  We discuss classical solutions of a graviton-dilaton-B_{\mu\nu}-tachyon
system. Both constant tachyon solutions, including AdS_3 solutions, and
space-dependent tachyon solutions are investigated, and their possible
implications to closed string tachyon condensation are argued. The stability
issue of the AdS_3 solutions is also discussed.


  We analytically examine the dynamics of quasi-particle modes occuring in a
Bose-Einstein condensate which is subject to a weak acceleration. It is shown
that the momentum of a quasi-particle mode is squeezed rather than accelerated.


  In this fourth paper of the series, I clarify the somewhat mysterious
relation between the large class of {\it orientation orbifolds} (with twisted
open-string CFT's at $\hat c=52$) and {\it orientifolds} (with untwisted open
strings at $c=26$), both of which have been associated to division by
world-sheet orientation-reversing automorphisms. In particular -- following a
spectral clue in the previous paper -- I show that, even as an {\it interacting
string system}, a certain half-integer-moded orientation orbifold-string system
is in fact equivalent to the archetypal orientifold. The subtitle of this
paper, that orientation orbifolds include and generalize standard orientifolds,
then follows because there are many other orientation orbifold-string systems
-- with higher fractional modeing -- which are not equivalent to untwisted
string systems.


  Current approaches for 3D scene graph prediction rely on labeled datasets to
train models for a fixed set of known object classes and relationship
categories. We present Open3DSG, an alternative approach to learn 3D scene
graph prediction in an open world without requiring labeled scene graph data.
We co-embed the features from a 3D scene graph prediction backbone with the
feature space of powerful open world 2D vision language foundation models. This
enables us to predict 3D scene graphs from 3D point clouds in a zero-shot
manner by querying object classes from an open vocabulary and predicting the
inter-object relationships from a grounded LLM with scene graph features and
queried object classes as context. Open3DSG is the first 3D point cloud method
to predict not only explicit open-vocabulary object classes, but also open-set
relationships that are not limited to a predefined label set, making it
possible to express rare as well as specific objects and relationships in the
predicted 3D scene graph. Our experiments show that Open3DSG is effective at
predicting arbitrary object classes as well as their complex inter-object
relationships describing spatial, supportive, semantic and comparative
relationships.


  Deep learning is currently the subject of intensive study. However,
fundamental concepts such as representations are not formally defined --
researchers "know them when they see them" -- and there is no common language
for describing and analyzing algorithms. This essay proposes an abstract
framework that identifies the essential features of current practice and may
provide a foundation for future developments.
  The backbone of almost all deep learning algorithms is backpropagation, which
is simply a gradient computation distributed over a neural network. The main
ingredients of the framework are thus, unsurprisingly: (i) game theory, to
formalize distributed optimization; and (ii) communication protocols, to track
the flow of zeroth and first-order information. The framework allows natural
definitions of semantics (as the meaning encoded in functions), representations
(as functions whose semantics is chosen to optimized a criterion) and grammars
(as communication protocols equipped with first-order convergence guarantees).
  Much of the essay is spent discussing examples taken from the literature. The
ultimate aim is to develop a graphical language for describing the structure of
deep learning algorithms that backgrounds the details of the optimization
procedure and foregrounds how the components interact. Inspiration is taken
from probabilistic graphical models and factor graphs, which capture the
essential structural features of multivariate distributions.


  Let $D$ be an oriented graph. The inversion of a set $X$ of vertices in $D$
consists in reversing the direction of all arcs with both ends in $X$. The
inversion number of $D$, denoted by ${\rm inv}(D)$, is the minimum number of
inversions needed to make $D$ acyclic. Denoting by $\tau(D)$, $\tau' (D)$, and
$\nu(D)$ the cycle transversal number, the cycle arc-transversal number and the
cycle packing number of $D$ respectively, one shows that ${\rm inv}(D) \leq
\tau' (D)$, ${\rm inv}(D) \leq 2\tau(D)$ and there exists a function $g$ such
that ${\rm inv}(D)\leq g(\nu(D))$. We conjecture that for any two oriented
graphs $L$ and $R$, ${\rm inv}(L\rightarrow R) ={\rm inv}(L) +{\rm inv}(R)$
where $L\rightarrow R$ is the dijoin of $L$ and $R$. This would imply that the
first two inequalities are tight. We prove this conjecture when ${\rm
inv}(L)\leq 1$ and ${\rm inv}(R)\leq 2$ and when ${\rm inv}(L) ={\rm inv}(R)=2$
and $L$ and $R$ are strongly connected. We also show that the function $g$ of
the third inequality satisfies $g(1)\leq 4$.
  We then consider the complexity of deciding whether ${\rm inv}(D)\leq k$ for
a given oriented graph $D$. We show that it is NP-complete for $k=1$, which
together with the above conjecture would imply that it is NP-complete for every
$k$. This contrasts with a result of Belkhechine et al. which states that
deciding whether ${\rm inv}(T)\leq k$ for a given tournament $T$ is
polynomial-time solvable.


  With more than four billion usage of cellular phones worldwide, mobile
advertising has become an attractive alternative to online advertisements. In
this paper, we propose a new targeted advertising policy for Wireless Service
Providers (WSPs) via SMS or MMS- namely {\em AdCell}. In our model, a WSP
charges the advertisers for showing their ads. Each advertiser has a valuation
for specific types of customers in various times and locations and has a limit
on the maximum available budget. Each query is in the form of time and location
and is associated with one individual customer. In order to achieve a
non-intrusive delivery, only a limited number of ads can be sent to each
customer. Recently, new services have been introduced that offer location-based
advertising over cellular network that fit in our model (e.g., ShopAlerts by
AT&T) .
  We consider both online and offline version of the AdCell problem and develop
approximation algorithms with constant competitive ratio. For the online
version, we assume that the appearances of the queries follow a stochastic
distribution and thus consider a Bayesian setting. Furthermore, queries may
come from different distributions on different times. This model generalizes
several previous advertising models such as online secretary problem
\cite{HKP04}, online bipartite matching \cite{KVV90,FMMM09} and AdWords
\cite{saberi05}. ...


  We describe a technique coupling standard rheology and ultrasonic imaging
with promising applications to characterization of soft materials under shear.
Plane wave imaging using an ultrafast scanner allows to follow the local
dynamics of fluids sheared between two concentric cylinders with frame rates as
high as 10,000 images per second, while simultaneously monitoring the shear
rate, shear stress, and viscosity as a function of time. The capacities of this
"rheo-ultrasound" instrument are illustrated on two examples: (i) the classical
case of the Taylor-Couette instability in a simple viscous fluid and (ii) the
unstable shear-banded flow of a non-Newtonian wormlike micellar solution.


  High-quality Extragalactic Legacy-field Monitoring (HELM) is a long-term
observing program that photometrically monitors several well-studied
extragalactic legacy fields with the Dark Energy Camera (DECam) imager on the
CTIO 4m Blanco telescope. Since Feb 2019, HELM has been monitoring regions
within COSMOS, XMM-LSS, CDF-S, S-CVZ, ELAIS-S1, and SDSS Stripe 82 with few-day
cadences in the $(u)gri(z)$ bands, over a collective sky area of $\sim 38$
deg${\rm ^2}$. The main science goal of HELM is to provide high-quality optical
light curves for a large sample of active galactic nuclei (AGNs), and to build
decades-long time baselines when combining past and future optical light curves
in these legacy fields. These optical images and light curves will facilitate
the measurements of AGN reverberation mapping lags, as well as studies of AGN
variability and its dependences on accretion properties. In addition, the
time-resolved and coadded DECam photometry will enable a broad range of science
applications from galaxy evolution to time-domain science. We describe the
design and implementation of the program and present the first data release
that includes source catalogs and the first $\sim 3.5$ years of light curves
during 2019A--2022A.


  The frequency comb created by a femtosecond mode-locked laser and a
microstructured fiber is used to phase coherently measure the frequencies of
both the Hg^+ and Ca optical standards with respect to the SI second as
realized at NIST. We find the transition frequencies to be f_Hg=1 064 721 609
899 143(10) Hz and f_Ca=455 986 240 494 158(26) Hz, respectively. In addition
to the unprecedented precision demonstrated here, this work is the precursor to
all-optical atomic clocks based on the Hg^+ and Ca standards. Furthermore, when
combined with previous measurements, we find no time variations of these atomic
frequencies within the uncertainties of |(df_Ca/dt)/f_Ca| < 8 x 10^{-14}
yr^{-1}, and |(df_Hg/dt)/f_Hg|< 30 x 10^{-14} yr^{-1}.


  Charge transport properties of thin films of sol--gel processed undoped and
Al-doped zinc oxide nanoparticles with variable doping level between 0.8 at%
and 10 at% were investigated. The X-ray diffraction studies revealed a decrease
of the average crystallite sizes in highly doped samples. We provide estimates
of the conductivity and the resulting charge carrier densities with respect to
the doping level. The increase of charge carrier density due to extrinsic
doping were compared to the accumulation of charge carriers in field effect
transistor structures. This allowed to assess the scattering effects due to
extrinsic doping on the electron mobility. The latter decreases from 4.6*10^-3
cm^2/Vs to 4.5*10^-4 cm^2/Vs with increasing doping density. In contrast, the
accumulation leads to an increasing mobility up to 1.5*10^-2 cm^2/Vs. The
potential barrier heights related to grain boundaries between the crystallites
were derived from temperature dependent mobility measurements. The extrinsic
doping initially leads to a grain boundary barrier height lowering, followed by
an increase due to doping-induced structural defects. We conclude that the
conductivity of sol--gel processed nanocrystalline ZnO:Al is governed by an
interplay of the enhanced charge carrier density and the doping-induced charge
carrier scattering effects, achieving a maximum at 0.8 at% in our case.


