Abstract:
  The prevalence of location tracking systems has resulted in large volumes of
spatiotemporal data generated every day. Addressing reachability queries on
such datasets is important for a wide range of applications (surveillance,
public health, social networks, etc.) A spatiotemporal reachability query
identifies whether a physical item (or information etc.) could have been
transferred from the source object $O_S$ to the target object $O_T$ during a
time interval $I$ (either directly, or through a chain of intermediate
transfers). In previous research on spatiotemporal reachability queries, the
number of such transfers is not limited, and the weight of a piece of
transferred information remains the same. This paper introduces novel
reachability queries, which assume a scenario of information decay. Such
queries arise when the value of information that travels through the chain of
intermediate objects decreases with each transfer. To address such queries
efficiently over large spatiotemporal datasets, we introduce the RICCdecay
algorithm. Further, the decay scenario leads to an important extension: if
there are many different sources of information, the aggregate value of
information an object can obtain varies. As a result, we introduce a top-k
reachability problem, identifying the k objects with the highest accumulated
information. We also present the RICCtopK algorithm that can efficiently
compute top-k reachability with transfer decay queries. An experimental
evaluation shows the efficiency of the proposed algorithms over previous
approaches.


Abstract:
  We prove a universal approximation property (UAP) for a class of ODENet and a
class of ResNet, which are used in many deep learning algorithms. The UAP can
be stated as follows. Let $n$ and $m$ be the dimension of input and output
data, and assume $m\leq n$. Then we show that ODENet width $n+m$ with any
non-polynomial continuous activation function can approximate any continuous
function on a compact subset on $\mathbb{R}^n$. We also show that ResNet has
the same property as the depth tends to infinity. Furthermore, we derive
explicitly the gradient of a loss function with respect to a certain tuning
variable. We use this to construct a learning algorithm for ODENet. To
demonstrate the usefulness of this algorithm, we apply it to a regression
problem, a binary classification, and a multinomial classification in MNIST.


Abstract:
  If C is a closed symmetric monoidal category, the Chu category Chu(C, g) over
C and an object g of it was defined by Chu, as a *-autonomous category
generated from C. Bishop introduced the category of complemented subsets of a
set, in order to overcome the problems generated by the use of negation in
constructive measure theory. Shulman mentions that Bishop's complemented
subsets correspond roughly to the Chu construction. In this paper we explain
this correspondence by showing that there is a Chu representation (a full
embedding) of the category of complemented subsets of a set X into Chu(Set, X x
X). A Chu representation of the category of Bishop spaces into Chu(Set, R) is
shown, as the constructive analogue to the standard Chu representation of the
category of topological spaces into Chu(Set, 2). In order to represent the
category of predicates (with objects pairs (X, A), where A is a subset of X,
and the category of complemented predicates (with objects pairs (X, A), where A
is a complemented subset of X, we generalise the Chu construction by defining
the Chu category over a cartesian closed category C and an endofunctor on C.
Finally, we introduce the antiparallel Grothendieck construction over a product
category and a contra-variant Set-valued functor on it of which the Chu
construction is a special case, in case C is a locally small, cartesian closed
category.


Abstract:
  Epidemiology models play a key role in understanding and responding to the
COVID-19 pandemic. In order to build those models, scientists need to
understand contributing factors and their relative importance. A large strand
of literature has identified the importance of airflow to mitigate droplets and
far-field aerosol transmission risks. However, the specific factors
contributing to higher or lower contamination in various settings have not been
clearly defined and quantified. As part of the MOAI project
(https://moaiapp.com), we are developing a privacy-preserving test and trace
app to enable infection cluster investigators to get in touch with patients
without having to know their identity. This approach allows involving users in
the fight against the pandemic by contributing additional information in the
form of anonymous research questionnaires. We first describe how the
questionnaire was designed, and the synthetic data was generated based on a
review we carried out on the latest available literature. We then present a
model to evaluate the risk exposition of a user for a given setting. We finally
propose a temporal addition to the model to evaluate the risk exposure over
time for a given user.


Abstract:
  Vector Addition Systems and equivalent Petri nets are a well established
models of concurrency. The central algorithmic problem for Vector Addition
Systems with a long research history is the reachability problem asking whether
there exists a run from one given configuration to another. We settle its
complexity to be Ackermann-complete thus closing the problem open for 45 years.
In particular we prove that the problem is $\mathcal{F}_k$-hard for Vector
Addition Systems with States in dimension $6k$, where $\mathcal{F}_k$ is the
$k$-th complexity class from the hierarchy of fast-growing complexity classes.


Abstract:
  In this paper, we present several new structures for the colored HOMFLY-PT
invariants of framed links. First, we prove the strong integrality property for
the normalized colored HOMFLY-PT invariants by purely using the HOMFLY-PT skein
theory developed by H. Morton and his collaborators. By this strong integrality
property, we immediately obtain several symmetric properties for the full
colored HOMFLY-PT invariants of links. Then, we apply our results to refine the
mathematical structures appearing in the Labastida-Mari\~no-Ooguri-Vafa (LMOV)
integrality conjecture for framed links. As another application of the strong
integrality, we obtain that the $q=1$ and $a=1$ specializations of the
normalized colored HOMFLY-PT invariant are well-defined link polynomials. We
find that a conjectural formula for the colored Alexander polynomial which is
the $a=1$ specialization of the normalized colored HOMFLY-PT invariant implies
that a special case of the LMOV conjecture for frame knot holds.


Abstract:
  In this article we present a novel discrete-time design approach which
reduces the deteriorating effects of sampling on stability and performance in
digitally controlled nonlinear mechanical systems. The method is motivated by
recent results for linear systems, where feedback imposes closed-loop behavior
that exactly represents the symplectic discretization of a desired target
system. In the nonlinear case, both the second order accurate representation of
the sampling process and the definition of the target dynamics stem from the
application of the implicit midpoint rule. The implicit nature of the resulting
state feedback requires the numerical solution of an in general nonlinear
system of algebraic equations in every sampling interval. For an implementation
with pure position feedback, the velocities/momenta have to be approximated in
the sampling instants, which gives a clear interpretation of our approach in
terms of the St\"ormer-Verlet integration scheme on a staggered grid. We
present discrete-time versions of impedance or energy shaping plus damping
injection control as well as computed torque tracking control. Both the
Hamiltonian and the Lagrangian perspective are adopted. Besides a linear
example to introduce the concept, the simulations with a planar two link robot
model illustrate the performance and stability gain compared to the discrete
implementations of continuous-time control laws. A short analysis of
computation times shows the real-time capability of our method.


Abstract:
  The non-Hermitian skin effect can be realized through asymmetric hopping
between forward and backward directions, where all the modes of the system are
localized at one edge of a finite 1D lattice. However, achieving such an
asymmetric hopping in optical systems is far from trivial. Here we show
theoretically that in a finite chain of 1D exciton-polariton micropillars with
symmetric hopping, the inherent non-linearity of the system can exhibit a
bi-skin effect, where the modes of the system are localized at the two edges of
the system. To show the topological origin of such modes, we calculate the
winding number.


Abstract:
  Can deep learning solve multiple tasks simultaneously, even when they are
unrelated and very different? We investigate how the representations of the
underlying tasks affect the ability of a single neural network to learn them
jointly. We present theoretical and empirical findings that a single neural
network is capable of simultaneously learning multiple tasks from a combined
data set, for a variety of methods for representing tasks -- for example, when
the distinct tasks are encoded by well-separated clusters or decision trees
over certain task-code attributes. More concretely, we present a novel analysis
that shows that families of simple programming-like constructs for the codes
encoding the tasks are learnable by two-layer neural networks with standard
training. We study more generally how the complexity of learning such combined
tasks grows with the complexity of the task codes; we find that combining many
tasks may incur a sample complexity penalty, even though the individual tasks
are easy to learn. We provide empirical support for the usefulness of the
learning bounds by training networks on clusters, decision trees, and SQL-style
aggregation.


Abstract:
  We prove the super-exponential decay of probabilities that there exist $n$
crossings of a given quadrilateral in a simple $\text{CLE}_\kappa(\Omega)$,
$\frac{8}{3}<\kappa\le 4$, as $n$ goes to infinity. Besides being of
independent interest, this also provides the missing ingredient in
arXiv:1809.00690 for proving the convergence of probabilities of cylindrical
events for the double-dimer loop ensembles to those for the nested
$\text{CLE}_4(\Omega)$.


Abstract:
  We present results from the first deployment of novel, high definition,
compact gas Time Projection Chambers (TPCs) with pixel chip readout as part of
the BEAST II beam background measurement project at SuperKEKB. The TPCs provide
detailed 3D imaging of ionization from neutron-induced nuclear recoils in a
helium and carbon dioxide target gas mixture at standard temperature and
pressure. We find excellent electron background rejection, leading to
background-free nuclear recoil measurements above 50 keVee, despite the extreme
high-background environment. We measure an angular resolution better than
20{\deg} for recoil tracks longer than 1.7 mm, corresponding to an average
ionization energy of ~100 keVee. We also obtain the 3D vector direction of
helium recoils by utilizing charge profile measurements along the recoil axis,
with a correct head/tail assignment efficiency of approximately 80%. With this
performance, we present comparisons between measured and simulated event rates,
recoil energy spectra, and directional distributions originating from beam-gas
and Touschek beam losses at SuperKEKB. We utilize head/tail recognition to
distinguish neutron components travelling with positive radial velocity in the
Belle II coordinate system from those with opposite directionality. Finally, we
present a novel method of discriminating beam-gas interactions from Touschek
beam losses that can eliminate the need for dedicated accelerator runs for
background measurements. This method is still statistics-limited. However,
future studies should be able to verify this method, which in turn could lead
to neutron background analysis runs symbiotic with normal Belle II operation.
The capabilities demonstrated here also suggest that high definition recoil
imaging in gas TPCs is applicable to low energy, low-background experiments,
such as directional dark matter searches.


Abstract:
  Curve reconstruction from unstructured points in a plane is a fundamental
problem with many applications that has generated research interest for
decades. Involved aspects like handling open, sharp, multiple and non-manifold
outlines, run-time and provability as well as potential extension to 3D for
surface reconstruction have led to many different algorithms. We survey the
literature on 2D curve reconstruction and then present an open-sourced
benchmark for the experimental study. Our unprecedented evaluation on a
selected set of planar curve reconstruction algorithms aims to give an overview
of both quantitative analysis and qualitative aspects for helping users to
select the right algorithm for specific problems in the field. Our benchmark
framework is available online to permit reproducing the results, and easy
integration of new algorithms.


