  Recent studies have shown promising results in utilizing multimodal large
language models (MLLMs) for computer vision tasks such as object detection and
semantic segmentation. However, many challenging video tasks remain
under-explored. Video-language tasks necessitate spatial and temporal
comprehension and require significant compute. Therefore, prior works have
developed complex, highly specialized architectures or leveraged additional
input signals such as video transcripts to best encode contextual and temporal
information, which limits their generality and can be impractical. One
particularly challenging task is video moment retrieval, which requires precise
temporal and contextual grounding. This work demonstrates the surprising
effectiveness of leveraging image-text pretrained MLLMs for moment retrieval.
We introduce Mr. BLIP (Mr. as in Moment Retrieval), a multimodal, single-stage
model that requires no expensive video-language pretraining, no additional
input signal (e.g., no transcript or audio), and has a simpler and more
versatile design than prior state-of-the-art methods. We achieve a new
state-of-the-art in moment retrieval on the widely used benchmarks
Charades-STA, QVHighlights, and ActivityNet Captions. Notably, we attain over
9% (absolute) higher Recall (at 0.5 and 0.7 IoU) on the challenging long-video
multi-moment QVHighlights benchmark. Our code is publicly available.


  Within the solar system, approximate realizations of the three-body problem
occur when a comet approaches a planet while being affected mainly by such a
planet and the Sun, and this configuration was investigated by Tisserand within
the framework of Newtonian gravity. The exact relativistic treatment of the
problem is not an easy task, but the present paper develops an approximate
calculational scheme which computes for the first time the tiny
effective-gravity correction to the equation of the surface for all points of
which it is equally advantageous to regard the heliocentric motion as being
perturbed by the attraction of Jupiter, or the jovicentric motion as being
perturbed by the attraction of the Sun. This analysis completes the previous
theoretical investigations of effective-gravity corrections to the Newtonian
analysis of three-body systems, and represents an intermediate step towards
relativistic effects on cometary motions.


  Given a numerical semigroup $S$ and a positive integer $d$, the fraction
$\frac{S}{d}=\{ x \in \mathbb{N} \ | \ dx \in S\}$ is again a numerical
semigroup. In this paper we determine a generating set for $\frac{S}{d}$ in
terms of the minimal generators of $S$ and provide sharp upper bounds for the
embedding dimension of $\frac{S}{d}$.


  This paper combines experimental data with simple mathematical models to
investigate the influence of spray formulation type and leaf character
(wettability) on shatter, bounce and adhesion of droplets impacting with
cotton, rice and wheat leaves. Impaction criteria that allow for different
angles of the leaf surface and the droplet impact trajectory are presented;
their predictions are based on whether combinations of droplet size and
velocity lie above or below bounce and shatter boundaries. In the experimental
component, real leaves are used, with all their inherent natural variability.
Further, commercial agricultural spray nozzles are employed, resulting in a
range of droplet characteristics. Given this natural variability, there is
broad agreement between the data and predictions. As predicted, the shatter of
droplets was found to increase as droplet size and velocity increased, and the
surface became harder to wet. Bouncing of droplets occurred most frequently on
hard to wet surfaces with high surface tension mixtures. On the other hand, a
number of small droplets with low impact velocity were observed to bounce when
predicted to lie well within the adhering regime. We believe this discrepancy
between the predictions and experimental data could be due to air layer effects
that were not taken into account in the current bounce equations. Other
discrepancies between experiment and theory are thought to be due to the
current assumption of a dry impact surface, whereas, in practice, the leaf
surfaces became increasingly covered with fluid throughout the spray test runs.


  The luminous efficiency of meteors is poorly known, but critical for
determining the meteoroid mass. We present an uncertainty analysis of the
luminous efficiency as determined by the classical ablation equations, and
suggest a possible method for determining the luminous efficiency of real
meteor events. We find that a two-term exponential fit to simulated lag data is
able to reproduce simulated luminous efficiencies reasonably well.


  The Gaia Archive provides access to observations of around a billion sky
sources. The primary access to this archive is via TAP services such as GACS
and ARI-Gaia, which allow execution of SQL-like queries against a large remote
database returning a result set of manageable size for client-side use. Such
services are generally used for extracting relatively small source lists
according to potentially complex selection criteria. But they can also be used
to obtain statistical information about all, or a large fraction of, the
observed sources by building histogram-like results.
  We examine here the practicalities of producing and consuming all-sky HEALPix
weighted density maps in this way for Gaia and other large datasets. We present
some modest requirements on TAP/RDBMS services to enable such queries, and
discuss visualisation and serialization options for the results including some
new capabilities in recent versions of TOPCAT.


  LS 2883/PSR B1259-63 is a high mass, eccentric gamma-ray binary that has
previously been observed to eject X-ray emitting material. We report the
results of recent Chandra observations near binary apastron in which a new
X-ray emitting clump of matter was detected. The clump has a high projected
velocity of $v_{\perp}\approx 0.07c$ and hard X-ray spectrum, which fits an
absorbed power-law model with $\Gamma=1.1\pm0.3$. Although clumps with similar
velocities and spectra were detected in some of the previous binary cycles, no
resolved clumps were seen near apastron in the preceding cycle of 2017-2021.


  We performed spectral analysis of Suzaku data of the galactic disk and
outflow regions of the starburst galaxy M82. Thermal modeling of the central
disk regions requires at least three temperature components. The Ly$\beta$ line
fluxes of O VIII and Ne X exceed those expected from a plasma in collisional
ionization equilibrium. The ratios of Ly$\beta$/Ly$\alpha$ lines for O VIII and
Ne X are higher than those of collisional ionization equilibrium, which may be
caused by the process of charge exchange. In the outflow wind region, the
spectra are well reproduced with two-temperature thermal models, and we have
derived the metal abundances of O, Ne, Mg, and Fe in the outflow. The ratios of
O/Fe, Ne/Fe, and Mg/Fe are about 2, 3, and 2, respectively, relative to the
solar value determined by Lodders (2003). Since there is no evidence of charge
exchange in outflow region, the metal abundances should be more reliable than
those in the central region. This abundance pattern indicates that starburst
activity enriches the outflow through SN II metal ejection into intergalactic
space.


  Inferring contextually-relevant and diverse commonsense to understand
narratives remains challenging for knowledge models. In this work, we develop a
series of knowledge models, DiffuCOMET, that leverage diffusion to learn to
reconstruct the implicit semantic connections between narrative contexts and
relevant commonsense knowledge. Across multiple diffusion steps, our method
progressively refines a representation of commonsense facts that is anchored to
a narrative, producing contextually-relevant and diverse commonsense inferences
for an input context. To evaluate DiffuCOMET, we introduce new metrics for
commonsense inference that more closely measure knowledge diversity and
contextual relevance. Our results on two different benchmarks, ComFact and
WebNLG+, show that knowledge generated by DiffuCOMET achieves a better
trade-off between commonsense diversity, contextual relevance and alignment to
known gold references, compared to baseline knowledge models.


  SN~2007D is a nearby (redshift $z = 0.023146$), luminous Type Ic supernova
(SN) having a narrow light curve (LC) and high peak luminosity. Previous
research based on the assumption that it was powered by the $^{56}$Ni cascade
decay suggested that the inferred $^{56}$Ni mass and the ejecta mass are $\sim
1.5$M$_{\odot}$ and $\sim 3.5$M$_{\odot}$, respectively. In this paper, we
employ some multiband LC models to model the $R$-band LC and the color ($V-R$)
evolution of SN~2007D to investigate the possible energy sources powering them.
We find that the pure $^{56}$Ni model is disfavored; the multiband LCs of
SN~2007D can be reproduced by a magnetar whose initial rotational period
$P_{0}$ and magnetic field strength $B_p$ are $7.28_{-0.21}^{+0.21}$ (or
$9.00_{-0.42}^{+0.32}$) ms and $3.10_{-0.35}^{+0.36}\times 10^{14}$ (or
$2.81_{-0.44}^{+0.43}\times 10^{14}$) G, respectively. By comparing the
spectrum of SN~2007D with that of some superluminous SNe (SLSNe), we find that
it might be a luminous SN like several luminous ``gap-filler" optical
transients that bridge ordinary and SLSNe, rather than a genuine SLSN.


  We adapt the direct approach to the semiclassical Bergman kernel asymptotics,
developed recently by A. Deleporte, J. Sj\"ostrand, and the first-named author
for real analytic exponential weights, to the smooth case. Similar to that
work, our approach avoids the use of the Kuranishi trick and it allows us to
construct the amplitude of the asymptotic Bergman projection by means of an
asymptotic inversion of an explicit Fourier integral operator.


  The recent measurement of the gravitational redshifts of galaxies in galaxy
clusters by Wojtak et al. has opened a new observational window on dark matter
and modified gravity. By stacking clusters this determination effectively used
the line of sight distortion of the cross-correlation function of massive
galaxies and lower mass galaxies to estimate the gravitational redshift profile
of clusters out to 4 Mpc/h. Here we use a halo model of clustering to predict
the distortion due to gravitational redshifts of the cross-correlation function
on scales from 1 - 100 Mpc/h. We compare our predictions to simulations and use
the simulations to make mock catalogues relevant to current and future galaxy
redshift surveys. Without formulating an optimal estimator, we find that the
full BOSS survey should be able to detect gravitational redshifts from
large-scale structure at the ~4 sigma level. Upcoming redshift surveys will
greatly increase the number of galaxies useable in such studies and the BigBOSS
and Euclid experiments should be capable of measurements with precision at the
few percent level. As has been recently pointed out by McDonald, Kaiser and
Zhao et al, other interesting effects including relativistic beaming and
transverse Doppler shift can add additional asymmetric distortions to the
correlation function. While these contributions are subdominant to the
gravitational redshift on large scales, they represent additional opportunities
to probe gravitational physics and indicate that many qualitatively new
measurements should soon be possible using large redshift surveys.


