Abstract:
  To remain adaptable to a dynamic environment, coordinated neural activity in
the brain must be simultaneously flexible and reliable. There is evidence that
the brain facilitates this adaptive response using highly-conserved
metabotropic neuromodulatory neurotransmitters, such as noradrenaline and
acetylcholine. While we understand how these neuromodulators alter individual
neuronal dynamics, precisely how neuromodulation operates in networks of
neurons to give rise to observed large-scale dynamics remains unknown. Here, we
investigate this disparity by demonstrating correspondence between adaptive
information processing modes - calculated on in vivo electrophysiological
recordings of bursting layer V neurons in awake mice - and fluctuations in
neuromodulatory tone - assessed by dynamic changes in pupil diameter. We
theoretically validate these results by creating a novel, biologically
plausible dual-compartment model of nonlinear layer V pyramidal neurons -
capable of both regular spike and bursting modes - that reproduce our main
empirical findings. We then demonstrate that the adrenergic and cholinergic
neuromodulatory systems shift the brain into a neuroprotective (i.e., slightly
subcritical) regime - assessed by the branching parameter - while facilitating
flexible and reliable dynamics, respectively. This unexpected result
demonstrates that the brain has circumvented the necessity to shift the system
closer to criticality for variability by differentially augmenting an intrinsic
neuronal nonlinearity. Our analyses establish that these distinct arms of the
ascending arousal system modulate ensembles of layer V pyramidal neurons to
augment critical dynamics and facilitate distinct adaptive information
processing modes within the brain.


Abstract:
  We give the geometric version of a construction of Colmez-Niziol which
establishes a comparison theorem between arithmetic p-adic nearby cycles and
syntomic sheaves. The local construction of the period isomorphism uses
$(\phi,\Gamma)$-modules theory and is obtained by reducing the period
isomorphism to a comparison theorem between cohomologies of Lie algebras. By
applying the method of "more general coordinates" used by Bhatt-Morrow-Scholze,
we construct a global isomorphism. In particular, we deduce the semi-stable
conjecture of Fontaine-Jannsen. This result was also proved by (among others)
Tsuji, via the Fontaine-Messing map, and by Cesnavicius and Koshikawa, which
generalized the proof of the crystalline conjecture by Bhatt, Morrow and
Scholze. We use the previous map to show that the period morphism of Tsuji and
the one of Cesnavicius-Koshikawa are the same.


Abstract:
  The blast wave of machine learning and artificial intelligence has also
reached the power systems community, and amid the frenzy of methods and
black-box tools that have been left in its wake, it is sometimes difficult to
perceive a glimmer of Occam's razor principle. In this letter, we use the unit
commitment problem (UCP), an NP-hard mathematical program that is fundamental
to power system operations, to show that simplicity must guide any strategy to
solve it, in particular those that are based on learning from past UCP
instances. To this end, we apply a naive algorithm to produce candidate
solutions to the UCP and show, using a variety of realistically sized power
systems, that we are able to find optimal or quasi-optimal solutions with
remarkable speedups. Our claim is thus that any sophistication of the learning
method must be backed up with a statistically significant improvement of the
results in this letter.


Abstract:
  Craniomaxillofacial reconstruction with patient-specific customized
craniofacial implants (CCIs) is most commonly performed for large-sized
skeletal defects. Because the exact size of skull resection may not be known
prior to the surgery, in the single-stage cranioplasty, a large CCI is
prefabricated and resized intraoperatively with a manual-cutting process
provided by a surgeon. The manual resizing, however, may be inaccurate and
significantly add to the operating time. This paper introduces a fast and
non-contact approach for intraoperatively determining the exact contour of the
skull resection and automatically resizing the implant to fit the resection
area. Our approach includes four steps: First, a patient's defect information
is acquired by a 3D scanner. Second, the scanned defect is aligned to the CCI
by registering the scanned defect to the reconstructed CT model. Third, a
cutting toolpath is generated from the contour of the scanned defect. Lastly,
the large CCI is resized by a cutting robot to fit the resection area according
to the given toolpath. To evaluate the resizing performance of our method, six
different resection shapes were used in the cutting experiments. We compared
the performance of our method to the performances of surgeon's manual resizing
and an existing technique which collects the defect contour with an optical
tracking system and projects the contour on the CCI to guide the manual
modification. The results show that our proposed method improves the resizing
accuracy by 56% compared to the surgeon's manual modification and 42% compared
to the projection method.


Abstract:
  Minimal linear codes are in one-to-one correspondence with special types of
blocking sets of projective spaces over a finite field, which are called strong
or cutting blocking sets. In this paper we prove an upper bound on the minimal
length of minimal codes of dimension $k$ over the $q$-element Galois field
which is linear in both $q$ and $k$, hence improve the previous superlinear
bounds. This result determines the minimal length up to a small constant
factor. We also improve the lower and upper bounds on the size of so called
higgledy-piggledy line sets in projective spaces and apply these results to
present improved bounds on the size of covering codes and saturating sets in
projective spaces as well. The contributions rely on geometric and
probabilistic arguments.


Abstract:
  We propose methods to infer properties of the execution environment of
machine learning pipelines by tracing characteristic numerical deviations in
observable outputs. Results from a series of proof-of-concept experiments
obtained on local and cloud-hosted machines give rise to possible forensic
applications, such as the identification of the hardware platform used to
produce deep neural network predictions. Finally, we introduce boundary samples
that amplify the numerical deviations in order to distinguish machines by their
predicted label only.


Abstract:
  Fast and affordable solutions for COVID-19 testing are necessary to contain
the spread of the global pandemic and help relieve the burden on medical
facilities. Currently, limited testing locations and expensive equipment pose
difficulties for individuals trying to be tested, especially in low-resource
settings. Researchers have successfully presented models for detecting COVID-19
infection status using audio samples recorded in clinical settings [5, 15],
suggesting that audio-based Artificial Intelligence models can be used to
identify COVID-19. Such models have the potential to be deployed on smartphones
for fast, widespread, and low-resource testing. However, while previous studies
have trained models on cleaned audio samples collected mainly from clinical
settings, audio samples collected from average smartphones may yield suboptimal
quality data that is different from the clean data that models were trained on.
This discrepancy may add a bias that affects COVID-19 status predictions. To
tackle this issue, we propose a multi-branch deep learning network that is
trained and tested on crowdsourced data where most of the data has not been
manually processed and cleaned. Furthermore, the model achieves state-of-art
results for the COUGHVID dataset [16]. After breaking down results for each
category, we have shown an AUC of 0.99 for audio samples with COVID-19 positive
labels.


Abstract:
  The unsteady, lineal translation of a solid spherical particle through
viscoelastic fluids described by the Johnson-Segalman and Giesekus models is
studied analytically. Solutions for the pressure and velocity fields as well as
the force on the particle are expanded as a power series in the Weissenberg
number. The momentum balance and constitutive equation are solved
asymptotically for a steadily translating particle up to second order in the
particle velocity, and rescaling of the pressure and velocity in the frequency
domain is used to relate the solutions for steady lineal translation to those
for unsteady lineal translation. The unsteady force at third order in the
particle velocity is then calculated through application of the Lorentz
reciprocal theorem, and it is shown that this weakly nonlinear contribution to
the force can be expressed as part of a Volterra series. Through a series of
examples, it is shown that a truncated representation of this Volterra series,
which can be manipulated to describe the velocity in terms of an imposed force,
is useful for analyzing specific time-dependent particle motions. Two examples
studied using this relationship are the force on a particle suddenly set into
motion and the velocity of a particle in response to a suddenly imposed steady
force. Additionally, the weakly nonlinear response of particle captured by a
harmonic trap moving lineally through the fluid is computed. This is an analog
to active microrheology experiments, and can be used to explain how weakly
nonlinear responses manifest in active microrheology experiments with spherical
probes.


Abstract:
  In a recent paper by Casado-Pascual [Phys. Rev. E \textbf{97}, 032219
(2018)], directed motion of a sphere immersed in a viscous fluid and subjected
to zero-average biharmonic forces is studied. The author explains the
dependence on the relative amplitude of the two harmonic components of the
average terminal velocity from the perspective of a general formalism. In this
Comment, this explanation is shown to be in general incorrect, while the theory
of ratchet universality together with the vibrational mechanics approach
provide a satisfactory explanation of major aspects of the observed phenomena.


Abstract:
  Mobile edge computing provides users with a cloud environment close to the
edge of the wireless network, supporting the computing intensive applications
that have low latency requirements. The combination of offloading with the
wireless communication brings new challenges. This paper investigates the
service caching problem during the long-term service offloading in the
user-centric wireless network. To meet the time-varying service demands of a
typical user, a cooperative service caching strategy in the unit of the base
station (BS) cluster is proposed. We formulate the caching problem as a
time-averaged completion delay minimization problem and transform it into
time-decoupled instantaneous problems with a virtual caching cost queue at
first. Then we propose a distributed algorithm which is based on the
consensus-sharing alternating direction method of multipliers to solve each
instantaneous problem. The simulations validate that the proposed online
distributed service caching algorithm can achieve the optimal time-averaged
completion delay of offloading tasks with the smallest caching cost in the unit
of a BS cluster.


Abstract:
  In this paper we introduce and study double tails of multiple zeta values. We
show, in particular, that they satisfy certain recurrence relations and deduce
from this a generalization of Euler's classical formula
$\zeta(2)=3\sum_{m=1}^\infty m^{-2}\left({2m\atop m}\right)^{-1}$ to all
multiple zeta values, as well as a new and very efficient algorithm for
computing these values.


Abstract:
  Experimental studies are prevalent in Evolutionary Computation (EC), and
concerns about the reproducibility and replicability of such studies have
increased in recent times, reflecting similar concerns in other scientific
fields. In this article, we discuss, within the context of EC, the different
types of reproducibility and suggest a classification that refines the badge
system of the Association of Computing Machinery (ACM) adopted by ACM
Transactions on Evolutionary Learning and Optimization
(https://dlnext.acm.org/journal/telo). We identify cultural and technical
obstacles to reproducibility in the EC field. Finally, we provide guidelines
and suggest tools that may help to overcome some of these reproducibility
obstacles.


