  The nature of the seeds of the observed high-z super-massive black holes
(SMBH) is unknown. Although different options have been proposed, involving
e.g. intermediate mass direct collapse black holes, BH remnants of massive
stars remain the most natural explanation. To identify the most favorable
conditions (if any) for their rapid growth, we study the accretion rate of a
M_BH = 100M_sun BH formed in a typical z = 10 galaxy under different conditions
(e.g. galaxy structure, BH initial position and velocity). We model the galaxy
baryonic content and follow the BH orbit and accretion history for 300 Myr (the
time span in 10 > z > 7), assuming the radiation-regulated accretion model by
Park & Ricotti (2013). We find that, within the limits of our model, BH seeds
cannot grow by more than 30 percent, suggesting that accretion on light-seed
models are inadequate to explain high-z SMBH. We also compute the X-ray
emission from such accreting stellar BH population in the (0.5, 8) keV band and
find it comparable to the one produced by high-mass X-ray binaries. This study
suggests that early BHs, by X-ray pre-heating of the intergalactic medium at
cosmic dawn, might leave a specific signature on the HI 21 cm line power
spectrum potentially detectable with SKA.


  Permissioned blockchains are supported by identified but individually
untrustworthy nodes, collectively maintaining a replicated ledger whose content
is trusted. The Hyperledger Fabric permissioned blockchain system targets
high-throughput transaction processing. Fabric uses a set of nodes tasked with
the ordering of transactions using consensus. Additional peers endorse and
validate transactions, and maintain a copy of the ledger. The ability to
quickly disseminate new transaction blocks from ordering nodes to all peers is
critical for both performance and consistency. Broadcast is handled by a gossip
protocol, using randomized exchanges of blocks between peers. We show that the
current implementation of gossip in Fabric leads to heavy tail distributions of
block propagation latencies, impacting performance, consistency, and fairness.
We contribute a novel design for gossip in Fabric that simultaneously optimizes
propagation time, tail latency and bandwidth consumption. Using a 100-node
cluster, we show that our enhanced gossip allows the dissemination of blocks to
all peers more than 10 times faster than with the original implementation,
while decreasing the overall network bandwidth consumption by more than 40%.
With a high throughput and concurrent application, this results in 17% to 36%
fewer invalidated transactions for different block sizes.


  A major challenge in cognitive science and AI has been to understand how
autonomous agents might acquire and predict behavioral and mental states of
other agents in the course of complex social interactions. How does such an
agent model the goals, beliefs, and actions of other agents it interacts with?
What are the computational principles to model a Theory of Mind (ToM)? Deep
learning approaches to address these questions fall short of a better
understanding of the problem. In part, this is due to the black-box nature of
deep networks, wherein computational mechanisms of ToM are not readily
revealed. Here, we consider alternative hypotheses seeking to model how the
brain might realize a ToM. In particular, we propose embodied and situated
agent models based on distributed adaptive control theory to predict actions of
other agents in five different game theoretic tasks (Harmony Game, Hawk-Dove,
Stag-Hunt, Prisoner's Dilemma and Battle of the Exes). Our multi-layer control
models implement top-down predictions from adaptive to reactive layers of
control and bottom-up error feedback from reactive to adaptive layers. We test
cooperative and competitive strategies among seven different agent models
(cooperative, greedy, tit-for-tat, reinforcement-based, rational, predictive
and other's-model agents). We show that, compared to pure reinforcement-based
strategies, probabilistic learning agents modeled on rational, predictive and
other's-model phenotypes perform better in game-theoretic metrics across tasks.
Our autonomous multi-agent models capture systems-level processes underlying a
ToM and highlight architectural principles of ToM from a control-theoretic
perspective.


  While the research community appears to have developed a consensus on the
methods of acquiring annotated data, design and training of CNNs, many
questions still remain to be answered. In this paper, we explore the following
questions that are critical to face recognition research: (i) Can we train on
still images and expect the systems to work on videos? (ii) Are deeper datasets
better than wider datasets? (iii) Does adding label noise lead to improvement
in performance of deep networks? (iv) Is alignment needed for face recognition?
We address these questions by training CNNs using CASIA-WebFace, UMDFaces, and
a new video dataset and testing on YouTube- Faces, IJB-A and a disjoint portion
of UMDFaces datasets. Our new data set, which will be made publicly available,
has 22,075 videos and 3,735,476 human annotated frames extracted from them.


  Shortcomings of randomized clinical trials are pronounced in urgent health
crises, when rapid identification of effective treatments is critical.
Leveraging short-term surrogates in real-world data (RWD) can guide
policymakers evaluating new treatments. In this paper, we develop novel
estimators for the proportion of treatment effect (PTE) on the true outcome
explained by a surrogate in RWD settings. We propose inverse probability
weighted and doubly robust (DR) estimators of an optimal transformation of the
surrogate and PTE by semi-nonparametrically modeling the relationship between
the true outcome and surrogate given baseline covariates. We show that our
estimators are consistent and asymptotically normal, and the DR estimator is
consistent when either the propensity score model or outcome regression model
is correctly specified. We compare our proposed estimators to existing
estimators and show a reduction in bias and gains in efficiency through
simulations. We illustrate the utility of our method in obtaining an
interpretable PTE by conducting a cross-trial comparison of two biologic
therapies for ulcerative colitis.


  Egorov's theorem for transversally elliptic operators, acting on sections of
a vector bundle over a compact foliated manifold, is proved. This theorem
relates the quantum evolution of transverse pseudodifferential operators
determined by a first order transversally elliptic operator with the
(classical) evolution of its symbols determined by the parallel transport along
the orbits of the associated transverse bicharacteristic flow. For a particular
case of a transverse Dirac operator, the transverse bicharacteristic flow is
shown to be given by the transverse geodesic flow and the parallel transport by
the parallel transport determined by the transverse Levi-Civita connection.
These results allow us to describe the noncommutative geodesic flow in
noncommutative geometry of Riemannian foliations.


  We present HelpViz, a tool for generating contextual visual mobile tutorials
from text-based instructions that are abundant on the web. HelpViz transforms
text instructions to graphical tutorials in batch, by extracting a sequence of
actions from each text instruction through an instruction parsing model, and
executing the extracted actions on a simulation infrastructure that manages an
array of Android emulators. The automatic execution of each instruction
produces a set of graphical and structural assets, including images, videos,
and metadata such as clicked elements for each step. HelpViz then synthesizes a
tutorial by combining parsed text instructions with the generated assets, and
contextualizes the tutorial to user interaction by tracking the user's progress
and highlighting the next step.
  Our experiments with HelpViz indicate that our pipeline improved tutorial
execution robustness and that participants preferred tutorials generated by
HelpViz over text-based instructions. HelpViz promises a cost-effective
approach for generating contextual visual tutorials for mobile interaction at
scale.


  We study logarithmically enhanced electromagnetic corrections to the decay
rate in the high dilepton invariant mass region as well as corrections to the
forward backward asymmetry (FBA) of the inclusive rare decay $\bar{B} \to X_s
\ell^+ \ell^-$. As expected, the relative effect of these corrections in the
high dilepton mass region is around -8% for the muonic final state and
therefore much larger than in the low dilepton mass region.
  We also present a complete phenomenological analysis, to improved NNLO
accuracy, of the dilepton mass spectrum and the FBA integrated in the low
dilepton mass region, including a new approach to the zero of the FBA. The
latter represents one of the most precise predictions in flavour physics with a
theoretical uncertainty of order 5%. We find $(q_0^2)_{\mu\mu} = (3.50 \pm
0.12) \gev^2$. For the high dilepton invariant mass region, we have ${\cal
B}(\bar B\to X_s\mu\mu)_{\rm high} = (2.40^{+0.69}_{-0.62}) \times 10^{-7}$.
The dominant uncertainty is due to the $1/m_b$ corrections and can be
significantly reduced in the future. For the low dilepton invariant mass
region, we confirm previous results up to small corrections.


  We present a microscopic theory of the equilibrium polariton condensate state
of a semiconductor quantum well in a planar optical cavity. The theory accounts
for the adjustment of matter excitations to the presence of a coherent photon
field, predicts effective polariton-polariton interaction strengths that are
weaker and condensate exciton fractions that are smaller than in the commonly
employed exciton-photon model, and yields effective Rabi coupling strengths
that depend on the detuning of the cavity photon energy relative to the bare
exciton energy. The dressed quasiparticle bands that appear naturally in the
theory provide a mechanism for electrical manipulation of polariton
condensates.


  We study a range of radio-frequency ion trap geometries and investigate the
effect of integrating dielectric cavity mirrors on their trapping potential. We
aim to identify ion trap and cavity configurations that are best suited for
achieving small cavity volumes and thus large ion-photon coupling as required
for scalable quantum information networks. In particular, we investigate the
trapping potential distortions caused by the dielectric material of the cavity
mirrors for different mirror orientations with respect to the trapping
electrodes, as well as for mirror misalignment. We also analyze the effect of
the mirror material properties such as dielectric constants and surface
conductivity, and study the effect of surface charges on the mirrors. The
smallest trapping potential distortions are found if the cavities are aligned
along the major symmetry axis of the electrode geometries. These cavity
configurations also appear to be the most stable with respect to any mirror
misalignment.


  We present Breizhcrops, a novel benchmark dataset for the supervised
classification of field crops from satellite time series. We aggregated label
data and Sentinel-2 top-of-atmosphere as well as bottom-of-atmosphere time
series in the region of Brittany (Breizh in local language), north-east France.
We compare seven recently proposed deep neural networks along with a Random
Forest baseline. The dataset, model (re-)implementations and pre-trained model
weights are available at the associated GitHub repository
(https://github.com/dl4sits/BreizhCrops) that has been designed with
applicability for practitioners in mind. We plan to maintain the repository
with additional data and welcome contributions of novel methods to build a
state-of-the-art benchmark on methods for crop type mapping.


  We show that an inhomogeneous defocusing nonlinearity that grows toward the
periphery in the positive and negative transverse directions at different rates
can support strongly asymmetric fundamental and multipole bright solitons,
which are stable in wide parameter regions. In the limiting case when
nonlinearity is uniform in one direction, solitons transform into stable domain
walls (fronts), with constant or oscillating intensity in the homogeneous
region, attached to a tail rapidly decaying in the direction of growing
nonlinearity.


