  We consider two independent binary i.i.d. random strings $X$ and $Y$ of equal
length $n$ and the optimal alignments according to a symmetric scoring
functions only. We decompose the space of scoring functions into five
components. Two of these components add a part to the optimal score which does
not depend on the alignment and which is asymptotically normal.
  We show that when we restrict the number of gaps sufficiently and add them
only into one sequence, then the alignment score can be decomposed into a part
which is normal and has order $O(\sqrt{n})$ and a part which is on a smaller
order and tends to a Tracy-Widom distribution. Adding gaps only into one
sequence is equivalent to aligning a string with its descendants in case of
mutations and deletes. For testing relatedness of strings, the normal part is
irrelevant, since it does not depend on the alignment hence it can be safely
removed from the test statistic.


  The exact enumeration of pure dimer coverings on the square lattice was
obtained by Kasteleyn, Temperley and Fisher in 1961. In this paper, we consider
the monomer-dimer covering problem (allowing multiple monomers) which is an
outstanding unsolved problem in lattice statistics. We have developed the state
matrix recursion method that allows us to compute the number of monomer--dimer
coverings and to know the partition function with monomer and dimer activities.
This method proceeds with a recurrence relation of so-called state matrices of
large size. The enumeration problem of pure dimer coverings and dimer coverings
with single boundary monomer is revisited in partition function forms. We also
provide the number of dimer coverings with multiple vacant sites. The related
Hosoya index and the asymptotic behavior of its growth rate are considered.
Lastly, we apply this method to the enumeration study of domino tilings of
Aztec diamonds and more generalized regions, so-called Aztec octagons and
multi-deficient Aztec octagons.


  Antiprotons, stored and cooled at low energies in a storage ring or at rest
in traps, are highly desirable for the investigation of a large number of basic
questions on fundamental interactions. This includes the static structure of
antiprotonic atomic systems and the time-dependent quantum dynamics of
correlated systems. The Antiproton Decelerator (AD) at CERN is currently the
worlds only low energy antiproton factory dedicated to antimatter experiments.
New antiproton facilities, such as the Extra Low ENergy Antiproton ring (ELENA)
at CERN and the Ultra-low energy Storage Ring (USR) at FLAIR, will open unique
possibilities. They will provide cooled, high quality beams of extra-low energy
antiprotons at intensities exceeding those achieved presently at the AD by
factors of ten to one hundred. These facilities, operating in the energy regime
between 100 keV down to 20 keV, face several design and beam dynamics
challenges, for example nonlinearities, space charge and scattering effects
limiting beam life time. Detailed investigations into the low energy and long
term beam dynamics have been carried out to address many of those challenges
towards the design optimisation. Results from these studies are presented in
this contribution, showing some examples for ELENA.


  In this paper, we consider context-awareness to enhance route reliability and
robustness in multi-hop cognitive networks. A novel context-aware route
discovery protocol is presented to enable secondary users to select the route
according to their QoS requirements. The protocol facilitates adjacent relay
selection under different criteria, such as shortest available path, route
reliability and relay reputation. New routing and security-based metrics are
defined to measure route robustness in spatial, frequency and temporal domains.
Secure throughput, defined as the percentage of traffic not being intercepted
in the network, is provided. The resources needed for trading are then obtained
by jointly optimizing secure throughput and trading price. Simulation results
show that when there is a traffic imbalance of factor 4 between the primary and
secondary networks, 4 channels are needed to achieve 90% link reliability and
99% secure throughput in the secondary network. Besides, when relay reputation
varies from 0.5 to 0.9, a 20% variation in the required resources is observed.


  Machine learning datasets have elicited concerns about privacy, bias, and
unethical applications, leading to the retraction of prominent datasets such as
DukeMTMC, MS-Celeb-1M, and Tiny Images. In response, the machine learning
community has called for higher ethical standards in dataset creation. To help
inform these efforts, we studied three influential but ethically problematic
face and person recognition datasets -- Labeled Faces in the Wild (LFW),
MS-Celeb-1M, and DukeMTM -- by analyzing nearly 1000 papers that cite them. We
found that the creation of derivative datasets and models, broader
technological and social change, the lack of clarity of licenses, and dataset
management practices can introduce a wide range of ethical concerns. We
conclude by suggesting a distributed approach to harm mitigation that considers
the entire life cycle of a dataset.


  We study ``soft leptogenesis'', a new mechanism of leptogenesis which does
not require flavour mixing among the right-handed neutrinos. Supersymmetry
soft-breaking terms give a small mass splitting between the CP-even and CP-odd
right-handed sneutrino states of a single generation and provide a CP-violating
phase sufficient to generate a lepton asymmetry. The mechanism is successful if
the lepton-violating soft bilinear coupling is unconventionally (but not
unnaturally) small. The values of the right-handed neutrino masses predicted by
soft leptogenesis can be low enough to evade the cosmological gravitino
problem.


  Approximate graph pattern mining (A-GPM) is an important data analysis tool
for many graph-based applications. There exist sampling-based A-GPM systems to
provide automation and generalization over a wide variety of use cases.
However, there are two major obstacles that prevent existing A-GPM systems
being adopted in practice. First, the termination mechanism that decides when
to end sampling lacks theoretical backup on confidence, and is unstable and
slow in practice. Second, they suffer poor performance when dealing with the
"needle-in-the-hay" cases, because a huge number of samples are required to
converge, given the extremely low hit rate of their fixed sampling schemes. We
build ScaleGPM, an accurate and fast A-GPM system that removes the two
obstacles. First, we propose a novel on-the-fly convergence detection mechanism
to achieve stable termination and provide theoretical guarantee on the
confidence, with negligible overhead. Second, we propose two techniques to deal
with the "needle-in-the-hay" problem, eager-verify and hybrid sampling. Our
eager-verify method improves sampling hit rate by pruning unpromising
candidates as early as possible. Hybrid sampling improves performance by
automatically choosing the better scheme between fine-grained and
coarse-grained sampling schemes. Experiments show that our online convergence
detection mechanism can detect convergence and results in stable and rapid
termination with theoretically guaranteed confidence. We show the effectiveness
of eager-verify in improving the hit rate, and the scheme-selection mechanism
in correctly choosing the better scheme for various cases. Overall, ScaleGPM
achieves a geomean average of 565x (up to 610169x) speedup over the
state-of-the-art A-GPM system, Arya. In particular, ScaleGPM handles
billion-scale graphs in seconds, where existing systems either run out of
memory or fail to complete in hours.


  A few decades earlier, Kirkaldy and Lane proposed an indirect method of
estimating the tracer and intrinsic diffusion coefficients in a ternary system
(without showing experimental verification), which is otherwise impossible
following the Kirkendall marker experiments. Subsequently, Manning proposed the
relations between the tracer and intrinsic diffusion coefficients in the
multicomponent system by extending the Onsager formalism (although could not be
estimated by intersecting the diffusion couples). By solving these issues in
this article, we have now proposed the equations and method for estimating
these parameters in pseudo-ternary diffusion couples in which diffusion paths
can be intersected in multicomponent space. We have chosen NiCoFeCr system for
verification of this method because of the availability of good quality
diffusion couple experiments and estimated tracer diffusion coefficients of all
the components measured by the radiotracer method. An excellent match is found
when the tracer diffusion coefficients estimated following the newly proposed
method are compared with the data estimated following the radiotracer method.
Following, the intrinsic diffusion coefficients are estimated experimentally in
a multicomponent system for the first time highlighting diffusional
interactions between the components. We have further shown that the intrinsic
diffusion coefficients are the same (if the vacancy wind effect is
negligible/neglected) when estimated from other types of diffusion couples
(pseudo-binary and body diagonal) in the same multi-component system. This
method can be now extended to the Al, Ga, Si containing systems in which the
estimation of tracer diffusion coefficients following the radiotracer method is
difficult/impossible because of various reasons.


  We study the occurrence of frequency synchronised states with tunable
emergent frequencies in a network of connected systems. This is achieved by the
interplay between time scales of nonlinear dynamical systems connected to form
a network, where out of N systems, m evolve on a slower time scale. In such
systems, in addition to frequency synchronised states, we also observe
amplitude death, synchronised clusters and multifrequency states. We report an
interesting cross over behaviour from fast to slow collective dynamics as the
number of slow systems m increases. The transition to amplitude death is
analysed in detail for minimal network configurations of 3 and 4 systems which
actually form possible motifs of the full network.


  We discuss here the famous paradox of a continuous power drainage from the
source at the input of an otherwise lossless infinite transmission line. The
solution of the paradox lies in the realization that in an open-circuit finite
transmission line/ladder network, there is an incident as well as a reflected
wave and the input impedence is determined by the superposition of both waves.
It is explicitly shown that the reactive input impedance of even a single
block, comprising say a simple LC circuit, is determined at all driving
frequencies from the superposition of incident and reflected waves, and that
the input impedance remains reactive in nature (i.e., an imaginary value) even
when additional blocks are added indefinitely. However in a ladder network or
transmission line, taken to be {\em infinite right from the beginning}, there
is no reflected wave (assuming the circuit to be ideal with no discontinuities
en route). Thus the source while continuously supplying power in the forward
direction, does not retrieve it from a reflected wave and unlike in the case of
a finite line, there is a net power loss. This apparently lost energy
ultimately appears in the electromagnetic fields in the reactive elements
(capacitances and inductances which to begin with had no such stored energy),
further down the line as the incident wave advances forward. It is also shown
that radiation plays absolutely no role in resolving this intriguing paradox.


  Let $X$ be a smooth projective variety over a field of characteristic $0$ and
$L$ an ample divisor. In this paper we study the Weak Lefschetz conjecture for
Chow groups using the technique employed by Grothendieck in his study of the
problem for Picard groups, and using Bloch's formula to interpret Chow groups
in terms of Milnor $K$- theory we prove the infinitesimal part of the
conjecture.


  The games of prediction with expert advice are considered in this paper. We
present some modification of Kalai and Vempala algorithm of following the
perturbed leader for the case of unrestrictedly large one-step gains. We show
that in general case the cumulative gain of any probabilistic prediction
algorithm can be much worse than the gain of some expert of the pool.
Nevertheless, we give the lower bound for this cumulative gain in general case
and construct a universal algorithm which has the optimal performance; we also
prove that in case when one-step gains of experts of the pool have ``limited
deviations'' the performance of our algorithm is close to the performance of
the best expert.


