  Coded aperture imaging systems have recently shown great success in
recovering scene depth and extending the depth-of-field. The ideal pattern,
however, would have to serve two conflicting purposes: 1) be broadband to
ensure robust deconvolution and 2) has sufficient zero-crossings for a high
depth discrepancy. This paper presents a simple but effective scene-adaptive
coded aperture solution to bridge this gap. We observe that the geometric
structures in a natural scene often exhibit only a few edge directions, and the
successive frames are closely correlated. Therefore we adopt a spatial
partitioning and temporal propagation scheme. In each frame, we address one
principal direction by applying depth-discriminative codes along it and
broadband codes along its orthogonal direction. Since within a frame only the
regions with edge direction corresponding to its aperture code behaves well, we
utilize the close among-frame correlation to propagate the high quality single
frame results temporally to obtain high performance over the whole image
lattice. To physically implement this scheme, we use a Liquid Crystal on
Silicon (LCoS) microdisplay that permits fast changing pattern codes. Firstly,
we capture the scene with a pinhole and analyze the scene content to determine
primary edge orientations. Secondly, we sequentially apply the proposed coding
scheme with these orientations in the following frames. Experiments on both
synthetic and real scenes show that our technique is able to combine advantages
of the state-of-the-art patterns for recovering better quality depth map and
all-focus images.


  We probe Penrose's five-fold symmetry and fractal behavior for atom H. With
radius r(H) derived from H mass m(H), H symmetry is governed by Euclid's golden
ratio phi=0,5(sqrt(5)-1), as proved with accurate H terms. A Hund-type Mexican
hat curve in the natural H spectrum points to mirrored antihydrogen Hbar. We
predict that term H 1S-3S, to be measured soon, is 2 922 743 278 654 kHz.


  We explore a detailed model in which the active galactic nucleus (AGN)
obscuration results from the extinction of AGN radiation in a global flow
driven by the pressure of infrared radiation on dust grains. We assume that
external illumination by UV and soft X-rays of the dusty gas located at
approximately 1pc away from the supermassive black hole is followed by a
conversion of such radiation into IR. Using 2.5D, time-dependent radiation
hydrodynamics simulations in a flux-limited diffusion approximation we find
that the external illumination can support a geometrically thick obscuration
via outflows driven by infrared radiation pressure in AGN with luminosities
greater than $0.05\, L_{\rm edd}$ and Compton optical depth, $\tau_{\rm
T}\gtrsim 1$.


  Exciton-plasmon polaritons (EPPs) are attractive both for the exploration of
fundamental phenomena and applications in nanophotonics. Previous studies of
EPPs mainly relied on far-field characterization. Here, using near-field
optical microscopy, we quantitatively characterize the dispersion of EPPs
existing in 13-nm-thick tungsten diselenide (WSe$_2$) deposited on a
monocrystalline gold platelet. We extract from our experimental data a Rabi
splitting of 81 meV, and an experimental effective polariton loss of 55 meV,
demonstrating that our system is in the strong-coupling regime. Furthermore, we
measure for the first time at visible wavelengths the propagation length of
these EPPs for each excitation energy of the dispersion relation. To
demonstrate the quantitative nature of our near-field method to obtain the full
complex-valued wavevector of EPPs, we use our near-field measurements to
predict, via the transfer matrix method, the far-field reflectivities across
the exciton resonance. These predictions are in excellent agreement with our
experimental far-field measurements. Our findings open the door towards the
full near-field study of light-manipulating devices at the nanoscale.


  A simple characterization is given of open subsets of a complex surface that
smoothly perturb to Stein open subsets. As applications, complex 2-space C^2
contains domains of holomorphy (Stein open subsets) that are exotic R^4's, and
others homotopy equivalent to the 2-sphere but cut out by smooth, compact
3-manifolds. Pseudoconvex embeddings of Brieskorn spheres and other 3-manifolds
into complex surfaces are constructed, as are pseudoconcave holomorphic
fillings (with disagreeing contact and boundary orientations). Pseudoconcave
complex structures on Milnor fibers are found. A byproduct of this construction
is a simple polynomial expression for the signature of the (p,q,npq-1) Milnor
fiber. Akbulut corks in complex surfaces can always be chosen to be
pseudoconvex or pseudoconcave submanifods. The main theorem is expressed via
Stein handlebodies (possibly infinite), which are defined holomorphically in
all dimensions by extending Stein theory to manifolds with noncompact boundary.


  A new redshift formula is obtained considering the longitudinal Doppler
effect in the de Sitter expanding universe where the relative geodesic motion
is governed by the Lorenzian isometries of our new de Sitter relativity [I. I.
Cot\u aescu, {Eur. Phys. J. C} {\bf 77} (2017) 485]. This formula combines in a
non-trivial manner the well-known cosmological contribution given by Lama\'
itre's law with that of the relative motion of the source with respect to a
fixed observer as in de Sitter relativity instead of special relativity. Other
related quantities as the dispersion relation, the propagation time of the
photon and the real distance between source and observer at the moment of
observation are discussed pointing out their specific features in the de Sitter
relativity.


  Recently, examples of an index theory for KMS states of circle actions were
discovered, \cite{CPR2,CRT}. We show that these examples are not isolated.
Rather there is a general framework in which we use KMS states for circle
actions on a C*-algebra A to construct Kasparov modules and semifinite spectral
triples. By using a residue construction analogous to that used in the
semifinite local index formula we associate to these triples a twisted cyclic
cocycle on a dense subalgebra of A. This cocycle pairs with the equivariant
KK-theory of the mapping cone algebra for the inclusion of the fixed point
algebra of the circle action in A. The pairing is expressed in terms of
spectral flow between a pair of unbounded self adjoint operators that are
Fredholm in the semifinite sense. A novel aspect of our work is the discovery
of an eta cocycle that forms a part of our twisted residue cocycle. To
illustrate our theorems we observe firstly that they incorporate the results in
\cite{CPR2,CRT} as special cases. Next we use the Araki-Woods III_\lambda
representations of the Fermion algebra to show that there are examples which
are not Cuntz-Krieger systems.


  An accurate force field is the key to the success of all molecular mechanics
simulations on organic polymers and biomolecules. Accuracy beyond density
functional theory is often needed to describe the intermolecular interactions,
while most correlated wavefunction (CW) methods are prohibitively expensive for
large molecules. Therefore, it posts a great challenge to develop an extendible
ab initio force field for large flexible organic molecules at CW level of
accuracy. In this work, we face this challenge by combining the physics-driven
nonbonding potential with a data-driven subgraph neural network bonding model
(named sGNN). Tests on polyethylene glycol polymer chains show that our
strategy is highly accurate and robust for molecules of different sizes.
Therefore, we can develop the force field from small molecular fragments (with
sizes easily accessible to CW methods) and safely transfer it to large
polymers, thus opening a new path to the next-generation organic force fields.


  We construct an equivalence between Hausmann's model of global stable
homotopy theory in terms of symmetric spectra on the one hand and spectral
Mackey functors in the sense of Barwick on a certain global effective Burnside
category on the other hand.
  Using this, we then give a new description of Schwede's global algebraic
$K$-theory by identifying the corresponding spectral Mackey functors with
certain categorically defined Mackey functors, making precise the slogan that
global algebraic $K$-theory is a coherent version of Swan $K$-theory.


  Safe exploration is essential for the practical use of reinforcement learning
(RL) in many real-world scenarios. In this paper, we present a generalized safe
exploration (GSE) problem as a unified formulation of common safe exploration
problems. We then propose a solution of the GSE problem in the form of a
meta-algorithm for safe exploration, MASE, which combines an unconstrained RL
algorithm with an uncertainty quantifier to guarantee safety in the current
episode while properly penalizing unsafe explorations before actual safety
violation to discourage them in future episodes. The advantage of MASE is that
we can optimize a policy while guaranteeing with a high probability that no
safety constraint will be violated under proper assumptions. Specifically, we
present two variants of MASE with different constructions of the uncertainty
quantifier: one based on generalized linear models with theoretical guarantees
of safety and near-optimality, and another that combines a Gaussian process to
ensure safety with a deep RL algorithm to maximize the reward. Finally, we
demonstrate that our proposed algorithm achieves better performance than
state-of-the-art algorithms on grid-world and Safety Gym benchmarks without
violating any safety constraints, even during training.


  Geological settings with reservoir characteristics include fractures with
different material and geometrical properties. Hence, numerical simulations in
applied geophysics demands for computational frameworks which efficiently allow
to integrate various fracture geometries in a porous medium matrix. This study
presents a modeling approach for single-phase flow in fractured porous media
and its application to different types of non-conforming mesh models. We
propose a combination of the Lagrange multiplier method with variational
transfer to allow for complex non-conforming geometries as well as hybrid- and
equi-dimensional models and discretizations of flow through fractured porous
media. The variational transfer is based on the $L^2$-projection and enables an
accurate and highly efficient parallel projection of fields between
non-conforming meshes (e.g.,\ between fracture and porous matrix domain). We
present the different techniques as a unified mathematical framework with a
practical perspective. By means of numerical examples we discuss both,
performance and applicability of the particular strategies. Comparisons of
finite element simulation results to widely adopted 2D benchmark cases show
good agreement and the dual Lagrange multiplier spaces show good performance.
In an extension to 3D fracture networks, we first provide complementary results
to a recently developed benchmark case, before we explore a complex scenario
which leverages the different types of fracture meshes. Complex and highly
conductive fracture networks are found more suitable in combination with
embedded hybrid-dimensional fractures. However, thick and blocking fractures
are better approximated by equi-dimensional embedded fractures and the
equi-dimensional mortar method, respectively.


