  We propose an approach considering the nonextensive effects in the context of
the Verlinde theory in order to address an extended cosmological model in the
context of viscous dark energy. Specifically, this model leads to a tiny
perturbation in the dynamics of the expansion of the universe through the
generalized Friedmann equations so-called the extended $\Lambda$CDM model. From
the observational test standpoint, we make a Bayesian analysis of the models of
bulk viscosity for dark energy which follows the Eckart theory of bulk
viscosity. These models are investigated through the context of both models
$\Lambda$CDM and extended $\Lambda$CDM. The Bayesian analysis is performed
using the data of CMB Distance priors, Baryon Acoustic Oscillations
Measurements, Cosmic Chronometers, and SNe Ia distance measurements.


  In this work we perform an observational data analysis on the energy momentum
squared gravity model. Possible solutions for matter density are obtained from
the model and their cosmological implications are studied. Some recent
observational data is used to constrain model parameters using statistical
techniques. We have used the cosmic chronometer and SNe Type-Ia Riess (292)
$H(z)-z$ data-sets in our study. Along with the data-sets we have also used
baryon acoustic oscillation (BAO) peak parameter and cosmic microwave
background (CMB) peak parameter to obtain bounds on the model parameters. Joint
analysis of the data with the above mentioned parameters have been performed to
obtain better results. For the statistical analysis we have used the
minimization technique of the $\chi^{2}$ statistic. Using this tool we have
constrained the free parameters of the model. Confidence contours have been
generated for the predicted values of the free parameters at the $66\%$, $90\%$
and $99\%$ confidence levels. Finally we have compared our analysis with the
union2 data sample presented by Amanullah et al.,2010 and the recently
published Pantheon data sample. Finally a multi-component model is investigated
by adding dust to a general cosmological fluid with equation of state $w=-1/3$.
The density parameters were studied and their values were found to comply with
the observational results.


  To adopt the soft hand exoskeleton to support activities of daily livings, it
is necessary to control finger joints precisely with the exoskeleton. The
problem of controlling joints to follow a given trajectory is called the
tracking control problem. In this study, we focus on the tracking control
problem of a human finger attached with thin McKibben muscles. To achieve
precise control with thin McKibben muscles, there are two problems: one is the
complex characteristics of the muscles, for example, non-linearity, hysteresis,
uncertainties in the real world, and the other is the difficulty in accessing a
precise model of the muscles and human fingers. To solve these problems, we
adopted DreamerV2, which is a model-based reinforcement learning method, but
the target trajectory cannot be generated by the learned model. Therefore, we
propose Tracker, which is an extension of DreamerV2 for the tracking control
problem. In the experiment, we showed that Tracker can achieve an approximately
81% smaller error than PID for the control of a two-link manipulator that
imitates a part of human index finger from the metacarpal bone to the proximal
bone. Tracker achieved the control of the third joint of the human index finger
with a small error by being trained for approximately 60 minutes. In addition,
it took approximately 15 minutes, which is less than the time required for the
first training, to achieve almost the same accuracy by fine-tuning the policy
pre-trained by the user's finger after taking off and attaching thin McKibben
muscles again as the accuracy before taking off.


  We present a new class of 3D black hole initial data sets for numerical
relativity. These data sets go beyond the axisymmetric, ``gravity wave plus
rotating black hole'' single black hole data sets by creating a dynamic,
distorted hole with adjustable distortion parameters in 3D. These data sets
extend our existing test beds for 3D numerical relativity, representing the
late stages of binary black hole collisions resulting from on-axis collision or
3D spiralling coalescence, and should provide insight into the physics of such
systems. We describe the construction of these sets, the properties for a
number of example cases, and report on progress evolving them.


  We prove that for non-linear L = L(R), the Lagrangians L and \hat L give
conformally equivalent fourth-order field equations being dual to each other.
The proof represents a new application of the fact that the operator
<D'Alembertian minus R/6> is conformally invariant.


  The magnetization reversal process in thin-film ferromagnetic elements with
surface anisotropy of various shapes and sizes is investigated by means of
numerical simulation. The dependence of the perpendicular and in-plane
hysteresis loops on the element thickness and the value of the surface
anisotropy constant is obtained. For sufficiently large values of the surface
anisotropy constant the magnetization reversal of thin-film elements is shown
to occur due to the nucleation of the buckling mode. For an elongated
rectangular element the nucleation field of the buckling mode is proportional
to the absolute value of the surface anisotropy constant, and inversely
proportional to the element thickness.


  A new hydrodynamic framework for particles with spin 1/2, based solely on the
conservation laws for charge, energy, momentum and angular momentum, is
discussed.


  A longitudinal double spin asymmetry for \pi^0 production has been measured
by the PHENIX collaboration. The asymmetry is sensitive to the polarized gluon
distribution and is indicated to be positive by theoretical predictions. We
study a correlation between behavior of the asymmetry and polarized gluon
distribution in neutral and charged pion production at RHIC.


  This paper puts forward the perspective that social play spaces are
opportunities to utilise both technology and body for the benefit of community
culture and engagement. Co-located social gaming coupled with tangible
interfaces offer active participant engagement and the development of the local
video game scene. This paper includes a descriptive account of Rabble Room
Arcade, an experimental social event combining custom-built physical interface
devices and multiplayer video games.


  We investigate the mass spectra of open heavy flavor mesons in an external
constant magnetic field within QCD sum rules. Spectral ans\"atze on the
phenomenological side are proposed in order to properly take into account
mixing effects between the pseudoscalar and vector channels, and the Landau
levels of charged mesons. The operator product expansion is implemented up to
dimension-5 operators. As a result, we find for neutral D mesons a significant
positive mass shift that goes beyond simple mixing effects. In contrast,
charged D mesons are further subject to Landau level effects, which together
with the mixing effects almost completely saturate the mass shifts obtained in
our sum rule analysis.


  Following Max Planck's hypothesis of quanta (quant-ph/0012069) and the matter
wave idea of Louis de Broglie (quant-ph/9911107), Erwin Schroedinger proposed,
at the beginning of 1926, the concept of wavefunction and wave equation for it.
Though endowed with a realistic undular interpretation by its father, the
wavefunction could not be considered as a real "matter wave" and has been
provided with only abstract, formally probabilistic interpretation. In this
paper we show how the resulting "mysteries" of usual theory are solved within
the unreduced, dynamically multivalued description of the underlying,
essentially nonlinear interaction process (quant-ph/9902015, quant-ph/9902016),
without artificial modification of the Schroedinger equation. The latter is
rigorously derived instead as universal expression of unreduced interaction
complexity. Causal, totally realistic wavefunction is obtained as a dynamically
probabilistic intermediate state of a simple system with interaction performing
dynamically discrete transitions between its localised, incompatible
"realisations" ("corpuscular" states). Causal wavefunction and Schroedinger
equation are then extended to arbitrary level of world dynamics. We outline
some applications of the obtained causal description, such as genuine quantum
chaos (quant-ph/9511034-36) and realistic quantum devices (physics/0211071),
and emphasize the basic difference of the proposed dynamically multivalued
theory from dynamically single-valued imitations of causality and complexity.
The causally complete wavefunction concept, representing the unified essence of
unreduced (multivalued) complex dynamics, provides a clear distinctive feature
of realistic science, absent in any its unitary imitation.


  The study of Deep Network (DN) training dynamics has largely focused on the
evolution of the loss function, evaluated on or around train and test set data
points. In fact, many DN phenomenon were first introduced in literature with
that respect, e.g., double descent, grokking. In this study, we look at the
training dynamics of the input space partition or linear regions formed by
continuous piecewise affine DNs, e.g., networks with (leaky)ReLU
nonlinearities. First, we present a novel statistic that encompasses the local
complexity (LC) of the DN based on the concentration of linear regions inside
arbitrary dimensional neighborhoods around data points. We observe that during
training, the LC around data points undergoes a number of phases, starting with
a decreasing trend after initialization, followed by an ascent and ending with
a final descending trend. Using exact visualization methods, we come across the
perplexing observation that during the final LC descent phase of training,
linear regions migrate away from training and test samples towards the decision
boundary, making the DN input-output nearly linear everywhere else. We also
observe that the different LC phases are closely related to the memorization
and generalization performance of the DN, especially during grokking.


  The observables in a single-channel $2$-body scattering problem remain
invariant once the amplitude is multiplied by an overall energy- and
angle-dependent phase. This invariance is known as the continuum ambiguity.
Also, mostly in truncated partial wave analyses (TPWAs), discrete ambiguities
originating from complex conjugation of roots are known to occur. In this note,
it is shown that the general continuum ambiguity mixes partial waves and that
for scalar particles, discrete ambiguities are just a subset of continuum
ambiguities with a specific phase. A numerical method is outlined briefly,
which can determine the relevant connecting phases.


  We discuss quantum two-block codes, a large class of CSS codes constructed
from two commuting square matrices.Interesting families of such codes are
generalized-bicycle (GB) codes and two-block group-algebra (2BGA) codes, where
a cyclic group is replaced with an arbitrary finite group, generally
non-abelian. We present code construction and give several expressions for code
dimension, applicable depending on whether the constituent group is cyclic,
abelian, or non-abelian. This gives a simple criterion for an essentially
non-abelian 2BGA code guaranteed not to be permutation-equivalent to such a
code based on an abelian group. We also give a lower bound on the distance
which, in particular, applies to the case when a 2BGA code reduces to a
hypergraph-product code constructed from a pair of classical group codes.


  Let k be an algebraically closed field. We show that the Cremona group of all
birational transformations of the projective plane P^2 over k is not a simple
group. The strategy makes use of hyperbolic geometry, geometric group theory,
and algebraic geometry to produce elements in the Cremona group that generate
non trivial normal subgroups.


  With the increased level of distributed generation and demand response comes
the need for associated mechanisms that can perform well in the face of
increasingly complex deregulated energy market structures. Using Lagrangian
duality theory, we develop a decentralized market mechanism that ensures that,
under the guidance of a market operator, self-interested market participants:
generation companies (GenCos), distribution companies (DistCos), and
transmission companies (TransCos), reach a competitive equilibrium. We show
that even in the presence of informational asymmetries and nonlinearities (such
as power losses and transmission constraints), the resulting competitive
equilibrium is Pareto efficient.


