  We prove that on B-free subshifts, with B satisfying the Erd\"os condition,
all cellular automata are determined by monotone sliding block codes. In
particular, this implies the validity of the Garden of Eden theorem for such
systems.


  MaNGA (Mapping Nearby Galaxies at APO) is a galaxy integral-field
spectroscopic survey within the fourth generation Sloan Digital Sky Survey
(SDSS-IV). It will be mapping the composition and kinematics of gas and stars
in 10,000 nearby galaxies, using 17 differently sized fiber bundles. MaNGA's
goal is to provide new insights in galaxy formation and evolution, and to
deliver a local benchmark for current and future high-redshift studies.


  We present CommunityWatch, an open-source system that enables timely and
accurate detection of BGP routing anomalies. CommunityWatch leverages meta-data
encoded by AS operators on their advertised routes through the BGP Communities
attribute. The BGP Communities values lack standardized semantics, offering the
flexibility to attach a wide range of information, including AS relationships,
location data, and route redistribution policies. Therefore, parsing and
correlating Community values and their dynamics enables the detection and
tracking of a variety of routing anomalies. We exhibit the efficacy of
CommunityWatch through the detection of three different types of anomalies:
infrastructure outages, route leaks, and traffic blackholing.


  Priors are essential for reconstructing images from noisy and/or incomplete
measurements. The choice of the prior determines both the quality and
uncertainty of recovered images. We propose turning score-based diffusion
models into principled image priors ("score-based priors") for analyzing a
posterior of images given measurements. Previously, probabilistic priors were
limited to handcrafted regularizers and simple distributions. In this work, we
empirically validate the theoretically-proven probability function of a
score-based diffusion model. We show how to sample from resulting posteriors by
using this probability function for variational inference. Our results,
including experiments on denoising, deblurring, and interferometric imaging,
suggest that score-based priors enable principled inference with a
sophisticated, data-driven image prior.


  We show that the duals of Banach algebras of scalar-valued bounded
holomorphic functions on the open unit ball $B_E$ of a Banach space $E$ lack
weak$^*$-strongly exposed points. Consequently, we obtain that some Banach
algebras of holomorphic functions on an arbitrary Banach space have the
Daugavet property which extends the observation of P. Wojtaszczyk. Moreover, we
present a new denseness result by proving that the set of norm-attaining
vector-valued holomorphic functions on the open unit ball of a dual Banach
space is dense provided that its predual space has the metric $\pi$-property.
Besides, we obtain several equivalent statements for the Banach space of
vector-valued homogeneous polynomials to be reflexive, which improves the
result of J. Mujica, J. A. Jaramillo and L. A. Moraes. As a byproduct, we
generalize some results on polynomial reflexivity due to J. Farmer.


  The output of an association rule miner is often huge in practice. This is
why several concise lossless representations have been proposed, such as the
"essential" or "representative" rules. We revisit the algorithm given by
Kryszkiewicz (Int. Symp. Intelligent Data Analysis 2001, Springer-Verlag LNCS
2189, 350-359) for mining representative rules. We show that its output is
sometimes incomplete, due to an oversight in its mathematical validation. We
propose alternative complete generators and we extend the approach to an
existing closure-aware basis similar to, and often smaller than, the
representative rules, namely the basis B*.


  The leading coefficient of the Alexander polynomial of a knot is the most
informative element in this invariant, and the growth of orders of the first
homology of cyclic branched covering spaces is also a familiar subject.
Accordingly, there are a lot of investigations into each subject. However,
there is no study which deal with the both subjects in a same context. In this
paper, we show that the two subjects are closely related in p-adic number
theory and dynamical systems.


  During the 2017 record-breaking burning season in Canada / United States,
intense wild fires raged during the first week of September in the Pacific
northwestern region (British Columbia, Alberta, Washington, Oregon, Idaho,
Montana and northern California) burning mostly temperate coniferous forests.
The heavy loads of smoke particles emitted in the atmosphere reached the
Iberian Peninsula (IP) a few days later on 7 and 8 September. Satellite imagery
allows to identify two main smoke clouds emitted during two different periods
that were injected and transported in the atmosphere at several altitude
levels. Columnar properties on 7 and 8 September at two Aerosol Robotic Network
(AERONET) mid-altitude, background sites in northern and southern Spain are:
aerosol optical depth (AOD) at 440 nm up to 0.62, Angstrom exponent of 1.6-1.7,
large dominance of small particles (fine mode fraction > 0.88), low absorption
AOD at 440 nm (<0.008) and large single scattering albedo at 440 nm (>0.98).
Profiles from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)
show the presence of smoke particles in the stratosphere during the transport,
whereas the smoke is only observed in the troposphere at its arrival over the
IP. Portuguese and Spanish ground lidar stations from the European Aerosol
Research Lidar Network / Aerosols, Clouds, and Trace gases Research
InfraStructure Network (EARLINET/ACTRIS) and the Micro-Pulse Lidar NETwork
(MPLNET) reveal smoke plumes with different properties: particle depolarization
ratio and color ratio, respectively, of 0.05 and 2.5 in the mid troposphere
(5-9 km) and of 0.10 and 3.0 in the upper troposphere (10-13 km). In the mid
troposphere the particle depolarization ratio does not seem time-dependent
during the transport whereas the color ratio seems to increase (larger
particles sediment first).


  Recent advances in large language models have shown that autoregressive
modeling can generate complex and novel sequences that have many real-world
applications. However, these models must generate outputs autoregressively,
which becomes time-consuming when dealing with long sequences. Hierarchical
autoregressive approaches that compress data have been proposed as a solution,
but these methods still generate outputs at the original data frequency,
resulting in slow and memory-intensive models. In this paper, we propose a
model based on the Hierarchical Recurrent Encoder Decoder (HRED) architecture.
This model independently encodes input sub-sequences without global context,
processes these sequences using a lower-frequency model, and decodes outputs at
the original data frequency. By interpreting the encoder as an implicitly
defined embedding matrix and using sampled softmax estimation, we develop a
training algorithm that can train the entire model without a high-frequency
decoder, which is the most memory and compute-intensive part of hierarchical
approaches. In a final, brief phase, we train the decoder to generate data at
the original granularity. Our algorithm significantly reduces memory
requirements for training autoregressive models and it also improves the total
training wall-clock time.


  For objects belonging to a known model set and observed through a prescribed
linear process, we aim at determining methods to recover linear quantities of
these objects that are optimal from a worst-case perspective. Working in a
Hilbert setting, we show that, if the model set is the intersection of two
hyperellipsoids centered at the origin, then there is an optimal recovery
method which is linear. It is specifically given by a constrained
regularization procedure whose parameters, short of being explicit, can be
precomputed by solving a semidefinite program. This general framework can be
swiftly applied to several scenarios: the two-space problem, the problem of
recovery from $\ell_2$-inaccurate data, and the problem of recovery from a
mixture of accurate and $\ell_2$-inaccurate data. With more effort, it can also
be applied to the problem of recovery from $\ell_1$-inaccurate data. For the
latter, we reach the conclusion of existence of an optimal recovery method
which is linear, again given by constrained regularization, under a
computationally verifiable sufficient condition. Experimentally, this condition
seems to hold whenever the level of $\ell_1$-inaccuracy is small enough. We
also point out that, independently of the inaccuracy level, the minimal
worst-case error of a linear recovery method can be found by semidefinite
programming.


  Amongst the derivatives of 4-biphenylcarboxylic acid and amino acid esters,
the crystal structure of 4-biphenylcarboxy-(L)-phenylalaninate is unusual owing
to its monoclinic symmetry within a pseudo-orthorhombic lattice. The distortion
is described by disparate rotational property around the chiral centers
($\varphi_{\mathrm{chiral}}$ $\simeq$ -129 degrees and 58 degrees) of the two
molecules in the asymmetric unit. Each of these molecules comprise of planar
biphenyl moieties ($\varphi_{\mathrm{biphenyl}}$ = 0 degrees). Using
temperature dependent single crystal X-ray diffraction experiments we show that
the compound undergoes a phase transition below $T$ $\sim$ 124 K that is
characterized by a commensurate modulation wave vector, $\mathbf{q}$ =
$\delta(101)$, $\delta$ = $\frac{1}{2}$. The (3+1) dimensional modulated
structure at $T$ = 100 K suggests that the phase transition drives the biphenyl
moieties towards non coplanar conformations with significant variation of
internal torsion ($\varphi^{\mathrm{max}}_{\mathrm{biphenyl}}$ $\leq$ $20$
degrees). These intramolecular rotations lead to dimerization of the molecular
stacks that are described predominantly by intermolecular tilts and small
variations in intermolecular distances. Atypical of modulated structures and
superstructures of biphenyl and other polyphenyls, the rotations of individual
molecules are asymmetric ($\Delta$$\varphi_{\mathrm{biphenyl}}$ $\approx$ 5
degrees) while $\varphi_{\mathrm{biphenyl}}$ of one independent molecule is two
to four times larger than the other. Crystal-chemical analysis and phase
relations in superspace suggest multiple competing factors involving
intramolecular steric factors, intermolecular H--C${\cdot}{\cdot}{\cdot}$C--H
contacts and weak C--H${\cdot}{\cdot}{\cdot}$O hydrogen bonds that govern the
distinctively unequal torsional property of the molecules.


  The Photonic hybRid EleCtromagnetic SolvEr (PRECISE) is a Matlab based
library to model large and complex photonics integrated circuits. Each circuit
is modularly described in terms of waveguide segments connected through
multiport nodes. Linear, nonlinear, and dynamical phenomena are simulated by
solving the system of differential equations describing the effect to be
considered. By exploiting the steady state approximation of the electromagnetic
field within each node device, the library can handle large and complex
circuits even on desktop PC. We show that the steady state assumption is
fulfilled in a broad number of applications and we compare its accuracy with
analytical model (coupled mode theory) and experimental results. PRECISE is
highly modular and easily extensible to handle equations different from those
already implemented and is, thus, a flexible tool to model the increasingly
complex photonic circuits.


  A novel hydraulic fracture (HF) formulation is introduced which accounts for
the hydraulically induced shear stress at the crack faces. It utilizes a
general form of the elasticity operator alongside a revised fracture
propagation condition based on the critical value of the energy release rate.
It is shown that the revised formulation describes the underlying physics of HF
in a more accurate way and is in agreement with the asymptotic behaviour of the
linear elastic fracture mechanics. A number of numerical simulations by means
of the universal HF algorithm previously developed in Wrobel & Mishuris (2015)
are performed in order to: i) compare the modified HF formulation with its
classic counterpart and ii) investigate the peculiarities of the former.
Computational advantages of the revised HF model are demonstrated. Asymptotic
estimations of the main solution elements are provided for the cases of small
and large toughness. The modified formulation opens new ways to analyse the
physical phenomenon of HF and also improves the reliability and efficiency of
its numerical simulations.


