Abstract:
  Iterating the skew RSK correspondence discovered by Sagan and Stanley in the
late '80s, we define a deterministic dynamics on the space of pairs of skew
Young tableaux $(P,Q)$. We find that this skew RSK dynamics displays
conservation laws which, in the picture of Viennot's shadow line construction,
identify generalizations of Greene invariants.
  The introduction of a novel realization of $0$-th Kashiwara operators reveals
that the skew RSK dynamics possesses symmetries induced by an affine bicrystal
structure,
  which, combined with connectedness properties of Demazure crystals, leads to
its linearization.
  Studying asymptotic evolution of the dynamics started from a pair of skew
tableaux $(P,Q)$, we discover a new bijection $\Upsilon : (P,Q) \mapsto (V,W;
\kappa, \nu)$. Here $(V,W)$ is a pair of
  vertically strict tableaux, i.e., column strict filling of Young diagrams
with no condition on rows, with shape prescribed by the Greene invariant,
$\kappa$ is an array of non-negative weights and $\nu$ is a partition.
  An application of this construction is the first bijective proof of Cauchy
and Littlewood identities involving $q$-Whittaker polynomials. New identities
relating sums of $q$-Whittaker and Schur polynomials are also presented.


Abstract:
  Recently, the EuS/InAs interface has attracted attention for the possibility
of inducing magnetic exchange correlations in a strong spin-orbit
semiconductor, which could be useful for topological quantum devices. We use
density functional theory (DFT) with a machine-learned Hubbard $U$ correction
[npj Comput. Mater. 6, 180 (2020)] to elucidate the effect of the bonding
configuration at the interface on the electronic structure. For all interface
configurations considered here, we find that the EuS valence band maximum (VBM)
lies below the InAs VBM. In addition, dispersed states emerge at the top of the
InAs VBM at the interface, which do not exist in either material separately.
These states are contributed mainly by the InAs layer adjacent to the
interface. They are localized at the interface and may be attributed to charge
transfer from the EuS to the InAs. The interface configuration affects the
position of the EuS VBM with respect to the InAs VBM, as well as the dispersion
of the interface state. For all interface configurations studied here, the
induced magnetic moment in the InAs is small. This suggests that this
interface, in its coherent form studied here, is not promising for inducing
equilibrium magnetic properties in InAs.


Abstract:
  The goal of mediation analysis is to study the effect of exposure on an
outcome interceded by a mediator. Two simple hypotheses are tested: the effect
of the exposure on the mediator, and the effect of the mediator on the outcome.
When either of these hypotheses is true, a predetermined significance level can
be assured. When both nulls are true, the same test becomes conservative.
Adaptively finding the correct scenario enables customizing the tests and
consequently enlarges their efficiency, which is most important in a multiple
testing framework. In this work, we link between adaptive two-stage procedures
and shrinkage estimators. We first study the properties of shrinkage
estimators, and characterize their behavior at different parameter points using
local asymptotics. We formulate theoretical results regarding shrinkage
estimators, compared to regular estimators. We then discuss the
multiple-testing framework and state results about using shrinkage estimator in
two-stage procedures for controlling the FWER. Taking advantage of these
theoretical results, we suggest a number of estimators and test statistics for
the two-stage mediation procedures. We then investigate their empirical FWER
and power, compared to regular estimators and tests, through simulations.


Abstract:
  3C 84 (NGC 1275) is the bright radio core of the Perseus Cluster. Even in the
absence of strong relativistic effects, the source has been detected at
Gamma-rays up to TeV energies. Despite its intensive study, the physical
processes responsible for the high-energy emission in the source remain
unanswered. We present a detailed kinematics study of the source and its
connection to Gamma-ray emission. The sub-parsec scale radio structure is
dominated by slow-moving features in both the eastern and western lanes of the
jet. The jet appears to have accelerated to its maximum speed within less than
125 000 gravitational radii. The fastest reliably detected speed in the jet was
~0.9 c. This leads to a minimum Lorentz factor of ~1.35. Our analysis suggests
the presence of multiple high-energy sites in the source. If Gamma-rays are
associated with kinematic changes in the jet, they are being produced in both
eastern and western lanes in the jet. Three Gamma-ray flares are
contemporaneous with epochs where the slowly moving emission region splits into
two sub-regions. We estimate the significance of these events being associated
as ~2-3 sigma. We tested our results against theoretical predictions for
magnetic reconnection-induced mini-jets and turbulence and find them
compatible.


Abstract:
  This paper presents a novel unsupervised abstractive summarization method for
opinionated texts. While the basic variational autoencoder-based models assume
a unimodal Gaussian prior for the latent code of sentences, we alternate it
with a recursive Gaussian mixture, where each mixture component corresponds to
the latent code of a topic sentence and is mixed by a tree-structured topic
distribution. By decoding each Gaussian component, we generate sentences with
tree-structured topic guidance, where the root sentence conveys generic
content, and the leaf sentences describe specific topics. Experimental results
demonstrate that the generated topic sentences are appropriate as a summary of
opinionated texts, which are more informative and cover more input contents
than those generated by the recent unsupervised summarization model
(Bra\v{z}inskas et al., 2020). Furthermore, we demonstrate that the variance of
latent Gaussians represents the granularity of sentences, analogous to Gaussian
word embedding (Vilnis and McCallum, 2015).


Abstract:
  In this letter, a novel framework is proposed for analyzing data offloading
in a multi-access edge computing system. Specifically, a two-phase algorithm,
is proposed, including two key phases: user association phase and task
offloading phase. In the first phase, a ruin theory-based approach is developed
to obtain the users association considering the users' transmission
reliability. Meanwhile, in the second phase, an optimization-based algorithm is
used to optimize the data offloading process. In particular, ruin theory is
used to manage the user association phase, and a ruin probability-based
preference profile is considered to control the priority of proposing users.
Here, ruin probability is derived by the surplus buffer space of each edge node
at each time slot. Giving the association results, an optimization problem is
formulated to optimize the amount of offloaded data aiming at minimizing the
energy consumption of users. Simulation results show that the developed
solutions guarantee system reliability under a tolerable value of surplus
buffer size and minimize the total energy consumption of all users.


Abstract:
  The relaxation dynamics of superexcited superfluid He nanodroplets is
thoroughly investigated by means of extreme-ultraviolet (XUV) femtosecond
electron and ion spectroscopy complemented by time-dependent density functional
theory (TDDFT). Three main paths leading to the emission of electrons and ions
are identified: Droplet autoionization, pump-probe photoionization, and
autoionization induced by re-excitation of droplets relaxing into levels below
the droplet ionization threshold. The most abundant product of both droplet
autoionization and photoionization is He$_2^+$, whereas the delayed appearance
of He$^+$ is indicative of the ejection of excited He atoms from the droplets.
The state-resolved time-dependent photoelectron spectra reveal that
intermediate excited states of the droplets are populated in the course of the
relaxation, terminating in the lowest-lying metastable singlet and triplet He
atomic states. The slightly faster relaxation of the triplet state compared to
the singlet state is in agreement with the simulation showing faster formation
of a bubble around a He atom in the triplet state.


Abstract:
  This paper presents a new hybrid architecture for voice activity detection
(VAD) incorporating both convolutional neural network (CNN) and bidirectional
long short-term memory (BiLSTM) layers trained in an end-to-end manner. In
addition, we focus specifically on optimising the computational efficiency of
our architecture in order to deliver robust performance in difficult
in-the-wild noise conditions in a severely under-resourced setting. Nested
k-fold cross-validation was used to explore the hyperparameter space, and the
trade-off between optimal parameters and model size is discussed. The
performance effect of a BiLSTM layer compared to a unidirectional LSTM layer
was also considered. We compare our systems with three established baselines on
the AVA-Speech dataset. We find that significantly smaller models with near
optimal parameters perform on par with larger models trained with optimal
parameters. BiLSTM layers were shown to improve accuracy over unidirectional
layers by $\approx$2% absolute on average. With an area under the curve (AUC)
of 0.951, our system outperforms all baselines, including a much larger ResNet
system, particularly in difficult noise conditions.


Abstract:
  This work explores how to design a single neural network that is capable of
adapting to multiple heterogeneous tasks of computer vision, such as image
segmentation, 3D detection, and video recognition. This goal is challenging
because network architecture designs in different tasks are inconsistent. We
solve this challenge by proposing Network Coding Propagation (NCP), a novel
"neural predictor", which is able to predict an architecture's performance in
multiple datasets and tasks. Unlike prior arts of neural architecture search
(NAS) that typically focus on a single task, NCP has several unique benefits.
(1) NCP can be trained on different NAS benchmarks, such as NAS-Bench-201 and
NAS-Bench-MR, which contains a novel network space designed by us for jointly
searching an architecture among multiple tasks, including ImageNet, Cityscapes,
KITTI, and HMDB51. (2) NCP learns from network codes but not original data,
enabling it to update the architecture efficiently across datasets. (3)
Extensive experiments evaluate NCP on object classification, detection,
segmentation, and video recognition. For example, with 17\% fewer FLOPs, a
single architecture returned by NCP achieves 86\% and 77.16\% on
ImageNet-50-1000 and Cityscapes respectively, outperforming its counterparts.
More interestingly, NCP enables a single architecture applicable to both image
segmentation and video recognition, which achieves competitive performance on
both HMDB51 and ADE20K compared to the singular counterparts. Code is available
at https://github.com/dingmyu/NCP}{https://github.com/dingmyu/NCP.


Abstract:
  Collective excitations of bound electron-hole pairs -- known as excitons --
are ubiquitous in condensed matter, emerging in systems as diverse as band
semiconductors, molecular crystals, and proteins. Recently, their existence in
strongly correlated electron materials has attracted increasing interest due to
the excitons' unique coupling to spin and orbital degrees of freedom. The
non-equilibrium driving of such dressed quasiparticles offers a promising
platform for realizing unconventional many-body phenomena and phases beyond
thermodynamic equilibrium. Here, we achieve this in the van der Waals
correlated insulator NiPS$_3$ by photoexciting its newly discovered
spin-orbit-entangled excitons that arise from Zhang-Rice states. By monitoring
the time evolution of the terahertz conductivity, we observe the coexistence of
itinerant carriers produced by exciton dissociation and the long-wavelength
antiferromagnetic magnon that coherently precesses in time. These results
demonstrate the emergence of a transient metallic state that preserves
long-range antiferromagnetism, a phase that cannot be reached by simply tuning
the temperature. More broadly, our findings open an avenue toward the
exciton-mediated optical manipulation of magnetism.


