  Depth completion aims to recover dense depth maps from sparse ones, where
color images are often used to facilitate this task. Recent depth methods
primarily focus on image guided learning frameworks. However, blurry guidance
in the image and unclear structure in the depth still impede their performance.
To tackle these challenges, we explore a repetitive design in our image guided
network to gradually and sufficiently recover depth values. Specifically, the
repetition is embodied in both the image guidance branch and depth generation
branch. In the former branch, we design a dense repetitive hourglass network
(DRHN) to extract discriminative image features of complex environments, which
can provide powerful contextual instruction for depth prediction. In the latter
branch, we present a repetitive guidance (RG) module based on dynamic
convolution, in which an efficient convolution factorization is proposed to
reduce the complexity while modeling high-frequency structures progressively.
Furthermore, in the semantic guidance branch, we utilize the well-known large
vision model, i.e., segment anything (SAM), to supply RG with semantic prior.
In addition, we propose a region-aware spatial propagation network (RASPN) for
further depth refinement based on the semantic prior constraint. Finally, we
collect a new dataset termed TOFDC for the depth completion task, which is
acquired by the time-of-flight (TOF) sensor and the color camera on
smartphones. Extensive experiments demonstrate that our method achieves
state-of-the-art performance on KITTI, NYUv2, Matterport3D, 3D60, VKITTI, and
our TOFDC.


  The relationship between microsystems and macrosystems is considered in the
context of quantum field formulation of statistical mechanics: it is argued
that problems on foundations of quantum mechanics can be solved relying on this
relationship. This discussion requires some improvement of non-equilibrium
statistical mechanics that is briefly presented.


  Suppose that a Lie algebra $L$ admits a finite Frobenius group of
automorphisms $FH$ with cyclic kernel $F$ and complement $H$ of order 2, such
that the fixed-point subalgebra of $F$ is trivial and the fixed-point
subalgebra of $H$ is metabelian. Then the derived length of $L$ is bounded by a
constant.


  We propose a new autocorrelation measure for functional time series that we
term spherical autocorrelation. It is based on measuring the average angle
between lagged pairs of series after having been projected onto the unit
sphere. This new measure enjoys several complimentary advantages compared to
existing autocorrelation measures for functional data, since it both 1)
describes a notion of sign or direction of serial dependence in the series, and
2) is more robust to outliers. The asymptotic properties of estimators of the
spherical autocorrelation are established, and are used to construct confidence
intervals and portmanteau white noise tests. These confidence intervals and
tests are shown to be effective in simulation experiments, and demonstrated in
applications to model selection for daily electricity price curves, and
measuring the volatility in densely observed asset price data.


  We introduce a new fractional derivative that generalizes the so-called
alternative fractional derivative recently proposed by Katugampola. We denote
this new differential operator by $\mathscr{D}_{M}^{\alpha,\beta }$, where the
parameter $\alpha$, associated with the order, is such that $0<\alpha<1$,
$\beta>0$ and $M$ is used to denote that the function to be derived involves a
Mittag-Leffler function with one parameter. This new derivative satisfies some
properties of integer-order calculus, e.g.\ linearity, product rule, quotient
rule, function composition and the chain rule. Besides as in the case of the
Caputo derivative, the derivative of a constant is zero. Because Mittag-Leffler
function is a natural generalization of the exponential function, we can extend
some of the classical results of integer-order calculus, namely: Rolle's
theorem, the mean value theorem and its extension. Further, when the order of
the derivative is $\alpha=1$ and the parameter of the Mittag-Leffler function
is also unitary, our definition is equivalent to the definition of the ordinary
derivative of order one. Finally, we present the corresponding fractional
integral from which, as a natural consequence, new results emerge which can be
interpreted as applications. Specifically, we generalize the inversion property
of the fundamental theorem of calculus and prove a theorem associated with the
classical integration by parts.


  During the upstroke of a normal eye blink, the upper lid moves and paints a
thin tear film over the exposed corneal and conjunctival surfaces. This thin
tear film may be modeled by a nonlinear fourth-order PDE derived from
lubrication theory. A challenge in the numerical simulation of this model is to
include both the geometry of the eye and the movement of the eyelid. A pair of
orthogonal and conformal maps transform a square into an approximate
representation of the exposed ocular surface of a human eye. A spectral
collocation method on the square produces relatively efficient solutions on the
eye-shaped domain via these maps. The method is demonstrated on linear and
nonlinear second-order diffusion equations and shown to have excellent accuracy
as measured pointwise or by conservation checks. Future work will use the
method for thin-film equations on the same type of domain.


  Recently developed classes of monocrystalline silicon solar microcells
(u-cell) can be assembled into modules with characteristics (i.e., mechanically
flexible forms, compact concentrator designs, and high-voltage outputs) that
would be impossible to achieve using conventional, wafer-based approaches. In
this paper, we describe a highly dense, uniform and non-periodic nanocone
forest structure of black silicon (bSi) created on optically-thin (30 um)
u-cells for broadband and omnidirectional light-trapping with a
lithography-free and high-throughput plasma texturizing process. With optimized
plasma etching conditions and a silicon nitride passivation layer, black
silicon u-cells, when embedded in a polymer waveguiding layer, display dramatic
increases of as much as 65.7% in short circuit current, as compared to a bare
silicon device. The conversion efficiency increases from 8% to 11.5% with a
small drop in open circuit voltage and fill factor.


  The McVittie-Plummer spacetime is a spherically symmetric inhomogeneous
cosmological model that represents a spherical star system embedded in a
standard FLRW cosmological model. We study the main physical properties of this
gravitational field. Regarding the interplay between the physics of the local
system and the expanding background, we employ the Misner-Sharp mass-energy
function to show that there is a relatively weak time-dependent general
relativistic coupling between the astrophysical system and the background FLRW
cosmological model. The coupling term is proportional to the inverse of the
scale factor and decreases as the universe expands.


  In this paper, we investigate the problem of embodied multi-agent
cooperation, where decentralized agents must cooperate given only partial
egocentric views of the world. To effectively plan in this setting, in contrast
to learning world dynamics in a single-agent scenario, we must simulate world
dynamics conditioned on an arbitrary number of agents' actions given only
partial egocentric visual observations of the world. To address this issue of
partial observability, we first train generative models to estimate the overall
world state given partial egocentric observations. To enable accurate
simulation of multiple sets of actions on this world state, we then propose to
learn a compositional world model for multi-agent cooperation by factorizing
the naturally composable joint actions of multiple agents and compositionally
generating the video. By leveraging this compositional world model, in
combination with Vision Language Models to infer the actions of other agents,
we can use a tree search procedure to integrate these modules and facilitate
online cooperative planning. To evaluate the efficacy of our methods, we create
two challenging embodied multi-agent long-horizon cooperation tasks using the
ThreeDWorld simulator and conduct experiments with 2-4 agents. The results show
our compositional world model is effective and the framework enables the
embodied agents to cooperate efficiently with different agents across various
tasks and an arbitrary number of agents, showing the promising future of our
proposed framework. More videos can be found at
https://vis-www.cs.umass.edu/combo/.


  In this paper we analyse and improve integer discrete flows for lossless
compression. Integer discrete flows are a recently proposed class of models
that learn invertible transformations for integer-valued random variables.
Their discrete nature makes them particularly suitable for lossless compression
with entropy coding schemes. We start by investigating a recent theoretical
claim that states that invertible flows for discrete random variables are less
flexible than their continuous counterparts. We demonstrate with a proof that
this claim does not hold for integer discrete flows due to the embedding of
data with finite support into the countably infinite integer lattice.
Furthermore, we zoom in on the effect of gradient bias due to the
straight-through estimator in integer discrete flows, and demonstrate that its
influence is highly dependent on architecture choices and less prominent than
previously thought. Finally, we show how different architecture modifications
improve the performance of this model class for lossless compression, and that
they also enable more efficient compression: a model with half the number of
flow layers performs on par with or better than the original integer discrete
flow model.


  New calculations of the quasi-bound state in the $K^- pp$ system using
Faddeev-type equations in AGS form with coupled $\bar{K}NN$ and $\pi \Sigma N$
channels were performed. A chiral $\bar{K}N$ potential together with
phenomenological models of $\bar{K}N$ interaction with one- and two-pole
structure of the $\Lambda(1405)$ resonance were used. All three potentials
reproduce experimental data on low-energy $K^- p$ scattering and kaonic
hydrogen with the same level of accuracy. New method of calculating the
subthreshold resonance position and width in a three-body system was proposed
and used together with the direct search of the resonance pole. We obtained
binding energy of the $K^-pp$ quasi-bound state $\sim 32$ MeV with the chirally
motivated and $47 - 54$ MeV with the phenomenological $\bar{K}N$ potentials.
The width is about $50$ MeV for the two-pole models of the interaction, while
the one-pole potential gives $\sim 65$ MeV width. The question of using an
energy dependent potential in few-body calculations is discussed in detail. It
is shown that ``self-consistent'' variational calculations using such a
potential are unable to produce a reasonable approximation to the exact result.


  A new Hamiltonian formulation for the fully nonlinear water-wave problem over
variable bathymetry is derived, using an exact, vertical series expansion of
the velocity potential, in conjunction with Luke's variational principle. The
obtained Euler-Lagrange equations contain infinite series and can rederive
various existing model equations upon truncation. In this paper, the infinite
series are summed up, resulting in two exact Hamiltonian equations for the
free-surface elevation and the free-surface potential, coupled with a
time-independent horizontal system of equations. The Dirichlet to Neumann
operator is given by a simple and versatile representation, which is valid for
any smooth fluid domain, not necessarily periodic in the horizontal
direction(s), without limitations on the steepness and deformation of the
seabed and the free surface. An efficient numerical scheme is presented and
applied to the case of one horizontal dimension, establishing the ability of
the new formulation to simulate strongly nonlinear and dispersive wave-bottom
interactions by comparison with experimental measurements.