  For Anderson tight-binding models in dimension $d$ with random on-site
energies $\epsilon_{\vec r}$ and critical long-ranged hoppings decaying
typically as $V^{typ}(r) \sim V/r^d$, we show that the strong multifractality
regime corresponding to small $V$ can be studied via the standard perturbation
theory for eigenvectors in quantum mechanics. The Inverse Participation Ratios
$Y_q(L)$, which are the order parameters of Anderson transitions, can be
written in terms of weighted L\'evy sums of broadly distributed variables (as a
consequence of the presence of on-site random energies in the denominators of
the perturbation theory). We compute at leading order the typical and
disorder-averaged multifractal spectra $\tau_{typ}(q)$ and $\tau_{av}(q)$ as a
function of $q$. For $q<1/2$, we obtain the non-vanishing limiting spectrum
$\tau_{typ}(q)=\tau_{av}(q)=d(2q-1)$ as $V \to 0^+$. For $q>1/2$, this method
yields the same disorder-averaged spectrum $\tau_{av}(q)$ of order $O(V)$ as
obtained previously via the Levitov renormalization method by Mirlin and Evers
[Phys. Rev. B 62, 7920 (2000)]. In addition, it allows to compute explicitly
the typical spectrum, also of order $O(V)$, but with a different $q$-dependence
$\tau_{typ}(q) \ne \tau_{av}(q)$ for all $q>q_c=1/2$. As a consequence, we find
that the corresponding singularity spectra $f_{typ}(\alpha)$ and
$f_{av}(\alpha)$ differ even in the positive region $f>0$, and vanish at
different values $\alpha_+^{typ} > \alpha_+^{av}$, in contrast to the standard
picture. We also obtain that the saddle value $\alpha_{typ}(q)$ of the Legendre
transform reaches the termination point $\alpha_+^{typ}$ where
$f_{typ}(\alpha_+^{typ})=0 $ only in the limit $q \to +\infty$.


  We suggest that the recently discovered charm-strange meson D_sJ(2632), with
unusual properties, could be a cyptoexotic tetraquark baryonium state
cdd_bars_bar. We predict other four narrow states, as Regge recurrences of
D_sJ(2632), below the possible baryon-antibaryon thresholds.


  Personalized hashtag recommendation methods aim to suggest users hashtags to
annotate, categorize, and describe their posts. The hashtags, that a user
provides to a post (e.g., a micro-video), are the ones which in her mind can
well describe the post content where she is interested in. It means that we
should consider both users' preferences on the post contents and their personal
understanding on the hashtags. Most existing methods rely on modeling either
the interactions between hashtags and posts or the interactions between users
and hashtags for hashtag recommendation. These methods have not well explored
the complicated interactions among users, hashtags, and micro-videos. In this
paper, towards the personalized micro-video hashtag recommendation, we propose
a Graph Convolution Network based Personalized Hashtag Recommendation (GCN-PHR)
model, which leverages recently advanced GCN techniques to model the complicate
interactions among <users, hashtags, micro-videos> and learn their
representations. In our model, the users, hashtags, and micro-videos are three
types of nodes in a graph and they are linked based on their direct
associations. In particular, the message-passing strategy is used to learn the
representation of a node (e.g., user) by aggregating the message passed from
the directly linked other types of nodes (e.g., hashtag and micro-video).
Because a user is often only interested in certain parts of a micro-video and a
hashtag is typically used to describe the part (of a micro-video) that the user
is interested in, we leverage the attention mechanism to filter the message
passed from micro-videos to users and hashtags, which can significantly improve
the representation capability. Extensive experiments have been conducted on two
real-world micro-video datasets and demonstrate that our model outperforms the
state-of-the-art approaches by a large margin.


  Nuclear magnetic resonance (NMR) is a fundamental bulk probe that provides
key information about the electronic properties of materials. Very recently,
the analysis of all available planar copper shift as well as relaxation data
proved that while the shifts cannot be understood in terms of a single
temperature dependent spin component, relaxation can be explained with one
dominating Fermi liquid-like component, without enhanced electronic spin
fluctuations. For the shifts, a doping dependent isotropic term, as well as
doping independent anisotropic term became obvious. Here we focus on planar
$^{17}$O NMR shifts and quadrupole splittings. Surprisingly, we find that they
demand, independently, a similar two-component scenario and confirm most of the
previous conclusions concerning the properties of the spin components, in
particular that a negative spin polarization survives in the superconducting
state. This should have consequences for the pairing scenario.


  The amorphous conducting carbon films have been prepared at three different
preparation temperatures with different boron-doping levels. The structural and
transport properties of the same have been studied. X-ray diffraction
measurements show that the 'd' value of the carbon depends both on atomic
percentage of B in the carbon network and also on the preparation temperature.
Doping of boron increases the structural graphitic ordering of the films
prepared at lower temperatures. On the contrary for the films prepared at
higher temperatures the ordering deteriorates as the boron content increases.
The d.c electrical transport measurements on these amorphous conducting carbon
films show, doping induced metal-insulator transition via critical regime, in
the temperature interval of 1.3 K to 300K. Also the films in the insulating
regime show a crossover from Mott to ES VRH for T < 55K. Additional support to
this transition is evident from negative magnetoresistance in VRH regime when
the sample is deep inside the insulating side of MI transition. The calculated
value of density of states at Fermi level shows a gradual change with
corresponding variation in boron doping level, indicating a change in the
number of conducting pi electrons due to substitutional doping of boron in the
carbon network. However for the films exhibiting critical behaviour, the
magnetic field dependence of magnetoresistance was not as predicted by the
available theoretical models. Various calculated parameters like localization
length, density of states at the Fermi level and coulomb gap for insulating
samples were calculated from the experimental data.


  Thin-film lithium niobate has shown promise for scalable applications ranging
from single-photon sources to high-bandwidth data communication systems.
Realization of the next generation high-performance classical and quantum
devices, however, requires much lower optical losses than the current state of
the art ($\sim$10 million). Unfortunately, material limitations of ion-sliced
thin-film lithium niobate have not been explored, and therefore it is unclear
how high-quality factor can be achieved in this platform. Here we evaluate the
material limited quality factor of thin-film lithium niobate photonic platform
can be as high as $Q\approx 1.8\times10^{8}$ at telecommunication wavelengths,
corresponding to a propagation loss of 0.2 dB/m.


  Here we present the results of benchmarking of a quantum processor based on
trapped $^{171}$Yb$^{+}$ ions by performing basic quantum machine learning
algorithms. Specifically, we carry out a supervised binary classification of
small-scale digits images, which are intentionally chosen so that they can be
classified with 100% accuracy, using a quantum-enhanced Support Vector Machine
algorithm with up to four qubits. In our work, we specifically consider
different types of quantum encodings of the dataset and different levels of
transpilation optimizations for the corresponding quantum circuits. For each
quantum encoding, we obtain a classifier that is of 100% accuracy on both
training and test sets, which demonstrates that the quantum processor can
correctly solve the basic classification task considered. As we expect, with
the increase of the capabilities quantum processors, they can become a useful
tool for machine learning.


  Simulation results for Mobile Ad-Hoc Networks (MANETs) are fundamentally
governed by the underlying Mobility Model. Thus it is imperative to find
whether events functionally dependent on the mobility model 'converge' to well
defined functions or constants. This shall ensure the long-run consistency
among simulation performed by disparate parties. This paper reviews a work on
the discrete Random Waypoint Mobility Model (RWMM), addressing its long run
stochastic stability. It is proved that each model in the targeted discrete
class of the RWMM satisfies Birkhoff's pointwise ergodic theorem [13], and
hence time averaged functions on the mobility model surely converge. We also
simulate the most common and general version of the RWMM to give insight into
its working.


  In this paper, we considier the limiting distribution of the maximum
interpoint Euclidean distance $M_n=\max _{1 \leq i<j \leq
n}\left\|\boldsymbol{X}_i-\boldsymbol{X}_j\right\|$, where $\boldsymbol{X}_1,
\boldsymbol{X}_2, \ldots, \boldsymbol{X}_n$ be a random sample coming from a
$p$-dimensional population with dependent sub-gaussian components. When the
dimension tends to infinity with the sample size, we proves that $M_n^2$ under
a suitable normalization asymptotically obeys a Gumbel type distribution. The
proofs mainly depend on the Stein-Chen Poisson approximation method and high
dimensional Gaussian approximation.


  We report the detection of off-pulse emission from two long period pulsars
PSR B0525+21 and PSR B2045-16 using the Giant Metrewave Radio Telescope (GMRT).
We recorded high time resolution interferometric data at 325 MHz and gated the
data offline to separate the on- and off-pulse sections of the pulsar. On
imaging the two temporal sections separately, we detected a point source in the
off-pulse images of both the pulsars coincident with the pulsar locations in
the on-pulse images. The long periods (3.75 and 1.96 s respectively) and low
spin-down energies (E ~ 10exp(31) erg s-1) of the two pulsars argue against a
pulsar wind nebula (PWN) origin for the off-pulse emission. The derived
estimates of the ISM particle density required to drive a PWN do not support
such an interpretation. A magnetospheric origin for the off-pulse emission
raises questions regarding the location of the emission region, which is an
important input into pulsar emission models.


  The composite Fermion (CF) picture offers a simple intuitive way of
understanding many of the surprising properties of a strongly interacting
two-dimensional electron fluid in a large magnetic field. The simple way in
which the mean field CF picture describes the low lying bands of angular
momentum multiplets for any value of the applied magnetic field is illustrated
and compared with the results of exact numerical diagonalization of small
systems. The justification of the success of the CF approach is discussed in
some detail, and a CF hierarchy picture of the incompressible quantum fluid
states is introduced. The CF picture is used to understand the energy spectrum
and photoluminescence of systems containing both electrons and valence band
holes.


  Hyper-luminous infrared galaxies (HyLIRGs) are the most extreme star-forming
systems observed in the early Universe, and their properties still elude
comprehensive understanding. We have undertaken a large XMM-Newton observing
program to probe the total accreting black hole population in three HyLIRGs at
z = 2.12, 3.25, and 3.55, gravitationally lensed by foreground galaxies.
Selected from the Planck All-Sky Survey to Analyze Gravitationally-lensed
Extreme Starbursts (PASSAGES), these HyLIRGs have apparent infrared
luminosities > E14 Lsun. Our observations revealed X-ray emission in each of
them. PJ1336+49 appears to be dominated by high-mass X-ray binaries (HMXBs).
Remarkably, the luminosity of this non-AGN X-ray emission exceeds by a factor
of about three the value obtained by calibration with local galaxies with much
lower star formation rates. This enhanced X-ray emission most likely highlights
the efficacy of dynamical HMXB production within compact clusters, which is an
important mode of star formation in HyLIRGs. The remaining two (PJ0116-24 and
PJ1053+60) morphologically and spectrally exhibit a compact X-ray component in
addition to the extended non-AGN X-ray emission, indicating the presence of
Active Galactic Nuclei (AGNs). The AGN appears to be centrally located in the
reconstructed source plane images of PJ0116-24, which manifests its
star-forming activity predominantly within an extended galactic disk. In
contrast, the AGN in the field of PJ1053+60 is projected 60 kpc away from the
extreme star-forming galaxy and could be ejected from it. These results
underline the synergistic potential of deep X-ray observations with strong
lensing for the study of high-energy astrophysical phenomena in HyLIRGs.


