  Level density and radiative strength functions which allow precise
reproduction of the two-step cascade intensity, gamma width of compound state
and cascade population of levels up to excitation energy of about 3.5 MeV were
determined using experimental data on the (n,2gamma) reaction. Level density in
these nuclei (like in other even-odd nuclei studied earlier) in wide excitation
energy interval is considerably less than that predicted by Fermi-gas model.
Enhancement of the radiative strength functions, caused by strong correlations
between cascade gamma-decay parameters, most probably, relates with the change
in ratio between the quasi-particle and collective components of the wave
functions of the cascade intermediate levels in the region of most strong
change in their density.


  Escalation in games is when agents keep playing forever. Based on formal
proofs we claim that if agents assume that resource are infinite, escalation is
rational.


  Boshernitzan found a decay condition on the measure of cylinder sets that
implies unique ergodicity for minimal subshifts. Interest in the properties of
subshifts satisfying this condition has grown recently, due to a connection
with the study of discrete Schr\"odinger operators. Of particular interest is
the question of how restrictive Boshernitzan's condition is. While it implies
zero topological entropy, our main theorem shows how to construct minimal
subshifts satisfying the condition whose factor complexity grows faster than
any pre-assigned subexponential rate. As an application, via a theorem of
Damanik and Lenz, we show that there is no subexponentially growing sequence
for which the spectra of all discrete Schr\"odinger operators associated with
subshifts whose complexity grows faster than the given sequence, have only
finitely many gaps.


  We reveal significant qualitative differences in the rigidity transition of
three types of disordered network materials: randomly diluted spring networks,
jammed sphere packings, and stress-relieved networks that are diluted using a
protocol that avoids the appearance of floppy regions. The marginal state of
jammed and stress-relieved networks are globally isostatic, while marginal
randomly diluted networks show both overconstrained and underconstrained
regions. When a single bond is added to or removed from these isostatic
systems, jammed networks become globally overconstrained or floppy, whereas the
effect on stress-relieved networks is more local and limited. These differences
are also reflected in the linear elastic properties and point to the highly
effective and unusual role of global self-organization in jammed sphere
packings.


  Understanding the number of individuals who have been infected with the novel
coronavirus SARS-CoV-2, and the extent to which social distancing policies have
been effective at limiting its spread, are critical for effective policy going
forward. Here we present estimates of the extent to which confirmed cases in
the United States undercount the true number of infections, and analyze how
effective social distancing measures have been at mitigating or suppressing the
virus. Our analysis uses a Bayesian model of COVID-19 fatalities with a
likelihood based on an underlying differential equation model of the epidemic.
We provide analysis for four states with significant epidemics: California,
Florida, New York, and Washington. Our short-term forecasts suggest that these
states may be following somewhat different trajectories for growth of the
number of cases and fatalities.


  Character-level string-to-string transduction is an important component of
various NLP tasks. The goal is to map an input string to an output string,
where the strings may be of different lengths and have characters taken from
different alphabets. Recent approaches have used sequence-to-sequence models
with an attention mechanism to learn which parts of the input string the model
should focus on during the generation of the output string. Both soft attention
and hard monotonic attention have been used, but hard non-monotonic attention
has only been used in other sequence modeling tasks such as image captioning
(Xu et al., 2015), and has required a stochastic approximation to compute the
gradient. In this work, we introduce an exact, polynomial-time algorithm for
marginalizing over the exponential number of non-monotonic alignments between
two strings, showing that hard attention models can be viewed as neural
reparameterizations of the classical IBM Model 1. We compare soft and hard
non-monotonic attention experimentally and find that the exact algorithm
significantly improves performance over the stochastic approximation and
outperforms soft attention. Code is available at https://github.
com/shijie-wu/neural-transducer.


  Air-Cherenkov telescopes have mapped the Galactic plane at TeV energies. Here
we evaluate the prospects for detecting the neutrino emission from sources in
the Galactic plane assuming that the highest energy photons originate from the
decay of pions, which yields a straightforward prediction for the neutrino flux
from the decay of the associated production of charged pions. Four promising
sources are identified based on having a large flux and a flat spectrum. We
subsequently evaluate the probability of their identification above the
atmospheric neutrino background in IceCube data as a function of time. We show
that observing them over the twenty-year lifetime of the instrumentation is
likely, and that some should be observable at the $3\,\sigma$ level with six
years of data. In the absence of positive results, we derive constraints on the
spectral index and cut-off energy of the sources, assuming a hadronic
acceleration mechanism.


  Topological entropy is a measure of complex dynamics. In this regard,
multimodal maps play an important role when it comes to study low-dimensional
chaotic dynamics or explain some features of higher dimensional complex
dynamics with conceptually simple models. In the first part of this paper an
analytical formula for the topological entropy of twice differentiable
multimodal maps is derived, and some basic properties are studied. This
expression involves the so-called min-max symbols, which are closely related to
the kneading symbols. Furthermore, its proof leads to a numerical algorithm
that simplifies a previous one also based on min-max symbols. In the second
part of the paper this new algorithm is used to compute the topological entropy
of different modal maps. Moreover, it compares favorably to the previous
algorithm when computing the topological entropy of the bi- and tri-modal maps
considered in the numerical simulations.


  Land use mapping is a fundamental yet challenging task in geographic science.
In contrast to land cover mapping, it is generally not possible using overhead
imagery. The recent, explosive growth of online geo-referenced photo
collections suggests an alternate approach to geographic knowledge discovery.
In this work, we present a general framework that uses ground-level images from
Flickr for land use mapping. Our approach benefits from several novel aspects.
First, we address the nosiness of the online photo collections, such as
imprecise geolocation and uneven spatial distribution, by performing location
and indoor/outdoor filtering, and semi- supervised dataset augmentation. Our
indoor/outdoor classifier achieves state-of-the-art performance on several
bench- mark datasets and approaches human-level accuracy. Second, we utilize
high-level semantic image features extracted using deep learning, specifically
convolutional neural net- works, which allow us to achieve upwards of 76%
accuracy on a challenging eight class land use mapping problem.


  We extend a previous analysis [PRL {\bf 80}, 4693 (1998)] of breakdown of
dynamical scale invariance in the coarsening of two-dimensional DLAs
(diffusion-limited aggregates) as described by the Cahn-Hilliard equation.
Existence of a second dynamical length scale, predicted earlier, is
established. Having measured the "solute mass" outside the cluster versus time,
we obtain a third dynamical exponent. An auxiliary problem of the dynamics of a
slender bar (that acquires a dumbbell shape) is considered. A simple scenario
of coarsening of fractal clusters with branching structure is suggested that
employs the dumbbell dynamics results. This scenario involves two dynamical
length scales: the characteristic width and length of the cluster branches. The
predicted dynamical exponents depend on the (presumably invariant) fractal
dimension of the cluster skeleton. In addition, a robust theoretical estimate
for the third dynamical exponent is obtained. Exponents found numerically are
in reasonable agreement with these predictions.


  This paper concerns branching simulation for weighted Kripke structures with
parametric weights. Concretely, we consider a weighted extension of branching
simulation where a single transitions can be matched by a sequence of
transitions while preserving the branching behavior. We relax this notion to
allow for a small degree of deviation in the matching of weights, inducing a
directed distance on states. The distance between two states can be used
directly to relate properties of the states within a sub-fragment of weighted
CTL. The problem of relating systems thus changes to minimizing the distance
which, in the general parametric case, corresponds to finding suitable
parameter valuations such that one system can approximately simulate another.
Although the distance considers a potentially infinite set of transition
sequences we demonstrate that there exists an upper bound on the length of
relevant sequences, thereby establishing the computability of the distance.


  A theoretical analysis is presented on possible effects of disorder by
dopants in high-temperature superconducting cuprate perovskites, to define
their basic spectra of spin and electronic excitations, and the subsequent
observable properties, especially doping dependence of superconducting order
parameter. The central point in the proposed physical picture is formation of
specific impurity subband within the insulating bandgap of initial undoped
material, serving as a source for the system metallization and further
transition into superconducting state with anisotropic order parameter.


  An interesting transient has been detected in one of our three Dark Energy
Camera deep fields. Observations of these deep fields take advantage of the
high red sensitivity of DECam on the Cerro Tololo Interamerican Observatory
Blanco telescope. The survey includes the Y band with rest wavelength 1430{\AA}
at z = 6. Survey fields (the Prime field 0555-6130, the 16hr field 1600-75 and
the SUDSS New Southern Field) are deeper in Y than other infrared surveys. They
are circumpolar, allowing all night to be used efficiently, exploiting the moon
tolerance of 1 micron observations to minimize conflict with the Dark Energy
Survey. As an i-band dropout (meaning that the flux decrement shortward of
Lyman alpha is in the i bandpass), the transient we report here is a supernova
candidate with z ~ 6, with a luminosity comparable to the brightest known
current epoch superluminous supernova (i.e., ~ 2 x 10^11 solar luminosities).


  In this paper we characterize when the product of two block Toeplitz
operators is a compact perturbation of a block Toeplitz operator on the Hardy
space of the open unit disk. Necessary and sufficient conditions are given for
the commutator of two block Toeplitz operators to be compact.


  A two-dimensional (2D) assembly of noninteracting, temperature-dependent,
composite-boson Cooper pairs (CPs) in chemical and thermal equilibrium with
unpaired fermions is examined in a binary boson-fermion statistical model as
the superconducting singularity temperature is approached from above. The model
is derived from {\it first principles} for the BCS model interfermion
interaction from three extrema of the system Helmholtz free energy (subject to
constant pairable-fermion number) with respect to: a) the pairable-fermion
distribution function; b) the number of excited (bosonic) CPs, i.e., with
nonzero total momenta--usually ignored in BCS theory--and with the appropriate
(linear, as opposed to quadratic) dispersion relation that arises from the
Fermi sea; and c) the number of CPs with zero total momenta. Compared with the
BCS theory condensate, higher singularity temperatures for the Bose-Einstein
condensate are obtained in the binary boson-fermion mixture model which are in
rough agreement with empirical critical temperatures for quasi-2D
superconductors


  The situation assessment problem is considered, in terms of object,
condition, activity, and plan recognition, based on data coming from the
real-word {em via} various sensors. It is shown that uncertainty issues are
linked both to the models and to the matching algorithm. Three different types
of uncertainties are identified, and within each one, the numerical and the
symbolic cases are distinguished. The emphasis is then put on purely symbolic
uncertainties: it is shown that they can be dealt with within a purely symbolic
framework resulting from a transposition of classical numerical estimation
tools.


