  The spatial location of cells within tissues and organs is crucial for the
manifestation of their specific functions.Spatial transcriptomics technology
enables comprehensive measurement of the gene expression patterns in tissues
while retaining spatial information. However, current popular spatial
transcriptomics techniques either have shallow sequencing depth or low
resolution. We present stEnTrans, a deep learning method based on Transformer
architecture that provides comprehensive predictions for gene expression in
unmeasured areas or unexpectedly lost areas and enhances gene expression in
original and inputed spots. Utilizing a self-supervised learning approach,
stEnTrans establishes proxy tasks on gene expression profile without requiring
additional data, mining intrinsic features of the tissues as supervisory
information. We evaluate stEnTrans on six datasets and the results indicate
superior performance in enhancing spots resolution and predicting gene
expression in unmeasured areas compared to other deep learning and traditional
interpolation methods. Additionally, Our method also can help the discovery of
spatial patterns in Spatial Transcriptomics and enrich to more biologically
significant pathways. Our source code is available at
https://github.com/shuailinxue/stEnTrans.


  We study an interacting $\lambda\,\phi^4_{\star}$ scalar field defined on
Snyder-de Sitter space. Due to the noncommutativity as well as the curvature of
this space, the renormalization of the two-point function differs from the
commutative case. In particular, we show that the theory in the limit of small
curvature and noncommutativity is described by a model similar to the
Grosse-Wulkenhaar one. Moreover, very much akin to what happens in the
Grosse-Wulkenhaar model, our computation demonstrates that there exists a fixed
point in the renormalization group flow of the harmonic and mass terms.


  A new superconformal mechanics with OSp(4|2) symmetry is obtained by gauging
the U(1) isometry of a superfield model. It is the one-particle case of the new
N=4 super Calogero model recently proposed in arXiv:0812.4276 [hep-th].
Classical and quantum generators of the osp(4|2) superalgebra are constructed
on physical states. As opposed to other realizations of N=4 superconformal
algebras, all supertranslation generators are linear in the odd variables,
similarly to the N=2 case. The bosonic sector of the component action is
standard one-particle (dilatonic) conformal mechanics accompanied by an
SU(2)/U(1) Wess-Zumino term, which gives rise to a fuzzy sphere upon
quantization. The strength of the conformal potential is quantized.


  We consider the dynamics of a small spherical particle driven through an
unbounded viscoelastic shear flow by an external force. We give analytical
solutions to both the mobility problem (velocity of forced particle) and the
resistance problem (force on fixed particle), valid to second order in the
dimensionless Deborah and Weissenberg numbers, which represent the elastic
relaxation time of the fluid relative to the rate of translation and the
imposed shear rate. We find a shear-induced lift at $O({\rm Wi})$, a modified
drag at $O({\rm De}^2)$ and $O({\rm Wi}^2)$, and a second lift that is
orthogonal to the first, at $O({\rm Wi}^2)$. The relative importance of these
effects depends strongly on the orientation of the forcing relative to the
shear. We discuss how these forces affect the terminal settling velocity in an
inclined shear flow. We also describe a new basis set of symmetric Cartesian
tensors, and demonstrate how they enable general tensorial perturbation
calculations such as the present theory. In particular this scheme allows us to
write down a solution to the inhomogenous Stokes equations, required by the
perturbation expansion, by a sequence of algebraic manipulations well suited to
computer implementation.


  Most of the research effort on image-based place recognition is designed for
urban environments. In bucolic environments such as natural scenes with low
texture and little semantic content, the main challenge is to handle the
variations in visual appearance across time such as illumination, weather,
vegetation state or viewpoints. The nature of the variations is different and
this leads to a different approach to describing a bucolic scene. We introduce
a global image descriptor computed from its semantic and topological
information. It is built from the wavelet transforms of the image semantic
edges. Matching two images is then equivalent to matching their semantic edge
descriptors. We show that this method reaches state-of-the-art image retrieval
performance on two multi-season environment-monitoring datasets: the
CMU-Seasons and the Symphony Lake dataset. It also generalises to urban scenes
on which it is on par with the current baselines NetVLAD and DELF.


  In-context Learning (ICL) has emerged as a powerful paradigm for performing
natural language tasks with Large Language Models (LLM) without updating the
models' parameters, in contrast to the traditional gradient-based finetuning.
The promise of ICL is that the LLM can adapt to perform the present task at a
competitive or state-of-the-art level at a fraction of the cost. The ability of
LLMs to perform tasks in this few-shot manner relies on their background
knowledge of the task (or task priors). However, recent work has found that,
unlike traditional learning, LLMs are unable to fully integrate information
from demonstrations that contrast task priors. This can lead to performance
saturation at suboptimal levels, especially for subjective tasks such as
emotion recognition, where the mapping from text to emotions can differ widely
due to variability in human annotations. In this work, we design experiments
and propose measurements to explicitly quantify the consistency of proxies of
LLM priors and their pull on the posteriors. We show that LLMs have strong yet
inconsistent priors in emotion recognition that ossify their predictions. We
also find that the larger the model, the stronger these effects become. Our
results suggest that caution is needed when using ICL with larger LLMs for
affect-centered tasks outside their pre-training domain and when interpreting
ICL results.


  We consider a class of sparse random matrices which includes the adjacency
matrix of the Erd\H{o}s-R\'enyi graph $\mathcal{G}(N,p)$. We show that if
$N^{\varepsilon} \leq Np \leq N^{1/3-\varepsilon}$ then all nontrivial
eigenvalues away from 0 have asymptotically Gaussian fluctuations. These
fluctuations are governed by a single random variable, which has the
interpretation of the total degree of the graph. This extends the result [19]
on the fluctuations of the extreme eigenvalues from $Np \geq N^{2/9 +
\varepsilon}$ down to the optimal scale $Np \geq N^{\varepsilon}$. The main
technical achievement of our proof is a rigidity bound of accuracy
$N^{-1/2-\varepsilon} \, (Np)^{-1/2}$ for the extreme eigenvalues, which avoids
the $(Np)^{-1}$-expansions from [9,19,24]. Our result is the last missing
piece, added to [8, 12, 19, 24], of a complete description of the eigenvalue
fluctuations of sparse random matrices for $Np \geq N^{\varepsilon}$.


  The electromagnetic transient following a binary neutron star merger is known
as a kilonova (KN). Owing to rapid expansion velocities and small ejecta
masses, KNe rapidly transition into the Non-Local Thermodynamic Equilibrium
(NLTE) regime. In this study, we present synthetic NLTE spectra of KNe from 5
to 20 days after merger using the \texttt{SUMO} spectral synthesis code. We
study three homogeneous composition, 1D multi-zone models with characteristic
electron fractions of $Y_e \sim 0.35, 0.25$ and $0.15$. We find that emission
features in the spectra tend to emerge in windows of reduced line blocking, as
the ejecta are still only partially transparent even at 20 days. For the $Y_e
\sim 0.35$ (lanthanide-free) ejecta, we find that the neutral and singly
ionised species of Rb, Sr, Y and Zr dominate the spectra, all with good
potential for identification. We directly test and confirm an impact of Sr on
the 10000 angstrom spectral region in lanthanide-free ejecta, but also see that
its signatures may be complex. We suggest the Rb I $\rm{5p^{1}}$- $\rm{5s^{1}}$
7900 angstrom transition as a candidate for the $\lambda_0 \sim$ 7500--7900
angstrom P-Cygni feature in AT2017gfo. For the $Y_e \sim 0.25$ and $0.15$
compositions, lanthanides are dominant in the spectral formation, in particular
Nd, Sm, and Dy. We identify key processes in KN spectral formation, notably
that scattering and fluorescence play important roles even up to 20 days after
merger, implying that the KN ejecta are not yet optically thin at this time.


  Anick proved that every q-mild Hopf algebra up to homotopy is isomorphic to a
primitively-generated chain Hopf algebra. We provide a new proof, that involves
extensive use of the Bockstein spectral sequence.


  This work discusses an important issue in the area of human resource
management by proposing a novel model for creation and evaluation of software
teams. The model consists of several assessments, including a technical test, a
quality of life test and a psychological-sociological test. Since the technical
test requires particular organizational specifications and cannot be examined
without reference to a specific company, only the sociological test and the
quality of life tests are extensively discussed in this work. Two strategies
are discussed for assigning roles in a project. Initially, six software
projects were selected, and after extensive analysis of the projects, two
projects were chosen and correctives actions were applied. An empirical
evaluation was also conducted to assess the model effectiveness. The
experimental results demonstrate that the application of the model improved the
productivity of project teams.


  Let A be a quasi-finite R-algebra (i.e., a direct limit of module finite
algebras) with identity. Let I_i, i=0,...,m, be two-sided ideals of A,
\GL_n(A,I_i) the principal congruence subgroup of level I_i in GL_n(A) and
E_n(A,I_i) be the relative elementary subgroup of level I_i. We prove a
multiple commutator formula
  [E_n(A,I_0),\GL_n(A,I_1),& \GL_n(A, I_2),..., \GL_n(A, I_m)] =
[E_n(A,I_0),E_n(A,I_1),E_n(A, I_2),..., E_n(A, I_m)],
  which is a broad generalization of the standard commutator formulas.


  We argue that the freezing transition scenario, previously explored in the
statistical mechanics of 1/f-noise random energy models, also determines the
value distribution of the maximum of the modulus of the characteristic
polynomials of large N x N random unitary (CUE) matrices. We postulate that our
results extend to the extreme values taken by the Riemann zeta-function zeta(s)
over sections of the critical line s=1/2+it of constant length and present the
results of numerical computations in support. Our main purpose is to draw
attention to possible connections between the statistical mechanics of random
energy landscapes, random matrix theory, and the theory of the Riemann zeta
function.