  Splotch is a rendering algorithm for exploration and visual discovery in
particle-based datasets coming from astronomical observations or numerical
simulations. The strengths of the approach are production of high quality
imagery and support for very large-scale datasets through an effective mix of
the OpenMP and MPI parallel programming paradigms. This article reports our
experiences in re-designing Splotch for exploiting emerging HPC architectures
nowadays increasingly populated with GPUs. A performance model is introduced
for data transfers, computations and memory access, to guide our re-factoring
of Splotch. A number of parallelization issues are discussed, in particular
relating to race conditions and workload balancing, towards achieving optimal
performances. Our implementation was accomplished by using the CUDA programming
paradigm. Our strategy is founded on novel schemes achieving optimized data
organisation and classification of particles. We deploy a reference simulation
to present performance results on acceleration gains and scalability. We
finally outline our vision for future work developments including possibilities
for further optimisations and exploitation of emerging technologies.


  Let $k$ and $\ell$ be positive integers. We prove that if $1 \leq \ell \leq
o_k(k^{6/5})$, then in every large enough graph $G$, the fraction of $k$-vertex
subsets that induce exactly $\ell$ edges is at most $1/e + o_k(1)$. Together
with a recent result of Kwan, Sudakov, and Tran, this settles a conjecture of
Alon, Hefetz, Krivelevich, and Tyomkyn.


  Many machine learning algorithms are based on the assumption that training
examples are drawn independently. However, this assumption does not hold
anymore when learning from a networked sample where two or more training
examples may share common features. We propose an efficient weighting method
for learning from networked examples and show the sample error bound which is
better than previous work.


  Remotely sensing the spatial distribution of exposed coal (EC) is significant
for understanding the footprints of mining activities. However, widely
applicable methods for the identification of EC surfaces remain inadequate
because the choices of recent methods confront the diverse EC types and
backgrounds. Therefore, this study proposed a new Automated Coal Mapping Index
(ACMI) which was empirically formulated by an iterative process of identifying
parameters that maximize the separability of EC and non-EC surfaces. The
performance of ACMI was tested in six study areas worldwide with different
landscape types and coal types. Based on the visual inspection, ACMI was more
effective in highlighting EC surfaces and suppressing non-EC surfaces than the
existing methods. Compared with the sample points obtained through direct
interpretation, ACMI obtained better EC mapping results than previous methods
with the F1 score and overall accuracy (OA) no less than 0.91 and 93.20% across
all the selected Landsat images of the study areas, respectively. In addition,
ACMI was demonstrated to have a stable optimal threshold and 0 can serve as its
default threshold. The default threshold makes EC mapping using ACMI an
automated process. The new index has the potential to support a variety of
mining-activity-related studies, such as the identification of mining
disturbances and illegal mining detection at multi-spatial-temporal scales.


  The Metric Traveling Salesman Problem (TSP) is a classical NP-hard
optimization problem. The double-tree shortcutting method for Metric TSP yields
an exponentially-sized space of TSP tours, each of which approximates the
optimal solution within at most a factor of 2. We consider the problem of
finding among these tours the one that gives the closest approximation, i.e.\
the \emph{minimum-weight double-tree shortcutting}. Previously, we gave an
efficient algorithm for this problem, and carried out its experimental
analysis. In this paper, we address the related question of the worst-case
approximation ratio for the minimum-weight double-tree shortcutting method. In
particular, we give lower bounds on the approximation ratio in some specific
metric spaces: the ratio of 2 in the discrete shortest path metric, 1.622 in
the planar Euclidean metric, and 1.666 in the planar Minkowski metric. The
first of these lower bounds is tight; we conjecture that the other two bounds
are also tight, and in particular that the minimum-weight double-tree method
provides a 1.622-approximation for planar Euclidean TSP.


  A generalization of the eight vertex model by means of higher spin
representations of the Sklyanin algebra is investigated by the quantum inverse
scattering method and the algebraic Bethe Ansatz. Under the well-known string
hypothesis low-lying excited states are considered and scattering phase shifts
of two physical particles are calculated. The $S$ matrix of two particle states
is shown to be proportional to the Baxter's elliptic $R$ matrix with a
different elliptic modulus from the original one.


  In this paper we study properties of solutions to stochastic differential
equations with Sobolev diffusion coefficients and singular drifts. The
properties we study include stability with respect to the coefficients, weak
differentiability with respect to starting points, and the Malliavin
differentiability with respect to sample paths. We also establish
Bismut-Elworthy-Li's formula for the solutions. As an application, we use the
stochastic Lagrangian representation of incompressible Navier-Stokes equations
given by Constantin-Iyer \cite{Co-Iy} to prove the local well-posedness of NSEs
in $\mR^d$ with initial values in the first order Sobolev space
$\mW^1_p(\mR^d;\mR^d)$ provided $p>d$.


  We present the results of combining Hubble Space Telescope optical photometry
with ground-based Ks-band photometry from the Gemini imagers NIRI and
FLAMINGOS-I to study the globular-cluster populations in four early-type
galaxies that are candidate remnants of recent mergers (NGC1700, NGC2865,
NGC4382, and NGC7727). These galaxies were chosen based on their blue colors
and fine structure, such as shells and ripples that are indicative of past
interactions. We fit the combined VIKs globular-cluster data with simple toy
models of mixed cluster populations that contain three subpopulations of
different age and metallicity. The fits, done via Chi-square mapping of the
parameter space, yield clear evidence for the presence of intermediate-age
clusters in each galaxy. We find that the ages of 1-2 Gyr for these
globular-cluster subpopulations are consistent with the previously estimated
merger ages for the host galaxies.


  Let $f$ denote length preserving function on words. A classical algorithm can
be considered as $T$ iterated applications of black box representing $f$,
beginning with input word $x$ of length $n$.
  It is proved that if $T=O(2^{n/(7+e)}), e >0$, and $f$ is chosen randomly
then with probability 1 every quantum computer requires not less than $T$
evaluations of $f$ to obtain the result of classical computation. It means that
the set of classical algorithms admitting quantum speeding up has probability
measure zero.
  The second result is that for arbitrary classical time complexity $T$ and $f$
chosen randomly with probability 1 every quantum simulation of classical
computation requires at least $\Omega (\sqrt {T})$ evaluations of $f$.


  We propose a three dimensional mechanical model of embryonic tissue dynamics.
Mechanically coupled adherent cells are represented as particles interconnected
with elastic beams which can exert non-central forces and torques. Tissue
plasticity is modeled by a stochastic process consisting of a connectivity
change (addition or removal of a single link) followed by a complete relaxation
to mechanical equilibrium. In particular, we assume that (i) two non-connected,
but adjacent particles can form a new link; and (ii) the lifetime of links is
reduced by tensile forces. We demonstrate that the proposed model yields a
realistic macroscopic elasto-plastic behavior and we establish how microscopic
model parameters affect the material properties at the macroscopic scale. Based
on these results, microscopic parameter values can be inferred from tissue
thickness, macroscopic elastic modulus and the magnitude and dynamics of
intercellular adhesion forces. In addition to their mechanical role, model
particles can also act as active simulation agents and modulate their
connectivity according to specific rules. As an example, anisotropic link
insertion and removal probabilities can give rise to local cell intercalation
and large scale convergent extension movements. The proposed stochastic
simulation of cell activities yields fluctuating tissue movements which exhibit
the same autocorrelation properties as empirical data from avian embryos.


  Plan recognition aims to discover target plans (i.e., sequences of actions)
behind observed actions, with history plan libraries or domain models in hand.
Previous approaches either discover plans by maximally "matching" observed
actions to plan libraries, assuming target plans are from plan libraries, or
infer plans by executing domain models to best explain the observed actions,
assuming complete domain models are available. In real world applications,
however, target plans are often not from plan libraries and complete domain
models are often not available, since building complete sets of plans and
complete domain models are often difficult or expensive. In this paper we view
plan libraries as corpora and learn vector representations of actions using the
corpora; we then discover target plans based on the vector representations. Our
approach is capable of discovering underlying plans that are not from plan
libraries, without requiring domain models provided. We empirically demonstrate
the effectiveness of our approach by comparing its performance to traditional
plan recognition approaches in three planning domains.


  We advocate for a mission concept study for a space telescope dedicated to
solar system science in Earth orbit. Such a study was recommended by the
Committee on Astrobiology and Planetary Science (CAPS) report "Getting Ready
for the Next Planetary Science Decadal Survey." The Mid-Decadal Review also
recommended NASA to assess the role and value of space telescopes for planetary
science. The need for high-resolution, UV-Visible capabilities is especially
acute for planetary science with the impending end of the Hubble Space
Telescope (HST); however, NASA has not funded a planetary telescope concept
study, and the need to assess its value remains. Here, we present potential
design options that should be explored to inform the decadal survey.


  We study a one-dimensional model of disordered electrons (also relevant for
random spin chains), which exhibits a delocalisation transition at
half-filling. Exact probability distribution functions for the Wigner time and
transmission coefficient are calculated. We identify and distinguish those
features of probability densities that are due to rare, trapping configurations
of the random potential from those which are due to the proximity to the
delocalisation transition.


  Differential resistance spectra (${\rm d}V/{\rm d}I-V$ characteristics) have
been measured for point-contacts between the heavy-fermion superconductor (HFS)
CePt$_{3}$Si and a normal metal. Some contacts show a peak at V=0 that is
characteristic of HFS coexisting with a magnetic order such as UPd$_2$Al$_3$,
UNi$_2$Al$_3$ and URu$_2$Si$_2$. The evolution of the peak occurs well above
the antiferromagnetic transition temperature $T_{{\rm N}}\sim$ 2.2 K, so that
the direct relationship with the magnetic transition is questionable. The
half-width of the peak seems to reflect the crystal field splitting or the
spin-wave gap as observed for the above-mentioned HFSs, possibly suggesting
that some common scattering process induces the zero-bias peaks in these
materials.


  An ensemble Green's function formalism, based on the von Neumann density
matrix approach, to calculate one-electron excitation spectra of a
many-electron system with degenerate ground states is proposed. A set of
iterative equations for the ensemble Green's function and self-energy is
derived and a simplest approximation corresponding to an ensemble $GW$
approximation is naturally obtained. The derivation is based on the Schwinger
functional derivative technique and does not assume any adiabatic connection
between a noninteracting and an interacting ground state.


