  It is proved that on a smooth algebraic variety, fibered into cubic surfaces
over the projective line and sufficiently ``twisted'' over the base, there is
only one pencil of rational surfaces -- that is, this very pencil of cubics. In
particular, this variety is non-rational; moreover, it can not be fibered into
rational curves. The proof is obtained by means of the method of maximal
singularities.


  Let $R$ be a ring with unity. The cozero-divisor graph of a ring $R$, denoted
by $\Gamma'(R)$, is an undirected simple graph whose vertices are the set of
all non-zero and non-unit elements of $R$, and two distinct vertices $x$ and
$y$ are adjacent if and only if $x \notin Ry$ and $y \notin Rx$. In this paper,
first we study the Laplacian spectrum of $\Gamma'(\mathbb{Z}_n)$. We show that
the graph $\Gamma'(\mathbb{Z}_{pq})$ is Laplacian integral. Further, we obtain
the Laplacian spectrum of $\Gamma'(\mathbb{Z}_n)$ for $n = p^{n_1}q^{n_2}$,
where $n_1, n_2 \in \mathbb{N}$ and $p, q$ are distinct primes. In order to
study the Laplacian spectral radius and algebraic connectivity of
$\Gamma'(\mathbb{Z}_n)$, we characterized the values of $n$ for which the
Laplacian spectral radius is equal to the order of $\Gamma'(\mathbb{Z}_n)$.
Moreover, the values of $n$ for which the algebraic connectivity and vertex
connectivity of $\Gamma'(\mathbb{Z}_n)$ coincide are also described. At the
final part of this paper, we obtain the Wiener index of $\Gamma'(\mathbb{Z}_n)$
for arbitrary $n$.


  Existing datasets for autonomous driving (AD) often lack diversity and
long-range capabilities, focusing instead on 360{\deg} perception and temporal
reasoning. To address this gap, we introduce Zenseact Open Dataset (ZOD), a
large-scale and diverse multimodal dataset collected over two years in various
European countries, covering an area 9x that of existing datasets. ZOD boasts
the highest range and resolution sensors among comparable datasets, coupled
with detailed keyframe annotations for 2D and 3D objects (up to 245m), road
instance/semantic segmentation, traffic sign recognition, and road
classification. We believe that this unique combination will facilitate
breakthroughs in long-range perception and multi-task learning. The dataset is
composed of Frames, Sequences, and Drives, designed to encompass both data
diversity and support for spatio-temporal learning, sensor fusion,
localization, and mapping. Frames consist of 100k curated camera images with
two seconds of other supporting sensor data, while the 1473 Sequences and 29
Drives include the entire sensor suite for 20 seconds and a few minutes,
respectively. ZOD is the only large-scale AD dataset released under a
permissive license, allowing for both research and commercial use. More
information, and an extensive devkit, can be found at https://zod.zenseact.com


  We investigated the evolution of the temperature-composition phase diagram of
Fe$_{1+y}$Te upon Se substitution. In particular, the effect of Se substitution
on the two-step, coupled magneto-structural transition in Fe$_{1+y}$Te single
crystals is investigated. To this end, the nominal Fe excess was kept at $y$ =
0.12. For low Se concentrations, the two magneto-structural transitions
displayed a tendency to merge. In spite of the high Fe-content,
superconductivity emerges for Se concentrations, $x \geq$ 0.1. We present a
temperature-composition phase diagram to demonstrate the interplay of
structure, magnetism, and superconductivity in these ternary Fe chalcogenides.


  The explosive availability of remote sensing images has challenged supervised
classification algorithms such as Support Vector Machines (SVM), as training
samples tend to be highly limited due to the expensive and laborious task of
ground truthing. The temporal correlation and spectral similarity between
multitemporal images have opened up an opportunity to alleviate this problem.
In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is
proposed for multitemporal remote sensing image classification. The approach
leverages the classifiers of previous images to reduce the required number of
training samples for the classifier training of an incoming image. For each
incoming image, a rough classifier is firstly predicted based on the temporal
trend of a set of previous classifiers. The predicted classifier is then
fine-tuned into a more accurate position with current training samples. This
approach can be applied progressively to sequential image data, with only a
small number of training samples being required from each image. Experiments
were conducted with Sentinel-2A multitemporal data over an agricultural area in
Australia. Results showed that the proposed SCT-SVM achieved better
classification accuracies compared with two state-of-the-art model transfer
algorithms. When training data are insufficient, the overall classification
accuracy of the incoming image was improved from 76.18% to 94.02% with the
proposed SCT-SVM, compared with those obtained without the assistance from
previous images. These results demonstrate that the leverage of a priori
information from previous images can provide advantageous assistance for later
images in multitemporal image classification.


  Automatic chart to text summarization is an effective tool for the visually
impaired people along with providing precise insights of tabular data in
natural language to the user. A large and well-structured dataset is always a
key part for data driven models. In this paper, we propose ChartSumm: a
large-scale benchmark dataset consisting of a total of 84,363 charts along with
their metadata and descriptions covering a wide range of topics and chart types
to generate short and long summaries. Extensive experiments with strong
baseline models show that even though these models generate fluent and
informative summaries by achieving decent scores in various automatic
evaluation metrics, they often face issues like suffering from hallucination,
missing out important data points, in addition to incorrect explanation of
complex trends in the charts. We also investigated the potential of expanding
ChartSumm to other languages using automated translation tools. These make our
dataset a challenging benchmark for future research.


  Adaptive loss function formulation is an active area of research and has
gained a great deal of popularity in recent years, following the success of
deep learning. However, existing frameworks of adaptive loss functions often
suffer from slow convergence and poor choice of weights for the loss
components. Traditionally, the elements of a multi-part loss function are
weighted equally or their weights are determined through heuristic approaches
that yield near-optimal (or sub-optimal) results. To address this problem, we
propose a family of methods, called SoftAdapt, that dynamically change function
weights for multi-part loss functions based on live performance statistics of
the component losses. SoftAdapt is mathematically intuitive, computationally
efficient and straightforward to implement. In this paper, we present the
mathematical formulation and pseudocode for SoftAdapt, along with results from
applying our methods to image reconstruction (Sparse Autoencoders) and
synthetic data generation (Introspective Variational Autoencoders).


  Spin wave nonreciprocity in domain walls (DWs) allows for unidirectional
signal processing in reconfigurable magnonic circuits. Using scanning
transmission x-ray microscopy (STXM), we examined coherently-excited magnons
propagating in Bloch-like DWs in amorphous Fe/Gd multilayers with perpendicular
magnetic anisotropy (PMA). Near 1 GHz we detected magnons with short
wavelengths down to $\lambda = 281$ nm in DWs whose minimum width amounted to
$\delta_{\rm DW} = 52$ nm. Consistent with micromagnetic simulations, the STXM
data reveal their nonreciprocal magnon band structures. We identified Bloch
points which disrupted the phase evolution of magnons and induced different
$\lambda$ adjacent to the topological defects. Our observations provide direct
evidence of nonreciprocal spin waves within Bloch-like DWs, serving as
programmable waveguides in magnonic devices with directed information flow.


  More and more stock trading strategies are constructed using deep
reinforcement learning (DRL) algorithms, but DRL methods originally widely used
in the gaming community are not directly adaptable to financial data with low
signal-to-noise ratios and unevenness, and thus suffer from performance
shortcomings. In this paper, to capture the hidden information, we propose a
DRL based stock trading system using cascaded LSTM, which first uses LSTM to
extract the time-series features from stock daily data, and then the features
extracted are fed to the agent for training, while the strategy functions in
reinforcement learning also use another LSTM for training. Experiments in DJI
in the US market and SSE50 in the Chinese stock market show that our model
outperforms previous baseline models in terms of cumulative returns and Sharp
ratio, and this advantage is more significant in the Chinese stock market, a
merging market. It indicates that our proposed method is a promising way to
build a automated stock trading system.


  Motivated by the experimentally observed high mobility of gold atoms on
graphene and their tendency to form nanometer-sized clusters, we present a
density functional theory study of the ground state structures of small gold
clusters on graphene, their mobility and clustering. Our detailed analysis of
the electronic structures identifies the opportunity to form strong gold-gold
bonds and the graphene mediated interaction of the pre-adsorbed fragments as
the driving forces behind gold's tendency to aggregate on graphene. While
clusters containing up to three gold atoms have one unambiguous ground state
structure, both gas phase isomers of a cluster with four gold atoms can be
found on graphene. In the gas phase the diamond shaped Au$_{4}^{D}$ cluster is
the ground state structure, whereas the Y shaped Au$_{4}^{Y}$ becomes the
actual ground state when adsorbed on graphene. As we show, both clusters can be
produced on graphene by two distinct clustering processes. We also studied in
detail the stepwise formation of a gold dimer out of two pre-adsorbed adatoms,
as well as the formation of Au$_{3}$. All reactions are exothermic and no
further activation barriers, apart from the diffusion barriers, were found. The
diffusion barriers of all studied clusters range from 4 to 36 meV, only, and
are substantially exceeded by the adsorption energies of -0.1 to -0.59 eV. This
explains the high mobility of Au$_{1-4}$ on graphene along the C-C bonds.


  We develop new approximate compilation schemes that significantly reduce the
expense of compiling the Quantum Approximate Optimization Algorithm (QAOA) for
solving the Max-Cut problem. Our main focus is on compilation with trapped-ion
simulators using Pauli-$X$ operations and all-to-all Ising Hamiltonian
$H_\text{Ising}$ evolution generated by Molmer-Sorensen or optical dipole force
interactions, though some of our results also apply to standard gate-based
compilations. Our results are based on principles of graph sparsification and
decomposition; the former reduces the number of edges in a graph while
maintaining its cut structure, while the latter breaks a weighted graph into a
small number of unweighted graphs. Though these techniques have been used as
heuristics in various hybrid quantum algorithms, there have been no guarantees
on their performance, to the best of our knowledge. This work provides the
first provable guarantees using sparsification and decomposition to improve
quantum noise resilience and reduce quantum circuit complexity.
  For quantum hardware that uses edge-by-edge QAOA compilations, sparsification
leads to a direct reduction in circuit complexity. For trapped-ion quantum
simulators implementing all-to-all $H_\text{Ising}$ pulses, we show that for a
$(1-\epsilon)$ factor loss in the Max-Cut approximation ($\epsilon>0)$, our
compilations improve the (worst-case) number of $H_\text{Ising}$ pulses from
$O(n^2)$ to $O(n\log(n/\epsilon))$ and the (worst-case) number of Pauli-$X$ bit
flips from $O(n^2)$ to $O\left(\frac{n\log(n/\epsilon)}{\epsilon^2}\right)$ for
$n$-node graphs. We demonstrate significant reductions in noise are obtained in
our new compilation approaches using theory and numerical calculations for
trapped-ion hardware. We anticipate these approximate compilation techniques
will be useful tools in a variety of future quantum computing experiments.