Abstract:
  A new unsupervised learning method of depth and ego-motion using multiple
masks from monocular video is proposed in this paper. The depth estimation
network and the ego-motion estimation network are trained according to the
constraints of depth and ego-motion without truth values. The main contribution
of our method is to carefully consider the occlusion of the pixels generated
when the adjacent frames are projected to each other, and the blank problem
generated in the projection target imaging plane. Two fine masks are designed
to solve most of the image pixel mismatch caused by the movement of the camera.
In addition, some relatively rare circumstances are considered, and repeated
masking is proposed. To some extent, the method is to use a geometric
relationship to filter the mismatched pixels for training, making unsupervised
learning more efficient and accurate. The experiments on KITTI dataset show our
method achieves good performance in terms of depth and ego-motion. The
generalization capability of our method is demonstrated by training on the
low-quality uncalibrated bike video dataset and evaluating on KITTI dataset,
and the results are still good.


Abstract:
  Unmanned aerial vehicles (UAVs) have been increasingly used in a wide area of
military and civilian applications such as data collection and monitoring. A
reliable network for command and control, communication, and data transfer is
crucial, not only for mission purposes but also for safety concerns. The
already deployed cellular networks are appropriate candidates for UAV
communication given the solid security and wide coverage of these networks.
However, the reliability of such networks needs a comprehensive investigation.
In this paper, we use the long-term evolution (LTE) network as the
infrastructure for drone communication and data transfer, in a rural area. We
study the communication characteristics of an LTE-connected drone during
low-altitude flights, for different altitudes and UAV speeds. We show that, in
such areas, the higher elevation benefits from a better signal quality and
experiences a fewer number of handover processes. Higher speed flights also
slightly degrade the communication performance.


Abstract:
  Relational and noSQL storages are developed for the fast processing of the
large data sets having a stable structure, while the ontologies are used to
rep-resent complex and dynamic sets of information of a limited size. In the
in-dustrial applications it is often needed to maintain the large warehouses of
data consolidated from various sources. The ontologies are useful to repre-sent
the structure of that data, but RDF triple stores are not well suitable for
storing it. We offer an approach and a system allowing to use the
opportuni-ties of fast storage engines along with the flexibility of
ontology-based data management tools, including SPARQL queries. The system
implements a multi-model data abstraction layer which allows working with the
data as if it is situated in RDF triple store, executes SPARQL queries over it
and ap-plies SHACL constraints and rules.


Abstract:
  In this paper we reformulate the problem of pricing options in a quantum
setting. Our proposed algorithm involves preparing an initial state,
representing the option price, and then evolving it using existing imaginary
time simulation algorithms. This way of pricing options boils down to mapping
an initial option price to a quantum state and then simulating the time
dependence in Wick's imaginary time space. We numerically verify our algorithm
for European options using a particular imaginary time evolution algorithm as
proof of concept and show how it can be extended to path dependent options like
Asian options. As the proposed method uses a hybrid variational algorithm, it
is bound to be relevant for near-term quantum computers.


Abstract:
  In this paper, we propose a dual-mixed formulation for stationary
viscoplastic flows with yield, such as the Bingham or the Herschel-Bulkley
flow. The approach is based on a Huber regularization of the viscosity term and
a two-fold saddle point nonlinear operator equation for the resulting weak
formulation. We provide the uniqueness of solutions for the continuous
formulation and propose a discrete scheme based on Arnold-Falk-Winther finite
elements. The discretization scheme yields a system of slantly differentiable
nonlinear equations, for which a semismooth Newton algorithm is proposed and
implemented. Local superlinear convergence of the method is also proved.
Finally, we perform several numerical experiments in two and three dimensions
to investigate the behavior and efficiency of the method.


Abstract:
  In this paper DeWitt's formalism for field theories is presented; it provides
a framework in which the quantization of fields possessing infinite dimensional
invariance groups may be carried out in a manifestly covariant
(non-Hamiltonian) fashion, even in curved space-time. Another important virtue
of DeWitt's approach is that it emphasizes the common features of apparently
very different theories such as Yang-Mills theories and General Relativity;
moreover, it makes it possible to classify all gauge theories in three
categories characterized in a purely geometrical way, i.e., by the algebra
which the generators of the gauge group obey; the geometry of such theories is
the fundamental reason underlying the emergence of ghost fields in the
corresponding quantum theories, too. These "tricky extra particles", as Feynman
called them in 1964, contribute to a physical observable such as the
stress-energy tensor, which can be expressed in terms of Feynman's Green
function itself. Therefore, an entire section is devoted to the study of the
Green functions of the neutron scalar meson: in flat space-time, the choice of
a particular Green's function is the choice of an integration contour in the
"momentum" space; in curved space-time the momentum space is no longer
available, and the definition of the different Green functions requires a
careful discussion itself. After the necessary introduction of bitensors, world
function and parallel displacement tensor, an expansion for the Feynman
propagator in curved space-time is obtained. Most calculations are explicitly
shown.


Abstract:
  The matching complex of a graph $G$ is a simplicial complex whose simplices
are matchings in $G$. In the last few years the matching complexes of grid
graphs have gained much attention among the topological combinatorists. In
2017, Braun and Hough obtained homological results related to the matching
complexes of $2 \times n$ grid graphs. Further in 2019, Matsushita showed that
the matching complexes of $2 \times n$ grid graphs are homotopy equivalent to a
wedge of spheres. In this article we prove that the matching complexes of
$3\times n$ grid graphs are homotopy equivalent to a wedge of spheres. We also
give the comprehensive list of the dimensions of spheres appearing in the
wedge.


Abstract:
  We present results of two-year photometric monitoring of Comet C/2011 J2
(LINEAR) that spans the time period from February 2013 through December 2014,
before and after perihelion passage. The observations were conducted with
broadband R filter. Analysis of observations of Comet C/2011 J2 (LINEAR) allows
estimating the nucleus radius as function of geometric albedo and
phase-darkening coefficient. Furthermore, our observations showed split of the
comet. Fragment (B) additional to the primary C/2011 J2 nucleus was
unambiguously detected; relative velocity is estimated to be near 0.17 ''/day.
We derive the Afrho parameter and estimate the dust production rate in Comet
C/2011 J2 (LINEAR) over the entire run of observations. We found a noticeable
increase in Afrho parameter between September 18, 2014 and November 5, 2014,
epoch when the nucleus presumably got split. On September 28, 2014, we observed
a transit of the 16-magnitude star (USNO-A2 1275-18299027) near nucleus of the
Comet C/2011 J2 (LINEAR). We retrieve the optical depth of the coma 0.034. The
filling factor f that corresponds to such optical depth is in good quantitative
agreement with the value that can be derived from the Afrho parameter under
reasonable assumption on geometric albedo of cometary dust.


Abstract:
  Evidence of triggered star formation at large spatial scales involving
stellar clusters is scarce. We investigate a Galactic region (l=130.0, b=0.35)
populated by several open stellar clusters that according to the last GAIA data
release, are located at a distance of about 2.9 kpc. By analyzing the
interstellar medium (ISM) at infrared, centimeter, and millimeter wavelengths
towards this group of clusters we discovered a shell of material of about 2
degree in size at the same distance. We suggest that the shell, mainly observed
at 12 um and in the Hi emission at 21 cm, was generated by the action of
massive stars belonging to clusters Berkeley 7 and UBC 414, that lie at its
center. Five clusters (MWSC0152, Czernik 6, Czernik 7, Berkeley 6, NGC 663, and
NGC 654) lie at the border of this shell. From the comparison between the
dynamical time of the discovered Hi shell and the analysis of the ages of
stellar populations in these clusters, we conclude that the expansion of the
shell could have triggered in the past the formation of stars in some of them.
We point out that in order to find physical evidence supporting a genetic
connection between stellar clusters, it is necessary not only to study the
individual clusters and their stellar populations, but also to investigate
their surrounding ISM at a large spatial scale.


Abstract:
  We present a detailed spectral-timing analysis of the Kilohertz
quasi-periodic oscillations (kHz QPOs) in Sco X-1 using the data of Rossi X-ray
Timing Explorer ($RXTE$) and the Hard X-ray Modulation Telescope
($Insight$-HXMT). The energy band with detectable kHz QPOs is studied for the
first time: on the horizontal branch, it is $\sim$ 6.89--24.01 keV and $\sim$
8.68--21.78 keV for the upper and lower kHz QPOs detected by $RXTE$, and $\sim$
9--27.5 keV for the upper kHz QPOs by $Insight$-HXMT; on the lower normal
branch, the energy band is narrower. The fractional root mean square (rms) of
the kHz QPOs increases with energy at lower energy, reaches a plateau at about
16 keV and 20 keV for the lower and upper peaks, and then levels off though
with a large uncertainty. The simulation of the deadtime effect of $RXTE$/PCA
shows that the deadtime does not affect much the search of the kHz QPOs but
makes the rms amplitude underestimated. No significant QPO is detected below
$\sim$ 6 keV as shown by the $RXTE$ data, implying that the kHz QPOs do not
originate from the black body emission of the accretion disk and neutron star
surface. In addition, with the combined analysis of the energy spectra and the
absolute rms spectra of kHz QPOs, we suggest that the kHz QPOs in Sco X-1
originate from the Comptonization of the inner part of the transition layer,
where the rotation sets the frequency and the inward bulk motion makes the
spectrum harder.


Abstract:
  We propose an architecture and training scheme to predict video frames by
explicitly modeling dis-occlusions and capturing the evolution of semantically
consistent regions in the video. The scene layout (semantic map) and motion
(optical flow) are decomposed into layers, which are predicted and fused with
their context to generate future layouts and motions. The appearance of the
scene is warped from past frames using the predicted motion in co-visible
regions; dis-occluded regions are synthesized with content-aware inpainting
utilizing the predicted scene layout. The result is a predictive model that
explicitly represents objects and learns their class-specific motion, which we
evaluate on video prediction benchmarks.


Abstract:
  We prove mixing convergence of the least squares estimator of autoregressive
parameters for supercritical Gaussian autoregressive processes of order 2
having real characteristic roots with different absolute values. We use an
appropriate random scaling such that the limit distribution is a
two-dimensional normal distribution concentrated on a one-dimensional ray
determined by the characteristic root having the larger absolute value.


