  Conventional virtual memory (VM) frameworks enable a virtual address to
flexibly map to any physical address. This flexibility necessitates large data
structures to store virtual-to-physical mappings, which leads to high address
translation latency and large translation-induced interference in the memory
hierarchy. On the other hand, restricting the address mapping so that a virtual
address can only map to a specific set of physical addresses can significantly
reduce address translation overheads by using compact and efficient translation
structures. However, restricting the address mapping flexibility across the
entire main memory severely limits data sharing across different processes and
increases data accesses to the swap space of the storage device, even in the
presence of free memory. We propose Utopia, a new hybrid virtual-to-physical
address mapping scheme that allows both flexible and restrictive hash-based
address mapping schemes to harmoniously co-exist in the system. The key idea of
Utopia is to manage physical memory using two types of physical memory
segments: restrictive and flexible segments. A restrictive segment uses a
restrictive, hash-based address mapping scheme that maps virtual addresses to
only a specific set of physical addresses and enables faster address
translation using compact translation structures. A flexible segment employs
the conventional fully-flexible address mapping scheme. By mapping data to a
restrictive segment, Utopia enables faster address translation with lower
translation-induced interference. Utopia improves performance by 24% in a
single-core system over the baseline system, whereas the best prior
state-of-the-art contiguity-aware translation scheme improves performance by
13%.


  With the aid of large-scale three-dimensional QED-PIC simulations, we
describe a realistic experimental configuration to measure collective effects
that couple strong field quantum electrodynamics to plasma kinetics. For two
counter propagating lasers interacting with a foil at intensities exceeding
$10^{22}$ Wcm$^{-2}$, a binary result occurs; when quantum effects are
included, a foil that classically would effectively transmit the laser pulse
becomes opaque. This is a dramatic change in plasma behavior, directly as a
consequence of the coupling of radiation reaction and pair production to plasma
dynamics.


  Fast (>700 km/s) and slow (~400 km/s) winds stream from the Sun, permeate the
heliosphere and influence the near-Earth environment. While the fast wind is
known to emanate primarily from polar coronal holes, the source of the slow
wind remains unknown. Here we identify possible sites of origin using a slow
solar wind source map of the entire Sun, which we construct from specially
designed, full- disk observations from the Hinode satellite, and a magnetic
field model. Our map provides a full-Sun observation that combines three key
ingredients for identifying the sources: velocity, plasma composition and
magnetic topology and shows them as solar wind composition plasma outflowing on
open magnetic field lines. The area coverage of the identified sources is large
enough that the sum of their mass contributions can explain a significant
fraction of the mass loss rate of the solar wind.


  The Lorentzian length of a timelike curve connecting both endpoints of a
classical computation is a function of the path taken through Minkowski
spacetime. The associated runtime difference is due to time-dilation: the
phenomenon whereby an observer finds that another's physically identical ideal
clock has ticked at a different rate than their own clock. Using ideas
appearing in the framework of computational complexity theory, time-dilation is
quantified as an algorithmic resource by relating relativistic energy to an
$n$th order polynomial time reduction at the completion of an observer's
journey. These results enable a comparison between the optimal quadratic
\emph{Grover speedup} from quantum computing and an $n=2$ speedup using
classical computers and relativistic effects. The goal is not to propose a
practical model of computation, but to probe the ultimate limits physics places
on computation.


  Intertwining of spin, charge and pairing correlations in the repulsive
two-dimensional Hubbard model is shown through unrestricted variational
calculations, with projected wavefunctions free of symmetry breaking. A
crossover from incommensurate antiferromagnetism to stripe order naturally
emerges in the hole-doped region when increasing the on-site coupling. Although
effective pairing interactions are identified, they are strongly fragmented in
several modes including d-wave pairing and more exotic channels related to an
underlying stripe. We demonstrate that the entanglement of a mean-field
wavefunction by symmetry restoration can largely account for interaction
effects.


  Linear codes are considered over the ring $\mathbb{Z}_4+v\mathbb{Z}_4$, where
$v^2=v$. Gray weight, Gray maps for linear codes are defined and MacWilliams
identity for the Gray weight enumerator is given. Self-dual codes, construction
of Euclidean isodual codes, unimodular complex lattices, MDS codes and MGDS
codes over $\mathbb{Z}_4+v\mathbb{Z}_4$ are studied. Cyclic codes and quadratic
residue codes are also considered. Finally, some examples for illustrating the
main work are given.


  Due to the incompatibility of the nonlinear realization of superconformal
symmetry and dilatation symmetry with the dilaton as the compensator field, in
the present paper it shows an alternative mechanism of spontaneous breaking the
N=2 superconformal symmetry to the N=0 case. By using the approach of nonlinear
transformations it is found that it leads to a space-filling brane theory with
Weyl scale W(1,3) symmetry. The dynamics of the resulting Weyl scale invariant
brane, along with that of other Nambu-Goldstone fields, is derived in terms of
the building blocks of the vierbein and the covariant derivative from the
Maurer-Cartan oneforms. A general coupling of the matter fields localized on
the brane world volume to these NG fields is also constructed.


  In this paper, we propose an algorithm combining the forward-backward
splitting method and the alternative projection method for solving the system
of splitting inclusion problem. We want to find a point in the interception of
a finite number of sets that we don't know, the solution of each component of
the system. The algorithm consists of approximate the sets involved in the
problem by separates halfspaces which are a known strategy. By finding these
halfspaces in each iteration we use only one inclusion problem of the system.
The iterations consist of two parts, the first contains an explicit Armijo-type
search in the spirit of the extragradient-like methods for variational
inequalities. In the iterative process, the operator forward-backward is
computed only one time for each inclusion problem, this represents a great
computational saving because the computational cost of this operator is nothing
cheap. The second part consists of special projection step, projecting in the
separating halfspace. The convergence analysis of the proposed scheme is given
assuming monotonicity all operators, without any Lipschitz continuity
assumption.


  This paper demonstrates the viability of perfectly undetectable affine
transformation attacks against robotic manipulators where intelligent attackers
can inject multiplicative and additive false data while remaining completely
hidden from system users. The attacker can implement these communication line
attacks by satisfying three Conditions presented in this work. These claims are
experimentally validated on a FANUC 6 degree of freedom manipulator by
comparing a nominal (non-attacked) trial and a detectable attack case against
three perfectly undetectable trajectory attack Scenarios: scaling, reflection,
and shearing. The results show similar observed end effector error for the
attack Scenarios and the nominal case, indicating that the perfectly
undetectable affine transformation attack method keeps the attacker perfectly
hidden while enabling them to attack manipulator trajectories.


  We compute the entanglement entropy of a massless spin $2$ field in a sphere
in flat Minkowski space. We describe the theory with a linearized metric
perturbation field $h_{\mu\nu}$ and decompose it in tensor spherical harmonics.
We fix the gauge such that a) the two dynamical modes for each angular momentum
decouple and have the dynamics of scalar spherical modes, and b) the
gauge-fixed field degrees of freedom inside the sphere represent gauge
invariant operators of the theory localized in the same region. In this way the
entanglement entropy turns out to be equivalent to the one of a pair of free
massless scalars where the contributions of the $l=0$ and $l=1$ modes have been
subtracted. The result for the coefficient of the universal logarithmic term is
$-61/45$ and coincides with the one computed using the mutual information.


  We analyse the noncommutative space underlying the quantum group SUq(2) from
the spectral point of view which is the basis of noncommutative geometry, and
show how the general theory developped in our joint work with H. Moscovici
applies to the specific spectral triple defined by Chakraborty and Pal. This
provides the pseudo-differential calculus, the Wodzciki-type residue, and the
local cyclic cocycle giving the index formula. The cochain whose coboundary is
the difference between the original Chern character and the local one is given
by the remainders in the rational approximation of the logarithmic derivative
of the Dedekind eta function. This specific example allows to illustrate the
general notion of locality in NCG. The formulas computing the residue are
"local". Locality by stripping all the expressions from irrelevant details
makes them computable. The key feature of this spectral triple is its
equivariance, i.e. the $SU_q(2)$-symmetry. We shall explain how this leads
naturally to the general concept of invariant cyclic cohomology in the
framework of quantum group symmetries.


  In this work we establish the posterior consistency for a parametrized family
of partially observed, fully dominated Markov models. As a main assumption, we
suppose that the prior distribution assigns positive probability to all
neighborhoods of the true parameter, for a distance induced by the expected
Kullback-Leibler divergence between the family members' Markov transition
densities. This assumption is easily checked in general. In addition, under
some additional, mild assumptions we show that the posterior consistency is
implied by the consistency of the maximum likelihood estimator. The latter has
recently been established also for models with non-compact state space. The
result is then extended to possibly non-compact parameter spaces and
non-stationary observations. Finally, we check our assumptions on examples
including the partially observed Gaussian linear model with correlated noise
and a widely used stochastic volatility model.


  Identifying sleep stages and patterns is an essential part of diagnosing and
treating sleep disorders. With the advancement of smart technologies, sensor
data related to sleeping patterns can be captured easily. In this paper, we
propose a Convolution Neural Network (CNN) architecture that improves the
classification performance. In particular, we benchmark the classification
performance from different methods, including traditional machine learning
methods such as Logistic Regression (LR), Decision Trees (DT), k-Nearest
Neighbour (k-NN), Naive Bayes (NB) and Support Vector Machine (SVM), on 3
publicly available sleep datasets. The accuracy, sensitivity, specificity,
precision, recall, and F-score are reported and will serve as a baseline to
simulate the research in this direction in the future.


  For an isolated assembly that comprises a system and its surrounding
reservoirs, the total entropy ($S_{a}$) always monotonically increases as time
elapses. This phenomenon is known as the second law of thermodynamics
($S_{a}\geq0$). Here we analytically prove that, unlike the entropy itself, the
entropy variation rate ($B=dS_{a}/dt$) defies the monotonicity for multiple
reservoirs ($n\geq2$). In other words, there always exist minima. For example,
when a system is heated by two reservoirs from $T=300\,K$ initially to
$T=400\,K$ at the final steady state, $B$ decreases steadily first. Then
suddenly it turns around and starts to increases at $387\,K$ until it reaches
its steady-state value, exhibiting peculiar dipping behaviors. In addition, the
crux of our work is the proof that a newly-defined variable, $B/T$, always
decreases. Our proof involves the Newton's law of cooling, in which the heat
transfer coefficient is assumed to be constant. These theoretical macro-scale
findings are validated by numerical experiments using the Crank-Nicholson
method, and are illustrated with practical examples. They constitute an
alternative to the traditional second-law statement, and may provide useful
references for the future micro-scale entropy-related research.


