  The min-max vehicle routing problem (min-max VRP) traverses all given
customers by assigning several routes and aims to minimize the length of the
longest route. Recently, reinforcement learning (RL)-based sequential planning
methods have exhibited advantages in solving efficiency and optimality.
However, these methods fail to exploit the problem-specific properties in
learning representations, resulting in less effective features for decoding
optimal routes. This paper considers the sequential planning process of min-max
VRPs as two coupled optimization tasks: customer partition for different routes
and customer navigation in each route (i.e., partition and navigation). To
effectively process min-max VRP instances, we present a novel attention-based
Partition-and-Navigation encoder (P&N Encoder) that learns distinct embeddings
for partition and navigation. Furthermore, we utilize an inherent symmetry in
decoding routes and develop an effective agent-permutation-symmetric (APS) loss
function. Experimental results demonstrate that the proposed
Decoupling-Partition-Navigation (DPN) method significantly surpasses existing
learning-based methods in both single-depot and multi-depot min-max VRPs. Our
code is available at


  Is it possible to draw a circle in Manhattan, using only its discrete network
of streets and boulevards? In this study, we will explore the construction and
properties of circular paths on an integer lattice, a discrete space where the
distance between two points is not governed by the familiar Euclidean metric,
but the Manhattan or taxicab distance, a metric linear in its coordinates. In
order to achieve consistency with the continuous ideal, we need to abandon
Euclid's very original definition of the circle in favour of a parametric
construction. Somewhat unexpectedly, we find that the Euclidean circle's
defining constant $\pi$ can be recovered in such a discrete setting.


  Fifth Generation (5G) telecommunication system is going to deliver a flexible
radio access network (RAN). Security functions such as authorization,
authentication and accounting (AAA) are expected to be distributed from central
clouds to edge clouds. We propose a novel architectural security solution that
applies to 5G networks. It is called Trust Zone (TZ) that is designed as an
enhancement of the 5G AAA in the edge cloud. TZ also provides an autonomous and
decentralized security policy for different tenants under variable network
conditions. TZ also initiates an ability of disaster cognition and extends the
security functionalities to a set of flexible and highly available emergency
services in the edge cloud.


  We consider a stochastic model of gene expression in which transcription
depends on a multistate promoter, including the famous two-state model and
refractory promoters as special cases, and focus on deriving the exact
stationary distribution. Building upon several successful approaches, we
present a more unified viewpoint that enables us to simplify and generalize
existing results. In particular, the original jump process is deeply related to
a multivariate piecewise-deterministic Markov process that may also be of
interest beyond the biological field. In a very particular case of promoter
configuration, this underlying process is shown to have a simple Dirichlet
stationary distribution. In the general case, the corresponding marginal
distributions extend the well-known class of Beta products, involving complex
parameters that directly relate to spectral properties of the promoter
transition matrix. Finally, we illustrate these results with biologically
plausible examples.


  We consider random walks on dynamical networks where edges appear and
disappear during finite time intervals. The process is grounded on three
independent stochastic processes determining the walker's waiting-time, the
up-time and down-time of edges activation. We first propose a comprehensive
analytical and numerical treatment on directed acyclic graphs. Once cycles are
allowed in the network, non-Markovian trajectories may emerge, remarkably even
if the walker and the evolution of the network edges are governed by memoryless
Poisson processes. We then introduce a general analytical framework to
characterize such non-Markovian walks and validate our findings with numerical
simulations.


  Two quantum theories are physically equivalent if they are related, not by a
unitary transformation, but by an isometric transformation. The conditions
under which a quantum canonical transformation is an isometric transformation
are given.


  This paper identifies factors that influence Kazakhstan's e-Government portal
usage. It determines challenges encountered by citizens while using the portal.
Targeted respondents for the web-based questionnaire survey were the citizens
of Kazakhstan. The technology acceptance model was used as a methodology to
measure attitude towards portal usage. In addition, this paper discusses the
barriers which were experienced by the respondents and can prevent the
successful adoption of e-Government initiative. The results of the analysis
demonstrate that awareness among citizens is high, i.e. the majority of people
have visited it at least once and they perceive the portal to be useful, but
only a limited percentage of citizens' use it on the regular basis. Further,
paper can be used to help IT managers of the portal to improve management of
informational content and maintain a more effective adoption among citizens.


  Sliding cable system with frictions is encountered in many engineering
applications. Such system is typically characterized by existences of complex
and varied motion states of different sliding nodes (pulleys), which leads to
significant difficulties for analysis. A very simple but effective linear
complementarity approach isdeveloped. Within this approach, the challenging and
significant issue of determinations of motion states of sliding nodes, as well
as calculations of their sliding lengths, can be handled in a standard linear
complementarity formulation, and can be solved using any available efficient
solver for linear complementarity problem (LCP). The proposed approach
eliminates the need for traditional cumbersome predictor-corrector-based
operations. It also opens a way to study the theory problem such as the
existence and uniqueness of the solution by using complementarity theories,
which is generally absent from existing literature given its complexity. The
uniqueness of the solution has been proved in this work, while the existence
issue remains open and deserves further investigation. Multi examples involving
both static and dynamic analyses are presented to demonstrate the effectiveness
of the proposed approach, as well as to reveal some novel phenomenon involved
in sliding cable system considering frictions. The results highlight the
ability of the method in accurately handling variegated motion states of
sliding nodes. The proposed approach, we think, has the potential to be a
popular method in dealing with multi-node sliding cable with frictions, given
its simplicity and effectiveness.


  We consider the asymptotic behavior of the mean square of truncations of the
Dirichlet series of $\zeta(s)^k$. We discuss the connections of this problem
with that of the variance of the divisor function in short intervals and in
arithmetic progressions, reviewing the recent results on this topic. Finally,
we show how these results can all be proved assuming a suitable version of the
moments conjecture.


  Neutral uranium (U I) is a very difficult atom for theoretical calculations
due to a large number of valence electrons, six, strong valence-valence and
valence-core correlations, high density of states, and relativistic effects.
Configuration-interaction many-body perturbation theory (CI-MBPT) can treat
efficiently valence-core correlations and relativistic effects, but because the
formalism was developed for Dirac-Hartree-Fock (DHF) starting potential that
does not contain valence electrons, quite large CI space is needed to
compensate for +6 charge of such a potential. Much more efficient is
relativistic configuration-interaction (RCI) approach which uses relatively
accurate starting DHF potential that includes some valence electrons to make
the valence electron Hamiltonian diagonally dominated for some states. Here we
report calculations of U I hyperfine constants of several low-energy states
using the RCI method with the starting potential that includes four f valence
electrons. With this starting potential, it is possible to use the
single-configuration approximation or small basis sets to obtain quite accurate
results for hyperfine structure constants. In fact, by scaling nuclear magnetic
moment, the agreement for 5 levels was within 5\%, and a new magnetic moment
can be recommended 0.43(2). The method can be further developed to include more
extensive data sets to improve accuracy and can be applied to other atoms and
for calculations of other properties, for example, relevant to fundamental
symmetry tests.


  We introduce symmetric states and quantum symmetric states on universal
unital free product C*-algebras an arbitrary unital C*-algebra A with itself
infinitely many times, as a generalization of the notions of exchangeable and
quantum exchangeable random variables. We prove existence of conditional
expectations onto tail algebras in various settings and we define a natural
C*-subalgebra of the tail algebra, called the tail C*-algebra. Extending and
building on the proof of the noncommutative de Finetti theorem of Koestler and
Speicher, we prove a de Finetti type theorem that characterizes quantum
symmetric states in terms of amalgamated free products over the tail
C*-algebra, and we provide a convenient description of the set of all quantum
symmetric states on the free product C*-algebra in terms of C*-algebras
generated by homomorphic images of A and the tail C*-algebra. This description
allows a characterization of the extreme quantum symmetric states. Similar
results are proved for the subset of tracial quantum symmetric states, though
in terms of von Neumann algebras and normal conditional expectations. The
central quantum symmetric states are those for which the tail algebra is in the
center of the von Neumann algebra, and we show that the central quantum
symmetric states form a Choquet simplex whose extreme points are the free
product states, while the tracial central quantum symmetric states form a
Choquet simplex whose extreme points are the free product traces.


  We give a list of possibilities for surfaces of general type with $p_g=0$
having an involution $i$ such that the bicanonical map of $S$ is not composed
with $i$ and $S/i$ is not rational. Some examples with $K^2=4,...,7$ are
constructed as double coverings of an Enriques surface. These surfaces have a
description as bidouble coverings of the plane.


  Suppose that $X$ is an analytic subvariety of a Stein manifold $M$ and that
$\varphi$ is a plurisubharmonic (psh) function on $X$ which is dominated by a
continuous psh exhaustion function $u$ of $M$. Given any number $c>1$, we show
that $\varphi$ admits a psh extension to $M$ which is dominated by $cu$ on $M$.
  We use this result to prove that any $\omega$-psh function on a subvariety of
the complex projective space is the restriction of a global $\omega$-psh
function, where $\omega$ is the Fubini-Study K\"ahler form.


  In this review we survey the literature on mean-field coupled maps. We start
with the early works from the physics literature, arriving to some recent
results from ergodic theory studying the thermodynamic limit of globally
coupled maps and the associated self-consistent transfer operators. We also
give few pointers to related research fields dealing with mean-field coupled
systems in continuous time, and applications.


  We construct a sofic approximation of $\mathbb F_2\times\mathbb F_2$ that is
not essentially a "branched cover" of a sofic approximation by homomorphisms.
This answers a question of L. Bowen.


  Quantum computers have the potential to explore the vast Hilbert space of
entangled states that play an important role in the behavior of strongly
interacting matter. This opportunity motivates reconsidering the Hamiltonian
formulation of gauge theories, with a suitable truncation scheme to render the
Hilbert space finite-dimensional. Conventional formulations lead to a Hilbert
space largely spanned by unphysical states; given the current inability to
perform large scale quantum computations, we examine here how one might
restrict wave function evolution entirely or mostly to the physical subspace.
We consider such constructions for the simplest of these theories containing
dynamical gauge bosons -- U(1) lattice gauge theory without matter in $d=2,3$
spatial dimensions -- and find that electric-magnetic duality naturally plays
an important role. We conclude that this approach is likely to significantly
reduce computational overhead in $d=2$ by a reduction of variables and by
allowing one to regulate magnetic fluctuations instead of electric. The former
advantage does not exist in $d=3$, but the latter might be important for
asymptotically-free gauge theories.