  We propose a method to efficiently equip the Segment Anything Model (SAM)
with the ability to generate regional captions. SAM presents strong
generalizability to segment anything while is short for semantic understanding.
By introducing a lightweight query-based feature mixer, we align the
region-specific features with the embedding space of language models for later
caption generation. As the number of trainable parameters is small (typically
in the order of tens of millions), it costs less computation, less memory
usage, and less communication bandwidth, resulting in both fast and scalable
training. To address the scarcity problem of regional caption data, we propose
to first pre-train our model on objection detection and segmentation tasks. We
call this step weak supervision pretraining since the pre-training data only
contains category names instead of full-sentence descriptions. The weak
supervision pretraining allows us to leverage many publicly available object
detection and segmentation datasets. We conduct extensive experiments to
demonstrate the superiority of our method and validate each design choice. This
work serves as a stepping stone towards scaling up regional captioning data and
sheds light on exploring efficient ways to augment SAM with regional semantics.
The project page, along with the associated code, can be accessed via
https://xk-huang.github.io/segment-caption-anything/.


  The RAON, a superconducting linear accelerator for RISP (Rare Isotope Science
Project), will use a charge stripper in order to increase the charge states of
the heavy ions for effective acceleration in the higher energy part of the
linac. The charge stripper affects the beam qualities by scattering when the
heavy ions go through the charge stripper. Moreover we have to select and
accelerate proper charge states between 77+ and 81+ for uranium beam case in
order to satisfy the beam power requirement at an IF (Inflight Fragmentation)
target. This work focuses on the beam optics affected by the charge stripper in
the 180-dgree bending section.


  Shear induced alignment of elongated particles is studied experimentally and
numerically. We show that shear alignment of ensembles of macroscopic particles
is comparable even on a quantitative level to simple molecular systems, despite
the completely different types of particle interactions. We demonstrate that
for dry elongated grains the preferred orientation forms a small angle with the
streamlines, independent of shear rate across three decades. For a given
particle shape, this angle decreases with increasing aspect ratio of the
particles. The shear-induced alignment results in a considerable reduction of
the effective friction of the granular material.


  In this paper we describe a new model for solidification with heat flux using
the phase field crystal (PFC) framework. The equations are thermodynamically
consistent, in the sense that the time rate of change of the entropy density is
positive in the bulk and at the boundaries of the domain of interest. The
resulting model consists of two equations, a heat-like equation and a
mass-conservation equation that describes how the atom density changes in time
and space. The model is simple, yet it can properly capture the variation in
the free energy landscape as the temperature is changed. We describe to
construct a temperature-atom-density phase diagram using this energy landscape,
and we give a simple demonstration of solidification using the model.


  Newton-Hooke group is the nonrelativistic limit of de Sitter (anti-de Sitter)
group, which can be enlarged with transformations that describe constant
acceleration, as well as central charges. We consider a higher order Lagrangian
that is quasi-invariant under the acceleration-extended Newton-Hooke symmetry,
and obtain the Schr\"{o}dinger equation quantizing the Hamiltonian
corresponding to its first order form. We show that the Schr\"{o}dinger
equation is invariant under the acceleration-extended Newton-Hooke
transformations. We also discuss briefly the exotic conformal Newton-Hooke
symmetry in 2+1 dimension.


  Extending our reduction construction in \cite{Hu} to the Hamiltonian action
of a Poisson Lie group, we show that generalized K\"ahler reduction exists even
when only one generalized complex structure in the pair is preserved by the
group action. We show that the constructions in string theory of the
(geometrical) $T$-duality with $H$-fluxes for principle bundles naturally arise
as reductions of factorizable Poisson Lie group actions. In particular, the
group may be non-abelian.


  In this paper I developed a classical model of elementary particle that is
associated with a membrane of finite size, surrounded by non-linear
electromagnetic field. The form of local interaction which lead to bounded
states of finite masses, charges and spins was constructed. To do this I added
Kaluza-Klein Lagrangian on the surface, which is associated with extended
particle, to quadratic in field potentials term (a la tachyon mass). This
ensures that Lagrangian remains an even function of the field, but spontaneous
symmetry breaking leads to nontrivial soliton-like solutions. I assumed that
the particle has axial symmetry and that the surface has only one degree of
freedom, i.e. is a disk with the radius determined from the equations of
motion. The solution of system of two non-linear partial differential equations
for the field potentials was obtained numerically by different methods. Several
solutions with increasing orders of leading field multipoles and disk radius
were obtained, and masses, electric charges and spins were calculated. In the
framework of this model the ratio of a charge square to a double spin, i.e. the
fine structure constant, which do not depend on parameters of the model, was
calculated.


  Confidence is an essential ingredient of success in a wide range of domains
ranging from job performance and mental health, to sports, business, and
combat. Some authors have suggested that not just confidence but
overconfidence-believing you are better than you are in reality-is advantageous
because it serves to increase ambition, morale, resolve, persistence, or the
credibility of bluffing, generating a self-fulfilling prophecy in which
exaggerated confidence actually increases the probability of success. However,
overconfidence also leads to faulty assessments, unrealistic expectations, and
hazardous decisions, so it remains a puzzle how such a false belief could
evolve or remain stable in a population of competing strategies that include
accurate, unbiased beliefs. Here, we present an evolutionary model showing
that, counter-intuitively, overconfidence maximizes individual fitness and
populations will tend to become overconfident, as long as benefits from
contested resources are sufficiently large compared to the cost of competition.
In contrast, "rational" unbiased strategies are only stable under limited
conditions. The fact that overconfident populations are evolutionarily stable
in a wide range of environments may help to explain why overconfidence remains
prevalent today, even if it contributes to hubris, market bubbles, financial
collapses, policy failures, disasters, and costly wars.


  We propose Universal Causality, an overarching framework based on category
theory that defines the universal property that underlies causal inference
independent of the underlying representational formalism used. More formally,
universal causal models are defined as categories consisting of objects and
morphisms between them representing causal influences, as well as structures
for carrying out interventions (experiments) and evaluating their outcomes
(observations). Functors map between categories, and natural transformations
map between a pair of functors across the same two categories. Abstract causal
diagrams in our framework are built using universal constructions from category
theory, including the limit or co-limit of an abstract causal diagram, or more
generally, the Kan extension. We present two foundational results in universal
causal inference. The first result, called the Universal Causality Theorem
(UCT), pertains to the universality of diagrams, which are viewed as functors
mapping both objects and relationships from an indexing category of abstract
causal diagrams to an actual causal model whose nodes are labeled by random
variables, and edges represent functional or probabilistic relationships. UCT
states that any causal inference can be represented in a canonical way as the
co-limit of an abstract causal diagram of representable objects. UCT follows
from a basic result in the theory of sheaves. The second result, the Causal
Reproducing Property (CRP), states that any causal influence of a object X on
another object Y is representable as a natural transformation between two
abstract causal diagrams. CRP follows from the Yoneda Lemma, one of the deepest
results in category theory. The CRP property is analogous to the reproducing
property in Reproducing Kernel Hilbert Spaces that served as the foundation for
kernel methods in machine learning.


  As automated driving technology advances, the role of the driver to resume
control of the vehicle in conditionally automated vehicles becomes increasingly
critical. In the SAE Level 3 or partly automated vehicles, the driver needs to
be available and ready to intervene when necessary. This makes it essential to
evaluate their readiness accurately. This article presents a comprehensive
analysis of driver readiness assessment by combining head pose features and
eye-tracking data. The study explores the effectiveness of predictive models in
evaluating driver readiness, addressing the challenges of dataset limitations
and limited ground truth labels. Machine learning techniques, including LSTM
architectures, are utilised to model driver readiness based on the
Spatio-temporal status of the driver's head pose and eye gaze. The experiments
in this article revealed that a Bidirectional LSTM architecture, combining both
feature sets, achieves a mean absolute error of 0.363 on the DMD dataset,
demonstrating superior performance in assessing driver readiness. The modular
architecture of the proposed model also allows the integration of additional
driver-specific features, such as steering wheel activity, enhancing its
adaptability and real-world applicability.


  Storing and release of a quantum light pulse in a medium of atoms in the
tripod configuration are studied. Two complementary sets of control fields are
defined, which lead to independent and complete photon release at two stages.
The system constitutes a new kind of a flexible beam splitter in which the
input and output ports concern photons of the same direction but well separated
in time. A new version of Hong-Ou-Mandel interference is discussed.


  Let $E$ be a homogeneous compact set, for instance a Cantor set of positive
length. Further let $\sigma$ be a positive measure with
$\text{supp}(\sigma)=E$. Under the condition that the absolutely continuous
part of $\sigma$ satisfies a Szeg\"o--type condition we give an asymptotic
representation, on and off the support, for the polynomials orthonormal with
respect to $\sigma$. For the special case that $E$ consists of a finite number
of intervals and that $\sigma$ has no singular component this is a nowaday well
known result of Widom. If $E=[a,b]$ it becomes a classical result due to
Szeg\"o and in case that there appears in addition a singular component, it is
due to Kolmogorov--Krein. In fact the results are presented for the more
general case that the orthogonality measure may have a denumerable set of
mass--points outside of $E$ which are supposed to accumulate on $E$ only and to
satisfy (together with the zeros of the associated Stieltjes function) the
free--interpolation Carleson--type condition. Up to the case of a finite number
of mass points this is even new for the single interval case. Furthermore, as a
byproduct of our representations, we obtain that the recurrence coefficients of
the orthonormal polynomials behave asymptotically almost periodic. Or in other
words the Jacobi matrices associated with the above discussed orthonormal
polynomials are compact perturbations of a one--sided restriction of almost
periodic Jacobi matrices with homogeneous spectrum. Our main tool is a theory
of Hardy spaces of character--automorphic functions and forms on Riemann
surfaces of Widom type, we use also some ideas of scattering theory for
one--dimensional Schr\"odinger equations.


  We first survey the known results on functional equations for the double
zeta-function of Euler type and its various generalizations. Then we prove two
new functional equations for double series of Euler-Hurwitz-Barnes type with
complex coefficients. The first one is of general nature, while the second one
is valid when the coefficients are Fourier coefficients of a cusp form.


