  Fostered by upcoming data from new generation observational campaigns, we are
about to enter a new era for the study of how galaxies form and evolve. The
unprecedented quantity of data that will be collected, from distances only
marginally grasped up to now, will require analysis tools designed to target
the specific physical peculiarities of the observed sources and handle
extremely large datasets. One powerful method to investigate the complex
astrophysical processes that govern the properties of galaxies is to model
their observed spectral energy distribution (SED) at different stages of
evolution and times throughout the history of the Universe. To address these
challenges, we have developed GalaPy, a new library for modelling and fitting
SEDs of galaxies from the X-ray to the radio band, as well as the evolution of
their components and dust attenuation/reradiation. GalaPy incorporates both
empirical and physically-motivated star formation histories, state-of-the-art
single stellar population synthesis libraries, a two-component dust model for
attenuation, an age-dependent energy conservation algorithm to compute dust
reradiation, and additional sources of stellar continuum such as synchrotron,
nebular/free-free emission and X-ray radiation from low and high mass binary
stars. GalaPy has a hybrid implementation that combines the high performance of
compiled C++ with the flexibility of Python, and exploits an object-oriented
design. It generates models on the fly without relying on templates, and
exploits fully Bayesian parameter space sampling. In this first work, we
introduce the project and showcase the photometric SED fitting tools already
available to users. The library is available on the Python Package Index (PyPI)
and comes with extensive online documentation and tutorials.


  We consider associative superalgebra realized on the smooth Grassmann-valued
functions with compact supports in R^n. The lower Hochschild cohomologies of
this superalgebra are found.


  Precise measurements of the spin structure functions of the proton
$g_1^p(x,Q^2)$ and deuteron $g_1^d(x,Q^2)$ are presented over the kinematic
range $0.0041 \leq x \leq 0.9$ and $0.18 $ GeV$^2$ $\leq Q^2 \leq 20$ GeV$^2$.
The data were collected at the HERMES experiment at DESY, in deep-inelastic
scattering of 27.6 GeV longitudinally polarized positrons off longitudinally
polarized hydrogen and deuterium gas targets internal to the HERA storage ring.
The neutron spin structure function $g_1^n$ is extracted by combining proton
and deuteron data. The integrals of $g_1^{p,d}$ at $Q^2=5$ GeV$^2$ are
evaluated over the measured $x$ range. Neglecting any possible contribution to
the $g_1^d$ integral from the region $x \leq 0.021$, a value of $0.330 \pm
0.011\mathrm{(theo.)}\pm0.025\mathrm{(exp.)}\pm 0.028$(evol.) is obtained for
the flavor-singlet axial charge $a_0$ in a leading-twist NNLO analysis.


  In this study, the impact of turbulent diffusion on mixing of biochemical
reaction models is explored by implementing and validating different models. An
original codebase called CHAD (Coupled Hydrodynamics and Anaerobic Digestion)
is extended to incorporate turbulent diffusion and validate it against results
from OpenFOAM with 2D Rayleigh-Taylor Instability and lid-driven cavity
simulations. The models are then tested for the applications with Anaerobic
Digestion - a widely used wastewater treatment method. The findings demonstrate
that the implemented models accurately capture turbulent diffusion when
provided with an accurate flow field. Specifically, a minor effect of chemical
turbulent diffusion on biochemical reactions within the anaerobic digestion
tank is observed, while thermal turbulent diffusion significantly influences
mixing. By successfully implementing turbulent diffusion models in CHAD, its
capabilities for more accurate anaerobic digestion simulations are enhanced,
aiding in optimizing the design and operation of anaerobic digestion reactors
in real-world wastewater treatment applications.


  $J/\psi$ measurements in $p+p$ and $d+Au$ collisions serve as crucial
references to understand the $J/\psi$ production in $Au+Au$ collisions at RHIC
where quark gluon plasma (QGP) is expected to be formed. They also provide
important clues to study various interesting phenomena such as the gluon
shadowing and color glass condensate. We report the latest results from PHENIX
experiment on $J/\psi$ production in $p+p$ and $d+Au$ collisions at forward,
backward and midrapidity.


  In this work, I study the non-linear QED effects to the Maxwell equations due
to the interaction of the electromagnetic waves with an external magnetic field
with arbitrary direction with respect to the direction of propagation of the
electromagnetic waves. I show that in the case when the external magnetic field
has a longitudinal component with respect to the electromagnetic wave direction
of propagation, a longitudinal photon state is generated in a magnetized
vacuum. The mixing of transverse and longitudinal photon states causes a
decrease in the group velocity with respect to the velocity of electromagnetic
waves in a vacuum. In a cosmological context, as far as a cosmic magnetic field
is concerned, the decrease of the group velocity of any form of electromagnetic
radiation would cause changes in several distance measures in cosmology. The
most affected of these distance measures turn out to be the comoving distance
and the luminosity distance. The absolute change in distance measures are
usually large but the relative changes are very small.


  We propose a new graph neural network (GNN) module, based on relaxations of
recently proposed geometric scattering transforms, which consist of a cascade
of graph wavelet filters. Our learnable geometric scattering (LEGS) module
enables adaptive tuning of the wavelets to encourage band-pass features to
emerge in learned representations. The incorporation of our LEGS-module in GNNs
enables the learning of longer-range graph relations compared to many popular
GNNs, which often rely on encoding graph structure via smoothness or similarity
between neighbors. Further, its wavelet priors result in simplified
architectures with significantly fewer learned parameters compared to competing
GNNs. We demonstrate the predictive performance of LEGS-based networks on graph
classification benchmarks, as well as the descriptive quality of their learned
features in biochemical graph data exploration tasks.


  Dual encoders and cross encoders have been widely used for image-text
retrieval. Between the two, the dual encoder encodes the image and text
independently followed by a dot product, while the cross encoder jointly feeds
image and text as the input and performs dense multi-modal fusion. These two
architectures are typically modeled separately without interaction. In this
work, we propose LoopITR, which combines them in the same network for joint
learning. Specifically, we let the dual encoder provide hard negatives to the
cross encoder, and use the more discriminative cross encoder to distill its
predictions back to the dual encoder. Both steps are efficiently performed
together in the same model. Our work centers on empirical analyses of this
combined architecture, putting the main focus on the design of the distillation
objective. Our experimental results highlight the benefits of training the two
encoders in the same network, and demonstrate that distillation can be quite
effective with just a few hard negative examples. Experiments on two standard
datasets (Flickr30K and COCO) show our approach achieves state-of-the-art dual
encoder performance when compared with approaches using a similar amount of
data.


  In brane world models our universe is considered as a brane imbedded into a
higher dimensional space. We discuss the behaviour of geodesics in the
Randall-Sundrum background and point out that free massive particles cannot
move along the brane only. The brane is repulsive, and matter will be expelled
from the brane into the extra dimension. This is rather undesirable, and hence
we study an alternative model with a non-compact extra dimension, but with an
attractive brane embedded into the higher dimensional space. We study the
linearized gravity equations and show that Newton's gravitational law is valid
on the brane also in the alternative background.


  The formation of brown dwarfs (BDs) due to the fragmentation of proto-stellar
disks undergoing pairwise encounters was investigated. High resolution allowed
the use of realistic initial disk models where both the vertical structure and
the local Jeans mass were resolved. The results show that objects with masses
ranging from giant planets to low mass stars can form during such encounters
from initially stable disks. The parameter space of initial spin-orbit
orientations and the azimuthal angles for each disk was explored. An upper
limit on the initial Toomre Q value of ~2 was found for fragmentation to occur.
Depending on the initial configuration, shocks, tidal-tail structures and mass
inflows were responsible for the condensation of disk gas. Retrograde disks
were generally more likely to fragment. When the interaction timescale was
significantly shorter than the disks' dynamical timescales, the proto-stellar
disks tended to be truncated without forming objects.
  The newly-formed objects had masses ranging from 0.9 to 127 Jupiter masses,
with the majority in the BD regime. They often resided in star-BD multiples and
in some cases also formed hierarchical orbiting systems. Most of them had large
angular momenta and highly flattened, disk-like shapes. The objects had radii
ranging from 0.1 to 10 AU. The disk gas was assumed to be locally isothermal,
appropriate for the short cooling times in extended proto-stellar disks, but
not for condensed objects. An additional case with explicit cooling that
reduced to zero for optically thick gas was simulated to test the extremes of
cooling effectiveness and it was still possible to form objects in this case.
Detailed radiative transfer is expected to lengthen the internal evolution
timescale for these objects, but not to alter our basic results.


  We describe a simple scheme that allows an agent to learn about its
environment in an unsupervised manner. Our scheme pits two versions of the same
agent, Alice and Bob, against one another. Alice proposes a task for Bob to
complete; and then Bob attempts to complete the task. In this work we will
focus on two kinds of environments: (nearly) reversible environments and
environments that can be reset. Alice will "propose" the task by doing a
sequence of actions and then Bob must undo or repeat them, respectively. Via an
appropriate reward structure, Alice and Bob automatically generate a curriculum
of exploration, enabling unsupervised training of the agent. When Bob is
deployed on an RL task within the environment, this unsupervised training
reduces the number of supervised episodes needed to learn, and in some cases
converges to a higher reward.


  This work provides a comparative analysis illustrating how Deep Learning (DL)
surpasses Machine Learning (ML) in addressing tasks within Internet of Things
(IoT), such as attack classification and device-type identification. Our
approach involves training and evaluating a DL model using a range of diverse
IoT-related datasets, allowing us to gain valuable insights into how adaptable
and practical these models can be when confronted with various IoT
configurations. We initially convert the unstructured network traffic data from
IoT networks, stored in PCAP files, into images by processing the packet data.
This conversion process adapts the data to meet the criteria of DL
classification methods. The experiments showcase the ability of DL to surpass
the constraints tied to manually engineered features, achieving superior
results in attack detection and maintaining comparable outcomes in device-type
identification. Additionally, a notable feature extraction time difference
becomes evident in the experiments: traditional methods require around 29
milliseconds per data packet, while DL accomplishes the same task in just 2.9
milliseconds. The significant time gap, DL's superior performance, and the
recognized limitations of manually engineered features, presents a compelling
call to action within the IoT community. This encourages us to shift from
exploring new IoT features for each dataset to addressing the challenges of
integrating DL into IoT, making it a more efficient solution for real-world IoT
scenarios.


