  As an important upstream task for many medical applications, supervised
landmark localization still requires non-negligible annotation costs to achieve
desirable performance. Besides, due to cumbersome collection procedures, the
limited size of medical landmark datasets impacts the effectiveness of
large-scale self-supervised pre-training methods. To address these challenges,
we propose a two-stage framework for one-shot medical landmark localization,
which first infers landmarks by unsupervised registration from the labeled
exemplar to unlabeled targets, and then utilizes these noisy pseudo labels to
train robust detectors. To handle the significant structure variations, we
learn an end-to-end cascade of global alignment and local deformations, under
the guidance of novel loss functions which incorporate edge information. In
stage II, we explore self-consistency for selecting reliable pseudo labels and
cross-consistency for semi-supervised learning. Our method achieves
state-of-the-art performances on public datasets of different body parts, which
demonstrates its general applicability.


  We study nestohedra $P_B$ corresponding to building sets $B$. It is shown
that every flag nestohedron can be obtained from a cube by successive shavings
faces of codimension 2. We receive new Delzant geometric realization of flag
nestohedra. The main result of the paper is that Gal's conjecture holds for
every flag nestohedron. Moreover, we get the exact estimation of
$\gamma$-vectors of $n$-dimensional flag nestohedra:
$0\leq\gamma_i(P_B)\leq\gamma_i(Pe^n)$.


  In this article we consider the $\alpha$--Euler equations in the exterior of
a small fixed disk of radius $\epsilon$. We assume that the initial potential
vorticity is compactly supported and independent of $\epsilon$, and that the
circulation of the unfiltered velocity on the boundary of the disk does not
depend on $\epsilon$. We prove that the solution of this problem converges, as
$\epsilon \to 0$, to the solution of a modified $\alpha$--Euler equation in the
full plane where an additional Dirac located at the center of the disk is
imposed in the potential vorticity.


  In this article, we study the convergence of algorithms for solving monotone
inclusions in the presence of adjoint mismatch. The adjoint mismatch arises
when the adjoint of a linear operator is replaced by an approximation, due to
computational or physical issues. This occurs in inverse problems, particularly
in computed tomography. In real Hilbert spaces, monotone inclusion problems
involving a maximally $\rho$-monotone operator, a cocoercive operator, and a
Lipschitzian operator can be solved by the Forward-Backward-Half-Forward and
the Forward-Douglas-Rachford-Forward methods. We investigate the case of a
mismatched Lipschitzian operator. We propose variants of the two aforementioned
methods to cope with the mismatch, and establish conditions under which the
weak convergence to a solution is guaranteed for these variants. The proposed
algorithms hence enable each iteration to be implemented with a possibly
iteration-dependent approximation to the mismatch operator, thus allowing this
operator to be modified at each iteration. Finally, we present numerical
experiments on a computed tomography example in material science, showing the
applicability of our theoretical findings.


  Data scarcity has become one of the main obstacles to developing supervised
models based on Artificial Intelligence in Computer Vision. Indeed, Deep
Learning-based models systematically struggle when applied in new scenarios
never seen during training and may not be adequately tested in non-ordinary yet
crucial real-world situations. This paper presents and publicly releases
CrowdSim2, a new synthetic collection of images suitable for people and vehicle
detection gathered from a simulator based on the Unity graphical engine. It
consists of thousands of images gathered from various synthetic scenarios
resembling the real world, where we varied some factors of interest, such as
the weather conditions and the number of objects in the scenes. The labels are
automatically collected and consist of bounding boxes that precisely localize
objects belonging to the two object classes, leaving out humans from the
annotation pipeline. We exploited this new benchmark as a testing ground for
some state-of-the-art detectors, showing that our simulated scenarios can be a
valuable tool for measuring their performances in a controlled environment.


  We provide upper bounds on the total number of irreducible factors, and in
particular irreducibility criteria for some classes of bivariate polynomials
$f(x,y)$ over an arbitrary field $\mathbb{K}$. Our results rely on information
on the degrees of the coefficients of $f$, and on information on the
factorization of the constant term and of the leading coefficient of $f$,
viewed as a polynomial in $y$ with coefficients in $\mathbb{K}[x]$. In
particular, we provide a generalization of the bivariate version of Perron's
irreducibility criterion, and similar results for polynomials in an arbitrary
number of indeterminates. The proofs use non-Archimedean absolute values, that
are suitable for finding information on the location of the roots of $f$ in an
algebraic closure of $\mathbb{K}(x)$.


  We develop a time-dependent variational Monte Carlo (t-VMC) method for
quantum dynamics of strongly correlated electrons. The t-VMC method has been
recently applied to bosonic systems and quantum spin systems. Here, we propose
a time-dependent trial wave function with many variational parameters, which is
suitable for nonequilibrium strongly correlated electron systems. As the trial
state, we adopt the generalized pair-product wave function with correlation
factors and quantum-number projections. This trial wave function has been
proven to accurately describe ground states of strongly correlated electron
systems. To show the accuracy and efficiency of our trial wave function in
nonequilibrium states as well, we present our benchmark results for relaxation
dynamics during and after interaction quench protocols of fermionic Hubbard
models. We find that our trial wave function well reproduces the exact results
for the time evolution of physical quantities such as energy, momentum
distribution, spin structure factor, and superconducting correlations. These
results show that the t-VMC with our trial wave function offers an efficient
and accurate way to study challenging problems of nonequilibrium dynamics in
strongly correlated electron systems.


  This paper analyses both nonlinear activation functions and spatial
max-pooling for Deep Convolutional Neural Networks (DCNNs) by means of the
algebraic basis of mathematical morphology. Additionally, a general family of
activation functions is proposed by considering both max-pooling and nonlinear
operators in the context of morphological representations. Experimental section
validates the goodness of our approach on classical benchmarks for supervised
learning by DCNN.


  We present our study of the evolution of brightest cluster galaxies based on
data from the Las Campanas Distant Cluster Survey (Gonzalez et al. 2001) and
HST optical and IR imaging. We briefly discuss the technique that enabled us to
use short (~ 3 min) exposures and a small (1m) telescope to efficiently survey
about 130 sq. deg. of sky for high-z galaxy clusters. Follow-up imaging is used
to construct a sample of several tens of BCGs at 0.3 < z < 0.9 with which to
explore their evolution. In particular, we examine the luminosity and color
evolution of these galaxies. We confirm the previous results that 1) BCGs in
different environments evolve differently (eg. Burke et al 2000), 2) BCGs,
particularly those in low-mass clusters, must be accreting significantly since
z ~ 1 (Aragon-Salamanca et al. 1998). From measurments of the colors vs.
redshift, we conclude that this accretion must consist of old stellar
populations. Using HST NICMOS and WFPC2 images, we find preliminary evidence
for evolution in the sizes of BCGs that is consistent with the accretion model
presented in standard hierarchical models (Aragon-Salamanca et al. 1998), but
puzzling differences between NICMOS and WFPC2 scale lengths precludes a
definitive conclusion.


  As gradual typing becomes increasingly popular in languages like Python and
TypeScript, there is a growing need to infer type annotations automatically.
While type annotations help with tasks like code completion and static error
catching, these annotations cannot be fully determined by compilers and are
tedious to annotate by hand. This paper proposes a probabilistic type inference
scheme for TypeScript based on a graph neural network. Our approach first uses
lightweight source code analysis to generate a program abstraction called a
type dependency graph, which links type variables with logical constraints as
well as name and usage information. Given this program abstraction, we then use
a graph neural network to propagate information between related type variables
and eventually make type predictions. Our neural architecture can predict both
standard types, like number or string, as well as user-defined types that have
not been encountered during training. Our experimental results show that our
approach outperforms prior work in this space by $14\%$ (absolute) on library
types, while having the ability to make type predictions that are out of scope
for existing techniques.


  The computational study of equilibria involving constraints on players'
strategies has been largely neglected. However, in real-world applications,
players are usually subject to constraints ruling out the feasibility of some
of their strategies, such as, e.g., safety requirements and budget caps.
Computational studies on constrained versions of the Nash equilibrium have lead
to some results under very stringent assumptions, while finding constrained
versions of the correlated equilibrium (CE) is still unexplored. In this paper,
we introduce and computationally characterize constrained Phi-equilibria -- a
more general notion than constrained CEs -- in normal-form games. We show that
computing such equilibria is in general computationally intractable, and also
that the set of the equilibria may not be convex, providing a sharp divide with
unconstrained CEs. Nevertheless, we provide a polynomial-time algorithm for
computing a constrained (approximate) Phi-equilibrium maximizing a given linear
function, when either the number of constraints or that of players' actions is
fixed. Moreover, in the special case in which a player's constraints do not
depend on other players' strategies, we show that an exact, function-maximizing
equilibrium can be computed in polynomial time, while one (approximate)
equilibrium can be found with an efficient decentralized no-regret learning
algorithm.


  We have carried out high magnetic field studies of single-crystalline
Li$_2$MnO$_3$, a honeycomb lattice antiferromagnet. Its magnetic phase diagram
was mapped out using magnetization measurements at applied fields up to 35 T.
Our results show that it undergoes two successive meta-magnetic transitions
around 9 T fields applied perpendicular to the ab-plane (along the c*-axis).
These phase transitions are completely absent in the magnetization measured
with field applied along the ab-plane. In order to understand this magnetic
phase diagram, we developed a mean-field model starting from the correct
Neel-type magnetic structure, consistent with our single crystal neutron
diffraction data at zero field. Our model calculations succeeded in explaining
the two meta-magnetic transitions that arise when Li$_2$MnO$_3$ enters two
different spin-flop phases from the zero field Neel phase.


  We present a powerful theorem for proving the irreducibility of tempered
unitary representations of the free group.


  We derive some new conditions for integrability of dynamically defined C^1
invariant splittings in arbitrary dimension and co-dimension. In particular we
prove that every 2-dimensional C^1 invariant decomposition on a 3-dimensional
manifold satisfying a volume domination condition is uniquely integrable. In
the special case of volume preserving diffeomorphisms we show that standard
dynamical domination is already sufficient to guarantee unique integrability.


