Abstract:
  When the available information is noisy zeroth-order (ZO) oracle, stochastic
approximation methods are popular for estimating the root of the multivariate
gradient equation. Inspired by the Stein's identity, this work establishes a
novel Hessian approximation scheme. We compare it alongside with second-order
simultaneous perturbation stochastic approximation (2SPSA). On the basis of the
almost sure convergence and the same convergence rate, 2SPSA requires four ZO
queries, while ours requires three ZO queries. Moreover, 2SPSA requires two
statistically independent perturbations and two differencing stepsizes, while
ours requires generating one perturbation vector only and tuning one
differencing stepsize only. Besides, the weighting mechanism for the Hessian
estimate is generalized and the smoothness restriction on the loss function is
relaxed compared to 2SPSA. Finally, we present numerical support for the
reduced per-iteration ZO query complexity.


Abstract:
  This article presents a state feedback control design strategy for the
stabilization of a vehicle along a reference collision avoidance maneuver. The
stabilization of the vehicle is achieved through a combination of steering,
acceleration and braking. A Linear Parameter-Varying (LPV) model is obtained
from the linearization of a non-linear model along the reference trajectory. A
robust state feedback control law is computed for the LPV model. Finally,
simulation results illustrate the stabilization of the vehicle along the
reference trajectory.


Abstract:
  It was shown that when one disposes of a parametric information of the
truncation distribution, the semiparametric estimator of the distribution
function for truncated data (Wang, 1989) is more efficient than the
nonparametric one. On the basis of this estimation method, we derive an
estimator for the tail index of Pareto-type distributions that are randomly
right-truncated and establish its consistency and asymptotic normality. The
finite sample behavior of the proposed estimator is carried out by simulation
study. We point out that, in terms of both bias and root of the mean squared
error, our estimator performs better than those based on nonparametric
estimation methods. An application to a real dataset of induction times of AIDS
diseases is given as well.


Abstract:
  Detached circumplanetary disks are unstable to tilting as a result of the
stellar tidal potential. We examine how a tilted circumplanetary disk affects
the evolution of the spin axis of an oblate planet. The disk is evolved using
time-dependent equations for linear wave-like warp evolution, including terms
representing the effect of the tidal potential and planetary oblateness. For a
disk with a sufficiently large mass, we find that the planet spin quickly
aligns to the misaligned disk. The tilt of the planetary spin axis then
increases on the same timescale as the disk. This can be an efficient mechanism
for generating primordial obliquity in giant planets. We suggest that directly
imaged exoplanets at large orbital radii, where the disk mass criterion is more
likely to be satisfied, could have significant obliquities due to the tilt
instability of their circumplanetary disks.


Abstract:
  A sequential recommender system aims to recommend attractive items to users
based on behaviour patterns. The predominant sequential recommendation models
are based on natural language processing models, such as the gated recurrent
unit, that embed items in some defined space and grasp the user's long-term and
short-term preferences based on the item embeddings. However, these approaches
lack fundamental insight into how such models are related to the user's
inherent decision-making process. To provide this insight, we propose a novel
recurrent cell, namely FaNC, from Freudian and Newtonian perspectives. FaNC
divides the user's state into conscious and unconscious states, and the user's
decision process is modelled by Freud's two principles: the pleasure principle
and reality principle. To model the pleasure principle, i.e., free-floating
user's instinct, we place the user's unconscious state and item embeddings in
the same latent space and subject them to Newton's law of gravitation.
Moreover, to recommend items to users, we model the reality principle, i.e.,
balancing the conscious and unconscious states, via a gating function. Based on
extensive experiments on various benchmark datasets, this paper provides
insight into the characteristics of the proposed model. FaNC initiates a new
direction of sequential recommendations at the convergence of psychoanalysis
and recommender systems.


Abstract:
  We present a novel generating function (GF) method for the self-condensing
vinyl polymerization (SCVP) system with any initial distribution of preexisted
polymers. Such a method was proven to be especially useful to investigate the
semi-batch SCVP system allowing a sequence of feeding operations during the
polymerization. Consequently, the number-, weight-, and z-average molecular
weights as well as polydispersity index of hyperbranched polymers can be
explicitly given, which are determined by predetermined feeding details and
conversions in each polymerization step. These analytical results are further
confirmed by the corresponding Monte Carlo simulation. Therefore the present GF
method has provided a unified treatment on the semi-batch SCVP system.
Accordingly, hyperbranched polymers with desired properties can be prepared by
designing feeding details and presetting conversions at each step based on the
present GF method.


Abstract:
  An inverse problem for a stationary heat transfer process is studied for a
totally isolated bar on its lateral surface, of negligible diameter, made up of
two consecutive sections of different, isotropic and homogeneous materials. At
the left boundary, a Dirichlet type condition is imposed that represents a
constant temperature source while a Robin type condition that models the heat
dissipation by convection is considered at the right one. Many articles in the
literature focus on thermal and stress analysis at the interface but no one is
dedicated to the estimation of the contact point location between the two
materials. In this work, it is assumed that the interface position is unknown.
A technique to determine it from a unique noisy flow measurement at the right
boundary is introduced. Necessary and sufficient conditions are derived in
order to obtain the estimation of the interface point from a heat flux measured
at the right boundary. Numerical solutions are obtained together with an
expression for the estimation error. Moreover, an elasticity analysis is
included to study the influence of data errors. The results show that our
approach is useful for determining the location of the materials interface.


Abstract:
  Fixed-order perturbative calculations of fiducial cross sections for two-body
decay processes at colliders show disturbing sensitivity to unphysically low
momentum scales and, in the case of $H\to \gamma \gamma$ in gluon fusion, poor
convergence. Such problems have their origins in an interplay between the
behaviour of standard experimental cuts at small transverse momenta ($p_t$) and
logarithmic perturbative contributions. We illustrate how this interplay leads
to a factorially divergent structure in the perturbative series that sets in
already from the first orders. We propose simple modifications of fiducial cuts
to eliminate their key incriminating characteristic, a linear dependence of the
acceptance on the Higgs or $Z$-boson $p_t$, replacing it with quadratic
dependence. This brings major improvements in the behaviour of the perturbative
expansion. More elaborate cuts can achieve an acceptance that is independent of
the Higgs $p_t$ at low $p_t$, with a variety of consequent advantages.


Abstract:
  Power laws have been found to describe a wide variety of natural (physical,
biological, astronomic, meteorological, geological) and man-made (social,
financial, computational) phenomena over a wide range of magnitudes, although
their underlying mechanisms are not always clear. In statistics, power law
distribution is often found to fit data exceptionally well when the normal
(Gaussian) distribution fails. Nevertheless, predicting power law phenomena is
notoriously difficult because some of its idiosyncratic properties such as lack
of well-defined average value, and potentially unbounded variance. TPL
(Taylor's power law), a power law first discovered to characterize the spatial
and/or temporal distribution of biological populations and recently extended to
describe the spatiotemporal heterogeneities (distributions) of human
microbiomes and other natural and artificial systems such as fitness
distribution in computational (artificial) intelligence. The power law with
exponential cutoff (PLEC) is a variant of power-law function that tapers off
the exponential growth of power-law function ultimately and can be particularly
useful for certain predictive problems such as biodiversity estimation and
turning-point prediction for COVID-19 infection/fatality. Here, we propose
coupling (integration) of TPL and PLEC to offer improved prediction quality of
certain power-law phenomena. The coupling takes advantages of variance
prediction using TPL and the asymptote estimation using PLEC and delivers
confidence interval for the asymptote. We demonstrate the integrated approach
to the estimation of potential (dark) biodiversity and turning point of
COVID-19 fatality. We expect this integrative approach should have wide
applications given the duel relationship between power law and normal
statistical distributions.


Abstract:
  The Central Molecular Zone (CMZ), a $\sim$200 pc sized region around the
Galactic Centre, is peculiar in that it shows a star formation rate (SFR) that
is suppressed with respect to the available dense gas. To study the SFR in the
CMZ, young stellar objects (YSOs) can be investigated. Here we present radio
observations of 334 2.2 $\mu$m infrared sources that have been identified as
YSO candidates. Our goal is to investigate the presence of centimetre
wavelength radio continuum counterparts to this sample of YSO candidates which
we use to constrain the current SFR in the CMZ. As part of the GLOSTAR survey,
D-configuration VLA data was obtained for the Galactic Centre, covering
-2$^{\circ}<l<$2$^{\circ}$ and -1$^{\circ}<b<$1$^{\circ}$, with a frequency
coverage of 4-8 GHz. We matched YSOs with radio continuum sources based on
selection criteria and classified these radio sources as potential HII regions
and determined their physical properties. Of the 334 YSO candidates, we found
35 with radio continuum counterparts. We find that 94 YSOs are associated with
dense dust condensations identified in the 870 $\mu$m ATLASGAL survey, of which
14 have a GLOSTAR counterpart. Of the 35 YSOs with radio counterparts, 11 are
confirmed as HII regions, based on their spectral indices and the literature.
We estimated their Lyman continuum photon flux in order to estimate the mass of
the ionising star. Combining these with known sources, the present-day SFR in
the CMZ is calculated to be $\sim$0.068 M$_{\odot}$ yr$^{-1}$, which is
$\sim$6.8$\%$ of the Galactic SFR. Candidate YSOs that lack radio counterparts
may not have yet evolved to the stage of exhibiting an HII region or,
conversely, are older and have dispersed their natal clouds. Since many lack
dust emission, the latter is more likely. Our SFR estimate in the CMZ is in
agreement with previous estimates in the literature.


Abstract:
  In this paper, we show that Orlicz--Sobolev spaces $W^{1,\phi}(\Omega)$ can
be characterized with the ACL- and ACC-characterizations. ACL stands for
absolutely continuous on lines and ACC for absolutely continuous on curves. Our
results hold under the assumptions that $C^1(\Omega)$ functions are dense in
$W^{1,\phi}(\Omega)$, and $\phi(x,\beta) \geq 1$ for some $\beta > 0$ and
almost every $x \in \Omega$. The results are new even in the special cases of
Orlicz and double phase growth.