  The remarkable progress in computer vision over the last few years is, by and
large, attributed to deep learning, fueled by the availability of huge sets of
labeled data, and paired with the explosive growth of the GPU paradigm. While
subscribing to this view, this book criticizes the supposed scientific progress
in the field and proposes the investigation of vision within the framework of
information-based laws of nature. Specifically, the present work poses
fundamental questions about vision that remain far from understood, leading the
reader on a journey populated by novel challenges resonating with the
foundations of machine learning. The central thesis is that for a deeper
understanding of visual computational processes, it is necessary to look beyond
the applications of general purpose machine learning algorithms and focus
instead on appropriate learning theories that take into account the
spatiotemporal nature of the visual signal.


  Let $G$ be a finite simple group of Lie type, and let $\pi_G$ be the
permutation representation of $G$ associated with the action of $G$ on itself
by conjugation. We prove that every irreducible representation of $G$ is a
constituent of $\pi_G$, unless $G=PSU_n(q)$ and $n$ is coprime to $2(q+1)$,
where precisely one irreducible representation fails. Let St be the Steinberg
representation of $G$. We prove that a complex irreducible representation of
$G$ is a constituent of the tensor square $St\otimes St$, with the same
exceptions as in the previous statement.


  We further study the security of the quantum bit commitment (QBC) protocol we
previously proposed [Phys. Rev. A 74, 022332 (2006).], by analyzing the reduced
density matrix \rho_{b}^{B} which describes the quantum state at Bob's side
corresponding to Alice's committed bit b. It is shown that Alice will find
\rho_{0}^{B}\perp \rho_{1}^{B} while the protocol remains concealing to Bob. On
the contrary, the existing no-go theorem of unconditionally secure QBC is based
on the condition \rho_{0}^{B}\simeq \rho_{1}^{B}. Thus the specific cheating
strategy proposed in the no-go theorem does not necessarily applies to our
protocol.


  In this paper we look at the problem of scheduling tasks on a
single-processor system, where each task requires unit time and must be
scheduled within a certain time window, and each task can be added to or
removed from the system at any time. On each operation, the system is allowed
to reschedule any tasks, but the goal is to minimize the number of rescheduled
tasks. Our main result is an allocator that maintains a valid schedule for all
tasks in the system if their time windows have constant size and reschedules
O(1/{\epsilon}*log(1/{\epsilon})) tasks on each insertion as {\epsilon}->0,
where {\epsilon} is a certain measure of the schedule flexibility of the
system. We also show that it is optimal for any allocator that works on
arbitrary instances. We also briefly mention a few variants of the problem,
such as if the tasks have time windows of difference sizes, for which we have
an allocator that we conjecture reschedules only 1 task on each insertion if
the schedule flexibility remains above a certain threshold.


  We calculate the probability of success of block-hiding mining strategies in
Bitcoin-like networks. These strategies involve building a secret branch of the
block-tree and publishing it opportunistically, aiming to replace the top of
the main branch and rip the reward associated with the secretly mined blocks.
We identify two types of block-hiding strategies and chart the parameter space
where those are more beneficial than the standard mining strategy described in
Nakamoto's paper. Our analysis suggests a generalization of the notion of the
relative hashing power as a measure for a miner's influence on the network.
Block-hiding strategies are beneficial only when this measure of influence
exceeds a certain threshold.


  We propose a method to adiabatically control an atomic ensemble using a
decoherence-free subspace (DFS) within a dissipative cavity. We can engineer a
specific eigenstate of the system's Lindblad jump operators by injecting a
field into the cavity which deconstructively interferes with the emission
amplitude of the ensemble. In contrast to previous adiabatic DFS proposals, our
scheme creates a DFS in the presence of collective decoherence. We therefore
have the ability to engineer states that have high multi-particle entanglements
which may be exploited for quantum information science or metrology. We further
demonstrate a more optimized driving scheme that utilizes the knowledge of
possible diabatic evolution gained from the so-called adiabatic criteria. This
allows us to evolve to a desired state with exceptionally high fidelity on a
time scale that does not depend on the number of atoms in the ensemble. By
engineering the DFS eigenstate adiabatically, our method allows for faster
state preparation than previous schemes that rely on damping into a desired
state solely using dissipation.


  A particular Fermi-point-splitting (FPS) model is proposed, which allows, for
finite values of the three-momentum, the group and phase velocities of the
muon-flavor neutrino in vacuum to be superluminal (i.e., muon-neutrino
velocities greater than c, the velocity of light in vacuum). Still, the
proposed FPS model has a front velocity equal to c for all particles, which is
known to be crucial for causality. If the muon-neutrino velocity is indeed
superluminal as claimed by OPERA, the model predicts that at least one of the
other two flavors of neutrinos has a subluminal velocity.


  We consider the problem of reconstruction of dielectrics from blind
backscattered experimental data. Experimental data were collected by a device,
which was built at University of North Carolina at Charlotte. This device sends
electrical pulses into the medium and collects the time resolved backscattered
data on a part of a plane. The spatially distributed dielectric constant
$\varepsilon_{r}(\mathbf{x}),\mathbf{x}\in \mathbb{R}^{3}$ is the unknown
coefficient of a wave-like PDE. This coefficient is reconstructed from those
data in blind cases. To do this, a globally convergent numerical method is
used.


  Quantum routing, the entanglement of an input quantum signal over multiple
output paths, will be an important aspect of future quantum networks.
Implementation of such routing in emerging quantum networks via the noisy
quantum devices currently under development is a distinct possibility. Quantum
error correction, suitable for the arbitrary noisy quantum channels experienced
in the routing process, will be required. In this work, we design a combined
circuit for quantum routing and quantum error correction, and carry out the
first implementation of such a circuit on a noisy real-world quantum device.
Under the assumption of statistical knowledge on the channel, we experimentally
verify the quantum nature of the error-corrected quantum routing by determining
the path-entanglement through quantum state tomography, measuring also its
probability of success. The quantum error correction deployed is identified as
successful in terms of improving the routing. Our experiments validate, for the
first time, that error-corrected quantum routing in near-term noisy
quantum-computing devices is feasible, and our detailed results provide a
quantum-routing benchmark for all near-term quantum hardware.


  In contrast to the neutral population cycles of the deterministic mean-field
Lotka--Volterra rate equations, including spatial structure and stochastic
noise in models for predator-prey interactions yields complex spatio-temporal
structures associated with long-lived erratic population oscillations.
Environmental variability in the form of quenched spatial randomness in the
predation rates results in more localized activity patches. Population
fluctuations in rare favorable regions in turn cause a remarkable increase in
the asymptotic densities of both predators and prey. Very intriguing features
are found when variable interaction rates are affixed to individual particles
rather than lattice sites. Stochastic dynamics with demographic variability in
conjunction with inheritable predation efficiencies generate non-trivial time
evolution for the predation rate distributions, yet with overall essentially
neutral optimization.


  We consider a coupled system of Schr\"odinger equations, arising in quantum
mechanics via the so-called time-dependent self-consistent field method. Using
Wigner transformation techniques we study the corresponding classical limit
dynamics in two cases. In the first case, the classical limit is only taken in
one of the two equations, leading to a mixed quantum-classical model which is
closely connected to the well-known Ehrenfest method in molecular dynamics. In
the second case, the classical limit of the full system is rigorously
established, resulting in a system of coupled Vlasov-type equations. In the
second part of our work, we provide a numerical study of the coupled
semi-classically scaled Schr\"odinger equations and of the mixed
quantum-classical model obtained via Ehrenfest's method. A second order (in
time) method is introduced for each case. We show that the proposed methods
allow time steps independent of the semi-classical parameter(s) while still
capturing the correct behavior of physical observables. It also becomes clear
that the order of accuracy of our methods can be improved in a straightforward
way.


  We study the distribution of partition function zeroes for the $XY$--model in
two dimensions. In particular we find the scaling behaviour of the end of the
distribution of zeroes in the complex external magnetic field plane in the
thermodynamic limit (the Yang--Lee edge) and the form for the density of these
zeroes. Assuming that finite--size scaling holds, we show that there have to
exist logarithmic corrections to the leading scaling behaviour of thermodynamic
quantities in this model. These logarithmic corrections are also manifest in
the finite--size scaling formulae and we identify them numerically. The method
presented here can be used to check the compatibility of scaling behaviour of
odd and even thermodynamic functions in other models too.


  In this paper we study the problem of finding a small safe set $S$ in a graph
$G$, i.e. a non-empty set of vertices such that no connected component of
$G[S]$ is adjacent to a larger component in $G - S$. We enhance our
understanding of the problem from the viewpoint of parameterized complexity by
showing that (1) the problem is W[2]-hard when parameterized by the pathwidth
$pw$ and cannot be solved in time $n^{o(pw)}$ unless the ETH is false, (2) it
admits no polynomial kernel parameterized by the vertex cover number $vc$
unless $\mathrm{PH} = \Sigma^{\mathrm{p}}_{3}$, but (3) it is fixed-parameter
tractable (FPT) when parameterized by the neighborhood diversity $nd$, and (4)
it can be solved in time $n^{f(cw)}$ for some double exponential function $f$
where $cw$ is the clique-width. We also present (5) a faster FPT algorithm when
parameterized by solution size.


  Let $M$ be a compact smooth manifold without boundary. Based on results by
Good and Meddaugh (2020), we prove that a strong distributional chaos is
$C^0$-generic in the space of continuous self-maps (resp. homeomorphisms) of
$M$. The results contain answers to questions by Li et al. (2016) and Moothathu
(2011) in the zero-dimensional case. A related counter-example on the chain
components under shadowing is also given.


  I discuss the energetics of synchrotron-emitting outflows, increasingly found
to be present in many different classes of X-ray binary systems. It is shown
that the outflow is likely to be comparable in power to the integrated X-ray
luminosity, traditionally taken to be an indicator of the global mass-transfer
rate. This is especially found to be the case in the (low/)hard states of black
hole candidate systems. I conclude that jets are extremely important,
energetically and dynamically, for the accretion process in the majority of
known X-ray binary systems.


