  Social networks are rarely observed in full detail. In many situations
properties are known for only a sample of the individuals in the network and it
is desirable to induce global properties of the full social network from this
"egocentric" network data. In the current paper we study a few different types
of egocentric data, and show what global network properties are consistent with
those egocentric data. Two global network properties are considered: the size
of the largest connected component in the network (the giant), and secondly,
the possible size of an epidemic outbreak taking place on the network, in which
transmission occurs only between network neighbours, and with probability $p$.
The main conclusion is that in most cases, egocentric data allow for a large
range of possible sizes of the giant and the outbreak. However, there is an
upper bound for the latter. For the case that the network is selected uniformly
among networks with prescribed egocentric data (satisfying some conditions),
the asymptotic size of the giant and the outbreak is characterised.


  In this work we present a useful way to introduce the octonionic projective
and hyperbolic plane through the use of Veronese vectors. Then we focus on
their relation with the exceptional Jordan algebra and show that the Veronese
vectors are the rank-one elements of the algebra. We then study groups of
motions over the octonionic plane recovering all real forms of G2, F4and E6
groups and finally give a classification of all octonionic and split-octonionic
planes as symmetric spaces.


  Let S be a closed oriented surface of genus $g\geq 0$ with $n\geq 0$
punctures and $3g-3+n\geq 5$. Let $Q$ be a connected component of a stratum in
the moduli space Q(S) of area one meromorphic quadratic differentials on S with
n simple poles at the punctures or in the moduli space H(S) of abelian
differentials on S if n=0. For a compact subset K of Q(S) or of H(S), we show
that the asymptotic growth rate of the number of periodic orbits for the
Teichmueller flow on Q which are entirely contained in Q-K is at least h(Q)-1
where h(Q)>0 is the complex dimension of Q.


  Upon reading Einstein's views on quantum incompleteness in publications or in
his correspondence after 1935 (the EPR paradox), one gets a very intense
feeling of deja-vu. Einstein presents a quantum hole argument, which somewhat
reminds of the hole argument in his 1914 "Entwurf" theory of general
relativity. PBR write in their paper, "An important step towards the derivation
of our result is the idea that the quantum state is physical if distinct
quantum states correspond to non-overlapping distributions for [physical
states]". PBR then conclude, "The general notion that two distinct quantum
states may describe the same state of reality, however, has a long history. For
example, in a letter to Schr\"odinger containing a variant of the famous EPR
(Einstein-Podolsky-Rosen) argument", and they refer to Einstein's quantum hole
argument. This short paper discusses the PBR theorem, and the connection
between the PBR argument and Einstein's argument and solution.


  We introduce MegaPose, a method to estimate the 6D pose of novel objects,
that is, objects unseen during training. At inference time, the method only
assumes knowledge of (i) a region of interest displaying the object in the
image and (ii) a CAD model of the observed object. The contributions of this
work are threefold. First, we present a 6D pose refiner based on a
render&compare strategy which can be applied to novel objects. The shape and
coordinate system of the novel object are provided as inputs to the network by
rendering multiple synthetic views of the object's CAD model. Second, we
introduce a novel approach for coarse pose estimation which leverages a network
trained to classify whether the pose error between a synthetic rendering and an
observed image of the same object can be corrected by the refiner. Third, we
introduce a large-scale synthetic dataset of photorealistic images of thousands
of objects with diverse visual and shape properties and show that this
diversity is crucial to obtain good generalization performance on novel
objects. We train our approach on this large synthetic dataset and apply it
without retraining to hundreds of novel objects in real images from several
pose estimation benchmarks. Our approach achieves state-of-the-art performance
on the ModelNet and YCB-Video datasets. An extensive evaluation on the 7 core
datasets of the BOP challenge demonstrates that our approach achieves
performance competitive with existing approaches that require access to the
target objects during training. Code, dataset and trained models are available
on the project page: https://megapose6d.github.io/.


  The present paper introduces a hybrid explicit-implicit topology optimization
method for shell-infill composite structure design. The proposed approach
effectively combines the advantages of the explicit Moving Morphable Component
(MMC) method, which describes structural topology only using a set of geometric
parameters, and the implicit Solid Isotropic Material with Penalization (SIMP)
method, which offers greater design freedom for characterizing the structural
features. Compared to the existing methods for shell-infill structure design,
the proposed approach can obtain optimized shell-infill structures with complex
infill topology without resorting to complex filtering/projection operations.
Numerical examples demonstrate the effectiveness of the proposed approach.


  Organisms modulate their fitness in heterogeneous environments by dispersing.
Prior work shows that there is selection against "unconditional" dispersal in
spatially heterogeneous environments. "Unconditional" means individuals
disperse at a rate independent of their location. We prove that if within-patch
fitness varies spatially and between two values temporally, then there is
selection for unconditional dispersal: any evolutionarily stable strategy (ESS)
or evolutionarily stable coalition (ESC) includes a dispersive phenotype.
Moreover, at this ESS or ESC, there is at least one sink patch (i.e. geometric
mean of fitness less than one) and no sources patches (i.e. geometric mean of
fitness greater than one). These results coupled with simulations suggest that
spatial-temporal heterogeneity due to abiotic forcing result in either an ESS
with a dispersive phenotype or an ESC with sedentary and dispersive phenotypes.
In contrast, spatial-temporal heterogeneity due to biotic interactions can
select for higher dispersal rates that ultimately spatially synchronize
population dynamics.


  Whittle index policy is a powerful tool to obtain asymptotically optimal
solutions for the notoriously intractable problem of restless bandits. However,
finding the Whittle indices remains a difficult problem for many practical
restless bandits with convoluted transition kernels. This paper proposes
NeurWIN, a neural Whittle index network that seeks to learn the Whittle indices
for any restless bandits by leveraging mathematical properties of the Whittle
indices. We show that a neural network that produces the Whittle index is also
one that produces the optimal control for a set of Markov decision problems.
This property motivates using deep reinforcement learning for the training of
NeurWIN. We demonstrate the utility of NeurWIN by evaluating its performance
for three recently studied restless bandit problems. Our experiment results
show that the performance of NeurWIN is significantly better than other RL
algorithms.


  Two of Lanczos' seminal contributions in physics that are not generally known
about in the physics community are discussed and commented in relation to more
recent investigations: First, his integral formulation of the Schroedinger
equation that he published just before Schroedinger himself published his
differential equation formulation of quantum mechanics, and, second, his
solution to the problem of equations of motion of material particles in the
theory of general relativity.


  The thermodynamical properties of heterogeneous DNA sequences are computed by
path integral techniques applied to a nonlinear model Hamiltonian. The base
pairs relative displacements are interpreted as time dependent paths whose
amplitudes are consistent with the model potential for the hydrogen bonds
between complementary strands. The portion of configuration space contributing
to the partition function is determined, at any temperature, by selecting the
ensemble of paths which fulfill the second law of thermodynamics. For a short
DNA fragment, the denaturation is signaled by a succession of peaks in the
specific heat plots while the entropy grows continuously versus $T$. Thus, the
opening of the double strand with bubble formation appears as a smooth
crossover due to base pair fluctuation effects which are accounted for by the
path integral method. The multistep transition is driven by the AT-rich regions
of the DNA fragment. The base pairs path ensemble shows an enhanced degree of
cooperativity at about the same temperatures for which the specific heat peaks
occur. These findings establish a link between microscopic and macroscopic
signatures of the transition. The fractions of mean base pair stretchings are
computed by varying the AT base pairs content and taking some threshold values
for the occurrence of the molecule denaturation.


  We have studied the vector meson spectral function (VMSF) in a hot and dense
medium within an effective QCD model namely the Nambu-Jona-Lasinio (NJL) and
its Polyakov Loop extended version (PNJL) with and without the effect of
isoscalar vector interaction (IVI). The effect of the IVI has been taken into
account using the ring approximation. We obtained the dilepton production rate
(DPR) using the VMSF and observed that at moderate temperature it is enhanced
in the PNJL model as compared to the NJL and Born rate due to the suppression
of color degrees of freedom.


  The present work proposes a deep-learning-based approach for the
classification of COVID-19 coughs from non-COVID-19 coughs and that can be used
as a low-resource-based tool for early detection of the onset of such
respiratory diseases. The proposed system uses the ResNet-50 architecture, a
popularly known Convolutional Neural Network (CNN) for image recognition tasks,
fed with the log-Mel spectrums of the audio data to discriminate between the
two types of coughs. For the training and validation of the proposed deep
learning model, this work utilizes the Track-1 dataset provided by the DiCOVA
Challenge 2021 organizers. Additionally, to increase the number of
COVID-positive samples and to enhance variability in the training data, it has
also utilized a large open-source database of COVID-19 coughs collected by the
EPFL CoughVid team. Our developed model has achieved an average validation AUC
of 98.88%. Also, applying this model on the Blind Test Set released by the
DiCOVA Challenge, the system has achieved a Test AUC of 75.91%, Test
Specificity of 62.50%, and Test Sensitivity of 80.49%. Consequently, this
submission has secured 16th position in the DiCOVA Challenge 2021 leader-board.


  Automated vehicles can implement strategies to drive with optimized fuel
efficiency. Therefore, automated driving is seen as a major advancement in
tackling climate change. However, with automated vehicles driving in cities and
other areas rife with other road users such as human drivers, pedestrians, or
cyclists, there is the potential for "stop-and-go" traffic. This would greatly
diminish the possibility of automated vehicles to drive fuel-efficient. We
suggest using external communication of automated vehicles to aid in ecological
driving by providing clues to other road users to show the intent and therefore
ultimately enable smoother traffic.