  Since the 1980's it has been becoming increasingly clear that the Solar
System's irregular satellites are collisionally evolved. We derive a general
model for the collisional evolution of an irregular satellite swarm and apply
it to the Solar System and extrasolar planets. Our model reproduces the Solar
System's complement of observed irregulars well, and suggests that the
competition between grain-grain collisions and Poynting-Robertson (PR) drag
helps set the fate of the dust. Because swarm collision rates decrease over
time the main dust sink can change with time, and may help unravel the
accretion history of synchronously rotating regular satellites that show
brightness asymmetries. Some level of dust must be present on AU scales around
the Solar System's giant planets, which we predict may be at detectable levels.
We also predict whether dust produced by extrasolar circumplanetary swarms can
be detected. The coronagraphic instruments on JWST will have the ability to
detect the dust generated by these swarms, which are most detectable around
planets that orbit at tens of AU from the youngest stars. Because the
collisional decay of swarms is relatively insensitive to planet mass, swarms
can be much brighter than their host planets and allow discovery of
Neptune-mass planets that would otherwise remain invisible. This dust may have
already been detected. The observations of the planet Fomalhaut b can be
explained as scattered light from dust produced by the collisional decay of an
irregular satellite swarm around a 10 Earth-mass planet. Such a swarm comprises
about 5 Lunar masses worth of irregular satellites. Finally, we consider what
happens if Fomalhaut b passes through Fomalhaut's main debris ring, which
allows the circumplanetary swarm to be replenished through collisions with ring
planetesimals. (abridged)


  Label propagation has proven to be a fast method for detecting communities in
complex networks. Recent work has also improved the accuracy and stability of
the basic algorithm, however, a general approach is still an open issue. We
propose different label propagation algorithms that convey two unique
strategies of community formation, namely, defensive preservation and offensive
expansion of communities. Furthermore, the strategies are combined in an
advanced label propagation algorithm that retains the advantages of both
approaches; and are enhanced with hierarchical community extraction, prominent
for the use on larger networks. The proposed algorithms were empirically
evaluated on different benchmarks networks with planted partition and on over
30 real-world networks of various types and sizes. The results confirm the
adequacy of the propositions and give promising grounds for future analysis of
(large) complex networks. Nevertheless, the main contribution of this work is
in showing that different types of networks (with different topological
properties) favor different strategies of community formation.


  Effects of Lie type noncommutativity on thermodynamic properties of a system
of free identical particles are investigated. A definition for finite volume of
the configuration space is given, and the grandcanonical partition function in
the thermodynamic limit is calculated. Two possible definitions for the
pressure are discussed, which are equivalent when the noncommutativity
vanishes. The thermodynamic observables are extracted from the partition
function. Different limits are discussed where either the noncommutativity or
the quantum effects are important. Finally specific cases are discussed where
the group is SU(2) or SO(3), and the partition function of a nondegenerate gas
is calculated.


  An event consistent with the signature expected for the rare kaon decay K+
--> pi+ nu anti-nu has been observed. In the pion momentum region examined,
211<P<230 MeV/c, the backgrounds are estimated to contribute 0.08 +/- 0.03
events. If the event is due to K+ --> pi+ nu anti-nu, the branching ratio is
(4.2 +9.7/-3.5) x E-10.


  Compression algorithms are widely used as they save memory without losing
data. However, elimination of redundant symbols and sequences in data leads to
a compression side channel. So far, compression attacks have only focused on
the compression-ratio side channel, i.e., the size of compressed data,and
largely targeted HTTP traffic and website content.
  In this paper, we present the first memory compression attacks exploiting
timing side channels in compression algorithms, targeting a broad set of
applications using compression. Our work systematically analyzes different
compression algorithms and demonstrates timing leakage in each. We present
Comprezzor,an evolutionary fuzzer which finds memory layouts that lead to
amplified latency differences for decompression and therefore enable remote
attacks. We demonstrate a remote covert channel exploiting small local timing
differences transmitting on average 643.25 bit/h over 14 hops over the
internet. We also demonstrate memory compression attacks that can leak secrets
bytewise as well as in dictionary attacks in three different case studies.
First, we show that an attacker can disclose secrets co-located and compressed
with attacker data in PHP applications using Memcached. Second, we present an
attack that leaks database records from PostgreSQL, managed by a Python-Flask
application, over the internet. Third, we demonstrate an attack that leaks
secrets from transparently compressed pages with ZRAM,the memory compression
module in Linux. We conclude that memory-compression attacks are a practical
threat.


  Bolometric detectors are used in particle physics experiments to search for
rare processes, such as neutrinoless double beta decay and dark matter
interactions. By operating at cryogenic temperatures, they are able to detect
particle energies from a few keV up to several MeV, measuring the temperature
rise produced by the energy released. This work focusses on the bolometers of
the CUORE experiment, which are made of TeO$_2$ crystals. The response of these
detectors is nonlinear with energy and changes with the operating temperature.
The noise depends on the working conditions and significantly affects the
energy resolution and the detection performances at low energies. We present a
software tool to simulate signal and noise of CUORE-like bolometers, including
effects generated by operating temperature drifts, nonlinearities and pileups.
The simulations agree well with data.


  As a final work to establish that frame flows for geometrically finite
hyperbolic manifolds of arbitrary dimensions are exponentially mixing with
respect to the Bowen-Margulis-Sullivan measure, this paper focuses on the case
with cusps. To prove this, we utilize the countably infinite symbolic coding of
the geodesic flow of Li-Pan and perform a frame flow version of Dolgopyat's
method \`{a} la Sarkar-Winter and Tsujii-Zhang. This requires the local
non-integrability condition and the non-concentration property but the
challenge in the presence of cusps is that the latter holds only on a large
proper subset of the limit set. To overcome this, we use a large deviation
property for symbolic recurrence to the large subset. It is proved by studying
the combinatorics of cusp excursions and using an effective renewal theorem as
in the work of Li; the latter uses the exponential decay of the transfer
operators for the geodesic flow of Li-Pan.


  We investigate the mechanism design problem faced by a principal who hires
\emph{multiple} agents to gather and report costly information. Then, the
principal exploits the information to make an informed decision. We model this
problem as a game, where the principal announces a mechanism consisting in
action recommendations and a payment function, a.k.a. scoring rule. Then, each
agent chooses an effort level and receives partial information about an
underlying state of nature based on the effort. Finally, the agents report the
information (possibly non-truthfully), the principal takes a decision based on
this information, and the agents are paid according to the scoring rule. While
previous work focuses on single-agent problems, we consider multi-agents
settings. This poses the challenge of coordinating the agents' efforts and
aggregating correlated information. Indeed, we show that optimal mechanisms
must correlate agents' efforts, which introduces externalities among the
agents, and hence complex incentive compatibility constraints and equilibrium
selection problems. First, we design a polynomial-time algorithm to find an
optimal incentive compatible mechanism. Then, we study an online problem, where
the principal repeatedly interacts with a group of unknown agents. We design a
no-regret algorithm that provides $\widetilde{\mathcal{O}}(T^{2/3})$ regret
with respect to an optimal mechanism, matching the state-of-the-art bound for
single-agent settings.


  Prediction of future observations is an important and challenging problem.
The two mainstream approaches for quantifying prediction uncertainty use
prediction regions and predictive distributions, respectively, with the latter
believed to be more informative because it can perform other prediction-related
tasks. The standard notion of validity, what we refer to here as Type-1
validity, focuses on coverage probability of prediction regions, while a notion
of validity relevant to the other prediction-related tasks performed by
predictive distributions is lacking. Here we present a new notion, called
Type-2 validity, relevant to these other prediction tasks. We establish
connections between Type-2 validity and coherence properties, and show that
imprecise probability considerations are required in order to achieve it. We go
on to show that both types of prediction validity can be achieved by
interpreting the conformal prediction output as the contour function of a
consonant plausibility measure. We also offer an alternative characterization
of conformal prediction, based on a new nonparametric inferential model
construction, wherein the appearance of consonance is natural, and prove its
validity.


  It is shows that some aspects of classic KPP-problem (1937) can be extended
to some fourth and sixth-order quasilinear parabolic equations.


  This paper solves the Sylvester equation in the form of AX+XB=C in a
distributed way, and proposes three distributed continuous-time algorithms for
three cases. We start with the basic algorithm for solving a least squares
solution of the equation, and then give a simplified algorithm for the case
when there is an exact solution to the equation, followed by an algorithm with
regularization case. Based on local information and appropriate communication
among neighbor agents, we solve the distributed computation problem of the
Sylvester equation from the optimization viewpoint, and we prove the
convergence of proposed algorithms to an optimal solution in three different
cases, with help of the convex optimization and semi-stability.


  In this paper we study the Cauchy problem for the wave equations for sums of
squares of left invariant vector fields on compact Lie groups and also for
hypoelliptic homogeneous left-invariant differential operators on graded Lie
groups (the positive Rockland operators), when the time-dependent propagation
speed satisfies a Log-Lipschitz condition. We prove the well-posedness in the
associated Sobolev spaces exhibiting a finite loss of regularity with respect
to the initial data, which is not true when the propagation speed is a ${\rm
H\ddot{o}lder}$ function. We also indicate an extension to general Hilbert
spaces. In the special case of the Laplacian on $\mathbb R^n$, the results boil
down to the celebrated result of Colombini-De Giorgi and Spagnolo.


  We have found a possibility to streamline the proof of the Treves' theorem
(Duke Math. J., 108, 251-295, 2001) on an algebraic characterization of the KdV
hierarchy which makes it significantly shorter, following essentially the logic
of the original proof.


  Recent results indicate that the grain size and crystallinity inferred from
observations of silicate features may be correlated with spectral type of the
central star and/or disk geometry. In this paper, we show that grain size, as
probed by the 10 um silicate feature peak-to-continuum and 11.3-to-9.8 um flux
ratios, is inversely proportional to log L_star. These trends can be understood
using a simple two-layer disk model for passive irradiated flaring disks,
CGPLUS. We find that the radius, R_10, of the 10 um silicate emission zone in
the disk goes as (L_star/L_sun)^0.56, with slight variations depending on disk
geometry (flaring angle, inner disk radius). The observed correlations,
combined with simulated emission spectra of olivine and pyroxene mixtures,
imply a grain size dependence on luminosity. Combined with the fact that R_10
is smaller for less luminous stars, this implies that the apparent grain size
of the emitting dust is larger for low-luminositysources. In contrast, our
models suggest that the crystallinity is only marginally affected, because for
increasing luminosity, the zone for thermal annealing (assumed to be at T>800
K) is enlarged by roughly the same factor as the silicate emission zone. The
observed crystallinity is affected by disk geometry, however, with increased
crystallinity in flat disks. The apparent crystallinity may also increase with
grain growth due to a corresponding increase in contrast between crystalline
and amorphous silicate emission bands.


