  There exist two uniquely defined $v=4$ states in systems within a $j=9/2$
subshell, which automatically conserve seniority and do not mix with other
states. Here I show that the partial conservation of seniority plays an
essential role in our understanding of the electric quadrupole transitions of
the semimagic nuclei involving $j=9/2$ subshells, including the long-lived
$8^{+}$ isomer in $^{94}$Ru. The effects of configuration mixing from
neighboring subshells on the structure of those unique states are analysed. It
is shown that a sharp transition from pure seniority coupling to a significant
mixture between the $v=2$ and $v=4$ states may be induced by the cross-orbital
non-diagonal interaction matrix elements. Such strong mixture is essential to
explain the observed E2 transition properties of $N=50$ isotones $^{96}$Pd and
$^{94}$Ru.


  Stochastic iterative algorithms such as the Kaczmarz and Gauss-Seidel methods
have gained recent attention because of their speed, simplicity, and the
ability to approximately solve large-scale linear systems of equations without
needing to access the entire matrix. In this work, we consider the setting
where we wish to solve a linear system in a large matrix X that is stored in a
factorized form, X = UV; this setting either arises naturally in many
applications or may be imposed when working with large low-rank datasets for
reasons of space required for storage. We propose a variant of the randomized
Kaczmarz method for such systems that takes advantage of the factored form, and
avoids computing X. We prove an exponential convergence rate and supplement our
theoretical guarantees with experimental evidence demonstrating that the
factored variant yields significant acceleration in convergence.


  With the proliferation of the adoption of renewable energy in powering data
centers, addressing the challenges of such energy sources has attracted
researchers from academia and industry. One of the challenging characteristics
of data centers with renewable energy is the intrinsic power fluctuation.
Fluctuation in renewable power supply inevitably requires adjusting
applications' power consumption, which can lead to undesirable performance
degradation. This paper investigates the possible control knobs to manage the
power and performance of a popular cloud workload, i.e., deep neural network
inference, under the fluctuating power supply. Through empirical profiling and
trace-driven simulations, we observe the different impact levels associated
with inference control knobs on throughput, under varying power supplies. Based
on our observations, we provide a list of future research directions to
leverage the control knobs to achieve high throughput.


  Mode-division multiplexing (MDM) is an emerging large-capacity data
communication technology utilizing orthogonal guiding modes as independent data
streams. One of the challenges of multimode waveguide routing in MDM systems is
decreasing the mode leakage of waveguide crossings. In this article, a square
Maxwell's fish-eye lens as waveguide crossing medium based on quasi-conformal
transformation optics is designed and implemented on the silicon-on-insulator
platform. Two approaches were taken to realize the designed lens: graded
photonic crystal and varying the thickness of Si slab waveguide.
Three-dimensional numerical simulations show that the designed multimode
waveguide crossing has an ultrawide bandwidth from 1260 to 1675 nm with a
compact footprint of only $3.77 \times 3.77 \mu m^2$. For the first three
transverse electric modes $(TE_0, TE_1, \: and \: TE_2)$, the designed
waveguide crossing exhibits an average insertion loss of 0.24, 0.55, and 0.45
dB and the crosstalk of less than -72, -61, and -27 dB, and a maximum return
loss of 54, 53, and 30 dB, respectively. The designed waveguide crossing
supports low distortion pulse transmission with a high fidelity factor of
0.9857. Furthermore, proposed method can be expanded to design waveguide
crossings with even higher number of supporting modes by increasing the size of
the lens.


  For an indecomposable module $M$ over a path algebra of a quiver of type
$\widetilde{\mathbb A}_n$, the Gabriel-Roiter measure gives rise to four new
numerical invariants; we call them the multiplicity, and the initial, periodic
and final parts. We describe how these invariants for $M$ and for its dual
specify the position of $M$ in the Auslander-Reiten quiver of the algebra.


  Multivariate binary distributions can be decomposed into products of
univariate conditional distributions. Recently popular approaches have modeled
these conditionals through neural networks with sophisticated weight-sharing
structures. It is shown that state-of-the-art performance on several standard
benchmark datasets can actually be achieved by training separate probability
estimators for each dimension. In that case, model training can be trivially
parallelized over data dimensions. On the other hand, complexity control has to
be performed for each learned conditional distribution. Three possible methods
are considered and experimentally compared. The estimator that is employed for
each conditional is LogitBoost. Similarities and differences between the
proposed approach and autoregressive models based on neural networks are
discussed in detail.


  In this study, we proposed a new end-to-end convolutional neural network,
called MS-SincResNet, for music genre classification. MS-SincResNet appends 1D
multi-scale SincNet (MS-SincNet) to 2D ResNet as the first convolutional layer
in an attempt to jointly learn 1D kernels and 2D kernels during the training
stage. First, an input music signal is divided into a number of fixed-duration
(3 seconds in this study) music clips, and the raw waveform of each music clip
is fed into 1D MS-SincNet filter learning module to obtain three-channel 2D
representations. The learned representations carry rich timbral, harmonic, and
percussive characteristics comparing with spectrograms, harmonic spectrograms,
percussive spectrograms and Mel-spectrograms. ResNet is then used to extract
discriminative embeddings from these 2D representations. The spatial pyramid
pooling (SPP) module is further used to enhance the feature discriminability,
in terms of both time and frequency aspects, to obtain the classification label
of each music clip. Finally, the voting strategy is applied to summarize the
classification results from all 3-second music clips. In our experimental
results, we demonstrate that the proposed MS-SincResNet outperforms the
baseline SincNet and many well-known hand-crafted features. Considering
individual 2D representation, MS-SincResNet also yields competitive results
with the state-of-the-art methods on the GTZAN dataset and the ISMIR2004
dataset. The code is available at https://github.com/PeiChunChang/MS-SincResNet


  We study the scattering of a particle from a bound pair in an effective field
theory using a distorted-wave renormalisation group method to find the
power-counting for the three-body force terms. We find that three-body terms
appear at lower orders than naively expected. They start with a marginal term
that varies as a logarithm rather than a power of the energy scales in the
problem. The marginal term has important implications for the three-body
problem in nuclear physics.


  Goulden and Jackson introduced a very powerful method to study the
distributions of certain consecutive patterns in permutations, words, and other
combinatorial objects which is now called the cluster method. There are a
number of natural classes of combinatorial objects which start with either
permutations or words and add additional restrictions. These include up-down
permutations, generalized Euler permutations, words with no consecutive
repeated letters, Young tableaux, and non-backtracking random walks. We develop
an extension of the cluster method which we call the {\em generalized cluster
method} to study the distribution of certain consecutive patterns in such
restricted combinatorial objects. In this paper, we focus on block patterns in
generalized Euler permutations.


  We are interested in the implications of a linearly autocorrelated driven
noise on the asymptotic behavior of the usual least squares estimator in a
stable autoregressive process. We show that the least squares estimator is not
consistent and we suggest a sharp analysis of its almost sure limiting value as
well as its asymptotic normality. We also establish the almost sure convergence
and the asymptotic normality of the estimated serial correlation parameter of
the driven noise. Then, we derive a statistical procedure enabling to test for
correlation of any order in the residuals of an autoregressive modelling,
giving clearly better results than the commonly used portmanteau tests of
Ljung-Box and Box-Pierce, and appearing to outperform the Breusch-Godfrey
procedure on small-sized samples.


  Many critical policy decisions, from strategic investments to the allocation
of humanitarian aid, rely on data about the geographic distribution of wealth
and poverty. Yet many poverty maps are out of date or exist only at very coarse
levels of granularity. Here we develop the first micro-estimates of wealth and
poverty that cover the populated surface of all 135 low and middle-income
countries (LMICs) at 2.4km resolution. The estimates are built by applying
machine learning algorithms to vast and heterogeneous data from satellites,
mobile phone networks, topographic maps, as well as aggregated and
de-identified connectivity data from Facebook. We train and calibrate the
estimates using nationally-representative household survey data from 56 LMICs,
then validate their accuracy using four independent sources of household survey
data from 18 countries. We also provide confidence intervals for each
micro-estimate to facilitate responsible downstream use. These estimates are
provided free for public use in the hope that they enable targeted policy
response to the COVID-19 pandemic, provide the foundation for new insights into
the causes and consequences of economic development and growth, and promote
responsible policymaking in support of the Sustainable Development Goals.


  We analyse the results of an Eulerian AMR cosmological simulation in order to
quantify the mass growth of galaxy clusters, exploring the differences between
dark matter and baryons. We have determined the mass assembly histories (MAHs)
of each of the mass components and computed several proxies for the
instantaneous mass accretion rate (MAR). The mass growth of both components is
clearly dominated by the contribution of major mergers, but high MARs can also
occur during smooth accretion periods. We explored the correlations between
MARs, merger events and clusters' environments, finding the mean densities in
$1 \leq r/R_{200m} \leq 1.5$ to correlate strongly with $\Gamma_{200m}$ in
massive clusters which undergo major mergers through their MAH. From the study
of the dark matter velocity profiles, we find a strong anticorrelation between
the MAR proxies $\Gamma_{200m}$ and $\alpha_{200m}$. Last, we present a novel
approach to study the angularly-resolved distribution of gas accretion flows in
simulations, which allows to extract and interpret the main contributions to
the accretion picture and to assess systematic differences between the
thermodynamical properties of each of these contributions using multipolar
analysis. We have preliminarily applied the method to the best
numerically-resolved cluster in our simulation. Amongst the most remarkable
results, we find that the gas infalling through the cosmic filaments has
systematically lower entropy compared to the isotropic component, but we do not
find a clear distinction in temperature.


  Neural networks (NN)-based learning algorithms are strongly affected by the
choices of initialization and data distribution. Different optimization
strategies have been proposed for improving the learning trajectory and finding
a better optima. However, designing improved optimization strategies is a
difficult task under the conventional landscape view. Here, we propose
persistent neurons, a trajectory-based strategy that optimizes the learning
task using information from previous converged solutions. More precisely, we
utilize the end of trajectories and let the parameters explore new landscapes
by penalizing the model from converging to the previous solutions under the
same initialization. Persistent neurons can be regarded as a stochastic
gradient method with informed bias where individual updates are corrupted by
deterministic error terms. Specifically, we show that persistent neurons, under
certain data distribution, is able to converge to more optimal solutions while
initializations under popular framework find bad local minima. We further
demonstrate that persistent neurons helps improve the model's performance under
both good and poor initializations. We evaluate the full and partial persistent
model and show it can be used to boost the performance on a range of NN
structures, such as AlexNet and residual neural network (ResNet).