  Results of a search for the pair production of photon-jets$-$collimated
groupings of photons$-$in the ATLAS detector at the Large Hadron Collider are
reported. Highly collimated photon-jets can arise from the decay of new, highly
boosted particles that can decay to multiple photons collimated enough to be
identified in the electromagnetic calorimeter as a single, photonlike energy
cluster. Data from proton-proton collisions at a center-of-mass energy of 13
TeV, corresponding to an integrated luminosity of 36.7 fb$^{-1}$, were
collected in 2015 and 2016. Candidate photon-jet pair production events are
selected from those containing two reconstructed photons using a set of
identification criteria much less stringent than that typically used for the
selection of photons, with additional criteria applied to provide improved
sensitivity to photon-jets. Narrow excesses in the reconstructed diphoton mass
spectra are searched for. The observed mass spectra are consistent with the
Standard Model background expectation. The results are interpreted in the
context of a model containing a new, high-mass scalar particle with narrow
width, $X$, that decays into pairs of photon-jets via new, light particles,
$a$. Upper limits are placed on the cross section times the product of
branching ratios $\sigma \times \mathcal{B}(X \rightarrow aa) \times \mathcal
{B}(a \rightarrow \gamma \gamma)^{2}$ for 200 GeV $< m_{X} <$ 2 TeV and for
ranges of $ m_a $ from a lower mass of 100 MeV up to between 2 and 10 GeV,
depending upon $ m_X $. Upper limits are also placed on $\sigma \times
\mathcal{B}(X \rightarrow aa) \times \mathcal {B}(a \rightarrow 3\pi^{0})^{2}$
for the same range of $ m_X $ and for ranges of $ m_a $ from a lower mass of
500 MeV up to between 2 and 10 GeV.


  CeCuAl3 and CeAuAl3, crystallizing in the non-centrosymmetric BaNiSn3
tetragonal structure, are known mainly for their unusual neutron scattering
spectra involving additional excitations ascribed to vibron quasi-bound quantum
state in CeCuAl3 and anti-crossing of phonon and crystal field excitations in
CeAuAl3. In this work, we present results of nuclear magnetic resonance
experiments on their lanthanum analogues - LaCuAl3 and LaAuAl3. The character
of nuclear magnetic resonance spectra of 139La, 27Al, and 65Cu measured in
LaAuAl3 and LaCuAl3 is dominated by electric quadrupole interaction. The
spectral parameters acquired from experimental data are confronted with values
obtained from the electronic structure calculations. The results show
remarkable diffrences for the two compounds. The 139La spectrum in LaAuAl3 can
be interpreted by a single spectral component corresponding to uniform
environment of La atoms in the crystal structure, whereas for LaCuAl3 the
spectrum decomposition yields a wide distribution of spectral parameters, which
is not possible to explain by a single La environment, and multiple
non-equivalent La positions in the crystal structure are required to interpret
the spectrum.


  We analyze features that resemble type i spicules in two different 3D
numerical simulations in which we include horizontal magnetic flux emergence in
a computational domain spanning the upper layers of the convection zone to the
lower corona. The two simulations differ mainly in the preexisting ambient
magnetic field strength and in the properties of the inserted flux tube. We use
the Oslo Staggered Code (OSC) to solve the full MHD equations with non-grey and
non-LTE radiative transfer and thermal conduction along the magnetic field
lines. We find a multitude of features that show a spatiotemporal evolution
that is similar to that observed in type i spicules, which are characterized by
parabolic height vs. time profiles, and are dominated by rapid upward motion at
speeds of 10-30 km/s, followed by downward motion at similar velocities. We
measured the parameters of the parabolic profile of the spicules and find
similar correlations between the parameters as those found in observations. The
values for height (or length) and duration of the spicules found in the
simulations are more limited in range than those in the observations. The
spicules found in the simulation with higher preexisting ambient field have
shorter length and smaller velocities. From the simulations, it appears that
these kinds of spicules can, in principle, be driven by a variety of mechanisms
that include p-modes, collapsing granules, magnetic energy release in the
photosphere and lower chromosphere and convective buffeting of flux
concentrations.


  This paper proposes a deep learning approach to channel sensing and downlink
hybrid beamforming for massive multiple-input multiple-output systems operating
in the time division duplex mode and employing either single-carrier or
multicarrier transmission. The conventional precoding design involves a
two-step process of first estimating the high-dimensional channel, then
designing the precoders based on such estimate. This two-step process is,
however, not necessarily optimal. This paper shows that by using a learning
approach to design the analog sensing and the hybrid downlink precoders
directly from the received pilots without the intermediate high-dimensional
channel estimation, the overall system performance can be significantly
improved. Training a neural network to design the analog and digital precoders
simultaneously is, however, difficult. Further, such an approach is not
generalizable to systems with different number of users. In this paper, we
develop a simplified and generalizable approach that learns the uplink sensing
matrix and downlink analog precoder using a deep neural network that decomposes
on a per-user basis, then designs the digital precoder based on the estimated
low-dimensional equivalent channel. Numerical comparisons show that the
proposed methodology results in significantly less training overhead and leads
to an architecture that generalizes to various system settings.


  In this work we develop a theory of interaction of randomly patterned
surfaces as a generic prototype model of protein-protein interactions. The
theory predicts that pairs of randomly superimposed identical (homodimeric)
random patterns have always twice as large magnitude of the energy fluctuations
with respect to their mutual orientation, as compared with pairs of different
(heterodimeric) random patterns. The amplitude of the energy fluctuations is
proportional to the square of the average pattern density, to the square of the
amplitude of the potential and its characteristic length, and scales linearly
with the area of surfaces. The greater dispersion of interaction energies in
the ensemble of homodimers implies that strongly attractive complexes of random
surfaces are much more likely to be homodimers, rather than heterodimers. Our
findings suggest a plausible physical reason for the anomalously high fraction
of homodimers observed in real protein interaction networks.


  The inspirals of stellar-mass compact objects into supermassive black holes
are some of the most important sources for LISA. Detection techniques based on
fully coherent matched filtering have been shown to be computationally
intractable. We describe an efficient and robust detection method that utilizes
the time-frequency evolution of such systems. We show that a typical extreme
mass ratio inspiral (EMRI) source could possibly be detected at distances of up
to ~2 Gpc, which would mean ~10s of EMRI sources can be detected per year using
this technique. We discuss the feasibility of using this method as a first step
in a hierarchical search.


  Nordhaus and Gaddum proved, for any graph G, that the chromatic number of G
plus the chromatic number of G complement is less than or equal to the number
of vertices in G plus 1. Finck characterized the class of graphs that satisfy
equality in this bound. In this paper, we provide a new characterization of
this class of graphs, based on vertex degrees, which yields a new
polynomial-time recognition algorithm and efficient computation of the
chromatic number of graphs in this class. Our motivation comes from our theorem
that generalizes the Nordhaus-Gaddum theorem to the distinguishing chromatic
number: for any graph G, the distinguishing chromatic number of G plus the
distinguishing chromatic number of G complement is less than or equal to the
number of vertices of G plus the distinguishing number of G. Finally, we
characterize those graphs that achieve equality in the sum upper bounds
simultaneously for both the chromatic number and for our distinguishing
chromatic number analog of the Nordhaus-Gaddum inequality.


  We investigate the potential of patent data for improving the antibody
humanness prediction using a multi-stage, multi-loss training process.
Humanness serves as a proxy for the immunogenic response to antibody
therapeutics, one of the major causes of attrition in drug discovery and a
challenging obstacle for their use in clinical settings. We pose the initial
learning stage as a weakly-supervised contrastive-learning problem, where each
antibody sequence is associated with possibly multiple identifiers of function
and the objective is to learn an encoder that groups them according to their
patented properties. We then freeze a part of the contrastive encoder and
continue training it on the patent data using the cross-entropy loss to predict
the humanness score of a given antibody sequence. We illustrate the utility of
the patent data and our approach by performing inference on three different
immunogenicity datasets, unseen during training. Our empirical results
demonstrate that the learned model consistently outperforms the alternative
baselines and establishes new state-of-the-art on five out of six inference
tasks, irrespective of the used metric.


  For primes $p$, we investigate an $\mathbb{F}_p$-version of simplicial volume
and compare these invariants with their siblings over other coefficient rings.
We will also consider the associated gradient invariants, obtained by
stabilisation along finite coverings. Throughout, we will discuss the relation
between such simplicial volumes and Betti numbers.


  Recent knowledge editing methods have primarily focused on modifying
structured knowledge in large language models, heavily relying on the
assumption that structured knowledge is stored as key-value pairs locally in
MLP layers or specific neurons. However, this task setting overlooks the fact
that a significant portion of real-world knowledge is stored in an unstructured
format, characterized by long-form content, noise, and a complex yet
comprehensive nature. The "knowledge locating" and "term-driven optimization"
techniques conducted from the assumption used in previous methods (e.g., MEMIT)
are ill-suited for unstructured knowledge. To address these challenges, we
propose a novel unstructured knowledge editing method, namely UnKE, which
extends previous assumptions in the layer dimension and token dimension.
Firstly, in the layer dimension, we discard the "knowledge locating" step and
treat first few layers as the key, which expand knowledge storage through
layers to break the "knowledge stored locally" assumption. Next, we replace
"term-driven optimization" with "cause-driven optimization" across all inputted
tokens in the token dimension, directly optimizing the last layer of the key
generator to perform editing to generate the required key vectors. By utilizing
key-value pairs at the layer level, UnKE effectively represents and edits
complex and comprehensive unstructured knowledge, leveraging the potential of
both the MLP and attention layers. Results on newly proposed unstructure
knowledge editing dataset (UnKEBench) and traditional structured datasets
demonstrate that UnKE achieves remarkable performance, surpassing strong
baselines.


  We consider the ground state of an atom in the framework of non-relativistic
qed. We show that the ground state as well as the ground state energy are
analytic functions of the coupling constant which couples to the vector
potential, under the assumption that the atomic Hamiltonian has a
non-degenerate ground state. Moreover, we show that the corresponding expansion
coefficients are precisely the coefficients of the associated
Raleigh-Schroedinger series. As a corollary we obtain that in a scaling limit
where the ultraviolet cutoff is of the order of the Rydberg energy the ground
state and the ground state energy have convergent power series expansions in
the fine structure constant $\alpha$, with $\alpha$ dependent coefficients
which are finite for $\alpha \geq 0$.