Abstract:
  Most artificial neural networks used for object detection and recognition are
trained in a fully supervised setup. This is not only very resource consuming
as it requires large data sets of labeled examples but also very different from
how humans learn. We introduce a setup in which an artificial agent first
learns in a simulated world through self-supervised exploration. Following
this, the representations learned through interaction with the world can be
used to associate semantic concepts such as different types of doors. To do
this, we use a method we call fast concept mapping which uses correlated firing
patterns of neurons to define and detect semantic concepts. This association
works instantaneous with very few labeled examples, similar to what we observe
in humans in a phenomenon called fast mapping. Strikingly, this method already
identifies objects with as little as one labeled example which highlights the
quality of the encoding learned self-supervised through embodiment using
curiosity-driven exploration. It therefor presents a feasible strategy for
learning concepts without much supervision and shows that through pure
interaction with the world meaningful representations of an environment can be
learned.


Abstract:
  The emergence and continued reliance on the Internet and related technologies
has resulted in the generation of large amounts of data that can be made
available for analyses. However, humans do not possess the cognitive
capabilities to understand such large amounts of data. Machine learning (ML)
provides a mechanism for humans to process large amounts of data, gain insights
about the behavior of the data, and make more informed decision based on the
resulting analysis. ML has applications in various fields. This review focuses
on some of the fields and applications such as education, healthcare, network
security, banking and finance, and social media. Within these fields, there are
multiple unique challenges that exist. However, ML can provide solutions to
these challenges, as well as create further research opportunities.
Accordingly, this work surveys some of the challenges facing the aforementioned
fields and presents some of the previous literature works that tackled them.
Moreover, it suggests several research opportunities that benefit from the use
of ML to address these challenges.


Abstract:
  We show that object detectors can hallucinate and detect missing objects;
potentially even accurately localized at their expected, but non-existing,
position. This is particularly problematic for applications that rely on visual
part verification: detecting if an object part is present or absent. We show
how popular object detectors hallucinate objects in a visual part verification
task and introduce the first visual part verification dataset: DelftBikes,
which has 10,000 bike photographs, with 22 densely annotated parts per image,
where some parts may be missing. We explicitly annotated an extra object state
label for each part to reflect if a part is missing or intact. We propose to
evaluate visual part verification by relying on recall and compare popular
object detectors on DelftBikes.


Abstract:
  Dark matter particles may interact with other dark matter particles via a new
force mediated by a dark photon, $A^{\prime}$, which would be the dark-sector
analog to the ordinary photon of electromagnetism. The dark photon can obtain a
highly suppressed mixing-induced coupling to the electromagnetic current,
providing a portal through which dark photons can interact with ordinary
matter. This review focuses on $A^{\prime}$ scenarios that are potentially
accessible to accelerator-based experiments. We summarize the existing
constraints placed by such experiments on dark photons, highlight what could be
observed in the near future, and discuss the major experimental challenges that
must be overcome to improve sensitivities.


Abstract:
  We introduce MADGRAD, a novel optimization method in the family of AdaGrad
adaptive gradient methods. MADGRAD shows excellent performance on deep learning
optimization problems from multiple fields, including classification and
image-to-image tasks in vision, and recurrent and bidirectionally-masked models
in natural language processing. For each of these tasks, MADGRAD matches or
outperforms both SGD and ADAM in test set performance, even on problems for
which adaptive methods normally perform poorly.


Abstract:
  We investigate the structure, dynamics, and chemistry of the molecule-rich
nebula around the stellar symbiotic system R Aqr, which is significantly
affected by the presence of a white dwarf (WD) companion. We study the effects
of the strong dynamical interaction between the AGB wind and the WD and of
photodissociation by the WD UV radiation on the circumstellar shells.
  We obtained high-quality ALMA maps of the 12CO J=2-1, J=3-2, and J=6-5 lines
and of 13CO J=3-2. The maps were analyzed by means of a heuristic 3D model that
is able to reproduce the observations. In order to interpret this description
of the molecule-rich nebula, we performed sophisticated calculations of
hydrodynamical interaction and photoinduced chemistry.
  We find that the CO-emitting gas is distributed within a relatively small
region <~ 1.5". Its structure consists of a central dense component plus
strongly disrupted outer regions, which seem to be parts of spiral arms that
are highly focused on the orbital plane. The structure and dynamics of these
spiral arms are compatible with our hydrodynamical calculations. We argue that
the observed nebula is the result of the dynamical interaction between the wind
and the gravitational attraction of the WD. We also find that UV emission from
the hot companion efficiently photodissociates molecules except in the densest
and best-shielded regions, that is, in the close surroundings of the AGB star
and some shreds of the spiral arms from which the detected lines come. We can
offer a faithful description of the distribution of nebular gas in this
prototypical source, which will be a useful template for studying material
around other tight binary systems.


Abstract:
  It is common for video-on-demand and music streaming services to adopt a user
interface composed of several recommendation lists, i.e. widgets or swipeable
carousels, each generated according to a specific criterion or algorithm (e.g.
most recent, top popular, recommended for you, editors' choice, etc.).
Selecting the appropriate combination of carousel has significant impact on
user satisfaction. A crucial aspect of this user interface is that to measure
the relevance a new carousel for the user it is not sufficient to account
solely for its individual quality. Instead, it should be considered that other
carousels will already be present in the interface. This is not considered by
traditional evaluation protocols for recommenders systems, in which each
carousel is evaluated in isolation, regardless of (i) which other carousels are
displayed to the user and (ii) the relative position of the carousel with
respect to other carousels. Hence, we propose a two-dimensional evaluation
protocol for a carousel setting that will measure the quality of a
recommendation carousel based on how much it improves upon the quality of an
already available set of carousels. Our evaluation protocol takes into account
also the position bias, i.e. users do not explore the carousels sequentially,
but rather concentrate on the top-left corner of the screen.
  We report experiments on the movie domain and notice that under a carousel
setting the definition of which criteria has to be preferred to generate a list
of recommended items changes with respect to what is commonly understood.


Abstract:
  Multi-label text classification refers to the problem of assigning each given
document its most relevant labels from the label set. Commonly, the metadata of
the given documents and the hierarchy of the labels are available in real-world
applications. However, most existing studies focus on only modeling the text
information, with a few attempts to utilize either metadata or hierarchy
signals, but not both of them. In this paper, we bridge the gap by formalizing
the problem of metadata-aware text classification in a large label hierarchy
(e.g., with tens of thousands of labels). To address this problem, we present
the MATCH solution -- an end-to-end framework that leverages both metadata and
hierarchy information. To incorporate metadata, we pre-train the embeddings of
text and metadata in the same space and also leverage the fully-connected
attentions to capture the interrelations between them. To leverage the label
hierarchy, we propose different ways to regularize the parameters and output
probability of each child label by its parents. Extensive experiments on two
massive text datasets with large-scale label hierarchies demonstrate the
effectiveness of MATCH over state-of-the-art deep learning baselines.


Abstract:
  Convolutional neural networks (CNNs) have achieved beyond human-level
accuracy in the image classification task and are widely deployed in real-world
environments. However, CNNs show vulnerability to adversarial perturbations
that are well-designed noises aiming to mislead the classification models. In
order to defend against the adversarial perturbations, adversarially trained
GAN (ATGAN) is proposed to improve the adversarial robustness generalization of
the state-of-the-art CNNs trained by adversarial training. ATGAN incorporates
adversarial training into standard GAN training procedure to remove obfuscated
gradients which can lead to a false sense in defending against the adversarial
perturbations and are commonly observed in existing GANs-based adversarial
defense methods. Moreover, ATGAN adopts the image-to-image generator as data
augmentation to increase the sample complexity needed for adversarial
robustness generalization in adversarial training. Experimental results in
MNIST SVHN and CIFAR-10 datasets show that the proposed method doesn't rely on
obfuscated gradients and achieves better global adversarial robustness
generalization performance than the adversarially trained state-of-the-art
CNNs.


Abstract:
  It is known that every surface-link, which is a closed surface embedded in
$4$-space, can be described as the closure of a $2$-dimensional braid providing
it is orientable. In this paper, we introduce a new method of describing a
surface-link using a braided surface, which we call a plat form. We prove that
every surface-link, not necessarily orientable, can be described in a plat
form. The plat index for a surface-link is defined. In classical knot theory,
the plat index of a link coincides with the bridge index. The plat index of a
surface-link we introduce here is an analogy of them.


Abstract:
  To determine whether some often-used lexical association measures assign high
scores to n-grams that chance could have produced as frequently as observed, we
used an extension of Fisher's exact test to sequences longer than two words to
analyse a corpus of four million words. The results, based on the
precision-recall curve and a new index called chance-corrected average
precision, show that, as expected, simple-ll is extremely effective. They also
show, however, that MI3 is more efficient than the other hypothesis tests-based
measures and even reaches a performance level almost equal to simple-ll for
3-grams. It is additionally observed that some measures are more efficient for
3-grams than for 2-grams, while others stagnate.


Abstract:
  Inertial sensor calibration plays a progressively important role in many
areas of research among which navigation engineering. By performing this task
accurately, it is possible to significantly increase general navigation
performance by correctly filtering out the deterministic and stochastic
measurement errors that characterize such devices. While different techniques
are available to model and remove the deterministic errors, there has been
considerable research over the past years with respect to modelling the
stochastic errors which have complex structures. In order to do the latter,
different replicates of these error signals are collected and a model is
identified and estimated based on one of these replicates. While this procedure
has allowed to improve navigation performance, it has not yet taken advantage
of the information coming from all the other replicates collected on the same
sensor. However, it has been observed that there is often a change of error
behaviour between replicates which can also be explained by different
(constant) external conditions under which each replicate was taken. Whatever
the reason for the difference between replicates, it appears that the model
structure remains the same between replicates but the parameter values vary. In
this work we therefore consider and study the properties of different
approaches that allow to combine the information from all replicates
considering this phenomenon, confirming their validity both in simulation
settings and also when applied to real inertial sensor error signals. By taking
into account parameter variation between replicates, this work highlights how
these approaches can improve the average navigation precision as well as obtain
reliable estimates of the uncertainty of the navigation solution.


