  Particle production in p+p and central Pb+Pb collisions at LHC is discussed
in the context of the statistical thermal model. For heavy-ion collisions,
predictions of various particle ratios are presented. The sensitivity of
several ratios on the temperature and the baryon chemical potential is studied
in detail, and some of them, which are particularly appropriate to determine
the chemical freeze-out point experimentally, are indicated. Considering
elementary interactions on the other hand, we focus on strangeness production
and its possible suppression. Extrapolating the thermal parameters to LHC
energy, we present predictions of the statistical model for particle yields in
p+p collisions. We quantify the strangeness suppression by the correlation
volume parameter and discuss its influence on particle production. We propose
observables that can provide deeper insight into the mechanism of strangeness
production and suppression at LHC.


  We perform a Markov Chain Monte Carlo (MCMC) analysis of a simple yet generic
multifield inflation model characterized by two scalar fields coupled to each
other and nonminimally coupled to gravity, fit to Planck 2018 cosmic microwave
background (CMB) data. In particular, model parameters are constrained by data
on the amplitude of the primordial power spectrum of scalar curvature
perturbations on CMB scales $A_s$, the spectral index $n_s$, and the ratio of
power in tensor to scalar modes $r$, with a prior that the primordial power
spectrum should also lead to primordial black hole (PBH) production sufficient
to account for the observed dark matter (DM) abundance. We find that $n_s$ in
particular controls the constraints on our model. Whereas previous studies of
PBH formation from an ultra-slow-roll phase of inflation have highlighted the
need for at least one model parameter to be highly fine-tuned, we identify a
degeneracy direction in parameter space such that shifts by $\sim 10\%$ of one
parameter can be compensated by comparable shifts in other parameters while
preserving a close fit between model predictions and observations. Furthermore,
we find this allowed parameter region produces observable gravitational wave
(GW) signals in the frequency ranges to which upcoming experiments are
projected to be sensitive, including Advanced LIGO and Virgo, the Einstein
Telescope (ET), Cosmic Explorer (CE), DECIGO, and LISA.


  The Finite Fourier Series (FFS) Shape-Based (SB) trajectory approximation
method has been used to rapidly generate initial trajectories that satisfy the
dynamics, trajectory boundary conditions, and limitation on maximum thrust
acceleration. The FFS SB approach solves a nonlinear programming problem (NLP)
in searching for feasible trajectories. This paper extends the development of
the FFS SB approach to generate sub optimal solutions. Specifically, the
objective function of the NLP problem is modified to include also a measure for
the time of flight. Numerical results presented in this paper show several
solutions that differ from those of the original FFS SB ones. The sub-optimal
trajectories generated using a time of flight minimization are shown to be
physically feasible trajectories and potential candidates for direct solvers.


  We consider the regular Lagrangian flow X associated to a bounded
divergence-free vector field b with bounded variation. We prove a
Lusin-Lipschitz regularity result for X and we show that the Lipschitz constant
grows at most linearly in time. As a consequence we deduce that both geometric
and analytical mixing have a lower bound of order $t^{-1}$ as $t \to \infty$.


  The watershed is a powerful tool for segmenting objects whose contours appear
as crest lines on a gradient image. The watershed transform associates to a
topographic surface a partition into catchment basins, defined as attraction
zones of a drop of water falling on the relief and following a line of steepest
descent. Unfortunately, catchment basins may overlap and do not form a
partition. Moreover, current watershed algorithms, being shortsighted, do not
correctly estimate the steepness of the downwards trajectories and overestimate
the overlapping zones of catchment basins. An arbitrary division of these zones
between adjacent catchment basin results in a poor localization of the
contours. We propose an algorithm without myopia, which considers the total
length of a trajectory for estimating its steepness. We first consider
topographic surfaces defined on node weighted graphs. The graphs are pruned in
order to eliminate all downwards trajectories which are not the steepest. An
iterative algorithm with simple neighborhood operations performs the pruning
and constructs the catchment basins. The algorithm is then adapted to gray tone
images. The graph structure itself is encoded as an image thanks to the fixed
neighborhood structure of grids. A pair of adaptative erosions and dilations
prune the graph and extend the catchment basins. As a result one obtains a
precise detection of the catchment basins and a graph of the steepest
trajectories. A last iterative algorithm allows to follow selected downwards
trajectories in order to detect particular structures such as rivers or thalweg
lines of the topographic surface.


  A novel fully bio-based Poly (Glycerol-Itaconic acid) (PGI) was designed and
highly efficiently synthesized by solvent-free polycondensation. The Poly
(ethylene glycol) (PEG) was used as the phase change materials (PCM) working
substance and encapsulated by the sustainable PGI supporter. PEG chains were
tightly encapsulated with the PGI supporting material mainly under hydrogen
bonds due to the structural compatibility between PGI and PEG. The PCMs can
achieve high form stability and high phase change enthalpies in the same kinds
of PCMs. Furthermore, the phase change temperatures and enthalpies of the PCMs
can be adjusted conveniently by regulating the PEG content and molecular
weight. Notably, this process extremely facilitates the realization of
efficient mass production due to the eco-friendly nature, high efficiency and
low cost.


  It was known from Metropolis et al. [J. Chem. Phys. 21 (1953) 1087--1092]
that one can sample from a distribution by performing Monte Carlo simulation
from a Markov chain whose equilibrium distribution is equal to the target
distribution. However, it took several decades before the statistical community
embraced Markov chain Monte Carlo (MCMC) as a general computational tool in
Bayesian inference. The usual reasons that are advanced to explain why
statisticians were slow to catch on to the method include lack of computing
power and unfamiliarity with the early dynamic Monte Carlo papers in the
statistical physics literature. We argue that there was a deeper reason,
namely, that the structure of problems in the statistical mechanics and those
in the standard statistical literature are different. To make the methods
usable in standard Bayesian problems, one had to exploit the power that comes
from the introduction of judiciously chosen auxiliary variables and collective
moves. This paper examines the development in the critical period 1980--1990,
when the ideas of Markov chain simulation from the statistical physics
literature and the latent variable formulation in maximum likelihood
computation (i.e., EM algorithm) came together to spark the widespread
application of MCMC methods in Bayesian computation.


  In this paper, we study a simple model of an orbiting protoplanet in a
central magnetospheric cavity, the entry into such a cavity having been
proposed as a mechanism for halting inward orbital migration. We have
calculated the gravitational interaction of the protoplanet with the
magnetosphere using a local model and determined the rate of evolution of the
orbit. The interaction is found to be determined by the outward flux of MHD
waves and thus the possibility of the existence of such waves in the cavity is
significant. The estimated orbital evolution rates due to gravitational and
other interactions with the magnetosphere are unlikely to be significant during
protoplanetary disk lifetimes.


  We have used the Sydney University Stellar Interferometer (SUSI) to measure
the angular diameter of the F9 V star beta Virginis. After correcting for limb
darkening and combining with the revised Hipparcos parallax, we derive a radius
of 1.703 +/- 0.022 R_sun (1.3%). We have also calculated the bolometric flux
from published measurements which, combined with the angular diameter, implies
an effective temperature of 6059 +/- 49 K (0.8%). We also derived the
luminosity of beta Vir to be L = 3.51 +/- 0.08 L_sun (2.1%). Solar-like
oscillations were measured in this star by Carrier et al. (2005) and using
their value for the large frequency separation yields the mean stellar density
with an uncertainty of about 2%. Our constraints on the fundamental parameters
of beta Vir will be important to test theoretical models of this star and its
oscillations.


  {}From the non-equilibrium critical relaxation study of the two-dimensional
Ising model, the dynamical critical exponent $z$ is estimated to be $2.165 \pm
0.010$ for this model. The relaxation in the ordered phase of this model is
consistent with $\exp (-\sqrt{t/\tau })$ behavior. The interface energy of the
three-dimensional Ising model is studied and the critical exponent of the
correlation length $\nu$ and the critical amplitude of the surface tension
$\sigma_0$ are estimated to be $0.6250\pm 0.025$ and $1.42\pm 0.04$,
respectively. A dynamic Monte Carlo renormalization group method is applied to
the equilibrium properties of the three-dimensional Ising model successfully.


  The internal resonator banjo, patented and first sold by Fred Bacon around
1906, remains something of a cult favorite and is still produced by some
independent luthiers. According to enthusiasts, the characteristic design
elements produce a sound that is mellower, richer, and of greater complexity
and presence than without them. Aspects of that sound are studied here,
comparing instruments that are otherwise identical and identifying physics
mechanisms that are likely responsible.


  Entangling operations are among the most important primitive gates employed
in quantum computing and it is crucial to ensure high-fidelity implementations
as systems are scaled up. We experimentally realize and characterize a simple
scheme to minimize errors in entangling operations related to the residual
excitation of mediating bosonic oscillator modes that both improves gate
robustness and provides scaling benefits in larger systems. The technique
employs discrete phase shifts in the control field driving the gate operation,
determined either analytically or numerically, to ensure all modes are
de-excited at arbitrary user-defined times. We demonstrate an average gate
fidelity of 99.4(2)% across a wide range of parameters in a system of
$^{171}\text{Yb}^{+}$ trapped ion qubits, and observe a reduction of gate error
in the presence of common experimental error sources. Our approach provides a
unified framework to achieve robustness against both static and time-varying
laser amplitude and frequency detuning errors. We verify these capabilities
through system-identification experiments revealing improvements in
error-susceptibility achieved in phase-modulated gates.


  Using the data sets of 17.3 pb$^{-1}$ collected at $\sqrt{s}=$ 3.773 GeV and
6.5 pb$^{-1}$ collected at $\sqrt{s}=$ 3.650 GeV with the BESII detector at the
BEPC collider, we have measured the observed cross sections for 18 exclusive
light hadron final states produced in $e^+e^-$ annihilation at the two energy
points.


  A domain decomposition method is proposed based on carefully chosen impedance
transmission operators for a hybrid formulation of the eddy current problem.
Preliminary analysis and numerical results are provided in the spherical case
showing the potential of these conditions in accelerating the convergence rate


  In this article, we give some necessary conditions for the concavity property
of minimal $L^2$ integrals degenerating to partial linearity, a charaterization
for the concavity degenerating to partial linearity for open Riemann surfaces,
and some relations between the concavity property for minimal $L^2$ integrals
and the log-convexity for Bergman kernels.