  The fast-reaction limit for reaction--diffusion systems modelling
predator--prey interactions is investigated. In the considered model, predators
exist in two possible states, namely searching and handling. The switching rate
between these two states happens on a much faster time scale than other
processes, leading to the consideration of the fast-reaction limit for the
corresponding systems. The rigorous convergence of the solution to the
fast-reaction system to the ones of the limiting cross-diffusion system has
been recently studied in [Conforto, Desvillettes, Soresina, NoDEA, 25(3):24,
2018]. In this paper, we extend these results by proving improved convergence
of solutions and slow manifolds. In particular, we prove that the slow manifold
converges strongly in all dimensions without additional assumptions, thanks to
use of a modified energy function. This consists in a unified approach since it
is applicable to both types of fast-reaction systems, namely with the
Lotka--Volterra and the Holling-type II terms.


  The influence of the outer boundary condition (OBC) on the dynamics and
radiation of optically thin accretion flow is investigated. Bremsstrahlung and
synchrotron radiations amplified by Comptonization are taken into account and
two-temperature plasma assumption is adopted. The three OBCs we adopted are the
temperatures of the electrons and ions and the specific angular momentum of the
accretion flow at a certain outer boundary. We find that when the general
parameters such as the mass accretion rate and the viscous parameter are fixed,
the peak flux at various bands such as radio, IR and X-ray, can differ by as
large as several orders of magnitude under different OBCs in our example. Our
results indicate that OBC is both dynamically and radiatively important
therefore should be regarded as a new ``parameter'' in accretion disk models.
We apply the above results to the compact radio source Sgr A* and find that the
discrepancy between the mass accretion rate favored by ADAF models in the
literature and that favored by the three dimensional hydrodynamical simulation
can be naturally resolved by seriously considering the outer boundary condition
of the accretion flow.


  We address the problem of Bayesian structure learning for domains with
hundreds of variables by employing non-parametric bootstrap, recursively. We
propose a method that covers both model averaging and model selection in the
same framework. The proposed method deals with the main weakness of
constraint-based learning---sensitivity to errors in the independence
tests---by a novel way of combining bootstrap with constraint-based learning.
Essentially, we provide an algorithm for learning a tree, in which each node
represents a scored CPDAG for a subset of variables and the level of the node
corresponds to the maximal order of conditional independencies that are encoded
in the graph. As higher order independencies are tested in deeper recursive
calls, they benefit from more bootstrap samples, and therefore more resistant
to the curse-of-dimensionality. Moreover, the re-use of stable low order
independencies allows greater computational efficiency. We also provide an
algorithm for sampling CPDAGs efficiently from their posterior given the
learned tree. We empirically demonstrate that the proposed algorithm scales
well to hundreds of variables, and learns better MAP models and more reliable
causal relationships between variables, than other state-of-the-art-methods.


  We study supersymmetric Wilson loops in $d=3$, $\mathcal{N}=3$ harmonic
superspace, leading to a construction of a supersymmetrized generalization of
the $\frac{1}{3}$-BPS Wilson loop for $\mathcal{N}=3$ gauge theories. This also
includes a generalization of the $\frac{1}{6}$-BPS loop for ABJM theory. We
perform a 'one-loop' computation of the vacuum expectation value of this
operator directly in superspace and compare with the known $\mathcal{N}=2$
localization results at large $N$. This comparison also lets us identify
certain fermionic contributions that do not receive any subleading corrections.


  We examine the properties of two-dimensional conformal field theories (CFTs)
with vanishing central charge based on the extended Kac-table for c_(9,6)=0
using a general ansatz for the stress energy tensor residing in a Jordan cell
of rank two. Within this setup we will derive the OPEs and two point functions
of the stress energy tensor T(z) and its logarithmic partner field t(z) and
illustrate this by a bosonic field realization. We will show why our approach
may be more promising than those chosen in the literature so far, including a
discussion on properties of the augmented minimal model with vanishing central
charge such as full conformal invariance of the vacuum as a state in an
irreducible representation, consequences on percolation from null vectors and
the structure of representations within the Kac table. Furthermore we will
present another solution to the c --> 0 catastrophe based on an logarithmic CFT
tensor model. As example, we consider a tensor product of the well-known c=-2
logarithmic CFT with a four-fold Ising model. We give an overview of the
possible configurations and various consequences on the two point functions and
the OPEs of the stress energy tensor T(z) and its logarithmic partner field
t(z). We will motivate that due to the full conformal invariance of the vacuum
at c=0, we have to assume a Jordan cell for the identity since t(z) is now a
descendant of a new h=0 field.


  Resolution in deep convolutional neural networks (CNNs) is typically bounded
by the receptive field size through filter sizes, and subsampling layers or
strided convolutions on feature maps. The optimal resolution may vary
significantly depending on the dataset. Modern CNNs hard-code their resolution
hyper-parameters in the network architecture which makes tuning such
hyper-parameters cumbersome. We propose to do away with hard-coded resolution
hyper-parameters and aim to learn the appropriate resolution from data. We use
scale-space theory to obtain a self-similar parametrization of filters and make
use of the N-Jet: a truncated Taylor series to approximate a filter by a
learned combination of Gaussian derivative filters. The parameter sigma of the
Gaussian basis controls both the amount of detail the filter encodes and the
spatial extent of the filter. Since sigma is a continuous parameter, we can
optimize it with respect to the loss. The proposed N-Jet layer achieves
comparable performance when used in state-of-the art architectures, while
learning the correct resolution in each layer automatically. We evaluate our
N-Jet layer on both classification and segmentation, and we show that learning
sigma is especially beneficial for inputs at multiple sizes.


  This paper deals with a class of optimal control problems which arises in
advertising models with Volterra Ornstein-Uhlenbeck process representing the
product goodwill. Such choice of the model can be regarded as a stochastic
modification of the classical Nerlove-Arrow model that allows to incorporate
both presence of uncertainty and empirically observed memory effects such as
carryover or distributed forgetting. We present an approach to solve such
optimal control problems based on an infinite dimensional lift which allows us
to recover Markov properties by formulating a optimization problem equivalent
to the original one in a Hilbert space. Such technique, however, requires the
Volterra kernel from the forward equation to have a representation of a
particular form that may be challenging to obtain in practice. We overcome this
issue for H\"older continuous kernels by approximating them with Bernstein
polynomials (which turn out to enjoy a simple representation of the required
type) and then solving the optimal control problem for the forward process with
approximated kernel instead of the original one. The approach is illustrated
with simulations.


  We investigate interplay between magnetic fluctuations and superconductivity
in the effective five-band Hubbard model for iron-oxypnictide superconductors
on the basis of the fluctuation-exchange approximation. As for the normal-state
properties, we find the pseudogap behavior in the NMR relaxation rate and the
spectral weight in the electron-doped region, while we cannot find such
behavior in the hole-doped region. The pseudogap behavior originates from the
band structure effect, that is, existence of high density of states just below
the Fermi level.
  Solving the superconducting Eliashberg equation, we find that the most
probable candidate for the pairing symmetry is the sign-changed s-wave
spin-singlet state. For small Hund's coupling J, the eigenvalue is not so
sensitive to carrier doping, and seems to be irrelevant with antiferromagnetic
(AF) spin fluctuation. We suggest that correlation between spin and
spin-quadrupole is important as the pairing mechanism as well as the AF
fluctuation.


  We define an isotopy invariant of embeddings N -> R^m of manifolds into
Euclidean space. This invariant together with the \alpha-invariant of
Haefliger-Wu is complete in the dimension range where the \alpha-invariant
could be incomplete. We also define parametric connected sum of certain
embeddings (analogous to surgery). This allows to obtain new completeness
results for the \alpha-invariant and the following estimation of isotopy
classes of embeddings.
  For the piecewise-linear category, a (3n-2m+2)-connected n-manifold N and
(4n+4)/3 < m < (3n+3)/2 each preimage of \alpha-invariant injects into a
quotient of H_{3n-2m+3}(N), where the coefficients are Z for m-n odd and Z_2
for m-n even.


  We demonstrate the application of artificial neural network (ANN) models to
reverse Monte Carlo based thermodynamic calculations. Adsorption isotherms are
generated for 2D square and triangular lattices. These lattices are considered
because of their importance to catalytic applications. In general,
configurational free energy terms that arise from adsorbate arrangements are
challenging to handle and are typically evaluated using computationally
expensive Monte Carlo simulations. We show that a combination of reverse Monte
Carlo (RMC) and ANN model can provide an accurate estimate of the
configurational free energy. The ANN model is trained/constructed using data
generated with the help of RMC simulations. Adsorption isotherms are accurately
obtained for a range of adsorbate-adsorbate interactions, coverages and
temperatures within few seconds on a desktop computer using this method. The
results are validated by comparing to MC calculations. Additionally, H
adsorption on Ni(100) surface is studied using the ANN/RMC approach.


  We consider bandwidth matrix selection for kernel density estimators (KDEs)
of density level sets in $\mathbb{R}^d$, $d \ge 2$. We also consider estimation
of highest density regions, which differs from estimating level sets in that
one specifies the probability content of the set rather than specifying the
level directly. This complicates the problem. Bandwidth selection for KDEs is
well studied, but the goal of most methods is to minimize a global loss
function for the density or its derivatives. The loss we consider here is
instead the measure of the symmetric difference of the true set and estimated
set. We derive an asymptotic approximation to the corresponding risk. The
approximation depends on unknown quantities which can be estimated, and the
approximation can then be minimized to yield a choice of bandwidth, which we
show in simulations performs well. We provide an R package lsbs for
implementing our procedure.


