Abstract:
  A major difficulty in debugging distributed systems lies in manually
determining which of the many available debugging tools to use and how to query
its logs. Our own study of a production debugging workflow confirms the
magnitude of this burden. This paper explores whether a machine-learning model
can assist developers in distributed systems debugging. We present Revelio, a
debugging assistant which takes user reports and system logs as input, and
outputs debugging queries that developers can use to find a bug's root cause.
The key challenges lie in (1) combining inputs of different types (e.g.,
natural language reports and quantitative logs) and (2) generalizing to unseen
faults. Revelio addresses these by employing deep neural networks to uniformly
embed diverse input sources and potential queries into a high-dimensional
vector space. In addition, it exploits observations from production systems to
factorize query generation into two computationally and statistically simpler
learning tasks. To evaluate Revelio, we built a testbed with multiple
distributed applications and debugging tools. By injecting faults and training
on logs and reports from 800 Mechanical Turkers, we show that Revelio includes
the most helpful query in its predicted list of top-3 relevant queries 96% of
the time. Our developer study confirms the utility of Revelio.


Abstract:
  References are an essential part of Wikipedia. Each statement in Wikipedia
should be referenced. In this paper, we explore the creation and collection of
references for new Wikipedia articles from an editors' perspective. We map out
the workflow of editors when creating a new article, emphasising how they
select references.


Abstract:
  We continue the study of partition functions of 5d supersymmetric theories on
manifolds taking the form of a twisted product $\mathcal{M}_3\times
\Sigma_{\mathfrak{g}}$ with $\Sigma_{\mathfrak{g}}$ denoting a Riemann surface
of genus $\mathfrak{g}$. The 5d theory compactified on $\Sigma_{\mathfrak{g}}$
leads to a novel class of 3d theories in IR, whose existence at large $N$ is
expected from holography. Focussing on $\mathcal{M}_3$ being $S^2\times S^1$
without or with a topological twist on the 2-sphere leads to the superconformal
index or topologically twisted index, respectively, for such a class of 3d
theories. We discuss the large $N$ limit of these partition functions and find
new relations between them and other well-known 5d partition functions, with
interesting consequences for the 3d indices.


Abstract:
  We derive novel results on the ergodic theory of irreducible, aperiodic
Markov chains. We show how to optimally steer the network flow to a stationary
distribution over a finite or infinite time horizon. Optimality is with respect
to an entropic distance between distributions on feasible paths. When the prior
is reversible, it shown that solutions to this discrete time and space steering
problem are reversible as well. A notion of temperature is defined for
Boltzmann distributions on networks, and problems analogous to cooling (in this
case, for evolutions in discrete space and time) are discussed.


Abstract:
  Temperature-dependent reflectivity measurements on the kagome metal
CsV$_3$Sb$_5$ in a broad frequency range of $50-20000$ cm$^{-1}$ down to $T$=10
K are reported. The charge-density wave (CDW) formed below $T_{\rm CDW}$ = 94 K
manifests itself in a prominent spectral-weight transfer from low to higher
energy regions. The CDW gap of 60-75 meV is observed at the lowest temperature
and shows significant deviations from an isotropic BCS-type mean-field
behavior. Absorption peaks appear at frequencies as low as 200 cm$^{-1}$ and
can be identified with interband transitions according to density-functional
calculations. The change in the interband absorption compared to KV$_3$Sb$_5$
reflects the inversion of band saddle points between the K and Cs compounds.
Additionally, a broader and strongly temperature-dependent absorption feature
is observed below 1000 cm$^{-1}$ and assigned to a displaced Drude peak. It
reflects localization effects on charge carriers.


Abstract:
  False Data Injection (FDI) attacks against powersystem state estimation are a
growing concern for operators.Previously, most works on FDI attacks have been
performedunder the assumption of the attacker having full knowledge ofthe
underlying system without clear justification. In this paper, wedevelop a
topology-learning-aided FDI attack that allows stealthycyber-attacks against AC
power system state estimation withoutprior knowledge of system information. The
attack combinestopology learning technique, based only on branch and bus
powerflows, and attacker-side pseudo-residual assessment to performstealthy FDI
attacks with high confidence. This paper, for thefirst time, demonstrates how
quickly the attacker can developfull-knowledge of the grid topology and
parameters and validatesthe full knowledge assumptions in the previous work.


Abstract:
  A rapidly growing bubble close to a free surface induces jetting: a central
jet protruding outwards and a crown surrounding it at later stages. While the
formation mechanism of the central jet is known and documented, that of the
crown remains unsettled. We perform axisymmetric simulations of the problem
using the free software program basilisk, where a finite-volume compressible
solver has been implemented, that uses a geometric Volume-of-Fluid method (VoF)
for the tracking of the interface. We show that the mechanism of crown
formation is a combination of a pressure distortion over the curved interface,
inducing flow focusing, and of a flow reversal, caused by the second expansion
of the toroidal bubble that drives the crown. The work culminates in a
parametric study with the Weber number, the Reynolds number, the pressure ratio
and the dimensionless bubble distance to the free surface as control
parameters. Their effects on both the central jet and the crown are explored.
For high Weber numbers, we observe the formation of weaker "secondary crowns",
highly correlated with the third oscillation cycle of the bubble.


Abstract:
  We prove that finitely generated virtually free groups are stable in
permutations. As an application, we show that almost-periodic
almost-automorphisms of labelled graphs are close to periodic automorphisms.


Abstract:
  Labeling data for modern machine learning is expensive and time-consuming.
Latent variable models can be used to infer labels from weaker,
easier-to-acquire sources operating on unlabeled data. Such models can also be
trained using labeled data, presenting a key question: should a user invest in
few labeled or many unlabeled points? We answer this via a framework centered
on model misspecification in method-of-moments latent variable estimation. Our
core result is a bias-variance decomposition of the generalization error, which
shows that the unlabeled-only approach incurs additional bias under
misspecification. We then introduce a correction that provably removes this
bias in certain cases. We apply our decomposition framework to three scenarios
-- well-specified, misspecified, and corrected models -- to 1) choose between
labeled and unlabeled data and 2) learn from their combination. We observe
theoretically and with synthetic experiments that for well-specified models,
labeled points are worth a constant factor more than unlabeled points. With
misspecification, however, their relative value is higher due to the additional
bias but can be reduced with correction. We also apply our approach to study
real-world weak supervision techniques for dataset construction.


Abstract:
  Having timely and fresh knowledge about the current state of information
sources is critical in a variety of applications. In particular, a status
update may arrive at the destination later than its generation time due to
processing and communication delays. The freshness of the status update at the
destination is captured by the notion of age of information. In this study, we
analyze a multiple sensing network with multiple sources, multiple servers, and
a monitor (destination). Each source corresponds to an independent piece of
information and its age is measured individually. Given a particular source,
the servers independently sense the source of information and send the status
update to the monitor. We assume that updates arrive at the servers according
to Poisson random processes. Each server sends its updates to the monitor
through a direct link, which is modeled as a queue. The service time to
transmit an update is considered to be an exponential random variable. We
examine both homogeneous and heterogeneous service and arrival rates for the
single-source case, and only homogeneous arrival and service rates for the
multiple-source case. We derive a closed-form expression for the average age of
information under a last-come-first-serve (LCFS) queue for a single source and
arbitrary number of homogeneous servers. Using a recursive method, we derive
the explicit average age of information for any number of sources and
homogeneous servers. We also investigate heterogeneous servers and a single
source, and present algorithms for finding the average age of information.


Abstract:
  We propose a new model to determine the ages and masses of red giant branch
(RGB) stars from the low-resolution large sky area multi-object fiber
spectroscopic telescope (LAMOST) spectra. The ages of RGB stars are difficult
to determine using classical isochrone fitting techniques in the
Hertzsprung-Russell diagram, because isochrones of RGB stars are tightly
crowned. With the help of the asteroseismic method, we can determine the masses
and ages of RGB stars accurately. Using the ages derived from the asteroseismic
method, we train a deep learning model based on DenseNet to calculate the ages
of RGB stars directly from their spectra. We then apply this model to determine
the ages of 512 272 RGB stars from LAMOST DR7 spectra (see
http://dr7.lamost.org/). The results show that our model can estimate the ages
of RGB stars from low-resolution spectra with an accuracy of 24.3%. The results
on the open clusters M 67, Berkeley 32, and NGC 2420 show that our model
performs well in estimating the ages of RGB stars. Through comparison, we find
that our method performs better than other methods in determining the ages of
RGB stars. The proposed method can be used in the stellar parameter pipeline of
upcoming large surveys such as 4MOST, WEAVES, and MOONS.


Abstract:
  We present SICK-NL (read: signal), a dataset targeting Natural Language
Inference in Dutch. SICK-NL is obtained by translating the SICK dataset of
Marelli et al. (2014)from English into Dutch. Having a parallel inference
dataset allows us to compare both monolingual and multilingual NLP models for
English and Dutch on the two tasks. In the paper, we motivate and detail the
translation process, perform a baseline evaluation on both the original SICK
dataset and its Dutch incarnation SICK-NL, taking inspiration from Dutch
skipgram embeddings and contextualised embedding models. In addition, we
encapsulate two phenomena encountered in the translation to formulate stress
tests and verify how well the Dutch models capture syntactic restructurings
that do not affect semantics. Our main finding is all models perform worse on
SICK-NL than on SICK, indicating that the Dutch dataset is more challenging
than the English original. Results on the stress tests show that models don't
fully capture word order freedom in Dutch, warranting future systematic
studies.


Abstract:
  In preparation for the Dark Energy Spectroscopic Instrument (DESI), a new top
end was installed on the Mayall 4-meter telescope at Kitt Peak National
Observatory. The refurbished telescope and the DESI instrument were
successfully commissioned on sky between 2019 October and 2020 March. Here we
describe the pointing, tracking and imaging performance of the Mayall telescope
equipped with its new DESI prime focus corrector, as measured by six guider
cameras sampling the outer edge of DESI's focal plane. Analyzing ~500,000
guider images acquired during commissioning, we find a median delivered image
FWHM of 1.1 arcseconds (in the r-band at 650 nm), with the distribution
extending to a best-case value of ~0.6 arcseconds. The point spread function is
well characterized by a Moffat profile with a power-law index of $\beta$ ~ 3.5
and little dependence of $\beta$ on FWHM. The shape and size of the PSF
delivered by the new corrector at a field angle of 1.57 degrees are very
similar to those measured with the old Mayall corrector on axis. We also find
that the Mayall achieves excellent pointing accuracy (several arcseconds RMS)
and minimal open-loop tracking drift (< 1 milliarcsecond per second),
improvements on the telecope's pre-DESI performance. In the future, employing
DESI's active focus adjustment capabilities will likely further improve the
Mayall/DESI delivered image quality.