  We investigate the effect of non-Gaussianity on the reconstruction of the
initial mass field from the Ly$\alpha$ forest. We show that the transmitted
flux of QSO absorption spectra are highly non-Gaussian in terms of the
statistics, the kurtosis spectrum and scale-scale correlation. These
non-Gaussianities can not be completely removed by the conventional algorithm
of Gaussianization, and the scale-scale correlations are largely retained in
the mass field recovered by the Gaussian mapping. Therefore, the mass power
spectrum recovered by the conventional algorithm is systematically lower than
the initial mass spectrum on scales at which the local scale-scale correlation
is substantial. To reduce the non-Gaussian contamination, we present two
methods. The first is to perform the Gaussianization scale-by-scale using the
discrete wavelet transform (DWT) decomposition. We show that the non-Gaussian
features of the Ly$\alpha$ forest basically will no longer exist in the
scale-by-scale Gaussianized mass field. The second method is to choose a proper
orthonormal basis (representation) to suppress the effect of the non-Gaussian
correlations. In the quasilinear regime of cosmic structure formation, the DWT
power spectrum is efficient for suppressing the non-Gaussian contamination.
These two methods significantly improve the recovery of the mass power spectrum
from the Ly$\alpha$ forest.


  A line bundle is immaculate if its cohomology vanishes in every dimension. We
give a criterion for when a smooth toric Deligne-Mumford stack has infinitely
many immaculate line bundles. This answers positively a question of Borisov and
Wang. As a byproduct, we describe the asymptotic behaviour of the collection of
immaculate line bundles.


  Although simplices are trivial from a linear optimization standpoint, the
simplex algorithm can exhibit quite complex behavior. In this paper we study
the behavior of max-slope pivot rules on (products of) simplices and describe
the associated pivot rule polytopes. For simplices, the pivot rule polytopes
are combinatorially isomorphic to associahedra. To prove this correspondence,
we interpret max-slope pivot rules in terms of the combinatorics of colliding
particles on a line. For prisms over simplices, we recover Stasheff's
multiplihedra. For products of two simplices we get new realizations of
constrainahedra, that capture the combinatorics of certain particle systems in
the plane.


  We review the construction of actions with supersymmetry on spaces with a
domain wall. The latter objects act as sources inducing a jump in the gauge
coupling constant. Despite these singularities, supersymmetry can be
formulated, maintaining its role as a square root of translations in this
singular space. The setup is designed for the application in five dimensions
related to the Randall-Sundrum (RS) scenario. The space has two domain walls.
We discuss the solutions of the theory with fixed scalars and full preserved
supersymmetry, in which case one of the branes can be pushed to infinity, and
solutions where half of the supersymmetries are preserved.


  The existence of ground states and (multiple) bound states to semilinear
time-independent Maxwell and Schr\"odinger equations, with or without
$L^2$-constraints, is investigated.


  This is a pedagogical account on reaction-diffusion systems and their
relationship with integrable quantum spin chains.
  Reaction-diffusion systems are paradigmatic examples of non-equilibrium
systems. Their long-time behaviour is strongly influenced through fluctuation
effects in low dimensions which renders the habitual mean-field cinetic
equations inapplicable. Starting from the master equation rewritten as a
Schr\"odinger equation with imaginary time, the associated quantum hamiltonian
of certain one-dimensional reaction-diffusion models is closely related to
integrable magnetic chains. The relationship with the Hecke algebra and its
quotients allows to identify integrable reaction-diffusion models and, through
the Baxterization procedure, relate them to the solutions of Yang-Baxter
equations which can be solved via the Bethe ansatz. Methods such as spectral
and partial integrability, free fermions, similarity transformations or
diffusion algebras are reviewed, with several concrete examples treated
explicitly. An outlook on how the recently-introduced concept of local scale
invariance might become useful in the description of non-equilibrium ageing
phenomena is presented, with particular emphasis on the kinetic Ising model
with Glauber dynamics.


  We have studied the zero and low-temperature behavior of anisotropic
alternating antiferromagnetic-ferromagnetic Heisenberg spin-1/2 chains in a
transverse magnetic field. Using the analytical spinless fermion approach, the
thermodynamic behavior of the model has been studied. We have introduced a new
order parameter to distinguish the gapless Luttinger liquid phase from the
other gapped phases. The exact diagonalization Lanczos results are also used to
compare with the spinless fermion ones. We have found a double peak structure
in the specific heat curves for the region between the two quantum critical
points. Using the numerical full diagonalization results, the existence of the
double peak structure is confirmed.


  This work explores the possibility of decoding Imagined Speech (IS) signals
which can be used to create a new design of Human-Computer Interface (HCI).
Since the underlying process generating EEG signals is unknown, various feature
extraction methods, along with different neural network (NN) models, are used
to approximate data distribution and classify IS signals. Based on the
experimental results, feed-forward NN model with ensemble and covariance matrix
transformed features showed the highest performance in comparison to other
existing methods. For comparison, three publicly available datasets were used.
We report a mean classification accuracy of 80% between rest and imagined
state, 96% and 80% for decoding long and short words on two datasets. These
results show that it is possible to differentiate brain signals (generated
during rest state) from the IS brain signals. Based on the experimental
results, we suggest that the word length and complexity can be used to decode
IS signals with high accuracy, and a BCI system can be designed with IS signals
for computer interaction. These ideas, and results give direction for the
development of a commercial level IS based BCI system, which can be used for
human-computer interaction in daily life.


  We discuss several examples of generating apparent singular points as a
result of differentiating particular homogeneous linear ordinary differential
equations with polynomial coefficients and formulate two general conjectures on
the generation and removal of apparent singularities in arbitrary Fuchsian
differential equations with polynomial coefficients. We consider a model
problem in polymer physics.


  BES Collaboration measured the $R$ values at 3.650, 3.6648 and 3.773 GeV, the
$R$ values at 68 energy points in the energy region between 3.650 and 3.872
GeV, the resonance parameters of $\psi(3686)$ and $\psi(3770)$, the branching
fractions for $\psi(3770)\to D^0\bar D^0, D^+D^-, D\bar D$ and non-$D\bar D$,
and the observed cross sections for some exclusive light hadron final states at
3.773 and 3.650 GeV. These measurements are made by analyzing the data sets
collected with the BESII detector at the BEPC collider.


  We investigate the buildup dynamics of broadband Q-switched noise-like pulse
(QS-NLP) driven by slow gain dynamics in a microfiber-based passively
mode-locked fiber laser. Based on shot-to-shot tracing of the transient optical
spectra and qualitatively reproduced numerial simulation, we demonstrate that
slow gain dynamics is deeply involved in the onset of such complex temporal and
spectral instabilities of QS-NLP. The proposed gain dynamics model could
contribute to deeper insight into such nonlinear phenomenon and transient
dynamics simulation in ultrafast fiber laser.


  In the representation theory of real reductive Lie groups, many objects have
finiteness properties. For example, the lengths of Verma modules and principal
series representations are finite, and more precisely, they are bounded. In
this paper, we introduce a notion of uniformly bounded families of holonomic
$\mathscr{D}$-modules to explain and find such boundedness properties.
  A uniform bounded family has good properties. For instance, the lengths of
modules in the family are bounded and the uniform boundedness is preserved by
direct images and inverse images. By the Beilinson--Bernstein correspondence,
we can deduce several boundedness results about the representation theory of
complex reductive Lie algebras from corresponding results of uniformly bounded
families of $\mathscr{D}$-modules. In this paper, we concentrate on proving
fundamental properties of uniformly bounded families, and preparing abstract
results for applications to the branching problem and harmonic analysis.


  Making use of theory of differentiable stacks, we study symplectic vortex
equations over a compact orbifold Riemann surface. We discuss the category of
representable morphisms from a compact orbifold Riemann surface to a quotient
stack. After that we define symplectic vortex equations over a compact orbifold
Riemann surface. We also discuss the moduli space of solutions to the equations
for linear actions of the circle group on the complex plane.


  This paper has been removed by arXiv administrators because it plagiarizes
gr-qc/0011027, gr-qc/0205028, and gr-qc/0303034.


  Inside cells of living organisms, actin filaments and microtubules
self-assemble and dissemble dynamically by incorporating actin or tubulin from
the cell plasma or releasing it into their tips' surroundings. Such
reaction-diffusion systems can show diffusion- or reaction-limited behaviour.
However, neither limit explains the experimental data: while the offset of the
linear relation between growth speed and bulk tubulin density contradicts the
diffusion limit, the surprisingly large variance of the growth speed rejects a
pure reaction limit. In this Letter, we accommodate both limits and use a
Doi-Peliti field-theory model to estimate how diffusive transport is perturbing
the chemical reactions at the filament tip. Furthermore, a crossover bulk
density is predicted at which the limiting process changes from chemical
reactions to diffusive transport. In addition, we explain and estimate larger
variances of the growth speed.


  Previous works show that a correlation among radio luminosity, X-ray
luminosity, and black hole (BH) mass from stellar-mass BHs in X-ray binaries to
supermassive BHs in active galactic nuclei (AGNs), which leads to the so-called
fundamental plane of BH activity. However, there are two competing explanations
for this fundamental plane, including the jet-dominated model and the disk-jet
model. Thus, the physical origin of this fundamental plane remains unknown. In
this paper, we show that the X-ray luminosities, radio luminosities and BH
masses of gamma-ray bursts (GRBs) and M82 X-1 also show a similar distribution.
The universal scaling law among stellar-mass, intermediate and supermassive BH
systems, together with the fact that radio and X-ray emission of GRBs
originates from relativistic jets, reveals that the fundamental plane of BH
activity is controlled by a jet, i.e., the radio and X-ray emission is mainly
from the jet. Our work also suggests that the jets are scale-invariant with
respect to the BH mass.


  We analyze the minority game for patients, and the results known from the
minority game are applied to the patient problem consulted at the department of
pediatric cardiology. We find numerically the standard deviation and the global
efficiency, similar to the El Farol bar problem. After the score equation and
the scaled utility are introduced, the dynamical behavior of our model is
discussed for particular strategies. Our result presented will be compared with
the well-known minority games.


  The following work is an exploration into certain topics in the broad world
of integrable models, both classical and quantum, and consists of two main
parts of roughly equal length. The first part, consisting of chapters 1-3,
concerns itself mainly with correlations between results in classical
hierarchies and quantum lattice models. The second part, consisting of chapters
4-6, deals almost entirely with deriving results concerned with quantum lattice
models.