  We report $^{125}$Te and $^{63,65}$Cu nuclear magnetic resonance (NMR)
studies on single crystals of staircase Kagom\'e antiferromagnet PbCu3TeO7
($T_{N1}\approx$ 36 K). A Curie constant as large as $\Theta$~140 K is obtained
by a Curie-Weiss fitting of the high-temperature Knight shift. The frustration
factor f (= $\Theta/T_N $)$\approx$ 4 implies intermediate frustration in this
system. From the high-temperature Knight shift data of $^{125}$Te, its
hyperfine coupling constant is estimated to be $^{125}A_{hf}$ = -67
kOe/${\mu}_B$, which suggests a strong interlayer coupling bridging the
neighboring Kagom\'e layers. At $T$= 2 K, we find two types of zero-field NMR
signals. One has no dependence on the history of the field treatment, which is
consistent with Cu NMR signals from inequivalent Cu sites with different
internal hyperfine fields. The other one is only seen after a field-cycling
process, which is consistent with domain wall contributions in this frustrated
antiferromagnet.


  The eclipsing binary CM Dra contains nearly identical red dwarfs of spectral
class dM4.5. Their masses and radii have been reported with unprecedentedly
small statistical errors. When compared with standard stellar models of
appropriate age (\approx4 Gyr), the empirical results indicate that both
components are larger in R, and lower in luminosity L, by several standard
deviations. Here, we attempt at first to model the components in the context of
standard stellar models using differ


  Theories developed by Tinto and Nora identify academic performance, learning
gains, and involvement in learning communities as significant facets of student
engagement that, in turn, support student persistence. Collaborative learning
environments, such as those employed in the Modeling Instruction introductory
physics course, provide structure for student engagement by encouraging
peer-to-peer interactions. Because of the inherently social nature of
collaborative learning, we examine student interactions in the classroom using
network analysis. We use centrality---a family of measures that quantify how
connected or "central" a particular student is within the classroom
network---to study student engagement longitudinally. Bootstrapped linear
regression modeling shows that students' centrality predicts future academic
performance over and above prior GPA for three out of four centrality measures
tested. In particular, we find that closeness centrality explains 28 % more of
the variance than prior GPA alone. These results confirm that student
engagement in the classroom is critical to supporting academic performance.
Furthermore, we find that this relationship for social interactions does not
emerge until the second half of the semester, suggesting that classroom
community develops over time in a meaningful way.


  To place the highly substructured stellar halos of the Milky Way and M31 in a
larger context of hierarchical galaxy formation, it is necessary to understand
the prevalence and properties of tidal substructure around external galaxies.
This chapter details the current state of our observational knowledge of
streams in galaxies in and beyond the Local Group, which are studied both in
resolved stellar populations and in integrated light. Modeling of individual
streams in extragalactic systems is hampered by our inability to obtain
resolved stellar kinematics in the streams, though many streams contain
alternate luminous kinematic tracers, such as globular clusters or planetary
nebulae. We compare the observed structures to the predictions of models of
galactic halo formation, which provide insight in the number and properties of
streams expected around Milky Way like galaxies. More specifically, we discuss
the inferences that can be made about stream progenitors based only on observed
morphologies. We expand our discussion to consider hierarchical accretion at
lower mass scales, in particular the observational evidence that substructure
exists on smaller mass scales and the effects accretion events may have on the
evolution of dwarf galaxies (satellite or isolated). Lastly, we discuss
potential correlations between the presence of substructure in the halo and the
structural properties of the disk. While many exciting discoveries have been
made of tidal substructures around external galaxies, the "global" questions of
galaxy formation and evolution via hierarchical accretion await a more complete
census of the low surface brightness outskirts of galaxies in and beyond the
Local Group.


  Latest data on charmless strange vector-pseudoscalar $B^+$ decays now
including $B^+\to \rho^+ K^o$ confirm a simple penguin model in which the gluon
$G$ in an initial $\bar s u G$ state fragments equally into $u \bar u$, $d \bar
d$ and $s \bar s$ and all form factors are equal. A search for possible
additional contributions shows only a few signals not obscured by experimental
errors whose implications are discussed. The experimental value of $0.25 \pm
0.11$ for the ratio of the branching ratios $BR(B^+ \to K^{*+}\eta)$ to
$BR(B^+\to K^{*+}\eta')$ confirms the parity selection rule prediction 0.32.
Large violations arise in a new sum rule for the sum of these branching ratios,
analogous to the similar pseudoscalar sum rule including $K^+\eta$ and
$K^+\eta'$. Indications for either an electroweak penguin contribution or
additional admixtures like instrinsic charm in the $\eta-\eta'$ system remain
to be clarified. An alternative symmetry description with new predictive power
clarifies the simple penguin approximation and presents new predictions which
can be tested experimentally. The fragmentation of the $\bar s u G$ state into
two mesons is described by a strong interaction S-matrix dominated by nonexotic
hadron resonances in multiparticle intermediate states.


  A model of saturated hyperelastic porous solids at large strains is
formulated and analysed. The material response is assumed to be of a
viscoelastic Kelvin-Voigt type and inertial effects are considered, too. The
flow of the diffusant is driven by the gradient of the chemical potential and
is coupled to the mechanics via the occurrence of swelling and squeezing.
Buoyancy effects due to the evolving mass density in a gravity field are
covered. Higher-order viscosity is also included, allowing for physically
relevant stored energies and local invertibility of the deformation. The whole
system is formulated in a fully Eulerian form in terms of rates. The energetics
of the model is discussed and the existence and regularity of weak solutions is
proved by a combined regularization-Galerkin approximation argument.


  We have developed a coherent Raman imaging platform using broadband coherent
anti-Stokes Raman scattering (BCARS) that provides an unprecedented combination
of speed, sensitivity, and spectral breadth. The system utilizes a unique
configuration of laser sources that probes the Raman spectrum over 3,000
cm$^{-1}$ and generates an especially strong response in the typically weak
Raman "fingerprint" region through heterodyne amplification of the anti-Stokes
photons with a large nonresonant background (NRB) while maintaining high
spectral resolution of $<$ 13 cm$^{-1}$. For histology and pathology, this
system shows promise in highlighting major tissue components in a
non-destructive, label-free manner. We demonstrate high-speed chemical imaging
in two- and three-dimensional views of healthy murine liver and pancreas
tissues and interfaces between xenograft brain tumors and the surrounding
healthy brain matter.


  We investigate the computational complexity of mining guarded clauses from
clausal datasets through the framework of inductive logic programming (ILP). We
show that learning guarded clauses is NP-complete and thus one step below the
$\sigma^P_2$-complete task of learning Horn clauses on the polynomial
hierarchy. Motivated by practical applications on large datasets we identify a
natural tractable fragment of the problem. Finally, we also generalise all of
our results to $k$-guarded clauses for constant $k$.


  Let $G$ be a graph on $n$ vertices and $1 \le k \le n$ a fixed integer. The
$k$-token graph of $G$ is the graph, $F_k(G)$, whose vertex set is equal to all
the $k$-subsets of $V(G)$; where two of them are adjacent whenever their
symmetric difference is an edge of $G$. In this paper we study the treewidth of
$F_k(G)$, when $G$ is a star, path or a complete graph. We show that in the
first two cases, the treewidth is of order $\Theta(n^{k-1})$, and of order
$\Theta(n^k)$ in the third case. We conjecture that our upper bound for the
treewidth of $F_k(K_n)$ is tight. This is particularly relevant since
$F_k(K_n)$ is isomorphic to the well known Johnson graph $J(n,k)$.


  Context. Ice lines are suggested to play a significant role in grain growth
and planetesimal formation in protoplanetary disks. Evaporation fronts directly
influence the gas and ice abundances of volatile species in the disk and
therefore the coagulation physics and efficiency and the chemical composition
of the resulting planetesimals.
  Aims. In this work we investigate the influence of the existence of the CO
ice line on the particle growth and on the distribution of CO in the disk.
  Methods. We include the possibility of tracking the CO content and/or other
volatiles in particles and in the gas in our existing dust coagulation and disk
evolution model and developed a method for evaporation and condensation of CO
using the Hertz-Knudsen equation. Our model does not include fragmentation,
yet, which will be part of further investigations.
  Results. We find no enhanced grain growth just outside the ice line where the
particle size is limited by radial drift. Instead we find a depletion of solid
material inside the ice line which is solely due to evaporation of the CO. Such
a depression inside the ice line may be observable and may help to quantify the
processes described in this work. Furthermore, we find that the viscosity and
diffusivity of the gas heavily influence the re-distribution of vaporized CO at
the ice line and can lead to an increase in the CO abundance by up to a factors
of a few in the region just inside the ice line. Depending on the strength of
the gaseous transport mechanisms the position of the ice line in our model can
change by up to 10 AU and consequently, the temperature at that location can
range from 21 K to 23 K.


  We show that if X is a Banach space without cotype, then every locally finite
metric space embeds metrically into X.


  The paper presents a pragmatic and practical multilayer network planning
approach based on a candidate lightpath auxiliary graph model. The paper
discusses, how this approach can be applied to offline network planning as well
as dynamic planning and provisioning of services.


  We report first-principles studies of electronic transport in monolayers of
Tour wires functionalized with different side groups. An analysis of the
scattering states and transmission eigenchannels suggests that the
functionalization does not strongly affect the resonances responsible for
current flow through the monolayer. However, functionalization has a
significant effect on the interactions within the monolayer, so that monolayers
with NO$_2$ side groups exhibit local minima associated with twisted
conformations of the molecules. We use our results to interpret observations of
negative differential resistance and molecular memory in monolayers of NO$_2$
functionalized molecules in terms of a twisting of the central ring induced by
an applied bias potential.


  We review progress in studying the solutions of SQCD in the presence of
explicit, soft SUSY breaking terms. Massive N=1 SQCD in the presence of a small
gaugino mass leads to a controlled solution which has interesting phase
structure with changing theta angle reminiscent of the QCD chiral Lagrangian.
Current attempts to test the solutions of pure glue SQCD on the lattice require
a theoretical understanding of the theory with small gaugino mass in order to
understand the approach to the SUSY point. We provide such a description making
predictions for the gaugino condensate and lightest bound state masses. Finally
we briefly review recent D-brane constructions of 4D gauge theories in string
theory including a non-supersymmetric configuration. We identify this
configuration with softly broken N=2 SQCD.