  The chalcogenide Bi$_2$Se$_3$ can attain the three dimensional (3D) Dirac
semimetal state under the influence of strain and microstrain. Here we report
the presnece of large linear magnetoresistance in such a Bi$_2$Se$_3$ crystal.
The magnetoresistance has quadratic form at low fields which crossovers to
linear above 4 T. The temperature dependence of magnetoresistance scales with
carrier mobility and the crossover field scales with inverse of mobility. Our
analysis suggest that the linear magnetoresistance in our system has a
classical origin and arises from the scattering of high mobility 3D Dirac
electrons from crystalline inhomogeneities. We observe that the charged
selenium vacancies are strongly screened by high mobility Dirac electrons and
the neutral crystalline defects are the main scattering center for transport
mechanism. Our analysis suggests that both the resistivity and the
magnetoresistance have their origin in scattering of charge carriers from
neutral defects.


  We consider solutions satisfying the zero Neumann boundary condition and a
linearized mean field game equation in $\Omega \times (0,T)$ whose principal
coefficients depend on the time and spatial variables with general Hamiltonian,
where $\Omega$ is a bounded domain in $\Bbb R^d$ and $(0,T)$ is the time
interval. We first prove the Lipschitz stability in $\Omega \times
(\varepsilon, T-\varepsilon)$ with given $\varepsilon>0$ for the determination
of the solutions by Dirichlet data on arbitrarily chosen subboundary of
$\partial\Omega$. Next we prove the Lipschitz stability for an inverse problem
of determining spatially varying factors of source terms and a coefficient by
extra boundary data and spatial data at intermediate time.


  China's stock market is the largest emerging market all over the world. It is
widely accepted that the Chinese stock market is far from efficiency and it
possesses possible linear and nonlinear dependence. We study the predictability
of returns in the Chinese stock market by employing the wild bootstrap
automatic variance ratio test and the generalized spectral test. We find that
the return predictability vary over time and significant return predictability
is observed around market turmoils. Our findings are consistent with the
Adaptive Markets Hypothesis and have practical implications for market
participants.


  The aim of this paper is to highlight and explore a traditional problem,
which is the minimum spanning tree, and finding the shortest-path in network
routing, by using Swarm Intelligence. This work to be considered as an
investigation topic with combination between operations research, discrete
mathematics, and evolutionary computing aiming to solve one of networking
problems.


  Adaptive methods such as Adam and RMSProp are widely used in deep learning
but are not well understood. In this paper, we seek a crisp, clean and precise
characterization of their behavior in nonconvex settings. To this end, we first
provide a novel view of adaptive methods as preconditioned SGD, where the
preconditioner is estimated in an online manner. By studying the preconditioner
on its own, we elucidate its purpose: it rescales the stochastic gradient noise
to be isotropic near stationary points, which helps escape saddle points.
Furthermore, we show that adaptive methods can efficiently estimate the
aforementioned preconditioner. By gluing together these two components, we
provide the first (to our knowledge) second-order convergence result for any
adaptive method. The key insight from our analysis is that, compared to SGD,
adaptive methods escape saddle points faster, and can converge faster overall
to second-order stationary points.


This note is a survey of $J$-spaces.


  Despite the general capabilities of Large Language Models (LLM), these models
still request fine-tuning or adaptation with customized data when meeting
specific business demands. However, this process inevitably introduces new
threats, particularly against the Fine-tuning based Jailbreak Attack (FJAttack)
under the setting of Language-Model-as-a-Service (LMaaS), where the model's
safety has been significantly compromised by fine-tuning users' uploaded
examples contain just a few harmful examples. Though potential defenses have
been proposed that the service providers can integrate safety examples into the
fine-tuning dataset to reduce safety issues, such approaches require
incorporating a substantial amount of data, making it inefficient. To
effectively defend against the FJAttack with limited safety examples under
LMaaS, we propose the Backdoor Enhanced Safety Alignment method inspired by an
analogy with the concept of backdoor attacks. In particular, service providers
will construct prefixed safety examples with a secret prompt, acting as a
"backdoor trigger". By integrating prefixed safety examples into the
fine-tuning dataset, the subsequent fine-tuning process effectively acts as the
"backdoor attack", establishing a strong correlation between the secret prompt
and safety generations. Consequently, safe responses are ensured once service
providers prepend this secret prompt ahead of any user input during inference.
Our comprehensive experiments demonstrate that through the Backdoor Enhanced
Safety Alignment with adding as few as 11 prefixed safety examples, the
maliciously fine-tuned LLMs will achieve similar safety performance as the
original aligned models without harming the benign performance. Furthermore, we
also present the effectiveness of our method in a more practical setting where
the fine-tuning data consists of both FJAttack examples and the fine-tuning
task data.


  (Abridged) Classification schemes for YSOs are based on evaluating the degree
of dissipation of the surrounding envelope, whose main effects are the
extinction of the optical radiation from the central YSO and re-emission in the
far-infrared. Since extinction is a property of column density along the line
of sight, the presence of a protoplanetary disk may lead to a misclassification
when the system is viewed edge-on.
  We performed radiative transfer calculations, using the axysimmetric 3D
radiative transfer codes RADMC and RADICAL, to show the effects of different
geometries on the main indicators of YSO evolutionary stage, like the slope of
the flux between 2 and 24mum, the bolometric temperature and the optical depth
of silicates and ices.
  We show that for systems viewed at intermediate angles the 'classical'
indicators of evolution accurately trace the envelope column density, and they
all agree with each other. On the other hand, edge-on system are misclassified
for inclinations larger than ~65deg. In particular, silicate emission, typical
of pre-main sequence stars with disks, turns into absorption when the disk
column density reaches 1e22cm-2, corresponding e.g. to a 5e-3 Msun flaring disk
viewed at 64deg. A similar effect is noticed in all the other classification
indicators studied alpha, Tbol, and the H2O and CO2 ices absorption strengths.
This misclassification has a big impact on the nature of the flat-spectrum
sources (alpha ~0), whose number can be explained by simple geometrical
arguments without invoking evolution. A reliable classification scheme using a
minimal number of observations is constituted by observations of the mm-flux
with both a single dish and an interferometer.


  We examine the distributions of eccentricity and host star metallicity of
exoplanets as a function of their mass. Planets with M sin i >~ 4 M_J have an
eccentricity distribution consistent with that of binary stars, while planets
with M sin i <~ 4 M_J are less eccentric than binary stars and more massive
planets. In addition, host star metallicities decrease with planet mass. The
statistical significance of both of these trends is only marginal with the
present sample of exoplanets. To account for these trends, we hypothesize that
there are two populations of gaseous planets: the low-mass population forms by
gas accretion onto a rock-ice core in a circumstellar disk and is more abundant
at high metalliticities, and the high-mass population forms directly by
fragmentation of a pre-stellar cloud. Planets of the first population form in
initially circular orbits and grow their eccentricities later, and may have a
mass upper limit from the total mass of the disk that can be accreted by the
core. The second population may have a mass lower limit resulting from
opacity-limited fragmentation. This would roughly divide the two populations in
mass, although they would likely overlap over some mass range. If most objects
in the second population form before the pre-stellar cloud becomes highly
opaque, they would have to be initially located in orbits larger than ~30 AU,
and would need to migrate to the much smaller orbits in which they are
observed. The higher mean orbital eccentricity of the second population might
be caused by the larger required intervals of radial migration, and the brown
dwarf desert might be due to the inability of high-mass brown dwarfs to migrate
inwards sufficiently in radius.


  The recent discovery of apparent cosmic acceleration has highlighted the
depth of our ignorance of the fundamental properties of nature. It is commonly
assumed that the explanation for acceleration must come from a new form of
energy dominating the cosmos - dark energy - or a modification of Einstein's
theory of Relativity. It is often overlooked, however, that a currently viable
alternative explanation of the data is radial inhomogeneity which alters the
Hubble diagram without any acceleration. This explanation is often ignored for
two reasons: radial inhomogeneity significantly complicates analysis and
predictions, and so the full details have not been investigated; and it is a
philosophically highly controversial idea, revoking as it does the long-held
Copernican Principle. To date, there has not been a general way of determining
the validity if the Copernican Principle -- that we live at a typical position
in the universe -- significantly weakening the foundations of cosmology as a
scientific endeavour. Here we present an observational test for the Copernican
assumption which can be automatically implemented while we search for dark
energy in the coming decade. Our test is entirely independent of any model for
dark energy or theory of gravity and thereby represents a model-independent
test of the Copernican Principle.


  Sexism in online content is a pervasive issue that necessitates effective
classification techniques to mitigate its harmful impact. Online platforms
often have sexist comments and posts that create a hostile environment,
especially for women and minority groups. This content not only spreads harmful
stereotypes but also causes emotional harm. Reliable methods are essential to
find and remove sexist content, making online spaces safer and more welcoming.
Therefore, the sEXism Identification in Social neTworks (EXIST) challenge
addresses this issue at CLEF 2024. This study aims to improve sexism
identification in bilingual contexts (English and Spanish) by leveraging
natural language processing models. The tasks are to determine whether a text
is sexist and what the source intention behind it is. We fine-tuned the
XLM-RoBERTa model and separately used GPT-3.5 with few-shot learning prompts to
classify sexist content. The XLM-RoBERTa model exhibited robust performance in
handling complex linguistic structures, while GPT-3.5's few-shot learning
capability allowed for rapid adaptation to new data with minimal labeled
examples. Our approach using XLM-RoBERTa achieved 4th place in the soft-soft
evaluation of Task 1 (sexism identification). For Task 2 (source intention), we
achieved 2nd place in the soft-soft evaluation.


  We present non-linear hydrodynamic pulsation models for OGLE-BLG-RRLYR-02792
- a 0.26M_sun pulsator, component of the eclipsing binary system, analysed
recently by Pietrzynski et al. The star's light and radial velocity curves
mimic that of classical RR Lyrae stars, except for the bump in the middle of
the ascending branch of the radial velocity curve. We show that the bump is
caused by the 2:1 resonance between the fundamental mode and the second
overtone - the same mechanism that causes the Hertzsprung bump progression in
classical Cepheids. The models allow to constrain the parameters of the star,
in particular to estimate its absolute luminosity (approx 33L_sun) and
effective temperature (approx 6970K, close to the blue edge of the instability
strip).
  We conduct a model survey for the new class of low mass pulsators similar to
OGLE-BLG-RRLYR-02792 - products of evolution in the binary systems. We compute
a grid of models with masses corresponding to half (and less) of the typical
mass of RR Lyrae variable, 0.20M_sun<=M<=0.30M_sun, and discuss the properties
of the resulting light and radial velocity curves. Resonant bump progression is
clear and may be used to distinguish such stars from classical RR Lyrae stars.
We present the Fourier decomposition parameters for the modelled light and
radial velocity curves. The expected values of the phi_31 Fourier phase for the
light curves differ significantly from that observed in RR Lyrae stars, which
is another discriminant of the new class.


