  It has been shown recently [PRL 102, 254503 (2009)] that in the plane-plane
configuration a mechanical resonator vibrating close to a rigid wall in a
simple fluid can be overdamped to a frozen regime. Here, by solving
analytically the Navier Stokes equations with partial slip boundary conditions
at the solid fluid interface, we develop a theoretical approach justifying and
extending these earlier findings. We show in particular that in the perfect
slip regime the above mentioned results are, in the plane-plane configuration,
very general and robust with respect to lever geometry considerations. We
compare the results with those obtained previously for the sphere moving
perpendicularly and close to a plane in a simple fluid and discuss in more
details the differences concerning the dependence of the friction forces with
the gap distance separating the moving object (i.e., plane or sphere) from the
fixed plane. Finally, we show that the submicron fluidic effect reported in the
reference above, and discussed further in the present work, can have dramatic
implications in the design of nano-electromechanical systems (NEMS).


  We investigate the cross-correlation between the cosmic infrared background
(CIB) and cosmic microwave background (CMB) anisotropies due to the integrated
Sachs-Wolfe (ISW) effect. We first describe the CIB anisotropies using a
linearly biased power spectrum, valid on the angular scales of interest. From
this, we derive the theoretical angular power spectrum of the CMB-CIB
cross-correlation for different instruments and frequencies. Our cross-spectra
show similarities in shape with usual CMB/galaxies cross-correlations. We
discuss the detectability of the ISW signal by performing a signal-to-noise
(SNR) analysis with our predicted spectra. Our results show that : (i) in the
ideal case of noiseless, full-sky maps, the significances obtained range from 6
to 7 sigmas depending on the frequency, with a maximum at 353 GHz (ii) in
realistic cases which account for the presence of noise including astrophysical
contaminents, the results depend strongly on the major contribution to the
noise term. They span from 2 to 5 sigmas, the most favorable frequency for
detection being 545 GHz. We also find that the joint use of all available
frequencies in the cross-correlation does not improve significantly the total
SNR, due to the high level of correlation of the CIB maps at different
frequencies.


  We study a generalization of the Random Energy Model to the case when the
number of exponential factors varies at random. Also a relation between REM and
the Erd"os-R'enyi limit theorem for maximums of partial sums is considered.


  We describe the special relativistic extension of the CRONOS code, which has
been used for studies of gamma-ray binaries in recent years. The code was
designed to be easily adaptable, allowing the user to easily change existing
functionalities or introduce new modules tailored to the problem at hand.
Numerically, the equations are treated using a finite-volume Godunov scheme on
rectangular grids, which currently support Cartesian, spherical, and
cylindrical coordinates. The employed reconstruction technique, the approximate
Riemann solver and the equation of state can be chosen dynamically by the user.
Further, the code was designed with stability and robustness in mind, detecting
and mitigating possible failures early on. We demonstrate the code's
capabilities on an extensive set of validation problems.


  Understanding vascular adaptation, namely what drives veins to shrink or
grow, is key for the self-organization of flow networks and their optimization.
From the top-down principle of minimizing flow dissipation at a fixed metabolic
cost within flow networks, flow shear rate resulting from the flows pervading
veins is hypothesized to drive vein adaptation. Yet, there is no bottom-up
derivation of how flow forces impact vein dynamics. From the physical principle
of force balance, shear rate acts parallel to vein walls, and hence, naively
shear rate could only stretch veins and not dilate or shrink them. We, here,
resolve this paradox by theoretically investigating force balance on a vein
wall in the context of the vascular network of the model organism Physarum
polycephalum. We propose, based on previous mechanical studies of cross-linked
gels, that shear induces a nonlinear, anisotropic response of the actomyosin
gel, making up vein walls that can indeed drive vein dilatation. Furthermore,
our force balance approach allows us to identify that shear feedback occurs
with a typical timescale and with a typical target shear rate that are not
universal properties of the material but instead depend smoothly on the
location of the vein within the network. In particular, the target shear rate
is related to the vein's hydrostatic pressure, which highlights the role of
pressure in vascular adaptation. Finally, since our derivation is based on
force balance and fluid mechanics, we believe our approach can be extended to
vascular adaptation in other organisms.


  The space spanned by theta series of adjoints of maximal even lattices of
exact level $N$ and determinant $N^2$ has the Weierstrass property and hence
allows to define extremality for arbitrary squarefree level $N$. We find
examples of such dual extremal lattices.


  Textual overlays are often used in social media videos as people who watch
them without the sound would otherwise miss essential information conveyed in
the audio stream. This is why extraction of those overlays can serve as an
important meta-data source, e.g. for content classification or retrieval tasks.
In this work, we present a robust method for extracting textual overlays from
videos that builds up on multiple neural network architectures. The proposed
solution relies on several processing steps: keyframe extraction, text
detection and text recognition. The main component of our system, i.e. the text
recognition module, is inspired by a convolutional recurrent neural network
architecture and we improve its performance using synthetically generated
dataset of over 600,000 images with text prepared by authors specifically for
this task. We also develop a filtering method that reduces the amount of
overlapping text phrases using Levenshtein distance and further boosts system's
performance. The final accuracy of our solution reaches over 80A% and is au
pair with state-of-the-art methods.


  Many real-world networks are characterized by directionality; however, the
absence of an appropriate Fourier basis hinders the effective implementation of
graph signal processing techniques.
  Inspired by discrete signal processing, where embedding a line digraph into a
cycle digraph facilitates the powerful Discrete Fourier Transform for signal
analysis, addressing the structural complexities of general digraphs can help
overcome the limitations of the Graph Fourier Transform (GFT) and unlock its
potential.
  The Discrete Fourier Transform (DFT) serves as a Graph Fourier Transform for
both cycle graphs and Cayley digraphs on the finite cyclic groups
$\mathbb{Z}_N$. We propose a systematic method to identify a class of such
Cayley digraphs that can encompass a given directed graph. By embedding the
directed graph into these Cayley digraphs and opting for envelope extensions
that naturally support the Graph Fourier Transform, the GFT functionalities of
these extensions can be harnessed for signal analysis.
  Among the potential envelopes, optimal performance is achieved by selecting
one that meets key properties. This envelope's structure closely aligns with
the characteristics of the original digraph. The Graph Fourier Transform of
this envelope is reliable in terms of numerical stability, and its columns
approximately form an eigenbasis for the adjacency matrix associated with the
original digraph.
  It is shown that the envelope extensions possess a convolution product, with
their GFT fulfilling the convolution theorem. Additionally, shift-invariant
graph filters (systems) are described as the convolution operator, analogous to
the classical case. This allows the utilization of systems for signal analysis.


  We present results obtained from X-ray observations of the anomalous X-ray
pulsar (AXP) 4U 0142+61 taken between 2000-2007 using XMM-Newton, Chandra and
Swift. In observations taken before 2006, the pulse profile is observed to
become more sinusoidal and the pulsed fraction increased with time. These
results confirm those derived using the Rossi X-ray Timing Explorer and expand
the observed evolution to energies below 2 keV. The XMM-Newton total flux in
the 0.5-10 keV band is observed to be nearly constant in observations taken
before 2006, while an increase of ~10% is seen afterwards and coincides with
the burst activity detected from the source in 2006-2007. After these bursts,
the evolution towards more sinusoidal pulse profiles ceased while the pulsed
fraction showed a further increase. No evidence for large-scale, long-term
changes in the emission as a result of the bursts is seen. The data also
suggest a correlation between the flux and hardness of the spectrum, with
brighter observations on average having a harder spectrum. As pointed out by
other authors, we find that the standard blackbody plus power-law model does
not provide the best spectral fit to the emission from 4U 0142+61. We also
report on observations taken with the Gemini telescope after two bursts. These
observations show source magnitudes consistent with previous measurements. Our
results demonstrate the wide range of X-ray variability characteristics seen in
AXPs and we discuss them in light of current emission models for these sources.


  Rejoinder to "The Essential Role of Pair Matching in Cluster-Randomized
Experiments, with Application to the Mexican Universal Health Insurance
Evaluation" [arXiv:0910.3752]


  Gaussian processes are flexible function approximators, with inductive biases
controlled by a covariance kernel. Learning the kernel is the key to
representation learning and strong predictive performance. In this paper, we
develop functional kernel learning (FKL) to directly infer functional
posteriors over kernels. In particular, we place a transformed Gaussian process
over a spectral density, to induce a non-parametric distribution over kernel
functions. The resulting approach enables learning of rich representations,
with support for any stationary kernel, uncertainty over the values of the
kernel, and an interpretable specification of a prior directly over kernels,
without requiring sophisticated initialization or manual intervention. We
perform inference through elliptical slice sampling, which is especially well
suited to marginalizing posteriors with the strongly correlated priors typical
to function space modelling. We develop our approach for non-uniform,
large-scale, multi-task, and multidimensional data, and show promising
performance in a wide range of settings, including interpolation,
extrapolation, and kernel recovery experiments.


  High resolution electron energy loss spectroscopy (HR-EELS) is utilized to
probe the surface spin canting in nanoparticles of two technologically
important magnetic materials, i.e. Fe3O4 and CoFe2O4 (CFO). A soft experimental
technique is developed that is capable of extracting EELS spectra with one
atomic plane resolution recorded in a single frame. This yields information at
different depth of the nanoparticle from the surface to the core regions with
high signal to noise ratio and without beam damage. This enables comparing the
fine structures between the surface and core regions of the nanoparticles. The
results confirm earlier observations of uniformly oriented spin canting
structure for CFO with additional information on atom site-selective spin
canting information. In case of Fe3O4 preferred canting orientation forming
core and shell structure is deduced. Unlike earlier reports based on polarized
spin-flip neutron scattering measurement, it is possible to narrow down the
possible canting angles for Fe3O4 (Td, Oh tilts 40{\deg}, 40{\deg}) and CFO
(Td, Oh tilts 17{\deg}, 17{\deg}) from the experimental spectra combined with
the first principle based calculation considering non-collinear magnetism. In
addition, the role of Dzyaloshinskii-Moriya interaction in stabilizing the spin
canting at the nanoparticle surface is discussed. The results demonstrate that
HREELS can be a powerful technique to probe the magnetic structure in
nano-dimensional systems and has advantages over neutron based techniques in
terms of superior spatial resolution, site specific information and easy of
sample preparation.