  In this paper, we study the existence of multiple solutions to a
Caffarelli-Kohn-Nirenberg type equation with asymptotically linear term at
infinity. In this case, the well-known Ambrosetti-Rabinowtz type condition
doesn't hold, hence it is difficult to verify the classical (PS)$_c$ condition.
To overcome this difficulty, we use an equivalent version of Cerami's
condition, which allows the more general existence result.


  The star formation rate (SFR) is a key parameter in the study of galaxy
evolution. The accuracy of SFR measurements at z~2 has been questioned
following a disagreement between observations and theoretical models. The
latter predict SFRs at this redshift that are typically a factor 4 or more
lower than the measurements. We present star-formation rates based on
calorimetric measurements of the far-infrared (FIR) luminosities for massive
1.5<z<2.5, normal star-forming galaxies (SFGs), which do not depend on
extinction corrections and/or extrapolations of spectral energy distributions.
The measurements are based on observations in GOODS-N with the Photodetector
Array Camera & Spectrometer (PACS) onboard Herschel, as part of the PACS
Evolutionary Probe (PEP) project, that resolve for the first time individual
SFGs at these redshifts at FIR wavelengths. We compare FIR-based SFRs to the
more commonly used 24 micron and UV SFRs. We find that SFRs from 24 micron
alone are higher by a factor of ~4-7.5 than the true SFRs. This overestimation
depends on luminosity: gradually increasing for log L(24um)>12.2 L_sun. The
SFGs and AGNs tend to exhibit the same 24 micron excess. The UV SFRs are in
closer agreement with the FIR-based SFRs. Using a Calzetti UV extinction
correction results in a mean excess of up to 0.3 dex and a scatter of 0.35 dex
from the FIR SFRs. The previous UV SFRs are thus confirmed and the mean excess,
while narrowing the gap, is insufficient to explain the discrepancy between the
observed SFRs and simulation predictions.


  By considering the continuum scaling limit of the $A_{4}$ RSOS lattice model
of Andrews-Baxter-Forrester with integrable boundaries, we derive excited state
TBA equations describing the boundary flows of the tricritical Ising model.
Fixing the bulk weights to their critical values, the integrable boundary
weights admit a parameter $\xi $ which plays the role of the perturbing
boundary field $\phi_{1,3}$ and induces the renormalization group flow between
boundary fixed points. The boundary TBA equations determining the RG flows are
derived in the $\mathcal{B}_{(1,2)}\to \mathcal{B}_{(2,1)}$ example. The
induced map between distinct Virasoro characters of the theory are specified in
terms of distribution of zeros of the double row transfer matrix.


  Handover rate is one of the most import metrics to instruct mobility
management and resource management in wireless cellular networks. In the
literature, the mathematical expression of handover rate has been derived for
homogeneous cellular network by both regular hexagon coverage model and
stochastic geometry model, but there has not been any reliable result for
heterogeneous cellular networks (HCNs). Recently, stochastic geometry modeling
has been shown to model well the real deployment of HCNs and has been
extensively used to analyze HCNs. In this paper, we give an effective handover
analysis for HCNs by stochastic geometry modeling, derive the mathematical
expression of handover rate by employing an infinitesimal method for a
generalized multi-tier scenario, discuss the result by deriving some meaningful
corollaries, and validate the analysis by computer simulation with multiple
walking models. By our analysis, we find that in HCNs the handover rate is
related to many factors like the base stations' densities and transmitting
powers, user's velocity distribution, bias factor, pass loss factor and etc.
Although our analysis focuses on the scenario of multi-tier HCNs, the
analytical framework can be easily extended for more complex scenarios, and may
shed some light for future study.


  The HST UV Survey of Globular Clusters (GCs) has investigated GCs and their
stellar populations. In previous papers of this series we have introduced a
pseudo two-color diagram, "chromosome map" (ChM), that maximises the separation
between the multiple populations. We have identified two main classes of GCs:
Type I (~83% of the objects) and Type II, both hosting two main groups of
stars, referred to in this series as first (1G) and second generation (2G).
Type II clusters exhibit two or more parallel sequences of 1G and 2G stars in
their ChMs. We exploit elemental abundances from literature to assign the
chemical composition to the distinct populations as identified on the ChMs of
29 GCs. We find that stars in different regions of the ChM have different
composition: 1G stars share the same light-element content as field stars,
while 2G stars are enhanced in N, Na and depleted in O. Stars enhanced in Al
and depleted in Mg populate the extreme regions of the ChM. We investigate the
color spread among 1G stars observed in many GCs, and find no evidence for
variations in light elements, whereas either a 0.1 dex Fe spread or a variation
in He remain to be verified. In the attempt of analysing the global properties
of the multiple populations, we have constructed a universal ChM, which
highlights that, though variegate, the phenomenon has some common pattern. The
universal ChM reveals a tight connection with Na, for which we have provided an
empirical relation. The additional ChM sequences typical of Type II GCs are
enhanced in metallicity and, often, in s elements. Omega Cen can be classified
as an extreme Type II GC, with a ChM displaying three main streams, each with
its own variations in chemical abundances. One of the most noticeable
differences is between the lower and upper streams, with the latter (associated
with higher He) having higher Fe and lower Li. We publicly release ChMs.


  While RFML is expected to be a key enabler of future wireless standards, a
significant challenge to the widespread adoption of RFML techniques is the lack
of explainability in deep learning models. This work investigates the use of CB
models as a means to provide inherent decision explanations in the context of
DL-based AMC. Results show that the proposed approach not only meets the
performance of single-network DL-based AMC algorithms, but provides the desired
model explainability and shows potential for classifying modulation schemes not
seen during training (i.e. zero-shot learning).


  Not only source catalogs are extracted from astronomy observations. Their sky
coverage is always carefully recorded and used in statistical analyses, such as
correlation and luminosity function studies. Here we present a novel method for
catalog matching, which inherently builds on the coverage information for
better performance and completeness. A modified version of the Zones Algorithm
is introduced for matching partially overlapping observations, where irrelevant
parts of the data are excluded up front for efficiency. Our design enables
searches to focus on specific areas on the sky to further speed up the process.
Another important advantage of the new method over traditional techniques is
its ability to quickly detect dropouts, i.e., the missing components that are
in the observed regions of the celestial sphere but did not reach the detection
limit in some observations. These often provide invaluable insight into the
spectral energy distribution of the matched sources but rarely available in
traditional associations.


  Concern of toxic compounds and their, potentially more harmful degradation
products, present in aquatic environment alarmed scientific community and
research on the development of novel technologies for wastewater treatment had
become of great interest. Up to this date, many papers pointed out the
challenges and limitations of conventional wastewater treatment and of some
advanced oxidation processes. Advanced technologies based on the use of
non-equilibrium or non-thermal plasma had been recognized as a possible
solution for, not only degradation, but for complete removal of recalcitrant
organic micropollutants. While previous review papers have been focused on
plasma physics and chemistry of different types of discharges for few organic
micropollutants, this paper brings comprehensive review of current knowledge on
the chemistry and degradation pathways by using different non-thermal plasma
types for several micropollutants classes, such as pharmaceuticals,
perfluorinated compounds, pesticides, phenols and dyes and points out some
major research gaps.


  Measurements of dijet $p_{\mathrm{T}}$ correlations in Pb+Pb and $pp$
collisions at a nucleon-nucleon centre-of-mass energy of
$\sqrt{s_{\mathrm{NN}}}=2.76\textrm{ TeV}$ are presented. The measurements are
performed with the ATLAS detector at the Large Hadron Collider using Pb+Pb and
$pp$ data samples corresponding to integrated luminosities of 0.14 nb$^{-1}$
and 4.0 pb$^{-1}$, respectively. Jets are reconstructed using the anti-$k_t$
algorithm with radius parameter values $R=0.3$ and $R=0.4$. A background
subtraction procedure is applied to correct the jets for the large underlying
event present in Pb+Pb collisions. The leading and sub-leading jet transverse
momenta are denoted $p_{\mathrm{T_{\mathrm{1}}}}$ and
$p_{\mathrm{T_{\mathrm{2}}}}$. An unfolding procedure is applied to the
two-dimensional ($p_{\mathrm{T_{\mathrm{1}}}}$, $p_{\mathrm{T_{\mathrm{2}}}}$)
distributions to account for experimental effects in the measurement of both
jets. Distributions of $(1/N)\mbox{$\mathrm{d}$} N/\mbox{$\mathrm{d}$}
x_{\mathrm{J}}$, where $x_{\mathrm{J}}=p_{\mathrm{T}_{2}}/p_{\mathrm{T}_{1}}$,
are presented as a function of $p_{\mathrm{T_{\mathrm{1}}}}$ and collision
centrality. The distributions are found to be similar in peripheral Pb+Pb
collisions and $pp$ collisions, but highly modified in central Pb+Pb
collisions. Similar features are present in both the $R=0.3$ and $R=0.4$
results, indicating that the effects of the underlying event are properly
accounted for in the measurement. The results are qualitatively consistent with
expectations from partonic energy loss models.


  We report a measurement of the Lambda_b lifetime in the exclusive decay
Lambda_b --> J/psi Lambda0 in p-pbar collisions at sqrt(s) = 1.96 TeV using an
integrated luminosity of 1.0 fb^{-1} of data collected by the CDF II detector
at the Fermilab Tevatron. Using fully reconstructed decays, we measure
tau(Lambda_b) = 1.593 ^{+0.083}_{-0.078} (stat.) +- 0.033 (syst.) ps. This is
the single most precise measurement of tau(Lambda_b) and is 3.2 sigma higher
than the current world average.


  We present the results of a search for deeply-eclipsing white dwarfs in the
ZTF Data Release 4. We identify nine deeply-eclipsing white dwarf candidates,
four of which we followed up with high-cadence photometry and spectroscopy.
Three of these systems show total eclipses in the ZTF data and our follow-up
APO 3.5-meter telescope observations. Even though the eclipse duration is
consistent with sub-stellar companions, our analysis shows that all four
systems contain a white dwarf with low-mass stellar companions of ~0.1 Msol. We
provide mass and radius constraints for both stars in each system based on our
photometric and spectroscopic fitting. Finally, we present a list of 41
additional eclipsing WD+M candidates identified in a preliminary search of ZTF
DR7, including 12 previously studied systems. We identify two new candidate
short-period, eclipsing, white dwarf-brown dwarf binaries within our sample of
41 WD+M candidates based on PanSTARRS colors.