  The 3C 273 and RX J1230.8+0115 sightlines probe the outskirts of the Virgo
Cluster at physical separations between the sightlines of 200-500 h_70 kpc. We
present an analysis of HST STIS echelle and FUSE UV spectroscopy of RX
J1230.8+0115 in which we detect five Lyman-alpha absorbers at Virgo distances.
One of these absorbers is a blend of two strong metal line absorbers coincident
in velocity with the highest neutral hydrogen column density absorber in the 3C
273 sightline ~350 h_70 kpc away. The consistency of the metal line column
density ratios in the RX J1230.8+0115 sightline allows us to determine the
ionization mechanism (photoionization) for these absorbers. While the low
signal-to-noise ratio of the FUSE spectrum limits our ability to model the
neutral hydrogen column density of these absorbers, we are able to constrain
them to be in the range 10^{16-17} cm^-2. The properties of these absorbers are
similar to those of the 3C 273 absorber studied by Tripp et al. However, the
inferred line-of-sight size for the 3C 273 absorber is only 70 pc, much smaller
than those inferred in RX J1230.8+0115, which are 10-30 h_70 kpc. The small
sizes of all three absorbers are at odds with the >~350 h_70 kpc minimum
transverse size implied by an application of the standard QSO line pairs
analysis. On the basis of absorber associations between these two sight lines
we conclude that a large-scale structure filament produces a correlated, not
contiguous, gaseous structure in this region of the Virgo Supercluster. These
data may indicate that we are detecting overdensities in the large scale
structure filaments in this region. Alternatively, the presence of a galaxy 71
h_70 kpc from a 3C 273 absorber may indicate that we have probed outflowing,
starburst driven shells of gas associated with nearby galaxies.


  We define a generalization of the Mullineux involution on multipartitions
using the theory of crystals for higher level Fock spaces. Our generalized
Mullineux involution turns up in representation theory via two important
derived functors on cyclotomic Cherednik category $\mathcal{O}$: Losev's
"$\kappa=0$" wall-crossing, and the Ringel duality.


  We propose and discuss a Bayesian procedure to estimate the average treatment
effect (ATE) for multilevel observations in the presence of confounding. We
focus on situations where the confounders may be latent (e.g., spatial latent
effects). This work is motivated by an interest in determining the causal
impact of directly observed therapy (DOT) on the successful treatment of
Tuberculosis (TB); the available data correspond to individual-level
information observed across different cities in a state in Brazil. We focus on
propensity score regression and covariate adjustment to balance the treatment
(DOT) allocation. We discuss the need to include latent local-level random
effects in the propensity score model to reduce bias in the estimation of the
ATE. A simulation study suggests that accounting for the multilevel nature of
the data with latent structures in both the outcome and propensity score models
has the potential to reduce bias in the estimation of causal effects.


  While most methods for solving mixed-integer optimization problems compute a
single optimal solution, a diverse set of near-optimal solutions can often lead
to improved outcomes. We present a new method for finding a set of diverse
solutions by emphasizing diversity within the search for near-optimal
solutions. Specifically, within a branch-and-bound framework, we investigated
parameterized node selection rules that explicitly consider diversity. Our
results indicate that our approach significantly increases the diversity of the
final solution set. When compared with two existing methods, our method runs
with similar runtime as regular node selection methods and gives a diversity
improvement between 12% and 190%. In contrast, popular node selection rules,
such as best-first search, in some instances performed worse than
state-of-the-art methods by more than 35% and gave an improvement of no more
than 130%. Further, we find that our method is most effective when diversity in
node selection is continuously emphasized after reaching a minimal depth in the
tree and when the solution set has grown sufficiently large. Our method can be
easily incorporated into integer programming solvers and has the potential to
significantly increase the diversity of solution sets.


  This paper explores the segmentation of very small medical objects with
significant clinical value. While Convolutional Neural Networks (CNNs),
particularly UNet-like models, and recent Transformers have shown substantial
progress in image segmentation, our empirical findings reveal their poor
performance in segmenting the small medical objects and lesions concerned in
this paper. This limitation may be attributed to information loss during their
encoding and decoding process. In response to this challenge, we propose a
novel model named EFCNet for small object segmentation in medical images. Our
model incorporates two modules: the Cross-Stage Axial Attention Module (CSAA)
and the Multi-Precision Supervision Module (MPS). These modules address
information loss during encoding and decoding procedures, respectively.
Specifically, CSAA integrates features from all stages of the encoder to
adaptively learn suitable information needed in different decoding stages,
thereby reducing information loss in the encoder. On the other hand, MPS
introduces a novel multi-precision supervision mechanism to the decoder. This
mechanism prioritizes attention to low-resolution features in the initial
stages of the decoder, mitigating information loss caused by subsequent
convolution and sampling processes and enhancing the model's global perception.
We evaluate our model on two benchmark medical image datasets. The results
demonstrate that EFCNet significantly outperforms previous segmentation methods
designed for both medical and normal images.


  We suggest that the physically irrelevant choice of a representative
worldline of a relativistic spinning particle should correspond to a gauge
symmetry in an action approach. Using a canonical formalism in special
relativity, we identify a (first-class) spin gauge constraint, which generates
a shift of the worldline together with the corresponding transformation of the
spin on phase space. An action principle is formulated for which a minimal
coupling to fields is straightforward. The electromagnetic interaction of a
monopole-dipole particle is constructed explicitly.


  We experimentally investigate the interplay of Turing and Faraday
(modulational) instabilities in a bistable passive nonlinear resonator. The
Faraday branch is induced via parametric resonance owing to a periodic
modulation of the resonator dispersion. We show that the bistable switching
dynamics is dramatically affected by the competition between the two
instability mechanisms, which dictates two completely novel scenarios. At low
detunings from resonance switching occurs between the stable stationary lower
branch and the Faraday-unstable upper branch, whereas at high detunings we
observe the crossover between the Turing and Faraday periodic structures. The
results are well explained in terms of the universal Lugiato-Lefever model.


  We prove diameter bounds for graphs having positive Ricci-curvature bound in
Bakry-Emery sense. One result using only curvature and maximal vertex degree is
sharp in case of hypercubes. The other result depends on an additional
dimension bound, but is independent of the vertex degree. In particular, the
second result is the first Bonnet-Myers type theorem for unbounded graph
Laplacians. Moreover, our results improve diameter bounds from [6] and [10].


  For almost a quarter of a century the Tevatron proton-antiproton collider was
the centrepiece of the world's high-energy physics program, from the start of
operation in December 1985 until it was overtaken by the LHC in 2011. The
initial design luminosity of the Tevatron was 1030 cm-2 s-1; however, as a
result of two decades of upgrades, the accelerator has been able to deliver
430-times higher luminosities to each of two high-luminosity experiments,
Collider Detector at Fermilab (CDF) and D0. On the way to record high
luminosities, many issues related to the electromagnetic beam-beam interaction
of colliding beams have been addressed. Below we present a short overview of
the beam-beam effects in the Tevatron.


  We revisit Newton's equation of motion in one dimension when the moving
particle has a variable mass m(x,t) depending both on position (x) and time
(t). Geometrically the mass function is identified with one of the metric
function in a 1+1-dimensional spacetime. As a reflection of the equivalence
principle geodesics equation gives the Newton's law of motion leaving the right
hand side to be supplemented by the external forces. The resulting equation
involves the speed of light so that our equation of motion addresses a wider
scope than the customary classical mechanics. In the limit of infinite light
speed which amounts to instantaneous interaction we recover the classical
results.


  Recent advances in deep neural models allow us to build reliable named entity
recognition (NER) systems without handcrafting features. However, such methods
require large amounts of manually-labeled training data. There have been
efforts on replacing human annotations with distant supervision (in conjunction
with external dictionaries), but the generated noisy labels pose significant
challenges on learning effective neural models. Here we propose two neural
models to suit noisy distant supervision from the dictionary. First, under the
traditional sequence labeling framework, we propose a revised fuzzy CRF layer
to handle tokens with multiple possible labels. After identifying the nature of
noisy labels in distant supervision, we go beyond the traditional framework and
propose a novel, more effective neural model AutoNER with a new Tie or Break
scheme. In addition, we discuss how to refine distant supervision for better
NER performance. Extensive experiments on three benchmark datasets demonstrate
that AutoNER achieves the best performance when only using dictionaries with no
additional human effort, and delivers competitive results with state-of-the-art
supervised benchmarks.


  We investigate the notion of a semi-retraction between two first order
structures (in typically different signatures) that was introduced by the
second author as a link between the Ramsey property and generalized
indiscernible sequences. We further these connections between combinatorics and
model theory, and look at semi-retractions through a new lens establishing
transfers of the Ramsey property and finite Ramsey degrees under quite general
conditions that are optimal as demonstrated by counterexamples. Finally, we
compare semi-retractions to the category theoretic notion of a pre-adjunction.


  We present a microscopic description of edge excitations in the quantum Hall
effect which is analogous to Feynman's theory of superfluids. Analytic
expressions for the excitation energies are derived in finite dots. Our
predictions are in excellent agreement with the results of a recent numerical
diagonalization. In the large $N$ limit the dispersion law is proportional to
$qlog{1\over q}$. For short range interactions the energy instead behaves as
$q^3$. The same results are also derived using hydrodynamic theory of
incompressible liquids.


  The degree of a point configuration is defined as the maximal codimension of
its interior faces. This concept is motivated from a corresponding
Ehrhart-theoretic notion for lattice polytopes and is related to neighborly
polytopes and the generalized lower bound theorem and, by Gale duality, to
Tverberg theory.
  The main results of this paper are a complete classification of point
configurations of degree 1, as well as a structure result on point
configurations whose degree is less than a third of the dimension. Statements
and proofs involve the novel notion of a weak Cayley decomposition, and imply
that the m-core of a set S of n points in R^r is contained in the set of
Tverberg points of order 3m-2(n-r) of S.