Abstract:
  The SNO+ experiment is located 2 km underground at SNOLAB in Sudbury, Canada.
A low background search for neutrinoless double beta ($0\nu\beta\beta$) decay
will be conducted using 780 tonnes of liquid scintillator loaded with 3.9
tonnes of natural tellurium, corresponding to 1.3 tonnes of $^{130}$Te. This
paper provides a general overview of the SNO+ experiment, including detector
design, construction of process plants, commissioning efforts, electronics
upgrades, data acquisition systems, and calibration techniques. The SNO+
collaboration is reusing the acrylic vessel, PMT array, and electronics of the
SNO detector, having made a number of experimental upgrades and essential
adaptations for use with the liquid scintillator. With low backgrounds and a
low energy threshold, the SNO+ collaboration will also pursue a rich physics
program beyond the search for $0\nu\beta\beta$ decay, including studies of geo-
and reactor antineutrinos, supernova and solar neutrinos, and exotic physics
such as the search for invisible nucleon decay. The SNO+ approach to the search
for $0\nu\beta\beta$ decay is scalable: a future phase with high
$^{130}$Te-loading is envisioned to probe an effective Majorana mass in the
inverted mass ordering region.


Abstract:
  Non-negative Matrix Factorization (NMF) is one of the most popular techniques
for data representation and clustering, and has been widely used in machine
learning and data analysis. NMF concentrates the features of each sample into a
vector, and approximates it by the linear combination of basis vectors, such
that the low-dimensional representations are achieved. However, in real-world
applications, the features are usually with different importances. To exploit
the discriminative features, some methods project the samples into the subspace
with a transformation matrix, which disturbs the original feature attributes
and neglects the diversity of samples. To alleviate the above problems, we
propose the Feature weighted Non-negative Matrix Factorization (FNMF) in this
paper. The salient properties of FNMF can be summarized as threefold: 1) it
learns the weights of features adaptively according to their importances; 2) it
utilizes multiple feature weighting components to preserve the diversity; 3) it
can be solved efficiently with the suggested optimization algorithm.
Performance on synthetic and real-world datasets demonstrate that the proposed
method obtains the state-of-the-art performance.


Abstract:
  The electrostatic theory of magnetoplasmons on a semi-infinite magnetized
electron gas is generalized to mixed Faraday-Voigt configurations. We analyze a
new type of electrostatic surface waves that is strongly direction-dependent,
and may be realized on narrow-gap semiconductors in the THz regime. A general
expression for the dispersion relation is presented, with its dependence on the
magnitude and orientation of the applied magnetic field. Remarkably, the group
velocity is always perpendicular to the phase velocity. Both velocity and
energy relations of the found magnetoplasmons are discussed in detail. In the
appropriate limits the known magnetoplasmons in the higher-symmetry Faraday and
Voigt configurations are recovered.


Abstract:
  Binary decision-making process is ubiquitous in social life and is of vital
significance in many real-world issues, ranging from public health to political
campaigns. While continuous opinion evolution independent of discrete choice
behavior has been extensively studied, few works unveil how the group binary
decision-making result is determined by the coupled dynamics of these two
processes. To this end, we propose an agent-based model to study the collective
behaviors of individual binary decision-making process through competitive
opinion dynamics on social networks. Three key factors are considered: bounded
confidence that describes the cognitive scope of the population, stubbornness
level that characterizes the opinion updating speed, and the opinion strength
that represents the asymmetry power or attractiveness of the two choices. We
find that bounded confidence plays an important role in determining competing
evolution results. As bounded confidence grows, population opinions experience
polarization to consensus, leading to the emergence of phase transition from
co-existence to winner-takes-all state under binary decisions. Of particular
interest, we show how the combined effects of bounded confidence and asymmetry
opinion strength may reverse the initial supportive advantage in competitive
dynamics. Notably, our model qualitatively reproduces the important dynamical
pattern during a brutal competition, namely, cascading collapse, as observed by
real data. Finally and intriguingly, we find that individual cognitive
heterogeneity can bring about randomness and unpredictability in binary
decision-making process, leading to the emergence of indeterministic
oscillation. Our results reveal how the diverse behavioral patterns of binary
decision-making can be interpreted by the complicated interactions of the
proposed elements, which provides important insights toward competitive
dynamics


Abstract:
  Special atom spaces have been around for quite awhile since the introduction
of atoms by R. Coifman in his seminal paper who led to another proof that the
dual of the Hardy space $H^1$ is in fact the space of functions of bounded
means oscillations (BMO). Special atom spaces enjoy quite a few attributes of
their own, among which the fact that they have an analytic extension to the
unit disc. Recently, an extension of special atom spaces to higher dimensions
was proposed, making ripe the possible exploration of the above extension in
higher dimensions. In this paper we propose an analytic characterization of
special atom spaces in higher dimensions.


Abstract:
  Nanofluids are suspensions of nanoparticles in a base heat-transfer liquid.
They have been widely investigated to boost heat transfer since they were
proposed in the 1990's. We present a statistical correlation analysis of
experimentally measured thermal conductivity of water-based nanofluids
available in the literature. The influences of particle concentration, particle
size, temperature and surfactants are investigated. For specific materials
(alumina, titania, copper oxide, copper, silica and silicon carbide), separate
analyses are performed. The conductivity increases with the concentration in
qualitative agreement with Maxwell's theory of homogeneous media. The
conductivity also increases with the temperature (in addition to the
improvement due to the increased conductivity with water). Surprisingly, only
silica nanofluids exhibit a statistically significant effect of particle size,
whereby smaller particles lead to faster heat transfer. Overall, the large
scatter in the experimental data prevents a compelling, unambiguous assessment
of these effects. Taken together, the results of our analysis suggest that more
comprehensive experimental characterizations of nanofluids are necessary to
estimate their practical potential.


Abstract:
  Human pose estimation is a fundamental yet challenging task in computer
vision, which aims at localizing human anatomical keypoints. However, unlike
human vision that is robust to various data corruptions such as blur and
pixelation, current pose estimators are easily confused by these corruptions.
This work comprehensively studies and addresses this problem by building
rigorous robust benchmarks, termed COCO-C, MPII-C, and OCHuman-C, to evaluate
the weaknesses of current advanced pose estimators, and a new algorithm termed
AdvMix is proposed to improve their robustness in different corruptions. Our
work has several unique benefits. (1) AdvMix is model-agnostic and capable in a
wide-spectrum of pose estimation models. (2) AdvMix consists of adversarial
augmentation and knowledge distillation. Adversarial augmentation contains two
neural network modules that are trained jointly and competitively in an
adversarial manner, where a generator network mixes different corrupted images
to confuse a pose estimator, improving the robustness of the pose estimator by
learning from harder samples. To compensate for the noise patterns by
adversarial augmentation, knowledge distillation is applied to transfer clean
pose structure knowledge to the target pose estimator. (3) Extensive
experiments show that AdvMix significantly increases the robustness of pose
estimations across a wide range of corruptions, while maintaining accuracy on
clean data in various challenging benchmark datasets.


Abstract:
  The processing of binary alloys consisting of ferromagnetic Fe and
antiferromagnetic Cr by severe plastic deformation (SPD) with different
chemical compositions has been investigated. Although the phase diagram
exhibits a large gap in the thermodynamical equilibrium at lower temperatures,
it is shown that techniques based on SPD help to overcome common processing
limits. Different processing routes including initial ball milling (BM) and arc
melting (AM) and a concatenation with annealing treatments prior to
high-pressure torsion (HPT) deformation are compared in this work.
Investigation of the deformed microstructures by electron microscopy and
synchrotron X-ray diffraction reveal homogeneous, nanocrystalline
microstructures for HPT deformed AM alloys. HPT deformation of powder blends
and BM powders leads to an exorbitant increase in hardness or an unusual fast
formation of a $\sigma$-phase and therefore impede successful processing.


Abstract:
  Policy optimization, which learns the policy of interest by maximizing the
value function via large-scale optimization techniques, lies at the heart of
modern reinforcement learning (RL). In addition to value maximization, other
practical considerations arise commonly as well, including the need of
encouraging exploration, and that of ensuring certain structural properties of
the learned policy due to safety, resource and operational constraints. These
considerations can often be accounted for by resorting to regularized RL, which
augments the target value function with a structure-promoting regularization
term. Focusing on an infinite-horizon discounted Markov decision process, this
paper proposes a generalized policy mirror descent (GPMD) algorithm for solving
regularized RL. As a generalization of policy mirror descent Lan (2021), the
proposed algorithm accommodates a general class of convex regularizers as well
as a broad family of Bregman divergence in cognizant of the regularizer in use.
We demonstrate that our algorithm converges linearly over an entire range of
learning rates, in a dimension-free fashion, to the global solution, even when
the regularizer lacks strong convexity and smoothness. In addition, this linear
convergence feature is provably stable in the face of inexact policy evaluation
and imperfect policy updates. Numerical experiments are provided to corroborate
the applicability and appealing performance of GPMD.


Abstract:
  There are now more Particle-in-Cell (PIC) codes than ever before that
researchers use to simulate intense laser-plasma interactions. To date, there
have been relatively few direct comparisons of these codes in the literature,
especially for relativistic intensity lasers interacting with thin overdense
targets. To address this we perform a code comparison of three PIC codes:
EPOCH, LSP, and WarpX for the problem of laser-driven ion acceleration in a
2D(3v) geometry for a $10^{20}$ W cm$^{-2}$ intensity laser. We examine the
plasma density, ion energy spectra, and laser-plasma coupling of the three
codes and find strong agreement. We also run the same simulation 20 times with
different random seeds to explore statistical fluctuations of the outputs. We
then compare the execution times and memory usage of the codes (without
"tuning" to improve performance) using between 1 and 48 processors on one node.
We provide input files to encourage larger and more frequent code comparisons
in this field.


Abstract:
  The abolitionist movement of the nineteenth-century United States remains
among the most significant social and political movements in US history.
Abolitionist newspapers played a crucial role in spreading information and
shaping public opinion around a range of issues relating to the abolition of
slavery. These newspapers also serve as a primary source of information about
the movement for scholars today, resulting in powerful new accounts of the
movement and its leaders. This paper supplements recent qualitative work on the
role of women in abolition's vanguard, as well as the role of the Black press,
with a quantitative text modeling approach. Using diachronic word embeddings,
we identify which newspapers tended to lead lexical semantic innovations -- the
introduction of new usages of specific words -- and which newspapers tended to
follow. We then aggregate the evidence across hundreds of changes into a
weighted network with the newspapers as nodes; directed edge weights represent
the frequency with which each newspaper led the other in the adoption of a
lexical semantic change. Analysis of this network reveals pathways of lexical
semantic influence, distinguishing leaders from followers, as well as others
who stood apart from the semantic changes that swept through this period. More
specifically, we find that two newspapers edited by women -- THE PROVINCIAL
FREEMAN and THE LILY -- led a large number of semantic changes in our corpus,
lending additional credence to the argument that a multiracial coalition of
women led the abolitionist movement in terms of both thought and action. It
also contributes additional complexity to the scholarship that has sought to
tease apart the relation of the abolitionist movement to the women's suffrage
movement, and the vexed racial politics that characterized their relation.


