Abstract:
  Accurate traffic state prediction is the foundation of transportation control
and guidance. It is very challenging due to the complex spatiotemporal
dependencies in traffic data. Existing works cannot perform well for multi-step
traffic prediction that involves long future time period. The spatiotemporal
information dilution becomes serve when the time gap between input step and
predicted step is large, especially when traffic data is not sufficient or
noisy. To address this issue, we propose a multi-spatial graph convolution
based Seq2Seq model. Our main novelties are three aspects: (1) We enrich the
spatiotemporal information of model inputs by fusing multi-view features (time,
location and traffic states) (2) We build multiple kinds of spatial
correlations based on both prior knowledge and data-driven knowledge to improve
model performance especially in insufficient or noisy data cases. (3) A
spatiotemporal attention mechanism based on reachability knowledge is novelly
designed to produce high-level features fed into decoder of Seq2Seq directly to
ease information dilution. Our model is evaluated on two real world traffic
datasets and achieves better performance than other competitors.


Abstract:
  Studying history involves many difficult tasks. Examples include searching
for proper data in a large event space, understanding stories of historical
events by time and space, and finding relationships among events that may not
be apparent. Instructors who extensively use well-organized and well-argued
materials (e.g., textbooks and online resources) can lead students to a narrow
perspective in understanding history and prevent spontaneous investigation of
historical events, with the students asking their own questions. In this work,
we proposed HisVA, a visual analytics system that allows the efficient
exploration of historical events from Wikipedia using three views: event, map,
and resource. HisVA provides an effective event exploration space, where users
can investigate relationships among historical events by reviewing and linking
them in terms of space and time. To evaluate our system, we present two usage
scenarios, a user study with a qualitative analysis of user exploration
strategies, and %expert feedback with in-class deployment results.


Abstract:
  We pursue a program to confront observations with inhomogeneous extensions of
the FLRW metric. The main idea is to test the Copernican principle rather than
assuming it a priori. We consider the $\Lambda$CDM model endowed with a
spherical $\Lambda$LTB inhomogeneity around us, that is, we assume isotropy and
test the hypothesis of homogeneity. We confront the $\Lambda$LTB model with the
latest available data from CMB, BAO, type Ia supernovae, local $H_0$, cosmic
chronometers, Compton y-distortion and kinetic Sunyaev-Zeldovich effect. We
find that these data can constrain tightly this extra inhomogeneity, almost to
the cosmic variance level: on scales $\gtrsim 100$ Mpc structures can have a
small non-Copernican effective contrast of just $\delta_L \sim 0.01$.
Furthermore, the constraints on the standard $\Lambda$CDM parameters are not
weakened after marginalizing over the parameters that model the local
structure, to which we assign ignorance priors. In other words, dropping the
Copernican principle assumption does not imply worse constraints on the
cosmological parameters. This positive result confirms that the present and
future data can be meaningfully analyzed within the framework of inhomogeneous
cosmology.


Abstract:
  Location prediction is of great importance in location-based applications for
the construction of the smart city. To our knowledge, existing models for
location prediction focus on users' preferences on POIs from the perspective of
the human side. However, modeling users' interests from the historical
trajectory is still limited by the data sparsity. Additionally, most of
existing methods predict the next location according to the individual data
independently, but the data sparsity makes it difficult to mine explicit
mobility patterns or capture the casual behavior for each user. To address the
issues above, we propose a novel Bi-direction Speculation and Dual-level
Association method (BSDA), which considers both users' interests in POIs and
POIs' appeal to users. Furthermore, we develop the cross-user and cross-POI
association to alleviate the data sparsity by similar users and POIs to enrich
the candidates. Experimental results on two public datasets demonstrate that
BSDA achieves significant improvements over state-of-the-art methods.


Abstract:
  We develop a new method of online inference for a vector of parameters
estimated by the Polyak-Ruppert averaging procedure of stochastic gradient
descent (SGD) algorithms. We leverage insights from time series regression in
econometrics and construct asymptotically pivotal statistics via random
scaling. Our approach is fully operational with online data and is rigorously
underpinned by a functional central limit theorem. Our proposed inference
method has a couple of key advantages over the existing methods. First, the
test statistic is computed in an online fashion with only SGD iterates and the
critical values can be obtained without any resampling methods, thereby
allowing for efficient implementation suitable for massive online data. Second,
there is no need to estimate the asymptotic variance and our inference method
is shown to be robust to changes in the tuning parameters for SGD algorithms in
simulation experiments with synthetic data.


Abstract:
  We develop a mathematical model, based on a system of ordinary differential
equations, to the upshot of farming alertness in crop pest administration,
bearing in mind plant biomass, pest, and level of control. Main qualitative
analysis of the proposed mathematical model, akin to both pest-free and
coexistence equilibrium points and stability analysis, is investigated. We show
that all solutions of the model are positive and bounded with initial
conditions in a certain significant set. The local stability of pest-free and
coexistence equilibria is shown using the Routh-Hurwitz criterion. Moreover, we
prove that when a threshold value is less than one, then the pest-free
equilibrium is locally asymptotically stable. To get optimum interventions for
crop pests, that is, to decrease the number of pests in the crop field, we
apply optimal control theory and find the corresponding optimal controls. We
establish existence of optimal controls and characterize them using
Pontryagin's minimum principle. Finally, we make use of numerical simulations
to illustrate the theoretical analysis of the proposed model, with and without
control measures.


Abstract:
  Two factors have proven to be very important to the performance of semantic
segmentation models: global context and multi-level semantics. However,
generating features that capture both factors always leads to high
computational complexity, which is problematic in real-time scenarios. In this
paper, we propose a new model, called Attention-Augmented Network (AttaNet), to
capture both global context and multilevel semantics while keeping the
efficiency high. AttaNet consists of two primary modules: Strip Attention
Module (SAM) and Attention Fusion Module (AFM). Viewing that in challenging
images with low segmentation accuracy, there are a significantly larger amount
of vertical strip areas than horizontal ones, SAM utilizes a striping operation
to reduce the complexity of encoding global context in the vertical direction
drastically while keeping most of contextual information, compared to the
non-local approaches. Moreover, AFM follows a cross-level aggregation strategy
to limit the computation, and adopts an attention strategy to weight the
importance of different levels of features at each pixel when fusing them,
obtaining an efficient multi-level representation. We have conducted extensive
experiments on two semantic segmentation benchmarks, and our network achieves
different levels of speed/accuracy trade-offs on Cityscapes, e.g., 71 FPS/79.9%
mIoU, 130 FPS/78.5% mIoU, and 180 FPS/70.1% mIoU, and leading performance on
ADE20K as well.


Abstract:
  The strong truncated Hamburger moment problem (STHMP) of degree
$(-2k_1,2k_2)$ asks to find necessary and sufficient conditions for the
existence of a positive Borel measure, supported on $\mathbb{R}\setminus
\{0\}$, such that $\beta_i=\int x^id\mu\; (-2k_1\leq i\leq 2k_2)$. Using the
solution of the truncated Hamburger moment problem and the properties of Hankel
matrices we solve the STHMP. Then, using the equivalence with the STHMP of
degree $(-2k,2k)$, we obtain the solution of the 2-dimensional truncated moment
problem (TMP) of degree $2k$ with variety $xy=1$, first solved by Curto and
Fialkow. Our addition to their result is the fact previously known only for
$k=2$, that the existence of a measure is equivalent to the existence of a flat
extension of the moment matrix. Further on, we solve the STHMP of degree
$(-2k_1,2k_2)$ with one missing moment in the sequence, i.e., $\beta_{-2k_1+1}$
or $\beta_{2k_2-1}$, which also gives the solution of the TMP with variety
$x^2y=1$ as a special case, first studied by Fialkow.


Abstract:
  Neural models excel at extracting statistical patterns from large amounts of
data, but struggle to learn patterns or reason about language from only a few
examples. In this paper, we ask: Can we learn explicit rules that generalize
well from only a few examples? We explore this question using program
synthesis. We develop a synthesis model to learn phonology rules as programs in
a domain-specific language. We test the ability of our models to generalize
from few training examples using our new dataset of problems from the
Linguistics Olympiad, a challenging set of tasks that require strong linguistic
reasoning ability. In addition to being highly sample-efficient, our approach
generates human-readable programs, and allows control over the generalizability
of the learnt programs.


Abstract:
  This work presents CLIPDraw, an algorithm that synthesizes novel drawings
based on natural language input. CLIPDraw does not require any training; rather
a pre-trained CLIP language-image encoder is used as a metric for maximizing
similarity between the given description and a generated drawing. Crucially,
CLIPDraw operates over vector strokes rather than pixel images, a constraint
that biases drawings towards simpler human-recognizable shapes. Results compare
between CLIPDraw and other synthesis-through-optimization methods, as well as
highlight various interesting behaviors of CLIPDraw, such as satisfying
ambiguous text in multiple ways, reliably producing drawings in diverse
artistic styles, and scaling from simple to complex visual representations as
stroke count is increased. Code for experimenting with the method is available
at:
https://colab.research.google.com/github/kvfrans/clipdraw/blob/main/clipdraw.ipynb


Abstract:
  We study learning dynamics in distributed production economies such as
blockchain mining, peer-to-peer file sharing and crowdsourcing. These economies
can be modelled as multi-product Cournot competitions or all-pay auctions
(Tullock contests) when individual firms have market power, or as Fisher
markets with quasi-linear utilities when every firm has negligible influence on
market outcomes. In the former case, we provide a formal proof that Gradient
Ascent (GA) can be Li-Yorke chaotic for a step size as small as $\Theta(1/n)$,
where $n$ is the number of firms. In stark contrast, for the Fisher market
case, we derive a Proportional Response (PR) protocol that converges to market
equilibrium. The positive results on the convergence of the PR dynamics are
obtained in full generality, in the sense that they hold for Fisher markets
with \emph{any} quasi-linear utility functions. Conversely, the chaos results
for the GA dynamics are established even in the simplest possible setting of
two firms and one good, and they hold for a wide range of price functions with
different demand elasticities. Our findings suggest that by considering
multi-agent interactions from a market rather than a game-theoretic
perspective, we can formally derive natural learning protocols which are stable
and converge to effective outcomes rather than being chaotic.