  To broaden the understanding of classical Cepheid structure, evolution and
atmospheres, we have extended our continuing secret lives of Cepheids program
by obtaining XMM/Chandra X-ray observations, and Hubble space telescope (HST) /
cosmic origins spectrograph (COS) FUV-UV spectra of the bright, nearby Cepheids
Polaris, {\delta} Cep and {\beta} Dor. Previous studies made with the
international ultraviolet explorer (IUE) showed a limited number of UV emission
lines in Cepheids. The well-known problem presented by scattered light
contamination in IUE spectra for bright stars, along with the excellent
sensitivity & resolution combination offered by HST/COS, motivated this study,
and the spectra obtained were much more rich and complex than we had ever
anticipated. Numerous emission lines, indicating 10^4 K up to ~3 x 10^5 K
plasmas, have been observed, showing Cepheids to have complex, dynamic outer
atmospheres that also vary with the photospheric pulsation period. The FUV line
emissions peak in the phase range {\phi} ~ 0.8-1.0 and vary by factors as large
as 10x. A more complete picture of Cepheid outer atmospheres is accomplished
when the HST/COS results are combined with X-ray observations that we have
obtained of the same stars with XMM-Newton & Chandra. The Cepheids detected to
date have X-ray luminosities of log Lx ~ 28.5-29.1 ergs/sec, and plasma
temperatures in the 2-8 x 10^6 K range. Given the phase-timing of the enhanced
emissions, the most plausible explanation is the formation of a
pulsation-induced shocks that excite (and heat) the atmospheric plasmas
surrounding the photosphere. A pulsation-driven {\alpha}^2 equivalent dynamo
mechanism is also a viable and interesting alternative. However, the tight
phase-space of enhanced emission (peaking near 0.8-1.0 {\phi}) favor the shock
heating mechanism hypothesis.


  [Abridged] We examine the physical properties of 173 cluster members in
CL1358+62 (z=0.33) from HST WFPC2 imaging taken in the F606W & F814W filters
(rest-frame B & V) over a 2.2x2.2 Mpc^2 field. Structural parameters are
measured for each member by fitting a PSF-convolved, two component model to
their 2D surface brightness distribution. We examine bulge+disk models using
three different bulge profiles (de Vaucouleurs, Sersic, and exponential), and
rigorously test the robustness of our results by analyzing several thousand
artificial galaxies in the same manner as the cluster data. Combining
structural parameters with ground-based spectroscopy, we test for correlations
between morphological characteristics, current star formation, total galaxy
colors, and cluster substructure. We find that: (1) Using a de Vaucouleurs
bulge, bulge-to-total ratios and Hubble type (-5<T<8) are strongly correlated
(99% CL), but the scatter is large and early-type spirals are not reliably
distinguished from E's and S0's based on (B/T). (2) The low luminosity
ellipticals in our sample are likely to be face-on S0 galaxies. (3) High galaxy
asymmetry and strong [OII] emission are strongly correlated for disk-dominated
members. (4) There exists a small population (~5%) of bulge-dominated members
whose significant [OII] emission (<-5A) suggest they harbor AGN. (5) At these
redshifts, determining the correct Sersic index can be highly unreliable.


  The inherent crystal anisotropy of hexagonal boron nitride (hBN) sustains
naturally hyperbolic phonon polaritons, i.e. polaritons that can propagate with
very large wavevectors within the material volume, thereby enabling optical
confinement to exceedingly small dimensions. Indeed, previous research has
shown that nanometer-scale truncated nanocone hBN cavities, with deep
subwavelength dimensions, support three-dimensionally confined optical modes in
the mid-infrared. Due to optical selection rules, only a few of many such modes
predicted theoretically have been observed experimentally via far-field
reflection and scattering-type scanning near-field optical microscopy. The
Photothermal induced resonance (PTIR) technique probes optical and vibrational
resonances overcoming weak far-field emission by leveraging an atomic force
microscope (AFM) probe to transduce local sample expansion due to light
absorption. Here we show that PTIR enables the direct observation of previously
unobserved, dark hyperbolic modes of hBN nanostructures. Leveraging these
optical modes could yield a new degree of control over the electromagnetic
near-field concentration, polarization and angular momentum in nanophotonic
applications.


  We use recent advances on the discretized sum-product problem to obtain new
bounds on the Hausdorff dimension of planar $(\alpha,2\alpha)$-Fursterberg
sets. This provides a quantitative improvement to the $2\alpha+\epsilon$ bound
of H\'era-Shmerkin-Yavicoli. In particular, we show that every
$1/2$-Furstenberg set has dimension at least $1 + 1/4536$.


  In this note, motivated by the Klebanov-Polyakov conjecture we investigate
the strongly coupled O(N) vector model at large $N$ on a squashed three-sphere
and its holographic relation to bulk gravity on asymptotically locally $AdS_4$
spaces. We present analytical results for the action of the field theory as the
squashing parameter $\alpha\to-1$, when the boundary becomes effectively one
dimensional. The dual bulk geometry is AdS-Taub-NUT space in the corresponding
limit. In this limit we solve the theory exactly and show that the action of
the strongly coupled boundary theory scales as $\ln(1+\alpha)/ (1+\alpha)^2$.
This result is remarkably close to the $-1/(1+\alpha)^2$ scaling of the
Einstein gravity action for AdS-Taub-NUT space. These results explain the
numerical agreement presented in hep-th/0503238, and the soft logarithmic
departure is interpreted as a prediction for the contribution due to higher
spin fields in the bulk $AdS_4$ geometry.


  We investigate the electronic structure and the optical absorption onset of
close-to-30\degree twisted hexagonal boron nitride bilayers. Our study is
carried out with a purposely developed tight-binding model validated against
DFT simulations. We demonstrate that approaching 30\degree (quasicrystal
limit), all bilayers sharing the same moir\'e supercell develop identical band
structures, irrespective of their stacking sequence. This band structure
features a bundle of flat bands laying slightly above the bottom conduction
state which is responsible for an intense peak at the onset of the absorption
spectrum. These results suggest the presence of strong, stable and
stacking-independent excitons in boron nitride 30\degree-twisted bilayers. By
carefully analyzing the electronic structure and its spatial distribution, we
elucidate the origin of these states as moir\'e-induced K-valley scattering due
to interlayer B$-$B coupling. We take advantage of the the physical
transparency of the tight-binding parameters to derive a simple triangular
model based on the B sublattice that accurately describes the emergence of the
bundle. Being our conclusions very general, we predict that a similar bundle
should emerge in other close-to-30{\degree} bilayers, like transition metal
dichalcogenides, shedding new light on the unique potential of 2D materials.


  Background: Here we investigate whether releasing COVID-19 vaccines in
limited quantities and at limited times boosted Italy's vaccination campaign in
2021. This strategy exploits insights from psychology and consumer marketing.
Methods: We built an original dataset covering 200 days of vaccination data in
Italy, including 'open day' events. Open-day events (in short: open days) are
instances where COVID-19 vaccines were released in limited quantities and only
for a specific day at a specified location (usually, a large pavilion or a
public building). Our dependent variables are the number of total and first
doses administered in proportion to the eligible population. Our key
independent variable is the presence of open-day events in a given region on a
specific day. We analyzed the data using regression with fixed effects for time
and region. The analysis was robust to alternative model specifications.
Findings: We find that when an open day event was organized, in proportion to
the eligible population, there was an average 0.39-0.44 percentage point
increase in total doses administered and a 0.30-0.33 percentage point increase
in first doses administered. These figures correspond to an average increase of
10,455-11,796 in total doses administered and 8,043-8,847 in the first doses
administered. Interpretation: Releasing vaccines in limited quantities and at
limited times by organizing open-day events was associated with an increase in
COVID-19 vaccinations in most Italian regions. These results call for wider
adoption of vaccination strategies based on the limited release of vaccines for
other infectious diseases or future pandemics.


  The simplest bounded potential is that of penetrable spheres, which takes a
positive finite value $\epsilon$ if the two spheres are overlapped, being 0
otherwise. In this paper we derive the cavity function to second order in
density and the fourth virial coefficient as functions of $T^*\equiv
k_BT/\epsilon$ (where $k_B$ is the Boltzmann constant and $T$ is the
temperature) for penetrable sphere fluids. The expressions are exact, except
for the function represented by an elementary diagram inside the core, which is
approximated by a polynomial form in excellent agreement with accurate results
obtained by Monte Carlo integration. Comparison with the hypernetted-chain
(HNC) and Percus-Yevick (PY) theories shows that the latter is better than the
former for $T^*\lesssim 1$ only. However, even at zero temperature (hard sphere
limit), the PY solution is not accurate inside the overlapping region, where no
practical cancelation of the neglected diagrams takes place. The exact fourth
virial coefficient is positive for $T^*\lesssim 0.73$, reaches a minimum
negative value at $T^*\approx 1.1$, and then goes to zero from below as
$1/{T^*}^4$ for high temperatures. These features are captured qualitatively,
but not quantitatively, by the HNC and PY predictions. In addition, in both
theories the compressibility route is the best one for $T^*\lesssim 0.7$, while
the virial route is preferable if $T^*\gtrsim 0.7$.


  We study the localization in the Hilbert space of a modified
Tomonaga-Luttinger model. For the standard version of this model, the states
are found to be extended in the basis of Slater determinants, representing the
eigenstates of the non-interacting system. The linear dispersion which leads to
the fact that these eigenstates are extended in the modified model is replaced
by one with random level spacings modeling the complicated one-particle spectra
of realistic models. The localization properties of the eigenstates are
studied. The interactions are simplified and an effective one-dimensional Lloyd
model is obtained. The effects of many-body energy correlations are studied
numerically. The eigenstates of the system are found to be localized in Fock
space for any strength of the interactions, but the localization is not
exponential.


  Deep reinforcement learning has achieved great strides in solving challenging
motion control tasks. Recently, there has been significant work on methods for
exploiting the data gathered during training, but there has been less work on
how to best generate the data to learn from. For continuous action domains, the
most common method for generating exploratory actions involves sampling from a
Gaussian distribution centred around the mean action output by a policy.
Although these methods can be quite capable, they do not scale well with the
dimensionality of the action space, and can be dangerous to apply on hardware.
We consider learning a forward dynamics model to predict the result,
($x_{t+1}$), of taking a particular action, ($u$), given a specific observation
of the state, ($x_{t}$). With this model we perform internal look-ahead
predictions of outcomes and seek actions we believe have a reasonable chance of
success. This method alters the exploratory action space, thereby increasing
learning speed and enables higher quality solutions to difficult problems, such
as robotic locomotion and juggling.