  This tutorial covers the use of absolute astrometry, in particular from the
combination of the Hipparcos and Gaia missions, to identify faint companions to
nearby stars and to measure the masses and orbits of those companions. Absolute
astrometry has been used with increasing success to discover new planets and
brown dwarfs and to measure masses and orbits for systems with periods as long
as centuries. This tutorial summarizes the nature of the underlying astrometric
data, the approach typically used to fit orbits, and the assumptions about that
data implicit throughout the process. It attempts to provide intuition for the
sensitivity of astrometry as a function of stellar and companion properties and
how the available constraints depend on the character and quantity of data
available. This tutorial is written for someone with some background in
astronomy but with no more than a minimal acquaintance with astrometry or orbit
fitting.


  We consider the decoherence of a black hole "Schr\"odinger cat"- a non-local
superposition of a Schwarzschild black hole in two distinct locations - due to
the Hawking radiation it inevitably emits. An environment interacting with a
system acquires information about its state, e.g. about its location. The
resulting decoherence is thought to be responsible for the emergence of the
classical realm of our Universe out of the quantum substrate. However, this
view of the emergence of the classical is sometimes dismissed as a consequence
of insufficient isolation and, hence, as non-fundamental (i.e., for practical
purposes only). A black hole can never be isolated from its own Hawking
radiation environment that carries information about its location. The
resulting decoherence rate turns out to be given by a surprisingly simple
equation that, remarkably (and in contrast to known cases of decoherence), does
not involve Planck's constant $\hbar$.


  Let $M$ be a compact connected complex manifold and $G$ a connected reductive
complex affine algebraic group. Let $E_G$ be a holomorphic principal
$G$--bundle over $M$ and $T\, \subset\, G$ a torus containing the connected
component of the center of $G$. Let $N$ (respectively, $C$) be the normalizer
(respectively, centralizer) of $T$ in $G$, and let $W$ be the Weyl group $N/C$
for $T$. We prove that there is a natural bijective correspondence between the
following two:
  Torus subbundles $\mathbb T$ of ${\rm Ad}(E_G)$ such that for some (hence
every) $x\, \in\, M$, the fiber ${\mathbb T}_x$ lies in the conjugacy class of
tori in ${\rm Ad}(E_G)$ determined by $T$.
  Quadruples of the form $(E_W,\, \phi,\, E'_C,\, \tau)$, where $\phi\, :\,
E_W\, \longrightarrow\, M$ is a principal $W$--bundle, $\phi^*E_G\, \supset\,
E'_C\, \stackrel{\psi}{\longrightarrow}\, E_W$ is a holomorphic reduction of
structure group of $\phi^* E_G$ to $C$, and $$ \tau\,:\, E'_C\times N \,
\longrightarrow\, E'_C $$ is a holomorphic action of $N$ on $E'_C$ extending
the natural action of $C$ on $E'_C$, such that the composition $\psi\circ\tau$
coincides with the composition of the quotient map $E'_C\times N\,
\longrightarrow\, (E'_C/C)\times (N/C)\,=\, (E'_C\times N)/(C\times C)$ with
the natural map $(E'_C/C)\times (N/C)\, \longrightarrow\, E_W$.
  The composition of maps $E'_C\, \longrightarrow\, E_W \, \longrightarrow\, M$
defines a principal $N$--bundle on $M$. This principal $N$--bundle $E_N$ is a
reduction of structure group of $E_G$ to $N$. Given a complex connection
$\nabla$ on $E_G$, we give a necessary and sufficient condition for $\nabla$ to
be induced by a connection on $E_N$. This criterion relates Hermitian--Einstein
connections on $E_G$ and $E'_C$ in a very precise manner.


  Query relevance ranking and sentence saliency ranking are the two main tasks
in extractive query-focused summarization. Previous supervised summarization
systems often perform the two tasks in isolation. However, since reference
summaries are the trade-off between relevance and saliency, using them as
supervision, neither of the two rankers could be trained well. This paper
proposes a novel summarization system called AttSum, which tackles the two
tasks jointly. It automatically learns distributed representations for
sentences as well as the document cluster. Meanwhile, it applies the attention
mechanism to simulate the attentive reading of human behavior when a query is
given. Extensive experiments are conducted on DUC query-focused summarization
benchmark datasets. Without using any hand-crafted features, AttSum achieves
competitive performance. It is also observed that the sentences recognized to
focus on the query indeed meet the query need.


  Event sensors output a stream of asynchronous brightness changes (called
``events'') at a very high temporal rate. Previous works on recovering the lost
intensity information from the event sensor data have heavily relied on the
event stream, which makes the reconstructed images non-photorealistic and also
susceptible to noise in the event stream. We propose to reconstruct
photorealistic intensity images from a hybrid sensor consisting of a low frame
rate conventional camera, which has the scene texture information, along with
the event sensor. To accomplish our task, we warp the low frame rate intensity
images to temporally dense locations of the event data by estimating a
spatially dense scene depth and temporally dense sensor ego-motion. The results
obtained from our algorithm are more photorealistic compared to any of the
previous state-of-the-art algorithms. We also demonstrate our algorithm's
robustness to abrupt camera motion and noise in the event sensor data.


  This is the second paper in a series presenting the results from a 500
$h^{-1}$Mpc large constrained hydro-dynamical simulation of the local Universe
(SLOW). The initial conditions are based on peculiar velocities derived from
the CosmicFlows-2 catalogue. The inclusion of galaxy formation treatment,
allows to directly predict observable properties of the Intra-Cluster Medium
(ICM) within galaxy clusters. Comparing the properties of observed galaxy
clusters within the local Universe with the properties of their simulated
counterparts, enables us to assess the effectiveness of the initial condition
constraints in accurately replicating the non-linear properties of the largest,
collapsed objects within the simulation. Based on the combination of several,
publicly available surveys, we identified 45 local Universe galaxy clusters in
SLOW, including the 13 most massive from the Planck SZ catalog and 70% of those
with $M_{500} > 2\times 10^{14}$ M$_{\odot}$. We then derived the probability
of the cross identification based on mass, X-ray luminosity, temperature and
Compton-y by comparing it to a random selection. In relation to previous
constrained simulations of the local volume, we found in SLOW a much larger
amount of replicated galaxy clusters, where their simulation based mass
prediction falls within the uncertainties of the observational mass estimates.
Comparing the median observed and simulated masses of our cross identified
sample allows to independently deduce a hydrostatic mass bias of
$(1-b)\approx0.87$. The SLOW constrained simulation of the local Universe
faithfully reproduces numerous fundamental characteristics of the galaxy
clusters within our local neighbourhood, opening a new avenue for studying the
formation and evolution of a large set of individual galaxy clusters as well as
testing our understanding of physical processes governing the ICM.


  We consider the real part $\Re(\zeta(s))$ of the Riemann zeta-function
$\zeta(s)$ in the half-plane $\Re(s) \ge 1$. We show how to compute accurately
the constant $\sigma_0 = 1.19\ldots$ which is defined to be the supremum of
$\sigma$ such that $\Re(\zeta(\sigma+it))$ can be negative (or zero) for some
real $t$. We also consider intervals where $\Re(\zeta(1+it)) \le 0$ and show
that they are rare. The first occurs for $t$ approximately 682112.9, and has
length about 0.05. We list the first fifty such intervals.


  We investigate the strain effect along armchair and zigzag directions on the
tunneling transport of Dirac fermions in graphene laser barrier through a time
dependent potential along y-axis. Our system is composed of three regions and
the central one is subjected to a deformation of strength S. Based on Dirac
equation and the Floquet approach, we determine the eigenvalues and
eigenspinors for each region. Using the boundary conditions at interfaces
together with the transfer matrix method we identify the transmission in the
different Floquet sideband states as function of the physical parameters. In
the strainless case, we show that the transmisson of central band decreases for
smaller values of the barrier width and rapidly oscillates with different
amplitude for larger ones. Whereas the transmission for the first sidebands
increases from zero and shows a damped oscillatory profile. It is found that
the number of oscillations in all transmission channels reduces with increasing
the strength of armchair strain but becomes more important by switching the
deformation to zigzag. Moreover, it is observed the appearance of Fano type
resonance peaks by altering the amplitude and the frequency of the laser field.


  This paper introduces a novel paradigm to impute missing data that combines a
decision tree with an auto-associative neural network (AANN) based model and a
principal component analysis-neural network (PCA-NN) based model. For each
model, the decision tree is used to predict search bounds for a genetic
algorithm that minimize an error function derived from the respective model.
The models' ability to impute missing data is tested and compared using HIV
sero-prevalance data. Results indicate an average increase in accuracy of 13%
with the AANN based model's average accuracy increasing from 75.8% to 86.3%
while that of the PCA-NN based model increasing from 66.1% to 81.6%.


  We study the negative gradient flow of the spinorial energy functional
(introduced by Ammann, Wei{\ss}, and Witt) on 3-dimensional Berger spheres. For
a certain class of spinors we show that the Berger spheres collapse to a
2-dimensional sphere. Moreover, for special cases, we prove that the
volume-normalized standard 3-sphere together with a Killing spinor is a stable
critical point of the volume-normalized version of the flow. Our results also
include an example of a critical point of the volume-normalized flow on the
3-sphere, which is not a Killing spinor.


  Lee-Wick-like scalar model near a Dirichlet plate is considered in this work.
The modified propagator for the scalar field due to the presence of a Dirichlet
boundary is computed, and the interaction between the plate and a point-like
scalar charge is analysed. The non-validity of the image method is investigated
and the results are compared with the corresponding ones obtained for the
Lee-Wick gauge field and for the standard Klein-Gordon field.


  It has long been known that strings wound around incontractible cycles can
play a vital role in cosmology. In particular, in a spacetime with toroidal
spatial hypersurfaces, the dynamics of the winding modes may help yield three
large spatial dimensions. However, toroidal compactifications are
phenomenologically unrealistic. In this paper we therefore take a first step
toward extending these cosmological considerations to $D$-dimensional toroidal
orbifolds. We use numerical simulation to study the timescales over which
"pseudo-wound" strings unwind on these orbifolds with trivial fundamental
group. We show that pseudo-wound strings can persist for many ``Hubble times''
in some of these spaces, suggesting that they may affect the dynamics in the
same way as genuinely wound strings. We also outline some possible extensions
that include higher-dimensional wrapped branes.


