  Context: We present COSTA (COld STream finder Algorithm), a novel algorithm
to search for cold kinematical substructures in the phase space of planetary
nebulae (PNe) and globular clusters (GCs) in the halo of massive galaxies and
intracluster regions. Aims: COSTA aims at detecting small sized, low velocity
dispersion streams, as the ones produced in recent interactions of dwarf
galaxies with the halo of more massive galaxies, including the ones sitting in
the central region of rich galaxy clusters. Methods: COSTA is based on a deep
friend-of-friend procedure that isolates groups of N particles with small
velocity dispersion (between 10 kms and $\sim$ 100 kms), using an iterative (n)
sigma-clipping over a defined number of (k) neighbor particles. The algorithm
has three parameters (k-n-N), plus a velocity dispersion cut-off, which defines
the "coldness" of the stream, that are set using Montecarlo realizations of the
sample under exam. Results: In this paper, we show the ability of COSTA to
recover simulated streams on mock data-sets of discrete kinematical tracers
with different sizes and measurement errors, from publicly available
hydrodynamical simulations. We also show the best algorithm set-up for a
realistic case of stream finding in the core of the Fornax cluster, for future
applications of COSTA to real populations of PNe and GCs. Conclusions: COSTA
can be generalized to all problems of finding small substructures in the phase
space of a limited sample of discrete tracers, provided that the algorithm is
trained on realistic mock observations reproducing the specific dataset under
exam.


  Despite a rise in the use of "learning by doing" pedagogical methods in
praxis, little is known as to how these methods improve learning outcomes. Here
we show that visual association cortex causally contributes to performance
benefits of a learning by doing method. This finding derives from transcranial
magnetic stimulation (TMS) and a gesture-enriched foreign language (L2)
vocabulary learning paradigm performed by 22 young adults. Inhibitory TMS of
visual motion cortex reduced learning outcomes for abstract and concrete
gesture-enriched words in comparison to sham stimulation. There were no TMS
effects on words learned with pictures. These results adjudicate between
opposing predictions of two neuroscientific learning theories: While
reactivation-based theories predict no functional role of visual motion cortex
in vocabulary learning outcomes, the current study supports the predictive
coding theory view that specialized sensory cortices precipitate
sensorimotor-based learning benefits.


  This article describes a search for low-mass axion-like particles (ALPs) at
the Large Hadron Collider (LHC). If ALPs were produced at the LHC via
gluon-gluon fusion and decayed to bosons, the energy dependence of the measured
diboson cross-sections would differ from the Standard Model expectation.
Measurements of $WW$ and $Z\gamma$ differential cross-sections by the ATLAS
collaboration are interpreted to constrain ALP couplings to $W$-, $Z$-bosons
and photons assuming gluon-gluon-fusion production.


  Achieving a truly accurate wavelength calibration of high-dispersion echelle
spectrographs is a challenging task but crucially needed for certain science
cases, e.g. to test for a possible variation of the fine-structure constant in
quasar spectra. One of the spectrographs best suited for this mission is
VLT/ESPRESSO. Nevertheless, previous studies have identified significant
discrepancies between the classical wavelength solutions and the one derived
independently from the laser frequency comb. The dominant parts of these
systematics were intra-order distortions, most-likely related to a deviation of
the instrumental line-spread function from the assumed Gaussian shape. Here, we
therefore present a study focused on a detailed modeling of the ESPRESSO
instrumental line-spread function. We demonstrate that it is strongly
asymmetric, non-Gaussian, different for the two slices and fibers, and varies
significantly along the spectral orders. Incorporating the determined
non-parametric model in the wavelength calibration process drastically improves
the wavelength calibration accuracy, reducing the discrepancies between the two
independent wavelength solutions from 50m/s to about 10m/s. The most striking
success is, however, that the different fibers and slices now provide fully
consistent measurements with a scatter of just a couple m/s. This demonstrates
that the instrument-related systematics can be nearly eliminated over most of
the spectral range by properly taking into account the complex shape of the
instrumental line-spread function and paves the way for further optimizations
of the wavelength calibration process.


  The design of the precoder the maximizes the mutual information in linear
vector Gaussian channels with an arbitrary input distribution is studied.
Precisely, the precoder optimal left singular vectors and singular values are
derived. The characterization of the right singular vectors is left, in
general, as an open problem whose computational complexity is then studied in
three cases: Gaussian signaling, low SNR, and high SNR. For the Gaussian
signaling case and the low SNR regime, the dependence of the mutual information
on the right singular vectors vanishes, making the optimal precoder design
problem easy to solve. In the high SNR regime, however, the dependence on the
right singular vectors cannot be avoided and we show the difficulty of
computing the optimal precoder through an NP-hardness analysis.


  Chiral molecules show differences in their chemical and optical properties
due to different spatial arrangements of the atoms in the two enantiomers. A
common way to optically differentiate them is to detect the disparity in the
absorption of light by the two enantiomers, i.e. the absorption circular
dichroism (CD). However, the CD of typical molecules is very small, limiting
the sensitivity of chiroptical analysis based on CD. Cavity ring-down
spectroscopy (CRDS) is a well-known ultrasensitive absorption spectroscopic
method for low-absorbing gas-phase samples because the multiple reflections of
light in the cavity greatly increase the absorption path. By inserting a prism
into the cavity, the optical mode undergoes total internal reflection (TIR) at
the prism surface and the evanescent wave (EW) enables the absorption detection
of condensed-phase samples within a very thin layer near the prism surface,
called EW-CRDS. Here, we propose an ultrasensitive chiral absorption
spectroscopy platform using a dielectric metasurface-assisted EW-CRDS. We
theoretically show that, upon linearly polarized and oblique incidence, the
metasurface exhibits minimum scattering and absorption loss, introduces
negligible polarization change, and locally converts the linearly polarized
light into near fields with finite optical chirality, enabling CD detection
with EW-CRDS that only works with linearly polarized light. We evaluate the
ring-down time in the presence of chiral molecules and determine the
sensitivity of the cavity as a function of total absorption from the molecules.
The findings open the avenue for an ultrasensitive thin film detection of the
chiral molecules using the CRDS techniques.


  A number of irregular moons of the Jovian planets have recently been
discovered. Most adequate way of their origin is capture, but detailed
mechanism is unknown. A few possibilities are discussed: collisions, gas drag,
tidal destruction of a binary asteroid. In this paper the capture process in
restricted four body problem (RFBP) is researched. The interaction with regular
satellite can be studied by this way as well as binary asteroid destruction in
Hill sphere of planet. The energetic criteria of ballistic capture are studied
and some numerical experiments are developed. It is confirmed, that capture in
four body problem is more probable on retrograde than on prograde orbit. In
according with our results, encounter with regular satellites is more effective
mechanism to create an irregular satellite population, than a binary asteroid
flyby.


  Suppose that $\mathcal{A}$ is an abelian category whose derived category
$\mathcal{D}(\mathcal{A})$ has $Hom$ sets and arbitrary (small) coproducts, let
$T$ be a (not necessarily classical) ($n$-)tilting object of $\mathcal{A}$ and
let $\mathcal{H}$ be the heart of the associated t-structure on
$\mathcal{D}(\mathcal{A})$. We show that the inclusion functor
$\mathcal{H}\hookrightarrow\mathcal{D}(\mathcal{A})$ extends to a triangulated
equivalence of unbounded derived categories
$\mathcal{D}(\mathcal{H})\stackrel{\cong}{\longrightarrow}\mathcal{D}(\mathcal{A})$.
The result admits a straightforward dualization to cotilting objects in abelian
categories whose derived category has $Hom$ sets and arbitrary products.


  We present the optical imaging polarimetric observations of comet C/2009
P1(Garradd) at three different phase angles e.g., 28.2$^\circ$, 28.1$^\circ$
and 21.6$^\circ$. The observations were carried out using IUCAA Faint Object
Spectrograph and Camera mounted on Cassegrain focus of the 2-m Telescope of
IGO, IUCAA, Pune in R$_{comet}$, R photometric bands, on 21 and 22 March 2012
and ARIES Imaging Polarimeter mounted on Cassegrain focus of the 1.04-m
Sampurnanand Telescope of ARIES, Nainital in R photometric band, on 23 May
2012. We show the presence of a jet activity in the rotational gradient treated
image of comet Garradd at phase angle 28.1$^\circ$. These jets are mainly
oriented towards the Sun and extended up to $\sim$ 5100 km from the cometary
photocenter. The antisolar extension of jet seems to be fainter, which is
extended up to $\sim$ 1800 km. It is found that the comet Garradd shows
negative polarization at phase angle 21.6$^\circ$. The degree of polarization
derived for Garradd is in good agreement with other comets at nearly similar
phase angles e.g., comets 67P/Churyumov-Gerasimenko, 22P/Kopff, 1P/Halley,
C/1990 K1 (Levy), 4P/Faye, and C/1995 O1 (Hale-Bopp) at phase angle $\sim$
28$^\circ$, and 47P/Ashbrook-Jackson at phase angle $\sim$ 21.6$^\circ$,
respectively. It is also found that the degree of polarization of dusty coma of
comet Garradd at phase angle $\sim$ 28$^\circ$ is high but, not as high as in
the case of comet Hale-Bopp.


  The paper investigates a hypothesis that our visual system groups visual cues
based on how they form a surface, or more specifically triangulation derived
from the visual cues. To test our hypothesis, we compare shape recognition with
three different representations of visual cues: a set of isolated dots
delineating the outline of the shape, a set of triangles obtained from Delaunay
triangulation of the set of dots, and a subset of Delaunay triangles excluding
those outside of the shape. Each participant was assigned to one particular
representation type and increased the number of dots (and consequentially
triangles) until the underlying shape could be identified. We compare the
average number of dots needed for identification among three types of
representations. Our hypothesis predicts that the results from the three
representations will be similar. However, they show statistically significant
differences. The paper also presents triangulation based algorithms for
reconstruction and recognition of a shape from a set of isolated dots.
Experiments showed that the algorithms were more effective and perceptually
agreeable than similar contour based ones. From these experiments, we conclude
that triangulation does affect our shape recognition. However, the surface
based approach presents a number of computational advantages over the contour
based one and should be studied further.


