  In contrast to the static operations of conventional semiconductor devices,
the dynamic conformational freedom in molecular devices opens up the
possibility of using molecules as new types of devices such as a molecular
conformational switch or for molecular data storage. Bistable molecules, with
e.g. two stable cis and trans isomeric configurations, could provide, once
clamped between two electrodes, a switching phenomenon in the nonequilibrium
current response. Here, we model molecular switch junctions formed at silicon
contacts and demonstrate the potential of tunable molecular switches in
electrode/molecule/electrode configurations. Using the non equilibrium Green
function approach implemented with the density-functional-based tight-binding
theory, a series of properties such as electron transmissions, I-V
characteristics in the different isomer-conformations, and potential energy
surfaces as a function of the reaction coordinates along the trans to cis
transition were calculated. Furthermore, in order to investigate stability of
molecular switches in ambient conditions, molecular dynamics (MD) simulations
at room temperature were performed and time- dependent fluctuations of the
conductance along the MD pathways were calculated. Our numerical results show
that the transmission spectra of the cis isomers are more conductive than trans
counterparts inside the bias window for all two model molecules. The
current-voltage characteristics consequently show the same trends.
Additionally, the calculations of time-dependent transmission fluctuations
along the MD pathways have shown that the transmission in cis isomers is always
significantly larger than that of trans counterparts showing that molecular
switches can be expected to work as robust molecular switching components.


  We rederive a popular nonsemisimple fusion algebra in the braided context,
from a Nichols algebra. Together with the decomposition that we find for the
product of simple Yetter-Drinfeld modules, this strongly suggests that the
relevant Nichols algebra furnishes an equivalence with the triplet W-algebra in
the (p,1) logarithmic models of conformal field theory. For this, the category
of Yetter-Drinfeld modules is to be regarded as an \textit{entwined} category
(the one with monodromy, but not with braiding).


  In this chapter we discuss the problem of enumerating distinct regular
expressions by size and the regular languages they represent. We discuss
various notions of the size of a regular expression that appear in the
literature and their advantages and disadvantages. We consider a formal
definition of regular expressions using a context-free grammar.
  We then show how to enumerate strings generated by an unambiguous
context-free grammar using the Chomsky-Sch\"utzenberger theorem. This theorem
allows one to construct an algebraic equation whose power series expansion
provides the enumeration. Classical tools from complex analysis, such as
singularity analysis, can then be used to determine the asymptotic behavior of
the enumeration.
  We use these algebraic and analytic methods to obtain asymptotic estimates on
the number of regular expressions of size n. A single regular language can
often be described by several regular expressions, and we estimate the number
of distinct languages denoted by regular expressions of size n. We also give
asymptotic estimates for these quantities. For the first few values, we provide
exact enumeration results.


  We discuss observations of the ion flux from a cloud of trapped metastable
helium atoms. Both Bose-Einstein condensates and thermal clouds were
investigated. The ion flux is compared to time-of-flight observations of the
expanded cloud. We show data concerning BEC formation and decay, as well as
measurements of two- and three-body ionization rate constants. We also discuss
possible improvements and extensions of our results.


  Recent developments for the delivery of proton and ion beam therapy have been
significant, and a number of technological solutions now exist for the creation
and utilisation of these particles for the treatment of cancer. In this paper
we review the historical development of particle accelerators used for external
beam radiotherapy and discuss the more recent progress towards more capable and
cost-effective sources of particles.


  In the paper, the transition probability density of isotropic $\alpha$-stable
stochastic process in a finite dimensional Euclidean space is considered. The
results of applying pseudo differential operators with respect spatial
variables to this function are estimated from the both side: above and below.
Operators in the consideration are defined by the symbols $|\lambda|^\varkappa$
and $\lambda|\lambda|^{\varkappa-1}$, where $\varkappa$ is some constant. The
first operator with negative sign is fractional Laplacian and the second one
multiplied by imaginary unit is fractional gradient.


  We analyze charmless two-body non-leptonic B decays $B \to PP, PV$ under the
framework of factorization assisted topological amplitude approach, where
$P(V)$ denotes a light pseudoscalar (vector) meson. Compared with the
conventional flavor diagram approach, we consider flavor $SU(3)$ breaking
effect assisted by factorization hypothesis for topological diagram amplitudes
of different decay modes, factorizing out the corresponding decay constants and
form factors. The non-perturbative parameters of topology diagram magnitudes
$\chi$ and strong phase $\phi$ are universal that can be extracted by $\chi^2$
fit from current abundant experimental data of charmless B decays. The number
of free parameters and the $\chi^2$ per degree of freedom are both reduced
comparing with previous analysis. With these best fitted parameters, we predict
branching fractions and $CP$ asymmetry parameters of nearly 100 $B_{u,d}$ and
$B_s$ decay modes. The long-standing $\pi \pi$ and $\pi K$-$CP$ puzzles are
resolved simultaneoulsy.


  By inferring the dynamic permittivity of different material media from the
observations and calculating dynamic electric dipole polarizabilties of the Li
through Cs alkali atoms, precise values of $C_3$ coefficients were estimated in
Phys. Rev. A {\bf 89}, 022511 (2014) and Phys. Lett. A {\bf 380}, 3366 (2016).
Since significant contribution towards the long range van der Waals potential
is given by the quadrupole polarization effects, we have estimated the $C_5$
coefficients in this work arising from the quadrupole polarization effects of
all the alkali atoms interacting with metal (Au), semiconductor (Si) and four
dielectric materials (SiO$_2$, SiN$_x$, YAG and sapphire). The required dynamic
electric quadrupole (E2) polarizabilities are evaluated by calculating E2
matrix elements of a large number of transitions in the alkali atoms by
employing a relativistic coupled-cluster method. Our finding shows that
contributions from the $C_5$ coefficients to the atom-wall interaction
potentials are pronounced at short distances (1$-$10 nm). The $C_3$
coefficients of Fr atom interacting with the above material media are also
reported. These results can be useful in understanding the interactions of
alkali atoms trapped in different material bodies during the high-precision
measurements.


  The nonequilibrium steady state of an infinite-range Ising model is studied.
The steady state is obtained by dividing the spins into two groups and
attaching them to two heat baths generating spin flips at different
temperatures. In the thermodynamic limit, the resulting dynamics can be solved
exactly, and the probability flow in the phase space can be visualized. We can
calculate the steady state fluctuations far from equilibrium and, in
particular, we find the exact probability distribution of the energy current in
both the high- and low-temperature phase.


  We study the ground state properties of the hole-doped three-band Hubbard
(Emery) model, describing the copper-oxygen planes of the cuprates, using
large-scale 2D tensor network calculations. Our simulations reveal a period 4
stripe state with spin and weak charge order over an extended doping range
beyond $\delta\sim0.12$ and stripes with larger periods at smaller doping. The
period 4 stripe exhibits coexisting $d$-wave superconductivity in the doping
range $0.15 \lesssim \delta < 0.25$, while at smaller doping around $\delta
\sim 1/8$ we find a strong competition between superconducting and
non-superconducting stripes, including also pair density-wave states with
alternating sign structure on neighboring stripes, suggesting that the fate of
superconductivity around 1/8 doping may be sensitive on the model parameters.


  We introduce a theoretical framework for understanding and predicting the
complexity of sequence classification tasks, using a novel extension of the
theory of Boolean function sensitivity. The sensitivity of a function, given a
distribution over input sequences, quantifies the number of disjoint subsets of
the input sequence that can each be individually changed to change the output.
We argue that standard sequence classification methods are biased towards
learning low-sensitivity functions, so that tasks requiring high sensitivity
are more difficult. To that end, we show analytically that simple lexical
classifiers can only express functions of bounded sensitivity, and we show
empirically that low-sensitivity functions are easier to learn for LSTMs. We
then estimate sensitivity on 15 NLP tasks, finding that sensitivity is higher
on challenging tasks collected in GLUE than on simple text classification
tasks, and that sensitivity predicts the performance both of simple lexical
classifiers and of vanilla BiLSTMs without pretrained contextualized
embeddings. Within a task, sensitivity predicts which inputs are hard for such
simple models. Our results suggest that the success of massively pretrained
contextual representations stems in part because they provide representations
from which information can be extracted by low-sensitivity decoders.


  The interdependence of electricity and natural gas markets is becoming a
major topic in energy research. Integrated energy models are used to assist
decision-making for businesses and policymakers addressing challenges of energy
transition and climate change. The analysis of complex energy systems requires
large-scale models, which are based on extensive databases, intertemporal
dynamics and a multitude of decision variables. Integrating such energy system
models results in increased system complexity. This complexity poses a
challenge for energy modellers to address multiple uncertainties that affect
both markets. Stochastic optimisation approaches enable an adequate
consideration of uncertainties in investment and operation planning; however,
stochastic modelling of integrated large-scale energy systems further scales
the level of complexity. In this paper, we combine integrated and stochastic
optimisation problems and parametrise our model for European electricity and
gas markets. We analyse and compare the impact of uncertain input parameters,
such as gas and electricity demand, renewable energy capacities and fuel and
CO2 prices, on the quality of the solution obtained in the integrated
optimisation problem. Our results quantify the value of encoding uncertainty as
a part of a model. While the methodological contribution should be of interest
for energy modellers, our findings are relevant for industry experts and
stakeholders with an empirical interest in the European energy system.


  The HyperCP experiment (Fermilab E871) was designed to search for rare
phenomena in the decays of charged strange particles, in particular CP
violation in $\Xi$ and $\Lambda$ hyperon decays with a sensitivity of
$10^{-4}$. Intense charged secondary beams were produced by 800 GeV/c protons
and momentum-selected by a magnetic channel. Decay products were detected in a
large-acceptance, high-rate magnetic spectrometer using multiwire proportional
chambers, trigger hodoscopes, a hadronic calorimeter, and a muon-detection
system. Nearly identical acceptances and efficiencies for hyperons and
antihyperons decaying within an evacuated volume were achieved by reversing the
polarities of the channel and spectrometer magnets. A high-rate
data-acquisition system enabled 231 billion events to be recorded in twelve
months of data-taking.


