  Learning the directed acyclic graph (DAG) structure of a Bayesian network
from observational data is a notoriously difficult problem for which many
hardness results are known. In this paper we propose a provably polynomial-time
algorithm for learning sparse Gaussian Bayesian networks with equal noise
variance --- a class of Bayesian networks for which the DAG structure can be
uniquely identified from observational data --- under high-dimensional
settings. We show that $O(k^4 \log p)$ number of samples suffices for our
method to recover the true DAG structure with high probability, where $p$ is
the number of variables and $k$ is the maximum Markov blanket size. We obtain
our theoretical guarantees under a condition called Restricted Strong Adjacency
Faithfulness, which is strictly weaker than strong faithfulness --- a condition
that other methods based on conditional independence testing need for their
success. The sample complexity of our method matches the information-theoretic
limits in terms of the dependence on $p$. We show that our method out-performs
existing state-of-the-art methods for learning Gaussian Bayesian networks in
terms of recovering the true DAG structure while being comparable in speed to
heuristic methods.


  Small-to-medium size enterprises (SMEs), including many startup firms, need
to manage interrelated flows of cash and inventories of goods. In this paper,
we model a firm that can finance its inventory (ordered or manufactured) with
loans in order to meet random demand which in general may not be time
stationary. The firm earns interest on its cash on hand and pays interest on
its debt. The objective is to maximize the expected value of the firm's
%working capital at the end of a finite planning horizon. Our study shows that
the optimal ordering policy is characterized by a pair of threshold variables
for each period as function of the initial state of the period. Further, upper
and lower bounds for the threshold values are developed using two
simple-to-compute ordering policies. Based on these bounds, we provide an
efficient algorithm to compute the two threshold values. Since the underlying
state space is two-dimensional which leads to high computational complexity of
the optimization algorithm, we also derive upper bounds for the optimal value
function by reducing the optimization problem to one dimension. Subsequently,
it is shown that policies of similar structure are optimal when the loan and
deposit interest rates are piecewise linear functions, when there is a maximal
loan limit and when unsatisfied demand is backordered. Finally, further
managerial insights are provided with numerical studies.


  In processing raster digital elevation models (DEMs) it is often necessary to
assign drainage directions over flats---that is, over regions with no local
elevation gradient. This paper presents an approach to drainage direction
assignment which is not restricted by a flat's shape, number of outlets, or
surrounding topography. Flow is modeled by superimposing a gradient away from
higher terrain with a gradient towards lower terrain resulting in a drainage
field exhibiting flow convergence, an improvement over methods which produce
regions of parallel flow. This approach builds on previous work by Garbrecht
and Martz (1997), but presents several important improvements. The improved
algorithm guarantees that flats are only resolved if they have outlets. The
algorithm does not require iterative application; a single pass is sufficient
to resolve all flats. The algorithm presents a clear strategy for identifying
flats and their boundaries. The algorithm is not susceptible to loss of
floating-point precision. Furthermore, the algorithm is efficient, operating in
O( N ) time whereas the older algorithm operates in O( N^(3/2) ) time. In
testing, the improved algorithm ran 6.5 times faster than the old for a 100 x
100 cell flat and 69 times faster for a 700 x 700 cell flat. In tests on actual
DEMs, the improved algorithm finished its processing 38--110 times sooner while
running on a single processor than a parallel implementation of the old
algorithm did while running on 16 processors. The improved algorithm is an
optimal, accurate, easy-to-implement drop-in replacement for the original.
Pseudocode is provided in the paper and working source code is provided in the
Supplemental Materials.


  We present the first high-redshift Hubble diagram for Type II-P supernovae
(SNe II-P) based upon five events at redshift up to z~0.3. This diagram was
constructed using photometry from the Canada-France-Hawaii Telescope Supernova
Legacy Survey and absorption line spectroscopy from the Keck observatory. The
method used to measure distances to these supernovae is based on recent work by
Hamuy & Pinto (2002) and exploits a correlation between the absolute brightness
of SNe II-P and the expansion velocities derived from the minimum of the Fe II
516.9 nm P-Cygni feature observed during the plateau phases. We present three
refinements to this method which significantly improve the practicality of
measuring the distances of SNe II-P at cosmologically interesting redshifts.
These are an extinction correction measurement based on the V-I colors at day
50, a cross-correlation measurement for the expansion velocity and the ability
to extrapolate such velocities accurately over almost the entire plateau phase.
We apply this revised method to our dataset of high-redshift SNe II-P and find
that the resulting Hubble diagram has a scatter of only 0.26 magnitudes, thus
demonstrating the feasibility of measuring the expansion history, with present
facilities, using a method independent of that based upon supernovae of Type
Ia.


  We propose a method for deriving equivalent one-dimensional models for
slender non-linear structures. The approach is designed to be broadly
applicable, and can handle in principle finite strains, finite rotations,
arbitrary cross-sections shapes, inhomogeneous elastic properties across the
cross-section, arbitrary elastic constitutive laws (possibly with low symmetry)
and arbitrary distributions of pre-strain, including finite pre-strain. It is
based on a kinematic parameterization of the actual configuration that makes
use of a center-line, a frame of directors, and local degrees of freedom
capturing the detailed shape of cross-sections. A relaxation method is applied
that holds the framed center-line fixed while relaxing the local degrees of
freedom; it is asymptotically valid when the macroscopic strain and the
properties of the rod vary slowly in the longitudinal direction. The outcome is
a one-dimensional strain energy depending on the apparent stretching, bending
and twisting strain of the framed center-line; the dependence on the strain
gradients is also captured, yielding an equivalent rod model that is
asymptotically exact to higher order. The method is presented in a fully
non-linear setting and it is verified against linear and weakly non-linear
solutions available from the literature.


  In this article we establish a large deviation principle for the family
{\nu_{\epsilon}:\epsilon \in (0,1)} of distributions of the scaled stochastic
processes {P_{-\log\sqrt{\epsilon}}Z_t}_{t\leq 1}, where (Z_t)_{t\in \lbrack
0,1]} is a square-integrable martingale over Brownian filtration and
(P_t)_{t\geq 0} is the Ornstein-Uhlenbeck semigroup. The rate function is
identified as well in terms of the Wiener-It\^{o} chaos decomposition of the
terminal value Z_{1}. The result is established by developing a continuity
theorem for large deviations, together with two essential tools, the
hypercontractivity of the Ornstein-Uhlenbeck semigroup and Lyons' continuity
theorem for solutions of Stratonovich type stochastic differential equations.


  This paper provides an alternative approach referred to as pseudo-predictor
feedback (PPF) for stabilization of linear systems with multiple input delays.
Differently from the traditional predictor feedback which is from the model
reduction appoint of view, the proposed PPF utilizes the idea of prediction by
generalizing the corresponding results for linear systems with a single input
delay to the case of multiple input delays. Since the PPF will generally lead
to distributed controllers, a truncated pseudopredictor feedback (TPPF)
approach is established instead which gives finite dimensional controllers. It
is shown that the TPPF can compensate arbitrarily large yet bounded delays as
long as the open-loop system is only polynomially unstable. The proposed TPPF
approach is then used to solve the consensus problems for multi-agent systems
characterized by linear systems with multiple input delays. Numerical examples
show the effectiveness of the proposed approach.


  Energy storage has exhibited great potential in providing flexibility in
power system to meet critical peak demand and thus reduce the overall
generation cost, which in turn stabilizes the electricity prices. In this work,
we exploit the opportunities for the independent system operator (ISO) to
invest and manage storage as public asset, which could systematically provide
benefits to the public. Assuming a quadratic generation cost structure, we
apply parametric analysis to investigate the ISO's problem of economic
dispatch, given variant quantities of storage investment. This investment is
beneficial to users on expectation. However, it may not necessarily benefit
everyone. We adopt the notion of marginal system cost impact (MCI) to measure
each user's welfare and show its relationship with the conventional locational
marginal price. We find interesting convergent characteristics for MCI.
Furthermore, we perform $k$-means clustering to classify users for effective
user profiling and conduct numerical studies on both prototype and IEEE test
systems to verify our theoretical conclusions.


  Using a nonperturbative method based on asymptotic behaviour of Wilson loops
we calculate masses of glueballs and corresponding Regge-trajectories. The
method contains no fitting parameters and the mass scale is fixed by the meson
Regge slope. Theoretical predictions for lowest glueball states are in a
perfect agreement with lattice results. The leading glueball trajectory and its
relation to the Pomeron is discussed in details. Important role of mixing
between glueball and q\bar q trajectories is emphasized.


  In this paper, we describe the result of our experiments on Algorithms for
the Food-Selection Problem, which is the fundamental problem first stated and
addressed in the seminal paper \cite{pigout}. Because the key aspect of any
experimental evaluation is the \textbf{reproducibility}, we detail deeply the
setup of all our experiments, thus leaving to the interested eater the
opportunity to reproduce all the results described in this paper. More
specifically, we describe all the answers we provided to the questions proposed
in \cite{pigout}: Where can I have dinner tonight? What is the typical Roman
cuisine that I should (not) miss? Where can I find the best coffee or gelato in
town?


  The surveillance, analysis and ultimately the efficient long-term prediction
and control of epidemic dynamics appear to be one of the major challenges
nowadays. Detailed atomistic mathematical models play an important role towards
this aim. In this work it is shown how one can exploit the Equation Free
approach and optimization methods such as Simulated Annealing to bridge
detailed individual-based epidemic simulation with coarse-grained,
systems-level, analysis. The methodology provides a systematic approach for
analyzing the parametric behavior of complex/ multi-scale epidemic simulators
much more efficiently than simply simulating forward in time. It is shown how
steady state and (if required) time-dependent computations, stability
computations, as well as continuation and numerical bifurcation analysis can be
performed in a straightforward manner. The approach is illustrated through a
simple individual-based epidemic model deploying on a random regular connected
graph. Using the individual-based microscopic simulator as a black box
coarse-grained timestepper and with the aid of Simulated Annealing I compute
the coarse-grained equilibrium bifurcation diagram and analyze the stability of
the stationary states sidestepping the necessity of obtaining explicit closures
at the macroscopic level under a pairwise representation perspective.