  We studied the edge states and transverse electron focusing in the presence
of spin-orbit interaction in a two dimensional electron gas. Assuming strong
spin-orbit coupling we derived semiclassical quantization conditions to
describe the dispersion of the edge states. Using the dispersion relation we
then make predictions about certain properties of the focusing spectrum.
Comparison of our analytical results with quantum mechanical transport
calculations reveals that certain features of the focusing spectrum can be
quite well understood in terms of the interference of the edge states while the
explanation of other features seems to require a different approach.


  Quantum annealing is getting increasing attention in combinatorial
optimization. The quantum processing unit by D-Wave is constructed to
approximately solve Ising models on so-called Chimera graphs. Ising models are
equivalent to quadratic unconstrained binary optimization (QUBO) problems and
maximum cut problems on the associated graphs. We have tailored branch-and-cut
as well as semidefinite programming algorithms for solving Ising models for
Chimera graphs to provable optimality and use the strength of these approaches
for comparing our solution values to those obtained on the current quantum
annealing machine D-Wave 2000Q. This allows for the assessment of the quality
of solutions produced by the D-Wave hardware. It has been a matter of
discussion in the literature how well the D-Wave hardware performs at its
native task, and our experiments shed some more light on this issue.


  The semantic capabilities of language models (LMs) have the potential to
enable rich analytics and reasoning over vast knowledge corpora. Unfortunately,
existing systems lack high-level abstractions to perform semantic queries at
scale. We introduce semantic operators, a declarative programming interface
that extends the relational model with composable AI-based operations for
semantic queries over datasets (e.g., sorting or aggregating records using
natural language criteria). Each operator can be implemented and optimized in
multiple ways, opening a rich space for execution plans similar to relational
operators. We implement our operators and several optimizations for them in
LOTUS, an open-source query engine with a Pandas-like API.
  We demonstrate LOTUS' effectiveness across a series of real applications,
including fact-checking, extreme multi-label classification, and search. We
find that LOTUS' programming model is highly expressive, capturing
state-of-the-art query pipelines with low development overhead. Specifically,
on the FEVER dataset, LOTUS' programs can reproduce FacTool, a recent
state-of-the-art fact-checking pipeline, in few lines of code, and implement a
new pipeline that improves accuracy by $9.5\%$, while offering $7-34\times$
lower execution time. In the extreme multi-label classification task on the
BioDEX dataset, LOTUS reproduces state-of-the art result quality with its join
operator, while providing an efficient algorithm that runs $800\times$ faster
than a naive join. In the search and ranking application, LOTUS allows a simple
composition of operators to achieve $5.9 - 49.4\%$ higher nDCG@10 than the
vanilla retriever and re-ranker, while also providing query efficiency, with
$1.67 - 10\times$ lower execution time than LM-based ranking methods used by
prior works. LOTUS is publicly available at
https://github.com/stanford-futuredata/lotus.


  Let h be a Real bundle, in the sense of Atiyah, over a space X. This is a
complex vector bundle together with an involution which is compatible with
complex conjugation. We use the fact that BU is equipped with a structure of
conjugation space, as defined by Hausmann, Holm, and Puppe, to construct
equivariant Chern classes in the Z/2-equivariant cohomology of X with twisted
integer coefficients. We show that these classes determine the
(non-equivariant) Chern classes of h, forgetting the involution on X, and the
Stiefel-Whitney classes of the real bundle of fixed points.


  Transient cloud servers such as Amazon Spot instances, Google Preemptible
VMs, and Azure Low-priority batch VMs, can reduce cloud computing costs by as
much as $10\times$, but can be unilaterally preempted by the cloud provider.
Understanding preemption characteristics (such as frequency) is a key first
step in minimizing the effect of preemptions on application performance,
availability, and cost. However, little is understood about temporally
constrained preemptions---wherein preemptions must occur in a given time
window. We study temporally constrained preemptions by conducting a large scale
empirical study of Google's Preemptible VMs (that have a maximum lifetime of 24
hours), develop a new preemption probability model, new model-driven resource
management policies, and implement them in a batch computing service for
scientific computing workloads. Our statistical and experimental analysis
indicates that temporally constrained preemptions are not uniformly
distributed, but are time-dependent and have a bathtub shape. We find that
existing memoryless models and policies are not suitable for temporally
constrained preemptions. We develop a new probability model for bathtub
preemptions, and analyze it through the lens of reliability theory. To
highlight the effectiveness of our model, we develop optimized policies for job
scheduling and checkpointing. Compared to existing techniques, our model-based
policies can reduce the probability of job failure by more than $2\times$. We
also implement our policies as part of a batch computing service for scientific
computing applications, which reduces cost by $5\times$ compared to
conventional cloud deployments and keeps performance overheads under $3\%$.


  We use the Barnard 68 dark globule as a test case for a spherically symmetric
PDR model exposed to low-UV radiation fields. With a roughly spherical
morphology and an accurately determined density profile, Barnard 68 is ideal
for this purpose. The processes governing the energy balance in the cloud
surface are studied in detail. We compare the spherically symmetric PDR model
by Stoerzer, Stutzki & Sternberg (1996) to observations of the three lowest
rotational transitions of 12CO, 13CO J = 2-1 and J = 3-2 as well as the [CI]
3P_1-3P_0 fine structure transition. We study the role of Polycyclic Aromatic
Hydrocarbons (PAHs) in the chemical network of the PDR model and consider the
impact of depletion as well as of a variation of the external FUV field. We
find it difficult to simultaneously model the observed 12CO and 13CO emission.
The 12CO and [CI] emission can be explained by a PDR model with a external FUV
field of 1-0.75 chi_0, but this model fails to reproduce the observed 13CO by a
factor of ~2. Adding PAHs to the chemical network increases the [CI] emission
by 50% in our model but makes [CII] very faint. CO depletion only slightly
reduces the 12CO and 13CO line intensity (by <10% and <20%, respectively).
Predictions for the [CII] 2P_3/2-2P_1/2, [CI] 3P_2-3P_1 and 12CO J= 5-4 and 4-3
transitions are presented. This allows a test of our model with future
observations (APEX, NANTEN2, HERSCHEL, SOFIA).


  It is shown that under reasonable assumptions a Drake-style equation can be
obtained for the probability that our universe is the result of a deliberate
simulation. Evaluating loose bounds for certain terms in the equation shows
that the probability is unlikely to be as high as previously reported in the
literature, especially in a scenario where the simulations are recursive.
Furthermore, we investigate the possibility of eavesdropping from the outside
of such a simulation and introduce a general attack that can circumvent
attempts at quantum cryptography inside the simulation, even if the quantum
properties of the simulation are genuine.


  An introduction to Extremal Optimization written for the Computer Simulation
Column in ``Computing in Science and Engineering'' (CISE).


  This paper provides a tutorial discussion on path analysis structure with
concept of structural equation modelling (SEM). The paper delivers an
introduction to path analysis technique and explain to how to deal with
analyzing the data with this kind of statistical methodology especially with a
mediator in the research model. The intended audience is statisticians,
mathematicians, or methodologists who either know about SEM or simple basic
statistics especially in regression and linear/nonlinear modeling, and Ph.D.
students in statistics, mathematics, management, psychology, and even computer
science.


  We study the real, bounded-variables process (X_n) defined by a k-term
recurrence relation X_{n+k} ={\phi}(X_n, ... , X_{n+k-1}). We prove the decay
of correlations, mainly under purely analytic hypotheses concerning the
function {\phi} and its partial derivatives.


  The recent outbreak of COVID-19 has led to urgent needs for reliable
diagnosis and management of SARS-CoV-2 infection. As a complimentary tool,
chest CT has been shown to be able to reveal visual patterns characteristic for
COVID-19, which has definite value at several stages during the disease course.
To facilitate CT analysis, recent efforts have focused on computer-aided
characterization and diagnosis, which has shown promising results. However,
domain shift of data across clinical data centers poses a serious challenge
when deploying learning-based models. In this work, we attempt to find a
solution for this challenge via federated and semi-supervised learning. A
multi-national database consisting of 1704 scans from three countries is
adopted to study the performance gap, when training a model with one dataset
and applying it to another. Expert radiologists manually delineated 945 scans
for COVID-19 findings. In handling the variability in both the data and
annotations, a novel federated semi-supervised learning technique is proposed
to fully utilize all available data (with or without annotations). Federated
learning avoids the need for sensitive data-sharing, which makes it favorable
for institutions and nations with strict regulatory policy on data privacy.
Moreover, semi-supervision potentially reduces the annotation burden under a
distributed setting. The proposed framework is shown to be effective compared
to fully supervised scenarios with conventional data sharing instead of model
weight sharing.


  For a finitely presented group, the word problem asks for an algorithm which
declares whether or not words on the generators represent the identity. The
Dehn function is a complexity measure of a direct attack on the word problem by
applying the defining relations. Dison & Riley showed that a "hydra phenomenon"
gives rise to novel groups with extremely fast growing (Ackermannian) Dehn
functions. Here we show that nevertheless, there are efficient (polynomial
time) solutions to the word problems of these groups. Our main innovation is a
means of computing efficiently with enormous integers which are represented in
compressed forms by strings of Ackermann functions.


  For a pair of points in a smooth locally convex surface in 3-space, its
mid-plane is the plane containing its mid-point and the intersection line of
the corresponding pair of tangent planes. In this paper we show that the limit
of mid-planes when one point tends to the other along a direction is the
Transon plane of the direction. Moreover, the limit of the envelope of
mid-planes is non-empty for at most six directions, and, in this case, it
coincides with the center of the Moutard's quadric. These results establish an
unexpected connection between these classical notions of affine differential
geometry and the apparently unrelated concept of envelope of mid-planes. We
call the limit of envelope of mid-planes the affine mid-planes evolute and
prove that, under some generic conditions, it is a regular surface in 3-space.


  We study generalized Bayesian inference under misspecification, i.e. when the
model is 'wrong but useful'. Generalized Bayes equips the likelihood with a
learning rate $\eta$. We show that for generalized linear models (GLMs),
$\eta$-generalized Bayes concentrates around the best approximation of the
truth within the model for specific $\eta \neq 1$, even under severely
misspecified noise, as long as the tails of the true distribution are
exponential. We derive MCMC samplers for generalized Bayesian lasso and
logistic regression and give examples of both simulated and real-world data in
which generalized Bayes substantially outperforms standard Bayes.


