Abstract:
  The general position number of a graph $G$ is the size of the largest set of
vertices $S$ such that no geodesic of $G$ contains more than two elements of
$S$. The monophonic position number of a graph is defined similarly, but with
`induced path' in place of `geodesic'. In this paper we investigate some
extremal problems for these parameters. Firstly we discuss the problem of the
smallest possible order of a graph with given general and monophonic position
numbers, with applications to a realisation result. We then solve a Tur\'{a}n
problem for the size of graphs with given order and position numbers and
characterise the possible diameters of graphs with given order and monophonic
position number. Finally we classify the graphs with given order and diameter
and largest possible general position number.


Abstract:
  We consider initial value problems of nonlinear dynamical systems, which
include physical parameters. A quantity of interest depending on the solution
is observed. A discretisation yields the trajectories of the quantity of
interest in many time points. We examine the mapping from the set of parameters
to the discrete values of the trajectories. An evaluation of this mapping
requires to solve an initial value problem. Alternatively, we determine an
approximation, where the evaluation requires low computation work, using a
concept of machine learning. We employ feedforward neural networks, which are
fitted to data from samples of the trajectories. Results of numerical
computations are presented for a test example modelling an electric circuit.


Abstract:
  Entanglement is a unique property of quantum systems and an essential
resource for many quantum technologies. The ability to transfer or swap
entanglement between systems is an important protocol in quantum information
science. Entanglement between photons forms the basis of distributed quantum
networks and the demonstration of photonic entanglement swapping is essential
for their realization. Here an experiment demonstrating entanglement swapping
from two independent multimode time-frequency entangled sources is presented,
resulting in multiple heralded temporal-mode Bell states. Entanglement in the
heralded states is verified by measuring conditional anti-correlated joint
spectra as well as quantum beating in two-photon interference. Our
proof-of-concept experiment is able to distinguish up to five orthogonal Bell
pairs within the same setup, limited in principle only by the entanglement of
the sources.


Abstract:
  Inhomogeneous big bang nucleosynthesis (BBN) produces a spatially
inhomogeneous distribution of element abundances at $T \sim 10^9$ K, but
subsequent element diffusion will tend to erase these inhomogeneities. We
calculate the cosmological comoving diffusion length for the BBN elements. This
diffusion length is limited by atomic scattering and is therefore dominated by
diffusion when the atoms are neutral, between the redshifts of recombination
and reionization. We find that the comoving diffusion length today is $d_{com}
\approx 70$ pc for all of the elements of interest except $^7$Li, for which
$d_{com}$ is an order of magnitude smaller because $^7$Li remains ionized
throughout the relevant epoch. This comoving diffusion length corresponds to a
substellar baryonic mass scale and is roughly equal to the horizon scale at
BBN. These results lend support to the possibility that inhomogeneities on
scales larger than the horizon at BBN could lead to a spatially inhomogeneous
distribution of elements today, while purely subhorizon fluctuations at BBN can
result only in a homogeneous element distribution at present.


Abstract:
  X-ray flux from the inner hot region around central compact object in a
binary system illuminates the upper surface of an accretion disc and it behaves
like a corona. This region can be photoionised by the illuminating radiation,
thus can emit different emission lines. We study those line spectra in black
hole X-ray binaries for different accretion flow parameters including its
geometry. The varying range of model parameters captures maximum possible
observational features. We also put light on the routinely observed Fe line
emission properties based on different model parameters, ionization rate, and
Fe abundances. We find that the Fe line equivalent width $W_{\rm E}$ decreases
with increasing disc accretion rate and increases with the column density of
the illuminated gas. Our estimated line properties are in agreement with
observational signatures.


Abstract:
  We extend and generalize the construction of Sturm-Liouville problems for a
family of Hamiltonians constrained to fulfill a third-order shape-invariance
condition and focusing on the "$-2x/3$" hierarchy of solutions to the fourth
Painlev\'e transcendent. Such a construction has been previously addressed in
the literature for some particular cases but we realize it here in the most
general case. The corresponding potential in the Hamiltonian operator is a
rationally extended oscillator defined in terms of the conventional Okamoto
polynomials, from which we identify three different zero-modes constructed in
terms of the generalized Okamoto polynomials. The third-order ladder operators
of the system reveal that the complete set of eigenfunctions is decomposed as a
union of three disjoint sequences of solutions, generated from a set of
three-term recurrence relations. We also identify a link between the
eigenfunctions of the Hamiltonian operator and a special family of exceptional
Hermite polynomial.


Abstract:
  Artificial intelligence-powered pocket-sized air robots have the potential to
revolutionize the Internet-of-Things ecosystem, acting as autonomous,
unobtrusive, and ubiquitous smart sensors. With a few cm$^{2}$ form-factor,
nano-sized unmanned aerial vehicles (UAVs) are the natural befit for indoor
human-drone interaction missions, as the pose estimation task we address in
this work. However, this scenario is challenged by the nano-UAVs' limited
payload and computational power that severely relegates the onboard brain to
the sub-100 mW microcontroller unit-class. Our work stands at the intersection
of the novel parallel ultra-low-power (PULP) architectural paradigm and our
general development methodology for deep neural network (DNN) visual pipelines,
i.e., covering from perception to control. Addressing the DNN model design,
from training and dataset augmentation to 8-bit quantization and deployment, we
demonstrate how a PULP-based processor, aboard a nano-UAV, is sufficient for
the real-time execution (up to 135 frame/s) of our novel DNN, called
PULP-Frontnet. We showcase how, scaling our model's memory and computational
requirement, we can significantly improve the onboard inference (top energy
efficiency of 0.43 mJ/frame) with no compromise in the quality-of-result vs. a
resource-unconstrained baseline (i.e., full-precision DNN). Field experiments
demonstrate a closed-loop top-notch autonomous navigation capability, with a
heavily resource-constrained 27-gram Crazyflie 2.1 nano-quadrotor. Compared
against the control performance achieved using an ideal sensing setup, onboard
relative pose inference yields excellent drone behavior in terms of median
absolute errors, such as positional (onboard: 41 cm, ideal: 26 cm) and angular
(onboard: 3.7$^{\circ}$, ideal: 4.1$^{\circ}$).


Abstract:
  We report on the preparation of flexible polymer composite films with aligned
metallic fillers comprised of atomic chain bundles of the quasi-one-dimensional
(1D) van der Waals material tantalum triselenide, TaSe3. The material
functionality, embedded at the nanoscale level, is achieved by mimicking the
design of an electromagnetic aperture grid antenna. The processed composites
employ chemically exfoliated TaSe3 nanowires as the grid building blocks
incorporated within the thin film. Filler alignment is achieved using the
"blade coating" method. Measurements conducted in the X-band frequency range
demonstrate that the electromagnetic transmission through such films can be
varied significantly by changing the relative orientations of the quasi-1D
fillers and the polarization of the electromagnetic wave. We argue that such
polarization-sensitive polymer films with quasi-1D fillers are applicable to
advanced electromagnetic interference shielding in future communication
systems.


Abstract:
  The cell wall serves as a mechanical protection for the fungi and controls
the traffic of molecules entering the cell. However, the optical role of the
cell wall has not been fully investigated. In this work, we use a computational
simulation to evaluate the optical response of the fungal cell wall. The main
advantage of this approach is that the scattering structure can be introduced
within the simulation via transmission electronic microscope (TEM) images of
its cross section which in this case correspond to real fungal cell walls. The
obtained results indicate that the reflectivity of the fungal cell wall depends
on the incident wavelength, and then, it modulates the light that reaches the
interior of the cell. This suggests that the light stimulus that reaches the
inner part of the cell could depend on the structural characteristics of the
cell wall. Additionally, regarding the analysis of color, our finding opens up
a new and interesting approach to investigate the ecological role of UV light
in relation to fungal organisms and their microhabitat.


Abstract:
  Motivated by deviations with respect to Standard Model predictions in $b\to
s\ell^+\ell^-$ decays, we evaluate the global significance of the new physics
hypothesis in this system by including the {\it look-elsewhere effect} for the
first time. We estimate the trial-factor with pseudo-experiments and find that
it can be as large as seven. We calculate the global significance for the new
physics hypothesis by considering the most general description of a
non-standard $b\to s\ell^+\ell^-$ amplitude of short-distance origin.
Theoretical uncertainties are treated in a highly conservative way by absorbing
the corresponding effects into a redefinition of the Standard Model amplitude.
Using the most recent measurements of LHCb, ATLAS and CMS, we obtain the global
significance to be $4.3$ standard deviations.


Abstract:
  Continuous determinantal point processes (DPPs) are a class of repulsive
point processes on $\mathbb{R}^d$ with many statistical applications. Although
an explicit expression of their density is known, this expression is too
complicated to be used directly for maximum likelihood estimation. In the
stationary case, an approximation using Fourier series has been suggested, but
it is limited to rectangular observation windows and no theoretical results
support it. In this contribution, we investigate a different way to approximate
the likelihood by looking at its asymptotic behaviour when the observation
window grows towards $\mathbb{R}^d$. This new approximation is not limited to
rectangular windows, is faster to compute than the previous one, does not
require any tuning parameter, and some theoretical justifications are provided.
The performances of the associated estimator are assessed in a simulation study
on standard parametric models on $\mathbb{R}^d$ and compare favourably to
common alternative estimation methods for continuous DPPs.


Abstract:
  Given a simple recursive function, we show how to extract from it a
reversible and an classical iterative part. Those parts can synchronously
cooperate under a Producer/Consumer pattern in order to implement the original
recursive function. The reversible producer is meant to run on reversible
hardware. We also discuss how to extend the extraction to a more general
compilation scheme.


Abstract:
  Since 2014 transfer learning has become the key driver for the improvement of
spatial saliency prediction; however, with stagnant progress in the last 3-5
years. We conduct a large-scale transfer learning study which tests different
ImageNet backbones, always using the same read out architecture and learning
protocol adopted from DeepGaze II. By replacing the VGG19 backbone of DeepGaze
II with ResNet50 features we improve the performance on saliency prediction
from 78% to 85%. However, as we continue to test better ImageNet models as
backbones (such as EfficientNetB5) we observe no additional improvement on
saliency prediction. By analyzing the backbones further, we find that
generalization to other datasets differs substantially, with models being
consistently overconfident in their fixation predictions. We show that by
combining multiple backbones in a principled manner a good confidence
calibration on unseen datasets can be achieved. This new model, "DeepGaze IIE",
yields a significant leap in benchmark performance in and out-of-domain with a
15 percent point improvement over DeepGaze II to 93% on MIT1003, marking a new
state of the art on the MIT/Tuebingen Saliency Benchmark in all available
metrics (AUC: 88.3%, sAUC: 79.4%, CC: 82.4%).