  E-learning has been continuously present in current educational discourse,
thanks to technological advances, learning methodologies and public or
organizational policies, among other factors. However, despite its boom and
dominance in various subject areas, this thematic does not yet exist in the
world system of publications. Therefore, works in this thematic end up being
published under related categories, particularly Education or categories within
subject area Computer Science, thus fragmenting and make invisible the existing
knowledge. This work is based on the hypothesis that the scientific
communication of e-learning has a sufficient degree of cohesion to be
considered as a thematic category in itself. From a bibliometric approach, its
scientific production was analyzed, obtaining the bibliographic data of SCOPUS
and SCImago, selecting its main descriptors and generating visualizations
through VOSViewer with the mapping overlay technique, to represent its set and
proximity. As a result, it was determined that a set of 219 publications show a
high bibliometric interrelation among its articles and these are presented
transversely between the social sciences, computer science and health. This set
serves as a channel of scientific communication and structure of knowledge on
the thematic and can therefore be considered as the basis for establishing the
"e-learning" thematic category in the world system of scientific publications,
contributing to the consolidation of the discipline, to its access and
development by researchers.


  We illustrate how to apply modern effective field theory techniques and
dimensional regularization to factorize the various scales which appear in
non-relativistic bound states at finite temperature. We focus here on the
simplest case: the hydrogen atom. We discuss in detail the interplay of the
hard, soft and ultrasoft scales of the non-relativistic system at zero
temperature with the additional scales induced at finite temperature. We also
comment on the implications of our results for heavy quarkonium bound states in
the quark gluon plasma.


  Weyl semimetals are topological materials with protected Weyl nodes in the
band structure. In these materials the surface states form open curves at the
Fermi surface, Fermi arcs in Weyl semimetals and drumhead states of nodal-line
semimetals. In this work we solve analitically the wave function of the surface
states in a generic continuous model describing Weyl and nodal-line type I-II
semimetals within a slab geometry. Depending on the values of the parameters,
different types of Fermi arcs and drumhead states appear. When the mass terms
are dominant with respect to the Fermi velocity in the Hamiltonian the decay of
the surface states become oscillatory. This property has important consequences
in the stability of surface states in a slab geometry This exact solution can
be used for a better understanding of the behaviour of Fermi Arcs in real
materials and their influence in transport and optical properties. We use these
solutions to study the Joint Density of States at the surface which can be used
to interpret quasi-particle interference data in scanning tunnneling microscope
experiments. We show that oscillatory decay can be distinguish from simple
exponential decay of the surface states in these experiments.


  Using Monte Carlo simulations we confirm that the rare-earth compound LiHoF4
is a very good realization of a dipolar Ising model. With only one free
parameter our calculations for the magnetization, specific heat and inverse
susceptibility match experimental data at a quantitative level in the single
Kelvin temperature range, including the ferromagnetic transition at 1.53 K.
Using parallel tempering methods and reaching system sizes up to 32000 dipoles
with periodic boundary conditions we are able to give strong direct evidence of
the logarithmic corrections predicted in renormalization group theory. Due to
the long range and angular dependence of the dipolar model sample shape and
domains play a crucial role in the ordered state. We go beyond Griffiths's
theorem and consider surface corrections arising in finite macroscopic samples
leading to a theory of magnetic domains. We predict that the ground-state
domain structure for cylinders with a demagnetization factor N>0 consists of
thin parallel sheets of opposite magnetization, with a width depending on the
demagnetization factor.


  An emerging problem in trustworthy machine learning is to train models that
produce robust interpretations for their predictions. We take a step towards
solving this problem through the lens of axiomatic attribution of neural
networks. Our theory is grounded in the recent work, Integrated Gradients (IG),
in axiomatically attributing a neural network's output change to its input
change. We propose training objectives in classic robust optimization models to
achieve robust IG attributions. Our objectives give principled generalizations
of previous objectives designed for robust predictions, and they naturally
degenerate to classic soft-margin training for one-layer neural networks. We
also generalize previous theory and prove that the objectives for different
robust optimization models are closely related. Experiments demonstrate the
effectiveness of our method, and also point to intriguing problems which hint
at the need for better optimization techniques or better neural network
architectures for robust attribution training.


  The non--Gaussian cold spot in the 1-year WMAP data, described in Vielva et
al. and Cruz et al., is analysed in detail in the present paper. First of all,
we perform a more rigorous calculation of the significance of the non-zero
kurtosis detected in WMAP maps by Vielva et al. in wavelet space, mainly
generated by the Spot. We confirm the robustness of that detection, since the
probability of obtaining this deviation by chance is 0.69%. Afterwards, the
morphology of the Spot is studied by applying Spherical Mexican Hat Wavelets
with different ellipticities. The shape of the Spot is found to be almost
circular. Finally, we discuss if the observed non-Gaussianity in wavelet space
can arise from bad subtracted foreground residues in the WMAP maps. We show
that the flat frequency dependence of the Spot cannot be explained by a thermal
Sunyaev-Zeldovich effect. Based on our present knowledge of Galactic foreground
emissions, we conclude that the significance of our detection is not affected
by Galactic residues in the region of the Spot. Considering different Galactic
foreground estimates, the probability of finding such a big cold spot in
Gaussian simulations is always below 1%.


  In the context of neural network models, overparametrization refers to the
phenomena whereby these models appear to generalize well on the unseen data,
even though the number of parameters significantly exceeds the sample sizes,
and the model perfectly fits the in-training data. A conventional explanation
of this phenomena is based on self-regularization properties of algorithms used
to train the data. In this paper we prove a series of results which provide a
somewhat diverging explanation. Adopting a teacher/student model where the
teacher network is used to generate the predictions and student network is
trained on the observed labeled data, and then tested on out-of-sample data, we
show that any student network interpolating the data generated by a teacher
network generalizes well, provided that the sample size is at least an explicit
quantity controlled by data dimension and approximation guarantee alone,
regardless of the number of internal nodes of either teacher or student
network.
  Our claim is based on approximating both teacher and student networks by
polynomial (tensor) regression models with degree depending on the desired
accuracy and network depth only. Such a parametrization notably does not depend
on the number of internal nodes. Thus a message implied by our results is that
parametrizing wide neural networks by the number of hidden nodes is misleading,
and a more fitting measure of parametrization complexity is the number of
regression coefficients associated with tensorized data. In particular, this
somewhat reconciles the generalization ability of neural networks with more
classical statistical notions of data complexity and generalization bounds. Our
empirical results on MNIST and Fashion-MNIST datasets indeed confirm that
tensorized regression achieves a good out-of-sample performance, even when the
degree of the tensor is at most two.


  We give an overview of different paradigms for control of quantum systems and
their applications, illustrated with specific examples. We further discuss the
implications of fault-tolerance requirements for quantum process engineering
using optimal control, and explore the possibilities for architecture
simplification and effective control using a minimum number of simple switch
actuators.


  Scalable quantum technologies will require an unprecedented combination of
precision and complexity for designing stable structures of well-controllable
quantum systems. It is a challenging task to find a suitable elementary
building block, of which a quantum network can be comprised in a scalable way.
Here we present the working principle of such a basic unit, engineered using
molecular chemistry, whose control and readout are executed using a nitrogen
vacancy (NV) center in diamond. The basic unit we investigate is a synthetic
polyproline with electron spins localized on attached molecular sidegroups
separated by a few nanometers. We demonstrate the readout and coherent
manipulation of very few ($\leq 6 $) of these $S=1/2$ electronic spin systems
and access their direct dipolar coupling tensor. Our results show, that it is
feasible to use spin-labeled peptides as a resource for a molecular-qubit based
network, while at the same time providing simple optical readout of single
quantum states through NV-magnetometry. This work lays the foundation for
building arbitrary quantum networks using well-established chemistry methods,
which has many applications ranging from mapping distances in single molecules
to quantum information processing.


  This paper describes an inverse shape design method for thermoelastic bodies.
With a known equilibrium shape as input, the focus of this paper is the
determination of the corresponding initial shape of a body undergoing thermal
expansion or contraction, as well as nonlinear elastic deformations. A
distinguishing feature of the described method lies in its capability to
approximately prescribe an initial heterogeneous temperature distribution as
well as an initial stress field even though the initial shape is unknown. At
the core of the method, there is a system of nonlinear partial differential
equations. They are discretized and solved with the finite element method or
isogeometric analysis. In order to better integrate the method with
application-oriented simulations, an iterative procedure is described that
allows fine-tuning of the results. The method was motivated by an inverse
cavity design problem in injection molding applications. Its use in this field
is specifically highlighted, but the general description is kept independent of
the application to simplify its adaptation to a wider range of use cases.


  We prove new results regarding the existence, uniqueness, (eventual)
boundedness, (total) stability and attractivity of the solutions of a class of
initial-boundary-value problems characterized by a quasi-linear third order
equation which may contain time-dependent coefficients. The class includes
equations arising in Superconductor Theory and in the Theory of Viscoelastic
Materials. In the proof we use a Liapunov functional V depending on two
parameters, which we adapt to the characteristics of the problem.