  While the field of NL2SQL has made significant advancements in translating
natural language instructions into executable SQL scripts for data querying and
processing, achieving full automation within the broader data science pipeline
- encompassing data querying, analysis, visualization, and reporting - remains
a complex challenge. This study introduces SageCopilot, an advanced,
industry-grade system system that automates the data science pipeline by
integrating Large Language Models (LLMs), Autonomous Agents (AutoAgents), and
Language User Interfaces (LUIs). Specifically, SageCopilot incorporates a
two-phase design: an online component refining users' inputs into executable
scripts through In-Context Learning (ICL) and running the scripts for results
reporting & visualization, and an offline preparing demonstrations requested by
ICL in the online phase. A list of trending strategies such as Chain-of-Thought
and prompt-tuning have been used to augment SageCopilot for enhanced
performance. Through rigorous testing and comparative analysis against
prompt-based solutions, SageCopilot has been empirically validated to achieve
superior end-to-end performance in generating or executing scripts and offering
results with visualization, backed by real-world datasets. Our in-depth
ablation studies highlight the individual contributions of various components
and strategies used by SageCopilot to the end-to-end correctness for data
sciences.


  Nonreciprocity and nonreciprocal optical devices play a vital role in modern
photonic technologies by enforcing one-way propagation of light. Most
nonreciprocal devices today are made from a special class of low-loss ferrites
that exhibit a magneto-optical response in the presence of an external static
magnetic field. While breaking transmission symmetry, ferrites fail to satisfy
the need for miniaturization of photonic circuitry due to weak character of
nonreciprocal responses at optical wavelengths and are not easy to integrate
into on-chip photonic systems. These challenges led to the emergence of
magnetic-free approaches relying on breaking time reversal symmetry, e.g. with
nonlinear effects modulating optical system in time. Here, we demonstrate an
all-optical approach to nonreciprocity based on nonlinear valley-selective
response in transition metal dichalcogenides (TMDs). This approach overcomes
the limitations of magnetic materials and it does not require an external
magnetic field. We provide experimental evidence of photoinduced nonreciprocity
in a monolayer WS2 pumped by circularly polarized light. Nonreciprocity stems
from valley-selective exciton-exciton interactions, giving rise to nonlinear
circular dichroism controlled by circularly polarized pump fields. Our
experimental results reveal a significant effect even at room temperature,
despite considerable intervalley-scattering, showing potential for practical
applications in magnetic-free nonreciprocal platforms. As an example, we
propose a device scheme to realize an optical isolator based on a pass-through
silicon nitride (SiN) ring resonator integrating the optically biased TMD
monolayer.


  Be/X-ray binaries comprise roughly two-thirds of the high-mass X-ray binaries
(HMXBs), which is a class of X-ray binaries that results from the high mass of
the companion or donor star (> 10 solar masses). Currently the formation and
evolution of X-ray producing Be binaries is a matter of great debate. Modelling
of these systems requires knowledge of Be star evolution and also consideration
of how the evolution changes when the star is in close proximity to a
companion. Within this work we complete a full population synthesis study of Be
binaries for the Galaxy. The results for the first time match aspects of the
observational data, most notably the observed upper limit to the period
distribution. We conclude that greater detailed studies on the evolution of Be
stars within X-ray binaries needs to be completed, so that rapid binary
evolution population synthesis packages may best evolve these systems.


  Optimization methods for long-horizon, dynamically feasible motion planning
in robotics tackle challenging non-convex and discontinuous optimization
problems. Traditional methods often falter due to the nonlinear characteristics
of these problems. We introduce a technique that utilizes learned
representations of the system, known as Polytopic Action Sets, to efficiently
compute long-horizon trajectories. By employing a suitable sequence of
Polytopic Action Sets, we transform the long-horizon dynamically feasible
motion planning problem into a Linear Program. This reformulation enables us to
address motion planning as a Mixed Integer Linear Program (MILP). We
demonstrate the effectiveness of a Polytopic Action-Set and Motion Planning
(PAAMP) approach by identifying swing-up motions for a torque-constrained
pendulum within approximately 0.75 milliseconds. This approach is well-suited
for solving complex motion planning and long-horizon Constraint Satisfaction
Problems (CSPs) in dynamic and underactuated systems such as legged and aerial
robots.


  This paper investigates phase transitions on the optimality gaps in Optimal
Power Flow (OPF) problem on real-world power transmission systems operated in
France. The experimental results study optimal power flow solutions for more
than 6000 scenarios on the networks with various load profiles, voltage
feasibility regions, and generation capabilities. The results show that
bifurcations between primal solutions and the QC, SOCP, and SDP relaxation
techniques frequently occur when approaching congestion points. Moreover, the
results demonstrate the existence of multiple bifurcations for certain
scenarios when load demands are increased uniformly. Preliminary analysis on
these bifurcations were performed.


  We extend conformal prediction methodology beyond the case of exchangeable
data. In particular, we show that a weighted version of conformal prediction
can be used to compute distribution-free prediction intervals for problems in
which the test and training covariate distributions differ, but the likelihood
ratio between these two distributions is known---or, in practice, can be
estimated accurately with access to a large set of unlabeled data (test
covariate points). Our weighted extension of conformal prediction also applies
more generally, to settings in which the data satisfies a certain weighted
notion of exchangeability. We discuss other potential applications of our new
conformal methodology, including latent variable and missing data problems.


  After 40 years we have no "low" theory of quasars to predict the atomic
absorption and emission features so abundant in quasar spectra. Here I take the
first step, using results selected for their diagnostic power to build an
"observational paradigm" unifying all the atomic features in quasars. This
paradigm bears a remarkable resemblance to the one I proposed in Elvis (2000,
astro-ph/0008064)! Yet all the results used here were not used in that earlier
study, with one exception. The Elvis (2000) model has been tested by the new
data and has survived, and so has been strengthened. The structure is readily
tested, is physically suggestive, and opens up many areas for detailed
modelling. From this I believe a truly predictive "low" theory of quasars will
emerge.


  Using the geodesic distance on the $n$-dimensional sphere, we study the
expected radius function of the Delaunay mosaic of a random set of points.
Specifically, we consider the partition of the mosaic into intervals of the
radius function and determine the expected number of intervals whose radii are
less than or equal to a given threshold. Assuming the points are not contained
in a hemisphere, the Delaunay mosaic is isomorphic to the boundary complex of
the convex hull in $\mathbb{R}^{n+1}$, so we also get the expected number of
faces of a random inscribed polytope. We find that the expectations are
essentially the same as for the Poisson-Delaunay mosaic in $n$-dimensional
Euclidean space. As proved by Antonelli and collaborators, an orthant section
of the $n$-sphere is isometric to the standard $n$-simplex equipped with the
Fisher information metric. It follows that the latter space has similar
stochastic properties as the $n$-dimensional Euclidean space. Our results are
therefore relevant in information geometry and in population genetics.


  The study group on data preservation in high energy physics, DPHEP, is moving
to a new collaboration structure, which will focus on the implementation of
preservation projects, such as those described in the group's large scale
report published in 2012. One such project is the development of a validation
framework, which checks the compatibility of evolving computing environments
and technologies with the experiments software for as long as possible, with
the aim of substantially extending the lifetime of the analysis software, and
hence of the usability of the data. The framework is designed to automatically
test and validate the software and data of an experiment against changes and
upgrades to the computing environment, as well as changes to the experiment
software itself. Technically, this is realised using a framework capable of
hosting a number of virtual machine images, built with different configurations
of operating systems and the relevant software, including any necessary
external dependencies.


  We describe modeling approaches to a "network" of connected enzyme-catalyzed
reactions, with added (bio)chemical processes that introduce biochemical
filtering steps into the functioning of such a biocatalytic cascade.
Theoretical expressions are derived that allow simple, few-parameter modeling
of processes concatenated in such cascades, both with and without filtering.
The modeling approach captures and explains features identified in earlier
studies of enzymatic processes considered as potential "network components" for
multi-step information/signal processing systems.


  In wavelength division multiplexing (WDM) schemes, splitters must be used to
combine and separate different wavelengths. Conventional splitters are fairly
large with footprints in hundreds to thousands of square microns, and
experimentally-demonstrated MMI-based and inverse-designed ultra-compact
splitters operate with only two channels and large channel spacing ($>$100 nm).
Here we inverse design and experimentally demonstrate a three-channel
wavelength demultiplexer with 40 nm spacing (1500 nm, 1540 nm, and 1580 nm)
with a footprint of 24.75 $\mu\mathrm{m}^2$. The splitter has a simulated peak
insertion loss of -1.55 dB with under -15 dB crosstalk and a measured peak
insertion loss of -2.29 dB with under -10.7 dB crosstalk.


  Following is a collection of lecture notes on D-branes, which may be used by
the reader as preparation for applications to modern research applications such
as: the AdS/CFT and other gauge theory/geometry correspondences, Matrix Theory
and stringy non-commutative geometry, etc. In attempting to be reasonably
self-contained, the notes start from classical point-particles and develop the
subject logically (but selectively) through classical strings, quantisation,
D-branes, supergravity, superstrings, string duality, including many detailed
applications. Selected focus topics feature D-branes as probes of both
spacetime and gauge geometry, highlighting the role of world-volume curvature
and gauge couplings, with some non-Abelian cases. Other advanced topics which
are discussed are the (presently) novel tools of research such as fractional
branes, the enhancon mechanism, D(ielectric)-branes and the emergence of the
fuzzy/non-commutative sphere.


  Social media data provides propitious opportunities for public health
research. However, studies suggest that disparities may exist in the
representation of certain populations (e.g., people of lower socioeconomic
status). To quantify and address these disparities in population
representation, we need demographic information, which is usually missing from
most social media platforms. Here, we propose an ensemble approach for
inferring demographics from social media data.
  Several methods have been proposed for inferring demographic attributes such
as, age, gender and race/ethnicity. However, most of these methods require
large volumes of data, which makes their application to large scale studies
challenging. We develop a scalable approach that relies only on user names to
predict gender. We develop three separate classifiers trained on data
containing the gender labels of 7,953 Twitter users from Kaggle.com. Next, we
combine predictions from the individual classifiers using a stacked
generalization technique and apply the ensemble classifier to a dataset of
36,085 geotagged foodborne illness related tweets from the United States.
  Our ensemble approach achieves an accuracy, precision, recall, and F1 score
of 0.828, 0.851, 0.852 and 0.837, respectively, higher than the individual
machine learning approaches. The ensemble classifier also covers any user with
an alphanumeric name, while the data matching approach, which achieves an
accuracy of 0.917, only covers 67% of users. Application of our method to
reports of foodborne illness in the United States highlights disparities in
tweeting by gender and shows that counties with a high volume of
foodborne-illness related tweets are heavily overrepresented by female Twitter
users.


