Abstract:
  Time series data is a collection of chronological observations which is
generated by several domains such as medical and financial fields. Over the
years, different tasks such as classification, forecasting, and clustering have
been proposed to analyze this type of data. Time series data has been also used
to study the effect of interventions over time. Moreover, in many fields of
science, learning the causal structure of dynamic systems and time series data
is considered an interesting task which plays an important role in scientific
discoveries. Estimating the effect of an intervention and identifying the
causal relations from the data can be performed via causal inference. Existing
surveys on time series discuss traditional tasks such as classification and
forecasting or explain the details of the approaches proposed to solve a
specific task. In this paper, we focus on two causal inference tasks, i.e.,
treatment effect estimation and causal discovery for time series data, and
provide a comprehensive review of the approaches in each task. Furthermore, we
curate a list of commonly used evaluation metrics and datasets for each task
and provide in-depth insight. These metrics and datasets can serve as
benchmarks for research in the field.


Abstract:
  The $\pi$-calculus is used as a model for programming languages. Its contexts
exhibit arbitrary concurrency, making them very discriminating. This may
prevent validating desirable behavioural equivalences in cases when more
disciplined contexts are expected. In this paper we focus on two such common
disciplines: sequentiality, meaning that at any time there is a single thread
of computation, and well-bracketing, meaning that calls to external services
obey a stack-like discipline. We formalise the disciplines by means of type
systems. The main focus of the paper is on studying the consequence of the
disciplines on behavioural equivalence. We define and study labelled
bisimilarities for sequentiality and well-bracketing. These relations are
coarser than ordinary bisimilarity. We prove that they are sound for the
respective (contextual) barbed equivalence, and also complete under a certain
technical condition. We show the usefulness of our techniques on a number of
examples, that have mainly to do with the representation of functions and
store.


Abstract:
  An analysis of the projectile motion in stagnant air is presented for an
evaporating respiratory micro-droplet which has been ejected from the mouth as
an isolated droplet. It is assumed that the air resistance is a nonlinear
function of the droplet's velocity and that the rate of decrease of the
droplet's external surface area depends only on the relative humidity and the
ambient temperature. The droplet's initial content is considered to be 98 wt%
water, 1 wt% salt and 1 wt% protein. The change of the average density of the
droplet due to water evaporation is determined, up to the instant when the
droplet reduces to its nucleus, consisting of salt and dry protein only. The
numerical solution of the governing differential equations of droplet's motion
gives the trajectories of different-sized droplets ejected at different
velocities and angles, and under different relative humidities and rates of
evaporation. The evaporation times are compared with the times for droplets to
reach the ground after being ejected from a given height. The maximum
horizontal and vertical distances reached by the droplet are evaluated in the
presence of wind and discussed in the context of possible infection spreading.


Abstract:
  We report an on-chip single mode microlaser with low-threshold fabricated on
Erbium doped lithium niobate on insulator (LNOI). The single mode laser
emission at 1550.5 nm wavelength is generated in a coupled photonic molecule,
which is facilitated by Vernier effect when pumping the photonic molecule at
970 nm. A threshold pump power as low as 200 uW is demonstrated thanks to the
high quality factor above 10^6. Moreover, the linewidth of the microlaser
reaches 4 kHz, which is the best result in LNOI microlasers. Such single mode
micro-laser lithographically fabricated on chip is highly in demand by photonic
community.


Abstract:
  Advances in reinforcement learning (RL) have resulted in recent breakthroughs
in the application of artificial intelligence (AI) across many different
domains. An emerging landscape of development environments is making powerful
RL techniques more accessible for a growing community of researchers. However,
most existing frameworks do not directly address the problem of learning in
complex operating environments, such as dense urban settings or defense-related
scenarios, that incorporate distributed, heterogeneous teams of agents. To help
enable AI research for this important class of applications, we introduce the
AI Arena: a scalable framework with flexible abstractions for distributed
multi-agent reinforcement learning. The AI Arena extends the OpenAI Gym
interface to allow greater flexibility in learning control policies across
multiple agents with heterogeneous learning strategies and localized views of
the environment. To illustrate the utility of our framework, we present
experimental results that demonstrate performance gains due to a distributed
multi-agent learning approach over commonly-used RL techniques in several
different learning environments.


Abstract:
  To study the resolution required for simulating gravitational fragmentation
with newly developed Lagrangian hydrodynamic schemes, Meshless Finite Volume
method (MFV) and Meshless Finite Mass method (MFM), we have performed a number
of simulations of the Jeans test and compared the results with both the
expected analytic solution and results from the more standard Lagrangian
approach: Smoothed Particle Hydrodynamics (SPH). We find that the different
schemes converge to the analytic solution when the diameter of a fluid element
is smaller than a quarter of the Jeans wavelength, $\lambda_\mathrm{J}$. Among
the three schemes, SPH/MFV shows the fastest/slowest convergence to the
analytic solution. Unlike the well-known behaviour of Eulerian schemes, none of
the Lagrangian schemes investigated displays artificial fragmentation when the
perturbation wavelength, $\lambda$, is shorter than $\lambda_\mathrm{J}$, even
at low numerical resolution. For larger wavelengths ($\lambda >
\lambda_\mathrm{J}$) the growth of the perturbation is delayed when it is not
well resolved. Furthermore, with poor resolution, the fragmentation seen with
the MFV scheme proceeds very differently compared to the converged solution.
All these results suggest that, when unresolved, the ratio of the magnitude of
hydrodynamic force to that of self-gravity at the sub-resolution scale is the
largest/smallest in MFV/SPH, the reasons for which we discussed in detail.
These tests are repeated to investigate the effect of kernels of higher-order
than the fiducial cubic spline. Our results indicate that the standard
deviation of the kernel is a more appropriate definition of the size of a fluid
element than its compact support radius.


Abstract:
  There is great interest in using near-term quantum computers to simulate and
study foundational problems in quantum mechanics and quantum information
science, such as the scrambling measured by an out-of-time-ordered correlator
(OTOC). Here we use an IBM Q processor, quantum error mitigation, and weaved
Trotter simulation to study high-resolution operator spreading in a 4-spin
Ising model as a function of space, time, and integrability. Reaching 4 spins
while retaining high circuit fidelity is made possible by the use of a
physically motivated fixed-node variant of the OTOC, allowing scrambling to be
estimated without overhead. We find clear signatures of ballistic operator
spreading in a chaotic regime, as well as operator localization in an
integrable regime. The techniques developed and demonstrated here open up the
possibility of using cloud-based quantum computers to study and visualize
scrambling phenomena, as well as quantum information dynamics more generally.


Abstract:
  With its high sensitivity, the Pyramid wavefront sensor (PyWFS) is becoming
an advantageous sensor for astronomical adaptive optics (AO) systems. However,
this sensor exhibits significant non-linear behaviours leading to challenging
AO control issues. In order to mitigate these effects, we propose to use, in
addition to the classical pyramid sensor, a focal plane image combined with a
convolutive description of the sensor to perform a fast tracking of the PyWFS
non-linearities, the so-called optical gains (OG). We show that this additional
focal plane imaging path only requires a small fraction of the total flux,
while representing a robust solution to estimate the PyWFS OG. Finally, we
demonstrate the gain brought by our method with the specific examples of
bootstrap and Non-Common Path Aberrations (NCPA) handling.


Abstract:
  Many unsupervised domain adaptation (UDA) methods exploit domain adversarial
training to align the features to reduce domain gap, where a feature extractor
is trained to fool a domain discriminator in order to have aligned feature
distributions. The discrimination capability of the domain classifier w.r.t the
increasingly aligned feature distributions deteriorates as training goes on,
thus cannot effectively further drive the training of feature extractor. In
this work, we propose an efficient optimization strategy named Re-enforceable
Adversarial Domain Adaptation (RADA) which aims to re-energize the domain
discriminator during the training by using dynamic domain labels. Particularly,
we relabel the well aligned target domain samples as source domain samples on
the fly. Such relabeling makes the less separable distributions more separable,
and thus leads to a more powerful domain classifier w.r.t. the new data
distributions, which in turn further drives feature alignment. Extensive
experiments on multiple UDA benchmarks demonstrate the effectiveness and
superiority of our RADA.


Abstract:
  The quest for safe high-energy batteries with "5V-class" cathodes and lithium
metal anodes drives research into solid electrolytes. However, reasons for the
large charge transfer resistances -- the major bottleneck of all-solid-state
batteries -- are still debated. In this article, we explore the processes in
incompressible solid electrolytes between blocking electrodes by theory-based
continuum modeling and numerical simulations. We investigate the experimentally
observed wide space-charge-zones in solid electrolytes, which are a possible
cause for the high interfacial resistances. On time scales relevant for battery
applications, we reduce our model equations. Analytic and numeric calculations
predict and study the actual structure of space-charge-layers in solid
electrolytes. To illustrate these dynamics and validate our model,
computational results are presented and compared with experimental
observations. Analog to semiconductors, we determine the material dependent,
asymmetric space-charge-layer width in the low temperature limit approximately.
This allows us to make an explicit statement about the influence of defect
concentrations and dielectric properties on the width of the
space-charge-layers in homogeneous solid electrolytes.


Abstract:
  We show that if an equivalence relation $E$ on a Polish space is a countable
union of smooth Borel subequivalence relations, then there is either a Borel
reduction of $E$ to a countable Borel equivalence relation on a Polish space or
a continuous embedding of $\mathbb{E}_1$ into $E$. We also establish related
results concerning countable unions of more general Borel equivalence
relations.


Abstract:
  The purpose of this note is to establish convergence of random walks on the
moduli space of Abelian differentials on compact Riemann surfaces in two
different modes: convergence of the $n$-step distributions from almost every
starting point in an affine invariant submanifold towards the associated affine
invariant measure, and almost sure pathwise equidistribution towards the affine
invariant measure on the $SL_2(\mathbb{R})$-orbit closure of an arbitrary
starting point. These are analogues to previous results for random walks on
homogeneous spaces.


