Abstract:
  Many characteristics of dwarf carbon stars are broadly consistent with a
binary origin, including mass transfer from an evolved companion. While the
population overall appears to have old-disc or halo kinematics, roughly
2$\,$per cent of these stars exhibit H$\alpha$ emission, which in low-mass
main-sequence stars is generally associated with rotation and relative youth.
Its presence in an older population therefore suggests either irradiation or
spin-up. This study presents time-series analyses of photometric and
radial-velocity data for seven dwarf carbon stars with H$\alpha$ emission. All
are shown to have photometric periods in the range 0.2--5.2$\,$d, and orbital
periods of similar length, consistent with tidal synchronisation. It is
hypothesised that dwarf carbon stars with emission lines are the result of
close-binary evolution, indicating that low-mass, metal-weak or metal-poor
stars can accrete substantial material prior to entering a common-envelope
phase.


Abstract:
  Many super-resolution (SR) models are optimized for high performance only and
therefore lack efficiency due to large model complexity. As large models are
often not practical in real-world applications, we investigate and propose
novel loss functions, to enable SR with high perceptual quality from much more
efficient models. The representative power for a given low-complexity generator
network can only be fully leveraged by strong guidance towards the optimal set
of parameters. We show that it is possible to improve the performance of a
recently introduced efficient generator architecture solely with the
application of our proposed loss functions. In particular, we use a Fourier
space supervision loss for improved restoration of missing high-frequency (HF)
content from the ground truth image and design a discriminator architecture
working directly in the Fourier domain to better match the target HF
distribution. We show that our losses' direct emphasis on the frequencies in
Fourier-space significantly boosts the perceptual image quality, while at the
same time retaining high restoration quality in comparison to previously
proposed loss functions for this task. The performance is further improved by
utilizing a combination of spatial and frequency domain losses, as both
representations provide complementary information during training. On top of
that, the trained generator achieves comparable results with and is 2.4x and
48x faster than state-of-the-art perceptual SR methods RankSRGAN and SRFlow
respectively.


Abstract:
  In this article, we investigate Maximum Likelihood Estimation with tools from
Tropical Geometry and Bernstein--Sato theory. We investigate the critical
points of very affine varieties and study their asymptotic behavior. We relate
these asymptotics to particular rays in the tropical variety as well as to
Bernstein--Sato ideals and give a connection to Maximum Likelihood Estimation
in Statistics.


Abstract:
  We introduce a novel method for discerning optical telescope images of stars
from those of galaxies using Gaussian processes (GPs). Although applications of
GPs often struggle in high-dimensional data modalities such as optical image
classification, we show that a low-dimensional embedding of images into a
metric space defined by the principal components of the data suffices to
produce high-quality predictions from real large-scale survey data. We develop
a novel method of GP classification hyperparameter training that scales
approximately linearly in the number of image observations, which allows for
application of GP models to large-size Hyper Suprime-Cam (HSC) Subaru Strategic
Program data. In our experiments we evaluate the performance of a principal
component analysis (PCA) embedded GP predictive model against other machine
learning algorithms including a convolutional neural network and an image
photometric morphology discriminator. Our analysis shows that our methods
compare favorably with current methods in optical image classification while
producing posterior distributions from the GP regression that can be used to
quantify object classification uncertainty. We further describe how
classification uncertainty can be used to efficiently parse large-scale survey
imaging data to produce high-confidence object catalogs.


Abstract:
  In this work we investigate greedy energy sequences on the unit circle for
the logarithmic and Riesz potentials. By definition, if $(a_n)_{n=0}^{\infty}$
is a greedy $s$-energy sequence on the unit circle, the Riesz potential
$U_{N,s}(x):=\sum_{k=0}^{N-1}|a_k-x|^{-s}$, $s>0$, generated by the first $N$
points of the sequence attains its minimum value at the point $a_{N}$, for
every $N\geq 1$. In the case $s=0$ we minimize instead the logarithmic
potential $U_{N,0}(x):=-\sum_{k=0}^{N-1}\log |a_{k}-x|$. We analyze the
asymptotic properties of these extremal values $U_{N,s}(a_N)$, studying
separately the cases $s=0$, $0<s<1$, $s=1$, and $s>1$. We obtain second-order
asymptotic formulas for $U_{N,s}(a_N)$ in the cases $s=0$, $0<s<1$, and $s=1$
(the corresponding first-order formulas are well known). A first-order result
for $s>1$ is proved, and it is shown that the normalized sequence
$U_{N,s}(a_N)/N^s$ is bounded and divergent in this case. We also consider,
briefly, greedy energy sequences in which the minimization condition is
required starting from the point $a_{p+1}$ (instead of the point $a_{1}$ as
previously stated), for some $p\geq 1$. For this more general class of greedy
sequences, we prove a first-order asymptotic result for $0\leq s<1$.


Abstract:
  We present here a self-consistent cosmological zoom-in simulation of a triple
supermassive black hole (SMBH) system forming in a complex multiple galaxy
merger. The simulation is run with an updated version of our code KETJU, which
is able to follow the motion of SMBHs down to separations of tens of
Schwarzschild radii while simultaneously modeling the large-scale astrophysical
processes in the surrounding galaxies, such as gas cooling, star formation, and
stellar and AGN feedback. Our simulation produces initially a SMBH binary
system for which the hardening process is interrupted by the late arrival of a
third SMBH. The KETJU code is able to accurately model the complex behavior
occurring in such a triple SMBH system, including the ejection of one SMBH to a
kiloparsec-scale orbit in the galaxy due to strong three-body interactions as
well as Lidov-Kozai oscillations suppressed by relativistic precession when the
SMBHs are in a hierarchical configuration. One pair of SMBHs merges $\sim
3\,\mathrm{Gyr}$ after the initial galaxy merger, while the remaining binary is
at a parsec-scale separation when the simulation ends at redshift $z=0$. We
also show that KETJU can capture the effects of the SMBH binaries and triplets
on the surrounding stellar population, which can affect the binary merger
timescales as the stellar density in the system evolves. Our results
demonstrate the importance of dynamically resolving the complex behavior of
multiple SMBHs in galactic mergers, as such systems cannot be readily modeled
using simple orbit-averaged semi-analytic models.


Abstract:
  Distribution grid agents are obliged to exchange and disclose their states
explicitly to neighboring regions to enable distributed optimal power flow
dispatch. However, the states contain sensitive information of individual
agents, such as voltage and current measurements. These measurements can be
inferred by adversaries, such as other participating agents or eavesdroppers.
To address the issue, we propose a privacy-preserving distributed optimal power
flow (OPF) algorithm based on partially homomorphic encryption (PHE). First of
all, we exploit the alternating direction method of multipliers (ADMM) to solve
the OPF in a distributed fashion. In this way, the dual update of ADMM can be
encrypted by PHE. We further relax the augmented term of the primal update of
ADMM with the $\ell_1$-norm regularization. In addition, we transform the
relaxed ADMM with the $\ell_1$-norm regularization to a semidefinite program
(SDP), and prove that this transformation is exact. The SDP can be solved
locally with only the sign messages from neighboring agents, which preserves
the privacy of the primal update. At last, we strictly prove the privacy
preservation guarantee of the proposed algorithm. Numerical case studies
validate the effectiveness and exactness of the proposed approach.


Abstract:
  Remote state preparation (RSP) provides a useful way of transferring quantum
information between two distant nodes based on the previously shared
entanglement. In this paper, we study RSP of an arbitrary single-photon state
in two degrees of freedom (DoFs). Using hyper-entanglement as a shared
resource, our first goal is to remotely prepare the single-photon state in
polarization and frequency DoFs and the second one is to reconstruct the
single-photon state in polarization and time-bin DoFs. In the RSP process, the
sender will rotate the quantum state in each DoF of the photon according to the
knowledge of the state to be communicated. By performing a projective
measurement on the polarization of the sender's photon, the original
single-photon state in two DoFs can be remotely reconstructed at the receiver's
quantum systems. This work demonstrates a novel capability for long-distance
quantum communication.


Abstract:
  In this present work, we have obtained a singularity-free spherically
symmetric stellar model with anisotropic pressure in the background of
Einstein's general theory of relativity. The Einstein's field equations have
been solved by exploiting Tolman {\em ansatz} [Richard C Tolman, Phys. Rev.
55:364, 1939] in $(3+1)$-dimensional space-time. Using observed values of mass
and radius of the compact star PSR J1903+327, we have calculated the numerical
values of all the constants from the boundary conditions. All the physical
characteristics of the proposed model have been discussed both analytically and
graphically. The new exact solution satisfies all the physical criteria for a
realistic compact star. The matter variables are regular and well behaved
throughout the stellar structure. Constraints on model parameters have been
obtained. All the energy conditions are verified with the help of graphical
representation. The stability condition of the present model has been described
through different testings.


Abstract:
  This paper describes the system submitted by our team, KBCNMUJAL, for Task 2
of the shared task Hate Speech and Offensive Content Identification in
Indo-European Languages (HASOC), at Forum for Information Retrieval Evaluation,
December 16-20, 2020, Hyderabad, India. The datasets of two Dravidian languages
Viz. Malayalam and Tamil of size 4000 observations, each were shared by the
HASOC organizers. These datasets are used to train the machine using different
machine learning algorithms, based on classification and regression models. The
datasets consist of tweets or YouTube comments with two class labels offensive
and not offensive. The machine is trained to classify such social media
messages in these two categories. Appropriate n-gram feature sets are extracted
to learn the specific characteristics of the Hate Speech text messages. These
feature models are based on TFIDF weights of n-gram. The referred work and
respective experiments show that the features such as word, character and
combined model of word and character n-grams could be used to identify the term
patterns of offensive text contents. As a part of the HASOC shared task, the
test data sets are made available by the HASOC track organizers. The best
performing classification models developed for both languages are applied on
test datasets. The model which gives the highest accuracy result on training
dataset for Malayalam language was experimented to predict the categories of
respective test data. This system has obtained an F1 score of 0.77. Similarly
the best performing model for Tamil language has obtained an F1 score of 0.87.
This work has received 2nd and 3rd rank in this shared Task 2 for Malayalam and
Tamil language respectively. The proposed system is named HASOC_kbcnmujal.