Abstract:
  We consider a chain of spinful fermions with nearest neighbor hopping in the
presence of a $XY$ antiferromagnetic interaction. The $XY$ term is mapped onto
a Kitaev chain at half-filling such that displays a bosonic zero mode
topologically protected and long-range order. As the strength of the hopping
amplitude is changed, the system undergoes a quantum phase transition from the
topological non-trivial to the trivial phase. We apply the finite-size scaling
method to determinate the phase diagram of the model.


Abstract:
  Image classification models can depend on multiple different semantic
attributes of the image. An explanation of the decision of the classifier needs
to both discover and visualize these properties. Here we present StylEx, a
method for doing this, by training a generative model to specifically explain
multiple attributes that underlie classifier decisions. A natural source for
such attributes is the StyleSpace of StyleGAN, which is known to generate
semantically meaningful dimensions in the image. However, because standard GAN
training is not dependent on the classifier, it may not represent these
attributes which are important for the classifier decision, and the dimensions
of StyleSpace may represent irrelevant attributes. To overcome this, we propose
a training procedure for a StyleGAN, which incorporates the classifier model,
in order to learn a classifier-specific StyleSpace. Explanatory attributes are
then selected from this space. These can be used to visualize the effect of
changing multiple attributes per image, thus providing image-specific
explanations. We apply StylEx to multiple domains, including animals, leaves,
faces and retinal images. For these, we show how an image can be modified in
different ways to change its classifier output. Our results show that the
method finds attributes that align well with semantic ones, generate meaningful
image-specific explanations, and are human-interpretable as measured in
user-studies.


Abstract:
  Multiscale transforms have become a key ingredient in many data processing
tasks. With technological development, we observe a growing demand for methods
to cope with non-linear data structures such as manifold values. In this paper,
we propose a multiscale approach for analyzing manifold-valued data using a
pyramid transform. The transform uses a unique class of downsampling operators
that enable a non-interpolating subdivision schemes as upsampling operators. We
describe this construction in detail and present its analytical properties,
including stability and coefficient decay. Next, we numerically demonstrate the
results and show the application of our method to denoising and anomaly
detection.


Abstract:
  We present first principles calculations of the two-particle excitation
spectrum of CrI$_3$ using many-body perturbation theory including spin-orbit
coupling. Specifically, we solve the Bethe-Salpeter equation, which is
equivalent to summing up all ladder diagrams with static screening and it is
shown that excitons as well as magnons can be extracted seamlessly from the
calculations. The resulting optical absorption spectrum as well as the magnon
dispersion agree very well with recent measurements and we extract the
amplitude for optical excitation of magnons resulting from spin-orbit
interactions. Importantly, the results do not rely on any assumptions on the
microscopic magnetic interactions such as Dzyaloshinskii-Moriya (DM), Kitaev or
biquadratic interactions and we obtain a model independent estimate of the gap
between acoustic and optical magnons of 0.3 meV. In addition, we resolve the
magnon wavefunction in terms of band transitions and show that the magnon
carries a spin that is significantly smaller than $\hbar$. This highlights the
importance of terms that do not commute with $S^z$ in any Heisenberg model
description.


Abstract:
  Detecting facial forgery images and videos is an increasingly important topic
in multimedia forensics. As forgery images and videos are usually compressed
into different formats such as JPEG and H264 when circulating on the Internet,
existing forgery-detection methods trained on uncompressed data often suffer
from significant performance degradation in identifying them. To solve this
problem, we propose a novel anti-compression facial forgery detection
framework, which learns a compression-insensitive embedding feature space
utilizing both original and compressed forgeries. Specifically, our approach
consists of three ideas: (i) extracting compression-insensitive features from
both uncompressed and compressed forgeries using an adversarial learning
strategy; (ii) learning a robust partition by constructing a metric loss that
can reduce the distance of the paired original and compressed images in the
embedding space; (iii) improving the accuracy of tampered localization with an
attention-transfer module. Experimental results demonstrate that, the proposed
method is highly effective in handling both compressed and uncompressed facial
forgery images.


Abstract:
  The aim of the present work is to introduce a lens whose faces are a conical
surface and a spherical surface. We illuminate this lens by a plane wavefront
and its associated refracted wavefronts, light rays and caustic are computed.
We find that the caustic has two branches. The first is constituted by two
segments of a line, one part of this caustic is real and the other one virtual.
The second branch of the caustic is a two-dimensional surface with a
singularity of the cusp ridge type. It is important to remark that the two
branches of the caustic are disconnected. Because of this property we believe
that using this optical element one could generate a scalar optical
accelerating beam in the region where the caustic is a two-dimensional surface
of revolution, and at the same time a scalar optical beam with similar
properties to the Bessel beam of zero order in the region were the real caustic
is a segment of a line along the optical axis.


Abstract:
  Luminescent metal-organic frameworks (MOFs) offer a multifunctional platform
for creating non-invasive sensors and tuneable optoelectronics. However,
fluorochromic materials that are photophysically resilient and show high
sensitivity towards different physical and chemical stimuli are scarce. We
report a facile host-guest nanoconfinement strategy to construct a fluorescent
hybrid material with multiple sensing capabilities. We design and fabricate a
new Guest@MOF material: comprising a zeolitic MOF (ZIF-71) as a nanoporous host
for encapsulating rhodamine B (RhB dye) guest molecules, resulting in the
RhB@ZIF-71 system with mechanochromic, thermochromic, and solvatochromic
sensing response. The fluorochromic sensing properties stem from the
nanoconfinement effect that ZIF-71 imposes on RhB monomers, yielding the H- or
J-type aggregates with tuneable photophysical and photochemical properties. For
mechanochromism, the external pressure causes an emission red shift in a linear
fashion, switching RhB guests from H-type to J-type aggregates through a shear
deformation. For thermochromism, we demonstrate a linear scaling as a function
of temperature due to the spatial restriction imposed on J-type aggregates
incarcerated in ZIF-71 pores. Harnessing the solvatochromism of RhB@ZIF-71, we
identified three diverse groups of volatile organic compounds. The multimodal
sensing response pave the way to smart applications like photonic pressure
sensors, non-invasive thermometers, and ultrasensitive chemosensors.


Abstract:
  The linear convex log-homotopy has been used in the derivation of particle
flow filters. One natural question is whether it is beneficial to consider
other forms of homotopy. We revisit this question by considering a general
linear form of log-homotopy for which we derive particle flow filters, validate
the distribution of flows, and obtain conditions for the stability of particle
flows. We then formulate the problem of stiffness mitigation as an optimal
control problem by minimizing the condition number of the Hessian matrix of the
posterior density function. The optimal homotopy can be efficiently obtained by
solving a one-dimensional second order two-point boundary value problem.
Compared with traditional matrix analysis based approaches to condition number
improvements such as scaling, this novel approach explicitly exploits the
special structure of the stochastic differential equations in particle flow
filters. The effectiveness of the proposed approach is demonstrated by a
numerical example.


Abstract:
  Let $R$ be a $G$-graded ring. In this article, we introduce two new concepts
on graded rings, namely, weakly graded rings and invertible graded rings, and
we discuss the relations between these concepts and several properties of
graded rings. Also, we study the concept of weakly crossed products and study
some properties defined on weakly crossed product and give the relationship
between this new concept and several properties of graded rings. Moreover, in
this article, we give a generalization for the concept of graded essential
submodules, and introduce the concept of graded semi-uniform modules which is a
generalization for the concept of graded uniform modules.


Abstract:
  One of the central problems in machine learning is domain adaptation. Unlike
past theoretical work, we consider a new model for subpopulation shift in the
input or representation space. In this work, we propose a provably effective
framework for domain adaptation based on label propagation. In our analysis, we
use a simple but realistic expansion assumption, proposed in
\citet{wei2021theoretical}. Using a teacher classifier trained on the source
domain, our algorithm not only propagates to the target domain but also
improves upon the teacher. By leveraging existing generalization bounds, we
also obtain end-to-end finite-sample guarantees on the entire algorithm. In
addition, we extend our theoretical framework to a more general setting of
source-to-target transfer based on a third unlabeled dataset, which can be
easily applied in various learning scenarios. Inspired by our theory, we adapt
consistency-based semi-supervised learning methods to domain adaptation
settings and gain significant improvements.


Abstract:
  The COVID pandemic and the measures which were taken had effect over the
mental health of persons. The current paper proposes a concept that supports
the performance of students by analyzing three ways of distance learning,
namely text, text and illustrations, including charts, video. An
electroencephalogram headset allows the detection of brainwaves and the
developed web application enhances the process of distance learning. The
electrodes of the headset are placed at contact with the user head and monitor
the activity of the left and right frontal regions, along with the temporal
lobe. Mood, focus, stress, relaxation, engagement, excitement and interest are
triggered as numerical values by using the headset. The users provide
information about their daily activities, including learning and evaluation
processes. According to the study, users had the highest long term attention
while using text and illustrations, followed by watching videos. This is caused
by the fact that the text contained the code for the programs which were
presented in the video. Also, the users feel comfortable while using the
application and they started to pay more attention to the connection between
stress, health, education and well being. The results triggered by the headset
had higher values while students studied for the first time using videos. When
they wanted to remember the information, the text and illustrations way of
learning was the best option. Based on the study outcomes, the instructional
design can be enhanced. Moreover, the results improved as the students became
more equilibrated and confident in themselves. Teachers, professors and parents
are able to collaborate and enhance training. While studying online under
lockdown, students have found the proposed solution to be good because their
inner state influences their productivity while solving problems.


Abstract:
  Three-, four-, and six-fold excitations have significantly extended the
subjects of condensed matter physics. There is an urgent need for a realistic
material that can have coexisting 3-, 4-, and 6-fold excitations. However,
these materials are uncommon because these excitations in electronic systems
are usually broken by spin-orbit coupling (SOC) and normally far from the Fermi
level. Unlike the case in electronic systems, the phonon systems with
negligible SOC effect, not constrained by the Pauli exclusion principle,
provide a feasible platform to realize these excitations in a wide frequency
range. Hence, in this work, we demonstrate by first-principle calculations and
symmetry analysis that perfect 3-, 4-, 6-fold excitations appear in the phonon
dispersion rather than the band structures of Zr3Ni3Sb4, which is a well-known
indirect-gap semiconductor with an Y3Au3Sb4-type structure. This material
features 3-fold quadratic contact triple-point phonon, 4-fold Dirac point
phonon, and 6-fold point phonon. Moreover, these nodal-point phonons are very
robust to uniform strain. Two obvious phonon surface arcs of the [001] plane
are extended in the whole Brillouin zone, which will facilitate their detection
in future experimental studies. The current work provides an ideal model to
investigate the rich excitations in a single material.