  In this paper we examine the effectiveness of several multi-arm bandit
algorithms when used as a trust system to select agents to delegate tasks to.
In contrast to existing work, we allow for recursive delegation to occur. That
is, a task delegated to one agent can be delegated onwards by that agent, with
further delegation possible until some agent finally executes the task. We show
that modifications to the standard multi-arm bandit algorithms can provide
improvements in performance in such recursive delegation settings.


  Context. There are more than 3000 known Galactic planetary nebulae (PNe), but
only 492 central stars of Galactic planetary nebulae (CSPN) have known spectral
types. It is vital to increase this number in order to have reliable
statistics, which will lead to an increase of our understanding of these
amazing objects.
  Aims. We aim to contribute to the knowledge of central stars of planetary
nebulae and stellar evolution.
  Methods. This observational study is based on Gemini Multi-Object
Spectrographs (GMOS) and with the Intermediate Dispersion Spectrograph (IDS) at
the Isaac Newton Telescope (INT) spectra of 78 CSPN. The objects were selected
because they did not have any previous classification, or the present
classification is ambiguous. These new high quality spectra allowed us to
identify the key stellar lines for determining spectral classification in the
Morgan-Keenan (MK) system.
  Results. We have acquired optical spectra of a large sample of CSPN. From the
observed targets, 50 are classified here for the first time while for 28 the
existing classifications have been improved. In seven objects we have
identified a P-Cygni profile at the He i lines. Six of these CSPN are late
O-type. The vast majority of the stars in the sample exhibit an absorption-type
spectrum, and in one case we have found wide emission lines typical of [WR]
stars. We give a complementary, and preliminary, classification criterion to
obtain the sub-type of the O(H)-type CSPN. Finally, we give a more realistic
value of the proportion of CSPN that are rich or poor in hydrogen.


  We develop a formulation for molecular dynamics, Langevin, and hybrid Monte
Carlo algorithms in the recently proposed generalized ensemble that is based on
a physically motivated realisation of Tsallis weights. The effectiveness of the
methods are tested with an energy function for a protein system. Simulations in
this generalized ensemble by the three methods are performed for a penta
peptide, Met-enkephalin. For each algorithm, it is shown that from only one
simulation run one can not only find the global-minimum-energy conformation but
also obtain probability distributions in canonical ensemble at any temperature,
which allows the calculation of any thermodynamic quantity as a function of
temperature.


  We consider bubble collisions in single scalar field theories with multiple
vacua. Recent work has argued that at sufficiently high impact velocities,
collisions between such bubble vacua are governed by 'free passage' dynamics in
which field interactions can be ignored during the collision, providing a
systematic process for populating local minima without quantum nucleation. We
focus on the time period that follows the bubble collision and provide evidence
that, for certain potentials, interactions can drive significant deviations
from the free-passage bubble profile, thwarting the production of bubbles with
different field values.


  Optomechanical (OM) cavities simultaneously localize photons and phonons,
thus enhancing their mutual interaction through radiation-pressure force. This
acousto-optic interaction can be quantified by means of the optical frequency
shift per mechanical displacement G. The aforesaid frequency shift can also be
related to the vacuum OM coupling rate, g0, where only photoelastic (PE) and
moving boundaries (MB) effects are commonly taken into account. However, the
thermo-optic (TO) and thermal expansion (ThE) effects may also play a role
since the material forming the OM cavity could be heated by the presence of
photons, which should naturally affect the mechanical properties of the cavity.
In this work, we introduce a new theoretical approach to determine how thermal
effects change the canonical OM coupling rate. To test the model, a complete
set of optical-thermal-mechanical simulations has been performed in two OM
crystal cavities fabricated from two different materials: silicon and diamond.
Our results lead us to conclude that there is a non-negligible thermal
correction that is always present as a negative shift to the OM coupling rate
that should be considered in order to predict more accurately the strength of
the OM interaction.


  We present extensive, well-sampled optical and ultraviolet photometry and
optical spectra of the Type Ia supernova (SN Ia) 2017hpa. The light curves
indicate that SN 2017hpa is a normal SN Ia with an absolute peak magnitude of
$M_{\rm max}^{B} \approx$ -19.12$\pm$0.11 mag and a post-peak decline rate \mb\
= 1.02$\pm$0.07 mag. According to the quasibolometric light curve, we derive a
peak luminosity of 1.25$\times$10$^{43}$ erg s$^{-1}$ and a $^{56}$Ni mass of
0.63$\pm$0.02 $M_{\odot}$. The spectral evolution of SN 2017hpa is similar to
that of normal SNe Ia, while it exhibits unusually rapid velocity evolution
resembling that of SN 1991bg-like SNe Ia or the high-velocity subclass of SNe
Ia, with a post-peak velocity gradient of $\sim$ 130$\pm$7 km s$^{-1}$
d$^{-1}$. Moreover, its early spectra ($t < -7.9$ d) show prominent
\CII~$\lambda$6580 absorption feature, which disappeared in near-maximum-light
spectra but reemerged at phases from $t \sim +8.7$ d to $t \sim +11.7$ d after
maximum light. This implies that some unburned carbon may mix deep into the
inner layer, and is supported by the low \CII~$\lambda$6580 to
\SiII~$\lambda$6355 velocity ratio ($\sim 0.81$) observed in SN 2017hpa. The
\OI~$\lambda$7774 line shows a velocity distribution like that of carbon. The
prominent carbon feature, low velocity seen in carbon and oxygen, and large
velocity gradient make SN 2017hpa stand out from other normal SNe Ia, and are
more consistent with predictions from a violent merger of two white dwarfs.
Detailed modelling is still needed to reveal the nature of SN 2017hpa.


  A geometric inequality among three triangles, originating in circle packing
problems, is introduced. In order to prove it, we reduce the original
formulation to the nonnegativity of a particular polynomial in four real
indeterminates. Techniques based on sum of squares decompositions, semidefinite
programming, and symmetry reduction are then applied to provide an easily
verifiable nonnegativity certificate.


  We use the Nernst effect to delineate the boundary of the pseudogap phase in
the temperature-doping phase diagram of cuprate superconductors. New data for
the Nernst coefficient $\nu(T)$ of YBa$_{2}$Cu$_{3}$O$_{y}$ (YBCO),
La$_{1.8-x}$Eu$_{0.2}$Sr$_x$CuO$_4$ (Eu-LSCO) and
La$_{1.6-x}$Nd$_{0.4}$Sr$_x$CuO$_4$ (Nd-LSCO) are presented and compared with
previous data including La$_{2-x}$Sr$_x$CuO$_4$ (LSCO). The temperature $T_\nu$
at which $\nu/T$ deviates from its high-temperature behaviour is found to
coincide with the temperature at which the resistivity deviates from its
linear-$T$ dependence, which we take as the definition of the pseudogap
temperature $T^\star$- in agreement with gap opening detected in ARPES data. We
track $T^\star$ as a function of doping and find that it decreases linearly vs
$p$ in all four materials, having the same value in the three LSCO-based
cuprates, irrespective of their different crystal structures. At low $p$,
$T^\star$ is higher than the onset temperature of the various orders observed
in underdoped cuprates, suggesting that these orders are secondary
instabilities of the pseudogap phase. A linear extrapolation of $T^\star(p)$ to
$p=0$ yields $T^\star(p\to 0)\simeq T_N(0)$, the N\'eel temperature for the
onset of antiferromagnetic order at $p=0$, suggesting that there is a link
between pseudogap and antiferromagnetism. With increasing $p$, $T^\star(p)$
extrapolates linearly to zero at $p\simeq p_{\rm c2}$, the critical doping
below which superconductivity emerges at high doping, suggesting that the
conditions which favour pseudogap formation also favour pairing. We also use
the Nernst effect to investigate how far superconducting fluctuations extend
above $T_{\rm c}$, as a function of doping, and find that a narrow fluctuation
regime tracks $T_{\rm c}$, and not $T^\star$. This confirms that the pseudogap
phase is not a form of precursor superconductivity.


  High resolution X-ray spectroscopy has revealed soft X-rays from high density
plasma in Classical T-Tauri stars (CTTSs), probably arising from the accretion
shock region. However, the mass accretion rates derived from the X-ray
observations are consistently lower than those derived from UV/optical/NIR
studies. We aim to test the hypothesis that the high density soft X-ray
emission is from accretion by analysing optical accretion tracers from an X-ray
selected sample of CTTSs in a homogeneous manner. We analyse optical spectra of
a sample of CTTSs and calculate the accretion rates based on measuring optical
emission lines. These are then compared to the accretion rates derived from the
X-ray spectroscopy. We find that, for each CTTS in our sample, the different
optical tracers predict mass accretion rates that agree within the errors,
albeit with a spread of ~1 order of magnitude. Typically, mass accretion rates
derived from Halpha and HeI 5876 Ang are larger than those derived from Hbeta,
Hgamma and OI. When comparisons of the optical mass accretion rates are made to
the X-ray derived mass accretion rates, we find that: a) the latter are always
lower (but by varying amounts); b) the latter range within a factor of ~2
around 2x10^{-10} M_odot yr^{-1}, despite the fact that the former span a range
of ~3 orders of magnitude. We suggest that the systematic underestimation of
the X-ray derived mass accretion rates could depend on the density distribution
inside the accretion streams, where the densest part of the stream is not
visible in the X-ray band because of the absorption by the stellar atmosphere.
We also suggest that a non-negligible optical depth of X-ray emission lines
produced by post-shock accreting plasma may explain the almost constant mass
accretion rates derived in X-rays if the effect is larger in stars with larger
optical mass accretion rates.