  By means of density functional theory based calculations, we study the role
of spin-orbit coupling in the new family of ABC hyperferroelectrics [Phys. Rev.
Lett. 112, 127601 (2014)]. We unveil an extremely rich physics strongly linked
to ferroelectric properties, ranging from the electric control of bulk Rashba
effect to the existence of a three dimensional topological insulator phase,
with concomitant topological surface states even in the ultrathin film limit.
Moreover, we predict that the topological transition, as induced by alloying,
is followed by a Weyl semi-metal phase of finite concentration extension, which
is robust against disorder, putting forward hyperferroelectrics as promising
candidates for spin-orbitronic applications.


  This note is an expanded and updated version of our entry with the same title
for the 2006 Encyclopedia of Mathematical Physics. We give a brief overview of
graded Poisson algebras, their main properties and their main applications, in
the contexts of super differentiable and of derived algebraic geometry.


  Assuming the newly observed structure $Z_{b}(10610)$ as a bottomonium-like
molecular state $B^{*}\bar{B}$, we calculate its mass in the framework of QCD
sum rules. The numerical result is $10.54\pm0.22 GeV$ for $B^{*}\bar{B}$, which
coincide with the mass of $Z_{b}(10610)$. This consolidates the statement made
by Belle Collaboration that the $Z_{b}(10610)$ resonance could be a
$B^{*}\bar{B}$ molecular state.


  The early stages of clot formation in blood vessels involve platelets
adhesion-aggregation. Although these mechanisms have been extensively studied,
gaps in their understanding still persist. We have performed detailed in-vitro
experiments and developed a numerical model to better describe and understand
this phenomenon. Unlike previous studies, we took into account both activated
and non-activated platelets, as well as the 3D nature of the aggregation
process. Our investigation reveals that blood albumin is a major parameter
limiting platelet adhesion and aggregation. Our results also show that the well
accepted Zydney-Colton shear-induced diffusivity is much too low to explain the
observed deposition rate. Simulations are in very good agreement with
observations and provide quantitative estimates of the adhesion and aggregation
rates that are hard to measure experimentally.


  Not so long ago, thermoelectricity in superconductors was believed to be
possible only by breaking explicitly the particle-hole symmetry. Recently, it
has been theoretically predicted that a superconducting tunnel junction can
develop bipolar thermoelectric phenomena in the presence of a large thermal
gradient owing to non-equilibrium spontaneous PH symmetry breaking. The
experimental realization of the first thermoelectric Josephson engine then
followed. Here, we give a more extended discussion and focus on the impact of
the Josephson contribution on thermoelectricity modulating the Cooper pairs
transport in a double-loop SQUID. When the Cooper pairs current prevails on the
quasiparticle one, the Josephson contribution short-circuits the junction
thereby screening the thermoelectric effect. We demonstrate that the
thermoelectric generation due to the pure quasiparticle transport is
phase-independent, once Josephson contribution is appropriately removed from
the net current measured. At the same time, we investigate an additional
metastable state at V\simeq0 determined by the presence of the Josephson
coupling, which peculiarly modifies the hysteretic behavior of our
thermoelectric engine realized. At the end, we also discuss how the
current-voltage characteristics are affected by the presence of multiple
thermoelectric elements, which improve the generated output power.


  In one dimension, the theory of the $G$-normal distribution is
well-developed, and many results from the classical setting have a nonlinear
counterpart. Significant challenges remain in multiple dimensions, and some of
what has already been discovered is quite nonintuitive. By answering several
classically-inspired questions concerning independence, covariance uncertainty,
and behavior under certain linear operations, we continue to highlight the
fascinating range of unexpected attributes of the multidimensional $G$-normal
distribution.


  In the past few years, several hints of lepton flavour universality (LFU)
violation have emerged in the $b \to c \tau \bar\nu$ and $b \to s \ell^+
\ell^-$ data. Quite recently, the Belle Collaboration has reported the first
measurement of the $D^*$ longitudinal polarization fraction in the $B \to D^*
\tau \bar\nu$ decay. Motivated by this intriguing result, together with the
recent measurements of $R_{J/\psi}$ and $\tau$ polarization, we study $b \to c
\tau \bar\nu$ decays in the Supersymmetry (SUSY) with $R$-parity violation
(RPV). We consider $B \to D^{(*)} \tau \bar\nu$, $B_c \to \eta_c \tau \bar\nu$,
$B_c \to J/\psi \tau \bar\nu$ and $\Lambda_b \to \Lambda_c \tau \bar\nu$ modes
and focus on the branching ratios, the LFU ratios, the forward-backward
asymmetries, polarizations of daughter hadrons and $\tau$ lepton. It is found
that the RPV SUSY can explain the $R_{D^{(*)}}$ anomalies at $2\sigma$ level,
after taking into account various flavour constraints. In the allowed parameter
space, the differential branching fractions and LFU ratios are largely enhanced
by the SUSY effects, especially in the large dilepton invariant mass region. In
addition, a lower bound $\mathcal B(B^+ \to K^+ \nu \bar\nu) > 7.37 \times
10^{-6}$ is obtained. These observables could provide testable signatures at
the High-Luminosity LHC and SuperKEKB, and correlate with direct searches for
SUSY.


  Models of the Sagittarius Stream have consistently found that the Milky Way
disc is oriented such that its short axis is along the intermediate axis of the
triaxial dark matter halo. We attempt to build models of disc galaxies in such
an `intermediate-axis orientation'. We do this with three models. In the first
two cases we simply rigidly grow a disc in a triaxial halo such that the disc
ends up perpendicular to the global intermediate axis. We also attempt to coax
a disc to form in an intermediate-axis orientation by producing a gas+dark
matter triaxial system with gas angular momentum about the intermediate axis.
In all cases we fail to produce systems which remain with stellar angular
momentum aligned with the halo's intermediate axis, even when the disc's
potential flattens the inner halo such that the disc is everywhere
perpendicular to the halo's local minor axis. For one of these unstable
simulations we show that the potential is even rounder than the models of the
Milky Way potential in the region probed by the Sagittarius Stream. We conclude
that the Milky Way's disc is very unlikely to be in an intermediate axis
orientation. However we find that a disc can persist off one of the principal
planes of the potential. We propose that the disc of the Milky Way must be
tilted relative to the principal axes of the dark matter halo. Direct
confirmation of this prediction would constitute a critical test of Modified
Newtonian Dynamics.


  An underwater in situ gamma ray spectrometer based on LaBr3 was developed and
optimized to monitor marine radioactivity. The intrinsic background mainly from
La138 and Ac227 of LaBr3 was well determined by low background measurement and
pulse shape discrimination method. A method of self-calibration using three
internal contaminant peaks was proposed to eliminate the peak shift during long
term monitoring. With experiments under different temperatures, the method was
proved to be helpful for maintaining long term stability. To monitor the marine
radioactivity, the spectrometer efficiency was calculated via water tank
experiment as well as Monte Carlo simulation.


  Accuracy of depth estimation from static images has been significantly
improved recently, by exploiting hierarchical features from deep convolutional
neural networks (CNNs). Compared with static images, vast information exists
among video frames and can be exploited to improve the depth estimation
performance. In this work, we focus on exploring temporal information from
monocular videos for depth estimation. Specifically, we take the advantage of
convolutional long short-term memory (CLSTM) and propose a novel
spatial-temporal CSLTM (ST-CLSTM) structure. Our ST-CLSTM structure can capture
not only the spatial features but also the temporal correlations/consistency
among consecutive video frames with negligible increase in computational cost.
Additionally, in order to maintain the temporal consistency among the estimated
depth frames, we apply the generative adversarial learning scheme and design a
temporal consistency loss. The temporal consistency loss is combined with the
spatial loss to update the model in an end-to-end fashion. By taking advantage
of the temporal information, we build a video depth estimation framework that
runs in real-time and generates visually pleasant results. Moreover, our
approach is flexible and can be generalized to most existing depth estimation
frameworks. Code is available at: https://tinyurl.com/STCLSTM


  The design of personalized cranial implants is a challenging and tremendous
task that has become a hot topic in terms of process automation with the use of
deep learning techniques. The main challenge is associated with the high
diversity of possible cranial defects. The lack of appropriate data sources
negatively influences the data-driven nature of deep learning algorithms.
Hence, one of the possible solutions to overcome this problem is to rely on
synthetic data. In this work, we propose three volumetric variations of deep
generative models to augment the dataset by generating synthetic skulls, i.e.
Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP),
WGAN-GP hybrid with Variational Autoencoder pretraining (VAE/WGAN-GP) and
Introspective Variational Autoencoder (IntroVAE). We show that it is possible
to generate dozens of thousands of defective skulls with compatible defects
that achieve a trade-off between defect heterogeneity and the realistic shape
of the skull. We evaluate obtained synthetic data quantitatively by defect
segmentation with the use of V-Net and qualitatively by their latent space
exploration. We show that the synthetically generated skulls highly improve the
segmentation process compared to using only the original unaugmented data. The
generated skulls may improve the automatic design of personalized cranial
implants for real medical cases.


  This paper is aimed at improving the performance of the treecode algorithm
for N-Body simulation by employing the NetSolve GridRPC programming model to
exploit the use of multiple clusters. N-Body is a classical problem, and
appears in many areas of science and engineering, including astrophysics,
molecular dynamics, and graphics. In the simulation of N-Body, the specific
routine for calculating the forces on the bodies which accounts for upwards of
90% of the cycles in typical computations is eminently suitable for obtaining
parallelism with GridRPC calls. It is divided among the compute nodes by
simultaneously calling multiple GridRPC requests to them. The performance of
the GridRPC implementation is then compared to that of the MPI version and
hybrid MPI-OpenMP version for the treecode algorithm on individual clusters.