Abstract:
  Dicke superradiance, i.e., the enhanced spontaneous emission of coherent
radiation, is often attributed to radiation emitted by synchronized dipoles
coherently oscillating in phase. At the same time, Dicke derived superradiance
assuming atoms in entangled Dicke states which do not display any dipole
moment. To shed light on this apparent paradox, we study the intensity
distribution arising from two identical two-level atoms prepared either in an
entangled Dicke state or in a separable atomic state with non-vanishing dipole
moment. We find that the two configurations produce similar far field intensity
patterns, however, stemming from fundamentally distinct types of coherence:
while in the second case the atoms display coherence among the individual
particles leading to Young-type interference as known from classical dipoles,
atoms in Dicke states possess collective coherence leading to enhanced
spontaneous emission. This demonstrates that the radiation generated by
synchronized dipoles and Dicke superradiance are fundamentally distinct
phenomena and have to be interpreted in different ways.


Abstract:
  Amino acids are building-blocks of proteins, basic constituents of all
organisms and essential to life on Earth. They are present in carbonaceous
chondrite meteorites and comets, but their origin is still unknown. Formation
of amino acids in the interstellar medium is posible via specific gas-phase
reactions in dark clouds, however sensitive radiosearches at millimeter
wavelengths have not revealed their existence yet. The mid-IR vibrational
spectra of amino acids provides an alternative path for their identification.
We present Spitzer spectroscopic observations in the star-forming region IC 348
of the Perseus Molecular Cloud showing the detection of mid-IR emission lines
consistent with the most intense laboratory bands of the three aromatic amino
acids, tyrosine, phenylalanine and tryptophan and the aliphatic amino acids
isoleucine and glycine. Estimates of column densities give values 10-100 times
higher for isoleucine and glycine than for the aromatic amino acids as in some
meteorites. The strongest bands of each amino acid are also found in the
combined spectrum of >30 interstellar locations in diverse star-forming regions
supporting the suggestion that amino acids are widely spread in interstellar
space. Future mid-IR searches for proteinogenic amino acids in protostars,
protoplanetary disks and in the interstellar medium will be key to establish an
exogenous origin of meteoritic amino acids and to understand how the prebiotic
conditions for life were set in the early Earth.


Abstract:
  In an attempt to better understand structural benefits and generalization
power of deep neural networks, we firstly present a novel graph theoretical
formulation of neural network models, including fully connected, residual
network (ResNet) and densely connected networks (DenseNet). Secondly, we extend
the error analysis of the population risk for two layer network
\cite{ew2019prioriTwo} and ResNet \cite{e2019prioriRes} to DenseNet, and show
further that for neural networks satisfying certain mild conditions, similar
estimates can be obtained. These estimates are a priori in nature since they
depend sorely on the information prior to the training process, in particular,
the bounds for the estimation errors are independent of the input dimension.


Abstract:
  Defensive deception techniques have emerged as a promising proactive defense
mechanism to mislead an attacker and thereby achieve attack failure. However,
most game-theoretic defensive deception approaches have assumed that players
maintain consistent views under uncertainty. They do not consider players'
possible, subjective beliefs formed due to asymmetric information given to
them. In this work, we formulate a hypergame between an attacker and a defender
where they can interpret the same game differently and accordingly choose their
best strategy based on their respective beliefs. This gives a chance for
defensive deception strategies to manipulate an attacker's belief, which is the
key to the attacker's decision making. We consider advanced persistent threat
(APT) attacks, which perform multiple attacks in the stages of the cyber kill
chain where both the attacker and the defender aim to select optimal strategies
based on their beliefs. Through extensive simulation experiments, we
demonstrated how effectively the defender can leverage defensive deception
techniques while dealing with multi-staged APT attacks in a hypergame in which
the imperfect information is reflected based on perceived uncertainty, cost,
and expected utilities of both attacker and defender, the system lifetime
(i.e., mean time to security failure), and improved false positive rates in
detecting attackers.


Abstract:
  We use a simulation-based modelling approach to analyse the anisotropic
clustering of the BOSS LOWZ sample over the radial range $0.4 \, h^{-1} \,
\mathrm{Mpc}$ to $63 \, h^{-1} \, \mathrm{Mpc}$, significantly extending what
is possible with a purely analytic modelling framework. Our full-scale analysis
yields constraints on the growth of structure that are a factor of two more
stringent than any other study on large scales at similar redshifts. We infer
$f \sigma_8 = 0.471 \pm 0.024$ at $z \approx 0.25$, and $f \sigma_8 = 0.431 \pm
0.025$ at $z \approx 0.40$; the corresponding $\Lambda$CDM predictions of the
Planck CMB analysis are $0.470 \pm 0.006$ and $0.476 \pm 0.005$, respectively.
Our results are thus consistent with Planck, but also follow the trend seen in
previous low-redshift measurements of $f \sigma_8$ falling slightly below the
$\Lambda$CDM+CMB prediction. We find that small and large radial scales yield
mutually consistent values of $f \sigma_8$, but there are $1-2.5 \sigma$ hints
of small scales ($< 10 \, h^{-1} \, \mathrm{Mpc}$) preferring lower values for
$f \sigma_8$ relative to larger scales. We analyse the constraining power of
the full range of radial scales, finding that most of the multipole information
about $f\sigma_8$ is contained in the scales $2 \, h^{-1} \, \mathrm{Mpc}
\lesssim s \lesssim 20 \, h^{-1} \, \mathrm{Mpc}$. Evidently, once the
cosmological information of the quasi-to-nonlinear regime has been harvested,
large-scale modes contain only modest additional information about structure
growth. Finally, we compare predictions for the galaxy-galaxy lensing amplitude
of the two samples against measurements from SDSS and assess the lensing-is-low
effect in light of our findings.


Abstract:
  This paper presents a systematic study the effects of compression on
hyperspectral pixel classification task. We use five dimensionality reduction
methods -- PCA, KPCA, ICA, AE, and DAE -- to compress 301-dimensional
hyperspectral pixels. Compressed pixels are subsequently used to perform
pixel-based classifications. Pixel classification accuracies together with
compression method, compression rates, and reconstruction errors provide a new
lens to study the suitability of a compression method for the task of
pixel-based classification. We use three high-resolution hyperspectral image
datasets, representing three common landscape units (i.e. urban, transitional
suburban, and forests) collected by the Remote Sensing and Spatial Ecosystem
Modeling laboratory of the University of Toronto. We found that PCA, KPCA, and
ICA post greater signal reconstruction capability; however, when compression
rate is more than 90\% those methods showed lower classification scores. AE and
DAE methods post better classification accuracy at 95\% compression rate,
however decreasing again at 97\%, suggesting a sweet-spot at the 95\% mark. Our
results demonstrate that the choice of a compression method with the
compression rate are important considerations when designing a hyperspectral
image classification pipeline.


Abstract:
  Conversational recommender systems (CRSs) have revolutionized the
conventional recommendation paradigm by embracing dialogue agents to
dynamically capture the fine-grained user preference. In a typical
conversational recommendation scenario, a CRS firstly generates questions to
let the user clarify her/his demands and then makes suitable recommendations.
Hence, the ability to generate suitable clarifying questions is the key to
timely tracing users' dynamic preferences and achieving successful
recommendations. However, existing CRSs fall short in asking high-quality
questions because: (1) system-generated responses heavily depends on the
performance of the dialogue policy agent, which has to be trained with huge
conversation corpus to cover all circumstances; and (2) current CRSs cannot
fully utilize the learned latent user profiles for generating appropriate and
personalized responses.
  To mitigate these issues, we propose the Knowledge-Based Question Generation
System (KBQG), a novel framework for conversational recommendation. Distinct
from previous conversational recommender systems, KBQG models a user's
preference in a finer granularity by identifying the most relevant relations
from a structured knowledge graph (KG). Conditioned on the varied importance of
different relations, the generated clarifying questions could perform better in
impelling users to provide more details on their preferences. Finially,
accurate recommendations can be generated in fewer conversational turns.
Furthermore, the proposed KBQG outperforms all baselines in our experiments on
two real-world datasets.


Abstract:
  The faithful irreducible $*$-representations of the $C^*$-algebra of the
quantum symplectic sphere $S_q^{4n-1}, n\geq 2$, have been investigated by
D'Andrea and Landi. They proved that the first $n-1$ generators are all zero
inside $C^*(S_q^{4n-1})$, for $n\geq 2$. The result is a generalisation of the
case where $n=2$, which was shown by Mikkelsen and Szyma\'nski.
  We will show that $C^*(S_q^{4n-1}), n\geq 2$ is isomorphic to a graph
$C^*$-algebra. From here it follows that $C^*(S_q^{4n-1})$ is isomorphic to the
quantum $(2(n+1)-1)$-sphere by Vaksman and Soibelman.


Abstract:
  Analyses based on quantum metrology have shown that the ability to localize
the positions of two incoherent point sources can be significantly enhanced
through the use of mode sorting. Here we theoretically and experimentally
investigate the effect of partial coherence on the sub-diffraction limit
localization of two sources based on parity sorting. With the prior information
of a negative and real-valued degree of coherence, higher Fisher information is
obtained than that for the incoherent case. Our results pave the way to
clarifying the role of coherence in quantum limited metrology.


Abstract:
  We analytically study the Fermi-gas formulation of sphere correlation
functions of the Coulomb branch operators for 3d $\mathcal{N}=4$ ADHM theory
with a gauge group $U(N)$, an adjoint hypermultiplet and $l$ hypermultiplets
which can describe a stack of $N$ M2-branes at $A_{l-1}$ singularities. We find
that the leading coefficients of the perturbative grand canonical correlation
functions are invariant under a hidden triality symmetry conjectured from the
twisted M-theory. The triality symmetry also helps us to fix the
next-to-leading corrections analytically.


Abstract:
  Three-dimensional face dense alignment and reconstruction in the wild is a
challenging problem as partial facial information is commonly missing in
occluded and large pose face images. Large head pose variations also increase
the solution space and make the modeling more difficult. Our key idea is to
model occlusion and pose to decompose this challenging task into several
relatively more manageable subtasks. To this end, we propose an end-to-end
framework, termed as Self-aligned Dual face Regression Network (SADRNet), which
predicts a pose-dependent face, a pose-independent face. They are combined by
an occlusion-aware self-alignment to generate the final 3D face. Extensive
experiments on two popular benchmarks, AFLW2000-3D and Florence, demonstrate
that the proposed method achieves significant superior performance over
existing state-of-the-art methods.


