  Viscoelastic surface rheology plays an important role in multiphase systems.
A typical example is the actin cortex which surrounds most animal cells. It
shows elastic properties for short time scales and behaves viscous for longer
time scales. Hence, realistic simulations of cell shape dynamics require a
model capturing the entire elastic to viscous spectrum. However, currently
there are no numerical methods to simulate deforming viscoelastic surfaces.
Thus models for the cell cortex, or other viscoelastic surfaces, are usually
based on assumptions or simplifications which limit their applicability. In
this paper we develop a first numerical approach for simulation of deforming
viscoelastic surfaces. To this end, we derive the surface equivalent of the
upper convected Maxwell model using the GENERIC formulation of nonequilibrium
thermodynamics. The model distinguishes between shear dynamics and dilatational
surface dynamics. The viscoelastic surface is embedded in a viscous fluid
modelled by the Navier-Stokes equation. Both systems are solved using Finite
Elements. The fluid and surface are combined using an Arbitrary
Lagrange-Eulerian (ALE) Method that conserves the surface grid spacing during
rotations and translations of the surface. We verify this numerical
implementation against analytic solutions and find good agreement. To
demonstrate its potential we simulate the experimentally observed tumbling and
tank-treading of vesicles in shear flow. We also supply a phase-diagram to
demonstrate the influence of the viscoelastic parameters on the behaviour of a
vesicle in shear flow. Finally, we explore cytokinesis as a future application
of the numerical method by simulating the start of cytokinesis using a
spatially dependent function for the surface tension.


  Detecting fashion landmarks is a fundamental technique for visual clothing
analysis. Due to the large variation and non-rigid deformation of clothes,
localizing fashion landmarks suffers from large spatial variances across poses,
scales, and styles. Therefore, understanding contextual knowledge of clothes is
required for accurate landmark detection. To that end, in this paper, we
propose a fashion landmark detection network with a global-local embedding
module. The global-local embedding module is based on a non-local operation for
capturing long-range dependencies and a subsequent convolution operation for
adopting local neighborhood relations. With this processing, the network can
consider both global and local contextual knowledge for a clothing image. We
demonstrate that our proposed method has an excellent ability to learn advanced
deep feature representations for fashion landmark detection. Experimental
results on two benchmark datasets show that the proposed network outperforms
the state-of-the-art methods. Our code is available at
https://github.com/shumming/GLE_FLD.


  In this paper, we investigate a cache updating system with a server
containing $N$ files, $K$ relays and $M$ users. The server keeps the freshest
versions of the files which are updated with fixed rates. Each relay can
download the fresh files from the server in a certain period of time. Each user
can get the fresh files from any relay as long as the relay has stored the
fresh versions of the requested files. Due to the limited storage capacity and
updating capacity of each relay, different cache designs will lead to different
average freshness of all updating files at users. In order to keep the average
freshness as large as possible in the cache updating system, we formulate an
average freshness-optimal cache updating problem (AFOCUP) to obtain an optimal
cache scheme. However, because of the nonlinearity of the AFOCUP, it is
difficult to seek out the optimal cache scheme. As a result, an linear
approximate model is suggested by distributing the total update rates
completely in accordance with the number of files in the relay in advance. Then
we utilize the greedy algorithm to search the optimal cache scheme that is
satisfied with the limited storage capacity of each relay. Finally, some
numerical examples are provided to illustrate the performance of the
approximate solution.


  This white paper briefly describes the astrophysics of ultra-compact
binaries, with emphasis of the challenges and opportunities in the next decade.


  In this proceedings we summarize state-of-the-art theoretical predictions for
the process of $t\bar{t}W^\pm$ production at the LHC. After a review of the
status of inclusive calculations, we discuss recent advancements in exclusive
predictions.


  We provide a closed-form expression for the motivic Kontsevich-Soibelman
invariant for M-theory in the background of the toric Calabi-Yau threefold
$K_{\mathbb{F}_0}$. This encodes the refined BPS spectrum of $SU(2)$ 5d ${\cal
N}=1$ Yang-Mills theory on $S^1\times \mathbb{R}^4$, corresponding to rank-zero
Donaldson-Thomas invariants for $K_{\mathbb{F}_0}$, anywhere on the Coulomb
branch.


  We present an adaptive event-triggered boundary control scheme for a
parabolic PDE-ODE system, where the reaction coefficient of the parabolic PDE,
and the system parameter of a scalar ODE, are unknown. In the proposed
controller, the parameter estimates, which are built by batch least-square
identification, are recomputed and the plant states are resampled
simultaneously. As a result, both the parameter estimates and the control input
employ piecewise-constant values. In the closed-loop system, the following
results are proved: 1) the absence of a Zeno phenomenon; 2) finite-time exact
identification of the unknown parameters under most initial conditions of the
plant (all initial conditions except a set of measure zero); 3) exponential
regulation of the plant states to zero. A simulation example is presented to
validate the theoretical result.


  We construct time-dependent multi-centre solutions to three-dimensional
general relativity with zero or negative cosmological constant. These solutions
correspond to dynamical systems of freely falling black holes and conical
singularities, with a multiply connected spacetime topology. Stationary
multi-black-hole solutions are possible only in the extreme black hole case.


  The quasi-persistent neutron star X-ray transient and eclipsing binary EXO
0748-676 recently started the transition to quiescence following an accretion
outburst that lasted more than 24 years. We report on two Chandra and twelve
Swift observations performed within five months after the end of the outburst.
The Chandra spectrum is composed of a soft, thermal component that fits to a
neutron star atmosphere model with kT^inf~0.12 keV, joined by a hard powerlaw
tail that contributes ~20% of the total 0.5-10 keV unabsorbed flux. The
combined Chandra/Swift data set reveals a relatively hot and luminous quiescent
system with a temperature of kT^inf~0.11-0.13 keV and a bolometric thermal
luminosity of ~8.1E33-1.6E34 (d/7.4 kpc)^2 erg/s. We discuss our results in the
context of cooling neutron star models.


  In this article we characterize measure theoretical eigenvalues of Toeplitz
Bratteli-Vershik minimal systems of finite topological rank which are not
associated to a continuous eigenfunction. Several examples are provided to
illustrate the different situations that can occur.


  In this paper a type D breakdown of the Navier Stokes (NS) in d=3 is
demonstrated. The element of the breakdown also occurs in the Euler equation.
We consider the fact that in d=2 Ladyzhenskaya found a generalized type B
solution. The discussion revolves around the notion, also found in quantum spin
theory, that in the behavior of a system can be quite different from the
behavior in d=2 dimensions. Concerning applications, our resolution of the
problem implies that e.g. hydrology problems formulated as a NS equation can
only be solved in computational approximation.


  In this note we present some results concerning the unirationality of the
algebraic variety $\cal{S}_{f}$ given by the equation \begin{equation*}
N_{K/k}(X_{1}+\alpha X_{2}+\alpha^2 X_{3})=f(t), \end{equation*} where $k$ is a
number field, $K=k(\alpha)$, $\alpha$ is a root of an irreducible polynomial
$h(x)=x^3+ax+b\in k[x]$ and $f\in k[t]$. We are mainly interested in the case
of pure cubic extensions, i.e. $a=0$ and $b\in k\setminus k^{3}$. We prove that
if $\op{deg}f=4$ and the variety $\cal{S}_{f}$ contains a $k$-rational point
$(x_{0},y_{0},z_{0},t_{0})$ with $f(t_{0})\neq 0$, then $\cal{S}_{f}$ is
$k$-unirational. A similar result is proved for a broad family of quintic
polynomials $f$ satisfying some mild conditions (for example this family
contains all irreducible polynomials). Moreover, the unirationality of
$\cal{S}_{f}$ (with non-trivial $k$-rational point) is proved for any
polynomial $f$ of degree 6 with $f$ not equivalent to the polynomial $h$
satisfying the condition $h(t)\neq h(\zeta_{3}t)$, where $\zeta_{3}$ is the
primitive third root of unity. We are able to prove the same result for an
extension of degree 3 generated by the root of polynomial $h(x)=x^3+ax+b\in
k[x]$, provided that $f(t)=t^6+a_{4}t^4+a_{1}t+a_{0}\in k[t]$ with
$a_{1}a_{4}\neq 0$.


  In the domain of machine learning algorithms, the significance of the loss
function is paramount, especially in supervised learning tasks. It serves as a
fundamental pillar that profoundly influences the behavior and efficacy of
supervised learning algorithms. Traditional loss functions, while widely used,
often struggle to handle noisy and high-dimensional data, impede model
interpretability, and lead to slow convergence during training. In this paper,
we address the aforementioned constraints by proposing a novel robust, bounded,
sparse, and smooth (RoBoSS) loss function for supervised learning. Further, we
incorporate the RoBoSS loss function within the framework of support vector
machine (SVM) and introduce a new robust algorithm named
$\mathcal{L}_{rbss}$-SVM. For the theoretical analysis, the
classification-calibrated property and generalization ability are also
presented. These investigations are crucial for gaining deeper insights into
the performance of the RoBoSS loss function in the classification tasks and its
potential to generalize well to unseen data. To empirically demonstrate the
effectiveness of the proposed $\mathcal{L}_{rbss}$-SVM, we evaluate it on $88$
real-world UCI and KEEL datasets from diverse domains. Additionally, to
exemplify the effectiveness of the proposed $\mathcal{L}_{rbss}$-SVM within the
biomedical realm, we evaluated it on two medical datasets: the
electroencephalogram (EEG) signal dataset and the breast cancer (BreaKHis)
dataset. The numerical results substantiate the superiority of the proposed
$\mathcal{L}_{rbss}$-SVM model, both in terms of its remarkable generalization
performance and its efficiency in training time.


  Process mining methods often analyze processes in terms of the individual
end-to-end process runs. Process behavior, however, may materialize as a
general state of many involved process components, which can not be captured by
looking at the individual process instances. A more holistic state of the
process can be determined by looking at the events that occur close in time and
share common process capacities. In this work, we conceptualize such behavior
using high-level events and propose a new framework for detecting and logging
such high-level events. The output of our method is a new high-level event log,
which collects all generated high-level events together with the newly assigned
event attributes: activity, case, and timestamp. Existing process mining
techniques can then be applied on the produced high-level event log to obtain
further insights. Experiments on both simulated and real-life event data show
that our method is able to automatically discover how system-level patterns
such as high traffic and workload emerge, propagate and dissolve throughout the
process.