  Spectroscopy of vortex bound states can provide valuable information on the
structure of the superconducting order parameter. Quasiparticle wavefunctions
are expected to leak out in the directions of gap minima or nodes, if they
exist, and scanning tunneling spectroscopy (STS) on these low-energy states
should probe the momentum dependence of the gap. Anisotropy can also arise from
band structure effects, however. We perform a quasiclassical calculation of the
density of states of a single vortex in an anisotropic superconductor, and show
that if the gap itself is not highly anisotropic, the Fermi surface anisotropy
dominates, preventing direct observation of superconducting gap features. This
serves as a cautionary message for the analysis of STS data on the vortex state
on Fe-based superconductors, in particular LiFeAs, which we treat explicitly.


  We consider the problem of topology recognition in wireless (radio) networks
modeled as undirected graphs. Topology recognition is a fundamental task in
which every node of the network has to output a map of the underlying graph
i.e., an isomorphic copy of it, and situate itself in this map. In wireless
networks, nodes communicate in synchronous rounds. In each round a node can
either transmit a message to all its neighbors, or stay silent and listen. At
the receiving end, a node $v$ hears a message from a neighbor $w$ in a given
round, if $v$ listens in this round, and if $w$ is its only neighbor that
transmits in this round. Nodes have labels which are (not necessarily
different) binary strings. The length of a labeling scheme is the largest
length of a label. We concentrate on wireless networks modeled by trees, and we
investigate two problems.
  \begin{itemize} \item What is the shortest labeling scheme that permits
topology recognition in all wireless tree networks of diameter $D$ and maximum
degree $\Delta$?
  \item What is the fastest topology recognition algorithm working for all
wireless tree networks of diameter $D$ and maximum degree $\Delta$, using such
a short labeling scheme? \end{itemize}
  We are interested in deterministic topology recognition algorithms. For the
first problem, we show that the minimum length of a labeling scheme allowing
topology recognition in all trees of maximum degree $\Delta \geq 3$ is
$\Theta(\log\log \Delta)$. For such short schemes, used by an algorithm working
for the class of trees of diameter $D\geq 4$ and maximum degree $\Delta \geq
3$, we show almost matching bounds on the time of topology recognition: an
upper bound $O(D\Delta)$, and a lower bound $\Omega(D\Delta^{\epsilon})$, for
any constant $\epsilon<1$.


  We present high angular resolution 1.3 mm continuum, methyl cyanide molecular
line, and 7 mm continuum observations made with the Submillimeter Array and the
Very Large Array, toward the most highly obscured and southern part of the
massive star forming region OMC1S located behind the Orion Nebula. We find two
flattened and rotating molecular structures with sizes of a few hundred
astronomical units suggestive of circumbinary molecular rings produced by the
presence of two stars with very compact circumstellar disks with sizes and
separations of about 50 AU, associated with the young stellar objects 139-409
and 134-411. Furthermore, these two circumbinary rotating rings are related to
two compact and bright {\it hot molecular cores}. The dynamic mass of the
binary systems obtained from our data are $\geq$ 4 M$_\odot$ for 139-409 and
$\geq$ 0.5 M$_\odot$ for 134-411. This result supports the idea that
intermediate-mass stars will form through {\it circumstellar disks} and
jets/outflows, as the low mass stars do. Furthermore, when intermediate-mass
stars are in multiple systems they seem to form a circumbinary ring similar to
those seen in young, multiple low-mass systems (e.g., GG Tau and UY Aur).


  Temporal variation of secondary cosmic rays (SCR) flux was measured during
the several full and new moon and days close to them at Department of Physics,
University of Mumbai, Mumbai (Geomagnetic latitude: 10.6 N), India. The
measurements were done by using NaI (Tl) scintillation detector with energy
threshold of 200 keV. The SCR flux shows sudden enhancement for approximately
about 2 hour in counts during couple of events out of all experimental
observations. The maximum Enhancement SCR flux is about 200% as compared to the
diurnal trend of SCR temporal variations. Weather parameters (temperature and
relative humidity) were continuously monitored during all observation. The
influences of geomagnetic field, interplanetary parameters and tidal effect on
SCR flux have been considered. Summed spectra corresponding to enhancement
duration indicates appearance of atmospheric radioactivity which shows single
gamma ray line. Detail investigation revealed the presence of radioactive Ar 41
. This measurements puts limitations on low energy SCR flux monitoring. This
paper will help many researcher who want to measure SCR flux during eclipses
and motivate to find unknown mechanism behind decrease/ increase during
solar/lunar eclipse.


  All indecomposable finite-dimensional representations of the homogeneous
Galilei group which when restricted to the rotation subgroup are decomposed to
spin 0, 1/2 and 1 representations are constructed and classified. These
representations are also obtained via contractions of the corresponding
representations of the Lorentz group. Finally the obtained representations are
used to derive a general Pauli anomalous interaction term and Darwin and
spin-orbit couplings of a Galilean particle interacting with an external
electric field.


  We present a new algorithm for discovering patterns in time series and other
sequential data. We exhibit a reliable procedure for building the minimal set
of hidden, Markovian states that is statistically capable of producing the
behavior exhibited in the data -- the underlying process's causal states.
Unlike conventional methods for fitting hidden Markov models (HMMs) to data,
our algorithm makes no assumptions about the process's causal architecture (the
number of hidden states and their transition structure), but rather infers it
from the data. It starts with assumptions of minimal structure and introduces
complexity only when the data demand it. Moreover, the causal states it infers
have important predictive optimality properties that conventional HMM states
lack. We introduce the algorithm, review the theory behind it, prove its
asymptotic reliability, use large deviation theory to estimate its rate of
convergence, and compare it to other algorithms which also construct HMMs from
data. We also illustrate its behavior on an example process, and report
selected numerical results from an implementation.


  For a prime number $p$ and a free pro-$p$ group $G$ on a totally ordered
basis $X$, we consider closed normal subgroups $G^\Phi$ of $G$ which are
generated by $p$-powers of iterated commutators associated with Lyndon words in
the alphabet $X$. We express the profinite cohomology group $H^2(G/G^\Phi)$
combinatorically, in terms of the shuffle algebra on $X$. This partly extends
existing results for the lower $p$-central and $p$-Zassenhaus filtrations of
$G$.


  Depression is a common and serious mood disorder that negatively affects the
patient's capacity of functioning normally in daily tasks. Speech is proven to
be a vigorous tool in depression diagnosis. Research in psychiatry concentrated
on performing fine-grained analysis on word-level speech components
contributing to the manifestation of depression in speech and revealed
significant variations at the phoneme-level in depressed speech. On the other
hand, research in Machine Learning-based automatic recognition of depression
from speech focused on the exploration of various acoustic features for the
detection of depression and its severity level. Few have focused on
incorporating phoneme-level speech components in automatic assessment systems.
In this paper, we propose an Artificial Intelligence (AI) based application for
clinical depression recognition and assessment from speech. We investigate the
acoustic characteristics of phoneme units, specifically vowels and consonants
for depression recognition via Deep Learning. We present and compare three
spectrogram-based Deep Neural Network architectures, trained on phoneme
consonant and vowel units and their fusion respectively. Our experiments show
that the deep learned consonant-based acoustic characteristics lead to better
recognition results than vowel-based ones. The fusion of vowel and consonant
speech characteristics through a deep network significantly outperforms the
single space networks as well as the state-of-art deep learning approaches on
the DAIC-WOZ database.


  The more precise extraction for the CKM matrix element |V_{cb}| in the heavy
quark effective field theory (HQEFT) of QCD is studied from both exclusive and
inclusive semileptonic B decays. The values of relevant nonperturbative
parameters up to order 1/m^2_Q are estimated consistently in HQEFT of QCD.
Using the most recent experimental data for B decay rates, |V_{cb}| is updated
to be |V_{cb}| = 0.0395 \pm 0.0011_{exp} \pm 0.0019_{th} from B\to D^{\ast} l
\nu decay and |V_{cb}| = 0.0434 \pm 0.0041_{exp} \pm 0.0020_{th} from B\to D l
\nu decay as well as |V_{cb}| = 0.0394 \pm 0.0010_{exp} \pm 0.0014_{th} from
inclusive B\to X_c l \nu decay.


  Laccases (benzenodiol: oxygen oxidoreductases; EC 1.10.3.2) are multicopper
oxidases of wide substrate specificity mainly found in white-rot fungi, which
are the only microorganisms able to degrade the whole wood components, but they
are also expressed in bacteria and higher plants. Laccases are used currently
in biotechnological processes because this enzyme oxidizes both phenolic and
non-phenolic lignin-related compounds as well as highly recalcitrant
environmental pollutants. In this work banana skin has been selected as a
supporting material for laccase produntion because of its high content in
carbohydrates, which due to their organic nature are easily metabolized by the
fungus. In addition, its content in ascorbic acid exerts an inhibitory effect
against bacteria. The activity of the produced laccase is tested in
decoloration studies.


  J.H.C. Whitehead's second free-group algorithm determines whether or not two
given elements of a free group lie in the same orbit of the automorphism group
of the free group. The algorithm involves certain connected graphs, and
Whitehead used three-manifold models to prove their connectedness; later,
Rapaport and Higgins & Lyndon gave group-theoretic proofs. Combined work of
Gersten, Stallings, and Hoare showed that the three-manifold models may be
viewed as graphs. We give the direct translation of Whitehead's topological
argument into the language of graph theory.


  Hair editing is an interesting and challenging problem in computer vision and
graphics. Many existing methods require well-drawn sketches or masks as
conditional inputs for editing, however these interactions are neither
straightforward nor efficient. In order to free users from the tedious
interaction process, this paper proposes a new hair editing interaction mode,
which enables manipulating hair attributes individually or jointly based on the
texts or reference images provided by users. For this purpose, we encode the
image and text conditions in a shared embedding space and propose a unified
hair editing framework by leveraging the powerful image text representation
capability of the Contrastive Language-Image Pre-Training (CLIP) model. With
the carefully designed network structures and loss functions, our framework can
perform high-quality hair editing in a disentangled manner. Extensive
experiments demonstrate the superiority of our approach in terms of
manipulation accuracy, visual realism of editing results, and irrelevant
attribute preservation. Project repo is https://github.com/wty-ustc/HairCLIP.