  The Instance Segmentation task, an extension of the well-known Object
Detection task, is of great help in many areas, such as precision agriculture:
being able to automatically identify plant organs and the possible diseases
associated with them, allows to effectively scale and automate crop monitoring
and its diseases control. To address the problem related to early disease
detection and diagnosis on vines plants, a new dataset has been created with
the goal of advancing the state-of-the-art of diseases recognition via instance
segmentation approaches. This was achieved by gathering images of leaves and
clusters of grapes affected by diseases in their natural context. The dataset
contains photos of 10 object types which include leaves and grapes with and
without symptoms of the eight more common grape diseases, with a total of
17,706 labeled instances in 1,092 images. Multiple statistical measures are
proposed in order to offer a complete view on the characteristics of the
dataset. Preliminary results for the object detection and instance segmentation
tasks reached by the models Mask R-CNN and R^3-CNN are provided as baseline,
demonstrating that the procedure is able to reach promising results about the
objective of automatic diseases' symptoms recognition.


  Recently, the LHCb Collaboration reported a new structure $P_{cs}(4459)$ with
a mass of 19 MeV below the $\Xi_c \bar{D}^{*} $ threshold. It may be a
candidate of molecular state from the $\Xi_c \bar{D}^{*} $ interaction. In the
current work, we perform a coupled-channel study of the $\Xi_c^*\bar{D}^*$,
$\Xi'_c\bar{D}^*$, $\Xi^*_c\bar{D}$, $\Xi_c\bar{D}^*$, $\Xi'_c\bar{D}$, and
$\Xi_c\bar{D}$ interactions in the quasipotential Bethe-Salpeter equation
approach. With the help of the heavy quark chiral effective Lagrangian, the
potential is constructed by light meson exchanges. Two $\Xi_c \bar{D}^{*} $
molecular states are produced with spin parities $ J^P=1/2^-$ and $3/2^- $. The
lower state with $3/2^-$ can be related to the observed $P_{cs}(4450)$ while
two-peak structure cannot be excluded. Within the same model, other strange
hidden-charm pentaquarks are also predicted. Two states with spin parities
$1/2^-$ and $3/2^-$ are predicted near the $\Xi'_c\bar{D}$, $\Xi_c\bar{D}$, and
$\Xi_c^*\bar{D}$ thresholds, respectively. As two states near $\Xi_c
\bar{D}^{*}$ threshold, two states are produced with $1/2^-$ and $3/2^-$ near
the $\Xi'_c\bar{D}^*$ threshold. The couplings of the molecular states to the
considered channels are also discussed. The experimental research of those
states are helpful to understand the origin and internal structure of the
$P_{cs}$ and $P_c$ states.


  In LiDAR-based 3D object detection for autonomous driving, the ratio of the
object size to input scene size is significantly smaller compared to 2D
detection cases. Overlooking this difference, many 3D detectors directly follow
the common practice of 2D detectors, which downsample the feature maps even
after quantizing the point clouds. In this paper, we start by rethinking how
such multi-stride stereotype affects the LiDAR-based 3D object detectors. Our
experiments point out that the downsampling operations bring few advantages,
and lead to inevitable information loss. To remedy this issue, we propose
Single-stride Sparse Transformer (SST) to maintain the original resolution from
the beginning to the end of the network. Armed with transformers, our method
addresses the problem of insufficient receptive field in single-stride
architectures. It also cooperates well with the sparsity of point clouds and
naturally avoids expensive computation. Eventually, our SST achieves
state-of-the-art results on the large scale Waymo Open Dataset. It is worth
mentioning that our method can achieve exciting performance (83.8 LEVEL 1 AP on
validation split) on small object (pedestrian) detection due to the
characteristic of single stride. Codes will be released at
https://github.com/TuSimple/SST


  The uncertainty of a quantum state is given by the composition of two
components. The first is called the quantum component and is given by the
probability distribution of an observable relative to the state. The second is
the classical component which is an uncertainty function that is applied to the
first component. We characterize uncertainty functions in terms of four axioms.
We then study four examples called variance, entropy, geometric and sine
uncertainty functions. The final section presents the general theory of state
uncertainty.


  The classical Kneser-Milnor theorem says that every closed oriented connected
3-dimensional manifold admits a unique connected sum decomposition into
manifolds that cannot be decomposed any further. We discuss to what degree such
decompositions exist in higher dimensions and we show that in many settings
uniqueness fails in higher dimensions.


  This paper proposes a parametric error analysis method for Goldschmidt
floating point division, which reveals how the errors of the intermediate
results accumulate and propagate during the Goldschmidt iterations. The
analysis is developed by separating the error terms with and without
convergence to zero, which are the key parts of the iterative approximate
value. The proposed method leads to a state-of-the-art wordlength reduction for
intermediate results during the Goldschmidt iterative computation. It enables
at least half of the calculation precision reduction for the iterative factor
to implement the rectangular multiplier in the divider through flexible
numerical method, which can also be applied in the analysis of Goldschmidt
iteration with iterative factors assigned under other ways for faster
convergence. Based on the proposed method, two proof-of-concept divider models
with different configurations are developed, which are verified by more than
100 billion random test vectors to show the correctness and tightness of the
proposed error analysis.


  Boolean functions with high algebraic immunity are important cryptographic
primitives in some stream ciphers. In this paper, two methodologies for
constructing binary minimal codes from sets, Boolean functions and vectorial
Boolean functions with high algebraic immunity are proposed. More precisely, a
general construction of new minimal codes using minimal codes contained in
Reed-Muller codes and sets without nonzero low degree annihilators is
presented. The other construction allows us to yield minimal codes from certain
subcodes of Reed-Muller codes and vectorial Boolean functions with high
algebraic immunity. Via these general constructions, infinite families of
minimal binary linear codes of dimension $m$ and length less than or equal to
$m(m+1)/2$ are obtained. In addition, a lower bound on the minimum distance of
the proposed minimal linear codes is established. Conjectures and open problems
are also presented. The results of this paper show that Boolean functions with
high algebraic immunity have nice applications in several fields such as
symmetric cryptography, coding theory and secret sharing schemes.


  Sharp features such as edges and corners play an important role in the
perception of 3D models. In order to capture them better, we propose quadric
loss, a point-surface loss function, which minimizes the quadric error between
the reconstructed points and the input surface. Computation of Quadric loss is
easy, efficient since the quadric matrices can be computed apriori, and is
fully differentiable, making quadric loss suitable for training point and mesh
based architectures. Through extensive experiments we show the merits and
demerits of quadric loss. When combined with Chamfer loss, quadric loss
achieves better reconstruction results as compared to any one of them or other
point-surface loss functions.


  We present a two-dimensional kinematic analysis for a sample of simulated
binary disc merger remnants with mass ratios 1:1 and 3:1. For the progenitor
discs we used pure stellar models as well as models with 10% of their mass in
gas. A multitude of phenomena also observed in real galaxies are found in the
simulations. These include misaligned rotation, embedded discs, gas rings,
counter-rotating cores and kinematic misaligned discs. Using the 2D maps we
illustrate projection effects and the change in properties of a merger remnant
when gas is included in the merger. We find that kinematic peculiar subsystems
are preferably formed in equal mass mergers. Equal-mass collisionless remnants
can show almost no rotation, regular rotation or strong kinematic misalignment.
The inclusion of gas makes the remnants appear more round(1:1) and
axisymmetric(3:1). Counter-Rotating Cores (CRCs) are almost exclusively formed
in equal-mass mergers with a dissipational component. 3:1 remnants show a much
more regular structure. We quantify these properties by applying the kinemetric
methods recently developed by Krajnovi\'c et al. This work will help to
understand observations of elliptical galaxies with 2D field spectrographs,
like SAURON.


  We present Meta MMO, a collection of many-agent minigames for use as a
reinforcement learning benchmark. Meta MMO is built on top of Neural MMO, a
massively multiagent environment that has been the subject of two previous
NeurIPS competitions. Our work expands Neural MMO with several computationally
efficient minigames. We explore generalization across Meta MMO by learning to
play several minigames with a single set of weights. We release the
environment, baselines, and training code under the MIT license. We hope that
Meta MMO will spur additional progress on Neural MMO and, more generally, will
serve as a useful benchmark for many-agent generalization.


  The blurry line between nefarious fake news and protected-speech satire has
been a notorious struggle for social media platforms. Further to the efforts of
reducing exposure to misinformation on social media, purveyors of fake news
have begun to masquerade as satire sites to avoid being demoted. In this work,
we address the challenge of automatically classifying fake news versus satire.
Previous work have studied whether fake news and satire can be distinguished
based on language differences. Contrary to fake news, satire stories are
usually humorous and carry some political or social message. We hypothesize
that these nuances could be identified using semantic and linguistic cues.
Consequently, we train a machine learning method using semantic representation,
with a state-of-the-art contextual language model, and with linguistic features
based on textual coherence metrics. Empirical evaluation attests to the merits
of our approach compared to the language-based baseline and sheds light on the
nuances between fake news and satire. As avenues for future work, we consider
studying additional linguistic features related to the humor aspect, and
enriching the data with current news events, to help identify a political or
social message.


  A Hamiltonian that approaches the study of the three-body problem in general
relativity is obtained. We use it to study the relativistic version of the
circular restricted three-body problem in which the first body is the heaviest
and the third body is a test-particle. We focus on the orbits around the 3:2
resonance. We show that, in spite of the notable difference between the
relativistic and Newtonian orbits, most of the resonant region is preserved.
Nevertheless, differently from the Newtonian case, the frequencies between the
second and the third body are no longer commensurable.


  We present a definition of cause and effect in terms of decision-theoretic
primitives and thereby provide a principled foundation for causal reasoning.
Our definition departs from the traditional view of causation in that causal
assertions may vary with the set of decisions available. We argue that this
approach provides added clarity to the notion of cause. Also in this paper, we
examine the encoding of causal relationships in directed acyclic graphs. We
describe a special class of influence diagrams, those in canonical form, and
show its relationship to Pearl's representation of cause and effect. Finally,
we show how canonical form facilitates counterfactual reasoning.