  The newly discovered Kagome superconductors $A$V$_3$Sb$_5$ ($A$ = K, Rb, Cs)
exhibit superconductivity, charge order, and band topology simultaneously. To
explore the intricate interplay between the superconducting and charge orders,
we investigate the doping evolution of superconductivity, charge-density-wave
(CDW) order, and band topology in doped topological kagome superconductors
Cs(V$_{1-x}$Ti$_x$)$_3$Sb$_5$ where the Ti-dopant introduces hole-like charge
carriers. Despite the absence of the CDW phase transition in doped compounds
even for the lowest doping level of $x=0.047$, the superconductivity survives
in all doped samples with enhanced critical temperatures. The high-resolution
angle-resolved photoemission spectroscopy (ARPES) measurements reveal that the
Ti-dopant in the kagome plane lowers the chemical potential, pushing the van
Hove singularity (VHS) at $M$ point above the Fermi level. First-principle
simulations corroborate the doping evolution of the band structure observed in
ARPES, and affirm that the CDW instability does not occur once the VHS is
pushed above the Fermi level, explaining the absence of the CDW ordering in our
doped samples Cs(V$_{1-x}$Ti$_x$)$_3$Sb$_5$. Our results demonstrate a
competition between the CDW and superconducting orders in the kagome-metal
superconductor CsV$_3$Sb$_5$, although the superconductivity is likely
inconsequential of the CDW order.


  We report the first reconstruction of the $B^{0} \to \pi^{0} \pi^{0}$ decay
mode at Belle II using samples of 2019 and 2020 data that correspond to 62.8
fb$^{-1}$ of integrated luminosity. We find $14.0^{+6.8}_{-5.6}$ signal decays,
corresponding to a significance of 3.4 standard deviations and determine a
branching ratio of $\mathcal{B}(B^{0} \rightarrow \pi^{0} \pi^{0}) =
[0.98^{+0.48}_{-0.39} \pm 0.27] \times 10^{-6}$. The results agree with
previous determinations and contribute important information to an early
assessment of detector performance and Belle II's potential for future
determinations of $\alpha/\phi_2$ using $B \rightarrow \pi \pi$ modes.


  Colonies of bacteria grown on thin agar plate exhibit fractal patterns as a
result of adaptation to their environments. The bacterial colony pattern
formation is regulated crucially by chemotaxis, the movement of cells along a
chemical concentration gradient. Here, the dynamics of pattern formation in
bacterial colony is investigated theoretically through a continuum model that
considers chemotaxis. In the case of the gradient sensed by the bacterium is
nearly uniform, the bacterial colony patterns are self-similar, which they look
the same at every scale. The scaling law of the bacterial colony growth has
been revealed explicitly. Chemotaxis biases the movement of bacterial
population in colony trend toward the chemical attractant. Moreover, the
bacterial colonies evolve long time as the traveling wave with sharp front.


  Lattice gas cellular automata (Lgca) are particular cellular automata that
imitate the behavior of par- ticles moving on a lattice. We used a particular
set of Lgca rules, called hpp, to mix bits in data blocks and obtain a
symmetric cryptographic algorithm. The encryption and decryption keys are the
positions of perturbation sites on the lattice (walls). Basically, this paper
presents an original way to perform cryp- tographic operations, based on
cellular automata. In this paper, we show several characteristics about our
algorithm: typical block size (2^(2n-1) ), key-length (2^n ), number of rounds
(2^(n+1) ). We also evaluate avalanche and strict avalanche properties with
respect to key and plain text. Finally, we highlight the underbellies of our
method and give clues to solve them.


  We obtain exact solutions of Dirac equation at zero kinetic energy for radial
power-law relativistic potentials. It turns out that these are the relativistic
extension of a subclass of exact solutions of Schrodinger equation with
two-term power-law potentials at zero energy. The latter is solved by point
canonical transformation of the exactly solvable problem of the three
dimensional oscillator. The wavefunction solutions are written in terms of the
confluent hypergeometric functions and almost always square integrable. For
most cases these solutions support bound states at zero energy. Some
exceptional unbounded states are normalizable for non-zero angular momentum.
Using a generalized definition, degeneracy of the nonrelativistic states is
demonstrated and the associated degenerate observable is defined.


  Let $f_1(x),\ldots,f_n(x)$ be some polynomials. The upper bound on the number
of $x\in\mathbb F_p$ such that $f_1(x),\ldots,f_n(x)$ are roots of unit of
order $t$ is obtained. This bound generalize the bound of the paper \cite{V-S}
to the case of polynomials of degrees greater than one. The bound is obtained
over fields of positive characteristic and over the complex field.


  This paper addresses a problem recently raised by Laurent and Nogueira about
inhomogeneous Diophantine approximation with coprime integers. As a corollary
of our main theorem we obtain an improvement of the best known exponent of
approximation in this problem, from 1/2 to 1-epsilon, for any epsilon>0.


  Given a Sasaki manifold S, we prove the Sasaki-Ricci flow converges
exponentially fast to a Sasaki-Einstein metric if one exists, provided the
automorphism group of the transverse holomorphic structure is trivial.


  Time-Sensitive Networking (TSN) is an enhancement of Ethernet which provides
various mechanisms for real-time communication. Time-triggered (TT) traffic
represents periodic data streams with strict real-time requirements. Amongst
others, TSN supports scheduled transmission of TT streams, i.e., the
transmission of their frames by end stations is coordinated in such a way that
none or very little queuing delay occurs in intermediate nodes. TSN supports
multiple priority queues per egress port. The TAS uses so-called gates to
explicitly allow and block these queues for transmission on a short periodic
timescale. The TAS is utilized to protect scheduled traffic from other traffic
to minimize its queuing delay. In this work, we consider scheduling in TSN
which comprises the computation of periodic transmission instants at end
stations and the periodic opening and closing of queue gates.
  In this paper, we first give a brief overview of TSN features and standards.
We state the TSN scheduling problem and explain common extensions which also
include optimization problems. We review scheduling and optimization methods
that have been used in this context. Then, the contribution of currently
available research work is surveyed. We extract and compile optimization
objectives, solved problem instances, and evaluation results. Research domains
are identified, and specific contributions are analyzed. Finally, we discuss
potential research directions and open problems.


  We present a comparative analysis of large-scale topological and evolutionary
properties of transcription networks in three species, the two distant bacteria
E. coli and B. subtilis, and the yeast S. cerevisiae. The study focuses on the
global aspects of feedback and hierarchy in transcriptional regulatory
pathways. While confirming that gene duplication has a significant impact on
the shaping of all the analyzed transcription networks, our results point to
distinct trends between the bacteria, where time constraints in the
transcription of downstream genes might be important in shaping the
hierarchical structure of the network, and yeast, which seems able to sustain a
higher wiring complexity, that includes the more feedback, intricate hierarchy,
and the combinatorial use of heterodimers made of duplicate transcription
factors.


  We consider the evolution of a spherically expanding plasma cloud, where
there is continuous injection of non-thermal electrons. We compute the time
dependent electron distribution and resultant photon spectra taking into
account synchrotron, adiabatic and inverse Compton cooling. This model is
different from previous works where, instead of a continuous injection of
particles, a short injection period was assumed. We apply this model to the
radio/optical knots in the large scale jets of AGN, detected in X-rays by {\it
Chandra} and find that the overall broadband spectral features can be
reproduced. It is shown that for some sources, constraints on the X-ray
spectral index (by a longer {\it Chandra} observation) will be able to
differentiate between the different models. This in turn will put a strong
constraint on the acceleration mechanism active in these sources.


  We study boundary value problems for bounded uniform domains in
$\mathbb{R}^n$, $n\geq 2$, with non-Lipschitz (and possibly fractal)
boundaries. We prove Poincar\'e inequalities with trace terms and uniform
constants for uniform $(\varepsilon,\infty)$-domains within bounded common
confinements. We then introduce generalized Dirichlet, Robin and Neumann
problems for Poisson type equations and prove the Mosco convergence of the
associated energy functionals along sequences of suitably converging domains.
This implies a stability result for weak solutions, and this also implies the
norm convergence of the associated resolvents and the convergence of the
corresponding eigenvalues and eigenfunctions. Based on our earlier work, we
prove compactness results for parametrized classes of admissible domains,
energy functionals and weak solutions. Using these results, we can verify the
existence of optimal shapes in these classes.


  Combining first-principles calculations with a technique for many-body
problems, we investigate properties of the transition metal oxide ${\rm
Sr_{2}VO_{4}}$ from the microscopic point of view. By using the local density
approximation (LDA), the high-energy band structure is obtained, while screened
Coulomb interactions are derived from the constrained LDA and the GW method.
The renormalization of the kinetic energy is determined from the GW method. By
these downfolding procedures, an effective Hamiltonian at low energies is
derived. Applying the path integral renormalization group method to this
Hamiltonian, we obtain ground state properties such as the magnetic and orbital
orders. Obtained results are consistent with experiments within available data.
We find that ${\rm Sr_{2}VO_{4}}$ is close to the metal-insulator transition.
Furthermore, because of the coexistence and competition of ferromagnetic and
antiferromgnetic exchange interactions in this system, an antiferromagnetic and
orbital-ordered state with a nontrivial and large unit cell structure is
predicted in the ground state. The calculated optical conductivity shows
characteristic shoulder structure in agreement with the experimental results.
This suggests an orbital selective reduction of the Mott gap.


  Precision premium, a concept in astrometry that was firstly presented by
Pascu in 1994, initially means that the relative positional measurement of the
Galilean satellites of Jupiter would be more accurate when their separations
are small. Correspondingly, many observations have been obtained of these
Galilean satellites since then. However, the exact range of the separation in
which precision premium takes effect is not clear yet, not to say the variation
of the precision with the separation. In this paper, the observations of open
cluster M35 are used to study precision premium and the newest star catalogue
Gaia DR2 is used in the data reduction. Our results show that precision premium
does work in about less than 100 arcsecs for two concerned objects, and the
relative positional precision can be well fitted by a sigmoidal function.
Observations of Uranian satellites are also reduced as an example of precision
premium.