  Data-Free Meta-Learning (DFML) aims to extract knowledge from a collection of
pre-trained models without requiring the original data, presenting practical
benefits in contexts constrained by data privacy concerns. Current DFML methods
primarily focus on the data recovery from these pre-trained models. However,
they suffer from slow recovery speed and overlook gaps inherent in
heterogeneous pre-trained models. In response to these challenges, we introduce
the Faster and Better Data-Free Meta-Learning (FREE) framework, which contains:
(i) a meta-generator for rapidly recovering training tasks from pre-trained
models; and (ii) a meta-learner for generalizing to new unseen tasks.
Specifically, within the module Faster Inversion via Meta-Generator, each
pre-trained model is perceived as a distinct task. The meta-generator can
rapidly adapt to a specific task in just five steps, significantly accelerating
the data recovery. Furthermore, we propose Better Generalization via
Meta-Learner and introduce an implicit gradient alignment algorithm to optimize
the meta-learner. This is achieved as aligned gradient directions alleviate
potential conflicts among tasks from heterogeneous pre-trained models.
Empirical experiments on multiple benchmarks affirm the superiority of our
approach, marking a notable speed-up (20$\times$) and performance enhancement
(1.42\% $\sim$ 4.78\%) in comparison to the state-of-the-art.


  We prove decidability of the boundedness problem for monadic least
fixed-point recursion based on positive monadic second-order (MSO) formulae
over trees. Given an MSO-formula phi(X,x) that is positive in X, it is
decidable whether the fixed-point recursion based on phi is spurious over the
class of all trees in the sense that there is some uniform finite bound for the
number of iterations phi takes to reach its least fixed point, uniformly across
all trees. We also identify the exact complexity of this problem. The proof
uses automata-theoretic techniques. This key result extends, by means of
model-theoretic interpretations, to show decidability of the boundedness
problem for MSO and guarded second-order logic (GSO) over the classes of
structures of fixed finite tree-width. Further model-theoretic transfer
arguments allow us to derive major known decidability results for boundedness
for fragments of first-order logic as well as new ones.


  High-dynamic-range (HDR) photography involves fusing a bracket of images
taken at different exposure settings in order to compensate for the low dynamic
range of digital cameras such as the ones used in smartphones. In this paper, a
method for automatically selecting the exposure settings of such images is
introduced based on the camera characteristic function. In addition, a new
fusion method is introduced based on an optimization formulation and weighted
averaging. Both of these methods are implemented on a smartphone platform as an
HDR app to demonstrate the practicality of the introduced methods. Comparison
results with several existing methods are presented indicating the
effectiveness as well as the computational efficiency of the introduced
solution.


  Modeling fractional data in various real life scenarios is a challenging
task. This paper consider situations where fractional data is observed on the
interval [0,1]. The unit-Lindley distribution has been discussed in the
literature where its support lies between 0 and 1. In this paper, we focus on
an inflated variant of the unit-Lindley distribution, where the inflation
occurs at both 0 and 1. Various properties of the inflated unit-Lindley
distribution are discussed and examined, including point estimation based on
the maximum likelihood method and interval estimation. Finally, extensive Monte
Carlo simulation and real-data analyses are carried out to compare the fit of
our proposed distribution along with some of the existing ones such as the
inflated beta and the inflated Kumaraswamy distributions.


  We discuss the formation of scalar hair on Gauss-Bonnet solitons and black
holes in 5-dimensional Anti-de Sitter (AdS) space-time. We present new results
on the static case and point out further details. We find that the presence of
the Gauss-Bonnet term has an influence on the pattern of soliton solutions for
small enough values of the electric charge. We also discuss rotating
Gauss-Bonnet black holes with and without scalar hair.


  Children typically learn the meanings of nouns earlier than the meanings of
verbs. However, it is unclear whether this asymmetry is a result of complexity
in the visual structure of categories in the world to which language refers,
the structure of language itself, or the interplay between the two sources of
information. We quantitatively test these three hypotheses regarding early verb
learning by employing visual and linguistic representations of words sourced
from large-scale pre-trained artificial neural networks. Examining the
structure of both visual and linguistic embedding spaces, we find, first, that
the representation of verbs is generally more variable and less discriminable
within domain than the representation of nouns. Second, we find that if only
one learning instance per category is available, visual and linguistic
representations are less well aligned in the verb system than in the noun
system. However, in parallel with the course of human language development, if
multiple learning instances per category are available, visual and linguistic
representations become almost as well aligned in the verb system as in the noun
system. Third, we compare the relative contributions of factors that may
predict learning difficulty for individual words. A regression analysis reveals
that visual variability is the strongest factor that internally drives verb
learning, followed by visual-linguistic alignment and linguistic variability.
Based on these results, we conclude that verb acquisition is influenced by all
three sources of complexity, but that the variability of visual structure poses
the most significant challenge for verb learning.


  This study presents a novel methodology incorporating safety constraints into
a robotic simulation during the training of deep reinforcement learning (DRL).
The framework integrates specific parts of the safety requirements, such as
velocity constraints, as specified by ISO 10218, directly within the DRL model
that becomes a part of the robot's learning algorithm. The study then evaluated
the efficiency of these safety constraints by subjecting the DRL model to
various scenarios, including grasping tasks with and without obstacle
avoidance. The validation process involved comprehensive simulation-based
testing of the DRL model's responses to potential hazards and its compliance.
Also, the performance of the system is carried out by the functional safety
standards IEC 61508 to determine the safety integrity level. The study
indicated a significant improvement in the safety performance of the robotic
system. The proposed DRL model anticipates and mitigates hazards while
maintaining operational efficiency. This study was validated in a testbed with
a collaborative robotic arm with safety sensors and assessed with metrics such
as the average number of safety violations, obstacle avoidance, and the number
of successful grasps. The proposed approach outperforms the conventional method
by a 16.5% average success rate on the tested scenarios in the simulations and
2.5% in the testbed without safety violations. The project repository is
available at https://github.com/ammar-n-abbas/sim2real-ur-gym-gazebo.


  We present an algorithm that fully reverses the shoebox image source method
(ISM), a popular and widely used room impulse response (RIR) simulator for
cuboid rooms introduced by Allen and Berkley in 1979. More precisely, given a
discrete multichannel RIR generated by the shoebox ISM for a microphone array
of known geometry, the algorithm reliably recovers the 18 input parameters.
These are the 3D source position, the 3 dimensions of the room, the
6-degrees-of-freedom room translation and orientation, and an absorption
coefficient for each of the 6 room boundaries. The approach builds on a
recently proposed gridless image source localization technique combined with
new procedures for room axes recovery and first-order-reflection
identification. Extensive simulated experiments reveal that near-exact recovery
of all parameters is achieved for a 32-element, 8.4-cm-wide spherical
microphone array and a sampling rate of 16~kHz using fully randomized input
parameters within rooms of size 2X2X2 to 10X10X5 meters. Estimation errors
decay towards zero when increasing the array size and sampling rate. The method
is also shown to strongly outperform a known baseline, and its ability to
extrapolate RIRs at new positions is demonstrated. Crucially, the approach is
strictly limited to low-passed discrete RIRs simulated using the vanilla
shoebox ISM. Nonetheless, it represents to our knowledge the first algorithmic
demonstration that this difficult inverse problem is in-principle fully
solvable over a wide range of configurations.


  Although both frequency utilization plans for VDE-SAT downlink are within the
frequency range from 156.0125 to 162.0375 MHz which belongs to the VHF maritime
mobile band, on land, this band is mostly allocated for conventional and
trunked land mobile systems by safety agencies, utilities and transportation
companies. The challenge lies in the fact that there is no existing regulatory
rule directly established for protection of the land system against the
satellite system or the like, and hence the evaluation of the potential impact
on the incumbent land systems becomes difficult if not impossible. A method
adopted in the current analysis is to place general restrictions on the
emissions from the satellite stations inferred from the existing regulatory
rules for interference protection between land systems specified by ITU and
ECC. The restrictions are expressed in terms of values of maximum allowed power
flux density (PFD) emitted by any space stations to the surface of the Earth at
all possible incident angles in a reference bandwidth that serves as a
protection mask for the land system such that the actual interference that the
land system experiences is no worse than that from another land mobile system
permitted by these regulations. This article examines the regulatory
constraints specified by ITU and ECC in these bands and its implications on the
PFD mask.


  The analyses of Titan's gravity field obtained by Cassini space mission
suggest the presence of an internal ocean beneath its icy surface. The
characterization of the geophysical parameters of the icy shell and the ocean
is important to constrain the evolution models of Titan. The knowledge of the
librations, that are periodic oscillations around a uniform rotational motion,
can bring piece of information on the interior parameters. The objective of
this paper is to study the librational response in longitude from an analytical
approach for Titan composed of a deep atmosphere, an elastic icy shell, an
internal ocean, and an elastic rocky core perturbed by the gravitational
interactions with Saturn. We start from the librational equations developed for
a rigid satellite in synchronous spin-orbit resonance. We introduce explicitly
the atmospheric torque acting on the surface computed from the Titan IPSL GCM
(Institut Pierre Simon Laplace General Circulation Model) and the periodic
deformations of elastic solid layers due to the tides. We investigate the
librational response for various interior models in order to compare and to
identify the influence of the geophysical parameters and the impact of the
elasticity. The main librations arise at two well-separated forcing frequency
ranges: low forcing frequencies dominated by the Saturnian annual and
semi-annual frequencies, and a high forcing frequency regime dominated by
Titan's orbital frequency around Saturn. We find that internal structure models
including an internal ocean with elastic solid layers lead to the same order of
libration amplitude than the oceanless models, which makes more challenging to
differentiate them by the interpretation of librational motion.


  Some features and predictions of a recently proposed model based on
diquark-antidiquark bound states are illustrated. Its ability in accomodating
newly discovered charmed resonances around 4 GeV is discussed.


  This is a status report on where PHENIX stands in terms of mapping out the
landscape of jet correlation in $p_T$, hadron species, $\sqrt{s}$. We discuss
separately high $p_T$ correlation results and low and intermediate $p_T$
correlation results. The former is sensitive to the quenching of the jet by
medium, the later allows detailed study of the medium response to the (di-)jet.