  This paper presents the first item response theory (IRT) analysis of the
national data set on introductory, general education, college-level astronomy
teaching using the Light and Spectroscopy Concept Inventory (LSCI). We used the
difference between students' pre- and post-instruction IRT-estimated abilities
as a measure of learning gain. This analysis provides deeper insights than
prior publications into both the LSCI as an instrument and into the
effectiveness of teaching and learning in introductory astronomy courses. Our
IRT analysis supports the classical test theory findings of prior studies using
the LSCI with this population. In particular, we found that students in classes
that used active learning strategies at least 25\% of the time had average
IRT-estimated learning gains that were approximately 1 logit larger than
students in classes that spent less time on active learning strategies. We also
found that instructors who want their classes to achieve an improvement in
abilities of average $\Delta \theta = 1$ logit must spend at least 25\% of
class time on active learning strategies. However, our analysis also powerfully
illustrates the lack of insight into student learning that is revealed by
looking at a single measure of learning gain, such as average $\Delta \theta$.
Educators and researchers should also examine the distributions of students'
abilities pre- and post-instruction in order to understand how many students
actually achieved an improvement in their abilities and whether or not a
majority of students have moved to post-abilities significantly greater than
the national average.


  We present results showing how water drops, produced by ink-jet printing,
spread on surfaces patterned with lattices of diamond or triangular posts.
Considering post widths typically ~7 m and lattice spacings between 15-40 m, we
observe drop shapes with 3,4 and 6-fold symmetry, depending on both the
symmetry of the lattice and the shape of the posts. This is a result of the
different mechanisms of interface pinning and depinning which depend on the
direction of the contact line motion with respect to the post shape. Lattice
Boltzmann simulations are used to describe these mechanisms in detail for
triangular posts. We also follow the motion of the contact line as the drops
evaporate showing that they tend to return to their original shape. To explain
this we show that the easy direction for movement is the same for spreading and
drying drops. We compare the behaviour of small drops with that of larger drops
created by jetting several drops at the same position. We find that the contact
line motion is unexpectedly insensitive to drop volume, even when a spherical
cap of fluid forms above the posts. The findings are relevant to microfluidic
applications and to the control of drop shapes in ink-jet printing.


  We consider prediction with expert advice for strongly convex and bounded
losses, and investigate trade-offs between regret and "variance" (i.e., squared
difference of learner's predictions and best expert predictions). With $K$
experts, the Exponentially Weighted Average (EWA) algorithm is known to achieve
$O(\log K)$ regret. We prove that a variant of EWA either achieves a negative
regret (i.e., the algorithm outperforms the best expert), or guarantees a
$O(\log K)$ bound on both variance and regret. Building on this result, we show
several examples of how variance of predictions can be exploited in learning.
In the online to batch analysis, we show that a large empirical variance allows
to stop the online to batch conversion early and outperform the risk of the
best predictor in the class. We also recover the optimal rate of model
selection aggregation when we do not consider early stopping. In online
prediction with corrupted losses, we show that the effect of corruption on the
regret can be compensated by a large variance. In online selective sampling, we
design an algorithm that samples less when the variance is large, while
guaranteeing the optimal regret bound in expectation. In online learning with
abstention, we use a similar term as the variance to derive the first
high-probability $O(\log K)$ regret bound in this setting. Finally, we extend
our results to the setting of online linear regression.


  Molecular spectroscopy in the mid-infrared portion of the electromagnetic
spectrum (3--25 um) has been a cornerstone interdisciplinary analytical
technique widely adapted across the biological, chemical, and physical
sciences. Applications range from understanding mesoscale trends in climate
science via atmospheric monitoring to microscopic investigations of cellular
biological systems via protein characterization. Here, we present a compact and
comprehensive approach to infrared spectroscopy incorporating the development
of broadband laser frequency combs across 3--27 um, encompassing the entire
mid-infrared, and direct electric-field measurement of the corresponding near
single-cycle infrared pulses of light. Utilizing this unified apparatus for
high-resolution and accurate frequency comb spectroscopy, we present the
infrared spectra of important atmospheric compounds such as ammonia and carbon
dioxide in the molecular fingerprint region. To further highlight the ability
to study complex biological systems, we present a broadband spectrum of a
monoclonal antibody reference material consisting of more than 20,000 atoms.
The absorption signature resolves the amide I and II vibrations, providing a
means to study secondary structures of proteins. The approach described here,
operating at the boundary of ultrafast physics and precision spectroscopy,
provides a table-top solution and a widely adaptable technique impacting both
applied and fundamental scientific studies.


  The semiclassical approach, successfully applied in the past to the
inelastic, inclusive electron scattering off nuclei, is extended to the
treatment of exclusive processes. The final states interaction is accounted for
in the mean field approximation, respecting the Pauli principle. The impact on
the exclusive cross section of the shape of the potential binding the nucleons
into the nucleus and of the distortion of the outgoing nucleon wave are
explored. The exclusive scattering is found to be quite sensitive to the mean
field final states interaction, unlike the inclusive one. Indeed we verify that
the latter is not affected, as implied by unitarity, by the distortion of the
outgoing nucleon wave except for the effect of relativity, which is modest in
the range of momenta up to about 500 MeV/c. Furthermore, depending upon the
correlations between the directions of the outgoing and of the initial nucleon,
the exclusive cross-section turns out to be remarkably sensitive to the shape
of the potential binding the nucleons. These correlations also critically
affect the domain in the missing energy-- missing momentum plane where the
exclusive process occurs.


  Supplementing the publications based on the first-light observations with the
German Receiver for Astronomy at Terahertz frequencies (GREAT) on SOFIA, we
present background information on the underlying heterodyne detector
technology. We describe the superconducting hot electron bolometer (HEB)
detectors that are used as frequency mixers in the L1 (1400 GHz), L2 (1900
GHz), and M (2500 GHz) channels of GREAT. Measured performance of the detectors
is presented and background information on their operation in GREAT is given.
Our mixer units are waveguide-based and couple to free-space radiation via a
feedhorn antenna. The HEB mixers are designed, fabricated, characterized, and
flight-qualified in-house. We are able to use the full intermediate frequency
bandwidth of the mixers using silicon-germanium multi-octave cryogenic
low-noise amplifiers with very low input return loss. Superconducting HEB
mixers have proven to be practical and sensitive detectors for high-resolution
THz frequency spectroscopy on SOFIA. We show that our niobium-titanium-nitride
(NbTiN) material HEBs on silicon nitride (SiN) membrane substrates have an
intermediate frequency (IF) noise roll-off frequency above 2.8 GHz, which does
not limit the current receiver IF bandwidth. Our mixer technology development
efforts culminate in the first successful operation of a waveguide-based HEB
mixer at 2.5 THz and deployment for radioastronomy. A significant contribution
to the success of GREAT is made by technological development, thorough
characterization and performance optimization of the mixer and its IF interface
for receiver operation on SOFIA. In particular, the development of an optimized
mixer IF interface contributes to the low passband ripple and excellent
stability, which GREAT demonstrated during its initial successful astronomical
observation runs.


  In this paper we study wave breaking in the unidirectional non-local wave
model describing the motion of a collision-free plasma in a magnetic field. By
analyzing the monotonicity and continuity properties of a system of the
Riccati-type differential inequalities involving the extremal slopes of flows,
we show a new sufficient condition on the initial data to exhibit wave
breaking. Moreover, the estimates of life span and wave breaking rate are
derived.


  Scheme uses garbage collection for heap memory management. Ideally, garbage
collectors should be able to reclaim all dead objects, i.e. objects that will
not be used in future. However, garbage collectors collect only those dead
objects that are not reachable from any program variable. Dead objects that are
reachable from program variables are not reclaimed.
  In this paper we describe our experiments to measure the effectiveness of
garbage collection in MIT/GNU Scheme. We compute the drag time of objects, i.e.
the time for which an object remains in heap memory after its last use. The
number of dead objects and the drag time together indicate opportunities for
improving garbage collection. Our experiments reveal that up to 26% of dead
objects remain in memory. The average drag time is up to 37% of execution time.
Overall, we observe memory saving potential ranging from 9% to 65%.


  We study the nonlinear diffusion equation $ u_t=\Delta\phi(u) $ on general
Euclidean domains, with homogeneous Neumann boundary conditions. We assume that
$ \phi^\prime(u) $ is bounded from below by $ |u|^{m_1-1} $ for small $ |u| $
and by $ |u|^{m_2-1} $ for large $|u|$, the two exponents $ m_1,m_2 $ being
possibly different and larger than one. The equality case corresponds to the
well-known porous medium equation. We establish sharp short- and long-time $
L^{q_0} $-$ L^\infty $ smoothing estimates: similar issues have widely been
investigated in the literature in the last few years, but the Neumann problem
with different powers had not been addressed yet. This work extends some
previous results in many directions.


  Identifiability concerns finding which unknown parameters of a model can be
estimated from given input-output data. If some subset of the parameters of a
model cannot be determined given input-output data, then we say the model is
unidentifiable. In past work we identified a class of models, that we call
identifiable cycle models, which are not identifiable but have the simplest
possible identifiable functions (so-called monomial cycles). Here we show how
to modify identifiable cycle models by adding inputs, adding outputs, or
removing leaks, in such a way that we obtain an identifiable model. We also
prove a constructive result on how to combine identifiable models, each
corresponding to strongly connected graphs, into a larger identifiable model.
We apply these theoretical results to several real-world biological models from
physiology, cell biology, and ecology.


  We show that for the multi-antenna Gaussian multi-access channel (MAC) and
broadcast channel (BC) with perfect feedback, the rate regions achieved by
linear-feedback coding schemes (called linear-feedback capacity regions)
coincide when the same total input-power constraint is imposed on both channels
and when the MAC channel matrices are the transposes of the BC channel
matrices. Such a pair of MAC and BC is called dual. We also identify
sub-classes of linear-feedback coding schemes that achieve the linear-feedback
capacity regions of these two channels and present multi-letter expressions for
the linear-feedback capacity regions. Moreover, within the two sub-classes of
coding schemes that achieve the linear-feedback capacity regions for a given
MAC and its dual BC, we identify for each MAC scheme a BC scheme and for each
BC scheme a MAC scheme so that the two schemes have same total input power and
achieve the same rate regions.


  We show that at a second order phase transition, of \phi^4 like system, a
necessary condition for streched exponential decay of the time structure factor
is obeyed. Using the ideas presented in this proof a crude estimate of the
decay of the structure factor is obtained and shown to yield stretched
exponential decay under very reasonable conditions.