  Resampling techniques are widely used in statistical inference and ensemble
learning, in which estimators' statistical properties are essential. However,
existing methods are computationally demanding, because repetitions of
estimation/learning via numerical optimization/integral for each resampled data
are required. In this study, we introduce a computationally efficient method to
resolve such problem: replicated vector approximate message passing. This is
based on a combination of the replica method of statistical physics and an
accurate approximate inference algorithm, namely the vector approximate message
passing of information theory. The method provides tractable densities without
repeating estimation/learning, and the densities approximately offer an
arbitrary degree of the estimators' moment in practical time. In the
experiment, we apply the proposed method to the stability selection method,
which is commonly used in variable selection problems. The numerical results
show its fast convergence and high approximation accuracy for problems
involving both synthetic and real-world datasets.


  The unique properties of central potential of the form $-\beta
e^{-r}r^{\gamma}$ were studied using the recently developed critical parameter
technique. The particular cases of $\gamma=0$ and $\gamma=-1$ yield,
respectively, the exponential and Yukawa potentials widely used in the atomic,
molecular and nuclear physics. We found different behavior of the energy levels
of this potential for three different ranges of the value of $\gamma$. For
$\gamma\geq0$ it was found that the energy of bound states with the same
principal quantum number $N$ decreases with increasing angular momentum $\ell$.
The Gaussian and Woods-Saxon potentials also show this behavior. On the
contrary, for $-2\leq\gamma\leq-1$ increasing $\ell$ gives a higher energy,
resembling the Hulthen potential. However, a potential with $-1<\gamma<0$
possesses mixed properties, which give rise to several interesting results. For
one, the order of energy levels with different quantum numbers is not preserved
when varying the parameter $\beta$. This leads to a quantum degeneracy of the
states, and in fact, for a given value of $\gamma$ we can find the values
$\beta_{thr}$ for which two energy levels with different quantum numbers
coincide. Another interesting phenomena is the possibility, for some values of
$\gamma$ in this range, for two new energy levels with different quantum
numbers to appear simultaneously when $\beta$ reaches their common critical
value.


  In a volume-limited sample of 63 ultracool dwarfs of spectral type M7--M9.5,
we have obtained high-resolution spectroscopy with UVES at the Very Large
Telescope and HIRES at Keck Observatory. In this first paper we introduce our
volume-complete sample from DENIS and 2MASS targets, and we derive radial
velocities and space motion. Kinematics of our sample are consistent with the
stars being predominantly members of the young disk. The kinematic age of the
sample is 3.1 Gyr. We find that six of our targets show strong Li lines
implying that they are brown dwarfs younger than several hundred million years.
Five of the young brown dwarfs were unrecognized before. Comparing the fraction
of Li detections to later spectral types, we see a hint of an unexpected local
maximum of this fraction at spectral type M9. It is not yet clear whether this
maximum is due to insufficient statistics, or to a combination of physical
effects including spectral appearance of young brown dwarfs, Li line formation,
and the star formation rate at low masses.


  We propose a method to calculate the $q\bar{q}$ light front wave functions
(LFWFs) of photon at low-virtuality, i.e., the light front amplitude of
$\gamma^*\rightarrow q\bar{q}$ at low $Q^2$, based on a light front projection
approach. We exemplify this method using a contact interaction model within
Dyson-Schwinger equations formalism and obtain the nonperturbative photon
$q\bar{q}$ LFWFs. In this case, we find the nonperturbative effects are encoded
in the enhanced quark mass and a dressing function of covariant quark-photon
vertex, as compared to the leading order quantum electrodynamics photon
$q\bar{q}$ LFWFs. We then use nonperturbative-effect modified photon $q\bar{q}$
LFWFs to study the inclusive deep inelastic scattering HERA data in the
framework of the color dipole model. The results demonstrate that the
theoretical description of data at low $Q^2$ can be significantly improved once
the nonperturbative corrections are included in the photon LFWFs.


  We propose a new kind of seismic diagram, based on the determination of the
locations of sharp acoustic features inside a star. We show that by combining
the information about the position of the base of the convective envelope or
the second helium ionisation zone with a measure of the average large
separation, it is possible to constrain the unknown parameters characterising
the physical processes in the stellar interior. We demonstrate the application
of this technique to the analysis of mock data for a CoRoT target star.


  We consider 2D networks composed of nodes initially linked by two-qubit mixed
states. In these networks we develop a global error correction scheme that can
generate distance-independent entanglement from arbitrary network geometries
using rank two states. By using this method and combining it with the concept
of percolation we also show that the generation of long distance entanglement
is possible with rank three states. Entanglement percolation and global error
correction have different advantages depending on the given situation. To
reveal the trade-off between them we consider their application on networks
containing pure states. In doing so we find a range of pure-state schemes, each
of which has applications in particular circumstances: For instance, we can
identify a protocol for creating perfect entanglement between two distant
nodes. However, this protocol can not generate a singlet between any two nodes.
On the other hand, we can also construct schemes for creating entanglement
between any nodes, but the corresponding entanglement fidelity is lower.


  We show that the Euclidean and Lorentzian EPRL vertex amplitudes of covariant
Loop Quantum Gravity are related through a ``Wick rotation'' of the real
Immirzi parameter to purely imaginary values. Our result follows from the
simultaneous analytic continuation of the algebras, group elements and unitary
irreducible representations of the gauge groups $Spin(4)$ and
$SL(2,\mathbb{C})$, applied to the decomposition of the two models in terms of
$SU(2)$ invariants and booster functions.


  We investigate thermodynamics of static and spherically symmetric black holes
(BHs) in the Horndeski theories. Because of the presence of the
higher-derivative interactions and the nonminimal derivative couplings of the
scalar field, the standard Wald entropy formula may not be directly applicable.
Hence, following the original formulation by Iyer and Wald, we obtain the
differentials of the BH entropy and the total mass of the system in the
Horndeski theories, which lead to the first-law of thermodynamics via the
conservation of the Hamiltonian. Our formulation covers the case of the static
and spherically symmetric BH solutions with the static scalar field and those
with the linearly time-dependent scalar field in the shift-symmetric Horndeski
theories. We then apply our results to explicit BH solutions in the Horndeski
theories. In the case of the conventional scalar-tensor theories and the
Einstein-scalar-Gauss-Bonnet theories, we recover the BH entropy obtained by
the Wald entropy formula. In the shift-symmetric theories, in the case of the
BH solutions with the static scalar field we show that the BH entropy follows
the ordinary area law even in the presence of the nontrivial profile of the
scalar field. On the other hand, in the case of the BH solutions where the
scalar field linearly depends on time, i.e., the stealth Schwarzschild and
Schwarzschild-(anti-) de Sitter solutions, the BH entropy also depends on the
profile of the scalar field. By use of the entropy, we find that there exists
some range of the parameters in which Schwarzschild$-$(AdS) BH with non-trivial
scalar field is thermodynamically stable than Schwarzschild$-$(AdS) BH without
scalar field in general relativity.


  The Transformed-Transformer family of distributions are the resulting family
of distributions as transformed from a random variable $T$ through another
transformer random variable $X$ using a weight function $\omega$ of the
cumulative distribution function of $X$. In this paper, we study different
stochastic ageing properties, as well as different stochastic orderings of this
family of distributions. We discuss the results with several well known
distributions.


  This document presents the contents of three lectures delivered by the author
at the Erd\H{o}s Center School ``Optimal Transport on Quantum Structures'',
Septemer 19-23, 2022 in Budapest, Hungary. It presents a fairly self contained
account of an active topic of current research, and this account should be
accessible to most graduate students, as befits lectures for a school. The main
results are known, but there a number of new proofs and some new results.


  So far, there have been plenty of literatures on the metric in the space of
probability distributions and quantum states. As for channels, however, only a
little had been known. In this paper, we impose monotonicity by concatenation
of channels before and after the given channel families, and invariance by
tensoring identity channels. Under these axioms, we identify the largest and
the smallest metrics. Also, we studied asymptotic theory of metric in parallel
and adaptive repetition settings, and applied them to the study of channel
estimation. First we express the achievable lower bound of the mean square
error (MSE) of an estimate by a monotone channel metric, and show this equals
O(1/n) for noisy channels, where n is the number of times of channel use. This
result shows Heisenberg rate, or O(1/n^2)-rate of the MSE observed in case of
estimation of unitary, collapses with very small arbitrary noise.


  Supervised deep learning needs a large amount of labeled data to achieve high
performance. However, in medical imaging analysis, each site may only have a
limited amount of data and labels, which makes learning ineffective. Federated
learning (FL) can learn a shared model from decentralized data. But traditional
FL requires fully-labeled data for training, which is very expensive to obtain.
Self-supervised contrastive learning (CL) can learn from unlabeled data for
pre-training, followed by fine-tuning with limited annotations. However, when
adopting CL in FL, the limited data diversity on each site makes federated
contrastive learning (FCL) ineffective. In this work, we propose two federated
self-supervised learning frameworks for volumetric medical image segmentation
with limited annotations. The first one features high accuracy and fits
high-performance servers with high-speed connections. The second one features
lower communication costs, suitable for mobile devices. In the first framework,
features are exchanged during FCL to provide diverse contrastive data to each
site for effective local CL while keeping raw data private. Global structural
matching aligns local and remote features for a unified feature space among
different sites. In the second framework, to reduce the communication cost for
feature exchanging, we propose an optimized method FCLOpt that does not rely on
negative samples. To reduce the communications of model download, we propose
the predictive target network update (PTNU) that predicts the parameters of the
target network. Based on PTNU, we propose the distance prediction (DP) to
remove most of the uploads of the target network. Experiments on a cardiac MRI
dataset show the proposed two frameworks substantially improve the segmentation
and generalization performance compared with state-of-the-art techniques.