Abstract:
  We show a unified physical picture of single cyclotron electron with
radiation-reaction, which bridges the classical electron models and quantum
mechanical self-consistent field theory. On a classical level, we suggest an
improved electrodynamical action, which build the classical electron models
into a first-principle framework. The link between dynamical defections and
non-physical action configurations emerges naturally. On a quantum level, a
self-consistent description for electron gyro-motion with self-force is
constructed in the Schr\"odinger-Maxwell theory. We derive a class of
asymptotic equations. The leading and next-to-leading orders give a good
analogue of a classical cyclotron electron, and the limit field theory avoids
classical electron induced defections gracefully. Beyond the Hamiltonian
perturbation theory, we use state-of-the-art geometric simulator to observe
single electron gyro-motions at quantum region. The non-linear and
non-perturbative features captured by simulations provide a complete physical
picture in a very wide range. We show an optimal complementary relation between
classical and quantum cyclotron electrons, and find a strange and inexplicable
electron chimera state existing at strong non-linear regions, which may be
observed in astrophysical environments and strong magnetic experiments.


Abstract:
  We study large uniform random maps with one face whose genus grows linearly
with the number of edges, which are a model of discrete hyperbolic geometry. In
previous works, several hyperbolic geometric features have been investigated.
In the present work, we study the number of short cycles in a uniform
unicellular map of high genus, and we show that it converges to a Poisson
distribution. As a corollary, we obtain the law of the systole of uniform
unicellular maps in high genus. We also obtain the asymptotic distribution of
the vertex degrees in such a map.


Abstract:
  Online property business or known as e-commerce is currently experiencing an
increase in home sales. Indonesia's e-commerce property business has positive
trending shown by the increasing sales of more than 500% from 2011 to 2015. A
prediction of the property price is important to help investors or the public
to have accurate information before buying property. One of the methods for
prediction is a classification based on several distinctive property industry
attributes, such as building size, land size, number of rooms, and location.
Today, data is easily obtained, there are many open data from E-commerce sites.
E-commerce contains information about homes and other properties advertised to
sell. People also regularly visit the site to find the right property or to
sell the property using price information which collectively available as open
data. To predict the property sales, this research employed two different
classification methods in Data Mining which are Decision Tree and k-NN
classification. We compare which model classification is better to predict
property price and their attributes. We use Indonesia's biggest property-based
e-commerce site Rumah123.com as our open data source, and choose location
Bandung in our experiment. The accuracy result of the decision tree is 75% and
KNN is 71%, other than that k-NN can explore more data patterns than the
Decision Tree.


Abstract:
  With neural architecture search methods gaining ground on manually designed
deep neural networks -even more rapidly as model sophistication escalates-, the
research trend shifts towards arranging different and often increasingly
complex neural architecture search spaces. In this conjuncture, delineating
algorithms which can efficiently explore these search spaces can result in a
significant improvement over currently used methods, which, in general,
randomly select the structural variation operator, hoping for a performance
gain. In this paper, we investigate the effect of different variation operators
in a complex domain, that of multi-network heterogeneous neural models. These
models have an extensive and complex search space of structures as they require
multiple sub-networks within the general model in order to answer to different
output types. From that investigation, we extract a set of general guidelines,
whose application is not limited to that particular type of model, and are
useful to determine the direction in which an architecture optimization method
could find the largest improvement. To deduce the set of guidelines, we
characterize both the variation operators, according to their effect on the
complexity and performance of the model; and the models, relying on diverse
metrics which estimate the quality of the different parts composing it.


Abstract:
  Purpose: To develop an algorithm for robust partial Fourier (PF)
reconstruction applicable to diffusion-weighted (DW) images with non-smooth
phase variations.
  Methods: Based on an unrolled proximal splitting algorithm, a neural network
architecture is derived which alternates between data consistency operations
and regularization implemented by recurrent convolutions. In order to exploit
correlations, multiple repetitions of the same slice are jointly reconstructed
under consideration of permutation-equivariance. The proposed method is trained
on DW liver data of 60 volunteers and evaluated on retrospectively and
prospectively sub-sampled data of different anatomies and resolutions. In
addition, the benefits of using a recurrent network over other unrolling
strategies is investigated.
  Results: Conventional PF techniques can be significantly outperformed in
terms of quantitative measures as well as perceptual image quality. The
proposed method is able to generalize well to brain data with contrasts and
resolution not present in the training set. The reduction in echo time (TE)
associated with prospective PF-sampling enables DW imaging with higher signal.
Also, the TE increase in acquisitions with higher resolution can be compensated
for. It can be shown that unrolling by means of a recurrent network produced
better results than using a weight-shared network or a cascade of networks.
  Conclusion: This work demonstrates that robust PF reconstruction of DW data
is feasible even at strong PF factors in applications with severe phase
variations. Since the proposed method does not rely on smoothness priors of the
phase but uses learned recurrent convolutions instead, artifacts of
conventional PF methods can be avoided.


Abstract:
  The increasingly widespread application of AI models motivates increased
demand for explanations from a variety of stakeholders. However, this demand is
ambiguous because there are many types of 'explanation' with different
evaluative criteria. In the spirit of pluralism, I chart a taxonomy of types of
explanation and the associated XAI methods that can address them. When we look
to expose the inner mechanisms of AI models, we develop
Diagnostic-explanations. When we seek to render model output understandable, we
produce Explication-explanations. When we wish to form stable generalizations
of our models, we produce Expectation-explanations. Finally, when we want to
justify the usage of a model, we produce Role-explanations that situate models
within their social context. The motivation for such a pluralistic view stems
from a consideration of causes as manipulable relationships and the different
types of explanations as identifying the relevant points in AI systems we can
intervene upon to affect our desired changes. This paper reduces the ambiguity
in use of the word 'explanation' in the field of XAI, allowing practitioners
and stakeholders a useful template for avoiding equivocation and evaluating XAI
methods and putative explanations.


Abstract:
  Relation extraction from text is an important task for automatic knowledge
base population. In this thesis, we first propose a syntax-focused multi-factor
attention network model for finding the relation between two entities. Next, we
propose two joint entity and relation extraction frameworks based on
encoder-decoder architecture. Finally, we propose a hierarchical entity graph
convolutional network for relation extraction across documents.


Abstract:
  We show that polarization singularities, generic for any complex vector field
but so far mostly studied for electromagnetic fields, appear naturally in
inhomogeneous yet monochromatic sound and water-surface (e.g., gravity or
capillary) wave fields in fluids or gases. The vector properties of these waves
are described by the velocity or displacement fields characterizing the local
oscillatory motion of the medium particles. We consider a number of examples
revealing C-points of purely circular polarization and polarization M\"{o}bius
strips (formed by major axes of polarization ellipses) around the C-points in
sound and gravity wave fields. Our results (i) offer a new readily accessible
platform for studies of polarization singularities and topological features of
complex vector wavefields and (ii) can play an important role in characterizing
vector (e.g., dipole) wave-matter interactions in acoustics and fluid
mechanics.


Abstract:
  Applying spectral dynamics methods to one typical $N$-body model with a
barlens, we dissect the modelled bar into separate components supported by
completely different types of orbits. We identify at least four components: a
narrow elongated bar, a boxy bar, and two components contributing to the
barlens. We analyse the vertical structure of all components that make up the
thick part of the bar, which has a boxy/peanut shape (B/P bulge). We show that
the `peanut' shape is mainly due to the orbits that assemble the boxy part of
the face-on bar. We associate the X-shape with the narrow and elongated bar.
The wider part of the barlens with square-like isophotes contributes to the
boxy shape of the B/P bulge when we observe the galaxy edge-on. However, the
part of the barlens with rounded isophotes in the face-on view is a rather flat
structure in the vertical direction without any significant off-centre
protrusions. Thus, for the first time, we demonstrate that the rounded face-on
barlens cannot be entirely associated with the B/P bulge.


Abstract:
  As is well-known, the dimension of the space spanned by the non-degenerate
invariant symmetric bilinear forms (NISes) on any simple finite-dimensional Lie
algebra or Lie superalgebra is equal to at most 1 if the characteristic of the
algebraically closed ground field is not 2.
  We prove that in characteristic 2, the superdimension of the space spanned by
NISes can be equal to 0, or 1, or $0|1$, or $1|1$; it is equal to $1|1$ if and
only if the Lie superalgebra is a queerification (defined in arXiv:1407.1695)
of a simple classically restricted Lie algebra with a NIS (for examples, mainly
in characteristic distinct from 2, see arXiv:1806.05505).
  We give examples of NISes on deformations (with both even and odd parameters)
of several simple finite-dimensional Lie superalgebras in characteristic 2.
  We also recall examples of multiple NISes on simple Lie algebras over
non-closed fields.


Abstract:
  Analyzing the structure of proteins is a key part of understanding their
functions and thus their role in biology at the molecular level. In addition,
design new proteins in a methodical way is a major engineering challenge. In
this work, we introduce a joint geometric-neural networks approach for
comparing, deforming and generating 3D protein structures. Viewing protein
structures as 3D open curves, we adopt the Square Root Velocity Function (SRVF)
representation and leverage its suitable geometric properties along with Deep
Residual Networks (ResNets) for a joint registration and comparison. Our
ResNets handle better large protein deformations while being more
computationally efficient. On top of the mathematical framework, we further
design a Geometric Variational Auto-Encoder (G-VAE), that once trained, maps
original, previously unseen structures, into a low-dimensional (latent)
hyper-sphere. Motivated by the spherical structure of the pre-shape space, we
naturally adopt the von Mises-Fisher (vMF) distribution to model our hidden
variables. We test the effectiveness of our models by generating novel protein
structures and predicting completions of corrupted protein structures.
Experimental results show that our method is able to generate plausible
structures, different from the structures in the training data.