  We propose Information-Theoretic Active Learning (ITAL), a novel batch-mode
active learning method for binary classification, and apply it for acquiring
meaningful user feedback in the context of content-based image retrieval.
Instead of combining different heuristics such as uncertainty, diversity, or
density, our method is based on maximizing the mutual information between the
predicted relevance of the images and the expected user feedback regarding the
selected batch. We propose suitable approximations to this computationally
demanding problem and also integrate an explicit model of user behavior that
accounts for possible incorrect labels and unnameable instances. Furthermore,
our approach does not only take the structure of the data but also the expected
model output change caused by the user feedback into account. In contrast to
other methods, ITAL turns out to be highly flexible and provides
state-of-the-art performance across various datasets, such as MIRFLICKR and
ImageNet.


  In the design of a diffraction-limited storage ring (DLSR) consisting of
compact multi-bend achromats (MBAs), it is challenging to simultaneously
achieve an ultralow emittance and a satisfactory nonlinear performance, due to
extremely large nonlinearities and limited tuning ranges of the element
parameters. Nevertheless, taking the High Energy Photon Source (HEPS) as an
example, we demonstrate that the potential of a DLSR design can be explored
with a successive and iterative implementation of the particle swarm
optimization (PSO) and multi-objective genetic algorithm (MOGA). It turns out
that with a hybrid MBA lattice, it is feasible for the HEPS to attain a natural
emittance of about 50 pm.rad, and meanwhile, realize a sufficient ring
acceptance for beam accumulation with an on-axis longitudinal injection scheme.
Particularly, this study indicates that a rational combination of the PSO and
MOGA is more effective than either of them alone in approaching the true global
optima, for an explorative multi-objective problem with many optimizing
variables and local optima.


  X-ray binaries provide exceptional laboratories for understanding the physics
of matter under the most extreme conditions. Until recently, there were few, if
any, observational constraints on the circumbinary environments of X-ray
binaries at $\sim$ 100-5000 AU scales; it remains unclear how the accretion
onto the compact objects or the explosions giving rise to the compact objects
interact with their immediate surroundings. Here, we present the first
high-contrast adaptive optics images of X-ray binaries. These observations
target all X-ray binaries within $\sim$ 3 kpc accessible with the Keck/NIRC2
vortex coronagraph. This paper focuses on one of the first key results from
this campaign: our images reveal the presence of 21 sources potentially
associated with the $\gamma$ Cassiopeiae analog high-mass X-ray binary RX
J1744.7$-$2713. By conducting different analyses - a preliminary proper motion
analysis, a color-magnitude diagram and a probability of chance alignment
calculation - we found that three of these 21 sources have a high probability
of being bound to the system. If confirmed, they would be in wide orbits
($\sim$ 450 AU to 2500 AU). While follow-up astrometric observations will be
needed in $\sim$ 5-10 years to confirm further the bound nature of these
detections, these discoveries emphasize that such observations may provide a
major breakthrough in the field. In fact, they would be useful not only for our
understanding of stellar multiplicity but also for our understanding of how
planets, brown dwarfs and stars can form even in the most extreme environments.


  We demonstrate that the recent measurement of the anomalous magnetic moment
of the muon and dark matter can be simultaneously explained within the Minimal
Supersymmetric Standard Model. Dark matter is a mostly-bino state, with the
relic abundance obtained via co-annihilations with either the sleptons or wino.
The most interesting regions of parameter space will be tested by the next
generation of dark matter direct detection experiments.


  In this paper, we study the embedded topology of smooth plane quartics and
its bitangent lines via two-graphs and apply it to construct interesting
examples for Zariski $m$-ple.


  Understanding of short-term synaptic depression (STSD) and other forms of
synaptic plasticity is a topical problem in neuroscience. Here we study the
role of STSD in the formation of complex patterns of brain rhythms. We use a
cortical circuit model of neural networks composed of irregular spiking
excitatory and inhibitory neurons having type 1 and 2 excitability and
stochastic dynamics. In the model, neurons form a sparsely connected network
and their spontaneous activity is driven by random spikes representing synaptic
noise. Using simulations and analytical calculations, we found that if the STSD
is absent, the neural network shows either asynchronous behavior or regular
network oscillations depending on the noise level. In networks with STSD,
changing parameters of synaptic plasticity and the noise level, we observed
transitions to complex patters of collective activity: mixed-mode and spindle
oscillations, bursts of collective activity, and chaotic behaviour.
Interestingly, these patterns are stable in a certain range of the parameters
and separated by critical boundaries. Thus, the parameters of synaptic
plasticity can play a role of control parameters or switchers between different
network states. However, changes of the parameters caused by a disease may lead
to dramatic impairment of ongoing neural activity. We analyze the chaotic
neural activity by use of the 0-1 test for chaos (Gottwald, G. & Melbourne, I.,
2004) and show that it has a collective nature.


  Schur modules give the irreducible polynomial representations of the general
linear group $\mathrm{GL}_t$. Viewing the symmetric group $\mathfrak{S}_t$ as a
subgroup of $\mathrm{GL}_t$, we may restrict Schur modules to $\mathfrak{S}_t$
and decompose the result into a direct sum of Specht modules, the irreducible
representations of $\mathfrak{S}_t$. We give an equivariant M\"{o}bius
inversion formula that we use to invert this expansion in the representation
ring for $\mathfrak{S}_t$ for $t$ large. In addition to explicit formulas in
terms of plethysms, we show the coefficients that appear alternate in sign by
degree. In particular, this allows us to define a new basis of symmetric
functions whose structure constants are stable Kronecker coefficients and which
expand with alternating signs into the Schur basis.


  High-dimensional state trajectories of state-space models pose challenges for
Bayesian inference. Particle Gibbs (PG) methods have been widely used to sample
from the posterior of a state space model. Basically, particle Gibbs is a
Particle Markov Chain Monte Carlo (PMCMC) algorithm that mimics the Gibbs
sampler by drawing model parameters and states from their conditional
distributions.
  This tutorial provides an introductory view on Particle Gibbs (PG) method and
its extensions and variants, and illustrates through several examples of
inference in non-linear state space models (SSMs). We also implement PG
Samplers in two different programming languages: Python and Rust. Comparison of
run-time performance of Python and Rust programs are also provided for various
PG methods.


  Shell model calculations have been carried out using the m-scheme numerical
code {\small ANTOINE} in order to elucidate the particular trend of the
isoscalar matrix elements, $M_0$, for $A=4n+2$ isobaric triplets ranging from
A=18 to A=42. The 2${_1^+}_{(T=1)}$ $\to$ 0${_1^+}_{(T=1)}$ transition
energies, reduced transition probabilities and isoscalar matrix elements are
predicted to a high degree of accuracy. The general agreement of $M_0$ between
those from mirror pairs and those from $T_Z=0$ nuclides support our shell model
calculations. The predicted results tie together recent experimental data, and
the trend of $M_0$ strength along the $sd$ and beginning of the $fp$ shells is
interpreted in terms of the dynamic shell structure. Certain discrepancies
arise at A=18 and A=38 isobaric triplets, which might be explained in terms of
core polarization effects and the low occupancy of the orbits at the extremes
of the $sd$ shell.


  In 2011, Aaronson gave a striking proof, based on quantum linear optics,
showing that the problem of computing the permanent of a matrix is #P-hard.
Aaronson's proof led naturally to hardness of approximation results for the
permanent, and it was arguably simpler than Valiant's seminal proof of the same
fact in 1979. Nevertheless, it did not prove that computing the permanent was
#P-hard for any class of matrices which was not previously known. In this
paper, we present a collection of new results about matrix permanents that are
derived primarily via these linear optical techniques.
  First, we show that the problem of computing the permanent of a real
orthogonal matrix is #P-hard. Much like Aaronson's original proof, this will
show that even a multiplicative approximation remains #P-hard to compute. The
hardness result even translates to permanents over finite fields, where the
problem of computing the permanent of an orthogonal matrix is ModpP-hard in the
finite field F_{p^4} for all primes p not equal to 2 or 3. Interestingly, this
characterization is tight: in fields of characteristic 2, the permanent
coincides with the determinant; in fields of characteristic 3, one can
efficiently compute the permanent of an orthogonal matrix by a nontrivial
result of Kogan.
  Finally, we use more elementary arguments to prove #P-hardness for the
permanent of a positive semidefinite matrix, which shows that certain
probabilities of boson sampling experiments with thermal states are hard to
compute exactly despite the fact that they can be efficiently sampled by a
classical computer.


  In this write-up of my SQM 2006 Theory Summary talk I focus on a selection of
key contributions which I consider to have a large impact on the current status
of the field of strangeness physics or which may have the potential to
significantly advance strangeness -- or in general flavor physics -- in the
near future.


  Due to the limitations of sensors, the transmission medium and the intrinsic
properties of ultrasound, the quality of ultrasound imaging is always not
ideal, especially its low spatial resolution. To remedy this situation, deep
learning networks have been recently developed for ultrasound image
super-resolution (SR) because of the powerful approximation capability.
However, most current supervised SR methods are not suitable for ultrasound
medical images because the medical image samples are always rare, and usually,
there are no low-resolution (LR) and high-resolution (HR) training pairs in
reality. In this work, based on self-supervision and cycle generative
adversarial network (CycleGAN), we propose a new perception consistency
ultrasound image super-resolution (SR) method, which only requires the LR
ultrasound data and can ensure the re-degenerated image of the generated SR one
to be consistent with the original LR image, and vice versa. We first generate
the HR fathers and the LR sons of the test ultrasound LR image through image
enhancement, and then make full use of the cycle loss of LR-SR-LR and HR-LR-SR
and the adversarial characteristics of the discriminator to promote the
generator to produce better perceptually consistent SR results. The evaluation
of PSNR/IFC/SSIM, inference efficiency and visual effects under the benchmark
CCA-US and CCA-US datasets illustrate our proposed approach is effective and
superior to other state-of-the-art methods.