  To effectively manage increasing knowledge graphs in various domains, a hot
research topic, knowledge graph storage management, has emerged. Existing
methods are classified to relational stores and native graph stores. Relational
stores are able to store large-scale knowledge graphs and convenient in
updating knowledge, but the query performance weakens obviously when the
selectivity of a knowledge graph query is large. Native graph stores are
efficient in processing complex knowledge graph queries due to its index-free
adjacent property, but they are inapplicable to manage a large-scale knowledge
graph due to limited storage budgets or inflexible updating process. Motivated
by this, we propose a dual-store structure which leverages a graph store to
accelerate the complex query process in the relational store. However, it is
challenging to determine what data to transfer from relational store to graph
store at what time. To address this problem, we formulate it as a Markov
Decision Process and derive a physical design tuner DOTIL based on
reinforcement learning. With DOTIL, the dual-store structure is adaptive to
dynamic changing workloads. Experimental results on real knowledge graphs
demonstrate that our proposed dual-store structure improves query performance
up to average 43.72% compared with the most commonly used relational stores.


  We present a multifrequency study of the transient $\gamma$-ray source 4FGL
J1544.3-0649, a blazar that exhibited a remarkable behaviour raising from the
state of an anonymous mid-intensity radio source, never detected at high
energies, to that of one of the brightest extreme blazars in the X-ray and
$\gamma$-ray sky. Our analysis shows that the averaged $\gamma$-ray spectrum is
well described by a powerlaw with a photon index of $1.87\pm0.04$, while the
flux above 100 MeV is $(8.0\pm0.9)\times10^{-9}\:{\rm photon\: cm^{-2}\:
s^{-1}}$, which increases during the active state of the source. The X-ray flux
and spectral slope are both highly variable, with the highest 2-10 keV flux
reaching $(1.28\pm0.05)\times10^{-10}\:{\rm erg\:cm^{-2}\:s^{-1}}$. On several
observations the X-ray spectrum hardened to the point implying as SED peak
moving to energies larger than 10 keV. As in many extreme blazars the broadband
spectral energy distribution can be described by a homogeneous one-zone
synchrotron-self-Compton leptonic model. We briefly discuss the potential
implications for high-energy multi-messenger astrophysics in case the dual
behaviour shown by 4FGL J1544.3-0649 does not represent an isolated case, but
rather a manifestation of a so far unnoticed relatively common phenomenon.


  The prevalence of ubiquitous location-aware devices and mobile Internet
enables us to collect massive individual-level trajectory dataset from users.
Such trajectory big data bring new opportunities to human mobility research but
also raise public concerns with regard to location privacy. In this work, we
present the Conditional Adversarial Trajectory Synthesis (CATS), a
deep-learning-based GeoAI methodological framework for privacy-preserving
trajectory data generation and publication. CATS applies K-anonymity to the
underlying spatiotemporal distributions of human movements, which provides a
distributional-level strong privacy guarantee. By leveraging conditional
adversarial training on K-anonymized human mobility matrices, trajectory global
context learning using the attention-based mechanism, and recurrent bipartite
graph matching of adjacent trajectory points, CATS is able to reconstruct
trajectory topology from conditionally sampled locations and generate
high-quality individual-level synthetic trajectory data, which can serve as
supplements or alternatives to raw data for privacy-preserving trajectory data
publication. The experiment results on over 90k GPS trajectories show that our
method has a better performance in privacy preservation, spatiotemporal
characteristic preservation, and downstream utility compared with baseline
methods, which brings new insights into privacy-preserving human mobility
research using generative AI techniques and explores data ethics issues in
GIScience.


  We study the Riesz Decomposition Property types of the lexicographic product
of two po-groups. Then we apply them to the study of pseudo effect algebras
which can be decomposed to a comparable system of non-void slices indexed by
some subgroup of real numbers. Finally, we present their representation by the
lexicographic product.


  We present a generative model for complex free-form structures such as
stroke-based drawing tasks. While previous approaches rely on sequence-based
models for drawings of basic objects or handwritten text, we propose a model
that treats drawings as a collection of strokes that can be composed into
complex structures such as diagrams (e.g., flow-charts). At the core of the
approach lies a novel autoencoder that projects variable-length strokes into a
latent space of fixed dimension. This representation space allows a relational
model, operating in latent space, to better capture the relationship between
strokes and to predict subsequent strokes. We demonstrate qualitatively and
quantitatively that our proposed approach is able to model the appearance of
individual strokes, as well as the compositional structure of larger diagram
drawings. Our approach is suitable for interactive use cases such as
auto-completing diagrams. We make code and models publicly available at
https://eth-ait.github.io/cose.


  We study a zero-temperature phase transition in the random field Ising model
on scale-free networks with the degree exponent $\gamma$. Using an analytic
mean-field theory, we find that the spins are always in the ordered phase for
$\gamma<3$. On the other hand, the spins undergo a phase transition from an
ordered phase to a disordered phase as the dispersion of the random fields
increases for $\gamma > 3$. The phase transition may be either continuous or
discontinuous depending on the shape of the random field distribution. We
derive the condition for the nature of the phase transition. Numerical
simulations are performed to confirm the results.


  While both extractive and generative readers have been successfully applied
to the Question Answering (QA) task, little attention has been paid toward the
systematic comparison of them. Characterizing the strengths and weaknesses of
the two readers is crucial not only for making a more informed reader selection
in practice but also for developing a deeper understanding to foster further
research on improving readers in a principled manner. Motivated by this goal,
we make the first attempt to systematically study the comparison of extractive
and generative readers for question answering. To be aligned with the
state-of-the-art, we explore nine transformer-based large pre-trained language
models (PrLMs) as backbone architectures. Furthermore, we organize our findings
under two main categories: (1) keeping the architecture invariant, and (2)
varying the underlying PrLMs. Among several interesting findings, it is
important to highlight that (1) the generative readers perform better in long
context QA, (2) the extractive readers perform better in short context while
also showing better out-of-domain generalization, and (3) the encoder of
encoder-decoder PrLMs (e.g., T5) turns out to be a strong extractive reader and
outperforms the standard choice of encoder-only PrLMs (e.g., RoBERTa). We also
study the effect of multi-task learning on the two types of readers varying the
underlying PrLMs and perform qualitative and quantitative diagnosis to provide
further insights into future directions in modeling better readers.


  In this paper we present a new characterization of the Sobolev space
$W^{1,p}$, $1<p<\infty$ which is a higher dimensional version of a result of
Waterman. We also provide a new and simplified proof of a recent result of
Alabern, Mateu and Verdera. Finally, we generalize the results to the case of
weighted Sobolev spaces with respect to a Muckenhoupt weight.


  Percolation of gold films of ~15 nm thickness was controlled to achieve the
largest openings during Au deposition. Gold was evaporated on 300-nm-thick
films of nanostructured porous and columnar SiO2, TiO2 and MgF2 which were
deposited by controlling the angle, rotation speed during film formation and
ambient pressure. The gold films were tested for SERS performance using
thiophenol reporter molecules which form a stable self-assembled monolayer on
gold. The phase retardation of these SERS substrates was up to 5% for
wavelengths in the visible spectral range, as measured by Stokes polarimetry.
The SERS intensity on gold percolation films can reach 10^3 counts/(mW.s) for
tight focusing in air, while back-side excitation through the substrate has
shown the presence of an additional SERS enhancement via the Fresnel near-field
mechanism.


  An ordered graph is a pair $\mathcal{G}=(G,\prec)$ where $G$ is a graph and
$\prec$ is a total ordering of its vertices. The ordered Ramsey number
$\overline{R}(\mathcal{G})$ is the minimum number $N$ such that every
$2$-coloring of the edges of the ordered complete graph on $N$ vertices
contains a monochromatic copy of $\mathcal{G}$.
  We show that for every integer $d \geq 3$, almost every $d$-regular graph $G$
satisfies $\overline{R}(\mathcal{G}) \geq
\frac{n^{3/2-1/d}}{4\log{n}\log{\log{n}}}$ for every ordering $\mathcal{G}$ of
$G$. In particular, there are 3-regular graphs $G$ on $n$ vertices for which
the numbers $\overline{R}(\mathcal{G})$ are superlinear in $n$, regardless of
the ordering $\mathcal{G}$ of $G$. This solves a problem of Conlon, Fox, Lee,
and Sudakov.
  On the other hand, we prove that every graph $G$ on $n$ vertices with maximum
degree 2 admits an ordering $\mathcal{G}$ of $G$ such that
$\overline{R}(\mathcal{G})$ is linear in $n$.
  We also show that almost every ordered matching $\mathcal{M}$ with $n$
vertices and with interval chromatic number two satisfies
$\overline{R}(\mathcal{M}) \geq cn^2/\log^2{n}$ for some absolute constant $c$.


  Deep late-time X-ray observations of the relativistic, engine-driven, type Ic
SN2012ap allow us to probe the nearby environment of the explosion and reveal
the unique properties of relativistic SNe. We find that on a local scale of
~0.01 pc the environment was shaped directly by the evolution of the progenitor
star with a pre-explosion mass-loss rate <5x10^-6 Msun yr-1 in line with GRBs
and the other relativistic SN2009bb. Like sub-energetic GRBs, SN2012ap is
characterized by a bright radio emission and evidence for mildly relativistic
ejecta. However, its late time (t~20 days) X-ray emission is ~100 times fainter
than the faintest sub-energetic GRB at the same epoch, with no evidence for
late-time central engine activity. These results support theoretical proposals
that link relativistic SNe like 2009bb and 2012ap with the weakest observed
engine-driven explosions, where the jet barely fails to breakout. Furthermore,
our observations demonstrate that the difference between relativistic SNe and
sub-energetic GRBs is intrinsic and not due to line-of-sight effects. This
phenomenology can either be due to an intrinsically shorter-lived engine or to
a more extended progenitor in relativistic SNe.


  The Bloch--Torrey operator $-h^2\Delta+e^{i\alpha}x_1$ on a bounded smooth
planar domain, subject to Dirichlet boundary conditions, is analyzed. Assuming
$\alpha\in\left[0,\frac{3\pi}{5}\right)$ and a non-degeneracy assumption on the
left-hand side of the domain, asymptotics of the eigenvalues with the smallest
real part in the limit $h \to 0$ are derived. The strategy is a backward
complex scaling and the reduction to a tensorized operator involving a real
Airy operator and a complex harmonic oscillator.


