Abstract:
  A differentiable curve y = y(x) is determined by its tangent lines and is
said to be the envelope of its tangent lines. The coefficients of the curve's
tangent lines form a curve in another space, called the dual space. There is a
transformation between the original x,y-space and the dual space, such that
points on the original curve and the curve of the coefficients of the tangent
lines are transformed into each other.The dual space and the transformation
depend upon the form that is used for the tangent lines. One choice is y = mx +
b, so that the coordinates in the dual space are m and b, where the curve
representing the tangent lines is b = b(m). Each point of the curve b = b(m) in
the dual space corresponds to a tangent line to the curve y = y(x) in
x,y-space. We present other choices of the form for the tangent lines and
explore techniques for finding a curve from the tangent lines, transformations
between the original space and a dual space and among dual spaces, the
differential equation, whose solutions are exactly the equations of the
equations of the tangent lines and the original curve. Geometric constructions
and other tools are used to find dual curves.


Abstract:
  Unsupervised domain adaptation aims to learn a task classifier that performs
well on the unlabeled target domain, by utilizing the labeled source domain.
Inspiring results have been acquired by learning domain-invariant deep features
via domain-adversarial training. However, its parallel design of task and
domain classifiers limits the ability to achieve a finer category-level domain
alignment. To promote categorical domain adaptation (CatDA), based on a joint
category-domain classifier, we propose novel losses of adversarial training at
both domain and category levels. Since the joint classifier can be regarded as
a concatenation of individual task classifiers respectively for the two
domains, our design principle is to enforce consistency of category predictions
between the two task classifiers. Moreover, we propose a concept of vicinal
domains whose instances are produced by a convex combination of pairs of
instances respectively from the two domains. Intuitively, alignment of the
possibly infinite number of vicinal domains enhances that of original domains.
We propose novel adversarial losses for vicinal domain adaptation (VicDA) based
on CatDA, leading to Vicinal and Categorical Domain Adaptation (ViCatDA). We
also propose Target Discriminative Structure Recovery (TDSR) to recover the
intrinsic target discrimination damaged by adversarial feature alignment. We
also analyze the principles underlying the ability of our key designs to align
the joint distributions. Extensive experiments on several benchmark datasets
demonstrate that we achieve the new state of the art.


Abstract:
  Finite Sample Smeariness (FSS) has been recently discovered. It means that
the distribution of sample Fr\'echet means of underlying rather unsuspicious
random variables can behave as if it were smeary for quite large regimes of
finite sample sizes. In effect classical quantile-based statistical testing
procedures do not preserve nominal size, they reject too often under the null
hypothesis. Suitably designed bootstrap tests, however, amend for FSS. On the
circle it has been known that arbitrarily sized FSS is possible, and that all
distributions with a nonvanishing density feature FSS. These results are
extended to spheres of arbitrary dimension. In particular all rotationally
symmetric distributions, not necessarily supported on the entire sphere feature
FSS of Type I. While on the circle there is also FSS of Type II it is
conjectured that this is not possible on higher-dimensional spheres.


Abstract:
  The main purpose of this paper is a wide generalization of one of the results
abstract algebraic geometry begins with, namely of the fact that the prime
spectrum $\mathrm{Spec}(R)$ of a unital commutative ring $R$ is always a
spectral (=coherent) topological space. In this generalization, which includes
several other known ones, the role of ideals of $R$ is played by elements of an
abstract complete lattice $L$ equipped with binary multiplication with
$xy\leqslant x\wedge y$ for all $x,y\in L$. In fact when no further conditions
on $L$ are required, the resulting space can be and is only shown to be sober,
and we discuss further conditions sufficient to make it spectral. This
discussion involves establishing various comparison theorems on so-called
prime, radical, solvable, and locally solvable elements of $L$; we also make
short additional remarks on semiprime elements. We consider categorical and
universal-algebraic applications involving general theory of commutators, and
an application to ideals in what we call the commutative world. The cases of
groups and of non-commutative rings are briefly considered separately.


Abstract:
  Most of state of the art methods applied on time series consist of deep
learning methods that are too complex to be interpreted. This lack of
interpretability is a major drawback, as several applications in the real world
are critical tasks, such as the medical field or the autonomous driving field.
The explainability of models applied on time series has not gather much
attention compared to the computer vision or the natural language processing
fields. In this paper, we present an overview of existing explainable AI (XAI)
methods applied on time series and illustrate the type of explanations they
produce. We also provide a reflection on the impact of these explanation
methods to provide confidence and trust in the AI systems.


Abstract:
  Recommender systems rely on user behavior data like ratings and clicks to
build personalization model. However, the collected data is observational
rather than experimental, causing various biases in the data which
significantly affect the learned model. Most existing work for recommendation
debiasing, such as the inverse propensity scoring and imputation approaches,
focuses on one or two specific biases, lacking the universal capacity that can
account for mixed or even unknown biases in the data. Towards this research
gap, we first analyze the origin of biases from the perspective of \textit{risk
discrepancy} that represents the difference between the expectation empirical
risk and the true risk. Remarkably, we derive a general learning framework that
well summarizes most existing debiasing strategies by specifying some
parameters of the general framework. This provides a valuable opportunity to
develop a universal solution for debiasing, e.g., by learning the debiasing
parameters from data. However, the training data lacks important signal of how
the data is biased and what the unbiased data looks like. To move this idea
forward, we propose \textit{AotoDebias} that leverages another (small) set of
uniform data to optimize the debiasing parameters by solving the bi-level
optimization problem with meta-learning. Through theoretical analyses, we
derive the generalization bound for AutoDebias and prove its ability to acquire
the appropriate debiasing strategy. Extensive experiments on two real datasets
and a simulated dataset demonstrated effectiveness of AutoDebias. The code is
available at \url{https://github.com/DongHande/AutoDebias}.


Abstract:
  We consider Cauchy problem of the Hartree-type nonlinear Dirac equation with
potentials given by $V_b(x) = \frac1{4\pi}\frac{e^{-b|x|}}{|x|}\, (b \ge 0)$.
In previous works, a standard argument is to utilise null form estimates in
order to prove global well-posedness for $H^s$-data, $s>0$. However, the null
structure inside the equations is not enough to attain the critical regularity.
We impose an extra regularity assumption with respect to the angular variable.
Firstly, we prove global well-posedness and scattering of Dirac equations with
Hartree-type nonlinearity for $b>0$ for small $L^2_x$-data with additional
angular regularity. We also show that only small amount of angular regularity
is required to obtain global existence of solutions. Secondly, we obtain
non-scattering result for a certain class of solutions with the Coulomb
potential $b=0$.


Abstract:
  A common approach for feature selection is to examine the variable importance
scores for a machine learning model, as a way to understand which features are
the most relevant for making predictions. Given the significance of feature
selection, it is crucial for the calculated importance scores to reflect
reality. Falsely overestimating the importance of irrelevant features can lead
to false discoveries, while underestimating importance of relevant features may
lead us to discard important features, resulting in poor model performance.
Additionally, black-box models like XGBoost provide state-of-the art predictive
performance, but cannot be easily understood by humans, and thus we rely on
variable importance scores or methods for explainability like SHAP to offer
insight into their behavior.
  In this paper, we investigate the performance of variable importance as a
feature selection method across various black-box and interpretable machine
learning methods. We compare the ability of CART, Optimal Trees, XGBoost and
SHAP to correctly identify the relevant subset of variables across a number of
experiments. The results show that regardless of whether we use the native
variable importance method or SHAP, XGBoost fails to clearly distinguish
between relevant and irrelevant features. On the other hand, the interpretable
methods are able to correctly and efficiently identify irrelevant features, and
thus offer significantly better performance for feature selection.


Abstract:
  We study the effect of characteristic size variation on the phonon thermal
transport in crystalline GeTe for a wide range of temperatures using the
first-principles density-functional method coupled with the kinetic collective
model approach. The characteristic size dependence of phonon thermal transport
reveals an intriguing collective phonon transport regime, located in between
the ballistic and the diffusive transport regimes. Therefore, systematic
investigations have been carried out to describe the signatures of phonon
hydrodynamics via the competitive effects between grain size and temperature. A
characteristic nonlocal length associated with phonon hydrodynamics and a heat
wave propagation length has been extracted. The connections between phonon
hydrodynamics and these length scales are discussed in terms of the Knudsen
number. Further, the scaling relation of thermal conductivity as a function of
characteristic size in the intermediate size range emerges as a crucial
indicator of the strength of the hydrodynamic behavior. A ratio concerning
normal and resistive scattering rates has been employed to understand these
different scaling relations, which seems to control the strength and prominent
visibility of the collective phonon transport in GeTe. This systematic
investigation emphasizes the importance of the competitive effects between
temperature and characteristic size on phonon hydrodynamics in GeTe, which can
lead to a better understanding of the generic behavior and consequences of the
phonon hydrodynamics and its controlling parameters in low-thermal conductivity
materials.


Abstract:
  We present a 5A-100um Spectral Energy Distribution (SED) of the ultracool
dwarf star TRAPPIST-1, obtained as part of the Mega-MUSCLES Treasury Survey.
The SED combines ultraviolet and blue-optical spectroscopy obtained with the
Hubble Space Telescope, X-ray spectroscopy obtained with XMM-Newton, and models
of the stellar photosphere, chromosphere, transition region and corona. A new
Differential Emission Measure model of the unobserved extreme-ultraviolet
spectrum is provided, improving on the Lyman alpha to EUV relations often used
to estimate the 100-911A flux from low-mass stars. We describe the observations
and models used, as well as the recipe for combining them into an SED. We also
provide a semi-empirical, noise-free model of the stellar ultraviolet spectrum
based on our observations for use in atmospheric modelling of the TRAPPIST-1
planets.


