  In this paper, a method to synthesize controllers using finite time
convergence control barrier functions guided by linear temporal logic
specifications for continuous time multi-agent dynamical systems is proposed.
Finite time convergence to a desired set in the state space is guaranteed under
the existence of a suitable finite time convergence control barrier function.
In addition, these barrier functions also guarantee forward invariance once the
system converges to the desired set. This allows us to formulate a theoretical
framework which synthesizes controllers for the multi-agent system. These
properties also enable us to solve the reachability problem in continuous time
by formulating a theorem on the composition of multiple finite time convergence
control barrier functions. This approach is more flexible than existing methods
and also allows for a greater set of feasible control laws. Linear temporal
logic is used to specify complex task specifications that need to be satisfied
by the multi-agent system. With this solution methodology, a control law is
synthesized that satisfies the given temporal logic task specification. Robotic
experiments are provided which were performed on the Robotarium multi-robot
testbed at Georgia Tech.


  We study the topological space of left-orderings of the braid group, and its
subspace of Nielsen-Thurston orderings. Our main result is that no
Nielsen-Thurston ordering is isolated in the space of braid orderings. In the
course of the proof, we classify the convex subgroups and calculate the
Conradian soul for any Nielsen-Thurston ordering of B_n. We also prove that for
a large class of Nielsen-Thurston orderings, including all those of infinite
type, a stronger result holds: they are approximated by their own conjugates.
On the other hand, we suggest an example of a Nielsen-Thurston ordering which
may not be approximated by its conjugates.


  We formulate an analog of Inverse Scattering Method for integrable systems on
noncommutative associative algebras. In particular we define Hamilton flows,
Casimir elements and noncommutative analog of the Lax matrix. The
noncommutative Lax element generates infinite family of commuting Hamilton
flows on an associative algebra. The proposed approach to integrable systems on
associative algebras satisfy certain universal property, in particular it
incorporates both classical and quantum integrable systems as well as provides
a basis for further generalization.
  We motivate our definition by explicit construction of noncommutative analog
of Lax matrix for a system of differential equations on associative algebra
recently proposed by Kontsevich. First we present these equations in the
Hamilton form by defining a bracket of Loday type on the group algebra of the
free group with two generators. To make the definition more constructive we
utilize (with certain generalizations) the Van den Bergh approach to Loday
brackets via double Poisson brackets. We show that there exists an infinite
family of commuting flows generated by the noncommutative Lax element.


  The electron sheath is a particular electron-rich sheath with negative net
charges where plasma potential is lower than the biased electrode. Here an
improved understanding of electron sheath theory is provided using both fluid
and kinetic approaches while elaborating on its implications for plasma-surface
interaction. A fluid model is first proposed considering the electron presheath
structure, avoiding the singularity in electron sheath Child-Langmuir law. The
latter is proved to underestimate the sheath potential. Subsequently, the
kinetic model of electron sheath is established, showing considerably different
sheath profiles in respect to the fluid model due to the electron velocity
distribution function and finite ion temperature. The model is then further
generalized involving a more realistic truncated ion velocity distribution
function. It is demonstrated that such distribution function yields a
super-thermal electron sheath whose entering velocity at sheath edge is greater
than that prescribed by the Bohm criterion, implying a potentially omitted
calibration issue in the probe measurement. Furthermore, an attempt is made to
incorporate the self-consistent presheath-sheath match within the kinetic
framework, showing a necessary compromise between realistic sheath entrance and
the inclusion of kinetic effects. In the end, the consequent secondary electron
emission due to sheath-accelerated plasma electrons in electron sheath are
analyzed, providing a sheath potential coupled with the plasma and wall
properties.


  Between space crystals and amorphous materials there exists a third class of
aperiodic structures which lack translational symmetry but reveal long-range
order. They are dubbed quasi-crystals and their formation, similarly as the
formation of space crystals, is related to spontaneous breaking of
translational symmetry of underlying Hamiltonians. Here, we investigate
spontaneous emergence of quasi-crystals in periodically driven systems. We
consider a quantum many-body system which is driven by a harmonically
oscillating force and show that interactions between particles result in
spontaneous self-reorganization of the motion of a quantum many-body system and
in the formation of a quasi-crystal structure in time.


  In the current era of stellar spectroscopic surveys, synthetic spectral
libraries are the basis for the derivation of stellar parameters and chemical
abundances. In this paper, we compare the stellar parameters determined using
five popular synthetic spectral grids (INTRIGOSS, FERRE, AMBRE, PHOENIX, and
MPIA/1DNLTE) with our convolutional neural network (CNN, $\texttt{StarNet}$).
The stellar parameters are determined for six physical properties (effective
temperature, surface gravity, metallicity, [$\alpha$/Fe], radial velocity, and
rotational velocity) given the spectral resolution, signal-to-noise, and
wavelength range of optical FLAMES-UVES spectra from the Gaia-ESO Survey. Both
CNN modelling and epistemic uncertainties are incorporated through training an
ensemble of networks. $\texttt{StarNet}$ training was also adapted to mitigate
differences between the synthetic grids and observed spectra by augmenting with
realistic observational signatures (i.e. resolution matching, wavelength
sampling, Gaussian noise, zeroing flux values, rotational and radial
velocities, continuum removal, and masking telluric regions). Using the
FLAMES-UVES spectra for FGK type dwarfs and giants as a test set, we quantify
the accuracy and precision of the stellar label predictions from
$\texttt{StarNet}$. We find excellent results over a wide range of parameters
when $\texttt{StarNet}$ is trained on the MPIA/1DNLTE synthetic grid, and
acceptable results over smaller parameter ranges when trained on the 1DLTE
grids. These tests also show that our CNN pipeline is highly adaptable to
multiple simulation grids.


  Soliton gases represent large random soliton ensembles in physical systems
that display integrable dynamics at the leading order. Despite significant
theoretical developments and observational evidence of ubiquity of soliton
gases in fluids and optical media their controlled experimental realization has
been missing. We report the first controlled synthesis of a dense soliton gas
in deep-water surface gravity waves using the tools of nonlinear spectral
theory (inverse scattering transform (IST)) for the one-dional focusing
nonlinear Schr\"odinger equation. The soliton gas is experimentally generated
in a one-dimensional water tank where we demonstrate that we can control and
measure the density of states, i. e. the probability density function
parametrizing the soliton gas in the IST spectral phase space. Nonlinear
spectral analysis of the generated hydrodynamic soliton gas reveals that the
density of states slowly changes under the influence of perturbative
higher-order effects that break the integrability of the wave dynamics.


  Sentiment Analysis (SA) refers to the task of associating a view polarity
(usually, positive, negative, or neutral; or even fine-grained such as slightly
angry, sad, etc.) to a given text, essentially breaking it down to a supervised
(since we have the view labels apriori) classification task. Although heavily
studied in resource-rich languages such as English thus pushing the SOTA by
leaps and bounds, owing to the arrival of the Transformer architecture, the
same cannot be said for resource-poor languages such as Bengali (BN). For a
language spoken by roughly 300 million people, the technology enabling them to
run trials on their favored tongue is severely lacking. In this paper, we
analyze the SOTA for SA in Bengali, particularly, Transformer-based models. We
discuss available datasets, their drawbacks, the nuances associated with
Bengali i.e. what makes this a challenging language to apply SA on, and finally
provide insights for future direction to mitigate the limitations in the field.


  Motivated by a recent experiment by Zelevinsky et al. [Phys. Rev. Lett. 96,
203201], we present the theory for photoassociation and optical Feshbach
resonances of atoms confined in a tight one-dimensional (1D) or two-dimensional
(2D) optical lattice. In the case of an alkaline-earth intercombination
resonance, the narrow natural width of the line makes it possible to observe
clear manifestations of the dimensionality, as well as some sensitivity to the
scattering length of the atoms. Among possible applications, a 2D lattice may
be used to increase the spectroscopic resolution by about one order of
magnitude. Furthermore, a 1D lattice induces a shift which provides a new way
of determining the strength of a resonance by spectroscopic measurements.


  This paper presents CORE, a conceptually simple, effective and
communication-efficient model for multi-agent cooperative perception. It
addresses the task from a novel perspective of cooperative reconstruction,
based on two key insights: 1) cooperating agents together provide a more
holistic observation of the environment, and 2) the holistic observation can
serve as valuable supervision to explicitly guide the model learning how to
reconstruct the ideal observation based on collaboration. CORE instantiates the
idea with three major components: a compressor for each agent to create more
compact feature representation for efficient broadcasting, a lightweight
attentive collaboration component for cross-agent message aggregation, and a
reconstruction module to reconstruct the observation based on aggregated
feature representations. This learning-to-reconstruct idea is task-agnostic,
and offers clear and reasonable supervision to inspire more effective
collaboration, eventually promoting perception tasks. We validate CORE on
OPV2V, a large-scale multi-agent percetion dataset, in two tasks, i.e., 3D
object detection and semantic segmentation. Results demonstrate that the model
achieves state-of-the-art performance on both tasks, and is more
communication-efficient.


  Seismic inversion refers to the process of estimating reservoir rock
properties from seismic reflection data. Conventional and machine
learning-based inversion workflows usually work in a trace-by-trace fashion on
seismic data, utilizing little to no information from the spatial structure of
seismic images. We propose a deep learning-based seismic inversion workflow
that models each seismic trace not only temporally but also spatially. This
utilizes information-relatedness in seismic traces in depth and spatial
directions to make efficient rock property estimations. We empirically compare
our proposed workflow with some other sequence modeling-based neural networks
that model seismic data only temporally. Our results on the SEAM dataset
demonstrate that, compared to the other architectures used in the study, the
proposed workflow is able to achieve the best performance, with an average
$r^{2}$ coefficient of 79.77\%.


  The variational theorem for the scattering length in the presence of the
dipole-dipole interaction is developed. The theorem is applied to the spinless
dipolar Bose gas in three dimensions. We calculated analytically the long-range
tails of the single-particle momentum distribution and static structure factor,
and the pair distribution function at short distances. The momentum
distribution is inversely proportional to $q^4$ with the anisotropic prefactor.
In the absence of the dipole-dipole interaction, Tan's adiabatic sweep theorem
is reproduced as a particular case. For the homogeneous dilute Bose gas, all
the relations are calculated analytically.


  We summarize our results on decoherence for short- to intermediate-time
dynamics of an externally controlled two-state quantum system - a qubit -
interacting with thermal bosonic environment. The developed approximation
schemes are illustrated for an adiabatic model with time-dependent gate
control, and for a model with rotating-wave gate function.