  Recently the possibility of generating nonclassical polariton states by means
of parametric scattering has been demonstrated. Excitonic polaritons propagate
in a complex interacting environment and contain real electronic excitations
subject to scattering events and noise affecting quantum coherence and
entanglement. Here we present a general theoretical framework for the realistic
investigation of polariton quantum correlations in the presence of coherent and
incoherent interaction processes. The proposed theoretical approach is based on
the {\em nonequilibrium quantum Langevin approach for open systems} applied to
interacting-electron complexes described within the dynamics controlled
truncation scheme. It provides an easy recipe to calculate multi-time
correlation functions which are key-quantities in quantum optics. As a first
application, we analyze the build-up of polariton parametric emission in
semiconductor microcavities including the influence of noise originating from
phonon induced scattering.


  We present what is to our knowledge the first implementation of a
Schr\"odinger cat states "breeding" operation, which allows an iterative growth
of these states. We thus report the experimental generation of a squeezed
Schr\"odinger cat state from two single photon Fock states, which can be seen
as cat states with zero amplitude. These Fock states are mixed on a symmetrical
beamsplitter and the generation is heralded by a homodyne measurement in one of
the two output arms. The output state has a fidelity of 61% with an even
squeezed Schr\"odinger cat state of amplitude $\alpha=1.63$. This hybrid
operation opens up new prospects in quantum optics as the protocol depicted
here can be iterated in order to produce new kind of mesoscopic states.


  It has been over ten years since Kane's influential proposal for a
silicon-based nuclear spin quantum computer using phosphorous donors. Since
then, silicon-based architectures have been refined as the experimental
challenges associated with the original proposal have become better understood,
while simultaneously a number of powerful and generic models for quantum
computation have emerged. Here, I discuss how the cluster state or "one-way"
model for quantum computing might be advantageously applied to donors in
silicon, with the potential to substantially reduce the practical requirements
of a successful implementation. The essence of the scheme is to use the
electron spin associated with a donor to weave an entangled network between 31P
donor nuclear spins. This resource has been shown to have exceptional coherence
times and supports universal quantum computation through local measurements on
the nuclear spins. Some of the key ingredients, such as global spin
manipulation, have been robustly established, while others, such as single spin
measurement, have seen much progress in recent years. A key challenge will be
the demonstration of electron transfer between donors that preserves spin
coherence.


  This paper focuses on the limitations of current over-parameterized shadow
removal models. We present a novel lightweight deep neural network that
processes shadow images in the LAB color space. The proposed network termed
"LAB-Net", is motivated by the following three observations: First, the LAB
color space can well separate the luminance information and color properties.
Second, sequentially-stacked convolutional layers fail to take full use of
features from different receptive fields. Third, non-shadow regions are
important prior knowledge to diminish the drastic color difference between
shadow and non-shadow regions. Consequently, we design our LAB-Net by involving
a two-branch structure: L and AB branches. Thus the shadow-related luminance
information can well be processed in the L branch, while the color property is
well retained in the AB branch. In addition, each branch is composed of several
Basic Blocks, local spatial attention modules (LSA), and convolutional filters.
Each Basic Block consists of multiple parallelized dilated convolutions of
divergent dilation rates to receive different receptive fields that are
operated with distinct network widths to save model parameters and
computational costs. Then, an enhanced channel attention module (ECA) is
constructed to aggregate features from different receptive fields for better
shadow removal. Finally, the LSA modules are further developed to fully use the
prior information in non-shadow regions to cleanse the shadow regions. We
perform extensive experiments on the both ISTD and SRD datasets. Experimental
results show that our LAB-Net well outperforms state-of-the-art methods. Also,
our model's parameters and computational costs are reduced by several orders of
magnitude. Our code is available at https://github.com/ngrxmu/LAB-Net.


  Relative Lempel-Ziv (RLZ) is a popular algorithm for compressing databases of
genomes from individuals of the same species when fast random access is
desired. With Kuruppu et al.'s (SPIRE 2010) original implementation, a
reference genome is selected and then the other genomes are greedily parsed
into phrases exactly matching substrings of the reference. Deorowicz and
Grabowski (Bioinformatics, 2011) pointed out that letting each phrase end with
a mismatch character usually gives better compression because many of the
differences between individuals' genomes are single-nucleotide substitutions.
Ferrada et al. (SPIRE 2014) then pointed out that also using relative pointers
and run-length compressing them usually gives even better compression. In this
paper we generalize Ferrada et al.'s idea to handle well also short insertions,
deletions and multi-character substitutions. We show experimentally that our
generalization achieves better compression than Ferrada et al.'s implementation
with comparable random-access times.


  We show that any Carnot group contains a closed nowhere dense set which has
measure zero but is not $\sigma$-porous with respect to the
Carnot-Carath\'eodory (CC) distance. In the first Heisenberg group we observe
that there exist sets which are porous with respect to the CC distance but not
the Euclidean distance and vice-versa. In Carnot groups we then construct a
Lipschitz function which is Pansu differentiable at no point of a given
$\sigma$-porous set and show preimages of open sets under the horizontal
gradient are far from being porous.


  Mass spectra and semileptonic decay rates of doubly heavy baryons are studied
in the framework of the relativistic quark model in the quark-diquark
approximation.


  Based on image moment theory, an approach for space-variant Shack-Hartmann
wavefront reconstruction is presented in this article. The relation between the
moment of a pair of subimages and the local transformation coefficients is
derived. The square guide 'star' is used to obtain a special solution from this
relation. The moment-based wavefront reconstruction has a reduced computational
complexity compared to the iteration-based algorithm. Image restorations are
executed by the tiling strategy with 5 $\times$ 5 PSFs as well as the
conventional strategy with a global average PSF. Visual and quantitative
evaluations support our approach.


  SrxBi2Se3 is a candidate topological superconductor but its superconductivity
requires the intercalation of Sr by into the van-der-Waals gaps of Bi2Se3. We
report the synthesis of SrxBi2Se3 thin films by molecular beam epitaxy, and we
characterize their structural, vibrational and electrical properties. X-ray
diffraction and Raman spectroscopy show evidence of substitutional Sr alloying
into the structure, while transport measurements allow us to correlate the
increasing Sr content with an increased n-type doping, but do not reveal
superconductivity down to 1.5K. Our results suggest that Sr predominantly
occupies sites within a quintuple layer, simultaneously substituting for Bi and
as an interstitial. Our results motivate future density functional studies to
further investigate the energetics of Sr substitution into Bi2Se3.


  In practice, observations are often contaminated by noise, making the
resulting sample covariance matrix to be an information-plus-noise-type
covariance matrix. Aiming to make inferences about the spectra of the
underlying true covariance matrix under such a situation, we establish an
asymptotic relationship that describes how the limiting spectral distribution
of (true) sample covariance matrices depends on that of
information-plus-noise-type sample covariance matrices. As an application, we
consider the inference about the spectra of integrated covolatility (ICV)
matrices of high-dimensional diffusion processes based on high-frequency data
with microstructure noise. The (slightly modified) pre-averaging estimator is
an information-plus-noise-type covariance matrix, and the aforementioned
result, together with a (generalized) connection between the spectral
distribution of true sample covariance matrices and that of the population
covariance matrix, enables us to propose a two-step procedure to estimate the
spectral distribution of ICV for a class of diffusion processes. An alternative
estimator is further proposed, which possesses two desirable properties: it
eliminates the impact of microstructure noise, and its limiting spectral
distribution depends only on that of the ICV through the standard
Mar\v{c}enko-Pastur equation. Numerical studies demonstrate that our proposed
methods can be used to estimate the spectra of the underlying covariance matrix
based on noisy observations.


  We prove that in the infinite speed-of-light limit (i.e., non-relativistic
and subhorizon limits), the relativistic fully nonlinear cosmological
perturbation equations in two gauge conditions, the zero-shear gauge and the
uniform-expansion gauge, exactly reproduce the Newtonian hydrodynamic
perturbation equations in the cosmological background; as a consequence, in the
same two gauge conditions, the Newtonian hydrodynamic equations are exactly
recovered in the Minkowsky background.


  One strategy to fit larger problems on NISQ devices is to exploit a tradeoff
between circuit width and circuit depth. Unfortunately, this tradeoff still
limits the size of tractable problems since the increased depth is often not
realizable before noise dominates. Here, we develop qubit-efficient quantum
algorithms for entanglement spectroscopy which avoid this tradeoff. In
particular, we develop algorithms for computing the trace of the n-th power of
the density operator of a quantum system, $Tr(\rho^n)$, (related to the R\'enyi
entropy of order n) that use fewer qubits than any previous efficient algorithm
while achieving similar performance in the presence of noise, thus enabling
spectroscopy of larger quantum systems on NISQ devices. Our algorithms, which
require a number of qubits independent of n, are variants of previous
algorithms with width proportional to n, an asymptotic difference. The crucial
ingredient in these new algorithms is the ability to measure and reinitialize
subsets of qubits in the course of the computation, allowing us to reuse qubits
and increase the circuit depth without suffering the usual noisy consequences.
We also introduce the notion of effective circuit depth as a generalization of
standard circuit depth suitable for circuits with qubit resets. This tool helps
explain the noise-resilience of our qubit-efficient algorithms and should aid
in designing future algorithms. We perform numerical simulations to compare our
algorithms to the original variants and show they perform similarly when
subjected to noise. Additionally, we experimentally implement one of our
qubit-efficient algorithms on the Honeywell System Model H0, estimating
$Tr(\rho^n)$ for larger n than possible with previous algorithms.


  In an idealized infinite crystal, the material properties are constrained by
the symmetries of its unit cell. Naturally, the point-group symmetry is broken
by the sample shape of any finite crystal, yet this is commonly unobservable in
macroscopic metals. To sense the shape-induced symmetry lowering in such
metals, long-lived bulk states originating from anisotropic Fermi surfaces are
needed. Here we show how strongly facetted Fermi surfaces and long
quasiparticle mean free paths present in microstructures of PdCoO2 yield an
in-plane resistivity anisotropy that is forbidden by symmetry on an infinite
hexagonal lattice. Bar shaped transport devices narrower than the mean free
path are carved from single crystals using focused ion beam (FIB) milling, such
that the ballistic charge carriers at low temperatures frequently collide with
both sidewalls defining a channel. Two symmetry-forbidden transport signatures
appear: the in-plane resistivity anisotropy exceeds a factor of 2, and
transverse voltages appear in zero magnetic field. We robustly identify the
channel direction as the source of symmetry breaking via ballistic Monte- Carlo
simulations and numerical solution of the Boltzmann equation.


