At the same time, a widely-observed empirical phenomenon is that in
training deep networks stochastic momentum appears to significantly improve
convergence time, variants of it have flourished in the development of other
popular update methods, e.g. ADAM [KB15], AMSGrad [RKK18], etc. Yet theoretical
justification for the use of stochastic momentum has remained a significant
open question. In this paper we propose an answer: stochastic momentum improves
deep network training because it modifies SGD to escape saddle points faster
and, consequently, to more quickly find a second order stationary point. Our
theoretical results also shed light on the related question of how to choose
the ideal momentum parameter--our analysis suggests that $\beta \in [0,1)$
should be large (close to 1), which comports with empirical findings. We also
provide experimental findings that further validate these conclusions.


  Porous electrode theory, pioneered by John Newman and collaborators, provides
a useful macroscopic description of battery cycling behavior, rooted in
microscopic physical models rather than empirical circuit approximations. The
theory relies on a separation of length scales to describe transport in the
electrode coupled to intercalation within small active material particles.
Typically, the active materials are described as solid solution particles with
transport and surface reactions driven by concentration fields, and the
thermodynamics are incorporated through fitting of the open circuit potential.
This approach has fundamental limitations, however, and does not apply to
phase-separating materials, for which the voltage is an emergent property of
inhomogeneous concentration profiles, even in equilibrium. Here, we present a
general theoretical framework for "multiphase porous electrode theory"
implemented in an open-source software package called "MPET", based on
electrochemical nonequilibrium thermodynamics. Cahn-Hilliard-type phase field
models are used to describe the solid active materials with suitably
generalized models of interfacial reaction kinetics. Classical concentrated
solution theory is implemented for the electrolyte phase, and Newman's porous
electrode theory is recovered in the limit of solid-solution active materials
with Butler-Volmer kinetics. More general, quantum-mechanical models of
Faradaic reactions are also included, such as Marcus-Hush-Chidsey kinetics for
electron transfer at metal electrodes, extended for concentrated solutions. The
full equations and numerical algorithms are described, and a variety of example
calculations are presented to illustrate the novel features of the software
compared to existing battery models.


  Dissipative soliton resonance (DSR) is a promising way for high-energy pulse
generation typically having a symmetrical square pulse profile. While this
method is well known, the impact of third order dispersion (TOD) on DSR is yet
to be fully addressed in the literature. In this article, the impact of TOD on
DSR is numerically investigated under the frame of the complex cubic-quintic
Ginzburg-Landau equation (CQGLE). Our numerical investigations indicate that
DSR can stably exist under TOD with nearly the same pulse amplitude, but with a
(significantly) different pulse duration. Depending on the value of chromatic
dispersion, the pulse duration can be notably longer or shorter due to the
presence of TOD. The TOD effect also alters the dependence of pulse duration on
the nonlinear gain. Another impact of TOD on DSR is that the DSR exists with an
asymmetric pulse profile, leading to steepening of one edge of the DSR pulse,
while flattening of the other. Our results indicate that TOD has a critical
role for realizing DSR in mode-locked lasers and it should be taken into
consideration during design and development of DSR-based lasers.


  The logit model is often used to analyze experimental data. However,
randomization does not justify the model, so the usual estimators can be
inconsistent. A consistent estimator is proposed. Neyman's non-parametric setup
is used as a benchmark. In this setup, each subject has two potential
responses, one if treated and the other if untreated; only one of the two
responses can be observed. Beside the mathematics, there are simulation
results, a brief review of the literature, and some recommendations for
practice.


  We study square loss in a realizable time-series framework with martingale
difference noise. Our main result is a fast rate excess risk bound which shows
that whenever a trajectory hypercontractivity condition holds, the risk of the
least-squares estimator on dependent data matches the iid rate order-wise after
a burn-in time. In comparison, many existing results in learning from dependent
data have rates where the effective sample size is deflated by a factor of the
mixing-time of the underlying process, even after the burn-in time.
Furthermore, our results allow the covariate process to exhibit long range
correlations which are substantially weaker than geometric ergodicity. We call
this phenomenon learning with little mixing, and present several examples for
when it occurs: bounded function classes for which the $L^2$ and
$L^{2+\epsilon}$ norms are equivalent, ergodic finite state Markov chains,
various parametric models, and a broad family of infinite dimensional
$\ell^2(\mathbb{N})$ ellipsoids. By instantiating our main result to system
identification of nonlinear dynamics with generalized linear model transitions,
we obtain a nearly minimax optimal excess risk bound after only a polynomial
burn-in time.


  Principal component analysis (PCA) is a widely used dimension reduction tool
in the analysis of many kind of high-dimensional data. It is used in signal
processing, mechanical engineering, psychometrics, and other fields under
different names. It still bears the same mathematical idea: the decomposition
of variation of a high dimensional object into uncorrelated factors or
components. However, in many of the above applications, one is interested in
capturing the tail variables of the data rather than variation around the mean.
Such applications include weather related event curves, expected shortfalls,
and speeding analysis among others. These are all high dimensional tail objects
which one would like to study in a PCA fashion. The tail character though
requires to do the dimension reduction in an asymmetric norm rather than the
classical $L_2$-type orthogonal projection. We develop an analogue of PCA in an
asymmetric norm. These norms cover both quantiles and expectiles, another tail
event measure. The difficulty is that there is no natural basis, no `principal
components', to the $k$-dimensional subspace found. We propose two definitions
of principal components and provide algorithms based on iterative least
squares. We prove upper bounds on their convergence times, and compare their
performances in a simulation study. We apply the algorithms to a Chinese
weather dataset with a view to weather derivative pricing


  Many problems in machine learning write as the minimization of a sum of
individual loss functions over the training examples. These functions are
usually differentiable but, in some cases, their gradients are not Lipschitz
continuous, which compromises the use of (proximal) gradient algorithms.
Fortunately, changing the geometry and using Bregman divergences can alleviate
this issue in several applications, such as for Poisson linear inverse
problems.However, the Bregman operation makes the aggregation of several points
and gradients more involved, hindering the distribution of computations for
such problems. In this paper, we propose an asynchronous variant of the Bregman
proximal-gradient method, able to adapt to any centralized computing system. In
particular, we prove that the algorithm copes with arbitrarily long delays and
we illustrate its behavior on distributed Poisson inverse problems.


  The emission spectrum of the supermassive compact dark object at the Galactic
center is calculated in the framework of standard thin accretion disk theory
assuming that the compact object is a neutrino ball of $2.6 \times
10^{6}M_{\odot}$ instead of a supermassive black hole. The neutrino ball
scenario could explain the observed radio and infrared emission spectrum of the
Galactic center for wavelengths between $\lambda=0.3 {\rm cm}$ and
$\lambda=10^{-3} {\rm cm}$, if the neutrino mass and the accretion rate fulfil
some constraints.


  In previous work, the authors introduced the notion of Q-Koszul algebras, as
a tool to "model" module categories for semisimple algebraic groups over fields
of large characteristics. Here we suggest the model extends to small
characteristics as well. In particular, we present several conjectures in the
modular representation theory of semisimple groups which these algebras
inspire. They provide a new world-view of modular representation theory,
potentially valid for some root systems in all characteristics. In fact, we
give a non-trivial example in which $p=2$. This paper begins a systematic study
of Q-Koszul algebras, viewed as interesting objects in their own right.


  The incomplete hippocampal inversion (IHI) is an atypical anatomical pattern
of the hippocampus. However, the hippocampus is not a homogeneous structure, as
it consists of segregated subfields with specific characteristics. While IHI is
not related to whole hippocampal volume, higher IHI scores have been associated
to smaller CA1 in aging. Although the segmentation of hippocampal subfields is
challenging due to their small size, there are algorithms allowing their
automatic segmentation. By using a Human Connectome Project dataset of healthy
young adults, we first tested the inter-reliability of two methods for
automatic segmentation of hippocampal subfields, and secondly, we explored the
relationship between IHI and subfield volumes. Results evidenced strong
correlations between volumes obtained thorough both segmentation methods.
Furthermore, higher IHI scores were associated to bigger subiculum and smaller
CA1 volumes. Here, we provide new insights regarding IHI subfields volumetry,
and we offer support for automatic segmentation inter-method reliability.