  A not-too-technical version of the paper: "A Granular Permutation-based
Representation of Complex Numbers and Quaternions: Elements of a Realistic
Quantum Theory" - Proc. Roy. Soc.A (2004) 460, 1039-1055. The phrase
"meteorological butterfly effect" is introduced to illustrate, not the familiar
loss of predictability in low-dimensional chaos, but the much less familiar and
much more radical paradigm of the finite-time predictability horizon,
associated with upscale transfer of uncertainty in certain multi-scale systems.
This motivates a novel reinterpretation of unit complex numbers (and
quaternions) in terms of a family of self-similar permutation operators. A
realistic deterministic kinematic reformulation of the foundations of quantum
theory is given using this reinterpretation of complex numbers. Using a
property of the cosine function not normally encountered in physics, that it is
irrational for all dyadic rational angles between 0 and pi/2, this
reformulation is shown to have the emergent property of counterfactual
indefiniteness and is therefore not non-locally causal.


  Stochastic gravity waves have been recently detected and characterised in
stars thanks to space asteroseismology and they may play an important role in
the evolution of stellar angular momentum. In this context, the observational
study of the CoRoT hot Be star HD 51452 suggests a potentially strong impact of
rotation on stochastic excitation of gravito-inertial waves in rapidly rotating
stars. In this work, we present our results on the action of the Coriolis
acceleration on stochastic wave excitation by turbulent convection. We study
the change of efficiency of this mechanism as a function of the waves' Rossby
number and we demonstrate that the excitation presents two different regimes
for super-inertial and sub-inertial frequencies. Consequences for rapidly
rotating early-type stars and the transport of angular momentum in their
interiors are discussed.


  Learning and equilibrium computation in games are fundamental problems across
computer science and economics, with applications ranging from politics to
machine learning. Much of the work in this area revolves around a simple
algorithm termed \emph{randomized weighted majority} (RWM), also known as
"Hedge" or "Multiplicative Weights Update," which is well known to achieve
statistically optimal rates in adversarial settings (Littlestone and Warmuth
'94, Freund and Schapire '99). Unfortunately, RWM comes with an inherent
computational barrier: it requires maintaining and sampling from a distribution
over all possible actions. In typical settings of interest the action space is
exponentially large, seemingly rendering RWM useless in practice.
  In this work, we refute this notion for a broad variety of \emph{structured}
games, showing it is possible to efficiently (approximately) sample the action
space in RWM in \emph{polylogarithmic} time. This gives the first efficient
no-regret algorithms for problems such as the \emph{(discrete) Colonel Blotto
game}, \emph{matroid congestion}, \emph{matroid security}, and basic
\emph{dueling games}. As an immediate corollary, we give a polylogarithmic time
meta-algorithm to compute approximate Nash Equilibria for these games that is
exponentially faster than prior methods in several important settings. Further,
our algorithm is the first to efficiently compute equilibria for more involved
variants of these games with general sums, more than two players, and, for
Colonel Blotto, multiple resource types.


  We consider the primordial gravity wave background produced by inflation. We
compute the small anisotropy produced by the primordial scalar fluctuations.


  The purpose of this paper is twofold. Firstly, to emphasise that the class of
Lie algebras with chain lattices of ideals are elementary blocks in the
embedding or decomposition of Lie algebras with finite lattice of ideals.
Secondly, to show that the number of Lie algebras of this class is large and
they support other types of Lie structures. Beginning with general examples and
algebraic decompositions, we focus on computational algorithms to build Lie
algebras in which the lattice of ideals is a chain. The chain condition forces
gradings on the nilradicals of this class of algebras. Our algorithms yield to
several positive naturally graded parametric families of Lie algebras. Further
generalizations and other kind of structures will also be discussed.


  Kreck and Yang Su recently gave counterexamples to a version of the Torelli
theorem for hyperk\"ahlerian manifolds as stated by Verbitsky. The initial
purpose of this document (which was prepared for a seminar talk) was to extract
the correct statement and to give a short proof of it. We also revisit a few of
its consequences, some of which are given new (shorter) proofs.


  We suggest a possible algorithm to calculate one-loop n-point functions
within a variant of light-front perturbation theory. The key ingredients are
the covariant Passarino-Veltman scheme and a surprising integration formula
that localises Feynman integrals at vanishing longitudinal momentum. The
resulting expressions are generalisations of Weinberg's infinite-momentum
results and are manifestly Lorentz invariant. For n = 2 and 3 we explicitly
show how to relate those to light-front integrals with standard energy
denominators. All expressions are rendered finite by means of transverse
dimensional regularisation.


  With the expansion of operational scale of supermarkets in China, the
vegetable market has grown considerably. The decision-making related to
procurement costs and allocation quantities of vegetables has become a pivotal
factor in determining the profitability of supermarkets. This paper analyzes
the relationship between pricing and allocation faced by supermarkets in
vegetable operations. Optimization algorithms are employed to determine
replenishment and pricing strategies. Linear regression is utilized to model
the historical data of various products, establishing the relationship between
sale prices and sales volumes for 61 products. By integrating historical data
on vegetable costs with time information based on the 24 solar terms, a cost
prediction model is trained using TCN-Attention. The Topis evaluation model
identifies the 32 most market-demanded products. A genetic algorithm is then
used to search for the globally optimized vegetable product allocation-pricing
decision.


  Resource Description Framework (RDF) has been widely used to represent
information on the web, while SPARQL is a standard query language to manipulate
RDF data. Given a SPARQL query, there often exist many joins which are the
bottlenecks of efficiency of query processing. Besides, the real RDF datasets
often reveal strong data sparsity, which indicates that a resource often only
relates to a few resources even the number of total resources is large. In this
paper, we propose a sparse matrix-based (SM-based) SPARQL query processing
approach over RDF datasets which con- siders both join optimization and data
sparsity. Firstly, we present a SM-based storage for RDF datasets to lift the
storage efficiency, where valid edges are stored only, and then introduce a
predicate- based hash index on the storage. Secondly, we develop a scalable
SM-based join algorithm for SPARQL query processing. Finally, we analyze the
overall cost by accumulating all intermediate results and design a query plan
generated algorithm. Besides, we extend our SM-based join algorithm on GPU for
parallelizing SPARQL query processing. We have evaluated our approach compared
with the state-of-the-art RDF engines over benchmark RDF datasets and the
experimental results show that our proposal can significantly improve SPARQL
query processing with high scalability.


  Covariant tensor representations of gl(m|n) occur as irreducible components
of tensor powers of the natural (m+n)-dimensional representation. We construct
a basis of each covariant representation and give explicit formulas for the
action of the generators of gl(m|n) in this basis. The basis has the property
that the natural Lie subalgebras gl(m) and gl(n) act by the classical
Gelfand-Tsetlin formulas. The main role in the construction is played by the
fact that the subspace of gl(m)-highest vectors in any finite-dimensional
irreducible representation of gl(m|n) carries a structure of an irreducible
module over the Yangian Y(gl(n)). One consequence is a new proof of the
character formula for the covariant representations first found by Berele and
Regev and by Sergeev.


  The neutron capture cross section of the unstable nucleus $^{186}$Re is
studied by investigating the inverse photodisintegration reaction
$^{187}$Re($\gamma$,n). The special interest of the {\it s}-process branching
point $^{186}$Re is related to the question of possible {\it s}-process
contributions to the abundance of the {\it r}-process chronometer nucleus
^{187}$Re. We use the photoactivation technique to measure photodisintegration
rates. Our experimental results are in good agreement with two different
statistical model calculations. Although the cross sections predicted by both
models for the inverse reaction $^{186}$Re(n,$\gamma$) is too low to remove the
overproduction of $^{186}$Os; the two predicted neutron-capture cross sections
differ by a factor of 2.4; this calls for future theoretical study.


  Counterterms that are not reducible to $\zeta_{n}$ are generated by ${}_3F_2$
hypergeometric series arising from diagrams for which triangle and uniqueness
relations furnish insufficient data. Irreducible double sums, corresponding to
the torus knots $(4,3)=8_{19}$ and $(5,3)=10_{124}$, are found in anomalous
dimensions at ${\rm O}(1/N^3)$ in the large-$N$ limit, which we compute
analytically up to terms of level 11, corresponding to 11 loops for
4-dimensional field theories and 12 loops for 2-dimensional theories.
High-precision numerical results are obtained up to 24 loops and used in Pad\'e
resummations of $\varepsilon$-expansions, which are compared with analytical
results in 3 dimensions. The ${\rm O}(1/N^3)$ results entail knots generated by
three dressed propagators in the master two-loop two-point diagram. At higher
orders in $1/N$ one encounters the uniquely positive hyperbolic 11-crossing
knot, associated with an irreducible triple sum. At 12 crossings, a pair of
3-braid knots is generated, corresponding to a pair of irreducible double sums
with alternating signs. The hyperbolic positive knots $10_{139}$ and $10_{152}$
are not generated by such self-energy insertions.


  In this paper we present a new Ant Colony Optimisation-based algorithm for
Sudoku, which out-performs existing methods on large instances. Our method
includes a novel anti-stagnation operator, which we call Best Value
Evaporation.


  Due to the node structure of the gap in a d-wave superconductor, the presence
of impurities generates a finite density of quasiparticle excitations at zero
temperature. Since these impurity-induced quasiparticles are both generated and
scattered by impurities, prior calculations indicate a universal limit (\Omega
-> 0, T -> 0) where the transport coefficients obtain scattering-independent
values, depending only on the velocity anisotropy v_f/v_2. We improve upon
prior results, including the contributions of vertex corrections and Fermi
liquid corrections in our calculations of universal limit electrical, thermal,
and spin conductivity. We find that while vertex corrections modify electrical
conductivity and Fermi liquid corrections renormalize both electrical and spin
conductivity, only thermal conductivity maintains its universal value,
independent of impurity scattering or Fermi liquid interactions. Hence, low
temperature thermal conductivity measurements provide the most direct means of
obtaining the velocity anisotropy for high T_c cuprate superconductors.