  We explore the radial distribution of star formation in galaxies in the SAMI
Galaxy Survey as a function of their local group environment. Using a sample of
galaxies in groups (with halo masses less than $ \simeq 10^{14} \,
\mathrm{M_{\odot}}$) from the Galaxy And Mass Assembly Survey, we find
signatures of environmental quenching in high-mass groups ($M_{G} > 10^{12.5}
\, \mathrm{M_{\odot}}$). The mean integrated specific star formation rate of
star-forming galaxies in high-mass groups is lower than for galaxies in
low-mass groups or that are ungrouped, with $\Delta \log(sSFR/\mathrm{yr^{-1}})
= 0.45 \pm 0.07$. This difference is seen at all galaxy stellar masses. In
high-mass groups, star-forming galaxies more massive than $M_{*} \sim 10^{10}
\, \mathrm{M_{\odot}}$ have centrally-concentrated star formation. These
galaxies also lie below the star-formation main sequence, suggesting they may
be undergoing outside-in quenching. Lower mass galaxies in high-mass groups do
not show evidence of concentrated star formation. In groups less massive than
$M_{G} = 10^{12.5} \, \mathrm{M_{\odot}}$ we do not observe these trends. In
this regime we find a modest correlation between centrally-concentrated star
formation and an enhancement in total star formation rate, consistent with
triggered star formation in these galaxies.


  In this paper, we present Adaptive Computation Steps (ACS) algo-rithm, which
enables end-to-end speech recognition models to dy-namically decide how many
frames should be processed to predict a linguistic output. The model that
applies ACS algorithm follows the encoder-decoder framework, while unlike the
attention-based mod-els, it produces alignments independently at the encoder
side using the correlation between adjacent frames. Thus, predictions can be
made as soon as sufficient acoustic information is received, which makes the
model applicable in online cases. Besides, a small change is made to the
decoding stage of the encoder-decoder framework, which allows the prediction to
exploit bidirectional contexts. We verify the ACS algorithm on a Mandarin
speech corpus AIShell-1, and it achieves a 31.2% CER in the online occasion,
compared to the 32.4% CER of the attention-based model. To fully demonstrate
the advantage of ACS algorithm, offline experiments are conducted, in which our
ACS model achieves an 18.7% CER, outperforming the attention-based counterpart
with the CER of 22.0%.


  Deep learning system have drawback that their output is not accompanied with
ex-planation. In a domain such as forensic handwriting verification it is
essential to provideexplanation to jurors. The goal of handwriting verification
is to find a measure of confi-dence whether the given handwritten samples are
written by the same or different writer.We propose a method to generate
explanations for the confidence provided by convolu-tional neural network (CNN)
which maps the input image to 15 annotations (features)provided by experts. Our
system comprises of: (1) Feature learning network (FLN),a differentiable
system, (2) Inference module for providing explanations. Furthermore,inference
module provides two types of explanations: (a) Based on cosine
similaritybetween categorical probabilities of each feature, (b) Based on
Log-Likelihood Ratio(LLR) using directed probabilistic graphical model. We
perform experiments using acombination of feature learning network (FLN) and
each inference module. We evaluateour system using XAI-AND dataset, containing
13700 handwritten samples and 15 cor-responding expert examined features for
each sample. The dataset is released for publicuse and the methods can be
extended to provide explanations on other verification taskslike face
verification and bio-medical comparison. This dataset can serve as the basis
and benchmark for future research in explanation based handwriting
verification. The code is available on github.


  It is known that a gamma ray burst (GRB) originating near the Earth could be
devastating to life. The mechanism of ozone depletion and subsequent increased
UVB exposure is the primary risk, but models also show increased nitrification
culminating in nitric acid rainout. These effects are also expected after
nearby supernovae and extreme solar proton events. In this work we considered
specifically whether the increased nitric acid rainout from such events is a
threat to modern terrestrial ecosystems. We also considered its potential
benefit to early terrestrial Paleozoic ecosystems. We used established critical
loads for nitrogen deposition in ecoregions of Europe and the US and compared
them with previously predicted values of nitric acid rainout from a typical GRB
within our galaxy. The predicted rainout was found to be too low to harm modern
ecosystems, however, it is large compared with probable nitrate flux onto land
prior to the invasion of plants. We suggest that this flux may have contributed
nutrients to this invasion if, as hypothesized, the end-Ordovician extinction
event were initiated by a GRB or other ionizing radiation event.


  We study the use of gradient descent with backtracking line search (GD-BLS)
to solve the noisy optimization problem
$\theta_\star:=\mathrm{argmin}_{\theta\in\mathbb{R}^d}
\mathbb{E}[f(\theta,Z)]$, imposing that the function
$F(\theta):=\mathbb{E}[f(\theta,Z)]$ is strictly convex but not necessarily
$L$-smooth. Assuming that $\mathbb{E}[\|\nabla_\theta
f(\theta_\star,Z)\|^2]<\infty$, we first prove that sample average
approximation based on GD-BLS allows to estimate $\theta_\star$ with an error
of size $\mathcal{O}_{\mathbb{P}}(B^{-0.25})$, where $B$ is the available
computational budget. We then show that we can improve upon this rate by
stopping the optimization process earlier when the gradient of the objective
function is sufficiently close to zero, and use the residual computational
budget to optimize, again with GD-BLS, a finer approximation of $F$. By
iteratively applying this strategy $J$ times, we establish that we can estimate
$\theta_\star$ with an error of size
$\mathcal{O}_{\mathbb{P}}(B^{-\frac{1}{2}(1-\delta^{J})})$, where
$\delta\in(1/2,1)$ is a user-specified parameter. More generally, we show that
if $\mathbb{E}[\|\nabla_\theta f(\theta_\star,Z)\|^{1+\alpha}]<\infty$ for some
known $\alpha\in (0,1]$ then this approach, which can be seen as a
retrospective approximation algorithm with a fixed computational budget, allows
to learn $\theta_\star$ with an error of size
$\mathcal{O}_{\mathbb{P}}(B^{-\frac{\alpha}{1+\alpha}(1-\delta^{J})})$, where
$\delta\in(2\alpha/(1+3\alpha),1)$ is a tuning parameter. Beyond knowing
$\alpha$, achieving the aforementioned convergence rates do not require to tune
the algorithms parameters according to the specific functions $F$ and $f$ at
hand, and we exhibit a simple noisy optimization problem for which stochastic
gradient is not guaranteed to converge while the algorithms discussed in this
work are.


  Quantum Chromodynamics has been studied extensively at Fermilab's Tevatron
collider. Between 1992 and 1996 the D{\O}and CDF experiments, each accumulated
approximately $100 \mathrm{pb}^{-1}$ of proton-antiproton collisions at a
center-of-mass energy $\sqrt{s}=1800 \mathrm{GeV}$ and $\sim 0.5
\mathrm{pb}^{-1}$ at $\sqrt{s}=630 \mathrm{GeV}$. In this paper, we present
selected recent measurements of higher order effects in QCD: multiple jet
production and subjet and charged particle multiplicities in quark and gluon
jets.


  In binary metallic systems like the Pd--Er alloys charged with hydrogen the
observed structure evolution exhibits complex dynamics. It is characterized by
non-monotonic time variations in an Er-rich fraction respect with an Er-poor
fraction observed experimentally. The present paper proposes a qualitative
model for this non-monotonic structure relaxation. We assume that the alloy
have crystalline defects which trap (or emit) an additional amount of Er atoms.
Hydrogen atoms into the alloy disturb the phase equilibrium as well as change
the defects capacity with respect to Er atoms. Both of these factors lead to
the spatial redistribution of Er atoms and cause the interface between the
Er-rich and the Er-poor phase to move. The competition of diffusion fluxes in
system is responsible for non-monotonic time variations, for example, in the
relative volume of the enriched phase. We have found the conditions when the
interface motion can change its direction several times during the system
relaxation to a new equilibrium state. From our point of view this effect is
the essence of the hydrogen induced non-monotonic relaxation observed in such
systems. The numerical simulation confirms the basic assumptions.


  The sustainability of urban environments is an increasingly relevant problem.
Air pollution plays a key role in the degradation of the environment as well as
the health of the citizens exposed to it. In this chapter we provide a review
of the methods available to model air pollution, focusing on the application of
machine-learning methods. In fact, machine-learning methods have proved to
importantly increase the accuracy of traditional air-pollution approaches while
limiting the development cost of the models. Machine-learning tools have opened
new approaches to study air pollution, such as flow-dynamics modelling or
remote-sensing methodologies.


  Quantum spin liquids have fascinated condensed matter physicists for decades
because of their unusual properties such as spin fractionalization and
long-range entanglement. Unlike conventional symmetry breaking the topological
order underlying quantum spin liquids is hard to detect experimentally. Even
theoretical models are scarce for which the ground state is established to be a
quantum spin liquid. The Kitaev honeycomb model and its generalizations to
other tri-coordinated lattices are chief counterexamples --- they are exactly
solvable, harbor a variety of quantum spin liquid phases, and are also relevant
for certain transition metal compounds including the polymorphs of
(Na,Li)$_2$IrO$_3$ Iridates and RuCl$_3$. In this review, we give an overview
of the rich physics of the Kitaev model, including 2D and 3D fractionalization
as well as dynamical correlations and behavior at finite temperatures. We
discuss the different materials, and argue how the Kitaev model physics can be
relevant even though most materials show magnetic ordering at low temperatures.


  Hardware injections are simulated gravitational-wave signals added to the
Laser Interferometer Gravitational-wave Observatory (LIGO). The detectors' test
masses are physically displaced by an actuator in order to simulate the effects
of a gravitational wave. The simulated signal initiates a control-system
response which mimics that of a true gravitational wave. This provides an
end-to-end test of LIGO's ability to observe gravitational waves. The
gravitational-wave analyses used to detect and characterize signals are
exercised with hardware injections. By looking for discrepancies between the
injected and recovered signals, we are able to characterize the performance of
analyses and the coupling of instrumental subsystems to the detectors' output
channels. This paper describes the hardware injection system and the recovery
of injected signals representing binary black hole mergers, a stochastic
gravitational wave background, spinning neutron stars, and sine-Gaussians.