  As unmanned aircraft systems (UASs) continue to integrate into the U.S.
National Airspace System (NAS), there is a need to quantify the risk of
airborne collisions between unmanned and manned aircraft to support regulation
and standards development. Developing and certifying collision avoidance
systems often rely on the extensive use of Monte Carlo collision risk analysis
simulations using probabilistic models of aircraft flight. To train these
models, high performance computing resources are required. We've prototyped a
high performance computing workflow designed and deployed on the Lincoln
Laboratory Supercomputing Center to process billions of observations of
aircraft. However, the prototype has various computational and storage
bottlenecks that limited rapid or more comprehensive analyses and models. In
response, we have developed a novel workflow to take advantage of various job
launch and task distribution technologies to improve performance. The workflow
was benchmarked using two datasets of observations of aircraft, including a new
dataset focused on the environment around aerodromes. Optimizing how the
workflow was parallelized drastically reduced the execution time from weeks to
days.


  Semiparametric exponential family proposed by Ning et al. (2017) is an
extension of the parametric exponential family to the case with a nonparametric
base measure function. Such a distribution family has potential application in
some areas such as high dimensional data analysis. However, the methodology for
achieving the semiparametric efficiency has not been proposed in the existing
literature. In this paper, we propose a profile likelihood to efficiently
estimate both parameter and nonparametric function. Due to the use of the least
favorable curve in the procedure of profile likelihood, the semiparametric
efficiency is achieved successfully and the estimation bias is reduced
significantly. Moreover, by making the most of the structure information of the
semiparametric exponential family, the estimator of the least favorable curve
has an explicit expression. It ensures that the newly proposed profile
likelihood can be implemented and is computationally simple. Simulation studies
can illustrate that our proposal is much better than the existing methodology
for most cases under study, and is robust to the different model conditions.


  Employing constraints derived from the microscopic theory of the strong
interaction, we estimate the zero-temperature phase structure of dense
isospin-asymmetric matter with two quark flavors. We find indications that
strong-interaction matter along trajectories relevant for astrophysical
applications undergoes a first-order phase transition from a
color-superconducting phase to an ungapped quark-matter phase when the density
is increased. Such a phase transition is found to be absent in
isospin-symmetric matter. Moreover, by taking into account constraints from
$\beta$-equilibrium, charge neutrality, and color neutrality, we provide an
estimate for the speed of sound in neutron-star matter. Notably, we observe
that the speed of sound in neutron-star matter exceeds the asymptotic value
associated with the noninteracting quark gas and even increases towards lower
densities across a wide range, in agreement with recent results for
isospin-symmetric matter. Considering results from studies based on chiral
effective field theory at low densities, our findings suggest the existence of
a maximum in the speed of sound for $n/n_0 \lesssim 10$, where $n_0$ is the
nuclear saturation density.


  To understand global changes in the Earth system, scientists must generalize
globally from observations made locally and regionally. In land change science
(LCS), local field-based observations are costly and time consuming, and
generally obtained by researchers working at disparate local and regional
case-study sites chosen for different reasons. As a result, global synthesis
efforts in LCS tend to be based on non-statistical inferences subject to
geographic biases stemming from data limitations and fragmentation. Thus, a
fundamental challenge is the production of generalized knowledge that links
evidence of the causes and consequences of local land change to global patterns
and vice versa. The GLOBE system was designed to meet this challenge. GLOBE
aims to transform global change science by enabling new scientific workflows
based on statistically robust, globally relevant integration of local and
regional observations using an online social-computational and geovisualization
system. Consistent with the goals of Digital Earth, GLOBE has the capability to
assess the global relevance of local case-study findings within the context of
over 50 global biophysical, land-use, climate, and socio-economic datasets. We
demonstrate the implementation of one such assessment - a representativeness
analysis - with a recently published meta-study of changes in swidden
agriculture in tropical forests. The analysis provides a standardized indicator
to judge the global representativeness of the trends reported in the
meta-study, and a geovisualization is presented that highlights areas for which
sampling efforts can be reduced and those in need of further study. GLOBE will
enable researchers and institutions to rapidly share, compare, and synthesize
local and regional studies within the global context, as well as contributing
to the larger goal of creating a Digital Earth.


  We study central Z-boson production accompanied by rapidity gaps on either
side as a way to gauge Higgs weak boson fusion production at the LHC. We
analyse the possible backgrounds for the bb-decay mode and show that these can
be substantially reduced. Special attention is paid to the evaluation of the
gap survival factor, which is the major source of theoretical uncertainty in
the rate of H, Z and W central production events with rapidity gaps.


  Sketch-to-image (S2I) translation plays an important role in image synthesis
and manipulation tasks, such as photo editing and colorization. Some specific
S2I translation including sketch-to-photo and sketch-to-painting can be used as
powerful tools in the art design industry. However, previous methods only
support S2I translation with a single level of density, which gives less
flexibility to users for controlling the input sketches. In this work, we
propose the first multi-level density sketch-to-image translation framework,
which allows the input sketch to cover a wide range from rough object outlines
to micro structures. Moreover, to tackle the problem of noncontinuous
representation of multi-level density input sketches, we project the density
level into a continuous latent space, which can then be linearly controlled by
a parameter. This allows users to conveniently control the densities of input
sketches and generation of images. Moreover, our method has been successfully
verified on various datasets for different applications including face editing,
multi-modal sketch-to-photo translation, and anime colorization, providing
coarse-to-fine levels of controls to these applications.


  We make an exact field theoretical computation of the conformal anomaly for
two-dimensional submanifold observables. By including a scalar field in the
definition for the Wilson surface, as appropriate for a spontaneously broken
A_1 theory, we get a conformal anomaly which is such that N times it is equal
to the anomaly that was computed in hep-th/9901021 in the large N limit and
which relied on the AdS-CFT correspondence. We also show how the spherical
surface observable can be expressed as a conformal anomaly.


  In recent years, substantial progress has been achieved in learning-based
reconstruction of 3D objects. At the same time, generative models were proposed
that can generate highly realistic images. However, despite this success in
these closely related tasks, texture reconstruction of 3D objects has received
little attention from the research community and state-of-the-art methods are
either limited to comparably low resolution or constrained experimental setups.
A major reason for these limitations is that common representations of texture
are inefficient or hard to interface for modern deep learning techniques. In
this paper, we propose Texture Fields, a novel texture representation which is
based on regressing a continuous 3D function parameterized with a neural
network. Our approach circumvents limiting factors like shape discretization
and parameterization, as the proposed texture representation is independent of
the shape representation of the 3D object. We show that Texture Fields are able
to represent high frequency texture and naturally blend with modern deep
learning techniques. Experimentally, we find that Texture Fields compare
favorably to state-of-the-art methods for conditional texture reconstruction of
3D objects and enable learning of probabilistic generative models for texturing
unseen 3D models. We believe that Texture Fields will become an important
building block for the next generation of generative 3D models.


  Let $f\colon X\to S$ be an extremal contraction from a threefold with only
terminal singularities to a surface. We study local analytic structure such
contractions near degenerate fiber $C$ in the case when $C$ is irreducible and
$X$ has on $C$ only one non-Gorenstein point.


  The optical properties of transition metal dichalcogenides have previously
been modified at the nanoscale by using mechanical and electrical
nanostructuring. However, a clear experimental picture relating the local
electronic structure with emission properties in such structures has so far
been lacking. Here, we use a combination of scanning tunneling microscopy (STM)
and near-field photoluminescence (nano-PL) to probe the electronic and optical
properties of single nano-bubbles in bilayer heterostructures of WSe2 on MoSe2.
We show from tunneling spectroscopy that there are electronic states deeply
localized in the gap at the edge of such bubbles, which are independent of the
presence of chemical defects in the layers. We also show a significant change
in the local bandgap on the bubble, with a continuous evolution to the edge of
the bubble over a length scale of ~20 nm. Nano-PL measurements observe a
continuous redshift of the interlayer exciton on entering the bubble, in
agreement with the band to band transitions measured by STM. We use
self-consistent Schr\"odinger-Poisson (SP) simulations to capture the essence
of the experimental results and find that strong doping in the bubble region is
a key ingredient to achieving the observed localized states, together with
mechanical strain.


  This paper describes TOBY, a visualization tool that helps a user explore the
contents of an academic survey paper. The visualization consists of four
components: a hierarchical view of taxonomic data in the survey, a document
similarity view in the space of taxonomic classes, a network view of citations,
and a new paper recommendation tool. In this paper, we will discuss these
features in the context of three separate deployments of the tool.


  One important problem in constructing the reduced dynamics of molecular
systems is the accurate modeling of the non-Markovian behavior arising from the
dynamics of unresolved variables. The main complication emerges from the lack
of scale separations, where the reduced dynamics generally exhibits pronounced
memory and non-white noise terms. We propose a data-driven approach to learn
the reduced model of multi-dimensional resolved variables that faithfully
retains the non-Markovian dynamics. Different from the common approaches based
on the direct construction of the memory function, the present approach seeks a
set of non-Markovian features that encode the history of the resolved
variables, and establishes a joint learning of the extended Markovian dynamics
in terms of both the resolved variables and these features. The training is
based on matching the evolution of the correlation functions of the extended
variables that can be directly obtained from the ones of the resolved
variables. The constructed model essentially approximates the multi-dimensional
generalized Langevin equation and ensures numerical stability without empirical
treatment. We demonstrate the effectiveness of the method by constructing the
reduced models of molecular systems in terms of both one-dimensional and
four-dimensional resolved variables.


  We define a class of partial orders on a Coxeter group associated with sets
of reflections. In special cases, these lie between the left weak order and the
Bruhat order. We prove that these posets are graded by the length function and
that the projections on the right parabolic quotients are always order
preserving. We also introduce the notion of $k$-Bruhat graph, $k$-absolute
length and $k$-absolute order, proposing some related conjectures and problems.


  A physical model of Geant4-based simulation of galactic cosmic ray (GCR)
particles interaction with the lunar surface matter has been developed to
investigate the production rate of cosmogenic nuclei. In this model the GCRs,
mainly very high energy protons and $\alpha$ particles, bombard the surface of
the Moon and produce many secondary particles such as protons and neutrons. The
energies of proton and neutron at different depths are recorded and saved into
ROOT files, and the analytical expressions for the differential proton and
neutron fluxes are obtained through the best-fit procedure under the ROOT
software. To test the validity of this model, we calculate the production rates
of long-lived nuclei $^{10}$Be and $^{26}$Al in the Apollo 15 long drill core
by combining the above differential fluxes and the newly evaluated spallation
reaction cross sections. Numerical results show that the theoretical production
rates agree quite well with the measured data. It means that this model works
well. Therefore, it can be expected that this model can be used to investigate
the cosmogenic nuclei in lunar samples returned by Chinese lunar exploration
program and can be extended to study other objects, such as the meteorites and
the Earth's atmosphere.