  If modern computers are sometimes superior to humans in some specialized
tasks such as playing chess or browsing a large database, they can't beat the
efficiency of biological vision for such simple tasks as recognizing and
following an object in a complex cluttered background. We present in this paper
our attempt at outlining the dynamical, parallel and event-based representation
for vision in the architecture of the central nervous system. We will
illustrate this on static natural images by showing that in a signal matching
framework, a L/LN (linear/non-linear) cascade may efficiently transform a
sensory signal into a neural spiking signal and we will apply this framework to
a model retina. However, this code gets redundant when using an over-complete
basis as is necessary for modeling the primary visual cortex: we therefore
optimize the efficiency cost by increasing the sparseness of the code. This is
implemented by propagating and canceling redundant information using lateral
interactions. We compare the efficiency of this representation in terms of
compression as the reconstruction quality as a function of the coding length.
This will correspond to a modification of the Matching Pursuit algorithm where
the ArgMax function is optimized for competition, or Competition Optimized
Matching Pursuit (COMP). We will in particular focus on bridging neuroscience
and image processing and on the advantages of such an interdisciplinary
approach.


  A self-adjoint element in a finite AW*-factor is spectrally symmetric, if its
spectral measure under the quasitrace is invariant under the change of
variables $t\longmapsto -t$. We show that if $\mathcal{A}$ is an AW*-factor of
type II_1, a self-djoint element in $\mathcal{A}$, without full support, has
quasitrace zero, if and only if it can be written as a sum of at most three
commuting spectrally symmetric elements.


  Recently, reconfigurable intelligent surface (RIS) has gained research and
development interests to modify wireless channel characteristics in order to
improve performance of wireless communications, especially when quality of the
line-of-sight channel is not that good. In this work, for the first time in the
literature, we have used simultaneously transmitting and reflecting RIS
(STAR-RIS) in non-orthogonal multiple-access visible light communication system
to improve performance of the system. Achievable rates of the users are derived
for two data recovery schemes, single-user detection (SUD) and successive
interference cancellation (SIC). Then, sum-rate optimization problem is
formulated for two operating modes of STAR-RIS, namely energy-splitting and
mode-switching cases. Moreover, a sequential parametric convex approximation
method is used to solve the sum-rate optimization problems. We have also
compared energy-splitting and mode-switching cases and showed that these two
modes have the same performance. Finally, numerical results for SUD and SIC
schemes and two benchmarking schemes, time-sharing and max-min fairness, are
presented and spectral- and energy-efficiency, number of STAR-RIS elements,
position of users and access point are discussed.


  The paper is a sequel to our previous work (Zhang et al. Phys. Rev. D 103,
062001 (2021)). For proposed geocentric space-based gravitational wave
detectors such as TianQin, gLISA, and GADFLI, the gravity-field disturbances,
i.e., the so called ``orbital noise'', from the Earth-Moon system on the
sensitive intersatellite laser interferometric measurements should be carefully
evaluated and taken into account in the concept studies. Based on TianQin, we
investigate how the effect, in terms of frequency spectra, varies with
different choices of orbital orientations and radii through single-variable
studies, and present the corresponding roll-off frequencies that may set the
lower bounds of the targeted detection bands. The results, including the
special cases of geostationary orbits (gLISA/GADFLI) and repeat orbits, can
provide a useful input to orbit and constellation design for future geocentric
missions.


  We construct the quantum versions of the monodromy matrices of KdV theory.
The traces of these quantum monodromy matrices, which will be called as ``${\bf
T}$-operators'', act in highest weight Virasoro modules. The ${\bf
T}$-operators depend on the spectral parameter $\lambda$ and their expansion
around $\lambda = \infty$ generates an infinite set of commuting Hamiltonians
of the quantum KdV system. The ${\bf T}$-operators can be viewed as the
continuous field theory versions of the commuting transfer-matrices of
integrable lattice theory. In particular, we show that for the values
$c=1-3{{(2n+1)^2}\over {2n+3}} , n=1,2,3,... $of the Virasoro central charge
the eigenvalues of the ${\bf T}$-operators satisfy a closed system of
functional equations sufficient for determining the spectrum. For the
ground-state eigenvalue these functional equations are equivalent to those of
massless Thermodynamic Bethe Ansatz for the minimal conformal field theory
${\cal M}_{2,2n+3}$; in general they provide a way to generalize the technique
of Thermodynamic Bethe Ansatz to the excited states. We discuss a
generalization of our approach to the cases of massive field theories obtained
by perturbing these Conformal Field Theories with the operator $\Phi_{1,3}$.
The relation of these ${\bf T}$-operators to the boundary states is also
briefly described.


  Simulating quantum circuits using classical computers can accelerate the
development and validation of quantum algorithms. Our newly developed
algorithm, variational quantum search (VQS), has shown an exponential advantage
over Grover's algorithm in the range from 5 to 26 qubits, in terms of circuit
depth, for searching unstructured databases. We need to further validate the
VQS for more than 26 qubits. Numerous simulators have been developed. However,
it is not clear which simulator is most suitable for executing VQS with many
qubits. To solve this issue, we implement a typical quantum circuit used in VQS
on eight mainstream simulators. Results show that the time and memory required
by most simulators increase exponentially with the number of qubits and that
Pennylane with GPU and Qulacs are the most suitable simulators for executing
VQS efficiently. Our results aid researchers in selecting suitable quantum
simulators without the need for exhaustive implementation, and we have made our
codes available for community contributions.


  Most V2X (Vehicle-to-Everything) applications rely on broadcasting awareness
messages known as CAM (Cooperative Awareness Messages) in ETSI or BSM (Basic
Safety Message) in SAE standards. A large number of studies have been devoted
to guarantee their reliable transmission. However, to date, the studies are
generally based on simplified data traffic models that generate awareness
messages at periodic intervals or with a constant message size. These models do
not accurately represent the real generation of CAM messages that follow
specific mobility-based rules. Using simplified and unrealistic traffic models
can significantly impact the results and validity of the studies, and hence
accurate models for the generation of awareness messages are necessary. This
paper proposes the first set of models that can realistically generate CAM
messages. The models have been created from real traces collected by two car
manufacturers in urban, sub-urban and highway test drives. The models are based
on mth order Markov sources, and model the size of CAMs and the time interval
between CAMs. The models are openly provided to the community and can be easily
integrated into any simulator.


  We present an approach for object segmentation in videos that combines
frame-level object detection with concepts from object tracking and motion
segmentation. The approach extracts temporally consistent object tubes based on
an off-the-shelf detector. Besides the class label for each tube, this provides
a location prior that is independent of motion. For the final video
segmentation, we combine this information with motion cues. The method
overcomes the typical problems of weakly supervised/unsupervised video
segmentation, such as scenes with no motion, dominant camera motion, and
objects that move as a unit. In contrast to most tracking methods, it provides
an accurate, temporally consistent segmentation of each object. We report
results on four video segmentation datasets: YouTube Objects, SegTrackv2,
egoMotion, and FBMS.


  We consider QCD at temperatures T near Tc, where the theory deconfines. We
distinguish between a "complete" Quark Gluon Plasma (QGP), where the vacuum
expectation value of the renormalized Polyakov loop is near unity, essentially
constant with T, and the "semi"-QGP, where the loop changes strongly with T.
Lattice simulations indicate that in QCD, there is a semi-QGP from below Tc to
a few times that. Using a semi-classical model, we compute the shear viscosity,
eta, to leading order in perturbation theory. We find that near Tc, where the
expectation value of the Polyakov loop is small, that eta/T^3 is suppressed by
two powers of the loop. For heavy ions, this suggests that during the initial
stages of the collision, hydrodynamic behavior at the LHC will be characterized
by a shear viscosity which is significantly larger than that at RHIC.


  Exact bounds for the positions of the branch points for cyclic coverings of
the $p$-adic projective line by Mumford curves are calculated in two ways.
Firstly, by using Fumiharu Kato's *-trees, and secondly by giving explicit
matrix representations of the Schottky groups corresponding to the Mumford
curves above the projective line through combinatorial group theory.


  The last years have seen a growing interest from mathematicians in Mahler
functions. This class of functions includes the generating series of the
automatic sequences. The present paper is concerned with the following problem,
which is omnipresent in combinatorics: a set of Mahler functions
$u_{1},...,u_{n}$ being given, are $u_{1},...,u_{n}$ and their successive
derivatives algebraically independent? In this paper, we give general criteria
ensuring an affirmative answer to this question. We apply our main results to
the generating series attached to the so-called Baum-Sweet and Rudin-Shapiro
automatic sequences. In particular, we show that these series are
hyperalgebraically independent, i.e., that these series and their successive
derivatives are algebraically independent. Our approach relies of the
parametrized difference Galois theory (in this context, the
algebro-differential relations between the solutions of a given Mahler equation
are reflected by a linear differential algebraic group).


  In this paper, we consider a multiple-input multiple-output broadcast channel
with limited feedback where all users share the feedback rates. Firstly, we
find the optimal feedback rate sharing strategy using zero-forcing transmission
scheme at the transmitter and random vector quantization at each user. We
mathematically prove that equal sharing of sum feedback size among all users is
the optimal strategy in the low signal-to-noise ratio (SNR) region, while
allocating whole feedback size to a single user is the optimal strategy in the
high SNR region. For the mid-SNR region, we propose a simple numerical method
to find the optimal feedback rate sharing strategy based on our analysis and
show that the equal allocation of sum feedback rate to a partial number of
users is the optimal strategy. It is also shown that the proposed simple
numerical method can be applicable to finding the optimal feedback rate sharing
strategy when different path losses of the users are taken into account. We
show that our proposed feedback rate sharing scheme can be extended to the
system with stream control and is still useful for the systems with other
techniques such as regularized zero-forcing and spherical cap codebook.