  The radio luminosity function (RLF) of radio galaxies and radio-loud quasars
is often modelled as a broken power-law. The break luminosity is close to the
dividing line between the two Fanaroff-Riley (FR) morphological classes for the
large-scale radio structure of these objects. We use an analytical model for
the luminosity and size evolution of FRII-type objects together with a simple
prescription for FRI-type sources to construct the RLF. We postulate that all
sources start out with an FRII-type morphology. Weaker jets subsequently
disrupt within the quasi-constant density cores of their host galaxies and
develop turbulent lobes of type FRI. With this model we recover the slopes of
the power laws and the break luminosity of the RLF determined from
observations. The rate at which AGN with jets of jet power $Q$ appear in the
universe is found to be proportional to $Q^{-1.6}$. The model also roughly
predicts the distribution of the radio lobe sizes for FRII-type objects, if the
radio luminosity of the turbulent jets drops significantly at the point of
disruption. We show that our model is consistent with recent ideas of two
distinct accretion modes in jet-producing AGN, if radiative efficiency of the
accretion process is correlated with jet power.


  The kinematics of two-neutron emission following the $\beta$-decay of
$^{11}$Li was investigated for the first time by detecting the two neutrons in
coincidence and by measuring their angle and energy. An array of
liquid-scintillator neutron detectors was used to reject cosmic-ray and
$\gamma$-ray backgrounds by pulse-shape discrimination. Cross-talk events in
which two detectors are fired by a single neutron were rejected using a filter
tested on the $\beta$-1n emitter $^9$Li. A large cross-talk rejection rate is
obtained ($> 95 \%$) over most of the energy range of interest. Application to
$^{11}$Li data leads to a significant number of events interpreted as
$\beta$-2n decay. A discrete neutron line at $\approx$ 2 MeV indicates
sequential two-neutron emission, possibly from the unbound state at 10.6 MeV
excitation energy in $^{11}$Be.


  Salce \cite{MR565595} introduced the notion of a \emph{cotorsion pair} of
classes of abelian groups, and asked whether every such pair is \emph{complete}
(i.e., has enough injectives and projectives). We prove that it is consistent,
relative to the consistency of Vop\v{e}nka's Principle (VP), that the answer is
affirmative. Combined with a previous result of Eklof-Shelah \cite{MR2031314},
this shows that Salce's Problem is independent of the ZFC axioms (modulo the
consistency of VP).


  Spin-orbit interaction (SOI) offers a nonferromagnetic scheme to realize spin
polarization through utilizing an electric field. Electrically tunable SOI
through electrostatic gates have been investigated, however, the relatively
weak and volatile tunability limit its practical applications in spintronics.
Here, we demonstrate the nonvolatile electric-field control of SOI via
constructing ferroelectric Rashba architectures, i.e., 2D Bi2O2Se/PMN-PT
ferroelectric field effect transistors. The experimentally observed weak
antilocalization (WAL) cusp in Bi2O2Se films implies the Rashba-type SOI that
arises from asymmetric confinement potential. Significantly, taking advantage
of the switchable ferroelectric polarization, the WAL-to-weak localization (WL)
transition trend reveals the competition between spin relaxation and dephasing
process, and the variation of carrier density leads to a reversible and
nonvolatile modulation of spin relaxation time and spin splitting energy of
Bi2O2Se films by this ferroelectric gating. Our work provides a scheme to
achieve nonvolatile control of Rashba SOI with the utilization of ferroelectric
remanent polarization.


  Only one carbon-rich (C-rich, hereinafter) Mira variable has so far been
suggested as a member of the Galactic bulge and this is in a symbiotic system.
Here we describe a method for selecting C-rich candidates from an infrared
colour-colour diagram, (J-Ks) vs ([9]-[18]). Follow-up low-resolution
spectroscopy resulted in the detection of 8 C-rich Mira variables from a sample
of36 candidates towards the Galactic bulge. Our near-infrared photometry
indicates that two of these, including the known symbiotic, are closer than the
main body of the bulge while a third is a known foreground object. Of the 5
bulge members, one shows He I and [O II] emission and is possibly another
symbiotic star. Our method is useful for identifying rare C-rich stars in the
Galactic bulge and elsewhere. The age of these C-rich stars and the
evolutionary process which produced them remain uncertain. They could be old
and the products of either binary mass transfer or mergers, i.e. the
descendants of blue stragglers, but we cannot rule out the possibility that
they belong to a small in-situ population of metal-poor intermediate age (less
than 5 Gyr) stars in the bulge or that they have been accreted from a dwarf
galaxy.


  The Book of the Dean of Lismore (BDL) is a 16th-century Scottish Gaelic
manuscript written in a non-standard orthography. In this work, we outline the
problem of transliterating the text of the BDL into a standardised orthography,
and perform exploratory experiments using Transformer-based models for this
task. In particular, we focus on the task of word-level transliteration, and
achieve a character-level BLEU score of 54.15 with our best model, a BART
architecture pre-trained on the text of Scottish Gaelic Wikipedia and then
fine-tuned on around 2,000 word-level parallel examples. Our initial
experiments give promising results, but we highlight the shortcomings of our
model, and discuss directions for future work.


  The PERCS system was designed by IBM in response to a DARPA challenge that
called for a high-productivity high-performance computing system. The IBM PERCS
architecture is a two level direct network having low diameter and high
bisection bandwidth. Mapping and routing strategies play an important role in
the performance of applications on such a topology. In this paper, we study
mapping strategies for PERCS architecture, that examine how to map tasks of a
given job on to the physical processing nodes. We develop and present
fundamental principles for designing good mapping strategies that minimize
congestion. This is achieved via a theoretical study of some common
communication patterns under both direct and indirect routing mechanisms
supported by the architecture.


  The motion of photons around black holes determines the shape of shadow and
match the ringdown properties of a perturbed black hole. Observations of
shadows and ringdown waveforms will reveal the nature of black holes. In this
paper, we study the motion of photons in a general parametrized metric beyond
the Kerr hypothesis. We investigated the radius and frequency of the photon
circular orbits on the equatorial plane and obtained fitted formula with varied
parameters. The Lyapunov exponent which connects to the decay rate of the
ringdown amplitude is also calculated. We also analyzed the shape of shadow
with full parameters of the generally axisymmetric metric. Our results imply
the potential constraint on black hole parameters by combining the Event
Horizon Telescope and gravitational wave observations in the future.


  We find generalized Jack polynomials for the group $SU(3)$ and verify that
their Selberg averages for several first levels are given by Nekrasov
functions. To compute the averages we derive recurrence relations for the
$sl_3$ Selberg integrals.


  Coordinate-transformation cloaking is based on the design of a metamaterial
shell made of an anisotropic, spatially inhomogeneous "transformation medium"
that allows rerouting the impinging wave around a given region of space. In its
original version, it is generally believed that, in the ideal limit, the
radiation cannot penetrate the cloaking shell (from outside to inside, and
viceversa). However, it was recently shown by Chen et al. that electromagnetic
fields may actually penetrate the cloaked region, provided that this region
contains double-negative transformation media which, via proper design, may be
in principle used to (partially or totally) "undo" the cloaking transformation,
thereby acting as an "anti-cloak." In this paper, we further elaborate this
concept, by considering a more general scenario of cloak/anti-cloak
interactions. Our full-wave analytical study provides new insightful results
and explores the effects of departure from ideality, suggesting also some novel
scenarios for potential applications.


  Phase transitions and effects of external noise on many body systems are one
of the main topics in physics. In mean field coupled nonlinear dynamical
stochastic systems driven by Brownian noise, various types of phase transitions
including nonequilibrium ones may appear. A Brownian motion is a special case
of L\'evy motion and the stochastic process based on the latter is an
alternative choice for studying cooperative phenomena in various fields.
Recently, fractional Fokker-Planck equations associated with L\'evy noise have
attracted much attention and behaviors of systems with double-well potential
subjected to L\'evy noise have been studied intensively. However, most of such
studies have resorted to numerical computation. We construct an {\it
analytically solvable model} to study the occurrence of phase transitions
driven by L\'evy stable noise.


  In this work we present a set-oriented path following method for the
computation of relative global attractors of parameter-dependent dynamical
systems. We start with an initial approximation of the relative global
attractor for a fixed parameter $\lambda_0$ computed by a set-oriented
subdivision method. By using previously obtained approximations of the
parameter-dependent relative global attractor we can track it with respect to a
one-dimensional parameter $\lambda > \lambda_0$ without restarting the whole
subdivision procedure. We illustrate the feasibility of the set-oriented path
following method by exploring the dynamics in models for shear flows during the
transition to turbulence.


  We study experimentally and theoretically polarization-dependent luminescence
from an ensemble of quantum-dot-like nanostructures with a very large in-plane
shape anisotropy (quantum dashes). We show that the measured degree of linear
polarization of the emitted light increases with the excitation power and
changes with temperature in a non-trivial way, depending on the excitation
conditions. Using an approximate model based on the k.p theory, we are able to
relate this degree of polarization to the amount of light hole admixture in the
exciton states which, in turn, depends on the symmetry of the envelope wave
function. Agreement between the measured properties and theory is reached under
assumption that the ground exciton state in a quantum dash is trapped in a
confinement fluctuation within the structure and thus localized in a much
smaller volume of much lower asymmetry than the entire nanostructure.


  To capture user preference, transformer models have been widely applied to
model sequential user behavior data. The core of transformer architecture lies
in the self-attention mechanism, which computes the pairwise attention scores
in a sequence. Due to the permutation-equivariant nature, positional encoding
is used to enhance the attention between token representations. In this
setting, the pairwise attention scores can be derived by both semantic
difference and positional difference. However, prior studies often model the
two kinds of difference measurements in different ways, which potentially
limits the expressive capacity of sequence modeling. To address this issue,
this paper proposes a novel transformer variant with complex vector attention,
named EulerFormer, which provides a unified theoretical framework to formulate
both semantic difference and positional difference. The EulerFormer involves
two key technical improvements. First, it employs a new transformation function
for efficiently transforming the sequence tokens into polar-form complex
vectors using Euler's formula, enabling the unified modeling of both semantic
and positional information in a complex rotation form.Secondly, it develops a
differential rotation mechanism, where the semantic rotation angles can be
controlled by an adaptation function, enabling the adaptive integration of the
semantic and positional information according to the semantic
contexts.Furthermore, a phase contrastive learning task is proposed to improve
the isotropy of contextual representations in EulerFormer. Our theoretical
framework possesses a high degree of completeness and generality. It is more
robust to semantic variations and possesses moresuperior theoretical properties
in principle. Extensive experiments conducted on four public datasets
demonstrate the effectiveness and efficiency of our approach.