Abstract:
  Artificial Intelligence (AI) covers a broad spectrum of computational
problems and use cases. Many of those implicate profound and sometimes
intricate questions of how humans interact or should interact with AIs.
Moreover, many users or future users do have abstract ideas of what AI is,
significantly depending on the specific embodiment of AI applications.
Human-centered-design approaches would suggest evaluating the impact of
different embodiments on human perception of and interaction with AI. An
approach that is difficult to realize due to the sheer complexity of
application fields and embodiments in reality. However, here XR opens new
possibilities to research human-AI interactions. The article's contribution is
twofold: First, it provides a theoretical treatment and model of human-AI
interaction based on an XR-AI continuum as a framework for and a perspective of
different approaches of XR-AI combinations. It motivates XR-AI combinations as
a method to learn about the effects of prospective human-AI interfaces and
shows why the combination of XR and AI fruitfully contributes to a valid and
systematic investigation of human-AI interactions and interfaces. Second, the
article provides two exemplary experiments investigating the aforementioned
approach for two distinct AI-systems. The first experiment reveals an
interesting gender effect in human-robot interaction, while the second
experiment reveals an Eliza effect of a recommender system. Here the article
introduces two paradigmatic implementations of the proposed XR testbed for
human-AI interactions and interfaces and shows how a valid and systematic
investigation can be conducted. In sum, the article opens new perspectives on
how XR benefits human-centered AI design and development.


Abstract:
  In recent years, recommender systems play a pivotal role in helping users
identify the most suitable items that satisfy personal preferences. As
user-item interactions can be naturally modelled as graph-structured data,
variants of graph convolutional networks (GCNs) have become a well-established
building block in the latest recommenders. Due to the wide utilization of
sensitive user profile data, existing recommendation paradigms are likely to
expose users to the threat of privacy breach, and GCN-based recommenders are no
exception. Apart from the leakage of raw user data, the fragility of current
recommenders under inference attacks offers malicious attackers a backdoor to
estimate users' private attributes via their behavioral footprints and the
recommendation results. However, little attention has been paid to developing
recommender systems that can defend such attribute inference attacks, and
existing works achieve attack resistance by either sacrificing considerable
recommendation accuracy or only covering specific attack models or protected
information. In our paper, we propose GERAI, a novel differentially private
graph convolutional network to address such limitations. Specifically, in
GERAI, we bind the information perturbation mechanism in differential privacy
with the recommendation capability of graph convolutional networks.
Furthermore, based on local differential privacy and functional mechanism, we
innovatively devise a dual-stage encryption paradigm to simultaneously enforce
privacy guarantee on users' sensitive features and the model optimization
process. Extensive experiments show the superiority of GERAI in terms of its
resistance to attribute inference attacks and recommendation effectiveness.


Abstract:
  This paper deals with the scarcity of data for training optical flow
networks, highlighting the limitations of existing sources such as labeled
synthetic datasets or unlabeled real videos. Specifically, we introduce a
framework to generate accurate ground-truth optical flow annotations quickly
and in large amounts from any readily available single real picture. Given an
image, we use an off-the-shelf monocular depth estimation network to build a
plausible point cloud for the observed scene. Then, we virtually move the
camera in the reconstructed environment with known motion vectors and rotation
angles, allowing us to synthesize both a novel view and the corresponding
optical flow field connecting each pixel in the input image to the one in the
new frame. When trained with our data, state-of-the-art optical flow networks
achieve superior generalization to unseen real data compared to the same models
trained either on annotated synthetic datasets or unlabeled videos, and better
specialization if combined with synthetic images.


Abstract:
  We introduce a protocol to transfer excitations between two noninteracting
qubits via purely dissipative processes (i.e., in the Lindblad master equation
there is no coherent interaction between the qubits). The fundamental
ingredients are the presence of collective (i.e. nonlocal) dissipation and
unbalanced local dissipation rates (the qubits dissipate at different rates).
The resulting quantum trajectories show that the measurement backaction changes
the system wave function and induces a passage of the excitation from one qubit
to the other. While similar phenomena have been witnessed for a non-Markovian
environment, here the dissipative quantum state transfer is induced by an
update of the observer knowledge of the wave function in the presence of a
Markovian (memoryless) environment -- this is a single quantum trajectory
effect. Beyond single quantum trajectories and postselection, such an effect
can be observed by histogramming the quantum jumps along several realizations
at different times. By investigating the effect of the temperature in the
presence of unbalanced local dissipation, we demonstrate that, if appropriately
switched on and off, the collective dissipator can act as a Maxwell's demon.
These effects are a generalized measure equivalent to the standard projective
measure description of quantum teleportation and Maxwell's demon. They can be
witnessed in state-of-the-art setups given the extreme experimental control in,
e.g., superconducting qubits or Rydberg atoms.


Abstract:
  The linear operator $c + (-\Delta)^{\alpha/2}$, where $c > 0$ and
$(-\Delta)^{\alpha/2}$ is the fractional Laplacian on the periodic domain,
arises in the existence of periodic travelling waves in the fractional
Korteweg--de Vries equation. We establish a relation of the Green's function of
this linear operator with the Mittag--Leffler function, which was previously
used in the context of Riemann--Liouville's and Caputo's fractional
derivatives. By using this relation, we prove that Green's function is strictly
positive and single-lobe (monotonically decreasing away from the maximum point)
for every $c > 0$ and every $\alpha \in (0,2]$. On the other hand, we argue
from numerical approximations that in the case of $\alpha \in (2,4]$, the
Green's function is positive and single-lobe for small $c$ and non-positive and
non-single lobe for large $c$.


Abstract:
  Combinatorial bandits with semi-bandit feedback generalize multi-armed
bandits, where the agent chooses sets of arms and observes a noisy reward for
each arm contained in the chosen set. The action set satisfies a given
structure such as forming a base of a matroid or a path in a graph. We focus on
the pure-exploration problem of identifying the best arm with fixed confidence,
as well as a more general setting, where the structure of the answer set
differs from the one of the action set. Using the recently popularized game
framework, we interpret this problem as a sequential zero-sum game and develop
a CombGame meta-algorithm whose instances are asymptotically optimal algorithms
with finite time guarantees. In addition to comparing two families of learners
to instantiate our meta-algorithm, the main contribution of our work is a
specific oracle efficient instance for best-arm identification with
combinatorial actions. Based on a projection-free online learning algorithm for
convex polytopes, it is the first computationally efficient algorithm which is
asymptotically optimal and has competitive empirical performance.


Abstract:
  We present StyleTune, a mobile app for interactive multi-level control of
neural style transfers that facilitates creative adjustments of style elements
and enables high output fidelity. In contrast to current mobile neural style
transfer apps, StyleTune supports users to adjust both the size and orientation
of style elements, such as brushstrokes and texture patches, on a global as
well as local level. To this end, we propose a novel stroke-adaptive
feed-forward style transfer network, that enables control over stroke size and
intensity and allows a larger range of edits than current approaches. For
additional level-of-control, we propose a network agnostic method for
stroke-orientation adjustment by utilizing the rotation-variance of CNNs. To
achieve high output fidelity, we further add a patch-based style transfer
method that enables users to obtain output resolutions of more than 20
Megapixel. Our approach empowers users to create many novel results that are
not possible with current mobile neural style transfer apps.


Abstract:
  We consider theoretically the influence of the flexoelectric coupling on the
spatial distribution and temperature behavior of spontaneous polarization for
several types of stable domain structure in thin ferroelectric films, such as
stripe domains and vortices.Finite element modelling (FEM) for BaTiO3 films and
analytical calculations within the Landau-Ginzburg-Devonshire approach reveals
that an out-of-plane polarization component can be very sensitive to the
flexoelectric coupling for periodic quasi-2D stripe domains and 3D
vortex-antivortex structures.However, the influence is rather different for
these structures. The flexoelectric coupling increases significantly the
amplitude of a small out-of-plane polarization component in the stripe domains,
and the "up" or "down" direction of the component is defined by the sign of the
flexoelectric coefficients.Concerning the vortex-antivortex pairs, their
antivortices with in-plane anti-circulation have smooth wide dipolar cores
through the entire film, whose shape and other features are almost insensitive
to the coupling.The vortices with in-plane vorticity have spike-like cores with
an out-of-plane quadrupolar moment induced by the flexoelectric coupling. The
cores are located near the film-dead layer interfaces.FEM results corroborated
by analytical calculations prove that a change of the flexoelectric coefficient
sign leads to a reorientation of the core axial polarization, making the
flexo-sensitive 3D vortices similar to the recently introduced "flexons" in
cylindrical nanoparticles.The relatively wide temperature range (from 200 to
400 K) of the flexo-sensitive vortices' existence gives us the hope that they
can be observed experimentally in thin ferroelectric films by scanning probe
and nonlinear optical microscopy methods.


Abstract:
  Definitions of new symbols merely abbreviate expressions in logical
frameworks, and no new facts (regarding previously defined symbols) should hold
because of a new definition. In Isabelle/HOL, definable symbols are types and
constants. The latter may be ad-hoc overloaded, i.e. have different definitions
for non-overlapping types. We prove that symbols that are independent of a new
definition may keep their interpretation in a model extension. This work
revises our earlier notion of model-theoretic conservative extension and
generalises an earlier model construction. We obtain consistency of theories of
definitions in higher-order logic (HOL) with ad-hoc overloading as a corollary.
Our results are mechanised in the HOL4 theorem prover.


Abstract:
  In this paper, we first generalize to any hyper-K\"ahler manifold with
nonzero third Betti number results proved by O'Grady for hyper-K\"ahler
manifolds of generalized Kummer type. In the second part, we restrict to
hyper-K\"ahler manifolds of generalized Kummer type and prove, using results of
Markman, that their Kuga-Satake correspondence is algebraic.


Abstract:
  While classical forms of stochastic gradient descent algorithm treat the
different coordinates in the same way, a framework allowing for adaptive (non
uniform) coordinate sampling is developed to leverage structure in data. In a
non-convex setting and including zeroth order gradient estimate, almost sure
convergence as well as non-asymptotic bounds are established. Within the
proposed framework, we develop an algorithm, MUSKETEER, based on a
reinforcement strategy: after collecting information on the noisy gradients, it
samples the most promising coordinate (all for one); then it moves along the
one direction yielding an important decrease of the objective (one for all).
Numerical experiments on both synthetic and real data examples confirm the
effectiveness of MUSKETEER in large scale problems.