  This paper empirically validates battery-less sensor activation via wireless
energy transmission to release sensors from wires and batteries. To seamlessly
extend the coverage and activate sensor nodes distributed in any indoor
environment, we proposed multi-point wireless energy transmission with carrier
shift diversity. In this scheme, multiple transmitters are employed to
compensate path-loss attenuation and orthogonal frequencies are allocated to
the multiple transmitters to avoid the destructive interference that occurs
when the same frequency is used by all transmitters. In our previous works, the
effectiveness of the proposed scheme was validated theoretically and also
empirically by using just a spectrum analyzer to measure the received power. In
this paper, we develop low-energy battery-less sensor nodes whose consumed
power and required received power for activation are respectively 142 uW and
400 uW. In addition, we conduct indoor experiments in which the received power
and activation of battery-less sensor node are simultaneously observed by using
the developed battery-less sensor node and a spectrum analyzer. The results
show that the coverage of single-point and multi-point wireless energy
transmission without carrier shift diversity are, respectively, 84.4% and
83.7%, while the coverage of the proposed scheme is 100%. It can be concluded
that the effectiveness of the proposed scheme can be verified by our
experiments using real battery-less sensor nodes.


  Information of many kinds is flowing in software projects and organizations.
Requirements have to flow from the customer to the developers. Testers need to
know the requirements as well. Boundary conditions and design decisions have to
be at the right place at the right time. Information flow analysis with FLOW
facilitates modeling of mode and route of the flow of information and
experience independent of the development methodology. Experience often acts as
a control factor, because experienced developers can process and route
information more efficiently. Therefore, experience needs to be at the right
place at the right time, too. However, most valuable experiences never get
documented. Since information and experience is flowing in agile as well as in
traditional environments, the FLOW method does not distinguish between agile
and traditional, but only between how the flows are shaped.
  ----
  In Softwareprojekten flie{\ss}en vielerlei Informationen. Anforderungen
m\"ussen vom Kunden zu den Entwicklern gelangen. Auch Tester m\"ussen die
Anforderungen kennen. Randbedingungen und Entwurfsentscheidungen m\"ussen zur
rechten Zeit am rechten Ort sein. Die Informationsflussanalyse mit FLOW
erm\"oglicht es, unabh\"angig von der Entwicklungsmethode zu modellieren, wie
und auf welchem Wege Informationen und Erfahrungen flie{\ss}en. Erfahrungen
spielen dabei oft die Rolle von Steuergr\"o{\ss}en, denn erfahrene Mitarbeiter
k\"onnen Informationen kompetenter bearbeiten und weiterleiten. Auch die
Erfahrungen m\"ussen in geeigneter Form zur rechten Zeit am rechten Ort sein.
Viele Erfahrungen werden aber nie dokumentiert. Da Informationen und
Erfahrungen sowohl in agilen als auch in traditionellen Umgebungen flie{\ss}en
m\"ussen, wird in FLOW ein Modell aufgebaut, das nicht nach agil, traditionell
oder anderen Bezeichnungen unterscheidet, sondern einzig danach, wie die
Fl\"usse gestaltet sind.


  In this paper, we develop some matrix Poisson's equations satisfied by the
mean and variance of the mixing time in an irreducible positive-recurrent
discrete-time Markov chain with infinitely-many levels, and provide a
computational framework for the solution to the matrix Poisson's equations by
means of the UL-type of $RG$-factorization as well as the generalized inverses.
In an important special case: the level-dependent QBD processes, we provide a
detailed computation for the mean and variance of the mixing time. Based on
this, we give new highlight on computation of the mixing time in the
block-structured Markov chains with infinitely-many levels through the
matrix-analytic method.


  The contagion threshold for diffusion of innovations is defined and
calculated in finite graphs (two-dimensional regular lattices, regular random
networks (RRNs), and two kinds of scale-free networks (SFNs)) with and without
the bilingual option. Without the bilingual option, degree inhomogeneity and
clustering enhance the contagion threshold in non-regular networks except for
those with an unrealistically small average degree. It is explained by the
friendship paradox and detour effect. We found the general boundary of the cost
that makes the bilingual option effective. With a low-cost bilingual option,
among regular lattices, SFNs, and RRNs, the contagion threshold is largest in
regular lattices and smallest in RRNs. The contagion threshold of regular
random networks is almost the same as that of the regular trees, which is the
minimum among regular networks. We show that the contagion threshold increases
by clustering with a low-cost bilingual option.


  We investigate the necessary condition for the existence of classical
Euclidean wormholes in a conformally non-invariant gravitational model
minimally coupled to an scalar field. It is shown that while the original Ricci
tensor with positive eigenvalues does not allow the Euclidean wormholes to
occur, under dynamical conformal transformations the Ricci tensor, with respect
to the original metric, is dynamically coupled with the conformal field and its
eigenvalues may become negative allowing the Euclidean wormholes to occur.
Therefore, it is conjectured that dynamical conformal transformations may
provide us with {\it effective} forms of matter sources leading to Euclidean
wormholes in conformally non-invariant systems.


  Estimating the rate of convergence of the empirical measure of an i.i.d.
sample to the reference measure is a classical problem in probability theory.
Extending recent results of Ambrosio, Stra and Trevisan on 2-dimensional
manifolds, in this paper we prove sharp asymptotic and nonasymptotic upper
bounds for the mean rate in the quadratic Wasserstein metric $W_2$ on a
$d$-dimensional compact Riemannian manifold. Under a smoothness assumption on
the reference measure, our bounds match the classical rate in the optimal
matching problem on the unit cube due to Ajtai, Koml\'os, Tusn\'ady and
Talagrand. The i.i.d. condition is relaxed to stationary samples with a mixing
condition. As an example of a nonstationary sample, we also consider the
empirical measure of a random walk on a compact Lie group. Surprisingly, on
semisimple groups random walks attain almost optimal rates even without a
spectral gap assumption. The proofs are based on Fourier analysis, and in
particular on a Berry-Esseen smoothing inequality for $W_2$ on compact
manifolds, a result of independent interest with a wide range of applications.


  Sparse principal component analysis (PCA) is an important technique for
dimensionality reduction of high-dimensional data. However, most existing
sparse PCA algorithms are based on non-convex optimization, which provide
little guarantee on the global convergence. Sparse PCA algorithms based on a
convex formulation, for example the Fantope projection and selection (FPS),
overcome this difficulty, but are computationally expensive. In this work we
study sparse PCA based on the convex FPS formulation, and propose a new
algorithm that is computationally efficient and applicable to large and
high-dimensional data sets. Nonasymptotic and explicit bounds are derived for
both the optimization error and the statistical accuracy, which can be used for
testing and inference problems. We also extend our algorithm to online learning
problems, where data are obtained in a streaming fashion. The proposed
algorithm is applied to high-dimensional gene expression data for the detection
of functional gene groups.


  Combining cosmic microwave background (CMB) data from Planck satellite data,
Baryon Acoustic Oscillations (BAO) measurements and Type Ia supernovae (SNe Ia)
data, we obtain the bounds on total neutrino masses $M_\nu$ with the
approximation of degenerate neutrino masses and for three dark energy models:
the cosmological constant ($\Lambda$CDM) model, a phenomenological emergent
dark energy (PEDE) model and a model-independent quintessential
parameterization (HBK). The bounds on the sum of neutrino masses $M_\nu$ depend
on the dark energy (DE) models. In the HBK model, we confirm the conclusion
from some previous work that the quintessence prior of dark energy tends to
tighten the cosmological constraint on $M_\nu$. On the other hand, the PEDE
model leads to larger $M_\nu$ and a nonzero lower bound. Besides, we also
explore the correlation between three different neutrino hierarchies and dark
energy models.


  Stars of $\sim$ 8 - 10 $M_{\odot}$ on the main-sequence form strongly
electron-degenerate O+Ne+Mg core and become super-AGB stars. If such an O+Ne+Mg
core grows to 1.38 $M_\odot$, electron captures on
$^{20}$Ne$(e,\nu_e)^{20}$F$(e,\nu_e)^{20}$O take place and ignite O-Ne
deflagration around the center. In this paper, we perform two-dimensional
hydrodynamics simulations of the propagation of the O-Ne flame to see whether
such a flame induces a collapse of the O+Ne+Mg core due to subsequent electron
capture behind the flame or triggers a thermonuclear explosion. We present a
series of models to explore how the outcome depends on model parameters for the
central density in the range from $10^{9.80}$ to $10^{10.20}$ g cm$^{-3}$,
flame structure of both centered and off-centered ignition kernels, special and
general relativistic effects, turbulent flame speed formula and the treatments
of laminar burning phase. We find that the O+Ne+Mg core obtained from stellar
evolutionary models has a high tendency to collapse into a neutron star. We
obtain the bifurcation between the electron-capture collapse and thermonuclear
explosion. We discuss the implication in nucleosynthesis and the possible
observational signals of this class of supernovae.


  This paper examines recovery of the wall-shear stress of a turbulent boundary
layer that has undergone a sudden transition from a rough to a smooth surface.
Early works of Antonia and Luxton questioned the reliability of standard
smooth-wall methods to measure wall-shear stress in such conditions, and
subsequent studies show significant disagreement depending on the approach used
to determine the wall-shear stress downstream. Here we address this by
utilising a collection of experimental databases at Re_\tau \approx 4100 that
have access to both `direct' and `indirect' measures of the wall-shear stress
to understand the recovery to equilibrium conditions to the new surface. Our
results reveal that the viscous region (z^+\lesssim 4) recovers almost
immediately to an equilibrium state with the new wall conditions, however, the
buffer region and beyond takes several boundary layer thicknesses before
recovering to equilibrium conditions, which is longer than previously thought.
A unique direct numerical simulation database of a wall-bounded flow with a
rough-to-smooth wall transition is employed to confirm these findings. In doing
so, we present evidence that any estimate of the wall-shear stress from the
mean velocity profile in the buffer region or further away from the wall tends
to underestimate its magnitude in the near vicinity of the rough-to-smooth
transition, and this is likely to be partly responsible for the large scatter
of recovery lengths to equilibrium conditions reported in the literature. Our
results also reveal that the smaller energetic scales in the near-wall region
recover to an equilibrium state associated with the new wall conditions within
one boundary layer thickness downstream of the transition, while the larger
energetic scales exhibit an over-energised state for several boundary layer
thicknesses downstream of the transition.


  In part I of this paper we studied additive decomposability of the set $\F_y$
of th $y$-smooth numbers and the multiplicative decomposability of the shifted
set $\g_y=\F_y+\{1\}$. In this paper, focusing on the case of 'large' functions
$y$, we continue the study of these problems. Further, we also investigate a
problem related to the m-decomposability of $k$-term sumsets, for arbitrary
$k$.