  This paper augments the reward received by a reinforcement learning agent
with potential functions in order to help the agent learn (possibly stochastic)
optimal policies. We show that a potential-based reward shaping scheme is able
to preserve optimality of stochastic policies, and demonstrate that the ability
of an agent to learn an optimal policy is not affected when this scheme is
augmented to soft Q-learning. We propose a method to impart potential based
advice schemes to policy gradient algorithms. An algorithm that considers an
advantage actor-critic architecture augmented with this scheme is proposed, and
we give guarantees on its convergence. Finally, we evaluate our approach on a
puddle-jump grid world with indistinguishable states, and the continuous state
and action mountain car environment from classical control. Our results
indicate that these schemes allow the agent to learn a stochastic optimal
policy faster and obtain a higher average reward.


  We discuss forward jet production data recently published by the H1 and ZEUS
collaborations at HERA. We review how several Monte-Carlo models compare to the
data. QCD calculations based on the BFKL formalism and on fixed NLO
perturbation theory with and without resolved virtual photons are described.


  The PREX and the CREX group reported their skin values. Using the Love-Franey
(LF) $t$-matrix folding model with the neutron and proton densities scaled to
the neutron radius $r_{\rm n}^{208}({\rm PREX2})$ and the proton radius of the
electron scattering, we found that the model reproduces $\sigma_R$ for p+
$^{208}$Pb scattering at $E_{\rm lab} = 534.1, 549, 806$MeV. Zenihiro {\it el
al. } deduce neutron radii $r_{\rm n}^{48,40}({\rm exp})$ from proton elastic
scattering, whereas we determine $r_{\rm m}^{\rm 40}({\rm exp})=3.361 \pm
0.075$fm from measured $\sigma_R$ for $^{4}$He+ $^{40}$Ca scattering. Our first
aim is to determine $r_{\rm skin}^{\rm 208}$ from measured $\sigma_R$ of
p+$^{208}$Pb scattering at $E_{\rm lab} = 534.1, 549, 806$MeV by using the LF
$t$-matrix folding model. Our second aim is to determine $r_{\rm skin}^{\rm
48}$ from the $r_{\rm m}^{\rm 40}({\rm exp})$ and $\Delta \equiv r_{\rm m}^{\rm
48}({\rm exp})-r_{\rm m}^{\rm 40}({\rm exp})$ that is evaluated from the
$r_{\rm n}^{48,40}({\rm exp})$ and the $r_{\rm p}^{48,40}({\rm exp})$
calculated with the isotope shift method based on the electron scattering. For
the first aim, we use the LF $t$-matrix model with the densities scaled from
the D1S-GHFB+AMP neutron density. The D1M-GHFB+AMP is used to estimate a
theoretical error. The resulting skin values are $r_{\rm skin}^{\rm 208}= 0.324
\pm 0.047$fm for D1S and $r_{\rm skin}^{\rm 208}({\rm exp})=0.333 \pm 0.047 $fm
for D1M. The $\Delta=0.109$fm and $r_{\rm m}^{\rm 40}({\rm exp})=3.361 \pm
0.075$fm yield $r_{\rm m}^{\rm 48}=3.470 \pm 0.075$fm, leading to $r_{\rm
skin}^{\rm 48}=0.144 \pm 0.075$fm. We conclude that $r_{\rm skin}^{208}({\rm
exp})=0.324 \pm (0.047)_{\rm exp} \pm (0.009)_{\rm th}~{\rm fm}$ for p
scattering at $E_{\rm lab} = 534.1, 549, 806$MeV. Our skin values are
consistent with the PREX2 and CREX values.


  Noisy Student Training (NST) has recently demonstrated extremely strong
performance in Automatic Speech Recognition(ASR). In this paper, we propose a
data selection strategy named LM Filter to improve the performance of NST on
non-target domain data in ASR tasks. Hypotheses with and without a Language
Model are generated and the CER differences between them are utilized as a
filter threshold. Results reveal that significant improvements of 10.4%
compared with no data filtering baselines. We can achieve 3.31% CER in
AISHELL-1 test set, which is best result from our knowledge without any other
supervised data. We also perform evaluations on the supervised 1000 hour
AISHELL-2 dataset and competitive results of 4.73% CER can be achieved.


  We investigate the gravonuclear instabilities reported by Bono et al.
(1997a,b) during the onset of helium-shell burning at the end of the
horizontal-branch (HB) phase. These instabilities are characterized by
relaxation oscillations within the helium shell which lead to loops in the
evolutionary tracks. We find the occurrence of these instabilities depends
critically on how the breathing pulses are suppressed near the end of the HB
phase. If they are suppressed by omitting the gravitational energy term in the
stellar structure equations, then the helium profile within the core at the end
of the HB phase will contain a broad region of varying helium abundance. The
helium-burning shell which forms in this region is too thick to be unstable,
and gravonuclear instabilities do not occur. If, on the other hand, the
breathing pulses are suppressed by prohibiting any increase in the central
helium abundance, then the final helium profile can exhibit a large
discontinuity at the edge of the helium-exhausted core. The helium shell which
forms just exterior to this discontinuity is then much thinner and can be
thermally unstable. Even in this case, however, the gravonuclear instabilities
disappear as soon as the nuclear burning broadens the helium shell into its
characteristic S-shape. We conclude that the gravonuclear instabilities found
by Bono et al. are a consequence of the ad hoc procedure used to suppress the
breathing pulses.


  Algorithms which compute locally optimal continuous designs often rely on a
finite design space or on repeatedly solving a complex non-linear program. Both
methods require extensive evaluations of the Jacobian Df of the underlying
model. These evaluations present a heavy computational burden. Based on the
Kiefer-Wolfowitz Equivalence Theorem we present a novel design of experiments
algorithm which computes optimal designs in a continuous design space. For this
iterative algorithm we combine an adaptive Bayes-like sampling scheme with
Gaussian process regression to approximate the directional derivative of the
design criterion. The approximation allows us to adaptively select new design
points on which to evaluate the model. The adaptive selection of the algorithm
requires significantly less evaluations of Df and reduces the runtime of the
computations. We show the viability of the new algorithm on two examples from
chemical engineering.


  This paper combines the power of deep-learning with the generalizability of
physics-based features, to present an advanced method for seismic
discrimination between earthquakes and explosions. The proposed method contains
two branches: a deep learning branch operating directly on seismic waveforms or
spectrograms, and a second branch operating on physics-based parametric
features. These features are high-frequency P/S amplitude ratios and the
difference between local magnitude (ML) and coda duration magnitude (MC). The
combination achieves better generalization performance when applied to new
regions than models that are developed solely with deep learning. We also
examined which parts of the waveform data dominate deep learning decisions
(i.e., via Grad-CAM). Such visualization provides a window into the black-box
nature of the machine-learning models and offers new insight into how the deep
learning derived models use data to make the decisions.


  Electrical and optical characterization of two-dimensional/three-dimensional
(2D/3D) p-molybdenum disulfide/n-gallium nitride (p-MoS2/n-GaN) heterojunction
diodes are reported. Devices were fabricated on high-quality, large-area p-MoS2
grown by chemical vapor deposition (CVD) on hexagonal sapphire substrates. The
processed devices were transferred onto GaN/sapphire substrates, and the
transferred films were characterized by X-ray diffraction (XRD) and atomic
force microscopy (AFM). On-axis XRD spectra and surface topology obtained from
AFM scans were consistent with previously grown high-quality, continuous MoS2
films. Current-voltage measurements of these diodes exhibited excellent
rectification, and capacitance-voltage measurements were used to extract a
conduction band offset of approximately 0.2 eV for the transferred MoS2/GaN
heterojunction. This conduction band offset was confirmed by internal
photoemission (IPE) measurements. The energy band lineup of the MoS2/GaN
heterojunction is proposed here. This work demonstrates the potential of 2D/3D
heterojunctions for novel device applications.


  In this talk we report on two physical processes, the photoproduction of
  $\rho$ mesons in nuclei and the $\pi^0 \pi^0$ photoproduction in nuclei at
low energies. In the first case the aim is to observe experimentally the
theoretically predicted changes on the $\rho $ properties in the nuclear
medium. In the second case one wishes to investigate the modifications of the
$\pi \pi$ interaction in the nuclear medium in the region of the $\sigma$
meson. In the first case we observe that it is quite difficult to see large
effects due to the $\rho$ modification in the medium because the detection of
the $\rho $ is done through the two pions which are produced in the $\Delta$
region and are largely distorted in the medium. In the second case, when the
I=0 part of the $\pi \pi$ interaction is substituted by the in medium $\pi \pi$
amplitude, we observe a very large shift of strength in the invariant mass
distribution to small values of the mass in $^{12}C$ and $^{208}Pb$ with
respect to the distribution of the elementary reaction. This spectacular shift
appears to be corroborated by recent experiments at Mainz reported in this same
Workshop.


  This study applies the binomial, k-binomial, rising k-binomial and falling
k-binomial transforms to the modified k-Fibonacci-like sequence. Also, the
Binet formulas and generating functions of the above mentioned four transforms
are newly found by the recurrence relations.


  The penetration depth of photoacoustic imaging in biological tissues has been
fundamentally limited by the strong optical attenuation when light is delivered
externally through the tissue surface. To address this issue, we previously
reported internal-illumination photoacoustic imaging using a customized
radial-emission optical fiber diffuser, which, however, has complex
fabrication, high cost, and non-uniform light emission. To overcome these
shortcomings, we have developed a new type of low-cost fiber diffusers based on
a graded-scattering method in which the optical scattering of the fiber
diffuser is gradually increased as the light travels. The graded scattering can
compensate for the optical attenuation and provide relatively uniform light
emission along the diffuser. We performed Monte Carlo numerical simulations to
optimize several key design parameters, including the number of scattering
segments, scattering anisotropy factor, divergence angle of the optical fiber,
and reflective index of the surrounding medium. These optimized parameters
collectively result in uniform light emission along the fiber diffuser and can
be flexibly adjusted to accommodate different applications. We fabricated and
characterized the prototype fiber diffuser made of agarose gel and intralipid.
Equipped with the new fiber diffuser, we performed thorough proof-of-concept
studies on ex vivo tissue phantoms and an in vivo swine model to demonstrate
the deep-imaging capability (~10 cm achieved ex vivo) of photoacoustic
tomography. We believe that the internal light delivery via the optimized fiber
diffuser is an effective strategy to image deep targets (e.g., kidney) in large
animals or humans.