  We use group size haloes identified with a ``friends of friends'' (FOF)
algorithm in a concordance $\Lambda \rm{CDM}$ GADGET2 (dark matter only)
simulation to investigate the dependence of halo properties on the environment
at $z=0$. The study is carried out using samples of haloes at different
distances from their nearest massive {\em cluster} halo. We find that the
fraction of haloes with substructure typically increases in high density
regions. The halo mean axial ratio $<c/a>$ also increases in overdense regions,
a fact which is true for the whole range of halo mass studied. This can be
explained as a reflection of an earlier halo formation time in high-density
regions, which gives haloes more time to evolve and become more spherical.
Moreover, this interpretation is supported by the fact that, at a given
halo-cluster distance, haloes with substructure are more elongated than their
equal mass counterparts with no substructure, reflecting that the virialization
(and thus sphericalization) process is interrupted by merger events. The
velocity dispersion of low mass haloes with strong substructure shows a
significant increase near massive clusters with respect to equal mass haloes
with low-levels of substructure or with haloes found in low-density
environments. The alignment signal between the shape and the velocity ellipsoid
principal axes decreases going from lower to higher density regions, while such
an alignment is stronger for haloes without substructure. We also find, in
agreement with other studies, a tendency of halo major axes to be aligned and
of minor axes to lie roughly perpendicular with the orientation of the filament
within which the halo is embedded, an effect which is stronger in the proximity
of the massive clusters.


  We introduce the multiple zeta functions with structures similar to those of
symmetric functions such as Schur $P$-, Schur $Q$-, symplectic and orthogonal
functions in the representation theory. We first consider their basic
properties such as a domain of absolute convergence. And then by restricting to
the truncated multiple zeta functions, we obtain the pfaffian expression of the
Schur $Q$-multiple zeta functions, the sum formula for Schur $P$- and Schur
$Q$-multiple zeta functions, the determinant expressions of symplectic and
orthogonal Schur multiple zeta functions under an assumption on variables.
Finally, we generalize those to the quasi-symmetric functions.


  Let $(R,\mathfrak{m}_R,k)$ be a one-dimensional complete local reduced
$k$-algebra over a field of characteristic zero. R. Berger conjectured that $R$
is regular if and only if the universally finite module of differentials
$\Omega_R$ is torsion free. When $R$ is a domain, we prove the conjecture in
several cases. Our techniques are primarily reliant on making use of the
valuation semi-group of $R$. First, we establish a method of verifying the
conjecture by analyzing the valuation semi-group of $R$ and orders of units of
the integral closure of $R$. We also prove the conjecture in the case when
certain monomials are missing from the monomial support of the defining ideal
of $R$. These monomials are based on the smallest power of $\mathfrak{m}_R$
that is contained within the conductor ideal. This also generalizes a previous
result of Corti\~nas, Geller and Weibel.


  Traffic flow forecasting is considered a critical task in the field of
intelligent transportation systems. In this paper, to address the issue of low
accuracy in long-term forecasting of spatial-temporal big data on traffic flow,
we propose an innovative model called Spatial-Temporal Retentive Network
(ST-RetNet). We extend the Retentive Network to address the task of traffic
flow forecasting. At the spatial scale, we integrate a topological graph
structure into Spatial Retentive Network(S-RetNet), utilizing an adaptive
adjacency matrix to extract dynamic spatial features of the road network. We
also employ Graph Convolutional Networks to extract static spatial features of
the road network. These two components are then fused to capture dynamic and
static spatial correlations. At the temporal scale, we propose the Temporal
Retentive Network(T-RetNet), which has been demonstrated to excel in capturing
long-term dependencies in traffic flow patterns compared to other time series
models, including Recurrent Neural Networks based and transformer models. We
achieve the spatial-temporal traffic flow forecasting task by integrating
S-RetNet and T-RetNet to form ST-RetNet. Through experimental comparisons
conducted on four real-world datasets, we demonstrate that ST-RetNet
outperforms the state-of-the-art approaches in traffic flow forecasting.


  We study the problem of parametric estimation for continuously observed
stochastic differential equation driven by fractional Brownian motion. Under
some assumptions on drift and diffusion coefficients, we construct maximum
likelihood estimator and establish its the asymptotic normality and moment
convergence of the drift parameter when a small dispersion coefficient
vanishes.


  Discoveries of exoplanets using the radial velocity method are progressively
reaching out to increasingly longer orbital periods as the duration of surveys
continues to climb. The improving sensitivity to potential Jupiter analogs is
revealing a diversity of orbital architectures that are substantially different
from that found in our solar system. An excellent example of this is the recent
discovery of HR 5183b; a giant planet on a highly eccentric ($e = 0.84$)
$\sim$75~year orbit. The presence of such giant planet orbits are intrinsically
interesting from the perspective of the dynamical history of planetary systems,
and also for examining the implications of on-going dynamical stability and
habitability of these systems. In this work, we examine the latter, providing
results of dynamical simulations that explore the stable regions that the
eccentric orbit of the HR 5183 giant planet allows to exist within the
Habitable Zone of the host star. Our results show that, despite the incredible
perturbing influence of the giant planet, there remain a narrow range of
locations within the Habitable Zone where terrestrial planets may reside in
long-term stable orbits. We discuss the effects of the giant planet on the
potential habitability of a stable terrestrial planet, including the modulation
of terrestrial planet eccentricities and the periodically spectacular view of
the giant planet from the terrestrial planet location.


  The recently proposed chameleonic extension of bigravity theory, by including
a scalar field dependence in the graviton potential, avoids several
fine-tunings found to be necessary in usual massive bigravity. In particular it
ensures that the Higuchi bound is satisfied at all scales, that no Vainshtein
mechanism is needed to satisfy solar-system experiments, and that the strong
coupling scale is always above the scale of cosmological interest all the way
up to the early universe. This paper extends the previous work by presenting a
stable example of cosmology in the chameleon bigravity model. We find a set of
initial conditions and parameters such that the derived stability conditions on
general flat Friedmann background are satisfied at all times. The evolution
goes through radiation dominated, matter dominated, and de Sitter eras. We
argue that the parameter space allowing for such a stable evolution may be
large enough to encompass an observationally viable evolution. We also argue
that our model satisfies all known constraints due to gravitational wave
observations so far and thus can be considered as a unique testing ground of
gravitational wave phenomenologies in bimetric theories of gravity.


  A central aim in metamaterial research is to engineer sub-wavelength unit
cells that give rise to desired effective-medium properties and parameters,
such as a negative refractive index. Ideally one can disregard the details of
the unit cell and employ the effective description instead. A popular strategy
to compensate for the inevitable losses in metallic components of metamaterials
is to add optical gain material. Here we study the quantum optics of such
loss-compensated metamaterials at frequencies for which effective parameters
can be unambiguously determined. We demonstrate that the usual effective
parameters are insufficient to describe the propagation of quantum states of
light. Furthermore, we propose a quantum-optical effective-medium theory
instead and show that it correctly predicts the properties of the light
emerging from loss-compensated metamaterials.


  We characterize infinitesimal generators on complete hyperbolic complex
manifolds without any regularity assumption on the Kobayashi distance. This
allows to prove a general Loewner type equation with regularity of any order
$d\in [1,+\infty ]$. Finally, based on these results, we focus on some open
problems naturally arising.


  In this article we study self-gravitating static solutions of the
Einstein-ScalarField system in arbitrary dimensions. We discuss the existence
and the non-existence of geodesically complete solutions depending on the form
of the scalar field potential $V(\phi)$, and provide full global geometric
estimates when the solutions exist. Our main results are summarised as follows.
For the Klein-Gordon field, namely when $V(\phi)=m^{2}|\phi|^{2}$, it is proved
that geodesically complete solutions have Ricci-flat spatial metric, have
constant lapse and are vacuum, (that is $\phi$ is constant and equal to zero if
$m\neq 0$). In particular, when the spatial dimension is three, the only such
solutions are either Minkowski or a quotient thereof (no nontrivial solutions
exist). When $V(\phi)=m^{2}|\phi|^{2}+2\Lambda$, that is, when a vacuum energy
or a cosmological constant is included, it is proved that no geodesically
complete solution exists when $\Lambda>0$, whereas when $\Lambda<0$ it is
proved that no non-vacuum geodesically complete solution exists unless
$m^{2}<-2\Lambda/(n-1)$, ($n$ is the spatial dimension) and the spatial
manifold is non-compact. The proofs are based on techniques in comparison
geometry \'a la Backry-Emery.


  We present a scheme to conditionally engineer an optical quantum system via
continuous-variable measurements. This scheme yields high-fidelity squeezed
single photon and superposition of coherent states, from input single and two
photon Fock states respectively. The input Fock state is interacted with an
ancilla squeezed vacuum state using a beam-splitter. We transform the quantum
system by post-selecting on the continuous-observable measurement outcome of
the ancilla state. We experimentally demonstrate the principles of this scheme
using displaced coherent states and measure experimentally fidelities that are
only achievable using quantum resources.


  Nonparametric two-sample testing is a classical problem in inferential
statistics. While modern two-sample tests, such as the edge count test and its
variants, can handle multivariate and non-Euclidean data, contemporary
gargantuan datasets often exhibit heterogeneity due to the presence of latent
subpopulations. Direct application of these tests, without regulating for such
heterogeneity, may lead to incorrect statistical decisions. We develop a new
nonparametric testing procedure that accurately detects differences between the
two samples in the presence of unknown heterogeneity in the data generation
process. Our framework handles this latent heterogeneity through a composite
null that entertains the possibility that the two samples arise from a mixture
distribution with identical component distributions but with possibly different
mixing weights. In this regime, we study the asymptotic behavior of weighted
edge count test statistic and show that it can be effectively re-calibrated to
detect arbitrary deviations from the composite null. For practical
implementation we propose a Bootstrapped Weighted Edge Count test which
involves a bootstrap-based calibration procedure that can be easily implemented
across a wide range of heterogeneous regimes. A comprehensive simulation study
and an application to detecting aberrant user behaviors in online games
demonstrates the excellent non-asymptotic performance of the proposed test.