Abstract:
  Principal component analysis has been used to reduce dimensionality of
datasets for a long time. In this paper, we will demonstrate that in mode
detection the components of smallest variance, the pettiest components, are
more important. We prove that when the data follows a multivariate normal
distribution, by implementing "pettiest component analysis" when the data is
normally distributed, we obtain boxes of optimal size in the sense that their
size is minimal over all possible boxes with the same number of dimensions and
given probability. We illustrate our result with a simulation revealing that
pettiest component analysis works better than its competitors.


Abstract:
  We prove that any minimal Lagrangian diffeomorphism between two closed
spherical surfaces with cone singularities is an isometry, without any
assumption on the multiangles of the two surfaces. As an application, we show
that every branched immersion of a closed surface of constant positive Gaussian
curvature in Euclidean three-space is a branched covering onto a round sphere,
thus generalizing the classical rigidity theorem of Liebmann to branched
immersions.


Abstract:
  The quasi-steady-state approximation is widely used to develop simplified
deterministic or stochastic models of enzyme catalyzed reactions. In
deterministic models, the quasi-steady-state approximation can be
mathematically justified from singular perturbation theory. For several closed
enzymatic reactions, the homologous extension of the quasi-steady-state
approximation to the stochastic regime, known as the stochastic
quasi-steady-state approximation, has been shown to be accurate under the
analogous conditions that permit the quasi-steady-state reduction of the
deterministic counterpart. However, it was recently demonstrated that the
extension of the stochastic quasi-steady-state approximation to an open
Michaelis--Menten reaction mechanism is only valid under a condition that is
far more restrictive than the qualifier that ensures the validity of its
corresponding deterministic quasi-steady-state approximation. In this paper, we
suggest a possible explanation for this discrepancy from the lens of geometric
singular perturbation theory. In so doing, we illustrate a misconception in the
application of the quasi-steady-state approximation: timescale separation does
not imply singular perturbation.


Abstract:
  In wireless network communication environments, Spectral Efficiency (SE) and
Energy Efficiency (EE) are among the major indicators used for evaluating
network performance. However, given the high demand for data rate services and
the exponential growth of energy consumption, SE and EE continue to elicit
increasing attention in academia and industries. Consequently, a study of the
trade-off between these metrics is imperative. In contrast with existing works,
this study proposes an efficient SE and EE trade-off algorithm for saving
energy in downlink Long Term Evolution (LTE) networks to concurrently optimize
SE and EE while considering battery life at the Base Station (BS). The scheme
is formulated as a Multi-objective Optimization Problem (MOP) and its Pareto
optimal solution is examined. In contrast with other algorithms that prolong
battery life by considering the idle state of a BS, thereby increasing average
delay and energy consumption, the proposed algorithm prolongs battery life by
adjusting the initial and final states of a BS to minimize the average delay
and the energy consumption. Similarly, the use of an omni-directional antenna
to spread radio signals to the user equipment in all directions causes high
interference and low spatial reuse. We propose using a directional antenna
instead of an omni-directional antenna by transmitting signals in one direction
which results in no or low interference and high spatial reuse. The proposed
scheme has been extensively evaluated through simulation, where simulation
results prove that the proposed scheme is efficiently able to decrease the
average response delay, improve SE, and minimize energy consumption.


Abstract:
  In this paper we present an open database for the development of detection
and enhancement algorithms of speech transmitted over HF radio channels. It
consists of audio samples recorded by various receivers at different locations
across Europe, all monitoring the same single-sideband modulated transmission
from a base station in Paderborn, Germany. Transmitted and received speech
signals are precisely time aligned to offer parallel data for supervised
training of deep learning based detection and enhancement algorithms. For the
task of speech activity detection two exemplary baseline systems are presented,
one based on statistical methods employing a multi-stage Wiener filter with
minimum statistics noise floor estimation, and the other relying on a deep
learning approach.


Abstract:
  We exhibit a Quillen equivalence between two model categories encoding the
homotopy theory of stratified spaces : the model category of filtered
simplicial sets, and that of filtered spaces. Additionally, we introduce a new
class of filtered spaces, that of vertical filtered CW-complexes, providing a
nice model for the homotopy category of stratified spaces.


Abstract:
  Graphically-rich applications such as games are ubiquitous with attractive
visual effects of Graphical User Interface (GUI) that offers a bridge between
software applications and end-users. However, various types of graphical
glitches may arise from such GUI complexity and have become one of the main
component of software compatibility issues. Our study on bug reports from game
development teams in NetEase Inc. indicates that graphical glitches frequently
occur during the GUI rendering and severely degrade the quality of
graphically-rich applications such as video games. Existing automated testing
techniques for such applications focus mainly on generating various GUI test
sequences and check whether the test sequences can cause crashes. These
techniques require constant human attention to captures non-crashing bugs such
as bugs causing graphical glitches. In this paper, we present the first step in
automating the test oracle for detecting non-crashing bugs in graphically-rich
applications. Specifically, we propose \texttt{GLIB} based on a code-based data
augmentation technique to detect game GUI glitches. We perform an evaluation of
\texttt{GLIB} on 20 real-world game apps (with bug reports available) and the
result shows that \texttt{GLIB} can achieve 100\% precision and 99.5\% recall
in detecting non-crashing bugs such as game GUI glitches. Practical application
of \texttt{GLIB} on another 14 real-world games (without bug reports) further
demonstrates that \texttt{GLIB} can effectively uncover GUI glitches, with 48
of 53 bugs reported by \texttt{GLIB} having been confirmed and fixed so far.


Abstract:
  Many automatic sequences, such as the Thue-Morse sequence or the
Rudin-Shapiro sequence, have some desirable features of pseudorandomness such
as a large linear complexity and a small well-distribution measure. However,
they also have some disastrous properties in view of certain applications. For
example, the majority of possible binary patterns never appears in automatic
sequences and their correlation measure of order 2 is extremely large.
  Certain subsequences, such as automatic sequences along squares, may keep the
good properties of the original sequence but avoid the bad ones.
  In this survey we investigate properties of pseudorandomness and
non-randomness of automatic sequences and their subsequences and present
results on their behaviour under several measures of pseudorandomness including
linear complexity, correlation measure of order $k$, expansion complexity and
normality. We also mention some analogs for finite fields.


Abstract:
  Micro aerial vehicles are widely being researched and employed due to their
relative low operation costs and high flexibility in various applications. We
study the under-actuated quadrotor perching problem, designing a trajectory
planner and controller which generates feasible trajectories and drives
quadrotors to desired state in state space. This paper proposes a trajectory
generating and tracking method for quadrotor perching that takes the advantages
of reinforcement learning controller and traditional controller. The trained
low-level reinforcement learning controller would manipulate quadrotor toward
the perching point in simulation environment. Once the simulated quadrotor has
successfully perched, the relative trajectory information in simulation will be
sent to tracking controller on real quadrotor and start the actual perching
task. Generating feasible trajectories via the trained reinforcement learning
controller requires less time, and the traditional trajectory tracking
controller could easily be modified to control the quadrotor and mathematically
analysis its stability and robustness. We show that this approach permits the
control structure of trajectories and controllers enabling such aggressive
maneuvers perching on vertical surfaces with high precision.


Abstract:
  Internet services leverage transport protocol port numbers to specify the
source and destination application layer protocols. While using port 0 is not
allowed in most transport protocols, we see a non-negligible share of traffic
using port 0 in the Internet. In this study, we dissect port 0 traffic to infer
its possible origins and causes using five complementing flow-level and
packet-level datasets. We observe 73 GB of port 0 traffic in one week of IXP
traffic, most of which we identify as an artifact of packet fragmentation. In
our packet-level datasets, most traffic is originated from a small number of
hosts and while most of the packets have no payload, a major fraction of
packets containing payload belong to the BitTorrent protocol. Moreover, we find
unique traffic patterns commonly seen in scanning. In addition to analyzing
passive traces, we also conduct an active measurement campaign to study how
different networks react to port 0 traffic. We find an unexpectedly high
response rate for TCP port 0 probes in IPv4, with very low response rates with
other protocol types. Finally, we will be running continuous port 0
measurements and providing the results to the measurement community.


Abstract:
  The performance of voice-controlled systems is usually influenced by accented
speech. To make these systems more robust, the frontend accent recognition (AR)
technologies have received increased attention in recent years. As accent is a
high-level abstract feature that has a profound relationship with the language
knowledge, AR is more challenging than other language-agnostic audio
classification tasks. In this paper, we use an auxiliary automatic speech
recognition (ASR) task to extract language-related phonetic features.
Furthermore, we propose a hybrid structure that incorporates the embeddings of
both a fixed acoustic model and a trainable acoustic model, making the
language-related acoustic feature more robust. We conduct several experiments
on the Accented English Speech Recognition Challenge (AESRC) 2020 dataset. The
results demonstrate that our approach can obtain a 6.57% relative improvement
on the validation set. We also get a 7.28% relative improvement on the final
test set for this competition, showing the merits of the proposed method.


Abstract:
  This presentation reports the first known data from a field mill mounted on a
ground-based robotic platform. The robot's motor and electrostatic charging of
its wheels do not perturb the field mill data, and electric field varies
smoothly whilst the robot is moving. Test measurements under a charged
polystyrene plate are reduced in variability by a factor of 2 compared to a
hand-held field mill. This technology has potential for autonomous measurements
in inaccessible or hazardous environments.


Abstract:
  NLP has a rich history of representing our prior understanding of language in
the form of graphs. Recent work on analyzing contextualized text
representations has focused on hand-designed probe models to understand how and
to what extent do these representations encode a particular linguistic
phenomenon. However, due to the inter-dependence of various phenomena and
randomness of training probe models, detecting how these representations encode
the rich information in these linguistic graphs remains a challenging problem.
In this paper, we propose a new information-theoretic probe, Bird's Eye, which
is a fairly simple probe method for detecting if and how these representations
encode the information in these linguistic graphs. Instead of using classifier
performance, our probe takes an information-theoretic view of probing and
estimates the mutual information between the linguistic graph embedded in a
continuous space and the contextualized word representations. Furthermore, we
also propose an approach to use our probe to investigate localized linguistic
information in the linguistic graphs using perturbation analysis. We call this
probing setup Worm's Eye. Using these probes, we analyze BERT models on their
ability to encode a syntactic and a semantic graph structure, and find that
these models encode to some degree both syntactic as well as semantic
information; albeit syntactic information to a greater extent.