  We present a simple method, not based on the transfer matrices, to prove
vanishing of dynamical transport exponents. The method is applied to long range
quasiperiodic operators.


  Many proteins that undergo sequential enzymatic modification in the Golgi
cisternae are displayed at the plasma membrane as cell identity markers. The
modified proteins, called glycans, represent a molecular code. The fidelity of
this glycan code is measured by how accurately the glycan synthesis machinery
realises the desired target glycan distribution for a particular cell type and
niche. In this paper, we quantitatively analyse the tradeoffs between the
number of cisternae and the number and specificity of enzymes, in order to
synthesize a prescribed target glycan distribution of a certain complexity. We
find that to synthesize complex distributions, such as those observed in real
cells, one needs to have multiple cisternae and precise enzyme partitioning in
the Golgi. Additionally, for fixed number of enzymes and cisternae, there is an
optimal level of specificity of enzymes that achieves the target distribution
with high fidelity. Our results show how the complexity of the target glycan
distribution places functional constraints on the Golgi cisternal number and
enzyme specificity.


  It is common in machine learning and statistics to use symmetries derived
from expert knowledge to simplify problems or improve performance, using
methods like data augmentation or penalties. In this paper we consider the
unsupervised and semi-supervised problems of learning such symmetries in a
distribution directly from data in a model-free fashion. We show that in the
worst case this problem is as difficult as the graph automorphism problem.
However, if we restrict to the case where the covariance matrix has unique
eigenvalues, then the eigenvectors will also be eigenvectors of the symmetry
transformation. If we further restrict to finding orthogonal symmetries, then
the eigenvalues will be either be 1 or -1, and the problem reduces to
determining which eigenvectors are which. We develop and compare theoretically
and empirically the effectiveness of different methods of selecting which
eigenvectors should have eigenvalue -1 in the symmetry transformation, and
discuss how to extend this approach to non-orthogonal cases where we have
labels


  Demand flexibility plays a pivotal role in modern power systems with high
penetration of variable energy resources. In recent years, one of the
fastest-growing flexible energy demands has been proof-of-work-based
cryptocurrency mining facilities. Due to their competitive ramping capabilities
and demonstrated flexibility, such fast-responding loads are capable of
participating in frequency regulation services for the grid while
simultaneously increasing their own operational revenue. In this paper, we
investigate the physical and economic viability of employing cryptocurrency
mining facilities to provide frequency regulation in large power systems. We
quantify mining facilities' operational profit, and propose a decision-making
framework to explore their optimal participation strategy and account for the
most influential factors. We employ real-world ERCOT ancillary services data in
our case study to investigate the conditions under which provision of frequency
regulation in the Texas grid is profitable. We also perform transient level
simulations using a synthetic Texas grid to demonstrate the competitiveness of
mining facilities at frequency regulation provision.


  In this paper, we consider the large time asymptotic behavior of solutions to
systems of two cubic nonlinear Klein-Gordon equations in one space dimension.
We classify the systems by studying the quotient set of a suitable subset of
systems by the equivalence relation naturally induced by the linear
transformation of the unknowns. It is revealed that the equivalence relation is
well described by an identification with a matrix. In particular, we
characterize some known systems in terms of the matrix and specify all systems
equivalent to them. An explicit reduction procedure from a given system in the
suitable subset to a model system, i.e., to a representative, is also
established. The classification also draws our attention to some model systems
which admit solutions with a new kind of asymptotic behavior. Especially, we
find new systems which admit a solution of which decay rate is worse than that
of a solution to the linear Klein-Gordon equation by logarithmic order.


  The variance of the Lyapunov exponent is calculated exactly in the
one-dimensional Anderson model with random site energies distributed according
to the Cauchy distribution. We derive an exact analytical criterion for the
validity of the single parameter scaling in this model. According to this
criterion, states with energies from the conduction band of the underlying
non-random system satisfy single parameter scaling when disorder is small
enough. At the same time, single parameter scaling is not valid for states
close to band boundaries and those outside of the original spectrum even in the
case of small disorder. The obtained results are applied to the Kronig-Penney
model with the potential in the form of periodically positioned
$\delta$-functions with random strengths. We show that an increase in the
disorder can restore single parameter scalingbehavior for states from band-gaps
of this model.


  The first science run of the LIGO and GEO gravitational wave detectors
presented the opportunity to test methods of searching for gravitational waves
from known pulsars. Here we present new direct upper limits on the strength of
waves from the pulsar PSR J1939+2134 using two independent analysis methods,
one in the frequency domain using frequentist statistics and one in the time
domain using Bayesian inference. Both methods show that the strain amplitude at
Earth from this pulsar is less than a few times $10^{-22}$.


  We consider the problem of assessing the importance of multiple variables or
factors from a dataset when side information is available. In principle, using
side information can allow the statistician to pay attention to variables with
a greater potential, which in turn, may lead to more discoveries. We introduce
an adaptive knockoff filter, which generalizes the knockoff procedure (Barber
and Cand\`es, 2015; Cand\`es et al., 2018) in that it uses both the data at
hand and side information to adaptively order the variables under study and
focus on those that are most promising. Adaptive knockoffs controls the
finite-sample false discovery rate (FDR) and we demonstrate its power by
comparing it with other structured multiple testing methods. We also apply our
methodology to real genetic data in order to find associations between genetic
variants and various phenotypes such as Crohn's disease and lipid levels. Here,
adaptive knockoffs makes more discoveries than reported in previous studies on
the same datasets.


  We investigate classical and quantum geometric information flow theories
(respectively, GIFs and QGIFs) when the geometric flow evolution and field
equations for nonholonomic Einstein systems, NES, are derived from
Perelman-Lyapunov type entropic type functionals. In this work, the term NES
encodes models of gravitational and matter fields interactions and their
geometric flow evolution subjected to nonholonomic (equivalently,
non-integrable, anholonomic) constraints. There are used canonical geometric
variables which allow a general decoupling and integration of systems of
nonlinear partial differential equations describing GIFs and QGIFs and (for
self-similar geometric flows) Ricci soliton type configurations. Our approach
is different from the methods and constructions elaborated for special classes
of solutions characterized by area--hypersurface entropy, related holographic
and dual gauge--gravity models, and conformal field theories, involving
generalizations of the Bekenstein-Hawking entropy and black hole
thermodynamics. We formulate the theory of QGIFs which in certain
quasi-classical limits encodes GIFs and models with flow evolution of NES.
There are analysed the most important properties (inequalities) for NES and
defined and computed QGIF versions of the von Neumann, relative and conditional
entropy; mutual information, (modified) entanglement and R\'{e}nyi entropy. We
construct explicit examples of generic off-diagonal exact and parametric
solutions describing stationary solitonic gravitational hierarchies and
deformations of black hole configurations. Finally, we show how Perelman's
entropy and geometric thermodynamic values, and extensions to GIF and QGIF
models can be computed for various new classes of exact solutions which cannot
be described following the Bekenstein-Hawking approach.


  In this paper, we present a low-complexity algorithm for detection in
high-rate, non-orthogonal space-time block coded (STBC) large-MIMO systems that
achieve high spectral efficiencies of the order of tens of bps/Hz. We also
present a training-based iterative detection/channel estimation scheme for such
large STBC MIMO systems. Our simulation results show that excellent bit error
rate and nearness-to-capacity performance are achieved by the proposed
multistage likelihood ascent search (M-LAS) detector in conjunction with the
proposed iterative detection/channel estimation scheme at low complexities. The
fact that we could show such good results for large STBCs like 16x16 and 32x32
STBCs from Cyclic Division Algebras (CDA) operating at spectral efficiencies in
excess of 20 bps/Hz (even after accounting for the overheads meant for pilot
based training for channel estimation and turbo coding) establishes the
effectiveness of the proposed detector and channel estimator. We decode perfect
codes of large dimensions using the proposed detector. With the feasibility of
such a low-complexity detection/channel estimation scheme, large-MIMO systems
with tens of antennas operating at several tens of bps/Hz spectral efficiencies
can become practical, enabling interesting high data rate wireless
applications.


  In these lectures presented at Baikal summer school on physics of elementary
particles and astrophysics 2011, I present a wide view of neutron star
astrophysics with special attention paid to young isolated compact objects and
studies of the properties of neutron star interiors using astronomical methods.


  In this work, a microscopic quantum mechanical model for gravitationally
induced decoherence introduced by Blencowe and Xu is investigated in the
context of neutrino oscillations. The focus is on the comparison with existing
phenomenological models and the physical interpretation of the decoherence
parameters in such models. The results show that for neutrino oscillations in
vacuum gravitationally induced decoherence can be matched with phenomenological
models with decoherence parameters of the form $\Gamma_{ij}\sim \Delta
m^4_{ij}E^{-2}$. When matter effects are included, the decoherence parameters
show a dependence on matter effects, which vary in the different layers of the
Earth, that can be explained with the form of the coupling between neutrinos
and the gravitational wave environment inspired by linearised gravity.
Consequently, in the case of neutrino oscillations in matter, the microscopic
model does not agree with many existing phenomenological models that assume
constant decoherence parameters in matter, and their existing bounds cannot be
used to further constrain the model considered here. The probabilities for
neutrino oscillations with constant and varying decoherence parameters are
compared and it is shown that the deviations can be up to 10%. On a theoretical
level, these different models can be characterised by a different choice of
Lindblad operators, with the model with decoherence parameters that do not
include matter effects being less suitable from the point of view of linearised
gravity.