  Sign language recognition (SLR) is a weakly supervised task that annotates
sign videos as textual glosses. Recent studies show that insufficient training
caused by the lack of large-scale available sign datasets becomes the main
bottleneck for SLR. Most SLR works thereby adopt pretrained visual modules and
develop two mainstream solutions. The multi-stream architectures extend
multi-cue visual features, yielding the current SOTA performances but requiring
complex designs and might introduce potential noise. Alternatively, the
advanced single-cue SLR frameworks using explicit cross-modal alignment between
visual and textual modalities are simple and effective, potentially competitive
with the multi-cue framework. In this work, we propose a novel contrastive
visual-textual transformation for SLR, CVT-SLR, to fully explore the pretrained
knowledge of both the visual and language modalities. Based on the single-cue
cross-modal alignment framework, we propose a variational autoencoder (VAE) for
pretrained contextual knowledge while introducing the complete pretrained
language module. The VAE implicitly aligns visual and textual modalities while
benefiting from pretrained contextual knowledge as the traditional contextual
module. Meanwhile, a contrastive cross-modal alignment algorithm is designed to
explicitly enhance the consistency constraints. Extensive experiments on public
datasets (PHOENIX-2014 and PHOENIX-2014T) demonstrate that our proposed CVT-SLR
consistently outperforms existing single-cue methods and even outperforms SOTA
multi-cue methods.


  Different aspects of Tau physics are discussed, both as a New Physics probe
and as a tool to study Standard Model physics. The goal of this Tau2014 opening
talk is to introduce (some of) the many research directions currently being
pursued with tau leptons, which will be discussed in great technical detail in
the following talks.


  We want to study the mid-infrared properties and the starburst and AGN
contributions, of 24um sources at z~2, through analysis of mid-infrared spectra
combined with millimeter, radio, and infrared photometry. Mid-infrared
spectroscopy allows us to recover accurate redshifts. A complete sample of 16
Spitzer-selected sources (ULIRGs) believed to be starbursts at z~2
("5.8um-peakers") was selected in the (0.5 sq.deg.) J1064+56 SWIRE Lockman Hole
field. These sources have S(24um)>0.5mJy, a stellar emission peak redshifted to
5.8um, and r'(Vega)>23. The entire sample was observed with the low resolution
units of the Spitzer/IRS infrared spectrograph. These sources have 1.2mm
observations with IRAM 30m/MAMBO and very deep 20cm observations from the VLA.
Nine of our sources also benefit from 350um observation and detection from
CSO/SHARC-II. The entire sample shows good quality IRS spectra dominated by
strong PAH features. The main PAH features at 6.2, 7.7, 8.6, and 11.3um have
high S/N average luminosities of 2.90, 10.38, 3.62, and 2.29x10^{10}Lsun,
respectively. We derived accurate redshifts spanning from 1.75 to 2.28. The
average of these redshifts is 2.017. This result confirms that the selection
criteria of "5.8um-peakers" associated with a strong detection at 24um are
reliable to select sources at z~2. We have analyzed the different correlations
between PAH emission and infrared, millimeter, and radio emission. Practically
all our sources are strongly dominated by starburst emission. We have also
defined two subsamples based on the equivalent width at 7.7um to investigate
AGN contributions. Our sample contains strong starbursts and represents a
particularly 24um-bright class of SMGs. The very good correlation between PAH
and far-IR luminosities is now confirmed in high-z starburst ULIRGs. These
sources show a small AGN contribution to the mid-IR, around ~20% in most cases.


  We introduce a new notion of $\boxast$-product of two integrable series with
coefficients in distinct Grothendieck rings of algebraic varieties, preserving
the integrability and commuting with the limit of rational series. In the same
context, we define a motivic multiple zeta function with respect to an ordered
family of regular functions, which is integrable and connects closely to
Denef-Loeser's motivic zeta functions. We also show that the $\boxast$-product
is associative in the class of motivic multiple zeta functions.
  Furthermore, a version of the Euler reflexion formula for motivic zeta
functions is nicely formulated to deal with the $\boxast$-product and motivic
multiple zeta functions, and it is proved using the theory of arc spaces. As an
application, taking the limit for the motivic Euler reflexion formula we
recover the well known motivic Thom-Sebastiani theorem.


  We study the gravitational wave (GW) production induced by the asymmetric
jets of gamma-ray bursts (GRBs). The asymmetric jets result in a recoil force
acted on the central compact object, whose motion leads to emission of GW.
Under reasonable assumptions and simplifications, we derive the analytic form
of the produce GWs. The amplitude of emitted GWs is estimated to be relatively
low, but possibility exists that they can be detected by future experiments
such as the Einstein Telescope. We find the dynamical properties of the central
object, which is difficult to be studied via the electromagnetic (EW) channel,
can be inferred by measuring the emitted GWs. Moreover, we find the emitted GWs
can be used determine whether the relativistic jets is launched by the neutrino
annihilation process or the Blandford-Znajek process, which cannot be clearly
distinguished by the current GRB observations. Our work manifests the
importance of the GW channel in multi-messenger astronomy. The physical
information encoded in the GW and EW emissions of an astrophysical object is
complementary to each other; in case some physics can not be effectively
investigated using the EW channel alone, including the GW channel can be very
helpful.


  HELAC is a FORTRAN based package that is able to compute efficiently helicity
amplitudes for arbitrary scattering processes within the standard electroweak
theory. The algorithm exploits the virtues of the Dyson-Schwinger equations as
compared to the traditional Feynman graph approach. All electroweak vertices
are included in both the unitary and Feynman gauges, and computations including
all mass effects are available. A version performing multi-precision
computations with arbitrary - user defined - accuracy is also included,
allowing access to any phase space point for arbitrary high energies.


  We present a first principle study of the electron-phonon (e-p) interaction
at the Be(0001) surface. The real and imaginary part of the e-p self energy are
calculated for the surface state in the binding energy range from the
$\bar{\Gamma}$ point to the Fermi level. Our calculation shows an overall good
agreement with several photoemission data measured at high and low
temperatures. Additionally, we show that the energy derivative of real part of
the self-energy presents a strong temperature and energy variation close to
$E_{F}$, making it difficult to measure its value just at $E_{F}$.


  We show that three dimensional cubes of any size can be tiled with trominoes
and, when necessary, one or two singletons in any positions. Cubes of side
length a multiple of three can always be tiled with trominoes (known), cubes of
side length congruent to 1 mod 3 can always be tiled with an arbitrary single
cube and trominoes, and cubes of side length congruent to 2 mod 3 can always be
tiled with two single cubes in arbitrary locations and trominoes.


  This paper introduces a new methodology for detecting anomalies in time
series data, with a primary application to monitoring the health of (micro-)
services and cloud resources. The main novelty in our approach is that instead
of modeling time series consisting of real values or vectors of real values, we
model time series of probability distributions over real values (or vectors).
This extension to time series of probability distributions allows the technique
to be applied to the common scenario where the data is generated by requests
coming in to a service, which is then aggregated at a fixed temporal frequency.
Our method is amenable to streaming anomaly detection and scales to monitoring
for anomalies on millions of time series. We show the superior accuracy of our
method on synthetic and public real-world data. On the Yahoo Webscope data set,
we outperform the state of the art in 3 out of 4 data sets and we show that we
outperform popular open-source anomaly detection tools by up to 17% average
improvement for a real-world data set.


  Humans and other animals behave as if we perform fast Bayesian inference
underlying decisions and movement control given uncertain sense data. Here we
show that a biophysically realistic model of the subthreshold membrane
potential of a single neuron can exactly compute the numerator in Bayes rule
for inferring the Poisson parameter of a sensory spike train. A simple network
of spiking neurons can construct and represent the Bayesian posterior density
of a parameter of an external cause that affects the Poisson parameter,
accurately and in real time.


  We give an extension of Sister Celine's method of proving hypergeometric sum
identities that allows it to handle a larger variety of input summands. We then
apply this to several problems. Some give new results, and some reprove already
known results in an automated way.


  Let $B^p_{\sigma}$, $1\le p<\infty$, $\sigma>0$, denote the space of all
$f\in L^p(\mathbb{R})$ such that the Fourier transform of $f$ (in the sense of
distributions) vanishes outside $[-\sigma,\sigma]$. The classical sampling
theorem states that each $f\in B^p_{\sigma}$ may be reconstructed exactly from
its sample values at equispaced sampling points $\{\pi
m/\sigma\}_{m\in\mathbb{Z}} $ spaced by $\pi /\sigma$. Reconstruction is also
possible from sample values at sampling points $\{\pi \theta m/\sigma\}_m $
with certain $1< \theta\le 2$ if we know $f(\theta\pi m/\sigma) $ and
$f'(\theta\pi m/\sigma)$, $m\in\mathbb{Z}$. In this paper we present sampling
series for functions of several variables. These series involves samples of
functions and their partial derivatives.


  Synthetic aperture imaging (SAI) is able to achieve the see through effect by
blurring out the off-focus foreground occlusions and reconstructing the
in-focus occluded targets from multi-view images. However, very dense
occlusions and extreme lighting conditions may bring significant disturbances
to the SAI based on conventional frame-based cameras, leading to performance
degeneration. To address these problems, we propose a novel SAI system based on
the event camera which can produce asynchronous events with extremely low
latency and high dynamic range. Thus, it can eliminate the interference of
dense occlusions by measuring with almost continuous views, and simultaneously
tackle the over/under exposure problems. To reconstruct the occluded targets,
we propose a hybrid encoder-decoder network composed of spiking neural networks
(SNNs) and convolutional neural networks (CNNs). In the hybrid network, the
spatio-temporal information of the collected events is first encoded by SNN
layers, and then transformed to the visual image of the occluded targets by a
style-transfer CNN decoder. Through experiments, the proposed method shows
remarkable performance in dealing with very dense occlusions and extreme
lighting conditions, and high quality visual images can be reconstructed using
pure event data.


  Previous studies of the evolution of gamma-ray burst spectra have generally
relied on fitting sequences of spectra. These studies usually were confined to
bright bursts and often lacked sufficient temporal resolution. We have
developed techniques which sacrifice spectral resolution for temporal
resolution. First, the crosscorrelations between intensity lightcurves in
different energy bands allow for the classification of the spectral evolution
of a large burst sample. Second, the energy correlation of pairs of counts
places limits on short-duration, narrowband emission.


  We perform a dynamical analysis for the exponential scalar field with
non-minimally derivative coupling. For the quintessence case, the stable fixed
points are the same with and without the non-minimally derivative coupling. For
the phantom case, the attractor with dark energy domination exists for the
minimal coupling only. For the non-minimally derivative coupling without the
standard canonical kinetic term, only the de-Sitter attractor exists, and the
dark matter solution is unstable.