  Language models (LMs) are known to suffer from forgetting of previously
learned examples when fine-tuned, breaking stability of deployed LM systems.
Despite efforts on mitigating forgetting, few have investigated whether, and
how forgotten upstream examples are associated with newly learned tasks.
Insights on such associations enable efficient and targeted mitigation of
forgetting. In this paper, we empirically analyze forgetting that occurs in $N$
upstream examples while the model learns $M$ new tasks and visualize their
associations with a $M \times N$ matrix. We empirically demonstrate that the
degree of forgetting can often be approximated by simple multiplicative
contributions of the upstream examples and newly learned tasks. We also reveal
more complicated patterns where specific subsets of examples are forgotten with
statistics and visualization. Following our analysis, we predict forgetting
that happens on upstream examples when learning a new task with matrix
completion over the empirical associations, outperforming prior approaches that
rely on trainable LMs. Project website:
https://inklab.usc.edu/lm-forgetting-prediction/


  DG Tau A, a class-II young stellar object (YSO) displays both thermal, and
non-thermal, radio emission associated with its bipolar jet. To investigate the
nature of this emission, we present sensitive ($\sigma\sim2\,\mu {\rm Jy
\,beam^{-1}}$), Karl G.\ Jansky Very Large Array (VLA) $6$ and $10\,{\rm GHz}$
observations. Over $3.81\,{\rm yr}$, no proper motion is observed towards the
non-thermal radio knot C, previously thought to be a bowshock. Its quasi-static
nature, spatially-resolved variability and offset from the central jet axis
supports a scenario whereby it is instead a stationary shock driven into the
surrounding medium by the jet. Towards the internal working surface, knot A, we
derive an inclination-corrected, absolute velocity of $258\pm23\, {\rm
km\,s^{-1}}$. DG Tau A's receding counterjet displays a spatially-resolved
increase in flux density, indicating a variable mass loss event, the first time
such an event has been observed in the counterjet. For this ejection, we
measure an ionised mass loss rate of $(3.7\pm1.0) \times 10^{-8}\, {\rm
M_\odot\, yr^{-1}}$ during the event. A contemporaneous ejection in the
approaching jet isn't seen, showing it to be an asymmetric process. Finally,
using radiative transfer modelling, we find that the extent of the radio
emission can only be explained with the presence of shocks, and therefore
reionisation, in the flow. Our modelling highlights the need to consider the
relative angular size of optically thick, and thin, radio emission from a jet,
to the synthesised beam, when deriving its physical conditions from its
spectral index.


  We show that the biomolecular exciton dynamics under the influence of slow
polarization fluctuations in the solvent cannot be described by approaches
which are perturbative in the system-bath coupling. For this, we compare
results for the decoherence rate of the exciton dynamics of a resumed
perturbation theory with numerically exact real-time path-integral results. We
find up to one order in magnitude difference in the decoherence rate for
realistically slow solvent environments even in the weak coupling regime, while
both results coincide for fast environmental noise. This shows explicitely the
nonperturbative influence of the bioenvironmental fluctuations and might render
current perturbative approaches to biomolecular exciton transport questionable.


  There remains an open question about the usefulness and the interpretation of
Machine learning (MLE) approaches for discrimination of spatial patterns of
brain images between samples or activation states. In the last few decades,
these approaches have limited their operation to feature extraction and linear
classification tasks for between-group inference. In this context, statistical
inference is assessed by randomly permuting image labels or by the use of
random effect models that consider between-subject variability. These
multivariate MLE-based statistical pipelines, whilst potentially more effective
for detecting activations than hypotheses-driven methods, have lost their
mathematical elegance, ease of interpretation, and spatial localization of the
ubiquitous General linear Model (GLM). Recently, the estimation of the
conventional GLM has been demonstrated to be connected to an univariate
classification task when the design matrix is expressed as a binary indicator
matrix. In this paper we explore the complete connection between the univariate
GLM and MLE \emph{regressions}. To this purpose we derive a refined statistical
test with the GLM based on the parameters obtained by a linear Support Vector
Regression (SVR) in the \emph{inverse} problem (SVR-iGLM). Subsequently, random
field theory (RFT) is employed for assessing statistical significance following
a conventional GLM benchmark. Experimental results demonstrate how parameter
estimations derived from each model (mainly GLM and SVR) result in different
experimental design estimates that are significantly related to the predefined
functional task. Moreover, using real data from a multisite initiative the
proposed MLE-based inference demonstrates statistical power and the control of
false positives, outperforming the regular GLM.


  Any two $(g; 0, 0, k)$-trisected closed 4-manifolds are related to each other
by cutting and regluing in regard to $\gamma$ curves by an element of the
Torelli group. Moreover, Lambert-Cole showed that this element can be replaced
by an element of the Johnson kernel. We consider similar situation in the
relative case. Furthermore, we relate this situation to cork twist.


  In this paper, we study the heat kernel associated to the intrinsic
sublaplacian on a quaternionic contact manifold considered as a subriemannian
manifold. More precisely, we explicitly compute the first two coefficients
$c_0$ and $c_1$ appearing in the small time asymptotics expansion of the heat
kernel on the diagonal. We show that the second coefficient $c_1$ depends
linearly on the qc scalar curvature $\kappa$. Finally we apply our results to
compact qc-Einstein manifolds and prove the spectral invariance of geometric
quantities in the subriemannian setting.


  In this article we discuss the application of the Heath-Jarrow-Morton
framework Heath et al. [26] to energy markets. The goal of the article is to
give a detailed overview of the topic, focusing on practical aspects rather
than on theory, which has been widely studied in literature. This work aims to
be a guide for practitioners and for all those who deal with the practical
issues of this approach to energy market. In particular, we focus on the
markets' structure, model calibration by dimension reduction with Principal
Component Analysis (PCA), Monte Carlo simulations and derivatives pricing. As
application, we focus on European power and gas markets: we calibrate the model
on historical futures quotations, we perform futures and spot simulations and
we analyze the results.


  We analyse dependence of the partition function on the boundary condition for
the longitudinal component of the electric field strength in gauge field
theories. In a physical gauge the Gauss law constraint may be resolved
explicitly expressing this component via an integral of the physical
transversal variables. In particular, we study quantum electrodynamics with an
external charge and SU(2) gluodynamics. We find that only a charge distribution
slowly decreasing at spatial infinity can produce a nontrivial dependence in
the Abelian theory. However, in gluodynamics for temperatures below some
critical value the partition function acquires a delta-function like dependence
on the boundary condition, which leads to colour confinement.


  Flow equations describe the evolution of the effective action $\Gamma_k$ in
the process of varying an infrared cutoff $k$. The presence of the infrared
cutoff explicitly breaks gauge and hence BRS invariance. We derive modified
Slavnov-Taylor identities, which are valid for nonvanishing $k$. They guarantee
the BRS invariance of $\Gamma_k$ for $k\to0$, and hence allow the study of
non-abelian gauge theories by integrating the flow equations. Within a
perturbative expansion of $\Gamma_k$, we derive an equation for a $k$ dependent
mass term for the gauge fields implied by the modified Slavnov-Taylor
identities.


  We study the possibility of using machine learning for the calculation of the
bounce action in quantum tunneling. Adopting supervised learning, we train
neural network to give the bounce action from a given potential. It is found
that, for one-dimensional tunneling, even a simple neural network performs at a
percent level.


  Can we analyze data without decompressing it? As our data keeps growing,
understanding the time complexity of problems on compressed inputs, rather than
in convenient uncompressed forms, becomes more and more relevant. Suppose we
are given a compression of size $n$ of data that originally has size $N$, and
we want to solve a problem with time complexity $T(\cdot)$. The naive strategy
of "decompress-and-solve" gives time $T(N)$, whereas "the gold standard" is
time $T(n)$: to analyze the compression as efficiently as if the original data
was small.
  We restrict our attention to data in the form of a string (text, files,
genomes, etc.) and study the most ubiquitous tasks. While the challenge might
seem to depend heavily on the specific compression scheme, most methods of
practical relevance (Lempel-Ziv-family, dictionary methods, and others) can be
unified under the elegant notion of Grammar Compressions. A vast literature,
across many disciplines, established this as an influential notion for
Algorithm design.
  We introduce a framework for proving (conditional) lower bounds in this
field, allowing us to assess whether decompress-and-solve can be improved, and
by how much. Our main results are:
  - The $O(nN\sqrt{\log{N/n}})$ bound for LCS and the $O(\min\{N \log N, nM\})$
bound for Pattern Matching with Wildcards are optimal up to $N^{o(1)}$ factors,
under the Strong Exponential Time Hypothesis. (Here, $M$ denotes the
uncompressed length of the compressed pattern.)
  - Decompress-and-solve is essentially optimal for Context-Free Grammar
Parsing and RNA Folding, under the $k$-Clique conjecture.
  - We give an algorithm showing that decompress-and-solve is not optimal for
Disjointness.


  ULYSSES is a python package that calculates the baryon asymmetry produced
from leptogenesis in the context of a type-I seesaw mechanism. The code solves
the semi-classical Boltzmann equations for points in the model parameter space
as specified by the user. We provide a selection of predefined Boltzmann
equations as well as a plugin mechanism for externally provided models of
leptogenesis. Furthermore, the ULYSSES code provides tools for
multi-dimensional parameter space exploration. The emphasis of the code is on
user flexibility and rapid evaluation. It is publicly available at
https://github.com/earlyuniverse/ulysses


  Susceptibility of neural networks to adversarial attack prompts serious
safety concerns for lane detection efforts, a domain where such models have
been widely applied. Recent work on adversarial road patches have successfully
induced perception of lane lines with arbitrary form, presenting an avenue for
rogue control of vehicle behavior. In this paper, we propose a modular lane
verification system that can catch such threats before the autonomous driving
system is misled while remaining agnostic to the particular lane detection
model. Our experiments show that implementing the system with a simple
convolutional neural network (CNN) can defend against a wide gamut of attacks
on lane detection models. With a 10% impact to inference time, we can detect
96% of bounded non-adaptive attacks, 90% of bounded adaptive attacks, and 98%
of patch attacks while preserving accurate identification at least 95% of true
lanes, indicating that our proposed verification system is effective at
mitigating lane detection security risks with minimal overhead.