Abstract:
  We employ non-perturbative renormalisation group methods to compute the full
momentum dependence of propagators in quantum gravity in general dimensions. We
disentangle all different graviton and Faddeev-Popov ghost modes and find
qualitative differences in the momentum dependence of their propagators. This
allows us to reconstruct the form factors quadratic in curvature from first
principles, which enter physical observables like scattering cross sections.
The results are qualitatively stable under variations of the gauge fixing
choice.


Abstract:
  Accretion onto black holes is an efficient mechanism in converting the gas
mass-energy into energetic outputs as radiation, wind and jet. Tidal disruption
events, in which stars are tidally torn apart and then accreted onto
supermassive black holes, offer unique opportunities of studying the accretion
physics as well as the wind and jet launching physics across different
accretion regimes. In this review, we systematically describe and discuss the
models that have been developed to study the accretion flows and jets in tidal
disruption events. A good knowledge of these physics is not only needed for
understanding the emissions of the observed events, but also crucial for
probing the general relativistic space-time around black holes and the
demographics of supermassive black holes via tidal disruption events.


Abstract:
  In computer-aided design (CAD), the ability to "reverse engineer" the
modeling steps used to create 3D shapes is a long-sought-after goal. This
process can be decomposed into two sub-problems: converting an input mesh or
point cloud into a boundary representation (or B-rep), and then inferring
modeling operations which construct this B-rep. In this paper, we present a new
system for solving the second sub-problem. Central to our approach is a new
geometric representation: the zone graph. Zones are the set of solid regions
formed by extending all B-Rep faces and partitioning space with them; a zone
graph has these zones as its nodes, with edges denoting geometric adjacencies
between them. Zone graphs allow us to tractably work with industry-standard CAD
operations, unlike prior work using CSG with parametric primitives. We focus on
CAD programs consisting of sketch + extrude + Boolean operations, which are
common in CAD practice. We phrase our problem as search in the space of such
extrusions permitted by the zone graph, and we train a graph neural network to
score potential extrusions in order to accelerate the search. We show that our
approach outperforms an existing CSG inference baseline in terms of geometric
reconstruction accuracy and reconstruction time, while also creating more
plausible modeling sequences.


Abstract:
  The new Coronavirus is spreading rapidly, and it has taken the lives of many
people so far. The virus has destructive effects on the human lung, and early
detection is very important. Deep Convolution neural networks are such powerful
tools in classifying images. Therefore, in this paper, a hybrid approach based
on a deep network is presented. Feature vectors were extracted by applying a
deep convolution neural network on the images, and useful features were
selected by the binary differential meta-heuristic algorithm. These optimized
features were given to the SVM classifier. A database consisting of three
categories of images such as COVID-19, pneumonia, and healthy included in 1092
X-ray samples was considered. The proposed method achieved an accuracy of
99.43%, a sensitivity of 99.16%, and a specificity of 99.57%. Our results
demonstrate that the suggested approach is better than recent studies on
COVID-19 detection with X-ray images.


Abstract:
  In a (parameterized) graph edge modification problem, we are given a graph
$G$, an integer $k$ and a (usually well-structured) class of graphs
$\mathcal{G}$, and ask whether it is possible to transform $G$ into a graph $G'
\in \mathcal{G}$ by adding and/or removing at most $k$ edges. Parameterized
graph edge modification problems received considerable attention in the last
decades.
  In this paper, we focus on finding small kernels for edge modification
problems. One of the most studied problems is the Cluster Editing problem, in
which the goal is to partition the vertex set into a disjoint union of cliques.
Even if this problem admits a $2k$ kernel [Cao, 2012], this kernel does not
reduce the size of most instances. Therefore, we explore the question of
whether linear kernels are a theoretical limit in edge modification problems,
in particular when the target graphs are very structured (such as a partition
into cliques for instance). We prove, as far as we know, the first sublinear
kernel for an edge modification problem. Namely, we show that Clique +
Independent Set Deletion, which is a restriction of Cluster Deletion, admits a
kernel of size $O(k/\log k)$.
  We also obtain small kernels for several other edge modification problems. We
prove that Split Addition (and the equivalent Split Deletion) admits a linear
kernel, improving the existing quadratic kernel of Ghosh et al. [Ghosh et al.,
2015]. We complement this result by proving that Trivially Perfect Addition
admits a quadratic kernel (improving the cubic kernel of Guo [Guo, 2007]), and
finally prove that its triangle-free version (Starforest Deletion) admits a
linear kernel, which is optimal under ETH.


Abstract:
  Motor disturbances can affect the interaction with dynamic objects, such as
catching a ball. A classification of clinical catching trials might give
insight into the existence of pathological alterations in the relation of arm
and ball movements. Accurate, but also early decisions are required to classify
a catching attempt before the catcher's first ball contact. To obtain
clinically valuable results, a significant decision confidence of at least 75%
is required. Hence, three competing objectives have to be optimized at the same
time: accuracy, earliness and decision-making confidence. Here we propose a
coupled classification and prediction approach for early time series
classification: a predictive, generative recurrent neural network (RNN)
forecasts the next data points of ball trajectories based on already available
observations; a discriminative RNN continuously generates classification
guesses based on the available data points and the unrolled sequence
predictions. We compare our approach, which we refer to as predictive
sequential classification (PSC), to state-of-the-art sequence learners,
including various RNN and temporal convolutional network (TCN) architectures.
On this hard real-world task we can consistently demonstrate the superiority of
PSC over all other models in terms of accuracy and confidence with respect to
earliness of recognition. Specifically, PSC is able to confidently classify the
success of catching trials as early as 123 milliseconds before the first ball
contact. We conclude that PSC is a promising approach for early time series
classification, when accurate and confident decisions are required.


Abstract:
  A generalised numerical semigroup (GNS) is a submonoid $S$ of $\mathbb{N}^d$
for which the complement $\mathbb{N}^d\setminus S$ is finite. The points in the
complement $\mathbb{N}^d\setminus S$ are called gaps. A gap $F$ is considered
Frobenius allowable if there is some relaxed monomial ordering on
$\mathbb{N}^d$ with respect to which $F$ is the largest gap. We characterise
the Frobenius allowable gaps of a GNS. A GNS that has only one Frobenius
allowable gap is called a Frobenius GNS. We estimate the number of Frobenius
GNS with a given Frobenius gap $F=(F^{(1)},\dots,F^{(d)})\in\mathbb{N}^d$ and
show that it is close to $\sqrt{3}^{(F^{(1)}+1)\cdots (F^{(d)}+1)}$ for large
$d$. We define notions of quasi-irreducibility and quasi-symmetry for GNS.
While in the case of $d=1$ these notions coincide with irreducibility and
symmetry of GNS, they are distinct in higher dimensions.


Abstract:
  Optical quantum interferometry represents the oldest example of quantum
metrology and it is at the source of quantum technologies. The original
squeezed state scheme is now a significant element of the last version of
gravitational wave detectors and various additional uses have been proposed.
  Further quantum enhanced schemes, from SU(1,1) interferometer to twin beam
correlation interferometry, have also reached the stage of proof of principle
experiments enlarging the field of experimental quantum interferometry and
paving the way to several further applications ranging from Planck scale
signals search to small effects detection. In this review paper I introduce
these experimental achievements, describing their schemes, advantages,
applications and possible further developments.


Abstract:
  I provide an axiomatization of stochastic utility over two periods, stating
testable necessary and sufficient conditions under which an agent's choice
behavior under exogenous menu selection can be modeled by a pair of random
utility functions. Although static random utility is characterized by a single
axiom, Block-Marschak nonnegativity, I demonstrate that an additional notion of
marginal consistency is needed for the two-period axiomatization. In
particular, when each period's choice set has size three, I restate the
characterization using the simpler axiom of stochastic regularity. I conclude
by stating several corollaries, including an axiomatization of stochastic
utility with full support and an axiomatization of n-period stochastic utility.


Abstract:
  This paper deals with the convergence of the Doi-Navier-Stokes model of
liquid crystals to the Ericksen-Leslie model in the limit of the Deborah number
tending to zero. While the literature has investigated this problem by means of
the Hilbert expansion method, we develop the moment method, i.e. a method that
exploits conservation relations obeyed by the collision operator. These are
non-classical conservation relations which are associated with a new concept,
that of Generalized Collision Invariant (GCI). In this paper, we develop the
GCI concept and relate it to geometrical and analytical structures of the
collision operator. Then, the derivation of the limit model using the GCI is
performed in an arbitrary number of spatial dimensions and with non-constant
and non-uniform polymer density. This non-uniformity generates new terms in the
Ericksen-Leslie model.


Abstract:
  We obtain a remarkable semi-analytic expression concerning the role of purely
tidal curvature on accelerated probes, revealing some novel insights into the
role of absolute vs. tidal acceleration in the response of such probes. The key
quantity we evaluate is the relation between geodesic ($\tau_{\rm geod}$) and
proper time ($\tau_{\rm acc}$) intervals between events on the probe
trajectory.
  This is obtained as a covariant power series in curvature using a combination
of analytical and numerical tools. A serendipitous observation then reveals
that one can $exactly$ sum all terms involving the $purely\;tidal$ component
${\mathscr E}_n= R_{abcd} \varepsilon^{ab} \varepsilon^{cd}$ of curvature, with
$\varepsilon^{ab}$ the bi-normal to the plane of motion: $$ \tau_{\rm geod} =
\frac{2}{\sqrt{{-\mathscr E}_n}} \sinh ^{-1}\Biggl[\sqrt{\frac{-{\mathscr
E}_n}{a^2-{\mathscr E}_n}} \sinh \left(\frac{1}{2} \sqrt{a^2-{\mathscr E}_n} \;
\tau_{\rm acc} \right) \Biggl] $$ For classical clocks, the above result
represents an interesting closed form contribution of tidal curvature to the
differential ageing of twins in the classic $Twin\;paradox$. For quantum
probes, it gives a thermal contribution to the $detector\;response$ with a
modified $Unruh\;temperature$ $$ [k_{\rm B} T]_{{\mathscr E}_n} = \frac{\hbar
\sqrt{a^2- {\mathscr E}_n }}{2 \pi} $$ As an operational tool, the
computational framework we present and the corresponding results should find
applications to a wide range of physical problems that involve measurements and
observations by use of accelerated probes in curved spacetimes.