  Spectrum cartography (SC), also known as radio map estimation (RME), aims at
crafting multi-domain (e.g., frequency and space) radio power propagation maps
from limited sensor measurements. While early methods often lacked theoretical
support, recent works have demonstrated that radio maps can be provably
recovered using low-dimensional models -- such as the block-term tensor
decomposition (BTD) model and certain deep generative models (DGMs) -- of the
high-dimensional multi-domain radio signals. However, these existing provable
SC approaches assume that sensors send real-valued (full-resolution)
measurements to the fusion center, which is unrealistic. This work puts forth a
quantized SC framework that generalizes the BTD and DGM-based SC to scenarios
where heavily quantized sensor measurements are used. A maximum likelihood
estimation (MLE)-based SC framework under a Gaussian quantizer is proposed.
Recoverability of the radio map using the MLE criterion are characterized under
realistic conditions, e.g., imperfect radio map modeling and noisy
measurements. Simulations and real-data experiments are used to showcase the
effectiveness of the proposed approach.


  We propose a new camera-based method of robot identification, tracking and
orientation estimation. The system utilises coloured lights mounted in a circle
around each robot to create unique colour sequences that are observed by a
camera. The number of robots that can be uniquely identified is limited by the
number of colours available, $q$, the number of lights on each robot, $k$, and
the number of consecutive lights the camera can see, $\ell$. For a given set of
parameters, we would like to maximise the number of robots that we can use. We
model this as a combinatorial problem and show that it is equivalent to finding
the maximum number of disjoint $k$-cycles in the de Bruijn graph
$\text{dB}(q,\ell)$.
  We provide several existence results that give the maximum number of cycles
in $\text{dB}(q,\ell)$ in various cases. For example, we give an optimal
solution when $k=q^{\ell-1}$. Another construction yields many cycles in larger
de Bruijn graphs using cycles from smaller de Bruijn graphs: if
$\text{dB}(q,\ell)$ can be partitioned into $k$-cycles, then
$\text{dB}(q,\ell)$ can be partitioned into $tk$-cycles for any divisor $t$ of
$k$. The methods used are based on finite field algebra and the combinatorics
of words.


  Focusing on the DNA-bridging nucleoid proteins Fis and H-NS, and integrating
several independent experimental and bioinformatic data sources, we investigate
the links between chromosomal spatial organization and global transcriptional
regulation. By means of a novel multi-scale spatial aggregation analysis, we
uncover the existence of contiguous clusters of nucleoid-perturbation sensitive
genes along the genome, whose expression is affected by a combination of
topological DNA state and nucleoid-shaping protein occupancy. The clusters
correlate well with the macrodomain structure of the genome. The most
significant of them lay symmetrically at the edges of the ter macrodomain and
involve all of the flagellar and chemotaxis machinery, in addition to key
regulators of biofilm formation, suggesting that the regulation of the physical
state of the chromosome by the nucleoid proteins plays an important role in
coordinating the transcriptional response leading to the switch between a
motile and a biofilm lifestyle.


  It is proposed to use the pinch technique (PT) to obtain the
gauge-independent thermal $\beta$ function $\beta_T$ in a hot Yang-Mills gas.
Calculations of the thermal $\beta$ function are performed at one-loop level in
four different gauges, (i) the background field method with an arbitrary gauge
parameter, (ii) the Feynman gauge, (iii) the Coulomb gauge, and (iv) the
temporal axial gauge. When the pinch contributions to the gluon self-energy are
included, the same result is derived for $\beta_T$ in all four cases.


  We report an intriguing debris disk towards the F3V star HD 15407A, in which
an extremely large amount of warm fine dust (~ 10^(-7) M_Earth) is detected.
The dust temperature is derived as ~ 500-600 K and the location of the debris
dust is estimated as 0.6-1.0 AU from the central star, a terrestrial planet
region. The fractional luminosity of the debris disk is ~ 0.005, which is much
larger than those predicted by steady-state models of the debris disk produced
by planetesimal collisions. The mid-infrared spectrum obtained by Spitzer
indicates the presence of abundant micron-sized silica dust, suggesting that
the dust comes from the surface layer of differentiated large rocky bodies and
might be trapped around the star.


  Let $A$ be a compact $d$-rectifiable set embedded in Euclidean space $\RR^p$,
$d\le p$. For a given continuous distribution $\sigma(x)$ with respect to
$d$-dimensional Hausdorff measure on $A$, our earlier results provided a method
for generating $N$-point configurations on $A$ that have asymptotic
distribution $\sigma (x)$ as $N\to \infty$; moreover such configurations are
"quasi-uniform" in the sense that the ratio of the covering radius to the
separation distance is bounded independent of $N$. The method is based upon
minimizing the energy of $N$ particles constrained to $A$ interacting via a
weighted power law potential $w(x,y)|x-y|^{-s}$, where $s>d$ is a fixed
parameter and $w(x,y)=\left(\sigma(x)\sigma(y)\right)^{-({s}/{2d})}$.
  Here we show that one can generate points on $A$ with the above mentioned
properties keeping in the energy sums only those pairs of points that are
located at a distance of at most $r_N=C_N N^{-1/d}$ from each other, with $C_N$
being a positive sequence tending to infinity arbitrarily slowly. To do this we
minimize the energy with respect to a varying truncated weight
$v_N(x,y)=\Phi\(\left|x-y\right|/r_N\)w(x,y)$, where $\Phi:(0,\infty)\to
[0,\infty)$ is a bounded function with $\Phi(t)=0$, $t\geq 1$, and $\lim_{t\to
0^+}\Phi(t)=1$. This reduces, under appropriate assumptions, the complexity of
generating $N$ point `low energy' discretizations to order $N C_N^d$
computations.


  We study Heegaard Floer homology and various related invariants (such as the
$h$-function) for two-component L-space links with linking number zero. For
such links, we explicitly describe the relationship between the $h$-function,
the Sato-Levine invariant and the Casson invariant. We give a formula for the
Heegaard Floer $d$-invariants of integral surgeries on two-component L-space
links of linking number zero in terms of the $h$-function, generalizing a
formula of Ni and Wu. As a consequence, for such links with unknotted
components, we characterize L-space surgery slopes in terms of the
$\nu^{+}$-invariants of the knots obtained from blowing down the components.
  We give a proof of a skein inequality for the $d$-invariants of $+1$
surgeries along linking number zero links that differ by a crossing change. We
also describe bounds on the smooth four-genus of links in terms of the
$h$-function, expanding on previous work of the second author, and use these
bounds to calculate the four-genus in several examples of links.


  A family of the billiard-type systems with zero Lyapunov exponent is
considered as an example of dynamics which is between the regular one and
chaotic mixing. This type of dynamics is called ``pseudochaos''. We demonstrate
how the fractional kinetic equation can be introduced for the pseudochaos and
how the main critical exponents of the fractional kinetics can be evaluated
from the dynamics. Problems related to pseudochaos are discussed: Poincare
recurrences, continued fractions, log-periodicity, rhombic billiards, and
others. Pseudochaotic dynamics and fractional kinetics can be applied to
streamlines or magnetic field lines behavior.


  Link Floer homology is an invariant for links which has recently been
described entirely in a combinatorial way. Originally constructed with mod 2
coefficients, it was generalized to integer coefficients thanks to a sign
refinement. In this paper, thanks to the spin extension of the permutation
group we give an alternative construction of the combinatorial link Floer chain
complex associated to a grid diagram with integer coefficients. We prove that
the filtered homology of this complex is an invariant for the link and that it
gives the previous sign refinement by means of a 2-cohomological class
corresponding to the spin extension of the permutation group.


  We propose and analyse a quantum electromechanical system composed of a
monolithic quartz bulk acoustic wave (BAW) oscillator coupled to a
superconducting transmon qubit via an intermediate LC electrical circuit.
Monolithic quartz oscillators offer unprecedentedly high effective masses and
quality factors for the investigation of mechanical oscillators in the quantum
regime. Ground-state cooling of such mechanical modes via resonant
piezoelectric coupling to an LC circuit, which is itself sideband cooled via
coupling to a transmon qubit, is shown to be feasible. The fluorescence
spectrum of the qubit, containing motional sideband contributions due to the
couplings to the oscillator modes, is obtained and the imprint of the
electromechanical steady-state on the spectrum is determined. This allows the
qubit to function both as a cooling resource for, and transducer of, the
mechanical oscillator. The results described are relevant to any hybrid quantum
system composed of a qubit coupled to two (coupled or uncoupled) thermal
oscillator modes.


  Gene duplication is believed to play a major role in the evolution of genomic
complexity. The presence of a duplicate removes the constraint of natural
selection upon the gene, leading to its likely loss of function or,
occasionally, the gain of a novel one. Alternately, a pleiotropic gene might
partition its functions among its duplicates, thus preserving both copies.
Duplicate genes is not a novelty for diploid genotypes, but only for
haplotypes. In this paper, we study the consequences of regulatory interactions
in diploid genotypes and explore how the context of allelic interactions gives
rise to dynamical phenotypes that enable duplicate genes to spread in a
population. The regulatory network we study is that of a single autoregulatory
activator gene, and the two copies of the gene diverge either as alleles in a
diploid species or as duplicates in haploids. These differences are in their
transcriptional ability -- either via alterations to its activating domain, or
to its cis-regulatory binding repertoire. When cis-regulatory changes are
introduced that partition multiple regulatory triggers among the duplicates, it
is shown that mutually exclusive expression states of the duplicates that
emerge are accompanied by a back-up facility: when a highly expressed gene is
deleted, the previously unexpressed duplicate copy compensates for it. The
diploid version of the regulatory network model can account for allele-specific
expression variants, and a model of inheritance of the haplotype network
enables us to trace the evolutionary consequence of heterozygous phenotypes.
This is modelled for the variations in the activating domain of one copy,
whereby stable as well as transiently bursting oscillations ensue in single
cells. The evolutionary model shows that these phenotypic states accessible to
a diploid, heterozygous genotype enable the spread of a duplicated haplotype.


  The binding-energy differences for $^{163}\mathrm{Ho}^{q+}$ and
$^{163}\mathrm{Dy}^{q+}$ ions with ionization degrees $q = 38$, $39$, and $40$
are calculated. The calculations are performed using the large-scale
relativistic configuration-interaction and relativistic coupled-clusters
methods. The contributions from quantum-electrodynamics, nuclear-recoil, and
frequency-dependent Breit-interaction effects are taken into account. The final
uncertainty does not exceed $1$ eV. Combining the obtained results with the
binding-energy difference for neutral atoms calculated in [Savelyev et al.,
Phys. Rev. A 105, 012806 (2022)], we get the secondary differences of the
ion-atom binding energies. These values can be used to evaluate the amount of
energy released in the electron capture process in $^{163}\mathrm{Ho}$ atom
(the $Q$ value), provided mass differences of highly charged ions
$^{163}\mathrm{Ho}^{q+}$ and $^{163}\mathrm{Dy}^{q+}$ is known from experiment.
The $Q$ value is required by experiments on the determination of the absolute
scale of the electron neutrino mass by studying the beta-decay process.