  The TeV-emitting BL Lac object Mkn 421 was observed with very long baseline
interferometry (VLBI) at three closely-spaced epochs one-month apart in
March-April 1998. The source was also monitored at very-high gamma-ray energies
(TeV measurements) during the same period in an attempt to search for
correlations between TeV variability and the evolution of the radio morphology
on parsec scales. While the VLBI maps show no temporal changes in the Mkn 421
VLBI jet, there is strong evidence of complex variability in both the total and
polarized fluxes of the VLBI core of Mkn 421 and in its spectrum over the
two-month span of our data. The high-energy measurements indicate that the
overall TeV activity of the source was rising during this period, with a
gamma-ray flare detected just three days prior to our second VLBI observing
run. Although no firm correlation can be established, our data suggest that the
two phenomena (TeV activity and VLBI core variability) are connected, with the
VLBI core at 22 GHz being the self-absorbed radio counterpart of synchrotron
self-Compton (SSC) emission at high energies. Based on the size of the VLBI
core, we could derive an upper limit of 0.1 pc (3 x 10**17 cm) for the
projected size of the SSC zone. This determination is the first model-free
estimate of the size of the gamma-ray emitting region in a blazar.


  An experimental setup for determining the electrical resistivity of several
types of thermoelectric materials over the temperature range 20 < T < 550 C is
described in detail. One resistivity measurement during temperature cycling is
also explained for Cu0.01Bi2Te2.7Se0.3 while a second measurement is made on
Yb0.35Co4Sb12 as a function of time at 400 C. Both measurements confirm that
the materials are thermally stable for the temperature range and time period
measured. Measurements made during temperature cycling show an irreversible
decrease in the electrical resistivity of Cu0.01Bi2Te2.7Se0.3 when the
measuring temperature exceeds the pressing temperature. Several other possible
uses of such a system include but are not limited to studying the effects of
annealing and/or oxidation as a function of both temperature and time.


  The exclusive photoproduction reaction gamma p -> Upsilon(1S) p has been
studied with the ZEUS detector in ep collisions at HERA using an integrated
luminosity of 468 pb^-1. The measurement covers the kinematic range 60<W<220
GeV and Q^2<1 GeV^2, where W is the photon-proton centre-of-mass energy and Q^2
is the photon virtuality. The exponential slope, b, of the t dependence of the
cross section, where t is the squared four-momentum transfer at the proton
vertex, has been measured, yielding b = 4.3 +2.0 -1.3 (stat.) +0.5 -0.6 (syst.)
GeV^-2. This constitutes the first measurement of the t dependence of the gamma
p -> Upsilon(1S) p cross section.


  Autonomous robotic systems are complex, hybrid, and often safety-critical;
this makes their formal specification and verification uniquely challenging.
Though commonly used, testing and simulation alone are insufficient to ensure
the correctness of, or provide sufficient evidence for the certification of,
autonomous robotics. Formal methods for autonomous robotics have received some
attention in the literature, but no resource provides a current overview. This
short paper summarises the contributions of Luckcuck 2019, which surveys the
state-of-the-art in formal specification and verification for autonomous
robotics.


  We present here a reverse engineering tool that can be used for information
retrieval and anti-malware techniques. Our main contribution is the design and
implementation of an instrumentation framework aimed at providing insight on
the emulation process. Sample emulation is achieved via translation of the
binary code to an intermediate representation followed by compilation and
execution. The design makes this a versatile tool that can be used for multiple
task such as information retrieval, reverse engineering, debugging, and
integration with anti-malware products.


  We present a Green's function method for the evaluation of the particle
density profile and of the higher moments of the one-body density matrix in a
mesoscopic system of N Fermi particles moving independently in a linear
potential. The usefulness of the method is illustrated by applications to a
Fermi gas confined in a harmonic potential well, for which we evaluate the
momentum flux and kinetic energy densities as well as their quantal mean-square
fluctuations. We also study some properties of the kinetic energy functional
E_{kin}[n(x)] in the same system. Whereas a local approximation to the kinetic
energy density yields a multi-valued function, an exact single-valued
relationship between the density derivative of E_{kin}[n(x)] and the particle
density n(x) is demonstrated and evaluated for various values of the number of
particles in the system.


  The zeta function of a K3 surface over a finite field satisfies a number of
obvious (archimedean and l-adic) and a number of less obvious (p-adic)
constraints. We consider the converse question, in the style of Honda-Tate:
given a function Z satisfying all these constraints, does there exist a K3
surface whose zeta-function equals Z? Assuming semi-stable reduction, we show
that the answer is yes if we allow a finite extension of the finite field. An
important ingredient in the proof is the construction of complex projective K3
surfaces with complex multiplication by a given CM field.


  A new family of minimum distance estimators for binary logistic regression
models based on $\phi$-divergence measures is introduced. The so called "pseudo
minimum phi-divergence estimator"(PM$\phi$E) family is presented as an
extension of "minimum phi-divergence estimator" (M$\phi$E) for general sample
survey designs and contains, as a particular case, the pseudo maximum
likelihood estimator (PMLE) considered in Roberts et al. \cite{r}. Through a
simulation study it is shown that some PM$\phi$Es have a better behaviour, in
terms of efficiency, than the PMLE.


  This paper studies reachability, coverability and inclusion problems for
Integer Vector Addition Systems with States (ZVASS) and extensions and
restrictions thereof. A ZVASS comprises a finite-state controller with a finite
number of counters ranging over the integers. Although it is folklore that
reachability in ZVASS is NP-complete, it turns out that despite their
naturalness, from a complexity point of view this class has received little
attention in the literature. We fill this gap by providing an in-depth analysis
of the computational complexity of the aforementioned decision problems. Most
interestingly, it turns out that while the addition of reset operations to
ordinary VASS leads to undecidability and Ackermann-hardness of reachability
and coverability, respectively, they can be added to ZVASS while retaining
NP-completness of both coverability and reachability.


  The quantum phase-space dynamics driven by hyperbolic P\"oschl-Teller (PT)
potentials is investigated in the context of the Weyl-Wigner quantum mechanics.
The obtained Wigner functions for quantum superpositions of ground and
first-excited states exhibit some non-classical and non-linear patterns which
are theoretically tested and quantified according to a non-gaussian continuous
variable framework. It comprises the computation of quantifiers of
non-classicality for an anharmonic two-level system where non-Liouvillian
features are identified through the phase-space portrait of quantum
fluctuations. In particular, the associated non-gaussian profiles are
quantified by measures of {\em kurtosis} and {\em negative entropy}. As
expected from the PT {\em quasi}-harmonic profile, our results suggest that
quantum wells can work as an experimental platform that approaches the gaussian
behavior in the investigation of the interplay between classical and quantum
scenarios. Furthermore, it is also verified that the Wigner representation
admits the construction of a two-particle bipartite quantum system of
continuous variables, $A$ and $B$, identified by $\sum_{i,j=0,1}^{i\neq j}\vert
i_{_A}\rangle\otimes\vert j_{_B}\rangle$, which are shown to be separable under
gaussian and non-gaussian continuous variable criteria.


  We study elastic manifolds in a N-dimensional random potential using
functional RG. We extend to N>1 our previous construction of a field theory
renormalizable to two loops. For isotropic disorder with O(N) symmetry we
obtain the fixed point and roughness exponent to next order in epsilon=4-d,
where d is the internal dimension of the manifold. Extrapolation to the
directed polymer limit d=1 allows some handle on the strong coupling phase of
the equivalent N-dimensional KPZ growth equation, and eventually suggests an
upper critical dimension of about 2.5.


  Decision-making in large imperfect information games is difficult. Thanks to
recent success in Poker, Counterfactual Regret Minimization (CFR) methods have
been at the forefront of research in these games. However, most of the success
in large games comes with the use of a forward model and powerful state
abstractions. In trick-taking card games like Bridge or Skat, large information
sets and an inability to advance the simulation without fully determinizing the
state make forward search problematic. Furthermore, state abstractions can be
especially difficult to construct because the precise holdings of each player
directly impact move values.
  In this paper we explore learning model-free policies for Skat from human
game data using deep neural networks (DNN). We produce a new state-of-the-art
system for bidding and game declaration by introducing methods to a) directly
vary the aggressiveness of the bidder and b) declare games based on expected
value while mitigating issues with rarely observed state-action pairs. Although
cardplay policies learned through imitation are slightly weaker than the
current best search-based method, they run orders of magnitude faster. We also
explore how these policies could be learned directly from experience in a
reinforcement learning setting and discuss the value of incorporating human
data for this task.


  Twisted homomorphisms of bialgebras are bialgebra homomorphisms from the
first into Drinfeld twistings of the second. They possess a composition
operation extending composition of bialgebra homomorphisms. Gauge
transformations of twists, compatible with adjacent homomorphisms, give rise to
gauge transformation of twisted homomorphisms, which behave nicely with respect
to compositions. Here we study (gauge classes of) twisted automorphisms of
cocommutative Hopf algebras. After revising well-known relations between
twists, twisted forms of bialgebras and $R$-matrices (for commutative
bialgebras) we describe twisted automorphisms of universal enveloping algebras.


  We have measured the relationships between HI mass, stellar mass and star
formation rate using the HI Parkes All Sky-Survey Catalogue (HICAT) and the
Wide-field Infrared Survey Explorer (WISE). Of the 3,513 HICAT sources, we find
3.4 micron counterparts for 2,896 sources (80%) and provide new WISE matched
aperture photometry for these galaxies. For our principal sample of spiral
galaxies with W1 $\le$ 10 mag and z $\le$ 0.01, we identify HI detections for
93% of the sample. We measure lower HI-stellar mass relationships that HI
selected samples that do not include spiral galaxies with little HI gas. Our
observations of the spiral sample show that HI mass increases with stellar mass
with a power-law index 0.35; however, this value is dependent on T-type, which
affects both the median and the dispersion of HI mass. We also observe an upper
limit on the HI gas fraction, which is consistent with a halo spin parameter
model. We measure the star formation efficiency of spiral galaxies to be
constant 10$^{-9.57}$ yr$^{-1}$ $\pm$ 0.4 dex for 2.5 orders of magnitude in
stellar mass, despite the higher stellar mass spiral showing evidence of
quenched star formation.


  A family of closed subsets of a topological space $X$ is called a (strict)
$Cld$-fan in $X$ if this family is (strictly) compact-finite but not locally
finite in $X$. Applications of (strict) $Cld$-fans are based on a simple
observation that $k$-spaces contain no $Cld$-fan and Ascoli spaces contain no
strict $Cld$-fan. In this paper we develop the machinery of (strict) fans and
apply it to detecting the $k$-space and Ascoli properties in spaces that
naturally appear in General Topology, Functional Analysis, and Topological
Algebra. In particular, we detect (generalized) metric spaces $X$ whose
functor-spaces, functions spaces, free (para)topological (abelian) groups, free
(locally convex) linear topological spaces, free (Lawson) topological
semilattices, and free (para)topological (Clifford, Abelian) inverse semigroups
are $k$-spaces or Ascoli spaces.