  The mathematical model of orthodox quantum mechanics has been critically
examined and some deficiencies have been summarized. The model based on the
extended Hilbert space and free of these shortages has been proposed;
parameters being until now denoted as "hidden" have been involved. Some earlier
arguments against a hidden-variable theory have been shown to be false, too. In
the known Einstein-Bohr controversy Einstein has been shown to be true. The
extended model seems to be strongly supported also by the polarization
experiments performed by us ten years ago.


  In this work we consider the two-dimensional Dirac operator with general
local singular interactions supported on a closed curve. A systematic study of
the interaction is performed by decomposing it into a linear combination of
four elementary interactions: electrostatic, Lorentz scalar, magnetic, and a
fourth one which can be absorbed by using unitary transformations. We address
the self-adjointness and the spectral description of the underlying Dirac
operator, and moreover we describe its approximation by Dirac operators with
regular potentials.


  A surface that is the pointwise sum of circles in Euclidean space is either
coplanar or contains no more than 2 circles through a general point. A surface
that is the pointwise product of circles in the unit-quaternions contains
either 2, 3, 4, or 5 circles through a general point. A surface in a
unit-sphere of any dimension that contains 2 great circles through a general
point contains either 4, 5, 6, or infinitely many circles through a general
point. These are some corollaries from our classification of translational and
great Darboux cyclides. We use the combinatorics associated to the set of low
degree curves on such surfaces modulo numerical equivalence.


  Using photometry from Data Release 10 of the northern footprint of the Sloan
Digital Sky Survey, we detect two new stellar streams with lengths of between
$25\arcdeg$ and $50\arcdeg$. The streams, which we designate Hermus and Hyllus,
are at distances of between 15 and 23 kpc from the Sun and pass primarily
through Hercules and Corona Borealis. Stars in the streams appear to be metal
poor, with [Fe/H] $\sim -2.3$, though we cannot rule out metallicities as high
as [Fe/H] = -1.2. While Hermus passes within $1\arcdeg$ (in projection) of the
globular cluster NGC 6229, a roughly one magnitude difference in distance
modulus, combined with no signs of connecting with NGC 6229's Roche lobe, argue
against any physical association between the two. Though the two streams almost
certainly had different progenitors, similarities in preliminary orbit
estimates suggest that those progenitors may themselves have been a product of
a single accretion event.


  We consider partially directed walks crossing a $L\times L$ square weighted
according to their length by a fugacity $t$. The exact solution of this model
is computed in three different ways, depending on whether $t$ is less than,
equal to or greater than 1. In all cases a complete expression for the dominant
asymptotic behaviour of the partition function is calculated. The model admits
a dilute to dense phase transition, where for $0 < t < 1$ the partition
function scales exponentially in $L$ whereas for $t>1$ the partition function
scales exponentially in $L^2$, and when $t=1$ there is an intermediate scaling
which is exponential in $L \log{L}$.


  The Feigenbaum constants $\alpha$ and $\delta$ for the three-site
antiferromagnetic Ising spin model on Husimi tree are calculated. It is shown
that the numerical values of these constants for this real physical system
coincide with the famous universal Feigenbaum constants with high accuracy. The
quantitative description from ordering to chaos is also obtained.


  We investigate convergence properties of generalized Walsh series associated
with signals $f\in L^1[0,1]$. We also show how the dependence of the
generalized Walsh bases on $N\times N$ unitary matrices allows for applications
in signal encoding and encryption, provided the signals are piece-wise constant
on $N$-adic subintervals of $[0,1]$.


  Learning concepts from natural high-dimensional data (e.g., images) holds
potential in building human-aligned and interpretable machine learning models.
Despite its encouraging prospect, formalization and theoretical insights into
this crucial task are still lacking. In this work, we formalize concepts as
discrete latent causal variables that are related via a hierarchical causal
model that encodes different abstraction levels of concepts embedded in
high-dimensional data (e.g., a dog breed and its eye shapes in natural images).
We formulate conditions to facilitate the identification of the proposed causal
model, which reveals when learning such concepts from unsupervised data is
possible. Our conditions permit complex causal hierarchical structures beyond
latent trees and multi-level directed acyclic graphs in prior work and can
handle high-dimensional, continuous observed variables, which is well-suited
for unstructured data modalities such as images. We substantiate our
theoretical claims with synthetic data experiments. Further, we discuss our
theory's implications for understanding the underlying mechanisms of latent
diffusion models and provide corresponding empirical evidence for our
theoretical insights.


  In this paper, we prove the existence of a countable family of regular
spherically symmetric self-similar solutions to focusing energy super-critical
semi-linear wave equations \begin{equation*}
  \partial_{tt}u-\Delta u=|u|^{p-1}u \qquad \text{in} \,\, \mathbb{R}^{N},
\end{equation*} where $N\geq 3$, $1+\frac{4}{N-2}<p$, and, if $N\geq 4$, $p
\leq 1+\frac{4}{N-3}$. This was previously known only in the case $N=3$, for
integer $p$ (see Bizo\'{n}, Maison and Wasserman \cite{BMW}). We also study the
asymptotics of these solutions.


  We present the problem of approximating the time-evolution operator
$e^{-i\hat{H}t}$ to error $\epsilon$, where the Hamiltonian $\hat{H}=(\langle
G|\otimes\hat{\mathcal{I}})\hat{U}(|G\rangle\otimes\hat{\mathcal{I}})$ is the
projection of a unitary oracle $\hat{U}$ onto the state $|G\rangle$ created by
another unitary oracle. Our algorithm solves this with a query complexity
$\mathcal{O}\big(t+\log({1/\epsilon})\big)$ to both oracles that is optimal
with respect to all parameters in both the asymptotic and non-asymptotic
regime, and also with low overhead, using at most two additional ancilla
qubits. This approach to Hamiltonian simulation subsumes important prior art
considering Hamiltonians which are $d$-sparse or a linear combination of
unitaries, leading to significant improvements in space and gate complexity,
such as a quadratic speed-up for precision simulations. It also motivates
useful new instances, such as where $\hat{H}$ is a density matrix. A key
technical result is `qubitization', which uses the controlled version of these
oracles to embed any $\hat{H}$ in an invariant $\text{SU}(2)$ subspace. A large
class of operator functions of $\hat{H}$ can then be computed with optimal
query complexity, of which $e^{-i\hat{H}t}$ is a special case.


  Quantum Field Theory is plagued by divergences in the attempt to calculate
physical quantities. Standard techniques of regularization and renormalization
are used to keep under control such a problem. In this paper we would like to
use a different scheme based on Modified Dispersion Relations (MDR) to remove
infinities appearing in one loop approximation in contrast to what happens in
conventional approaches. In particular, we apply the MDR regularization to the
computation of the entropy of a Schwarzschild black hole from one side and the
Zero Point Energy (ZPE) of the graviton from the other side. The graviton ZPE
is connected to the cosmological constant by means of of the Wheeler-DeWitt
equation.


  We present the analytical and numerical results of a random walk on the
family of small-world graphs. The average access time shows a crossover from
the regular to random behavior with increasing distance from the starting point
of the random walk. We introduce an {\em independent step approximation}, which
enables us to obtain analytic results for the average access time. We observe a
scaling relation for the average access time in the degree of the nodes. The
behavior of average access time as a function of $p$, shows striking similarity
with that of the {\em characteristic length} of the graph. This observation may
have important applications in routing and switching in networks with large
number of nodes.


  Nuclear binding energies are investigated in two variants of the Skyrme
model: the first replaces the usual Skyrme term with a term that is sixth order
in derivatives, and the second includes a potential that is quartic in the pion
fields. Solitons in the first model are shown to deviate significantly from
ans\"atze previously assumed in the literature. The binding energies obtained
in both models are lower than those obtained from the standard Skyrme model,
and those obtained in the second model are close to the experimental values.


  We present a new class of metrics for unrooted phylogenetic $X$-trees derived
from the Gromov-Hausdorff distance for (compact) metric spaces. These metrics
can be efficiently computed by linear or quadratic programming. They are robust
under NNI-operations, too. The local behavior of the metrics shows that they
are different from any formerly introduced metrics. The performance of the
metrics is briefly analised on random weighted and unweighted trees as well as
random caterpillars.


  Machine learning models are often implemented in cohort with humans in the
pipeline, with the model having an option to defer to a domain expert in cases
where it has low confidence in its inference. Our goal is to design mechanisms
for ensuring accuracy and fairness in such prediction systems that combine
machine learning model inferences and domain expert predictions. Prior work on
"deferral systems" in classification settings has focused on the setting of a
pipeline with a single expert and aimed to accommodate the inaccuracies and
biases of this expert to simultaneously learn an inference model and a deferral
system. Our work extends this framework to settings where multiple experts are
available, with each expert having their own domain of expertise and biases. We
propose a framework that simultaneously learns a classifier and a deferral
system, with the deferral system choosing to defer to one or more human experts
in cases of input where the classifier has low confidence. We test our
framework on a synthetic dataset and a content moderation dataset with biased
synthetic experts, and show that it significantly improves the accuracy and
fairness of the final predictions, compared to the baselines. We also collect
crowdsourced labels for the content moderation task to construct a real-world
dataset for the evaluation of hybrid machine-human frameworks and show that our
proposed learning framework outperforms baselines on this real-world dataset as
well.


  Detection and description of keypoints from an image is a well-studied
problem in Computer Vision. Some methods like SIFT, SURF or ORB are
computationally really efficient. This paper proposes a solution for a
particular case study on object recognition of industrial parts based on
hierarchical classification. Reducing the number of instances leads to better
performance, indeed, that is what the use of the hierarchical classification is
looking for. We demonstrate that this method performs better than using just
one method like ORB, SIFT or FREAK, despite being fairly slower.