  We evaluate empirically a scheme for combining classifiers, known as stacked
generalization, in the context of anti-spam filtering, a novel cost-sensitive
application of text categorization. Unsolicited commercial e-mail, or "spam",
floods mailboxes, causing frustration, wasting bandwidth, and exposing minors
to unsuitable content. Using a public corpus, we show that stacking can improve
the efficiency of automatically induced anti-spam filters, and that such
filters can be used in real-life applications.


  High-precision mass measurements of exotic $^{95-97}$Ag isotopes close to the
$N = Z$ line have been conducted with the JYFLTRAP double Penning trap mass
spectrometer, with the silver ions produced using the recently commissioned
inductively-heated hot cavity catcher laser ion source at the Ion Guide Isotope
Separator On-Line facility. The atomic mass of $^{95}$Ag was directly
determined for the first time. In addition, the atomic masses of
$\beta$-decaying 2$^+$ and 8$^+$ states in $^{96}$Ag have been identified and
measured for the first time, and the precision of the $^{97}$Ag mass has been
improved. The newly measured masses, with a precision of $\approx$ 1 keV/c$^2$,
have been used to investigate the $N =$ 50 neutron shell closure confirming it
to be robust. Empirical shell-gap and pairing energies determined with the new
ground-state mass data are compared with the state-of-the-art \textit{ab
initio} calculations with various chiral effective field theory Hamiltonians.
The precise determination of the excitation energy of the $^{96m}$Ag isomer in
particular serves as a benchmark for \textit{ab initio} predictions of nuclear
properties beyond the ground state, specifically for odd-odd nuclei situated in
proximity to the proton dripline below $^{100}$Sn. In addition, density
functional theory (DFT) calculations and configuration-interaction shell-model
(CISM) calculations are compared with the experimental results. All theoretical
approaches face challenges to reproduce the trend of nuclear ground-state
properties in the silver isotopic chain across the $N =$50 neutron shell and
toward the proton drip-line.


  Orbital angular momentum (OAM) of photons, as a new fundamental degree of
freedom, has excited a great diversity of interest, because of a variety of
emerging applications. Arbitrarily tunable OAM has gained much attention, but
its creation remains still a tremendous challenge. We demonstrate the
realization of well-controlled arbitrary OAM in both theory and experiment. We
present the concept of general OAM, which extends the OAM carried by the scalar
vortex field to the OAM carried by the azimuthally varying polarized vector
field. The arbitrary OAM has the same characteristics as the well-defined
integer OAM: intrinsic OAM, uniform local OAM and intensity ring, and
propagation stability. The arbitrary OAM has unique natures: it is allowed to
be flexibly tailored and the radius of the focusing ring can have various
choices for a desired OAM, which are of great significance to the benefit of
surprising applications of the arbitrary OAM.


  The use of Internet of Things (IoT) devices in homes and the immediate
proximity of an individual communicates to create Personal IoT (PIoT) networks.
The exploratory study of PIoT is in its infancy, which will explore the
expansion of new use cases, service requirements, and the proliferation of PIoT
devices. This article provides a big picture of PIoT architecture, vision, and
future research scope.


  The potential formation of multi-mega-ampere beams of relativistic "runaway"
electrons (REs) during sudden terminations of tokamak plasmas poses a
significant challenge to the tokamak's development as a fusion energy source.
Here, we use state-of-the-art modeling of disruption magnetohydrodynamics
coupled with a self-consistent evolution of RE generation and transport to show
that a non-axisymmetric in-vessel coil will passively prevent RE beam formation
during disruptions in the SPARC tokamak, a compact, high-field, high-current
device capable of achieving a fusion gain Q > 2 in deuterium-tritium plasmas.


  We show that for local alternatives to uniformity which are determined by a
sequence of square integrable densities the moderate deviation (MD) theorem for
the corresponding Neyman-Pearson statistic does not hold in the full range for
all unbounded densities. We give a sufficient condition under which MD theorem
holds. The proof is based on Mogulskii's inequality.


  We show how Leibnitz.s indiscernibility principle and Gentzen's original work
lead to extensions of the sequent calculus to first order logic with equality
and investigate the cut elimination property. Furthermore we discuss and
improve the nonlengthening property of Lifshitz and Orevkov.


  We present a novel method for probing the alignment of the molecular axis of
an ultracold, nonpolar dimer. These results are obtained using diatomic
$^{87}\textrm{Rb}_2$ molecules in the vibrational ground state of the lowest
triplet potential $a^3\Sigma_u^+$ trapped in a 3D optical lattice. We measure
the molecular polarizabilities, which are directly linked to the alignment,
along each of the $x$, $y$, and $z$ directions of the lab coordinate system. By
preparing the molecules in various, precisely defined rotational quantum states
we can control the degree of alignment of the molecular axis with high
precision over a large range. Furthermore, we derive the dynamical
polarizabilities for a laser wavelength of $1064.5\:\textrm{nm}$ parallel and
orthogonal to the molecular axis of the dimer, $\alpha_\parallel=(8.9 \pm
0.9)\times10^3\:\textrm{a.u.}$ and $\alpha_\perp=(0.9 \pm
0.4)\times10^3\:\textrm{a.u.}$, respectively. Our findings highlight that the
depth of an optical lattice strongly depends on the rotational state of the
molecule which has to be considered in collision experiments. The present work
paves the way for reaction studies between aligned molecules in the ultracold
temperature regime.


  The supersymmetry preserving mu parameter in SUSY theories is naively
expected to be of order the Planck scale while phenomenology requires it to be
of order the weak scale. This is the famous SUSY mu problem. Its solution
involves two steps: 1. first forbid mu, perhaps via some symmetry, and then 2.
re-generate it of order the scale of soft SUSY breaking terms. However, present
LHC limits suggest the soft breaking scale m_{soft} lies in the multi-TeV
regime whilst naturalness requires mu~ m_{W,Z,h}~ 100 GeV so that a Little
Hierarchy (LH) appears with mu << m_{soft}. We review twenty previously devised
solutions to the SUSY mu problem and re-evaluate them in light of whether they
are apt to support the LH. We organize the twenty solutions according to: 1.
solutions from supergravity/superstring constructions, 2. extended MSSM
solutions, 3. solutions from an extra local U(1)' and 4. solutions involving
Peccei-Quinn (PQ) symmetry and axions. Early solutions would invoke a global
Peccei-Quinn symmetry to forbid the mu term while relating the mu solution to
solving the strong CP problem via the axion. We discuss the gravity-safety
issue pertaining to global symmetries and the movement instead toward local
gauge symmetries or R-symmetries, either continuous or discrete. At present,
discrete R-symmetries of order M (Z_M^R) which emerge as remnants of Lorentz
symmetry of compact dimensions seem favored. Even so, a wide variety of
regenerative mechanisms are possible, some of which relate to other issues such
as the strong CP problem or the generation of neutrino masses. We also discuss
the issue of experimental verification or falsifiability of various solutions
to the mu problem. Almost all solutions seem able to accommodate the LH.


  In this paper, two large-scale fading path loss models are presented based on
indoor and outdoor channel measurements at 73 GHz. The line-of-sight
millimeter-wave propagation measurement campaigns were uniquely conducted
within the indoor and outdoor environments at an airport setting, i.e., the
Boise Airport. The channel measurements were made with directional transmit and
receive antennas with a 24 dBi gain at different receive antenna heights. From
the measured data, we obtained the parameters of two path loss models, i.e.,
the close-in reference distance model (CIM) and the floating-intercept model
(FIM). Results show that the path loss exponents estimated from the CIM are
very close to that of the free-space path loss model, while the FIM provides a
better fit to the measurement data.


  It is shown, subject to the abc-conjecture, that
  \[\sum_{n\le N}\exp(2\pi i\alpha n^3)\ll_{\epsilon,\alpha}N^{5/7+\epsilon}\]
for any $\epsilon>0$ and any quadratic irrational $\alpha$.


  We propose to achieve joint detections of frequency and direction of arrival
in wideband using single sensor based on an active metasurface with
programmable transmission states of pass and stop. By integrating two PIN
diodes with the opposite directions into the proposed single-layer and
ultrathin meta-atom, the transmission performance with 10 dB difference between
the pass and stop states is realized in the bandwidth from 5.9 GHz to 8.8 GHz
using field-circuit co-simulations. Accordingly, random receiving patterns are
generated by controlling the programmable metasurface composed of the
switchable meta-atoms. Afterwards, the frequency and direction information of
sources located in the far field are detected using the modified algorithm of
estimating signal parameters via rotational invariance techniques (ESPRIT) and
the compressive sensing method, respectively. A sample of the programmable
metasurface is fabricated and the voltage control system is built up
correspondingly. To entirely verify the validity of the proposed method, we
conduct three kinds of experiments with one single source, double sources with
different frequencies, and double sources with the same frequency,
respectively. In all cases, the source information of frequency and direction
has been detected preciously in measurements in the frequency band from 6.2 GHz
to 8.8 GHz.


  For low angular momentum axially symmetric accretion flow maintained in
hydrostatic equilibrium along the vertical direction, the value of the Mach
number at the critical points deviates from unity, resulting in the
non-isomorphism of the critical and the sonic points. This introduces several
undesirable complexities while analytically dealing with the stationary
integral accretion solutions and the corresponding phase portraits. We propose
that introduction of an effective dynamical sound speed may resolve the issue
in an elegant way. We linear perturb the full spacetime-dependent general
relativistic Euler and the continuity equations governing the structure and the
dynamics of accretion disc in vertical equilibrium around Schwarzschild black
holes and identify the sonic metric embedded within the stationary background
flow. Such metric describes the propagation of the linear acoustic perturbation
inside the accretion flow. We construct the wave equation corresponding to that
acoustic perturbation and find the speed of propagation of such perturbation.
We finally show that the ordinary thermodynamic sound speed should be
substituted by the speed of propagation of the linear acoustic wave which has
been obtained through the dynamical perturbation. Such substitution will make
the value of Mach number at the critical point to be equal to unity. Use of the
aforementioned effective sound speed will lead to a modified stationary disc
structure where the critical and the sonic points will be identical.