Abstract:
  In the field of topology optimization, the homogenization approach has been
revived as an important alternative to the established, density-based methods
because it can represent the microstructural design at a much finer
length-scale than the computational grid. The optimal microstructure for a
single load case is an orthogonal rank-3 laminate. A rank-3 laminate can be
described in terms of frame fields, which are also an important tool for mesh
generation in both 2D and 3D.
  We propose a method for generating multi-laminar structures from frame
fields. Rather than relying on integrative approaches that find a
parametrization based on the frame field, we find stream surfaces, represented
as point clouds aligned with frame vectors, and we solve an optimization
problem to find well-spaced collections of such stream surfaces. The stream
surface tracing is unaffected by the presence of singularities outside the
region of interest. Neither stream surface tracing nor selecting well-spaced
surface rely on combed frame fields.
  In addition to stream surface tracing and selection, we provide two methods
for generating structures from stream surface collections. One of these methods
produces volumetric solids by summing basis functions associated with each
point of the stream surface collection. The other method reinterprets point
sampled stream surfaces as a spatial twist continuum and produces a
hexahedralization by dualizing a graph representing the structure.
  We demonstrate our methods on several frame fields produced using the
homogenization approach for topology optimization, boundary-aligned, algebraic
frame fields, and frame fields computed from closed-form expressions.


Abstract:
  We present a logic named L_{LF} whose intended use is to formalize properties
of specifications developed in the dependently typed lambda calculus LF. The
logic is parameterized by the LF signature that constitutes the specification.
Atomic formulas correspond to typing derivations relative to this signature.
The logic includes a collection of propositional connectives and quantifiers.
Quantification ranges over expressions that denote LF terms and LF contexts.
Quantifiers of the first variety are qualified by simple types that describe
the functional structure associated with the variables they bind; deeper,
dependency related properties are expressed by the body of the formula.
Context-level quantifiers are qualified by context schemas that identify
patterns of declarations out of which actual contexts may be constructed. The
semantics of variable-free atomic formulas is articulated via the derivability
in LF of the judgements they encode. Propositional constants and connectives
are understood in the usual manner and the meaning of quantifiers is explicated
through substitutions of expressions that adhere to the type qualifications.
The logic is complemented by a proof system that enables reasoning that is
sound with respect to the described semantics. The main novelties of the proof
system are the provision for case-analysis style reasoning about LF judgements,
support for inductive reasoning over the heights of LF derivations and the
encoding of LF meta-theorems. The logic is motivated by the paradigmatic
example of type assignment in the simply-typed lambda calculus and the proof
system is illustrated through the formalization of a proof of type uniqueness
for this calculus.


Abstract:
  In the pilot-wave theory of quantum mechanics particles have definite
positions and velocities and the system evolves deterministically. The velocity
of a particle is determined by the wave function of the system (the guidance
equation) and the wave function evolves according to Schrodinger's equation. In
this paper I first construct a Hamiltonian that gives Schrodinger's equation
and the guidance equation for the particle. I then find the Hamiltonian for a
relativistic particle in Dirac's theory and for a quantum scalar field.


Abstract:
  Music is an essential component in our everyday lives and experiences, as it
is a way that we use to express our feelings, emotions and cultures. In this
study, we explore the association between music genre preferences, demographics
and moral values by exploring self-reported data from an online survey
administered in Canada. Participants filled in the moral foundations
questionnaire, while they also provided their basic demographic information,
and music preferences. Here, we predict the moral values of the participants
inferring on their musical preferences employing classification and regression
techniques. We also explored the predictive power of features estimated from
factor analysis on the music genres, as well as the generalist/specialist (GS)
score for revealing the diversity of musical choices for each user. Our results
show the importance of music in predicting a person's moral values (.55-.69
AUROC); while knowledge of basic demographic features such as age and gender is
enough to increase the performance (.58-.71 AUROC).


Abstract:
  Where information grows abundant, attention becomes a scarce resource. As a
result, agents must plan wisely how to allocate their attention in order to
achieve epistemic efficiency. Here, we present a framework for multi-agent
epistemic planning with attention, based on Dynamic Epistemic Logic (DEL, a
powerful formalism for epistemic planning). We identify the framework as a
fragment of standard DEL, and consider its plan existence problem. While in the
general case undecidable, we show that when attention is required for learning,
all instances of the problem are decidable.


Abstract:
  A conceptual model can be used to manage complexity in both the design and
implementation phases of the system development life cycle. Such a model
requires a firm grasp of the abstract principles on which a system is based, as
well as an understanding of the high-level nature of the representation of
entities and processes. In this context, models can have distinct architectural
characteristics. This paper discusses model multiplicity (e.g., unified
modeling language [UML]), model singularity (e.g., object-process methodology
[OPM], thinging machine [TM]), and a heterogeneous model that involves
multiplicity and singularity. The basic idea of model multiplicity is that it
is not possible to present all views in a single representation, so a number of
models are used, with each model representing a different view. The model
singularity approach uses only a single unified model that assimilates its
subsystems into one system. This paper is concerned with current approaches,
especially in software engineering texts, where multimodal UML is introduced as
the general-purpose modeling language (i.e., UML is modeling). In such a
situation, we suggest raising the issue of multiplicity versus singularity in
modeling. This would foster a basic appreciation of the UML advantages and
difficulties that may be faced during modeling, especially in the educational
setting. Furthermore, we advocate the claim that a multiplicity of views does
not necessitate a multiplicity of models. The model singularity approach can
represent multiple views (static, behavior) without resorting to a collection
of multiple models with various notations. We present an example of such a
model where the static representation is developed first. Then, the dynamic
view and behavioral representations are built by incorporating a decomposition
strategy interleaved with the notion of time.


Abstract:
  The demand for on-device document recognition systems increases in
conjunction with the emergence of more strict privacy and security
requirements. In such systems, there is no data transfer from the end device to
a third-party information processing servers. The response time is vital to the
user experience of on-device document recognition. Combined with the
unavailability of discrete GPUs, powerful CPUs, or a large RAM capacity on
consumer-grade end devices such as smartphones, the time limitations put
significant constraints on the computational complexity of the applied
algorithms for on-device execution.
  In this work, we consider document location in an image without prior
knowledge of the document content or its internal structure. In accordance with
the published works, at least 5 systems offer solutions for on-device document
location. All these systems use a location method which can be considered
Hough-based. The precision of such systems seems to be lower than that of the
state-of-the-art solutions which were not designed to account for the limited
computational resources.
  We propose an advanced Hough-based method. In contrast with other approaches,
it accounts for the geometric invariants of the central projection model and
combines both edge and color features for document boundary detection. The
proposed method allowed for the second best result for SmartDoc dataset in
terms of precision, surpassed by U-net like neural network. When evaluated on a
more challenging MIDV-500 dataset, the proposed algorithm guaranteed the best
precision compared to published methods. Our method retained the applicability
to on-device computations.


Abstract:
  Financial regulatory agencies are struggling to manage the systemic risks
attributed to negative economic shocks. Preventive interventions are prominent
to eliminate the risks and help to build a more resilient financial system.
Although tremendous efforts have been made to measure multi-risk severity
levels, understand the contagion behaviors and other risk management problems,
there still lacks a theoretical framework revealing what and how regulatory
intervention measurements can mitigate systemic risk. Here we demonstrate
regshock, a practical visual analytical approach to support the exploration and
evaluation of financial regulation measurements. We propose risk-island, an
unprecedented risk-centered visualization algorithm to help uncover the risk
patterns while preserving the topology of financial networks. We further
propose regshock, a novel visual exploration and assessment approach based on
the simulation-intervention-evaluation analysis loop, to provide a heuristic
surgical intervention capability for systemic risk mitigation. We evaluate our
approach through extensive case studies and expert reviews. To our knowledge,
this is the first practical systemic method for the financial network
intervention and risk mitigation problem; our validated approach potentially
improves the risk management and control capabilities of financial experts.


Abstract:
  Observations of the Galactic Center supermassive black hole Sagittarius A*
(Sgr A*) with very long baseline interferometry (VLBI) are affected by
interstellar scattering along our line of sight. At long radio observing
wavelengths ($\gtrsim1\,$cm), the scattering heavily dominates image
morphology. At 3.5 mm (86 GHz), the intrinsic source structure is no longer
sub-dominant to scattering, and thus the intrinsic emission from Sgr A* is
resolvable with the Global Millimeter VLBI Array (GMVA). Long-baseline
detections to the phased Atacama Large Millimeter/submillimeter Array (ALMA) in
2017 provided new constraints on the intrinsic and scattering properties of Sgr
A*, but the stochastic nature of the scattering requires multiple observing
epochs to reliably estimate its statistical properties. We present new
observations with the GMVA+ALMA, taken in 2018, which confirm non-Gaussian
structure in the scattered image seen in 2017. In particular, the ALMA-GBT
baseline shows more flux density than expected for an anistropic Gaussian
model, providing a tight constraint on the source size and an upper limit on
the dissipation scale of interstellar turbulence. We find an intrinsic source
extent along the minor axis of $\sim100\,\mu$as both via extrapolation of
longer wavelength scattering constraints and direct modeling of the 3.5 mm
observations. Simultaneously fitting for the scattering parameters, we find an
at-most modestly asymmetrical (major-to-minor axis ratio of $1.5\pm 0.2$)
intrinsic source morphology for Sgr A*.