  We construct a moduli space $\mathsf{LP}_G$ of $\mathrm{SL}_2$-parameters
over $\mathbb{Q}$, and show that it has good geometric properties (e.g.
explicitly parametrized geometric connected components and smoothness). We
construct a Jacobson--Morozov morphism $\mathsf{JM}\colon
\mathsf{LP}_G\to\mathsf{WDP}_G$ (where $\mathsf{WDP}_G$ is the moduli space of
Weil--Deligne parameters considered by several other authors). We show that
$\mathsf{JM}$ is an isomorphism over a dense open of $\mathsf{WDP}_G$, that it
induces an isomorphism between the discrete loci
$\mathsf{LP}^{\mathrm{disc}}_G\to\mathsf{WDP}_G^{\mathrm{disc}}$, and that for
any $\mathbb{Q}$-algebra $A$ it induces a bijection between Frobenius
semi-simple equivalence classes in $\mathsf{LP}_G(A)$ and Frobenius semi-simple
equivalence classes in $\mathsf{WDP}_G(A)$ with constant (up to conjugacy)
monodromy operator.


  We present a computational study of the adhesive and structural properties of
the Al/Al2O3 interfaces as building blocks of the Metal-Insulator-Metal (MIM)
tunnel devices, where electron transport is accomplished via tunnelling
mechanism through the sandwiched insulating barrier. The main goal of this
paper is to understand, on the atomic scale, the role of the geometrical
details in the formation of the tunnel barrier profiles. To provide reliable
results, we carefully assess the accuracy of the traditional methods used to
examine Al/Al2O3 interfaces. These are the most widely employed
exchange-correlation functionals, LDA, PBE and PW91, the Universal Binding
Energy Relation (UBER) for predicting equilibrium interfacial distances and
adhesion energies, and the ideal work of separation as a measure of junction
stability. Finally, we perform a detailed analysis of the atomic and
interplanar relaxations in each junction. Our results imply that the structural
irregularities on the surface of the Al film have a significant contribution to
lowering the tunnel barrier height, while interplanar relaxations in the Al
film, away from the immediate interface do not have a notable impact on the
tunnelling properties. On the other hand, up to 5-7 layers of Al2O3 may be
involved in shaping the tunnel barriers. Interplanar relaxations of these
layers depend on the geometry of the interface and may result in the net
contraction by 13% relative to the corresponding thickness in the bulk oxide.
This is a significant amount as the tunnelling probability depends
exponentially on the barrier width.


  The FORS Deep Field project is a multi-colour, multi-object spectroscopic
investigation of an approx. 7 times 7 region near the south galactic pole based
mostly on observations carried out with the FORS instruments attached to the
VLT telescopes. It includes the QSO Q 0103-260 (z = 3.36). The goal of this
study is to improve our understanding of the formation and evolution of
galaxies in the young Universe. In this paper the field selection, the
photometric observations, and the data reduction are described. The source
detection and photometry of objects in the FORS Deep Field is discussed in
detail. A combined B and I selected UBgRIJKs photometric catalog of 8753
objects in the FDF is presented and its properties are briefly discussed. The
formal 50% completeness limits for point sources, derived from the co-added
images, are 25.64, 27.69, 26.86, 26.68, 26.37, 23.60 and 21.57 in U, B, g, R,
I, J and Ks (Vega-system), respectively. A comparison of the number counts in
the FORS Deep Field to those derived in other deep field surveys shows very
good agreement.


  We present cosmology results from a blinded joint analysis of cosmic shear,
$\xi_{\pm}(\vartheta)$, galaxy-galaxy weak lensing, $\Delta\!\Sigma(R)$, and
projected galaxy clustering, $w_{\rm p}(R)$, measured from the Hyper
Suprime-Cam three-year (HSC-Y3) shape catalog and the Sloan Digital Sky Survey
(SDSS) DR11 spectroscopic galaxy catalog - a 3$\times$2pt cosmology analysis.
We define luminosity-cut samples of SDSS galaxies to serve as the tracers of
$w_{\rm p}$ and as the lens samples for $\Delta\!\Sigma$ in three spectroscopic
redshift bins spanning the range $0.15<z<0.7$. For the $\xi_{\pm}$ and
$\Delta\!\Sigma$ measurements, we use a single source sample over 416 deg$^2$,
selected from HSC-Y3 based on having photometric redshifts (photo-$z$) greater
than 0.75. For cosmological parameter inference, we use Dark Emulator combined
with a halo occupation distribution prescription to model $w_{\rm p}$ and
$\Delta\!\Sigma$ down to quasi-nonlinear scales. In our baseline analysis we
employ an uninformative flat prior of the residual photo-$z$ error to model a
residual bias in the mean redshift of HSC source galaxies. We obtain a robust
constraint on the cosmological parameters for the flat $\Lambda$CDM model:
$S_8=\sigma_8(\Omega_{\rm m}/0.3)^{0.5}=0.763^{+0.040}_{-0.036}$ (68% C.I.), or
the best-constrained parameter given by $S'_8=\sigma_8(\Omega_{\rm
m}/0.3)^{0.22}=0.721\pm 0.028$, determined with about 4% fractional precision.
Our HSC-Y3 data exhibits about 2.5$\sigma$ tension with the Planck inferred
$S_8$ value for the $\Lambda$CDM model, and hints at a non-zero residual
photo-$z$ bias implying that the true mean redshift of the HSC galaxies at
$z\gtrsim 0.75$ is higher than that implied by the original photo-$z$
estimates.


  We propose the realization of photonic circuits whose dynamics is governed by
advanced-retarded differential equations. Beyond their mathematical interest,
these photonic configurations enable the implementation of quantum feedback and
feedforward without requiring any intermediate measurement. We show how this
protocol can be applied to implement interesting delay effects in the quantum
regime, as well as in the classical limit. Our results elucidate the potential
of the protocol as a promising route towards integrated quantum control systems
on a chip.


  We discuss in this paper the subleading contact interactions, or
counterterms, in the 3P0 channel of nucleon-nucleon scattering up to O(Q^3),
where, already at leading order, Weinberg's original power counting (WPC)
scheme fails to fulfill renormalization group invariance due to the singular
attraction of one-pion exchange. Treating the subleading interactions as
perturbations and using renormalization group invariance as the criterion, we
investigate whether WPC, although missing the leading order, could prescribe
correct subleading counterterms. We find that the answer is negative and,
instead, that the structure of counterterms agrees with a modified version of
naive dimensional analysis. Using 3P0 as an example, we also study the cutoffs
where the subleading potential can be iterated together with the leading one.


  The momentum operator representation of nonrelativistic anyons is developed
in the Chern - Simons formulation of fractional statistics. The connection
between anyons and the q-deformed bosonic algebra is established.


  It is well accepted that the solar cycle originates from a
magnetohydrodynamics dynamo deep inside the Sun. Many dynamo models have long
been proposed based on a lot of observational constraints. In this paper, using
342 NSO/Kitt Peak solar synoptic charts we study the solar cycle phases in
different solar latitudinal zones to set further constraints. Our results can
be summarized as follows. (1) The variability of solar polar regions' area has
a correlation with total unsigned magnetic flux in advance of 5 years. (2) The
high-latitude region mainly appears unipolar in the whole solar cycle and its
flux peak time lags sunspot cycle for 3 years. (3) For the activity belt, it is
not surprised that its phase be the same as sunspot's. (4) The flux peak time
of the low-latitude region shifts forward with an average gradient of 32.2
$day/deg$. These typical characteristics may provide some hints for
constructing an actual solar dynamo.


  We investigate the parity-violating analyzing power in neutron capture on the
proton at thermal energies in the framework of chiral effective field theory.
By combining this analysis with a previous analysis of parity violation in
proton-proton scattering, we are able to extract the size of the weak
pion-nucleon coupling constant. The uncertainty is significant and dominated by
the experimental error which is expected to be reduced soon.


  Interpretability methods for deep neural networks mainly focus on the
sensitivity of the class score with respect to the original or perturbed input,
usually measured using actual or modified gradients. Some methods also use a
model-agnostic approach to understanding the rationale behind every prediction.
In this paper, we argue and demonstrate that local geometry of the model
parameter space relative to the input can also be beneficial for improved
post-hoc explanations. To achieve this goal, we introduce an interpretability
method called "geometrically-guided integrated gradients" that builds on top of
the gradient calculation along a linear path as traditionally used in
integrated gradient methods. However, instead of integrating gradient
information, our method explores the model's dynamic behavior from multiple
scaled versions of the input and captures the best possible attribution for
each input. We demonstrate through extensive experiments that the proposed
approach outperforms vanilla and integrated gradients in subjective and
quantitative assessment. We also propose a "model perturbation" sanity check to
complement the traditionally used "model randomization" test.


  The thermally activated proton diffusion in BaZr0.9Y0.1O3-{\delta} was
studied with electrochemical impedance spectroscopy (IS) and quasi-elastic
neutron scattering (QENS) in the temperature range from 300 K to 900 K. The
diffusivities for the bulk material and the grain boundaries as obtained by IS
obey an Arrhenius law with activation energies of 0.46 eV and 1.21 eV,
respectively. The activation energies obtained by IS for the bulk are 0.26 eV
above 700 K and 0.46 eV, below 700 K. The total diffusivity as obtained by IS
is by one order of magnitude lower than the microscopic diffusivity as obtained
by QENS. The activation energies obtained by QENS are 0.13 eV above 700 K and
0.04 eV, below 700 K. At about 700 K, the diffusion constants for IS and QENS
have a remarkable crossover, suggesting two processes with different activation
energies.


  We present Contextual Query Rewrite (CQR) a dataset for multi-domain
task-oriented spoken dialogue systems that is an extension of the Stanford
dialog corpus (Eric et al., 2017a). While previous approaches have addressed
the issue of diverse schemas by learning candidate transformations (Naik et
al., 2018), we instead model the reference resolution task as a user query
reformulation task, where the dialog state is serialized into a natural
language query that can be executed by the downstream spoken language
understanding system. In this paper, we describe our methodology for creating
the query reformulation extension to the dialog corpus, and present an initial
set of experiments to establish a baseline for the CQR task. We have released
the corpus to the public [1] to support further research in this area.