Abstract:
  We discuss cross-link relations between the $\pi$ and $\rho$-meson channels
emerging from two different descriptions of the QCD vacuum: Instanton physics
and QCD sum rules with nonlocal condensates (NLC). We derive in both schemes an
intriguing linear relation between the $\pi$ and the $\rho^\|$-meson
distribution amplitudes in terms of their conformal coefficients and work out
the specific impact of the scalar NLC in these two channels. Using a simple
model with Gaussian decay of the scalar NLC, we are able to relate it to the
moments of the pion non-singlet parton distribution function measurable in
experiment -- a highly nontrivial result. The implications for the pion and the
$\rho^\|$-meson DAs entailed by the obtained cross-link relations are outlined
in terms of two generic scenarios.


Abstract:
  In order for an autonomous robot to efficiently explore an unknown
environment, it must account for uncertainty in sensor measurements, hazard
assessment, localization, and motion execution. Making decisions for maximal
reward in a stochastic setting requires value learning and policy construction
over a belief space, i.e., probability distribution over all possible
robot-world states. However, belief space planning in a large spatial
environment over long temporal horizons suffers from severe computational
challenges. Moreover, constructed policies must safely adapt to unexpected
changes in the belief at runtime. This work proposes a scalable value learning
framework, PLGRIM (Probabilistic Local and Global Reasoning on Information
roadMaps), that bridges the gap between (i) local, risk-aware resiliency and
(ii) global, reward-seeking mission objectives. Leveraging hierarchical belief
space planners with information-rich graph structures, PLGRIM addresses
large-scale exploration problems while providing locally near-optimal coverage
plans. We validate our proposed framework with high-fidelity dynamic
simulations in diverse environments and on physical robots in Martian-analog
lava tubes.


Abstract:
  We present a gauge formulation of the special affine algebra extended to
include an antisymmetric tensorial generator belonging to the tensor
representation of the special linear group. We then obtain a Maxwell modified
metric affine gravity action with a cosmological constant term. We find the
field equations of the theory and show that the theory reduces to an
Einstein-like equation for metric affine gravity with the source added to the
gravity equations with cosmological constant \mu contains linear contributions
from the new gauge fields. The reduction of the Maxwell metric affine gravity
to Riemann-Cartan one is discussed and the shear curvature tensor corresponding
to the symmetric part of the special linear connection is identified with the
dark energy. Furthermore, the new gauge fields interpreted as geometrical
inflaton vector fields which drive accelerated expansion.


Abstract:
  The global spread of COVID-19, the disease caused by the novel coronavirus
SARS-CoV-2, has cast a significant threat to mankind. As the COVID-19 situation
continues to evolve, predicting localized disease severity is crucial for
advanced resource allocation. This paper proposes a method named COURAGE
(COUnty aggRegation mixup AuGmEntation) to generate a short-term prediction of
2-week-ahead COVID-19 related deaths for each county in the United States,
leveraging modern deep learning techniques. Specifically, our method adopts a
self-attention model from Natural Language Processing, known as the transformer
model, to capture both short-term and long-term dependencies within the time
series while enjoying computational efficiency. Our model fully utilizes
publicly available information of COVID-19 related confirmed cases, deaths,
community mobility trends and demographic information, and can produce
state-level prediction as an aggregation of the corresponding county-level
predictions. Our numerical experiments demonstrate that our model achieves the
state-of-the-art performance among the publicly available benchmark models.


Abstract:
  We reconsider the problem of bounding higher derivative couplings in
consistent weakly coupled gravitational theories, starting from general
assumptions about analyticity and Regge growth of the S-matrix. Higher
derivative couplings are expected to be of order one in the units of the UV
cutoff. Our approach justifies this expectation and allows to prove precise
bounds on the order one coefficients. Our main tool are dispersive sum rules
for the S-matrix. We overcome the difficulties presented by the graviton pole
by measuring couplings at small impact parameter, rather than in the forward
limit. We illustrate the method in theories containing a massless scalar
coupled to gravity, and in theories with maximal supersymmetry.


Abstract:
  Tactile sensing is important for robots to perceive the world as it captures
the texture and hardness of the object in contact and is robust to illumination
and colour variances. However, due to the limited sensing area and the
resistance of the fixed surface, current tactile sensors have to tap the
tactile sensor on target object many times when assessing a large surface,
i.e., pressing, lifting up and shifting to another region. This process is
ineffective and time consuming. It is also undesirable to drag such sensors as
this often damages the sensitive membrane of the sensor or the object. To
address these problems, we propose a cylindrical optical tactile sensor named
TouchRoller that can roll around its center axis. It maintains being in contact
with the assessed surface throughout the entire motion, which allows for
measuring the object continuously and effectively. Extensive experiments show
that the TouchRoller sensor can cover a textured surface of 8cm*11cm in a short
time of 10s, much more effectively than a flat optical tactile sensor (in
196s). The reconstructed map of the texture from the collected tactile images
has a high Structural Similarity Index (SSIM) of 0.31 on average, when compared
with the visual texture. In addition, the contacts on the sensor can be
localised with a low localisation error, 2.63mm in the center regions and
7.66mm on average. The proposed sensor will enable the fast assessment of large
surfaces with high-resolution tactile sensing, and also the effective
collection of tactile images.


Abstract:
  Local available quantum correlations (LAQC), as defined by Mundarain et al.,
are analyzed for two subsets of 2-qubit X states. We start by studying X-states
that are symmetric under the exchange of subsystems, that is, those with the
same non-null local Bloch vector. We also analyze the subset of states that are
anti-symmetric under subsystem exchange, that is, those that have non-null
local Bloch vectors with an equal magnitude but opposite direction. We present
various examples and compare the obtained results to concurrence as an
entanglement measure and with quantum discord. We have also included markovian
decoherence, with the analysis of amplitude damping decoherence for Werner
states. As was previously observed for depolarization and phase damping
decoherence, LAQC did not exhibit sudden death behavior for Werner states under
amplitude damping decoherence.


Abstract:
  We demonstrate the low temperature suppression of the platinum (Pt) spin
Nernst angle in bilayers consisting of the antiferromagnetic insulator hematite
($\alpha$-Fe$_2$O$_3$) and Pt upon measuring the transverse spin Nernst
magnetothermopower (TSNM). We show that the observed signal stems from the
interplay between the interfacial spin accumulation in Pt originating from the
spin Nernst effect and the orientation of the N\'eel vector of
$\alpha$-Fe$_2$O$_3$, rather than its net magnetization. Since the latter is
negligible in an antiferromagnet, our device is superior to ferromagnetic
structures, allowing to unambiguously distinguish the TSNM from thermally
excited magnon transport (TMT), which usually dominates in ferri/ferromagnets
due to their non-zero magnetization. Evaluating the temperature dependence of
the effect, we observe a vanishing TSNM below ~100 K. We compare these results
with theoretical calculations of the temperature dependent spin Nernst
conductivity and find excellent agreement. This provides evidence for a
vanishing spin Nernst angle of Pt at low temperatures and the dominance of
extrinsic contributions to the spin Nernst effect.


Abstract:
  We report on the non-destructive measurement of Young's modulus of thin-film
single crystal beta gallium oxide (beta-Ga2O3) out of its nanoscale mechanical
structures by measuring their fundamental mode resonance frequencies. From the
measurements, we extract Young's modulus in (100) plane, EY,(100) =
261.4+/-20.6 GPa, for beta-Ga2O3 nanoflakes synthesized by low-pressure
chemical vapor deposition (LPCVD), and Young's modulus in [010] direction,
EY,[010] = 245.8+/-9.2 GPa, for beta-Ga2O3 nanobelts mechanically cleaved from
bulk beta-Ga2O3 crystal grown by edge-defined film-fed growth (EFG) method. The
Young's moduli extracted directly on nanomechanical resonant device platforms
are comparable to theoretical values from first-principle calculations and
experimentally extracted values from bulk crystal. This study yields important
quantitative nanomechanical properties of beta-Ga2O3 crystals, and helps pave
the way for further engineering of beta-Ga2O3 micro/nanoelectromechanical
systems (M/NEMS) and transducers.


Abstract:
  As passenger vehicle technologies have advanced, so have their capabilities
to avoid obstacles, especially with developments in tires, suspensions,
steering, as well as safety technologies like ABS, ESC, and more recently, ADAS
systems. However, environments around passenger vehicles have also become more
complex, and dangerous. There have previously been studies that outline driver
tendencies and performance capabilities when attempting to avoid obstacles
while driving passenger vehicles. Now that autonomous vehicles are being
developed with obstacle avoidance capabilities, it is important to target
performance that meets or exceeds that of human drivers. This manuscript
highlights systems that are crucial for an emergency obstacle avoidance
maneuver (EOAM) and identifies the state-of-the-art for each of the related
systems, while considering the nuances of traveling at highway speeds. Some of
the primary EOAM-related systems/areas that are discussed in this review are:
general path planning methods, system hierarchies, decision-making, trajectory
generation, and trajectory-tracking control methods. After concluding remarks,
suggestions for future work which could lead to an ideal EOAM development, are
discussed.


Abstract:
  We use an empirical relation to measure the HI scale height of relatively HI
rich galaxies using 21-cm observations. The galaxies were selected from the
BLUEDISK, THINGS and VIVA surveys. We aim to compare the thickness of the HI
layer of unusually HI rich with normal spiral galaxies and find any correlation
between the HI scale height with other galaxies properties. We found that on
average the unusually HI rich galaxies have similar HI disk thickness to the
control sample and the galaxies selected from the THINGS and VIVA surveys
within their uncertainties. Our result also show that the average thickness of
the neutral hydrogen inside the optical disk is correlated with the atomic gas
fraction inside the optical disk with a scatter of ~ 0.22 dex. A correlation is
also found between the HI scale height with the atomic-to-molecular gas ratio
which indicates the link between star formation and the vertical distribution
of HI which is consistent with previous studies. This new scaling relation
between the HI scale height and atomic gas fraction will allow us to predict
the HI scale height of a large number of galaxies but a larger sample is needed
to decrease the scatter.


Abstract:
  We define a new measure of causation from a fluctuation-response theorem for
Kullback-Leibler divergences, based on the information-theoretic cost of
perturbations. This information response has both the invariance properties
required for an information-theoretic measure and the physical interpretation
of a propagation of perturbations. In linear systems, the information response
reduces to the transfer entropy, providing a connection between Fisher and
mutual information.