Abstract:
  We propose a robust calibration pipeline that optimises the selection of
calibration samples for the estimation of calibration parameters that fit the
entire scene. We minimise user error by automating the data selection process
according to a metric, called Variability of Quality (VOQ) that gives a score
to each calibration set of samples. We show that this VOQ score is correlated
with the estimated calibration parameter's ability to generalise well to the
entire scene, thereby overcoming the overfitting problems of existing
calibration algorithms. Our approach has the benefits of simplifying the
calibration process for practitioners of any calibration expertise level and
providing an objective measure of the quality for our calibration pipeline's
input and output data. We additionally use a novel method of assessing the
accuracy of the calibration parameters. It involves computing reprojection
errors for the entire scene to ensure that the parameters are well fitted to
all features in the scene. Our proposed calibration pipeline takes 90s, and
obtains an average reprojection error of 1-1.2cm, with standard deviation of
0.4-0.5cm over 46 poses evenly distributed in a scene. This process has been
validated by experimentation on a high resolution, software definable lidar,
Baraja Spectrum-Scan; and a low, fixed resolution lidar, Velodyne VLP-16. We
have shown that despite the vast differences in lidar technologies, our
proposed approach manages to estimate robust calibration parameters for both.
Our code and data set used for this paper are made available as open-source.


Abstract:
  We study the homotopy type of the space of the unitary group
$\operatorname{U}_1(C^\ast_u(|\mathbb{Z}^n|))$ of the uniform Roe algebra
$C^\ast_u(|\mathbb{Z}^n|)$ of $\mathbb{Z}^n$. We show that the stabilizing map
$\operatorname{U}_1(C^\ast_u(|\mathbb{Z}^n|))\to\operatorname{U}_\infty(C^\ast_u(|\mathbb{Z}^n|))$
is a homotopy equivalence. Moreover, when $n=1,2$, we determine the homotopy
type of $\operatorname{U}_1(C^\ast_u(|\mathbb{Z}^n|))$, which is the product of
the unitary group $\operatorname{U}_1(C^\ast(|\mathbb{Z}^n|))$ (having the
homotopy type of $\operatorname{U}_\infty(\mathbb{C})$ or $\mathbb{Z}\times
B\operatorname{U}_\infty(\mathbb{C})$ depending on the parity of $n$) of the
Roe algebra $C^\ast(|\mathbb{Z}^n|)$ and rational Eilenberg--MacLane spaces.


Abstract:
  We analyze the effect of polarized diffuse emission in the calibration of
wide-beam mm-wave polarimeters, when using the Crab Nebula as a reference
source for both polarized brightness and polarization angle. We show that, for
CMB polarization experiments aiming at detecting B-mode in a scenario with a
tensor to scalar ratio $r \sim 0.001$, wide (a few degrees in diameter),
precise ($\sigma_Q$ , $\sigma_U$ $\sim$ 20 $\mu$$K_{CMB}$ arcmin), high angular
resolution ($< \mathrm{FWHM}$) reference maps are needed to properly take into
account the effects of diffuse polarized emission and avoid significant bias in
the calibration.


Abstract:
  Enzyme is the major workhorse to carry out the diverse cellular functions. It
catalyzes the biological reactions with a high specificity, with its topology
playing a crucial role. For ecologically safe production of numerous
bioproducts including drugs and chemicals, we have been striving to design the
industrially useful enzyme molecules with highly improved catalytic capability.
As the sequence space is enormous for an enzyme, its quick and effective
exploration is quite improbable for the mutagenesis studies whose accuracy is
greatly reliant on the prior information of the mutated sites and the extent of
rigorous screening of the mutant libraries. Although directed evolution methods
significantly aid the construction of a functionally improved molecule, their
credibility depends on the successful excavation of the functionally similar
sequence space in the available databases, encompassing billions of proteins.
As deep learning methods aid us to extensively uncover the underlying network
of all the key catalytic positions without any experimental data, their
implementation has reliably increased the accuracy of directed evolution. The
chapter comprehensively explains data mining and deep learning methods to
further showcase their importance in enzyme engineering methods. The key
biological and algorithmic limitations of these deep learning methodologies are
lastly highlighted.


Abstract:
  The time dependence of the temperature during the reheating process is
studied. We consider the thermal feedback effects of the produced particles on
the effective dissipation rate of the inflaton field, which can lead to
enhanced production of particles. We parameterize the temperature dependence of
the dissipation rate in terms of a Taylor expansion containing the vacuum decay
rate and the thermal terms. By solving the Boltzmann equations for the energy
densities of the inflaton and radiation, we provide analytic estimates for a
general power law dependence on the temperature. In this way we describe the
entire reheating process. The maximum temperature of the reheating process and
its dependence on model parameters are studied in different cases. The impact
of the thermal feedback effects on the expansion history of the universe and
the cosmic microwave background (CMB) is discussed. We also discuss the range
of validity of our approach.


Abstract:
  In spite of much recent research in the area, it is still unclear whether
subject-area question-answering data is useful for machine reading
comprehension (MRC) tasks. In this paper, we investigate this question. We
collect a large-scale multi-subject multiple-choice question-answering dataset,
ExamQA, and use incomplete and noisy snippets returned by a web search engine
as the relevant context for each question-answering instance to convert it into
a weakly-labeled MRC instance. We then propose a self-teaching paradigm to
better use the generated weakly-labeled MRC instances to improve a target MRC
task. Experimental results show that we can obtain +5.1% in accuracy on a
multiple-choice MRC dataset, C^3, and +3.8% in exact match on an extractive MRC
dataset, CMRC 2018 over state-of-the-art MRC baselines, demonstrating the
effectiveness of our framework and the usefulness of large-scale subject-area
question-answering data for different types of machine reading comprehension
tasks.


Abstract:
  A fully implicit particle-in-cell method for handling the
$v_\parallel$-formalism of electromagnetic gyrokinetics has been implemented in
XGC. By choosing the $v_\parallel$-formalism, we avoid introducing the
non-physical skin terms in Amp\`{e}re's law, which are responsible for the
well-known ``cancellation problem" in the $p_\parallel$-formalism. The
$v_\parallel$-formalism, however, is known to suffer from a numerical
instability when explicit time integration schemes are used due to the
appearance of a time derivative in the particle equations of motion from the
inductive component of the electric field. Here, using the conventional $\delta
f$ scheme, we demonstrate that our implicitly discretized algorithm can provide
numerically stable simulation results with accurate dispersive properties. We
verify the algorithm using a test case for shear Alfv\'{e}n wave propagation in
addition to a case demonstrating the ITG-KBM transition. The ITG-KBM transition
case is compared to results obtained from other $\delta f$ gyrokinetic
codes/schemes, whose verification has already been archived in the literature.


Abstract:
  An important problem in causal inference is to break down the total effect of
a treatment on an outcome into different causal pathways and to quantify the
causal effect in each pathway. For instance, in causal fairness, the total
effect of being a male employee (i.e., treatment) constitutes its direct effect
on annual income (i.e., outcome) and the indirect effect via the employee's
occupation (i.e., mediator). Causal mediation analysis (CMA) is a formal
statistical framework commonly used to reveal such underlying causal
mechanisms. One major challenge of CMA in observational studies is handling
confounders, variables that cause spurious causal relationships among
treatment, mediator, and outcome. Conventional methods assume sequential
ignorability that implies all confounders can be measured, which is often
unverifiable in practice. This work aims to circumvent the stringent sequential
ignorability assumptions and consider hidden confounders. Drawing upon proxy
strategies and recent advances in deep learning, we propose to simultaneously
uncover the latent variables that characterize hidden confounders and estimate
the causal effects. Empirical evaluations using both synthetic and
semi-synthetic datasets validate the effectiveness of the proposed method. We
further show the potentials of our approach for causal fairness analysis.


Abstract:
  Rapid rise in income inequality in India is a serious concern. While the
emphasis is on inclusive growth, it seems difficult to tackle the problem
without looking at the intricacies of the problem. Social mobility is one such
important tool which helps in reaching the cause of the problem and focuses on
bringing long term equality in the country. The purpose of this study is to
examine the role of social background and education attainment in generating
occupation mobility in the country. By applying an extended version of the RC
association model to 68th round (2011-12) of the Employment and Unemployment
Survey by the National Sample Survey Office of India, we found that the role of
education is not important in generating occupation mobility in India, while
social background plays a critical role in determining one's occupation. This
study successfully highlights the strong intergenerational occupation
immobility in the country and also the need to focus on education. In this
regard, further studies are needed to uncover other crucial factors limiting
the growth of individuals in the country.


Abstract:
  The parameterisation proposed by Casas and Ibarra in the year 2001 have shown
promising role in the extraction of neutrino Yukawa coupling which is a basic
ingredient of the seesaw mechanism generating neutrino mass. We pay special
attention in establishing the crucial role of the Casas-Ibarra (CI)
parameterisation in presence of two different orthogonal matrices, $R=
\textbf{O} \,\rm e^{i {\bf A}}$ and $R= \textbf{O} \,\rm e^{{\bf A}}$ in order
to investigate flavored leptogenesis. In the light of these two choices of the
orthogonal matrix we examine the connection between the low energy and high
energy CP violations along with certain interesting predictions on the low
energy parameters namely, the lightest neutrino mass and the Dirac CP phase
($\delta$). Considering the right handed neutrino (RHN) mass window to be
$10^8$ GeV, we show that Dirac phase leptogenesis is possible with the choices
of these two orthogonal matrices. We choose a nearly degenerate spectrum for
the RHN masses for having a successful leptogenesis. We also emphasize on
presenting a range of the matrix elements of the skew symmetric matrix $
\textbf{A}$. The results obtained in the present analysis underline the
importance of understanding the status of CP violation in the low energy
sector. We also discuss the phenomenological implications of these two case
studies in the context of LFV considering the $\mu \rightarrow e\gamma$ decay
process.