  The near-infrared shape of the big blue bump component in quasar spectra has
been essentially unknown. It usually cannot be observed directly, due to the
strong hot dust emission which dominates quasar spectra longward of ~1micron.
However this is quite an important part of the spectrum theoretically. At least
bare disk models provide quite a robust prediction for the overall continuum
shape in the near-infrared. Self-gravity should become important in the outer,
near-infrared emitting regions of the putative disk, possibly leaving a
signature of disk truncation in the near-infrared. We propose here that this
important part of the spectrum can be revealed for the first time by observing
polarized flux from normal quasars. At least in some polarized quasars, the
emission lines are all unpolarized and so the polarized flux should originate
interior to the broad line region, and therefore also interior to the dust
emitting region. This can then be used to eliminate the dust emission. We
present the results of near-infrared polarimetry for such three quasars
(Ton202, 4C37.43, B2 1208+32). The data for Ton202 have the highest S/N, and
the near-infrared polarized flux in this case is measured to have quite a blue
shape, nu^+0.42+-0.29 in F_nu, intriguingly consistent with the simple
multi-temperature black body, bare disk prediction of nu^+1/3. All these data,
although still with quite low S/N for the other two objects, demonstrate the
unique potential of the technique with future better data. We also present
similar data for other quasars and radio galaxies, and briefly discuss the
nature of the polarization.


  This work focuses on mitigating two limitations in the joint learning of
local feature detectors and descriptors. First, the ability to estimate the
local shape (scale, orientation, etc.) of feature points is often neglected
during dense feature extraction, while the shape-awareness is crucial to
acquire stronger geometric invariance. Second, the localization accuracy of
detected keypoints is not sufficient to reliably recover camera geometry, which
has become the bottleneck in tasks such as 3D reconstruction. In this paper, we
present ASLFeat, with three light-weight yet effective modifications to
mitigate above issues. First, we resort to deformable convolutional networks to
densely estimate and apply local transformation. Second, we take advantage of
the inherent feature hierarchy to restore spatial resolution and low-level
details for accurate keypoint localization. Finally, we use a peakiness
measurement to relate feature responses and derive more indicative detection
scores. The effect of each modification is thoroughly studied, and the
evaluation is extensively conducted across a variety of practical scenarios.
State-of-the-art results are reported that demonstrate the superiority of our
methods.


  We present a theoretical model for driving jets by accretion onto Kerr black
holes and try to give an answer to the following question: How much energy
could be extracted from a rotating black hole and its accretion disk in order
to power relativistic jets in Active Galactic Nuclei?


  I continue the study of a renormalizable four-dimensional generally covariant
Yang-Mills-like action, which depends on the Lorentzian complex structure of
spacetime and not its metric. The field equations and their integrability
conditions are written down explicitly. The model is studied with the presence
of two static external sources in the trivial cylindrical complex structure.
The energy of two static "colored" sources is found to increase linearly with
respect to their distance, providing an explicit proof of their perturbative
confinement. In the present model, confinement is not a concequence of the
non-Abelian character of the gauge group, but it is implied by the complex
structure dependence of the model.


  Let $(M,g,\si)$ be a compact Riemannian spin manifold of dimension $\geq 2$.
For any metric $\tilde g$ conformal to $g$, we denote by $\tilde\lambda$ the
first positive eigenvalue of the Dirac operator on $(M,\tilde g,\si)$. We show
that $$\inf_{\tilde{g} \in [g]} \tilde\lambda \Vol(M,\tilde g)^{1/n} \leq (n/2)
\Vol(S^n)^{1/n}.$$ This inequality is a spinorial analogue of Aubin's
inequality, an important inequality in the solution of the Yamabe problem. The
inequality is already known in the case $n \geq 3$ and in the case $n = 2$,
$\ker D=\{0\}$. Our proof also works in the remaining case $n=2$, $\ker D\neq
\{0\}$. With the same method we also prove that any conformal class on a
Riemann surface contains a metric with $2\tilde\lambda^2\leq \tilde\mu$, where
$\tilde\mu$ denotes the first positive eigenvalue of the Laplace operator.


  This paper is the first work to propose a network to predict a structured
uncertainty distribution for a synthesized image. Previous approaches have been
mostly limited to predicting diagonal covariance matrices. Our novel model
learns to predict a full Gaussian covariance matrix for each reconstruction,
which permits efficient sampling and likelihood evaluation.
  We demonstrate that our model can accurately reconstruct ground truth
correlated residual distributions for synthetic datasets and generate plausible
high frequency samples for real face images. We also illustrate the use of
these predicted covariances for structure preserving image denoising.


  Emotion recognition using EEG has been widely studied to address the
challenges associated with affective computing. Using manual feature extraction
methods on EEG signals results in sub-optimal performance by the learning
models. With the advancements in deep learning as a tool for automated feature
engineering, in this work, a hybrid of manual and automatic feature extraction
methods has been proposed. The asymmetry in different brain regions is captured
in a 2D vector, termed the AsMap, from the differential entropy features of EEG
signals. These AsMaps are then used to extract features automatically using a
convolutional neural network model. The proposed feature extraction method has
been compared with differential entropy and other feature extraction methods
such as relative asymmetry, differential asymmetry and differential caudality.
Experiments are conducted using the SJTU emotion EEG dataset and the DEAP
dataset on different classification problems based on the number of classes.
Results obtained indicate that the proposed method of feature extraction
results in higher classification accuracy, outperforming the other feature
extraction methods. The highest classification accuracy of 97.10% is achieved
on a three-class classification problem using the SJTU emotion EEG dataset.
Further, this work has also assessed the impact of window size on
classification accuracy.


  Recent measurements of the Luminosity Function (LF) of galaxies in the Epoch
of Reionization (EoR, $z\gsim6$) indicate a very steep increase of the number
density of low-mass galaxies populating the LF faint-end. However, as star
formation in low-mass halos can be easily depressed or even quenched by
ionizing radiation, a turnover is expected at some faint UV magnitudes. Using a
physically-motivated analytical model, we quantify reionization feedback
effects on the LF faint-end shape. We find that if reionization feedback is
neglected, the power-law Schechter parameterization characterizing the LF
faint-end remains valid up to absolute UV magnitude $\sim -9$. If instead
radiative feedback is strong enough that quenches star formation in halos with
circular velocity smaller than 50 km s$^{-1}$, the LF starts to drop at
absolute UV magnitude $\sim -15$, i.e. slightly below the detection limits of
current (unlensed) surveys at $z\sim5$. The LFs may rise again at higher
absolute UV magnitude, where, as a result of interplay between reionization
process and galaxy formation, most of the galaxy light is from relic stars
formed before the EoR. We suggest that the galaxy number counts data,
particularly in lensed fields, can put strong constraints on reionization
feedback. In models with stronger reionization feedback, stars in galaxies with
absolute UV magnitude higher than $\sim -13$ and smaller than $\sim - 8$ are
typically older. Hence, the stellar age - UV magnitude relation can be used as
an alternative feedback probe.


  A section of a Hamiltonian system is a hypersurface in the phase space of the
system, usually representing a set of one-sided constraints (e.g. a boundary,
an obstacle or a set of admissible states). In this paper we give local
classification results for all typical singularities of sections of regular
(non-singular) Hamiltonian systems, a problem equivalent to the classification
of typical singularities of Hamiltonian systems with one-sided constraints. In
particular we give a complete list of exact normal forms with functional
invariants, and we show how these are related/obtained by the symplectic
classification of mappings with prescribed (Whitney-type) singularities,
naturally defined on the reduced phase space of the Hamiltonian system.


  We use a large sample of $\sim 350,000$ galaxies constructed by combining the
UKIDSS UDS, VIDEO/CFHT-LS, UltraVISTA/COSMOS and GAMA survey regions to probe
the major merging histories of massive galaxies ($>10^{10}\ \mathrm{M}_\odot$)
at $0.005 < z < 3.5$. We use a method adapted from that presented in
Lopez-Sanjuan et al. (2014) using the full photometric redshift probability
distributions, to measure pair $\textit{fractions}$ of flux-limited, stellar
mass selected galaxy samples using close-pair statistics. The pair fraction is
found to weakly evolve as $\propto (1+z)^{0.8}$ with no dependence on stellar
mass. We subsequently derive major merger $\textit{rates}$ for galaxies at $>
10^{10}\ \mathrm{M}_\odot$ and at a constant number density of $n > 10^{-4}$
Mpc$^{-3}$, and find rates a factor of 2-3 smaller than previous works,
although this depends strongly on the assumed merger timescale and likelihood
of a close-pair merging. Galaxies undergo approximately 0.5 major mergers at $z
< 3.5$, accruing an additional 1-4 $\times 10^{10}\ \mathrm{M}_\odot$ in the
process. Major merger accretion rate densities of $\sim 2 \times 10^{-4}$
$\mathrm{M}_\odot$ yr$^{-1}$ Mpc$^{-3}$ are found for number density selected
samples, indicating that direct progenitors of local massive
($>10^{11}\mathrm{M}_\odot$) galaxies have experienced a steady supply of
stellar mass via major mergers throughout their evolution. While pair fractions
are found to agree with those predicted by the Henriques et al. (2014)
semi-analytic model, the Illustris hydrodynamical simulation fails to
quantitatively reproduce derived merger rates. Furthermore, we find major
mergers become a comparable source of stellar mass growth compared to
star-formation at $z < 1$, but is 10-100 times smaller than the SFR density at
higher redshifts.


  We study the space ${\mathcal X}^{d}(G)$ of pictures of a graph $G$ in
complex projective $d$-space. The main result is that the homology groups (with
integer coefficients) of ${\mathcal X}^{d}(G)$ are completely determined by the
Tutte polynomial of $G$. One application is a criterion in terms of the Tutte
polynomial for independence in the {\it $d$-parallel matroids} studied in
combinatorial rigidity theory. For certain special graphs called
\defterm{orchards}, the picture space is smooth and has the structure of an
iterated projective bundle. We give a Borel presentation of the cohomology ring
of the picture space of an orchard, and use this presentation to develop an
analogue of the classical Schubert calculus.


  We make a comparison for thirteen dark energy (DE) models by using current
cosmological observations, including type Ia supernova, baryon acoustic
oscillations, and cosmic microwave background. To perform a systematic and
comprehensive analysis, we consider three statistics methods of SNIa, including
magnitude statistic (MS), flux statistic (FS), and improved flux statistic
(IFS), as well as two kinds of BAO data. In addition, Akaike information
criteria (AIC) and Bayesian information criteria (BIC) are used to assess the
worth of each model. We find that: (1) The thirteen models can be divided into
four grades by performing cosmology-fits. The cosmological constant model,
which is most favored by current observations, belongs to grade one;
$\alpha$DE, constant $w$ and generalized Chaplygin gas models belong to grade
two; Chevalliear-Polarski-Linder (CPL) parametrization, Wang parametrization,
doubly coupled massive gravity, new generalized Chaplygin gas and holographic
DE models belong to grade three; agegraphic DE, Dvali-Gabadadze-Porrati, Vacuum
metamorphosis and Ricci DE models, which are excluded by current observations,
belong to grade four. (2) For parameter estimation, adopting IFS yields the
biggest $\Omega_m$ and the smallest $h$ for all the models. In contrast, using
different BAO data does not cause significant effects. (3) IFS has the
strongest constraint ability on various DE models. For examples, adopting IFS
yields the smallest value of $\Delta$AIC for all the models; in addition,
making use of this technique yields the biggest figure of merit for CPL and
Wang parametrizations.