  A set of theoretical results on infrared absorption and inelastic (Raman)
scattering of light in semiconductor quantum dots is reviewed. When it is
possible, qualitative comparisons with recent experimental results are
presented.


  We propose an attention-injective deformable convolutional network called
ADCrowdNet for crowd understanding that can address the accuracy degradation
problem of highly congested noisy scenes. ADCrowdNet contains two concatenated
networks. An attention-aware network called Attention Map Generator (AMG) first
detects crowd regions in images and computes the congestion degree of these
regions. Based on detected crowd regions and congestion priors, a multi-scale
deformable network called Density Map Estimator (DME) then generates
high-quality density maps. With the attention-aware training scheme and
multi-scale deformable convolutional scheme, the proposed ADCrowdNet achieves
the capability of being more effective to capture the crowd features and more
resistant to various noises. We have evaluated our method on four popular crowd
counting datasets (ShanghaiTech, UCF_CC_50, WorldEXPO'10, and UCSD) and an
extra vehicle counting dataset TRANCOS, and our approach beats existing
state-of-the-art approaches on all of these datasets.


  We review recent computations of neutral pion photoproduction and Compton
scattering on the deuteron in baryon chiral perturbation theory. Progress in
extracting the neutron electric dipole amplitude, which is relevant in neutral
pion photoproduction, and the neutron polarizabilities, which are relevant in
Compton scattering, is discussed.


  Einstein's relativity theory appears to be very accurate, but at times
equally puzzling. On the one hand, electromagnetic radiation must have zero
rest mass in order to propagate at the speed of light, but on the other hand,
since it definitely carries momentum and energy, it has non-zero inertial mass.
Hence, by the principle of equivalence, it must have non-zero gravitational
mass, and so, light must be heavy. In this paper, no new results will be
derived, but a possibly surprising perspective on the above paradox is given.


  We study theoretically weak localization of excitons in atomically-thin
transition metal dichalcogenides. The constructive interference of excitonic de
Broglie waves on the trajectories forming closed loops results in a decrease of
the exciton diffusion coefficient. We calculate the interference contribution
to the diffusion coefficient for the experimentally relevant situation of
exciton scattering by acoustic phonons and static disorder. For the acoustic
phonon scattering, the quantum interference becomes more and more important
with increasing the temperature. Our estimates show that the quantum
contribution to the diffusion coefficient is considerable for the
state-of-the-art monolayer and bilayer transition metal dichalcogenides.


  Using a single circular Rydberg atom, we have prepared two modes of a
superconducting cavity in a maximally entangled state. The two modes share a
single photon. This entanglement is revealed by a second atom probing, after a
delay, the correlations between the two modes. This experiment opens
interesting perspectives for quantum information manipulation and fundamental
tests of quantum theory.


  We implement and investigate a method for measuring departures from
scale-invariance, both scale-dependent as well as scale-free, in the primordial
power spectrum of density perturbations using cosmic microwave background (CMB)
C_l data and a principal component analysis technique. The primordial power
spectrum is decomposed into a dominant scale-invariant Gaussian adiabatic
component plus a series of orthonormal modes whose detailed form only depends
the noise model for a particular CMB experiment. However, in general these
modes are localised across wavenumbers with 0.01 < k < 0.2 Mpc^-1, displaying
rapid oscillations on scales corresponding the acoustic peaks where the
sensitivity to primordial power spectrum is greatest. The performance of this
method is assessed using simulated data for the Planck satellite, and the full
cosmological plus power spectrum parameter space is integrated out using Markov
Chain Monte Carlo. As a proof of concept we apply this data compression
technique to the current CMB data from WMAP, ACBAR, CBI and VSA. We find no
evidence for the breaking of scale-invariance from measurements of four PCA
mode amplitudes, which is translated to a constraint on the scalar spectral
index n_S(k_0=0.04 Mpc^-1)=0.94+-0.04 in accordance with WMAP studies.


  It is well known that a hyperbolic domain in the complex plane has uniformly
perfect boundary precisely when the product of its hyperbolic density and the
distance function to its boundary has a positive lower bound. We extend this
characterization to a hyperbolic domain in the Riemann sphere in terms of the
spherical metric.


  We consider Hamiltonian closures of the Vlasov equation using the phase-space
moments of the distribution function. We provide some conditions on the
closures imposed by the Jacobi identity. We completely solve some families of
examples. As a result, we show that imposing that the resulting reduced system
preserves the Hamiltonian character of the parent model shapes its phase space
by creating a set of Casimir invariants as a direct consequence of the Jacobi
identity.


  For a continuous map on the unit interval or circle, we define the
bifurcation set to be the collection of those interval holes whose surviving
set is sensitive to arbitrarily small changes of their position. By assuming a
global perspective and focusing on the geometric and topological properties of
this collection rather than the surviving sets of individual holes, we obtain a
novel topological invariant for one-dimensional dynamics.
  We provide a detailed description of this invariant in the realm of
transitive maps and observe that it carries fundamental dynamical information.
In particular, for transitive non-minimal piecewise monotone maps, the
bifurcation set encodes the topological entropy and strongly depends on the
behavior of the critical points.


  In this article, we investigate whether exchange rate risk is priced. We use
a multivariate GARCH-in-Mean specification and test alternative conditional
international CAPM versions. Our results support strongly the international
asset-pricing model that includes exchange rate risk for both developed and
emerging stock markets. However, there are important time and cross-country
variations in the relative size and dynamics of different risk premia.


  Boudou and the authors have recently introduced the intuitionistic temporal
logic $\sf ITL^e$ and shown it to be decidable. In this article we show that
the `henceforth'-free fragment of this logic is complete for the class of
non-deterministic quasimodels introduced by Fern\'andez-Duque. From this and
results of Boudou, Romero and the authors, we conclude that this fragment is
also complete for the standard semantics of $\sf ITL^e$ over the class of
expanding posets.


  The time-dependent transmission coefficient for the Kramers problem exhibits
different behaviors in different parameter regimes. In the high friction regime
it decays monotonically ("non-adiabatic"), and in the low friction regime it
decays in an oscillatory fashion ("energy-diffusion-limited"). The generalized
Kramers problem with an exponential memory friction exhibits an additional
oscillatory behavior in the high friction regime ("caging"). In this paper we
consider an oscillatory memory kernel, which can be associated with a model in
which the reaction coordinate is linearly coupled to a nonreactive coordinate,
which is in turn coupled to a heat bath. We recover the non-adiabatic and
energy-diffusion-limited behaviors of the transmission coefficient in
appropriate parameter regimes, and find that caging is not observed with an
oscillatory memory kernel. Most interestingly, we identify a new regime in
which the time-dependent transmission coefficient decays via a series of rather
sharp steps followed by plateaus ("stair-like"). We explain this regime and its
dependence on the various parameters of the system.


  Knowledge graphs are large, useful, but incomplete knowledge repositories.
They encode knowledge through entities and relations which define each other
through the connective structure of the graph. This has inspired methods for
the joint embedding of entities and relations in continuous low-dimensional
vector spaces, that can be used to induce new edges in the graph, i.e., link
prediction in knowledge graphs. Learning these representations relies on
contrasting positive instances with negative ones. Knowledge graphs include
only positive relation instances, leaving the door open for a variety of
methods for selecting negative examples. In this paper we present an empirical
study on the impact of negative sampling on the learned embeddings, assessed
through the task of link prediction. We use state-of-the-art knowledge graph
embeddings -- \rescal , TransE, DistMult and ComplEX -- and evaluate on
benchmark datasets -- FB15k and WN18. We compare well known methods for
negative sampling and additionally propose embedding based sampling methods. We
note a marked difference in the impact of these sampling methods on the two
datasets, with the "traditional" corrupting positives method leading to best
results on WN18, while embedding based methods benefiting the task on FB15k.


  We consider the behaviour of a critical system in the presence of a gradient
perturbation of the couplings. In the direction of the gradient an interface
region separates the ordered phase from the disordered one. We develop a
scaling theory for the density profiles induced by the gradient perturbation
which involves a characteristic length given by the width of the interface
region. The scaling predictions are tested in the framework of the mean-field
Ginzburg-Landau theory. Then we consider the Ising quantum chain in a linearly
varying transverse field which corresponds to the extreme anisotropic limit of
a classical two-dimensional Ising model. The quantum Hamiltonian can be
diagonalized exactly in the scaling limit where the eigenvalue problem is the
same as for the quantum harmonic oscillator. The energy density, the
magnetization profile and the two-point correlation function are studied either
analytically or by exact numerical calculations. Their scaling behaviour are in
agreement with the predictions of the scaling theory.


  In this paper, we investigate the minimality of the map $\frac{x}{\|x\|}$
from the euclidean unit ball $\mathbf{B}^n$ to its boundary $\mathbb{S}^{n-1}$
for weighted energy functionals of the type $E\_{p,f}=
\int\_{\mathbf{B}^n}f(r)\|\nabla u\|^p dx$, where $f$ is a non-negative
function. We prove that in each of the two following cases: i) $p=1$ and $f$ is
non-decreasing, i)) $p$ is an integer, $p \leq n-1$ and $f= r^{\alpha}$ with
$\alpha \geq 0$, the map $\frac{x}{\|x\|}$ minimizes $E\_{p,f}$ among the maps
in $W^{1,p}(\mathbf{B}^n, \mathbb{S}^{n-1})$ which coincide with
$\frac{x}{\|x\|}$ on $\partial \mathbf{B}^n$. We also study the case where $
f(r)= r^{\alpha}$ with $-n+2 < \alpha < 0$ and prove that $\frac{x}{\|x\|}$
does not minimize $E\_{p,f}$ for $\alpha$ close to $-n+2$ and when $n \geq 6$,
for $\alpha$ close to $4-n$.