Abstract:
  Evolutionary optimization algorithms, including particle swarm optimization
(PSO), have been successfully applied in oil industry for production planning
and control. Such optimization studies are quite challenging due to large
number of decision variables, production scenarios, and subsurface
uncertainties. In this work, a multi-stage, multi-swarm PSO (MS2PSO) is
proposed to fix certain issues with canonical PSO algorithm such as premature
convergence, excessive influence of global best solution, and oscillation.
  Multiple experiments are conducted using Olympus benchmark to compare the
efficacy of algorithms. Canonical PSO hyperparameters are first tuned to
prioritize exploration in early phase and exploitation in late phase. Next, a
two-stage multi-swarm PSO (2SPSO) is used where multiple-swarms of the first
stage collapse into a single swarm in the second stage. Finally, MS2PSO with
multiple stages and multiple swarms is used in which swarms recursively
collapse after each stage. Multiple swarm strategy ensures that diversity is
retained within the population and multiple modes are explored. Staging ensures
that local optima found during initial stage does not lead to premature
convergence. Optimization test case comprises of 90 control variables and a
twenty year period of flow simulation. It is observed that different algorithm
designs have their own benefits and drawbacks. Multiple swarms and stages help
algorithm to move away from local optima, but at the same time they may also
necessitate larger number of iterations for convergence. Both 2SPSO and MS2PSO
are found to be helpful for problems with high dimensions and multiple modes
where greater degree of exploration is desired.


Abstract:
  Linked Data Fragments (LDFs) refer to Web interfaces that allow for accessing
and querying Knowledge Graphs on the Web. These interfaces, such as SPARQL
endpoints or Triple Pattern Fragment servers, differ in the SPARQL expressions
they can evaluate and the metadata they provide. Client-side query processing
approaches have been proposed and are tailored to evaluate queries over
individual interfaces. Moreover, federated query processing has focused on
federations with a single type of LDF interface, typically SPARQL endpoints. In
this work, we address the challenges of SPARQL query processing over
federations with heterogeneous LDF interfaces. To this end, we formalize the
concept of federations of Linked Data Fragment and propose a framework for
federated querying over heterogeneous federations with different LDF
interfaces. The framework comprises query decomposition, query planning, and
physical operators adapted to the particularities of different LDF interfaces.
Further, we propose an approach for each component of our framework and
evaluate them in an experimental study on the well-known FedBench benchmark.
The results show a substantial improvement in performance achieved by devising
these interface-aware approaches exploiting the capabilities of heterogeneous
interfaces in federations.


Abstract:
  We demonstrate the use of Adaptive Stress Testing to detect and address
potential vulnerabilities in a financial environment. We develop a simplified
model for credit card fraud detection that utilizes a linear regression
classifier based on historical payment transaction data coupled with business
rules. We then apply the reinforcement learning model known as Adaptive Stress
Testing to train an agent, that can be thought of as a potential fraudster, to
find the most likely path to system failure -- successfully defrauding the
system. We show the connection between this most likely failure path and the
limits of the classifier and discuss how the fraud detection system's business
rules can be further augmented to mitigate these failure modes.


Abstract:
  This paper presents a physics-based constitutive equation to predict the
thermo-chemical aging in elastomers. High-temperature oxidation in elastomers
is a complex phenomenon. The macromolecular network of elastomers'
microstructures undergoes chain scission and crosslinking under high
temperature and oxygen diffusion conditions. In this work, we modify the
network stiffness and the chain extensibility in the Arruda-Boyce well-known
eight-chain constitutive equation to incorporate the additional Helmholtz free
energy due to network changes in elastomers' microstructures. The effect of
network evolution due to aging in changing the shear modulus and the number of
chain monomers is considered. The modification is based on chemical
characterization tests, namely the equilibrium swelling experiment to measure
the crosslink density evolution. The developed constitutive equation predicts
the mechanical responses of thermo-chemically aged elastomers independent of
any mechanical tests on aged samples. The proposed constitutive equation is
validated with respect to a comprehensive set of experimental data available in
the literature that were designed to capture thermo-chemical aging effects in
elastomers. The comparison showed that the constitutive equation can accurately
predict the intermittent tensile tests based on crosslink density evolution
input. The developed constitutive equation is physics-based, simple, and
includes minimal material parameters.


Abstract:
  We are interested in the Klein-Gordon-Zakharov equations in
$\mathbb{R}^{1+3}$, and we aim to show that the energy for the global solution
to the equations is uniformly bounded, and we do not require the compactness
assumption on the initial data. To achieve these goals, the key is to apply
Alinhac's ghost weight energy estimates adapted to the Klein-Gordon equations.


Abstract:
  In this paper we develop and significantly extend the thermal phase change
model, introduced in [12], describing the process of paraffinic wax layer
formation on the interior wall of a circular pipe transporting heated oil, when
subject to external cooling. In particular we allow for the natural dependence
of the solidifying paraffinic wax conductivity on local temperature. We are
able to develop a complete theory, and provide efficient numerical
computations, for this extended model. Comparison with recent experimental
observations is made, and this, together with recent reviews of the physical
mechanisms associated with wax layer formation, provide significant support for
the thermal model considered here.


Abstract:
  Biphoton frequency comb (BFC) having quantum entanglement in a high
dimensional system is widely applicable to quantum communication and quantum
computation. However, a dozen mode realized so far has not been enough to
realize its full potential. Here, we show a massive-mode BFC with polarization
entanglement experimentally realized by a nonlinear optical waveguide
resonator. The generated BFC at least 1400 modes is broad and dense, that
strongly enhances the advantage of BFC. We also demonstrated a versatile
property of the present BFC, which enables us to prepare both the
frequency-multiplexed entangled photon pair and the high dimensional
hyperentangled one. The versatile, stable and highly efficient system with the
massive-mode BFC will open up a large-scale photonic quantum information
platform.


Abstract:
  To unfold the tremendous amount of multimedia data uploaded daily to social
media platforms, effective topic modeling techniques are needed. Existing work
tends to apply topic models on written text datasets. In this paper, we propose
a topic extractor on video transcripts. Exploiting neural word embeddings
through graph-based clustering, we aim to improve usability and semantic
coherence. Unlike most topic models, this approach works without knowing the
true number of topics, which is important when no such assumption can or should
be made. Experimental results on the real-life multimodal dataset MuSe-CaR
demonstrates that our approach GraphTMT extracts coherent and meaningful topics
and outperforms baseline methods. Furthermore, we successfully demonstrate the
applicability of our approach on the popular Citysearch corpus.


Abstract:
  We present infrared spectral indices (1.0-2.3 um) of Galactic late-type
giants and red supergiants (RSGs). We used existing and new spectra obtained at
resolution power R=2000 with SpeX on the IRTF telescope. While a large CO
equivalent width (EW), at 2.29 um ([CO, 2.29]>45 AA) is a typical signature of
RSGs later than spectral type M0, [CO] of K-type RSGs and giants are similar.
In the [CO, 2.29] versus [Mg I, 1.71] diagram, RSGs of all spectral types can
be distinguished from red giants, because the Mg I line weakens with increasing
temperature and decreasing gravity. We find several lines that vary with
luminosity, but not temperature: Si I (1.59 um), Sr (1.033 um), Fe+Cr+Si+CN
(1.16 um), Fe+Ti (1.185 um), Fe+Ti (1.196 um), Ti+Ca (1.28 um), and Mn (1.29
um). Good markers of CN enhancement are the Fe+Si+CN line at 1.087 um and CN
line at 1.093 um. Using these lines, at the resolution of SpeX, it is possible
to separate RSGs and giants. Contaminant O-rich Mira and S-type AGBs are
recognized by strong molecular features due to water vapor features, TiO band
heads, and/or ZrO absorption. Among the 42 candidate RSGs that we observed, all
but one were found to be late-types. 21 have EWs consistent with those of RSGs,
16 with those of O-rich Mira AGBs, and one with an S-type AGB. These infrared
results open new, unexplored, potential for searches at low-resolution of RSGs
in the highly obscured innermost regions of the Milky Way.


Abstract:
  Games potentially provide a wealth of knowledge about our shared cultural
past and the development of human civilisation, but our understanding of early
games is incomplete and often based on unreliable reconstructions. This paper
describes the Digital Ludeme Project, a five-year research project currently
underway that aims to address such issues using modern computational
techniques.


Abstract:
  Images captured in snowy days suffer from noticeable degradation of scene
visibility, which degenerates the performance of current vision-based
intelligent systems. Removing snow from images thus is an important topic in
computer vision. In this paper, we propose a Deep Dense Multi-Scale Network
(\textbf{DDMSNet}) for snow removal by exploiting semantic and geometric
priors. As images captured in outdoor often share similar scenes and their
visibility varies with depth from camera, such semantic and geometric
information provides a strong prior for snowy image restoration. We incorporate
the semantic and geometric maps as input and learn the semantic-aware and
geometry-aware representation to remove snow. In particular, we first create a
coarse network to remove snow from the input images. Then, the coarsely
desnowed images are fed into another network to obtain the semantic and
geometric labels. Finally, we design a DDMSNet to learn semantic-aware and
geometry-aware representation via a self-attention mechanism to produce the
final clean images. Experiments evaluated on public synthetic and real-world
snowy images verify the superiority of the proposed method, offering better
results both quantitatively and qualitatively.


Abstract:
  The advanced performance of depth estimation is achieved by the employment of
large and complex neural networks. While the performance is still being
continuously improved, we argue that the depth estimation has to be efficient
as well since it is a preliminary requirement for real-world applications.
However, fast depth estimation tends to lower the performance as the trade-off
between the model's capacity and accuracy. In this paper, we aim to achieve
accurate depth estimation with a light-weight network. To this end, we first
introduce a highly compact network that can estimate a depth map in real-time.
We then develop a knowledge distillation paradigm to further improve the
performance. We observe that many scenarios have the same scene scales in
real-world, yielding similar depth histograms, thus they are potentially
valuable and applicable to develop a better learning strategy. Therefore, we
propose to employ auxiliary unlabeled/labeled data to improve knowledge
distillation. Through extensive and rigorous experiments, we show that our
method can achieve comparable performance against state-of-the-of-art methods
with only 1% parameters, and outperforms previous light-weight methods in terms
of inference accuracy, computational efficiency and generalizability.