  We reveal the relevance between the nonlocality and the focusing/defocusing
states in nonlocally nonlinear media, and predict a novel phenomenon that the
self-focusing/self-defocusing property of the optical beam in the nonlocally
nonlinear medium with a sine-oscillation response function depends on its
degree of nonlocality. The transition from the focusing nonlinearity to the
defocusing nonlinearity of the nonlinear refractive index will happen when the
degree of nonlocality of the system goes cross a critical value, and vise
verse. Bright and dark soliton solutions are obtained, respectively, in the
focusing state and in the defocusing state, and their stabilities are also
discussed. It is mentioned that such a phenomenon might be experimentally
realized in the nematic liquid crystal with negative dielectric anisotropy or
in the quadratic nonlinear medium.


  Accurate accounting of particle number and 4-momentum in radiative transfer
may be facilitated by the use of transport equations that allow transparent
conversion between volume and surface integrals in both spacetime and momentum
space. Such conservative formulations of general relativistic radiative
transfer in multiple spatial dimensions are presented, and their relevance to
core-collapse supernova simulations described.


  We derive the main classical gravitational tests for a recently found vacuum
solution with spin and dilation charges in the framework of Metric-Affine gauge
theory of gravity. Using the results of the perihelion precession of the star
S2 by the GRAVITY collaboration and the gravitational redshift of Sirius B
white dwarf we constrain the corrections provided by the torsion and
nonmetricity fields for these effects.


  Coherent controlization, i.e., coherent conditioning of arbitrary single- or
multi-qubit operations on the state of one or more control qubits, is an
important ingredient for the flexible implementation of many algorithms in
quantum computation. This is of particular significance when certain
subroutines are changing over time or when they are frequently modified, such
as in decision-making algorithms for learning agents. We propose a scheme to
realize coherent controlization for any number of superconducting qubits
coupled to a microwave resonator. For two and three qubits, we present an
explicit construction that is of high relevance for quantum learning agents. We
demonstrate the feasibility of our proposal, taking into account loss,
dephasing, and the cavity self-Kerr effect.


  Graph neural networks (GNNs) have shown great potential in learning on
graphs, but they are known to perform sub-optimally on link prediction tasks.
Existing GNNs are primarily designed to learn node-wise representations and
usually fail to capture pairwise relations between target nodes, which proves
to be crucial for link prediction. Recent works resort to learning more
expressive edge-wise representations by enhancing vanilla GNNs with structural
features such as labeling tricks and link prediction heuristics, but they
suffer from high computational overhead and limited scalability. To tackle this
issue, we propose to learn structural link representations by augmenting the
message-passing framework of GNNs with Bloom signatures. Bloom signatures are
hashing-based compact encodings of node neighborhoods, which can be efficiently
merged to recover various types of edge-wise structural features. We further
show that any type of neighborhood overlap-based heuristic can be estimated by
a neural network that takes Bloom signatures as input. GNNs with Bloom
signatures are provably more expressive than vanilla GNNs and also more
scalable than existing edge-wise models. Experimental results on five standard
link prediction benchmarks show that our proposed model achieves comparable or
better performance than existing edge-wise GNN models while being 3-200
$\times$ faster and more memory-efficient for online inference.


  In this paper we study a new variant of graph arboricity, which requires all
the forests to have the same number of edges (up to a difference of 1). We
prove that the new variant, which we call equitable arboricity, is equivalent
to ordinary arboricity. In other words we show that any arborescent
decomposition of a graph can be transformed into an equitable one without
modifying the number of used forests.


  The output of a 200 kHz, 34 W, 300 fs Yb amplifier is compressed to 31 fs
with > 88 % efficiency to reach a peak power of 2.5 GW, which to date is a
record for a single-stage bulk multi-pass cell. Despite operation 80 times
above the critical power for self-focusing in bulk material, the setup
demonstrates excellent preservation of the input beam quality. Extensive beam
and pulse characterizations are performed to show that the compressed pulses
are promising drivers for high harmonic generation and nonlinear optics in
gases or solids.


  We present the arguments suggesting that time is emergent in quantum gravity
and discuss extensively, but without any technical detail, the many aspects
that can be involved in such emergence. We refer to both the physical issues
that need to be tackled, by quantum gravity formalisms, to realize concretely
this emergent picture of time, and the conceptual challenges that have to be
addressed in parallel to achieve a proper understanding of it.


  In a recent work we have proven the existence of degenerate solutions to the
Dirac equation, corresponding to an infinite number of different
electromagnetic fields, providing also some examples regarding massless
particles. In the present article our results are extended significantly,
providing degenerate solutions to the Dirac equation for particles with
arbitrary mass, which, under certain conditions, could be interpreted as pairs
of particles (or antiparticles) moving in a potential barrier with energy equal
to the height of the barrier and spin opposite to each other. We calculate the
electromagnetic fields corresponding to these solutions, providing also some
examples regarding both spatially constant electromagnetic fields and
electromagnetic waves. Further, we discuss some potential applications of our
work, mainly regarding the control of the particles outside the potential
barrier, without affecting their state inside the barrier. Finally, we study
the effect of small perturbations to the degenerate solutions, showing that our
results are still valid, in an approximate sense, provided that the amplitude
of the electromagnetic fields corresponding to the exact degenerate solutions
is sufficiently small.


  We consider a smectic-A* in a capillary with surface anchoring that favors
parallel alignment. If the bulk phase of the smectic is the standard
twist-grain-boundary phase of chiral smectics, then there will be a critical
radius below which the smectic will not have any topological defects. Above
this radius a single screw dislocation in the center of the capillary will be
favored. Along with surface anchoring, a magnetic field will also suppress the
formation of a screw dislocation. In this note, we calculate the critical field
at which a defect is energetically preferred as a function of the surface
anchoring strength and the capillary radius. Experiments at a few different
radii could thus determine the anchoring strength.


  We construct Zachary space in $\R^\infty$ and find that this is a Banach
space of functions of bounded mean oscillation with order $p, 1\leq p \leq
\infty$ containing the function of bounded mean oscillation $BMO[\R_I^\infty]$
as a dense continuous embedding. As an application of $\R_I^\infty$ we
construction $\mcB,$ where $\mcB $ is separable Banach space and finally we
construct $\mcZ^p[\mcB]$.


  In this paper we consider deterministic limits of molecular stochastic
systems with finite and infinite degrees of freedom. The method to obtain the
deterministic vector field is based on the continuum limit of such microscopic
systems which has been derived in [11]. With the aid of the theory we finally
develop a new approach for molecular systems that describe typical enzyme
kinetics or other interactions between molecular machines like genetic elements
and smaller communicating molecules. In contrast to the literature on enzyme
kinetics the resulting deterministic functional responses are not derived by
time-scale arguments on the macroscopic level, but are a result of time scaling
transition rates on the discrete microscopic level. We present several examples
of common functional responses found in the literature, like Michaelis-Menten
and Hill equation. We finally give examples of more complex but typical
macro-molecular machinery.


  We show that galaxy ellipticity estimation for weak gravitational lensing
with unweighted image moments reduces to the problem of measuring a combination
of the means of three independent normal random variables. Under very general
assumptions, the intrinsic image moments of sources can be recovered from
observations including effects such as the point-spread function and
pixellation. Gaussian pixel noise turns these into three jointly normal random
variables, the means of which are algebraically related to the ellipticity. We
show that the random variables are approximately independent with known
variances, and provide an algorithm for making them exactly independent. Once
the framework is developed, we derive general properties of the ellipticity
estimation problem, such as the signal-to-noise ratio, a generic form of an
ellipticity estimator, and Cram\'er-Rao lower bounds for an unbiased estimator.
We then derive the unbiased ellipticity estimator using unweighted image
moments. We find that this unbiased estimator has a poorly behaved distribution
and does not converge in practical applications, but demonstrates how to derive
and understand the behaviour of new moment-based ellipticity estimators.


  Dependent Object Types (DOT) is intended to be a core calculus for modelling
Scala. Its distinguishing feature is abstract type members, fields in objects
that hold types rather than values. Proving soundness of DOT has been
surprisingly challenging, and existing proofs are complicated, and reason about
multiple concepts at the same time (e.g. types, values, evaluation). To serve
as a core calculus for Scala, DOT should be easy to experiment with and extend,
and therefore its soundness proof needs to be easy to modify.
  This paper presents a simple and modular proof strategy for reasoning in DOT.
The strategy separates reasoning about types from other concerns. It is centred
around a theorem that connects the full DOT type system to a restricted variant
in which the challenges and paradoxes caused by abstract type members are
eliminated. Almost all reasoning in the proof is done in the intuitive world of
this restricted type system. Once we have the necessary results about types, we
observe that the other aspects of DOT are mostly standard and can be
incorporated into a soundness proof using familiar techniques known from other
calculi.
  Our paper comes with a machine-verified version of the proof in Coq.


The existence of ring-like structures in exact hopfion solutions is shown.


  The action of recently proposed formulation of Einstein Theory of Gravitation
is written according to 3+1 decomposition of the space-time variables. The
result coincides with known formulation of Dirac and Arnowitt-Deser-Misner.


  Farms produce hundreds of thousands of data points on the ground daily.
Farming technique which combines farming practices with the insights uncovered
in these data points using AI technology is called precision farming. Precision
farming technology augments and extends farmers' deep knowledge about their
land, making production more sustainable and profitable. As part of the larger
effort at Microsoft for empowering agricultural labor force to be more
productive and sustainable, this paper presents the AI technology for
predicting micro-climate conditions on the farm.
  This article is a chapter in publication by Food and Agriculture Organization
of the United Nations and International Telecommunication Union Bangkok, 2021.
This publication on artificial intelligence (AI) for agriculture is the fifth
in the E-agriculture in Action series, launched in 2016 and jointly produced by
FAO and ITU. It aims to raise awareness about existing AI applications in
agriculture and to inspire stakeholders to develop and replicate the new ones.
Improvement of capacity and tools for capturing and processing data and
substantial advances in the field of machine learning open new horizons for
data-driven solutions that can support decision-making, facilitate supervision
and monitoring, improve the timeliness and effectiveness of safety measures
(e.g. use of pesticides), and support automation of many resource-consuming
tasks in agriculture. This publication presents the reader with a collection of
informative applications highlighting various ways AI is used in agriculture
and offering valuable insights on the implementation process, success factors,
and lessons learnt.