  In this paper, we study the following degenerate critical elliptic equations
with anisotropic coefficients $$ -div(|x_{N}|^{2\alpha}\nabla
u)=K(x)|x_{N}|^{\alpha\cdot 2^{*}(s)-s}|u|^{2^{*}(s)-2}u {in} \mathbb{R}^{N} $$
where $x=(x_{1},...,x_{N})\in\mathbb{R}^{N},$ $N\geq 3,$ $\alpha>1/2,$ $0\leq
s\leq 2$ and $2^{*}(s)=2(N-s)/(N-2).$ Some basic properties of the degenerate
elliptic operator $-div(|x_{N}|^{2\alpha}\nabla u)$ are investigated and some
regularity, symmetry and uniqueness results for entire solutions of this
equation are obtained. We also get some variational identities for solutions of
this equation. As a consequence, we obtain some nonexistence results for
solutions of this equation.


  The growth in the deployment of Internet of Things (IoT) devices in various
industries required the use of IoT platforms to manage, automate and control
devices. This introduced different commercial and open source IoT platforms for
developers and researchers to deploy. As a result, selecting one of these
platforms for a specific application and use case became a challenge. In this
study, a guideline for selecting an open source platform is presented. The
process starts by identifying a list of functional requirements that would
reflect the requirements of an IoT system in general. This list of requirements
is used to compare between four major open source platforms: 1) OM2M (OneM2M
standard), 2) IoTivity (OCF standard), LwM2M (OMA SpecWorks LwM2M standard),
and 4) FIWARE (FIWARE standard). The purpose of this comparison is to indicate
the capability and limitations of the different platforms and how they satisfy
each requirement. Afterwards, two examples are presented to demonstrate how
this guideline is used to select the most suitable platform for an e-health and
a smart city use case. This includes how to define each use case and all the
required information that could affect the process of selecting the most
suitable platform for the development of the IoT platform.


  Active turbulence in dense active systems is characterized by high vorticity
on a length scale that is large compared to that of individual entities. We
describe the properties of active turbulence as momentum propagation is
screened by frictional damping. As friction is increased, the spacing between
the walls in the nematic director field decreases as a consequence of the more
rapid velocity decays. This leads to, first, a regime with more walls and an
increased number of topological defects, and then to a jammed state in which
the walls deliminate bands of opposing flow, analogous to the shear bands
observed in passive complex fluids.


  Let ${\mathcal N}$ be a nest on a complex Banach space $X$ and let $\mbox{
Alg}{\mathcal N}$ be the associated nest algebra. We say that an operator $Z\in
\mbox{ Alg}{\mathcal N}$ is an all-derivable point of $\mbox{ Alg}{\mathcal N}$
if every linear map $\delta$ from $\mbox{ Alg}{\mathcal N}$ into itself
derivable at $Z$ (i.e. $\delta$ satisfies $\delta(A)B+A\delta(B)=\delta(Z)$ for
any $A,B \in \mbox{ Alg}{\mathcal N}$ with $AB=Z$) is a derivation. In this
paper, it is shown that every injective operator and every operator with dense
range in $\mbox{Alg}{\mathcal N}$ are all-derivable points of
$\mbox{Alg}{\mathcal N}$ without any additional assumption on the nest.


  Deep Reinforcement Learning (DRL) has become increasingly powerful in recent
years, with notable achievements such as Deepmind's AlphaGo. It has been
successfully deployed in commercial vehicles like Mobileye's path planning
system. However, a vast majority of work on DRL is focused on toy examples in
controlled synthetic car simulator environments such as TORCS and CARLA. In
general, DRL is still at its infancy in terms of usability in real-world
applications. Our goal in this paper is to encourage real-world deployment of
DRL in various autonomous driving (AD) applications. We first provide an
overview of the tasks in autonomous driving systems, reinforcement learning
algorithms and applications of DRL to AD systems. We then discuss the
challenges which must be addressed to enable further progress towards
real-world deployment.


  A major cause of irreversible visual impairment is angle-closure glaucoma,
which can be screened through imagery from Anterior Segment Optical Coherence
Tomography (AS-OCT). Previous computational diagnostic techniques address this
screening problem by extracting specific clinical measurements or handcrafted
visual features from the images for classification. In this paper, we instead
propose to learn from training data a discriminative representation that may
capture subtle visual cues not modeled by predefined features. Based on
clinical priors, we formulate this learning with a presented Multi-Context Deep
Network (MCDN) architecture, in which parallel Convolutional Neural Networks
are applied to particular image regions and at corresponding scales known to be
informative for clinically diagnosing angle-closure glaucoma. The output
feature maps of the parallel streams are merged into a classification layer to
produce the deep screening result. Moreover, we incorporate estimated clinical
parameters to further enhance performance. On a clinical AS-OCT dataset, our
system is validated through comparisons to previous screening methods.


  Flooding due to Hurricane Florence led to billions of dollars in damage and
nearly a hundred deaths in North Carolina. These damages and fatalities can be
avoided with proper prevention and preparation. Modelling such flooding events
can provide insight and precaution based on principles of fluid dynamics and
GIS technology. Using topography and other geographic data from USGS, HEC-RAS
can solve the Shallow Water Equations over flooding areas to assist the study
of inundation patterns. Simulation results from HEC-RAS agree with observations
from NOAA in the flooding area studied. Modeled results from specific locations
affected by Hurricane Florence near Neuse River, NC are compared with
observations. While overall pattern of inundation is agreeable between model
results and observations, there are also differences at very specific
locations. Higher resolution topography data and precipitation data over the
flooding area may improve the simulation result and reduce the differences.


  We present here quantitative versions in 1 dimension of Faltings'theorem
according to which the set of the K-rational points (where K is a given number
field) of an abelian variety A definied over K, which are close (with respect
to a v-adic distance on K) to some K-subvariety X of A, but not belonging to X,
is finite. We treat more exactly the case where A is an elliptic curve and X is
reduced to a point of A and we give (in this case) explicit bounds for the
cardinal of the exceptional finite set. We consider also, more generally,
instead of only one place v of K, a finite set S of places of K and the
distance from the point of A to X taking into account of all places of S.


  In arXiv:1905.07734 we presented a construction that is an analogue of
Pontryagin's for proper maps in stable dimensions. This gives a bijection
between the cobordism set of framed embedded compact submanifolds in
$W\times\mathbb{R}^n$ for a given manifold $W$ and a large enough number $n$,
and the homotopy classes of proper maps from $W\times\mathbb{R}^n$ to
$\mathbb{R}^{k+n}$. In the present paper we generalise this result in a similar
way as Thom's construction generalises Pontryagin's. In other words, we present
a bijection between the cobordism set of submanifolds embedded in
$W\times\mathbb{R}^n$ with normal bundles induced from a given bundle
$\xi\oplus\varepsilon^n$, and the homotopy classes of proper maps from
$W\times\mathbb{R}^n$ to a space $U(\xi\oplus\varepsilon^n)$ that depends on
the given bundle. An important difference between Thom's construction and ours
is that we also consider cobordisms of non-compact manifolds after indroducing
a suitable notion of cobordism relation for these.


  We study properties of metric segments in the class of all metric spaces
considered up to an isometry, endowed with Gromov--Hausdorff distance. On the
isometry classes of all compact metric spaces, the Gromov-Hausdorff distance is
a metric. A metric segment is a class that consists of points lying between two
given ones. By von Neumann--Bernays--G\"odel (NBG) axiomatic set theory, a
proper class is a "monster collection", e.g., the collection of all cardinal
sets. We prove that any metric segment in the proper class of isometry classes
of all metric spaces with the Gromov-Hausdorff distance is a proper class if
the segment contains at least one metric space at positive distances from the
segment endpoints. In addition, we show that the restriction of a
non-degenerated metric segment to compact metric spaces is a non-compact set.


  It is proven that the physical measure for the two-dimensional Yang-Mills
theory is purely singular with respect to the kinematical Ashtekar-Lewandowski
measure. For this, an explicit decomposition of the gauge orbit space into
supports of these two measures is given. Finally, the results are extended to
more general (e.g. confining) theories. Such a singularity implies, in
particular, that the standard method of determining the physical measure via
``exponential of minus the action times kinematical measure'' is not
applicable.


  This study analyzes political interactions in the European Parliament (EP) by
considering how the political agenda of the plenary sessions has evolved over
time and the manner in which Members of the European Parliament (MEPs) have
reacted to external and internal stimuli when making Parliamentary speeches. It
does so by considering the context in which speeches are made, and the content
of those speeches. To detect latent themes in legislative speeches over time,
speech content is analyzed using a new dynamic topic modeling method, based on
two layers of matrix factorization. This method is applied to a new corpus of
all English language legislative speeches in the EP plenary from the period
1999-2014. Our findings suggest that the political agenda of the EP has evolved
significantly over time, is impacted upon by the committee structure of the
Parliament, and reacts to exogenous events such as EU Treaty referenda and the
emergence of the Euro-crisis have a significant impact on what is being
discussed in Parliament.


  We present the results from three gravitational-wave searches for coalescing
compact binaries with component masses above 1$\mathrm{M}_\odot$ during the
first and second observing runs of the Advanced gravitational-wave detector
network. During the first observing run (O1), from September $12^\mathrm{th}$,
2015 to January $19^\mathrm{th}$, 2016, gravitational waves from three binary
black hole mergers were detected. The second observing run (O2), which ran from
November $30^\mathrm{th}$, 2016 to August $25^\mathrm{th}$, 2017, saw the first
detection of gravitational waves from a binary neutron star inspiral, in
addition to the observation of gravitational waves from a total of seven binary
black hole mergers, four of which we report here for the first time: GW170729,
GW170809, GW170818 and GW170823. For all significant gravitational-wave events,
we provide estimates of the source properties. The detected binary black holes
have total masses between $18.6_{-0.7}^{+3.2}\mathrm{M}_\odot$, and
$84.4_{-11.1}^{+15.8} \mathrm{M}_\odot$, and range in distance between
$320_{-110}^{+120}$ Mpc and $2840_{-1360}^{+1400}$ Mpc. No neutron star - black
hole mergers were detected. In addition to highly significant
gravitational-wave events, we also provide a list of marginal event candidates
with an estimated false alarm rate less than 1 per 30 days. From these results
over the first two observing runs, which include approximately one
gravitational-wave detection per 15 days of data searched, we infer merger
rates at the 90% confidence intervals of $110\, -\, 3840$
$\mathrm{Gpc}^{-3}\,\mathrm{y}^{-1}$ for binary neutron stars and $9.7\, -\,
101$ $\mathrm{Gpc}^{-3}\,\mathrm{y}^{-1}$ for binary black holes assuming fixed
population distributions, and determine a neutron star - black hole merger rate
90% upper limit of $610$ $\mathrm{Gpc}^{-3}\,\mathrm{y}^{-1}$.