Abstract:
  The entropy of $1/16$-th BPS AdS$_5$ black holes can be microscopically
accounted for by the superconformal index of the $\mathcal{N}=4$
super-Yang-Mills theory. One way to compute this is through a Cardy-like limit
of a formula for the index obtained in [1] using the "$S$-transformation" of
the elliptic $\Gamma$ function. In this paper, we derive more general
$SL(3,\mathbb{Z})$ modular properties of the elliptic $\Gamma$ function. We
then use these properties to obtain a three integer parameter family of
generalized Cardy-like limits of the $\mathcal{N}=4$ superconformal index. From
these limits, we obtain entropy formulae that have a similar form as that of
the original AdS$_5$ black hole, up to an overall rescaling of the entropy. We
interpret this both on the field theory and the gravitational side. Finally, we
comment on how our work suggests a generalization of the Farey tail to four
dimensions.


Abstract:
  Two-dimensional Dirac semimetals have attracted much attention because of
their linear energy dispersion and non-trivial Berry phase. Graphene-like 2D
Dirac materials are gapless only within certain approximations, e.g., if
spin-orbit coupling (SOC) is neglected. It has recently been reported that
materials with nonsymmorphic crystal lattice possess symmetry-enforced
Dirac-like band dispersion around certain high-symmetry momenta even in the
presence of SOC. Here we calculate the optical absorption coefficient of
nonsymmorphic semimetals, such as $\alpha$-bismuthene, which hosts two
anisotropic Dirac cones with different Fermi velocities along $x$ and $y$
directions.We find that the optical absorption coefficient depends strongly on
the anisotropy factor and the photon polarization. When a magnetic field is
applied perpendicular to the plane of the material, the absorption coefficient
also depends on an internal parameter we termed the mixing angle of the band
structure. We further find that an in-plane magnetic field, while leaving the
system gapless, can induce a Van-Hove singularity in the joint density of
states: this causes a significant enhancement of the optical absorption at the
frequency of the singularity for one direction of polarization but not for the
orthogonal one, making the optical properties even more strongly dependent on
polarization. Due to the anisotropy present in our model, the Dirac cones at
two high-symmetry momenta in the Brillouin zone contribute very differently to
the optical absorbance. Consequently, it might be possible to preferentially
populate one valley or the other by varying photon polarization and frequency.
These results suggest that nonsymmorphic 2D Dirac semimetals are excellent
candidate materials for tunable magneto-optic devices.


Abstract:
  Large language models have become increasingly difficult to train because of
the growing computation time and cost. In this work, we present SRU++, a
highly-efficient architecture that combines fast recurrence and attention for
sequence modeling. SRU++ exhibits strong modeling capacity and training
efficiency. On standard language modeling tasks such as Enwik8, Wiki-103 and
Billion Word datasets, our model obtains better bits-per-character and
perplexity while using 3x-10x less training cost compared to top-performing
Transformer models. For instance, our model achieves a state-of-the-art result
on the Enwik8 dataset using 1.6 days of training on an 8-GPU machine. We
further demonstrate that SRU++ requires minimal attention for near
state-of-the-art performance. Our results suggest jointly leveraging fast
recurrence with little attention as a promising direction for accelerating
model training and inference.


Abstract:
  The multiphase flow of droplets is widespread, both at the industrial and the
microscale, for both biological and non-biological applications alike. But the
ensemble interactions of such systems are inherently nonlinear and complex,
compounded by interfacial effects, making it a difficult many-body problem for
theory. In comparison, the self-assembly dynamics of solid particles in flow
have long been described and successfully exploited in the field of inertial
microfluidics, where particle crystals can be realized from inertial forces and
hydrodynamic interactions. Here, we report novel self-assembly dynamics of
liquid drops in confined microfluidic channels that contrast starkly with the
established paradigm of inertial microfluidics: higher inertia leads to better
spatial ordering. Instead, we find that the conventional straight wall channel
geometry not only fails to achieve regular spatial ordering for drops but
actually exacerbates it with increasing inertia. Conversely, an asymmetric
serpentine geometry is able to achieve long-range, periodic spatial ordering
over length scales that are at least 3 orders of magnitude greater than the
drop diameter, particularly at low inertia. Experimentally, we are able to
decouple droplet generation from ordering, enabling independent variation of
number density, confinement, inertia, and surfactant concentration. We find the
inertia-dependent emergence of preferred drop separations and show for the
first time that Marangoni effects can influence the longitudinal ordering of
multidrop arrays. These results present a largely unexplored direction for
inertial microfluidics but also show the potential of its unification with
droplet microfluidics. In particular, the utility of passively restoring
uniform drop spacing on-chip is a key requirement for the streamlined
integration of incubation and drop-by-drop interrogation capabilities.


Abstract:
  This work is a continuation of [Y. Fittouhi and A. Joseph, Weierstrass
Sections for Parabolic adjoint action in type $A$]. Let $G$ be an irreducible
simple algebraic group and $B$ a Borel subgroup of $G$. Let $\mathfrak n$ be
the Lie algebra of the nilradical of $B$. Consider an irreducible subgroup $P$
of $G$ containing $B$. Let $P'$ be the derived group of $P$. Let $\mathfrak m$
be the Lie algebra of the nilradical of $P$. A theorem of Richardson asserts
that the algebra $\mathbb C[\mathfrak m]^{P'}$ of $P$ semi-invariants is
multiplicity-free. A linear subvariety $e+V$ such that the restriction map
induces an isomorphism of $\mathbb C[\mathfrak m]^{P'}$ onto $\mathbb C[e+V]$
is called a Weierstrass section for the action of $P'$ on $\mathfrak m$. Here
in type $A$ such a section is constructed, but in better form than that given
in Sect. 4, loc cit. Yet the main difference is a complete change of emphasis
from the construction of a Weierstrass section, to its application.
  Let $\mathscr N$ be the nilfibre relative to this action. From the
construction of a Weierstrass section $e+V$, it is shown that $e \in \mathscr
N$. Then $P.e$ is contained in a unique irreducible component $\mathscr C$ of
$\mathscr N$. The structure of $e+V$ is used to give a rather explicit
description of $\mathscr C$ as a $B$ saturation set, that is of the form
$\overline{B.\mathfrak u}$, where $\mathfrak u$ is a subalgebra of $\mathfrak
n$ . This algebra is not necessarily complemented by a subalgebra in $\mathfrak
n$ and so $\overline{B.\mathfrak u}$ is not necessarily an orbital variety
closure (hence Lagrangian) but it can be.
  It is shown that $\mathscr C$ need not contain a dense $P$ orbit and this by
a purely theoretical analysis. This occurs for an appropriate parabolic in
$A_{10}$ and is possibly the simplest example. In this particular case
$\mathscr C$ is not an orbital variety closure.


Abstract:
  In 2006, during a meeting of a working group of scientists in La Jolla,
California at The Neurosciences Institute (NSI), Gerald Edelman described a
roadmap towards the creation of a Conscious Artifact. As far as I know, this
roadmap was not published. However, it did shape my thinking and that of many
others in the years since that meeting. This short paper, which is based on my
notes taken during the meeting, describes the key steps in this roadmap. I
believe it is as groundbreaking today as it was more than 15 years ago.


Abstract:
  Peer code review has been found to be effective in identifying security
vulnerabilities. However, despite practicing mandatory code reviews, many Open
Source Software (OSS) projects still encounter a large number of post-release
security vulnerabilities, as some security defects escape those. Therefore, a
project manager may wonder if there was any weakness or inconsistency during a
code review that missed a security vulnerability. Answers to this question may
help a manager pinpointing areas of concern and taking measures to improve the
effectiveness of his/her project's code reviews in identifying security
defects. Therefore, this study aims to identify the factors that differentiate
code reviews that successfully identified security defects from those that
missed such defects. With this goal, we conduct a case-control study of
Chromium OS project. Using multi-stage semi-automated approaches, we build a
dataset of 516 code reviews that successfully identified security defects and
374 code reviews where security defects escaped. The results of our empirical
study suggest that the are significant differences between the categories of
security defects that are identified and that are missed during code reviews. A
logistic regression model fitted on our dataset achieved an AUC score of 0.91
and has identified nine code review attributes that influence identifications
of security defects. While time to complete a review, the number of mutual
reviews between two developers, and if the review is for a bug fix have
positive impacts on vulnerability identification, opposite effects are observed
from the number of directories under review, the number of total reviews by a
developer, and the total number of prior commits for the file under review.


Abstract:
  In the non-uniform $k$-center problem, the objective is to cover points in a
metric space with specified number of balls of different radii. Chakrabarty,
Goyal, and Krishnaswamy [ICALP 2016, Trans. on Algs. 2020] (CGK, henceforth)
give a constant factor approximation when there are two types of radii. In this
paper, we give a constant factor approximation for the two radii case in the
presence of outliers. To achieve this, we need to bypass the technical barrier
of bad integrality gaps in the CGK approach. We do so using "the ellipsoid
method inside the ellipsoid method": use an outer layer of the ellipsoid method
to reduce to stylized instances and use an inner layer of the ellipsoid method
to solve these specialized instances. This idea is of independent interest and
could be applicable to other problems.
  Keywords: Approximation, Clustering, Outliers, and Round-or-Cut.


Abstract:
  We consider the combinatorial bandits problem, where at each time step, the
online learner selects a size-$k$ subset $s$ from the arms set $\mathcal{A}$,
where $\left|\mathcal{A}\right| = n$, and observes a stochastic reward of each
arm in the selected set $s$. The goal of the online learner is to minimize the
regret, induced by not selecting $s^*$ which maximizes the expected total
reward. Specifically, we focus on a challenging setting where 1) the reward
distribution of an arm depends on the set $s$ it is part of, and crucially 2)
there is \textit{no total order} for the arms in $\mathcal{A}$.
  In this paper, we formally present a reward model that captures set-dependent
reward distribution and assumes no total order for arms. Correspondingly, we
propose an Upper Confidence Bound (UCB) algorithm that maintains UCB for each
individual arm and selects the arms with top-$k$ UCB. We develop a novel regret
analysis and show an $O\left(\frac{k^2 n \log T}{\epsilon}\right)$
gap-dependent regret bound as well as an $O\left(k^2\sqrt{n T \log T}\right)$
gap-independent regret bound. We also provide a lower bound for the proposed
reward model, which shows our proposed algorithm is near-optimal for any
constant $k$. Empirical results on various reward models demonstrate the broad
applicability of our algorithm.