  We have used the star camera on the WIRE satellite to observe the K0 III star
Alpha UMa, and we report the apparent detection of 10 oscillation modes. The
lowest frequency mode is at 1.82 microhertz, and appears to be the fundamental
mode. The mean spacing between the mode frequencies is 2.94 microhertz, which
implies that all detected modes are radial. The mode frequencies are consistent
with the physical parameters of a K0 III star, if we assume that only radial
modes are excited. Mode amplitudes are 100 -- 400 micromagnitudes, which is
consistent with the scaling relation of Kjeldsen & Beddinge (1995).


  We prove that, for a two-dimensional Riemannian manifold, the Ricci flow is
obtained by a Wiener process.


  We consider the competition of magnetic and charge ordering in high-Tc
cuprates within the framework of the simplified static 2D spin-pseudospin
model. This model is equivalent to the 2D dilute antiferromagnetic (AFM) Ising
model with charged impurities. We present the mean-field results for the system
under study and make a brief comparison with classical Monte Carlo (MC)
calculations. Numerical simulations show that the cases of strong exchange and
strong charge correlation differ qualitatively. For a strong exchange, the AFM
phase is unstable with respect to the phase separation (PS) into the charge and
spin subsystems, which behave like immiscible quantum liquids. An analytical
expression was obtained for the PS temperature.


  Adversarial attacks alter NLP model predictions by perturbing test-time
inputs. However, it is much less understood whether, and how, predictions can
be manipulated with small, concealed changes to the training data. In this
work, we develop a new data poisoning attack that allows an adversary to
control model predictions whenever a desired trigger phrase is present in the
input. For instance, we insert 50 poison examples into a sentiment model's
training set that causes the model to frequently predict Positive whenever the
input contains "James Bond". Crucially, we craft these poison examples using a
gradient-based procedure so that they do not mention the trigger phrase. We
also apply our poison attack to language modeling ("Apple iPhone" triggers
negative generations) and machine translation ("iced coffee" mistranslated as
"hot coffee"). We conclude by proposing three defenses that can mitigate our
attack at some cost in prediction accuracy or extra human annotation.


  The thermofield dynamics of the D = 10 heterotic thermal string theory is
described in proper reference to the thermal duality symmetry as well as the
thermal stability of modular invariance in association with the global phase
structure of the D = 10 heterotic thermal string ensemble.


  We prove that the classical $W$-algebra associated to a nilpotent orbit in a
simple Lie-algebra can be constructed by preforming bihamiltonian,
Drinfeld-Sokolov or Dirac reductions.
  We conclude that the classical $W$-algebra depends only on the nilpotent
orbit but not on the choice of a good grading or an isotropic subspace.
  In addition, using this result we prove again that the transverse Poisson
structure to a nilpotent orbit is polynomial and we better clarify the relation
between classical and finite $W$-algebras.


  The likelihood function represents statistical evidence in the context of
data and a probability model. Considerable theory has demonstrated that
evidence strength for different parameter values can be interpreted from the
ratio of likelihoods at different points on the likelihood curve. The
likelihood function can, however, be unknown or difficult to compute; e.g. for
genetic association studies with a binary outcome in large multi-generational
families. Composite likelihood is a convenient alternative to using the real
likelihood and here we show composite likelihoods have valid evidential
interpretation. We show that composite likelihoods, with a robust adjustment,
have two large sample performance properties that enable reliable evaluation of
relative evidence for different values on the likelihood curve: (1) The
composite likelihood function will support the true value over the false value
by an arbitrarily large factor; and (2) the probability of favouring a false
value over a true value with high probability is small and bounded. Using an
extensive simulation study, and in a genetic association analysis of reading
disability in large complex pedigrees, we show that the composite approach
yields valid statistical inference. Results are compared to analyses using
generalized estimating equations and show similar inference is obtained,
although the composite approach results in a full likelihood solution that
provides additional complementary information.


  We consider a sample of ten GRBs with long lasting ($\gtrsim10^2\rm\,sec$)
emission detected by Fermi/LAT and for which X-ray data around $1\,$day are
also available. We assume that both the X-rays and the GeV emission are
produced by electrons accelerated at the external forward shock, and show that
the X-ray and the GeV fluxes lead to very different estimates of the initial
kinetic energy of the blast wave. The energy estimated from GeV is on average
$\sim50$ times larger than the one estimated from X-rays. We model the data
(accounting also for optical detections around $1\,$day, if available) to
unveil the reason for this discrepancy and find that good modelling within the
forward shock model is always possible and leads to two possibilities: either
the X-ray emitting electrons (unlike the GeV emitting electrons) are in the
slow cooling regime or ii) the X-ray synchrotron flux is strongly suppressed by
Compton cooling, whereas, due to the Klein-Nishina suppression, this effect is
much smaller at GeV energies. In both cases the X-ray flux is no longer a
robust proxy for the blast wave kinetic energy. On average, both cases require
weak magnetic fields ($10^{-6}\lesssim \epsilon_B \lesssim 10^{-3}$) and
relatively large isotropic kinetic blast wave energies
$10^{53}\rm\,erg<E_{0,kin}<10^{55}\rm\,erg$ corresponding to large lower limits
on the collimated energies, in the range
$10^{52}\rm\,erg<E_{\theta,kin}<5\times10^{52}\rm\,erg$ for an ISM environment
with $n\sim 1\mbox{cm}^{-3}$ and
$10^{52}\rm\,erg<E_{\theta,kin}<10^{53}\rm\,erg$ for a wind environment with
$A_* \sim 1$. These energies are larger than those estimated from the X-ray
flux alone, and imply smaller inferred values of the prompt efficiency
mechanism, reducing the efficiency requirements on the still uncertain
mechanism responsible for prompt emission.


  The transient fluctuation of the prosperity of firms in a network economy is
investigated with an abstract stochastic model. The model describes the profit
which firms make when they sell materials to a firm which produces a product
and the fixed cost expense to the firms to produce those materials and product.
The formulae for this model are parallel to those for population dynamics. The
swinging changes in the fluctuation in the transient state from the initial
growth to the final steady state are the consequence of a topology-dependent
time trial competition between the profitable interactions and expense. The
firm in a sparse random network economy is more likely to go bankrupt than
expected from the value of the limit of the fluctuation in the steady state,
and there is a risk of failing to reach by far the less fluctuating steady
state.


  Deep learning techniques have emerged as a promising approach to highly
accelerated MRI. However, recent reconstruction challenges have shown several
drawbacks in current deep learning approaches, including the loss of fine image
details even using models that perform well in terms of global quality metrics.
In this study, we propose an end-to-end deep learning framework for image
reconstruction and pathology detection, which enables a clinically aware
evaluation of deep learning reconstruction quality. The solution is
demonstrated for a use case in detecting meniscal tears on knee MRI studies,
ultimately finding a loss of fine image details with common reconstruction
methods expressed as a reduced ability to detect important pathology like
meniscal tears. Despite the common practice of quantitative reconstruction
methodology evaluation with metrics such as SSIM, impaired pathology detection
as an automated pathology-based reconstruction evaluation approach suggests
existing quantitative methods do not capture clinically important
reconstruction outcomes.


  We investigate the evolution of dust formed in Population III supernovae
(SNe) by considering its transport and processing by sputtering within the SN
remnants (SNRs). We find that the fates of dust grains within SNRs heavily
depend on their initial radii $a_{\rm ini}$. For Type II SNRs expanding into
the ambient medium with density of $n_{\rm H,0} = 1$ cm$^{-3}$, grains of
$a_{\rm ini} < 0.05$ $\mu$m are detained in the shocked hot gas and are
completely destroyed, while grains of $a_{\rm ini} > 0.2$ $\mu$m are injected
into the surrounding medium without being destroyed significantly. Grains with
$a_{\rm ini}$ = 0.05-0.2 $\mu$m are finally trapped in the dense shell behind
the forward shock. We show that the grains piled up in the dense shell enrich
the gas up to 10$^{-6}-10^{-4}$ $Z_\odot$, high enough to form low-mass stars
with 0.1-1 $M_\odot$. In addition, [Fe/H] in the dense shell ranges from -6 to
-4.5, which is in good agreement with the ultra-metal-poor stars with [Fe/H] <
-4. We suggest that newly formed dust in a Population III SN can have great
impacts on the stellar mass and elemental composition of Population II.5 stars
formed in the shell of the SNR.


  We show a new lower bound on the sample complexity of $(\varepsilon,
\delta)$-differentially private algorithms that accurately answer statistical
queries on high-dimensional databases. The novelty of our bound is that it
depends optimally on the parameter $\delta$, which loosely corresponds to the
probability that the algorithm fails to be private, and is the first to
smoothly interpolate between approximate differential privacy ($\delta > 0$)
and pure differential privacy ($\delta = 0$).
  Specifically, we consider a database $D \in \{\pm1\}^{n \times d}$ and its
\emph{one-way marginals}, which are the $d$ queries of the form "What fraction
of individual records have the $i$-th bit set to $+1$?" We show that in order
to answer all of these queries to within error $\pm \alpha$ (on average) while
satisfying $(\varepsilon, \delta)$-differential privacy, it is necessary that
$$ n \geq \Omega\left( \frac{\sqrt{d \log(1/\delta)}}{\alpha \varepsilon}
\right), $$ which is optimal up to constant factors. To prove our lower bound,
we build on the connection between \emph{fingerprinting codes} and lower bounds
in differential privacy (Bun, Ullman, and Vadhan, STOC'14).
  In addition to our lower bound, we give new purely and approximately
differentially private algorithms for answering arbitrary statistical queries
that improve on the sample complexity of the standard Laplace and Gaussian
mechanisms for achieving worst-case accuracy guarantees by a logarithmic
factor.