  By means of computer simulations we study how droplets of hard, rod-like
particles optimize their shape and internal structure under the influence of
the osmotic compression caused by the presence of spherical particles that act
as depletion agents. At sufficiently high osmotic pressures the rods that make
up the drops spontaneously align to turn them into uniaxial nematic liquid
crystalline droplets. The nematic droplets or "tactoids" that are formed this
way are not spherical but elongated, resulting from the competition between the
anisotropic surface tension and the elastic deformation of the director field.
In agreement with recent theoretical predictions we find that sufficiently
small tactoids have a uniform director field, whilst large ones are
characterized by a bipolar director field. From the shape and director-field
transformation of the droplets we are able to estimate the surface anchoring
strength and an average of the elastic constants of the hard-rod nematic.


  An experimentally feasible magnetometer based on a dual-coupling
optomechanical system is proposed, where the radiation-pressure coupling
transduces the magnetic signal to the optical phase, and the quadratic
optomechanical interaction induces a periodic squeezing effect. The latter not
only amplifies the signal to be measured, but also accelerates the signal
transducing rate characterized by an experimentally observable phase
accumulation efficiency. In the vicinity of opto-mechanical decoupled time, the
ultimate bound to the estimability of magnetic signal is proportional to
$\exp(-6r)$, and then the optimized accuracy of estimation can be enhanced
nearly 3 orders with a controllable squeezing parameter $r<1$. Moreover, our
proposal is robust against the mechanical thermal noise, and the sensitivity of
a specific measurement can reach to the order of $10^{-17}{\rm T/\sqrt{Hz}}$ in
the presence of dissipations and without ground state cooling of mechanical
oscillator. Our proposal fundamentally broadens the fields of quantum metrology
and cavity optomechanics, with potential application for on-chip magnetic
detection with high precision.


  Context. It has recently been proposed that the surface composition of icy
main-belt asteroids (B-,C-,Cb-,Cg-,P-,and D-types) may be consistent with that
of chondritic porous interplanetary dust particles (CPIDPs). Aims. In the light
of this new association, we re-examine the surface composition of a sample of
asteroids belonging to the Themis family in order to place new constraints on
the formation and evolution of its parent body. Methods. We acquired NIR
spectral data for 15 members of the Themis family and complemented this dataset
with existing spectra in the visible and mid-infrared ranges to perform a
thorough analysis of the composition of the family. Assuming end-member
minerals and particle sizes (<2\mum) similar to those found in CPIDPs, we used
a radiative transfer code adapted for light scattering by small particles to
model the spectral properties of these asteroids. Results. Our best-matching
models indicate that most objects in our sample possess a surface composition
that is consistent with the composition of CP IDPs.We find ultra-fine grained
Fe-bearing olivine glasses to be among the dominant constituents. We further
detect the presence of minor fractions of Mg-rich crystalline silicates. The
few unsuccessfully matched asteroids may indicate the presence of interlopers
in the family or objects sampling a distinct compositional layer of the parent
body. Conclusions. The composition inferred for the Themis family members
suggests that the parent body accreted from a mixture of ice and anhydrous
silicates (mainly amorphous) and subsequently underwent limited heating. By
comparison with existing thermal models that assume a 400km diameter
progenitor, the accretion process of the Themis parent body must have occurred
relatively late (>4Myr after CAIs) so that only moderate internal heating
occurred in its interior, preventing aqueous alteration of the outer shell.


  An increasing number of CS researchers are employed in academic non-CS
departments where publication output is measured in terms of journal impact
factors. To foster recognition of publications in peer-reviewed CS conference
proceedings, we analyzed more than 40,000 CS publications and computed journal
impact factors for 88 top-ranking conferences across a representative range of
fields, finding that some conferences have impact factors corresponding to
those of high-ranking journals.


  We call a knot in the 3-sphere $SU(2)$-simple if all representations of the
fundamental group of its complement which map a meridian to a trace-free
element in $SU(2)$ are binary dihedral. This is a generalisation of being a
2-bridge knot. Pretzel knots with bridge number $\geq 3$ are not
$SU(2)$-simple. We provide an infinite family of knots $K$ with bridge number
$\geq 3$ which are $SU(2)$-simple.
  One expects the instanton knot Floer homology $I^\natural(K)$ of a
$SU(2)$-simple knot to be as small as it can be -- of rank equal to the knot
determinant $\det(K)$. In fact, the complex underlying $I^\natural(K)$ is of
rank equal to $\det(K)$, provided a genericity assumption holds that is
reasonable to expect. Thus formally there is a resemblance to strong L-spaces
in Heegaard Floer homology. For the class of $SU(2)$-simple knots that we
introduce this formal resemblance is reflected topologically: The branched
double covers of these knots are strong L-spaces. In fact, somewhat
surprisingly, these knots are alternating. However, the Conway spheres are
hidden in any alternating diagram.
  With the methods we use, we show that an integer homology 3-sphere which is a
graph manifold always admits irreducible representations of its fundamental
group.


  We present a study of what may be called an intrinsic metric for a general
regular Dirichlet form. For such forms we then prove a Rademacher type theorem.
For strongly local forms we show existence of a maximal intrinsic metric (under
a weak continuity condition) and for Dirichlet forms with an absolutely
continuous jump kernel we characterize intrinsic metrics by bounds on certain
integrals. We then turn to applications on spectral theory and provide for
(measure perturbation of) general regular Dirichlet forms an
Allegretto-Piepenbrinck type theorem, which is based on a ground state
transform, and a Shnol type theorem. Our setting includes Laplacian on
manifolds, on graphs and $\alpha$-stable processes.


  Most active galactic nuclei (AGN) lack prominent jets, and show modest radio
emission and significant X-ray emission which arises mainly from the galactic
core, very near from the central black hole. We use a quantitative scenario of
such core-dominated radio-quiet AGN, which attributes a substantial fraction of
the X-ray emission to the presence of abortive jets involving the collision of
gas blobs in the core. Here we investigate the consequences of the acceleration
of protons in the shocks from such collisions. We find that protons will be
accelerated up to energies above the pion photoproduction threshold on both the
X-rays and the UV photons from the accretion disk. The secondary charged pions
decay, producing neutrinos. We predict significant fluxes of TeV-PeV neutrinos,
and show that the AMANDA II detector is already constraining several important
astrophysical parameters of these sources. Larger cubic kilometer detectors
such as IceCube will be able to detect such neutrinos in less than one year of
operation, or otherwise rule out this scenario.


  On the twisted Fock spaces $ \mathcal{F}^\lambda(\mathbb{C}^{2n}) $ we
consider two types of convolution operators $ S_\varphi $ and $
\widetilde{S}_\varphi $ associated to an element $ \varphi \in
\mathcal{F}^\lambda(\mathbb{C}^{2n}) .$ We find a necessary and sufficient
condition on $ \varphi $ so that $ S_\varphi $ (resp. $ \widetilde{S}_\varphi $
) is bounded on $ \mathcal{F}^\lambda(\mathbb{C}^{2n}).$ We show that for any
given non constant $ \varphi $ at least one of these two operators is
unbounded.


  Let $A$ and $B$ be finite-dimensional $k$-algebras over a field $k$ such that
$A/\rad(A)$ and $B/\rad(B)$ are separable. In this note, we consider how to
transfer a stable equivalence of Morita type between $A$ and $B$ to that
between $eAe$ and $fBf$, where $e$ and $f$ are idempotent elements in $A$ and
in $B$, respectively. In particular, if the Auslander algebras of two
representation-finite algebras $A$ and $B$ are stably equivalent of Morita
type, then $A$ and $B$ themselves are stably equivalent of Morita type. Thus,
combining a result with Liu and Xi, we see that two representation-finite
algebras $A$ and $B$ over a perfect field are stably equivalent of Morita type
if and only if their Auslander algebras are stably equivalent of Morita type.
Moreover, since stable equivalence of Morita type preserves $n$-cluster tilting
modules, we extend this result to $n$-representation-finite algebras and
$n$-Auslander algebras studied by Iyama.


  The magnitude of four independent amplitudes are determined in charged kaon
photoproduction in the energy range of 1500 to 2300MeV incident photon.
Differential cross section and three polarization parameters are required for
such amplitudes reconstruction at different kaon scattering angles. This
analysis has indicated that the magnitudes of spin-flip amplitudes of the
target nucleon dominate over the non-flip ones in all energies and scattering
angles. The results have been compared with the solution of phase shift
analysis sp98 at all energies. This analysis indicates that here is a good
agreement between the present work and the results of phase shift analysis at
all energies and scattering angles.


  Resource is an important constraint when deploying Deep Neural Networks
(DNNs) on mobile and edge devices. Existing works commonly adopt the cell-based
search approach, which limits the flexibility of network patterns in learned
cell structures. Moreover, due to the topology-agnostic nature of existing
works, including both cell-based and node-based approaches, the search process
is time consuming and the performance of found architecture may be sub-optimal.
To address these problems, we propose AutoShrink, a topology-aware Neural
Architecture Search(NAS) for searching efficient building blocks of neural
architectures. Our method is node-based and thus can learn flexible network
patterns in cell structures within a topological search space. Directed Acyclic
Graphs (DAGs) are used to abstract DNN architectures and progressively optimize
the cell structure through edge shrinking. As the search space intrinsically
reduces as the edges are progressively shrunk, AutoShrink explores more
flexible search space with even less search time. We evaluate AutoShrink on
image classification and language tasks by crafting ShrinkCNN and ShrinkRNN
models. ShrinkCNN is able to achieve up to 48% parameter reduction and save 34%
Multiply-Accumulates (MACs) on ImageNet-1K with comparable accuracy of
state-of-the-art (SOTA) models. Specifically, both ShrinkCNN and ShrinkRNN are
crafted within 1.5 GPU hours, which is 7.2x and 6.7x faster than the crafting
time of SOTA CNN and RNN models, respectively.


  Employing a large dataset (at most, the order of n = 10^6), this study
attempts enhance the literature on the comparison between regression and
machine learning (ML)-based rent price prediction models by adding new
empirical evidence and considering the spatial dependence of the observations.
The regression-based approach incorporates the nearest neighbor Gaussian
processes (NNGP) model, enabling the application of kriging to large datasets.
In contrast, the ML-based approach utilizes typical models: extreme gradient
boosting (XGBoost), random forest (RF), and deep neural network (DNN). The
out-of-sample prediction accuracy of these models was compared using Japanese
apartment rent data, with a varying order of sample sizes (i.e., n = 10^4,
10^5, 10^6). The results showed that, as the sample size increased, XGBoost and
RF outperformed NNGP with higher out-of-sample prediction accuracy. XGBoost
achieved the highest prediction accuracy for all sample sizes and error
measures in both logarithmic and real scales and for all price bands (when n =
10^5 and 10^6). A comparison of several methods to account for the spatial
dependence in RF showed that simply adding spatial coordinates to the
explanatory variables may be sufficient.