  We assume that the Stefan problem with undercooling has a classical solution
until the moment of contact of free boundaries and the free boundaries have
finite velocities until the contact. Under these assumptions, we construct a
smooth approximation of the global solution of the Stefan problem with
undercooling, which, until the contact, gives the classical solution mentioned
above and, after the contact, gives a solution which is the solution of the
heat conduction equation.


  Interpreting camera data is key for autonomously acting systems, such as
autonomous vehicles. Vision systems that operate in real-world environments
must be able to understand their surroundings and need the ability to deal with
novel situations. This paper tackles open-world semantic segmentation, i.e.,
the variant of interpreting image data in which objects occur that have not
been seen during training. We propose a novel approach that performs accurate
closed-world semantic segmentation and, at the same time, can identify new
categories without requiring any additional training data. Our approach
additionally provides a similarity measure for every newly discovered class in
an image to a known category, which can be useful information in downstream
tasks such as planning or mapping. Through extensive experiments, we show that
our model achieves state-of-the-art results on classes known from training data
as well as for anomaly segmentation and can distinguish between different
unknown classes.


  Stellar streams are sensitive tracers of the gravitational potential, which
is typically assumed to be static in the inner Galaxy. However, massive mergers
like Gaia-Sausage-Enceladus can impart torques on the stellar disk of the Milky
Way that result in the disk tilting at rates of up to 10-20 deg/Gyr. Here, we
demonstrate the effects of disk tilting on the morphology and kinematics of
stellar streams. Through a series of numerical experiments, we find that
streams with nearby apocenters $(r_{\rm apo} \lesssim 20~\rm{kpc})$ are
sensitive to disk tilting, with the primary effect being changes to the
stream's on-sky track and width. Interestingly, disk tilting can produce both
more diffuse streams and more narrow streams, depending on the orbital
inclination of the progenitor and the direction in which the disk is tilting.
Our model of Pal 5's tidal tails for a tilting rate of 15 deg/Gyr is in
excellent agreement with the observed stream's track and width, and reproduces
the extreme narrowing of the trailing tail. We also find that failure to
account for a tilting disk can bias constraints on shape parameters of the
Milky Way's local dark matter distribution at the level of 5-10%, with the
direction of the bias changing for different streams. Disk tilting could
therefore explain discrepancies in the Milky Way's dark matter halo shape
inferred using different streams.


  We present simulations of a large array of imaging atmospheric Cherenkov
telescopes (IACTs), for which the size of the array footprint is much larger
than the size of the Cherenkov lightpool. To evaluate limitations of the
imaging atmospheric Cherenkov technique, the array is simulated under the
assumption of ideal optics, having infinite resolution of the photon arrival
direction, which makes our conclusions independent of any particular telescope
implementation. The primary characteristics of the array performance, gamma-ray
trigger efficiency, photon energy at the peak of the detection rate, and
angular resolution are calculated as a function of the parameters of the array:
telescope spacing, telescope aperture, and camera pixelation. We discuss
implication of the results for the design of the next generation ground-based
gamma-ray observatory.


  Despite decades of research, SE lacks widely accepted models (that offer
precise quantitative stable predictions) about what factors most influence
software quality. This paper provides a promising result showing such stable
models can be generated using a new transfer learning framework called
"STABILIZER". Given a tree of recursively clustered projects (using project
meta-data), STABILIZER promotes a model upwards if it performs best in the
lower clusters (stopping when the promoted model performs worse than the models
seen at a lower level).
  The number of models found by STABILIZER is minimal: one for defect
prediction (756 projects) and less than a dozen for project health (1628
projects). Hence, via STABILIZER, it is possible to find a few projects which
can be used for transfer learning and make conclusions that hold across
hundreds of projects at a time. Further, the models produced in this manner
offer predictions that perform as well or better than the prior
state-of-the-art.
  To the best of our knowledge, STABILIZER is order of magnitude faster than
the prior state-of-the-art transfer learners which seek to find conclusion
stability, and these case studies are the largest demonstration of the
generalizability of quantitative predictions of project quality yet reported in
the SE literature.
  In order to support open science, all our scripts and data are online at
https://github.com/Anonymous633671/STABILIZER.


  We examine a computational geometric problem concerning the structure of
polymers. We model a polymer as a polygonal chain in three dimensions. Each
edge splits the polymer into two subchains, and a dihedral rotation rotates one
of these chains rigidly about this edge. The problem is to determine, given a
chain, an edge, and an angle of rotation, if the motion can be performed
without causing the chain to self-intersect. An Omega(n log n) lower bound on
the time complexity of this problem is known.
  We prove that preprocessing a chain of n edges and answering n dihedral
rotation queries is 3SUM-hard, giving strong evidence that solving n queries
requires Omega(n^2) time in the worst case. For dynamic queries, which also
modify the chain if the requested dihedral rotation is feasible, we show that
answering n queries is by itself 3SUM-hard, suggesting that sublinear query
time is impossible after any amount of preprocessing.


  A new organic superconductor, possibly with formula $K_3$ p-terphenyl, has
been discovered by Wang, Gao, Huang and Chen, reaching a very high $T_c$ of
about 120~K. Besides a clear diamagnetic signal, most other details such as
stoichiometry and structure are yet unknown. However, pristine p-terphenyl has
a familiar $P2_1/a$ staggered bimolecular structure, and it can be reasonably
assumed that a similar bimolecular structure could be retained by hypothetical
$K_3$ p-terphenyl. We point out that the resulting 2-narrow band metal would
support the same $s_{\pm}$ superconductivity recently proposed for doped
polycyclic aromatic hydrocarbons such as La-phenanthrene or $K_3$-picene. In
that model, narrow bands, a large Hubbard $U$ and the neighbourhood of a Mott
transition enhance superconductivity rather than damaging it.


  We present theoretical evidence that the exclusive/inclusive ratio of
semileptonic $\Lambda_b$-decays exceeds that of semileptonic B-decays where the
experimental exclusive/inclusive ratio amounts to about 66%. We start from the
observation that the spectator quark model provides a lower bound on the
leading order Isgur-Wise function of the $\Lambda_b \to \Lambda_c$ transition
in terms of the corresponding $B \to D,D^*$ mesonic Isgur-Wise function. Using
experimental data for the $B \to D,D^*$ mesonic Isgur-Wise functions this bound
is established. Applying a Bethe-Salpeter model including spectator quark
interactions and a QCD sum rule estimate of the $\Lambda_b \to \Lambda_c$
transition form factor which satisfy the spectator quark model bound we predict
the exclusive/inclusive ratio of semileptonic $\Lambda_b$ decay rates to lie in
a range between 0.81 and 0.92. We also provide an upper bound on the baryonic
Isgur-Wise function which is determined from the requirement that the exclusive
rate should not exceed the inclusive rate.


  Network science provides effective tools to model and analyze complex
systems. However, the increasing size of real-world networks becomes a major
hurdle in order to understand their structure and topological features.
Therefore, mapping the original network into a smaller one while preserving its
information is an important issue. Extracting the so-called backbone of a
network is a very challenging problem that is generally handled either by
coarse-graining or filter-based methods. Coarse-graining methods reduce the
network size by grouping similar nodes, while filter-based methods prune the
network by discarding nodes or edges based on a statistical property. In this
paper, we propose and investigate two filter-based methods exploiting the
overlapping community structure in order to extract the backbone in weighted
networks. Indeed, highly connected nodes (hubs) and overlapping nodes are at
the heart of the network. In the first method, called "overlapping nodes ego
backbone", the backbone is formed simply from the set of overlapping nodes and
their neighbors. In the second method, called "overlapping nodes and hubs
backbone", the backbone is formed from the set of overlapping nodes and the
hubs. For both methods, the links with the lowest weights are removed from the
network as long as a backbone with a single connected component is preserved.
Experiments have been performed on real-world weighted networks originating
from various domains (social, co-appearance, collaboration, biological, and
technological) and different sizes. Results show that both backbone extraction
methods are quite similar. Furthermore, comparison with the most influential
alternative filtering method demonstrates the greater ability of the proposed
backbones extraction methods to uncover the most relevant parts of the network.


  We propose a simple dissipative system with purely cubic defocusing
nonlinearity and nonuniform linear gain that can support stable localized
dissipative vortex solitons with high topological charges without the
utilization of competing nonlinearities and nonlinear gain or losses.
Localization of such solitons is achieved due to an intriguing mechanism when
defocusing nonlinearity stimulates energy flow from the ring-like region with
linear gain to the periphery of the medium where energy is absorbed due to
linear background losses. Vortex solitons bifurcate from linear gain-guided
vortical modes with eigenvalues depending on topological charges that become
purely real only at specific gain amplitudes. Increasing gain amplitude leads
to transverse expansion of vortex solitons, but simultaneously it usually also
leads to stability enhancement. Increasing background losses allows creation of
stable vortex solitons with high topological charges that are usually prone to
instabilities in conservative and dissipative systems. Propagation of the
perturbed unstable vortex solitons in this system reveals unusual dynamical
regimes, when instead of decay or breakup, the initial state transforms into
stable vortex soliton with lower or sometimes even with higher topological
charge. Our results suggest an efficient mechanism for the formation of
nonlinear excited vortex-carrying states with suppressed destructive azimuthal
modulational instabilities in a simple setting relevant to a wide class of
systems, including polaritonic systems, structured microcavities, and lasers.