Abstract:
  Unbalanced optimal power flow refers to a class of optimization problems
subject to the steady state physics of three-phase power grids with
nonnegligible phase unbalance. Significant progress on this problem has been
made on the mathematical modelling side of unbalanced OPF, however there is a
lack of information on implementation aspects as well as data sets for
benchmarking. One of the key problems is the lack of definitions of current and
voltage bounds across different classes of representations of the power flow
equations. Therefore, this tutorial-style paper summarizes the structural
features of the unbalanced (optimal) power problem for three-phase systems. The
resulting nonlinear complex-value matrix formulations are presented for both
the bus injection and branch flow formulation frameworks, which typically
cannot be implemented as-is in optimization toolboxes. Therefore, this paper
also derives the equivalent real-value formulations, and discusses challenges
related to the implementation in optimization modeling toolboxes. The derived
formulations can be re-used easily for continuous and discrete optimization
problems in distribution networks for a variety of operational and planning
problems. Finally, bounds are derived for all variables involved, to further
the development of benchmarks for unbalanced optimal power flow, where
consensus on bound semantics is a pressing need. We believe benchmarks remain a
cornerstone for the development and validation of scalable and reproducible
optimization models and tools. The soundness of the derivations is confirmed
through numerical experiments, validated w.r.t. OpenDSS for IEEE test feeders
with 3x3 impedance matrices.


Abstract:
  Recently, convolutional neural network (CNN) based image super-resolution
(SR) methods have achieved significant performance improvement. However, most
CNN-based methods mainly focus on feed-forward architecture design and neglect
to explore the feedback mechanism, which usually exists in the human visual
system. In this paper, we propose feedback pyramid attention networks (FPAN) to
fully exploit the mutual dependencies of features. Specifically, a novel
feedback connection structure is developed to enhance low-level feature
expression with high-level information. In our method, the output of each layer
in the first stage is also used as the input of the corresponding layer in the
next state to re-update the previous low-level filters. Moreover, we introduce
a pyramid non-local structure to model global contextual information in
different scales and improve the discriminative representation of the network.
Extensive experimental results on various datasets demonstrate the superiority
of our FPAN in comparison with the state-of-the-art SR methods.


Abstract:
  Models pre-trained on multiple languages have shown significant promise for
improving speech recognition, particularly for low-resource languages. In this
work, we focus on phoneme recognition using Allosaurus, a method for
multilingual recognition based on phonetic annotation, which incorporates
phonological knowledge through a language-dependent allophone layer that
associates a universal narrow phone-set with the phonemes that appear in each
language. To evaluate in a challenging real-world scenario, we curate phone
recognition datasets for Bukusu and Saamia, two varieties of the Luhya language
cluster of western Kenya and eastern Uganda. To our knowledge, these datasets
are the first of their kind. We carry out similar experiments on the dataset of
an endangered Tangkhulic language, East Tusom, a Tibeto-Burman language variety
spoken mostly in India. We explore both zero-shot and few-shot recognition by
fine-tuning using datasets of varying sizes (10 to 1000 utterances). We find
that fine-tuning of Allosaurus, even with just 100 utterances, leads to
significant improvements in phone error rates.


Abstract:
  R-parity conserving supersymmetric models with right-handed (RH) neutrinos
are very appealing since they could naturally explain neutrino physics and also
provide a good dark matter (DM) candidate such as the lightest supersymmetric
particle (LSP). In this work we consider the next-to-minimal supersymmetric
standard model (NMSSM) plus RH neutrino superfields, with effective Majorana
masses dynamically generated at the electroweak scale (EW). We perform a scan
of the relevant parameter space and study both possible DM candidates: RH
sneutrino and neutralino. Especially for the case of RH sneutrino DM we analyse
the intimate relation between both candidates to obtain the correct amount of
relic density. Besides the well-known resonances, annihilations through scalar
quartic couplings and coannihilation mechanisms with all kind of neutralinos,
are crucial. Finally, we present the impact of current and future direct and
indirect detection experiments on both DM candidates.


Abstract:
  We propose a novel approach to summarization based on Bayesian deep learning.
We approximate Bayesian summary generation by first extending state-of-the-art
summarization models with Monte Carlo dropout and then using them to perform
multiple stochastic forward passes. This method allows us to improve
summarization performance by simply using the median of multiple stochastic
summaries. We show that our variational equivalents of BART and PEGASUS can
outperform their deterministic counterparts on multiple benchmark datasets. In
addition, we rely on Bayesian inference to measure the uncertainty of the model
when generating summaries. Having a reliable uncertainty measure, we can
improve the experience of the end user by filtering out generated summaries of
high uncertainty. Furthermore, our proposed metric could be used as a criterion
for selecting samples for annotation, and can be paired nicely with active
learning and human-in-the-loop approaches.


Abstract:
  Retrograde Rossby waves, measured to have significant amplitudes in the Sun,
likely have notable implications for various solar phenomena. Rossby waves
create small-amplitude, very-low frequency motions (on the order of the
rotation rate and lower), which in turn shift the resonant frequencies and
eigenfunctions of the acoustic modes of the Sun. The detection of even
azimuthal orders Rossby modes using mode coupling presents additional
challenges and prior work therefore only focused on odd orders. Here, we
successfully extend the methodology to measure even azimuthal orders as well.
We analyze 4 and 8 years of Helioseismic and Magnetic Imager (HMI) data and
consider coupling between different-degree acoustic modes (of separations 1 and
3 in harmonic degree). The technique uses couplings between different frequency
bins to capture the temporal variability of the Rossby modes. We observe
significant power close to the theoretical dispersion relation for sectoral
Rossby modes (where the azimuthal order is same as harmonic degree, s = |t|).
Our results are consistent with prior measurements of Rossby modes with
azimuthal orders over the range t = 4 to 16 with maximum power occurring at
mode t = 8. The amplitudes of these modes vary from 1 to 2 m/s. We place an
upper bound of 0.2 m/s on the sectoral t = 2 mode, which we do not detect in
our measurements. This effort adds credence to the mode-coupling methodology in
helioseismology


Abstract:
  We study lattices acting on $\textrm{CAT}(0)$ spaces via their commensurated
subgroups. To do this we introduce the notions of a graph of lattices and a
complex of lattices giving graph and complex of group splittings of
$\textrm{CAT}(0)$ lattices. Using this framework we characterise irreducible
uniform $(\textrm{Isom}(\mathbb{E}^n)\times T)$-lattices by $C^\ast$-simplicity
and the failure of virtual fibring and biautomaticity. We construct
non-residually finite uniform lattices acting on arbitrary products of right
angled buildings and non-biautomatic lattices acting on the product of
$\mathbb{E}^n$ and a right-angled building. We investigate the residual
finiteness, $L^2$-cohomology, and $C^\ast$-simplicity of $\textrm{CAT}(0)$
lattices more generally. Along the way we prove that many right angled Artin
groups with rank $2$ centre are not quasi-isometrically rigid.


Abstract:
  Large annotated lung sound databases are publicly available and might be used
to train algorithms for diagnosis systems. However, it might be a challenge to
develop a well-performing algorithm for small non-public data, which have only
a few subjects and show differences in recording devices and setup. In this
paper, we use transfer learning to tackle the mismatch of the recording setup.
This allows us to transfer knowledge from one dataset to another dataset for
crackle detection in lung sounds. In particular, a single input convolutional
neural network (CNN) model is pre-trained on a source domain using ICBHI 2017,
the largest publicly available database of lung sounds. We use log-mel
spectrogram features of respiratory cycles of lung sounds. The pre-trained
network is used to build a multi-input CNN model, which shares the same network
architecture for respiratory cycles and their corresponding respiratory phases.
The multi-input model is then fine-tuned on the target domain of our
self-collected lung sound database for classifying crackles and normal lung
sounds. Our experimental results show significant performance improvements of
9.84% (absolute) in F-score on the target domain using the multi-input CNN
model based on transfer learning for crackle detection in adventitious lung
sound classification task.


Abstract:
  Recently, models have been shown to predict the effects of unexpected
situations, e.g., would cloudy skies help or hinder plant growth? Given a
context, the goal of such situational reasoning is to elicit the consequences
of a new situation (st) that arises in that context. We propose a method to
iteratively build a graph of relevant consequences explicitly in a structured
situational graph (st-graph) using natural language queries over a finetuned
language model (M). Across multiple domains, CURIE generates st-graphs that
humans find relevant and meaningful in eliciting the consequences of a new
situation. We show that st-graphs generated by CURIE improve a situational
reasoning end task (WIQA-QA) by 3 points on accuracy by simply augmenting their
input with our generated situational graphs, especially for a hard subset that
requires background knowledge and multi-hop reasoning.


Abstract:
  Different types of malicious activities have been flagged in multiple
permissionless blockchains such as bitcoin, Ethereum etc. While some malicious
activities exploit vulnerabilities in the infrastructure of the blockchain,
some target its users through social engineering techniques. To address these
problems, we aim at automatically flagging blockchain accounts that originate
such malicious exploitation of accounts of other participants. To that end, we
identify a robust supervised machine learning (ML) algorithm that is resistant
to any bias induced by an over representation of certain malicious activity in
the available dataset, as well as is robust against adversarial attacks. We
find that most of the malicious activities reported thus far, for example, in
Ethereum blockchain ecosystem, behaves statistically similar. Further, the
previously used ML algorithms for identifying malicious accounts show bias
towards a particular malicious activity which is over-represented. In the
sequel, we identify that Neural Networks (NN) holds up the best in the face of
such bias inducing dataset at the same time being robust against certain
adversarial attacks.