  We study two Hamiltonian models, based on infrared approximations which
render them solvable, in order to obtain an operator formulation of the
soft-photon corrections to the scattering of a single electron, as given in
Quantum Electrodynamics by the method of Feynman's diagrams. The first model is
based on the same approximations of the Pauli-Fierz Hamiltonian, the second one
stems from an expansion in powers of the four-momentum transfer, along the
lines of Bloch and Nordsieck. For both models, the dynamics of the charge is
accounted for by suitably chosen classical currents, interacting with the
quantum e.m. potential. M\"oller operators, preserving respectively the Hilbert
scalar product, for the Coulomb-gauge formulation of the models, and an
indefinite metric, for the formulation of the models in the
Feynman-Gupta-Bleuler gauge, are obtained in the presence of an infrared
cutoff, with the help of suitable renormalization counterterms. We show that
the soft-photon corrections to the electron scattering under consideration are
reproduced by suitable matrix elements of the M\"oller operators pertaining to
the model "of the Bloch-Nordsieck type", both in the FGB gauge and in the
Coulomb gauge. Further, we prove that if one assumes that the charged particle
is non relativistic and employs a dipole approximation, the resulting
low-energy radiative corrections admit an operator formulation as well, in
terms of the M\"oller operators of the model "of Pauli-Fierz type", but lack
the invariance property with respect to the gauge employed in their
calculation. The reason why such a discrepancy occurs is finally traced back in
full generality, also in connection with the Gupta-Bleuler formulation of
non-relativistic models.


  Data sharding, a technique for partitioning and distributing data among
multiple servers or nodes, offers enhancements in the scalability, performance,
and fault tolerance of extensive distributed systems. Nonetheless, this
strategy introduces novel challenges, including load balancing among shards,
management of node failures and data loss, and adaptation to evolving data and
workload patterns. This paper proposes an innovative approach to tackle these
challenges by empowering self-healing nodes with adaptive data sharding.
Leveraging concepts such as self-replication, fractal regeneration, sentient
data sharding, and symbiotic node clusters, our approach establishes a dynamic
and resilient data sharding scheme capable of addressing diverse scenarios and
meeting varied requirements. Implementation and evaluation of our approach
involve a prototype system simulating a large-scale distributed database across
various data sharding scenarios. Comparative analyses against existing data
sharding techniques highlight the superior scalability, performance, fault
tolerance, and adaptability of our approach. Additionally, the paper delves
into potential applications and limitations, providing insights into the future
research directions that can further advance this innovative approach.


  Despite recent advances in vehicle safety technologies, road traffic
accidents still pose a severe threat to human lives and have become a leading
cause of premature deaths. In particular, crosswalks present a major threat to
pedestrians, but we lack dense behavioral data to investigate the risks they
face. Therefore, we propose a comprehensive analytical model for pedestrian
potential risk using video footage gathered by road security cameras deployed
at such crossings. The proposed system automatically detects vehicles and
pedestrians, calculates trajectories by frames, and extracts behavioral
features affecting the likelihood of potentially dangerous scenes between these
objects. Finally, we design a data cube model by using the large amount of the
extracted features accumulated in a data warehouse to perform multidimensional
analysis for potential risk scenes with levels of abstraction, but this is
beyond the scope of this paper, and will be detailed in a future study. In our
experiment, we focused on extracting the various behavioral features from
multiple crosswalks, and visualizing and interpreting their behaviors and
relationships among them by camera location to show how they may or may not
contribute to potential risk. We validated feasibility and applicability by
applying it in multiple crosswalks in Osan city, Korea.


  If a group $G$ is the union of proper subgroups $H_1, \dots, H_k$, we say
that the collection $\{H_1, \dots H_k \}$ is a cover of $G$, and the size of a
minimal cover (supposing one exists) is the covering number of $G$, denoted
$\sigma(G)$. Mar\'oti showed that $\sigma(S_n) = 2^{n-1}$ for $n$ odd and
sufficiently large, and he also gave asymptotic bounds for $n$ even. In this
paper, we determine the exact value of $\sigma(S_n)$ when $n$ is divisible by
$6$.


  A rigorous homogenization theory of metamaterials -- artificial periodic
structures judiciously designed to control the propagation of electromagnetic
waves -- is developed. All coarse-grained fields are unambiguously defined and
effective parameters are then derived without any heuristic assumptions. The
theory is an amalgamation of two concepts: Smith & Pendry's physical insight
into field averaging and the mathematical framework of
Whitney-Nedelec-Bossavit-Kotiuga interpolation. All coarse-grained fields are
defined via Whitney forms and satisfy Maxwell's equations exactly. The new
approach is illustrated with several analytical and numerical examples and
agrees well with the established results (e.g. the Maxwell-Garnett formula and
the zero cell-size limit) within the range of applicability of the latter. The
sources of approximation error and the respective suitable error indicators are
clearly identified, along with systematic routes for improving the accuracy
further. The proposed approach should be applicable in areas beyond
metamaterials and electromagnetic waves -- e.g. in acoustics and elasticity.


  We determine analytically the phase diagram of the toric code model in a
parallel magnetic field which displays three distinct regions. Our study relies
on two high-order perturbative expansions in the strong- and weak-field limit,
as well as a large-spin analysis. Calculations in the topological phase
establish a quasiparticle picture for the anyonic excitations. We obtain two
second-order transition lines that merge with a first-order line giving rise to
a multicritical point as recently suggested by numerical simulations. We
compute the values of the corresponding critical fields and exponents that
drive the closure of the gap. We also give the one-particle dispersions of the
anyonic quasiparticles inside the topological phase.


  We discuss how singular can cardinals be in absence of the axiom of choice.
We show that, contrasting with known negative consistency results (of Gitik and
others), certain positive results are provable. Then we pose some problems.


  This paper provides theoretical insights into high-dimensional binary
classification with class-conditional noisy labels. Specifically, we study the
behavior of a linear classifier with a label noisiness aware loss function,
when both the dimension of data $p$ and the sample size $n$ are large and
comparable. Relying on random matrix theory by supposing a Gaussian mixture
data model, the performance of the linear classifier when $p,n\to \infty$ is
shown to converge towards a limit, involving scalar statistics of the data.
Importantly, our findings show that the low-dimensional intuitions to handle
label noise do not hold in high-dimension, in the sense that the optimal
classifier in low-dimension dramatically fails in high-dimension. Based on our
derivations, we design an optimized method that is shown to be provably more
efficient in handling noisy labels in high dimensions. Our theoretical
conclusions are further confirmed by experiments on real datasets, where we
show that our optimized approach outperforms the considered baselines.


  As an application of the method of Thue-Siegel, we will resolve a conjecture
of Walsh to the effect that the Diophantine equation $aX^{4} - bY^2=1$, for
fixed positive integers $a$ and $b$, possesses at most two solutions in
positive integers $X$ and $Y$. Since there are infinitely many pairs $(a,b)$
for which two such solutions exist, this result is sharp.


  Framed combinatorial topology is a recent approach to tame geometry which
expresses higher-dimensional stratified spaces via tractable combinatorial
data. The resulting theory of spaces is well-behaved and computable. In this
paper we extend FCT by allowing labelling systems of meshes and trusses in
$\infty$-categories, and build an alternative model of FCT by constructing
$\infty$-categories that classify meshes and trusses. This will serve as the
foundation for future work on models of higher categories based on generalised
string diagrams and the study of generalised tangles.


  This paper presents a multiple learner algorithm called the 'Three Ensemble
Clustering 3EC' algorithm that classifies unlabeled data into quality clusters
as a part of unsupervised learning. It offers the flexibility to explore the
context of new clusters formed by an ensemble of algorithms based on internal
validation indices.
  It is worth mentioning that the input data set is considered to be a cluster
of clusters. An anomaly can possibly manifest as a cluster as well. Each
partitioned cluster is considered to be a new data set and is a candidate to
explore the most optimal algorithm and its number of partition splits until a
predefined stopping criteria is met. The algorithms independently partition the
data set into clusters and the quality of the partitioning is assessed by an
ensemble of internal cluster validation indices. The 3EC algorithm presents the
validation index scores from a choice of algorithms and its configuration of
partitions and it is called the Tau Grid. 3EC chooses the most optimal score.
The 3EC algorithm owes its name to the two input ensembles of algorithms and
internal validation indices and an output ensemble of final clusters.
  Quality plays an important role in this clustering approach and it also acts
as a stopping criteria from further partitioning. Quality is determined based
on the quality of the clusters provided by an algorithm and its optimal number
of splits. The 3EC algorithm determines this from the score of the ensemble of
validation indices. The user can configure the stopping criteria by providing
quality thresholds for the score range of each of the validation indices and
the optimal size of the output cluster. The users can experiment with different
sets of stopping criteria and choose the most 'sensible group' of quality
clusters


  We demonstrate that future market correlation structure can be predicted with
high out-of-sample accuracy using a multiplex network approach that combines
information from social media and financial data. Market structure is measured
by quantifying the co-movement of asset prices returns, while social structure
is measured as the co-movement of social media opinion on those same assets.
Predictions are obtained with a simple model that uses link persistence and
link formation by triadic closure across both financial and social media
layers. Results demonstrate that the proposed model can predict future market
structure with up to a 40\% out-of-sample performance improvement compared to a
benchmark model that assumes a time-invariant financial correlation structure.
Social media information leads to improved models for all settings tested,
particularly in the long-term prediction of financial market structure.
Surprisingly, financial market structure exhibited higher predictability than
social opinion structure.


  We present Chen-Ricci inequality and improved Chen-Ricci inequality for
curvature like tensors. Applying our improved Chen-Ricci inequality we study
Lagrangian and Kaehlerian slant submanifolds of complex space forms and
C-totally real submanifolds of Sasakian space forms.


  Binary evolution predicts a population of helium core (M < 0.5 Msol) white
dwarfs (WDs) that are slowly accreting hydrogen-rich material from low mass
main sequence or brown dwarf donors with orbital periods less than four hours.
Four binaries are presently known in the Milky Way that will reach such a
mass-transferring state in a few Gyr. Despite these predictions and
observations of progenitor binaries, there are still no secure cases of helium
core WDs among the mass-transferring cataclysmic variables (CVs). This led us
to calculate the fate of He WDs once accretion begins at a rate Mdot < 1e-10
Msol/yr set by angular momentum losses. We show here that the cold He core
temperatures (T_c < 1e7 K) and low Mdot result in ~ 1e-3 Msol of accumulated
H-rich material at the onset of the thermonuclear runaway. Shara and
collaborators noted that these large accumulated masses may lead to
exceptionally long classical nova (CN) events. For a typical donor star of 0.2
Msol, such binaries will only yield a few hundred CNe, making these events rare
amongst all CNe. We calculate the reheating of the accreting WD, allowing a
comparison to the measured WD effective temperatures in quiescent dwarf novae
and raising the possibility that WD seismology may be the best way to confirm
the presence of a He WD. We also find that a very long (> 1000 yr) stable
burning phase occurs after the CN outburst, potentially explaining enigmatic
short orbital period supersoft sources like RX J0537-7034 (P_orb = 3.5 hr) and
1E 0035.4-7230 (P_orb = 4.1 hr).