  Context: Designing software is an activity in which software developers think
and make design decisions that shape the structure and behavior of software
products. Designing software is one of the least understood software
engineering activities. In a collaborative design setting, various types of
distances can lead to challenges and effects that potentially affect how
software is designed. Objective: To contribute to a better understanding of
collaborative software design, we investigate how geographic distance affects
its design thinking and the creativity of its discussions. Method: To this end,
we conducted a multiple-case study exploring the design thinking and creativity
of co-located and distributed software developers in a collaborative design
setting. Results: Compared to co-located developers, distributed developers
spend less time on exploring the problem space, which could be related to
different socio-technical challenges, such as lack of awareness and common
understanding. Distributed development does not seem to affect the creativity
of their activities. Conclusion: Developers engaging in collaborative design
need to be aware that problem space exploration is reduced in a distributed
setting. Unless distributed teams take compensatory measures, this could
adversely affect the development. Regarding the effect distance has on
creativity, our results are inconclusive and further studies are needed.


  Beam collisions with a crossing angle at the interaction point have been
applied in high intensity colliders to reduce the effects of parasitic
collisions which induce emittance growth and beam lifetime deterioration. The
crossing angle causes the geometrical reduction of the luminosity. Crab cavity
can be one of the most promising ways to compensate the crossing angle and to
realize effective head-on collisions. Moreover, the crab crossing mitigates the
synchro-betatron resonances due to the crossing angle. Crab cavity experiment
in SPS is proposed for deciding on a full crab-cavity implementation in LHC. In
this paper, we investigate the effects of crab crossing on beam dynamics and
its life time with the global scheme.


  Hydrogen embrittlement of uranium, which arises due to the formation of a
structurally weak pyrophoric hydride, poses a major safety risk in material
applications. Previous experiments have shown that hydriding begins on top or
near the surface (i.e., subsurface) of a-uranium. However, the fundamental
molecular-level mechanism of this process remains unknown. In this work,
starting from pristine {\alpha}-U bulk and surfaces, we present a systematic
investigation of possible mechanisms for formation of the metal hydride.
Specifically, we address this problem by examining the individual steps of
hydrogen embrittlement, including surface adsorption, subsurface absorption,
and the inter-layer diffusion of atomic hydrogen. Furthermore, by examining
these processes across different facets, we highlight the importance of both
(1) hydrogen monolayer coverage and (2) applied tensile strain on hydriding
kinetics. Taken together, by studying previously overlooked phenomena, this
study provides foundational insights into the initial steps of this overall
complex process. We anticipate that this work will guide near-term future
development of multiscale kinetic models for uranium hydriding and
subsequently, identify potential strategies to mitigate this undesired process.


  We propose an extension of the definition of vertex algebras in arbitrary
space-time dimensions together with their basic structure theory. An one-to-one
correspondence between these vertex algebras and axiomatic quantum field theory
(QFT) with global conformal invariance (GCI) is constructed.


  We construct an endomorphism of the Jiang-Su algebra $\mathcal{Z}$ which does
not admit a conditional expectation. This answers a question in the
testamentary homework by E. Kirchberg. As an application, it is shown that any
unital separable nuclear $\mathcal{Z}$-absorbing C*-algebra is
non-transportable in the Cuntz algebra $\mathcal{O}_2$.


  In this paper, we propose a new primal-dual algorithm for minimizing $f(x) +
g(x) + h(Ax)$, where $f$, $g$, and $h$ are proper lower semi-continuous convex
functions, $f$ is differentiable with a Lipschitz continuous gradient, and $A$
is a bounded linear operator. The proposed algorithm has some famous
primal-dual algorithms for minimizing the sum of two functions as special
cases. E.g., it reduces to the Chambolle-Pock algorithm when $f = 0$ and the
proximal alternating predictor-corrector when $g = 0$. For the general convex
case, we prove the convergence of this new algorithm in terms of the distance
to a fixed point by showing that the iteration is a nonexpansive operator. In
addition, we prove the $O(1/k)$ ergodic convergence rate in the primal-dual
gap. With additional assumptions, we derive the linear convergence rate in
terms of the distance to the fixed point. Comparing to other primal-dual
algorithms for solving the same problem, this algorithm extends the range of
acceptable parameters to ensure its convergence and has a smaller per-iteration
cost. The numerical experiments show the efficiency of this algorithm.


  We give an excellent approximation of the average number of spins of a
simplified version of a two-player version of the game Dreidel. We also make a
conjecture on the average number of spins of the full version of the game.


  We review basic computational techniques for simulations of various magnetic
properties of solids. Several applications to compute magnetic anisotropy
energy, spin wave spectra, magnetic susceptibilities and temperature dependent
magnetisations for a number of real systems are presented for illustrative
purposes.


  Recent trends of ab initio studies and progress in methodologies for
electronic structure calculations of strongly correlated electron systems are
discussed. The interest for developing efficient methods is motivated by recent
discoveries and characterizations of strongly correlated electron materials and
by requirements for understanding mechanisms of intriguing phenomena beyond a
single-particle picture. A three-stage scheme is developed as renormalized
multi-scale solvers (RMS) utilizing the hierarchical electronic structure in
the energy space. It provides us with an ab initio downfolding of the global
band structure into low-energy effective models followed by low-energy solvers
for the models. The RMS method is illustrated with examples of several
materials. In particular, we overview cases such as dynamics of semiconductors,
transition metals and its compounds including iron-based superconductors and
perovskite oxides, as well as organic conductors of kappa-ET type.


  The SU$_q$(2) rotator model is used for describing the $\beta_1$- and
$\gamma_1$-bands of even-even rare earth and actinide collective nuclei. Good
results are obtained in nuclei with valence pair number $N>10$. It is shown
that in the excited bands the violation of the exact SU(2) symmetry is
generally stronger than in the ground state bands, indicating the presence of a
nonadiabatic perturbation caused by the excited vibrational degrees of freedom.
The physical content of the parameter $q$ is discussed. Predictions of the
SU$_q$(2) model for B(E2) intraband transitions in excited bands are presented
and the need for specific experimental data is pointed out.


  In the present paper we develop an approach to obtain sharp spectral
asymptotics for Steklov type problems on planar domains with corners. Our main
focus is on the two-dimensional sloshing problem, which is a mixed
Steklov-Neumann boundary value problem describing small vertical oscillations
of an ideal fluid in a container or in a canal with a uniform cross-section. We
prove a two-term asymptotic formula for sloshing eigenvalues. In particular,
this confirms a conjecture posed by Fox and Kuttler in 1983. We also obtain
similar eigenvalue asymptotics for other related mixed Steklov type problems,
and discuss applications to the study of Steklov spectral asymptotics on
polygons.


  Large Language Models (LLMs) employing Chain-of-Thought (CoT) prompting have
broadened the scope for improving multi-step reasoning capabilities. We
generally divide multi-step reasoning into two phases: path generation to
generate the reasoning path(s); and answer calibration post-processing the
reasoning path(s) to obtain a final answer. However, the existing literature
lacks systematic analysis on different answer calibration approaches. In this
paper, we summarize the taxonomy of recent answer calibration techniques and
break them down into step-level and path-level strategies. We then conduct a
thorough evaluation on these strategies from a unified view, systematically
scrutinizing step-level and path-level answer calibration across multiple
paths. Experimental results reveal that integrating the dominance of both
strategies tends to derive optimal outcomes. Our study holds the potential to
illuminate key insights for optimizing multi-step reasoning with answer
calibration.


  Using Reshetikhin's construction for multiparametric quantum algebras we
obtain the associated multiparametric quantum spin chains. We show that under
certain restrictions these models can be mapped to quantum spin chains with
twisted boundary conditions. We illustrate how this general formalism applies
to construct multiparametric versions of the supersymmetric t-J and U models.


  The LIGO/Virgo detected a gravitational wave (GW) event, named
GW200224_222234 (a.k.a. S200224ca) and classified as a binary-black-hole
coalescence, on February 24, 2020. Given its relatively small localization
skymap (71 deg$^2$ for a 90% credible region; revised to 50 deg$^2$ in GWTC-3),
we performed target-of-opportunity observations using the Subaru/Hyper
Suprime-Cam (HSC) in the $r2$- and $z$-bands. Observations were conducted on
February 25 and 28 and March 23, 2020, with the first epoch beginning 12.3 h
after the GW detection. The survey covered the highest probability sky area of
56.6 deg$^2$, corresponding to a 91% probability. This was the first deep
follow-up ($m_{r}\gtrsim24, m_{z}\gtrsim23$) for a binary-black-hole merger
covering $>$90% of the localization. By performing image subtraction and
candidate screening including light curve fitting with transient templates and
examples, we found 22 off-nucleus transients that were not ruled out as the
counterparts of GW200224_222234 with only our Subaru/HSC data. We also
performed GTC/OSIRIS spectroscopy of the probable host galaxies for five
candidates; two are likely to be located within the 3D skymap, whereas the
others are not. In conclusion, 19 transients remain as possible optical
counterparts of GW200224_222234; however, we could not identify a unique
promising counterpart. If there are no counterparts in the remaining
candidates, the upper limits of optical luminosity are $\nu L_{\nu} <
5.2^{+2.4}_{-1.9}\times 10^{41}$ erg s$^{-1}$ and $\nu L_{\nu} <
1.8^{+0.8}_{-0.6}\times 10^{42}$ erg s$^{-1}$ in the $r2$- and $z$-bands,
respectively, at $\sim$12 h after GW detection. We also discuss improvements in
the strategies of optical follow-ups for future GW events.


  We investigate a certain class of solvable metric Lie algebras. For this
purpose a theory of twofold extensions associated to an orthogonal
representation of an abelian Lie algebra is developed. Among other things, we
obtain a classification scheme for indecomposable metric Lie algebras with
maximal isotropic centre and the classification of metric Lie algebras of index
2.