Abstract:
  The use of external background knowledge can be beneficial for the task of
matching schemas or ontologies automatically. In this paper, we exploit six
general-purpose knowledge graphs as sources of background knowledge for the
matching task. The background sources are evaluated by applying three different
exploitation strategies. We find that explicit strategies still outperform
latent ones and that the choice of the strategy has a greater impact on the
final alignment than the actual background dataset on which the strategy is
applied. While we could not identify a universally superior resource, BabelNet
achieved consistently good results. Our best matcher configuration with
BabelNet performs very competitively when compared to other matching systems
even though no dataset-specific optimizations were made.


Abstract:
  The recent development of metasurfaces has motivated their potential use for
improving the performance of wireless communication networks by manipulating
the propagation environment through nearly-passive sub-wavelength scattering
elements arranged on a surface. However, most studies of this technology focus
on reflective metasurfaces, i.e., the surface reflects the incident signals
towards receivers located on the same side of the transmitter, which restricts
the coverage to one side of the surface. In this article, we introduce the
concept of intelligent omni-surface (IOS), which is able to serve mobile users
on both sides of the surface to achieve full-dimensional communications by
jointly engineering its reflective and refractive properties. The working
principle of the IOS is introduced and a novel hybrid beamforming scheme is
proposed for IOS-based wireless communications. Moreover, we present a
prototype of IOS-based wireless communications and report experimental results.
Furthermore, potential applications of the IOS to wireless communications
together with relevant research challenges are discussed.


Abstract:
  Diverse domains of science and engineering use parameterised mechanistic
models. Engineers and scientists can often hypothesise several rival models to
explain a specific process or phenomenon. Consider a model discrimination
setting where we wish to find the best mechanistic, dynamic model candidate and
the best model parameter estimates. Typically, several rival mechanistic models
can explain the available data, so design of dynamic experiments for model
discrimination helps optimally collect additional data by finding experimental
settings that maximise model prediction divergence. We argue there are two main
approaches in the literature for solving the optimal design problem: (i) the
analytical approach, using linear and Gaussian approximations to find
closed-form expressions for the design objective, and (ii) the data-driven
approach, which often relies on computationally intensive Monte Carlo
techniques. Olofsson et al. (ICML 35, 2018) introduced Gaussian process (GP)
surrogate models to hybridise the analytical and data-driven approaches, which
allowed for computationally efficient design of experiments for discriminating
between black-box models. In this study, we demonstrate that we can extend
existing methods for optimal design of dynamic experiments to incorporate a
wider range of problem uncertainty. We also extend the Olofsson et al. (2018)
method of using GP surrogate models for discriminating between dynamic
black-box models. We evaluate our approach on a well-known case study from
literature, and explore the consequences of using GP surrogates to approximate
gradient-based methods.


Abstract:
  Machine learning has achieved much success on supervised learning tasks with
large sets of well-annotated training samples. However, in many practical
situations, such strong and high-quality supervision provided by training data
is unavailable due to the expensive and labor-intensive labeling process.
Automatically identifying and recognizing object categories in a large volume
of unlabeled images with weak supervision remains an important, yet unsolved
challenge in computer vision. In this paper, we propose a novel machine
learning framework, artificial perceptual learning (APL), to tackle the problem
of weakly supervised image categorization. The proposed APL framework is
constructed using state-of-the-art machine learning algorithms as building
blocks to mimic the cognitive development process known as infant
categorization. We develop and illustrate the proposed framework by
implementing a wide-field fine-grain ecological survey of tree species over an
8,000-hectare area of the El Yunque rainforest in Puerto Rico. It is based on
unlabeled high-resolution aerial images of the tree canopy. Misplaced
ground-based labels were available for less than 1% of these images, which
serve as the only weak supervision for this learning framework. We validate the
proposed framework using a small set of images with high quality human
annotations and show that the proposed framework attains human-level cognitive
economy.


Abstract:
  Lee waves generated by stratified flow over rough bottom topography in the
ocean extract momentum and energy from the geostrophic flow, causing drag and
enhancing turbulence and mixing in the interior ocean when they break. Inviscid
linear theory is generally used to predict the generation rate of lee waves,
but the location and mechanism of wave breaking leading to eventual dissipation
of energy and irreversible mixing are poorly constrained. In this study, a
linear model with viscosity, diffusivity, and an upper boundary is used to
demonstrate the potential importance of the surface in reflecting lee wave
energy back into the interior, making the case for treating lee waves as a full
water column process. In the absence of critical levels, it is shown that lee
waves can be expected to interact with the upper ocean, resulting in enhanced
vertical velocities and dissipation and mixing near the surface. The impact of
the typical oceanic conditions of increasing background velocity and
stratification with height above bottom are investigated and shown to
contribute to enhanced upper ocean vertical velocities and mixing.


Abstract:
  Recently, several weakly supervised learning methods have been devoted to
utilize bounding box supervision for training deep semantic segmentation
models. Most existing methods usually leverage the generic proposal generators
(\eg, dense CRF and MCG) to produce enhanced segmentation masks for further
training segmentation models. These proposal generators, however, are generic
and not specifically designed for box-supervised semantic segmentation, thereby
leaving some leeway for improving segmentation performance. In this paper, we
aim at seeking for a more accurate learning-based class-agnostic pseudo mask
generator tailored to box-supervised semantic segmentation. To this end, we
resort to a pixel-level annotated auxiliary dataset where the class labels are
non-overlapped with those of the box-annotated dataset. For learning pseudo
mask generator from the auxiliary dataset, we present a bi-level optimization
formulation. In particular, the lower subproblem is used to learn
box-supervised semantic segmentation, while the upper subproblem is used to
learn an optimal class-agnostic pseudo mask generator. The learned pseudo
segmentation mask generator can then be deployed to the box-annotated dataset
for improving weakly supervised semantic segmentation. Experiments on PASCAL
VOC 2012 dataset show that the learned pseudo mask generator is effective in
boosting segmentation performance, and our method can further close the
performance gap between box-supervised and fully-supervised models. Our code
will be made publicly available at
https://github.com/Vious/LPG_BBox_Segmentation .


Abstract:
  As interest in autonomous driving increases, efforts are being made to meet
requirements for the high-level automation of vehicles. In this context, the
functionality inside the vehicle cabin plays a key role in ensuring a safe and
pleasant journey for driver and passenger alike. At the same time, recent
advances in the field of artificial intelligence (AI) have enabled a whole
range of new applications and assistance systems to solve automated problems in
the vehicle cabin. This paper presents a thorough survey on existing work that
utilizes AI methods for use-cases inside the driving cabin, focusing, in
particular, on application scenarios related to (1) driving safety and (2)
driving comfort. Results from the surveyed works show that AI technology has a
promising future in tackling in-cabin tasks within the autonomous driving
aspect.


Abstract:
  The widespread adoption of nonlinear Receding Horizon Control (RHC)
strategies by industry has led to more than 30 years of intense research
efforts to provide stability guarantees for these methods. However, current
theoretical guarantees require that each (generally nonconvex) planning problem
can be solved to (approximate) global optimality, which is an unrealistic
requirement for the derivative-based local optimization methods generally used
in practical implementations of RHC. This paper takes the first step towards
understanding stability guarantees for nonlinear RHC when the inner planning
problem is solved to first-order stationary points, but not necessarily global
optima. Special attention is given to feedback linearizable systems, and a
mixture of positive and negative results are provided. We establish that, under
certain strong conditions, first-order solutions to RHC exponentially stabilize
linearizable systems. Crucially, this guarantee requires that state costs
applied to the planning problems are in a certain sense `compatible' with the
global geometry of the system, and a simple counter-example demonstrates the
necessity of this condition. These results highlight the need to rethink the
role of global geometry in the context of optimization-based control.


Abstract:
  The third annual International Applied Category Theory Conference (ACT2020)
was planned to take place at MIT in Cambridge, Massachusetts USA. However, the
global COVID-19 pandemic made the prospect of holding a large in-person meeting
impossible, and the event was thus held completely online. Holding the talks
online had the new benefits of reducing carbon footprint, being inclusive of
people from more parts of the world, and producing higher-quality video talks,
which have been posted online for posterity.
  The ACT2020 contributions spanned a broad spectrum of application areas,
including databases, dynamical systems, functional programming, game theory,
lenses, neuroscience, probabilistic programming, natural language processing,
quantum mechanics, and cyberphysical systems. Papers featured a broad range of
categorical techniques.
  Papers in this Proceedings volume represents about half of the talks
presented at ACT2020. Being included in the proceedings vs. not is not an
indication of talk quality, but instead almost exclusively the choice of the
authors, e.g. to present work already published elsewhere.


Abstract:
  X-ray emission generated through solar-wind charge exchange (SWCX) is known
to contaminate X-ray observation data, the amount of which is often significant
or even dominant, particularly in the soft X-ray band, when the main target is
comparatively weak diffuse sources, depending on the space weather during the
observation. In particular, SWCX events caused by interplanetary coronal mass
ejections (ICMEs) tend to be spectrally rich and to provide critical
information about the metal abundance in the ICME plasma. We analyzed the
SN1006 background data observed with Suzaku on 2005 September 11 shortly after
an X6-class solar flare, signatures of which were separately detected together
with an associated ICME. We found that the data include emission lines from a
variety of highly ionized ions generated through SWCX. The relative abundances
of the detected ions were found to be consistent with those in past ICME-driven
SWCX events. Thus, we conclude that this event was ICME-driven. In addition, we
detected a sulfur XVI line for the first time as one from the SWCX emission,
the fact of which suggests that it is the most spectrally-rich SWCX event ever
observed. We suggest that observations of ICME-driven SWCX events can provide a
unique probe to study the population of highly-ionized ions in the plasma,
which is difficult to measure in currently-available in-situ observations.