Abstract:
  Advancing the state-of-the-art in large-scale biomedical semantic indexing
and question answering is the main focus of the BioASQ challenge. BioASQ
organizes respective tasks where different teams develop systems that are
evaluated on the same benchmark datasets that represent the real information
needs of experts in the biomedical domain. This paper presents an overview of
the ninth edition of the BioASQ challenge in the context of the Conference and
Labs of the Evaluation Forum (CLEF) 2021. In this year, a new question
answering task, named Synergy, is introduced to support researchers studying
the COVID-19 disease and measure the ability of the participating teams to
discern information while the problem is still developing. In total, 42 teams
with more than 170 systems were registered to participate in the four tasks of
the challenge. The evaluation results, similarly to previous years, show a
performance gain against the baselines which indicates the continuous
improvement of the state-of-the-art in this field.


Abstract:
  A lot of work has been done towards reconstructing the 3D facial structure
from single images by capitalizing on the power of Deep Convolutional Neural
Networks (DCNNs). In the recent works, the texture features either correspond
to components of a linear texture space or are learned by auto-encoders
directly from in-the-wild images. In all cases, the quality of the facial
texture reconstruction is still not capable of modeling facial texture with
high-frequency details. In this paper, we take a radically different approach
and harness the power of Generative Adversarial Networks (GANs) and DCNNs in
order to reconstruct the facial texture and shape from single images. That is,
we utilize GANs to train a very powerful facial texture prior \edit{from a
large-scale 3D texture dataset}. Then, we revisit the original 3D Morphable
Models (3DMMs) fitting making use of non-linear optimization to find the
optimal latent parameters that best reconstruct the test image but under a new
perspective. In order to be robust towards initialisation and expedite the
fitting process, we propose a novel self-supervised regression based approach.
We demonstrate excellent results in photorealistic and identity preserving 3D
face reconstructions and achieve for the first time, to the best of our
knowledge, facial texture reconstruction with high-frequency details.


Abstract:
  Narrow beams are key to wireless communications in millimeter wave frequency
bands. Beam alignment (BA) allows the base station (BS) to adjust the direction
and width of the beam used for communication. During BA, the BS transmits a
number of scanning beams covering different angular regions. The goal is to
minimize the expected width of the uncertainty region (UR) that includes the
angle of departure of the user. Conventionally, in interactive BA, it is
assumed that the feedback corresponding to each scanning packet is received
prior to transmission of the next one. However, in practice, the feedback delay
could be larger because of propagation or system constraints. This paper
investigates BA strategies that operate under arbitrary fixed feedback delays.
This problem is analyzed through a source coding prospective where the feedback
sequences are viewed as source codewords. It is shown that these codewords form
a codebook with a particular characteristic which is used to define a new class
of codes called d-unimodal codes. By analyzing the properties of these codes, a
lower bound on the minimum achievable expected beamwidth is provided. The
results reveal potential performance improvements in terms of the BA duration
it takes to achieve a fixed expected width of the UR over the state-of-the-art
BA methods which do not consider the effect of delay.


Abstract:
  We study two disjoint universes in an entangled pure state. When only one
universe contains gravity, the path integral for the $n^{\text{th}}$ R\'enyi
entropy includes a wormhole between the $n$ copies of the gravitating universe,
leading to a standard "island formula" for entanglement entropy consistent with
unitarity of quantum information. When both universes contain gravity,
gravitational corrections to this configuration lead to a violation of
unitarity. However, the path integral is now dominated by a novel wormhole with
$2n$ boundaries connecting replica copies of both universes. The analytic
continuation of this contribution involves a quotient by $\mathbb{Z}_n$ replica
symmetry, giving a cylinder connecting the two universes. When entanglement is
large, this configuration has an effective description as a "swap wormhole", a
geometry in which the boundaries of the two universes are glued together by a
"swaperator". This description allows precise computation of a generalized
entropy-like formula for entanglement entropy. The quantum extremal surface
computing the entropy lives on the Lorentzian continuation of the cylinder/swap
wormhole, which has a connected Cauchy slice stretching between the universes
-- a realization of the ER=EPR idea. The new wormhole restores unitarity of
quantum information.


Abstract:
  The mechanisms for photodissociation of methyl halides (CH$_3$X, X= Cl, Br,
I) have been studied for these molecules when adsorbed on thin films of
C$_6$H$_6$ or C$_6$F$_6$ on copper single crystals, using time-of-flight
spectroscopy with 248nm and 193nm light. For CH$_3$Cl and CH$_3$Br monolayers
adsorbed on C$_6$H$_6$, two photodissociation pathways can be identified:
neutral photodissociation similar to the gas-phase, and a dissociative electron
attachment (DEA) pathway due to photoelectrons from the metal. The same methyl
halides adsorbed on a C$_6$F$_6$ thin film display only neutral
photodissociation, with the DEA pathway entirely absent due to intermolecular
quenching via a LUMO-derived electronic band in the C$_6$F$_6$ thin film. For
CH$_3$I adsorbed on a C$_6$F$_6$ thin film, illumination with 248nm light
results in CH$_3$ photofragments departing due to neutral photodissociation via
the A-band absorption. When CH$_3$I monolayers on C$_6$H$_6$ thin films are
illuminated at the same wavelength, additional new photodissociation pathways
are observed that are due to absorption in the molecular film with energy
transfer leading to dissociation of the CH$_3$I molecules adsorbed on top. The
proposed mechanism for this photodissociation is via a charge-transfer complex
for the C$_6$H$_6$ layer and adsorbed CH$_3$I.


Abstract:
  Let $R$ be an associative ring with unit $1$, and $a, b, c\in R$ satisfy
$a(ba)^{2}=abaca=acaba=(ac)^{2}a$, this paper proves that $1-ac$ has
generalized Drazin inverse (Drazin inverse, pseudo Drazin inverse,
respectively) if and only if $1-ba$ has generalized Drazin inverse (Drazin
inverse, pseudo Drazin inverse, respectively). In particular, we obtain new
common spectral properties for $ac$ and $ba$ in Banach algebras. As
applications, new extension of Jacobson's lemma for B-Fredholm elements and
generalized Fredholm elements in rings is established.


Abstract:
  Understanding dynamics of an outbreak like that of COVID-19 is important in
designing effective control measures. This study aims to develop an agent based
model that compares changes in infection progression by manipulating different
parameters in a synthetic population. Model input includes population
characteristics like age, sex, working status etc. of each individual and other
factors influencing disease dynamics. Depending on number of epicentres of
infection, location of primary cases, sensitivity, proportion of asymptomatic
and frequency or duration of lockdown, our simulator tracks every individual
and hence infection progression through community over time.
  In a closed community of 10000 people, it is seen that without any lockdown,
number of cases peak around 6th week and wanes off around 15th week. If primary
case is located inside dense population cluster like slums, cases peak early
and wane off slowly. With introduction of lockdown, cases peak at slower rate.
If sensitivity of identifying infection decreases, cases and deaths increase.
Number of cases declines with increase in proportion of asymptomatic cases. The
model is robust and provides reproducible estimates with realistic parameter
values. It also guides in identifying measures to control outbreak in a
community. It is flexible in accommodating different parameters like
infectivity period, yield of testing, socio-economic strata, daily travel,
awareness level, population density, social distancing, lockdown etc. and can
be tailored to study other infections with similar transmission pattern.


Abstract:
  In this paper we discuss the Morse potential on a quantum computer. The Morse
potential is useful to describe diatomic molecules and has a finite number of
bound states which can be measured through spectroscopy. It is also a example
of an exactly soluble potential using supersymmetric quantum mechanics. Using
the the supersymmetric quantum mechanics formalism one can derive a heirachy of
Hamiltonians such that the ground state of the next rung on the heirarchy yeids
the first excited state of the hamiltonian below it. Using this method one can
determine all the states of the Morse potential by calculating all the ground
states of the sequence of Hamiltonians in the heirarchy. We use the IBM QISKit
software together with the Variational Quantum Eiegensolver (VQE) algorithm to
calculate the ground state and first excited state energy of the Morse
potential and find agreement with the exact expression for the bound state
energies of the Morse Potential. We analyze different optimizers to study the
numerical effect on the calculations. Finally we perform quantum computations
for diatomic and triatomic molecules to illustrate the application of these
techniques on near term quantum computers and find excellent agreement with
experimental data.


Abstract:
  Principal component analysis (PCA) is a classical and ubiquitous method for
reducing data dimensionality, but it is suboptimal for heterogeneous data that
are increasingly common in modern applications. PCA treats all samples
uniformly so degrades when the noise is heteroscedastic across samples, as
occurs, e.g., when samples come from sources of heterogeneous quality. This
paper develops a probabilistic PCA variant that estimates and accounts for this
heterogeneity by incorporating it in the statistical model. Unlike in the
homoscedastic setting, the resulting nonconvex optimization problem is not
seemingly solved by singular value decomposition. This paper develops a
heteroscedastic probabilistic PCA technique (HePPCAT) that uses efficient
alternating maximization algorithms to jointly estimate both the underlying
factors and the unknown noise variances. Simulation experiments illustrate the
comparative speed of the algorithms, the benefit of accounting for
heteroscedasticity, and the seemingly favorable optimization landscape of this
problem. Real data experiments on environmental air quality data show that
HePPCAT can give a better PCA estimate than techniques that do not account for
heteroscedasticity.


Abstract:
  This research was motivated by studying anti-drug antibody (ADA) formation
and its potential impact on long-term benefit of a biologic treatment in a
randomized controlled trial, in which ADA status was not only unobserved in the
control arm but also in a subset of patients from the experimental treatment
arm. Recent literature considers the principal stratum estimand strategy to
estimate treatment effect in groups of patients defined by an intercurrent
status, i.e. in groups defined by a post-randomization variable only observed
in one arm and potentially associated with the outcome. However, status
information might be missing even for a non-negligible number of patients in
the experimental arm. For this setting, a novel weighted principal stratum
approach is presented: Data from patients with missing intercurrent event
status were re-weighted based on baseline covariates and additional
longitudinal information. A theoretical justification of the proposed approach
is provided for different types of outcomes, and assumptions allowing for
causal conclusions on treatment effect are specified and investigated.
Simulations demonstrated that the proposed method yielded valid inference and
was robust against certain violations of assumptions. The method was shown to
perform well in a clinical study with ADA status as an intercurrent event.