  To understand the process of pattern formation in a low-density granular
flow, we propose a simple particle model. This model considers spherical
particles moving over an inclined flat surface based on three forces: gravity
as the driving force, repulsive force due to particle collision, and drag force
as the particle-- interaction through the ambient fluid. Numerical simulations
of this model are conducted in two different types of two-dimensional planes,
i.e., the monolayer was treated. In the horizontal plane parallel to the slope,
particles aggregate at the moving front of the granular flow; and subsequently,
flow instability occurs as a wavy pattern. This flow pattern is caused by the
interparticle interaction arising from the drag force. Additionally, a vortex
convection of particles is formed inside the aggregations. Meanwhile, in the
vertical plane on the slope, particle aggregation is also found at the moving
front of the granular flow. The aggregation resembles a head--tail structure,
where the frontal angle against the slope approaches 60 degree from a larger
angle as time progresses. Comparing the numerical result by varying the
particle size, the qualitative dynamics of the granular flow are independent of
size. To elucidate this reason, we perform a nondimensionalization for this
model. The result indicates that our model can be simplified to dimensionless
equations with one dimensionless parameter that represents the ratio of the
gravity term to the excluded volume effect.


  In this paper we discuss the consequences of including a new heavy
right-handed neutrino singlet $N_R$ in the littlest Higgs model. This new state
is not connected with the light neutrinos {\it via} the seesaw mechanism. A
very interesting property of this extended model is the full coupling of the
new neutral gauge boson $A_H$ to $N_R$, giving large total cross sections and
suggesting a wide range of experimental search for the $N_R$ at the p p
collider CERN-LHC and future electron-positron collider ILC.


  We consider the problem of decompressing the Lempel--Ziv 77 representation of
a string $S$ of length $n$ using a working space as close as possible to the
size $z$ of the input. The folklore solution for the problem runs in $O(n)$
time but requires random access to the whole decompressed text. Another
folklore solution is to convert LZ77 into a grammar of size $O(z\log(n/z))$ and
then stream $S$ in linear time. In this paper, we show that $O(n)$ time and
$O(z)$ working space can be achieved for constant-size alphabets. On general
alphabets of size $\sigma$, we describe (i) a trade-off achieving
$O(n\log^\delta \sigma)$ time and $O(z\log^{1-\delta}\sigma)$ space for any
$0\leq \delta\leq 1$, and (ii) a solution achieving $O(n)$ time and
$O(z\log\log (n/z))$ space. The latter solution, in particular, dominates both
folklore algorithms for the problem. Our solutions can, more generally, extract
any specified subsequence of $S$ with little overheads on top of the linear
running time and working space. As an immediate corollary, we show that our
techniques yield improved results for pattern matching problems on
LZ77-compressed text.


  One the base of Maxwell and Dirac equations the one biquaternionic model of
electro-gravimagnetic (EGM) fields is considered. The closed system of
biquaternionic wave equations is constructed for determination of free system
of electric and gravimagnetic charges and currents and generated by them
EGM-field. By using generalized functions theory the fundamental and regular
solutions of this system are determined and some of them are considered
(spinors, plane waves, shock EGMwaves and others). The properties of these
solutions are investigated.


  We propose how to control the THz radiation generated by fast moving
Josephson vortices in spatially modulated (either along the c-axis or the
ab-plane) samples of Bi-2212 and related superconducting compounds. We show
that the JVs moving in a subset of weaker junctions can generate
out-of-ab-plane and outside-the-cone Cherenkov radiation. The ab-plane
modulation of superconducting properties (achieved, for instance, by ion
irradiation lithography) can result in transition radiation within certain
frequency windows, i.e., allowing the design of tunable THz emitters and THz
photonic crystals.


  Medical health care centers are envisioned as a promising paradigm to handle
the massive volume of data of COVID-19 patients using artificial intelligence
(AI). Traditionally, AI techniques often require centralized data collection
and training the model in a single organization, which is most common weakness
due to the privacy and security of raw data communication. To solve this
challenging task, we propose a blockchain-based federated learning framework
that provides collaborative data training solutions by coordinating multiple
hospitals to train and share encrypted federated models without leakage of data
privacy. The blockchain ledger technology provides the decentralization of
federated learning model without any central server. The proposed homomorphic
encryption scheme encrypts and decrypts the gradients of model to preserve the
privacy. More precisely, the proposed framework: i) train the local model by a
novel capsule network to segmentation and classify COVID-19 images, ii) then
use the homomorphic encryption scheme to secure the local model that encrypts
and decrypts the gradients, and finally the model is shared over a
decentralized platform through proposed blockchain-based federated learning
algorithm. The integration of blockchain and federated learning leads to a new
paradigm for medical image data sharing in the decentralized network. The
conducted experimental resultsdemonstrate the performance of the proposed
scheme.


  We study the optimal investment-consumption problem for a member of defined
contribution plan during the decumulation phase. For a fixed annuitization
time, to achieve higher final annuity, we consider a variable consumption rate.
Moreover, to have a minimum guarantee for the final annuity, a safety level for
the wealth process is considered. To solve the stochastic optimal control
problem via dynamic programming, we obtain a Hamilton-Jacobi-Bellman (HJB)
equation on a bounded domain. The existence and uniqueness of classical
solutions are proved through the dual transformation. We apply the finite
difference method to find numerical approximations of the solution of the HJB
equation. Finally, the simulation results for the optimal
investment-consumption strategies, optimal wealth process and the final annuity
for different admissible ranges of consumption are given. Furthermore, by
taking into account the market present value of the cash flows before and after
the annuitization, we compare the outcomes of different scenarios.


  This tutorial discusses methodology for causal inference using longitudinal
modified treatment policies. This method facilitates the mathematical
formalization, identification, and estimation of many novel parameters, and
mathematically generalizes many commonly used parameters, such as the average
treatment effect. Longitudinal modified treatment policies apply to a wide
variety of exposures, including binary, multivariate, and continuous, and can
accommodate time-varying treatments and confounders, competing risks,
loss-to-follow-up, as well as survival, binary, or continuous outcomes.
Longitudinal modified treatment policies can be seen as an extension of static
and dynamic interventions to involve the natural value of treatment, and, like
dynamic interventions, can be used to define alternative estimands with a
positivity assumption that is more likely to be satisfied than estimands
corresponding to static interventions. This tutorial aims to illustrate several
practical uses of the longitudinal modified treatment policy methodology,
including describing different estimation strategies and their corresponding
advantages and disadvantages. We provide numerous examples of types of research
questions which can be answered using longitudinal modified treatment policies.
We go into more depth with one of these examples--specifically, estimating the
effect of delaying intubation on critically ill COVID-19 patients' mortality.
We demonstrate the use of the open-source R package lmtp to estimate the
effects, and we provide code on https://github.com/kathoffman/lmtp-tutorial.


  Here we study the electronic properties of cuprate/manganite interfaces. By
means of atomic resolution electron microscopy and spectroscopy, we produce a
subnanometer scale map of the transition metal oxidation state profile across
the interface between the high $T_c$ superconductor YBa$_2$Cu$_3$O$_{7-\delta}$
and the colossal magnetoresistance compound (La,Ca)MnO$_3$. A net transfer of
electrons from manganite to cuprate with a peculiar non-monotonic charge
profile is observed. Model calculations rationalize the profile in terms of the
competition between standard charge transfer tendencies (due to band mismatch),
strong chemical bonding effects across the interface, and Cu substitution into
the Mn lattice, with different characteristic length scales.


  The total photodetachment cross section of a linear triatomic anion is
derived for arbitrary laser polarization direction. The cross section is shown
to be strongly oscillatory when the laser polarization direction is parallel to
the axis of the system; the oscillation amplitude decreases and vanishes as the
angle between the laser polarization and the anion axis increases and becomes
perpendicular to the axis. The average cross section over the orientations of
the triatomic system is also obtained. The cross section of the triatomic anion
is compared with the cross section of a two-center system. We find there are
two oscillation frequencies in the triatomic anion in contrast to only one
oscillation frequency in the two-center case. Closed-orbit theory is used to
explain the oscillations.


  Inventory management problems with periodic and controllable resets occur in
the context of managing water storage in the developing world and retailing
limited-time availability products. In this paper, we consider a set of
sequential decision problems in which the decision-maker must not only balance
holding and shortage costs but discard all inventory before a fixed number of
decision epochs, with the option for an early inventory reset. Finding optimal
policies using dynamic programming for these problems is particularly
challenging since the resulting value functions are non-convex. Moreover, this
structure cannot be easily analyzed using existing extended definitions, such
as $K$-convexity. Our key contribution is to present sufficient conditions that
ensure the optimal policy has an easily interpretable structure that
generalizes the well-known $(s, S)$ policy from the operations literature.
Furthermore, we demonstrate that the optimal policy has a four-threshold
structure under these rather mild conditions. We then conclude with
computational experiments, thereby illustrating the policy structures that can
be extracted in several inventory management scenarios.


  In recent years, cosmic observational data have reported that our present
universe is undergoing an accelerated expansion, which has been termed as
mysterious "dark energy" phenomena, that is, the origin of dark energy has not
been determined yet. According to our previous work that a new equation of
state \cite{X.Shan and H.Chen1993} can be employed to explain the dark energy,
and we are very interested in investigating the astrophysical scale properties
of dark energy based on the new cosmological fluids given that the universe is
filled with a dark energy fluid everywhere. Hence, in this paper, we study the
exact solutions of spherically-symmetrical Einstein field equations describing
wormholes supported by phantom energy from Shan-Chen (SC) fluids by considering
an obvious relation between the transversal pressure and energy density which
is different from our previous work \cite{111}. We have still investigated the
important case $\psi\approx1$ which corresponds to the " saturation effect ",
and this regime corresponds to an effective form of " asymptotic freedom ",
occurring at cosmological rather than subnuclear scales. Then we find out two
solutions by making some special choices for the shape function $b(r)$ and
discuss the singularities of the solutions and find that the spacetimes are
both geodesically incomplete. At the same time, it is worth noting that whether
the solutions are geodesically complete or incomplete depends on the
appropriate choice of the shape function $b(r)$ through our works. In addition,
we match our interior solutions to the exterior Schwarzschild solutions and
calculate out the total mass of the wormhole when $r\leq a$.