  We investigate transition to synchronization in Sakaguchi-Kuramoto (SK) model
on complex networks analytically as well as numerically. Natural frequencies of
a percentage ($f$) of higher degree nodes of the network are assumed to be
correlated with their degrees and that of the remaining nodes are drawn from
some standard distribution namely Lorenz distribution. The effects of variation
of $f$ and phase frustration parameter $\alpha$ on transition to
synchronization are investigated in detail. Self-consistent equations involving
critical coupling strength ($\lambda_c$) and group angular velocity
($\Omega_c$) at the onset of synchronization have been derived analytically in
the thermodynamic limit. For the detailed investigation we considered SK model
on scale-free as well as Erd\H{o}s-R\'{e}nyi (ER) networks. Interestingly
explosive synchronization (ES) has been observed in both the networks for
different ranges of values of $\alpha$ and $f$. For scale-free networks, as the
value of $f$ is set within $10\% \leq f \leq 70\%$, the range of the values of
$\alpha$ for existence of the ES is greatly enhanced compared to the fully
degree-frequency correlated case. On the other hand, for random networks, ES
observed in a narrow window of $\alpha$ when the value of $f$ is taken within
$30\% \leq f \leq 50\%$. In all the cases critical coupling strengths for
transition to synchronization computed from the analytically derived
self-consistent equations show a very good agreement with the numerical
results.


  Combinatorial Hantzsche-Wendt groups G(n) were defined by W. Craig and P. A.
Linnell. For n = 2 it is a fundamental group of 3-dimensional oriented flat
manifold with no cyclic holonomy group. We calculate the Hilbert-Poincare
series of G(n), n > 0 with Q and F2 coefficients. Moreover, we prove that a
cohomological dimension of G(n) is equal to n + 1. Some other properties of
this group are also considered.


Final version. To appear in Discrete and Continuous Dynamical Systems - A.


  The limit theorems of discrete- and continuous-time quantum walks on the line
have been intensively studied. We show a relation among limit distributions of
quantum walks, Heun differential equations and Gauss differential equations.
Indeed, we derive the second-order Fucksian differential equations which limit
density functions of quantum walks satisfy. Moreover, using both differential
equations, we discuss a relationship between discrete- and continuous-time
quantum walks. Taking suitable limit, we can transform a Heun equation obtained
from the limit density function of the discrete-time quantum walk to a Gauss
equation given by that of the continuous-time quantum walk.


  We propose a zero-shot learning relation classification (ZSLRC) framework
that improves on state-of-the-art by its ability to recognize novel relations
that were not present in training data. The zero-shot learning approach mimics
the way humans learn and recognize new concepts with no prior knowledge. To
achieve this, ZSLRC uses advanced prototypical networks that are modified to
utilize weighted side (auxiliary) information. ZSLRC's side information is
built from keywords, hypernyms of name entities, and labels and their synonyms.
ZSLRC also includes an automatic hypernym extraction framework that acquires
hypernyms of various name entities directly from the web. ZSLRC improves on
state-of-the-art few-shot learning relation classification methods that rely on
labeled training data and is therefore applicable more widely even in
real-world scenarios where some relations have no corresponding labeled
examples for training. We present results using extensive experiments on two
public datasets (NYT and FewRel) and show that ZSLRC significantly outperforms
state-of-the-art methods on supervised learning, few-shot learning, and
zero-shot learning tasks. Our experimental results also demonstrate the
effectiveness and robustness of our proposed model.


  It is anticipated that a number of techniques to measure the top quark mass
at the LHC will yield m_top with uncertainties of about 0.5-1 percent. These
uncertainties are mostly theoretical; they are usually estimated using parton
shower Monte Carlo programs whose reliability at this level of precision is
difficult to assess. The goal of this paper is to contrast those estimates with
the results of NLO QCD computations for a few observables, often discussed in
the context of high-precision top quark mass measurements at the LHC. In
particular, we study the NLO QCD corrections to the invariant mass distribution
of a charged lepton and a B-meson in lepton+jets channels. In the dilepton
channel we investigate the invariant mass distribution of a charged lepton and
a b-jet, the average energy of the two leptons and the average energy of the
b-jets from top decays.


  Segmentation of pancreas is important for medical image analysis, yet it
faces great challenges of class imbalance, background distractions and
non-rigid geometrical features. To address these difficulties, we introduce a
Deep Q Network(DQN) driven approach with deformable U-Net to accurately segment
the pancreas by explicitly interacting with contextual information and extract
anisotropic features from pancreas. The DQN based model learns a
context-adaptive localization policy to produce a visually tightened and
precise localization bounding box of the pancreas. Furthermore, deformable
U-Net captures geometry-aware information of pancreas by learning geometrically
deformable filters for feature extraction. Experiments on NIH dataset validate
the effectiveness of the proposed framework in pancreas segmentation.


  We show that the set $\s(R)$ of shift-isomorphism classes of semidualizing
complexes over a local ring $R$ admits a nontrivial metric. We investigate the
interplay between the metric and several algebraic operations. Motivated by the
dagger duality isometry, we prove the following: If $K,L$ are homologically
bounded below and degreewise finite $R$-complexes such that $K\lotimes_R
K\lotimes_R L$ is semidualizing, then $K$ is shift-isomorphic to $R$. In
investigating the existence of nontrivial open balls in $\s(R)$, we prove that
$\s(R)$ contains elements that are not comparable in the reflexivity ordering
if and only if it contains at least three distinct elements.


  This work is aimed at demonstrating the possibility to construct new
exactly-solvable stochastic systems by use of the extended supersymmetric
quantum mechanics ($N=4 SUSY QM$) formalism. A feature of the proposed approach
consists in $N=4 SUSY QM$ the fact that probability densities and so obtained
new potentials, which enter the Langevin equation, have a parametric freedom.
The latter allows one to change the potentials form without changing the
temporal behavior of the dispersion function.


  We show that, when considering the anisotropic scaling factors and their
derivatives as affine variables, the coefficients of the heat kernel expansion
of the Dirac-Laplacian on $SU(2)$ Bianchi IX metrics are algebro-geometric
periods of motives of complements in affine spaces of unions of quadrics and
hyperplanes. We show that the motives are mixed Tate and we provide an explicit
computation of their Grothendieck classes.


  This paper discusses linearized models of hydropower plants (HPPs). First, it
reviews state-of-the-art models and discusses their non-linearities, then it
proposes suitable linearization strategies for the plant head, discharge, and
turbine torque. It is shown that neglecting the dependency of the hydroacoustic
resistance on the discharge leads to a linear formulation of the hydraulic
circuits model. For the turbine, a numerical linearization based on a
first-order Taylor expansion is proposed. Model performance is evaluated for a
medium- and a low-head HPP with a Francis and Kaplan turbine, respectively.
Perspective applications of these linear models are in the context of efficient
model predictive control of HPPs based on convex optimization.


  Let $D$ be a domain obtained by removing, out of the unit disk $\{z:|z|<1\}$,
finitely many mutually disjoint closed disks, and for each integer $n\geq 0$,
let $P_n(z)=z^n+\cdots$ be the monic $n$th-degree polynomial satisfying the
planar orthogonality condition $\int_D P_n(z)\overline{z^m}dxdy=0$, $0\leq
m<n$. Under a certain assumption on the domain $D$, we establish asymptotic
expansions and formulae that describe the behavior of $P_n(z)$ as $n\to\infty$
at every point $z$ of the complex plane. We also give an asymptotic expansion
for the squared norm $\int_D|P_n|^2dxdy$.


  Crop phenology is crucial information for crop yield estimation and
agricultural management. Traditionally, phenology has been observed from the
ground; however Earth observation, weather and soil data have been used to
capture the physiological growth of crops. In this work, we propose a new
approach for the within-season phenology estimation for cotton at the field
level. For this, we exploit a variety of Earth observation vegetation indices
(derived from Sentinel-2) and numerical simulations of atmospheric and soil
parameters. Our method is unsupervised to address the ever-present problem of
sparse and scarce ground truth data that makes most supervised alternatives
impractical in real-world scenarios. We applied fuzzy c-means clustering to
identify the principal phenological stages of cotton and then used the cluster
membership weights to further predict the transitional phases between adjacent
stages. In order to evaluate our models, we collected 1,285 crop growth ground
observations in Orchomenos, Greece. We introduced a new collection protocol,
assigning up to two phenology labels that represent the primary and secondary
growth stage in the field and thus indicate when stages are transitioning. Our
model was tested against a baseline model that allowed to isolate the random
agreement and evaluate its true competence. The results showed that our model
considerably outperforms the baseline one, which is promising considering the
unsupervised nature of the approach. The limitations and the relevant future
work are thoroughly discussed. The ground observations are formatted in an
ready-to-use dataset and will be available at
https://github.com/Agri-Hub/cotton-phenology-dataset upon publication.


  Network coding is known as a promising approach to improve wireless network
performance. How to discover the coding opportunity in relay nodes is really
important for it. There are more coding chances, there are more times it can
improve network throughput by network coding operation. In this paper, an
extended network coding opportunity discovery scheme (ExCODE) is proposed,
which is realized by appending the current node ID and all its 1-hop neighbors'
IDs to the packet. ExCODE enables the next hop relay node to know which nodes
else have already overheard the packet, so it can discover the potential coding
opportunities as much as possible. ExCODE expands the region of discovering
coding chance to n-hops, and have more opportunities to execute network coding
operation in each relay node. At last, we implement ExCODE over the AODV
protocol, and efficiency of the proposed mechanism is demonstrated with NS2
simulations, compared to the existing coding opportunity discovery scheme.


  We present a systematic study on the heavily Cr doped iron pnictides
BaFe$_{1.9-x}$Ni$_{0.1}$Cr$_{x}$As$_{2}$ by using elastic neutron scattering,
high-resolution synchrotron X-ray diffraction (XRD), resistivity and Hall
transport measurements. When the Cr concentration increases from $x=$ 0 to 0.8,
neutron diffraction experiments suggest that the collinear antiferromagnetism
persists in the whole doping range, where the N\'{e}el temperature $T_N$
coincides with the tetragonal-to-orthorhombic structural transition temperature
$T_s$, and both of them keeps around 35 K. The magnetic ordered moment, on the
other hand, increases within increasing $x$ until $x=$ 0.5, and then decreases
with further increasing $x$. Detailed refinement of the powder XRD patterns
reveals that the Cr substitutions actually stretch the FeAs$_4$ tetrahedron
along the $c-$axis and lift the arsenic height away Fe-Fe plane. Transport
results indicate that the charge carriers become more localized upon Cr doping,
then changes from electron-type to hole-type around $x=$ 0.5. Our results
suggest that the ordered moment and the ordered temperature of static magnetism
in iron pnictides can be decoupled and tuned separately by chemical doping.