Abstract:
  The streaming instability (SI) is a mechanism to aerodynamically concentrate
solids in protoplanetary disks and trigger the formation of planetesimals. The
SI produces strong particle clumping if the ratio of solid to gas surface
density -- an effective metallicity -- exceeds a critical value. This critical
value depends on particle sizes and disk conditions such as radial
drift-inducing pressure gradients and levels of turbulence. To quantify these
thresholds, we perform a suite of vertically-stratified SI simulations over a
range of dust sizes and metallicities. We find a critical metallicity as low as
0.4% for the optimum particle sizes and standard radial pressure gradients
(normalized value of $\Pi = 0.05$). This sub-Solar metallicity is lower than
previous results due to improved numerical methods and computational effort. We
discover a sharp increase in the critical metallicity for small solids, when
the dimensionless stopping time (Stokes number) is $\leq 0.01$. We provide
simple fits to the size-dependent SI clumping threshold, including
generalizations to different disk models and levels of turbulence. We also find
that linear, unstratified SI growth rates are a surprisingly poor predictor of
particle clumping in non-linear, stratified simulations, especially when the
finite resolution of simulations is considered. Our results widen the parameter
space for the SI to trigger planetesimal formation.


Abstract:
  In this note we give two small results concerning the correlations of the
Selberg sieve weights. We then use these estimates to derive a new
(conditional) lower bound on the variance of the primes in short intervals, and
also on the so-called `form factor' for the pair correlations of the zeros of
the Riemann zeta function. Our bounds ultimately rely on the estimates of
Bettin--Chandee for trilinear Kloosterman fractions.


Abstract:
  The ability to capture complex linguistic structures and long-term
dependencies among words in the passage is essential for discourse-level
relation extraction (DRE) tasks. Graph neural networks (GNNs), one of the
methods to encode dependency graphs, have been shown effective in prior works
for DRE. However, relatively little attention has been paid to receptive fields
of GNNs, which can be crucial for cases with extremely long text that requires
discourse understanding. In this work, we leverage the idea of graph pooling
and propose to use pooling-unpooling framework on DRE tasks. The pooling branch
reduces the graph size and enables the GNNs to obtain larger receptive fields
within fewer layers; the unpooling branch restores the pooled graph to its
original resolution so that representations for entity mention can be
extracted. We propose Clause Matching (CM), a novel linguistically inspired
graph pooling method for NLP tasks. Experiments on two DRE datasets demonstrate
that our models significantly improve over baselines when modeling long-term
dependencies is required, which shows the effectiveness of the
pooling-unpooling framework and our CM pooling method.


Abstract:
  Feed recommendation models are widely adopted by numerous feed platforms to
encourage users to explore the contents they are interested in. However, most
of the current research simply focus on targeting user's preference and lack
in-depth study of avoiding objectionable contents to be frequently recommended,
which is a common reason that let user detest. To address this issue, we
propose a Deep Latent Emotion Network (DLEN) model to extract latent
probability of a user preferring a feed by modeling multiple targets with
semi-supervised learning. With this method, the conflicts of different targets
are successfully reduced in the training phase, which improves the training
accuracy of each target effectively. Besides, by adding this latent state of
user emotion to multi-target fusion, the model is capable of decreasing the
probability to recommend objectionable contents to improve user retention and
stay time during online testing phase. DLEN is deployed on a real-world
multi-task feed recommendation scenario of Tencent QQ-Small-World with a
dataset containing over a billion samples, and it exhibits a significant
performance advantage over the SOTA MTL model in offline evaluation, together
with a considerable increase by 3.02% in view-count and 2.63% in user stay-time
in production. Complementary offline experiments of DLEN model on a public
dataset also repeat improvements in various scenarios. At present, DLEN model
has been successfully deployed in Tencent's feed recommendation system.


Abstract:
  We consider the problem of jointly estimating expectation values of many
Pauli observables, a crucial subroutine in variational quantum algorithms.
Starting with randomized measurements, we propose an efficient derandomization
procedure that iteratively replaces random single-qubit measurements with fixed
Pauli measurements; the resulting deterministic measurement procedure is
guaranteed to perform at least as well as the randomized one. In particular,
for estimating any $L$ low-weight Pauli observables, a deterministic
measurement on only of order $\log(L)$ copies of a quantum state suffices. In
some cases, for example when some of the Pauli observables have a high weight,
the derandomized procedure is substantially better than the randomized one.
Specifically, numerical experiments highlight the advantages of our
derandomized protocol over various previous methods for estimating the
ground-state energies of small molecules.


Abstract:
  This study integrates the educators digital competency (DC), as an individual
characteristic construct of the task-technology fit (TTF) theory, to examine a
better fit between Moodle using and teaching task, and to investigate its
effect on both Moodles utilization and their task performance. For assessing
our proposed hypotheses, an online survey was conducted with 238 teaching staff
from different departments of universities in Malaysia. Using Structural
Equation Modelling (SEM), our analysis revealed that all the proposed
components (i.e., technology literacy, knowledge deepening, presentation
skills, and professional skills) of digital competency significantly influenced
the TTF. The Task-Technology Fit was also found as an influential construct,
which positively and significantly affected both Moodles utilization and
teachers task performance. Besides, Moodles utilization was confirmed to be a
substantial determinant of the performance impact. In the end, this study
included limitations and future directions based on how the study's
contribution can support academics and practitioners for assessing and
understanding what particular components of digital competency impact TTF,
which in turn may influence the systems utilization and performance impact.


Abstract:
  Having recognized gender bias as a major issue affecting current translation
technologies, researchers have primarily attempted to mitigate it by working on
the data front. However, whether algorithmic aspects concur to exacerbate
unwanted outputs remains so far under-investigated. In this work, we bring the
analysis on gender bias in automatic translation onto a seemingly neutral yet
critical component: word segmentation. Can segmenting methods influence the
ability to translate gender? Do certain segmentation approaches penalize the
representation of feminine linguistic markings? We address these questions by
comparing 5 existing segmentation strategies on the target side of speech
translation systems. Our results on two language pairs (English-Italian/French)
show that state-of-the-art sub-word splitting (BPE) comes at the cost of higher
gender bias. In light of this finding, we propose a combined approach that
preserves BPE overall translation quality, while leveraging the higher ability
of character-based segmentation to properly translate gender.


Abstract:
  In this paper we recreate, and improve, the binary classification method for
particles proposed in Roe et al. (2005) paper "Boosted decision trees as an
alternative to artificial neural networks for particle identification". Such
particles are tau neutrinos, which we will refer to as background, and
electronic neutrinos: the signal we are interested in. In the original paper
the preferred algorithm is a Boosted decision tree. This is due to its low
effort tuning and good overall performance at the time. Our choice for
implementation is a deep neural network, faster and more promising in
performance. We will show how, using modern techniques, we are able to improve
on the original result, both in accuracy and in training time.


Abstract:
  We report extensive numerical simulations of different models of 2D polymer
rings with internal elasticity. We monitor the dynamical behavior of the rings
as a function of the packing fraction, to address the effects of particle
deformation on the collective response of the system. In particular, we compare
three different models: (i) a recently investigated model [Gnan \& Zaccarelli,
Nat. Phys. 15, 683 (2019)], where an inner hertzian field providing the
internal elasticity acts on the monomers of the ring, (ii) the same model where
the effect of such a field on the center of mass is balanced by opposite forces
and (iii) a semi-flexible model where an angular potential between adjacent
monomers induces strong particle deformations. By analyzing the dynamics of the
three models, we find that, in all cases, there exists a direct link between
the system fragility and particle asphericity. Among the three, only the first
model displays anomalous dynamics in the form of a super-diffusive behavior of
the mean squared displacement and of a compressed exponential relaxation of the
density auto-correlation function. We show that this is due to the combination
of internal elasticity and the out-of-equilibrium force self-generated by each
ring, both of which are necessary ingredients to induce such peculiar behavior
often observed in experiments of colloidal gels. These findings reinforce the
role of particle deformation, connected to internal elasticity, in driving the
dynamical response of dense soft particles.


Abstract:
  We consider a wireless federated learning system where multiple data holder
edge devices collaborate to train a global model via sharing their parameter
updates with an honest-but-curious parameter server. We demonstrate that the
inherent hardware-induced distortion perturbing the model updates of the edge
devices can be exploited as a privacy-preserving mechanism. In particular, we
model the distortion as power-dependent additive Gaussian noise and present a
power allocation strategy that provides privacy guarantees within the framework
of differential privacy. We conduct numerical experiments to evaluate the
performance of the proposed power allocation scheme under different levels of
hardware impairments.


Abstract:
  Recent data protection regulations (such as GDPR and CCPA) grant consumers
various rights, including the right to access, modify or delete any personal
information collected about them (and retained) by a service provider. To
exercise these rights, one must submit a verifiable consumer request proving
that collected data indeed pertains to them. This action is relatively
straightforward for consumers with active accounts with a service provider at
the time of data collection, since they can use standard (e.g., password-based)
means of authentication to validate their requests. However, a major conundrum
arises from the need to support consumers without accounts to exercise their
rights. To this end, some service providers began requiring these accountless
consumers to reveal and prove their identities (e.g., using government-issued
documents, utility bills or credit card numbers) as part of issuing a
verifiable consumer request. While understandable as a short-term cure, this
approach is, at the same time, cumbersome and expensive for service providers
as well as very privacy-invasive for consumers. Consequently, there is a strong
need to provide better means of authenticating requests from accountless
consumers. To achieve this, we propose VICEROY, a privacy-preserving and
scalable framework for producing proofs of data ownership, which can be used as
a basis for verifiable consumer requests. Building upon existing web techniques
and features (e.g., cookies), VICEROY allows accountless consumers to interact
with service providers, and later prove -- in a privacy-preserving manner --
that they are the same person, with minimal requirements for both parties. We
design and implement VICEROY with the emphasis on security/privacy,
deployability and usability. We also thoroughly assess its practicality via
extensive experiments.