Abstract:
  The largest anisotropy in the cosmic microwave background (CMB) is the 3 mK
kinematic dipole reflecting our motion with respect to the CMB frame and
pointed in the direction $(l, b) = (264^\circ, +48^\circ)$ in Galactic
coordinates. We introduce the concept of the ring of attraction (RA), which is
orthogonal to the axis of the kinematic dipole. These directions overlap with
the zone of percolation for the kinematic dipole, where its amplitude almost
vanishes. We show that along this ring are oriented the directions of the
dipole modulation of the CMB, and positions of the peaks responsible for
generation of parity asymmetry. This coincidence is peculiar at around the 3
sigma level. We analyzed the "interaction" of low multipoles of the CMB with RA
and showed that for odd modes there is a sequence of peaks in the RA direction.
These peaks correlate with each other for different multipoles and result in
mutual amplification of the odd $\ell$ signal for the first 30 multipoles. Our
method sheds new light on the nature of parity asymmetry. It consists of the
deficit of symmetrically located and equal in amplitude peaks in the CMB map in
comparison with asymmetric peaks.


Abstract:
  In this work we investigate whether a multilayered planet can be approximated
as a homogeneous planet, and in particular how well the dissipation rate of a
multilayered planet can be reproduced with a homogeneous rheology. We study the
case of a stratified body with an icy crust that, according to recent studies,
displays a double peak feature in the tidal response that cannot be reproduced
with a homogeneous planet with an Andrade rheology. We revisit the problem with
a slightly more complex rheology for the homogeneous body, the Sundberg-Cooper
rheology, which naturally has a double peak feature, and apply the model to the
TRAPPIST-1e planet. Our results compare very well with the results obtained
when employing a multilayered model, showing that it is possible to approximate
the behavior of a multilayer icy planet with a homogeneous planet using the
Sundberg-Cooper rheology. This highlights the fact that we do not need the
complexity of the multilayer planet model in order to estimate the tidal
dissipation of an icy planet.


Abstract:
  The recently commissioned Dark Energy Spectroscopic Instrument (DESI) will
measure the expansion historyof the universe using the Baryon Acoustic
Oscillation technique. The spectra of 35 million galaxies and quasarsover 14000
sq deg will be measured during the life of the experiment. A new prime focus
corrector for theKPNO Mayall telescope delivers light to 5000 fiber optic
positioners. The fibers in turn feed ten broad-bandspectrographs. We describe
key aspects and lessons learned from the development, delivery and installation
ofthe fiber system at the Mayall telescope.


Abstract:
  Presence of noise in the labels of large scale facial expression datasets has
been a key challenge towards Facial Expression Recognition (FER) in the wild.
During early learning stage, deep networks fit on clean data. Then, eventually,
they start overfitting on noisy labels due to their memorization ability, which
limits FER performance. This work proposes an effective training strategy in
the presence of noisy labels, called as Consensual Collaborative Training (CCT)
framework. CCT co-trains three networks jointly using a convex combination of
supervision loss and consistency loss, without making any assumption about the
noise distribution. A dynamic transition mechanism is used to move from
supervision loss in early learning to consistency loss for consensus of
predictions among networks in the later stage. Inference is done using a single
network based on a simple knowledge distillation scheme. Effectiveness of the
proposed framework is demonstrated on synthetic as well as real noisy FER
datasets. In addition, a large test subset of around 5K images is annotated
from the FEC dataset using crowd wisdom of 16 different annotators and reliable
labels are inferred. CCT is also validated on it. State-of-the-art performance
is reported on the benchmark FER datasets RAFDB (90.84%) FERPlus (89.99%) and
AffectNet (66%). Our codes are available at https://github.com/1980x/CCT.


Abstract:
  In this paper we present some very recent results regarding existence,
uniqueness, and multiplicity of solutions for quasilinear elliptic equations
and systems, exhibiting both singular and convective reaction terms. The
importance of boundary conditions (Dirichlet, Neumann, or Robin) is also
discussed. Existence is achieved via sub-supersolution and truncation
techniques, fixed point theory, nonlinear regularity, and set-valued analysis,
while uniqueness and multiplicity are obtained by monotonicity arguments.


Abstract:
  Infinite Gray code has been introduced by Tsuiki~\cite{ts} as a
redundancy-free representation of the reals. In applications the signed digit
representation is mostly used which has maximal redundancy. Tsuiki presented a
functional program converting signed digit code into infinite Gray code.
Moreover, he showed that infinite Gray code can effectively be converted into
signed digit code, but the program needs to have some non-deterministic
features (see also \cite{tsug}). Berger and Tsuiki~\cite{btifp,bt} reproved the
result in a system of formal first-order intuitionistic logic extended by
inductive and co-inductive definitions, as well as some new logical connectives
capturing concurrent behaviour. The programs extracted from the proofs are
exactly the ones given by Tsuiki. In order to do so, co-inductive predicates
$\bS$ and $\bG$ are defined and the inclusion $\bS \subseteq \bG$ is derived.
For the converse inclusion the new logical connectives are used to introduce a
concurrent version $\S_{2}$ of $S$ and $\bG \subseteq \bS_{2}$ is shown. What
one is looking for, however, is an equivalence proof of the involved concepts.
One of the main aims of the present paper is to close the gap. A concurrent
version $\bG^{*}$ of $\bG$ and a modification $\bS^{*}$ of $\bS_{2}$ are
presented such that $\bS^{*} = \bG^{*}$. A crucial tool in \cite{btifp} is a
formulation of the Archimedean property of the real numbers as an induction
principle. We introduce a concurrent version of this principle which allows us
to prove that $\bS^{*}$ and $\bG^{*}$ coincide. A further central contribution
is the extension of the above results to the hyperspace of non-empty compact
subsets of the reals.


Abstract:
  Ultra-hot Jupiters are the hottest exoplanets discovered so far. Observations
begin to provide insight into the composition of their extended atmospheres and
their chemical day/night asymmetries. Both are strongly affected by cloud
formation. We explore trends in cloud properties for a sample of five giant gas
planets: WASP-43b, WASP-18b, HAT-P-7b, WASP-103b, and WASP-121b. This provides
a reference frame for cloud properties for the JWST targets WASP-43b and
WASP-121b. We further explore chemically inert tracers to observe geometrical
asymmetries, and if the location of inner boundary of a 3D GCM matters for the
clouds that form. The large day/night temperature differences of ultra-hot
Jupiters cause large chemical asymmetries: cloud-free days but cloudy nights,
atomic vs. molecular gases and respectively different mean molecular weights,
deep thermal ionospheres vs. low-ionised atmospheres, undepleted vs enhanced
C/O. WASP-18b, as the heaviest planet in the sample, has the lowest global C/O.
The global climate may be considered as similar amongst ultra-hot Jupiters, but
different to that of hot gas giants. The local weather, however, is individual
for each planet since the local thermodynamic conditions, and hence the local
cloud and gas properties, differ. The morning and the evening terminator of
ultra-hot Jupiters will carry signatures of their strong chemical asymmetry
such that ingress/egress asymmetries can be expected. An increased C/O ratio is
a clear sign of cloud formation, making cloud modelling a necessity when
utilizing C/O (or other mineral ratios) as tracer for planet formation. The
changing geometrical extension of the atmosphere from the day to the nightside
may be probed through chemically inert species like helium. Ultra-hot Jupiters
are likely to develop deep atmospheric ionospheres which may impact the
atmosphere dynamics through MHD processes.


Abstract:
  We consider the problem of determining the achievable region of parameters
for universal $1 \to 2$ asymmetric quantum cloning. Measuring the cloning
performance with the figure of merit of singlet fraction, we show that the
physical region is a union of ellipses in the plane. Equivalently, we
characterize the parameter region of quantum state compatibility of two
possibly different isotropic states, considering, for the first time, negative
singlet fractions.


Abstract:
  Unmanned Aerial Vehicles (UAVs) are an emerging computation platform known
for their safety-critical need. In this paper, we conduct an empirical study on
a widely used open-source UAV software framework, Paparazzi, with the goal of
understanding the safety-critical concerns of UAV software from a bottom-up
developer-in-the-field perspective. We set our focus on the use of Bounding
Functions (BFs), the runtime checks injected by Paparazzi developers on the
range of variables. Through an in-depth analysis on BFs in the Paparazzi
autopilot software, we found a large number of them (109 instances) are used to
bound safety-critical variables essential to the cyber-physical nature of the
UAV, such as its thrust, its speed, and its sensor values. The novel
contributions of this study are two fold. First, we take a static approach to
classify all BF instances, presenting a novel datatype-based 5-category
taxonomy with fine-grained insight on the role of BFs in ensuring the safety of
UAV systems. Second, we dynamically evaluate the impact of the BF uses through
a differential approach, establishing the UAV behavioral difference with and
without BFs. The two-pronged static and dynamic approach together illuminates a
rarely studied design space of safety-critical UAV software systems.


Abstract:
  In Schubert Puzzles and Integrability I we proved several "puzzle rules" for
computing products of Schubert classes in K-theory (and sometimes equivariant
K-theory) of d-step flag varieties. The principal tool was "quantum
integrability", in several variants of the Yang--Baxter equation; this let us
recognize the Schubert structure constants as q->0 limits of certain matrix
entries in products of R- (and other) matrices of quantized affine algebra
representations. In the present work we give direct cohomological
interpretations of those same matrix entries but at finite q: they compute
products of "motivic Segre classes", closely related to K-theoretic
Maulik--Okounkov stable classes living on the cotangent bundles of the flag
varieties. Without q->0, we avoid some divergences that blocked fuller
understanding of d=3,4. The puzzle computations are then explained (in
cohomology onlyin this work, not K-theory) in terms of Lagrangian convolutions
between Nakajima quiver varieties. More specifically, the conormal bundle to
the diagonal inclusion of a flag variety factors through a quiver variety that
is not a cotangent bundle, and it is on that intermediate quiver variety that
the R-matrix calculation occurs.