  Sliding window approaches have been widely used for object recognition tasks
in recent years. They guarantee an investigation of the entire input image for
the object to be detected and allow a localization of that object. Despite the
current trend towards deep neural networks, sliding window methods are still
used in combination with convolutional neural networks. The risk of overlooking
an object is clearly reduced compared to alternative detection approaches which
detect objects based on shape, edges or color. Nevertheless, the sliding window
technique strongly increases the computational effort as the classifier has to
verify a large number of object candidates. This paper proposes a sliding
window approach which also uses depth information from a stereo camera. This
leads to a greatly decreased number of object candidates without significantly
reducing the detection accuracy. A theoretical investigation of the
conventional sliding window approach is presented first. Other publications to
date only mentioned rough estimations of the computational cost. A mathematical
derivation clarifies the number of object candidates with respect to parameters
such as image and object size. Subsequently, the proposed disparity sliding
window approach is presented in detail. The approach is evaluated on pedestrian
detection with annotations and images from the KITTI object detection
benchmark. Furthermore, a comparison with two state-of-the-art methods is made.
Code is available in C++ and Python https://github.com/julimueller/
disparity-sliding-window.


  In this paper, we investigate the Beris-Edwards system for both biaxial and
uniaxial $Q$-tensors with a general Landau-de Gennes energy density depending
on four non-zero elastic constants. We prove existence of the strong solution
of the Beris-Edwards system for uniaxial $Q$-tensors up to a maximal time.
Furthermore, we prove that the strong solutions of the Beris-Edwards system for
biaxial $Q$-tensors converge smoothly to the solution of the Beris-Edwards
system for uniaxial $Q$-tensors up to its maximal existence time.


  We consider a cosmological model with an interaction between dark matter and
dark energy which leaves the background cosmology unaffected and only affects
the evolution of the perturbations. This is achieved by introducing a coupling
given in terms of the relative velocities of dark matter and dark energy. This
interaction has the distinctive feature of appearing predominantly on small
scales, where peculiar velocities can become important. We confront the
predictions of the model to cosmological observations and find a potential
alleviation of the known tension in the amplitude of density perturbations as
measured by low redshift galaxy surveys and the Planck data. The model also
predicts a shift in the turnover of the matter power spectrum which does not
depend on the horizon at equality (fixed by the background cosmology and, thus,
unaffected by the perturbations) and is entirely due to the interaction between
dark matter and dark energy. A bias in the peculiar velocity between baryons
and dark matter is also shown to be a unique feature of this type of
interactions in the dark sector.


  A new primary torque control concept for hydrostatics mobile machines was
introduced in 2018. The mentioned concept controls the pressure in a closed
circuit by changing the angle of the hydraulic pump to achieve the desired
pressure based on a feedback system. Thanks to this concept, a series of
advantages are expected. However, while working in a Y cycle, the primary
torque-controlled wheel loader has worse performance in efficiency compared to
secondary controlled earthmover due to lack of recuperation ability.
Alternatively, we use deep learning algorithms to improve machines'
regeneration performance. In this paper, we firstly make a potential analysis
to show the benefit by utilizing the regeneration process, followed by
proposing a series of CRDNNs, which combine CNN, RNN, and DNN, to precisely
detect Y cycles. Compared to existing algorithms, the CRDNN with bi-directional
LSTMs has the best accuracy, and the CRDNN with LSTMs has a comparable
performance but much fewer training parameters. Based on our dataset including
119 truck loading cycles, our best neural network shows a 98.2% test accuracy.
Therefore, even with a simple regeneration process, our algorithm can improve
the holistic efficiency of mobile machines up to 9% during Y cycle processes if
primary torque concept is used.


  The B modes generated by the lensing of CMB polarization are a primary target
for the upcoming generation of experiments and can potentially constrain
quantities such as the neutrino mass and dark energy equation of state. The net
sample variance on the small scale B modes out to l=2000 exceeds Gaussian
expectations by a factor of 10 reflecting the variance of the larger scale
lenses that generate them. It manifests itself as highly correlated band powers
with correlation coefficients approaching 70% for wide bands of Delta l/l
\~0.25. It will double the total variance for experiments that achieve a
sensitivity of approximately 4 uK-arcmin and a beam of several arcminutes or
better. This non-Gaussianity must be taken into account in the analysis of
experiments that go beyond first detection.


  In order to construct quantum $[[n,0,d]]$ codes for $(n,d)=(56,15)$,
$(57,15)$, $(58,16)$, $(63,16)$, $(67,17)$, $(70,18)$, $(71,18)$, $(79,19)$,
$(83,20)$, $(87,20)$, $(89,21)$, $(95,20)$, we construct self-dual additive
$\mathbb{F}_4$-codes of length $n$ and minimum weight $d$ from circulant
graphs. The quantum codes with these parameters are constructed for the first
time.


  Many machine learning problems can be formulated as consensus optimization
problems which can be solved efficiently via a cooperative multi-agent system.
However, the agents in the system can be unreliable due to a variety of
reasons: noise, faults and attacks. Providing erroneous updates leads the
optimization process in a wrong direction, and degrades the performance of
distributed machine learning algorithms. This paper considers the problem of
decentralized learning using ADMM in the presence of unreliable agents. First,
we rigorously analyze the effect of erroneous updates (in ADMM learning
iterations) on the convergence behavior of multi-agent system. We show that the
algorithm linearly converges to a neighborhood of the optimal solution under
certain conditions and characterize the neighborhood size analytically. Next,
we provide guidelines for network design to achieve a faster convergence. We
also provide conditions on the erroneous updates for exact convergence to the
optimal solution. Finally, to mitigate the influence of unreliable agents, we
propose \textsf{ROAD}, a robust variant of ADMM, and show its resilience to
unreliable agents with an exact convergence to the optimum.


  We supplement a very recent paper of G. Markowsky concerned with the expected
exit times of Brownian motion from planar domains. Besides the use of conformal
mapping, we apply results from potential theory. We treat the case of a
wedge-shaped region exactly, subsuming an example of Markowsky. A number of
other results are presented, including for a half disc, $n$-grams, and a
variety of other regions.


  We revisit the issues on the thermodynamic property of stellar
self-gravitating system arising from Tsallis' non-extensive entropy. Previous
papers (Taruya & Sakagami, Physica A 307 (2002) 185 (cond-mat/0107494); ibid.
(2002) in press (cond-mat/0204315)) have revealed that the extremum-state of
Tsallis entropy characterized by the so-called stellar polytrope has consistent
thermodynamic structure, which predicts the thermodynamic instability due to
the negative specific heat. However, their analyses heavily relies on the old
Tsallis formalism using standard linear mean values. In this paper, extending
our previous study, we focus on the equilibrium structure based on the standard
framework by means of the normalized q-expectation values. It then turns out
that the new extremum-state of Tsallis entropy essentially remains unchanged
from the previous result, i.e., the stellar quasi-equilibrium distribution can
be described by the stellar polytrope. While the thermodynamic stability for a
system confined in an adiabatic wall completely agrees with the previous study
and thereby the stability/instability criterion remains unchanged, the
stability analysis reveals a new equilibrium property for the system surrounded
by a thermal bath. In any case, the stability/instability criteria are
consistently explained from the presence of negative specific heat and within
the formalism, the stellar polytrope is characterized as a plausible
non-extensive meta-equilibrium state.


  Gradients are natural first order differential operators depending on
Riemannian metrics. The principal symbols of them are related to the enveloping
algebra and higher Casimir elements. We give certain relations in the
enveloping algebra, which induce not only identities for higher Casimir
elements but also all Bochner-Weitzenb\"ock formulas for gradients. As
applications, we give some vanishing theorems.


  We study various moduli spaces of local Shtukas in the setting of Fargues'
program for $GL_n$. In certain cases, this gives us an explicit description of
the spectral action which was recently introduced by Fargues and Scholze. This
description sheds light to the categorical local Langlands program for $GL_n$
and allows us to construct Hecke eigensheaves associated to certain $\ell$-adic
Weil representations of rank $n$ and to prove some parts of Fargues'
conjecture. Moreover, by using this description, we can prove new cases of the
Harris-Viehmann conjecture for non-basic Rapoport-Zink spaces and compute some
parts of the cohomology of the Igusa varieties associated to $GL_n$.


  The phase diagram of Ca$_{2-x}$Sr$_x$RuO$_4$ has been studied by neutron
diffraction on powder and single-crystalline samples. The experiments reveal
antiferromagnetic order and structural distortions characterized by tilts and
rotations of the RuO$_6$-octahedra. There is strong evidence that the
structural details of the isovalent samples tune the magnetic as well as the
electronic behavior. In particular we observe for low Sr-concentration a metal
insulator transition associated with a structural change and magnetic ordering.


  Interactions between quasiparticles mediated by a surrounding environment are
ubiquitous and lead to a range of important effects from collective modes of
low temperature quantum gases, superconductivity, to the interaction between
elementary particles at high energies. This perspective article is motivated by
experimental progress in the fields of quantum degenerate atomic gases, cavity
QED, and two-dimensional (2D) semi-conductors, which enable a systematic
exploration of mediated interactions in new settings and regimes. We first
describe how to microscopically calculate the quasiparticle interaction using
perturbation theory, diagrammatics, and the path integral, highlighting the key
role played by the quantum statistics of the quasiparticles. Recent theoretical
and experimental insights into quasiparticle and mediated interactions in
general obtained from atomic gases are then discussed, after which we focus on
hybrid light-atom systems where a remarkable long range photon mediated
interaction can be realised. Next, we describe new and puzzling results
regarding the interaction between quasiparticles in 2D semiconductors. We then
discuss how mediated interactions open up ways to realise new quantum phases in
atomic and hybrid atom-photon systems as well as 2D semiconductors, and the
perspective ends by posing some open questions and outlook.


  Under unitary time evolution, expectation values of physically reasonable
observables often evolve towards the predictions of equilibrium statistical
mechanics. The eigenstate thermalization hypothesis (ETH) states that this is
also true already for individual energy eigenstates. Here we aim at elucidating
the emergence of ETH for observables that can realistically be measured due to
their high degeneracy, such as local, extensive or macroscopic observables. We
bisect this problem into two parts, a condition on the relative overlaps and
one on the relative phases between the eigenbases of the observable and
Hamiltonian.