  Nematode species are well-known for their invariant cell lineage pattern
during development. Combining knowledge about the fate specification induced by
asymmetric division and the anti-correlation between cell cycle length and cell
volume in Caenorhabditis elegans, we propose a model to simulate lineage
initiation by altering cell volume segregation ratio in each division, and
quantify the derived pattern's performance in proliferation speed, fate
diversity and space robustness. The stereotypic pattern in C. elegans embryo is
found to be one of the most optimal solutions taking minimum time to achieve
the cell number before gastrulation, by programming asymmetric division as a
strategy.


  High integration of intermittent renewable energy sources (RES), in
particular wind power, has created complexities in power system operations. On
the other hand, large fleets of Electric Vehicles (EVs) are expected to have
great impact on electricity consumption, and uncoordinated charging process
will add load uncertainty and further complicate the grid scheduling. In this
paper, we propose a smart charging approach that uses the flexibility of EV
owners to absorb the fluctuations in the output of RES in a vehicle-to-grid
(V2G) setup. We propose an optimal scheduling algorithm for charge/discharge of
aggregated EV fleets to maximize the integration of wind generation as well as
minimize the charging cost for EV owners. Challenges for people participation
in V2G, such as battery degradation and feeling insecure for unexpected events,
are also addressed. We first formulate a static model using mixed-integer
quadratic programming (MIQP) with multi-objective optimization assuming that
every parameter of the model is known a day ahead of scheduling. Subsequently,
we formulate a dynamic (dis)charging schedule after EVs arrive into the system
with updated information about EV availabilities, wind generation forecast, and
energy price in real-time, using rolling-horizon optimization. Simulations
using a group of 100 EVs in a micro-grid with wind as primary resource
demonstrate significant increase in wind utilization and reduction in charging
cost compared to uncontrolled charging scenario.


  Using the color-magnetic interaction Hamiltonian with SU(3) flavor symmetry
breaking, we perform a schematic study of the masses of the $J^P=1^+$
tetraquarks in the anti-decuplet representation. After diagonalizing the mass
matrix, we find the $ud\bar s\bar s$ tetraquark lies around 1347 MeV. It decays
into $K^+K^0\pi^0, K^+K^+\pi^-, K^0K^0\pi^+$ via P-wave. The dual suppression
from the not-so-big three-body phase space and P-wave decay barrier may render
this exotic state rather narrow. Future experimental exclusion of this state
will cast doubt on the validity of applying the simple color-magnetic
Hamiltonian to the multiquark system.


  We revisit acceleration-induced nonlocal electrodynamics and the phenomenon
of photon spin-rotation coupling. The kernel of the theory for the
electromagnetic field tensor involves parity violation under the assumption of
linearity of the field kernel in the acceleration tensor. However, we show that
parity conservation can be maintained by extending the field kernel to include
quadratic terms in the acceleration tensor. The field kernel must vanish in the
absence of acceleration; otherwise, a general dependence of the kernel on the
acceleration tensor cannot be theoretically excluded. The physical implications
of the quadratic kernel are briefly discussed.


  We investigate the partial orderings of the form (P(X),\subset), where X is a
countable binary relational structure and P(X) the set of the domains of its
isomorphic substructures and show that if the components of X are maximally
embeddable and satisfy an additional condition related to connectivity, then
the poset (P(X),\subset) is forcing equivalent to a finite power of
(P(\omega)/Fin)^+, or to (P(\omega \times \omega)/(Fin \times Fin))^+, or to
the direct product (P(\Delta)/ED_fin)^+ \times ((P(\omega)/Fin)^+)^n, for some
n \in \omega. In particular we obtain forcing equivalents of the posets of
copies of countable equivalence relations, disconnected ultrahomogeneous graphs
and some partial orderings.


  Motivated by the concept of Quasi-Sliding Mode (QSM) in discrete-time
systems, this paper presents a novel approach that relaxes the requirement of
ubiquitous exact sliding motion in continuous-time systems, aiming to achieve
an approximation- and chattering-free quasi-sliding mode controller (QSMC). The
proposed QSMC provides robust and chattering-free control for an unknown
nonlinear system without the need for intricate control methods, learning
agents, or system identification tools. Furthermore, comprehensive simulation
studies have been conducted to validate the effectiveness of the proposed
strategy.


  Conditions are established under which Fredholmness, Coburn's property and
one- or two-sided invertibility are shared by a Toeplitz operator with matrix
symbol $G$ and the Toeplitz operator with scalar symbol $\det G$. These results
are based on one-sided invertibility criteria for rectangular matrices over
appropriate commutative rings and related scalar corona type problems.


  Soft materials often exhibit a distinctive power-law viscoelastic response
arising from broad distribution of time-scales present in their complex
internal structure. A promising tool to accurately describe the rheological
behaviour of soft materials is fractional calculus. However, its use in the
scientific community remains limited due to the unusual notation and
non-trivial properties of fractional operators. This review aims to provide a
clear and accessible description of fractional viscoelastic models for a broad
audience, and to demonstrate the ability of these models to deliver a unified
approach for the characterisation of power-law materials. The use of a
consistent framework for the analysis of rheological data would help classify
the empirical behaviours of soft and biological materials, and better
understand their response.


  Assessing the quality of 3D printed models before they are printed remains a
challeng- ing problem, particularly when considering point cloud-based models.
This paper introduces an approach to quality assessment, which uses techniques
from the field of Topological Data Analy- sis (TDA) to compute a topological
abstraction of the eventual printed model. Two main tools of TDA, Mapper and
persistent homology, are used to analyze both the printed space and empty space
created by the model. This abstraction enables investigating certain qualities
of the model, with respect to print quality, and identifies potential anomalies
that may appear in the final product.


  We investigate the formation of molecular clouds from atomic gas by using
three-dimensional magnetohydrodynamic simulations, including non-equilibrium
chemical reactions and heating/cooling processes. We consider super-Alfv\'enic
head-on colliding flows of atomic gas possessing the two-phase structure that
consists of HI clouds and surrounding warm diffuse gas. We examine how the
formation of molecular clouds depends on the angle $\theta$ between the
upstream flow and the mean magnetic field. We find that there is a critical
angle $\theta_\mathrm{cr}$ above which the shock-amplified magnetic field
controls the post-shock gas dynamics. If the atomic gas is compressed almost
along the mean magnetic field ($\theta\ll\theta_\mathrm{cr}$), super-Alfv\'enic
anisotropic turbulence is maintained by the accretion of the highly
inhomogeneous upstream atomic gas. As a result, a greatly extended
turbulence-dominated post-shock layer is generated. Around $\theta\sim
\theta_\mathrm{cr}$, the shock-amplified magnetic field weakens the post-shock
turbulence, leading to a dense post-shock layer. For $\theta\gg
\theta_\mathrm{cr}$, the strong magnetic pressure suppresses the formation of
cold dense clouds. Efficient molecular cloud formation is expected if $\theta$
is less than a few times $\theta_\mathrm{cr}$. Developing an analytic model and
performing a parameter survey, we obtain an analytic formula for the critical
angle as a function of the mean density, collision speed, and field strength of
the upstream atomic gas. The critical angle is found to be less than $\sim
15^\circ$ as long as the field strength is larger than $1~\mu$G, indicating
that the probability of occurrence of compression with
$\theta<\theta_\mathrm{cr}$ is limited if shock waves come from various
directions.


  In this paper, the holographic dark energy in Brans-Dicke theory is
confronted by cosmic observations from SN Ia, BAO and CMB shift parameter. The
best fit parameters are found in $1\sigma$ region:
$\Omega_{h0}=0.683^{+0.035}_{-0.038}$, $c=0.605^{+0.138}_{-0.107}$ and
$\alpha=0.00662^{+0.00477}_{-0.00467}$ (equivalently
$\omega=905.690^{+637.906}_{-651.471}$ which is less the solar system bound and
consistent with other constraint results). With these best fit values of the
parameters, it is found the universe is undergoing accelerated expansion, and
the current value of equation of state of holographic dark energy
$w_{h0}=-1.246^{+0.191}_{-0.144}$ which is phantom like in Brans-Dicke theory.
The evolution of effective Newton's constant is also explored.


  A novel vehicle traction algorithm solving the traction force allocation
problem based on vehicle center point motion feedback controller is proposed in
this paper. The center point motion feedback control system proposed utilizes
individual wheel torque actuation assuming all wheels are individually driven.
The approach presented is an alternative to the various direct
optimization-based traction force/torque allocation schemes. The proposed
system has many benefits, such as significant reduction of the algorithm
complexity by merging most traction system functionalities into one. Such a
system enables significant simplification, unification, and standardization of
powertrain control design. Moreover, many signals needed by conventional
traction force allocation methods are not required to be measured or estimated
with the proposed approach, which are among others vehicle mass, wheel loading
(normal force), and vehicle center of gravity location. Vehicle center point
trajectory setpoints and measurements are transformed to each wheel, where the
tracking is ensured using the wheel torque actuation. The proposed control
architecture performance and analysis are shown using the nonlinear twin-track
vehicle model implemented in Matlab $\&$ Simulink environment. The performance
is then validated using high fidelity FEE CTU in Prague EFORCE formula model
implemented in IPG CarMaker environment with selected test scenarios. Finally,
the results of the proposed control allocation are compared to the
state-of-the-art approach.