  We present an introduction to cosmic inflation in the context of Palatini
gravity, which is an interesting alternative to the usual metric theory of
gravity. In the latter case only the metric $g_{\mu\nu}$ determines the
geometry of space-time, whereas in the former case both the metric and the
space-time connection $\Gamma^\lambda_{\mu\nu}$ are a priori independent
variables - a choice which can lead to a theory of gravity different from the
metric one. In scenarios where the field(s) responsible for cosmic inflation
are coupled non-minimally to gravity or the gravitational sector is otherwise
extended, assumptions of the underlying gravitational degrees of freedom can
have a big impact on the observational consequences of inflation. We
demonstrate this explicitly by reviewing several interesting and well-motivated
scenarios including Higgs inflation, $R^2$ inflation, and $\xi$-attractor
models. We also discuss some prospects for future research and argue why
$r=10^{-3}$ is a particularly important goal for future missions that search
for signatures of primordial gravitational waves.


  We consider boundary roughness for the ``droplet'' created when supercritical
two-dimensional Bernoulli percolation is conditioned to have an open dual
circuit surrounding the origin and enclosing an area at least $l^2$, for large
$l$. The maximum local roughness is the maximum inward deviation of the droplet
boundary from the boundary of its own convex hull; we show that for large $l$
this maximum is at least of order $l^{1/3}(\log l)^{-2/3}$. This complements
the upper bound of order $l^{1/3}(\log l)^{2/3}$ known for the average local
roughness. The exponent 1/3 on $l$ here is in keeping with predictions from the
physics literature for interfaces in two dimensions.


  One of the big challenges of developing interactive statistical applications
is the management of the data pipeline, which controls transformations from
data to plot. The user's interactions needs to be propagated through these
modules and reflected in the output representation at a fast pace. Each
individual module may be easy to develop and manage, but the dependency
structure can be quite challenging. The MVC (Model/View/Controller) pattern is
an attempt to solve the problem by separating the user's interaction from the
representation of the data. In this paper we discuss the paradigm of reactive
programming in the framework of the MVC architecture and show its applicability
to interactive graphics. Under this paradigm, developers benefit from the
separation of user interaction from the graphical representation, which makes
it easier for users and developers to extend interactive applications. We show
the central role of reactive data objects in an interactive graphics system,
implemented as the R package cranvas, which is freely available on GitHub and
the main developers include the authors of this paper.


  Characterization of crystallographic lattices is an important tool in
structure solution, crystallographic database searches and clustering of
diffraction images in serial crystallography. Characterization of lattices by
Niggli-reduced cells (based on the three shortest non-coplanar lattice edge
vectors) or by Delaunay-reduced cells (based on four edge vectors summing to
zero and all meeting at obtuse or right angles) are commonly used. The Niggli
cell derives from Minkowski reduction. The Delaunay cell derives from Selling
reduction. All are related to the Wigner-Seitz (or Dirichlet, or Voronoi) cell
of the lattice, which consists of the points at least as close to a chosen
lattice point than they are to any other lattice point. Starting from a
Niggli-reduced cell, the Dirichlet cell is characterized by the planes
determined by thirteen lattice half-edges: the midpoints of the three Niggli
cell edges, the six Niggli cell face diagonals and the four body-diagonals, but
seven of the edge lengths are sufficient: three edge lengths, the three shorter
of each pair of face-diagonal lengths and the shortest body-diagonal length,
from which the Niggli-reduced cell may be recovered.


  A local hidden variable model exploiting the detection loophole to reproduce
exactly the quantum correlation of the singlet state is presented. The model is
shown to be compatible with both the CHSH and the CH Bell inequalities.
Moreover, it bears the same rotational symmetry as spins. The reason why the
model can reproduce the quantum correlation without violating the Bell theorem
is that in the model the efficiency of the detectors depends on the local
hidden variable. On average the detector efficiency is limited to 75%.


  The glueballs lead to gluon and QCD monopole condensations as by-products of
color confinement. A color dielectric function $G(|\phi|)$ coupled with Abelian
gauge field is properly defined to mediate the glueball interactions at
confining regime after {\it spontaneous symmetry breaking} (SSB) of the gauge
symmetry. The particles are expected to form through quark gluon plasma (QGP)
hadronization phase where the free quarks and gluons start clamping together to
form hadrons. The QCD-like vacuum
$\langle\eta^2m_\eta^2F^{\mu\nu}F_{\mu\nu}\rangle$, confining potential
$V_c(r)$, string tension $\sigma$, penetration depth $\lambda$, superconducting
and normal monopole densities ($n_s\,n_n$) and the effective masses
($m_\eta^{2}$ and $m_A^{2}$) will be investigated at finite temperature $T$. We
also calculate the strong `running' coupling $\alpha_s$ and subsequently the
QCD $\beta$-function. {\it Dual superconducting} nature of the QCD vacuum will
be investigated based on monopole condensation.


  Growth is a known driver of cellular dynamics in a range of dense aggregates
from bacterial colonies to developing tissues to tumors. Hence, universal
physical principles underlying these dynamics are of great interdisciplinary
interest. Here, we study the emergent dynamics arising from the interplay of
growth, steric repulsion and motility in a minimal agent-based model of
exponentially growing three-dimensional spheroids. Our results show that,
without cell motility, deterministic motion caused by overall volume expansion
dominates the dynamics of individual cells in the radial direction, while
growth and division lead to cellular-scale diffusive motion in the tangential
direction, whose magnitude is largely independent of expansion velocity.
Despite this small-scale diffusion, we show that cell lineages are subject to
confinement in their local environment, quenching weak cell motility. At higher
motility, we find a transient regime of tangential superdiffusivity, which is
accompanied by global mixing of cells. A quantitative analysis reveals a
diverging mixing time scale at the transition, reminiscent of glassy dynamics.
Our study highlights the complex interaction of local cell division and
motility with global expansion, mediated exclusively by mechanics. The
observations may serve as a baseline for the identification of phenomena caused
by additional biological mechanisms in experiments on tissue spheroids, which
have become a popular model system in biophysics and other fields. The mixing
dynamics might also be relevant for competition or tumor progression.


  We examine subhaloes and galaxies residing in a simulated LCDM galaxy cluster
($M^{\rm crit}_{200}=1.1\times10^{15}M_\odot/h$) produced by hydrodynamical
codes ranging from classic Smooth Particle Hydrodynamics (SPH), newer SPH
codes, adaptive and moving mesh codes. These codes use subgrid models to
capture galaxy formation physics. We compare how well these codes reproduce the
same subhaloes/galaxies in gravity only, non-radiative hydrodynamics and full
feedback physics runs by looking at the overall subhalo/galaxy distribution and
on an individual objects basis. We find the subhalo population is reproduced to
within $\lesssim10\%$ for both dark matter only and non-radiative runs, with
individual objects showing code-to-code scatter of $\lesssim0.1$ dex, although
the gas in non-radiative simulations shows significant scatter. Including
feedback physics significantly increases the diversity. Subhalo mass and
$V_{max}$ distributions vary by $\approx20\%$. The galaxy populations also show
striking code-to-code variations. Although the Tully-Fisher relation is similar
in almost all codes, the number of galaxies with $10^{9}M_\odot/h\lesssim
M_*\lesssim 10^{12}M_\odot/h$ can differ by a factor of 4. Individual galaxies
show code-to-code scatter of $\sim0.5$ dex in stellar mass. Moreover, strong
systematic differences exist, with some codes producing galaxies $70\%$ smaller
than others. The diversity partially arises from the inclusion/absence of AGN
feedback. Our results combined with our companion papers demonstrate that
subgrid physics is not just subject to fine-tuning, but the complexity of
building galaxies in all environments remains a challenge. We argue even basic
galaxy properties, such as the stellar mass to halo mass, should be treated
with errors bars of $\sim0.2-0.4$ dex.