  Ultra-Diffuse galaxies are gravitational systems with quite varied
properties. On one hand we have cases like DF2 and DF4, both observed by the
Dragonfly Array Telescope, claimed to be highly-deficient in dark matter. On
the other hand, we have also observed dark matter dominated UDGs, such as DF44,
estimated to be at $99\%$ dark. Such variety of behaviors might be a problem
for both the standard dark matter paradigm and for alternative theories of
gravity. Here we consider a modified gravity theory belonging to the family of
Degenerate Higher-Order Scalar Tensor theories to study the internal kinematics
of both DF4 and DF44. The peculiarity of the chosen model is the partial
breaking of its Vaishtein screening mechanism for which it might have an
influence not only on cosmological but also on astrophysical scales. We
consider two different possibilities: one in which the model only plays the
role of dark energy; and another one in which it might also mimic a sort of
effective dark matter. We get conflicting results. For DF4 we confirm that the
galaxy dynamics might be successfully described even only by a stellar
component and that, at least at the scale which are probed, the content of dark
matter is quite low. In addition to that, we show that the DHOST model is
totally consistent with data and is statistically equivalent to a standard
General Relativity dark matter scenario, and it might even replace dark matter.
On the contrary, DF44 requires dark matter both in General Relativity and in
our DHOST model. When the latter is considered only as a cosmological dark
energy fluid, it is statistically fully reliable and equivalent to General
Relativity. But when we try to use it to substitute dark matter, although we
get good fits to the data, the constraints on the theoretical parameters are in
sharp contrast with those derived from more stringent probes from the stellar
scales.


  Suppressing charge recombination is key for organic solar cells to become
commercial reality. However, there is still no conclusive picture of how
recombination losses are influenced by the complex nanoscale morphology. Here,
new insight is provided by revisiting the P3HT:PCBM blend, which is still one
of the best performers regarding reduced recombination. By changing small
details in the annealing procedure, two model morphologies were prepared that
vary in phase separation, molecular order and phase purity, as revealed by
electron tomography and optical spectroscopy. Both systems behave very
similarly with respect to charge generation and transport, but differ
significantly in bimolecular recombination. Only the system containing P3HT
aggregates of high crystalline quality and purity is found to achieve
exceptionally low recombination rates. The high-quality aggregates support
charge delocalization, which assists the re-dissociation of interfacial
charge-transfer states formed upon the encounter of free carriers. For devices
with the optimized morphology, an exceptional long hole diffusion length is
found, which allows them to work as Shockley-type solar cells even in thick
junctions of 300 nm. In contrast, the encounter rate and the size of the
phase-separated domains appears to be less important.


  We analyze an interacting particle system with a Markov evolution of
birth-and-death type. We have shown that a local competition mechanism
(realized via a density dependent mortality) leads to a globally regular
behavior of the population in course of the stochastic evolution.


  Different from universal object detection, referring expression comprehension
(REC) aims to locate specific objects referred to by natural language
expressions. The expression provides high-level concepts of relevant visual and
contextual patterns, which vary significantly with different expressions and
account for only a few of those encoded in the REC model. This leads us to a
question: do we really need the entire network with a fixed structure for
various referring expressions? Ideally, given an expression, only
expression-relevant components of the REC model are required. These components
should be small in number as each expression only contains very few visual and
contextual clues. This paper explores the adaptation between expressions and
REC models for dynamic inference. Concretely, we propose a neat yet efficient
framework named Language Adaptive Dynamic Subnets (LADS), which can extract
language-adaptive subnets from the REC model conditioned on the referring
expressions. By using the compact subnet, the inference can be more economical
and efficient. Extensive experiments on RefCOCO, RefCOCO+, RefCOCOg, and
Referit show that the proposed method achieves faster inference speed and
higher accuracy against state-of-the-art approaches.


  On the occasion of its 25th anniversary, we revise Chen's derivative rule for
electron tunneling [C.J. Chen, Phys. Rev. B 42, 8841 (1990)] for the purpose of
computationally efficient simulations of scanning tunneling microscopy (STM)
based on first principles electronic structure data. The revised model allows
the weighting of tunneling matrix elements of different tip orbital characters
by an arbitrary energy independent choice or based on energy dependent
weighting coefficients obtained by an expansion of the tip single electron
wavefunctions/density of states projected onto the tip apex atom. Tip-orbital
interference in the STM junction is included in the model by construction and
can be analyzed quantitatively. As a further advantage, arbitrary tip
geometrical orientations are included in the revised model by rotating the
coordinate system of the tip apex using Euler angles and redefining the
weighting coefficients of the tunneling matrix elements. We demonstrate the
reliability of the model by applying it to two functionalized surfaces of
recent interest where quantum interference effects play an important role in
the STM imaging process: N-doped graphene and a magnetic Mn2H complex on the
Ag(111) surface. We find that the proposed tunneling model is 25 times faster
than the Bardeen method concerning computational time, while maintaining good
agreement. Our results show that the electronic structure of the tip has a
considerable effect on STM images, and the Tersoff-Hamann model does not always
provide sufficient results in view of quantum interference effects. For both
studied surfaces we highlight the importance of interference between s and pz
tip orbitals that can cause a significant contrast change in the STM images.


  We derive analytic results for scalar massless bosonic vacuum sum-integrals
at two loops. Building upon a recent factorization proof of massive two-loop
vacuum integrals, we are able to solve the corresponding Matsubara sums and map
the result onto one-loop structures, thereby proving factorization also in the
sum-integral setting. Analytic results are provided for generic integer-valued
propagator- and numerator-powers of the class of sum-integrals under
consideration, allowing to eliminate them from any perturbative expansion,
dramatically simplifying the evaluation of some observables encountered e.g. in
hot QCD.


  We study the status of the reactor antineutrino anomaly in light of recent
reactor flux models obtained with the conversion and summation methods. We
present a new improved calculation of the IBD yields of the standard
Huber-Mueller (HM) model and those of the new models. We show that the reactor
rates and the fuel evolution data are consistent with the predictions of the
Kurchatov Institute (KI) conversion model and with those of the Estienne-Fallot
(EF) summation model, leading to a plausible robust demise of the reactor
antineutrino anomaly. We show that the results of several goodness of fit tests
favor the KI and EF models over other models that we considered. We also
discuss the implications of the new reactor flux models for short-baseline
neutrino oscillations due to active-sterile oscillations. We show that reactor
data give upper bounds on active-sterile neutrino mixing that are not very
different for the reactor flux models under consideration and are in tension
with the large mixing required by the Gallium anomaly that has been refreshed
by the recent results of the BEST experiment.


  Feedback is indispensable in galaxy formation. However, lacking resolutions,
cosmological simulations often use ad hoc feedback parameters. Conversely,
small-box simulations, while better resolving the feedback, cannot capture gas
evolution beyond the simulation domain. We aim to bridge the gap by
implementing small-box results of supernovae-driven outflows into dark matter
halo-scale simulations and studying their impact on large scales. Galactic
outflows are multiphase, but small-box simulations show that the hot phase
(T$\approx$ 10$^{6-7}$ K) carries the majority of energy and metals. We
implement hot outflows in idealized simulations of the Milky Way halo, and
examine how they impact the circumgalactic medium (CGM). In this paper, we
discuss the case when the star formation surface density is low and therefore
the emerging hot outflows are gravitationally bound by the halo. We find that
outflows form a large-scale, metal-enriched atmosphere with fountain motions.
As hot gas accumulates, the inner atmosphere becomes "saturated". Cool gas
condenses, with a rate balancing the injection of the hot outflows. This
balance leads to a universal density profile of the hot atmosphere, independent
of mass outflow rate. The atmosphere has a radially-decreasing temperature,
naturally producing the observed X-ray luminosity and column densities of O VI,
O VII, O VIII. The self-regulated atmosphere has a baryon and a metal mass of
$(0.5-1.2)\times 10^{10}M_\odot$ and $(0.6-1.4)\times 10^8 M_\odot$,
respectively, small compared to the ``missing" baryons and metals from the
halo. We conjecture that the missing materials reside at even larger radii,
ejected by more powerful outflows in the past.


  We study the Bochner-Riesz problem for the twisted Laplacian $\mathcal L$ on
$\mathbb R^2$. For $p\in [1, \infty]\setminus\{2\}$, it has been conjectured
that the Bochner-Riesz means $S_\lambda^\delta(\mathcal L) f$ of order $\delta$
converges in $L^p$ for every $f\in L^p$ if and only if $\delta>
\max(0,|(p-2)/p|-1/2)$. We prove the conjecture by obtaining uniform $L^p$
bounds on $S_\lambda^\delta(\mathcal L)$ up to the sharp summability indices.


  Understanding how the brain responds to sensory inputs is challenging: brain
recordings are partial, noisy, and high dimensional; they vary across sessions
and subjects and they capture highly nonlinear dynamics. These challenges have
led the community to develop a variety of preprocessing and analytical (almost
exclusively linear) methods, each designed to tackle one of these issues.
Instead, we propose to address these challenges through a specific end-to-end
deep learning architecture, trained to predict the brain responses of multiple
subjects at once. We successfully test this approach on a large cohort of
magnetoencephalography (MEG) recordings acquired during a one-hour reading
task. Our Deep Recurrent Encoding (DRE) architecture reliably predicts MEG
responses to words with a three-fold improvement over classic linear methods.
To overcome the notorious issue of interpretability of deep learning, we
describe a simple variable importance analysis. When applied to DRE, this
method recovers the expected evoked responses to word length and word
frequency. The quantitative improvement of the present deep learning approach
paves the way to better understand the nonlinear dynamics of brain activity
from large datasets.


  We study regularity and numerical methods for two-sided fractional diffusion
equations with a lower-order term.
  We show that the regularity of the solution in weighted Sobolev spaces can be
greatly improved compared to that in standard Sobolev spaces.
  With this regularity, we improve higher-order convergence of a spectral
Galerkin method. We present a spectral Petrov-Galerkin method and provide an
optimal error estimate for the Petrov-Galerkin method.
  Numerical results are presented to verify our theoretical convergence orders.


  We consider the interaction of a magnetically trapped Bose-Einstein
condensate of Rubidium atoms with the stationary microwave radiation field
sustained by a coplanar waveguide resonator. This coupling allows for the
measurement of the magnetic field of the resonator by means of counting the
atoms that fall out of the condensate due to hyperfine transitions to
non-trapped states. We determine the quantum efficiency of this detection
scheme and show that weak microwave fields at the single-photon level can be
sensed with an integration time on the order of a second.


  We show that the presence of a two-dimensional inertial manifold for an
ordinary differential equation in ${\mathbb R}^{n}$ permits reducing the
problem of determining asymptotically orbitally stable limit cycles to the
Poincare--Bendixson theory. In the case $n=3$ we implement such a scenario for
a model of a satellite rotation around a celestial body of small mass and for a
biochemical model.