Abstract:
  Particle showers in dielectric media produce radio signals which are used for
the detection of both ultra-high energy cosmic rays and neutrinos with energies
above a few PeV. The amplitude, polarization, and spectrum of these short,
broadband radio pulses allow us to draw conclusions about the primary particles
that caused them, as well as the mechanics of shower development and radio
emission. However, confidently reconstructing the radio signals can pose a
challenge, as they are often obscured by background noise. Information Field
Theory offers a robust approach to this challenge by using Bayesian inference
to calculate the most likely radio signal, given the recorded data. In this
paper, we describe the application of Information Field Theory to radio signals
from particle showers in both air and ice and demonstrate how accurately pulse
parameters can be obtained from noisy data.


Abstract:
  Wave propagation problems have many applications in physics and engineering,
and the stochastic effects are important in accurately modeling them due to the
uncertainty of the media. This paper considers and analyzes a fully discrete
finite element method for a class of nonlinear stochastic wave equations, where
the diffusion term is globally Lipschitz continuous while the drift term is
only assumed to satisfy weaker conditions as in [11]. The novelties of this
paper are threefold. First, the error estimates cannot not be directly obtained
if the numerical scheme in primal form is used. The numerical scheme in mixed
form is introduced and several H\"{o}lder continuity results of the strong
solution are proved, which are used to establish the error estimates in both
$L^2$ norm and energy norms. Second, two types of discretization of the
nonlinear term are proposed to establish the $L^2$ stability and energy
stability results of the discrete solutions. These two types of discretization
and proper test functions are designed to overcome the challenges arising from
the stochastic scaling in time issues and the nonlinear interaction. These
stability results play key roles in proving the probability of the set on which
the error estimates hold approaches one. Third, higher order moment stability
results of the discrete solutions are proved based on an energy argument and
the underlying energy decaying property of the method. Numerical experiments
are also presented to show the stability results of the discrete solutions and
the convergence rates in various norms.


Abstract:
  Microcavity-polaritons in two-dimensional lattice geometries have been used
to study a wide range of interesting physics. Meanwhile, organic materials have
shown great promise on the road towards polaritonic devices, as the strong
binding energy of their Frenkel excitons permits room temperature condensation
and lasing. Whilst there are theoretical treatments of the condensation
processes in planar organic microcavities, models of lattice geometries are
lacking. Here, we introduce a model for describing the dynamics of lattices of
zero-dimensional organic microcavities, where the dominant condensation
mechanism involves the emission of a vibrational phonon. The vibronic resonance
provides a tool for targeted condensation in a particular eigenmode of the
system, which we highlight by examining a dimer and a dimerised chain. For the
dimer, we observe a double resonance in the condensation efficiency that arises
from tuning the condensate-reservoir detuning in to resonance with either the
symmetric or antisymmetric mode. This selective condensation was also exploited
in the dimerised chain, to condense the system into the topological edge states
under homogeneous pumping of all cavities. We also showed an interesting
signature of nonreciprocal transport when pumping a single cavity in the chain,
where the direction of propagation depends on the sublattice being pumped.


Abstract:
  The motivation for this thesis was to recast quantum self-testing [MY98,MY04]
in operational terms. The result is a category-theoretic framework for
discussing the following general question: How do different implementations of
the same input-output process compare to each other? In the proposed framework,
an input-output process is modelled by a causally structured channel in some
fixed theory, and its implementations are modelled by causally structured
dilations formalising hidden side-computations. These dilations compare through
a pre-order formalising relative strength of side-computations. Chapter 1
reviews a mathematical model for physical theories as semicartesian symmetric
monoidal categories. Many concrete examples are discussed, in particular
quantum and classical information theory. The key feature is that the model
facilitates the notion of dilations. Chapter 2 is devoted to the study of
dilations. It introduces a handful of simple yet potent axioms about dilations,
one of which (resembling the Purification Postulate [CDP10]) entails a duality
theorem encompassing a large number of classic no-go results for quantum
theory. Chapter 3 considers metric structure on physical theories, introducing
in particular a new metric for quantum channels, the purified diamond distance,
which generalises the purified distance [TCR10,Tom12] and relates to the Bures
distance [KSW08a]. Chapter 4 presents a category-theoretic formalism for
causality in terms of '(constructible) causal channels' and 'contractions'. It
simplifies aspects of the formalisms [CDP09,KU17] and relates to traces in
monoidal categories [JSV96]. The formalism allows for the definition of 'causal
dilations' and the establishment of a non-trivial theory of such dilations.
Chapter 5 realises quantum self-testing from the perspective of chapter 4, thus
pointing towards the first known operational foundation for self-testing.


Abstract:
  Human Activity Recognition (HAR), based on machine and deep learning
algorithms is considered one of the most promising technologies to monitor
professional and daily life activities for different categories of people
(e.g., athletes, elderly, kids, employers) in order to provide a variety of
services related, for example to well-being, empowering of technical
performances, prevention of risky situation, and educational purposes. However,
the analysis of the effectiveness and the efficiency of HAR methodologies
suffers from the lack of a standard workflow, which might represent the
baseline for the estimation of the quality of the developed pattern recognition
models. This makes the comparison among different approaches a challenging
task. In addition, researchers can make mistakes that, when not detected,
definitely affect the achieved results. To mitigate such issues, this paper
proposes an open-source automatic and highly configurable framework, named
B-HAR, for the definition, standardization, and development of a baseline
framework in order to evaluate and compare HAR methodologies. It implements the
most popular data processing methods for data preparation and the most commonly
used machine and deep learning pattern recognition models.


Abstract:
  For a simple graph $G$, denote by $n$, $\Delta(G)$, and $\chi'(G)$ its order,
maximum degree, and chromatic index, respectively. A connected class 2 graph
$G$ is edge-chromatic critical if $\chi'(G-e)<\Delta(G)+1$ for every edge $e$
of $G$. Define $G$ to be overfull if $|E(G)|>\Delta(G) \lfloor n/2 \rfloor$.
Clearly, overfull graphs are class 2 and any graph obtained from a regular
graph of even order by splitting a vertex is overfull. Let $G$ be an $n$-vertex
connected regular class 1 graph with $\Delta(G) >n/3$. Hilton and Zhao in 1997
conjectured that if $G^*$ is obtained from $G$ by splitting one vertex of $G$
into two vertices, then $G^*$ is edge-chromatic critical, and they verified the
conjecture for graphs $G$ with $\Delta(G)\ge \frac{n}{2}(\sqrt{7}-1)\approx
0.82n$. The graph $G^*$ is easily verified to be overfull, and so the hardness
of the conjecture lies in showing that the deletion of every of its edge
decreases the chromatic index. Except in 2002, Song showed that the conjecture
is true for a special class of graphs $G$ with $\Delta(G)\ge \frac{n}{2}$, no
other progress on this conjecture had been made. In this paper, we confirm the
conjecture for graphs $G$ with $\Delta(G) \ge 0.75n$.


Abstract:
  In this short note we consider RBSDE with Lipschitz drivers and barrier
processes that are optional and right upper semicontinuous. We treat the case
when the barrier can be represented as a decreasing limit of cadlag barriers.
We combine well known existence results for cadlag barriers with comparison
arguments for the control process to construct solutions. Finally, we highlight
the connection of such RBSDEs with usual cadlag BSDEs.


Abstract:
  The recently commissioned Dark Energy Spectroscopic Instrument (DESI) will
measure the expansion history of the Universe using the Baryon Acoustic
Oscillation technique. The spectra of 35 million galaxies and quasars over
14000 sqdeg will be measured during the life of the experiment. A new prime
focus corrector for the KPNO Mayall telescope delivers light to 5000 fiber
optic positioners. The fibers in turn feed ten broad-band spectrographs. A
consortium of Aix-Marseille University (AMU) and CNRS laboratories (LAM, OHP
and CPPM) together with LPNHE (CNRS, IN2P3, Sorbonne Universit\'e and
Universit\'e de Paris) and the WINLIGHT Systems company based in Pertuis
(France), were in charge of integrating and validating the performance
requirements of the ten full spectrographs, equipped with their cryostats,
shutters and other mechanisms. We present a summary of our activity which
allowed an efficient validation of the systems in a short-time schedule. We
detail the main results. We emphasize the benefits of our approach and also its
limitations.


Abstract:
  The inclusive as well as differential cross section of the associated
production of top-antitop quark pairs and a $Z$ boson ($t\bar{t}Z$) is measured
in final states with exactly three or four isolated leptons (electrons or
muons). For this purpose, the full LHC Run 2 dataset of proton-proton
collisions recorded by the ATLAS detector from $2015$ to $2018$, which
corresponds to an integrated luminosity of $139\,\text{fb}^{-1}$, is used. The
inclusive production cross section is measured to be
$\sigma_{t\bar{t}Z}=1.05\pm0.05\,(\text{stat.})\,\pm0.09\,(\text{syst.})\,\text{pb}$,
which is in agreement with the most precise Standard Model theoretical
prediction. Absolute and normalised differential cross section measurements are
performed as a function of various kinematic variables in order to probe the
kinematics of the $t\bar{t}Z$ system within both parton- and particle-level
phase spaces.


Abstract:
  Recently, a novel type of a multiscale simulation, called Relative Resolution
(RelRes), was introduced. In a single system, molecules switch their resolution
in terms of their relative separation, with near neighbors interacting via
fine-grained potentials yet far neighbors interacting via coarse-grained
potentials; notably, these two potentials are analytically parameterized by a
multipole approximation. This multiscale approach is consequently able to
correctly retrieve across state space, the structural and thermal, as well as
static and dynamic, behavior of various nonpolar mixtures. Our current work
focuses on the practical implementation of RelRes in LAMMPS, specifically for
the commonly used Lennard-Jones potential. By examining various correlations
and properties of several alkane liquids, including complex solutions of
alternate cooligomers and block copolymers, we confirm the validity of this
automated LAMMPS algorithm. Most importantly, we demonstrate that this RelRes
implementation gains almost an order of magnitude in computational efficiency,
as compared with conventional simulations. We thus recommend this novel LAMMPS
algorithm for anyone studying systems governed by Lennard-Jones interactions.