  The spectrum of Weakly-Interacting-Massive-Particle (WIMP) dark matter
generically possesses bound states when the WIMP mass becomes sufficiently
large relative to the mass of the electroweak gauge bosons. The presence of
these bound states enhances the annihilation rate via resonances in the
Sommerfeld enhancement, but they can also be produced directly with the
emission of a low-energy photon. In this work we compute the rate for SU(2)
triplet dark matter (the wino) to bind into WIMPonium -- which is possible via
single-photon emission for wino masses above 5 TeV for relative velocity v <
O(10^{-2}) -- and study the subsequent decays of these bound states. We present
results with applications beyond the wino case, e.g. for dark matter inhabiting
a nonabelian dark sector; these include analytic capture and transition rates
for general dark sectors in the limit of vanishing force carrier mass,
efficient numerical routines for calculating positive and negative-energy
eigenstates of a Hamiltonian containing interactions with both massive and
massless force carriers, and a study of the scaling of bound state formation in
the short-range Hulthen potential. In the specific case of the wino, we find
that the rate for bound state formation is suppressed relative to direct
annihilation, and so provides only a small correction to the overall
annihilation rate. The soft photons radiated by the capture process and by
bound state transitions could permit measurement of the dark matter's quantum
numbers; for wino-like dark matter, such photons are rare, but might be
observable by a future ground-based gamma-ray telescope combining large
effective area and a low energy threshold.


  Currently, grid and glass methods are the two most popular choices to
generate uniform particle distributions (i.e., pre-initial conditions) for
cosmological $N$-body simulations. In this article, we introduce an alternative
method called the capacity constrained Voronoi tessellation (CCVT), which
originates from computer graphics. As a geometrical equilibrium state, a CCVT
particle configuration satisfies two constraints: (i) the volume of the Voronoi
cell associated with each particle is equal; (ii) every particle is in the
center-of-mass position of its Voronoi cell. We show that the CCVT
configuration is uniform and isotropic, follows perfectly the minimal power
spectrum, $P(k)\propto k^4$, and is quite stable under gravitational
interactions. It is natural to incorporate periodic boundary conditions during
CCVT making, therefore, we can obtain a larger CCVT by tiling with a small
periodic CCVT. When applying the CCVT pre-initial condition to cosmological
$N$-body simulations, we show that it plays as good as grid and glass schemes.
The CCVT method will be helpful in studying the numerical convergence of
pre-initial conditions in cosmological simulations. It can also be used to set
up pre-initial conditions in smoothed-particle hydrodynamics simulations.


  This paper explores a class of non-linear constitutive relations for
materials with memory in the framework of covariant macroscopic Maxwell theory.
Based on earlier models for the response of hysteretic ferromagnetic materials
to prescribed slowly varying magnetic background fields, generalized models are
explored that are applicable to accelerating hysteretic magneto-electric
substances coupled self-consistently to Maxwell fields. Using a parameterized
model consistent with experimental data for a particular material that exhibits
purely ferroelectric hysteresis when at rest in a slowly varying electric
field, a constitutive model is constructed that permits a numerical analysis of
its response to a driven harmonic electromagnetic field in a rectangular
cavity. This response is then contrasted with its predicted response when set
in uniform rotary motion in the cavity.


  We study the topological Hall effect in a diffusive ferromagnetic metal thin
film by solving a Boltzmann transport equation in the presence of spin-flip
scattering. A generalized spin diffusion equation is derived, which contains an
additional source term associated with the gradient of the emergent magnetic
field that arises, and its solution shows that spin accumulation may build up
in the vicinity of the magnetic skyrmions. We show that the spin accumulation
gives rise to a spin polarized diffusion current that in general suppresses
that bulk topological Hall current. Only when the spin diffusion length is much
smaller than the skyrmion size, the topological Hall resistivity approaches to
the one originally derived by Bruno \textit{et al.} [Phys. Rev. Lett.
\textbf{93}, 096806 (2004)]. We derive a general expression of the TH
resistivity that applies to thin-film geometries with spin-flip scattering, and
show that the corrections to the TH resistivity become large when the size of
room temperature skyrmions is further reduced to tens of nanometers.


  The demand for high-speed digital communication such as data, video, and the
broadband Internet increases, the required throughput of the modules in
communications systems will also increase. In this paper we present an
instruction system, which works on the basis of a wavelength division multiplex
(WDM) system in the visible spectrum. It is specialised for the academic
training at universities to demonstrate the principles of the WDM techniques.
It works platform independent in combination with active modules in the
training description, short inline videos and interactive diagrams. The system
consists of LEDs in different wavelengths using analog and digital signals.


  A new solution of Einstein's vacuum field equations is discovered which
appears as a generalization of the well-known Ozsvath-Schucking solution and
explains its source of curvature which has otherwise remained hidden.
Curiously, the new solution has a vanishing Kretschmann scalar and is
singularity-free despite being curved.
  The discovery of the new solution is facilitated by a new insight which
reveals that it is always possible to define the source of curvature in a
vacuum solution in terms of some dimensional parameters. As the parameters
vanish, so does the curvature. The new insight also helps to make the vacuum
solutions Machian.


  In many wireless communication systems, radios are subject to a duty cycle
constraint, that is, a radio only actively transmits signals over a fraction of
the time. For example, it is desirable to have a small duty cycle in some low
power systems; a half-duplex radio cannot keep transmitting if it wishes to
receive useful signals; and a cognitive radio needs to listen and detect
primary users frequently. This work studies the capacity of scalar
discrete-time Gaussian channels subject to duty cycle constraint as well as
average transmit power constraint. An idealized duty cycle constraint is first
studied, which can be regarded as a requirement on the minimum fraction of
nontransmissions or zero symbols in each codeword. A unique discrete input
distribution is shown to achieve the channel capacity. In many situations,
numerically optimized on-off signaling can achieve much higher rate than
Gaussian signaling over a deterministic transmission schedule. This is in part
because the positions of nontransmissions in a codeword can convey information.
Furthermore, a more realistic duty cycle constraint is studied, where the extra
cost of transitions between transmissions and nontransmissions due to pulse
shaping is accounted for. The capacity-achieving input is no longer independent
over time and is hard to compute. A lower bound of the achievable rate as a
function of the input distribution is shown to be maximized by a first-order
Markov input process, the distribution of which is also discrete and can be
computed efficiently. The results in this paper suggest that, under various
duty cycle constraints, departing from the usual paradigm of intermittent
packet transmissions may yield substantial gain.


  The magnitude of spin accumulation created in semiconductors by electrical
injection of spin-polarized electrons from a ferromagnetic tunnel contact is
investigated, focusing on how the spin signal detected in a Hanle measurement
varies with the thickness of the tunnel oxide. An extensive set of
spin-transport data for Si and Ge magnetic tunnel devices reveals a scaling
with the tunnel resistance that violates the core feature of available
theories, namely, the linear proportionality of the spin voltage to the
injected spin current density. Instead, an anomalous scaling of the spin signal
with the tunnel resistance is observed, following a power-law with an exponent
between 0.75 and 1 over 6 decades. The scaling extends to tunnel resistance
values larger than 10$^{9}$ $\Omega\mu m^2$, far beyond the regime where the
classical impedance mismatch plays a role. This scaling is incompatible with
existing theory for direct tunnel injection of spins into the semiconductor. It
also demonstrates conclusively that the large spin signal does not originate
from two-step tunneling via localized states near the oxide/semiconductor
interface. Control experiments on devices with a non-magnetic metal (Ru)
electrode, instead of the semiconductor, exhibit no Hanle spin signal, showing
that spin accumulation in localized states within the tunnel barrier is also
not responsible. Control devices in which the spin current is removed by
inserting a non-magnetic interlayer exhibit no Hanle signals either, proving
that the spin signals observed in the standard devices are genuine and
originate from spin-polarized tunneling and the resulting spin accumulation.
Altogether, the scaling results suggest that the spin signal is proportional to
the applied bias voltage, rather than the (spin) current.


  Human face-to-face communication is a complex multimodal signal. We use words
(language modality), gestures (vision modality) and changes in tone (acoustic
modality) to convey our intentions. Humans easily process and understand
face-to-face communication, however, comprehending this form of communication
remains a significant challenge for Artificial Intelligence (AI). AI must
understand each modality and the interactions between them that shape human
communication. In this paper, we present a novel neural architecture for
understanding human communication called the Multi-attention Recurrent Network
(MARN). The main strength of our model comes from discovering interactions
between modalities through time using a neural component called the
Multi-attention Block (MAB) and storing them in the hybrid memory of a
recurrent component called the Long-short Term Hybrid Memory (LSTHM). We
perform extensive comparisons on six publicly available datasets for multimodal
sentiment analysis, speaker trait recognition and emotion recognition. MARN
shows state-of-the-art performance on all the datasets.


  We construct the Hilbert compactification of the universal moduli space of
semistable vector bundles over smooth curves. The Hilbert compactification is
the GIT quotient of some open part of an appropriate Hilbert scheme of curves
in a Grassmannian. It has all the properties asked for by Teixidor.


  In this article we study the stable marriage game induced by the
men-proposing Gale-Shapley algorithm. Our setting is standard: all the lists
are complete and the matching mechanism is the men-proposing Gale-Shapley
algorithm. It is well known that in this setting, men cannot cheat, but women
can. In fact, Teo, Sethuraman and Tan \cite{TST01}, show that there is a
polynomial time algorithm to obtain, for a given strategy (the set of all
lists) $Q$ and a woman $w$, the best partner attainable by changing her list.
However, what if the resulting matching is not stable with respect to $Q$?
Obviously, such a matching would be vulnerable to further manipulation, but is
not mentioned in \cite{TST01}. In this paper, we consider (safe) manipulation
that implies a stable matching in a most general setting. Specifically, our
goal is to decide for a given $Q$, if w can manipulate her list to obtain a
strictly better partner with respect to the true strategy $P$ (which may be
different from $Q$), and also the outcome is a stable matching for $P$.


  We investigate exceptional sheaves on the Hirzebruch surface $\mathbb{F}_2$,
as the first attempt toward the classification of exceptional objects on weak
del Pezzo surfaces.