Abstract:
  A key way to construct complex distributed systems is through modular
composition of linearizable concurrent objects. A prominent example is shared
registers, which have crash-tolerant implementations on top of message-passing
systems, allowing the advantages of shared memory to carry over to
message-passing. Yet linearizable registers do not always behave properly when
used inside randomized programs. A strengthening of linearizability, called
strong linearizability, has been shown to preserve probabilistic behavior, as
well as other hypersafety properties. In order to exploit composition and
abstraction in message-passing systems, it is crucial to know whether there
exist strongly-linearizable implementations of registers in message-passing.
This paper answers the question in the negative: there are no
strongly-linearizable fault-tolerant message-passing implementations of
multi-writer registers, max-registers, snapshots or counters. This result is
proved by reduction from the corresponding result by Helmi et al. The reduction
is a novel extension of the BG simulation that connects shared-memory and
message-passing, supports long-lived objects, and preserves strong
linearizability. The main technical challenge arises from the discrepancy
between the potentially minuscule fraction of failures to be tolerated in the
simulated message-passing algorithm and the large fraction of failures that can
afflict the simulating shared-memory system. The reduction is general and can
be viewed as the inverse of the ABD simulation of shared memory in
message-passing.


Abstract:
  Motivated by the recent observation of anomalous electron-positron angular
correlations in the decay of the 18.15 MeV 1+ excited states in 8Be, we
reexamine in detail the Standard Model expectations for these angular
correlations. The 18.15 MeV state is above particle threshold, and several
multipoles can contribute to its $e^+e^-$ decay. We present the general
theoretical expressions for $e^+e^-$ angular distributions for nuclear decay by
C0, C1, C2 M1, E1, and E2 multipoles, and we examine their relative
contribution to the $e^+e^-$ decay of 8Be at 18.15 MeV. We find that this
resonance is dominated by M1 and E1 decay, and that the ratio of M1 to E1
strength is a strong function of energy. This is in contract to the original
analysis of the $e^+e^-$ angular distributions, where the M1/E1 ratio was
assumed to be a constant over the energy region Ep = 0:8-1:2 MeV. We find that
the existence of a `bump' in the measured angular distribution is strongly
dependent on the assumed M1/E1 ratio, with the present analysis finding the
measured large-angle contributions to the $e^+e^-$ angular distribution to be
lower than expectation. Thus, in the current analysis we find no evidence for
axion decay in the 18.15 MeV resonance region of 8Be.


Abstract:
  The use of magneto-optical techniques to tune the plasmonic response of
nanostructures is a hot topic in active plasmonics, with fascinating
implications for several plasmon-based applications and devices. For this
emerging field, called magnetoplasmonics, plasmonic nanomaterials with strong
optical response to magnetic field are desired, which is generally challenging
to achieve with pure noble metals. To overcome this issue, several efforts have
been carried out to design and tailor the magneto-optical response of metal
nanostructures, mainly by combining plasmonic and magnetic materials in a
single nanostructure. In this tutorial we focus our attention on
magnetoplasmonic effects in purely plasmonic nanostructures, as they are a
valuable model system allowing for an easier rationalization of
magnetoplasmonic effects. The most common magneto-optical experimental methods
employed to measure these effects are introduced, followed by a review of the
major experimental observations that are discussed within the framework of an
analytical model developed for the rationalization of magnetoplasmonic effects.
Different materials are discussed, from noble metals to novel plasmonic
materials, such as heavily doped semiconductors.


Abstract:
  This paper addresses the problem of assessing trajectory quality in
conditions when no ground truth poses are available or when their accuracy is
not enough for the specific task - for example, small-scale mapping in outdoor
scenes. In our work, we propose a no-reference metric, Mutually Orthogonal
Metric (MOM), that estimates the quality of the map from registered point
clouds via the trajectory poses. MOM strongly correlates with full-reference
trajectory metric Relative Pose Error, making it a trajectory benchmarking tool
on setups where 3D sensing technologies are employed. We provide a mathematical
foundation for such correlation and confirm it statistically in synthetic
environments. Furthermore, since our metric uses a subset of points from
mutually orthogonal surfaces, we provide an algorithm for the extraction of
such subset and evaluate its performance in synthetic CARLA environment and on
KITTI dataset. The code of the proposed metric is publicly available as
pip-package.


Abstract:
  Object detection plays an important role in self-driving cars for security
development. However, mobile systems on self-driving cars with limited
computation resources lead to difficulties for object detection. To facilitate
this, we propose a compiler-aware neural pruning search framework to achieve
high-speed inference on autonomous vehicles for 2D and 3D object detection. The
framework automatically searches the pruning scheme and rate for each layer to
find a best-suited pruning for optimizing detection accuracy and speed
performance under compiler optimization. Our experiments demonstrate that for
the first time, the proposed method achieves (close-to) real-time, 55ms and
99ms inference times for YOLOv4 based 2D object detection and PointPillars
based 3D detection, respectively, on an off-the-shelf mobile phone with minor
(or no) accuracy loss.


Abstract:
  Spatio-temporal counts of infectious disease cases often contain an excess of
zeros. With existing zero inflated count models applied to such data it is
difficult to quantify space-time heterogeneity in the effects of disease spread
between areas. Also, existing methods do not allow for separate dynamics to
affect the reemergence and persistence of the disease. As an alternative, we
develop a new zero-state coupled Markov switching negative binomial model,
under which the disease switches between periods of presence and absence in
each area through a series of partially hidden nonhomogeneous Markov chains
coupled between neighboring locations. When the disease is present, an
autoregressive negative binomial model generates the cases with a possible 0
representing the disease being undetected. Bayesian inference and prediction is
illustrated using spatio-temporal counts of dengue fever cases in Rio de
Janeiro, Brazil.


Abstract:
  Verifying quantum systems has attracted a lot of interests in the last
decades. In this paper, we initialised the model checking of quantum
continuous-time Markov chain (QCTMC). As a real-time system, we specify the
temporal properties on QCTMC by signal temporal logic (STL). To effectively
check the atomic propositions in STL, we develop a state-of-art real root
isolation algorithm under Schanuel's conjecture; further, we check the general
STL formula by interval operations with a bottom-up fashion, whose query
complexity turns out to be linear in the size of the input formula by calling
the real root isolation algorithm. A running example of an open quantum walk is
provided to demonstrate our method.


Abstract:
  We employ the inductive structure of determinantal varieties to calculate the
weight filtration on local cohomology modules with determinantal support. We
show that the weight of a simple composition factor is uniquely determined by
its support and cohomological degree. As a consequence, we obtain the
equivariant structure of the Hodge filtration on each local cohomology module,
and we provide a formula for its generation level. In the case of square
matrices, we express the Hodge filtration in terms of the Hodge ideals for the
determinant hypersurface. As an application, we describe a recipe for
calculating the mixed Hodge module structure on any iteration of local
cohomology functors with determinantal support.


Abstract:
  Human motion prediction aims to forecast future human poses given a prior
pose sequence. The discontinuity of the predicted motion and the performance
deterioration in long-term horizons are still the main challenges encountered
in current literature. In this work, we tackle these issues by using a compact
manifold-valued representation of human motion. Specifically, we model the
temporal evolution of the 3D human poses as trajectory, what allows us to map
human motions to single points on a sphere manifold. To learn these
non-Euclidean representations, we build a manifold-aware Wasserstein generative
adversarial model that captures the temporal and spatial dependencies of human
motion through different losses. Extensive experiments show that our approach
outperforms the state-of-the-art on CMU MoCap and Human 3.6M datasets. Our
qualitative results show the smoothness of the predicted motions.


Abstract:
  Mixture-of-Experts (MoE) models can achieve promising results with outrageous
large amount of parameters but constant computation cost, and thus it has
become a trend in model scaling. Still it is a mystery how MoE layers bring
quality gains by leveraging the parameters with sparse activation. In this
work, we investigate several key factors in sparse expert models. We observe
that load imbalance may not be a significant problem affecting model quality,
contrary to the perspectives of recent studies, while the number of sparsely
activated experts $k$ and expert capacity $C$ in top-$k$ routing can
significantly make a difference in this context. Furthermore, we take a step
forward to propose a simple method called expert prototyping that splits
experts into different prototypes and applies $k$ top-$1$ routing. This
strategy improves the model quality but maintains constant computational costs,
and our further exploration on extremely large-scale models reflects that it is
more effective in training larger models. We push the model scale to over $1$
trillion parameters and implement it on solely $480$ NVIDIA V100-32GB GPUs, in
comparison with the recent SOTAs on $2048$ TPU cores. The proposed giant model
achieves substantial speedup in convergence over the same-size baseline.


Abstract:
  We present a method of defining projectors in the virtual Temperley-Lieb
algebra, that generalizes the Jones-Wenzl projectors in Temperley-Lieb algebra.
We show that the projectors have similar properties with the Jones-Wenzl
projectors, and contain an extra property which is associated with the virtual
generator elements, that is, the product of a projector with a virtual
generator is unchanged. We also show the uniqueness of the projector $f_n$ in
terms of its axiomatic properties in the virtual Temperley-Lieba algebra
$VTL_n(d)$. Finally, we find the coefficients of $f_n$ and give an explicit
formula for the projector $f_n$.


Abstract:
  We develop a novel Monte Carlo algorithm for the vector consisting of the
supremum, the time at which the supremum is attained and the position of an
exponentially tempered L\'{e}vy process. The algorithm, based on the increments
of the process without tempering, converges geometrically fast (as a function
of the computational cost) for discontinuous and locally Lipschitz functions of
the vector. We prove that the corresponding multilevel Monte Carlo estimator
has optimal computational complexity (i.e. of order $\epsilon^{-2}$ if the mean
squared error is at most $\epsilon^{2}$) and provide its central limit theorem
(CLT). Using the CLT we construct confidence intervals for barrier option
prices and various risk measures based on drawdown under the tempered stable
(CGMY) model calibrated/estimated on real-world data. We provide non-asymptotic
and asymptotic comparisons of our algorithm with existing approximations,
leading to rule-of-thumb guidelines for users to the best method for a given
set of parameters, and illustrate its performance with numerical examples.