Abstract:
  The updated H-band spectral line list (from \lambda 15,000 - 17,000\AA)
adopted by the Apache Point Observatory Galactic Evolution Experiment (APOGEE)
for the SDSS IV Data Release 16 (DR16) is presented here. The APOGEE line list
is a combination of atomic and molecular lines with data from laboratory,
theoretical, and astrophysical sources. Oscillator strengths and damping
constants are adjusted using high signal-to-noise, high-resolution spectra of
the Sun and alpha Boo (Arcturus) as "standard stars". Updates to the DR16 line
list, when compared to the previous DR14 version, are the inclusion of
molecular H_2O and FeH lines, as well as a much larger (by a factor of ~4)
atomic line list, which includes significantly more transitions with hyperfine
splitting. More recent references and line lists for the crucial molecules CO
and OH were used, as well as for C_2 and SiH. In contrast to DR14, DR16
contains measurable lines from the heavy neutron-capture elements cerium (as Ce
II), neodymium (as Nd II), and ytterbium (as Yb II), as well as one line from
rubidium (as Rb I), that may be detectable in a small fraction of APOGEE red
giants.


Abstract:
  In this note we construct a measure $\mu$ on a $\sigma$-algebra $\mathcal{M}$
of subsets of the positive real axis, $\mathbb{R}_{>0}$, with the following
multiplicative property: \[
  \mu \left( \bigcup_j E_j \right) = \prod_j \mu(E_j) \] for every countable
collection $\{ E_j \}$ of pairwise disjoint sets of $\mathcal{M}$. For them, we
apply the Carath\'eodory's procedure to the triplet $\left( \mathbb{R}_{>0},
\cdot \, , \tau \right)$, where $\cdot$ is the product of R and $\tau$ is the
usual topology on $\mathbb{R}_{>0}$. We conclude this note describing the
connection between this multiplicative measure $\mu$ and the Lebesgue measure.


Abstract:
  Agent-Based Models are a powerful class of computational models widely used
to simulate complex phenomena in many different application areas. However, one
of the most critical aspects, poorly investigated in the literature, regards an
important step of the model credibility assessment: solution verification. This
study overcomes this limitation by proposing a general verification framework
for Agent-Based Models that aims at evaluating the numerical errors associated
with the model. A step-by-step procedure, which consists of two main
verification studies (deterministic and stochastic model verification), is
described in detail and applied to a specific mission critical scenario: the
quantification of the numerical approximation error for UISS-TB, an ABM of the
human immune system developed to predict the progression of pulmonary
tuberculosis. Results provide indications on the possibility to use the
proposed model verification workflow to systematically identify and quantify
numerical approximation errors associated with UISS-TB and, in general, with
any other ABMs.


Abstract:
  We have realized quantized charge pumping using non-adiabatic single-electron
pumps in dopant-free GaAs two-dimensional electron gases (2DEGs). The
dopant-free III-V platform allows for ambipolar devices, such as p-i-n
junctions, that could be combined with such pumps to form electrically-driven
single photon sources. Our pumps operate at up to 0.95 GHz and achieve
remarkable performance considering the relaxed experimental conditions:
one-gate pumping in zero magnetic field and temperatures up to 5K, driven by a
simple RF sine waveform. Fitting to a universal decay cascade model yields
values for the figure of merit $\delta$ that compare favorably to reported
modulation-doped GaAs pumps operating under similar conditions. The devices
reported here are already suitable for optoelectronics applications, and with
further improvement could offer a route to a current standard that does not
require sub-Kelvin temperatures and high magnetic fields.


Abstract:
  To shift the computational burden from real-time to offline in delay-critical
power systems applications, recent works entertain the idea of using a deep
neural network (DNN) to predict the solutions of the AC optimal power flow
(AC-OPF) once presented load demands. As network topologies may change,
training this DNN in a sample-efficient manner becomes a necessity. To improve
data efficiency, this work utilizes the fact OPF data are not simple training
labels, but constitute the solutions of a parametric optimization problem. We
thus advocate training a sensitivity-informed DNN (SI-DNN) to match not only
the OPF optimizers, but also their partial derivatives with respect to the OPF
parameters (loads). It is shown that the required Jacobian matrices do exist
under mild conditions, and can be readily computed from the related primal/dual
solutions. The proposed SI-DNN is compatible with a broad range of OPF solvers,
including a non-convex quadratically constrained quadratic program (QCQP), its
semidefinite program (SDP) relaxation, and MATPOWER; while SI-DNN can be
seamlessly integrated in other learning-to-OPF schemes. Numerical tests on
three benchmark power systems corroborate the advanced generalization and
constraint satisfaction capabilities for the OPF solutions predicted by an
SI-DNN over a conventionally trained DNN, especially in low-data setups.


Abstract:
  Measuring the respiratory signal from a video based on body motion has been
proposed and recently matured in products for video health monitoring. The core
algorithm for this measurement is the estimation of tiny chest/abdominal
motions induced by respiration, and the fundamental challenge is motion
sensitivity. Though prior arts reported on the validation with real human
subjects, there is no thorough/rigorous benchmark to quantify the sensitivities
and boundary conditions of motion-based core respiratory algorithms that
measure sub-pixel displacement between video frames. In this paper, we designed
a setup with a fully-controllable physical phantom to investigate the essence
of core algorithms, together with a mathematical model incorporating two motion
estimation strategies and three spatial representations, leading to six
algorithmic combinations for respiratory signal extraction. Their promises and
limitations are discussed and clarified via the phantom benchmark. The insights
gained in this paper are intended to improve the understanding and applications
of camera-based respiration measurement in health monitoring.


Abstract:
  We explore the sensitivity offered by a global network of cosmic ray
detectors to a novel, unobserved phenomena: widely separated simultaneous
extended air showers. Existing localized observatories work independently to
observe individual showers, offering insight into the source and nature of
ultra-high energy cosmic rays. However no current observatory is large enough
to provide sensitivity to anticipated processes such as the GZ effect or
potential new physics that generate simultaneous air showers separated by
hundreds to thousands of kilometers. A global network of consumer electronics
(the CRAYFIS experiment), may provide a novel opportunity for observation of
such phenomena. Two user scenarios are explored. In the first, with maximal
user adoption, we find that statistically significant discoveries of
spatially-separated but coincident showers are possible within a couple years.
In the second, more practical adoption model with $10^6$ active devices, we
find a worldwide CRAYFIS to be sensitive to novel "burst" phenomena where many
simultaneous EASs occur at once.


Abstract:
  We report an in-depth study of the reduction of graphene oxide (GO) by
in-situ thermal transmission electron microscopy (TEM) analysis. In-situ
heating high-resolution TEM (HRTEM) imaging and electron energy-loss
spectroscopy (EELS) measurements have been combined to identify the
transformations of different oxygen functional groups, the desorption of
physisorbed and chemisorbed water and the graphitization as a function of the
temperature in the range from 70 up to 1200{\deg}C. A model for the general
removal of water and OFGs is proposed based on different chemical and physical
parameters that have been monitored. All this unique information provides a
detailed roadmap of the thermal behaviour of GO at an extended range of
temperature. This is not only of interest to understand the thermal reduction
process of GO but also of critical relevance to the response of GO in
applications when exposed to thermal effects.


Abstract:
  Multibeam high throughput satellite (MB-HTS) systems will play a key role in
delivering broadband services to a large number of users with diverse Quality
of Service (QoS) requirements. This paper focuses on MB-HTS where the same
spectrum is re-used by all user links and, in particular, we propose a novel
user scheduling design capable to provide guarantees in terms of individual QoS
requirements while maximizing the system throughput. This is achieved by
precoding to mitigate mutual interference. The combinatorial optimization
structure requires an extremely high cost to obtain the global optimum even
with a reduced number of users. We, therefore, propose a heuristic algorithm
yielding a good local solution and tolerable computational complexity,
applicable for large-scale networks. Numerical results demonstrate the
effectiveness of our proposed algorithm on scheduling many users with better
sum throughput than the other benchmarks. Besides, the QoS requirements for all
scheduled users are guaranteed.


Abstract:
  The problem of saddle-point avoidance for non-convex optimization is quite
challenging in large scale distributed learning frameworks, such as Federated
Learning, especially in the presence of Byzantine workers. The celebrated
cubic-regularized Newton method of \cite{nest} is one of the most elegant ways
to avoid saddle-points in the standard centralized (non-distributed) setup. In
this paper, we extend the cubic-regularized Newton method to a distributed
framework and simultaneously address several practical challenges like
communication bottleneck and Byzantine attacks. Note that the issue of
saddle-point avoidance becomes more crucial in the presence of Byzantine
machines since rogue machines may create \emph{fake local minima} near the
saddle-points of the loss function, also known as the saddle-point attack.
Being a second order algorithm, our iteration complexity is much lower than the
first order counterparts. Furthermore we use compression (or sparsification)
techniques like $\delta$-approximate compression for communication efficiency.
We obtain theoretical guarantees for our proposed scheme under several settings
including approximate (sub-sampled) gradients and Hessians. Moreover, we
validate our theoretical findings with experiments using standard datasets and
several types of Byzantine attacks, and obtain an improvement of $25\%$ with
respect to first order methods in iteration complexity.


Abstract:
  Let $(S_t)_{t\geq 0}$ be the running maximum of a standard Brownian motion
$(B_t)_{t\geq 0}$ and $T_m:=\inf\{t; \, mS_t<t\},\, m>0$. In this note we
calculate the joint distribution of $T_m$ and $B_{T_m}$. The motivation for our
work comes from a mathematical model for animal foraging. We also present
results for Brownian motion with drift.


Abstract:
  Computer mediated conversations (e.g., videoconferencing) is now the new
mainstream media. How would credibility be impacted if one could change their
race on the fly in these environments? We propose an approach using Deepfakes
and a supporting GAN architecture to isolate visual features and alter racial
perception. We then crowd-sourced over 800 survey responses to measure how
credibility was influenced by changing the perceived race. We evaluate the
effect of showing a still image of a Black person versus a still image of a
White person using the same audio clip for each survey. We also test the effect
of showing either an original video or an altered video where the appearance of
the person in the original video is modified to appear more White. We measure
credibility as the percent of participant responses who believed the speaker
was telling the truth. We found that changing the race of a person in a static
image has negligible impact on credibility. However, the same manipulation of
race on a video increases credibility significantly (61\% to 73\% with p $<$
0.05). Furthermore, a VADER sentiment analysis over the free response survey
questions reveals that more positive sentiment is used to justify the
credibility of a White individual in a video.


