Abstract:
  Learning vector representations for words is one of the most fundamental
topics in NLP, capable of capturing syntactic and semantic relationships useful
in a variety of downstream NLP tasks. Vector representations can be limiting,
however, in that typical scoring such as dot product similarity intertwines
position and magnitude of the vector in space. Exciting innovations in the
space of representation learning have proposed alternative fundamental
representations, such as distributions, hyperbolic vectors, or regions. Our
model, Word2Box, takes a region-based approach to the problem of word
representation, representing words as $n$-dimensional rectangles. These
representations encode position and breadth independently and provide
additional geometric operations such as intersection and containment which
allow them to model co-occurrence patterns vectors struggle with. We
demonstrate improved performance on various word similarity tasks, particularly
on less common words, and perform a qualitative analysis exploring the
additional unique expressivity provided by Word2Box.


Abstract:
  The calcium superhydrides are synthesized at high pressure of 180 GPa and
1000 degree high temperatures. Superconductivity with Tc onset about 210 K is
realized in thus obtained hydrogen rich calcium compounds at high pressure. The
critical magnetic field Hc2(T) is estimated to around 166 T in the Ginzburg
Landau model.


Abstract:
  In the field of light-matter interactions, it is often assumed that a
classical light field that interacts with a quantum particle remains almost
unchanged and thus contains nearly no information about the manipulated
particles. To investigate the validity of this assumption, we develop and
theoretically analyze a simple Gedankenexperiment which involves the
interaction of a coherent state with a quantum particle in an optical cavity.
We quantify the resulting alteration of the light field by measuring the
fidelity of its initial and equilibrium states. Using Bayesian inference, we
demonstrate the information transfer through photon measurements. In addition,
we employ the concepts of quantum entropy and mutual information to quantify
the entropy transfer from the particle to the light field. In the weak coupling
limit, we validate the usually assumed negligible alteration of the light field
and entropy transfer. In the strong coupling limit, however, we observe that
the information of the initial particle state can be fully encoded in the light
field, even for large photon numbers. Nevertheless, we show that spontaneous
emission is a sufficient mechanism for removing the entropy initially stored in
the particle. Our analysis provides a deeper understanding of the entropy
exchange between quantum matter and classical light.


Abstract:
  In this article, we study a divisor function in an arbitrary number field
akin to Koshliakov's work on Vorono\"{\dotlessi} summation formula. More
precisely, we generalize Koshliakov's kernel and Koshliakov's transform over
any number field to obtain identities for the Lambert series associated to the
divisor function in an arbitrary number field.


Abstract:
  Domain adaptation methods face performance degradation in object detection,
as the complexity of tasks require more about the transferability of the model.
We propose a new perspective on how CNN models gain the transferability,
viewing the weights of a model as a series of motion patterns. The directions
of weights, and the gradients, can be divided into domain-specific and
domain-invariant parts, and the goal of domain adaptation is to concentrate on
the domain-invariant direction while eliminating the disturbance from
domain-specific one. Current UDA object detection methods view the two
directions as a whole while optimizing, which will cause domain-invariant
direction mismatch even if the output features are perfectly aligned. In this
paper, we propose the domain-specific suppression, an exemplary and
generalizable constraint to the original convolution gradients in
backpropagation to detach the two parts of directions and suppress the
domain-specific one. We further validate our theoretical analysis and methods
on several domain adaptive object detection tasks, including weather, camera
configuration, and synthetic to real-world adaptation. Our experiment results
show significant advance over the state-of-the-art methods in the UDA object
detection field, performing a promotion of $10.2\sim12.2\%$ mAP on all these
domain adaptation scenarios.


Abstract:
  In laser metal deposition (LMD) the height of the deposited track can vary
within and between layers, causing significant deviations during the process
evolution. Previous works have shown that in certain conditions a
self-stabilizing mechanism occurs, maintaining a regular height growth and a
constant standoff distance between the part and the deposition nozzle. Here we
analyze the link between the powder catchment efficiency and the deposition
height stability. To this purpose, a monitoring system was developed to study
the deposition in different process conditions, using inline measurements of
the specimen weight in combination with the layer height information obtained
with coaxial optical triangulation. An analytical model was used to estimate
the deposition efficiency in real-time from the height monitoring and the
process parameters, which was verified by the direct mass measurements. The
results show that the track height stabilization is associated to a reduction
of the powder catchment efficiency, which is governed by the melt pool relative
position with respect to the powder cone and the laser beam. For a given set of
parameters, the standoff distance can be estimated to achieve the highest
powder catchment efficiency and a regular height through the build direction.


Abstract:
  Circular colliders have the advantage of delivering collisions to multiple
interaction points, which allow different detector designs to be studied and
optimized - up to four for FCC-ee. On the one hand, the detectors must satisfy
the constraints imposed by the invasive interaction region layout. On the other
hand, the performance of heavy-flavour tagging, of particle identification, of
tracking and particle-flow reconstruction, and of lepton, jet, missing energy
and angular resolution, need to match the physics programme and the exquisite
statistical precision offered by FCC-ee. During the FCC feasibility study
(2021-2025), benchmark physics processes will be used to determine, via
appropriate simulations, the requirements on the detector performance or design
that must be satisfied to ensure that the systematic uncertainties of the
measurements are commensurate with their statistical precision. The usage of
the data themselves, in order to reach the challenging goals on the stability
and on the alignment of the detector, in particular for the programme at and
around the Z peak, will also be studied. In addition, the potential for
discovering very weakly coupled new particles, in decays of Z or Higgs bosons,
could motivate dedicated detector designs that would increase the efficiency
for reconstructing the unusual signatures of such processes. These studies are
a crucial input to the further optimization of the two concepts described in
the Conceptual Design Report, CLD, and IDEA, and to the development of new
concepts which might actually prove to be better adapted to the FCC-ee physics
programme, or parts thereof.


Abstract:
  We calculate the electric conductivity $\sigma$ in deconfined QCD matter
using a holographic QCD model, i.e., the Sakai-Sugimoto Model with varying
magnetic field $B$ and chiral anomaly strength. After confirming that our
estimated $\sigma$ for $B=0$ is consistent with the lattice-QCD results, we
study the case with $B\neq 0$ in which the coefficient $\alpha$ in the
Chern-Simons term controls the chiral anomaly strength. Our results imply that
the transverse conductivity, $\sigma_\perp$, is suppressed to be $\lesssim
70\%$ at $B\sim 1\,\mathrm{GeV}^2$ as compared to the $B=0$ case when the
temperature is fixed as $T= 0.2\,\mathrm{GeV}$. Since the Sakai-Sugimoto Model
has massless fermions, the longitudinal conductivity, $\sigma_\parallel$, with
$B\neq 0$ should diverge due to production of the matter chirality. Yet, it is
possible to extract a regulated part out from $\sigma_\parallel$ with an extra
condition to neutralize the matter chirality. This regulated quantity is
interpreted as an Ohmic part of $\sigma_\parallel$. We show that the
longitudinal Ohmic conductivity increases with increasing $B$ for small
$\alpha$, while it is suppressed with larger $B$ for physical $\alpha=3/4$ due
to anomaly induced interactions.


Abstract:
  We present systematic large-scale shell-model calculations for Rn isotopes
with $A=$ 207 to 216. For the $^{207-212}$Rn isotopes, we perform calculations
with KHH7B interaction, while for $^{213-216}$Rn isotopes with KHPE and KHH7B
interactions. The calculated energies and electromagnetic properties are
compared with the available experimental data and predicted where experimental
data are not available. We also suggest spins and parities of several
unconfirmed states available from the recent experimental data. Comprehensive
study of several isomeric states from the calculated shell-model configurations
and half-lives is also reported.


Abstract:
  Among non-Hermitian systems, pseudo-Hermitian phases represent a special
class of physical models characterized by real energy spectra and by the
absence of non-Hermitian skin effects. Here, we show that several
pseudo-Hermitian phases in two and three dimensions can be built by employing
$q$-deformed matrices, which are related to the representation of deformed
algebras. Through this algebraic approach we present and study the
pseudo-Hermitian version of well known Hermitian topological phases, raging
from two-dimensional Chern insulators and time-reversal-invariant topological
insulators to three-dimensional Weyl semimetals and chiral topological
insulators. We analyze their topological bulk states through non-Hermitian
generalizations of Abelian and non-Abelian tensor Berry connections and quantum
metric. Although our pseudo-Hermitian models and their Hermitian counterparts
share the same topological invariants, their band geometries are different. We
indeed show that some of our pseudo-Hermitian phases naturally support
nearly-flat topological bands, opening the route to the study of
pseudo-Hermitian strongly-interacting systems. Finally, we provide an
experimental protocol to realize our models and measure the full non-Hermitian
quantum geometric tensor in synthetic matter.


Abstract:
  At present, people usually use some methods based on convolutional neural
networks (CNNs) for Electroencephalograph (EEG) decoding. However, CNNs have
limitations in perceiving global dependencies, which is not adequate for common
EEG paradigms with a strong overall relationship. Regarding this issue, we
propose a novel EEG decoding method that mainly relies on the attention
mechanism. The EEG data is firstly preprocessed and spatially filtered. And
then, we apply attention transforming on the feature-channel dimension so that
the model can enhance more relevant spatial features. The most crucial step is
to slice the data in the time dimension for attention transforming, and finally
obtain a highly distinguishable representation. At this time, global averaging
pooling and a simple fully-connected layer are used to classify different
categories of EEG data. Experiments on two public datasets indicate that the
strategy of attention transforming effectively utilizes spatial and temporal
features. And we have reached the level of the state-of-the-art in
multi-classification of EEG, with fewer parameters. As far as we know, it is
the first time that a detailed and complete method based on the transformer
idea has been proposed in this field. It has good potential to promote the
practicality of brain-computer interface (BCI). The source code can be found
at: \textit{https://github.com/anranknight/EEG-Transformer}.