  We present deep near-IR images of high redshift radio galaxies obtained with
NIRC on the Keck I telescope. In most cases, the near-IR data sample rest
wavelengths at ~4000 Angstroms, free of strong emission lines. At z > 3, the
rest frame optical morphologies generally have faint, large-scale emission
surrounding multiple components of ~10 kpc size. The brightest of the small
knots are often aligned with the radio structures. At z < 3, the morphologies
change dramatically, showing single, compact structures without radio-aligned
features. The sizes and luminosities of the individual components in the z > 3
radio galaxies are similar to those of the radio-quiet star-forming galaxies
discovered at z ~ 3 by the Lyman dropout technique. The rest frame optical
colors of the z > 3 radio galaxies are consistent with models in which recent
star formation dominates the observed IR light, and in one case (4C 41.17) we
have direct spectroscopic evidence for massive star formation (Dey et al.
1997a). Our results suggest that the z > 3 radio galaxies evolve into very
massive elliptical galaxies at 2 < z < 3, in qualitative agreement with the
hierarchical model of galaxy formation. We also discuss the Hubble diagram of
radio galaxies, the possibility of a radio power dependence in the K-z
relation, and the implications for radio galaxy formation.


  Planar spin-transfer devices with dominating easy-plane anisotropy can be
described by an effective one-dimensional equation for the in-plane angle. Such
a description provides an intuitive qualitative understanding of the magnetic
dynamics. We give a detailed derivation of the effective planar equation and
use it to describe magnetic switching in devices with tilted polarizer.


  Nonlinear spectroscopy and microscopy techniques are ubiquitous in a wide
range of applications across physics and biology. However, these usually rely
on high-powered pulsed laser systems. A promising alternative is to exploit
entangled two-photon absorption (ETPA), which can lead to tens of orders of
magnitude lower incident flux rates than in conventional two-photon absorption
(TPA) schemes. However, the role of different entangled degrees of freedom in
ETPA was unclear following recent experimental studies, when compared to
earlier theoretical works. Here, we first demonstrate a linear dependence of
the ETPA rate with the photon-pair flux, a clear signature of ETPA, and
estimate the first values for the concentration-dependent ETPA cross-section
for Rhodamine 6G.We then investigate the signature of energy-time entanglement
and polarization dependence in the ETPA fluorescence rate and demonstrate a
strong dependence of the signal on the inter-photon delay that reflects the
coherence time of the entangled two-photon wave-packet.


  Aligned text-image encoders such as CLIP have become the de facto model for
vision-language tasks. Furthermore, modality-specific encoders achieve
impressive performances in their respective domains. This raises a central
question: does an alignment exist between uni-modal vision and language
encoders since they fundamentally represent the same physical world? Analyzing
the latent spaces structure of vision and language models on image-caption
benchmarks using the Centered Kernel Alignment (CKA), we find that the
representation spaces of unaligned and aligned encoders are semantically
similar. In the absence of statistical similarity in aligned encoders like
CLIP, we show that a possible matching of unaligned encoders exists without any
training. We frame this as a seeded graph-matching problem exploiting the
semantic similarity between graphs and propose two methods - a Fast Quadratic
Assignment Problem optimization, and a novel localized CKA metric-based
matching/retrieval. We demonstrate the effectiveness of this on several
downstream tasks including cross-lingual, cross-domain caption matching and
image classification. Code available at github.com/mayug/0-shot-llm-vision.


  We describe the geometrical ladder of equations for Abelian bundles and
gerbes, as well as higher generalisations, in terms of the cohomology of an
operator that combines de Rham and Cech cohomology.


  We introduce \textsc{PoliteRewrite} -- a dataset for polite language rewrite
which is a novel sentence rewrite task. Compared with previous text style
transfer tasks that can be mostly addressed by slight token- or phrase-level
edits, polite language rewrite requires deep understanding and extensive
sentence-level edits over an offensive and impolite sentence to deliver the
same message euphemistically and politely, which is more challenging -- not
only for NLP models but also for human annotators to rewrite with effort. To
alleviate the human effort for efficient annotation, we first propose a novel
annotation paradigm by a collaboration of human annotators and GPT-3.5 to
annotate \textsc{PoliteRewrite}. The released dataset has 10K polite sentence
rewrites annotated collaboratively by GPT-3.5 and human, which can be used as
gold standard for training, validation and test; and 100K high-quality polite
sentence rewrites by GPT-3.5 without human review. We wish this work (The
dataset (10K+100K) will be released soon) could contribute to the research on
more challenging sentence rewrite, and provoke more thought in future on
resource annotation paradigm with the help of the large-scaled pretrained
models.


  This article is intended as a compendium and guide to the variety of Bell
Inequality derivations that have appeared in the literature in recent years,
classifying them into six broad categories, revealing the underlying, often
hidden, assumption common to each - semifactuality. Evaluation of the attendant
conditional brings to light a significant EPR loophole that has not appeared in
the literature. Semantics for the inequality in the ongoing philosophic debate
that led to its discovery is discussed.


  This paper addresses the classification of Arabic text data in the field of
Natural Language Processing (NLP), with a particular focus on Natural Language
Inference (NLI) and Contradiction Detection (CD). Arabic is considered a
resource-poor language, meaning that there are few data sets available, which
leads to limited availability of NLP methods. To overcome this limitation, we
create a dedicated data set from publicly available resources. Subsequently,
transformer-based machine learning models are being trained and evaluated. We
find that a language-specific model (AraBERT) performs competitively with
state-of-the-art multilingual approaches, when we apply linguistically informed
pre-training methods such as Named Entity Recognition (NER). To our knowledge,
this is the first large-scale evaluation for this task in Arabic, as well as
the first application of multi-task pre-training in this context.


  While learned video codecs have demonstrated great promise, they have yet to
achieve sufficient efficiency for practical deployment. In this work, we
propose several novel ideas for learned video compression which allow for
improved performance for the low-latency mode (I- and P-frames only) along with
a considerable increase in computational efficiency. In this setting, for
natural videos our approach compares favorably across the entire R-D curve
under metrics PSNR, MS-SSIM and VMAF against all mainstream video standards
(H.264, H.265, AV1) and all ML codecs. At the same time, our approach runs at
least 5x faster and has fewer parameters than all ML codecs which report these
figures.
  Our contributions include a flexible-rate framework allowing a single model
to cover a large and dense range of bitrates, at a negligible increase in
computation and parameter count; an efficient backbone optimized for ML-based
codecs; and a novel in-loop flow prediction scheme which leverages prior
information towards more efficient compression.
  We benchmark our method, which we call ELF-VC (Efficient, Learned and
Flexible Video Coding) on popular video test sets UVG and MCL-JCV under metrics
PSNR, MS-SSIM and VMAF. For example, on UVG under PSNR, it reduces the BD-rate
by 44% against H.264, 26% against H.265, 15% against AV1, and 35% against the
current best ML codec. At the same time, on an NVIDIA Titan V GPU our approach
encodes/decodes VGA at 49/91 FPS, HD 720 at 19/35 FPS, and HD 1080 at 10/18
FPS.


