Abstract:
  Neutrino oscillations constitute the first experimental confirmation of new
physics, as they require a new piece that is absent in the Standard Model,
non-zero neutrino masses. Questions such as how these masses are generated or
why they are so small compared to other masses, are still under investigation.
In this thesis, we study radiative neutrino mass models, a class of models
where small neutrino masses are generated naturally, while the energy scale at
which new physics appears is still low enough to be tested by current and
future experiments, offering a falsifiable window to test beyond the Standard
Model physics. In these models, neutrino masses are forbidden at the
tree-level, but allowed at a certain order in loops. Consequently, we get
masses suppressed by loop factors without requiring a large new physics scale
and/or small couplings. In particular, we study the systematic classifications
of the $d=7$ Majorana neutrino mass operator at one-loop order, the Weinberg
operator at three-loop order and the $d=4$ Dirac neutrino mass models at the
two-loop level. We then move to more specific models and mechanisms, analysing
the connection between the generation of radiative neutrino masses and the
stability of dark matter from a model-independent point of view, and examine a
particular realisation of the type-I seesaw which relies on the radiative
generation of Dirac Yukawa coupling. Finally, we analyse the phenomenology of
some of the models discussed in the classifications, as well as a very
particular case of neutrinoless double-$\beta$ decay where electrons are
emitted with opposite chiralities along with a light scalar (Majoron).


Abstract:
  The multi-color passband CCD light curves of ASAS J124343+1531.7 and LINEAR
2323566 were first obtained by the 0.84-m Ritchey-Chr\'{e}tien telescope with
follow up observations by the WIYN 0.90m Cassegrain telescope. The data from
the $Transiting\quad Exoplanet\quad Survey\quad Satellite\quad (TESS)$ of ASAS
J124343+1531.7 was also applied for subsequent analysis. By analyzing the data
through the W-D program, their mass ratios and fill-out factors were determined
as 3.758, 1.438 and 31.8$\%$, 14.9$\%$, respectively. ASAS J124343+1531.7 is a
W-subtype median contact binary, while LINEAR 2323566 is a W-subtype shallow
contact binary, and the asymmetric light curves prove that they both have the
O'Connell effect, which is generally explained by magnetic activity. The
equivalent widths (EWs) of H$_\alpha$ lines were calculated, which show they
certainly have magnetic activity. Moreover, LINEAR 2323566 has a stronger
magnetic activity. The analysis of orbital period changes shows that ASAS
J124343+1531.7 has a trend of secular period increase, which is generally
explained by the mass transfer from the less massive to the more massive star.
According to the estimated absolute parameters, their evolutionary states are
discussed. The two components of ASAS J124343+1531.7 are both main sequence
stars. While for LINEAR 2323566, the more massive star is a main sequence star,
the less massive star has evolved out of main sequence and is over-luminous and
over-sized.


Abstract:
  We demonstrate that a conditional wavefunction theory enables a unified and
efficient treatment of the equilibrium structure and nonadiabatic dynamics of
correlated electron-ion systems. The conditional decomposition of the many-body
wavefunction formally recasts the full interacting wavefunction of a closed
system as a set of lower dimensional (conditional) coupled `slices'. We
formulate a variational wavefunction ansatz based on a set of conditional
wavefunction slices, and demonstrate its accuracy by determining the structural
and time-dependent response properties of the hydrogen molecule. We then extend
this approach to include time-dependent conditional wavefunctions, and address
paradigmatic nonequilibrium processes including strong-field molecular
ionization, laser driven proton transfer, and Berry phase effects induced by a
conical intersection. This work paves the road for the application of
conditional wavefunction theory in equilibrium and out of equilibrium ab-initio
molecular simulations of finite and extended systems.


Abstract:
  We consider symmetric positive definite preconditioners for multiple
saddle-point systems of block tridiagonal form, which can be applied within the
MINRES algorithm. We describe such a preconditioner for which the
preconditioned matrix has only two distinct eigenvalues, 1 and -1, when the
preconditioner is applied exactly. We discuss the relative merits of such an
approach compared to a more widely studied block diagonal preconditioner, and
specify the computational work associated with applying the new preconditioner
inexactly. Numerical results validate our theoretical findings.


Abstract:
  Transient non-gaussian noise in gravitational wave detectors, commonly
referred to as glitches, pose challenges for inference of the astrophysical
properties of detected signals when the two are coincident in time. Current
analyses aim towards modeling and subtracting the glitches from the data using
a flexible, morphology-independent model in terms of sine-gaussian wavelets
before the signal source properties are inferred using templates for the
compact binary signal. We present a new analysis of gravitational wave data
that contain both a signal and glitches by simultaneously modeling the compact
binary signal in terms of templates and the instrumental glitches using
sine-gaussian wavelets. The model for the glitches is generic and can thus be
applied to a wide range of glitch morphologies without any special tuning. The
simultaneous modeling of the astrophysical signal with templates allows us to
efficiently separate the signal from the glitches, as we demonstrate using
simulated signals injected around real O2 glitches in the two LIGO detectors.
We show that our new proposed analysis can separate overlapping glitches and
signals, estimate the compact binary parameters, and provide ready-to-use
glitch-subtracted data for downstream inference analyses.


Abstract:
  We investigate the gravitational perturbations of the Schwarzschild black
hole in the nonlocal gravity model recently proposed by Deser and Woodard
(DW-II). The analysis is performed in the localized version in which the
nonlocal corrections are represented by some auxiliary scalar fields. We find
that the nonlocal corrections do not affect the axial gravitational
perturbations, and hence the axial modes are completely identical to those in
General Relativity (GR). However, the polar modes get different from their GR
counterparts when the scalar fields are excited at the background level. In
such a case the polar modes are sourced by an additional massless scalar mode
and, as a result, the isospectrality between the axial and the polar modes
breaks down. We also perform a similar analysis for the predecessor of this
model (DW-I) and arrive at the same conclusion for it.


Abstract:
  Human enterprise often suffers from direct negative effects caused by
jellyfish blooms. The investigation of a prior jellyfish monitoring system
showed that it was unable to reliably perform in a cross validation setting,
i.e. in new underwater environments. In this paper, a number of enhancements
are proposed to the part of the system that is responsible for object
classification. First, the training set is augmented by adding synthetic data,
making the deep learning classifier able to generalise better. Then, the
framework is enhanced by employing a new second stage model, which analyzes the
outputs of the first network to make the final prediction. Finally, weighted
loss and confidence threshold are added to balance out true and false
positives. With all the upgrades in place, the system can correctly classify
30.16% (comparing to the initial 11.52%) of all spotted jellyfish, keep the
amount of false positives as low as 0.91% (comparing to the initial 2.26%) and
operate in real-time within the computational constraints of an autonomous
embedded platform.


Abstract:
  We propose three constructions of classically verifiable non-interactive
zero-knowledge proofs and arguments (CV-NIZK) for QMA in various preprocessing
models.
  - We construct a CV-NIZK for QMA in the quantum secret parameter model where
a trusted setup sends a quantum proving key to the prover and a classical
verification key to the verifier. It is information theoretically sound and
zero-knowledge.
  - Assuming the quantum hardness of the learning with errors problem, we
construct a CV-NIZK for QMA in a model where a trusted party generates a CRS
and the verifier sends an instance-independent quantum message to the prover as
preprocessing. This model is the same as one considered in the recent work by
Coladangelo, Vidick, and Zhang (CRYPTO '20).
  Our construction has the so-called dual-mode property, which means that there
are two computationally indistinguishable modes of generating CRS, and we have
information theoretical soundness in one mode and information theoretical
zero-knowledge property in the other. This answers an open problem left by
Coladangelo et al, which is to achieve either of soundness or zero-knowledge
information theoretically. To the best of our knowledge, ours is the first
dual-mode NIZK for QMA in any kind of model.
  - We construct a CV-NIZK for QMA with quantum preprocessing in the quantum
random oracle model. This quantum preprocessing is the one where the verifier
sends a random Pauli-basis states to the prover. Our construction uses the
Fiat-Shamir transformation. The quantum preprocessing can be replaced with the
setup that distributes Bell pairs among the prover and the verifier, and
therefore we solve the open problem by Broadbent and Grilo (FOCS '20) about the
possibility of NIZK for QMA in the shared Bell pair model via the Fiat-Shamir
transformation.


Abstract:
  We characterize the equality between ultradifferentiable function classes
defined in terms of abstractly given weight matrices and in terms of the
corresponding matrix of associated weight functions by using new growth
indices. These indices, defined by means of weight sequences and (associated)
weight functions, are extending the notion of O-regular variation to a mixed
setting. Hence we are extending the known comparison results concerning classes
defined in terms of a single weight sequence and of a single weight function
and give also these statements an interpretation expressed in O-regular
variation.


Abstract:
  Causal discovery methods seek to identify causal relations between random
variables from purely observational data, as opposed to actively collected
experimental data where an experimenter intervenes on a subset of correlates.
One of the seminal works in this area is the Inferred Causation algorithm,
which guarantees successful causal discovery under the assumption of a
conditional independence (CI) oracle: an oracle that can states whether two
random variables are conditionally independent given another set of random
variables. Practical implementations of this algorithm incorporate statistical
tests for conditional independence, in place of a CI oracle. In this paper, we
analyze the sample complexity of causal discovery algorithms without a CI
oracle: given a certain level of confidence, how many data points are needed
for a causal discovery algorithm to identify a causal structure? Furthermore,
our methods allow us to quantify the value of domain expertise in terms of data
samples. Finally, we demonstrate the accuracy of these sample rates with
numerical examples, and quantify the benefits of sparsity priors and known
causal directions.


Abstract:
  We prove a result which provides a link between the decomposition of
parabolically induced representations and the Bushnell--Kutzko theory of
typical representations. As an application, we show that there exists a
well-defined inertial Langlands correspondence which respects the monodromy
action of L-parameters, under some standard conjectures regarding the local
Langlands correspondence. To allow for potential applications of this inertial
Langlands correspondence, we also provide a complete construction of the set of
typical representations, giving a parametrization of these in terms of the
structure of the Bruhat--Tits building of $G$.