  Imagine we could do asteroseismology of large samples of OB-type stars by
using just one spectrum per target. That would be great! But this is probably a
crazy and stupid idea. Or maybe not. Maybe we have the possibility to open a
new window to investigate stellar oscillations in massive stars that has been
in front of us for many years, but has not attracted very much our attention:
the characterization and understanding of the so-called macroturbulent
broadening in OB-type stars.


  We refine a technique to measure the absorption corrected ultraviolet (UV)
luminosity of starburst galaxies using rest frame UV quantities alone, and
apply it to U-dropouts at z ~ 3 found in the Hubble Deep Field. The method is
based on an observed correlation between the ratio of far infrared (FIR) to UV
fluxes with spectral slope \beta (a UV color). A simple fit to this relation
allows the UV flux absorbed by dust to be calculated, and hence the dust-free
UV luminosity to be determined. IUE spectra and IRAS fluxes of local starbursts
are used to calibrate the ratio in terms of 1600 Angstrom absorption A_{1600},
and the transformation from broad band color to \beta. We show that the recent
marginal and non-detections of HDF U-dropouts in the radio and sub-mm are
consistent with our calculated A_{1600}. This is also true of observations of
the ratio of optical emission line flux to UV flux density in the brightest
U-dropouts. This latter ratio turns out not to be a good indicator of dust
extinction. In U-dropouts, absolute magnitude correlates with \beta: brighter
galaxies are redder, as is observed in local starburst galaxies. This suggests
that a mass-metallicity relationship is in place by z ~ 3. The UV luminosity
function of U-dropouts extends up to M_{1600,0} ~ -24 ABmag corresponding to a
star formation rate ~ 200 \Msun/yr (H_0 = 50 km/s/Mpc, q_0 = 0.5). The
absorption-corrected UV luminosity density at z ~ 3 is \rho_{1600,0} =>
1.4x10^{27} erg/s/Hz/Mpc^3; still a lower limit since completeness corrections
have not been done and because only galaxies with A_{1600} < ~3.6 mag are blue
enough to be selected as U-dropouts. The luminosity weighted mean dust
absorption factor of our sample is 5.4 +/- 0.9$ at 1600\AA (abridged).


  Observations of the nebula associated with the WO star in the galaxy IC 1613
are presented. The observations were carried out with a scanning Interferometer
Fabry-Perot in H_alpha at the 6m Special Astrophysical Observatory telescope;
narrow-band H_alpha and [OIII] images were obtained with the 4-m KPNO
telescope. The monochromatic H_alpha image clearly reveals a giant bipolar
shell structure outside the bright nebula S3. The sizes of the southeastern and
northwestern shells are 112x77 pc and (186-192)x(214-224) pc, respectively. We
have studied the object's kinematics for the first time and found evidence for
expansion of both shells. The expansion velocities of the southeastern and
northwestern shells exceed 50 and 70 km/s, respectively. We revealed a
filamentary structure of the shells and several compact features in the S3
core. A scenario is proposed for the formation of the giant bipolar structure
by the stellar wind from the central WO star located at the boundary of a dense
``supercavity'' in the galaxy's H I distribution.


  A one-electron Schroedinger equation based on special one-electron potentials
for atoms is shown to exist that produces orbitals for an arbitrary molecule
that are sufficiently accurate to be used without modification to construct
single- and multi-determinant wavefunctions. The exact Hamiltonian is used to
calculate the energy variationally and to generate configuration interaction
expansions. Earlier work on equilibrium geometries is extended to larger basis
sets and molecular dissociation. For a test set of molecules representing
different bonding environments, a single set of invariant atomic potentials
gives wavefunctions with energies that deviate from configuration interaction
energies based on SCF orbitals by less than 0.04 eV per bond or valence
electron pair. On a single diagonalization of the Fock matrix, the
corresponding errors are reduced 0.01 eV. Atomization energies are also in good
agreement with CI values based on canonical SCF orbitals. Configuration
interaction applications to single bond dissociations of water and glycine, and
multiple bond dissociations of ethylene and oxygen produce dissociation energy
curves in close agreement with CI calculations based on canonical SCF orbitals
for the entire range of internuclear distances.


  A new search of globular clusters for millisecond pulsars is in progress at
Parkes. In this paper we describe the motivation, the new hardware and software
systems adopted, the survey plan and the preliminary results. So far, we have
discovered ten new millisecond pulsars in four clusters for which no associated
pulsars were previously known.


  We establish the well-posedness of the Neumann problem for stochastic
conservation laws with multiplicative noise. As a major step for establishing
the uniqueness of the kinetic solution to the referred problem we establish the
new strong trace property for stochastic conservation laws. Existence of
kinetic solutions is proved through the vanishing viscosity method and the
detailed analysis of the corresponding stochastic parabolic problem is also
made here for the first time, as far as the authors know.


  We calculate the anomalous magnetic moment of anyons in three dimensional
$CP^{N-1}$ model with a Chern-Simons term in various limits in $1/N$ expansion.
We have found that for anyons of infinite mass the gyromagnetic ratio
($g$-factor) is 2 up to the next-to-leading order in $1/N$. Our result supports
a recent claim that the $g$-factor of nonrelativistic anyons is exactly two. We
also found that for $-{8\over\pi }<\theta<0$, the electromagnetic interation
between two identical aynons of large mass are attractive.


  In the recent years, the interest of individual users in modern electric
vehicles (EVs) has grown exponentially. An EV has two major components, which
make it different from traditional vehicles, first is its environment friendly
nature because of being electric, and second is the interconnection ability of
these vehicles because of modern information and communication technologies
(ICTs). Both of these features are playing a key role in the development of
EVs, and both academia and industry personals are working towards development
of modern protocols for EV networks. All these interactions, whether from
energy perspective or from communication perspective, both are generating a
tremendous amount of data every day. In order to get most out of this data
collected from EVs, research works have highlighted the use of machine/deep
learning techniques for various EV applications. This interaction is quite
fruitful, but it also comes with a critical concern of privacy leakage during
collection, storage, and training of vehicular data. Therefore, alongside
developing machine/deep learning techniques for EVs, it is also critical to
ensure that they are resilient to private information leakage and attacks. In
this paper, we begin with the discussion about essential background on EVs and
privacy preservation techniques, followed by a brief overview of privacy
preservation in EVs using machine learning techniques. Particularly, we also
focus on an in-depth review of the integration of privacy techniques in EVs and
highlighted different application scenarios in EVs. Alongside this, we provide
a a very detailed survey of current works on privacy preserving machine/deep
learning techniques used for modern EVs. Finally, we present the certain
research issues, critical challenges, and future directions of research for
researchers working in privacy preservation in EVs.


  The conservativity theorem for nested relational calculus implies that query
expressions can freely use nesting and unnesting, yet as long as the query
result type is a flat relation, these capabilities do not lead to an increase
in expressiveness over flat relational queries. Moreover, Wong showed how such
queries can be translated to SQL via a constructive rewriting algorithm. While
this result holds for queries over either set or multiset semantics, to the
best of our knowledge, the questions of conservativity and normalization have
not been studied for queries that mix set and bag collections, or provide
duplicate-elimination operations such as SQL's
$\mathtt{SELECT}~\mathtt{DISTINCT}$. In this paper we formalize the problem,
and present partial progress: specifically, we introduce a calculus with both
set and multiset collection types, along with natural mappings from sets to
bags and vice versa, present a set of valid rewrite rules for normalizing such
queries, and give an inductive characterization of a set of queries whose
normal forms can be translated to SQL. We also consider examples that do not
appear straightforward to translate to SQL, illustrating that the relative
expressiveness of flat and nested queries with mixed set and multiset semantics
remains an open question.


  While code review is central to the software development process, it can be
tedious and expensive to carry out. In this paper, we investigate whether and
how Large Language Models (LLMs) can aid with code reviews. Our investigation
focuses on two tasks that we argue are fundamental to good reviews: (i)
flagging code with security vulnerabilities and (ii) performing software
functionality validation, i.e., ensuring that code meets its intended
functionality. To test performance on both tasks, we use zero-shot and
chain-of-thought prompting to obtain final ``approve or reject''
recommendations. As data, we employ seminal code generation datasets (HumanEval
and MBPP) along with expert-written code snippets with security vulnerabilities
from the Common Weakness Enumeration (CWE). Our experiments consider a mixture
of three proprietary models from OpenAI and smaller open-source LLMs. We find
that the former outperforms the latter by a large margin. Motivated by
promising results, we finally ask our models to provide detailed descriptions
of security vulnerabilities. Results show that 36.7% of LLM-generated
descriptions can be associated with true CWE vulnerabilities.


  In conventional Kerr- and Faraday microscopy the sample is illuminated with
plane-polarised light and a magnetic domain contrast is generated by an
analyser making use of the Kerr- or Faraday rotation. In this paper we
demonstrate possibilities of analyser-free magneto-optical microscopy based on
magnetisation-dependent intensity modulations of the light: (i) The transverse
Kerr effect can be applied for in-plane magnetised material, demonstrated for
an FeSi sheet. (ii) Illuminating the same sample with circularly polarised
light leads to a domain contrast with a different symmetry as the conventional
Kerr contrast. (iii) Circular polarisation can also be used for perpendicularly
magnetised material, demonstrated for a garnet film and an ultrathin CoFeB
film. (iv) Plane-polarised light at a specific angle can be employed for both,
in-plane and perpendicular media. (v) Perpendicular light incidence leads to a
domain contrast on in-plane materials that is quadratic in the magnetisation
and to a domain boundary contrast. (vi) Domain contrast can even be obtained
without polariser. In cases (ii) and (iii), the contrast is generated by MCD
(Magnetic Circular Dichroism), while MLD (Magnetic Linear Dichroism) is
responsible for the contrast in case (v). The domain boundary contrast is due
to the magneto-optical gradient effect in metallic samples. A domain boundary
contrast can also arise due to interference of phase-shifted magneto-optical
amplitudes. An explanation of these contrast phenomena is provided in terms of
Maxwell-Fresnel theory.