  Addressing fairness in artificial intelligence (AI), particularly in medical
AI, is crucial for ensuring equitable healthcare outcomes. Recent efforts to
enhance fairness have introduced new methodologies and datasets in medical AI.
However, the fairness issue under the setting of domain transfer is almost
unexplored, while it is common that clinics rely on different imaging
technologies (e.g., different retinal imaging modalities) for patient
diagnosis. This paper presents FairDomain, a pioneering systemic study into
algorithmic fairness under domain shifts, employing state-of-the-art domain
adaptation (DA) and generalization (DG) algorithms for both medical
segmentation and classification tasks to understand how biases are transferred
between different domains. We also introduce a novel plug-and-play fair
identity attention (FIA) module that adapts to various DA and DG algorithms to
improve fairness by using self-attention to adjust feature importance based on
demographic attributes. Additionally, we curate the first fairness-focused
dataset with two paired imaging modalities for the same patient cohort on
medical segmentation and classification tasks, to rigorously assess fairness in
domain-shift scenarios. Excluding the confounding impact of demographic
distribution variation between source and target domains will allow clearer
quantification of the performance of domain transfer models. Our extensive
evaluations reveal that the proposed FIA significantly enhances both model
performance accounted for fairness across all domain shift settings (i.e., DA
and DG) with respect to different demographics, which outperforms existing
methods on both segmentation and classification. The code and data can be
accessed at https://ophai.hms.harvard.edu/datasets/harvard-fairdomain20k.


  This paper considers the backstepping design of state feedback controllers
for coupled linear parabolic partial integro-differential equations (PIDEs) of
Volterra-type with distinct diffusion coefficients, spatially-varying
parameters and mixed boundary conditions. The corresponding target system is a
cascade of parabolic PDEs with local couplings allowing a direct specification
of the closed-loop stability margin. The determination of the state feedback
controller leads to kernel equations, which are a system of coupled linear
second-order hyperbolic PIDEs with spatially-varying coefficients and rather
unusual boundary conditions. By extending the method of successive
approximations for the scalar case to the considered system class, the
well-posedness of these kernel equations is verified by providing a
constructive solution procedure. This results in a systematic method for the
backstepping control of coupled parabolic PIDEs as well as PDEs. The
applicability of the new backstepping design method is confirmed by the
stabilization of two coupled parabolic PIDEs with Dirichlet/Robin unactuated
boundaries and a coupled Neumann/Dirichlet actuation.


  For a fixed Feynman graph one can consider Feynman integrals with all
possible powers of propagators and try to reduce them, by linear relations, to
a finite subset of integrals, the so-called master integrals. Up to now, there
are numerous examples of reduction procedures resulting in a finite number of
master integrals for various families of Feynman integrals. However, up to now
it was just an empirical fact that the reduction procedure results in a finite
number of irreducible integrals. It this paper we prove that the number of
master integrals is always finite.


  How can we effectively develop speech technology for languages where no
transcribed data is available? Many existing approaches use no annotated
resources at all, yet it makes sense to leverage information from large
annotated corpora in other languages, for example in the form of multilingual
bottleneck features (BNFs) obtained from a supervised speech recognition
system. In this work, we evaluate the benefits of BNFs for subword modeling
(feature extraction) in six unseen languages on a word discrimination task.
First we establish a strong unsupervised baseline by combining two existing
methods: vocal tract length normalisation (VTLN) and the correspondence
autoencoder (cAE). We then show that BNFs trained on a single language already
beat this baseline; including up to 10 languages results in additional
improvements which cannot be matched by just adding more data from a single
language. Finally, we show that the cAE can improve further on the BNFs if
high-quality same-word pairs are available.


  The dynamics of the cavitation bubble collapse is a fundamental issue for the
bubble collapse application and prevention. In present work, the modified
forcing scheme for the pseudopotential multi-relaxation-time lattice Boltzmann
model developed by Li Q. et al. is adopted to develop a cavitation bubble
collapse model. In the respects of coexistence curves and Laplace law
verification, the improved pseudopotential multi-relaxation-time lattice
Boltzmann model is investigated. The independence between the kinematic
viscosity and the thermodynamic consistency, surface tension is founded. By
homogeneous and heterogeneous cavitation simulation, the capability of the
present model to describe the cavitation bubble development as well as the
cavitation inception is verified. The bubble collapse between two parallel
walls is simulated. The dynamic process of collapsing bubble is consistent with
the results from experiments and simulations by other numerical method. It is
demonstrated that the present pseudopotential multi-relaxation-time lattice
Boltzmann model is available and efficient, and the lattice Boltzmann method is
an alternative tool for collapsing bubble modeling.


  In Analog-to-digital (A/D) conversion, signal decimation has been proven to
greatly improve the efficiency of data storage while maintaining high accuracy.
When one couples signal decimation with the $\Sigma\Delta$ quantization scheme,
the reconstruction error decays exponentially with respect to the bit-rate. In
this study, similar results have been proven for finite unitarily generated
frames. We have devised a process called alternative decimation on finite
frames that is compatible with $\Sigma\Delta$ quantization up to the second
order. In both cases, decimation results in exponential error decay with
respect to the bit usage.


  We consider the propagation of a matter wavepacket of two-level atoms through
a square potential created by a super-Gaussian laser beam. We explore the
matter wave analog of Goos-H\"{a}nchen shift within the framework of atom
optics where the roles of atom and light is exchanged with respect to
conventional optics. Using a vector theory, where atoms are treated as
particles possessing two internal spin components, we show that not only large
negative but also large positive Goos-H\"{a}nchen shifts can occur in the
reflected atomic beam.


  In network interdiction problems, evaders (e.g., hostile agents or data
packets) may be moving through a network towards targets and we wish to choose
locations for sensors in order to intercept the evaders before they reach their
destinations. The evaders might follow deterministic routes or Markov chains,
or they may be reactive}, i.e., able to change their routes in order to avoid
sensors placed to detect them. The challenge in such problems is to choose
sensor locations economically, balancing security gains with costs, including
the inconvenience sensors inflict upon innocent travelers. We study the
objectives of 1) maximizing the number of evaders captured when limited by a
budget on sensing cost and 2) capturing all evaders as cheaply as possible. We
give optimal sensor placement algorithms for several classes of special graphs
and hardness and approximation results for general graphs, including for
deterministic or Markov chain-based and reactive or oblivious evaders. In a
similar-sounding but fundamentally different problem setting posed by
Rubinstein and Glazer where both evaders and innocent travelers are reactive,
we again give optimal algorithms for special cases and hardness and
approximation results on general graphs.


  Normalizing Flows (NF) are powerful likelihood-based generative models that
are able to trade off between expressivity and tractability to model complex
densities. A now well established research avenue leverages optimal transport
(OT) and looks for Monge maps, i.e. models with minimal effort between the
source and target distributions. This paper introduces a method based on
Brenier's polar factorization theorem to transform any trained NF into a more
OT-efficient version without changing the final density. We do so by learning a
rearrangement of the source (Gaussian) distribution that minimizes the OT cost
between the source and the final density. We further constrain the path leading
to the estimated Monge map to lie on a geodesic in the space of
volume-preserving diffeomorphisms thanks to Euler's equations. The proposed
method leads to smooth flows with reduced OT cost for several existing models
without affecting the model performance.


  We study families of Galois covers of curves of positive genus. It is known
that under a numerical condition these families yield Shimura subvarieties
generically contained in the Jacobian locus. We prove that there are only 6
families satisfying this condition, all of them in genus 2,3 or 4. We also show
that these families admit two fibrations in totally geodesic subvarieties,
generalizing a result of Grushevsky and M\"oller. Countably many of these
fibres are Shimura. Thus the Jacobian locus contains infinitely many Shimura
subvarieties of positive dimension of any $g \leq 4$.


  We propose a novel framework for exploring weak and $L_2$ generalization
errors of algorithms through the lens of differential calculus on the space of
probability measures. Specifically, we consider the KL-regularized empirical
risk minimization problem and establish generic conditions under which the
generalization error convergence rate, when training on a sample of size $n$,
is $\mathcal{O}(1/n)$. In the context of supervised learning with a one-hidden
layer neural network in the mean-field regime, these conditions are reflected
in suitable integrability and regularity assumptions on the loss and activation
functions.


  Main contribution of this paper is an investigation of expressive power of
the database category DB. An object in this category is a database-instance
(set of n-ary relations). Morphisms are not functions but have complex tree
structures based on a set of complex query computations. They express the
semantics of view-based mappings between databases. The higher (logical) level
scheme mappings between databases, usually written in some high expressive
logical language, may be functorially translated into this base "computation"
DB category . The behavioral point of view for databases is assumed, with
behavioural equivalence of databases corresponding to isomorphism of objects in
DB category. The introduced observations, which are view-based computations
without side-effects, are based (from Universal algebra) on monad endofunctor
T, which is the closure operator for objects and for morphisms also. It was
shown that DB is symmetric (with a bijection between arrows and objects)
2-category, equal to its dual, complete and cocomplete. In this paper we
demonstrate that DB is concrete, locally small and finitely presentable.
Moreover, it is enriched over itself monoidal symmetric category with a tensor
products for matching, and has a parameterized merging database operation. We
show that it is an algebraic lattice and we define a database metric space and
a subobject classifier: thus, DB category is a monoidal elementary topos.


  HADES is the only operating dielectron spectrometer in the energy regime 1-2
AGeV. The physics program aims at a systematic investigation of dielectron
production in heavy ion as well as elementary and pion-induced reactions.
  This contribution highlights recent results on electron pair production in
C12+C12 collisions at an incident energy of 2 AGeV with HADES. The measured
pair production probabilities span over five orders of magnitude. Dalitz decays
of pi0 and eta account for all the yield up to 0.15 GeV/c2, but for only about
50% above this invariant mass. The excess yield compared to the hadronic
cocktail between the pi0-Dalitz and the rho/omega invariant-mass region is in
agreement with the former DLS result if one assumes that it scales with beam
energy like pion production. Preliminary results from C12+C12 collisions at an
incident energy of 1 AGeV support this scenario.