  We present a search for a new particle $T'$ decaying to top quark via
$T^{\prime} \rightarrow t + X$, where $X$ is an invisible particle. In a data
sample with 4.8 fb$^{-1}$ of integrated luminosity collected by the CDF II
detector at Fermilab in $p\bar{p}$ collisions with $\sqrt{s}=1.96$ TeV, we
search for pair production of $T'$ in the lepton+jets channel, $p\bar{p}
\rightarrow t\bar{t} +X+X \rightarrow \ell\nu b q q' b + X +X$. We interpret
our results primarily in terms of a model where $T'$ are exotic fourth
generation quarks and $X$ are dark matter particles. Current direct and
indirect bounds on such exotic quarks restrict their masses to be between 300
and 600 GeV$/c^2$, the dark matter particle mass being anywhere below
$m_{T^{\prime}}$. The data are consistent with standard model expectations, and
we set 95% confidence level limits on the generic production of $T'\bar{T'}
\rightarrow t\bar{t}+X+X$. We apply these limits to the dark matter model and
exclude the fourth generation exotic quarks $T'$ at 95% confidence level up to
$m_{T'}=360$ GeV$/c^{2}$ for $m_X\le 100$ GeV$/c^2$.


  We address the issue of fermionic particle creation in cosmological bouncing
models governed by General Relativity, but where the bounce itself takes place
due to quantum effects. If the energy scale of the bounce is not very close to
the Planck energy, the Wheeler-DeWitt approach can be used to furnish sensible
singularity-free background models with a contracting phase preceding an
expanding phase in which the fermions evolve. The fermionic fields considered
are massive, neutral and minimally coupled to gravity. We are particularly
interested in neutrinos, neutrons and heavy neutrinos motivated by theories
beyond the Standard Model of Particle Physics. We perform a numerical analysis
for a bouncing model containing radiation and a pressureless fluid. The results
reveal that the fermionic production is very small, with no back-reaction,
unless the fermions are very heavy with masses up to $10^9$GeV. Hence,
investigations concerning baryogenesis in such bouncing models should either go
beyond the minimal coupling between gravity and the fermionic fields considered
here, or assume the existence of such heavy fermions as a starting point.


  Nano-fabrication in silicon, arguably the most important material for modern
technology, has been limited exclusively to its surface. Existing lithographic
methods cannot penetrate the wafer surface without altering it, whereas
emerging laser-based subsurface or in-chip fabrication remains at > 1
micrometer resolution. In addition, available methods do not allow positioning
or modulation with sub-micron precision deep inside the wafer. The fundamental
difficulty of breaking these dimensional barriers are two-fold, i.e., complex
nonlinear effects inside the wafer and the inherent diffraction limit for laser
light. Here, we overcome these challenges by using structured-laser-beams and
exploiting preformed subsurface structures as seed, in order to establish the
first controlled nano-fabrication capability inside silicon. We demonstrate
buried nano-structures of feature sizes down to 100 nm +- 25 nm, with
sub-wavelength and multi-dimensional control; thereby improving the
state-of-the-art by an order-of-magnitude. In order to showcase the photonic
capabilities, we fabricated the first in-chip nano-photonic elements, i.e.,
nano-gratings with record diffraction efficiency and spectral control. The
reported advance is an important step towards in-chip nano-photonic systems,
micro-/nano-fluidics & NEMS/MEMS, and 3D electronic-photonic integration.


  In serine proteases (SP's), the H-bond between His-57 and Asp-102, and that
between Gly-193 and the transition state intermediate play a crucial role for
enzymatic function. To shed light on the nature of these interactions, we have
carried out ab initio molecular dynamics simulations on complexes representing
adducts between the reaction intermediate and elastase (one protein belonging
to the SP family). Our calculations indicate the presence of a low--barrier
H-bond between His-57 and Asp-102, in complete agreement with NMR experiments
on enzyme--transition state analog complexes. Comparison with an ab initio
molecular dynamics simulation on a model of the substrate--enzyme adduct
indicates that the Gly-193--induced strong stabilization of the intermediate is
accomplished by charge/dipole interactions and not by H-bonding as previously
suggested. Inclusion of the protein electric field in the calculations does not
affect significantly the charge distribution.


  Let F be a 4-regular graph with an Euler system C. We introduce a simple way
to modify the interlacement matrix of C so that every circuit partition P of F
has an associated modified interlacement matrix M(C,P). If C and C' are Euler
systems of F then M(C,C') and M(C',C) are inverses, and for any circuit
partition P, M(C',P)=M(C',C)M(C,P). This machinery allows for short proofs of
several results regarding the linear algebra of interlacement.


  We construct a noncommutative Cartan calculus on any braided commutative
algebra and study its applications in noncommutative geometry. The braided Lie
derivative, insertion and de Rham differential are introduced and related via
graded braided commutators, also incorporating the braided Schouten-Nijenhuis
bracket. The resulting braided Cartan calculus generalizes the Cartan calculus
on smooth manifolds and the twisted Cartan calculus. While it is a necessity of
derivation based Cartan calculi on noncommutative algebras to employ central
bimodules our approach allows to consider bimodules over the full underlying
algebra. Furthermore, equivariant covariant derivatives and metrics on braided
commutative algebras are discussed. In particular, we prove the existence and
uniqueness of an equivariant Levi-Civita covariant derivative for any fixed
non-degenerate equivariant metric. Operating in a symmetric braided monoidal
category we argue that Drinfel'd twist deformation corresponds to gauge
equivalences of braided Cartan calculi. The notions of equivariant covariant
derivative and metric are compatible with the Drinfel'd functor as well.
Moreover, we project braided Cartan calculi to submanifold algebras and prove
that this process commutes with twist deformation.


  The colored Jones polynomial is a series of one variable Laurent polynomials
J(K,n) associated with a knot K in 3-space. We will show that for an
alternating knot K the absolute values of the first and the last three leading
coefficients of J(K,n) are independent of n when n is sufficiently large.
Computation of sample knots indicates that this should be true for any fixed
leading coefficient of the colored Jones polynomial for alternating knots. As a
corollary we get a Volume-ish Theorem for the colored Jones Polynomial.


  A widely considered characteristic of extra-solar planetary systems has been
a seeming tendency for major axes of adjacent orbits to librate in stable
configurations. Based on a new catalog of extra-solar planets (Butler et al.
2006) and our numerical integrations, we find that such small amplitude
oscillations are actually not common, but in fact quite rare; most pairs of
planets' major axes a re consistenet with circulating relative to one another.
However, the new result s are consistent with studies that find that two-planet
systems tend to lie near a separatrix between libration and circulation.
Similarly, in systems of more than two planets, many adjacent orbits lie near a
separatrix that divides modes of circulation.


  We prove the discrete Lusternik-Schnirelmann theorem telling that tcat(G)
less or equal to crit(G) for a general simple graph G=(V,E). It relates the
minimal number tcat(G) of in G contractible graphs covering G, with crit(G),
the minimal number of critical points which an injective function f on the
vertex set V can have. We also prove that the cup length cup(G) is less or
equal to tcat(G) which is valid also for any finite simple graph. If cat(G) is
the minimal tcat(H) among all graphs H homotopic to G and cri(G) is the minimal
crit(H) among all graphs H homotopic to G, we get a relation between three
homotopy invariants: an algebraic quantity (cup), a topological quantity (cat)
and an analytic quantity (cri).


  The biomaterials exploitation in a sophisticated manner can provide extensive
opportunities for experimentation in the field of interdisciplinary and
multidisciplinary scientific research. Owing to the unique features of this
trendy area, research scientists have been directed/redirected their interests
in bio-based biomaterials for targeted applications in different sectors of the
modern world. The present manuscript highlights the novel perspectives of
biomaterials as a trendy source to engineer functional entities in numerous
geometries for pharmaceuticals, cosmeceuticals, nutraceuticals, and other
biotechnological or biomedical applications.


  This paper presents a collection of highly comparable web corpora of
Slovenian, Croatian, Bosnian, Montenegrin, Serbian, Macedonian, and Bulgarian,
covering thereby the whole spectrum of official languages in the South Slavic
language space. The collection of these corpora comprises a total of 13 billion
tokens of texts from 26 million documents. The comparability of the corpora is
ensured by a comparable crawling setup and the usage of identical crawling and
post-processing technology. All the corpora were linguistically annotated with
the state-of-the-art CLASSLA-Stanza linguistic processing pipeline, and
enriched with document-level genre information via the Transformer-based
multilingual X-GENRE classifier, which further enhances comparability at the
level of linguistic annotation and metadata enrichment. The genre-focused
analysis of the resulting corpora shows a rather consistent distribution of
genres throughout the seven corpora, with variations in the most prominent
genre categories being well-explained by the economic strength of each language
community. A comparison of the distribution of genre categories across the
corpora indicates that web corpora from less developed countries primarily
consist of news articles. Conversely, web corpora from economically more
developed countries exhibit a smaller proportion of news content, with a
greater presence of promotional and opinionated texts.


  Diffusion models have marked a significant breakthrough in the synthesis of
semantically coherent images. However, their extensive noise estimation
networks and the iterative generation process limit their wider application,
particularly on resource-constrained platforms like mobile devices. Existing
post-training quantization (PTQ) methods have managed to compress diffusion
models to low precision. Nevertheless, due to the iterative nature of diffusion
models, quantization errors tend to accumulate throughout the generation
process. This accumulation of error becomes particularly problematic in
low-precision scenarios, leading to significant distortions in the generated
images. We attribute this accumulation issue to two main causes: error
propagation and exposure bias. To address these problems, we propose a
timestep-aware correction method for quantized diffusion model, which
dynamically corrects the quantization error. By leveraging the proposed method
in low-precision diffusion models, substantial enhancement of output quality
could be achieved with only negligible computation overhead. Extensive
experiments underscore our method's effectiveness and generalizability. By
employing the proposed correction strategy, we achieve state-of-the-art (SOTA)
results on low-precision models.


  Long-range exchange and correlation effects, responsible for the failure of
currently used approximate density functionals in describing van der Waals
forces, are taken into account explicitly after a separation of the
electron-electron interaction in the Hamiltonian into short- and long-range
components. We propose a "range-separated hybrid" functional based on a local
density approximation for the short-range exchange-correlation energy, combined
with a long-range exact exchange energy. Long-range correlation effects are
added by a second-order perturbational treatment. The resulting scheme is
general and is particularly well-adapted to describe van der Waals complexes,
like rare gas dimers.