Abstract:
  With recent progress in the field of Explainable Artificial Intelligence
(XAI) and increasing use in practice, the need for an evaluation of different
XAI methods and their explanation quality in practical usage scenarios arises.
For this purpose, we present VitrAI, which is a web-based service with the goal
of uniformly demonstrating four different XAI algorithms in the context of
three real life scenarios and evaluating their performance and
comprehensibility for humans. This work reveals practical obstacles when
adopting XAI methods and gives qualitative estimates on how well different
approaches perform in said scenarios.


Abstract:
  Extra dimensions can be utilized to simplify problems in classical mechanics,
offering new insights. Here we show a simple example of how the motion of a
test particle under the influence of an inverse-quadratic potential in 1D is
equivalent to that of another test particle moving freely in 2D Euclidean space
and 3D Minkowskian space.


Abstract:
  We design a neural network to extract and process features from absorption
images taken of one-dimensional Bose gases in the quasi-condensate regime.
Specifically, the network is trained to predict both the temperature of single
realizations of the system and the uncertainty thereof. For multiple
realizations, the individual predictions can be combined in an estimate of the
mean temperature, improving precision. We benchmark our model on both simulated
and experimentally measured data and compare it to the established method of
density ripples thermometry. We find the predictions of the two methods
compatible, although the neural network reaches similar precision needing much
fewer realizations, thus highlighting the efficiency gain achievable when
incorporating neural networks into analysis of data from cold gas experiments.
Further, we study feature maps to reveal which local features of the condensate
are extracted by the network and how said features correlate with properties of
the system. A similar analysis could be employed to uncover physical relations
in more complex systems.


Abstract:
  Art is a variety of human activities that include the production of visual,
auditory, or performing objects that express the creativity, creative concepts,
or technological abilities of the artist, intended primarily for their beauty
or emotional power to be appreciated. The renaissance of historic and forgotten
art has been made possible by modern developments in Artificial Intelligence.
Techniques for Computer Vision have long been related to such arts. Style
Transfer using Neural Networks refers to optimization techniques, where a
content image and a style image are taken and blended such that it feels like
the content image is reconstructed in the style image color palette. This paper
implements the Style Transfer using three different Neural Networks in form of
an application that is accessible to the general population thereby reviving
interest in lost art styles.


Abstract:
  We study the dynamics of the one-dimensional $\varepsilon$-dependent
Cahn-Hilliard / Allen-Cahn equation within a neighborhood of an equilibrium of
$N$ transition layers, that in general does not conserve mass. Two different
settings are considered which differ in that, for the second, we impose a
mass-conservation constraint in place of one of the zero-mass flux boundary
conditions at $x=1$. Motivated by the study of Carr and Pego on the layered
metastable patterns of Allen-Cahn in [10], and by this of Bates and Xun in [5]
for the Cahn-Hilliard equation, we implement an $N$-dimensional, and a
mass-conservative $ N-1$-dimensional manifold respectively; therein, a
metastable state with $N$ transition layers is approximated. We then determine,
for both cases, the essential dynamics of the layers (ode systems with the
equations of motion), expressed in terms of local coordinates relative to the
manifold used. In particular, we estimate the spectrum of the linearized
Cahn-Hilliard / Allen-Cahn operator, and specify wide families of
$\varepsilon$-dependent weights $\delta(\varepsilon)$, $\mu(\varepsilon)$,
acting at each part of the operator, for which the dynamics are stable and rest
exponentially small in $\varepsilon$. Our analysis enlightens the role of mass
conservation in the classification of the general mixed problem into two main
categories where the solution has a profile close to Allen-Cahn, or, when the
mass is conserved, close to the Cahn-Hilliard solution.


Abstract:
  We study actions of pointed Hopf algebras in the $\ZZ$-graded setting. Our
main result classifies inner-faithful actions of generalized Taft algebras on
quantum generalized Weyl algebras which respect the $\ZZ$-grading. We also show
that generically the invariant rings of Taft actions on quantum generalized
Weyl algebras are commutative Kleinian singularities.


Abstract:
  Quantum computing is among the most significant technologies to emerge in
recent decades, offering the promise of paradigm-shifting computational
capacity with significant ethical consequences. On a technical level, the
unique features of quantum computation have consequences for the imposition of
fairness and ethical constraints on computation. Despite its significance,
little if no structured research has been undertaken into the ethical
implications of quantum technologies. In this paper, we fill this gap in the
literature by presenting a roadmap for ethical quantum computing setting out
prospective research programmes. We situate quantum ethics at the
cross-disciplinary intersection of quantum information science, technology
ethics and moral philosophy. We summarise the key elements of quantum
information processing relevant to ethical analysis and set-out taxonomies for
use by researchers considering the ethics of quantum technologies. In doing so,
we inaugurate the cross-disciplinary field of the ethics of quantum computing.


Abstract:
  In the case that user profiles are not available, the recommendation based on
anonymous session is particularly important, which aims to predict the items
that the user may click at the next moment based on the user's access sequence
over a while. In recent years, with the development of recurrent neural
network, attention mechanism, and graph neural network, the performance of
session-based recommendation has been greatly improved. However, the previous
methods did not comprehensively consider the context dependencies and
short-term interest first of the session. Therefore, we propose a context-aware
short-term interest first model (CASIF).The aim of this paper is improve the
accuracy of recommendations by combining context and short-term interest. In
CASIF, we dynamically construct a graph structure for session sequences and
capture rich context dependencies via graph neural network (GNN), latent
feature vectors are captured as inputs of the next step. Then we build the
short-term interest first module, which can to capture the user's general
interest from the session in the context of long-term memory, at the same time
get the user's current interest from the item of the last click. In the end,
the short-term and long-term interest are combined as the final interest and
multiplied by the candidate vector to obtain the recommendation probability.
Finally, a large number of experiments on two real-world datasets demonstrate
the effectiveness of our proposed method.


Abstract:
  Subspace codes are collections of subspaces of a projective space such that
any two subspaces satisfy a pairwise minimum distance criterion. Recent results
have shown that it is possible to construct optimal $(5,3)$ subspace codes from
pairs of partial spreads in the projective space $\mathrm{PG}(4,q)$ over the
finite field $ \mathbb{F}_q $, termed doubling codes. We have utilized a
complete classification of maximal partial line spreads in $\mathrm{PG}(4,2)$
in literature to establish the types of the spreads in the doubling code
instances obtained from two recent constructions of optimum $(5,3)_q$ codes,
restricted to $ \mathbb{F}_2 $. Further we present a new characterization of a
subclass of binary doubling codes based on the intersection patterns of key
subspaces in the pair of constituent spreads.


Abstract:
  Generalised linear models for multi-class classification problems are one of
the fundamental building blocks of modern machine learning tasks. In this
manuscript, we characterise the learning of a mixture of $K$ Gaussians with
generic means and covariances via empirical risk minimisation (ERM) with any
convex loss and regularisation. In particular, we prove exact asymptotics
characterising the ERM estimator in high-dimensions, extending several previous
results about Gaussian mixture classification in the literature. We exemplify
our result in two tasks of interest in statistical learning: a) classification
for a mixture with sparse means, where we study the efficiency of $\ell_1$
penalty with respect to $\ell_2$; b) max-margin multi-class classification,
where we characterise the phase transition on the existence of the multi-class
logistic maximum likelihood estimator for $K>2$. Finally, we discuss how our
theory can be applied beyond the scope of synthetic data, showing that in
different cases Gaussian mixtures capture closely the learning curve of
classification tasks in real data sets.


Abstract:
  In its observation band, the Laser Interferometer Space Antenna (LISA) will
simultaneously observe stochastic gravitational-wave background (SGWB) signals
of different origins; orbitally modulated waveforms from galactic white dwarf
binaries, a binary black hole produced background, and possibly a
cosmologically produced SGWB. We simulate the emission of gravitational waves
from galactic white dwarf binaries based on the Lamberts \cite{Lamberts}
distributions and determine a complex waveform from the galactic foreground. We
generate the modulated galactic signal detected by LISA due to its orbital
motion, and present a data analysis strategy to address it. The Fisher
Information and Markov Chain Monte Carlo methods give an estimate of the LISA
noise and parameters for the different signal sources. We simultaneously
estimate the galactic foreground, the astrophysical and cosmological
backgrounds, and estimate detection limits for the future LISA observation of
the SGWB in the spectral domain with the 3 LISA channels $ A $, $ E $ and $ T
$. In the context of the expected astrophysical background and a galactic
foreground, a cosmological background energy density of about $
\Omega_{GW,Cosmo} \approx 8 \times 10^{-13} $ could be detected by LISA with
our spectral separation strategy.


Abstract:
  Recommending cold-start items is a long-standing and fundamental challenge in
recommender systems. Without any historical interaction on cold-start items, CF
scheme fails to use collaborative signals to infer user preference on these
items. To solve this problem, extensive studies have been conducted to
incorporate side information into the CF scheme. Specifically, they employ
modern neural network techniques (e.g., dropout, consistency constraint) to
discover and exploit the coalition effect of content features and collaborative
representations. However, we argue that these works less explore the mutual
dependencies between content features and collaborative representations and
lack sufficient theoretical supports, thus resulting in unsatisfactory
performance. In this work, we reformulate the cold-start item representation
learning from an information-theoretic standpoint. It aims to maximize the
mutual dependencies between item content and collaborative signals.
Specifically, the representation learning is theoretically lower-bounded by the
integration of two terms: mutual information between collaborative embeddings
of users and items, and mutual information between collaborative embeddings and
feature representations of items. To model such a learning process, we devise a
new objective function founded upon contrastive learning and develop a simple
yet effective Contrastive Learning-based Cold-start Recommendation
framework(CLCRec). In particular, CLCRec consists of three components:
contrastive pair organization, contrastive embedding, and contrastive
optimization modules. It allows us to preserve collaborative signals in the
content representations for both warm and cold-start items. Through extensive
experiments on four publicly accessible datasets, we observe that CLCRec
achieves significant improvements over state-of-the-art approaches in both
warm- and cold-start scenarios.