  The notion of a (metric) modular on an arbitrary set and the corresponding
modular space, more general than a metric space, were introduced and studied
recently by the author [V. V. Chistyakov, Metric modulars and their
application, Dokl. Math. 73(1) (2006) 32-35, and Modular metric spaces, I:
Basic concepts, Nonlinear Anal. 72(1) (2010) 1-14]. In this paper we establish
a fixed point theorem for contractive maps in modular spaces. It is related to
contracting rather ``generalized average velocities'' than metric distances,
and the successive approximations of fixed points converge to the fixed points
in a weaker sense as compared to the metric convergence.


  Let $\Omega:=\left( a,b\right) \subset\mathbb{R}$, $m\in L^{1}\left(
\Omega\right) $ and $\lambda>0$ be a real parameter. Let $\mathcal{L}$ be the
differential operator given by $\mathcal{L}u:=-\phi\left( u^{\prime}\right)
^{\prime}+r\left( x\right) \phi\left( u\right) $, where $\phi
:\mathbb{R\rightarrow R}$ is an odd increasing homeomorphism and $0\leq r\in
L^{1}\left( \Omega\right) $. We study the existence of positive solutions for
problems of the form $\mathcal{L}u=\lambda m\left( x\right) f\left( u\right)$
in $\Omega,$ $u=0$ on $\partial\Omega$, where $f:\left[ 0,\infty\right)
\rightarrow\left[ 0,\infty\right) $ is a continuos function which is, roughly
speaking, sublinear with respect to $\phi$. Our approach combines the sub and
supersolution method with some estimates on related nonlinear problems. We
point out that our results are new even in the cases $r\equiv0$ and/or
$m\geq0$.


  By numerical simulation, we study the classical magnetoresistance of
two-dimensional electrons in the presence of weak short range scattering. A
critical magnetic field defines the percolation threshold, above which the
longitudinal resistance vanishes. Unexpectedely, just below this threshold we
find a shrp narrow peak, where the resistance may increase 15 times compared to
its zero-field value. By considering the complex topology of the effective
potential landscape for the center of the cyclotron circle, we show that this
phenomenon is related to infinite equipotential lines, which exists only in a
narrow magnetic field interval below the percolation threshold


  The influences of an impurity on the spin and the charge transport of
one-dimensional antisymmetric spin filter are investigated using bosonization
and Keldysh formulation and the results are highlighted against those of
spinful Luttinger liquids. Due to the dependence of the electron spin
orientation on wave number the spin transport is not affected by the impurity,
while the charge transport is essentially identical with that of spinless
one-dimensional Luttinger liquid.


  The Casimir problem is usually posed as the response of a fluctuating quantum
field to externally imposed boundary conditions. In reality, however, no
interaction is strong enough to enforce a boundary condition on all frequencies
of a fluctuating field. We construct a more physical model of the situation by
coupling the fluctuating field to a smooth background potential that implements
the boundary condition in a certain limit. To study this problem, we develop
general new methods to compute renormalized one--loop quantum energies and
energy densities. We use analytic properties of scattering data to compute
Green's functions in time--independent background fields at imaginary momenta.
Our calculational method is particularly useful for numerical studies of
singular limits because it avoids terms that oscillate or require cancellation
of exponentially growing and decaying factors. To renormalize, we identify
potentially divergent contributions to the Casimir energy with low orders in
the Born series to the Green's function. We subtract these contributions and
add back the corresponding Feynman diagrams, which we combine with counterterms
fixed by imposing standard renormalization conditions on low--order Green's
functions. The resulting Casimir energy and energy density are finite
functionals for smooth background potentials. In general, however, the Casimir
energy diverges in the boundary condition limit. This divergence is real and
reflects the infinite energy needed to constrain a fluctuating field on all
energy scales; renormalizable quantum field theories have no place for ad hoc
surface counterterms. We apply our methods to simple examples to illustrate
cases where these subtleties invalidate the conclusions of the boundary
condition approach.


  In retrospective assessments, internet news reports have been shown to
capture early reports of unknown infectious disease transmission prior to
official laboratory confirmation. In general, media interest and reporting
peaks and wanes during the course of an outbreak. In this study, we quantify
the extent to which media interest during infectious disease outbreaks is
indicative of trends of reported incidence. We introduce an approach that uses
supervised temporal topic models to transform large corpora of news articles
into temporal topic trends. The key advantages of this approach include,
applicability to a wide range of diseases, and ability to capture disease
dynamics - including seasonality, abrupt peaks and troughs. We evaluated the
method using data from multiple infectious disease outbreaks reported in the
United States of America (U.S.), China and India. We noted that temporal topic
trends extracted from disease-related news reports successfully captured the
dynamics of multiple outbreaks such as whooping cough in U.S. (2012), dengue
outbreaks in India (2013) and China (2014). Our observations also suggest that
efficient modeling of temporal topic trends using time-series regression
techniques can estimate disease case counts with increased precision before
official reports by health organizations.


  This paper proposes a new robust data-driven control method for linear
systems with bounded disturbances, where the system model and disturbances are
unknown. Due to disturbances, accurately determining the true system becomes
challenging using the collected dataset. Therefore, instead of designing
controllers directly for the unknown true system, an available approach is to
design controllers for all systems compatible with the dataset. To overcome the
limitations of using a single dataset and benefit from collecting more data,
multiple datasets are employed in this paper. Furthermore, a new iterative
method is developed to address the challenges of using multiple datasets. Based
on this method, this paper develops an offline and online robust data-driven
iterative control method, respectively. Compared to the existing robust
data-driven controller method, both proposed control methods iteratively
utilize multiple datasets in the controller design process. This allows for the
incorporation of numerous datasets, potentially reducing the conservativeness
of the designed controller. Particularly, the online controller is iteratively
designed by continuously incorporating online collected data into the
historical data to construct new datasets. Lastly, the effectiveness of the
proposed methods is demonstrated using a batch reactor.


  We present first results from dynamical Chirally Improved (CI) fermion
simulations for the axial charge $G_A$ of various hadrons. We work with 16^3x32
lattices of spatial extent 2.4 fm and use the variational method with a
suitable basis of Jacobi-smeared interpolators to suppress contaminations from
excited states.


  Various questions on Lie ideals of C*-algebras are investigated. They fall
roughly under the following topics: relation of Lie ideals to closed two-sided
ideals; Lie ideals spanned by special classes of elements such as commutators,
nilpotents, and the range of polynomials; characterization of Lie ideals as
similarity invariant subspaces.


  One of the main challenges in Heavy Energy Physics is to make fast analysis
of high amount of experimental and simulated data. At LHC-CERN one p-p event is
approximate 1 Mb in size. The time taken to analyze the data and obtain fast
results depends on high computational power. The main advantage of using
GPU(Graphic Processor Unit) programming over traditional CPU one is that
graphical cards bring a lot of computing power at a very low price. Today a
huge number of application(scientific, financial etc) began to be ported or
developed for GPU, including Monte Carlo tools or data analysis tools for High
Energy Physics. In this paper, we'll present current status and trends in HEP
using GPU.


  We analyse the properties of the early-type dwarf galaxy population in the
Hydra I cluster. We investigate the galaxy luminosity function (LF), the
colour-magnitude relation (CMR), and the magnitude-surface brightness relation
down to M_V~-10 mag. Another goal of this study is to find candidates for
ultra-compact dwarf galaxies (UCDs). Two spectroscopic surveys performed with
Magellan I/LDSS2 and VLT/VIMOS, as well as deep VLT/FORS1 images in V and I
bands were examined. We identify cluster members by radial velocity
measurements and select other cluster galaxy candidates by their morphology.
One possible UCD candidate with M_V=-13.26 mag is found. Our sample of 100
morphologically selected dwarf galaxies defines a CMR that extends the CMR of
the giant cluster galaxies to the magnitude limit of our survey (M_V~-10 mag).
It matches the relations found for the Local Group and the Fornax cluster
dwarfs almost perfectly. The Hydra I dwarfs also follow a magnitude-surface
brightness relation similar to that of the LG dwarfs. Moreover, we observe a
continuous relation for dwarf galaxies and giant early-type galaxies when
plotting the central surface brightness \mu_0 of a S\'ersic model vs. the
galaxy magnitude. The effective radius is found to be largely independent of
the luminosity for M_V>-18 mag, being R_e~0.8 kpc. We derive a very flat
faint-end slope of the LF (\alpha = -1.13 \pm 0.04) from fitting a Schechter
function, whereas fitting a power law for M_V>-14 mag gives \alpha = -1.40 \pm
0.18. Our findings suggest that early-type dwarf and giant galaxies are the
same class of objects. The similarity of the dwarf galaxy scaling relations to
other environments implies that internal processes could be more important for
their global photometric properties than external influences. (abridged)


  This paper presents a method to control the probability density distribution
of a swarm of vehicles via velocity fields. The proposed approach synthesizes
smooth velocity fields, which specify a desired velocity as a function of time
and position in a decentralized manner i.e., each agent calculates the desired
velocity locally by utilizing the number of agents within a prescribed
communication distance. Swarm converges to the desired/commanded density
distribution by following the velocity field. The local information consists,
only, of agents' positions and it is utilized to estimate the density around
each agent. Local density estimation is performed by using kernel density
estimation. Then the local density estimates and the desired density are
utilized to generate the velocity field, which propagates the swarm probability
density distribution via the well-known heat equation. The key advantage of
using smooth velocity fields to control swarm density with respect to our
earlier Markov chain based probabilistic control methods is that the agents
move more smoothly in an organized manner and their relative velocities go to
zero as they get closer to each other, i.e., they facilitate conflict/collision
avoidance. The desired density distribution, which is commanded to the local
controllers, can be computed by using a Markov chain that propagates the
desired density distribution within prescribed requirements. Along with
convergence and stability analysis, the effectiveness of the approach is
illustrated via numerical examples.


  We study the rheology of bidisperse non-Brownian suspensions using
particle-based simulation, mapping the viscosity as a function of the size
ratio of the species, their relative abundance, and the overall solid content.
The variation of the viscosity with applied stress exhibits shear thickening
phenomenology irrespective of composition, though the stress-dependent limiting
solids fraction governing the viscosity and its divergence point are
non-monotonic in the mixing ratio. Contact force data demonstrate an asymmetric
exchange in dominant stress contribution from large-large to small-small
particle contacts as the mixing ratio of the species evolves. Combining a prior
model for shear thickening with one for composition-dependent jamming, we
obtain a full description of the rheology of bidisperse non-Brownian
suspensions capable of predicting effects such as the viscosity reduction
observed upon adding small particle fines to a suspension of large particles.