  In this paper we study the convergence of a fully discrete Crank-Nicolson
Galerkin scheme for the initial value problem associated with the fractional
Korteweg-de Vries (KdV) equation, which involves the fractional Laplacian and
non-linear convection terms. Our proof relies on the Kato type local smoothing
effect to estimate the localized $H^{\alpha/2}$-norm of the approximated
solution, where $\alpha \in [1,2)$. We demonstrate that the scheme converges
strongly in $L^2(0,T;L^2_{loc}(\mathbb{R}))$ to a weak solution of the
fractional KdV equation provided the initial data in $L^2(\mathbb{R})$.
Assuming the initial data is sufficiently regular, we obtain the rate of
convergence for the numerical scheme. Finally, the theoretical convergence
rates are justified numerically through various numerical illustrations.


  I study the relationship between the likelihood of a violent domestic
conflict and the risk that such a conflict "externalizes" (i.e. spreads to
another country by creating an international dispute). I consider a situation
in which a domestic conflict between a government and a rebel group has the
potential to externalize. I show that the risk of externalization increases the
likelihood of a peaceful outcome, but only if the government is sufficiently
powerful relative to the rebels, the risk of externalization is sufficiently
high, and the foreign actor who can intervene in the domestic conflict is
sufficiently uninterested in material costs and benefits. I show how this model
helps to understand the recent and successful peace process between the
Colombian government and the country's most powerful rebel group, the
Revolutionary Armed Forces of Colombia (FARC).


  In the synthesis of metallic nanoparticles in microemulsions, we hypothesized
that particle size is mainly controlled by the reaction rate. Thus, the changes
observed on the particle sizes as reaction conditions, such as concentrations,
temperature, type of surfactant used, etc., are varied should not be correlated
directly to the modification of those conditions but indirectly to the changes
they produce on the reaction rates. By means of time resolved UV-vis
spectroscopy, we measured the reaction rates in the production of Pd
nanoparticles inside microemulsions at different reactant concentrations,
keeping all the other parameters constant. The measured reaction rates were
then correlated with the particle sizes measured by transmission electron
microscopy (TEM). We found that nanoparticle size increases linearly as the
reaction rates increases, independently of the actual reactant concentrations.
We proposed that the kinetics is controlled mainly by the diffusion of the
reducing agent through the surfactant monolayer covering the microemulsion
membrane. With this model, we predicted that particle size should depend
indirectly, via the reaction kinetics, on the micelle radius (v0 ~ r^-3), the
water volume (v0~vw^3) and the total microemulsion volume (v0~vT^-3), and
temperature (Arrhenius). Some of these predictions were explored in this
article.


  The present-day Universe is highly magnetized, even though the first magnetic
seed fields were most probably extremely weak. To explain the growth of the
magnetic field strength over many orders of magnitude fast amplification
processes need to operate. The most efficient mechanism known today is the
small-scale dynamo, which converts turbulent kinetic energy into magnetic
energy leading to an exponential growth of the magnetic field. The efficiency
of the dynamo depends on the type of turbulence indicated by the slope of the
turbulence spectrum v(l) \propto l^{theta}, where v(l) is the eddy velocity at
a scale l. We explore turbulent spectra ranging from incompressible Kolmogorov
turbulence with theta = 1/3 to highly compressible Burgers turbulence with
theta = 1/2. In this work we analyze the properties of the small-scale dynamo
for low magnetic Prandtl numbers Pm, which denotes the ratio of the magnetic
Reynolds number, Rm, to the hydrodynamical one, Re. We solve the Kazantsev
equation, which describes the evolution of the small-scale magnetic field,
using the WKB approximation. In the limit of low magnetic Prandtl numbers the
growth rate is proportional to Rm^{(1-theta)/(1+theta)}. We furthermore discuss
the critical magnetic Reynolds number Rm_crit, which is required for
small-scale dynamo action. The value of Rm_crit is roughly 100 for Kolmogorov
turbulence and 2700 for Burgers. Furthermore, we discuss that Rm_crit provides
a stronger constraint in the limit of low Pm than it does for large Pm. We
conclude that the small-scale dynamo can operate in the regime of low magnetic
Prandtl numbers, if the magnetic Reynolds number is large enough. Thus, the
magnetic field amplification on small scales can take place in a broad range of
physical environments and amplify week magnetic seed fields on short
timescales.


  We determine the locations of unintentional quantum dots (U-QDs) in a silicon
nanowire with a precision of a few nanometers by comparing the capacitances to
multiple gates with a capacitance simulation. Because we observe U-QDs in the
same location of the wire in multiple devices, their cause is likely to be an
unintended consequence of the fabrication, not random atomic-scale defects as
is typically assumed. The locations of the U-QDs appear consistent with
conduction band modulation from strain from the oxide and the gates. This
allows us to suggest methods to reduce the frequency of U-QDs.


  We predict theoretically that surface of an optical lattice imprinted in
defocusing nonlinear media can support shock, or kink waves. Such new surface
waves contain a modulationally stable pedestal and are strongly localized at
the edge of the optical lattice due to Bragg-type reflection. The kink
steepness and localization degree can be controlled by the lattice depth. We
found two types of kinks, which exhibit distinct stability properties for each
finite gap in the lattice spectrum. Our findings open the way to experimental
observation of optical surface kink waves.


  This is a survey paper dealing with holomorphic G-structures and holomorphic
Cartan geometries on compact complex manifolds. Our emphasis is on the foliated
case: holomorphic foliations with transverse (branched or generalized)
holomorphic Cartan geometries.


  A proposed strategy for determining the deceleration parameter entails
measuring the deviation from a linear (Hubble) distance-red shift relation.
However, even at moderate red shifts, z > 0.2, the deviation does not depend on
q_0 alone, but also on the equation-of-state of the universe.


  Current top-leading solutions for video object segmentation (VOS) typically
follow a matching-based regime: for each query frame, the segmentation mask is
inferred according to its correspondence to previously processed and the first
annotated frames. They simply exploit the supervisory signals from the
groundtruth masks for learning mask prediction only, without posing any
constraint on the space-time correspondence matching, which, however, is the
fundamental building block of such regime. To alleviate this crucial yet
commonly ignored issue, we devise a correspondence-aware training framework,
which boosts matching-based VOS solutions by explicitly encouraging robust
correspondence matching during network learning. Through comprehensively
exploring the intrinsic coherence in videos on pixel and object levels, our
algorithm reinforces the standard, fully supervised training of mask
segmentation with label-free, contrastive correspondence learning. Without
neither requiring extra annotation cost during training, nor causing speed
delay during deployment, nor incurring architectural modification, our
algorithm provides solid performance gains on four widely used benchmarks,
i.e., DAVIS2016&2017, and YouTube-VOS2018&2019, on the top of famous
matching-based VOS solutions.


  Bosonic fields can give rise to self-gravitating structures. These are
interesting hypothetical new "dark matter stars" and good descriptions of dark
matter haloes if the fields are very light. We study the dynamical response of
Newtonian boson stars (NBS) when excited by external matter (stars, planets or
black holes) in their vicinities. Our setup can describe the interaction
between a massive black hole and the surrounding environment, shortly after the
massive body has undergone a "kick", due to the collapse of baryonic matter at
the galactic center, or dark matter depletion as a reaction to an inspiralling
binary. We perform the first self-consistent calculation of dynamical friction
acting on moving bodies in these backgrounds. Binaries close to coalescence
"stir" the NBS core, and backreaction affects gravitational waveforms at
leading $-6PN$ order with respect to the dominant quadrupolar term; the
coefficient is too small to allow detection by next-generation interferometers.
We also show that the gravitational collapse to a supermassive black hole at
the center of a NBS is accompanied by only a small change in the surrounding
core. The NBS eventually gets accreted, but for astrophysical parameters this
occurs only after several Hubble times.


  In this article, we derive multiple polylogarithms from multiple zeta values
by using a recursive Riemann-Hilbert problem of additive type. Furthermore we
show that this Riemann-Hilbert problem is regarded as an inverse problem for
the connection problem of the KZ equation of one variable, so that the
fundamental solutions to the equation are derived from the Drinfel'd associator
by using a Riemann-Hilbert problem of multiplicative type. These results say
that the duality relation for the Drinfel'd associator can be interpreted as
the solvability condition for this inverse problem.


  This paper relates both to the metaphysics of probability and to the physics
of time asymmetry. Using the formalism of decoherent histories, it investigates
whether intuitions about intrinsic time directedness that are often associated
with probability can be justified in the context of no-collapse approaches to
quantum mechanics. The standard (two-vector) approach to time symmetry in the
decoherent histories literature is criticised, and an alternative approach is
proposed, based on two decoherence conditions ('forwards' and 'backwards')
within the one-vector formalism. In turn, considerations of forwards and
backwards decoherence and of decoherence and recoherence suggest that a
time-directed interpretation of probabilities, if adopted, should be both
contingent and perspectival.


  Precision robotic manipulation tasks (insertion, screwing, precisely pick,
precisely place) are required in many scenarios. Previous methods achieved good
performance on such manipulation tasks. However, such methods typically require
tedious calibration or expensive sensors. 3D/RGB-D cameras and torque/force
sensors add to the cost of the robotic application and may not always be
economical. In this work, we aim to solve these but using only weak-calibrated
and low-cost webcams. We propose Binocular Alignment Learning (BAL), which
could automatically learn the eye-hand coordination and points alignment
capabilities to solve the four tasks. Our work focuses on working with unknown
eye-hand coordination and proposes different ways of performing eye-in-hand
camera calibration automatically. The algorithm was trained in simulation and
used a practical pipeline to achieve sim2real and test it on the real robot.
Our method achieves a competitively good result with minimal cost on the four
tasks.


  Resource allocation is considered for cooperative transmissions in
multiple-relay wireless networks. Two auction mechanisms, SNR auctions and
power auctions, are proposed to distributively coordinate the allocation of
power among multiple relays. In the SNR auction, a user chooses the relay with
the lowest weighted price. In the power auction, a user may choose to use
multiple relays simultaneously, depending on the network topology and the
relays' prices. Sufficient conditions for the existence (in both auctions) and
uniqueness (in the SNR auction) of the Nash equilibrium are given. The fairness
of the SNR auction and efficiency of the power auction are further discussed.
It is also proven that users can achieve the unique Nash equilibrium
distributively via best response updates in a completely asynchronous manner.