  The goal of this paper is to develop a measure for characterizing complex
dependence between stationary time series that cannot be captured by
traditional measures such as correlation and coherence. Our approach is to use
copula models of functionals of the Fourier coefficients which is a
generalization of coherence. Here, we use standard parametric copula models
with a single parameter both from elliptical and Archimedean families. Our
approach is to analyze changes in local field potentials in the rat cortex
prior to and immediately following the onset of stroke. We present the
necessary theoretical background, the multivariate models and an illustration
of our methodology on these local field potential data. Simulations with
non-linear dependent data show information that were missed by not taking into
account dependence on specific frequencies. Moreover, these simulations
demonstrate how our proposed method captures more complex non-linear dependence
between time series. Finally, we illustrate our copula-based approach in the
analysis of local field potentials of rats.


  In this paper the double-sided Talor's approximations are used to obtain
generalisations and improvements of some trigonometric inequalities.


  We examine Cr isotopes at the drip line, where surface effects related to the
existence of a weakly bound $s1/2$ state are known to be important and tightly
connected with the pairing phenomenon (anti-halo effect). For these
weakly-bound isotopes, we evaluate the ground state to ground state two-neutron
transfer probabilities within a mean-field-based approach. An important part of
the discussion is devoted to the analysis of several procedures that can be
employed to constrain the parameters of a phenomenological pairing interaction.
The parameters are first adjusted to reproduce the experimental gaps evaluated
with the five-point formula. This choice has however some consequences on the
evolution of the pairing correlations along the isotopic chains and, in
particular, at shell closures. Other procedures are then followed (adjustment
on a theoretical pairing gap at mid-shell and on the two-neutron separation
energies).
  For the transfer probabilities, we discuss the effects associated to
different choices of the spatial localization of the pairing interaction. We
indicate that the analysis of pair-transfer reactions for such cases (where the
last bound state is a low-$l$ state in a weakly bound nucleus) may improve our
understanding of two aspects: the spatial distribution of pairing correlations
in nuclei and the general problem of the persistence of pairing at the drip
lines.


  The article provides a review of the publications on the current trends and
developments in Dempster-Shafer theory and its different applications in
science, engineering, and technologies. The review took account of the
following provisions with a focus on some specific aspects of the theory.
Firstly, the article considers the research directions whose results are known
not only in scientific and academic community but understood by a wide circle
of potential designers and developers of advanced engineering solutions and
technologies. Secondly, the article shows the theory applications in some
important areas of human activity such as manufacturing systems, diagnostics of
technological processes, materials and products, building and construction,
product quality control, economic and social systems. The particular attention
is paid to the current state of research in the domains under consideration
and, thus, the papers published, as a rule, in recent years and presenting the
achievements of modern research on Dempster-Shafer theory and its application
are selected and analyzed.


  It is argued that our universe happens to be in a state of statistical
equilibrium at the hidden-variable level, such that nonlocality is masked by
quantum noise. To account for this 'quantum equilibrium', we outline a
subquantum statistical mechanics in configuration space, and an H-theorem
analogous to the classical coarse-graining H-theorem. Foundational issues in
statistical mechanics are addressed, and an alternative explanation based on
'typicality' is criticised. An estimate is given for the relaxation timescale,
and an illustrative numerical simulation is provided. Assuming the universe
began in quantum non-equilibrium, we sketch a scenario in which relaxation is
suppressed at very early times by the rapid expansion of space, raising the
possibility that deviations from quantum theory could survive to the present
day for relic cosmological particles that decoupled sufficiently early. It is
concluded that quantum noise is a remnant of the big bang and, like the
microwave background, should be probed experimentally. Possible tests with
relic particles are briefly discussed.


  The next-generation Very Large Array (ngVLA) is an astronomical observatory
planned to operate at centimeter wavelengths (25 to 0.26 centimeters,
corresponding to a frequency range extending from 1.2 to 116 GHz). The
observatory will be a synthesis radio telescope constituted of approximately
244 reflector antennas each of 18 meters diameter, and 19 reflector antennas
each of 6 meters diameter, operating in a phased or interferometric mode. We
provide a technical overview of the Reference Design of the ngVLA. This
Reference Design forms a baseline for a technical readiness assessment and the
construction and operations cost estimate of the ngVLA. The concepts for major
system elements such as the antenna, receiving electronics, and central signal
processing are presented.


  The bright low-mass X-ray binary (LMXB) GX 13+1 is one of the most peculiar
Galactic binary systems. A periodicity of 24.27 d with a formal statistical
error of 0.03 d was observed in its power spectrum density obtained with RXTE
All Sky Monitor (ASM) data spanning 14 years. Starting from a recent study,
indicating GX 13+1 as a possible dipping source candidate, we systematically
searched for periodic dips in the X-ray light curves of GX 13+1 from 1996 up to
2013 using RXTE/ASM, and MAXI data to determine for the first time the X-ray
orbital ephemeris of GX 13+1. We searched for a periodic signal in the ASM and
MAXI light curves, finding a common periodicity of 24.53 d. We folded the 1.3-5
keV and 5-12.1 keV ASM light curves and the 2-4 and 4-10 keV MAXI light curves
at the period of 24.53 d finding a periodic dip. To refine the value of the
period we used the timing technique dividing the ASM light curve in eight
intervals and the MAXI light curve in two intervals, obtaining four and two dip
arrival times from the ASM and MAXI light curves, respectively. We improved the
X-ray position of GX 13+1 using a recent Chandra observation. The new X-ray
position is discrepant by $\sim 7\arcsec$ from the previous one, while it is
compatible with the infrared and radio counterpart positions. We detected an
X-ray dip, that is totally covered by the Chandra observation, in the light
curve of GX 13+1 and showed, a-posteriori, that it is a periodic dip. We
obtained seven dip arrival times from ASM, MAXI, and Chandra light curves. We
calculated the delays of the detected dip arrival times with respect to the
expected times for a 24.52 d periodicity. Fitting the delays with a linear
function we find that the orbital period and the epoch of reference of GX 13+1
are 24.5274(2) days and 50,086.79(3) MJD, respectively.(Abridged)


  Topic lifecycle analysis on Twitter, a branch of study that investigates
Twitter topics from their birth through lifecycle to death, has gained immense
mainstream research popularity. In the literature, topics are often treated as
one of (a) hashtags (independent from other hashtags), (b) a burst of keywords
in a short time span or (c) a latent concept space captured by advanced text
analysis methodologies, such as Latent Dirichlet Allocation (LDA). The first
two approaches are not capable of recognizing topics where different users use
different hashtags to express the same concept (semantically related), while
the third approach misses out the user's explicit intent expressed via
hashtags. In our work, we use a word embedding based approach to cluster
different hashtags together, and the temporal concurrency of the hashtag
usages, thus forming topics (a semantically and temporally related group of
hashtags).We present a novel analysis of topic lifecycles with respect to
communities. We characterize the participation of social communities in the
topic clusters, and analyze the lifecycle of topic clusters with respect to
such participation. We derive first-of-its-kind novel insights with respect to
the complex evolution of topics over communities and time: temporal morphing of
topics over hashtags within communities, how the hashtags die in some
communities but morph into some other hashtags in some other communities (that,
it is a community-level phenomenon), and how specific communities adopt to
specific hashtags. Our work is fundamental in the space of topic lifecycle
modeling and understanding in communities: it redefines our understanding of
topic lifecycles and shows that the social boundaries of topic lifecycles are
deeply ingrained with community behavior.


  Convolutional networks reach top quality in pixel-level video object
segmentation but require a large amount of training data (1k~100k) to deliver
such results. We propose a new training strategy which achieves
state-of-the-art results across three evaluation datasets while using 20x~1000x
less annotated data than competing methods. Our approach is suitable for both
single and multiple object segmentation. Instead of using large training sets
hoping to generalize across domains, we generate in-domain training data using
the provided annotation on the first frame of each video to synthesize ("lucid
dream") plausible future video frames. In-domain per-video training data allows
us to train high quality appearance- and motion-based models, as well as tune
the post-processing stage. This approach allows to reach competitive results
even when training from only a single annotated frame, without ImageNet
pre-training. Our results indicate that using a larger training set is not
automatically better, and that for the video object segmentation task a smaller
training set that is closer to the target domain is more effective. This
changes the mindset regarding how many training samples and general
"objectness" knowledge are required for the video object segmentation task.


  We present the first determination of the strong coupling constant from a fit
of next-to-next-to-leading order QCD predictions to event-shape variables,
measured in $e^+e^-$ annihilations at LEP. The data have been collected by the
ALEPH detector at centre-of-mass energies between 91 and 206 GeV. Compared to
results of next-to-leading order fits we observe that the central fit values
are lower by about 10%, with considerably reduced scatter among the results
obtained with different event-shape variables. The dominant systematic
uncertainty from renormalization scale variations is reduced by a factor of
two. By combining the results for several event-shape variables and
centre-of-mass energies, we find \alpha_s(M_Z^2) =
0.1240+-0.0008(stat)+-0.0010(exp)+-0.0011(had)+-0.0029(theo).


  The Mixture-of-Experts (MoE) layer, a sparsely-activated model controlled by
a router, has achieved great success in deep learning. However, the
understanding of such architecture remains elusive. In this paper, we formally
study how the MoE layer improves the performance of neural network learning and
why the mixture model will not collapse into a single model. Our empirical
results suggest that the cluster structure of the underlying problem and the
non-linearity of the expert are pivotal to the success of MoE. To further
understand this, we consider a challenging classification problem with
intrinsic cluster structures, which is hard to learn using a single expert. Yet
with the MoE layer, by choosing the experts as two-layer nonlinear
convolutional neural networks (CNNs), we show that the problem can be learned
successfully. Furthermore, our theory shows that the router can learn the
cluster-center features, which helps divide the input complex problem into
simpler linear classification sub-problems that individual experts can conquer.
To our knowledge, this is the first result towards formally understanding the
mechanism of the MoE layer for deep learning.