  Halo merger trees describe the hierarchical mass assembly of dark matter
haloes, and are the backbone for modeling galaxy formation and evolution.
Merger trees constructed using Monte Carlo algorithms based on the extended
Press-Schechter (EPS) formalism are complementary to those extracted from
N-body simulations, and have the advantage that they are not trammeled by
limited numerical resolution and uncertainties in identifying (sub)haloes and
linking them between snapshots. This paper compares multiple EPS-based merger
tree algorithms to simulation results using four diagnostics: progenitor mass
function (PMF), mass assembly history (MAH), merger rate per descendant halo,
and the unevolved subhalo mass function (USMF). In general, algorithms based on
spherical collapse yield major-merger rates that are too high by a factor of
two, resulting in MAHs that are systematically offset. Assuming ellipsoidal
collapse solves most of these issues, but the particular algorithm investigated
here that incorporates ellipsoidal collapse dramatically overpredicts the
minor-merger rate for massive haloes. The only algorithm in our comparison that
yields MAHs, merger rates, and USMFs in good agreement with simulations, is
that by Parkinson et al. (2008). However, this is not a true EPS-based
algorithm as it draws its progenitor masses from a PMF calibrated against
simulations, rather than `predicted' by EPS. Finally we emphasize that the
benchmarks used to test the EPS algorithms are obtained from simulations and
are hampered by significant uncertainties themselves. In particular, MAHs and
halo merger rates obtained from simulations by different authors reveal
discrepancies that easily exceed 50 percent, even when based on the same
simulation. Given this status quo, merger trees constructed using the Parkinson
et al. algorithm are as accurate as those extracted from N-body simulations.


  The recent developments of artificial intelligence increase its capability
for the creation of arts in both largely autonomous and collaborative contexts.
In both contexts, Ai aims to imitate, combine, and extend existing artistic
styles, and can transform creative practices. In our ongoing research, we
investigate such Creative-Ai from sustainability and ethical perspectives. The
two main focus areas are understanding the environmental sustainability aspects
(material, practices) in the context of artistic processes that involve
Creative-Ai, and ethical issues related to who gets to be involved in the
creation process (power, authorship, ownership). This paper provides an outline
of our ongoing research in these two directions. We will present our
interdisciplinary approach, which combines interviews, workshops, online
ethnography, and energy measurements, to address our research questions: How is
Creative-Ai currently used by artist communities, and which future applications
do artists imagine? When Ai is applied to creating art, how might it impact the
economy and environment? And, how can answers to these questions guide
requirements for intellectual property regimes for Creative-Ai?


  Symmetry-protected topological (SPT) states are short-range entangled states
with a symmetry G. They belong to a new class of quantum states of matter which
are classified by the group cohomology $H^{d+1}(G,\mathbb{R}/\mathbb{Z})$ in
d-dimensional space. In this paper, we propose a class of symmetry- protected
topological invariants that may allow us to fully characterize SPT states with
a symmetry group G (ie allow us to measure the cocycles in
$H^{d+1}(G,\mathbb{R}/\mathbb{Z})$ that characterize the SPT states). We give
an explicit and detailed construction of symmetry-protected topological
invariants for 2+1D SPT states. Such a construction can be directly generalized
to other dimensions.


  In this article, we solve the strong openness conjecture on the multiplier
ideal sheaves for the plurisubharmonic functions posed by Demailly. We prove
two conjectures about the growth of the volumes of the sublevel sets of
plurisubharmonic functions related to the complex singularity exponents and
quasi-plurisubharmonic functions related to the jumping numbers, which were
posed by Demailly-Koll\'{a}r and Jonsson-Mustat\u{a} respectively. We give a
new proof of a lower semicontinuity conjecture posed by Demailly-Koll\'{a}r
without using the ACC conjecture. Other applications by combining with
well-known results are also mentioned.


  This paper presents a comparison of six machine learning (ML) algorithms:
GRU-SVM (Agarap, 2017), Linear Regression, Multilayer Perceptron (MLP), Nearest
Neighbor (NN) search, Softmax Regression, and Support Vector Machine (SVM) on
the Wisconsin Diagnostic Breast Cancer (WDBC) dataset (Wolberg, Street, &
Mangasarian, 1992) by measuring their classification test accuracy and their
sensitivity and specificity values. The said dataset consists of features which
were computed from digitized images of FNA tests on a breast mass (Wolberg,
Street, & Mangasarian, 1992). For the implementation of the ML algorithms, the
dataset was partitioned in the following fashion: 70% for training phase, and
30% for the testing phase. The hyper-parameters used for all the classifiers
were manually assigned. Results show that all the presented ML algorithms
performed well (all exceeded 90% test accuracy) on the classification task. The
MLP algorithm stands out among the implemented algorithms with a test accuracy
of ~99.04%.


  The TGRS experiment on board the Wind spacecraft has many advantages as a sky
monitor --- broad field of view (~2 pi) centered on the south ecliptic pole),
long life (1994-present), and stable low background and continuous coverage due
to Wind's high altitude high eccentricity orbit. The Ge detector has sufficient
energy resolution (3-4 keV at 511 keV) to resolve a cosmic positron
annihilation line from the strong background annihilation line from beta-decays
induced by cosmic ray impacts on the instrument, if the cosmic line is
Doppler-shifted by this amount. Such lines (blueshifted) are predicted from
nucleosynthesis in classical novae. We have searched the entire TGRS database
for 1995-1997 for this line, with negative results. In principle such a search
could yield an unbiased upper limit on the highly-uncertain Galactic nova rate.
We carefully examined the times around the known nova events during this
period, also with negative results. The upper limit on the nova line flux in a
6-hr interval is typically <3.8 E-3 photon/(cm2 s) at 4.6 sigma. We performed
the same analysis for times around the outburst of Nova Vel 1999, obtaining a
worse limit due to recent degradation of the detector response caused by cosmic
ray induced damage.


  We demonstrate the capability of accurate time transfer using optical fibers
over long distances utilizing a dark fiber and hardware which is usually
employed in two-way satellite time and frequency transfer (TWSTFT). Our time
transfer through optical fiber (TTTOF) system is a variant of the standard
TWSTFT by employing an optical fiber in the transmission path instead of
free-space transmission of signals between two ground stations through
geostationary satellites. As we use a dark fiber there are practically no
limitations to the bandwidth of the transmitted signals so that we can use the
highest chip-rate of the binary phase-shift modulation available from the
commercial equipment. This leads to an enhanced precision compared to satellite
time transfer where the occupied bandwidth is limited for cost reasons. The
TTTOF system has been characterized and calibrated in a common clock experiment
at PTB, and the combined calibration uncertainty is estimated as 74 ps. In a
second step the remote part of the system was operated at Leibniz Universitaet
Hannover, Institut fuer Quantenoptik (IQ) separated by 73 km from PTB in
Braunschweig. In parallel, a GPS time transfer link between Braunschweig and
Hannover was established, and both links connected a passive hydrogen maser at
IQ with the reference time scale UTC(PTB) maintained in PTB. The results
obtained with both links agree within the 1-sigma uncertainty of the GPS link
results, which is estimated as 0.72 ns. The fiber link exhibits a nearly
10-fold improved stability compared to the GPS link, and assessment of its
performance has been limited by the properties of the passive maser.


  Under the diffusion scaling and a scaling assumption on the microscopic
component, a non-classical fluid dynamic system was derived in \cite{BGLY} that
is related to the system of ghost effect derived in \cite{Sone-2} in different
settings. This paper aims to justify this limit system for a non-trivial
background profile with slab symmetry. The result reveals not only the
diffusion phenomena in the temperature and density, but also the flow of higher
order in Knudsen number due to the gradient of the temperature. Precisely, we
show that the solution to the Boltzmann equation converges to a diffusion wave
with decay rates in both Knudsen number and time.


  We report an experimental study of a one-dimensional quintuple-quantum-dot
array integrated with two quantum dot charge sensors in an InAs nanowire. The
device is studied by measuring double quantum dots formed consecutively in the
array and corresponding charge stability diagrams are revealed with both direct
current measurements and charge sensor signals. The one-dimensional
quintuple-quantum-dot array are then tuned up and its charge configurations are
fully mapped out with the two charge sensors. The energy level of each dot in
the array can be controlled individually by using a compensated gate
architecture (i.e., "virtual gate"). After that, four dots in the array are
selected to form two double quantum dots and ultra strong inter-double-dot
interaction is obtained. A theoretical simulation based on a 4-dimensional
Hamiltonian confirms the strong coupling strength between the two double
quantum dots. The highly controllable one-dimensional quantum dot array
achieved in this work is expected to be valuable for employing InAs nanowires
to construct advanced quantum hardware in the future.


  This article establishes the boundary H\"{o}lder continuity of stable
solutions to semilinear elliptic problems in the optimal range of dimensions $n
\leq 9$, for $C^{1,1}$ domains. We consider equations $- L u = f(u)$ in a
bounded $C^{1,1}$ domain $\Omega \subset \mathbb{R}^n$, with $u = 0$ on
$\partial \Omega$, where $L$ is a linear elliptic operator with variable
coefficients and $f \in C^1$ is nonnegative, nondecreasing, and convex. The
stability of $u$ amounts to the nonnegativity of the principal eigenvalue of
the linearized equation $- L - f'(u)$. Our result is new even for the
Laplacian, for which [Cabr\'{e}, Figalli, Ros-Oton, and Serra, Acta Math. 224
(2020)] proved the H\"{o}lder continuity in $C^3$ domains.


  We use holography to study the spontaneous condensation of a neutral order
parameter in a (2+1)-dimensional field theory at zero-temperature and finite
density, dual to the electron star background of Hartnoll and Tavanfar. An
appealing feature of this field theory is the emergence of an IR Lifshitz
fixed-point with a finite dynamical critical exponent $z$, which is due to the
strong interaction between critical bosonic degrees of freedom and a finite
density of fermions (metallic quantum criticality). We show that under some
circumstances the electron star background develops a neutral scalar hair whose
holographic interpretation is that the boundary field theory undergoes a
quantum phase transition, with a Berezinski-Kosterlitz-Thouless character, to a
phase with a neutral order parameter. Including the backreaction of the bulk
neutral scalar on the background, we argue that the two phases across the
quantum critical point have different $z$, a novelty that exists in certain
quantum phase transitions in condensed matter systems. We also analyze the
system at finite temperature and find that the phase transition becomes, as
expected, second-order. Embedding the neutral scalar into a higher form, a
variety of interesting phases could potentially be realized for the boundary
field theory. Examples which are of particular interest to condensed matter
physics include an antiferromagnetic phase where a vector condenses and break
the spin symmetry, a quadrupole nematic phase which involves the condensation
of a symmetric traceless tensor breaking rotational symmetry, or different
phases of a system with competing order parameters.