  Observations at 1 au have confirmed that enhancements in measured energetic
particle fluxes are statistically associated with "rough" magnetic fields,
i.e., fields having atypically large spatial derivatives or increments, as
measured by the Partial Variance of Increments (PVI) method. One way to
interpret this observation is as an association of the energetic particles with
trapping or channeling within magnetic flux tubes, possibly near their
boundaries. However, it remains unclear whether this association is a transport
or local effect; i.e., the particles might have been energized at a distant
location, perhaps by shocks or reconnection, or they might experience local
energization or re-acceleration. The Parker Solar Probe (PSP), even in its
first two orbits, offers a unique opportunity to study this statistical
correlation closer to the corona. As a first step, we analyze the separate
correlation properties of the energetic particles measured by the \isois
instruments during the first solar encounter. The distribution of time
intervals between a specific type of event, i.e., the waiting time, can
indicate the nature of the underlying process. We find that the \isois
observations show a power-law distribution of waiting times, indicating a
correlated (non-Poisson) distribution. Analysis of low-energy \isois data
suggests that the results are consistent with the 1 au studies, although we
find hints of some unexpected behavior. A more complete understanding of these
statistical distributions will provide valuable insights into the origin and
propagation of solar energetic particles, a picture that should become clear
with future PSP orbits.


  We show that inverse Compton scattering of cosmic-microwave-background and
starlight photons by cosmic-ray electrons in the interstellar and intergalactic
space explains well the spectrum and intensity of the diffuse gamma-ray
background radiation (GBR), which was measured by EGRET aboard the Compton
Gamma Ray Observatory (CGRO) in directions away from the Galactic disk and
centre. The Gamma Ray Large Area Space Telescope (GLAST) will be able to
separate the Galactic foreground from the extragalactic gamma-rays, and to
provide stringent tests of the theory.


  To solve a text-based game, an agent needs to formulate valid text commands
for a given context and find the ones that lead to success. Recent attempts at
solving text-based games with deep reinforcement learning have focused on the
latter, i.e., learning to act optimally when valid actions are known in
advance. In this work, we propose to tackle the first task and train a model
that generates the set of all valid commands for a given context. We try three
generative models on a dataset generated with Textworld. The best model can
generate valid commands which were unseen at training and achieve high $F_1$
score on the test set.


  We show that the Cao-Li cryptosystem proposed in \cite{CL1} is not secure.
Its private key can be reconstructed from its public key using elementary means
such as LU-decomposition and Euclidean algorithm.


  Wikipedia is the largest online encyclopedia that allows anyone to edit
articles. In this paper, we propose the use of deep learning to detect vandals
based on their edit history. In particular, we develop a multi-source
long-short term memory network (M-LSTM) to model user behaviors by using a
variety of user edit aspects as inputs, including the history of edit reversion
information, edit page titles and categories. With M-LSTM, we can encode each
user into a low dimensional real vector, called user embedding. Meanwhile, as a
sequential model, M-LSTM updates the user embedding each time after the user
commits a new edit. Thus, we can predict whether a user is benign or vandal
dynamically based on the up-to-date user embedding. Furthermore, those user
embeddings are crucial to discover collaborative vandals.


  A detailed investigation of hadronic interactions is performed using
$\pi^-$-mesons with energies in the range 2--10 GeV incident on a high
granularity silicon-tungsten electromagnetic calorimeter. The data were
recorded at FNAL in 2008. The region in which the $\pi^-$-mesons interact with
the detector material and the produced secondary particles are characterised
using a novel track-finding algorithm that reconstructs tracks within hadronic
showers in a calorimeter in the absence of a magnetic field. The principle of
carrying out detector monitoring and calibration using secondary tracks is also
demonstrated.


  Rotating massive stars with initial progenitor masses $M_{\rm prog} \sim$ 25
$M_{\odot}$ -- $\sim$140 $M_{\odot}$ can leave rapidly rotating black holes to
become collapsars. The black holes and the surrounding accretion disks may
develop powerful jets by magneto-hydrodynamics instabilities. The propagation
of the jet in the stellar envelope provides the necessary shock heating for
triggering nucleosynthesis unseen in canonical core-collapse supernovae. Yet,
the energy budget of the jet and its effects on the final chemical abundance
pattern are unclear. In this exploratory work, we present a survey on the
parameter dependence of collapsar nucleosynthesis on jet energetics. We use the
zero-metallicity star with $M_{\rm prog} \sim$ 40 $M_{\odot}$ as the
progenitor. The parameters include the jet duration, its energy deposition
rate, deposited energy, and the opening angle. We examine the correlations of
following observables: (1) the ejecta and remnant masses, (2) the energy
deposition efficiency, (3) the $^{56}$Ni production and its correlation with
the ejecta velocity, deposited energy, and the ejected mass, (4) the Sc-Ti-V
correlation as observed in metal-poor stars, and (5) the [Zn/Fe] ratio as
observed in some metal-poor stars. We also provide the chemical abundance table
of these explosion models for the use of the galactic chemical evolution and
stellar archaeology.


  The shape of 3$d$-orbitals often governs the electronic and magnetic
properties of correlated transition metal oxides. In the superconducting
cuprates, the planar confinement of the $d_{x^2-y^2}$ orbital dictates the
two-dimensional nature of the unconventional superconductivity and a competing
charge order. Achieving orbital-specific control of the electronic structure to
allow coupling pathways across adjacent planes would enable direct assessment
of the role of dimensionality in the intertwined orders. Using Cu-$L_3$ and
Pr-$M_5$ resonant x-ray scattering and first-principles calculations, we report
a highly correlated three-dimensional charge order in Pr-substituted
YBa$_2$Cu$_3$O$_{7}$, where the Pr $f$-electrons create a direct orbital bridge
between CuO$_2$ planes. With this, we demonstrate that interplanar orbital
engineering can be used to surgically control electronic phases in correlated
oxides and other layered materials.


  In this paper, we examine the (LRS) Bianchi-type-I cosmological model with
holographic dark energy. The exact solutions to the corresponding field
equations are obtained by using generalized hybrid expansion law (HEL). The EoS
parameter $\omega$ for DE is found to be time dependent and redshift dependent
and its exiting range for derived model is agreeing well with the current
observations. Here we likewise apply two mathematical diagnostics, the
statefinder ({r, s}) and $\omega_{d}-\omega^{'}_{d}$ plane to segregate HDE
model from the $\Lambda CDM$ model. Here the $\omega_{d}-\omega^{'}_{d}$
diagnostic trajectories is the good tool to classifying the dynamical DE model.
We found that our model lies in both thawing region and freezing region. We
have also construct the potential as well as dynamics of the quintessence and
tachyon scalar field. Some physical and geometric properties of this model
along with the physical acceptability of cosmological solution have been
discussed in detail.


  We have undertaken a survey of archived, pointed ROSAT PSPC data for blazars
by correlating the ROSAT WGACAT database with several publicly available radio
catalogs, restricting our candidate list to serendipitous flat radio spectrum
sources (alpha_r <= 0.70). Here we discuss our survey methods, identification
procedure and first results. Our survey is found to be ~ 95% efficient at
finding flat-spectrum radio-loud quasars (FSRQs, 59 of our first 85 IDs) and BL
Lacertae objects (22 of our first 85 IDs), a figure which is comparable to or
greater than that achieved by other radio and X-ray survey techniques.
  The identifications presented here show that all previous samples of blazars
(even when taken together) did not representatively survey the blazar
population, missing critical regions of (L_X,L_R) parameter space within which
large fractions of the blazar population lie. Particularly important is the
identification of a large population of FSRQs (>~ 25% of DXRBS FSRQs) with
ratios of X-ray to radio luminosity >~ 10^-6 (alpha_rx <~ 0.78). In addition,
due to our greater sensitivity, DXRBS has already more than doubled the number
of FSRQs in complete samples with 5 GHz (radio) luminosities between 10^31.5
and 10^33.5 erg/s/Hz and fills in the region of parameter space between X-ray
selected and radio-selected samples of BL Lacs. DXRBS is the very first sample
to contain statistically significant numbers of blazars at low luminosities,
approaching what should be the lower end of the FSRQ luminosity function.


  Contextual information can have a substantial impact on the performance of
visual tasks such as semantic segmentation, object detection, and geometric
estimation. Data stored in Geographic Information Systems (GIS) offers a rich
source of contextual information that has been largely untapped by computer
vision. We propose to leverage such information for scene understanding by
combining GIS resources with large sets of unorganized photographs using
Structure from Motion (SfM) techniques. We present a pipeline to quickly
generate strong 3D geometric priors from 2D GIS data using SfM models aligned
with minimal user input. Given an image resectioned against this model, we
generate robust predictions of depth, surface normals, and semantic labels. We
show that the precision of the predicted geometry is substantially more
accurate other single-image depth estimation methods. We then demonstrate the
utility of these contextual constraints for re-scoring pedestrian detections,
and use these GIS contextual features alongside object detection score maps to
improve a CRF-based semantic segmentation framework, boosting accuracy over
baseline models.


  We compute gauge theories of the Lorentz group. We discuss non-interacting,
and interacting fermionic systems. The interacting system combines a local with
a global Lorentz group, i.e, discusses a $SO(3,1)_{l}\times
SO(3,1)_{g}$-theory. We compute the equations of motion and conservation laws
for the fermionic matter current. The core of our work is the prediction of
some new form of monopoles we call 'Dirac-Clifford-'t Hooft-Polyakov'-monopole.
It resides in a state similar to color-flavor locking. Dirac-Clifford-'t
Hooft-Polyakov-monopoles are invariant under global Lorentz transformations and
are predicted to form vortices. The theory is renormalizable, since all
Goldstone-Nambu modes are converted into massive vector gauge fields.


  We propose a fibre bundle formulation of the mathematical base of
relativistic quantum mechanics. At the present stage the bundle form of the
theory is equivalent to its conventional one, but it admits new types of
generalizations in different directions. In the bundle description the
wavefunctions are replaced with (state) sections (covariant approach) or
liftings of paths (equivalently: sections along paths) (time-dependent
approach) of a suitably chosen vector bundle over space-time whose (standard)
fibre is the space of the wavefunctions. Now the quantum evolution is described
as a linear transportation of the state sections/liftings in the (total) bundle
space. The equations of these transportations turn to be the bundle versions of
the corresponding relativistic wave equations. Connections between the
(retarded) Green functions of these equations and the evolution operators and
transports are found. Especially the Dirac and Klein-Gordon equations are
considered.