  Decoherence is a major obstacle to the preparation of topological order in
noisy intermediate-scale quantum devices. Here, we show that decoherence can
also give rise to new types of topological order. Specifically, we construct
concrete examples by proliferating fermionic anyons in the toric code via local
quantum channels. The resulting mixed states retain long-range entanglement,
which manifests in the nonzero topological entanglement negativity, though the
topological quantum memory is destroyed by decoherence. By comparison to the
gapless spin liquid in pure states, we show that the identified states
represent a novel intrinsic mixed-state quantum topological order, which has no
counterpart in pure states. Through the lens of quantum anomalies of 1-form
symmetries, we then provide general constructions of intrinsic mixed-state
quantum topological order, and reveal the existence of non-bosonic deconfined
anyons as another key feature of these novel phases. The extended meaning and
characterization of deconfined excitations and their statistics in mixed states
are clarified. Moreover, when these deconfined anyons have nontrivial braiding
statistics, we prove that the mixed states %must be bipartite long-range
entangled for any bipartition. That is, such mixed states cannot be prepared
via finite-depth local quantum channels from any bipartite separable states. We
further demonstrate our construction using the decohered Kitaev honeycomb model
and the decohered double semion model. In the latter case, a surprising
scenario arises where decoherence gives rise to additional types of deconfined
anyons.


  In this article, we review the analytical and numerical approaches for
computing the phase space structures in two degrees-of-freedom Hamiltonian
systems that arise in chemical reactions. In particular, these phase space
structures are the unstable periodic orbit associated with an index-1 saddle,
the periodic orbit dividing surface, and the stable and unstable invariant
manifolds of the unstable periodic orbit. We review the approaches in the
context of a two degrees-of-freedom Hamiltonian with a quartic potential
coupled with a quadratic potential. We derive the analytical form of the phase
space structures for the integrable case and visualize their geometry on the
three dimensional energy surface. We then investigate the bifurcation of the
dividing surface due to the changes in the parameters of the potential energy.
We also review the numerical method of \emph{turning point} and present its new
modification called the \emph{turning point based on configuration difference}
for computing the unstable periodic orbit in two degrees-of-freedom systems.
These methods are implemented in the open-source python package,
UPOsHam~\cite{Lyu2020}.


  We present a comparison review that evaluates popular techniques for garment
draping for 3D fashion design, virtual try-ons, and animations. A comparative
study is performed between various methods for garment draping of clothing over
the human body. These include numerous models, such as physics and machine
learning based techniques, collision handling, and more. Performance
evaluations and trade-offs are discussed to ensure informed decision-making
when choosing the most appropriate approach. These methods aim to accurately
represent deformations and fine wrinkles of digital garments, considering the
factors of data requirements, and efficiency, to produce realistic results. The
research can be insightful to researchers, designers, and developers in
visualizing dynamic multi-layered 3D clothing.


  Random linear network coding (RLNC) unicast protocol is analyzed over a
rapidly-changing network topology. We model the probability mass function (pmf)
of the dissemination time as a sequence of independent geometric random
variables whose success probability changes with every successful reception of
an innovative packet. We derive a tight approximation of the average networked
innovation probability conditioned on network dimension increase. We show
through simulations that our approximations for the average dissemination time
and its pmf are tight. We then propose to use a RLNC-based broadcast
dissemination protocol over a general dynamic topology where nodes are chosen
for transmission based on average innovative information that they can provided
to the rest of the network. Simulation results show that information
disseminates considerably faster as opposed to standard RLNC algorithm where
nodes are chosen uniformly at random.


  In this paper we consider the fourth order operators A1u := (au")" and A2u :=
au"" in divergence form and non divergence form, respectively, where a, defined
in [0, 1] with values in R+, degenerates in an interior point of the interval.
Using the semigroup technique, under suitable assumptions on a, we study the
generation property of these operators associated to generalized Wentzell
boundary conditions, proving the well posedness of the corresponding parabolic
problems.


  K-means is one of the most widely used clustering models in practice. Due to
the problem of data isolation and the requirement for high model performance,
how to jointly build practical and secure K-means for multiple parties has
become an important topic for many applications in the industry. Existing work
on this is mainly of two types. The first type has efficiency advantages, but
information leakage raises potential privacy risks. The second type is provable
secure but is inefficient and even helpless for the large-scale data sparsity
scenario. In this paper, we propose a new framework for efficient
sparsity-aware K-means with three characteristics. First, our framework is
divided into a data-independent offline phase and a much faster online phase,
and the offline phase allows to pre-compute almost all cryptographic
operations. Second, we take advantage of the vectorization techniques in both
online and offline phases. Third, we adopt a sparse matrix multiplication for
the data sparsity scenario to improve efficiency further. We conduct
comprehensive experiments on three synthetic datasets and deploy our model in a
real-world fraud detection task. Our experimental results show that, compared
with the state-of-the-art solution, our model achieves competitive performance
in terms of both running time and communication size, especially on sparse
datasets.


  We investigate systems of bosonic particles at zero temperature in triangular
and hexagonal optical lattice potentials in the framework of the Bose-Hubbard
model. Employing the process-chain approach, we obtain accurate values for the
boundaries between the Mott insulating phase and the superfluid phase. These
results can serve as reference data for both other approximation schemes and
upcoming experiments. Since arbitrary integer filling factors g are amenable to
our technique, we are able to monitor the behavior of the critical hopping
parameters with increasing filling. We also demonstrate that the g-dependence
of these exact parameters is described almost perfectly by a scaling relation
inferred from the mean-field approximation.


  This paper exposes the underlying mechanism for obtaining second integral
moments of $GL_2$ automorphic $L$--functions over an arbitrary number field.
Here, moments for $GL_2$ are presented in a form enabling application of the
structure of adele groups and their representation theory. To the best of our
knowledge, this is the first formulation of integral moments in
adele-group-theoretic terms, distinguishing global and local issues, and
allowing uniform application to number fields. When specialized to the field of
rational numbers $\Bbb{Q}$, we recover the classical results.


  We present results for the aging dynamics of a jammed 2D colloidal system
obtained with molecular dynamics simulations. We performed extensive
simulations to gather detailed statistics about rare rearrangement events. With
a simple criterion for identifying irreversible events based on Voronoi
tessellations, we find that the rate of those events decelerates
hyperbolically. We track the probability density function for particle
displacements, the van-Hove function, with sufficient statistics as to reveal
its two-time dependence that is indicative of aging. Those displacements,
measured from a waiting time $t_{w}$ after the quench up to times
$t=t_{w}+\Delta t$, exhibit a data collapse as a function of $\Delta t/t_{w}$.
These findings can be explained comprehensively as manifestations of "record
dynamics", i.e., a relaxation dynamic driven by record-breaking fluctuations.
We show that an on-lattice model of a colloid that was built on record dynamics
indeed reproduces the experimental results in great detail.


  We derive an exact equation governing two-particle backwards mean-squared
dispersion for both deterministic and stochastic tracer particles in turbulent
flows. For the deterministic trajectories, we probe the consequences of our
formula for short times and arrive at approximate expressions for the
mean-squared dispersion which involve second order structure functions of the
velocity and acceleration fields. For the stochastic trajectories, we
analytically compute an exact $t^3$ contribution to the squared separation of
stochastic paths. We argue that this contribution appears also for
deterministic paths at long times and present direct numerical simulation
results for incompressible Navier-Stokes flows to support this claim. We also
numerically compute the probability distribution of particle separations for
the deterministic paths and the stochastic paths and show their strong
self-similar nature.


  The automated segmentation of Intracranial Arteries (IA) in Digital
Subtraction Angiography (DSA) plays a crucial role in the quantification of
vascular morphology, significantly contributing to computer-assisted stroke
research and clinical practice. Current research primarily focuses on the
segmentation of single-frame DSA using proprietary datasets. However, these
methods face challenges due to the inherent limitation of single-frame DSA,
which only partially displays vascular contrast, thereby hindering accurate
vascular structure representation. In this work, we introduce DIAS, a dataset
specifically developed for IA segmentation in DSA sequences. We establish a
comprehensive benchmark for evaluating DIAS, covering full, weak, and
semi-supervised segmentation methods. Specifically, we propose the vessel
sequence segmentation network, in which the sequence feature extraction module
effectively captures spatiotemporal representations of intravascular contrast,
achieving intracranial artery segmentation in 2D+Time DSA sequences. For
weakly-supervised IA segmentation, we propose a novel scribble learning-based
image segmentation framework, which, under the guidance of scribble labels,
employs cross pseudo-supervision and consistency regularization to improve the
performance of the segmentation network. Furthermore, we introduce the random
patch-based self-training framework, aimed at alleviating the performance
constraints encountered in IA segmentation due to the limited availability of
annotated DSA data. Our extensive experiments on the DIAS dataset demonstrate
the effectiveness of these methods as potential baselines for future research
and clinical applications. The dataset and code are publicly available at
https://doi.org/10.5281/zenodo.11396520 and https://github.com/lseventeen/DIAS.


  We study a parametrically modulated oscillator that has two stable states of
vibrations at half the modulation frequency $\omega_F$. Fluctuations of the
oscillator lead to interstate switching. A comparatively weak additional field
can strongly affect the switching rates, because it changes the switching
activation energies. The change is linear in the field amplitude. When the
additional field frequency $\omega_d$ is $\omega_F/2$, the field makes the
populations of the vibrational states different thus lifting the states
symmetry. If $\omega_d$ differs from $\omega_F/2$, the field modulates the
state populations at the difference frequency, leading to fluctuation-mediated
wave mixing. For an underdamped oscillator, the change of the activation energy
displays characteristic resonant peaks as a function of frequency.