  Knowledge distillation (KD) has been extensively studied in single-label
image classification. However, its efficacy for multi-label classification
remains relatively unexplored. In this study, we firstly investigate the
effectiveness of classical KD techniques, including logit-based and
feature-based methods, for multi-label classification. Our findings indicate
that the logit-based method is not well-suited for multi-label classification,
as the teacher fails to provide inter-category similarity information or
regularization effect on student model's training. Moreover, we observe that
feature-based methods struggle to convey compact information of multiple labels
simultaneously. Given these limitations, we propose that a suitable dark
knowledge should incorporate class-wise information and be highly correlated
with the final classification results. To address these issues, we introduce a
novel distillation method based on Class Activation Maps (CAMs), which is both
effective and straightforward to implement. Across a wide range of settings,
CAMs-based distillation consistently outperforms other methods.


  String and M-theory realizations of brane world supersymmetry breaking
scenarios are considered in which visible sector Standard Model fields are
confined on a brane, with hidden sector supersymmetry breaking isolated on a
distant brane. In calculable examples with an internal manifold of any volume
the Kahler potential generically contains brane--brane non-derivative contact
interactions coupling the visible and hidden sectors and is not of the no-scale
sequestered form. This leads to non-universal scalar masses and without
additional assumptions about flavor symmetries may in general induce dangerous
sflavor violation even though the Standard Model and supersymmetry branes are
physically separated. Deviations from the sequestered form are dictated by bulk
supersymmetry and can in most cases be understood as arising from exchange of
bulk supergravity fields between branes or warping of the internal geometry.
Unacceptable visible sector tree-level tachyons arise in many models but may be
avoided in certain classes of compactifications. Anomaly mediated and gaugino
mediated contributions to scalar masses are sub-dominant except in special
circumstances such as a flat or AdS pure five--dimensional bulk geometry
without bulk vector multiplets.


  Human personality traits are the key drivers behind our decision-making,
influencing our life path on a daily basis. Inference of personality traits,
such as Myers-Briggs Personality Type, as well as an understanding of
dependencies between personality traits and users' behavior on various social
media platforms is of crucial importance to modern research and industry
applications. The emergence of diverse and cross-purpose social media avenues
makes it possible to perform user personality profiling automatically and
efficiently based on data represented across multiple data modalities. However,
the research efforts on personality profiling from multi-source multi-modal
social media data are relatively sparse, and the level of impact of different
social network data on machine learning performance has yet to be
comprehensively evaluated. Furthermore, there is not such dataset in the
research community to benchmark. This study is one of the first attempts
towards bridging such an important research gap. Specifically, in this work, we
infer the Myers-Briggs Personality Type indicators, by applying a novel
multi-view fusion framework, called "PERS" and comparing the performance
results not just across data modalities but also with respect to different
social network data sources. Our experimental results demonstrate the PERS's
ability to learn from multi-view data for personality profiling by efficiently
leveraging on the significantly different data arriving from diverse social
multimedia sources. We have also found that the selection of a machine learning
approach is of crucial importance when choosing social network data sources and
that people tend to reveal multiple facets of their personality in different
social media avenues. Our released social multimedia dataset facilitates future
research on this direction.


  We consider randomized computation of continuous data in the sense of
Computable Analysis. Our first contribution formally confirms that it is no
loss of generality to take as sample space the Cantor space of infinite FAIR
coin flips. This extends [Schr\"oder&Simpson'05] and [Hoyrup&Rojas'09]
considering sequences of suitably and adaptively BIASED coins.
  Our second contribution is concerned with 1D Brownian Motion (aka Wiener
Process), a probability distribution on the space of continuous functions
f:[0,1]->R with f(0)=0 whose computability has been conjectured
[Davie&Fouch\'e'13; arXiv:1409.4667,S6]. We establish that this (higher-type)
random variable is computable iff some/every computable family of moduli of
continuity (as ordinary random variables) has a computable probability
distribution with respect to the Wiener Measure.


  As the deployment of NLP systems in critical applications grows, ensuring the
robustness of large language models (LLMs) against adversarial attacks becomes
increasingly important. Large language models excel in various NLP tasks but
remain vulnerable to low-cost adversarial attacks. Focusing on the domain of
conversation entailment, where multi-turn dialogues serve as premises to verify
hypotheses, we fine-tune a transformer model to accurately discern the
truthfulness of these hypotheses. Adversaries manipulate hypotheses through
synonym swapping, aiming to deceive the model into making incorrect
predictions. To counteract these attacks, we implemented innovative fine-tuning
techniques and introduced an embedding perturbation loss method to
significantly bolster the model's robustness. Our findings not only emphasize
the importance of defending against adversarial attacks in NLP but also
highlight the real-world implications, suggesting that enhancing model
robustness is critical for reliable NLP applications.


  Negation scope has been annotated in several English and Chinese corpora, and
highly accurate models for this task in these languages have been learned from
these annotations. Unfortunately, annotations are not available in other
languages. Could a model that detects negation scope be applied to a language
that it hasn't been trained on? We develop neural models that learn from
cross-lingual word embeddings or universal dependencies in English, and test
them on Chinese, showing that they work surprisingly well. We find that
modelling syntax is helpful even in monolingual settings and that cross-lingual
word embeddings help relatively little, and we analyse cases that are still
difficult for this task.


  Economic periods and financial crises have highlighted the importance of
evaluating financial markets to investors and researchers in recent decades.


  A simple model of nucleons coupled to angular momentum zero (s-pairs)
occupying the valance shell of a semi-magic nuclei is considered. The model has
a separable, orbit dependent pairing interaction which dominates over the
kinetic term. It is shown that such an interaction leads to an exactly solvable
model whose (0+) eigenstates and energies can be computed very easily with the
help of the algebraic Bethe ansatz method. It is also shown that the model has
a supersymmetry which connects the spectra of some semimagic nuclei. The
results obtained from this model for the semimagic Ni isotopes from 58Ni to
68Ni are given. In addition, a new and easier technique for calculating the
energy eigenvalues from the Bethe ansatz equations is also presented.


  The (co)homology theory of n-ary (co)compositions is a functor associating to
$n$-ary (co)composition a complex. We present unified approach to the
cohomology theory of coassociative and Lie coalgebras and for $2n$-ary
cocompositions. This approach points to a possible generalization.


  In this work we reconstruct the probability current in phase space of
out-of-equilibrium microwave circuits. This is achieved by a statistical
analysis of short-time correlations in time domain measurements. It allows us
to check locally in phase space the violation of detailed balance or the
presence of fluctuation loops. We present the data analysis methods and
experimental results for several microwave circuits driven by two noise sources
in the 4-8GHz frequency range.


  Radio Frequency Identification (RFID) is a modern communication technology,
which provides authentication and identification through a nonphysical contact.
Recently, the use of this technology is almost developed in healthcare
environments. Although RFID technology can prepare sagacity in systems, privacy
and security issues ought to be considered before. Recently, in 2015, Li et al.
proposed SRTA, a hash-based RFID authentication protocol in medication
verification for healthcare. In this paper, we study this protocol and show
that SRTA protocol is vulnerable to traceability, impersonation and Dos
attacks. So it does not provide the privacy and security of RFID end users.
Therefore, we propose an improved secure and efficient RFID authentication
protocol to enhance the performance of Li et al. method. Our analyze show that
the existing weaknesses of SRTA protocol are eliminated in our proposed
protocol.


  The purpose of this article is to define and examine graded almost prime
ideals over a non-commutative graded ring, and consider some cases where all
graded right ideals of a non-commutative graded ring are graded almost prime.


  Initial value problems for the integrable discrete equations on quad-graphs
are investigated. A geometric criterion of the well-posedness of such a problem
is found. The effects of the interaction of the solutions with the localized
defects in the regular square lattice are discussed for the discrete potential
KdV and linear wave equations. The examples of kinks and solitons on various
quad-graphs, including quasiperiodic tilings, are presented.


  Many basic key distribution schemes specifically tuned to wireless sensor
networks have been proposed in the literature. Recently, several researchers
have proposed schemes in which they have used group-based deployment models and
assumed predeployment knowledge of the expected locations of nodes. They have
shown that these schemes achieve better performance than the basic schemes, in
terms of connectivity, resilience against node capture and storage
requirements. But in many situations expected locations of nodes are not
available. In this paper we propose a solution which uses the basic scheme, but
does not use group-based deployment model and predeployment knowledge of the
locations of nodes, and yet performs better than schemes which make the
aforementioned assumptions.
  In our scheme, groups are formed after deployment of sensor nodes, on the
basis of their physical locations, and the nodes sample keys from disjoint key
pools. Compromise of a node affects secure links with other nodes that are part
of its group only. Because of this reason, our scheme performs better than the
basic schemes and the schemes using predeployment knowledge, in terms of
connectivity, storage requirement, and security. Moreover, the post-deployment
key generation process completes sooner than in schemes like LEAP+.


  Sequential Monte Carlo samplers represent a compelling approach to posterior
inference in Bayesian models, due to being parallelisable and providing an
unbiased estimate of the posterior normalising constant. In this work, we
significantly accelerate sequential Monte Carlo samplers by adopting the L-BFGS
Hessian approximation which represents the state-of-the-art in full-batch
optimisation techniques. The L-BFGS Hessian approximation has only linear
complexity in the parameter dimension and requires no additional posterior or
gradient evaluations. The resulting sequential Monte Carlo algorithm is
adaptive, parallelisable and well-suited to high-dimensional and multi-modal
settings, which we demonstrate in numerical experiments on challenging
posterior distributions.


  We observe that gradients computed via the reparameterization trick are in
direct correspondence with solutions of the transport equation in the formalism
of optimal transport. We use this perspective to compute (approximate) pathwise
gradients for probability distributions not directly amenable to the
reparameterization trick: Gamma, Beta, and Dirichlet. We further observe that
when the reparameterization trick is applied to the Cholesky-factorized
multivariate Normal distribution, the resulting gradients are suboptimal in the
sense of optimal transport. We derive the optimal gradients and show that they
have reduced variance in a Gaussian Process regression task. We demonstrate
with a variety of synthetic experiments and stochastic variational inference
tasks that our pathwise gradients are competitive with other methods.