Abstract:
  A thermophysical model was developed to test the viability of 57 candidate
hollow-forming volatiles within the hollow-formation model framework of Blewett
et al. (2013). We find that the thermophysical properties of elemental sulfur
(S) combined with the abundance of S on Mercury, make it the most likely
hollow-forming volatile explored in this study. We propose a novel model for
hollow formation in which a subsurface heat source drives sulfur-rich systems
that deposit volatiles (importantly, S) in the near-surface at night within a
"sulfur permafrost zone", and daytime solar heating drives sublimation to form
hollows.


Abstract:
  Recent advances in appearance-based models have shown improved eye tracking
performance in difficult scenarios like occlusion due to eyelashes, eyelids or
camera placement, and environmental reflections on the cornea and glasses. The
key reason for the improvement is the accurate and robust identification of eye
parts (pupil, iris, and sclera regions). The improved accuracy often comes at
the cost of labeling an enormous dataset, which is complex and time-consuming.
This work presents two semi-supervised learning frameworks to identify
eye-parts by taking advantage of unlabeled images where labeled datasets are
scarce. With these frameworks, leveraging the domain-specific augmentation and
novel spatially varying transformations for image segmentation, we show
improved performance on various test cases. For instance, for a model trained
on just 48 labeled images, these frameworks achieved an improvement of 0.38%
and 0.65% in segmentation performance over the baseline model, which is trained
only with the labeled dataset.


Abstract:
  We present Ly$\alpha$ and ultraviolet-continuum (UV) luminosity functions
(LFs) of galaxies and active galactic nuclei (AGN) at $z=2.0-3.5$ determined by
the un-targetted optical spectroscopic survey of the Hobby-Eberly Telescope
Dark Energy Experiment (HETDEX). We combine deep Subaru imaging with HETDEX
spectra resulting in $11.4$ deg$^2$ of fiber-spectra sky coverage, obtaining
$18320$ galaxies spectroscopically identified with Ly$\alpha$ emission, $2126$
of which host type 1 AGN showing broad (FWHM$~>1000$ km s$^{-1}$) Ly$\alpha$
emission lines. We derive the Ly$\alpha$ (UV) LF over 2 orders of magnitude
covering bright galaxies and AGN in $\log
L_\mathrm{Ly\alpha}/\mathrm{[erg~s^{-1}]}=43.3-45.5$ ($-27<M_\mathrm{UV}<-20$)
by the $1/V_\mathrm{max}$ estimator. Our results reveal the bright-end hump of
the Ly$\alpha$ LF is composed of type 1 AGN. In conjunction with previous
spectroscopic results at the faint end, we measure a slope of the best-fit
Schechter function to be $\alpha_\mathrm{Sch}=-1.70^{+0.13}_{-0.14}$, which
indicates $\alpha_\mathrm{Sch}$ steepens from $z=2-3$ towards high redshift.
Our UV LF agrees well with previous AGN UV LFs, and extends to faint-AGN and
bright-galaxy regimes. The number fraction of Ly$\alpha$-emitting objects
($X_\mathrm{LAE}$) increases from $M_\mathrm{UV}^*\sim-21$ to bright magnitude
due to the contribution of type 1 AGN, while previous studies claim that
$X_\mathrm{Ly\alpha}$ decreases from faint magnitude to $M_\mathrm{UV}^*$,
suggesting a valley in the $X_\mathrm{Ly\alpha}-$magnitude relation at
$M_\mathrm{UV}^*$. Comparing our UV LF of type 1 AGN at $z=2-3$ with those at
$z=0$, we find that the number density of faint ($M_\mathrm{UV}>-21$) type 1
AGN increases from $z\sim2$ to $z\sim0$ as opposed to the evolution of bright
($M_\mathrm{UV}<-21$) type 1 AGN, suggesting the AGN downsizing in the
rest-frame UV luminosity.


Abstract:
  The old, solar metallicity open cluster Messier 67 has long been considered a
lynchpin in the study and understanding of the structure and evolution of
solar-type stars. The same is arguably true for stellar remnants - the white
dwarf population of M67 provides crucial observational data for understanding
and interpreting white dwarf populations and evolution. In this work, we
determine the white dwarf masses and derive their progenitor star masses using
high signal-to-noise spectroscopy of warm ($\gtrsim10,000$ K) DA white dwarfs
in the cluster. From this we are able to derive each white dwarf's position on
the initial-final mass relation, with an average $M_{\mathrm WD} = 0.60\pm 0.01
M_{\odot}$ and progenitor mass $M_i = 1.52\pm 0.04 M_{\odot}$. These values are
fully consistent with recently published linear and piecewise linear fits to
the semi-empirical initial-final mass relation and provide a crucial, precise
anchor point for the initial-final mass relation for solar-metallicity,
low-mass stars. The mean mass of M67 white dwarfs is also consistent with the
sharp narrow peak in the local field white dwarf mass distribution, indicating
that a majority of recently-formed field white dwarfs come from stars with
progenitor masses of $\approx 1.5 M_{\odot}$. Our results enable more precise
modeling of the Galactic star formation rate encoded in the field WD mass
distribution.


Abstract:
  Optimization in the latent space of variational autoencoders is a promising
approach to generate high-dimensional discrete objects that maximize an
expensive black-box property (e.g., drug-likeness in molecular generation,
function approximation with arithmetic expressions). However, existing methods
lack robustness as they may decide to explore areas of the latent space for
which no data was available during training and where the decoder can be
unreliable, leading to the generation of unrealistic or invalid objects. We
propose to leverage the epistemic uncertainty of the decoder to guide the
optimization process. This is not trivial though, as a naive estimation of
uncertainty in the high-dimensional and structured settings we consider would
result in high estimator variance. To solve this problem, we introduce an
importance sampling-based estimator that provides more robust estimates of
epistemic uncertainty. Our uncertainty-guided optimization approach does not
require modifications of the model architecture nor the training process. It
produces samples with a better trade-off between black-box objective and
validity of the generated samples, sometimes improving both simultaneously. We
illustrate these advantages across several experimental settings in digit
generation, arithmetic expression approximation and molecule generation for
drug design.


Abstract:
  We derive an invariance principle for the lift to the rough path topology of
stochastic processes with delayed regenerative increments under an optimal
moment condition. An interesting feature of the result is the emergence of area
anomaly, a correction term in the second level of the limiting rough path which
is identified as the average stochastic area on a regeneration interval. A few
applications include random walks in random environment and additive
functionals of recurrent Markov chains. The result is formulated in the
p-variation settings, where a rough Donsker Theorem is available under the
second moment condition. The key renewal theorem is applied to obtain an
optimal moment condition.


Abstract:
  A group-theoretical structure in a perturbative expansion of the Wilson loops
in the 3d Chern-Simons theory with $SU(N)$ gauge group is studied in symmetric
approach. A special basis in the center of the universal enveloping algebra
$ZU(\mathfrak{sl}_N)$ is introduced. This basis allows one to present group
factors in an arbitrary irreducible finite-dimensional representation.
Developed methods have wide applications, the most straightforward and evident
ones are mentioned. Namely, Vassiliev invariants of higher orders are computed,
a conjecture about existence of new symmetries of the colored HOMFLY
polynomials is stated, and the recently discovered tug-the-hook symmetry of the
colored HOMFLY polynomial is proved.


Abstract:
  We investigate large-scale latent variable models (LVMs) for neural story
generation -- an under-explored application for open-domain long text -- with
objectives in two threads: generation effectiveness and controllability. LVMs,
especially the variational autoencoder (VAE), have achieved both effective and
controllable generation through exploiting flexible distributional latent
representations. Recently, Transformers and its variants have achieved
remarkable effectiveness without explicit latent representation learning, thus
lack satisfying controllability in generation. In this paper, we advocate to
revive latent variable modeling, essentially the power of representation
learning, in the era of Transformers to enhance controllability without hurting
state-of-the-art generation effectiveness. Specifically, we integrate latent
representation vectors with a Transformer-based pre-trained architecture to
build conditional variational autoencoder (CVAE). Model components such as
encoder, decoder and the variational posterior are all built on top of
pre-trained language models -- GPT2 specifically in this paper. Experiments
demonstrate state-of-the-art conditional generation ability of our model, as
well as its excellent representation learning capability and controllability.


Abstract:
  Two general constructions of linear codes with functions over finite fields
have been extensively studied in the literature. The first one is given by
$\mathcal{C}(f)=\left\{ {\rm Tr}(af(x)+bx)_{x \in \mathbb{F}_{q^m}^*}: a,b \in
\mathbb{F}_{q^m} \right\}$, where $q$ is a prime power, $\bF_{q^m}^*=\bF_{q^m}
\setminus \{0\}$, $\tr$ is the trace function from $\bF_{q^m}$ to $\bF_q$, and
$f(x)$ is a function from $\mathbb{F}_{q^m}$ to $\mathbb{F}_{q^m}$ with
$f(0)=0$. Almost bent functions, quadratic functions and some monomials on
$\bF_{2^m}$ were used in the first construction, and many families of binary
linear codes with few weights were obtained in the literature. This paper
studies some punctured codes of these binary codes. Several families of binary
linear codes with few weights and new parameters are obtained in this paper.
Several families of distance-optimal binary linear codes with new parameters
are also produced in this paper.


Abstract:
  Spherical nanoparticles (NPs) of size 14 nm, made of intermetallic Fe2CoAl
(FCA) Heusler alloy, are synthesized via the co-precipitation and thermal
deoxidization method. X-ray diffraction (XRD) and selected area electron
diffraction (SAED) patterns confirm that the present nanoalloy is crystallized
in A2-disordered cubic Heusler structure. Magnetic field (H) and temperature
(T) dependent magnetization (M) results reveal that the NPs are soft
ferromagnetic (FM) with high saturation magnetization (Ms) and Curie
temperature (Tc). Fe2CoAl nanoalloy does not follow the Slater Pauling (SP)
rule, possibly because of the disorder present in the system. We also
investigate its magnetic phase transition (MPT) and magnetocaloric (MC)
properties. The peak value of the magnetic entropy change vs T curve at a
magnetic field change of 20 kOe corresponds to about 2.65 J/kg-K, and the
observed value of refrigeration capacity (RCP) is as large as 44 J/kg,
suggesting a large heat conversion in magnetic refrigeration cycle. The Arrott
plot and the nature of the universal curve accomplish that the FM to
paramagnetic (PM) phase transition in Fe2CoAl nanoalloy is of second-order. The
present study suggests that the Fe2CoAl nanoscale system is proficient, useful
and a good candidate for the spintronics application and opens up a window for
further research on full-Heusler based magnetic refrigerants.