Abstract:
  Oxygen consumption (VO$_2$) provides established clinical and physiological
indicators of cardiorespiratory function and exercise capacity. However, VO$_2$
monitoring is largely limited to specialized laboratory settings, making its
widespread monitoring elusive. Here, we investigate temporal prediction of
VO$_2$ from wearable sensors during cycle ergometer exercise using a temporal
convolutional network (TCN). Cardiorespiratory signals were acquired from a
smart shirt with integrated textile sensors alongside ground-truth VO$_2$ from
a metabolic system on twenty-two young healthy adults. Participants performed
one ramp-incremental and three pseudorandom binary sequence exercise protocols
to assess a range of VO$_2$ dynamics. A TCN model was developed using causal
convolutions across an effective history length to model the time-dependent
nature of VO$_2$. Optimal history length was determined through minimum
validation loss across hyperparameter values. The best performing model encoded
218 s history length (TCN-VO$_2$ A), with 187 s, 97 s, and 76 s yielding less
than 3% deviation from the optimal validation loss. TCN-VO$_2$ A showed strong
prediction accuracy (mean, 95% CI) across all exercise intensities (-22
ml.min$^{-1}$, [-262, 218]), spanning transitions from low-moderate (-23
ml.min$^{-1}$, [-250, 204]), low-high (14 ml.min$^{-1}$, [-252, 280]),
ventilatory threshold-high (-49 ml.min$^{-1}$, [-274, 176]), and maximal (-32
ml.min$^{-1}$, [-261, 197]) exercise. Second-by-second classification of
physical activity across 16090 s of predicted VO$_2$ was able to discern
between vigorous, moderate, and light activity with high accuracy (94.1%). This
system enables quantitative aerobic activity monitoring in non-laboratory
settings across a range of exercise intensities using wearable sensors for
monitoring exercise prescription adherence and personal fitness.


Abstract:
  We introduce the concept of quasi-inverse of quantum and classical channels,
prove general properties of these inverses and determine them for a large class
of channels acting in an arbitrary finite dimension. Therefore we extend the
previous results of [1] to arbitrary dimensional channels and to the classical
domain. We demonstrate how application of the proposed scheme can increase on
the average the fidelity between a given random pure state and its image
transformed by the quantum channel followed by its quasi-inversion.


Abstract:
  The notion of an Evolutional Deep Neural Network (EDNN) is introduced for the
solution of partial differential equations (PDE). The parameters of the network
are trained to represent the initial state of the system only, and are
subsequently updated dynamically, without any further training, to provide an
accurate prediction of the evolution of the PDE system. In this framework, the
network parameters are treated as functions with respect to the appropriate
coordinate and are numerically updated using the governing equations. By
marching the neural network weights in the parameter space, EDNN can predict
state-space trajectories that are indefinitely long, which is difficult for
other neural network approaches. Boundary conditions of the PDEs are treated as
hard constraints, are embedded into the neural network, and are therefore
exactly satisfied throughout the entire solution trajectory. Several
applications including the heat equation, the advection equation, the Burgers
equation, the Kuramoto Sivashinsky equation and the Navier-Stokes equations are
solved to demonstrate the versatility and accuracy of EDNN. The application of
EDNN to the incompressible Navier-Stokes equation embeds the divergence-free
constraint into the network design so that the projection of the momentum
equation to solenoidal space is implicitly achieved. The numerical results
verify the accuracy of EDNN solutions relative to analytical and benchmark
numerical solutions, both for the transient dynamics and statistics of the
system.


Abstract:
  We reveal that finite-size solid acoustic resonators can support genuine
bound states in the continuum (BICs) completely localized inside the resonator.
The developed theory provides the multipole classification of such BICs in the
resonators of various shapes. It is shown how breaking of the resonator's
symmetry turns BICs into quasi-BICs manifesting themselves in the scattering
spectra as high-Q Fano resonances. We believe that the revealed novel states
will push the performance limits of acoustic devices and will serve as high-Q
building blocks for acoustic sensors, antennas, and topological acoustic
structures.


Abstract:
  The 2-dimensional global rigidity has been shown to be equivalent to
3-connectedness and redundant rigidity by a combination of two results due to
Jackson and Jord\'an, and Connelly, respectively. By the characterization, a
theorem of Lov\'asz and Yemini implies that every $6$-connected graph is
redundantly rigid, and thus globally rigid. The 6-connectedness is best
possible, since there exist infinitely many 5-connected non-rigid graphs.
Jackson, Servatius and Servatius used the idea of ``essential connectivity''
and proved that every 4-connected ``essentially 6-connected'' graph is
redundantly rigid and thus global rigid. Since 3-connectedness is a necessary
condition of global rigidity, it is interesting to study 3-connected graphs for
redundant rigidity and thus globally rigidity. We utilize a different
``essential connectivity'', and prove that every 3-connected essentially
9-connected graph is redundantly rigid and thus globally rigid. The essential
9-connectedness is best possible. Under this essential connectivity, we also
prove that every 4-connected essentially 6-connected graph is redundantly rigid
and thus global rigid. Our proofs are based on discharging arguments.


Abstract:
  The question of whether quark matter exists in neutron stars is a long
standing one. Generally one finds that a first order phase transition from
baryons to quarks softens the equation of state so much that the star would
collapse into a black hole. We consider a crossover equation of state, similar
to the crossover that is found in lattice QCD studies at finite temperature and
zero or small baryon chemical potentials. We find that with reasonable
parameters it may be possible to support neutron stars up to about 2.2 solar
masses. In that case 1 to 10% of the pressure would be contributed by quark
matter in the central core of the highest mass stars.


Abstract:
  The $n$th term of an automatic sequence is the output of a deterministic
finite automaton fed with the representation of $n$ in a suitable numeration
system. In this paper, instead of considering automatic sequences built on a
numeration system with a regular numeration language, we consider these built
on languages associated with trees having periodic labeled signatures and, in
particular, rational base numeration systems. We obtain two main
characterizations of these sequences. The first one is concerned with $r$-block
substitutions where $r$ morphisms are applied periodically. In particular, we
provide examples of such sequences that are not morphic. The second
characterization involves the factors, or subtrees of finite height, of the
tree associated with the numeration system and decorated by the terms of the
sequence.


Abstract:
  Data science has employed great research efforts in developing advanced
analytics, improving data models and cultivating new algorithms. However, not
many authors have come across the organizational and socio-technical challenges
that arise when executing a data science project: lack of vision and clear
objectives, a biased emphasis on technical issues, a low level of maturity for
ad-hoc projects and the ambiguity of roles in data science are among these
challenges. Few methodologies have been proposed on the literature that tackle
these type of challenges, some of them date back to the mid-1990, and
consequently they are not updated to the current paradigm and the latest
developments in big data and machine learning technologies. In addition, fewer
methodologies offer a complete guideline across team, project and data &
information management. In this article we would like to explore the necessity
of developing a more holistic approach for carrying out data science projects.
We first review methodologies that have been presented on the literature to
work on data science projects and classify them according to the their focus:
project, team, data and information management. Finally, we propose a
conceptual framework containing general characteristics that a methodology for
managing data science projects with a holistic point of view should have. This
framework can be used by other researchers as a roadmap for the design of new
data science methodologies or the updating of existing ones.


Abstract:
  Can we change the shape of a domain without altering its sizes? By
introducing a size-invariant shape transformation, we propose the existence and
explore the consequences of a new type of physical effect appearing at the
quantum scales, which we call here as "quantum shape effect". By completely
separating the shape effects from size effects, we show that shape alone
becomes a control parameter on the thermodynamic state functions of confined
systems at nanoscale. We develop an overlapped quantum boundary layer method to
analytically predict the quantum shape effects, reducing a thermodynamic
problem into a geometric one and revealing the profound link between the
geometry and thermodynamics at the quantum scales. Furthermore, we introduce
the isoformal, shape preserving, process which opens up the possibility of a
new generation of thermodynamic cycles operating at nanoscale with unique
features. As a whole, this thesis constitutes the proposition and a
comprehensive investigation of the theory, construction of the methodology and
exploration of the applications of quantum shape effects in thermodynamics.


Abstract:
  In this paper, we study a cosmological model inspired in the axionic matter
with two canonical scalar fields $\phi_1$ and $\phi_2$ interacting through a
term added to its potential. Introducing novel dynamical variables, and a
dimensionless time variable, the resulting dynamical system is studied. The
main difficulties arising in the standard dynamical systems approach, where
expansion normalized dynamical variables are usually adopted, are due to the
oscillations entering the nonlinear system through the Klein-Gordon (KG)
equations. This motivates the analysis of the oscillations using methods from
the theory of averaging nonlinear dynamical systems. We prove that
time-dependent systems, and their corresponding time-averaged versions, have
the same late-time dynamics. Then, we study the time-averaged system using
standard techniques of dynamical systems. We present numerical simulations as
evidence of such behavior.


Abstract:
  Continuous fuzzing is an increasingly popular technique for automated quality
and security assurance. Google maintains OSS-Fuzz: a continuous fuzzing service
for open source software. We conduct the first empirical study of OSS-Fuzz,
analyzing 23,907 bugs found in 316 projects. We examine the characteristics of
fuzzer-found faults, the lifecycles of such faults, and the evolution of
fuzzing campaigns over time. We find that OSS-Fuzz is often effective at
quickly finding bugs, and developers are often quick to patch them. However,
flaky bugs, timeouts, and out of memory errors are problematic, people rarely
file CVEs for security vulnerabilities, and fuzzing campaigns often exhibit
punctuated equilibria, where developers might be surprised by large spikes in
bugs found. Our findings have implications on future fuzzing research and
practice.


Abstract:
  We investigate the magnetic interactions in triangular rare-earth
delafossites materials NaYbO$_2$ and NaYbS$_2$ via first-principles
calculations. The calculated Curie-Weiss temperatures are in good agreement
with experiments. We perform classical Monte Carlo simulations of the two
compounds using the extracted exchange parameters. We find that if only the
nearest neighbor interactions are considered, the magnetic ground states of
NaYbO$_2$ and NaYbS$_2$ are a stripe and a planar 120\degree~ N\'{e}el state,
respectively. The simulated transition temperatures are much higher than the
lowest experimental temperatures, where no magnetic ordering was observed.
However, we show by adding suitable second neighbor interactions, the {\it
classical} magnetic ground state of NaYbO$_2$ becomes to the $Z_2$ vortex
phase, and the simulated specific heat $C_v$ are very similar to the
experimental observations, with no obvious phase transition down to the
extremely low temperature.