Abstract:
  Insurers are increasingly adopting more demand-based strategies to
incorporate the indirect effect of premium changes on their policyholders'
willingness to stay. However, since in practice both insurers' renewal premia
and customers' responses to these premia typically depend on the customer's
level of risk, it remains challenging in these strategies to determine how to
properly control for this confounding. We therefore consider a causal inference
approach in this paper to account for customer price sensitivities and to
deduce optimal, multi-period profit maximizing premium renewal offers. More
specifically, we extend the discrete treatment framework of Guelman and
Guill\'en (2014) by Extreme Gradient Boosting, or XGBoost, and by multiple
imputation to better account for the uncertainty in the counterfactual
responses. We additionally introduce the continuous treatment framework with
XGBoost to the insurance literature to allow identification of the exact
optimal renewal offers and account for any competition in the market by
including competitor offers. The application of the two treatment frameworks to
a Dutch automobile insurance portfolio suggests that a policy's competitiveness
in the market is crucial for a customer's price sensitivity and that XGBoost is
more appropriate to describe this than the traditional logistic regression.
Moreover, an efficient frontier of both frameworks indicates that substantially
more profit can be gained on the portfolio than realized, also already with
less churn and in particular if we allow for continuous rate changes. A
multi-period renewal optimization confirms these findings and demonstrates that
the competitiveness enables temporal feedback of previous rate changes on
future demand.


Abstract:
  A high-quality factor microwave resonator in the presence of a strong
magnetic field could have a wide range of applications, such as axion dark
matter searches where the two aspects must coexist to enhance the experimental
sensitivity. We introduce a polygon-shaped cavity design with bi-axially
textured YBa$_{2}$Cu$_{3}$O$_{7-x}$ superconducting tapes covering the entire
inner wall. Using a 12-sided polygon cavity, we obtain substantially improved
quality factors of the TM$_{010}$ mode at 6.9 GHz at 4 K with respect to a
copper cavity and observe no considerable degradation in the presence of
magnetic fields up to 8 T. This corresponds to the first demonstration of
practical applications of superconducting radio frequency technology for axion
and other research areas requiring low loss in a strong magnetic field. We
address the importance of the successful demonstration and discuss further
improvements.


Abstract:
  We propose a floating tunable coupler that does not rely on direct
qubit-qubit coupling capacitances to achieve the zero-coupling condition. We
show that the polarity of the qubit-coupler couplings can be engineered to
offset the otherwise constant qubit-qubit coupling and attain the zero-coupling
condition when the coupler frequency is above or below the qubit frequencies.
We experimentally demonstrate these two operating regimes of the tunable
coupler by implementing symmetric and asymmetric configurations of the
coupler's superconducting pads with respect to the qubits. Such a floating
tunable coupler provides flexibility in designing large-scale quantum
processors while reducing the always-on residual couplings.


Abstract:
  Objective: To investigate the impact of different logistic regression
estimators applied to RDS samples obtained by simulation and real data.
Methods: Four simulated populations were created combining different
connectivity models, levels of clusterization and infection processes. Each
subject in the population received two attributes, only one of them related to
the infection process. From each population, RDS samples with different sizes
were obtained. Similarly, RDS samples were obtained from a real-world dataset.
Three logistic regression estimators were applied to assess the association
between the attributes and the infection status, and subsequently the observed
coverage of each was measured. Results: The type of connectivity had more
impact on estimators performance than the clusterization level. In simulated
datasets, unweighted logistic regression estimators emerged as the best option,
although all estimators showed a fairly good performance. In the real dataset,
the performance of weighted estimators presented some instabilities, making
them a risky option. Conclusion: An unweighted logistic regression estimator is
a reliable option to be applied to RDS samples, with similar performance to
random samples and, therefore, should be the preferred option.


Abstract:
  In situ thermal transport measurement of flowing fluid could be useful for
the characterization and diagnosis of practical thermal systems such as fluid
heat exchangers and thermal energy storage systems. Despite abundant reports on
the ex-situ thermal conductivity measurement of stagnant fluids, a suitable
technique for the thermal conductivity measurement of flowing fluid has been
rarely reported. This paper presents the thermal conductivity measurement of
flowing fluid within a pipe using a non-contact modulated photothermal
radiometry (MPR) technique, where the surface of the pipe is heated by an
intensity-modulated laser and the heat diffuses into the fluid with suitable
modulation frequency. We design a tube section with small wall thickness
suitable for the MPR measurements to maximize the sensitivity of the thermal
response to the fluid properties while minimizing the lateral heat spreading
effect. Intrinsic thermal conductivity of different fluids was obtained within
a proper range of frequency and flow velocity where the forced convection
effect is negligible. The forced convection effect became prominent at high
flowing velocity and at low modulation frequency, leading to overestimated
thermal conductivity of fluid. It is found that the intrinsic thermal
conductivity could be obtained when the flow velocity is less than 100 mm/sec
and ReD1/2Pr1/3 < 100 for DI water and Xceltherm oil under the specified
experimental conditions, where Re_D is the Reynolds number and Pr is the
Prandtl number.


Abstract:
  Let $K$ be a field. The \'etale open topology on the $K$-points $V(K)$ of a
$K$-variety $V$ was introduced in our previous work. The \'etale open topology
is non-discrete if and only if $K$ is large. If $K$ is separably, real,
$p$-adically closed then the \'etale open topology agrees with the Zariski,
order, valuation topology, respectively. We show that existentially definable
sets in perfect large fields behave well with respect to this topology: such
sets are finite unions of \'etale open subsets of Zariski closed sets. This
implies that existentially definable sets in arbitrary perfect large fields
enjoy some of the well-known topological properties of definable sets in
algebraically, real, and $p$-adically closed fields. We introduce and study the
class of \'ez fields: $K$ is \'ez if $K$ is large and every definable set is a
finite union of \'etale open subsets of Zariski closed sets. This should be
seen as a generalized notion of model completeness for large fields.
Algebraically closed, real closed, $p$-adically closed, and bounded
$\mathrm{PAC}$ fields are \'ez. (In particular pseudofinite fields and infinite
algebraic extensions of finite fields are \'ez.) We develop the basics of a
theory of definable sets in \'ez fields. This gives a uniform approach to the
theory of definable sets across all characteristic zero local fields and a new
topological theory of definable sets in bounded $\mathrm{PAC}$ fields. We also
show that some prominent examples of possibly non-model complete
model-theoretically tame fields (characteristic zero Henselian fields and
Frobenius fields) are \'ez.


Abstract:
  Microwave pulse sequences are the basis of coherent manipulation of the
electronic spin ground state in nitrogen-vacancy (NV) centers. In this work we
demonstrate stimulated Raman transitions (SRT) and stimulated Raman adiabatic
passage (STIRAP), two ways to drive the dipole-forbidden transition between two
spin sublevels in the electronic triplet ground state of the NV center. This is
achieved by a multitone Raman microwave pulse which simultaneously drives two
detuned transitions via a virtual level for SRT or via two adiabatic and
partially overlapping resonant microwave pulses for STIRAP. We lay the
theoretical framework of SRT and STIRAP dynamics and verify experimentally the
theoretical predictions of population inversion by observing the
dipole-forbidden transition in the ground state of a single NV center. A
comparison of the two schemes showed a better robustness and success of the
spin swap for STIRAP as compared to SRT.


Abstract:
  Does engagement with opposing views help break down ideological `echo
chambers'; or does it backfire and reinforce them? This question remains
critical as academics, policymakers and activists grapple with the question of
how to regulate political discussion on social media. In this study, we
contribute to the debate by examining the impact of opposing views within a
major climate change skeptic online community on Reddit. A large sample of
posts (N = 3000) was manually coded as either dissonant or consonant which
allowed the automated classification of the full dataset of more than 50,000
posts, with codes inferred from linked websites. We find that ideologically
dissonant submissions act as a stimulant to activity in the community: they
received more attention (comments) than consonant submissions, even though they
received lower scores through up-voting and down-voting. Users who engaged with
dissonant submissions were also more likely to return to the forum. Consistent
with identity theory, confrontation with opposing views triggered activity in
the forum, particularly among users that are highly engaged with the community.
In light of the findings, theory of social identity and echo chambers is
discussed and enhanced.


Abstract:
  At present, more than a billion closed-circuit television (CCTV) cameras are
watching the world. These cameras garner a lot of visual information that is
often processed and stored in remote and centralized cloud servers. Multiple
occasions have revealed that this traditional approach is plagued with security
and privacy breaches. The breaches could be the interception of raw videos
while in transit to distant surveillance analytics centers (SAC), infiltration
to cameras and network video records (NVR), or abuse of cameras and stored
videos. Hence, the traditional video surveillance system (VSS) cannot guarantee
the protection of the privacy of individuals caught on CCTV cameras. Wherefore,
this paper proposes a Secure and Privacy-preserving Stored surveillance video
sharing (SePriS) mechanism for authorized users/nodes based on smart contracts,
blockchain (BC), and the enciphering of video frames using DAB, a mechanism
developed based on discrete cosine transform (DCT), advanced encryption
standard (AES), and block shuffling (BS) algorithm. The BC-based solution
creates an environment auspicious for creating decentralized, reliable SACs and
storage sites with secure and privacy-aware sharing of stored surveillance
videos across SAC nodes and by law enforcers, police departments, and courts
securely connected to the SAC nodes. The experiments and analyses validate that
the proposed BC-based SePriS solution achieves the design purpose.