  Since the inception of Industry 4.0 in 2012, emerging technologies have
enabled the acquisition of vast amounts of data from diverse sources such as
machine tools, robust and affordable sensor systems with advanced information
models, and other sources within Smart Manufacturing Systems (SMS). As a
result, the amount of data that is available in manufacturing settings has
exploded, allowing data-hungry tools such as Artificial Intelligence (AI) and
Machine Learning (ML) to be leveraged. Time-series analytics has been
successfully applied in a variety of industries, and that success is now being
migrated to pattern recognition applications in manufacturing to support higher
quality products, zero defect manufacturing, and improved customer
satisfaction. However, the diverse landscape of manufacturing presents a
challenge for successfully solving problems in industry using time-series
pattern recognition. The resulting research gap of understanding and applying
the subject matter of time-series pattern recognition in manufacturing is a
major limiting factor for adoption in industry. The purpose of this paper is to
provide a structured perspective of the current state of time-series pattern
recognition in manufacturing with a problem-solving focus. By using an ontology
to classify and define concepts, how they are structured, their properties, the
relationships between them, and considerations when applying them, this paper
aims to provide practical and actionable guidelines for application and
recommendations for advancing time-series analytics.


  We present the first non-trivial fully dynamic algorithm maintaining exact
single-source distances in unweighted graphs. This resolves an open problem
stated by Sankowski [COCOON 2005] and van den Brand and Nanongkai [FOCS 2019].
Previous fully dynamic single-source distances data structures were all
approximate, but so far, non-trivial dynamic algorithms for the exact setting
could only be ruled out for polynomially weighted graphs (Abboud and
Vassilevska Williams, [FOCS 2014]). The exact unweighted case remained the main
case for which neither a subquadratic dynamic algorithm nor a quadratic lower
bound was known.
  Our dynamic algorithm works on directed graphs, is deterministic, and can
report a single-source shortest paths tree in subquadratic time as well. Thus
we also obtain the first deterministic fully dynamic data structure for
reachability (transitive closure) with subquadratic update and query time. This
answers an open problem of van den Brand, Nanongkai, and Saranurak [FOCS 2019].
  Finally, using the same framework we obtain the first fully dynamic data
structure maintaining all-pairs $(1+\epsilon)$-approximate distances within
non-trivial sub-$n^\omega$ worst-case update time while supporting optimal-time
approximate shortest path reporting at the same time. This data structure is
also deterministic and therefore implies the first known non-trivial
deterministic worst-case bound for recomputing the transitive closure of a
digraph.


  We describe singular diffusion in bounded subsets $\Omega$ of $\mathbb{R}^n$
by form methods and characterize the associated operator. We also prove
positivity and contractivity of the corresponding semigroup. This results in a
description of a stochastic process moving according to classical diffusion in
one part of $\Omega$, where jumps are allowed through the rest of $\Omega$.


  String theory in 4 dimensions has the unique feature that a topological term,
the oriented self-intersection number, can be added to the usual action. It has
been suggested that the corresponding theory of random surfaces wold be free
from the problem encountered in the scaling of the string tension.
Unfortunately, in the usual dynamical triangulation it is not clear how to
write such a term. We show that for random surfaces on a hypercubic lattice
however, the analogue of the oriented self-intersection number $I[\s]$ can be
defined and computed in a straightforward way. Furthermore, $I[\s]$ has a
genuine topological meaning in the sense that it is invariant under the
discrete analogue of continuous deformations. The resulting random surface
model is no longer free and may lead to a non trivial continuum limit.


  As the number of electronic components in the car increases, the requirement
for the higher data transmission scheme among them is on the sharp rise.
Controller area network (CAN) has been widely adopted to support the in-car
communications needs but the data rate is far below what other schemes such as
Ethernet and optical fibers can offer. A new scheme for enhancing the speed of
CAN network has been proposed, where carrier modulated signal is introduced on
top of the existing CAN signal whereby the data rate can be enhanced over
100Mbps. The proposed scheme is compatible with the existing CAN network and
accordingly enables seamless upgrade of the existing network to support high
speed demand using CAN protocol.


  Differential privacy is a mathematical concept that provides an
information-theoretic security guarantee. While differential privacy has
emerged as a de facto standard for guaranteeing privacy in data sharing, the
known mechanisms to achieve it come with some serious limitations. Utility
guarantees are usually provided only for a fixed, a priori specified set of
queries. Moreover, there are no utility guarantees for more complex - but very
common - machine learning tasks such as clustering or classification. In this
paper we overcome some of these limitations. Working with metric privacy, a
powerful generalization of differential privacy, we develop a polynomial-time
algorithm that creates a private measure from a data set. This private measure
allows us to efficiently construct private synthetic data that are accurate for
a wide range of statistical analysis tools. Moreover, we prove an
asymptotically sharp min-max result for private measures and synthetic data for
general compact metric spaces. A key ingredient in our construction is a new
superregular random walk, whose joint distribution of steps is as regular as
that of independent random variables, yet which deviates from the origin
logarithmicaly slowly.


  A novel spin texture formed by Cooper pair spins is found theoretically with
a phase string attached by half-quantized vortices at both ends in a unit cell
and characterized by its topologically rich vortex structure in a spin-triplet
pairing. It is stable at an intermediate field region sandwiched by two
conventional singular vortex phases below and above it. The d-vector direction
of this spin texture is tilted from the principal crystal axes, whose spin
susceptibility is neither the normal Pauli one \c{hi}N nor zero, describing
microscopically the process of the d-vector rotation phenomena observed
recently in UTe2. We compare the spin texture and singular vortex state in
relation to the quasi-particle structure with Majorana zero modes for STM, the
nuclear spin resonance spectral line width for NMR and {\mu}SR, and the vortex
form factors for SANS to facilitate the identification of the pairing symmetry
in UTe2.


  We explore how to encode more than a qubit in vanadyl porphyrin molecules
hosting a electronic spin 1/2 coupled to a nuclear spin 7/2. The spin
Hamiltonian and its parameters, as well as the spin dynamics, have been
determined via a combination of electron paramagnetic resonance, heat capacity,
magnetization and on-chip magnetic spectroscopy experiments performed on single
crystals. We find low temperature spin coherence times of micro-seconds and
spin relaxation times longer than a second. For sufficiently strong magnetic
fields (B larger than 0.1 T, corresponding to resonance frequencies of 9 to 10
GHz) these properties make vanadyl porphyrin molecules suitable qubit
realizations. The presence of multiple equispaced nuclear spin levels then
merely provides 8 alternatives to define the 0 and 1 basis states. For lower
magnetic fields (below 0.1 T), and lower frequencies (smaller than 2 GHz), we
find spectroscopic signatures of a sizeable electronuclear entanglement. This
effect generates a larger set of allowed transitions between different
electronuclear spin states and removes their degeneracies. Under these
conditions, we show that each molecule fulfills the conditions to act as a
universal 4-qubit processor or, equivalently, as a d = 16 qudit. These findings
widen the catalogue of chemically designed systems able to implement
non-trivial quantum functionalities, such as quantum simulations and,
especially, quantum error correction at the molecular level.


  A complete list of all transitive symplectic manifolds of the Poincar\'e and
Galilei group in 1+2 dimensions is given.


  Structural and electronic properties comparison between anatase, rutile and
monoclinic TiO2(B) titania polymorphs in bulk and related nanostructures
simulated by two-dimensional slabs has been performed by means of density
functional theory calculations. TiO2(B) was included due to experimental
results where it appears as a metastable phase during thermal annealing of low
content sodium layered titanate nanostructures obtained via hydrothermal
synthesis. Band gaps and surface energies are discussed confronting results
obtained by Plane Waves (PW) and Localized Basis Set methods (LCNAO), observing
an excellent agreement between both methodologies when considering floating
basis to calculate surfaces energies using LCAO. The computed formation energy
for the TiO2(B) (001) slab, as has been previously reported by Vittadini A. et
al., J. Phys. Chem. C 2009, 113 (44), 18973-18977, is in the order of the one
calculated for anatasa (101) which represents one of the most stable TiO2
surfaces. The adsorption of formic acid on (001) and (100) - less stable -
TiO2(B) dry facets was investigated as the first step for further evaluation of
TiO2(B) nanostructures for dye sensitized solar cells (DSSC) applications. The
results show that the bidentate dissociated configuration is the most stable
for both surfaces being the computed adsorption energy 0.76 eV and 1.35 eV for
(001) and (100) respectively. These results differ from what was previously
found for dry anatasa (101) where monodentate coordination prevails. The
evaluated reactivity of TiO2(B) slabs is comparable with anatase and indicates
that it could be a good adsorbent for common dyes used for Dye Sensitized Solar
Cells purposes.


  The macroscopic dynamics of a droplet impacting a solid is crucially
determined by the intricate air dynamics occurring at the vanishingly small
length scale between droplet and substrate prior to direct contact. Here we
investigate the inverse problem, namely the role of air for the impact of a
horizontal flat disk onto a liquid surface, and find an equally significant
effect. Using an in-house experimental technique, we measure the free surface
deflections just before impact, with a precision of a few micrometers. Whereas
stagnation pressure pushes down the surface in the center, we observe a lift-up
under the edge of the disk, which sets in at a later stage, and which we show
to be consistent with a Kelvin-Helmholtz instability of the water-air
interface.


  Flight delays hurt airlines, airports, and passengers. Their prediction is
crucial during the decision-making process for all players of commercial
aviation. Moreover, the development of accurate prediction models for flight
delays became cumbersome due to the complexity of air transportation system,
the number of methods for prediction, and the deluge of flight data. In this
context, this paper presents a thorough literature review of approaches used to
build flight delay prediction models from the Data Science perspective. We
propose a taxonomy and summarize the initiatives used to address the flight
delay prediction problem, according to scope, data, and computational methods,
giving particular attention to an increased usage of machine learning methods.
Besides, we also present a timeline of significant works that depicts
relationships between flight delay prediction problems and research trends to
address them.
  The published version of this paper is made available at
\url{https://doi.org/10.1080/01441647.2020.1861123}.
  Please cite as:
  L. Carvalho, A. Sternberg, L. Maia Gon\c{c}alves, A. Beatriz Cruz, J.A.
Soares, D. Brand\~ao, D. Carvalho, e E. Ogasawara, 2020, On the relevance of
data science for flight delay research: a systematic review, Transport Reviews


  We consider the problem of change-point estimation of the instantaneous phase
of an observed time series. Such change points, or phase shifts, can be markers
of information transfer in complex systems; their analysis occurring in
geology, biology and physics, but most notably in neuroscience. We develop two
non-parametric approaches to this problem: the cumulative summation (CUSUM) and
phase derivative (PD) estimators. In general the CUSUM estimator has higher
power for identifying single shift events, while the PD estimator has better
temporal resolution for multiple ones. A system of weakly coupled Rossler
attractors provides an application in which there are high levels of systematic
and time-dependent noise. Shift identification is also performed on beta-band
activity from electroencephalogram recordings of a visual attention task, an
unsupervised application which requires high temporal resolution.