  The Seiberg-Witten equations are defined on certain complex line bundles over
smooth oriented four manifolds. When the base manifold is a complex Kahler
surface, the Seiberg-Witten equations are essentially the Abelian vortex
equations. Using known non-abelian generalizations of the vortex equations as a
guide, we explore some non-abelian versions of the Seiberg-Witten equations. We
also make some comments about the differences between the vortex equations that
have previously appeared in the literature and those that emerge as Kahler
versions of Seiberg-witten type equations.


  We prove that any three linearly independent pure quantum states can always
be locally distinguished with nonzero probability regardless of their
dimension, entanglement, or multipartite structure. Almost always, all three
states can be unambiguously identified. The only exceptional case, where one
state is locally knowable but the other two are not, is found among multi-qubit
states.


  Two people may claim both to be naturalists, but have divergent conceptions
of basic elements of the natural world which lead them to mean different things
when they talk about laws of nature, or states, or the role of mathematics in
physics. These disagreements do not much affect the ordinary practice of
science which is about small subsystems of the universe, described or explained
against a background, idealized to be fixed. But these issues become crucial
when we consider including the whole universe within our system, for then there
is no fixed background to reference observables to. I argue here that the key
issue responsible for divergent versions of naturalism and divergent approaches
to cosmology is the conception of time. One version, which I call temporal
naturalism, holds that time, in the sense of the succession of present moments,
is real, and that laws of nature evolve in that time. This is contrasted with
timeless naturalism, which holds that laws are immutable and the present moment
and its passage are illusions. I argue that temporal naturalism is empirically
more adequate than the alternatives, because it offers testable explanations
for puzzles its rivals cannot address, and is likely a better basis for solving
major puzzles that presently face cosmology and physics.
  This essay also addresses the problem of qualia and experience within
naturalism and argues that only temporal naturalism can make a place for qualia
as intrinsic qualities of matter.


  We compute the first and second cohomology groups with coefficients in the
adjoint module of frobeniusian model algebras whose parameters move in a dense
open subset of $\mathbb{C}^{p-1}$, and obtain upper bounds for the dimension of
cohomology groups of frobeniusian Lie algebras. Moreover, it is shown that for
a dense open subset of $\mathbb{C}^{p-1}$ the deformations of model algebras
also belong to the family. Therefore any frobeniusian non-model algebra
contracts on some element of the model whose parameters move on a finite union
of hyperplanes. Further applications as the nullity of Rim's quadratic map
  $sq_{1}$ are obtained.


  The mechanism of electron-hole separation in organic solar cells is currently
hotly debated. Recent experimental work suggests that these charges can
separate on extremely short timescales (<100 fs). This can be understood in
terms of delocalised transport within fullerene aggregates, which is thought to
emerge on short timescales before vibronic relaxation induces polaron
formation. However, in the optimal heterojunction morphology, electrons and
holes will often re-encounter each other before reaching the electrodes. If
such charges trap and cannot separate, then device efficiency will suffer. Here
we extend the theory of ultrafast charge separation to incorporate polaron
formation, and find that the same delocalised transport used to explain
ultrafast charge separation can account for the suppression of nongeminate
recombination in the best devices.


  The Work Disability Functional Assessment Battery (WD-FAB) is a
multidimensional item response theory (IRT) instrument designed for assessing
work-related mental and physical function based on responses to an item bank.
In prior iterations it was developed using traditional means -- linear
factorization and null hypothesis statistical testing for item
partitioning/selection, and finally, posthoc calibration of disjoint
unidimensional IRT models. As a result, the WD-FAB, like many other IRT
instruments, is a posthoc model. Its item partitioning, based on exploratory
factor analysis, is blind to the final nonlinear IRT model and is not performed
in a manner consistent with goodness of fit to the final model. In this
manuscript, we develop a Bayesian hierarchical model for self-consistently
performing the following simultaneous tasks: scale factorization, item
selection, parameter identification, and response scoring. This method uses
sparsity-based shrinkage to obviate the linear factorization and null
hypothesis statistical tests that are usually required for developing
multidimensional IRT models, so that item partitioning is consistent with the
ultimate nonlinear factor model. We also analogize our multidimensional IRT
model to probabilistic autoencoders, specifying an encoder function that
amortizes the inference of ability parameters from item responses. The encoder
function is equivalent to the "VBE" step in a stochastic variational Bayesian
expectation maximization (VBEM) procedure that we use for approxiamte Bayesian
inference on the entire model. We use the method on a sample of WD-FAB item
responses and compare the resulting item discriminations to those obtained
using the traditional posthoc method.


  We prove that for any positive integer c and any s > 0 there are
representations of c as a sum a+b of two coprime positive integers a, b, such
that the respective radicals are all greater than K(s)R(c)^(1-s)c^2. For the
reprasentations in question, this is a stronger result than the abc-conjecture,
which postulates that these representations are greater than k(s)c^1/(1+s).


  When an electromagnetic transverse wave propagates through an inhomogeneous
plasma so that its electric field has a component in the direction of the
background density gradient, there appears a disbalance of charge in every
plasma layer, caused by the density gradient. Due to this some additional
longitudinal electric field component appears in the direction of the wave
vector. This longitudinal field may couple with the usual electrostatic
longitudinal perturbations like the ion acoustic, electron Langmuir, and ion
plasma waves. As a result, these standard electrostatic waves are modified and
in addition to this a completely new low-frequency mode appears. Some basic
features of the coupling and modification of the ion acoustic wave, and
properties of the new mode are discussed here, in ordinary electron-ion and in
pair plasmas.


  A macroscopic model-based approach for estimation of the traffic state,
specifically of the (total) density and flow of vehicles, is developed for the
case of "mixed" traffic, i.e., traffic comprising both ordinary and connected
vehicles. The development relies on the following realistic assumptions: (i)
The density and flow of connected vehicles are known at the (local or central)
traffic monitoring and control unit on the basis of their regularly reported
positions, and (ii) the average speed of conventional vehicles is roughly equal
to the average speed of connected vehicles. Thus, complete traffic state
estimation (for arbitrarily selected segments in the network) may be achieved
by merely estimating the percentage of connected vehicles with respect to the
total number of vehicles. A model is derived, which describes the dynamics of
the percentage of connected vehicles, utilizing only well-known conservation
law equations that describe the dynamics of the density of connected vehicles
and of the total density of all vehicles. Based on this model, which is a
linear time-varying system, an estimation algorithm for the percentage of
connected vehicles is developed employing a Kalman filter. The estimation
methodology is validated through simulations using a second-order macroscopic
traffic flow model as ground truth for the traffic state. The approach calls
for a minimum of spot sensor-based total flow measurements {according to a
variety of possible location configurations.


  The inner boundary of a black hole accretion disk is often set to the
marginally stable circular orbit (or the innermost stable circular orbit, ISCO)
around the black hole. It is important for the theories of black hole accretion
disks and their applications to astrophysical black hole systems.
Traditionally, the marginally stable circular orbit is obtained by considering
the equatorial motion of a test particle around a black hole. However, in
reality the accretion flow around black holes consists of fluid, in which the
pressure often plays an important role. Here we consider the influence of fluid
pressure on the location of marginally stable circular orbit around black
holes. It is found that when the temperature of the fluid is so low that the
thermal energy of a particle is much smaller than its rest energy, the location
of marginally stable circular orbit is almost the same as that in the test
particle case. However, we demonstrate that in some special cases the
marginally stable circular orbit can be different when the fluid pressure is
large and the thermal energy becomes non-negligible comparing with the rest
energy. We present our results for both the cases of non-spinning and spinning
black holes. The influences of our results on the black hole spin parameter
measurement in X-ray binaries and the energy release efficiency of accretion
flows around black holes are discussed.


  We report two decentralized multi-agent cooperative localization algorithms
in which, to reduce the communication cost, inter-agent state estimate
correlations are not maintained but accounted for implicitly. In our first
algorithm, to guarantee filter consistency, we account for unknown inter-agent
correlations via an upper bound on the joint covariance matrix of the agents.
In the second method, we use an optimization framework to estimate the unknown
inter-agent cross-covariance matrix. In our algorithms, each agent localizes
itself in a global coordinate frame using a local filter driven by local dead
reckoning and occasional absolute measurement updates, and opportunistically
corrects its pose estimate whenever it can obtain relative measurements with
respect to other mobile agents. To process any relative measurement, only the
agent taken the measurement and the agent the measurement is taken from need to
communicate with each other. Consequently, our algorithms are decentralized
algorithms that do not impose restrictive network-wide connectivity condition.
Moreover, we make no assumptions about the type of agents or relative
measurements. We demonstrate our algorithms in simulation and a
robotic~experiment.


  A frequency-calibrated SCINet (FC-SCINet) equalizer is proposed for
down-stream 100G PON with 28.7 dB path loss. At 5 km, FC-SCINet improves the
BER by 88.87% compared to FFE and a 3-layer DNN with 10.57% lower complexity.


  In this paper we present a general tool to handle the presence of zero
dynamics which are asymptotically but not locally exponentially stable in
problems of robust nonlinear stabilization by output feedback. We show how it
is possible to design locally Lipschitz stabilizers under conditions which only
rely upon a partial detectability assumption on the controlled plant, by
obtaining a robust stabilizing paradigm which is not based on design of
observers and separation principles. The main design idea comes from recent
achievements in the field of output regulation and specifically in the design
of nonlinear internal models.


  Architecture sizes for neural networks have been studied widely and several
search methods have been offered to find the best architecture size in the
shortest amount of time possible. In this paper, we study compact neural
network architectures for binary classification and investigate improvements in
speed and accuracy when favoring overcomplete architecture candidates that have
a very high-dimensional representation of the input. We hypothesize that an
overcomplete model architecture that creates a relatively high-dimensional
representation of the input will be not only be more accurate but would also be
easier and faster to find. In an NxM search space, we propose an online
traversal algorithm that finds the best architecture candidate in O(1) time for
best case and O(N) amortized time for average case for any compact binary
classification problem by using k-completeness as heuristics in our search. The
two other offline search algorithms we implement are brute force traversal and
diagonal traversal, which both find the best architecture candidate in O(NxM)
time. We compare our new algorithm to brute force and diagonal searching as a
baseline and report search time improvement of 52.1% over brute force and of
15.4% over diagonal search to find the most accurate neural network
architecture when given the same dataset. In all cases discussed in the paper,
our online traversal algorithm can find an accurate, if not better,
architecture in significantly shorter amount of time.