  Finding a path free from obstacles that poses minimal risk is critical for
safe navigation. People who are sighted and people who are visually impaired
require navigation safety while walking on a sidewalk. In this research we
developed an assistive navigation on a sidewalk by integrating sensory inputs
using reinforcement learning. We trained a Sidewalk Obstacle Avoidance Agent
(SOAA) through reinforcement learning in a simulated robotic environment. A
Sidewalk Obstacle Conversational Agent (SOCA) is built by training a natural
language conversation agent with real conversation data. The SOAA along with
SOCA was integrated in a prototype device called augmented guide (AG).
Empirical analysis showed that this prototype improved the obstacle avoidance
experience about 5% from a base case of 81.29%


  Quality inspection applications in industry are required to move towards a
zero-defect manufacturing scenario, withnon-destructive inspection and
traceability of 100 % of produced parts. Developing robust fault detection and
classification modelsfrom the start-up of the lines is challenging due to the
difficulty in getting enough representative samples of the faulty patternsand
the need to manually label them. This work presents a methodology to develop a
robust inspection system, targeting thesepeculiarities, in the context of solar
cell manufacturing. The methodology is divided into two phases: In the first
phase, an anomalydetection model based on a Generative Adversarial Network
(GAN) is employed. This model enables the detection and localizationof
anomalous patterns within the solar cells from the beginning, using only
non-defective samples for training and without anymanual labeling involved. In
a second stage, as defective samples arise, the detected anomalies will be used
as automaticallygenerated annotations for the supervised training of a Fully
Convolutional Network that is capable of detecting multiple types offaults. The
experimental results using 1873 EL images of monocrystalline cells show that
(a) the anomaly detection scheme can beused to start detecting features with
very little available data, (b) the anomaly detection may serve as automatic
labeling in order totrain a supervised model, and (c) segmentation and
classification results of supervised models trained with automatic labels
arecomparable to the ones obtained from the models trained with manual labels.


  Observations of gamma-ray burst (GRB) afterglows have yielded tantalizing
hints that supernovae (SNe) and GRBs are related. The case had been
circumstantial, though, relying on irregularities in the light curve or the
colors of the afterglow. I will present observations of the optical afterglow
of GRB 030329. The early spectra show a power-law continuum, consistent with
other GRB afterglows. After approximately one week, broad peaks in the spectrum
developed that were remarkably similar to those seen in the spectra of the
peculiar Type Ic SN 1998bw. This is the first direct, spectroscopic
confirmation that at least some GRBs arise from SNe.


  Phased microphone arrays are used widely in the applications for acoustic
source localization. Deconvolution approaches such as DAMAS successfully
overcome the spatial resolution limit of the conventional delay-and-sum (DAS)
beamforming method. However deconvolution approaches require high computational
effort compared to conventional DAS beamforming method. This paper presents a
novel method that serves to improve the efficiency of DAMAS via wavelet
compression computational grid rather than via optimizing DAMAS algorithm. In
this method, the efficiency of DAMAS increases with compression ratio. This
method can thus save lots of run time in industrial applications for sound
source localization, particularly when sound sources are just located in a
small extent compared with scanning plane and a band of angular frequency needs
to be calculated. In addition, this method largely retains the spatial
resolution of DAMAS on original computational grid, although with a minor
deficiency that the occurrence probability of aliasing increasing slightly for
complicated sound source.


  The MAJORANA DEMONSTRATOR is searching for neutrinoless double-beta decay in
76Ge using arrays of point-contact germanium detectors operating at the Sanford
Underground Research Facility. Background results in the neutrinoless
double-beta decay region of interest from data taken during construction,
commissioning, and the start of full operations have been recently published. A
pulse shape analysis cut applied to achieve this result, named AvsE, is
described in this paper. This cut is developed to remove events whose waveforms
are typical of multi-site energy deposits while retaining (90 +/- 3.5)% of
single-site events. This pulse shape discrimination is based on the
relationship between the maximum current and energy, and tuned using 228Th
calibration source data. The efficiency uncertainty accounts for variation
across detectors, energy, and time, as well as for the position distribution
difference between calibration and $0\nu\beta\beta$ events, established using
simulations.


  The simple ultraviolet absorption spectrum of thiophene is investigated using
a combination of a vibronic coupling model Hamiltonian with multi-configuration
time-dependent Hartree quantum dynamics simulations. The model includes five
states and all twenty-one vibrations, with potential surfaces calculated at the
complete active space with second-order perturbation (CASPT2) level of theory.
The model includes terms up to seventh-order to describe the diabatic
potentials. The resulting spectrum is in excellent agreement to the
experimentally measured spectrum of Holland et al [PCCP (2014) 16: 21629]. The,
until now not understood, spectral features are assigned, with a combination of
strongly coupled vibrations and vibronic coupling between the states giving
rise to a progression of triplets on the rising edge of the broad spectrum. The
analysis of the underlying dynamics indicates that population transfer between
all states takes place on a sub-100 fs timescale, with ring-opening occurring
at longer times. The model thus provides a starting point for further
investigations into the complicated photo-excited dynamics of this key
hetero-aromatic molecule.


  We use 14-year baseline images obtained with the Wide Field Planetary Camera
2 on board the Hubble Space telescope to derive a proper motion for one of the
Milky Way's most distant dwarf spheroidal companions, Leo II, relative to an
extragalactic background reference frame. Astrometric measurements are
performed in the effective point spread function (ePSF) formalism using our own
developed code. An astrometric reference grid is defined using 3,224 stars that
are members of Leo II that are brighter than magnitude 25 in the F814W band. We
identify 17 compact extra-galactic sources, for which we measure a systemic
proper motion relative to this stellar reference grid. We derive a proper
motion [\mu_{\alpha},\mu_{\delta}]=[+104+/-113,-33+/-151] microarcseconds/yr
for Leo II in the heliocentric reference frame. Though marginally detected, the
proper motion yields constraints on the orbit of Leo II. Given a distance of
230 Kpc and a heliocentric radial velocity +79 km/s, and after subtraction of
the solar motion, our measurement indicates a total orbital motion
266.1+/-128.7 km/s in the Galactocentric reference frame, with a radial
component +21.5+/-4.3 km/s and tangential component 265.2+/-129.4 km/s. The
small radial component indicates that Leo II either has a low-eccentricity
orbit, or is currently close to perigalacticon or apogalacticon distance. We
see evidence for systematic errors in the astrometry of the extragalactic
sources which, while close to being point sources, are slightly resolved in the
HST images. We argue that more extensive observations at later epochs will be
necessary to better constrain the proper motion of Leo II. We provide a
detailed catalog of the stellar and extragalactic sources identified in the HST
data which should provide a solid early-epoch reference for future astrometric
measurements.


  The statistical analysis of parsec scale region of radio galaxies is crucial
to obtain information on the nature of their central engine. To this purpose,
we defined and observed the Bologna Complete Sample (BCS) which is unbiased
with respect to the orientation of the nuclear relativistic jet being selected
from low-frequency samples. The BCS is a complete sample of 94 nearby (z<0.1)
radio galaxies that are well studied targets with literature kiloparsec data.
For all of them, we collected parsec scale information asking new VLBI (VLBA
and EVN) observations. Statistical results on their properties in radio band
are presented. From the estimates of the Doppler factor and viewing angles, we
discuss the connection with the available gamma-ray data. Finally, we show how
future observations with Fermi could reveal new important detections of some of
the BCS sources.


  We study spatial correlation functions of charmonium in 2+1 flavor QCD using
an improved staggered formulation. Contrary to the temporal correlation
functions the spatial correlation functions exhibit a strong temperature
dependence above the QCD transition temperature. Above this temperature they
are sensitive to temporal boundary conditions. Both features become significant
at a temperature close to 1.5 Tc and suggest corresponding modifications of
charmonium spectral functions.


  Generative adversarial networks, or GANs, commonly display unstable behavior
during training. In this work, we develop a principled theoretical framework
for understanding the stability of various types of GANs. In particular, we
derive conditions that guarantee eventual stationarity of the generator when it
is trained with gradient descent, conditions that must be satisfied by the
divergence that is minimized by the GAN and the generator's architecture. We
find that existing GAN variants satisfy some, but not all, of these conditions.
Using tools from convex analysis, optimal transport, and reproducing kernels,
we construct a GAN that fulfills these conditions simultaneously. In the
process, we explain and clarify the need for various existing GAN stabilization
techniques, including Lipschitz constraints, gradient penalties, and smooth
activation functions.


  Recent progress in model selection raises the question of the fundamental
limits of these techniques. Under specific scrutiny has been model selection
for general contextual bandits with nested policy classes, resulting in a
COLT2020 open problem. It asks whether it is possible to obtain simultaneously
the optimal single algorithm guarantees over all policies in a nested sequence
of policy classes, or if otherwise this is possible for a trade-off
$\alpha\in[\frac{1}{2},1)$ between complexity term and time:
$\ln(|\Pi_m|)^{1-\alpha}T^\alpha$. We give a disappointing answer to this
question. Even in the purely stochastic regime, the desired results are
unobtainable. We present a Pareto frontier of up to logarithmic factors
matching upper and lower bounds, thereby proving that an increase in the
complexity term $\ln(|\Pi_m|)$ independent of $T$ is unavoidable for general
policy classes. As a side result, we also resolve a COLT2016 open problem
concerning second-order bounds in full-information games.


  We present a comprehensive study of the properties of the off-resonant
conductance spectrum in oligomer nanojunctions between graphitic electrodes. By
employing first-principle-based methods and the Landauer approach of quantum
transport, we identify how the electronic structure of the molecular junction
components is reflected in electron transport across such systems. For
virtually all energies within the conduction gap of the corresponding idealised
polymer chain, we show that: a) the inverse decay length of the tunnelling
conductance is intrinsically defined by the complex-band structure of the
molecular wire despite ultrashort oligomer lengths of few monomer units, and b)
the contact conductance crucially depends on both the local density of states
on the metal side and the realised interfacial contact.


  In this paper we overview some results on the hydrogen atom in external
static uniform magnetic fields. We focus on the case of very strong magnetic
field, B>>B_0=2.3x10^9 Gauss, use various approximate models and, particularly,
in the adiabatic approximation have calculated exactly the integral defining
the effective potential. This potential appears to be finite at z=0. Our
consideration of the problem of highly magnetized atoms and molecules is
motivated by the recently developed MagneGas technology by Santilli
(http://www.magnegas.com). The ground state electron charge distribution of the
hydrogen atom in an intense magnetic field is of a toroidal form, in agreement
with that studied by Santilli. This physical picture is at the foundation of
the new chemical species of magnecules proposed by Santilli.


  It is discussed how the worldwide tourist arrivals, about 10% of world's
domestic product, form a largely heterogeneous and directed complex network.
Remarkably the random network of connectivity is converted into a scale-free
network of intensities. The importance of weights on network connections is
brought into discussion. It is also shown how strategic positioning
particularly benefit from market diversity and that interactions among
countries prevail on a technological and economic pattern, questioning the
backbones of traveling driving forces.