  There are many sources of interference encountered in the
electroencephalogram (EEG) recordings, specifically ocular, muscular, and
cardiac artifacts. Rejection of EEG artifacts is an essential process in EEG
analysis since such artifacts cause many problems in EEG signals analysis. One
of the most challenging issues in EEG denoising processes is removing the
ocular artifacts where Electrooculographic (EOG), and EEG signals have an
overlap in both frequency and time domains. In this paper, we build and train a
deep learning model to deal with this challenge and remove the ocular artifacts
effectively. In the proposed scheme, we convert each EEG signal to an image to
be fed to a U-NET model, which is a deep learning model usually used in image
segmentation tasks. We proposed three different schemes and made our U-NET
based models learn to purify contaminated EEG signals similar to the process
used in the image segmentation process. The results confirm that one of our
schemes can achieve a reliable and promising accuracy to reduce the Mean square
error between the target signal (Pure EEGs) and the predicted signal (Purified
EEGs).


  The gauge coupling constants in the electroweak standard model can be written
as mass ratios, e.g. the coupling constant for isospin interactions
$g_2^2=2{m_W^2\over m^2}\sim 2({80\over169})^2\sim{1\over 2.3}$ with the mass
of the charged weak boson and the mass parameter characterizing the ground
state degeneracy. A theory is given which relates the two masses in such a
ratio to invariants which characterize the representations of a noncompact
nonabelian group with real rank 2. The two noncompact abelian subgroups are
operations for time and for a hyperbolicposition space in a model for
spacetime, homogeneous under dilation and Lorentz group action. The
representations of the spacetime model embed the bound state representations of
hyperbolic position space as seen in the nonrelativistic hydrogen atom.
Interactions like Coulomb or Yukawa interactions are described by Lie algebra
representation coefficients.A quantitative determination of the ratio of the
invariants for position and time related operations, determined by the
spacetime representation, gives the right order of magnitude for the gauge
coupling constants.


  Given a polyanalytic function, we show that the corresponding Toeplitz
operator on the Bergman space of the unit disc can be expressed as a quotient
of certain differential operators with holomorphic coefficients. This enables
us to obtain several operator theoretic results including a criterion for
invertibility of a Toeplitz operator.


  We study the tailoring of structured random graph ensembles to real networks,
with the objective of generating precise and practical mathematical tools for
quantifying and comparing network topologies macroscopically, beyond the level
of degree statistics. Our family of ensembles can produce graphs with any
prescribed degree distribution and any degree-degree correlation function, its
control parameters can be calculated fully analytically, and as a result we can
calculate (asymptotically) formulae for entropies and complexities, and for
information-theoretic distances between networks, expressed directly and
explicitly in terms of their measured degree distribution and degree
correlations.


  We analyze the effects of finite chemical potential on spontaneous breaking
of internal symmetries within the class of relativistic field theories
described by the linear sigma model. Special attention is paid to the emergence
of ``abnormal'' Goldstone bosons with quadratic dispersion relation. We show
that their presence is tightly connected to nonzero density of the Noether
charges, and formulate a general counting rule. The general results are
demonstrated on an SU(3)xU(1) invariant model with an SU(3)-sextet scalar
field, which describes one of the color-superconducting phases of QCD.


  It is assumed that neutrino-nucleon scattering at ultra-high energies
effectively proceeds through excitations of leptoquarks in neutrino-quark
subprocesses. This approach reproduces the behavior of the energy dependence of
the ultra-high energy neutrino-nucleon scattering cross sections and allows to
estimate masses as well as the decay widths of the involved leptoquarks. For
instance, this leads to the leptoquark mass $1353\pm230$ GeV in a way
independent on the leptoquark quantum numbers. The discovery potential of the
LHC for the leptoquarks is evaluated.


  We study the module categories of a tilted algebra C and the corresponding
cluster-tilted algebra B. In particular, we study which $\tau$-rigid C-modules
are also $\tau$-rigid B-modules.


  This study examines strategic issues in fair rank-minimizing mechanisms,
which choose an assignment that minimizes the average rank of object types to
which agents are assigned and satisfy a fairness property called equal
treatment of equals. As one of these fair mechanisms, the uniform
rank-minimizing mechanism is considered. We particularly focus on the case
where agents can refuse their assignment and obtain the outside option instead.
Without the refusal option, if the uniform rank-minimizing mechanism is used,
truth-telling is not strategically dominated by any strategies. However, if
agents have the option, then an outside option demotion strategy -- revealing
all object types as acceptable without changing the ranking of truly acceptable
ones -- strategically dominates truth-telling. Moreover, we show that adopting
this strategy may lead to inefficient assignments. To counter this, we propose
the modified uniform rank-minimizing mechanism, though it may lead agents to
strategically reduce the number of acceptable types.


  Graphene oxide (GO) is an important intermediate to prepare graphene and it
is also a versatile material with various applications. However, despite its
importance, the detailed structure of GO is still unclear. For example,
previous theoretical studies based on energetics have suggested that hydroxyl
chain is an important structural motif of GO, which, however, is found to be
contrary to nuclear magnetic resonance (NMR) experiment. In this study, we
check both thermodynamic and kinetic aspects missed previously. First
principles thermodynamics gives a free energy based stability ordering similar
to that based on energetics, and hydroxyl chain is thus thermodynamically still
favorable. At the same time, by checking the calculated vibrational
frequencies, we note that hydroxyl chain structure is also inconsistent with
infrared experiment. Therefore, kinetics during GO synthesis is expected to
make an important role in GO structure. Transition state calculations predict
large energy barriers between local minima, which suggests that experimentally
obtained GO has a kinetically constrained structure.


  This paper explores the implications of blocking duality---pioneered by
Fulkerson et al.---in the context of $p$-modulus on networks. Fulkerson's
blocking duality is an analogue on networks to the method of conjugate families
of curves in the plane. The technique presented here leads to a general
framework for studying families of objects on networks; each such family has a
corresponding dual family whose $p$-modulus is essentially the reciprocal of
the original family's. As an application, we give a modulus-based proof for the
fact that effective resistance is a metric on graphs. This proof immediately
generalizes to yield a family of graph metrics, depending on the parameter $p$,
that continuously interpolates among the shortest-path metric, the effective
resistance metric, and the mincut ultrametric. In a second application, we
establish a connection between Fulkerson's blocking duality and the
probabilistic interpretation of modulus. This connection, in turn, provides a
straightforward proof of several monotonicity properties of modulus that
generalize known monotonicity properties of effective resistance. Finally, we
use this framework to expand on a result of Lov\'asz in the context of randomly
weighted graphs.


  Open-ended learning benefits immensely from the use of symbolic methods for
goal representation as they offer ways to structure knowledge for efficient and
transferable learning. However, the existing Hierarchical Reinforcement
Learning (HRL) approaches relying on symbolic reasoning are often limited as
they require a manual goal representation. The challenge in autonomously
discovering a symbolic goal representation is that it must preserve critical
information, such as the environment dynamics. In this work, we propose a
developmental mechanism for subgoal discovery via an emergent representation
that abstracts (i.e., groups together) sets of environment states that have
similar roles in the task. We create a HRL algorithm that gradually learns this
representation along with the policies and evaluate it on navigation tasks to
show the learned representation is interpretable and results in data
efficiency.


  We introduce lattice models with explicit N=2 supersymmetry. In these
interacting models, the supersymmetry generators Q^+ and Q^- yield the
Hamiltonian H={Q^+,Q^-} on any graph. The degrees of freedom can be described
as either fermions with hard cores, or as quantum dimers. The Hamiltonian of
our simplest model contains a hopping term and a repulsive potential, as well
as the hard-core repulsion. We discuss these models from a variety of
perspectives: using a fundamental relation with conformal field theory, via the
Bethe ansatz, and using cohomology methods. The simplest model provides a
manifestly-supersymmetric lattice regulator for the supersymmetric point of the
massless 1+1-dimensional Thirring (Luttinger) model. We discuss the
ground-state structure of this same model on more complicated graphs, including
a 2-leg ladder, and discuss some generalizations.


  Recent work by Brock et al. (2018) suggests that Generative Adversarial
Networks (GANs) benefit disproportionately from large mini-batch sizes.
Unfortunately, using large batches is slow and expensive on conventional
hardware. Thus, it would be nice if we could generate batches that were
effectively large though actually small. In this work, we propose a method to
do this, inspired by the use of Coreset-selection in active learning. When
training a GAN, we draw a large batch of samples from the prior and then
compress that batch using Coreset-selection. To create effectively large
batches of 'real' images, we create a cached dataset of Inception activations
of each training image, randomly project them down to a smaller dimension, and
then use Coreset-selection on those projected activations at training time. We
conduct experiments showing that this technique substantially reduces training
time and memory usage for modern GAN variants, that it reduces the fraction of
dropped modes in a synthetic dataset, and that it allows GANs to reach a new
state of the art in anomaly detection.


  We present a theoretical construction for closest-to-classical wave packets
localized in both angular and radial coordinates and moving on a keplerian
orbit. The method produces a family of elliptical squeezed states for the
planar Coulomb problem that minimize appropriate uncertainty relations in
radial and angular coordinates. The time evolution of these states is studied
for orbits with different semimajor axes and eccentricities. The elliptical
squeezed states may be useful for a description of the motion of Rydberg wave
packets excited by short-pulsed lasers in the presence of external fields,
which experiments are attempting to produce. We outline an extension of the
method to include certain effects of quantum defects appearing in the
alkali-metal atoms used in experiments.


  A method is suggested to deduce the anisotropy in neutral pions by measuring
the azimuthal anisotropy of photons in ultra-relativistic nuclear collisions.
The ratio of the estimated anisotropy in photons to the anisotropy in neutral
pions is seen to scale with a parameter which depends on photon multiplicity
and anisotropy. This parameter can be determined from experimental data.