Abstract:
  We study self-supervised video representation learning, which is a
challenging task due to 1) lack of labels for explicit supervision; 2)
unstructured and noisy visual information. Existing methods mainly use
contrastive loss with video clips as the instances and learn visual
representation by discriminating instances from each other, but they need a
careful treatment of negative pairs by either relying on large batch sizes,
memory banks, extra modalities or customized mining strategies, which
inevitably includes noisy data. In this paper, we observe that the consistency
between positive samples is the key to learn robust video representation.
Specifically, we propose two tasks to learn the appearance and speed
consistency, respectively. The appearance consistency task aims to maximize the
similarity between two clips of the same video with different playback speeds.
The speed consistency task aims to maximize the similarity between two clips
with the same playback speed but different appearance information. We show that
optimizing the two tasks jointly consistently improves the performance on
downstream tasks, e.g., action recognition and video retrieval. Remarkably, for
action recognition on the UCF-101 dataset, we achieve 90.8\% accuracy without
using any extra modalities or negative pairs for unsupervised pretraining,
which outperforms the ImageNet supervised pretrained model. Codes and models
will be available.


Abstract:
  Modern scientific research has become largely a cooperative activity in the
Internet age. We build a simulation model to understand the population-level
creativity based on the heuristic ant colony algorithm. Each researcher has two
heuristic parameters characterizing the goodness of his own judgments and his
trust on literature. In a population with all kinds of researchers, we find
that as the problem scale increases, the contributor distribution significantly
shifts from the independent regime of relying on one's own judgments to the
cooperative regime of more closely following the literature. The distribution
also changes with the stage of the research problem and the computing power
available. Our work provides some preliminary understanding and guidance for
the dynamical process of cooperative scientific research in various
disciplines.


Abstract:
  Let $K$ be the function field of a curve $C$ over a field $\mathbb{F}$ of
either odd or zero characteristic. Following the work by Serre and Mason on
$\mathrm{SL}_2$, we study the action of arithmetic subgroups of
$\mathrm{SU}(3)$ on its corresponding Bruhat-Tits tree associated to a suitable
completion of $K$. More precisely, we prove that the quotient graph "looks like
a spider", in the sense that it is the union of a set of cuspidal rays (the
"legs"), parametrized by an explicit Picard group, that are attached to a
connected graph (the "body"). We use this description in order to describe
these arithmetic subgroups as amalgamated products and study their homology. In
the case where $\mathbb{F}$ is a finite field, we use a result by Bux, K\"ohl
and Witzel in order to prove that the "body" is a finite graph, which allows us
to get even more precise applications.


Abstract:
  This paper investigates the problem of straight-line path following for
magnetic helical microswimmers. The control objective is to make the helical
microswimmer to converge to a straight line without violating the step-out
frequency constraint. The proposed feedback control solution is based on an
optimal decision strategy (ODS) that is cast as a trust-region subproblem
(TRS), i.e., a quadratic program over a sphere. The ODS-based control strategy
minimizes the difference between the microrobot velocity and an integral
line-of-sight (ILOS)-based reference vector field while respecting the magnetic
saturation constraints and ensuring the absolute continuity of the control
input. Due to the embedded integral action in the reference vector field, the
microswimmer will follow the desired straight line by compensating for the
drift effect of the environmental disturbances as well as the microswimmer
weight.


Abstract:
  In \cite{APSIII} Atiyah, Patodi and Singer introduced spectral flow for
elliptic operators on odd dimensional compact manifolds. They argued that it
could be computed from the Fredholm index of an elliptic operator on a manifold
of one higher dimension. A general proof of this fact was produced by
Robbin-Salamon \cite{RS95}. In \cite{GLMST}, a start was made on extending
these ideas to operators with some essential spectrum as occurs on non-compact
manifolds. The new ingredient introduced there was to exploit scattering theory
following the fundamental paper \cite{Pu08}. These results do not apply to
differential operators directly, only to pseudo-differential operators on
manifolds, due to the restrictive assumption that spectral flow is considered
between an operator and {its perturbation by a relatively trace-class
operator}. In this paper we extend the main results of these earlier papers to
spectral flow between an operator and a perturbation satisfying a higher
$p^{th}$ Schatten class condition for $0\leq p<\infty$, thus allowing
differential operators on manifolds of any dimension $d<p+1$. In fact our main
result does not assume any ellipticity or Fredholm properties at all and proves
an operator theoretic trace formula motivated by \cite{BCPRSW, CGK16}. This
leads us to introduce a notion of `generalised spectral flow' for such paths
and to investigate its properties.


Abstract:
  We consider the generalized Good-Boussinesq model in one dimension, with
power nonlinearity and data in the energy space $H^1\times L^2$. This model has
solitary waves with speeds $-1<c<1$. When $|c|$ approaches 1, Bona and Sachs
showed orbital stability of such waves. It is well-known from a work of Liu
that for small speeds solitary waves are unstable. In this paper we consider in
more detail the long time behavior of zero speed solitary waves, or standing
waves. By using virial identities, in the spirit of Kowalczyk, Martel and
Mu\~noz, we construct and characterize a manifold of even-odd initial data
around the standing wave for which there is asymptotic stability in the energy
space.


Abstract:
  Given a smooth manifold $M$ (with or without boundary), in this paper we
establish a global functional calculus (without the standard assumption that
the operators are classical pseudo-differential operators) and the G\r{a}rding
inequality for global pseudo-differential operators associated with boundary
value problems. The analysis that we follow is free of local coordinate
systems. Applications of the G\r{a}rding inequality to the global solvability
for a class of evolution problems are also considered.


Abstract:
  The minimum-time path for intercepting a moving target with a prescribed
impact angle is studied in the paper. The candidate paths from Pontryagin's
maximum principle are analyzed, so that each candidate is related to a zero of
a real-valued function. It is found that the real-valued functions or their
first-order derivatives can be converted to polynomials of at most fourth
degree. As a result, each canidate path can be computed within a constant time
by embedding a standard polynomial solver into the typical bisection method.
The control strategy along the shortest candidate eventually gives rise to the
time-optimal guidance law. Finally, the developments of the paper is
illustrated and verified by three numerical examples.


Abstract:
  NASA's TESS mission is expected to discover hundreds of M dwarf planets.
However, few studies focus on how planets form around low-mass stars. We aim to
better characterize the formation process of M dwarf planets to fill this gap
and aid in the interpretation of TESS results. We use ten sets of N-body planet
formation simulations which vary in whether a gas disc is present, initial
range of embryo semi-major axes, and initial solid surface density profile.
Each simulation begins with 147 equal-mass embryos around a 0.2 solar mass star
and runs for 100 Myr. We find that planets form rapidly, with most collisions
occurring within the first 1 Myr. The presence of a gas disc reduces the final
number of planets relative to a gas-free environment and causes planets to
migrate inward. We find that roughly a quarter of planetary systems experience
their final giant impact inside the gas disc, suggesting that some super-Earths
may be able to reaccrete an extended gaseous envelope after their final giant
impact, though these may be affected by additional process such as
photoevaporation. In addition, we find that the final distribution of planets
does not retain a memory of the slope of the initial surface density profile,
regardless of whether or not a gas disc is present. Thus, our results suggest
that present-day observations are unlikely to provide sufficient information to
accurately reverse-engineer the initial distribution of solids.


Abstract:
  The understanding of complex quantum many-body systems has been vastly
boosted by tensor network (TN) methods. Among others, excitation spectrum and
long-range interacting systems can be studied using TNs, where one however
confronts the intricate summation over an extensive number of tensor diagrams.
Here, we introduce a set of generating functions, which encode the diagrammatic
summations as leading order series expansion coefficients. Combined with
automatic differentiation, the generating function allows us to solve the
problem of TN diagrammatic summation. We illustrate this scheme by computing
variational excited states and dynamical structure factor of a quantum spin
chain, and further investigating entanglement properties of excited states.
Extensions to infinite size systems and higher dimension are outlined.


Abstract:
  The dynamic response of a parametric system constituted by a spin precessing
in a time dependent magnetic field is studied by means of a perturbative
approach that unveils unexpected features, and is then experimentally
validated. The first-order analysis puts in evidence different regimes: beside
a tailorable low-pass-filter behaviour, a band-pass response with interesting
potential applications emerges. Extending the analysis to the second
perturbation order permits to study the response to generically oriented fields
and to characterize several non-linear features in the behaviour of such kind
of systems.


Abstract:
  Flexible manufacturing has been the trend in the area of the modern chemical
process nowadays. One of the essential characteristics of flexible
manufacturing is to track time-varying target trajectories (e.g. diversity and
quantity of products). A possible tool to achieve time-varying targets is
contraction theory. However, the contraction theory was developed for
continuous time systems and there lacks analysis and synthesis tools for
discrete-time systems. This article develops a systematic approach to
discrete-time contraction analysis and control synthesis using Discrete-time
Control Contraction Metrics (DCCM) which can be implemented using Sum of Square
(SOS) programming. The proposed approach is demonstrated by illustrative
example.


Abstract:
  We study two interacting identical run and tumble particles (RTP's) in one
dimension. Each particle is driven by a telegraphic noise, and in some cases,
also subjected to a thermal white noise with a corresponding diffusion constant
$D$. We are interested in the stationary bound state formed by the two RTP's in
the presence of a mutual attractive interaction. The distribution of the
relative coordinate $y$ indeed reaches a steady state that we characterize in
terms of the solution of a second-order differential equation. We obtain the
explicit formula for the stationary probability $P(y)$ of $y$ for two examples
of interaction potential $V(y)$. The first one corresponds to $V(y) \sim |y|$.
In this case, for $D=0$ we find that $P(y)$ contains a delta function part at
$y=0$, signaling a strong clustering effect, together with a smooth exponential
component. For $D>0$, the delta function part broadens, leading instead to weak
clustering. The second example is the harmonic attraction $V(y) \sim y^2$ in
which case, for $D=0$, $P(y)$ is supported on a finite interval. We unveil an
interesting relation between this two-RTP model with harmonic attraction and a
three-state single RTP model in one dimension, as well as with a four-state
single RTP model in two dimensions. We also provide a general discussion of the
stationary bound state, including examples where it is not unique, e.g., when
the particles cannot cross due to an additional short-range repulsion.