  Alkaline-earth-like transition-metal atoms such as Zn and Cd are promising
candidates for precision measurements and quantum many-body physics
experiments. Here, we theoretically investigate the properties of diatomic
molecules containing these closed-shell atoms. We calculate potential energy
curves, permanent electric dipole moments, and spectroscopic constants for
molecules consisting of either a Zn or Cd atom interacting with an alkali-metal
(Li, Na, K, Rb, Cs, Fr) or alkaline-earth-metal (Be, Mg, Ca, Sr, Ba, Ra) atom.
We use the ab initio electronic structure coupled cluster method with single,
double, and triple excitations combined with large Gaussian basis sets and
small-core relativistic energy-consistent pseudopotentials for heavier atoms.
We predict that the studied molecules in the ground electronic state are
chemically reactive weakly bound van der Waals complexes with small permanent
electric dipole moments. The present results may be useful for spectroscopy and
application of the studied molecules in modern ultracold physics and chemistry
experiments.


  We present high-quality J, H and K photometry of four Small Magellanic Cloud
stellar clusters with intermediate ages in the 1-7 Gyr range (namely NGC 339,
361, 416 and 419) . We obtained deep Color-Magnitude Diagrams to study the
evolved sequences and providing a detailed census of the Red Giant Branch
(RGB), Asymptotic Giant Branch (AGB) and Carbon star populations in each
cluster and their contribution to the total cluster light. We find that in the
5-7 Gyr old clusters AGB stars account for ~6 % of the total light in K-band,
Carbon stars are lacking and RGB stars account for ~45 % of the total
bolometric luminosity. These empirical findings are in good agreement with the
theoretical predictions. Finally, we derived photometric metallicities computed
by using the properties of the RGB and finding an iron content of [Fe/H]=-1.18,
-1.08, -0.99 and -0.96 dex for NGC 339, 361, 416 and 419 respectively.


  Graphene as a two-dimensional (2D) topological Dirac semimetal has attracted
much attention for its outstanding properties and potential applications.
However, three-dimensional (3D) topological semimetals for carbon materials are
still rare. Searching for such materials with salient physics has become a new
direction for carbon research. Here, using first-principles calculations and
tight-binding modeling, we study three types of 3D graphene networks whose
properties inherit those of Dirac electrons in graphene. In the band structures
of these materials, two flat Weyl surfaces appear in the Brillouin zone (BZ),
which straddle the Fermi level and are robust against external strain. When the
networks are cut, the resulting lower-dimensional slabs and nanowires remain to
be semimetallic with Weyl line-like and point-like Fermi surfaces,
respectively. Between the Weyl lines, flat surface bands emerge with strong
magnetism when each surface carbon atom is passivated by one hydrogen atom. The
robustness of these structures can be traced back to a bulk topological
invariant, ensured by the sublattice symmetry, and to the one-dimensional (1D)
Weyl semimetal behavior of the zigzag carbon chain, which has been the common
backbone to all these structures. The flat Weyl-surface semimetals may enable
applications in correlated electronics, as well as in energy storage, molecular
sieve, and catalysis because of their good stability, porous geometry, and
large superficial area.


  We construct a family of bipartite states of arbitrary dimension whose
eigenvalues of the partially transposed matrix can be inferred directly from
the block structure of the global density matrix. We identify from this several
subfamilies in which the PPT criterion is both necessary and sufficient. A
sufficient criterion of separability is obtained, which is fundamental for the
discussion. We show how several examples of states known to be classifiable by
the PPT criterion indeed belong to this general set. Possible uses of these
states in numerical analysis of entanglement and in the search of PPT bound
entangled states are briefly discussed.


  Distance matrices of graphs were introduced by Graham and Pollack in 1971 to
study a problem in communications. Since then, there has been extensive
research on the distance matrices of graphs -- a 2014 survey by Aouchiche and
Hansen on spectra of distance matrices of graphs lists more than 150
references. In the last ten years, variants such as the distance Laplacian, the
distance signless Laplacian, and the normalized distance Laplacian matrix of a
graph have been studied. After a brief description of the early history of the
distance matrix and its motivating problem, this survey focuses on comparing
and contrasting techniques and results for the four types of distance matrices.
Digraphs are treated separately after the discussion of graphs, including
discussion of similarities and differences between graphs and digraphs. New
results are presented that complement existing results, including results for
some the matrices on unimodality of characteristic polynomials for graphs,
preservation of parameters by cospectrality for graphs, and bounds on spectral
radii for digraphs.


  The thin, extended planes of satellite galaxies detected around both the
Milky Way and Andromeda are not a natural prediction of the LCDM paradigm.
Galaxies in these distinct planes may have formed and evolved in a different
way (e.g., tidally) to their off-plane neighbours. If this were the case, one
would expect the on- and off-plane dwarf galaxies in Andromeda to have
experienced different evolutionary histories, which should be reflected by the
chemistries, dynamics, and star formation histories of the two populations. In
this work, we present new, robust kinematic observations for 2 on-plane M31
dSphs (And XVI and XVII) and compile and compare all available observational
metrics for the on- and off-plane dwarfs to search for a signal that would
corroborate such a hypothesis. We find that, barring their spatial alignment,
the on- and off-plane Andromeda dwarf galaxies are indistinguishable from one
another, arguing against vastly different formative and evolutionary histories
for these two populations.


  This project aims to break down large pathology images into small tiles and
then cluster those tiles into distinct groups without the knowledge of true
labels, our analysis shows how difficult certain aspects of clustering tumorous
and non-tumorous cells can be and also shows that comparing the results of
different unsupervised approaches is not a trivial task. The project also
provides a software package to be used by the digital pathology community, that
uses some of the approaches developed to perform unsupervised unsupervised tile
classification, which could then be easily manually labelled.
  The project uses a mixture of techniques ranging from classical clustering
algorithms such as K-Means and Gaussian Mixture Models to more complicated
feature extraction techniques such as deep Autoencoders and Multi-loss
learning. Throughout the project, we attempt to set a benchmark for evaluation
using a few measures such as completeness scores and cluster plots.
  Throughout our results we show that Convolutional Autoencoders manages to
slightly outperform the rest of the approaches due to its powerful internal
representation learning abilities. Moreover, we show that Gaussian Mixture
models produce better results than K-Means on average due to its flexibility in
capturing different clusters. We also show the huge difference in the
difficulties of classifying different types of pathology textures.


  An approximation model based on convolutional neural networks (CNNs) is
proposed for flow field predictions. The CNN is used to predict the velocity
and pressure field in unseen flow conditions and geometries given the pixelated
shape of the object. In particular, we consider Reynolds Averaged Navier-Stokes
(RANS) flow solutions over airfoil shapes. The CNN can automatically detect
essential features with minimal human supervision and shown to effectively
estimate the velocity and pressure field orders of magnitude faster than the
RANS solver, making it possible to study the impact of the airfoil shape and
operating conditions on the aerodynamic forces and the flow field in near-real
time. The use of specific convolution operations, parameter sharing, and
robustness to noise are shown to enhance the predictive capabilities of CNN. We
explore the network architecture and its effectiveness in predicting the flow
field for different airfoil shapes, angles of attack, and Reynolds numbers.


  This paper presents a comprehensive study of the performance of Sezawa
surface acoustic wave (SAW) devices in SweGaN QuanFINE ultrathin GaN/SiC
platform, reaching frequencies above 14 GHz for the first time. Sezawa mode
frequency scaling is achieved due to the elimination of the thick buffer layer
typically present in epitaxial GaN technology. Finite element analysis (FEA) is
first performed to find the range of frequencies over which the Sezawa mode is
supported in the grown structure. Transmission lines and resonance cavities
driven with Interdigital Transducers (IDTs) are designed, fabricated, and
characterized. Modified Mason circuit models are developed for each class of
devices to extract critical performance metrics. We observe a strong
correlation between measured and simulated dispersion of the phase velocity
(vp) and piezoelectric coupling coefficient (k^2). Maximum k^2 of 0.61% and
frequency-quality factor product (f.Qm) of 6x10^12 1/s are achieved for Sezawa
resonators at 11 GHz, with a minimum propagation loss of 0.26 dB/wavelength for
the two-port devices. Sezawa modes are observed at frequencies spanning up to
14.3 GHz, achieving a record high in GaN microelectromechanical systems (MEMS)
to the best of the authors' knowledge.


  The unification of relativity and thermodynamics has been a subject of
considerable debate over the last 100 years. The reasons for this are twofold:
(i) Thermodynamic variables are nonlocal quantities and, thus, single out a
preferred class of hyperplanes in spacetime. (ii) There exist different,
seemingly equally plausible ways of defining heat and work in relativistic
systems. These ambiguities led, for example, to various proposals for the
Lorentz transformation law of temperature. Traditional 'isochronous'
formulations of relativistic thermodynamics are neither theoretically
satisfactory nor experimentally feasible. Here, we demonstrate how these
deficiencies can be resolved by defining thermodynamic quantities with respect
to the backward-lightcone of an observation event. This approach yields novel,
testable predictions and allows for a straightforward-extension of
thermodynamics to General Relativity. Our theoretical considerations are
illustrated through three-dimensional relativistic many-body simulations.


  The difference in the transmission for left and right circularly polarised
light though thin films on substrates in a magnetic field is used to obtain the
magnetic circular dichroism of the film. However there are reflections at all
the interfaces and these are also different for the two polarisations and
generate the polar Kerr signal. In this paper the contribution to the
differences to the total transmission from the transmission across interfaces
as well as the differences in absorption in the film and the substrate are
calculated. This gives a guide to when it is necessary to evaluate these
corrections in order to obtain the real MCD from a measure of the differential
transmission due to differential absorption in the film.


  Artificial intelligence (AI) in its various forms finds more and more its way
into complex distributed systems. For instance, it is used locally, as part of
a sensor system, on the edge for low-latency high-performance inference, or in
the cloud, e.g. for data mining. Modern complex systems, such as connected
vehicles, are often part of an Internet of Things (IoT). To manage complexity,
architectures are described with architecture frameworks, which are composed of
a number of architectural views connected through correspondence rules. Despite
some attempts, the definition of a mathematical foundation for architecture
frameworks that are suitable for the development of distributed AI systems
still requires investigation and study. In this paper, we propose to extend the
state of the art on architecture framework by providing a mathematical model
for system architectures, which is scalable and supports co-evolution of
different aspects for example of an AI system. Based on Design Science
Research, this study starts by identifying the challenges with architectural
frameworks. Then, we derive from the identified challenges four rules and we
formulate them by exploiting concepts from category theory. We show how
compositional thinking can provide rules for the creation and management of
architectural frameworks for complex systems, for example distributed systems
with AI. The aim of the paper is not to provide viewpoints or architecture
models specific to AI systems, but instead to provide guidelines based on a
mathematical formulation on how a consistent framework can be built up with
existing, or newly created, viewpoints. To put in practice and test the
approach, the identified and formulated rules are applied to derive an
architectural framework for the EU Horizon 2020 project ``Very efficient deep
learning in the IoT" (VEDLIoT) in the form of a case study.