  We investigate explosive nucleosynthesis in a non-rotating 15$M_\odot$ star
with solar metallicity that explodes by a neutrino-heating supernova (SN)
mechanism aided by both standing accretion shock instability (SASI) and
convection. To trigger explosions in our two-dimensional hydrodynamic
simulations, we approximate the neutrino transport with a simple light-bulb
scheme and systematically change the neutrino fluxes emitted from the
protoneutron star. By a post-processing calculation, we evaluate abundances and
masses of the SN ejecta for nuclei with the mass number $\le 70$ employing a
large nuclear reaction network. Aspherical abundance distributions, which are
observed in nearby core-collapse SN remnants, are obtained for the non-rotating
spherically-symmetric progenitor, due to the growth of low-mode SASI. Abundance
pattern of the supernova ejecta is similar to that of the solar system for
models whose masses ranges $(0.4-0.5) \Ms$ of the ejecta from the inner region
($\le 10,000\km$) of the precollapse core. For the models, the explosion
energies and the \nuc{Ni}{56} masses are $ \simeq 10^{51} \rm erg$ and
$(0.05-0.06) \Ms$, respectively; their estimated baryonic masses of the neutron
star are comparable to the ones observed in neutron-star binaries. These
findings may have little uncertainty because most of the ejecta is composed by
matter that is heated via the shock wave and has relatively definite
abundances. The abundance ratios for Ne, Mg, Si and Fe observed in Cygnus loop
are well reproduced with the SN ejecta from an inner region of the $15\Ms$
progenitor.


  This paper presents a novel multitask multiple kernel learning framework that
efficiently learns the kernel weights leveraging the relationship across
multiple tasks. The idea is to automatically infer this task relationship in
the \textit{RKHS} space corresponding to the given base kernels. The problem is
formulated as a regularization-based approach called \textit{Multi-Task
Multiple Kernel Relationship Learning} (\textit{MK-MTRL}), which models the
task relationship matrix from the weights learned from latent feature spaces of
task-specific base kernels. Unlike in previous work, the proposed formulation
allows one to incorporate prior knowledge for simultaneously learning several
related tasks. We propose an alternating minimization algorithm to learn the
model parameters, kernel weights and task relationship matrix. In order to
tackle large-scale problems, we further propose a two-stage \textit{MK-MTRL}
online learning algorithm and show that it significantly reduces the
computational time, and also achieves performance comparable to that of the
joint learning framework. Experimental results on benchmark datasets show that
the proposed formulations outperform several state-of-the-art multitask
learning methods.


  UHECRs have attracted a lot of attention due to their challengingly high
energies and their potential value to constrain physical processes and
astrophysical parameters in the most energetic sources of the universe. Current
detectors have failed to detect significant anisotropies which had been
expected to allow source identification. Some indications about the UHECR
composition, which may become heavier at the highest energies, has even put
into question the possibility that such a goal could be achieved soon.
  We investigate the potential value of a new-generation detector, with 10
times larger exposure, to overcome the current situation and make significant
progress in the detection of anisotropies and thus in the study of UHECRs. We
take as an example the expected performances of the JEM-EUSO, assuming a
uniform full-sky coverage with a total exposure of 300,000 km2 sr yr.
  We simulate realistic UHECR sky maps for a wide range of possible
astrophysical scenarios allowed by the current constraints, taking into account
the energy losses and photo-dissociation of the UHECRs, as well as their
deflections by magnetic fields. These sky maps, built for the expected
statistics of JEM-EUSO as well as for the current Auger statistics, as a
reference, are analyzed from the point of view of their intrinsic anisotropies,
using the two-point correlation function. A statistical study of the resulting
anisotropies is performed for each astrophysical scenario, varying the UHECR
source composition and spectrum as well as the source density.
  We find that significant anisotropies are expected to be detected by a
next-generation UHECR detector, for essentially all the astrophysical scenarios
studied, and give precise, quantitative meaning to this statement. Our results
show that a gain of one order of magnitude in exposure would make a significant
difference compared to the existing detectors.


  We introduce a computational Maxwell-Bloch framework for investigating
out-of-equilibrium optical emitters in open systems. To do so, we compute the
pulse-induced dynamics of each emitter from fundamental light-matter
interactions and self-consistently calculate their radiative coupling,
including phase inhomogeneity from propagation effects. This semiclassical
framework is applied to open quantum dots systems with different densities and
dipolar coupling. We observe signatures of superradiant behavior, such as
directionality and faster decay, as well as subradiant emission. We compare and
discuss the computed light emission obtained with our method and a Master
equation approach. Our framework enables quantitative investigations of large
optical ensembles in the time domain and could be used to design new systems
with enhanced superradiant and subradiant properties.


  Nanowire field-effect sensors have recently been developed for label-free
detection of biomolecules. In this work, we introduce a computational technique
based on Bayesian estimation to determine the physical parameters of the sensor
and, more importantly, the properties of the analyte molecules. To that end, we
first propose a PDE based model to simulate the device charge transport and
electrochemical behavior. Then, the adaptive Metropolis algorithm with delayed
rejection (DRAM) is applied to estimate the posterior distribution of unknown
parameters, namely molecule charge density, molecule density, doping
concentration, and electron and hole mobilities. We determine the device and
molecules properties simultaneously, and we also calculate the molecule density
as the only parameter after having determined the device parameters. This
approach makes it possible not only to determine unknown parameters, but it
also shows how well each parameter can be determined by yielding the
probability density function (pdf).


  Off-topic spoken response detection, the task aiming at predicting whether a
response is off-topic for the corresponding prompt, is important for an
automated speaking assessment system. In many real-world educational
applications, off-topic spoken response detectors are required to achieve high
recall for off-topic responses not only on seen prompts but also on prompts
that are unseen during training. In this paper, we propose a novel approach for
off-topic spoken response detection with high off-topic recall on both seen and
unseen prompts. We introduce a new model, Gated Convolutional Bidirectional
Attention-based Model (GCBiA), which applies bi-attention mechanism and
convolutions to extract topic words of prompts and key-phrases of responses,
and introduces gated unit and residual connections between major layers to
better represent the relevance of responses and prompts. Moreover, a new
negative sampling method is proposed to augment training data. Experiment
results demonstrate that our novel approach can achieve significant
improvements in detecting off-topic responses with extremely high on-topic
recall, for both seen and unseen prompts.


  Suspended Bernal-stacked graphene multilayers up to an unexpectedly large
thickness exhibit a broken-symmetry ground state, whose origin remains to be
understood. Here we show that a finite-temperature second order phase
transition occurs in multilayers whose critical temperature Tc increases from
12 K in bilayers to 100 K in heptalayers. A comparison of the data to a
phenomenological model inspired by a mean field approach suggests that the
transition is associated with the appearance of a self-consistent valley- and
spin-dependent staggered potential changing sign from one layer to the next,
appearing at Tc and increasing upon cooling. The systematic evolution with
thickness of several measured quantities imposes constraints on any microscopic
theory aiming to analyze the nature of electronic correlations in this system.


  The purpose of this erratum and addendum is to correct the errors in [1]. It
consists of five components:
  1. Lemma 7.1 and Proposition 7.2 are wrong and discarded;
  2. A new proof of existence $\lambda(\xi)$ in (7.1) without Proposition 7.2;
  3. Definition of a new bijection in Theorem 5.2 and a proof by a new
technique;
  4. A new proof of Theorem 5.5 based on the new bijection in Theorem 5.2;
  5. Correction to the list of exceptional simple pairs in Proposition 3.1.
  The main results of [1] remain true as stated. We also add a final remark on
generalization.


  Novel low-power wireless technologies and IoT applications open the door to
the Industrial Internet of Things (IIoT). In this new paradigm, Wireless Sensor
Networks (WSNs) must fulfil, despite energy and transmission power limitations,
the challenging communication requirements of advanced manufacturing processes
and technologies. In industrial networks, this is possible thanks to the
availability of network infrastructure and the presence of a network
coordinator that efficiently allocates the available radio resources. In this
work, we consider a WSN that simultaneously transmits measurements of Networked
Control Systems' (NCSs) dynamics to remote state estimators over a shared
packet-erasure channel. We develop a minimum transmission power control (TPC)
policy for the coordination of the wireless medium by formulating an infinite
horizon Markov decision process (MDP) optimization problem. We compute the
policy using an approximate value iteration algorithm and provide an extensive
evaluation of its parameters in different interference scenarios and NCSs
dynamics. The evaluation results present a comprehensive characterization of
the algorithm's performance, proving that it can flexibly adapt to arbitrary
use cases.


  Hubble Space Telescope (HST) photometry is providing an extensive analysis of
globular clusters (GCs). In particular, the pseudo two-colour diagram dubbed
'chromosome map (ChM)' allowed to detect and characterize their multiple
populations with unprecedented detail. The main limitation of these studies is
the small field of view of HST, which makes it challenging to investigate some
important aspects of the multiple populations, such as their spatial
distributions and the internal kinematics in the outermost cluster regions. To
overcome this limitation, we analyse state-of-art wide-field photometry of 43
GCs obtained from ground-based facilities. We derived high-resolution reddening
maps and corrected the photometry for differential reddening when needed. We
use photometry in the U, B, and I bands to introduce the $\Delta c_{\rm U,B,I}$
vs. $\Delta_{\rm B,I}$ ChM of red-giant branch (RGB) and asymptotic-giant
branch (AGB) stars. We demonstrate that this ChM, which is built with wide-band
ground-based photometry, is an efficient tool to identify first- and
second-generation stars (1G and 2G) over a wide field of view. To illustrate
its potential, we derive the radial distribution of multiple populations in NGC
288 and infer their chemical composition. We present the ChMs of RGB stars in
29 GCs and detect a significant degree of variety. The fraction of 1G and 2G
stars, the number of subpopulations, and the extension of the ChMs
significantly change from one cluster to another. Moreover, the metal-poor and
metal-rich stars of Type II GCs define distinct sequences in the ChM. We
confirm the presence of extended 1G sequences.