Abstract:
  The purpose of the article is to generalize the concept of approximate
Birkhoff-James orthogonality, in the semi-Hilbertian structure. Given a
positive operator $ A $ on a Hilbert space $ \mathbb{H}, $ we define $
(\epsilon,A)- $approximate orthogonality and $ (\epsilon,A)- $approximate
orthogonality in the sense of Chmieli$\acute{n}$ski and establish a relation
between them. We also characterize $ (\epsilon,A)- $approximate orthogonality
in the sense of Chmieli$\acute{n}$ski for $A$-bounded and $A$-bounded compact
operators. We further generalize the concept of right symmetric and left
symmetric operators on a Hilbert space. The utility of these notions are
illustrated by extending some of the previous results obtained by various
authors in the setting of Hilbert spaces.


Abstract:
  Cyberbullying or Online harassment detection on social media for various
major languages is currently being given a good amount of focus by researchers
worldwide. Being the seventh most speaking language in the world and increasing
usage of online platform among the Bengali speaking people urge to find
effective detection technique to handle the online harassment. In this paper,
we have proposed binary and multiclass classification model using hybrid neural
network for bully expression detection in Bengali language. We have used 44,001
users comments from popular public Facebook pages, which fall into five classes
- Non-bully, Sexual, Threat, Troll and Religious. We have examined the
performance of our proposed models from different perspective. Our binary
classification model gives 87.91% accuracy, whereas introducing ensemble
technique after neural network for multiclass classification, we got 85%
accuracy.


Abstract:
  Quasi-conformal models are an appealing scenario that can offer naturally a
strongly supercooled phase transition and a period of thermal inflation in the
early Universe. A crucial aspect for the viability of these models is how the
Universe escapes from the supercooled state. One possibility is that thermal
inflation phase ends by nucleation and percolation of true vacuum bubbles. This
route is not, however, always efficient. In such case another escape mechanism,
based on the growth of quantum fluctuations of the scalar field that eventually
destabilize the false vacuum, becomes relevant. We study both of these cases in
detail in a simple yet representative model. We determine the duration of the
thermal inflation, the curvature power spectrum generated for the scales that
exit horizon during the thermal inflation, and the stochastic gravitational
wave background from the phase transition. We show that these gravitational
waves provide an observable signal from the thermal inflation in almost the
entire parameter space of interest. Furthermore, the shape of the gravitational
wave spectrum can be used to ascertain how the Universe escaped from
supercooling.


Abstract:
  Element-selective techniques are central for the understanding of ultrafast
spin dynamics in multi-element materials like magnetic alloys. Recently,
though, it turned out that the commonly used technique of transverse
magneto-optical Kerr effect (T-MOKE) in the EUV range may have linearity issues
including unwanted cross talk between different elemental signals. This problem
can be sizeable, which puts recent observations of ultrafast spin transfer from
Fe to Ni sites in FeNi alloys into question. In this study, we investigate the
Fe-to-Ni spin transfer in a cross-talk-free time-resolved X-ray magnetic
circular dichroism (XMCD) experiment with a reliable time reference. We find a
very similar Fe and Ni dynamics with XMCD as with T-MOKE from identical
samples. Considering the non-linearities of the T-MOKE response, the agreement
with our findings appears fortuitous. We discuss possible reasons why T-MOKE
seems to give accurate results in this case. Our data provide the ongoing
discussion about ultrafast spin-transfer mechanisms in FeNi systems with a
sound experimental basis.


Abstract:
  A three-way (resp., two-way) two-dimensional automaton has a read-only input
head that moves in three (resp., two) directions on a finite array of cells
labelled by symbols of the input alphabet. Restricting the input head movement
of a two-dimensional automaton results in a model that is weaker in terms of
recognition power.
  In this paper, we introduce the notion of "degrees of restriction" for
two-dimensional automata, and we develop sets of extended two-dimensional
automaton models that allow for some bounded number of restricted moves. We
establish recognition hierarchies for both deterministic and nondeterministic
extended three-way two-dimensional automata, and we find similar hierarchies
for both deterministic and nondeterministic extended two-way two-dimensional
automata. We also prove incomparability results between nondeterministic and
deterministic extended three-way two-dimensional automata. Lastly, we consider
closure properties for some operations on languages recognized by extended
three-way two-dimensional automata.


Abstract:
  We present examples of nearly integrable analytic Hamiltonian systems with
several strong diffusion properties: topological weak mixing and diffusion at
all times. These examples are obtained by AbC constructions with several
frequencies.


Abstract:
  This paper describes an application of the Cartographer graph SLAM stack as a
pose sensor in a UAV feedback control loop, with certain application-specific
changes in the SLAM stack such as smoothing of the optimized pose. Pose
estimation is performed by fusing 3D LiDAR/IMU-based proprioception with GPS
position measurements by means of pose graph optimisation. Moreover, partial
environment maps built from the LiDAR data (submaps) within the Cartographer
SLAM stack are marshalled into OctoMap, an Octree-based voxel map
implementation. The OctoMap is further used for navigation tasks such as path
planning and obstacle avoidance.


Abstract:
  Many schools in large urban districts have more applicants than seats.
Centralized school assignment algorithms ration seats at over-subscribed
schools using randomly assigned lottery numbers, non-lottery tie-breakers like
test scores, or both. The New York City public high school match illustrates
the latter, using test scores and other criteria to rank applicants at
``screened'' schools, combined with lottery tie-breaking at unscreened
``lottery'' schools. We show how to identify causal effects of school
attendance in such settings. Our approach generalizes regression discontinuity
methods to allow for multiple treatments and multiple running variables, some
of which are randomly assigned. The key to this generalization is a local
propensity score that quantifies the school assignment probabilities induced by
lottery and non-lottery tie-breakers. The local propensity score is applied in
an empirical assessment of the predictive value of New York City's school
report cards. Schools that receive a high grade indeed improve SAT math scores
and increase graduation rates, though by much less than OLS estimates suggest.
Selection bias in OLS estimates is egregious for screened schools.


Abstract:
  For Cyper-Physical Production Systems (CPPS), localization is becoming
increasingly important as wireless and mobile devices are considered an
integral part. While localizing targets in a wireless communication system
based on the Received Signal Strength Indicators (RSSIs) is a usual solution,
it is limited by sensor quality. We consider the scenario of a car moving in
and out of a chamber and propose to use a particle filter for sensor fusion,
allowing us to incorporate non-idealities in our model and achieve a
high-quality position estimate. Then, we use Machine Learning (ML) to classify
the vehicle position. Our results show that the location output of the particle
filter is a better input to the classifiers than the raw RSSI data, and we
achieve improved accuracy while simultaneously reducing the number of features
that the ML has to consider. We also compare the performance of multiple ML
algorithms and show that SVMs provide the overall best performance for the
given task.


Abstract:
  Despite the surging development and utilization of IoT devices, the security
of IoT devices is still in infancy. The security pitfalls of IoT devices have
made it easy for hackers to take over IoT devices and use them for malicious
activities like botnet attacks. With the rampant emergence of IoT devices,
botnet attacks are surging. The botnet attacks are not only catastrophic for
IoT device users but also for the rest of the world. Therefore, there is a
crucial need to identify and mitigate the possible threats in IoT devices
during the design phase. Threat modelling is a technique that is used to
identify the threats in the earlier stages of the system design activity. In
this paper, we propose a threat modelling approach to analyze and mitigate the
botnet attacks in an IoT smart home use case. The proposed methodology
identifies the development-level and application-level threats in smart home
use case using STRIDE and VAST threat modelling methods. Moreover, we
reticulate the identified threats with botnet attacks. Finally, we propose the
mitigation techniques for all identified threats including the botnet threats.


Abstract:
  In this paper, we present an algorithmic approach to design and construct
planar truss structures based on symmetric lattices using modular elements. The
method of assembly is similar to Leonardo grids as they both rely on the
property of interlocking. In theory, our modular elements can be assembled by
the same type of binary operations. Our modular elements embody the principle
of geometric interlocking, a principle recently introduced in literature that
allows for pieces of an assembly to be interlocked in a way that they can
neither be assembled nor disassembled unless the pieces are subjected to
deformation or breakage. We demonstrate that breaking the pieces can indeed
facilitate the effective assembly of these pieces through the use of a simple
key-in-hole concept. As a result, these modular elements can be assembled
together to form an interlocking structure, in which the locking pieces apply
the force necessary to hold the entire assembly together.


Abstract:
  Epidemic models often reflect characteristic features of infectious spreading
processes by coupled non-linear differential equations considering different
states of health (such as Susceptible, Infected, or Recovered). This
compartmental modeling approach, however, delivers an incomplete picture of the
dynamics of epidemics, as it neglects stochastic and network effects, and also
the role of the measurement process, on which the estimation of epidemiological
parameters and incidence values relies. In order to study the related issues,
we extend established epidemiological spreading models with a model of the
measurement (i.e. testing) process, considering the problems of false positives
and false negatives as well as biased sampling. Studying a model-generated
ground truth in conjunction with simulated observation processes (virtual
measurements) allows one to gain insights into the limitations of purely
data-driven methods to assess the epidemic situation. We conclude that epidemic
monitoring, simulation, and forecasting are wicked problems, as applying a
conventional data-driven approach to a complex system with non-linear dynamics,
network effects, and uncertainty can be misleading. Nevertheless, some of the
errors can be corrected for, using scientific knowledge of the spreading
dynamics and the measurement process. We conclude that such corrections should
generally be part of epidemic monitoring, modeling, and forecasting efforts.


Abstract:
  We focus on the development of diagnostic tools and an R package called MNB
for a multivariate negative binomial (MNB) regression model for detecting
atypical and influential subjects. The MNB model is deduced from a Poisson
mixed model in which the random intercept follows the generalized log-gamma
(GLG) distribution. The MNB model for correlated count data leads to an MNB
regression model that inherits the features of a hierarchical model to
accommodate the intraclass correlation and the occurrence of overdispersion
simultaneously. The asymptotic consistency of the dispersion parameter
estimator depends on the asymmetry of the GLG distribution. Inferential
procedures for the MNB regression model are simple, although it can provide
inconsistent estimates of the asymptotic variance when the correlation
structure is misspecified. We propose the randomized quantile residual for
checking the adequacy of the multivariate model, and derive global and local
influence measures from the multivariate model to assess influential subjects.
Finally, two applications are presented in the data analysis section. The code
for installing the MNB package and the code used in the two examples is
exhibited in the Appendix.