Abstract:
  The field of neuromorphic computing promises extremely low-power and
low-latency sensing and processing. Challenges in transferring learning
algorithms from traditional artificial neural networks (ANNs) to spiking neural
networks (SNNs) have so far prevented their application to large-scale, complex
regression tasks. Furthermore, realizing a truly asynchronous and fully
neuromorphic pipeline that maximally attains the abovementioned benefits
involves rethinking the way in which this pipeline takes in and accumulates
information. In the case of perception, spikes would be passed as-is and
one-by-one between an event camera and an SNN, meaning all temporal integration
of information must happen inside the network. In this article, we tackle these
two problems. We focus on the complex task of learning to estimate optical flow
from event-based camera inputs in a self-supervised manner, and modify the
state-of-the-art ANN training pipeline to encode minimal temporal information
in its inputs. Moreover, we reformulate the self-supervised loss function for
event-based optical flow to improve its convexity. We perform experiments with
various types of recurrent ANNs and SNNs using the proposed pipeline.
Concerning SNNs, we investigate the effects of elements such as parameter
initialization and optimization, surrogate gradient shape, and adaptive
neuronal mechanisms. We find that initialization and surrogate gradient width
play a crucial part in enabling learning with sparse inputs, while the
inclusion of adaptivity and learnable neuronal parameters can improve
performance. We show that the performance of the proposed ANNs and SNNs are on
par with that of the current state-of-the-art ANNs trained in a self-supervised
manner.


Abstract:
  We analyze experimentally and theoretically the transport spectra of a gated
lateral GaAs double quantum dot containing two holes. The strong spin-orbit
interaction present in the hole subband lifts the Pauli spin blockade and
allows to map out the complete spectra of the two-hole system. By performing
measurements in both source-drain voltage directions, at different detunings
and magnetic fields, we carry out quantitative fitting to a Hubbard two-site
model accounting for the tunnel coupling to the leads and the spin-flip
relaxation process. We extract the singlet-triplet gap and the magnetic field
corresponding to the singlet-triplet transition in the double-hole ground
state. Additionally, at the singlet-triplet transition we find a resonant
enhancement (in the blockaded direction) and suppression of current (in the
conduction direction). The current enhancement stems from the multiple
resonance of two-hole levels, opening several conduction channels at once. The
current suppression arises from the quantum interference of spin-conserving and
spin flipping tunneling processes.


Abstract:
  In this contribution, the estimates for the response of time delay systems
with nonlinear homogeneous right-hand side of degree strictly greater than one
are constructed. The existing results obtained via the Lyapunov--Razumikhin
approach are reminded. Their proofs, revisited in the appendix, lead to
explicit expressions of the involved constants. Based on a recently introduced
Lyapunov--Krasovskii functional and known estimates of the domain of
attraction, we present new estimates of the system response. We compare both
approaches and discuss the illustrative examples.


Abstract:
  Let G be a combinatorial graph with vertices V and edges E. A proper coloring
of G is an assignment of colors to the vertices such that no edge connects two
vertices of the same color. These are the colorings considered in the famous
Four Color Theorem. It turns out that the number of proper colorings of G using
t colors is a polynomial in t, called the chromatic polynomial of G. This
polynomial has many wonderful properties. It also has the surprising habit of
appearing in contexts which, a priori, have nothing to do with graph coloring.
We will survey three such instances involving acyclic orientations, hyperplane
arrangements, and increasing forests. In addition, connections to symmetric
functions and algebraic geometry will be mentioned.


Abstract:
  Annotated medical images are typically rarer than labeled natural images
since they are limited by domain knowledge and privacy constraints. Recent
advances in transfer and contrastive learning have provided effective solutions
to tackle such issues from different perspectives. The state-of-the-art
transfer learning (e.g., Big Transfer (BiT)) and contrastive learning (e.g.,
Simple Siamese Contrastive Learning (SimSiam)) approaches have been
investigated independently, without considering the complementary nature of
such techniques. It would be appealing to accelerate contrastive learning with
transfer learning, given that slow convergence speed is a critical limitation
of modern contrastive learning approaches. In this paper, we investigate the
feasibility of aligning BiT with SimSiam. From empirical analyses, different
normalization techniques (Group Norm in BiT vs. Batch Norm in SimSiam) are the
key hurdle of adapting BiT to SimSiam. When combining BiT with SimSiam, we
evaluated the performance of using BiT, SimSiam, and BiT+SimSiam on CIFAR-10
and HAM10000 datasets. The results suggest that the BiT models accelerate the
convergence speed of SimSiam. When used together, the model gives superior
performance over both of its counterparts. We hope this study will motivate
researchers to revisit the task of aggregating big pre-trained models with
contrastive learning models for image analysis.


Abstract:
  Inertial cavitation in soft matter is an important phenomenon featured in a
wide array of biological and engineering processes. Recent advances in
experimental, theoretical, and numerical techniques have provided access into a
world full of nonlinear physics, yet most of our quantitative understanding to
date has been centered on a spherically symmetric description of the cavitation
process. However, cavitation bubble growth and collapse rarely occur in a
perfectly symmetrical fashion, particularly in soft materials. Predicting the
onset of dynamically arising, non-spherical instabilities has remained a
significant, unresolved challenge in part due to the additional constitutive
complexities introduced by the surrounding nonlinear viscoelastic solid. Here,
we provide a new theoretical model capable of accurately predicting the onset
of non-spherical instability shapes of a bubble in a soft material by
explicitly accounting for all pertinent nonlinear interactions between the
fluid-like cavitation bubble and the solid-like surroundings. Comparison
against high-resolution experimental images from laser-induced cavitation
events in a polyacrylamide (PA) hydrogel show excellent agreement.
Interestingly, and consistent with experimental findings, our model predicts
the emergence of various dynamic instability shapes for hoop stretch ratios
greater than one in contrast to most quasi-static investigations. Our new
theoretical framework not only provides unprecedented insight into the
cavitation dynamics in a soft solid, but it also provides a quantitative means
of interpreting bubble dynamics relevant to a wide array of engineering and
medical applications as well as natural phenomena.


Abstract:
  Growing individualization of products up to lot-size-1 and high volatility of
product mixes lead to new challenges in the manufacturing domain, including the
need for frequent reconfiguration of the system and reacting to changing
orders. Thus, apart from functional aspects, safety aspects of the production
system as well as product quality assurance aspects must be addressed for
flexible and reconfigurable manufacturing systems at runtime. To cope with the
mentioned challenges, we present an integrated model-based approach SQUADfps
(machine Safety and product QUAlity for flexible proDuction systems) to support
the automatic conduct of the risk assessment of flexible production scenarios
in terms of safety as well as the process-FMEA to ensure that the requirements
w.r.t. the quality of the production process and the resulting product are met.
Our approach is based on a meta-model which captures all information needed to
conduct both risk assessment and process-FMEA dynamically during the runtime,
and thus enables flexible manufacturing scenarios with frequent changes of the
production system and orders up to a lot-size of one while guaranteeing safety
and product quality requirements. The automatically generated results will
assist human in making further decisions. To demonstrate the feasibility of our
approach, we apply it to a case study.


Abstract:
  We present an experimental and theoretical study of core-level ionization of
small hetero- and homo-nuclear molecules employing circularly polarized light
and address molecular-frame photoelectron angular distributions in the light's
polarization plane (CP-MFPADs). We find that the main forward-scattering peaks
of CP-MFPADs are slightly tilted with respect to the molecular axis. We show
that this tilt angle can be directly connected to the molecular bond length by
a simple, universal formula. The extraction of the bond length becomes more
accurate as the photoelectron energy is increased. We apply the derived formula
to several examples of CP-MFPADs of C 1s and O 1s photoelectrons of CO, which
have been measured experimentally or obtained by means of ab initio modeling.
The photoelectron kinetic energies range from 70 to 1000~eV and the extracted
bond lengths agree well with the known bond length of the CO molecule in its
ground state. In addition, we discuss the influence of the back-scattering
contribution that is superimposed over the analyzed forward-scattering peak in
case of homo-nuclear diatomic molecules as N$_2$.


Abstract:
  Secure wireless access in ultra-reliable low-latency communications (URLLC),
which is a critical aspect of 5G security, has become increasingly important
due to its potential support of grant-free configuration. In grant-free URLLC,
precise allocation of different pilot resources to different users that share
the same time-frequency resource is essential for the next generation NodeB
(gNB) to exactly identify those users under access collision and to maintain
precise channel estimation required for reliable data transmission. However,
this process easily suffers from attacks on pilots. We in this paper propose a
quantum learning based nonrandom superimposed coding method to encode and
decode pilots on multidimensional resources, such that the uncertainty of
attacks can be learned quickly and eliminated precisely. Particularly,
multiuser pilots for uplink access are encoded as distinguishable subcarrier
activation patterns (SAPs) and gNB decodes pilots of interest from observed
SAPs, a superposition of SAPs from access users, by joint design of attack mode
detection and user activity detection though a quantum learning network (QLN).
We found that the uncertainty lies in the identification process of codeword
digits from the attacker, which can be always modelled as a black-box model,
resolved by a quantum learning algorithm and quantum circuit. Novel analytical
closed-form expressions of failure probability are derived to characterize the
reliability of this URLLC system with short packet transmission. Simulations
how that our method can bring ultra-high reliability and low latency despite
attacks on pilots.


Abstract:
  The ability to reconstruct the kinematic parameters of hand movement using
non-invasive electroencephalography (EEG) is essential for strength and
endurance augmentation using exosuit/exoskeleton. For system development, the
conventional classification based brain computer interface (BCI) controls
external devices by providing discrete control signals to the actuator. A
continuous kinematic reconstruction from EEG signal is better suited for
practical BCI applications. The state-of-the-art multi-variable linear
regression (mLR) method provides a continuous estimate of hand kinematics,
achieving maximum correlation of upto 0.67 between the measured and the
estimated hand trajectory. In this work, three novel source aware deep learning
models are proposed for motion trajectory prediction (MTP). In particular,
multi layer perceptron (MLP), convolutional neural network - long short term
memory (CNN-LSTM), and wavelet packet decomposition (WPD) CNN-LSTM are
presented. Additional novelty of the work includes utilization of brain source
localization (using sLORETA) for the reliable decoding of motor intention
mapping (channel selection) and accurate EEG time segment selection.
Performance of the proposed models are compared with the traditionally utilised
mLR technique on the real grasp and lift (GAL) dataset. Effectiveness of the
proposed framework is established using the Pearson correlation coefficient and
trajectory analysis. A significant improvement in the correlation coefficient
is observed when compared with state-of-the-art mLR model. Our work bridges the
gap between the control and the actuator block, enabling real time BCI
implementation.