Abstract:
  Scene text detection and recognition have been well explored in the past few
years. Despite the progress, efficient and accurate end-to-end spotting of
arbitrarily-shaped text remains challenging. In this work, we propose an
end-to-end text spotting framework, termed PAN++, which can efficiently detect
and recognize text of arbitrary shapes in natural scenes. PAN++ is based on the
kernel representation that reformulates a text line as a text kernel (central
region) surrounded by peripheral pixels. By systematically comparing with
existing scene text representations, we show that our kernel representation can
not only describe arbitrarily-shaped text but also well distinguish adjacent
text. Moreover, as a pixel-based representation, the kernel representation can
be predicted by a single fully convolutional network, which is very friendly to
real-time applications. Taking the advantages of the kernel representation, we
design a series of components as follows: 1) a computationally efficient
feature enhancement network composed of stacked Feature Pyramid Enhancement
Modules (FPEMs); 2) a lightweight detection head cooperating with Pixel
Aggregation (PA); and 3) an efficient attention-based recognition head with
Masked RoI. Benefiting from the kernel representation and the tailored
components, our method achieves high inference speed while maintaining
competitive accuracy. Extensive experiments show the superiority of our method.
For example, the proposed PAN++ achieves an end-to-end text spotting F-measure
of 64.9 at 29.2 FPS on the Total-Text dataset, which significantly outperforms
the previous best method. Code will be available at: https://git.io/PAN.


Abstract:
  Detecting and segmenting salient objects from given image scenes has received
great attention in recent years. A fundamental challenge in training the
existing deep saliency detection models is the requirement of large amounts of
annotated data. While gathering large quantities of training data becomes cheap
and easy, annotating the data is an expensive process in terms of time, labor
and human expertise. To address this problem, this paper proposes to learn the
effective salient object detection model based on the manual annotation on a
few training images only, thus dramatically alleviating human labor in training
models. To this end, we name this task as the few-cost salient object detection
and propose an adversarial-paced learning (APL)-based framework to facilitate
the few-cost learning scenario. Essentially, APL is derived from the self-paced
learning (SPL) regime but it infers the robust learning pace through the
data-driven adversarial learning mechanism rather than the heuristic design of
the learning regularizer. Comprehensive experiments on four widely-used
benchmark datasets demonstrate that the proposed method can effectively
approach to the existing supervised deep salient object detection models with
only 1k human-annotated training images. The project page is available at
https://github.com/hb-stone/FC-SOD.


Abstract:
  In a recent paper it was shown how a matrix S-lemma can be applied to
construct controllers from noisy data. The current paper complements these
results by proving a matrix version of the classical Finsler's lemma. This
matrix Finsler's lemma provides a tractable condition under which all matrix
solutions to a quadratic equality also satisfy a quadratic inequality. We will
apply this result to bridge known data-driven control design techniques for
both exact and noisy data, thereby revealing a more general theory. The result
is also applied to data-driven control of Lur'e systems.


Abstract:
  We consider the single eigenvalue fluctuations of random matrices of general
Wigner-type, under a one-cut assumption on the density of states. For
eigenvalues in the bulk, we prove that the asymptotic fluctuations of a single
eigenvalue around its classical location are Gaussian with a universal variance
which agrees with the GOE and GUE cases.
  Our method is based on a dynamical approach to mesoscopic linear spectral
statistics which reduces their behavior on short scales to that on larger
scales. We prove a central limit theorem for linear spectral statistics on
larger scales via resolvent techniques and show that for certain classes of
test functions, the leading order contribution to the variance is universal,
agreeing with the GOE/GUE cases.


Abstract:
  Due to the vulnerability of deep neural networks to adversarial examples,
numerous works on adversarial attacks and defenses have been burgeoning over
the past several years. However, there seem to be some conventional views
regarding adversarial attacks and object detection approaches that most
researchers take for granted. In this work, we bring a fresh perspective on
those procedures by evaluating the impact of universal perturbations on object
detection at a class-level. We apply it to a carefully curated data set related
to autonomous driving. We use Faster-RCNN object detector on images of five
different categories: person, car, truck, stop sign and traffic light from the
COCO data set, while carefully perturbing the images using Universal Dense
Object Suppression algorithm. Our results indicate that person, car, traffic
light, truck and stop sign are resilient in that order (most to least) to
universal perturbations. To the best of our knowledge, this is the first time
such a ranking has been established which is significant for the security of
the data sets pertaining to autonomous vehicles and object detection in
general.


Abstract:
  Suppose that $(T,\star)$ is a groupoid with a left identity such that each
element $a\in T$ has a left inverse. Then $T$ is called a \textit{gyrogroup} if
and only if $(i)$ there exists a function $gyr:T\times T\longrightarrow Aut(T)$
such that for all $a,b,c\in T$, $a\star(b\star c)= (a\star b)\star gyr[a,b]c$,
where $gyr[a,b]c=gyr(a,b)(c)$; and $(ii)$ for all $a,b\in T$,
$gyr[a,b]=gyr[a\star b,b]$. In this paper, the structure of normal
subgyrogroups of certain gyrogroups are investigated.


Abstract:
  Attention-based encoder-decoder (AED) models learn an implicit internal
language model (ILM) from the training transcriptions. The integration with an
external LM trained on much more unpaired text usually leads to better
performance. A Bayesian interpretation as in the hybrid autoregressive
transducer (HAT) suggests dividing by the prior of the discriminative acoustic
model, which corresponds to this implicit LM, similarly as in the hybrid hidden
Markov model approach. The implicit LM cannot be calculated efficiently in
general and it is yet unclear what are the best methods to estimate it. In this
work, we compare different approaches from the literature and propose several
novel methods to estimate the ILM directly from the AED model. Our proposed
methods outperform all previous approaches. We also investigate other methods
to suppress the ILM mainly by decreasing the capacity of the AED model,
limiting the label context, and also by training the AED model together with a
pre-existing LM.


Abstract:
  As announced "Intrinsic mirror symmetry and punctured invariants" in 2016, we
construct and prove consistency of the canonical wall structure. This
construction starts with a log Calabi-Yau pair (X,D) and produces a wall
structure, as defined by Gross-Hacking-Siebert. Roughly put, the canonical wall
structure is a data structure which encodes an algebro-geometric analogue of
counts of Maslov index zero disks. These enumerative invariants are defined in
terms of the punctured invariants of Abramovich-Chen-Gross-Siebert. There are
then two main theorems of the paper. First, we prove consistency of the
canonical wall structure, so that the canonical wall structure gives rise to a
mirror family. Second, we prove that this mirror family coincides with the
intrinsic mirror constructed in our paper "Intrinsic mirror symmetry". While
the setup of this paper is narrower than that of the latter paper, it gives a
more detailed description of the mirror.


Abstract:
  We study an interesting and challenging problem, learning any part of a
Bayesian network (BN) structure. In this challenge, it will be computationally
inefficient using existing global BN structure learning algorithms to find an
entire BN structure to achieve the part of a BN structure in which we are
interested. And local BN structure learning algorithms encounter the false edge
orientation problem when they are directly used to tackle this challenging
problem. In this paper, we first present a new concept of Expand-Backtracking
to explain why local BN structure learning methods have the false edge
orientation problem, then propose APSL, an efficient and accurate Any Part of
BN Structure Learning algorithm. Specifically, APSL divides the V-structures in
a Markov blanket (MB) into two types: collider V-structure and non-collider
V-structure, then it starts from a node of interest and recursively finds both
collider V-structures and non-collider V-structures in the found MBs, until the
part of a BN structure in which we are interested are oriented. To improve the
efficiency of APSL, we further design the APSL-FS algorithm using Feature
Selection, APSL-FS. Using six benchmark BNs, the extensive experiments have
validated the efficiency and accuracy of our methods.


Abstract:
  The impact parameter dependence of color charge correlators in the proton is
obtained from the light front formalism in light cone gauge. We include NLO
corrections due to the $|qqqg\rangle$ Fock state via light-cone perturbation
theory. Near the center of the proton, the $b$-dependence of the correlations
is very different from a "transverse profile function". The resulting
$t$-dependence of exclusive $J/\Psi$ photoproduction transitions from
exponential to power law at $|t| \approx 1$ GeV$^2$. This prediction could be
tested at upcoming DIS facilities or in nucleus-proton ultraperipheral
collisions (UPCs).


Abstract:
  Today, human security analysts collapse under the sheer volume of alerts they
have to triage during investigations. The inability to cope with this load,
coupled with a high false positive rate of alerts, creates alert fatigue. This
results in failure to detect complex attacks, such as advanced persistent
threats (APTs), because they manifest over long time frames and attackers tread
carefully to evade detection mechanisms. In this paper, we contribute a new
method to synthesize attack graphs from state machines. We use the network
direction to derive potential attack stages from single and meta-alerts and
model resulting attack scenarios in a kill chain state machine (KCSM). Our
algorithm yields a graphical summary of the attack, APT scenario graphs, where
nodes represent involved hosts and edges infection activity. We evaluate the
feasibility of our approach in multiple experiments based on the
CSE-CIC-IDS2018 data set. We obtain up to 446 458 singleton alerts that our
algorithm condenses into 700 APT scenario graphs resulting in a reduction of up
to three orders of magnitude. This reduction makes it feasible for human
analysts to effectively triage potential incidents. An evaluation on the same
data set, in which we embedded a synthetic yet realistic APT campaign, supports
the applicability of our approach of detecting and contextualizing complex
attacks. The APT scenario graphs constructed by our algorithm correctly link
large parts of the APT campaign and present a coherent view to support the
human analyst in further analyses.


Abstract:
  We present a systematic study of higher-order exchange interactions beyond
the pair-wise Heisenberg exchange in transition-metal trilayers based on
density functional theory calculations. We show that these terms can play an
important role in magnetic trilayers composed of a single hexagonal Fe or Co
atomic layer sandwiched between $4d$ and $5d$ transition-metal layers. We study
the dependence of the biquadratic and the three-site and four-site four spin
interaction on the band filling of the $4d$ and $5d$ layers as well as the
stacking sequence, i.e. fcc vs. hcp stacking. Our calculations reveal
relatively small higher-order interactions for Co based trilayers. For Fe based
trilayers with a Rh or Ir layer the higher-order terms can be on the same order
of magnitude as pair-wise Heisenberg exchange. The trends obtained for
freestanding trilayers are used to understand the higher-order interactions in
ultrathin film systems on surfaces that are experimentally accessible. It is
shown that hcp Rh/Fe/Ir(111) and hcp-Rh/Fe/Rh(111) exhibit the largest values
for the biquadratic and the three-site four spin interaction of all systems
under study. We further demonstrate that the three-site four spin interaction
is responsible for the experimentally observed change of the magnetic ground
state of Rh/Fe/Ir(111) from a spin spiral (single-Q) for fcc-Rh to a 2Q state
for hcp-Rh. We find similar trends for Rh/Fe/Rh(111), i.e. replacing the Ir
surface by the isoelectronic Rh surface. For Rh/Co/Ir(111), we obtain a
negative value for the four-site four spin interaction which will lead to a
reduced stability of magnetic skyrmions which are metastable in this film at
zero magnetic field. In contrast, for Pd/Fe/Ir(111), the four-site four spin
interaction is positive which leads to an enhanced stability of skyrmions.