Abstract:
  Boolean Satisfiability (SAT) is a well-known NP-complete problem. Despite
this theoretical hardness, SAT solvers based on Conflict Driven Clause Learning
(CDCL) can solve large SAT instances from many important domains. CDCL learns
clauses from conflicts, a technique that allows a solver to prune its search
space. The selection heuristics in CDCL prioritize variables that are involved
in recent conflicts. While only a fraction of decisions generate any conflicts,
many generate multiple conflicts.
  In this paper, we study conflict-generating decisions in CDCL in detail. We
investigate the impact of single conflict (sc) decisions, which generate only
one conflict, and multi-conflict (mc) decisions which generate two or more. We
empirically characterize these two types of decisions based on the quality of
the learned clauses produced by each type of decision. We also show an
important connection between consecutive clauses learned within the same mc
decision, where one learned clause triggers the learning of the next one
forming a chain of clauses. This leads to the consideration of similarity
between conflicts, for which we formulate the notion of conflictsproximity as a
similarity measure. We show that conflicts in mc decisions are more closely
related than consecutive conflicts generated from sc decisions. Finally, we
develop Common Reason Variable Reduction (CRVR) as a new decision strategy that
reduces the selection priority of some variables from the learned clauses of mc
decisions. Our empirical evaluation of CRVR implemented in three leading
solvers demonstrates performance gains in benchmarks from the main track of SAT
Competition-2020.


Abstract:
  Resolving a linear combination of point sources from their band-limited
Fourier data is a fundamental problem in imaging and signal processing. With
the incomplete Fourier data and the inevitable noise in the measurement, there
is a fundamental limit on the separation distance between point sources that
can be resolved. This is the so-called resolution limit problem.
Characterization of this resolution limit is still a long-standing puzzle
despite the prevalent use of the classic Rayleigh limit. It is well-known that
Rayleigh limit is heuristic and its drawbacks become prominent when dealing
with data that is subjected to delicate processing, as is what modern
computational imaging methods do. Therefore, more precise characterization of
the resolution limit becomes increasingly necessary with the development of
data processing methods. For this purpose, we developed a theory of
"computational resolution limit" for both number detection and support recovery
in one dimension in [arXiv:2003.02917[cs.IT], arXiv:1912.05430[eess.IV]]. In
this paper, we extend the one-dimensional theory to multi-dimensions. More
precisely, we define and quantitatively characterize the "computational
resolution limit" for the number detection and support recovery problems in a
general k-dimensional space. Our results indicate that there exists a phase
transition phenomenon regarding to the super-resolution factor and the
signal-to-noise ratio in each of the two recovery problems. Our main results
are derived using a subspace projection strategy. Finally, to verify the
theory, we proposed deterministic subspace projection based algorithms for the
number detection and support recovery problems in dimension two and three. The
numerical results confirm the phase transition phenomenon predicted by the
theory.


Abstract:
  The relaxation of out-of-time-ordered correlators (OTOCs) has been studied as
a mean to characterize the scrambling properties of a quantum system. We show
that the presence of local conserved quantities typically results in, at the
fastest, an algebraic relaxation of the OTOC provided (i) the dynamics is local
and (ii) the system follows the eigenstate thermalization hypothesis. Our
result relies on the algebraic scaling of the infinite-time value of OTOCs with
system size, which is typical in thermalizing systems with local conserved
quantities, and on the existence of finite speed of propagation of correlations
for finite-range-interaction systems. We show that time-independence of the
Hamiltonian is not necessary as the above conditions (i) and (ii) can occur in
time-dependent systems, both periodic or aperiodic. We also remark that our
result can be extended to systems with power-law interactions.


Abstract:
  This paper introduces a new video-and-language dataset with human actions for
multimodal logical inference, which focuses on intentional and aspectual
expressions that describe dynamic human actions. The dataset consists of 200
videos, 5,554 action labels, and 1,942 action triplets of the form <subject,
predicate, object> that can be translated into logical semantic
representations. The dataset is expected to be useful for evaluating multimodal
inference systems between videos and semantically complicated sentences
including negation and quantification.


Abstract:
  We explicitly construct global attractors of fully nonlinear parabolic
equations. The attractors are decomposed as equilibria (time independent
solutions) and heteroclinic orbits (solutions that converge to distinct
equilibria backwards and forwards in time). In particular, we state necessary
and sufficient conditions for the occurrence of heteroclinics between
hyperbolic equilibria, which is accompanied by a method that compute such
conditions.


Abstract:
  In the Milky Way, $\sim$18 Wolf-Rayet+O (WR+O) binaries are known with
estimates of their stellar and orbital parameters. Whereas black hole+O (BH+O)
binaries are thought to evolve from the former, only one such system is known
in the Milky Way. To resolve this disparity, it was suggested that upon core
collapse, the WR stars receive large kicks such that most of the binaries are
disrupted. We reassess this issue, with emphasis on the uncertainty in the
formation of an accretion disk around wind-accreting BHs in BH+O binaries,
which is key to identifying such systems. We follow the methodology of previous
work and apply an improved analytic criterion for the formation of an accretion
disk around wind accreting BHs. We then use stellar models to predict the
properties of the BH+O binaries which are expected to descend from the observed
WR+O binaries, if the WR stars would form BHs without a natal kick. We find
that disk formation depends sensitively on the O stars' wind velocity, the
specific angular momentum carried by the wind, the efficiency of angular
momentum accretion by the BH, and the spin of the BH. We show that the
assumption of a low wind velocity may lead to predicting that most of the BH+O
star binaries will have an extended X-ray bright period. However, this is not
the case when typical wind velocities of O stars are considered. We find that a
high spin of the BH can boost the duration of the X-ray active phase as well as
the X-ray brightness during this phase, producing a strong bias for detecting
high mass BH binaries in X-rays with high BH spin parameters. We conclude that
large BH formation kicks are not required to understand the sparsity of X-ray
bright BH+O stars in the Milky Way. Probing for a population of X-ray silent
BH+O systems with alternative methods can inform us about BH kicks and the
conditions for high energy emission from high mass BH binaries. (Abridged)


Abstract:
  Achieving accurate and robust global situational awareness of a complex
time-evolving field from a limited number of sensors has been a longstanding
challenge. This reconstruction problem is especially difficult when sensors are
sparsely positioned in a seemingly random or unorganized manner, which is often
encountered in a range of scientific and engineering problems. Moreover, these
sensors can be in motion and can become online or offline over time. The key
leverage in addressing this scientific issue is the wealth of data accumulated
from the sensors. As a solution to this problem, we propose a data-driven
spatial field recovery technique founded on a structured grid-based
deep-learning approach for arbitrary positioned sensors of any numbers. It
should be noted that the na\"ive use of machine learning becomes prohibitively
expensive for global field reconstruction and is furthermore not adaptable to
an arbitrary number of sensors. In the present work, we consider the use of
Voronoi tessellation to obtain a structured-grid representation from sensor
locations enabling the computationally tractable use of convolutional neural
networks. One of the central features of the present method is its
compatibility with deep-learning based super-resolution reconstruction
techniques for structured sensor data that are established for image
processing. The proposed reconstruction technique is demonstrated for unsteady
wake flow, geophysical data, and three-dimensional turbulence. The current
framework is able to handle an arbitrary number of moving sensors, and thereby
overcomes a major limitation with existing reconstruction methods. The
presented technique opens a new pathway towards the practical use of neural
networks for real-time global field estimation.


Abstract:
  This work aims at intensifying text-independent speaker identification
performance in real application situations such as noisy and emotional talking
conditions. This is achieved by incorporating two different modules: a
Computational Auditory Scene Analysis CASA based pre-processing module for
noise reduction and cascaded Gaussian Mixture Model Convolutional Neural
Network GMM-CNN classifier for speaker identification followed by emotion
recognition. This research proposes and evaluates a novel algorithm to improve
the accuracy of speaker identification in emotional and highly-noise
susceptible conditions. Experiments demonstrate that the proposed model yields
promising results in comparison with other classifiers when Speech Under
Simulated and Actual Stress SUSAS database, Emirati Speech Database ESD, the
Ryerson Audio-Visual Database of Emotional Speech and Song RAVDESS database and
the Fluent Speech Commands database are used in a noisy environment.


Abstract:
  While nanoscale color generations have been studied for years, high
performance transmission structural colors, simultaneously equipped with large
gamut, high resolution, low loss and optical multiplexing abilities, still
remain as a hanging issue. Here, beneficial from metasurfaces, we demonstrate a
silicon metasurface embedded Fabry-P\'erot cavity (meta-FP cavity), with
polydimethylsiloxanes (PDMS) surrounding media and silver film mirrors. By
changing the planar geometries of the embedded nanopillars, the meta-FP cavity
provides transmission colors with ultra large gamut of 194% sRGB and ultrahigh
resolution of 141111 DPI, along with considerably average transmittance of 43%
and more than 300% enhanced angular tolerance. Such high density allows
two-dimensional color mixing at diffraction limit scale. The color gamut and
the resolution can be flexibly tuned and improved by modifying the silver film
thickness and the lattice period. The polarization manipulation ability of the
metasurface also enables arbitrary color arrangement between cyan and red for
two orthogonal linear polarization states, at deep subwavelength scale. Our
proposed cavities can be used in filters, printings, optical storages and many
other applications in need of high quality and density colors.


Abstract:
  Astronomers require efficient automated detection and classification
pipelines when conducting large-scale surveys of the (optical) sky for variable
and transient sources. Such pipelines are fundamentally important, as they
permit rapid follow-up and analysis of those detections most likely to be of
scientific value. We therefore present a deep learning pipeline based on the
convolutional neural network architecture called $\texttt{MeerCRAB}$. It is
designed to filter out the so called 'bogus' detections from true astrophysical
sources in the transient detection pipeline of the MeerLICHT telescope. Optical
candidates are described using a variety of 2D images and numerical features
extracted from those images. The relationship between the input images and the
target classes is unclear, since the ground truth is poorly defined and often
the subject of debate. This makes it difficult to determine which source of
information should be used to train a classification algorithm. We therefore
used two methods for labelling our data (i) thresholding and (ii) latent class
model approaches. We deployed variants of $\texttt{MeerCRAB}$ that employed
different network architectures trained using different combinations of input
images and training set choices, based on classification labels provided by
volunteers. The deepest network worked best with an accuracy of 99.5$\%$ and
Matthews correlation coefficient (MCC) value of 0.989. The best model was
integrated to the MeerLICHT transient vetting pipeline, enabling the accurate
and efficient classification of detected transients that allows researchers to
select the most promising candidates for their research goals.