  The mystery of the infamous sign problem in quantum Monte Carlo (QMC)
simulations mightily restricts applications of the method in fermionic and
frustrated systems. A recent work [Science \textbf{375}, 418 (2022)] made a
remarkable breakthrough in the sign problem by pointing out that the sign can
be used to probe phase transition. In this work, we proposed a general argument
based on the definition of the sign that is related to the difference in free
energy between the original and reference systems to clarify that the sign
problem and phase transition cannot always be strictly related. The sign can
exactly probe phase transition only if the free energy in the reference system
is flat under variable parameters, which is almost impossible to design.
Generally speaking, the conclusion that the sign can probe phase transition is
survivorship bias without universality. To solve this problem, we define a
modified sign that excludes the influence of the reference system, which can
probe the phase transition strictly. The work gives an unbiased solution for
detecting phase transition by the new modified sign.


  We show how the formal Wirthmuller isomorphism theorem proven in
"Isomorphisms between left and right adjoints", by Fausk, Hu, and May,
simplifies the proof of the Wirthmuller isomorphism in equivariant stable
homotopy theory. Other examples from equivariant stable homotopy theory show
that the hypotheses of the formal Wirthmuller and formal Grothendieck
isomorphism theorems in the cited paper cannot be weakened.


  We examine, in a quantum mechanical setting, the Hilbert space representation
of the BRST symmetry associated with Schwinger-Keldysh path integrals. This
structure had been postulated to encode important constraints on influence
functionals in coarse-grained systems with dissipation, or in open quantum
systems. Operationally, this entails uplifting the standard Schwinger-Keldysh
two-copy formalism into superspace by appending BRST ghost degrees of freedom.
These statements were previously argued at the level of the correlation
functions. We provide herein a complementary perspective by working out the
Hilbert space structure explicitly. Our analysis clarifies two crucial issues
not evident in earlier works: firstly, certain background ghost insertions
necessary to reproduce the correct Schwinger-Keldysh correlators arise
naturally. Secondly, the Schwinger-Keldysh difference operators are
systematically dressed by the ghost bilinears, which turn out to be necessary
to give rise to a consistent operator algebra. We also elaborate on the
structure of the final state (which is BRST closed) and the future boundary
condition of the ghost fields.


  U-duality plays a special role in the study of the microscopic degrees of
freedom of supersymmetric black holes. To be consistent with duality, the black
hole quantum degeneracy must obey special arithmetic properties, which are
non-perturbative in nature. In this work, we study these properties from a
holographic point of view, establishing a connection between arithmetic
properties of Kloosterman sums and quantum gravity in $\text{AdS}_2$ space. To
this end, we consider the entropy of black holes that carry non-primitive
charges, in both $\mathcal{N}=8$ and $\mathcal{N}=4$ four dimensional
compactifications; our analysis includes all the perturbative and
non-perturbative bulk quantum corrections. The key result relies on special
arithmetic properties of generalized Kloosterman sums that we develop. These
are a generalization of the known Selberg identity of classical Kloosterman
sums. In both the $\mathcal{N}=8$ and $\mathcal{N}=4$ examples, we recover,
from the bulk quantum gravity, the non-primitive answer which is a sum over the
primitive degeneracies, depending non-trivially on the discrete duality
invariants. In particular, for the $\mathcal{N}=4$ case we show that the
quantum gravity answer reproduces the dependence on the torsion invariant
$I=\text{gcd}(Q\wedge P)$, in agreement with the microscopic formulas. For the
$\mathcal{N}=8$ case, we solve a puzzle related to U-duality invariance of the
supergravity answer and the corresponding one-eighth BPS degeneracy.


  Fingerprint recognition systems are widely deployed in various real-life
applications as they have achieved high accuracy. The widely used applications
include border control, automated teller machine (ATM), and attendance
monitoring systems. However, these critical systems are prone to spoofing
attacks (a.k.a presentation attacks (PA)). PA for fingerprint can be performed
by presenting gummy fingers made from different materials such as silicone,
gelatine, play-doh, ecoflex, 2D printed paper, 3D printed material, or latex.
Biometrics Researchers have developed Presentation Attack Detection (PAD)
methods as a countermeasure to PA. PAD is usually done by training a machine
learning classifier for known attacks for a given dataset, and they achieve
high accuracy in this task. However, generalizing to unknown attacks is an
essential problem from applicability to real-world systems, mainly because
attacks cannot be exhaustively listed in advance. In this survey paper, we
present a comprehensive survey on existing PAD algorithms for fingerprint
recognition systems, specifically from the standpoint of detecting unknown PAD.
We categorize PAD algorithms, point out their advantages/disadvantages, and
future directions for this area.


  We develop computationally affordable and encoding independent gradient
evaluation procedures for unitary coupled-cluster type operators, applicable on
quantum computers. We show that, within our framework, the gradient of an
expectation value with respect to a parameterized n-fold fermionic excitation
can be evaluated by four expectation values of similar form and size, whereas
most standard approaches based on the direct application of the
parameter-shift-rule come with an associated cost of O(2^(2n)) expectation
values. For real wavefunctions, this cost can be further reduced to two
expectation values. Our strategies are implemented within the open-source
package tequila and allow blackboard style construction of differentiable
objective functions. We illustrate initial applications for electronic ground
and excited states.


  In the framework of a toy model which possesses the main features of QCD in
the high energy limit, we conduct a numerical study of scattering amplitudes
constructed from parton splittings and projectile-target multiple interactions,
in a way that unitarizes the amplitudes without however explicit saturation in
the wavefunction of the incoming states. This calculation is performed in two
different ways. One of these formulations, the closest to field theory,
involves the numerical resummation of a factorially divergent series, for which
we develop appropriate numerical tools. We accurately compare the properties of
the resulting amplitudes with what would be expected if saturation were
explicitly included in the evolution of the states. We observe that the
amplitudes have similar properties in a small but finite range of rapidity in
the beginning of the evolution, as expected. Some of the features of
reaction-diffusion processes are already present in that range, even when
saturation is left out of the model.


  As Artificial Intelligence (AI) making advancements in medical
decision-making, there is a growing need to ensure doctors develop appropriate
reliance on AI to avoid adverse outcomes. However, existing methods in enabling
appropriate AI reliance might encounter challenges while being applied in the
medical domain. With this regard, this work employs and provides the validation
of an alternative approach -- majority voting -- to facilitate appropriate
reliance on AI in medical decision-making. This is achieved by a
multi-institutional user study involving 32 medical professionals with various
backgrounds, focusing on the pathology task of visually detecting a pattern,
mitoses, in tumor images. Here, the majority voting process was conducted by
synthesizing decisions under AI assistance from a group of pathology doctors
(pathologists). Two metrics were used to evaluate the appropriateness of AI
reliance: Relative AI Reliance (RAIR) and Relative Self-Reliance (RSR). Results
showed that even with groups of three pathologists, majority-voted decisions
significantly increased both RAIR and RSR -- by approximately 9% and 31%,
respectively -- compared to decisions made by one pathologist collaborating
with AI. This increased appropriateness resulted in better precision and recall
in the detection of mitoses. While our study is centered on pathology, we
believe these insights can be extended to general high-stakes decision-making
processes involving similar visual tasks.


  We use a set of Stackel potentials to obtain a local approximation for an
effective third integral in axisymmetric systems. We present a study on the
feasibility and effectiveness of this approach. We have applied it to three
trial potentials of various flattenings, corresponding to nearly ellipsoidal,
disky and boxy density isophotes. In all three cases, a good fit to the
potential requires only a small set of Stackel potentials, and the associated
Stackel third integral provides a very satisfactory, yet analytically simple,
approximation to the trial potentials effective third integral.


  We compute the supersymmetric contribution to
  $R_{b}\equiv \Gamma (Z\to b{\bar b})/\Gamma (Z\to {\rm hadrons})$ in a
variety of supergravity models. We find $R^{\rm susy}_b\lsim0.0004$, which does
not shift significantly the Standard Model prediction
  ($R^{\rm SM}_b=0.2162$ for $m_t=160\GeV$). An improvement in experimental
precision by a factor of four would be required to be sensitive to such an
effect.


  Big data, encompassing extensive datasets, has seen rapid expansion, notably
with a considerable portion being textual data, including strings and texts.
Simple compression methods and standard data structures prove inadequate for
processing these datasets, as they require decompression for usage or consume
extensive memory resources. Consequently, this motivation has led to the
development of compressed data structures that support various queries for a
given string, typically operating in polylogarithmic time and utilizing
compressed space proportional to the string's length. Notably, the suffix array
(SA) query is a critical component in implementing a suffix tree, which has a
broad spectrum of applications.
  A line of research has been conducted on (especially, static) compressed data
structures that support the SA query. A common finding from most of the studies
is the suboptimal space efficiency of existing compressed data structures.
Kociumaka, Navarro, and Prezza [IEEE Trans. Inf. Theory 2023] have made a
significant contribution by introducing an asymptotically minimal space
requirement, $O\left(\delta \log\frac{n\log\sigma}{\delta\log n} \log n
\right)$ bits ($\delta$-optimal space), sufficient to represent any string of
length $n$, with an alphabet size of $\sigma$, and substring complexity
$\delta$, serving as a measure of repetitiveness. More recently, Kempa and
Kociumaka [FOCS 2023] presented $\delta$-SA, a compressed data structure
supporting SA queries in $\delta$-optimal space. However, the data structures
introduced thus far are static.
  We present the first dynamic compressed data structure that supports the SA
query and update in polylogarithmic time and $\delta$-optimal space. More
precisely, it can answer SA queries and perform updates in $O(\log^7 n)$ and
expected $O(\log^8 n)$ time, respectively, using an expected $\delta$-optimal
space.


  Conformal gravity theory can explain observed flat rotation curves of
galaxies without invoking hypothetical dark matter. Within this theory, we
obtain a generic formula for the sizes of galaxies exploiting the stability
criterion of circular orbits. It is found that different galaxies have
different finite sizes uniquely caused by the assumed quadratic potential of
cosmological origin. Observations on where circular orbits might actually
terminate could thus be very instructive in relation to the galactic sizes
predicted here.