  We have investigated magneto-transport properties in a single crystal of
pyrochore-type Nd2Ir2O7. The metallic conduction is observed on the
antiferromagnetic domain walls of the all-in all-out type Ir-5d moment ordered
insulating bulk state, that can be finely controlled by external magnetic field
along [111]. On the other hand, an applied field along [001] induces the bulk
phase transition from insulator to semimetal as a consequence of the
field-induced modification of Nd-4f and Ir-5d moment configurations. A
theoretical calculation consistently describing the experimentally observed
features suggests a variety of exotic topological states as functions of
electron correlation and Ir-5d moment orders which can be finely tuned by
choice of rare-earth ion and by magnetic field, respectively.


  I consider the problem of learning an optimal path graphical model from data
and show the problem to be NP-hard for the maximum likelihood and minimum
description length approaches and a Bayesian approach. This hardness result
holds despite the fact that the problem is a restriction of the polynomially
solvable problem of finding the optimal tree graphical model.


  We present WiGest: a system that leverages changes in WiFi signal strength to
sense in-air hand gestures around the user's mobile device. Compared to related
work, WiGest is unique in using standard WiFi equipment, with no
modi-fications, and no training for gesture recognition. The system identifies
different signal change primitives, from which we construct mutually
independent gesture families. These families can be mapped to distinguishable
application actions. We address various challenges including cleaning the noisy
signals, gesture type and attributes detection, reducing false positives due to
interfering humans, and adapting to changing signal polarity. We implement a
proof-of-concept prototype using off-the-shelf laptops and extensively evaluate
the system in both an office environment and a typical apartment with standard
WiFi access points. Our results show that WiGest detects the basic primitives
with an accuracy of 87.5% using a single AP only, including through-the-wall
non-line-of-sight scenarios. This accuracy in-creases to 96% using three
overheard APs. In addition, when evaluating the system using a multi-media
player application, we achieve a classification accuracy of 96%. This accuracy
is robust to the presence of other interfering humans, highlighting WiGest's
ability to enable future ubiquitous hands-free gesture-based interaction with
mobile devices.


  Segmentation of medical images is an essential part in the process of
diagnostics. Physicians require an automatic, robust and valid results. Hidden
Markov Random Fields (HMRF) provide powerful model. This latter models the
segmentation problem as the minimization of an energy function. Cuckoo search
(CS) algorithm is one of the recent nature-inspired meta-heuristic algorithms.
It has shown its efficiency in many engineering optimization problems. In this
paper, we use three cuckoo search algorithm to achieve medical image
segmentation.


  In this talk I first discuss predictions based on QCD sum rules and on
hadronic models for the properties of vector mesons in the nuclear medium. I
then describe possible experimental signatures and show detailed predictions
for dilepton invariant mass spectra with a special emphasis on nuclear
reactions involving elementary incoming beams and nuclear targets. I will in
particular illustrate that the sensitivity of pion and photon induced reactions
to in-medium effects is comparable to that of heavy-ion reactions.


  A manifestly Lorentz-covariant formulation of Loop Quantum Gravity (LQG) is
given in terms of finite-dimensional representations of the Lorentz group. The
formulation accounts for discrete symmetries, such as parity and time-reversal,
and it establishes a link with Wigner classification of particles. The
resulting quantum model can be seen as LQG with the internal $SU(2)\otimes
SU(2)$ group and it is free of the Immirzi parameter, while the scalar
constraint is just the Euclidean part.


  Coupling a qubit coherently to an ensemble is the basis for collective
quantum memories. A driven quantum dot can deterministically excite low-energy
collective modes of a nuclear spin ensemble in the presence of lattice strain.
We propose to gate a quantum state transfer between this central electron and
these low-energy excitations -- spin waves -- in the presence of a strong
magnetic field, where the nuclear coherence time is long. We develop a
microscopic theory capable of calculating the exact time evolution of the
strained electron-nuclear system. With this, we evaluate the operation of
quantum state storage and show that fidelities up to 90% can be reached with a
modest nuclear polarisation of only 50%. These findings demonstrate that
strain-enabled nuclear spin waves are a highly suitable candidate for quantum
memory.


  Experimental data is often affected by uncontrolled variables that make
analysis and interpretation difficult. For spatiotemporal systems, this problem
is further exacerbated by their intricate dynamics. Modern machine learning
methods are particularly well-suited for analyzing and modeling complex
datasets, but to be effective in science, the result needs to be interpretable.
We demonstrate an unsupervised learning technique for extracting interpretable
physical parameters from noisy spatiotemporal data and for building a
transferable model of the system. In particular, we implement a
physics-informed architecture based on variational autoencoders that is
designed for analyzing systems governed by partial differential equations
(PDEs). The architecture is trained end-to-end and extracts latent parameters
that parameterize the dynamics of a learned predictive model for the system. To
test our method, we train our model on simulated data from a variety of PDEs
with varying dynamical parameters that act as uncontrolled variables. Numerical
experiments show that our method can accurately identify relevant parameters
and extract them from raw and even noisy spatiotemporal data (tested with
roughly 10% added noise). These extracted parameters correlate well (linearly
with $R^2 > 0.95$) with the ground truth physical parameters used to generate
the datasets. We then apply this method to nonlinear fiber propagation data,
generated by an ab-initio simulation, to demonstrate its capabilities on a more
realistic dataset. Our method for discovering interpretable latent parameters
in spatiotemporal systems will allow us to better analyze and understand
real-world phenomena and datasets, which often have unknown and uncontrolled
variables that alter the system dynamics and cause varying behaviors that are
difficult to disentangle.


  Programming errors in Ethereum smart contracts can result in catastrophic
financial losses from stolen cryptocurrency. While vulnerability detectors can
prevent vulnerable contracts from being deployed, this does not mean that such
contracts will not be deployed. Once a vulnerable contract is instantiated on
the blockchain and becomes the target of attacks, the identification of exploit
transactions becomes indispensable in assessing whether it has been actually
exploited and identifying which malicious or subverted accounts were involved.
  In this work, we study the problem of post-factum investigation of Ethereum
attacks using Indicators of Compromise (IoCs) specially crafted for use in the
blockchain. IoC definitions need to capture the side-effects of successful
exploitation in the context of the Ethereum blockchain. Therefore, we define a
model for smart contract execution, comprising multiple abstraction levels that
mirror the multiple views of code execution on a blockchain. Subsequently, we
compare IoCs defined across the different levels in terms of their
effectiveness and practicality through EtherClue, a prototype tool for
investigating Ethereum security incidents. Our results illustrate that
coarse-grained IoCs defined over blocks of transactions can detect exploit
transactions with less computation; however, they are contract-specific and
suffer from false negatives. On the other hand, fine-grained IoCs defined over
virtual machine instructions can avoid these pitfalls at the expense of
increased computation which are nevertheless applicable for practical use.


  We report the electromotive force (EMF) properties generated in 3d-transition
ferromagnetic metal (FM = Fe, Co, and Ni80Fe20) films themselves under their
ferromagnetic resonance (FMR). For Fe and Co films, the EMF due to the
anomalous-Hall effect is dominantly generated under their FMR. Meanwhile, for a
Ni80Fe20 film, the EMF due to the inverse spin-Hall effect in the Ni80Fe20 film
itself under the FMR is mainly generated. This tendency is qualitatively
explained with differences of the spin polarization, the spin Hall
conductivity, the anomalous Hall conductivity, the magnetization saturation,
and the resistivity of the FM films.


  We consider higher dimensional generalisations of normal almost contact
structures, the so called f.pk-structures where parallelism spans a Lie algebra
g (f.pk-g-structures). Two types of these structures are discussed. In the
first case, we construct an almost complex structure on a product manifold
mirroring K-structures. We show that the natural normality condition can be
satisfied only when g is abelian. The second case we consider is when the Lie
algebra in question is 3-dimensional, but the almost complex structure on a
product is constructed in a different manner. In both cases the normality
conditions are expressed in terms of the structure tensors.


  In this article we collect results obtained by the authors jointly with other
authors and we discuss old and new ideas. In particular we discuss
singularities of the exponential map, completeness and homogeneity for
Riemannian Hilbert quotient manifolds. We also extend a Theorem due to Nomizu
and Ozeki to infinite dimensional Riemannian Hilbert manifolds.


  Dynamics in correlated quantum matter is a hard problem, as its exact
solution generally involves a computational effort that grows exponentially
with the number of constituents. While a remarkable progress has been witnessed
in recent years for one-dimensional systems, much less has been achieved for
interacting quantum models in higher dimensions, since they incorporate an
additional layer of complexity. In this work, we employ a variational method
that allows for an efficient and controlled computation of the dynamics of
quantum many-body systems in one and higher dimensions. The approach presented
here introduces a variational class of wavefunctions based on complex networks
of classical spins akin to artificial neural networks, which can be constructed
in a controlled fashion. We provide a detailed prescription for such
constructions and illustrate their performance by studying quantum quenches in
one- and two-dimensional models. In particular, we investigate the
nonequilibrium dynamics of a genuinely interacting two-dimensional lattice
gauge theory, the quantum link model, for which we have recently shown --
employing the technique discussed thoroughly in this paper -- that it features
disorder-free localization dynamics [P. Karpov et al., Phys. Rev. Lett. 126,
130401 (2021)]. The present work not only supplies a framework to address
purely theoretical questions but also could be used to provide a theoretical
description of experiments in quantum simulators, which have recently seen an
increased effort targeting two-dimensional geometries. Importantly, our method
can be applied to any quantum many-body system with a well-defined classical
limit.