  Ni$_3$Al and Ni$_3$Ga are closely related materials on opposite sides of a
ferromagnetic quantum critical point. The Stoner factor of Ni is virtually the
same in both compounds and the density of states is larger in Ni$_3$Ga. So,
according to the Stoner theory, it should be more magnetic, and, in LDA
calculations, it is. However, experimentally, it is a paramagnet, while
Ni$_3$Al is an itinerant ferromagnet. We show that the critical spin
fluctuations are stronger than in Ni$_3$Ga, due to a weaker q-dependence of the
susceptibility, and this effect is strong enough to reverse the trend. The
approach combines LDA calculations with the Landau theory and the
fluctuation-dissipation theorem using the same momentum cut-off for both
materials. The calculations provide evidence for strong, beyond LDA, spin
fluctuations associated with the critical point in both materials, but stronger
in Ni$_3$Ga than in Ni$_3$Al.


  We consider space-times which are asymptotically flat at spacelike infinity,
i^0. It is well known that, in general, one cannot have a smooth differentiable
structure at i^0, but have to use direction dependent structures. Instead of
the oftenly used C^{>1}-differentiabel structure, we suggest a weaker
differential structure, a C^{1^+} structure. The reason for this is that we
have not seen any completions of the Schwarzschild space-time which is C^{>1}
in both spacelike and null directions at {i^0}. In a C^{1^+} structure all
directions can be treated equal, at the expense of logarithmic singularities at
{i^0}. We show that, in general, the relevant part of the curvature tensor, the
Weyl part, is free from these singularities, and that the (rescaled) Weyl
tensor has a certain symmetry.


  Coulomb corrections to perturbation theory for producing electron-positron
pairs in ultrarelativistic heavy ion collisions are considered in a
part-analytical, part-numerical approach. Production probabilities are reduced
from perturbation theory with increasing charge of the colliding heavy ions, as
has been previously argued in the literature. It is shown here that the
reduction from perturbation theory comes from the appropriate physical spatial
cutoff of the electromagnetic potentials arising from the colliding
ultrarelativistic heavy ions.


Using methods of ergodic theory we prove as in the title.


  We investigate the energy of the gravitational wave from a binary black hole
merger by the coalescence of two Kerr black holes with an orbital angular
momentum. The coalescence is constructed to be consistent with particle
absorption in the limit in which the primary black hole is sufficiently large
compared with the secondary black hole. In this limit, we analytically obtain
an effective gravitational spin--orbit interaction dependent on the alignments
of the angular momenta. Then, binary systems with various parameters including
equal masses are numerically analyzed. According to the numerical analysis, the
energy of the gravitational wave still depends on the effective interactions,
as expected from the analytical form. In particular, we ensure that the final
black hole obtains a large portion of its spin angular momentum from the
orbital angular momentum of the initial binary black hole. To estimate the
angular momentum released by the gravitational wave in the actual binary black
hole, we apply our results to observations at the Laser Interferometer
Gravitational-Wave Observatory: GW150914, GW151226, GW170104, GW170608, and
GW170814.


  Runtime assurance (RTA) addresses the problem of keeping an autonomous system
safe while using an untrusted (or experimental) controller. This can be done
via logic that explicitly switches between the untrusted controller and a
safety controller, or logic that filters the input provided by the untrusted
controller. While several tools implement specific instances of RTAs, there is
currently no framework for evaluating different approaches. Given the
importance of the RTA problem in building safe autonomous systems, an
evaluation tool is needed. In this paper, we present the RTAEval framework as a
low code framework that can be used to quickly evaluate different RTA logics
for different types of agents in a variety of scenarios. RTAEval is designed to
quickly create scenarios, run different RTA logics, and collect data that can
be used to evaluate and visualize performance. In this paper, we describe
different components of RTAEval and show how it can be used to create and
evaluate scenarios involving multiple aircraft models.


  Particle Flow Algorithms (PFAs) have been proposed as a method of improving
the jet energy resolution of future colliding beam detectors. PFAs require
calorimeters with high granularity to enable three-dimensional imaging of
events. The Calorimeter for the Linear Collider Collaboration (CALICE) is
developing and testing prototypes of such highly segmented calorimeters. In
this context, a large prototype of a Digital Hadron Calorimeter (DHCAL) was
developed and constructed by a group led by Argonne National Laboratory. The
DHCAL consists of 52 layers, instrumented with Resistive Plate Chambers (RPCs)
and interleaved with steel absorber plates. The RPCs are read out by 1 x 1 cm2
pads with a 1-bit resolution (digital readout). The DHCAL prototype has
approximately 480,000 readout channels. This talk reports on the design,
construction and commissioning of the DHCAL. The DHCAL was installed at the
Fermilab Test Beam Facility in fall 2010 and data was collected through the
summer 2011.


  A difficult task to deal with is the analytical treatment of models composed
by three real scalar fields, once their equations of motion are in general
coupled and hard to be integrated. In order to overcome this problem we
introduce a methodology to construct three-field models based on the so-called
"extension method". The fundamental idea of the procedure is to combine three
one-field systems in a non-trivial way, to construct an effective three scalar
field model. An interesting scenario where the method can be implemented is
within inflationary models, where the Einstein-Hilbert Lagrangian is coupled
with the scalar field Lagrangian. We exemplify how a new model constructed from
our method can lead to non-trivial behaviors for cosmological parameters.


  Fluid dynamics is traditionally thought to apply only to systems near local
equilibrium. In this case, the effective theory of fluid dynamics can be
constructed as a gradient series. Recent applications of resurgence suggest
that this gradient series diverges, but can be Borel-resummed, giving rise to a
hydrodynamic attractor solution which is well defined even for large gradients.
Arbitrary initial data quickly approaches this attractor via non-hydrodynamic
mode decay. This suggests the existence of a new theory of far-from-equilibrium
fluid dynamics. In this work, the framework of fluid dynamics far from local
equilibrium for conformal system is introduced, and the hydrodynamic attractor
solutions for rBRSSS, kinetic theory in the relaxation time approximation, and
strongly-coupled N=4 SYM are identified for a system undergoing Bjorken flow.


  The general seven-dimensional maximal supergravity is presented. Its
universal Lagrangian is described in terms of an embedding tensor which can be
characterized group-theoretically. The theory generically combines vector,
two-form and three-form tensor fields that transform into each other under an
intricate set of nonabelian gauge transformations. The embedding tensor encodes
the proper distribution of the degrees of freedom among these fields. In
addition to the kinetic terms the vector and tensor fields contribute to the
Lagrangian with a unique gauge invariant Chern-Simons term. This new
formulation encompasses all possible gaugings. Examples include the sphere
reductions of M theory and of the type IIA/IIB theories with gauge groups
SO(5), CSO(4,1), and SO(4), respectively.


  This paper combines experimental and modelling studies to provide a detailed
examination of the influence of porosity volume fraction and morphology on the
polarisation-electric field response of ferroelectric materials. The broadening
of the electric field distribution and a decrease in the electric field
experienced by the ferroelectric ceramic medium due to the presence of
low-permittivity pores is examined and its implications on the shape of the
hysteresis loop, remnant polarisation and coercive field is discussed. The
variation of coercive field with porosity level is seen to be complex and is
attributed to two competing mechanisms where at high porosity levels the
influence of the broadening of the electric field distribution dominates, while
at low porosity levels an increase in the compliance of the matrix is more
important. This new approach to understanding these materials enables the
seemingly conflicting observations in the existing literature to be clarified
and provides an effective approach to interpret the influence of pore fraction
and morphology on the polarisation behaviour of ferroelectrics. Such
information provides new insights in the interpretation of the physical
properties of porous ferroelectric materials to inform future effort in the
design of ferroelectric materials for piezoelectric sensor, actuator, energy
harvesting, and transducer applications.


  Attribute-Based Access Control (ABAC) and Relationship-based access control
(ReBAC) provide a high level of expressiveness and flexibility that promote
security and information sharing, by allowing policies to be expressed in terms
of attributes of and chains of relationships between entities. Algorithms for
learning ABAC and ReBAC policies from legacy access control information have
the potential to significantly reduce the cost of migration to ABAC or ReBAC.
  This paper presents the first algorithms for mining ABAC and ReBAC policies
from access control lists (ACLs) and incomplete information about entities,
where the values of some attributes of some entities are unknown. We show that
the core of this problem can be viewed as learning a concise three-valued logic
formula from a set of labeled feature vectors containing unknowns, and we give
the first algorithm (to the best of our knowledge) for that problem.


  We present ten new equilibrium solutions to plane Couette flow in small
periodic cells at low Reynolds number (Re) and two new traveling-wave
solutions. The solutions are continued under changes of Re and spanwise period.
We provide a partial classification of the isotropy groups of plane Couette
flow and show which kinds of solutions are allowed by each isotropy group. We
find two complementary visualizations particularly revealing. Suitably chosen
sections of their 3D-physical space velocity fields are helpful in developing
physical intuition about coherent structures observed in low Re turbulence.
Projections of these solutions and their unstable manifolds from their
infinite-dimensional state space onto suitably chosen 2- or 3-dimensional
subspaces reveal their interrelations and the role they play in organizing
turbulence in wall-bounded shear flows.


  Making serverless computing widely applicable requires detailed performance
understanding. Although contemporary benchmarking approaches exist, they report
only coarse results, do not apply distributed tracing, do not consider
asynchronous applications, and provide limited capabilities for (root cause)
analysis. Addressing this gap, we design and implement ServiBench, a serverless
benchmarking suite. ServiBench (i) leverages synchronous and asynchronous
serverless applications representative of production usage, (ii) extrapolates
cloud-provider data to generate realistic workloads, (iii) conducts
comprehensive, end-to-end experiments to capture application-level performance,
(iv) analyzes results using a novel approach based on (distributed) serverless
tracing, and (v) supports comprehensively serverless performance analysis. With
ServiBench, we conduct comprehensive experiments on AWS, covering five common
performance factors: median latency, cold starts, tail latency, scalability,
and dynamic workloads. We find that the median end-to-end latency of serverless
applications is often dominated not by function computation but by external
service calls, orchestration, or trigger-based coordination. We release
collected experimental data under FAIR principles and ServiBench as a tested,
extensible open-source tool.