Abstract:
  This paper presents a new vision Transformer, called Swin Transformer, that
capably serves as a general-purpose backbone for computer vision. Challenges in
adapting Transformer from language to vision arise from differences between the
two domains, such as large variations in the scale of visual entities and the
high resolution of pixels in images compared to words in text. To address these
differences, we propose a hierarchical Transformer whose representation is
computed with \textbf{S}hifted \textbf{win}dows. The shifted windowing scheme
brings greater efficiency by limiting self-attention computation to
non-overlapping local windows while also allowing for cross-window connection.
This hierarchical architecture has the flexibility to model at various scales
and has linear computational complexity with respect to image size. These
qualities of Swin Transformer make it compatible with a broad range of vision
tasks, including image classification (87.3 top-1 accuracy on ImageNet-1K) and
dense prediction tasks such as object detection (58.7 box AP and 51.1 mask AP
on COCO test-dev) and semantic segmentation (53.5 mIoU on ADE20K val). Its
performance surpasses the previous state-of-the-art by a large margin of +2.7
box AP and +2.6 mask AP on COCO, and +3.2 mIoU on ADE20K, demonstrating the
potential of Transformer-based models as vision backbones. The hierarchical
design and the shifted window approach also prove beneficial for all-MLP
architectures. The code and models are publicly available
at~\url{https://github.com/microsoft/Swin-Transformer}.


Abstract:
  The modification of the hard core of jets in a dense QCD medium is studied.
In particular, we consider partons which possess a virtuality somewhat larger
than the multiple scattering scale of the medium ($\hat{q} \tau$, where
$\hat{q}$ is the transverse broadening jet transport coefficient, and $\tau$ is
the formation length of a particular emission). We delineate the region of
parameter space where the higher-twist approach is applicable, and derive the
in-medium DGLAP evolution equation. We study a region in parameter space where
this is the dominant mechanism of energy loss. We argue that such a regime is
pervasive in most cases of jets in $A$-$A$ and future $e$-$A$ collisions, and
controls the modification of the hard core of jets and the leading single
particle spectrum at high transverse momentum ($p_\mathrm{T}$).


Abstract:
  We present a search for continuous gravitational-wave emission due to r-modes
in the pulsar PSR J0537-6910 using data from the LIGO-Virgo Collaboration
observing run O3. PSR J0537-6910 is a young energetic X-ray pulsar and is the
most frequent glitcher known. The inter-glitch braking index of the pulsar
suggests that gravitational-wave emission due to r-mode oscillations may play
an important role in the spin evolution of this pulsar. Theoretical models
confirm this possibility and predict emission at a level that can be probed by
ground-based detectors. In order to explore this scenario, we search for r-mode
emission in the epochs between glitches by using a contemporaneous timing
ephemeris obtained from NICER data. We do not detect any signals in the
theoretically expected band of 86-97 Hz, and report upper limits on the
amplitude of the gravitational waves. Our results improve on previous amplitude
upper limits from r-modes in J0537-6910 by a factor of up to 3 and place
stringent constraints on theoretical models for r-mode driven spin-down in PSR
J0537-6910, especially for higher frequencies at which our results reach below
the spin-down limit defined by energy conservation.


Abstract:
  Maritime autonomous transportation has played a crucial role in the
globalization of the world economy. Deep Reinforcement Learning (DRL) has been
applied to automatic path planning to simulate vessel collision avoidance
situations in open seas. End-to-end approaches that learn complex mappings
directly from the input have poor generalization to reach the targets in
different environments. In this work, we present a new strategy called
state-action rotation to improve agent's performance in unseen situations by
rotating the obtained experience (state-action-state) and preserving them in
the replay buffer. We designed our model based on Deep Deterministic Policy
Gradient, local view maker, and planner. Our agent uses two deep Convolutional
Neural Networks to estimate the policy and action-value functions. The proposed
model was exhaustively trained and tested in maritime scenarios with real maps
from cities such as Montreal and Halifax. Experimental results show that the
state-action rotation on top of the CVN consistently improves the rate of
arrival to a destination (RATD) by up 11.96% with respect to the Vessel
Navigator with Planner and Local View (VNPLV), as well as it achieves superior
performance in unseen mappings by up 30.82%. Our proposed approach exhibits
advantages in terms of robustness when tested in a new environment, supporting
the idea that generalization can be achieved by using state-action rotation.


Abstract:
  In this article, we give explicit calculations for the family Floer mirrors
of some non-compact Calabi-Yau surfaces. We compare it with the mirror
construction of Gross-Hacking-Keel for suitably chosen log Calabi-Yau pairs and
the rank two cluster varieties of finite type. In particular, the
analytifications of the later two give partial compactifications of the family
Floer mirrors that we computed.


Abstract:
  Neural style transfer, allowing to apply the artistic style of one image to
another, has become one of the most widely showcased computer vision
applications shortly after its introduction. In contrast, related tasks in the
music audio domain remained, until recently, largely untackled. While several
style conversion methods tailored to musical signals have been proposed, most
lack the 'one-shot' capability of classical image style transfer algorithms. On
the other hand, the results of existing one-shot audio style transfer methods
on musical inputs are not as compelling. In this work, we are specifically
interested in the problem of one-shot timbre transfer. We present a novel
method for this task, based on an extension of the vector-quantized variational
autoencoder (VQ-VAE), along with a simple self-supervised learning strategy
designed to obtain disentangled representations of timbre and pitch. We
evaluate the method using a set of objective metrics and show that it is able
to outperform selected baselines.


Abstract:
  We present the first multi-task learning model -- named PhoNLP -- for joint
Vietnamese part-of-speech (POS) tagging, named entity recognition (NER) and
dependency parsing. Experiments on Vietnamese benchmark datasets show that
PhoNLP produces state-of-the-art results, outperforming a single-task learning
approach that fine-tunes the pre-trained Vietnamese language model PhoBERT
(Nguyen and Nguyen, 2020) for each task independently. We publicly release
PhoNLP as an open-source toolkit under the Apache License 2.0. Although we
specify PhoNLP for Vietnamese, our PhoNLP training and evaluation command
scripts in fact can directly work for other languages that have a pre-trained
BERT-based language model and gold annotated corpora available for the three
tasks of POS tagging, NER and dependency parsing. We hope that PhoNLP can serve
as a strong baseline and useful toolkit for future NLP research and
applications to not only Vietnamese but also the other languages. Our PhoNLP is
available at: https://github.com/VinAIResearch/PhoNLP


Abstract:
  We develop a novel method for carrying out model selection for Bayesian
autoencoders (BAEs) by means of prior hyper-parameter optimization. Inspired by
the common practice of type-II maximum likelihood optimization and its
equivalence to Kullback-Leibler divergence minimization, we propose to optimize
the distributional sliced-Wasserstein distance (DSWD) between the output of the
autoencoder and the empirical data distribution. The advantages of this
formulation are that we can estimate the DSWD based on samples and handle
high-dimensional problems. We carry out posterior estimation of the BAE
parameters via stochastic gradient Hamiltonian Monte Carlo and turn our BAE
into a generative model by fitting a flexible Dirichlet mixture model in the
latent space. Consequently, we obtain a powerful alternative to variational
autoencoders, which are the preferred choice in modern applications of
autoencoders for representation learning with uncertainty. We evaluate our
approach qualitatively and quantitatively using a vast experimental campaign on
a number of unsupervised learning tasks and show that, in small-data regimes
where priors matter, our approach provides state-of-the-art results,
outperforming multiple competitive baselines.


Abstract:
  We present ARCH, a computational pathology (CP) multiple instance captioning
dataset to facilitate dense supervision of CP tasks. Existing CP datasets focus
on narrow tasks; ARCH on the other hand contains dense diagnostic and
morphological descriptions for a range of stains, tissue types and pathologies.
Using intrinsic dimensionality estimation, we show that ARCH is the only CP
dataset to (ARCH-)rival its computer vision analog MS-COCO Captions. We
conjecture that an encoder pre-trained on dense image captions learns
transferable representations for most CP tasks. We support the conjecture with
evidence that ARCH representation transfers to a variety of pathology sub-tasks
better than ImageNet features or representations obtained via self-supervised
or multi-task learning on pathology images alone. We release our best model and
invite other researchers to test it on their CP tasks.


Abstract:
  Using the state-of-the-art SKA precursor, the MeerKAT radio telescope, we
explore the limits to precision pulsar timing of millisecond pulsars achievable
due to pulse stochasticity (jitter). We report new jitter measurements in 15 of
the 29 pulsars in our sample and find that the levels of jitter can vary
dramatically between them. For some, like the 2.2~ms pulsar PSR J2241--5236, we
measure an implied jitter of just $\sim$ 4~ns/hr, while others like the 3.9~ms
PSR J0636--3044 are limited to $\sim$ 100 ns/hr. While it is well known that
jitter plays a central role to limiting the precision measurements of arrival
times for high signal-to-noise ratio observations, its role in the measurement
of dispersion measure (DM) has not been reported, particularly in broad-band
observations. Using the exceptional sensitivity of MeerKAT, we explored this on
the bright millisecond pulsar PSR J0437--4715 by exploring the DM of literally
every pulse. We found that the derived single pulse DMs vary by typically
0.0085 cm$^{-3}$ pc from the mean, and that the best DM estimate is limited by
the differential pulse jitter across the band. We postulate that all
millisecond pulsars will have their own limit on DM precision which can only be
overcome with longer integrations. Using high-time resolution filterbank data
of 9 $\mu$s, we also present a statistical analysis of single pulse
phenomenology. Finally, we discuss optimization strategies for the MeerKAT
pulsar timing program and its role in the context of the International Pulsar
Timing Array (IPTA).