  Procedural content generation (PCG) is a growing field, with numerous
applications in the video game industry and great potential to help create
better games at a fraction of the cost of manual creation. However, much of the
work in PCG is focused on generating relatively straightforward levels in
simple games, as it is challenging to design an optimisable objective function
for complex settings. This limits the applicability of PCG to more complex and
modern titles, hindering its adoption in industry. Our work aims to address
this limitation by introducing a compositional level generation method that
recursively composes simple low-level generators to construct large and complex
creations. This approach allows for easily-optimisable objectives and the
ability to design a complex structure in an interpretable way by referencing
lower-level components. We empirically demonstrate that our method outperforms
a non-compositional baseline by more accurately satisfying a designer's
functional requirements in several tasks. Finally, we provide a qualitative
showcase (in Minecraft) illustrating the large and complex, but still coherent,
structures that were generated using simple base generators.


  We use the conformal approach to numerical relativity to evolve hyperboloidal
gravitational wave data without any symmetry assumptions. Although our grid is
finite in space and time, we cover the whole future of the initial data in our
calculation, including future null and future timelike infinity.


  Prepared domain specific datasets plays an important role to supervised
learning approaches. In this article a new sentence dataset for software
quality-in-use is proposed. Three experts were chosen to annotate the data
using a proposed annotation scheme. Then the data were reconciled in a (no
match eliminate) process to reduce bias. The Kappa, k statistics revealed an
acceptable level of agreement; moderate to substantial agreement between the
experts. The built data can be used to evaluate software quality-in-use models
in sentiment analysis models. Moreover, the annotation scheme can be used to
extend the current dataset.


  A recent experiment showed that, contrary to theoretical predictions, beyond
a cutoff point grinding coffee more finely results in lower extraction. One
potential explanation for this is that fine grinding promotes non-uniform
extraction in the coffee bed. We investigate the possibility that this could
occur due the interaction between dissolution and flow promoting uneven
extraction. A low dimensional model in which there are two possible pathways
for flow is derived and analysed. This model shows that, below a critical grind
size, there is a decreasing extraction with decreasing grind size as is seen
experimentally. In the model this is due to a complicated interplay between an
initial imbalance in the porosities and permeabilities of the two pathways
which is increased by flow and extraction, leading to the complete extraction
of all soluble coffee from one pathway.


  We present a theory of information expressed solely in terms of which
transformations of physical systems are possible and which are impossible -
i.e. in constructor-theoretic terms. Although it includes conjectured laws of
physics that are directly about information, independently of the details of
particular physical instantiations, it does not regard information as an a
priori mathematical or logical concept, but as something whose nature and
properties are determined by the laws of physics alone. It does not suffer from
the circularity at the foundations of existing information theory (namely that
information and distinguishability are each defined in terms of the other). It
explains the relationship between classical and quantum information, and
reveals the single, constructor-theoretic property underlying the most
distinctive phenomena associated with the latter, including the lack of
in-principle distinguishability of some states, the impossibility of cloning,
the existence of pairs of variables that cannot simultaneously have sharp
values, the fact that measurement processes can be both deterministic and
unpredictable, the irreducible perturbation caused by measurement, and
entanglement (locally inaccessible information).


  We consider an analog of particle production in a quartic $O(N)$ quantum
oscillator with time-dependent frequency, which is a toy model of particle
production in the dynamical Casimir effect and de Sitter space. We calculate
exact quantum averages, Keldysh propagator, and particle number using two
different methods. First, we employ a kind of rotating wave approximation to
estimate these quantities for small deviations from stationarity. Second, we
extend these results to arbitrarily large deviations using the
Schwinger-Keldysh diagrammatic technique. We show that in strongly
nonstationary situations, including resonant oscillations, loop corrections to
the tree-level expressions effectively result in an additional degree of
freedom, $N \to N + \frac{3}{2}$, which modifies the average number and energy
of created particles.


  We consider complex K3 surfaces with a non-symplectic group acting trivially
on the algebraic cycles. Vorontsov and Kondo classified those K3 surfaces with
transcendental lattice of minimal rank. The purpose of this note is to study
the Galois representations associated to these K3 surfaces. The rank of the
transcendental lattices is even and varies from 2 to 20, excluding 8 and 14. We
show that these K3 surfaces are dominated by Fermat surfaces, and hence they
are all of CM type. We will establish the modularity of the Galois
representations associated to them. Also we discuss mirror symmetry for these
K3 surfaces in the sense of Dolgachev, and show that a mirror K3 surface exists
with one exception.


  Real-world object detection models should be cheap and accurate. Knowledge
distillation (KD) can boost the accuracy of a small, cheap detection model by
leveraging useful information from a larger teacher model. However, a key
challenge is identifying the most informative features produced by the teacher
for distillation. In this work, we show that only a very small fraction of
features within a ground-truth bounding box are responsible for a teacher's
high detection performance. Based on this, we propose Prediction-Guided
Distillation (PGD), which focuses distillation on these key predictive regions
of the teacher and yields considerable gains in performance over many existing
KD baselines. In addition, we propose an adaptive weighting scheme over the key
regions to smooth out their influence and achieve even better performance. Our
proposed approach outperforms current state-of-the-art KD baselines on a
variety of advanced one-stage detection architectures. Specifically, on the
COCO dataset, our method achieves between +3.1% and +4.6% AP improvement using
ResNet-101 and ResNet-50 as the teacher and student backbones, respectively. On
the CrowdHuman dataset, we achieve +3.2% and +2.0% improvements in MR and AP,
also using these backbones. Our code is available at
https://github.com/ChenhongyiYang/PGD.


  We propose an estimation procedure for discrete choice models of
differentiated products with possibly high-dimensional product attributes. In
our model, high-dimensional attributes can be determinants of both mean and
variance of the indirect utility of a product. The key restriction in our model
is that the high-dimensional attributes affect the variance of indirect
utilities only through finitely many indices. In a framework of the
random-coefficients logit model, we show a bound on the error rate of a
$l_1$-regularized minimum distance estimator and prove the asymptotic linearity
of the de-biased estimator.


  We model physical signals using elements of the algebra of split octonions
over the field of real numbers. Elementary particles are corresponded to the
special elements of the algebra that nullify octonionic norms (zero divisors).
It is shown that the standard model particle spectrum naturally follows from
the classification of the independent primitive zero divisors of split
octonions.


  Speaker adaptation, which involves cloning voices from unseen speakers in the
Text-to-Speech task, has garnered significant interest due to its numerous
applications in multi-media fields. Despite recent advancements, existing
methods often struggle with inadequate speaker representation accuracy and
overfitting, particularly in limited reference speeches scenarios. To address
these challenges, we propose an Agile Speaker Representation Reinforcement
Learning strategy to enhance speaker similarity in speaker adaptation tasks.
ASRRL is the first work to apply reinforcement learning to improve the modeling
accuracy of speaker embeddings in speaker adaptation, addressing the challenge
of decoupling voice content and timbre. Our approach introduces two action
strategies tailored to different reference speeches scenarios. In the
single-sentence scenario, a knowledge-oriented optimal routine searching RL
method is employed to expedite the exploration and retrieval of refinement
information on the fringe of speaker representations. In the few-sentence
scenario, we utilize a dynamic RL method to adaptively fuse reference speeches,
enhancing the robustness and accuracy of speaker modeling. To achieve optimal
results in the target domain, a multi-scale fusion scoring mechanism based
reward model that evaluates speaker similarity, speech quality, and
intelligibility across three dimensions is proposed, ensuring that improvements
in speaker similarity do not compromise speech quality or intelligibility. The
experimental results on the LibriTTS and VCTK datasets within mainstream TTS
frameworks demonstrate the extensibility and generalization capabilities of the
proposed ASRRL method. The results indicate that the ASRRL method significantly
outperforms traditional fine-tuning approaches, achieving higher speaker
similarity and better overall speech quality with limited reference speeches.


  We point out that the elliptic genus of the K3 surface has a natural
decomposition in terms of dimensions of irreducible representations of the
largest Mathieu group M_24. The reason is yet a mystery.


  Deep neural network pruning and quantization techniques have demonstrated it
is possible to achieve high levels of compression with surprisingly little
degradation to test set accuracy. However, this measure of performance conceals
significant differences in how different classes and images are impacted by
model compression techniques. We find that models with radically different
numbers of weights have comparable top-line performance metrics but diverge
considerably in behavior on a narrow subset of the dataset. This small subset
of data points, which we term Pruning Identified Exemplars (PIEs) are
systematically more impacted by the introduction of sparsity. Compression
disproportionately impacts model performance on the underrepresented long-tail
of the data distribution. PIEs over-index on atypical or noisy images that are
far more challenging for both humans and algorithms to classify. Our work
provides intuition into the role of capacity in deep neural networks and the
trade-offs incurred by compression. An understanding of this disparate impact
is critical given the widespread deployment of compressed models in the wild.


  The Chandra X-ray Satellite already observed several deep fields, including
the two 1 Megasec exposures of the Chandra Deep Field South (CDFS) and North.
We review here the main findings from the CDFS. The LogN-LogS relations show
the resolution of the X-ray background into point sources at the level of
83-99% in the 1-2 keV band and 65-98% in the 2-10 keV band, given the
uncertainties in the unresolved value. The so called ``spectral paradox'' is
solved by a hard, faint population of sources constituted mostly by nearby
(z<1) absorbed (Type II) AGNs with hard-band luminosities L~10^{42}-10^{44}
erg/s. When comparing these results to other deep fields in the X-ray band, we
find that the AGNs detected in 0.1 deg^2 of the CDFS are representative of the
AGNs population as a whole. However, we also noticed an excess in the hard
counts in two Chandra deep fields. If we include this excess and average it
among the observed fields, the total contribution of the XRB can grow of about
7%. Finally, we discuss briefly the properties of the Intra Cluster Medium
imaged at high z, showing no evolution in clusters properties up to z~1.


  Duality is considered for the perturbation theory by deriving, given a series
solution in a small parameter, its dual series with the development parameter
being the inverse of the other. A dual symmetry in perturbation theory is
identified. It is then shown that the dual to the Dyson series in quantum
mechanics is given by a recent devised series having the adiabatic
approximation as leading order. A simple application of this result is given by
rederiving a theorem for strongly perturbed quantum systems.