Abstract:
  The associated production of a single-top with opposite-sign same-flavor
(OSSF) di-leptons, $pp \to t \ell^+ \ell^-$ and $ pp \to t \ell^+ \ell^- + j$
($j=$light jet), can lead to striking tri-lepton $pp \to \ell^\prime \ell^+
\ell^- + X$ and di-lepton $pp \to \ell^+ \ell^- + j_b + X$ ($j_b=b$-jet) events
at the LHC, after the top decays. Although these rather generic multi-lepton
signals are flavor-blind, they can be generated by new 4-Fermi flavor changing
(FC) $u_i t \ell \ell$ scalar, vector and tensor interactions ($u_i \in u,c$),
which we study in this paper; we match the FC $u_i t \ell \ell$ 4-Fermi terms
to the SMEFT operators and also to different types of FC underlying heavy
physics. The main backgrounds to these di- and tri-lepton signals arise from $t
\bar t$, $Z$+jets and $VV$ ($V=W,Z$) production, but they can be essentially
eliminated with a sufficiently high invariant mass selection on the OSSF
di-leptons, $m_{\ell^+ \ell^-}^{\tt min}(OSSF) > 1$ TeV; the use of $b$-tagging
as an additional selection in the di-lepton final state case also proves very
useful. We find, for example, that the expected 95\% CL bounds on the scale of
a tensor(vector) $u t \mu \mu$ interaction, with the current $\sim 140$
fb$^{-1}$ of LHC data, are $\Lambda < 5(3.2) $ TeV or $\Lambda < 4.1(2.7)$ TeV,
if analyzed via the di-muon $\mu^+ \mu^- + j_b$ signal or the $e \mu^+ \mu^-$
tri-lepton one, respectively. The expected reach at the HL-LHC with 3000
fb$^{-1}$ of data is $\Lambda < 7.1(4.7)$ TeV and $\Lambda < 2.4(1.5)$ TeV for
the corresponding $u t \mu \mu$ and $c t \mu \mu$ operators. We also study the
potential sensitivity at future 27 TeV and 100 TeV high-energy LHC successors
and also discuss the possible implications of this class of FC 4-Fermi
effective interactions on lepton non-universality tests at the LHC.


Abstract:
  Round-the-clock monitoring of human behavior and emotions is required in many
healthcare applications which is very expensive but can be automated using
machine learning (ML) and sensor technologies. Unfortunately, the lack of
infrastructure for collection and sharing of such data is a bottleneck for ML
research applied to healthcare. Our goal is to circumvent this bottleneck by
simulating a human body in virtual environment. This will allow generation of
potentially infinite amounts of shareable data from an individual as a function
of his actions, interactions and emotions in a care facility or at home, with
no risk of confidentiality breach or privacy invasion. In this paper, we
develop for the first time a system consisting of computational models for
synchronously synthesizing skeletal motion, electrocardiogram, blood pressure,
respiration, and skin conductance signals as a function of an open-ended set of
actions and emotions. Our experimental evaluations, involving user studies,
benchmark datasets and comparison to findings in the literature, show that our
models can generate skeletal motion and physiological signals with high
fidelity. The proposed framework is modular and allows the flexibility to
experiment with different models. In addition to facilitating ML research for
round-the-clock monitoring at a reduced cost, the proposed framework will allow
reusability of code and data, and may be used as a training tool for ML
practitioners and healthcare professionals.


Abstract:
  Given a natural number $n \geq 4$ we show that there exists infinitely many
polynomials $f_{n}(x):= \prod_{i=1}^{n} (x^{2} - a_{i})$ such that (i)
$f_{n}(x)$ has a root modulo every positive integer, (ii) $f_{n}(x)$ has no
rational roots, and (iii) every proper divisor of $f_{n}(x)$ fails to have root
modulo some positive integer. We exhibit a process to explicitly construct such
$f_{n}$ and this process demonstrates that the set of natural numbers $a_{n}$,
such that the polynomial $f_{n}(x):= \prod_{i=1}^{n} (x^{2} - a_{i})$ satisfies
the properties (i), (ii) and (iii), is of positive asymptotic density in
$\mathbb{N}$.


Abstract:
  We prove a number of results regarding odd values of the Ramanujan
$\tau$-function. For example, we prove the existence of an effectively
computable positive constant $\kappa$ such that if $\tau(n)$ is odd and $n \ge
25$ then either \[
  P(\tau(n)) \; > \; \kappa \cdot \frac{\log\log\log{n}}{\log\log\log\log{n}}
\] or there exists a prime $p \mid n$ with $\tau(p)=0$. Here $P(m)$ denotes the
largest prime factor of $m$. We also solve the equation $\tau(n)=\pm 3^{b_1}
5^{b_2} 7^{b_3} 11^{b_4}$ and the equations $\tau(n)=\pm q^b$ where $3\le q <
100$ is prime and the exponents are arbitrary nonnegative integers. We make use
of a variety of methods, including the Primitive Divisor Theorem of Bilu,
Hanrot and Voutier, bounds for solutions to Thue--Mahler equations due to
Bugeaud and Gy\H{o}ry, and the modular approach via Galois representations of
Frey-Hellegouarch elliptic curves.


Abstract:
  We propose a novel method to reconstruct high-resolution three-dimensional
mass maps using data from photometric weak-lensing surveys. We apply an
adaptive LASSO algorithm to perform a sparsity-based reconstruction on the
assumption that the underlying cosmic density field is represented by a sum of
Navarro-Frenk-White halos. We generate realistic mock galaxy shape catalogues
by considering the shear distortions from isolated halos for the configurations
matched to Subaru Hyper Suprime-Cam Survey with its photometric redshift
estimates. We show that the adaptive method significantly reduces line-of-sight
smearing that is caused by the correlation between the lensing kernels at
different redshifts. Lensing clusters with lower mass limits of $10^{14.0}
h^{-1}M_{\odot}$, $10^{14.7} h^{-1}M_{\odot}$, $10^{15.0} h^{-1}M_{\odot}$ can
be detected with 1.5-$\sigma$ confidence at the low ($z<0.3$), median ($0.3\leq
z< 0.6$) and high ($0.6\leq z< 0.85$) redshifts, respectively, with an average
false detection rate of 0.022 deg$^{-2}$. The estimated redshifts of the
detected clusters are systematically lower than the true values by $\Delta z
\sim 0.03$ for halos at $z\leq 0.4$, but the relative redshift bias is below
$0.5\%$ for clusters at $0.4<z\leq 0.85$. The standard deviation of the
redshift estimation is $0.092$. Our method enables direct three-dimensional
cluster detection with accurate redshift estimates.


Abstract:
  Following our work [Phys. Rev. Lett. 125, 020401 (2020)], we discuss a
semiclassical description of one-dimensional quantum tunneling through
multibarrier potentials in terms of complex time. We start by defining a
complex-extended continuum level density of unbound systems and show its
relation to a complex time shift of the transmitted wave. While the real part
of the level density and time shift describes the passage of the particle
through classically allowed coordinate regions, the imaginary part is connected
with an instanton-like picture of the tunneling through forbidden regions. We
describe singularities in the real and imaginary parts of the level density and
time shift caused by stationary points of the tunneling potential, and show
that they represent a dual extension of excited-state quantum phase transitions
from bound to continuum systems. Using the complex scaling method, we
numerically verify the predicted effects in several tunneling potentials.


Abstract:
  This concept paper aims to provide a brief outline of quantum computers,
explore existing methods of quantum image classification techniques, so
focusing on remote sensing applications, and discuss the bottlenecks of
performing these algorithms on currently available open source platforms.
Initial results demonstrate feasibility. Next steps include expanding the size
of the quantum hidden layer and increasing the variety of output image options.


Abstract:
  A composite drag control (CDC) combining the opposition (OC) and spanwise
opposed wall-jet forcing (SOJF) methods is studied in a turbulent channel flow
via direct numerical simulation of the incompressible Navier-Stokes equations.
A maximum drag reduction of about 33% is obtained for CDC -- much higher than
that produced by either individual method (namely, 19% for SOJF and 23% for
OC). Due to the small power input required for both OC and SOJF methods, a
significant net power saving (about 32%) is achieved via CDC. Flow analysis
shows that CDC can take advantage of both OC and SOJF methods to better
suppress drag producing, near-wall turbulent structures -- vortices and
streaks. In particular, due to the presence of the large-scale coherent swirls
generated by SOJF, it is more effective than OC in suppressing the random
turbulence. Moreover, due to the OC's role in suppressing random small-scale
turbulence, CDC requires weaker large-scale coherent swirls than those using
SOJF only -- hence decreasing the drag contribution associated with large-scale
swirls. In summary, our results suggest prospects of employing composite
control strategy for effective skin friction drag reduction, particularly at
very high Reynolds numbers.


Abstract:
  We study the fundamental problem of allocating indivisible goods to agents
with additive preferences. We consider eliciting from each agent only a ranking
of her $k$ most preferred goods instead of her full cardinal valuations. We
characterize the value of $k$ needed to achieve envy-freeness up to one good
and approximate maximin share guarantee, two widely studied fairness notions.
We also analyze the multiplicative loss in social welfare incurred due to the
lack of full information with and without the fairness requirements.