Abstract:
  The advent of social media platforms has been a catalyst for the development
of digital photography that engendered a boom in vision applications. With this
motivation, we introduce a large-scale dataset termed 'Photozilla', which
includes over 990k images belonging to 10 different photographic styles. The
dataset is then used to train 3 classification models to automatically classify
the images into the relevant style which resulted in an accuracy of ~96%. With
the rapid evolution of digital photography, we have seen new types of
photography styles emerging at an exponential rate. On that account, we present
a novel Siamese-based network that uses the trained classification models as
the base architecture to adapt and classify unseen styles with only 25 training
samples. We report an accuracy of over 68% for identifying 10 other distinct
types of photography styles. This dataset can be found at
https://trisha025.github.io/Photozilla/


Abstract:
  We show that bulk operators lying between the outermost extremal surface and
the asymptotic boundary admit a simple boundary reconstruction in the classical
limit. This is the converse of the Python's lunch conjecture, which proposes
that operators with support between the minimal and outermost (quantum)
extremal surfaces - e.g. the interior Hawking partners - are highly complex.
Our procedure for reconstructing this "simple wedge" is based on the HKLL
construction, but uses causal bulk propagation of perturbed boundary conditions
on Lorentzian timefolds to expand the causal wedge as far as the outermost
extremal surface. As a corollary, we establish the Simple Entropy proposal for
the holographic dual of the area of a marginally trapped surface as well as a
similar holographic dual for the outermost extremal surface. We find that the
simple wedge is dual to a particular coarse-grained CFT state, obtained via
averaging over all possible Python's lunches. An efficient quantum circuit
converts this coarse-grained state into a "simple state" that is
indistinguishable in finite time from a state with a local modular Hamiltonian.
Under certain circumstances, the simple state modular Hamiltonian generates an
exactly local flow; we interpret this result as a holographic dual of black
hole uniqueness.


Abstract:
  Two recent large data releases for the Atacama Cosmology Telescope (ACT),
called DR4 and DR5, are available for public access. These data include
temperature and polarization maps that cover nearly half the sky at arcminute
resolution in three frequency bands; lensing maps and component-separated maps
covering ~ 2,100 deg^2 of sky; derived power spectra and cosmological
likelihoods; a catalog of over 4,000 galaxy clusters; and supporting ancillary
products including beam functions and masks. The data and products are
described in a suite of ACT papers; here we provide a summary. In order to
facilitate ease of access to these data we present a set of Jupyter IPython
notebooks developed to introduce users to DR4, DR5, and the tools needed to
analyze these data. The data products (excluding simulations) and the set of
notebooks are publicly available on the NASA Legacy Archive for Microwave
Background Data Analysis (LAMBDA); simulation products are available on the
National Energy Research Scientific Computing Center (NERSC).


Abstract:
  The nuclear obscurer of Active Galactic Nuclei (AGN) is poorly understood in
terms of its origin, geometry and dynamics. We investigate whether physically
motivated geometries emerging from hydro-radiative simulations can be
differentiated with X-ray reflection spectroscopy. For two new geometries, the
radiative fountain model of Wada (2012) and a warped disk, we release spectral
models produced with the ray tracing code XARS. We contrast these models with
spectra of three nearby AGN taken by NuSTAR and Swift/BAT. Along heavily
obscured sight-lines, the models present different 4-20keV continuum spectra.
These can be differentiated by current observations. Spectral fits of the
Circinus Galaxy favor the warped disk model over the radiative fountain, and
clumpy or smooth torus models. The necessary reflector (NH>10^25/cm^2) suggests
a hidden population of heavily Compton-thick AGN amongst local galaxies. X-ray
reflection spectroscopy is a promising pathway to understand the nuclear
obscurer in AGN.


Abstract:
  In an effort to find an effective interaction which can consistently be used
for both the mean-field part and the residual part in beyond mean-field
theories, properties of the Skyrme interactions as a residual interaction are
investigated. The time-dependent density-matrix theory (TDDM) is used as a
beyond mean-field theory and the ground states of $^{16}$O and $^{40}$Ca are
calculated using the five standard parametrizations of the Skyrme interaction
which differs in density and momentum dependence. It is found that the Skyrme
interaction which has strong density dependence and weak momentum dependence
induces substantial ground-state correlations comparable to the results of
other theoretical calculations.


Abstract:
  Due to road traffic accidents, 6941 Filipinos died in 2010, and thousands
more were wounded or disabled. Head and neck injuries are the main cause of
death, severe injury, and motorcycle users disabilities. Motorcycle users make
up a large proportion of those on the road who were killed. The main purpose of
the study is to develop an MCU Based Motorcycle System for Optimum Road Safety
with Anti-theft Capability that will help motorcycle riders to be safe while
travelling in national roads. The researchers will be using the prototyping
methodology where in a prototype is built according to the initial requirements
gathered from the motorists themselves. The expected result of the proposed
methodology is the system will be utilizing the different function of each
modules to ensure that the riders will be able to detect and avoid possible
danger while on the road. As a result of different literature in relation to
each module, the system is expected to provide a new leap to ensure the safety
of all riders here in the Philippines. Future studies will ensure the
development of the system, provide testing and improve the functionality of the
system depending on the test result. Due to the high increase in the number of
cars and motorcycle travelling on national road, the percentage of accidents
also is getting higher. In line with that, the proposed system is expected to
lessen the percentage of accident by avoiding the possible cause of it.


Abstract:
  We present a microscopic quantum theory of light-matter interaction in
pristine sheets of two-dimensional semiconductors coupled to localized
electromagnetic resonators such as optical nanocavities or plasmonic particles.
The light-matter interaction breaks the translation symmetry of excitons in the
two-dimensional lattice, and we find that this symmetry-breaking interaction
leads to the formation of a localized exciton state, which mimics the spatial
distribution of the electromagnetic field of the resonator. The localized
exciton state is in turn coupled to an environment of residual exciton states.
We quantify the influence of the environment and find that it is most
pronounced for small lateral confinement length scales of the electromagnetic
field in the resonator, and that environmental effects can be neglected if this
length scale is sufficiently large. The microscopic theory provides a
physically appealing derivation of the coupled oscillator models widely used to
model experiments on these types of systems, in which all observable quantities
are directly derived from the material parameters and the properties of the
resonant electromagnetic field. As a consistency check, we show that the theory
recovers the results of semiclassical electromagnetic calculations and
experimental measurements of the excitonic dielectric response in the linear
excitation limit. The theory, however, is not limited to linear response, and
in general describes nonlinear exciton-exciton interactions in the localized
exciton state, thereby providing a powerful means of investigating the
nonlinear optical response of such systems.


Abstract:
  Hidden Markov models (HMMs) and their extensions have proven to be powerful
tools for classification of observations that stem from systems with temporal
dependence as they take into account that observations close in time are likely
generated from the same state (i.e.\ class). When information on the classes of
the observations is available in advanced, supervised methods can be applied.
In this paper, we provide details for the implementation of four models for
classification in a supervised learning context: HMMs, hidden semi-Markov
models (HSMMs), autoregressive-HMMs, and autoregressive-HSMMs. Using
simulations, we study the classification performance under various degrees of
model misspecification to characterize when it would be important to extend a
basic HMM to an HSMM. As an application of these techniques we use the models
to classify accelerometer data from Merino sheep to distinguish between four
different behaviors of interest. In particular in the field of movement
ecology, collection of fine-scale animal movement data over time to identify
behavioral states has become ubiquitous, necessitating models that can account
for the dependence structure in the data. We demonstrate that when the aim is
to conduct classification, various degrees of model misspecification of the
proposed model may not impede good classification performance unless there is
high overlap between the state-dependent distributions, that is, unless the
observation distributions of the different states are difficult to
differentiate.


Abstract:
  In this study, we propose a novel heuristic two-step algorithm for shared
ridehailing in which users can share their rides with only one more user. The
algorithm, which is centrally formulated, starts with matching users and
creating a set of passenger pairs in step 1 and is followed by solving an
assignment problem to assign passenger pairs to the vehicles. To solve the
problem of high computational time in dynamic ride-matching problems, we
propose a distributed system that is based on vehicle to infrastructure (V2I)
and infrastructure to infrastructure (I2I) communication. To evaluate the
distributed system's performance, we compare it with the proposed centralized
ridehailing algorithm. Both centralized and distributed systems are implemented
in a micro-traffic simulator to assess their performance and their impact on
traffic congestion. Downtown Toronto road network was chosen as the study area.
Based on our obtained results, the service rate of the distributed system was
91.59% which is close to 95.80% in the centralized system. However, the
distributed system yielded much lower computational time compared to
centralized. Furthermore, the scalability of the distributed system was shown
by testing it on a small network and comparing with the entire network.


Abstract:
  Results on the existence, uniqueness and strict comparison for solutions to a
BSDE driven by a multi-dimensional RCLL martingale are established. The goal is
to develop a general multi-asset framework encompassing a wide spectrum of
nonlinear financial models with jumps, including as particular cases the setups
studied by Peng and Xu \cite{PX2009,PX2010} and Dumitrescu et al.
\cite{DGQS2018} who dealt with BSDEs driven by a one-dimensional Brownian
motion and a purely discontinuous martingale with a single jump.


Abstract:
  The Chow group of zero cycles in the moduli space of stable pointed curves of
genus zero is isomorphic to the integer additive group. Let $M$ be monomial in
this Chow group. If no two factors of $M$ fulfill a particular quadratic
relation, then the monomial can be represented equivalently by a specific tree;
otherwise, $M$ is mapped to zero under the stated isomorphism. Starting from
this tree representation, we introduce a graphical algorithm for computing the
corresponding integer for $M$ under the aforementioned isomorphism. The
algorithm is linear with respect to the size of the tree.


Abstract:
  Various work has suggested that the memorability of an image is consistent
across people, and thus can be treated as an intrinsic property of an image.
Using computer vision models, we can make specific predictions about what
people will remember or forget. While older work has used now-outdated deep
learning architectures to predict image memorability, innovations in the field
have given us new techniques to apply to this problem. Here, we propose and
evaluate five alternative deep learning models which exploit developments in
the field from the last five years, largely the introduction of residual neural
networks, which are intended to allow the model to use semantic information in
the memorability estimation process. These new models were tested against the
prior state of the art with a combined dataset built to optimize both
within-category and across-category predictions. Our findings suggest that the
key prior memorability network had overstated its generalizability and was
overfit on its training set. Our new models outperform this prior model,
leading us to conclude that Residual Networks outperform simpler convolutional
neural networks in memorability regression. We make our new state-of-the-art
model readily available to the research community, allowing memory researchers
to make predictions about memorability on a wider range of images.