  When faced with learning a set of inter-related tasks from a limited amount
of usable data, learning each task independently may lead to poor
generalization performance. Multi-Task Learning (MTL) exploits the latent
relations between tasks and overcomes data scarcity limitations by co-learning
all these tasks simultaneously to offer improved performance. We propose a
novel Multi-Task Multiple Kernel Learning framework based on Support Vector
Machines for binary classification tasks. By considering pair-wise task
affinity in terms of similarity between a pair's respective feature spaces, the
new framework, compared to other similar MTL approaches, offers a high degree
of flexibility in determining how similar feature spaces should be, as well as
which pairs of tasks should share a common feature space in order to benefit
overall performance. The associated optimization problem is solved via a block
coordinate descent, which employs a consensus-form Alternating Direction Method
of Multipliers algorithm to optimize the Multiple Kernel Learning weights and,
hence, to determine task affinities. Empirical evaluation on seven data sets
exhibits a statistically significant improvement of our framework's results
compared to the ones of several other Clustered Multi-Task Learning methods.


  Oxygen isotope substitution is performed in the perovskite cobalt oxide
(Pr$_{0.7}$Sm$_{0.3}$)$_{0.7}$Ca$_{0.3}$CoO${_3}$ which shows a sharp spin
state transition from the intermediate spin (IS) state to the low spin (LS)
state at a certain temperature. The transition temperature of the spin state
up-shifts with the substitution of $^{16}O$ by $^{18}$O from the resistivity
and magnetic susceptibility measurements. The up-shift value is 6.8 K and an
oxygen isotope exponent ($\alpha_S$) is about -0.8. The large oxygen isotope
effect indicates strong electron-phonon coupling in this material. The
substitution of $^{16}$O by $^{18}$O leads to a decrease in the frequency of
phonon and an increase in the effective mass of electron ($m$$^\ast$), so that
the bandwidth W is decreased and the energy difference between the different
spin states is increased. This is the reason why the $T_s$ is shifted to high
temperature with oxygen isotopic exchange.


  A graph is said to be diameter-$k$-critical if its diameter is $k$ and
removal of any of its edges increases its diameter. A beautiful conjecture by
Murty and Simon, says that every diameter-2-critical graph of order $n$ has at
most $\lfloor n^2/4\rfloor$ edges and equality holds only for $K_{\lceil n/2
\rceil,\lfloor n/2 \rfloor }$. Haynes et al. proved that the conjecture is true
for $\Delta\geq 0.7n$. They also proved that for $n>2000$, if $\Delta \geq
0.6789n$ then the conjecture is true. We will improve this bound by showing
that the conjecture is true for every $n$ if $\Delta\geq\ 0.676n$.


  It is well known that tuna fish in the ocean can dynamically morph their
median fins to achieve optimal hydrodynamic performance, e.g. linear
acceleration and maneuverability. In this study, based on the previous studies
about the median fin's hydrodynamic effects focusing on tethered conditions, we
continue to explore the hydrodynamic function of tuna morphing dorsal fin in
free swimming conditions for better approaching real-life situations.Here, we
developed a tuna-inspired robotic fish platform that can swim independently in
three dimensions, equipped with a biomimetic morphing dorsal fin magnetically
attached to the robotic fish. Based on the free-swimming robotic fish platform,
we investigated how the erected dorsal fin affects the speed, cost of transport
(COT), and robotic fish's yaw angle at different frequencies and amplitudes.
The erected dorsal fin plays a positive role in improving the yaw stability of
robotic fish. However, it shows little influence on the speed and COT in our
test. This remains to be further investigated in the future.


  Recently a class of molecular graphs, called altans, became a focus of
attention of several theoretical chemists and mathematicians. In this paper we
study primary iterated altans and show, among other things, their connections
with nanotubes and nanocaps. The question of classification of bipartite altans
is also addressed. Using the results of Gutman we are able to enumerate
Kekul\'e structures of several nanocaps of arbitrary length.


  Multimodal sentiment analysis (MSA) draws increasing attention with the
availability of multimodal data. The boost in performance of MSA models is
mainly hindered by two problems. On the one hand, recent MSA works mostly focus
on learning cross-modal dynamics, but neglect to explore an optimal solution
for unimodal networks, which determines the lower limit of MSA models. On the
other hand, noisy information hidden in each modality interferes the learning
of correct cross-modal dynamics. To address the above-mentioned problems, we
propose a novel MSA framework \textbf{M}odulation \textbf{M}odel for
\textbf{M}ultimodal \textbf{S}entiment \textbf{A}nalysis ({$ M^3SA $}) to
identify the contribution of modalities and reduce the impact of noisy
information, so as to better learn unimodal and cross-modal dynamics.
Specifically, modulation loss is designed to modulate the loss contribution
based on the confidence of individual modalities in each utterance, so as to
explore an optimal update solution for each unimodal network. Besides, contrary
to most existing works which fail to explicitly filter out noisy information,
we devise a modality filter module to identify and filter out modality noise
for the learning of correct cross-modal embedding. Extensive experiments on
publicly datasets demonstrate that our approach achieves state-of-the-art
performance.


  The problem of optical character recognition, OCR, has been widely discussed
in the literature. Having a hand-written text, the program aims at recognizing
the text. Even though there are several approaches to this issue, it is still
an open problem. In this paper we would like to propose an approach that uses
K-nearest neighbors algorithm, and has the accuracy of more than 90%. The
training and run time is also very short.


  The action of four-dimensional Horndeski gravity coupled to Born-Infeld
electromagnetic fields is given via the Kaluza-Klein process. Dyonic black hole
solution of the theory is constructed. The metric is devoid of singularity at
the origin independent of the parameter selections, this property is different
from the one of Einstein-Born-Infeld black holes. Thermodynamics of the black
hole is studied, thermodynamic quantities are calculated and the first law is
checked to be satisfied. Thermodynamic phase transitions of the black holes are
studied in extended phase space.


  We give a new semi-combinatorial proof for the equality of the number of
ballot permutations of length $n$ and the number of odd order permutations of
length $n$, which is due to Bernardi, Duplantier and Nadeau. Spiro conjectures
that the descent number of ballot permutations and certain cyclic weight of odd
order permutations of the same length are equi-distributed. We present a
bijection to establish a Toeplitz property for ballot permutations with any
fixed number of descents, and a Toeplitz property for odd order permutations
with any fixed cyclic weight. This allows us to refine Spiro's conjecture by
tracking the neighbors of the largest letter in permutations.


  We present updated constraints on the cosmological 3D power spectrum of
carbon monoxide CO(1-0) emission in the redshift range $2.4$-$3.4$. The
constraints are derived from the two first seasons of Carbon monOxide Mapping
Array Project (COMAP) Pathfinder line-intensity mapping observations aiming to
trace star-formation during the Epoch of Galaxy Assembly. These results improve
on the previous Early Science (ES) results through both increased data volume
and improved data processing methodology. On the methodological side, we now
perform cross-correlations between groups of detectors (''feed-groups''), as
opposed to cross-correlations between single feeds, and this new feed-group
pseudo power spectrum (FGPXS) is constructed to be more robust against
systematic effects. In terms of data volume, the effective mapping speed is
significantly increased due to an improved observational strategy as well as
better data selection methodology. The updated spherically- and field-averaged
FGPXS, $\tilde{C}(k)$, is consistent with zero, at a probability-to-exceed of
around $34\,\%$, with an excess of $2.7\,\sigma$ in the most sensitive bin. Our
power spectrum estimate is about an order of magnitude more sensitive in our
six deepest bins across ${0.09\,\mathrm{Mpc}^{-1} < k <
0.73\,\mathrm{Mpc}^{-1}}$, as compared to the feed-feed pseudo power spectrum
(FPXS) of COMAP ES. Each of these bins individually constrains the CO power
spectrum to ${kP_\mathrm{CO}(k)< 2400-4900\,\mathrm{\mu K^2 Mpc^{2}}}$ at
$95\,\%$ confidence. To monitor potential contamination from residual
systematic effects, we analyze a set of 312 difference-map null tests and find
that these are consistent with the instrumental noise prediction. In sum, these
results provide the strongest direct constraints on the cosmological 3D CO(1-0)
power spectrum published to date.


  This paper demonstrates the groundwork for the structure and nature of
Human-Robot Cognitive Coupling.The human mind is best at associating objects,
while digital devices can only compare. Successful communication between robot
and human agents involves translating human thought into unique numbers that
can be compared, and translating these numbers back into a form that humans can
understand. A cognitive robot is one capable of interpreting its own
operational data. Working from a D&D project standpoint with a telerobot, a
system is designed to utilize sensor data, extract usable profiles and
information from them, and then apply them as a means of self-testing for
nominal/off-nominal operations. If operations are considered off-nominal, the
telerobot begins a sequence of recalling and comparing previous off-nominal
occurrences to determine potential corrections. In this manner, a telerobot can
apply previous 'experiences' in order to learn without the need for operator
input. In the cognitive telerobotics, knowledge base is an important component
to store required information. In this paper, a tool-centric knowledge base
with learning capability is presented. In this paper, the knowledge can be
updated based on the reinforcement learning algorithm at both global and local
levels and knowledge fusion by coupling with operator's cognition.


  Totally asymmetric exclusion processes (TASEP) with open boundaries are known
to exhibit moving shocks or delocalised domain walls (DDW) for sufficiently
small equal injection and extraction rates. In contrast
  TASEPs in an inhomogeneous ring have been shown to display pinned shocks or
localised domain walls (LDW) under similar conditions [see, e.g., H. Hinsch and
E. Frey, {\em Phys. Rev. Lett.} {\bf 97}, 095701 (2006)]. By studying periodic
exclusion processes composed of a driven (TASEP) and a diffusive segments, we
uncover smooth transitions between LDW and DDW; the latter mimics DDWs in an
open TASEP, controlled essentially by the fluctuations in the diffusive
segment. Mean-field theory together with Monte Carlo simulations are employed
to characterize the emerging nonequilibrium steady states. Our studies provide
an explicit route to control the degree of shock fluctuations in periodic
systems, and should be relevant in cell biological transport where the
availability of molecular motors is the rate limiting constraint.


  We show that every smooth cubic hypersurface X in P^{n+1}, n> 1 is
algebraically elliptic in Gromov's sense. This gives the first examples of
non-rational projective manifolds elliptic in Gromov's sense. We also deduce
that the punctured affine cone over X is elliptic.