  Studying low-likelihood high-impact extreme weather events in a warming world
is a significant and challenging task for current ensemble forecasting systems.
While these systems presently use up to 100 members, larger ensembles could
enrich the sampling of internal variability. They may capture the long tails
associated with climate hazards better than traditional ensemble sizes. Due to
computational constraints, it is infeasible to generate huge ensembles
(comprised of 1,000-10,000 members) with traditional, physics-based numerical
models. In this two-part paper, we replace traditional numerical simulations
with machine learning (ML) to generate hindcasts of huge ensembles. In Part I,
we construct an ensemble weather forecasting system based on Spherical Fourier
Neural Operators (SFNO), and we discuss important design decisions for
constructing such an ensemble. The ensemble represents model uncertainty
through perturbed-parameter techniques, and it represents initial condition
uncertainty through bred vectors, which sample the fastest growing modes of the
forecast. Using the European Centre for Medium-Range Weather Forecasts
Integrated Forecasting System (IFS) as a baseline, we develop an evaluation
pipeline composed of mean, spectral, and extreme diagnostics. Using
large-scale, distributed SFNOs with 1.1 billion learned parameters, we achieve
calibrated probabilistic forecasts. As the trajectories of the individual
members diverge, the ML ensemble mean spectra degrade with lead time,
consistent with physical expectations. However, the individual ensemble
members' spectra stay constant with lead time. Therefore, these members
simulate realistic weather states, and the ML ensemble thus passes a crucial
spectral test in the literature. The IFS and ML ensembles have similar Extreme
Forecast Indices, and we show that the ML extreme weather forecasts are
reliable and discriminating.


  New observations show that most of the flattened diffuse elliptical (dE)
galaxies are essentially isotropic rotators. This supports the idea that dEs
have evolved from fast rotating dIrr systems or late-type spirals after their
gas was expelled by SN-driven winds and stripped by ram pressure against the
intergalactic medium.


  The successful measurements of the smallest neutrino mixing angle,
$\theta_{13}$, in 2012 by the short (1$\sim$2 km) baseline reactor neutrinos
experiments, Daya Bay, RENO, and Double Chooz, have triggered a golden age of
neutrino physics. The three experiments have been improving the $\theta_{13}$
measurements by accumulating event statistics and reducing systematic
uncertainties. Now the $\theta_{13}$ measurement is the most precise one among
the mixing angles in the Pontecorvo-Maki-Nakagawa-Sakata matrix. The most
updated $\theta_{13}$ and $\Delta m^{2}_{ee}$ measurements from these
experiments are reported here as well as the 5 MeV excess, absolute reactor
neutrino flux and sterile neutrino search. The best final precision on the
sin$^{2}2\theta_{13}$ ($|\Delta m^2_{ee}|$) measurement is expected to be
$\sim$3\% ($\sim$3\%). A combined analysis from the three experiments will
reduce the uncertainty and the relevant activity has started recently.


  Camera with a fisheye or ultra-wide lens covers a wide field of view that
cannot be modeled by the perspective projection. Serious fisheye lens
distortion in the peripheral region of the image leads to degraded performance
of the existing head pose estimation models trained on undistorted images. This
paper presents a new approach for head pose estimation that uses the knowledge
of head location in the image to reduce the negative effect of fisheye
distortion. We develop an end-to-end convolutional neural network to estimate
the head pose with the multi-task learning of head pose and head location. Our
proposed network estimates the head pose directly from the fisheye image
without the operation of rectification or calibration. We also created a
fisheye-distorted version of the three popular head pose estimation datasets,
BIWI, 300W-LP, and AFLW2000 for our experiments. Experiments results show that
our network remarkably improves the accuracy of head pose estimation compared
with other state-of-the-art one-stage and two-stage methods.


  Quantum computing is emerging as a new computational paradigm with the
potential to transform several research fields, including quantum chemistry.
However, current hardware limitations (including limited coherence times, gate
infidelities, and limited connectivity) hamper the straightforward
implementation of most quantum algorithms and call for more noise-resilient
solutions. In quantum chemistry, the limited number of available qubits and
gate operations is particularly restrictive since, for each molecular orbital,
one needs, in general, two qubits. In this study, we propose an explicitly
correlated Ansatz based on the transcorrelated (TC) approach, which transfers
-- without any approximation -- correlation from the wavefunction directly into
the Hamiltonian, thus reducing the number of resources needed to achieve
accurate results with noisy, near-term quantum devices. In particular, we show
that the exact transcorrelated approach not only allows for more shallow
circuits but also improves the convergence towards the so-called basis set
limit, providing energies within chemical accuracy to experiment with smaller
basis sets and, therefore, fewer qubits. We demonstrate our method by computing
bond lengths, dissociation energies, and vibrational frequencies close to
experimental results for the hydrogen dimer and lithium hydride using just 4
and 6 qubits, respectively. Conventional methods require at least ten times
more qubits for the same accuracy.


  Modern online services continuously generate data at very fast rates. This
continuous flow of data encompasses content - e.g., posts, news, products,
comments -, but also user feedback - e.g., ratings, views, reads, clicks -,
together with context data - user device, spatial or temporal data, user task
or activity, weather. This can be overwhelming for systems and algorithms
designed to train in batches, given the continuous and potentially fast change
of content, context and user preferences or intents. Therefore, it is important
to investigate online methods able to transparently adapt to the inherent
dynamics of online services. Incremental models that learn from data streams
are gaining attention in the recommender systems community, given their natural
ability to deal with the continuous flows of data generated in dynamic, complex
environments. User modeling and personalization can particularly benefit from
algorithms capable of maintaining models incrementally and online.
  The objective of this workshop is to foster contributions and bring together
a growing community of researchers and practitioners interested in online,
adaptive approaches to user modeling, recommendation and personalization, and
their implications regarding multiple dimensions, such as evaluation,
reproducibility, privacy and explainability.


  We report on a search for new particles in the diphoton channel using a data
sample of $p\bar p$ collisions at $\sqrt{s} = 1.96$ TeV collected by the CDF II
detector at the Fermilab Tevatron, with an integrated luminosity of 5.4
fb$^{-1}$. The diphoton invariant mass spectrum of the data agrees well with
the standard model expectation. We set upper limits on the production cross
section times branching ratio for the Randall-Sundrum graviton, as a function
of diphoton mass. We subsequently derive lower limits on the graviton mass of
459 GeV$/c^{2}$ and 963 GeV$/c^{2}$, at the 95% confidence level, for coupling
parameters ($k/\bar{M}_{Pl}$) of 0.01 and 0.1 respectively.


  Let $G$ be a graph on $n$ vertices of maximum degree $\Delta$. We show that,
for any $\delta > 0$, the down-up walk on independent sets of size $k \leq
(1-\delta)\alpha_c(\Delta)n$ mixes in time $O_{\Delta,\delta}(k\log{n})$,
thereby resolving a conjecture of Davies and Perkins in an optimal form. Here,
$\alpha_{c}(\Delta)n$ is the NP-hardness threshold for the problem of counting
independent sets of a given size in a graph on $n$ vertices of maximum degree
$\Delta$. Our mixing time has optimal dependence on $k,n$ for the entire range
of $k$; previously, even polynomial mixing was not known. In fact, for $k =
\Omega_{\Delta}(n)$ in this range, we establish a log-Sobolev inequality with
optimal constant $\Omega_{\Delta,\delta}(1/n)$.
  At the heart of our proof are three new ingredients, which may be of
independent interest. The first is a method for lifting
$\ell_\infty$-independence from a suitable distribution on the discrete cube --
in this case, the hard-core model -- to the slice by proving stability of an
Edgeworth expansion using a multivariate zero-free region for the base
distribution. The second is a generalization of the Lee-Yau induction to prove
log-Sobolev inequalities for distributions on the slice with considerably less
symmetry than the uniform distribution. The third is a sharp decomposition-type
result which provides a lossless comparison between the Dirichlet form of the
original Markov chain and that of the so-called projected chain in the presence
of a contractive coupling.


  A data warehouse efficiently prepares data for effective and fast data
analysis and modelling using machine learning algorithms. This paper discusses
existing solutions for the Data Extraction, Transformation, and Loading (ETL)
process and automation for algorithmic trading algorithms. Integrating the Data
Warehouses and, in the future, the Data Lakes with the Machine Learning
Algorithms gives enormous opportunities in research when performance and data
processing time become critical non-functional requirements.


  The lepton pair production in ultra-peripheral collisions (UPC) is studied in
the classical field approximation. We derive a general form of the cross
section in terms of photon distributions which depend on the transverse
momentum and coordinate based on the wave packet form of nuclear wave
functions. Such a general form of the cross section in the classical field
approximation contains the results of the generalized equivalent photon
approximation (EPA) as well as the corrections beyond EPA in the Born
approximation. By rewriting the general form of the cross section in light-cone
coordinates, we find a good connection with the transverse momentum dependent
distribution (TMD) factorization formalism in the Born approximation. Our
numerical results are consistent with current experimental data.


  The parametric behavior of regular periodic arrays of rising bubbles is
investigated with the aid of 2-dimensional BGK Lattice-Boltzmann (LB)
simulators. The Recursive Projection Method is implemented and coupled to the
LB simulators, accelerating their convergence towards what we term coarse
steady states. Efficient stability/bifurcation analysis is performed by
computing the leading eigenvalues/eigenvectors of the coarse time stepper. Our
approach constitutes the basis for system-level analysis of processes modeled
through microscopic simulations.