  Fine-grained visual categorization (FGVC) aims to discriminate similar
subcategories, whose main challenge is the large intraclass diversities and
subtle inter-class differences. Existing FGVC methods usually select
discriminant regions found by a trained model, which is prone to neglect other
potential discriminant information. On the other hand, the massive interactions
between the sequence of image patches in ViT make the resulting class-token
contain lots of redundant information, which may also impacts FGVC performance.
In this paper, we present a novel approach for FGVC, which can simultaneously
make use of partial yet sufficient discriminative information in environmental
cues and also compress the redundant information in class-token with respect to
the target. Specifically, our model calculates the ratio of high-weight regions
in a batch, adaptively adjusts the masking threshold and achieves moderate
extraction of background information in the input space. Moreover, we also use
the Information Bottleneck~(IB) approach to guide our network to learn a
minimum sufficient representations in the feature space. Experimental results
on three widely-used benchmark datasets verify that our approach can achieve
outperforming performance than other state-of-the-art approaches and baseline
models.


  The relationship between jet properties and the underlying geometry of the
medium produced in heavy ion collisions can be explored through a measurement
of the correlation between the axes of reconstructed jets and the reaction
plane, defined as "jet v2." Such a measurement provides information on the
pathlength dependence of medium-induced parton energy loss and may also be used
to assess biases in jet-finding methods. We present first measurements of jet
v2 in sqrt(sNN) = 200 GeV Au+Au collisions in the STAR experiment at RHIC. In
order to reduce the artificial jet - event plane bias, which results from jet
fragments being included in the event plane calculation, detectors at forward
pseudorapidity are used to determine the event plane when measuring the v2 of
reconstructed jets at mid-rapidity. These measurements demonstrate a non-zero
jet v2, which is indicative of pathlength-dependent parton energy loss.


  We show that a Calogero-Sutherland type model with anharmonic interactions of
fourth and sixth orders leads to the matrix model corresponding to the branched
polymers. We also show that by suitably modifying this model one can also
obtain N-particle problems which are connected to matrix models corresponding
to the pure gravity phase as well as corresponding to the transition point
between the soap bubble and the branched polymer phase.


  With the rapid global spread of COVID-19, more and more data related to this
virus is becoming available, including genomic sequence data. The total number
of genomic sequences that are publicly available on platforms such as GISAID is
currently several million, and is increasing with every day. The availability
of such \emph{Big Data} creates a new opportunity for researchers to study this
virus in detail. This is particularly important with all of the dynamics of the
COVID-19 variants which emerge and circulate. This rich data source will give
us insights on the best ways to perform genomic surveillance for this and
future pandemic threats, with the ultimate goal of mitigating or eliminating
such threats. Analyzing and processing the several million genomic sequences is
a challenging task. Although traditional methods for sequence classification
are proven to be effective, they are not designed to deal with these specific
types of genomic sequences. Moreover, most of the existing methods also face
the issue of scalability. Previous studies which were tailored to coronavirus
genomic data proposed to use spike sequences (corresponding to a subsequence of
the genome), rather than using the complete genomic sequence, to perform
different machine learning (ML) tasks such as classification and clustering.
However, those methods suffer from scalability issues. In this paper, we
propose an approach called Spike2Vec, an efficient and scalable feature vector
representation for each spike sequence that can be used for downstream ML
tasks. Through experiments, we show that Spike2Vec is not only scalable on
several million spike sequences, but also outperforms the baseline models in
terms of prediction accuracy, F1 score, etc.


  We report measurements of the far infrared transmission of superconducting
YBa$_2$Cu$_3$O$_{7-\delta }$ thin films from 5 cm$^{-1}$ to 200 cm$^{-1}$ in
fields up to 14$T$. A Kramers-Kronig analysis of the magneto-transmission
spectrum yields the magneto-conductivity tensor. The result shows that the
magneto-conductivity of YBa$_2$Cu$_3$O$_{7-\delta }$ is dominated by three
terms: a London term, a low frequency Lorentzian ($\omega _1\approx $ 3 cm$%
^{-1}$) of width $\Gamma _1=$ 10 cm$^{-1}$ and a finite frequency Lorentzian of
width $\Gamma _2=$ 17 cm$^{-1}$ at $\omega _2=$ 24 cm$^{-1}$ in the hole
cyclotron resonance active mode of circular polarization.\\


  Motivated by conjectures in Mirror Symmetry, we continue the study of the
real Monge--Amp\`ere operator on the boundary of a simplex. This can be
formulated in terms of optimal transport, and we consider, more generally, the
problem of optimal transport between symmetric probability measures on the
boundary of a simplex and of the dual simplex. For suitably regular measures,
we obtain regularity properties of the transport map, and of its convex
potential. To do so, we exploit boundary regularity results for optimal
transport maps by Caffarelli, together with the symmetries of the simplex.


  Radiologists highly desire fully automated versatile AI for medical imaging
interpretation. However, the lack of extensively annotated large-scale
multi-disease datasets has hindered the achievement of this goal. In this
paper, we explore the feasibility of leveraging language as a naturally
high-quality supervision for chest CT imaging. In light of the limited
availability of image-report pairs, we bootstrap the understanding of 3D chest
CT images by distilling chest-related diagnostic knowledge from an extensively
pre-trained 2D X-ray expert model. Specifically, we propose a language-guided
retrieval method to match each 3D CT image with its semantically closest 2D
X-ray image, and perform pair-wise and semantic relation knowledge
distillation. Subsequently, we use contrastive learning to align images and
reports within the same patient while distinguishing them from the other
patients. However, the challenge arises when patients have similar semantic
diagnoses, such as healthy patients, potentially confusing if treated as
negatives. We introduce a robust contrastive learning that identifies and
corrects these false negatives. We train our model with over 12,000 pairs of
chest CT images and radiology reports. Extensive experiments across multiple
scenarios, including zero-shot learning, report generation, and fine-tuning
processes, demonstrate the model's feasibility in interpreting chest CT images.


  We discuss the geometry of Laplacian eigenfunctions $-\Delta \phi = \lambda
\phi$ on compact manifolds $(M,g)$ and combinatorial graphs $G=(V,E)$. The
'dual' geometry of Laplacian eigenfunctions is well understood on
$\mathbb{T}^d$ (identified with $\mathbb{Z}^d$) and $\mathbb{R}^n$ (which is
self-dual). The dual geometry is of tremendous role in various fields of pure
and applied mathematics. The purpose of our paper is to point out a notion of
similarity between eigenfunctions that allows to reconstruct that geometry. Our
measure of 'similarity' $ \alpha(\phi_{\lambda}, \phi_{\mu})$ between
eigenfunctions $\phi_{\lambda}$ and $\phi_{\mu}$ is given by a global average
of local correlations $$ \alpha(\phi_{\lambda}, \phi_{\mu})^2 = \|
\phi_{\lambda} \phi_{\mu} \|_{L^2}^{-2}\int_{M}{ \left( \int_{M}{ p(t,x,y)(
\phi_{\lambda}(y) - \phi_{\lambda}(x))( \phi_{\mu}(y) - \phi_{\mu}(x)) dy}
\right)^2 dx},$$ where $p(t,x,y)$ is the classical heat kernel and $e^{-t
\lambda} + e^{-t \mu} = 1$. This notion recovers all classical notions of
duality but is equally applicable to other (rough) geometries and graphs; many
numerical examples in different continuous and discrete settings illustrate the
result.


  Determining the degree of confidence of deep learning model in its prediction
is an open problem in the field of natural language processing. Most of the
classical methods for uncertainty estimation are quite weak for text
classification models. We set the task of obtaining an uncertainty estimate for
neural networks based on the Transformer architecture. A key feature of such
mo-dels is the attention mechanism, which supports the information flow between
the hidden representations of tokens in the neural network. We explore the
formed relationships between internal representations using Topological Data
Analysis methods and utilize them to predict model's confidence. In this paper,
we propose a method for uncertainty estimation based on the topological
properties of the attention mechanism and compare it with classical methods. As
a result, the proposed algorithm surpasses the existing methods in quality and
opens up a new area of application of the attention mechanism, but requires the
selection of topological features.


  The Gallai-Ramsey number $gr_{k}(K_{3}: H_{1}, H_{2}, \cdots, H_{k})$ is the
smallest integer $n$ such that every $k$-edge-colored $K_{n}$ contains either a
rainbow $K_3$ or a monochromatic $H_{i}$ in color $i$ for some $i\in [k]$. We
find the largest star that can be removed from $K_n$ such that the underlying
graph is still forced to have a rainbow $K_3$ or a monochromatic $H_{i}$ in
color $i$ for some $i\in [k]$. Thus, we define the star-critical Gallai-Ramsey
number $gr_{k}^{*}(K_3: H_{1}, H_{2}, \cdots, H_{k})$ as the smallest integer
$s$ such that every $k$-edge-colored $K_{n}-K_{1, n-1-s}$ contains either a
rainbow $K_3$ or a monochromatic $H_{i}$ in color $i$ for some $i\in [k]$. When
$H=H_{1}=\cdots=H_{k}$, we simply denote $gr_{k}^{*}(K_{3}: H_{1}, H_{2},
\cdots, H_{k})$ by $gr_{k}^{*}(K_{3}: H)$. We determine the star-critical
Gallai-Ramsey numbers for complete graphs and some small graphs. Furthermore,
we show that $gr_{k}^{*}(K_3: H)$ is exponential in $k$ if $H$ is not
bipartite, linear in $k$ if $H$ is bipartite but not a star and constant (not
depending on $k$) if $H$ is a star.


  This chapter is a short pedagogical introduction to the use of quantum logic
for the simulation of complex quantum systems, including a simulation example
on actual quantum hardware.


  In-medium properties of the low-lying baryons are studied in the quark-meson
coupling (QMC) model, focusing on the $\Sigma_b$ and $\Xi_b$ baryons. It is
predicted that the Lorentz-scalar effective mass of $\Sigma_b$ becomes smaller
than that of $\Xi_b$ at moderate nuclear matter density, and as the density
increases, namely, $m^*_{\Sigma_b} < m^*_{\Xi_b}$, although in vacuum
$m_{\Sigma_b} > m_{\Xi_b}$. We also study the effects of the repulsive
Lorentz-vector potentials on the excitation energies of these bottom baryons.


  The little Grothendieck theorem for Banach spaces says that every bounded
linear operator between $C(K)$ and $\ell_2$ is 2-summing. However, it is shown
in \cite{J05} that the operator space analogue fails. Not every cb-map $v : \K
\to OH$ is completely 2-summing. In this paper, we show an operator space
analogue of Maurey's theorem : Every cb-map $v : \K \to OH$ is $(q,cb)$-summing
for any $q>2$ and hence admits a factorization $\|v(x)\| \leq c(q) \|v\|_{cb}
\|axb\|_q$ with $a,b$ in the unit ball of the Schatten class $S_{2q}$.