Abstract:
  Rapid growth in scientific data and a widening gap between computational
speed and I/O bandwidth makes it increasingly infeasible to store and share all
data produced by scientific simulations. Instead, we need methods for reducing
data volumes: ideally, methods that can scale data volumes adaptively so as to
enable negotiation of performance and fidelity tradeoffs in different
situations. Multigrid-based hierarchical data representations hold promise as a
solution to this problem, allowing for flexible conversion between different
fidelities so that, for example, data can be created at high fidelity and then
transferred or stored at lower fidelity via logically simple and mathematically
sound operations. However, the effective use of such representations has been
hindered until now by the relatively high costs of creating, accessing,
reducing, and otherwise operating on such representations. We describe here
highly optimized data refactoring kernels for GPU accelerators that enable
efficient creation and manipulation of data in multigrid-based hierarchical
forms. We demonstrate that our optimized design can achieve up to 264 TB/s
aggregated data refactoring throughput -- 92% of theoretical peak -- on 1024
nodes of the Summit supercomputer. We showcase our optimized design by applying
it to a large-scale scientific visualization workflow and the MGARD lossy
compression software.


Abstract:
  Heat kernels arise in a variety of contexts including probability, geometry,
and functional analysis; the automorphic heat kernel is particularly important
in number theory and string theory. The typical construction of an automorphic
heat kernel as a Poincar\'{e} series presents analytic difficulties, which can
be dealt with in special cases (e.g. hyperbolic spaces) but are often
sidestepped in higher rank by restricting to the compact quotient case. In this
paper, we present a new approach, using global automorphic Sobolev theory, a
robust framework for solving automorphic PDEs that does not require any
simplifying assumptions about the rank of the symmetric space or the
compactness of the arithmetic quotient. We construct an automorphic heat kernel
via its automorphic spectral expansion in terms of cusp forms, Eisenstein
series, and residues of Eisenstein series. We then prove uniqueness of the
automorphic heat kernel as an application of operator semigroup theory.
Finally, we prove the smoothness of the automorphic heat kernel by proving that
its automorphic spectral expansion converges in the $C^\infty$-topology.


Abstract:
  We complete the description of automorphism groups of all nonsplit extensions
of elementary abelian $2$-groups by $PSL_2(q)$, with $q$ odd, for an
irreducible induced action. An application of this result to the theory of
$\pi$-submaximal subgroups is given.


Abstract:
  We propose a simple recursive algorithm that allows the computation of the
first- and second-order derivatives with respect to the inputs of an arbitrary
deep feed forward neural network (DFNN). The algorithm naturally incorporates
the derivatives with respect to the network parameters. To test the algorithm,
we apply it to the study of the quantum mechanical variational problem for few
cases of simple potentials, modeling the ground-state wave function in terms of
a DFNN.


Abstract:
  We present a statistical testing framework to detect if a given machine
learning classifier fails to satisfy a wide range of group fairness notions.
The proposed test is a flexible, interpretable, and statistically rigorous tool
for auditing whether exhibited biases are intrinsic to the algorithm or due to
the randomness in the data. The statistical challenges, which may arise from
multiple impact criteria that define group fairness and which are discontinuous
on model parameters, are conveniently tackled by projecting the empirical
measure onto the set of group-fair probability models using optimal transport.
This statistic is efficiently computed using linear programming and its
asymptotic distribution is explicitly obtained. The proposed framework can also
be used to test for testing composite fairness hypotheses and fairness with
multiple sensitive attributes. The optimal transport testing formulation
improves interpretability by characterizing the minimal covariate perturbations
that eliminate the bias observed in the audit.


Abstract:
  It is well-known that the densest lattice sphere packings also typically have
the largest kissing numbers. The sphere packing density maximization problem is
known to have a solution among well-rounded lattices, of which the integer
lattice $\mathbb{Z}^n$ is the simplest example. The integer lattice is also an
example of a generic well-rounded lattice, i.e., a well-rounded lattice with a
minimal kissing number. However, the integer lattice has the worst density
among well-rounded lattices. In this paper, the problem of constructing
explicit generic well-rounded lattices with dense sphere packings is
considered. To this end, so-called tame lattices recently introduced by Damir
et al. are utilized. Tame lattices came to be as a generalization of the ring
of integers of certain abelian number fields. The sublattices of tame lattices
constructed in this paper are shown to always result in either a generic
well-rounded lattice or the lattice $A_n$, with density ranging between that of
$\mathbb{Z}^n$ and $A_n$. Further, explicit deformations of some known densest
lattice packings are constructed, yielding a family of generic well-rounded
lattices with densities arbitrarily close to the optimum. In addition to being
an interesting mathematical problem on its own, the constructions are also
motivated from a more practical point of view. Namely, the constructed lattices
provide good candidates for lattice codes used in secure wireless
communications.


Abstract:
  The quantum anomalous Hall (QAH) effect in magnetic topological insulator
(TI) represents a new state of matter originated from the interplay between
topology and magnetism. The defining characteristics of the QAH ground state
are the quantized Hall resistivity ($\rho_{yx}$) and vanishing longitudinal
resistivity ($\rho_{xx}$) in the absence of external magnetic field. A
fundamental question concerning the QAH effect is whether it is merely a
zero-magnetic-field quantum Hall (QH) effect, or if it can host unique quantum
phases and phase transitions that are unavailable elsewhere. The most dramatic
departure of the QAH systems from other QH systems lies in the strong magnetic
disorders that induce spatially random magnetization. Because disorder and
magnetism play pivotal roles in the phase diagram of two-dimensional electron
systems, the high degree of magnetic disorders in QAH systems may create novel
phases and quantum critical phenomena. In this work, we perform systematic
transport studies of a series of magnetic TIs with varied strength of magnetic
disorders. We find that the ground state of QAH effect can be categorized into
two distinct classes: the QAH insulator and anomalous Hall (AH) insulator
phases, as the zero-magnetic-field counterparts of the QH liquid and Hall
insulator in the QH systems. In the low disorder limit of the QAH insulator
regime, we observe a universal quantized longitudinal resistance $\rho_{xx} =
h/e^{2}$ at the coercive field. In the AH insulator regime, we find that a
magnetic field can drive it to the QAH insulator phase through a quantum
critical point with distinct scaling behaviors from that in the QH phase
transition. We propose that the transmission between chiral edge states at
domain boundaries, tunable by disorder and magnetic fields, is the key for
determining the QAH ground state.


Abstract:
  In this article we deal with the free Banach lattice $FBL\langle \mathbb{L}
\rangle$ generated by a lattice $\mathbb{L}$. We prove that if $FBL\langle
\mathbb{L} \rangle$ is projective then $\mathbb{L}$ has a maximum and a
minimum. On the other hand, we show that if $\mathbb{L}$ has maximum and
minimum then $FBL\langle \mathbb{L} \rangle$ is $2$-lattice isomorphic to a
$C(K)$-space. As a consequence, $FBL\langle \mathbb{L} \rangle$ is projective
if and only if it is lattice isomorphic to a $C(K)$-space with $K$ being an
absolute neighborhood retract. As an application, we characterize those
linearly ordered sets and Boolean algebras for which the corresponding free
Banach lattice is projective.


Abstract:
  In this work, we focus on two crucial components in the cross-domain
text-to-SQL semantic parsing task: schema linking and value filling. To
encourage the model to learn better encoding ability, we propose a column
selection auxiliary task to empower the encoder with the relevance matching
capability by using explicit learning targets. Furthermore, we propose two
value filling methods to build the bridge from the existing zero-shot semantic
parsers to real-world applications, considering most of the existing parsers
ignore the values filling in the synthesized SQL. With experiments on Spider,
our proposed framework improves over the baselines on the execution accuracy
and exact set match accuracy when database contents are unavailable, and
detailed analysis sheds light on future work.


Abstract:
  The routing number is a graph invariant introduced by Alon, Chung, and Graham
in 1994, and it has been studied for trees and other classes of graphs such as
hypercubes. It gives the minimum number of routing steps needed to sort a set
of distinct tokens, placed one on each vertex, where each routing step swaps a
set of disjoint pairs of adjacent tokens. Our main theorem generalizes the
known estimate that a rectangular grid graph R with width w(R) and height h(R)
has routing number rt(R) in O(w(R)+h(R)). We show that for the subgraph P of
the infinite square lattice enclosed by any convex polygon, its routing number
rt(P) is in O(w(P)+h(P)).


Abstract:
  Diodes made of heterostructures of the 2D material graphene and conventional
3D materials are reviewed in this manuscript. Several applications in high
frequency electronics and optoelectronics are highlighted. In particular,
advantages of metal-insulator-graphene (MIG) diodes over conventional
metal-insulator-metal diodes are discussed with respect to relevant
figures-of-merit. The MIG concept is extended to 1D diodes. Several
experimentally implemented radio frequency circuit applications with MIG diodes
as active elements are presented. Furthermore, graphene-silicon Schottky diodes
as well as MIG diodes are reviewed in terms of their potential for
photodetection. Here, graphene-based diodes have the potential to outperform
conventional photodetectors in several key figures-of-merit, such as overall
responsivity or dark current levels. Obviously, advantages in some areas may
come at the cost of disadvantages in others, so that 2D/3D diodes need to be
tailored in application-specific ways.