Abstract:
  We construct families of smooth functions
$H\colon\mathbb{R}^{n+1}\to\mathbb{R}$ such that the Euclidean $(n+1)$-space is
completely filled by not necessarily round hyperspheres of mean curvature $H$
at every point.


Abstract:
  We prove that if $A$ is a string algebra then there are not three irreducible
morphisms between indecomposable $A$-modules such that its composition belongs
to $\Re^{6} \backslash \Re^{7}$, whenever the compositions of two of them are
not in $\Re^{3}$. Moreover, for any positive integer $n \geq 3$, we show that
there are $n$ irreducible morphisms such that their composition is in
$\Re^{n+4} \backslash \Re^{n+5}$.


Abstract:
  Coronary artery disease (CAD) remains the world's leading cause of mortality
and the disease burden is continually expanding as the population ages.
Recently, the MR-INFORM randomised trial has demonstrated that the management
of patients with stable CAD can be guided by stress perfusion cardiovascular
magnetic resonance (CMR) imaging and it is non-inferior to the using the
invasive reference standard of fractional flow reserve. The benefits of using
stress perfusion CMR include that it is non-invasive and significantly reduces
the number of unnecessary coronary revascularisations. As compared to other
ischaemia tests, it boasts a high spatial resolution and does not expose the
patient to ionising radiation. However, the main limitation of stress perfusion
CMR is that the diagnostic accuracy is highly dependent on the level of
training of the operator, resulting in the test only being performed routinely
in experienced tertiary centres. The clinical translation of stress perfusion
CMR would be greatly aided by a fully automated, user-independent, quantitative
evaluation of myocardial blood flow. This thesis presents major steps towards
this goal: robust motion correction, automated image processing, reliable
quantitative modelling, and thorough validation. The motion correction scheme
makes use of data decomposition techniques, such as robust principal component
analysis, to mitigate the difficulties in image registration caused by the
dynamic contrast enhancement. The motion corrected image series are input to a
processing pipeline which leverages the recent advances in image processing
facilitated by deep learning. The pipeline utilises convolutional neural
networks to perform a series of computer vision tasks including myocardial
segmentation and right ventricular insertion point detection...


Abstract:
  Context: In spiral galaxies, star formation tends to trace features of the
spiral pattern, including arms, spurs, feathers, and branches. However, in our
own Milky Way, it has been challenging to connect individual star-forming
regions to their larger Galactic environment owing to our perspective from
within the disk. One feature in nearly all modern models of the Milky Way is
the Sagittarius Arm, located inward of the Sun with a pitch angle of ~12 deg.
Aims: We map the 3D locations and velocities of star-forming regions in a
segment of the Sagittarius Arm using young stellar objects (YSOs) from the
Spitzer/IRAC Candidate YSO (SPICY) catalog to compare their distribution to
models of the arm. Methods: Distances and velocities for these objects are
derived from Gaia EDR3 astrometry and molecular line surveys. We infer
parallaxes and proper motions for spatially clustered groups of YSOs and
estimate their radial velocities from the velocities of spatially associated
molecular clouds. Results: We identify 25 star-forming regions in the Galactic
longitude range l~4.0-18.5 deg arranged in a narrow, ~1 kpc long linear
structure with a high pitch angle of $\psi = 56$ deg and a high aspect ratio of
~7:1. This structure includes massive star-forming regions such as M8, M16,
M17, and M20. The motions in the structure are remarkably coherent, with
velocities in the direction of Galactic rotation of $240\pm3$ km/s (slightly
higher than average) and slight drifts toward the Galactic center (-4.3 km/s)
and in the negative Z direction (-2.9 km/s). The rotational shear experienced
by the structure is 4.6 km/s/kpc. Conclusions: The observed 56 deg pitch angle
is remarkably high for a segment of the Sagittarius Arm. We discuss possible
interpretations of this feature as a substructure within the lower pitch angle
Sagittarius Arm, as a spur, or as an isolated structure.


Abstract:
  Photoacoustic imaging is a new non-destructive medical imaging technology
based on photoacoustic effect. It can reflect the difference of light
absorption energy by detecting photoacoustic signal. At present, the analysis
methods of photoacoustic signals in biological tissues can be divided into two
categories, namely, time-domain analysis of signals and frequency-domain
analysis of signals. In time domain analysis, the envelope of the received
photoacoustic signal is usually used to reconstruct the image. However, due to
the influence of various external factors, the time domain signal cannot
accurately reflect the characteristics of the absorber itself. Here,
photoacoustic spectrum analysis was performed by using k-Wave to obtains the
relationship between the structure, size, density of the absorber and the
photoacoustic spectrum. Firstly, the relationship between the size of absorber
and the photoacoustic spectrum is studied, and the slope and intercept are used
to analyze the spectrum. Conversely, the relationship was used to predict the
size of the absorber Finally, we used this relationship to predict the size of
blood vessels.


Abstract:
  Afshani, Barbay and Chan (2017) introduced the notion of instance-optimal
algorithm in the order-oblivious setting. An algorithm A is instance-optimal in
the order-oblivious setting for a certain class of algorithms A* if the
following hold:
  - A takes as input a sequence of objects from some domain;
  - for any instance $\sigma$ and any algorithm A' in A*, the runtime of A on
$\sigma$ is at most a constant factor removed from the runtime of A' on the
worst possible permutation of $\sigma$. If we identify permutations of a
sequence as representing the same instance, this essentially states that A is
optimal on every possible input (and not only in the worst case).
  We design instance-optimal algorithms for the problem of reporting, given a
bichromatic set of points in the plane S, all pairs consisting of points of
different color which span an empty axis-aligned rectangle (or reporting all
points which appear in such a pair). This problem has applications for
training-set reduction in nearest-neighbour classifiers. It is also related to
the problem consisting of finding the decision boundaries of a euclidean
nearest-neighbour classifier, for which Bremner et al. (2005) gave an optimal
output-sensitive algorithm.
  By showing the existence of an instance-optimal algorithm in the
order-oblivious setting for this problem we push the methods of Afshani et al.
closer to their limits by adapting and extending them to a setting which
exhibits highly non-local features. Previous problems for which
instance-optimal algorithms were proven to exist were based solely on local
relationships between points in a set.


Abstract:
  Mobility-as-a-Service (MaaS) is an emerging business model driven by the
concept of "Everything-as-a-Service" and enabled through mobile internet
technologies. In the context of economic deregulation, a MaaS system consists
of a typical two-sided market, where travelers and transportation service
providers (TSPs) are two groups of agents interacting with each other through a
MaaS platform. In this study, we propose a modeling and optimization framework
for the regulation of two-sided MaaS markets. We consider a name-your-own-price
(NYOP)-auction mechanism where travelers submit purchase-bids to accommodate
their travel demand via MaaS platform, and TSPs submit sell-bids to supply
mobility resources for the MaaS platform in exchange for payments. We cast this
problem as a single-leader multi-follower game (SLMFG) where the leader is the
MaaS regulator and two groups of follower problems represent the travelers and
the TSPs. The MaaS regulator aims to maximize its profits by optimizing
operations. In response to the MaaS regulator's decisions, travelers (resp.
TSPs) adjust their participation level in the MaaS platform to minimize their
travel costs (resp. maximize their profits). We analyze cross-group network
effects in the MaaS market, and formulate SLMFGs without and with network
effects leading to mixed-integer linear bilevel programming and mixed-integer
quadratic bilevel programming problems, respectively. We propose customized
branch-and-bound algorithms based on strong duality reformulations to solve
these SLMFGs. Extensive numerical experiments conducted on large scale
simulation instances generated from realistic mobility data highlight that the
performance of the proposed algorithms is significantly superior to a
benchmarking approach, and provide meaningful managerial insights for the
regulation of two-sided MaaS markets in practice.


Abstract:
  Continual learning aims to provide intelligent agents that are capable of
learning continually a sequence of tasks, building on previously learned
knowledge. A key challenge in this learning paradigm is catastrophically
forgetting previously learned tasks when the agent faces a new one. Current
rehearsal-based methods show their success in mitigating the catastrophic
forgetting problem by replaying samples from previous tasks during learning a
new one. However, these methods are infeasible when the data of previous tasks
is not accessible. In this work, we propose a new pseudo-rehearsal-based
method, named learning Invariant Representation for Continual Learning (IRCL),
in which class-invariant representation is disentangled from a conditional
generative model and jointly used with class-specific representation to learn
the sequential tasks. Disentangling the shared invariant representation helps
to learn continually a sequence of tasks, while being more robust to forgetting
and having better knowledge transfer. We focus on class incremental learning
where there is no knowledge about task identity during inference. We
empirically evaluate our proposed method on two well-known benchmarks for
continual learning: split MNIST and split Fashion MNIST. The experimental
results show that our proposed method outperforms regularization-based methods
by a big margin and is better than the state-of-the-art pseudo-rehearsal-based
method. Finally, we analyze the role of the shared invariant representation in
mitigating the forgetting problem especially when the number of replayed
samples for each previous task is small.


Abstract:
  Multiple-choice/multiple-response (MCMR) items (i.e., multiple-choice
questions for which there may be more than one correct response) can be a
valuable tool for assessment. Like traditional multiple-choice/single-response
questions, they are easy to grade; but MCMR items may provide more information
about student reasoning by probing multiple facets of reasoning in a single
problem context. Because MCMR items are infrequently used, best practices for
their implementation are not established. In this paper, we describe the
administration of MCMR items on an online, research-based assessment. We
discuss possible differences in performance on MCMR items that may result from
differences in administration method (in-person vs. online). This work is
presented as a potential first step toward establishing best-practices for the
administration of MCMR items on online assessments.