Abstract:
  Annihilation dynamics plays a fundamental role in the baryon$-$antibaryon
interaction (B$-\rm{\overline{B}}$) at low-energy and its strength and range
are crucial in the assessment of possible baryon bound states. Experimental
data on annihilation cross sections are available for the p$-\rm{\overline{p}}$
system but not in the low relative momentum region. Data regarding the
B$\rm{\overline{B}}$ interaction with strange degrees of freedom are extremely
scarce or absent, hence the modeling of the annihilation contributions is
mainly based on nucleon$-$antinucleon (N$-\rm{\overline{N}}$) results, when
available. In this letter we present a measurement of the
p$-\rm{\overline{p}}$, p$-\overline{\Lambda}\oplus\rm{\overline{p}}-\Lambda$
and $\Lambda-\overline{\Lambda}$ interaction using correlation functions in the
relative momentum space in high-multiplicity triggered pp collisions at
$\sqrt{s} = 13$ TeV recorded by ALICE at the LHC. In the p$-\rm{\overline{p}}$
system the couplings to the mesonic channels in different partial waves are
extracted by adopting a coupled-channel approach with recent $\chi\rm{EFT}$
potentials. The inclusion of these inelastic channels provides good agreement
with the data, showing a significant presence of the annihilation term down to
zero momentum. Predictions obtained using the Lednick\'y$-$Lyuboshits formula
and scattering parameters obtained from heavy-ion collisions, hence mainly
sensitive to elastic processes, are compared with the experimental
p$-\overline{\Lambda}\oplus\rm{\overline{p}}-\Lambda$ and
$\Lambda-\overline{\Lambda}$ correlations. The model describes the
$\Lambda-\overline{\Lambda}$ data and underestimates the
p$-\overline{\Lambda}\oplus\rm{\overline{p}}-\Lambda$ data in the region of
momenta below 200 MeV/$c$. The observed deviation indicates a different
contribution of annihilation channels to the two systems containing strange
hadrons.


Abstract:
  For a monoidal $\infty$-category $\mathcal{M}$ with colimits, we study
colimits of $\mathcal{M}$-functors $\mathcal{A}\to\mathcal{B}$ where
$\mathcal{B}$ is left-tensored over $\mathcal{M}$ and $\mathcal{A}$ is an
$\mathcal{M}$-enriched category. We prove that the enriched Yoneda embedding
$Y:\mathcal{A}\to P_{\mathcal{M}}(\mathcal{A})$ yields a universal
$\mathcal{M}$-functor and, in the case when $\mathcal{A}$ has a certain
monoidal structure, the category of enriched presheaves
$P_{\mathcal{M}}(\mathcal{A})$ inherits the same monoidal structure.


Abstract:
  A model to determine the dose rate of a planar alpha-emitting surface, has
been developed. The approach presented is a computationally efficient
mathematical model using stopping range data from the Stopping Ranges of Ions
in Matter (SRIM) software. The alpha dose rates as a function of distance from
irradiated UO2 spent fuel surfaces were produced for bench-marking with
previous modelling attempts. This method is able to replicate a Monte Carlo
(MCNPX) study of an irradiated UO2 fuel surface within 0.6 % of the resulting
total dose rate and displays a similar dose profile.


Abstract:
  The D68 ringlet is the innermost feature in Saturn's rings. Four clumps that
appeared in D68 around 2014 remained evenly spaced about 30 degrees apart and
moved very slowly relative to each other from 2014 up until the last
measurements were taken in 2017. D68's narrowness and the distribution of
clumps could either indicate that we have a collection of source bodies in a
co-orbital configuration or imply that an outside force confines the observed
dust and any source bodies. In this paper we explore the possibility that these
four clumps arose from four source bodies in a co-orbital configuration. We
find that there are no solutions with four masses that produce the observed
spacings. We therefore consider whether an unseen fifth co-orbital object could
account for the discrepancies in the angular separations and approach a stable
stationary configuration. We find a range of solutions for five co-orbital
objects where their mass ratios depend on the assumed location of the fifth
mass. Numerical simulations of five co-orbitals are highly sensitive to initial
conditions, especially for the range of masses we would expect the D68 clumps
to have. The fragility of our D68 co-orbital system model implies that there is
probably some outside force confining the material in this ringlet.


Abstract:
  Discovering speaker independent acoustic units purely from spoken input is
known to be a hard problem. In this work we propose an unsupervised speaker
normalization technique prior to unit discovery. It is based on separating
speaker related from content induced variations in a speech signal with an
adversarial contrastive predictive coding approach. This technique does neither
require transcribed speech nor speaker labels, and, furthermore, can be trained
in a multilingual fashion, thus achieving speaker normalization even if only
few unlabeled data is available from the target language. The speaker
normalization is done by mapping all utterances to a medoid style which is
representative for the whole database. We demonstrate the effectiveness of the
approach by conducting acoustic unit discovery with a hidden Markov model
variational autoencoder noting, however, that the proposed speaker
normalization can serve as a front end to any unit discovery system.
Experiments on English, Yoruba and Mboshi show improvements compared to using
non-normalized input.


Abstract:
  A beautiful idea about the incompatibility of Physical Context(IPC) was
introduced in Phys. Rev. A 102, 050201(R) (2020). Here, a context is defined as
a set of a quantum state and two sharp rank-one measurements, and the
incompatibility of physical context is defined as the leakage of information
while implementing those two measurements successively in that quantum state.
In this work, we show the limitations in their approach. The three primary
limitations are that, (i) their approach is not positive operator-valued
measurements and (ii), they restrict information-theoretic agents Alice, Eve
and Bob to specific quantum operations and do not consider most general quantum
operations i.e., quantum instruments and (iii), their measure of IPC can take
negative values in specific cases in a more general scenario which implies the
limitation of their information measure. Thereby, we have introduced a
generalization and modification to their approach in more general and
convenient way, such that this idea is well-defined for generic measurements,
without these limitations. We also present a comparison of the measure of the
IPC through their and our method. Lastly, we show, how the IPC reduces in the
presence of memory using our modification, which further validates our
approach.


Abstract:
  Generative models are typically trained on grid-like data such as images. As
a result, the size of these models usually scales directly with the underlying
grid resolution. In this paper, we abandon discretized grids and instead
parameterize individual data points by continuous functions. We then build
generative models by learning distributions over such functions. By treating
data points as functions, we can abstract away from the specific type of data
we train on and construct models that are agnostic to discretization. To train
our model, we use an adversarial approach with a discriminator that acts on
continuous signals. Through experiments on a wide variety of data modalities
including images, 3D shapes and climate data, we demonstrate that our model can
learn rich distributions of functions independently of data type and
resolution.


Abstract:
  Industry 4.0 introduces new use cases, with more and more mobile devices
appearing in the industrial landscape. These applications require both new
technologies and smooth integration into existing brownfield deployments.
Emerging mobile use cases can be divided into optional mobile and mandatory
mobile, where the first point considers the use of wireless communications due
to soft criteria such as cost savings and the second means use cases that
cannot be covered by wireline technologies due to their movement, such as AGVs.
For most industrial applications, high determinism, E2E latency and
synchronicity are most important. Therefore, we provide a common table, based
on these requirements, listing both existing and emerging mobile use cases.
Since time synchronization is particularly demanding for wireless use cases, we
propose a concept for a simple but precise synchronization in IEEE 802.11 WLAN
and a suitable integration using TSN in combination with OPC UA technology as
examples. Furthermore, the concept is evaluated with the help of a testbed
utilizing state-of-the-art hardware. This means that this concept can be
directly applied in existing industry solutions. It can be shown that the
concept is already suitable for a wide range of the mandatory mobile
applications.


Abstract:
  Cardiovascular diseases and heart failures in particular are the main cause
of non-communicable disease mortality in the world. Constant patient monitoring
enables better medical treatment as it allows practitioners to react on time
and provide the appropriate treatment. Telemedicine can provide constant remote
monitoring so patients can stay in their homes, only requiring medical sensing
equipment and network connections. A limiting factor for telemedical centers is
the amount of patients that can be monitored simultaneously. We aim to increase
this amount by implementing a decision support system. This paper investigates
a machine learning model to estimate a risk score based on patient vital
parameters that allows sorting all cases every day to help practitioners focus
their limited capacities on the most severe cases. The model we propose reaches
an AUCROC of 0.84, whereas the baseline rule-based model reaches an AUCROC of
0.73. Our results indicate that the usage of deep learning to improve the
efficiency of telemedical centers is feasible. This way more patients could
benefit from better health-care through remote monitoring.


Abstract:
  Molecular dynamics calculations of inelastic collisions of atomic oxygen with
molecular nitrogen are known to show orders of magnitude discrepancies with
experimental results in the range from room temperature to many thousands of
degrees Kelvin. In this work, we have achieved an unprecedented quantitative
agreement with experiments even at low temperature, by including a
non-adiabatic treatment involving vibronic states on newly developed potential
energy surfaces. This result paves the way to the calculation of accurate and
detailed databases of vibrational energy exchange rates for this collisional
system. This is bound to have an impact on air plasma simulations in a wide
range of conditions and on the development of Very Low Earth Orbit (VLEO)
satellites, operating in the low thermosphere, objects of great technological
interest due to their potential at a competitive cost.


Abstract:
  We present a unifying framework in which both the $\nu$-Tamari lattice,
introduced by Pr\'eville-Ratelle and Viennot, and principal order ideals in
Young's lattice indexed by lattice paths $\nu$, are realized as the dual graphs
of two combinatorially striking triangulations of a family of flow polytopes
which we call the $\nu$-caracol flow polytopes. The first triangulation gives a
new geometric realization of the $\nu$-Tamari complex introduced by Ceballos,
Padrol and Sarmiento. We use the second triangulation to show that the
$h^*$-vector of the $\nu$-caracol flow polytope is given by the $\nu$-Narayana
numbers, extending a result of M\'esz\'aros when $\nu$ is a staircase lattice
path. Our work generalizes and unifies results on the dual structure of two
subdivisions of a polytope studied by Pitman and Stanley.