  The prediction of time series is a challenging task relevant in such diverse
applications as analyzing financial data, forecasting flow dynamics or
understanding biological processes. Especially chaotic time series that depend
on a long history pose an exceptionally difficult problem. While machine
learning has shown to be a promising approach for predicting such time series,
it either demands long training time and much training data when using deep
recurrent neural networks. Alternative, when using a reservoir computing
approach it comes with high uncertainty and typically a high number of random
initializations and extensive hyper-parameter tuning when using a reservoir
computing approach. In this paper, we focus on the reservoir computing approach
and propose a new mapping of input data into the reservoir's state space.
Furthermore, we incorporate this method in two novel network architectures
increasing parallelizability, depth and predictive capabilities of the neural
network while reducing the dependence on randomness. For the evaluation, we
approximate a set of time series from the Mackey-Glass equation, inhabiting
non-chaotic as well as chaotic behavior and compare our approaches in regard to
their predictive capabilities to echo state networks and gated recurrent units.
For the chaotic time series, we observe an error reduction of up to $85.45\%$
and up to $87.90\%$ in contrast to echo state networks and gated recurrent
units respectively. Furthermore, we also observe tremendous improvements for
non-chaotic time series of up to $99.99\%$ in contrast to existing approaches.


  We study a double quantum dot system coherently coupled to an electromagnetic
resonator. A current through the dot system can create a population inversion
in the dot levels and, within a narrow resonance window, a lasing state in the
resonator. The lasing state correlates with the transport properties. On one
hand, this allows probing the lasing state via a current measurement. On the
other hand, the resulting narrow current peak allows resolving small
differences in the dot properties, e.g., a small difference in the Zeeman
splittings of the two dots. For realistic situations relaxation processes have
pronounced consequences. Remarkably, they may even enhance the resolution
between different spin states by releasing a trapped population in the
off-resonant spin channel.


  In the coming decade, up to 100 000 satellites in large constellations could
be launched into low Earth orbit. The satellites will introduce a variety of
negative impacts on astronomy observatories and science, which vary from
negligible to very disruptive depending on the type of instrument, the position
of the science target, and the nature of the constellation. Since the launch of
the first batch of SpaceX's Starlink constellation in 2019, the astronomy
community has made substantial efforts to analyse the problem and to engage
with satellite operators and government agencies. This article presents a short
summary of the simulations of impacts on ESO's optical and infrared facilities
and ALMA, as well as the conducted observational campaigns to assess the
brightness of satellites. It also discusses several activities to identify
policy solutions at the international and national level.


  We prove the factoriality of the following nodal threefolds: a complete
intersection of hypersurfaces $F$ and $G\subset\mathbb{P}^{5}$ of degree $n$
and $k$ respectively, where $G$ is smooth, $|\mathrm{Sing}(F\cap
G)|\leqslant(n+k-2)(n-1)/5$, $n\geqslant k$; a double cover of a smooth
hypersurface $F\subset\mathbb{P}^{4}$ of degree $n$ branched over a surface
that is cut out on $F$ by a hypersurface $G$ of degree $2r\geqslant n$, and
$|\mathrm{Sing}(F\cap G)|\leqslant(2r+n-2)r/4$.


  Incident management for large cloud services is a complex and tedious process
and requires significant amount of manual efforts from on-call engineers
(OCEs). OCEs typically leverage data from different stages of the software
development lifecycle [SDLC] (e.g., codes, configuration, monitor data, service
properties, service dependencies, trouble-shooting documents, etc.) to generate
insights for detection, root causing and mitigating of incidents. Recent
advancements in large language models [LLMs] (e.g., ChatGPT, GPT-4, Gemini)
created opportunities to automatically generate contextual recommendations to
the OCEs assisting them to quickly identify and mitigate critical issues.
However, existing research typically takes a silo-ed view for solving a certain
task in incident management by leveraging data from a single stage of SDLC. In
this paper, we demonstrate that augmenting additional contextual data from
different stages of SDLC improves the performance of two critically important
and practically challenging tasks: (1) automatically generating root cause
recommendations for dependency failure related incidents, and (2) identifying
ontology of service monitors used for automatically detecting incidents. By
leveraging 353 incident and 260 monitor dataset from Microsoft, we demonstrate
that augmenting contextual information from different stages of the SDLC
improves the performance over State-of-The-Art methods.


  This paper presents AutoMarks, an automated and transferable watermarking
framework that leverages graph neural networks to reduce the watermark search
overheads during the placement stage. AutoMarks's novel automated watermark
search is accomplished by (i) constructing novel graph and node features with
physical, semantic, and design constraint-aware representation; (ii) designing
a data-efficient sampling strategy for watermarking fidelity label collection;
and (iii) leveraging a graph neural network to learn the connectivity between
cells and predict the watermarking fidelity on unseen layouts. Extensive
evaluations on ISPD'15 and ISPD'19 benchmarks demonstrate that our proposed
automated methodology: (i) is capable of finding quality-preserving watermarks
in a short time; and (ii) is transferable across various designs, i.e.,
AutoMarks trained on one layout is generalizable to other benchmark circuits.
AutoMarks is also resilient against potential watermark removal and forging
attacks


  We derive new limits on the value of the cosmological constant, $\Lambda$,
based on the Einstein bending of light by systems where the lens is a distant
galaxy or a cluster of galaxies. We use an amended lens equation in which the
contribution of $\Lambda$ to the Einstein deflection angle is taken into
account and use observations of Einstein radii around several lens systems. We
use in our calculations a Schwarzschild-de Sitter vacuole exactly matched into
a Friedmann-Robertson-Walker background and show that a $\Lambda$-contribution
term appears in the deflection angle within the lens equation. We find that the
contribution of the $\Lambda$-term to the bending angle is larger than the
second-order term for many lens systems. Using these observations of bending
angles, we derive new limits on the value of $\Lambda$. These limits constitute
the best observational upper bound on $\Lambda$ after cosmological constraints
and are only two orders of magnitude away from the value determined by those
cosmological constraints.


  Traditionally, object tracking and segmentation are treated as two separate
problems and solved independently. However, in this paper, we argue that
tracking and segmentation are actually closely related and solving one should
help the other. On one hand, the object track, which is a set of bounding boxes
with one bounding box in every frame, would provide strong high-level guidance
for the target/background segmentation task. On the other hand, the object
segmentation would separate object from other objects and background, which
will be useful for determining track locations in every frame. We propose a
novel framework which combines online multiple target tracking and segmentation
in a video. In our approach, the tracking and segmentation problems are coupled
by Lagrange dual decomposition, which leads to more accurate segmentation
results and also \emph{helps resolve typical difficulties in multiple target
tracking, such as occlusion handling, ID-switch and track drifting}. To track
targets, an individual appearance model is learned for each target via
structured learning and network flow is employed to generate tracks from
densely sampled candidates. For segmentation, multi-label Conditional Random
Field (CRF) is applied to a superpixel based spatio-temporal graph in a segment
of video to assign background or target labels to every superpixel. The
experiments on diverse sequences show that our method outperforms
state-of-the-art approaches for multiple target tracking as well as
segmentation.


  We revisit a recent paper of Fialowski and Iohara. They compute the homology
of the Lie algebra $\mathfrak{gl}(\infty,R)$ for $R$ an associative unital
algebra over a field of characteristic zero. We explain how to obtain
essentially the same results by a completely different method.


  ``Extended Ensemble Monte Carlo''is a generic term that indicates a set of
algorithms which are now popular in a variety of fields in physics and
statistical information processing. Exchange Monte Carlo (Metropolis-Coupled
Chain, Parallel Tempering), Simulated Tempering (Expanded Ensemble Monte
Carlo), and Multicanonical Monte Carlo (Adaptive Umbrella Sampling) are typical
members of this family. Here we give a cross-disciplinary survey of these
algorithms with special emphasis on the great flexibility of the underlying
idea. In Sec.2, we discuss the background of Extended Ensemble Monte Carlo. In
Sec.3, 4 and 5, three types of the algorithms, i.e., Exchange Monte Carlo,
Simulated Tempering, Multicanonical Monte Carlo are introduced. In Sec.6, we
give an introduction to Replica Monte Carlo algorithm by Swendsen and Wang.
Strategies for the construction of special-purpose extended ensembles are
discussed in Sec.7. We stress that an extension is not necessary restricted to
the space of energy or temperature. Even unphysical (unrealizable)
configurations can be included in the ensemble, if the resultant fast mixing of
the Markov chain offsets the increasing cost of the sampling procedure.
Multivariate (multi-component) extensions are also useful in many examples. In
Sec.8, we give a survey on extended ensembles with a state space whose
dimensionality is dynamically varying. In the appendix, we discuss advantages
and disadvantages of three types of extended ensemble algorithms.


  We propose a general theoretical framework, using two layers of ancilla
qubits, for deconfined criticality between a Fermi liquid with a large Fermi
surface, and a pseudogap metal with a small Fermi surface of electron-like
quasiparticles. The pseudogap metal can be a magnetically ordered metal, or a
fractionalized Fermi liquid (FL*) without magnetic order. A critical 'ghost'
Fermi surface emerges (alongside the large electron Fermi surface) at the
transition, with the ghost fermions carrying neither spin nor charge, but
minimally coupled to $(U(1) \times U(1))/Z_2$ or $(SU(2) \times U(1))/Z_2$
gauge fields. The $(U(1) \times U(1))/Z_2$ case describes simultaneous Kondo
breakdown and onset of magnetic order: the two gauge fields induce nearly equal
attractive and repulsive interactions between ghost Fermi surface excitations,
and this competition controls the quantum criticality. Away from the transition
on the pseudogap side, the ghost Fermi surface absorbs part of the large
electron Fermi surface, and leads to a jump in the Hall co-efficient. We also
find an example of an "unnecessary quantum critical point" between a metal with
spin density wave order, and a metal with local moment magnetic order. The
ghost fermions contribute an enhanced specific heat near the transition, and
could also be detected in other thermal probes. We relate our results to the
phases of correlated electron compounds.