Abstract:
  Highly accurate profiles of consumers daily energy usage are reported to
power grid via smart meters which enables smart grid to effectively regulate
power demand and supply. However, consumers energy consumption pattern can
reveal personal and sensitive information regarding their lifestyle. Therefore,
to ensure users privacy, differentially distributed noise is added to the
original data. This technique comes with a trade off between privacy of the
consumer versus utility of the data in terms of providing services like
billing, Demand Response schemes, and Load Monitoring. In this paper, we
propose a technique - Differential Privacy with Noise Cancellation Technique
(DPNCT) - to maximize utility in aggregated load monitoring and fair billing
while preserving users privacy by using noise cancellation mechanism on
differentially private data. We introduce noise to the sensitive data stream
before it leaves smart meters in order to guarantee privacy at individual
level. Further, we evaluate the effects of different periodic noise cancelling
schemes on privacy and utility i.e., billing and load monitoring. Our proposed
scheme outperforms the existing scheme in terms of preserving the privacy while
accurately calculating the bill.


Abstract:
  In point of fact the Indian tradition in mathematics is long and glorious. It
dates back to earliest times, and indeed many of the Indian discoveries from
5000 years ago correspond rather naturally to modern mathematical results.


Abstract:
  Over half of disk galaxies are barred, yet the mechanisms for bar formation
and the life-time of bar buckling remain poorly understood. In simulations, a
thin bar undergoes a rapid (<1 Gyr) event called "buckling," during which the
inner part of the bar is asymmetrically bent out of the galaxy plane and
eventually thickens, developing a peanut/X-shaped profile when viewed side-on.
Through analyzing stellar kinematics of N-body model snapshots of a galaxy
before, during, and after the buckling phase, we confirm a distinct quadrupolar
pattern of out-of-plane stellar velocities in nearly face-on galaxies. This
kinematic signature of buckling allows us to identify five candidates of
currently buckling bars among 434 barred galaxies in the Mapping Nearby
Galaxies at Apache Point Observatory (MaNGA) Survey, an integral field unit
(IFU) spectroscopic survey that measures the composition and kinematic
structure of nearby galaxies. The frequency of buckling events detected is
consistent with the 0.5-1 Gyr timescale predicted by simulations. The five
candidates we present more than double the total number of candidate buckling
bars, and are the only ones found using the kinematic signature.


Abstract:
  The difficulty of solving a multi-objective optimization problem is impacted
by the number of objectives to be optimized. The presence of many objectives
typically introduces a number of challenges that affect the choice/design of
optimization algorithms. This paper investigates the drivers of these
challenges from two angles: (i) the influence of the number of objectives on
problem characteristics and (ii) the practical behavior of commonly used
procedures and algorithms for coping with many objectives. In addition to
reviewing various drivers, the paper makes theoretical contributions by
quantifying some drivers and/or verifying these drivers empirically by carrying
out experiments on multi-objective NK landscapes and other typical benchmarks.
We then make use of our theoretical and empirical findings to derive practical
recommendations to support algorithm design. Finally, we discuss remaining
theoretical gaps and opportunities for future research in the area of multi-
and many-objective optimization.


Abstract:
  Searching large digital repositories can be extremely frustrating, as common
list-based formats encourage users to adopt a convenience-sampling approach
that favours chance discovery and random search, over meaningful exploration.
We have designed a methodology that allows users to visually and thematically
explore corpora, while developing personalised holistic reading strategies. We
describe the results of a three-phase qualitative study, in which experienced
researchers used our interactive visualisation approach to analyse a set of
publications and select relevant themes and papers. Using in-depth
semi-structured interviews and stimulated recall, we found that users: (i)
selected papers that they otherwise would not have read, (ii) developed a more
coherent reading strategy, and (iii) understood the thematic structure and
relationships between papers more effectively. Finally, we make six design
recommendations to enhance current digital repositories that we have shown
encourage users to adopt a more holistic and thematic research approach.


Abstract:
  Detection of semantic contradictory sentences is one of the most challenging
and fundamental issues for NLP applications such as recognition of textual
entailments. Contradiction in this study includes different types of semantic
confrontation, such as conflict and antonymy. Due to lack of sufficient data to
apply precise machine learning and specifically deep learning methods to
Persian and other low resource languages, rule-based approaches that can
function similarly to these systems will be of a great interest. Also recently,
emergence of new methods such as transfer learning, has opened up the
possibility of deep learning for low-resource languages. Considering two above
points, in this study, along with a simple rule-base baseline, a novel
rule-base system for identifying semantic contradiction along with a Bert base
deep contradiction detection system for Persian texts have been introduced. The
rule base system has used frequent rule mining method to extract appropriate
contradiction rules using a development set. Extracted rules are tested for
different categories of contradictory sentences. In this system the maximum
f-measure among contradiction categories is obtained for negation about 90% and
the average F-measure of system for all classes is about 76% which outperforms
other algorithms on Persian texts. On the other hand, because of medium
performance of rule base system for some categories of contradiction, we use a
Bert base deep learning system using our translated dataset; with average
F-measure of 73. Our hybrid system has f-measure of about 80.


Abstract:
  In this work, we study thermal conductivity (k) of BC5, an ultra-hard
diamondlike semiconductor material, using first-principles computations and
analyze the effect of both isotopic disorder as well as length scale
dependence. k of isotopically pure BC5 is computed to be 169 Wm- 1K-1 (along
a-axis) at 300K; this high k is found to be due to the high frequencies and
phonon group velocities of both acoustic and optical phonons owing to the light
atomic mass of Carbon (C) and Boron (B) atoms and strong C-C and B-C bonds. We
also observe a dominance of optical phonons (~ 54%) over acoustic phonons in
heat conduction at higher temperatures (~500 K). This unusually high
contribution of optical phonons is found to be due to a unique effect in BC5
related to a weaker temperature dependence of optical phonon scattering rates
relative to acoustic phonons. The effect is explained in terms of high
frequencies of optical phonons causing decay into other high frequency phonons,
where low phonon populations cause the decay term to become insensitive to
temperature. The effect further leads to high nanoscale thermal conductivity of
77 Wm-1K-1 at 100 nm length scale due to optical phonon meanfreepaths being in
nanometer regime. These results provide avenues for application of BC5 in
nanoscale thermal management.


Abstract:
  In this paper, a novel adaptive smooth disturbance observer-based fast
finite-time adaptive backstepping control scheme is presented for the attitude
tracking of the 3-DOF helicopter system subject to compound disturbances.
First, an adaptive smooth disturbance observer (ASDO) is proposed to estimate
the composite disturbance, which owns the characteristics of smooth output,
fast finite-time convergence, and adaptability to the disturbance of unknown
derivative boundary. Then, a finite-time backstepping control protocol is
construct to drive the elevation and pitch angles to track reference
trajectories. To tackle the "explosion of complexity" and "singularity"
problems in the conventional backstepping design framework, a fast finite-time
command filter (FFTCF) is utilized to estimate the virtual control signal and
its derivative. Moreover, a fractional power-based auxiliary dynamic system is
introduced to compensate the error caused by the FFTCF estimation. Furthermore,
an improved fractional power-based adaptive law with the $\sigma $-modification
term is designed to attenuate the observer approximation error, such that the
tracking performance is further enhanced. In terms of the fast finite-time
stability theory, the signals of the closed-loop system are all fast
finite-time bounded while the attitude tracking errors can fast converge to a
sufficiently small region of the origin in finite time. Finally, a contrastive
numerical simulation is carried out to validate the effectiveness and
superiority of the designed control scheme.


Abstract:
  THz pulses are generated from femtosecond pulse-excited
ferromagnetic/nonmagnetic spintronic heterostructures via inverse spin Hall
effect. The contribution from ultrafast demagnetization/remagnetization is
extremely weak, in the comparison. The highest possible THz signal strength
from spintronic THz emitters is limited by the optical damage threshold of the
corresponding heterostructures. The THz generation efficiency does not saturate
with the excitation fluence even up till the damage threshold. Bilayer (Fe,
CoFeB)/(Pt, Ta) based FM/NM spintronic heterostructures have been studied for
an optimized performance for THz generation when pumped by sub-50 fs amplified
laser pulses at 800 nm. Among them, CoFeB/Pt is the best combination for an
efficient THz source. The optimized FM/NM spintronic heterostructure on a
quartz substrate, having alpha-phase Ta as the nonmagnetic layer, show the
highest damage threshold as compared to those with Pt, irrespective of their
generation efficiency. The damage threshold of the Fe/Ta heterostructure on
quartz substrate is ~85 GW/cm2.


Abstract:
  A dark matter search project needs and extremely low background radiation
detector since the expected event rate of dark matter is less than a few events
in one year in one tonne of the detector mass. The authors developed a highly
radiopure NaI(Tl) crystal to search for dark matter. The best combination of
the purification methods was developed, resulting $^{\mathrm{nat}}$K and
$^{210}$Pb were less than 20 ppb and 5.7 $\mu$Bq/kg, respectively.
  The authors will construct a large volume detector system with high-purity
NaI(Tl) crystals. The design and the performance of the prototype detector
module will be reported in this article.


Abstract:
  In Phys. Rev. Lett. 120, 200603 (2018), a segmented XXZ spin chain with zero
anisotropy in one half and a large anisotropy on the other half gave rise to a
spin current rectification which is perfect in the thermodynamic limit. Here we
extend the previous study to segmented chains with interacting integrable as
well as non-integrable halves, considering even cases in which no ballistic
transport can emerge in either half. We demonstrate that, also in this more
general case, it is possible to obtain giant rectification when the two
interacting half chains are sufficiently different. We also show that the
mechanism causing this effect is the emergence of an energy gap in the
excitation spectrum of the out-of-equilibrium insulating steady state in one of
the two biases. Finally we demonstrate that in the thermodynamic limit there is
no perfect rectification when each of the two half chains is interacting.