  In this paper, we developed the system for recognizing the orchid species by
using the images of flower. We used MSRM (Maximal Similarity based on Region
Merging) method for segmenting the flower object from the background and
extracting the shape feature such as the distance from the edge to the centroid
point of the flower, aspect ratio, roundness, moment invariant, fractal
dimension and also extract color feature. We used HSV color feature with
ignoring the V value. To retrieve the image, we used Support Vector Machine
(SVM) method. Orchid is a unique flower. It has a part of flower called lip
(labellum) that distinguishes it from other flowers even from other types of
orchids. Thus, in this paper, we proposed to do feature extraction not only on
flower region but also on lip (labellum) region. The result shows that our
proposed method can increase the accuracy value of content based flower image
retrieval for orchid species up to $\pm$ 14%. The most dominant feature is
Centroid Contour Distance, Moment Invariant and HSV Color. The system accuracy
is 85,33% in validation phase and 79,33% in testing phase.


  It is greatly expected that the relic neutrino background from past
supernovae is detected by Superkamiokande (SK) which is now under construction.
We calculate the spectrum and the event rate at SK systematically by using the
results of simulations of a supernova explosion and reasonable supernova rates.
We also investigate the effect of a cosmological constant, $\Lambda$, on the
spectrum, since some recent cosmological observations strongly suggest the
existence of $\Lambda$. We find following results. 1) The spectrum has a peak
at about 3 MeV, which is much lower than that of previous estimate ($6 \sim$ 10
MeV). 2) The event rate at SK in the range from 10 MeV to 50 MeV, where the
relic neutrinos from past supernovae is dominant, is about $25 \, {h_{50}}^2 \,
\left( \frac{R_{SN}}{0.1 {\rm yr^{-1}}} \right) \left(\frac{n_G \,
h_{50}^{-3}}{0.02{\rm Mpc}^{-3}}\right)$ events per year, where $R_{SN}$ is the
supernova rate in a galaxy, $n_G$ is the number density of galaxies, and
$h_{50} = H_0 /$ (50km/s/Mpc), where $H_0$ is the Hubble constant. 3) The event
rate is almost insensitive to $\Lambda$. The flux increases in the low energy
side ($< 10$ MeV) with increasing $\Lambda$, but decreases in the high energy
side (10 MeV $<$) in models in which the integrated number of supernovae in one
galaxy is fixed.


  Manifold distances are very effective tools for visual object recognition.
However, most of the traditional manifold distances between images are based on
the pixel-level comparison and thus easily affected by image rotations and
translations. In this paper, we propose a new manifold distance to model the
dissimilarities between visual objects based on the Complex Wavelet Structural
Similarity (CW-SSIM) index. The proposed distance is more robust to rotations
and translations of images than the traditional manifold distance and the
CW-SSIM index based distance. In addition, the proposed distance is combined
with the $k$-medoids clustering method to derive a new clustering method for
visual object categorization. Experiments on Coil-20, Coil-100 and Olivetti
Face Databases show that the proposed distance measure is better for visual
object categorization than both the traditional manifold distances and the
CW-SSIM index based distances.


  Uncertainty is an inherent property of any complex system, especially those
that integrate physical parts or operate in real environments. In this paper,
we focus on the Digital Twins of adaptive systems, which are particularly
complex to design, verify, and optimize. One of the problems of having two
systems (the physical one and its digital replica) is that their behavior may
not always be consistent. In addition, both twins are normally subject to
different types of uncertainties, which complicates their comparison. In this
paper we propose the explicit representation and treatment of the uncertainty
of both twins, and show how this enables a more accurate comparison of their
behaviors. Furthermore, this allows us to reduce the overall system uncertainty
and improve its behavior by properly averaging the individual uncertainties of
the two twins. An exemplary incubator system is used to illustrate and validate
our proposal.


  Excitons are composite bosons that can feature spin singlet and triplet
states. In usual semiconductors, without an additional spin-flip mechanism,
triplet excitons are extremely inefficient optical emitters. Large spin-orbit
coupling in transition metal dichalcogenides (TMDs) couples circularly
polarized light to excitons with selective valley and spin. Here, we
demonstrate electrically controlled brightening of spin-triplet interlayer
excitons in a MoSe$_2$/WSe$_2$ TMD van der Waals (vdW) heterostructure. The
atomic registry of vdW layers in TMD heterostructures provides a quasi-angular
momentum to interlayer excitons, enabling emission from otherwise dark
spin-triplet excitons. Employing magnetic field, we show that photons emitted
by triplet and singlet excitons in the same valley have opposite chirality. We
also measure effective exciton g-factors, presenting direct and quantitative
evidence of triplet interlayer excitons. We further demonstrate gate tuning of
the relative photoluminescence intensity between singlet and triplet charged
excitons. Electrically controlled emission between singlet and triplet excitons
enables a route for optoelectronic devices that can configure excitonic chiral,
spin, and valley quantum states.


  We consider strongly-correlated systems described by the multi-orbital
Hubbard model in the atomic limit and obtain exact expressions for the chemical
potential and thermopower. We show that these expressions reduce to the Heikes
formula in the appropriate limits ($k_BT \gg U$) and ($k_BT \ll U$) and obtain
the full temperature dependence in between these regimes. We also investigate
the effect of a magnetic field introduced through a Zeeman term and observe
that the thermopower of the multi-orbital Hubbard model displays spikes as a
function of magnetic field at certain special values of the field. This effect
might be observable in experiments for materials with a large magnetic
coupling.


  The Astrophysics Source Code Library (ASCL; ascl.net) is a free online
registry of codes used in astronomy research; it currently contains over 900
codes and is indexed by ADS. The ASCL has recently moved a new infrastructure
into production. The new site provides a true database for the code entries and
integrates the WordPress news and information pages and the discussion forum
into one site. Previous capabilities are retained and permalinks to ascl.net
continue to work. This improvement offers more functionality and flexibility
than the previous site, is easier to maintain, and offers new possibilities for
collaboration. This presentation covers these recent changes to the ASCL.


  The base surface $B$ of a Lagrangian fibration $X\twoheadrightarrow B$ of a
projective, irreducible symplectic fourfold $X$ is shown to be isomorphic to
${\mathbb P}^2$.


  An expression in the form of an easily computed integral is given for the
determinant of the scalar GJMS operator on an odd--dimensional sphere.
Manipulation yields a sum formula for the logdet in terms of the logdets of the
ordinary conformal Laplacian for other dimensions. This is formalised and
expanded by an analytical treatment of the integral which produces an explicit
combinatorial expression directly in terms of the Riemann zeta function, and
$\log2$. An incidental byproduct is a (known) expression for the central
factorial coefficients in terms of higher Bernoulli numbers.


  In this paper, we address the boundary detection task motivated by the
ambiguities in current definition of edge detection. To this end, we generate a
large database consisting of more than 10k images (which is 20x bigger than
existing edge detection databases) along with ground truth boundaries between
459 semantic classes including both foreground objects and different types of
background, and call it the PASCAL Boundaries dataset, which will be released
to the community. In addition, we propose a novel deep network-based
multi-scale semantic boundary detector and name it Multi-scale Deep Semantic
Boundary Detector (M-DSBD). We provide baselines using models that were trained
on edge detection and show that they transfer reasonably to the task of
boundary detection. Finally, we point to various important research problems
that this dataset can be used for.


  The existence of doublets in single-cell RNA sequencing (scRNA-seq) data
poses a great challenge in downstream data analysis. Computational
doublet-detection methods have been developed to remove doublets from scRNA-seq
data. Yet, the default hyperparameter settings of those methods may not provide
optimal performance. Here, we propose a strategy to tune hyperparameters for a
cutting-edge doublet-detection method. We utilize a full factorial design to
explore the relationship between hyperparameters and detection accuracy on 16
real scRNA-seq datasets. The optimal hyperparameters are obtained by a response
surface model and convex optimization. We show that the optimal hyperparameters
provide top performance across scRNA-seq datasets under various biological
conditions. Our tuning strategy can be applied to other computational
doublet-detection methods. It also offers insights into hyperparameter tuning
for broader computational methods in scRNA-seq data analysis.


  Systematic effects caused by the Berry (geometric) phases in an
electric-dipole-moment experiment in an all-electric storage ring are
considered. We analyze the experimental setup when the spin is frozen and local
longitudinal and vertical electric fields alternate. Due to the Berry phases,
the spin rotates about the radial axis. The corresponding systematic error is
rather important while it can be canceled with clockwise and counterclockwise
beams. The Berry phases also lead to the spin rotation about the radial axis.
This effect can be canceled with clockwise and counterclockwise beams as well.
The sign of the azimuthal component of the angular velocity of the spin
precession depends on the starting point where the spin orientation is perfect.
The radial component of this quantity keeps its value and sign for each
starting point. When the longitudinal and vertical electric fields are joined
in the same sections without any alternation, the systematic error due to the
geometric phases does not appear. However, another systematic effect of the
spin rotation about the azimuthal axis takes place and has opposite signs for
clockwise and counterclockwise beams.


  Harris's correlation inequality states that any two monotone functions on the
Boolean hypercube are positively correlated. Talagrand \cite{Talcorr} started a
line of works in search of quantitative versions of this fact by providing a
lower bound on the correlation in terms of the influences of the functions. A
famous conjecture of Chv\'{a}tal \cite{Chvatal} was found by Friedgut, Kahn,
Kalai and Keller \cite{FKKK} to be equivalent to a certain strengthening of
Talagrand's bound, conjectured to hold true when one of the functions is
antipodal (hence $g(x) = 1-g(-x)$). Motivated by this conjecture, we strengthen
some of those bounds by giving estimates that also involve the second order
Fourier coefficients of the functions. In particular we show that in the bounds
due to Talagrand and due to Keller, Mossel and Sen \cite{KMS14}, a logarithmic
factor can be replaced by its square root when one of the functions is
antipodal. Our proofs follow a different route than the ones in the literature,
and the analysis is carried out in the Gaussian setting.


  Layered transition-metal tellurides have attracted considerable attention
because of their rich physics; for example, tungsten ditelluride WTe2 exhibits
extremely large magnetoresistance; the tritelluride ZrTe3 shows a charge
density wave transition at low temperature; and the pentatelluride ZrTe5
displays an enigmatic resistivity anomaly and large thermoelectric power.
Recently some transition-metal tellurides are predicted to be quantum spin Hall
insulators (e.g. ZrTe5 and HfTe5) or Weyl semimetals (e.g. WTe2 and MoTe2) and
were subjected to intensive investigations. Here, we report on the discovery of
superconductivity in hafnium pentatelluride HfTe5 under high pressure. We
observe two structural phase transitions and metallization with
superconductivity developing at around 5 GPa. A maximal critical temperature of
4.8 K is attained at a pressure of 20 GPa, and superconductivity persists up to
the maximum pressure in our study (42 GPa). Theoretical calculations indicate
that the superconductivity develops mainly in the Te atom layers at medium
pressure and in the Te atom chains at high pressure.