  We calculate the shadowing of sea quarks and gluons and show that the
shadowing of gluons is {\it not} simply given by the sea quark shadowing,
especially at small $x$. The calculations are done in the lab frame approach by
using the generalized vector meson dominance model. Here the virtual photon
turns into a hadronic fluctuation long before the nucleus. The subsequent
coherent interaction with more than one nucleon in the nucleus leads to the
depletion $\sigma (\gamma^* A) < A\sigma (\gamma^* N)$ known as shadowing. A
comparison of the shadowing of quarks to E665 data for $^{40}Ca$ and $^{207}Pb$
shows good agreement.


  In this paper we generalize the 1-bit matrix completion problem to higher
order tensors. We prove that when $r=O(1)$ a bounded rank-$r$, order-$d$ tensor
$T$ in $\mathbb{R}^{N} \times \mathbb{R}^{N} \times \cdots \times
\mathbb{R}^{N}$ can be estimated efficiently by only $m=O(Nd)$ binary
measurements by regularizing its max-qnorm and M-norm as surrogates for its
rank. We prove that similar to the matrix case, i.e., when $d=2$, the sample
complexity of recovering a low-rank tensor from 1-bit measurements of a subset
of its entries is the same as recovering it from unquantized measurements.
Moreover, we show the advantage of using 1-bit tensor completion over
matricization both theoretically and numerically. Specifically, we show how the
1-bit measurement model can be used for context-aware recommender systems.


  Experiments based on noble liquids and solid state cryogenic detectors have
had a leading role in the direct detection of dark matter. But smaller scale
projects can help to explore the new dark matter landscape with advanced,
ultra-sensitive detectors based on recently developed technologies. Here, the
physics case of different types of small scale dark matter experiments will be
presented and many of them will be reviewed, highlighting the detection
techniques and summarizing their properties, results and status.


  This work constitutes the second part of a series of studies that aim to
utilise tools from Hamiltonian mechanics to investigate the motion of an
extended body in general relativity. The first part of this work [Refs. [1, 2]]
constructed a ten-dimensional, covariant Hamiltonian framework encompassing all
the linear-in-spin corrections to the geodesic motion in arbitrary spacetime.
This framework was proven to be integrable in the Schwarzschild and Kerr
spacetimes, specifically. The present work translates this abstract
integrability result into tangible applications for linear-in-spin Hamiltonian
dynamics of a compact object in a Schwarzschild spacetime. In particular, a
canonical system of coordinates is constructed explicitly, which exploits the
spherical symmetry of the Schwarzschild spacetime. These coordinates are based
on a relativistic generalization of the classical Andoyer variables of
Newtonian rigid body motion. This canonical setup allows us to derive
ready-to-use formulae for action-angle coordinates and gauge-invariant
Hamiltonian frequencies, which automatically include all linear-in-spin
effects. No external parameters or ad hoc choices are necessary, and the
framework can be used to find complete solutions by quadrature of generic
(bound or unbound), linear-in-spin orbits, including orbital inclination,
precession and eccentricity, as well as spin precession. The efficacy of the
formalism is demonstrated here in the context of circular orbits with arbitrary
spin and orbital precession, with the results validated against known results
in the literature.


  Combinatorial characterisations are obtained of symmetric and anti-symmetric
infinitesimal rigidity for two-dimensional frameworks with reflectional
symmetry in the case of norms where the unit ball is a quadrilateral and where
the reflection acts freely on the vertex set. At the framework level, these
characterisations are given in terms of induced monochrome subgraph
decompositions, and at the graph level they are given in terms of sparsity
counts and recursive construction sequences for the corresponding signed
quotient graphs.


  We provide a structural generalization of a theorem by Kleiman--Piene,
concerning the enumerative geometry of nodal curves in a complete linear system
|L| on a smooth projective surface S. Provided that r, the number of nodes, is
sufficiently small compared to the ampleness of the linear system, we show
that, under certain assumptions, the number of r-nodal curves passing through
points in general position on S is given by a Bell polynomial in universally
defined integers a_i(S,L), which we identify, using classical intersection
theory, as linear, integral polynomials evaluated in four basic Chern numbers.
Furthermore, we provide a decomposition of the a_i as a sum of three terms with
distinct geometric interpretations, and discuss the relationship between these
polynomials and Kazarian's Thom polynomials for multisingularities of maps.


  We consider a disordered graphene layer with anisotropic Rashba spin-orbit
coupling subjected to a longitudinal electric field. Using the linear response
theory we calculate current-induced spin polarization including in-plane normal
and parallel components with respect to the electric field direction. Unlike
the case of isotropic Rashba spin-orbit where the normal component of spin
polarization is linear in terms of Fermi energy around the Dirac point,
anisotropic Rashba spin-orbit can result in non-linear dependence of this
component at such energies within the Lifshitz points. Furthermore, we show
that anisotropic Rashba interaction allows for tuning the direction of spin
polarization from perpendicular direction to the parallel one such that for
certain values of Rashba parameters the magnitudes of both components can also
be quenched. The effect of carriers scattering on randomly distributed
non-magnetic disorders is also taken into account by calculating vertex
correction. This results in modification of spin polarization components
depending on the relative strength of Rashba parameters.


  As a promising framework for resolving partial differential equations (PDEs),
physics-informed neural networks (PINNs) have received widespread attention
from industrial and scientific fields. However, lack of expressive ability and
initialization pathology issues are found to prevent the application of PINNs
in complex PDEs. In this work, we propose Element-wise Multiplication Based
Physics-informed Neural Networks (EM-PINNs) to resolve these issues. The
element-wise multiplication operation is adopted to transform features into
high-dimensional, non-linear spaces, which effectively enhance the expressive
capability of PINNs. Benefiting from element-wise multiplication operation,
EM-PINNs can eliminate the initialization pathologies of PINNs. The proposed
structure is verified on various benchmarks. The results show that EM-PINNs
have strong expressive ability.


  With increasingly sophisticated large language models (LLMs), the potential
for abuse rises drastically. As a submission to the Swiss AI Safety Prize, we
present a novel type of metamorphic malware leveraging LLMs for two key
processes. First, LLMs are used for automatic code rewriting to evade
signature-based detection by antimalware programs. The malware then spreads its
copies via email by utilizing an LLM to socially engineer email replies to
encourage recipients to execute the attached malware. Our submission includes a
functional minimal prototype, highlighting the risks that LLMs pose for
cybersecurity and underscoring the need for further research into intelligent
malware.


  Most video super-resolution methods focus on restoring high-resolution video
frames from low-resolution videos without taking into account compression.
However, most videos on the web or mobile devices are compressed, and the
compression can be severe when the bandwidth is limited. In this paper, we
propose a new compression-informed video super-resolution model to restore
high-resolution content without introducing artifacts caused by compression.
The proposed model consists of three modules for video super-resolution:
bi-directional recurrent warping, detail-preserving flow estimation, and
Laplacian enhancement. All these three modules are used to deal with
compression properties such as the location of the intra-frames in the input
and smoothness in the output frames. For thorough performance evaluation, we
conducted extensive experiments on standard datasets with a wide range of
compression rates, covering many real video use cases. We showed that our
method not only recovers high-resolution content on uncompressed frames from
the widely-used benchmark datasets, but also achieves state-of-the-art
performance in super-resolving compressed videos based on numerous quantitative
metrics. We also evaluated the proposed method by simulating streaming from
YouTube to demonstrate its effectiveness and robustness. The source codes and
trained models are available at
https://github.com/google-research/google-research/tree/master/comisr.


  We investigate both experimentally and numerically the impact of liquid drops
on deep pools of aqueous glycerol solutions with variable pool viscosity and
air pressure. With this approach we are able to address drop impacts on
substrates that continuously transition from low-viscosity liquids to almost
solids. We show that the generic corolla spreading out from the impact point
consists of two distinct sheets, namely an ejecta sheet fed by the drop liquid
and a second sheet fed by the substrate liquid, which evolve on separated
timescales. These two sheets contribute to a varying extent to the corolla
overall dynamics and splashing, depending in particular on the viscosity ratio
between the two liquids.


  Colorization of gray-scale images relies on prior color information.
Examplar-based methods use a color image as source of such information. Then
the colors of the source image are transferred to the gray-scale image. In the
literature, this transfer is mainly guided by texture descriptors. Face images
usually contain few texture so that the common approaches frequently fail. In
this paper we propose a new method based on image morphing. This technique is
able to compute a correspondence map between images with similar shapes. It is
based on the geometric structure of the images rather than textures which is
more reliable for faces. Our numerical experiments show that our morphing based
approach clearly outperforms state-of-the-art methods.


  In the framework of bilinear control of the Schr\"odinger equation with
bounded control operators, it has been proved that the reachable set has a
dense complemement in ${\cal S}\cap {\cal H}^{2}$. Hence, in this setting,
exact quantum control in infinite dimensions is not possible. On the other hand
it is known that there is a simple choice of operators which, when applied to
an arbitrary state, generate dense orbits in Hilbert space. Compatibility of
these two results is established in this paper and, in particular, it is proved
that the closure of the reachable set of bilinear control by bounded operators
is dense in ${\cal S}\cap {\cal H}^{2}$. The requirements for controllability
in infinite dimensions are also related to the properties of the infinite
dimensional unitary group.


  We study scaling properties and topological aspects of the 2--d O(3)
non--linear $\sigma$--model on the lattice with the parametrized fixed point
action recently proposed by P.~Hasenfratz and F.~Niedermayer. The behavior of
the mass gap confirms the good properties of scaling of the fixed point action.
Concerning the topology, lattice classical solutions are proved to be very
stable under local minimization of the action; this outcome ensures the
reliability of the cooling method for the computation of the topological
susceptibility, which indeed reproduces the results of the field theoretical
approach. Disagreement is instead observed with a different approach in which
the fixed point topological charge operator is used: we argue that the
discrepancy is related to the ultraviolet dominated nature of the model.