Abstract:
  PdCoO2 , belonging to a family of triangular oxides called delafossite, is
one of the most conducting oxides. Its in-plane conductivity is comparable to
those of the best metals, and exhibits hydrodynamic electronic transport with
extremely long mean free path at cryogenic temperatures. Nonetheless, it is
nonmagnetic despite the presence of the cobalt ion. Here, we show that a mild
hydrogenation process reduces PdCoO2 thin films to an atomically-mixed alloy of
PdCo with strong out-of-plane ferromagnetism and sign-tunable anomalous Hall
effect. Considering that many other compounds remain little affected under a
similar hydrogenation condition, this discovery may provide a route to creating
novel spintronic heterostructures combining strong ferromagnetism, involving
oxides and other functional materials.


Abstract:
  In the present work, NIR, LIBS, Raman and XRD techniques have been
complementarily used to carry out a comprehensive characterization of a
terrestrial analogue selected from the Chesapeake Bay Impact Structure (CBIS).
The obtained data clearly highlight the key role of Raman spectroscopy in the
detection of minor and trace compounds, through which inferences about
geological processes occurred in the CBIS can be extrapolated. Beside the use
of commercial systems, further Raman analyses were performed by the Raman Laser
Spectrometer (RLS) ExoMars Simulator. This instrument represents the most
reliable tool to effectively predict the scientific capabilities of the
ExoMars/Raman system that will be deployed on Mars in 2021. By emulating the
analytical procedures and operational restrictions established by the ExoMars
mission rover design, it was proved that the RLS ExoMars Simulator is able to
detect the amorphization of quartz, which constitutes an analytical clue of the
impact origin of craters. On the other hand, the detection of barite and
siderite, compounds crystallizing under hydrothermal conditions, helps to
indirectly confirm the presence of water in impact targets. Furthermore, the
RLS ExoMars Simulator capability of performing smart molecular mappings was
also evaluated. According to the obtained results, the algorithms developed for
its operation provide a great analytical advantage over most of the automatic
analysis systems employed by commercial Raman instruments, encouraging its
application for many additional scientific and commercial purposes.


Abstract:
  We propose a field-based design for dielectric antennas to interface diamond
color centers with a Gaussian propagating far field. This antenna design
enables an efficient spin-photon interface with a Purcell factor exceeding 400
and a 93% mode overlap to a 0.4 numerical aperture far-field Gaussian mode. The
antenna design is robust to fabrication imperfections, such as variations in
the dimensions of the dielectric perturbations and the emitter dipole location.
The field-based dielectric antenna design provides an efficient free-space
interface to closely packed arrays of quantum memories for multiplexed quantum
repeaters, arrayed quantum sensors, and modular quantum computers.


Abstract:
  In a wireless network that conveys status updates from sources (i.e.,
sensors) to destinations, one of the key issues studied by existing literature
is how to design an optimal source sampling strategy on account of the
communication constraints which are often modeled as queues. In this paper, an
alternative perspective is presented -- a novel status-aware communication
scheme, namely \emph{parallel communications}, is proposed which allows sensors
to be communication-agnostic. Specifically, the proposed scheme can determine,
based on an online prediction functionality, whether a status packet is worth
transmitting considering both the network condition and status prediction, such
that sensors can generate status packets without communication constraints. We
evaluate the proposed scheme on a Software-Defined-Radio (SDR) test platform,
which is integrated with a collaborative autonomous driving simulator, i.e.,
Simulation-of-Urban-Mobility (SUMO), to produce realistic vehicle control
models and road conditions. The results show that with online status
predictions, the channel occupancy is significantly reduced, while guaranteeing
low status recovery error. Then the framework is applied to two scenarios: a
multi-density platooning scenario, and a flight formation control scenario.
Simulation results show that the scheme achieves better performance on the
network level, in terms of keeping the minimum safe distance in both vehicle
platooning and flight control.


Abstract:
  The recently proposed action spotting task consists in finding the exact
timestamp in which an event occurs. This task fits particularly well for soccer
videos, where events correspond to salient actions strictly defined by soccer
rules (a goal occurs when the ball crosses the goal line). In this paper, we
devise a lightweight and modular network for action spotting, which can
simultaneously predict the event label and its temporal offset using the same
underlying features. We enrich our model with two training strategies: the
first one for data balancing and uniform sampling, the second for masking
ambiguous frames and keeping the most discriminative visual cues. When tested
on the SoccerNet dataset and using standard features, our full proposal exceeds
the current state of the art by 3 Average-mAP points. Additionally, it reaches
a gain of more than 10 Average-mAP points on the test set when fine-tuned in
combination with a strong 2D backbone.


Abstract:
  Biomedical research is intensive in processing information in the previously
published papers. This motivated a lot of efforts to provide tools for text
mining and information extraction from PDF documents over the past decade. The
*nix (Unix/Linux) operating systems offer many tools for working with text
files, however, very few such tools are available for processing the contents
of PDF files. This paper reports our effort to develop shell script utilities
for *nix systems with the core functionality focused on viewing and searching
multiple PDF documents combining logical and regular expressions, and enabling
more reliable text extraction from PDF documents with subsequent manipulation
of the resulting blocks of text. Furthermore, a procedure for extracting the
most frequently occurring multi-word phrases was devised and then demonstrated
on several scientific papers in life sciences. Our experiments revealed that
the procedure is surprisingly robust to deficiencies in text extraction and the
actual scoring function used to rank the phrases in terms of their importance
or relevance. The keyword relevance is strongly context dependent, the word
stemming did not provide any recognizable advantage, and the stop-words should
only be removed from the beginning and the end of phrases. In addition, the
developed utilities were used to convert the list of acronyms and the index
from a PDF e-book into a large list of biochemical terms which can be exploited
in other text mining tasks. All shell scripts and data files are available in a
public repository named \pp\ on the Github. The key lesson learned in this work
is that semi-automated methods combining the power of algorithms with the
capabilities of research experience are the most promising for improving the
research efficiency.


Abstract:
  Sampling-based model-predictive control (MPC) is a promising tool for
feedback control of robots with complex, non-smooth dynamics, and cost
functions. However, the computationally demanding nature of sampling-based MPC
algorithms has been a key bottleneck in their application to high-dimensional
robotic manipulation problems in the real world. Previous methods have
addressed this issue by running MPC in the task space while relying on a
low-level operational space controller for joint control. However, by not using
the joint space of the robot in the MPC formulation, existing methods cannot
directly account for non-task space related constraints such as avoiding joint
limits, singular configurations, and link collisions. In this paper, we develop
a system for fast, joint space sampling-based MPC for manipulators that is
efficiently parallelized using GPUs. Our approach can handle task and joint
space constraints while taking less than 8ms~(125Hz) to compute the next
control command. Further, our method can tightly integrate perception into the
control problem by utilizing learned cost functions from raw sensor data. We
validate our approach by deploying it on a Franka Panda robot for a variety of
dynamic manipulation tasks. We study the effect of different cost formulations
and MPC parameters on the synthesized behavior and provide key insights that
pave the way for the application of sampling-based MPC for manipulators in a
principled manner. We also provide highly optimized, open-source code to be
used by the wider robot learning and control community. Videos of experiments
can be found at: https://sites.google.com/view/manipulation-mpc


Abstract:
  Three state-of-the-art statistical ranking methods for forecasting football
matches are combined with several other predictors in a hybrid machine learning
model. Namely an ability estimate for every team based on historic matches; an
ability estimate for every team based on bookmaker consensus; average
plus-minus player ratings based on their individual performances in their home
clubs and national teams; and further team covariates (e.g., market value, team
structure) and country-specific socio-economic factors (population, GDP). The
proposed combined approach is used for learning the number of goals scored in
the matches from the four previous UEFA EUROs 2004-2016 and then applied to
current information to forecast the upcoming UEFA EURO 2020. Based on the
resulting estimates, the tournament is simulated repeatedly and winning
probabilities are obtained for all teams. A random forest model favors the
current World Champion France with a winning probability of 14.8% before
England (13.5%) and Spain (12.3%). Additionally, we provide survival
probabilities for all teams and at all tournament stages.


Abstract:
  Deep neural networks have received considerable attention in clinical
imaging, particularly with respect to the reduction of radiation risk. Lowering
the radiation dose by reducing the photon flux inevitably results in the
degradation of the scanned image quality. Thus, researchers have sought to
exploit deep convolutional neural networks (DCNNs) to map low-quality, low-dose
images to higher-dose, higher-quality images thereby minimizing the associated
radiation hazard. Conversely, computed tomography (CT) measurements of
geomaterials are not limited by the radiation dose. In contrast to the human
body, however, geomaterials may be comprised of high-density constituents
causing increased attenuation of the X-Rays. Consequently, higher dosage images
are required to obtain an acceptable scan quality. The problem of prolonged
acquisition times is particularly severe for micro-CT based scanning
technologies. Depending on the sample size and exposure time settings, a single
scan may require several hours to complete. This is of particular concern if
phenomena with an exponential temperature dependency are to be elucidated. A
process may happen too fast to be adequately captured by CT scanning. To
address the aforementioned issues, we apply DCNNs to improve the quality of
rock CT images and reduce exposure times by more than 60\%, simultaneously. We
highlight current results based on micro-CT derived datasets and apply transfer
learning to improve DCNN results without increasing training time. The approach
is applicable to any computed tomography technology. Furthermore, we contrast
the performance of the DCNN trained by minimizing different loss functions such
as mean squared error and structural similarity index.


Abstract:
  We study theoretically spin current generation from a band insulator with PT
symmetry, which is associated with Zener tunneling in strong dc electric
fields. Each band in this system is doubly degenerate with opposite spins, but
spin rotational symmetry is not preserved in general. We consider the condition
for spin current generation in connection with the nature of the wave function,
which ultimately depends on a geometric quantity known as the shift vector.
From an analysis of a two-band model, we find that the shift vector is
necessary for spin current generation in a PT symmetric system. We also present
zigzag chain models that have shift vectors, and confirm from numerical
calculations that a nonzero tunneling spin current occurs in spin-degenerate
systems.