  Simple representation for the average value of the W-boson one-loop
polarization tensor in a magnetic field B=const, calculated in the ground state
of the tree-level spectrum, is derived. It corresponds to Demeur's formula for
electron in QED. The energy of this state, describing effective particle mass,
is computed by solving the Schwinger-Dyson equation. As application, we
investigate the effective mass squared at the threshold of the tree-level
instability, $B \to B_c = m^2/e$, and show that it is positive. In this way the
stability of the W-boson spectrum is established. Some peculiarities of the
results obtained and other applications are discussed.


  Recently, lane detection has made great progress in autonomous driving. RESA
(REcurrent Feature-Shift Aggregator) is based on image segmentation. It
presents a novel module to enrich lane feature after preliminary feature
extraction with an ordinary CNN. For Tusimple dataset, there is not too
complicated scene and lane has more prominent spatial features. On the basis of
RESA, we introduce the method of position embedding to enhance the spatial
features. The experimental results show that this method has achieved the best
accuracy 96.93% on Tusimple dataset.


  Lung cancer is one of the prevalence diseases in the world which cause many
deaths. Detecting early stages of lung cancer is so necessary. So, modeling and
simulating some intelligent medical systems is an essential which can help
specialist to accurately determine and diagnose the disease. So this paper
contributes a new lung cancer detection model in CT images which use machine
learning methods. There are three steps in this model: noise reduction
(pre-processing), segmentation (middle-processing) and optimize segmentation
for detect exact are of nodules. This article use some filters for noise
reduction and then use Independent Recurrent Neural Networks (IndRNN) as deep
learning methods for segmentation which optimize and tune by Genetic Algorithm.
The results represented that the proposed method can detect exact area of
nodules in CT images.


  Image dehazing has become one of the crucial preprocessing steps for any
computer vision task. Most of the dehazing methods try to estimate the
transmission map along with the atmospheric light to get the dehazed image in
the image domain. In this paper, we propose a novel end-to-end architecture
that directly estimates dehazed image in Krawtchouk transform domain. For this
a customized Krawtchouk Convolution Layer (KCL) in the architecture is added.
KCL is constructed using Krawtchouk basis functions which converts the image
from the spatial domain to the Krawtchouk transform domain. Another convolution
layer is added at the end of the architecture named as Inverse Krawtchouk
Convolution Layer (IKCL) which converts the image back to the spatial domain
from the transform domain. It has been observed that the haze is mainly present
in lower frequencies of hazy images, wherein the Krawtchouk transform helps to
analyze the high and low frequencies of the images separately. We have divided
our architecture into two branches, the upper branch deals with the higher
frequencies while the lower branch deals with the lower frequencies of the
image. The lower branch is made deeper in terms of the layers as compared to
the upper branch to address the haze present in the lower frequencies. Using
the proposed Orthogonal Transform based Generative Adversarial Network (OTGAN)
architecture for image dehazing, we were able to achieve competitive results
when compared to the present state-of-the-art methods.


  Standard likelihood penalties to learn Gaussian graphical models are based on
regularising the off-diagonal entries of the precision matrix. Such methods,
and their Bayesian counterparts, are not invariant to scalar multiplication of
the variables, unless one standardises the observed data to unit sample
variances. We show that such standardisation can have a strong effect on
inference and introduce a new family of penalties based on partial
correlations. We show that the latter, as well as the maximum likelihood, $L_0$
and logarithmic penalties are scale invariant. We illustrate the use of one
such penalty, the partial correlation graphical LASSO, which sets an $L_{1}$
penalty on partial correlations. The associated optimization problem is no
longer convex, but is conditionally convex. We show via simulated examples and
in two real datasets that, besides being scale invariant, there can be
important gains in terms of inference.


  Impact loads from waves on vessels and coastal structures are complex and may
involve wave breaking, which has made these loads difficult to estimate
numerically or empirically. Results from previous experiments have shown a wide
range of forces and pressures measured from breaking and nonbreaking waves,
with no clear trend between wave characteristics and the localized forces and
pressures that they generate. In 2008, a canonical breaking wave impact data
set was obtained at the Naval Surface Warfare Center, Carderock Division, by
measuring the distribution of impact pressures of incident nonbreaking and
breaking waves on one face of a cube. This experimental effort was sponsored by
the Office of Naval Research (ONR), under the Dynamics of Interacting Platforms
Program, Program Manager Dr. Ron Joslin. The effects of wave height,
wavelength, face orientation, face angle, and submergence depth were
investigated. Additionally, a limited number of runs were made at low forward
speeds, ranging from about 0.5 to 2 knots (0.26 to 1.03 m/s).


  In this paper, we extend the DC Calculus introduced by Perelman on finite
dimensional Alexandrov spaces with curvature bounded below. Among other things,
our results allow us to define the Hessian and the Laplacian of DC functions
(including distance functions as a particular instance) as a measure-valued
tensor and a Radon measure respectively. We show that these objects share
various properties with their analogues on smooth Riemannian manifolds.


  Comparing probability distributions is at the crux of many machine learning
algorithms. Maximum Mean Discrepancies (MMD) and Wasserstein distances are two
classes of distances between probability distributions that have attracted
abundant attention in past years. This paper establishes some conditions under
which the Wasserstein distance can be controlled by MMD norms. Our work is
motivated by the compressive statistical learning (CSL) theory, a general
framework for resource-efficient large scale learning in which the training
data is summarized in a single vector (called sketch) that captures the
information relevant to the considered learning task. Inspired by existing
results in CSL, we introduce the H\"older Lower Restricted Isometric Property
and show that this property comes with interesting guarantees for compressive
statistical learning. Based on the relations between the MMD and the
Wasserstein distances, we provide guarantees for compressive statistical
learning by introducing and studying the concept of Wasserstein regularity of
the learning task, that is when some task-specific metric between probability
distributions can be bounded by a Wasserstein distance.


  We have investigated the $\rho^{0}$ meson photoproduction in ultraperipheral
isobaric collisions between $_{44}^{96}\textrm{Ru}+_{44}^{96}\textrm{Ru}$ and
$_{40}^{96}\textrm{Zr}+_{40}^{96}\textrm{Zr}$ at $\sqrt{s_{NN}}=200$ GeV,
employing the dipole model with the equivalent photon approximation. By
implementing the Woods-Saxon distribution to represent the nuclear mass
density, which is derived from density functional theory with an inclusion of
nuclear deformation effects, we have calculated the transverse momentum $q_{T}$
spectra in isobaric collisions. We observe the characteristic dip behavior in
these spectra, indicative of diffraction phenomena in high-energy physics. We
notice that the deformation effects cause a nearly linear increase with
$q_{T}^{2}$ for $q_{T}^{2}\lesssim0.015$ $\textrm{GeV}^{2}$, aligning with
experimental observations. We offer a simple explanation for the observed
behavior in these spectra by introducing the effective width of the nuclei in
the thickness function. We also extend our discussion on the $\rho^{0}$ meson
photoproduction with the targets $^{63}\textrm{Cu}$,$^{197}\textrm{Au}$, and
$^{238}\textrm{U}$.


  We consider the Lam\'e system of linear elasticity when the inclusion has the
extreme elastic constants. We show that the solutions to the Lam\'e system
converge in appropriate $H^1$-norms when the shear modulus tends to infinity
(the other modulus, the compressional modulus is fixed), and when the bulk
modulus and the shear modulus tend to zero. Using this result, we show that the
asymptotic expansion of the displacement vector in the presence of small
inclusion is uniform with respect to Lam\'e parameters.


  We propose a second order differential calculus to analyze the regularity and
the stability properties of the distribution semigroup associated with
McKean-Vlasov diffusions. This methodology provides second order Taylor type
expansions with remainder for both the evolution semigroup as well as the
stochastic flow associated with this class of nonlinear diffusions.
Bismut-Elworthy-Li formulae for the gradient and the Hessian of the
integro-differential operators associated with these expansions are also
presented. The article also provides explicit Dyson-Phillips expansions and a
refined analysis of the norm of these integro-differential operators. Under
some natural and easily verifiable regularity conditions we derive a series of
exponential decays inequalities with respect to the time horizon. We illustrate
the impact of these results with a second order extension of the
Alekseev-Gr{\"o}bner lemma to nonlinear measure valued semigroups and
interacting diffusion flows. This second order perturbation analysis provides
direct proofs of several uniform propagation of chaos properties w.r.t. the
time parameter, including bias, fluctuation error estimate as well as
exponential concentration inequalities.


  This study first reconstructs three deep learning powered stock trading
models and their associated strategies that are representative of distinct
approaches to the problem and established upon different aspects of the many
theories evolved around deep learning. It then seeks to compare the performance
of these strategies from different perspectives through trading simulations ran
on three scenarios when the benchmarks are kept at historical low points for
extended periods of time. The results show that in extremely adverse market
climates, investment portfolios managed by deep learning powered algorithms are
able to avert accumulated losses by generating return sequences that shift the
constantly negative CSI 300 benchmark return upward. Among the three, the LSTM
model's strategy yields the best performance when the benchmark sustains
continued loss.


  The diffusion, penetration and intercalation of metallic atomic dopants is an
important question for various graphite applications in engineering and
nanotechnology. We have performed systematic first-principles calculations of
the behaviour of ruthenium nanoclusters on a graphene monolayer and
intercalated into a bilayer. Our computational results show that at a
sufficiently high density of single Ru atom interstitials, intercalated atoms
can shear the surrounding lattice to an AA stacking configuration, an effect
which weakens with increasing cluster size. Moreover, the interlayer stacking
configuration, in turn, has a significant effect on cluster diffusion. We
therefore find different trends in diffusivity as a function of cluster size
and interlayer stacking. For monolayer graphene and an AA graphene bilayer, the
formation of small clusters generally lowers diffusion barriers, while the
opposite behaviour is found for the preferred AB stacking configuration. These
results demonstrate that conditions of local impurity concentration and
interlayer disregistry are able to regulate the diffusivity of metallic
impurities in graphite.