  We combine new Herschel/SPIRE sub-millimeter observations with existing
multiwavelength data to investigate the dust scaling relations of the Herschel
Reference Survey, a magnitude-, volume-limited sample of ~300 nearby galaxies
in different environments. We show that the dust-to-stellar mass ratio
anti-correlates with stellar mass, stellar mass surface density and NUV-r
colour across the whole range of parameters covered by our sample. Moreover,
the dust-to-stellar mass ratio decreases significantly when moving from late-
to early-type galaxies. These scaling relations are similar to those observed
for the HI gas-fraction, supporting the idea that the cold dust is tightly
coupled to the cold atomic gas component in the interstellar medium. We also
find a weak increase of the dust-to-HI mass ratio with stellar mass and colour
but no trend is seen with stellar mass surface density. By comparing galaxies
in different environments we show that, although these scaling relations are
followed by both cluster and field galaxies, HI-deficient systems have, at
fixed stellar mass, stellar mass surface density and morphological type
systematically lower dust-to-stellar mass and higher dust-to-HI mass ratios
than HI-normal/field galaxies. This provides clear evidence that dust is
removed from the star-forming disk of cluster galaxies but the effect of the
environment is less strong than what is observed in the case of the HI disk.
Such effects naturally arise if the dust disk is less extended than the HI and
follows more closely the distribution of the molecular gas phase, i.e., if the
dust-to-atomic gas ratio monotonically decreases with distance from the
galactic center.


  We consider a system of nonlinear partial differential equations that
describes an age-structured population inhabiting several temporally varying
patches. We prove existence and uniqueness of solution and analyze its
large-time behavior in cases when the environment is constant and when it
changes periodically. A pivotal assumption is that individuals can disperse and
that each patch can be reached from every other patch, directly or through
several intermediary patches. We introduce the net reproductive operator and
characteristic equations for time-independent and periodical models and prove
that permanency is defined by the net reproductive rate for the whole system.
If the net reproductive rate is less or equal to one, extinction on all patches
is imminent. Otherwise, permanency on all patches is guaranteed. The proof is
based on a new approach to analysis of large-time stability.


  The theory of rational choice assumes that when people make decisions they do
so in order to maximize their utility. In order to achieve this goal they ought
to use all the information available and consider all the choices available to
choose an optimal choice. This paper investigates what happens when decisions
are made by artificially intelligent machines in the market rather than human
beings. Firstly, the expectations of the future are more consistent if they are
made by an artificially intelligent machine and the decisions are more rational
and thus marketplace becomes more rational.


  We discuss the effect of an (ab)-surface on the melting transition of the
pancake-vortex lattice in a layered superconductor within a density functional
theory approach. Both discontinuous and continuous surface melting are
predicted for this system, although the latter scenario occupies the major part
of the low-field phase diagram. The formation of a quasi-liquid layer below the
bulk melting temperature inhibits the appearance of a superheated solid phase,
yielding an asymmetric hysteretic behavior which has been seen in experiments.


  The main features of fermion condensation quantum phase transition (FCQPT),
which are distinctive in several aspects from that of conventional quantum
phase transition (CQPT), are considered. We show that in contrast to CQPT,
whose physics in quantum critical region is dominated by thermal and quantum
fluctuations and characterized by the absence of quasiparticles, the physics of
a Fermi system near FCQPT or undergone FCQPT is controlled by the system of
quasiparticles resembling the Landau quasiparticles. Contrary to the Landau
quasiparticles, the effective mass of these quasiparticles strongly depends on
the temperature, magnetic fields, density, etc. This system of quasiparticles
having general properties determines the universal behavior of the Fermi system
in question. As a result, the universal behavior persists up to relatively high
temperatures comparatively to the case when such a behavior is determined by
CQPT. We analyze striking recent measurements of specific heat, charge and heat
transport used to study the nature of magnetic field-induced QCP in
heavy-fermion metal CeCoIn$_5$ and show that the observed facts are in good
agreement with our scenario based on FCQPT and certainly seem to rule out the
critical fluctuations related with CQPT. Our general consideration suggests
that FCQPT and the emergence of novel quasiparticles near and behind FCQPT and
resembling the Landau quasiparticles are distinctive features intrinsic to
strongly correlated substances.


  We consider a variant of the classical linear quadratic Gaussian regulator
(LQG) in which penalties on the endpoint state are replaced by the
specification of the terminal state distribution. The resulting theory
considerably differs from LQG as well as from formulations that bound the
probability of violating state constraints. We develop results for optimal
state-feedback control in the two cases where i) steering of the state
distribution is to take place over a finite window of time with minimum energy,
and ii) the goal is to maintain the state at a stationary distribution over an
infinite horizon with minimum power. For both problems the distribution of
noise and state are Gaussian. In the first case, we show that provided the
system is controllable, the state can be steered to any terminal Gaussian
distribution over any specified finite time-interval. In the second case, we
characterize explicitly the covariance of admissible stationary state
distributions that can be maintained with constant state-feedback control. The
conditions for optimality are expressed in terms of a system of dynamically
coupled Riccati equations in the finite horizon case and in terms of algebraic
conditions for the stationary case. In the case where the noise and control
share identical input channels, the Riccati equations for finite-horizon
steering become homogeneous and can be solved in closed form. The present paper
is largely based on our recent work in arxiv.org/abs/1408.2222,
arxiv.org/abs/1410.3447 and presents an overview of certain key results.


  The relative fixity of a permutation group is the maximum proportion of the
points fixed by a non-trivial element of the group and the relative fixity of a
graph is the relative fixity of its automorphism group, viewed as a permutation
group on the vertex-set of the graph. We prove in this paper that the relative
fixity of connected $2$-arc-transitive graphs of a fixed valence tends to $0$
as the number of vertices grows to infinity. We prove the same result for the
class of arc-transitive graphs of a fixed prime valence, and more generally,
for any class of arc-transitive locally-$L$ graphs, where $L$ is a fixed
quasiprimitive graph-restrictive permutation group.


  We give an example of a non-trivial linear operad that only admits trivial
coalgebras and give sufficient conditions ensuring that the cofree coalgebra
functor be faithful.


  Working from a Poisson-Gaussian noise model, a multi-sample extension of the
Photon Counting Histogram Expectation Maximization (PCH-EM) algorithm is
derived as a general-purpose alternative to the Photon Transfer (PT) method.
This algorithm is derived from the same model, requires the same experimental
data, and estimates the same sensor performance parameters as the time-tested
PT method, all while obtaining lower uncertainty estimates. It is shown that as
read noise becomes large, multiple data samples are necessary to capture enough
information about the parameters of a device under test, justifying the need
for a multi-sample extension. An estimation procedure is devised consisting of
initial PT characterization followed by repeated iteration of PCH-EM to
demonstrate the improvement in estimate uncertainty achievable with PCH-EM;
particularly in the regime of Deep Sub-Electron Read Noise (DSERN). A
statistical argument based on the information theoretic concept of sufficiency
is formulated to explain how PT data reduction procedures discard information
contained in raw sensor data, thus explaining why the proposed algorithm is
able to obtain lower uncertainty estimates of key sensor performance parameters
such as read noise and conversion gain. Experimental data captured from a CMOS
quanta image sensor with DSERN is then used to demonstrate the algorithm's
usage and validate the underlying theory and statistical model. In support of
the reproducible research effort, the code associated with this work can be
obtained on the MathWorks File Exchange (Hendrickson et al., 2024).


  We consider a Sturm--Liouville boundary value problem in a boun\-ded domain
$\cD$ of $\mathbb{R}^n$. By this is meant that the differential equation is
given by a second order elliptic operator of divergent form in $\cD$ and the
boundary conditions are of Robin type on $\partial \cD$. The first order term
of the boundary operator is the oblique derivative whose coefficients bear
discontinuities of the first kind. Applying the method of weak perturbation of
compact self-adjoint operators and the method of rays of minimal growth, we
prove the completeness of root functions related to the boundary value problem
in Lebesgue and Sobolev spaces of various types.