Abstract:
  We analyze the average fidelity (say, F) and the fidelity deviation (say, D)
in noisy-channel quantum teleportation. Here, F represents how well
teleportation is performed on average and D quantifies whether the
teleportation is performed impartially on the given inputs, that is, the
condition of universality. Our analysis results prove that the achievable
maximum average fidelity ensures zero fidelity deviation, that is, perfect
universality. This structural trait of teleportation is distinct from those of
other limited-fidelity probabilistic quantum operations, for instance,
universal-NOT or quantum cloning. This feature is confirmed again based on a
tighter relationship between F and D in the qubit case. We then consider
another realistic noise model where F decreases and D increases due to
imperfect control. To alleviate such deterioration, we propose a
machine-learning-based algorithm. We demonstrate by means of numerical
simulations that the proposed algorithm can stabilize the system. Notably, the
recovery process consists solely of the maximization of F, which reduces the
control time, thus leading to a faster cure cycle.


Abstract:
  We address an outstanding problem that represents a critical roadblock in the
development of the Majorana-based topological qubit using
semiconductor-superconductor hybrid structures: the quantitative
characterization of disorder effects generated by the unintentional presence of
charge impurities within the hybrid device. Given that disorder can have
far-reaching consequences for the Majorana physics, but is intrinsically
difficult to probe experimentally in a hybrid structure, providing a
quantitative theoretical description of disorder effects becomes essential. To
accomplish this task, we develop a microscopic theory that (i) provides a
quantitative characterization of the effective potential generated by a charge
impurity embedded inside a semiconductor wire proximity-coupled to a
superconductor layer by solving self-consistently the associated
three-dimensional Schr\"odinger-Poisson problem, (ii) describes the low-energy
physics of the hybrid structure in the presence of s-wave superconductivity,
spin-orbit coupling, Zeeman splitting, and disorder arising from multiple
charge impurities by using the results of (i) within a standard free fermion
approach, and (iii) links the microscopic results to experimentally observable
features by generating tunneling differential conductance maps as function of
the control parameters (e.g., Zeeman field and chemical potential). We find
that charge impurities lead to serious complications regarding the realization
and observation of Majorana zero modes, which have direct implications for the
development of Majorana-based qubits. More importantly, our work provides a
clear direction regarding what needs to be done for progress in the field,
including specific materials quality and semiconductor purity targets that must
be achieved to create a topological qubit.


Abstract:
  The Minkowski question-mark function $?(x)$ is a continuous strictly
increasing function defined on $[0,1]$ interval. It is well known fact that the
derivative of this function, if exists, can take only two values: $0$ and
$+\infty$. It is also known that the value of the derivative $?'(x)$ at the
point $x=[0;a_1,a_2,\ldots,a_t,\ldots]$ is connected with the limit behavior of
the arithmetic mean $(a_1+a_2+\ldots+a_t)/t$. Particularly, N. Moshchevitin and
A. Dushistova showed that if $a_1+a_2+\ldots+a_t<\kappa_1 t$, where $\kappa_1 =
2\log\bigl({\frac{1+\sqrt{5}}{2}}\bigr)/\log{2}= 1.3884\ldots$, then
$?'(x)=+\infty$. They also proved that the constant $\kappa_1$ is
non-improvable. We consider a dual problem: how small can be the quantity
$a_1+a_2+\ldots+a_t-\kappa_1 t$ if $?'(x)=0$? We obtain the non-improvable
estimates of this quantity.


Abstract:
  We sharpen to nearly optimal the known asymptotic and explicit bounds for the
number of $\mathbb{F}_q$-rational points on a geometrically irreducible
hypersurface over a (large) finite field. The proof involves a Bertini-type
probabilistic combinatorial technique. Namely, we study the number of
$\mathbb{F}_q$-points on the intersection of the given hypersurface with a
random plane.


Abstract:
  Computational notebooks have become the tool of choice for many data
scientists and practitioners for performing analyses and disseminating results.
Despite their increasing popularity, the research community cannot yet count on
a large, curated dataset of computational notebooks. In this paper, we fill
this gap by introducing KGTorrent, a dataset of Python Jupyter notebooks with
rich metadata retrieved from Kaggle, a platform hosting data science
competitions for learners and practitioners with any levels of expertise. We
describe how we built KGTorrent, and provide instructions on how to use it and
refresh the collection to keep it up to date. Our vision is that the research
community will use KGTorrent to study how data scientists, especially
practitioners, use Jupyter Notebook in the wild and identify potential
shortcomings to inform the design of its future extensions.


Abstract:
  This work studies the role of inter-user device-to-device (D2D) cooperation
for improving physical-layer secret communication in multi-user downlink
systems. It is assumed that there are out-of-band D2D channels, on each of
which a selected legitimate user transmits an amplified version of the received
downlink signal to other legitimate users. A key technical challenge for
designing such systems is that eavesdroppers can overhear downlink as well as
D2D cooperation signals. We tackle the problem of jointly optimizing the
downlink precoding, artificial noise covariance, and amplification coefficients
that maximize the minimum rate. An iterative alternating optimization algorithm
is proposed based on the matrix fractional programming. Numerical results
confirm the performance gains of the proposed D2D cooperation scheme compared
to benchmark secret communication schemes.


Abstract:
  In this paper, we extend the reinterpreted discrete fracture model for flow
simulation of fractured porous media containing flow blocking barriers on
non-conforming meshes. The methodology of the approach is to modify the
traditional Darcy's law into the hybrid-dimensional Darcy's law where fractures
and barriers are represented as Dirac-delta functions contained in the
permeability tensor and resistance tensor, respectively. As a natural extension
of the reinterpreted discrete fracture model for highly conductive fractures,
this model is able to account for the influence of both highly conductive
fractures and blocking barriers accurately on non-conforming meshes. The local
discontinuous Galerkin (LDG) method is employed to accommodate the form of the
hybrid-dimensional Darcy's law and the nature of the pressure/flux
discontinuity. The performance of the model is demonstrated by several
numerical tests.


Abstract:
  In this paper, we consider several possible ways to set up Heterogeneous
Multiscale Methods for the Landau-Lifshitz equation with a highly oscillatory
diffusion coefficient, which can be seen as a means to modeling rapidly varying
ferromagnetic materials. We then prove estimates for the errors introduced when
approximating the relevant quantity in each of the models given a periodic
problem, using averaging in time and space of the solution to a corresponding
micro problem. In our setup, the Landau-Lifshitz equation with highly
oscillatory coefficient is chosen as the micro problem for all models. We then
show that the averaging errors only depend on $\varepsilon$, the size of the
microscopic oscillations, as well as the size of the averaging domain in time
and space and the choice of averaging kernels.


Abstract:
  Reusable decoys offer a cost-effective alternative to the single-use hardware
commonly applied to protect surface assets from threats. Such decoys portray
fake assets to lure threats away from the true asset. To deceive a threat, a
decoy first has to position itself such that it can break the radar lock.
Considering multiple simultaneous threats, this paper introduces an approach
for controlling multiple decoys to minimise the time required to break the
locks of all the threats. The method includes the optimal allocation of one
decoy to every threat with an assignment procedure that provides local position
constraints to guarantee collision avoidance and thereby decouples the control
of the decoys. A crude model of a decoy with uncertainty is considered for
motion planning. The task of a decoy reaching a state in which the lock of the
assigned threat can be broken is formulated as a temporal logic specification.
To this end, the requirements to complete the task are modelled as time-varying
set-membership constraints. The temporal and logical combination of the
constraints is encoded in a mixed-integer optimisation problem. To demonstrate
the results a simulated case study is provided.


Abstract:
  In this paper, we present Talaria, a novel permissioned blockchain simulator
that supports numerous protocols and use cases, most notably in supply chain
management. Talaria extends the capability of BlockSim, an existing blockchain
simulator, to include permissioned blockchains and serves as a foundation for
further private blockchain assessment. Talaria is designed with both practical
Byzantine Fault Tolerance (pBFT) and simplified version of Proof-of-Authority
consensus protocols, but can be revised to include other permissioned protocols
within its modular framework. Moreover, Talaria is able to simulate different
types of malicious authorities and a variable daily transaction load at each
node. In using Talaria, business practitioners and policy planners have an
opportunity to measure, evaluate, and adapt a range of blockchain solutions for
commercial operations.


Abstract:
  Let L be a general length function for modules over a Noetherian ring R. We
use L to define Hilbert series and polynomials for R[X]-modules. The leading
term of any such polynomial is an invariant of R[X]-modules, which refines the
algebraic entropy.


Abstract:
  We study quasi periodic and frequency locked states that can occur in a
sinusoidally driven linear harmonic oscillator in the special relativistic
regime. We show how the shift in natural frequency of the oscillator with
increasing relativistic effects leads to frequency locking or quasi periodicity
and the chaotic states that arise due to the increasing non linearity. We find
the same system can have multi-stable states in the presence of small damping.
We also report an enhancement of chaos in the relativistic Henon-Heiles system.


Abstract:
  High-resolution manometry (HRM) is the primary procedure used to diagnose
esophageal motility disorders. Its interpretation and classification includes
an initial evaluation of swallow-level outcomes and then derivation of a
study-level diagnosis based on Chicago Classification (CC), using a tree-like
algorithm. This diagnostic approach on motility disordered using HRM was
mirrored using a multi-stage modeling framework developed using a combination
of various machine learning approaches. Specifically, the framework includes
deep-learning models at the swallow-level stage and feature-based machine
learning models at the study-level stage. In the swallow-level stage, three
models based on convolutional neural networks (CNNs) were developed to predict
swallow type, swallow pressurization, and integrated relaxation pressure (IRP).
At the study-level stage, model selection from families of the
expert-knowledge-based rule models, xgboost models and artificial neural
network(ANN) models were conducted, with the latter two model designed and
augmented with motivation from the export knowledge. A simple model-agnostic
strategy of model balancing motivated by Bayesian principles was utilized,
which gave rise to model averaging weighted by precision scores. The averaged
(blended) models and individual models were compared and evaluated, of which
the best performance on test dataset is 0.81 in top-1 prediction, 0.92 in top-2
predictions. This is the first artificial-intelligence-style model to
automatically predict CC diagnosis of HRM study from raw multi-swallow data.
Moreover, the proposed modeling framework could be easily extended to
multi-modal tasks, such as diagnosis of esophageal patients based on clinical
data from both HRM and functional luminal imaging probe panometry (FLIP).