  The Sudbury Neutrino Observatory (SNO) used an array of 3He proportional
counters to measure the rate of neutral-current interactions in heavy water and
precisely determined the total active (nu_x) 8B solar neutrino flux. This
technique is independent of previous methods employed by SNO. The total flux is
found to be 5.54(+0.33/-0.31 stat, +0.36/-0.34 syst) x 10^6 cm^-2 s^-1, in
agreement with previous measurements and standard solar models. A global
analysis of solar and reactor neutrino results yields Delta m^2 =
7.59(+0.19/-0.21) x 10^-5 eV^2 and theta = 34.4(+1.3/-1.2) degrees. The
uncertainty on the mixing angle has been reduced from SNO's previous results.


  A multigraph G is triangle decomposable if its edge set can be partitioned
into subsets, each of which induces a triangle of G, and rationally triangle
decomposable if its triangles can be assigned rational weights such that for
each edge e of G, the sum of the weights of the triangles that contain e equals
1. We present a necessary and sufficient condition for a planar multigraph to
be triangle decomposable. We also show that if a simple planar graph is
rationally triangle decomposable, then it has such a decomposition using only
weights 0,1 and 1/2. This result provides a characterization of rationally
triangle decomposable simple planar graphs. Finally, if G is a multigraph with
the complete graph of order 4 as underlying graph, we give necessary and
sufficient conditions on the multiplicities of its edges for G to be triangle
and rationally triangle decomposable.


  We present two 1/8 supersymmetric intersecting p-brane solutions of
11-dimensional supergravity which upon compactification to four dimensions
reduce to extremal dyonic black holes with finite area of horizon. The first
solution is a configuration of three intersecting 5-branes with an extra
momentum flow along the common string. The second describes a system of two
2-branes and two 5-branes. Related (by compactification and T-duality) solution
of type IIB theory corresponds to a completely symmetric configuration of four
intersecting 3-branes. We suggest methods for counting the BPS degeneracy of
three intersecting 5-branes which, in the macroscopic limit, reproduce the
Bekenstein-Hawking entropy.


  We characterize $k$--colorability of a simplicial graph via the intrinsic
algebraic structure of the associated right-angled Artin group. As a
consequence, we show that a certain problem about the existence of
homomorphisms from right-angled Artin groups to products of free groups is
NP--complete.


  The hull $H(C)$ of a linear code $C$ is defined by $H(C)=C \cap C^\perp$. A
linear code with a complementary dual (LCD) is a linear code with $H(C)=\{0\}$.
The dimension of the hull of a code is an invariant under permutation
equivalence. For binary and ternary codes the dimension of the hull is also
invariant under monomial equivalence and we show that this invariant is
determined by the extended weight enumerator of the code.\\ The hull of a code
is not invariant under monomial equivalence if $q\geq 4$. We show that every
${\mathbb F}_q $-linear code is monomial equivalent with an LCD code in case $q
\geq 4$. The proof uses techniques from Gr\"obner basis theory. We conclude
that if there exists an ${\mathbb F}_q $-linear code with parameters
$[n,k,d]_q$ and $q \geq 4$, then there exists also a LCD code with the same
parameters. Hence this holds for optimal and MDS codes. In particular there
exist LCD codes that are above the Gilbert-Varshamov bound if $q$ is a square
and $q\geq 49$ by the existence of such codes that are algebraic geometric.\\
Similar results are obtained with respect to Hermitian LCD codes.


  We construct various systems of coherent states (SCS) on the
$O(D)$-equivariant fuzzy spheres $S^d_\Lambda$ ($d=1,2$, $D=d\!+\!1$)
constructed in [G. Fiore, F. Pisacane, J. Geom. Phys. 132 (2018), 423-451] and
study their localizations in configuration space as well as angular momentum
space. These localizations are best expressed through the $O(D)$-invariant
square space and angular momentum uncertainties
$(\Delta\boldsymbol{x})^2,(\Delta\boldsymbol{L})^2$ in the ambient Euclidean
space $\mathbb{R}^D$. We also determine general bounds (e.g. uncertainty
relations from commutation relations) for
$(\Delta\boldsymbol{x})^2,(\Delta\boldsymbol{L})^2$, and partly investigate
which SCS may saturate these bounds. In particular, we determine
$O(D)$-equivariant systems of optimally localized coherent states, which are
the closest quantum states to the classical states (i.e. points) of $S^d$. We
compare the results with their analogs on commutative $S^d$. We also show that
on $S^2_\Lambda$ our optimally localized states are better localized than those
on the Madore-Hoppe fuzzy sphere with the same cutoff $\Lambda$.


  It was recently shown that a massive thin shell that is sandwiched between a
flat interior and an exterior geometry given by the outgoing Vaidya metric
becomes null in a finite proper time. We investigate this transition for a
general spherically-symmetric metric outside the shell and find that it occurs
generically. Once the shell is null its persistence on a null trajectory can be
ensured by several mechanisms that we describe. Using the outgoing Vaidya
metric as an example we show that if a dust shell acquires surface pressure on
its transition to a null trajectory it can evade the Schwarzschild radius
through its collapse. Alternatively, the pressureless collapse may continue if
the exterior geometry acquires a more general form.


  We develop an extension of subhalo abundance matching (SHAM) capable of
accurately reproducing the real and redshift-space clustering of galaxies in a
state-of-the-art hydrodynamical simulation. Our method uses a low-resolution
gravity-only simulation and it includes orphan and tidal disruption
prescriptions for satellite galaxies, and a flexible amount of galaxy assembly
bias. Furthermore, it includes recipes for star formation rate (SFR) based on
the dark matter accretion rate. We test the accuracy of our model against
catalogues of stellar-mass- and SFR-selected galaxies in the TNG300
hydrodynamic simulation. By fitting a small number of free parameters, our
extended SHAM reproduces the projected correlation function and redshift-space
multipoles for number densities $10^{-3} - 10^{-2}\, h^{3}{\rm Mpc}^{-3}$, at
$z=1$ and $z=0$, and for scales $r \in [0.3 - 20] h^{-1}{\rm Mpc}$.
Simultaneously, the SHAM results also retrieve the correct halo occupation
distribution, the level of galaxy assembly bias, and higher-order statistics
present in the TNG300 galaxy catalogues. As an application, we show that our
model simultaneously fits the projected correlation function of the SDSS in 3
disjoint stellar mass bins, with an accuracy similar to that of TNG300
galaxies. This SHAM extension can be used to get accurate clustering prediction
even when using low and moderate-resolution simulations.


  Taking advantage of the conformal equivalence of f(R) theories of gravity
with General Relativity coupled to a scalar field we generalize the Israel
junction conditions for this class of theories by direct integration of the
field equations. We suggest a specific non-minimal coupling of matter to
gravity which opens the possibility of a new class of braneworld scenarios.


  This paper deals with the spectral element modeling of seismic wave
propagation at the global scale. Two aspects relevant to low-frequency studies
are particularly emphasized. First, the method is generalized beyond the
Cowling approximation in order to fully account for the effects of
self-gravitation. In particular, the perturbation of the gravity field outside
the Earth is handled by a projection of the spectral element solution onto the
basis of spherical harmonics. Second, we propose a new formulation inside the
fluid which allows to account for an arbitrary density stratification. It is
based upon a decomposition of the displacement into two scalar potentials, and
results in a fully explicit fluid-solid coupling strategy. The implementation
of the method is carefully detailed and its accuracy is demonstrated through a
series of benchmark tests.


  Efficient channel state information at transmitter (CSIT) for frequency
division duplex (FDD) massive MIMO can facilitate its backward compatibility
with existing FDD cellular networks. To date, several CSIT estimation schemes
have been proposed for FDD single-cell massive MIMO systems, but they fail to
consider inter-cell-interference (ICI) and suffer from downlink pilot
contamination in multi-cell scenario. To solve this problem, this paper
proposes a compressive sensing (CS)-based CSIT estimation scheme to combat ICI
in FDD multi-cell massive MIMO systems. Specifically, angle-domain massive MIMO
channels exhibit the common sparsity over different subcarriers, and such
sparsity is partially shared by adjacent users. By exploiting these sparsity
properties, we design the pilot signal and the associated channel estimation
algorithm under the framework of CS theory, where the channels associated with
multiple adjacent BSs can be reliably estimated with low training overhead for
downlink pilot decontamination. Simulation results verify the good downlink
pilot decontamination performance of the proposed solution compared to its
conventional counterparts in multi-cell FDD massive MIMO.


  Our research topic is Human segmentation with static camera. This topic can
be divided into three sub-tasks, which are object detection, instance
identification and segmentation. These sub-tasks are three closely related
subjects. The development of each subject has great impact on the other two
fields. In this literature review, we will first introduce the background of
human segmentation and then talk about issues related to the above three fields
as well as how they interact with each other.


  We investigate the formation and evolution of misaligned accretion discs
around the secondary component of a binary through mass transfer driven by
Kozai-Lidov oscillations of the circumprimary disc's eccentricity and
inclination. We perform SPH simulations to study the amount of mass transferred
to the secondary star as a function of both the disc and binary parameters. For
the range of parameters we explore, we find that increasing the disc aspect
ratio, viscosity parameter and initial inclination as well as decreasing the
binary mass ratio leads to larger amount of mass transfer, up to a maximum of
about ten per cent of the initial mass of the primary disc. The circumsecondary
disc forms with a high eccentricity and a high inclination and is also able to
undergo KL oscillations. The circumsecondary disc oscillations have a shorter
period than those in the disc around the primary. We find that some of the
material that escapes the Roche-lobe of the two components forms a narrow
misaligned circumbinary accretion disc. This study has implications for disc
evolution in young binary star systems.


  Modern single-cell flow and mass cytometry technologies measure the
expression of several proteins of the individual cells within a blood or tissue
sample. Each profiled biological sample is thus represented by a set of
hundreds of thousands of multidimensional cell feature vectors, which incurs a
high computational cost to predict each biological sample's associated
phenotype with machine learning models. Such a large set cardinality also
limits the interpretability of machine learning models due to the difficulty in
tracking how each individual cell influences the ultimate prediction. We
propose using Kernel Mean Embedding to encode the cellular landscape of each
profiled biological sample. Although our foremost goal is to make a more
transparent model, we find that our method achieves comparable or better
accuracies than the state-of-the-art gating-free methods through a simple
linear classifier. As a result, our model contains few parameters but still
performs similarly to deep learning models with millions of parameters. In
contrast with deep learning approaches, the linearity and sub-selection step of
our model makes it easy to interpret classification results. Analysis further
shows that our method admits rich biological interpretability for linking
cellular heterogeneity to clinical phenotype.