Abstract:
  The most promising avenue for detecting primordial gravitational waves from
cosmic inflation is through measurements of degree-scale CMB $B$-mode
polarisation. This approach must face the challenge posed by gravitational
lensing of the CMB, which obscures the signal of interest. Fortunately, the
lensing effects can be partially removed by combining high-resolution $E$-mode
measurements with an estimate of the projected matter distribution. For
near-future experiments, the best estimate of the latter will arise from
co-adding internal reconstructions (derived from the CMB itself) with external
tracers of the large-scale structure such as the cosmic infrared background
(CIB). In this work, we characterise how foregrounds impact the delensing
procedure when CIB intensity, $I$, is used as the matter tracer. We find that
higher-point functions of the CIB and Galactic dust such as $\langle BEI
\rangle_{c}$ and $\langle EIEI \rangle_{c}$ can, in principle, bias the power
spectrum of delensed $B$-modes. After estimating the dust residuals in
currently-available CIB maps and upcoming, foreground-cleaned Simons
Observatory CMB data, we find, using non-Gaussian dust simulations, that the
bias to any primordial signal is small compared to statistical errors for
ground-based experiments, but might be significant for space-based experiments
probing very large angular scales. However, mitigation techniques based on
multi-frequency cleaning appear to be very effective. We also show, by means of
an analytic model, that the bias arising from the higher-point functions of the
CIB itself ought to be negligible.


Abstract:
  The Directed Grid Theorem, stating that there is a function $f$ such that a
directed graphs of directed treewidth at least $f(k)$ contains a directed grid
of size at least $k$ as a butterfly minor, after being a conjecture for nearly
20 years, has been proven in 2015 by Kawarabayashi and Kreutzer. However, the
function $f$ obtained in the proof is very fast growing.
  In this work, we show that if one relaxes directed grid to bramble of
constant congestion, one can obtain a polynomial bound. More precisely, we show
that for every $k \geq 1$ there exists $t = \mathcal{O}(k^{48} \log^{13} k)$
such that every directed graph of directed treewidth at least $t$ contains a
bramble of congestion at most $8$ and size at least $k$.


Abstract:
  This paper presents a framework that allows online
dynamic-stability-constrained optimal trajectory planning of a mobile
manipulator robot working on rough terrain. First, the kinematics model of a
mobile manipulator robot, and the Zero Moment Point (ZMP) stability measure are
presented as theoretical background. Then, a sampling-based quasi-static
planning algorithm modified for stability guarantee and traction optimization
in continuous dynamic motion is presented along with a mathematical proof. The
robot's quasi-static path is then used as an initial guess to warm-start a
nonlinear optimal control solver which may otherwise have difficulties finding
a solution to the stability-constrained formulation efficiently. The
performance and computational efficiency of the framework are demonstrated
through an application to a simulated timber harvesting mobile manipulator
machine working on varying terrain. The results demonstrate feasibility of
online trajectory planning on varying terrain while satisfying the dynamic
stability constraint.


Abstract:
  Using matrix model, Mironov and Morozov recently gave a formula which
represents Kontsevich-Witten tau-function as a linear expansion of Schur
Q-polynomials. In this paper, we will show directly that the Q-polynomial
expansion in this formula satisfies the Virasoro constraints, and consequently
obtain a proof of this formula without using matrix model. We also give a proof
for Alexandrov's conjecture that Kontsevich-Witten tau-function is a
hypergeometric tau-function of the BKP hierarchy after re-scaling.


Abstract:
  Mid-infrared photometry of the Wolf-Rayet star HD 38030 in the Large
Magellanic Cloud from the NEOWISE-R mission show it to have undergone a
dust-formation episode in 2018 and the dust to have cooled in 2019-20. New
spectroscopy with the MagE spectrograph on the Magellan I Baade Telescope in
2019 and 2020 show absorption lines attributable to a companion of type near
O9.7III-IV. We found a significant shift in the radial velocity of the C IV
5801-12 blend compared with the RVs measured in 1984 and 1993. The results
combine to suggest that HD 38030 is a colliding-wind binary having short-lived
dust formation episodes, like the Galactic systems WR 140 and WR 19, but at
intervals in excess of 20 yr.


Abstract:
  There is increasing appetite for analysing multiple network data. This is
different to analysing traditional data sets, where now each observation in the
data comprises a network. Recent technological advancements have allowed the
collection of this type of data in a range of different applications. This has
inspired researchers to develop statistical models that most accurately
describe the probabilistic mechanism that generates a network population and
use this to make inferences about the underlying structure of the network data.
Only a few studies developed to date consider the heterogeneity that can exist
in a network population. We propose a Mixture of Measurement Error Models for
identifying clusters of networks in a network population, with respect to
similarities detected in the connectivity patterns among the networks' nodes.
Extensive simulation studies show our model performs well in both clustering
multiple network data and inferring the model parameters. We further apply our
model on two real world multiple network data sets resulting from the fields of
Computing (Human Tracking Systems) and Neuroscience.


Abstract:
  NEO is one of the top public chains worldwide. We focus on its backbone
consensus protocol, called delegated Byzantine Fault Tolerance (dBFT). The dBFT
protocol has been adopted by a variety of blockchain systems such as ONT. dBFT
claims to guarantee the security when no more than $f = \lfloor \frac{n}{3}
\rfloor$ nodes are Byzantine, where $n$ is the total number of consensus
participants. However, we identify attacks to break the claimed security. In
this paper, we show our results by providing a security analysis on its dBFT
protocol. First, we evaluate NEO's source code and formally present the
procedures of dBFT via the state machine replication (SMR) model. Next, we
provide a theoretical analysis with two example attacks. These attacks break
the security of dBFT with no more than $f$ nodes. Then, we provide
recommendations on how to fix the system against the identified attacks. The
suggested fixes have been accepted by the NEO official team. Finally, we
further discuss the reasons causing such issues, the relationship with current
permissioned blockchain systems, and the scope of potential influence.


Abstract:
  Over-parameterized models can perfectly learn various types of data
distributions, however, generalization error is usually lower for real data in
comparison to artificial data. This suggests that the properties of data
distributions have an impact on generalization capability. This work focuses on
the search space defined by the input data and assumes that the correlation
between labels of neighboring input values influences generalization. If
correlation is low, the randomness of the input data space is high leading to
high generalization error. We suggest to measure the randomness of an input
data space using Maurer's universal. Results for synthetic classification tasks
and common image classification benchmarks (MNIST, CIFAR10, and Microsoft's
cats vs. dogs data set) find a high correlation between the randomness of input
data spaces and the generalization error of deep neural networks for binary
classification problems.


Abstract:
  Background and aim: Most of the Mixed Reality models used in the surgical
telepresence are suffering from discrepancies in the boundary area and
spatial-temporal inconsistency due to the illumination variation in the video
frames. The aim behind this work is to propose a new solution that helps
produce the composite video by merging the augmented video of the surgery site
and the virtual hand of the remote expertise surgeon. The purpose of the
proposed solution is to decrease the processing time and enhance the accuracy
of merged video by decreasing the overlay and visualization error and removing
occlusion and artefacts. Methodology: The proposed system enhanced the mean
value cloning algorithm that helps to maintain the spatial-temporal consistency
of the final composite video. The enhanced algorithm includes the 3D mean value
coordinates and improvised mean value interpolant in the image cloning process,
which helps to reduce the sawtooth, smudging and discolouration artefacts
around the blending region. Results: As compared to the state of the art
solution, the accuracy in terms of overlay error of the proposed solution is
improved from 1.01mm to 0.80mm whereas the accuracy in terms of visualization
error is improved from 98.8% to 99.4%. The processing time is reduced to 0.173
seconds from 0.211 seconds. Conclusion: Our solution helps make the object of
interest consistent with the light intensity of the target image by adding the
space distance that helps maintain the spatial consistency in the final merged
video.


Abstract:
  In hierarchical structure formation, metal-poor stars in and around the Milky
Way (MW) originate primarily from mergers of lower-mass galaxies. A common
expectation is therefore that metal-poor stars should have isotropic,
dispersion-dominated orbits that do not correlate strongly with the MW disk.
However, recent observations of stars in the MW show that metal-poor ([Fe/H] <
-2) stars are preferentially on prograde orbits with respect to the disk. Using
the FIRE-2 suite of cosmological zoom-in simulations of MW/M31-mass galaxies,
we investigate the prevalence and origin of prograde metal-poor stars. Almost
all (11 of 12) of our simulations have metal-poor stars on preferentially
prograde orbits today and throughout most of their history: we thus predict
that this is a generic feature of MW/M31-mass galaxies. The typical
prograde-to-retrograde ratio is ~2:1, which depends weakly on stellar
metallicity at Fe/H] < -1. These trends predicted by our simulations agree well
with MW observations. Prograde metal-poor stars originate largely from a single
LMC/SMC-mass gas-rich merger 7 - 12.5 Gyr ago, which deposited existing
metal-poor stars and significant gas on an orbital vector that sparked the
formation of and/or shaped the orientation of a long-lived stellar disk, giving
rise to a prograde bias for all low-metallicity stars. We find sub-dominant
contributions from in-situ stars formed in the host galaxy before this merger,
and in some cases, additional massive mergers. We find few clear correlations
between any properties of our MW/M31-mass galaxies at z = 0 and the degree of
this prograde bias as a result of diverse merger scenarios.


Abstract:
  High precision astrometry aims at source position determination to a very
small fraction of the diffraction image size, in high SNR regime. One of the
key limitations to such goal is the optical response variation of the telescope
over a sizeable FOV, required to ensure bright reference objects to any
selected target. The issue translates into severe calibration constraints,
and/or the need for complex telescope and focal plane metrology. We propose an
innovative system approach derived from the established TMA telescope concept,
extended to achieve high filling factor of an annular field of view around the
optical axis of the telescope. The proposed design is a very compact, 1 m class
telescope compatible with modern CCD and CMOS detectors (EFL = 15 m). We
describe the concept implementation guidelines and the optical performance of
the current optical design. The diffraction limited FOV exceeds 1.25 square
degrees, and the detector occupies the best 0.25 square degree with 66 devices.