  We model the behavior of a big (Brazil) nut in a medium of smaller nuts with
a stochastic asymmetric simple exclusion dynamics of a polymer-monomer lattice
system. The polymer or `rod' can move up or down in an external negative field,
occupying N horizontal lattice sites where the monomers cannot enter. The
monomers (at most one per site) or `fluid particles' are moving symmetrically
in the horizontal plane and asymmetrically in the vertical direction, also with
a negative field. For a fixed position of the rod, this lattice fluid is in
equilibrium with a vertical height profile reversible for the monomers' motion.
Upon `shaking' (speeding up the monomers) the motion of the `rod' dynamically
decouples from that of the monomers resulting in a reversible random walk for
the rod around an average height proportional to log N.


  Traffic correlation attacks to de-anonymize Tor users are possible when an
adversary is in a position to observe traffic entering and exiting the Tor
network. Recent work has brought attention to the threat of these attacks by
network-level adversaries (e.g., Autonomous Systems). We perform a historical
analysis to understand how the threat from AS-level traffic correlation attacks
has evolved over the past five years. We find that despite a large number of
new relays added to the Tor network, the threat has grown. This points to the
importance of increasing AS-level diversity in addition to capacity of the Tor
network.
  We identify and elaborate on common pitfalls of AS-aware Tor client design
and construction. We find that succumbing to these pitfalls can negatively
impact three major aspects of an AS-aware Tor client -- (1) security against
AS-level adversaries, (2) security against relay-level adversaries, and (3)
performance. Finally, we propose and evaluate a Tor client -- Cipollino --
which avoids these pitfalls using state-of-the-art in network-measurement. Our
evaluation shows that Cipollino is able to achieve better security against
network-level adversaries while maintaining security against relay-level
adversaries and


  We report a Raman scattering study of low-temperature, pressure-induced
melting of the CDW phase of 1T-TiSe2. Our Raman scattering measurements reveal
that the collapse of the CDW state occurs in three stages: (i) For P<5 kbar,
the pressure dependence of the CDW amplitude mode energies and intensities are
indicative of a ``crystalline'' CDW regime; (ii) for 5 < P < 25 kbar, there is
a decrease in the CDW amplitude mode energies and intensities with increasing
pressure that suggests a regime in which the CDW softens, and may decouple from
the lattice; and (iii) for P>25 kbar, the absence of amplitude modes reveals a
melted CDW regime.


  In the context of minimal seesaw framework, we study the implications of
Dirac and Majorana mass matrices in which two rigid properties coexist, namely,
equalities among mass matrix elements and texture zeros. In the first part of
the study, we discuss general possibilities of the Dirac and Majorana mass
matrices for neutrinos with such hybrid structures. We then classify the mass
matrices into realistic textures which are compatible with global neutrino
oscillation data and unrealistic ones which do not comply with the data. Among
the large number of general possibilities, we find that only 6 patterns are
consistent with the observations at the level of the most minimal number of
free parameters. These solutions have only 2 adjustable parameters, so that all
the mixing angles can be described in terms of the two mass differences or pure
numbers. We analyze these textures in detail and discuss their impacts for
future neutrino experiments and for leptogenesis.


  A Schwarzschild Black Hole (BH) is the gravitational field due to a neutral
point mass, and it turns out that the gravitational mass of a neutral point
mass: $M=0$ (Arnowitt, Deser, Misner, PRL 4, 375, 1960). The same result is
also suggested by Janis, Newman, and Winicour (PRL 20, 878, 1968). In 1969, Bel
gave an explicit proof that for a Schwarzschild BH, $M=0$ (Bel, JMP 10, 1051,
1969). The same result follows from the fact the timelike geodesic of a test
particle would turn null if it would ever arrive at an event horizon (Mitra,
FPL, 2000, 2002). Non-occurrrence of trapped surfaces in continued
gravitational collapse too demands $M=0$ for black hole (Mitra, Pramana, 73,
615, 2009).
  Physically, for a point mass at $R=0$, one expects ${\it Ric} \sim M \delta
(R=0)$ (Narlikar \& Padmanabhan, Found. Phys., 18, 659, 1988). But the black
hole solution is obtained from ${\it Ric} =0$. Again this is the most direct
proof that $M=0$ for a Schwarzschild black hole. Implication of this result is
that the observed massive black hole candidates are non-singular quasi black
holes or black hole mimickers which can possess strong magnetic fields as has
been observed. The echoes from LIGO signals, if true, may be the direct
evidence that the pertinent compact objects are BH mimickers and not true
vacuum BHs.


  Amorphous solids have excess soft modes in addition to the phonon modes
described by the Debye theory. Recent numerical results show that if the phonon
modes are carefully removed, the density of state of the excess soft modes
exhibit universal quartic scaling, independent of the interaction potential,
preparation protocol, and spatial dimensions. We hereby provide a theoretical
framework to describe this universal scaling behavior. For this purpose, we
extend the mean-field theory to include the effects of finite dimensional
fluctuation. Based on a semi-phenomenological argument, we show that mean-field
quadratic scaling is replaced by the quartic scaling in finite dimensions.
Furthermore, we apply our formalism to explain the pressure and protocol
dependence of the excess soft modes.


  We study the effect of random scattering in quantum walks on a finite graph
and compare it with the effect of repeated measurements. To this end, a
constructive approach is employed by introducing a localized and a delocalized
basis for the underlying Hilbert space. This enables us to design Hamiltonians
whose eigenvectors are either localized or delocalized. By presenting some
specific examples we demonstrate that the localization of eigenvectors
restricts the transition probabilities on the graph and leads to dark states in
the monitored evolution. We conclude that repeated measurements as well as
random scattering provide efficient tools for controlling quantum walks.


  We present a hybrid Eulerian-Lagrangian method for the direct simulation of
three-dimensional, heterogeneous structures made of soft fibers and immersed in
incompressible viscous fluids. Fiber-based organization of matter is pervasive
in nature and engineering, from biological architectures made of cilia, hair,
muscles or bones to polymers, composite materials or soft robots. In nature,
many such structures are adapted to manipulate flows for feeding, locomotion or
energy harvesting, through mechanisms that are often not fully understood.
While simulations can support the analysis (and subsequent translational
engineering) of these systems, extreme fibers' aspect-ratios, large elastic
deformations and two-way coupling with three-dimensional flows, all render the
problem numerically challenging. To address this, we couple Cosserat rod
theory, which exploits fibers' slenderness to capture their dynamics in
one-dimensional, accurate fashion, with vortex methods via a penalty immersed
boundary technique. The favorable properties of the resultant hydroelastic
solver are demonstrated against a battery of benchmarks, and further showcased
in a range of multi-physics scenarios, involving magnetic actuation, viscous
streaming, biomechanics, multi-body interaction, and self-propulsion.


  The rise of machine-to-machine communications has rekindled the interest in
random access protocols as a support for a massive number of uncoordinatedly
transmitting devices. The legacy ALOHA approach is developed under a collision
model, where slots containing collided packets are considered as waste.
However, if the common receiver (e.g., base station) is capable to store the
collision slots and use them in a transmission recovery process based on
successive interference cancellation, the design space for access protocols is
radically expanded. We present the paradigm of coded random access, in which
the structure of the access protocol can be mapped to a structure of an
erasure-correcting code defined on graph. This opens the possibility to use
coding theory and tools for designing efficient random access protocols,
offering markedly better performance than ALOHA. Several instances of coded
random access protocols are described, as well as a case study on how to
upgrade a legacy ALOHA system using the ideas of coded random access.


  The vulnerability of false data injection attacks on real-time electricity
pricing for the power grid market has been recently explored. Previous work has
focused on the impact caused by attackers that compromise pricing signals and
send false prices to a subset of consumers. In this paper we extend previous
work by considering a more powerful and general adversary model, a new analysis
method based on sensitivity functions, and by proposing several countermeasures
that can mitigate the negative impact of these attacks. Countermeasures include
adding a low-pass filter to the pricing signals, selecting the time interval
between price updates, selecting parameters of the controller, designing robust
control algorithms, and by detecting anomalies in the behavior of the system.


  We present the first constraints on cosmology from the Dark Energy Survey
(DES), using weak lensing measurements from the preliminary Science
Verification (SV) data. We use 139 square degrees of SV data, which is less
than 3\% of the full DES survey area. Using cosmic shear 2-point measurements
over three redshift bins we find $\sigma_8 (\Omega_{\rm m}/0.3)^{0.5} = 0.81
\pm 0.06$ (68\% confidence), after marginalising over 7 systematics parameters
and 3 other cosmological parameters. We examine the robustness of our results
to the choice of data vector and systematics assumed, and find them to be
stable. About $20$\% of our error bar comes from marginalising over shear and
photometric redshift calibration uncertainties. The current state-of-the-art
cosmic shear measurements from CFHTLenS are mildly discrepant with the
cosmological constraints from Planck CMB data; our results are consistent with
both datasets. Our uncertainties are $\sim$30\% larger than those from CFHTLenS
when we carry out a comparable analysis of the two datasets, which we attribute
largely to the lower number density of our shear catalogue. We investigate
constraints on dark energy and find that, with this small fraction of the full
survey, the DES SV constraints make negligible impact on the Planck
constraints. The moderate disagreement between the CFHTLenS and Planck values
of $\sigma_8 (\Omega_{\rm m}/0.3)^{0.5}$ is present regardless of the value of
$w$.


  In this paper we give an algorithm for counting the number of all independent
sets in a given graph which works in time $O^*(1.1394^n)$ for subcubic graphs
and in time $O^*(1.2369^n)$ for general graphs, where $n$ is the number of
vertices in the instance graph, and polynomial space. The result comes from
combining two well known methods "Divide and Conquer" and "Measure and
Conquer". We introduce this new concept of Divide, Measure and Conquer method
and expect it will find applications in other problems.
  The algorithm of Bj\"orklund, Husfeldt and Koivisto for graph colouring with
our algorithm used as a subroutine has complexity $O^*(2.2369^n)$ and is
currently the fastest graph colouring algorithm in polynomial space.


  We examine the power spectrum $P_n$ of the $n$th harmonic for
Garfinkle-Vachaspati cosmic strings, which correspond to planar rectangular
loops. While these loop tractories are self-intersecting, a slightly perturbed
non-self-intersecting form of these trajectories has been suggested as a
generic end state of cosmic string loop fragmentation. We find that $P_n$
scales as $n^{-2} \ln n$, rather than the expected $n^{-2}$ for kink-kink
collisions. This result is demonstrated analytically for even $n$ in square
loops and numerically for all other cases. At lowest order, the effect of loop
decays is to further enhance $P_n$ at large $n$ relative to its value at small
$n$. The consequences for relic stochastic background radiation along with the
caveats pertaining to our results are discussed.