Abstract:
  The growth of internet users in Indonesia gives an impact on many aspects of
daily life, including commerce. Indonesian small-medium enterprises took this
advantage of new media to derive their activity by the meaning of online
commerce. Until now, there is no known practical implementation of how to
predict their sales and revenue using their historical transaction. In this
paper, we build a sales prediction model on the Indonesian footwear industry
using real-life data crawled on Tokopedia, one of the biggest e-commerce
providers in Indonesia. Data mining is a discipline that can be used to gather
information by processing the data. By using the method of classification in
data mining, this research will describe patterns of the market and predict the
potential of the region in the national market commodities. Our approach is
based on the classification decision tree. We managed to determine predicted
the number of items sold by the viewers, price, and type of shoes.


Abstract:
  We shall prove a weak law of large numbers for the uncorrelated (see
Definition 3.1) fuzzy random variable sequence with respect to the uniform
Hausdorff metric $d_H^{\infty}$, which is an extension of weak law of large
numbers for independent fuzzy random variables.


Abstract:
  We propose a robust universal approach to identify multiple dynamical states,
including stationary and travelling chimera states based on an adaptive
coherence measure. Our approach allows automatic disambiguation of synchronized
clusters, travelling waves, chimera states, and asynchronous regimes. In
addition, our method can determine the number of clusters in the case of
cluster synchronization. We further couple our approach with a new speed
calculation method for travelling chimeras. We validate our approach by an
example of a ring network of type II Morris-Lecar neurons with asymmetrical
nonlocal inhibitory connections where we identify a rich repertoire of coherent
and wave states. We propose that the method is robust for the networks of phase
oscillators and extends to a general class of relaxation oscillator networks.


Abstract:
  Predicting outcomes, such as survival or metastasis for individual cancer
patients is a crucial component of precision oncology. Machine learning (ML)
offers a promising way to exploit rich multi-modal data, including clinical
information and imaging to learn predictors of disease trajectory and help
inform clinical decision making. In this paper, we present a novel graph-based
approach to incorporate imaging characteristics of existing cancer spread to
local lymph nodes (LNs) as well as their connectivity patterns in a prognostic
ML model. We trained an edge-gated Graph Convolutional Network (Gated-GCN) to
accurately predict the risk of distant metastasis (DM) by propagating
information across the LN graph with the aid of soft edge attention mechanism.
In a cohort of 1570 head and neck cancer patients, the Gated-GCN achieves AUROC
of 0.757 for 2-year DM classification and $C$-index of 0.725 for lifetime DM
risk prediction, outperforming current prognostic factors as well as previous
approaches based on aggregated LN features. We also explored the importance of
graph structure and individual lymph nodes through ablation experiments and
interpretability studies, highlighting the importance of considering individual
LN characteristics as well as the relationships between regions of cancer
spread.


Abstract:
  Harnessing flexibility from distributed energy resources (DER) to participate
in various markets while accounting for relevant technical and commercial
constraints is essential for the development of low-carbon grids. However,
there is no clear definition or even description of the salient features of
aggregated DER flexibility, including its steady-state and dynamic features and
how these are impacted by network constraints and market requirements. This
paper proposes a comprehensive DER flexibility modelling and characterisation
framework that is based on the concept of nodal operating envelope (NOE). In
particular, capacity, ramp, duration and cost are identified as key flexibility
metrics and associated with different but consistent NOEs describing
capability, feasibility, ramp, duration, economic, technical and commercial
flexibility features. These NOEs, which conceptually arise from a Venn diagram,
can be built via optimal power flow (OPF) analysis, visualised in the
active-reactive power space, and used by different stakeholders. Results from a
canonical test system and a real distribution system illustrate the value and
applicability of the proposed framework to model and characterise provision of
flexibility and market services from DER for different use cases.


Abstract:
  We present a model for the configuration of noninteracting material that is
ejected in a continuous manner from an atmosphereless gravitating body for a
given distribution of sources. The model is applicable to material on bound or
unbound trajectories and to steady and nonsteady modes of ejection. For a jet
that is inclined to the surface normal, we related the distributions of
ejection direction, velocity, and size to the phase-space number density at the
distance from the source body. Integrating over velocity space, we obtained an
expression from which we inferred the density, flux, or optical depth of the
ejected material. As examples for the application of the code, we calculate
profiles of the dust density in the Enceladus plume, the pattern of mass
deposition rates around a plume on Europa, and images of optical depth
following the nonstationary emission of material from a volcano on Io. We make
the source code of a Fortran-95 implementation of the model freely available.


Abstract:
  We consider Takahashi's categorical interpretation of the Berglund-Hubsch
mirror symmetry conjecture for invertible polynomials in the case of chain
polynomials. Our strategy is based on a stronger claim that the relevant
categories satisfy a recursion of directed $A_{\infty}$-categories, which may
be of independent interest. We give a full proof of this claim on the B-side.
On the A-side we give a detailed sketch of an argument, which falls short of a
full proof because of certain missing foundational results in Fukaya-Seidel
categories, most notably a generation statement.


Abstract:
  The changes of magnetic properties upon heat treatment of a metastable
supersaturated solid solution processed by severe plastic deformation are
investigated by in-situ AC-hysteresis measurements. Data are analyzed in the
framework of dynamic loss theory, with correlative investigations of the
microstructural properties. The evolution of hysteresis upon annealing points
out that the single-phase supersaturated solid solution remains stable up to
400{\deg}C, then hindering of domain wall motion sets in at this temperature.
At 600{\deg}C, a multi phase microstructure is present, causing a significant
increase in coercivity.


Abstract:
  The dynamics of large systems of coupled oscillators is a subject of
increasing importance with prominent applications in several areas such as
physics and biology. The Kuramoto model, where a set of oscillators move around
a circle representing their phases, is a paradigm in this field, exhibiting a
continuous transition between disordered and synchronous motion. Reinterpreting
the oscillators as rotating unit vectors, the model was extended to allow
vectors to move on the surface of D-dimensional spheres, with $D=2$
corresponding to the original model. It was shown that the transition to
synchronous dynamics was discontinuous for odd D, raising a lot of interest.
Inspired by results in 2D, Ott et al proposed an ansatz for density function
describing the oscillators and derived equations for the ansatz parameters,
effectively reducing the dimensionality of the system. Here we take a different
approach for the 3D system and construct an ansatz based on spherical harmonics
decomposition of the distribution function. Our result differs significantly
from that proposed in Ott's work and leads to similar but simpler equations
determining the dynamics of the order parameter. We derive the phase diagram of
equilibrium solutions for several distributions of natural frequencies and find
excellent agreement with simulations. We also compare the dynamics of the order
parameter with numerical simulations and with the previously derived equations,
finding good agreement in all cases. We believe our approach can be generalized
to higher dimensions and help to achieve complexity reduction in other systems
of equations.


Abstract:
  We study PBW bases of the untwisted quantum loop group $U_q(L\mathfrak{g})$
(in the Drinfeld new presentation) using the combinatorics of loop words, by
generalizing the treatment of [26,27,40] in the finite type case. As an
application, we prove that Enriquez' homomorphism [10] from the positive half
of the quantum loop group to the trigonometric degeneration of
Feigin-Odesskii's elliptic algebra [13] associated to $\mathfrak{g}$ is an
isomorphism.


Abstract:
  A modular proof-theoretic framework was recently developed to prove Craig
interpolation for normal modal logics based on generalizations of sequent
calculi (e.g., nested sequents, hypersequents, and labelled sequents). In this
paper, we turn to uniform interpolation, which is stronger than Craig
interpolation. We develop a constructive method for proving uniform
interpolation via nested sequents and apply it to reprove the uniform
interpolation property for normal modal logics $\mathsf{K}$, $\mathsf{D}$, and
$\mathsf{T}$. We then use the know-how developed for nested sequents to apply
the same method to hypersequents and obtain the first direct proof of uniform
interpolation for $\mathsf{S5}$ via a cut-free sequent-like calculus. While our
method is proof-theoretic, the definition of uniform interpolation for nested
sequents and hypersequents also uses semantic notions, including bisimulation
modulo an atomic proposition.


Abstract:
  In this article, we particularly address the problem of assessing the impact
of clinical stage and age on the specific survival times of men with breast
cancer when cure is a possibility, where there is also the interest of
explaining this impact on different quantiles of the survival times. To this
end, we developed a quantile regression model for survival data in the presence
of long-term survivors based on the generalized distribution of Gompertz in a
defective version, which is conveniently reparametrized in terms of the q-th
quantile and then linked to covariates via a logarithm link function. This
proposal allows us to obtain how each variable affects the survival times in
different quantiles. In addition, we are able to study the effects of
covariates on the cure rate as well. We consider Markov Chain Monte Carlo
(MCMC) methods to develop a Bayesian analysis in the proposed model and we
evaluate its performance through a Monte Carlo simulation study. Finally, we
illustrate the advantages of our model in a data set about male breast cancer
from Brazil.


Abstract:
  Side-channel attacks extracting sensitive data from implementations have been
considered a major threat to the security of cryptographic schemes. This has
elevated the need for improved designs by embodying countermeasures, with
masking being the most prominent example. To formally verify the security of a
masking scheme, numerous attack models have been developed to capture the
physical properties of the information leakage as well as the capabilities of
the adversary. With regard to these models, extensive research has been
performed to realize masking schemes. These research efforts have led to
significant progress in the development of security assessment methodologies
and further initiated standardization activities. However, since the majority
of this work is theoretical, it is challenging for the more practice-oriented
hardware security community to fully grasp and contribute to. To bridge the
gap, these advancements are reviewed and discussed in this survey, mainly from
the perspective of hardware security. In doing so, a clear taxonomy is provided
that is helpful for a systematic treatment of the masking-related topics. By
giving an extensive overview of the existing methods, this survey (1) provides
a research landscape of circuit masking for newcomers to the field, (2) offers
guidelines on which attack model and verification tool to choose when designing
masking schemes, and (3) identifies interesting new research directions where
masking models and assessment tools can be applied. Thus, this survey serves as
an essential reference for hardware security practitioners interested in the
theory behind masking techniques, the tools useful to verify the security of
masked circuits, and their potential applications.