Abstract:
  We introduce and study $\ell_p$-norm-multiway-cut: the input here is an
undirected graph with non-negative edge weights along with $k$ terminals and
the goal is to find a partition of the vertex set into $k$ parts each
containing exactly one terminal so as to minimize the $\ell_p$-norm of the cut
values of the parts. This is a unified generalization of min-sum multiway cut
(when $p=1$) and min-max multiway cut (when $p=\infty$), both of which are
well-studied classic problems in the graph partitioning literature. We show
that $\ell_p$-norm-multiway-cut is NP-hard for constant number of terminals and
is NP-hard in planar graphs. On the algorithmic side, we design an $O(\log^2
n)$-approximation for all $p\ge 1$. We also show an integrality gap of
$\Omega(k^{1-1/p})$ for a natural convex program and an
$O(k^{1-1/p-\epsilon})$-inapproximability for any constant $\epsilon>0$
assuming the small set expansion hypothesis.


Abstract:
  In this paper, we propose a unifying framework incorporating several
momentum-related search directions for solving strongly monotone variational
inequalities. The specific combinations of the search directions in the
framework are made to guarantee the optimal iteration complexity bound of
$\mathcal{O}\left(\kappa\ln(1/\epsilon)\right)$ to reach an
$\epsilon$-solution, where $\kappa$ is the condition number. This framework
provides the flexibility for algorithm designers to train -- among different
parameter combinations -- the one that best suits the structure of the problem
class at hand. The proposed framework includes the following iterative points
and directions as its constituents: the extra-gradient, the optimistic gradient
descent ascent (OGDA) direction (aka "optimism"), the "heavy-ball" direction,
and Nesterov's extrapolation points. As a result, all the afore-mentioned
methods become the special cases under the general scheme of extra points. We
also specialize this approach to strongly convex minimization, and show that a
similar extra-point approach achieves the optimal iteration complexity bound of
$\mathcal{O}(\sqrt{\kappa}\ln(1/\epsilon))$ for this class of problems.


Abstract:
  We study the transport properties of generic out-of-equilibrium quantum
systems connected to fermionic reservoirs. We develop a new method, based on an
expansion of the current in terms of the inverse system size and out of
equilibrium formulations such as the Keldysh technique and the Meir-Wingreen
formula. Our method allows a simple and compact derivation of the current for a
large class of systems showing diffusive/ohmic behavior. In addition, we obtain
exact solutions for a large class of quantum stochastic Hamiltonians (QSHs)
with time and space dependent noise, using a self consistent Born diagrammatic
method in the Keldysh representation. We show that these QSHs exhibit diffusive
regimes which are encoded in the Keldysh component of the single particle
Green's function. The exact solution for these QSHs models confirms the
validity of our system size expansion ansatz, and its efficiency in capturing
the transport properties. We consider in particular three fermionic models: i)
a model with local dephasing ii) the quantum simple symmetric exclusion process
model iii) a model with long-range stochastic hopping. For i) and ii) we
compute the full temperature and dephasing dependence of the conductance of the
system, both for two- and four-points measurements. Our solution gives access
to the regime of finite temperature of the reservoirs which could not be
obtained by previous approaches. For iii), we unveil a novel
ballistic-to-diffusive transition governed by the range and the nature (quantum
or classical) of the hopping. As a by-product, our approach equally describes
the mean behavior of quantum systems under continuous measurement.


Abstract:
  Although lattice gases composed by $k$NN particles, forbidding up to their
$k$th nearest neighbors of being occupied, have been widely investigated in
literature, the location and the universality class of the fluid-columnar
transition in the 2NN model on the square lattice is still a topic of debate.
Here, we present grand-canonical solutions of this model on Husimi lattices
built with diagonal square lattices, with $2L(L+1)$ sites, for $L \leqslant 7$.
The systematic sequence of mean-field solutions confirms the existence of a
continuous transition in this system and extrapolations of the critical
chemical potential $\mu_{2,c}(L)$ and particle density $\rho_{2,c}(L)$ to $L
\rightarrow \infty$ yield estimates of these quantities in close agreement with
previous results for the 2NN model on the square lattice. To confirm the
reliability of this approach we employ it also for the 1NN model, where very
accurate estimates for the critical parameters $\mu_{1,c}$ and $\rho_{1,c}$ --
for the fluid-solid transition in this model on the square lattice -- are found
from extrapolations of data for $L \leqslant 6$. The non-classical critical
exponents for these transitions are investigated through the coherent anomaly
method (CAM), which in the 1NN case yields $\beta$ and $\nu$ differing by at
most 6\% from the expected Ising exponents. For the 2NN model, the CAM analysis
is somewhat inconclusive, because the exponents sensibly depend on the value of
$\mu_{2,c}$ used to calculate them. Notwithstanding, our results suggest that
$\beta$ and $\nu$ are considerably larger than the Ashkin-Teller exponents
reported in numerical studies of the 2NN system.


Abstract:
  In Standard Model (SM) Higgs Boson pair production initiated by photons
($\gamma \gamma \to h h$) is loop-generated process and thereby very sensitive
to any new couplings and particles that may come in loops. The Composite Higgs
Models provide an alternate mechanism to address the hierarchy problem of SM
where Higgs instead of being an elementary field could be a bound state of a
strongly interacting sector. These set of models apart from modifying the SM
Higgs couplings could also introduce new effective couplings that can have
substantial impact on the loop processes. In this work we have studied the
impact of such modifications by Composite Higgs models in $\gamma\gamma \to h
h$ production process.


Abstract:
  We shift the paradigm of feedback control from the control of quantum states
to the control of phase transitions in quantum systems. We show that feedback
allows tuning the universality class of phase transitions via modifying its
critical exponent. We expand our previous treatment [D. A. Ivanov, T. Yu.
Ivanova, S. F. Caballero-Benitez, and I. B. Mekhov, Phys. Rev. Lett. 124,
010603 (2020)] of Dicke model and go beyond the approximation of adiabatically
eliminated light field. Both linearized and nonlinear models of spin ensembles
are considered. The tunability of quantum fluctuations near the critical point
by the feedbacks of nontrivial shapes is explained by considering the
fluctuation spectra and the system behavior at single quantum trajectories.


Abstract:
  This paper addresses the problem of 3D human body shape and pose estimation
from RGB images. Recent progress in this field has focused on single images,
video or multi-view images as inputs. In contrast, we propose a new task: shape
and pose estimation from a group of multiple images of a human subject, without
constraints on subject pose, camera viewpoint or background conditions between
images in the group. Our solution to this task predicts distributions over SMPL
body shape and pose parameters conditioned on the input images in the group. We
probabilistically combine predicted body shape distributions from each image to
obtain a final multi-image shape prediction. We show that the additional body
shape information present in multi-image input groups improves 3D human shape
estimation metrics compared to single-image inputs on the SSP-3D dataset and a
private dataset of tape-measured humans. In addition, predicting distributions
over 3D bodies allows us to quantify pose prediction uncertainty, which is
useful when faced with challenging input images with significant occlusion. Our
method demonstrates meaningful pose uncertainty on the 3DPW dataset and is
competitive with the state-of-the-art in terms of pose estimation metrics.


Abstract:
  Probability theory is far from being the most general mathematical theory of
uncertainty. A number of arguments point at its inability to describe
second-order ('Knightian') uncertainty. In response, a wide array of theories
of uncertainty have been proposed, many of them generalisations of classical
probability. As we show here, such frameworks can be organised into clusters
sharing a common rationale, exhibit complex links, and are characterised by
different levels of generality. Our goal is a critical appraisal of the current
landscape in uncertainty theory.


Abstract:
  In the metallic pyrochlore Nd$_2$Mo$_2$O$_7$, the conducting Molybdenum
sublattice adopts canted, yet nearly collinear ferromagnetic order with nonzero
scalar spin chirality. The chemical potential may be controlled by replacing
Nd$^{3+}$ with Ca$^{2+}$, while introducing only minimal additional disorder to
the conducting states. Here, we demonstrate the stability of the canted
ferromagnetic state, including the tilting angle of Molybdenum spins, in
(Nd$_{1-x}$Ca$_{x}$)$_2$Mo$_2$O$_7$ (NCMO) with $x\le 0.15$ using magnetic
susceptibility measurements. Mo-Mo and Mo-Nd magnetic couplings both change
sign above $x=0.22$, where the canted ferromagnetic state gives way to a
spin-glass metallic region. Contributions to the Curie-Weiss law from two
magnetic sublattices are separated systematically.


Abstract:
  The traditional notion of capacity studied in the context of memoryless
network communication builds on the concept of block-codes and requires that,
for sufficiently large blocklength n, all receiver nodes simultaneously decode
their required information after n channel uses. In this work, we generalize
the traditional capacity region by exploring communication rates achievable
when some receivers are required to decode their information before others, at
different predetermined times; referred here as the "time-rate" region. Through
a reduction to the standard notion of capacity, we present an inner-bound on
the time-rate region. The time-rate region has been previously studied and
characterized for the memoryless broadcast channel (with a sole common message)
under the name "static broadcasting".


Abstract:
  The chemical and physical evolution of starless and pre-stellar cores are of
paramount importance to understanding the process of star formation. The Taurus
Molecular Cloud cores TMC 1-C and TMC 1-CP share similar initial conditions and
provide an excellent opportunity to understand the evolution of the pre-stellar
core phase. We investigated the evolutionary stage of starless cores based on
observations towards the prototypical dark cores TMC 1-C and TMC 1-CP, mapping
them in the CS $3\rightarrow 2$, C$^{34}$S $3\rightarrow 2$, $^{13}$CS
$2\rightarrow 1$, DCN $1\rightarrow 0$, DCN $2\rightarrow 1$, DNC $1\rightarrow
0$, DNC $2\rightarrow 1$, DN$^{13}$C $1\rightarrow 0$, DN$^{13}$C $2\rightarrow
1$, N$_2$H$^+$ $1\rightarrow 0$, and N$_2$D$^+$ $1\rightarrow 0$ transitions.
We performed a multi-transitional study of CS and its isotopologs, DCN, and DNC
lines to characterize the physical and chemical properties of these cores. We
studied their chemistry using the state-of-the-art gas-grain chemical code
Nautilus and pseudo time-dependent models to determine their evolutionary
stage. Observational diagnostics seem to indicate that TMC 1-C is in a later
evolutionary stage than TMC 1-CP, with a chemical age $\sim$1 Myr. TMC 1-C
shows signs of being an evolved core at the onset of star formation, while TMC
1-CP appears to be in an earlier evolutionary stage due to a more recent
formation or, alternatively, a collapse slowed down by a magnetic support.