  The Italian government has been one of the most responsive to COVID-19
emergency, through the adoption of quick and increasingly stringent measures to
contain the outbreak. Despite this, Italy has suffered a huge human and social
cost, especially in Lombardy. The aim of this paper is dual: i) first, to
investigate the reasons of the case fatality rate (CFR) differences across
Italian 20 regions and 107 provinces, using a multivariate OLS regression
approach; and ii) second, to build a taxonomy of provinces with similar
mortality risk of COVID-19, by using the Ward hierarchical agglomerative
clustering method. I considered health system metrics, environmental pollution,
climatic conditions, demographic variables, and three ad hoc indexes that
represent the health system saturation. The results showed that overall health
care efficiency, physician density, and average temperature helped to reduce
the CFR. By the contrary, population aged 70 and above, car and firm density,
level of air pollutants (NO2, O3, PM10, and PM2.5), relative average humidity,
COVID-19 prevalence, and all three indexes of health system saturation were
positively associated with the CFR. Population density, social vertical
integration, and altitude were not statistically significant. In particular,
the risk of dying increases with age, as 90 years old and above had a
three-fold greater risk than the 80 to 89 years old and four-fold greater risk
than 70 to 79 years old. Moreover, the cluster analysis showed that the highest
mortality risk was concentrated in the north of the country, while the lowest
risk was associated with southern provinces. Finally, since prevalence and
health system saturation indexes played the most important role in explaining
the CFR variability, a significant part of the latter may have been caused by
the massive stress of the Italian health system.


  In this paper, we study a new generalization of the Lorentz cone, called the
Monotone Extended Second Order Cone (MESOC). We investigate basic properties of
MESOC including computation of its Lyapunov rank and proving its reducibility.
Moreover, we show that in an ambient space, a cylinder is an isotonic
projection set with respect to MESOC. We also examine a Nonlinear
Complementarity Problem on a cylinder, which is equivalent to a suitable Mixed
Complementarity Problem and provide a computational example illustrating
applicability of MESOC.


  The transparent formulation of explanation methods is essential for
elucidating the predictions of neural networks, which are typically black-box
models. Layer-wise Relevance Propagation (LRP) is a well-established method
that transparently traces the flow of a model's prediction backward through its
architecture by backpropagating relevance scores. However, the conventional LRP
does not fully consider the existence of skip connections, and thus its
application to the widely used ResNet architecture has not been thoroughly
explored. In this study, we extend LRP to ResNet models by introducing
Relevance Splitting at points where the output from a skip connection converges
with that from a residual block. Our formulation guarantees the conservation
property throughout the process, thereby preserving the integrity of the
generated explanations. To evaluate the effectiveness of our approach, we
conduct experiments on ImageNet and the Caltech-UCSD Birds-200-2011 dataset.
Our method achieves superior performance to that of baseline methods on
standard evaluation metrics such as the Insertion-Deletion score while
maintaining its conservation property. We will release our code for further
research at https://5ei74r0.github.io/lrp-for-resnet.page/


  We measure the tidal alignment of the major axes of Luminous Red Galaxies
(LRGs) from the Legacy Imaging Survey and use it to infer the artificial
redshift-space distortion signature that will arise from an
orientation-dependent, surface-brightness selection in the Dark Energy
Spectroscopic Instrument (DESI) survey. Using photometric redshifts to
down-weight the shape-density correlations due to weak lensing, we measure the
intrinsic tidal alignment of LRGs. Separately, we estimate the net polarization
of LRG orientations from DESI's fiber-magnitude target selection to be of order
10^-2 along the line of sight. Using these measurements and a linear tidal
model, we forecast a 0.5% fractional decrease on the quadrupole of the 2-point
correlation function for projected separations of 40-80 Mpc/h. We also use a
halo catalog from the Abacus Summit cosmological simulation suite to reproduce
this false quadrupole.


  This article presents a new three-degree-of-freedom (3-DOF) parallel
mechanism (PM) with two translations and one rotation (2T1R), designed based on
the topological design theory of the parallel mechanism using position and
orientation characteristics (POC). The PM is primarily intended for use in
package sorting and delivery. The mobile platform of the PM moves along a
translation axis, picks up objects from a conveyor belt, and tilts them to
either side of the axis. We first calculate the PM's topological
characteristics, such as the degree of freedom (DOF) and the degree of
coupling, and provide its topological analytical formula to represent the
topological information of the PM. Next, we solve the direct and inverse
kinematic models based on the kinematic modelling principle using the
topological features. The models are purely analytic and are broken down into a
series of quadratic equations, making them suitable for use in an industrial
robot. We also study the singular configurations to identify the serial and
parallel singularities. Using the decoupling properties, we size the mechanism
to address the package sorting and depositing problem using an algebraic
approach. To determine the smallest segment lengths, we use a cylindrical
algebraic decomposition to solve a system with inequalities.


  We present a systematic study on the influence of epitaxial strain and hole
concentration on the magnetic anisotropy in (Ga,Mn)As at 4.2 K. The strain was
gradually varied over a wide range from tensile to compressive by growing a
series of (Ga,Mn)As layers with 5% Mn on relaxed graded (In,Ga)As/GaAs
templates with different In concentration. The hole density, the Curie
temperature, and the relaxed lattice constant of the as-grown and annealed
(Ga,Mn)As layers turned out to be essentially unaffected by the strain.
Angle-dependent magnetotransport measurements performed at different magnetic
field strengths were used to probe the magnetic anisotropy. The measurements
reveal a pronounced linear dependence of the uniaxial out-of-plane anisotropy
on both strain and hole density. Whereas the uniaxial and cubic in-plane
anisotropies are nearly constant, the cubic out-of-plane anisotropy changes
sign when the magnetic easy axis flips from in-plane to out-of-plane. The
experimental results for the magnetic anisotropy are quantitatively compared
with calculations of the free energy based on a mean-field Zener model. An
almost perfect agreement between experiment and theory is found for the
uniaxial out-of-plane and cubic in-plane anisotropy parameters of the as-grown
samples. In addition, magnetostriction constants are derived from the
anisotropy data.


  New phase transition phenomena have recently been discovered for the
stochastic block model, for the special case of two non-overlapping symmetric
communities. This gives raise in particular to new algorithmic challenges
driven by the thresholds. This paper investigates whether a general phenomenon
takes place for multiple communities, without imposing symmetry.
  In the general stochastic block model $\text{SBM}(n,p,Q)$, $n$ vertices are
split into $k$ communities of relative size $\{p_i\}_{i \in [k]}$, and vertices
in community $i$ and $j$ connect independently with probability
$\{Q_{i,j}\}_{i,j \in [k]}$. This paper investigates the partial and exact
recovery of communities in the general SBM (in the constant and logarithmic
degree regimes), and uses the generality of the results to tackle overlapping
communities.
  The contributions of the paper are: (i) an explicit characterization of the
recovery threshold in the general SBM in terms of a new divergence function
$D_+$, which generalizes the Hellinger and Chernoff divergences, and which
provides an operational meaning to a divergence function analog to the
KL-divergence in the channel coding theorem, (ii) the development of an
algorithm that recovers the communities all the way down to the optimal
threshold and runs in quasi-linear time, showing that exact recovery has no
information-theoretic to computational gap for multiple communities, in
contrast to the conjectures made for detection with more than 4 communities;
note that the algorithm is optimal both in terms of achieving the threshold and
in having quasi-linear complexity, (iii) the development of an efficient
algorithm that detects communities in the constant degree regime with an
explicit accuracy bound that can be made arbitrarily close to 1 when a
prescribed signal-to-noise ratio (defined in term of the spectrum of
$\diag(p)Q$) tends to infinity.


  External sorting is at the core of many operations in large-scale database
systems, such as ordering and aggregation queries for large result sets,
building indexes, sort-merge joins, duplicate removal, sharding, and record
clustering. Unlike in-memory sorting, these algorithms need to work together
with the OS and the filesystem to efficiently utilize system resources and
minimize disk I/O.
  In this paper we describe ELSAR: a parallel external sorting algorithm that
uses an innovative paradigm based on a learned data distribution model. The
algorithm leverages the model to arrange the input records into mutually
exclusive, monotonic, and equi-depth partitions that, once sorted, can simply
be concatenated to form the output. This method completely eliminates the need
for multi-way file merging, which is typically used in external sorting.
  We present thorough benchmarks for uniform and skewed datasets in various
storage media, where we measure the sorting rates, size scalability, and energy
efficiency of ELSAR and other sorting algorithms. We observed that ELSAR has up
to 1.65x higher sorting rates than the next-best external sort (Nsort) on SSD
drives and 5.31x higher than the GNU coreutils' sort utility on Intel Optane
non-volatile memory. In addition, ELSAR supersedes the current winner of the
SortBenchmark for the most energy-efficient external string sorting algorithm
by an impressive margin of 41%.
  These results reinforce the premise that novel learning-enhanced algorithms
can provide remarkable performance benefits over traditional ones.


  Electric conductivity and dielectric permeability of the non-degenerate
electronic gas for the collisional plasmas under arbitrary degree of
degeneration of electron gas is found. The kinetic equation of Wigner - Vlasov
- Boltzmann with collision integral in relaxation form BGK (Bhatnagar, Gross
and Krook) in coordinate space is used. Dielectric permeability with using of
the relaxation equation in the momentum space has been received by Mermin.
Comparison with Mermin's formula has been realized. It is shown, that in the
limit when Planck's constant tends to zero expression for dielectric
permeability passes in the classical.


  We examine the process $B \to X_s \gamma$ in minimal supersymmetry (SUSY)
with general squark flavor mixings. We include all relevant next--to--leading
order (NLO) QCD corrections and dominant NLO SUSY effects from the gluino. We
find that gluino--squark corrections to down--type quark masses induce large
NLO corrections to the dominant Wilson coefficients whose size is often similar
to those at LO, especially at large $\tan\beta$. For $\mu>0$, destructive
interference, and suppression by the renormalization group running lead to a
``focusing effect'' of reducing the size of gluino corrections to the branching
ratio, and also of reducing the LO sensitivity to flavor mixings among squarks.
Constraints from $B(B \to X_s \gamma)$ on the SUSY--breaking scale can become
significantly weakened relative to the minimal flavor violation case, even, at
large $\tan\beta$, for small flavor mixings. The case of $\mu<0$ can also
becomes allowed.