  In this paper, we compute the derivations of the positive part of the
two-parameter quantum group of type $G_2$ by embedding it into a quantum torus.
We also show that the Hochschild cohomology group of degree $1$ of this algebra
is a two dimensional vector space over the complex field.


  We study magneto-optical (MO) properties of a semi-Dirac nanoribbon in
presence of a perpendicular magnetic field using Kernel Polynomial Method (KPM)
based on the Keldysh formalism in the experimental (terahertz frequency)
regime. For comparison, we have also included results for the Dirac systems as
well, so that the interplay of the band structure deformation and MO
conductivities can be studied. We have found that the MO conductivity shows
features in the semi-Dirac system that are quite distinct from the Dirac case
near the ultra-violet and the visible regimes. The real parts of the
longitudinal conductivities, namely Re($\sigma_{xx}$) and Re($\sigma_{yy}$)
(which are different in the semi-Dirac case, as opposed to a Dirac one) present
a series of resonance peaks as a function of the incident photon energy. We
have also found that the absorption peaks corresponding to the $y$-direction
are larger (roughly one order of magnitude) than those corresponding to the
$x$-direction for the semi-Dirac case. In the case of the Hall conductivity,
that is, Re($\sigma_{xy}$), there are extra peaks in the spectra compared to
the Dirac case which originate from the distinct optical transitions of the
carriers from one Landau level to another. We have also explored how the
carrier concentration influences the MO conductivities. In the semi-Dirac case,
there is the emergence of additional peaks yet again in the absorption spectrum
underscoring the presence of an asymmetric dispersion compared to the Dirac
case. Further, we have explored the interplay between the polarization of the
incident beam and the features of the absorption spectra which can be probed in
experiments. Finally, we evaluate the MO activity of the medium by computing
the Faraday rotation angle, $\theta_{F}$.


  There are two alternative historical laws of rolling resistance formulated by
French scientist Coulomb and Dupuit. It has been decided to verify
experimentally again, which of these laws describes freely rolling ball
bearings on a hard surface better. An inducement to carrying out the
measurements was the idea of the constant thickness of roadbed, which is
consistent with Dupuit's theory. Measurements have been done using the damped
oscillations in the pendulum bearings. Results have shown better consistency
with the Coulomb's theory with small, but measurable deviations. These
deviations were successfully explained by the so called "Cobblestones model".
Parameters designated by this model have been successfully verified by the
surface roughness's profile measurement. An additional theoretical aspect of
this work is distinguishing two types of rolling friction force: dynamical and
kinematical in an analogy to two types of specific heat capacity in the
thermodynamics of gases.


  We present results for the spectrum of excited mesons obtained from temporal
correlations of spatially-extended single-hadron and multi-hadron operators
computed in lattice QCD. The stochastic LapH algorithm is implemented on
anisotropic, dynamical lattices for isovectors for pions of mass $390$ MeV. A
large correlation matrix with single-particle and two-particle probe operators
is diagonalized to identify resonances. The masses of excited states in the
$I=1, S=0, T_{1u}^+$ channel as well as the mixing of single and multi-particle
probe operators are presented.


  It is shown that the results of the paper by Contreras et al. [Contreras, G.,
Nunez, L. A., Percoco, U. "Ricci Collineations for Non-degenerate, Diagonal and
Spherically Symmetric Ricci Tensors" (2000) Gen. Rel. Grav. 32, 285-294]
concerning the Ricci Collineations in spherically symmetric space-times with
non-degenerate and diagonal Ricci tensor do not cover all possible cases.
Furthermore the complete algebra of Ricci Collineations of certain
Robertson-Walker metrics of vanishing spatial curvature are given.


  Following E. Wigner's original vision, we prove that sampling the eigenvalue
gaps within the bulk spectrum of a fixed (deformed) Wigner matrix $H$ yields
the celebrated Wigner-Dyson-Mehta universal statistics with high probability.
Similarly, we prove universality for a monoparametric family of deformed Wigner
matrices $H+xA$ with a deterministic Hermitian matrix $A$ and a fixed Wigner
matrix $H$, just using the randomness of a single scalar real random variable
$x$. Both results constitute quenched versions of bulk universality that has so
far only been proven in annealed sense with respect to the probability space of
the matrix ensemble.


  We propose a mirror model for ordinary and dark matter that assumes a new
SU(3) gauge group of transformations, as a natural extension of the Standard
Model (SM). A close study of big bang nucleosynthesis, baryon asymmetries,
cosmic microwave background bounds, galaxy dynamics, together with the Standard
Model assumptions, help us to set a limit on the mass and width of the new
gauge boson. The cross section for the elastic scattering of a dark proton by
an ordinary proton is estimated and compare to the WIMP--nucleon experimental
upper bounds. It is observed that all experimental bounds for the various cross
sections can be accommodated consistently within the gauge model. We also
suggest a way for direct detection of the new gauge boson via one example of a
SM forbidden process: $e^+ + p \rightarrow \mu^+ + X$, where $X = \Lambda$ or
$\Lambda_c$.


  The translation of brain dynamics into natural language is pivotal for
brain-computer interfaces (BCIs). With the swift advancement of large language
models, such as ChatGPT, the need to bridge the gap between the brain and
languages becomes increasingly pressing. Current methods, however, require
eye-tracking fixations or event markers to segment brain dynamics into
word-level features, which can restrict the practical application of these
systems. To tackle these issues, we introduce a novel framework, DeWave, that
integrates discrete encoding sequences into open-vocabulary EEG-to-text
translation tasks. DeWave uses a quantized variational encoder to derive
discrete codex encoding and align it with pre-trained language models. This
discrete codex representation brings forth two advantages: 1) it realizes
translation on raw waves without marker by introducing text-EEG contrastive
alignment training, and 2) it alleviates the interference caused by individual
differences in EEG waves through an invariant discrete codex with or without
markers. Our model surpasses the previous baseline (40.1 and 31.7) by 3.06% and
6.34%, respectively, achieving 41.35 BLEU-1 and 33.71 Rouge-F on the ZuCo
Dataset. This work is the first to facilitate the translation of entire EEG
signal periods without word-level order markers (e.g., eye fixations), scoring
20.5 BLEU-1 and 29.5 Rouge-1 on the ZuCo Dataset.


  Dense pose estimation is a dense 3D prediction task for instance-level human
analysis, aiming to map human pixels from an RGB image to a 3D surface of the
human body. Due to a large amount of surface point regression, the training
process appears to be easy to collapse compared to other region-based human
instance analyzing tasks. By analyzing the loss formulation of the existing
dense pose estimation model, we introduce a novel point regression loss
function, named Dense Points} loss to stable the training progress, and a new
balanced loss weighting strategy to handle the multi-task losses. With the
above novelties, we propose a brand new architecture, named UV R-CNN. Without
auxiliary supervision and external knowledge from other tasks, UV R-CNN can
handle many complicated issues in dense pose model training progress, achieving
65.0% $AP_{gps}$ and 66.1% $AP_{gpsm}$ on the DensePose-COCO validation subset
with ResNet-50-FPN feature extractor, competitive among the state-of-the-art
dense human pose estimation methods.


  We show how to derive (variants of) Michell truss theory in two and three
dimensions rigorously as the vanishing weight limit of optimal design problems
in linear elasticity in the sense of $\Gamma$-convergence. We improve our
previous results in that our treatment here includes the three dimensional case
and that we allow for more general boundary conditions and applied forces.


  We count the number of zeros of the holomorphic odd weight Eisenstein series
in all $\mathrm{SL}_2(\mathbb{Z})$-translates of the standard fundamental
domain.


  The spin-half XXZ model on the linear chain and the square lattice are
examined with the extended coupled cluster method (ECCM) of quantum many-body
theory. We are able to describe both the Ising-Heisenberg phase and the
XY-Heisenberg phase, starting from known wave functions in the Ising limit and
at the phase transition point between the XY-Heisenberg and ferromagnetic
phases, respectively, and by systematically incorporating correlations on top
of them. The ECCM yields good numerical results via a diagrammatic approach,
which makes the numerical implementation of higher-order truncation schemes
feasible. In particular, the best non-extrapolated coupled cluster result for
the sublattice magnetization is obtained, which indicates the employment of an
improved wave function. Furthermore, the ECCM finds the expected qualitatively
different behaviours of the linear chain and the square lattice cases.


  Recent theory has predicted large temperature differences between the
in-plane (LA and TA) and out-of-plane (ZA) acoustic phonon baths in
locally-heated suspended graphene. To verify these predictions, and their
implications for understanding the nonequilibrium thermodynamics of 2D
materials, experimental techniques are needed. Here, we present a method to
determine the acoustic phonon bath temperatures from the frequency-dependent
mechanical response of suspended graphene to a power modulated laser. The
mechanical motion reveals two counteracting contributions to the thermal
expansion force, that are attributed to fast positive thermal expansion by the
in-plane phonons and slower negative thermal expansion by the out-of-plane
phonons. The magnitude of the two forces reveals that the in-plane and flexural
acoustic phonons are at very different temperatures in the steady-state, with
typically observed values of the ratio $\Delta T_{\mathrm{LA+TA}}/\Delta
T_{\mathrm{ZA}}$ between 0.2 and 3.7. These deviations from the generally used
local thermal equilibrium assumption ($\Delta T_{\mathrm{LA+TA}}=\Delta
T_{\mathrm{ZA}}$) can affect the experimental analysis of thermal properties of
2D materials.


  Social media could provide valuable information to support decision making in
crisis management, such as in accidents, explosions and fires. However, much of
the data from social media are images, which are uploaded in a rate that makes
it impossible for human beings to analyze them. Despite the many works on image
analysis, there are no fire detection studies on social media. To fill this
gap, we propose the use and evaluation of a broad set of content-based image
retrieval and classification techniques for fire detection. Our main
contributions are: (i) the development of the Fast-Fire Detection method
(FFDnR), which combines feature extractor and evaluation functions to support
instance-based learning, (ii) the construction of an annotated set of images
with ground-truth depicting fire occurrences -- the FlickrFire dataset, and
(iii) the evaluation of 36 efficient image descriptors for fire detection.
Using real data from Flickr, our results showed that FFDnR was able to achieve
a precision for fire detection comparable to that of human annotators.
Therefore, our work shall provide a solid basis for further developments on
monitoring images from social media.