Abstract:
  It is well-known that GPS is vulnerable to signal spoofing attacks. Although
several spoofing detection techniques exist, they are incapable of mitigation
and recovery from stealthy attackers. In this work, we present SemperFi, a
single antenna GPS receiver capable of tracking legitimate GPS satellite
signals and estimating the true location even during a spoofing attack. The
main challenge in building SemperFi is, unlike most wireless systems where
\emph{the data} contained in the wireless signals is important, GPS relies on
the time of arrival (ToA) of satellite signals. SemperFi is capable of
distinguishing spoofing signals and recovering legitimate GPS signals that are
even completely overshadowed by a strong adversary. We exploit the short-term
stability of inertial sensors to identify the spoofing signal and extend the
successive interference cancellation algorithm to preserve the legitimate
signal's ToA. We implement SemperFi in GNSS-SDR, an open-source
software-defined GNSS receiver, and evaluate its performance using UAV
simulators, real drones, a variety of real-world GPS datasets, and various
embedded platforms. Our evaluation results indicate that in many scenarios,
SemperFi can identify adversarial peaks by executing flight patterns that are
less than 50 m long and recover the true location within 10 seconds (Jetson
Xavier). We show that our receiver is secure against stealthy attackers who
exploit inertial sensor errors and execute seamless takeover attacks. We design
SemperFi as a pluggable module capable of generating a spoofer-free GPS signal
for processing on any commercial-off-the-shelf GPS receiver available today.
Finally, we release our implementation to the community for usage and further
research.


Abstract:
  We present an implementation of the trimmed serendipity finite element
family, using the open source finite element package Firedrake. The new
elements can be used seamlessly within the software suite for problems
requiring $H^1$, \hcurl, or \hdiv-conforming elements on meshes of squares or
cubes. To test how well trimmed serendipity elements perform in comparison to
traditional tensor product elements, we perform a sequence of numerical
experiments including the primal Poisson, mixed Poisson, and Maxwell cavity
eigenvalue problems. Overall, we find that the trimmed serendipity elements
converge, as expected, at the same rate as the respective tensor product
elements while being able to offer significant savings in the time or memory
required to solve certain problems.


Abstract:
  The multi-color CCD photometric study of 27 Delta Scuti stars is presented.
By using approximately three years of photometric observations, we obtained the
times of maxima and magnitude changes during the observation time interval for
each star. The ephemerides of our Delta Scuti stars were calculated based on
the Markov Chain Monte Carlo (MCMC) method using the observed times of maxima
and the period of the stars' oscillations. We used the Gaia EDR3 parallaxes to
calculate the luminosities and also the absolute magnitudes of these Delta
Scuti stars. The fundamental physical parameters of all the stars in our sample
such as masses and radii were estimated. We determined the pulsation modes of
the stars based on the pulsation constants. Moreover, the period-luminosity
(P-L) relation of Delta Scuti stars was investigated and discussed. Then, by
using a machine learning classification, new P-L relations for fundamental and
overtone modes are presented.


Abstract:
  GPS technology has revolutionized the way we localize and navigate outdoors.
However, the poor reception of GPS signals in buildings makes it unsuitable for
indoor localization. WiFi fingerprinting-based indoor localization is one of
the most promising ways to meet this demand. Unfortunately, most work in the
domain fails to resolve challenges associated with deployability on
resource-limited embedded devices. In this work, we propose a compression-aware
and high-accuracy deep learning framework called CHISEL that outperforms the
best-known works in the area while maintaining localization robustness on
embedded devices.


Abstract:
  Inferring social relations from dialogues is vital for building emotionally
intelligent robots to interpret human language better and act accordingly. We
model the social network as an And-or Graph, named SocAoG, for the consistency
of relations among a group and leveraging attributes as inference cues.
Moreover, we formulate a sequential structure prediction task, and propose an
$\alpha$-$\beta$-$\gamma$ strategy to incrementally parse SocAoG for the
dynamic inference upon any incoming utterance: (i) an $\alpha$ process
predicting attributes and relations conditioned on the semantics of dialogues,
(ii) a $\beta$ process updating the social relations based on related
attributes, and (iii) a $\gamma$ process updating individual's attributes based
on interpersonal social relations. Empirical results on DialogRE and MovieGraph
show that our model infers social relations more accurately than the
state-of-the-art methods. Moreover, the ablation study shows the three
processes complement each other, and the case study demonstrates the dynamic
relational inference.


Abstract:
  In this paper, we consider the renormalization operator $\mathcal R$ for
multimodal maps. We prove the renormalization operator $\mathcal R$ is a
self-homeomorphism on any totally $\mathcal R$-invariant set. As a corollary,
we prove the existence of the full renormalization horseshoe for multimodal
maps.


Abstract:
  The interplay between quadrupole and octupole degrees of freedom is discussed
in a series of neutron-rich actinides and superheavy nuclei with $92 \le$ Z
$\le 110$ and $186 \le$ N $\le 202$. In addition to the static
Hartree-Fock-Bogoliubov approach, dynamical beyond-mean-field correlations are
taken into account via both parity restoration and symmetry-conserving
Generator Coordinate Method calculations based on the Gogny-D1M energy density
functional. Physical properties such as correlation energies, negative-parity
excitation energies as well as reduced transition probabilities $B(E1)$ and
$B(E3)$ are discussed in detail. It is shown that, for the studied nuclei, the
quadrupole-octupole coupling is weak and to a large extent the properties of
negative parity states can be reasonably well described in terms of the
octupole degree of freedom alone.


Abstract:
  BINGO (Baryon Acoustic Oscillations from Integrated Neutral Gas
Observations.) is a radio telescope designed to survey from 980 MHz to 1260
MHz, observe the neutral Hydrogen (HI) 21-cm line and detect BAO (Baryon
Acoustic Oscillation) signal with Intensity Mapping technique. Here we present
our method to generate mock maps of the 21-cm Intensity Mapping signal covering
the BINGO frequency range and related test results. (Abridged)


Abstract:
  Domain discretization is an essential part of the solution procedure in
numerical simulations. Meshless methods simplify the domain discretization to
positioning of nodes in the interior and on the boundary of the domain.
However, generally speaking, the shape of the boundary is often undefined and
thus needs to be constructed before it can be discretized with a desired
internodal spacing. Domain shape construction is far from trivial and is the
main challenge of this paper. We tackle the simulation of moving boundary
problems where the lack of domain shape information can introduce difficulties.
We present a solution for 2D surface reconstruction from discretization points
using cubic splines and thus providing a surface description anywhere in the
domain. We also demonstrate the presented algorithm in a simulation of
phase-change-like problem.


Abstract:
  The key idea of power-domain non-orthogonal multiple access (NOMA) is to
exploit the superposition coding (SC) combined with successive interference
cancellation (SIC) technique (called SC-SIC) while reducing the receivers'
complexity as well as error propagation. Actually, NOMA suggests a
low-complexity scheme, where users are grouped into multiple clusters operating
in isolated resource blocks, and SC-SIC is performed among users within each
cluster. In this paper, we propose a globally optimal joint intra- and
inter-cluster power allocation for any arbitrary user grouping to maximize
users' sum-rate. In this algorithm, we exploit the closed-form of optimal
intra-cluster power allocation obtained in our previous work. Then, by
transforming network-NOMA to an equivalent virtual network-OMA, we show that
the optimal power allocation can be obtained based on the very fast
water-filling algorithm. Interestingly, we observe that each NOMA cluster acts
as a virtual OMA user whose effective channel gain is obtained in closed form.
Also, each virtual OMA user requires a minimum power to satisfy the minimum
rate demand of its real multiplexed user. In simulation results, we evaluate
the performance gap between fully SC-SIC, NOMA, and OMA, in terms of users
sum-rate, and outage probability.


Abstract:
  Understanding the formation of star clusters with low star-formation
efficiency (SFE) is very important to know about the star-formation history. In
N-body models of star cluster evolution after gas expulsion, the Plummer model
with outer power law density profile has been used massively. We study the
impact of the density profile slopes on the survivability of the low-SFE star
clusters after instantaneous gas expulsion. We compare cases when stellar
cluster has Plummer profile and Dehnen profiles with cusp of different slopes
at the time of formation. We determine the corresponding density profile of the
residual gas for a given global SFE, assuming that our model clusters formed
with a constant efficiency per free-fall time and hence have shallower density
profile of gas than that of stars. We perform direct $N$-body simulations of
evolution of clusters initially in virial equilibrium within gas potential
after gas removal. We find that the violent relaxation lasts no longer than
20~Myr independently of the density profile power law slopes. Dehnen model
clusters survive after violent relaxation with significantly lower SFEs when
the global SFE measured within the Jacobi radius or within a half-mass radius.
Dehnen $\gamma=0$ model clusters show similar final bound fraction with the
Plummer model clusters if global SFE is measured within 10 scale radii. The
final bound fraction increases with $\gamma$ values for a given global SFE. We
conclude that Dehnen clusters better resist the consequences of the violent
relaxation followed the instantaneous gas expulsion than the Plummer clusters.
Thus the shallower the outer density slope of the low-SFE clusters, the better
for their survivability after gas expulsion. Among Dehnen clusters we find that
the steeper the inner slope (cusp) the higher the bound mass fraction is
retained after violent relaxation for a given global SFE.


Abstract:
  Deep neural networks are vulnerable to input deformations in the form of
vector fields of pixel displacements and to other parameterized geometric
deformations e.g. translations, rotations, etc. Current input deformation
certification methods either 1. do not scale to deep networks on large input
datasets, or 2. can only certify a specific class of deformations, e.g. only
rotations. We reformulate certification in randomized smoothing setting for
both general vector field and parameterized deformations and propose
DeformRS-VF and DeformRS-Par, respectively. Our new formulation scales to large
networks on large input datasets. For instance, DeformRS-Par certifies rich
deformations, covering translations, rotations, scaling, affine deformations,
and other visually aligned deformations such as ones parameterized by
Discrete-Cosine-Transform basis. Extensive experiments on MNIST, CIFAR10, and
ImageNet show competitive performance of DeformRS-Par achieving a certified
accuracy of $39\%$ against perturbed rotations in the set
$[-10\degree,10\degree]$ on ImageNet.


Abstract:
  We study the motion of charged particles in a family of five-dimensional
solutions describing either a black hole or topological star with a fifth
compact dimension stabilised by a magnetic flux. The particle's trajectory is
shown to move along the surface of a Poincar\'{e} cone. The radial motion shows
a rich structure where the existence of various bound, plunging, or escaping
trajectories depend on the constants of motion. Curves of energy and angular
momentum corresponding to spherical orbits show a swallow-tail structure highly
reminiscent to phase transitions of thermodynamics. When the momentum along the
compact direction is varied, the is a critical point beyond which the
swallow-tail kink disappears and becomes a smooth curve.