  The glassy behavior observed in the pyrochlore magnet Y2Mo2O7, where the
magnetic Mo^{4+} ions interact predominantly via isotropic nearest neighbor
antiferromagnetic exchange, possibly with additional weak disorder, is a
distinct class of spin glass systems where frustration is mostly geometrical. A
model proposed to describe such a spin glass behavior is the Heisenberg model
on a pyrochlore lattice with random but strictly antiferromagnetic exchange
disorder. In this paper, we provide compelling numerical evidence from
extensive Monte Carlo simulations which show that the model exhibits a finite
temperature spin glass transition and thus is a realization of a spin glass
induced by random weak disorder from spin liquid. From our results, we are led
to suggest that the spin glass state of Y2Mo2O7 is driven by effective strong
disorder.


  We study K-stability properties of a smooth Fano variety X using
non-Archimedean geometry, specifically the Berkovich analytification of X with
respect to the trivial absolute value on the ground field. More precisely, we
view K-semistability and uniform K-stability as conditions on the space of
plurisubharmonic (psh) metrics on the anticanonical bundle of X. Using the
non-Archimedean Calabi-Yau theorem and the Legendre transform, this allows us
to give a new proof that K-stability is equivalent to Ding stability. By
choosing suitable psh metrics, we also recover the valuative criterion of
K-stability by Fujita and Li. Finally, we study the asymptotic Fubini-Study
operator, which associates a psh metric to any graded filtration (or norm) on
the anticanonical ring. Our results hold for arbitrary smooth polarized
varieties, and suitable adjoint/twisted notions of K-stability and Ding
stability. They do not rely on the Minimal Model Program.


  The numerically exact evaluation of the van der Waals interaction, also known
as Casimir interaction when including retardation effects, constitutes a
challenging task. We present a new approach based on the plane-wave basis and
demonstrate that it possesses advantages over the more commonly used multipole
basis. The rotational symmetry of the plane-sphere and sphere-sphere geometries
can be exploited by means of a discrete Fourier transform. The new technique is
applied to a study of the interaction between a colloid particle made of
polystyrene or mercury and another polystyrene sphere or a polystyrene wall in
an aqueous solution. Special attention is paid to the influence of screening
caused by a variable salt concentration in the medium. It is found that in
particular for low salt concentrations the error implied by the proximity force
approximation is larger than usually assumed. For a mercury droplet, a
repulsive interaction is found for sufficiently large distances provided
screening is negligible. We emphasize that the effective Hamaker parameter
depends significantly on the scattering geometry on which it is based.


  The nonlinearities of current-voltage characteristics (IVC) of
superconducting niobium point contacts for voltages of the order of the
characteristic phonon frequencies are investigated. It is shown that in
limiting by dirty contacts ${{l}_{1}}\ll d$ , (${{l}_{1}}$ is the momentum
mean-free path of electrons), features are observed on the IVC corresponding to
maxima in the first or second derivatives and are situated at energies close to
the characteristic phonon energies. These features are due to the energy
dependence of the excess current, since the nonlinearity of the IVC in the
normal state is several times smaller in absolute magnitude, and its second
derivative is monotonic. For a contact diameter $d$ of the order of several
tens of angstroms (the contact resistance is of the order of 100 $\Omega $),
the thermal effects can be neglected. It is assumed that in the region of the
constriction, the characteristic lengths of the system satisfy the relation
$\xi \gtrsim d>{{\Lambda }_{\varepsilon }}$ (${{\Lambda }_{\varepsilon }}$ is
the diffusion length for energy relaxation of the electrons, $\xi$ is the
coherence length) which makes possible inelastic transitions of electrons
between pair chemical potentials differing in energy by an account $eV$ ($V$ is
the voltage drop across the contact)


  We present a complete analysis of K -> 3 pi gamma decays to O(p^4) in the
low-energy expansion of the Standard Model. We employ the notion of
"generalized bremsstrahlung" to take full advantage of experimental information
on the corresponding non-radiative K -> 3 pi decays.


  The data acquisition console is an important component of the EAST data
acquisition system which provides unified data acquisition and long-term data
storage for diagnostics. The data acquisition console is used to manage the
data acquisition configuration information and control the data acquisition
workflow. The data acquisition console has been developed many years, and with
increasing of data acquisition nodes and emergence of new control nodes, the
function of configuration management has become inadequate. It is going to
update the configuration management function of data acquisition console. The
upgraded data acquisition console based on LabVIEW should be oriented to the
data acquisition administrator, with the functions of managing data acquisition
nodes, managing control nodes, setting and publishing configuration parameters,
batch management, database backup, monitoring the status of data acquisition
nodes, controlling the data acquisition workflow, and shot simulation data
acquisition test. The upgraded data acquisition console has been designed and
under testing recently.


  We show that quantum theory allows for transformations of black boxes that
cannot be realized by inserting the input black boxes within a circuit in a
pre-defined causal order. The simplest example of such a transformation is the
classical switch of black boxes, where two input black boxes are arranged in
two different orders conditionally on the value of a classical bit. The quantum
version of this transformation-the quantum switch-produces an output circuit
where the order of the connections is controlled by a quantum bit, which
becomes entangled with the circuit structure. Simulating these transformations
in a circuit with fixed causal structure requires either postselection, or an
extra query to the input black boxes.


  Let $G$ be a graph each component of which has order at least 3, and let $G$
have order $n$, size $m$, total domination number $\gamma_t$ and maximum degree
$\Delta(G)$. Let $\Delta = 3$ if $\Delta(G) = 2$ and $\Delta = \Delta (G)$ if
$\Delta(G) \ge 3$. It is known [J. Graph Theory 49 (2005), 285--290; J. Graph
Theory 54 (2007), 350--353] that $m \le \Delta (n- \gamma_t)$. In this paper we
characterize the extremal graphs $G$ satisfying $m = \Delta (n- \gamma_t)$.


  As a fundamental yet significant process in personalized recommendation,
candidate generation and suggestion effectively help users spot the most
suitable items for them. Consequently, identifying substitutable items that are
interchangeable opens up new opportunities to refine the quality of generated
candidates. When a user is browsing a specific type of product (e.g., a laptop)
to buy, the accurate recommendation of substitutes (e.g., better equipped
laptops) can offer the user more suitable options to choose from, thus
substantially increasing the chance of a successful purchase. However, existing
methods merely treat this problem as mining pairwise item relationships without
the consideration of users' personal preferences. Moreover, the substitutable
relationships are implicitly identified through the learned latent
representations of items, leading to uninterpretable recommendation results. In
this paper, we propose attribute-aware collaborative filtering (A2CF) to
perform substitute recommendation by addressing issues from both
personalization and interpretability perspectives. Instead of directly
modelling user-item interactions, we extract explicit and polarized item
attributes from user reviews with sentiment analysis, whereafter the
representations of attributes, users, and items are simultaneously learned.
Then, by treating attributes as the bridge between users and items, we can
thoroughly model the user-item preferences (i.e., personalization) and
item-item relationships (i.e., substitution) for recommendation. In addition,
A2CF is capable of generating intuitive interpretations by analyzing which
attributes a user currently cares the most and comparing the recommended
substitutes with her/his currently browsed items at an attribute level. The
recommendation effectiveness and interpretation quality of A2CF are
demonstrated via extensive experiments on three real datasets.


  Imagine an online work environment where researchers have direct and
immediate access to myriad data sources and tools and data management
resources, useful throughout the research lifecycle. This is our vision for the
next generation of the Dataverse Network: an Open Science Platform (OSP). For
the first time, researchers would be able to seamlessly access and create
primary and derived data from a variety of sources: prior research results,
public data sets, harvested online data, physical instruments, private data
collections, and even data from other standalone repositories. Researchers
could recruit research participants and conduct research directly on the OSP,
if desired, using readily available tools. Researchers could create private or
shared workspaces to house data, access tools, and computation and could
publish data directly on the platform or publish elsewhere with persistent,
data citations on the OSP. This manuscript describes the details of an Open
Science Platform and its construction. Having an Open Science Platform will
especially impact the rate of new scientific discoveries and make scientific
findings more credible and accountable.


  With the rapid advancement of AI applications, the growing needs for data
privacy and model robustness have highlighted the importance of machine
unlearning, especially in thriving graph-based scenarios. However, most
existing graph unlearning strategies primarily rely on well-designed
architectures or manual process, rendering them less user-friendly and posing
challenges in terms of deployment efficiency. Furthermore, striking a balance
between unlearning performance and framework generalization is also a pivotal
concern. To address the above issues, we propose \underline{\textbf{M}}utual
\underline{\textbf{E}}volution \underline{\textbf{G}}raph
\underline{\textbf{U}}nlearning (MEGU), a new mutual evolution paradigm that
simultaneously evolves the predictive and unlearning capacities of graph
unlearning. By incorporating aforementioned two components, MEGU ensures
complementary optimization in a unified training framework that aligns with the
prediction and unlearning requirements. Extensive experiments on 9 graph
benchmark datasets demonstrate the superior performance of MEGU in addressing
unlearning requirements at the feature, node, and edge levels. Specifically,
MEGU achieves average performance improvements of 2.7\%, 2.5\%, and 3.2\%
across these three levels of unlearning tasks when compared to state-of-the-art
baselines. Furthermore, MEGU exhibits satisfactory training efficiency,
reducing time and space overhead by an average of 159.8x and 9.6x,
respectively, in comparison to retraining GNN from scratch.


  There have been many matching pursuit algorithms (MPAs) which handle the
sparse signal recovery problem a.k.a. compressed sensing (CS). In the MPAs, the
correlation computation step has a dominant computational complexity. In this
letter, we propose a new fast correlation computation method when we use some
classes of partial unitary matrices as the sensing matrix. Those partial
unitary matrices include partial Fourier matrices and partial Hadamard matrices
which are popular sensing matrices. The proposed correlation computation method
can be applied to almost all MPAs without causing any degradation of their
recovery performance. And, for most practical parameters, the proposed method
can reduce the computational complexity of the MPAs substantially.


  We consider the dispersion energy of a pair of dipoles embedded in a metallic
waveguide with transverse dimension $a$ smaller than the characteristic dipolar
wavelength. We find that $a$ sets the scale that separates retarded,
Casimir-Polder-like, from quasistatic, van der Waals-like, interactions.
Whereas in the retarded regime, the energy decays exponentially with
inter-dipolar distance, typical of evanescent waves, in the van der Waals
regime, the known free-space result is obtained. This short-range scaling
implies that the additivity of the dispersion interactions inside a waveguide
extends to denser media, along with modifications to related Casimir effects in
such structures.