  Maximum entropy (Maxent) models are a class of statistical models that use
the maximum entropy principle to estimate probability distributions from data.
Due to the size of modern data sets, Maxent models need efficient optimization
algorithms to scale well for big data applications. State-of-the-art algorithms
for Maxent models, however, were not originally designed to handle big data
sets; these algorithms either rely on technical devices that may yield
unreliable numerical results, scale poorly, or require smoothness assumptions
that many practical Maxent models lack. In this paper, we present novel
optimization algorithms that overcome the shortcomings of state-of-the-art
algorithms for training large-scale, non-smooth Maxent models. Our proposed
first-order algorithms leverage the Kullback-Leibler divergence to train
large-scale and non-smooth Maxent models efficiently. For Maxent models with
discrete probability distribution of $n$ elements built from samples, each
containing $m$ features, the stepsize parameters estimation and iterations in
our algorithms scale on the order of $O(mn)$ operations and can be trivially
parallelized. Moreover, the strong $\ell_{1}$ convexity of the
Kullback--Leibler divergence allows for larger stepsize parameters, thereby
speeding up the convergence rate of our algorithms. To illustrate the
efficiency of our novel algorithms, we consider the problem of estimating
probabilities of fire occurrences as a function of ecological features in the
Western US MTBS-Interagency wildfire data set. Our numerical results show that
our algorithms outperform the state of the arts by one order of magnitude and
yield results that agree with physical models of wildfire occurrence and
previous statistical analyses of wildfire drivers.


  Contemporary scientific exchanges are international, yet language continues
to be a persistent barrier to scientific communication, particularly for
non-native English-speaking scholars. Since the ability to absorb knowledge has
a strong impact on how researchers create new scientific knowledge, a
comprehensive access to and understanding of both domestic and international
scientific publications is essential for scientific performance. This study
explores the effect of absorbed knowledge on research impact by analyzing the
relationship between the language diversity of cited references and the number
of citations received by the citing paper. Chinese social sciences are taken as
the research object, and the data, 950,302 papers published between 1998 and
2013 with 8,151,327 cited references, were collected from the Chinese Social
Sciences Citation Index. Results show that there is a stark increase in the
consumption of foreign language material within the Chinese social science
community, and English material accounts for the vast majority of this
consumption. Papers with foreign language references receive significantly more
citations than those without, and the citation advantage of these
internationalized work holds when we control for characteristics of the citing
papers. However, the citation advantage has decreased from 1998 to 2008,
largely as an artifact of the increased number of papers citing foreign
language material. After 2008, the decline of the citation advantage subsided
and became relatively stable, which suggests that incorporating foreign
language literature continues to increase scientific impact, even as the
scientific community itself becomes increasingly international. However,
internationalization is not without concerns: the work closes with a discussion
of the benefits and potential problems of the lack of linguistic diversity in
scientific communication.


  The spectrometer aboard INTEGRAL, SPI, has the capability to detect the
signature of polarised emission from a bright gamma-ray source. GRB 041219a is
the most intense burst localised by INTEGRAL and is an ideal candidate for such
a study. Polarisation can be measured using multiple events scattered into
adjacent detectors because the Compton scatter angle depends on the
polarisation of the incoming photon. A search for linear polarisation in the
most intense pulse of duration 66 seconds and in the brightest 12 seconds of
GRB 041219a was performed in the 100-350keV, 100-500keV and 100keV-1MeV energy
ranges. The multiple event data from the spectrometer was analysed and compared
with the predicted instrument response obtained from Monte-Carlo simulations
using the GEANT 4 INTEGRAL mass model. The chi^2 distribution between the real
and simulated data as a function of the percentage polarisation and
polarisation angle was calculated for all three energy ranges. The degree of
linear polarisation in the brightest pulse of duration 66s was found to be
63+/-31% at an angle of 70+/-14 degrees in the 100-350keV energy range. The
degree of polarisation was also constrained in the brightest 12s of the GRB and
a polarisation fraction of 96+/-40% at an angle of 60+/-14 degrees was
determined over the same energy range. However, despite extensive analysis and
simulations, a systematic effect that could mimic the weak polarisation signal
could not be definitively excluded. Our results over several energy ranges and
time intervals are consistent with a polarisation signal of about 60% at a low
level of significance (2 sigma). We conclude that the procedure described here
demonstrates the effectiveness of using SPI as a polarimeter, and is a viable
method of measuring polarisation levels in intense gamma--ray bursts.


  Complex period variations of five W UMa type binaries (AB And, OO Aql, DK
Cyg, V566 Oph, U Peg) were investigated by analyzing their O--C diagrams, and
several common features were found. Four of the five systems show secular
period variations at a constant rate on the order of |dP / dt 1/P | ~ 10E-7
1/yr. In the case of AB And, OO Aql, and U Peg a high-amplitude, nearly
one-century long quasi-sinusoidal pattern was also found. It might be explained
as light-time effect, or by some magnetic phenomena, although the mathematical,
and consequently the physical, parameters of these fits are very problematic,
as the obtained periods are very close to the length of the total data range.
The most interesting feature of the studied O--C diagrams is a low amplitude
(2-4 x 10E-3 d) modulation with a period around 18--20 yr in four of the five
cases. This phenomenon might be indirect evidence of some magnetic cycle in
late-type overcontact binaries as an analog to the observed activity cycles in
RS CVn systems.


  Modern large-scale date centres, such as those used for cloud computing
service provision, are becoming ever-larger as the operators of those data
centres seek to maximise the benefits from economies of scale. With these
increases in size comes a growth in system complexity, which is usually
problematic. There is an increased desire for automated "self-star"
configuration, management, and failure-recovery of the data-centre
infrastructure, but many traditional techniques scale much worse than linearly
as the number of nodes to be managed increases. As the number of nodes in a
median-sized data-centre looks set to increase by two or three orders of
magnitude in coming decades, it seems reasonable to attempt to explore and
understand the scaling properties of the data-centre middleware before such
data-centres are constructed. In [1] we presented SPECI, a simulator that
predicts aspects of large-scale data-centre middleware performance,
concentrating on the influence of status changes such as policy updates or
routine node failures. [...]. In [1] we used a first-approximation assumption
that such subscriptions are distributed wholly at random across the data
centre. In this present paper, we explore the effects of introducing more
realistic constraints to the structure of the internal network of
subscriptions. We contrast the original results [...] exploring the effects of
making the data-centre's subscription network have a regular lattice-like
structure, and also semi-random network structures resulting from parameterised
network generation functions that create "small-world" and "scale-free"
networks. We show that for distributed middleware topologies, the structure and
distribution of tasks carried out in the data centre can significantly
influence the performance overhead imposed by the middleware.


  We classify superpotentials for the Hamiltonian system corresponding to the
cohomogeneity one gradient Ricci soliton equations. Aside from recovering known
examples of superpotentials for steady solitons, we find a new superpotential
on a specific case of the B\'erard-Bergery-Calabi ansatz. The latter is used to
obtain an explicit formula for a steady complete soliton with an equidistant
family of hypersurfaces given by circle bundles over $S^2\times S^2$. There are
no superpotentials in the non-steady case in dimensions greater than 2, even if
polynomial coefficients are allowed. We also briefly discuss generalised first
integrals and the limitations of some known methods of finding them.


  We study the problem of folding a polyomino $P$ into a polycube $Q$, allowing
faces of $Q$ to be covered multiple times. First, we define a variety of
folding models according to whether the folds (a) must be along grid lines of
$P$ or can divide squares in half (diagonally and/or orthogonally), (b) must be
mountain or can be both mountain and valley, (c) can remain flat (forming an
angle of $180^\circ$), and (d) must lie on just the polycube surface or can
have interior faces as well. Second, we give all the inclusion relations among
all models that fold on the grid lines of $P$. Third, we characterize all
polyominoes that can fold into a unit cube, in some models. Fourth, we give a
linear-time dynamic programming algorithm to fold a tree-shaped polyomino into
a constant-size polycube, in some models. Finally, we consider the triangular
version of the problem, characterizing which polyiamonds fold into a regular
tetrahedron.


  Our main result is that chaos in dimension $n+1$ is a one-dimensional
geometrical object embedded in a geometrical object of dimension $n$ which
corresponds to a $n$ dimensional object which is either singular or
non-singular. Our main result is then that this chaos occurs in the first case
as either on an isolated or non-isolated singularity. In the first case this
chaos is either boundary chaos or spherical chaos which is what happens also in
the non-singular case. In the case of an isolated singular geometry one has
chaos which can either be boundary, spherical or tubular chaos. We furthermore
prove that the prime numbers display quantum behaviour.


  We investigate a statistical anisotropy on the Cosmic Microwave Background
(CMB) bispectrum, which can be generated from the primordial non-Gaussianity
induced by quantum fluctuations of a vector field. We find new configurations
in the multipole space of the CMB bispectrum given by $\ell_1 = \ell_2 + \ell_3
+ 2, |\ell_2 - \ell_3| - 2$ and their permutations, which violate the
rotational invariance, such as an off-diagonal configuration in the CMB power
spectrum. We also find that in a model presented by Yokoyama and Soda (2008),
the amplitude of the statistically anisotropic bispectrum in the above
configurations becomes as large as that in other configurations such as $\ell_1
= \ell_2 + \ell_3$. As a result, it might be possible to detect these
contributions in future experiments, which would give us novel information
about the physics of the early Universe.


  We study the \lq \lq symmetry integral\rq \rq, \thinspace say $I_f$, of some
arithmetic functions $f:\N \rightarrow \R$; we obtain from lower bounds of
$I_f$ (for a large class of arithmetic functions $f$) lower bounds for the \lq
\lq Selberg integral\rq \rq \thinspace of $f$, say $J_f$ (both these integrals
give informations about $f$ in almost all the short intervals $[x-h,x+h]$, when
$N\le x\le 2N$). In particular, when \thinspace $f=d_k$, the divisor function
(having Dirichlet series \thinspace $\zeta^k$, with \thinspace $\zeta$
\thinspace the Riemann zeta function), where $k\ge 3$ is integer, we give lower
bounds for the Selberg integrals, say \thinspace $J_k=J_{d_k}$, of the
\thinspace $d_k$. We apply elementary methods (Cauchy inequality to get Large
Sieve type bounds) in order to give $I_f$ lower bounds.


  In this work, a generic rigorous Bayesian formalism is introduced to predict
the most likely path of any ion crossing a medium between two detection points.
The path is predicted based on a combination of the particle scattering in the
material and measurements of its initial and final position, direction and
energy. The path estimate's precision is compared to the Monte Carlo simulated
path. Every ion from hydrogen to carbon is simulated in two scenarios to
estimate the accuracy achievable: one where the range is fixed and one where
the initial velocity is fixed. In the scenario where the range is kept
constant, the maximal root-mean-square error between the estimated path and the
Monte Carlo path drops significantly between the proton path estimate (0.50 mm)
and the helium path estimate (0.18 mm), but less so up to the carbon path
estimate (0.09 mm). In the scenario where the initial velocity is kept
constant, helium have systematically the minimal root-mean-square error
throughout the path. As a result, helium is found to be the optimal particle
for ion imaging.