  Hybrid methods that utilize both content and rating information are commonly
used in many recommender systems. However, most of them use either handcrafted
features or the bag-of-words representation as a surrogate for the content
information but they are neither effective nor natural enough. To address this
problem, we develop a collaborative recurrent autoencoder (CRAE) which is a
denoising recurrent autoencoder (DRAE) that models the generation of content
sequences in the collaborative filtering (CF) setting. The model generalizes
recent advances in recurrent deep learning from i.i.d. input to non-i.i.d.
(CF-based) input and provides a new denoising scheme along with a novel
learnable pooling scheme for the recurrent autoencoder. To do this, we first
develop a hierarchical Bayesian model for the DRAE and then generalize it to
the CF setting. The synergy between denoising and CF enables CRAE to make
accurate recommendations while learning to fill in the blanks in sequences.
Experiments on real-world datasets from different domains (CiteULike and
Netflix) show that, by jointly modeling the order-aware generation of sequences
for the content information and performing CF for the ratings, CRAE is able to
significantly outperform the state of the art on both the recommendation task
based on ratings and the sequence generation task based on content information.


  The concept of updating a probability distribution in the light of new
evidence lies at the heart of statistics and machine learning. Pearl's and
Jeffrey's rule are two natural update mechanisms which lead to different
outcomes, yet the similarities and differences remain mysterious. This paper
clarifies their relationship in several ways: via separate descriptions of the
two update mechanisms in terms of probabilistic programs and sampling
semantics, and via different notions of likelihood (for Pearl and for Jeffrey).
Moreover, it is shown that Jeffrey's update rule arises via variational
inference. In terms of categorical probability theory, this amounts to an
analysis of the situation in terms of the behaviour of the multiset functor,
extended to the Kleisli category of the distribution monad.


  Measuring the similarity between patches in images is a fundamental building
block in various tasks. Naturally, the patch-size has a major impact on the
matching quality, and on the consequent application performance. Under the
assumption that our patch database is sufficiently sampled, using large patches
(e.g. 21-by-21) should be preferred over small ones (e.g. 7-by-7). However,
this "dense-sampling" assumption is rarely true; in most cases large patches
cannot find relevant nearby examples. This phenomenon is a consequence of the
curse of dimensionality, stating that the database-size should grow
exponentially with the patch-size to ensure proper matches. This explains the
favored choice of small patch-size in most applications.
  Is there a way to keep the simplicity and work with small patches while
getting some of the benefits that large patches provide? In this work we offer
such an approach. We propose to concatenate the regular content of a
conventional (small) patch with a compact representation of its (large)
surroundings - its context. Therefore, with a minor increase of the dimensions
(e.g. with additional 10 values to the patch representation), we
implicitly/softly describe the information of a large patch. The additional
descriptors are computed based on a self-similarity behavior of the patch
surrounding.
  We show that this approach achieves better matches, compared to the use of
conventional-size patches, without the need to increase the database-size.
Also, the effectiveness of the proposed method is tested on three distinct
problems: (i) External natural image denoising, (ii) Depth image
super-resolution, and (iii) Motion-compensated frame-rate up-conversion.


  In this paper, we generalize finite depth wavelet scattering transforms,
which we formulate as $\Lb^q(\mathbb{R}^n)$ norms of a cascade of continuous
wavelet transforms (or dyadic wavelet transforms) and contractive
nonlinearities. We then provide norms for these operators, prove that these
operators are well-defined, and are Lipschitz continuous to the action of $C^2$
diffeomorphisms in specific cases. Lastly, we extend our results to formulate
an operator invariant to the action of rotations $R \in \text{SO}(n)$ and an
operator that is equivariant to the action of rotations of $R \in
\text{SO}(n)$.


  The variational reduced density matrix theory has been recently applied with
great success to models within the truncated doubly-occupied configuration
interaction space, which corresponds to the seniority zero subspace.
Conservation of the seniority quantum number restricts the Hamiltonians to be
based on the SU(2) algebra. Among them there is a whole family of exactly
solvable Richardson-Gaudin pairing Hamiltonians. We benchmark the variational
theory against two different exactly solvable models, the
Richardson-Gaudin-Kitaev and the reduced BCS Hamiltonians. We obtain exact
numerical results for the so-called PQGT N-representability conditions in both
cases for systems that go from 10 to 100 particles. However, when random
single-particle energies as appropriate for small superconducting grains are
considered, the exactness is lost but still a high accuracy is obtained.


  The single transverse spin asymmetry, A_N, of the p-carbon elastic scattering
process in the Coulomb Nuclear Interference (CNI) region was measured using an
ultra thin carbon target and polarized proton beam in the Relativistic Heavy
Ion Collider (RHIC) at Brookhaven National Laboratory (BNL). In 2004, data were
collected to calibrate the p-carbon process at two RHIC energies (24 GeV, 100
GeV). A_N was obtained as a function of momentum transfer -t. The results were
fit with theoretical models which allow us to assess the contribution from a
hadronic spin flip amplitude.


  Security function outsourcing has witnessed both research and deployment in
the recent years. While most existing services take a straight-forward approach
of cloud hosting, on-path transit networks (such as ISPs) are increasingly more
interested in offering outsourced security services to end users. Recent
proposals (such as SafeBricks and mbTLS) have made it possible to outsource
sensitive security applications to untrusted, arbitrary networks, rendering
on-path security function outsourcing more promising than ever. However, to
provide on-path security function outsourcing, there is one crucial component
that is still missing -- a practical end-to-end network protocol. Thus, the
discovery and orchestration of multiple capable and willing transit networks
for user-requested security functions have only been assumed in many studies
without any practical solutions. In this work, we propose Opsec, an end-to-end
security-outsourcing protocol that fills this gap and brings us closer to the
vision of on-path security function outsourcing. Opsec automatically discovers
one or more transit ISPs between a client and a server, and requests
user-specified security functions efficiently. When designing Opsec, we
prioritize the practicality and applicability of this new end-to-end protocol
in the current Internet. Our proof-of-concept implementation of Opsec for web
sessions shows that an end user can easily start a new web session with a few
clicks of a browser plug-in, to specify a series of security functions of her
choice. We show that it is possible to implement such a new end-to-end service
model in the current Internet for the majority of the web services without any
major changes to the standard protocols (e.g., TCP, TLS, HTTP) and the existing
network infrastructure (e.g., ISP's routing primitives).


  We present surface photometry and stellar kinematics of IC 3167, a dwarf
galaxy hosting a lopsided weak bar and infalling into the Virgo cluster. We
measured the bar radius and strength from broad-band imaging and bar pattern
speed by applying the Tremaine-Weinberg method to stellar-absorption
integral-field spectroscopy. We derived the ratio of the corotation radius to
bar radius (R = 1.7 + 0.5 - 0.3) from stellar kinematics and bar pattern speed.
The probability that the bar is rotating slowly is more than twice as likely as
that the bar is fast. This allows us to infer that the formation of this bar
was triggered by the ongoing interaction rather than to internal processes.


  We develop a theory of tunneling spectroscopy in superconducting topological
insulator (STI), i.e. superconducting state of a carrier-doped topological
insulator. Based on the quasi-classical approximation, we obtain an analytical
expression of the energy dispersion of surface Andreev bound states (ABSs) and
a compact formula for tunneling conductance of normal metal/STI junctions. The
obtained compact formula of tunneling conductance makes the analysis of
experimental data easy. As an application of our theory, we study tunneling
conductance in Cu$_x$Bi$_2$Se$_3$. We reveal that our theory reproduces the
previous results by Yamakage {\it et al} [Phys. Rev. B 85, 180509(R) (2012)].
We further study magneto-tunneling spectroscopy in the presence of external
magnetic fields, where the energy of quasiparticles is shifted by the Doppler
effect. By rotating magnetic fields in the basal plane, we can distinguish
between two different topological superconducting states with and without point
nodes, in their magneto-tunneling spectra.


  This paper investigates the spectral efficiency of multi-cell massive
multiple-input multiple-output systems with Ricean fading that utilize the
linear maximal-ratio combining detector. We firstly present closed-form
expressions for the effective signal-to-interference-plus-noise ratio (SINR)
with the least squares and minimum mean squared error (MMSE) estimation
methods, respectively, which apply for any number of base-station antennas $M$
and any Ricean $K$-factor. Also, the obtained results can be particularized in
Rayleigh fading conditions when the Ricean $K$-factor is equal to zero. In the
following, novel exact asymptotic expressions of the effective SINR are derived
in the high $M$ and high Ricean $K$-factor regimes. The corresponding analysis
shows that pilot contamination is removed by the MMSE estimator when we
consider both infinite $M$ and infinite Ricean $K$-factor, while the pilot
contamination phenomenon persists for the rest of cases. All the theoretical
results are verified via Monte-Carlo simulations.


  The healthcare environment is commonly referred to as "information-rich" but
also "knowledge poor". Healthcare systems collect huge amounts of data from
various sources: lab reports, medical letters, logs of medical tools or
programs, medical prescriptions, etc. These massive sets of data can provide
great knowledge and information that can improve the medical services, and
overall the healthcare domain, such as disease prediction by analyzing the
patient's symptoms or disease prevention, by facilitating the discovery of
behavioral factors for diseases. Unfortunately, only a relatively small volume
of the textual eHealth data is processed and interpreted, an important factor
being the difficulty in efficiently performing Big Data operations. In the
medical field, detecting domain-specific multi-word terms is a crucial task as
they can define an entire concept with a few words. A term can be defined as a
linguistic structure or a concept, and it is composed of one or more words with
a specific meaning to a domain. All the terms of a domain create its
terminology. This chapter offers a critical study of the current, most
performant solutions for analyzing unstructured (image and textual) eHealth
data. This study also provides a comparison of the current Natural Language
Processing and Deep Learning techniques in the eHealth context. Finally, we
examine and discuss some of the current issues, and we define a set of research
directions in this area.