Abstract:
  In this work, we analyze the noncommutative three-dimensional Coulomb
potential problem. For this purpose, we used the Kustaanheimo-Stiefel mapping
to write the Schr\"odinger equation for Coulomb potential in a solvable way.
Then, the noncommutative hydrogen-like atoms were treated, and their energy
levels were found. In addition, we estimate a bound for the noncommutativity
parameter.


Abstract:
  The demand for high-performance anomaly detection techniques of IoT data
becomes urgent, especially in industry field. The anomaly identification and
explanation in time series data is one essential task in IoT data mining. Since
that the existing anomaly detection techniques focus on the identification of
anomalies, the explanation of anomalies is not well-solved. We address the
anomaly explanation problem for multivariate IoT data and propose a 3-step
self-contained method in this paper. We formalize and utilize the domain
knowledge in our method, and identify the anomalies by the violation of
constraints. We propose set-cover-based anomaly explanation algorithms to
discover the anomaly events reflected by violation features, and further
develop knowledge update algorithms to improve the original knowledge set.
Experimental results on real datasets from large-scale IoT systems verify that
our method computes high-quality explanation solutions of anomalies. Our work
provides a guide to navigate the explicable anomaly detection in both IoT fault
diagnosis and temporal data cleaning.


Abstract:
  Policy evaluation is an important instrument for the comparison of different
algorithms in Reinforcement Learning (RL). Yet even a precise knowledge of the
value function $V^{\pi}$ corresponding to a policy $\pi$ does not provide
reliable information on how far is the policy $\pi$ from the optimal one. We
present a novel model-free upper value iteration procedure $({\sf UVIP})$ that
allows us to estimate the suboptimality gap $V^{\star}(x) - V^{\pi}(x)$ from
above and to construct confidence intervals for $V^\star$. Our approach relies
on upper bounds to the solution of the Bellman optimality equation via
martingale approach. We provide theoretical guarantees for ${\sf UVIP}$ under
general assumptions and illustrate its performance on a number of benchmark RL
problems.


Abstract:
  We introduce a new class of collider-type observables in conformal field
theories which we call generalized event shapes. They are defined as matrix
elements of light-ray operators that are sensitive to the longitudinal, or
time-dependent, structure of the state produced in the collision. Generalized
event shapes can be studied using both correlation functions and scattering
amplitudes. They are infrared finite and smoothly transit over to the familiar
event shapes. We compute them in planar ${\cal N}=4$ super-Yang-Mills theory at
weak and strong coupling, and study their physical properties. We show that at
strong coupling both the stringy and quantum-gravitational corrections to the
energy-energy correlation exhibit longitudinal broadening that manifests itself
through the presence of long-time tails in the energy flux measured by the
detectors.


Abstract:
  Electrical control of magnetism has great potential for low-power spintronics
applications and the newly discovered two-dimensional van-der-Waals magnetic
materials are promising systems for this type of applications. In fact, it has
been recently shown experimentally that upon electrostatic doping by electrons
bilayer CrI3 undergoes an antiferromagnetic-ferromagnetic (AFM-FM) phase
transition, even in the absence of magnetic field. Doping by holes, on the
other hand, does not induce the same transition in the experiment, which points
to an intrinsic asymmetry in the hole and electron doping that limits the
control of the transition by doping. We here show, based on first-principles
calculations, that the asymmetry originates in the relativistic nature of the
valence-band-edge states of the pristine bilayer, which inhibits the magnetic
transition upon hole doping. Based on this finding, we propose an approach to
engineer this system so that it displays the AFM-FM transition for both hole
and electron doping by using moderate uniaxial strain along the soft direction
of the bilayer.


Abstract:
  We consider the problem of sequentially allocating resources in a censored
semi-bandits setup, where the learner allocates resources at each step to the
arms and observes loss. The loss depends on two hidden parameters, one specific
to the arm but independent of the resource allocation, and the other depends on
the allocated resource. More specifically, the loss equals zero for an arm if
the resource allocated to it exceeds a constant (but unknown) arm dependent
threshold. The goal is to learn a resource allocation that minimizes the
expected loss. The problem is challenging because the loss distribution and
threshold value of each arm are unknown. We study this setting by establishing
its `equivalence' to Multiple-Play Multi-Armed Bandits (MP-MAB) and
Combinatorial Semi-Bandits. Exploiting these equivalences, we derive optimal
algorithms for our problem setting using known algorithms for MP-MAB and
Combinatorial Semi-Bandits. The experiments on synthetically generated data
validate the performance guarantees of the proposed algorithms.


Abstract:
  We prove uniqueness and existence theorems for four-dimensional
asymptotically flat, Ricci-flat, gravitational instantons with a torus
symmetry. In particular, we prove that such instantons are uniquely
characterised by their rod structure, which is data that encodes the fixed
point sets of the torus action. Furthermore, we establish that for every
admissible rod structure there exists an instanton that is smooth up to
possible conical singularities at the axes of symmetry. The proofs involve
adapting the methods that are used to establish black hole uniqueness theorems,
to a harmonic map formulation of Ricci-flat metrics with torus symmetry, where
the target space is directly related to the metric (rather than auxiliary
potentials). We also give an elementary proof of the nonexistence of
asymptotically flat toric half-flat instantons. Finally, we derive a general
set of identities that relate asymptotic invariants such as the mass to the rod
structure.


Abstract:
  Relay-assisted free-space optical (FSO) communication systems are exploited
as a means to mitigate the limiting effects of the turbulence induced
atmospheric scintillation. However, conventional ground relays are stationary,
and their optimal placement is not always feasible. Due to their mobility and
flexibility, unmanned aerial vehicles (UAVs) provide new opportunities for FSO
relaying systems. In this paper, a hovering UAV-based serial FSO
decode-and-forward relaying system is investigated. In the channel modelling
for such a system, four types of impairments (i.e., atmospheric loss,
atmospheric turbulence, pointing error, and link interruption due to
angle-of-arrival fluctuation) are considered. Based on the proposed channel
model, a tractable expression for the probability density function of the total
channel gain is obtained. Closed-form expressions of the link outage
probability and end-to-end outage probability are derived. Asymptotic outage
performance bounds for each link and the overall system are also presented to
reveal insights into the impacts of different impairments. To improve system
performance, we optimize the beam width, field-of-view and UAVs' locations.
Numerical results show that the derived theoretical expressions are accurate to
evaluate the outage performance of the system. Moreover, the proposed
optimization schemes are efficient and can improve performance significantly.


Abstract:
  The problem of anomaly detection in astronomical surveys is becoming
increasingly important as data sets grow in size. We present the results of an
unsupervised anomaly detection method using a Wasserstein generative
adversarial network (WGAN) on nearly one million optical galaxy images in the
Hyper Suprime-Cam (HSC) survey. The WGAN learns to generate realistic HSC-like
galaxies that follow the distribution of the data set; anomalous images are
defined based on a poor reconstruction by the generator and outlying features
learned by the discriminator. We find that the discriminator is more attuned to
potentially interesting anomalies compared to the generator, and compared to a
simpler autoencoder-based anomaly detection approach, so we use the
discriminator-selected images to construct a high-anomaly sample of
$\sim$13,000 objects. We propose a new approach to further characterize these
anomalous images: we use a convolutional autoencoder to reduce the
dimensionality of the residual differences between the real and
WGAN-reconstructed images and perform UMAP clustering on these. We report
detected anomalies of interest including galaxy mergers, tidal features, and
extreme star-forming galaxies. A follow-up spectroscopic analysis of one of
these anomalies is detailed in the Appendix; we find that it is an unusual
system most likely to be a metal-poor dwarf galaxy with an extremely blue,
higher-metallicity HII region. We have released a catalog with the WGAN anomaly
scores; the code and catalog are available at
https://github.com/kstoreyf/anomalies-GAN-HSC, and our interactive
visualization tool for exploring the clustered data is at
https://weirdgalaxi.es.


Abstract:
  We consider all 16 unary operations that, given a homogeneous binary relation
R, define a new one by a boolean combination of xRy and yRx. Operations can be
composed, and connected by pointwise-defined logical junctors. We consider the
usual properties of relations, and allow them to be lifted by prepending an
operation.
  We investigate extensional equality between lifted properties (e.g. a
relation is connex iff its complement is asymmetric), and give a table to
decide this equality. Supported by a counter-example generator and a resolution
theorem prover, we investigate all 3-atom implications between lifted
properties, and give a sound and complete axiom set for them (containing e.g.
"if R's complement is left Euclidean and R is right serial, then R's symmetric
kernel is left serial").


Abstract:
  It is conceivable that an RNA virus could use a polysome, that is, a string
of ribosomes covering the RNA strand, to protect the genetic material from
degradation inside a host cell. This paper discusses how such a virus might
operate, and how its presence might be detected by ribosome profiling. There
are two possible forms for such a polysomally protected virus, depending upon
whether just the forward strand or both the forward and complementary strands
can be encased by ribosomes (these will be termed type 1 and type 2,
respectively). It is argued that in the type 2 case the viral RNA would evolve
an ambigrammatic property, whereby the viral genes are free of stop codons in a
reverse reading frame (with forward and reverse codons aligned). Recent
observations of ribosome profiles of ambigrammatic narnavirus sequences are
consistent with our predictions for the type 2 case.


Abstract:
  Diversity in patent language is growing and makes finding synonyms for
conducting patent searches more and more challenging. In addition to that, most
approaches for dealing with diverse patent language are based on manual search
and human intuition. In this paper, a word embedding approach using statistical
analysis of human labeled data to produce accurate and language independent
word vectors for technical terms is introduced. This paper focuses on the
explanation of the idea behind the statistical analysis and shows first
qualitative results. The resulting algorithm is a development of the former
EQMania UG (eqmania.com) and can be tested under eqalice.com until April 2021.


Abstract:
  Silicene is a promising 2D Dirac material as a building block for van der
Waals heterostructures (vdWHs). Here we investigate the electronic properties
of hexagonal boron nitride/silicene (BN/Si) vdWHs using first-principles
calculations. We calculate the energy band structures of BN/Si/BN
heterostructures with different rotation angles and find that the electronic
properties of silicene are retained and protected robustly by the BN layers. In
BN/Si/BN/Si/BN heterostructure, we find that the band structure near the Fermi
energy is sensitive to the stacking configurations of the silicene layers due
to interlayer coupling. The coupling is reduced by increasing the number of BN
layers between the silicene layers and becomes negligible in BN/Si/(BN)3/Si/BN.
In (BN)n/Si superlattices, the band structure undergoes a conversion from Dirac
lines to Dirac points by increasing the number of BN layers between the
silicene layers. Calculations of silicene sandwiched by other 2D materials
reveal that silicene sandwiched by low-carbon-doped boron nitride or HfO2 is
semiconducting.