  Montmorillonite-based barriers are key elements of the engineered barrier
systems (EBS) in geological disposal facilities (GDF). Their performance at
temperatures above 100 {\deg}C is not sufficiently understood to assess the
possibility of raising the temperature limits in GDF designs that could reduce
construction costs and CO2 footprint. The present work provides new fundamental
insights through molecular dynamics (MD) simulations of Na-montmorillonite's
water-clay interactions and swelling pressure at temperatures 298-500 K and
basal spacings of 1.5-3.5 nm. At temperatures above 100 {\deg}C, the swelling
behaviour is governed by the attractive van der Waals force and the repulsive
hydration force instead of the repulsive electrostatic (double layer) force.
The swelling pressure reduction with increasing temperature is related to the
weakened hydration repulsion and electric double layer repulsion, which result
from the deterioration of the interlayer water layer structure and the
shrinkage of the electric double layer. The applicability and breakdown of the
classic Derjaguin-Landau-Verwey-Overbeek (DLVO) theory at elevated temperatures
are examined. By excluding the osmotic contribution in the DLVO theory, the
summation of the van der Waals interaction in DLVO and an additional non-DLVO
hydration interaction can predict our MD system's swelling under high
temperatures. The findings of this study provide a fundamental understanding of
the swelling behaviour and the underlying molecular-level mechanisms of the
clay microstructure under extreme conditions.


  We report on the low-temperature ($T <$ 120 K) thermal expansion of the
bismuth-based topological semimetal $\beta$-PtBi$_2$ and topological
superconductor $\beta$-Bi$_2$Pd candidates. The linear thermal-expansion
coefficient of tetragonal $\beta$-Bi$_2$Pd shows a pronounced anisotropy
between the $a$- and $c$-axis while the volume thermal-expansion coefficient
$\alpha_V$($\beta$-Bi$_2$Pd) is considerable larger than
$\alpha_V$($\beta$-PtBi$_2$). The coefficient $\alpha_V$($\beta$-PtBi$_2$)
nearly matches the experimental specific heat, from which a Debye temperature
$\theta_D =$ 199 K is obtained. On the other hand, $\alpha_V$($\beta$-Bi$_2$Pd)
reasonably fits the Debye model with $\theta_D =$ 138 K, extracted from the
low-temperature specific heat. An almost constant Gr\"uneisen parameter $\Gamma
\approx$ 2 is obtained for both compounds. No magnetostriction is observed in
any of both compounds up to $\mu_0 H =$ 16 T. We compare our results with other
Bi-based topological materials.


  In this article, we provide explicit bounds for the prime counting function
$\theta(x)$ in all ranges of $x$. The bounds for the error term for $\theta
(x)- x$ are of the shape $\epsilon x$ and $\frac{c_k x}{(\log x)^k}$, for
$k=1,\ldots,5$. Tables of values for $\epsilon$ and $c_k$ are provided.


  We consider the problem of estimating the parameters a Gaussian Mixture Model
with K components of known weights, all with an identity covariance matrix. We
make two contributions. First, at the population level, we present a sharper
analysis of the local convergence of EM and gradient EM, compared to previous
works. Assuming a separation of $\Omega(\sqrt{\log K})$, we prove convergence
of both methods to the global optima from an initialization region larger than
those of previous works. Specifically, the initial guess of each component can
be as far as (almost) half its distance to the nearest Gaussian. This is
essentially the largest possible contraction region. Our second contribution
are improved sample size requirements for accurate estimation by EM and
gradient EM. In previous works, the required number of samples had a quadratic
dependence on the maximal separation between the K components, and the
resulting error estimate increased linearly with this maximal separation. In
this manuscript we show that both quantities depend only logarithmically on the
maximal separation.


  The vortex in the $(2+1)$-dimensional $\mathrm{O}(2)$ model is studied via
numerical simulations in a fully non-perturbative lattice regularization. We
compute the vortex condensate and susceptibility to determine its critical
exponents and a renormalized condensate in the continuum limit. Together with
recent results on the vortex mass, this gives a complete picture of the scaling
behaviour of the vortex operator in this model and sheds light on the
statistical mechanics of topological excitations.


  Charge Coupled Devices (CCD) are used for reactor neutrino experiments and
already shown their potential in constraining new physics models. The prospect
of a Skipper-CCD experiment looking for standard and beyond standard model
physics (BSM) in a nuclear reactor has been recently evaluated for different
benchmark scenarios. Here we report the installation of the first 2 g
Skipper-CCD inside the containment building of a 2 GW$_{th}$ nuclear power
plant, positioned 12 meters from the center of the reactor core. We discuss the
challenges involved in the commissioning of the detector and present data
acquired during reactor ON and reactor OFF periods, with the detector operating
with a sub-electron readout noise of 0.17 e-. The ongoing efforts to improve
sensitivities to CEvNS and BSM interaction are also discussed.


  Markov Chain Monte Carlo (MCMC) methods are employed to sample from a given
distribution of interest, whenever either the distribution does not exist in
closed form, or, if it does, no efficient method to simulate an independent
sample from it is available. Although a wealth of diagnostic tools for
convergence assessment of MCMC methods have been proposed in the last two
decades, the search for a dependable and easy to implement tool is ongoing. We
present in this article a criterion based on the principle of detailed balance
which provides a qualitative assessment of the convergence of a given chain.
The criterion is based on the behaviour of a one-dimensional statistic, whose
asymptotic distribution under the assumption of stationarity is derived; our
results apply under weak conditions and have the advantage of being completely
intuitive. We implement this criterion as a stopping rule for simulated
annealing in the problem of finding maximum likelihood estimators for
parameters of a 20-component mixture model. We also apply it to the problem of
sampling from a 10-dimensional funnel distribution via slice sampling and the
Metropolis-Hastings algorithm. Furthermore, based on this convergence criterion
we define a measure of efficiency of one algorithm versus another.


  We investigate the possibility to explain the MiniBooNE anomaly by CPT and
Lorentz symmetry violating neutrino-antineutrino oscillations in a two
generation framework. We work with four non-zero CPT-violating parameters that
allow for resonant enhancements in neutrino-antineutrino oscillation phenomena
in vacuo which are suitably described in terms of charge conjugation
eigenstates of the system. We study the relation between the flavor, charge
conjugation and mass eigenbasis of neutrino-antineutrino oscillations and
examine the interplay between the available CPT-violating parameter space and
possible resonance structures.


  The deformable and continuum nature of soft robots promises versatility and
adaptability. However, control of modular, multi-limbed soft robots for
terrestrial locomotion is challenging due to the complex robot structure,
actuator mechanics and robot-environment interaction. Traditionally, soft robot
control is performed by modeling kinematics using exact geometric equations and
finite element analysis. The research presents an alternative, model-free,
data-driven, reinforcement learning inspired approach, for controlling
multi-limbed soft material robots. This control approach can be summarized as a
four-step process of discretization, visualization, learning and optimization.
The first step involves identification and subsequent discretization of key
factors that dominate robot-environment, in turn, the robot control. Graph
theory is used to visualize relationships and transitions between the
discretized states. The graph representation facilitates mathematical
definition of periodic control patterns (simple cycles) and locomotion gaits.
Rewards corresponding to individual arcs of the graph are weighted displacement
and orientation change for robot state-to-state transitions. These rewards are
specific to surface of locomotion and are learned. Finally, the control
patterns result from optimization of reward dependent locomotion task (e.g.
translation) cost function. The optimization problem is an Integer Linear
Programming problem which can be quickly solved using standard solvers. The
framework is generic and independent of type of actuator, soft material
properties or the type of friction mechanism, as the control exists in the
robot's task space. Furthermore, the data-driven nature of the framework
imparts adaptability to the framework toward different locomotion surfaces by
re-learning rewards.


  The unusually narrow features in the fluorescence from Rubidium-85 driven by
cooling and repumper laser fields, reported in an earlier experiment [1] are
explained on the basis of a four-level density matrix calculation. Quantum
effects alter the efficiency of atom transfer by the probe (repumper) laser to
the levels connected by the pump (cooling) laser. This combined with the double
resonance condition [1], results in velocity selection from co-propagating and
counter propagating probe and pump beams resulting in narrow fluorescence peaks
from a thermal gas at room temperature.


  Prompt learning has been widely adopted to efficiently adapt vision-language
models (VLMs), e.g. CLIP, for few-shot image classification. Despite their
success, most prompt learning methods trade-off between classification accuracy
and robustness, e.g. in domain generalization or out-of-distribution (OOD)
detection. In this work, we introduce Global-Local Prompts (GalLoP), a new
prompt learning method that learns multiple diverse prompts leveraging both
global and local visual features. The training of the local prompts relies on
local features with an enhanced vision-text alignment. To focus only on
pertinent features, this local alignment is coupled with a sparsity strategy in
the selection of the local features. We enforce diversity on the set of prompts
using a new ``prompt dropout'' technique and a multiscale strategy on the local
prompts. GalLoP outperforms previous prompt learning methods on accuracy on
eleven datasets in different few shots settings and with various backbones.
Furthermore, GalLoP shows strong robustness performances in both domain
generalization and OOD detection, even outperforming dedicated OOD detection
methods. Code and instructions to reproduce our results will be open-sourced.


  In an experiment in the Institute of Continuous Media Mechanics in Perm
(Russia) an non--stationary screw dynamo is intended to be realized with a
helical flow of liquid sodium in a torus. The flow is necessarily turbulent,
that is, may be considered as a mean flow and a superimposed turbulence. In
this paper the induction processes of the turbulence are investigated within
the framework of mean--field electrodynamics. They imply of course a part which
leads to an enhanced dissipation of the mean magnetic field. As a consequence
of the helical mean flow there are also helical structures in the turbulence.
They lead to some kind of $\alpha$--effect, which might basically support the
screw dynamo. The peculiarity of this $\alpha$--effect explains measurements
made at a smaller version of the device envisaged for the dynamo experiment.
The helical structures of the turbulence lead also to other effects, which in
combination with a rotational shear are potentially capable of dynamo action. A
part of them can basically support the screw dynamo. Under the conditions of
the experiment all induction effects of the turbulence prove to be rather weak
in comparison to that of the main flow. Numerical solutions of the mean--field
induction equation show that all the induction effects of the turbulence
together let the screw dynamo threshold slightly, at most by one per cent,
rise. The numerical results give also some insights into the action of the
individual induction effects of the turbulence.


  We prove that the relation $E_0(\rho, W^{-}) + E_0(\rho, W^{+}) \geq 2
E_0(\rho, W)$ holds for any binary input discrete memoryless channel $W$, and
$\rho \geq 0$.