Abstract:
  With the development of medical computer-aided diagnostic systems, pulmonary
artery-vein(A/V) separation plays a crucial role in assisting doctors in
preoperative planning for lung cancer surgery. However, distinguishing arterial
from venous irrigation in chest CT images remains a challenge due to the
similarity and complex structure of the arteries and veins. We propose a novel
method for automatic separation of pulmonary arteries and veins from chest CT
images. The method consists of three parts. First, global connection
information and local feature information are used to construct a complete
topological tree and ensure the continuity of vessel reconstruction. Second,
the Twin-Pipe network proposed can automatically learn the differences between
arteries and veins at different levels to reduce classification errors caused
by changes in terminal vessel characteristics. Finally, the topology optimizer
considers interbranch and intrabranch topological relationships to maintain
spatial consistency to avoid the misclassification of A/V irrigations. We
validate the performance of the method on chest CT images. Compared with manual
classification, the proposed method achieves an average accuracy of 96.2% on
noncontrast chest CT. In addition, the method has been proven to have good
generalization, that is, the accuracies of 93.8% and 94.8% are obtained for CT
scans from other devices and other modes, respectively. The result of pulmonary
artery-vein obtained by the proposed method can provide better assistance for
preoperative planning of lung cancer surgery.


Abstract:
  In early January 2020, after China reported the first cases of the new
coronavirus (SARS-CoV-2) in the city of Wuhan, unreliable and not fully
accurate information has started spreading faster than the virus itself.
Alongside this pandemic, people have experienced a parallel infodemic, i.e., an
overabundance of information, some of which misleading or even harmful, that
has widely spread around the globe. Although Social Media are increasingly
being used as information source, Web Search Engines, like Google or Yahoo!,
still represent a powerful and trustworthy resource for finding information on
the Web. This is due to their capability to capture the largest amount of
information, helping users quickly identify the most relevant, useful, although
not always the most reliable, results for their search queries. This study aims
to detect potential misleading and fake contents by capturing and analysing
textual information, which flow through Search Engines. By using a real-world
dataset associated with recent CoViD-19 pandemic, we first apply re-sampling
techniques for class imbalance, then we use existing Machine Learning
algorithms for classification of not reliable news. By extracting lexical and
host-based features of associated Uniform Resource Locators (URLs) for news
articles, we show that the proposed methods, so common in phishing and
malicious URLs detection, can improve the efficiency and performance of
classifiers. Based on these findings, we suggest that the use of both textual
and URLs features can improve the effectiveness of fake news detection methods.


Abstract:
  The iron-based superconductor FeTe$_{1-x}$Se$_{x}$ has attracted considerable
attention as a candidate topological superconductor owing to a unique
combination of topological surface states and bulk high-temperature
superconductivity. The superconducting properties of as-grown single crystals,
however, are highly variable and synthesis dependent due to excess interstitial
iron impurities incorporated during growth. Here we report a novel
physicochemical process for pumping this interstitial iron out of the
FeTe$_{1-x}$Se$_{x}$ matrix and achieving bulk superconductivity. Our method
should have significant value for the synthesis of high-quality single crystals
of FeTe$_{1-x}$Se$_{x}$ with large superconducting volume fractions.


Abstract:
  Algorithms for exchange of kidneys is one of the key successful applications
in market design, artificial intelligence, and operations research. Potent
immunosuppressant drugs suppress the body's ability to reject a transplanted
organ up to the point that a transplant across blood- or tissue-type
incompatibility becomes possible. In contrast to the standard kidney exchange
problem, we consider a setting that also involves the decision about which
recipients receive from the limited supply of immunosuppressants that make them
compatible with originally incompatible kidneys. We firstly present a general
computational framework to model this problem. Our main contribution is a range
of efficient algorithms that provide flexibility in terms of meeting meaningful
objectives. Motivated by the current reality of kidney exchanges using
sophisticated mathematical-programming-based clearing algorithms, we then
present a general but scalable approach to optimal clearing with
immunosuppression; we validate our approach on realistic data from a large
fielded exchange.


Abstract:
  The thermodynamic and physical properties of the Laves phase Fe2Mo have been
investigated using the finite-temperature quantum mechanical calculations
within the frame of the density functional theory (DFT). All relevant free
energy contributions including electronic, vibrational and magnetic excitations
are considered. The quasi-harmonic Debye - Gruneisen theory is used. In this
work, a new method of searching a thermal expansion path of compounds is
proposed. It allows to reduce the problem to a one-dimensional case and
minimize the free energy in one variable, in volume. The heat capacity, thermal
expansion, elastic constants and bulk modulus are modelled. The calculated
results analyzed and are in an agreement with the available experimental data.
It is shown that magnetic entropy must be considered on equal footing with
vibrational and electronic energies to reliably predict stability of Fe2Mo.


Abstract:
  The bulk band topology of symmetry invariant adiabatic systems in the
thermodynamic limit are considered to be determined by the hopping energy. In
this work, we present that in closed classical systems, due to generalized
chiral symmetry broken, the on-site energy cannot always be regarded as
identical and can crucially impact the topological properties of the systems.
Based on a finite one-dimensional chain, we demonstrate that the non-equivalent
on-site energy of bulk lattices affects the topological phases of the bands,
and the on-site energy of end lattices affects the existence of the topological
states. Along these lines, the correspondence with generalized chiral symmetry
in acoustic system is rigorously proposed. Our work provides a new degree of
freedom for topological classical systems, and can be generalized to
higher-dimensions and non-Hermitian conditions.


Abstract:
  Quasi-Periodic Eruptions (QPEs) are extreme high-amplitude bursts of X-ray
radiation recurring every few hours and originating near the central
supermassive black holes in galactic nuclei. It is currently unknown what
triggers these events, how long they last and how they are connected to the
physical properties of the inner accretion flows. Previously, only two such
sources were known, found either serendipitously or in archival data, with
emission lines in their optical spectra classifying their nuclei as hosting an
actively accreting supermassive black hole. Here we present the detection of
QPEs in two further galaxies, obtained with a blind and systematic search over
half of the X-ray sky. The optical spectra of these galaxies show no signature
of black hole activity, indicating that a pre-existing accretion flow typical
of active nuclei is not required to trigger these events. Indeed, the periods,
amplitudes and profiles of the newly discovered QPEs are inconsistent with
current models that invoke radiation-pressure driven accretion disk
instabilities. Instead, QPEs might be driven by an orbiting compact object.
Furthermore, their observed properties require the mass of the secondary object
to be much smaller than the main body and future X-ray observations may
constrain possible changes in the period due to orbital evolution. This
scenario could make QPEs a viable candidate for the electromagnetic
counterparts of the so-called extreme mass ratio inspirals, with considerable
implications for multi-messenger astrophysics and cosmology.


Abstract:
  Using 6.32 fb$^{-1}$ of electron-positron collision data recorded by the
BESIII detector at center-of-mass energies between 4.178 and 4.226~GeV, we
present the first search for the decay $D_s^+\to a_0(980)^0
e^+\nu_e,\,a_0(980)^0\rightarrow \pi^0\eta$, which could proceed via
$a_0(980)$-$f_0(980)$ mixing. No significant signal is observed. An upper limit
of $1.2 \times 10^{-4}$ at the $90\%$ confidence level is set on the product of
the branching fractions of $D_{s}^{+}\to a_0(980)^0 e^+\nu_e$ and
$a_0(980)^0\rightarrow \pi^0\eta$ decays.


Abstract:
  Disease transmission is studied through disciplines like epidemiology,
applied mathematics, and statistics. Mathematical simulation models for
transmission have implications in solving public and personal health
challenges. The SIR model uses a compartmental approach including dynamic and
nonlinear behavior of transmission through three factors: susceptible,
infected, and removed (recovered and deceased) individuals. Using the Lambert W
Function, we propose a framework to study solutions of the SIR model. This
demonstrates the applications of COVID-19 transmission data to model the spread
of a real-world disease. Different models of disease including the SIR, SIRmp
and SEIRpqr model are compared with respect to their ability to predict disease
spread. Physical distancing impacts and personal protection equipment use are
discussed with relevance to the COVID-19 spread.


Abstract:
  One fundamental limitation of blockchain-based smart contracts is that they
execute in a closed environment. Thus, they only have access to data and
functionality that is already on the blockchain, or is fed into the blockchain.
Any interactions with the real world need to be mediated by a bridge service,
which is called an oracle. As decentralized applications mature, oracles are
playing an increasingly prominent role. With their evolution comes more
attacks, necessitating greater attention to their trust model. In this
systemization of knowledge paper (SoK), we dissect the design alternatives for
oracles, showcase attacks, and discuss attack mitigation strategies.


Abstract:
  The scope of automotive functions has grown from a single-vehicle as an
entity to multiple vehicles working together as an entity, referred to as
cooperative driving. The current automotive safety standard, ISO 26262, is
designed for single vehicles. With the increasing number of cooperative driving
capable vehicles on the road, it is now imperative to systematically assess the
functional safety of architectures of these vehicles. Many methods are proposed
to assess architectures with respect to different quality attributes in the
software architecture domain, but to the best of our knowledge, functional
safety assessment of automotive architectures is not explored in the
literature. We present a method, that leverages existing research in software
architecture and safety engineering domains, to check whether the functional
safety requirements for a cooperative driving scenario are fulfilled in the
technical architecture of a vehicle. We apply our method on a real-life
academic prototype for a cooperative driving scenario, platooning, and discuss
our insights.


Abstract:
  Local distinguishability of sets of generalized Bell states (GBSs) is
investigated. We first clarify the conditions such that a set of GBSs can be
locally transformed to a certain type of GBS set that is easily distinguishable
within local operations and one-way classical communication. We then show that,
if the space dimension $d$ is a prime, these conditions are necessary and
sufficient for sets of $d$ GBSs in $\mathbb{C}^d \otimes \mathbb{C}^d$ to be
locally distinguishable. Thus we obtain a simple computable criterion for local
distinguishability of sets of $d$ GBSs in prime dimension $d$.