  Recent experimental studies have found that magnetic impurities deposited on
superconducting monolayer NbSe$_2$ generate coupled Yu-Shiba-Rusinov bound
states. Here we consider ferromagnetic chains of impurities which induce a
Yu-Shiba-Rusinov band and harbor Majorana bound states at the chain edges. We
show that these topological phases are stabilized by strong Ising spin-orbit
coupling in the monolayer and examine the conditions under which Majorana
phases appear as a function of distance between impurities, impurity spin
projection, orientation of chains on the surface of the monolayer, and strength
of magnetic exchange energy between impurity and superconductor.


  We present a system to support generalized SQL workload analysis and
management for multi-tenant and multi-database platforms. Workload analysis
applications are becoming more sophisticated to support database
administration, model user behavior, audit security, and route queries, but the
methods rely on specialized feature engineering, and therefore must be
carefully implemented and reimplemented for each SQL dialect, database system,
and application. Meanwhile, the size and complexity of workloads are increasing
as systems centralize in the cloud. We model workload analysis and management
tasks as variations on query labeling, and propose a system design that can
support general query labeling routines across multiple applications and
database backends. The design relies on the use of learned vector embeddings
for SQL queries as a replacement for application-specific syntactic features,
reducing custom code and allowing the use of off-the-shelf machine learning
algorithms for labeling. The key hypothesis, for which we provide evidence in
this paper, is that these learned features can outperform conventional feature
engineering on representative machine learning tasks. We present the design of
a database-agnostic workload management and analytics service, describe
potential applications, and show that separating workload representation from
labeling tasks affords new capabilities and can outperform existing solutions
for representative tasks, including workload sampling for index recommendation
and user labeling for security audits.


  A formal derivation is presented of the energy transfer rate between
radiation and matter due to the scattering of an isotropic distribution of
resonant photons. The derivation is developed in the context of the two-level
atom in the absence of collisions and radiative transitions to and from the
continuum, but includes the full angle-averaged redistribution function for
photon scattering. The result is compared with previous derivations, all of
which have been based on the Fokker-Planck approximation to the radiative
transfer equation. A new Fokker-Planck approximation, including an extension to
higher (post-diffusive) orders, is derived to solve the radiative transfer
equation, and time-dependent numerical solutions are found. The relaxation of
the colour temperature to the matter temperature is computed as the radiation
field approaches statistical equilibrium through scattering. The results are
discussed in the context of the Wouthuysen-Field mechanism for coupling the
21cm spin temperature of neutral hydrogen to the kinetic temperature of the gas
through LyA scattering. The evolution of the heating rate is also computed, and
shown to diminish as the gas approaches statistical equilibrium.


  In the dynamical gauge-Higgs unification of electroweak interactions in the
Randall-Sundrum warped spacetime the Higgs boson mass is predicted in the range
120 GeV -- 290 GeV, provided that the spacetime structure is determined at the
Planck scale. Couplings of quarks and leptons to gauge bosons and their
Kaluza-Klein (KK) excited states are determined by the masses of quarks and
leptons. All quarks and leptons other than top quarks have very small couplings
to the KK excited states of gauge bosons. The universality of weak interactions
is slightly broken by magnitudes of $10^{-8}$, $10^{-6}$ and $10^{-2}$ for
$\mu$-$e$, $\tau$-$e$ and $t$-$e$, respectively. Yukawa couplings become
substantially smaller than those in the standard model, by a factor $|\cos
\onehalf \theta_W|$ where $\theta_W$ is the non-Abelian Aharonov-Bohm phase
(the Wilson line phase) associated with dynamical electroweak symmetry
breaking.


  We show that a strong laser pulse combined with a strong x-ray pulse can be
employed in a detection scheme for characterizing high-energy $\gamma$-ray
pulses down to the zeptosecond timescale. The scheme employs streak imaging
technique built upon the high-energy process of electron-positron pair
production in vacuum through the collision of a test pulse with intense laser
pulses. The role of quantum radiation reaction in multiphoton Compton
scattering process and limitations imposed by it on the detection scheme are
examined.


  Centralized coded caching and delivery is studied for a radio access
combination network (RACN), whereby a set of $H$ edge nodes (ENs), connected to
a cloud server via orthogonal fronthaul links with limited capacity, serve a
total of $K$ user equipments (UEs) over wireless links. Each user, equipped
with a cache of size $\mu_R N F$ bits, is connected to a distinct set of $r$
ENs each of which equipped with a cache of size $\mu_T N F$ bits, where
$\mu_T$, $\mu_R \in [0,1]$ are the fractional cache capacities of the UEs and
the ENs, respectively. The objective is to minimize the normalized delivery
time (NDT. Three coded caching and transmission schemes are considered, namely
the\textit{ MDS-IA}, \textit{soft-transfer} and \textit{ zero-forcing (ZF)}
schemes. The achievable NDT for MDS-IA scheme is presented for $r=2$ and
arbitrary fractional cache sizes $\mu_T$ and $\mu_R$, and also for arbitrary
value of $r$ and fractional cache size $\mu_T$ when the cache capacity of the
UE is above a certain threshold. The achievable NDT for the soft-transfer
scheme is presented for arbitrary $r$ and arbitrary fractional cache sizes
$\mu_T$ and $\mu_R$. The last scheme utilizes ZF between the ENs and the UEs
without the participation of the cloud server in the delivery phase. The
achievable NDT for this scheme is presented for an arbitrary value of $r$ when
the total cache size at a pair of UE and EN is sufficient to store the whole
library, i.e., $\mu_T+\mu_R \geq 1$. The results indicate that the fronthaul
capacity determines which scheme achieves a better performance in terms of the
NDT, and the soft-transfer scheme becomes favorable as the fronthaul capacity
increases.


  In massive gravity and bigravity, spin-2 interactions are defined in terms of
a square root matrix that involves two metrics. In this work, the interactions
are constructed using a congruence matrix between the metrics. It is
established that the primary square root matrix function is the only power
series solution to the equations of motion for the congruence. Moreover, the
shift vector redefinition that is used in the bimetric ghost-free proofs
follows from the $N+1$ form of the equations of motion. The analysis also gives
an insight into the vielbein formulation of spin-2 interactions since the
bimetric formulation in terms of a congruence is algebraically equivalent to
the unconstrained vielbein formulation.


  Open-access microcavities are a powerful tool to enhance light-matter
interactions for solid-state quantum and nano systems and are key to advance
applications in quantum technologies. For this purpose, the cavities should
simultaneously meet two conflicting requirements - full tunability to cope with
spatial and spectral inhomogeneities of a material, and highest stability under
operation in a cryogenic environment to maintain resonance conditions. To
tackle this challenge, we have developed a fully-tunable, open-access,
fiber-based Fabry-P\'erot microcavity platform which can be operated also under
increased noise levels in a closed-cycle cryostat. It comprises custom-designed
monolithic micro- and nanopositioning elements with up to mm-scale travel range
that achieve a passive cavity length stability at low temperature of only 15 pm
rms in a closed-cycle cryostat, and 5 pm in a more quiet flow cryostat. This
can be further improved by active stabilization, and even higher stability is
obtained under direct mechanical contact between the cavity mirrors, yielding
0:8 pm rms during the quiet phase of the closed-cycle cryo cooler. The platform
provides operation of cryogenic cavities with high finesse and small mode
volume for strong enhancement of light-matter interactions, opening up novel
possibilities for experiments with a great variety of quantum and nano
materials.


  We present a systematic expansion of all constraint equations in canonical
quantum gravity up to the order of the inverse Planck mass squared. It is
demonstrated that this method generates the conventional Feynman diagrammatic
technique involving graviton loops and vertices. It also reveals explicitly the
back reaction effects of quantized matter and graviton vacuum polarization.
This provides an explicit correspondence between the frameworks of canonical
and covariant quantum gravity in the semiclassical limit.


  The laws of thermodynamics allow work extraction from a single heat bath
provided that the entropy decrease of the bath is compensated for by another
part of the system. We propose a thermodynamic quantum engine that exploits
this principle and consists of two electrons on a double quantum dot (QD). The
engine is fueled by providing it with singlet spin states, where the electron
spins on different QDs are maximally entangled, and its operation involves only
changing the tunnel coupling between the QDs. Work can be extracted since the
entropy of an entangled singlet is lower than that of a thermal (mixed) state,
although they look identical when measuring on a single QD. We show that the
engine is an optimal thermodynamic engine in the long-time limit. In addition,
we include a microscopic description of the bath and analyze the engine's
finite-time performance using experimentally relevant parameters.


  We present a simple method to derive the semiclassical equations of motion
for a spinning particle in a gravitational field. We investigate the cases of
classical, rotating particles (pole-dipole particles), as well as particles
with intrinsic spin. We show that, starting with a simple Lagrangian, one can
derive equations for the spin evolution and momentum propagation in the
framework of metric theories of gravity and in theories based on a
Riemann-Cartan geometry (Poincare gauge theory), without explicitly referring
to matter current densities (spin and energy-momentum). Our results agree with
those derived from the multipole expansion of the current densities by the
conventional Papapetrou method and from the WKB analysis for elementary
particles.


  In d=2+1 dimensions, there exist field theories which are non-relativistic
and superconformal. These theories describe two species of anyons, whose spins
differ by 1/2, interacting in a harmonic trap. We compute the dimensions of
chiral primary operators. These operators receive large anomalous dimensions
which are related to the unusual angular momentum properties of anyons.
Surprisingly, we find that the dimensions of some chiral primary operators
violate the unitarity bound and we trace this to the fact that the associated
wavefunctions become non-normalisable. We also study BPS non-perturbative
states in this theory: these are Jackiw-Pi vortices. We show that these emerge
at exactly the point where perturbative operators hit the unitarity bound. To
describe the low-energy dynamics of these vortices, we construct a novel type
of supersymmetric gauged linear sigma model.


  We study analytically the $d$-wave holographic superconductors with Lifshitz
scaling in the presence of external magnetic field. The vortex lattice
solutions of the model have also been obtained with different Lifshitz scaling.
Our results imply that holographic $d$-wave superconductor is indeed a type II
one even for different Lifshitz scaling. This is the same as the conventional
$d$-wave superconductors in the Ginzburg-Landau theory.
  Our results also indicate that the dynamical exponent $z$ has no effect to
the shape of the vortex lattice even after higher order corrections (away from
the phase transition point $B_c$) are included. However, it has effects on the
upper critical magnetic field $B_{c_2}$ through the fact that a larger $z$
results in a smaller $B_{c_2}$ and therefore influences the size (characterized
by $r_0\equiv 1/\sqrt{B_{c_2}}$) of the vortex lattices. Furthermore, close
comparisons between our results and those of the Ginzburg-Landau theory reveal
the fact that the upper critical magnetic field $B_{c_2}$ is inversely
proportional to the square of the superconducting coherence length $\xi$,
regardless of the anisotropy between space and time.


  miniBELEN-10A is a modular and transportable moderated neutron counter with a
nearly flat detection efficiency up to 8 MeV. The detector was designed to
carry out measurements of (alpha, n) reactions in the context of the
Measurement of Alpha Neutron Yields (MANY) project. In this work we present the
results of the commissioning of miniBELEN-10A using the relatively well-known
thick-target neutron yields from 27Al(alpha, n)30P.