  Joint analysis of multiple biomarker images and tissue morphology is
important for disease diagnosis, treatment planning and drug development. It
requires cross-staining comparison among Whole Slide Images (WSIs) of
immuno-histochemical and hematoxylin and eosin (H&E) microscopic slides.
However, automatic, and fast cross-staining alignment of enormous gigapixel
WSIs at single-cell precision is challenging. In addition to morphological
deformations introduced during slide preparation, there are large variations in
cell appearance and tissue morphology across different staining. In this paper,
we propose a two-step automatic feature-based cross-staining WSI alignment to
assist localization of even tiny metastatic foci in the assessment of lymph
node. Image pairs were aligned allowing for translation, rotation, and scaling.
The registration was performed automatically by first detecting landmarks in
both images, using the scale-invariant image transform (SIFT), followed by the
fast sample consensus (FSC) protocol for finding point correspondences and
finally aligned the images. The Registration results were evaluated using both
visual and quantitative criteria using the Jaccard index. The average Jaccard
similarity index of the results produced by the proposed system is 0.942 when
compared with the manual registration.


  The growth function is the generating function for sizes of spheres around
the identity in Cayley graphs of groups. We present a novel method to calculate
growth functions for automatic groups with normal form recognizing automata
that recognize a single normal form for each group element, and are at most
context free in complexity: context free grammars can be translated into
algebraic systems of equations, whose solutions represent generating functions
of their corresponding non-terminal symbols. This approach allows us to
seamlessly introduce weightings on the growth function: assign different or
even distinct weights to each of the generators in an underlying presentation,
such that this weighting is reflected in the growth function. We recover known
growth functions for small braid groups, and calculate growth functions that
weight each generator in an automatic presentation of the braid groups
according to their lengths in braid generators.


  With increasing interest in mmWave and THz communication systems, an unmanned
aerial vehicle (UAV)-mounted intelligent reflecting surface (IRS) has been
suggested as a key enabling technology to establish robust line-of-sight (LoS)
connections with ground nodes owing to their free mobility and high altitude,
especially for emergency and disaster response. This paper investigates a
secure offloading system, where the UAV-mounted IRS assists the offloading
procedures between ground users and an access point (AP) acting as an edge
cloud. In this system, the users except the intended recipients in the
offloading process are considered as potential eavesdroppers. The system aims
to achieve the minimum total energy consumption of battery-limited ground user
devices under constraints for secure offloading accomplishment and operability
of UAV-mounted IRS, which is done by optimizing the transmit power of ground
user devices, the trajectory and phase shift matrix of UAV-mounted IRS, and the
offloading ratio between local execution and edge computing based on the
successive convex approximation (SCA) algorithms. Numerical results show that
the proposed algorithm can provide the considerable energy savings compared
with local execution and partial optimizations.


  The feasibility of thermally driven spin injectors built from half-metallic
Heusler alloys inserted between aluminum leads was investigated by means of
{\em ab initio} calculations of the thermodynamic equilibrium and electronic
transport. We have focused on two main issues and found that: (i) the interface
between Al and the closely lattice-matched Heusler alloys of type Co$_2$Ti$Z$
($Z=$ Si or Ge) is stable under various growth conditions; and (ii) the
conventional and spin-dependent Seebeck coefficients in such heterojunctions
exhibit a strong dependence on both the spacer and the atomic composition of
the Al/Heusler interface. The latter quantity gives a measure of the spin
accumulation and varies between $+8$~$\mu$V/K and $-3$~$\mu$V/K near $300$~K,
depending on whether a Ti-Ge or a Co-Co plane makes the contact between Al and
Co$_2$TiGe in the trilayer. Our results show that it is in principle possible
to tailor the spin-caloric effects by a targeted growth control of the samples.


  Force fluctuations exhibited in focal adhesions (FAs) that connect a cell to
its extracellular environment, point to the complex role of the underlying
machinery that controls cell migration. To elucidate the explicit role of
myosin motors in the temporal traction force oscillations, we vary the
contractility of these motors in a dynamical model based on the molecular
clutch hypothesis. As the contractility is lowered, effected both by changing
the motor velocity and the rate of attachment/detachment, we show analytically
in an experimentally relevant parameter space that the system goes from
decaying oscillations to stable limit cycle oscillations through a
supercritical Hopf bifurcation. As a function of motor activity and the number
of clutches, the system exhibits a wide array of dynamical states. We
corroborate our analytical results with stochastic simulations of the
motor-clutch system. We obtain limit cycle oscillations in the parameter regime
as predicted by our model. The frequency range of oscillations in the average
clutch and motor deformation compares well with experimental results.


  We perform a series of time dependent Magnetohydrodynamic simulations of the
HD 189733 star-planet system in order to predict radio transit modulations due
to the interaction between the stellar wind and planetary magnetic field. The
simulation combines a model for the stellar corona and wind with an exoplanet
that is orbiting the star in a fully dynamic, time-dependent manner. Our
simulations generate synthetic radio images that enable us to obtain synthetic
radio lightcurves in different frequencies. We find a clear evidence for the
planetary motion in the radio light curves. Moreover, we find specific repeated
features in the light curves that are attributed to the passage of the
planetary magnetosphere in front of the star during transit. More importantly,
we find a clear dependence in the magnitude and phase of these lightcurve
features on the strength of the planetary magnetic field. Our work demonstrates
that if radio transits could be observed, they could indeed provide information
about the magnetic field strength of the transiting exoplanet. Future work to
parameterize these lightcurve features and their dependence on the planetary
field strength would provide tools to search for these features in radio
observations datasets. As we only consider the thermal radio emission from the
host star for our study, very sensitive radio interferometers are necessary to
detect these kinds of planetary transit in radio.


  The impact of culture in visual emotion perception has recently captured the
attention of multimedia research. In this study, we pro- vide powerful
computational linguistics tools to explore, retrieve and browse a dataset of
16K multilingual affective visual concepts and 7.3M Flickr images. First, we
design an effective crowdsourc- ing experiment to collect human judgements of
sentiment connected to the visual concepts. We then use word embeddings to
repre- sent these concepts in a low dimensional vector space, allowing us to
expand the meaning around concepts, and thus enabling insight about
commonalities and differences among different languages. We compare a variety
of concept representations through a novel evaluation task based on the notion
of visual semantic relatedness. Based on these representations, we design
clustering schemes to group multilingual visual concepts, and evaluate them
with novel metrics based on the crowdsourced sentiment annotations as well as
visual semantic relatedness. The proposed clustering framework enables us to
analyze the full multilingual dataset in-depth and also show an application on
a facial data subset, exploring cultural in- sights of portrait-related
affective visual concepts.


  Computer vision can accelerate ecology research by automating the analysis of
raw imagery from sensors like camera traps, drones, and satellites. However,
computer vision is an emerging discipline that is rarely taught to ecologists.
This work discusses our experience teaching a diverse group of ecologists to
prototype and evaluate computer vision systems in the context of an intensive
hands-on summer workshop. We explain the workshop structure, discuss common
challenges, and propose best practices. This document is intended for computer
scientists who teach computer vision across disciplines, but it may also be
useful to ecologists or other domain experts who are learning to use computer
vision themselves.


  While the kinematics of galaxies up to z~3 have been characterized in detail,
only a handful of galaxies at high redshift (z>4) have been examined in such a
way. The Atacama Large Millimeter/submillimeter Array (ALMA) Large Program to
INvestigate [CII] at Early times (ALPINE) survey observed a statistically
significant sample of 118 star-forming main sequence galaxies at z=4.4-5.9 in
[CII]158um emission, increasing the number of such observations by nearly 10x.
A preliminary qualitative classification of these sources revealed a diversity
of kinematic types (i.e., rotators, mergers, and dispersion-dominated systems).
In this work, we supplement the initial classification by applying quantitative
analyses to the ALPINE data: a tilted ring model (TRM) fitting code (3DBarolo),
a morphological classification (Gini-M20), and a set of disk identification
criteria. Of the 75 [CII]-detected ALPINE galaxies, 29 are detected at
sufficient significance and spatial resolution to allow for TRM fitting and the
derivation of morphological and kinematic parameters. These 29 sources
constitute a high-mass subset of the ALPINE sample (M_*>10^9.5Msol). We
robustly classify 14 of these sources (six rotators, five mergers, and three
dispersion-dominated systems); the remaining sources showing complex behaviour.
By exploring the G-M20 of z>4 rest-frame FIR and [CII] data for the first time,
we find that our 1"~6kpc resolution data alone are insufficient to separate
galaxy types. We compare the rotation curves and dynamical mass profiles of the
six ALPINE rotators to the two previously detected z~4-6 unlensed main sequence
rotators, finding high rotational velocities (~50-250km/s) and a diversity of
rotation curve shapes.