  We calculate QCD corrections to transversely polarized Drell-Yan process at a
measured $Q_T$ of the produced lepton pair in the dimensional regularization
scheme. The $Q_T$ distribution is discussed resumming soft gluon effects
relevant for small $Q_T$.


  We propose a new theory of dark matter based on axion physics and
cosmological phase transitions. We show that theories in which a gauge coupling
increases through a first-order phase transition naturally result in `axion
relic pockets': regions of relic false vacua stabilised by the pressure from a
kinematically trapped, hot axion gas. Axion relic pockets provide a viable and
highly economical theory of dark matter: the macroscopic properties of the
pockets depend only on a single parameter (the phase transition temperature).
We describe the formation, evolution and present-day properties of axion relic
pockets, and outline how their phenomenology is distinct from existing dark
matter paradigms. We briefly discuss how laboratory experiments and
astronomical observations can be used to test the theory, and identify
gamma-ray observations of magnetised, dark-matter-dense environments as
particularly promising.


  Thermal processes are one of the most common systems in the industry, making
its understanding a mandatory skill for control engineers. So, multiple efforts
are focused on developing low-cost and portable experimental training rigs
recreating the thermal process dynamics and controls, usually limited to SISO
or low order 2x2 MIMO systems. This paper presents PHELP, a low-cost, portable,
and high order MIMO educational platform for uniformity temperature control
training. The platform is composed of an array of 16 Peltier modules as heating
elements, with a lower heating and cooling times, resulting in a 16x16 high
order MIMO system. A low-cost real-time infrared thermal camera is employed as
a temperature feedback sensor instead of a standard thermal sensor, ideal for
high order MIMO system sensing and temperature distribution tracking. The
control algorithm is developed in Matlab/Simulink and employs an Arduino board
in hardware in the loop configuration to apply the control action to each
Peltier module in the array. A temperature control experiment is performed,
showing that the platform is suitable for teaching and training experiences not
only in the classroom but also for engineers in the industry. Furthermore,
various abnormal conditions can be introduced so that smart control engineering
features can be tested.


  We introduce a framework and develop a theory of transitory queueing models.
These are models that are not only non-stationary and time-varying but also
have other features such as the queueing system operates over finite time, or
only a finite population arrives. Such models are relevant in many real-world
settings, from queues at post-offces, DMV, concert halls and stadia to
out-patient departments at hospitals. We develop fluid and diffusion limits for
a large class of transitory queueing models. We then introduce three specific
models that fit within this framework, namely, the Delta(i)/GI/1 model, the
conditioned G/GI/1 model, and an arrival model of scheduled traffic with epoch
uncertainty. We show that asymptotically these models are distributionally
equivalent, i.e., they have the same fluid and diffusion limits. We note that
our framework provides the first ever way of analyzing the standard G/GI/1
model when we condition on the number of arrivals. In obtaining these results,
we provide generalizations and extensions of the Glivenko-Cantelli and Donskers
Theorem for empirical processes with triangular arrays. Our analysis uses the
population acceleration technique that we introduce and develop. This may be
useful in analysis of other non-stationary and non-ergodic queuing models.


  We report the first detection of molecular hydrogen (H$_{2}$) absorption in
the Lyman-$\alpha$ emission line profiles of two classical T Tauri stars
(CTTSs), DF Tau and V4046 Sgr, observed by \emph{HST}/COS. This absorption is
the energy source for many of the Lyman-band H$_{2}$ fluorescent lines commonly
seen in the far-ultraviolet spectra of CTTSs. We find that the absorbed energy
in the H$_{2}$ pumping transitions from a portion of the Lyman-$\alpha$ line
significantly differ from the amount of energy in the resulting fluorescent
emission. By assuming additional absorption in the H I Lyman-$\alpha$ profile
along our light of sight, we can correct the H$_{2}$ absorption/emission ratios
so that they are close to unity. The required H I absorption for DF Tau is at a
velocity close to the radial velocity of the star, consistent with H I
absorption in the edge-on disk and interstellar medium. For V4046 Sgr, a nearly
face-on system, the required absorption is between +100 km/s and +290 km/s,
most likely resulting from H I gas in the accretion columns falling onto the
star.


  We consider the Derivative NLS equation with general quadratic
nonlinearities. In \cite{be2} the first author has proved a sharp small data
local well-posedness result in Sobolev spaces with a decay structure at
infinity in dimension $n = 2$. Here we prove a similar result for large initial
data in all dimensions $n \geq 2$.


  New products must be formulated rapidly to succeed in the global formulated
product market; however, key product indicators (KPIs) can be complex, poorly
understood functions of the chemical composition and processing history.
Consequently, scale-up must currently undergo expensive trial-and-error
campaigns. To accelerate process flow diagram (PFD) optimisation and knowledge
discovery, this work proposed a novel digital framework to automatically
quantify process mechanisms by integrating symbolic regression (SR) within
model-based design of experiments (MBDoE). Each iteration, SR proposed a Pareto
front of interpretable mechanistic expressions, and then MBDoE designed a new
experiment to discriminate between them while balancing PFD optimisation. To
investigate the framework's performance, a new process model capable of
simulating general formulated product synthesis was constructed to generate
in-silico data for different case studies. The framework could effectively
discover ground-truth process mechanisms within a few iterations, indicating
its great potential for use within the general chemical industry for digital
manufacturing and product innovation.


  Binary neutron star mergers are promising sources of gravitational waves for
ground-based detectors such as Advanced LIGO. Neutron-rich material ejected by
these mergers may also be the main source of r-process elements in the
Universe, while radioactive decays in the ejecta can power bright
electromagnetic post-merger signals. Neutrino-matter interactions play a
critical role in the evolution of the composition of the ejected material,
which significantly impacts the outcome of nucleosynthesis and the properties
of the associated electromagnetic signal. In this work, we present a simulation
of a binary neutron star merger using an improved method for estimating the
average neutrino energies in our energy-integrated neutrino transport scheme.
These energy estimates are obtained by evolving the neutrino number density in
addition to the neutrino energy and flux densities. We show that significant
changes are observed in the composition of the polar ejecta when comparing our
new results with earlier simulations in which the neutrino spectrum was assumed
to be the same everywhere in optically thin regions. In particular, we find
that material ejected in the polar regions is less neutron rich than previously
estimated. Our new estimates of the composition of the polar ejecta make it
more likely that the color and timescale of the electromagnetic signal depend
on the orientation of the binary with respect to an observer's line-of-sight.
These results also indicate that important observable properties of neutron
star mergers are sensitive to the neutrino energy spectrum, and may need to be
studied through simulations including a more accurate, energy-dependent
neutrino transport scheme.


  A soft-max function has two main efficiency measures: (1) approximation -
which corresponds to how well it approximates the maximum function, (2)
smoothness - which shows how sensitive it is to changes of its input. Our goal
is to identify the optimal approximation-smoothness tradeoffs for different
measures of approximation and smoothness. This leads to novel soft-max
functions, each of which is optimal for a different application. The most
commonly used soft-max function, called exponential mechanism, has optimal
tradeoff between approximation measured in terms of expected additive
approximation and smoothness measured with respect to R\'enyi Divergence. We
introduce a soft-max function, called "piecewise linear soft-max", with optimal
tradeoff between approximation, measured in terms of worst-case additive
approximation and smoothness, measured with respect to $\ell_q$-norm. The
worst-case approximation guarantee of the piecewise linear mechanism enforces
sparsity in the output of our soft-max function, a property that is known to be
important in Machine Learning applications [Martins et al. '16, Laha et al.
'18] and is not satisfied by the exponential mechanism. Moreover, the
$\ell_q$-smoothness is suitable for applications in Mechanism Design and Game
Theory where the piecewise linear mechanism outperforms the exponential
mechanism. Finally, we investigate another soft-max function, called power
mechanism, with optimal tradeoff between expected \textit{multiplicative}
approximation and smoothness with respect to the R\'enyi Divergence, which
provides improved theoretical and practical results in differentially private
submodular optimization.


  This article advances the hypothesis that the heightened eschatological
sensitivity evident among the historians writing in the 5th century and its
weaker echos in the time of Charlemagne were caused by the irregularities of
the the lunisolar calendar and its particular realization, the Easter calendar.
The lunisolar calendar that Christians used for the calculation of the date of
the Easter had a number of key periods when the cycles of the Sun and the Moon
came in sync in relationship to the beginning of the count and thus produced an
effect of the times repeating themselves or ending with the nearly precise
astronomical repetition. It is shown that Late Antique scholars who were
actively involved in the construction of the Christian history's chronology
were limited in their choices by the astronomical peculiarities of the
Earth-Moon system. The total conjunctions of the astronomical Solar and Lunar
calendars took place, some within the 1st century CE, and the next one, in 483
CE. This was also a special year because the lunar calendar lost one day. Thus
the 5th century was the time of heightened expectations of whether the calendar
and the Moon's showings will repeat those that accompanied the birth of Jesus.
The Full Supermoon (or whatever phase it was on December 25th, 1 BCE) may have
repeated in 410 CE (the entry of Goths into Rome), in 467 CE and in 476 CE (the
Fall of the Roman Empire), marking the coming of the time very similar to
Jesus' birth. The Full Moon was supposed to repeat December 25th, 800 CE and in
the year 1000 CE. This may have determined the setting of the biblical calendar
in a way that put the birth of Christ on 5199 CE (making the year 800 CE, the
year of the Full Supermoon or of its phase on December 25th, 1 BCE) a critical
turning point.


  Network-based analyses of high-throughput genomics data provide a holistic,
systems-level understanding of various biological mechanisms for a common
population. However, when estimating multiple networks across heterogeneous
sub-populations, varying sample sizes pose a challenge in the estimation and
inference, as network differences may be driven by differences in power. We are
particularly interested in addressing this challenge in the context of
proteomic networks for related cancers, as the number of subjects available for
rare cancer (sub-)types is often limited. We develop NExUS (Network Estimation
across Unequal Sample sizes), a Bayesian method that enables joint learning of
multiple networks while avoiding artefactual relationship between sample size
and network sparsity. We demonstrate through simulations that NExUS outperforms
existing network estimation methods in this context, and apply it to learn
network similarity and shared pathway activity for groups of cancers with
related origins represented in The Cancer Genome Atlas (TCGA) proteomic data.