  A Kramers pair of helical edge states in quantum spin Hall effect (QSHE) is
robust against normal dephasing but not robust to spin dephasing. In our work,
we provide an effective spin dephasing mechanism in the puddles of
two-dimensional (2D) QSHE, which is simulated as quantum dots modeled by 2D
massive Dirac Hamiltonian. We demonstrate that the spin dephasing effect can
originate from the combination of the Rashba spin-orbit coupling and
electron-phonon interaction, which gives rise to inelastic backscattering in
edge states within the topological insulator quantum dots, although the
time-reversal symmetry is preserved throughout. Finally, we discuss the
tunneling between extended helical edge states and local edge states in the QSH
quantum dots, which leads to backscattering in the extended edge states. These
results can explain the more robust edge transport in InAs/GaSb QSH systems.


  We review the mathematically rigorous formulation of the quantum theory of a
linear field propagating in a globally hyperbolic spacetime. This formulation
is accomplished via the algebraic approach, which, in essence, simultaneously
admits all states in all possible (unitarily inequivalent) Hilbert space
constructions. The physically nonsingular states are restricted by the
requirement that their two-point function satisfy the Hadamard condition, which
insures that the ultra-violet behavior of the state be similar to that of the
vacuum state in Minkowski spacetime, and that the expected stress-energy tensor
in the state be finite. We briefly review the Unruh and Hawking effects from
the perspective of the theoretical framework adopted here. A brief discussion
also is given of several open issues and questions in quantum field theory in
curved spacetime regarding the treatment of ``back-reaction", the validity of
some version of the ``averaged null energy condition'', and the formulation and
properties of quantum field theory in causality violating spacetimes.


  There exist combable groups in which the conjugacy problem is unsolvable. The
isomorphism problem is unsolvable for certain recursive sequences of finite
presentations of combable groups.


  This work provides the foundation for the finite element analysis of an
elliptic problem which is the rotational analogue of the $p$-Laplacian and
which appears as a model of the magnetic induction in a high-temperature
superconductor operating near it's critical current. Whereas the function
theory for the $p$-Laplacian requires standard results in $L^p$ Sobolev spaces,
this problem requires an extension to $L^p$ spaces of the well-known $L^2$
theory for divergence free vector fields, as used in the finite element method
applied to incompressible flows and electromagnetic radiation. Among other
things, the analysis requires extensions to $L^p$ of the well-known
$H(\operatorname{div}; \Omega)$ and $H(\operatorname{curl};\Omega)$, extensions
of traces and Green's theorem, a Helmholtz decomposition and finally a
Friedrich's inequality. In this paper, we provide a proof of the existence and
uniqueness of weak solutions of our so-called $p$-CurlCurl problem. In a
companion paper, the analysis is extended to treat continuous and finite
element solutions of the nonlinear parabolic problem whose spatial term is the
$p$-CurlCurl operator. Many of the results presented here are either already
known, known in slightly different forms or are proven with the help of
techniques that are already well-known. The main novelty of this paper appears
to be the structured form of this $L^p$ theory and our form of the Helmholtz
decomposition and of the Friedrich's inequality. In this respect, we note that
some of these results can be found in the works of M. Dauge, M. Mitrea and I.
Mitrea.


  In this paper the potential for the discovery of new physics in the exclusive
decay $\bar{B}_d \to \bar{K}^{*0} \mu^+ \mu^-$ is discussed. Attention is paid
to constructing observables which are protected from uncertainties in QCD form
factors and at the same time observe the symmetries of the angular
distribution. We discuss the sensitivity to new physics in the observables
including the effect of CP-violating phases.


  We propose a simple continuum model to interpret the shearing motion of
dense, dry and cohesion-less granular media. Compressibility, dilatancy and
Coulomb-like friction are the three basic ingredients. The granular stress is
split into a rate-dependent part representing the rebound-less impacts between
grains and a rate-independent part associated with long-lived contacts. Because
we consider stationary flows only, the grain compaction and the grain velocity
are the two main variables. The predicted velocity and compaction profiles are
in apparent agreement with the experimental or numerical results concerning
free-surface shear flows as well as confined shear flows


  Proceedings of the 5th International Workshop on Software Engineering Methods
in Spreadsheets (SEMS'18), held on October 1st, 2018, in Lisbon, Portugal, and
co-located with the 2018 IEEE Symposium on Visual Languages and Human-Centric
Computing (VL/HCC).


  The closedness condition for real geodesics on n-dimensional ellipsoids is in
general transcendental in the parameters (semiaxes of the ellipsoid and
constants of motion). We show that it is algebraic in the parameters if and
only if both the real and the imaginary geodesics are closed and we
characterize such double--periodicity condition via real hyperelliptic
tangential coverings. We prove the density of algebraically closed geodesics on
n-dimensional ellipsoids with respect to the natural topology in the
(2n)-dimensional real parameter space. In particular, the approximating
sequence of algebraic closed geodesics on the approximated ellipsoids may be
chosen so to share the same values of the length and of the real period vector
as the limiting closed geodesic on the limiting ellipsoid.
  Finally, for real doubly-periodic geodesics on triaxial ellipsoids, we show
how to evaluate algebraically the period mapping and we present some explicit
examples of families of algebraically closed geodesics.


  In this paper, we first discuss the main types of noise in a typical
pump-probe system, and then focus specifically on terahertz time domain
spectroscopy (THz-TDS) setups. We then introduce four statistical models for
the noisy pulses obtained in such systems, and detail rigorous mathematical
algorithms to de-noise such traces, find the proper averages and characterise
various types of experimental noise. Finally, we perform a comparative analysis
of the performance, advantages and limitations of the algorithms by testing
them on the experimental data collected using a particular THz-TDS system
available in our laboratories. We conclude that using advanced statistical
models for trace averaging results in the fitting errors that are significantly
smaller than those obtained when only a simple statistical average is used.


  We report the automatic detection of 11 transients (7 possible supernovae and
4 active galactic nuclei candidates) within the Zwicky Transient Facility
fourth data release (ZTF DR4), all of them observed in 2018 and absent from
public catalogs. Among these, three were not part of the ZTF alert stream. Our
transient mining strategy employs 41 physically motivated features extracted
from both real light curves and four simulated light curve models (SN Ia, SN
II, TDE, SLSN-I). These features are input to a k-D tree algorithm, from which
we calculate the 15 nearest neighbors. After pre-processing and selection cuts,
our dataset contained approximately a million objects among which we visually
inspected the 105 closest neighbors from seven of our brightest, most
well-sampled simulations, comprising 89 unique ZTF DR4 sources. Our result
illustrates the potential of coherently incorporating domain knowledge and
automatic learning algorithms, which is one of the guiding principles directing
the SNAD team. It also demonstrates that the ZTF DR is a suitable testing
ground for data mining algorithms aiming to prepare for the next generation of
astronomical data.


  We present the first experimental observations of scale-free behavior in the
bubble footprint distribution during the boiling crisis of water, in pool and
flow boiling conditions. We formulate a continuum percolation model that
elucidates how the scale-free behavior emerges from the near-wall stochastic
interaction of bubbles and provides a criterion to predict the boiling crisis.
It also offers useful insights on how to engineer surfaces that enhance the
critical heat flux limit.


  In the present paper we deal with the issue of finding the self-adjoint
extensions of a $p^4$-corrected Hamiltonian. The importance of this subject
lies on the application of the concepts of quantum mechanics to the
minimal-length scale scenario which describes an effective theory of quantum
gravity. We work in a finite one dimensional interval and we give the explicit
$U(4)$ parametrization that leads to the self-adjoint extensions. Once the
parametrization is known, we can choose appropriate $U(4)$ matrices to model
physical problems. As examples, we discuss the infinite square-well, periodic
conditions, anti-periodic conditions and periodic conditions up to a prescribed
phase. We hope that the parametrization we found will contribute to model other
interesting physical situations in further works.


  We provide direct elementary proofs of several explicit expressions for
Bernoulli numbers and Bernoulli polynomials. As a byproduct of our method of
proof, we provide natural definitions for generalized Bernoulli numbers and
polynomials of complex order.


  Using precise galaxy stellar mass function measurements in the COSMOS field
we determine the stellar-to-halo mass relationship (SHMR) using a parametric
abundance matching technique. The unique combination of size and highly
complete stellar mass estimates in COSMOS allows us to determine the SHMR over
a wide range of halo masses from $z\sim0.2$ to $z\sim5$. At $z\sim 0.2$ the
ratio of stellar-to-halo mass content peaks at a characteristic halo mass
$M_{\rm h} =10^{12} M_\odot$ and declines at higher and lower halo masses. This
characteristic halo mass increases with redshift reaching $M_{\rm h} =10^{12.5}
M_\odot$ at $z\sim2.3$ and remaining flat up to $z=4$. We considered the
principal sources of uncertainty in our stellar mass measurements and also the
variation in halo mass estimates in the literature. We show that our results
are robust to these sources of uncertainty and explore likely explanation for
differences between our results and those published in the literature. The
steady increase in characteristic halo mass with redshift points to a scenario
where cold gas inflows become progressively more important in driving
star-formation at high redshifts but larger samples of massive galaxies are
needed to rigorously test this hypothesis.