  We give a description of Gray AC^{\perp} manifolds $(M,g)$ whose Ricci tensor
has two eigenvalues of multiplicity 1 and dim M-1.


  Online hatred is a growing concern on many social media platforms. To address
this issue, different social media platforms have introduced moderation
policies for such content. They also employ moderators who can check the posts
violating moderation policies and take appropriate action. Academicians in the
abusive language research domain also perform various studies to detect such
content better. Although there is extensive research in abusive language
detection in English, there is a lacuna in abusive language detection in low
resource languages like Hindi, Urdu etc. In this FIRE 2021 shared task -
"HASOC- Abusive and Threatening language detection in Urdu" the organizers
propose an abusive language detection dataset in Urdu along with threatening
language detection. In this paper, we explored several machine learning models
such as XGboost, LGBM, m-BERT based models for abusive and threatening content
detection in Urdu based on the shared task. We observed the Transformer model
specifically trained on abusive language dataset in Arabic helps in getting the
best performance. Our model came First for both abusive and threatening content
detection with an F1scoreof 0.88 and 0.54, respectively.


  Large language models (LLMs) have garnered considerable attention for their
proficiency in tackling intricate tasks, particularly leveraging their
capacities for zero-shot and in-context learning. However, their utility has
been predominantly restricted to general tasks due to an absence of
domain-specific knowledge. This constraint becomes particularly pertinent in
the realm of protein engineering, where specialized expertise is required for
tasks such as protein function prediction, protein evolution analysis, and
protein design, with a level of specialization that existing LLMs cannot
furnish. In response to this challenge, we introduce \textsc{ProteinEngine}, a
human-centered platform aimed at amplifying the capabilities of LLMs in protein
engineering by seamlessly integrating a comprehensive range of relevant tools,
packages, and software via API calls. Uniquely, \textsc{ProteinEngine} assigns
three distinct roles to LLMs, facilitating efficient task delegation,
specialized task resolution, and effective communication of results. This
design fosters high extensibility and promotes the smooth incorporation of new
algorithms, models, and features for future development. Extensive user
studies, involving participants from both the AI and protein engineering
communities across academia and industry, consistently validate the superiority
of \textsc{ProteinEngine} in augmenting the reliability and precision of deep
learning in protein engineering tasks. Consequently, our findings highlight the
potential of \textsc{ProteinEngine} to bride the disconnected tools for future
research in the protein engineering domain.


  In previous work a novel Edge Lightweight Searchable Attribute-based
encryption (ELSA) method was proposed to support Industry 4.0 and specifically
Industrial Internet of Things applications. In this paper, we aim to improve
ELSA by minimising the lookup table size and summarising the data records by
integrating Machine Learning (ML) methods suitable for execution at the edge.
This integration will eliminate records of unnecessary data by evaluating added
value to further processing. Thus, resulting in the minimization of both the
lookup table size, the cloud storage and the network traffic taking full
advantage of the edge architecture benefits. We demonstrate our mini-ELSA
expanded method on a well-known power plant dataset. Our results demonstrate a
reduction of storage requirements by 21% while improving execution time by
1.27x.


  Transition states or quantum states of zero energy appear at the boundary
between the discrete part of the spectrum of negative energies and the
continuum part of positive energy states. As such, transition states can be
regarded as a limiting case of a bound state with vanishing binding energy,
emerging for a particular set of critical potential parameters. In this work we
study the properties of these critical parameters for short range central
potentials. To this end we develop two exact methods and also utilize the first
and second order WKB approximations. Using these methods we have calculated the
critical parameters for several widely used central potentials.
  The general analytic expressions for the asymptotic representations of the
critical parameters were derived for cases where either the orbital quantum
number $l$ or the number $n$ of bound states approaches infinity.
  The above mathematical models enable us to answer the following physical
(quantum mechanical) questions: i) what is the number of bound states for a
given central potential and given orbital quantum number $l$; ii) what is the
maximum value of $l$ which can provide a bound state for the given central
potential; iii) what is the order of energy levels for the given form of the
central potential.
  It is revealed that the ordering of energy levels depends on the potential
singularity at the origin.


  Non-orthogonal multiple access (NOMA) has emerged as a promising radio access
technique for enabling the performance enhancements promised by the
fifth-generation (5G) networks in terms of connectivity, low latency, and high
spectrum efficiency. In the NOMA uplink, successive interference cancellation
(SIC) based detection with device clustering has been suggested. In the case of
multiple receive antennas, SIC can be combined with the minimum mean-squared
error (MMSE) beamforming. However, there exists a tradeoff between the NOMA
cluster size and the incurred SIC error. Larger clusters lead to larger errors
but they are desirable from the spectrum efficiency and connectivity point of
view. We propose a novel online learning based detection for the NOMA uplink.
In particular, we design an online adaptive filter in the sum space of linear
and Gaussian reproducing kernel Hilbert spaces (RKHSs). Such a sum space design
is robust against variations of a dynamic wireless network that can deteriorate
the performance of a purely nonlinear adaptive filter. We demonstrate by
simulations that the proposed method outperforms the MMSE-SIC based detection
for large cluster sizes.


  Applying changes to an input speech signal to change the perceived speaker of
speech to a target while maintaining the content of the input is a challenging
but interesting task known as Voice conversion (VC). Over the last few years,
this task has gained significant interest where most systems use data-driven
machine learning models. Doing the conversion in a low-latency real-world
scenario is even more challenging constrained by the availability of
high-quality data. Data augmentations such as pitch shifting and noise addition
are often used to increase the amount of data used for training machine
learning based models for this task. In this paper we explore the efficacy of
common data augmentation techniques for real-time voice conversion and
introduce novel techniques for data augmentation based on audio and voice
transformation effects as well. We evaluate the conversions for both male and
female target speakers using objective and subjective evaluation methodologies.


  We comment on the paper ``Numerical study of the SWKB condition of novel
classes of exactly solvable systems'' [Y. Nasuda and N. Sawado, Mod. Phys.
Lett. A 36, 2150025 (2021)]. We show that it misrepresents our prior work [J.
Bougie, A. Gangopadhyaya and C. Rasinariu, J. Phys. A: Math. Theor. 51, 375202
(2018)], and clarify this misunderstanding.


  Fat-tree networks have been widely adopted to High Performance Computing
(HPC) clusters and to Data Center Networks (DCN). These parallel systems
usually have a large number of servers and hosts, which generate large volumes
of highly-volatile traffic. Thus, distributed load-balancing routing design
becomes critical to achieve high bandwidth utilization, and low-latency packet
delivery. Existing distributed designs rely on remote congestion feedbacks to
address congestion, which add overheads to collect and react to network-wide
congestion information. In contrast, we propose a simple but effective
load-balancing scheme, called Dynamic Randomized load-Balancing (DRB), to
achieve network-wide low levels of path collisions through local-link
adjustment which is free of communications and cooperations between switches.
First, we use D-mod-k path selection scheme to allocate default paths to all
source-destination (S-D) pairs in a fat-tree network, guaranteeing low levels
of path collision over downlinks for any set of active S-D pairs. Then, we
propose Threshold-based Two-Choice (TTC) randomized technique to balance uplink
traffic through local uplink adjustment at each switch. We theoretically show
that the proposed TTC for the uplink-load balancing in a fat-tree network have
a similar performance as the two-choice technique in the area of randomized
load balancing. Simulation results show that DRB with TTC technique achieves a
significant improvement over many randomized routing schemes for fat-tree
networks.


  We present a temperature dependent study of the thermal excitation of
magnonic spin currents in two-sublattice magnetic materials. Using atomistic
spin model simulations, we study the local magnetization profiles in the
vicinity of a temperature step in antiferromagnets, as well as in ferrimagnets.
It is shown that the strength of the excitation of the spin currents in these
systems scales with the derivative of the magnetization with respect to the
temperature.


  Amino acids occurring in proteins are, with rare exceptions, exclusively of
the L-configuration. Among the many scenarios put forward to explain the origin
of this chiral homogeneity (i.e., homochirality), one involves the asymmetric
photolysis of amino acids present in space, triggered by circularly polarized
UV radiation. The recent observation of circularly polarized light (CPL) in the
Orion OMC-1 star-forming region (Bailey et al. 1998, Science 281, 672) has been
presented as providing a strong validation of this scenario. The present paper
reviews the situation. It is stressed for example that one important condition
for the asymmetric photolysis by CPL to be at the origin of the terrestrial
homochirality of natural amino acids is generally overlooked, namely, the
asymmetric photolysis should favour the L-enantiomer for ALL the primordial
amino acids involved in the genesis of life (i.e., biogenic amino acids).
Although this condition is probably satisfied for aliphatic amino acids, some
non-aliphatic amino acids like tryptophan and proline may violate the condition
and thus invalidate the asymmetric photolysis scenario, assuming they were
among the primordial amino acids. Alternatively, if CPL photolysis in space is
indeed the source of homochirality of amino acids, then tryptophan and proline
may be crossed out from the list of biogenic amino acids.


  In this paper, we investigate the diversity aspect of paraphrase generation.
Prior deep learning models employ either decoding methods or add random input
noise for varying outputs. We propose a simple method Diverse Paraphrase
Generation (D-PAGE), which extends neural machine translation (NMT) models to
support the generation of diverse paraphrases with implicit rewriting patterns.
Our experimental results on two real-world benchmark datasets demonstrate that
our model generates at least one order of magnitude more diverse outputs than
the baselines in terms of a new evaluation metric Jeffrey's Divergence. We have
also conducted extensive experiments to understand various properties of our
model with a focus on diversity.