  The spatial scan statistic is widely used in epidemiology and medical studies
as a tool to identify hotspots of diseases. The classical spatial scan
statistic assumes the number of disease cases in different locations have
independent Poisson distributions, while in practice the data may exhibit
overdispersion and spatial correlation. In this work, we examine the behavior
of the spatial scan statistic when overdispersion and spatial correlation are
present, and propose a modified spatial scan statistic to account for that.
Some theoretical results are provided to demonstrate that ignoring the
overdispersion and spatial correlation leads to an increased rate of false
positives, which is verified through a simulation study. Simulation studies
also show that our modified procedure can substantially reduce the rate of
false alarms. Two data examples involving brain cancer cases in New Mexico and
chickenpox incidence data in France are used to illustrate the practical
relevance of the modified procedure.


  Text simplification aims to make technical texts more accessible to laypeople
but often results in deletion of information and vagueness. This work proposes
InfoLossQA, a framework to characterize and recover simplification-induced
information loss in form of question-and-answer (QA) pairs. Building on the
theory of Question Under Discussion, the QA pairs are designed to help readers
deepen their knowledge of a text. We conduct a range of experiments with this
framework. First, we collect a dataset of 1,000 linguist-curated QA pairs
derived from 104 LLM simplifications of scientific abstracts of medical
studies. Our analyses of this data reveal that information loss occurs
frequently, and that the QA pairs give a high-level overview of what
information was lost. Second, we devise two methods for this task: end-to-end
prompting of open-source and commercial language models, and a natural language
inference pipeline. With a novel evaluation framework considering the
correctness of QA pairs and their linguistic suitability, our expert evaluation
reveals that models struggle to reliably identify information loss and applying
similar standards as humans at what constitutes information loss.


  We review the last year progress in understanding supersymmetric SU(2)
Yang-Mills quantum mechanics in four and ten space-time dimensions. The four
dimensional system is now well under control and the precise spectrum is
obtained in all channels. In D=10 some new results are also available.


  Virtual try-on (VTO) applications aim to improve the online shopping
experience by allowing users to preview garments, before making purchase
decisions. However, many VTO tools fail to consider the crucial relationship
between a garment's size and the user's body size, often employing a
one-size-fits-all approach when visualizing a clothing item. This results in
poor size recommendations and purchase decisions leading to increased return
rates. To address this limitation, we introduce SiCo, an online VTO system,
where users can upload images of themselves and visualize how different sizes
of clothing would look on their body to help make better-informed purchase
decisions. Our user study shows SiCo's superiority over baseline VTO. The
results indicate that our approach significantly enhances user ability to gauge
the appearance of outfits on their bodies and boosts their confidence in
selecting clothing sizes that match desired goals. Based on our evaluation, we
believe our VTO design has the potential to reduce return rates and enhance the
online clothes shopping experience. Our code is available at
https://github.com/SherryXTChen/SiCo.


  Expository documents are vital resources for conveying complex information to
readers. Despite their usefulness, writing expository text by hand is a
challenging process that requires careful content planning, obtaining facts
from multiple sources, and the ability to clearly synthesize these facts. To
ease these burdens, we propose the task of expository text generation, which
seeks to automatically generate an accurate and stylistically consistent
expository text for a topic by intelligently searching a knowledge source. We
solve our task by developing IRP, a framework that overcomes the limitations of
retrieval-augmented models and iteratively performs content planning, fact
retrieval, and rephrasing. Through experiments on three diverse,
newly-collected datasets, we show that IRP produces factual and organized
expository texts that accurately inform readers.


  Multi-state models are increasingly being used to model complex
epidemiological and clinical outcomes over time. It is common to assume that
the models are Markov, but the assumption can often be unrealistic. The Markov
assumption is seldomly checked and violations can lead to biased estimation for
many parameters of interest. As argued by Datta and Satten (2001), the
Aalen-Johansen estimator of occupation probabilities is consistent also in the
non-Markov case. Putter and Spitoni (2018) exploit this fact to construct a
consistent estimator of state transition probabilities, the landmark
Aalen-Johansen estimator, which does not rely on the Markov assumption. A
disadvantage of landmarking is data reduction, leading to a loss of power. This
is problematic for less traveled transitions, and undesirable when such
transitions indeed exhibit Markov behaviour. Using a framework of partially
non-Markov multi-state models we suggest a hybrid landmark Aalen-Johansen
estimator for transition probabilities. The proposed estimator is a compromise
between regular Aalen-Johansen and landmark estimation, using transition
specific landmarking, and can drastically improve statistical power. The
methods are compared in a simulation study and in a real data application
modelling individual transitions between states of sick leave, disability,
education, work and unemployment. In the application, a birth cohort of 184951
Norwegian men are followed for 14 years from the year they turn 21, using data
from national registries.


  Electronic skin, a class of wearable electronic sensors that mimic the
functionalities of human skin, has made remarkable success in applications
including health monitoring, human-machine interaction and
electronic-biological interfaces. While electronic skin continues to achieve
higher sensitivity and faster response, its ultimate performance is
fundamentally limited by the nature of low-frequency AC currents in electronic
circuitries. Here we demonstrate highly sensitive optical skin (O-skin) in
which the primary sensory elements are optically driven. The simple
construction of the sensors is achieved by embedding glass micro/nanofibers
(MNFs) in thin layers of polydimethylsiloxane (PDMS). Enabled by the highly
sensitive power-leakage response of the guided modes from the MNF upon external
stimuli, our optical sensors show ultrahigh sensitivity (1870/kPa), low
detection limit (7 mPa) and fast response (10 microseconds) for pressure
sensing, significantly exceeding the performance metrics of state-of-the-art
electronic skins. Electromagnetic interference (EMI)-free detection of
high-frequency vibrations, wrist pulse and human voice are realized. Moreover,
a five-sensor optical data glove and a 2x2-MNF tactile sensor are demonstrated.
Our results pave the way toward wearable optical devices ranging from
ultrasensitive flexible sensors to optical skins.


  The Multi Unit Spectroscopic Explorer (MUSE) is an integral field
spectrograph on the Very Large Telescope Unit Telescope 4, capable of laser
guide star assisted and tomographic adaptive optics using the GALACSI module.
Its observing capabilities include a wide field (1 square arcmin), ground layer
AO mode (WFM-AO) and a narrow field (7.5"x7.5"), laser tomography AO mode
(NFM-AO). The latter has had several upgrades in the 4 years since
commissioning, including an optimisation of the control matrices for the AO
system and a new sub-electron noise detector for its infra-red low order
wavefront sensor. We set out to quantify the NFM-AO system performance by
analysing $\sim$230 spectrophotometric standard star observations taken over
the last 3 years. To this end we expand upon previous work, designed to
facilitate analysis of the WFM-AO system performance. We briefly describe the
framework that will provide a user friendly, semi-automated way for system
performance monitoring during science operations. We provide the results of our
performance analysis, chiefly through the measured Strehl ratio and full width
at half maximum (FWHM) of the core of the point spread function (PSF) using two
PSF models, and correlations with atmospheric conditions. These results will
feed into a range of applications, including providing a more accurate
prediction of the system performance as implemented in the exposure time
calculator, and the associated optimization of the scientific output for a
given set of limiting atmospheric conditions.


  In this paper, we present GALEX-SDSS-WISE Legacy Catalog (GSWLC), a catalog
of physical properties (stellar masses, dust attenuations and star formation
rates (SFRs)) of ~700,000 galaxies with SDSS redshifts below 0.3. GSWLC
contains galaxies within the GALEX footprint, regardless of a UV detection,
covering 90% of SDSS. The physical properties were obtained from UV/optical SED
fitting following Bayesian methodology of Salim et al. (2007), with
improvements such as blending corrections for low-resolution UV photometry,
flexible dust attenuation laws, and emission line corrections. GSWLC includes
mid-IR SFRs derived from IR templates based upon 22 micron WISE observations.
These estimates are independent of UV/optical SED fitting, in order to separate
possible systematics. The paper argues that the comparison of specific SFRs
(SSFRs) is more informative and physically motivated than the comparison of
SFRs. SSFRs resulting from the UV/optical SED fitting are compared to the
mid-IR SSFRs, and to SSFRs from three published catalogs. For "main sequence"
galaxies with no AGN contribution all SSFRs are in very good agreement (within
0.1 dex on average). In particular, the widely-used aperture-corrected SFRs
from MPA/JHU catalog show no systematic offsets, in contrast to some
integral-field spectroscopy results. For galaxies below the main sequence (log
SSFR<-11), mid-IR (S)SFRs based on fixed luminosity-SFR conversion are severely
biased (up to 2 dex) because the dust is primarily heated by old stars.
Furthermore, mid-IR (S)SFRs are overestimated by up to 0.6 dex for galaxies
with AGN, presumably due to non-stellar dust heating. UV/optical (S)SFRs are
thus preferred to IR-based (S)SFRs for quenched galaxies and those which host
AGN.


  Reasoning is a critical ability towards complete visual understanding. To
develop machine with cognition-level visual understanding and reasoning
abilities, the visual commonsense reasoning (VCR) task has been introduced. In
VCR, given a challenging question about an image, a machine must answer
correctly and then provide a rationale justifying its answer. The methods
adopting the powerful BERT model as the backbone for learning joint
representation of image content and natural language have shown promising
improvements on VCR. However, none of the existing methods have utilized
commonsense knowledge in visual commonsense reasoning, which we believe will be
greatly helpful in this task. With the support of commonsense knowledge,
complex questions even if the required information is not depicted in the image
can be answered with cognitive reasoning. Therefore, we incorporate commonsense
knowledge into the cross-modal BERT, and propose a novel Knowledge Enhanced
Visual-and-Linguistic BERT (KVL-BERT for short) model. Besides taking visual
and linguistic contents as input, external commonsense knowledge extracted from
ConceptNet is integrated into the multi-layer Transformer. In order to reserve
the structural information and semantic representation of the original
sentence, we propose using relative position embedding and mask-self-attention
to weaken the effect between the injected commonsense knowledge and other
unrelated components in the input sequence. Compared to other task-specific
models and general task-agnostic pre-training models, our KVL-BERT outperforms
them by a large margin.