  This paper proposes a simple self-calibration method for the internal time
synchronization of MEMS(Micro-electromechanical systems) LiDAR during research
and development. Firstly, we introduced the problem of internal time
misalignment in MEMS lidar. Then, a robust Minimum Vertical Gradient(MVG) prior
is proposed to calibrate the time difference between the laser and MEMS mirror,
which can be calculated automatically without any artificial participation or
specially designed cooperation target. Finally, actual experiments on MEMS
LiDARs are implemented to demonstrate the effectiveness of the proposed method.
It should be noted that the calibration can be implemented in a simple
laboratory environment without any ranging equipment and artificial
participation, which greatly accelerate the progress of research and
development in practical applications.


  As news and social media exhibit an increasing amount of manipulative
polarized content, detecting such propaganda has received attention as a new
task for content analysis. Prior work has focused on supervised learning with
training data from the same domain. However, as propaganda can be subtle and
keeps evolving, manual identification and proper labeling are very demanding.
As a consequence, training data is a major bottleneck. In this paper, we tackle
this bottleneck and present an approach to leverage cross-domain learning,
based on labeled documents and sentences from news and tweets, as well as
political speeches with a clear difference in their degrees of being
propagandistic. We devise informative features and build various classifiers
for propaganda labeling, using cross-domain learning. Our experiments
demonstrate the usefulness of this approach, and identify difficulties and
limitations in various configurations of sources and targets for the transfer
step. We further analyze the influence of various features, and characterize
salient indicators of propaganda.


  We present a study of charmed hadron transverse momentum distribution from
p+p collisions at $\sqrt{s_{NN}}=200$ GeV using the PYTHIA Monte Carlo event
generator. The PHENIX tuned PYTHIA parameter set based on electron measurements
alone yields a $D$ meson $p_{T}$ distribution softer than the preliminary STAR
experimental data from d+Au collisions scaled by the number of binary
collisions. In order to match the STAR $p_{T}$ spectrum, the higher order QCD
effects in PYTHIA have to be taken into account, and a fragmentation function
of charm quarks much harder than the default Peterson function is needed.
Electrons from beauty quark semi-leptonic decays are found significantly below
the contribution from charm quark decays for the $p_{T}$ region up to 8 GeV/c
in our modified fragmentation scheme, while in the default fragmentation scheme
the B decay electrons dominate at $p_{T}$ above 4 GeV/c. We propose an
experimental measurement of electron and charged hadron correlation to
differentiate these scenarios.


  This article is the first bibliometric analysis of the 708 lectures published
by The Librarians and Archivists National Congress between 1985 and 2012,
having been developed markers for production, productivity, institutional
origin and thematic analysis, in a quantitative, relational and diachronic
perspective. Its results show a dynamic congress, essentially national and
professional, with a strong majority of individual authorships, even with the
recent growth of the ratio of collaborations. In its thematic approach,
emphasis is given to public services of information, with the greatest focus
being on libraries, while still giving relevance to reflections on professional
and academic training in the area of Information Sciences, and also following
the most recent technological developments.


  Learning a good speaker embedding is important for many automatic speaker
recognition tasks, including verification, identification and diarization. The
embeddings learned by softmax are not discriminative enough for open-set
verification tasks. Angular based embedding learning target can achieve such
discriminativeness by optimizing angular distance and adding margin penalty. We
apply several different popular angular margin embedding learning strategies in
this work and explicitly compare their performance on Voxceleb speaker
recognition dataset. Observing the fact that encouraging inter-class
separability is important when applying angular based embedding learning, we
propose an exclusive inter-class regularization as a complement for angular
based loss. We verify the effectiveness of these methods for learning a
discriminative embedding space on ASV task with several experiments. These
methods together, we manage to achieve an impressive result with 16.5%
improvement on equal error rate (EER) and 18.2% improvement on minimum
detection cost function comparing with baseline softmax systems.


  Multiple carrier frequency offsets (CFOs) present in the uplink of orthogonal
frequency division multiple access (OFDMA) systems adversely affect subcarrier
orthogonality and impose a serious performance loss. In this paper, we propose
the application of time domain receiver windowing to concentrate the leakage
caused by CFOs to a few adjacent subcarriers with almost no additional
computational complexity. This allows us to approximate the interference matrix
with a quasi-banded matrix by neglecting small elements outside a certain band
which enables robust and computationally efficient signal detection. The
proposed CFO compensation technique is applicable to all types of subcarrier
assignment techniques. Simulation results show that the quasi-banded
approximation of the interference matrix is accurate enough to provide almost
the same bit error rate performance as that of the optimal solution. The
excellent performance of our proposed method is also proven through running an
experiment using our FPGA-based system setup.


  The formation of a phase of matter can be associated with the spontaneous
breaking of a symmetry. For crystallization, this broken symmetry is the
spatial translation symmetry, as the atoms spontaneously localize in a periodic
fashion. In analogy to spatial crystals, the spontaneous breaking of temporal
translation symmetry results in the formation of time crystals. While recent
and on-going experiments on driven isolated systems aim to minimize dissipative
processes, as it is an undesired source of decay, well-designed dissipation has
been put forth as a constitutive ingredient in the formation of dissipative
time crystals (DTCs). Here, we present the first experimental realisation of a
DTC, implemented in an atom-cavity system. Its defining feature is a period
doubled switching between distinct chequerboard density wave patterns, induced
by controlled cavity-dissipation and cavity-mediated interactions. We
demonstrate the robustness of this phase against system parameter changes and
temporal perturbations of the driving. Our work provides a framework for
realising phases of matter with spatiotemporal order in presence of
dissipation. We note that this is the natural environment of matter, and
therefore shapes its physical phenomena profoundly, making its study
imperative.


  Metal thiophosphates (MTPs) are a large family of 2D materials that exhibit
large structural and chemical diversity. They also show promise for
applications in energy harvesting and photodetection. Strain and defect
engineering have previously been demonstrated as useful mechanisms to tune
several properties of MTPs such as resistivity, magnetic state, and electronic
band gap. However, the effect of these stimuli on engineering tunable light
emission in MTPs remains unexplored. Here, we show experimentally that
structural defects in metal thiophosphate AgScP$_2$S$_6$ are prominent in
exhibiting photoluminescence, which is likely driven by the
defect-state-to-conduction-band transitions and can be further tuned by
temperature-induced strain gradients.


  Battery life is an increasingly urgent challenge for today's untethered VR
and AR devices. However, the power efficiency of head-mounted displays is
naturally at odds with growing computational requirements driven by better
resolution, refresh rate, and dynamic ranges, all of which reduce the sustained
usage time of untethered AR/VR devices. For instance, the Oculus Quest 2, under
a fully-charged battery, can sustain only 2 to 3 hours of operation time. Prior
display power reduction techniques mostly target smartphone displays. Directly
applying smartphone display power reduction techniques, however, degrades the
visual perception in AR/VR with noticeable artifacts. For instance, the
"power-saving mode" on smartphones uniformly lowers the pixel luminance across
the display and, as a result, presents an overall darkened visual perception to
users if directly applied to VR content.
  Our key insight is that VR display power reduction must be cognizant of the
gaze-contingent nature of high field-of-view VR displays. To that end, we
present a gaze-contingent system that, without degrading luminance, minimizes
the display power consumption while preserving high visual fidelity when users
actively view immersive video sequences. This is enabled by constructing a
gaze-contingent color discrimination model through psychophysical studies, and
a display power model (with respect to pixel color) through real-device
measurements. Critically, due to the careful design decisions made in
constructing the two models, our algorithm is cast as a constrained
optimization problem with a closed-form solution, which can be implemented as a
real-time, image-space shader. We evaluate our system using a series of
psychophysical studies and large-scale analyses on natural images. Experiment
results show that our system reduces the display power by as much as 24% with
little to no perceptual fidelity degradation.


  We present a complete set of local unitary invariants for generic multi-qubit
systems which gives necessary and sufficient conditions for two states being
local unitary equivalent. These invariants are canonical polynomial functions
in terms of the generalized Bloch representation of the quantum states. In
particular, we prove that there are at most 12 polynomial local unitary
invariants for two-qubit states and at most 90 polynomials for three-qubit
states. Comparison with Makhlin's 18 local unitary invariants is given for
two-quibit systems.


  Integrated radio-spectrum of Cas A in continuum was analyzed with special
emphasis on possible high frequency spectral curvature. We conclude that the
most probable scenario is that Planck's new data reveal the imprint of
non-linear particle acceleration in the case of this young Galactic supernova
remnant (SNR).


  The leading twist parametrization of dipion mass distribution in hard
exclusive reactions is proposed. Its parameters are related to quark
distributions (usual and skewed) in the pion and to distributions amplitudes of
mesons ($\pi$, $\rho$, etc.). We show that measurements of the shape of dipion
mass distribution in hard exclusie reactions can give important information
about partonic structure of the pion. The expression for the amplitude of the
reaction $\gamma^*\gamma\to \pi\pi$ near the threshold in terms of singlet
quark distribution in the pion is presented.


  We derive a number of equivalent criterions for the variable exponent Hardy
type inequality |\frac{1}{x}\int_{0}^{x}f(t)dt|_{L^{p(.)}(0,1)}\leq
C|f|_{L^{p(.)}(0,1)}; f\geq 0. to hold, whenever the exponent $p:(0,1)\to
(1,\infty)$ is increasing or decreasing near small neighborhood of the origin.


  Deep learning architectures have achieved state-of-the-art (SOTA) performance
on computer vision tasks such as object detection and image segmentation. This
may be attributed to the use of over-parameterized, monolithic deep learning
architectures executed on large datasets. Although such architectures lead to
increased accuracy, this is usually accompanied by a large increase in
computation and memory requirements during inference. While this is a non-issue
in traditional machine learning pipelines, the recent confluence of machine
learning and fields like the Internet of Things has rendered such large
architectures infeasible for execution in low-resource settings. In such
settings, previous efforts have proposed decision cascades where inputs are
passed through models of increasing complexity until desired performance is
achieved. However, we argue that cascaded prediction leads to increased
computational cost due to wasteful intermediate computations. To address this,
we propose PaSeR (Parsimonious Segmentation with Reinforcement Learning) a
non-cascading, cost-aware learning pipeline as an alternative to cascaded
architectures. Through experimental evaluation on real-world and standard
datasets, we demonstrate that PaSeR achieves better accuracy while minimizing
computational cost relative to cascaded models. Further, we introduce a new
metric IoU/GigaFlop to evaluate the balance between cost and performance. On
the real-world task of battery material phase segmentation, PaSeR yields a
minimum performance improvement of 174% on the IoU/GigaFlop metric with respect
to baselines. We also demonstrate PaSeR's adaptability to complementary models
trained on a noisy MNIST dataset, where it achieved a minimum performance
improvement on IoU/GigaFlop of 13.4% over SOTA models. Code and data are
available at https://github.com/scailab/paser .