Abstract:
  Let $\mathbb{K}$ be a field and $R = \mathbb{K}[x_1, \ldots, x_n]$. We obtain
an improved upper bound for asymptotic resurgence of squarefree monomial ideals
in $R$. We study the effect on the resurgence when sum, product and
intersection of ideals are taken. We obtain sharp upper and lower bounds for
the resurgence and asymptotic resurgence of cover ideals of finite simple
graphs in terms of associated combinatorial invariants. We also explicitly
compute the resurgence and asymptotic resurgence of cover ideals of several
classes of graphs. We characterize a graph being bipartite in terms of the
resurgence and asymptotic resurgence of edge and cover ideals. We also compute
explicitly the resurgence and asymptotic resurgence of edge ideals of some
classes of graphs.


Abstract:
  We introduce a simple but powerful technique to study processes driven by two
or more reinforcement mechanisms in competition. We apply our method to two
types of models: to non conservative zero range processes on finite graphs, and
to multi-particle random walks with positive and negative reinforcement on the
edges. The results hold for a broad class of reinforcement functions, including
those with superlinear growth. Our technique consists in a comparison of the
original processes with suitable reference models. To implement the comparison
we estimate a Radon-Nikodym derivative on a carefully chosen set of
trajectories. Our results describe the almost surely long time behaviour of the
processes. We also prove a phase transition depending on the strength of the
reinforcement functions.


Abstract:
  Context: Magnetic null points are associated with high-energy coronal
phenomena such as solar flares and are often sites of reconnection and particle
acceleration. Dynamic twisting of a magnetic null point can generate a
Kelvin-Helmholtz instability (KHI) within its fan plane and, under continued
twisting, can instigate spine-fan reconnection and an associated collapse of
the null point.
  Aim: This article aims to compare the effects of isotropic and anisotropic
viscosity in simulations of the KHI and collapse in a dynamically twisted
magnetic null point.
  Methods: Simulations were performed using the 3D magnetohydrodynamics code
Lare3d with a custom anisotropic viscosity module. A pair of high resolution
simulations was performed, one using isotropic viscosity and another using
anisotropic viscosity, keeping all other factors identical, and the results
analysed in detail. A further parameter study was performed over a range of
values for viscosity and resistivity.
  Results: Both viscosity models permit the growth of the Kelvin-Helmholtz
instability and the eventual collapse of the null point. Over all studied
parameters, anisotropic viscosity allows a faster growing instability, while
isotropic viscosity damps the instability to the extent of stabilisation in
some cases. Although the viscous heating associated with anisotropic viscosity
is generally smaller, the ohmic heating dominates and is enhanced by the
current sheets generated by the instability, leading to a greater overall
heating rate when using anisotropic viscosity. The collapse of the null point
occurs significantly sooner when anisotropic viscosity is employed.


Abstract:
  International Classification of Disease (ICD) coding procedure which refers
to tagging medical notes with diagnosis codes has been shown to be effective
and crucial to the billing system in medical sector. Currently, ICD codes are
assigned to a clinical note manually which is likely to cause many errors.
Moreover, training skilled coders also requires time and human resources.
Therefore, automating the ICD code determination process is an important task.
With the advancement of artificial intelligence theory and computational
hardware, machine learning approach has emerged as a suitable solution to
automate this process. In this project, we apply a transformer-based
architecture to capture the interdependence among the tokens of a document and
then use a code-wise attention mechanism to learn code-specific representations
of the entire document. Finally, they are fed to separate dense layers for
corresponding code prediction. Furthermore, to handle the imbalance in the code
frequency of clinical datasets, we employ a label distribution aware margin
(LDAM) loss function. The experimental results on the MIMIC-III dataset show
that our proposed model outperforms other baselines by a significant margin. In
particular, our best setting achieves a micro-AUC score of 0.923 compared to
0.868 of bidirectional recurrent neural networks. We also show that by using
the code-wise attention mechanism, the model can provide more insights about
its prediction, and thus it can support clinicians to make reliable decisions.
Our code is available online (https://github.com/biplob1ly/TransICD)


Abstract:
  The aim of this article is to relate the discrete quantum walk on
$\mathbb{Z}$ with the continuous Schr\"odinger operator on $\mathbb{R}$ in the
scattering problem. Each point of $\mathbb{Z}$ is associated with a barrier of
the potential, and the coin operator of the quantum walk is determined by the
transfer matrix between bases of WKB solutions on the classically allowed
regions of both sides of the barrier. This correspondence enables us to
represent each entry of the scattering matrix of the Schr\"odinger operator as
a countable sum of probability amplitudes associated with the paths of the
quantum walker. In particular, the barrier-top scattering corresponds to the
Hadamard walk in the semiclassical limit.


Abstract:
  A viable quantum theory does not allow curvature invariant terms of different
higher orders to be accommodated in the gravitational action. We show that
there is indeed a conflict between the curvature squared and Gauss-Bonnet
squared terms from the point of view of hermiticity. This means one should
choose either, in addition to the Einstein-Hilbert term, but never the two
together. We explore early cosmic evolution with Gauss-Bonnet squared term.


Abstract:
  In the scanning transmission electron microscope, fast-scanning and
frame-averaging are two widely used approaches for reducing electron-beam
damage and increasing image signal-noise ratio which require no additional
specialised hardware. Unfortunately, for scans with short pixel dwell-times
(less than 5 ${\mu}$s), line flyback time represents an increasingly wasteful
overhead. Although beam exposure during flyback causes damage while yielding no
useful information, scan-coil hysteresis means that eliminating it entirely
leads to unacceptably distorted images. In this work, we reduce this flyback to
an absolute minimum by calibrating and correcting for this hysteresis in
postprocessing. Substantial improvements in dose-efficiency can be realised (up
to 20 %), while crystallographic and spatial fidelity is maintained for
displacement/strain measurement.


Abstract:
  Radiation-dust driven outflows, where radiation pressure on dust grains
accelerates gas, occur in many astrophysical environments. Almost all previous
numerical studies of these systems have assumed that the dust was
perfectly-coupled to the gas. However, it has recently been shown that the dust
in these systems is unstable to a large class of 'resonant drag instabilities'
(RDIs) which de-couple the dust and gas dynamics and could qualitatively change
the nonlinear outcome of these outflows. We present the first simulations of
radiation-dust driven outflows in stratified, inhomogeneous media, including
explicit grain dynamics and a realistic spectrum of grain sizes and charge,
magnetic fields and Lorentz forces on grains (which dramatically enhance the
RDIs), Coulomb and Epstein drag forces, and explicit radiation transport
allowing for different grain absorption and scattering properties. In this
paper we consider conditions resembling giant molecular clouds (GMCs), HII
regions, and distributed starbursts, where optical depths are modest ($\lesssim
1$), single-scattering effects dominate radiation-dust coupling, Lorentz forces
dominate over drag on grains, and the fastest-growing RDIs are similar, such as
magnetosonic and fast-gyro RDIs. These RDIs generically produce strong
size-dependent dust clustering, growing nonlinear on timescales that are much
shorter than the characteristic times of the outflow. The instabilities produce
filamentary and plume-like or 'horsehead' nebular morphologies that are
remarkably similar to observed dust structures in GMCs and HII regions.
Additionally, in some cases they strongly alter the magnetic field structure
and topology relative to filaments. Despite driving strong micro-scale dust
clumping which leaves some gas 'behind,' an order-unity fraction of the gas is
always efficiently entrained by dust.


Abstract:
  Infectious diseases are a significant threat to human society which was over
sighted before the incidence of COVID-19, although according to the report of
the World Health Organisation (WHO) about 4.2 million people die annually due
to infectious disease. Due to recent COVID-19 pandemic, more than 2 million
people died during 2020 and 96.2 million people got affected by this
devastating disease. Recent research shows that applying individual
interactions and movements data could help managing the pandemic though
modelling the spread of infectious diseases on social contact networks.
Infectious disease spreading can be explained with the theories and methods of
diffusion processes where a dynamic phenomena evolves on networked systems. In
the modelling of diffusion process, it is assumed that contagious items spread
out in the networked system through the inter-node interactions. This resembles
spreading of infectious virus, e.g. spread of COVID-19, within a population
through individual social interactions. The evolution behaviours of the
diffusion process are strongly influenced by the characteristics of the
underlying system and the mechanism of the diffusion process itself. Thus,
spreading of infectious disease can be explained how people interact with each
other and by the characteristics of the disease itself. This paper presenters
the relevant theories and methodologies of diffusion process that can be used
to model the spread of infectious diseases.


Abstract:
  The nearby elliptical galaxy IC4296 has produced a large (510 kpc)
low-luminosity radio source with typical FR I core/jet/lobe morphology. The
unprecedented combination of brightness sensitivity, dynamic range, and angular
resolution of a new 1.28 GHz MeerKAT continuum image reveals striking new
morphological features which we call threads, ribbons, and rings. The threads
are faint narrow emission features originating where helical Kelvin-Helmholtz
instabilities disrupt the main radio jets. The ribbons are smooth regions
between the jets and the lobes, and they appear to be relics of jets powered by
earlier activity that have since come into pressure equilibrium. Vortex rings
in the outer portions of the lobes and their backflows indicate that the
straight outer jets and ribbons are inclined by $i = 60 \pm 5^\circ$ from the
line-of-sight, in agreement with photometric, geometric, and gas-dynamical
estimates of inclination angles near the nucleus.