  While remarkable success has been achieved in weakly-supervised object
localization (WSOL), current frameworks are not capable of locating objects of
novel categories in open-world settings. To address this issue, we are the
first to introduce a new weakly-supervised object localization task called
OWSOL (Open-World Weakly-Supervised Object Localization). During training, all
labeled data comes from known categories and, both known and novel categories
exist in the unlabeled data. To handle such data, we propose a novel paradigm
of contrastive representation co-learning using both labeled and unlabeled data
to generate a complete G-CAM (Generalized Class Activation Map) for object
localization, without the requirement of bounding box annotation. As no class
label is available for the unlabelled data, we conduct clustering over the full
training set and design a novel multiple semantic centroids-driven contrastive
loss for representation learning. We re-organize two widely used datasets,
i.e., ImageNet-1K and iNatLoc500, and propose OpenImages150 to serve as
evaluation benchmarks for OWSOL. Extensive experiments demonstrate that the
proposed method can surpass all baselines by a large margin. We believe that
this work can shift the close-set localization towards the open-world setting
and serve as a foundation for subsequent works. Code will be released at
https://github.com/ryylcc/OWSOL.


  The Cornell Electron-positron Storage Ring (CESR) has been converted from a
High Energy Physics electron-positron collider to operate as a dedicated
synchrotron light source for the Cornell High Energy Synchrotron Source (CHESS)
and to conduct accelerator physics research as a test accelerator, capable of
studying topics relevant to future damping rings, colliders and light sources.
Some of the specific topics that were targeted for the initial phase of
operation of the storage ring in this mode, labeled CesrTA (CESR as a Test
Accelerator), included 1) tuning techniques to produce low emittance beams, 2)
the study of electron cloud development in a storage ring and 3) intra-beam
scattering effects. The complete conversion of CESR to CesrTA occurred over a
several year period, described elsewhere. As a part of this conversion the CESR
beam position monitoring (CBPM) system was completely upgraded to provide the
needed instrumental capabilities for these studies. This paper describes the
new CBPM system hardware, its function and representative measurements
performed by the upgraded system.


  Recent signs of a Standard Model-like Higgs at 125 GeV point towards large
A-terms in the MSSM. This presents special challenges for gauge mediation,
which by itself predicts vanishing A-terms at the messenger scale. In this
paper, we review the general problems that arise when extending gauge mediation
to achieve large A-terms, and the mechanisms that exist to overcome them. Using
these mechanisms, we construct weakly-coupled models of low-scale gauge
mediation with extended Higgs-messenger couplings that generate large A-terms
at the messenger scale and viable mu/B_mu-terms. Our models are simple,
economical, and complete realizations of supersymmetry at the weak scale.


  Theory predicts that graphene under uniaxial compressive strain in an
armchair direction should undergo a topological phase transition from a
semimetal into an insulator. Due to the change of the hopping integrals under
compression, both Dirac points shift away from the corners of the Brillouin
zone towards each other. For sufficiently large strain, the Dirac points merge
and an energy gap appears. However, such a topological phase transition has not
yet been observed in normal graphene (due to its large stiffness) neither in
any other electronic system. We show numerically and analytically that such a
merging of the Dirac points can be observed in electronic artificial graphene
created from a two-dimensional electron gas by application of a triangular
lattice of repulsive antidots. Here, the effect of strain is modeled by tuning
the distance between the repulsive potentials along the armchair direction. Our
results show that the merging of the Dirac points should be observable in a
recent experiment with molecular graphene.


  Magnetic skyrmions have attracted considerable interest, especially after
their recent experimental demonstration at room temperature in multilayers. The
robustness, nanoscale size and non-volatility of skyrmions have triggered a
substantial amount of research on skyrmion-based low-power, ultra-dense
nanocomputing and neuromorphic systems such as artificial synapses.
Room-temperature operation is required to integrate skyrmionic synapses in
practical future devices. Here, we numerically propose a nanoscale skyrmionic
synapse composed of magnetic multilayers that enables room-temperature device
operation tailored for optimal synaptic resolution. We demonstrate that when
embedding such multilayer skyrmionic synapses in a simple spiking neural
network (SNN) with unsupervised learning via the spike-timing-dependent
plasticity rule, we can achieve only a 78% classification accuracy in the MNIST
handwritten data set under realistic conditions. We propose that this
performance can be significantly improved to about 98.61% by using a deep SNN
with supervised learning. Our results illustrate that the proposed skyrmionic
synapse can be a potential candidate for future energy-efficient neuromorphic
edge computing.


  This paper addresses the problem of large-scale image retrieval. We propose a
two-layer fusion method which takes advantage of global and local cues and
ranks database images from coarse to fine (C2F). Departing from the previous
methods fusing multiple image descriptors simultaneously, C2F is featured by a
layered procedure composed by filtering and refining. In particular, C2F
consists of three components. 1) Distractor filtering. With holistic
representations, noise images are filtered out from the database, so the number
of candidate images to be used for comparison with the query can be greatly
reduced. 2) Adaptive weighting. For a certain query, the similarity of
candidate images can be estimated by holistic similarity scores in
complementary to the local ones. 3) Candidate refining. Accurate retrieval is
conducted via local features, combining the pre-computed adaptive weights.
Experiments are presented on two benchmarks, \emph{i.e.,} Holidays and Ukbench
datasets. We show that our method outperforms recent fusion methods in terms of
storage consumption and computation complexity, and that the accuracy is
competitive to the state-of-the-arts.


  We compute the topological charge and its susceptibility in finite
temperature (2+1)-flavor QCD on the lattice applying a gradient flow method.
With the Iwasaki gauge action and nonperturbatively $O(a)$-improved Wilson
quarks, we perform simulations on a fine lattice
with~$a\simeq0.07\,\mathrm{fm}$ at a heavy $u$, $d$ quark mass with
$m_\pi/m_\rho\simeq0.63$ but approximately physical $s$ quark mass with
$m_{\eta_{ss}}/m_\phi\simeq0.74$. In a temperature range
from~$T\simeq174\,\mathrm{MeV}$ ($N_t=16$) to $697\,\mathrm{MeV}$ ($N_t=4$), we
study two topics on the topological susceptibility. One is a comparison of
gluonic and fermionic definitions of the topological susceptibility. Because
the two definitions are related by chiral Ward-Takahashi identities, their
equivalence is not trivial for lattice quarks which violate the chiral symmetry
explicitly at finite lattice spacings. The gradient flow method enables us to
compute them without being bothered by the chiral violation. We find a good
agreement between the two definitions with Wilson quarks. The other is a
comparison with a prediction of the dilute instanton gas approximation, which
is relevant in a study of axions as a candidate of the dark matter in the
evolution of the Universe. We find that the topological susceptibility shows a
decrease in $T$ which is consistent with the predicted $\chi_\mathrm{t}(T)
\propto (T/T_{\rm pc})^{-8}$ for three-flavor QCD even at low temperature
$T_{\rm pc} < T\le1.5 T_{\rm pc}$.


  We discuss general properties of classical string field theories with
symmetric vertices in the context of deformation theory. For a given conformal
background there are many string field theories corresponding to different
decomposition of moduli space of Riemann surfaces. It is shown that any
classical open string field theories on a fixed conformal background are
$A_\infty$-quasi-isomorphic to each other. This indicates that they have
isomorphic moduli space of classical solutions. The minimal model theorem in
$A_\infty$-algebras plays a key role in these results. Its natural and
geometric realization on formal supermanifolds is also given. The same results
hold for classical closed string field theories, whose algebraic structure is
governed by $L_\infty$-algebras.


  An (additive) commutative monoid is called atomic if every given
non-invertible element can be written as a sum of atoms (i.e., irreducible
elements), in which case, such a sum is called a factorization of the given
element. The number of atoms (counting repetitions) in the corresponding sum is
called the length of the factorization. Following Geroldinger and Zhong, we say
that an atomic monoid $M$ is a length-finite factorization monoid if each $b
\in M$ has only finitely many factorizations of any prescribed length. An
additive submonoid of $\mathbb{R}_{\ge 0}$ is called a positive monoid.
Factorizations in positive monoids have been actively studied in recent years.
The main purpose of this paper is to give a better understanding of the
non-unique factorization phenomenon in positive monoids through the lens of the
length-finite factorization property. To do so, we identify a large class of
positive monoids which satisfy the length-finite factorization property. Then
we compare the length-finite factorization property to the bounded and the
finite factorization properties, which are two properties that have been
systematically investigated for more than thirty years.


  The deep neural network (DNN) has been proven effective in various domains.
However, they often struggle to perform well on certain minority groups during
inference, despite showing strong performance on the majority of data groups.
This is because over-parameterized models learned \textit{bias attributes} from
a large number of \textit{bias-aligned} training samples. These bias attributes
are strongly spuriously correlated with the target variable, causing the models
to be biased towards spurious correlations (i.e., \textit{bias-conflicting}).
To tackle this issue, we propose a novel \textbf{re}weighted \textbf{s}parse
\textbf{t}raining framework, dubbed as \textit{\textbf{REST}}, which aims to
enhance the performance of biased data while improving computation and memory
efficiency. Our proposed REST framework has been experimentally validated on
three datasets, demonstrating its effectiveness in exploring unbiased
subnetworks. We found that REST reduces the reliance on spuriously correlated
features, leading to better performance across a wider range of data groups
with fewer training and inference resources. We highlight that the
\textit{REST} framework represents a promising approach for improving the
performance of DNNs on biased data, while simultaneously improving computation
and memory efficiency. By reducing the reliance on spurious correlations, REST
has the potential to enhance the robustness of DNNs and improve their
generalization capabilities. Code is released at
\url{https://github.com/zhao1402072392/REST}


  We introduce and investigate the ribbon groupoid associated with a Garside
group. Under a technical hypothesis, we prove that this category is a Garside
groupoid. We decompose this groupoid into a semi-direct product of two of its
parabolic subgroupoids and provide a groupoid presentation. In order to
established the latter result, we describe quasi-centralizers in Garside
groups. All results hold in the particular case of Artin-Tits groups of
spherical type.