  The generalized uncertainty principle discloses a self-complete
characteristic of gravity, namely the possibility of masking any curvature
singularity behind an event horizon as a result of matter compression at the
Planck scale. In this paper we extend the above reasoning in order to overcome
some current limitations to the framework, including the absence of a
consistent metric describing such Planck-scale black holes. We implement a
minimum-size black hole in terms of the extremal configuration of a neutral
non-rotating metric, which we derived by mimicking the effects of the
generalized uncertainty principle via a short scale modified version of
Einstein gravity. In such a way, we find a self-consistent scenario that
reconciles the self-complete character of gravity and the generalized
uncertainty principle.


  This work addresses the task of long-term person re-identification.
Typically, person re-identification assumes that people do not change their
clothes, which limits its applications to short-term scenarios. To overcome
this limitation, we investigate long-term person re-identification, which
considers both clothes-changing and clothes-consistent scenarios. In this
paper, we propose a novel framework that effectively learns and utilizes both
global and local information. The proposed framework consists of three streams:
global, local body part, and head streams. The global and head streams encode
identity-relevant information from an entire image and a cropped image of the
head region, respectively. Both streams encode the most distinct, less
distinct, and average features using the combinations of adversarial erasing,
max pooling, and average pooling. The local body part stream extracts
identity-related information for each body part, allowing it to be compared
with the same body part from another image. Since body part annotations are not
available in re-identification datasets, pseudo-labels are generated using
clustering. These labels are then utilized to train a body part segmentation
head in the local body part stream. The proposed framework is trained by
backpropagating the weighted summation of the identity classification loss, the
pair-based loss, and the pseudo body part segmentation loss. To demonstrate the
effectiveness of the proposed method, we conducted experiments on three
publicly available datasets (Celeb-reID, PRCC, and VC-Clothes). The
experimental results demonstrate that the proposed method outperforms the
previous state-of-the-art method.


  Depression recognition based on physiological signals such as functional
near-infrared spectroscopy (fNIRS) and electroencephalogram (EEG) has made
considerable progress. However, most existing studies ignore the
complementarity and semantic consistency of multimodal physiological signals
under the same stimulation task in complex spatio-temporal patterns. In this
paper, we introduce a multimodal physiological signals representation learning
framework using Siamese architecture via multiscale contrasting for depression
recognition (MRLMC). First, fNIRS and EEG are transformed into different but
correlated data based on a time-domain data augmentation strategy. Then, we
design a spatio-temporal contrasting module to learn the representation of
fNIRS and EEG through weight-sharing multiscale spatio-temporal convolution.
Furthermore, to enhance the learning of semantic representation associated with
stimulation tasks, a semantic consistency contrast module is proposed, aiming
to maximize the semantic similarity of fNIRS and EEG. Extensive experiments on
publicly available and self-collected multimodal physiological signals datasets
indicate that MRLMC outperforms the state-of-the-art models. Moreover, our
proposed framework is capable of transferring to multimodal time series
downstream tasks.


  In this article we define a generalization of the domino shuffling algorithm
for tilings of the Aztec diamond to the interacting $k$-tilings recently
introduced by S. Corteel, A. Gitlin, and the first author. We describe the
algorithm both in terms of dynamics on a system of colored particles and as
operations on the dominos themselves.


  Given a family $\mathcal{F}=\{A_1,\dots,A_s\}$ of subsets of $\mathbb{Z}_n$,
define $\Delta \mathcal{F}$ to be the multiset of all (cyclic) distances
dist$(x,y)$, where $\{x,y\} \subset A_i$, $x \neq y$, for some $i=1,\dots,s$.
Taking inspiration from a Euclidean distance problem of Erd\H{o}s, we say that
$\mathcal{F}$ is Erd\H{o}s-deep if the multiplicities of distances that occur
in $\Delta \mathcal{F}$ are precisely $1,2,\dots,k-1$ for some integer $k$. In
the case $s=1$, it is known that a modular arithmetic progression in
$\mathbb{Z}_n$ achieves this property (under mild conditions); conversely, APs
are the only such sets, except for one sporadic case when $n=6$. Here, we
consider in detail the case $s=2$. In particular, we classify Erd\H{o}s-deep
pairs $\{A_1,A_2\}$ when each $A_i$ is an arithmetic progression in
$\mathbb{Z}_n$. We also give a construction of a much wider class of
Erd\H{o}s-deep families $\{A_1,\dots,A_s\}$ when $s$ is a square integer.


  Having a precise knowledge of the dispersal ability of a population in a
heterogeneous environment is of critical importance in agroecology and
conservation biology as it can provide management tools to limit the effects of
pests or to increase the survival of endangered species. In this paper, we
propose a mechanistic-statistical method to estimate space-dependent diffusion
parameters of spatially-explicit models based on stochastic differential
equations, using genetic data. Dividing the total population into
subpopulations corresponding to different habitat patches with known allele
frequencies, the expected proportions of individuals from each subpopulation at
each position is computed by solving a system of reaction-diffusion equations.
Modelling the capture and genotyping of the individuals with a statistical
approach, we derive a numerically tractable formula for the likelihood function
associated with the diffusion parameters.
  In a simulated environment made of three types of regions, each associated
with a different diffusion coefficient, we successfully estimate the diffusion
parameters with a maximum-likelihood approach. Although higher genetic
differentiation among subpopulations leads to more accurate estimations, once a
certain level of differentiation has been reached, the finite size of the
genotyped population becomes the limiting factor for accurate estimation.


  We systematically study the optical field localization in an active
three-dimensional (3D) disordered percolating system with light nanoemitters
incorporated in percolating clusters. An essential feature of such a hybrid
medium is that the clusters are combined into a fractal radiation pattern, in
which light is simultaneously emitted and scattered by the disordered
structures. Theoretical considerations, based on systematic 3D simulations,
reveal nontrivial dynamics in the form of propagation of localized field
bunches in the percolating material. We obtain the length of the field
localization and dynamical properties of such states as functions of the
occupation probability of the disordered clusters. A transition between the
dynamical states and narrow point-like fields pinned to the emitters is found.
The theoretical analysis of the fractal field properties is followed by an
experimental study of the light generation by nanoemitters incorporated in the
percolating clusters. The experimental results corroborate theoretical
predictions.


  The possibility that the asymptotic quasi-normal mode (QNM) frequencies can
be used to obtain the Bekenstein-Hawking entropy for the Schwarzschild black
hole -- commonly referred to as Hod's conjecture -- has received considerable
attention. To test this conjecture, using monodromy technique, attempts have
been made to analytically compute the asymptotic frequencies for a large class
of black hole spacetimes. In an earlier work, two of the current authors
computed the high frequency QNMs for scalar perturbations of $(D+2)$
dimensional spherically symmetric, asymptotically flat, single horizon
spacetimes with generic power-law singularities. In this work, we extend these
results to asymptotically non-flat spacetimes. Unlike the earlier analyses, we
treat asymptotically flat and de Sitter spacetimes in a unified manner, while
the asymptotic anti-de Sitter spacetimes is considered separately. We obtain
master equations for the asymptotic QNM frequency for all the three cases. We
show that for all the three cases, the real part of the asymptotic QNM
frequency -- in general -- is not proportional to ln(3) thus indicating that
the Hod's conjecture may be restrictive.


  Nonlocal conservation laws are used to describe various realistic instances
of crowd behaviors. First, a basic analytic framework is established through an
"ad hoc" well posedness theorem for systems of nonlocal conservation laws in
several space dimensions interacting non locally with a system of ODEs.
Numerical integrations show possible applications to the interaction of
different groups of pedestrians, and also with other "agents".


  We present a detailed quark-model study of pion photoproduction within the
effective Lagrangian approach. Cross sections and single-polarization
observables are investigated for the four charge channels, $\gamma p\to \pi^+
n$, $\gamma n\to \pi^- p$, $\gamma p\to \pi^0 p$, and $\gamma n\to \pi^0 n$.
Leaving the $\pi N\Delta$ coupling strength to be a free parameter, we obtain a
reasonably consistent description of these four channels from threshold to the
first resonance region. Within this effective Lagrangian approach, strongly
constrainted by the quark model, we consider the issue of double-counting which
may occur if additional {\it t}-channel contributions are included.


  This paper introduces an efficient high-order numerical method for solving
the 1D stationary Schr\"odinger equation in the highly oscillatory regime.
Building upon the ideas from [2], we first analytically transform the given
equation into a smoother (i.e. less oscillatory) equation. By developing
sufficiently accurate quadratures for several (iterated) oscillatory integrals
occurring in the Picard approximation of the solution, we obtain a one-step
method that is third order w.r.t. the step size. The accuracy and efficiency of
the method are illustrated through several numerical examples.


  Water solid interfacial reactions are critical to understanding corrosion.
More specifically, it is notoriously difficult to determine how water and solid
interact beyond the initial chemisorption to induce the surface dissolution.
Here, we report atomic-scale mechanisms of the elementary steps during SiC
hydrothermal corrosion, from the initial surface attack to surface dissolution.
We find that hydrogen scission reactions play a vital role in breaking Si-C
bonds, regardless of the surface orientations. Stable silica layer does not
form on the surface, but the newly identified chemical reactions on SiC are
analogous to those observed during the dissolution of silica. SiC is dissolved
directly into the water as soluble silicic acid. The rate of hydrothermal
corrosion determined based on the calculated reaction activation energies is
consistent with available experimental data. Our work sheds new light on
understanding and interpreting the experimental observations and it provides
foundation for design of materials that are resistant to corrosion in water.


  Probabilistic modelling has been an essential tool in medical image analysis,
especially for analyzing brain Magnetic Resonance Images (MRI). Recent deep
learning techniques for estimating high-dimensional distributions, in
particular Variational Autoencoders (VAEs), opened up new avenues for
probabilistic modeling. Modelling of volumetric data has remained a challenge,
however, because constraints on available computation and training data make it
difficult effectively leverage VAEs, which are well-developed for 2D images. We
propose a method to model 3D MR brain volumes distribution by combining a 2D
slice VAE with a Gaussian model that captures the relationships between slices.
We do so by estimating the sample mean and covariance in the latent space of
the 2D model over the slice direction. This combined model lets us sample new
coherent stacks of latent variables to decode into slices of a volume. We also
introduce a novel evaluation method for generated volumes that quantifies how
well their segmentations match those of true brain anatomy. We demonstrate that
our proposed model is competitive in generating high quality volumes at high
resolutions according to both traditional metrics and our proposed evaluation.