  Van der Waals like behavior of $f(R)$ AdS black holes is revisited via two
point correlation function, which is dual to the geodesic length in the bulk.
The equation of motion constrained by the boundary condition is solved
numerically and both the effect of boundary region size and $f(R)$ gravity are
probed. Moreover, an analogous specific heat related to $\delta L$ is
introduced. It is shown that the $T-\delta L$ graphs of $f(R)$ AdS black holes
exhibit reverse van der Waals like behavior just as the $T-S$ graphs do. Free
energy analysis is carried out to determine the first order phase transition
temperature $T_*$ and the unstable branch in $T-\delta L$ curve is removed by a
bar $T=T_*$. It is shown that the first order phase transition temperature is
the same at least to the order of $10^{-10}$ for different choices of the
parameter $b$ although the values of free energy vary with $b$. Our result
further supports the former finding that charged $f(R)$ AdS black holes behave
much like RN-AdS black holes. We also check the analogous equal area law
numerically and find that the relative errors for both the cases $\theta_0=0.1$
and $\theta_0=0.2$ are small enough. The fitting functions between $ \log\mid T
-T_c\mid$ and $\log\mid\delta L-\delta L_c\mid $ for both cases are also
obtained. It is shown that the slope is around 3, implying that the critical
exponent is about $2/3$. This result is in accordance with those in former
literatures of specific heat related to the thermal entropy or entanglement
entropy.


  For human children as well as machine learning systems, a key challenge in
learning a word is linking the word to the visual phenomena it describes. We
explore this aspect of word learning by using the performance of computer
vision systems as a proxy for the difficulty of learning a word from visual
cues. We show that the age at which children acquire different categories of
words is correlated with the performance of visual classification and
captioning systems, over and above the expected effects of word frequency. The
performance of the computer vision systems is correlated with human judgments
of the concreteness of words, which are in turn a predictor of children's word
learning, suggesting that these models are capturing the relationship between
words and visual phenomena.


  We discuss the possibility of having gravity ``localized'' in dimension d in
a system where gauge bosons propagate in dimension d+1. In such a circumstance
- depending on the rate of falloff of the field strengths in d dimensions - one
might expect the gauge symmetry in d+1 dimensions to behave like a global
symmetry in d dimensions, despite the presence of gravity. Naive extrapolation
of warped long-wavelength solutions of general relativity coupled to scalars
and gauge fields suggests that such an effect might be possible. However, in
some basic realizations of such solutions in M theory, we find that this effect
does not persist microscopically. It turns over either to screening or the
Higgs mechanism at long distances in the d-dimensional description of the
system. We briefly discuss the physics of charged objects in this type of
system.


  The ultraviolet luminosity evolution of star-forming galaxies is explored
from the theoretical point of view, especially focusing on the theory of UV
energetics in simple and composite stellar populations and its relationship to
the star formation rate and other main evolutionary parameters.
  Galaxy emission below 3000 Angstroms directly correlates with actual star
formation, not depending on the total mass of the system. A straightforward
calibration is obtained, in this sense, from the theoretical models at 1600,
2000 and 2800 Angstroms, and a full comparison is carried out with IUE data and
other balloon-borne observations for local galaxies.
  The claimed role of late-type systems as prevailing contributors to the
cosmic UV background is reinforced by our results; at 2000 Angstroms Im
irregulars are found in fact nearly four orders of magnitude brighter than
ellipticals, per unit luminous mass.
  The role of dust absorption in the observation of high-redshift galaxies is
assessed, comparing model output and observed spectral energy distribution of
local galaxy samples. Similar to what we observe in our own galaxy, a quick
evolution in the dust environment might be envisaged in primeval galaxies, with
an increasing fraction of luminous matter that would escape the regions of
harder and "clumpy" dust absorption on a timescale of some 10^7 yr, comparable
with the lifetime of stars of 5-10 solar masses.


  In this paper, we consider degenerate poly-Bernoulli numbers and polynomials
associated with polylogarithmic function and p-adic invariant integral on Zp.
By using umbral calculus, we derive some identities of those numbers and
polynomials


  In this letter, dark energy is obtained using dual roles of the Ricci scalar
(as a physical field as well as geometry). Dark energy density, obtained here,
mimics phantom and the derived Friedmann equation contains a term $\rho^2_{\rm
de}/2 \lambda$ with $\rho_{\rm de}$ being the dark energy density and $\lambda$
being the cosmic tension. It is like brane-gravity inspired Friedmann equation,
which arises without using the brane-gravity theory. It is found that
acceleration is transient for $\lambda < 0$, but for $\lambda > 0$, expansion
is found to encounter big-rip problem. It is shown that this problem can be
avoided if dark energy behaves as barotropic fluid and generalized Chaplygin
gas simultaneously. Moreover, time for transition (from deceleration to
acceleration of the universe) is derived as a function of equation of state
parameter ${\rm w}_{\rm de} = p_[rm de}/\rho_{\rm de}$ with $p_{\rm de}$ being
the pressure for dark energy fluid..


  In the context of the upcoming SRG/eROSITA survey, we present an N-body
simulation-based mock catalogue for X-ray selected AGN samples. The model
reproduces the observed hard X-ray AGN luminosity function (XLF) and the soft
X-ray logN-logS from redshift 0 to 6. The XLF is reproduced to within $\pm5\%$
and the logN-logS to within $\pm20\%$. We develop a joint X-ray -- optical
extinction and classification model. We adopt a set of empirical spectral
energy distributions to predict observed magnitudes in the UV, optical and NIR.
With the latest eROSITA all sky survey sensitivity model, we create a
high-fidelity full-sky mock catalogue of X-ray AGN. It predicts their
distributions in right ascension, declination, redshift and fluxes. Using
empirical medium resolution optical spectral templates and an exposure time
calculator, we find that $1.1\times10^6$ ($4\times10^5$) fiber-hours are needed
to follow-up spectroscopically from the ground the detected X-ray AGN with an
optical magnitude $21<r<22.8$ ($22.8<r<25$) with a 4-m (8-m) class multi-object
spectroscopic facility. We find that future clustering studies will measure the
AGN bias to the percent level at redshift $z<1.2$ and should discriminate
possible scenarios of galaxy-AGN co-evolution. We predict the accuracy to which
the baryon acoustic oscillation standard ruler will be measured using X-ray
AGN: better than 3\% for AGN between redshift 0.5 to 3 and better than 1\%
using the Ly$\alpha$ forest of X-ray QSOs discovered between redshift 2 and 3.
eROSITA will provide an outstanding set of targets for future galaxy evolution
and cosmological studies.


  Unsupervised domain adaption aims to learn a powerful classifier for the
target domain given a labeled source data set and an unlabeled target data set.
To alleviate the effect of `domain shift', the major challenge in domain
adaptation, studies have attempted to align the distributions of the two
domains. Recent research has suggested that generative adversarial network
(GAN) has the capability of implicitly capturing data distribution. In this
paper, we thus propose a simple but effective model for unsupervised domain
adaption leveraging adversarial learning. The same encoder is shared between
the source and target domains which is expected to extract domain-invariant
representations with the help of an adversarial discriminator. With the labeled
source data, we introduce the center loss to increase the discriminative power
of feature learned. We further align the conditional distribution of the two
domains to enforce the discrimination of the features in the target domain.
Unlike previous studies where the source features are extracted with a fixed
pre-trained encoder, our method jointly learns feature representations of two
domains. Moreover, by sharing the encoder, the model does not need to know the
source of images during testing and hence is more widely applicable. We
evaluate the proposed method on several unsupervised domain adaption benchmarks
and achieve superior or comparable performance to state-of-the-art results.


  Spectral observations of classical T Tauri stars show a wide range of line
profiles, many of which reveal signs of matter inflow and outflow. Halpha is
the most commonly observed line profile due to its intensity, and it is highly
dependent on the characteristics of the surrounding environment of these stars.
Our aim is to analyze how the Halpha line profile is affected by the various
parameters of our model which contains both the magnetospheric and disk wind
contributions to the Halpha flux. We used a dipolar axisymmetric stellar
magnetic field to model the stellar magnetosphere and a modified Blandford &
Payne model was used in our disk wind region. A three-level atom with continuum
was used to calculate the required Hydrogen level populations. We use the
Sobolev approximation and a ray-by-ray method to calculate the integrated line
profile. Through an extensive study of the model parameter space, we have
investigated the contribution of many of the model parameters on the calculated
line profiles. Our results show that the Halpha line is strongly dependent on
the densities and temperatures inside the magnetosphere and the disk wind
region. The bulk of the flux comes, most of the time, from the magnetospheric
component for standard classical T Tauri stars parameters, but the disk wind
contribution becomes more important as the mass accretion rate, the
temperatures and densities inside the disk wind increase. We have also found
that most of the disk wind contribution to the Halpha line is emitted at the
innermost region of the disk wind. Models that take into consideration both
inflow and outflow of matter are a necessity to fully understand and describe
classical T Tauri stars.


  Context. Knowing the exact shape of the UV luminosity function of
high-redshift galaxies is important in order to understand the star formation
history of the early universe. However, the uncertainties, especially at the
faint and bright ends of the LFs, are still significant.
  Aims. In this paper, we study the UV luminosity function of redshift z = 2.5
- 4.5 galaxies in 2.38 deg^2 of ALHAMBRA data with I <= 24. Thanks to the large
area covered by ALHAMBRA, we particularly constrain the bright end of the
luminosity function. We also calculate the cosmic variance and the
corresponding bias values for our sample and derive their host dark matter halo
masses.
  Methods. We use a novel methodology based on redshift and magnitude
probability distribution functions (PDFs). This methodology robustly takes into
account the uncertainties due to redshift and magnitude errors, shot noise and
cosmic variance, and models the luminosity function in two dimensions (z; M_UV
).
  Results. We find an excess of bright ~ M*_UV galaxies as compared to the
studies based on broad-band photometric data. However, our results agree well
with the luminosity function of the magnitude-selected spectroscopic VVDS data.
We measure high bias values, b ~ 8 - 10, that are compatible with the previous
measurements considering the redshifts and magnitudes of our galaxies and
further reinforce the real high-redshift nature of our bright galaxies.
  Conclusions. We call into question the shape of the luminosity function at
its bright end; is it a double power-law as suggested by the recent broad-band
photometric studies or rather a brighter Schechter function, as suggested by
our multi-filter analysis and the spectroscopic VVDS data.


  This paper presents a smart meter phase identification algorithm for two
cases: meter-phase-label-known and meter-phase-label-unknown. To improve the
identification accuracy, a data segmentation method is proposed to exclude data
segments that are collected when the voltage correlation between smart meters
on the same phase are weakened. Then, using the selected data segments, a
hierarchical clustering method is used to calculate the correlation distances
and cluster the smart meters. If the phase labels are unknown, a
Connected-Triple-based Similarity (CTS) method is adapted to further improve
the phase identification accuracy of the ensemble clustering method. The
methods are developed and tested on both synthetic and real feeder data sets.
Simulation results show that the proposed phase identification algorithm
outperforms the state-of-the-art methods in both accuracy and robustness.