  Detecting the thermal emission from double neutron star merger events is a
challenging task because of the quick fading of the observed flux. In order to
create an efficient observing strategy for their observing method it is crucial
to know their intrinsic rate. Unfortunately, the numerous models existing today
predict this rate on a vary wide range. Hence, our goal in this paper is to
investigate the effect of different level of approximations on the relative
rate predictions. Also, we study the effect of distinct ejecta mass lay-outs on
the light curve. We find that the ratio of the expected kilonova detections of
the spherical to axisymmetrical models is 6:1 (or 2:1, depending on the input
parameter set applied in our work). Nevertheless, the light curve shape is only
slightly affected by the various ejecta alignments. This means that different
ejecta layouts can produce light curves with similar shapes making it a
challenging task to infer the structure of the matter outflow. Thus, we
conclude that the uncertainty in the rate predictions arising from the various
ejecta mass distribution models is negligible compared to the errors present in
other input parameters (e.g. binary neutron star merger rate). In addition, we
show that up to moderate redshifts (z < 0.2) the redshift distribution type
(observed or uniform in volume) does not affect the expected relative rate
estimations.


  We have investigated skyrmions in N-component quantum Hall systems. We find
that SU(N) skyrmions are the lowest energy charged excitations for filling
factors \nu = 1,2,...,N-1 for small enough symmetry breaking terms. N>2
skyrmions can be realized in Si QH systems based on the (110) or (111)
interfaces of Si, or perhaps in Si (100) systems, where the spin and valley
isospin together provide an SU(4)-symmetry, or in multilayer QH systems. We
also present Hartree-Fock results for a phenomenological easy-axis
SU(2)-breaking model appropriate to valley degeneracy.


  We demonstrate transport and evaporative cooling of several atomic clouds in
a chain of magnetic Ioffe-Pritchard traps moving at a low speed ($<1$~m/s). The
trapping scheme relies on the use of a magnetic guide for transverse
confinement and of magnets fixed on a conveyor belt for longitudinal trapping.
This experiment introduces a new approach for parallelizing the production of
Bose-Einstein condensates as well as for the realization of a continuous atom
laser.


  Online advertising driven by auctions brings billions of dollars in revenue
for social networking services and e-commerce platforms. GSP auctions, which
are simple and easy to understand for advertisers, have almost become the
benchmark for ad auction mechanisms in the industry. However, most GSP-based
industrial practices assume that the user click only relies on the ad itself,
which overlook the effect of external items, referred to as externalities.
Recently, DNA has attempted to upgrade GSP with deep neural networks and models
local externalities to some extent. However, it only considers set-level
contexts from auctions and ignores the order and displayed position of ads,
which is still suboptimal. Although VCG-based multi-slot auctions (e.g., VCG,
WVCG) make it theoretically possible to model global externalities (e.g., the
order and positions of ads and so on), they lack an efficient balance of both
revenue and social welfare. In this paper, we propose novel auction mechanisms
named Neural Multi-slot Auctions (NMA) to tackle the above-mentioned
challenges. Specifically, we model the global externalities effectively with a
context-aware list-wise prediction module to achieve better performance. We
design a list-wise deep rank module to guarantee incentive compatibility in
end-to-end learning. Furthermore, we propose an auxiliary loss for social
welfare to effectively reduce the decline of social welfare while maximizing
revenue. Experiment results on both offline large-scale datasets and online A/B
tests demonstrate that NMA obtains higher revenue with balanced social welfare
than other existing auction mechanisms (i.e., GSP, DNA, WVCG) in industrial
practice, and we have successfully deployed NMA on Meituan food delivery
platform.


  I discuss the possibility that the quark-gluon plasma at strong coupling
admits a description in terms of a black hole in asymptotically anti-de Sitter
space.


  In this paper we address the question of the optimal design for the Purcell
3-link swim-mer. More precisely we investigate the best link length ratio which
maximizes its displace-ment. The dynamics of the swimmer is expressed as an
ODE, using the Resistive Force Theory [13]. Among a set of optimal strategies
of deformation (strokes), we provide an asymptotic estimate of the displacement
for small deformations, from which we derive the optimal link ratio. Numerical
simulations are in good agreement with this theoretical esti-mate, and also
cover larger amplitudes of deformation. Compared with the classical design of
the Purcell swimmer, we observe a gain in displacement of roughly 60%.


  Another manifestation of the Aharonov-Bohm effect is introduced to chemistry,
in fact to nuclear dynamics and high-resolution molecular spectroscopy. As
demonstrated, the overall rotation of a symmetric-top molecule influences the
dynamics of an internal vibrational motion in a way that is analogous to the
presence of a solenoid carrying magnetic flux. To a good approximation, the
low-energy rovibrational energy-level structure of the quasistructural
molecular ion H5+ can be understood entirely in terms of this effect.


  Exact models for Bianchi VI0 spacetimes with multiple scalar fields with
exponential potentials have been derived and analysed. It has been shown that
these solutions, when they exist, attract neighbouring solutions in the two
cases corresponding to interacting and non-interacting fields. Unlike the
results obtained in a previous work dealing with the late-time inflationary
behaviour of Bianchi VI0 cosmologies, the knowledge of exact solutions has made
possible to study in detail the occurrence of inflation before the asymptotic
regime. As happened in preceding works, here as well inflation is more likely
to happen with a higher number of non-interacting fields or a lower number of
interacting scalar fields.


  Bifurcations of one dimensional dynamical systems are discussed based on some
ultradiscrete equations. The ultradiscrete equations are derived from normal
forms of one-dimensional nonlinear differential equations, each of which has
saddle-node, transcritical, or supercritical pitchfork bifurcations. An
additional bifurcation, which is similar to flip bifurcation, is found in
ultradiscrete equations for supercritical pitchfork bifurcation. Dynamical
properties of these ultradiscrete bifurcations can be characterized with
graphical analysis. As an example of application of our treatment, we focus on
an ultradiscrete equation of FitzHugh-Nagumo model, and discuss its dynamical
properties.


  The branching ratio of the lightest Higgs decay into $b\bar b$,
$\tau\bar\tau$ and $c\bar c$ is sensitive to supersymmetric effects. We include
in this work the effects of CP phases on the Higgs decays. Specifically we
compute the deviation of the CP phase dependent branching ratio from the
standard model result. The analysis includes the full one loop corrections of
fermion masses including CP phases involving the gluino, the chargino and the
neutralino exchanges. The analysis shows that the supersymmetric effects with
CP phases can change the branching ratios by as much as 100% for the lightest
Higgs decay into $b\bar b$ and $\tau\bar\tau$ with similar results holding for
the heavier Higgs decays. A detailed analysis is also given of the effects of
CP phases on the Higgs decays into $c\bar c$. The deviations of $R_{b/\tau}$
and $R_{b/c}$ from the standard model result are investigated as a possible
signature of supersymmetry and CP effects. Thus a measurement of the decays of
the higgs into $b\bar b$, $\tau\bar \tau$ and $c\bar c$ may provide important
clues regarding the existence of supersymmetry and CP phases.


  Bose-Einstein condensation (BEC) in a gas has now been achieved. Alkali atoms
($^{87}Rb$, $^{23}Na$ and $^{7}Li$) have been cooled to the point of
condensation (temperature of 100 nK) using laser cooling and trapping, followed
by magnetic trapping and evaporative cooling. This important experimental
result has also renewed the interest on theoretical studies of BEC. In this
contribution we discuss the statistical mechanics of the trapped BEC at zero
temperature. In particular, we study the stability of the condensate by using a
variational method with local and non-local interaction between the particles.


  The `theoretical' existence of traversable Lorentzian wormholes in the
classical, macroscopic world is plagued by the violation of the well--known
energy conditions of General Relativity. In this brief article we show : (i)
how the extent of violation can be quantified using certain volume integrals
(ii) whether this `amount of violation' can be minimised for some specific
cut--and--paste geometric constructions. Examples and possibilities are also
outlined.


  We review recent work on the structure of the fermion mass matrices in
supergravity effective superstrings. They are generally given at low energy by
non-trivial functions of the gauge singlet moduli fields. Interesting
structures appear in particular if they are homogeneous functions of zero
degree in the moduli. In this case we find Yukawa matrices very similar to the
ones obtained by imposing a $U(1)$ family symmetry to reproduce the observed
hierarchy of masses and mixing angles. The role of the $U(1)$ symmetry is
played here by the modular symmetry. The Flavor Changing Neutral Currents
effects at the Planck scale coming from the soft terms are identically zero. A
dynamical scenario is discussed which allow to generate the observed
hierarchies.


  Copula-based time series models implicitly assume a finite Markov order. In
reality a time series may not follow the Markov property. We modify the
copula-based time series models by introducing a moving aggregate (MAG) part
into the model updating equation. The functional form of the MAG-part is given
as the inverse of a conditional copula. The resulting MAG-modified
Autoregressive Copula-Based Time Series model (MAGMAR-Copula) is discussed in
detail and distributional properties are derived in a D-vine framework. The
model nests the classical ARMA model as well as the copula-based time series
model. The modeling performance is compared with the model from
\cite{mcneil2022time} modeling US inflation. Our model is competitive in terms
of information criteria. It is a generalization of ARMA and also copula-based
time series models and is in spirit similar to other moving average time series
models such as ARMA and GARCH.


  A study of ray trajectories was undertaken for the Tamm medium which
represents the spacetime of a cosmic spinning string, under the
geometric-optics approximation. Our numerical studies revealed that: (i) rays
never cross the string's boundary; (ii) the Tamm medium supports evanescent
waves in regions of phase space that correspond to those regions of the
string's spacetime which could support closed timelike curves; and (iii) a
spinning string can be slightly visible while a non-spinning string is almost
perfectly invisible.