Abstract:
  Measures of face identification proficiency are essential to ensure accurate
and consistent performance by professional forensic face examiners and others
who perform face identification tasks in applied scenarios. Current proficiency
tests rely on static sets of stimulus items, and so, cannot be administered
validly to the same individual multiple times. To create a proficiency test, a
large number of items of "known" difficulty must be assembled. Multiple tests
of equal difficulty can be constructed then using subsets of items. Here, we
introduce a proficiency test, the Triad Identity Matching (TIM) test, based on
stimulus difficulty measures based on Item Response Theory (IRT). Participants
view face-image "triads" (N=225) (two images of one identity and one image of a
different identity) and select the different identity. In Experiment 1,
university students (N=197) showed wide-ranging accuracy on the TIM test.
Furthermore, IRT modeling demonstrated that the TIM test produces items of
various difficulty levels. In Experiment 2, IRT-based item difficulty measures
were used to partition the TIM test into three equally "easy" and three equally
"difficult" subsets. Simulation results indicated that the full set, as well as
curated subsets, of the TIM items yielded reliable estimates of subject
ability. In summary, the TIM test can provide a starting point for developing a
framework that is flexible, calibrated, and adaptive to measure proficiency
across various ability levels (e.g., professionals or populations with face
processing deficits)


Abstract:
  Deep Spiking Neural Networks (SNNs) present optimization difficulties for
gradient-based approaches due to discrete binary activation and complex
spatial-temporal dynamics. Considering the huge success of ResNet in deep
learning, it would be natural to train deep SNNs with residual learning.
Previous Spiking ResNet mimics the standard residual block in ANNs and simply
replaces ReLU activation layers with spiking neurons, which suffers the
degradation problem and can hardly implement residual learning. In this paper,
we propose the spike-element-wise (SEW) ResNet to realize residual learning in
deep SNNs. We prove that the SEW ResNet can easily implement identity mapping
and overcome the vanishing/exploding gradient problems of Spiking ResNet. We
evaluate our SEW ResNet on ImageNet, DVS Gesture, and CIFAR10-DVS datasets, and
show that SEW ResNet outperforms the state-of-the-art directly trained SNNs in
both accuracy and time-steps. Moreover, SEW ResNet can achieve higher
performance by simply adding more layers, providing a simple method to train
deep SNNs. To our best knowledge, this is the first time that directly training
deep SNNs with more than 100 layers becomes possible. Our codes are available
at https://github.com/fangwei123456/Spike-Element-Wise-ResNet.


Abstract:
  We generalize leading-order asymptotics of a form of the heat content of a
submanifold (van den Berg & Gilkey 2015) to the setting of time-dependent
diffusion processes in the limit of vanishing diffusivity. Such diffusion
processes arise naturally when advection-diffusion processes are viewed in
Lagrangian coordinates. We prove that as diffusivity $\varepsilon$ goes to
zero, the diffusive transport out of a material set $S$ under the
time-dependent, mass-preserving advection-diffusion equation with initial
condition given by the characteristic function $\mathds{1}_S$, is
$\sqrt{\varepsilon/\pi} d\overline{A}(\partial S) + o(\sqrt{\varepsilon})$. The
surface measure $d\overline A$ is that of the so-called geometry of mixing, as
introduced in (Karrasch & Keller, 2020). We apply our result to the
characterisation of coherent structures in time-dependent dynamical systems.


Abstract:
  Instance segmentation of planar regions in indoor scenes benefits visual SLAM
and other applications such as augmented reality (AR) where scene understanding
is required. Existing methods built upon two-stage frameworks show satisfactory
accuracy but are limited by low frame rates. In this work, we propose a
real-time deep neural architecture that estimates piece-wise planar regions
from a single RGB image. Our model employs a variant of a fast single-stage CNN
architecture to segment plane instances. Considering the particularity of the
target detected, we propose Fast Feature Non-maximum Suppression (FF-NMS) to
reduce the suppression errors resulted from overlapping bounding boxes of
planes. We also utilize a Residual Feature Augmentation module in the Feature
Pyramid Network (FPN). Our method achieves significantly higher frame-rates and
comparable segmentation accuracy against two-stage methods. We automatically
label over 70,000 images as ground truth from the Stanford 2D-3D-Semantics
dataset. Moreover, we incorporate our method with a state-of-the-art planar
SLAM and validate its benefits.


Abstract:
  To date, formal models of collective intelligence have lacked a plausible
mathematical description of the relationship between local-scale interactions
between highly autonomous sub-system components (individuals) and global-scale
behavior of the composite system (the collective). In this paper we use the
Active Inference Formulation (AIF), a framework for explaining the behavior of
any non-equilibrium steady state system at any scale, to posit a minimal
agent-based model that simulates the relationship between local
individual-level interaction and collective intelligence (operationalized as
system-level performance). We explore the effects of providing baseline AIF
agents (Model 1) with specific cognitive capabilities: Theory of Mind (Model
2); Goal Alignment (Model 3), and Theory of Mind with Goal Alignment (Model 4).
These stepwise transitions in sophistication of cognitive ability are motivated
by the types of advancements plausibly required for an AIF agent to persist and
flourish in an environment populated by other AIF agents, and have also
recently been shown to map naturally to canonical steps in human cognitive
ability. Illustrative results show that stepwise cognitive transitions increase
system performance by providing complementary mechanisms for alignment between
agents' local and global optima. Alignment emerges endogenously from the
dynamics of interacting AIF agents themselves, rather than being imposed
exogenously by incentives to agents' behaviors (contra existing computational
models of collective intelligence) or top-down priors for collective behavior
(contra existing multiscale simulations of AIF). These results shed light on
the types of generic information-theoretic patterns conducive to collective
intelligence in human and other complex adaptive systems.


Abstract:
  Tunnelling lies at the heart of quantum mechanics and is a fundamental
process in attosecond science, molecular biology, and quantum devices. Whether
tunnelling takes time and how a microscopic particle transits through a barrier
have been debated since the early days of quantum mechanics. The time required
for an electron to tunnel through an atomic potential barrier has been measured
with attosecond angular streaking (attoclock), and a recent work on the
hydrogen atom claimed that electron tunnelling is instantaneous. However, the
time required for Rb atoms to tunnel through an optical potential barrier has
been measured to be on the order of milliseconds with a recent Larmor clock
measurement. The essence of electron and atom tunnelling is identical, but the
reason for the contradictory conclusions remains unknown. Here, we demonstrate
that the sub-barrier potential interaction is the root of the nonzero
tunnelling delay time. We reveal that the wave-particle duality of a tunnelling
electron must be fully taken into account when decoding the tunnelling delay
time from the attoclock measurements. Based on energy-resolved attoclock
measurements, we show that the tunnelling delay time of an electron ranges from
24 to 58 attoseconds, and the counterintuitive result that an electron with a
lower energy may spend less time in the barrier is consistent with the
velocity-dependent tunnelling time of atoms in Larmor clock measurements. Our
results unify the tunnelling time of microscopic particles by highlighting the
classically forbidden sub-barrier potential interactions of matter waves.


Abstract:
  After examining the Feynman diagrams corresponding to the $\bar D^{(*)}
\Sigma_c^{(*)}$, $\bar D^{(*)} \Lambda_c$, $D^{(*)} \bar K^{*}$, and $D^{(*)}
\bar D^{(*)}$ hadronic molecular states, we propose a possible binding
mechanism induced by shared light quarks. This mechanism is similar to the
covalent bond in chemical molecules induced by shared electrons. We use the
method of QCD sum rules to calculate its corresponding light-quark-exchange
diagrams, and the obtained results indicate a model-independent hypothesis: the
light-quark-exchange interaction is attractive when the shared light quarks are
totally antisymmetric so that obey the Pauli principle. We build a toy model
with four parameters to formulize this picture, and estimate binding energies
of some possibly-existing covalent hadronic molecules. A unique feature of this
picture is that binding energies of the $(I)J^P = (0)1^+$ $D\bar B^*/D^* \bar
B$ hadronic molecules are much larger than those of the $(I)J^P = (0)1^+$
$DD^*/\bar B \bar B^*$ ones, while the $(I)J^P = (1/2)1/2^+$ $\bar D
\Sigma_c/\bar D \Sigma_b/B \Sigma_c/B \Sigma_b$ hadronic molecules have similar
binding energies.


Abstract:
  We generally expect quantum systems to thermalize and satisfy the eigenstate
thermalization hypothesis (ETH), which states that finite energy density
eigenstates are thermal. However, some systems, such as many-body localized
systems and systems with quantum many-body scars, violate ETH and have
high-energy athermal eigenstates. In systems with scars, most eigenstates
thermalize, but a few atypical scar states do not. Scar states can give rise to
a periodic revival when time-evolving particular initial product states, which
can be detected experimentally. Recently, a family of spin Hamiltonians was
found with magnetically ordered 3-colored eigenstates that are quantum
many-body scars [Lee et al. Phys. Rev. B 101, 241111(2020)]. These models can
be realized in any lattice that can be tiled by triangles, such as the
triangular or kagome lattices, and have been shown to have close connections to
the physics of quantum spin liquids in the Heisenberg kagome antiferromagnet.
In this work, we introduce a generalized family of $n$-colored Hamiltonians
with "spiral colored" eigenstates made from $n$-spin motifs such as polygons or
polyhedra. We show how these models can be realized in many different lattice
geometries and provide numerical evidence that they can exhibit quantum
many-body scars with periodic revivals that can be observed by time-evolving
simple product states. The simple structure of these Hamiltonians makes them
promising candidates for future experimental studies of quantum many-body
scars.