  When proxies (external instruments) used to identify target structural shocks
are weak, inference in proxy-SVARs (SVAR-IVs) is nonstandard and the
construction of asymptotically valid confidence sets for the impulse responses
of interest requires weak-instrument robust methods. In the presence of
multiple target shocks, test inversion techniques require extra restrictions on
the proxy-SVAR parameters other those implied by the proxies that may be
difficult to interpret and test. We show that frequentist asymptotic inference
in these situations can be conducted through Minimum Distance estimation and
standard asymptotic methods if the proxy-SVAR can be identified by using
`strong' instruments for the non-target shocks; i.e. the shocks which are not
of primary interest in the analysis. The suggested identification strategy
hinges on a novel pre-test for the null of instrument relevance based on
bootstrap resampling which is not subject to pre-testing issues, in the sense
that the validity of post-test asymptotic inferences is not affected by the
outcomes of the test. The test is robust to conditionally heteroskedasticity
and/or zero-censored proxies, is computationally straightforward and applicable
regardless of the number of shocks being instrumented. Some illustrative
examples show the empirical usefulness of the suggested identification and
testing strategy.


  In recent years there has been a growing interest in the statistical
properties of surfaces growing under deposition of material. Yet it is clear
that a theory describing the evolution of a surface should at the same time
describe the properties of the bulk buried underneath. Clearly, the structure
of the bulk is relevant for many practical purposes, such as the transport of
electric current in devices, transport of fluids in geological formations and
stress transmission in granular systems. The present paper demonstrates
explicitly how models describing deposition can provide us with information on
the structure of the bulk. Comparison of an analytic model with a simulation of
a discrete growth model reveals an interesting long range tail in the
density-density correlation in the direction of deposition.


  We do not know how to align a very intelligent AI agent's behavior with human
interests. I investigate whether -- absent a full solution to this AI alignment
problem -- we can build smart AI agents which have limited impact on the world,
and which do not autonomously seek power. In this thesis, I introduce the
attainable utility preservation (AUP) method. I demonstrate that AUP produces
conservative, option-preserving behavior within toy gridworlds and within
complex environments based off of Conway's Game of Life. I formalize the
problem of side effect avoidance, which provides a way to quantify the side
effects an agent had on the world. I also give a formal definition of
power-seeking in the context of AI agents and show that optimal policies tend
to seek power. In particular, most reward functions have optimal policies which
avoid deactivation. This is a problem if we want to deactivate or correct an
intelligent agent after we have deployed it. My theorems suggest that since
most agent goals conflict with ours, the agent would very probably resist
correction. I extend these theorems to show that power-seeking incentives occur
not just for optimal decision-makers, but under a wide range of decision-making
procedures.


  We perform a calculation of dense and hot nuclear matter where the mean
interaction between nucleons is described by in-medium effective fields and
where we employ analytical approximations of the Fermi integrals. We generalize
a previous work where we have addressed the case of the Fermi gas model with
in-medium effective mass. In the present work, we fully treat the in-medium
interaction by considering both its contribution to the in-medium effective
fields, which can be subsumed by the mass in some cases, and to the potential
term. Our formalism is general and could be applied to relativistic and
nonrelativistic approaches. It is illustrated for different popular models --
Skyrme, nonlinear, and density-dependent relativistic mean-field models --, but
also for the metamodel, and it provides a clear understanding of the in-medium
correction to the pressure, which is present in the case of the Skyrme and
metamodel but is not for the relativistic ones. For the Fermi integrals, we
compare the analytical approximation to the, so-called, ``exact'' numerical
calculations in order to quantitatively estimate the accuracy of the
approximation.


  WloopPHI is a Python code that expands the features of WIEN2k, a
full-potential all-electron density functional theory package, by the
characterization of Weyl semimetals. It enables the calculation of the
chirality (or "monopole charge") associated with Weyl nodes and nodal lines.
The theoretical methodology for the calculation of the chirality is based on an
extended Wilson loop method and a Berry phase approach. We validate the code
using TaAs, which is a well-characterized Weyl semimetal, both theoretically
and experimentally. Afterwards, we applied the method to the characterization
of YRh$_6$Ge$_4$ and found two sets of Weyl points (ca. 0.2 eV below the Fermi
energy) together with a topological nodal line (protected by mirror symmetry)
crossing the Fermi energy and mapped their chiralities.


  Let $\mathcal{L}$ be the space of complex-valued functions $f$ on the set of
vertices $T$ of an rooted infinite tree rooted at $o$ such that the difference
of the values of $f$ at neighboring vertices remains bounded throughout the
tree, and let $\mathcal{L}_{\textbf{w}}$ be the set of functions $f\in
\mathcal{L}$ such that $|f(v)-f(v^-)|=O(|v|^{-1})$, where $|v|$ is the distance
between $o$ and $v$ and $v^-$ is the neighbor of $v$ closest to $o$. In this
article, we characterize the bounded and the compact multiplication operators
between $\mathcal{L}$ and $\mathcal{L}_{\textbf{w}}$, and provide operator norm
and essential norm estimates. Furthermore, we characterize the bounded and
compact multiplication operators between $\mathcal{L}_{\textbf{w}}$ and the
space $L^\infty$ of bounded functions on $T$ and determine their operator norm
and their essential norm. We establish that there are no isometries among the
multiplication operators between these spaces.


  It has been conjectured that varieties of general type do not admit nowhere
vanishing holomorphic one-forms. We confirm this conjecture for smooth minimal
varieties and for varieties whose Albanese variety is simple.


  We compute the exact John--Nirenberg constant of ${\rm BMO}^p((0,1))$ for
$1\le p\le 2,$ which has been known only for $p=1$ and $p=2.$ We also show that
this constant is attained in the weak-type John--Nirenberg inequality and
obtain a sharp lower estimate for the distance in ${\rm BMO}^p$ to $L^\infty.$
These results rely on sharp $L^p$- and weak-type estimates for logarithms of
$A_\infty$ weights, which in turn use the exact expressions for the
corresponding Bellman functions.


  Increasing the visibility of nighttime hazy images is challenging because of
uneven illumination from active artificial light sources and haze
absorbing/scattering. The absence of large-scale benchmark datasets hampers
progress in this area. To address this issue, we propose a novel synthetic
method called 3R to simulate nighttime hazy images from daytime clear images,
which first reconstructs the scene geometry, then simulates the light rays and
object reflectance, and finally renders the haze effects. Based on it, we
generate realistic nighttime hazy images by sampling real-world light colors
from a prior empirical distribution. Experiments on the synthetic benchmark
show that the degrading factors jointly reduce the image quality. To address
this issue, we propose an optimal-scale maximum reflectance prior to
disentangle the color correction from haze removal and address them
sequentially. Besides, we also devise a simple but effective learning-based
baseline which has an encoder-decoder structure based on the MobileNet-v2
backbone. Experiment results demonstrate their superiority over
state-of-the-art methods in terms of both image quality and runtime. Both the
dataset and source code will be available at https://github.com/chaimi2013/3R.


  Early olfactory pathway responses to the presentation of an odor exhibit
remarkably similar dynamical behavior across phyla from insects to mammals, and
frequently involve transitions among quiescence, collective network
oscillations, and asynchronous firing. We hypothesize that the time scales of
fast excitation and fast and slow inhibition present in these networks may be
the essential element underlying this similar behavior, and design an
idealized, conductance-based integrate-and-fire (I&F) model to verify this
hypothesis via numerical simulations. To better understand the mathematical
structure underlying the common dynamical behavior across species, we derive a
firing-rate (FR) model and use it to extract a slow passage through a
saddle-node-on-an-invariant-circle (SNIC) bifurcation structure. We expect this
bifurcation structure to provide new insights into the understanding of the
dynamical behavior of neuronal assemblies and that a similar structure can be
found in other sensory systems.


  Semi-annihilation is a generic feature of particle dark matter that is most
easily probed by cosmic ray experiments. We explore models where the
semi-annihilation cross section is enhanced at late times and low temperatures
by the presence of an s-channel resonance near threshold. The relic density is
then sensitive to the evolution of the dark matter temperature, and we compute
expressions for the associated Boltzmann equation valid in general
semi-annihilating models. At late times, a self-heating effect warms the dark
matter, allowing number-changing processes to remain effective long after
kinetic decoupling of the dark and visible sectors. This allows the
semi-annihilation signal today to be enhanced by up to five orders of magnitude
over the thermal relic cross section. As a case study, we apply this to a dark
matter explanation of the positron excess seen by AMS-02. We see that unlike
annihilating dark matter, our model has no difficulty fitting the data while
also giving the correct relic density. However, constraints from the CMB and
$\gamma$-rays from the galactic centre do restrict the preferred regions of
parameter space.


  Image-amplifying optical cavities offer an intriguing platform for nonlinear
optical processing of two-dimensional signals, with potential applications in
optical information processing and optically implemented artificial neural
networks. Here, we analyze the performance of a self-imaging degenerate cavity
via numerical simulations and show the existence of a cavity size-dependent
minimum spread in the transverse mode resonances. This non-degeneracy, in turn,
leads to an inherent trade-off between the amplification and the fidelity of
the intracavity image as the cavity finesse changes. Our results point to a
promising path to nonlinear image processing in a low-power, small-form-factor
optical cavity.


  In many industrial processes, such as power generation, chemical production,
and waste management, accurately monitoring industrial burner flame
characteristics is crucial for safe and efficient operation. A key step
involves separating the flames from the background through binary segmentation.
Decades of machine vision research have produced a wide range of possible
solutions, from traditional image processing to traditional machine learning
and modern deep learning methods. In this work, we present a comparative study
of multiple segmentation approaches, namely Global Thresholding, Region
Growing, Support Vector Machines, Random Forest, Multilayer Perceptron, U-Net,
and DeepLabV3+, that are evaluated on a public benchmark dataset of industrial
burner flames. We provide helpful insights and guidance for researchers and
practitioners aiming to select an appropriate approach for the binary
segmentation of industrial burner flames and beyond. For the highest accuracy,
deep learning is the leading approach, while for fast and simple solutions,
traditional image processing techniques remain a viable option.


  An additive-multiplicative magic square is a square grid of numbers whose
rows, columns, and long diagonals all have the same sum (called the magic sum)
and the same product (called the magic product). There are numerous open
problems about magic squares by Christian Boyer on multimagie.com. One such
problem is to construct or prove the impossibility of a $5 \times 5$ or $6
\times 6$ additive-multiplicative magic square of distinct positive integers.
Here, we present a possible approach to this problem and some partial results.
We observe that such a square can be described by a form determined by the
prime factorizations of its entries and that identifying these forms might be
helpful in finding such a square or ruling out specific magic products.