Abstract:
  Open clusters are central elements of our understanding of the Galactic disk
evolution, as an accurate determination of their parameters leads to an
unbiased picture of our Galaxy's structure. Extending the analysis towards
fainter magnitudes in cluster sequences has a significant impact on the derived
fundamental parameters, such as extinction and total mass. We perform a
homogeneous analysis of six open stellar clusters in the Galactic disk using
kinematic and photometric information from the Gaia DR2 and VVV surveys:
NGC6067, NGC6259, NGC4815, Pismis18, Trumpler23, and Trumpler20. We implement
two coarse-to-fine characterization methods: first, we employ Gaussian mixture
models to tag fields around each open cluster in the proper motion space, and
then we apply an unsupervised machine learning method to make the membership
assignment to each cluster. For the studied clusters, with ages in the
$\sim$120-1900 Myr range, we report an increase of $\sim$45 % new member
candidates on average in our sample. The data-driven selection approach of
cluster members makes our catalog a valuable resource for testing stellar
evolutionary models and for assessing the cluster low-to-intermediate mass
populations. This study is the first of a series intended to homogeneously
reveal open cluster near-infrared sequences.


Abstract:
  In this article, we propose the fractional lower order covariance method
(FLOC) for estimating the parameters of vector autoregressive process (VAR) of
order $p$, $p\geq 1$ with symmetric stable noise. Further, we show the
efficiency, accuracy, and simplicity of our methods through Monte-Carlo
simulation.


Abstract:
  With the rise of AI in SE, researchers have shown how AI can be applied to
assist software developers in a wide variety of activities. However, it has not
been accompanied by a complementary increase in labelled datasets, which is
required in many supervised learning methods. Several studies have been using
crowdsourcing platforms to collect labelled training data in recent years.
However, research has shown that the quality of labelled data is unstable due
to participant bias, knowledge variance, and task difficulty. Thus, we present
CodeLabeller, a web-based tool that aims to provide a more efficient approach
in handling the process of labelling Java source files at scale by improving
the data collection process throughout, and improving the degree of reliability
of responses by requiring each labeller to attach a confidence rating to each
of their responses. We test CodeLabeller by constructing a corpus of over a
thousand source files obtained from a large collection of opensource Java
projects, and labelling each Java source file with their respective design
patterns and summaries. Apart from assisting researchers to crowdsource a
labelled dataset, the tool has practical applicability in software engineering
education and assists in building expert ratings for software artefacts. This
paper discusses the motivation behind the creation of CodeLabeller, the
intended users, a tool demonstration and its UI, its implementation, benefits,
and lastly, the evaluation through a user study and in-practice usage.


Abstract:
  We report the identification of symmetry-enforced nodal planes (NPs) in CoSi
providing the missing topological charges in an entire network of
band-crossings comprising in addition multifold degeneracies and Weyl points,
such that the fermion doubling theorem is satisfied. In our study we have
combined measurements of Shubnikov-de Haas (SdH) oscillations in CoSi with
material-specific calculations of the electronic structure and Berry curvature,
as well as a general analysis of the band topology of space group (SG) 198. The
observation of two nearly dispersionless SdH frequency branches provides
unambiguous evidence of four Fermi surface sheets at the R point that reflect
the symmetry-enforced orthogonality of the underlying wave functions at the
intersections with the NPs. Hence, irrespective of the spin-orbit coupling
strength, SG198 features always six- and fourfold degenerate crossings at R and
$\Gamma$ that are intimately connected to the topological charges distributed
across the network.


Abstract:
  Context: Software startups are an essential source of innovation and
software-intensive products. The need to understand product development in
startups and to provide relevant support are highlighted in software research.
While state-of-the-art literature reveals how startups develop their software,
the reasons why they adopt these activities are underexplored. Objective: This
study investigates the tactics behind software engineering (SE) activities by
analyzing key engineering events during startup journeys. We explore how
entrepreneurial mindsets may be associated with SE knowledge areas and with
each startup case. Method: Our theoretical foundation is based on causation and
effectuation models. We conducted semi-structured interviews with 40 software
startups. We used two-round open coding and thematic analysis to describe and
identify entrepreneurial software development patterns. Additionally, we
calculated an effectuation index for each startup case. Results: We identified
621 events merged into 32 codes of entrepreneurial logic in SE from the sample.
We found a systemic occurrence of the logic in all areas of SE activities.
Minimum Viable Product (MVP), Technical Debt (TD), and Customer Involvement
(CI) tend to be associated with effectual logic, while testing activities at
different levels are associated with causal logic. The effectuation index
revealed that startups are either effectuation-driven or mixed-logics-driven.
Conclusions: Software startups fall into two types that differentiate between
how traditional SE approaches may apply to them. Effectuation seems the most
relevant and essential model for explaining and developing suitable SE
practices for software startups.


Abstract:
  Deep neural networks (DNNs) are known vulnerable to backdoor attacks, a
training time attack that injects a trigger pattern into a small proportion of
training data so as to control the model's prediction at the test time.
Backdoor attacks are notably dangerous since they do not affect the model's
performance on clean examples, yet can fool the model to make incorrect
prediction whenever the trigger pattern appears during testing. In this paper,
we propose a novel defense framework Neural Attention Distillation (NAD) to
erase backdoor triggers from backdoored DNNs. NAD utilizes a teacher network to
guide the finetuning of the backdoored student network on a small clean subset
of data such that the intermediate-layer attention of the student network
aligns with that of the teacher network. The teacher network can be obtained by
an independent finetuning process on the same clean subset. We empirically
show, against 6 state-of-the-art backdoor attacks, NAD can effectively erase
the backdoor triggers using only 5\% clean training data without causing
obvious performance degradation on clean examples. Code is available in
https://github.com/bboylyg/NAD.


Abstract:
  The rapid deployment of distributed energy resources (DERs) in distribution
networks has brought challenges to balance the system and stabilize frequency.
DERs have the ability to provide frequency regulation; however, existing
dynamic frequency simulation tools-which were developed mainly for the
transmission system-lack the capability to simulate distribution network
dynamics with high penetrations of DERs. Although electromagnetic transient
(EMT) simulation tools can simulate distribution network dynamics, the
computation efficiency limits their use for large-scale
transmission-and-distribution (T&D) simulations. This paper presents an
efficient T&D dynamic frequency co-simulation framework for DER frequency
response based on the HELICS platform and existing off-the-shelf simulators.
The challenge of synchronizing frequency between the transmission network and
DERs hosted in the distribution network is approached by detailed modeling of
DERs in frequency dynamic models while DER phasor models are also preserved in
the distribution networks. Thereby, local voltage constraints can be respected
when dispatching the DER power for frequency response. The DER frequency
responses (primary and secondary)-are simulated in case studies to validate the
proposed framework. Lastly, fault-induced delayed voltage recovery (FIDVR)
event of a large system is presented to demonstrate the efficiency and
effectiveness of the overall framework.


Abstract:
  This paper describes a technique for the autonomous mission planning of
unmanned aerial system swarms. Given a swarm operating in a known area, a
central command system generates measurements from the swarm. If those
measurements indicate changes to the mission situation such as target movement,
the swarm planning is updated to reflect the new situation and guidance updates
are broadcast to the swarm. The primary algorithms featured in this work are A*
pathfinding and the Generalized Labeled Multi-Bernoulli multi-target tracking
method.


Abstract:
  Recently, experiments have been reported where researchers were able to
perform high dynamic range (HDR) tomography in a heuristic fashion, by fusing
multiple tomographic projections. This approach to HDR tomography has been
inspired by HDR photography and inherits the same disadvantages. Taking a
computational imaging approach to the HDR tomography problem, we here suggest a
new model based on the Modulo Radon Transform (MRT), which we rigorously
introduce and analyze. By harnessing a joint design between hardware and
algorithms, we present a single-shot HDR tomography approach, which to our
knowledge, is the only approach that is backed by mathematical guarantees.
  On the hardware front, instead of recording the Radon Transform projections
that my potentially saturate, we propose to measure modulo values of the same.
This ensures that the HDR measurements are folded into a lower dynamic range.
On the algorithmic front, our recovery algorithms reconstruct the HDR images
from folded measurements. Beyond mathematical aspects such as injectivity and
inversion of the MRT for different scenarios including band-limited and
approximately compactly supported images, we also provide a first
proof-of-concept demonstration. To do so, we implement MRT by experimentally
folding tomographic measurements available as an open source data set using our
custom designed modulo hardware. Our reconstruction clearly shows the
advantages of our approach for experimental data. In this way, our MRT based
solution paves a path for HDR acquisition in a number of related imaging
problems.


Abstract:
  Due to the different photosensitive properties of infrared and visible light,
the registered RGB-T image pairs shot in the same scene exhibit quite different
characteristics. This paper proposes a siamese infrared and visible light
fusion Network (SiamIVFN) for RBG-T image-based tracking. SiamIVFN contains two
main subnetworks: a complementary-feature-fusion network (CFFN) and a
contribution-aggregation network (CAN). CFFN utilizes a two-stream multilayer
convolutional structure whose filters for each layer are partially coupled to
fuse the features extracted from infrared images and visible light images. CFFN
is a feature-level fusion network, which can cope with the misalignment of the
RGB-T image pairs. Through adaptively calculating the contributions of infrared
and visible light features obtained from CFFN, CAN makes the tracker robust
under various light conditions. Experiments on two RGB-T tracking benchmark
datasets demonstrate that the proposed SiamIVFN has achieved state-of-the-art
performance. The tracking speed of SiamIVFN is 147.6FPS, the current fastest
RGB-T fusion tracker.