  Long-context language models (LCLMs) have the potential to revolutionize our
approach to tasks traditionally reliant on external tools like retrieval
systems or databases. Leveraging LCLMs' ability to natively ingest and process
entire corpora of information offers numerous advantages. It enhances
user-friendliness by eliminating the need for specialized knowledge of tools,
provides robust end-to-end modeling that minimizes cascading errors in complex
pipelines, and allows for the application of sophisticated prompting techniques
across the entire system. To assess this paradigm shift, we introduce LOFT, a
benchmark of real-world tasks requiring context up to millions of tokens
designed to evaluate LCLMs' performance on in-context retrieval and reasoning.
Our findings reveal LCLMs' surprising ability to rival state-of-the-art
retrieval and RAG systems, despite never having been explicitly trained for
these tasks. However, LCLMs still face challenges in areas like compositional
reasoning that are required in SQL-like tasks. Notably, prompting strategies
significantly influence performance, emphasizing the need for continued
research as context lengths grow. Overall, LOFT provides a rigorous testing
ground for LCLMs, showcasing their potential to supplant existing paradigms and
tackle novel tasks as model capabilities scale.


  We use Richter's $2$-primary proof of Gray's conjecture to give a homotopy
decomposition of the fibre $\Omega^3S^{17}\{2\}$ of the $H$-space squaring map
on the triple loop space of the $17$-sphere. This induces a splitting of the
mod-$2$ homotopy groups $\pi_\ast(S^{17}; \mathbb{Z}/2\mathbb{Z})$ in terms of
the integral homotopy groups of the fibre of the double suspension
$E^2:S^{2n-1} \to \Omega^2S^{2n+1}$ and refines a result of Cohen and Selick,
who gave similar decompositions for $S^5$ and $S^9$. We relate these
decompositions to various Whitehead products in the homotopy groups of mod-$2$
Moore spaces and Stiefel manifolds to show that the Whitehead square $[i_{2n},
i_{2n}]$ of the inclusion of the bottom cell of the Moore space $P^{2n+1}(2)$
is divisible by $2$ if and only if $2n=2, 4, 8$ or $16$.


  The absolute mass value of $^{168}$Yb has been directly determined with the
JYFLTRAP Penning trap mass spectrometer at the Ion Guide Isotope Separator
On-Line (IGISOL) facility. A more precise value of the mass of $^{168}$Yb is
needed to extract possible signatures of beyond standard model physics from
high-precision isotope shift measurements of Yb atomic transition frequencies.
The measured mass-excess value, ME($^{168}$Yb) = $-$61579.846(94) keV, is 12
times more precise and deviates from the Atomic Mass Evaluation 2016 value by
1.7$\sigma$. The impact on precision isotope shift studies of the stable Yb
isotopes is discussed.


  Scoring play games were first studied by Fraser Stewart for his PhD thesis.
He showed that under the disjunctive sum, scoring play games are partially
ordered, but do not have the same "nice" structure of normal play games. In
this paper I will be considering scoring play games under three different
operators given by John Conway and William Stromquist and David Ullman, namely
the conjunctive sum, selective sum and sequential join.


  We focus on a protocol for auditable restoration of distributed systems. The
need for such protocol arises due to conflicting requirements (e.g., access to
the system should be restricted but emergency access should be provided). One
can design such systems with a tamper detection approach (based on the
intuition of "break the glass door"). However, in a distributed system, such
tampering, which are denoted as auditable events, is visible only for a single
node. This is unacceptable since the actions they take in these situations can
be different than those in the normal mode. Moreover, eventually, the auditable
event needs to be cleared so that system resumes the normal operation.
  With this motivation, in this paper, we present a protocol for auditable
restoration, where any process can potentially identify an auditable event.
Whenever a new auditable event occurs, the system must reach an "auditable
state" where every process is aware of the auditable event. Only after the
system reaches an auditable state, it can begin the operation of restoration.
Although any process can observe an auditable event, we require that only
"authorized" processes can begin the task of restoration. Moreover, these
processes can begin the restoration only when the system is in an auditable
state. Our protocol is self-stabilizing and has bounded state space. It can
effectively handle the case where faults or auditable events occur during the
restoration protocol. Moreover, it can be used to provide auditable restoration
to other distributed protocol.


  We use a result of Hellus about generalized local duality to describe some
generalized Matlis duals for certain quasi-\F-modules. Furthermore, we apply
this description to obtain examples for non-artinian local cohomology modules
by the theory of \F-modules. In particular, we get a new view on Hartshorne's
counterexample for a conjecture by Grothendieck about the finiteness of
$Hom_R(R/I,H^i_I(R))$ for a noetherian local Ring $R$ and an ideal $I \subseteq
R$.


  There is an ever-growing need for predictive models for the
elasto-viscoplastic deformation of solids. Our goal in this paper is to
incorporate recently developed out-of-equilibrium statistical concepts into a
thermodynamically consistent, finite-deformation, continuum framework for
deforming amorphous solids. The basic premise is that the configurational
degrees of freedom of the material --- the part of the internal energy/entropy
that corresponds to mechanically stable microscopic configurations --- are
characterized by a configurational temperature that might differ from that of
the vibrational degrees of freedom, which equilibrate rapidly with an external
heat bath. This results in an approximate internal energy decomposition into
weakly interacting configurational and vibrational subsystems, which exchange
energy following a Fourier-like law, leading to a thermomechanical framework
permitting two well-defined temperatures. In this framework, internal variables
that carry information about the state of the material equilibrate with the
configurational subsystem, are explicitly associated with energy and entropy of
their own, and couple to a viscoplastic flow rule. The coefficients that
determine the rate of flow of entropy and heat between different internal
systems are proposed to explicitly depend on the rate of irreversible
deformation. As an application of this framework, we discuss two constitutive
models for the response of glassy materials, a simple phenomenological model
and a model related to the concept of Shear-Transformation-Zones as the basis
for internal variables. The models account for several salient features of
glassy deformation phenomenology. Directions for future investigation are
briefly discussed.


  Due to their interdisciplinary nature, devices for controlled-environment
agriculture have the possibility to turn into ideal tools not only to conduct
research on plant phenology but also to create curricula in a wide range of
disciplines. Controlled-environment devices are increasing their
functionalities as well as improving their accessibility. Traditionally,
building one of these devices from scratch implies knowledge in fields such as
mechanical engineering, digital electronics, programming, and energy
management. However, the requirements of an effective controlled environment
device for personal use brings new constraints and challenges. This paper
presents the OpenAg Personal Food Computer (PFC); a low cost desktop size
platform, which not only targets plant phenology researchers but also
hobbyists, makers, and teachers from elementary to high-school levels (K-12).
The PFC is completely open-source and it is intended to become a tool that can
be used for collective data sharing and plant growth analysis. Thanks to its
modular design, the PFC can be used in a large spectrum of activities.


  We prove matching upper and lower bounds for the average of the 6-torsion of
class groups of quadratic fields. Furthermore, we count the number of integer
solutions on an affine quartic surface.


  The interior structure of an exoplanet is hidden from direct view yet likely
plays a crucial role in influencing the habitability of the Earth analogues.
Inferences of the interior structure are impeded by a fundamental degeneracy
that exists between any model comprising more than two layers and observations
constraining just two bulk parameters: mass and radius. In this work, we show
that although the inverse problem is indeed degenerate, there exists two
boundary conditions that enables one to infer the minimum and maximum core
radius fraction, CRFmin and CRFmax. These hold true even for planets with light
volatile envelopes, but require the planet to be fully differentiated and that
layers denser than iron are forbidden. With both bounds in hand, a marginal CRF
can also be inferred by sampling in-between. After validating on the Earth, we
apply our method to Kepler-36b and measure CRFmin = (0.50 +/- 0.07), CRFmax =
(0.78 +/- 0.02), and CRFmarg = (0.64 +/- 0.11), broadly consistent with the
Earth's true CRF value of 0.55. We apply our method to a suite of hypothetical
measurements of synthetic planets to serve as a sensitivity analysis. We find
that CRFmin and CRFmax have recovered uncertainties proportional to the
relative error on the planetary density, but CRFmarg saturates to between 0.03
and 0.16 once delta rho/rho drops below 1-2 per cent. This implies that mass
and radius alone cannot provide any better constraints on internal composition
once bulk density constraints hit around a per cent, providing a clear target
for observers.


  Nuclear forces violating parity and time reversal invariance (${\cal P},{\cal
T}$-odd) produce ${\cal P},{\cal T}$-odd nuclear moments, for example, the
nuclear Schiff moment. In turn, this moment can induce the electric dipole
moment in the atom. The nuclear Schiff moment is predicted to be enhanced in
nuclei with static quadrupole and octupole deformation. The analogous
suggestion of the enhanced contribution to the Schiff moment from the soft
collective quadrupole and octupole vibrations in spherical nuclei is tested in
this article in the framework of the quasiparticle random phase approximation
with separable quadrupole and octupole forces applied to the odd $^{217-221}$Ra
and $^{217-221}$Rn isotopes. We confirm the existence of the enhancement effect
due to the soft modes. However, in the standard approximation the enhancement
is strongly reduced by a small weight of the corresponding "particle + phonon"
component in a complicated wave function of a soft nucleus. The perspectives of
a better description of the structure of heavy soft nuclei are discussed.


  Linear Partial Differential Equations (PDEs) govern the spatial-temporal
dynamics of physical systems that are essential to building modern technology.
When working with linear PDEs, designing a physical system for a specific
outcome is difficult and costly due to slow and expensive explicit simulation
of PDEs and the highly nonlinear relationship between a system's configuration
and its behavior. In this work, we prove a parametric form that certain
physical quantities in the Fourier domain must obey in linear PDEs, named the
CZP (Constant-Zeros-Poles) framework. Applying CZP to antenna design, an
industrial application using linear PDEs (i.e., Maxwell's equations), we derive
a sample-efficient parametric surrogate model that directly predicts its
scattering coefficients without explicit numerical PDE simulation. Combined
with a novel image-based antenna representation and an attention-based neural
network architecture, CZP outperforms baselines by 10% to 25% in terms of test
loss and also is able to find 2D antenna designs verifiable by commercial
software with $33\%$ greater success than baselines, when coupled with
sequential search techniques like reinforcement learning.


  We present the first large-scale simulations of an ultracold, neutral plasma,
produced by photoionization of laser-cooled xenon atoms, from creation to
initial expansion, using classical molecular dynamics methods with open
boundary conditions. We reproduce many of the experimental findings such as the
trapping efficiency of electrons with increased ion number, a minimum electron
temperature achieved on approach to the photoionization threshold, and
recombination into Rydberg states of anomalously-low principal quantum number.
In addition, many of these effects establish themselves very early in the
plasma evolution ($\sim$ ns) before present experimental observations begin.