  In the context of the little Higgs models, we discuss single production of
the new charged gauge bosons in association with top quark at the $CERN$ Large
Hadron Collider$(LHC)$. We find that the new charged gauge bosons $W_{H}^{-}$
and $X^{-}$, which are predicted by the littlest Higgs model and the SU(3)
simple model, respectively, can be abundantly produced at the $LHC$. However,
since the main backgrounds coming from the processes $pp\to t\bar{t}+X$ and
$pp\to tW^{-}+X$ are very large, the values of the ratios $N_{W}$ and $N_{X}$
are very small in most of the parameter space. It is only possible to detect
the signal of the gauge boson $W_{H}^{-}$ via the process $pp\to gb+X\to
tW_{H}^{-}+X$ at the $LHC$ in a small region of the parameter space.


  We study the dynamical stability and fates of hierarchical (in semi-major
axis) two-planet systems with arbitrary eccentricities and mutual inclinations.
We run a large number of long-term numerical integrations and use the Support
Vector Machine algorithm to search for an empirical boundary that best
separates stable systems from systems experiencing either ejections or
collisions with the star. We propose the following new criterion for dynamical
stability: $a_{\rm out}(1-e_{\rm out})/[a_{\rm in}(1+e_{\rm
in})]>2.4\left[\max(\mu_{\rm in},\mu_{\rm out})\right]^{1/3}(a_{\rm out}/a_{\rm
in})^{1/2}+1.15$, which should be applicable to planet-star mass ratios
$\mu_{\rm in},\mu_{\rm out}=10^{-4}-10^{-2}$, integration times up to $10^8$
orbits of the inner planet, and mutual inclinations $\lesssim40^\circ$. Systems
that do not satisfy this condition by a margin of $\gtrsim0.5$ are expected to
be unstable, mostly leading to planet ejections if $\mu_{\rm in}>\mu_{\rm
out}$, while slightly favoring collisions with the star for $\mu_{\rm
in}<\mu_{\rm out}$. We use our numerical integrations to test other stability
criteria that have been proposed in the literature and show that our stability
criterion performs significantly better for the range of system parameters that
we have explored.


  The Multi-Agent Pathfinding (MAPF) problem involves finding a set of
conflict-free paths for a group of agents confined to a graph. In typical MAPF
scenarios, the graph and the agents' starting and ending vertices are known
beforehand, allowing the use of centralized planning algorithms. However, in
this study, we focus on the decentralized MAPF setting, where the agents may
observe the other agents only locally and are restricted in communications with
each other. Specifically, we investigate the lifelong variant of MAPF, where
new goals are continually assigned to the agents upon completion of previous
ones. Drawing inspiration from the successful AlphaZero approach, we propose a
decentralized multi-agent Monte Carlo Tree Search (MCTS) method for MAPF tasks.
Our approach utilizes the agent's observations to recreate the intrinsic Markov
decision process, which is then used for planning with a tailored for
multi-agent tasks version of neural MCTS. The experimental results show that
our approach outperforms state-of-the-art learnable MAPF solvers. The source
code is available at https://github.com/AIRI-Institute/mats-lp.


  In this paper, we present our first experiments in text-to-articulation
prediction, using ultrasound tongue image targets. We extend a traditional
(vocoder-based) DNN-TTS framework with predicting PCA-compressed ultrasound
images, of which the continuous tongue motion can be reconstructed in synchrony
with synthesized speech. We use the data of eight speakers, train fully
connected and recurrent neural networks, and show that FC-DNNs are more
suitable for the prediction of sequential data than LSTMs, in case of limited
training data. Objective experiments and visualized predictions show that the
proposed solution is feasible and the generated ultrasound videos are close to
natural tongue movement. Articulatory movement prediction from text input can
be useful for audiovisual speech synthesis or computer-assisted pronunciation
training.


  The topologically engineered complex Schwarzites architecture has been used
to build novel and unique structural components with a high specific strength.
The mechanical properties of these building blocks can be further tuned,
reinforcing with stronger and high surface area architecture. In the current
work, we have built six different Schwarzites structures with multiple
interlocked layers, which we named architecturally interlocked
petal-schwarzites (AIPS). These complex structures are 3D printed into
macroscopic dimensions and compressed using uniaxial compression. The
experimental results show a strong dependency of mechanical response on the
number of layers and topology of the layers. Fully atomistic molecular dynamics
compressive simulations were also carried out, and the results are in good
agreement with experimental observations. They can explain the underlying AIPS
mechanism of high specific strength and energy absorption. The proposed
approach opens a new perspective on developing new 3D-printed materials with
tunable and enhanced mechanical properties.


  We show that every complete metric space is homeomorphic to the precise locus
of zeros of an entire analytic map from a Hilbert space to a Banach space. As a
corollary, every complete separable metric space is homeomorphic to the precise
locus of zeros of an entire analytic map between two separable complex Hilbert
spaces. AmS TeX 2.1.


  A major challenge in the development of a fully automated vehicle is to
enable a large variety of users to use the vehicle independently and safely.
Particular demands arise from user groups who rely on human assistance when
using conventional cars. For the independent use of a vehicle by such groups,
the vehicle must compensate for the absence of an accompanying person, whose
actions and decisions ensure the accompanied person's safety even in unknown
situations. The resulting requirements cannot be fulfilled only by the
geometric design of the vehicle and the nature of its control elements. Special
user needs must be taken into account in the entire automation of the vehicle.
In this paper, we describe requirements for compensating for the absence of an
accompanying person and show how required functions can be located in a
hierarchical functional system architecture of an automated vehicle. In
addition, we outline the relevance of the vehicle's operational design domain
in this context and present a use case for the described functionalities.


  Optical communications technology has made enormous and steady progress for
several decades, providing the key resource in our increasingly
information-driven society and economy. Much of this progress has been in
finding innovative ways to increase the data carrying capacity of a single
optical fibre. In this search, researchers have explored (and close to
maximally exploited) every available degree of freedom, and even commercial
systems now utilize multiplexing in time, wavelength, polarization, and phase
to speed more information through the fibre infrastructure. Conspicuously, one
potentially enormous source of improvement has however been left untapped in
these systems: fibres can easily support hundreds of spatial modes, but today's
commercial systems (single-mode or multi-mode) make no attempt to use these as
parallel channels for independent signals.


  The Gaussian scale mixture model (GSM) is a simple yet powerful probabilistic
generative model of natural image patches. In line with the well-established
idea that sensory processing is adapted to the statistics of the natural
environment, the GSM has also been considered a model of the early visual
system, as a reasonable "first-order" approximation of the internal model that
the primary visual cortex (V1) implements. According to this view, neural
activities in V1 represent the posterior distribution under the GSM given a
particular visual stimulus. Indeed, (approximate) inference under the GSM has
successfully accounted for various nonlinearities in the mean (trial-average)
responses of V1 neurons, as well as the dependence of (across-trial) response
variability with stimulus contrast found in V1 recordings. However, previous
work almost exclusively relied on numerical simulations to obtain these
results. Thus, for a deeper insight into the realm of possible behaviours the
GSM can (and cannot) exhibit and predict, here we present analytical
derivations for the limiting behaviour of the mean and (co)variance of the GSM
posterior at very low and very high contrast levels. These results should guide
future work exploring neural circuit dynamics appropriate for implementing
inference under the GSM.