Abstract:
  Graph Convolutional Networks (GCNs) and subsequent variants have been
proposed to solve tasks on graphs, especially node classification tasks. In the
literature, however, most tricks or techniques are either briefly mentioned as
implementation details or only visible in source code. In this paper, we first
summarize some existing effective tricks used in GCNs mini-batch training.
Based on this, two novel tricks named GCN_res Framework and Embedding Usage are
proposed by leveraging residual network and pre-trained embedding to improve
baseline's test accuracy in different datasets. Experiments on Open Graph
Benchmark (OGB) show that, by combining these techniques, the test accuracy of
various GCNs increases by 1.21%~2.84%. We open source our implementation at
https://github.com/ytchx1999/PyG-OGB-Tricks.


Abstract:
  We construct special cycles on the moduli stack of unitary shtukas. We prove
an identity between (1) the r-th central derivative of non-singular Fourier
coefficients of a normalized Siegel--Eisenstein series, and (2) the degree of
special cycles of "virtual dimension 0" on the moduli stack of unitary shtukas
with r legs. This may be viewed as a function-field analogue of the
Kudla-Rapoport Conjecture, that has the additional feature of encompassing all
higher derivatives of the Eisenstein series.


Abstract:
  In this paper, we use homological techniques and a theorem of Thurston to
study homological finiteness of $\text{BDiff}(M, \text{rel }\partial)$ when $M$
is a reducible $3$-manifold with a non-empty boundary that has distinct
irreducible factors. Kontsevich conjectured that $\text{BDiff}(M, \text{rel
}\partial)$ has the homotopy type of a finite CW complex for all $3$-manifolds
with non-empty boundary. Hatcher-McCullough proved this conjecture when $M$ is
irreducible. We show that when $M$ is reducible with non-empty boundary that
has distinct irreducible factors, $\text{BDiff}(M, \text{rel }\partial)$ is
homology isomorphic to a finite CW-complex.


Abstract:
  Magnetic clouds (MCs) are flux-rope magnetic structures forming a subset of
solar coronal mass ejections which have significant space weather impacts. The
geoeffectiveness of MCs depends on their properties which evolve during their
interplanetary passage. Based on an analysis of observations spanning two solar
cycles we establish that MCs interacting with the ambient solar wind magnetic
field (i.e., heliospheric open flux) lose a substantial amount of their initial
magnetic flux via magnetic reconnection, which in some cases, reduce their
geoeffectiveness. We find a linear correlation between the eroded flux of MCs
and solar open flux which is consistent with the scenario that MC erosion is
mediated via the local heliospheric magnetic field draping around an MC during
its interplanetary propagation. The solar open flux is governed by the sunspot
cycle. This work therefore uncovers a hitherto unknown pathway for solar cycle
modulation of the properties of MCs.


Abstract:
  Recent developments in surgical robotics have led to new advancements in the
automation of surgical sub-tasks such as suturing, soft tissue manipulation,
tissue tensioning and cutting. However, integration of dynamics to optimize
these control policies for the variety of scenes encountered in surgery remains
unsolved. Towards this effort, we investigate the integration of differentiable
fluid dynamics to optimizing a suction tool's trajectory to clear the surgical
field from blood as fast as possible. The fully differentiable fluid dynamics
is integrated with a novel suction model for effective model predictive control
of the tool. The differentiability of the fluid model is crucial because we
utilize the gradients of the fluid states with respect to the suction tool
position to optimize the trajectory. Through a series of experiments, we
demonstrate how, by incorporating fluid models, the trajectories generated by
our method can perform as good as or better than handcrafted human-intuitive
suction policies. We also show that our method is adaptable and can work in
different cavity conditions while using a single handcrafted strategy fails.


Abstract:
  In recent years, network function virtualization and a software defined focus
has allowed networks to become flexible and extensible in ways not possible
previously. Although network modeling tools such as NS-2, NS-3, and OMNet++
have been extended with modules and code to support the absolute latest
wireless protocols and medium access standards - there has been a growing gap
in simulation of layers above medium access which recent 5G use cases are
designed to support. In this thesis, I extend research into the topic of
application layer modeling in wireless networks, with focus on upcoming
deployments of enhanced vehicle services. In particular, I begin in discussing
the evolution of vehicle network standards and use cases, along with the most
recent initiative `Project 5GCar`; funded by the European Commission. I go
narrower, from the macro trend of vehicular network standards evolution, to
compare the state of the art in simulation stacks, designed to support
application models in vehicle networks. Within this comparison, simulation
environments OVNIS, Veins, iTetris, and VSimRTI are compared for their
capabilities. Finally, to measure the qualitative performance of application
layer modeling in vehicle networks, I take the cooperative maneuver use case,
presented under Project 5GCar; to design an autonomous merge algorithm -
completing the steps required to program and model the application in vehicles
using the VSimRTI simulation stack.


Abstract:
  With the popularity of mobile devices, mobile applications have become an
essential part of people's lives. To provide secure mobile application download
channels for users, various modern app markets are maintained by different
companies. For example, Google maintains Google Play for Android users, while
Apple maintains App Store for iOS, iPadOS, and MacOS users. Though app markets
have come up with strict policies which impose restrictions on developers to
avoid the potential harmful applications, we still have quite limited knowledge
on the process of app vetting and the status of potential harmful apps. To fill
this gap, this paper takes the initiative to conduct a large-scale and
longitudinal study of removed apps in the iOS app store. Our analysis reveals
that although most of the removed apps are low-quality apps, a number of them
are quite popular. Furthermore, the mis-behaviors of these apps are reflected
on app metadata, which makes it possible to distinguish potential harmful apps.


Abstract:
  In this paper, we propose a novel bidirectional multiscale feature
aggregation (BMFA) network with attentional fusion modules for text-independent
speaker verification. The feature maps from different stages of the backbone
network are iteratively combined and refined in both a bottom-up and top-down
manner. Furthermore, instead of simple concatenation or element-wise addition
of feature maps from different stages, an attentional fusion module is designed
to compute the fusion weights. Experiments are conducted on the NIST SRE16 and
VoxCeleb1 datasets. The experimental results demonstrate the effectiveness of
the bidirectional aggregation strategy and show that the proposed attentional
fusion module can further improve the performance.


Abstract:
  This work identifies information-theoretic quantities that are closely
related to the required list size for successive cancellation list (SCL)
decoding to implement maximum-likelihood decoding. It also provides an
approximation for these quantities that can be computed efficiently for very
long codes. There is a concentration around the mean of the logarithm of the
required list size for sufficiently large block lengths. We further provide a
simple method to estimate the mean via density evolution for the binary erasure
channel (BEC). Simulation results for the binary-input additive white Gaussian
noise channel as well as the BEC demonstrate the accuracy of the mean estimate.
A modified Reed-Muller code with dynamic frozen bits performs very close to the
random coding union (RCU) bound down to the block error rate of $10^{-5}$ under
SCL decoding with list size $L=128$ when the block length is $N=128$. The
analysis shows how to modify the design to improve the performance when a more
practical list size, e.g., $L=32$, is adopted while keeping the performance
with $L=128$ unchanged. For the block length of $N=512$, a design performing
within $0.4$ dB from the RCU bound down to the block error rate of $10^{-6}$
under an SCL decoder with list size $L=1024$ is provided. The design is
modified using the new guidelines so that the performance improves with
practical list sizes, e.g., $L\in\{8,32,128\}$, outperforming 5G designs.


Abstract:
  Content: Qualitative and ad-hoc descriptions of observations of the
"undergraduate" and "graduate" effects. Comparisons between the classical
models of both undergraduate and graduate systems separately lead to the
conclusion that a quantized model is inevitably required to explain certain
effects such as "Spooky Action At A Seminar" and the FTR (Food Travel Ratio)
among other select observed phenomena. Two regimes of dynamics in systems are
explored as well as interactions of composite systems. An attempt is made to
refine the undergraduate/graduate statistical theory in order to explain the
common equilibrium systems known as "labs". Finally this work will begin the
discussion on the mysterious ultra-long range "Advisor" interaction, which
seems to contradict the theory of undergraduate/graduate interaction as well as
specific graduate/graduate interactions, and graduate/research interactions in
erratic ways.


Abstract:
  This paper is focused on performing a new method for solving linear and
nonlinear higher-order boundary value problems (HBVPs). This direct numerical
method based on spectral method. The trial function of this method is the Monic
Chebyshev polynomials (MCPs). This method was relying on derivative of MCPs
which explicit in the series expansion. The advantage of this method is solved
HBVPs without transforming it to a system of lower-order ordinary differential
equations (ODEs). This method supported by examples of HBVPs in wide
application. The mentioned examples showed that the proposed method is
efficient and accurate.


Abstract:
  We show that Floquet engineering using bicircular light (BCL) is a versatile
way to control magnetic symmetries and topology in materials. The electric
field of BCL, which is a superposition of two circularly polarized light waves
with frequencies that are integer multiples of each other, traces out a rose
pattern in the polarization plane that can be chosen to break selective
symmetries, including spatial inversion. Using a realistic low-energy model, we
theoretically demonstrate that the three-dimensional Dirac semimetal
Cd$_3$As$_2$ is a promising platform for BCL Floquet engineering. Without
strain, BCL irradiation induces a transition to a non-centrosymmetric magnetic
Weyl semimetal phase with tunable energy separation between the Weyl nodes. In
the presence of strain, we predict the emergence of a magnetic topological
crystalline insulator with exotic unpinned surface Dirac states that are
protected by a combination of twofold rotation and time-reversal $(2')$ and can
be controlled by light.


Abstract:
  The Standard Model(SM) vacuum is unstable for the measured values of the top
Yukawa coupling and Higgs mass. Here we study the issue of vacuum stability
when neutrino masses are generated through spontaneous low-scale lepton number
violation. In the simplest dynamical inverse seesaw, the SM Higgs has two
siblings: a massive CP-even scalar plus a massless Nambu-Goldstone boson,
called majoron. For TeV scale breaking of lepton number, Higgs bosons can have
a sizeable decay into the invisible majorons. We examine the interplay and
complementarity of vacuum stability and perturbativity restrictions, with
collider constraints on visible and invisible Higgs boson decay channels. This
simple framework may help guiding further studies, for example, at the proposed
FCC facility.