  With growing popularity of mobile Internet, providing secure wireless
services has become a critical issue. Physical layer security (PHY-security)
has been recognized as an effective means to enhance wireless security by
exploiting wireless medium characteristics, e.g., fading, noise, and
interference. A particularly interesting PHY-security technology is cooperative
relay due to the fact that it helps to provide distributed diversity and
shorten access distance. This article offers a tutorial on various
multi-antenna relaying technologies to improve security at physical layer. The
state of the art research results on multi-antenna relay aided PHY-security as
well as some secrecy performance optimization schemes are presented. In
particular, we focus on large-scale MIMO (LS-MIMO) relaying technology, which
is effective to tackle various challenging issues for implementing wireless
PHY-security, such as short-distance interception without eavesdropper channel
state information (CSI) and with imperfect legitimate CSI. Moreover, the future
directions are identified for further enhancement of secrecy performance.


  Fossil groups are galaxy structures that probably underwent a nearly complete
fusion of all intermediate magnitude galaxies into a single large central
dominant galaxy. However, the formation and evolution processes of these
structures are still not well understood. In order to test this scenario and
its implications we studied the fossil group RXJ1119.7+2126, based on available
spectroscopy of the galaxies in the low density large scale region around the
fossil group and deep B and R band imaging of its close vicinity and three
comparison fields. We used spectroscopic data to investigate the degree of
isolation of RXJ1119.7+2126 in terms of bright neighbour galaxies. The imaging
data were used to derive the color magnitude relation and select faint galaxies
statistically belonging to this structure. The structure appears as a very
isolated group exhibiting a red sequence in the color magnitude relation with
characteristics close to the red sequences already observed for other fossil
groups. All these results can be interpreted consistently in the framework of
the building up process generally proposed for fossil groups.


  For a stationary set S subseteq omega_1 and a ladder system C over S, a new
type of gaps called C-Hausdorff is introduced and investigated. We describe a
forcing model of ZFC in which, for some stationary set S, for every ladder C
over S, every gap contains a subgap that is C-Hausdorff. But for every ladder E
over omega_1 setminus S there exists a gap with no subgap that is E-Hausdorff.
A new type of chain condition, called polarized chain condition, is introduced.
We prove that the iteration with finite support of polarized c.c.c posets is
again a polarized c.c.c poset.


  Despite the many studies on stellar nucleosynthesis published so far, the
scenario for the production of Cu in stars remains elusive. In particular, it
is still debated whether copper originates mostly in massive stars or type Ia
supernovae. To answer this question, we compute self-consistent chemical
evolution models taking into account the results of updated stellar
nucleosynthesis. By contrasting copper evolution in Omega Cen and the Milky
Way, we end up with a picture where massive stars are the major responsible for
the production of Cu in Omega Cen as well as the Galactic disc.


  Autonomous exploration of unknown environments using a team of mobile robots
demands distributed perception and planning strategies to enable efficient and
scalable performance. Ideally, each robot should update its map and plan its
motion not only relying on its own observations, but also considering the
observations of its peers. Centralized solutions to multi-robot coordination
are susceptible to central node failure and require a sophisticated
communication infrastructure for reliable operation. Current decentralized
active mapping methods consider simplistic robot models with linear-Gaussian
observations and Euclidean robot states. In this work, we present a distributed
multi-robot mapping and planning method, called Riemannian Optimization for
Active Mapping (ROAM). We formulate an optimization problem over a graph with
node variables belonging to a Riemannian manifold and a consensus constraint
requiring feasible solutions to agree on the node variables. We develop a
distributed Riemannian optimization algorithm that relies only on one-hop
communication to solve the problem with consensus and optimality guarantees. We
show that multi-robot active mapping can be achieved via two applications of
our distributed Riemannian optimization over different manifolds: distributed
estimation of a 3-D semantic map and distributed planning of SE(3) trajectories
that minimize map uncertainty. We demonstrate the performance of ROAM in
simulation and real-world experiments using a team of robots with RGB-D
cameras.


  Many network applications, e.g., industrial control, demand Ultra-Low Latency
(ULL). However, traditional packet networks can only reduce the end-to-end
latencies to the order of tens of milliseconds. The IEEE 802.1 Time Sensitive
Networking (TSN) standard and related research studies have sought to provide
link layer support for ULL networking, while the emerging IETF Deterministic
Networking (DetNet) standards seek to provide the complementary network layer
ULL support. This article provides an up-to-date comprehensive survey of the
IEEE TSN and IETF DetNet standards and the related research studies. The survey
of these standards and research studies is organized according to the main
categories of flow concept, flow synchronization, flow management, flow
control, and flow integrity. ULL networking mechanisms play a critical role in
the emerging fifth generation (5G) network access chain from wireless devices
via access, backhaul, and core networks. We survey the studies that
specifically target the support of ULL in 5G networks, with the main categories
of fronthaul, backhaul, and network management. Throughout, we identify the
pitfalls and limitations of the existing standards and research studies. This
survey can thus serve as a basis for the development of standards enhancements
and future ULL research studies that address the identified pitfalls and
limitations.


  We will propose a derivation of the correspondence between certain gauge
theories with N=2 supersymmetry and conformal field theory discovered by Alday,
Gaiotto and Tachikawa in the spirit of Seiberg-Witten theory. Based on certain
results from the literature we argue that the quantum theory of the moduli
spaces of flat SL(2,R)-connections represents a non-perturbative "skeleton" of
the gauge theory, protected by supersymmetry. It follows that instanton
partition functions can be characterized as solutions to a Riemann-Hilbert type
problem. In order to solve it, we describe the quantization of the moduli
spaces of flat connections explicitly in terms of two natural sets of Darboux
coordinates. The kernel describing the relation between the two pictures
represents the solution to the Riemann Hilbert problem, and is naturally
identified with the Liouville conformal blocks.


  Switchbacks (SBs) are localized structures in the solar wind containing
deflections of the magnetic field direction relative to the background solar
wind magnetic field. The amplitudes of the magnetic field deflection angles for
different SBs vary from ~40 to ~160-170 degrees. Alignment of the perturbations
of the magnetic field and the bulk solar wind velocity is observed inside SBs
and causes spiky enhancements of the radial bulk velocity inside SBs. We have
investigated the deviations of SB perturbations from Alfv\'enicity by
evaluating the distribution of the parameter defined as the ratio of the
parallel to $\Delta\vec{B}$ component of $\Delta\vec{V}$ to $\Delta\vec{V}_A =
\Delta\vec{B}/4\pi n_i m_i$ inside SBs, i.e. $\alpha=V_{||}/|\Delta\vec{V}_A|$
( $\alpha=\Delta\vec{V}/|\Delta\vec{V}_A|$ when $\Delta\vec{V}~\Delta\vec{B}$),
which quantifies the deviation of the perturbation from an Alfv\'enic one.
Based on Parker Solar Probe (PSP) observations, we show that $\alpha$ inside
SBs has systematically lower values than it has in the pristine solar wind:
$\alpha$ inside SBs observed during PSP Encounter 1 were distributed in a range
of 0.2-0.9. The upper limit on $\alpha$ is constrained by the requirement that
the jump in velocity across the switchback boundary be less than the local
Alfv\'en speed. This prevents the onset of shear flow instabilities. The
consequence is that the perturbation of the proton bulk velocity in SBs with
deflection greater than 60 degrees cannot reach $\alpha=1$ (the Alfv\'enicity
condition) and the highest possible $\alpha$ for a SB with the full reversal of
B is 0.5. These results have consequences for the interpretation of switchbacks
as large amplitude Alfv\'en waves.


  We compare the two main techniques used for estimating the state of a
physical system from unknown measurements: standard detector tomography and
data-pattern tomography. Adopting linear inversion as a fair benchmark, we show
that the difference between these two protocols can be traced back to the
nonexistence of the reverse-order law for pseudoinverses. We capitalize on this
fact to identify regimes where the data-pattern approach outperforms the
standard one and vice versa. We corroborate these conclusions with numerical
simulations of relevant examples of quantum state tomography.


  HTCondor has been very successful in managing globally distributed,
pleasantly parallel scientific workloads, especially as part of the Open
Science Grid. HTCondor system design makes it ideal for integrating compute
resources provisioned from anywhere, but it has very limited native support for
autonomously provisioning resources managed by other solutions. This work
presents a solution that allows for autonomous, demand-driven provisioning of
Kubernetes-managed resources. A high-level overview of the employed
architectures is presented, paired with the description of the setups used in
both on-prem and Cloud deployments in support of several Open Science Grid
communities. The experience suggests that the described solution should be
generally suitable for contributing Kubernetes-based resources to existing
HTCondor pools.


  We investigate a projection-based reduced-order model of the steady
incompressible Navier-Stokes equations for moderate Reynolds numbers. In
particular, we construct an "embedded" reduced basis space, by applying proper
orthogonal decomposition to the Shifted Boundary Method, a high-fidelity
embedded method recently developed. We focus on the geometrical parametrization
through level-set geometries, using a fixed Cartesian background geometry and
the associated mesh. This approach avoids both remeshing and the development of
a reference domain formulation, as typically done in fitted mesh finite element
formulations. Two-dimensional computational examples for one and
three-parameter dimensions are presented to validate the convergence and the
efficacy of the proposed approach.


  Non-Hermitian systems with parity-time ($\mathcal{PT}$) symmetry give rise to
exceptional points (EPs) with exceptional properties that arise due to the
coalescence of eigenvectors. Such systems have been extensively explored in the
classical domain, where second or higher order EPs have been proposed or
realized. In contrast, quantum information studies of $\mathcal{PT}$-symmetric
systems have been confined to systems with a two-dimensional Hilbert space.
Here by using a single-photon interferometry setup, we simulate quantum
dynamics of a four-dimensional $\mathcal{PT}$-symmetric system across a
fourth-order exceptional point. By tracking the coherent, non-unitary evolution
of the density matrix of the system in $\mathcal{PT}$-symmetry unbroken and
broken regions, we observe the entropy dynamics for both the entire system, and
the gain and loss subsystems. Our setup is scalable to the higher-dimensional
$\mathcal{PT}$-symmetric systems, and our results point towards the rich
dynamics and critical properties.