  In this paper we show that an enriched continuum model of the micromorphic
type (Relaxed Micromorphic Model) can be used to model metamaterials' response
in view of their use for meta-structural design. We focus on the fact that the
reduced model's structure, coupled with the introduction of well-posed
interface conditions, allows us to easily test different combinations of
metamaterials' and classical-materials bricks, so that we can eventually end-up
with the conception of a meta-structure acting as a mechanical diode for
low/medium frequencies and as a total screen for higher frequencies. Thanks to
the reduced model's structure, we are also able to optimize this meta-structure
so that the diode-behaviour is enhanced for both "pressure" and "shear"
incident waves and for all possible angles of incidence.


  AdaBelief, one of the current best optimizers, demonstrates superior
generalization ability compared to the popular Adam algorithm by viewing the
exponential moving average of observed gradients. AdaBelief is theoretically
appealing in that it has a data-dependent $O(\sqrt{T})$ regret bound when
objective functions are convex, where $T$ is a time horizon. It remains however
an open problem whether the convergence rate can be further improved without
sacrificing its generalization ability. %on how to exploit strong convexity to
further improve the convergence rate of AdaBelief. To this end, we make a first
attempt in this work and design a novel optimization algorithm called
FastAdaBelief that aims to exploit its strong convexity in order to achieve an
even faster convergence rate. In particular, by adjusting the step size that
better considers strong convexity and prevents fluctuation, our proposed
FastAdaBelief demonstrates excellent generalization ability as well as superior
convergence. As an important theoretical contribution, we prove that
FastAdaBelief attains a data-dependant $O(\log T)$ regret bound, which is
substantially lower than AdaBelief. On the empirical side, we validate our
theoretical analysis with extensive experiments in both scenarios of strong and
non-strong convexity on three popular baseline models. Experimental results are
very encouraging: FastAdaBelief converges the quickest in comparison to all
mainstream algorithms while maintaining an excellent generalization ability, in
cases of both strong or non-strong convexity. FastAdaBelief is thus posited as
a new benchmark model for the research community.


  Neutron spin rotation is expected from quark-quark weak interactions in the
Standard Model, which induce weak interactions among nucleons that violate
parity. We present the results from an experiment searching for the effect of
parity violation via the spin rotation of polarized neutrons in a liquid
$^{4}$He medium. The value for the neutron spin rotation angle per unit length
in $^{4}$He, $d\phi/dz =(+2.1 \pm 8.3 (stat.) \pm 2.9 (sys.))\times10^{-7}$
rad/m, is consistent with zero. The result agrees with the best current
theoretical estimates of the size of nucleon-nucleon weak amplitudes from other
experiments and with the expectations from recent theoretical approaches to
weak nucleon-nucleon interactions. In this paper we review the theoretical
status of parity violation in the $\vec{n}+^{4}$He system and discuss details
of the data analysis leading to the quoted result. Analysis tools are presented
that quantify systematic uncertainties in this measurement and that are
expected to be essential for future measurements.


  Let $k$ be a field. We consider triples $(V,U,T)$, where $V$ is a finite
dimensional $k$-space, $U$ a subspace of $V$ and $T \:V \to V$ a linear
operator with $T^n = 0$ for some $n$, and such that $T(U) \subseteq U$. Thus,
$T$ is a nilpotent operator on $V$, and $U$ is an invariant subspace with
respect to $T$.
  We will discuss the question whether it is possible to classify these
triples. These triples $(V,U,T)$ are the objects of a category with the
Krull-Remak-Schmidt property, thus it will be sufficient to deal with
indecomposable triples. Obviously, the classification problem depends on $n$,
and it will turn out that the decisive case is $n=6.$ For $n < 6$, there are
only finitely many isomorphism classes of indecomposables triples, whereas for
$n > 6$ we deal with what is called ``wild'' representation type, so no
complete classification can be expected.
  For $n=6$, we will exhibit a complete description of all the indecomposable
triples.


  Magneto-oscillations in kinetic and magnetic response functions of a few
underdoped cuprates are perhaps one of the most striking observations since
many probes of underdoped cuprates clearly point to a non Fermi-liquid normal
state. Their observation in the vortex state well below the upper critical
field raises a doubt concerning their normal state origin. Here I propose an
explanation of the magneto-oscillations as emerging from the quantum
interference of the vortex lattice and checkerboard modulations of the electron
density of states revealed by STM with atomic resolution in some cuprate
superconductors. The checkerboard effectively pins the vortex lattice, when the
period of the latter is commensurate with the period of the checkerboard. This
condition yields 1/\sqrt{B} periodicity of the response functions versus
magnetic field B, rather than 1/B periodicity of conventional normal state
oscillations. Our solution of the Gross-Pitaevskii-type equation for composed
charged bosons accounting for the d-wave symmetry of the order-parameter and
its checkerboard modulations describes well changes in resonant frequency of
the tunnel-diode oscillator circuit with YBa2Cu4O8 and the oscillatory part of
the Hall resistance and magnetic susceptibility in the mixed state of
YBa2Cu3O6.5.


  Several theories exist to explain the source of the bright, millisecond
duration pulses known as fast radio bursts (FRBs). If the progenitors of FRBs
are non-cataclysmic, such as giant pulses from pulsars, pulsar-planet binaries,
or magnetar flares, FRB emission may be seen to repeat. We have undertaken a
survey of the fields of eight known FRBs from the High Time Resolution Universe
survey to search for repeating pulses. Although no repeat pulses were detected
the survey yielded the detection of a new FRB, described in Petroff et al.
(2015a). From our observations we rule out periodic repeating sources with
periods P $\leq$ 8.6 hours and rule out sources with periods 8.6 < P < 21 hours
at the 90% confidence level. At P $\geq$ 21 hours our limits fall off as ~1/P.
Dedicated and persistent observations of FRB source fields are needed to rule
out repetition on longer timescales, a task well-suited to next generation
wide-field transient detectors.


  The interstellar medium is enriched primarily by matter ejected from evolved
low and intermediate mass stars. The outflows from these stars create a
circumstellar envelope in which a rich gas-phase and dust-nucleation chemistry
takes place. We observed the nearest carbon-rich evolved star, IRC+10216, using
the PACS (55-210 {\mu}m) and SPIRE (194-672 {\mu}m) spectrometers on board
Herschel. We find several tens of lines from SiS and SiO, including lines from
the v=1 vibrational level. For SiS these transitions range up to J=124-123,
corresponding to energies around 6700K, while the highest detectable transition
is J=90-89 for SiO, which corresponds to an energy around 8400K. Both species
trace the dust formation zone of IRC+10216, and the broad energy ranges
involved in their detected transitions permit us to derive the physical
properties of the gas and the particular zone in which each species has been
formed. This allows us to check the accuracy of chemical thermodynamical
equilibrium models and the suggested depletion of SiS and SiO due to accretion
onto dust grains.


  The "characteristic age" of a pulsar usually is considered to approximate its
true age, but this assumption has led to some puzzling results, including the
fact that many pulsars with small characteristic ages have no associated
supernova remnants. The pulsar B1757-24 is located just beyond the edge of a
supernova remnant; the properties of the system indicate that the pulsar was
born at the centre of the remnant, but that it has subsequently overtaken the
expanding blast-wave. With a characteristic age of 16,000 yr, this implies an
expected proper motion by the pulsar of 63-80 milliarcsec per year. Here we
report observations of the nebula surrounding the pulsar which limit its proper
motion to less than 25 mas/yr, implying a minimum age of 39,000 yr. A more
detailed analysis argues for a true age as great as 170,000 yr, significantly
larger than the characteristic age. From this result and other discrepancies
associated with pulsars, we conclude that characteristic ages seriously
underestimate the true ages of pulsars.


  We present a new theoretical framework for designing linear parameter varying
controllers in the polynomial chaos framework. We assume the scheduling
variable to be random and apply polynomial chaos approach to synthesize the
controller for the resulting linear stochastic dynamical system. Two algorithms
are presented that minimize the performance objective with respect to the
stochastic system. The first algorithm is based on Galerkin projection and the
second algorithm is based on stochastic collocation. LPV controllers from both
the algorithms are shown to outperform classical LPV designs with respect to
regulator design for nonlinear missile system.


  The most common nonlinear deformations of the su(2) Lie algebra, introduced
by Polychronakos and Ro\v cek, involve a single arbitrary function of J_0 and
include the quantum algebra su_q(2) as a special case. In the present
contribution, less common nonlinear deformations of su(2), introduced by
Delbecq and Quesne and involving two deforming functions of J_0, are reviewed.
Such algebras include Witten's quadratic deformation of su(2) as a special
case. Contrary to the former deformations, for which the spectrum of J_0 is
linear as for su(2), the latter give rise to exponential spectra, a property
that has aroused much interest in connection with some physical problems.
Another interesting algebra of this type, denoted by ${\cal A}^+_q(1)$, has two
series of (N+1)-dimensional unitary irreducible representations, where N=0, 1,
2, .... To allow the coupling of any two such representations, a generalization
of the standard Hopf axioms is proposed. The resulting algebraic structure,
referred to as a two-colour quasitriangular Hopf algebra, is described.


  We present the perturbative parts of the structure functions F_2^c and F_L^c
for a gluon target having nonzero transverse momentum squared at order \alpha
_s. The results of the double convolution (with respect to the Bjorken variable
x and the transverse momentum) of the perturbative part and the unintegrated
gluon densities are compared with HERA experimental data for F_2^c. The
contribution from F_L^c structure function ranges 10\div30% of that of F_2^c at
the HERA kinematical range.


  Understanding symmetry-breaking states of materials is a major challenge in
the modern physical sciences. Quantum atmosphere proposed recently sheds light
on the hidden world of these symmetry broken patterns. But the requirements for
exquisite sensitivity to the small shift and tremendous spatial resolution to
local information pose huge obstacles to its experimental manifestation. In our
experiment, we prepare time-reversal-symmetry conserved and broken quantum
atmosphere of a single nuclear spin and successfully observe their symmetry
properties. Our work proves in principle that finding symmetry patterns from
quantum atmosphere is conceptually viable. It also opens up entirely new
possibilities in the potential application of quantum sensing in material
diagnosis.