  The current integrated circuit (IC) technology based on conventional MOS-FET
(metal-oxide-semiconductor field-effect transistor) is approaching the limit of
miniaturization with increasing demand on energy. Several analog circuit
applications based on graphene FETs have been demonstrated with less components
comparing to the conventional technology. However, low on/off current ratio
caused by the semimetal nature of graphene has severely hindered its practical
applications. Here we report a graphene/MoTe2 van der Waals (vdW) vertical
transistor with V-shaped ambipolar field effect transfer characteristics to
overcome this challenge. Investigations on temperature dependence of transport
properties reveal that gate tunable asymmetric barriers of the devices are
account for the ambipolar behaviors. Furthermore, to demonstrate the analog
circuit applications of such vdW vertical transistors, we successfully realized
output polarity controllable (OPC) amplifier and frequency doubler. These
results enable vdW heterojunction based electronic devices to open up new
possibilities for wide perspective in telecommunication field.


  Performance of clustering algorithms is evaluated with the help of accuracy
metrics. There is a great diversity of clustering algorithms, which are key
components of many data analysis and exploration systems. However, there exist
only few metrics for the accuracy measurement of overlapping and
multi-resolution clustering algorithms on large datasets. In this paper, we
first discuss existing metrics, how they satisfy a set of formal constraints,
and how they can be applied to specific cases. Then, we propose several
optimizations and extensions of these metrics. More specifically, we introduce
a new indexing technique to reduce both the runtime and the memory complexity
of the Mean F1 score evaluation. Our technique can be applied on large datasets
and it is faster on a single CPU than state-of-the-art implementations running
on high-performance servers. In addition, we propose several extensions of the
discussed metrics to improve their effectiveness and satisfaction to formal
constraints without affecting their efficiency. All the metrics discussed in
this paper are implemented in C++ and are available for free as open-source
packages that can be used either as stand-alone tools or as part of a
benchmarking system to compare various clustering algorithms.


  In this study, we analyse the global stability of the equilibrium in a
departure time choice problem using a game-theoretic approach that deals with
atomic users. We first formulate the departure time choice problem as a
strategic game in which atomic users select departure times to minimise their
trip cost; we call this game the 'departure time choice game'. The concept of
the epsilon-Nash equilibrium is introduced to ensure the existence of
pure-strategy equilibrium corresponding to the departure time choice
equilibrium in conventional fluid models. Then, we prove that the departure
time choice game is a weakly acyclic game. By analysing the convergent better
responses, we clarify the mechanisms of global convergence to equilibrium. This
means that the epsilon-Nash equilibrium is achieved by sequential better
responses of users, which are departure time changes to improve their own
utility, in an appropriate order. Specifically, the following behavioural rules
are important to ensure global convergence: (i) the adjustment of the departure
time of the first user departing from the origin to the corresponding
equilibrium departure time and (ii) the fixation of users to their equilibrium
departure times in order (starting with the earliest). Using convergence
mechanisms, we construct evolutionary dynamics under which global stability is
guaranteed. We also investigate the stable and unstable dynamics studied in the
literature based on convergence mechanisms, and gain insight into the factors
influencing the different stability results. Finally, numerical experiments are
conducted to demonstrate the theoretical results.


  We introduce warped product skew semi-invariant submanifolds of order $1$ of
a locally product Riemannian manifold. We give a necessary and sufficient
condition for skew semi-invariant submanifold of order 1 to be a locally warped
product. We also prove that the invariant distribution which is involved in the
definition of the submanifold is integrable under some restrictions. Moreover,
we find an inequality between the warping function and the squared norm of the
second fundamental form for such submanifolds. Equality case is also discussed.


  Polytope theory has produced a great number of remarkably simple and complete
characterization results for face-number sets or f-vector sets of classes of
polytopes. We observe that in most cases these sets can be described as the
intersection of a semi-algebraic set with an integer lattice. Such
"semi-algebraic sets of lattice points" have not received much attention, which
is surprising in view of a close connection to Hilbert's Tenth problem, which
deals with their projections.
  We develop proof techniques in order to show that, despite the observations
above, some f-vector sets are NOT semi-algebraic sets of lattice points. This
is then proved for the set of all pairs $(f_1,f_2)$ of 4-dimensional polytopes,
the set of all f-vectors of simplicial $d$-polytopes for $d\ge6$, and the set
of all f-vectors of general $d$-polytopes for $d\ge6$. For the f-vector set of
all 4-polytopes this remains open.


  We examine the properties of the Fe emission lines that arise near 6.4 keV in
the ASCA spectra of AGN. Our emphasis is on the Seyfert 1 galaxies where broad
and apparently complex Fe K alpha emission is observed. We consider various
origins for the line but focus on the pros and cons for line emitting accretion
disk models. We develop a simple model of an illuminated disk capable of
producing both X-ray and optical lines from a disk. The model is able to
reproduce the observed Fe K alpha FWHM ratio as well as the radii of maximum
emissivity implied by the profile redshifts. The overall profile shapes however
do not fit well the predictions of our disk illumination model nor do we derive
always consistent disk inclinations for the two lines. We conclude that the
evidence for and against an accretion disk origin for the Fe K alpha emission
is equal at best. The bulk of the data requires a very disparate set of line
fits which shed little light on a coherent physical model. We briefly consider
alternatives to disk emission models and show that a simple bicone model can
reproduce the FE line profiles equally well.


  The integration of artificial intelligence (AI) into daily life, particularly
through information retrieval and recommender systems, has necessitated
advanced user modeling and profiling techniques to deliver personalized
experiences. These techniques aim to construct accurate user representations
based on the rich amounts of data generated through interactions with these
systems. This paper presents a comprehensive survey of the current state,
evolution, and future directions of user modeling and profiling research. We
provide a historical overview, tracing the development from early stereotype
models to the latest deep learning techniques, and propose a novel taxonomy
that encompasses all active topics in this research area, including recent
trends. Our survey highlights the paradigm shifts towards more sophisticated
user profiling methods, emphasizing implicit data collection, multi-behavior
modeling, and the integration of graph data structures. We also address the
critical need for privacy-preserving techniques and the push towards
explainability and fairness in user modeling approaches. By examining the
definitions of core terminology, we aim to clarify ambiguities and foster a
clearer understanding of the field by proposing two novel encyclopedic
definitions of the main terms. Furthermore, we explore the application of user
modeling in various domains, such as fake news detection, cybersecurity, and
personalized education. This survey serves as a comprehensive resource for
researchers and practitioners, offering insights into the evolution of user
modeling and profiling and guiding the development of more personalized,
ethical, and effective AI systems.


  We argue that in classical and quantum theories of gravity the configuration
space and Hilbert space may not be constructible through any finite procedure.
If this is the case then the "problem of time" in quantum cosmology may be a
pseudo-problem, because the argument that time disappears from the theory
depends on constructions that cannot be realized by any finite beings that live
in the universe. We propose an alternative formulation of quantum cosmological
theories in which it is only necessary to predict the amplitudes for any given
state to evolve to a finite number of possible successor states. The space of
accessible states of the system is then constructed as the universe evolves
from any initial state. In this kind of formulation of quantum cosmology time
and causality are built in at the fundamental level. An example of such a
theory is the recent path integral formulation of quantum gravity of
Markopoulou and Smolin, but there are a wide class of theories of this type.


  This paper focuses on isochronicity of linear center perturbed by a
polynomial. Isochronicity of a linear center perturbed by a degree four and
degree five polynomials is studied, several new isochronous centers are found.
For homogeneous isochronous perturbations, a first integral and a linearizing
change of coordinates are presented. Moreover, a family of Abel polynomial
systems is also considered. By investigations until degree 10 we prove the
existence of a unique isochronous center. These results are established using a
computer implementation based on Urabe theorem.


  This article deals with study of the steady flow and heat transfer
characteristics of Sisko fluid over a rotating infinite disk. The flow and heat
transfer aspects are thoroughly investigated encompassing highly shear
thinning/thickening Sisko fluids. The modeled boundary layer equations are
reduced to a system of nonlinear ordinary differential equations using the
appropriate transformation. The resulting equations are then solved numerically
by shooting method in the domain . The numerical data for the velocity and
temperature fields are graphically sketched and effects of the relevant
parameters are discussed in detail. In addition, the velocity gradients at the
disk surface and the local Nusselt number for different values of the
pertaining parameters are given in tabulated form. Further, the flow and
temperature fields of power-law and Newtonian fluids are also compared with
those Sisko fluid. Moreover, a comparison with previously published work, as a
special case of the problem, has been provided and the results are found to be
in excellent agreement.


  Precise characterization of quantum devices is usually achieved with quantum
tomography. However, most methods which are currently widely used in
experiments, such as maximum likelihood estimation, lack a well-justified error
analysis. Promising recent methods based on confidence regions are difficult to
apply in practice or yield error bars which are unnecessarily large. Here, we
propose a practical yet robust method for obtaining error bars. We do so by
introducing a novel representation of the output of the tomography procedure,
the "quantum error bars". This representation is (i) concise, being given in
terms of few parameters, (ii) intuitive, providing a fair idea of the "spread"
of the error, and (iii) useful, containing the necessary information for
constructing confidence regions. The statements resulting from our method are
formulated in terms of a figure of merit, such as the fidelity to a reference
state. We present an algorithm for computing this representation and provide
ready-to-use software. Our procedure is applied to actual experimental data
obtained from two superconducting qubits in an entangled state, demonstrating
the applicability of our method.


  This work studies the semantic representations learned by BERT for compounds,
that is, expressions such as sunlight or bodyguard. We build on recent studies
that explore semantic information in Transformers at the word level and test
whether BERT aligns with human semantic intuitions when dealing with
expressions (e.g., sunlight) whose overall meaning depends -- to a various
extent -- on the semantics of the constituent words (sun, light). We leverage a
dataset that includes human judgments on two psycholinguistic measures of
compound semantic analysis: lexeme meaning dominance (LMD; quantifying the
weight of each constituent toward the compound meaning) and semantic
transparency (ST; evaluating the extent to which the compound meaning is
recoverable from the constituents' semantics). We show that BERT-based measures
moderately align with human intuitions, especially when using contextualized
representations, and that LMD is overall more predictable than ST. Contrary to
the results reported for 'standard' words, higher, more contextualized layers
are the best at representing compound meaning. These findings shed new light on
the abilities of BERT in dealing with fine-grained semantic phenomena.
Moreover, they can provide insights into how speakers represent compounds.