Abstract:
  In 2017, the Event Horizon Telescope (EHT) Collaboration succeeded in
capturing the first direct image of the center of the M87 galaxy. The
asymmetric ring morphology and size are consistent with theoretical
expectations for a weakly accreting supermassive black hole of mass
approximately 6.5 x 10^9 M_solar. The EHTC also partnered with several
international facilities in space and on the ground, to arrange an extensive,
quasi-simultaneous multi-wavelength campaign. This Letter presents the results
and analysis of this campaign, as well as the multi-wavelength data as a legacy
data repository. We captured M87 in a historically low state, and the core flux
dominates over HST-1 at high energies, making it possible to combine core flux
constraints with the more spatially precise very long baseline interferometry
data. We present the most complete simultaneous multi-wavelength spectrum of
the active nucleus to date, and discuss the complexity and caveats of combining
data from different spatial scales into one broadband spectrum. We apply two
heuristic, isotropic leptonic single-zone models to provide insight into the
basic source properties, but conclude that a structured jet is necessary to
explain M87's spectrum. We can exclude that the simultaneous gamma-ray emission
is produced via inverse Compton emission in the same region producing the EHT
mm-band emission, and further conclude that the gamma-rays can only be produced
in the inner jets (inward of HST-1) if there are strongly particle-dominated
regions. Direct synchrotron emission from accelerated protons and secondaries
cannot yet be excluded.


Abstract:
  Availability of domain-specific datasets is an essential problem in object
detection. Maritime vessel detection of inshore and offshore datasets is no
exception, there is a limited number of studies addressing this need. For that
reason, we collected a dataset of images of maritime vessels taking into
account different factors: background variation, atmospheric conditions,
illumination, visible proportion, occlusion and scale variation. Vessel
instances (including 9 types of vessels), seamarks and miscellaneous floaters
were precisely annotated: we employed a first round of labelling and
subsequently, we used the CSRT [1] tracker to trace inconsistencies and relabel
inadequate label instances. Moreover, we evaluated the the out-of-the-box
performance of four prevalent object detection algorithms (Faster R-CNN [2],
R-FCN [3], SSD [4] and EfficientDet [5]). The algorithms were previously
trained on the Microsoft COCO dataset. We compare their accuracy based on
feature extractor and object size. Our experiments show that Faster R-CNN with
Inception-Resnet v2 outperforms the other algorithms, except in the large
object category where EfficientDet surpasses the latter.


Abstract:
  This paper presents a computational framework that generates ensemble
predictive mechanics models with uncertainty quantification (UQ). We first
develop a causal discovery algorithm to infer causal relations among
time-history data measured during each representative volume element (RVE)
simulation through a directed acyclic graph (DAG). With multiple plausible sets
of causal relationships estimated from multiple RVE simulations, the
predictions are propagated in the derived causal graph while using a deep
neural network equipped with dropout layers as a Bayesian approximation for
uncertainty quantification. We select two representative numerical examples
(traction-separation laws for frictional interfaces, elastoplasticity models
for granular assembles) to examine the accuracy and robustness of the proposed
causal discovery method for the common material law predictions in civil
engineering applications.


Abstract:
  Super-resolution (SR) methods have seen significant advances thanks to the
development of convolutional neural networks (CNNs). CNNs have been
successfully employed to improve the quality of endomicroscopy imaging. Yet,
the inherent limitation of research on SR in endomicroscopy remains the lack of
ground truth high-resolution (HR) images, commonly used for both supervised
training and reference-based image quality assessment (IQA). Therefore,
alternative methods, such as unsupervised SR are being explored. To address the
need for non-reference image quality improvement, we designed a novel zero-shot
super-resolution (ZSSR) approach that relies only on the endomicroscopy data to
be processed in a self-supervised manner without the need for ground-truth HR
images. We tailored the proposed pipeline to the idiosyncrasies of
endomicroscopy by introducing both: a physically-motivated Voronoi downscaling
kernel accounting for the endomicroscope's irregular fibre-based sampling
pattern, and realistic noise patterns. We also took advantage of video
sequences to exploit a sequence of images for self-supervised zero-shot image
quality improvement. We run ablation studies to assess our contribution in
regards to the downscaling kernel and noise simulation. We validate our
methodology on both synthetic and original data. Synthetic experiments were
assessed with reference-based IQA, while our results for original images were
evaluated in a user study conducted with both expert and non-expert observers.
The results demonstrated superior performance in image quality of ZSSR
reconstructions in comparison to the baseline method. The ZSSR is also
competitive when compared to supervised single-image SR, especially being the
preferred reconstruction technique by experts.


Abstract:
  We present a modified version of the Judd-Ofelt theory, which describes the
intensities of f-f transitions by trivalent lanthanide ions (Ln$^{3+}$) in
solids. In our model, the properties of the dopant are calculated with
well-established atomic-structure techniques, while the influence of the
crystal-field potential is described by three adjustable parameters. By
applying our model to europium (Eu$^{3+}$), well-known to challenge the
standard Judd-Ofelt theory, we are able to give a physical insight into all the
transitions within the ground electronic configuration, and also to reproduce
quantitatively experimental absorption oscillator strengths. Our model opens
the possibility to interpret polarized-light transitions between individual
levels of the ion-crystal system.


Abstract:
  The interfacial structures and interactions of two-dimensional (2D) materials
on solid substrates are of fundamental importance for the fabrication and
application of 2D materials. However, selection of a suitable solid substrate
to grow 2D material, determination and control of the 2D material-substrate
interface remain a big challenge due to the large diversity of possible
configurations. Here, we propose a computational framework to select an
appropriate substrate for epitaxial growth of 2D material and to predict
possible 2D material-substrate interface structures and orientations using
density functional theory calculations performed for all non-equivalent atomic
structures satisfying the symmetry constraints. The approach was validated by
the correct prediction of three experimentally reported 2D material-substrate
interface systems with only the given information of two parent materials.
Several possible interface configurations are also proposed based on this
approach. We therefore construct a database that contains these interface
systems and has been continuously expanding. This database serves as
preliminary guidance for epitaxial growth and stabilization of new materials in
experiments.


Abstract:
  We study the sensitivity of the Fermilab Short-Baseline Neutrino (SBN)
experiments, MicroBooNE, ICARUS, and SBND, to MeV- to GeV-scale inelastic dark
matter interacting through a dark photon mediator. These models provide
interesting scenarios of light thermal dark matter, which, while challenging to
probe with direct and indirect detection experiments, are amenable to
accelerator-based searches. We consider production of the dark sector states
with both the Fermilab Booster 8 GeV and NuMI 120 GeV proton beams and study
the signatures of scattering and decay of the heavy excited dark state in the
SBN detectors. These distinct signatures probe complementary regions of
parameter space. All three experiments will be able to cover new ground, with
an excellent near-term opportunity to search for cosmologically motivated
targets explaining the observed dark matter abundance.


Abstract:
  Modern logics of dependence and independence are based on team semantics,
which means that formulae are evaluated not on a single assignment of values to
variables, but on a set of such assignments, called a team. This leads to high
expressive power, on the level of existential second-order logic. As an
alternative, Baltag and van Benthem have proposed a local variant of dependence
logic, called logic of functional dependence (LFD). While its semantics is also
based on a team, the formulae are evaluated locally on just one of its
assignments, and the team just serves as the supply of the possible assignments
that are taken into account in the evaluation process. This logic thus relies
on the modal perspective of generalized assignments semantics, and can be seen
as a fragment of first-order logic. For the variant of LFD without equality,
the satisfiability problem is decidable.
  We extend the idea of localising logics of dependence and independence in a
systematic way, taking into account local variants of standard atomic
dependency properties: besides dependence and independence, also inclusion,
exclusion, and anonymity. We study model-theoretic and algorithmic questions of
the localised logics, and also resolve some of the questions that had been left
open by Baltag and van Benthem. In particular, we study decidability issues of
the local logics, and prove that satisfiability of LFD with equality is
undecidable. Further, we establish characterisation theorems via appropriate
notions of bisimulation and study the complexity of model checking problems for
these logics.


Abstract:
  Liouville closed $H$-fields are ordered differential fields whose ordering
and derivation interact in a natural way and where every linear differential
equation of order $1$ has a nontrivial solution. (The introduction gives a
precise definition.) For a Liouville closed $H$-field $K$ with small derivation
we show: $K$ has the Intermediate Value Property for differential polynomials
iff $K$ is elementarily equivalent to the ordered differential field of
transseries. We also indicate how this applies to Hardy fields.


Abstract:
  Spectral CT offers enhanced material discrimination over single-energy
systems and enables quantitative estimation of basis material density images.
Water/iodine decomposition in contrast-enhanced CT is one of the most
widespread applications of this technology in the clinic. However, low
concentrations of iodine can be difficult to estimate accurately, limiting
potential clinical applications and/or raising injected contrast agent
requirements. We seek high-sensitivity spectral CT system designs which
minimize noise in water/iodine density estimates. In this work, we present a
model-driven framework for spectral CT system design optimization to maximize
material separability. We apply this tool to optimize the sensitivity spectra
on a spectral CT test bench using a hybrid design which combines source kVp
control and k-edge filtration. Following design optimization, we scanned a
water/iodine phantom with the hybrid spectral CT system and performed
dose-normalized comparisons to two single-technique designs which use only kVp
control or only kedge filtration. The material decomposition results show that
the hybrid system reduces both standard deviation and crossmaterial noise
correlations compared to the designs where the constituent technologies are
used individually.


Abstract:
  The ability to detect and segment moving objects in a scene is essential for
building consistent maps, making future state predictions, avoiding collisions,
and planning. In this paper, we address the problem of moving object
segmentation from 3D LiDAR scans. We propose a novel approach that pushes the
current state of the art in LiDAR-only moving object segmentation forward to
provide relevant information for autonomous robots and other vehicles. Instead
of segmenting the point cloud semantically, i.e., predicting the semantic
classes such as vehicles, pedestrians, roads, etc., our approach accurately
segments the scene into moving and static objects, i.e., also distinguishing
between moving cars vs. parked cars. Our proposed approach exploits sequential
range images from a rotating 3D LiDAR sensor as an intermediate representation
combined with a convolutional neural network and runs faster than the frame
rate of the sensor. We compare our approach to several other state-of-the-art
methods showing superior segmentation quality in urban environments.
Additionally, we created a new benchmark for LiDAR-based moving object
segmentation based on SemanticKITTI. We published it to allow other researchers
to compare their approaches transparently and we furthermore published our
code.