Abstract:
  We propose a novel mechanism for the photo-induced superconducting-like
response recently reported in cuprates and other strongly correlated materials.
This mechanism relies on quantum-fluctuating bosons consisting of electron
pairs. With periodic drive, the electron pairs and vacancies of pairs form a
coherent non-equilibrium condensate, different from conventional
superconductors, yet showing superconducting-like response in some regimes even
with dissipation. Unlike the case of driven fermionic bands which results in
the familiar Floquet bands with hybridization gaps, for driven bosons the "gap"
opens up in the momentum direction, resulting in a resonant region in momentum
space where the eigenvalues are complex. We give a simple physical argument why
this picture leads to a "perfect conductor" which exhibits superconducting-like
frequency-dependent conductivity but no Meissner response. While our model is
quite general, in the case of cuprates, quantum-fluctuating pair density wave
in the pseudogap region may serve as the origin of the quantum-fluctuating
electron pairs.


Abstract:
  A playtest is the process in which human testers are recruited to play video
games and to reveal software bugs. Manual testing is expensive and
time-consuming, especially when there are many mobile games to test and every
software version requires for extensive testing before being released. Existing
testing frameworks (e.g., Android Monkey) are limited because they adopt no
domain knowledge to play games. Learning-based tools (e.g., Wuji) involve a
huge amount of training data and computation before testing any game. This
paper presents LIT -- our lightweight approach to generalize playtesting
tactics from manual testing, and to adopt the generalized tactics to automate
game testing. LIT consists of two phases. In Phase I, while a human plays an
Android game app G for a short period of time (e.g., eight minutes), \tool
records the user's actions (e.g., swipe) and the scene before each action.
Based on the collected data, LIT generalizes a set of \emph{context-aware,
abstract playtesting tactics} which describe under what circumstances, what
actions can be taken to play the game. In Phase II, LIT tests G based on the
generalized tactics. Namely, given a randomly generated game scene, LIT
searches match for the abstract context of any inferred tactic; if there is a
match, LIT customizes the tactic and generates a feasible event to play the
game. Our evaluation with nine games shows LIT to outperform two
state-of-the-art tools. This implies that by automating playtest, LIT will
significantly reduce manual testing and boost the quality of game apps.


Abstract:
  This article is devoted to studying individual ergodic theorems for
subsequential weighted ergodic averages on the noncommutative Lp-spaces
associated to a semifinite von Neumann algebra M. In particular, we establish
the convergence of these averages along sequences with density one and certain
types of block sequences with positive lower density, and we extend known
results along uniform sequences in the sense of Brunel and Keane.


Abstract:
  We design a fast algorithm that computes, for a given linear differential
operator with coefficients in $Z[x ]$, all the characteristic polynomials of
its p-curvatures, for all primes $p < N$ , in asymptotically quasi-linear bit
complexity in N. We discuss implementations and applications of our algorithm.
We shall see in particular that the good performances of our algorithm are
quickly visible.


Abstract:
  Few-shot learning aims to learn a classifier using a few labelled instances
for each class. Metric-learning approaches for few-shot learning embed
instances into a high-dimensional space and conduct classification based on
distances among instance embeddings. However, such instance embeddings are
usually shared across all episodes and thus lack the discriminative power to
generalize classifiers according to episode-specific features. In this paper,
we propose a novel approach, namely \emph{Episode Adaptive Embedding Network}
(EAEN), to learn episode-specific embeddings of instances. By leveraging the
probability distributions of all instances in an episode at each channel-pixel
embedding dimension, EAEN can not only alleviate the overfitting issue
encountered in few-shot learning tasks, but also capture discriminative
features specific to an episode. To empirically verify the effectiveness and
robustness of EAEN, we have conducted extensive experiments on three widely
used benchmark datasets, under various combinations of different generic
embedding backbones and different classifiers. The results show that EAEN
significantly improves classification accuracy about $10\%$ to $20\%$ in
different settings over the state-of-the-art methods.


Abstract:
  We investigate the properties of foliated gauge fields and construct several
foliated field theories in 3+1d that describe foliated fracton orders both with
and without matter, including the recent hybrid fracton models. These field
theories describe Abelian or non-Abelian gauge theories coupled to foliated
gauge fields, and they fall into two classes of models that we call the
electric models and the magnetic models. We show that these two classes of
foliated field theories enjoy a duality. We also construct a model (using
foliated gauge fields and an exactly solvable lattice Hamiltonian model) for a
subsystem-symmetry protected topological (SSPT) phase, which is analogous to a
one-form symmetry protected topological phase, with the subsystem symmetry
acting on codimension-two subregions. We construct the corresponding gauged
SSPT phase as a foliated two-form gauge theory. Some instances of the gauged
SSPT phase are a variant of the X-cube model with the same ground state
degeneracy and the same fusion, but different particle statistics.


Abstract:
  Security is one of the main challenges of the Internet of Things (IoT). IoT
devices are mainly powered by low-cost microcontrollers (MCUs) that typically
lack basic hardware security mechanisms to separate security-critical
applications from less critical components. Recently, Arm has started to
release Cortex-M MCUs enhanced with TrustZone technology (i.e., TrustZone-M), a
system-wide security solution aiming at providing robust protection for IoT
devices. Trusted Execution Environments (TEEs) relying on TrustZone hardware
have been perceived as safe havens for securing mobile devices. However, for
the past few years, considerable effort has gone into unveiling hundreds of
vulnerabilities and proposing a collection of relevant defense techniques to
address several issues. While new TEE solutions built on TrustZone-M start
flourishing, the lessons gathered from the research community appear to be
falling short, as these new systems are trapping into the d\'ej\`a vu pitfalls
of the past. In this paper, we present uTango, the first multi-world TEE for
modern IoT devices. uTango proposes a novel architecture aiming at tackling the
major architectural deficiencies currently affecting TrustZone(-M)-assisted
TEEs. In particular, we leverage the very same TrustZone hardware primitives
used by dual-world implementations to create multiple, equally-secure execution
environments within the normal world. We demonstrate the benefits of uTango by
conducting an extensive evaluation on a real TrustZone-M hardware platform,
i.e., Arm Musca-B1. uTango will be open-sourced and freely available on GitHub
in hopes of engaging academia and industry on securing the foreseeable trillion
IoT devices.


Abstract:
  We address the exploitation of an optical parametric oscillator (OPO) in the
task of mitigating, at least partially, phase noise produced by phase
diffusion. In particular, we analyze two scenarios where phase diffusion is
typically present. The first one is the estimation of the phase of an optical
field, while the second involves a quantum communication protocol based on
phase shift keying. In both cases, we prove that an OPO may lead to a partial
or full compensation of the noise.


Abstract:
  Recent advances in the high sensitivity spectroscopy have made it possible,
in combination with accurate theoretical predictions, to observe for the first
time very weak electric quadrupole transitions in a polar polyatomic molecule
of water. Here we present accurate theoretical predictions of the complete
quadrupole ro-vibrational spectrum of a non-polar molecule CO$_2$, important in
atmospheric and astrophysical applications. Our predictions are validated by
recent cavity enhanced absorption spectroscopy measurements and are used to
assign few weak features in the recent ExoMars ACS MIR spectroscopic
observations of the martian atmosphere. Predicted quadrupole transitions appear
in some of the mid-infrared CO$_2$ and water vapor transparency regions, making
them important for detection and characterization of the minor absorbers in
water- and CO$_2$-rich environments, such as present in the atmospheres of
Earth, Venus and Mars.


Abstract:
  The kinematic features of a centaur-type humanoid platform, combined with a
powerful actuation, enable the experimentation of a variety of agile and
dynamic motions. However, the higher number of degrees-of-freedom and the
increased weight of the system, compared to the bipedal and quadrupedal
counterparts, pose significant research challenges in terms of computational
load and real implementation. To this end, this work presents a control
architecture to perform agile actions, conceived for torque-controlled
platforms, which decouples for computational purposes offline optimal control
planning of lower-body primitives, based on a template kinematic model, and
online control of the upper-body motion to maintain balance. Three stabilizing
strategies are presented, whose performance is compared in two types of
simulated jumps, while experimental validation is performed on a half-squat
jump using the CENTAURO robot.


Abstract:
  We extend the calculation of dark matter direct detection rates via
electronic transitions in general dielectric crystal targets, combining
state-of-the-art density functional theory calculations of electronic band
structures and wave functions near the band gap, with semi-analytic
approximations to include additional states farther away from the band gap. We
show, in particular, the importance of all-electron reconstruction for
recovering large momentum components of electronic wave functions, which,
together with the inclusion of additional states, has a significant impact on
direct detection rates, especially for heavy mediator models and at
$\mathcal{O}(10\,\text{eV})$ and higher energy depositions. Applying our
framework to silicon and germanium (that have been established already as
sensitive dark matter detectors), we find that our extended calculations can
appreciably change the detection prospects. Our calculational framework is
implemented in an open-source program $\texttt{EXCEED-DM}$ (EXtended
Calculation of Electronic Excitations for Direct detection of Dark Matter), to
be released in an upcoming publication.


Abstract:
  The concept of spin ice can be extended to a general graph. We study the
degeneracy of spin ice graph on arbitrary interaction structures via graph
theory. Via the mapping of spin ices to the Ising model, we clarify whether the
inverse mapping is possible via a modified Krausz construction. From the gauge
freedom of frustrated Ising systems, we derive exact, general results about
frustration and degeneracy. We demonstrate for the first time that every spin
ice graph, with the exception of the 1D Ising model, is degenerate. We then
study how degeneracy scales in size, using the mapping between Eulerian trails
and spin ice manifolds, and a permanental identity for the number of Eulerian
orientations. We show that the Bethe permanent technique provides both an
estimate and a lower bound to the frustration of spin ices on arbitrary graphs
of even degree. While such technique can be used also to obtain an upper bound,
we find that in all the examples we studied but one, another upper bound based
on Schrijver inequality is tighter.