  In this paper, we generalize the Lyndon-Shirshov words to Lyndon-Shirshov
$\Omega$-words on a set $X$ and prove that the set of all non-associative
Lyndon-Shirshov $\Omega$-words forms a linear basis of the free Lie
$\Omega$-algebra on the set $X$. From this, we establish Gr\"{o}bner-Shirshov
bases theory for Lie $\Omega$-algebras. As applications, we give
Gr\"{o}bner-Shirshov bases for free $\lambda$-Rota-Baxter Lie algebras, free
modified $\lambda$-Rota-Baxter Lie algebras and free Nijenhuis Lie algebras and
then linear bases of such three free algebras are obtained.


  This paper tackles the risk averse multi-armed bandits problem when incurred
losses are non-stationary. The conditional value-at-risk (CVaR) is used as the
objective function. Two estimation methods are proposed for this objective
function in the presence of non-stationary losses, one relying on a weighted
empirical distribution of losses and another on the dual representation of the
CVaR. Such estimates can then be embedded into classic arm selection methods
such as epsilon-greedy policies. Simulation experiments assess the performance
of the arm selection algorithms based on the two novel estimation approaches,
and such policies are shown to outperform naive benchmarks not taking
non-stationarity into account.


  The database of Prof. Rogers (1887), which includes wheat prices in England
in the Middle Ages, was used to search for a possible influence of solar
activity on the wheat market. We present a conceptual model of possible modes
for sensitivity of wheat prices to weather conditions, caused by solar cycle
variations, and compare expected price fluctuations with price variations
recorded in medieval England.
  We compared statistical properties of the intervals between wheat price
bursts during years 1249-1703 with statistical properties of the intervals
between minimums of solar cycles during years 1700-2000. We show that
statistical properties of these two samples are similar, both for
characteristics of the distributions and for histograms of the distributions.
We analyze a direct link between wheat prices and solar activity in the 17th
Century, for which wheat prices and solar activity data (derived from 10Be
isotope) are available. We show that for all 10 time moments of the solar
activity minimums the observed prices were higher than prices for the
correspondent time moments of maximal solar activity (100% sign correlation, on
a significance level < 0.2%). We consider these results as a direct evidence of
the causal connection between wheat prices bursts and solar activity.


  This paper is a sequel to an earlier paper devoted to multiple, multicycle
nova evolution models (Prialnik & Kovetz 1995, first paper of the series),
which showed that the different characteristics of nova outbursts can be
reproduced by varying the values of three basic and independent parameters: the
white dwarf mass-M_{WD}, the temperature of its isothermal core-T_{WD} and the
mass transfer rate-Mdot. Apart from being the largest computational classical
novae parameter-space survey, we show here that the parameter space is
constrained by several analytical considerations and find its limiting
surfaces. Consequently, we extend the grid of multicycle nova evolution models
presented in Paper I almost to its limits, adding multicycle nova outburst
calculations for a considerable number of new parameter combinations. In
particular, the extended parameter pace that produces nova eruptions includes
low mass transfer rates down to 5e-13 Msun/yr, and more models for low T_{WD}.
Resulting characteristics of these runs are added to the former parameter
combination results, to provide a full grid spanning the entire parameter space
for Carbon-Oxygen white dwarfs. The full grid covers the entire range of
observed nova characteristics, even those of peculiar objects, which have not
been numerically reproduced until now. Most remarkably, runs for very low Mdot
lead to very high values for some characteristics, such as outburst amplitude
A>~20, high super-Eddington luminosities at maximum, heavy element abundance of
the ejecta Z_{ej}~0.63 and high ejected masses m_{ej}~7e-4 Msun.


  The class of Constitutive Artificial Neural Networks (CANNs) represents a new
approach of neural networks in the field of constitutive modeling. So far,
CANNs have proven to be a powerful tool in predicting elastic and inelastic
material behavior. However, the specification of inelastic constitutive
artificial neural networks (iCANNs) to capture plasticity remains to be
discussed. We present the extension and application of an iCANN to the
inelastic phenomena of plasticity. This includes the prediction of a
formulation for the elastic and plastic Helmholtz free energies, the inelastic
flow rule, and the yield condition that defines the onset of plasticity. Thus,
we learn four feed-forward networks in combination with a recurrent neural
network and use the second Piola-Kirchhoff stress measure for training. The
presented formulation captures both, associative and non-associative
plasticity. In addition, the formulation includes kinematic hardening effects
by introducing the plastic Helmholtz free energy. This opens the range of
application to a wider class of materials. The capabilities of the presented
framework are demonstrated by training on artificially generated data of models
for perfect plasticity of von-Mises type, tension-compression asymmetry, and
kinematic hardening. We observe already satisfactory results for training on
one load case only while extremely precise agreement is found for an increase
in load cases. In addition, the performance of the specified iCANN was
validated using experimental data of X10CrMoVNb9-1 steel. Training has been
performed on both, uniaxial tension and cyclic loading, separately and the
predicted results are then validated on the opposing set. The results underline
that the autonomously discovered material model is capable to describe and
predict the underlying experimental data.


  We investigate the control landscapes of closed, finite level quantum systems
beyond the dipole approximation by including a polarizability term in the
Hamiltonian. Theoretical analysis is presented for the $n$ level case and
formulas for singular controls, which are candidates for landscape traps, are
compared to their analogues in the dipole approximation. A numerical analysis
of the existence of traps in control landscapes beyond the dipole approximation
is made in the four level case. A numerical exploration of these control
landscapes is achieved by generating many random Hamiltonians which include a
term quadratic in a single control field. The landscapes of such systems are
found numerically to be trap free in general. This extends a great body of
recent work on typical landscapes of quantum systems where the dipole
approximation is made. We further investigate the relationship between the
magnitude of the polarizability and the magnitude of the controls resulting
from optimization. It is shown numerically that including a polarizability term
in an otherwise uncontrollable system removes traps from the landscapes of a
specific family of systems by restoring controllability. We numerically assess
the effect of a random polarizability term on the know example of a three level
system with a second order trap in its control landscape. It is found that the
addition of polarizability removes the trap from the landscape. The
implications for laboratory control are discussed.


  Chandrasekhar separated the Dirac equation for spinning and massive particles
in Kerr geometry into radial and angular parts. Here we solve the complete wave
equation and find out how the Dirac wave scatters off Kerr black holes. The
eigenfunctions, eigenvalues and reflection and transmission co-efficients are
computed. We compare the solutions with several parameters to show how a
spinning black hole distinguishes mass and energy of incoming waves. Very close
to the horizon the solutions become independent of the particle parameters
indicating an universality of the behaviour.


  We examine the optimal mass transport problem in $\mathbb{R}^{n}$ between
densities having independent compact support by considering the geometry of a
continuous interpolating support boundary in space-time within which the mass
density evolves according to the fluid dynamical framework of Benamou and
Brenier. We treat the geometry of this space--time embedding in terms of
points, vectors, and sets in
$\mathbb{R}^{n+1}\!=\mathbb{R}\times\mathbb{R}^{n}$ and blend the mass density
and velocity as well into a space-time solenoidal vector field ${\bf
W}\;|\;{\bf \Omega\subset}\mathbb{R}^{n+1}\!\to\mathbb{R}^{n+1}$ over compact
sets ${\bf \Omega}$ . We then formulate a coupled gradient descent approach
containing separate evolution steps for $\partial{\bf \Omega}$ and ${\bf W}$.


  In the current monocular depth research, the dominant approach is to employ
unsupervised training on large datasets, driven by warped photometric
consistency. Such approaches lack robustness and are unable to generalize to
challenging domains such as nighttime scenes or adverse weather conditions
where assumptions about photometric consistency break down.
  We propose DeFeat-Net (Depth & Feature network), an approach to
simultaneously learn a cross-domain dense feature representation, alongside a
robust depth-estimation framework based on warped feature consistency. The
resulting feature representation is learned in an unsupervised manner with no
explicit ground-truth correspondences required.
  We show that within a single domain, our technique is comparable to both the
current state of the art in monocular depth estimation and supervised feature
representation learning. However, by simultaneously learning features, depth
and motion, our technique is able to generalize to challenging domains,
allowing DeFeat-Net to outperform the current state-of-the-art with around 10%
reduction in all error measures on more challenging sequences such as nighttime
driving.


  The order parameter of a finite system with a spontaneously broken continuous
global symmetry acts as a quantum mechanical rotor. Both antiferromagnets with
a spontaneously broken $SU(2)_s$ spin symmetry and massless QCD with a broken
$SU(2)_L \times SU(2)_R$ chiral symmetry have rotor spectra when considered in
a finite volume. When an electron or hole is doped into an antiferromagnet or
when a nucleon is propagating through the QCD vacuum, a Berry phase arises from
a monopole field and the angular momentum of the rotor is quantized in
half-integer units.


  The outlook for producing useful practical devices within the paradigm of
magnonics rests on our ability to emit, control and detect coherent exchange
spin waves on the nanoscale. Here, we argue that all these key functionalities
can be delivered by chiral magnonic resonators - soft magnetic elements
chirally coupled, via magneto-dipole interaction, to magnonic media nearby.
Starting from the basic principles of chiral coupling, we outline how they
could be used to construct devices and explore underpinning physics, ranging
from basic logic gates to field programmable gate arrays, in-memory computing,
and artificial neural networks, and extending from one- to two- and
three-dimensional architectures.


  We present a simple novel scheme that converts a Gaussian beam into an
approximated radially polarized beam using coherent polarization manipulation
together with Poynting walk-off in birefringent crystals. Our scheme alleviates
the interferometric stability required by previous schemes that implemented
this coherent mode summation using Mach-Zehnder-like interferometers. A
symmetrical arrangement of two walk-off crystals with a half-wave plate, allows
coherence control even when the laser has short temporal coherence length. We
generated 14 watts of radially polarized beam from an Ytterbium fiber laser,
only limited by the available fiber laser power.