  The problem with Bekenstein-Hawking entropy of black hole can be resolved
with quantum gravity theory with Dp-branes as supergravity solutions of type
IIB string theory. Dp-brane solutions of type IIB are a direct analog of the
Schwarzschild charged hole, so called black p-branes. The coincidence of the
black p-brane metrics and ten-dimensional metrics of N-parallel D3-branes was
used from the viewpoint of the Azumaya structure on D-branes connected with
deformation of the classical moduli space. Applying Rosenberg theorem we can
classify Hilbert spaces of N coinciding Dp-branes as vector bundles through
K-functor.


  We establish an intriguing connection between quantum phase transitions and
bifurcations in the ground-state fidelity per lattice site, and construct the
universal order parameter for quantum Ising model in a transverse magnetic
field on an infinite-size square lattice in two spatial dimensions, a
prototypical model with symmetry breaking order. This is achieved by computing
ground-state wave functions in the context of the tensor network algorithm
based on the infinite projected entangled-pair state representation. Our
finding is applicable to any systems with symmetry breaking order, as a result
of the fact that, in the conventional Landau-Ginzburg-Wilson paradigm, a
quantum system undergoing a phase transition is characterized in terms of
spontaneous symmetry breaking captured by a local order parameter. In addition,
a bifurcation in the reduced fidelity between two different reduced density
matrices is also discussed.


  In this paper we study quantum state transfer (also called quantum tunneling)
on graphs when there is a potential function on the vertex set. We present two
main results. First, we show that for paths of length greater than three, there
is no potential on the vertices of the path for which perfect state transfer
between the endpoints can occur. In particular, this answers a question raised
by Godsil in Section 20 of [8]. Second, we show that if a graph has two
vertices that share a common neighborhood, then there is a potential on the
vertex set for which perfect state transfer will occur between those two
vertices. This gives numerous examples where perfect state transfer does not
occur without the potential, but adding a potential makes perfect state
transfer possible. In addition, we investigate perfect state transfer on graph
products, which gives further examples where perfect state transfer can occur.


  Discriminative dictionary learning (DDL) has recently gained significant
attention due to its impressive performance in various pattern classification
tasks. However, the locality of atoms is not fully explored in conventional DDL
approaches which hampers their classification performance. In this paper, we
propose a locality constraint dictionary learning with support vector
discriminative term (LCDL-SV), in which the locality information is preserved
by employing the graph Laplacian matrix of the learned dictionary. To jointly
learn a classifier during the training phase, a support vector discriminative
term is incorporated into the proposed objective function. Moreover, in the
classification stage, the identity of test data is jointly determined by the
regularized residual and the learned multi-class support vector machine.
Finally, the resulting optimization problem is solved by utilizing the
alternative strategy. Experimental results on benchmark databases demonstrate
the superiority of our proposed method over previous dictionary learning
approaches on both hand-crafted and deep features. The source code of our
proposed LCDL-SV is accessible at https://github.com/yinhefeng/LCDL-SV


  We introduce a novel self-attention mechanism, which we call CSA (Chromatic
Self-Attention), which extends the notion of attention scores to attention
_filters_, independently modulating the feature channels. We showcase CSA in a
fully-attentional graph Transformer CGT (Chromatic Graph Transformer) which
integrates both graph structural information and edge features, completely
bypassing the need for local message-passing components. Our method flexibly
encodes graph structure through node-node interactions, by enriching the
original edge features with a relative positional encoding scheme. We propose a
new scheme based on random walks that encodes both structural and positional
information, and show how to incorporate higher-order topological information,
such as rings in molecular graphs. Our approach achieves state-of-the-art
results on the ZINC benchmark dataset, while providing a flexible framework for
encoding graph structure and incorporating higher-order topology.


  The black hole area spectrum has been studied in the framework of tunneling
mechanism, where a Generalized Uncertainty Principle (GUP) has been considered.
The results implies in a non evenly spaced spectrum for the black hole area
which becomes increasingly spaced as the black hole evaporates.


  We study a supergauge transformation of a complex superfield which generates
a chiral superfield in two-dimensional ${\cal N}=(2,2)$ theory. This complex
superfield is referred to as the prepotential of the chiral superfield. Since
there exist redundant component fields in the prepotential, we remove some of
them by a gauge-fixing condition. This situation is parallel to that of a
vector superfield. In order to obtain a suitable configuration of the GLSM for
the exotic five-brane which gives rise to a nongeometric background, we impose
a relatively relaxed gauge-fixing condition. It turns out that the gauge-fixed
prepotential is different from a semichiral superfield whose scalar field
represents a coordinate of generalized K\"{a}hler geometry.


  In recent years proper motion measurements have been added to the set of
observational constraints on the current properties of Galactic X-ray binaries.
We develop an analysis that allows us to consider all this available
information and reconstruct the full evolutionary history of X-ray binaries
back to the time of compact object formation. This analysis accounts for mass
transfer through the ongoing X-ray phase, tidal circularization before the
onset of Roche-lobe overflow, motion through the Galactic potential after the
formation of the compact object, and binary orbital dynamics and hydrodynamic
modeling of the core collapse. We apply the analysis to the soft X-ray
transient GRO J1655-40 and, for the first time, use its full 3D peculiar
velocity constraints right after core collapse instead of lower limits on the
current space velocity given by the present-day radial velocity. We find that
the system has remained within 200pc from the Galactic plane throughout its
entire life time and that the mass loss and a kick possibly associated with the
black hole (BH) formation imparted a kick velocity of 45-115 km/s to the
binary's center of mass. Right after BH formation, the system consists of a
3.5-6.3 Msun BH and a 2.3-4 Msun main-sequence star. At the onset of the X-ray
phase the donor is still on the main sequence. We find that a symmetric BH
formation event cannot be formally excluded, but that the associated system
parameters are only marginally consistent with the currently observed binary
properties. BH formation mechanisms involving an asymmetric supernova explosion
with associated BH kick velocities of a few tens of km/s, on the other hand,
satisfy the constraints much more comfortably. We also derive an upper limit on
the BH kick magnitude of 210 km/s. (abridged)


  Let $\beta>1$, $I$ be the unite interval $[0,1)$ and $\phi$ be an integer
function defined on $\mathbb{N}\setminus\{0\}$ satisfying $1\leq\phi(n)\leq n$.
Denote by $A_\phi(x,\beta)$ the Erd\"{o}s-R\'{e}nyi average of $x\in I$
associated with the function $\phi$ in $\beta$-expansion and $I_\beta$ the
range of $A_\phi(x,\beta)$ for $x\in I$. For the level set \begin{align*}
  ER_\phi^\beta(\alpha)=\left\{x\in I\colon
A_\phi(x,\beta)=\alpha\right\},\quad\text{where}\ \alpha\in I_\beta,
\end{align*} in this paper we will determine its Hausdorff dimension under the
assumption $\phi(n)\to\infty$ as $n\to\infty$ and $\phi$ is the integer part of
some slowly varying sequence. Besides, a generalization to the classic work
\cite{Be} of Besicovitch is also given in $\beta$-expansion.


  We first develop some basic facts about hypergeometric sheaves on the
multiplicative group ${\mathbb G}_m$ in characteristic $p >0$. Certain of their
Kummer pullbacks extend to irreducible local systems on the affine line in
characteristic $p>0$. One of these, of rank $23$ in characteristic $p=3$, turns
out to have the Conway group $\mathrm{Co}_2$, in its irreducible orthogonal
representation of degree $23$, as its arithmetic and geometric monodromy
groups.


  Let $\A$ and $\B$ be unital Banach algebras and $\phi\colon\A\to\B$ be a
unital continuous homomorphism. We prove that if $\phi$ is relatively spectral
(i.e., there is a dense subalgebra $X$ of $\A$ such that
$\sp_\B(\phi(a))=\sp_\A(a)$ for every $a\in X$) and has dense range, then
$\phi$ induces monomorphisms from $K_i(\A)$ to $K_i(\B)$, $i=0,1$.


  Control and manipulation of electric current and, especially, its degree of
spin polarization (spin filtering) across single molecules are currently of
great interest in the field of molecular spintronics. We explore one possible
strategy based on the modification of nanojunction symmetry which can be
realized, for example, by a mechanical strain. Such modification can activate
new molecular orbitals which were inactive before due to their orbital mismatch
with the electrode's conduction states. This can result in several important
consequences such as (i) quantum interference effects appearing as Fano-like
features in electron transmission and (ii) the change in molecular level
hybridization with the electrode's states. We argue that the symmetry change
can affect very differently two majority- and minority-spin conductances and
thus alter significantly the resulting spin-filtering ratio as the junction
symmetry is modified. We illustrate the idea for two basic molecular junctions:
Ni/benzene/Ni (perpendicular vs tilted orientations) and Ni/Si chain/Ni (zigzag
vs linear chains). In both cases, one highest occupied molecular orbital (HOMO)
and one lowest unoccupied molecular orbital (LUMO) (out of HOMO and LUMO
doublets) are important. In particular, their destructive interference with
other orbitals leads to dramatic suppression of majority-spin conductance in
low-symmetry configurations. For a minority-spin channel, on the contrary, the
conductance is strongly enhanced when the symmetry is lowered due to an
increase in hybridization strength. We believe that our results may offer a
potential route for creating molecular devices with a large on-off ratio of
spin polarization via quantum interference effects.


  We revisit the Frank-Wolfe (FW) optimization under strongly convex constraint
sets. We provide a faster convergence rate for FW without line search, showing
that a previously overlooked variant of FW is indeed faster than the standard
variant. With line search, we show that FW can converge to the global optimum,
even for smooth functions that are not convex, but are quasi-convex and
locally-Lipschitz. We also show that, for the general case of (smooth)
non-convex functions, FW with line search converges with high probability to a
stationary point at a rate of $O\left(\frac{1}{t}\right)$, as long as the
constraint set is strongly convex -- one of the fastest convergence rates in
non-convex optimization.