  Magnetohydrodynamic interactions between plasma flows and magnetic fields is
fundamental to the origin and sustenance of the 11-year sunspot cycle. These
processes are intrinsically three-dimensional (3D) in nature. Our goal is to
construct a 3D solar dynamo model that on the one hand captures the buoyant
emergence of tilted bipolar sunspot pairs, and on the other hand produces
cyclic large-scale field reversals mediated via surface flux-transport
processes -- that is, the Babcock-Leighton mechanism. We perform kinematic
dynamo simulations where the prescribed velocity field is a combination of
solar-like differential rotation and meridional circulation, along with a
parametrized turbulent diffusivity. We use a novel methodology for modeling
magnetic buoyancy through field-strength-dependent 3D helical up-flows that
results in the formation of tilted bipolar sunspots. The bipolar spots produced
in our simulations participate in the process of poloidal-field generation
through the Babcock-Leighton mechanism, resulting in self-sustained and
periodic large-scale magnetic field reversal. Our parameter space study varying
the amplitude of the meridional flow, the convection zone diffusivity, and
parameters governing the efficiency of the magnetic buoyancy mechanism reveal
their relative roles in determining properties of the sunspot cycle such as
amplitude, period, and dynamical memory relevant to solar cycle prediction. We
also derive a new dynamo number for the Babcock-Leighton solar dynamo mechanism
which reasonably captures our model dynamics. This study elucidates the
relative roles of different flux-transport processes in the Sun's convection
zone in determining the properties and physics of the sunspot cycle and could
potentially lead to realistic, data-driven 3D dynamo models for solar-activity
predictions and exploration of stellar magnetism and starspot formation in
other stars.


  We have managed to correlate the stability constants of complex formation,
which can be registered in equilibrium state, to the dynamic characteristic of
the complex lifetime. Thus, the principal concept of molecular biophysics
regarding biomolecule, structure-dynamics-function can be reformatted as
structure-stability-function. It should be specially noted that such an
approach highly simplifies end widens the time interval of investigation of
dynamic characteristics of macromolecules.
  Study of hydration energy and hydration number by kinetic curves of water
desorption from Na-DNA humidified fibers by glow discharge atomic spectral
analysis of hydrogen allowed us to reveal that after transition of DNA from B
form to unordered state the activation energy of hydrated water desorption
increases by 0.65kcal/Mole of water. This increase of energy is 0.1 kcal/Mole
for transition from B to C form (from 20 to 10 water molecules per nucleotide)
and 0.55 kcal/Mole for the one from C form to A unordered state (from 10 to 5
molecules per nucleotide).
  Simultaneous use of UDS and AES allows identifying the number of Wrong
Watson-Crick pairs in DNA. The quantitative analysis is based on increase of
informativity of UDS via its combination with atomic-emission spectroscopy. The
number of the total metal-induced tautomerization makes six hundred per million
base pairs of DNA. Such approach enables estimation of risk factors during
clinical check-ups and evaluation of DNA suitability for nanotechnological
purposes.


  Recent adoption of deep learning methods to the field of machine lipreading
research gives us two options to pursue to improve system performance. Either,
we develop end-to-end systems holistically or, we experiment to further our
understanding of the visual speech signal. The latter option is more difficult
but this knowledge would enable researchers to both improve systems and apply
the new knowledge to other domains such as speech therapy. One challenge in
lipreading systems is the correct labeling of the classifiers. These labels map
an estimated function between visemes on the lips and the phonemes uttered.
Here we ask if such maps are speaker-dependent? Prior work investigated
isolated word recognition from speaker-dependent (SD) visemes, we extend this
to continuous speech. Benchmarked against SD results, and the isolated words
performance, we test with RMAV dataset speakers and observe that with
continuous speech, the trajectory between visemes has a greater negative effect
on the speaker differentiation.


  Instant messaging (IM) has changed the way people communicate with each
other. However, the interactive and instant nature of these applications (apps)
made them an attractive choice for malicious cyber activities such as phishing.
The forensic examination of IM apps for modern Windows 8.1 (or later) has been
largely unexplored, as the platform is relatively new. In this paper, we seek
to determine the data remnants from the use of two popular Windows Store
application software for instant messaging, namely Facebook and Skype on a
Windows 8.1 client machine. This research contributes to an in-depth
understanding of the types of terrestrial artefacts that are likely to remain
after the use of instant messaging services and application software on a
contemporary Windows operating system. Potential artefacts detected during the
research include data relating to the installation or uninstallation of the
instant messaging application software, log-in and log-off information, contact
lists, conversations, and transferred files.


  In this work we present a new approach on studying dynamical systems.
Combining the two ways of expressing the uncertainty, using probabilistic
theory and credibility theory, we have research the generalized fractional
hybrid equations. We have introduced the concepts of generalized fractional
Wiener process, generalized fractional Liu process and the combination between
those two, generalized fractional hybrid process. Corresponding generalized
fractional stochastic, respectively fuzzy, respectively hybrid dynamical
systems were defined. We applied the theory for generalized fractional hybrid
Hamilton-Pontryagin (HP) equation, generalized fractional Hamiltonian
equations. From the general fractional hybrid Hamiltonian equations, fractional
Langevin equations were found and numerical simulations were done.


  A new derivation of the five-dimensional Myers-Perry black-hole metric as a
2-soliton solution on a non-flat background is presented. It is intended to be
an illustration of how the well-known Belinski-Zakharov method can be applied
to find solutions of the Einstein equations in D-dimensional space-time with
D-2 commuting Killing vectors using the complete integrability of this system.
The method appears also to be promising for the analysis of the uniqueness
questions for higher-dimensional black holes.


  Nonlinear systems driven by noise and periodic forces with more than one
frequency exhibit the phenomenon of Ghost Stochastic Resonance (GSR) found in a
wide and disparate variety of fields ranging from biology to geophysics. The
common novel feature is the emergence of a "ghost" frequency in the system's
output which it is absent in the input. As reviewed here, the uncovering of
this phenomenon helped to understand a range of problems, from the perception
of pitch in complex sounds or visual stimuli, to the explanation of climate
cycles. Recent theoretical efforts show that a simple mechanism with two
ingredients are at work in all these observations. The first one is the linear
interference between the periodic inputs and the second a nonlinear detection
of the largest constructive interferences, involving a noisy threshold. These
notes are dedicated to review the main aspects of this phenomenon, as well as
its different manifestations described on a bewildering variety of systems
ranging from neurons, semiconductor lasers, electronic circuits to models of
glacial climate cycles.


  Random Forest has become one of the most popular tools for feature selection.
Its ability to deal with high-dimensional data makes this algorithm especially
useful for studies in neuroimaging and bioinformatics. Despite its popularity
and wide use, feature selection in Random Forest still lacks a crucial
ingredient: false positive rate control. To date there is no efficient,
principled and computationally light-weight solution to this shortcoming. As a
result, researchers using Random Forest for feature selection have to resort to
using heuristically set thresholds on feature rankings. This article builds an
approximate probabilistic model for the feature selection process in random
forest training, which allows us to compute an estimated false positive rate
for a given threshold on selection frequency. Hence, it presents a principled
way to determine thresholds for the selection of relevant features without any
additional computational load. Experimental analysis with synthetic data
demonstrates that the proposed approach can limit false positive rates on the
order of the desired values and keep false negative rates low. Results show
that this holds even in the presence of a complex correlation structure between
features. Its good statistical properties and light-weight computational needs
make this approach widely applicable to feature selection for a wide-range of
applications.


  Wave mixing is an archetypical phenomenon in bosonic systems. In
optomechanics, the bi-directional conversion between electromagnetic waves or
photons at optical frequencies and elastic waves or phonons at radio
frequencies is building on precisely this fundamental principle. Surface
acoustic waves provide a versatile interconnect on a chip and, thus, enable the
optomechanical control of remote systems. Here, we report on the coherent
nonlinear three-wave mixing between the coherent fields of two radio frequency
surface acoustic waves and optical laser photons via the dipole transition of a
single quantum dot exciton. In the resolved sideband regime, we demonstrate
fundamental acoustic analogues of sum and difference frequency generation
between the two SAWs and employ phase matching to deterministically enhance or
suppress individual sidebands. This bi-directional transfer between the
acoustic and optical domains is described by theory which fully takes into
account direct and virtual multi-phonon processes. Finally, we show that the
precision of the wave mixing is limited by the frequency accuracy of modern
radio frequency electronics.


  We investigate structural aspects of JT gravity through its BF description.
In particular, we provide evidence that JT gravity should be thought of as (a
coset of) the noncompact subsemigroup SL$^+$(2,R) BF theory. We highlight
physical implications, including the famous sinh Plancherel measure. Exploiting
this perspective, we investigate JT gravity on more generic manifolds with
emphasis on the edge degrees of freedom on entangling surfaces and
factorization. It is found that the one-sided JT gravity degrees of freedom are
described not just by a Schwarzian on the asymptotic boundary, but also include
frozen SL$^+$(2,R) degrees of freedom on the horizon, identifiable as JT
gravity black hole states. Configurations with two asymptotic boundaries are
linked to 2d Liouville CFT on the torus surface.


  Analytic form has been obtained for four-dimensional black holes with a
minimal Hawking temperature in a theory with cosmological constant, dilaton and
gauge fields. In general dimensions, black hole solutions are shown to exist
and their asymptotic behaviors are obtained. In theories of ten dimension, N
coincident D3-branes as the boundary of an $AdS_5$ space are constructed by
embedding black D3-branes, with a five-dimensional compactified space of
negligible size if N is large, which provide natural realizations of the
Randall-Sundrum scenario. For this $AdS_{5}$ background, the cosmological
constant is a higher order perturbation and its effect on the spectra of
standard model fields on the branes can be calculated.


  The heating of magnetized plasma by propagation of Alfven waves is calculated
as a function of the magnetic field spectral density. The results can be
applied to evaluate the heating power of the solar corona at known data from
satellites' magnetometers. This heating rate can be incorporated in global
models for heating of the solar corona and creation of the solar wind. The
final formula for the heating power is illustrated with a model spectral
density of the magnetic field obtained by analysis of the Voyager 1 mission
results. The influence of high frequency dissipative modes is also taken into
account and it is concluded that for evaluation of the total coronal heating it
is necessary to know the spectral density of the fluctuating component of the
magnetic field up to the frequency of electron-proton collisions.


  In this work, the task of pixel-wise semantic segmentation in the context of
self-driving with a goal to reduce the inference time is explored. Fully
Convolutional Network (FCN-8s, FCN-16s, and FCN-32s) with a VGG16 encoder
architecture and skip connections is trained and validated on the Cityscapes
dataset. Numerical investigations are carried out for several inference
optimization techniques built into TensorFlow and TensorRT to quantify their
impact on the inference time and network size. Finally, the trained network is
ported on to an embedded platform (Nvidia Jetson TX1) and the inference time,
as well as the total energy consumed for inference across hardware platforms,
are compared.