  This preprint has been withdrawn, as the evidence for persistent currents
found with the "infinite-size" DMRG algorithm has not been reproduced with the
(presumably more accurate) "finite-size" version of the algorithm (M. Troyer
and U. Schollwoeck, private communiation). A much clearer example of long-range
order in orbital currents is presented in: J. O. Fjaerestad and J. B. Marston,
``Staggered Orbital Currents in the Half-Filled Two-Leg Ladder,''
cond-mat/0107094.


  We consider interacting electrons in a one dimensional lattice with an
incommensurate Aubry-Andre' potential in the regime when the single-particle
eigenstates are localized. We rigorously establish persistence of ground state
localization in presence of weak many-body interaction, for almost all the
chemical potentials. The proof uses a quantum many body extension of methods
adopted for the stability of tori of nearly integrable hamiltonian systems, and
relies on number-theoretic properties of the potential incommensurate
frequency.


  We present an alternative proof of the Alexander-Hirschowitz Theorem in
dimension 3 using degenerations of toric varieties.


  We define the Global Centre Symmetry set (GCS) of a smooth closed
m-dimensional submanifold M of R^n, $n \leq 2m$, which is an affinely invariant
generalization of the centre of a k-sphere in R^{k+1}. The GCS includes both
the centre symmetry set defined by Janeczko and the Wigner caustic defined by
Berry. We develop a new method for studying generic singularities of the GCS
which is suited to the case when M is lagrangian in R^{2m} with canonical
symplectic form. The definition of the GCS, which slightly generalizes one by
Giblin and Zakalyukin, is based on the notion of affine equidistants, so, we
first study singularities of affine equidistants of Lagrangian submanifolds,
classifying all the stable ones. Then, we classify the affine-Lagrangian stable
singularities of the GCS of Lagrangian submanifolds and show that, already for
smooth closed convex curves in R^2, many singularities of the GCS which are
affine stable are not affine-Lagrangian stable.


  We relate here the vacuum structure of chiral symmetry breaking to a wide
class of low energy hadronic properties to examine consistency with
experimental results. Phase transition of chiral symmetry breaking is described
through explicit vacuum realignment. We find that for the present ansatz,
vacuum structure of the light quark sector can be determined from $f_\pi=92$
MeV, which then yields that $\Gamma(\pi^0\rightarrow 2 \gamma)=8.3$ eV and,
$R_{ch}=0.67$ fms, in agreement with experiments. This amounts to three
independent `observations' of the vacuum structure of the light quark sector.
For the calculation of $\Gamma(\pi^0\rightarrow 2\gamma)$, we do not use the
anomaly equation - local gauge invariance for vacuum realignment becomes
adequate. The above width is sensitive to the vacuum structure, and can give
evidence regarding progress towards chiral symmetry restoration in QGP. The
corresponding results for $s$ quark sector are moderately good. We also exploit
similar ideas for heavy quarks, but here the experimental data are rare, and
the theoretical techniques are likely to need refinements.


  New physics theories often depend on a large number of free parameters. The
precise values of those parameters in some cases drastically affect the
resulting phenomenology of fundamental physics processes, while in others
finite variations can leave it basically invariant at the level of detail
experimentally accessible. When designing a strategy for the analysis of
experimental data in the search for a signal predicted by a new physics model,
it appears advantageous to categorize the parameter space describing the model
according to the corresponding kinematical features of the final state. A
multi-dimensional test statistic can be used to gauge the degree of similarity
in the kinematics of different models; a clustering algorithm using that metric
may then allow the division of the space into homogeneous regions, each of
which can be successfully represented by a benchmark point. Searches targeting
those benchmark points are then guaranteed to be sensitive to a large area of
the parameter space. In this document we show a practical implementation of the
above strategy for the study of non-resonant production of Higgs boson pairs in
the context of extensions of the standard model with anomalous couplings of the
Higgs bosons. A non-standard value of those couplings may significantly enhance
the Higgs pair production cross section, such that the process could be
detectable with the data that the Large Hadron Collider will collect in Run 2.


  On a smooth, closed Riemannian manifold $(M,g)$ of dimension $n\ge3$ with
positive scalar curvature and not conformally diffeomorphic to the standard
sphere, we prove that the only conformal metrics to $g$ with constant
Q-curvature of order 4 are the metrics $\lambda g$ with $\lambda>0$ constant.


  With the growing popularity of deep-learning based NLP models, comes a need
for interpretable systems. But what is interpretability, and what constitutes a
high-quality interpretation? In this opinion piece we reflect on the current
state of interpretability evaluation research. We call for more clearly
differentiating between different desired criteria an interpretation should
satisfy, and focus on the faithfulness criteria. We survey the literature with
respect to faithfulness evaluation, and arrange the current approaches around
three assumptions, providing an explicit form to how faithfulness is "defined"
by the community. We provide concrete guidelines on how evaluation of
interpretation methods should and should not be conducted. Finally, we claim
that the current binary definition for faithfulness sets a potentially
unrealistic bar for being considered faithful. We call for discarding the
binary notion of faithfulness in favor of a more graded one, which we believe
will be of greater practical utility.


  The paper focuses on the calibration of serial industrial robots using
partial pose measurements. In contrast to other works, the developed advanced
robot calibration technique is suitable for geometrical and elastostatic
calibration. The main attention is paid to the model parameters identification
accuracy. To reduce the impact of measurement errors, it is proposed to use
directly position measurements of several points instead of computing
orientation of the end-effector. The proposed approach allows us to avoid the
problem of non-homogeneity of the least-square objective, which arises in the
classical identification technique with the full-pose information. The
developed technique does not require any normalization and can be efficiently
applied both for geometric and elastostatic identification. The advantages of a
new approach are confirmed by comparison analysis that deals with the
efficiency evaluation of different identification strategies. The obtained
results have been successfully applied to the elastostatic parameters
identification of the industrial robot employed in a machining work-cell for
aerospace industry.


  Face detection has achieved great success using the region-based methods. In
this report, we propose a region-based face detector applying deep networks in
a fully convolutional fashion, named Face R-FCN. Based on Region-based Fully
Convolutional Networks (R-FCN), our face detector is more accurate and
computational efficient compared with the previous R-CNN based face detectors.
In our approach, we adopt the fully convolutional Residual Network (ResNet) as
the backbone network. Particularly, We exploit several new techniques including
position-sensitive average pooling, multi-scale training and testing and
on-line hard example mining strategy to improve the detection accuracy. Over
two most popular and challenging face detection benchmarks, FDDB and WIDER
FACE, Face R-FCN achieves superior performance over state-of-the-arts.


  Well designed lattice-Boltzmann codes exploit the essentially embarrassingly
parallel features of the algorithm and so can be run with considerable
efficiency on modern supercomputers. Such scalable codes permit us to simulate
the behaviour of increasingly large quantities of complex condensed matter
systems. In the present paper, we present some preliminary results on the large
scale three-dimensional lattice-Boltzmann simulation of binary immiscible fluid
flows through a porous medium derived from digitised x-ray microtomographic
data of Bentheimer sandstone, and from the study of the same fluids under
shear. Simulations on such scales can benefit considerably from the use of
computational steering and we describe our implementation of steering within
the lattice-Boltzmann code, called LB3D, making use of the RealityGrid steering
library. Our large scale simulations benefit from the new concept of capability
computing, designed to prioritise the execution of big jobs on major
supercomputing resources. The advent of persistent computational grids promises
to provide an optimal environment in which to deploy these mesoscale simulation
methods, which can exploit the distributed nature of compute, visualisation and
storage resources to reach scientific results rapidly; we discuss our work on
the grid-enablement of lattice-Boltzmann methods in this context.