  In banking practice, rating transition matrices have become the standard
approach of deriving multi-year probabilities of default (PDs) from one-year
PDs, the latter normally being available from Basel ratings. Rating transition
matrices have gained in importance with the newly adopted IFRS 9 accounting
standard. Here, the multi-year PDs can be used to calculate the so-called
expected credit losses (ECL) over the entire lifetime of relevant credit
assets. A typical approach for estimating the rating transition matrices relies
on calculating empirical rating migration counts and frequencies from rating
history data. For small portfolios, however, this approach often leads to zero
counts and high count volatility, which makes the estimations unreliable and
unstable, and can also produce counter-intuitive prediction patterns such as
non-parallel/crossing forward PD patterns. This paper proposes a structural
model which overcomes these problems. We make a plausible assumption of an
underlying autoregressive mean-reverting ability-to-pay process. With only
three parameters, this sparse process can well describe an entire typical
rating transition matrix, provided the one-year PDs of the rating classes are
specified. The transition probabilities produced by the structural approach are
well-behaved by design. The approach significantly reduces the statistical
degrees of freedom of the estimated transition probabilities, which makes the
rating transition matrix more reliable for small portfolios. The approach can
be applied to data with as few as 50 observed rating transitions. Moreover, the
approach can be efficiently applied to data consisting of continuous PDs (prior
to rating discretization). In the IFRS 9 context, the approach offers an
additional merit: it can easily account for the macroeconomic adjustments,
which are required by the IFRS 9 accounting standard.


  In this research work the quantum theory of Ur-alternatives of Carl Friedrich
von Weizs\"acker is considered. In This theory all objects and interactions in
nature are constituted by abstract purely quantum logical relations in time.
Such structures can be devided into binary alternatives, which are called
Ur-Alternatives with respect to this principle meaning, and this leads to a
tensor space of many Ur-alternatives. The indistinguishability of the
Ur-alternatives yields two Hilbert spaces, the tensor space of symmetric and
antisymmetric states under permutations of the Ur-alternatives. It is shown how
the structures in the symmetric space can be represented in a three dimensional
real position space connected to time according to special relativity.
Accordingly this theory obtains ultimately a deep understanding of the
EPR-paradox, since logic is independent of topological structures and causal
relations in space. The antisymmetric space, which because of the Pauli
principle contains much less degress of freedem, yields the additional quantum
numbers of the elementary particles. Based on the demand for the highest
possible symmetry in the tensor space, the Yang-Mills gauge theories of the
standard model as well as a translational gauge formulation of general
relativity in a purely quantum logical description are derived. The equations
determine entanglements of states containing many Ur-alternatives and this
concept is the analogue to classical pointwise interactions between separated
objects. This means that the main structures of the SU(N)-interactions of the
standard model of elementary particle physics as well as gravitation according
to general relativiy are unified in a purely quantum theoretical conceptional
framework. Finally it is derived an extended version of the E8-group, the
biggest exceptional Lie-group.


  Ultrasound (US) imaging is widely used for anatomical structure inspection in
clinical diagnosis. The training of new sonographers and deep learning based
algorithms for US image analysis usually requires a large amount of data.
However, obtaining and labeling large-scale US imaging data are not easy tasks,
especially for diseases with low incidence. Realistic US image synthesis can
alleviate this problem to a great extent. In this paper, we propose a
generative adversarial network (GAN) based image synthesis framework. Our main
contributions include: 1) we present the first work that can synthesize
realistic B-mode US images with high-resolution and customized texture editing
features; 2) to enhance structural details of generated images, we propose to
introduce auxiliary sketch guidance into a conditional GAN. We superpose the
edge sketch onto the object mask and use the composite mask as the network
input; 3) to generate high-resolution US images, we adopt a progressive
training strategy to gradually generate high-resolution images from
low-resolution images. In addition, a feature loss is proposed to minimize the
difference of high-level features between the generated and real images, which
further improves the quality of generated images; 4) the proposed US image
synthesis method is quite universal and can also be generalized to the US
images of other anatomical structures besides the three ones tested in our
study (lung, hip joint, and ovary); 5) extensive experiments on three large US
image datasets are conducted to validate our method. Ablation studies,
customized texture editing, user studies, and segmentation tests demonstrate
promising results of our method in synthesizing realistic US images.


  Deep learning has made remarkable progress recently, largely due to the
availability of large, well-labeled datasets. However, the training on such
datasets elevates costs and computational demands. To address this, various
techniques like coreset selection, dataset distillation, and dataset
quantization have been explored in the literature. Unlike traditional
techniques that depend on uniform sample distributions across different
classes, our research demonstrates that maintaining performance is feasible
even with uneven distributions. We find that for certain classes, the variation
in sample quantity has a minimal impact on performance. Inspired by this
observation, an intuitive idea is to reduce the number of samples for stable
classes and increase the number of samples for sensitive classes to achieve a
better performance with the same sampling ratio. Then the question arises: how
can we adaptively select samples from a dataset to achieve optimal performance?
In this paper, we propose a novel active learning based adaptive sampling
strategy, Dataset Quantization with Active Learning based Adaptive Sampling
(DQAS), to optimize the sample selection. In addition, we introduce a novel
pipeline for dataset quantization, utilizing feature space from the final stage
of dataset quantization to generate more precise dataset bins. Our
comprehensive evaluations on the multiple datasets show that our approach
outperforms the state-of-the-art dataset compression methods.


  Biomimetics has played a key role in the evolution of artificial neural
networks. Thus far, in silico metaphors have been dominated by concepts from
neuroscience and cognitive psychology. In this paper we introduce a different
type of biomimetic model, one that borrows concepts from the immune system, for
designing robust deep neural networks. This immuno-mimetic model leads to a new
computational biology framework for robustification of deep neural networks
against adversarial attacks. Within this Immuno-Net framework we define a
robust adaptive immune-inspired learning system (Immuno-Net RAILS) that
emulates, in silico, the adaptive biological mechanisms of B-cells that are
used to defend a mammalian host against pathogenic attacks. When applied to
image classification tasks on benchmark datasets, we demonstrate that
Immuno-net RAILS results in improvement of as much as 12.5% in adversarial
accuracy of a baseline method, the DkNN-robustified CNN, without appreciable
loss of accuracy on clean data.


  Intrinsic, two-dimensional (2D) ferromagnetic semiconductors are an important
class of materials for spintronics applications. Cr$_2$X$_2$Te$_6$ (X = Si and
Ge) semiconductors show 2D Ising-like ferromagnetism, which is preserved in
few-layer devices. The maximum magnetic entropy change associated with the
critical properties around the ferromagnetic transition for Cr$_2$Si$_2$Te$_6$
$-\Delta S_M^{max} \sim$ 5.05 J kg$^{-1}$ K$^{-1}$ is much larger than $-\Delta
S_M^{max} \sim$ 2.64 J kg$^{-1}$ K$^{-1}$ for Cr$_2$Ge$_2$Te$_6$ with an
out-of-plane field change of 5 T. The rescaled $-\Delta S_M(T,H)$ curves
collapse onto a universal curve independent of temperature and field for both
materials. This indicates similar critical behavior and 2D Ising magnetism,
confirming the magnetocrystalline anisotropy that could preserve the long-range
ferromagnetism in few-layers of Cr$_2$X$_2$Te$_6$.


  To date, various neural methods have been proposed for causal effect
estimation based on observational data, where a default assumption is the same
distribution and availability of variables at both training and inference
(i.e., runtime) stages. However, distribution shift (i.e., domain shift) could
happen during runtime, and bigger challenges arise from the impaired
accessibility of variables. This is commonly caused by increasing privacy and
ethical concerns, which can make arbitrary variables unavailable in the entire
runtime data and imputation impractical. We term the co-occurrence of domain
shift and inaccessible variables runtime domain corruption, which seriously
impairs the generalizability of a trained counterfactual predictor. To counter
runtime domain corruption, we subsume counterfactual prediction under the
notion of domain adaptation. Specifically, we upper-bound the error w.r.t. the
target domain (i.e., runtime covariates) by the sum of source domain error and
inter-domain distribution distance. In addition, we build an adversarially
unified variational causal effect model, named VEGAN, with a novel two-stage
adversarial domain adaptation scheme to reduce the latent distribution
disparity between treated and control groups first, and between training and
runtime variables afterwards. We demonstrate that VEGAN outperforms other
state-of-the-art baselines on individual-level treatment effect estimation in
the presence of runtime domain corruption on benchmark datasets.


  Many modern applications involve accessing and processing graphical data,
i.e. data that is naturally indexed by graphs. Examples come from internet
graphs, social networks, genomics and proteomics, and other sources. The
typically large size of such data motivates seeking efficient ways for its
compression and decompression. The current compression methods are usually
tailored to specific models, or do not provide theoretical guarantees. In this
paper, we introduce a low-complexity lossless compression algorithm for sparse
marked graphs, i.e. graphical data indexed by sparse graphs, which is capable
of universally achieving the optimal compression rate in a precisely defined
sense. In order to define universality, we employ the framework of local weak
convergence, which allows one to make sense of a notion of stochastic processes
for sparse graphs. Moreover, we investigate the performance of our algorithm
through some experimental results on both synthetic and real-world data.


  Lenses are a well-established structure for modelling bidirectional
transformations, such as the interactions between a database and a view of it.
Lenses may be symmetric or asymmetric, and may be composed, forming the
morphisms of a monoidal category. More recently, the notion of a learner has
been proposed: these provide a compositional way of modelling supervised
learning algorithms, and again form the morphisms of a monoidal category. In
this paper, we show that the two concepts are tightly linked. We show both that
there is a faithful, identity-on-objects symmetric monoidal functor embedding a
category of asymmetric lenses into the category of learners, and furthermore
there is such a functor embedding the category of learners into a category of
symmetric lenses.


  We achieve a bosonization of one-dimensional ideal gas of exclusion
statistics $\lambda$ at low temperatures, resulting in a new variant of $c=1$
conformal field theory with compactified radius $R=\sqrt{1/\lambda}$. These
ideal excluson gases exactly reproduce the low-$T$ critical properties of
Luttinger liquids, so they can be used to characterize the fixed points of the
latter. Generalized ideal gases with mutual statistics and non-ideal gases with
Luttinger-type interactions have also similar behavior, controlled by an
effective statistics varying in a fixed-point line.