Abstract:
  It was found that, although isovalent, Rh substituted for Ir in Sr$_2$IrO$_4$
may trap one electron inducing effective hole doping of Ir sites. Transport and
thermoelectric measurements on Sr$_2$Ir$_{1-x}$Rh$_x$O$_4$ single crystals
presented here reveal the existence of an electron-like contribution to
transport, in addition to the hole-doped one. As no electron band shows up in
ARPES measurements, this points to the possibility that this hidden electron
may delocalize in disordered clusters.


Abstract:
  Anticipating the maximum magnitude of injection-triggered earthquakes is
highly valuable for the safe and efficient exploitation of geoenergies. The
recent work by Li et al.(2021) reached the conclusion that unexpectedly large
injection-triggered earthquakes are primarily caused by large pre-existing
critical shear stresses on seismogenic faults. Also of great interest is the
proposal of the ratio of fault slip to dilation as an index to anticipate the
fault rupture energetics. However, their fluid injection experiments were
conducted under fully drained conditions, where the fluid pressure distribution
on the fault plane is always uniform. As already pointed out by the authors,
local fluid pressure perturbations inside faults under locally undrained
conditions also have the potential to cause unexpectedly large seismic events.
Here we add more datasets and possible explanations for this viewpoint, which
has not been explored in such detail by Li et al.(2021).


Abstract:
  We describe a congruence property of solvable polynomials over Q, based on
the irreducibility of cyclotomic polynomials over number fields that meet
certain conditions.


Abstract:
  We consider $\beta$-smooth (satisfies the generalized Holder condition with
parameter $\beta > 2$) stochastic convex optimization problem with zero-order
one-point oracle. The best known result was arXiv:2006.07862: $\mathbb{E}
\left[f(\overline{x}_N) - f(x^*)\right] = \tilde{\mathcal{O}}
\left(\dfrac{n^{2}}{\gamma N^{\frac{\beta-1}{\beta}}} \right)$ in
$\gamma$-strongly convex case, where $n$ is the dimension. In this paper we
improve this bound: $\mathbb{E} \left[f(\overline{x}_N) - f(x^*)\right] =
\tilde{\mathcal{O}} \left(\dfrac{n^{2-\frac{1}{\beta}}}{\gamma
N^{\frac{\beta-1}{\beta}}} \right).$


Abstract:
  The beauty hadron decays is unique laboratory to study charmonium and
charmonium-like states, such as the $\chi_{c1}(3872)$ meson, other exotic
states and the tensor D-wave $\psi_2(3823$) states. However the nature of many
exotic charmonium-like candidates are still unknown. The most recent LHCb
results related to b-hadron decays to charmonium states and obtained using
large data samples collected during the Run 1 and Run 2 periods are presented.
This includes the most precise determination of the mass and width of the
$\chi_{c1}(3872)$ state using the $B^{+} \to J/\psi\pi^{+}\pi^{-}K^{+}$ decays,
observation of a resonant structure denoted as X(4740) in the $J/\psi \phi$
mass spectrum from $B^{0}_{s} \to J/\psi\pi^{+}\pi^{-}K^{+}K^{-}$ decays and
the precise measurement of the $B^{0}_{s}$ meson mass.


Abstract:
  This paper provides a zeroth-order optimisation framework for non-smooth and
possibly non-convex cost functions with matrix parameters that are real and
symmetric. We provide complexity bounds on the number of iterations required to
ensure a given accuracy level for both the convex and non-convex case. The
derived complexity bounds for the convex case are less conservative than
available bounds in the literature since we exploit the symmetric structure of
the underlying matrix space. Moreover, the non-convex complexity bounds are
novel for the class of optimisation problems we consider. The utility of the
framework is evident in the suite of applications that use symmetric matrices
as tuning parameters. Of primary interest here is the challenge of tuning the
gain matrices in model predictive controllers, as this is a challenge known to
be inhibiting industrial implementation of these architectures. To demonstrate
the framework we consider the problem of MIMO diesel air-path control, and
consider implementing the framework iteratively ``in-the-loop'' to reduce
tracking error on the output channels. Both simulations and experimental
results are included to illustrate the effectiveness of the proposed framework
over different engine drive cycles.


Abstract:
  We perform a general reduction of an M2-brane on a space-time that admits a
null Killing vector leading to fundamental string and D2-brane action in
Newton-Cartan background.


Abstract:
  We propose a new framework, Translation between Augmented Natural Languages
(TANL), to solve many structured prediction language tasks including joint
entity and relation extraction, nested named entity recognition, relation
classification, semantic role labeling, event extraction, coreference
resolution, and dialogue state tracking. Instead of tackling the problem by
training task-specific discriminative classifiers, we frame it as a translation
task between augmented natural languages, from which the task-relevant
information can be easily extracted. Our approach can match or outperform
task-specific models on all tasks, and in particular, achieves new
state-of-the-art results on joint entity and relation extraction (CoNLL04, ADE,
NYT, and ACE2005 datasets), relation classification (FewRel and TACRED), and
semantic role labeling (CoNLL-2005 and CoNLL-2012). We accomplish this while
using the same architecture and hyperparameters for all tasks and even when
training a single model to solve all tasks at the same time (multi-task
learning). Finally, we show that our framework can also significantly improve
the performance in a low-resource regime, thanks to better use of label
semantics.


Abstract:
  We consider the problem of optimal transportation with general cost between a
empirical measure and a general target probability on R d , with d $\ge$ 1. We
extend results in [19] and prove asymptotic stability of both optimal transport
maps and potentials for a large class of costs in R d. We derive a central
limit theorem (CLT) towards a Gaussian distribution for the empirical
transportation cost under minimal assumptions, with a new proof based on the
Efron-Stein inequality and on the sequential compactness of the closed unit
ball in L 2 (P) for the weak topology. We provide also CLTs for empirical
Wassertsein distances in the special case of potential costs | $\bullet$ | p ,
p > 1.


Abstract:
  Physics-based character animation has seen significant advances in recent
years with the adoption of Deep Reinforcement Learning (DRL). However,
DRL-based learning methods are usually computationally expensive and their
performance crucially depends on the choice of hyperparameters. Tuning
hyperparameters for these methods often requires repetitive training of control
policies, which is even more computationally prohibitive. In this work, we
propose a novel Curriculum-based Multi-Fidelity Bayesian Optimization framework
(CMFBO) for efficient hyperparameter optimization of DRL-based character
control systems. Using curriculum-based task difficulty as fidelity criterion,
our method improves searching efficiency by gradually pruning search space
through evaluation on easier motor skill tasks. We evaluate our method on two
physics-based character control tasks: character morphology optimization and
hyperparameter tuning of DeepMimic. Our algorithm significantly outperforms
state-of-the-art hyperparameter optimization methods applicable for
physics-based character animation. In particular, we show that hyperparameters
optimized through our algorithm result in at least 5x efficiency gain comparing
to author-released settings in DeepMimic.


Abstract:
  Atom probe tomography is a powerful tools in investigating nanostructures
such as interfaces and nanoparticles in material science. Advanced analysis
tools are particularly useful for analyzing these nanostructures characterized
very often by curved shapes. However, these tools are very limited for complex
materials with non-negligible peak overlaps in their respective mass-to-charge
ratio spectra. Usually, an analyst solves peak overlaps in the bulk regions but
the behavior at interfaces is rarely considered. Therefore, in this work we
demonstrate how the proximity histogram generated for a specific interface can
be corrected by using the natural abundances of isotopes. This leads to
overlap-solved proximity histograms with a resolution of up to 0.1 nm. This
work expands on previous work that showed the advantage of a maximum likelihood
peak overlap solving. The corrected proximity histograms together with the
maximum likelihood peak overlap algorithm were implemented in an user-friendly
software suite called EPOSA.


Abstract:
  Instance-level image retrieval is the task of searching in a large database
for images that match an object in a query image. To address this task, systems
usually rely on a retrieval step that uses global image descriptors, and a
subsequent step that performs domain-specific refinements or reranking by
leveraging operations such as geometric verification based on local features.
In this work, we propose Reranking Transformers (RRTs) as a general model to
incorporate both local and global features to rerank the matching images in a
supervised fashion and thus replace the relatively expensive process of
geometric verification. RRTs are lightweight and can be easily parallelized so
that reranking a set of top matching results can be performed in a single
forward-pass. We perform extensive experiments on the Revisited Oxford and
Paris datasets, and the Google Landmarks v2 dataset, showing that RRTs
outperform previous reranking approaches while using much fewer local
descriptors. Moreover, we demonstrate that, unlike existing approaches, RRTs
can be optimized jointly with the feature extractor, which can lead to feature
representations tailored to downstream tasks and further accuracy improvements.
The code and trained models are publicly available at
https://github.com/uvavision/RerankingTransformer.


Abstract:
  There is a growing trend regarding perceiving personal data as a commodity.
Existing studies have built frameworks and theories about how to determine an
arbitrage-free price of a given query according to the privacy loss quantified
by differential privacy. However, those studies have assumed that data buyers
can purchase query answers with the arbitrary privacy loss of data owners,
which may not be valid under strict privacy regulations and data owners'
increasing privacy concerns. In this paper, we study how to empower data owners
to control privacy loss in data trading. First, we propose a framework for
trading personal data that enables data owners to bound their personalized
privacy losses. Second, since bounded privacy losses indicate bounded utilities
of query answers, we propose a reasonable relaxation of arbitrage freeness
named partial arbitrage freeness, i.e., the guarantee of arbitrage-free pricing
only for a limited range of utilities, which provides more possibilities for
our market design. Third, to avoid arbitrage, we propose a general method for
ensuring arbitrage freeness under personalized differential privacy. Fourth, to
fully utilize data owners' personalized privacy loss bounds, we propose privacy
budget allocation techniques to allocate privacy losses for queries under
arbitrage freeness. Finally, we conduct experiments to verify the effectiveness
of our proposed trading protocols.