Abstract:
  Using finite difference operators, we define a notion of boundary and surface
measure for configuration sets under Poisson measures. A Margulis-Russo type
identity and a co-area formula are stated with applications to deviation
inequalities and functional inequalities, and bounds are obtained on the
associated isoperimetric constants.


Abstract:
  Programming by Example (PBE) is a program synthesis paradigm in which the
synthesizer creates a program that matches a set of given examples. In many
applications of such synthesis (e.g., program repair or reverse engineering),
we are to reconstruct a program that is close to a specific target program, not
merely to produce some program that satisfies the seen examples. In such
settings, we wish that the synthesized program generalizes well, i.e., has as
few errors as possible on the unobserved examples capturing the target function
behavior. In this paper, we propose the first framework (called SynGuar) for
PBE synthesizers that guarantees to achieve low generalization error with high
probability. Our main contribution is a procedure to dynamically calculate how
many additional examples suffice to theoretically guarantee generalization. We
show how our techniques can be used in 2 well-known synthesis approaches: PROSE
and STUN (synthesis through unification), for common string-manipulation
program benchmarks. We find that often a few hundred examples suffice to
provably bound generalization error below $5\%$ with high ($\geq 98\%$)
probability on these benchmarks. Further, we confirm this empirically: SynGuar
significantly improves the accuracy of existing synthesizers in generating the
right target programs. But with fewer examples chosen arbitrarily, the same
baseline synthesizers (without SynGuar) overfit and lose accuracy.


Abstract:
  Mode competition is conventionally regarded as an unwanted phenomenon that
induces instability of laser emission. Here, we experimentally demonstrate that
the random dynamics behavior of mode competition provides hidden information of
the intracavity topology. We introduced the concept of self-assembled
biophotonics networks, where human insulin protein with network topology was
sandwiched in a Fabry-Perot (FP) cavity. The optical coupling between insulin
fibrils and enables strong mode competition. Thanks to the optical feedback
provided by the FP cavity, the interaction between the light and insulin
network was enhanced. Subtle changes in the network will cause significant
changes in laser mode dynamics. By recording the transverse mode dynamics, we
reconstruct a graph that reveals the optical correlation in the insulin
network. Our results indicate that the graph represents an intrinsic feature
and can sensitivity respond to the stimulus. The unclonable and sensitively
responsive property of the biophotonic lasing network can potentially be
employed in applications in sensing and information encryption.


Abstract:
  Intensive research has been conducted on the verification and validation of
deep neural networks (DNNs), aiming to understand if, and how, DNNs can be
applied to safety critical applications. However, existing verification and
validation techniques are limited by their scalability, over both the size of
the DNN and the size of the dataset. In this paper, we propose a novel
abstraction method which abstracts a DNN and a dataset into a Bayesian network
(BN). We make use of dimensionality reduction techniques to identify hidden
features that have been learned by hidden layers of the DNN, and associate each
hidden feature with a node of the BN. On this BN, we can conduct probabilistic
inference to understand the behaviours of the DNN processing data. More
importantly, we can derive a runtime monitoring approach to detect in
operational time rare inputs and covariate shift of the input data. We can also
adapt existing structural coverage-guided testing techniques (i.e., based on
low-level elements of the DNN such as neurons), in order to generate test cases
that better exercise hidden features. We implement and evaluate the BN
abstraction technique using our DeepConcolic tool available at
https://github.com/TrustAI/DeepConcolic.


Abstract:
  Aiming at the local overload of multi-controller deployment in
software-defined networks, a load balancing mechanism of SDN controller based
on reinforcement learning is designed. The initial paired migrate-out domain
and migrate-in domain are obtained by calculating the load ratio deviation
between the controllers, a preliminary migration triplet, contains migration
domain mentioned above and a group of switches which are subordinated to the
migrate-out domain, makes the migration efficiency reach the local optimum.
Under the constraint of the best efficiency of migration in the whole and
without migration conflict, selecting multiple sets of triples based on
reinforcement learning, as the final migration of this round to attain the
global optimal controller load balancing with minimum cost. The experimental
results illustrate that the mechanism can make full use of the controllers'
resources, quickly balance the load between controllers, reduce unnecessary
migration overhead and get a faster response rate of the packet-in request.


Abstract:
  In this paper, we use a complete model of classical primordial standard
clocks as an example to develop a methodology of directly comparing numerical
predictions from complicated multi-field feature models with the Planck data,
including the Planck 2018 Plik unbinned likelihood and the statistically most
powerful CamSpec 2020 likelihood for temperature and polarization data. As this
two-field inflationary model offers a plethora of primordial feature spectra
that represent combinations of sharp and resonance feature signals
non-trivially distributed over extended cosmological scales, its data
comparison has not been satisfactorily addressed by previous attempts using
analytical templates. The method of this paper, consisting of numerical
prediction, effective parameter construction and nested sampling data
comparison, allows us to efficiently explore every possible spectra from the
model. We classify the resulting feature candidates in three different
frequency ranges. We use the Bayesian evidences to assess the statistical
significance of the candidates over the baseline model, taking into account the
effect of additional parameters and the look-elsewhere effect. Although none of
the candidates is statistically significant, the methodology of this paper can
be used to facilitate the future model-building and data-screening process of
primordial features, and the candidates can be subjected to further tests with
data from the upcoming cosmic microwave background polarization observations
and galaxy surveys.


Abstract:
  Compared to the general news domain, information extraction (IE) from
biomedical text requires much broader domain knowledge. However, many previous
IE methods do not utilize any external knowledge during inference. Due to the
exponential growth of biomedical publications, models that do not go beyond
their fixed set of parameters will likely fall behind. Inspired by how humans
look up relevant information to comprehend a scientific text, we present a
novel framework that utilizes external knowledge for joint entity and relation
extraction named KECI (Knowledge-Enhanced Collective Inference). Given an input
text, KECI first constructs an initial span graph representing its initial
understanding of the text. It then uses an entity linker to form a knowledge
graph containing relevant background knowledge for the the entity mentions in
the text. To make the final predictions, KECI fuses the initial span graph and
the knowledge graph into a more refined graph using an attention mechanism.
KECI takes a collective approach to link mention spans to entities by
integrating global relational information into local representations using
graph convolutional networks. Our experimental results show that the framework
is highly effective, achieving new state-of-the-art results in two different
benchmark datasets: BioRelEx (binding interaction detection) and ADE (adverse
drug event extraction). For example, KECI achieves absolute improvements of
4.59% and 4.91% in F1 scores over the state-of-the-art on the BioRelEx entity
and relation extraction tasks.


Abstract:
  For a Banach space $X$ we demonstrate the equivalence of the following two
properties:
  (1) $X$ is B-convex (that is, possesses a nontrivial infratype), and
  (2) if ${F: [0,1] \to 2^{X} \setminus \{\varnothing\}}$ is a {multifunction},
$\mathrm{conv} F$ denotes the mapping $t \mapsto \mathrm{conv} F(t)$, then the
Riemann integrability of $\mathrm{conv} F$ is equivalent to the Riemann
integrability of $F$.
  For multifunctions with compact values the Riemann integrability of
$\mathrm{conv} F$ is equivalent to the Riemann integrability of $F$ without any
restrictions on the Banach space $X$.


Abstract:
  This letter presents a novel framework termed DistSTN for the task of
synthetic aperture radar (SAR) automatic target recognition (ATR). In contrast
to the conventional SAR ATR algorithms, DistSTN considers a more challenging
practical scenario for non-cooperative targets whose aspect angles for training
are incomplete and limited in a partial range while those of testing samples
are unlimited. To address this issue, instead of learning the pose invariant
features, DistSTN newly involves an elaborated feature disentangling model to
separate the learned pose factors of a SAR target from the identity ones so
that they can independently control the representation process of the target
image. To disentangle the explainable pose factors, we develop a pose
discrepancy spatial transformer module in DistSTN to characterize the intrinsic
transformation between the factors of two different targets with an explicit
geometric model. Furthermore, DistSTN develops an amortized inference scheme
that enables efficient feature extraction and recognition using an
encoder-decoder mechanism. Experimental results with the moving and stationary
target acquisition and recognition (MSTAR) benchmark demonstrate the
effectiveness of our proposed approach. Compared with the other ATR algorithms,
DistSTN can achieve higher recognition accuracy.


Abstract:
  We give necessary and/or sufficient conditions for stochastic stability of
second-order linear autonomous systems with parameters, which are perturbed by
a random process of the "white noise" type. The Ito's and Stratonovich's forms
of stochastic differential equations representing the system with white noise
are considered. The investigation of stochastic stability of the systems
considered is based on the construction of special Lyapunov functions in the
quadratic forms. The bifurcation value of the white noise intensity at which a
system first becomes unstable is presented in the analytical expression. As an
example a damped harmonic oscillator with randomly perturbed parameters is
considered.


Abstract:
  Human ratings are one of the most prevalent methods to evaluate the
performance of natural language processing algorithms. Similarly, it is common
to measure the quality of sentences generated by a natural language generation
model using human raters. In this paper, we argue for exploring the use of
subjective evaluations within the process of training language generation
models in a multi-task learning setting. As a case study, we use a
crowd-authored dialogue corpus to fine-tune six different language generation
models. Two of these models incorporate multi-task learning and use subjective
ratings of lines as part of an explicit learning goal. A human evaluation of
the generated dialogue lines reveals that utterances generated by the
multi-tasking models were subjectively rated as the most typical, most moving
the conversation forward, and least offensive. Based on these promising first
results, we discuss future research directions for incorporating subjective
human evaluations into language model training and to hence keep the human user
in the loop during the development process.


Abstract:
  We continue the study of the octagon form factor which helps to evaluate a
class of four-point correlation functions in $\mathcal{N}=4$ SYM theory. The
octagon is characterised, besides the kinematical parameters, by a "bridge" of
$\ell$ propagators connecting two non-adjacent operators. In this paper we
construct an operator representation of the octagon with finite bridge as an
expectation value in the Fock space of free complex fermions. The bridge $\ell$
appears as the level of filling of the Dirac sea. We obtain determinant
identities relating octagons with different bridges, which we derive from the
expression of the octagon in terms of discrete fermionic oscillators. The
derivation is based on the existence of a previously conjectured similarity
transformation, which we find here explicitly.