  Recent observations of quasars show high line-flux ratios in their broad
emission lines and the ratios appear to be independent of redshift up to $z
\gtrsim 6$, which indicate that the broad-line regions of these early quasars
are surprisingly metal-rich. Here, we revisit the chemical evolution of
high-redshift quasars by adding a new ingredient, i.e., the neutrino-dominated
accretion flows (NDAFs) with outflows, on top of the conventional core-collapse
supernovae (CCSNe). In the presence of the chemical contribution from NDAFs
with outflows, the total metal mass (i.e., the summation of the conventional
CCSN and NDAFs with outflows) per CCSN depends weakly upon the mass of the
progenitor star if the mass is in the range of $\sim 25-55~M_{\odot}$. We model
the chemical evolution by adopting a improved open-box model with three typical
initial mass functions (IMFs). We find that, with the additional chemical
contribution from NDAFs with outflows, the quasar metallicity can be enriched
more rapidly in the very early Universe ($z \sim 10$) and reaches higher
saturation than the no-NDAF case at $z \sim 8$, after which they evolve slowly
with redshift. The quasar metallicity can reach $\sim 20~Z_{\odot}$ ($Z_\odot$
denotes the metallicity of the Sun; and $\sim 20\%$ of which is produced by
NDAF outflows) at $z \sim 8$ for the ``top-heavy'' IMF model in
\citet{Toyouchi2022}, which readily explains the quasar observations on the
super-solar metal abundance and redshift-independent evolution.


  The idea of holography in gravity arose from the fact that the entropy of
black holes is given by their surface area. The holography encountered in
gauge/gravity duality has no such relation however; the boundary surface can be
placed at an arbitrary location in AdS space and its area does not give the
entropy of the bulk. The essential issues are also different between the two
cases: in black holes we get Hawking radiation from the `holographic surface'
which leads to the information issue, while in gauge/gravity duality there is
no such radiation from the boundary surface. To resolve the information paradox
we need to show that there are real degrees of freedom at the horizon of the
hole; this is achieved by the fuzzball construction. While the fuzzball has no
interior to the horizon, we argue that an auxiliary spacetime can be
constructed to continue the collective dynamics of fuzzball for times of order
the crossing time; this is an analogue of `complementarity'.


  We devise a prescription to utilize a novel convergent expansion in the
strong-asymptotic regime for the Stieltjes integral and its generalizations
[Galapon E.A Proc.R.Soc A 473, 20160567(2017)] to sum the associated divergent
series of Stieltjes across all asymptotic regimes. The novel expansion makes
use of the divergent negative-power moments which we treated as Hadamard's
finite part integrals. The result allowed us to compute the ground-state energy
of the quartic, sextic anharmonic oscillators as well as the $\mathcal{PT}$
symmetric cubic oscillator, and the funnel potential across all perturbation
regimes from a single expansion that is built from the divergent weak-coupling
perturbation series and incorporates the known leading-order strong-coupling
behavior of the spectra.


  It is conjectured that all decomposable (i.e. interior can be triangulated
without adding new vertices) polyhedra with vertices in convex position are
infinitesimally rigid and only recently has it been shown that this is indeed
true under an additional assumption of codecomposability (i.e. the interior of
the difference between the convex hull and the polyhedron itself can be
triangulated without adding new vertices).
  One major set of tools for studying infinitesimal rigidity happens to be
throughout the (negative) Hessian $M_T$ of the discrete Hilbert-Einstein
functional. Besides its theoretical importance, it provides the necessary
machinery to tackle the problem experimentally. To search for potential
counterexamples to the conjecture, one constructs an explicit family of
so-called $T$-polyhedra, all of which are weakly convex and decomposable, while
being non-codecomposable. Since infinitesimal rigidity is equivalent to a
non-degenerate $M_T$, one can let Mathematica search for the eigenvalues of
$M_T$ and gather experimental evidence that such a flexible, weakly convex and
decomposable $T$-polyhedron may not exist.


  Advancements in Sonar image capture have enabled researchers to apply
sophisticated object identification algorithms in order to locate targets of
interest in images such as mines. Despite progress in this field, modern sonar
automatic target recognition (ATR) approaches lack robustness to the amount of
noise one would expect in real-world scenarios, the capability to handle
blurring incurred from the physics of image capture, and the ability to excel
with relatively few training samples. We address these challenges by adapting
modern sparsity-based techniques with dictionaries comprising of training from
each class. We develop new discriminative (as opposed to generative) sparse
representations which can help automatically classify targets in Sonar imaging.
Using a simulated SAS data set from the Naval Surface Warfare Center (NSWC), we
obtained compelling classification rates for multi-class problems even in cases
with considerable noise and sparsity in training samples.


  Let $M$ be a complete Riemannian manifold, $N\in \NN$ and $p\ge 1$. We prove
that almost everywhere on $x=(x_1,...,x_N)\in M^N$ for Lebesgue measure in
$M^N$, the measure $\di \mu(x)=\f1N\sum_{k=1}^N\d_{x_k}$ has a unique $p$-mean
$e_p(x)$. As a consequence, if $X=(X_1,...,X_N)$ is a $M^N$-valued random
variable with absolutely continuous law, then almost surely $\mu(X(\om))$ has a
unique $p$-mean. In particular if $(X_n)_{n\ge 1}$ is an independent sample of
an absolutely continuous law in $M$, then the process
$e_{p,n}(\om)=e_p(X_1(\om),..., X_n(\om))$ is well-defined. Assume $M$ is
compact and consider a probability measure $\nu$ in $M$. Using partial
simulated annealing, we define a continuous semimartingale which converges to
the set of minimizers of the integral of distance at power $p$ with respect to
$\nu$. When the set is a singleton, it converges to the $p$-mean.


  This work attempts to combine the strengths of two major technologies that
have matured over the last three decades: global mixed-integer nonlinear
optimization and branch-and-price. We consider a class of generally nonconvex
mixed-integer nonlinear programs (MINLPs) with linear complicating constraints
and integer linking variables. If the complicating constraints are removed, the
problem becomes easy to solve, e.g. due to decomposable structure. Integrality
of the linking variables allows us to apply a discretization approach to derive
a Dantzig-Wolfe reformulation and solve the problem to global optimality using
branch-and-price. It is a remarkably simple idea; but to our surprise, it has
barely found any application in the literature. In this work, we show that many
relevant problems directly fall or can be reformulated into this class of
MINLPs. We present the branch-and-price algorithm and demonstrate its
effectiveness (and sometimes ineffectiveness) in an extensive computational
study considering multiple large-scale problems of practical relevance, showing
that, in many cases, orders-of-magnitude reductions in solution time can be
achieved.


  We probe the quantum speed limit (QSL) of an electron when it is trapped in a
non-uniform magnetic field. We show that the QSL increases to a large value,
but within the regime of causality, by choosing a proper variation in magnetic
fields. We also probe the dependence of QSL on spin of electron and find that
it is higher for spin-down electron in the relativistic regime. This can be
useful in achieving a faster speed of transmission of quantum information.
Further, we use the Bremermann--Bekenstein bound to find a critical magnetic
field that bridges the gap between non-relativistic and relativistic treatments
and relates to the stability of matter. An analytical framework is developed.
We also provide a plausible experimental design to supplement our theory.


  In this paper, we consider the problem of certifying the robustness of neural
networks to perturbed and adversarial input data. Such certification is
imperative for the application of neural networks in safety-critical
decision-making and control systems. Certification techniques using convex
optimization have been proposed, but they often suffer from relaxation errors
that void the certificate. Our work exploits the structure of ReLU networks to
improve relaxation errors through a novel partition-based certification
procedure. The proposed method is proven to tighten existing linear programming
relaxations, and asymptotically achieves zero relaxation error as the partition
is made finer. We develop a finite partition that attains zero relaxation error
and use the result to derive a tractable partitioning scheme that minimizes the
worst-case relaxation error. Experiments using real data show that the
partitioning procedure is able to issue robustness certificates in cases where
prior methods fail. Consequently, partition-based certification procedures are
found to provide an intuitive, effective, and theoretically justified method
for tightening existing convex relaxation techniques.


  Aluminum-oxygen reaction is important in many highly energetic, high pressure
generating systems. Recent experiments with nanostructured thermites suggest
that oxidation of aluminum nanoparticles occurs in a few microseconds. Such
rapid reaction cannot be explained by a conventional diffusion-based mechanism.
We present a rapid oxidation model of a spherical aluminum nanoparticle, using
Cabrera-Mott moving boundary mechanism, and taking self-heating into account.
In our model, electric potential solves the nonlinear Poisson equation. In
contrast with the Coulomb potential, a "double-layer" type solution for the
potential and self-heating leads to enhanced oxidation rates. At maximal
reaction temperature of 2000 C, our model predicts overall oxidation time scale
in microseconds range, in agreement with experimental evidence.


  This paper discusses one of the most significant challenges of
next-generation big data (BD) federation platforms, namely, Hadoop access
control. Privacy and security on a federation scale remain significant concerns
among practitioners. Hadoop's current primitive access control presents
security concerns and limitations, such as the complexity of deployment and the
consumption of resources. However, this major concern has not been a subject of
intensive study in the literature. This paper critically reviews and
investigates these security limitations and provides a framework called BD
federation access broker to address 8 main security limitations. This paper
proposes the federated access control reference model (FACRM) to formalize the
design of secure BD solutions within the Apache Hadoop stack. Furthermore, this
paper discusses the implementation of the access broker and its usefulness for
security breach detection and digital forensics investigations. The efficiency
of the proposed access broker has not sustainably affected the performance
overhead. The experimental results show only 1\% of each 100 MB read/write
operation in a WebHDFS. Overall, the findings of the paper pave the way for a
wide range of revolutionary and state-of-the-art enhancements and future trends
within Hadoop stack security and privacy.


  We give a necessary and sufficient condition in order for a hyperplane
arrangement to be of Torelli type, namely that it is recovered as the set of
unstable hyperplanes of its Dolgachev sheaf of logarithmic differentials.
Decompositions and semistability of non-Torelli arrangements are investigated.


  We construct two combinatorially equivalent line arrangements in the complex
projective plane such that the fundamental groups of their complements are not
isomorphic. The proof uses a new invariant of the fundamental group of the
complement to a line arrangement of a given combinatorial type with respect to
isomorphisms inducing the canonical isomorphism of the first homology groups.


  In this paper, we propose a multiplex proportional-integral approach, for
solving consensus problems in networks of heterogeneous nodes dynamics affected
by constant disturbances. The proportional and integral actions are deployed on
two different layers across the network, each with its own topology. Sufficient
conditions for convergence are derived that depend upon the structure of the
network, the parameters characterizing the control layers and the node
dynamics. The effectiveness of the theoretical results is illustrated using a
power network model as a representative example.