  Black-box attack methods aim to infer suitable attack patterns to targeted
DNN models by only using output feedback of the models and the corresponding
input queries. However, due to lack of prior and inefficiency in leveraging the
query and feedback information, existing methods are mostly query-intensive for
obtaining effective attack patterns. In this work, we propose a meta attack
approach that is capable of attacking a targeted model with much fewer queries.
Its high queryefficiency stems from effective utilization of meta learning
approaches in learning generalizable prior abstraction from the previously
observed attack patterns and exploiting such prior to help infer attack
patterns from only a few queries and outputs. Extensive experiments on MNIST,
CIFAR10 and tiny-Imagenet demonstrate that our meta-attack method can
remarkably reduce the number of model queries without sacrificing the attack
performance. Besides, the obtained meta attacker is not restricted to a
particular model but can be used easily with a fast adaptive ability to attack
a variety of models.The code of our work is available at
https://github.com/dydjw9/MetaAttack_ICLR2020/.


  A study of the transverse acoustic phonons on nano-structured AlN films has
been carried out by using high-resolution micro-Brillouin spectroscopy. Dense
films have been deposited by radio frequency (r.f.) magnetron sputtering under
ultra high vacuum at room temperature. Films with different morphologies were
prepared and investigated by transmission electron microscopy and Brillouin
Spectroscopy (equiaxed nano-sized, nano-columnar grains and amorphous phase).
Results show a dependence of the transverse modes on the nano-structure. The
nano-columnar film exhibits two transversal modes as expected for the AlN
w{\"u}rtzite while the equiaxed nano-sized and the amorphous films only exhibit
one isotropic transverse mode as expected in amorphous materials. One important
result is that the sound propagation velocity in the AlN amorphous phase is
higher than the one in the non-textured nano-crystalline phase. This phenomenon
has, however, already been observed in ferroelectric ceramics.


  The increasing presence of robots alongside humans, such as in human-robot
teams in manufacturing, gives rise to research questions about the kind of
behaviors people prefer in their robot counterparts. We term actions that
support interaction by reducing future interference with others as supportive
robot actions and investigate their utility in a co-located manipulation
scenario. We compare two robot modes in a shared table pick-and-place task: (1)
Task-oriented: the robot only takes actions to further its own task objective
and (2) Supportive: the robot sometimes prefers supportive actions to
task-oriented ones when they reduce future goal-conflicts. Our experiments in
simulation, using a simplified human model, reveal that supportive actions
reduce the interference between agents, especially in more difficult tasks, but
also cause the robot to take longer to complete the task. We implemented these
modes on a physical robot in a user study where a human and a robot perform
object placement on a shared table. Our results show that a supportive robot
was perceived as a more favorable coworker by the human and also reduced
interference with the human in the more difficult of two scenarios. However, it
also took longer to complete the task highlighting an interesting trade-off
between task-efficiency and human-preference that needs to be considered before
designing robot behavior for close-proximity manipulation scenarios.


  We investigate the application of efficient recursive numerical integration
strategies to models in lattice gauge theory from quantum field theory. Given
the coupling structure of the physics problems and the group structure within
lattice cubature rules for numerical integration, we show how to approach these
problems efficiently by means of Fast Fourier Transform techniques. In
particular, we consider applications to the quantum mechanical rotor and
compact U(1) lattice gauge theory, where the physical dimensions are two and
three. This proceedings article reviews our results presented in J. Comput.
Phys 443 (2021) 110527.


  This paper primarily focuses on analyzing the problems and proposing
solutions for the probabilistic truncation protocol in existing PPML works from
the perspectives of accuracy and efficiency. In terms of accuracy, we reveal
that precision selections recommended in some of the existing works are
incorrect. We conduct a thorough analysis of their open-source code and find
that their errors were mainly due to simplified implementation, more
specifically, fixed numbers are used instead of random numbers in probabilistic
truncation protocols. Based on this, we provide a detailed theoretical analysis
to validate our views. We propose a solution and a precision selection
guideline for future works. Regarding efficiency, we identify limitations in
the state-of-the-art comparison protocol, Bicoptor's (S\&P 2023) DReLU
protocol, which relies on the probabilistic truncation protocol and is heavily
constrained by the security parameter to avoid errors, significantly impacting
the protocol's performance. To address these challenges, we introduce the first
non-interactive deterministic truncation protocol, replacing the original
probabilistic truncation protocol. Additionally, we design a non-interactive
modulo switch protocol to enhance the protocol's security. Finally, we provide
a guideline to reduce computational and communication overhead by using only a
portion of the bits of the input, i.e., the key bits, for DReLU operations
based on different model parameters. With the help of key bits, the performance
of our DReLU protocol is further improved. We evaluate the performance of our
protocols on three GPU servers, and achieve a 10x improvement in DReLU
protocol, and a 6x improvement in the ReLU protocol over the state-of-the-art
work Piranha-Falcon (USENIX Sec 22). Overall, the performance of our end-to-end
(E2E) privacy-preserving machine learning (PPML) inference is improved by 3-4
times.


  This work presents a simple proof that the moduli space of complete integral
Gorenstein curves with a prescribed symmetric Weierstrass semigroup becomes a
weighted projective space, even for fields of positive characteristic, when the
associated monomial curve is a local complete intersection.


  Stationary states of Navier-Stokes fluids have been proposed to be described
equivalently by several alternative equations, besides the NS equation itself.
In particular equivalence between the NS evolution and a reversible. It is
natural to test whether, assuming the Chaotic Hypothesis, the Fluctuation
Theorem can be applied to the reversible flows. Here an example is provided
which also leads to the possibility of testing the prediction of the
fluctuation theorem even in systems evolving irreversibly.


  The purpose of this paper is to construct a generalized r-matrix structure of
finite dimensional systems and an approach to obtain the algebro-geometric
solutions of integrable nonlinear evolution equations (NLEEs). Our starting
point is a generalized Lax matrix instead of usual Lax pair. The generalized
r-matrix structure and Hamiltonian functions are presented on the basis of
fundamental Poisson bracket. It can be clearly seen that various nonlinear
constrained (c-) and restricted (r-) systems, such as the c-AKNS, c-MKdV,
c-Toda, r-Toda, c-Levi, etc, are derived from the reduction of this structure.
All these nonlinear systems have {\it r}-matrices, and are completely
integrable in Liouville's sense. Furthermore, our generalized structure is
developed to become an approach to obtain the algebro-geometric solutions of
integrable NLEEs. Finally, the two typical examples are considered to
illustrate this approach: the infinite or periodic Toda lattice equation and
the AKNS equation with the condition of decay at infinity or periodic boundary.


  We consider the Poisson equation with homogeneous Dirichlet conditions in a
family of domains in $R^{n}$ indexed by a small parameter $\epsilon$. The
domains depend on $\epsilon$ only within a ball of radius proportional to
$\epsilon$ and, as $\epsilon$ tends to zero, they converge in a self-similar
way to a domain with a conical boundary singularity. We construct an expansion
of the solution as a series of fractional powers of $\epsilon$, and prove that
it is not just an asymptotic expansion as $\epsilon\to0$, but that, for small
values of $\epsilon$, it converges normally in the Sobolev space $H^{1}$. The
phenomenon that solutions to boundary value problems on singularly perturbed
domains may have convergent expansions is the subject of the Functional
Analytic Approach by Lanza de Cristoforis and his collaborators. This approach
was originally adopted to study small holes shrinking to interior points of a
smooth domain and heavily relies on integral representations obtained through
layer potentials. To relax all regularity assumptions, we forgo boundary layer
potentials and instead exploit expansions in terms of eigenfunctions of the
Laplace-Beltrami operator on the intersection of the cone with the unit sphere.
Our analysis is based on a two-scale cross-cutoff ansatz for the solution.
Specifically, we write the solution as a sum of a function in the slow variable
multiplied by a cutoff function depending on the fast variable, plus a function
in the fast variable multiplied by a cutoff function depending on the slow
variable. While the cutoffs are considered fixed, the two unknown functions are
solutions to a $2\times2$ system of partial differential equations that depend
on $\epsilon$ in a way that can be analyzed in the framework of generalized
power series when the right-hand side of the Poisson equation vanishes in a
neighborhood of the perturbation.


  We argue that de Sitter universes with a small cosmological constant are
entropically favored to have three spatial dimensions. The conclusion relies on
the causal-patch description of de Sitter space, where fiducial observers
experience local thermal equilibrium up to a stretched horizon, on the
holographic principle, and on some assumptions about the nature of gravity and
the constituents of Hawking/Unruh radiation.


  Spectral components of continuous squeezed fields are entangled. In this
article we review and clarify this phenomenon by analyzing systematically the
relations between the correlations of modes filtered from stationary continuous
fields and the cross power spectrum between the operators of the corresponding
spectral components. Moreover, we study the specific spectral components that
are filtered in homodyne or heterodyne detections and their entanglement
properties. In particular, we establish the equivalence between two-mode
squeezing variance and logarithmic negativity for the spectral components of
continuous stationary fields, thereby demonstrating that the measurement of the
homodyne or heterodyne spectrum is, in fact, a direct measurement of the
logarithmic negativity between specific spectral modes. As an illustrative
example, we apply these concepts to the analysis of entanglement in
ponderomotive squeezing.


  The $k$-dimensional Weisfeiler-Leman procedure ($k$-WL), which colors
$k$-tuples of vertices in rounds based on the neighborhood structure in the
graph, has proven to be immensely fruitful in the algorithmic study of Graph
Isomorphism. More generally, it is of fundamental importance in understanding
and exploiting symmetries in graphs in various settings. Two graphs are
$k$-WL-equivalent if the $k$-dimensional Weisfeiler-Leman procedure produces
the same final coloring on both graphs. 1-WL-equivalence is known as fractional
isomorphism of graphs, and the $k$-WL-equivalence relation becomes finer as $k$
increases.
  We investigate to what extent standard graph parameters are preserved by
$k$-WL-equivalence, focusing on fractional graph packing numbers. The integral
packing numbers are typically NP-hard to compute, and we discuss applicability
of $k$-WL-invariance for estimating the integrality gap of the LP relaxation
provided by their fractional counterparts.


  The typical size of computational meshes needed for realistic geometries and
high-speed flow conditions makes Computational Fluid Dynamics (CFD) impractical
for full-mission performance prediction and control. Reduced-Order Models
(ROMs) in low-speed aerodynamics have come a long way in terms of
reconstructing coherent flow patterns, thus enabling aerodynamic performance
prediction. While many examples of ROMs exist for low-speed aerodynamics, there
is no such broad literature for high-speed flows. We propose to use
physics-enhanced neural fields for prediction of the steady, supersonic flow
around a rocket for resolving: the bow shock profile, the boundary layer
gradients and the wake over a range of incidences. This approach can lead to
the construction of a computationally inexpensive, continuous aerothermodynamic
model of a rocket at realistic flight conditions with applications to
aerodynamic design and mission analysis and control. The use of neural fields
allows to quickly and efficiently sweep the Angle of Attack (AoA) in a
continuous manner, as compared to traditional CFD which requires running
simulations for each discrete incidence value.