Abstract:
  Let $A$ be an $n\times n$ matrix and let $\vee^k A$ be its $k$-th symmetric
tensor product. We express the normalized trace of $\vee^k A$ as an integral of
the $k$-th powers of the numerical values of $A$ over the unit sphere
$\mathbb{S}^{n}$ of $\mathbb{C}^{n}$ with respect to the normalized Euclidean
surface measure. Equivalently, this expression in turn can be interpreted as an
integral representation for the (normalized) complete symmetric polynomials
over $\mathbb{C}^n$. As applications, we present a new proof for the MacMahon
Master Theorem in enumerative combinatorics. Then, our next application deals
with a generalization of the work of Cuttler et al. in \cite{cuttler}
concerning the monotonicity of products of complete symmetric polynomials. In
the process, we give a solution to an open problem that was raised by I.
Roven\c{t}a and L. E. Temereanca in \cite{roventa}.


Abstract:
  Communicating the robot state is vital to creating an efficient and
trustworthy collaboration between humans and collaborative robots (cobots).
Standard approaches for Robot-to-human communication face difficulties in
industry settings, e.g., because of high noise levels or certain visibility
requirements. Therefore, this paper presents zoomorphic gestures based on dog
body language as a possible alternative for communicating the state of
appearance-constrained cobots. For this purpose, we conduct a visual
communication benchmark comparing zoomorphic gestures, abstract gestures, and
light displays. We investigate the modalities regarding intuitive
understanding, user experience, and user preference. In a first user study (n =
93), we evaluate our proposed design guidelines for all visual modalities. A
second user study (n = 214) constituting the benchmark indicates that intuitive
understanding and user experience are highest for both gesture-based
modalities. Furthermore, zoomorphic gestures are considerably preferred over
other modalities. These findings indicate that zoomorphic gestures with their
playful nature are especially suitable for novel users and may decrease initial
inhibitions.


Abstract:
  Measuring and promoting policy diversity is critical for solving games with
strong non-transitive dynamics where strategic cycles exist, and there is no
consistent winner (e.g., Rock-Paper-Scissors). With that in mind, maintaining a
pool of diverse policies via open-ended learning is an attractive solution,
which can generate auto-curricula to avoid being exploited. However, in
conventional open-ended learning algorithms, there are no widely accepted
definitions for diversity, making it hard to construct and evaluate the diverse
policies. In this work, we summarize previous concepts of diversity and work
towards offering a unified measure of diversity in multi-agent open-ended
learning to include all elements in Markov games, based on both Behavioral
Diversity (BD) and Response Diversity (RD). At the trajectory distribution
level, we re-define BD in the state-action space as the discrepancies of
occupancy measures. For the reward dynamics, we propose RD to characterize
diversity through the responses of policies when encountering different
opponents. We also show that many current diversity measures fall in one of the
categories of BD or RD but not both. With this unified diversity measure, we
design the corresponding diversity-promoting objective and population
effectivity when seeking the best responses in open-ended learning. We validate
our methods in both relatively simple games like matrix game, non-transitive
mixture model, and the complex \textit{Google Research Football} environment.
The population found by our methods reveals the lowest exploitability, highest
population effectivity in matrix game and non-transitive mixture model, as well
as the largest goal difference when interacting with opponents of various
levels in \textit{Google Research Football}.


Abstract:
  We report the feasibility of using magnetoentropic mapping for the rapid
identification of magnetic cycloid and skyrmion phases in uniaxial systems,
based on the GaV4S8 and GaV4Se8 model skyrmion hosts with easy-axis and
easy-plane anisotropies respectively. We show that these measurements can be
interpreted with the help of a simple numerical model for the spin Hamiltonian
to yield unambiguous assignments for both single phase regions and phase
boundaries. In the two lacunar spinel chemistries, we obtain excellent
agreement between the measured magnetoentropic features and a minimal spin
Hamiltonian built on Heisenberg exchange, single-ion anisotropy, and
anisotropic Dzyaloshinskii-Moriya interactions. In particular, we identify
characteristic high-entropy behavior in the cycloid phase that serves as a
precursor to the formation of skyrmions at elevated temperatures and is a
readily-measurable signature of this phase transition. Our results demonstrate
that rapid magnetoentropic mapping guided by numerical modeling is an effective
means of understanding the complex magnetic phase diagrams innate to skyrmion
hosts. One notable exception is the observation of an anomalous,
low-temperature high-entropy state in the easy-plane system GaV$_4$Se$_8$,
which is not captured in the numerical model. Possible origins of this state
are discussed.


Abstract:
  As more and more data being created every day, all of it can help take better
decisions with data analysis. It is not different from data generated in
financial markets. Here we examine the process of how the global economy is
affected by the market sentiment influenced by the micro-blogging data (tweets)
of American President Donald Trump. The news feed is gathered from The Guardian
and Bloomberg from the period between December 2016 and October 2019, which are
used to further identify the potential tweets that influenced the markets as
measured by changes in equity indices.


Abstract:
  A conjecture of Alon, Krivelevich, and Sudakov states that, for any graph
$F$, there is a constant $c_F > 0$ such that if $G$ is an $F$-free graph of
maximum degree $\Delta$, then $\chi(G) \leq c_F \Delta / \log\Delta$. Alon,
Krivelevich, and Sudakov verified this conjecture for a class of graphs $F$
that includes all bipartite graphs. Moreover, it follows from recent work by
Davies, Kang, Pirot, and Sereni that if $G$ is $K_{t,t}$-free, then $\chi(G)
\leq (t + o(1)) \Delta / \log\Delta$ as $\Delta \to \infty$. We improve this
bound to $(1+o(1)) \Delta/\log \Delta$, making the constant factor independent
of $t$. We further extend our result to the DP-coloring setting (also known as
correspondence coloring), introduced by Dvo\v{r}\'ak and Postle.


Abstract:
  We propose the first general-purpose gradient-based attack against
transformer models. Instead of searching for a single adversarial example, we
search for a distribution of adversarial examples parameterized by a
continuous-valued matrix, hence enabling gradient-based optimization. We
empirically demonstrate that our white-box attack attains state-of-the-art
attack performance on a variety of natural language tasks. Furthermore, we show
that a powerful black-box transfer attack, enabled by sampling from the
adversarial distribution, matches or exceeds existing methods, while only
requiring hard-label outputs.


Abstract:
  Various fonts give different impressions, such as legible, rough, and
comic-text.This paper aims to analyze the correlation between the local shapes,
or parts, and the impression of fonts. By focusing on local shapes instead of
the whole letter shape, we can realize letter-shape independent and more
general analysis. The analysis is performed by newly combining SIFT and
DeepSets, to extract an arbitrary number of essential parts from a particular
font and aggregate them to infer the font impressions by nonlinear regression.
Our qualitative and quantitative analyses prove that (1)fonts with similar
parts have similar impressions, (2)many impressions, such as legible and rough,
largely depend on specific parts, (3)several impressions are very irrelevant to
parts.


Abstract:
  We investigate the focal plane wavefront sensing technique, known as Phase
Diversity, at the scientific focal plane of a segmented mirror telescope with
an adaptive optics (AO) system. We specifically consider an optical system
imaging a point source in the context of (i) an artificial source within the
telescope structure and (ii) from AO-corrected images of a bright star. From
our simulations, we reliably disentangle segmented telescope phasing errors
from non-common path aberrations (NCPA) for both a theoretical source and
on-sky, AO-corrected images where we have simulated the Keck/NIRC2 system. This
quantification from on-sky images is appealing, as it's sensitive to the
cumulative wavefront perturbations of the entire optical train; disentanglement
of phasing errors and NCPA is therefore critical, where any potential
correction to the primary mirror from an estimate must contain minimal NCPA
contributions. Our estimates require a one-minute sequence of short-exposure,
AO-corrected images; by exploiting a slight modification to the AO-loop, we
find that 75 defocused images produces reliable estimates. We demonstrate a
correction from our estimates to the primary and deformable mirror results in a
wavefront error reduction of up to 67% and 65% for phasing errors and NCPA,
respectively. If the segment phasing errors on the Keck primary are on the
order of ~130 nm RMS, we show we can improve the H-band Strehl ratio by up to
10% by using our algorithm. We conclude our technique works well to estimate
NCPA alone from on-sky images, suggesting it is a promising method for any
AO-system.


Abstract:
  In recent years, there has been an explosion of machine learning techniques
for turbulence closure modeling, though many rely on augmenting existing
models. While this has proven successful in single-phase flows, it breaks down
for multiphase flows, particularly when the system dynamics are controlled by
two-way coupling between the phases. In this work, we propose an approach that
blends sparse regression and gene expression programming (GEP) to generate
closed-form algebraic models from simulation data. Sparse regression is used to
determine a minimum set of functional groups required to capture the physics
and GEP is used to automate the formulation of the coefficients and
dependencies on operating conditions. The framework is demonstrated on a
canonical gas--solid flow in which two-way coupling generates and sustains
fluid-phase turbulence.


Abstract:
  Many applications of Visual SLAM, such as augmented reality, virtual reality,
robotics or autonomous driving, require versatile, robust and precise
solutions, most often with real-time capability. In this work, we describe
OV$^{2}$SLAM, a fully online algorithm, handling both monocular and stereo
camera setups, various map scales and frame-rates ranging from a few Hertz up
to several hundreds. It combines numerous recent contributions in visual
localization within an efficient multi-threaded architecture. Extensive
comparisons with competing algorithms shows the state-of-the-art accuracy and
real-time performance of the resulting algorithm. For the benefit of the
community, we release the source code:
\url{https://github.com/ov2slam/ov2slam}.