Abstract:
  When applying machine learning in safety-critical systems, a reliable
assessment of the uncertainy of a classifier is required. However, deep neural
networks are known to produce highly overconfident predictions on
out-of-distribution (OOD) data and even if trained to be non-confident on OOD
data one can still adversarially manipulate OOD data so that the classifer
again assigns high confidence to the manipulated samples. In this paper we
propose a novel method where from first principles we combine a certifiable OOD
detector with a standard classifier into an OOD aware classifier. In this way
we achieve the best of two worlds: certifiably adversarially robust OOD
detection, even for OOD samples close to the in-distribution, without loss in
prediction accuracy and close to state-of-the-art OOD detection performance for
non-manipulated OOD data. Moreover, due to the particular construction our
classifier provably avoids the asymptotic overconfidence problem of standard
neural networks.


Abstract:
  In the cases where there is no Sobolev-type or Gagliardo-Nirenberg-type
fractional estimate involving $\lvert u\rvert_{W^{s,p}}$, we establish
alternative estimates where the strong $L^p$ norms are replaced by Lorentz
norms.


Abstract:
  Large language models have led to remarkable progress on many NLP tasks, and
researchers are turning to ever-larger text corpora to train them. Some of the
largest corpora available are made by scraping significant portions of the
internet, and are frequently introduced with only minimal documentation. In
this work we provide some of the first documentation for the Colossal Clean
Crawled Corpus (C4; Raffel et al., 2020), a dataset created by applying a set
of filters to a single snapshot of Common Crawl. We begin by investigating
where the data came from, and find a significant amount of text from unexpected
sources like patents and US military websites. Then we explore the content of
the text itself, and find machine-generated text (e.g., from machine
translation systems) and evaluation examples from other benchmark NLP datasets.
To understand the impact of the filters applied to create this dataset, we
evaluate the text that was removed, and show that blocklist filtering
disproportionately removes text from and about minority individuals. Finally,
we conclude with some recommendations for how to created and document web-scale
datasets from a scrape of the internet.


Abstract:
  In the Gaia era, the membership analysis and parameter determination of open
clusters (OCs) are more accurate. We performed a census of OC's classical
Cepheids based on Gaia Early Data Release 3 (EDR3) and obtained a sample of 33
OC Cepheids fulfilling the constraints of the spatial position, proper motion,
parallax and evolution state. 13 of 33 OC Cepheids are newly discovered. Among
them, CM Sct is the first first-crossing Cepheids with direct evidence of
evolution. DP Vel is likely a fourth- or fifth-crossing Cepheids. Based on
independent distances from OCs, W_1-band period-luminosity relation of Cepheids
is determined with a 3.5% accuracy: <MW1> = -(3.274 +- 0.090) log P - (-2.567
+- 0.080). The Gaia-band period-Wesenheit relation agrees well with Ripepi et
al. (2019). A direct period-age relation for fundamental Cepheids are also
determined based on OC's age, that is log t = -(0.638 +- 0.063) log P + (8.569
+- 0.057).


Abstract:
  We propose an ultracold-atom setting where a fermionic superfluidity with
attractive s-wave interaction is uploaded in a non-Hermitian Lieb optical
lattice. The existence of a real-energy flat band solution is revealed. We show
that the interplay between the skin effect and flat-band localization leads to
exotic localization properties. We develop a multiband mean-field description
of this system and use both order parameters and superfluid weight to describe
the phase transition. A relation between the superfluid weight and
non-Hermitian quantum metric of the quantum states manifold is built. We find
non-monotone criticality depending on the non-Hermiticity, and the
non-reciprocity prominently enhances the phase coherence of the pairing field,
suggesting ubiquitous critical behavior of the non-Hermitian fermionic
superfluidity.


Abstract:
  We study the large time behaviour of the reaction-diffsuion equation
$\partial_t u=\Delta u +f(u)$ in spatial dimension $N$, when the nonlinear term
is bistable and the initial datum is compactly supported. We prove the
existence of a Lipschitz function $s^\infty$ of the unit sphere, such that
$u(t,x)$ converges uniformly in $\mathbb{R}^N$, as $t$ goes to infinity, to
  $U_{c_*}\bigg(|x|-c_*t + \frac{N-1}{c_*} \mathrm{ln}t +
s^\infty\Big(\frac{x}{|x|}\Big)\bigg)$, where $U_{c*}$ is the unique 1D
travelling profile. This extends earlier results that identified the locations
of the level sets of the solutions with $o_{t\to+\infty}(t)$ precision, or
identified precisely the level sets locations for almost radial initial data.


Abstract:
  This document provides recommendations to the Government of Canada and the
Canadian Space Agency in response to their call for feedback on the future of
Canadian space exploration. The report focuses on how the construction and
long-term placement of mega-constellations of satellites into Earth orbit will
affect astronomy and the view of the night sky by all peoples, with attention
to all Canadians. The broader discussion highlights several environmental
concerns associated with the construction and maintenance of these
mega-constellations. The eight recommendations here address ways that Canada
can play a role in mitigating some of these negative effects through national
and international initiatives. In drafting the recommendations, we take the
approach that space needs to be developed sustainably. In this regard, we use
the Brundtland Report's definition: "Sustainable development is the development
that meets the needs of the present without compromising the ability of future
generations to meet their own needs." Thus, all recommendations here are made
with the intent of minimizing the negative consequences of mega-constellations,
while also recognizing that their development will continue.


Abstract:
  We study the online stochastic matching problem. Consider a bipartite graph
with offline vertices on one side, and with i.i.d.online vertices on the other
side. The offline vertices and the distribution of online vertices are known to
the algorithm beforehand. The realization of the online vertices, however, is
revealed one at a time, upon which the algorithm immediately decides how to
match it. For maximizing the cardinality of the matching, we give a
$0.711$-competitive online algorithm, which improves the best previous ratio of
$0.706$. When the offline vertices are weighted, we introduce a
$0.7009$-competitive online algorithm for maximizing the total weight of the
matched offline vertices, which improves the best previous ratio of $0.662$.
  Conceptually, we find that the analysis of online algorithms simplifies if
the online vertices follow a Poisson process, and establish an approximate
equivalence between this Poisson arrival model and online stochstic matching.
Technically, we propose a natural linear program for the Poisson arrival model,
and demonstrate how to exploit its structure by introducing a converse of
Jensen's inequality. Moreover, we design an algorithmic amortization to replace
the analytic one in previous work, and as a result get the first
vertex-weighted online stochastic matching algorithm that improves the results
in the weaker random arrival model.


Abstract:
  Growing advancements in reinforcement learning has led to advancements in
control theory. Reinforcement learning has effectively solved the inverted
pendulum problem and more recently the double inverted pendulum problem. In
reinforcement learning, our agents learn by interacting with the control system
with the goal of maximizing rewards. In this paper, we explore three such
reward functions in the cart position problem. This paper concludes that a
discontinuous reward function that gives non-zero rewards to agents only if
they are within a given distance from the desired position gives the best
results.


Abstract:
  We propose a data-driven method for synthesizing a static analyzer to detect
side-channel information leaks in cryptographic software. Compared to the
conventional way of manually crafting such a static analyzer, which can be
labor intensive, error prone and suboptimal, our learning-based technique is
not only automated but also provably sound. Our analyzer consists of a set of
type-inference rules learned from the training data, i.e., example code
snippets annotated with ground truth. Internally, we use syntax-guided
synthesis (SyGuS) to generate new features and decision tree learning (DTL) to
generate type-inference rules based on these features. We guarantee soundness
by formally proving each learned rule via a technique called Datalog query
containment checking. We have implemented our technique in the LLVM compiler
and used it to detect power side channels in C programs. Our results show that,
in addition to being automated and provably sound during synthesis, the learned
analyzer also has the same empirical accuracy as two state-of-the-art, manually
crafted analyzers while being 300X and 900X faster, respectively.


Abstract:
  In combinatorics on words, a word $w$ over an alphabet $\Sigma$ is said to
avoid a pattern $p$ over an alphabet $\Delta$ if there is no factor $f$ of $w$
such that $f=h(p)$ where $h:\Delta^*\to\Sigma^*$ is a non-erasing morphism. A
pattern $p$ is said to be $k$-avoidable if there exists an infinite word over a
$k$-letter alphabet that avoids $p$. A pattern is \emph{doubled} if every
variable occurs at least twice. Doubled patterns are known to be $3$-avoidable.
Currie, Mol, and Rampersad have considered a generalized notion which allows
variable occurrences to be reversed. That is, $h(V^R)$ is the mirror image of
$h(V)$ for every $V\in\Delta$. We show that doubled patterns with reversal are
$3$-avoidable. We also show that for every doubled pattern $p$, the growth rate
of ternary words avoiding $p$ is at least the growth rate of ternary
square-free words. A previous version of this paper containing only the first
result has been presented at WORDS 2021.


Abstract:
  In humans and animals, curriculum learning -- presenting data in a curated
order - is critical to rapid learning and effective pedagogy. Yet in machine
learning, curricula are not widely used and empirically often yield only
moderate benefits. This stark difference in the importance of curriculum raises
a fundamental theoretical question: when and why does curriculum learning help?
  In this work, we analyse a prototypical neural network model of curriculum
learning in the high-dimensional limit, employing statistical physics methods.
Curricula could in principle change both the learning speed and asymptotic
performance of a model. To study the former, we provide an exact description of
the online learning setting, confirming the long-standing experimental
observation that curricula can modestly speed up learning. To study the latter,
we derive performance in a batch learning setting, in which a network trains to
convergence in successive phases of learning on dataset slices of varying
difficulty. With standard training losses, curriculum does not provide
generalisation benefit, in line with empirical observations. However, we show
that by connecting different learning phases through simple Gaussian priors,
curriculum can yield a large improvement in test performance. Taken together,
our reduced analytical descriptions help reconcile apparently conflicting
empirical results and trace regimes where curriculum learning yields the
largest gains. More broadly, our results suggest that fully exploiting a
curriculum may require explicit changes to the loss function at curriculum
boundaries.