  We mainly study numerical integration of real valued functions defined on the
$d$-dimensional unit cube with all partial derivatives up to some finite order
$r\ge1$ bounded by one. It is well known that optimal algorithms that use $n$
function values achieve the error rate $n^{-r/d}$, where the hidden constant
depends on $r$ and $d$. Here we prove explicit error bounds without hidden
constants and, in particular, show that the optimal order of the error is $\min
\bigl\{1, d \, n^{-r/d}\bigr\}$, where now the hidden constant only depends on
$r$, not on $d$. For $n=m^d$, this optimal order can be achieved by (tensor)
product rules.
  We also provide lower bounds for integration defined over an arbitrary open
domain of volume one. We briefly discuss how lower bounds for integration may
be applied for other problems such as multivariate approximation and
optimization.


  In this paper we give various characterizations of quasiopen sets and
quasicontinuous functions on metric spaces. For complete metric spaces equipped
with a doubling measure supporting a p-Poincar\'e inequality we show that
quasiopen and p-path open sets coincide. Under the same assumptions we show
that all Newton-Sobolev functions on quasiopen sets are quasicontinuous.


  We study possible non-standard $tuZ$ and $tcZ$ couplings, which induce
flavor-changing neutral-current decays of the top quark, in the
effective-Lagrangian framework. The corresponding interaction Lagrangian comes
from several $SU(3) \times SU(2) \times U(1)$ invariant dimension-6 effective
operators, and it includes four independent complex-number coupling constants.
Constraints on those non-standard coupling constants in each interaction are
derived by using the present experimental limits of the branching fractions for
$t \to u Z$ and $t \to c Z$ processes. Expected improvements of the constraints
at future facilities are discussed as well. It is also pointed out that some
correlations hold among those constrained coupling constants.


  In this paper we study long distance modifications of gravity obtained by
considering actions that are singular in the limit of vanishing curvature. In
particular, we showed in a previous publication that models that include
inverse powers of curvature invariants that diverge for r->0 in the
Schwarzschild geometry, recover an acceptable weak field limit at short
distances from sources. We study then the linearisation of generic actions of
the form L=F[R,P,Q] where P=R_{ab}R^{ab} and Q=R_{abcd}R^{abcd}. We show that
for the case in which F[R,P,Q]=F[R,Q-4P], the theory is ghost free. Assuming
this is the case, in the models that can explain the acceleration of the
Universe without recourse to Dark Energy there is still an extra scalar field
in the spectrum besides the massless spin two graviton. The mass of this extra
excitation is of the order of the Hubble scale in vacuum. We nevertheless
recover Einstein gravity at short distances because the mass of this scalar
field depends on the background in such a way that it effectively decouples
when one gets close to any source. Remarkably, for the values of the parameters
necessary to explain the cosmic acceleration the induced modifications of
gravity are suppressed at the Solar System level but can be important for
systems like a galaxy.


  We use consistent truncations in supergravity to show that the backreaction
of $N$ rotating M5-branes wrapped on a Riemann surface leads to asymptotically
AdS$_5$ black hole solutions of 11d supergravity. We discuss the thermodynamic
properties of these black holes focusing on their supersymmetric limit. The
Bekenstein-Hawking entropy of the supersymmetric black holes scales as $N^3$
and can be reproduced by the superconformal index of the holographically dual
4d $\mathcal{N}=1$ SCFTs of class $\mathcal{S}$.


  We work with the notion of apparent/trapping horizons for spherically
symmetric, dynamical spacetimes: these are quasi-locally defined, simply based
on the behaviour of congruence of light rays. We show that the sign of the
dynamical Hayward-Kodama surface gravity is dictated by the inner/outer nature
of the horizon. Using the tunneling method to compute Hawking Radiation, this
surface gravity is then linked to a notion of temperature, up to a sign that is
dictated by the future/past nature of the horizon. Therefore two sign effects
are conspiring to give a positive temperature for the black hole case and the
expanding cosmology, whereas the same quantity is negative for white holes and
contracting cosmologies. This is consistent with the fact that, in the latter
cases, the horizon does not act as a separating membrane, and Hawking emission
should not occur.


  The role of gravitational instability-driven turbulence in determining the
structure and evolution of disk galaxies, and the extent to which gravity
rather than feedback can explain galaxy properties, remains an open question.
To address it, we present high resolution adaptive mesh refinement simulations
of Milky Way-like isolated disk galaxies, including realistic heating and
cooling rates and a physically motivated prescription for star formation, but
no form of star formation feedback. After an initial transient, our galaxies
reach a state of fully-nonlinear gravitational instability. In this state,
gravity drives turbulence and radial inflow. Despite the lack of feedback, the
gas in our galaxy models shows substantial turbulent velocity dispersions,
indicating that gravitational instability alone may be able to power the
velocity dispersions observed in nearby disk galaxies on 100 pc scales.
Moreover, the rate of mass transport produced by this turbulence approaches
$\sim 1$ $M_\odot$ yr$^{-1}$ for Milky Way-like conditions, sufficient to fully
fuel star formation in the inner disks of galaxies. In a companion paper we add
feedback to our models, and use the comparison between the two cases to
understand what galaxy properties depend sensitively on feedback, and which can
be understood as the product of gravity alone. All of the code, initial
conditions, and simulation data for our model are publicly available.


  Let $G$ be a finite group of Lie type $E_6$ over $F_q$ (adjoint or simply
connected) and $W$ be the Weyl group of $G$. We describe maximal tori $T$ such
that $T$ has a complement in its algebraic normalizer $N(G,T)$. It is well
known that for each maximal torus $T$ of $G$ there exists an element $w\in W$
such that $N(G,T)/T\simeq C_W(w)$. When $T$ does not have a complement
isomorphic to $C_W(w)$, we show that $w$ has a lift in $N(G,T)$ of the same
order.


  Fission at low excitation energy is an ideal playground to probe the impact
of nuclear structure on nuclear dynamics. While the importance of structural
effects in the nascent fragments is well-established in the (trans-)actinide
region, the observation of asymmetric fission in several neutron-deficient
pre-actinides can be explained by various mechanisms. To deepen our insight
into that puzzle, an innovative approach based on inverse kinematics and an
enhanced version of the VAMOS++ heavy-ion spectrometer was implemented at the
GANIL facility, Caen. Fission of $^{178}$Hg was induced by fusion of $^{124}$Xe
and $^{54}$Fe. The two fragments were detected in coincidence using VAMOS++
supplemented with a new SEcond Detection arm. For the first time in the
pre-actinide region, access to the pre-neutron mass and total kinetic energy
distributions, and the simultaneous isotopic identification of one the fission
fragment, was achieved. The present work describes the experimental approach,
and discusses the pre-neutron observables in the context of an extended
asymmetric-fission island located south-west of $^{208}Pb. A comparison with
different models is performed, demonstrating the importance of this "new"
asymmetric-fission island for elaborating on driving effects in fission.


  This paper is a brief report to our submission to the VIPriors Object
Detection Challenge. Object Detection has attracted many researchers' attention
for its full application, but it is still a challenging task. In this paper, we
study analysis the characteristics of the data, and an effective data
enhancement method is proposed. We carefully choose the model which is more
suitable for training from scratch. We benefit a lot from using softnms and
model fusion skillfully.


  In this work we undertake an extension of various aspects of the potential
theory of Dirichlet forms from locally compact spaces to noncommutative
C*-algebras with trace. In particular we introduce finite-energy states,
potentials and multipliers of Dirichlet spaces. We prove several results among
which the celebrated Deny's embedding theorem and the Deny's inequality, the
fact that the carre' du champ of bounded potentials are finite-energy
functionals and the relative supply of multipliers.


  We describe a new, low-cost system designed to provide multi-sensor remote
condition monitoring of modern scientific laboratories, as well as to allow
users to perform actions from remote locations in case of detection of
specified events. The system is battery operated and does not require the
presence of a Local Area Network (LAN) or WiFi (which are typically not
available in case of, e.g. power losses), as it exploits the growing
infrastructure of Internet of Things (IoT) Low Power Wide Area Networks
(LPWAN). In particular our system exploits the new SigFox
ultra-narrow-bandwidth (UNB) infrastructure, and provides for a bidirectional
link between the instrumentation and the remote user even in case of power line
outages, which are among the most critical situations that a scientific
laboratory can withstand. The system can detect the occurrence of predefined
events in very short times, and either autonomously react with a series of
predefined actions, also allowing a remote user to timely perform additional
actions on the system through an user-friendly smartphone application or via a
browser interface. The system also embeds a novel power-loss detection
architecture, which detects power line failures in less than 2 ms. We provide a
full characterization of the prototype, including reaction times, connection
latencies, sensors sensitivity, and power consumption.


  Owing to the interaction between the layers, the twisted bilayer
two-dimensional materials exhibit numerous unique optical and electronic
properties different from the monolayer counterpart, and have attracted
tremendous interests in current physical research community. By means of
first-principles and tight-binding model calculations, the electronic
properties of twisted bilayer biphenylene carbon are systematically
investigated in this paper. The results indicate that the effect of twist will
not only leads to a phase transition from semiconductor to metal, but also an
adjustable band gap in BPC (0 meV to 120 meV depending on the twist angle).
Moreover, unlike the twisted bilayer graphene (TBG), the flat bands in twisted
BPC are no longer restricted by "magic angles", i.e., abnormal flat bands could
be appeared as well at several specific large angles in addition to the small
angles. The charge density of these flat bands possesses different local modes,
indicating them might be derived from different stacked modes and host
different properties. The exotic physical properties presented in this work
foreshow twisted BPC a promising material for the application of terahertz and
infrared photodetectors and the exploration of strong correlation.


  In this paper, the connections among $1$-loop Feynman integrands of a large
variety of theories with massless external states are further investigated. The
work includes two parts. First, we construct a new class of differential
operators which transmute the $1$-loop gravitational Feynman integrands to
$1$-loop Yang-Mills Feynman integrands. The new operators are commutable with
the integration of loop momentum, thus the corresponding transmutational
relations hold at not only the integrands level, but also the $1$-loop
amplitudes level. Secondly, by using $1$-loop level transmutational relations,
together with some general requirements such as gauge and Lorentz invariance,
we derive the expansions of the Feynman integrands of one theory to those of
other theories. The unified web for expansions is established, including a wide
range of theories which are gravitational theory, Einstein-Yang-Mills theory,
Einstein-Maxwell theory, Born-Infeld theory, pure Yang-Mills theory,
Yang-Mills-scalar theory, special Yang-Mills-scalar theory, Dirac-Born-Infeld
theory, extended Dirac-Born-Infeld theory, special Galileon theory, non-linear
sigma model. The systematic rules for evaluating coefficients in the expansions
are provided, and the duality between transmutational relations and expansions
is shown.