  This paper considers method of creation of an advisor and indicator based on
the spectral stochastic analysis model, both with linear and non-linear
approximation. The problem of entrance to one or another trade position is
solved on the basis of combined analysis of dynamics of quotations of all
currency pairs, what allows to actively hedge open positions.


  We present a model for the aperiodic variablity of accreting Black Hole
Candidates (BHC) along with model light curves. According to the model this
variability is the combined outcome of random (Poisson) injection of soft
photons near the center of an extended {\sl inhomogeneous} distribution of hot
electrons (similar to those advocated by the ADAF or ADIOS flows) and the
stochastic nature of Compton scattering which converts these soft photons into
the observed high energy radiation. Thus, the timing properties (PSD, lags,
coherence) of the BHC light curves reflect, to a large extent, the properties
of the scattering medium (which in this approximation acts as a combination of
a {\sl linear} amplifier/filter) and they can be used to probe its structure,
most notably the density profile of the scattering medium. The model accounts
well for the observed PSDs and lags and also the reduction in the RMS
variability and the increase in the characteristic PSD frequencies with
increasing source luminosity. The electron density profiles obtained to date
are consistent mainly with those of ADIOS but also with pure ADAF flows.


  We have grown and characterized 30 nm thick YBa$_2$Cu$_3$O$_{7-\delta}$
(YBCO) films, deposited by pulsed laser deposition on both MgO (110) and
SrTiO$_3$ (001) substrates, which induce opposite strain to the superconducting
layer. By carefully tuning the in-situ post-annealing oxygen pressure, we
achieved, in a reproducible way, films at different oxygen doping, spanning
from the slightly overdoped down to the strongly underdoped region of the phase
diagram. The transport properties of the films, investigated through resistance
versus temperature measurements, are in perfect qualitative agreement with
single crystals. Starting from these films, we have also successfully
fabricated nanowires with widths down to 65 nm, at different oxygen doping. The
nanostructures exhibit characteristic temperatures (as the critical temperature
$T_{\mathrm{c}}$ and the pseudogap temperature $T^*$) similar to those of the
as-grown films and carry critical current densities $J_{\mathrm{c}}$ close to
the critical depairing value, limited by vortex entry. This implies that the
superconducting and the normal state properties of underdoped YBCO are
preserved in our films, and they can be studied as a function of the
dimensionality of the system, down to the nanoscale.


  Humans work together to solve common problems by having discussions,
explaining, and agreeing or disagreeing with each other. Similarly, if a system
can have discussions with humans when solving tasks, it can improve the
system's performance and reliability. In previous research on explainability,
it has only been possible for the system to make predictions and for humans to
ask questions about them rather than having a mutual exchange of opinions. This
research aims to create a dataset and computational framework for systems that
discuss and refine their predictions through dialogue. Through experiments, we
show that the proposed system can have beneficial discussions with humans
improving the accuracy by up to 25 points in the natural language inference
task.


  Empirical observations indicate striking similarities among locomotion in
terrestrial animals, birds, and fish, but unifying physical grounds are
lacking. When applied to efficient locomotion, the analytical mechanics
principle of minimum action yields two patterns of mechanical similarity via
two explicit spatiotemporal coherent states. In steady locomotory modes, the
slow muscles determining maximal optimum speeds maintain universal intrinsic
muscular pressure. Otherwise, maximal speeds are due to constant mass-dependent
stiffness of fast muscles generating a uniform force field, exceeding
gravitation. Being coherent in displacements, velocities and forces, the body
appendages of animals are tuned to natural propagation frequency through the
state-dependent elastic muscle moduli.
  Key words: variational principle of minimum action (04.20.Fy), locomotion
(87.19.ru), biomechanics (87.85.G-).


  We conduct a preliminary investigation into the levels of congestion in New
Delhi, motivated by concerns due to rapidly growing vehicular congestion in
Indian cities. First, we provide statistical evidence for the rising congestion
levels on the roads of New Delhi from taxi GPS traces. Then, we estimate the
economic costs of congestion in New Delhi. In particular, we estimate the
marginal and the total costs of congestion. In calculating the marginal costs,
we consider the following factors: (i) productivity loss, (ii) air pollution
costs, and (iii) costs due to accidents. In calculating the total costs, in
addition to the above factors, we also estimate the costs due to the wastage of
fuel. We also project the associated costs due to productivity loss and air
pollution till 2030. The projected traffic congestion costs for New Delhi comes
around 14658 million US$/yr for the year 2030. The key takeaway from our
current study is that costs due to productivity loss, particularly from buses,
dominates the overall economic costs. Additionally, the expected increase in
fuel wastage makes a strong case for intelligent traffic management systems.


  In this paper, we introduce operator geodesically convex and operator
convex-log functions and characterize some properties of them. Then apply these
classes of functions to present several operator Azc\'{e}l and Minkowski type
inequalities extending some known results. The concavity counterparts are also
considered.


  This paper proposes a set of piecewise Toeplitz matrices as the linear
mapping/sensing operator $\mathcal{A}: \mathbf{R}^{n_1 \times n_2} \rightarrow
\mathbf{R}^M$ for recovering low rank matrices from few measurements. We prove
that such operators efficiently encode the information so there exists a unique
reconstruction matrix under mild assumptions. This work provides a significant
extension of the compressed sensing and rank minimization theory, and it
achieves a tradeoff between reducing the memory required for storing the
sampling operator from $\mathcal{O}(n_1n_2M)$ to $\mathcal{O}(\max(n_1,n_2)M)$
but at the expense of increasing the number of measurements by $r$. Simulation
results show that the proposed operator can recover low rank matrices
efficiently with a reconstruction performance close to the cases of using
random unstructured operators.


  The changeover from first-order to second-order phase transitions in q-state
Potts models is obtained at q_c=2 in spatial dimension d=3 and essentially at
q_c=4 in d=2, using a physically intuited simple adaptation of the
Migdal-Kadanoff renormalization-group transformation. This simple procedure
yields the latent heats at the first-order phase transitions. In both d=2 and
3, the calculated phase transition temperatures, respectively compared with the
exact self-duality and Monte Carlo results, are dramatically improved.


  It is shown that the wave function describes the state of the statistical
ensemble E[S] of individual particles, or the statistical average particle <S>.
This result follows from the fact that in the classical limit h=0 the
Schroedinger equation turns to the dynamic equations for the statistical
ensemble of classical particles. The statement that the wave function describes
the state of an individual particle is incompatible with the quantum mechanics
formalism. It is shown that the statistical interpretation of quantum mechanics
is a corollary of the fact, that the QM formalism is the technique of the
statistical ensemble description, restricted by constraints of the dynamic
equation linearity.


  We analyze the convergence rate of a simplified version of a popular Gibbs
sampling method used for statistical discovery of gene regulatory binding
motifs in DNA sequences. This sampler satisfies a very strong form of
ergodicity (uniform). However, we show that, due to multimodality of the
posterior distribution, the rate of convergence often decreases exponentially
as a function of the length of the DNA sequence. Specifically, we show that
this occurs whenever there is more than one true repeating pattern in the data.
In practice there are typically multiple such patterns in biological data, the
goal being to detect the most well-conserved and frequently-occurring of these.
Our findings match empirical results, in which the motif-discovery Gibbs
sampler has exhibited such poor convergence that it is used only for finding
modes of the posterior distribution (candidate motifs) rather than for
obtaining samples from that distribution. Ours are some of the first meaningful
bounds on the convergence rate of a Markov chain method for sampling from a
multimodal posterior distribution, as a function of statistical quantities like
the number of observations.


  A summary of the W-boson properties measured by the four LEP collaborations
is presented here. These properties are updated to take into account the most
recent results presented at the ICHEP98 conference.


  The degree to which interstellar grains align with respect to the
interstellar magnetic field depends on disaligning as well as aligning
mechanisms. For decades, it was assumed that disalignment was due primarily to
the random angular impulses a grain receives when colliding with gas-phase
atoms. Recently, a new disalignment mechanism has been considered, which may be
very potent for a grain that has a time-varying electric dipole moment and
drifts across the magnetic field. We provide quantitative estimates of the
disalignment times for silicate grains with size > approximately 0.1 micron.
These appear to be shorter than the time-scale for alignment by radiative
torques, unless the grains contain superparamagnetic inclusions.


  Nasopharyngeal carcinoma (NPC) is a prevalent and clinically significant
malignancy that predominantly impacts the head and neck area. Precise
delineation of the Gross Tumor Volume (GTV) plays a pivotal role in ensuring
effective radiotherapy for NPC. Despite recent methods that have achieved
promising results on GTV segmentation, they are still limited by lacking
carefully-annotated data and hard-to-access data from multiple hospitals in
clinical practice. Although some unsupervised domain adaptation (UDA) has been
proposed to alleviate this problem, unconditionally mapping the distribution
distorts the underlying structural information, leading to inferior
performance. To address this challenge, we devise a novel Sourece-Free Active
Domain Adaptation (SFADA) framework to facilitate domain adaptation for the GTV
segmentation task. Specifically, we design a dual reference strategy to select
domain-invariant and domain-specific representative samples from a specific
target domain for annotation and model fine-tuning without relying on
source-domain data. Our approach not only ensures data privacy but also reduces
the workload for oncologists as it just requires annotating a few
representative samples from the target domain and does not need to access the
source data. We collect a large-scale clinical dataset comprising 1057 NPC
patients from five hospitals to validate our approach. Experimental results
show that our method outperforms the UDA methods and achieves comparable
results to the fully supervised upper bound, even with few annotations,
highlighting the significant medical utility of our approach. In addition,
there is no public dataset about multi-center NPC segmentation, we will release
code and dataset for future research.


  We use the Kempf-Lascoux-Weyman geometric technique in order to determine the
minimal free resolutions of some orbit closures of quivers. As a consequence,
we obtain that for Dynkin quivers orbit closures of 1-step representations are
normal with rational singularities. For Dynkin quivers of type $A$, we describe
explicit minimal generators of the defining ideals of orbit closures of 1-step
representations. Using this, we provide an algorithm for type $A$ quivers for
describing an efficient set of generators of the defining ideal of the orbit
closure of any representation.