Abstract:
  Most modern operating systems have adopted the one-to-one thread model to
support fast execution of threads in both multi-core and single-core systems.
This thread model, which maps the kernel-space and user-space threads in a
one-to-one manner, supports quick thread creation and termination in
high-performance server environments. However, the performance of time-critical
threads is degraded when multiple threads are being run in low-end CE devices
with limited system resources. When a CE device runs many threads to support
diverse application functionalities, low-level hardware specifications often
lead to significant resource contention among the threads trying to obtain
system resources. As a result, the operating system encounters challenges, such
as excessive thread context switching overhead, execution delay of
time-critical threads, and a lack of virtual memory for thread stacks. This
paper proposes a state-of-the-art Thread Evolution Kit (TEK) that consists of
three primary components: a CPU Mediator, Stack Tuner, and Enhanced Thread
Identifier. From the experiment, we can see that the proposed scheme
significantly improves user responsiveness (7x faster) under high CPU
contention compared to the traditional thread model. Also, TEK solves the
segmentation fault problem that frequently occurs when a CE application
increases the number of threads during its execution.


Abstract:
  Abundances and partitioning of ices and gases produced by gas-grain chemistry
are governed by adsorption and desorption on grains. Understanding
astrophysical observations rely on laboratory measurements of adsorption and
desorption rates on dust grains analogs. On flat surfaces, gas adsorption
probabilities (or sticking coefficients) have been found close to unity for
most gases. Here we report a strong decrease of the sticking coefficients of
H2O and CO2 on substrates more akin to cosmic dust, such as submicrometer-sized
particles of carbon and olivine, bare or covered with ice. This effect results
from the local curvature of the grains, and then extends to larger grains made
of aggregated small particles, such as fluffy or porous dust in more evolved
media (e.g. circumstellar disks). The main astrophysical implication is that
accretion rates of gases are reduced accordingly, slowing the growth of cosmic
ices. Furthermore, volatile species that are not adsorbed on a grain at their
freeze-out temperature will pertain in the gas phase, which will impact gas-ice
partitions. We also found that thermal desorption of H2O is not modified by
grains size, and thus the snowlines temperature should be independent on the
dust size distribution.


Abstract:
  Within a new paradigm for communications on the nanoscale, high-frequency
surface acoustic waves are becoming effective data carrier and encoder. On-chip
communications require acoustic wave propagation along nano-corrugated surfaces
which strongly scatter traditional Rayleigh waves. Here we propose the delivery
of information using subsurface acoustic waves with hypersound frequencies ~20
GHz, which is a nanoscale analogue of subsurface sound waves in the ocean. A
bunch of subsurface hypersound modes is generated by pulsed optical excitation
in a multilayer semiconductor structure with a metallic nanograting on top. The
guided hypersound modes propagate coherently beneath the nanograting, retaining
the surface imprinted information, on a distance of more than 50 {\mu}m which
essentially exceeds the propagation length of Rayleigh waves. The concept is
suitable for interfacing single photon emitters, such as buried quantum dots,
carrying coherent spin excitations in magnonic devices, and encoding the
signals for optical communications at the nanoscale.


Abstract:
  The emergence of novel differential rotation laws that can reproduce the
rotational profile of binary neutron star merger remnants has opened the way
for the construction of equilibrium models with properties that resemble those
of remnants in numerical simulations. We construct models of merger remnants,
using a recently introduced 4-parameter differential rotation law and three
tabulated, zero-temperature equations of state. The models have angular momenta
that are determined by empirical relations, constructed through numerical
simulations. After a systematic exploration of the parameter space of merger
remnant equilibrium sequences, which includes the determination of turning
points along constant angular momentum sequences, we find that a particular
rotation law can reproduce the threshold mass to prompt collapse to a black
hole with a relative difference of only $\sim 1\%$ with respect to numerical
simulations, in all cases considered. Furthermore, our results indicate a
possible correlation between the compactness of equilibrium models of remnants
at the threshold mass and the compactness of maximum-mass nonrotating models.
Another key prediction of binary neutron star merger simulations is a
relatively slowly rotating inner region, where the angular velocity $\Omega$
(as measured by an observer at infinity) is mostly due to the frame dragging
angular velocity $\omega$. In our investigation of the parameter space of the
adopted differential rotation law, we naturally find quasi-spherical (Type A)
remnant models with this property. Our investigation clarifies the impact of
the differential rotation law and of the equation of state on key properties of
binary neutron star remnants and lays the groundwork for including thermal
effects in future studies.


Abstract:
  The spherical Fourier-Bessel (SFB) decomposition is a natural choice for the
radial/angular separation that allows optimal extraction of cosmological
information from large volume galaxy surveys. In this paper we develop a SFB
power spectrum estimator that allows the measurement of the largest angular and
radial modes with the next generation of galaxy surveys. The code measures the
pseudo-SFB power spectrum, and takes into account mask, selection function,
pixel window, and shot noise. We show that the local average effect is
significant only in the largest-scale mode, and we provide an analytical
covariance matrix. By imposing boundary conditions at the minimum and maximum
radius encompassing the survey volume, the estimator does not suffer from the
numerical instabilities that have proven challenging in the past. The estimator
is demonstrated on simplified Roman-like, SPHEREx-like, and Euclid-like mask
and selection functions. For intuition and validation, we also explore the SFB
power spectrum in the Limber approximation. We release the associated public
code written in Julia.


Abstract:
  Modern recording technologies now enable simultaneous recording from large
numbers of neurons. This has driven the development of new statistical models
for analyzing and interpreting neural population activity. Here we provide a
broad overview of recent developments in this area. We compare and contrast
different approaches, highlight strengths and limitations, and discuss
biological and mechanistic insights that these methods provide.


Abstract:
  Spectral functions on Euclidean Jordan algebras arise frequently in convex
models. Despite the success of primal-dual conic interior point solvers, there
has been little work on enabling direct support for spectral cones, i.e. proper
nonsymmetric cones defined from epigraphs and perspectives of spectral
functions. We propose simple logarithmically homogeneous barriers for spectral
cones and we derive efficient, numerically stable procedures for evaluating
barrier oracles such as inverse Hessian operators. For two useful classes of
spectral cones - the root-determinant cones and the matrix monotone derivative
cones - we show that the barriers are self-concordant, with nearly optimal
parameters. We implement these cones and oracles in our open source solver
Hypatia, and we write simple, natural formulations for four applied problems.
Our computational benchmarks demonstrate that Hypatia often solves the natural
formulations more efficiently than advanced solvers such as MOSEK 9 solve
equivalent extended formulations written using only the cones these solvers
support.


Abstract:
  Computing the periods of variable objects is well-known to be computationally
expensive. Modern astronomical catalogs contain a significant number of
observed objects. Therefore, even if the period ranges for particular classes
of objects are well-constrained due to expected physical properties, periods
must be derived for a tremendous number of objects. In this paper, we propose a
GPU-accelerated Lomb-Scargle period finding algorithm that computes periods for
single objects or for batches of objects as is necessary in many data
processing pipelines. We demonstrate the performance of several optimizations,
including comparing the use of shared and global memory GPU kernels and using
multiple CUDA streams to copy periodogram data from the GPU to the host. Also,
we quantify the difference between 32-bit and 64-bit floating point precision
on two classes of GPUs, and show that the performance degradation of using
64-bit over 32-bit is greater on the CPU than a GPU designed for scientific
computing. We find that the GPU algorithm achieves superior performance over
the baseline parallel CPU implementation, achieving a speedup of up to
174.53$\times$. The Vera C. Rubin Observatory will carry out the Legacy Survey
of Space and Time (LSST). We perform an analysis that shows we can derive the
rotation periods of batches of Solar System objects at LSST scale in near
real-time, which will be employed in a future LSST event broker. All source
code has been made publicly available.


Abstract:
  We present photometric and spectroscopic observations of the nearby Type Ia
SN 2019yvq, from its discovery $\sim$1 day after explosion to $\sim$100 days
after its peak brightness. This SN exhibits several unusual features, most
notably an extremely bright UV excess seen within $\sim$5 days of its
explosion. As seen in Swift UV data, this early excess outshines its "peak"
brightness, making this object more extreme than other SNe with early UV/blue
excesses (e.g. iPTF14atg and SN 2017cbv). In addition, it was underluminous
($M_B=-18.4$), relatively quickly declining ($\Delta m_{15}(B)=1.35$), and
shows red colors past its early blue bump. Unusual (although not unprecedented)
spectral features include extremely broad-lined and high-velocity Si
absorption. Despite obvious differences in peak spectra, we classify SN 2019yvq
as a transitional member of the 02es-like subclass due to its similarities in
several respects (e.g. color, peak luminosity, peak Ti, nebular [Ca II]). We
model this dataset with a variety of published models, including SN ejecta -
companion shock interaction and sub-Chandrasekhar mass WD double detonation
models. Radio constraints from the VLA place an upper limit of $(4.5 - 20)
\times 10^{-8}$ M$_{\odot}$/yr on the mass-loss rate from a symbiotic
progenitor, which does not exclude a red giant or main sequence companion.
Ultimately we find that no one model can accurately replicate all aspects of
the dataset, and further we find that the ubiquity of early excesses in
02es-like SNe Ia requires a progenitor system that is capable of producing
isotropic UV flux, ruling out some models for this class of objects.


Abstract:
  The literature on ranking from ordinal data is vast, and there are several
ways to aggregate overall preferences from pairwise comparisons between
objects. In particular, it is well known that any Nash equilibrium of the zero
sum game induced by the preference matrix defines a natural solution concept
(winning distribution over objects) known as a von Neumann winner. Many
real-world problems, however, are inevitably multi-criteria, with different
pairwise preferences governing the different criteria. In this work, we
generalize the notion of a von Neumann winner to the multi-criteria setting by
taking inspiration from Blackwell's approachability. Our framework allows for
non-linear aggregation of preferences across criteria, and generalizes the
linearization-based approach from multi-objective optimization.
  From a theoretical standpoint, we show that the Blackwell winner of a
multi-criteria problem instance can be computed as the solution to a convex
optimization problem. Furthermore, given random samples of pairwise
comparisons, we show that a simple plug-in estimator achieves near-optimal
minimax sample complexity. Finally, we showcase the practical utility of our
framework in a user study on autonomous driving, where we find that the
Blackwell winner outperforms the von Neumann winner for the overall
preferences.