  The Evidential regression network (ENet) estimates a continuous target and
its predictive uncertainty without costly Bayesian model averaging. However, it
is possible that the target is inaccurately predicted due to the gradient
shrinkage problem of the original loss function of the ENet, the negative log
marginal likelihood (NLL) loss. In this paper, the objective is to improve the
prediction accuracy of the ENet while maintaining its efficient uncertainty
estimation by resolving the gradient shrinkage problem. A multi-task learning
(MTL) framework, referred to as MT-ENet, is proposed to accomplish this aim. In
the MTL, we define the Lipschitz modified mean squared error (MSE) loss
function as another loss and add it to the existing NLL loss. The Lipschitz
modified MSE loss is designed to mitigate the gradient conflict with the NLL
loss by dynamically adjusting its Lipschitz constant. By doing so, the
Lipschitz MSE loss does not disturb the uncertainty estimation of the NLL loss.
The MT-ENet enhances the predictive accuracy of the ENet without losing
uncertainty estimation capability on the synthetic dataset and real-world
benchmarks, including drug-target affinity (DTA) regression. Furthermore, the
MT-ENet shows remarkable calibration and out-of-distribution detection
capability on the DTA benchmarks.


  Weak-lensing searches for galaxy clusters are plagued by low completeness and
purity, severely limiting their usefulness for constraining cosmological
parameters with the cluster mass function. A significant fraction of `false
positives' are due to projection of large-scale structure and as such carry
information about the matter distribution. We demonstrate that by constructing
a "peak function", in analogy to the cluster mass function, cosmological
parameters can be constrained. To this end we carried out a large number of
cosmological N-body simulations in the \Omega_m-\sigma_8 plane to study the
variation of this peak function. We demonstrate that the peak statistics is
able to provide constraints competitive with those obtained from cosmic-shear
tomography from the same data set. By taking the full cross-covariance between
the peak statistics and cosmic shear into account, we show that the combination
of both methods leads to tighter constraints than either method alone can
provide.


  We present the results of X-ray and near-infrared (NIR) observations of
L1448, a star-forming region in the Perseus cloud complex using the Chandra
X-ray Observatory and the 4 m telescope at the Kitt Peak National Observatory.
We detect 72 X-ray sources in a ~17 arcmin x 17 arcmin region with a ~68 ks
ACIS exposure, for which we conduct follow-up NIR imaging observations in a
concentric ~11 arcmin x 11 arcmin region with FLAMINGOS down to m_Ks ~ 17 mag.
Twelve X-ray sources have NIR or optical counterparts. By plotting X-ray mean
energy versus NIR to X-ray flux ratio, the X-ray sources are clearly separated
into two groups. The X-ray spectral and temporal features as well as NIR
magnitudes and colors indicate that one group mainly consists of young stellar
objects (YSOs) in the cloud and the other of background extragalactic sources.
Ten X-ray-emitting YSO candidates are thus newly identified, which are low-mass
or brown dwarf mass sources from their NIR magnitudes. In addition, a possible
X-ray signal is found from a mid-infrared protostar L1448 IRS 3(A). The lack of
detection of this source in our deep NIR images indicates that this source has
a very steep spectral slope of > 3.2 in 2--10 micron.


  Motivated by recent experiments, we investigate the scattering properties of
percolation clusters generated by numerical simulations on a three dimensional
cubic lattice. Individual clusters of given size are shown to present a fractal
structure up to a scale of order their extent, even far away from the
percolation threshold $p_c$. The influence of inter-cluster correlations on the
structure factor of assemblies of clusters selected by an invasion phenomenon
is studied in detail. For invasion from bulk germs, we show that the scattering
properties are determined by three length scales, the correlation length $\xi$,
the average distance between germs $d_g$, and the spatial scale probed by
scattering, set by the inverse of the scattering wavevector $Q$. At small
scales, we find that the fractal structure of individual clusters is retained,
the structure factor decaying as $Q^{-d_f}$. At large scales, the structure
factor tends to a limit, set by the smaller of $\xi$ and $d_g$, both below and
above $p_c$. We propose approximate expressions reproducing the simulated
structure factor for arbitrary $\xi$, $d_g$, and $Q$, and illustrate how they
can be used to avoid to resort to costly numerical simulations. For invasion
from surfaces, we find that, at $p_c$, the structure factor behaves as
$Q^{-d_f}$ at all $Q$, i.e. the fractal structure is retained at arbitrarily
large scales. Results away from $p_c$ are compared to the case of bulk germs.
Our results can be applied to discuss light or neutrons scattering experiments
on percolating systems. This is illustrated in the context of evaporation from
porous materials.


  A realistic computer-simulation of a breast computed tomography (CT) system
and subject is constructed. The model is used to investigate the optimal number
of views for the scan given a fixed total X-ray fluence. The reconstruction
algorithm is based on accurate solution to a constrained, TV-minimization
problem, which has received much interest recently for sparse-view CT data.


  Maintaining stable fluxes for multiple source elements is a challenging task
when the source materials have significantly different oxygen affinities in a
complex-oxide molecular-beam-epitaxy (MBE) environment. Considering that Sr is
one of the most easily oxidized and widely used element in various complex
oxides, we took Sr as a probe to investigate the flux stability problem in a
number of different conditions. Source oxidation was less for higher flux,
extended port geometry, and un-melted source shape. The extended port geometry
also eliminated the flux transient after opening a source shutter as observed
in the standard port. We also found that the source oxidation occurred more
easily on the crucible wall than on the surface of the source material. Atomic
oxygen, in spite of its stronger oxidation effectiveness, did not make any
difference in source oxidation as compared to molecular oxygen in this
geometry. Our results may provide a guide for solutions to the source oxidation
problem in oxide-MBE system.


  Humans have an inherent ability to learn novel concepts from only a few
samples and generalize these concepts to different situations. Even though
today's machine learning models excel with a plethora of training data on
standard recognition tasks, a considerable gap exists between machine-level
pattern recognition and human-level concept learning. To narrow this gap, the
Bongard problems (BPs) were introduced as an inspirational challenge for visual
cognition in intelligent systems. Despite new advances in representation
learning and learning to learn, BPs remain a daunting challenge for modern AI.
Inspired by the original one hundred BPs, we propose a new benchmark
Bongard-LOGO for human-level concept learning and reasoning. We develop a
program-guided generation technique to produce a large set of
human-interpretable visual cognition problems in action-oriented LOGO language.
Our benchmark captures three core properties of human cognition: 1)
context-dependent perception, in which the same object may have disparate
interpretations given different contexts; 2) analogy-making perception, in
which some meaningful concepts are traded off for other meaningful concepts;
and 3) perception with a few samples but infinite vocabulary. In experiments,
we show that the state-of-the-art deep learning methods perform substantially
worse than human subjects, implying that they fail to capture core human
cognition properties. Finally, we discuss research directions towards a general
architecture for visual reasoning to tackle this benchmark.


  In this paper we define $n$th order Hessian structures on manifolds and study
them. In particular, when $n = 3$, we make a detailed study and establish a
one-to-one correspondence between {\it third-order Hessian structures} and a
{\it certain class of connections} on the second-order tangent bundle of a
manifold. Further, we show that a connection on the tangent bundle of a
manifold induces a connection on the second-order tangent bundle. Also we
define second-order geodesics of special second-order connection which gives a
geometric characterization of symmetric third-order Hessian structures.


  Spherically symmetric brane black holes have tidal charge, which modifies
both weak and strong lensing characteristics. Even if lensing measurements are
in agreement with a Schwarzschild lens, the margin of error of the detecting
instrument allows for a certain tidal charge. In this paper we derive the
respective constraint on the tidal charge of the supermassive black hole (SMBH)
in the center of our galaxy, from the radius of the first relativistic Einstein
ring, emerging in strong lensing. We find that even if general relativistic
predictions are confirmed by high precision strong lensing measurements, SMBHs
could have a much larger tidal charge, than the Sun or neutron stars.


  Abhyankar showed that for a finite tame extension $L_1/K$ and a finite
extension $L_2/K$ of $\mathfrak{P}$-adic fields, the condition $[\nu L_1 : \nu
K]$ divides $[\nu L_2 : \nu K]$ is sufficient to eliminate ramification, that
is, $L_1 \cdot L_2 / L_2$ is unramified. In this paper, we show that the above
condition is not sufficient in the case of an arbitrary henselian valued field.
We construct a counterexample illustrating that fact. We also give a necessary
and sufficient condition for the elimination of tame ramification of a
henselian field after a finite extension of the base field.


  Superpixel segmentation algorithms are to partition an image into
perceptually coherence atomic regions by assigning every pixel a superpixel
label. Those algorithms have been wildly used as a preprocessing step in
computer vision works, as they can enormously reduce the number of entries of
subsequent algorithms. In this work, we propose an alternative superpixel
segmentation method based on Gaussian mixture model (GMM) by assuming that each
superpixel corresponds to a Gaussian distribution, and assuming that each pixel
is generated by first randomly choosing one distribution from several Gaussian
distributions which are defined to be related to that pixel, and then the pixel
is drawn from the selected distribution. Based on this assumption, each pixel
is supposed to be drawn from a mixture of Gaussian distributions with unknown
parameters (GMM). An algorithm based on expectation-maximization method is
applied to estimate the unknown parameters. Once the unknown parameters are
obtained, the superpixel label of a pixel is determined by a posterior
probability. The success of applying GMM to superpixel segmentation depends on
the two major differences between the traditional GMM-based clustering and the
proposed one: data points in our model may be non-identically distributed, and
we present an approach to control the shape of the estimated Gaussian functions
by adjusting their covariance matrices. Our method is of linear complexity with
respect to the number of pixels. The proposed algorithm is inherently parallel
and can get faster speed by adding simple OpenMP directives to our
implementation. According to our experiments, our algorithm outperforms the
state-of-the-art superpixel algorithms in accuracy and presents a competitive
performance in computational efficiency.