  Learning how to generate descriptions of images or videos received major
interest both in the Computer Vision and Natural Language Processing
communities. While a few works have proposed to learn a grounding during the
generation process in an unsupervised way (via an attention mechanism), it
remains unclear how good the quality of the grounding is and whether it
benefits the description quality. In this work we propose a movie description
model which learns to generate description and jointly ground (localize) the
mentioned characters as well as do visual co-reference resolution between pairs
of consecutive sentences/clips. We also propose to use weak localization
supervision through character mentions provided in movie descriptions to learn
the character grounding. At training time, we first learn how to localize
characters by relating their visual appearance to mentions in the descriptions
via a semi-supervised approach. We then provide this (noisy) supervision into
our description model which greatly improves its performance. Our proposed
description model improves over prior work w.r.t. generated description quality
and additionally provides grounding and local co-reference resolution. We
evaluate it on the MPII Movie Description dataset using automatic and human
evaluation measures and using our newly collected grounding and co-reference
data for characters.


  A treatment of many-electron polar and $s-d(f)$ exchange models is carried
out in connection with the development of the theory of magnetism of transition
and rare-earth metals, as well as their compounds. Particular emphasis is
placed on the derivation of the many-electron Heisenberg, Hubbard, Anderson and
$t-J$ models, as well as the internal relationship between them. Among the
topics discussed are many-electron approaches in describing systems of $d$- and
$f$-electrons, atomic representation of Hubbard's $X$-operators, slave-particle
representations, the problem of strong itinerant magnetism and the formation of
local moments, the role of non-quasiparticle (incoherent states). The
application of these concepts to strongly correlated systems, in particular, to
half-metallic ferromagnets and Kondo lattices, is discussed. A comparison is
performed with modern field-theoretical and topological approaches.


  We study a family of (possibly non topological) deformations of $BF$ theory
for the Lie algebra obtained by quadratic extension of $\mathfrak{so}(3,1)$ by
an orthogonal module. The resulting theory, called quadratically extended
General Relativity (qeGR), is shown to be classically equivalent to certain
models of gravity with dynamical torsion. The classical equivalence is shown to
promote to a stronger notion of equivalence within the Batalin--Vilkovisky
formalism. In particular, both Palatini--Cartan gravity and a deformation
thereof by a dynamical torsion term, called (quadratic) generalised Holst
theory, are recovered from the standard Batalin--Vilkovisky formulation of qeGR
by elimination of generalised auxiliary fields.


  In this paper, we consider a distributed stochastic optimization problem
where the goal is to minimize the time average of a cost function subject to a
set of constraints on the time averages of related stochastic processes called
penalties. We assume that the state of the system is evolving in an independent
and non-stationary fashion and the "common information" available at each node
is distributed and delayed. Such stochastic optimization is an integral part of
many important problems in wireless networks such as scheduling, routing,
resource allocation and crowd sensing. We propose an approximate distributed
Drift- Plus-Penalty (DPP) algorithm, and show that it achieves a time average
cost (and penalties) that is within epsilon > 0 of the optimal cost (and
constraints) with high probability. Also, we provide a condition on the
convergence time t for this result to hold. In particular, for any delay D >= 0
in the common information, we use a coupling argument to prove that the
proposed algorithm converges almost surely to the optimal solution. We use an
application from wireless sensor network to corroborate our theoretical
findings through simulation results.


  We investigate the conditions required for planet formation via gravitational
instability (GI) and protoplanetary disk (PPD) fragmentation around M-dwarfs.
Using a suite of 64 SPH simulations with $10^6$ particles, the parameter space
of disk mass, temperature, and radius is explored, bracketing reasonable values
based on theory and observation. Our model consists of an equilibrium, gaseous,
and locally isothermal disk orbiting a central star of mass $M_*=M_{sol}/3$.
Disks with a minimum Toomre $Q$ of $Q_{min} \lesssim 0.9$ will fragment and
form gravitationally bound clumps. Some previous literature has found $Q_{min}
< 1.3-1.5$ to be sufficient for fragmentation. Increasing disk height tends to
stabilize disks, and when incorporated into $Q$ as $Q_{eff}\propto
Q(H/R)^\alpha$ for $\alpha=0.18$ is sufficient to predict fragmentation. Some
discrepancies in the literature regarding $Q_{crit}$ may be due to different
methods of generating initial conditions (ICs). A series of 15 simulations
demonstrates that perturbing ICs slightly out of equilibrium can cause disks to
fragment for higher $Q$. Our method for generating ICs is presented in detail.
We argue that GI likely plays a role in PPDs around M-dwarfs and that disk
fragmentation at large radii is a plausible outcome for these disks.


  This article offers a modern perspective which exposes the many contributions
of Leray in his celebrated work on the Navier--Stokes equations from 1934.
Although the importance of his work is widely acknowledged, the precise
contents of his paper are perhaps less well known. The purpose of this article
is to fill this gap. We follow Leray's results in detail: we prove local
existence of strong solutions starting from divergence-free initial data that
is either smooth, or belongs to $H^1$, $L^2\cap L^p$ (with $p\in(3,\infty]$),
as well as lower bounds on the norms $\| \nabla u (t) \|_2$, $\| u(t) \|_p$
($p\in(3,\infty]$) as $t$ approaches a putative blow-up time. We show global
existence of a weak solution and weak-strong uniqueness. We present Leray's
characterisation of the set of singular times for the weak solution, from which
we deduce that its upper box-counting dimension is at most $\tfrac{1}{2}$.
Throughout the text we provide additional details and clarifications for the
modern reader and we expand on all ideas left implicit in the original work,
some of which we have not found in the literature. We use some modern
mathematical tools to bypass some technical details in Leray's work, and thus
expose the elegance of his approach.


  The parametric global minimum cut problem concerns a graph $G = (V,E)$ where
the cost of each edge is an affine function of a parameter $\mu \in
\mathbb{R}^d$ for some fixed dimension $d$. We consider the problems of finding
the next breakpoint in a given direction, and finding a parameter value with
maximum minimum cut value. We develop strongly polynomial algorithms for these
problems that are faster than a naive application of Megiddo's parametric
search technique. Our results indicate that the next breakpoint problem is
easier than the max value problem.


  Gaze-based interactions offer a potential way for users to naturally engage
with mixed reality (XR) interfaces. Black-box machine learning models enabled
higher accuracy for gaze-based interactions. However, due to the black-box
nature of the model, users might not be able to understand and effectively
adapt their gaze behaviour to achieve high quality interaction. We posit that
explainable AI (XAI) techniques can facilitate understanding of and interaction
with gaze-based model-driven system in XR. To study this, we built a real-time,
multi-level XAI interface for gaze-based interaction using a deep learning
model, and evaluated it during a visual search task in XR. A between-subjects
study revealed that participants who interacted with XAI made more accurate
selections compared to those who did not use the XAI system (i.e., F1 score
increase of 10.8%). Additionally, participants who used the XAI system adapted
their gaze behavior over time to make more effective selections. These findings
suggest that XAI can potentially be used to assist users in more effective
collaboration with model-driven interactions in XR.


  Viscoelastic rate-type fluids are popular models of choice in many
applications involving flows of fluid-like materials with complex
micro-structure. A well-developed mathematical theory for the most of these
classical fluid models is however missing. The main purpose of this study is to
provide a complete proof of long-time and large-data existence of weak
solutions to unsteady internal three-dimensional flows of Giesekus fluids
subject to a no-slip boundary condition. As a new auxiliary tool, we provide
the identification of certain biting limits in the parabolic setting, presented
here within the framework of evolutionary Stokes problems. We also generalize
the long-time and large-data existence result to higher dimensions, to
viscoelastic models with multiple relaxation mechanisms and to viscoelastic
models with different type of dissipation.


  I discuss recent results from Jefferson Lab on the measurement of inclusive
spin structure functions in the nucleon resonance region using polarized
electron beams and polarized targets. Results on the first moment of the spin
structure function for protons and neutrons are discussed, as well as the
Bjorken integral. I will argue that the helicity structure of individual
resonances plays a vital role in understanding the nucleon's spin response in
the domain of strong interaction QCD, and must be considered in any analysis of
the nucleon spin structure at low and intermediate photon virtuality.


  A theorem of L. Caffarelli implies the existence of a map pushing forward a
source Gaussian measure to a target measure which is more log-concave than the
source one, which contracts Euclidean distance (in fact, Caffarelli showed that
the optimal-transport Brenier map $T_{opt}$ is a contraction in this case). We
generalize this result to more general source and target measures, using a
condition on the third derivative of the potential, using two different proofs.
The first uses a map $T$, whose inverse is constructed as a flow along an
advection field associated to an appropriate heat-diffusion process. The
contraction property is then reduced to showing that log-concavity is preserved
along the corresponding diffusion semi-group, by using a maximum principle for
parabolic PDE. In particular, Caffarelli's original result immediately follows
by using the Ornstein-Uhlenbeck process and the Pr\'ekopa--Leindler Theorem.
The second uses the map $T_{opt}$ by generalizing Caffarelli's argument,
employing in addition further results of Caffarelli. As applications, we obtain
new correlation and isoperimetric inequalities.


  We report the first experimental observation of non-adiabatic field-free
orientation of a heteronuclear diatomic molecule (CO) induced by an intense
two-color (800 and 400 nm) femtosecond laser field. We monitor orientation by
measuring fragment ion angular distributions after Coulomb explosion with an
800 nm pulse. The orientation of the molecules is controlled by the relative
phase of the two-color field. The results are compared to quantum mechanical
rigid rotor calculations. The demonstrated method can be applied to study
molecular frame dynamics under field-free conditions in conjunction with a
variety of spectroscopy methods, such as high-harmonic generation, electron
diffraction and molecular frame photoemission.