  The Lichnerowicz conjecture asserts that all harmonic manifolds are either
flat or locally symmetric spaces of rank 1. This conjecture has been proved by
Z.I. Szabo for harmonic manifolds with compact universal cover. E. Damek and F.
Ricci provided examples showing that in the noncompact case the conjecture is
wrong. However, such manifolds do not admit a compact quotient. The
classification of all noncompact harmonic spaces is still a very difficult open
problem.
  In this paper we provide a survey on recent results on noncompact simply
connected harmonic manifolds, and we also prove many new results, both for
general noncompact harmonic manifolds and for noncompact harmonic manifolds
with purely exponential volume growth.


  In this paper we show the relation between robust transitivity and robust
ergodicity for conservative diffeomorphisms. In dimension 2 robustly transitive
systems are robustly ergodic. For the three dimensional case, we define it
almost robust ergodicity and prove that generically robustly transitive systems
are almost robustly ergodic, if the Lyapunov exponents are nonzero. We also
show in higher dimensions, that under some conditions robust transitivity
implies robust ergodicity.


  We study the gravitational wave signals emitted from phase-transition induced
collapses of rapidly rotating neutron stars to strange stars by performing 3D
numerical simulations. Our preliminary results suggest that the complete
conversion of neutron stars to strange stars would occur within a fraction of
millisecond. We also find that the gravitational waves generated from the
collapse process may be detectable by the advanced LIGO for reasonable source
distance. In addition, the study such gravitational wave signals would put
useful constraint on the parameters of QCD.


  We discuss the prompt emission of GRBs, allowing for $\gamma\gamma$ pair
production and synchrotron self-absorption. The observed hard spectra suggest
heavy pair-loading in GRBs. The re-emission of the generated pairs results in
the energy transmission from high-energy gamma-rays to long-wavelength
radiation. Due to strong self-absorption, the synchrotron radiation by pairs is
in optically thick regime, showing a thermal-like spectral bump in the
extreme-ultraviolet/soft X-ray band, other than the peak from the main burst.
Recently, the prompt soft X-ray emission of GRB 031203 was detected thanks to
the discovery of a delayed dust echo, and it seems to be consistent with the
model prediction of a double-peak structure. The confirmation of the
thermal-like feature and the double-peak structure by observation would
indicate that the dominant radiation mechanism in GRBs is synchrotron rather
than inverse-Compton radiation.


  We study the zero-dispersion limit of the Calogero-Moser derivative NLS
equation $$i\partial_tu+\partial_x^2 u \pm\,2D\Pi(|u|^2)u=0, \qquad
x\in\mathbb{R},$$ starting from an initial data $u_0\in L^2_+(\mathbb{R})\cap
L^\infty (\mathbb{R}),$ where $D=-i\partial_x,$ and $\Pi$ is the Szeg\H{o}
projector defined as $\widehat{\Pi
u}(\xi)=1_{[0,+\infty)}(\xi)\widehat{u}(\xi).$ We characterize the
zero-dispersion limit solution by an explicit formula. Moreover, we identify
it, in terms of the branches of the multivalued solution of the inviscid
Burgers-Hopf equation. Finally, we infer that it satisfies a maximum principle.


  We study the effect of different heating rates of a dilute Bose gas confined
in a quasi-1D finite, leaky box. An optical kicked-rotor is used to transfer
energy to the atoms while two repulsive optical beams are used to confine the
atoms. The average energy of the atoms is localized after a large number of
kicks and the system reaches a nonequilibrium steady state. A numerical
simulation of the experimental data suggests that the localization is due to
energetic atoms leaking over the barrier. Our data also indicates a correlation
between collisions and the destruction of the Bose-Einstein condensate
fraction.


  The Frank-Wolfe algorithm has regained much interest in its use in
structurally constrained machine learning applications. However, one major
limitation of the Frank-Wolfe algorithm is the slow local convergence property
due to the zig-zagging behavior. We observe that this zig-zagging phenomenon
can be viewed as an artifact of discretization, as when the method is viewed as
an Euler discretization of a continuous time flow, that flow does not zig-zag.
For this reason, we propose multistep Frank-Wolfe variants based on
discretizations of the same flow whose truncation errors decay as
$O(\Delta^p)$, where $p$ is the method's order. This strategy "stabilizes" the
method, and allows tools like line search and momentum to have more benefit.
However, in terms of a convergence rate, our result is ultimately negative,
suggesting that no Runge-Kutta-type discretization scheme can achieve a better
convergence rate than the vanilla Frank-Wolfe method. We believe that this
analysis adds to the growing knowledge of flow analysis for optimization
methods, and is a cautionary tale on the ultimate usefulness of multistep
methods.


  A broad family of phase transitions in the closed as well as open quantum
systems is known to be mediated by a non-Hermitian degeneracy (a.k.a.
exceptional point, EP) of the Hamiltonian. In the EP limit, in general, the
merger of an $N-$plet of the energy eigenvalues is accompanied by a parallel
(though not necessarily complete) degeneracy of eigenstates (forming an
EP-asociated $K-$plet; in mathematics, $K$ is called the geometric multiplicity
of the EP). In the literature, unfortunately, only the benchmark matrix models
with $K=1$ can be found. In our paper the gap is filled: the EP-mediated
quantum phase transitions with $K>1$ are called "clustered", and a family of
benchmark models admitting such a clustering phenomenon is proposed and
described. For the sake of maximal simplicity our attention is restricted to
the real perturbed-harmonic-oscillator-type N by N matrix Hamiltonians which
are exactly solvable and in which the perturbation is multiparametric (i.e.,
maximally variable) and antisymmetric (i.e., maximally non-Hermitian). A
labeling (i.e., an exhaustive classification) of these models is provided by a
specific partitioning of N.


  Current home appliances are capable to execute a limited number of voice
commands such as turning devices on or off, adjusting music volume or light
conditions. Recent progress in machine reasoning gives an opportunity to
develop new types of conversational user interfaces for home appliances. In
this paper, we apply state-of-the-art visual reasoning model and demonstrate
that it is feasible to ask a smart fridge about its contents and various
properties of the food with close-to-natural conversation experience. Our
visual reasoning model answers user questions about existence, count, category
and freshness of each product by analyzing photos made by the image sensor
inside the smart fridge. Users may chat with their fridge using off-the-shelf
phone messenger while being away from home, for example, when shopping in the
supermarket. We generate a visually realistic synthetic dataset to train
machine learning reasoning model that achieves 95% answer accuracy on test
data. We present the results of initial user tests and discuss how we modify
distribution of generated questions for model training based on
human-in-the-loop guidance. We open source code for the whole system including
dataset generation, reasoning model and demonstration scripts.


  Remote photoplethysmography (rPPG) has been widely applied to measure heart
rate from face videos. To increase the generalizability of the algorithms,
domain generalization (DG) attracted increasing attention in rPPG. However,
when rPPG is extended to simultaneously measure more vital signs (e.g.,
respiration and blood oxygen saturation), achieving generalizability brings new
challenges. Although partial features shared among different physiological
signals can benefit multi-task learning, the sparse and imbalanced target label
space brings the seesaw effect over task-specific feature learning. To resolve
this problem, we designed an end-to-end Mixture of Low-rank Experts for
multi-task remote Physiological measurement (PhysMLE), which is based on
multiple low-rank experts with a novel router mechanism, thereby enabling the
model to adeptly handle both specifications and correlations within tasks.
Additionally, we introduced prior knowledge from physiology among tasks to
overcome the imbalance of label space under real-world multi-task physiological
measurement. For fair and comprehensive evaluations, this paper proposed a
large-scale multi-task generalization benchmark, named Multi-Source Synsemantic
Domain Generalization (MSSDG) protocol. Extensive experiments with MSSDG and
intra-dataset have shown the effectiveness and efficiency of PhysMLE. In
addition, a new dataset was collected and made publicly available to meet the
needs of the MSSDG.


  A subset of the highly excited eigenstates of thiophosgene (SCCl$_{2}$) near
the dissociation threshold are analyzed using sensitive measures of quantum
ergodicity. We find several localized eigenstates, suggesting that the
intramolecular vibrational energy flow dynamics is nonstatistical even at such
high levels of excitations. The results are consistent with recent observations
of sharp spectral features in the stimulated emission spectra of SCCl$_{2}$


  We define and investigate a class of Koszul quasi-hereditary algebras for
which there is a natural equivalence between the bounded derived category of
graded modules and the bounded derived category of graded modules over (a
proper version of) the extension algebra of standard modules. Examples of such
algebras include, in particular, the multiplicity free blocks of the BGG
category $\mathcal{O}$, and some quasi-hereditary algebras with Cartan
decomposition in the sense of K{\"o}nig.


  In this paper, we study the global dynamics of a chemostat model with a
single nutrient and several competing species. Growth rates are not required to
be proportional to food uptakes. The model was studied by Fiedler and Hsu [J.
Math. Biol. (2009) 59:233-253]. These authors prove the nonexistence of
periodic orbits, by means of a multi-dimensional Bendixon-Dulac criterion. Our
approach is based on the construction of Lyapunov functions. The Lyapunov
functions extend those used by Hsu [SIAM J. Appl. Math. (1978) 34:760-763] and
by Wolkowicz and Lu [SIAM J. Appl. Math. (1997) 57:1019-1043] in the case when
growth rates are proportional to food uptakes.


  We implement a quantum random number generator based on a balanced homodyne
measurement of vacuum fluctuations of the electromagnetic field. The digitized
signal is directly processed with a fast randomness extraction scheme based on
a linear feedback shift register. The random bit stream is continuously read in
a computer at a rate of about 480 Mbit/s and passes an extended test suite for
random numbers.


  We have developed a simple algorithm for defining a single proxy state which
accounts for state truncation in the sum-over-states calculations of the
dispersion of the molecular hyperpolarizabilities. The transition strengths
between the proxy state and the truncated set of states are determined using
the Thomas-Reiche-Kuhn sum rules. In addition to the sum rules, this method
requires as an input the off-resonant polarizability. This proxy state method
can augment experimentally determined parameters or finite-state theories to
allow for a more accurate prediction of the nonlinear optical properties of
molecular systems. We benchmark this approach by comparison with exact
perturbation calculations of one-dimensional power law potentials.