Abstract:
  We consider dual-primal isogeometric tearing and interconnection (IETI-DP)
solvers for multi-patch geometries in Isogeometric Analysis. Recently, the
authors have published a convergence analysis for those solvers that is
explicit in both the grid size and the spline degree for conforming
discretizations of two dimensional computational domains. In the present paper,
we shortly revisit these results and provide numerical experiments that
indicate that similar results may hold for three dimensional domains.


Abstract:
  Localization is a characteristic phenomenon of space-inhomogeneous quantum
walks in one dimension, where particles remain localized around their initial
position. The existence of eigenvalues of time evolution operators is a
necessary and sufficient condition for the occurrence of localization, and
their associated eigenvectors are deeply related to the amount of localization,
i.e., the probability that the walker stays around the starting position in the
long-time limit. In a previous study by authors, the eigenvalues of two-phase
quantum walks with one defect were studied using a transfer matrix, which
focused on the occurrence of localization (Quantum Inf. Process 20(5), 2021).
In this paper, we introduce the analytical method to calculate eigenvectors
using the transfer matrix and also extend our results to characterize
eigenvalues not only for two-phase quantum walks with one defect but also for a
more general space-inhomogeneous model.
  With these results, we quantitatively evaluate localization and study the
strong trapping property by deriving the time-averaged limit distributions of
five models studied previously.


Abstract:
  Darboux theorem in symplectic geometry is the crux of emergent gravity in
which the gravitational metric emerges from a noncommutative U(1)-theory.
Topological T-duality, on the other hand, is a relation between two a priori
different backgrounds (with different geometries, different fluxes and even
topologically distinct manifolds) which nevertheless behave identically from a
physical point of view. For us these backgrounds are principal torus bundles on
the same base manifold. In this article we review how these theories can be
naturally understood in the light of generalized geometry. Generalized geometry
provides an unifying framework for such a systematic approach and gives rise to
the group of Courant automorphism $Diff(M) \ltimes \Omega_{closed}^{2}(M)$ for
the $TM \oplus T^*M$ bundle. Here we propose a novel geometric construction for
the T-dual of an emergent gravity theory implemented between the
$\mathbb{T}$-bundles and this duality is realized using the
Gualtieri-Cavalcanti map that establishes an isomorphism between Courant
algebroids. In the case of flat spacetime we obtain that, under mild
assumptions, the T-dual of emergent gravity is again an emergent theory of
gravity. In the general case we obtained formulas for the T-dual of an emergent
metric in a $\mathbb{T}^2$-fibration, however due to the appearance of H-flux
after T-dualizing, the theory thus obtained can no longer be considered in the
usual framework of emergent gravity. This motivates the study of emergent
gravity with non trivial H-flux.


Abstract:
  Urban Air Mobility, the scenario where hundreds of manned and Unmanned
Aircraft System (UAS) carry out a wide variety of missions (e.g. moving humans
and goods within the city), is gaining acceptance as a transportation solution
of the future. One of the key requirements for this to happen is safely
managing the air traffic in these urban airspaces. Due to the expected density
of the airspace, this requires fast autonomous solutions that can be deployed
online. We propose Learning-'N-Flying (LNF) a multi-UAS Collision Avoidance
(CA) framework. It is decentralized, works on-the-fly and allows autonomous UAS
managed by different operators to safely carry out complex missions,
represented using Signal Temporal Logic, in a shared airspace. We initially
formulate the problem of predictive collision avoidance for two UAS as a
mixed-integer linear program, and show that it is intractable to solve online.
Instead, we first develop Learning-to-Fly (L2F) by combining: a) learning-based
decision-making, and b) decentralized convex optimization-based control. LNF
extends L2F to cases where there are more than two UAS on a collision path.
Through extensive simulations, we show that our method can run online
(computation time in the order of milliseconds), and under certain assumptions
has failure rates of less than 1% in the worst-case, improving to near 0% in
more relaxed operations. We show the applicability of our scheme to a wide
variety of settings through multiple case studies.


Abstract:
  By revising the application of the open quantum system approach to the early
universe and extending it to the conditions beyond the Markovian approximation,
we obtain a new non-Markovian quantum Boltzmann equation. Throughout the paper,
we also develop an extension of the quantum Boltzmann equation to describe the
processes that are irreversible at the macroscopic level. This new kinetic
equation is, in principle, applicable to a wide variety of processes in the
early universe. For instance, using this equation one can accurately study the
microscopic influence of a cosmic environment on a system of cosmic background
photons or stochastic gravitational waves. In this paper, we apply the
non-Markovian quantum Boltzmann equation to study the damping of gravitational
waves propagating in a medium consisting of decoupled ultra-relativistic
neutrinos. For such a system, we study the time evolution of the intensity and
the polarization of the gravitational waves. It is shown that, in contrast to
intensity and linear polarization which are damped, the circular polarization
(V-mode) of the gravitational wave (if present) is amplified by propagating
through such a medium.


Abstract:
  Genetic programming is the practice of evolving formulas using crossover and
mutation of genes representing functional operations. Motivated by genetic
evolution we develop and solve two combinatorial games, and we demonstrate some
advantages and pitfalls of using genetic programming to investigate Grundy
values. We conclude by investigating a combinatorial game whose ruleset and
starting positions are inspired by genetic structures.


Abstract:
  We prove a large-deviation principle (LDP) for the sample paths of jump
Markov processes in the small noise limit when, possibly, all the jump rates
vanish uniformly, but slowly enough, in a region of the state space. We further
discuss the optimality of our assumptions on the decay of the jump rates. As a
direct application of this work we relax the assumptions needed for the
application of LDPs to, e.g., Chemical Reaction Network dynamics, where
vanishing reaction rates arise naturally particularly the context of mass
action kinetics.


Abstract:
  The multidisciplinarity of robotics creates a need for robust integration
methodologies that can facilitate the adoption of state-of-the-art research
components in an industrial application. Unfortunately, there are no clear,
community accepted guidelines or standards that define the integration of such
components in a single robotic system. In this paper, we propose a methodology
that assesses the software components of a candidate system on the basis of the
effort required to integrate them and the impact their integration will have on
a target system. We demonstrate how this methodology can be applied using an
industrial tool packing system as an example. The system integrates a wide
range of both in-house and third-party research outputs and software
components. We prove the effectiveness of our approach by evaluating system
performance with an experimental benchmark that assesses the robustness,
reliability and operational speed of the system for the given packing task. We
also demonstrate how our methodology can be used to predict the amount of
integration time required for a component. The proposed integration methodology
can be applied to any robotic system to facilitate its transition from the
research to an industrial environment.


Abstract:
  The interaction of Galactic-Centre (GC) super bubbles (GSB) with the gaseous
disc and halo of the Milky Way is investigated using radio continuum, X-ray, HI
and CO line surveys. The radio North Polar Spur (NPS) constitutes the brightest
eastern ridge of GSB, brightening towards the galactic plane and reaching $ l =
22\deg, \ b = + 2\deg$ at the sharpest end, where it intersects the tangential
direction of the 3-kpc expanding ring and crater. Examination of the spur
ridges reveals that the entire GSB, including the NPS and its counter spurs,
constitutes a GC-symmetrical $\Omega /$\rotatebox[origin=c]{180}{$\Omega$}
shape. The thickness and gas density of the HI and CO discs are shown to
increase sharply from the inside (lower longitude) to the outside of the 3-kpc
crater. Formation of crater is explained by the sweeping of the upper layer of
disc gas by the shock wave from the GC by the explosion $ \sim 10 $ My ago with
the emitted energy of several $10 ^ {55} $ ergs. Based on the discussion, a
unified view on the structure and formation mechanism of GSB is presented.


Abstract:
  Causal Optimal Transport (COT) results from imposing a temporal causality
constraint on classic optimal transport problems, which naturally generates a
new concept of distances between distributions on path spaces. The first
application of the COT theory for sequential learning was given in Xu et al.
(2020), where COT-GAN was introduced as an adversarial algorithm to train
implicit generative models optimized for producing sequential data. Relying on
Xu et al. (2020), the contribution of the present paper is twofold. First, we
develop a conditional version of COT-GAN suitable for sequence prediction. This
means that the dataset is now used in order to learn how a sequence will evolve
given the observation of its past evolution. Second, we improve on the
convergence results by working with modifications of the empirical measures via
a specific type of quantization due to Backhoff et al. (2020). The resulting
quantized conditional COT-GAN algorithm is illustrated with an application for
video prediction.


Abstract:
  Following an eruptive accretion event in NGC6334I-MM1, flares in the various
maser species, including water masers, were triggered. We report the observed
relative proper motion of the highly variable water masers associated with the
massive star-forming region, NGC6334I. High velocity H$_2$O maser proper
motions were detected in 5 maser clusters, CM2-W2 (bow-shock structure),
MM1-W1, MM1-W3, UCHII-W1 and UCHII-W3. The overall average of the derived
relative proper motion is 85 km s$^{-1}$. This mean proper motion is in
agreement with the previous results from VLA multi-epoch observations. Our
position and velocity variance and co-variance matrix analyses of the maser
proper motions show its major axis to have a position angle of $-$79.4$^\circ$,
cutting through the dust cavity around MM1B and aligned in the
northwest-southeast direction. We interpret this as the axis of the jet driving
the CM2 shock and the maser motion. The complicated proper motions in MM1-W1
can be explained by the combined influence of the MM1 northeast-southwest
bipolar outflow, CS(6-5) north-south collimated bipolar outflow, and the radio
jet. The relative proper motions of the H$_2$O masers in UCHII-W1 are likely
not driven by the jets of MM1B protostar but by MM3-UCHII. Overall, the
post-accretion burst relative proper motions of the H$_2$O masers trace shocks
of jet motion.