  Although Cloud Computing promises to lower IT costs and increase users'
productivity in everyday life, the unattractive aspect of this new technology
is that the user no longer owns all the devices which process personal data. To
lower scepticism, the project SensorCloud investigates techniques to understand
and compensate these adoption barriers in a scenario consisting of cloud
applications that utilize sensors and actuators placed in private places. This
work provides an interdisciplinary overview of the social and technical core
research challenges for the trustworthy integration of sensor and actuator
devices with the Cloud Computing paradigm. Most importantly, these challenges
include i) ease of development, ii) security and privacy, and iii) social
dimensions of a cloud-based system which integrates into private life. When
these challenges are tackled in the development of future cloud systems, the
attractiveness of new use cases in a sensor-enabled world will considerably be
increased for users who currently do not trust the Cloud.


  The impact of nuclear structure has garnered considerable attention in the
high-energy nuclear physics community in recent years. This work focuses on
studying the potential nuclear cluster structure in $^{16}\text{O}$ nuclei
using anisotropic flow observables in $\rm O+O$ collisions at 200 GeV.
Employing an improved AMPT model with various cluster structure configurations,
we find that an extended effective parton formation time is necessary to align
with the recent STAR experimental data. In addition, we reveal that the
presented flow observables serve as sensitive probes for differentiating
configurations of $\alpha$-clustering of $^{16}\text{O}$ nuclei. The systematic
AMPT calculations presented in this paper, along with comprehensive comparisons
to forthcoming experimental measurements at RHIC and the LHC, pave the way for
a novel approach to investigate the $\alpha$-clustering structure of
$^{16}\text{O}$ nuclei using $\rm O+O$ collisions at the ultra-relativistic
energies.


  For the $a_0(1450)$, considered as the $q\bar{q} 1^3P_0$ state,
"experimental" tensor splitting, $c_{exp}=(-150\pm 40$) MeV, appears to be in
contradiction with conventional theory of fine structure. There is no such
discrepancy if the $a_0(980)$ belongs to the $1^3P_J q\bar{q}$ multiplet. The
hadronic shift of the $a_0(980)$ is shown to be strongly dependent on the value
of the strong coupling in spin-dependent interaction.


  Minimal unitary representation of SO*(8) = SO(6,2) realized over the Hilbert
space of functions of five variables and its deformations labeled by the spin t
of an SU(2) subgroup correspond to massless conformal fields in six dimensions
as was shown in arXiv:1005.3580. In this paper we study the minimal unitary
supermultiplet of OSp(8*|2N) with the even subgroup SO*(8) x USp(2N) and its
deformations using quasiconformal methods. We show that the minimal unitary
supermultiplet of OSp(8*|2N) admits deformations labeled uniquely by the spin t
of an SU(2) subgroup of the little group SO(4) of lightlike vectors in six
dimensions. We construct the deformed minimal unitary representations and show
that they correspond to massless 6D conformal supermultiplets. The minimal
unitary supermultiplet of OSp(8*|4) is the massless supermultiplet of (2,0)
conformal field theory that is believed to be dual to M-theory on AdS_7 x S^4.
We study its deformations in further detail and show that they are isomorphic
to the doubleton supermultiplets constructed by using twistorial oscillators.


  Knowing a user's next cell allows more efficient resource allocation and
enables new location-aware services. To anticipate the cell a user will
hand-over to, we introduce a new machine learning based prediction system.
Therein, we formulate the prediction as a classification problem based on
information that is readily available in cellular networks. Using only Channel
State Information (CSI) and handover history, we perform classification by
embedding Support Vector Machines (SVMs) into an efficient pre-processing
structure. Simulation results from a Manhattan Grid scenario and from a
realistic radio map of downtown Frankfurt show that our system provides timely
prediction at high accuracy.


  In April to May 2023, the superBIT telescope was lifted to the Earth's
stratosphere by a helium-filled super-pressure balloon, to acquire astronomical
imaging from above (99.5% of) the Earth's atmosphere. It was launched from New
Zealand then, for 40 days, circumnavigated the globe five times at a latitude
40 to 50 degrees South. Attached to the telescope were four 'DRS' (Data
Recovery System) capsules containing 5 TB solid state data storage, plus a GNSS
receiver, Iridium transmitter, and parachute. Data from the telescope were
copied to these, and two were dropped over Argentina. They drifted 61 km
horizontally while they descended 32 km, but we predicted their descent vectors
within 2.4 km: in this location, the discrepancy appears irreducible below 2 km
because of high speed, gusty winds and local topography. The capsules then
reported their own locations to within a few metres. We recovered the capsules
and successfully retrieved all of superBIT's data - despite the telescope
itself being later destroyed on landing.


  This paper extends the well-known source coding problem of multiple
descriptions, in its general and basic setting, to analog source-channel coding
scenarios. Encoding-decoding functions that optimally map between the (possibly
continuous valued) source and the channel spaces are numerically derived. The
main technical tool is a non-convex optimization method, namely, deterministic
annealing, which has recently been successfully used in other mapping
optimization problems. The obtained functions exhibit several interesting
structural properties, map multiple source intervals to the same interval in
the channel space, and consistently outperform the known competing mapping
techniques.


  The recently discovered high temperature superconductor F-doped LaFeAsO and
related compounds represent a new class of superconductors with the highest
transition temperature (Tc) apart from the cuprates. The studies ongoing
worldwide are revealing that these Fe-based superconductors are forming a
unique class of materials that are interesting from the viewpoint of
applications. To exploit the high potential of the Fe-based superconductors for
device applications, it is indispensable to establish a process that enables
the growth of high quality thin films. Efforts of thin film preparation started
soon after the discovery of Fe-based superconductors, but none of the earlier
attempts had succeeded in an in-situ growth of a superconducting film of
LnFeAs(O,F) (Ln=lanthanide), which exhibits the highest Tc to date among the
Fe-based superconductors. Here, we report on the successful growth of
NdFeAs(O,F) thin films on GaAs substrates, which showed well-defined
superconducting transitions up to 48 K without the need of an ex-situ heat
treatment.


  The growing interest in tin-halide semiconductors for photovoltaic
applications demands an in-depth knowledge of the fundamental properties of its
constituents, starting from the smallest monomers entering the initial stages
of formation. In this first-principles work based on time-dependent
density-functional theory, we investigate the structural, electronic, and
optical properties of tin-halide molecules SnX$_n^{2-n}$, with $n=1,2,3,4$ and
X = Cl, Br, I, simulating these compounds in vacuo as well as in an implicit
solvent. We find that structural properties are very sensitive to the halogen
species while the charge distribution is also affected by stoichiometry. The
ionicity of the Sn-X bond is confirmed by the Bader charge analysis albeit
charge displacement plots point to more complex metal-halide coordination.
Particular focus is posed on the neutral molecules SnX$_2$, for which
electronic and optical properties are discussed in detail. Band gaps and
absorption onset decrease with increasing size of the halogen species and
despite general common features, each molecule displays peculiar optical
signatures. Our results are elaborated in the context of experimental and
theoretical literature, including the more widely studied lead-halide analogs,
aiming to contribute with microscopic insight to a better understanding of
tin-halide perovskites.


  We address the problem of the fermion species doubling on the Lattice. Our
strategy is to factorize the fermion doubles from the action. The mass term of
the Dirac-Wilson action is changed. In this case the extra roots which appear
in the action of free fermions in the moment representation are independent of
the mass and can be factorized from the fermion propagator. However the gauge
couplings suffer from the pathological ghost poles which are common to
non-local actions. This action can be used to find a solution of the Ginsparg
Wilson relation, which is cured from the non-local pathology. Finally we
compare this factorized action with solutions of The Ginsparg Wilson relation.
We find that the present is equivalent to the Zenkin action, and that is not
quite as local as the Neuberger action.


  Images in the medical domain are fundamentally different from the general
domain images. Consequently, it is infeasible to directly employ general domain
Visual Question Answering (VQA) models for the medical domain. Additionally,
medical images annotation is a costly and time-consuming process. To overcome
these limitations, we propose a solution inspired by self-supervised
pretraining of Transformer-style architectures for NLP, Vision and Language
tasks. Our method involves learning richer medical image and text semantic
representations using Masked Language Modeling (MLM) with image features as the
pretext task on a large medical image+caption dataset. The proposed solution
achieves new state-of-the-art performance on two VQA datasets for radiology
images -- VQA-Med 2019 and VQA-RAD, outperforming even the ensemble models of
previous best solutions. Moreover, our solution provides attention maps which
help in model interpretability. The code is available at
https://github.com/VirajBagal/MMBERT


  In this work, we argue that the implications of Pseudo and Quantum Random
Number Generators (PRNG and QRNG) inexplicably affect the performances and
behaviours of various machine learning models that require a random input.
These implications are yet to be explored in Soft Computing until this work. We
use a CPU and a QPU to generate random numbers for multiple Machine Learning
techniques. Random numbers are employed in the random initial weight
distributions of Dense and Convolutional Neural Networks, in which results show
a profound difference in learning patterns for the two. In 50 Dense Neural
Networks (25 PRNG/25 QRNG), QRNG increases over PRNG for accent classification
at +0.1%, and QRNG exceeded PRNG for mental state EEG classification by +2.82%.
In 50 Convolutional Neural Networks (25 PRNG/25 QRNG), the MNIST and CIFAR-10
problems are benchmarked, in MNIST the QRNG experiences a higher starting
accuracy than the PRNG but ultimately only exceeds it by 0.02%. In CIFAR-10,
the QRNG outperforms PRNG by +0.92%. The n-random split of a Random Tree is
enhanced towards and new Quantum Random Tree (QRT) model, which has differing
classification abilities to its classical counterpart, 200 trees are trained
and compared (100 PRNG/100 QRNG). Using the accent and EEG classification
datasets, a QRT seemed inferior to a RT as it performed on average worse by
-0.12%. This pattern is also seen in the EEG classification problem, where a
QRT performs worse than a RT by -0.28%. Finally, the QRT is ensembled into a
Quantum Random Forest (QRF), which also has a noticeable effect when compared
to the standard Random Forest (RF)... ABSTRACT SHORTENED DUE TO ARXIV LIMIT


  The binding of clusters of metal nanoparticles is partly electrostatic. We
address difficulties in calculating the electrostatic energy when high charging
energies limit the total charge to a single quantum, entailing unequal
potentials on the particles. We show that the energy at small separation $h$
has a strong logarithmic dependence on $h$. We give a general law for the
strength of this logarithmic correction in terms of a) the energy at contact
ignoring the charge quantization effects and b) an adjacency matrix specifying
which spheres of the cluster are in contact and which is charged. We verify the
theory by comparing the predicted energies for a tetrahedral cluster with an
explicit numerical calculation.