Abstract:
  Existing scene understanding systems mainly focus on recognizing the visible
parts of a scene, ignoring the intact appearance of physical objects in the
real-world. Concurrently, image completion has aimed to create plausible
appearance for the invisible regions, but requires a manual mask as input. In
this work, we propose a higher-level scene understanding system to tackle both
visible and invisible parts of objects and backgrounds in a given scene.
Particularly, we built a system to decompose a scene into individual objects,
infer their underlying occlusion relationships, and even automatically learn
which parts of the objects are occluded that need to be completed. In order to
disentangle the occluded relationships of all objects in a complex scene, we
use the fact that the front object without being occluded is easy to be
identified, detected, and segmented. Our system interleaves the two tasks of
instance segmentation and scene completion through multiple iterations, solving
for objects layer-by-layer. We first provide a thorough experiment using a new
realistically rendered dataset with ground-truths for all invisible regions. To
bridge the domain gap to real imagery where ground-truths are unavailable, we
then train another model with the pseudo-ground-truths generated from our
trained synthesis model. We demonstrate results on a wide variety of datasets
and show significant improvement over the state-of-the-art.


Abstract:
  As predictive models are deployed into the real world, they must increasingly
contend with strategic behavior. A growing body of work on strategic
classification treats this problem as a Stackelberg game: the decision-maker
"leads" in the game by deploying a model, and the strategic agents "follow" by
playing their best response to the deployed model. Importantly, in this
framing, the burden of learning is placed solely on the decision-maker, while
the agents' best responses are implicitly treated as instantaneous. In this
work, we argue that the order of play in strategic classification is
fundamentally determined by the relative frequencies at which the
decision-maker and the agents adapt to each other's actions. In particular, by
generalizing the standard model to allow both players to learn over time, we
show that a decision-maker that makes updates faster than the agents can
reverse the order of play, meaning that the agents lead and the decision-maker
follows. We observe in standard learning settings that such a role reversal can
be desirable for both the decision-maker and the strategic agents. Finally, we
show that a decision-maker with the freedom to choose their update frequency
can induce learning dynamics that converge to Stackelberg equilibria with
either order of play.


Abstract:
  Monte Carlo simulation has been employed to investigate the magnetic
properties and phase diagrams of ferrimagnetic mixed-spin (1/2, 1) triangular
Ising nanotube with core-shell structure. In particular, the effect of the
exchange couplings and the temperature on the magnetic and thermodynamic
properties, hysteresis loops as well as the compensation temperature have been
discussed in detail. Moreover, the effects of the single-ion anisotropy, as
well as external magnetic field, have been examined. The threshold values of
the exchange couplings and single-ion anisotropy have been found, determining
whether the system exhibits a compensation temperature. We have found that the
appearance of the compensation temperature is strongly linked with the system
parameters. Moreover, we have obtained the double and triple hysteresis loops
for certain physical parameters in the considered magnetic system.


Abstract:
  Label smoothing (LS) is an arising learning paradigm that uses the positively
weighted average of both the hard training labels and uniformly distributed
soft labels. It was shown that LS serves as a regularizer for training data
with hard labels and therefore improves the generalization of the model. Later
it was reported LS even helps with improving robustness when learning with
noisy labels. However, we observe that the advantage of LS vanishes when we
operate in a high label noise regime. Puzzled by the observation, we proceeded
to discover that several proposed learning-with-noisy-labels solutions in the
literature instead relate more closely to negative label smoothing (NLS), which
defines as using a negative weight to combine the hard and soft labels! We show
that NLS differs substantially from LS in their achieved model confidence. To
differentiate the two cases, we will call LS the positive label smoothing
(PLS), and this paper unifies PLS and NLS into generalized label smoothing
(GLS). We provide understandings for the properties of GLS when learning with
noisy labels. Among other established properties, we theoretically show NLS is
considered more beneficial when the label noise rates are high. We provide
extensive experimental results on multiple benchmarks to support our findings
too.


Abstract:
  Fake news detection is an important task for increasing the credibility of
information on the media since fake news is constantly spreading on social
media every day and it is a very serious concern in our society. Fake news is
usually created by manipulating images, texts, and videos. In this paper, we
present a novel method for detecting fake news by fusing multimodal features
derived from textual and visual data. Specifically, we used a pre-trained BERT
model to learn text features and a VGG-19 model pre-trained on the ImageNet
dataset to extract image features. We proposed a scale-dot product attention
mechanism to capture the relationship between text features and visual
features. Experimental results showed that our approach performs better than
the current state-of-the-art method on a public Twitter dataset by 3.1%
accuracy.


Abstract:
  Given the prevalence of missing data in modern statistical research, a broad
range of methods is available for any given imputation task. How does one
choose the 'best' imputation method in a given application? The standard
approach is to select some observations, set their status to missing, and
compare prediction accuracy of the methods under consideration of these
observations. Besides having to somewhat artificially mask observations, a
shortcoming of this approach is that imputations based on the conditional mean
will rank highest if predictive accuracy is measured with quadratic loss. In
contrast, we want to rank highest an imputation that can sample from the true
conditional distributions. In this paper, we develop a framework called
"Imputation Scores" (I-Scores) for assessing missing value imputations. We
provide a specific I-Score based on density ratios and projections, that is
applicable to discrete and continuous data. It does not require to mask
additional observations for evaluations and is also applicable if there are no
complete observations. The population version is shown to be proper in the
sense that the highest rank is assigned to an imputation method that samples
from the correct conditional distribution. The propriety is shown under the
missing completely at random (MCAR) assumption but is also shown to be valid
under missing at random (MAR) with slightly more restrictive assumptions. We
show empirically on a range of data sets and imputation methods that our score
consistently ranks true data high(est) and is able to avoid pitfalls usually
associated with performance measures such as RMSE. Finally, we provide the
R-package Iscores available on CRAN with an implementation of our method.


Abstract:
  We consider the sub-Riemannian $3$-sphere $(\mathbb{S}^3,g_h)$ obtained by
restriction of the Riemannian metric of constant curvature $1$ to the planar
distribution orthogonal to the vertical Hopf vector field. It is known that
$(\mathbb{S}^3,g_h)$ contains a family of spherical surfaces
$\{\mathcal{S}_\lambda\}_{\lambda\geq 0}$ with constant mean curvature
$\lambda$. In this work we first prove that the two closed half-spheres of
$\mathcal{S}_0$ with boundary $C_0=\{0\}\times\mathbb{S}^1$ minimize the
sub-Riemannian area among compact $C^1$ surfaces with the same boundary. We
also see that the only $C^2$ solutions to this Plateau problem are vertical
translations of such half-spheres. Second, we establish that the closed
$3$-ball enclosed by a sphere $\mathcal{S}_\lambda$ with $\lambda>0$ uniquely
solves the isoperimetric problem in $(\mathbb{S}^3,g_h)$ for $C^1$ sets inside
a vertical solid tube and containing a horizontal section of the tube. The
proofs mainly rely on calibration arguments.


Abstract:
  In this article we present a functional central limit theorem for the paths
of the directed random polymer in space dimension two in the subcritical
intermediate disorder regime. More precisely, diffusively rescaled polymer
paths tested against bounded continuous functions converge in probability to
their Brownian motion counterparts, when taking the weak disorder limit. So far
analogous results have only been established for $d\neq 2$. Moreover, we show
convergence in probability of the finite-dimensional marginal distributions of
the polymer measure to the ones of Brownian motion. Along the way, we prove a
local limit theorem which allows us to factorise the point-to-point partition
function of the directed polymer into a product of two point-to-plane partition
functions.


Abstract:
  High Altitude Platform Station (HAPS) has the potential to provide global
wireless connectivity and data services such as high-speed wireless backhaul,
industrial Internet of things (IoT), and public safety for large areas not
served by terrestrial networks. A unified HAPS design is desired to support
various use cases and a wide range of requirements. In this paper, we present
two architecture designs of the HAPS system: i) repeater based HAPS, and ii)
base station based HAPS, which are both viable technical solutions. The energy
efficiency is analyzed and compared between the two architectures using
consumption factor theory. The system performance of these two architectures is
evaluated through Monte Carlo simulations and is characterized in metrics of
spectral efficiency using LTE band 1 for both single-cell and multi-cell cases.
Both designs can provide good downlink spectral efficiency and coverage, while
the uplink coverage is significantly limited by UE transmit power and antenna
gain. Using directional antennas at the UEs can improve the system performance
for both downlink and uplink.


Abstract:
  Unsourced random access is a novel communication paradigm designed for
handling a large number of uncoordinated users that sporadically transmit very
short messages. Under this model, coded compressed sensing (CCS) has emerged as
a low-complexity scheme that exhibits good error performance. Yet, one of the
challenges faced by CCS pertains to disentangling a large number of codewords
present on a single factor graph. To mitigate this issue, this article
introduces a modified CCS scheme whereby active devices stochastically
partition themselves into groups that utilize separate sampling matrices with
low cross-coherence for message transmission. At the receiver, ideas from the
field of compressed demixing are employed for support recovery, and separate
factor graphs are created for message disambiguation in each cluster. This
reduces the number of active users on a factor graph, which improves
performance significantly in typical scenarios. Indeed, coded demixing reduces
the probability of error as the number of groups increases, up to a point.
Findings are supported with numerical simulations.


Abstract:
  Cloud applications are increasingly shifting from large monolithic services,
to large numbers of loosely-coupled, specialized microservices. Despite their
advantages in terms of facilitating development, deployment, modularity, and
isolation, microservices complicate resource management, as dependencies
between them introduce backpressure effects and cascading QoS violations.
  We present Sinan, a data-driven cluster manager for interactive cloud
microservices that is online and QoS-aware. Sinan leverages a set of scalable
and validated machine learning models to determine the performance impact of
dependencies between microservices, and allocate appropriate resources per tier
in a way that preserves the end-to-end tail latency target. We evaluate Sinan
both on dedicated local clusters and large-scale deployments on Google Compute
Engine (GCE) across representative end-to-end applications built with
microservices, such as social networks and hotel reservation sites. We show
that Sinan always meets QoS, while also maintaining cluster utilization high,
in contrast to prior work which leads to unpredictable performance or
sacrifices resource efficiency. Furthermore, the techniques in Sinan are
explainable, meaning that cloud operators can yield insights from the ML models
on how to better deploy and design their applications to reduce unpredictable
performance.