  Constructing valid prediction intervals rather than point estimates is a
well-established approach for uncertainty quantification in the regression
setting. Models equipped with this capacity output an interval of values in
which the ground truth target will fall with some prespecified probability.
This is an essential requirement in many real-world applications where simple
point predictions' inability to convey the magnitude and frequency of errors
renders them insufficient for high-stakes decisions. Quantile regression is a
leading approach for obtaining such intervals via the empirical estimation of
quantiles in the (non-parametric) distribution of outputs. This method is
simple, computationally inexpensive, interpretable, assumption-free, and
effective. However, it does require that the specific quantiles being learned
are chosen a priori. This results in (a) intervals that are arbitrarily
symmetric around the median which is sub-optimal for realistic skewed
distributions, or (b) learning an excessive number of intervals. In this work,
we propose Relaxed Quantile Regression (RQR), a direct alternative to quantile
regression based interval construction that removes this arbitrary constraint
whilst maintaining its strengths. We demonstrate that this added flexibility
results in intervals with an improvement in desirable qualities (e.g. mean
width) whilst retaining the essential coverage guarantees of quantile
regression.


  The Axelrod model is a spatial stochastic model for the dynamics of cultures
which, similarly to the voter model, includes social influence, but differs
from the latter by also accounting for another social factor called homophily,
the tendency to interact more frequently with individuals who are more similar.
Each individual is characterized by its opinions about a finite number of
cultural features, each of which can assume the same finite number of states.
Pairs of adjacent individuals interact at a rate equal to the fraction of
features they have in common, thus modeling homophily, which results in the
interacting pair having one more cultural feature in common, thus modeling
social influence. It has been conjectured based on numerical simulations that
the one-dimensional Axelrod model clusters when the number of features exceeds
the number of states per feature. In this article, we prove this conjecture for
the two-state model with an arbitrary number of features.


  Lip reading aims to predict speech based on lip movements alone. As it
focuses on visual information to model the speech, its performance is
inherently sensitive to personal lip appearances and movements. This makes the
lip reading models show degraded performance when they are applied to unseen
speakers due to the mismatch between training and testing conditions. Speaker
adaptation technique aims to reduce this mismatch between train and test
speakers, thus guiding a trained model to focus on modeling the speech content
without being intervened by the speaker variations. In contrast to the efforts
made in audio-based speech recognition for decades, the speaker adaptation
methods have not well been studied in lip reading. In this paper, to remedy the
performance degradation of lip reading model on unseen speakers, we propose a
speaker-adaptive lip reading method, namely user-dependent padding. The
user-dependent padding is a speaker-specific input that can participate in the
visual feature extraction stage of a pre-trained lip reading model. Therefore,
the lip appearances and movements information of different speakers can be
considered during the visual feature encoding, adaptively for individual
speakers. Moreover, the proposed method does not need 1) any additional layers,
2) to modify the learned weights of the pre-trained model, and 3) the speaker
label of train data used during pre-train. It can directly adapt to unseen
speakers by learning the user-dependent padding only, in a supervised or
unsupervised manner. Finally, to alleviate the speaker information
insufficiency in public lip reading databases, we label the speaker of a
well-known audio-visual database, LRW, and design an unseen-speaker lip reading
scenario named LRW-ID.


  Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning
toolbox and have led to many breakthroughs in Artificial Intelligence. These
networks have mostly been developed for regular Euclidean domains such as those
supporting images, audio, or video. Because of their success, CNN-based methods
are becoming increasingly popular in Cosmology. Cosmological data often comes
as spherical maps, which make the use of the traditional CNNs more complicated.
The commonly used pixelization scheme for spherical maps is the Hierarchical
Equal Area isoLatitude Pixelisation (HEALPix). We present a spherical CNN for
analysis of full and partial HEALPix maps, which we call DeepSphere. The
spherical CNN is constructed by representing the sphere as a graph. Graphs are
versatile data structures that can act as a discrete representation of a
continuous manifold. Using the graph-based representation, we define many of
the standard CNN operations, such as convolution and pooling. With filters
restricted to being radial, our convolutions are equivariant to rotation on the
sphere, and DeepSphere can be made invariant or equivariant to rotation. This
way, DeepSphere is a special case of a graph CNN, tailored to the HEALPix
sampling of the sphere. This approach is computationally more efficient than
using spherical harmonics to perform convolutions. We demonstrate the method on
a classification problem of weak lensing mass maps from two cosmological models
and compare the performance of the CNN with that of two baseline classifiers.
The results show that the performance of DeepSphere is always superior or equal
to both of these baselines. For high noise levels and for data covering only a
smaller fraction of the sphere, DeepSphere achieves typically 10% better
classification accuracy than those baselines. Finally, we show how learned
filters can be visualized to introspect the neural network.


  Consider an optimization problem with $n$ binary variables and $d+1$ linear
objective functions. Each valid solution $x \in \{0,1\}^n$ gives rise to an
objective vector in $\R^{d+1}$, and one often wants to enumerate the Pareto
optima among them. In the worst case there may be exponentially many Pareto
optima; however, it was recently shown that in (a generalization of) the
smoothed analysis framework, the expected number is polynomial in $n$.
Unfortunately, the bound obtained had a rather bad dependence on $d$; roughly
$n^{d^d}$. In this paper we show a significantly improved bound of $n^{2d}$.
  Our proof is based on analyzing two algorithms. The first algorithm, on input
a Pareto optimal $x$, outputs a "testimony" containing clues about $x$'s
objective vector, $x$'s coordinates, and the region of space $B$ in which $x$'s
objective vector lies. The second algorithm can be regarded as a {\em
speculative} execution of the first -- it can uniquely reconstruct $x$ from the
testimony's clues and just \emph{some} of the probability space's outcomes. The
remainder of the probability space's outcomes are just enough to bound the
probability that $x$'s objective vector falls into the region $B$.


  It is well known that Hopf-fibre T-duality and uplift takes the D1-D5
near-horizon into a class of $AdS_3 \times S^2$ geometries in 11D where the
internal space is a Calabi-Yau three-fold. Moreover, supersymmetry dictates
that Calabi-Yau is the only permissible $SU(3)$-structure manifold.
Generalising this duality chain to non-Abelian isometries, a strong parallel
exists, resulting in the first explicit example of a class of $AdS_3 \times
S^2$ geometries with $SU(2)$-structure. Furthermore, the non-Abelian T-dual of
$AdS_3 \times S^3 \times S^3 \times S^1$ results in a new supersymmetric $AdS_3
\times S^2$ geometry, which falls outside of all known classifications. We
explore the basic properties of the holographic duals associated to the new
backgrounds. We compute the central charges and show that they are compatible
with a large $\mathcal{N}=4$ superconformal algebra in the infra-red.


  Reflected diffusions naturally arise in many problems from applications
ranging from economics and mathematical biology to queueing theory. In this
paper we consider a class of infinite time-horizon singular stochastic control
problems for a general one-dimensional diffusion that is reflected at zero. We
assume that exerting control leads to a state-dependent instantaneous reward,
whereas reflecting the diffusion at zero gives rise to a proportional cost with
constant marginal value. The aim is to maximize the total expected reward,
minus the total expected cost of reflection. We show that depending on the
properties of the state-dependent instantaneous reward we can have
qualitatively different kinds of optimal strategies. The techniques employed
are those of stochastic control and of the theory of linear diffusions.


  By modifying the method of Bruss and Peres, we construct two new families of
entangled two qutrit states. For all density matrices in these families the
(i,j)th entry is 0 for i+j odd. The first family depends on 27 independent real
parameters and includes both PPT and NPT states. The second family consists of
PPT entangled states. The number of independent real parameters of this family
is at least 11.


  The rank modulation scheme has been proposed for efficient writing and
storing data in non-volatile memory storage. Error-correction in the rank
modulation scheme is done by considering permutation codes. In this paper we
consider codes in the set of all permutations on $n$ elements, $S_n$, using the
Kendall $\tau$-metric. The main goal of this paper is to derive new bounds on
the size of such codes. For this purpose we also consider perfect codes,
diameter perfect codes, and the size of optimal anticodes in the Kendall
$\tau$-metric, structures which have their own considerable interest. We prove
that there are no perfect single-error-correcting codes in $S_n$, where $n>4$
is a prime or $4\leq n\leq 10$. We present lower bounds on the size of optimal
anticodes with odd diameter. As a consequence we obtain a new upper bound on
the size of codes in $S_n$ with even minimum Kendall $\tau$-distance. We
present larger single-error-correcting codes than the known ones in $S_5$ and
$S_7$.


  The nonlinear equations describing all the nonsingular pencils of metrics of
constant Riemannian curvature are derived and the integrability of these
nonlinear equations by the method of inverse scattering problem is proved. It
is proved that all the nonsingular pairs of compatible metrics of constant
Riemannian curvature are described by special integrable reductions of
nonlinear equations defining orthogonal curvilinear coordinate systems in the
spaces of constant curvature.


  We report the first science observations and results obtained with the
"extended" SMA (eSMA), which is composed of the SMA (Submillimeter Array), JCMT
(James Clerk Maxwell Telescope) and CSO (Caltech Submillimeter Observatory).
Redshifted absorptions at z=0.886 of CI (^3P_1 - ^3P_0) were observed with the
eSMA with an angular resolution of 0.55"x0.22" at 1.1 mm toward the
southwestern image of the remarkable lensed quasar PKS 1830-211, but not toward
the northeastern component at a separation of ~1". Additionally, SMA
observations of CO, 13CO and C18O (all J=4-3) were obtained toward this object:
CO was also detected toward the SW component, but none of the isotopologues
were. This is the first time [CI] is detected in this object, allowing the
first direct determination of relative abundances of neutral atomic carbon to
CO in the molecular clouds of a spiral galaxy at z>0.1. The [CI] and CO
profiles can be decomposed into two and three velocity components respectively.
We derive C/CO column density ratios ranging from <0.5 (representative of dense
cores) to ~2.5 (close to translucent clouds values). This could indicate that
we are seeing environments with different physical conditions or that we are
witnessing chemical evolution of regions where C has not completely been
converted into CO.