  A discussion is given on the interpretation and physical importance of the
Minkowski momentum in macroscopic electrodynamics (essential for the
Abraham-Minkowski problem). We focus on the following two facets: (1) Adopting
a simple dielectric model where the refractive index $n$ is constant, we
demonstrate by means of a mapping procedure how the electromagnetic field in a
medium can be mapped into a corresponding field in vacuum. This mapping was
presented many years ago [I. Brevik and B. Lautrup, Mat. Fys. Medd. Dan. Vid.
Selsk {\bf 38}(1), 1 (1970)], but is apparently not well known. A
characteristic property of this procedure is that it shows how natural the
Minkowski energy-momentum tensor fits into the canonical formalism. Especially
the spacelike character of the electromagnetic total four-momentum for a
radiation field (implying negative electromagnetic energy in some inertial
frames), so strikingly demonstrated in the Cherenkov effect, is worth
attention. (2) Our second objective is to give a critical analysis of some
recent experiments on electromagnetic momentum. Care must here be taken in the
interpretations: it is easy to be misled and conclude that an experiment is
important for the energy-momentum problem, while what is demonstrated
experimentally is merely the action of the Abraham-Minkowski force acting in
surface layers or inhomogeneous regions. The Abraham-Minkowski force is common
for the two energy-momentum tensors and carries no information about field
momentum. As a final item, we propose an experiment that might show the
existence of the Abraham force at high frequencies. This would eventually be a
welcome optical analogue to the classic low-frequency 1975 Lahoz-Walker
experiment.


  The similarity between the distributions of spins for galaxies ($\lambda_{\rm
g}$) and for dark-matter haloes ($\lambda_{\rm h}$), indicated both by
simulations and observations, is naively interpreted as a one-to-one
correlation between the spins of a galaxy and its host halo. This is used to
predict galaxy sizes in semi-analytic models via $R_{\rm e}\simeq\lambda_{\rm
h} R_{\rm v}$, with $R_{\rm e}$ the half-mass radius of the galaxy and $R_{\rm
v}$ the halo radius. Utilizing two different suites of zoom-in cosmological
simulations, we find that $\lambda_{\rm g}$ and $\lambda_{\rm h}$ are in fact
only barely correlated, especially at $z\geq 1$. A general smearing of this
correlation is expected based on the different spin histories, where the more
recently accreted baryons through streams gain and then lose significant
angular momentum compared to the gradually accumulated dark matter. Expecting
the spins of baryons and dark matter to be correlated at accretion into $R_{\rm
v}$, the null correlation at the end reflects an anti-correlation between
$\lambda_{\rm g}/\lambda_{\rm h}$ and $\lambda_{\rm h}$, which can partly arise
from mergers and a compact star-forming phase that many galaxies undergo. On
the other hand, the halo and galaxy spin vectors tend to be aligned, with a
median $\cos\theta=0.6$-0.7 between galaxy and halo, consistent with
instreaming within a preferred plane. The galaxy spin is better correlated with
the spin of the inner halo, but this largely reflects the effect of the baryons
on the halo. Following the null spin correlation, $\lambda_{\rm h}$ is not a
useful proxy for $R_{\rm e}$. While our simulations reproduce a general
relation of the sort $R_{\rm e}=AR_{\rm vir}$, in agreement with observational
estimates, the relation becomes tighter with $A=0.02(c/10)^{-0.7}$, where $c$
is the halo concentration, which in turn introduces a dependence on mass and
redshift.


  In this report we investigate fundamental requirements for the application of
classifier patching on neural networks. Neural network patching is an approach
for adapting neural network models to handle concept drift in nonstationary
environments. Instead of creating or updating the existing network to
accommodate concept drift, neural network patching leverages the inner layers
of the network as well as its output to learn a patch that enhances the
classification and corrects errors caused by the drift. It learns (i) a
predictor that estimates whether the original network will misclassify an
instance, and (ii) a patching network that fixes the misclassification. Neural
network patching is based on the idea that the original network can still
classify a majority of instances well, and that the inner feature
representations encoded in the deep network aid the classifier to cope with
unseen or changed inputs. In order to apply this kind of patching, we evaluate
different engagement layers and patch architectures in this report, and find a
set of generally applicable heuristics, which aid in parametrizing the patching
procedure.


  We constrain the quasar contribution to cosmic reionization based on our deep
optical survey of z~6 quasars down to z_R=24.15 using Subaru/Suprime-Cam in
three UKIDSS-DXS fields covering 6.5 deg^2. In Kashikawa et al. (2015), we
select 17 quasar candidates and report our initial discovery of two
low-luminosity quasars (M_1450~ -23) from seven targets, one of which might be
a Lyman alpha emitting galaxy. From an additional optical spectroscopy, none of
the four candidates out of the remaining ten turn out to be genuine quasars.
Moreover, the deeper optical photometry provided by the Hyper Suprime-Cam
Subaru Strategic Program (HSC-SSP) shows that, unlike the two already-known
quasars, the i-z and z-y colors of the last six candidates are consistent with
M- or L-type brown dwarfs. Therefore, the quasar luminosity function (QLF) in
the previous paper is confirmed. Compiling QLF measurements from the literature
over a wide magnitude range, including an extremely faint AGN candidate from
Parsa et al. (2017}, to fit them with a double power-law, we find that the
best-fit faint-end slope is alpha=-2.04^+0.33_-0.18 (-1.98^+0.48_-0.21) and
characteristic magnitude is M_1450^*=-25.8^+1.1_-1.9 (-25.7^+1.0_-1.8) in the
case of two (one) quasar detection. Our result suggests that, if the QLF is
integrated down to M_1450=-18, quasars produce ~1-12% of the ionizing photons
required to ionize the whole universe at z~6 with 2sigma confidence level,
assuming that the escape fraction is f_esc=1 and the IGM clumpy factor is C=3.
Even when the systematic uncertainties are taken into account, our result
supports the scenario that quasars are the minor contributors of reionization.


  Datasets are essential to apply AI algorithms to Cyber Physical System (CPS)
Security. Due to scarcity of real CPS datasets, researchers elected to generate
their own datasets using either real or virtualized testbeds. However, unlike
other AI domains, a CPS is a complex system with many interfaces that determine
its behavior. A dataset that comprises merely a collection of sensor
measurements and network traffic may not be sufficient to develop resilient AI
defensive or offensive agents. In this paper, we study the \emph{elements} of
CPS security datasets required to capture the system behavior and interactions,
and propose a dataset architecture that has the potential to enhance the
performance of AI algorithms in securing cyber physical systems. The framework
includes dataset elements, attack representation, and required dataset
features. We compare existing datasets to the proposed architecture to identify
the current limitations and discuss the future of CPS dataset generation using
testbeds.


  In the standard geometric approach to a measure of entanglement of a pure
state, $\sin^2\theta$ is used, where $\theta$ is the angle between the state to
the closest separable state of products of normalized qubit states. We consider
here a generalization of this notion to separable states consisting of products
of unnormalized states of different dimension. In so doing, the entanglement
measure $\sin^2\theta$ is found to have an interpretation as the distance
between the state to the closest separable state. We also find the components
of the closest separable state and its norm have an interpretation in terms of,
respectively, the eigenvectors and eigenvalues of the reduced density matrices
arising in the Schmidt decomposition of the state vector.


  Neutron stars and white dwarfs are both dense remnants of post-main-sequence
stars. Pulsars, magnetars and strongly magnetised white dwarfs have all been
seen to been observed to exhibit coherent, pulsed radio emission in relation to
their rotational period. Recently, a new type of radio long period transient
(LPT) has been discovered. The bright radio emission of LPTs resembles that of
radio pulsars and magnetars. However, they pulse on timescales (minutes) much
longer than previously seen. While minute timescales are common rotation
periods for white dwarfs, LPTs are much brighter than the known pulsating white
dwarfs, and dipolar radiation from isolated (as opposed to binary) magnetic
white dwarfs has yet to be observed. Here, we report the discovery of a new
$\sim$421~s LPT, CHIME J0630+25, using the CHIME/FRB and CHIME/Pulsar
instruments. We used standard pulsar timing techniques and obtained a
phase-coherent timing solution which yielded limits on the inferred magnetic
field and characteristic age. CHIME J0630+25 is remarkably nearby ($170 \pm
80$~pc), making it the closest LPT discovered to date.


  We show that universal rogue wave patterns exist in integrable systems. These
rogue patterns comprise fundamental rogue waves arranged in shapes such as
triangle, pentagon and heptagon, with a possible lower-order rogue wave at the
center. These patterns appear when one of the internal parameters in bilinear
expressions of rogue waves gets large. Analytically, these patterns are
determined by the root structures of the Yablonskii-Vorob'ev polynomial
hierarchy through a linear transformation. Thus, the induced rogue patterns in
the space-time plane are simply the root structures of Yablonskii-Vorob'ev
polynomials under actions such as dilation, rotation, stretch, shear and
translation. As examples, these universal rogue patterns are explicitly
determined and graphically illustrated for the generalized derivative nonlinear
Schr\"odinger equations, the Boussinesq equation, and the Manakov system.
Similarities and differences between these rogue patterns and those reported
earlier in the nonlinear Schr\"odinger equation are discussed.


  Here we report the fabrication and quantum transport measurements of gates
controlled parallel-coupled double quantum dot on both bilayer and single layer
graphene. It is shown that the interdot coupling strength of the parallel
double dots can be effectively tuned from weak to strong regime by both the
in-plane plunger gates and back gate. All the relevant energy scales and
parameters of the graphene parallel-coupled double dot can be extracted from
the honeycomb charge stability diagrams revealed through the transport
measurements.


  We show how to efficiently solve problems involving a quantitative measure,
here called energy, as well as a qualitative acceptance condition, expressed as
a B\"uchi or Parity objective, in finite weighted automata and in one-clock
weighted timed automata. Solving the former problem and extracting the
corresponding witness is our main contribution and is handled by a modified
version of the Bellman-Ford algorithm interleaved with Couvreur's algorithm.
The latter problem is handled via a reduction to the former relying on the
corner-point abstraction. All our algorithms are freely available and
implemented in a tool based on the open-source platforms TChecker and~Spot.