Abstract:
  In this letter we report the existence of nondegenerate fundamental bright
soliton solution for coupled multi-component nonlinear Schr\"{o}dinger
equations of Manakov type. To derive this class of nondegenerate vector soliton
solutions, we adopt the Hirota bilinear method with appopriate general class of
seed solutions. Very interestingly the obtained nondegenerate fundamental
soliton solution of the $N$-coupled nonlinear Schr\"{o}dinger (CNLS) system
admits multi-hump natured intensity profiles. We explicitly demonstrate this
specific property by considering the nondegenerate soliton solutions for $3$
and $4$-CNLS systems. We also point out the existence of a special class of
partially nondegenerate soliton solutions by imposing appropriate restrictions
on the wavenumbers in the already obtained completely nondegenerate soliton
solution. Such class of soliton solutions can also exhibit multi-hump profile
structures. Finally, we present the stability analysis of nondegenerate
fundamental soliton of the $3$-CNLS system as an example. The numerical results
confirm the stability of triple-humped profile nature against perturbations of
5\% and 10\% white noise. The multi-hump nature of nondegenerate fundamental
soliton solution will be usefull in multi-level optical communication
applications with enhanced flow of data in multi-mode fibers.


Abstract:
  This paper introduces Broad Infinity, a new and arguably intuitive axiom
scheme. It states that "broad numbers", which are three-dimensional trees whose
growth is controlled, form a set. If the Axiom of Choice is assumed, then Broad
Infinity is equivalent to the Ord-is-Mahlo scheme: every closed unbounded class
of ordinals contains a regular ordinal.
  Whereas the axiom of Infinity leads to generation principles for sets and
families and ordinals, Broad Infinity leads to more advanced versions of these
principles. The paper relates these principles under various prior assumptions:
the Axiom of Choice, the Law of Excluded Middle, and weaker assumptions.


Abstract:
  It is commonly assumed that topological phase transitions in topological
superconductors are accompanied by a closing of the topological gap or a change
of the symmetry of the system. We demonstrate that an unconventional
topological phase transition with neither gap closing nor a change of symmetry
is possible. We consider a nanoscopic length ladder of atoms on a
superconducting substrate, comprising self-organized magnetic moments coupled
to itinerant electrons. For a range of conditions, the ground state of such a
system prefers helical magnetic textures, self-sustaining topologically
nontrivial phase. Abrupt changes in the magnetic order as a function of induced
superconducting pairing or chemical potential can cause topological phase
transitions without closing the topological gap. Furthermore, the ground state
prefers either parallel or anti-parallel configurations along the rungs, and
the anti-parallel configuration causes an emergent time reversal asymmetry
protecting Kramer's pair's of Majorana zero modes, but in a BDI topological
superconductor. We determine the topological invariant and inspect the boundary
Majorana zero modes.


Abstract:
  Chorus emission in planetary magnetospheres is taken as working paradigm to
motivate a short tutorial trip through theoretical plasma physics methods and
their applications. Starting from basic linear theory, readers are first made
comfortable with whistler wave packets and their propagation in slowly varying
weakly nonuniform media, such as the Earth's magnetosphere, where they can be
amplified by a population of supra-thermal electrons. The nonlinear dynamic
description of energetic electrons in the phase space in the presence of
self-consistently evolving whistler fluctuation spectrum is progressively
introduced by addressing renormalization of the electron response and spectrum
evolution equations. Analytical and numerical results on chorus frequency
chirping are obtained and compared with existing observations and particle in
cell simulations. Finally, the general theoretical framework constructed during
this short trip through chorus physics is used to draw analogies with condensed
matter and laser physics as well as magnetic confinement fusion research.
Discussing these analogies ultimately presents plasma physics as an exciting
cross-disciplinary field to study.


Abstract:
  A set of unified relativistic mean-field equations of state for hyperonic
compact stars recently built in [M. Fortin, Ad. R. Raduta, S. Avancini, and C.
Providencia, Phys. Rev. D {\bf 101}, 034017 (2020)] is used to study the
thermal evolution of non-magnetized and non-rotating spherically-symmetric
isolated and accreting neutron stars under different hypothesis concerning
proton $S$-wave superfluidity.
  These equations of state have been obtained in the following way: the slope
of the symmetry energy is in agreement with experimental data; the coupling
constants of $\Lambda$ and $\Xi$-hyperons are determined from experimental
hypernuclear data; uncertainties in the nucleon-$\Sigma$ interaction potential
are accounted for; current constraints on the lower bound of the maximum
neutron star mass are satisfied.
  Within the considered set of equations of state, the presence of hyperons is
essential for the description of the cooling/heating curves.
  One of the conclusions we reach is that the criterion of best agreement with
observational data leads to different equations of states and proton $S$-wave
superfluidity gaps when applied separately for isolated neutron stars and
accreting neutron stars in quiescence.
  This means that at least in one situation the traditional simulation
framework that we employ is not complete and/or the equations of state are
inappropriate.
  Another result is that, considering equations of state which do not allow for
nucleonic dUrca or allow for it only in very massive NS, the low luminosity of
SAX J1808 requires a repulsive $\Sigma$-hyperon potential in symmetric nuclear
matter in the range $U_\Sigma^{(N)}\approx 10-30$ MeV. This range of values for
$U_\Sigma^{(N)} $ is also supported by the criterion of best agreement with all
available data from INS and XRT.


Abstract:
  We show that, if $w_1, \ldots , w_6$ are words which are not an identity of
any (non-abelian) finite simple group, then $w_1(G)w_2(G) \cdots w_6(G) = G$
for all (non-abelian) finite simple groups $G$. In particular, for every word
$w$, either $w(G)^6 = G$ for all finite simple groups, or $w(G)=1$ for some
finite simple groups. These theorems follow from more general results we obtain
on characteristic collections of finite groups and their covering numbers,
which are of independent interest and have additional applications.


Abstract:
  Online stores often utilize product relationships such as bundles and
substitutes to improve their catalog quality and guide customers through myriad
choices. Entity resolution using pairwise product matching models offers a
means of inferring relationships between products. In mature data repositories,
the relationships may be mostly correct but require incremental improvements
owing to errors in the original data or in the entity resolution system. It is
critical to devise incremental entity resolution (IER) approaches for improving
the health of relationships. However, most existing research on IER focuses on
the addition of new products or information into existing relationships.
Relatively little research has been done for detecting low quality within
current relationships.
  This paper proposes a novel method for identifying inconsistent clusters
(IC), existing groups of related products that do not belong together. We
propose to treat the identification of inconsistent clusters as a supervised
learning task which predicts whether a graph of products with similarities as
weighted edges should be partitioned into multiple clusters. In this case, the
problem becomes a classification task on weighted graphs and represents an
interesting application area for modern tools such as Graph Neural Networks
(GNNs). We demonstrate that existing Message Passing neural networks perform
well at this task, exceeding traditional graph processing techniques. We also
develop a novel message aggregation scheme for Message Passing Neural Networks
that further improves the performance of GNNs on this task. We apply the model
to synthetic datasets, a public benchmark dataset, and an internal application.
Our results demonstrate the value of graph classification in IER and the
ability of graph neural networks to develop useful representations for graph
partitioning.


Abstract:
  We classify transcendental entire functions that are compositions of a
polynomial and the exponential for which all singular values escape on disjoint
rays. We focus on the case where the escape is degenerate in the sense that
points from different singular orbits are arbitrarily close to each other. As
in the general case we employ an iteration procedure on an infinite-dimensional
Teichm\"uller space, analogously to the Thurston's classical Topological
Characterization of Rational Functions, but for an infinite set of marked
points.


Abstract:
  Motivated by recent anomalies in FCNC $b\to s\ell^+\ell^-$, we study $B_1\to
B_2\ell^+\ell^-(\ell=e,\mu,\tau)$ semi-leptonic weak decays with the SU(3)
flavor symmetry, where $B_{1,2}$ are the spin-1/2 baryons of single bottomed
anti-triplet $T_{b3}$, single charmed anti-triplet $T_{c3}$ or light baryon
octet $T_{8}$. Using the SU(3) irreducible representation approach, we first
obtain the amplitude relations among different decays, and then predict the
relevant not-yet measured observables of these decays. (a) We calculate the
branching ratios of the $T_{b3}\to T_8 \mu^+\mu^-$ and $T_{b3}\to T_8
\tau^+\tau^-$ in the whole $q^2$ region and in the different $q^2$ bins by the
measurement of $\Lambda^0_b\to \Lambda^0 \mu^+\mu^-$. Many of them are obtained
for the first time. In addition, the longitudinal polarization fractions and
the leptonic forward-backward asymmetries of all $T_{b3}\to T_{8}\ell^+\ell^-$
decays are very similar to each other in certain $q^2$ bin due to the SU(3)
flavor symmetry. (b) We analyze the upper limits of $B(T_{c3}\to
T_{8}\ell^+\ell^-)$ by using the experimental upper limits of $B(\Lambda^+_c\to
p\mu^+\mu^-)$ and $B(\Lambda^+_c\to pe^+e^-)$, and find the experimental upper
limit of $B(\Lambda^+_c\to p\mu^+\mu^-)$ giving effective bounds on the
relevant SU(3) flavor symmetry parameters. The predictions of $B(\Xi^0_c \to
\Xi^0e^+e^-)$ and $B(\Xi^0_c \to \Xi^0\mu^+\mu^-)$ will be different between
the single-quark transition dominant contributions and the W-exchange dominant
ones. (c) As for $T_{8}\to T'_8 \ell^+\ell^-$ decays, we analyze the
single-quark transition contributions and the W-exchange contributions by using
two measurements of $ B(\Xi^0\to \Lambda^0 e^+e^-)$ and $ B(\Sigma^+\to
p\mu^+\mu^-)$, and give the branching ratio predictions by assuming either
single-quark transition dominant contributions or the W-exchange dominant ones.


Abstract:
  Facial expressions are an ideal means of communicating one's emotions or
intentions to others. This overview will focus on human facial expression
recognition as well as robotic facial expression generation. In case of human
facial expression recognition, both facial expression recognition on predefined
datasets as well as in real time will be covered. For robotic facial expression
generation, hand coded and automated methods i.e., facial expressions of a
robot are generated by moving the features (eyes, mouth) of the robot by hand
coding or automatically using machine learning techniques, will also be
covered. There are already plenty of studies that achieve high accuracy for
emotion expression recognition on predefined datasets, but the accuracy for
facial expression recognition in real time is comparatively lower. In case of
expression generation in robots, while most of the robots are capable of making
basic facial expressions, there are not many studies that enable robots to do
so automatically.