Abstract:
  I present a scheme to obtain a 2 to 40 GeV low emittance muon beam, not
requiring cooling and within today's technological resources, to be used for
early commissioning of muon accelerator projects, or alternatively dedicated
muon and neutrino parameter measurements. In particular, a muon rate of 5x10^4
mu/s in a normalized transverse emittance of 5 um at 22 GeV, and energy spread
of 1 GeV obtained from O(10^11) e+/s on target at 44 GeV. This emittance is
below the expected results of advanced emittance cooling techniques for muons
produced from protons-on-target, and represents an alternative for the duration
of complete muon cooling studies. The scheme has beam designed to adjust the
muon beam energy in the GeV energy range to the needs for precise parameter
measurements of muons and neutrinos. Although the rate is small compared to
other muon sources, it does not seem to represent a big limitation for its
usage. Furthermore, the muon rate could be in principle increased
proportionally to the availability of higher positron rates, already foreseen
for future collider projects.


Abstract:
  This article is motivated by the objective of providing a new analytically
tractable and fully frequentist framework to characterize and implement
regression trees while also allowing a multivariate (potentially high
dimensional) response. The connection to regression trees is made by a high
dimensional model with dynamic mean vectors over multi-dimensional change axes.
Our theoretical analysis is carried out under a single two dimensional change
point setting. An optimal rate of convergence of the proposed estimator is
obtained, which in turn allows existence of limiting distributions.
Distributional behavior of change point estimates are split into two distinct
regimes, the limiting distributions under each regime is then characterized, in
turn allowing construction of asymptotically valid confidence intervals for
$2d$-location of change. All results are obtained under a high dimensional
scaling $s\log^2 p=o(T_wT_h),$ where $p$ is the response dimension, $s$ is a
sparsity parameter, and $T_w,T_h$ are sampling periods along change axes. We
characterize full regression trees by defining a multiple multi-dimensional
change point model. Natural extensions of the single $2d$-change point
estimation methodology are provided. Two applications, first on segmentation of
{\it Infra-red astronomy satellite (IRAS)} data and second to segmentation of
digital images are provided. Methodology and theoretical results are supported
with monte-carlo simulations.


Abstract:
  New hardware architectures open up immense opportunities for supercomputer
simulations. However, programming techniques for different architectures vary
significantly, which leads to the necessity of developing and supporting
multiple code versions, each being optimized for specific hardware features.
The oneAPI framework, recently introduced by Intel, contains a set of
programming tools for the development of portable codes that can be compiled
and fine-tuned for CPUs, GPUs, FPGAs, and accelerators. In this paper, we
report on the experience of porting the implementation of Boris particle pusher
to oneAPI. Boris particle pusher is one of the most demanding computational
stages of the Particle-in-Cell method, which, in particular, is used for
supercomputer simulations of laser-plasma interactions. We show how to adapt
the C++ implementation of the particle push algorithm from the Hi-Chi project
to the DPC++ programming language and report the performance of the code on
high-end Intel CPUs (Xeon Platinum 8260L) and Intel GPUs (P630 and Iris Xe
Max). It turned out that our C++ code can be easily ported to DPC++. We found
that on CPUs the resulting DPC++ code is only ~10% on average inferior to the
optimized C++ code. Moreover, the code is compiled and run on new Intel GPUs
without any specific optimizations and shows the expected performance, taking
into account the parameters of the hardware.


Abstract:
  In this article, we show that in the usual type-I seesaw framework, augmented
solely by a neutrino portal interaction, the dark matter (DM) relic density can
be created through freeze-in in a manner fully determined by the seesaw
interactions and the DM particle mass. This simple freeze-in scenario, where
dark matter is not a seesaw state, proceeds through slow, seesaw-induced decays
of Higgs, $W$ and $Z$ bosons. We identify two scenarios, one of which predicts
the existence of an observable neutrino line.


Abstract:
  In this note, approaches towards local $p$-adic and global adelic quantum
gates are proposed.


Abstract:
  We present a framework to generate synthetic historical documents with
precise ground truth using nothing more than a collection of unlabeled
historical images. Obtaining large labeled datasets is often the limiting
factor to effectively use supervised deep learning methods for Document Image
Analysis (DIA). Prior approaches towards synthetic data generation either
require expertise or result in poor accuracy in the synthetic documents. To
achieve high precision transformations without requiring expertise, we tackle
the problem in two steps. First, we create template documents with
user-specified content and structure. Second, we transfer the style of a
collection of unlabeled historical images to these template documents while
preserving their text and layout. We evaluate the use of our synthetic
historical documents in a pre-training setting and find that we outperform the
baselines (randomly initialized and pre-trained). Additionally, with visual
examples, we demonstrate a high-quality synthesis that makes it possible to
generate large labeled historical document datasets with precise ground truth.


Abstract:
  Multi-source-extractors are functions that extract uniform randomness from
multiple (weak) sources of randomness. Quantum multi-source-extractors were
considered by Kasher and Kempe (for the quantum-independent-adversary and the
quantum-bounded-storage-adversary), Chung, Li and Wu (for the
general-entangled-adversary) and Arnon-Friedman, Portmann and Scholz (for the
quantum-Markov-adversary). One of the main objectives of this work is to unify
all the existing quantum multi-source adversary models. We propose two new
models of adversaries: 1) the quantum-measurement-adversary (qm-adv), which
generates side-information using entanglement and on post-measurement and 2)
the quantum-communication-adversary (qc-adv), which generates side-information
using entanglement and communication between multiple sources. We show that, 1.
qm-adv is the strongest adversary among all the known adversaries, in the sense
that the side-information of all other adversaries can be generated by qm-adv.
2. The (generalized) inner-product function (in fact a general class of
two-wise independent functions) continues to work as a good extractor against
qm-adv with matching parameters as that of Chor and Goldreich. 3. A
non-malleable-extractor proposed by Li (against classical-adversaries)
continues to be secure against quantum side-information. This result implies a
non-malleable-extractor result of Aggarwal, Chung, Lin and Vidick with uniform
seed. We strengthen their result via a completely different proof to make the
non-malleable-extractor of Li secure against quantum side-information even when
the seed is not uniform. 4. A modification (working with weak sources instead
of uniform sources) of the Dodis and Wichs protocol for privacy-amplification
is secure against active quantum adversaries. This strengthens on a recent
result due to Aggarwal, Chung, Lin and Vidick which uses uniform sources.


Abstract:
  Even when neural networks are widely used in a large number of applications,
they are still considered as black boxes and present some difficulties for
dimensioning or evaluating their prediction error. This has led to an
increasing interest in the overlapping area between neural networks and more
traditional statistical methods, which can help overcome those problems. In
this article, a mathematical framework relating neural networks and polynomial
regression is explored by building an explicit expression for the coefficients
of a polynomial regression from the weights of a given neural network, using a
Taylor expansion approach. This is achieved for single hidden layer neural
networks in regression problems. The validity of the proposed method depends on
different factors like the distribution of the synaptic potentials or the
chosen activation function. The performance of this method is empirically
tested via simulation of synthetic data generated from polynomials to train
neural networks with different structures and hyperparameters, showing that
almost identical predictions can be obtained when certain conditions are met.
Lastly, when learning from polynomial generated data, the proposed method
produces polynomials that approximate correctly the data locally.


Abstract:
  Solar flares convert magnetic energy into thermal and non-thermal plasma
energy, the latter implying particle acceleration of charged particles such as
protons. Protons are injected out of the coronal acceleration region and can
interact with dense plasma in the lower solar atmosphere, producing mesons that
subsequently decay into gamma rays and neutrinos at O(MeV-GeV) energies. We
present the results of the first search for GeV neutrinos emitted during solar
flares carried out with the IceCube Neutrino Observatory. While the experiment
was originally designed to detect neutrinos with energies between 10 GeV and a
few PeV, a new approach allowing for a O(GeV) energy threshold will be
presented. The resulting limits allow us to constrain some of the theoretical
estimates of the expected neutrino flux.


Abstract:
  A large majority of American adults get at least some of their news from the
Internet. Even though many online news products have the goal of informing
their users about the news, they lack scalable and reliable tools for measuring
how well they are achieving this goal, and therefore have to resort to noisy
proxy metrics (e.g., click-through rates or reading time) to track their
performance.
  As a first step towards measuring news informedness at a scale, we study the
problem of quiz-style multiple-choice question generation, which may be used to
survey users about their knowledge of recent news. In particular, we formulate
the problem as two sequence-to-sequence tasks: question-answer generation (QAG)
and distractor, or incorrect answer, generation (DG). We introduce NewsQuizQA,
the first dataset intended for quiz-style question-answer generation,
containing 20K human written question-answer pairs from 5K news article
summaries. Using this dataset, we propose a series of novel techniques for
applying large pre-trained Transformer encoder-decoder models, namely PEGASUS
and T5, to the tasks of question-answer generation and distractor generation.
  We show that our models outperform strong baselines using both automated
metrics and human raters. We provide a case study of running weekly quizzes on
real-world users via the Google Surveys platform over the course of two months.
We found that users generally found the automatically generated questions to be
educational and enjoyable. Finally, to serve the research community, we are
releasing the NewsQuizQA dataset.


Abstract:
  We present Contingency Model Predictive Control (CMPC), a motion planning and
control framework that optimizes performance objectives while simultaneously
maintaining a contingency plan -- an alternate trajectory that avoids a
potential hazard. By preserving the existence of a feasible avoidance
trajectory, CMPC anticipates emergency and keeps the controlled system in a
safe state that is selectively robust to the identified hazard. We accomplish
this by adding an additional prediction horizon in parallel to the typical
Model Predictive Control (MPC) horizon. This extra horizon is constrained to
guarantee safety from the contingent threat and is coupled to the nominal
horizon at its first command. Thus, the two horizons negotiate to compute
commands that are both optimized for performance and robust to the contingent
event. This article presents a linear formulation for CMPC, illustrates its key
features on a toy problem, and then demonstrates its efficacy experimentally on
a full-size automated road vehicle that encounters a realistic pop-out
obstacle. Contingency MPC approaches potential emergencies with safe,
intuitive, and interpretable behavior that balances conservatism with incentive
for high performance operation.