  Could John Kerry have gained votes in the 2004 Presidential election by more
clearly distinguishing himself from George Bush on economic policy? At first
thought, the logic of political preferences would suggest not: the Republicans
are to the right of most Americans on economic policy, and so in a
one-dimensional space with party positions measured with no error, the optimal
strategy for the Democrats would be to stand infinitesimally to the left of the
Republicans. The median voter theorem suggests that each party should keep its
policy positions just barely distinguishable from the opposition. In a
multidimensional setting, however, or when voters vary in their perceptions of
the parties' positions, a party can benefit from putting some daylight between
itself and the other party on an issue where it has a public-opinion advantage
(such as economic policy for the Democrats). We set up a plausible theoretical
model in which the Democrats could achieve a net gain in votes by moving to the
left on economic policy, given the parties' positions on a range of issue
dimensions. We then evaluate this model based on survey data on voters'
perceptions of their own positions and those of the candidates in 2004. Under
our model, it turns out to be optimal for the Democrats to move slightly to the
right but staying clearly to the left of the Republicans' current position on
economic issues.


  The motion of a small compact object (SCO) in a background spacetime is
investigated further in a class of model nonlinear scalar field theories having
a perturbative structure analogous to the General Relativistic description of
extreme mass ratio inspirals (EMRIs). We derive regular expressions for the
scalar perturbations generated by the SCO's motion valid through third order in
$\epsilon$, the size of the SCO to the background curvature length scale. Our
expressions are compared to those calculated through second order in $\epsilon$
by Rosenthal in [E. Rosenthal, CQG 22, S859 (2005)] and found to agree but our
procedure for regularizing the scalar perturbations is considerably simpler.
Following the Detweiler-Whiting (DW) scheme, we use our regular expressions for
the field and derive the regular self-force corrections through third order. We
find agreement with our previous derivation based on a variational principle of
an effective action for the worldline associated with the SCO thus
demonstrating the internal consistency of our formalism. This also explicitly
demonstrates that the DW decomposition of Green's functions is a valid and
practical method of self force computation at higher orders in perturbation
theory and, as we show in an appendix, at all orders in perturbation theory.
Finally, we identify a master source from which all other physically relevant
quantities are derivable. Knowing the master source perturbatively allows one
to construct the waveform measured by an observer, the regular part of the
field on the worldline, the regular part of the self force, and orbital
quantities such as shifts of the innermost stable circular orbit, etc. The
existence of a master source together with the regularization methods
implemented in this series should be indispensable for derivations of
higher-order gravitational self force corrections.


  Computational intelligence-based ocean characteristics forecasting
applications, such as Significant Wave Height (SWH) prediction, are crucial for
avoiding social and economic loss in coastal cities. Compared to the
traditional empirical-based or numerical-based forecasting models, "soft
computing" approaches, including machine learning and deep learning models,
have shown numerous success in recent years. In this paper, we focus on
enabling the deep learning model to learn both short-term and long-term
spatial-temporal dependencies for SWH prediction. A Wavelet Graph Neural
Network (WGNN) approach is proposed to integrate the advantages of wavelet
transform and graph neural network. Several parallel graph neural networks are
separately trained on wavelet decomposed data, and the reconstruction of each
model's prediction forms the final SWH prediction. Experimental results show
that the proposed WGNN approach outperforms other models, including the
numerical models, the machine learning models, and several deep learning
models.


  With the rise of large language models (LLMs), recent works have leveraged
LLMs to improve the performance of click-through rate (CTR) prediction.
However, we argue that a critical obstacle remains in deploying LLMs for
practical use: the efficiency of LLMs when processing long textual user
behaviors. As user sequences grow longer, the current efficiency of LLMs is
inadequate for training on billions of users and items. To break through the
efficiency barrier of LLMs, we propose Behavior Aggregated Hierarchical
Encoding (BAHE) to enhance the efficiency of LLM-based CTR modeling.
Specifically, BAHE proposes a novel hierarchical architecture that decouples
the encoding of user behaviors from inter-behavior interactions. Firstly, to
prevent computational redundancy from repeated encoding of identical user
behaviors, BAHE employs the LLM's pre-trained shallow layers to extract
embeddings of the most granular, atomic user behaviors from extensive user
sequences and stores them in the offline database. Subsequently, the deeper,
trainable layers of the LLM facilitate intricate inter-behavior interactions,
thereby generating comprehensive user embeddings. This separation allows the
learning of high-level user representations to be independent of low-level
behavior encoding, significantly reducing computational complexity. Finally,
these refined user embeddings, in conjunction with correspondingly processed
item embeddings, are incorporated into the CTR model to compute the CTR scores.
Extensive experimental results show that BAHE reduces training time and memory
by five times for CTR models using LLMs, especially with longer user sequences.
BAHE has been deployed in a real-world system, allowing for daily updates of 50
million CTR data on 8 A100 GPUs, making LLMs practical for industrial CTR
prediction.


  We consider layered two-dimensional Ising and directed walk models and show
that the two problems are inherently related. The information about the
zero-field thermodynamical properties of the Ising model is contained into the
transfer matrix of the directed walk. For several hierarchical and aperiodic
distributions of the couplings, critical exponents for the two problems are
obtained exactly through renormalization.


  This index contains the proceedings submitted to the 38th International
Cosmic Ray Conference (ICRC 2023) in the name of the CTA consortium.


  A consistent approach in forming the 0.7 structure by using a quantum dot
rather than a quantum point contact is demonstrated. With this scheme, it was
possible to tune on and off the 0.7 structure. The 0.7 structure continuously
evolved into a normal integer conductance plateau by varying the tuning
condition. Unlike the conventional 0.7 plateau, the new 0.7 structure was
observed even at low electron temperatures down to 100 mK, with unprecedented
flatness. From our results, it is concluded that electron interference should
be taken into consideration to explain the 0.7 structure.


  There is a growing understanding that transport properties of complex oxides
and individual molecules are dominated by polaron physics. In superconducting
oxides the long-range Froehlich and short-range Jahn-Teller electron-phonon
interactions bind carriers into real space pairs - small bipolarons with
surprisingly low mass but sufficient binding energy, while the long-range
Coulomb repulsion keeps bipolarons apart preventing their clustering. The
bipolaron theory numerically explains high Tc values without any fitting
parameters and describes other key features of the cuprates. The same approach
provides a new insite into the theory of transport through molecular nanowires
and quantum dots (MQD). Attractive polaron-polaron correlations lead to a
"switching" phenomenon in the current-voltage characteristics of MQD. The
degenerate MQD with strong electron-vibron coupling has two stable current
states (a volatile memory), which might be useful in molecular electronics.


  Mach's "fixed stars" are actually not fixed at all. The distant clusters of
galaxies are not only receding from each observer but they are also
accelerating since the rate of cosmological expansion is not constant. If the
distant cosmic masses in someway constitute the frame of inertial reference
then an additional force should be generated among local bodies in reaction to
the apparent cosmological accelerations of the distant galaxies.


  A generalized finite element method is proposed for solving a heterogeneous
reaction-diffusion equation with a singular perturbation parameter
$\varepsilon$, based on locally approximating the solution on each subdomain by
solution of a local reaction-diffusion equation and eigenfunctions of a local
eigenproblem. These local problems are posed on some domains slightly larger
than the subdomains with oversampling size $\delta^{\ast}$. The method is
formulated at the continuous level as a direct discretization of the continuous
problem and at the discrete level as a coarse-space approximation for its
standard FE discretizations. Exponential decay rates for local approximation
errors with respect to $\delta^{\ast}/\varepsilon$ and $\delta^{\ast}/h$ (at
the discrete level with $h$ denoting the fine FE mesh size) and with the local
degrees of freedom are established. In particular, it is shown that the method
at the continuous level converges uniformly with respect to $\varepsilon$ in
the standard $H^{1}$ norm, and that if the oversampling size is relatively
large with respect to $\varepsilon$ and $h$ (at the discrete level), the
solutions of the local reaction-diffusion equations provide good local
approximations for the solution and thus the local eigenfunctions are not
needed. Numerical results are provided to verify the theoretical results.