  Tensor completion aims at filling the missing or unobserved entries based on
partially observed tensors. However, utilization of the observed tensors often
raises serious privacy concerns in many practical scenarios. To address this
issue, we propose a solid and unified framework that contains several
approaches for applying differential privacy to the two most widely used tensor
decomposition methods: i) CANDECOMP/PARAFAC~(CP) and ii) Tucker decompositions.
For each approach, we establish a rigorous privacy guarantee and meanwhile
evaluate the privacy-accuracy trade-off. Experiments on synthetic and
real-world datasets demonstrate that our proposal achieves high accuracy for
tensor completion while ensuring strong privacy protections.


  In this paper we show that the quantum channel between two inertial observers
who transmit quantum information by sending realistic photonic wave packets is
a well-studied channel in quantum Shannon theory -- the Pauli channel. The
parameters of the Pauli channel and therefore its classical and quantum
capacity depend on the magnitude of the Lorentz boost relating the two
observers. The most striking consequence is that two inertial observers whose
Pauli channel has initially zero quantum capacity can achieve nonzero quantum
communication rates (reaching in principle its maximal value equal to one) by
applying a boost in the right direction. This points to a fundamental
connection between quantum channel capacities and special relativity.


  Motivated by the need for computationally tractable spatial methods in
neuroimaging studies, we develop a distributed and integrated framework for
estimation and inference of Gaussian process model parameters with
ultra-high-dimensional likelihoods. We propose a shift in viewpoint from whole
to local data perspectives that is rooted in distributed model building and
integrated estimation and inference. The framework's backbone is a
computationally and statistically efficient integration procedure that
simultaneously incorporates dependence within and between spatial resolutions
in a recursively partitioned spatial domain. Statistical and computational
properties of our distributed approach are investigated theoretically and in
simulations. The proposed approach is used to extract new insights on autism
spectrum disorder from the Autism Brain Imaging Data Exchange.


  In the problem of quantum state discrimination, one has to determine by
measurements the state of a quantum system, based on the a priori side
information that the true state is one of two given and completely known
states, rho or sigma. In general, it is not possible to decide the identity of
the true state with certainty, and the optimal measurement strategy depends on
whether the two possible errors (mistaking rho for sigma, or the other way
around) are treated as of equal importance or not. Results on the quantum
Chernoff and Hoeffding bounds and the quantum Stein's lemma show that, if
several copies of the system are available then the optimal error probabilities
decay exponentially in the number of copies, and the decay rate is given by a
certain statistical distance between rho and sigma (the Chernoff distance, the
Hoeffding distances, and the relative entropy, respectively). While these
results provide a complete solution to the asymptotic problem, they are not
completely satisfying from a practical point of view. Indeed, in realistic
scenarios one has access only to finitely many copies of a system, and
therefore it is desirable to have bounds on the error probabilities for finite
sample size. In this paper we provide finite-size bounds on the so-called Stein
errors, the Chernoff errors, the Hoeffding errors and the mixed error
probabilities related to the Chernoff and the Hoeffding errors.


  Complex social systems are composed of interconnected individuals whose
interactions result in group behaviors. Optimal control of a real-world complex
system has many applications, including road traffic management, epidemic
prevention, and information dissemination. However, such real-world complex
system control is difficult to achieve because of high-dimensional and
non-linear system dynamics, and the exploding state and action spaces for the
decision maker. Prior methods can be divided into two categories:
simulation-based and analytical approaches. Existing simulation approaches have
high-variance in Monte Carlo integration, and the analytical approaches suffer
from modeling inaccuracy. We adopted simulation modeling in specifying the
complex dynamics of a complex system, and developed analytical solutions for
searching optimal strategies in a complex network with high-dimensional
state-action space. To capture the complex system dynamics, we formulate the
complex social network decision making problem as a discrete event decision
process. To address the curse of dimensionality and search in high-dimensional
state action spaces in complex systems, we reduce control of a complex system
to variational inference and parameter learning, introduce Bethe entropy
approximation, and develop an expectation propagation algorithm. Our proposed
algorithm leads to higher system expected rewards, faster convergence, and
lower variance of value function in a real-world transportation scenario than
state-of-the-art analytical and sampling approaches.


  Artin glueings provide a way to reconstruct a frame from a closed sublocale
and its open complement. We show that Artin glueings can be described as the
weakly Schreier split extensions in the category of frames with finite-meet
preserving maps. These extensions correspond to meet-semilattice homomorphisms
between frames, yielding an extension bifunctor. Finally, we discuss Baer sums,
the induced order structure on extensions and the failure of the split short
five lemma.


  Learning concepts that are consistent with human perception is important for
Deep Neural Networks to win end-user trust. Post-hoc interpretation methods
lack transparency in the feature representations learned by the models. This
work proposes a guided learning approach with an additional concept layer in a
CNN- based architecture to learn the associations between visual features and
word phrases. We design an objective function that optimizes both prediction
accuracy and semantics of the learned feature representations. Experiment
results demonstrate that the proposed model can learn concepts that are
consistent with human perception and their corresponding contributions to the
model decision without compromising accuracy. Further, these learned concepts
are transferable to new classes of objects that have similar concepts.


  This paper studies the joint estimation problem of a discrete choice model
and the arrival rate of potential customers when unobserved stock-out events
occur. In this paper, we generalize [Anupindi et al., 1998] and [Conlon and
Mortimer, 2013] in the sense that (1) we work with generic choice models, (2)
we allow arbitrary numbers of products and stock-out events, and (3) we
consider the existence of the null alternative, and estimates the overall
arrival rate of potential customers. In addition, we point out that the
modeling in [Conlon and Mortimer, 2013] is problematic, and present the correct
formulation.


  Several methods have been explored for automating parts of Systematic Mapping
(SM) and Systematic Review (SR) methodologies. Challenges typically evolve
around the gaps in semantic understanding of text, as well as lack of domain
and background knowledge necessary to bridge that gap. In this paper we
investigate possible ways of automating parts of the SM/SR process, i.e. that
of extracting keywords and key-phrases from scientific documents using
unsupervised methods, which are then used as a basis to construct the
corresponding Classification Scheme using semantic key-phrase clustering
techniques. Specifically, we explore the effect of ensemble scores measure in
key-phrase extraction, we explore semantic network based word embedding in
embedding representation of phrase semantics and finally we also explore how
clustering can be used to group related key-phrases. The evaluation is
conducted on a dataset of publications pertaining the domain of "Explainable
AI" which we constructed using standard publicly available digital libraries
and sets of indexing terms (keywords). Results shows that: ensemble ranking
score does improve the key-phrase extraction performance. Semantic-network
based word embedding based on the ConceptNet Semantic Network has similar
performance with contextualized word embedding, however the former are
computationally more efficient. Finally Semantic key-phrase clustering at
term-level can group similar terms together that can be suitable for
classification scheme.


  Ricci-like solitons with potential Reeb vector field are introduced and
studied on almost contact B-metric manifolds. The cases of Sasaki-like
manifolds and torse-forming potentials have been considered. In these cases, it
is proved that the manifold admits a Ricci-like soliton if and only if the
structure is Einstein-like. Explicit examples of Lie groups as 3- and
5-dimensional manifolds with the structures studied are provided.


  In 2004, de Mathan and Teuli\'e stated the $p$-adic Littlewood Conjecture
($p$-$LC$) in analogy with the classical Littlewood Conjecture. Given a field
$\mathbb{K}$ and an irreducible polynomial $p(t)$ with coefficients in
$\mathbb{K}$, $p$-$LC$ admits a natural analogue over function fields,
abbreviated to $p(t)$-$LC$ (and to $t$-$LC$ when $p(t)=t$).
  In this paper, an explicit counterexample to $p(t)$-$LC$ is found over fields
of characteristic 5. Furthermore, it is conjectured that this Laurent series
disproves $p(t)$-$LC$ over all fields of characteristic $p\equiv 1 \mod 4$.
This fills a gap left by a breakthrough paper from Adiceam, Nesharim and Lunnon
(2022) in which they conjecture $t$-$LC$ does not hold over all complementary
fields of characteristic $p\equiv 3\mod 4$ and proving this in the case $p=3$.
Supported by computational evidence, this provides a complete picture on how
$p(t)$-$LC$ is expected to behave over all fields with characteristic not equal
to 2. Furthermore, the counterexample to $t$-$LC$ over fields of characteristic
3 found by Adiceam, Nesharim and Lunnon is proven to also hold over fields of
characteristic 7 and 11, which provides further evidence to the aforementioned
conjecture.
  Following previous work in this area, these results are achieved by building
upon combinatorial arguments and are computer assisted. A new feature of the
present work is the development of an efficient algorithm (implemented in
Python) that combines the theory of automatic sequences with Diophantine
approximation over function fields. This algorithm is expected to be useful for
further research around Littlewood-type conjectures over function fields.


  The Hartree-Fock equations are modified to directly yield Wannier functions
following a proposal of Shukla et al. [Chem. Phys. Lett. 262, 213-218 (1996)].
This approach circumvents the a posteriori application of the Wannier
transformation to Bloch functions. I give a novel and rigorous derivation of
the relevant equations by introducing an orthogonalizing potential to ensure
the orthogonality among the resulting functions. The properties of these,
so-called a priori Wannier functions, are analyzed and the relation of the
modified Hartree-Fock equations to the conventional, Bloch-function-based
equations is elucidated. It is pointed out that the modified equations offer a
different route to maximally localized Wannier functions. Their computational
solution is found to involve an effort that is comparable to the effort for the
solution of the conventional equations. Above all, I show how a priori Wannier
functions can be obtained by a modification of the Kohn-Sham equations of
density-functional theory.


  Modern deep neural networks rely on overparameterization to achieve
state-of-the-art generalization. But overparameterized models are
computationally expensive. Network pruning is often employed to obtain less
demanding models for deployment. Fine-grained pruning removes individual
weights in parameter tensors and can achieve a high model compression ratio
with little accuracy degradation. However, it introduces irregularity into the
computing dataflow and often does not yield improved model inference efficiency
in practice. Coarse-grained model pruning, while realizing satisfactory
inference speedup through removal of network weights in groups, e.g. an entire
filter, often lead to significant accuracy degradation. This work introduces
the cross-channel intragroup (CCI) sparsity structure, which can prevent the
inference inefficiency of fine-grained pruning while maintaining outstanding
model performance. We then present a novel training algorithm designed to
perform well under the constraint imposed by the CCI-Sparsity. Through a series
of comparative experiments we show that our proposed CCI-Sparsity structure and
the corresponding pruning algorithm outperform prior art in inference
efficiency by a substantial margin given suited hardware acceleration in the
future.