  Neural semantic parsing has achieved impressive results in recent years, yet
its success relies on the availability of large amounts of supervised data. Our
goal is to learn a neural semantic parser when only prior knowledge about a
limited number of simple rules is available, without access to either annotated
programs or execution results. Our approach is initialized by rules, and
improved in a back-translation paradigm using generated question-program pairs
from the semantic parser and the question generator. A phrase table with
frequent mapping patterns is automatically derived, also updated as training
progresses, to measure the quality of generated instances. We train the model
with model-agnostic meta-learning to guarantee the accuracy and stability on
examples covered by rules, and meanwhile acquire the versatility to generalize
well on examples uncovered by rules. Results on three benchmark datasets with
different domains and programs show that our approach incrementally improves
the accuracy. On WikiSQL, our best model is comparable to the SOTA system
learned from denotations.


  The spin evolution of isolated neutron stars (NSs) is dominatd by their
magnetic fields. The measured braking indices of young NSs show that the
spin-down mechanism due to magnetic dipole radiation with constant magnetic
fields is inadequate. Assuming that the NS magnetic field is buried by
supernova fallback matter and re-emerges after accretion stops, we carry out
Monte-Carlo simulation of the evolution of young NSs, and show that most of the
pulsars have the braking indices ranging from -1 to 3. The results are
compatible with the observational data of NSs associated with supernova
remnants. They also suggest that the initial spin periods of NSs might occupy a
relatively wide range.


  An expression is derived for calculating microcanonical-ensemble averages of
the kinetic energies of atoms of different types in clusters isolated from the
environment. This expression is a natural generalization of the solution to the
problem of hard spheres with different masses to a system with a many-particle
interatomic interaction potential. The dynamics of a C8H8 cubane is simulated
numerically. The data on the numerical simulation confirm the validity of the
results obtained.


  Modeling quantum interference in the presence of dissipation is a critical
aspect of quantum technologies. Including dissipation into the model of a
linear device enables for assesing the detrimental impact of photon loss, as
well as for studying dissipation-driven quantum state transformations. However,
establishing the input-output relations characterizing quantum interference at
a general lossy N-port network poses important theoretical challenges. Here, we
propose a general procedure based on the singular value decomposition (SVD),
which allows for the efficient calculation of the input-output relations for
any arbitrary lossy linear device. In addition, we show how the SVD provides an
intuitive description of the principle of operation of linear optical devices.
We illustrate the applicability of our method by evaluating the input-output
relations of popular reciprocal and nonreciprocal lossy linear devices,
including devices with singular and nilpotent scattering matrices. We expect
that our procedure will motivate future research on quantum interference in
complex devices, as well as the realistic modelling of photon loss in linear
lossy devices.


  We propose a new variable selection algorithm, subsample-ordered least-angle
regression (solar), and its coordinate descent generalization, solar-cd. Solar
re-constructs lasso paths using the $L_0$ norm and averages the resulting
solution paths across subsamples. Path averaging retains the ranking
information of the informative variables while averaging out sensitivity to
high dimensionality, improving variable selection stability, efficiency, and
accuracy. We prove that: (i) with a high probability, path averaging perfectly
separates informative variables from redundant variables on the average $L_0$
path; (ii) solar variable selection is consistent and accurate; and (iii) the
probability that solar omits weak signals is controllable for finite sample
size. We also demonstrate that: (i) solar yields, with less than $1/3$ of the
lasso computation load, substantial improvements over lasso in terms of the
sparsity (64-84\% reduction in redundant variable selection) and accuracy of
variable selection; (ii) compared with the lasso safe/strong rule and variable
screening, solar largely avoids selection of redundant variables and rejection
of informative variables in the presence of complicated dependence structures;
(iii) the sparsity and stability of solar conserves residual degrees of freedom
for data-splitting hypothesis testing, improving the accuracy of post-selection
inference on weak signals with limited $n$; (iv) replacing lasso with solar in
bootstrap selection (e.g., bolasso or stability selection) produces a
multi-layer variable ranking scheme that improves selection sparsity and
ranking accuracy with the computation load of only one lasso realization; and
(v) given the computation resources, solar bootstrap selection is substantially
faster (98\% lower computation time) than the theoretical maximum speedup for
parallelized bootstrap lasso (confirmed by Amdahl's law).


  We consider stochastic bandit problems with a continuous set of arms and
where the expected reward is a continuous and unimodal function of the arm. No
further assumption is made regarding the smoothness and the structure of the
expected reward function. For these problems, we propose the Stochastic
Pentachotomy (SP) algorithm, and derive finite-time upper bounds on its regret
and optimization error. In particular, we show that, for any expected reward
function $\mu$ that behaves as $\mu(x)=\mu(x^\star)-C|x-x^\star|^\xi$ locally
around its maximizer $x^\star$ for some $\xi, C>0$, the SP algorithm is
order-optimal. Namely its regret and optimization error scale as
$O(\sqrt{T\log(T)})$ and $O(\sqrt{\log(T)/T})$, respectively, when the time
horizon $T$ grows large. These scalings are achieved without the knowledge of
$\xi$ and $C$. Our algorithm is based on asymptotically optimal sequential
statistical tests used to successively trim an interval that contains the best
arm with high probability. To our knowledge, the SP algorithm constitutes the
first sequential arm selection rule that achieves a regret and optimization
error scaling as $O(\sqrt{T})$ and $O(1/\sqrt{T})$, respectively, up to a
logarithmic factor for non-smooth expected reward functions, as well as for
smooth functions with unknown smoothness.


  In this paper we prove that several operator algebras are completely
isomorphic to each other; e.g., the $C^*_\lambda(F_k)$, $k\geq 2$, the
$C^*$-algebras generated by the regular left representation $\lambda:F_k\to
B(\ell_2(F_k))$, are completely isomorphic to each other.
  We also study the ``non-commutative'' analytic spaces introduced by G.
Popescu [Po], and give applications to Popescu's version of Von Neumann's
inequality.


  Suppose that $\mathcal{C}$ is a class of groups consisting only of periodic
groups and $\mathfrak{P}(\mathcal{C})^{\prime}$ is the set of prime numbers
each of which does not divide the order of any element of a
$\mathcal{C}$-group. A subgroup $Y$ of a group $X$ is called a)
$\mathcal{C}$-separable in this group if, for each $x \in X \setminus Y$, there
exists a homomorphism $\sigma$ of $X$ onto a group from $\mathcal{C}$ such that
$x\sigma \notin Y\sigma$; b) $\mathfrak{P}(\mathcal{C})^{\prime}$-isolated in
$X$ if, for any $x \in X$, $q \in \mathfrak{P}(\mathcal{C})^{\prime}$, the
inclusion $x^{q} \in Y$ implies that $x \in Y$. It is easy to see that if $Y$
is $\mathcal{C}$-separable in $X$, then it is
$\mathfrak{P}(\mathcal{C})^{\prime}$-isolated in this group. Let us say that
$X$ has the property $\mathcal{C}\mbox{-}\mathfrak{Sep}$ if all its
$\mathfrak{P}(\mathcal{C})^{\prime}$-isolated subgroups are
$\mathcal{C}$-separable. We find a condition that is sufficient for a nilpotent
group $N$ to have the property $\mathcal{C}\mbox{-}\mathfrak{Sep}$ provided
$\mathcal{C}$ is a root class (i.e., it contains non-trivial groups and is
closed under taking subgroups, extensions, and Cartesian products of the form
$\prod_{v \in V}U_{v}$, where $U, V \in \mathcal{C}$ and $U_{v}$ is an
isomorphic copy of $U$ for each $v \in V$). We also prove that if $N$ is
torsion-free, then the indicated condition is necessary for this group to have
$\mathcal{C}\mbox{-}\mathfrak{Sep}$.


  Let a word be a sequence of $n$ i.i.d. integer random variables. The
perimeter $P$ of the word is the number of edges of the word, seen as a
polyomino. In this paper, we present a probabilistic approach to the
computation of the moments of $P$. This is applied to uniform and geometric
random variables. We also show that, asymptotically, the distribution of $P$ is
Gaussian and, seen as a stochastic process, the perimeter converges in
distribution to a Brownian motion


  In symplectic mechanics, the magnetic term describing the interaction between
a charged particle and an external magnetic field has to be introduced by hand.
On the contrary, in generalised complex geometry, such magnetic terms in the
symplectic form arise naturally by means of B-transformations. Here we prove
that, regarding classical phase space as a generalised complex manifold, the
transformation law for the symplectic form under the action of a weak magnetic
field gives rise to Dirac's prescription for Poisson brackets in the presence
of constraints.


  We propose a new model of nonlinear electrodynamics with three parameters.
Born-Infeld electrodynamics and exponential electrodynamics are particular
cases of this model. The phenomenon of vacuum birefringence is studied. We show
that there is not singularity of the electric field at the origin of point-like
charged particles. The corrections to Coulomb's law at $r\rightarrow\infty$ are
obtained. We calculate the total electrostatic energy of charges for different
parameters of the model which is finite.


  The Beijing Electron-Positron Collider (BEPC) has been running for both high
energy physics (HEP) and synchrotron radiation (SR) researches since 1989. Good
performance of BEPC accomplishes a lot of achievements in the tau-charm region
over the past decade, and in many fields of SR applications as well. As an
upgrading scheme from BEPC, the BEPCII project was approved by the Chinese
government in the early time of this year, with a micro-beta scheme and
multi-bunch collision in a doublering machine installed in the current BEPC
tunnel. The design work of the BEPCII is being carried on. This paper will
describe the main progresses on the design of the BEPCII in the field of
accelerator physics.


  We show that there exist smooth surfaces violating Generic Vanishing in any
characteristic $p \geq 3$. As a corollary, we recover a result of Hacon and
Kov\'acs, producing counterexamples to Generic Vanishing in dimension 3 and
higher.