  Fact-checking is the process of evaluating the veracity of claims (i.e.,
purported facts). In this opinion piece, we raise an issue that has received
little attention in prior work -- that some claims are far more difficult to
fact-check than others. We discuss the implications this has for both practical
fact-checking and research on automated fact-checking, including task
formulation and dataset design. We report a manual analysis undertaken to
explore factors underlying varying claim difficulty and identify several
distinct types of difficulty. We motivate this new claim difficulty prediction
task as beneficial to both automated fact-checking and practical fact-checking
organizations.


  Most single-molecule transport experiments produce large and stochastic
datasets containing a wide range of behaviors, presenting both a challenge to
their analysis, but also an opportunity for discovering new physical insights.
Recently, several unsupervised clustering algorithms have been developed to
help extract and separate distinct features from single-molecule transport
data. However, these clustering approaches have been primarily designed and
used to extract major dataset components, and are consequently likely to
struggle with identifying very rare features and behaviors which may
nonetheless contain physically meaningful information. In this work, we thus
introduce a completely new analysis framework specifically designed for rare
event detection in single-molecule break junction data to help unlock such
information and provide a new perspective with different implicit assumptions
than clustering. Our approach leverages the concept of correlations of breaking
traces with their own history to robustly identify paths through
distance-conductance space that correspond to reproducible rare behaviors. As
both a demonstrative and important example, we focus on rare conductance
plateaus for short molecules, which can be essentially invisible when examining
raw data. We show that our grid-based correlation tools successfully and
reproducibly locate these rare plateaus in real experimental datasets,
including in situations that traditional clustering approaches find
challenging. This result enables a broader variety of molecules to be
considered in the future, and suggests that our new approach is a useful tool
for detecting rare yet meaningful behaviors in single molecule transport data
more generally.


  This work draws inspiration from three important sources of research on
dissimilarity-based clustering and intertwines those three threads into a
consistent principled functorial theory of clustering. Those three are the
overlapping clustering of Jardine and Sibson, the functorial approach of
Carlsson and M\'{e}moli to partition-based clustering, and the Isbell/Dress
school's study of injective envelopes. Carlsson and M\'{e}moli introduce the
idea of viewing clustering methods as functors from a category of metric spaces
to a category of clusters, with functoriality subsuming many desirable
properties. Our first series of results extends their theory of functorial
clustering schemes to methods that allow overlapping clusters in the spirit of
Jardine and Sibson. This obviates some of the unpleasant effects of chaining
that occur, for example with single-linkage clustering. We prove an equivalence
between these general overlapping clustering functors and projections of weight
spaces to what we term clustering domains, by focusing on the order structure
determined by the morphisms. As a specific application of this machinery, we
are able to prove that there are no functorial projections to cut metrics, or
even to tree metrics. Finally, although we focus less on the construction of
clustering methods (clustering domains) derived from injective envelopes, we
lay out some preliminary results, that hopefully will give a feel for how the
third leg of the stool comes into play.


  We study FeII and Balmer hydrogen emission lines observed simultaneously of
18 southern Be stars. We use the self-absorption-curve method (SAC) to
determine the optical depth regime of FeII emission lines and to derive first
insights on the physical properties of their forming regions.


  Cyclic boundaries are used in many branches of physics and mathematics,
typically to assist the approximation of a large space. We show that when
determining the performance of planar, fault-tolerant, topological quantum
error correction, using cyclic boundaries leads to a significant underestimate
of the logical error rate. We present cyclic and non-cyclic surface code
simulations exhibiting this discrepancy, and analytic formulae precisely
reproducing the observed behavior in the limit of low physical error. These
asymptotic formulae are then used to prove that the underestimate is
exponentially large in the code distance d at any fixed physical error rate p
below the threshold error rate p_th.


  The Fermi-LAT survey provides a large sample of blazars selected on the
strength of their inverse Compton emission. We cross-correlate the first
Fermi-LAT catalogue with the CRATES radio catalogue and use this sample to
investigate whether blazar gamma-ray luminosities are influenced by the
availability of external photons to be up-scattered. Using the 8.4 GHz flux
densities of their compact radio cores as a proxy for their jet power, we
calculate their Compton Efficiency parameters, which measure the ability of
jets to convert power in the form of ultra-relativistic electrons into Compton
gamma-rays. We find no clear differences in Compton efficiencies between BL Lac
objects and FSRQs and no anti-correlation between Compton efficiency and
synchrotron peak frequency. This suggests that the scattering of external
photons is energetically unimportant compared to the synchrotron self-Compton
process. These results contradict the predictions of the blazar sequence.


  Despite more than 30 years of searches, the compact object in Supernova (SN)
1987A has not yet been detected. We present new limits on the compact object in
SN 1987A using millimeter, near-infrared, optical, ultraviolet, and X-ray
observations from ALMA, VLT, HST, and Chandra. The limits are approximately 0.1
mJy ($0.1\times 10^{-26}$ erg s$^{-1}$ cm$^{-2}$ Hz$^{-1}$) at 213 GHz, 1 Lsun
($6\times 10^{-29}$ erg s$^{-1}$ cm$^{-2}$ Hz$^{-1}$) in optical if our
line-of-sight is free of ejecta dust, and $10^{36}$ erg s$^{-1}$ ($2\times
10^{-30}$ erg s$^{-1}$ cm$^{-2}$ Hz$^{-1}$) in 2-10 keV X-rays. Our X-ray
limits are an order of magnitude less constraining than previous limits because
we use a more realistic ejecta absorption model based on three-dimensional
neutrino-driven SN explosion models (presented in an accompanying article). The
allowed bolometric luminosity of the compact object is 22 Lsun if our
line-of-sight is free of ejecta dust, or 138 Lsun if dust-obscured. Depending
on assumptions, these values limit the effective temperature of a neutron star
to <4-8 MK and do not exclude models, which typically are in the range 3-4 MK.
For the simplest accretion model, the accretion rate for an efficiency $\eta$
is limited to $< 10^{-11} \eta^{-1}$ Msun yr$^{-1}$, which excludes most
predictions. For pulsar activity modeled by a rotating magnetic dipole in
vacuum, the limit on the magnetic field strength ($B$) for a given spin period
($P$) is $B < 10^{14} P^2$ G s$^{-2}$. By combining information about radiation
reprocessing and geometry, it is likely that the compact object is a
dust-obscured thermally-emitting neutron star, which may appear as a region of
higher-temperature ejecta dust emission.


  We present a classification of $W$ algebras and superalgebras arising in
Abelian as well as non Abelian Toda theories. Each model, obtained from a
constrained WZW action, is related with an $Sl(2)$ subalgebra (resp. $OSp(1|2)$
superalgebra) of a simple Lie algebra (resp. superalgebra) $\cg$. However, the
determination of an $U(1)_Y$ factor, commuting with $Sl(2)$ (resp. $OSp(1|2)$),
appears, when it exists, particularly useful to characterize the corresponding
$W$ algebra. The (super) conformal spin contents of each $W$ (super)algebra is
performed. The class of all the superconformal algebras (i.e. with conformal
spins $s\leq2$) is easily obtained as a byproduct of our general results.


  Generalized relative entropy, monotone Riemannian metrics, geodesic distance,
and trace distance are all known to decrease under the action of quantum
channels. We give some new bounds on, and relationships between, the maximal
contraction for these quantities.


  Epidemic containment is a major concern when confronting large-scale
infections in complex networks. Many works have been devoted to analytically
understand how to restructure the network to minimize the impact of major
outbreaks of infections at large scale. In many cases, the strategies consist
in the isolation of certain nodes, while less attention has been paid to the
intervention on links. In epidemic spreading, links inform about the
probability of carrying the contagion of the disease from infected to
susceptible individuals. Note that these states depend on the full structure of
the network, and its determination is not straightforward from the knowledge of
nodes' states. Here, we confront this challenge and propose a set of
discrete-time governing equations \rev{which} can be closed and analyzed,
assessing the contribution of links to spreading processes in complex networks.
Our approach allows a scheme for the \rev{containment} of epidemics, based on
deactivating the most important links in transmitting the disease. The model is
validated in synthetic and real networks, obtaining an accurate determination
of the epidemic incidence and the critical thresholds. Epidemic containment
based on links' deactivation promises to be an effective tool to maintain
functionality on networks while controlling the spread of diseases, as for
example in air transportation networks.


  Instanton-dyons, also known as instanton-monopoles or instanton-quarks, are
topological constituents of the instantons at nonzero temperature and holonomy.
We perform numerical simulations of the ensemble of interacting dyons for the
SU(2) pure gauge theory. Unlike previous studies, we focus on back reaction on
the holonomy and the issue of confinement. We calculate the free energy as a
function of the holonomy and the dyon densities, using standard Metropolis
Monte Carlo and integration over parameter methods. We observe that as the
temperature decreases and the dyon density grows, its minimum indeed moves from
small holonomy to the value corresponding to confinement. We then report
various parameters of the self-consistent ensembles as a function of
temperature, and investigate the role of inter-particle correlations.


  It is well known that in perturbation theory existence of quasi-degenerate
states can rearrange the order counting. For a heavy quarkonium system,
naively, enhanced effects ($l$-changing mixing effects) could contribute
already to the first-order and third-order corrections to the wave function and
the energy level, respectively, in expansion in $\alpha_s$. We present a
formulation and note that the corresponding (lowest-order) corrections vanish
due to absence of the relevant off-diagonal matrix elements. As a result, in
the quarkonium energy level and leptonic decay width, the enhanced effects are
expected to appear, respectively, in the fifth- and fourth-order corrections
and beyond.


  A narrow Gaussian excluded volume potential, which tends to a delta-function
repulsive potential in the limit of a width parameter d* going to zero, has
been used to examine the universal consequences of excluded volume interactions
on the equilibrium and linear viscoelastic properties of dilute polymer
solutions. Brownian dynamics simulations data, acquired for chains of finite
length, has been extrapolated to the limit of infinite chain length to obtain
model independent predictions. The success of the method in predicting well
known aspects of static solution properties suggests that it can be used as a
systematic means by which the influence of solvent quality on both equilibrium
and non-equilibrium properties can be studied.