Abstract:
  In this paper, we focus on category-level 6D pose and size estimation from
monocular RGB-D image. Previous methods suffer from inefficient category-level
pose feature extraction which leads to low accuracy and inference speed. To
tackle this problem, we propose a fast shape-based network (FS-Net) with
efficient category-level feature extraction for 6D pose estimation. First, we
design an orientation aware autoencoder with 3D graph convolution for latent
feature extraction. The learned latent feature is insensitive to point shift
and object size thanks to the shift and scale-invariance properties of the 3D
graph convolution. Then, to efficiently decode category-level rotation
information from the latent feature, we propose a novel decoupled rotation
mechanism that employs two decoders to complementarily access the rotation
information. Meanwhile, we estimate translation and size by two residuals,
which are the difference between the mean of object points and ground truth
translation, and the difference between the mean size of the category and
ground truth size, respectively. Finally, to increase the generalization
ability of FS-Net, we propose an online box-cage based 3D deformation mechanism
to augment the training data. Extensive experiments on two benchmark datasets
show that the proposed method achieves state-of-the-art performance in both
category- and instance-level 6D object pose estimation. Especially in
category-level pose estimation, without extra synthetic data, our method
outperforms existing methods by 6.3% on the NOCS-REAL dataset.


Abstract:
  We study past-directed extendibility of
Friedmann-Lema\^{i}tre-Robertson-Walker (FLRW) and Bianchi type I spacetimes
with a scale factor vanishing in the past. We give criteria for determining
whether a boundary for past-directed incomplete geodesics is a parallelly
propagated curvature singularity, which cannot necessarily be read off from
scalar curvature invariants. It is clarified that, for incomplete FLRW
spacetime to avoid the singularity, the spacetime necessarily reduces to the
Milne universe or flat de Sitter universe toward the boundary. For incomplete
Bianchi type I spacetime to be free of singularity, it is necessary that the
spacetime asymptotically fits into the product of the extendible isotropic
geometry (Milne or flat de Sitter) and flat space, or, anisotropic spacetime
with specific power law scale factors. Furthermore, we investigate in detail
the time-dependence of the scale factor compatible with the extendibility in
both spacetimes beyond the leading order.


Abstract:
  This paper proposes a learning reference governor (LRG) approach to enforce
state and control constraints in systems for which an accurate model is
unavailable, and this approach enables the reference governor to gradually
improve command tracking performance through learning while enforcing the
constraints during learning and after learning is completed. The learning can
be performed either on a black-box type model of the system or directly on the
hardware. After introducing the LRG algorithm and outlining its theoretical
properties, this paper investigates LRG application to fuel truck (tank truck)
rollover avoidance. Through simulations based on a fuel truck model that
accounts for liquid fuel sloshing effects, we show that the proposed LRG can
effectively protect fuel trucks from rollover accidents under various operating
conditions.


Abstract:
  Insider threats are the cyber attacks from within the trusted entities of an
organization. Lack of real-world data and issue of data imbalance leave insider
threat analysis an understudied research area. To mitigate the effect of skewed
class distribution and prove the potential of multinomial classification
algorithms for insider threat detection, we propose an approach that combines
generative model with supervised learning to perform multi-class classification
using deep learning. The generative adversarial network (GAN) based insider
detection model introduces Conditional Generative Adversarial Network (CGAN) to
enrich minority class samples to provide data for multi-class anomaly
detection. The comprehensive experiments performed on the benchmark dataset
demonstrates the effectiveness of introducing GAN derived synthetic data and
the capability of multi-class anomaly detection in insider activity analysis.
Moreover, the method is compared with other existing methods against different
parameters and performance metrics.


Abstract:
  Ambient backscatter communication (BackCom) is faced with the challenge that
a single BackCom device can occupy multiple orthogonal resource blocks
unintentionally. As a result, in order to avoid co-channel interference, a
conventional approach is to serve multiple BackCom devices in different time
slots, which reduces both spectral efficiency and connectivity. This letter
demonstrates that the use of non-orthogonal multiple access (NOMA) can
efficiently improve the system throughput and support massive connectivity in
ambient BackCom networks. In particular, two transceiver design approaches are
developed in the letter to realize different tradeoffs between system
performance and complexity.


Abstract:
  In this letter, we performed molecular dynamics simulations of ice nucleation
on a rigid surface model of cubic zinc blende structure with different surface
dipole strength and orientation. It follows that, despite the excellent lattice
match between cubic ice and substrates, the ice nucleation happened only when
the interfacial water molecules (IWs) have the same or similar orientations as
that of the water molecules in cubic ice. The free energy landscapes revealed
that, for substrates with improper dipole strength/orientation, large free
energy barriers arose to prevent the dipole of IWs rotating to the right
orientation to trigger ice formation. Our results suggest that the traditional
concept of lattice match, the similarity of lattice length between a substrate
and the new-formed crystalline, should be extended to a broader match include
the similarity between the molecular orientations of the interfacial component
and the component in the specific new-formed crystalline face.


Abstract:
  Based on symmetry consideration, quasi-one-dimensional (1D) objects, relevant
to numerous observables or phenomena, can be classified into eight different
types. We provide various examples of each 1D type, and discuss their Symmetry
Operational Similarity (SOS) relationships, which are often permutable. A
number of recent experimental observations, including current-induced
magnetization in polar or chiral conductors, non-linear Hall effect in polar
conductors, spin-polarization of tunneling current to chiral conductors, and
ferro-rotational domain imaging with linear gyration are discussed in terms of
(permutable) SOS. In addition, based on (permutable) SOS, we predict a large
number of new phenomena in low symmetry materials that can be experimentally
verified in the future.


Abstract:
  Since March 2020, the environment surrounding football has changed
dramatically because of the COVID-19 pandemic. After a few months' break,
re-scheduled matches were held behind closed doors without spectators. The main
objective of this study is a quantitative evaluation of ``crowd effects'' on
home advantage, using the results of these closed matches. The proposed
analysis uses pairwise comparison method to reduce the effects caused by the
unbalanced schedule. The following conclusions were drawn from the statistical
hypothesis tests conducted in this study: In four major European leagues, the
home advantage is reduced in closed matches compared to than in the normal
situation, i.e., with spectators. The reduction amounts among leagues were
different. For example, in Germany, the home advantage was negative during the
closed-match period. On the other hand,in England,statistically significant
differences in home advantage were not observed between closed matches and
normal situation.


Abstract:
  Random scattering of photons in disordered one-dimensional solids gives rise
to an exponential suppression of transmission, which is known as Anderson
localization. Here, we experimentally study Anderson localization in a
superconducting waveguide quantum electrodynamics system comprising eight
individually tunable qubits coupled to a photonic continuum of a waveguide.
Employing the qubit frequency control, we artificially introduce frequency
disorder to the system and observe an exponential suppression of the
transmission coefficient in the vicinity of its subradiant dark modes. The
localization length decreases with the disorder strength, which we control
in-situ by varying individual qubit frequencies. Employing a one-dimensional
non-interacting model of coupled qubits and photons, we are able to support and
complement the experimental results. The difference between our investigation
and previous studies of localization in qubit arrays is the coupling via a
common waveguide, allowing us to explore the localization of mediating photons
in an intrinsically open system. The experiment opens the door to the study of
various localization phenomena on a new platform, which offers a high degree of
control and readout possibilities.


Abstract:
  This paper studies the estimation of network connectedness with focally
sparse structure. We try to uncover the network effect with a flexible sparse
deviation from a predetermined adjacency matrix. To be more specific, the
sparse deviation structure can be regarded as latent or misspecified linkages.
To obtain high-quality estimator for parameters of interest, we propose to use
a double regularized high-dimensional generalized method of moments (GMM)
framework. Moreover, this framework also facilitates us to conduct the
inference. Theoretical results on consistency and asymptotic normality are
provided with accounting for general spatial and temporal dependency of the
underlying data generating processes. Simulations demonstrate good performance
of our proposed procedure. Finally, we apply the methodology to study the
spatial network effect of stock returns.


Abstract:
  Globally, 7-10 billion tonnes of waste are produced annually, including
300-500 million tonnes of hazardous wastes (HW)--explosive, flammable, toxic,
corrosive, and infective ones. About 10 % of these HW are traded through a
world-wide waste web (W4). The volume of HW traded through the W4 in the last
30 years has grown by 500 % and will continue to grow, creating serious legal,
economic, environmental and health problems at global scale. Here we
investigate the tip of the iceberg of the W4 by studying networks of 108
categories of wastes traded among 163 countries in the period 2003-2009.
Although, most of the HW were traded between developed nations, a
disproportionate asymmetry existed in the flow of waste from developed to
developing countries. Using a dynamical model we simulate how waste congestion
propagates through the W4. We identify 32 countries with poor environmental
performance which are at high risk of waste congestion. Therefore, they are a
threat of improper handling and disposal of HW. We found contamination by heavy
metals (HM), by volatile organic compounds (VOC) and/or by persistent organic
pollutants (POP), which were used as chemical fingerprints (CF) of the improper
handling of HW in 94 % of these countries.


Abstract:
  In this work we define some map-germs, called elementary joins, for the
purpose of producing new ${\mathcal A}$-finite map-germs from them. In
particular, we describe a general form of an ${\mathcal A}$-finite monomial map
from $(\mathbb{C}^n,0)$ to $(\mathbb{C}^{p},0)$ for $p\geq 2n$ of any corank in
terms of elementary join maps. Our main tools are the delta invariant and some
invariants of curves.


Abstract:
  We present a quantum kernel method for high-dimensional data analysis using
Google's universal quantum processor, Sycamore. This method is successfully
applied to the cosmological benchmark of supernova classification using real
spectral features with no dimensionality reduction and without vanishing kernel
elements. Instead of using a synthetic dataset of low dimension or
pre-processing the data with a classical machine learning algorithm to reduce
the data dimension, this experiment demonstrates that machine learning with
real, high dimensional data is possible using a quantum processor; but it
requires careful attention to shot statistics and mean kernel element size when
constructing a circuit ansatz. Our experiment utilizes 17 qubits to classify 67
dimensional data - significantly higher dimensionality than the largest prior
quantum kernel experiments - resulting in classification accuracy that is
competitive with noiseless simulation and comparable classical techniques.