Abstract:
  A measurement of the magnitude of the electric dipole moment of the electron
(eEDM) larger than that predicted by the Standard Model (SM) of particle
physics is expected to have a huge impact on the search for physics beyond the
SM. Polar diatomic molecules containing heavy elements experience enhanced
sensitivity to parity ($P$) and time-reversal ($T$)-violating phenomena, such
as the eEDM and the scalar-pseudoscalar (S-PS) interaction between the nucleons
and the electrons, and are thus promising candidates for measurements. The
NL-\textit{e}EDM collaboration is preparing an experiment to measure the eEDM
and S-PS interaction in a slow beam of cold BaF molecules [Eur. Phys. J. D, 72,
197 (2018)]. Accurate knowledge of the electronic structure parameters, $W_d$
and $W_s$, connecting the eEDM and the S-PS interaction to the measurable
energy shifts is crucial for the interpretation of these measurements.
  In this work we use the finite field relativistic coupled cluster approach to
calculate the $W_d$ and $W_s$ parameters in the ground state of the BaF
molecule. Special attention was paid to providing a reliable theoretical
uncertainty estimate based on investigations of the basis set, electron
correlation, relativistic effects and geometry. Our recommended values of the
two parameters, including conservative uncertainty estimates, are 3.13 $\pm$
$0.12 \times 10^{24}\frac{\text{Hz}}{e\cdot \text{cm}}$ for $W_d$ and 8.29
$\pm$ 0.12 kHz for $W_s$.


Abstract:
  In globally distributed projects, virtual teams are often partially
dispersed. One common setup occurs when several members from one company work
with a large outsourcing vendor based in another country. Further, the
introduction of the popular BizDevOps concept has increased the necessity to
cooperate across departments and reduce the age-old disconnection between the
business strategy and technical development. Establishing a good collaboration
in partially distributed BizDevOps teams requires extensive collaboration and
communication techniques. Nowadays, a common approach is to rely on
collaboration through pull requests and frequent communication on Slack. To
investigate barriers for pull requests in distributed teams, we examined an
organization located in Scandinavia where cross-functional BizDevOps teams
collaborated with off-site team members in India. Data were collected by
conducting 14 interviews, observing 23 entire days with the team, and observing
37 meetings. We found that the pull-request approach worked very well locally
but not across sites. We found barriers such as domain complexity, different
agile processes (timeboxed vs. flow-based development), and employee turnover.
Using an intellectual capital lens on our findings, we discuss barriers and
positive and negative effects on the success of the pull-request approach.


Abstract:
  Heterogeneous information networks(HINs) become popular in recent years for
its strong capability of modelling objects with abundant information using
explicit network structure. Network embedding has been proved as an effective
method to convert information networks into lower-dimensional space, whereas
the core information can be well preserved. However, traditional network
embedding algorithms are sub-optimal in capturing rich while potentially
incompatible semantics provided by HINs. To address this issue, a novel
meta-path-based HIN representation learning framework named mSHINE is designed
to simultaneously learn multiple node representations for different meta-paths.
More specifically, one representation learning module inspired by the RNN
structure is developed and multiple node representations can be learned
simultaneously, where each representation is associated with one respective
meta-path. By measuring the relevance between nodes with the designed objective
function, the learned module can be applied in downstream link prediction
tasks. A set of criteria for selecting initial meta-paths is proposed as the
other module in mSHINE which is important to reduce the optimal meta-path
selection cost when no prior knowledge of suitable meta-paths is available. To
corroborate the effectiveness of mSHINE, extensive experimental studies
including node classification and link prediction are conducted on five
real-world datasets. The results demonstrate that mSHINE outperforms other
state-of-the-art HIN embedding methods.


Abstract:
  The Linearization Theorem for proper Lie groupoids organizes and generalizes
several results for classic geometries. Despite the various approaches and
recent works on the subject, the problem of understanding invariant
linearization remains somehow open. We address it here, by first giving a
counter-example to a previous conjecture, and then proving a sufficient
criterion that uses compatible complete metrics and covers the case of proper
group actions. We also show a partial converse that fixes and extends previous
results in the literature.


Abstract:
  Chromatin is known to be organised into multiple domains of varying sizes and
compaction. While these domains are often imagined as static structures, they
are highly dynamic and show cell-to-cell variability. Since processes such as
gene regulation and DNA replication occur in the context of these domains, it
is important to understand their organization, fluctuation and dynamics. To
simulate chromatin domains, one requires knowledge of interaction strengths
among chromatin segments. Here, we derive interaction strength parameters from
experimentally known contact maps, and use it to predict chromatin organization
and dynamics. Taking $\alpha$-globin domain as an example, we investigate its
3D organization, size/shape fluctuations, and dynamics of different segments
within a domain, accounting for hydrodynamic effects. Perturbing the
interaction strengths systematically, we quantify how epigenetic changes can
alter the spatio-temporal nature of the domains. Computing
distance-distributions and relaxation times for different chromatin states, we
show that weak and strong interactions cooperatively determine the organization
of the domains and how the solid-like and liquid-like nature of chromatin gets
altered as we vary epigenetic states. Quantifying dynamics of chromatin
segments within a domain, we show how the competition between polymer entropy
and interaction energy influence the timescales of loop formation and
maintenance of stable loops.


Abstract:
  We study a system of two non-interacting quantum wires with fermions of
opposite chirality with a point contact junction at the origin across which
tunneling can take place when an arbitrary time-dependent bias between the
wires is applied. We obtain the exact dynamical non-equilibrium Green function
by solving Dyson's equation analytically. Both the space-time dependent two and
four-point functions are written down in a closed form in terms of simple
functions of position and time. This allows us to obtain, among other things,
the I-V characteristics for an arbitrary time-dependent bias. Our method is a
superior alternative to competing approaches to non-equilibrium as we are able
to account for transient phenomena as well as the steady state. We study the
approach to steady state by computing the time evolution of the equal-time
one-particle Green function. We also show how the presence of the point-contact
affects the density profile of the electrons by inducing Friedel oscillations
in the system and how a bias influences these oscillations. We then consider
the case of a finite bandwidth in the point contact and calculate the
non-equilibrium transport properties which exhibit non-Markovian behaviour.
When a subsequently constant bias is suddenly switched on, the current shows a
transient build up before approaching its steady state value in contrast to the
infinite bandwidth case. This transient property is consistent with numerical
simulations of lattice systems using time-dependent DMRG (tDMRG) suggesting
thereby that this transient build up is merely due to the presence of a short
distance cutoff in the problem description and not on the other details.


Abstract:
  Machine learning algorithms deployed on edge devices must meet certain
resource constraints and efficiency requirements. Random Vector Functional Link
(RVFL) networks are favored for such applications due to their simple design
and training efficiency. We propose a modified RVFL network that avoids
computationally expensive matrix operations during training, thus expanding the
network's range of potential applications. Our modification replaces the
least-squares classifier with the Generalized Learning Vector Quantization
(GLVQ) classifier, which only employs simple vector and distance calculations.
The GLVQ classifier can also be considered an improvement upon certain
classification algorithms popularly used in the area of Hyperdimensional
Computing. The proposed approach achieved state-of-the-art accuracy on a
collection of datasets from the UCI Machine Learning Repository - higher than
previously proposed RVFL networks. We further demonstrate that our approach
still achieves high accuracy while severely limited in training iterations
(using on average only 21% of the least-squares classifier computational
costs).


Abstract:
  Machine learning and wireless communication technologies are jointly
facilitating an intelligent edge, where federated edge learning (FEEL) is a
promising training framework. As wireless devices involved in FEEL are resource
limited in terms of communication bandwidth, computing power and battery
capacity, it is important to carefully schedule them to optimize the training
performance. In this work, we consider an over-the-air FEEL system with analog
gradient aggregation, and propose an energy-aware dynamic device scheduling
algorithm to optimize the training performance under energy constraints of
devices, where both communication energy for gradient aggregation and
computation energy for local training are included. The consideration of
computation energy makes dynamic scheduling challenging, as devices are
scheduled before local training, but the communication energy for over-the-air
aggregation depends on the l2-norm of local gradient, which is known after
local training. We thus incorporate estimation methods into scheduling to
predict the gradient norm. Taking the estimation error into account, we
characterize the performance gap between the proposed algorithm and its offline
counterpart. Experimental results show that, under a highly unbalanced local
data distribution, the proposed algorithm can increase the accuracy by 4.9% on
CIFAR-10 dataset compared with the myopic benchmark, while satisfying the
energy constraints.


Abstract:
  In video-based dynamic point cloud compression (V-PCC), 3D point clouds are
projected onto 2D images for compressing with the existing video codecs.
However, the existing video codecs are originally designed for natural visual
signals, and it fails to account for the characteristics of point clouds. Thus,
there are still problems in the compression of geometry information generated
from the point clouds. Firstly, the distortion model in the existing
rate-distortion optimization (RDO) is not consistent with the geometry quality
assessment metrics. Secondly, the prediction methods in video codecs fail to
account for the fact that the highest depth values of a far layer is greater
than or equal to the corresponding lowest depth values of a near layer. This
paper proposes an advanced geometry surface coding (AGSC) method for dynamic
point clouds (DPC) compression. The proposed method consists of two modules,
including an error projection model-based (EPM-based) RDO and an occupancy
map-based (OM-based) merge prediction. Firstly, the EPM model is proposed to
describe the relationship between the distortion model in the existing video
codec and the geometry quality metric. Secondly, the EPM-based RDO method is
presented to project the existing distortion model on the plane normal and is
simplified to estimate the average normal vectors of coding units (CUs).
Finally, we propose the OM-based merge prediction approach, in which the
prediction pixels of merge modes are refined based on the occupancy map.
Experiments tested on the standard point clouds show that the proposed method
achieves an average 9.84\% bitrate saving for geometry compression.


Abstract:
  Interacting fermion systems in one dimension, which in the low energy
approximation are described by Luttinger liquid theory, can be reformulated as
systems of weakly interacting particles with fractional exchange statistics.
This is shown by use of Landau's Fermi liquid theory, with quasiparticles
interpreted as adiabatically dressed fermions. An application of this method is
included, where boundary excitations of a two-dimensional quantum Hall electron
system are studied.


Abstract:
  In this paper we introduce a novel objective prior distribution levering on
the connections between information, divergence and scoring rules. In
particular, we do so from the starting point of convex functions representing
information in density functions. This provides a natural route to proper local
scoring rules using Bregman divergence. Specifically, we determine the prior
which solves setting the score function to be a constant. While in itself this
provides motivation for an objective prior, the prior also minimizes a
corresponding information criterion.