  Anecdotal evidence of cannabis use by professional programmers abounds.
Recent studies have found that some professionals regularly use cannabis while
programming even for work-related tasks. However, accounts of the impacts of
cannabis on programming vary widely and are often contradictory. For example,
some programmers claim that it impairs their ability to generate correct
solutions while others claim it enhances creativity and focus. There remains a
need for an empirical understanding of the true impacts of cannabis on
programming. This paper presents the first controlled observational study of
the effects of cannabis on programming ability. Based on a within-subjects
design with over 70 participants, we find that at ecologically valid dosages,
cannabis significantly impairs programming performance. Programs implemented
while high contain more bugs and take longer to write (p < 0.05), a small to
medium effect (0.22 <= d <= 0.44). We also did not find any evidence that high
programmers generate more divergent solutions. However, programmers can
accurately assess differences in their programming performance (r = 0.59), even
when under the influence of cannabis. We hope that this research will
facilitate evidence-based policies and help developers make informed decisions
regarding cannabis use while programming.


  Vision Transformers (ViTs) have successfully been applied to image
classification problems where large annotated datasets are available. On the
other hand, when fewer annotations are available, such as in biomedical
applications, image augmentation techniques like introducing image variations
or combinations have been proposed. However, regarding ViT patch sampling, less
has been explored outside grid-based strategies. In this work, we propose
Random Vision Transformer Tokens (RaViTT), a random patch sampling strategy
that can be incorporated into existing ViTs. We experimentally evaluated RaViTT
for image classification, comparing it with a baseline ViT and state-of-the-art
(SOTA) augmentation techniques in 4 datasets, including ImageNet-1k and
CIFAR-100. Results show that RaViTT increases the accuracy of the baseline in
all datasets and outperforms the SOTA augmentation techniques in 3 out of 4
datasets by a significant margin +1.23% to +4.32%. Interestingly, RaViTT
accuracy improvements can be achieved even with fewer tokens, thus reducing the
computational load of any ViT model for a given accuracy value.


  We determine the bosonic part of the superstring sigma model Lagrangian on
$\eta$-deformed AdS5 x S5, and use it to compute the perturbative world-sheet
scattering matrix of bosonic particles of the model. We then compare it with
the large string tension limit of the q-deformed S-matrix and find exact
agreement.


  It is known that the gravitational collapse of a dust ball results in naked
singularity formation from an initial density profile which is physically
reasonable. In this paper, we show that explosive radiation is emitted during
the formation process of the naked singularity.


  We give the full supersymmetric and kappa-symmetric actions for the Dirichlet
p-branes, including their coupling to background superfields of ten-dimensional
type IIA and IIB supergravity.


  In this paper, we discuss the benefits of ground-based, adaptive optics (AO)
aided observations for star formation research. After outlining the general
advantages, we present results obtained during the ALFA science demonstration
programme in 1999. These results underline the absolute necessity of AO
assistance for almost any kind of observations regarding star formation
regions.


  The aim of speech enhancement is to improve speech signal quality and
intelligibility from a noisy microphone signal. In many applications, it is
crucial to enable processing with small computational complexity and minimal
requirements regarding access to future signal samples (look-ahead). This paper
presents signal-based causal DCCRN that improves online single-channel speech
enhancement by reducing the required look-ahead and the number of network
parameters. The proposed modifications include complex filtering of the signal,
application of overlapped-frame prediction, causal convolutions and
deconvolutions, and modification of the loss function. Results of performed
experiments indicate that the proposed model with overlapped signal prediction
and additional adjustments, achieves similar or better performance than the
original DCCRN in terms of various speech enhancement metrics, while it reduces
the latency and network parameter number by around 30%.


  Query understanding plays a key role in exploring users' search intents and
facilitating users to locate their most desired information. However, it is
inherently challenging since it needs to capture semantic information from
short and ambiguous queries and often requires massive task-specific labeled
data. In recent years, pre-trained language models (PLMs) have advanced various
natural language processing tasks because they can extract general semantic
information from large-scale corpora. Therefore, there are unprecedented
opportunities to adopt PLMs for query understanding. However, there is a gap
between the goal of query understanding and existing pre-training strategies --
the goal of query understanding is to boost search performance while existing
strategies rarely consider this goal. Thus, directly applying them to query
understanding is sub-optimal. On the other hand, search logs contain user
clicks between queries and urls that provide rich users' search behavioral
information on queries beyond their content. Therefore, in this paper, we aim
to fill this gap by exploring search logs. In particular, to incorporate search
logs into pre-training, we first construct a query graph where nodes are
queries and two queries are connected if they lead to clicks on the same urls.
Then we propose a novel graph-enhanced pre-training framework, GE-BERT, which
can leverage both query content and the query graph. In other words, GE-BERT
can capture both the semantic information and the users' search behavioral
information of queries. Extensive experiments on various query understanding
tasks have demonstrated the effectiveness of the proposed framework.


  Earlier described model amplitudes are used in this paper to evaluate both
cross sections and density matrices for two-photon mediated resonance
production in e^+e^- collisions. All 25 q\bar{q} low-lying ^1S_0, ^3P_J and
^1D_2 resonances can thus be treated. Two independent methods are described to
obtain the resonance production density matrices and cross sections. These
density matrices combined with a resonance decay density matrix give the
detailed angular distributions of the resonance decay products. For two
particular decays, \chi_{c2},\chi_{c1}\to\gamma J/\psi the details are given.
Several numerical results are presented as well.


  Our multi-wavelength model GRASIL for the SED of galaxies is described, in
particular the recent extension to the radio and X-ray range. With our model we
can study different aspects of galaxy evolution by exploiting all available
spectral observations, where different emission components dominate.


  Recent observations of current-induced magnetization switching at
ferromagnet/normal-conductor interfaces have important consequences for future
magnetic memory technology. In one interpretation, the switching originates
from carriers with spin-dependent scattering giving rise to a relativistic
anti-damping spin-orbit torque (SOT) in structures with broken space-inversion
symmetry. The alternative interpretation combines the relativistic spin Hall
effect (SHE), making the normal-conductor an injector of a spin-current, with
the non-relativistic spin-transfer torque (STT) in the ferromagnet. Remarkably,
the SHE in these experiments originates from the Berry phase effect in the band
structure of a clean crystal and the anti-damping STT is also based on a
disorder-independent transfer of spin from carriers to magnetization. Here we
report the observation of an anti-damping SOT stemming from an analogous Berry
phase effect to the SHE. The SOT alone can therefore induce magnetization
dynamics based on a scattering-independent principle. The ferromagnetic
semiconductor (Ga,Mn)As we use has a broken space-inversion symmetry in the
crystal. This allows us to consider a bare ferromagnetic element which
eliminates by design any SHE related contribution to the spin torque. We
provide an intuitive picture of the Berry phase origin of the anti-damping SOT
and a microscopic modeling of measured data.


  Electric dipole transitions between different cavity polariton branches or
between dressed atomic states with the same excitation number are strictly
forbidden in centro-symmetric systems. For doped quantum wells in semiconductor
microcavities, the strong coupling between an intersubband transition in the
conduction band and a cavity mode produces two branches of intersubband cavity
polaritons, whose normal-mode energy splitting is tunable and can be in the
terahertz region. Here, we show that, by using asymmetric quantum wells, it is
possible to have allowed dipolar transitions between different polaritonic
branches, leading to the emission of terahertz photons. We present a quantum
field theory for such a system and predict that high-efficiency, widely tunable
terahertz lasing can be obtained.


  The rare radiative $B$-meson decay $B^-\to\gamma\ell^-\bar\nu$ and the
radiative Higgs-boson decay $h\to\gamma\gamma$ mediated by light-quark loops
both receive large logarithmic corrections in QCD, which can be resummed using
factorization theorems derived in soft-collinear effective theory. In these
factorization theorems the same radiative jet function appears, which is a
central object in the study of factorization beyond the leading order in scale
ratios. We calculate this function at two-loop order both in momentum space and
in a dual space, where its renormalization-group evolution takes on a simpler
form. We also derive the two-loop anomalous dimension of the jet function and
present the exact solution to its evolution equation at two-loop order. Another
important outcome of our analysis is the explicit form of the two-loop
anomalous dimension of the $B$-meson light-cone distribution amplitude in
momentum space.


  Some critical comments on the prevailing model of star-disk interaction are
made, in particular, on the rotating nature of the magnetic field lines and on
the application of the magnetohydrodynamic frozen-field theorem to the disk
plasma. As an alternative, a unipolar induction model is proposed, where the
magnetic field is stationary in space and the stellar unipolar electric field
$\vec E_{*}$ on the surface is uploaded to the magnetosphere. Through the
Poynting vector, the star and the magnetosphere form a coupled system where the
total angular momentum, consisting the mechanical one of the star, the
electromagnetic one of the magnetosphere, and the mechanical one of the plasma
in the magnetosphere, is conserved. The stellar interaction with the accretion
disk is through the projection of the unipolar electric field $\vec E_{*}$ onto
the disk via the equipotential field lines, generating a disk current and
consequently toroidal fields with opposite signs on both sides of the disk,
with return current loops via the stellar surface. As a result, magnetic flux
is added to the magnetospheric field in the northern and southern hemispheres
with the disk current sheet as the boundary condition. This makes the star-disk
system an astrophysical site where intense magnetic fields are generated
through the rotational energy of the star. Angular momentum extraction from
this star-disk system happens as the magnetic flux in the magnetosphere
increases to the point that exceeds the current carrying capacity of the disk,
leading to a mega scale magnetic eruption sending Poynting fluxes to space
either isotropically or beamed.


  It is shown that $n$ points and $e$ lines in the complex Euclidean plane
${\mathbb C}^2$ determine $O(n^{2/3}e^{2/3}+n+e)$ point-line incidences. This
bound is the best possible, and it generalizes the celebrated theorem by
Szemer\'edi and Trotter about point-line incidences in the real Euclidean plane
${\mathbb R}^2$.


  Adopting the strong field limit approach, we investigate the strong
gravitational lensing of a spherically symmetric spacetime in the
Einstein-Proca theory. With the strong field limit coefficient, three
observable quantities are obtained, which are the innermost relativistic image,
the deflection angle and the ratio of the flux. Comparing the observable value
and the theoretical value of the strong gravitational lensing, we can verify
the effectiveness of the strong gravitational lensing model.