Abstract:
  Previous X-ray studies of the Perseus Cluster, consisting of 85 Suzaku
pointings along eight azimuthal directions, revealed a particularly steep
decrease in the projected temperature profile near the virial radius (~r200)
towards the northwest (NW). To further explore this shock candidate, another 4
Suzaku observations on the NW edge of the Perseus Cluster have been obtained.
These deeper data were designed to provide the best possible control of
systematic uncertainties in the spectral analysis. Using the combined Suzaku
observations, we have carefully investigated this interesting region by
analyzing the spectra of various annuli and extracting projected thermodynamic
profiles. We find that the projected temperature profile shows a break near
r200, indicating a shock with M = 1.9+-0.3. Corresponding discontinuities are
also found in the projected emission measure and the density profiles at the
same location. This evidence of a shock front so far away from the cluster
center is unprecedented, and may provide a first insight into the properties of
large-scale virial shocks which shape the process of galaxy cluster growth.


Abstract:
  On any metric space, I provide an intrinsic characterization of those
complex-valued functions which are uniform limits of Lipschitz functions. There
are applications to function theory on complete Riemannian manifolds and, in
particular, on bounded symmetric domains in complex Euclidean space.


Abstract:
  Sparse regression and classification estimators capable of group selection
have application to an assortment of statistical problems, from multitask
learning to sparse additive modeling to hierarchical selection. This work
introduces a class of group-sparse estimators that combine group subset
selection with group lasso or ridge shrinkage. We develop an optimization
framework for fitting the nonconvex regularization surface and present
finite-sample error bounds for estimation of the regression function. Our
methods and analyses accommodate the general setting where groups overlap. As
an application of group selection, we study sparse semiparametric modeling, a
procedure that allows the effect of each predictor to be zero, linear, or
nonlinear. For this task, the new estimators improve across several metrics on
synthetic data compared to alternatives. Finally, we demonstrate their efficacy
in modeling supermarket foot traffic and economic recessions using many
predictors. All of our proposals are made available in the scalable
implementation grpsel.


Abstract:
  Electrical Impedance Tomography gives rise to the severely ill-posed
Calder\'on problem of determining the electrical conductivity distribution in a
bounded domain from knowledge of the associated Dirichlet-to-Neumann map for
the governing equation. The uniqueness and stability questions for the
three-dimensional problem were largely answered in the affirmative in the
1980's using complex geometrical optics solutions, and this led further to a
direct reconstruction method relying on a non-physical scattering transform. In
this paper, the reconstruction problem is taken one step further towards
practical applications by considering data contaminated by noise. Indeed, a
regularization strategy for the three-dimensional Calder\'on problem is
presented based on a suitable and explicit truncation of the scattering
transform. This gives a certified, stable and direct reconstruction method that
is robust to small perturbations of the data. Numerical tests on simulated
noisy data illustrate the feasibility and regularizing effect of the method,
and suggest that the numerical implementation performs better than predicted by
theory.


Abstract:
  We present PyTorch Geometric Temporal a deep learning framework combining
state-of-the-art machine learning algorithms for neural spatiotemporal signal
processing. The main goal of the library is to make temporal geometric deep
learning available for researchers and machine learning practitioners in a
unified easy-to-use framework. PyTorch Geometric Temporal was created with
foundations on existing libraries in the PyTorch eco-system, streamlined neural
network layer definitions, temporal snapshot generators for batching, and
integrated benchmark datasets. These features are illustrated with a
tutorial-like case study. Experiments demonstrate the predictive performance of
the models implemented in the library on real world problems such as
epidemiological forecasting, ridehail demand prediction and web-traffic
management. Our sensitivity analysis of runtime shows that the framework can
potentially operate on web-scale datasets with rich temporal features and
spatial structure.


Abstract:
  In this paper, we use the homogeneous $q$-operators [J. Difference Equ. Appl.
{\bf20 } (2014), 837--851.] to derive Rogers formulas, extended Rogers formulas
and Srivastava-Agarwal type bilinear generating functions for Cigler's
polynomials [J. Difference Equ. Appl. {\bf 24} (2018), 479--502.]. Finally, we
also derive two interesting transformation formulas between ${}_2\Phi_1, \,
{}_2\Phi_2$ and ${}_3\Phi_2$.


Abstract:
  The possibility of a non-gravitational interaction between the dark matter
and the dark energy has been reconstructed using some recent datasets. The
crucial aspect is that the interaction is not parametrized at the outset, but
rather reconstructed directly from the data in a non-parametric way. The Cosmic
Chronometer Hubble data, the Pantheon Supernova compilation of CANDELS and
CLASH Multy-Cycle Treasury programs obtained by the HST, and the Baryon
Acoustic Oscillation Hubble data have been considered in this work. The widely
accepted Gaussian Process is used for the reconstruction. The results clearly
indicate that a no interaction scenario is quite a possibility. Also, the
interaction, if any, is not really significant at the present epoch. The
direction of the flow of energy is clearly from the dark energy to the dark
matter which is consistent with the thermodynamic requirement.


Abstract:
  We introduce Attention Free Transformer (AFT), an efficient variant of
Transformers that eliminates the need for dot product self attention. In an AFT
layer, the key and value are first combined with a set of learned position
biases, the result of which is multiplied with the query in an element-wise
fashion. This new operation has a memory complexity linear w.r.t. both the
context size and the dimension of features, making it compatible to both large
input and model sizes. We also introduce AFT-local and AFT-conv, two model
variants that take advantage of the idea of locality and spatial weight sharing
while maintaining global connectivity. We conduct extensive experiments on two
autoregressive modeling tasks (CIFAR10 and Enwik8) as well as an image
recognition task (ImageNet-1K classification). We show that AFT demonstrates
competitive performance on all the benchmarks, while providing excellent
efficiency at the same time.


Abstract:
  We develop a numerical method based on matrix product states for simulating
quantum many-body systems at finite temperatures without importance sampling
and evaluate its performance in spin 1/2 systems. Our method is an extension of
the random phase product state (RPPS) approach introduced recently [T. Iitaka,
arXiv:2006.14459]. We show that the original RPPS approach often gives
unphysical values for thermodynamic quantities even in the Heisenberg chain. We
find that by adding the operation of Trotter gates to the RPPS, the sampling
efficiency of the approach significantly increases and its results are
consistent with those of the purification approach. We also apply our method to
a frustrated spin 1/2 system to exemplify that it can simulate a system in
which the purification approach fails.


Abstract:
  We consider a distributionally robust second-order stochastic dominance
constrained optimization problem. We require the dominance constraints hold
with respect to all probability distributions in a Wasserstein ball centered at
the empirical distribution. We adopt the sample approximation approach to
develop a linear programming formulation that provides a lower bound. We
propose a novel split-and-dual decomposition framework which provides an upper
bound. We establish quantitative convergency for both lower and upper
approximations given some constraint qualification conditions. To efficiently
solve the non-convex upper bound problem, we use a sequential convex
approximation algorithm. Numerical evidences on a portfolio selection problem
valid the convergency and effectiveness of the proposed two approximation
methods.


Abstract:
  This work studies the spectral convergence of graph Laplacian to the
Laplace-Beltrami operator when the graph affinity matrix is constructed from
$N$ random samples on a $d$-dimensional manifold embedded in a possibly high
dimensional space. By analyzing Dirichlet form convergence and constructing
candidate approximate eigenfunctions via convolution with manifold heat kernel,
we prove that, with Gaussian kernel, one can set the kernel bandwidth parameter
$\epsilon \sim (\log N/ N)^{1/(d/2+2)}$ such that the eigenvalue convergence
rate is $N^{-1/(d/2+2)}$ and the eigenvector convergence in 2-norm has rate
$N^{-1/(d+4)}$; When $\epsilon \sim N^{-1/(d/2+3)}$, both eigenvalue and
eigenvector rates are $N^{-1/(d/2+3)}$. These rates are up to a $\log N$ factor
and proved for finitely many low-lying eigenvalues. The result holds for
un-normalized and random-walk graph Laplacians when data are uniformly sampled
on the manifold, as well as the density-corrected graph Laplacian (where the
affinity matrix is normalized by the degree matrix from both sides) with
non-uniformly sampled data. As an intermediate result, we prove new point-wise
and Dirichlet form convergence rates for the density-corrected graph Laplacian.
Numerical results are provided to verify the theory.


Abstract:
  We discuss the construction of a nuclear Energy Density Functional (EDF) from
ab initio calculations, and we advocate the need of a methodical approach that
is free from ad hoc assumptions. The equations of state (EoS) of symmetric
nuclear and pure neutron matter are computed using the chiral NNLO$_{\rm sat}$
and the phenomenological AV4$^\prime$+UIX$_{c}$ Hamiltonians as inputs in the
Self-consistent Green's Function (SCGF) and Auxiliary Field Diffusion Monte
Carlo (AFDMC) methods, respectively. We propose a convenient parametrization of
the EoS as a function of the Fermi momentum and fit it on the SCGF and AFDMC
calculations. We apply the ab initio-based EDF to carry out an analysis of the
binding energies and charge radii of different nuclei in the local density
approximation. The NNLO$_{\rm sat}$-based EDF produces encouraging results,
whereas the AV4$^\prime$+UIX$_{c}$-based one is farther from experiment.
Possible explanations of these different behaviors are suggested, and the
importance of gradient and spin-orbit terms is analyzed. Our work paves the way
for a practical and systematic way to merge ab initio nuclear theory and DFT,
while at the same time it sheds light on some of the critical aspects of this
procedure.


Abstract:
  Quantum mechanics has so far not been tested for mechanical objects at the
scale of the Planck mass $\sqrt{\hbar c/ G} \simeq 22\,\mu\mathrm{g}$. We
present an experiment where a 1 mm quartz micropillar resonating at 3.6 MHz
with an effective mass of 30 $\mu$g is cooled to 500 mK with a dilution
refrigerator, and further optomechanically sideband-cooled to an effective
temperature of 3 mK, corresponding to a mode thermal occupancy of 20 phonons.
This nearly 1000-fold increase in the mass of an optomechanical system with
respect to previous experiments near the quantum ground state opens new
perspectives in the exploration of the quantum/classical border.