  Virtual flow meters, mathematical models predicting production flow rates in
petroleum assets, are useful aids in production monitoring and optimization.
Mechanistic models based on first-principles are most common, however,
data-driven models exploiting patterns in measurements are gaining popularity.
This research investigates a hybrid modeling approach, utilizing techniques
from both the aforementioned areas of expertise, to model a well production
choke. The choke is represented with a simplified set of first-principle
equations and a neural network to estimate the valve flow coefficient.
Historical production data from the petroleum platform Edvard Grieg is used for
model validation. Additionally, a mechanistic and a data-driven model are
constructed for comparison of performance. A practical framework for
development of models with varying degree of hybridity and stochastic
optimization of its parameters is established. Results of the hybrid model
performance are promising albeit with considerable room for improvements.


  This paper introduces a generalised 3rd-order Spectral Representation Method
for the simulation of multi-dimensional stochastic fields with asymmetric
non-linearities. The simulated random fields satisfy a prescribed Power
Spectrum and Bispectrum. The general d-dimensional simulation equations are
presented and the method is applied to simulate 2D and 3D random fields. The
differences between samples generated by the proposed methodology and the
existing classical Spectral Representation Method are analysed. An important
feature of this methodology is that the formula can be implemented efficiently
with the Fast Fourier Transform, details of which are presented. Computational
savings are shown to grow exponentially with dimensionality as a testament of
the scalability of the simulation methodology.


  The behavior of classical monochromatic waves in stationary media is shown to
be ruled by a novel, frequency-dependent function which we call Wave Potential,
and which we show to be encoded in the structure of the Helmholtz equation. An
exact, Hamiltonian, ray-based kinematical treatment, reducing to the usual
eikonal approximation in the absence of Wave Potential, shows that its presence
induces a mutual, perpendicular ray-coupling, which is the one and only cause
of wave-like phenomena such as diffraction and interference. The Wave
Potential, whose discovery does already constitute a striking novelty in the
case of classical waves, turns out to play an even more important role in the
quantum case. Recalling, indeed, that the time-independent Schroedinger
equation (associating the motion of mono-energetic particles with stationary
monochromatic matter waves) is itself a Helmholtz-like equation, the exact,
ray-based treatment developed in the classical case is extended - without
resorting to statistical concepts - to the exact, trajectory-based Hamiltonian
dynamics of mono-energetic point-like particles. Exact, classical-looking
particle trajectories may be defined, contrary to common belief, and turn out
to be perpendicularly coupled by an exact, energy-dependent Wave Potential,
similar in the form, but not in the physical meaning, to the statistical,
energy-independent "Quantum Potential" of Bohm's theory, which is affected, as
is well known, by the practical necessity of representing particles by means of
statistical wave packets, moving along probability flux lines. This result,
together with the connection shown to exist between Wave Potential and
Uncertainty Principle, allows a novel, non-probabilistic interpretation of Wave
Mechanics.


  We present a coupled model describing growth of microalgae in a raceway
cultivation process, accounting for hydrodynamics. Our approach combines a
biological model (based on the Han model) and shallow water dynamics equations
that model the fluid into the raceway. We then describe an optimization
procedure dealing with the topography to maximize the biomass production over
one cycle (one lap of the raceway). The results show that non-flat topographies
enhance microalgal productivity.


  In this note we give a glimpse of the fractional Laplacian. In particular, we
bring several definitions of this non-local operator and series of proofs of
its properties. It is structured in a way as to show that several of those
properties are natural extensions of their local counterparts, with some key
differences.


  We present an investigation of game-like simulations for physics teaching. We
report on the effectiveness of the interactive simulation "Real Time
Relativity" for learning special relativity. We argue that the simulation not
only enhances traditional learning, but also enables new types of learning that
challenge the traditional curriculum. The lessons drawn from this work are
being applied to the development of a simulation for enhancing the learning of
quantum mechanics.


  On February 16, 2021, an artificial object was recorded by the Spanish Meteor
Network (SPMN) moving slowly over the Mediterranean. From the astrometric
measurements, we identify this event as the reentry engine burn of a SpaceX
Falcon 9 launch vehicle's upper stage. To study this event in detail, we
adapted the plane intersection method for near-straight meteoroid trajectories
to analyze slow and curved orbits associated with artificial objects. To
corroborate our results, we approximated the orbital elements for the upper
stage using four pieces of "debris" cataloged by the U.S. Government Combined
Space Operations Center (CSpOC). Based on these calculations, we also estimated
the possible deorbit hazard zone using the MSISE90 model atmosphere. We warn of
the interference that these artificial bolides might have in fireball studies.
In addition, given that artificial bolides will be probably more frequent in
the future, we point out the new role that ground-based detection networks can
play in the monitoring of potentially hazardous artificial objects in
near-Earth space and determining the strewn field of artificial space debris.


  We propose a compact atom interferometry scheme for measuring weak,
time-dependent accelerations. Our proposal uses an ensemble of dilute trapped
bosons with two internal states that couple to a synthetic gauge field with
opposite charges. The trapped gauge field couples spin to momentum to allow
time dependent accelerations to be continuously imparted on the internal
states. We generalize this system to reduce noise and estimate the sensitivity
of such a system to be S~10^-7 m / s^2 / Hz^1/2.


  Using the semi-analytic method proposed by \citet{Esma91} we calculate the
abundances of the light elements produced during primordial nucleosynthesis
assuming that the gauge coupling constants of the fundamental interactions may
vary. We analyze the dependence of the nucleon masses, nuclear binding energies
and cross sections involved in the calculation of the abundances with the
fundamental constants assuming the chiral limit of QCD. The abundances of light
elements as a function of the fundamental constants are obtained. Finally,
using the observational data of $\De$, $\Het$, $\He$ and $\Li$ we estimate
constraints on the variation of the fundamental constants between the
primordial nucleosynthesis and the present. All observational abundances and
the WMAP estimate of the baryon density, can be fitted to the theoretical
predictions with varying coupling constants. The possible systematic errors in
the observational data, precludes from stronger conclusions.


  Highly accurate results for the homogeneous electron gas (HEG) have only been
achieved to date within a diffusion Monte Carlo (DMC) framework. Here, we
introduce a newly developed stochastic technique, Full Configuration
Interaction Quantum Monte Carlo (FCIQMC), which samples the exact wavefunction
expanded in plane wave Slater determinants. Despite the introduction of a basis
set incompleteness error, we obtain a finite-basis energy which is
significantly, and variationally lower than any previously published work for
the 54-electron HEG at $r_s$ = 0.5 a.u., in a Hilbert space of $10^{108}$
Slater determinants. At this value of $r_s$, as well as of 1.0 a.u., we remove
the remaining basis set incompleteness error by extrapolation, yielding results
comparable or better than state-of-the-art DMC backflow energies. In doing so,
we demonstrate that it is possible to yield highly accurate results with the
FCIQMC method in sizable periodic systems.


  The security of integrated circuits (ICs) can be broken by sophisticated
physical attacks relying on failure analysis methods. Optical probing is one of
the most prominent examples of such attacks, which can be accomplished in a
matter of days, even with limited knowledge of the IC under attack.
Unfortunately, few countermeasures are proposed in the literature, and none has
been fabricated and tested in practice. These countermeasures usually require
changing the standard cell libraries and, thus, are incompatible with digital
and programmable platforms, such as field programmable gate arrays (FPGAs). In
this work, we shift our attention from preventing the attack to detecting and
responding to it. We introduce LaserEscape, the first fully digital and
FPGA-compatible countermeasure to detect and mitigate optical probing attacks.
LaserEscape incorporates digital delay-based sensors to reliably detect the
physical alteration on the fabric caused by laser beam irradiations in real
time. Furthermore, as a response to the attack, LaserEscape deploys real-time
hiding approaches using randomized hardware reconfigurability. It realizes 1)
moving target defense (MTD) to physically move the sensitive circuity under
attack out of the probing field of focus to protect secret keys and 2)
polymorphism to logically obfuscate the functionality of the targeted circuit
to counter function extraction and reverse engineering attempts. We demonstrate
the effectiveness and resiliency of our approach by performing optical probing
attacks on protected and unprotected designs on a 28-nm FPGA. Our results show
that optical probing attacks can be reliably detected and mitigated without
interrupting the chip's operation.


  We present follow-up observations at 5, 8 and 30 GHz of the K-band Northern
Wide Survey (KNoWS) 20 GHz Bright Sample, performed with the 32-m Medicina
Radio Telescope and the 32-m Torun Radio Telescope. The KNoWS sources were
selected in the Northern Polar Cap (Delta > 72 deg) and have a flux density
limit S(20GHz) = 115 mJy. We include NVSS 1.4 GHz measurements to derive the
source radio spectra between 1.4 and 30 GHz. Based on optical identifications,
68 per cent of the sources are QSOs, and 27 per cent are radio galaxies. A
redshift measurement is available for 58 per cent of the sources. The radio
spectral properties of the different source populations are found to be in
agreement with those of other high-frequency selected samples.


  Sigma model ($\alpha^{\prime}$) corrections to the confining string
background are obtained. The main result is that the Poincar\'e invariant
ansatz is maintained. Physical conditions for the dissapearance of the naked
singularity are discussed.


  Quantifier elimination theorems show that each formula in a certain theory is
equivalent to a formula of a specific form -- usually a quantifier-free one,
sometimes in an extended language. Model theoretic embedding tests are a
frequently used tool for proving such results without providing an explicit
algorithm.
  We explain how proof mining methods can be adapted to apply to embedding
tests, and provide two explicit examples, giving algorithms for theories of
algebraic and real closed fields with a distinguished small subgroup
corresponding to the embedding test proofs given by van den Dries and
G\"unaydin.