  Consider an agent exploring an unknown graph in search of some goal state. As
it walks around the graph, it learns the nodes and their neighbors. The agent
only knows where the goal state is when it reaches it. How do we reach this
goal while moving only a small distance? This problem seems hopeless, even on
trees of bounded degree, unless we give the agent some help. This setting with
''help'' often arises in exploring large search spaces (e.g., huge game trees)
where we assume access to some score/quality function for each node, which we
use to guide us towards the goal. In our case, we assume the help comes in the
form of distance predictions: each node $v$ provides a prediction $f(v)$ of its
distance to the goal vertex. Naturally if these predictions are correct, we can
reach the goal along a shortest path. What if the predictions are unreliable
and some of them are erroneous? Can we get an algorithm whose performance
relates to the error of the predictions?
  In this work, we consider the problem on trees and give deterministic
algorithms whose total movement cost is only $O(OPT + \Delta \cdot ERR)$, where
$OPT$ is the distance from the start to the goal vertex, $\Delta$ the maximum
degree, and the $ERR$ is the total number of vertices whose predictions are
erroneous. We show this guarantee is optimal. We then consider a ''planning''
version of the problem where the graph and predictions are known at the
beginning, so the agent can use this global information to devise a search
strategy of low cost. For this planning version, we go beyond trees and give an
algorithms which gets good performance on (weighted) graphs with bounded
doubling dimension.


  We study the problem of Robust Least Squares Regression (RLSR) where several
response variables can be adversarially corrupted. More specifically, for a
data matrix X \in R^{p x n} and an underlying model w*, the response vector is
generated as y = X'w* + b where b \in R^n is the corruption vector supported
over at most C.n coordinates. Existing exact recovery results for RLSR focus
solely on L1-penalty based convex formulations and impose relatively strict
model assumptions such as requiring the corruptions b to be selected
independently of X.
  In this work, we study a simple hard-thresholding algorithm called TORRENT
which, under mild conditions on X, can recover w* exactly even if b corrupts
the response variables in an adversarial manner, i.e. both the support and
entries of b are selected adversarially after observing X and w*. Our results
hold under deterministic assumptions which are satisfied if X is sampled from
any sub-Gaussian distribution. Finally unlike existing results that apply only
to a fixed w*, generated independently of X, our results are universal and hold
for any w* \in R^p.
  Next, we propose gradient descent-based extensions of TORRENT that can scale
efficiently to large scale problems, such as high dimensional sparse recovery
and prove similar recovery guarantees for these extensions. Empirically we find
TORRENT, and more so its extensions, offering significantly faster recovery
than the state-of-the-art L1 solvers. For instance, even on moderate-sized
datasets (with p = 50K) with around 40% corrupted responses, a variant of our
proposed method called TORRENT-HYB is more than 20x faster than the best L1
solver.


  Using the Advanced Camera for Surveys on the Hubble Space Telescope, we have
obtained deep optical images reaching stars well below the oldest main sequence
turnoff in the spheroid, tidal stream, and outer disk of the Andromeda Galaxy.
We have reconstructed the star formation history in these fields by comparing
their color-magnitude diagrams to a grid of isochrones calibrated to Galactic
globular clusters observed in the same bands. Each field exhibits an extended
star formation history, with many stars younger than 10 Gyr but few younger
than 4 Gyr. Considered together, the star counts, kinematics, and population
characteristics of the spheroid argue against some explanations for its
intermediate-age, metal-rich population, such as a significant contribution
from stars residing in the disk or a chance intersection with the stream's
orbit. Instead, it is likely that this population is intrinsic to the inner
spheroid, whose highly-disturbed structure is clearly distinct from the
pressure-supported metal-poor halo that dominates farther from the galaxy's
center. The stream and spheroid populations are similar, but not identical,
with the stream's mean age being ~1 Gyr younger; this similarity suggests that
the inner spheroid is largely polluted by material stripped from either the
stream's progenitor or similar objects. The disk population is considerably
younger and more metal-rich than the stream and spheroid populations, but not
as young as the thin disk population of the solar neighborhood; instead, the
outer disk of Andromeda is dominated by stars of age 4 - 8 Gyr, resembling the
Milky Way's thick disk. The disk data are inconsistent with a population
dominated by ages older than 10 Gyr, and in fact do not require any stars older
than 10 Gyr.


  In this work we present numerical results concerning a time-delayed reservoir
computing scheme, where its single nonlinear node, is a Quantum-Dot spin
polarized Vertical Cavity Surface-Emitting Laser (QD s-VCSEL). The proposed
photonic neuromorphic scheme, exploits the complex temporal dynamics of
multiple energy states present in quantum dot materials and uses emission from
two discrete wavebands and two polarization states, so as to enhance
computational efficiency. The benchmark task used for this architecture, is the
equalization of a distorted 25Gbaud PAM-4 signal after 50Km of transmission at
1550nm. Results confirm that although typical ground-state emitting quantum-dot
nodes offer limited performance, due to bandwidth limitations; by exploiting
dual emission, we achieved a one-hundred-fold improvement in bit-error rate.
This performance boost can pave the way for the infiltration of quantum-dot
based devices in high-speed demanding neuromorphic driven applications.


  The second order quantum decoherence (SOQDC)is proposed as a novel
description for the loss of quantum coherence only reflected by second order
quantum correlations. By calculating the two-time correlation function, the
phenomenon of SOQDC is studied in details for a simple model, a two boson
system interacting with a reservoir composed of one or many bosons. The second
order quantum decoherence effects can be observed in the sketched cavity QED
experiment.


  We investigate long time asymptotics of the modified Camassa-Holm equation in
three transition zones under a nonzero background. The first transition zone
lies between the soliton region and the first oscillatory region, the second
one lies between the second oscillatory region and the fast decay region, and
possibly, the third one, namely, the collisionless shock region, that bridges
the first transition region and the first oscillatory region. Under a low
regularity condition on the initial data, we obtain Painlev\'e-type asymptotic
formulas in the first two transition regions, while the transient asymptotics
in the third region involves the Jacobi theta function. We establish our
results by performing a $\bar{\partial}$ nonlinear steepest descent analysis to
the associated Riemann-Hilbert problem.


  Finding optimal configurations for Stream Processing Systems (SPS) is a
challenging problem due to the large number of parameters that can influence
their performance and the lack of analytical models to anticipate the effect of
a change. To tackle this issue, we consider tuning methods where an
experimenter is given a limited budget of experiments and needs to carefully
allocate this budget to find optimal configurations. We propose in this setting
Bayesian Optimization for Configuration Optimization (BO4CO), an auto-tuning
algorithm that leverages Gaussian Processes (GPs) to iteratively capture
posterior distributions of the configuration spaces and sequentially drive the
experimentation. Validation based on Apache Storm demonstrates that our
approach locates optimal configurations within a limited experimental budget,
with an improvement of SPS performance typically of at least an order of
magnitude compared to existing configuration algorithms.


  Estimating reliable geometric model parameters from the data with severe
outliers is a fundamental and important task in computer vision. This paper
attempts to sample high-quality subsets and select model instances to estimate
parameters in the multi-structural data. To address this, we propose an
effective method called Latent Semantic Consensus (LSC). The principle of LSC
is to preserve the latent semantic consensus in both data points and model
hypotheses. Specifically, LSC formulates the model fitting problem into two
latent semantic spaces based on data points and model hypotheses, respectively.
Then, LSC explores the distributions of points in the two latent semantic
spaces, to remove outliers, generate high-quality model hypotheses, and
effectively estimate model instances. Finally, LSC is able to provide
consistent and reliable solutions within only a few milliseconds for general
multi-structural model fitting, due to its deterministic fitting nature and
efficiency. Compared with several state-of-the-art model fitting methods, our
LSC achieves significant superiority for the performance of both accuracy and
speed on synthetic data and real images. The code will be available at
https://github.com/guobaoxiao/LSC.


  Aims. Adaptive optics images are used to test the hypothesis that the
explosive BN/KL outflow from the Orion OMC1 cloud core was powered by the
dynamical decay of a non-hierarchical system of massive stars. Methods.
Narrow-band H2, [Fe II], and broad-band Ks obtained with the Gemini South
multi-conjugate adaptive optics (AO) system GeMS and near-infrared imager GSAOI
are presented. The images reach resolutions of 0.08 to 0.10", close to the
0.07" diffraction limit of the 8-meter telescope at 2.12 microns. Comparison
with previous AO-assisted observations of sub-fields and other ground-based
observations enable measurements of proper motions and the investigation of
morphological changes in H2 and [Fe II] features with unprecedented precision.
The images are compared with numerical simulations of compact, high-density
clumps moving ~1000 times their own diameter through a lower density medium at
Mach 1000. Results. Several sub-arcsecond H2 features and many [Fe II]
'fingertips' on the projected outskirts of the flow show proper motions of ~300
km/s. High-velocity, sub-arcsecond H2 knots ('bullets') are seen as far as 140"
from their suspected ejection site. If these knots propagated through the dense
Orion A cloud, their survival sets a lower bound on their densities of order
10^7 cm^-3, consistent with an origin within a few au of a massive star and
accelerated by a final multi-body dynamic encounter that ejected the BN object
and radio source I from OMC1 about 500 years ago. Conclusions. Over 120
high-velocity bow-shocks propagating in nearly all directions from the OMC1
cloud core provide evidence for an explosive origin for the BN/KL outflow
triggered by the dynamic decay of a non-hierarchical system of massive stars.
Such events may be linked to the origin of runaway, massive stars.


  Large multilingual models trained with self-supervision achieve
state-of-the-art results in a wide range of natural language processing tasks.
Self-supervised pretrained models are often fine-tuned on parallel data from
one or multiple language pairs for machine translation. Multilingual
fine-tuning improves performance on low-resource languages but requires
modifying the entire model and can be prohibitively expensive. Training a new
adapter on each language pair or training a single adapter on all language
pairs without updating the pretrained model has been proposed as a
parameter-efficient alternative. However, the former does not permit any
sharing between languages, while the latter shares parameters for all languages
and is susceptible to negative interference. In this paper, we propose training
language-family adapters on top of mBART-50 to facilitate cross-lingual
transfer. Our approach outperforms related baselines, yielding higher
translation scores on average when translating from English to 17 different
low-resource languages. We also show that language-family adapters provide an
effective method to translate to languages unseen during pretraining.