Abstract:
  Let $(\Omega, \mu)$ be a probability space endowed with an ergodic action,
$\tau$ of $( {\mathbb R} ^n, +)$. Let $H(x,p; \omega)=H_\omega(x,p)$ be a
smooth Hamiltonian on $T^* {\mathbb R} ^n$ parametrized by $\omega\in \Omega$
and such that $ H(a+x,p;\tau_a\omega)=H(x,p;\omega)$. We consider for an
initial condition $f\in C^0 ( {\mathbb R}^n)$, the family of variational
solutions of the stochastic Hamilton-Jacobi equations $$\left\{ \begin{aligned}
\frac{\partial u^{ \varepsilon }}{\partial t}(t,x;\omega)+H\left (\frac{x}{
\varepsilon } , \frac{\partial u^\varepsilon }{\partial x}(t,x;\omega);\omega
\right )=0 &\\ u^\varepsilon (0,x;\omega)=f(x)& \end{aligned} \right .$$ Under
some coercivity assumptions on $p$ -- but without any convexity assumption --
we prove that for a.e. $\omega \in \Omega$ we have $C^0-\lim
u^{\varepsilon}(t,x;\omega)=v(t,x)$ where $v$ is the variational solution of
the homogenized equation $$\left\{ \begin{aligned} \frac{\partial v}{\partial
t}(x)+{\overline H}\left (\frac{\partial v }{\partial x}(x) \right )=0 &\\ v
(0,x)=f(x)& \end{aligned} \right.$$


Abstract:
  Various microorganisms and some mammalian cells are able to swim in viscous
fluids by performing nonreciprocal body deformations, such as rotating attached
flagella or by distorting their entire body. In order to perform chemotaxis,
i.e. to move towards and to stay at high concentrations of nutrients, they
adapt their swimming gaits in a nontrivial manner. We propose a model how
microswimmers are able to autonomously adapt their shape in order to swim in
one dimension towards high field concentrations using an internal decision
making machinery modeled by an artificial neural network. We present two
methods to measure chemical gradients, spatial and temporal sensing, as known
for swimming mammalian cells and bacteria, respectively. Using the NEAT genetic
algorithm surprisingly simple neural networks evolve which control the shape
deformations of the microswimmer and allow them to navigate in static and
complex time-dependent chemical environments. By introducing noisy signal
transmission in the neural network the well-known biased run-and-tumble motion
emerges. Our work demonstrates that the evolution of a simple internal
decision-making machinery, which we can fully interpret and is coupled to the
environment, allows navigation in diverse chemical landscapes. These findings
are of relevance for intracellular biochemical sensing mechanisms of single
cells, or for the simple nervous system of small multicellular organisms such
as C. elegans.


Abstract:
  In this paper, novel dissensus algorithms based on the Oja principal
component analysis (PCA) flow are proposed to model opinion dynamics on the
unit sphere. The information of the covariance formed by the opinion state of
each agent is used to achieve a dissensus equilibrium on unsigned graphs. This
differs from most of the existing work where antagonistic interactions
represented by negative weights in signed graphs are used to achieve a
dissensus equilibrium. The nonlinear algorithm is analyzed under both constant
covariance and time-varying covariance leading to different behaviors.
Stability analysis for the unstable consensus and stable dissensus equilibria
is provided under various conditions. The performance of the algorithm is
illustrated through a simulation experiment of a multi-agent system.


Abstract:
  The notion of an orthogonality space was recently rediscovered as an
effective means to characterise the essential properties of quantum logic. The
approach can be considered as minimalistic; solely the aspect of mutual
exclusiveness is taken into account. In fact, an orthogonality space is simply
a set endowed with a symmetric and irreflexive binary relation. If the rank is
at least $4$ and if a certain combinatorial condition holds, these relational
structures can be shown to give rise in a unique way to Hermitian spaces. In
this paper, we focus on the finite case. In particular, we investigate
orthogonality spaces of rank at most $3$.


Abstract:
  In the current era of Noisy Intermediate-Scale Quantum (NISQ) technology, the
practical use of quantum computers remains inhibited by our inability to aptly
decouple qubits from their environment to mitigate computational errors. In
this work, we introduce an approach by which knowledge of a qubit's initial
quantum state and the standard parameters describing its decoherence can be
leveraged to mitigate the noise present during the execution of a single-qubit
gate. We benchmark our protocol using cloud-based access to IBM quantum
processors. On ibmq_rome, we demonstrate a reduction of the single-qubit error
rate by $38\%$, from $1.6 \times 10 ^{-3}$ to $1.0 \times 10 ^{-3}$, provided
the initial state of the input qubit is known. On ibmq_bogota, we prove that
our protocol will never decrease gate fidelity, provided the system's $T_1$ and
$T_2$ times have not drifted above $100$ times their assumed values. The
protocol can be used to reduce quantum state preparation errors, as well as to
improve the fidelity of quantum circuits for which some knowledge of the
qubits' intermediate states can be inferred. This work presents a pathway to
using information about noise levels and quantum state distributions to
significantly reduce error rates associated with quantum gates via optimized
decomposition into native gates.


Abstract:
  Words are malleable objects, influenced by events that are reflected in
written texts. Situated in the global outbreak of COVID-19, our research aims
at detecting semantic shifts in social media language triggered by the health
crisis. With COVID-19 related big data extracted from Twitter, we train
separate word embedding models for different time periods after the outbreak.
We employ an alignment-based approach to compare these embeddings with a
general-purpose Twitter embedding unrelated to COVID-19. We also compare our
trained embeddings among them to observe diachronic evolution. Carrying out
case studies on a set of words chosen by topic detection, we verify that our
alignment approach is valid. Finally, we quantify the size of global semantic
shift by a stability measure based on back-and-forth rotational alignment.


Abstract:
  The increasing cost, energy demand, and environmental issues has led many
researchers to find approaches for energy monitoring, and hence energy
conservation. The emerging technologies of Internet of Things (IoT) and Machine
Learning (ML) deliver techniques that have the potential to efficiently
conserve energy and improve the utilization of energy consumption. Smart Home
Energy Management Systems (SHEMSs) have the potential to contribute in energy
conservation through the application of Demand Response (DR) in the residential
sector. In this paper, we propose appliances Operation Modes Identification
using Cycles Clustering (OMICC) which is SHEMS fundamental approach that
utilizes the sensed residential disaggregated power consumption in supporting
DR by providing consumers the opportunity to select lighter appliance operation
modes. The cycles of the Single Usage Profile (SUP) of an appliance are
extracted and reformed into features in terms of clusters of cycles. These
features are then used to identify the operation mode used in every occurrence
using K-Nearest Neighbors (KNN). Operation modes identification is considered a
basis for many potential smart DR applications within SHEMS towards the
consumers or the suppliers


Abstract:
  We focus on cone-shaped nano- and microparticles, which have recently been
found to show particularly strong propulsion when they are exposed to a
traveling ultrasound wave, and study based on direct acoustofluidic computer
simulations how their propulsion depends on the cones' aspect ratio. The
simulations reveal that the propulsion velocity and even its sign are very
sensitive to the aspect ratio, where short particles move forward whereas
elongated particles move backward. Furthermore, we identify a cone shape that
allows for a particularly large propulsion speed. Our results contribute to the
understanding of the propulsion of ultrasound-propelled colloidal particles,
suggest a method for separation and sorting of nano- and microcones concerning
their aspect ratio, and provide useful guidance for future experiments and
applications.


Abstract:
  Management of heat during charging and discharging of Li-ion batteries is
critical for their safety, reliability, and performance. Understanding the
thermal conductivity of the materials comprising batteries is crucial for
controlling the temperature and temperature distribution in batteries. This
work provides systemic quantitative measurements of the thermal conductivity of
three important classes of solid electrolytes (oxides, sulfides, and halides)
over the temperature range 150-350 K. Studies include the oxides
Li1.5Al0.5Ge1.5(PO4)3 and Li6.4La3Zr1.4Ta0.6O12, sulfides Li2S-P2S5, Li6PS5Cl,
and Na3PS4, and halides Li3InCl6 and Li3YCl6. Thermal conductivities of sulfide
and halide solid electrolytes are in the range 0.45-0.70 W m-1 K-1; thermal
conductivities of Li6.4La3Zr1.4Ta0.6O12 and Li1.5Al0.5Ge1.5(PO4)3 are 1.4 W m-1
K-1 and 2.2 W m-1 K-1, respectively. For most of the solid electrolytes studied
in this work, the thermal conductivity increases with increasing temperature;
i.e., the thermal conductivity has a glass-like temperature dependence. The
measured room-temperature thermal conductivities agree well with the calculated
minimum thermal conductivities indicating the phonon mean-free-paths in these
solid electrolytes are close to an atomic spacing. We attribute the low,
glass-like thermal conductivity of the solid electrolytes investigated to the
combination of their complex crystal structures and the atomic-scale disorder
induced by the materials processing methods that are typically needed to
produce high ionic conductivities.


Abstract:
  Normalizing flows are a widely used class of latent-variable generative
models with a tractable likelihood. Affine-coupling (Dinh et al, 2014-16)
models are a particularly common type of normalizing flows, for which the
Jacobian of the latent-to-observable-variable transformation is triangular,
allowing the likelihood to be computed in linear time. Despite the widespread
usage of affine couplings, the special structure of the architecture makes
understanding their representational power challenging. The question of
universal approximation was only recently resolved by three parallel papers
(Huang et al.,2020;Zhang et al.,2020;Koehler et al.,2020) -- who showed
reasonably regular distributions can be approximated arbitrarily well using
affine couplings -- albeit with networks with a nearly-singular Jacobian. As
ill-conditioned Jacobians are an obstacle for likelihood-based training, the
fundamental question remains: which distributions can be approximated using
well-conditioned affine coupling flows?
  In this paper, we show that any log-concave distribution can be approximated
using well-conditioned affine-coupling flows. In terms of proof techniques, we
uncover and leverage deep connections between affine coupling architectures,
underdamped Langevin dynamics (a stochastic differential equation often used to
sample from Gibbs measures) and H\'enon maps (a structured dynamical system
that appears in the study of symplectic diffeomorphisms). Our results also
inform the practice of training affine couplings: we approximate a padded
version of the input distribution with iid Gaussians -- a strategy which
Koehler et al.(2020) empirically observed to result in better-conditioned
flows, but had hitherto no theoretical grounding. Our proof can thus be seen as
providing theoretical evidence for the benefits of Gaussian padding when
training normalizing flows.


Abstract:
  We study the binary phase separation in active model B, on a two-dimensional
substrate with inhomogeneous activity. The activity was introduced with a
maximum value at the center of the box and spread as a Bivariate-Gaussian
distribution as we move away from the center. The system was studied for three
different intensities of the distribution. Towards the boundary of the box,
activity is zero or the model is similar to the passive model B. We start from
the random homogeneous distribution of density of particles, and the system
evolves towards a structured distribution of density. With time, density starts
to phase separately with maximum density at the center of the box and decreases
as we move away from the center of the box. The width of the density profile at
the center increases as a power lawexponent{\alpha}(t) remains close between
2/3 to 3/4 up to some moderate time and then decays to zero in the steady
state. Hence, our result shows the response of density in an active binary
system with respect to the patterned substrate. It can be used to design
devices useful for the trapping and segregation of active particles.