Abstract:
  This paper proposes a novel online measurement-based Wide-Area Voltage
Control (WAVC) method using Phasor Measurement Unit (PMU) data in power systems
with Flexible AC Transmission System (FACTS) devices. As opposed to previous
WAVC methods, the proposed WAVC does not require any model knowledge or the
participation of all buses and considers both active and reactive power
perturbations. Specifically, the proposed WAVC method exploits the regression
theorem of the Ornstein-Uhlenbeck process to estimate the sensitivity matrices
through PMU data online, which are further used to design and apply the voltage
regulation by updating the reference points of FACTS devices. Numerical results
on the IEEE 39- Bus and IEEE 68-Bus systems demonstrate that the proposed
model-free WAVC can provide effective voltage control in various network
topologies, different combinations of voltage-controlled and
voltage-uncontrolled buses, under measurement noise, and in case of missing
PMUs. Particularly, the proposed WAVC algorithm may outperform the model-based
WAVC when an undetected topology change happens.


Abstract:
  We propose a method of Category-level 6D Object Pose and Size Estimation
(COPSE) from a single depth image, without external pose-annotated real-world
training data. While previous works exploit visual cues in RGB(D) images, our
method makes inferences based on the rich geometric information of the object
in the depth channel alone. Essentially, our framework explores such geometric
information by learning the unified 3D Orientation-Consistent Representations
(3D-OCR) module, and further enforced by the property of Geometry-constrained
Reflection Symmetry (GeoReS) module. The magnitude information of object size
and the center point is finally estimated by Mirror-Paired Dimensional
Estimation (MPDE) module. Extensive experiments on the category-level NOCS
benchmark demonstrate that our framework competes with state-of-the-art
approaches that require labeled real-world images. We also deploy our approach
to a physical Baxter robot to perform manipulation tasks on unseen but
category-known instances, and the results further validate the efficacy of our
proposed model. Our videos are available in the supplementary material.


Abstract:
  The use of natural language interfaces in the field of human-computer
interaction is undergoing intense study through dedicated scientific and
industrial research. The latest contributions in the field, including deep
learning approaches like recurrent neural networks, the potential of
context-aware strategies and user-centred design approaches, have brought back
the attention of the community to software-based dialogue systems, generally
known as conversational agents or chatbots. Nonetheless, and given the novelty
of the field, a generic, context-independent overview on the current state of
research of conversational agents covering all research perspectives involved
is missing. Motivated by this context, this paper reports a survey of the
current state of research of conversational agents through a systematic
literature review of secondary studies. The conducted research is designed to
develop an exhaustive perspective through a clear presentation of the
aggregated knowledge published by recent literature within a variety of
domains, research focuses and contexts. As a result, this research proposes a
holistic taxonomy of the different dimensions involved in the conversational
agents' field, which is expected to help researchers and to lay the groundwork
for future research in the field of natural language interfaces.


Abstract:
  We study the large $N$ matrix model for the index of 4d $\mathcal{N}=4$
Yang-Mills theory and its truncations to understand the dual AdS$_5$ black
holes. Numerical studies of the truncated models provide insights on the black
hole physics, some of which we investigate analytically with the full
Yang-Mills matrix model. In particular, we find many branches of saddle points
which describe the known black hole solutions. We analytically construct the
saddle points dual to the small black holes whose sizes are much smaller than
the AdS radius. They include the asymptotically flat BMPV black holes embedded
in large AdS with novel thermodynamic instabilities.


Abstract:
  In this paper, we propose a Thompson Sampling algorithm for \emph{unimodal}
bandits, where the expected reward is unimodal over the partially ordered arms.
To exploit the unimodal structure better, at each step, instead of exploration
from the entire decision space, our algorithm makes decision according to
posterior distribution only in the neighborhood of the arm that has the highest
empirical mean estimate. We theoretically prove that, for Bernoulli rewards,
the regret of our algorithm reaches the lower bound of unimodal bandits, thus
it is asymptotically optimal. For Gaussian rewards, the regret of our algorithm
is $\mathcal{O}(\log T)$, which is far better than standard Thompson Sampling
algorithms. Extensive experiments demonstrate the effectiveness of the proposed
algorithm on both synthetic data sets and the real-world applications.


Abstract:
  Designing flat sheets that can be made to deform into 3D shapes is an area of
intense research with applications in micromachines, soft robotics, and medical
implants. Thus far, such sheets were designed to adopt a single target shape.
Here, we show that through anisotropic deformation applied inhomogenously
throughout a sheet, it is possible to design a single sheet that can deform
into multiple surface geometries upon different actuations. The key to our
approach is development of an analytical method for solving this multi-valued
inverse problem. Such sheets open the door to fabricating machines that can
perform complex tasks through cyclic transitions between multiple shapes. As a
proof of concept we design a simple swimmer capable of moving through a fluid
at low Reynolds numbers.


Abstract:
  High throughput sequencing (HTS)-based technology enables identifying and
quantifying non-culturable microbial organisms in all environments. Microbial
sequences have enhanced our understanding of the human microbiome, the soil and
plant environment, and the marine environment. All molecular microbial data
pose statistical challenges due to contamination sequences from reagents, batch
effects, unequal sampling, and undetected taxa. Technical biases and
heteroscedasticity have the strongest effects, but different strains across
subjects and environments also make direct differential abundance testing
unwieldy. We provide an introduction to a few statistical tools that can
overcome some of these difficulties and demonstrate those tools on an example.
We show how standard statistical methods, such as simple hierarchical mixture
and topic models, can facilitate inferences on latent microbial communities. We
also review some nonparametric Bayesian approaches that combine visualization
and uncertainty quantification. The intersection of molecular microbial biology
and statistics is an exciting new venue. Finally, we list some of the important
open problems that would benefit from more careful statistical method
development.


Abstract:
  Dimensions was introduced as an alternative bibliometric database to the
well-established Web of Science (WoS) and Scopus, however all three databases
have fundamental differences in coverage and content, resultant from their
owners' indexation philosophies. In light of these differences, we explore
here, using a citation network analysis and assessment of normalised citation
impact of "duplicate" publications, whether the three databases offer
structurally different perspectives of the bibliometric landscape or if they
are essentially homogenous substitutes. Our citation network analysis of core
and exclusive 2016-2018 publications revealed a large set of core publications
indexed in all three databases that are highly self-referential. In comparison,
each database selected a set of exclusive publications that appeared to hold
similarly low levels of relevance to the core set and to one another, with
slightly more internal communication between exclusive publications in Scopus
and Dimensions than WoS. Our comparison of normalised citations for 41,848
publications indexed in all three databases found that German sectors were
valuated as more impactful in Scopus and Dimensions compared to WoS,
particularly for sectors with an applied research focus. We conclude that the
databases do present structurally different perspectives, although Scopus and
Dimensions with their additional circle of applied research vary more from the
more base research-focused WoS than they do from one another.


Abstract:
  We consider the $B^0_s \to \mu^{+} \mu^{-} \gamma$ effective lifetime, and
the related CP-phase sensitive quantity $A_{\Delta\Gamma_s}^{\mu \mu \gamma}$,
as a way to obtain qualitatively new insights on the current $B$-decay
discrepancies. Through a fit comparing pre- to post-Moriond-2021 data we
identify a few theory benchmark scenarios addressing these discrepancies, and
featuring large CP violation in addition. We then explore the possibility of
telling apart these scenarios with $A_{\Delta\Gamma_s}^{\mu \mu \gamma}$, once
resonance-modeling and form-factor uncertainties are taken into account. We do
so in both regions of low and high invariant di-lepton mass-squared $q^2$. For
low $q^2$, we show how to shape the integration range in order to reduce the
impact of the $\phi$-resonance modelling on the $A_{\Delta\Gamma_s}^{\mu \mu
\gamma}$ prediction. For high $q^2$, we find that the corresponding pollution
from broad-charmonium resonances has a surprisingly small effect on
$A_{\Delta\Gamma_s}^{\mu \mu \gamma}$. This is due to a number of
cancellations, that can be traced back to the complete dominance of
semi-leptonic operator contributions for high $q^2$ -- at variance with low
$q^2$ -- and to $A_{\Delta\Gamma_s}^{\mu \mu \gamma}$ behaving like a
ratio-of-amplitudes observable. Our study suggests that
$A_{\Delta\Gamma_s}^{\mu \mu \gamma}$ is -- especially at high $q^2$ -- a
potentially valuable probe of short-distance CP-violating effects in the very
same Wilson coefficients that are associated to current $b \to s$
discrepancies. Its discriminating power, however, relies on progress in
form-factor uncertainties. Interestingly, high $q^2$ is the region where $B^0_s
\to \mu^{+} \mu^{-} \gamma$ is already being accessed experimentally, and the
region where form factors are more accessible through non-perturbative QCD
methods.


Abstract:
  The electromagnetic process $e^{+}e^{-}\to p\bar{p}$ is studied with the
initial-state-radiation technique using 7.5 fb$^{-1}$ of data collected by the
BESIII experiment at seven energy points from 3.773 to 4.600 GeV. The Born
cross section and the effective form factor of the proton are measured from the
production threshold to 3.0 GeV/$c^{2}$ using the $p\bar{p}$ invariant-mass
spectrum. The ratio of electric and magnetic form factors of the proton is
determined from the analysis of the proton-helicity angular distribution.


Abstract:
  Past and current research has typically focused on ensuring that search
technology for the classroom serves children. In this paper, we argue for the
need to broaden the research focus to include teachers and how search
technology can aid them. In particular, we share how furnishing a
behind-the-scenes portal for teachers can empower them by providing a window
into the spelling, writing, and concept connection skills of their students.


Abstract:
  In this paper, we argue that modern pre-integration methods for inertial
measurement units (IMUs) are accurate enough to ignore the drift for short time
intervals. This allows us to consider a simplified camera model, which in turn
admits further intrinsic calibration. We develop the first-ever solver to
jointly solve the relative pose problem with unknown and equal focal length and
radial distortion profile while utilizing the IMU data. Furthermore, we show
significant speed-up compared to state-of-the-art algorithms, with small or
negligible loss in accuracy for partially calibrated setups. The proposed
algorithms are tested on both synthetic and real data, where the latter is
focused on navigation using unmanned aerial vehicles (UAVs). We evaluate the
proposed solvers on different commercially available low-cost UAVs, and
demonstrate that the novel assumption on IMU drift is feasible in real-life
applications. The extended intrinsic auto-calibration enables us to use
distorted input images, making tedious calibration processes obsolete, compared
to current state-of-the-art methods.