  We present the design and performance of the multi-object fiber spectrographs
for the Sloan Digital Sky Survey (SDSS) and their upgrade for the Baryon
Oscillation Spectroscopic Survey (BOSS). Originally commissioned in Fall 1999
on the 2.5-m aperture Sloan Telescope at Apache Point Observatory, the
spectrographs produced more than 1.5 million spectra for the SDSS and SDSS-II
surveys, enabling a wide variety of Galactic and extra-galactic science
including the first observation of baryon acoustic oscillations in 2005. The
spectrographs were upgraded in 2009 and are currently in use for BOSS, the
flagship survey of the third-generation SDSS-III project. BOSS will measure
redshifts of 1.35 million massive galaxies to redshift 0.7 and Lyman-alpha
absorption of 160,000 high redshift quasars over 10,000 square degrees of sky,
making percent level measurements of the absolute cosmic distance scale of the
Universe and placing tight constraints on the equation of state of dark energy.
  The twin multi-object fiber spectrographs utilize a simple optical layout
with reflective collimators, gratings, all-refractive cameras, and
state-of-the-art CCD detectors to produce hundreds of spectra simultaneously in
two channels over a bandpass covering the near ultraviolet to the near
infrared, with a resolving power R = \lambda/FWHM ~ 2000. Building on proven
heritage, the spectrographs were upgraded for BOSS with volume-phase
holographic gratings and modern CCD detectors, improving the peak throughput by
nearly a factor of two, extending the bandpass to cover 360 < \lambda < 1000
nm, and increasing the number of fibers from 640 to 1000 per exposure. In this
paper we describe the original SDSS spectrograph design and the upgrades
implemented for BOSS, and document the predicted and measured performances.


  This review starts out with a brief history of the photino-gluino
phenomenology. Next, it describes difficulties encountered in the construction
of Feynman amplitudes when dealing with Majorana fermions and outlines a
procedure that circumvents this difficulty by means of a prescription based on
a well-defined fermion flow without explicit charge-conjugation matrices at the
vertex equations. Finally, as an illustration, it calculates the cross section
for the production of photinos and gluinos.


  Understanding the magnetic properties of the various Mn doping configurations
that can be encountered in $2H$-MoS$_2$ monolayer could be beneficial for its
use in spintronics. Using density functional theory plus Hubbard U (DFT$+$U)
approach, we study how a single isolated, double- and triple-substitution
configurations of Mn atoms within a MoS$_2$ monolayer could contribute to its
total magnetization. We find that the doping-configuration plays a critical
role in stabilizing a ferromagnetic state in a Mn-doped MoS$_2$ monolayer.
Indeed, the Mn-Mn magnetic interaction is found to be ferromagntic and strong
for Mn in equidistant substitution positions where the separation average range
of 6-11 {\AA}. The strongest ferromagnetic interaction is found when
substitutions are in second nearest neighbors Mo-sites of the armchair chain.
Clustering is energetically favorable but it strongly reduces the ferromagnetic
exchange energies. Our results suggest that ordering the Mn dopants on MoS$_2$
monolayer is needed to increase its potential ferromagnetism.


  In this paper we study the dynamics near the equilibrium point of a family of
Hamiltonian systems in the neighborhood of a $0^2 iw$ resonance. The existence
of a family of periodic orbits surrounding the equilibrium is well-known and we
show here the existence of homoclinic connections with several loops for every
periodic orbit close to the origin, except the origin itself. To prove this
result, we first show a Hamiltonian normal form theorem inspired by the
Elphick-Tirapegui-Brachet-Coullet-Iooss normal form. We then use a local
Hamiltonian normalization relying on a result of Moser. We obtain the result of
existence of homoclinic orbits by geometrical arguments based on the low
dimension and with the aid of a KAM theorem which allows to confine the loops.
The same problem was studied before for reversible non Hamiltonian
vectorfields, and the splitting of the homoclinic orbits lead to exponentially
small terms which prevent the existence of homoclinic connections to
exponentially small periodic orbits. The same phenomenon occurs here but we get
round this difficulty thanks to geometric arguments specific to Hamiltonian
systems and by studying homoclinic orbits with many loops.


  It has recently been suggested that the driven lattice gas should be
described by a novel field theory in the limit of infinite drive. We review the
original and the new field theory, invoking several well-documented key
features of the microscopics. Since the new field theory fails to reproduce
these characteristics, we argue that it cannot serve as a viable description of
the driven lattice gas. Recent results, for the critical exponents associated
with this theory, are re-analyzed and shown to be incorrect.


  IHEP Protvino has pioneered the wide practical use of bent crystals as
optical elements in high-energy beams for beam extraction and deflection on
permanent basis since 1989. In the course of IHEP experiments, crystal
channeling has been developed into efficient instrument for particle steering
at accelerators, working in predictable, reliable manner with beams of very
high intensity over years. Crystal systems extract 70 GeV protons from IHEP
main ring with efficiency of 85% at intensity of 1.E12, basing on multi-pass
mechanism of channeling proposed theoretically and realised experimentally at
IHEP. Today, six locations on the IHEP 70-GeV main ring of the accelerator
facility are equipped by crystal extraction systems, serving mostly for routine
applications rather than for research and allowing a simultaneous run of
several particle physics experiments, thus significantly enriching the IHEP
physics program. The long successful history of large-scale crystal
exploitation at IHEP should help to incorporate channeling crystals into
accelerator systems worldwide in order to create unique systems for beam
delivery. We report recent results from the research and exploitation of
crystal extraction systems at IHEP.


  We report on the diffusive-ballistic thermal conductance of multi-moded
single-crystal silicon beams measured below 1 K. It is shown that the phonon
mean-free-path $\ell$ is a strong function of the surface roughness
characteristics of the beams. This effect is enhanced in diffuse beams with
lengths much larger than $\ell$, even when the surface is fairly smooth, 5-10
nm rms, and the peak thermal wavelength is 0.6 $\mu$m. Resonant phonon
scattering has been observed in beams with a pitted surface morphology and
characteristic pit depth of 30 nm. Hence, if the surface roughness is not
adequately controlled, the thermal conductance can vary significantly for
diffuse beams fabricated across a wafer. In contrast, when the beam length is
of order $\ell$, the conductance is dominated by ballistic transport and is
effectively set by the beam area. We have demonstrated a uniformity of $\pm$8%
in fractional deviation for ballistic beams, and this deviation is largely set
by the thermal conductance of diffuse beams that support the
micro-electro-mechanical device and electrical leads. In addition, we have
found no evidence for excess specific heat in single-crystal silicon membranes.
This allows for the precise control of the device heat capacity with normal
metal films. We discuss the results in the context of the design and
fabrication of large-format arrays of far-infrared and millimeter wavelength
cryogenic detectors.


  We apply the renormalized singles with correlation (RSc) Green's function in
the $GW$ approximation to calculate accurate quasiparticle (QP) energies and
orbitals. The RSc Green's function includes all orders of singles contributions
from the associated density functional approximation (DFA) and treats higher
order contributions in a perturbative manner. The
$G_{\text{RSc}}W_{\text{RSc}}$ method uses the RSc Green's function as the new
starting point and calculates the screened interaction with the RSc Green's
function. The $G_{\text{RSc}}W_0$ methods fixes the screened interaction at the
DFA level. For the calculations of ionization potentials in the GW100 set,
$G_{\text{RSc}}W_{\text{RSc}}$ significantly reduces the dependence on the
starting point of DFAs used and provides accurate results with the mean
absolute error (MAE) of $0.34$ eV comparable to ev$GW$. For the calculations of
core-level binding energies in the CORE65 set, $G_{\text{RSc}}W_{\text{RSc}}$
slightly overestimates the results because of underscreening, but
$G_{\text{RSc}}W_0$ with GGA functionals provides the optimal accuracy of
$0.40$ eV MAE comparable to ev$GW_0$. We also show that
$G_{\text{RSc}}W_{\text{RSc}}$ predicts accurate dipole moments of small
molecules. These two methods, $G_{\text{RSc}}W_{\text{RSc}}$ and
$G_{\text{RSc}}W_0$, are computationally much favorable than any flavor of
self-consistent $GW$ methods. Thus, the RSc approach is promising for making
$GW$ and other Green's function methods efficient and robust.


  We explore the utility of qutrits and qubits for simulating the flavor
dynamics of dense neutrino systems. The evolution of such systems impacts some
important astrophysical processes, such as core-collapse supernovae and the
nucleosynthesis of heavy nuclei. Many-body simulations require classical
resources beyond current computing capabilities for physically relevant system
sizes. Quantum computers are therefore a promising candidate to efficiently
simulate the many-body dynamics of collective neutrino oscillations. Previous
quantum simulation efforts have primarily focused on properties of the
two-flavor approximation due to their direct mapping to qubits. Here, we
present new quantum circuits for simulating three-flavor neutrino systems on
qutrit- and qubit-based platforms, and demonstrate their feasibility by
simulating systems of two, four and eight neutrinos on IBM and Quantinuum
quantum computers.


  Recent results of Kahle and Miller give a method of constructing primary
decompositions of binomial ideals by first constructing "mesoprimary
decompositions" determined by their underlying monoid congruences. Monoid
congruences (and therefore, binomial ideals) can present many subtle behaviors
that must be carefully accounted for in order to produce general results, and
this makes the theory complicated. In this paper, we examine their results in
the presence of a positive $A$-grading, where certain pathologies are avoided
and the theory becomes more accessible. Our approach is algebraic: while key
notions for mesoprimary decomposition are developed first from a combinatorial
point of view, here we state definitions and results in algebraic terms, which
are moreover significantly simplified due to our (slightly) restricted setting.
In the case of toral components (which are well-behaved with respect to the
$A$-grading), we are able to obtain further simplifications under additional
assumptions. We also provide counterexamples to two open questions, identifying
(i) a binomial ideal whose hull is not binomial, answering a question of
Eisenbud and Sturmfels, and (ii) a binomial ideal $I$ for which
$I_\text{toral}$ is not binomial, answering a question of Dickenstein, Miller
and the first author.