  High auditory sensitivity, sharp frequency selectivity, and otoacoustic
emissions are signatures of active amplification of the cochlea. The human ear
can also detect very large amplitude sound without being damaged as long as the
exposed time is not too long. The outer hair cells are believed as the best
candidate for the active force generator of the mammalian cochlea. In this
paper, we propose a new model for the basilar membrane oscillation which
successfully describes both the active and the protective mechanisms by
employing an energy depot concept and a critical velocity of the basilar
membrane. One of the main results is that thermal noise in the absence of
external stimulation can be amplified leading to the spontaneous basilar
membrane oscillation. The compressive response of the basilar membrane at the
characteristic frequency and the dynamic response to the stimulation are
consistent with the experimental results as expected. Our model also shows the
nonlinear distortion of the response of the basilar membrane.


  A graph homomorphism from the rooted $d$-branching tree $\phi: T^d \to H$ is
said to be cold if the values of $\phi$ for vertices arbitrarily far away from
the root can restrict the value of $\phi$ at the root. Warmth is a graph
parameter that measures the non-existence of cold maps. We study warmth of
random graphs $G(n,p)$, and for every $d \ge 1$, we exhibit a nearly-sharp
threshold for the existence of cold maps. As a corollary, for
$p=O(n^{-\alpha})$ warmth of $G(n,p)$ is concentrated on at most two values. As
another corollary, a conjecture of Lov\'asz relating mobility to chromatic
number holds for "almost all" graphs. Finally, our results suggest new
conjectures relating graph parameters from statistical physics with graph
parameters from equivariant topology.


  Heavy fermion materials gain high electronic masses and expand Fermi surfaces
when the high-temperature localized f electrons become itinerant and hybridize
with the conduction band at low temperatures. However, despite the common
application of this model, direct microscopic verification remains lacking.
Here we report high-resolution angle-resolved photoemission spectroscopy
measurements on CeCoIn5, a prototypical heavy fermion compound, and reveal the
long-sought band hybridization and Fermi surface expansion. Unexpectedly, the
localized-to-itinerant transition occurs at surprisingly high temperatures, yet
f electrons are still largely localized at the lowest temperature. Moreover,
crystal field excitations likely play an important role in the anomalous
temperature dependence. Our results paint an comprehensive unanticipated
experimental picture of the heavy fermion formation in a periodic multi-level
Anderson/Kondo lattice, and set the stage for understanding the emergent
properties in related materials.


  A search for the Standard Model Higgs boson at the Large Hadron Collider
(LHC) running at a centre-of-mass energy of 7 TeV is reported, based on a total
integrated luminosity of up to 40 pb^-1 collected by the ATLAS detector in
2010. Several Higgs boson decay channels: H -> {\gamma}{\gamma}, H -> ZZ (*) ->
llll, H -> ZZ -> ll{\nu}{\nu}, H -> ZZ -> llqq, H -> W W (*) -> l{\nu}l{\nu}
and H -> W W -> l{\nu}qq (l is e, {\mu}) are combined in a mass range from 110
GeV to 600 GeV. The highest sensitivity is achieved in the mass range between
160 GeV and 170 GeV, where the expected 95% CL exclusion sensitivity is at
Higgs boson production cross sections 2.3 times the Standard Model prediction.
Upper limits on the cross section for its production are determined. Models
with a fourth generation of heavy leptons and quarks with Standard Model-like
couplings to the Higgs boson are also investigated and are excluded for a Higgs
boson mass in the range from 140 GeV to 185 GeV.


  Cellular cortex, which is a highly viscous thin cytoplasmic layer just below
the cell membrane, controls the cell's mechanical properties, which can be
characterized by a hydrodynamic length scale $\ell$. Cells actively regulate
$\ell$ via the activity of force generating molecules, such as myosin II. Here
we develop a general theory for such systems through coarse-grained
hydrodynamic approach including activity in the static description of the
system providing an experimentally accessible parameter and elucidate the
detailed mechanism of how a living system can actively self-regulate its
hydrodynamic length scale, controlling the rigidity of the system. Remarkably,
we find that $\ell$, as a function of activity, behaves universally and roughly
inversely proportional to the activity of the system. Our theory rationalizes a
number of experimental findings on diverse systems and comparison of our theory
with existing experimental data show good agreement.


  We outline two alternative schemes to perform numerical calculations in
quantum field theory. In principle, both of these approaches are better suited
to study phase structure than conventional Monte Carlo. The first method,
Source Galerkin, is based on a numerical analysis of the Schwinger-Dyson
equations using modern computer techniques. The nature of this approach makes
dealing with fermions relatively straightforward, particularly since we can
work on the continuum. Its ultimate success in non-trivial dimensions will
depend on the power of a propagator expansion scheme which also greatly
simplifies numerical calculation of traditional perturbation graphs. The second
method extends Monte Carlo approaches by introducing a procedure to deal with
rapidly oscillating integrals.


  In this paper, we present an invariant perturbation theory of the adiabatic
process based on the concepts of U(1)-invariant adiabatic orbit and
U(1)-invariant adiabatic expansion. As its application, we propose and discuss
new adiabatic approximation conditions.


  We consider tiles of some fixed size, with an associated weighting on the
shapes of tile, of total mass 1. We study the pressure, $p$, of tilings with
those tiles; the pressure, one over the volume times the logarithm of the
partition function. (The quantity we define as "pressure" could, perhaps
equally harmoniously with physics notation, be called "entropy per volume",
neither nomenclature is "correct".) We let $\hat p^0$ (easy to compute) be the
pressure in the limit of absolute smoothness (the weighting function is
constant). Then as smoothness, suitably defined, increases, $p$ converges to
$\hat p^0$, uniformly in the volume. It is the uniformity requirement that
makes the result non-trivial. This seems like a very basic result in the theory
of pressure of tilings. Though at the same time, perhaps non-glamorous, being
bereft of geometry and not very difficult. The problem arose for us out of
study of a problem in mathematical physics, associated to a model of
ferromagnetism.


  After a short introduction to the generalized uncertainty principle (GUP), we
review some of the physical predictions of the GUP, and we focus in particular
on the bounds that present experimental tests can put on the value of the
deformation parameter $\beta$. We also describe a theoretical value computed
for $\beta$, and comment on the vast parameter region still unexplored, and to
be probed by future experiments.


  This comment is to show that our simulation data, based on our theory and
method in Ref. [J. Phys. B 41, 055401 (2008)], are also in agreement with the
experimental data presented for $D_{p}-D_{s}$ in Ref. [Phys. Rev. Lett.
\textbf{109}, 213901 (2012)]. We also demonstrate how to show the effect of
spatial coherence on the GH shifts in this comment, therefore we disagree with
the claims in Ref. [Phys. Rev. Lett. \textbf{109}, 213901 (2012)].


  In this paper, we study the semi-classical strings in AdS_4*CP^3 spacetime.
We construct various kinds of string solutions, including the point-like,
circular, folded and pulsating strings. For the circular and folded strings, we
figure out their field theory dual operators. In particular, we discuss the
anomalous dimensions of the corresponding operators from decoupled SU(2)*SU(2)
spin chain. We find that in the large angular momentum limit, the field theory
and string theory results are in very good agreement up to an interpolating
function of coupling constant.


  Standard multidimensional scaling takes as input a dissimilarity matrix of
general term $\delta _{ij}$ which is a numerical value. In this paper we input
$\delta _{ij}=[\underline{\delta _{ij}},\overline{\delta _{ij}}]$ where
$\underline{\delta _{ij}}$ and $\overline{\delta _{ij}}$ are the lower bound
and the upper bound of the ``dissimilarity'' between the stimulus/object $S_i$
and the stimulus/object $S_j$ respectively. As output instead of representing
each stimulus/object on a factorial plane by a point, as in other
multidimensional scaling methods, in the proposed method each stimulus/object
is visualized by a rectangle, in order to represent dissimilarity variation. We
generalize the classical scaling method looking for a method that produces
results similar to those obtained by Tops Principal Components Analysis. Two
examples are presented to illustrate the effectiveness of the proposed method.


  A typical task for classical and quantum computing in chemistry is finding a
potential energy surface (PES) along a reaction coordinate, which involves
solving the quantum chemistry problem for many points along the reaction path.
Developing algorithms to accomplish this task on quantum computers has been an
active area of development, yet finding all the relevant eigenstates along the
reaction coordinate remains a difficult problem, and determining PESs is thus a
costly proposal. In this paper, we demonstrate the use of a eigenvector
continuation -- a subspace expansion that uses a few eigenstates as a basis --
as a tool for rapidly exploring potential energy surfaces. We apply this to
determining the binding PES or torsion PES for several molecules of varying
complexity. In all cases, we show that the PES can be captured using relatively
few basis states; suggesting that a significant amount of (quantum)
computational effort can be saved by making use of already calculated ground
states in this manner.


  Variational Quantum Circuits (VQCs), or the so-called quantum
neural-networks, are predicted to be one of the most important near-term
quantum applications, not only because of their similar promises as classical
neural-networks, but also because of their feasibility on near-term noisy
intermediate-size quantum (NISQ) machines. The need for gradient information in
the training procedure of VQC applications has stimulated the development of
auto-differentiation techniques for quantum circuits. We propose the first
formalization of this technique, not only in the context of quantum circuits
but also for imperative quantum programs (e.g., with controls), inspired by the
success of differentiable programming languages in classical machine learning.
In particular, we overcome a few unique difficulties caused by exotic quantum
features (such as quantum no-cloning) and provide a rigorous formulation of
differentiation applied to bounded-loop imperative quantum programs, its
code-transformation rules, as well as a sound logic to reason about their
correctness. Moreover, we have implemented our code transformation in OCaml and
demonstrated the resource-efficiency of our scheme both analytically and
empirically. We also conduct a case study of training a VQC instance with
controls, which shows the advantage of our scheme over existing
auto-differentiation for quantum circuits without controls.


  We present a representation for describing transition models in complex
uncertain domains using relational rules. For any action, a rule selects a set
of relevant objects and computes a distribution over properties of just those
objects in the resulting state given their properties in the previous state. An
iterative greedy algorithm is used to construct a set of deictic references
that determine which objects are relevant in any given state. Feed-forward
neural networks are used to learn the transition distribution on the relevant
objects' properties. This strategy is demonstrated to be both more versatile
and more sample efficient than learning a monolithic transition model in a
simulated domain in which a robot pushes stacks of objects on a cluttered
table.