  Quantum scale invariance in the UV has been recently advocated as an
attractive way of solving the gauge hierarchy problem arising in the Standard
Model. We explore the cosmological signatures at the electroweak scale when the
breaking of scale invariance originates from a hidden sector and is mediated to
the Standard Model by gauge interactions (Gauge Mediation). These scenarios,
while being hard to distinguish from the Standard Model at LHC, can give rise
to a strong electroweak phase transition leading to the generation of a large
stochastic gravitational wave background in possible reach of future
space-based detectors such as eLISA and BBO. This relic would be the
cosmological imprint of the breaking of scale invariance in Nature.


  We perform a large parameter survey of 112 collisionless N-body simulations
of binary mergers of disk-galaxies with mass ratios of 1:1, 2:1, 3:1, and 4:1
using the special purpose hardware GRAPE. The basic properties of the remnants
correlate with the mass ratio of the progenitor disks. We find that about 80%
of the equal-mass merger simulations lead to slowly rotating remnants with
significant minor-axis rotation. One half of all projected 1:1 remnants shows
boxy and the other half shows disky isophotes. Remnants with mass ratios of 3:1
and 4:1 all rotate fast and show a small amount of minor-axis rotation. About
90% of the projected 3:1 and 4:1 remnants show disky isophotes. 2:1 remnants
show intermediate properties. Projection effects lead to a large spread in the
data in good agreement with observations. They do not change the fundamental
kinematical differences between equal-mass and unequal-mass merger remnants.
The results of this study weaken the disk merger scenario as the possible
formation mechanism of massive boxy giant ellipticals. In general, the
properties of equal-mass (and 2:1) merger remnants are consistent with the
observed population of giant ellipticals in the intermediate mass regime
between low mass fast rotating disky and bright massive boxy giant ellipticals.
3:1 and 4:1 merger remnants, however, are in very good agreement with the class
of low luminosity, fast rotating giant elliptical galaxies. Binary mergers of
disk galaxies are therefore still very good candidates for being the main
formation mechanism for intermediate and low mass giant ellipticals. The
homogeneous class of massive boxy ellipticals most likely formed by a different
process.


  Fast interpolation-grid frameworks facilitate an efficient and flexible
evaluation of higher-order predictions for any choice of parton distribution
functions or value of the strong coupling $\alpha_s$. They constitute an
essential tool for the extraction of parton distribution functions and Standard
Model parameters, as well as studies of the dependence of cross sections on the
renormalisation and factorisation scales. The APPLfast project provides a
generic interface between the parton-level Monte Carlo generator NNLOJET and
both the APPLgrid and the fastNLO libraries for the grid interpolation. The
extension of the project to include hadron-hadron collider processes at
next-to-next-to-leading order in perturbative QCD is presented, together with
an application for jet production at the LHC.


  Surface termination and interfacial interactions are critical for advanced
solid-state quantum applications. In this paper, we demonstrate that atomic
layer deposition (ALD) can both provide valuable insight on the chemical
environment of the surface, having sufficient sensitivity to distinguish
between the common diamond (001) surface termination types and passivate these
interfaces as desired. We selected diamond substrates exhibiting both smooth
and anomalously rough surfaces to probe the effect of morphology on ALD
nucleation. We use high resolution in situ spectroscopic ellipsometry to
monitor the surface reaction with sub-angstrom resolution, to evaluate the
nucleation of an ALD Al2O3 process as a function of different ex and in situ
treatments to the diamond surface. In situ water dosing and high vacuum
annealing provided the most favorable environment for nucleation of
dimethylaluminum isopropoxide and water ALD. Hydrogen termination passivated
both smooth and rough surfaces while triacid cleaning passivated the smooth
surface only, with striking effectiveness.


  Individuals with visual impairments, encompassing both partial and total
difficulties in visual perception, are referred to as visually impaired (VI)
people. An estimated 2.2 billion individuals worldwide are affected by visual
impairments. Recent advancements in multi-modal large language models (MLLMs)
have showcased their extraordinary capabilities across various domains. It is
desirable to help VI individuals with MLLMs' great capabilities of visual
understanding and reasoning. However, it is challenging for VI people to use
MLLMs due to the difficulties in capturing the desirable images to fulfill
their daily requests. For example, the target object is not fully or partially
placed in the image. This paper explores how to leverage MLLMs for VI
individuals to provide visual-question answers. VIAssist can identify undesired
images and provide detailed actions. Finally, VIAssist can provide reliable
answers to users' queries based on the images. Our results show that VIAssist
provides +0.21 and +0.31 higher BERTScore and ROUGE scores than the baseline,
respectively.


  In this talk I briefly review the recent progress in calculation of the high
order corrections to the parameters of the nonrelativistic heavy
quark-antiquark system in the effective theory approach.


  We study the viability of regions of large $\tan \beta$ within the framework
of Fat Higgs/$\lambda$-SUSY Models. We compute the one-loop effective potential
to find the corrections to the Higgs boson mass due to the heavy non-standard
Higgs bosons. As the tree level contribution to the Higgs boson mass is
suppressed at large $\tan \beta$, these one-loop corrections are crucial to
raising the Higgs boson mass to the measured LHC value. By raising the Higgsino
and singlino mass parameters, typical electroweak precision constraints can
also be avoided. We illustrate these new regions of Fat Higgs/$\lambda$-SUSY
parameter space by finding regions of large $\tan \beta$ that are consistent
with all experimental constraints including direct dark matter detection
experiments, relic density limits and the invisible decay width of the $Z$
boson. We find that there exist regions around $\lambda = 1.25, \tan \beta =
50$ and a uniform psuedo-scalar $4~{\rm TeV} \lsim M_A \lsim 8$~TeV which are
consistent will all present phenomenological constraints. In this region the
dark matter relic abundance and direct detection limits are satisfied by a
lightest neutralino that is mostly bino or singlino. As an interesting aside we
also find a region of low $\tan \beta$ and small singlino mass parameter where
a well-tempered neutralino avoids all cosmological and direct detection
constraints.


  We show that many of the unusual properties of the one-dimensional random
quantum Ising model are shared also by dilute quantum Ising systems in the
vicinity of a certain quantum transition in {\em any} dimension $d > 1$. Thus,
while these properties are not an artifact of $d=1$, they do require special
circumstances in higher dimensions.


  We propose that systems exhibiting compositional patterning at the nanoscale,
so far assumed to be due to some kind of ordered phase segregation, can be
understood instead in terms of coherent, single phase ordering of minority
motifs, caused by some constrained drive for uniformity. The essential features
of this type of arrangements can be reproduced using a superspace construction
typical of uniformity-driven orderings, which only requires the knowledge of
the modulation vectors observed in the diffraction patterns. The idea is
discussed in terms of a simple two dimensional lattice-gas model that simulates
a binary system in which the dilution of the minority component is favored.
This simple model already exhibits a hierarchy of arrangements similar to the
experimentally observed nano-chessboard and nano-diamond patterns, which are
described as occupational modulated structures with two independent modulation
wave vectors and simple step-like occupation modulation functions.


  A comparison of X-ray observations of the Cassiopeia A supernova remnant
taken in 2000, 2002, and 2004 with the Chandra ACIS-S3 reveals the presence of
several small scale features (<= 10 arcsec) which exhibit significant intensity
changes over a 4 year time frame. Here we report on the variability of six
features, four of which show count rate increases from ~ 10% to over 90%, and
two which show decreases of ~ 30% -- 40%. While extracted 1-4.5 keV X-ray
spectra do not reveal gross changes in emission line strengths, spectral fits
using non-equilibrium ionization, metal-rich plasma models indicate increased
or decreased electron temperatures for features showing increasing or
decreasing count rates, respectively. Based on the observed count rate changes
and the assumption that the freely expanding ejecta has a velocity of ~ 5000
km/s at the reverse shock front, we estimate the unshocked ejecta to have
spatial scale variations of 0.02 - 0.03 pc, which is consistent with the X-ray
emitting ejecta belonging to a more diffuse component of the supernova ejecta
than that seen in the optically emitting ejecta, which have spatial scales ~
0.001 pc.


  The Belle II detector at the SuperKEKB accelerator observed the first
collisions in April this year. Until mid-summer, the first commissioning run
uses a reduced version of the full vertex detector. Nevertheless, this phase is
an excellent opportunity to improve and test the alignment and calibration
procedures being prepared for the first physics runs starting in spring 2019.
  The procedure presented is based on Millepede II tool to solve the large
minimization problem emerging in the track-based alignment and calibration of
the pixel and strip detectors, the drift chamber or the muon system. The first
alignment of the vertex detector was performed quickly after the first
collisions and further improvements are expected with more data and with
inclusion of other sub-detectors into the procedure.
  This contribution will show overview and status of the Millepede alignment
and calibration procedure of the Belle 2 sub-detectors, after first collisions
and the plans for full physics run.


  In the investigation of the evolution of cosmological perturbations in
inflationary universe models the behavior of perturbations during the reheating
stage is the most unclear point. In particular in the early reheating phase in
which a rapidly oscillating scalar field dominates the energy density, the
behavior of perturbations are not known well because their evolution equation
expressed in terms of the curvature perturbation becomes singular. In this
paper it is shown that in spite of this singular behavior of the evolution
equation the Bardeen parameter stays constant in a good accuracy during this
stage for superhorizon-scale perturbations except for a sequence of negligibly
short intervals around the zero points of the time derivative of the scalar
field. This justifies the conventional formula relating the amplitudes of
quantum fluctuations during inflation and those of adiabatic perturbations at
horizon crossing in the Friedmann stage, except for possible corrections
produced by the energy transfer from the scalar field to radiation in the late
stage of reheating. It is further shown that outside the above sequence of time
intervals the behavior of the perturbations coincides in a good accuracy with
that for a perfect fluid system obtained from the original scalar field system
by the WKB approximation and a spacetime averaging over a Hubble horizon scale.