  We elaborate on the dynamics of ionized interstellar medium in the presence
of hidden photon dark matter. Our main focus is the ultra-light regime, where
the hidden photon mass is smaller than the plasma frequency in the Milky Way.
We point out that as a result of the Galactic plasma shielding direct detection
of ultra-light photons in this mass range is especially challenging. However,
we demonstrate that ultra-light hidden photon dark matter provides a powerful
heating source for the ionized interstellar medium. This results in a strong
bound on the kinetic mixing between hidden and regular photons all the way down
to the hidden photon masses of order $10^{-20}$ eV.


  Quantum Correlations are studied extensively in quantum information domain.
Entanglement Measures and Quantum Discord are good examples of these actively
studied correlations. Detection of violation in Bell inequalities is also a
widely active area in quantum information theory world. In this work, we
revisit the problem of analyzing the behavior of quantum correlations and
violation of Bell inequalities in noisy channels. We extend the problem defined
in [1] by observing the changes in negativity measure, quantum discord and a
modified version of Horodecki measure for violation of Bell inequalities under
amplitude damping, phase damping and depolarizing channels. We report different
interesting results for each of these correlations and measures. All these
correlations and measures decrease under decoherence channels, but some changes
are very dramatical comparing to others. We investigate also separability
conditions of example studied states.


  We study the phase diagram of 5-dimensional $SU(2)$ Yang-Mills theory on the
lattice. We consider two extensions of the fundamental plaquette Wilson action
in the search for the continuous phase transition suggested by the $4+\epsilon$
expansion. The extensions correspond to new terms in the action: i) a unit size
plaquette in the adjoint representation or ii) a two-unit sided square
plaquette in the fundamental representation. We use Monte Carlo to sample the
first and second derivative of the entropy near the confinement phase
transition, with lattices up to $12^{5}$. While we exclude the presence of a
second order phase transition in the parameter space we sampled for model i),
our data is not conclusive in some regions of the parameter space of model ii).


  The aim of this paper is to investigate Green's function for parabolic and
elliptic systems satisfying a possibly nonlocal Robin-type boundary condition.
We construct Green's function for parabolic systems with time-dependent
coefficients satisfying a possibly nonlocal Robin-type boundary condition
assuming that weak solutions of the system are locally H\"{o}lder continuous in
the interior of the domain, and as a corollary we construct Green's function
for elliptic system with a Robin-type condition. Also, we obtain Gaussian bound
for Robin Green's function under an additional assumption that weak solutions
of Robin problem are locally bounded up to the boundary. We provide some
examples satisfying such a local boundedness property, and thus have Gaussian
bounds for their Green's functions.


  A phenomenological model using fluid theory is developed to show that the
decay of two seemingly independent nonlinear structures namely the dust void
and dust soliton strongly depends on the plasma ionization parameter in an
unmagnetized complex plasma system. Numerical solution of model equations has
shown that the evolution of dust voids and their subsequent decay in a time
frame is intimately related with the ionization parameter. A similar result
also holds good in case of dust solitons where stability of soliton is found to
depend critically upon ionization parameter. Most importantly, it is observed
that time of the collapse of a dust soliton precedes the onset time of a dust
void decay and therefore soliton decay acts as a precursor for void decay to
occur in a given dusty plasma system.


  Spin-pumping from a ferromagnetic insulator into a high-$T_c$ superconductor
with a $d$-wave superconducting order parameter has recently been
experimentally observed. Such unconventional superconducting order is known to
produce interfacial bound-states for certain crystallographic orientations.
Here, we present a methodology which can be used to study spin-pumping into
unconventional superconductors, including the role of interfacial bound-states.
As an example, we determine how the crystallographic orientation of the
$d$-wave order parameter relative the interface changes the spin-pumping
effect. We find that the spin-pumping effect is slightly enhanced at low
temperatures for orientations hosting interfacial bound-states compared to
other superconducting states. However, the spin-pumping effect does not show a
coherence peak close to $T_c$ for such orientations, and instead remains
smaller than the normal state value for all $T$. For orientations not hosting
interfacial bound-states, we find that the pumped spin current can be increased
to several times the normal-state spin current at frequencies that are small
compared to the superconducting gap. Our results show that the spin-pumping
dependency on frequency and temperature changes qualitatively depending on the
crystallographic orientation of unconventional superconducting order parameters
relative the interface.


  2-Ethyhexyl nitrate (2-EHN) is a major additive of fuel which is used to
comply with the cetane number of diesel. Because of its wide use and possible
accidental release, 2-EHN is a potential pollutant of the environment. In this
study, Mycobacterium austroafricanum IFP 2173 was selected among several
strains as the best 2-EHN degrader. The 2-EHN biodegradation rate was increased
in biphasic cultures where the hydrocarbon was dissolved in an inert
non-aqueous phase liquid (NAPL), suggesting that the transfer of the
hydrophobic substrate to the cells was a growth-limiting factor. Carbon balance
calculation as well as organic carbon measurement indicated a release of
metabolites in the culture medium. Further analysis by gas chromatography
revealed that a single metabolite accumulated during growth. This metabolite
had a molecular mass of 114 Da as determined by GC/MS and was provisionally
identified as 4-ethyldihydrofuran-2(3H)-one by LC-MS/MS analysis.
Identification was confirmed by analysis of the chemically synthesized lactone.
Based on these results, a plausible catabolic pathway is proposed whereby 2-EHN
is converted to 4-ethyldihydrofuran-2(3H)-one, which cannot be metabolised
further by strain IFP 2173. This putative pathway provides an explanation for
the low energetic efficiency of 2-EHN degradation and its poor
biodegradability.


  We introduce a numerical method and python package,
https://github.com/andillio/CHiMES, that simulates quantum systems initially
well approximated by mean field theory using a second order extension of the
classical field approach. We call this the field moment expansion method. In
this way, we can accurately approximate the evolution of first and second field
moments beyond where the mean field theory breaks down. This allows us to
estimate the quantum breaktime of a classical approximation without any
calculations external to the theory. We investigate the accuracy of the field
moment expansion using a number of well studied quantum test problems.
Interacting Bosonic systems similar to scalar field dark matter are chosen as
test problems. We find that successful application of this method depends on
two conditions: the quantum system must initially be well described by the
classical theory, and that the growth of the higher order moments be
hierarchical.


  High quality epidemiological modelling is essential in order to combat the
spread of infectious diseases. In this contribution, we present SimPLoID, an
epidemiological modelling framework based on the probabilistic logic
programming language ProbLog. SimPLoiI combines concepts from compartmental
modelling, such as the classic Susceptible-Infected-Recovered (SIR) model, with
network-based modelling. As a proof of concept, SimPLoID showcases the
potential of declarative probabilistic logic programming for a natural,
flexible and compact expression of infectious disease dynamics. In particular,
its modularity makes it easily extendable in the face of changing requirements.
This application area benefits especially from the precisely specified
semantics of the ProbLog language and from the well-maintained engines, which
support a variety of query types from exact inference to Monte Carlo
simulation. We also provide a domain-specific language designed for researchers
not trained in programming, which is compiled to ProbLog clauses within an
interactive Python application.


  In the present paper, we constructed the $p$-adic $L$-function of Bianchi
modular form. Also we proved that the first homology groups are generated by
the special Bianchi modular symbols. As a corollary, the $\mu$-invariant of
some isotopic component of the $p$-adic $L$-function vanishes for Bianchi
newforms and a positive proportion of ordinary primes. Also we obtain the
residual non-vanishing result of the integral $L$-values.


  We study the theta map which assigns to a real quadratic form its theta
series. We introduce two invariants reflecting whether the differential of the
theta map vanishes or is degenerate. We provide examples of lattices where this
differential is zero. These invariants turn out to be modular forms for
integral lattices. We illustrate this in the rank two case.


  We consider the asymptotic behaviour of positive solutions u of the conformal
scalar curvature equation, \Delta u + n(n-2)/4 u^{(n+2)(n-2) = 0, in the
neighbourhood of isolated singularities in the standard Euclidean ball.
Although asymptotic radial symmetry for such solutions was proved some time
ago, by Caffarelli, Gidas and Spruck, we present a much simpler and more
geometric derivation of this fact. We also discuss a refinement, showing that
any such solution is asymptotic to one of the deformed radial singular
solutions. Finally we give some applications of these refined asymptotics,
first to computing the global Pohozaev invariants of solutions on the sphere
with isolated singularities, and then to the regularity of the moduli space of
all such solutions.


  Planets are expected to conclude their growth through a series of giant
impacts: energetic, global events that significantly alter planetary
composition and evolution. Computer models and theory have elucidated the
diverse outcomes of giant impacts in detail, improving our ability to interpret
collision conditions from observations of their remnants. However, many open
questions remain, as even the formation of the Moon, a widely suspected
giant-impact product for which we have the most information, is still debated.
We review giant-impact theory, the diverse nature of giant-impact outcomes, and
the governing physical processes. We discuss the importance of computer
simulations, informed by experiments, for accurately modeling the impact
process. Finally, we outline how the application of probability theory and
computational advancements can assist in inferring collision histories from
observations, and we identify promising opportunities for advancing
giant-impact theory in the future.
  $\bullet$ Giant impacts exhibit diverse possible outcomes leading to changes
in planetary mass, composition, and thermal history depending on the
conditions.
  $\bullet$ Improvements to computer simulation methodologies and new
laboratory experiments provide critical insights into the detailed outcomes of
giant impacts.
  $\bullet$ When colliding planets are similar in size, they can merge or
escape one another with roughly equal probability, but with different effects
on their resulting masses, densities, and orbits.
  $\bullet$ Different sequences of giant impacts can produce similar planets,
encouraging the use of probability theory to evaluate distinct formation
hypotheses.


  The process e+ e- -> phi -> K+ K- has been studied with the CMD-2 detector
using about 542 000 events detected in the center-of-mass energy range from
1.01 to 1.034 GeV. The systematic error of the cross section is estimated to be
2.2%. The phi(1020) meson parameters in the phi -> K+K- decay channel have been
measured: sigma_0(phi-> K+K-) = 2016 +- 8 +- 44 nb, m(phi) = 1019.441 +- 0.008
+- 0.080 MeV/c2, Gamma(phi) = 4.24 +- 0.02 +- 0.03 MeV, B(e+e-)B(K+K-) = (14.27
+- 0.05 +- 0.31)*10(-5).