  We have discovered that room temperature liquid metal is capable of
penetrating through macro- and microporous materials by applying a voltage. In
this work, we demonstrate the liquid metal penetration effect in various porous
materials such as tissue paper, thick and fine sponges, fabrics, and meshes.
The penetration effect mimics one of the three well-known superfluid properties
of liquid helium superfluid that only occur at near-zero Kelvin. The underlying
mechanism is that the high surface tension of liquid metal can be significantly
reduced to near-zero due to the voltage induced oxidation of the liquid metal
surface in a solution. It is the extremely low surface tension and gravity that
cause the liquid metal to superwet the solid surface, leading to the
penetration phenomena. Our findings offer new opportunities for novel
microfluidic applications and could promote further discovery of more exotic
fluid states of liquid metals.


  A time scale is a procedure for accurately and continuously marking the
passage of time. It is exemplified by Coordinated Universal Time (UTC), and
provides the backbone for critical navigation tools such as the Global
Positioning System (GPS). Present time scales employ microwave atomic clocks,
whose attributes can be combined and averaged in a manner such that the
composite is more stable, accurate, and reliable than the output of any
individual clock. Over the past decade, clocks operating at optical frequencies
have been introduced which are orders of magnitude more stable than any
microwave clock. However, in spite of their great potential, these optical
clocks cannot be operated continuously, which makes their use in a time scale
problematic. In this paper, we report the development of a hybrid
microwave-optical time scale, which only requires the optical clock to run
intermittently while relying upon the ensemble of microwave clocks to serve as
the flywheel oscillator. The benefit of using clock ensemble as the flywheel
oscillator, instead of a single clock, can be understood by the Dick-effect
limit. This time scale demonstrates for the first time sub-nanosecond accuracy
for a few months, attaining a fractional frequency uncertainty of 1.45*10-16 at
30 days and reaching the 10-17 decade at 50 days, with respect to UTC. This
time scale significantly improves the accuracy in timekeeping and could change
the existing time-scale architectures.


  Coronal-Line Forest Active Galactic Nuclei (CLiF AGN) are characterized by
strong, high-ionization lines, which are in contrast to what is found in
typical AGNs. Here, we carry out an infrared analysis aimed at understanding
the spectral energy distribution of six sources from this group. In this work,
the properties of the dusty torus for these objects are analyzed. To this
purpose, we infer the physical and geometrical properties of the dust structure
that surrounds the central region by fitting with models the spectral energy
distribution (SED) of CLiF AGNs in the infrared. For this analysis, we compare
the results of three models: CLUMPY, SKIRTOR and CAT3D-WIND. Using the Bayesian
information criterion, SKIRTOR was found to have the most robust fit to the
SEDs in five out of six galaxies. The remaining object was best fitted with
CLUMPY. The results indicate that these objects are preferentially Type~I
sources, supporting the detection of broad components in the permitted lines,
likely associated with the BLR in the near-infrared (NIR) spectra. The best SED
fitting indicates that the line of sight gives access to the view of the
central source for these objects, but the amount of dusty clouds in the same
direction is high, suggesting the hypothesis that they obscure the emission of
the continuum produced by the central source and that the obscuration makes the
coronal lines to not overlap with the continuum.


  We prove stability estimates for the Bakry-Emery bound on Poincar\'e and
logarithmic Sobolev constants of uniformly log-concave measures. In particular,
we improve the quantitative bound in a result of De Philippis and Figalli
asserting that if a $1$-uniformly log-concave measure has almost the same
Poincar\'e constant as the standard Gaussian measure, then it almost splits off
a Gaussian factor, and establish similar new results for logarithmic Sobolev
inequalities. As a consequence, we obtain dimension-free stability estimates
for Gaussian concentration of Lipschitz functions. The proofs are based on
Stein's method, optimal transport, and an approximate integration by parts
identity relating measures and approximate optimizers in the associated
functional inequality.


  We present a general framework for analyzing inhomogeneous (color-)
superconducting phases in mean-field approximation without restriction to the
Ginzburg-Landau approach. As a first application, we calculate real gap
functions with general one-dimensional periodic structures for a
3+1-dimensional toy model having two fermion species. The resulting solutions
are energetically favored against homogeneous superconducting (BCS) and normal
conducting phases in a window for the chemical potential difference which is
about twice as wide as for the most simple plane-wave ansatz ("Fulde-Ferrell
phase"). At the lower end of this window, we observe the formation of a soliton
lattice and a continuous phase transition to the BCS phase. At the higher end
of the window the gap functions are sinusoidal, and the transition to the
normal conducting phase is of first order. We also discuss the quasiparticle
excitation spectrum in the inhomogeneous phase. Finally, we compare the gap
functions with the known analytical solutions of the 1+1-dimensional theory.


  Previous studies have shown that WISE-selected hyperluminous, hot
dust-obscured galaxies (Hot DOGs) are powered by highly dust-obscured, possibly
Compton-thick AGNs. High obscuration provides us a good chance to study the
host morphology of the most luminous AGNs directly. We analyze the host
morphology of 18 Hot DOGs at $z\sim3$ using Hubble Space Telescope/WFC3
imaging. We find that Hot DOGs have a high merger fraction ($62\pm 14 \%$). By
fitting the surface brightness profiles, we find that the distribution of
S\'ersic indices in our Hot DOG sample peaks around 2, which suggests that most
of Hot DOGs have transforming morphologies. We also derive the AGN bolometric
luminosity ($\sim10^{14}L_\odot$) of our Hot DOG sample by using IR SEDs
decomposition. The derived merger fraction and AGN bolometric luminosity
relation is well consistent with the variability-based model prediction (Hickox
et al. 2014). Both the high merger fraction in IR-luminous AGN sample and
relatively low merger fraction in UV/optical-selected, unobscured AGN sample
can be expected in the merger-driven evolutionary model. Finally, we conclude
that Hot DOGs are merger-driven and may represent a transit phase during the
evolution of massive galaxies, transforming from the dusty starburst dominated
phase to the unobscured QSO phase.


  Social networks play an important role in people's daily socialization,
particularly through social media platforms, which have become key channels for
communication and information dissemination. The digital ecosystem does not
only evolve communication on multi-network (like TV, social media, and online
newspapers) but also provides the social researcher with useful data to explain
social-complex dynamics. Our work focus on cultural dynamics-reactions that
occurred during the 2020 Emilia-Romagna elections'' in Italy, where a
stronghold culture felt in danger of losing against the strong populism and
Euro-scepticism present in digital ecosystems. We would like to show how the
interaction between parts of the society, during cultural and/or political
shifting, can lead to or induce emerging behaviour from society, creating
groups that react against or improve the status quo. We developed a word-entry
network based on three different levels of participation: pro, con, and
neutral. We have analyzed the tweets collected (as text) with the word
embedding tools, to see, the most used words (which may suggest the main
topics) and the most related words among the various groups. We show how a
careful analysis of groups through networks, can give important information
about the current event.


  We discuss an experimentally amenable class of two-particle states of motion
giving rise to nonlocal spatial interference under position measurements. Using
the concept of modular variables, we derive a separability criterion which is
violated by these non-Gaussian states. While we focus on the free motion of
material particles, the presented results are valid for any pair of canonically
conjugate continuous variable observables and should apply to a variety of
bipartite interference phenomena.


  A long-term study of the elements Mg, Al, Si, P, Ca, S, Cl, Fe and Mn in
leaves is in progress. The objective of this study is to develop a week-by-week
profile of these elements in leaves during several growing seasons. The profile
includes the following information: (1) Which elements each tree collects in
its leaves. (2) The location in the leaf with the highest concentration, top
side, under side or interior. (3) The week during the growing season when each
element first appears in the leaves of each tree. (4) The change in the
relative concentration from week to week. (5) The source of the element i.e.,
deposition from the atmosphere or the root system of the tree. This information
is profile for each year and will be correlated with environmental conditions
for that year. Leaves are collected weekly from first unfolding in early spring
until leaf drop in the fall. They are from the 31 trees and 26 species in
Broome County, NY. From time to time leaves from most of the 26 species are
being randomly collected from trees growing throughout the northeastern US.


  The central engines of active galactic nuclei (AGN) contain cold, dense
material as well as hot X-ray emitting gas. The standard paradigm for the
engine geometry is a cold thin disc sandwiched between hot X-ray coronae.
Strong support for this geometry in Seyferts comes from the study of
fluorescent iron line profiles, although the evidence is not ubiquitously air
tight. The thin disc model of line profiles in AGN and in X-ray binaries should
be bench marked against other plausible possibilities. One proposed alternative
is an engine consisting of dense clouds embedded in an optically thin,
geometrically thick X-ray emitting engine. This model is further motivated by
studies of geometrically thick engines such as advection dominated accretion
flows (ADAFs). Here we compute the reprocessed iron line profiles from dense
clouds embedded in geometrically thick, optically thin X-ray emitting discs
near a Schwarzchild black hole. We consider a range of cloud distributions and
disc solutions, including ADAFs, pure radial infall, and bipolar outflows. We
find that such models can reproduce line profiles similar to those from
geometrically thin, optically thick discs and might help alleviate some of the
problems encountered from the latter.


  In this article we finish the classification of actions of torus
homeomorphisms on the fine curve graph initiated by Bowden, Hensel, Mann,
Militon, and Webb in \cite{BHMMW}. This is made by proving that if $f \in
\mathrm{Homeo}(\mathbb{T}^2)$, then $f$ acts elliptically on
$C^{\dagger}(\mathbb{T}^2)$ if and only if $f$ has bounded deviation from some
$v \in \mathbb{Q}^2 \setminus \left\{0\right\}$. The proof involves some kind
of slow rotation sets for torus homeomorphisms.


  We address the task of generating query suggestions for task-based search.
The current state of the art relies heavily on suggestions provided by a major
search engine. In this paper, we solve the task without reliance on search
engines. Specifically, we focus on the first step of a two-stage pipeline
approach, which is dedicated to the generation of query suggestion candidates.
We present three methods for generating candidate suggestions and apply them on
multiple information sources. Using a purpose-built test collection, we find
that these methods are able to generate high-quality suggestion candidates.