  We present the results of a spatially-resolved spectroscopic analysis of the
galaxy cluster Abell 496 with the S3 chip on-board the Chandra satellite. We
confirm the presence of a central positive temperature gradient consistent with
a cooling flow, but with a minimum gas temperature of ~0.5-0.9 keV. The cluster
also exhibits sharp edges in gas density and temperature which are consistent
with "cold front" substructures. The iron abundance profile is not radially
symmetric relative to the cluster center. Towards the direction of the most
prominent (northerly) cold front, the iron abundance is roughly flat, with
nearly solar values. In the opposite (southerly) direction from the center, the
iron abundance distribution shows an "off-center" peak. Various abundance
ratios suggest that the heavy elements in the central regions of the cluster
are dominated by SN Ia ejecta. However, for radii greater than 100 kpc, the
abundance ratios vary in such a way that different abundance ratios provide
very different estimates of the proportion of SN Ia/II ejecta. Nonetheless,
observed abundances and abundance ratios are continuous across the cold fronts,
which suggests that the cold fronts are not likely to be the result of a
subcluster merger. We suggest instead that the cold fronts in A496 are caused
by "sloshing" of the central cooling flow gas, induced by the motion of the cD
about the cluster center.


  We study low-energy nucleon Compton scattering in the framework of baryon
chiral perturbation theory (B$\chi$PT) with pion, nucleon, and $\Delta$(1232)
degrees of freedom, up to and including the next-to-next-to-leading order
(NNLO). We include the effects of order $p^2$, $p^3$ and $p^4/\varDelta$, with
$\varDelta\approx 300$ MeV the $\Delta$-resonance excitation energy. These are
all "predictive" powers in the sense that no unknown low-energy constants enter
until at least one order higher (i.e, $p^4$). Estimating the theoretical
uncertainty on the basis of natural size for $p^4$ effects, we find that
uncertainty of such a NNLO result is comparable to the uncertainty of the
present experimental data for low-energy Compton scattering. We find an
excellent agreement with the experimental cross section data up to at least the
pion-production threshold. Nevertheless, for the proton's magnetic
polarizability we obtain a value of $(4.0\pm 0.7)\times 10^{-4}$ fm$^3$, in
significant disagreement with the current PDG value. Unlike the previous
$\chi$PT studies of Compton scattering, we perform the calculations in a
manifestly Lorentz-covariant fashion, refraining from the heavy-baryon (HB)
expansion. The difference between the lowest order HB$\chi$PT and B$\chi$PT
results for polarizabilities is found to be appreciable. We discuss the chiral
behavior of proton polarizabilities in both HB$\chi$PT and B$\chi$PT with the
hope to confront it with lattice QCD calculations in a near future. In studying
some of the polarized observables, we identify the regime where their naive
low-energy expansion begins to break down, thus addressing the forthcoming
precision measurements at the HIGS facility.


  We point out that the presence of energetic cosmic rays above the GZK cutoff
may be explained by fundamental non-linearities in quantum mechanics at the
Plank level.


  Asteroid (596) Scheila was the first object for which the immediate aftermath
of an inter-asteroidal collision was observed. In Dec. 2010, the 113 km-sized
asteroid was impacted by a smaller asteroid of less than 100 m in diameter. The
scale of the impactor was established by observations of fading ejecta plumes.
Comparison of the light curves obtained before and after the impact allowed us
to assess how much of Scheila's surface was altered. Cratering physics based on
the impactor size suggests that the size of the affected area is larger than
expected, (effective radii of 3.5 -- 10 km depending on the change in the
albedo of the surface). Similar but more localized albedo changes have been
observed on Vesta and the Martian moons, but are not understood. Empirical laws
describing ejecta blankets however indicate that at distances between 3.5 -- 10
km from the crater, Scheila's surface would be covered by a thin layer 2 mm to
2 cm thick. This dusting, possibly mixed with bright impactor material may be
enough to explain to observed brightness increase. Our results show that
sub-critical impacts may play a significant role in processing the surfaces of
asteroids. The large effect of small impacts on asteroidal light curves
complicate shape modeling.


  The afterglow emission from a spreading jet expanding in a circumstellar
cloud is discussed. Prompt X-ray radiation and a strong UV flash from the
reverse shock produced by the interaction of the jet with the cloud may destroy
and clear the dust out to about 30 pc within the initial solid angle of the
jet. As the sideways expansion of the jet becomes significant, most of the
optical radiation from the high-latitude part of the jet may be absorbed by the
dust outside the initial solid angle of the jet, but only the radiation from
the part within the initial solid angle can be observed. We analytically show
that the flux of the observational radiation decays as $\propto t^{-(p+1)}$
(where $p$ is the power-law index of the electron distribution) in the
relativistic phase. This preliminary result motivates us to perform numerical
calculations. Our results show that one break in the optical afterglow ligh
curve extends over a factor of $\sim 3$ in time rather than one decade in time
in the previous jet model. These results may provide a way to judge whether
GRBs locate in dense clouds or not. Finally, we carry out a detailed modelling
for the R-band afterglow of GRB 000926.


  Resource-scale quantification of energy in water supplies is a basis for
regional-scale governance of water systems and national- and global-scale
assessments of the "water-energy-emission" nexus. But a physically based
approach for this quantification remains lacking. Here, applying recently
emerged complex system principles, we develop "scaling analysis" (SA), a
complex network-based methodology for quantifying energies and energy-nexus
properties in water supplies at the resource or regional scale. Conceiving
water supply systems as networks embedded in regionally self-organizing complex
systems, SA explores the complex system laws of skewed or scaled size abundance
(decreasing system abundance with increasing system size) and allometric energy
scaling (decreasing system energy intensity with increasing system size) as
unifying formulation for profiling system size distribution and predicting
system energies from system sizes. Overcoming challenges facing traditional
approaches from transscale and unbounded system size distributions and scarcity
of energy data, SA represents physically based, data-driven predictions. We
empirically demonstrate SA and test its predictions for the water supply
systems of the Great Lakes, one of the world's most geographically expansive
water resources.


  We present a mechanism to produce indistinguishable single-photon pulses on
demand from an optical cavity. The sequences of two laser pulses generate, at
the two Raman transitions of a four-level atom, the same cavity-mode photons
without repumping of the atom between photon generations. Photons are emitted
from the cavity with near-unit efficiency in well-defined temporal modes of
identical shapes controlled by the laser fields. The second order correlation
function reveals the single-photon nature of the proposed source. A realistic
setup for the experimental implementation is presented.


  The edit distance between two graphs on the same labeled vertex set is the
size of the symmetric difference of the edge sets. The distance between a
graph, $G$, and a hereditary property, ${\cal H}$, is the minimum of the
distance between $G$ and each $G'\in{\cal H}$. The edit distance function of
${\cal H}$ is a function of $p\in[0,1]$ and is the limit of the maximum
normalized distance between a graph of density $p$ and ${\cal H}$.
  This paper develops a method, called localization, for computing the edit
distance function of various hereditary properties. For any graph $H$, ${\rm
Forb}(H)$ denotes the property of not having an induced copy of $H$. This paper
gives some results regarding estimation of the function for an arbitrary
hereditary property. This paper also gives the edit distance function for ${\rm
Forb}(H)$, where $H$ is a cycle on 9 or fewer vertices.


  We prove the birational superrigidity and the nonrationality of a cyclic
triple cover of $\mathbb{P}^{2n}$ branched over a nodal hypersurface of degree
$3n$ for $n\ge 2$. In particular, the obtained result solves the problem of the
birational superrigidity of smooth cyclic triple spaces. We also consider
certain relevant problems.


  Colon cancer is a prevalent gastrointestinal malignancy arising in the colon.
Ulcerative colitis(UC) is one of the risk factors of colorectal cancer. The
detection of under-expressed biomarkers and molecular mechanisms in UC and
colon cancer can lead to effective management of colitis-associated cancer. A
total of two mRNA expression datasets (GSE87473 and GSE44076) were downloaded
from the Gene Expression Omnibus (GEO) database. GEO2R was used to screen
differentially expressed genes (DEGs) between extensive ulcerative colitis
samples and healthy samples, limited ulcerative colitis samples and healthy
samples, and colon cancer samples and healthy samples. In extensive ulcerative
colitis, limited ulcerative colitis and colon cancer groups, 95,69 and 635
under-expressed genes with adjusted p-value<0.05 and log(2) fold change<-2 were
detected respectively. Using Cytoscape software, the genes with degree> 15
including CLCA1, SLC26A3, SI, KIT, HPGDS, NR1H4, ADIPOQ, PPARGC1A, GCG, MS4A12,
GUCA2A and FABP1 were screened as hub under-expressed genes in colon cancer. In
extensive ulcerative colitis, the genes with degree>5 including ABCB1, ABCG2,
UGT1A6, CYP2B6 and AQP8 were identified as hub genes. Moreover, the genes
including NR1H4, CYP2B6, ABCB1, ABCG2, UGT2A3 and PLA2G12B were detected as hub
genes with degree>5 in limited ulcerative colitis. According to inclusion
criteria and venn diagram, the downregulated gene NR1H4 was common gene in
limited ulcerative colitis and colon cancer. The current in silico study showed
that downregulation of CLCA1, PPARGC1A and AQP8 genes may increase cancer cell
invasion and metastasis ability.


  We prove the existence of the total length process for the genealogical tree
of a population model with random size given by a quadratic stationary
continuous-state branching processes. We also give, for the one-dimensional
marginal, its Laplace transform as well as the fluctuation of the corresponding
convergence. This result is to be compared with the one obtained by
Pfaffelhuber and Wakolbinger for constant size population associated to the
Kingma coalescent. We also give a time reversal property of the number of
ancestors process at all time, and give a description of the so-called lineage
tree in this model.


  Timed model checking has been extensively researched in recent years. Many
new formalisms with time extensions and tools based on them have been
presented. On the other hand, Explicit-Time Description Methods aim to verify
real-time systems with general untimed model checkers. Lamport presented an
explicit-time description method using a clock-ticking process (Tick) to
simulate the passage of time together with a group of global variables for time
requirements. This paper proposes a new explicit-time description method with
no reliance on global variables. Instead, it uses rendezvous synchronization
steps between the Tick process and each system process to simulate time. This
new method achieves better modularity and facilitates usage of more complex
timing constraints. The two explicit-time description methods are implemented
in DIVINE, a well-known distributed-memory model checker. Preliminary
experiment results show that our new method, with better modularity, is
comparable to Lamport's method with respect to time and memory efficiency.


  We prove internal controllability in arbitrary time, for small data, for
quasi-linear Hamiltonian NLS equations on the circle. We use a procedure of
reduction to constant coefficients up to order zero and HUM method to prove the
controllability of the linearized problem. Then we apply a
Nash-Moser-H\"ormander implicit function theorem as a black box.