Abstract:
  This paper presents a new approach to statistical similarity assessment based
on sequence alignment. The algorithm performs mutual matching of two random
sequences by successively searching for common elements and by applying
sequence breaks to matchless elements in the function of exponential cost. As a
result, sequences varying significantly generate a high-cost alignment, while
for low-cost sequences the introduced interruptions allow inferring the nature
of sequences dependence. The most important advantage of the algorithm is an
easy interpretation of the obtained results based on two parameters: stretch
ratio and stretch cost. The operation of the method has been simulation tested
and verified with the use of real data obtained from hardware random number
generators. The proposed solution ensures simple implementation enabling the
integration of hardware solutions, and operation based on only two sequences of
any length predisposes the method to online testing.


Abstract:
  Segmenting unseen object instances in cluttered environments is an important
capability that robots need when functioning in unstructured environments.
While previous methods have exhibited promising results, they still tend to
provide incorrect results in highly cluttered scenes. We postulate that a
network architecture that encodes relations between objects at a high-level can
be beneficial. Thus, in this work, we propose a novel framework that refines
the output of such methods by utilizing a graph-based representation of
instance masks. We train deep networks capable of sampling smart perturbations
to the segmentations, and a graph neural network, which can encode relations
between objects, to evaluate the perturbed segmentations. Our proposed method
is orthogonal to previous works and achieves state-of-the-art performance when
combined with them. We demonstrate an application that uses uncertainty
estimates generated by our method to guide a manipulator, leading to efficient
understanding of cluttered scenes. Code, models, and video can be found at
https://github.com/chrisdxie/rice .


Abstract:
  Thermal emission is a universal phenomenon of stochastic electromagnetic
emission from an object composed of arbitrary materials at elevated
temperatures. A defining feature of this emission is the monotonic and rapid
growth of its intensity with the object's temperature for most known materials.
This growth originates from the Bose-Einstein statistics of the thermal
photonic field. The fact that the material's ability to absorb and emit light
may change with temperature, however, is often ignored. Here, we carry out a
theoretical study of thermal emission from structures incorporating ensembles
of two-level media. We investigate this effect in a range of geometries
including thin films and compact nanoparticles, and establish the general
dependencies in the evolution of thermal emission from systems including
two-level media. Thermal emission from such structures is essentially
Non-Planckian and exhibits a universal asymptotic behavior in the limit of high
temperatures. These results might have important implications for the design of
thermal energy harvesting and thermal vision systems.


Abstract:
  In this paper, we analyze multi-dimensional Weyl almost periodic type
functions in Lebesgue spaces with variable exponents. The introduced classes
seem to be new and not considered elsewhere even in the constant coefficient
case. We provide certain applications to the abstract Volterra
integro-differential equations in Banach spaces.


Abstract:
  Recently, various convolutions based on continuous or discrete kernels for
point cloud processing have been widely studied, and achieve impressive
performance in many applications, such as shape classification, scene
segmentation and so on. However, they still suffer from some drawbacks. For
continuous kernels, the inaccurate estimation of the kernel weights constitutes
a bottleneck for further improving the performance; while for discrete ones,
the kernels represented as the points located in the 3D space are lack of rich
geometry information. In this work, rather than defining a continuous or
discrete kernel, we directly embed convolutional kernels into the learnable
potential fields, giving rise to potential convolution. It is convenient for us
to define various potential functions for potential convolution which can
generalize well to a wide range of tasks. Specifically, we provide two simple
yet effective potential functions via point-wise convolution operations.
Comprehensive experiments demonstrate the effectiveness of our method, which
achieves superior performance on the popular 3D shape classification and scene
segmentation benchmarks compared with other state-of-the-art point convolution
methods.


Abstract:
  The study of inner and cyclic functions in $\ell^p_A$ spaces requires a
better understanding of the zeros of the so-called optimal polynomial
approximants.
  We determine that a point of the complex plane is the zero of an optimal
polynomial approximant for some element of $\ell^p_A$ if and only if it lies
outside of a closed disk (centered at the origin) of a particular radius which
depends on the value of $p$. We find the value of this radius for $p\neq 2$. In
addition, for each positive integer $d$ there is a polynomial $f_d$ of degree
at most $d$ that minimizes the modulus of the root of its optimal linear
polynomial approximant. We develop a method for finding these extremal
functions $f_d$ and discuss their properties. The method involves the Lagrange
multiplier method and a resulting dynamical system.


Abstract:
  We propose and investigate the intrinsically thinnest transistor concept: a
monolayer ballistic heterojunction bipolar transistor based on a lateral
heterostructure of transition metal dichalcogenides. The device is
intrinsically thinner than a Field Effect Transistor because it does not need a
top or bottom gate, since transport is controlled by the electrochemical
potential of the base electrode. As typical of bipolar transistors, the
collector current undergoes a tenfold increase for each 60 mV increase of the
base voltage over several orders of magnitude at room temperature, without
sophisticated optimization of the electrostatics. We present a detailed
investigation based on self-consistent simulations of electrostatics and
quantum transport for both electron and holes of a pnp device using MoS$_2$ for
the 10-nm base and WSe$_2$ for emitter and collector. Our three-terminal device
simulations confirm the working principle and a large current modulation
I$_\text{ON}$/I$_\text{OFF}\sim 10^8$ for $\Delta V_{\rm EB}=0.5$ V. Assuming
ballistic transport, we are able to achieve a current gain $\beta\sim$ 10$^4$
over several orders of magnitude of collector current and a cutoff frequency up
to the THz range. Exploration of the rich world of bipolar nanoscale device
concepts in 2D materials is promising for their potential applications in
electronics and optoelectronics.


Abstract:
  Aeolian sediment transport is observed to occur on Mars as well as other
extraterrestrial environments, generating ripples and dunes as on Earth. The
search for terrestrial analogues of planetary bedforms, as well as
environmental simulation experiments able to reproduce their formation in
planetary conditions, are powerful ways to question our understanding of
geomorphological processes towards unusual environmental conditions. Here, we
perform sediment transport laboratory experiments in a closed-circuit wind
tunnel placed in a vacuum chamber and operated at extremely low pressures to
show that Martian conditions belong to a previously unexplored saltation
regime. The threshold wind speed required to initiate saltation is only
quantitatively predicted by state-of-the art models up to a density ratio
between grain and air of $4 \times 10^5$, but unexpectedly falls to much lower
values for higher density ratios. In contrast, impact ripples, whose emergence
is continuously observed on the granular bed over the whole pressure range
investigated, display a characteristic wavelength and propagation velocity
essentially independent of pressure. A comparison of these findings with
existing models suggests that sediment transport at low Reynolds number but
high grain-to-fluid density ratio may be dominated by collective effects
associated with grain inertia in the granular collisional layer.


Abstract:
  In this paper, we study the linear stability properties of perturbations
around the homogeneous Couette flow for a 2D isentropic compressible fluid in
the domain $\mathbb{T}\times \mathbb{R}$. In the inviscid case there is a
generic Lyapunov type instability for the density and the irrotational
component of the velocity field. More precisely, we prove that their $L^2$ norm
grows as $t^{1/2}$ and this confirms previous observations in the physics
literature. Instead, the solenoidal component of the velocity field experience
inviscid damping, meaning that it decays to zero even in the absence of
viscosity. For a viscous compressible fluid, we show that the perturbations may
have a transient growth of order $\nu^{-1/6}$ (with $\nu^{-1}$ being
proportional to the Reynolds number) on a time-scale $\nu^{-1/3}$, after which
it decays exponentially fast. This phenomenon is also called enhanced
dissipation and our result appears to be the first to detect this mechanism for
a compressible fluid, where an exponential decay for the density is not a
priori trivial given the absence of dissipation in the continuity equation.


Abstract:
  We present the task of differential face morph attack detection using a
conditional generative network (cGAN). To determine whether a face image in an
identification document, such as a passport, is morphed or not, we propose an
algorithm that learns to implicitly disentangle identities from the morphed
image conditioned on the trusted reference image using the cGAN. Furthermore,
the proposed method can also recover some underlying information about the
second subject used in generating the morph. We performed experiments on AMSL
face morph, MorGAN, and EMorGAN datasets to demonstrate the effectiveness of
the proposed method. We also conducted cross-dataset and cross-attack detection
experiments. We obtained promising results of 3% BPCER @ 10% APCER on
intra-dataset evaluation, which is comparable to existing methods; and 4.6%
BPCER @ 10% APCER on cross-dataset evaluation, which outperforms
state-of-the-art methods by at least 13.9%.


Abstract:
  With the surge of online meetings, it has become more critical than ever to
provide high-quality speech audio and live captioning under various noise
conditions. However, most monaural speech enhancement (SE) models introduce
processing artifacts and thus degrade the performance of downstream tasks,
including automatic speech recognition (ASR). This paper proposes a multi-task
training framework to make the SE models unharmful to ASR. Because most ASR
training samples do not have corresponding clean signal references, we
alternately perform two model update steps called SE-step and ASR-step. The
SE-step uses clean and noisy signal pairs and a signal-based loss function. The
ASR-step applies a pre-trained ASR model to training signals enhanced with the
SE model. A cross-entropy loss between the ASR output and reference
transcriptions is calculated to update the SE model parameters. Experimental
results with realistic large-scale settings using ASR models trained on
75,000-hour data show that the proposed framework improves the word error rate
for the SE output by 11.82% with little compromise in the SE quality.
Performance analysis is also carried out by changing the ASR model, the data
used for the ASR-step, and the schedule of the two update steps.


Abstract:
  Road vehicles play an important role in the UK's energy systems and are a
critical component in reducing the reliance on fossil fuels and mitigating
emissions. A dynamic model of vehicle fleet, based on predator-prey concepts,
is presented. It allows to predict the evolution of the hydrogen based
vehicle's role in the UK's vehicle fleet and the sensitivity of this growth to
the supply chain. In addition to this, this model is used to predict the demand
of hydrogen for the passenger vehicle fleet for various scenarios. A key result
is that the amount of hydrogen required to support a huge network of hydrogen
based vehicles is currently not practical, regarding the infrastructure
required and the cost to build such an infrastructure. In order to mitigate
that, the policy focus should move primarily from hydrogen based vehicles to
first encompass range extenders in the transport energy system.