  Thermoacoustic systems are complex systems where the interactions between the
hydrodynamic, acoustic and heat release rate fluctuations lead to diverse
dynamics such as chaos, intermittency, and ordered dynamics. Such complex
interactions cause catastrophically high-amplitude acoustic pressure
oscillations and the emergence of order in the spatio-temporal dynamics,
referred to as thermoacoustic instability. In this work, we use multilayer
networks to study the spatial pattern of inter-subsystem interactions between
the vorticity dynamics and thermoacoustic power generated due to
acoustically-coupled combustion in a bluff-body stabilized turbulent dump
combustor. We construct a two-layered network where the layers represent the
thermoacoustic power and vorticity fields. The inter-layer links are determined
using cross-variable short-window correlations between vorticity and
thermoacoustic power fluctuations at any two locations in the flow field.
Analyzing the topology of inter-layer networks, using network properties such
as degree correlations and link-rank distributions, helps us infer the spatial
inhomogeneities in inter-subsystem interactions and unravel the fluid
mechanical processes involved during different dynamical states. We show that,
during chaotic dynamics, interactions between subsystems are non-localized and
spread throughout the flow field of the combustor. During the state of
thermoacoustic instability (order), we find that intense interactions occur in
between regions of coherent vortex shedding and thermoacoustic power generation
and we understand that these processes are strongly and locally coupled.
Moreover, we discover that such dense inter-layer connections emerge in spatial
pockets in the dump plane of the combustor during the state of intermittency
much prior to the onset of order.


  We apply a "color" tripole ansatz for describing the $D$-term of the proton.
By fitting the experimental data of the vector meson J/$\psi$ and $\phi$
photoproductions near the thresholds, we firstly obtained the gluonic $D$-term
of the proton. $D_g(0)$ is estimated to be $-2.16\pm0.42$ for J/$\psi$ and
$-1.31\pm0.48$ for $\phi$, and the mechanical root mean square radius of proton
is estimated to be $0.61\pm0.29$ fm for $\phi$ and $0.42\pm0.11$ fm for
J/$\psi$.


  During disasters, crisis, and emergencies the public relies on online
services provided by official authorities to receive timely alerts, trustworthy
information, and access to relief programs. It is therefore crucial for the
authorities to reduce risks when accessing their online services. This includes
catering to secure identification of service, secure resolution of name to
network service, and content security and privacy as a minimum base for
trustworthy communication.
  In this paper, we take a first look at Alerting Authorities (AA) in the US
and investigate security measures related to trustworthy and secure
communication. We study the domain namespace structure, DNSSEC penetration, and
web certificates. We introduce an integrative threat model to better understand
whether and how the online presence and services of AAs are harmed. As an
illustrative example, we investigate 1,388 Alerting Authorities. We observe
partial heightened security relative to the global Internet trends, yet find
cause for concern as about 78% of service providers fail to deploy measures of
trustworthy service provision. Our analysis shows two major shortcomings.
First, how the DNS ecosystem is leveraged: about 50% of organizations do not
own their dedicated domain names and are dependent on others, 55% opt for
unrestricted-use namespaces, which simplifies phishing, and less than 4% of
unique AA domain names are secured by DNSSEC, which can lead to DNS poisoning
and possibly to certificate misissuance. Second, how Web PKI certificates are
utilized: 15% of all hosts provide none or invalid certificates, thus cannot
cater to confidentiality and data integrity, 64% of the hosts provide domain
validation certification that lack any identity information, and shared
certificates have gained on popularity, which leads to fate-sharing and can be
a cause for instability.


  We study how gravitational duality acts on rotating solutions, using the
Kerr-NUT black hole as an example. After properly reconsidering how to take
into account both electric (i.e. mass-like) and magnetic (i.e. NUT-like)
sources in the equations of general relativity, we propose a set of definitions
for the dual Lorentz charges. We then show that the Kerr-NUT solution has
non-trivial such charges. Further, we clarify in which respect Kerr's source
can be seen as a mass M with a dipole of NUT charges.


  Behaviour of a weekly self-interacting scalar field with a small mass in the
de Sitter background is investigated using the stochastic approach (including
the case of a double-well interaction potential). Existence of the de Sitter
invariant equilibrium quantum state of the scalar field in the presence of
interaction is shown for any sign of the mass term. The stochastic approach is
further developed to produce a method of calculation of an arbitrary
anomalously large correlation function of the scalar field in the de Sitter
background, and expressions for the two-point correlation function in the
equilibrium state, correlation time and spatial physical correlation radius are
presented. The latter does not depend on time that implies that the
characteristic size of domains with positive and negative values of the scalar
field remains the same on average in the equilibrium state in spite of the
expansion of the $t=const$ hypersurface of the de Sitter space-time.


  We present a proposal for the estimation of B\"uttiker-Landauer traversal
time based on the visibility of transmission current. We analyze the tunneling
phenomena with a time-dependent potential and obtain the time-dependent
transmission current. We found that the visibility is directly connected to the
traversal time. Furthermore, this result is valid not only for rectangular
potential barrier but also for general form of potential to which the WKB
approximation is applicable . We compared these results with the numerical
values obtained from the simulation of Nelson's quantum mechanics. Both of them
fit together and it shows our method is very effective to measure
experimentally the traversal time.


  Models for predicting the time of a future event are crucial for risk
assessment, across a diverse range of applications. Existing time-to-event
(survival) models have focused primarily on preserving pairwise ordering of
estimated event times, or relative risk. Model calibration is relatively under
explored, despite its critical importance in time-to-event applications. We
present a survival function estimator for probabilistic predictions in
time-to-event models, based on a neural network model for draws from the
distribution of event times, without explicit assumptions on the form of the
distribution. This is done like in adversarial learning, but we achieve
learning without a discriminator or adversarial objective. The proposed
estimator can be used in practice as a means of estimating and comparing
conditional survival distributions, while accounting for the predictive
uncertainty of probabilistic models. Extensive experiments show that the
proposed model outperforms existing approaches, trained both with and without
adversarial learning, in terms of both calibration and concentration of
time-to-event distributions.


  The multi-layered nature of societal developments poses a challenge in the
teaching and learning of history of mathematics. As an attempt to tackle this
challenge we experimented with the use of Knowledge-Maps. The focus of our
interest lies on the use of these Maps to support meaningful learning (Ausubel,
2000) as well as on their role as facilitating tools in oral exams. In this
article we seek to relay practical experiences we have made with Knowledge-Maps
in our teaching and to report findings of an exploratory study we conducted in
a seminar about history of mathematics.


  It is shown that the notion of fundamental elements can be extended to_any_,
i.e. not necessarily homaloidal, web of rational surfaces in a
three-dimensional projective space. A Cremonian space-time can then be viewed
as an_emergent_ phenomenon when the condition of "homaloidity" of the
corresponding web is satisfied. The point is illustrated by a couple of
particular types of "almost-homaloidal" webs of quadratic surfaces. In the
first case, the quadrics have a line and two distinct points in common and the
corresponding pseudo-Cremonian manifold is endowed with just two spatial
dimensions. In the second case, the quadrics share six distinct points, no
three of them collinear, that lie in quadruples in three different planes, and
the corresponding pseudo-Cremonian configuration features three time
dimensions. In both the cases, the limiting process of the emergence of generic
Cremonian space-times is explicitly demonstrated.


  Kademlia is currently the most widely used searching algorithm in P2P
(peer-to-peer) networks. This work studies an essential question about Kademlia
from a mathematical perspective: how long does it take to locate a node in the
network? To answer it, we introduce a random graph K and study how many steps
are needed to locate a given vertex in K using Kademlia's algorithm, which we
call the routing time. Two slightly different versions of K are studied. In the
first one, vertices of K are labelled with fixed IDs. In the second one,
vertices are assumed to have randomly selected IDs. In both cases, we show that
the routing time is about c*log(n), where n is the number of nodes in the
network and c is an explicitly described constant.


  A picture of thermal QCD phase change based on the analogy with
metal-to-insulator transition of Anderson type was proposed in the past. In
this picture, a low-$T$ thermal state is akin to a metal with deeply infrared
(IR) Dirac modes abundant and extended, while a high-$T$ state is akin to an
insulator with IR modes depleted and localized below a mobility edge
$\lambda_{\text A} > 0$. Here we argue that, while $\lambda_{\text A}$ exists
in QCD, a high-$T$ state is not an insulator in such an analogy. Rather, it is
a critical state arising due to a new singular mobility edge at $\lambda_{\text
IR}=0$. This new mobility edge appears upon the transition into the recently
proposed IR phase. As a key part of such a metal-to-critical scenario, we
present evidence using pure-glue QCD that deeply infrared Dirac modes in the IR
phase extend to arbitrarily long distances. This is consistent with our
previous suggestion that the IR phase supports scale invariance in the
infrared. We discuss the role of Anderson-like aspects in this thermal regime
and emphasize that the combination of gauge field topology and disorder plays a
key role in shaping its IR physics. Our conclusions are conveyed by the
structure of Dirac spectral non-analyticities.


  We compute spin-orbit torques (SOTs) in strained PtMnSb from first
principles. We consider both tetragonal strain and shear strain. We find a
strong linear dependence of the field-like SOTs on these strains, while the
antidamping SOT is only moderately sensitive to shear strain and even
insensitive to tetragonal strain. We also study the dependence of the SOT on
the magnetization direction. In order to obtain analytical expressions suitable
for fitting our numerical \textit{ab-initio} results we derive a general
expansion of the SOT in terms of all response tensors that are allowed by
crystal symmetry. Our expansion includes also higher-order terms beyond the
usually considered lowest order. We find that the dependence on the strain is
much smaller for the higher-order terms than for the lowest order terms. In
order to judge the sensitivity of the SOT on the exchange correlation potential
we compute the SOT in both GGA and LDA. We find that the higher-order terms
depend significantly on the exchange-correlation potential, while the lowest
order terms are insensitive to it. Since the higher-order terms are small in
comparison to the lowest order terms the total SOT is insensitive to the
exchange correlation potential in strained PtMnSb.


  This paper uncovers and exploits a link between a central object in harmonic
analysis, the so-called Schur functions, and the very hot topic of symmetry
protected topological phases of quantum matter. This connection is found in the
setting of quantum walks, i.e. quantum analogs of classical random walks. We
prove that topological indices classifying symmetry protected topological
phases of quantum walks are encoded by matrix Schur functions built out of the
walk. This main result of the paper reduces the calculation of these
topological indices to a linear algebra problem: calculating symmetry indices
of finite-dimensional unitaries obtained by evaluating such matrix Schur
functions at the symmetry protected points $\pm1$. The Schur representation
fully covers the complete set of symmetry indices for 1D quantum walks with a
group of symmetries realizing any of the symmetry types of the tenfold way. The
main advantage of the Schur approach is its validity in the absence of
translation invariance, which allows us to go beyond standard Fourier methods,
leading to the complete classification of non-translation invariant phases for
typical examples.