  Be stars are main-sequence massive stars with emission features in their
spectrum, which originates in circumstellar gaseous discs. Even though the
viscous decretion disc (VDD) model can satisfactorily explain most
observations, two important physical ingredients, namely the magnitude of the
viscosity ($\alpha$) and the disk mass injection rate, remain poorly
constrained. The light curves of Be stars that undergo events of disc formation
and dissipation offer an opportunity to constrain these quantities. A pipeline
was developed to model these events that uses a grid of synthetic light curves,
computed from coupled hydrodynamic and radiative transfer calculations. A
sample of 54 Be stars from the OGLE survey of the Small Magellanic Cloud (SMC)
was selected for this study. Because of the way our sample was selected (bright
stars with clear disc events), it likely represents the densest discs in the
SMC. Like their siblings in the Galaxy, the mass of the disc in the SMC
increases with the stellar mass. The typical mass and angular momentum loss
rates associated with the disk events are of the order of $\sim$$10^{-10}\,
M_\odot\,\mathrm{yr^{-1}}$ and $\sim$$5\times 10^{36}\, \mathrm{g\, cm^{2}\,
s^{-2}}$, respectively. The values of $\alpha$ found in this work are typically
of a few tenths, consistent with recent results in the literature and with the
ones found in dwarf novae, but larger than current theory predicts. Considering
the sample as a whole, the viscosity parameter is roughly two times larger at
build-up ($\left\langle\alpha_\mathrm{bu}\right\rangle = 0.63$) than at
dissipation ($\left\langle\alpha_\mathrm{d}\right\rangle = 0.26$). Further work
is necessary to verify whether this trend is real or a result of some of the
model assumptions.


  We prove and extend a conjecture of Kempe, Kleinberg, and Tardos (KKT) on the
spread of influence in social networks. A social network can be represented by
a directed graph where the nodes are individuals and the edges indicate a form
of social relationship. A simple way to model the diffusion of ideas,
innovative behavior, or ``word-of-mouth'' effects on such a graph is to
consider an increasing process of ``infected'' (or active) nodes: each node
becomes infected once an activation function of the set of its infected
neighbors crosses a certain threshold value. Such a model was introduced by KKT
in \cite{KeKlTa:03,KeKlTa:05} where the authors also impose several natural
assumptions: the threshold values are (uniformly) random; and the activation
functions are monotone and submodular. For an initial set of active nodes $S$,
let $\sigma(S)$ denote the expected number of active nodes at termination. Here
we prove a conjecture of KKT: we show that the function $\sigma(S)$ is
submodular under the assumptions above. We prove the same result for the
expected value of any monotone, submodular function of the set of active nodes
at termination.


  A key feature of collaboration in science and software development is to have
a {\em log} of what and how is being done - for private use and reuse and for
sharing selected parts with collaborators, which most often today are
distributed geographically on an ever larger scale. Even better if this log is
{\em automatic}, created on the fly while a scientist or software developer is
working in a habitual way, without the need for extra efforts. The {\tt CAVES}
and {\tt CODESH} projects address this problem in a novel way, building on the
concepts of {\em virtual state} and {\em virtual transition} to provide an
automatic persistent logbook for sessions of data analysis or software
development in a collaborating group. A repository of sessions can be
configured dynamically to record and make available the knowledge accumulated
in the course of a scientific or software endeavor. Access can be controlled to
define logbooks of private sessions and sessions shared within or between
collaborating groups.


  We study a scenario that large non-Gaussianity arises from the baryon
asymmetry of the Universe. There are baryogenesis scenarios containing a light
scalar field, which may result in baryonic isocurvature perturbations with some
amount of non-Gaussianity. As an explicit example we consider the Affleck-Dine
mechanism and show that a flat direction of the supersymmeteric standard model
can generate large non-Gaussianity in the curvature perturbations, satisfying
the observational constraints on the baryonic isocurvature perturbations. The
sign of a non-linearity parameter, f_{NL}, is negative, if the Affleck-Dine
mechanism accounts for the observed baryon asymmetry; otherwise it can be
either positive or negative.


  In this paper,we prove the following Myers-type theorem: if $(M^n,g)$, $n\geq
3$, is an n-dimensional complete locally conformally flat Riemannian manifold
with bounded Ricci curvature satisfying the Ricci pinching condition $Rc\geq
\epsilon Rg>0$, where $\epsilon>0$ is an uniform constant, then $M^n$ must be
compact.


  In this paper, we study a class of fractional optimal control problems. A
necessary condition for the existence of an optimal control is provided in the
literature. It is commonly given as the existence of a solution of a fractional
Pontryagin's system and the proof is based on the introduction of a Lagrange
multiplier. Assuming an additional condition on these problems, we suggest a
new presentation of this result with a proof using only classical mathematical
tools adapted to the fractional case: calculus of variations, Gronwall's Lemma,
Cauchy-Lipschitz Theorem and stability under perturbations of differential
equations. In this paper, we furthermore provide a way in order to transit from
a classical optimal control problem to its fractional version via the
Stanislavsky's formalism. We also solve a strict fractional example allowing to
test numerical schemes. Finally, we state a fractional Noether's theorem giving
the existence of an explicit constant of motion for fractional Pontryagin's
systems admitting a symmetry.


  In computational design and fabrication, neural networks are becoming
important surrogates for bulky forward simulations. A long-standing,
intertwined question is that of inverse design: how to compute a design that
satisfies a desired target performance? Here, we show that the piecewise linear
property, very common in everyday neural networks, allows for an inverse design
formulation based on mixed-integer linear programming. Our mixed-integer
inverse design uncovers globally optimal or near optimal solutions in a
principled manner. Furthermore, our method significantly facilitates emerging,
but challenging, combinatorial inverse design tasks, such as material
selection. For problems where finding the optimal solution is not desirable or
tractable, we develop an efficient yet near-optimal hybrid optimization.
Eventually, our method is able to find solutions provably robust to possible
fabrication perturbations among multiple designs with similar performances.


  Domain adaptation is an effective solution to data scarcity in low-resource
scenarios. However, when applied to token-level tasks such as bioNER, domain
adaptation methods often suffer from the challenging linguistic characteristics
that clinical narratives possess, which leads to unsatisfactory performance. In
this paper, we present a simple yet effective hardness-guided domain adaptation
(HGDA) framework for bioNER tasks that can effectively leverage the domain
hardness information to improve the adaptability of the learnt model in
low-resource scenarios. Experimental results on biomedical datasets show that
our model can achieve significant performance improvement over the recently
published state-of-the-art (SOTA) MetaNER model


  A characteristic value formulation of the Weyl double copy leads to an
asymptotic formulation. We find that the Weyl double copy holds asymptotically
in cases where the full solution is algebraically general, using rotating STU
supergravity black holes as an example. The asymptotic formulation provides
clues regarding the relation between asymptotic symmetries that follows from
the double copy. Using the C-metric as an example, we show that a previous
interpretation of this gravity solution as a superrotation has a single copy
analogue relating the appropriate Li\'enard-Wiechert potential to a large gauge
transformation.


  We propose a GPU-based iterative method for accelerated elastodynamic
simulation with the log-barrier-based contact model. While Newton's method is a
conventional choice for solving the interior-point system, the presence of
ill-conditioned log barriers often necessitates a direct solution at each
linearized substep and costs substantial storage and computational overhead.
Moreover, constraint sets that vary in each iteration present additional
challenges in algorithm convergence. Our method employs a novel
barrier-augmented Lagrangian method to improve system conditioning and solver
efficiency by adaptively updating an augmentation constraint sets. This enables
the utilization of a scalable, inexact Newton-PCG solver with sparse GPU
storage, eliminating the need for direct factorization. We further enhance PCG
convergence speed with a domain-decomposed warm start strategy based on an
eigenvalue spectrum approximated through our in-time assembly. Demonstrating
significant scalability improvements, our method makes simulations previously
impractical on 128 GB of CPU memory feasible with only 8 GB of GPU memory and
orders-of-magnitude faster. Additionally, our method adeptly handles stiff
problems, surpassing the capabilities of existing GPU-based interior-point
methods. Our results, validated across various complex collision scenarios
involving intricate geometries and large deformations, highlight the
exceptional performance of our approach.


  We use a semianalytic approach that is calibrated to N-body simulations to
study the evolution of self-interacting dark matter cores in galaxies. We
demarcate the regime where the temporal evolution of the core density follows a
well-defined track set by the initial halo parameters and the cross section.
Along this track, the central density reaches a minimum value set by the
initial halo density. Further evolution leads to an outward heat transfer,
inducing gravothermal core collapse such that the core shrinks as its density
increases. We show that the time scale for the core collapse is highly
sensitive to the outer radial density profile. Satellite galaxies with
significant mass loss due to tidal stripping should have larger central
densities and significantly faster core collapse compared to isolated halos.
Such a scenario could explain the dense and compact cores of dwarf galaxies in
the Local Group like Tucana (isolated from the Milky Way), the classical Milky
Way satellite Draco, and some of the ultrafaint satellites. If the ultimate
fate of core collapse is black hole formation, then the accelerated time scale
provides a new mechanism for creating intermediate mass black holes.


  We discuss the cosmological constant problem using the properties of a
freely-suspended two-dimensional condensed-matter film, i.e., an explicit
realization of a 2D brane. The large contributions of vacuum fluctuations to
the surface tension of this film are cancelled in equilibrium by the
thermodynamic potential arising from the conservation law for particle number.
In short, the surface tension of the film vanishes in equilibrium due to a
thermodynamic identity. This 2D brane can be generalized to a 4D brane with
gravity. For the 4D brane, the analogue of the 2D surface tension is the 4D
cosmological constant, which is also nullified in full equilibrium. The 4D
brane theory provides an alternative description of the phenomenological
$q$-theory of the quantum vacuum. As for other realizations of the vacuum
variable $q$, such as the 4-form field-strength realization, the main
ingredient is the conservation law for the variable $q$, which makes the vacuum
a self-sustained system. For a vacuum within this class, the nullification of
the cosmological constant takes place automatically in equilibrium. Out of
equilibrium, the cosmological constant can be as large as suggested by naive
estimates based on the summation of zero-point energies. In this brane
description, $q$-theory also corresponds to a generalization of unimodular
gravity.