Abstract:
  Chest X-rays are the most commonly available and affordable radiological
examination for screening thoracic diseases. According to the domain knowledge
of screening chest X-rays, the pathological information usually lay on the lung
and heart regions. However, it is costly to acquire region-level annotation in
practice, and model training mainly relies on image-level class labels in a
weakly supervised manner, which is highly challenging for computer-aided chest
X-ray screening. To address this issue, some methods have been proposed
recently to identify local regions containing pathological information, which
is vital for thoracic disease classification. Inspired by this, we propose a
novel deep learning framework to explore discriminative information from lung
and heart regions. We design a feature extractor equipped with a multi-scale
attention module to learn global attention maps from global images. To exploit
disease-specific cues effectively, we locate lung and heart regions containing
pathological information by a well-trained pixel-wise segmentation model to
generate binarization masks. By introducing element-wise logical AND operator
on the learned global attention maps and the binarization masks, we obtain
local attention maps in which pixels are $1$ for lung and heart region and $0$
for other regions. By zeroing features of non-lung and heart regions in
attention maps, we can effectively exploit their disease-specific cues in lung
and heart regions. Compared to existing methods fusing global and local
features, we adopt feature weighting to avoid weakening visual cues unique to
lung and heart regions. Evaluated by the benchmark split on the publicly
available chest X-ray14 dataset, the comprehensive experiments show that our
method achieves superior performance compared to the state-of-the-art methods.


Abstract:
  Artificial Intelligence (AI) and the regulation thereof is a topic that is
increasingly being discussed within various fora. Various proposals have been
made in literature for defining regulatory bodies and/or related regulation. In
this paper, we present a pragmatic approach for providing a technology
assurance regulatory framework. To the best knowledge of the authors this work
presents the first national AI technology assurance legal and regulatory
framework that has been implemented by a national authority empowered through
law to do so. In aim of both providing assurances where required and not
stifling innovation yet supporting it, herein it is proposed that such
regulation should not be mandated for all AI-based systems and that rather it
should primarily provide a voluntary framework and only be mandated in sectors
and activities where required and as deemed necessary by other authorities for
regulated and critical areas.


Abstract:
  Power grids play a very important role in delivering electrical energy to
homes, industries and other places that require it. Because of this increased
demand they are facing a great challenge of voltage variations. This happens
due to varied use of energy-consuming devices and appliances like electric
vehicles, industrial consumption, occasional peak in energy demands etc. For
these fluctuations in demands, it becomes extremely important to monitor the
conditions at which the power grid operates. Once these conditions are known,
the energy production can be manipulated to meet the demand. It has been found
that the existing Power System State Estimation (PSSE) techniques may not be
good in producing optimal Performance. Moreover, they are also expensive in
terms of computational processing. To address this problem, this research
proposes a state estimation method for power grids using Convolutional Neural
Networks (CNN). It was found that the model produced an RMSE of 2.57 x 10-4,
which was comparatively accurate than one of previous studies involved in
making the estimation using a Prox Linear Model (2.97 x 10-4).
  Furthermore, the research also proposes Power System State Forecasting for
improving system awareness and resilience. The forecasting is carried out using
a model of Recurrent Neural Network (RNN). This model helps in accounting for
long-term nonlinear aspects present in data and based on that it does the
forecasting. The proposed model forecasted with a RMSE of 2.53 x 10-3, which is
comparatively equal to the previous study mentioned above (2.59 x 10-3).


Abstract:
  We use X-ray Active Galactic Nuclei (AGN) observed by the Chandra X-ray
Observatory within the 9.3 deg$^2$ Bo$\rm \ddot{o}$tes field of the NDWFS to
study whether there is a correlation between X-ray luminosity (L$_X$) and star
formation rate (SFR) of the host galaxy, at $\rm 0.5<z<2.0$, with respect to
the position of the galaxy to the main sequence (SFR$_{norm}$). About half of
the sources in the X-ray sample have spectroscopic redshifts. We also construct
a reference galaxy catalogue. For both datasets, we use photometric data from
optical to the far infrared, compiled by the HELP project and apply spectral
energy distribution (SED) fitting, using the X-CIGALE code. We exclude
quiescent sources from both the X-ray and the reference samples. We also
account for the mass completeness of our dataset, in different redshifts bins.
Our analysis highlights the importance of studying the SFR-L$_X$ relation, in a
uniform manner, taking into account the systematics and selection effects. Our
results suggest that, in less massive galaxies ($\rm log\,[M_*(M_\odot)] \sim
11$), AGN enhances the SFR of the host galaxy by $\sim 50\%$ compared to non
AGN systems. A flat relation is observed for the most massive galaxies.
SFR$_{norm}$ does not evolve with redshift. The results, although tentative,
are consistent with a scenario in which, in less massive systems, both AGN and
star formation (SF) are fed by cold gas, supplied by a merger event. In more
massive galaxies, the flat relation could be explained by a different SMBH
fuelling mechanism that is decoupled from the star formation of the host galaxy
(e.g. hot diffuse gas). Finally, we compare the host galaxy properties of X-ray
absorbed and unabsorbed sources. Our results show no difference which suggests
that X-ray absorption is not linked with the properties of the galaxy.


Abstract:
  The abundance of semiconductors in our smartphones, computers, fiber optic
junctions, cars, light sources, photovoltaic and thermoelectric cells results
from the possibilities of controlling their properties through doping,
lighting, and applying various fields. This paper, a part of the volume
celebrating 100 years of the Polish Physical Society, presents a biased
selection of worthwhile results obtained by researchers at the Institute of
Physics, Polish Academy of Sciences relevant, as seen today, to topological
matter and spintronics. Comprehensive studies, combining materials development,
experimental investigations, and theoretical description of narrow-gap and
dilute-magnetic semiconductors have been especially significant in this
context. This survey also emphasizes, in an autobiographical tone, a half of a
century of the author's intellectual emotions accompanying the rise of ideas
and quantitative theories, allowing identifying the physics behind ongoing and
future observations.


Abstract:
  The instability of financial system issues might trigger a bank failure,
evoke spillovers, and generate contagion effects which negatively impacted the
financial system, ultimately on the economy. This phenomenon is the result of
the highly interconnected banking transaction. The banking transactions network
is considered as a financial architecture backbone. The strong
interconnectedness between banks escalates contagion disruption spreading over
the banking network and trigger the entire system collapse. This far, the
financial instability is generally detected using macro approach mainly the
uncontrolled transaction deficits amount and unpaid foreign debt. This research
proposes financial instability detection in another point of view, through the
macro view where the banking network structure are explored globally and micro
view where focuses on the detailed network patterns called motif. Network
triadic motif patterns used as a denomination to detect financial instability.
The most related network triadic motif changes related to the instability
period are determined as a detector. We explore the banking network behavior
under financial instability phenomenon along with the major religious event in
Indonesia, Eid al-Fitr. We discover one motif pattern as the financial
instability underlying detector. This research helps to support the financial
system stability supervision.


Abstract:
  Multimodal VAEs seek to model the joint distribution over heterogeneous data
(e.g.\ vision, language), whilst also capturing a shared representation across
such modalities. Prior work has typically combined information from the
modalities by reconciling idiosyncratic representations directly in the
recognition model through explicit products, mixtures, or other such
factorisations. Here we introduce a novel alternative, the MEME, that avoids
such explicit combinations by repurposing semi-supervised VAEs to combine
information between modalities implicitly through mutual supervision. This
formulation naturally allows learning from partially-observed data where some
modalities can be entirely missing -- something that most existing approaches
either cannot handle, or do so to a limited extent. We demonstrate that MEME
outperforms baselines on standard metrics across both partial and complete
observation schemes on the MNIST-SVHN (image-image) and CUB (image-text)
datasets. We also contrast the quality of the representations learnt by mutual
supervision against standard approaches and observe interesting trends in its
ability to capture relatedness between data.


Abstract:
  Particle transport and localization phenomena in condensed-matter systems can
be modeled using a tight-binding lattice Hamiltonian. The ideal experimental
emulation of such a model utilizes simultaneous, high-fidelity control and
readout of each lattice site in a highly coherent quantum system. Here, we
experimentally study quantum transport in one-dimensional and two-dimensional
tight-binding lattices, emulated by a fully controllable $3 \times 3$ array of
superconducting qubits. We probe the propagation of entanglement throughout the
lattice and extract the degree of localization in the Anderson and
Wannier-Stark regimes in the presence of site-tunable disorder strengths and
gradients. Our results are in quantitative agreement with numerical simulations
and match theoretical predictions based on the tight-binding model. The
demonstrated level of experimental control and accuracy in extracting the
system observables of interest will enable the exploration of larger,
interacting lattices where numerical simulations become intractable.


Abstract:
  Typical user-friendly renderings of knowledge graphs are visualisations and
natural language text. Within the latter HCI solution approach, data-driven
natural language generation systems receive increased attention, but they are
often outperformed by template-based systems due to suffering from errors such
as content dropping, hallucination, or repetition. It is unknown which of those
errors are associated significantly with low quality judgements by humans who
the text is aimed for, which hampers addressing errors based on their impact on
improving human evaluations. We assessed their possible association with an
experiment availing of expert and crowdsourced evaluations of human authored
text, template generated text, and sequence-to-sequence model generated text.
The results showed that there was no significant association between human
authored texts with errors and the low human judgements of naturalness and
quality. There was also no significant association between machine learning
generated texts with dropped or hallucinated slots and the low human judgements
of naturalness and quality. Thus, both approaches appear to be viable options
for designing a natural language interface for knowledge graphs.


Abstract:
  The stochastic frontier model with heterogeneous technical efficiency
explained by exoge-nous variables is augmented with a spatial-temporal
component, a generalization relaxing the panel independence assumption in a
panel data. The estimation procedure takes advantage of additivity in the
model, computational advantages over maximum likelihood estimation of
parameters is exhibited. The spatial-temporal component can improve estimates
of technical efficiency in a production frontier that is usually biased
downwards. We present a test to veri-fy model assumptions that facilitates
estimation of parameters.