  Designing the user interface (UI) of mobile applications in the enterprise is
performed in many cases by the application users who represent the business
needs. This is based on existing developed enterprise services that can be
accessed by these applications. Design the UI of the mobile applications
require programming environments that are as much as possible codeless and easy
to use. In this paper, we present NitroGen which is a cloud-based
platform-independent tool that provides with no installation, a consumable
integrated set of capabilities to construct mobile solutions aiming at reducing
development and maintenance costs. We further illustrate how to use NitroGen
showing its visual touch-based mostly codeless way of programming. NitroGen can
easily connect to back-end services thus enable fast and facile development in
enterprises.


  We study heat kernel convergence of induced subgraphs with Neumann boundary
conditions. We first establish convergence of the resulting semigroups to the
Neumann semigroup in $\ell^2$. While convergence to the Neumann semigroup
always holds, convergence to the Dirichlet semigroup in $\ell^2$ turns out to
be equivalent to the coincidence of the Dirichlet and Neumann semigroups while
convergence in $\ell^1$ is equivalent to stochastic completeness. We then
investigate the Feller property for the Neumann semigroup via generalized
solutions and give applications to graphs satisfying a condition on the edges
as well as birth-death chains.


  We generalize the theory of critical groups from graphs to simplicial
complexes. Specifically, given a simplicial complex, we define a family of
abelian groups in terms of combinatorial Laplacian operators, generalizing the
construction of the critical group of a graph. We show how to realize these
critical groups explicitly as cokernels of reduced Laplacians, and prove that
they are finite, with orders given by weighted enumerators of simplicial
spanning trees. We describe how the critical groups of a complex represent flow
along its faces, and sketch another potential interpretation as analogues of
Chow groups.


  Winograd is generally utilized to optimize convolution performance and
computational efficiency because of the reduced multiplication operations, but
the reliability issues brought by winograd are usually overlooked. In this
work, we observe the great potential of winograd convolution in improving
neural network (NN) fault tolerance. Based on the observation, we evaluate
winograd convolution fault tolerance comprehensively from different
granularities ranging from models, layers, and operation types for the first
time. Then, we explore the use of inherent fault tolerance of winograd
convolution for cost-effective NN protection against soft errors. Specifically,
we mainly investigate how winograd convolution can be effectively incorporated
with classical fault-tolerant design approaches including triple modular
redundancy (TMR), fault-aware retraining, and constrained activation functions.
According to our experiments, winograd convolution can reduce the
fault-tolerant design overhead by 55.77\% on average without any accuracy loss
compared to standard convolution, and further reduce the computing overhead by
17.24\% when the inherent fault tolerance of winograd convolution is
considered. When it is applied on fault-tolerant neural networks enhanced with
fault-aware retraining and constrained activation functions, the resulting
model accuracy generally shows significant improvement in presence of various
faults.


  We present general results for one-dimensional systems of point charges
(signed point measures) on the line with a translation invariant distribution
$\mu$ for which the variance of the total charge in an interval is uniformly
bounded (instead of increasing with the interval length). When the charges are
restricted to multiples of a common unit, and their average charge density does
not vanish, then the boundedness of the variance implies translation-symmetry
breaking --- in the sense that there exists a function of the charge
configuration that is nontrivially periodic under translations --- and hence
that $\mu$ is not ``mixing.'' Analogous results are formulated also for one
dimensional lattice systems under some constraints on the values of the charges
at the lattice sites and their averages. The general results apply to
one-dimensional Coulomb systems, and to certain spin chains, putting on common
grounds different instances of symmetry breaking encountered there.


  Designing distributed, fast and reliable wireless consensus protocols is
instrumental in enabling mission-critical decentralized systems, such as
robotic networks in the industrial Internet of Things (IIoT), drone swarms in
rescue missions, and so forth. However, chasing both low-latency and
reliability of consensus protocols is a challenging task. The problem is
aggravated under wireless connectivity that may be slower and less reliable,
compared to wired connections. To tackle this issue, we investigate fundamental
relationships between consensus latency and reliability through the lens of
wireless connectivity, and co-design communication and consensus protocols for
low-latency and reliable decentralized systems. Specifically, we propose a
novel communication-efficient distributed consensus protocol, termed Random
Representative Consensus (R2C), and show its effectiveness under gossip and
broadcast communication protocols. To this end, we derive a closed-form
end-to-end (E2E) latency expression of the R2C that guarantees a target
reliability, and compare it with a baseline consensus protocol, referred to as
Referendum Consensus (RC). The result shows that the R2C is faster compared to
the RC and more reliable compared when co-designed with the broadcast protocol
compared to that with the gossip protocol.


  Interactive molecular graphics applications facilitate analysis of three
dimensional protein structures. Naturally, non-interactive 2-D snapshots of the
protein structures do not convey the same level of geometric detail. Several
2-D visualization methods have been in use to summarize structural information,
including contact maps and 2-D cartoon views. We present a new approach for 2-D
visualization of protein structures where amino acid residues are displayed on
a circle and spatially close residues are depicted by links. Furthermore,
residue-specific properties, such as conservation, accessibility, temperature
factor, can be displayed as plots on the same circular view.


  Bi-linear feature learning models, like the gated autoencoder, were proposed
as a way to model relationships between frames in a video. By minimizing
reconstruction error of one frame, given the previous frame, these models learn
"mapping units" that encode the transformations inherent in a sequence, and
thereby learn to encode motion. In this work we extend bi-linear models by
introducing "higher-order mapping units" that allow us to encode
transformations between frames and transformations between transformations.
  We show that this makes it possible to encode temporal structure that is more
complex and longer-range than the structure captured within standard bi-linear
models. We also show that a natural way to train the model is by replacing the
commonly used reconstruction objective with a prediction objective which forces
the model to correctly predict the evolution of the input multiple steps into
the future. Learning can be achieved by back-propagating the multi-step
prediction through time. We test the model on various temporal prediction
tasks, and show that higher-order mappings and predictive training both yield a
significant improvement over bi-linear models in terms of prediction accuracy.


  We study a model of two concentric onedimensional rings with incommensurate
areas $A_1$ and $A_2$, in a constant magnetic field. The two rings are coupled
by a nonhomogeneous inter-ring tunneling amplitude, which makes the
one-particle spectrum chaotic. For noninteracting particles the energy of the
many-body ground state and the first excited state exhibit random fluctuations
characterized by the Wigner-Dyson statistics. In contrast, we show that the
electron-electron interaction orders the magnetic field dependence of these
quantities, forcing them to become periodic functions, with period $ \propto
1/(A_1 + A_2)$. In such a strongly correlated system the only possible source
of disorder comes from charge fluctuations, which can be controlled by a
tunable inter-ring gate voltage.


  In this short note, using the Kendall-Cranston coupling, we study on K\"ahler
(resp. quaternion K\"ahler) manifolds first eigenvalue estimates in terms of
dimension, diameter, and lower bounds on the holomorphic (resp. quaternionic)
sectional curvature.


  We present the results of a study of different statistical methods currently
used in the literature to analyse the (micro)variability of active galactic
nuclei (AGNs) from ground-based optical observations. In particular, we focus
on the comparison between the results obtained by applying the so-called C and
F statistics, which are based on the ratio of standard deviations and
variances, respectively. The motivation for this is that the implementation of
these methods leads to different and contradictory results, making the
variability classification of the light curves of a certain source dependent on
the statistics implemented.
  For this purpose, we re-analyse the results on an AGN sample observed along
several sessions with the 2.15m "Jorge Sahade" telescope (CASLEO), San Juan,
Argentina. For each AGN we constructed the nightly differential light curves.
We thus obtained a total of 78 light curves for 39 AGNs, and we then applied
the statistical tests mentioned above, in order to re-classify the variability
state of these light curves and in an attempt to find the suitable statistical
methodology to study photometric (micro)variations. We conclude that, although
the $C$ criterion is not proper a statistical test, it could still be a
suitable parameter to detect variability and that its application allows us to
get more reliable variability results, in contrast with the F test.


  This study investigates the problem of communication for a network composed
of two half-duplex parallel relays with additive white Gaussian noise. Two
protocols, i.e., \emph{Simultaneous} and \emph{Successive} relaying, associated
with two possible relay orderings are proposed. The simultaneous relaying
protocol is based on \emph{Dynamic Decode and Forward (DDF)} scheme. For the
successive relaying protocol: (i) a \emph{Non-Cooperative} scheme based on the
\emph{Dirty Paper Coding (DPC)}, and (ii) a \emph{Cooperative} scheme based on
the \emph{Block Markov Encoding (BME)} are considered. Furthermore, the
composite scheme of employing BME at one relay and DPC at another always
achieves a better rate when compared to the \emph{Cooperative} scheme. A
\emph{"Simultaneous-Successive Relaying based on Dirty paper coding scheme"
(SSRD)} is also proposed. The optimum ordering of the relays and hence the
capacity of the half-duplex Gaussian parallel relay channel in the low and high
signal-to-noise ratio (SNR) scenarios is derived. In the low SNR scenario, it
is revealed that under certain conditions for the channel coefficients, the
ratio of the achievable rate of the simultaneous relaying based on DDF to the
cut-set bound tends to be 1. On the other hand, as SNR goes to infinity, it is
proved that successive relaying, based on the DPC, asymptotically achieves the
capacity of the network.


  Inhomogeneity generated waves, discovered more than a decade ago, play an
important role in processes like energy transfer, turbulence generation,
heating, etc. To understand the nature of these waves we developed the
formalism that looks for the short-term changes in the initial waveform due to
the linear interaction of the initial disturbance and inhomogeneities in the
background. Some aspects of inhomogeneity generated waves in the pressure
balanced structures have already been studied in earlier papers, presented at
the scientific gatherings, or proposed to different government funded agencies.
Here, my intention is to demonstrate an existence of inhomogeneity generated
waves in the pressure balanced structures in the simple analytical form that
provide insight about them.


  Hashing has played a pivotal role in large-scale image retrieval. With the
development of Convolutional Neural Network (CNN), hashing learning has shown
great promise. But existing methods are mostly tuned for classification, which
are not optimized for retrieval tasks, especially for instance-level retrieval.
In this study, we propose a novel hashing method for large-scale image
retrieval. Considering the difficulty in obtaining labeled datasets for image
retrieval task in large scale, we propose a novel CNN-based unsupervised
hashing method, namely Unsupervised Triplet Hashing (UTH). The unsupervised
hashing network is designed under the following three principles: 1) more
discriminative representations for image retrieval; 2) minimum quantization
loss between the original real-valued feature descriptors and the learned hash
codes; 3) maximum information entropy for the learned hash codes. Extensive
experiments on CIFAR-10, MNIST and In-shop datasets have shown that UTH
outperforms several state-of-the-art unsupervised hashing methods in terms of
retrieval accuracy.