  We review the present activities related to hypernuclear production in
hadronic in-medium reactions at intermediate energies. The status of
theoretical predictions and experimental evidences for the in-medium formation
of bound superstrange matter is discussed. Heavy-ion collisions and
antiproton-induced reactions at energies close to strangeness production
thresholds create the conditions of hypermatter formation. This allows to
understand better in-medium hyperon interactions and sets constraints on the
strangeness sector of the nuclear equation of state.


  We have found a family of solvable nineteen vertex model with statistical
configurations invariant by the time reversal symmetry within a systematic
study of the respective Yang-Baxter relation. The Boltzmann weights sit on a
degree seven algebraic threefold which is shown birationally equivalent to the
three-dimensional projective space. This permits to write parameterized
expressions for both the transition operator and the $\mathrm{R}$-matrix
depending on three independent affine spectral parameters. The Hamiltonian
limit tells us that the azimuthal magnetic field term is connected with the
asymmetry among two types of spectral variables. The absence of magnetic field
defines a physical submanifold whose geometrical properties are remarkably
shown to be governed by a quartic $\mathrm{K}3$ surface. This expands
considerably the class of irrational manifolds that could emerge in the theory
of quantum integrable models.


  In this paper we consider two recovery problems based on information given
with an error. First is the problem of optimal recovery of the class $W^T_q =
\{(t_1h_1,t_2h_2,\ldots)\in \ell_q\,:\,\|h\|_q\leqslant 1\}$, where $1\le q <
\infty$ and $t_1\geqslant t_2\geqslant \ldots \geqslant 0$, in the space
$\ell_q$ when in the capacity of inexact information we know either the first
$n\in\mathbb{N}$ elements of a sequence with an error measured in the space of
finite sequences $\ell_r^n$, $0 < r \le \infty$, or a sequence itself is known
with an error measured in the space $\ell_r$. The second is the problem of
optimal recovery of scalar products acting on Cartesian product $W^{T,S}_{p,q}$
of classes $W^T_p$ and $W^S_q$, where $1 < p,q < \infty$, $\frac{1}{p} +
\frac{1}{q} = 1$ and $s_1\ge s_2\ge \ldots \ge 0$, when in the capacity of
inexact information we know the first $n$ coordinate-wise products $x_1y_1,
x_2y_2,\ldots,x_ny_m$ of the element $x\times y \in W^{T,S}_{p,q}$ with an
error measured in the space $\ell_r^n$. We find exact solutions to above
problems and construct optimal methods of recovery. As an application of our
results we consider the problem of optimal recovery of classes in Hilbert
spaces by Fourier coefficients known with an error measured in the space
$\ell_p$ with $p > 2$.


  (Abridge) Near-infrared spectroscopy is used to study the kinematics and
excitation mechanisms of the H2 and [FeII] gas in a sample of AGN. The H2 lines
are unresolved in all objects in which they were detected while the [FeII]
lines have widths implying gas velocities of up to 650 km/s. This suggests
that, very likely, the H2 and [FeII] emission does not originate from the same
parcel of gas. Molecular H2 were detected in 90% of the sample, including PG
objects, indicating detectavel amounts of molecular material even in objects
with low levels of circumnuclear starburst activity. The data favors thermal
excitation for the H2 lines. Indeed, in NGC3227, Mrk766, NGC4051 and NGC4151,
the molecular emission is found to be purely thermal. This result is also
confirmed by the rather similar vibrational and rotational temperatures in the
objects for which they were derived. [FeII] lines are detected in all of the
AGN. The [FeII] 1.254mu/Pa-beta ratio is compatible with excitation of the
[FeII] by the active nucleus, but in Mrk 766 it implies a stellar origin. A
correlation between H2/Br-gamma and [FeII]/Pa-beta is found. We confirm that it
is a useful diagnostic tool in the NIR to separate emitting line objects by
their level of nuclear activity. X-ray excitation models are able to explain
the observed H2 and part of the [FeII] emission. Most likely, a combination of
X-ray heating, shocks driven by the radio jet, and circumnuclear star formation
contributes, in different proportions, to the H2 and [FeII] emission. In most
of our spectra, the [FeII] 1.257mu/1.644mu ratio is found to be 30% lower than
the intrinsic value based on current atomic data. This implies either than the
extinction towards the [FeII] emitting clouds is very similar in most objects
or there are possible inaccuracies in the A-values in the [FeII] transitions.


  We mapped 3 mm continuum and line emission from the starburst galaxy M82
using the Combined Array for Research in Millimeter-wave Astronomy. We targeted
the HCN, HCO$^{+}$, HNC, CS and HC$_{3}$N lines, but here we focus on the HCN
and HCO$^{+}$ emission. The map covers a field of 1.2' with a ~5" resolution.
The HCN and HCO$^{+}$ observations are combined with single dish images. The
molecular gas in M82 had been previously found to be distributed in a molecular
disk, coincident with the central starburst, and a galactic scale outflow which
originates in the central starburst. With the new short spacings-corrected maps
we derive some of the properties of the dense molecular gas in the base of the
outflow. From the HCN and HCO$^{+}$ J=(1-0) line emission, and under the
assumptions of the gas being optically thin and in local thermodynamic
equilibrium, we place lower limits to the amount of dense molecular gas in the
base of the outflow. The lower limits are $7\times10^{6}$ $M_{\odot}$ and
$21\times10^{6}$ $M_{\odot}$, or $\gtrsim2\%$ of the total molecular mass in
the outflow. The kinematics and spatial distribution of the dense gas outside
the central starburst suggests that it is being expelled through chimneys.
Assuming a constant outflow velocity, the derived outflow rate of dense
molecular gas is $\geq0.3$ $M_{\odot}$ yr$^{-1}$, which would lower the
starburst lifetime by $\geq5\%$. The energy required to expel this mass of
dense gas is $(1-10)\times10^{52}$ erg.


  We consider two entangled accelerating qubits coupled with real scalar
fields, each described by the Unruh-Wald model. It is demonstrated that because
of the Unruh effect, the bipartite entanglement of the two qubits suddenly dies
when the acceleration of one or more qubits are large enough. We also consider
three entangled accelerating qubits in GHZ state and in W state, with equal
acceleration-frequency ratio, and found that in either state, the tripartite
entanglement suddenly dies at a certain value of acceleration-frequency ratio.
The equivalence between the Rindler metric and the Schwarzchild metric in the
vicinity of the horizon of a black hole implies that for the two entangled
qubits outside a black hole, the entanglement suddenly dies when one or both of
the qubits are close enough to the horizon, while for the three entangled
qubits in GHZ or W state, the tripartite entanglement suddenly dies when these
qubits are close enough to the horizon.


  In recent years, deep learning has become a breakthrough technique in
assisting medical image diagnosis. Supervised learning using convolutional
neural networks (CNN) provides state-of-the-art performance and has served as a
benchmark for various medical image segmentation and classification. However,
supervised learning deeply relies on large-scale annotated data, which is
expensive, time-consuming, and even impractical to acquire in medical imaging
applications. Active Learning (AL) methods have been widely applied in natural
image classification tasks to reduce annotation costs by selecting more
valuable examples from the unlabeled data pool. However, their application in
medical image segmentation tasks is limited, and there is currently no
effective and universal AL-based method specifically designed for 3D medical
image segmentation. To address this limitation, we propose an AL-based method
that can be simultaneously applied to 2D medical image classification,
segmentation, and 3D medical image segmentation tasks. We extensively validated
our proposed active learning method on three publicly available and challenging
medical image datasets, Kvasir Dataset, COVID-19 Infection Segmentation
Dataset, and BraTS2019 Dataset. The experimental results demonstrate that our
PCDAL can achieve significantly improved performance with fewer annotations in
2D classification and segmentation and 3D segmentation tasks. The codes of this
study are available at https://github.com/ortonwang/PCDAL.


  This is an expository paper. We prove the Cannon-Thurston property for
bounded geometry surface groups with or without punctures. We prove three
theorems, due to Cannon-Thurston, Minsky and Bowditch. The proofs are culled
out of earlier work of the author.


  We solve the three-body problem of an ultracold Fermi gas with parabolic
confinement length $a_\perp$ and 3D scattering length $a$. On the two-body
level, there is a Feshbach-type resonance at $a_\perp/a\approx 1.46$, and a
dimer state for arbitrary $a_\perp/a$. The three-body problem is shown to be
universal, an d described by the atom-dimer scattering length $a_{ad}$ and a
range parameter $b_{ad}$. In the dimer limit $a_\perp/a\gg 1$, we find a
repulsive zero-range atom-dimer interaction. For $a_\perp/a\ll -1$, however,
the potential has long range, with $a_{ad}>0$ and $b_{ad}\gg a_{ad}$. There is
no trimer state, and despite $a_{ad}=0$ at $a_\perp/a\approx 2.6$, there is no
resonance enhancement of the interaction.


  In this paper, we generalize a result of Satoh to show that for any odd
natural $n$, the connected sum of the $n$-twist spun sphere of a knot $K$ and
an unknotted projective plane in the 4-sphere is equivalent to the same
unknotted projective plane. We additionally provide a fix to a small error in
Satoh's proof of the case that $K$ is a 2-bridge knot.


  We study the electronic state of the doped Mott-Hubbard insulator within
Dynamical Mean Field Theory. The evolution of the finite temperature spectral
functions as a function of doping show large redistributions of spectral weight
in both antiferromagnetic and paramagnetic phases. In particular, a metallic
antiferromagnetic state is obtained with a low frequency Slater-splitted
quasiparticle peak coexisting with Hubbard bands. In the high temperature
paramagnetic metallic phase, upon reducing doping, the system has a crossover
through a ``bad metal'' state characterized by an anomalous shift of the
quasiparticle peak away from the Fermi energy. We find that the {\it charge}
compressibility of the antiferromagnetic metal is dramatically enhanced upon
approaching the second order N\'eel line.


  We consider three dimensional conformal field theories living on a stack of N
anti-M2 branes at the tip of eight-dimensional supersymmetric cones. The
corresponding supergravity solution is obtained by changing sign to the
four-form in the Freund-Rubin solution representing M2 branes ("skew-whiffing"
transformation) and it is known to be stable. The existence of these non
supersymmetric, stable field theories, at least in the large N limit, is a
peculiarity of the AdS4/CFT3 correspondence with respect to the usual
AdS5/CFT4, and it is worthwhile to study it. We analyze in detail the KK
spectrum of the skew-whiffed solution associated with S^7/Z_k and we speculate
on the natural field content for a candidate non-supersymmetric dual field
theory.