  Accurate predictions of material properties within the chemical space of
transition metal fluorides (TMFs), using density functional theory (DFT) is
important for advancing several technological applications. The
state-of-the-art semi-local exchange-correlation functionals within DFT include
the strongly constrained and appropriately normed (SCAN) and the restored
regularized SCAN (r$^2$SCAN), both of which are meta generalized gradient
approximation (metaGGA) functionals. Both SCAN and r$^2$SCAN are susceptible to
self-interaction errors (SIEs) while modelling correlated $d$ electrons of
transition metals. Hence, in this work, we evaluate the accuracy of both
functionals in estimating properties of TMFs, including redox enthalpies,
lattice geometries, on-site magnetic moments, and band gaps. We observe both
SCAN and r$^2$SCAN to exhibit poor accuracy in estimating fluorination
enthalpies among TMFs, attributable to SIEs among the $d$ electrons. Thus, we
derive optimal Hubbard $U$ corrections for both functionals based on
experimental fluorination enthalpies of binary TMFs. Note that the linear
response theory yielded unphysical $U$ values for V, Fe, and Ni fluorides.
While adding the optimal $U$ to the metaGGA functionals does not significantly
affect the lattice volumes and magnetic moments, it does significantly increase
the calculated band gaps. Also, we calculate the average Na intercalation
voltage in Mn, Fe, Co, and Ni fluorides as a transferability check of our
optimal $U$ values. Overall, we recommend using the Hubbard $U$ correction to
improve predictions of redox enthalpies in other TMFs, while for band gap
predictions, we suggest using the non-corrected functionals. Finally, our study
should advance the accuracy of DFT-based screening studies to unearth novel
TMFs, which can be used in various applications, including energy storage,
catalysis, and magnetic devices.


  We prove new results regarding the existence of positive solutions for a
nonlinear periodic boundary value problem related to the Liebau phenomenon. As
a consequence we obtain new sufficient conditions for the existence of a pump
in a simple model. Our methodology relies on the use of classical fixed point
index. Some examples are provided to illustrate our theory. We improve and
complement previous results in the literature.


  Equilibration of polymer melts containing highly entangled long polymer
chains in confinement or with free surfaces is a challenge for computer
simulations. We approach this problem by first studying polymer melts based on
the soft-sphere coarse-grained model confined between two walls with periodic
boundary conditions in two directions parallel to the walls. Then we apply
backmapping to reinsert the microscopic details of the underlying bead-spring
model. Tuning the strength of the wall potential, the monomer density of
confined polymer melts in equilibrium is kept at the bulk density even near the
walls. In a weak confining regime, we observe the same conformational
properties of chains as in the bulk melt showing that our confined polymer
melts have reached their equilibrated state. Our methodology provides an
efficient way of equilibrating large polymer films with different thicknesses
and is not confined to a specific underlying microscopic model. Switching off
the wall potential in the direction perpendicular to the walls, enables to
study free-standing highly entangled polymer films or polymer films with one
supporting substrate.


  We construct an explicit example of a generalized Lie 3-algebra from the
octonions. In combination with the result of arXiv:0807.0808, this gives rise
to a three-dimensional N=2 Chern-Simons-matter theory with exceptional gauge
group G_2 and with global symmetry SU(4)\times U(1). This gives a possible
candidate for the theory on multiple M2-branes with G_2 gauge symmetry.


  We study periodic, two-dimensional, gravity-capillary traveling wave
solutions to a viscous shallow water system posed on an inclined plane. While
thinking of the Reynolds and Bond numbers as fixed and finite, we vary the
speed of the traveling frame and the degree of the incline and identify a set
of the latter two parameters that classifies from which combinations nontrivial
and small amplitude solution curves originate. Our principal technical tools
are a combination of the implicit function theorem and a local multiparameter
bifurcation theorem. To the best of the author's knowledge, this paper
constitutes the first construction and mathematical study of properly two
dimensional examples of viscous roll waves.


  For each $n=0,1,2,\ldots$ the central trinomial coefficient $T_n$ is the
coefficient of $x^n$ in the expansion of $(x^2+x+1)^n$. In 2016 the second
author conjectured that for any prime $p>3$ and positive integer $n$ the
quotient $(T_{pn}-T_n)/(pn)^2$ is a $p$-adic integer. In this paper we confirm
this conjecture and prove further that
$$\frac{T_{pn}-T_n}{(pn)^2}\equiv\frac{T_{n-1}}6\left(\frac
p3\right)B_{p-2}\left(\frac13\right)\pmod p,$$ where $(\frac p3)$ is the
Legendre symbol and $B_{p-2}(x)$ is the Bernoulli polynomial of degree $p-2$.


  We study the space of orthogonally additive $n$-homogeneous polynomials on
$C(K)$. There are two natural norms on this space. First, there is the usual
supremum norm of uniform convergence on the closed unit ball. As every
orthogonally additive $n$-homogeneous polynomial is regular with respect to the
Banach lattice structure, there is also the regular norm. These norms are
equivalent, but have significantly different geometric properties. We
characterise the extreme points of the unit ball for both norms, with different
results for even and odd degrees. As an application, we prove a Banach-Stone
theorem. We conclude with a classification of the exposed points.


  Young & massive stellar clusters (SCs) are a potential source of galactic
cosmic rays up to very high energies as a result of two possible acceleration
scenarios. Collective stellar winds from massive member stars form a wind-blown
bubble with a termination shock (TS) at which particle acceleration to PeV
energies may occur. Furthermore, shock acceleration may occur at SNRs expanding
inside the bubble. By applying a model of CR acceleration at both the wind TS
and SNR shocks to catalogues of known SCs derived from Gaia DR2, we identify
the most promising targets to search for evidence of PeVatron activity.
Predictions for the secondary fluxes of gamma-ray and neutrino emission are
derived based on particle acceleration at the collective wind TS and the
subsequent hadronic interactions with the surrounding medium. Predictions from
our modelling under baseline and optimistic scenarios are compared to data,
finding consistent results. We estimate the detection prospects for future
gamma-ray and neutrino experiments. We find that degree-scale angular sizes of
the wind-blown bubbles are typical, that may pose a challenge for experimental
detection. A shortlist of the most promising candidates is provided, with an
anticipated flux range. Of order 10 SCs may be detectable with future
facilities, and 1-5 could be currently operating as PeVatrons. Of these, three
gamma-ray detected SCs have data within our predicted range. Our model can
consistently describe gamma-ray measurements of SC emission. Several further
as-yet-undetected SCs offer promising targets for future observations, although
the flux range allowed by our model can be large (> factor 10). The large
angular size of the wind-blown bubble may lead to low surface brightness
emission, worsening the problem of source confusion. Nevertheless, we discuss
how further work will help to constrain SCs as PeVatron candidates. (abridged)


  We perform an exact and asymptotic analysis of the model of $n$ vicious
walkers interacting with a wall via contact potentials, a model introduced by
Brak, Essam and Owczarek. More specifically, we study the partition function of
watermelon configurations which start on the wall, but may end at arbitrary
height, and their mean number of contacts with the wall. We improve and extend
the earlier (partially non-rigorous) results by Brak, Essam and Owczarek,
providing new exact results, and more precise and more general asymptotic
results, in particular full asymptotic expansions for the partition function
and the mean number of contacts. Furthermore, we relate this circle of problems
to earlier results in the combinatorial and statistical literature.


  We propose the ``short'' version of q-deformed differential calculus on the
light-cone using twistor representation. The commutation relations between
coordinates and momenta are obtained. The quasi-classical limit introduced
gives an exact shape of the off-shell shifting.


  We report low-temperature electronic transport measurements performed in two
multi-terminal Corbino samples formed in GaAs/Al-GaAs two-dimensional electron
gases (2DEG) with both ultra-high electron mobility ($\gtrsim 20\times 10^6$
$cm^2/Vs)$ and with distinct electron density of $1.7$ and $3.6\times
10^{11}~cm^{-2}$. In both Corbino samples, a non-monotonic behavior is observed
in the temperature dependence of the resistance below 1~$K$. Surprisingly, a
sharp {\it decrease} in resistance is observed with {\it increasing}
temperature in the sample with lower electron density, whereas an opposite
behavior is observed in the sample with higher density. To investigate further,
transport measurements were performed in large van der Pauw samples having
identical heterostructures, and as expected they exhibit resistivity that is
monotonic with temperature. Finally, we discuss the results in terms of various
lengthscales leading to ballistic and hydrodynamic electronic transport, as
well as a possible Gurzhi effect.


  A general solution for vacancy-mediated diffusion in the
dilute-vacancy/dilute-solute limit for arbitrary crystal structures is derived
from the master equation. A general numerical approach to the vacancy lattice
Green function reduces to the sum of a few analytic functions and numerical
integration of a smooth function over the Brillouin zone for arbitrary
crystals. The Dyson equation solves for the Green function in the presence of a
solute with arbitrary but finite interaction range to compute the transport
coefficients accurately, efficiently and automatically, including cases with
very large differences in solute-vacancy exchange rates. The methodology takes
advantage of the space group symmetry of a crystal to reduce the complexity of
the matrix inversion in the Dyson equation. An open-source implementation of
the algorithm is available, and numerical results are presented for the
convergence of the integration error of the bare vacancy Green function, and
tracer correlation factors for a variety of crystals including wurtzite
(hexagonal diamond) and garnet.


  Autism is increasing in prevalence and is a neurodevelopmental disorder
characterised by impairments in communication skills and social behaviour.
Connectomes enable a systems-level representation of the brain with recent
interests in understanding the distributed nature of higher order cognitive
function using modules or subnetworks. By dividing the connectome according to
a central component of the brain critical for its function (it's hub), we
investigate network organisation in autism from hub through to peripheral
subnetworks. We complement this analysis by extracting features of energy
transport computed from heat kernels fitted with increasing time steps. This
heat kernel framework is advantageous as it can capture the energy transported
in all direct and indirect pathways between pair-wise regions over 'time', with
features that have correspondence to small-world properties. We apply our
framework to resting-state functional MRI connectomes from a large, publically
available autism dataset, ABIDE. We show that energy propagating through the
brain over time are different between subnetworks, and that heat kernel
features significantly differ between autism and controls. Furthermore, the hub
was functionally preserved and similar to controls, however, increasing
statistical significance between groups was found in increasingly peripheral
subnetworks. Our results support the increasing opinion of non-hub regions
playing an important role in functional organisation. This work shows that
analysing autism by subnetworks with the heat kernel reflects the atypical
activations in peripheral regions as alterations in energy dispersion and may
provide useful features towards understanding the distributed impact of this
disorder on the functional connectome.