Abstract:
  Diffuse gliomas are highly infiltrative tumors whose early diagnosis and
follow-up usually rely on magnetic resonance imaging (MRI). However, the
limited sensitivity of this technique makes it impossible to directly assess
the extent of the glioma cell invasion, leading to sub-optimal treatment
planing. Reaction-diffusion growth models have been proposed for decades to
extrapolate glioma cell infiltration beyond margins visible on MRI and predict
its spatial-temporal evolution. These models nevertheless require an initial
condition, that is the tumor cell density values at every location of the brain
at diagnosis time. Several works have proposed to relate the tumor cell density
function to abnormality outlines visible on MRI but the underlying assumptions
have never been verified so far. In this work we propose to verify these
assumptions by stereotactic histological analysis of a non-operated brain with
glioblastoma using a tailored 3D-printed slicer. Cell density maps are computed
from histological slides using a deep learning approach. The density maps are
then registered to a postmortem MR image and related to an MR-derived geodesic
distance map to the tumor core. The relation between the edema outlines visible
on T2 FLAIR MRI and the distance to the core is also investigated. Our results
suggest that (i) the previously suggested exponential decrease of the tumor
cell density with the distance to the tumor core is not unreasonable but (ii)
the edema outlines may in general not correspond to a cell density iso-contour
and (iii) the commonly adopted tumor cell density value at these outlines is
likely overestimated. These findings highlight the limitations of using
conventional MRI to derive glioma cell density maps and point out the need of
validating other methods to initialize reaction-diffusion growth models and
make them usable in clinical practice.


Abstract:
  Clustering is a fundamental problem in machine learning where distance-based
approaches have dominated the field for many decades. This set of problems is
often tackled by partitioning the data into K clusters where the number of
clusters is chosen apriori. While significant progress has been made on these
lines over the years, it is well established that as the number of clusters or
dimensions increase, current approaches dwell in local minima resulting in
suboptimal solutions. In this work, we propose a new set of distance threshold
methods called Theta-based Algorithms (ThetA). Via experimental comparisons and
complexity analyses we show that our proposed approach outperforms existing
approaches in: a) clustering accuracy and b) time complexity. Additionally, we
show that for a large class of problems, learning the optimal threshold is
straightforward in comparison to learning K. Moreover, we show how ThetA can
infer the sparsity of datasets in higher dimensions.


Abstract:
  This paper deals with random sensors initially randomly deployed on the line
according to general random process and on the plane according to two
independent general random processes. The mobile robot with carrying capacity
$k$ placed at the origin point is to move the sensors to achieve the general
scheduling requirement such as coverage, connectivity and thus to satisfy the
desired communication property in the network. We study tradeoffs between the
energy consumption in robot's movement, the numbers of sensors $n$, the sensor
range $r$, the interference distance $s$, and the robot capacity $k$ until
completion of the coverage simultaneously with interference scheduling task. In
this work, we obtain upper bounds for the energy consumption in robot's
movement and obtain the sharp decrease in the total movement cost of the robot
so as to provide the coverage simultaneously with interference requirement.


Abstract:
  Parkinson's Disease (PD) is a chronic, degenerative disorder which leads to a
range of motor and cognitive symptoms. PD diagnosis is a challenging task since
its symptoms are very similar to other diseases such as normal ageing and
essential tremor. Much research has been applied to diagnosing this disease.
This project aims to automate the PD diagnosis process using deep learning,
Recursive Neural Networks (RNN) and Convolutional Neural Networks (CNN), to
differentiate between healthy and PD patients. Besides that, since different
datasets may capture different aspects of this disease, this project aims to
explore which PD test is more effective in the discrimination process by
analysing different imaging and movement datasets (notably cube and spiral
pentagon datasets). In addition, this project evaluates which dataset type,
imaging or time series, is more effective in diagnosing PD.


Abstract:
  In informal learning scenarios the popularity of multimedia content, such as
video tutorials or lectures, has significantly increased. Yet, the users'
interactions, navigation behavior, and consequently learning outcome, have not
been researched extensively. Related work in this field, also called search as
learning, has focused on behavioral or text resource features to predict
learning outcome and knowledge gain. In this paper, we investigate whether we
can exploit features representing multimedia resource consumption to predict of
knowledge gain (KG) during Web search from in-session data, that is without
prior knowledge about the learner. For this purpose, we suggest a set of
multimedia features related to image and video consumption. Our feature
extraction is evaluated in a lab study with 113 participants where we collected
data for a given search as learning task on the formation of thunderstorms and
lightning. We automatically analyze the monitored log data and utilize
state-of-the-art computer vision methods to extract features about the seen
multimedia resources. Experimental results demonstrate that multimedia features
can improve KG prediction. Finally, we provide an analysis on feature
importance (text and multimedia) for KG prediction.


Abstract:
  A proper channel modeling methodology that characterizes the statistics of
extreme events is key in the design of a system at an ultra-reliable regime of
operation. The strict constraint of ultra-reliability corresponds to the packet
error rate (PER) in the range of $10^{-9}-10^{-5}$ within the acceptable
latency on the order of milliseconds. Extreme value theory (EVT) is a robust
framework for modeling the statistical behavior of extreme events in the
channel data. In this paper, we propose a methodology based on EVT to model the
extreme events of a non-stationary wireless channel for the ultra-reliable
regime of operation. This methodology includes techniques for splitting the
channel data sequence into multiple groups concerning the environmental factors
causing non-stationarity, and fitting the lower tail distribution of the
received power in each group to the generalized Pareto distribution (GPD). The
proposed approach also consists of optimally determining the time-varying
threshold over which the tail statistics are derived as a function of time, and
assessing the validity of the derived Pareto model. Finally, the proposed
approach chooses the best model with minimum complexity that represents the
time variation behavior of the non-stationary channel data sequence. Based on
the data collected within the engine compartment of Fiat Linea under various
engine vibrations and driving scenarios, we demonstrate the capability of the
proposed methodology in providing the best fit to the extremes of the
non-stationary data, significantly outperforming the channel modeling approach
in characterizing the extreme events with the stationary channel assumption.


Abstract:
  What is the role of topology in the propagation of quantum light in photonic
lattices? We address this question by studying the propagation of squeezed
states in a topological one-dimensional waveguide array, benchmarking our
results with those for a topologically trivial localized state, and studying
their robustness against disorder. Specifically, we study photon statistics,
one-mode and two-mode squeezing, and entanglement generation when the localized
state is excited with squeezed light. These quantum properties inherit the
shape of the localized state but, more interestingly, and unlike in the
topologically trivial case, we find that propagation of squeezed light in a
topologically protected state robustly preserves the phase of the squeezed
quadrature as the system evolves. We show how this latter topological advantage
can be harnessed for quantum information protocols.


Abstract:
  Batch codes are a useful notion of locality for error correcting codes,
originally introduced in the context of distributed storage and cryptography.
Many constructions of batch codes have been given, but few lower bound
(limitation) results are known, leaving gaps between the best known
constructions and best known lower bounds. Towards determining the optimal
redundancy of batch codes, we prove a new lower bound on the redundancy of
batch codes. Specifically, we study (primitive, multiset) linear batch codes
that systematically encode $n$ information symbols into $N$ codeword symbols,
with the requirement that any multiset of $k$ symbol requests can be obtained
in disjoint ways. We show that such batch codes need $\Omega(\sqrt{Nk})$
symbols of redundancy, improving on the previous best lower bounds of
$\Omega(\sqrt{N}+k)$ at all $k=n^\varepsilon$ with $\varepsilon\in(0,1)$. Our
proof follows from analyzing the dimension of the order-$O(k)$ tensor of the
batch code's dual code.


Abstract:
  Cosmic rays with energies up to a few PeV are known to be accelerated within
the Milky Way. Traditionally, it has been presumed that supernova remnants were
the main source of very-high-energy cosmic rays but theoretically it is
difficult to get protons to PeV energies and observationally there simply is no
evidence to support the remnants as sources of hadrons with energies above a
few tens of TeV. One possible source of protons with those energies is the
Galactic Center region. Here we report observations of 1-100 TeV gamma rays
coming from the 'Cygnus Cocoon', which is a superbubble surrounding a region of
OB2 massive star formation. These gamma rays are likely produced by 10-1000 TeV
freshly accelerated CRs originating from the enclosed star forming region
Cygnus OB2. Hitherto it was not known that such regions could accelerate
particles to these energies. The measured flux is likely originated by hadronic
interactions. The spectral shape and the emission profile of the Cocoon changes
from GeV to TeV energies, which reveals the transport of cosmic particles and
historical activity in the superbubble.


Abstract:
  It is well known that physical-layer key generation methods enable wireless
devices to harvest symmetric keys by accessing the randomness offered by the
wireless channels. Although two-user key generation is well understood, group
secret-key (GSK) generation, wherein more than two nodes in a network generate
secret-keys, still poses open problems. Recently, Manish Rao et al., have
proposed the Algebraic Symmetrically Quantized GSK (A-SQGSK) protocol for a
network of three nodes wherein the nodes share quantized versions of the
channel realizations over algebraic rings, and then harvest a GSK. Although
A-SQGSK protocol guarantees confidentiality of common randomness to an
eavesdropper, we observe that the key-rate of the protocol is poor since only
one channel in the network is used to harvest GSK. Identifying this limitation,
in this paper, we propose an opportunistic selection method wherein more than
one wireless channel is used to harvest GSKs without compromising the
confidentiality feature, thereby resulting in remarkable improvements in the
key-rate. Furthermore, we also propose a log-likelihood ratio (LLR) generation
method for the common randomness observed at various nodes, so that the
soft-values are applied to execute LDPC codes based reconciliation to reduce
the bit mismatches among the nodes.


Abstract:
  Computer security and user privacy are critical issues and concerns in the
digital era due to both increasing users and threats to their data. Separate
issues arise between generic cybersecurity guidance (i.e., protect all user
data from malicious threats) and the individualistic approach of privacy (i.e.,
specific to users and dependent on user needs and risk perceptions). Research
has shown that several security- and privacy-focused vulnerabilities are
technological (e.g., software bugs (Streiff, Kenny, Das, Leeth, & Camp, 2018),
insecure authentication (Das, Wang, Tingle, & Camp, 2019)), or behavioral
(e.g., sharing passwords (Das, Dingman, & Camp, 2018); and compliance (Das,
Dev, & Srinivasan, 2018) (Dev, Das, Rashidi, & Camp, 2019)). This panel
proposal addresses a third category of sociotechnical vulnerabilities that can
and sometimes do arise from non-inclusive design of security and privacy. In
this panel, we will address users' needs and desires for privacy. The panel
will engage in in-depth discussions about value-sensitive design while focusing
on potentially vulnerable populations, such as older adults, teens, persons
with disabilities, and others who are not typically emphasized in general
security and privacy concerns. Human factors have a stake in and ability to
facilitate improvements in these areas.