Abstract:
  Autism is one of the most important neurological disorders which leads to
problems in a person's social interactions. Improvement of brain imaging
technologies and techniques help us to build brain structural and functional
networks. Finding networks topology pattern in each of the groups (autism and
healthy control) can aid us to achieve an autism disorder screening model. In
the present study, we have utilized the genetic algorithm to extract a
discriminative sub-network that represents differences between two groups
better. In the fitness evaluation phase, for each sub-network, a machine
learning model was trained using various entropy features of the sub-network
and its performance was measured. Proper model performance implies extracting a
good discriminative sub-network. Network entropies can be used as network
topological descriptors. The evaluation results indicate the acceptable
performance of the proposed screening method based on extracted discriminative
sub-networks and the machine learning models succeeded in obtaining a maximum
accuracy of 73.1% in structural networks of the UCLA dataset, 82.2% in
functional networks of the UCLA dataset, and 66.1% in functional networks of
ABIDE datasets.


Abstract:
  Physical properties of two-dimensional van der Waals (vdWs) structures depend
sensitively on both stacking orders and interlayer interactions. Yet, in most
cases studied to date, the interlayer interaction is considered to be a static
property of the vdWs structures. Here we demonstrate that applying a scanning
tunneling microscopy (STM) tip pulse on twisted bilayer graphene (TBG) can
induce sub-Angstrom fluctuations of the interlayer separation in the TBG, which
are equivalent to dynamic vertical external pressure of about 10 GPa on the
TBG. The sub-Angstrom fluctuations of the interlayer separation result in large
oscillations of the energy separations between two van Hove singularities
(VHSs) in the TBG. The period of the oscillations of the VHSs spacing is
extremely long, about 500-1000 seconds, attributing to tip-induced local stress
in the atomic-thick TBG. Our result provides an efficient method to tune and
measure the physical properties of the vdWs structures dynamically.


Abstract:
  Effective Field Theories are an established framework to bridge the gap
between UV and low energy theories. In the context of the Standard Model, the
bottom-up approach extends its operator set and thus equips us to astutely
probe its observables while encapsulating indirect evidence of unknown high
scale theories. While the top-down approach, on the other hand, employs
functional techniques to integrate out the heavy fields from a BSM Lagrangian
leading to a set of SMEFT operators. An intricate interplay of the two
approaches enhances the efficacy of the SMEFT in making meaningful predictions
while providing a platform for conducting a coherent comparison of new physics
scenarios. However, while the bottom-up approach fails to indicate the origin
of the effective operators, the top-down approach is highly dependent on the
specific model assumptions of the UV theory. We, for the first time, are
proposing a diagrammatic approach to establish selection criteria for the
allowed heavy field representations corresponding to each SMEFT operator. This,
in turn, paves the way to construct observable driven new physics models. While
we take a predominantly minimalistic approach, we also highlight the necessity
for non-minimal interactions for certain operators.


Abstract:
  Let $\Omega\subset\mathbb{C}$ be a bounded domain such that there exists an
algebraic harmonic function of degree two vanishing on the boundary of
$\Omega.$ Then we show that the Khavinson-Shapiro conjecture holds for
$\Omega:$ if the Dirichlet problem on $\Omega$ with all polynomial boundary
data have polynomial solutions, then $\Omega$ must be an ellipse. We also prove
that if there exists a rational function with a singularity in $\Omega$, such
that the Dirichlet problem for its restriction on $\partial\Omega$ along with
all polynomial functions have rational solutions, then $\Omega$ must be a disc.
This generalizes a well-known result by Bell, Ebenfelt, Khavinson, and Shapiro.
Our proofs are purely algebraic.


Abstract:
  We explore the clustering of galaxy groups in the Galaxy and Mass Assembly
(GAMA) survey to investigate the dependence of group bias and profile on
separation scale and group mass. Due to the inherent uncertainty in estimating
the group selection function, and hence the group auto-correlation function, we
instead measure the projected galaxy--group cross-correlation function. We find
that the group profile has a strong dependence on scale and group mass on
scales $r_\bot \lesssim 1 h^{-1} \mathrm{Mpc}$. We also find evidence that the
most massive groups live in extended, overdense, structures. In the first
application of marked clustering statistics to groups, we find that group-mass
marked clustering peaks on scales comparable to the typical group radius of
$r_\bot \approx 0.5 h^{-1} \mathrm{Mpc}$. While massive galaxies are associated
with massive groups, the marked statistics show no indication of galaxy mass
segregation within groups. We show similar results from the IllustrisTNG
simulations and the L-Galaxies model, although L-Galaxies shows an enhanced
bias and galaxy mass dependence on small scales.


Abstract:
  We present a row reduction algorithm to compute the barcode decomposition of
persistence modules. This algorithm dualises the standard persistence one and
clarifies the symmetry between clear and compress optimisations.


Abstract:
  Let us consider a semilinear boundary value problem $ - \Delta u= f(x,u),$ in
$\Omega,$ with Dirichlet boundary conditions, where $ \Omega \subset
\mathbb{R}^N $, $N> 2,$ is a bounded smooth domain. We provide sufficient
conditions guarantying that semi-stable weak positive solutions to subcritical
semilinear elliptic equations are smooth in any dimension, and as a
consequence, classical solutions. By a subcritical nonlinearity we mean
$f(x,s)/s^\frac{N+2}{N-2} \to 0$ as $s\to\infty$, including non-power
nonlinearities, and enlarging the class of subcritical nonlinearities, which is
usually reserved for power like nonlinearities.


Abstract:
  In this paper, we obtain two Lichnerowicz type formulas for the Dirac-Witten
operators. And we give the proof of Kastler-Kalau-Walze type theorems for the
Dirac-Witten operators on 4-dimensional and 6- dimensional compact manifolds
with (resp.without) boundary


Abstract:
  In this paper we investigate iteration of maps on lattices and the
corresponding polynomial-like iterative equation. Since a lattice need not have
a metric space structure, neither the Schauder fixed point theorem nor the
Banach fixed point theorem is available. Using Tarski's fixed point theorem, we
prove the existence of order-preserving solutions on convex complete
sublattices of Riesz spaces. Further, in $\mathbb{R}^n$ and $\mathbb{R}$,
special cases of Riesz space, we discuss upper semi-continuous solutions and
integrable solutions respectively. Finally, we indicate more special cases of
Riesz space for discussion on the iterative equation.


Abstract:
  In the present paper we deal with parabolic fractional initial-boundary value
problems of Sturm Liouville type in an interval and in a general star graph. We
first give several existence, uniqueness and regularity results of weak and
very-weak solutions. We prove the existence and uniqueness of solutions to a
quadratic boundary optimal control problem and provide a characterization of
the optimal contol via the Euler Lagrange first order optimality conditions. We
then investigate the analogous problems for a fractional Sturm Liouville
problem in a general star graph with mixed Dirichlet and Neumann boundary
controls. The existence and uniqueness of minimizers, and the characterization
of the first order optimality conditions are obtained in a general star graph
by using the method of Lagrange multipliers.


Abstract:
  We propose a one-dimensional (1D) model for the three-dimensional(3D)
incompressible ideal magnetohydrodynamics. We establish a regularity criterion
of the Beale-Kato-Majda type for this 1D model. Without the stretching effect,
the model with only transport effect equipped with a proper sign is shown to
have global in time strong solution. Some numerical simulations suggest that
solutions of this model with smooth periodic initial data do not tend to
develop singularities at finite time.


Abstract:
  Networked systems are widely applicable in real-world scenarios such as
social networks, infrastructure networks, and biological networks. Among those
applications, we are interested in social networks due to their complexity and
popularity. One crucial task on the social network is to recommend new content
based on special characteristics of the graph structure. In this project, we
aim to enhance the recommender systems by preventing the recommendations from
leaning towards contents from closed communities. To counteract the bias, we
will consider information dissemination across network as a metric to assess
the recommendation for contents e.g. new connections and news feed. We use
academic collaboration network and user-item interaction datasets from Yelp to
simulate an environment for connection recommendations and to validate the
proposed algorithm.


Abstract:
  Axisymmetric disks of eccentric orbits in near-Keplerian potentials are
unstable to an out-of-plane buckling. Recently, Zderic et al. (2020) showed
that an idealized disk saturates to a lopsided mode. Here we show that this
apsidal clustering also occurs in a primordial scattered disk in the outer
solar system which includes the orbit-averaged gravitational influence of the
giant planets. We explain the dynamics using Lynden-Bell (1979)'s mechanism for
bar formation in galaxies. We also show surface density and line of sight
velocity plots at different times during the instability, highlighting the
formation of concentric circles and spiral arms in velocity space.


Abstract:
  Many machine learning frameworks have been proposed and used in wireless
communications for realizing diverse goals. However, their incapability of
adapting to the dynamic wireless environment and tasks and of self-learning
limit their extensive applications and achievable performance. Inspired by the
great flexibility and adaptation of primate behaviors due to the brain
cognitive mechanism, a unified cognitive learning (CL) framework is proposed
for the dynamic wireless environment and tasks. The mathematical framework for
our proposed CL is established. Using the public and authoritative dataset, we
demonstrate that our proposed CL framework has three advantages, namely, the
capability of adapting to the dynamic environment and tasks, the self-learning
capability and the capability of 'good money driving out bad money' by taking
modulation recognition as an example. The proposed CL framework can enrich the
current learning frameworks and widen the applications.


Abstract:
  We discuss renormalization group flows in two-dimensional quantum field
theories with (0,2) supersymmetry. We focus on theories with UV described by a
Landau-Ginzburg Lagrangian and use the chiral algebra to constrain the IR
dynamics. We present examples where the structure of the chiral algebra is
incompatible with unitarity of the IR superconformal theory and discuss the
implications of this result for programs of classifying (0,2) SCFTs as
endpoints of flows from simple Lagrangian theories.


Abstract:
  In this paper, we study the maximum edge augmentation problem in directed
Laplacian networks to improve their robustness while preserving lower bounds on
their strong structural controllability (SSC). Since adding edges could
adversely impact network controllability, the main objective is to maximally
densify a given network by selectively adding missing edges while ensuring that
SSC of the network does not deteriorate beyond certain levels specified by the
SSC bounds. We consider two widely used bounds: first is based on the notion of
zero forcing (ZF), and the second relies on the distances between nodes in a
graph. We provide an edge augmentation algorithm that adds the maximum number
of edges in a graph while preserving the ZF-based SSC bound, and also derive a
closed-form expression for the exact number of edges added to the graph. Then,
we examine the edge augmentation problem while preserving the distance-based
bound and present a randomized algorithm that guarantees an approximate
solution with high probability. Finally, we numerically evaluate and compare
these edge augmentation solutions.