  High noise and low spatial resolution are two key confounding factors that
limit the qualitative and quantitative accuracy of PET images. AI models for
image denoising and deblurring are becoming increasingly popular for
post-reconstruction enhancement of PET images. We present here a detailed
review of recent efforts for AI-based PET image enhancement with a focus on
network architectures, data types, loss functions, and evaluation metrics. We
also highlight emerging areas in this field that are quickly gaining
popularity, identify barriers to large-scale adoption of AI models for PET
image enhancement, and discuss future directions.


  Ransomware, a fearsome and rapidly evolving cybersecurity threat, continues
to inflict severe consequences on individuals and organizations worldwide.
Traditional detection methods, reliant on static signatures and application
behavioral patterns, are challenged by the dynamic nature of these threats.
This paper introduces three primary contributions to address this challenge.
First, we introduce a ransomware emulator. This tool is designed to safely
mimic ransomware attacks without causing actual harm or spreading malware,
making it a unique solution for studying ransomware behavior. Second, we
demonstrate how we use this emulator to create storage I/O traces. These traces
are then utilized to train machine-learning models. Our results show that these
models are effective in detecting ransomware, highlighting the practical
application of our emulator in developing responsible cybersecurity tools.
Third, we show how our emulator can be used to mimic the I/O behavior of
existing ransomware thereby enabling safe trace collection. Both the emulator
and its application represent significant steps forward in ransomware detection
in the era of machine-learning-driven cybersecurity.


  From 1873 to 1897, Georg Cantor worked on developing set theory, and despite
a strong initial resistance, it rapidly became accepted as the foundation of
mathematics. In this work, however, we'll demonstrate that Cantor's use of
infinity is inconsistent with Calculus. Since both the cardinal and the ordinal
numbers are to blame, we'll introduce the new infinite number $\lambda$ to
remedy this situation. By developing its arithmetical properties for each basic
operation, we'll show that infinity can be quantified with precision, and that
no set, however large, is uncountable. Moreover, by working with the number
$\lambda$, we'll resolve several long-standing paradoxes in mathematics, such
as the Riemann Rearrangement Theorem and the Banach-Tarski paradox.


  Video Frame Interpolation synthesizes non-existent images between adjacent
frames, with the aim of providing a smooth and consistent visual experience.
Two approaches for solving this challenging task are optical flow based and
kernel-based methods. In existing works, optical flow based methods can provide
accurate point-to-point motion description, however, they lack constraints on
object structure. On the contrary, kernel-based methods focus on structural
alignment, which relies on semantic and apparent features, but tends to blur
results. Based on these observations, we propose a structure-motion based
iterative fusion method. The framework is an end-to-end learnable structure
with two stages. First, interpolated frames are synthesized by structure-based
and motion-based learning branches respectively, then, an iterative refinement
module is established via spatial and temporal feature integration. Inspired by
the observation that audiences have different visual preferences on foreground
and background objects, we for the first time propose to use saliency masks in
the evaluation processes of the task of video frame interpolation. Experimental
results on three typical benchmarks show that the proposed method achieves
superior performance on all evaluation metrics over the state-of-the-art
methods, even when our models are trained with only one-tenth of the data other
methods use.


  We present the first measurement of individual cluster mass estimates using
weak lensing size and flux magnification. Using data from the HST-STAGES survey
of the A901/902 supercluster we detect the four known groups in the
supercluster at high significance using magnification alone. We discuss the
application of a fully Bayesian inference analysis, and investigate a broad
range of potential systematics in the application of the method. We compare our
results to a previous weak lensing shear analysis of the same field finding the
recovered signal-to-noise of our magnification-only analysis to range from 45%
to 110% of the signal-to-noise in the shear-only analysis. On a case-by-case
basis we find consistent magnification and shear constraints on cluster virial
radius, and finding that for the full sample, magnification constraints to be a
factor $0.77 \pm 0.18$ lower than the shear measurements.


  This paper studies the transmit beamforming in a downlink integrated sensing
and communication (ISAC) system, where a base station (BS) equipped with a
uniform linear array (ULA) sends combined information-bearing and dedicated
radar signals to simultaneously perform downlink multiuser communication and
radar target sensing. Under this setup, we maximize the radar sensing
performance (in terms of minimizing the beampattern matching errors or
maximizing the minimum weighted beampattern gains), subject to the
communication users' minimum signal-to-interference-plus-noise ratio (SINR)
requirements and the BS's transmit power constraints. In particular, we
consider two types of communication receivers, namely Type-I and Type-II
receivers, which do not have and do have the capability of cancelling the
interference from the {\emph{a-priori}} known dedicated radar signals,
respectively. Under both Type-I and Type-II receivers, the beampattern matching
and minimum weighted beampattern gain maximization problems are globally
optimally solved via applying the semidefinite relaxation (SDR) technique
together with the rigorous proof of the tightness of SDR for both Type-I and
Type-II receivers under the two design criteria. It is shown that at the
optimality, radar signals are not required with Type-I receivers under some
specific conditions, while radar signals are always needed to enhance the
performance with Type-II receivers. Numerical results show that the minimum
weighted beampattern gain maximization leads to significantly higher
beampattern gains at the worst-case sensing angles with a much lower
computational complexity than the beampattern matching design. We show that by
exploiting the capability of canceling the interference caused by the radar
signals, the case with Type-II receivers results in better sensing performance
than that with Type-I receivers and other conventional designs.


  We propose a new approach to value function approximation which combines
linear temporal difference reinforcement learning with subspace identification.
In practical applications, reinforcement learning (RL) is complicated by the
fact that state is either high-dimensional or partially observable. Therefore,
RL methods are designed to work with features of state rather than state
itself, and the success or failure of learning is often determined by the
suitability of the selected features. By comparison, subspace identification
(SSID) methods are designed to select a feature set which preserves as much
information as possible about state. In this paper we connect the two
approaches, looking at the problem of reinforcement learning with a large set
of features, each of which may only be marginally useful for value function
approximation. We introduce a new algorithm for this situation, called
Predictive State Temporal Difference (PSTD) learning. As in SSID for predictive
state representations, PSTD finds a linear compression operator that projects a
large set of features down to a small set that preserves the maximum amount of
predictive information. As in RL, PSTD then uses a Bellman recursion to
estimate a value function. We discuss the connection between PSTD and prior
approaches in RL and SSID. We prove that PSTD is statistically consistent,
perform several experiments that illustrate its properties, and demonstrate its
potential on a difficult optimal stopping problem.


  We show a uniqueness theorem for charged rotating black holes in the bosonic
sector of five-dimensional minimal supergravity. More precisely, under the
assumptions of the existence of two commuting axial isometries and spherical
topology of horizon cross-sections, we prove that an asymptotically flat,
stationary charged rotating black hole with finite temperature in
five-dimensional Einstein-Maxwell-Chern-Simons theory is uniquely characterized
by the mass, charge, and two independent angular momenta and therefore is
described by the five-dimensional Cvetic-Youm solution with equal charges. We
also discuss a generalization of our uniqueness theorem for spherical black
holes to the case of black rings.


  We address the following question of neural network identifiability: Suppose
we are given a function $f:\mathbb{R}^m\to\mathbb{R}^n$ and a nonlinearity
$\rho$. Can we specify the architecture, weights, and biases of all
feed-forward neural networks with respect to $\rho$ giving rise to $f$?
Existing literature on the subject suggests that the answer should be yes,
provided we are only concerned with finding networks that satisfy certain
"genericity conditions". Moreover, the identified networks are mutually related
by symmetries of the nonlinearity. For instance, the $\tanh$ function is odd,
and so flipping the signs of the incoming and outgoing weights of a neuron does
not change the output map of the network. The results known hitherto, however,
apply either to single-layer networks, or to networks satisfying specific
structural assumptions (such as full connectivity), as well as to specific
nonlinearities. In an effort to answer the identifiability question in greater
generality, we consider arbitrary nonlinearities with potentially complicated
affine symmetries, and we show that the symmetries can be used to find a rich
set of networks giving rise to the same function $f$. The set obtained in this
manner is, in fact, exhaustive (i.e., it contains all networks giving rise to
$f$) unless there exists a network $\mathcal{A}$ "with no internal symmetries"
giving rise to the identically zero function. This result can thus be
interpreted as an analog of the rank-nullity theorem for linear operators. We
furthermore exhibit a class of "$\tanh$-type" nonlinearities (including the
tanh function itself) for which such a network $\mathcal{A}$ does not exist,
thereby solving the identifiability question for these nonlinearities in full
generality. Finally, we show that this class contains nonlinearities with
arbitrarily complicated symmetries.


  We consider the motion of incompressible viscous fluids bounded above by a
free surface and below by a solid surface in the $N$-dimensional Euclidean
space for $N\geq 2$ when the gravity is not taken into account. The aim of this
paper is to show the global solvability of the Naiver-Stokes equations with a
free surface, describing the above-mentioned motion, in the maximal
$L_p\text{-}L_q$ regularity class. Our approach is based on the maximal
$L_p\text{-}L_q$ regularity with exponential stability for the linearized
equations, and solutions to the original nonlinear problem are also
exponentially stable.


  Lighting design in indoor environments is of primary importance for at least
two reasons: 1) people should perceive an adequate light; 2) an effective
lighting design means consistent energy saving. We present the Invisible Light
Switch (ILS) to address both aspects. ILS dynamically adjusts the room
illumination level to save energy while maintaining constant the light level
perception of the users. So the energy saving is invisible to them. Our
proposed ILS leverages a radiosity model to estimate the light level which is
perceived by a person within an indoor environment, taking into account the
person position and her/his viewing frustum (head pose). ILS may therefore dim
those luminaires, which are not seen by the user, resulting in an effective
energy saving, especially in large open offices (where light may otherwise be
ON everywhere for a single person). To quantify the system performance, we have
collected a new dataset where people wear luxmeter devices while working in
office rooms. The luxmeters measure the amount of light (in Lux) reaching the
people gaze, which we consider a proxy to their illumination level perception.
Our initial results are promising: in a room with 8 LED luminaires, the energy
consumption in a day may be reduced from 18585 to 6206 watts with ILS
(currently needing 1560 watts for operations). While doing so, the drop in
perceived lighting decreases by just 200 lux, a value considered negligible
when the original illumination level is above 1200 lux, as is normally the case
in offices.