Abstract:
  We provide a new formulation of the Local Friendliness no-go theorem of Bong
et al [Nat. Phys. 16, 1199 (2020)] from fundamental causal principles,
providing another perspective on how it puts strictly stronger bounds on
quantum reality than Bell's theorem. In particular, quantum causal models have
been proposed as a way to maintain a peaceful coexistence between quantum
mechanics and relativistic causality, while respecting Leibniz's methodological
principle. This works for Bell's theorem but does not work for the Local
Friendliness no-go theorem, which considers an extended Wigner's Friend
scenario. More radical conceptual renewal is required; we suggest that cleaving
to Leibniz's principle requires extending relativity to events themselves.


Abstract:
  When solving two-player zero-sum games, multi-agent reinforcement learning
(MARL) algorithms often create populations of agents where, at each iteration,
a new agent is discovered as the best response to a mixture over the opponent
population. Within such a process, the update rules of "who to compete with"
(i.e., the opponent mixture) and "how to beat them" (i.e., finding best
responses) are underpinned by manually developed game theoretical principles
such as fictitious play and Double Oracle. In this paper, we introduce a novel
framework -- Neural Auto-Curricula (NAC) -- that leverages meta-gradient
descent to automate the discovery of the learning update rule without explicit
human design. Specifically, we parameterise the opponent selection module by
neural networks and the best-response module by optimisation subroutines, and
update their parameters solely via interaction with the game engine, where both
players aim to minimise their exploitability. Surprisingly, even without human
design, the discovered MARL algorithms achieve competitive or even better
performance with the state-of-the-art population-based game solvers (e.g.,
PSRO) on Games of Skill, differentiable Lotto, non-transitive Mixture Games,
Iterated Matching Pennies, and Kuhn Poker. Additionally, we show that NAC is
able to generalise from small games to large games, for example training on
Kuhn Poker and outperforming PSRO on Leduc Poker. Our work inspires a promising
future direction to discover general MARL algorithms solely from data.


Abstract:
  It is suggested that low-light image enhancement realizes one-to-many mapping
since we have different definitions of NORMAL-light given application scenarios
or users' aesthetic. However, most existing methods ignore subjectivity of the
task, and simply produce one result with fixed brightness. This paper proposes
a neural network for multi-level low-light image enhancement, which is
user-friendly to meet various requirements by selecting different images as
brightness reference. Inspired by style transfer, our method decomposes an
image into two low-coupling feature components in the latent space, which
allows the concatenation feasibility of the content components from low-light
images and the luminance components from reference images. In such a way, the
network learns to extract scene-invariant and brightness-specific information
from a set of image pairs instead of learning brightness differences. Moreover,
information except for the brightness is preserved to the greatest extent to
alleviate color distortion. Extensive results show strong capacity and
superiority of our network against existing methods.


Abstract:
  We reproduce the electronic properties of FeSe in the high-temperature phase
within an ab initio framework that includes screened Fock exchange and local
dynamical correlations. We robustly capture the experimental band structure, as
long as the system is in the Hund's metal phase. In particular, we account for
the shrinking of the Fermi pockets and the sinking below the Fermi level of the
hole pocket with $xy$ orbital character. This entails the elusive correct
estimate of the Sommerfeld coefficient, and supports the interpretation of
non-compensated Fermi pockets seen in ARPES in terms of surface electron
doping. More stringently, our modeling matches well the experimental interband
optical spectrum, and captures qualitatively the temperature dependence of the
thermoelectric power, extremely sensitive to the details of the bands around
the Fermi level.


Abstract:
  Adaptive optics (AO) systems using tomographic estimation of
three-dimensional structure of atmospheric turbulence requires vertical
atmospheric turbulence profile, which describes turbulence strength as a
function of altitude as a prior information. We propose a novel method to
reconstruct the profile by applying Multi Aperture Scintillation Sensor (MASS)
method to scintillation data obtained by a Shack-Hartmann wavefront sensor
(SH-WFS). Compared to the traditional MASS, which uses atmospheric
scintillation within 4 concentric annular apertures, the new method utilizes
scintillation in several hundreds of spatial patterns, which are created by
combinations of SH-WFS subapertures. Accuracy of the turbulence profile
reconstruction is evaluated with Bayesian inference, and it is confirmed that
turbulence profile with more than 10 layers can be reconstructed thanks to the
large number of constraints. We demonstrate the new method with a SH-WFS
attached to the 50 cm telescope at Tohoku university and confirm that general
characteristics of atmospheric turbulence profile is reproduced.


Abstract:
  Client contribution evaluation, also known as data valuation, is a crucial
approach in federated learning(FL) for client selection and incentive
allocation. However, due to restrictions of accessibility of raw data, only
limited information such as local weights and local data size of each client is
open for quantifying the client contribution. Using data size from available
information, we introduce an empirical evaluation method called Federated
Client Contribution Evaluation through Accuracy Approximation(FedCCEA). This
method builds the Accuracy Approximation Model(AAM), which estimates a
simulated test accuracy using inputs of sampled data size and extracts the
clients' data quality and data size to measure client contribution. FedCCEA
strengthens some advantages: (1) enablement of data size selection to the
clients, (2) feasible evaluation time regardless of the number of clients, and
(3) precise estimation in non-IID settings. We demonstrate the superiority of
FedCCEA compared to previous methods through several experiments: client
contribution distribution, client removal, and robustness test to partial
participation.


Abstract:
  In this paper, we consider a single top quark production in association with
a neutral gauge boson (Z boson or Photon) at the future electron-positron
colliders. We utilize these channels to probe the top quark Flavour Changing
Neutral Currents (FCNC) interactions in $tqZ$ and $tq\gamma$ vertices. We
perform two separate analyses for top-Z-jet and top-$\gamma$-jet channels. The
Standard Model Effective Field Theory (SMEFT) approach is employed to search
for these anomalous couplings. We consider parton showering, hadronization and
fast detector simulation in our study and use a cut-and-count technique to
separate the signal from the Standard Model (SM) background processes. The
upper limits on the FCNC couplings at $95\%$ confidence level for the different
integrated luminosities are obtained. It is shown that the future lepton
collider would be able to probe the FCNC branching fractions to
$\rm{Br}(t\rightarrow q \gamma)< 10^{-4}$ and $\rm{Br}(t\rightarrow q Z) <
10^{-4}$ with $3$ $\rm{ab}^{-1}$of integrated luminosity of data at a center of
mass energy of $350$ GeV.


Abstract:
  For the biharmonic Steklov eigenvalue problem considered in this paper, we
show that among all bounded Euclidean domains of class $C^{1}$ with fixed
measure, the ball maximizes the first positive eigenvalue.


Abstract:
  We use the quantum Fisher information (QFI) to diagnose a dynamical phase
transition (DPT) in a closed quantum system, which is usually defined in terms
of non-analytic behaviour of a time-averaged order parameter. Employing the
Lipkin-Meshkov-Glick model as an illustrative example, we find that the DPT
correlates with a peak in the QFI that can be explained by a generic connection
to an underlying excited-state quantum phase transition that also enables us to
also relate the scaling of the QFI with the behaviour of the order parameter.
Motivated by the QFI as a quantifier of metrologically useful correlations and
entanglement, we also present a robust interferometric protocol that can enable
DPTs as a platform for quantum-enhanced sensing.


Abstract:
  Vision transformer has achieved competitive performance on a variety of
computer vision applications. However, their storage, run-time memory, and
computational demands are hindering the deployment to mobile devices. Here we
present a vision transformer pruning approach, which identifies the impacts of
dimensions in each layer of transformer and then executes pruning accordingly.
By encouraging dimension-wise sparsity in the transformer, important dimensions
automatically emerge. A great number of dimensions with small importance scores
can be discarded to achieve a high pruning ratio without significantly
compromising accuracy. The pipeline for vision transformer pruning is as
follows: 1) training with sparsity regularization; 2) pruning dimensions of
linear projections; 3) fine-tuning. The reduced parameters and FLOPs ratios of
the proposed algorithm are well evaluated and analyzed on ImageNet dataset to
demonstrate the effectiveness of our proposed method.


Abstract:
  Social media are digitalising massive amounts of users' cognitions in terms
of timelines and emotional content. Such Big Data opens unprecedented
opportunities for investigating cognitive phenomena like perception,
personality and information diffusion but requires suitable interpretable
frameworks. Since social media data come from users' minds, worthy candidates
for this challenge are cognitive networks, models of cognition giving structure
to mental conceptual associations. This work outlines how cognitive network
science can open new, quantitative ways for understanding cognition through
online media, like: (i) reconstructing how users semantically and emotionally
frame events with contextual knowledge unavailable to machine learning, (ii)
investigating conceptual salience/prominence through knowledge structure in
social discourse; (iii) studying users' personality traits like
openness-to-experience, curiosity, and creativity through language in posts;
(iv) bridging cognitive/emotional content and social dynamics via multilayer
networks comparing the mindsets of influencers and followers. These
advancements combine cognitive-, network- and computer science to understand
cognitive mechanisms in both digital and real-world settings but come with
limitations concerning representativeness, individual variability and data
integration. Such aspects are discussed along the ethical implications of
manipulating socio-cognitive data. In the future, reading cognitions through
networks and social media can expose cognitive biases amplified by online
platforms and relevantly inform policy making, education and markets about
massive, complex cognitive trends.


Abstract:
  We study the effects of introducing a feedback channel in the two-receiver
erasure source-broadcast problem in which a binary equiprobable source is to be
sent over an erasure broadcast channel to two receivers subject to erasure
distortion constraints. The receivers each require a certain fraction of a
source sequence, and we are interested in the minimum latency, or transmission
time, required to serve them all. We first show that for a two-user broadcast
channel, a point-to-point outer bound can always be achieved. We further show
that the point-to-point outer bound can also be achieved if only one of the
users, the stronger user, has a feedback channel. Our coding scheme relies on a
hybrid approach that combines transmitting both random linear combinations of
source symbols as well as a retransmission strategy.