  Context: Quantum computing (QC) represents a paradigm shift in computational
capabilities, presenting unique challenges in requirements engineering (RE).
The complexity of quantum systems and rapid technological advancements
necessitate a comprehensive understanding of the current state and future
trajectories in RE for QC. Objective: A protocol for carrying out a systematic
literature review about the evidence for identifying and analyzing the
challenges in RE for QC software. It seeks to evaluate the current
methodologies employed in this domain and propose a forward-looking perspective
on the evolution of these methodologies to meet future industry and academic
needs. Method: This protocol employs a structured approach to search and
analyze relevant literature systematically, according to Barbara Kitchenham's
guidelines. Results: A validated protocol to conduct a systematic review. The
protocol is expected to yield diverse literature spanning theoretical
frameworks, empirical studies, and methodological advancements in RE for QC. It
will highlight the current challenges, opportunities, and future directions,
offering insights into the field's academic and practical aspects. Conclusions:
The systematic review aims to provide a nuanced understanding of the RE
landscape in QC. It will offer valuable insights for academic researchers,
industry professionals, software engineers, industry analysts, and educators,
shaping the future discourse in QC development.


  We present the results of infrared (IR; 2.5--160 um) observations of the
supernova remnant (SNR) Kes 17 based on the data obtained with the AKARI and
Spitzer satellites. We first detect bright continuum emission of its western
shell in the mid- and far-IR wavebands together with its near-IR molecular line
emission. We also detect hidden mid-IR emission of its southern shell after
subtraction of the background emission in this region. The far-IR luminosity of
the western shell is ~ 8100 L_sun, which makes Kes 17 one of the few SNRs of
significant far-IR emission. The fittings of the spectral energy distribution
indicate the existence of two dust components: ~ 79 K (hot) and ~ 27 K (cold)
corresponding to the dust mass of ~ 6.2x10^{-4} M_sun and ~ 6.7 M_sun,
respectively. We suggest that the hot component represents the dust emission of
the material swept up by the SNR to its western and southern boundaries,
compatible with the distribution of radio continuum emission overlapping the
mid-IR emission in the western and southern shells. The existence of hot (~
2,000 K), shocked dense molecular gas revealed by the near-IR molecular line
emission in the western shell, on the other hand, suggests that the cold dust
component represents the dust emission related to the interaction between the
SNR and nearby molecular gas. The excitation conditions of the molecular gas
appear to be consistent with those from shocked, clumpy admixture gas of
different temperatures. We discuss three possibilities for the origin of the
bright far-IR emission of the cold dust in the western shell: the emission of
dust in the inter-clump medium of shocked molecular clouds, the emission of
dust in evaporating flows of molecular clouds engulfed by hot gas, and the
emission of dust of nearby molecular clouds illuminated by radiative shocks.


  In this paper, we investigate the $p$-rank of Jacobian of cyclotomic function
field $K_m$. Our goal in this paper is to give the necessary and sufficient
condition $m$ such that $K_m$ is ordinary.


  We show that when $\set{X_j}$ is a sequence of independent (but not
necessarily identically distributed) random variables which satisfies a
condition similar to the Lindeberg condition, the properly normalized geometric
sum $\sum_{j=1}^{\nu_p}X_j$ (where $\nu_p$ is a geometric random variable with
mean $1/p$) converges in distribution to a Laplace distribution as $p\to 0$.
The same conclusion holds for the multivariate case. This theorem provides a
reason for the ubiquity of the double power law in economic and financial data.


  We prove the necessary and sufficient conditions for practical stability of
nonlinear dynamical system at general phase restrictions. In such a case the
Lyapunov function is nondifferentiable. But if the set of initial data is
starry compact, then it is possible building Lyapunov function which belongs to
differentiable functions class. We also estimate the optimal sets of initial
conditions in structural forms for linear system and concrete phase
restrictions.


  The European Space Agency Rosetta/Philae mission to Comet
67P/Churyumov-Gerasimenko in 2014-2016 is the most complete and diverse
investigation of a comet carried out thus far. Yet, many physical and chemical
properties of the comet remain uncertain or unknown, and cometary activity is
still not a well-understood phenomenon. We here attempt to place constraints on
the nucleus abundances and sublimation front depths of H2O and CO2 ice, and to
reconstruct how the nucleus evolved throughout the perihelion passage. We
employ the thermophysical modelling code 'Numerical Icy Minor Body evolUtion
Simulator', or NIMBUS, to search for conditions under which the observed H2O
and CO2 production rates are simultaneously reproduced before and after
perihelion. We find that the refractories to water-ice mass ratio of relatively
pristine nucleus material is mu~1, that airfall material has mu~2, and that the
molar abundance of CO2 relative H2O near 30 per cent. The dust mantle thickness
is typically < 2 cm. The average CO2 sublimation front depths near aphelion
were ~3.8 m and ~1.9 m on the northern and southern hemispheres, respectively,
but varied substantially with time. We propose that airfall material is
subjected to substantial fragmentation and pulverisation due to thermal fatigue
during the aphelion passage. Sub-surface compaction of material due to CO2
activity near perihelion seems to have reduced the diffusivity in a measurable
way.


  Minimum-weight perfect matching (MWPM) has been been the primary classical
algorithm for error correction in the surface code, since it is of low runtime
complexity and achieves relatively low logical error rates [Phys. Rev. Lett.
108, 180501 (2012)]. A Markov chain Monte Carlo (MCMC) algorithm [Phys. Rev.
Lett. 109, 160503 (2012)] is able to achieve lower logical error rates and
higher thresholds than MWPM, but requires a classical runtime complexity which
is super-polynomial in L, the linear size of the code. In this work we present
an MCMC algorithm that achieves significantly lower logical error rates than
MWPM at the cost of a polynomially increased classical runtime complexity. For
error rates p close to the threshold, our algorithm needs a runtime complexity
which is increased by O(L^2) relative to MWPM in order to achieve a lower
logical error rate. If p is below an L-dependent critical value, no increase in
the runtime complexity is necessary any longer. For p->0, the logical error
rate achieved by our algorithm is exponentially smaller (in L) than that of
MWPM, without requiring an increased runtime complexity. Our algorithm allows
for trade-offs between runtime and achieved logical error rates as well as for
parallelization, and can be also used to correct in the case of imperfect
stabilizer measurements.


  The LHC is well on track toward the discovery or exclusion of a light
Standard Model (SM)-like Higgs boson. Such a Higgs has a very small SM width
and can easily have large branching fractions to physics beyond the SM, making
Higgs decays an excellent opportunity to observe new physics. Decays into
collider-invisible particles are particularly interesting as they are
theoretically well motivated and relatively clean experimentally. In this work
we estimate the potential of the 7 and 8 TeV LHC to observe an invisible Higgs
branching fraction. We analyze three channels that can be used to directly
study the invisible Higgs branching ratio at the 7 TeV LHC: an invisible Higgs
produced in association with (i) a hard jet; (ii) a leptonic Z; and (iii)
forward tagging jets. We find that the last channel, where the Higgs is
produced via weak boson fusion, is the most sensitive, allowing branching
fractions as small as 40% to be probed at 20 inverse fb for masses in the range
between 120 and 170 GeV, including in particular the interesting region around
125 GeV. We provide an estimate of the 8 TeV LHC sensitivity to an
invisibly-decaying Higgs produced via weak boson fusion and find that the reach
is comparable to but not better than the reach at the 7 TeV LHC. We further
estimate the discovery potential at the 8 TeV LHC for cases where the Higgs has
substantial branching fractions to both visible and invisible final states.


  A simple process for chemical vapor deposition of ultra SD single wall carbon
nanotubes has been developed. In this process, an iron nitrate nonahydrate
solution in isopropyl alcohol with a concentration of 400 ug/mlit was used to
catalyze nanoparticles formation on an oxidized silicon wafer. The oxide on the
substrate was made of a thick layer of wet oxide sandwiched between tow thin
layers of dry oxide. The process results in semiconducting single-walled carbon
nanotubes (SWNTs) with diameter of less than 0.7nm and more than 1ev band gap
energy, which are amongst the smallest diameters of SWNTs ever reported.


  Autonomous driving systems require huge amounts of data to train. Manual
annotation of this data is time-consuming and prohibitively expensive since it
involves human resources. Therefore, active learning emerged as an alternative
to ease this effort and to make data annotation more manageable. In this paper,
we introduce a novel active learning approach for object detection in videos by
exploiting temporal coherence. Our active learning criterion is based on the
estimated number of errors in terms of false positives and false negatives. The
detections obtained by the object detector are used to define the nodes of a
graph and tracked forward and backward to temporally link the nodes. Minimizing
an energy function defined on this graphical model provides estimates of both
false positives and false negatives. Additionally, we introduce a synthetic
video dataset, called SYNTHIA-AL, specially designed to evaluate active
learning for video object detection in road scenes. Finally, we show that our
approach outperforms active learning baselines tested on two datasets.


  We construct a family of $\frac{(q-1)}{2}$-ovoids of $Q(4,q)$, the parabolic
quadric of $\textup{PG}(4,q)$, for $q\equiv 3\pmod 4$. The existence of
$\frac{(q-1)}{2}$-ovoids of $Q(4,q)$ was only known for $q=3, 7,$ or $11$. Our
construction provides the first infinite family of $\frac{(q-1)}{2}$-ovoids of
$Q(4,q)$.Along the way, we also give a construction of $\frac{q+1}{2}$-ovoids
in $Q(4,q)$ for $q\equiv 1\pmod 4$.


  The main goal of this paper is to develop a theory of inference of player
valuations from observed data in the generalized second price auction without
relying on the Nash equilibrium assumption. Existing work in Economics on
inferring agent values from data relies on the assumption that all participant
strategies are best responses of the observed play of other players, i.e. they
constitute a Nash equilibrium. In this paper, we show how to perform inference
relying on a weaker assumption instead: assuming that players are using some
form of no-regret learning. Learning outcomes emerged in recent years as an
attractive alternative to Nash equilibrium in analyzing game outcomes, modeling
players who haven't reached a stable equilibrium, but rather use algorithmic
learning, aiming to learn the best way to play from previous observations. In
this paper we show how to infer values of players who use algorithmic learning
strategies. Such inference is an important first step before we move to testing
any learning theoretic behavioral model on auction data. We apply our
techniques to a dataset from Microsoft's sponsored search ad auction system.