  Measurement is a fundamental building block of numerous scientific models and
their creation. This is in particular true for data driven science. Due to the
high complexity and size of modern data sets, the necessity for the development
of understandable and efficient scaling methods is at hand. A profound theory
for scaling data is scale-measures, as developed in the field of formal concept
analysis. Recent developments indicate that the set of all scale-measures for a
given data set constitutes a lattice and does hence allow efficient exploring
algorithms. In this work we study the properties of said lattice and propose a
novel scale-measure exploration algorithm that is based on the well-known and
proven attribute exploration approach. Our results motivate multiple
applications in scale recommendation, most prominently (semi-)automatic
scaling.


  The derivation trees of a tree adjoining grammar provide a first insight into
the sentence semantics, and are thus prime targets for generation systems. We
define a formalism, feature-based regular tree grammars, and a translation from
feature based tree adjoining grammars into this new formalism. The translation
preserves the derivation structures of the original grammar, and accounts for
feature unification.


  We produce two identical keys using, for the first time, entangled trinary
quantum systems (qutrits) for quantum key distribution. The advantage of
qutrits over the normally used binary quantum systems is an increased coding
density and a higher security margin. The qutrits are encoded into the orbital
angular momentum of photons, namely Laguerre-Gaussian modes with azimuthal
index l +1, 0 and -1, respectively. The orbital angular momentum is controlled
with phase holograms. In an Ekert-type protocol the violation of a
three-dimensional Bell inequality verifies the security of the generated keys.
A key is obtained with a qutrit error rate of approximately 10 %.


  A major challenge when describing the origin of life is to explain "how
instructional information control systems emerge naturally and spontaneously
from mere molecular dynamics". So far, no one has clarified how information
control emerged ab initio and how primitive control mechanisms in life might
have evolved, becoming increasingly refined. Based on recent experimental
results showing that chemical computation does not require the presence of
life-related chemistry, we elucidate the origin and early evolution of
information handling by chemical automata, from information processing
(computation) to information storage (memory) and information transmission
(communication) and later digital messengers, covering at the same time its
syntactic, semantic and pragmatic flavors. In contrast to other theories that
assume the existence of initial complex structures, our representation starts
from trivial self-replicators whose interaction leads to the arising of more
powerful molecular machines. By describing precisely the primordial transitions
in chemistry-based computation, our framework is capable of explaining the
above-mentioned gaps and can be translated to other models of computation,
which allow us to explore biological phenomena at multiple spatial and temporal
scales. Being compatible with the free energy principle, we have developed a
computational enactivist theoretical framework that could be able to describe
from the origin of life to high-level cognition, as if it were a purely
constructivist narrative. At the end of our manuscript, we propose some ways to
extend our ideas, including experimental validation of our theory (both in
vitro and in silico).


  We have studied theoretically, using density functional theory, several
materials properties when going from one C layer in graphene to two and three g
raphene layers and on to graphite. The properties we have focused on are the
elastic constants, electronic structure (energy bands and density of state s),
and the dielectric properties. For any of the properties we have investigated
the modification due to an increase in the number of graphene layers is within
a few percent. Our results are in agreement with the analysis presented
recently by Kopelevich and Esquinazi (unpublished).


  In this {\color{red}{paper}} we discuss about the $ap-$Henstock-Kurzweil
integrable functions on a topological vector spaces. Basic results of
$ap-$Henstock-Kurzweil integrable functions are discussed here. We discuss the
equivalence of the $ap-$Henstock-Kurzweil integral on a topological vector
spaces and the vector valued $ap-$Henstock-Kurzweil integral. Finally, several
convergence theorems are studied.


  Fact knowledge memorization is crucial for Large Language Models (LLM) to
generate factual and reliable responses. However, the behaviors of LLM fact
memorization remain under-explored. In this paper, we analyze the scaling laws
for LLM's fact knowledge and LLMs' behaviors of memorizing different types of
facts. We find that LLMs' fact knowledge capacity has a linear and negative
exponential law relationship with model size and training epochs, respectively.
Estimated by the built scaling law, memorizing the whole Wikidata's facts
requires training an LLM with 1000B non-embed parameters for 100 epochs,
suggesting that using LLMs to memorize all public facts is almost implausible
for a general pre-training setting. Meanwhile, we find that LLMs can generalize
on unseen fact knowledge and its scaling law is similar to general
pre-training. Additionally, we analyze the compatibility and preference of
LLMs' fact memorization. For compatibility, we find LLMs struggle with
memorizing redundant facts in a unified way. Only when correlated facts have
the same direction and structure, the LLM can compatibly memorize them. This
shows the inefficiency of LLM memorization for redundant facts. For preference,
the LLM pays more attention to memorizing more frequent and difficult facts,
and the subsequent facts can overwrite prior facts' memorization, which
significantly hinders low-frequency facts memorization. Our findings reveal the
capacity and characteristics of LLMs' fact knowledge learning, which provide
directions for LLMs' fact knowledge augmentation.


  Explaining the decision-making process of machine learning models is crucial
for ensuring their reliability and fairness. One popular explanation form
highlights key input features, such as i) tokens (e.g., Shapley Values and
Integrated Gradients), ii) interactions between tokens (e.g., Bivariate Shapley
and Attention-based methods), or iii) interactions between spans of the input
(e.g., Louvain Span Interactions). However, these explanation types have only
been studied in isolation, making it difficult to judge their respective
applicability. To bridge this gap, we propose a unified framework that
facilitates a direct comparison between highlight and interactive explanations
comprised of four diagnostic properties. Through extensive analysis across
these three types of input feature explanations--each utilizing three different
explanation techniques--across two datasets and two models, we reveal that each
explanation type excels in terms of different diagnostic properties. In our
experiments, highlight explanations are the most faithful to a model's
prediction, and interactive explanations provide better utility for learning to
simulate a model's predictions. These insights further highlight the need for
future research to develop combined methods that enhance all diagnostic
properties.


  Inspired by the structure of top-down derived models endowed with modular
flavor symmetries, we investigate the yet phenomenologically unexplored binary
dihedral group 2D_3. After building the vector-valued modular forms in the
representations of 2D_3 with small modular weights, we systematically classify
all (Dirac and Majorana) mass textures of fermions with fractional modular
weights and all possible 2+1-family structures. This allows us to explore the
parameter space of fermion models based on 2D_3, aiming at a description of
both quarks and leptons with a minimal number of parameters and best
compatibility with observed data. We consider the separate possibilities of
neutrino masses generated by either a type-I seesaw mechanism or the Weinberg
operator. We identify a model that, besides fitting all known flavor
observables, delivers predictions for six not-yet measured parameters and
favors normal-ordered neutrino masses generated by the Weinberg operator. It
would be interesting to figure out whether it is possible to embed our model
within a top-down scheme, such as T2/Z4 heterotic orbifold compactifications.


  This paper is concerned with the effect of parameter fluctuations with a
characteristic waiting time in coupled R\"{o}ssler oscillators. We show that
the averaged error in synchronization that is introduced due to a fluctuating
parameter is proportional to the waiting time and the amplitude of the
fluctuations. It is also shown that coupling strength beyond a threshold value
does not have any significant effect on improving the quality of
synchronization when the fluctuations posses considerable waiting time.


  We present the Generalized Borel Transform (GBT). This new approach allows
one to obtain approximate solutions of Laplace/Mellin transform valid in both,
perturbative and non perturbative regimes. We compare the results provided by
the GBT for a solvable model of quantum mechanics with those provided by
standard techniques, as the conventional Borel sum, or its modified versions.
We found that our approach is very efficient for obtaining both the low and the
high energy behavior of the model.


  It has been shown recently that changing the fluidic properties of a drug can
improve its efficacy in ablating solid tumors. We develop a modeling framework
for tumor ablation, and present the simplest possible model for drug diffusion
in a spherical tumor with leaky boundaries and assuming cell death eventually
leads to ablation of that cell effectively making the two quantities
numerically equivalent. The death of a cell after a given exposure time depends
on both the concentration of the drug and the amount of oxygen available to the
cell. Higher oxygen availability leads to cell death at lower drug
concentrations. It can be assumed that a minimum concentration is required for
a cell to die, effectively connecting diffusion with efficacy. The
concentration threshold decreases as exposure time increases, which allows us
to compute dose-response curves. Furthermore, these curves can be plotted at
much finer time intervals compared to that of experiments, which is used to
produce a dose-threshold-response surface giving an observer a complete picture
of the drug's efficacy for an individual. In addition, since the diffusion,
leak coefficients, and the availability of oxygen is different for different
individuals and tumors, we produce artificial replication data through
bootstrapping to simulate error. While the usual data-driven model with
Sigmoidal curves use 12 free parameters, our mechanistic model only has two
free parameters, allowing it to be open to scrutiny rather than forcing
agreement with data. Even so, the simplest model in our framework, derived
here, shows close agreement with the bootstrapped curves, and reproduces well
established relations, such as Haber's rule.