Abstract:
  We consider robust variants of the standard optimal transport, named robust
optimal transport, where marginal constraints are relaxed via Kullback-Leibler
divergence. We show that Sinkhorn-based algorithms can approximate the optimal
cost of robust optimal transport in
$\widetilde{\mathcal{O}}(\frac{n^2}{\varepsilon})$ time, in which $n$ is the
number of supports of the probability distributions and $\varepsilon$ is the
desired error. Furthermore, we investigate a fixed-support robust barycenter
problem between $m$ discrete probability distributions with at most $n$ number
of supports and develop an approximating algorithm based on iterative Bregman
projections (IBP). For the specific case $m = 2$, we show that this algorithm
can approximate the optimal barycenter value in
$\widetilde{\mathcal{O}}(\frac{mn^2}{\varepsilon})$ time, thus being better
than the previous complexity
$\widetilde{\mathcal{O}}(\frac{mn^2}{\varepsilon^2})$ of the IBP algorithm for
approximating the Wasserstein barycenter.


Abstract:
  CP4 3HDM is a unique three-Higgs-doublet model equipped with a higher-order
CP symmetry in the scalar and Yukawa sector. Based on a single assumption (the
minimal model with a CP-symmetry of order 4 and no accidental symmetry), it
leads to a remarkable correlation between its scalar and Yukawa sectors, which
echoes in its phenomenology. A recent scan of the parameter space of CP4 3HDM
under the assumption of scalar alignment identified a few dozens of points
which passed many flavour constraints. In the present work we show, however,
that almost all of these points are now ruled out by the recent LHC searches of
$t \to H^+ b$ with subsequent hadronic decays of $H^+$. Apart from a few points
with charged Higgses heavier than the top quark, only one point survives all
the checks, the model with an exotic, non-2HDM-like generation pattern of $H^+$
couplings with quarks. One can expect many more points with exotic $H^+$
couplings to quarks if the scalar alignment assumption is relaxed.


Abstract:
  In many real applications such as the data integration, social network
analysis, and the Semantic Web, the entity resolution (ER) is an important and
fundamental problem, which identifies and links the same real-world entities
from various data sources. While prior works usually consider ER over static
and complete data, in practice, application data are usually collected in a
streaming fashion, and often incur missing attributes (due to the inaccuracy of
data extraction techniques). Therefore, in this paper, we will formulate and
tackle a novel problem, topic-aware entity resolution over incomplete data
streams (TER-iDS), which online imputes incomplete tuples and detects pairs of
topic-related matching entities from incomplete data streams. In order to
effectively and efficiently tackle the TER-iDS problem, we propose an effective
imputation strategy, carefully design effective pruning strategies, as well as
indexes/synopsis, and develop an efficient TER-iDS algorithm via index joins.
Extensive experiments have been conducted to evaluate the effectiveness and
efficiency of our proposed TER-iDS approach over real data sets.


Abstract:
  We review the recent progress in Higgs inflation focusing on Higgs-$R^2$
inflation, primordial black hole production and the $R^3$ term.


Abstract:
  In many sequential decision-making problems, the individuals are split into
several batches and the decision-maker is only allowed to change her policy at
the end of batches. These batch problems have a large number of applications,
ranging from clinical trials to crowdsourcing. Motivated by this, we study the
stochastic contextual bandit problem for general reward distributions under the
batched setting. We propose the BatchNeuralUCB algorithm which combines neural
networks with optimism to address the exploration-exploitation tradeoff while
keeping the total number of batches limited. We study BatchNeuralUCB under both
fixed and adaptive batch size settings and prove that it achieves the same
regret as the fully sequential version while reducing the number of policy
updates considerably. We confirm our theoretical results via simulations on
both synthetic and real-world datasets.


Abstract:
  The magnetic properties of Fe$_3$O$_4$ nanoparticle assemblies have been
investigated in detail through a combination of vibrating sample magnetometry
and muon spin relaxation ($\mu$SR) techniques. Two samples with average
particle sizes of 5 nm and 20 nm, respectively, were studied. For both samples,
the magnetometry and $\mu$SR results exhibit clear signatures of the
superparmagnetic state at high temperature and the magnetically blocked state
at low temperature. The $\mu$SR data demonstrate that the transition from the
superparamagnetic to the blocked state occurs gradually throughout the sample
volume over a broad temperature range due to the finite particle size
distribution of each sample. The transition occurs between approximately 3 K
and 45 K for the 5 nm sample and 150 K and 300 K for the 20 nm sample. The
magnetometry and $\mu$SR data are further analyzed to yield estimates of
microscopic magnetic parameters including the nanoparticle spin-flip activation
energy $E_A$, magnetic anisotropy $K$, and intrinsic nanoparticle spin reversal
attempt time $\tau_0$. These results highlight the complementary information
about magnetic nanoparticles that can be obtained by bulk magnetic probes such
as magnetometry and local magnetic probes such as $\mu$SR.


Abstract:
  We propose a universal quantum circuit design that can estimate any arbitrary
one-dimensional periodic functions based on the corresponding Fourier
expansion. The quantum circuit contains N-qubits to store the information on
the different N-Fourier components and $M+2$ auxiliary qubits with $M =
\lceil{\log_2{N}}\rceil$ for control operations. The desired output will be
measured in the last qubit $q_N$ with a time complexity of the computation of
$O(N^2\lceil \log_2N\rceil^2)$. We illustrate the approach by constructing the
quantum circuit for the square wave function with accurate results obtained by
direct simulations using the IBM-QASM simulator. The approach is general and
can be applied to any arbitrary periodic function.


Abstract:
  We study primary submodules and primary decompositions from a differential
and computational point of view. Our main theoretical contribution is a general
structure theory and a representation theorem for primary submodules of an
arbitrary finitely generated module over a polynomial ring. We characterize
primary submodules in terms of differential operators and punctual Quot
schemes. Moreover, we introduce and implement an algorithm that computes a
minimal differential primary decomposition for a module.


Abstract:
  This study presents an application of machine learning (ML) methods for
detecting the presence of stenoses and aneurysms in the human arterial system.
Four major forms of arterial disease -- carotid artery stenosis (CAS),
subclavian artery stenosis (SAC), peripheral arterial disease (PAD), and
abdominal aortic aneurysms (AAA) -- are considered. The ML methods are trained
and tested on a physiologically realistic virtual patient database (VPD)
containing 28,868 healthy subjects, which is adapted from the authors previous
work and augmented to include the four disease forms. Six ML methods -- Naive
Bayes, Logistic Regression, Support Vector Machine, Multi-layer Perceptron,
Random Forests, and Gradient Boosting -- are compared with respect to
classification accuracies and it is found that the tree-based methods of Random
Forest and Gradient Boosting outperform other approaches. The performance of ML
methods is quantified through the F1 score and computation of sensitivities and
specificities. When using all the six measurements, it is found that maximum F1
scores larger than 0.9 are achieved for CAS and PAD, larger than 0.85 for SAS,
and larger than 0.98 for both low- and high-severity AAAs. Corresponding
sensitivities and specificities are larger than 90% for CAS and PAD, larger
than 85% for SAS, and larger than 98% for both low- and high-severity AAAs.
When reducing the number of measurements, it is found that the performance is
degraded by less than 5% when three measurements are used, and less than 10%
when only two measurements are used for classification. For AAA, it is shown
that F1 scores larger than 0.85 and corresponding sensitivities and
specificities larger than 85% are achievable when using only a single
measurement. The results are encouraging to pursue AAA monitoring and screening
through wearable devices which can reliably measure pressure or flow-rates


Abstract:
  We show that the natural Glauber dynamics mixes rapidly and generates a
random proper edge-coloring of a graph with maximum degree $\Delta$ whenever
the number of colors is at least $q\geq (\frac{10}{3} + \epsilon)\Delta$, where
$\epsilon>0$ is arbitrary and the maximum degree satisfies $\Delta \geq C$ for
a constant $C = C(\epsilon)$ depending only on $\epsilon$. For edge-colorings,
this improves upon prior work \cite{Vig99, CDMPP19} which show rapid mixing
when $q\geq (\frac{11}{3}-\epsilon_0 ) \Delta$, where $\epsilon_0 \approx
10^{-5}$ is a small fixed constant. At the heart of our proof, we establish a
matrix trickle-down theorem, generalizing Oppenheim's influential result, as a
new technique to prove that a high dimensional simplical complex is a local
spectral expander.


Abstract:
  Given a graph $G$, the burning number of $G$ is the smallest integer $k$ for
which there are vertices $x_1, x_2,\ldots,x_k$ such that $(x_1,x_2,\ldots,x_k)$
is a burning sequence of $G$. It has been shown that the graph burning problem
is NP-complete, even for trees with maximum degree three, or linear forests. A
$t$-unicyclic graph is a unicycle graph with exactly one vertex of degree
greater than $2$. In this paper, we first present the bounds for the burning
number of $t$-unicyclic graphs, and then use the burning numbers of linear
forests with at most three components to determine the burning number of all
$t$-unicyclic graphs for $t\le 2$.


Abstract:
  Agricultural production using high technology is an inevitable trend in
Vietnam. Especially for material crops which typically need large growing
areas, wireless sensor networks has been clearly playing a significant role in
increasing productivity, monitoring pests and diseases, mitigating the impact
of climate change, and reducing the direct labor of cultivators. This paper
constructs an experimental model of agricultural crop field monitoring using a
combination of LoRa wireless sensor networks and unmanned aerial vehicles to
collect data on conditions of weather and soil, plant health, which helps
growers easily making right decisions on solutions for irrigation, pest
treatment, and fertilization with the currently planted crops. The system has
been developed and experimentized in the field to evaluate some basic features
and justified the stability and reliability of the obtained data.