  We have investigated the spin fluctuations in the langasite compound
Ba3NbFe3Si2O14 in both the ordered state and as a function of temperature. The
low temperature magnetic structure is defined by a spiral phase characterized
by magnetic Bragg peaks at q=(0,0,tau ~ 1/7) onset at TN=27 K as previously
reported by Marty et al. The nature of the fluctuations and temperature
dependence of the order parameter is consistent with a classical second order
phase transition for a two dimensional triangular antiferromagnet. We will show
that the physical properties and energy scales including the ordering
wavevector, Curie-Weiss temperature, and the spin-waves can be explained
through the use of only symmetric exchange constants without the need for the
Dzyaloshinskii-Moriya interaction. This is accomplished through a set of
``helical" exchange pathways along the c direction imposed by the chiral
crystal structure and naturally explains the magnetic diffuse scattering which
displays a strong vector chirality up to high temperatures well above the
ordering temperature. This illustrates a strong coupling between magnetic and
crystalline chirality in this compound.


  In this paper, we prove that any bubble-sheet oval for the mean curvature
flow in $\mathbb{R}^4$, up to scaling and rigid motion, either is the
$\textrm{O}(2)\times \textrm{O}(2)$-symmetric ancient oval constructed by
Hershkovits and the fourth author, or belongs to the one-parameter family of
$\mathbb{Z}_2^2\times \textrm{O}(2)$-symmetric ancient ovals constructed by the
third and fourth author. In particular, this seems to be the first instance of
a classification result for geometric flows that are neither cohomogeneity-one
nor selfsimilar.


  We discuss recent theoretical results on diffractive deeply inelastic
scattering, focusing on the partonic picture of diffraction in configuration
space and the predictions for the beta behavior and the scaling violation.


  Ptychography employs a set of diffraction patterns that capture redundant
information about an illuminated specimen as a localized beam is moved over the
specimen. The robustness of this method comes from the redundancy of the
dataset that in turn depends on the amount of oversampling and the form of the
illumination. Although the role of oversampling in ptychography is fairly well
understood, the same cannot be said of the illumination structure. This paper
provides a vector space model of ptychography that accounts for the
illumination structure in a way that highlights the role of the Wigner
distribution function in iterative ptychography.


  A novel data-driven modal decomposition of fluid flow is proposed comprising
key features of POD and DMD. The first mode is the normalized real or imaginary
part of the DMD mode which minimizes the time-averaged residual. The N-th mode
is defined recursively in an analogous manner based on the residual of an
expansion using the first N-1 modes. The resulting recursive DMD (RDMD) modes
are orthogonal by construction, retain pure frequency content and aim at low
residual. RDMD is applied to transient cylinder wake data and is benchmarked
against POD and optimized DMD (Chen et al. 2012) for the same snapshot
sequence. Unlike POD modes, RDMD structures are shown to have pure frequency
content while retaining a residual of comparable order as POD. In contrast to
DMD with exponentially growing or decaying oscillatory amplitudes, RDMD clearly
identifies initial, maximum and final fluctuation levels. Intriguingly, RDMD
outperforms both POD and DMD in the limit cycle resolution from the same
snaphots. RDMD is proposed as an attractive alternative to POD and DMD for
empirical Galerkin models, with nonlinear transient dynamics as a niche
application.


  We have carried out a multi-species study of a region which has had previous
measurements of strong magnetic fields through the CN Zeeman effect in order to
to explore the relationship between CN and N$_2$H$^+$, both of which have
evidence that they remain in the gas phase at densities of 10$^5$ - 10$^6$
cm$^{-3}$. To achieve this we map the 1 arcmin$^2$ region around the UCHII
region of W3(OH) using the Combined Array for Millimeter-wave Astronomy
(CARMA). Approximately 105 hours of data were collected in multiple array
configurations to produce maps with an effective resolution of $\sim$
2.5\arcsec at high signal-to-noise in CN, C$^{18}$O, HCN, HCO$^+$, N$_2$H$^+$,
and two continuum bands (91.2 GHz and 112 GHz). These data allow us to compare
tracer molecules associated with both low and high density regions to infer gas
properties. We determine that CARMA resolves out approximately 35% of the CN
emission around W3(OH) when compared with spectra obtained from the IRAM-30
meter telescope. The presence of strong absorption lines towards the continuum
source in three of the molecular transitions infers the presence of a cold,
dark, optically thick region in front of the continuum source. In addition, the
presence of high-velocity emission lines near the continuum source shows the
presence of hot clumpy emission behind the continuum source. These data
determine that future high-resolution interferometric CN Zeeman measurements
which cannot currently be performed (due to technical limitations of current
telescopes) are feasible. We confirm that CN is indeed a good tracer for high
density regions; with certain objects such as W3(OH) it appears to be a more
accurate tracer than N$_2$H$^+$.


  Named Entity Recognition (NER) is the task of identifying spans that
represent entities in sentences. Whether the entity spans are nested or
discontinuous, the NER task can be categorized into the flat NER, nested NER,
and discontinuous NER subtasks. These subtasks have been mainly solved by the
token-level sequence labelling or span-level classification. However, these
solutions can hardly tackle the three kinds of NER subtasks concurrently. To
that end, we propose to formulate the NER subtasks as an entity span sequence
generation task, which can be solved by a unified sequence-to-sequence
(Seq2Seq) framework. Based on our unified framework, we can leverage the
pre-trained Seq2Seq model to solve all three kinds of NER subtasks without the
special design of the tagging schema or ways to enumerate spans. We exploit
three types of entity representations to linearize entities into a sequence.
Our proposed framework is easy-to-implement and achieves state-of-the-art
(SoTA) or near SoTA performance on eight English NER datasets, including two
flat NER datasets, three nested NER datasets, and three discontinuous NER
datasets.


  In the standard model of core-collapse supernova (CCSN), all neutrinos are
assumed to be in pure flavor eigenstates in CCSN cores, but the assumption
becomes invalid if neutrino distributions are unstable to flavor conversions.
In this paper, we present a study of the occurrences of two representative
neutrino-flavor instabilities, fast- and collisional flavor instabilities, in
the cooling phase of proto-neutron star (PNS) from 1- to 50 seconds. We follow
the long-term evolution of a PNS under spherically symmetric and quasi-static
approximations, in which the matter profile is determined by solving the
Tolman-Oppenheimer-Volkoff equation with neutrino feedback under the treatment
of multi-group flux limited diffusion. For the stability analysis of neutrino
flavor conversions, we recompute neutrino distributions using Monte Carlo
transport in order to obtain the full angular distribution needed to compute
the dispersion relations. We find no signs of flavor conversions in our models;
the physical reason is thoroughly investigated. We also argue that the negative
conclusion in flavor conversions could be changed qualitatively if
multi-dimensional effects are included, as similar to cases in the earlier
phase of CCSN.


  Plane EM waves transmitted through nano-corrugated metallic thin films
produce evanescent waves which include the information on the nano-structures.
The production of the evanescent waves at the metallic surface are analyzed. A
microsphere located above the metallic surface collects the evanescent waves
which are converted into propagating waves. The equations for the refraction at
the boundary of the microsphere and the use of Snell's law for evanescent waves
are developed. The magnification of the nano-structure images is explained by a
geometric optics description, but the high resolution is related to the
evanescent waves properties.


  Understanding the dynamics of road networks has theoretical implications for
urban science and practical applications for sustainable long-term planning.
Various generative models to explain road network growth have been introduced
in the literature. We propose in this paper a systematic benchmark of such
models integrating different paradigms (spatial interactions, cost-benefit
compromises, biological network growth), focusing on the feasible space of
generated network measures. We find a quantitatively high complementarity
between the different models. This confirms the necessity of a plurality of
urban models, in line with integrative approaches to urban systems.


  In this paper, we give a list of strongly tempered BZSV quadruples. This
gives a conceptual explanation of many existing Rankin-Selberg integrals and
period integrals. It also proposes many new interesting period integrals to
study.


  The National Robotics Initiative (NRI) was launched 2011 and is about to
celebrate its 5 year anniversary. In parallel with the NRI, the robotics
community, with support from the Computing Community Consortium, engaged in a
series of road mapping exercises. The first version of the roadmap appeared in
September 2009; a second updated version appeared in 2013. While not directly
aligned with the NRI, these road-mapping documents have provided both a useful
charting of the robotics research space, as well as a metric by which to
measure progress. This report sets forth a perspective of progress in robotics
over the past five years, and provides a set of recommendations for the future.
The NRI has in its formulation a strong emphasis on co-robot, i.e., robots that
work directly with people. An obvious question is if this should continue to be
the focus going forward? To try to assess what are the main trends, what has
happened the last 5 years and what may be promising directions for the future a
small CCC sponsored study was launched to have two workshops, one in Washington
DC (March 5th, 2016) and another in San Francisco, CA (March 11th, 2016). In
this report we brief summarize some of the main discussions and observations
from those workshops. We will present a variety of background information in
Section 2, and outline various issues related to progress over the last 5 years
in Section 3. In Section 4 we will outline a number of opportunities for moving
forward. Finally, we will summarize the main points in Section 5.


  The talent scheduling problem is a simplified version of the real-world film
shooting problem, which aims to determine a shooting sequence so as to minimize
the total cost of the actors involved. In this article, we first formulate the
problem as an integer linear programming model. Next, we devise a
branch-and-bound algorithm to solve the problem. The branch-and-bound algorithm
is enhanced by several accelerating techniques, including preprocessing,
dominance rules and caching search states. Extensive experiments over two sets
of benchmark instances suggest that our algorithm is superior to the current
best exact algorithm. Finally, the impacts of different parameter settings are
disclosed by some additional experiments.


  This paper describes how to apply self-attention with relative positional
encodings to the task of relation extraction. We propose to use the
self-attention encoder layer together with an additional position-aware
attention layer that takes into account positions of the query and the object
in the sentence. The self-attention encoder also uses a custom implementation
of relative positional encodings which allow each word in the sentence to take
into account its left and right context. The evaluation of the model is done on
the TACRED dataset. The proposed model relies only on attention (no recurrent
or convolutional layers are used), while improving performance w.r.t. the
previous state of the art.


  Constraint Satisfaction Problems (CSPs) play a central role in many
applications in Artificial Intelligence and Operations Research. In general,
solving CSPs is NP-complete. The structure of CSPs is best described by
hypergraphs. Therefore, various forms of hypergraph decompositions have been
proposed in the literature to identify tractable fragments of CSPs. However,
also the computation of a concrete hypergraph decomposition is a challenging
task in itself. In this paper, we report on recent progress in the study of
hypergraph decompositions and we outline several directions for future
research.