Abstract:
  The effects of second-neighbor interactions in Kekule patterned graphene
electronic properties are studied starting from a tight-binding Hamiltonian.
Thereafter, a low-energy effective Hamiltonian is obtained by projecting the
high energy bands at the Gamma point into the subspace defined by the Kekule
wave vector. The spectrum of the low energy Hamiltonian is in excellent
agreement with the one obtained from a numerical diagonalization of the full
tight-binding Hamiltonian. The main effect of the second-neighbour interaction
is that a set of bands gains an effective mass and a shift in energy, thus
lifting the degeneracy of the conduction bands at the Dirac point. This band
structure is akin to a spin-one Dirac cone, a result expected for honeycomb
lattices with a distinction between one third of the atoms in one sublattice.
Finally, we present a study of Kekule patterned graphene nanoribbons. This
shows that the previous effects are enhanced as the width decreases. Moreover,
edge states become dispersive, as expected due to second neighbors interaction,
but here the Kek-Y bond texture results in an hybridization of both edge
states. The present study shows the importance of second neighbors in realistic
models of Kekule patterned graphene, specially at surfaces.


Abstract:
  When can a unimodular random planar graph be drawn in the Euclidean or the
hyperbolic plane in a way that the distribution of the random drawing is
isometry-invariant? This question was answered for one-ended unimodular graphs
in earlier work of Benjamini and Tim\'ar, using the fact that such graphs
automatically have locally finite (simply connected) drawings into the plane.
For the case of graphs with multiple ends the question was left open. We
provide a graph theoretic characterization of graphs that have a locally finite
embedding into the plane. Then we prove that such graphs do have a locally
finite invariant embedding into the Euclidean or the hyperbolic plane,
depending on whether the graph is amenable or not.


Abstract:
  Consider a multitype coalescent process in which each block has a colour in
$\{1,\ldots,d\}$. Individual blocks may change colour, and some number of
blocks of various colours may merge to form a new block of some colour. We show
that if the law of a multitype coalescent process is invariant under
permutations of blocks of the same colour, has consistent Markovian
projections, and has asychronous mergers, then it is a multitype
$\Lambda$-coalescent: a process in which single blocks may change colour, two
blocks of like colour may merge to form a single block of that colour, or large
mergers across various colours happen at rates governed by a $d$-tuple of
measures on the unit cube $[0,1]^d$. We go on to identify when such processes
come down from infinity. Our framework generalises Pitman's celebrated
classification theorem for singletype coalescent processes, and provides a
unifying setting for numerous examples that have appeared in the literature
including the seed-bank model, the island model and the coalescent structure of
continuous-state branching processes.


Abstract:
  In strong-field physics experiments with intense lasers, it is of paramount
importance to single-shot diagnose the temporal contrast between laser pulse
peak and its noise pedestal. This allows fast optimization of pulse contrast
and meaningful comparison with theory for each pulse shot, and it can help new
outcomes from clean laser-plasma interactions. Thus far, high contrast ratios
up to ~10^10, required by present petawatt (PW) class lasers, have been
accessible in both generation and single-shot characterization. However,
ultrahigh contrast ~10^13, required by the planned 200-PW lasers, challenges
intense laser technology and remains an open question. This paper reports on
the first demonstration of such an ultrahigh-contrast measurement by adapting
single-shot cross-correlator (SSCC). We introduce an ultrafast method that
enables to determine the SSCC detection limit. Our strategy mimics the test
laser having known ultrahigh contrast in the measurement frame of time-to-space
mapping. The ultimate contrast-measurement limit of 10^13 is achieved, which
corresponds to the highest pulse intensity set by SSCC damage threshold and the
lowest noise pedestal set by single-photon detection. As a consequence, photon
noise in the detection is observed and increases as the noise pedestal reduces.
The demonstrated measurement ability at the photon noise limit is applied to a
high-contrast laser system based on second-harmonic generation and optical
parametric chirped-pulse amplification, suggesting accessible of ultrahigh
contrast pulses.


Abstract:
  In the late 1990's, observations of 93 Type Ia supernovae were analysed in
the framework of the FLRW cosmology assuming these to be `standard(isable)
candles'. It was thus inferred that the Hubble expansion rate is accelerating
as if driven by a positive Cosmological Constant $\Lambda$. This is still the
only direct evidence for the `dark energy' that is the dominant component of
the standard $\Lambda$CDM cosmological model. Other data such as BAO, CMB
anisotropies, stellar ages, the rate of structure growth, etc are all
`concordant' with this model but do not provide independent evidence for
accelerated expansion. Analysis of a larger sample of 740 SNe Ia shows that
these are not quite standard candles, and highlights the "corrections" applied
to analyse the data in the FLRW framework. The latter holds in the reference
frame in which the CMB is isotropic, whereas observations are made in our
heliocentric frame in which the CMB has a large dipole anisotropy. This is
assumed to be of kinematic origin i.e. due to our non-Hubble motion driven by
local inhomogeneity in the matter distribution. The $\Lambda$CDM model predicts
how this peculiar velocity should fall off as the averaging scale is raised and
the universe becomes sensibly homogeneous. However observations of the local
`bulk flow' are inconsistent with this expectation and convergence to the CMB
frame is not seen. Moreover the kinematic interpretation implies a
corresponding dipole in the sky distribution of high redshift quasars, which is
rejected by observations at 4.9$\sigma$. The acceleration of the Hubble
expansion rate is also anisotropic at 3.9$\sigma$ and aligned with the bulk
flow. Thus dark energy may be an artefact of analysing data assuming that we
are idealised observers in an FLRW universe, when in fact the real universe is
inhomogeneous and anisotropic out to distances large enough to impact on
cosmological analyses.


Abstract:
  Li metal is an ideal anode material for use in state-of-the-art secondary
batteries. However, Li-dendrite growth is a safety concern and results in low
coulombic efficiency, which significantly restricts the commercial application
of Li secondary batteries. Unfortunately, the Li deposition (growth) mechanism
is poorly understood on the atomic scale. Here, we used machine learning to
construct a Li potential model with quantum-mechanical computational accuracy.
Molecular dynamics simulations in this study with this model revealed two
self-healing mechanisms in a large Li-metal system, viz. surface self-healing
and bulk self-healing, and identified three Li-dendrite morphologies under
different conditions, viz. "needle", "mushroom", and "hemisphere". Finally, we
introduce the concepts of local current density and variance in local current
density to supplement the critical current density when evaluating the
probability of self-healing.


Abstract:
  Lockdowns imposed in most of the countries were lifted following a decline in
the COVID-19 cases towards May-June 2020. A recent surge (second wave) in the
COVID-19 cases in Europe and other temperate countries as compared to the
tropical regions suggests the likely role of solar radiation. We hypothesized
that ultraviolet radiation's effect might be a significant factor moderating
the spread of the COVID-19 across countries. Regression analysis was done for
the UV radiation data for seven hotspot cities (New Delhi, Mumbai, Milan,
Madrid, New York, Melbourne and Sydney) with the daily COVID-19 cases. Global
erythemal UV radiation values were lower during winter and higher during
summer. In general, the daily new COVID-19 cases registered were higher during
the winter months having low UV radiation dose (0.5-3.7 kJ m-2). Cases began to
decline with summer onset that corresponded to increased UV radiation (2.5-6.7
kJ m-2). Our studies suggest that the natural UV radiation could be a strong
determining factor moderating the spread of COVID-19 cases. The importance of
UV radiation in natural sunlight as a disinfectant for SARS-CoV-2 cannot be
ignored since the lockdowns were lifted; further, it can be considered as a
factor for COVID management.


Abstract:
  We present a system to measure the ripeness of fruit with a hyperspectral
camera and a suitable deep neural network architecture. This architecture did
outperform competitive baseline models on the prediction of the ripeness state
of fruit. For this, we recorded a data set of ripening avocados and kiwis,
which we make public. We also describe the process of data collection in a
manner that the adaption for other fruit is easy. The trained network is
validated empirically, and we investigate the trained features. Furthermore, a
technique is introduced to visualize the ripening process.


Abstract:
  We introduce and describe the $2$-category $\mathsf{Grt}_{\flat}$ of
Grothendieck categories and flat morphisms between them. First, we show that
the tensor product of locally presentable linear categories $\boxtimes$
restricts nicely to $\mathsf{Grt}_{\flat}$. Then, we characterize exponentiable
objects with respect to $\boxtimes$: these are continuous Grothendieck
categories. In particular, locally finitely presentable Grothendieck categories
are exponentiable. Consequently, we have that, for a quasi-compact
quasi-separated scheme $X$, the category of quasi-coherent sheaves
$\mathsf{Qcoh}(X)$ is exponentiable. Finally, we provide a family of examples
and concrete computations of exponentials.


Abstract:
  Recent observations indicated that the photoelectric conversion properties of
perovskite materials are intimately related to the presence of superlattice
structures and other unusual nanoscale features in them. The low dimensional or
mixed dimensional halide perovskite family are found to be more efficient
materials for device application compared to 3-dimensional halide perovskites.
The emergence of perovskite solar cell has revolutionized the solar cell
industry because of their flexible architecture and rapidly increased
efficiency. Tuning the dielectric constant, charge separation are the main
objective in designing a photovoltaic device that can be explored using
2-dimensional perovskite family. Thus, revisiting the fundamental properties of
perovskite crystals could reveal further possibilities for recognizing these
improvements towards device functionality. In this context, this review
discusses the material properties of 2-dimensional halide perovskite and
related optoelectronic devices aiming particularly for solar cell application.


Abstract:
  Let $K$ be a compact convex set and $m$ be a positive integer. The covering
functional of $K$ with respect to $m$ is the smallest $\lambda\in[0,1]$ such
that $K$ can be covered by $m$ translates of $\lambda K$. Estimations of the
covering functionals of convex hulls of two or more compact convex sets are
presented. It is proved that, if a three-dimensional convex body $K$ is the
convex hull of two compact convex sets having no interior points, then the
least number $c(K)$ of smaller homothetic copies of $K$ needed to cover $K$ is
not greater than $8$ and $c(K)=8$ if and only if $K$ is a parallelepiped.