Abstract:
  The hypothesis of self-organized criticality explains the existence of
long-range `space-time' correlations, observed inseparably in many natural
dynamical systems. A simple link between these correlations is yet unclear,
particularly in fluctuations at `external drive' time scales. As an example, we
consider a class of sandpile models displaying non-trivial correlations.
Employing the scaling methods, we demonstrate the computation of spatial
correlation by establishing a link between local and global temporal
correlations.


Abstract:
  Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from
sentences, where each triplet includes an entity, its associated sentiment, and
the opinion span explaining the reason for the sentiment. Most existing
research addresses this problem in a multi-stage pipeline manner, which
neglects the mutual information between such three elements and has the problem
of error propagation. In this paper, we propose a Semantic and Syntactic
Enhanced aspect Sentiment triplet Extraction model (S3E2) to fully exploit the
syntactic and semantic relationships between the triplet elements and jointly
extract them. Specifically, we design a Graph-Sequence duel representation and
modeling paradigm for the task of ASTE: we represent the semantic and syntactic
relationships between word pairs in a sentence by graph and encode it by Graph
Neural Networks (GNNs), as well as modeling the original sentence by LSTM to
preserve the sequential information. Under this setting, we further apply a
more efficient inference strategy for the extraction of triplets. Extensive
evaluations on four benchmark datasets show that S3E2 significantly outperforms
existing approaches, which proves our S3E2's superiority and flexibility in an
end-to-end fashion.


Abstract:
  The use of Z-pinch facilities makes it possible to carry out well-controlled
and diagnosable laboratory experiments to study laboratory jets with scaling
parameters close to those of the jets from young stars. This makes it possible
to observe processes that are inaccessible to astronomical observations. Such
experiments are carried out at the PF-3 facility ("plasma focus", Kurchatov
Institute), in which the emitted plasma emission propagates along the drift
chamber through the environment at a distance of one meter. The paper presents
the results of experiments with helium, in which a successive release of two
ejections was observed. An analysis of these results suggests that after the
passage of the first supersonic ejection, a region with a low concentration is
formed behind it, the so-called vacuum trace, due to which the subsequent
ejection practically does not experience environmental resistance and
propagates being collimated. The numerical modeling of the propagation of two
ejections presented in the paper confirms this point of view. Using scaling
laws and appropriate numerical simulations of astrophysical ejections, it is
shown that this effect can also be significant for the jets of young stars.


Abstract:
  In this master thesis, we extend results from classical simple homotopy
theory to the world of stratified homotopy theory. To obtain a well-established
framework to work in, we prove a series of results on two model categories of
simplicial sets and topological spaces, both equipped with a notion of
filtration, introduced by Sylvain Douteau in his PHD thesis. In particular, we
show that there is a fully faithful embedding of homotopy categories from the
(finite) filtered simplicial into the filtered topological setting. We also use
these results to characterize the morphisms in the topological filtered
homotopy category between filtered spaces that are triangulable and stratified
in some very general sense as stratified homotopy classes of stratum preserving
maps. Then, moving on to simple homotopy theory, we propose a class of
combinatorial elementary expansions for filtered simplicial sets that
generalize both the classical ones as well as a class of stratified expansions
suggested by Banagl et al. We show that they fulfill a series of axioms,
suggested by Eckmann and Siebenmann for the construction of simple homotopy
theory. In doing so, we obtain a combinatorially defined Whitehead group and
torsion for filtered simplicial sets (and hence also for triangulable
stratified spaces). We then begin a detailed investigation of their formal
properties, proving for example that a Mayer-Vietoris formula holds. Next, we
apply our results on stratified homotopy theory to obtain a more topological
description of the Whitehead group and to generalize the Whitehead torsion to
arbitrary stratum preserving maps of triangulated filtered spaces. Finally, we
prove that our simple homotopy theory is a generalization of the classical one,
in the sense that it agrees with the latter when one considers trivially
filtered simplicial sets as CW-complexes.


Abstract:
  The Thue number $\pi(G)$ of a graph $G$ is the minimum number of colors
needed to color $G$ without creating a square on a path of $G$. For a graph
class $C$, $\pi(C)$ is the supremum of $\pi(G)$ over the graphs $G\in C$. The
Thue number has been investigated for famous minor-closed classes:
$\pi(tree)=4$, $7\le\pi(outerplanar)\le12$, and $11\le\pi(planar)\le768$.
Following a suggestion of Grytczuk, we consider the generalized parameters
$\pi_k(C)$ such that only squares of period at least $k$ must be avoided. Thus,
$\pi(C)=\pi_1(C)$. We show that $\pi_5(tree)=2$, $\pi_2(tree)=3$, and
$\pi_k(planar)\ge11$ for every fixed $k$.


Abstract:
  Pedestrian trajectory prediction in urban scenarios is essential for
automated driving. This task is challenging because the behavior of pedestrians
is influenced by both their own history paths and the interactions with others.
Previous research modeled these interactions with pooling mechanisms or
aggregating with hand-crafted attention weights. In this paper, we present the
Social Interaction-Weighted Spatio-Temporal Convolutional Neural Network
(Social-IWSTCNN), which includes both the spatial and the temporal features. We
propose a novel design, namely the Social Interaction Extractor, to learn the
spatial and social interaction features of pedestrians. Most previous works
used ETH and UCY datasets which include five scenes but do not cover urban
traffic scenarios extensively for training and evaluation. In this paper, we
use the recently released large-scale Waymo Open Dataset in urban traffic
scenarios, which includes 374 urban training scenes and 76 urban testing scenes
to analyze the performance of our proposed algorithm in comparison to the
state-of-the-art (SOTA) models. The results show that our algorithm outperforms
SOTA algorithms such as Social-LSTM, Social-GAN, and Social-STGCNN on both
Average Displacement Error (ADE) and Final Displacement Error (FDE).
Furthermore, our Social-IWSTCNN is 54.8 times faster in data pre-processing
speed, and 4.7 times faster in total test speed than the current best SOTA
algorithm Social-STGCNN.


Abstract:
  Mobile phone operators have begun the roll-out of 5G networks, deploying
massive MIMO base stations. Commercial product ranges start with 16 independent
radio chains connected to a large-scale antenna system to exploit both channel
hardening and favourable propagation in order to obtain increased spectral
efficiency. In this work, the cumulative distribution function describing the
gain for large-scale antenna systems considering spatial and spectral diversity
is evaluated empirically in terms of a fading margin and compared to an
analytical maximum diversity reference system. This allows for a simple
investigation of the trade-off between deployment size and exploitation of
channel hardening. For the considered site-specific measurement data, little
additional diversity is harvested with systems larger than 32 antenna elements.


Abstract:
  Transmission Electron Microscopy (TEM) is a powerful tool for imaging
material structure and characterizing material chemistry. Recent advances in
data collection technology for TEM have enabled high-volume and high-resolution
data collection at a microsecond frame rate. Taking advantage of these advances
in data collection rates requires the development and application of data
processing tools, including image analysis, feature extraction, and streaming
data processing techniques. In this paper, we highlight a few areas in
materials science that have benefited from combining signal processing and
statistical analysis with data collection capabilities in TEM and present a
future outlook on opportunities of integrating signal processing with automated
TEM data analysis.


Abstract:
  Under the global pandemic of COVID-19, building an automated framework that
quantifies the severity of COVID-19 and localizes the relevant lesion on chest
X-ray images has become increasingly important. Although pixel-level lesion
severity labels, e.g. lesion segmentation, can be the most excellent target to
build a robust model, collecting enough data with such labels is difficult due
to time and labor-intensive annotation tasks. Instead, array-based severity
labeling that assigns integer scores on six subdivisions of lungs can be an
alternative choice enabling the quick labeling. Several groups proposed deep
learning algorithms that quantify the severity of COVID-19 using the
array-based COVID-19 labels and localize the lesions with explainability maps.
To further improve the accuracy and interpretability, here we propose a novel
Vision Transformer tailored for both quantification of the severity and
clinically applicable localization of the COVID-19 related lesions. Our model
is trained in a weakly-supervised manner to generate the full probability maps
from weak array-based labels. Furthermore, a novel progressive self-training
method enables us to build a model with a small labeled dataset. The
quantitative and qualitative analysis on the external testset demonstrates that
our method shows comparable performance with radiologists for both tasks with
stability in a real-world application.


Abstract:
  For proper planning, operation, control, and protection of the power system,
the development of a suitable steady-state mathematical model of FACTS devices
is a key issue. The Fast and Flexible Holomorphic Embedding (FFHE) method
converges faster and provides the flexibility to use any state as an initial
guess. But to investigate the effect and ability of FACTS devices using FFHE
technique, it is necessary to develop an embedded system for these devices.
Therefore, this paper presents an FFHE-based embedded system for VSC-based
FACTS controllers, such as SSSC and IPFC. The embedded system is also proposed
for their controlling modes.
  The introduced embedded system is flexible which allows to take any state as
an initial guess instead of fixed state, which leads towards the reduced
runtime and decreases the required number of terms, as compared to standard
HELM. To demonstrate the effectiveness and practicability, the proposed
FFHE-based models of FACTS devices have been tested for several cases. Further,
the developed recursive formulas for power balance equations, devices' physical
constraints, and their controlling modes are thoroughly investigated and
examined. From several tests, it is found that the proposed FFHE-based FACTS
models require less execution time and reduce the error at higher rate.


Abstract:
  VR/AR technology is a key enabler for new ways of immersively experiencing
cultural heritage artifacts based on their virtual counterparts obtained from a
digitization process. In this paper, we focus on enriching VR-based object
inspection by additional haptic feedback, thereby creating tangible cultural
heritage experiences. For this purpose, we present an approach for interactive
and collaborative VR-based object inspection and annotation. Our system
supports high-quality 3D models with accurate reflectance characteristics while
additionally providing haptic feedback regarding the object shape features
based on a 3D printed replica. The digital object model in terms of a printable
representation of the geometry as well as reflectance characteristics are
stored in a compact and streamable representation on a central server, which
streams the data to remotely connected users/clients. The latter can jointly
perform an interactive inspection of the object in VR with additional haptic
feedback through the 3D printed replica. Evaluations regarding system
performance, visual quality of the considered models as well as insights from a
user study indicate an improved interaction, assessment and experience of the
considered objects.