Abstract:
  An array of low-frequency dipole antennas on the lunar farside surface will
probe a unique, unexplored epoch in the early Universe called the Dark Ages. It
begins at Recombination when neutral hydrogen atoms formed, first revealed by
the cosmic microwave background. This epoch is free of stars and astrophysics,
so it is ideal to investigate high energy particle processes including dark
matter, early Dark Energy, neutrinos, and cosmic strings. A NASA-funded study
investigated the design of the instrument and the deployment strategy from a
lander of 128 pairs of antenna dipoles across a 10 kmx10 km area on the lunar
surface. The antenna nodes are tethered to the lander for central data
processing, power, and data transmission to a relay satellite. The array, named
FARSIDE, would provide the capability to image the entire sky in 1400 channels
spanning frequencies from 100 kHz to 40 MHz, extending down two orders of
magnitude below bands accessible to ground-based radio astronomy. The lunar
farside can simultaneously provide isolation from terrestrial radio frequency
interference, the Earth's auroral kilometric radiation, and plasma noise from
the solar wind. It is thus the only location within the inner solar system from
which sky noise limited observations can be carried out at sub-MHz frequencies.
Through precision calibration via an orbiting beacon and exquisite foreground
characterization, the farside array would measure the Dark Ages global 21-cm
signal at redshifts z~35-200. It will also be a pathfinder for a larger 21-cm
power spectrum instrument by carefully measuring the foreground with high
dynamic range.


Abstract:
  For a Galois extension $K/F$ with $\text{char}(K)\neq 2$ and $\text{Gal}(K/F)
\simeq \mathbb{Z}/2\mathbb{Z}\oplus\mathbb{Z}/2\mathbb{Z}$, we determine the
$\mathbb{F}_2[\text{Gal}(K/F)]$-module structure of $K^\times/K^{\times 2}$.
Although there are an infinite number of (pairwise non-isomorphic)
indecomposable
$\mathbb{F}_2[\mathbb{Z}/2\mathbb{Z}\oplus\mathbb{Z}/2\mathbb{Z}]$-modules, our
decomposition includes at most $9$ indecomposable types. This paper marks the
first time that the Galois module structure of power classes of a field has
been fully determined when the modular representation theory allows for an
infinite number of indecomposable types.


Abstract:
  While coursework provides undergraduate data science students with some
relevant analytic skills, many are not given the rich experiences with data and
computing they need to be successful in the workplace. Additionally, students
often have limited exposure to team-based data science and the principles and
tools of collaboration that are encountered outside of school. In this paper,
we describe the DSC-WAV program, an NSF-funded data science workforce
development project in which teams of undergraduate sophomores and juniors work
with a local non-profit organization on a data-focused problem. To help
students develop a sense of agency and improve confidence in their technical
and non-technical data science skills, the project promoted a team-based
approach to data science, adopting several processes and tools intended to
facilitate this collaboration. Evidence from the project evaluation, including
participant survey and interview data, is presented to document the degree to
which the project was successful in engaging students in team-based data
science, and how the project changed the students' perceptions of their
technical and non-technical skills. We also examine opportunities for
improvement and offer insight to other data science educators who may want to
implement a similar team-based approach to data science projects at their own
institutions.


Abstract:
  Tensor computations overwhelm traditional general-purpose computing devices
due to the large amounts of data and operations of the computations. They call
for a holistic solution composed of both hardware acceleration and software
mapping. Hardware/software (HW/SW) co-design optimizes the hardware and
software in concert and produces high-quality solutions. There are two main
challenges in the co-design flow. First, multiple methods exist to partition
tensor computation and have different impacts on performance and energy
efficiency. Besides, the hardware part must be implemented by the intrinsic
functions of spatial accelerators. It is hard for programmers to identify and
analyze the partitioning methods manually. Second, the overall design space
composed of HW/SW partitioning, hardware optimization, and software
optimization is huge. The design space needs to be efficiently explored.
  To this end, we propose an agile co-design approach HASCO that provides an
efficient HW/SW solution to dense tensor computation. We use tensor syntax
trees as the unified IR, based on which we develop a two-step approach to
identify partitioning methods. For each method, HASCO explores the hardware and
software design spaces. We propose different algorithms for the explorations,
as they have distinct objectives and evaluation costs. Concretely, we develop a
multi-objective Bayesian optimization algorithm to explore hardware
optimization. For software optimization, we use heuristic and Q-learning
algorithms. Experiments demonstrate that HASCO achieves a 1.25X to 1.44X
latency reduction through HW/SW co-design compared with developing the hardware
and software separately.


Abstract:
  We have searched nearly 40,000 inorganic solids in the Inorganic Crystal
Structural Database to identify compounds containing a transition metal or rare
earth kagom\'e sublattice, a geometrically magnetically frustrated lattice,
ultimately identifying $\sim$500 materials. A broad analysis of the chemical
and structural trends of these materials shows three types of kagom\'e sheet
stacking and several classes of magnetic complexity. Following the search and
classification, we rapidly screen the magnetic properties of a subset of the
materials using density functional theory to eliminate those that are unlikely
to exhibit magnetic frustration. From the results of our computational
screening, we rediscover six materials that have previously been explored for
their low temperature magnetic behavior, albeit showing symmetry breaking
distortions, spin glass behavior, or magnetic ordering. However, all are
materials with antiferromagnetic behavior, which we correctly predict. Finally,
we also report three materials that appear to be unexplored for their magnetic
properties.


Abstract:
  Multiple-instance learning is a subset of weakly supervised learning where
labels are applied to sets of instances rather than the instances themselves.
Under the standard assumption, a set is positive only there is if at least one
instance in the set which is positive.
  This paper introduces a series of multiple-instance learning benchmarks
generated from MNIST, Fashion-MNIST, and CIFAR10. These benchmarks test the
standard, presence, absence, and complex assumptions and provide a framework
for future benchmarks to be distributed. I implement and evaluate several
multiple-instance learning techniques against the benchmarks. Further, I
evaluate the Noisy-And method with label noise and find mixed results with
different datasets. The models are implemented in TensorFlow 2.4.1 and are
available on GitHub. The benchmarks are available from PyPi as mil-benchmarks
and on GitHub.


Abstract:
  To any combinatorial triangulation $T$ of a square, there is an associated
polynomial relation $p_T$ among the areas of the triangles of $T$. With the
goal of understanding this polynomial, we consider polynomials obtained from
$p_T$ by choosing $l$ of its variables and specializing $p_T$ to these
variables by zeroing out the remaining variables. We show that for fixed $l$,
the set ${\mathcal E}_l$ of integer polynomials that appear as irreducible
factors of such specializations is finite. We compute this area encyclopedia
${\mathcal E}_l$ for $l\leq 4$. We also show that in any dissection of a square
into $l$ triangles, the areas of the triangles must satisfy a polynomial in
${\mathcal E}_l$. Our results are obtained by studying the rational map that
associates to each drawing of $T$ the tuple of areas of the triangles in that
drawing. By analyzing the ways of approaching the base locus, we derive
restrictions on points of the closure of the image of this map.


Abstract:
  BERT-based text ranking models have dramatically advanced the
state-of-the-art in ad-hoc retrieval, wherein most models tend to consider
individual query-document pairs independently. In the mean time, the importance
and usefulness to consider the cross-documents interactions and the
query-specific characteristics in a ranking model have been repeatedly
confirmed, mostly in the context of learning to rank. The BERT-based ranking
model, however, has not been able to fully incorporate these two types of
ranking context, thereby ignoring the inter-document relationships from the
ranking and the differences among queries. To mitigate this gap, in this work,
an end-to-end transformer-based ranking model, named Co-BERT, has been proposed
to exploit several BERT architectures to calibrate the query-document
representations using pseudo relevance feedback before modeling the relevance
of a group of documents jointly. Extensive experiments on two standard test
collections confirm the effectiveness of the proposed model in improving the
performance of text re-ranking over strong fine-tuned BERT-Base baselines. We
plan to make our implementation open source to enable further comparisons.


Abstract:
  The multilinear Pagerank model [Gleich, Lim and Yu, 2015] is a tensor-based
generalization of the Pagerank model. Its computation requires solving a system
of polynomial equations that contains a parameter $\alpha \in [0,1)$. For
$\alpha \approx 1$, this computation remains a challenging problem, especially
since the solution may be non-unique. Extrapolation strategies that start from
smaller values of $\alpha$ and `follow' the solution by slowly increasing this
parameter have been suggested; however, there are known cases where these
strategies fail, because a globally continuous solution curve cannot be defined
as a function of $\alpha$. In this paper, we improve on this idea, by employing
a predictor-corrector continuation algorithm based on a more general
representation of the solutions as a curve in $\mathbb{R}^{n+1}$. We prove
several global properties of this curve that ensure the good behavior of the
algorithm, and we show in our numerical experiments that this method is
significantly more reliable than the existing alternatives.


Abstract:
  As defined in [1], a Hausdorff space is strongly anti-Urysohn (in short: SAU)
if it has at least two non-isolated points and any two infinite} closed subsets
of it intersect. Our main result answers the two main questions of [1] by
providing a ZFC construction of a locally countable SAU space of cardinality
$2^{\mathfrak{c}}$. The construction hinges on the existence of
$2^{\mathfrak{c}}$ weak P-points in $\omega^*$, a very deep result of Ken
Kunen.
  It remains open if SAU spaces of cardinality $> 2^{\mathfrak{c}}$ could
exist, while it was shown in [1] that $2^{2^{\mathfrak{c}}}$ is an upper bound.
Also, we do not know if crowded SAU spaces, i.e. ones without any isolated
points, exist in ZFC but we obtained the following consistency results
concerning such spaces.
  (1) It is consistent that $\mathfrak{c}$ is as large as you wish and there is
a locally countable and crowded SAU space of cardinality $\mathfrak{c}^+$.
  (2) It is consistent that both $\mathfrak{c}$ and $2^\mathfrak{c}$ are as
large as you wish and there is a crowded SAU space of cardinality
$2^\mathfrak{c}$.
  (3) For any uncountable cardinal ${\kappa}$ the following statements are
equivalent:
  (i) ${\kappa}=cof({[{\kappa}]}^{{\omega}},\subseteq)$.
  (ii) There is a locally countable and crowded SAU space of size ${\kappa}$ in
the generic extension obtained by adding $\kappa$ Cohen reals.
  (iii) There is a locally countable and countably compact $T_1$-space of size
${\kappa}$ in some CCC generic extension.
  [1] I. Juhasz, L. Soukup, and Z. Szentmiklossy, Anti-Urysohn spaces, Top.
Appl., 213 (2016), pp. 8--23.