  Tests of gauge coupling unification require knowledge of thresholds between
the weak scale and the high scale of unification. If these scales are far
separated, as is the case in most unification scenarios considered in the
literature, the task can be factorized into IR and UV analyses. We advocate
"$\Delta\lambda$ plots" as an efficient IR analysis projected to the high
scale. The data from these plots gives an immediate qualitative guide to the
size of threshold corrections needed at the high scale (e.g., the indices of
high-scale representations) and provides precise quantitative data needed to
test the viability of hypothesized high-scale unification theories. Such an
approach shows more clearly the reasonable prospects of non-supersymmetric
grand unification in large rank groups, and also shows the low summed values of
high-scale threshold corrections required for supersymmetric unification. The
latter may imply tuned cancellations of high-scale thresholds in theories based
on weak-scale supersymmetry. For that reason we view non-supersymmetric
unification to be just as viable as supersymmetric unification when confining
ourselves only to the question of reasonable high-scale threshold corrections
needed for exact unification. We illustrate these features for a
non-supersymmetric $SO(10)$ graund unified theory and a supersymmetric $SU(5)$
theory.


  The mass and distance functions of free-floating planets (FFPs) would give
major insights into the formation and evolution of planetary systems, including
any systematic differences between those in the disk and bulge. We show that
the only way to measure the mass and distance of individual FFPs over a broad
range of distances is to observe them simultaneously from two observatories
separated by $D\sim {\cal O}(0.01\,AU)$ (to measure their microlens parallax
$\pi_{\rm E}$) and to focus on the finite-source point-lens (FSPL) events
(which yield the Einstein radius $\theta_{\rm E}$). By combining the existing
KMTNet 3-telescope observatory with a 0.3m $4\,{\rm deg}^2$ telescope at L2, of
order 130 such measurements could be made over four years, down to about $M\sim
6\,M_\oplus$ for bulge FFPs and $M\sim 0.7\,M_\oplus$ for disk FFPs. The same
experiment would return masses and distances for many bound planetary systems.
A more ambitious experiment, with two 0.5m satellites (one at L2 and the other
nearer Earth) and similar camera layout but in the infrared, could measure
masses and distances of sub-Moon mass objects, and thereby probe (and
distinguish between) genuine sub-Moon FFPs and sub-Moon ``dwarf planets'' in
exo-Kuiper Belts and exo-Oort Clouds.


  This attempt to "derive" space is part of the Random Dynamics project. The
Random Dynamics philosophy is that what we observe at our low energy level can
be interpreted as some Taylor tail of the physics taking place at a higher
energy level, and all the concepts like numbers, space, symmetry, as well as
the known physical laws, emerge from a "fundamental world machinery" being a
most general, random mathematical structure.
  Here we concentrate on obtaining spacetime in such a Random Dynamics way.
Because of quantum mechanics, we get space identified with about half the
dimension of the phase space of a very extended wave packet, which we call "the
Snake".
  In the last section we also explain locality from diffeomorphism symmetry.


  In this paper we describe, formalize, implement, and experimentally evaluate
a novel transaction re-identification attack against official foreign-trade
statistics releases in Brazil. The attack's goal is to re-identify the
importers of foreign-trade transactions (by revealing the identity of the
company performing that transaction), which consequently violates those
importers' fiscal secrecy (by revealing sensitive information: the value and
volume of traded goods). We provide a mathematical formalization of this fiscal
secrecy problem using principles from the framework of quantitative information
flow (QIF), then carefully identify the main sources of imprecision in the
official data releases used as auxiliary information in the attack, and model
transaction re-construction as a linear optimization problem solvable through
integer linear programming (ILP). We show that this problem is NP-complete, and
provide a methodology to identify tractable instances. We exemplify the
feasibility of our attack by performing 2,003 transaction re-identifications
that in total amount to more than \$137M, and affect 348 Brazilian companies.
Further, since similar statistics are produced by other statistical agencies,
our attack is of broader concern.


  Fashion knowledge helps people to dress properly and addresses not only
physiological needs of users, but also the demands of social activities and
conventions. It usually involves three mutually related aspects of: occasion,
person and clothing. However, there are few works focusing on extracting such
knowledge, which will greatly benefit many downstream applications, such as
fashion recommendation. In this paper, we propose a novel method to
automatically harvest fashion knowledge from social media. We unify three tasks
of occasion, person and clothing discovery from multiple modalities of images,
texts and metadata. For person detection and analysis, we use the off-the-shelf
tools due to their flexibility and satisfactory performance. For clothing
recognition and occasion prediction, we unify the two tasks by using a
contextualized fashion concept learning module, which captures the dependencies
and correlations among different fashion concepts. To alleviate the heavy
burden of human annotations, we introduce a weak label modeling module which
can effectively exploit machine-labeled data, a complementary of clean data. In
experiments, we contribute a benchmark dataset and conduct extensive
experiments from both quantitative and qualitative perspectives. The results
demonstrate the effectiveness of our model in fashion concept prediction, and
the usefulness of extracted knowledge with comprehensive analysis.


  We propose a harmonic linear response (HLR) method to calculate the phonon
dispersion relations of two-dimensional (2D) layers from equilibrium
simulations at finite temperature. This HLR approach is based on the linear
response of the system, as derived from the analysis of its centroid density in
equilibrium path integral simulations. In the classical limit, this approach is
closely related to those methods that study vibrational properties by the
diagonalization of the covariance matrix of atomic fluctuations. The validity
of the method is tested in the calculation of the phonon dispersion relations
of a graphene monolayer, a graphene bilayer, and graphane. Anharmonic effects
in the phonon dispersion relations of graphene are demonstrated by the
calculation of the temperature dependence of the following observables: the
kinetic energy of the carbon atoms, the vibrational frequency of the optical
$E_{2g}$ mode, and the elastic moduli of the layer.


  Although Federated Learning (FL) is promising to enable collaborative
learning among Artificial Intelligence of Things (AIoT) devices, it suffers
from the problem of low classification performance due to various heterogeneity
factors (e.g., computing capacity, memory size) of devices and uncertain
operating environments. To address these issues, this paper introduces an
effective FL approach named AdaptiveFL based on a novel fine-grained width-wise
model pruning strategy, which can generate various heterogeneous local models
for heterogeneous AIoT devices. By using our proposed reinforcement
learning-based device selection mechanism, AdaptiveFL can adaptively dispatch
suitable heterogeneous models to corresponding AIoT devices on the fly based on
their available resources for local training. Experimental results show that,
compared to state-of-the-art methods, AdaptiveFL can achieve up to 16.83%
inference improvements for both IID and non-IID scenarios.


  In [Arch. Ration. Mech. Anal. 213 (2014), 981-991] it has been proved that in
the Newtonian $N$-body problem, given a minimal central configuration $a$ and
an arbitrary configuration $x$, there exists a completely parabolic orbit
starting on $x$ and asymptotic to the homothetic parabolic motion of $a$,
furthermore such an orbit is a free time minimizer of the action functional. In
this article we extend this result in abundance of completely parabolic motions
by proving that under the same hypothesis it is possible to get that the
completely parabolic motion starting at $x$ has zero angular momentum. We
achieve this by characterizing the rotation invariant weak KAM solutions as
those defining a lamination on the configuration space by free time minimizers
with zero angular momentum.


  Sketching algorithms or sketches have emerged as a promising alternative to
the traditional packet sampling-based network telemetry solutions. At a high
level, they are attractive because of their high resource efficiency and
accuracy guarantees. While there have been significant recent advances in
various aspects of sketching for networking tasks, many fundamental challenges
remain unsolved that are likely stumbling blocks for adoption. Our contribution
in this paper is in identifying and formulating these research challenges
across the ecosystem encompassing network operators, platform
vendors/developers, and algorithm designers. We hope that these serve as a
necessary fillip for the community to enable the broader adoption of
sketch-based telemetry.


  In paper I of this series we discuss how magnification bias distorts the 3D
correlation function by enhancing the observed correlation in the line-of-sight
(LOS) orientation, especially on large scales. This lensing anisotropy is
distinctive, making it possible to separately measure the galaxy-galaxy,
galaxy-magnification {\it and} magnification-magnification correlations. Here
we extend the discussion to the power spectrum and also to redshift space. In
real space, pairs oriented close to the LOS direction are not protected against
nonlinearity even if the pair separation is large; this is because nonlinear
fluctuations can enter through gravitational lensing at a small transverse
separation (or i.e. impact parameter). The situation in Fourier space is
different: by focusing on a small wavenumber $k$, as is usually done, linearity
is guaranteed because both the LOS and transverse wavenumbers must be small.
This is why magnification distortion of the galaxy correlation appears less
severe in Fourier space. Nonetheless, the effect is non-negligible, especially
for the transverse Fourier modes, and should be taken into account in
interpreting precision measurements of the galaxy power spectrum, for instance
those that focus on the baryon oscillations. The lensing induced anisotropy of
the power spectrum has a shape that is distinct from the more well known
redshift space anisotropies due to peculiar motions and the Alcock-Paczynski
effect. The lensing anisotropy is highly localized in Fourier space while
redshift space distortions are more spread out. This means that one could
separate the magnification bias component in real observations, implying that
potentially it is possible to perform a gravitational lensing measurement
without measuring galaxy shapes.


  Apart from the few tens of stellar-mass black holes discovered in binary
systems, an order of $10^8$ isolated black holes (IBHs) are believed to be
lurking in our Galaxy. Although some IBHs are able to accrete matter from the
interstellar medium, the accretion flow is usually weak and thus radiatively
inefficient, which results in significant material outflow. We study electron
acceleration generated by the shock formed between this outflow and the
surrounding material, and the subsequent radio synchrotron emission from
accelerated electrons. By numerically calculating orbits of IBHs to obtain
their spatial and velocity distributions, we estimate the number of IBHs
detectable by surveys using SKA1-mid (SKA2) as $\sim 30$ ($\sim 700$) for the
most optimistic case. The SKA's parallax measurements may accurately give their
distances, possibly shedding light on the properties of the black holes in our
Galaxy.