  Computing the eigenvectors and eigenvalues of a perturbed matrix can be
remarkably difficult when the unperturbed matrix has repeated eigenvalues. In
this work we show how the limiting eigenvectors and eigenvalues of a symmetric
matrix $K(\varepsilon)$ as $\varepsilon \to 0$ can be obtained relatively
easily from successive Schur complements, provided that the entries scale in
different orders of $\varepsilon$. If the matrix does not directly exhibit this
structure, we show that putting the matrix into a ``generalised kernel form''
can be very informative. The resulting formulas are much simpler than classical
expressions obtained from complex integrals involving the resolvent. We apply
our results to the problem of computing the eigenvalues and eigenvectors of
kernel matrices in the ``flat limit'', a problem that appears in many
applications in statistics and approximation theory. In particular, we prove a
conjecture from [SIAM J. Matrix Anal. Appl., 2021, 42(1):17--57] which connects
the eigenvectors of kernel matrices to multivariate orthogonal polynomials.


  Let $d \geq 1$ be an integer. From a set of $d$-dimensional vectors, we
obtain a $d$-\dpg\ by letting each vector $\va^u$ correspond to a vertex $u$
and by adding an edge between two vertices $u$ and $v$ if and only if their dot
product $\va^{u} \cdot \va^{v} \geq t$, for some fixed, positive threshold~$t$.
Dot product graphs can be used to model social networks. Recognizing a $d$-dot
product graph is known to be \NP-hard for all fixed $d\geq 2$. To understand
the position of $d$-dot product graphs in the landscape of graph classes, we
consider the case $d=2$, and investigate how $2$-dot product graphs relate to a
number of other known graph classes including a number of well-known classes of
intersection graphs.


  Giant dipole resonance (GDR) is one of the fundamental collective excitation
modes in nucleus. Continuous efforts have been made to the evaluation of GDR
key parameters in different nuclear data libraries. We introduced multitask
learning (MTL) approach to learn and reproduce the evaluated experimental data
of GDR key parameters, including both GDR energies and widths. Compared to the
theoretical GDR parameters in RIPL-3 library, the accuracies of MTL approach
are almost doubled for 129 nuclei with experimental data. The significant
improvement is largely due to the right classification of unimodal nuclei and
bimodal nuclei by the classification neural network. Based on the good
performance of the neural network approach, an extrapolation to 79 nuclei
around the $\beta$-stability line without experimental data is made, which
provides an important reference to future experiments and data evaluations. The
successful application of MTL approach in this work further proofs the
feasibility of studying multi-output physical problems with multitask neural
network in nuclear physics domain.


  Fabrication of graphene field effect transistor is described which uses an
intrinsic graphene on the surface of as deposited hydrogenated amorphous carbon
films. Ambipolar characteristic has been demonstrated typical for graphene
devices, which changes to unipolar characteristic if the surface graphene was
etched in oxygen plasma. Because amorphous carbon films can be growth easily,
with unlimited dimensions and no transfer of graphene is necessary, this can
open new perspective for graphene electronics.


  In this letter, we investigate a UAV-enabled communication system, where a
UAV is deployed to communicate with the ground node (GN) in the presence of
multiple jammers. We aim to maximize the energy efficiency (EE) of the UAV by
optimizing its trajectory, subject to the UAV's mobility constraints. However,
the formulated problem is difficult to solve due to the non-convex and
fractional form of the objective function. Thus, we propose an iterative
algorithm based on successive convex approximation (SCA) technique and
Dinkelbach's algorithm to solve it. Numerical results show that the proposed
algorithm can strike a better balance between the throughput and energy
consumption by the optimized trajectory and thus improve the EE significantly
as compared to the benchmark algorithms.


  We develop a novel framework of bounded rationality under cognitive frictions
that studies learning over optimal behavior through both deliberative reasoning
and accumulated experiences. Using both types of information, agents engage in
Bayesian non-parametric estimation of the unknown action value function.
Reasoning signals are produced internally through mental deliberation, subject
to a cognitive cost. Experience signals are the observed utility outcomes at
previous actions. Agents' subjective estimation uncertainty, which evolves
through information accumulation, modulates the two modes of learning in a
state- and history-dependent way. We discuss how the model draws on and bridges
conceptual, methodological and empirical insights from both economics and the
cognitive sciences literature on reinforcement learning.


  How unique are the inflationary predictions for the cosmic microwave
anisotropy pattern? In this paper, it is asked whether an arbitrary causal
source for perturbations in the standard hot big bang could effectively mimic
the predictions of the simplest inflationary models. A surprisingly simple
example of a `scaling' causal source is found to closely reproduce the
inflationary predictions. This letter extends the work of a previous paper
(ref. 6) to a full computation of the anisotropy pattern, including the Sachs
Wolfe integral. I speculate on the possible physics behind such a source.


  With privacy legislation empowering the users with the right to be forgotten,
it has become essential to make a model amenable for forgetting some of its
training data. However, existing unlearning methods in the machine learning
context can not be directly applied in the context of distributed settings like
federated learning due to the differences in learning protocol and the presence
of multiple actors. In this paper, we tackle the problem of federated
unlearning for the case of erasing a client by removing the influence of their
entire local data from the trained global model. To erase a client, we propose
to first perform local unlearning at the client to be erased, and then use the
locally unlearned model as the initialization to run very few rounds of
federated learning between the server and the remaining clients to obtain the
unlearned global model. We empirically evaluate our unlearning method by
employing multiple performance measures on three datasets, and demonstrate that
our unlearning method achieves comparable performance as the gold standard
unlearning method of federated retraining from scratch, while being
significantly efficient. Unlike prior works, our unlearning method neither
requires global access to the data used for training nor the history of the
parameter updates to be stored by the server or any of the clients.


  The DeGiorgi classes $[DG]_p(E;\gamma)$, defined in (1.1)${}_{\pm}$ below
encompass, solutions of quasilinear elliptic equations with measurable
coefficients as well as minima and Q-minima of variational integrals. For these
classes we present some new results (\S 2 and \S 3.1), and some known facts
scattered in the literature (\S 3-\S 5), and formulate some open issues (\S 6).


  Let $S=\{S_t\}_{t\geq0}$ be the semigroup generated on $L_2(\Ri^d)$ by a
self-adjoint, second-order, divergence-form, elliptic operator $H$ with
Lipschitz continuous coefficients. Further let $\Omega$ be an open subset of
$\Ri^d$ with Lipschitz continuous boundary $\partial\Omega$. We prove that $S$
leaves $L_2(\Omega)$ invariant if, and only if, the capacity of the boundary
with respect to $H$ is zero or if, and only if, the energy flux across the
boundary is zero. The global result is based on an analogous local result.


  The organization of the axonal cytoskeleton is a key determinant of the
normal function of an axon, which is a long thin projection away from a neuron.
Under normal conditions two axonal cytoskeletal polymers microtubules and
neurofilaments align longitudinally in axons and are interspersed in axonal
cross-sections. However, in many neurotoxic and neurodegenerative disorders,
microtubules and neurofilaments segregate apart from each other, with
microtubules and membranous organelles clustered centrally and neurofilaments
displaced to the periphery. This striking segregation precedes abnormal and
excessive neurofilament accumulation in these diseases, which in turn leads to
focal axonal swellings. While neurofilament accumulation suggests the
impairment of neurofilament transport along axons, the underlying mechanism of
their segregation from microtubules remains poorly understood for over 30
years. To address this question, we developed a stochastic multiscale model for
the cross-sectional distribution of microtubules and neurofilaments in axons.
The model describes microtubules, neurofilaments and organelles as interacting
particles in a 2D cross-section, and incorporates the stochastic interactions
these particles through molecular motors. Simulations of the model demonstrate
that organelles can pull nearby microtubules together, and in the absence of
neurofilament transport, this mechanism gradually segregates microtubules from
neurofilaments on a time scale of hours, similar to that observed in toxic
neuropathies. This suggests that the microtubule-neurofilament segregation is
simply a consequence of the selective impairment of neurofilament transport.
The model generates the experimentally testable prediction that the rate and
extent of segregation will be dependent on the sizes of the moving organelles
as well as the density of their traffic.


  A tame ideal is an ideal $I$ such that the blowup of the affine space
$\mathbb{A}_k^n$ along $I$ is regular. In this paper, we give a combinatorial
characterization of tame squarefree monomial ideals. More precisely, we show
that a square free monomial ideal is tame if and only if the corresponding
clutter is a union of some isolated vertices and a complete $d$-partite
$d$-uniform clutter. It turns out that a squarefree monomial ideal is tame, if
and only if the facets of its Stanley-Reisner complex have mutually disjoint
complements. Also, we characterize all monomial ideals generated in degree at
most 2 which are tame. Finally, we prove that tame squarefree ideals are of
fiber type.


  The leading-twist valence-quark distribution function in the pion is obtained
at a low normalization scale of an order of the inverse average size of an
instanton $\rho_c$. The momentum dependent quark mass and the quark-pion vertex
are constructed in the framework of the instanton liquid model, using a gauge
invariant approach. The parameters of instanton vacuum, the effective instanton
radius and quark mass, are related to the vacuum expectation values of the
lowest dimension quark-gluon operators and to the pion low energy observables.
An analytic expression for the quark distribution function in the pion for a
general vertex function is derived. The results are QCD evolved to higher
momentum-transfer values, and reasonable agreement with phenomenological
analyzes of the data on parton distributions for the pion is found.


  The purpose of this letter is to define a distance on the underlying phase
space of a chaotic map, based on natural invariant density of the map. It is
observed that for logistic map this distance is equivalent to Wootters'
statistical distance. This distance becomes the Euclidean distance for a map
with constant invariant density.


  The effect of the Coulomb repulsion of holes on the Cooper instability in an
ensemble of spin-polaron quasiparticles has been analyzed, taking into account
the peculiarities of the crystallographic structure of the CuO$_2$ plane, which
are associated with the presence of two oxygen ions and one copper ion in the
unit cell, as well as the strong spin-fermion coupling. The investigation of
the possibility of implementation superconducting phases with d-wave and s-wave
pairing of the order parameter symmetry has shown that in the entire doping
region only the d-wave pairing satisfies the self-consistency equations, while
there is no solution for the s-wave pairing. This result completely corresponds
to the experimental data on cuprate HTSC. It has been demonstrated analytically
that the intersite Coulomb interaction does not affect the superconducting
d-wave pairing, because its Fourier transform $V_q$ does not appear in the
kernel of the corresponding integral equation.