  Standardized body region labelling of individual images provides data that
can improve human and computer use of medical images. A CNN-based classifier
was developed to identify body regions in CT and MRI. 17 CT (18 MRI) body
regions covering the entire human body were defined for the classification
task. Three retrospective databases were built for the AI model training,
validation, and testing, with a balanced distribution of studies per body
region. The test databases originated from a different healthcare network.
Accuracy, recall and precision of the classifier was evaluated for patient age,
patient gender, institution, scanner manufacturer, contrast, slice thickness,
MRI sequence, and CT kernel. The data included a retrospective cohort of 2,934
anonymized CT cases (training: 1,804 studies, validation: 602 studies, test:
528 studies) and 3,185 anonymized MRI cases (training: 1,911 studies,
validation: 636 studies, test: 638 studies). 27 institutions from primary care
hospitals, community hospitals and imaging centers contributed to the test
datasets. The data included cases of all genders in equal proportions and
subjects aged from a few months old to +90 years old. An image-level prediction
accuracy of 91.9% (90.2 - 92.1) for CT, and 94.2% (92.0 - 95.6) for MRI was
achieved. The classification results were robust across all body regions and
confounding factors. Due to limited data, performance results for subjects
under 10 years-old could not be reliably evaluated. We show that deep learning
models can classify CT and MRI images by body region including lower and upper
extremities with high accuracy.


  Motivated by the recent LHCb announcement of a $3.1\sigma$ violation of
lepton-flavor universality in the ratio $R_K=\Gamma(B\to
K\mu^+\mu^-)/\Gamma(B\to K e^+ e^-)$, we present an updated, comprehensive
analysis of the flavor anomalies seen in both neutral-current ($b\to
s\ell^+\ell^-$) and charged-current ($b\to c\tau\bar\nu$) decays of $B$ mesons.
Our study starts from a model-independent effective field-theory approach and
then considers both a simplified model and a UV-complete extension of the
Standard Model featuring a vector leptoquark $U_1$ as the main mediator of the
anomalies. We show that the new LHCb data corroborate the emerging pattern of a
new, predominantly left-handed, semileptonic current-current interaction with a
flavor structure respecting a (minimally) broken $U(2)^5$ flavor symmetry. New
aspects of our analysis include a combined analysis of the semileptonic
operators involving tau leptons, including in particular the important
constraint from $B_s$--$\bar B_s$ mixing, a systematic study of the effects of
right-handed leptoquark couplings and of deviations from minimal
flavor-symmetry breaking, a detailed analysis of various rare $B$-decay modes
which would provide smoking-gun signatures of this non-standard framework (LFV
decays, di-tau modes, and $B\to K^{(*)}\nu\bar\nu$), and finally an updated
analysis of collider bounds on the leptoquark mass and couplings.


  In recent years, instruction tuning has gained increasing attention and
emerged as a crucial technique to enhance the capabilities of Large Language
Models (LLMs). To construct high-quality instruction datasets, many instruction
processing approaches have been proposed, aiming to achieve a delicate balance
between data quantity and data quality. Nevertheless, due to inconsistencies
that persist among various instruction processing methods, there is no standard
open-source instruction processing implementation framework available for the
community, which hinders practitioners from further developing and advancing.
To facilitate instruction processing research and development, we present
EasyInstruct, an easy-to-use instruction processing framework for LLMs, which
modularizes instruction generation, selection, and prompting, while also
considering their combination and interaction. EasyInstruct is publicly
released and actively maintained at https://github.com/zjunlp/EasyInstruct,
along with an online demo app and a demo video for quick-start, calling for
broader research centered on instruction data and synthetic data.


  Due to the complexity of cancer, clustering algorithms have been used to
disentangle the observed heterogeneity and identify cancer subtypes that can be
treated specifically. While kernel based clustering approaches allow the use of
more than one input matrix, which is an important factor when considering a
multidimensional disease like cancer, the clustering results remain hard to
evaluate and, in many cases, it is unclear which piece of information had which
impact on the final result. In this paper, we propose an extension of multiple
kernel learning clustering that enables the characterization of each identified
patient cluster based on the features that had the highest impact on the
result. To this end, we combine feature clustering with multiple kernel
dimensionality reduction and introduce FIPPA, a score which measures the
feature cluster impact on a patient cluster. Results: We applied the approach
to different cancer types described by four different data types with the aim
of identifying integrative patient subtypes and understanding which features
were most important for their identification. Our results show that our method
does not only have state-of-the-art performance according to standard measures
(e.g., survival analysis), but, based on the high impact features, it also
produces meaningful explanations for the molecular bases of the subtypes. This
could provide an important step in the validation of potential cancer subtypes
and enable the formulation of new hypotheses concerning individual patient
groups. Similar analysis are possible for other disease phenotypes.


  A static, spherically symmetric, asymptotically flat spacetime may allow for
circular, closed null-geodesics which are said to belong to a photon sphere. In
the context of gravitational lensing in the strong deflection regime, the
presence of a photon sphere leads to an unbounded angle of deflection of light
(multiple turns) and formation of relativistic images. In this paper, we show
that photon spheres may form in some configurations of boson stars constructed
with a free massive complex scalar field nonminimally coupled to gravity.
Assuming that the boson star is transparent to light, photon spheres would give
raise not only to phenomena in the realm of strong gravitational lensing, but
also to considerably increased photon flux in the central region of the star,
relative to the flux in its surroundings.


  Present quantum computers often work with distinguishable qubits as their
computational units. In order to simulate indistinguishable fermionic
particles, it is first required to map the fermionic state to the state of the
qubits. The Bravyi-Kitaev Superfast (BKSF) algorithm can be used to accomplish
this mapping. The BKSF mapping has connections to quantum error correction and
opens the door to new ways of understanding fermionic simulation in a
topological context. Here, we present the first detailed exposition of BKSF
algorithm for molecular simulation. We provide the BKSF transformed qubit
operators and report on our implementation of the BKSF fermion-to-qubits
transform in OpenFermion. In this initial study of the hydrogen molecule, we
have compared BKSF, Jordan-Wigner and Bravyi-Kitaev transforms under the
Trotter approximation. We considered different orderings of the exponentiated
terms and found lower Trotter errors than previously reported for Jordan-Wigner
and Bravyi-Kitaev algorithms. These results open the door to further study of
the BKSF algorithm for quantum simulation.


  Artificial neural networks (ANNs) have gained a well-deserved popularity
among machine learning tools upon their recent successful applications in
image- and sound processing and classification problems. ANNs have also been
applied for predicting the family or function of a protein, knowing its residue
sequence. Here we present two new ANNs with multi-label classification ability,
showing impressive accuracy when classifying protein sequences into 698 UniProt
families (AUC=99.99%) and 983 Gene Ontology classes (AUC=99.45%).


  We report progress on nuclear structure functions, especially on their
nuclear modifications and a new tensor structure function for the deuteron. To
understand nuclear structure functions is an important step toward describing
nuclei and QCD matters from low to high densities and from low to high energies
in terms of fundamental quark and gluon degrees of freedom beyond conventional
hadron and nuclear physics. It is also practically important for understanding
new phenomena in high-energy heavy-ion collisions at RHIC and LHC. Furthermore,
since systematic errors of current neutrino-oscillation experiments are
dominated by uncertainties of neutrino-nucleus interactions, such studies are
valuable for finding new physics beyond current framework. Next, a new
tensor-polarized structure function $b_1$ is discussed for the deuteron. There
was a measurement by HERMES; however, its data are inconsistent with the
conventional convolution estimate based on the standard deuteron model with
D-state admixture. This fact suggests that a new hadronic phenomenon should
exist in the tensor-polarized deuteron at high energies, and it will be
experimentally investigated at JLab from the end of 2010's.


  We present new interferometric observations of the continuum emission at mm
wavelengths in the Seyfert galaxies NGC1068 and NGC3147. Three mm continuum
peaks are detected in NGC1068, one centered on the core, one associated with
the jet and the third one with the counter-jet. This is the first significant
detection of the radio jet and counter-jet at mm wavelengths in NGC1068. While
the fluxes of the jet components agree with a steep spectral index extrapolated
from cm-wavelengths, the core fluxes indicate a turnover of the inverted cm-
into a steep mm-spectrum at roughly 50GHz which is most likely caused by
electron-scattered synchrotron emission. As in NGC 1068, the spectrum of the
pointlike continuum source in NGC3147 also shows a turnover between cm and
mm-wavelengths at 25GHz resulting from synchrotron self-absorption different to
NGC1068. This strongly resembles the spectrum of SgrA*, the weakly active
nucleus of our own galaxy, and M81*, a link between SgrA* and Seyfert galaxies
in terms of activity sequence, which may display a similar turnover.


  The construction of affine interval exchange maps with wandering intervals
that are semi-conjugate with a given self-similar interval exchange map is
strongly related with the existence of the so called minimal sequences
associated with local potentials, which are certain elements of the
substitution subshift arising from the given interval exchange map. In this
article, under the condition called unique representation property, we
characterize such minimal sequences for potentials coming from non-real
eigenvalues of the substitution matrix. We also give conditions on the slopes
of the affine extensions of a self-similar interval exchange map that determine
whether it exhibits a wandering interval or not.


  Observations of individual bursts chosen by the vagaries of telescope
availability demonstrated that bursts are not standard candles and that their
apparent energy can be as great as 10^54 erg. However, determining the
distribution of their apparent energy (and of other burst properties) requires
the statistical analysis of a well-defined burst sample; the sample definition
includes the threshold for including a burst in the sample. Thus optical groups
need to the criteria behind the decision to search for a spectroscopic
redshift. Currently the burst samples are insufficient to choose between
lognormal and power law functional forms of the distribution, and the parameter
values for these functional forms differ between burst samples. Similarly, the
actual intensity distribution may be broader than observed, with a low energy
tail extending below the detection threshold.