  The special form of an electric discharge - "burn-through" in regard to the
ball lightning problem has been considered. Having been carried out within
closed volume, this mode of erosive discharge has shown the series of clear
differences consisting in the high gas density, formation of the metal-polymer
aerogel, the percolating current and unusual features of plasma's exhaust. The
conditions for its stable origination and the main stages of the discharge
process have been ascertained.


  We solve analytically the renormalization-group equation for the potential of
the O(N)-symmetric scalar theory in the large-N limit and in dimensions 2<d<4,
in order to look for nonperturbative fixed points that were found numerically
in a recent study. We find new real solutions with singularities in the higher
derivatives of the potential at its minimum, and complex solutions with branch
cuts along the negative real axis.


  The electronic transport of graphene p-n junctions under perpendicular
magnetic field is investigated in theory. Under low magnetic field, the
transport is determined by the resonant tunneling of Landau levels and
conductance versus magnetic field shows a Shubnikov-de Haas oscillation. At
higher magnetic field, the p-n junction subjected to the quasi-classical regime
and the formation of snake states results in periodical backscattering and
transmission as magnetic field varies. The conductance oscillation pattern is
mediated both by magnetic field and the carrier concentration on bipolar
regions. For medium magnetic field between above two regimes, the combined
contributions of resonant tunneling, snake states oscillation and Aharanov-Bohm
interference induce irregular oscillation of conductance. At very high magnetic
field, the system is subjected to quantum Hall regime. Under disorder, the
quantum tunneling at low magnetic field is slightly affected and the
oscillation of snake states at higher magnetic field is suppressed. In the
quantum Hall regime, the conductance is a constant as predicted by the mixture
rule.


  We establish several characterizations of Anosov representations of word
hyperbolic groups into real reductive Lie groups, in terms of a Cartan
projection or Lyapunov projection of the Lie group. Using a properness
criterion of Benoist and Kobayashi, we derive applications to proper actions on
homogeneous spaces of reductive groups.


  This paper proposes a control scheme for the quality-fair delivery of several
encoded video streams to mobile users sharing a common wireless resource. Video
quality fairness, as well as similar delivery delays are targeted among
streams. The proposed controller is implemented within some aggregator located
near the bottleneck of the network. The transmission rate among streams is
adapted based on the quality of the already encoded and buffered packets in the
aggregator. Encoding rate targets are evaluated by the aggregator and fed back
to each remote video server (fully centralized solution), or directly evaluated
by each server in a distributed way (partially distributed solution). Each
encoding rate target is adjusted for each stream independently based on the
corresponding buffer level or buffering delay in the aggregator. Communication
delays between the servers and the aggregator are taken into account. The
transmission and encoding rate control problems are studied with a
control-theoretic perspective. The system is described with a multi-input
multi-output model. Proportional Integral (PI) controllers are used to adjust
the video quality and control the aggregator buffer levels. The system
equilibrium and stability properties are studied. This provides guidelines for
choosing the parameters of the PI controllers. Experimental results show the
convergence of the proposed control system and demonstrate the improvement in
video quality fairness compared to a classical transmission rate fair streaming
solution and to a utility max-min fair approach.


  We consider the Casimir effect between two parallel plates localized on a
brane. In order to properly compute the contribution to the Casimir energy due
to any higher dimensional field, it is necessary to take into account the
localization properties of the KK modes. When no massless mode appears in the
spectrum, the correction to the Casimir energy is exponentially suppressed.
When a massless mode is present in the spectrum, the correction to the Casimir
energy can be, in principle, sizeable. Here we illustrate a new method to
compute the correction to the Casimir energy between two parallel plates,
localized on a brane. The Casimir energy is suppressed by two factors: at
lowest order in $\varepsilon$, the correction comes entirely from the massive
mode and turns out to be exponentially suppressed; the next-to-leading order
correction in $\varepsilon$ follows, instead, a power-law suppression due to
the small wave function overlap of the zero-mode with matter confined on the
visible brane. Generic comments on the constraints on new physics that may
arise from Casimir force experiments are also made.


  The aim of the present paper is three folds. For a reproducing kernel Hilbert
space $\mathcal{A}$ (R.K.H.S) and a $\sigma-$finite measure space
$(M_{1},d\mu_{1})$ for which the corresponding $L^{2}-$space is a separable
Hilbert space, we first build an isometry of Bargmann type as an integral
transform from $L^{2}(M_{1},d\mu_{1})$ into $\mathcal{A}$. Secondly, in the
case where there exists a $\sigma-$finite measure space $(M_{2},d\mu_{2})$ such
that the Hilbert space $L^{2}(M_{2},d\mu_{2})$ is separable and
$\mathcal{A}\subset L^{2}(M_{2},d\mu_{2})$ the inverse isometry is also given
in an explicit form as an integral transform. As consequence, we recover some
classical isometries of Bargmann type. Thirdly, for the classical Dirichlet
space as R.K.H.S, we elaborate a new isometry of Bargmann type. Furthermore,
for this Dirichlet space, we give a new characterization, as harmonic space of
a single second order elliptic partial differential operator for which, we
present some spectral properties. Finally, we extend the same results to a
class of generalized Bergman-Dirichlet space.


  The paper presents improved mathematical models and methods for statistical
regularities in the behavior of some important characteristics of
precipitation: duration of a wet period, maximum daily and total precipitation
volumes within a such period. The asymptotic approximations are deduced using
limit theorems for statistics constructed from samples with random sizes having
the generalized negative binomial (GNB) distribution. It demonstrates excellent
concordance with the empirical distribution of the duration of wet periods
measured in days. The asymptotic distribution of the maximum daily
precipitation volume within a wet period turns out to be a tempered scale
mixture of the gamma distribution with the scale factor having the Weibull
distribution, whereas the asymptotic approximation to the total precipitation
volume for a wet period turns out to be the generalized gamma (GG)
distribution. Two approaches to the definition of abnormally extremal
precipitation are presented. The first approach is based on an excess of a
certain quantile of the asymptotic distribution of the maximum daily
precipitation. The second approach is based on the GG model for the total
precipitation volume. The corresponding statistical test is compared with a
previously proposed one based on tha classical gamma distribution using real
precipitation data.


  Methods for random-effects meta-analysis require an estimate of the
between-study variance, $\tau^2$. The performance of estimators of $\tau^2$
(measured by bias and coverage) affects their usefulness in assessing
heterogeneity of study-level effects, and also the performance of related
estimators of the overall effect. For the effect measure log-response-ratio
(LRR, also known as the logarithm of the ratio of means, RoM), we review four
point estimators of $\tau^2$ (the popular methods of DerSimonian-Laird (DL),
restricted maximum likelihood, and Mandel and Paule (MP), and the less-familiar
method of Jackson), four interval estimators for $\tau^2$ (profile likelihood,
Q-profile, Biggerstaff and Jackson, and Jackson), five point estimators of the
overall effect (the four related to the point estimators of $\tau^2$ and an
estimator whose weights use only study-level sample sizes), and seven interval
estimators for the overall effect (four based on the point estimators for
$\tau^2$, the Hartung-Knapp-Sidik-Jonkman (HKSJ) interval, a modification of
HKSJ that uses the MP estimator of $\tau^2$ instead of the DL estimator, and an
interval based on the sample-size-weighted estimator). We obtain empirical
evidence from extensive simulations of data from lognormal distributions.


  Multiple, sequential mergers are unavoidable in the hierarchical build-up
picture of galaxies, in particular for the minor mergers that are frequent and
highly likely to have occured several times for most present-day galaxies.
However, the effect of repeated minor mergers on galactic structure and
evolution has not been studied systematically so far. We present a numerical
study of multiple, subsequent, minor galaxy mergers, with various mass ratios
ranging from 4:1 to 50:1. The N-body simulations include gas dynamics and star
formation. We study the morphological and kinematical properties of the
remnants, and show that several so-called "minor" mergers can lead to the
formation of elliptical-like galaxies that have global morphological and
kinematical properties similar to that observed in real elliptical galaxies.
The properties of these systems are compared with those of elliptical galaxies
produced by the standard scenario of one single major merger. We thus show that
repeated minor mergers can theoretically form elliptical galaxies without major
mergers, and can be more frequent than major mergers, in particular at moderate
redshift. This process must then have formed some elliptical galaxies seen
today, and could in particular explain the high boxiness of massive
ellipticals, and some fundamental relations observed in ellipticals. In
addition, because repeated minor mergers, even at high mass ratios, destroy
disks into spheroids, these results indicate that spiral galaxies cannot have
grown only by a succession of minor mergers.


  We consider a non singular origin for the Universe starting from an Einstein
static Universe in the framework of a theory which uses two volume elements
$\sqrt{-{g}}d^{4}x$ and $\Phi d^{4}x$, where $\Phi $ is a metric independent
density, also curvature, curvature square terms, first order formalism and for
scale invariance a dilaton field $\phi$ are considered in the action. In the
Einstein frame we also add a cosmological term that parametrizes the zero point
fluctuations. The resulting effective potential for the dilaton contains two
flat regions, for $\phi \rightarrow \infty$ relevant for the non singular
origin of the Universe and $\phi \rightarrow -\infty$, describing our present
Universe. Surprisingly, avoidance of singularities and stability as $\phi
\rightarrow \infty$ imply a positive but small vacuum energy as $\phi
\rightarrow -\infty$. Zero vacuum energy density for the present universe is
the "threshold" for universe creation. This requires a modified emergent
universe scenario, where the universe although very old, it does have a
beginning.


  We study the boundary behavior of solutions to parabolic double-phase
equations through the celebrated Wiener's sufficiency criterion. The analysis
is conducted for cylindrical domains and the regularity up to the lateral
boundary is shown in terms of either its $p$ or $q$ capacity, depending on
whether the phase vanishes at the boundary or not. Eventually we obtain a fine
boundary estimate that, when considering uniform geometric conditions as
density or fatness, leads us to the boundary H\"older continuity of solutions.
In particular, the double-phase elicits new questions on the definition of an
adapted capacity.