  The dynamical behaviour of domain boundaries between different realizations
of the vacuum of scalar fields with spontaneously broken phases is
investigated. They correspond to zero-modes of the Goldstone fields, moving
with the speed of light, and turn out to be accompanied by strongly oscillating
gravitational fields. In certain space-time topologies this leads to a
quantization condition for the symmetry breaking scale in terms of the Planck
mass.


  We obtain weighted $L^2$ Strichartz estimates for Schr\"odinger equations
$i\partial_tu+(-\Delta)^{a/2}u=F(x,t)$, $u(x,0)=f(x)$, of general orders $a>1$
with radial data $f,F$ with respect to the spatial variable $x$, whenever the
weight is in a Morrey-Campanato type class. This is done by making use of a
useful property of maximal functions of the weights together with
frequency-localized estimates which follow from using bilinear interpolation
and some estimates of Bessel functions. As consequences, we give an affirmative
answer to a question posed in \cite{BBCRV} concerning weighted homogeneous
Strichartz estimates, and improve previously known Morawetz estimates. We also
apply the weighted $L^2$ estimates to the well-posedness theory for the
Schr\"odinger equations with time-dependent potentials in the class.


  The superconducting properties of polycrystalline Sr0.6K0.4Fe2As2 were
strongly influenced by Ag doping (Supercond. Sci. Technol. 23 (2010) 025027).
Ag addition is mainly dominated by silver diffusing, so the annealing process
is one of the essential factors to achieve high quality Ag doped
Sr0.6K0.4Fe2As2. In this paper, the optimal annealing conditions were studied
for Ag doped Sr0.6K0.4Fe2As2 bulks prepared by a one-step solid reaction
method. It is found that the annealing temperature has a strong influence on
the superconducting properties, especially on the critical current density Jc.
As a result, higher heat treatment temperature (~900C) is helpful in diffusing
Ag and reducing the impurity phase gathered together to improve the grain
connectivity. In contrast, low-temperature sintering is counterproductive for
Ag doped samples. These results clearly suggest that annealing at ~900C is
necessary for obtaining high Jc Ag-doped samples.


  In this paper we prove that given a pair $(X,D)$ of a threefold $X$ and a
boundary divisor $D$ with mild singularities, if $(K_X+D)$ is movable, then the
orbifold second Chern class $c_2$ of $(X,D)$ is pseudo-effective. This
generalizes the classical result of Miyaoka on the pseudo-effectivity of $c_2$
for minimal models. As an application we give a simple solution to Kawamata's
effective non-vanishing conjecture in dimension $3$, where we prove that
$H^0(X, K_X+H)\neq 0$, whenever $K_X+H$ is nef and $H$ is an ample, effective,
reduced Cartier divisor. Furthermore, we study Lang-Vojta's conjecture for
codimension one subvarieties and prove that minimal varieties of general type
have only finitely many Fano, Calabi-Yau or Abelian subvarieties of codimension
one, mildly singular, whose classes belong to the movable cone.


  We present near-infrared spectra of ``A2'', the primary counter arc to the
gravitationally lensed galaxy MS1512-cB58. The spectra showredshifted H-alpha,
[NII], [OIII], and H-beta at z=2.728 +/- 0.001. We observe the same
H-alpha/[OIII] ratio as cB58, which together with the redshift confirms that A2
is indeed another image of a single background galaxy. Published lensing
reconstruction reports that A2 is a magnification of the entire source, while
cB58 is an image of only a part. At marginal significance, A2 shows higher line
to continuum ratios than cB58 (by a factor of about 2), suggesting a
non-uniform ratio of young to old stars across the galaxy. We observe a second
emission line source in the slit. This object, ``W5'', is predicted to be a
lensed image of another galaxy at a redshift similar to cB58. W5 is blueshifted
from cB58 by about 400 km/s and has a significantly lower H-alpha/[OIII] ratio,
confirming that it is an image of a different background galaxy in a group with
cB58. The H-alpha emission line in W5 implies a star formation rate of 6
Msun/yr (H_0 = 70 km/s/Mpc, Omega_M=0.3, Omega_L=0.7), after correcting for
lensing magnification.


  We give an isomorphism test that runs in time $n^{\operatorname{polylog}(h)}$
on all $n$-vertex graphs excluding some $h$-vertex vertex graph as a
topological subgraph. Previous results state that isomorphism for such graphs
can be tested in time $n^{\operatorname{polylog}(n)}$ (Babai, STOC 2016) and
$n^{f(h)}$ for some function $f$ (Grohe and Marx, SIAM J. Comp., 2015).
  Our result also unifies and extends previous isomorphism tests for graphs of
maximum degree $d$ running in time $n^{\operatorname{polylog}(d)}$ (SIAM J.
Comp., 2023) and for graphs of Hadwiger number $h$ running in time
$n^{\operatorname{polylog}(h)}$ (SIAM J. Comp., 2023).


  We discuss the worldsheet sigma-model whose target space is the $d+1$
dimensional Euclidean Schwarzschild black hole. We argue that in the limit
where the Hawking temperature of the black hole, $T$, approaches the Hagedorn
temperature, $T_H$, it can be described in terms of a generalized version of
the Horowitz-Polchinski effective theory. For $d\geq6$, where the
Horowitz-Polchinski EFT [1,2] does not have suitable solutions, the modified
effective Lagrangian allows one to study the black hole CFT in an expansion in
powers of $d-6$ and $T_H-T$. At $T=T_H$, the sigma model is non-trivial for all
$d>6$. It exhibits an enhanced $SU(2)$ symmetry, and is described by a
non-abelian Thirring model with a radially dependent coupling. The resulting
picture connects naturally to the results of [3-5], that relate Schwarzschild
black holes in flat spacetime at large $d$ to the two dimensional black hole.
We also discuss an analogous open string system, in which the black hole is
replaced by a system of two separated D-branes connected by a throat. In this
system, the asymptotic separation of the branes plays the role of the inverse
temperature. At the critical separation, the system is described by a
Kondo-type model, which again exhibits an enhanced $SU(2)$ symmetry. At large
$d$, the brane system gives rise to the hairpin brane [6].


  We studied the effect of disorder on the superconducting properties of
polycrystalline MgB_2 by specific-heat measurements. In the pristine state,
these measurements give a bulk confirmation of the presence of two
superconducting gaps with 2 Delta 0 / k_B T_c = 1.3 and 3.9 with nearly equal
weights. The scattering introduced by irradiation suppresses T_c and tends to
average the two gaps although less than predicted by theory. We also found that
by a suitable irradiation process by fast neutrons, a substantial bulk increase
of dH_{c2}/dT at T_c can be obtained without sacrificing more than a few
degrees in T_c. The upper critical field of the sample after irradiation
exceeds 28 T at T goes to 0 K.


  Mediation is a process, in which both parties agree to resolve their dispute
by negotiating over alternative solutions presented by a mediator. In order to
construct such solutions, mediation brings more information and knowledge, and,
if possible, resources to the negotiation table. The contribution of this paper
is the automated mediation machinery which does that. It presents an
argumentation-based mediation approach that extends the logic-based approach to
argumentation-based negotiation involving BDI agents. The paper describes the
mediation algorithm. For comparison it illustrates the method with a case study
used in an earlier work. It demonstrates how the computational mediator can
deal with realistic situations in which the negotiating agents would otherwise
fail due to lack of knowledge and/or resources.


  We analyze 5 epochs of NICER data of the black hole X-ray binary MAXI
J1820+070 during the bright hard-to-soft state transition in its 2018 outburst
with both reflection spectroscopy and Fourier-resolved timing analysis. We
confirm the previous discovery of reverberation lags in the hard state, and
find that the frequency range where the (soft) reverberation lag dominates
decreases with the reverberation lag amplitude increasing during the
transition, suggesting an increasing X-ray emitting region, possibly due to an
expanding corona. By jointly fitting the lag-energy spectra in a number of
broad frequency ranges with the reverberation model reltrans, we find the
increase in reverberation lag is best described by an increase in the X-ray
coronal height. This result, along with the finding that the corona contracts
in the hard state, suggests a close relationship between spatial extent of the
X-ray corona and the radio jet. We find the corona expansion (as probed by
reverberation) precedes a radio flare by ~5 days, which may suggest that the
hard-to-soft transition is marked by the corona expanding vertically and
launching a jet knot that propagates along the jet stream at relativistic
velocities.


  We characterize the dynamical behavior of a clamped flexible fiber immersed
in wall turbulence over a wide range of natural frequencies by means of direct
numerical simulations. Only two flapping states are possible: one where the
fiber oscillates at the characteristic frequency of the largest turbulent
eddies and another where the natural structural response dominates. The former
is obtained in the more flexible cases, while the latter in the more rigid
ones. We observe here for the first time that in the turbulence dominated
regime the fiber always sways at a frequency proportional to the largest scale
of the flow, regardless of its structural parameters. The hindrance of the
clamp to the wall prevents the synchronization of the fiber with turbulent
eddies of comparable size.


  Transformer-based large language models (LLMs) are now deployed to hundreds
of millions of users. LLM inference is commonly performed on batches of
sequences that share a prefix, such as few-shot examples or a chatbot system
prompt. Decoding in this large-batch setting can be bottlenecked by the
attention operation, which reads large key-value (KV) caches from memory and
computes inefficient matrix-vector products for every sequence in the batch. In
this work, we introduce Hydragen, a hardware-aware exact implementation of
attention with shared prefixes. Hydragen computes attention over the shared
prefix and unique suffixes separately. This decomposition enables efficient
prefix attention by batching queries together across sequences, reducing
redundant memory reads and enabling the use of hardware-friendly matrix
multiplications. Our method can improve end-to-end CodeLlama-13b throughput by
up to 32x against competitive baselines, with speedup growing with the batch
size and shared prefix length. Hydragen also enables the use of very long
shared contexts: with a large batch size, increasing the prefix length from 1K
to 16K tokens decreases Hydragen throughput by less than 15%, while the
throughput of baselines drops by over 90%. Hydragen generalizes beyond simple
prefix-suffix decomposition and can be applied to tree-based prompt sharing
patterns, allowing us to further reduce inference time on competitive
programming problems by 55%.


  This is an exposition of the Donaldson geometric flow on the space of
symplectic forms on a closed smooth four-manifold, representing a fixed
cohomology class. The original work appeared in [1].


  This paper was submitted to Phys. Rev. Lett in an earlier version. This
version was published in my book. It was delayed in posting by a clerical
error.