  We consider a cooperative multi-agent system consisting of a team of agents
with decentralized information. Our focus is on the design of symmetric (i.e.
identical) strategies for the agents in order to optimize a finite horizon team
objective. We start with a general information structure and then consider some
special cases. The constraint of using symmetric strategies introduces new
features and complications in the team problem. For example, we show in a
simple example that randomized symmetric strategies may outperform
deterministic symmetric strategies. We also discuss why some of the known
approaches for reducing agents' private information in teams may not work under
the constraint of symmetric strategies. We then adopt the common information
approach for our problem and modify it to accommodate the use of symmetric
strategies. This results in a common information based dynamic program where
each step involves minimization over a single function from the space of an
agent's private information to the space of probability distributions over
actions. We present specialized models where private information can be reduced
using simple dynamic program based arguments.


  We consider the transport through a system of three coupled quantum dots in a
perpendicular magnetic field. At zero field, destructive interference can trap
an electron in a dark state -- a coherent superposition of dot states that
completely blocks current flow. The magnetic field can disrupt this
interference giving rise to oscillations in the current and its higher-order
statistics as the field is increased. These oscillations have a period of
either the flux-quantum or half the flux-quantum, depending on the dot
geometry. We give results for the stationary current and for the shotnoise and
skewness at zero and finite frequency.


  The aim of this paper is to establish necessary and sufficient algorithmic
conditions to guarantee that an algebra is actually a 3-dimensional skew
polynomial algebra in the sense of Bell and Smith.


  Quantum mechanical foundations of the polarized neutron phase shift
experiment are discussed. The fact that the neutron retains its ground state
throughout the experiment is shown to be crucial for the phase shift obtained.


  Three dimensional (3D) Dirac semimetals are 3D analogue of graphene, which
display Dirac points with linear dispersion in k-space, stabilized by crystal
symmetry. Cd3As2 and Na3Bi were predicted to be 3D Dirac semimetals and were
subsequently demonstrated by photoemission experiments. As unveiled by
transport measurements, several exotic phases, such as Weyl semimetals,
topological insulators, and topological superconductors, can be deduced by
breaking time reversal or inversion symmetry. Here, we reported a facile and
scalable chemical vapor deposition method to fabricate high-quality Dirac
semimetal Cd3As2 microbelts, they have shown ultrahigh mobility up to 1.15*10^5
cm^2/V s and pronounced Shubnikov-de Haas oscillations. Such extraordinary
features are attributed to the suppression of electron backscattering. This
research opens a new avenue for the scalable fabrication of Cd3As2 materials
towards exciting electronic applications of 3D Dirac semimetals.


Consider an ambient medium and a heterogeneous entity composed of a
bidimensional material surrounded by a thin membrane. The electromagnetic
constants of these materials are different. By analogy with biological cells,
we call this entity a cell. We study the asymptotic behavior of the electric
field in the transverse magnetic (TM) mode, when the thickness of the membrane
tends to zero. The membrane is of thickness of hf(s), with s a curvilinear
coordinate. We provide a rigorous derivation of the first two terms of the
asymptotic expansion for h tending to zero. In the membrane, these terms are
given explicitly in local coordinates in terms of the boundary data and of the
function f, while outside the membrane they are the solutions of a scalar
Helmholtz equation with appropriate boundary and transmissions conditions given
explicitly in terms of the boundary data and of the above function f. We prove
that the remainder terms are of order O(h3/2). In addition, if the
complex dielectric permittivity in the membrane, denoted by z_m, tends to zero
faster than h, we give the difference between the exact solution and the above
asymptotic with z_m = 0; it is of order O(h^{3/2}+|z_m|).


  We examine the nature and statistical properties of electron-electron
collisions in the recollision process in a strong laser field. The separation
of the double ionization yield into sequential and nonsequential components
leads to a bell-shaped curve for the nonsequential probability and a
monotonically rising one for the sequential process. We identify key features
of the nonsequential process and connect our findings in a simplified model
which reproduces the knee shape for the probability of double ionization with
laser intensity and associated trends.


  The effects of edge hydrogenation and hydroxylation on the relative stability
and electronic properties of hexagonal boron nitride quantum dots (h-BNQDs) are
investigated. Zigzag edge hydroxylation is found to result in considerable
energetic stabilization of h-BNQDs as well as a reduction of their electronic
gap with respect to their hydrogenated counterparts. The application of an
external in-plane electric field leads to a monotonous decrease of the gap.
When compared to their edge-hydrogenated counterparts, significantly lower
field intensities are required to achieve full gap closure of the zigzag edge
hydroxylated h-BNQDs. These results indicate that edge chemistry may provide a
viable route for the design of stable and robust electronic devices based on
nanoscale hexagonal boron-nitride systems.


  There is a class of supersymmetric models which is well-motivated by hints of
evidence for SUSY and consistent with all existing data. It is important to
study the predictions of these models. They are characterized by M(N3) > M(C1)
> M(snu) > M(N1) (where Ni and Ci are neutralino and chargino mass
eigenstates), |mu| ~< M1 ~< M2 ~= M(Z), mu < 0, and tan(beta) near 1. Their LEP
signatures are mostly unusual. Most produced superpartners are invisible! A
good signature is two photons plus large missing energy. There are also excess
events at large recoil mass in the single photon plus nothing channel. We list
the main signatures for charginos, stops, etc., which are also likely to be
unconventional. This class of models will be definitively tested at LEP194 with
100 pb^{-1} per detector, and almost definitively tested at LEP184.


  This work studies optimal solar charging for solar-powered self-sustainable
UAV communication networks, considering the day-scale time-variability of solar
radiation and user service demand. The objective is to optimally trade off
between the user coverage performance and the net energy loss of the network by
proactively assigning UAVs to serve, charge, or land. Specifically, the studied
problem is first formulated into a time-coupled mixed-integer non-convex
optimization problem, and further decoupled into two sub-problems for
tractability. To solve the challenge caused by time-coupling, deep
reinforcement learning (DRL) algorithms are respectively designed for the two
sub-problems. Particularly, a relaxation mechanism is put forward to overcome
the "dimension curse" incurred by the large discrete action space in the second
sub-problem. At last, simulation results demonstrate the efficacy of our
designed DRL algorithms in trading off the communication performance against
the net energy loss, and the impact of different parameters on the tradeoff
performance.


  We present observations with the Cosmic Origins Spectrograph onboard the
Hubble Space Telescope of eight compact star-forming galaxies at redshifts
z=0.02811-0.06540, with low oxygen abundances 12+log(O/H)=7.43-7.82 and
extremely high emission-line flux ratios O32=[OIII]5007/[OII]3727~22-39, aiming
to study the properties of Ly-alpha emission in such conditions. We find a
diversity in Ly-alpha properties. In five galaxies Ly-alpha emission line is
strong, with equivalent width (EW) in the range 45-190A. In the remaining
galaxies, weak Ly-alpha emission with EW(Ly-alpha)~2-7A is superposed on a
broad Ly-alpha absorption line, indicating a high neutral hydrogen column
density N(HI)~(1-3)x10^21 cm^-2. We examine the relation between the Ly-alpha
escape fraction fesc(Ly-alpha) and the Lyman continuum escape fraction
fesc(LyC), using direct measures of the latter in eleven low-redshift LyC
leakers, to verify whether fesc(Ly-alpha) can be an indirect measure of
escaping LyC radiation. The usefulness of O32, of the Ly-alpha equivalent width
EW(Ly-alpha) and of the Ly-alpha peak separation Vsep as indirect indicators of
Ly-alpha leakage is also discussed. It is shown that there is no correlation
between O32 and fesc(Ly-alpha). We find an increase of fesc(Ly-alpha) with
increasing EW(Ly-alpha) for EW(Ly-alpha)<100A, but for higher EW(Ly-alpha)>150A
the fesc(Ly-alpha) is nearly constant attaining the value of ~0.25. We find an
anticorrelation between fesc(Ly-alpha) and Vsep, though not as tight as the one
found earlier between fesc(LyC) and Vsep. This finding makes Vsep a promising
indirect indicator of both the Ly-alpha and ionizing radiation leakage.


  The bispectrum can quantify the non-Gussianity present in the redshifted
21-cm signal produced by the neutral hydrogen (HI) during the epoch of
reionization (EoR). Motivated by this, we perform a comprehensive study of the
EoR 21-cm bispectrum using simulated signals. Given a model of reionization, we
demonstrate the behaviour of the bispectrum for all unique triangles in $k$
space. For ease of identification of the unique triangles, we parametrize the
$k$-triangle space with two parameters, namely the ratio of the two arms of the
triangle ($n=k_2/k_1$) and the cosine of the angle between them
($\cos{\theta}$). Furthermore, for the first time, we quantify the impact of
the redshift space distortions (RSD) on the spherically averaged EoR 21-cm
bispectrum in the entire unique triangle space. We find that the real space
signal bispectra for small and intermediate $k_1$-triangles ($k_1 \leq 0.6
\,{\rm Mpc^{-1}}$) is negative in most of the unique triangle space. It takes a
positive sign for squeezed, stretched and linear $k_1$-triangles, specifically
for large $k_1$ values ($k_1 \geq 0.6 \,{\rm Mpc^{-1}}$). The RSD affects both
the sign and magnitude of the bispectra significantly. It changes
(increases/decreases) the magnitude of the bispectra by $50-100\%$ without
changing its sign (mostly) during the entire period of the EoR for small and
intermediate $k_1$-triangles. For larger $k_1$-triangles, RSD affects the
magnitude by $100-200\%$ and also flips the sign from negative to positive. We
conclude that it is important to take into account the impact of RSD for a
correct interpretation of the EoR 21-cm bispectra.


  We review the equations determining the photon sphere radius and the black
hole shadow radius, and calculate the cosmological constant corrections arising
when the dark energy action has the usual form, and when dark energy arises
from a Weyl scaling invariant dark energy action. For black hole targets of the
Event Horizon Telescope, the corrections are very small.