Abstract:
  We formulate a general, signature-independent form of the law of the excluded
middle and prove that a logic is semisimple if and only if it enjoys this law,
provided that it satisfies a weak form of the so-called inconsistency lemma of
Raftery. We then show that this equivalence can be used to provide simple
syntactic proofs of the theorems of Kowalski and Kracht characterizing the
semisimple varieties of FLew-algebras and Boolean algebras with operators, and
to extend them to FLe-algebras and Heyting algebras with operators. Moreover,
under stronger assumptions this correspondence works at the level of individual
models: the semisimple models of such a logic are precisely those which satisfy
an axiomatic form of the law of the excluded middle, and a Glivenko-like
connection obtains between the logic and its extension by the axiom of the
excluded middle. This in particular subsumes the well-known Glivenko theorems
relating intuitionistic and classical logic and the modal logics S4 and S5. As
a consequence, we also obtain a description of the subclassical substructural
logics which are Glivenko related to classical logic.


Abstract:
  Novelty Detection methods identify samples that are not representative of a
model's training set thereby flagging misleading predictions and bringing a
greater flexibility and transparency at deployment time. However, research in
this area has only considered Novelty Detection in the offline setting.
Recently, there has been a growing realization in the computer vision community
that applications demand a more flexible framework - Continual Learning - where
new batches of data representing new domains, new classes or new tasks become
available at different points in time. In this setting, Novelty Detection
becomes more important, interesting and challenging. This work identifies the
crucial link between the two problems and investigates the Novelty Detection
problem under the Continual Learning setting. We formulate the Continual
Novelty Detection problem and present a benchmark, where we compare several
Novelty Detection methods under different Continual Learning settings.
  We show that Continual Learning affects the behaviour of novelty detection
algorithms, while novelty detection can pinpoint insights in the behaviour of a
continual learner. We further propose baselines and discuss possible research
directions. We believe that the coupling of the two problems is a promising
direction to bring vision models into practice.


Abstract:
  In this paper, we prove that the class of all special Gelfand--Dorfman
algebras (GD-algebras) is closed with respect to homomorphisms and thus forms a
variety. We also prove that every 2-dimensional GD-algebra is special. For the
latter, we give a technical method to find all special identities of
GD-algebras and compute the degree 6 component of the Gr\"obner basis for the
shuffle operad constructed on the symmetric operad governing the class of
GD-algebras.


Abstract:
  This paper uses new and recently introduced methodologies to study the
similarity in the dynamics and behaviours of cryptocurrencies and equities
surrounding the COVID-19 pandemic. We study two collections; 45
cryptocurrencies and 72 equities, both independently and in conjunction. First,
we examine the evolution of cryptocurrency and equity market dynamics, with a
particular focus on their change during the COVID-19 pandemic. We demonstrate
markedly more similar dynamics during times of crisis. Next, we apply recently
introduced methods to contrast trajectories, erratic behaviours, and extreme
values among the two multivariate time series. Finally, we introduce a new
framework for determining the persistence of market anomalies over time.
Surprisingly, we find that although cryptocurrencies exhibit stronger
collective dynamics and correlation in all market conditions, equities behave
more similarly in their trajectories, extremes, and show greater persistence in
anomalies over time.


Abstract:
  Intrinsic tame filling functions are quasi-isometry invariants that are
refinements of the intrinsic diameter function of a group. The main purpose of
this paper is to show that every finite presentation of a group has an
intrinsic tame filling function that is equivalent to its intrinsic diameter
function. We also introduce some alternative filling functions--based on
concepts similar to those used to define intrinsic tame filling functions--that
are potential proper refinements of the intrinsic diameter function.


Abstract:
  With the goal of making deep learning more label-efficient, a growing number
of papers have been studying active learning (AL) for deep models. However,
there are a number of issues in the prevalent experimental settings, mainly
stemming from a lack of unified implementation and benchmarking. Issues in the
current literature include sometimes contradictory observations on the
performance of different AL algorithms, unintended exclusion of important
generalization approaches such as data augmentation and SGD for optimization, a
lack of study of evaluation facets like the labeling efficiency of AL, and
little or no clarity on the scenarios in which AL outperforms random sampling
(RS). In this work, we present a unified re-implementation of state-of-the-art
AL algorithms in the context of image classification via our new open-source AL
toolkit DISTIL, and we carefully study these issues as facets of effective
evaluation. On the positive side, we show that AL techniques are $2\times$ to
$4\times$ more label-efficient compared to RS with the use of data
augmentation. Surprisingly, when data augmentation is included, there is no
longer a consistent gain in using BADGE, a state-of-the-art approach, over
simple uncertainty sampling. We then do a careful analysis of how existing
approaches perform with varying amounts of redundancy and number of examples
per class. Finally, we provide several insights for AL practitioners to
consider in future work, such as the effect of the AL batch size, the effect of
initialization, the importance of retraining the model at every round, and
other insights.


Abstract:
  We present an analysis of the dispersed spectral data from 11 epochs (March
2011 to September 2018) of supernova remnant (SNR) 1987A observations performed
with Chandra. These observations were performed with the High Energy
Transmission Grating (HETG) as part of our ongoing Chandra monitoring campaign
of SNR 1987A, whose 1st-order dispersed spectrum provides a significantly
greater energy resolution than the previously-published 0th-order spectrum. Our
data sets with moderate exposure times of ~50-70ks per epoch cover the time
period between deep Chandra HETG observations (with individual exposures
>200ks) taken in March 2011 and March 2018. These data have a much higher
cadence than the widely-spaced deep high-resolution spectra, at the expense of
total exposure time. While statistical uncertainties are large due to low
photon count statistics in the observed 1st-order spectra, we find that
spectral model parameters are generally in line with the shock wave propagating
into the medium beyond the dense inner ring, as suggested by Frank et al. 2016.
As the reverse shock begins ionizing the heavier elements of the supernova
ejecta interior to the equatorial ring, spectral fit parameters are expected to
change as the chemical makeup and physical properties of the shocked gas
evolve. Based on our broadband spectral model fits, we find that abundance
values appear to be constant in this time period. While our results are
somewhat limited due to photon statistics, we demonstrate the utility of the
dispersed HETG spectral analysis that can be performed with our regular Chandra
monitoring observations of SNR 1987A.


Abstract:
  In this paper we study sharp pointwise inequalities for maximal operators. In
particular, we strengthen DeVore's inequality for the moduli of smoothness and
a logarithmic variant of Bennett--DeVore--Sharpley's inequality for
rearrangements. As a consequence, we improve the classical Stein--Zygmund
embedding deriving $\dot{B}^{d/p}_\infty L_{p,\infty}(\mathbb{R}^d)
\hookrightarrow \text{BMO}(\mathbb{R}^d)$ for $1 < p < \infty$. Moreover, these
results are also applied to establish new Fefferman--Stein inequalities,
Calder\'on--Scott type inequalities, and extrapolation estimates. Our approach
is based on the limiting interpolation techniques.


Abstract:
  Sparse-view computed tomography (CT) is known as a widely used approach to
reduce radiation dose while accelerating imaging through lowered projection
views and correlated calculations. However, its severe imaging noise and
streaking artifacts turn out to be a major issue in the low dose protocol. In
this paper, we propose a dual-domain deep learning-based method that breaks
through the limitations of currently prevailing algorithms that merely process
single image slices. Since the scanned object usually contains a high degree of
spatial continuity, the obtained consecutive imaging slices embody rich
information that is largely unexplored. Therefore, we establish a cascade model
named LS-AAE which aims to tackle the above problem. In addition, in order to
adapt to the social trend of lightweight medical care, our model adopts the
inverted residual with linear bottleneck in the module design to make it mobile
and lightweight (reduce model parameters to one-eighth of its original) without
sacrificing its performance. In our experiments, sparse sampling is conducted
at intervals of 4{\deg}, 8{\deg} and 16{\deg}, which appears to be a
challenging sparsity that few scholars have attempted before. Nevertheless, our
method still exhibits its robustness and achieves the state-of-the-art
performance by reaching the PSNR of 40.305 and the SSIM of 0.948, while
ensuring high model mobility. Particularly, it still exceeds other current
methods when the sampling rate is one-fourth of them, thereby demonstrating its
remarkable superiority.


Abstract:
  The Boolean Hidden Matching (BHM) problem, introduced in a seminal paper of
Gavinsky et. al. [STOC'07], has played an important role in the streaming lower
bounds for graph problems such as triangle and subgraph counting, maximum
matching, MAX-CUT, Schatten $p$-norm approximation, maximum acyclic subgraph,
testing bipartiteness, $k$-connectivity, and cycle-freeness. The one-way
communication complexity of the Boolean Hidden Matching problem on a universe
of size $n$ is $\Theta(\sqrt{n})$, resulting in $\Omega(\sqrt{n})$ lower bounds
for constant factor approximations to several of the aforementioned graph
problems. The related (and, in fact, more general) Boolean Hidden Hypermatching
(BHH) problem introduced by Verbin and Yu [SODA'11] provides an approach to
proving higher lower bounds of $\Omega(n^{1-1/t})$ for integer $t\geq 2$.
Reductions based on Boolean Hidden Hypermatching generate distributions on
graphs with connected components of diameter about $t$, and basically show that
long range exploration is hard in the streaming model of computation with
adversarial arrivals.
  In this paper we introduce a natural variant of the BHM problem, called noisy
BHM (and its natural noisy BHH variant), that we use to obtain higher than
$\Omega(\sqrt{n})$ lower bounds for approximating several of the aforementioned
problems in graph streams when the input graphs consist only of components of
diameter bounded by a fixed constant. We also use the noisy BHM problem to show
that the problem of classifying whether an underlying graph is isomorphic to a
complete binary tree in insertion-only streams requires $\Omega(n)$ space,
which seems challenging to show using BHM or BHH alone.