Abstract:
  Experimental and theoretical studies of colloidal nanoparticles have
primarily focused on accurate characterization and simulation of observable
characteristics, such as resonant wavelength. In this Letter, we tackle the
optimal design of colloidal-nanoparticle ensembles: what is the largest
possible optical response, which designs might achieve them, and can such
response be experimentally demonstrated? We combine theory and experiment to
answer each of these questions. We derive general bounds on the maximum
cross-sections per volume, and we apply an analytical antenna model to show
that resonant nanorods should nearly achieve such bounds. We use a modified
seed-mediated synthesis approach to synthesize ensembles of gold nanorods with
small polydispersity, i.e., small variations in size and aspect ratio.
Polydispersity is the key determinant of how closely such ensembles can
approach their respective bounds yet is difficult to characterize
experimentally without near-field measurements. We show that a certain
"extinction metric," connecting extinction cross-section per volume with the
radiative efficiencies of the nanoparticles, offers a quantitative prediction
of polydispersity via quantities that can be rapidly measured with far-field
characterization tools. Our predictions apply generally across all plasmonic
materials and offers a roadmap to the largest possible optical response of
nanoparticle ensembles.


Abstract:
  The problem of adaptive Kalman filtering for a discrete observable linear
time-varying system with unknown noise covariance matrices is addressed in this
paper. The measurement difference autocovariance method is used to formulate a
linear least squares cost function containing the measurements and the process
and measurement noise covariance matrices. Subsequently, a Riemannian
trust-region optimization approach is designed to minimize the least squares
cost function and ensure symmetry and positive definiteness for the estimates
of the noise covariance matrices. The noise covariance matrix estimates, under
sufficient excitation of the system, are shown to converge to their unknown
true values. Saliently, the exponential stability and convergence guarantees
for the proposed adaptive Kalman filter to the optimal Kalman filter with known
noise covariance matrices is shown to be achieved under the relatively mild
assumptions of uniform observability and uniform controllability. Numerical
simulations on a linear time-varying system demonstrate the effectiveness of
the proposed adaptive filtering algorithm.


Abstract:
  We establish fundamental limits of tracking a moving target over the unit
cube under the framework of 20 questions with measurement-dependent noise. In
this problem, there is an oracle who knows the instantaneous location of a
target. Our task is to query the oracle as few times as possible to accurately
estimate the trajectory of the moving target, whose initial location and
velocity is \emph{unknown}. We study the case where the oracle's answer to each
query is corrupted by random noise with query-dependent discrete distribution.
In our formulation, the performance criterion is the resolution, which is
defined as the maximal absolute value between the true location and estimated
location at each discrete time during the searching process. We are interested
in the minimal resolution of any non-adaptive searching procedure with a finite
number of queries and derive approximations to this optimal resolution via the
second-order asymptotic analysis.


Abstract:
  Given a $t$-$(v, k, \lambda)$ design, $\mathcal{D}=(X,\mathcal{B})$, a
zero-sum $n$-flow of $\mathcal{D}$ is a map $f : \mathcal{B}\longrightarrow
\{\pm1,\ldots, \pm(n-1)\}$ such that for any point $x\in X$, the sum of $f$
over all blocks incident with $x$ is zero. For a positive integer $k$, we find
a zero-sum $k$-flow for an STS$(u w)$ and for an STS$(2v+7)$ for $v\equiv
1~(\mathrm{mod}~4)$, if there are STS$(u)$, STS$(w)$ and STS$(v)$ such that the
STS$(u)$ and STS$(v)$ both have a zero-sum $k$-flow. In 2015, it was
conjectured that for $v>7$ every STS$(v)$ admits a zero-sum $3$-flow. Here, it
is shown that many cyclic STS$(v)$ have a zero-sum $3$-flow. Also, we
investigate the existence of zero-sum flows for some Steiner quadruple systems.


Abstract:
  Trusted computing defines how to securely measure, store, and verify the
integrity of software controlling a computer. One of the major challenges that
make them hard to be applied in practice is the issue with software updates.
Specifically, an operating system update causes the integrity violation because
it changes the well-known initial state trusted by remote verifiers, such as
integrity monitoring systems. Consequently, the integrity monitoring of remote
computers becomes unreliable due to the high amount of false positives. We
address this problem by adding an extra level of indirection between the
operating system and software repositories. We propose a trusted software
repository (TSR), a secure proxy that overcomes the shortcomings of previous
approaches by sanitizing software packages. Sanitization consists of modifying
unsafe installation scripts and adding digital signatures in a way software
packages can be installed in the operating system without violating its
integrity. TSR leverages shielded execution, i.e., Intel SGX, to achieve
confidentiality and integrity guarantees of the sanitization process. TSR is
transparent to package managers, and requires no changes in the software
packages building and distributing processes. Our evaluation shows that running
TSR inside SGX is practical; since it induces only ~1.18X performance overhead
during package sanitization compared to the native execution without SGX. TSR
supports 99.76% of packages available in the main and community repositories of
Alpine Linux while increasing the total repository size by 3.6%.


Abstract:
  During magnetic reconnection in collisionless space plasma, the electron
fluid decouples from the magnetic field within narrow current layers, and
theoretical models for this process can be distinguished in terms of their
predicted current layer widths. From theory, the off-diagonal stress in the
electron pressure tensor is related to thermal non-circular orbit motion of
electrons around the magnetic field lines. This stress becomes significant when
the width of the reconnecting current layer approaches the small characteristic
length scale of the electron motion. To aid in situ spacecraft and numerical
investigations of reconnection, the structure of the electron diffusion region
is here investigated using the Terrestrial Reconnection EXperiment (TREX). In
agreement with the closely matched kinetic simulations, laboratory observations
reveal the presence of electron-scale current layer widths. Although the layers
are modulated by a current-driven instability, 3D simulations demonstrate that
it is the off-diagonal stress that is responsible for breaking the frozen-in
condition of the electron fluid.


Abstract:
  Here, we have analysed a GARCH(1,1) model with the aim to fit higher order
moments for different companies' stock prices. When we assume a gaussian
conditional distribution, we fail to capture any empirical data when fitting
the first three even moments of financial time series. We show instead that a
double gaussian conditional probability distribution better captures the higher
order moments of the data. To demonstrate this point, we construct regions
(phase diagrams), in the fourth and sixth order standardised moment space,
where a GARCH(1,1) model can be used to fit these moments and compare them with
the corresponding moments from empirical data for different sectors of the
economy. We found that the ability of the GARCH model with a double gaussian
conditional distribution to fit higher order moments is dictated by the time
window our data spans. We can only fit data collected within specific time
window lengths and only with certain parameters of the conditional double
gaussian distribution. In order to incorporate the non-stationarity of
financial series, we assume that the parameters of the GARCH model have time
dependence.


Abstract:
  Over the last few years, we have witnessed tremendous progress on many
subtasks of autonomous driving, including perception, motion forecasting, and
motion planning. However, these systems often assume that the car is accurately
localized against a high-definition map. In this paper we question this
assumption, and investigate the issues that arise in state-of-the-art autonomy
stacks under localization error. Based on our observations, we design a system
that jointly performs perception, prediction, and localization. Our
architecture is able to reuse computation between both tasks, and is thus able
to correct localization errors efficiently. We show experiments on a
large-scale autonomy dataset, demonstrating the efficiency and accuracy of our
proposed approach.


Abstract:
  Berezinskii-Kosterlitz-Thouless transition of the classical XY model is
re-investigated, combining the Tensor Network Renormalization (TNR) and the
Level Spectroscopy method based on the finite-size scaling of the Conformal
Field Theory. By systematically analyzing the spectrum of the transfer matrix
of the systems of various moderate sizes which can be accurately handled with a
finite bond dimension, we determine the critical point removing the logarithmic
corrections. This improves the accuracy by an order of magnitude over previous
studies including those utilizing TNR. Our analysis also gives a visualization
of the celebrated Kosterlitz Renormalization Group flow based on the numerical
data.


Abstract:
  Many popular learning-rate schedules for deep neural networks combine a
decaying trend with local perturbations that attempt to escape saddle points
and bad local minima. We derive convergence guarantees for bandwidth-based
step-sizes, a general class of learning rates that are allowed to vary in a
banded region. This framework includes many popular cyclic and non-monotonic
step-sizes for which no theoretical guarantees were previously known. We
provide worst-case guarantees for SGD on smooth non-convex problems under
several bandwidth-based step sizes, including stagewise $1/\sqrt{t}$ and the
popular step-decay (constant and then drop by a constant), which is also shown
to be optimal. Moreover, we show that its momentum variant converges as fast as
SGD with the bandwidth-based step-decay step-size. Finally, we propose novel
step-size schemes in the bandwidth-based family and verify their efficiency on
several deep neural network training tasks.


Abstract:
  In this letter, we propose a whole-body control strategy for humanoid robots
in multi-contact settings that enables switching between fixed and sliding
contacts under active balance. We compute, in real-time, a safe center-of-mass
position and wrench distribution of the contact points based on the Chebyshev
center. Our solution is formulated as a quadratic programming problem without a
priori computation of balance regions. We assess our approach with experiments
highlighting switches between fixed and sliding contact modes in multi-contact
configurations. A humanoid robot demonstrates such contact interchanges from
fully-fixed to multi-sliding and also shuffling of the foot. The scenarios
illustrate the performance of our control scheme in achieving the desired
forces, CoM position attractor, and planned trajectories while actively
maintaining balance.


Abstract:
  It is known that the membership in a given reproducing kernel Hilbert space
(RKHS) of the samples of a Gaussian process $X$ is controlled by a certain
nuclear dominance condition. However, it is less clear how to identify a
"small" set of functions (not necessarily a vector space) that contains the
samples. This article presents a general approach for identifying such sets. We
use scaled RKHSs, which can be viewed as a generalisation of Hilbert scales, to
define the sample support set as the largest set which is contained in every
element of full measure under the law of $X$ in the $\sigma$-algebra induced by
the collection of scaled RKHS. This potentially non-measurable set is then
shown to consist of those functions that can be expanded in terms of an
orthonormal basis of the RKHS of the covariance kernel of $X$ and have their
squared basis coefficients bounded away from zero and infinity, a result
suggested by the Karhunen-Lo\`{e}ve theorem.


Abstract:
  The use of Internet of Things (IoT) devices in homes and the immediate
proximity of an individual communicates to create Personal IoT (PIoT) networks.
The exploratory study of PIoT is in its infancy, which will explore the
expansion of new use cases, service requirements, and the proliferation of PIoT
devices. This article provides a big picture of PIoT architecture, vision, and
future research scope.