  In this paper knowledge based planning has been revolutionized via a novel
mathematical model which converts three dimensional dose distribution (3D3)
prediction to a clinical utilizable IMRT treatment plan. Presented model has
benefited from both prescribed dose as well as predicted dose and its objective
function includes both quadratic and linear phrases, so it was called QuadLin
model. The model has been run on the data of 30 patients with head and neck
cancer randomly selected from the Open KBP dataset. For each patient, there are
19 sets of dose prediction data in this database. Therefore, a total of 570
problems have been solved in the CVX framework and the results have been
evaluated by two plan quality approaches: 1- DVH points differences, and 2-
satisfied clinical criteria. The results of the current study indicate a strong
significant improvement in clinical indicators compared to the reference plan
of the dataset, 3D3 predictions, as well as the results of previous researches.
Accordingly, on average for 570 problems and total ROIs, clinical indicators
have improved by more than 21% and 15% compared to the predicted dose and
previous research, respectively.


  In this paper we classify the maximal subsemigroups of the \emph{full
transformation semigroup} $\Omega^\Omega$, which consists of all mappings on
the infinite set $\Omega$, containing certain subgroups of the symmetric group
$\sym(\Omega)$ on $\Omega$. In 1965 Gavrilov showed that there are five maximal
subsemigroups of $\Omega^\Omega$ containing $\sym(\Omega)$ when $\Omega$ is
countable and in 2005 Pinsker extended Gavrilov's result to sets of arbitrary
cardinality.
  We classify the maximal subsemigroups of $\Omega^\Omega$ on a set $\Omega$ of
arbitrary infinite cardinality containing one of the following subgroups of
$\sym(\Omega)$: the pointwise stabiliser of a non-empty finite subset of
$\Omega$, the stabiliser of an ultrafilter on $\Omega$, or the stabiliser of a
partition of $\Omega$ into finitely many subsets of equal cardinality. If $G$
is any of these subgroups, then we deduce a characterisation of the mappings
$f,g\in \Omega^\Omega$ such that the semigroup generated by $G\cup \{f,g\}$
equals $\Omega^\Omega$.


  In this paper, we present several new congruences on the $q$-trinomial
coefficients introduced by Andrews and Baxter. A new congruence on sums of
central $q$-binomial coefficients is also established.


  A novel concept of quantum random access memory (qRAM) employing a quantum
walk is provided. Our qRAM relies on a bucket brigade scheme to access the
memory cells. Introducing a bucket with chirality left and right as a quantum
walker, and considering its quantum motion on a full binary tree, we can
efficiently deliver the bucket to the designated memory cells, and fill the
bucket with the desired information in the form of quantum superposition
states. Our procedure has several advantages. First, we do not need to place
any quantum devices at the nodes of the binary tree, and hence in our qRAM
architecture, the cost to maintain the coherence can be significantly reduced.
Second, our scheme is fully parallelized. Consequently, only O(n) steps are
required to access and retrieve O(2n) data in the form of quantum superposition
states. Finally, the simplicity of our procedure may allow the design of qRAM
with simpler structures.


  With the rising prominence of WiFi in common spaces, efforts have been made
in the robotics community to take advantage of this fact by incorporating WiFi
signal measurements in indoor SLAM (Simultaneous Localization and Mapping)
systems. SLAM is essential in a wide range of applications, especially in the
control of autonomous robots. This paper describes recent work in the
development of WiFi-based localization and addresses the challenges currently
faced in achieving WiFi-based geometric mapping. Inspired by the field of
research into k-visibility, this paper presents the concept of inverse
k-visibility and proposes a novel algorithm that allows robots to build a map
of the free space of an unknown environment, essential for planning,
navigation, and avoiding obstacles. Experiments performed in simulated and
real-world environments demonstrate the effectiveness of the proposed
algorithm.


  With the development of deep neural networks, automatic music composition has
made great progress. Although emotional music can evoke listeners' different
emotions and it is important for artistic expression, only few researches have
focused on generating emotional music. This paper presents EmotionBox -an
music-element-driven emotional music generator that is capable of composing
music given a specific emotion, where this model does not require a music
dataset labeled with emotions. Instead, pitch histogram and note density are
extracted as features that represent mode and tempo respectively to control
music emotions. The subjective listening tests show that the Emotionbox has a
more competitive and balanced performance in arousing a specified emotion than
the emotion-label-based method.


  Deep learning on point clouds plays a vital role in a wide range of
applications such as autonomous driving and AR/VR. These applications interact
with people in real-time on edge devices and thus require low latency and low
energy. Compared to projecting the point cloud to 2D space, directly processing
the 3D point cloud yields higher accuracy and lower #MACs. However, the
extremely sparse nature of point cloud poses challenges to hardware
acceleration. For example, we need to explicitly determine the nonzero outputs
and search for the nonzero neighbors (mapping operation), which is unsupported
in existing accelerators. Furthermore, explicit gather and scatter of sparse
features are required, resulting in large data movement overhead. In this
paper, we comprehensively analyze the performance bottleneck of modern point
cloud networks on CPU/GPU/TPU. To address the challenges, we then present
PointAcc, a novel point cloud deep learning accelerator. PointAcc maps diverse
mapping operations onto one versatile ranking-based kernel, streams the sparse
computation with configurable caching, and temporally fuses consecutive dense
layers to reduce the memory footprint. Evaluated on 8 point cloud models across
4 applications, PointAcc achieves 3.7X speedup and 22X energy savings over RTX
2080Ti GPU. Co-designed with light-weight neural networks, PointAcc rivals the
prior accelerator Mesorasi by 100X speedup with 9.1% higher accuracy running
segmentation on the S3DIS dataset. PointAcc paves the way for efficient point
cloud recognition.


  Automatic modulation classification enables intelligent communications and it
is of crucial importance in today's and future wireless communication networks.
Although many automatic modulation classification schemes have been proposed,
they cannot tackle the intra-class diversity problem caused by the dynamic
changes of the wireless communication environment. In order to overcome this
problem, inspired by face recognition, a novel automatic modulation
classification scheme is proposed by using the multi-scale network in this
paper. Moreover, a novel loss function that combines the center loss and the
cross entropy loss is exploited to learn both discriminative and separable
features in order to further improve the classification performance. Extensive
simulation results demonstrate that our proposed automatic modulation
classification scheme can achieve better performance than the benchmark schemes
in terms of the classification accuracy. The influence of the network
parameters and the loss function with the two-stage training strategy on the
classification accuracy of our proposed scheme are investigated.


  Green's formulas for elliptic cone differential operators are established.
This is done by an accurate description of the maximal domain of an elliptic
cone differential operator and its formal adjoint, thereby utilizing the
concept of a discrete asymptotic type. From this description, the singular
coefficients replacing the boundary traces in classical Green's formulas are
deduced.


  Fine-grained entity typing is a challenging task with wide applications.
However, most existing datasets for this task are in English. In this paper, we
introduce a corpus for Chinese fine-grained entity typing that contains 4,800
mentions manually labeled through crowdsourcing. Each mention is annotated with
free-form entity types. To make our dataset useful in more possible scenarios,
we also categorize all the fine-grained types into 10 general types. Finally,
we conduct experiments with some neural models whose structures are typical in
fine-grained entity typing and show how well they perform on our dataset. We
also show the possibility of improving Chinese fine-grained entity typing
through cross-lingual transfer learning.


  We report in this paper the temperature and mangetic field dependence of the
conductance in the polycrystalline film of titanium nitride, before and after
heating at ambient conditions. The difference between the two films is the room
temperature sheet resistance which remains within 15 percent and both the films
show superconducting transition at lower temperatures. The zero field and the
high field data, respectively, corresponds to the superconducting and the
normal states. Both the films display Atshuler-Aronov zero bias anamoly in
their normal states, and the superconducting gap openeing up at low fields.
However the heated film has a smaller gap owing to more pronounced zero bias
suppression of the density of states. The normal states in both the films are
similar to the quasi-2d-disordered metal and its behavior is studied with
temperature. Our data suggests that the zero bias anamoly suppresses the
superconducting gap with increase in the disorder.


  We studied a thin superconducting NbN film in magnetic fields up to 8 T above
the zero- temperature limit by means of time-domain terahertz and scanning
tunneling spectroscopies in order to understand the vortex response. Scanning
tunneling spectroscopy was used to determine the optical gap and the upper
critical field of the sample. The obtained values were subsequently used to fit
the terahertz complex conductivity spectra in the magnetic field in the Faraday
geometry above the zero temperature limit. These spectra are best described in
terms of the Coffey-Clem self-consistent solution of a modified London equation
in the flux creep regime.


  This work presents a novel technique that performs both orientation and
distance localization of a sound source in a three-dimensional (3D) space using
only the interaural time difference (ITD) cue, generated by a newly-developed
self-rotational bi-microphone robotic platform. The system dynamics is
established in the spherical coordinate frame using a state-space model. The
observability analysis of the state-space model shows that the system is
unobservable when the sound source is placed with elevation angles of $90$ and
$0$ degree. The proposed method utilizes the difference between the azimuth
estimates resulting from respectively the 3D and the two-dimensional models to
check the zero-degree-elevation condition and further estimates the elevation
angle using a polynomial curve fitting approach. Also, the proposed method is
capable of detecting a $90$-degree elevation by extracting the zero-ITD signal
'buried' in noise. Additionally, a distance localization is performed by first
rotating the microphone array to face toward the sound source and then shifting
the microphone perpendicular to the source-robot vector by a predefined
distance of a fixed number of steps. The integrated rotational and
translational motions of the microphone array provide a complete orientation
and distance localization using only the ITD cue. A novel robotic platform
using a self-rotational bi-microphone array was also developed for unmanned
ground robots performing sound source localization. The proposed technique was
first tested in simulation and was then verified on the newly-developed robotic
platform. Experimental data collected by the microphones installed on a KEMAR
dummy head were also used to test the proposed technique. All results show the
effectiveness of the proposed technique.


  We introduce a method to reduce the study of the topology of a simplicial
complex to that of a simpler one. We give some applications of this method to
complexes arising from graphs. As a consequence, we answer some questions
raised in [R.Ehrenborg, G.Hetyei, European J.Combin.27 (2006), no.6, 906-923]
on the independence complex and the dominance complex. The techniques used come
mainly from monomial ideal theory.


  The recent technological progress in acquisition, modeling and processing of
3D data leads to the proliferation of a large number of 3D objects databases.
Consequently, the techniques used for content based 3D retrieval has become
necessary. In this paper, we introduce a new method for 3D objects recognition
and retrieval by using a set of binary images CLI (Characteristic level
images). We propose a 3D indexing and search approach based on the similarity
between characteristic level images using Hu moments for it indexing. To
measure the similarity between 3D objects we compute the Hausdorff distance
between a vectors descriptor. The performance of this new approach is evaluated
at set of 3D object of well known database, is NTU (National Taiwan University)
database.