Abstract:
  Phase can be reliably estimated from a single diffracted intensity image, if
a faithful prior information about the object is available. Examples include
amplitude bounds, object support, sparsity in the spatial or a transform
domain, deep image prior and the prior learnt from the labelled datasets by a
deep neural network. Deep learning facilitates state of art reconstruction
quality but requires a large labelled dataset (ground truth-measurement pair
acquired in the same experimental conditions) for training. To alleviate this
data requirement problem, this letter proposes a zero-shot learning method. The
letter demonstrates that the object-prior learnt by a deep neural network while
being trained for a denoising task can also be utilized for the phase
retrieval, if the diffraction physics is effectively enforced on the network
output. The letter additionally demonstrates that the incorporation of total
variation in the proposed zero-shot framework facilitates the reconstruction of
similar quality in lesser time (e.g. ~8.5 fold, for a test reported in this
letter).


Abstract:
  We consider coincidence Reidemeister zeta functions for tame endomorphism
pairs of nilpotent groups of finite rank, shedding new light on the subject by
means of profinite completion techniques.
  In particular, we provide a closed formula for coincidence Reidemeister
numbers for iterations of endomorphism pairs of torsion-free nilpotent groups
of finite rank, based on a weak commutativity condition, which derives from
simultaneous triangularisability on abelian sections. Furthermore, we present
results in support of a P\'olya-Carlson dichotomy between rationality and a
natural boundary for the analytic behaviour of the zeta functions in question.


Abstract:
  This study identifies and proposes techniques to alleviate two key
bottlenecks to executing deep neural networks in trusted execution environments
(TEEs): page thrashing during the execution of convolutional layers and the
decryption of large weight matrices in fully-connected layers. For the former,
we propose a novel partitioning scheme, y-plane partitioning, designed to (i)
provide consistent execution time when the layer output is large compared to
the TEE secure memory; and (ii) significantly reduce the memory footprint of
convolutional layers. For the latter, we leverage quantization and compression.
In our evaluation, the proposed optimizations incurred latency overheads
ranging from 1.09X to 2X baseline for a wide range of TEE sizes; in contrast,
an unmodified implementation incurred latencies of up to 26X when running
inside of the TEE.


Abstract:
  In this article, we propose a new hypothesis testing method for directed
acyclic graph (DAG). While there is a rich class of DAG estimation methods,
there is a relative paucity of DAG inference solutions. Moreover, the existing
methods often impose some specific model structures such as linear models or
additive models, and assume independent data observations. Our proposed test
instead allows the associations among the random variables to be nonlinear and
the data to be time-dependent. We build the test based on some highly flexible
neural networks learners. We establish the asymptotic guarantees of the test,
while allowing either the number of subjects or the number of time points for
each subject to diverge to infinity. We demonstrate the efficacy of the test
through simulations and a brain connectivity network analysis.


Abstract:
  TOI-216 hosts a pair of warm, large exoplanets discovered by the TESS
Mission. These planets were found to be in or near the 2:1 resonance, and both
of them exhibit transit timing variations (TTVs). Precise characterization of
the planets' masses and radii, orbital properties, and resonant behavior can
test theories for the origins of planets orbiting close to their stars.
Previous characterization of the system using the first six sectors of TESS
data suffered from a degeneracy between planet mass and orbital eccentricity.
Radial velocity measurements using HARPS, FEROS, and PFS break that degeneracy,
and an expanded TTV baseline from TESS and an ongoing ground-based transit
observing campaign increase the precision of the mass and eccentricity
measurements. We determine that TOI-216c is a warm Jupiter, TOI-216b is an
eccentric warm Neptune, and that they librate in the 2:1 resonance with a
moderate libration amplitude of 60 +/- 2 degrees; small but significant free
eccentricity of 0.0222 +0.0005/-0.0003 for TOI-216b; and small but significant
mutual inclination of 1.2-3.9 degrees (95% confidence interval). The libration
amplitude, free eccentricity, and mutual inclination imply a disturbance of
TOI-216b before or after resonance capture, perhaps by an undetected third
planet.


Abstract:
  The selection of time step plays a crucial role in improving stability and
efficiency in the Discontinuous Galerkin (DG) solution of hyperbolic
conservation laws on adaptive moving meshes that typically employs explicit
stepping. A commonly used selection of time step has been based on CFL
conditions established for fixed and uniform meshes. This work provides a
mathematical justification for those time step selection strategies used in
practical adaptive DG computations. A stability analysis is presented for a
moving mesh DG method for linear scalar conservation laws. Based on the
analysis, a new selection strategy of the time step is proposed, which takes
into consideration the coupling of the $\alpha$-function (that is related to
the eigenvalues of the Jacobian matrix of the flux and the mesh movement
velocity) and the heights of the mesh elements. The analysis also suggests
several stable combinations of the choices of the $\alpha$-function in the
numerical scheme and in the time step selection. Numerical results obtained
with a moving mesh DG method for Burgers' and Euler equations are presented.


Abstract:
  Based on first-principles calculations including a Coulomb repulsion term, we
identify trends in the electronic reconstruction of $A$NiO$_2$/SrTiO$_3$(001)
($A=$ Pr, La) and $A$CuO$_2$/SrTiO$_3$(001) ($A=$ Ca, Sr). Common to all cases
is the emergence of a quasi-two-dimensional electron gas (q2DEG) in
SrTiO$_3$(001), albeit the higher polarity mismatch at the interface of
nickelates vs. cuprates to the nonpolar SrTiO$_3(001)$ substrate (${3+}/0$ vs.
${2+}/0$) results in an enhanced q2DEG carrier density. The simulations reveal
a significant dependence of the interfacial Ti $3d_{xy}$ band bending on the
rare-earth ion in the nickelate films, being $20$-$30\%$ larger for PrNiO$_2$
and NdNiO$_2$ than for LaNiO$_2$. Contrary to expectations from the formal
polarity mismatch, the electrostatic doping in the films is twice as strong in
cuprates as in nickelates. We demonstrate that the depletion of the self-doping
rare-earth $5d$ states enhances the similarity of nickelate and cuprate Fermi
surfaces in film geometry, reflecting a single hole in the Ni and Cu
$3d_{x^2-y^2}$ orbitals. Finally, we show that NdNiO$_2$ films grown on a polar
NdGaO$_3(001)$ substrate feature a simultaneous suppression of q2DEG formation
as well as Nd~$5d$ self-doping.


Abstract:
  The great success of modern machine learning models on large datasets is
contingent on extensive computational resources with high financial and
environmental costs. One way to address this is by extracting subsets that
generalize on par with the full data. In this work, we propose a general
framework, GRAD-MATCH, which finds subsets that closely match the gradient of
the training or validation set. We find such subsets effectively using an
orthogonal matching pursuit algorithm. We show rigorous theoretical and
convergence guarantees of the proposed algorithm and, through our extensive
experiments on real-world datasets, show the effectiveness of our proposed
framework. We show that GRAD-MATCH significantly and consistently outperforms
several recent data-selection algorithms and achieves the best
accuracy-efficiency trade-off. GRAD-MATCH is available as a part of the CORDS
toolkit: \url{https://github.com/decile-team/cords}.


Abstract:
  Molecular dynamics calculations were preformed on organic crystals
4-N,N-dimethylamino-4'-N'-methyl-stilbazolium tosylate (DAST) and
4-N,N-dimethylamino-4'-N'-methylstilbazolium 2,4,6-trimethylbenzenesulfonate
(DSTMS). Vibrational modes of the structures were investigated to examine the
single unit cell phononic contribution of the organic crystals to their
terahertz generating capabilities. Linear optical properties were also
calculated from snapshots of the molecular dynamics structures through
Green-Kubo relations, and compared with experimental transmission.


Abstract:
  Crater chronologies are a fundamental tool to assess relative and absolute
ages of planetary surfaces when direct radiometric dating is not available.
Martian crater chronologies are derived from lunar crater spatial densities on
terrains with known radiometric ages, and thus they critically depend on the
extrapolation Moon to Mars. This extrapolation requires knowledge of the time
evolution of the impact flux, including contributions from various impactor
populations, factors that are not trivially connected to the dynamical
evolution of the early Solar System. In this paper, we will present a new
martian crater chronology based on current dynamical models, and consider the
main sources of uncertainties. The new martian crater chronology is discussed
using two interesting applications: Jezero crater's dark terrain (relevant to
the NASA Mars 2020 mission) and the southern heavily cratered highlands.
[abridged]


Abstract:
  Thurston obtained a combinatorial characterization for generic branched
self-coverings that preserve the orientation of the oriented 2-sphere by
associating a planar graph to them [arXiv:1502.04760]. In this work, the
Thurston result is generalized to any branched covering of the oriented
2-sphere. To achieve that the notion of local balance introduced by Thurston is
generalized. As an application, a new proof for a Theorem of
Eremenko-Gabrielov-Mukhin-Tarasov-Varchenko [MR1888795], [MR2552110] is
obtained. This theorem corresponded to a special case of the B. \& M. Shapiro
conjecture. In this case, it refers to generic rational functions stating that
a generic rational function $ R : \mathbb{C}\mathbb{P}^1 \rightarrow
\mathbb{C}\mathbb{P}^1$ with only real critical points can be transformed by
post-composition with an automorphism of $\mathbb{C}\mathbb{P}^1$ into a
quotient of polynomials with real coefficients. Operations against balanced
graphs are introduced.


Abstract:
  Computer vision is increasingly effective at segmenting objects in images and
videos; however, scene effects related to the objects -- shadows, reflections,
generated smoke, etc -- are typically overlooked. Identifying such scene
effects and associating them with the objects producing them is important for
improving our fundamental understanding of visual scenes, and can also assist a
variety of applications such as removing, duplicating, or enhancing objects in
video. In this work, we take a step towards solving this novel problem of
automatically associating objects with their effects in video. Given an
ordinary video and a rough segmentation mask over time of one or more subjects
of interest, we estimate an omnimatte for each subject -- an alpha matte and
color image that includes the subject along with all its related time-varying
scene elements. Our model is trained only on the input video in a
self-supervised manner, without any manual labels, and is generic -- it
produces omnimattes automatically for arbitrary objects and a variety of
effects. We show results on real-world videos containing interactions between
different types of subjects (cars, animals, people) and complex effects,
ranging from semi-transparent elements such as smoke and reflections, to fully
opaque effects such as objects attached to the subject.