Abstract:
  Marine microorganisms must cope with complex flow patterns and even
turbulence as they navigate the ocean. To survive they must avoid predation and
find efficient energy sources. A major difficulty in analysing possible
survival strategies is that the time series of environmental cues in non-linear
flow is complex, and that it depends on the decisions taken by the organism.
One way of determining and evaluating optimal strategies is reinforcement
learning. In a proof-of-principle study, Colabrese et al. [Phys. Rev. Lett.
(2017)] used this method to find out how a micro-swimmer in a vortex flow can
navigate towards the surface as quickly as possible, given a fixed swimming
speed. The swimmer measured its instantaneous swimming direction and the local
flow vorticity in the laboratory frame, and reacted to these cues by swimming
either left, right, up, or down. However, usually a motile microorganism
measures the local flow rather than global information, and it can only react
in relation to the local flow, because in general it cannot access global
information (such as up or down in the laboratory frame). Here we analyse
optimal strategies with local signals and actions that do not refer to the
laboratory frame. We demonstrate that symmetry-breaking is required in order to
learn vertical migration in a meaningful way. Using reinforcement learning we
analyse the emerging strategies for different sets of environmental cues that
microorganisms are known to measure.


Abstract:
  We present the second public data release (DR) of the VISTA EXtension to
Auxiliary Surveys (VEXAS), where we classify objects into stars, galaxies and
quasars based on an ensemble of machine learning algorithms. The aim of VEXAS
is to build the widest multi-wavelength catalogue, providing reference
magnitudes, colours and morphological information for a large number of
scientific uses. We apply an ensemble of 32 different machine learning models,
based on three different algorithms and on different magnitude sets, training
samples and classification problems on the three VEXAS DR1 optical+infrared
(IR) tables. The tables were created in DR1 cross-matching VISTA near-IR data
with WISE far-IR data and with optical magnitudes from the Dark Energy Survey
(VEXAS-DESW), the Sky Mapper Survey (VEXAS-SMW), and the PanSTARRS (VEXAS-PSW).
We assemble a large table of spectroscopically confirmed objects (415 628
unique objects), based on the combination of 6 different spectroscopic surveys
that we use for training. We develop feature imputation to classify also
objects for which magnitudes in one or more bands are missing. We classify in
total ~90 million objects in the Southern Hemisphere. Among these,~62.9M
(~52.6M) are classified as 'high confidence' ('secure') stars, ~920k (~750k) as
'high confidence' ('secure') quasars and ~34.8M (~34.1M) as 'high confidence'
('secure') galaxies, with probabilities $p_{\rm class}\ge 0.7$ ($p_{\rm
class}\ge 0.9$). The density of high-confidence extragalactic objects varies
strongly with the survey depth: at $p_{\rm class}\ge 0.7$, there are
111/deg$^2$ quasars in the VEXAS-DESW footprint and 103/deg$^2$ in the
VEXAS-PSW footprint, while only 10.7/deg$^2$ in the VEXAS-SM footprint.
Improved depth in the midIR and coverage in the optical and nearIR are needed
for the SM footprint that is not already covered by DESW and PSW.


Abstract:
  In the last years we have witnessed the fields of geosciences and remote
sensing and artificial intelligence to become closer. Thanks to both the
massive availability of observational data, improved simulations, and
algorithmic advances, these disciplines have found common objectives and
challenges to advance the modeling and understanding of the Earth system.
Despite such great opportunities, we also observed a worrying tendency to
remain in disciplinary comfort zones applying recent advances from artificial
intelligence on well resolved remote sensing problems. Here we take a position
on research directions where we think the interface between these fields will
have the most impact and become potential game changers. In our declared agenda
for AI on Earth sciences, we aim to inspire researchers, especially the younger
generations, to tackle these challenges for a real advance of remote sensing
and the geosciences.


Abstract:
  Recently, the BESIII Collaboration reported a resonance $Y(2040)$ with
$M=2034\pm13\pm9$ MeV and $\Gamma=234\pm30\pm25$ MeV in the process of
$e^+e^-\rightarrow\omega\pi^0$. In addition, new measurements with much higher
precision for the $\rho(1900)$ and $\rho(2150)$ states are obtained by the
BESIII and BABAR Collaborations. In this work, we perform a systematic study on
the mass spectrum of the excited $\rho$ resonances using the modified
Godfrey-Isgur model, and the strong decays of $Y(2040)$, $\rho(1900)$, and
$\rho(2150)$ within the $^3P_0$ model.We find that $Y(2040)$, $\rho(1900)$, and
$\rho(2150)$ can be interpreted as the $\rho(2^3D_1)$, $\rho(3^3S_1)$, and
$\rho(4^3S_1)$ states, respectively. Meanwhile, the mass and strong decays of
the $\rho(3^3D_1)$ state are predicted as well, which could be helpful to
search for this state in future.


Abstract:
  We compare the spectrum of the stochastic gravitational wave background
produced in several models of cosmic strings with the common-spectrum process
recently reported by NANOGrav. We discuss theoretical uncertainties in
computing such a background, and show that despite such uncertainties, cosmic
strings remain a good explanation for the potential signal, but the
consequences for cosmic string parameters depend on the model. Superstrings
could also explain the signal, but only in a restricted parameter space where
their network behavior is effectively identical to that of ordinary cosmic
strings.


Abstract:
  Power system N-x contingency analysis has inherent challenges due to its
combinatorial characteristic where outages grow exponentially with the increase
of x and N. To address these challenges, this paper proposes a method that
utilizes Line Outage Distribution Factors (LODFs) and group betweenness
centrality to identify subsets of critical branches. The proposed LODF metrics
are used to select the high-impact branches. Based on each selected branch, the
approach constructs the subgraph with parameters of distance and search level,
while using branches' LODF metrics as the weights. A key innovation of this
work is the use of the distance and search level parameters, which allow the
subgraph to identify the most coupled critical elements that may be far away
from a selected branch. The proposed approach is validated using the 200- and
500-bus test cases, and results show that the proposed approach can identify
multiple N-x contingencies that cause violations.


Abstract:
  Neural shape representations have recently shown to be effective in shape
analysis and reconstruction tasks. Existing neural network methods require
point coordinates and corresponding normal vectors to learn the implicit level
sets of the shape. Normal vectors are often not provided as raw data,
therefore, approximation and reorientation are required as pre-processing
stages, both of which can introduce noise. In this paper, we propose a
divergence guided shape representation learning approach that does not require
normal vectors as input. We show that incorporating a soft constraint on the
divergence of the distance function favours smooth solutions that reliably
orients gradients to match the unknown normal at each point, in some cases even
better than approaches that use ground truth normal vectors directly.
Additionally, we introduce a novel geometric initialization method for
sinusoidal shape representation networks that further improves convergence to
the desired solution. We evaluate the effectiveness of our approach on the task
of surface reconstruction and show state-of-the-art performance compared to
other unoriented methods and on-par performance compared to oriented methods.


Abstract:
  We study how parallelism can speed up quantum simulation. A parallel quantum
algorithm is proposed for simulating the dynamics of a large class of
Hamiltonians with good sparse structures, called uniform-structured
Hamiltonians, including various Hamiltonians of practical interest like local
Hamiltonians and Pauli sums. Given the oracle access to the target sparse
Hamiltonian, in both query and gate complexity, the running time of our
parallel quantum simulation algorithm measured by the quantum circuit depth has
a doubly (poly-)logarithmic dependence $\operatorname{polylog}\log(1/\epsilon)$
on the simulation precision $\epsilon$. This presents an exponential
improvement over the dependence $\operatorname{polylog}(1/\epsilon)$ of
previous optimal sparse Hamiltonian simulation algorithm without parallelism.
To obtain this result, we introduce a novel notion of parallel quantum walk,
based on Childs' quantum walk. The target evolution unitary is approximated by
a truncated Taylor series, which is obtained by combining these quantum walks
in a parallel way. A lower bound $\Omega(\log \log (1/\epsilon))$ is
established, showing that the $\epsilon$-dependence of the gate depth achieved
in this work cannot be significantly improved.
  Our algorithm is applied to simulating three physical models: the Heisenberg
model, the Sachdev-Ye-Kitaev model and a quantum chemistry model in second
quantization. By explicitly calculating the gate complexity for implementing
the oracles, we show that on all these models, the total gate depth of our
algorithm has a $\operatorname{polylog}\log(1/\epsilon)$ dependence in the
parallel setting.


Abstract:
  The recursively-constructed family of Mandelbrot matrices $M_n$ for $n=1$,
$2$, $\ldots$ have nonnegative entries (indeed just $0$ and $1$, so each $M_n$
can be called a binary matrix) and have eigenvalues whose negatives $-\lambda =
c$ give periodic orbits under the Mandelbrot iteration, namely $z_k =
z_{k-1}^2+c$ with $z_0=0$, and are thus contained in the Mandelbrot set. By the
Perron--Frobenius theorem, the matrices $M_n$ have a dominant real positive
eigenvalue, which we call $\rho_n$. This article examines the eigenvector
belonging to that dominant eigenvalue and its fractal-like structure, and
similarly examines (with less success) the dominant singular vectors of $M_n$
from the singular value decomposition.


Abstract:
  Time Reversal Symmetry (TRS) broken topological phases provide gapless
surface states protected by topology, regardless of additional internal
symmetries, spin or valley degrees of freedom. Despite the numerous
demonstrations of 2D topological phases, few examples of 3D topological systems
with TRS breaking exist. In this article, we devise a general strategy to
design 3D Chern insulating (3D CI) cubic photonic crystals in a weakly TRS
broken environment with orientable and arbitrarly large Chern vectors. The
designs display topologically protected chiral and unidirectional surface
states with disjoint equifrequency loops. The resulting crystals present the
following novel characteristics: First, by increasing the Chern number,
multiple surface states channels can be supported. Second, the Chern vector can
be oriented along any direction simply changing the magnetization axis, opening
up larger 3D CI/3D CI interfacing possibilities as compared to 2D. Third, by
lowering the TRS breaking requirements, the system is ideal for realistic
photonic applications where the magnetic response is weak.


Abstract:
  This paper investigates the capability of long-baseline experiments, which
are making use of neutrinos that are coming from Japan Proton Accelerator
Research Complex (J-PARC), in establishing the unitarity of active-neutrino
mixing by ruling out the non-unitary mixing scheme as a function of true values
of CP-violating phase $\delta_{\mathrm{CP}}$. It is found that T2HK can
establish unitarity of active neutrino mixing at above 2$\sigma$ C.L.
irrespective of neutrino mass hierarchy and true value of $\delta_{CP}$, if
non-unitary (NU) parameter $\alpha_{21}$ is of the order of $10^{-2}$. Further,
this paper is also discuss the bound on NU parameter in 21 sector and
sensitivity limit of these experiments in determining NU parameter. It is found
that the bounds on $\left(\alpha_{21}/2\right)$ are 0.028, 0.0026, 0.005 at
2$\sigma$ C.L. respectively for T2K, T2HK, and T2HKK. Moreover, it is also
found that the sensitivity limit of T2HK on NU parameter is far better than
that of both T2HKK and T2K.