  Leading three-baryon forces are derived within SU(3) chiral effective field
theory. Three classes of irreducible diagrams contribute: three-baryon contact
terms, one-meson exchange and two-meson exchange diagrams. We provide the
minimal non-relativistic terms of the chiral Lagrangian, that contribute to
these diagrams. SU(3) relations are given for the strangeness S=0 and -1
sectors. In the strangeness-zero sector we recover the well-known three-nucleon
forces from chiral effective field theory. Explicit expressions for the
lambda-nucleon-nucleon chiral potential in isospin space are presented.


  Galaxy source counts in the infrared provide strong constraints on the
evolution of the bolometric energy output from distant galaxy populations. We
present the results from deep 24 micron imaging from Spitzer surveys, which
include approximately 50,000 sources to an 80% completeness of 60 uJy. The 24
micron counts rapidly rise at near-Euclidean rates down to 5 mJy, increase with
a super-Euclidean rate between 0.4 - 4 mJy, and converge below 0.3 mJy. The 24
micron counts exceed expectations from non-evolving models by a factor >10 at
0.1 mJy. The peak in the differential number counts corresponds to a population
of faint sources that is not expected from predictions based on 15 micron
counts from ISO. We argue that this implies the existence of a previously
undetected population of infrared-luminous galaxies at z ~ 1-3. Integrating the
counts to 60 uJy, we derive a lower limit on the 24 micron background intensity
of 1.9 +/- 0.6 nW m-2 sr-1 of which the majority (~ 60%) stems from sources
fainter than 0.4 mJy. Extrapolating to fainter flux densities, sources below 60
uJy contribute 0.8 {+0.9/-0.4} nW m-2 sr-1 to the background, which provides an
estimate of the total 24 micron background of 2.7 {+1.1/-0.7} nW m-2 sr-1.


  We report the results of a full-Stokes survey of all four 18 cm OH lines in
77 OH megamasers (OHMs) using the Arecibo Observatory. This is the first survey
of OHMs that included observations of the OH satellite lines; only 4 of the 77
OHMs have existing satellite line observations in the literature. In 5 sources,
satellite line emission is detected, with 3 of the 5 sources re-detections of
previously published sources. The 2 sources with new detections of satellite
line emission are IRAS F10173+0829, which was detected at 1720 MHz, and IRAS
F15107+0724, for which both the 1612 MHz and 1720 MHz lines were detected. In
IRAS F15107+0724, the satellite lines are partially conjugate, as 1720 MHz
absorption and 1612 MHz emission have the same structure at some velocities
within the source, along with additional broader 1612 MHz emission. This is the
first observed example of conjugate satellite lines in an OHM. In the remaining
sources, no satellite line emission is observed. The detections and upper
limits are generally consistent with models of OHM emission in which all of the
18 cm OH lines have the same excitation temperature. There is no evidence for a
significant population of strong satellite line emitters among OHMs.


  In this paper, we study an impact of Leibniz algebras on the algebraic
structure of $\mathbb{N}$-graded vertex algebras. We provide easy ways to
characterize indecomposable non-simple $\mathbb{N}$-graded vertex algebras
$\oplus_{n=0}^{\infty}V_{(n)}$ such that $\dim V_{(0)}\geq 2$. Also, we examine
the algebraic structure of $\mathbb{N}$-graded vertex algebras
$V=\oplus_{n=0}^{\infty}V_{(n)}$ such that $\dim~V_{(0)}\geq 2$ and $V_{(1)}$
is a (semi)simple Leibniz algebra that has $sl_2$ as its Levi factor. We show
that under suitable conditions this type of vertex algebra is indecomposable
but not simple. Along the way we classify vertex algebroids associated with
(semi)simple Leibniz algebras that have $sl_2$ as their Levi factor.


  The nature of vertical heating of disk stars in the inner as well as the
outer region of disk galaxies is studied. The galactic bar (which is the
strongest non-axisymmetric pattern in the disk) is shown to be a potential
source of vertical heating of the disk stars in the inner region. Using a
nearly self-consistent high-resolution N-body simulation of disk galaxies, the
growth rate of the bar potential is found to be positively correlated with the
vertical heating exponent in the inner region of galaxies. We also characterize
the vertical heating in the outer region where the disk dynamics is often
dominated by the presence of transient spiral waves and mild bending waves. Our
simulation results suggest that the non-axisymmetric structures are capable of
producing the anisotropic heating of the disk stars.


  This talk presents details of the calculation of two-loop corrections to
heavy baryon currents containing one heavy quark in leading order of the
$1/m_Q$-expansion, i.e. in the limit of Heavy Quark Symmetry (HQS). The
calculations lead to the determination of the anomalous dimension of these
currents. I also discuss problems with different $\gamma_5$-schemes and their
solution by the finite renormalization procedure.


  We perform linear stability analyses (LSA) and direct numerical simulations
(DNS) to investigate the influence of the dynamic viscosity on viscous
fingering (VF) instability in miscible slices. Selecting the characteristic
scales appropriately the importance of the magnitude of the dynamic viscosity
of individual fluids on VF in miscible slice has been shown in the context of
the transient interfacial tension. Further, we have confirmed this result for
immiscible fluids and manifest the similarities between VF in immiscible and
miscible slices with transient interfacial tension. In a more general setting,
the findings of this letter will be very useful for multiphase viscous flow, in
which the momentum balance equation contains an additional stress term free
from the dynamic viscosity.


  Observations of stars and pulsars orbiting the black hole in the center of
the Milky Way offer the potential of measuring not only the mass of the black
hole but also its spin and quadrupole moment, thereby providing observational
verification of the no-hair theorem. The relativistic effects that will allow
us to measure these higher moments of the gravitational field, however, are
very small and may be masked by drag forces that stars and pulsars experience
orbiting within the hot, tenuous plasma that surrounds the black hole. The
properties of this plasma at large distances from the central object have been
measured using observations of the extended X-ray emission that surrounds the
point source. At distances comparable to the black-hole event horizon, the
properties of the accretion flow have been constrained using observations of
its long-wavelength emission and polarization, as well as of the size of the
emitting region at 1.3 mm. I use models of the plasma density and temperature
at various distances from the black hole to investigate the effect of
hydrodynamic drag forces on future measurements of the higher moments of its
gravitational field. I find that hydrodynamic drag does not preclude
measurements of the black hole spin and quadrupole moment using high-resolution
observations of stars and pulsars that orbit within a few thousand
gravitational radii from its horizon.


  [Alecu et al.: Graph functionality, JCTB2021] define functionality, a graph
parameter that generalizes graph degeneracy. They research the relation of
functionality to many other graph parameters (tree-width, clique-width,
VC-dimension, etc.). Extending their research, we prove logarithmic lower bound
for functionality of random graph $G(n,p)$ for large range of $p$. Previously
known graphs have functionality logarithmic in number of vertices. We show that
for every graph $G$ on $n$ vertices we have $\mathrm{fun}(G) \le O(\sqrt{ n
\log n})$ and we give a nearly matching $\Omega(\sqrt{n})$-lower bound provided
by projective planes.
  Further, we study a related graph parameter \emph{symmetric difference}, the
minimum of $|N(u) \Delta N(v)|$ over all pairs of vertices of the ``worst
possible'' induced subgraph. It was observed by Alecu et al. that
$\mathrm{fun}(G) \le \mathrm{sd}(G)+1$ for every graph $G$. We compare
$\mathrm{fun}$ and $\mathrm{sd}$ for the class $\mathrm{INT}$ of interval
graphs and $\mathrm{CA}$ of circular-arc graphs. We let $\mathrm{INT}_n$ denote
the $n$-vertex interval graphs, similarly for $\mathrm{CA}_n$. Alecu et al.
ask, whether $\mathrm{fun}(\mathrm{INT})$ is bounded. Dallard et al. answer
this positively in a recent preprint. On the other hand, we show that
$\Omega(\sqrt[4]{n}) \leq \mathrm{sd}(\mathrm{INT}_n) \leq O(\sqrt[3]{n})$. For
the related class $\mathrm{CA}$ we show that $\mathrm{sd}(\mathrm{CA}_n) =
\Theta(\sqrt{n})$. We propose a follow-up question: is
$\mathrm{fun}(\mathrm{CA})$ bounded?


  This paper presents a novel approach to procedural generation of urban maps
for First Person Shooter (FPS) games. A multi-agent evolutionary system is
employed to place streets, buildings and other items inside the Unity3D game
engine, resulting in playable video game levels. A computational agent is
trained using machine learning techniques to capture the intent of the game
designer as part of the multi-agent system, and to enable a semi-automated
aesthetic selection for the underlying genetic algorithm.


  We study the exclusive flavor chancing neutral current process
Lambda(b)-->Lambda neutrino antineutrino in the leptophobic Z-prime model,
where charged leptons do not couple to the extra Z-prime boson. The branching
ratio, as well as, the longitudinal,transversal and normal polarizations are
calculated. It has been shown that all these physical observables are very
sensitive to the existence of new physics beyond the standard model and their
experimental measurements can give valuable information about it.


  Deep Learning has been successfully applied to many application domains, yet
its advantages have been slow to emerge for time series forecasting. For
example, in the well-known Makridakis (M) Competitions, hybrids of traditional
statistical or machine learning techniques have only recently become the top
performers. With the recent architectural advances in deep learning being
applied to time series forecasting (e.g., encoder-decoders with attention,
transformers, and graph neural networks), deep learning has begun to show
significant advantages. Still, in the area of pandemic prediction, there remain
challenges for deep learning models: the time series is not long enough for
effective training, unawareness of accumulated scientific knowledge, and
interpretability of the model. To this end, the development of foundation
models (large deep learning models with extensive pre-training) allows models
to understand patterns and acquire knowledge that can be applied to new related
problems before extensive training data becomes available. Furthermore, there
is a vast amount of knowledge available that deep learning models can tap into,
including Knowledge Graphs and Large Language Models fine-tuned with scientific
domain knowledge. There is ongoing research examining how to utilize or inject
such knowledge into deep learning models. In this survey, several
state-of-the-art modeling techniques are reviewed, and suggestions for further
work are provided.