  Positronium (Ps) reaction rates (k) with weak Acceptors (Ac) leading to the
formation of Ps-Ac Complexes show several intriguing features: non-monotonic
temperature dependence of k (departing from the usual Arrhenius paradigm),
considerable variablity of k with respect to different solvents, and anomalies
in response to external pressure at ambient temperature (large changes of k in
some media and hardly any in others). We explain all these phenomena, through
the introduction of the novel concept of a critical surface tension, which
unifies observations in diverse non-polar solvents at different temperatures
and pressures.


  Motivated by recent observations that non-concatenated ring polymers in their
dense solution exhibit a glass-like dynamics, we propose a free volume
description of the motion of such rings based on the notion of topological
volume. We first construct a phenomenological free energy which enables one to
qnaitify the degree of topological crowding measured by the coordination
number. Then we pinpoint a key role of the cooperative dynamics of neighboring
rings, which is responsible for an anomalous dependence of the global
structural relaxation (diffusion) time on ring length. Predictions on molecular
weight dependence of both static (ring size, coordination number) and dynamic
(relaxation time, diffusion coefficient) quantities are in very good agreement
with reported numerical simulations. Throughout the discussion, the
entanglement length $N_e$ is assumed to be a unique characteristic length for
the topological constraint, hence, all the physical quantities are universally
described in terms of the rescaled chain length $N/N_e$. Finally, we discuss
how the dense solution of rings is analogous yet different from ordinary glassy
systems.


  The purpose of order-of-addition (OofA) experiments is to identify the best
order in a sequence of m components in a system or treatment. Such experiments
may be analysed by various regression models, the most popular ones being based
on pairwise ordering (PWO) factors or on component-position (CP) factors. This
paper reviews these models and extensions and proposes a new class of models
based on response surface (RS) regression using component position numbers as
predictor variables. Using two published examples, it is shown that RS models
can be quite competitive. In case of model uncertainty, we advocate the use of
model averaging for analysis. The averaging idea leads naturally to a design
approach based on a compound optimality criterion assigning weights to each
candidate model.


  We describe a sufficient condition for actions constructed in projective
superspace to possess an SU(2) R-symmetry. We check directly that this
condition implies that the corresponding hyperkahler varieties, constructed by
means of the generalized Legendre transform, have a Swann bundle structure.


  Modeling of physical human-robot collaborations is generally a challenging
problem due to the unpredictive nature of human behavior. To address this
issue, we present a data-efficient reinforcement learning framework which
enables a robot to learn how to collaborate with a human partner. The robot
learns the task from its own sensorimotor experiences in an unsupervised
manner. The uncertainty of the human actions is modeled using Gaussian
processes (GP) to implement action-value functions. Optimal action selection
given the uncertain GP model is ensured by Bayesian optimization. We apply the
framework to a scenario in which a human and a PR2 robot jointly control the
ball position on a plank based on vision and force/torque data. Our
experimental results show the suitability of the proposed method in terms of
fast and data-efficient model learning, optimal action selection under
uncertainties and equal role sharing between the partners.


  Successful partnerships between researchers, experts and public health
authorities has been critical to navigate the challenges of the Covid-19
pandemic worldwide. In Costa Rica, we constructed a multilayer network model
that incorporates a diverse contact structure for each individual (node). The
different layers which constitute the individual's contact structure include:
family, friends, and sporadic interactions. Different scenarios were
constructed to forecast and have a better understanding of the possible routes
of the pandemic in the country, given the information that was available at the
time and the different measures implemented by the health authorities of the
country. Strong collaboration within our diverse team allowed using the model
to tailor advice on contingency measures to health authorities. The model
helped develop informed strategies to prepare the public health system in Costa
Rica. The development, evolution and applications of a multilayer network model
of Covid-19 in the adoption of sanitary measures in Costa Rica has been an
example of the potential of such partnerships.


  In this paper, we prove the global rigidity of sphere packings on
3-dimensional manifolds. This is a 3-dimensional analogue of the rigidity
theorem of Andreev-Thurston and was conjectured by Cooper and Rivin. We also
prove a global rigidity result using a combinatorial scalar curvature
introduced by Ge and the author.


  For most diseases, building large databases of labeled genetic data is an
expensive and time-demanding task. To address this, we introduce genetic
Generative Adversarial Networks (gGAN), a semi-supervised approach based on an
innovative GAN architecture to create large synthetic genetic data sets
starting with a small amount of labeled data and a large amount of unlabeled
data. Our goal is to determine the propensity of a new individual to develop
the severe form of the illness from their genetic profile alone. The proposed
model achieved satisfactory results using real genetic data from different
datasets and populations, in which the test populations may not have the same
genetic profiles. The proposed model is self-aware and capable of determining
whether a new genetic profile has enough compatibility with the data on which
the network was trained and is thus suitable for prediction. The code and
datasets used can be found at https://github.com/caio-davi/gGAN.


  We study the problem of testing, using only a single sample, between mean
field distributions (like Curie-Weiss, Erd\H{o}s-R\'enyi) and structured Gibbs
distributions (like Ising model on sparse graphs and Exponential Random
Graphs). Our goal is to test without knowing the parameter values of the
underlying models: only the \emph{structure} of dependencies is known. We
develop a new approach that applies to both the Ising and Exponential Random
Graph settings based on a general and natural statistical test. The test can
distinguish the hypotheses with high probability above a certain threshold in
the (inverse) temperature parameter, and is optimal in that below the threshold
no test can distinguish the hypotheses.
  The thresholds do not correspond to the presence of long-range order in the
models. By aggregating information at a global scale, our test works even at
very high temperatures.
  The proofs are based on distributional approximation and sharp concentration
of quadratic forms, when restricted to Hamming spheres. The restriction to
Hamming spheres is necessary, since otherwise any scalar statistic is useless
without explicit knowledge of the temperature parameter. At the same time, this
restriction radically changes the behavior of the functions under
consideration, resulting in a much smaller variance than in the independent
setting; this makes it hard to directly apply standard methods (i.e., Stein's
method) for concentration of weakly dependent variables. Instead, we carry out
an additional tensorization argument using a Markov chain that respects the
symmetry of the Hamming sphere.


  Multi-modality fusion technologies have greatly improved the performance of
neural network-based Video Description/Caption, Visual Question Answering (VQA)
and Audio Visual Scene-aware Dialog (AVSD) over the recent years. Most previous
approaches only explore the last layers of multiple layer feature fusion while
omitting the importance of intermediate layers. To solve the issue for the
intermediate layers, we propose an efficient Quaternion Block Network (QBN) to
learn interaction not only for the last layer but also for all intermediate
layers simultaneously. In our proposed QBN, we use the holistic text features
to guide the update of visual features. In the meantime, Hamilton quaternion
products can efficiently perform information flow from higher layers to lower
layers for both visual and text modalities. The evaluation results show our QBN
improved the performance on VQA 2.0, even though using surpass large scale BERT
or visual BERT pre-trained models. Extensive ablation study has been carried
out to testify the influence of each proposed module in this study.


  The Decision Model and Notation (DMN) is a recent OMG standard for the
elicitation and representation of decision models, and for managing their
interconnection with business processes. DMN builds on the notion of decision
tables, and their combination into more complex decision requirements graphs
(DRGs), which bridge between business process models and decision logic models.
DRGs may rely on additional, external business knowledge models, whose
functioning is not part of the standard. In this work, we consider one of the
most important types of business knowledge, namely background knowledge that
conceptually accounts for the structural aspects of the domain of interest, and
propose decision knowledge bases (DKBs), which semantically combine DRGs
modeled in DMN, and domain knowledge captured by means of first-order logic
with datatypes. We provide a logic-based semantics for such an integration, and
formalize different DMN reasoning tasks for DKBs. We then consider background
knowledge formulated as a description logic ontology with datatypes, and show
how the main verification tasks for DMN in this enriched setting can be
formalized as standard DL reasoning services, and actually carried out in
ExpTime. We discuss the effectiveness of our framework on a case study in
maritime security.


  To enhance robustness of complex networked systems, a simple method is
introducing reinforced nodes which always function during failure propagation.
A random scheme of node reinforcement can be considered as a benchmark for
finding an optimal reinforcement solution. Yet there still lacks a systematic
evaluation on how node reinforcement affects network structure at a mesoscopic
level upon failures. Here we study this problem through the lens of $K$-cores
of networks. Based on an analytical percolation framework, we first show that,
on uncorrelated random graphs, with a critical size of reinforced nodes, an
abrupt emergence of $K$-cores is smoothed out to a continuous one, and a
detailed phase diagram is derived. We then show that, with a cost-benefit
analysis on random reinforcement, for proper weight factors in cost functions
with constant and increasing marginal costs, a gain function shows a
unimodality, thus we can analytically find an optimal reinforcement fraction by
locating the maximal gain. In all, our framework offers a gain-oriented
analytical perspective to designing robust interconnected systems.


  We study the static responses of cold quark matter in the intermediate
baryonic density region (characterized by a chemical potential $\mu$) in the
presence of a strong magnetic field. We consider in particular, the so-called
Magnetic Dual Chiral Density Wave (MDCDW) phase, which is materialized by an
inhomogeneous condensate formed by a particle-hole pair. It is shown, that the
MDCDW phase is more stable in the weak-coupling regime than the one considered
in the magnetic catalysis of chiral symmetry braking phenomenon (MC$\chi$SB)
and even than the chiral symmetric phase that was expected to be realized at
sufficiently high baryonic chemical potential. The different components of the
photon polarization operator of the MDCDW phase in the one-loop approximation
are calculated. We found that in the MDCDW phase there is no Debye screening
neither Meissner effect in the lowest-Landau-level approximation. The obtained
Debye length depends on the amplitude $m$ and modulation $b$ of the
inhomogeneous condensate and it is only different from zero if the relation $|
\mu -b| > m$ holds. But, we found that in the region of interest this
inequality is not satisfied. Thus, no Debye screening takes place under those
conditions. On the other hand, since the particle-hole condensate is
electrically neutral, the U(1) electromagnetic group is not broken by the
ground state and consequently there is no Meissner effect. These results can be
of interest for the astrophysics of neutron stars.