Abstract:
  Two-dimensional (2D) semiconductors have attracted tremendous interests as
natural passivation and atomically thin channels that could facilitate
continued transistor scaling. However, air-stable 2D semiconductors with high
performance were quite elusive. Recently, extremely air-stable MoSi2N4
monolayer had been successfully fabricated [Hong et al., Science 369, 670
(2020)]. To further reveal its potential applications in the sub-5 nm MOSFETs,
there is an urgent need to develop integrated circuits. Here we report
first-principles quantum transport simulations on the performance limits of n-
and p-type sub-5 nm monolayer (ML) MoSi2N4 metal-oxide-semiconductor FETs
(MOSFETs). We find that the on-state current in the MoSi2N4 MOSFETs can be
effectively manipulated by the length of gate and underlap (UL), as well as the
doping concentration. Very strikingly, we also find that the n-type devices the
optimized on-state current can reach up to 1390 and 1025 uA/um for the high
performance (HP) and low power (LP) applications, respectively, both of which
satisfy the International Technology Roadmap for Semiconductors (ITRS)
requirements. Whereas, the optimized on-state current can meet the LP
application (348 uA/um) for the p-type devices. Finally, we find that the
MoSi2N4 MOSFETs have ultra-low subthreshold swing and power delay product,
which have potential to realize the high speed and low power consumption
devices. Our results show that MoSi2N4 is an ideal 2D channel material for
future competitive ultrascaled devices.


Abstract:
  Beyond assigning the correct class, an activity recognition model should also
be able to determine, how certain it is in its predictions. We present the
first study of how welthe confidence values of modern action recognition
architectures indeed reflect the probability of the correct outcome and propose
a learning-based approach for improving it. First, we extend two popular action
recognition datasets with a reliability benchmark in form of the expected
calibration error and reliability diagrams. Since our evaluation highlights
that confidence values of standard action recognition architectures do not
represent the uncertainty well, we introduce a new approach which learns to
transform the model output into realistic confidence estimates through an
additional calibration network. The main idea of our Calibrated Action
Recognition with Input Guidance (CARING) model is to learn an optimal scaling
parameter depending on the video representation. We compare our model with the
native action recognition networks and the temperature scaling approach - a
wide spread calibration method utilized in image classification. While
temperature scaling alone drastically improves the reliability of the
confidence values, our CARING method consistently leads to the best uncertainty
estimates in all benchmark settings.


Abstract:
  The study of the structural behaviour of pure and multi-component lipids at
various temperatures and the interaction of these multi-component lipids with
pharmaceutically important drugs carry huge importance. Here, we investigated
the phase behaviour of the pure PSPC
(1-palmitoyl-2-stearoyl-sn-glycero-3-phosphocholine), and multicomponent PSPC
and
DSPE-PEG2000(1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N[amino(polyethylene
glycol)-2000]) membranes at seven different temperatures ranging from 280 K to
360 K, and calculated their structural properties. We observe a transition from
the gel phase to the liquid crystalline phase between 320 K and 330 K in
agreement with experimental reports for pure PSPC. PSPC remained in the tilted
gel phase L\b{eta}$'$ at 320 K and 310 K, entered the 'mixed ordered' domain
with a partially interdigitated region at 300 K, and finally formed the sub gel
phase at 280 K. We studied the self-assembly for the multicomponent PSPC and
DSPE-PEG2000 membranes and found the coexistence of ordered and disordered
phases at 320 K. In comparison to the pure PSPC, for multicomponent system,
this transition was gradual, and a complete liquid crystalline to gel phase
transformation occurred between 320 K and 310 K. We further studied the
interaction of Paclitaxel with pure PSPC and PEGylated multicomponent lipid
bilayers using umbrella sampling technique and observed PEG promotes the
interaction of Paclitaxel with the later one in comparison to the former. Above
the bilayer transition temperature, Paclitaxel interacts more with the bilayer
and enters inside the bilayer easily for both systems. Understanding of
structural and interaction behaviour of the PEGylated multicomponent lipid
bilayers with Paclitaxel will help explore Paclitaxel based drug applications
in the future.


Abstract:
  For most of the anchor-based detectors, Intersection over Union(IoU) is
widely utilized to assign targets for the anchors during training. However, IoU
pays insufficient attention to the closeness of the anchor's center to the
truth box's center. This results in two problems: (1) only one anchor is
assigned to most of the slender objects which leads to insufficient supervision
information for the slender objects during training and the performance on the
slender objects is hurt; (2) IoU can not accurately represent the alignment
degree between the receptive field of the feature at the anchor's center and
the object. Thus during training, some features whose receptive field aligns
better with objects are missing while some features whose receptive field
aligns worse with objects are adopted. This hurts the localization accuracy of
models. To solve these problems, we firstly design Gaussian Guided IoU(GGIoU)
which focuses more attention on the closeness of the anchor's center to the
truth box's center. Then we propose GGIoU-balanced learning method including
GGIoU-guided assignment strategy and GGIoU-balanced localization loss. The
method can assign multiple anchors for each slender object and bias the
training process to the features well-aligned with objects. Extensive
experiments on the popular benchmarks such as PASCAL VOC and MS COCO
demonstrate GGIoU-balanced learning can solve the above problems and
substantially improve the performance of the object detection model, especially
in the localization accuracy.


Abstract:
  We propose a new framework -- Square Root Principal Component Pursuit -- for
low-rank matrix recovery from observations corrupted with noise and outliers.
Inspired by the square root Lasso, this new formulation does not require prior
knowledge of the noise level. We show that a single, universal choice of the
regularization parameter suffices to achieve reconstruction error proportional
to the (a priori unknown) noise level. In comparison, previous formulations
such as stable PCP rely on noise-dependent parameters to achieve similar
performance, and are therefore challenging to deploy in applications where the
noise level is unknown. We validate the effectiveness of our new method through
experiments on simulated and real datasets. Our simulations corroborate the
claim that a universal choice of the regularization parameter yields near
optimal performance across a range of noise levels, indicating that the
proposed method outperforms the (somewhat loose) bound proved here.


Abstract:
  Ordinal embedding aims at finding a low dimensional representation of objects
from a set of constraints of the form "item $j$ is closer to item $i$ than item
$k$". Typically, each object is mapped onto a point vector in a low dimensional
metric space. We argue that mapping to a density instead of a point vector
provides some interesting advantages, including an inherent reflection of the
uncertainty about the representation itself and its relative location in the
space. Indeed, in this paper, we propose to embed each object as a Gaussian
distribution. We investigate the ability of these embeddings to capture the
underlying structure of the data while satisfying the constraints, and explore
properties of the representation. Experiments on synthetic and real-world
datasets showcase the advantages of our approach. In addition, we illustrate
the merit of modelling uncertainty, which enriches the visual perception of the
mapped objects in the space.


Abstract:
  Graph Neural Networks (GNNs) are the first choice for learning algorithms on
graph data. GNNs promise to integrate (i) node features as well as (ii) edge
information in an end-to-end learning algorithm. How does this promise work out
practically? In this paper, we study to what extend GNNs are necessary to solve
prominent graph classification problems. We find that for graph classification,
a GNN is not more than the sum of its parts. We also find that, unlike
features, predictions with an edge-only model do not always transfer to GNNs.


Abstract:
  Moving Object Detection (MOD) is a crucial task for the Autonomous Driving
pipeline. MOD is usually handled via 2-stream convolutional architectures that
incorporates both appearance and motion cues, without considering the
inter-relations between the spatial or motion features. In this paper, we
tackle this problem through multi-head attention mechanisms, both across the
spatial and motion streams. We propose MODETR; a Moving Object DEtection
TRansformer network, comprised of multi-stream transformer encoders for both
spatial and motion modalities, and an object transformer decoder that produces
the moving objects bounding boxes using set predictions. The whole architecture
is trained end-to-end using bi-partite loss. Several methods of incorporating
motion cues with the Transformer model are explored, including two-stream RGB
and Optical Flow (OF) methods, and multi-stream architectures that take
advantage of sequence information. To incorporate the temporal information, we
propose a new Temporal Positional Encoding (TPE) approach to extend the Spatial
Positional Encoding(SPE) in DETR. We explore two architectural choices for
that, balancing between speed and time. To evaluate the our network, we perform
the MOD task on the KITTI MOD [6] data set. Results show significant 5% mAP of
the Transformer network for MOD over the state-of-the art methods. Moreover,
the proposed TPE encoding provides 10% mAP improvement over the SPE baseline.


Abstract:
  We present the results of direct simulation of the expansionof a
two-component ultracold plasmafor various numbers of particles, densities, and
electron temperatures. A description of the expansionprocess common to all
plasma parameters is given. After the escape of fast electrons from the
plasmacloud, the excess positive charge is localized at the outer boundary, in
a narrow layer. This layerhas a characteristic front shape with a sharp drop in
the charge concentration. The charged layerretains the remaining electrons
during the entire expansion process. As the plasma expands, thespeed of
movement of the charged layer becomes constant and significantly exceeds the
sonic speedof ions. In addition, the dependence of the radial velocity of ions
on the radius acquires a self-similarcharacter long before the final stage of
expansion. On the basis of the calculation results, equationsand self-similar
solutions are obtained. General dependences on plasma parameters are
determined,which are compared with experimental data.


Abstract:
  This work addresses hand mesh recovery from a single RGB image. In contrast
to most of the existing approaches where the parametric hand models are
employed as the prior, we show that the hand mesh can be learned directly from
the input image. We propose a new type of GAN called Im2Mesh GAN to learn the
mesh through end-to-end adversarial training. By interpreting the mesh as a
graph, our model is able to capture the topological relationship among the mesh
vertices. We also introduce a 3D surface descriptor into the GAN architecture
to further capture the 3D features associated. We experiment two approaches
where one can reap the benefits of coupled groundtruth data availability of
images and the corresponding meshes, while the other combats the more
challenging problem of mesh estimations without the corresponding groundtruth.
Through extensive evaluations we demonstrate that the proposed method
outperforms the state-of-the-art.