Abstract:
  Routine ultraviolet imaging of the Sun's upper atmosphere shows the
spectacular manifestation of solar activity; yet we remain blind to its main
driver, the magnetic field. Here we report unprecedented spectropolarimetric
observations of an active region plage and its surrounding enhanced network,
showing circular polarization in ultraviolet (Mg II $h$ & $k$ and Mn I) and
visible (Fe I) lines. We infer the longitudinal magnetic field from the
photosphere to the very upper chromosphere. At the top of the plage
chromosphere the field strengths reach more than 300 gauss, strongly correlated
with the Mg II $k$ line core intensity and the electron pressure. This unique
mapping shows how the magnetic field couples the different atmospheric layers
and reveals the magnetic origin of the heating in the plage chromosphere.


Abstract:
  We present a theoretical investigation of electron states hosted by magnetic
domain walls on the 3D topological insulator surface. The consideration
includes the domain walls with distinct vectorial and spatial textures. The
study is carried out on the basis of the Hamiltonian for quasi-relativistic
fermions by using a continual approach and tight-binding calculations. We
derive the spectral characteristics and spatial localization of the the
one-dimensional low-energy states appearing at the domain walls. The antiphase
domain walls are shown to generate the topologically protected chiral states
with linear dispersion, the group velocity and spin-polarization direction of
which depend on an easy axis orientation. In the case of an easy plane
anisotropy, we predict a realization of a dispersionless state, flat band in
the energy spectrum, that is spin-polarized along the surface normal.
Modification of the surface states in the multi-domain case, which is
approximated by a periodic set of domain walls, is described as well. We find
that the magnetic domain walls with complex internal texture, such as
N\'eel-like or Bloch-like walls, also host the topological states, although
their spectrum and spin structure can be changed compared with the sharp wall
case.


Abstract:
  We present the discovery of J22564-5910, a new type of hot subdwarf (sdB)
which shows evidence of gas present in the system and has shallow, multi-peaked
hydrogen and helium lines which vary in shape over time. All observational
evidence points towards J22564-5910 being observed very shortly after the
merger phase that formed it. Using high-resolution, high signal-to-noise
spectroscopy, combined with multi-band photometry, Gaia astrometry, and TESS
light curves, we aim to interpret these unusual spectral features. The
photometry, spectra and light curves are all analyzed, and their results are
combined in order to support our interpretation of the observations: the likely
presence of a magnetic field combined with gas features around the sdB. Based
on the triple-peaked H lines, the magnetic field strength is estimated and, by
using the shellspec code, qualitative models of gas configurations are fitted
to the observations. All observations can either be explained by a magnetic
field of ~650 kG which enables the formation of a centrifugal magnetosphere, or
a non-magnetic hot subdwarf surrounded by a circumstellar gas disk/torus. Both
scenarios are not mutually exclusive and both can be explained by a recent
merger. J22564-5910 is the first object of its kind. It is a rapidly spinning
sdB with gas still present in the system. It is the first post-merger star
observed this early after the merger event, and as such is very valuable system
to test merger theories. If the magnetic field can be confirmed, it is not only
the first magnetic sdB, but it hosts the strongest magnetic field ever found in
a pre-white dwarf object. Thus, it could represent the long-sought for
immediate ancestor of strongly magnetic WDs.


Abstract:
  In this paper we study the role of planarity in generalized scattering
amplitudes, through several closely interacting structures in combinatorics,
algebraic and tropical geometry.
  The generalized biadjoint scalar amplitude, introduced recently by
Cachazo-Early-Guevara-Mizera (CEGM), is a rational function of homogeneous
degree $-(k-1)(n-k-1)$ in $\binom{n}{k}-n$ independent variables; its poles can
be constructed directly from the rays of the positive tropical Grassmannian.
  We introduce for each pair of integers $(k,n)$ with $2\le k\le n-2$ a system
of generalized positive roots which arises as a specialization of the planar
basis of kinematic invariants. We prove that the higher root polytope
$\mathcal{R}^{(k)}_{n-k}$ has volume the k-dimensional Catalan number
$C^{(k)}_{n-k}$, via a flag unimodular triangulation into simplices, in
bijection with noncrossing collections of $k$-element subsets. We also give a
bijection between certain positroidal subdivisions, called tripods, of the
hypersimplex $\Delta_{3,n}$ and noncrossing pairs of 3-element subsets that are
not weakly separated.
  We show that the facets of the Planar Kinematics (PK) polytope, introduced
recently by Cachazo and the author, are exactly the $\binom{n}{k}-n$
generalized positive roots. We show that the PK specialization of the
generalized biadjoint amplitude evaluates to $C^{(k)}_{n-k}$.
  Looking forward, we give defining equations and conjecture explicit solutions
using $(\mathbb{CP}^{n-k-1})^{\times (k-1)}$ via a notion of compatibility
degree for noncrossing collections, for a two parameter family of generalized
worldsheet associahedra $\mathcal{W}^+_{k,n}$. These specialize when $k=2$ to a
certain dihedrally invariant partial compactification of the configuration
space $M_{0,n}$ of $n$ distinct points in $\mathbb{CP}^{1}$. Many detailed
examples are given throughout to motivate future work.


Abstract:
  We study the stationary reflected Brownian motion in a non-convex wedge,
which, compared to its convex analogue model, has been much rarely analyzed in
the probabilistic literature. We prove that its stationary distribution can be
found by solving a two dimensional vector boundary value problem (BVP) on a
single curve for the associated Laplace transforms. The reduction to this kind
of vector BVP seems to be new in the literature. As a matter of comparison, one
single boundary condition is sufficient in the convex case. When the parameters
of the model (drift, reflection angles and covariance matrix) are symmetric
with respect to the bisector line of the cone, the model is reducible to a
standard reflected Brownian motion in a convex cone. Finally, we construct a
one-parameter family of distributions, which surprisingly provides, for any
wedge (convex or not), one particular example of stationary distribution of a
reflected Brownian motion.


Abstract:
  Multicopter swarms with decentralized structure possess the nature of
flexibility and robustness, while efficient spatial-temporal trajectory
planning still remains a challenge. This report introduces decentralized
spatial-temporal trajectory planning, which puts a well-formed trajectory
representation named MINCO into multi-agent scenarios. Our method ensures
high-quality local planning for each agent subject to any constraint from
either the coordination of the swarm or safety requirements in cluttered
environments. Then, the local trajectory generation is formulated as an
unconstrained optimization problem that is efficiently solved in milliseconds.
Moreover, a decentralized asynchronous mechanism is designed to trigger the
local planning for each agent. A systematic solution is presented with detailed
descriptions of careful engineering considerations. Extensive benchmarks and
indoor/outdoor experiments validate its wide applicability and high quality.
Our software will be released for the reference of the community.


Abstract:
  We report on detailed in-situ distributed temperature measurements inside a
high power fiber amplifier. The deducted thermal load and the TMI-threshold of
a commercial LMA fiber with 25 micron core and 400 micron cladding was measured
at various seed wavelengths. By matching these results with detailed
simulations we show that photodarkening has a negligible impact on the thermal
load and, therefore, on the TMI threshold in this fiber.


Abstract:
  Images captured under low-light conditions manifest poor visibility, lack
contrast and color vividness. Compared to conventional approaches, deep
convolutional neural networks (CNNs) perform well in enhancing images. However,
being solely reliant on confined fixed primitives to model dependencies,
existing data-driven deep models do not exploit the contexts at various spatial
scales to address low-light image enhancement. These contexts can be crucial
towards inferring several image enhancement tasks, e.g., local and global
contrast, brightness and color corrections; which requires cues from both local
and global spatial extent. To this end, we introduce a context-aware deep
network for low-light image enhancement. First, it features a global context
module that models spatial correlations to find complementary cues over full
spatial domain. Second, it introduces a dense residual block that captures
local context with a relatively large receptive field. We evaluate the proposed
approach using three challenging datasets: MIT-Adobe FiveK, LoL, and SID. On
all these datasets, our method performs favorably against the state-of-the-arts
in terms of standard image fidelity metrics. In particular, compared to the
best performing method on the MIT-Adobe FiveK dataset, our algorithm improves
PSNR from 23.04 dB to 24.45 dB.


Abstract:
  In this paper, a new randomized response technique aimed at protecting
respondents' privacy is proposed. It is designed for estimating the population
total, or the population mean, of a quantitative characteristic. It provides
a~high degree of protection to the interviewed individuals, hence it may be
favorably perceived by them and increase their willingness to cooperate.
Instead of revealing the true value of the characteristic under investigation,
a respondent only states whether the value is greater (or smaller) than
a~number which is selected by him/her at random, and is unknown to the
interviewer. For each respondent this number, a sort of individual threshold,
is generated as a pseudorandom number from the uniform distribution. Further,
two modifications of the proposed technique are presented. The first
modification assumes that the interviewer also knows the generated random
number. The second modification deals with the issue that, for certain
variables, such as income, it may be embarrassing for the respondents to report
either high or low values. Thus, depending on the value of the pseudorandom
lower bound, the respondent is asked different questions to avoid being
embarrassed. The suggested approach is applied in detail to the simple random
sampling without replacement, but it can also be applied to many currently used
sampling schemes, including cluster sampling, two-stage sampling, etc. Results
of simulations illustrate the behavior of the proposed procedure.


Abstract:
  We present a slowly rotating generalization of a black hole modeled by a Solv
horizon geometry, in five dimensional General Relativity. A separable ansatz
compatible with Einstein equations is proposed, which is integrated in terms of
hypergeometric and exponential functions. The solution highly simplifies after
requiring to maintain the same asymptotic behaviour as the non-rotating black
hole and to have finite, non-vanishing charges. We compute the charges of the
solution and also comment on its importance for studying the phase space of the
theory in this sector.