  Recently, Bruinier and Ono classified cusp forms $f(z) := \sum_{n=0}^{\infty}
a_f(n)q ^n \in S_{\lambda+1/2}(\Gamma_0(N),\chi)\cap \mathbb{Z}[[q]]$ that does
not satisfy a certain distribution property for modulo odd primes $p$. In this
paper, using Rankin-Cohen Bracket, we extend this result to modular forms of
half integral weight for primes $p \geq 5$. As applications of our main theorem
we derive distribution properties, for modulo primes $p\geq5$, of traces of
singular moduli and Hurwitz class number. We also study an analogue of Newman's
conjecture for overpartitions.


  The evolution of galaxies is driven strongly by dynamical processes including
internal instabilities, tidal interactions and mergers. The cluster environment
is a useful laboratory for studying these effects. I present recent results on
simulations of interacting populations of spiral and elliptical galaxies in the
cosmological collapse of a cluster, showing the formation of the central,
brightest cluster galaxies through merging, the effect of tidal interactions
and merging on galaxy morphology over cosmic history, and the distribution and
kinematics of the tidal debris field. (See
http://www.cita.utoronto.ca/~dubinski/rutgers98 for extensive figures and
animations.)


  We present an approach to proving control theorems for overconvergent
automorphic forms on some Harris-Taylor unitary Shimura varieties based on a
comparison between the rigid coho- mology of the multiplicative ordinary locus
and the rigid cohomology of the overlying Igusa tower, the latter which may be
computed using the Harris-Taylor version of the Langlands-Kottwitz method. We
also prove a higher level version, generalizing work of Coleman.


  We compute the ground-state phase diagram of the Hubbard and frustrated
Hubbard models on the square lattice with density matrix embedding theory using
clusters of up to 16 sites. We provide an error model to estimate the
reliability of the computations and complexity of the physics at different
points in the diagram. We find superconductivity in the ground-state as well as
competition between inhomogeneous charge, spin, and pairing states at low
doping. The estimated errors in the study are below T$_c$ in the cuprates and
on the scale of contributions in real materials that are neglected in the
Hubbard model.


  We propose a framework, named the postselected inflation framework, to obtain
converging outer approximations of the sets of probability distributions that
are compatible with classical multi-network scenarios. Here, a network is a
bilayer directed acyclic graph with a layer of sources of classical randomness,
a layer of agents, and edges specifying the connectivity between the agents and
the sources. A multi-network scenario is a list of such networks, together with
a specification of subsets of agents using the same strategy. An outer
approximation of the set of multi-network correlations provides means to
certify the infeasibility of a list of agent outcome distributions. We
furthermore show that the postselected inflation framework is mathematically
equivalent to the standard inflation framework: in that respect, our results
allow to gain further insights into the convergence proof of the inflation
hierarchy of Navascu\`es and Wolfe [arXiv:1707.06476], and extend it to the
case of multi-network scenarios.


  In this work, we theoretically study the heat flow between two $1+1$d chiral
gapless systems connected by a point contact. With a small temperature gradient
between the two, we find that the ratio between fluctuations of the heat
current and the heat current itself is proportional to the scaling dimension --
a universal number that characterizes the distribution of the particles
tunneling through the point contact. We adopt two different approaches,
scattering theory and conformal field theory, to calculate this ratio and see
that their results agree. Our findings are useful for probing not only
fractional charge excitations in fractional quantum Hall states but also
neutral ones in non-Abelian phases.


  Three-term recurrences have infused stupendous amount of research in a broad
spectrum of the sciences, such as orthogonal polynomials (in special functions)
and lattice paths (in enumerative combinatorics). Among these are the Lucas
polynomials, which have seen a recent true revival. In this paper one of the
themes of investigation is the specialization to the Pell and Delannoy numbers.
The underpinning motivation comprises primarily of divisibility and symmetry.
One of the most remarkable findings is a structural decomposition of the Lucas
polynomials into what we term as flat and sharp analogs.


  It has been 5 years since the Computing Community Consortium (CCC) Workshop
on Next Steps in Quantum Computing, and significant progress has been made in
closing the gap between useful quantum algorithms and quantum hardware. Yet
much remains to be done, in particular in terms of mitigating errors and moving
towards error-corrected machines. As we begin to transition from the
Noisy-Intermediate Scale Quantum (NISQ) era to a future of fault-tolerant
machines, now is an opportune time to reflect on how to apply what we have
learned thus far and what research needs to be done to realize computational
advantage with quantum machines.


  Consider a large social network with possibly severe degree heterogeneity and
mixed-memberships. We are interested in testing whether the network has only
one community or there are more than one communities. The problem is known to
be non-trivial, partially due to the presence of severe degree heterogeneity.
We construct a class of test statistics using the numbers of short paths and
short cycles, and the key to our approach is a general framework for canceling
the effects of degree heterogeneity. The tests compare favorably with existing
methods. We support our methods with careful analysis and numerical study with
simulated data and a real data example.


  The thermodynamic properties of dark energy fluids described by an equation
of state parameter $\omega=p/\rho$ are rediscussed in the context of FRW type
geometries. Contrarily to previous claims, it is argued here that the phantom
regime $\omega<-1$ is not physically possible since that both the temperature
and the entropy of every physical fluids must be always positive definite. This
means that one cannot appeal to negative temperature in order to save the
phantom dark energy hypothesis as has been recently done in the literature.
Such a result remains true as long as the chemical potential is zero. However,
if the phantom fluid is endowed with a non-null chemical potential, the phantom
field hypothesis becomes thermodynamically consistent, that is, there are
macroscopic equilibrium states with $T>0$ and $S>0$ in the course of the
Universe expansion. Further, the negative value of the chemical potential
resulting from the entropy constraint ($S>0$) suggests a bosonic massless
nature to the phantom particles.


  Researchers today have found out the importance of Artificial Intelligence,
and Machine Learning in our daily lives, as well as they can be used to improve
the quality of our lives as well as the cities and nations alike. An example of
this is that it is currently speculated that ML can provide ways to relieve
workers as it can predict effective working schedules and patterns which
increase the efficiency of the workers. Ultimately this is leading to a
Work-Life Balance for the workers. But how is this possible? It is practically
possible with the Machine Learning algorithms to predict, calculate the factors
affecting the feelings of the worker's work-life balance. In order to actually
do this, a sizeable amount of 12,756 people's data has been taken under
consideration. Upon analysing the data and calculating under various factors,
we have found out the correlation of various factors and WLB(Work-Life Balance
in short). There are some factors that have to be taken into serious
consideration as they play a major role in WLB. We have trained 80% of our data
with Random Forest Classifier, SVM and Naive Bayes algorithms. Upon testing,
the algorithms predict the WLB with 71.5% as the best accuracy.


  Gamma rays from a solar flare in Active Region 10039 on 23 July 2002 with the
RHESSI spacecraft spectrometer indicate that the CNO cycle occurs at the solar
surface, in electrical discharges along closed magnetic loops. At the two feet
of the loop, H ions are accelerated to energy levels that surpass Coulomb
barriers for the C-12[H-1, gamma]N-13 and N-14[H-1, gamma]O-15 reactions. First
x-rays appear along the discharge path. Next annihilation of positrons from
N-13 and O-15 [half-life = 10 m and 2 m] produce bright spots of 0.511 MeV
gammas at the loop feet. As C-13 increases from positron decay of N-13, the
C-13[He-4, n]O-16 reaction produces neutrons and then the 2.2 MeV emission line
appears from n-capture on H-1. These results suggest that the CNO cycle changed
the N-15/N-14 ratio in the solar wind and at the solar surface over geologic
time, and this ratio may contain an important historical record of climate
changes related to sunspot activity.


  The MAMMOTH-I Nebula at redshift 2.3 is one of the largest known Ly-alpha
nebulae in the Universe, spanning ~440 kpc. Enormous Ly-alpha nebulae like
MAMMOTH-I typically trace the densest and most active regions of galaxy
formation. Using sensitive low-surface-brightness observations of CO(1-0) with
the Very Large Array, we trace the cold molecular gas in the inner 150 kpc of
the MAMMOTH-I Nebula. CO is found in four regions that are associated with
either galaxies or groups of galaxies that lie inside the nebula. In three of
the regions, the CO stretches up to ~30 kpc into the circum-galactic medium
(CGM). In the centermost region, the CO has a very low velocity dispersion
(FWHM$_{\rm CO}$ ~ 85 km/s), indicating that this gas is dynamically cold. This
dynamically cold gas coincides with diffuse restframe optical light in the CGM
around a central group of galaxies, as discovered with the Hubble Space
Telescope. We argue that this likely represents cooling of settled and enriched
gas in the center of MAMMOTH-I. This implies that the dynamically cold gas in
the CGM, rather than the obscured AGN, marks the core of the potential well of
this Ly-alpha nebula. In total, the CO in the MAMMOTH-I Nebula traces a
molecular gas mass of M$_{\rm H2}$ ~ 1.4 ($\alpha_{\rm CO}$/3.6) $\times$
10$^{11}$ M$_{\odot}$, with roughly 50% of the CO(1-0) emission found in the
CGM. Our results add to the increasing evidence that extended reservoirs of
molecular gas exist in the CGM of massive high-z galaxies and proto-clusters.


  Some recent precision experiments in low energy muon physics are discussed.
Spectroscopy on the muonium atom, the bound state of a positve muon and an
electron, has provided precise tests of standard theory and yielded most
precise values of important fundamental constants. A search for spontaneous
muonium to antimuonium conversion test lepton flavour conservation and yields
most stringent limits on parameters in several speculative models. The muon
magnetic anomaly may contain hints to new physics and is a relevant calibration
point for numerous models beyond standard theory. Since most precision
experiments are limited by the particle fluxes at present muon sources,
possibilities in this field are shown which will emerge at upgraded present
facilities or new accelerator complexes under construction or planning. At such
places novel techniques would be enabled.


  This report presents VLSAT-1 (an acronym for "Very Large Boolean
SATisfiability problems"), the first part of a benchmark suite to be used in
scientificexperiments and software competitions addressing SAT-solving
issues.VLSAT-1 contains 100~benchmarks of increasing complexity, proposed in
DIMACSCNF format under a permissive Creative Commons license. These benchmarks
havebeen used by the 2020 International Competition on Model Counting.