Abstract:
  For unsupervised image-to-image translation, we propose a discriminator
architecture which focuses on the statistical features instead of individual
patches. The network is stabilized by distribution matching of key statistical
features at multiple scales. Unlike the existing methods which impose more and
more constraints on the generator, our method facilitates the shape deformation
and enhances the fine details with a greatly simplified framework. We show that
the proposed method outperforms the existing state-of-the-art models in various
challenging applications including selfie-to-anime, male-to-female and glasses
removal.


Abstract:
  A neural ordinary differential equations network (ODE-Net)-enabled
reachability method (Neuro-Reachability) is devised for the dynamic
verification of networked microgrids (NMs) with unidentified subsystems and
heterogeneous uncertainties. Three new contributions are presented: 1) An
ODENet-enabled dynamic model discovery approach is devised to construct the
data-driven state-space model which preserves the nonlinear and differential
structure of the NMs system; 2) A physics-data-integrated (PDI) NMs model is
established, which empowers various NM analytics; and 3) A
conformance-empowered reachability analysis is developed to enhance the
reliability of the PDI-driven dynamic verification. Extensive case studies
demonstrate the efficacy of the ODE-Net-enabled method in microgrid dynamic
model discovery, and the effectiveness of the Neuro-Reachability approach in
verifying the NMs dynamics under multiple uncertainties and various operational
scenarios.


Abstract:
  We consider interacting Bose particles in an external local potential. It is
shown that large class of external quasicrystal potentials cannot sustain any
type of Bose-Einstein condensates. Accordingly, at spatial dimensions $D\leq 2$
in such quasicrystal potentials a supersolid is not possible via Bose-Einstein
condensates at finite temperatures. The latter also hold true for the
two-dimensional Fibonacci tiling. However, supersolids do arise at $D\leq 2$
via Bose-Einstein condensates from infinitely long-range, nonlocal
interparticle potentials.


Abstract:
  Weighted timed games are two-player zero-sum games played in a timed
automaton equipped with integer weights. We consider optimal reachability
objectives, in which one of the players, that we call Min, wants to reach a
target location while minimising the cumulated weight. While knowing if Min has
a strategy to guarantee a value lower than a given threshold is known to be
undecidable (with two or more clocks), several conditions, one of them being
the divergence, have been given to recover decidability. In such weighted timed
games (like in untimed weighted games in the presence of negative weights), Min
may need finite memory to play (close to) optimally. This is thus tempting to
try to emulate this finite memory with other strategic capabilities. In this
work, we allow the players to use stochastic decisions, both in the choice of
transitions and of timing delays. We give for the first time a definition of
the expected value in weighted timed games, overcoming several theoretical
challenges. We then show that, in divergent weighted timed games, the
stochastic value is indeed equal to the classical (deterministic) value, thus
proving that Min can guarantee the same value while only using stochastic
choices, and no memory.


Abstract:
  Noisy intermediate-scale quantum (NISQ) computers could solve
quantum-mechanical simulation problems that are beyond the capabilities of
classical computers. However, NISQ devices experience significant errors which,
if not corrected, can render physical quantities measured in these simulations
inaccurate or meaningless. Here we describe a method of reducing these errors
which is tailored to quantum algorithms for simulating fermionic systems. The
method is based on executing quantum circuits in the model of fermionic linear
optics, which are known to be efficiently simulable classically, to infer the
relationship between exact and noisy measurement outcomes, and hence undo the
effect of noise. We validated our method by applying it to the VQE algorithm
for estimating ground state energies of instances of the Fermi-Hubbard model.
In classical numerical simulations of 12-qubit examples with physically
realistic levels of depolarising noise, errors were reduced by a factor of
around 34 compared with the uncorrected case. Smaller experiments on quantum
hardware demonstrate an average reduction in errors by a factor of 10 or more.


Abstract:
  Ecology and evolution under changing environments are important in many
subfields of biology with implications for medicine. Here, we explore an
example: the consequences of fluctuating environments on the emergence of
antibiotic resistance, which is an immense and growing problem. Typically, high
doses of antibiotics are employed to eliminate the infection quickly and
minimize the time under which resistance may emerge. However, this strategy may
not be optimal. Since competition can reduce fitness and resistance typically
has a reproductive cost, resistant mutants' fitness can depend on their
environment. Here we show conditions under which environmental varying fitness
can be exploited to prevent the emergence of resistance. We develop a
stochastic Lotka-Volterra model of a microbial system with competing
phenotypes: a wild strain susceptible to the antibiotic, and a mutant strain
that is resistant. We investigate the impact of various pulsed applications of
antibiotics on population suppression. Leveraging competition, we show how a
strategy of environmental switching can suppress the infection while avoiding
resistant mutants. We discuss limitations of the procedure depending on the
microbe and pharmacodynamics and methods to ameliorate them.


Abstract:
  We demonstrate that a periodic waveguide comprising of uniform lossless
segments together with discrete gain and radiating elements supports
exceptional points of degeneracy (EPDs). We provide analytical expressions for
all possible conditions that guarantee the occurrence of an EPD, i.e., the
coalescence of eigenvalues and eigenvectors. We show that EPDs are not only
achieved using symmetric gain and radiation periodic loading, but they are also
obtained using asymmetric gain and radiation loss conditions. We illustrate the
characteristics of the degenerate electromagnetic modes, showing the dispersion
diagram and discussing the tunability of the EPD frequency. We show a special
condition, we refer to it as parity-time (PT)-glide symmetry, which leads to a
degeneracy that is occurring at all frequencies of operation. The class of EPDs
proposed in this work is very promising for many applications that incorporate
discrete-distributed coherent sources and radiation-loss elements; operating in
the vicinity of such special degeneracy conditions leads to potential
performance enhancement in a variety of microwave and optical resonators,
antennas, and devices and can be extended to a new class of active integrated
antenna arrays and radiating laser arrays.


Abstract:
  Due to the significant importance of Big Data analysis, especially in
business-related topics such as improving services, finding potential
customers, and selecting practical approaches to manage income and expenses,
many companies attempt to collaborate with scientists to find how, why, and
what they should analysis. In this work, we would like to compare and discuss
two different approaches that employed in business analysis topic in Big Data
with more consideration on how they utilized Spark. Both studies have
investigated Churn Prediction as their case study for their proposed approaches
since it is an essential topic in business analysis for companies to recognize
a customer intends to leave or stop using their services. Here, we focus on
Apache Spark since it has provided several solutions to handle a massive amount
of data in recent years efficiently. This feature in Spark makes it one of the
most robust candidate tools to upfront with a Big Data problem, particularly
time and resource are concerns.


Abstract:
  Rural electrification demands the use of inexpensive technologies such as
single wire earth return (SWER) networks. There is a steadily growing energy
demand from remote consumers, and the capacity of existing lines may become
inadequate soon. Furthermore, high impedance arcing faults (HIF) from SWER
lines can cause catastrophic bushfires such as the 2009 Black Saturday event.
As a solution, reliable remote electricity networks can be established through
breaking the existing systems down into microgrids, and existing SWER lines can
be utilised to interconnect those microgrids. The development of such reliable
networks with better energy demand management will rely on having an integrated
network-wide condition monitoring system. As the first contribution of this
thesis, a distributed online monitoring platform is developed that incorporates
power quality monitoring, real-time HIF identification and transient
classification in SWER network. Artificial Intelligence (AI) based techniques
are developed to classify faults and transients. The proposed approach
demonstrates higher HIF detection accuracy (98.67%) and reduced detection
latency (115.2 ms). Secondly, a remote consumer load identification methodology
is developed to detect the load type from its transients. An edge
computing-based architecture is proposed to facilitate the high-frequency
analysis for load identification. The proposed approach is evaluated in
real-time, and it achieves an average accuracy of 98% in identifying different
loads. Finally, a deep neural network-based energy disaggregation framework is
developed to separate the load specific energy usage from an aggregated signal.
The proposed framework is evaluated using a real-world data set. It improves
the signal aggregate error by 44% and mean aggregate error by 19% in comparison
with the state-of-the-art techniques.


Abstract:
  With the invention of the COVID-19 vaccine, shipping and distributing are
crucial in controlling the pandemic. In this paper, we build a mean-field
variational problem in a spatial domain, which controls the propagation of
pandemic by the optimal transportation strategy of vaccine distribution. Here
we integrate the vaccine distribution into the mean-field SIR model designed in
our previous paper arXiv:2006.01249. Numerical examples demonstrate that the
proposed model provides practical strategies in vaccine distribution on a
spatial domain.


Abstract:
  We use double-resonant two-photon laser spectroscopy to measure the dynamic
scalar polarizability of the rubidium 5$D_{3/2}$ level,
$\alpha^{S}_{5D_{3/2}}$, at a wavelength of $\lambda=1064$~nm. Since $\lambda$
is shorter than the photoionization (PI) limit of the Rb 5$D_{3/2}$ level,
$\alpha^{S}_{5D_{3/2}}$ depends on both bound-bound and bound-free transition
matrix elements. The level also undergoes significant broadening due to PI. The
$1064$-nm field is applied in the form of a deep optical lattice
($\sim10^{5}$~photon recoils) generated by an in-vacuum field-enhancement
cavity. In our spectroscopic method, we use known dynamic polarizabilities to
eliminate the need to measure the light intensity. Our method yields, in atomic
units, $\alpha^{S}_{5D_{3/2}}=-524(17)$, in agreement with estimates.
Additionally, we extract the $5D_{3/2}$ photoionization cross section $\sigma$
at $1064$~nm from spectral linewidths; we find $\sigma=44(1)$~Mb.


Abstract:
  Out-of-distribution (OOD) detection is essential to prevent anomalous inputs
from causing a model to fail during deployment. While improved OOD detection
methods have emerged, they often rely on the final layer outputs and require a
full feedforward pass for any given input. In this paper, we propose a novel
framework, multi-level out-of-distribution detection MOOD, which exploits
intermediate classifier outputs for dynamic and efficient OOD inference. We
explore and establish a direct relationship between the OOD data complexity and
optimal exit level, and show that easy OOD examples can be effectively detected
early without propagating to deeper layers. At each exit, the OOD examples can
be distinguished through our proposed adjusted energy score, which is both
empirically and theoretically suitable for networks with multiple classifiers.
We extensively evaluate MOOD across 10 OOD datasets spanning a wide range of
complexities. Experiments demonstrate that MOOD achieves up to 71.05%
computational reduction in inference, while maintaining competitive OOD
detection performance.