  We introduce a geometric operation, which we call the relative Whitney trick,
that removes a single double point between properly immersed surfaces in a
$4$-manifold with boundary. Using the relative Whitney trick we prove that
every link in a homology sphere is homotopic to a link that is topologically
slice in a contractible topological $4$-manifold. We further prove that any
link in a homology sphere is order $k$ Whitney tower concordant to a link in
$S^3$ for all $k$. Finally, we explore the minimum Gordian distance from a link
in $S^3$ to a homotopically trivial link. Extending this notion to links in
homology spheres, we use the relative Whitney trick to make explicit
computations for 3-component links and establish bounds in general.


  A mean-field model explaining most of the details in the magnetic phase
diagram of ErNi_2B_2C is presented. The low-temperature magnetic properties are
found to be dominated by the appearance of long-period commensurate structures.
The stable structure at low temperatures and zero field is found to have a
period of 40 layers along the a direction, and upon cooling it undergoes a
first-order transition at T_C = 2.3 K to a different 40-layered structure
having a net ferromagnetic component of about 0.4 mu_B/Er. The
neutron-diffraction patterns predicted by the two 40-layered structures, above
and below T_C, are in agreement with the observations of Choi et al.


  Generalized sorting problem, also known as sorting with forbidden
comparisons, was first introduced by Huang et al. together with a randomized
algorithm which requires $\tilde O(n^{3/2})$ probes. We study this problem with
additional predictions for all pairs of allowed comparisons as input. We
propose a randomized algorithm which uses $O(n \log n+w)$ probes with high
probability and a deterministic algorithm which uses $O(nw)$ probes, where $w$
is the number of mistakes made by prediction.


  The ability to create high-quality lateral p-n junctions at nanometer length
scales is essential for the next generation of two-dimensional (2D) electronic
and plasmonic devices. Using a charge-transfer heterostructure consisting of
graphene on $\alpha$-RuCl$_3$, we conduct a proof-of-concept study
demonstrating the existence of intrinsic nanoscale lateral p-n junctions in the
vicinity of graphene nanobubbles. Our multi-pronged experimental approach
incorporates scanning tunneling microscopy (STM) and spectroscopy (STS) and
scattering-type scanning near-field optical microscopy ($\textit{s}$-SNOM) in
order to simultaneously probe both the electronic and optical responses of
nanobubble p-n junctions. Our STM and STS results reveal that p-n junctions
with a band offset of more than 0.6 eV can be achieved over lateral length
scale of less than 3 nm, giving rise to a staggering effective in-plane field
in excess of 10$^8$ V/m. Concurrent $\textit{s}$-SNOM measurements confirm the
utility of these nano-junctions in plasmonically-active media, and validate the
use of a point-scatterer formalism for modeling surface plasmon polaritons
(SPPs). Model $\textit{ab initio}$ density functional theory (DFT) calculations
corroborate our experimental data and reveal a combination of sub-angstrom and
few-angstrom decay processes dictating the dependence of charge transfer on
layer separation. Our study provides experimental and conceptual foundations
for the use of charge-transfer interfaces such as graphene/$\alpha$-RuCl$_3$ to
generate p-n nano-junctions.


  Superradiant instabilities of spinning black holes can be used to impose
strong constraints on ultralight bosons, thus turning black holes into
effective particle detectors. However, very little is known about the
development of the instability and whether its nonlinear time evolution accords
to the linear intuition. For the first time, we attack this problem by studying
the impact of gravitational-wave emission and gas accretion on the evolution of
the instability. Our quasi-adiabatic, fully-relativistic analysis shows that:
(i) gravitational-wave emission does not have a significant effect on the
evolution of the black hole, (ii) accretion plays an important role and (iii)
although the mass of the scalar cloud developed through superradiance can be a
sizeable fraction of the black-hole mass, its energy-density is very low and
backreaction is negligible. Thus, massive black holes are well described by the
Kerr geometry even if they develop bosonic clouds through superradiance. Using
Monte Carlo methods and very conservative assumptions, we provide strong
support to the validity of the linearized analysis and to the bounds of
previous studies.


  The large volumes of sequencing data required to sample complex environments
deeply pose new challenges to sequence analysis approaches. De novo metagenomic
assembly effectively reduces the total amount of data to be analyzed but
requires significant computational resources. We apply two pre-assembly
filtering approaches, digital normalization and partitioning, to make large
metagenome assemblies more comput\ ationaly tractable. Using a human gut mock
community dataset, we demonstrate that these methods result in assemblies
nearly identical to assemblies from unprocessed data. We then assemble two
large soil metagenomes from matched Iowa corn and native prairie soils. The
predicted functional content and phylogenetic origin of the assembled contigs
indicate significant taxonomic differences despite similar function. The
assembly strategies presented are generic and can be extended to any
metagenome; full source code is freely available under a BSD license.


  We use dialgebras, generalising both algebras and coalgebras, as a complement
of the standard coalgebraic framework, aimed at describing the semantics of an
interactive system by the means of reaction rules. In this model, interaction
is built-in, and semantic equivalence arises from it, instead of being
determined by a (possibly difficult) understanding of the side effects of a
component in isolation. Behavioural equivalence in dialgebras is determined by
how a given process interacts with the others, and the obtained observations.
We develop a technique to inter-define categories of dialgebras of different
functors, that in particular permits us to compare a standard coalgebraic
semantics and its dialgebraic counterpart. We exemplify the framework using the
CCS and the pi-calculus. Remarkably, the dialgebra giving semantics to the
pi-calculus does not require the use of presheaf categories.


  In practical asynchronous bi-directional relaying, symbols transmitted by two
sources cannot arrive at the relay with perfect frame and symbol alignments and
the asynchronous multiple-access channel (MAC) should be seriously considered.
Recently, Lu et al. proposed a Tanner-graph representation of the
symbol-asynchronous MAC with rectangular-pulse shaping and further developed
the message-passing algorithm for optimal decoding of the symbol-asynchronous
physical-layer network coding. In this paper, we present a general channel
model for the asynchronous MAC with arbitrary pulse-shaping. Then, the Bahl,
Cocke, Jelinek, and Raviv (BCJR) algorithm is developed for optimal decoding of
the asynchronous MAC channel. For Low-Density Parity-Check (LDPC)-coded BPSK
signalling over the symbol-asynchronous MAC, we present a formal log-domain
generalized sum-product-algorithm (Log-G-SPA) for efficient decoding.
Furthermore, we propose to use cyclic codes for combating the
frame-asynchronism and the resolution of the relative delay inherent in this
approach can be achieved by employing the simple cyclic-redundancy-check (CRC)
coding technique. Simulation results demonstrate the effectiveness of the
proposed approach.


  Given a spatio-temporal network (ST network) where edge properties vary with
time, a time-sub-interval minimum spanning tree (TSMST) is a collection of
minimum spanning trees of the ST network, where each tree is associated with a
time interval. During this time interval, the total cost of tree is least among
all the spanning trees. The TSMST problem aims to identify a collection of
distinct minimum spanning trees and their respective time-sub-intervals under
the constraint that the edge weight functions are piecewise linear. This is an
important problem in ST network application domains such as wireless sensor
networks (e.g., energy efficient routing). Computing TSMST is challenging
because the ranking of candidate spanning trees is non-stationary over a given
time interval. Existing methods such as dynamic graph algorithms and kinetic
data structures assume separable edge weight functions. In contrast, we propose
novel algorithms to find TSMST for large ST networks by accounting for both
separable and non-separable piecewise linear edge weight functions. The
algorithms are based on the ordering of edges in edge-order-intervals and
intersection points of edge weight functions.


  Jumbled indexing is the problem of indexing a text $T$ for queries that ask
whether there is a substring of $T$ matching a pattern represented as a Parikh
vector, i.e., the vector of frequency counts for each character. Jumbled
indexing has garnered a lot of interest in the last four years. There is a
naive algorithm that preprocesses all answers in $O(n^2|\Sigma|)$ time allowing
quick queries afterwards, and there is another naive algorithm that requires no
preprocessing but has $O(n\log|\Sigma|)$ query time. Despite a tremendous
amount of effort there has been little improvement over these running times.
  In this paper we provide good reason for this. We show that, under a
3SUM-hardness assumption, jumbled indexing for alphabets of size $\omega(1)$
requires $\Omega(n^{2-\epsilon})$ preprocessing time or $\Omega(n^{1-\delta})$
query time for any $\epsilon,\delta>0$. In fact, under a stronger 3SUM-hardness
assumption, for any constant alphabet size $r\ge 3$ there exist describable
fixed constant $\epsilon_r$ and $\delta_r$ such that jumbled indexing requires
$\Omega(n^{2-\epsilon_r})$ preprocessing time or $\Omega(n^{1-\delta_r})$ query
time.


  We study the fronts that appear when a shear-thickening suspension is
submitted to a sudden driving force at a boundary. Using a
quasi-one-dimensional experimental geometry, we extract the front shape and the
propagation speed from the suspension flow field and map out their dependence
on applied shear. We find that the relation between stress and velocity is
quadratic, as is generally true for inertial effects in liquids, but with a
pre-factor that can be much larger than the material density. We show that
these experimental findings can be explained by an extension of the Wyart-Cates
model, which was originally developed to describe steady-state
shear-thickening. This is achieved by introducing a sole additional parameter:
the characteristic strain scale that controls the crossover from start-up
response to steady-state behavior. The theoretical framework we obtain unifies
both transient and steady-state properties of shear-thickening materials.


  The authors study the geometry of lightlike hypersurfaces on a
four-dimensional manifold $(M, c)$ endowed with a pseudoconformal structure $c
= CO (2, 2)$. They prove that a lightlike hypersurface $V \subset (M, c)$ bears
a foliation formed by conformally invariant isotropic geodesics and two
isotropic distributions tangent to these geodesics, and that these two
distributions are integrable if and only if $V$ is totally umbilical. The
authors also indicate how, using singular points and singular submanifolds of a
lightlike hypersurface $V \subset (M, c)$, to construct an invariant
normalization of $V$ intrinsically connected with $V$.