  Recent breakthroughs in the field of semi-supervised learning have achieved
results that match state-of-the-art traditional supervised learning methods.
Most successful semi-supervised learning approaches in computer vision focus on
leveraging huge amount of unlabeled data, learning the general representation
via data augmentation and transformation, creating pseudo labels, implementing
different loss functions, and eventually transferring this knowledge to more
task-specific smaller models. In this paper, we aim to conduct our analyses on
three different aspects of SimCLR, the current state-of-the-art semi-supervised
learning framework for computer vision. First, we analyze properties of
contrast learning on fine-tuning, as we understand that contrast learning is
what makes this method so successful. Second, we research knowledge
distillation through teacher-forcing paradigm. We observe that when the teacher
and the student share the same base model, knowledge distillation will achieve
better result. Finally, we study how transfer learning works and its
relationship with the number of classes on different data sets. Our results
indicate that transfer learning performs better when number of classes are
smaller.


  Consider a finite and simple graph $G=(V,E)$ with maximum degree $\Delta$. A
strong Roman dominating function over the graph $G$ is understood as a map $f :
V (G)\rightarrow \{0, 1,\ldots , \left\lceil \frac{\Delta}{2}\right\rceil+ 1\}$
which carries out the condition stating that all the vertices $v$ labeled
$f(v)=0$ are adjacent to at least one another vertex $u$ that satisfies
$f(u)\geq 1+ \left\lceil \frac{1}{2}\vert N(u)\cap V_0\vert \right\rceil$, such
that $V_0=\{v \in V \mid f(v)=0 \}$ and the notation $N(u)$ stands for the open
neighborhood of $u$. The total version of one strong Roman dominating function
includes the additional property concerning the not existence of vertices of
degree zero in the subgraph of $G$, induced by the set of vertices labeled with
a positive value. The minimum possible value for the sum
$\omega(f)=f(V)=\sum_{v\in V} f(v)$ (also called the weight of $f$), taken
amongst all existent total strong Roman dominating functions $f$ of $G$, is
called the total strong Roman domination number of $G$, denoted by
$\gamma_{StR}^t(G)$. This total and strong version of the Roman domination
number (for graphs) is introduced in this research, and the study of its
mathematical properties is therefore initiated. For instance, we establish
upper bounds for such parameter, and relate it with several parameters related
to vertex domination in graphs, from which we remark the standard domination
number, the total version of the standard domination number and the (strong)
Roman domination number. In addition, among other results, we show that for any
tree $T$ of order $n(T)\ge 3$, with maximum degree $\Delta(T)$ and $s(T)$
support vertices, $\gamma_{StR}^t(T)\ge \left\lceil
\frac{n(T)+s(T)}{\Delta(T)}\right\rceil+1$.


  The concordance of standard big bang nucleosynthesis theory and the related
observations of the light element isotopes (including some new higher \he4
abundances) will be reviewed. Implications of BBN on chemical evolution, dark
matter and constraints on particle properties will be discussed.


  The present paper studies a quantitative version of the transversality
theorem. More precisely, given a continuous function $g\in
\mathcal{C}([0,1]^d,\mathbb{R}^m)$ and a global smooth manifold $W\subset
\mathbb{R}^m$ of dimension $p$, we establish a quantitative estimate on the
$(d+p-m)$-dimensional Hausdorff measure of the set
$\mathcal{Z}_{W}^{g}=\left\{x\in [0,1]^d: g(x)\in W\right\}$. The obtained
result is applied to quantify the total number of shock curves in weak entropy
solutions to scalar conservation laws with uniformly convex fluxes in one space
dimension.


  We develop a generalization of the singlet sector valence bond basis
projection algorithm of Sandvik, Beach, and Evertz (A. W. Sandvik, Phys. Rev.
Lett. 95, 207203 (2005); K. S. D. Beach and A. W. Sandvik, Nucl. Phys. B750,
142 (2006); A. W. Sandvik and H. G. Evertz, arXiv:0807.0682, unpublished.) to
cases in which the ground state of an antiferromagnetic Hamiltonian has total
spin $S_{tot} = 1/2$ in a finite size system. We explain how various ground
state expectation values may be calculated by generalizations of the estimators
developed in the singlet case, and illustrate the power of the method by
calculating the ground state spin texture and bond energies in a $L \times L$
Heisenberg antiferromagnet with $L$ odd and free boundaries.


  A phenomenological Dyson-Schwinger equation approach to QCD, formalised in
terms of a QCD based model field theory, is used to calculate the
electromagnetic charge radius of the pion. The contributions from the quark
core and pion loop, as defined in this approach, are identified and compared.
It is shown explicitly that the divergence of the charge radius in the chiral
limit is due to the pion loop and that, at the physical value of the pion mass,
this loop contributes less than 15\% to $\langle r^2_\pi\rangle$; i.e. the
quark core is the dominant determining characteristic for the pion. This
suggests that quark based models which fail to reproduce the $\ln\,m_\pi$
divergence of $\langle r^2_\pi\rangle$ may nevertheless incorporate the
dominant characteristic of the pion: its quark core.


  Traditional change detection methods usually follow the image differencing,
change feature extraction and classification framework, and their performance
is limited by such simple image domain differencing and also the hand-crafted
features. Recently, the success of deep convolutional neural networks (CNNs)
has widely spread across the whole field of computer vision for their powerful
representation abilities. In this paper, we therefore address the remote
sensing image change detection problem with deep learning techniques. We
firstly propose an end-to-end dual-branch architecture, termed as the W-Net,
with each branch taking as input one of the two bi-temporal images as in the
traditional change detection models. In this way, CNN features with more
powerful representative abilities can be obtained to boost the final detection
performance. Also, W-Net performs differencing in the feature domain rather
than in the traditional image domain, which greatly alleviates loss of useful
information for determining the changes. Furthermore, by reformulating change
detection as an image translation problem, we apply the recently popular
Generative Adversarial Network (GAN) in which our W-Net serves as the
Generator, leading to a new GAN architecture for change detection which we call
CDGAN. To train our networks and also facilitate future research, we construct
a large scale dataset by collecting images from Google Earth and provide
carefully manually annotated ground truths. Experiments show that our proposed
methods can provide fine-grained change detection results superior to the
existing state-of-the-art baselines.


  The data warehousing is becoming increasingly important in terms of strategic
decision making through their capacity to integrate heterogeneous data from
multiple information sources in a common storage space, for querying and
analysis. So it can evolve into a multi-tier structure where parts of the
organization take information from the main data warehouse into their own
systems. These may include analysis databases or dependent data marts. As the
data warehouse evolves and the organization gets better at capturing
information on all interactions with the customer. Data warehouse can track
customer interactions over the whole of the customer's lifetime.


  Two-dimensional Molecular Dynamics simulations are used to model the free
surface flow of spheres falling down an inclined chute. The interaction between
the particles in our model is assumed to be subjected to the Hertzian contact
force and normal as well as shear friction. The stream of particles shows a
characteristic height profile, consisting of layers of different types of
fluidization. The numerically observed flow properties agree qualitatively with
experimental results.


  We investigate the scattering phenomena in two dimensions produced by a
general finite-range nonseparable potential. This situation can appear either
in a Cartesian geometry or in a heterostructure with cylindrical symmetry.
Increasing the dimensionality of the scattering problem new processes as the
scattering between conducting channels and the scattering from conducting to
evanescent channels are allowed. For certain values of the energy called
resonance energy the transmission through the scattering region changes
dramatically in comparison with an one-dimensional problem. If the potential
has an attractive character even the evanescent channels can be seen as dips of
the total transmission. The multi-channel current scattering matrix is
determined using its representation in terms of the R-matrix. The resonant
transmission peaks are characterized quantitatively through the poles of the
current scattering matrix. Detailed maps of the localization probability
density sustain the physical interpretation of the resonances. Our formalism is
applied to a quantum dot in a two-dimensional electron gas and to a conical
quantum dot dot embedded inside a cylindrical nanowire.


  Knowledge-intensive language understanding tasks require Language Models
(LMs) to integrate relevant context, mitigating their inherent weaknesses, such
as incomplete or outdated knowledge. Nevertheless, studies indicate that LMs
often ignore the provided context as it can conflict with the pre-existing LM's
memory learned during pre-training. Moreover, conflicting knowledge can already
be present in the LM's parameters, termed intra-memory conflict. Existing works
have studied the two types of knowledge conflicts only in isolation. We
conjecture that the (degree of) intra-memory conflicts can in turn affect LM's
handling of context-memory conflicts. To study this, we introduce the DYNAMICQA
dataset, which includes facts with a temporal dynamic nature where a fact can
change with a varying time frequency and disputable dynamic facts, which can
change depending on the viewpoint. DYNAMICQA is the first to include real-world
knowledge conflicts and provide context to study the link between the different
types of knowledge conflicts. With the proposed dataset, we assess the use of
uncertainty for measuring the intra-memory conflict and introduce a novel
Coherent Persuasion (CP) score to evaluate the context's ability to sway LM's
semantic output. Our extensive experiments reveal that static facts, which are
unlikely to change, are more easily updated with additional context, relative
to temporal and disputable facts.


  Recently, Granville and Soundararajan have made fundamental breakthroughs in
the study of character sums. Building on their work and using estimates on
short character sums developed by Graham-Ringrose and Iwaniec, we improve the
Polya-Vinogradov inequality for characters with smooth conductor.


  We review recent developments in the theory of Thompson group representations
related to knot theory.


  Near-infrared observations of line emission from excited H2 and in the
continuum are reported in the direction of the Orion molecular cloud OMC1,
using the European Southern Observatory Very Large Telescope UT4, equipped with
the NAOS adaptive optics system and the CONICA infrared array camera. Spatial
resolution has been achieved at close to the diffraction limit of the telescope
(0.08" - 0.12") and images show a wealth of morphological detail. Structure is
not fractal but shows two preferred scale sizes of 2.4" (1100 AU) and 1.2" (540
AU), where the larger scale may be associated with star formation.


  In this paper, the pinching problems of complete $\lambda$-hypersurfaces in a
Euclidean space $\mathbb R^{n+1}$ are studied. By making use of the Sobolev
inequality, we prove a global pinching theorem of complete
$\lambda$-hypersurfaces in a Euclidean space $\mathbb R^{n+1}$.