  It is a high-quality algorithm for hierarchical clustering of large software
source code. This effectively allows to break the complexity of tens of
millions lines of source code, so that a human software engineer can comprehend
a software system at high level by means of looking at its architectural
diagram that is reconstructed automatically from the source code of the
software system. The architectural diagram shows a tree of subsystems having
OOP classes in its leaves (in the other words, a nested software
decomposition). The tool reconstructs the missing
(inconsistent/incomplete/inexistent) architectural documentation for a software
system from its source code. This facilitates software maintenance: change
requests can be performed substantially faster. Simply speaking, this unique
tool allows to lift the comprehensible grain of object-oriented software
systems from OOP class-level to subsystem-level. It is estimated that a
commercial tool, developed on the basis of this work, will reduce software
maintenance expenses 10 times on the current needs, and will allow to implement
next-generation software systems which are currently too complex to be within
the range of human comprehension, therefore can't yet be designed or
implemented. Implemented prototype in Open Source:
http://sourceforge.net/p/insoar/code-0/1/tree/


  The convective overshoot mixing plays an important role in stellar structure
and evolution. However, the overshoot mixing is a long standing problem. The
uncertainty of the overshoot mixing is one of the most uncertain factors in
stellar physics. As it is well known, the convective and overshoot mixing is
determined by the radial chemical component flux. In this paper, a local model
of the radial chemical component flux is established based on the hydrodynamic
equations and some model assumptions. The model is tested in stellar models.
The main conclusions are as follows. (i) The local model shows that the
convective and overshoot mixing could be regarded as a diffusion process, and
the diffusion coefficient for different chemical element is the same. However,
if the non-local terms, i.e., the turbulent convective transport of radial
chemical component flux, are taken into account, the diffusion coefficient for
each chemical element should be in general different. (ii) The diffusion
coefficient of convective / overshoot mixing shows different behaviors in
convection zone and in overshoot region because the characteristic length scale
of the mixing is large in the convection zone and small in the overshoot
region. The overshoot mixing should be regarded as a weak mixing process. (iii)
The result of the diffusion coefficient of mixing is tested in stellar models.
It is found that a single choice of our central mixing parameter leads to
consistent results for a solar convective envelope model as well as for core
convection models of stars with mass from 2M to 10M.


  Vera C. Rubin Observatory, through the Legacy Survey of Space and Time
(LSST), will allow us to derive a panchromatic view of variability in young
stellar objects (YSOs) across all relevant timescales. Indeed, both short-term
variability (on timescales of hours to days) and long-term variability (months
to years), predominantly driven by the dynamics of accretion processes in
disk-hosting YSOs, can be explored by taking advantage of the multi-band
filters option available in Rubin LSST, in particular the $u,g,r,i$ filters
that enable us to discriminate between photospheric stellar properties and
accretion signatures. The homogeneity and depth of sky coverage that will be
achieved with LSST will provide us with a unique opportunity to characterize
the time evolution of disk accretion as a function of age and varying
environmental conditions (e.g. field crowdedness, massive neighbors,
metallicity), by targeting different star-forming regions. In this contribution
to the Rubin LSST Survey Strategy Focus Issue, we discuss how implementing a
dense observing cadence to explore short-term variability in YSOs represents a
key complementary effort to the Wide-Fast-Deep observing mode that will be used
to survey the sky over the full duration of the main survey ($\approx$10
years). The combination of these two modes will be vital to investigate the
connection between the inner disk dynamics and longer-term eruptive variability
behaviors, such as those observed on EXor objects.


  The following article introduces a new parametric synthesis algorithm for
sound textures inspired by existing methods used for visual textures. Using a
2D Convolutional Neural Network (CNN), a sound signal is modified until the
temporal cross-correlations of the feature maps of its log-spectrogram resemble
those of a target texture. We show that the resulting synthesized sound signal
is both different from the original and of high quality, while being able to
reproduce singular events appearing in the original. This process is performed
in the time domain, discarding the harmful phase recovery step which usually
concludes synthesis performed in the time-frequency domain. It is also
straightforward and flexible, as it does not require any fine tuning between
several losses when synthesizing diverse sound textures. A way of extending the
synthesis in order to produce a sound of any length is also presented, after
which synthesized spectrograms and sound signals are showcased. We also discuss
on the choice of CNN, on border effects in our synthesized signals and on
possible ways of modifying the algorithm in order to improve its current long
computation time.


  In this paper we study an evolution of low-temperature thermodynamical
quantities for an electron gas with a $ \delta $-function attraction as the
system crosses over from weak-coupling (BCS-type) to strong-coupling
(Bose-type) superconductivity in three and two dimensions.


  Fictitious play is a natural dynamic for equilibrium play in zero-sum games,
proposed by [Brown 1949], and shown to converge by [Robinson 1951]. Samuel
Karlin conjectured in 1959 that fictitious play converges at rate
$O(1/\sqrt{t})$ with the number of steps $t$. We disprove this conjecture
showing that, when the payoff matrix of the row player is the $n \times n$
identity matrix, fictitious play may converge with rate as slow as
$\Omega(t^{-1/n})$.


  The bicategory of normal functors between W*-categories is monoidally
equivalent to the bicategory of W*-bimodules.


  High-metallicity pollution is common in white dwarf (WD) stars hosting
remnant planetary systems. However, they rarely have detectable debris
accretion discs, possibly because much of the influx is fast steeply-infalling
debris in star-grazing orbits, producing a more tenuous signature than a slowly
accreting disk. Processes governing such deposition between the Roche radius
and photosphere have so far received little attention and we model them here
analytically by extending recent work on sun-grazing comets to WD systems. We
find that the evolution of cm-to-km size (a_0) infallers most strongly depends
on two combinations of parameters, which effectively measure sublimation rate
and binding strength. We then provide an algorithm to determine the fate of
infallers for any WD, and apply the algorithm to four limiting combinations of
hot versus cool (young/old) WDs with snowy (weak, volatile) versus rocky
(strong, refractory) infallers. We find: (i) Total sublimation above the
photosphere befalls all small infallers across the entire WD temperature (T_WD)
range, the threshold size rising with T_WD and 100X larger for rock than snow.
(ii) All very large objects fragment tidally regardless of T_WD: for rock, a_0
>= 10^5 cm; for snow, a_0 >= 10^3 -- 3x10^4 cm across all WD cooling ages.
(iii) A considerable range of a_0 avoids fragmentation and total sublimation,
yielding impacts or grazes with cold WDs. This range narrows rapidly with
increasing T_WD, especially for snowy bodies. Finally, we discuss briefly how
the various forms of deposited debris may finally reach the photosphere surface
itself.


  We prove that if two knots are concordant, their involutive knot Floer
complexes satisfy a certain type of stable equivalence.


  Vision-centric occupancy networks, which represent the surrounding
environment with uniform voxels with semantics, have become a new trend for
safe driving of camera-only autonomous driving perception systems, as they are
able to detect obstacles regardless of their shape and occlusion. Modern
occupancy networks mainly focus on reconstructing visible voxels from object
surfaces with voxel-wise semantic prediction. Usually, they suffer from
inconsistent predictions of one object and mixed predictions for adjacent
objects. These confusions may harm the safety of downstream planning modules.
To this end, we investigate panoptic segmentation on 3D voxel scenarios and
propose an instance-aware occupancy network, PanoSSC. We predict foreground
objects and backgrounds separately and merge both in post-processing. For
foreground instance grouping, we propose a novel 3D instance mask decoder that
can efficiently extract individual objects. we unify geometric reconstruction,
3D semantic segmentation, and 3D instance segmentation into PanoSSC framework
and propose new metrics for evaluating panoptic voxels. Extensive experiments
show that our method achieves competitive results on SemanticKITTI semantic
scene completion benchmark.


  Even though the problem of network topology design is often studied as a
"clean-slate" optimization, in practice most service-provider and enterprise
networks are designed incrementally over time. This evolutionary process is
driven by changes in the underlying parameters and constraints (the
"environment") and it aims to minimize the modification cost after each change
in the environment. In this paper, we first formulate the incremental design
approach (in three variations), and compare that with the more traditional
optimized design approach in which the objective is to minimize the total
network cost. We evaluate the cost overhead and evolvability of incremental
design under two network expansion models (random and gradual), comparing
incremental and optimized networks in terms of cost, topological similarity,
delay and robustness. We find that even though incremental design has some cost
overhead, that overhead does not increase as the network grows. Also, it is
less costly to evolve an existing network than to design it "from scratch" as
long as the network expansion factor is less than a critical value.


  Since model selection is ubiquitous in data analysis, reproducibility of
statistical results demands a serious evaluation of reliability of the employed
model selection method, no matter what label it may have in terms of good
properties. Instability measures have been proposed for evaluating model
selection uncertainty. However, low instability does not necessarily indicate
that the selected model is trustworthy, since low instability can also arise
when a certain method tends to select an overly parsimonious model. F- and
G-measures have become increasingly popular for assessing variable selection
performance in theoretical studies and simulation results. However, they are
not computable in practice. In this work, we propose an estimation method for
F- and G-measures and prove their desirable properties of uniform consistency.
This gives the data analyst a valuable tool to compare different variable
selection methods based on the data at hand. Extensive simulations are
conducted to show the very good finite sample performance of our approach. We
further demonstrate the application of our methods using several micro-array
gene expression data sets, with intriguing findings.


  Optically active spin defects in wide-bandgap materials have many potential
applications in quantum information and quantum sensing. Spin defects in
two-dimensional layered van der Waals materials are just emerging to be
investigated. Here we demonstrate that optically-addressable spin ensembles in
hexagonal boron nitride (hBN) can be generated by femtosecond laser
irradiation. We observe optically detected magnetic resonance (ODMR) of hBN
spin defects created by laser irradiation. We show that the creation of spin
defects in hBN is strongly affected by the pulse energy of the femtosecond
laser. When the laser pulse number is less than a few thousand, the pulse
number only affects the density of defects but not the type of defects. With
proper laser parameters, spin defects can be generated with a high probability
of success. Our work provides a convenient way to create spin defects in hBN by
femtosecond laser writing, which shows promising prospects for quantum
technologies.


  In this paper, we investigate a unified dark fluid model with fast transition
and entropic perturbations. An effective sound speed is designated as an
additional free model parameter when the entropic perturbations are included,
and if the entropic perturbations are zero, the effective sound speed will
decrease to the adiabatic sound speed. In order to analyze the viability of the
unified model, we calculate the squared Jeans wave number with the entropic
perturbations. Furthermore, by using the Markov Chain Monte Carlo method, we
perform a global fitting for the unified dark fluid model from the type Ia
supernova Union 2.1, baryon acoustic oscillation and the full information of
cosmic microwave background measurement given by the WMAP 7-yr data points. The
constrained results favor a small effective sound speed. Compared to the
$\Lambda$CDM, it is found that the cosmic observations do not favor the
phenomenon of fast transition for the unified dark fluid model.