  Gaussian process regression in its most simplified form assumes normal
homoscedastic noise and utilizes analytically tractable mean and covariance
functions of predictive posterior distribution using Gaussian conditioning. Its
hyperparameters are estimated by maximizing the evidence, commonly known as
type II maximum likelihood estimation. Unfortunately, Bayesian inference based
on Gaussian likelihood is not robust to outliers, which are often present in
the observational training data sets. To overcome this problem, we propose a
robust process model in the Gaussian process framework with the likelihood of
observed data expressed as the Huber probability distribution. The proposed
model employs weights based on projection statistics to scale residuals and
bound the influence of vertical outliers and bad leverage points on the latent
functions estimates while exhibiting a high statistical efficiency at the
Gaussian and thick tailed noise distributions. The proposed method is
demonstrated by two real world problems and two numerical examples using
datasets with additive errors following thick tailed distributions such as
Students t, Laplace, and Cauchy distribution.


  As large language models (LLMs) continue to make significant strides, their
better integration into agent-based simulations offers a transformational
potential for understanding complex social systems. However, such integration
is not trivial and poses numerous challenges. Based on this observation, in
this paper, we explore architectures and methods to systematically develop
LLM-augmented social simulations and discuss potential research directions in
this field. We conclude that integrating LLMs with agent-based simulations
offers a powerful toolset for researchers and scientists, allowing for more
nuanced, realistic, and comprehensive models of complex systems and human
behaviours.


  Semantic segmentation using fine-resolution remotely sensed images plays a
critical role in many practical applications, such as urban planning,
environmental protection, natural and anthropogenic landscape monitoring, etc.
However, the automation of semantic segmentation, i.e., automatic
categorization/labeling and segmentation is still a challenging task,
particularly for fine-resolution images with huge spatial and spectral
complexity. Addressing such a problem represents an exciting research field,
which paves the way for scene-level landscape pattern analysis and decision
making. In this paper, we propose an approach for automatic land segmentation
based on the Feature Pyramid Network (FPN). As a classic architecture, FPN can
build a feature pyramid with high-level semantics throughout. However,
intrinsic defects in feature extraction and fusion hinder FPN from further
aggregating more discriminative features. Hence, we propose an Attention
Aggregation Module (AAM) to enhance multi-scale feature learning through
attention-guided feature aggregation. Based on FPN and AAM, a novel framework
named Attention Aggregation Feature Pyramid Network (A2-FPN) is developed for
semantic segmentation of fine-resolution remotely sensed images. Extensive
experiments conducted on three datasets demonstrate the effectiveness of our A2
-FPN in segmentation accuracy. Code is available at
https://github.com/lironui/A2-FPN.


  In this chapter, we review the eikonal technique to analytically derive
approximate quasi-normal mode frequencies of black holes. We first review the
procedure in General Relativity and extend it to theories beyond General
Relativity. As an example of the latter, we focus on scalar Gauss-Bonnet
gravity in which a scalar field can couple to the Gauss-Bonnet invariant in an
arbitrary way in the action. In this theory, metric and scalar perturbations
are coupled in the polar sector, forcing the isospectrality to break and making
the eikonal calculation more complex. We show that the analytic estimates can
accurately capture the numerical behavior of the quasi-normal mode frequencies,
especially when the coupling constant of the theory is small.


  IRAS04325+2402 (herafter IRAS04325) is a complex protostellar system hosting
two young stellar objects (AB and C in the following) at a separation of
1250AU. Here we present new deep Gemini imaging and spectroscopy for the system
covering the wavelength regime from 1-12mu as well as Sub-Millimeter Array
interferometry at 870mu, in combination with Spitzer and literature data.
Object AB is a low-mass star with a disk/envelope system and an outflow cavity,
which is prominently seen in infrared images. Object C, previously suspected to
be a brown dwarf, is likely a very low mass star, with an effective temperature
of ~3400K. It features an edge-on disk and an elongated envelope, and shows
strong indications for accretion and ejection activity. Both objects are likely
to drive parsec-scale molecular outflows. The two objects are embedded in an
isolated, dense molecular cloud core. High extinction, lack of X-ray emission,
and relatively high bolometric luminosity argue for a very young age below
1Myr. The disk/outflow systems of AB and C are misaligned by ~60deg against
each other and by 80 and 40deg against the orbital plane of the binary. The
system might be a good case for primordial misalignment, as opposed to
misalignment caused by dynamical interactions, because the outflow direction is
constant and the realignment timescale is likely larger than the system age.
This favours turbulent fragmentation, rather than rotational fragmentation, as
the formation scenario. We show that the spectral energy distributions and
images for the two objects can be reproduced with radiative transfer models for
disk/envelope systems. Our analysis provides reassurance in the established
paradigm for the structure and early evolution of YSOs, but stresses the
importance of developing 3D models with sophisticated dust chemistry
(abridged).


  In this study, we use Genetic Programming (GP) to compose new optimization
benchmark functions. Optimization benchmarks have the important role of showing
the differences between evolutionary algorithms, making it possible for further
analysis and comparisons. We show that the benchmarks generated by GP are able
to differentiate algorithms better than human-made benchmark functions. The
fitness measure of the GP is the Wasserstein distance of the solutions found by
a pair of optimizers. Additionally, we use MAP-Elites to both enhance the
search power of the GP and also illustrate how the difference between
optimizers changes by various landscape features. Our approach provides a novel
way to automate the design of benchmark functions and to compare evolutionary
algorithms.


  Visual In-Context Learning (VICL) is a prevailing way to transfer visual
foundation models to new tasks by leveraging contextual information contained
in in-context examples to enhance learning and prediction of query sample. The
fundamental problem in VICL is how to select the best prompt to activate its
power as much as possible, which is equivalent to the ranking problem to test
the in-context behavior of each candidate in the alternative set and select the
best one. To utilize more appropriate ranking metric and leverage more
comprehensive information among the alternative set, we propose a novel
in-context example selection framework to approximately identify the global
optimal prompt, i.e. choosing the best performing in-context examples from all
alternatives for each query sample. Our method, dubbed Partial2Global, adopts a
transformer-based list-wise ranker to provide a more comprehensive comparison
within several alternatives, and a consistency-aware ranking aggregator to
generate globally consistent ranking. The effectiveness of Partial2Global is
validated through experiments on foreground segmentation, single object
detection and image colorization, demonstrating that Partial2Global selects
consistently better in-context examples compared with other methods, and thus
establish the new state-of-the-arts.


  There is a perfect concordance between Friedmann's cosmological models and
the correspondent (purely gravitational interactions and negligible pressure)
Newtonian models. This renders quite intuitive the fact that in general
relativity no motion of bodies generates gravitational waves.


  We study the spin Hall effect (SHE) in graphene using a realistic
multi-orbital tight-binding model that includes the atomic spin-orbit
interaction. The SHE is found to be induced by the spin-dependent Aharonov-Bohm
phase. In the metallic case, the calculated values for the spin Hall
conductivity (SHC) are much smaller than the quantized Hall conductivity for
realistic parameter values of metallic graphene. In the insulating case,
quantization of the SHC is violated due to the multi-orbital effect. The
present study suggests that the SHE in a honeycomb lattice is enhanced by
chemical doping, such as the substitution of carbon atoms with boron atoms.


  Despite the success of deep learning in computer vision, algorithms to
recognize subtle and small objects (or regions) is still challenging. For
example, recognizing a baseball or a frisbee on a ground scene or a bone
fracture in an X-ray image can easily result in overfitting, unless a huge
amount of training data is available. To mitigate this problem, we need a way
to force a model should identify subtle regions in limited training data. In
this paper, we propose a simple but efficient supervised augmentation method
called Cut\&Remain. It achieved better performance on various medical image
domain (internally sourced- and public dataset) and a natural image domain
(MS-COCO$_s$) than other supervised augmentation and the explicit guidance
methods.
  In addition, using the class activation map, we identified that the
Cut\&Remain methods drive a model to focus on relevant subtle and small regions
efficiently. We also show that the performance monotonically increased along
the Cut\&Remain ratio, indicating that a model can be improved even though only
limited amount of Cut\&Remain is applied for, so that it allows low supervising
(annotation) cost for improvement.


  With the proliferation of social media, the detection of fake news has become
a critical issue that poses a significant threat to society. The dissemination
of fake information can lead to social harm and damage the credibility of
information. To address this issue, deep learning has emerged as a promising
approach, especially with the development of natural language processing (NLP).
This study addresses the problem of detecting fake news on social media, which
poses a significant challenge to society. This study proposes a new approach
named GANM for fake news detection that employs NLP techniques to encode nodes
for news context and user content and uses three graph convolutional networks
to extract features and aggregate users' endogenous and exogenous information.
The GANM employs a unique global attention mechanism with memory to learn the
structural homogeneity of news dissemination networks. The approach achieves
good results on a real dataset.


  Self-consistent approaches to superfluid many-fermion systems in 3-dimensions
(and subsequent time-dependent approaches) require a large number of
diagonalizations of very large dimension hermitian matrices, which results in
enormous computational costs. We present an approach based on the shifted
conjugate-orthogonal conjugate-gradient (COCG) method for the evaluation of the
Green's function, from which we subsequently extract various densities
(particle number, spin, current, kinetic energy, etc.) of a nuclear system
needed in self-consistent approaches. The approach eschews the construction of
the quasiparticle wavefunctions and their corresponding quasiparticle energies,
which are never explicitly needed in any density functional approaches. As
benchmarks we present calculations for nuclei with axial symmetry, including
the ground state of spherical (magic or semi-magic) and axially deformed
nuclei, the saddle-point in the $^{240}$Pu constrained fission path, and a
vortex in the neutron star crust.


  In this note we give the definition of the "doubling operation" for simple
polytopes, find the formula for the h-polynomial of new polytope.As an
application of this operation we establish the relationship between
moment-angle manifolds and their real analogues and prove the toral rank
conjecture for moment-angle manifolds Z_P.


  For a Markovian dynamics on discrete states, the logarithmic ratio of
waiting-time distributions between two successive, instantaneous transitions in
forward and backward direction is a measure of time-irreversibility. It thus
serves as an entropy estimator, which is exact in the case of a uni-cyclic
network. We adopt this framework to overdamped Langevin dynamics, where such
transitions have finite duration. By introducing milestones based on the
observation of a particle at at least three points, we identify an entropy
estimator that becomes exact for driven motion along a one-dimensional
potential.