  Cryptovirological augmentations present an immediate, incomparable threat.
Over the last decade, the substantial proliferation of crypto-ransomware has
had widespread consequences for consumers and organisations alike. Established
preventive measures perform well, however, the problem has not ceased. Reverse
engineering potentially malicious software is a cumbersome task due to platform
eccentricities and obfuscated transmutation mechanisms, hence requiring
smarter, more efficient detection strategies. The following manuscript presents
a novel approach for the classification of cryptographic primitives in compiled
binary executables using deep learning. The model blueprint, a DCNN, is
fittingly configured to learn from variable-length control flow diagnostics
output from a dynamic trace. To rival the size and variability of contemporary
data compendiums, hence feeding the model cognition, a methodology for the
procedural generation of synthetic cryptographic binaries is defined, utilising
core primitives from OpenSSL with multivariate obfuscation, to draw a vastly
scalable distribution. The library, CryptoKnight, rendered an algorithmic pool
of AES, RC4, Blowfish, MD5 and RSA to synthesis combinable variants which are
automatically fed in its core model. Converging at 91% accuracy, CryptoKnight
is successfully able to classify the sample algorithms with minimal loss.


  In this paper we deal with further recent developments, strictly connected to
the recent result obtained by Basile, Nota, Pezzotti and Pulvirenti. We
consider the Lorentz gas out of equilibrium in a weak coupling regime. Each
obstacle of the Lorentz gas generates a smooth radially symmetric potential
with compact support. We prove that the macroscopic current in the stationary
state is given by the Fick's law of diffusion. The diffusion coefficient is
given by the Green-Kubo formula associated to the generator of the diffusion
process dictated by the linear Landau equation.


  We prove that if in a spacetime endowed with a merely continuous metric, a
complete partial Cauchy hypersurface has nonempty Cauchy horizon, then the
horizon is caused by the presence of almost closed causal curves behind it or
by the influence of points at infinity. This statement is related to strong
cosmic censorship and a conjecture of Wald. In this light, Wald's conjecture
can be reformulated as a PDE problem about the location of Cauchyh horizons
inside black hole interiors.


  The dichromatic number of an oriented graph is the minimum size of a
partition of its vertices into acyclic induced subdigraphs. We prove that
oriented graphs with no induced directed path on six vertices and no triangle
have bounded dichromatic number. This is one (small) step towards the general
conjecture asserting that for every oriented tree T and every integer k, any
oriented graph that does not contain an induced copy of T nor a clique of size
k has dichromatic number at most some function of k and T.


  Lunar impact glasses, quenched melts produced during cratering events on the
Moon, have the potential to provide not only compositional information about
both the local and regional geology of the Moon but also information about the
impact flux over time. We present in this paper the results of 73 new 40Ar/39Ar
analyses of well-characterized, inclusion-free lunar impact glasses and
demonstrate that size, shape, chemical composition, fraction of radiogenic 40Ar
retained, and cosmic ray exposure (CRE) ages are important for 40Ar/39Ar
investigations of these samples. Specifically, analyses of lunar impact glasses
from the Apollo 14, 16, and 17 landing sites indicate that retention of
radiogenic 40Ar is a strong function of post-formation thermal history in the
lunar regolith, size, and chemical composition. Based on the relationships
presented in this paper, lunar impact glasses with compositions and sizes
sufficient to have retained 90% of their radiogenic Ar during 750 Ma of cosmic
ray exposure at time-integrated temperatures of up to 290 K have been
identified and are likely to have yielded reliable 40Ar/39Ar ages of formation.
Additionally, ~50% of the identified impact glass spheres have formation ages
of <500 Ma, while ~75% of the identified lunar impact glass shards and spheres
have ages of formation <2000 Ma. The observed age-frequency distribution of
lunar impact glasses may reflect two processes: (i) diminished preservation due
to spontaneous shattering with age; and (ii) preservation of a remnant
population of impact glasses from the tail end of the terminal lunar
bombardment having 40Ar/39Ar ages up to 3800 Ma. A protocol is described for
selecting and analyzing lunar impact glasses.


  Tight limits on the photon mass have been set through analyzing the arrival
time differences of photons with different frequencies originating from the
same astrophysical source. However, all these constraints have relied on using
the first-order Taylor expansion of the dispersion due to a nonzero photon
mass. In this work, we present an analysis of the nonzero photon mass
dispersion with the second-order derivative of Taylor series. If the arrival
time delay corrected for all known effects (including the first-order delay
time due to the plasma and photon mass effects) is assumed to be dominated by
the second-order term of the nonzero photon mass dispersion, a conservative
upper limit on the photon mass can be estimated. Here we show that the
dedispersed pulses with the second-order time delays from the Crab pulsar and
the fast radio burst FRB 180916B pose strict limits on the photon mass, i.e.,
$m_{\gamma,2} \leq5.7\times10^{-46}\;{\rm kg}\simeq3.2\times10^{-10}\; {\rm
eV}/c^{2}$ and $m_{\gamma,2} \leq6.0\times10^{-47}\;{\rm
kg}\simeq3.4\times10^{-11}\; {\rm eV}/c^{2}$, respectively. This is the first
time to study the possible second-order photon mass effect.


  For a graph $G$, we show that if $mad(G)<m$, then $\chi'_\ell(G)\leq
\Delta+1$ where $m$ depends upon $\Delta$ and $\chi'_\ell(G)$ is the
list-chromatic index of $G$. When $\Delta\leq 20$ the value of $m$ is close to
$\frac{1}{2}\Delta$, but as $\Delta$ increases $m$ becomes asymptotic to about
$\frac{1}{4}\Delta+5$.


  We propose to explore a cascade extreme Adaptive optics (ExAO) approach with
a second stage based on a Zernike wavefront sensor (ZWFS) for exoplanet imaging
and spectroscopy. Most exoplanet imagers currently use a single-stage ExAO to
correct for the effects of atmospheric turbulence and produce high-Strehl
images of observed stars in the near-infrared. While such systems enable the
observation of warm gaseous companions around nearby stars, adding a
second-stage AO enables to push the wavefront correction further and possibly
observe colder or smaller planets. This approach is currently investigated in
different exoplanet imagers (VLT/SPHERE, Mag-AOX, Subaru/SCExAO) by considering
a Pyramid wavefront sensor (PWFS) in the second arm to measure the residual
atmospheric turbulence left from the first stage. Since these aberrations are
expected to be very small (a few tens of nm in the near-infrared domain), we
propose to investigate an alternative approach based on the ZWFS. This sensor
is a promising concept with a small capture range to estimate residual
wavefront errors thanks to its large sensitivity, simple phase reconstruction
and easiness of implementation. In this contribution, we perform preliminary
tests on the GHOST testbed at ESO to validate this approach experimentally.
Additional experiments with petalling effects are also showed, giving promising
wavefront correction results. Finally, we briefly discuss a first comparison
between PWFS-based and ZWFS-based second-stage AO to draw preliminary
conclusions on the interests of both schemes for exoplanet imaging and
spectroscopy with the upgrade of the current exoplanet imagers and the
envisioned ExAO instruments for ELTs.


  We investigated the Cu/Ni-O$_2$ bilayer multi-orbital Hubbard model with
Cu/Ni impurity approximation embedded in the O lattice by incorporating the
3d$^{8}$ multiplet structure coupled to a full O-2p band. For this simplified
model of describing the local electronic structure of hole doped homo-bilayer
Cu(Ni)O$_2$-Cu(Ni)O$_2$ and hetero-bilayer CuO$_2$-NiO$_2$, we demonstrate that
the homo-bilayer system hosts rich phase diagram with inversion symmetry
breaking between two layers at strong enough interlayer hybridization,
especially the coexistence of two-hole singlet and triplet states in opposite
layers. Besides, we show that the interlayer hybridization in hetero-bilayer
systems destabilizes the Zhang-Rice singlets or 3d$^{8}$ triplets within each
layer via charge transfer between layers. The relevance of these electronic
redistribution to the bilayer or interface systems of cuprates and/or
nickelates are discussed.


  We develop a two-part reconstruction framework for signal recovery in
compressed sensing (CS), where a fast algorithm is applied to provide partial
recovery in Part 1, and a CS algorithm is applied to complete the residual
problem in Part 2. Partitioning the reconstruction process into two
complementary parts provides a natural trade-off between runtime and
reconstruction quality. To exploit the advantages of the two-part framework, we
propose a Noisy-Sudocodes algorithm that performs two-part reconstruction of
sparse signals in the presence of measurement noise. Specifically, we design a
fast algorithm for Part 1 of Noisy-Sudocodes that identifies the zero
coefficients of the input signal from its noisy measurements. Many existing CS
algorithms could be applied to Part 2, and we investigate approximate message
passing (AMP) and binary iterative hard thresholding (BIHT). For
Noisy-Sudocodes with AMP in Part 2, we provide a theoretical analysis that
characterizes the trade-off between runtime and reconstruction quality. In a
1-bit CS setting where a new 1-bit quantizer is constructed for Part 1 and BIHT
is applied to Part 2, numerical results show that the Noisy-Sudocodes algorithm
improves over BIHT in both runtime and reconstruction quality.


  Many current and future exoplanet missions are pushing to infrared (IR)
wavelengths where the flux contrast between the planet and star is more
favorable (Deming et al. 2009), and the impact of stellar magnetic activity is
decreased. Indeed, a recent analysis of starspots and faculae found these forms
of stellar activity do not substantially impact the transit signatures or
science potential for FGKM stars with JWST (Zellem et al. 2017). However, this
is not true in the case of flares, which I demonstrate can be a hinderance to
transit studies in this note.


  We consider new series expansions for variants of the so-termed ordinary
geometric square series generating functions originally defined in the recent
article titled "Square Series Generating Function Transformations" (arXiv:
1609.02803). Whereas the original square series transformations article adapts
known generating function transformations to construct integral representations
for these square series functions enumerating the square powers of $q^{n^2}$
for some fixed non-zero $q$ with $|q| < 1$, we study the expansions of these
special series through power series generated by Jacobi-type continued
fractions, or J-fractions. We prove new exact expansions of the $h^{th}$
convergents to these continued fraction series and show that the limiting case
of these convergent generating functions exists.
  We also prove new infinite $q$-series representations of special square
series expansions involving square-power terms of the series parameter $q$, the
$q$-Pochhammer symbol, and double sums over the $q$-binomial coefficients.
Applications of the new results we prove within the article include new
$q$-series representations for the ordinary generating functions of the special
sequences, $r_p(n)$, and $\sigma_1(n)$, as well as parallels to the examples of
the new integral representations for theta functions, series expansions of
infinite products and partition function generating functions, and related
unilateral special function series cited in the first square series
transformations article.