  This work deals with the presence of localized static structures in the real
line, described by relativistic real scalar fields in two spacetime dimensions.
We consider models featuring both standard and modified kinematics, where we
employ two intriguing potentials supporting defect solutions. The first
potential can transform kink into compacton in the standard framework, while
the second one is based on the inflationary Starobinsky model. Interesting
possibilities unseen in previous investigations are described, in particular,
for the case related to the Starobinsky potential. The addressed potentials are
inserted into a broader framework, and so the extended models are described by
a wider set of solutions. This investigation also reveals the presence of the
twinlike behavior for a specific compact configuration, which solves two
distinct models.


  To understand the origin of radio emission in radio-quiet AGN and
differentiate between the contributions from star formation, AGN accretion, and
jets, we have observed a nearby sample of Seyfert galaxies along with a
comparison sample of starburst galaxies using the EVLA in full-polarization
mode in the B-array configuration. The radio morphologies of the Seyfert
galaxies show lobe/bubble-like features or prominent cores in radio emission
whereas the starburst galaxies show radio emission spatially coincident with
the star-forming regions seen in optical images. There is tentative evidence
that Seyferts tend to show more polarized structures than starburst galaxies at
the resolution of our observations. We find that unlike a sample of Seyfert
galaxies hosting kilo-parsec scale radio (KSR) emission, starburst galaxies
with superwinds do not show radio-excess compared to the radio-FIR correlation.
This suggests that shock acceleration is not adequate to explain the excess
radio emission seen in Seyferts and hence most likely have a jet-related
origin. We also find that the [O III] luminosity of the Seyferts is correlated
with the off-nuclear radio emission from the lobes, whereas it is not well
correlated with the total emission which also includes the core. This suggests
strong jet-medium interaction, which in turn limits the jet/lobe extents in
Seyferts. We find that the power contribution of AGN jet, AGN accretion, and
star formation is more or less comparable in our sample of Seyfert galaxies. We
also find indications of episodic AGN activity in many of our Seyfert galaxies.


  Aims. The collimation of relativistic jets in galaxies is a poorly understood
process. Detailed radio studies of the jet collimation region have been
performed so far in few individual objects, providing important constraints for
jet formation models. However, the extent of the collimation zone as well as
the nature of the external medium possibly confining the jet are still debated.
Methods. In this article we present a multi-frequency and multi-scale analysis
of the radio galaxy NGC 315, including the use of mm-VLBI data up to 86 GHz,
aimed at revealing the evolution of the jet collimation profile. We then
consider results from the literature to compare the jet expansion profile in a
sample of 27 low-redshift sources, mainly comprising radio galaxies and BL
Lacs, classified based on the accretion properties as low-excitation (LEG) and
high-excitation (HEG) galaxies. Results.The jet collimation in NGC 315 is
completed on sub-parsec scales. A transition from a parabolic to conical jet
shape is detected at $z_{t}=0.58\pm0.28$ parsecs or ${\sim}5\times 10^3$
Schwarzschild radii ($R_{S}$) from the central engine, a distance which is much
smaller than the Bondi radius, $r_{B}{\sim}92$ $\rm pc$, estimated based on
X-ray data. The jet in this and in few other LEGs in our sample may be
initially confined by a thick disk extending out to ${\sim}10^3$-$10^4$
$R_{S}$. A comparison between the mass-scaled jet expansion profiles of all
sources indicates that jets in HEGs are surrounded by thicker disk-launched
sheaths and collimate on larger scales with respect to jets in LEGs. These
results suggest that disk winds play an important role in the jet collimation
mechanism, particularly in high-luminosity sources. The impact of winds for the
origin of the FRI/FRII dichotomy in radio galaxies is also discussed.


  As part of an extensive survey of diffuse interstellar bands (DIBs), we have
obtained optical spectra of the moderately reddened B5V star HD 62542, which is
known to have an unusual UV extinction curve of the type usually identified
with dark clouds. The typically strongest of the commonly catalogued DIBs
covered by the spectra -- those at 5780, 5797, 6270, 6284, and 6614 A -- are
essentially absent in this line of sight, in marked contrast with other lines
of sight of similar reddening. We compare the HD 62542 line of sight with
others exhibiting a range of extinction properties and molecular abundances and
interpret the weakness of the DIBs as an extreme case of deficient DIB
formation in a dense cloud whose more diffuse outer layers have been stripped
away. We comment on the challenges these observations pose for identifying the
carriers of the diffuse bands.


  This paper introduces a new open-source tool for the dynamic analyzer
Valgrind. The tool measures the amount of memory that is actively being used by
a process at any given point in time. While there exist numerous tools to
measure the memory requirements of a process, the vast majority only focuses on
metrics like resident or proportional set sizes, which include memory that was
once claimed, but is momentarily disused. Consequently, such tools do not
permit drawing conclusions about how much cache or RAM a process actually
requires at each point in time, and thus cannot be used for performance
debugging. The few tools which do measure only actively used memory, however,
have limitations in temporal resolution and introspection. In contrast, our
tool offers an easy way to compute the memory that has recently been accessed
at any point in time, reflecting how cache and RAM requirements change over
time. In particular, this tool computes the set of memory references made
within a fixed time interval before any point in time, known as the working
set, and captures call stacks for interesting peaks in the working set size. We
first introduce the tool, then we run some examples comparing the output from
our tool with similar memory tools, and we close with a discussion of
limitations


  Evaluating change in ranked term importance in a growing corpus is a powerful
tool for understanding changes in vocabulary usage. In this paper, we analyze a
corpus of free-response answers where 33,993 LGBTQ Generation Z respondents
from age 13 to 24 in the United States are asked to self-describe their sexual
orientation. We observe that certain labels, such as bisexual, pansexual, and
lesbian, remain equally important across age groups. The importance of other
labels, such as homosexual, demisexual, and omnisexual, evolve across age
groups. Although Generation Z is often stereotyped as homogenous, we observe
noticeably different label usage when self-describing sexual orientation within
it. We urge that interested parties must routinely survey the most important
sexual orientation labels to their target audience and refresh their materials
(such as demographic surveys) to reflect the constantly evolving LGBTQ
community and create an inclusive environment.


  Noise is a fundamental problem in learning theory with huge effects in the
application of Machine Learning (ML) methods, due to real world data tendency
to be noisy. Additionally, introduction of malicious noise can make ML methods
fail critically, as is the case with adversarial attacks. Thus, finding and
developing alternatives to improve robustness to noise is a fundamental problem
in ML. In this paper, we propose a method to deal with noise: mitigating its
effect through the use of data abstractions. The goal is to reduce the effect
of noise over the model's performance through the loss of information produced
by the abstraction. However, this information loss comes with a cost: it can
result in an accuracy reduction due to the missing information. First, we
explored multiple methodologies to create abstractions, using the training
dataset, for the specific case of numerical data and binary classification
tasks. We also tested how these abstractions can affect robustness to noise
with several experiments that explore the robustness of an Artificial Neural
Network to noise when trained using raw data \emph{vs} when trained using
abstracted data. The results clearly show that using abstractions is a viable
approach for developing noise robust ML methods.


  Topological insulators are bulk semiconductors that manifest in-gap massless
Dirac surface states due to the topological bulk-boundary correspondence
principle [1-3]. These surface states have been a subject of tremendous ongoing
interest, due both to their intrinsic properties and to higher order emergence
phenomena that can be achieved by manipulating the interface environment
[4-11]. Here, angle resolved photoemission (ARPES) spectromicroscopy and
supplementary scanning tunneling microscopy (STM) are performed on the model
topological insulator Bi2Se3 to investigate the interplay of crystallographic
inhomogeneity with the topologically ordered bulk and surface band structure.
Quantitative analysis methods are developed to obtain key spectroscopic
information in spite of a limited dwell time on each measured point. Band
energies are found to vary on the scale of 50 meV across the sample surface,
enabling single-sample measurements that are analogous to a multi-sample doping
series (termed a "binning series"). Focusing separately on the surface and bulk
electrons reveals a nontrivial hybridization-like interplay between
fluctuations in the surface and bulk state energetics.


  We study the superconformal index of five-dimensional SCFTs and the sphere
partition function of four-dimensional gauge theories with eight supercharges
in the presence of co-dimension two half-BPS defects. We derive a prescription
which is valid for defects which can be given a "vortex construction", i.e. can
be defined by RG flow from vortex configurations in a larger theory. We test
the prescription against known results and expected dualities. We employ our
prescription to develop a general computational strategy for defects defined by
coupling the bulk degrees of freedom to a Gauged Linear Sigma Model living in
co-dimension two.


  In this paper we prove and discuss some new $\left(H_{p},weak-L_{p}\right) $
type inequalities of maximal operators of Vilenkin-N\"orlund means with
monotone coefficients. We also apply these results to prove a.e. convergence of
such Vilenkin-N\"orlund means. It is also proved that these results are the
best possible in a special sense. As applications, both some well-known and new
results are pointed out.


  Observational evidence indicates that the role of gas is secondary to that of
gravity in the formation of the most luminous spheroids inhabiting the centres
of galaxy associations, as originally conjectured in the late 80's/early 90's.
However, attempts to explain the origin of the Fundamental Plane (FP) of
massive early-type galaxies (ETGs) -- a tilted version of the scaling relation
connecting the size, velocity dispersion and mass of virialized homologous
systems -- based on sequences of pairwise mergers, have systematically
concluded that dissipation cannot be ignored. We use controlled simulations of
the previrialization stage of galaxy groups to show that multiple collisionless
merging is capable of creating realistic first-ranked galaxies. Our mock
remnants define a thin FP that perfectly fits data from all kinds of giant ETGs
in the local volume, showing the existence of a unified relationship for these
systems. High-ranked galaxies occupy in the FP different areas than standard
objects, a segregation which is viewed essentially as zero-point offsets in the
2D correlations arising from standard projections of this plane. Our findings
make a strong case for considering hierarchical dissipationless merging a
viable route for the formation of the largest galaxies in the universe.