  Medical imaging plays a pivotal role in diagnosis and treatment in clinical
practice. Inspired by the significant progress in automatic image captioning,
various deep learning (DL)-based methods have been proposed to generate
radiology reports for medical images. Despite promising results, previous works
overlook the uncertainties of their models and are thus unable to provide
clinicians with the reliability/confidence of the generated radiology reports
to assist their decision-making. In this paper, we propose a novel method to
explicitly quantify both the visual uncertainty and the textual uncertainty for
DL-based radiology report generation. Such multi-modal uncertainties can
sufficiently capture the model confidence degree at both the report level and
the sentence level, and thus they are further leveraged to weight the losses
for more comprehensive model optimization. Experimental results have
demonstrated that the proposed method for model uncertainty characterization
and estimation can produce more reliable confidence scores for radiology report
generation, and the modified loss function, which takes into account the
uncertainties, leads to better model performance on two public radiology report
datasets. In addition, the quality of the automatically generated reports was
manually evaluated by human raters and the results also indicate that the
proposed uncertainties can reflect the variance of clinical diagnosis.


  We investigate advection dominated, transsonic accretion flows in the
vicinity of a Kerr black hole. We take into account all relativistic effects in
the dynamics of the flow and in the propagation of light. We assume the matter
to be weakly magnetized and cool via the thermal synchrotron and Bremsstrahlung
radiation. We include also the effects of Comptonization. We calculate the
spectra of radiation as seen by observers located at different positions
relative to the equatorial plane of the disk. The radiation emitted by the
accreting matter is anisotropic and observers near the equatorial plane
register a higher energy flux. This effect is more pronounced in the case of
slowly rotating black holes. We calculate also the shape of a hypothetical
gamma line, which may be produced by the thermonuclear reactions in the inner
part of the flow. The line is strongly broadened, but the fact that the flow is
quasi-spherical removes the two-peak shape of the line seen in the spectra
emitted from thin, Keplerian disks. The kinematics of the advection dominated
flows is not unique (as opposed to Keplerian disks or spherical free-fall) and
it would probably be difficult to find strong limits on source models using the
spectral observations.


  Associated absorption lines (AALs) are valuable probes of the gaseous
environments near quasars. Here we discuss high-resolution (6.7 km/s) spectra
of the AALs in the radio-loud quasar 3C 191 (redshift z=1.956). The measured
AALs have ionizations ranging from Mg I to N V, and multi-component profiles
that are blueshifted by ~400 to ~1400 km/s relative to the quasar's broad
emission lines. These data yield the following new results. 1) The density
based on Si II*/Si II lines is ~300 cm-3, implying a distance of ~28 kpc from
the quasar if the gas is photoionized. 2) The characteristic flow time is thus
\~3 x 10^7 yr. 3) Strong Mg I AALs identify neutral gas with very low
ionization parameter and high density. We estimate n_H > 5 x 10^4 cm-3 in this
region, compared to ~15 cm-3 where the N V lines form. 4) The total column
density is N_H < 4 x 10^18 cm-2 in the neutral gas and N_H ~ 2 x 10^20 cm-2 in
the moderately ionized regions. 5) The total mass in the AAL outflow is M ~ 2 x
10^9 Mo, assuming a global covering factor (as viewed from the quasar) of ~10%
>. 6) The absorbing gas only partially covers the background light source(s)
along our line(s) of sight, requiring absorption in small clouds or filaments
<0.01 pc across. The ratio N_H/n_H implies that the clouds have radial (line-
of-sight) thicknesses <0.2 pc. These properties might characterize a sub-class
of AALs that are physically related to quasars but form at large distances. We
propose a model for the absorber in which pockets of dense neutral gas are
surrounded by larger clouds of generally lower density and higher ionization.
This outflowing material might be leftover from a blowout associated with a
nuclear starburst, the onset of quasar activity or a past broad absorption line
(BAL) wind phase.


  The kernel of a pair of linear systems is studied in the framework of
commutative ring theory with applications to behavioral perspective of linear
systems


  We report on Monte Carlo studies of the kinetic exchange model for (III,Mn)V
ferromagnetic semiconductors in which S=5/2 local moments, representing Mn^{2+}
ions, are exchange coupled to band electrons. We treat the Mn^{2+}$ spin
orientations as classical degrees of freedom and use the Hybrid Monte Carlo
algorithm to explore thermodynamically important Mn spin configurations. The
critical temperature T_c of the model is unambiguously signalled in our
finite-size simulations by pronounced peaks in fluctuations of both Mn and band
carrier total spins. The T_c's we obtain are, over much of the model's
parameter space, substantially smaller than those estimated using mean-field
theory. When mean-field theory fails, short-range magnetic order and finite
{\em local} carrier spin polarisation are present for temperatures
substantially larger than T_{c}. For the simplest version of the model, which
has a single parabolic band with effective mass m^*, the dependence of T_{c} on
m^* is sublinear at large masses, in disagreement with the mean-field theory
result T_{c} \propto m^*. For the carrier densities studied, results obtained
with a six-band Luttinger model band Hamiltonian are in good agreement with
those obtained for a model with a single parabolic band whose effective mass is
equal to the Kohn-Luttinger model heavy-hole mass.


  In linear models it is common to have situations where several regression
coefficients are zero. In these situations a common tool to perform regression
is a variable selection operator. One of the most common such operators is the
LASSO operator, which promotes point estimates which are zero. The LASSO
operator and similar approaches, however, give little in terms of easily
interpretable parameters to determine the degree of variable selectivity. In
this paper we propose a new family of selection operators which builds on the
geometry of LASSO but which yield an easily interpretable way to tune
selectivity. These operators correspond to Bayesian prior densities and hence
are suitable for Bayesian inference. We present some examples using simulated
and real data, with promising results.


  Phase separation of multicomponent liquid mixtures plays an integral part in
many processes ranging from industry to cellular biology. In many cases the
morphology of coexisting phases is crucially linked to the function of the
separated mixture, yet it is unclear what determines morphology when multiple
phases are present. We developed a graph theory approach to predict the
topology of coexisting phases from a given set of surface energies (forward
problem), enumerate all topologically distinct morphologies, and reverse
engineer conditions for surface energies that produce the target morphology
(inverse problem).


  Recent experiments and simulations have revealed glassy features in the
cytoplasm, living tissues as well as dense assemblies of self propelled
colloids. This leads to a fundamental question: how do these non-equilibrium
(active) amorphous materials differ from conventional passive glasses, created
either by lowering temperature or by increasing density? To address this we
investigate the aging behaviour after a quench to an almost arrested state of a
model active glass former, a Kob-Andersen glass in two dimensions. Each
constituent particle is driven by a constant propulsion force whose direction
diffuses over time. Using extensive molecular dynamics simulations we reveal
rich aging behaviour of this dense active matter system: short persistence
times of the active forcing lead to effective thermal aging; in the opposite
limit we find a two-step aging process with active athermal aging at short
times followed by activity-driven aging at late times. We develop a dedicated
simulation method that gives access to this long-time scaling regime for highly
persistent active forces.


  We use the theory of Yennie, Frautschi and Suura to realize, via Monte Carlo
methods, the process $f\,\bbbarf\to f'\,\bbbarf'+n\gamma$ at SSC and LHC
energies, where $f$ and $f'$ are quarks or leptons. QED infrared divergences
are canceled to all orders in perturbation theory. The resulting Monte Carlo
event generator, SSC-YFS2, is used to study the effects of initial-state photon
radiation on these processes in the SSC environment. Sample Monte Carlo data
are presented and discussed. We find that the respective multiple-photon
effects must be taken into account in discussing precise predictions for SSC
physics processes.


  A theoretical expression for the drag on a spherical bubble is derived for
the entire range from very viscous to inertial flow conditions. It is based on
a solution for only that part of the velocity profile that determines the drag.
It is assumed the surface of the bubble has a zero tangential stress condition.
Excellent agreement with a previously proposed empirical model by Mei et al. is
obtained. This shows that a theoretical framework with relatively simple
physics can still predict the terminal velocity of a spherical bubble
accurately. To the best of our knowledge, this is one of the few models in
fluid dynamics to predict drag on an object for a range of Reynolds numbers
that spans many orders of magnitude.


  In condensed matter systems, zero-dimensional or one-dimensional Majorana
modes can be realized respectively as the end and edge states of
one-dimensional and two-dimensional topological superconductors. In this
$\textit{top-down}$ approach, $(d-1)$-dimensional Majorana modes are obtained
as the boundary states of a topologically nontrivial $d$-dimensional bulk. In a
$\textit{bottom-up}$ approach instead, $d$-dimensional Majorana modes in a
$d$-dimensional system can be realized as the continuous limit of a periodic
lattice of coupled $(d-1)$-dimensional Majorana modes. We illustrate this idea
by considering one-dimensional proximitized superconductors with
spatially-modulated potential or magnetic fields. The ensuing inhomogeneous
topological state exhibits one-dimensional counterpropagating Majorana modes
with finite dispersion, and with a Majorana gap that can be controlled by
external fields. In the massless case, the Majorana modes have opposite
Majorana polarizations and pseudospins, are conformally invariant, and realize
centrally extended quantum mechanical supersymmetry. The supersymmetry exhibits
spontaneous partial breaking. Consequently, the massless Majorana fermion can
be identified as a Goldstino, i.e., the Nambu-Goldstone fermion associated with
the spontaneously broken supersymmetry.


  The assistance of an intense optical laser pulse on electron-positron pair
production by the Breit-Wheeler and Schwinger processes in XFEL fields is
analyzed. The impact of a laser beam on high-energy photon collisions with XFEL
photons consists in a phase space redistribution of the pairs emerging in the
Breit-Wheeler sub-process. We provide numerical examples of the differential
cross section for parameters related to the European XFEL. Analogously, the
Schwinger type pair production in pulsed fields with oscillating components
referring to a superposition of optical laser and XFEL frequencies is
evaluated. The residual phase space distribution of created pairs is sensitive
to the pulse shape and may differ significantly from transiently achieved mode
occupations.


  Let $\mathcal{T}_n$ be the set of trees with $n$ vertices. Suppose that each
tree in $\mathcal{T}_n$ is equally likely. We show that the number of different
rooted trees of a tree equals $(\mu_r+o(1))n$ for almost every tree of
$\mathcal{T}_n$, where $\mu_r$ is a constant. As an application, we show that
the number of any given pattern in $\mathcal{T}_n$ is also asymptotically
normally distributed with mean $\sim \mu_M n$ and variance $\sim \sigma_M n$,
where $\mu_M, \sigma_M$ are some constants related to the given pattern. This
solves an open question claimed in Kok's thesis.