  Let $P$ be a generalized laplacian on $R^{2n+1}$. It is known that $P$ is the
generating functional of semigroups of measures $\mu_{t}$ on the Heisenberg
group $H^{n}$ and $\nu_{t}$ on the Abelian group $R^{2n+1}$. Under some
smoothness and growth conditions on the functional $P$ expressed in terms of
its Abelian Fourier transform $\widehat{P}$ we show that the semigroup
$\mu_{t}$ is a kind of perturbation of the semigroup $\nu_{t}$. More precisely,
we give pointwise estimates for the difference of the densities of the measures
$\mu_{t}$ and $\nu_{t}$.
  As a consequence we get a description of the asymptotic behavior at the
origin or pointwise estimates for the densities of the semigroup of measures on
the Heisenberg group which is an analogue (via generating functional) of the
symmetrized gamma (gamma-variance) semigroup on $R^{2n+1}$.
  The main tool is a symbolic calculus for convolution operators on the
Heisenberg group.


  The exponential growth of the blockchain size has become a major contributing
factor that hinders the decentralisation of blockchain and its potential
implementations in data-heavy applications. In this paper, we propose segment
blockchain, an approach that segmentises blockchain and enables nodes to only
store a copy of one blockchain segment. We use \emph{PoW} as a membership
threshold to limit the number of nodes taken by an Adversary---the Adversary
can only gain at most $n/2$ of nodes in a network of $n$ nodes when it has
$50\%$ of the calculation power in the system (the Nakamoto blockchain security
threshold). A segment blockchain system fails when an Adversary stores all
copies of a segment, because the Adversary can then leave the system, causing a
permanent loss of the segment. We theoretically prove that segment blockchain
can sustain a $(AD/n)^m$ failure probability when the Adversary has no more
than $AD$ number of nodes and every segment is stored by $m$ number of nodes.
The storage requirement is mostly shrunken compared to the traditional design
and therefore making the blockchain more suitable for data-heavy applications.


  We present a survey of recent results of the functional analysis allowing to
solve PDEs in a large class of domains with irregular boundaries. We extend the
previously introduced concept of admissible domains with a d-set boundary on
the domains with the boundaries on which the measure is not necessarily Ahlfors
regular d-measure. This gives a generalization of Rellich-Kondrachov theorem
and the compactness of the trace operator, allowing to obtain, as for a regular
classical case the unicity/existence of weak solutions of Poisson boundary
valued problem with the Robin boundary condition and to obtain the usual
properties of the associated spectral problem.


  Deep learning models have exhibited remarkable performance across various
domains. Nevertheless, the burgeoning model sizes compel edge devices to
offload a significant portion of the inference process to the cloud. While this
practice offers numerous advantages, it also raises critical concerns regarding
user data privacy. In scenarios where the cloud server's trustworthiness is in
question, the need for a practical and adaptable method to safeguard data
privacy becomes imperative. In this paper, we introduce Ensembler, an
extensible framework designed to substantially increase the difficulty of
conducting model inversion attacks for adversarial parties. Ensembler leverages
model ensembling on the adversarial server, running in parallel with existing
approaches that introduce perturbations to sensitive data during colloborative
inference. Our experiments demonstrate that when combined with even basic
Gaussian noise, Ensembler can effectively shield images from reconstruction
attacks, achieving recognition levels that fall below human performance in some
strict settings, significantly outperforming baseline methods lacking the
Ensembler framework.


  Proton captures on Mg isotopes play an important role in the Mg-Al cycle
active in stellar H shell burning. In particular, the strengths of low-energy
resonances with E < 200 keV in 25Mg(p,gamma)26Al determine the production of
26Al and a precise knowledge of these nuclear data is highly desirable.
Absolute measurements at such low-energies are often very difficult and
hampered by gamma-ray background as well as changing target stoichiometry
during the measurements. The latter problem can be partly avoided using higher
energy resonances of the same reaction as a normalization reference. Hence the
parameters of suitable resonances have to be studied with adequate precision.
In the present work we report on new measurements of the resonance strengths
omega_gamma of the E = 214, 304, and 326 keV resonances in the reactions
24Mg(p,gamma)25Al, 25Mg(p,gamma)26Al, and 26Mg(p,gamma)27Al, respectively.
These studies were performed at the LUNA facility in the Gran Sasso underground
laboratory using multiple experimental techniques and provided results with a
higher accuracy than previously achieved.


  Large astronomical databases obtained from sky surveys such as the
SuperCOSMOS Sky Survey (SSS) invariably suffer from spurious records coming
from artefactual effects of the telescope, satellites and junk objects in orbit
around earth and physical defects on the photographic plate or CCD. Though
relatively small in number these spurious records present a significant problem
in many situations where they can become a large proportion of the records
potentially of interest to a given astronomer. Accurate and robust techniques
are needed for locating and flagging such spurious objects, and we are
undertaking a programme investigating the use of machine learning techniques in
this context. In this paper we focus on the four most common causes of unwanted
records in the SSS: satellite or aeroplane tracks, scratches, fibres and other
linear phenomena introduced to the plate, circular halos around bright stars
due to internal reflections within the telescope and diffraction spikes near to
bright stars. Appropriate techniques are developed for the detection of each of
these. The methods are applied to the SSS data to develop a dataset of spurious
object detections, along with confidence measures, which can allow these
unwanted data to be removed from consideration. These methods are general and
can be adapted to other astronomical survey data.


  In this work we consider local minimizers (in the topology of transport
distances) of the interaction energy associated to a repulsive-attractive
potential. We show how the imensionality of the support of local minimizers is
related to the repulsive strength of the potential at the origin.


  The Markov Theorem Without Stabilization (MTWS) established the existence of
a calculus of braid isotopies that can be used to move between closed braid
representatives of a given oriented link type without having to increase the
braid index by stabilization. Although the calculus is extensive there are
three key isotopies that were identified and analyzed---destabilization,
exchange moves and elementary braid preserving flypes. One of the critical open
problems left in the wake of the MTWS is the "recognition
problem"---determining when a given closed $n$-braid admits a specified move of
the calculus. In this note we give an algorithmic solution to the recognition
problem for these three key isotopies of the MTWS calculus. The algorithm is
"directed" by a complexity measure that can be {\em monotonically simplified}
by the application of "elementary moves".


  We introduce Gradient Particle Magnetohydrodynamics (GPM), a new Lagrangian
method for magnetohydrodynamics based on gradients corrected for the locally
disordered particle distribution. The development of a numerical code for MHD
simulation using the GPM algorithm is outlined. Validation tests simulating
linear and nonlinear sound waves, linear MHD waves, advection of magnetic
fields in a magnetized vortex, hydrodynamical shocks, and three-dimensional
collapse are presented, demonstrating the viability of an MHD code using GPM.
The characteristics of a GPM code are discussed and possible avenues for
further development and refinement are mentioned. We conclude with a view of
how GPM may complement other methods currently in development for the next
generation of computational astrophysics.


  The BG-rank BG($\pi$) of an integer partition $\pi$ is defined as
$$\text{BG}(\pi) := i-j$$ where $i$ is the number of odd-indexed odd parts and
$j$ is the number of even-indexed odd parts of $\pi$. In a recent work, Fu and
Tang ask for a direct combinatorial proof of the following identity of
Berkovich and Uncu
$$B_{2N+\nu}(k,q)=q^{2k^2-k}\left[\begin{matrix}2N+\nu\\N+k\end{matrix}\right]_{q^2}$$
for any integer $k$ and non-negative integer $N$ where $\nu\in \{0,1\}$,
$B_N(k,q)$ is the generating function for partitions into distinct parts less
than or equal to $N$ with BG-rank equal to $k$ and
$\left[\begin{matrix}a+b\\b\end{matrix}\right]_q$ is a Gaussian binomial
coefficient. In this paper, we provide a combinatorial proof of Berkovich and
Uncu's identity along the lines of Vandervelde and Fu and Tang's idea.


  We study the limiting case of the Krichever construction of orthogonal
curvilinear coordinate systems when the spectral curve becomes singular. We
show that the case when the curve is reducible and all its irreducible
components are rational curves the construction procedure reduces to solving
systems of linear equations and simple computations with elementary functions.
Therewith we demonstrate how such well-known coordinate systems as the polar
coordinates, the cylindrical coordinates in the three-space, and the spherical
coordinates in the Euclidean spaces fit in this scheme.


  Our research focuses on few-shot fine-grained image classification, which
faces two major challenges: appearance similarity of fine-grained objects and
limited number of samples. To preserve the appearance details of images,
traditional feature reconstruction networks usually enhance the representation
ability of key features by spatial feature reconstruction and minimizing the
reconstruction error. However, we find that relying solely on a single type of
feature is insufficient for accurately capturing inter-class differences of
fine-grained objects in scenarios with limited samples. In contrast, the
introduction of channel features provides additional information dimensions,
aiding in better understanding and distinguishing the inter-class differences
of fine-grained objects. Therefore, in this paper, we design a new Hybrid
Feature Collaborative Reconstruction Network (HFCR-Net) for few-shot
fine-grained image classification, which includes a Hybrid Feature Fusion
Process (HFFP) and a Hybrid Feature Reconstruction Process (HFRP). In HFRP, we
fuse the channel features and the spatial features. Through dynamic weight
adjustment, we aggregate the spatial dependencies between arbitrary two
positions and the correlations between different channels of each image to
increase the inter-class differences. Additionally, we introduce the
reconstruction of channel dimension in HFRP. Through the collaborative
reconstruction of channel dimension and spatial dimension, the inter-class
differences are further increased in the process of support-to-query
reconstruction, while the intra-class differences are reduced in the process of
query-to-support reconstruction. Ultimately, our extensive experiments on three
widely used fine-grained datasets demonstrate the effectiveness and superiority
of our approach.


  Using a sample of $1.31$ billion $J/\psi$ events accumulated with the BESIII
detector at the BEPCII collider, we report the observation of the decay $J/\psi
\rightarrow \phi\pi^{0}$, which is the first evidence for a doubly
Okubo-Zweig-Iizuka suppressed electromagnetic $J/\psi$ decay. A clear structure
is observed in the $K^{+} K^{-}$ mass spectrum around 1.02 GeV/$c^2$, which can
be attributed to interference between $J/\psi \rightarrow \phi\pi^{0}$ and
$J/\psi \rightarrow K^{+}K^{-}\pi^{0}$ decays. Due to this interference, two
possible solutions are found. The corresponding measured values of the
branching fraction of $J/\psi \to \phi\pi^{0}$ are $[2.94 \pm
0.16\text{(stat.)} \pm 0.16\text{(syst.)}] \times 10^{-6}$ and $[1.24 \pm
0.33\text{(stat.)} \pm 0.30\text{(syst.)}] \times 10^{-7}$.