Abstract:
  The aim of this paper is to introduce a translation operator associated to
the canonical Fourier Bessel transform $\mathcal{F}_{\nu}^{\mathbf{m}}$ and
study some of the important properties. We derive a convolution product for
this transform and as application we study the heat equation and the heat
semigroup related to $\Delta_{\nu}^{\mathbf{m}}.$


Abstract:
  Naturally occurring radionuclide $^{210}$Pb ($T_{1/2}$=22.2 y) is an
important source of background in rare event searches, such as neutrinoless
double-$\beta$ decay and dark matter direct detection experiments. When a
sample mass of hundreds of grams is available, $\gamma$-counting measurements
can be performed. However, there are other cases where only grams of sample can
be used. For these cases, better sensitivities are required.
  In this paper, in collaboration with the Astroparticle Physics group at
Carleton University, the capabilities of the A.E. Lalonde AMS Laboratory at the
University of Ottawa for $^{210}$Pb measurements are discussed. PbF$_{2}$ and
PbO targets were used, selecting in the low energy sector, respectively,
(PbF$_{3}$)$^{-}$ or (PbO$_{2}$)$^{-}$ ions.
  For fluoride targets, the blank $^{210}$Pb/$^{206}$Pb ratio was in the
10$^{-14}$ to 10$^{-13}$ range, but current output was lower and less stable.
For oxide targets, current output showed better stability, despite a
significant difference in current output for commercial PbO and processed
samples, and background studies suggested a background not much higher than
that of the fluoride targets. Both target materials showed, therefore, good
performance for $^{210}$Pb AMS assay.
  Measurements of Kapton films, an ultra-thin polymer material, where masses
available are typically just several grams, were performed. 90% C.L. upper
limits for the $^{210}$Pb specific activity in the range of 0.74-2.8 Bq/kg were
established for several Kapton HN films.


Abstract:
  This paper builds the clustering model of measures of market microstructure
features which are popular in predicting stock returns. In a 10-second
time-frequency, we study the clustering structure of different measures to find
out the best ones for predicting. In this way, we can predict more accurately
with a limited number of predictors, which removes the noise and makes the
model more interpretable.


Abstract:
  ARES (Accelerator Research Experiment at SINBAD) is a linear accelerator at
the SINBAD (Short INnovative Bunches and Accelerators at DESY) facility at
DESY. ARES was designed to combine reproducible beams from conventional
RF-based accelerator technology with novel but still experimental acceleration
techniques. It aims to produce high brightness ultra-short electron bunches in
the range of sub fs to few fs, at a beam energy of 100-150 MeV, suitable for
injection into novel acceleration experiments like Dielectric Laser
Acceleration (DLA) and Laser driven Plasma Acceleration (LPA). This paper
reports the conceptual design and simulations of a final focus system for
injecting into a LPA experiment at ARES, including permanent magnetic
quadrupoles (PMQ), sufficient longitudinal space for collinear laser and
electron transport, space for required diagnostics and a LPA setup.
Space-charge effects play a significant role and are included. Simulation
results on the focusing of the ARES electron bunches into and their transport
through the laser-driven plasma are presented. The effects of several errors
have been simulated and are reported.


Abstract:
  We propose UCBMQ, Upper Confidence Bound Momentum Q-learning, a new algorithm
for reinforcement learning in tabular and possibly stage-dependent, episodic
Markov decision process. UCBMQ is based on Q-learning where we add a momentum
term and rely on the principle of optimism in face of uncertainty to deal with
exploration. Our new technical ingredient of UCBMQ is the use of momentum to
correct the bias that Q-learning suffers while, at the same time, limiting the
impact it has on the second-order term of the regret. For UCBMQ , we are able
to guarantee a regret of at most $O(\sqrt{H^3SAT}+ H^4 S A )$ where $H$ is the
length of an episode, $S$ the number of states, $A$ the number of actions, $T$
the number of episodes and ignoring terms in poly$log(SAHT)$. Notably, UCBMQ is
the first algorithm that simultaneously matches the lower bound of
$\Omega(\sqrt{H^3SAT})$ for large enough $T$ and has a second-order term (with
respect to the horizon $T$) that scales only linearly with the number of states
$S$.


Abstract:
  In this work we present the design decisions and advantages for accomplishing
cross mesh format checkpoint-restart in community code FLASH-X. AMReX and
Paramesh are the two AMR mesh formats developed and supported by FLASH-X. We
also highlight strong and weak scaling study of existing HDF5 I/O checkpoint
writing along with new ideas and results (presented during talk) for utilizing
heterogeneous compute architectures for improved I/O performance.


Abstract:
  The ability of an autonomous vehicle to perform 3D tracking is essential for
safe planing and navigation in cluttered environments. The main challenges for
multi-object tracking (MOT) in autonomous driving applications reside in the
inherent uncertainties regarding the number of objects, when and where the
objects may appear and disappear, and uncertainties regarding objects' states.
Random finite set (RFS) based approaches can naturally model these
uncertainties accurately and elegantly, and they have been widely used in
radar-based tracking applications. In this work, we developed an RFS-based MOT
framework for 3D LiDAR data. In partiuclar, we propose a Poisson
multi-Bernoulli mixture (PMBM) filter to solve the amodal MOT problem for
autonomous driving applications. To the best of our knowledge, this represents
a first attempt for employing an RFS-based approach in conjunction with 3D
LiDAR data for MOT applications with comprehensive validation using challenging
datasets made available by industry leaders. The superior experimental results
of our PMBM tracker on public Waymo and Argoverse datasets clearly illustrate
that an RFS-based tracker outperforms many state-of-the-art deep learning-based
and Kalman filter-based methods, and consequently, these results indicate a
great potential for further exploration of RFS-based frameworks for 3D MOT
applications.


Abstract:
  A toroidal dipole moment appears independent of the electric and magnetic
dipole moment in the multipole expansion of electrodynamics. It arises
naturally from vortex-like arrangements of spins. Observing and controlling
spontaneous long-range orders of toroidal moments are highly promising for
spintronics but remain challenging. Here we demonstrate that a vortex-like spin
configuration with a staggered arrangement of toroidal moments, a ferritoroidal
state, is realized in a chiral triangular-lattice magnet BaCoSiO4. Upon
applying a magnetic field, we observe multi-stair toroidal transitions
correlating directly with metamagnetic transitions. We establish a
first-principles microscopic Hamiltonian that explains both the formation of
toroidal states and the metamagnetic toroidal transition as a combined effect
of the magnetic frustration and the Dzyaloshinskii-Moriya interactions allowed
by the crystallographic chirality in BaCoSiO4.


Abstract:
  In this report, we introduce the technical details of our submission to the
VIPriors object detection challenge. Our solution is based on mmdetction of a
strong baseline open-source detection toolbox. Firstly, we introduce an
effective data augmentation method to address the lack of data problem, which
contains bbox-jitter, grid-mask, and mix-up. Secondly, we present a robust
region of interest (ROI) extraction method to learn more significant ROI
features via embedding global context features. Thirdly, we propose a
multi-model integration strategy to refinement the prediction box, which
weighted boxes fusion (WBF). Experimental results demonstrate that our approach
can significantly improve the average precision (AP) of object detection on the
subset of the COCO2017 dataset.


Abstract:
  In this work, we present an in-depth and systematic analysis using tools such
as local interpretable model-agnostic explanations (LIME) (arXiv:1602.04938)
and divergence measures to analyze what changes lead to improvement in
performance in fine tuned models for synthetic aperture sonar (SAS) data. We
examine the sensitivity to factors in the fine tuning process such as class
imbalance. Our findings show not only an improvement in seafloor texture
classification, but also provide greater insight into what features play
critical roles in improving performance as well as a knowledge of the
importance of balanced data for fine tuning deep learning models for seafloor
classification in SAS imagery.


Abstract:
  Improvement of the classical gravity with the running gravitational coupling
obtained from asymptotically safe gravity, is a good way of considering the
effects of quantum gravity. This is usually done for metric theories of
gravity. Here we investigate the effects of such an improvement for pure affine
theories of gravity. To motivate the approach, we first consider the effects of
quantum improvement on the connection using metric theory and investigate the
effects on the causal structure of black hole solution. Next in the framework
of Schr\"odinger-Eddington affine theory, the general way of affine improvement
is presented and a spherically symmetric solution is obtained and compared with
other ways of improvement.


Abstract:
  Recently, there has been a rising surge of momentum for deep representation
learning in hyperbolic spaces due to theirhigh capacity of modeling data like
knowledge graphs or synonym hierarchies, possessing hierarchical structure. We
refer to the model as hyperbolic deep neural network in this paper. Such a
hyperbolic neural architecture potentially leads to drastically compact model
withmuch more physical interpretability than its counterpart in Euclidean
space. To stimulate future research, this paper presents acoherent and
comprehensive review of the literature around the neural components in the
construction of hyperbolic deep neuralnetworks, as well as the generalization
of the leading deep approaches to the Hyperbolic space. It also presents
current applicationsaround various machine learning tasks on several publicly
available datasets, together with insightful observations and identifying
openquestions and promising future directions.


Abstract:
  If a binary liquid mixture, composed of two alternative species with equal
amounts, is quenched from a high temperature to a low temperature, below the
critical point of demixing, then the mixture will phase separate through a
process known as spinodal decomposition. However, if the two alternative
species are allowed to interconvert, either naturally (e.g. the equilibrium
interconversion of enantiomers) or forcefully (e.g. via an external source of
energy or matter), then the process of phase separation may drastically change.
In this case, depending on the nature of interconversion, two phenomena could
be observed: either phase amplification, the growth of one phase at the expense
of another stable phase, or microphase separation, the formation of nongrowing
(steady-state) microphase domains. In this work, we generalize the
Cahn-Hilliard theory of spinodal decomposition to include molecular
interconversion of species and describe the physical properties of systems
undergoing either phase amplification or microphase separation. We apply the
developed theory to describe the simulation results of three atomistic models
which demonstrate phase amplification and/or microphase separation. We also
discuss the application of our approach to phase transitions in polyamorphic
liquids. Lastly, we describe the effects of fluctuations of the order parameter
in the critical region on phase amplification and microphase separation.