  Finding a maximal independent set (MIS) in a graph is a cornerstone task in
distributed computing. The local nature of an MIS allows for fast solutions in
a static distributed setting, which are logarithmic in the number of nodes or
in their degrees. The result trivially applies for the dynamic distributed
model, in which edges or nodes may be inserted or deleted. In this paper, we
take a different approach which exploits locality to the extreme, and show how
to update an MIS in a dynamic distributed setting, either \emph{synchronous} or
\emph{asynchronous}, with only \emph{a single adjustment} and in a single
round, in expectation. These strong guarantees hold for the \emph{complete
fully dynamic} setting: Insertions and deletions, of edges as well as nodes,
gracefully and abruptly. This strongly separates the static and dynamic
distributed models, as super-constant lower bounds exist for computing an MIS
in the former.
  Our results are obtained by a novel analysis of the surprisingly simple
solution of carefully simulating the greedy \emph{sequential} MIS algorithm
with a random ordering of the nodes. As such, our algorithm has a direct
application as a $3$-approximation algorithm for correlation clustering. This
adds to the important toolbox of distributed graph decompositions, which are
widely used as crucial building blocks in distributed computing.
  Finally, our algorithm enjoys a useful \emph{history-independence} property,
meaning the output is independent of the history of topology changes that
constructed that graph. This means the output cannot be chosen, or even biased,
by the adversary in case its goal is to prevent us from optimizing some
objective function.


  We prove new upper and lower bounds for sample complexity of finding an
$\epsilon$-optimal policy of an infinite-horizon average-reward Markov decision
process (MDP) given access to a generative model. When the mixing time of the
probability transition matrix of all policies is at most $t_\mathrm{mix}$, we
provide an algorithm that solves the problem using
$\widetilde{O}(t_\mathrm{mix} \epsilon^{-3})$ (oblivious) samples per
state-action pair. Further, we provide a lower bound showing that a linear
dependence on $t_\mathrm{mix}$ is necessary in the worst case for any algorithm
which computes oblivious samples. We obtain our results by establishing
connections between infinite-horizon average-reward MDPs and discounted MDPs of
possible further utility.


  Fateev's sausage sigma models in two and three dimensions are known to be
integrable. We study their stability under RG flow in the target space by using
results from the mathematics of Ricci flow. We show that the three dimensional
sausage is unstable, whereas the two dimensional sausage appears to be stable
at least at leading order as it approaches the sphere. We speculate that the
stability results obtained are linked to the classification of ancient
solutions to Ricci flow (i.e., sigma models which are nonperturbative in the
IR) in two and three dimensions. We also describe a class of perturbations of
the three dimensional sausage (with the same continuous symmetries) which
remarkably decouple. This indicates that there could be a new solution to RG
flow which is described at least perturbatively as a deformation of the
sausage.


  This study proposes a novel multi-objective integer programming model for a
collision-free discrete drone path planning problem. Considering the
possibility of bypassing obstacles or flying above them, this study aims to
minimize the path length, energy consumption, and maximum path risk
simultaneously. The static environment is represented as 3D grid cells. Due to
the NP-hardness nature of the problem, several state-of-theart evolutionary
multi-objective optimization (EMO) algorithms with customized crossover and
mutation operators are applied to find a set of non-dominated solutions. The
results show the effectiveness of applied algorithms in solving several
generated test cases.


  Radio continuum observations detect non-thermal synchrotron and thermal
bremsstrahlung radiation. Separation of the two different emission components
is crucial to study the properties of diffuse interstellar medium. The Cygnus X
region is one of the most complex areas in the radio sky which contains a
number of massive stars and HII regions on the diffuse thermal and non-thermal
background. More supernova remnants are expected to be discovered. We aim to
develop a method which can properly separate the non-thermal and thermal radio
continuum emission and apply it to the Cygnus X region. The result can be used
to study the properties of different emission components and search for new
supernova remnants in the complex. Multi-frequency radio continuum data from
large-scale surveys are used to develop a new component separation method.
Spectral analysis is done pixel by pixel for the non-thermal synchrotron
emission with a realistic spectral index distribution and a fixed spectral
index of beta = -2.1 for the thermal bremsstrahlung emission. With the new
method, we separate the non-thermal and thermal components of the Cygnus X
region at an angular resolution of 9.5arcmin. The thermal emission component is
found to comprise 75% of the total continuum emission at 6cm. Thermal diffuse
emission, rather than the discrete HII regions, is found to be the major
contributor to the entire thermal budget. A smooth non-thermal emission
background of 100 mK Tb is found. We successfully make the large-extent known
supernova remnants and the HII regions embedded in the complex standing out,
but no new large SNRs brighter than Sigma_1GHz = 3.7 x 10^-21 W m^-2 Hz^-1
sr^-1 are found.


  We use linear perturbation theory to study perturbations in dynamical dark
energy models. We compare quintessence and tachyonic dark energy models with
identical background evolution. We write the corresponding equations for
different models in a form that makes it easier to see that the two models are
very hard to distinguish in the linear regime, especially for models with $(1 +
w) \ll 1$. We use Cosmic Microwave Background data and parametric
representations for the two models to illustrate that they cannot be
distinguished for the same background evolution with existing observations.
Further, we constrain tachyonic models with the Planck data. We do this
analysis for exponential and inverse square potentials and find that the
intrinsic parameters of the potentials remain very weakly constrained. In
particular, this is true in the regime allowed by low redshift observations.


  We exhibit an alternative method for solving inhomogeneous second--order
linear ordinary dynamic equations on time scales, based on reduction of order
rather than variation of parameters. Our form extends recent (and
long-standing) analysis on $\R$ to a new form for difference equations, quantum
equations, and arbitrary dynamic equations on time scales.


  We construct examples of smooth proper rigid-analytic varieties admitting
formal model with projective special fiber and violating Hodge symmetry for
cohomology in degrees $\geq 3$. This answers negatively a question raised by
Hansen and Li.


  The connection between thunderstorms and relativistic runaway electron
avalanches is an important topic that has attracted the attention of many
researchers. Among other things, there are a lot of various simulations of the
dynamics of electron avalanches. This article was written mostly in response to
the article "The critical avalanche of runaway electrons" by Evgeny Oreshkin et
al, which shows rather large numbers for an estimate of the number of runaway
electrons, but it also contains the results of our own simulation and
comparison with other papers.


  In non-relativistic as well as in special relativistic quantum theory, {\em
mass} and {\em charge} are {\em pure numbers} appearing in various (quantum)
operators and admit {\em any values}, {\it ie}, values for these quantities are
to be prescribed {\em by hand}. This is, in a theory of probability,
understandable since we need to {\em assume} some {\em intrinsic} properties of
the object we are calculating the probability about. Then, if we {\em specify},
in some satisfactory manner, mass and charge for a point of the space in a
suitable general-relativistic framework, the quantum theoretical framework
could, in principle, be {\em obtainable} within it. Heuristic arguments are
presented to show that a {\em natural unification} of the quantum theory and
the general theory of relativity is achievable in this manner.


  This paper provides empirical concerns about post-hoc explanations of
black-box ML models, one of the major trends in AI explainability (XAI), by
showing its lack of interpretability and societal consequences. Using a
representative consumer panel to test our assumptions, we report three main
findings. First, we show that post-hoc explanations of black-box model tend to
give partial and biased information on the underlying mechanism of the
algorithm and can be subject to manipulation or information withholding by
diverting users' attention. Secondly, we show the importance of tested
behavioral indicators, in addition to self-reported perceived indicators, to
provide a more comprehensive view of the dimensions of interpretability. This
paper contributes to shedding new light on the actual theoretical debate
between intrinsically transparent AI models and post-hoc explanations of
black-box complex models-a debate which is likely to play a highly influential
role in the future development and operationalization of AI systems.


  In this paper we investigate two variants of $\alpha$-stable processes,
namely tempered stable subordinators and modified tempered stable process as
well as their renormalization. We study the weak convergence in the Skorohod
space and prove that they satisfy the uniform tightness condition. Finally,
applications to the $\alpha$-dependence of the solutions of SDEs driven by
these processes are discussed.


  We investigate numerically the orbital evolution of massive extrasolar
planets within central cavities of their parent protoplanetary discs. Assuming
that they arrive at the inner edge of the disc due to type II migration, we
show that they spiral further in. We find that in magnetospheric cavities more
massive planets stop migrating at a larger distance from the edge of the disc.
This effect may qualitatively explain the correlation between masses and
orbital periods found for massive planets with P shorter than 5 days.


  This work is focused on finding G-optimal designs theoretically for kriging
models with two-dimensional inputs and separable exponential covariance
structures. For design comparison, the notion of evenness of two-dimensional
grid designs is developed. The mathematical relationship between the design and
the supremum of the mean squared prediction error ($SMSPE$) function is studied
and then optimal designs are explored for both prospective and retrospective
design scenarios. In the case of prospective designs, the new design is
developed before the experiment is conducted and the regularly spaced grid is
shown to be the G-optimal design. The retrospective designs are constructed by
adding or deleting points from an already existing design. Deterministic
algorithms are developed to find the best possible retrospective designs (which
minimizes the $SMSPE$). It is found that a more evenly spread design under the
G-optimality criterion leads to the best possible retrospective design. For all
the cases of finding the optimal prospective designs and the best possible
retrospective designs, both frequentist and Bayesian frameworks have been
considered. The proposed methodology for finding retrospective designs is
illustrated with a methane flux monitoring design.


  We study continuous finite element dicretizations for one dimensional
hyperbolic partial differential equations. The main contribution of the paper
is to provide a fully discrete spectral analysis, which is used to suggest
optimal values of the CFL number and of the stabilization parameters involved
in different types of stabilization operators. In particular, we analyze the
streamline-upwind Petrov-Galerkin (SUPG) stabilization technique, the
continuous interior penalty (CIP) stabilization method and the local projection
stabilization (LPS). Three different choices for the continuous finite element
space are compared: Bernstein polynomials, Lagrangian polynomials on equispaced
nodes, and Lagrangian polynomials on Gauss-Lobatto cubature nodes. For the last
choice, we only consider inexact quadrature based on the formulas corresponding
to the degrees of freedom of the element, which allows to obtain a fully
diagonal mass matrix. We also compare different time stepping strategies,
namely Runge-Kutta (RK), strong stability preserving RK (SSPRK) and deferred
correction time integration methods. The latter allows to alleviate the
computational cost as the mass matrix inversion is replaced by the high order
correction iterations. To understand the effects of these choices, both
time-continuous and fully discrete Fourier analysis are performed. These allow
to compare all the different combinations in terms of accuracy and stability,
as well as to provide suggestions for optimal values discretization parameters
involved. The results are thoroughly verified numerically both on linear and
non-linear problems, and error-CPU time curves are provided. Our final
conclusions suggest that cubature elements combined with SSPRK and CIP or LPS
stabilization are the most promising combinations.