  Ensemble forecasts of weather and climate are subject to systematic biases in
the ensemble mean and variance, leading to inaccurate estimates of the forecast
mean and variance. To address these biases, ensemble forecasts are
post-processed using statistical recalibration frameworks. These frameworks
often specify parametric probability distributions for the verifying
observations. A common choice is the Normal distribution with mean and variance
specified by linear functions of the ensemble mean and variance. The parameters
of the recalibration framework are estimated from historical archives of
forecasts and verifying observations. Often there are relatively few forecasts
and observations available for parameter estimation, and so the fitted
parameters are also subject to uncertainty. This artefact is usually ignored.
This study reviews analytic results that account for parameter uncertainty in
the widely used Model Output Statistics recalibration framework. The predictive
bootstrap is used to approximate the parameter uncertainty by resampling in
more general frameworks such as Non-homogeneous Gaussian Regression. Forecasts
on daily, seasonal and annual time scales are used to demonstrate that
accounting for parameter uncertainty in the recalibrated predictive
distributions leads to probability forecasts that are more skilful and reliable
than those in which parameter uncertainty is ignored. The improvements are
attributed to more reliable tail probabilities of the recalibrated forecast
distributions.


  In this paper we study a two-level branching model for virus populations
under cell division. We assume that the cells are carrying virus populations
which evolve as a branching particle system with competition, while the cells
split according to a Yule process thereby dividing their virus populations into
two independently evolving sub-populations.
  We then assume that sizes of the virus populations are huge and characterize
the fast branching rate and huge population density limit as the solution of a
well-posed martingale problem. While verifying tightness is quite standard, we
provide a Feynman-Kac duality relation to conclude uniqueness. Moreover, the
duality relation allows for a further study of the long term behavior of the
model.


  In single-particle cryo-electron microscopy (cryo-EM), K-means clustering
algorithm is widely used in unsupervised 2D classification of projection images
of biological macromolecules. 3D ab initio reconstruction requires accurate
unsupervised classification in order to separate molecular projections of
distinct orientations. Due to background noise in single-particle images and
uncertainty of molecular orientations, traditional K-means clustering algorithm
may classify images into wrong classes and produce classes with a large
variation in membership. Overcoming these limitations requires further
development on clustering algorithms for cryo-EM data analysis. We propose a
novel unsupervised data clustering method building upon the traditional K-means
algorithm. By introducing an adaptive constraint term in the objective
function, our algorithm not only avoids a large variation in class sizes but
also produces more accurate data clustering. Applications of this approach to
both simulated and experimental cryo-EM data demonstrate that our algorithm is
a significantly improved alterative to the traditional K-means algorithm in
single-particle cryo-EM analysis.


  In this paper, following the work of Chen, L\"u and Pope, we present the
general metric for Kerr-(A)dS black holes with two rotations. The corresponding
Klein-Gordon equation is separated explicitly, from which we develop
perturbative expansions for the angular eigenvalues in powers of the rotation
parameters with $D\geq 6$.


  We research the location of the zeros of the Eisenstein series and the
modular functions from the Hecke type Faber polynomials associated with the
normalizers of congruence subgroups which are of genus zero and of level at
most twelve.
  In Part I, we will consider the general theory of modular functions for the
normalizers.


  We derive the topological susceptibility to the one-loop order in the chiral
effective theory of QCD, for an arbitrary number of flavors.


  We give necessary and sufficient conditions under which the reproducing
kernel of a Pontryagin space of $d \times 1$ vector polynomials is determined
by a generalized Nevanlinna pair of $d \times d$ matrix polynomials.


  Despite the potential benefits of data augmentation for mitigating the data
insufficiency, traditional augmentation methods primarily rely on the prior
intra-domain knowledge. On the other hand, advanced generative adversarial
networks (GANs) generate inter-domain samples with limited variety. These
previous methods make limited contributions to describing the decision
boundaries for binary classification. In this paper, we propose a distance
guided GAN (DisGAN) which controls the variation degrees of generated samples
in the hyperplane space. Specifically, we instantiate the idea of DisGAN by
combining two ways. The first way is vertical distance GAN (VerDisGAN) where
the inter-domain generation is conditioned on the vertical distances. The
second way is horizontal distance GAN (HorDisGAN) where the intra-domain
generation is conditioned on the horizontal distances. Furthermore, VerDisGAN
can produce the class-specific regions by mapping the source images to the
hyperplane. Experimental results show that DisGAN consistently outperforms the
GAN-based augmentation methods with explainable binary classification. The
proposed method can apply to different classification architectures and has
potential to extend to multi-class classification.


  The pursuit of excellence seems to be the True North of academia. What is
meant by excellence? Can excellence be measured? This article discusses the
concept of excellence in the context of research and competition.


  We discuss holographic models of extremal and non-extremal black holes in
contact with a bath in d dimensions, based on a brane world model introduced in
arXiv:2006.04851. The main benefit of our setup is that it allows for a high
degree of analytic control as compared to previous work in higher dimensions.
We show that the appearance of quantum extremal islands in those models is a
consequence of the well-understood phase transition of RT surfaces, and does
not make any direct reference to ensemble averaging. For non-extremal black
holes the appearance of quantum extremal islands has the right behaviour to
avoid the information paradox in any dimension. We further show that for these
models the calculation of the full Page curve is possible in any dimension. The
calculation reduces to numerically solving two ODEs. In the case of extremal
black holes in higher dimensions, we find no quantum extremal islands for a
wide range of parameters. In two dimensions, our results agree with
arXiv:1910.11077 at leading order; however a finite UV cutoff introduced by the
brane results in subleading corrections. For example, these corrections result
in the quantum extremal surfaces moving further outward from the horizon, and
shifting the Page transition to a slightly earlier time.


  We discuss analytic solutions describing magnetically charged black branes in
$d$ dimensional AdS space. Focusing on $d=5$, we study the response of the
brane to an external short lived electric field. We argue that when the theory
possesses an 't Hooft anomaly then at sufficiently low temperature a long lived
oscillatory current will be observed long after the electric field has been
turned off. We demonstrate this ``anomalous resonance'' effect via a numerical
study.


  We construct the so-called quasiregular representations of the group
$B_0^{\mathbb N}({\mathbb F}_p)$ of infinite upper triangular matrices with
coefficients in a finite field and give the criteria of theirs irreducibility
in terms of the initial measure. These representations are particular case of
the Koopman representation hence, we find new conditions of its irreducibility.
Since the field ${\mathbb F}_p$ is compact some new operators in the commutant
emerges. Therefore, the Ismagilov conjecture in the case of the finite field
should be corrected.


  Recently, the CIII] and CIV emission lines have been observed in galaxies in
the early Universe ($z>5$), providing new ways to measure their redshift and
study their stellar populations and AGN. We explore the first blind CII], CIII]
and CIV survey ($z\sim0.68, 1.05, 1.53$, respectively) presented in Stroe et
al. (2017). We derive luminosity functions (LF) and study properties of CII],
CIII] and CIV line emitters through comparisons to the LFs of H$\alpha$ and
Ly$\alpha$ emitters, UV selected star forming (SF) galaxies and quasars at
similar redshifts. The CII] LF at $z\sim0.68$ is equally well described by a
Schechter or a power-law LF, characteristic of a mixture of SF and AGN
activity. The CIII] LF ($z\sim1.05$) is consistent to a scaled down version of
the Schechter H$\alpha$ and Ly$\alpha$ LF at their redshift, indicating a SF
origin. In stark contrast, the CIV LF at $z\sim1.53$ is well fit by a
power-law, quasar-like LF. We find that the brightest UV sources ($M_{UV}<-22$)
will universally have CIII] and CIV emission. However, on average, CIII] and
CIV are not as abundant as H$\alpha$ or Ly$\alpha$ emitters at the same
redshift, with cosmic average ratios of $\sim0.02-0.06$ to H$\alpha$ and
$\sim0.01-0.1$ to intrinsic Ly$\alpha$. We predict that the CIII] and CIV lines
can only be truly competitive in confirming high redshift candidates when the
hosts are intrinsically bright and the effective Ly$\alpha$ escape fraction is
below 1 per cent. While CIII] and CIV were proposed as good tracers of young,
relatively low-metallicity galaxies typical of the early Universe, we find
that, at least at $z\sim1.5$, CIV is exclusively hosted by AGN/quasars,
especially at large line equivalent widths.


  Determining the extent to which a patient is benefiting from cancer therapy
is challenging. Criteria for quantifying the extent of "tumor response"
observed within a few cycles of treatment have been established for various
types of solid as well as hematologic malignancies. These measures comprise the
primary endpoints of phase II trials. Regulatory approvals of new cancer
therapies, however, are usually contingent upon the demonstration of superior
overall survival with randomized evidence acquired with a phase III trial
comparing the novel therapy to an appropriate standard of care treatment. With
nearly two thirds of phase III oncology trials failing to achieve statistically
significant results, researchers continue to refine and propose new surrogate
endpoints. This article presents a Bayesian framework for studying
relationships among treatment, patient subgroups, tumor response and survival.
Combining classical components of mediation analysis with Bayesian model
averaging (BMA), the methodology is robust to model mis-specification among
various possible relationships among the observable entities. Posterior
inference is demonstrated via application to a randomized controlled phase III
trial in metastatic colorectal cancer. Moreover, the article details posterior
predictive distributions of survival and statistical metrics for quantifying
the extent of direct and indirect, or tumor response mediated, treatment
effects.


  We investigate the extent to which the commonly used standard pairwise
contact potential can be used to identify the native fold of a protein. Ideally
one would hope that a universal energy function exists, for which the native
folds of all proteins are the respective ground states. Here we pose a much
more restricted question: is it possible to find a set of contact parameters
for which the energy of the native contact map of a single protein (crambin) is
lower than that of all possible physically realizable decoy maps. We seek such
a set of parameters by perceptron learning, a procedure which is guaranteed to
find such a set if it exists. We found that it is extremely hard (and most
probably, impossible) to fine tune contact parameters that will assign all
alternative conformations higher energy than that of the native map. This
finding clearly indicates that it is impossible to derive a general pairwise
contact potential that can be used to fold any given protein. Inclusion of
additional energy terms, such as hydrophobic (solvation), hydrogen bond or
multi-body interactions may help to attain foldability within specific
structural families.