  Most saliency estimation methods aim to explicitly model low-level
conspicuity cues such as edges or blobs and may additionally incorporate
top-down cues using face or text detection. Data-driven methods for training
saliency models using eye-fixation data are increasingly popular, particularly
with the introduction of large-scale datasets and deep architectures. However,
current methods in this latter paradigm use loss functions designed for
classification or regression tasks whereas saliency estimation is evaluated on
topographical maps. In this work, we introduce a new saliency map model which
formulates a map as a generalized Bernoulli distribution. We then train a deep
architecture to predict such maps using novel loss functions which pair the
softmax activation function with measures designed to compute distances between
probability distributions. We show in extensive experiments the effectiveness
of such loss functions over standard ones on four public benchmark datasets,
and demonstrate improved performance over state-of-the-art saliency methods.


  We develop a pairing mean-field theory to describe the quantum dynamics of
the dissociation of molecular Bose-Einstein condensates into their constituent
bosonic or fermionic atoms. We apply the theory to one, two, and
three-dimensional geometries and analyze the role of dimensionality on the atom
production rate as a function of the dissociation energy. As well as
determining the populations and coherences of the atoms, we calculate the
correlations that exist between atoms of opposite momenta, including the column
density correlations in 3D systems. We compare the results with those of the
undepleted molecular field approximation and argue that the latter is most
reliable in fermionic systems and in lower dimensions. In the bosonic case we
compare the pairing mean-field results with exact calculations using the
positive-$P$ stochastic method and estimate the range of validity of the
pairing mean-field theory. Comparisons with similar first-principle simulations
in the fermionic case are currently not available, however, we argue that the
range of validity of the present approach should be broader for fermions than
for bosons in the regime where Pauli blocking prevents complete depletion of
the molecular condensate.


  We study the effects of numerical quadrature rules on error convergence rates
when solving Maxwell-type variational problems via the curl-conforming or edge
finite element method. A complete {\em a priori} error analysis for the case of
bounded polygonal and curved domains with non-homogeneous coefficients is
provided. We detail sufficient conditions with respect to mesh refinement and
precision for the quadrature rules so as to guarantee convergence rates
following that of exact numerical integration. On curved domains, we isolate
the error contribution to numerical quadrature rules.


  Turbulence is a major factor limiting the achievement of better tokamak
performance as it enhances the transport of particles, momentum and heat which
hinders the foremost objective of tokamaks. Hence, understanding and possibly
being able to control turbulence in tokamaks is of paramount importance, not to
mention our intellectual curiosity of it.


  Crowdsourcing has been widely used recently as an alternative to traditional
annotations that is costly and usually done by experts. However, crowdsourcing
tasks are not interesting by themselves, therefore, combining tasks with game
will increase both participants motivation and engagement. In this paper, we
have proposed a gamified crowdsourcing platform called TeleCrowd based on
Telegram Messenger to use its social power as a base platform and facilitator
for accomplishing crowdsourcing projects. Furthermore, to evaluate the
performance of the proposed platform, we ran an experimental crowdsourcing
project consisting of 500 informal Persian sentences in which participants were
supposed to provide candidates that were the formal equivalent of sentences or
qualify other candidates by upvoting or downvoting them. In this study, 2700
candidates and 21000 votes were submitted by the participants and a parallel
dataset using candidates with the highest points, sum of their upvotes and
downvotes, as the best candidates was built. As the evaluation, BLEU score of
0.54 was achieved on the collected dataset which shows that our proposed
platform can be used to create large corpora. Also, this platform is highly
efficient in terms of time period and cost price in comparison with other
related works, because the whole duration of the project was 28 days at a cost
of 40 dollars.


  Implicational bases (IBs) are a common representation of finite closure
systems and lattices, along with meet-irreducible elements. They appear in a
wide variety of fields ranging from logic and databases to Knowledge Space
Theory. Different IBs can represent the same closure system. Therefore, several
IBs have been studied, such as the canonical and canonical direct bases. In
this paper, we investigate the $D$-base, a refinement of the canonical direct
base. It is connected with the $D$-relation, an essential tool in the study of
free lattices. The $D$-base demonstrates desirable algorithmic properties, and
together with the $D$-relation, it conveys essential properties of the
underlying closure system. Hence, computing the $D$-base and the $D$-relation
of a closure system from another representation is crucial to enjoy its
benefits. However, complexity results for this task are lacking. In this paper,
we give algorithms and hardness results for the computation of the $D$-base and
$D$-relation. Specifically, we establish the $NP$-completeness of finding the
$D$-relation from an arbitrary IB; we give an output-quasi-polynomial time
algorithm to compute the $D$-base from meet-irreducible elements; and we obtain
a polynomial-delay algorithm computing the $D$-base from an arbitrary IB. These
results complete the picture regarding the complexity of identifying the
$D$-base and $D$-relation of a closure system.


  We have conducted a near-infrared monitoring campaign at the UK InfraRed
Telescope (UKIRT), of the Local Group spiral galaxy M33 (Triangulum). In this
paper, we present the dust and gas mass-loss rates by the pulsating Asymptotic
Giant Branch (AGB) stars and red supergiants (RSGs) across the stellar disc of
M33.


  One could begin a study like the present one by simply postulating that our
universe is four-dimensional. There are ample reasons for doing this.
Experience, observation and experiment all point to the fact that we inhabit a
four-dimensional universe. Another approach would be to show that
four-dimensions arise naturally from a reasonable model of the universe or
multiverse. After reviewing the causal set approach to discrete quantum gravity
in Section~1, we shall discuss the emergence of four-dimensions in Section~2.
We shall see that certain patterns of four arise that suggest the introduction
of a 4-dimensional discrete manifold. In the later sections we shall discuss
some consequences of this introduced framework. In particular, we will show
that quantum amplitudes can be employed to describe a multiverse dynamics.
Moreover, a natural unitary operator together with energy, position and
momentum operators will be introduced and their properties studied.


  The generation of novelty is central to any creative endeavor. Novelty
generation and the relationship between novelty and individual hedonic value
have long been subjects of study in social psychology. However, few studies
have utilized large-scale datasets to quantitatively investigate these issues.
Here we consider the domain of American cinema and explore these questions
using a database of films spanning a 70 year period. We use crowdsourced
keywords from the Internet Movie Database as a window into the contents of
films, and prescribe novelty scores for each film based on occurrence
probabilities of individual keywords and keyword-pairs. These scores provide
revealing insights into the dynamics of novelty in cinema. We investigate how
novelty influences the revenue generated by a film, and find a relationship
that resembles the Wundt-Berlyne curve. We also study the statistics of keyword
occurrence and the aggregate distribution of keywords over a 100 year period.


  A graph is NIC-planar if it admits a drawing in the plane with at most one
crossing per edge and such that two pairs of crossing edges share at most one
common end vertex. NIC-planarity generalizes IC-planarity, which allows a
vertex to be incident to at most one crossing edge, and specializes
1-planarity, which only requires at most one crossing per edge.
  We characterize embeddings of maximal NIC-planar graphs in terms of
generalized planar dual graphs. The characterization is used to derive tight
bounds on the density of maximal NIC-planar graphs which ranges between
3.2(n-2) and 3.6(n-2). Further, we prove that optimal NIC-planar graphs with
3.6(n-2) edges have a unique embedding and can be recognized in linear time,
whereas the general recognition problem of NIC-planar graphs is NP-complete. In
addition, we show that there are NIC-planar graphs that do not admit right
angle crossing drawings, which distinguishes NIC-planar from IC-planar graphs.


  Context: Machine Learning (ML) has been at the heart of many innovations over
the past years. However, including it in so-called 'safety-critical' systems
such as automotive or aeronautic has proven to be very challenging, since the
shift in paradigm that ML brings completely changes traditional certification
approaches.
  Objective: This paper aims to elucidate challenges related to the
certification of ML-based safety-critical systems, as well as the solutions
that are proposed in the literature to tackle them, answering the question 'How
to Certify Machine Learning Based Safety-critical Systems?'.
  Method: We conduct a Systematic Literature Review (SLR) of research papers
published between 2015 to 2020, covering topics related to the certification of
ML systems. In total, we identified 217 papers covering topics considered to be
the main pillars of ML certification: Robustness, Uncertainty, Explainability,
Verification, Safe Reinforcement Learning, and Direct Certification. We
analyzed the main trends and problems of each sub-field and provided summaries
of the papers extracted.
  Results: The SLR results highlighted the enthusiasm of the community for this
subject, as well as the lack of diversity in terms of datasets and type of
models. It also emphasized the need to further develop connections between
academia and industries to deepen the domain study. Finally, it also
illustrated the necessity to build connections between the above mention main
pillars that are for now mainly studied separately.
  Conclusion: We highlighted current efforts deployed to enable the
certification of ML based software systems, and discuss some future research
directions.


  This paper investigates a joint beamforming design in a multiuser
multiple-input single-output (MISO) communication network aided with an
intelligent reflecting surface (IRS) panel. The symbol-level precoding (SLP) is
adopted to enhance the system performance by exploiting the multiuser
interference (MUI) with consideration of bounded channel uncertainty. The joint
beamforming design is formulated into a nonconvex worst-case robust programming
to minimize the transmit power subject to single-to-noise ratio (SNR)
requirements. To address the challenges due to the constant modulus and the
coupling of the beamformers, we first study the single-user case. Specifically,
we propose and compare two algorithms based on the semidefinite relaxation
(SDR) and alternating optimization (AO) methods, respectively. It turns out
that the AO-based algorithm has much lower computational complexity but with
almost the same power to the SDR-based algorithm. Then, we apply the AO
technique to the multiuser case and thereby develop an algorithm based on the
proximal gradient descent (PGD) method. The algorithm can be generalized to the
case of finite-resolution IRS and the scenario with direct links from the
transmitter to the users. Numerical results show that the SLP can significantly
improve the system performance. Meanwhile, 3-bit phase shifters can achieve
near-optimal power performance.