  In Keplerian accretion disks, turbulence and magnetic fields may be jointly
excited through a subcritical dynamo process involving the magnetorotational
instability (MRI). High-resolution simulations exhibit a tendency towards
statistical self-organization of MRI dynamo turbulence into large-scale cyclic
dynamics. Understanding the physical origin of these structures, and whether
they can be sustained and transport angular momentum efficiently in
astrophysical conditions, represents a significant theoretical challenge. The
discovery of simple periodic nonlinear MRI dynamo solutions has recently proven
useful in this respect, and has notably served to highlight the role of
turbulent magnetic diffusion in the seeming decay of the dynamics at low
magnetic Prandtl number Pm (magnetic diffusivity larger than viscosity), a
common regime in accretion disks. The connection between these simple
structures and the statistical organization reported in turbulent simulations
remained elusive, though. Here, we report the numerical discovery in moderate
aspect ratio Keplerian shearing boxes of new periodic, incompressible,
three-dimensional nonlinear MRI dynamo solutions with a larger dynamical
complexity reminiscent of such simulations. These "chimera" cycles are
characterized by multiple MRI-unstable dynamical stages, but their basic
physical principles of self-sustainment are nevertheless identical to those of
simpler cycles found in azimuthally elongated boxes. In particular, we find
that they are not sustained at low Pm either due to subcritical turbulent
magnetic diffusion. These solutions offer a new perspective into the transition
from laminar to turbulent instability-driven dynamos, and may prove useful to
devise improved statistical models of turbulent accretion disk dynamos.


  English. This document is designed to study the data structures that can be
used in the Apache Spark framework and to evaluate the best performing ones to
implement solutions, in particular we will evaluate advantages / disadvantages
deriving from the use of Dataset for job creation. The observation of the
results provides further support in evaluating the use of Dataset as an
alternative to RDD, in order to understand its strengths and weaknesses. The
examination of the results is possible thanks to specifically designed and
implemented in Java 1.8 language. The execution of the jobs, entrusted to a
suitable distributed environment, will end with the comparison between
execution times and results obtained.
  Italiano. Il presente documento nasce allo scopo di studiare le strutture
dati utilizzabili nel framework Apache Spark e valutare quelle pi\`u
performanti per implementare soluzioni; valuteremo in articolare i vantaggi /
svantaggi derivanti dall'utilizzo dei Dataset nella progettazione dei job.
L'osservazione dei risultati fornisce ulteriore supporto nel valutare
l'utilizzo dei Dataset in alternativa a RDD, al fine di comprederne i punti di
forza e di debolezza. L'esame dei risultati \`e possibile in virt\`u di due
casi appositamente pensati e implementati in linguaggio Java 1.8. L'esecuzione
dei job, affidata a un adeguato ambiente distribuito, si concluder\`a con il
confronto tra tempi di esecuzione e risultati ottenuti.


  A novel optimization procedure for the generation of stability polynomials of
stabilized explicit Runge-Kutta methods is devised. Intended for
semidiscretizations of hyperbolic partial differential equations, the herein
developed approach allows the optimization of stability polynomials with more
than hundred stages. A potential application of these high degree stability
polynomials are problems with locally varying characteristic speeds as found in
non-uniformly refined meshes and different wave speeds.
  To demonstrate the applicability of the stability polynomials we construct 2N
storage many-stage Runge-Kutta methods that match their designed second order
of accuracy when applied to a range of linear and nonlinear hyperbolic PDEs
with smooth solutions. The methods are constructed to reduce the amplification
of round off errors which becomes a significant concern for these many-stage
methods.


  Due to the large value of the scattering length in nuclear systems, standard
density--functional theories based on effective interactions usually fail to
reproduce the nuclear Fermi liquid behavior both at very low densities and
close to equilibrium. Guided on one side by the success of the Skyrme density
functional and, on the other side, by resummation techniques used in Effective
Field Theories for systems with large scattering lengths, a new energy--density
functional is proposed. This functional, adjusted on microscopic calculations,
reproduces the nuclear equations of state of neutron and symmetric matter at
various densities. Furthermore, it provides reasonable saturation properties as
well as an appropriate density dependence for the symmetry energy.


  Ship detection using high-resolution remote sensing images is an important
task, which contribute to sea surface regulation. The complex background and
special visual angle make ship detection relies in high quality datasets to a
certain extent. However, there is few works on giving both precise
classification and accurate location of ships in existing ship detection
datasets. To further promote the research of ship detection, we introduced a
new fine-grained ship detection datasets, which is named as FGSD. The dataset
collects high-resolution remote sensing images that containing ship samples
from multiple large ports around the world. Ship samples were fine categorized
and annotated with both horizontal and rotating bounding boxes. To further
detailed the information of the dataset, we put forward a new representation
method of ships' orientation. For future research, the dock as a new class was
annotated in the dataset. Besides, rich information of images were provided in
FGSD, including the source port, resolution and corresponding GoogleEarth' s
resolution level of each image. As far as we know, FGSD is the most
comprehensive ship detection dataset currently and it'll be available soon.
Some baselines for FGSD are also provided in this paper.


  We introduce a new quantum key distribution protocol that uses d-level
quantum systems to encode an alphabet with c letters. It has the property that
the error rate introduced by an intercept-and-resend attack tends to one as the
numbers c and d increase. In dimension d=2, when the legitimate parties use a
complete set of three mutually unbiased bases, the protocol achieves a quantum
bit error rate of 57.1%. This represents a significant improvement over the 25%
quantum bit error rate achieved in the BB84 protocol or 33% in the six-state
protocol.


  Conventional approaches to image-text retrieval mainly focus on indexing
visual objects appearing in pictures but ignore the interactions between these
objects. Such objects occurrences and interactions are equivalently useful and
important in this field as they are usually mentioned in the text. Scene graph
presentation is a suitable method for the image-text matching challenge and
obtained good results due to its ability to capture the inter-relationship
information. Both images and text are represented in scene graph levels and
formulate the retrieval challenge as a scene graph matching challenge. In this
paper, we introduce the Local and Global Scene Graph Matching (LGSGM) model
that enhances the state-of-the-art method by integrating an extra graph
convolution network to capture the general information of a graph.
Specifically, for a pair of scene graphs of an image and its caption, two
separate models are used to learn the features of each graph's nodes and edges.
Then a Siamese-structure graph convolution model is employed to embed graphs
into vector forms. We finally combine the graph-level and the vector-level to
calculate the similarity of this image-text pair. The empirical experiments
show that our enhancement with the combination of levels can improve the
performance of the baseline method by increasing the recall by more than 10% on
the Flickr30k dataset.


  Inverse optimization describes a process that is the "reverse" of traditional
mathematical optimization. Unlike traditional optimization, which seeks to
compute optimal decisions given an objective and constraints, inverse
optimization takes decisions as input and determines an objective and/or
constraints that render these decisions approximately or exactly optimal. In
recent years, there has been an explosion of interest in the mathematics and
applications of inverse optimization. This paper provides a comprehensive
review of both the methodological and application-oriented literature in
inverse optimization.


  In a recent study, Lee et al. presented new photometric follow-up timing
observations of the semi-detached binary system SZ Herculis and proposed the
existence of two hierarchical cirumbinary companions. Based on the light-travel
time effect, the two low-mass M-dwarf companions are found to orbit the binary
pair on moderate to high eccentric orbits. The derived periods of these two
companions are close to a 2:1 mean-motion orbital resonance. We have studied
the stability of the system using the osculating orbital elements as presented
by Lee et al. Results indicate an orbit-crossing architecture exhibiting
short-term dynamical instabilities leading to the escape of one of the proposed
companions. We have examined the system's underlying model parameter-space by
following a Monte Carlo approach and found an improved fit to the timing data.
A study of the stability of our best-fitting orbits also indicates that the
proposed system is generally unstable. If the observed anomalous timing
variations of the binary period is due to additional circumbinary companions,
then the resulting system should exhibit a long-term stable orbital
configuration much different from the orbits suggested by Lee et al. We,
therefore, suggest that based on Newtonian-dynamical considerations, the
proposed quadruple system cannot exist. To uncover the true nature of the
observed period variations of this system, we recommend future photometric
follow-up observations that could further constrain eclipse-timing variations
and/or refine light-travel time models.


  The problem of visual object tracking has traditionally been handled by
variant tracking paradigms, either learning a model of the object's appearance
exclusively online or matching the object with the target in an offline-trained
embedding space. Despite the recent success, each method agonizes over its
intrinsic constraint. The online-only approaches suffer from a lack of
generalization of the model they learn thus are inferior in target regression,
while the offline-only approaches (e.g., convolutional siamese trackers) lack
the target-specific context information thus are not discriminative enough to
handle distractors, and robust enough to deformation. Therefore, we propose an
online module with an attention mechanism for offline siamese networks to
extract target-specific features under L2 error. We further propose a filter
update strategy adaptive to treacherous background noises for discriminative
learning, and a template update strategy to handle large target deformations
for robust learning. Effectiveness can be validated in the consistent
improvement over three siamese baselines: SiamFC, SiamRPN++, and SiamMask.
Beyond that, our model based on SiamRPN++ obtains the best results over six
popular tracking benchmarks and can operate beyond real-time.


  The common envelope evolution (CEE) is vital in forming short orbital period
compact binaries. It covers many objects, such as double compact merging
binaries, type Ia supernovae progenitors, binary pulsars, and X-ray binaries.
Knowledge about the common envelope (CE) eject efficiency still needs to be
improved, though progress has been made recently. Short orbital period hot
subdwarf B star plus white dwarf binaries are the most straightforward samples
to constrain CEE physics. We apply the known orbital period-white dwarf
relation to constrain the sdB progenitor of seven sdB+WD binaries with a known
inclination angle. The average value of the CE efficiency parameter is 0.32,
which is consistent with previous studies. However, the CE efficiency might not
be a constant but is a function of the initial mass ratio based on
well-constrained sdB progenitor mass and evolutionary stage. Our results can be
used as physical inputs for binary population synthesis simulations on related
objects. A similar method can also be applied to study other short orbital
period WD binaries.


  In this paper we study the spectra of bounded self-adjoint linear operators
that are related to finite Hilbert transforms $\mathcal{H}_L:L^2([b_L,0])\to
L^2([0,b_R])$ and $\mathcal{H}_R:L^2([0,b_R])\to L^2([b_L,0])$. These operators
arise when one studies the interior problem of tomography. The diagonalization
of $\mathcal{H}_R,\mathcal{H}_L$ has been previously obtained, but only
asymptotically when $b_L\neq-b_R$. We implement a novel approach based on the
method of matrix Riemann-Hilbert problems (RHP) which diagonalizes
$\mathcal{H}_R,\mathcal{H}_L$ explicitly. We also find the asymptotics of the
solution to a related RHP and obtain error estimates.