Abstract:
  Deep-learning (DL) has emerged as a powerful machine-learning technique for
several classic problems encountered in generic wireless communications.
Specifically, random Fourier Features (RFF) based deep-learning has emerged as
an attractive solution for several machine-learning problems; yet there is a
lacuna of rigorous results to justify the viability of RFF based DL-algorithms
in general. To address this gap, we attempt to analytically quantify the
viability of RFF based DL. Precisely, in this paper, analytical proofs are
presented demonstrating that RFF based DL architectures have lower
approximation-error and probability of misclassification as compared to
classical DL architectures. In addition, a new distribution-dependent RFF is
proposed to facilitate DL architectures with low training-complexity. Through
computer simulations, the practical application of the presented analytical
results and the proposed distribution-dependent RFF, are depicted for various
machine-learning problems encountered in next-generation communication systems
such as: a) line of sight (LOS)/non-line of sight (NLOS) classification, and b)
message-passing based detection of low-density parity check codes (LDPC) codes
over nonlinear visible light communication (VLC) channels. Especially in the
low training-data regime, the presented simulations show that significant
performance gains are achieved when utilizing RFF maps of observations. Lastly,
in all the presented simulations, it is observed that the proposed
distribution-dependent RFFs significantly outperform RFFs, which make them
useful for potential machine-learning/DL based applications in the context of
next-generation communication systems.


Abstract:
  We present a projection-based framework for solving a
thermodynamically-consistent Cahn-Hilliard Navier-Stokes system that models
two-phase flows. In this work we extend the fully implicit method presented in
Khanwale et al. [{\it A fully-coupled framework for solving Cahn-Hilliard
Navier-Stokes equations: Second-order, energy-stable numerical methods on
adaptive octree based meshes.}, arXiv:2009.06628 (2020)], to a block iterative
hybrid method. We use a projection-based semi-implicit time discretization for
the Navier-Stokes and a fully-implicit time discretization for the
Cahn-Hilliard equation. We use a conforming continuous Galerkin (cG) finite
element method in space equipped with a residual-based variational multiscale
(RBVMS) formulation. Pressure is decoupled using a projection step, which
results in two linear positive semi-definite systems for velocity and pressure,
instead of the saddle point system of a pressure-stabilized method. All the
linear systems are solved using an efficient and scalable algebraic multigrid
(AMG) method. We deploy this approach on a massively parallel numerical
implementation using parallel octree-based adaptive meshes. The overall
approach allows the use of relatively large time steps with much faster
time-to-solve. We present comprehensive numerical experiments showing detailed
comparisons with results from the literature for canonical cases, including the
single bubble rise and Rayleigh-Taylor instability.


Abstract:
  We present a novel task scheduling scheme for accelerating computational
applications involving distributed iterative processes that are executed on
networked computing resources. Such an application consists of multiple tasks,
each of which outputs data at each iteration to be processed by neighboring
tasks; these dependencies between the tasks can be represented as a directed
graph. We first mathematically formulate the problem as a Binary Quadratic
Program (BQP), accounting for both computation and communication costs. We show
that the problem is NP-hard. We then relax the problem as a Semi-Definite
Program (SDP) and utilize a randomized rounding technique based on sampling
from a suitably-formulated multi-variate Gaussian distribution. Furthermore, we
derive the expected value of bottleneck time. Finally, we apply our proposed
scheme on gossip-based federated learning as an application of iterative
processes. Through numerical evaluations on the MNIST and CIFAR-10 datasets, we
show that our proposed approach outperforms well-known scheduling techniques
from distributed computing. In particular, for arbitrary settings, we show that
it reduces bottleneck time by $91\%$ compared to HEFT and $84\%$ compared to
throughput HEFT.


Abstract:
  This work focuses on complete 3D facial geometry prediction, including 3D
facial alignment via 3D face modeling and face orientation estimation using the
proposed multi-task, multi-modal, and multi-representation landmark refinement
network (M$^3$-LRN). Our focus is on the important facial attributes, 3D
landmarks, and we fully utilize their embedded information to guide 3D facial
geometry learning. We first propose a multi-modal and multi-representation
feature aggregation for landmark refinement. Next, we are the first to study
3DMM regression from sparse 3D landmarks and utilize multi-representation
advantage to attain better geometry prediction. We attain the state of the art
from extensive experiments on all tasks of learning 3D facial geometry. We
closely validate contributions of each modality and representation. Our results
are robust across cropped faces, underwater scenarios, and extreme poses.
Specially we adopt only simple and widely used network operations in M$^3$-LRN
and attain a near 20\% improvement on face orientation estimation over the
current best performance. See our project page here.


Abstract:
  Major cause of midvehicle collision is due to the distraction of drivers in
both the Front and rear-end vehicle witnessed in dense traffic and high speed
road conditions. In view of this scenario, a crash detection and collision
avoidance algorithm coined as Midvehicle Collision Detection and Avoidance
System (MCDAS) is proposed to evade the possible crash at both ends of the host
vehicle. The method based upon Constant Velocity (CV) model specifically,
addresses two scenarios, the first scenario encompasses two sub-scenario
namely, a) A rear-end collision avoidance mechanism that accelerates the host
vehicle under no front-end vehicle condition and b) Curvilinear motion based on
front and host vehicle offset (position), whilst, the other scenario deals with
parallel parking issues. The offset based curvilinear motion of the host
vehicle plays a vital role in threat avoidance from the front-end vehicle. A
desired curvilinear strategy on left and right sides is achieved by the host
vehicle with concern of possible CV to avoid both end collisions. In this
methodology, path constraint is applicable for both scenarios with required
direction. Monte Carlo analysis of MCDAS covering vehicle kinematics
demonstrated acute discrimination with consistent performance for the collision
validated on simulated with real-time data.


Abstract:
  This paper presents abstract harmonic analysis foundations for structure of
covariant function algebras of invariant characters of normal subgroups.
Suppose that $G$ is a locally compact group and $N$ is a closed normal subgroup
of $G$. Let $\xi:N\to\mathbb{T}$ be a continuous $G$-invariant character, $1\le
p<\infty$, and $L_\xi^p(G,N)$ be the $L^p$-space of all covariant functions of
$\xi$ on $G$. We study structure of covariant convolution in $L^p_\xi(G,N)$. It
is proved that $L^1_\xi(G,N)$ is a Banach $*$-algebra and $L^p_\xi(G,N)$ is a
Banach $L^1_\xi(G,N)$-module. We then investigate the theory of covariant
convolutions for the case of characters of canonical normal subgroups in
semi-direct product groups. The paper is concluded by realization of the theory
in the case of different examples.


Abstract:
  The Martian Moons Exploration (MMX) spacecraft is a JAXA mission to Mars and
its moons Phobos and Deimos. MMX will carry the Circum-Martian Dust Monitor
(CMDM) which is a newly developed light-weight ($\mathrm{650\,g}$) large area
($\mathrm{1\,m^2}$) dust impact detector. Cometary meteoroid streams (also
referred to as trails) exist along the orbits of comets, forming fine
structures of the interplanetary dust cloud. The streams consist predominantly
of the largest cometary particles (with sizes of approximately
$\mathrm{100\,\mu m}$ to 1~cm) which are ejected at low speeds and remain very
close to the comet orbit for several revolutions around the Sun. The
Interplanetary Meteoroid Environment for eXploration (IMEX) dust streams in
space model is a new and recently published universal model for cometary
meteoroid streams in the inner Solar System. We use IMEX to study the detection
conditions of cometary dust stream particles with CMDM during the MMX mission
in the time period 2024 to 2028. The model predicts traverses of 12 cometary
meteoroid streams with fluxes of $\mathrm{100\,\mu m}$ and bigger particles of
at least $\mathrm{10^{-3}\,m^{-2}\,day^{-1}}$ during a total time period of
approximately 90~days. The highest flux of $\mathrm{0.15\,m^{-2}\,day^{-1}}$ is
predicted for comet 114P/Wiseman-Skiff in October 2026. With its large
detection area and high sensitivity CMDM will be able to detect cometary
meteoroid streams en route to Phobos. Our simulation results for the Mars
orbital phase of MMX also predict the occurrence of meteor showers in the
Martian atmosphere which may be observable from the Martian surface with
cameras on board landers or rovers. Finally, the IMEX model can be used to
study the impact hazards imposed by meteoroid impacts on to large-area
spacecraft structures that will be particularly necessary for crewed deep space
missions.


Abstract:
  Deep learning model (primarily convolutional networks and LSTM) for time
series classification has been studied broadly by the community with the wide
applications in different domains like healthcare, finance, industrial
engineering and IoT. Meanwhile, Transformer Networks recently achieved frontier
performance on various natural language processing and computer vision tasks.
In this work, we explored a simple extension of the current Transformer
Networks with gating, named Gated Transformer Networks (GTN) for the
multivariate time series classification problem. With the gating that merges
two towers of Transformer which model the channel-wise and step-wise
correlations respectively, we show how GTN is naturally and effectively
suitable for the multivariate time series classification task. We conduct
comprehensive experiments on thirteen dataset with full ablation study. Our
results show that GTN is able to achieve competing results with current
state-of-the-art deep learning models. We also explored the attention map for
the natural interpretability of GTN on time series modeling. Our preliminary
results provide a strong baseline for the Transformer Networks on multivariate
time series classification task and grounds the foundation for future research.


Abstract:
  Depression is one of the most common mental illness problems, and the
symptoms shown by patients are not consistent, making it difficult to diagnose
in the process of clinical practice and pathological research. Although
researchers hope that artificial intelligence can contribute to the diagnosis
and treatment of depression, the traditional centralized machine learning needs
to aggregate patient data, and the data privacy of patients with mental illness
needs to be strictly confidential, which hinders machine learning algorithms
clinical application. To solve the problem of privacy of the medical history of
patients with depression, we implement federated learning to analyze and
diagnose depression. First, we propose a general multi-view federated learning
framework using multi-source data, which can extend any traditional machine
learning model to support federated learning across different institutions or
parties. Secondly, we adopt late fusion methods to solve the problem of
inconsistent time series of multi-view data. Finally, we compare the federated
framework with other cooperative learning frameworks in performance and discuss
the related results.


Abstract:
  While the theory of labeled well-quasi-order has received significant
attention in the graph setting, it has not yet been considered in the context
of permutation patterns. We initiate this study here, and using labeled
well-quasi-order are able to subsume and extend all of the well-quasi-order
results in the permutation patterns literature. Connections to the graph
setting are emphasized throughout. In particular, we establish that a
permutation class is labeled well-quasi-ordered if and only if its
corresponding graph class is also labeled well-quasi-ordered.