  Suggestion mining is increasingly becoming an important task along with
sentiment analysis. In today's cyberspace world, people not only express their
sentiments and dispositions towards some entities or services, but they also
spend considerable time sharing their experiences and advice to fellow
customers and the product/service providers with two-fold agenda: helping
fellow customers who are likely to share a similar experience, and motivating
the producer to bring specific changes in their offerings which would be more
appreciated by the customers. In our current work, we propose a hybrid deep
learning model to identify whether a review text contains any suggestion. The
model employs semi-supervised learning to leverage the useful information from
the large amount of unlabeled data. We evaluate the performance of our proposed
model on a benchmark customer review dataset, comprising of the reviews of
Hotel and Electronics domains. Our proposed approach shows the F-scores of
65.6% and 65.5% for the Hotel and Electronics review datasets, respectively.
These performances are significantly better compared to the existing
state-of-the-art system.


  Composite S-brane solutions in multidimensional gravity with scalar fields
and fields of forms related to Toda-like systems are presented. These solutions
are defined on a product manifold R_{*} x M_1 x ... x M_n, where R_{*} is a
time manifold, M_1 is an Einstein manifold and M_i (i > 1) are Ricci-flat
manifolds. Certain examples of S-brane solutions related to A_1 + ... + A_1,
A_m Toda chains and those with "block-orthogonal" intersections (e.g. SM-brane
solutions) are singled out. Under certain restrictions imposed a Kasner-like
asymptotical behaviour of the solutions is shown.


  The dynamics of interacting quantized vortex filaments in a rotating trapped
Bose-Einstein condensate, which is in the Thomas-Fermi regime at zero
temperature and described by the Gross-Pitaevskii equation, is considered in
the hydrodynamic "anelastic" approximation. In the presence of a smoothly
inhomogeneous array of filaments (vortex lattice), a non-canonical Hamiltonian
equation of motion is derived for the macroscopically averaged vorticity, with
taking into account the spatial non-uniformity of the equilibrium condensate
density determined by the trap potential. A minimum of the corresponding
Hamiltonian describes a static configuration of deformed vortex lattice against
a given density background. The minimum condition is reduced to a vector
nonlinear partial differential equation of the second order, for which some
approximate and exact solutions are found. It is shown that if the condensate
density has an anisotropic Gaussian profile then equation of motion for the
averaged vorticity admits solutions in the form of a spatially uniform vector
with a nontrivial time dependence. An integral representation is obtained for
the matrix Green function determining the non-local Hamiltonian of a system of
arbitrary shaped vortex filaments in a condensate with Gaussian density. ... A
simple approximate expression for the two-dimensional Green function is
suggested at rather arbitrary density profile, and its successful comparison to
the exact result in the Gaussian case is done. Approximate equations of motion
are derived which describe a long-wave dynamics of interacting vortex filaments
in condensates with the density depending on the transverse coordinates only.


  The study of quantum channels is the fundamental field and promises wide
range of applications, because any physical process can be represented as a
quantum channel transforming an initial state into a final state. Inspired by
the method performing non-unitary operator by the linear combination of unitary
operations, we proposed a quantum algorithm for the simulation of universal
single-qubit channel, described by a convex combination of 'quasiextreme'
channels corresponding to four Kraus operators, and is scalable to arbitrary
higher dimension. We demonstrate the whole algorithm experimentally using the
universal IBM cloud quantum computer and study properties of different qubit
quantum channels. We illustrate the quantum capacity of the general qubit
quantum channels, which quantifies the amount of quantum information that can
be protected. The behaviour of quantum capacity in different channels reveal
which types of noise processes can support information transmission, and which
types are too destructive to protect information. There is a general agreement
between the theoretical predictions and the experiments, which strongly
supported our method. By realizing arbitrary qubit channel, this work provides
a universal way to explore various properties of quantum channel and novel
prospect of quantum communication.


  The severe acute respiratory syndrome coronavirus type-2 (SARS-CoV-2) caused
a global pandemic and immensely affected the global economy. Accurate,
cost-effective, and quick tests have proven substantial in identifying infected
people and mitigating the spread. Recently, multiple alternative platforms for
testing coronavirus disease 2019 (COVID-19) have been published that show high
agreement with current gold standard real-time polymerase chain reaction
(RT-PCR) results. These new methods do away with nasopharyngeal (NP) swabs,
eliminate the need for complicated reagents, and reduce the burden on RT-PCR
test reagent supply. In the present work, we have designed an artificial
intelligence-based (AI) testing method to provide confidence in the results.
Current AI applications for COVID-19 studies often lack a biological foundation
in the decision-making process, and our AI approach is one of the earliest to
leverage explainable AI (X-AI) algorithms for COVID-19 diagnosis using mass
spectrometry. Here, we have employed X-AI to explain the decision-making
process on a local (per-sample) and global (all samples) basis underscored by
biologically relevant features. We evaluated our technique with data extracted
from human gargle samples and achieved a testing accuracy of 94.12%. Such
techniques would strengthen the relationship between AI and clinical
diagnostics by providing biomedical researchers and healthcare workers with
trustworthy and, most importantly, explainable test results


  The next generation of space-based networks will contain optical clocks
embedded within satellites. To fully realize the capabilities of such clocks,
high-precision clock synchronization across the networks will be necessary.
Current experiments have shown the potential for classical frequency combs to
synchronize remote optical clocks over free-space. However, these classical
combs are restricted in precision to the standard quantum limit. Quantum
frequency combs, however, which exhibit quantum properties such as squeezing
and entanglement, provide pathways for going beyond the standard quantum limit.
Here, we present our perspective on the prospects for practical clock
synchronization in space using both classical and quantum frequency combs. We
detail the current outcomes achievable with a classical frequency comb approach
to synchronization, before quantifying the potential outcomes offered by
quantum frequency combs. Challenges to be overcome in deploying frequency combs
in space are presented, and the implications of almost-perfect synchronization
for future space-based applications and experiments discussed.


  We calculate the chirally improved (CI) Landau gauge quark propagator in two
flavor lattice Quantum Chromodynamics and study thereon the effects of Dirac
low-mode removal. The application of tree-level improvement of the propagator
and tree-level correction of the lattice dressing functions removes the leading
discretization artifacts. We find the dynamically generated mass in the
infrared domain of the mass function to dissolve continuously with the
reduction level and furthermore we observe strong suppression of the
wave-function renormalization function for small momenta.


  Recent works showed how low-density parity-check (LDPC) erasure correcting
codes, under maximum likelihood (ML) decoding, are capable of tightly
approaching the performance of an ideal maximum-distance-separable code on the
binary erasure channel. Such result is achievable down to low error rates, even
for small and moderate block sizes, while keeping the decoding complexity low,
thanks to a class of decoding algorithms which exploits the sparseness of the
parity-check matrix to reduce the complexity of Gaussian elimination (GE). In
this paper the main concepts underlying ML decoding of LDPC codes are recalled.
A performance analysis among various LDPC code classes is then carried out,
including a comparison with fixed-rate Raptor codes. The results show that LDPC
and Raptor codes provide almost identical performance in terms of decoding
failure probability vs. overhead.


  We demonstrate a two-crystal mid-infrared dual-wavelength optical parametric
oscillator, synchronously pumped by a high power femtosecond Yb:fiber laser.
The singly-resonant ring cavity, containing two periodically poled lithium
niobate crystals, is capable of generating two synchronized idler wavelengths,
independently tunable over 30 THz in the 2.9 - 4.2 {\mu}m wavelength region,
due to the cascaded quadratic nonlinear effect. The independent tunability of
the two idlers makes the optical parametric oscillator a promising source for
ultrafast pulse generation towards the THz wavelength region, based on
different frequency generation. In addition, the observed frequency doubled
idler within the crystal indicates the possibility to realize a broadband
optical self-phase locking between pump, signal, idler and higher order
generated parametric lights.


  We study the evolution of disc galaxies in group environments under the
effect of both the global tidal field and close-encounters between galaxies,
using controlled N-body simulations of isolated mergers. We find that
close-range encounters between galaxies are less frequent and less damaging to
disc galaxies than originally expected, since they mostly occur when group
members have lost a significant fraction of their initial mass to tidal
stripping. We also find that group members mostly affect disc galaxies
indirectly by modifying their common global tidal field. Different initial
orbital parameters of group members introduce a significant "scatter" in the
evolution of general properties of disc galaxies around a "median" evolution
that is similar to when only the effect of the global tidal field is included.
Close-encounters introduce a high variability in the properties of disc
galaxies, even slowing their evolution in some cases, and could wash out
correlations between galaxy properties and the group total mass. The combined
effect of the global tidal field and close-encounters appears to be inefficient
at forming/enhancing central stellar bulges. This implies that bulges of S0
galaxies should be mostly composed by young stars, which is consistent with
recent observations.


  What large-scale cohesive behaviors -- desirable or dangerous -- can suddenly
emerge from systems with interacting humans, machinery and software including
AI? When will they emerge? How will they evolve and be controlled? Here we
offer some answers to these urgent questions by introducing an aggregation
model that accounts for entities' inter- and intra-species diversities. It
yields a novel multi-dimensional generalization of existing aggregation
physics. We derive exact analytic solutions for the time-to-cohesion and
growth-of-cohesion for two species, and some generalizations for an arbitrary
number of species. These solutions reproduce -- and offer a microscopic
explanation for -- an anomalous nonlinear growth feature observed in related
real-world systems, e.g. Hamas-Hezbollah online support, human-machine team
interactions, AI-determined topic coherence. A key takeaway is that good and
bad 'surprises' will appear increasingly quickly as humans-machinery-AI etc.
mix more -- but the theory offers a rigorous approach for understanding and
controlling this.