  A lip-syncing deepfake is a digitally manipulated video in which a person's
lip movements are created convincingly using AI models to match altered or
entirely new audio. Lip-syncing deepfakes are a dangerous type of deepfakes as
the artifacts are limited to the lip region and more difficult to discern. In
this paper, we describe a novel approach, LIP-syncing detection based on mouth
INConsistency (LIPINC), for lip-syncing deepfake detection by identifying
temporal inconsistencies in the mouth region. These inconsistencies are seen in
the adjacent frames and throughout the video. Our model can successfully
capture these irregularities and outperforms the state-of-the-art methods on
several benchmark deepfake datasets. Code is available at
https://github.com/skrantidatta/LIPINC


  Consider a real line equipped with a (not necessarily intrinsic) distance. We
deal with the minimum-weight perfect matching problem for a complete graph
whose points are located on the line and whose edges have weights equal to
distances along the line. This problem is closely related to one-dimensional
Monge-Kantorovich trasnport optimization. The main result of the present note
is a "bottom-up" recursion relation for weights of partial minimum-weight
matchings.


  This is a survey of the historical development of the Spectral Standard Model
and beyond, starting with the ground breaking paper of Alain Connes in 1988
where he observed that there is a link between Higgs fields and finite
noncommutative spaces. We present the important contributions that helped in
the search and identification of the noncommutative space that characterizes
the fine structure of space-time. The nature and properties of the
noncommutative space are arrived at by independent routes and show the
uniqueness of the Spectral Standard Model at low energies and the Pati-Salam
unification model at high energies.


  Image dehazing poses significant challenges in environmental perception.
Recent research mainly focus on deep learning-based methods with single
modality, while they may result in severe information loss especially in
dense-haze scenarios. The infrared image exhibits robustness to the haze,
however, existing methods have primarily treated the infrared modality as
auxiliary information, failing to fully explore its rich information in
dehazing. To address this challenge, the key insight of this study is to design
a visible-infrared fusion network for image dehazing. In particular, we propose
a multi-scale Deep Structure Feature Extraction (DSFE) module, which
incorporates the Channel-Pixel Attention Block (CPAB) to restore more spatial
and marginal information within the deep structural features. Additionally, we
introduce an inconsistency weighted fusion strategy to merge the two modalities
by leveraging the more reliable information. To validate this, we construct a
visible-infrared multimodal dataset called AirSim-VID based on the AirSim
simulation platform. Extensive experiments performed on challenging real and
simulated image datasets demonstrate that VIFNet can outperform many
state-of-the-art competing methods. The code and dataset are available at
https://github.com/mengyu212/VIFNet_dehazing.


  The model of interaction between learning and evolutionary optimization is
designed and investigated. The evolving population of modeled organisms is
considered. The mechanism of the genetic assimilation of the acquired features
during a number of generations of Darwinian evolution is studied. It is shown
that the genetic assimilation takes place as follows: phenotypes of modeled
organisms move towards the optimum at learning; then the selection takes place;
genotypes of selected organisms also move towards the optimum. The hiding
effect is also studied; this effect means that strong learning can inhibit the
evolutionary search for the optimal genotype. The mechanism of influence of the
learning load on the interaction between learning and evolution is analyzed. It
is shown that the learning load can lead to a significant acceleration of
evolution.


  The main goal of this note is to provide evidence that the first rational
homology of the Johnson subgroup $K_{g,1}$ of the mapping class group of a
genus g surface with one marked point is finite-dimensional. Building on work
of Dimca-Papadima, we use symplectic representation theory to show that, for
all $g > 3$, the completion of $H_1(K_{g,1},\mathbb{Q})$ with respect to the
augmentation ideal in the rational group algebra of $\mathbb{Z}^{2g}$ is
finite-dimensional. We also show that the terms of the Johnson filtration of
the mapping class group have infinite-dimensional rational homology in some
degrees in almost all genera, generalizing a result of Akita.


  Every polynomial of the form $P=(x+1)(x^{n-1}+c_1x^{n-2}+\cdots +c_{n-1})$ is
representable as Schur-Szeg\H{o} composition of $n-1$ polynomials of the form
$(x+1)^{n-1}(x+a_i)$, where the numbers $a_i$ are unique up to permutation. We
give necessary and sufficient conditions upon the possible values of the
$8$-vector whose components are the number of positive, zero, negative and
complex roots of a real polynomial $P$ and the number of positive, zero,
negative and complex among the quantities $a_i$ corresponding to $P$. A similar
result is proved about entire functions of the form $e^xR$, where $R$ is a
polynomial.


  Using data taken during the year 2000 with the NA48 detector at the CERN SPS,
a search for the CP violating decay K_S -> 3 pi0 has been performed. From a fit
to the lifetime distribution of about 4.9 million reconstructed K0/K0bar -> 3
pi0 decays, the CP violating amplitude eta_000 = A(K_S -> 3 pi0)/A(K_L -> 3
pi0) has been found to be Re(eta_000) = -0.002 +- 0.011 +- 0.015 and
Im(eta_000) = -0.003 +- 0.013 +- 0.017. This corresponds to an upper limit on
the branching fraction of Br(K_S -> 3 pi0) < 7.4 x 10^-7 at 90% confidence
level. The result is used to improve knowledge of Re(epsilon) and the CPT
violating quantity Im(delta) via the Bell-Steinberger relation.


  We investigate choice principles in the Weihrauch lattice for finite sets on
the one hand, and convex sets on the other hand. Increasing cardinality and
increasing dimension both correspond to increasing Weihrauch degrees. Moreover,
we demonstrate that the dimension of convex sets can be characterized by the
cardinality of finite sets encodable into them. Precisely, choice from an n+1
point set is reducible to choice from a convex set of dimension n, but not
reducible to choice from a convex set of dimension n-1. Furthermore we consider
searching for zeros of continuous functions. We provide an algorithm producing
3n real numbers containing all zeros of a continuous function with up to n
local minima. This demonstrates that having finitely many zeros is a strictly
weaker condition than having finitely many local extrema. We can prove 3n to be
optimal.


  A distributive lattice $L$ with minimum element $0$ is called decomposable if
$a$ and $b$ are not comparable elements in $L$ then there exist
$\overline{a},\overline{b}\in L$ such that $a=\overline{a}\vee(a\wedge b),
b=\overline{b}\vee(a\wedge b)$ and $\overline{a}\wedge \overline{b}=0$. The
main purpose of this paper is to study the structure of decomposable lattices
determined by their prime ideals. The properties for five special decomposable
lattices are derived.


  In this paper we present a numerical study of plasma jets produced by intense
laser matter interactions. Through this study we hope to better understand
astrophysical jets and their recent experimental simulations in the laboratory.
We paid special attention to radiation cooling and the interaction of the jet
with ambient gas. Four cases are presented in this paper; two of them deal with
the propagation of jets in vacuum, while in the other two the propagation takes
place in the ambient gas. Available experimental results are reproduced to good
accuracy in the vacuum case. For jets in ambient gas, we find that the
existence of the surrounding gas confines the jet into a narrow cylindrical
shape so that both the density and temperature of the jet remain high enough
for effective radiation cooling. As a result, a collimated plasma jet is formed
in these cases. The dimensionless parameters characterizing the laboratory jets
and protostellar jets have overlapping domains. We also discuss the cooling
lengths for our model and compare them with the corresponding values in the
astrophysical jets. A plasma jet in the ambient gas experiment is proposed
which is within the reach of present day technology, and can be relevant to
astrophysical phenomena.


  Toral introduced so-called cooperative Parrondo games, in which there are N
players (3 or more) arranged in a circle. At each turn one player is randomly
chosen to play. He plays either game A or game B. Game A results in a win or
loss of one unit based on the toss of a fair coin. Game B results in a win or
loss of one unit based on the toss of a biased coin, with the amount of the
bias depending on whether none, one, or two of the player's two nearest
neighbors have won their most recent games. Game A is fair, so the games are
said to exhibit the Parrondo effect if game B is losing or fair and the random
mixture (1/2)(A+B) is winning. With the parameter space being the unit cube, we
investigate the region in which the Parrondo effect appears. Explicit formulas
can be found if N=3,4,5,6 and exact computations can be carried out if
N=7,8,9,...,19, at least. We provide numerical evidence suggesting that the
Parrondo region has nonzero volume in the limit as N approaches infinity.


  This paper employs a powerful argument, called an algorithmic argument, to
prove lower bounds of the quantum query complexity of a multiple-block ordered
search problem in which, given a block number i, we are to find a location of a
target keyword in an ordered list of the i-th block. Apart from much studied
polynomial and adversary methods for quantum query complexity lower bounds, our
argument shows that the multiple-block ordered search needs a large number of
nonadaptive oracle queries on a black-box model of quantum computation that is
also supplemented with advice. Our argument is also applied to the notions of
computational complexity theory: quantum truth-table reducibility and quantum
truth-table autoreducibility.


  I introduce a generic method for inference on entire quantile and regression
quantile processes in the presence of a finite number of large and arbitrarily
heterogeneous clusters. The method asymptotically controls size by generating
statistics that exhibit enough distributional symmetry such that randomization
tests can be applied. The randomization test does not require ex-ante matching
of clusters, is free of user-chosen parameters, and performs well at
conventional significance levels with as few as five clusters. The method tests
standard (non-sharp) hypotheses and can even be asymptotically similar in
empirically relevant situations. The main focus of the paper is inference on
quantile treatment effects but the method applies more broadly. Numerical and
empirical examples are provided.


  Let $D\subset \mathbb{R}^d$ be a bounded Lipschitz domain, $\omega$ be a high
order modulus of continuity and let $T$ be a convolution Calder\'{o}n-Zygmund
operator. We characterize the bounded restricted operators $T_D$ on the Zygmund
space $\mathcal{C}_{\omega}(D)$. The characterization is based on properties of
$T_D P$ for appropriate polynomials $P$ restricted to $D$.