Abstract:
  Fingerspelling, in which words are signed letter by letter, is an important
component of American Sign Language. Most previous work on automatic
fingerspelling recognition has assumed that the boundaries of fingerspelling
regions in signing videos are known beforehand. In this paper, we consider the
task of fingerspelling detection in raw, untrimmed sign language videos. This
is an important step towards building real-world fingerspelling recognition
systems. We propose a benchmark and a suite of evaluation metrics, some of
which reflect the effect of detection on the downstream fingerspelling
recognition task. In addition, we propose a new model that learns to detect
fingerspelling via multi-task training, incorporating pose estimation and
fingerspelling recognition (transcription) along with detection, and compare
this model to several alternatives. The model outperforms all alternative
approaches across all metrics, establishing a state of the art on the
benchmark.


Abstract:
  We propose a dynamic encoder transducer (DET) for on-device speech
recognition. One DET model scales to multiple devices with different
computation capacities without retraining or finetuning. To trading off
accuracy and latency, DET assigns different encoders to decode different parts
of an utterance. We apply and compare the layer dropout and the collaborative
learning for DET training. The layer dropout method that randomly drops out
encoder layers in the training phase, can do on-demand layer dropout in
decoding. Collaborative learning jointly trains multiple encoders with
different depths in one single model. Experiment results on Librispeech and
in-house data show that DET provides a flexible accuracy and latency trade-off.
Results on Librispeech show that the full-size encoder in DET relatively
reduces the word error rate of the same size baseline by over 8%. The
lightweight encoder in DET trained with collaborative learning reduces the
model size by 25% but still gets similar WER as the full-size baseline. DET
gets similar accuracy as a baseline model with better latency on a large
in-house data set by assigning a lightweight encoder for the beginning part of
one utterance and a full-size encoder for the rest.


Abstract:
  Charge-$4e$ superconductivity as a novel phase of matter remains elusive so
far. Here we show that charge-$4e$ phase can arise as a vestigial order above
the nematic superconducting transition temperature in time-reversal-invariant
nematic superconductors. On the one hand, the nontrivial topological defect --
nematic vortex -- is energetically favored over the superconducting phase
vortex when the nematic stiffness is less than the superfluid stiffness;
consequently the charge-$4e$ phase emerges by proliferation of nematic vortices
upon increasing temperatures. On the other hand, the Ginzburg-Landau theory of
the nematic superconductors has two distinct decoupling channels to either
charge-$4e$ orders or nematic orders; by analyzing the competition between the
effective mass of the charge-$4e$ order and the cubic potential of the nematic
order, we find a sizable regime where the charge-$4e$ order is favored. These
two analysis consistently show that nematic superconductors can provide a
promising route to realize charge-$4e$ phases, which may apply to candidate
nematic superconductors such as PbTaSe$_2$ and twisted bilayer graphene.


Abstract:
  In search for the possible astrophysical sources behind origination of the
diverse gamma-ray bursts, cluster analyses are performed to find homogeneous
groups, which discover an intermediate group other than the conventional short
and long bursts. However, very recently, few studies indicate a possibility of
the existence of more than three (namely five) groups. Therefore, in this
paper, fuzzy clustering is conducted on the gamma-ray bursts from the final
'Burst and Transient Source Experiment' catalog to cross-check the significance
of these new groups. Meticulous study on individual bursts based on their
memberships in the fuzzy clusters confirms the previously well-known three
groups against the newly found five.


Abstract:
  As reading on mobile devices is becoming more ubiquitous, content is consumed
in shorter intervals and is punctuated by frequent interruptions. In this work,
we explore the best way to mitigate the effects of reading interruptions on
longer text passages. Our hypothesis is that short summaries of either
previously read content (reviews) or upcoming content (previews) will help the
reader re-engage with the reading task. Our target use case is for students who
study using electronic textbooks and who are frequently mobile. We present a
series of pilot studies that examine the benefits of different types of
summaries and their locations, with respect to variations in text content and
participant cohorts. We find that users prefer reviews after an interruption,
but that previews shown after interruptions have a larger positive influence on
comprehension. Our work is a first step towards smart reading applications that
proactively provide text summaries to mitigate interruptions on the go.


Abstract:
  Crack-like objects that propagate along frictional interfaces,
i.e.~frictional shear cracks, play a major role in a broad range of frictional
phenomena. Such frictional cracks are commonly assumed to feature the universal
square root near-edge singularity of ideal shear cracks, as predicted by Linear
Elastic Fracture Mechanics. Here we show that this is not the generic case due
to the intrinsic dependence of the frictional strength on the slip rate, even
if the bodies forming the frictional interface are identical and predominantly
linear elastic. Instead, frictional shear cracks feature unconventional
singularities characterized by a singularity order $\xi$ that differs from the
conventional $-\tfrac{1}{2}$ one. It is shown that $\xi$ depends on the
friction law, on the propagation speed and on the symmetry mode of loading. We
discuss the general structure of a theory of unconventional singularities,
along with their implications for the energy balance and dynamics of frictional
cracks. Finally, we present explicit calculations of $\xi$ and the associated
near-edge fields for linear viscous-friction -- which may emerge as a
perturbative approximation of nonlinear friction laws or on its own -- for both
in-plane (mode-II) and anti-plane (mode-III) shear loadings.


Abstract:
  Protoplanetary disks drive some of the formation process (e.g., accretion,
gas dissipation, formation of structures, etc.) of stars and planets.
Understanding such physical processes is one of the main astrophysical
questions. HD 163296 is an interesting young stellar object for which infrared
and sub-millimeter observations have shown a prominent circumstellar disk with
gaps plausibly created by forming planets. This study aims at characterizing
the morphology of the inner disk in HD 163296 with multi-epoch near-infrared
interferometric observations performed with GRAVITY at the Very Large Telescope
Interferometer (VLTI). Our goal is to depict the K-band (lambda_0 ~ 2.2 um)
structure of the inner rim with milliarcsecond (sub-au) angular resolution. Our
data is complemented with archival PIONIER (H-band; lambda_0 ~ 1.65 um) data of
the source. We performed a Gradient Descent parametric model fitting to recover
the sub-au morphology of our source. Our analysis shows the existence of an
asymmetry in the disk surrounding the central star of HD 163296. We confirm
variability of the disk structure in the inner ~2 mas (0.2 au). While
variability of the inner disk structure in this source has been suggested by
previous interferometric studies, this is the first time that it is confirmed
in the H- and K-bands by using a complete analysis of the closure phases and
squared visibilities over several epochs. Because of the separation from the
star, position changes, and persistence of this asymmetric structure on
timescales of several years, we argue that it is a dusty feature (e.g., a
vortex or dust clouds), probably, made by a mixing of sillicate and carbon dust
and/or refractory grains, inhomogeneously distributed above the mid-plane of
the disk.


Abstract:
  The explosive growth of bandwidth hungry Internet applications has led to the
rapid development of new generation mobile network technologies that are
expected to provide broadband access to the Internet in a pervasive manner. For
example, 6G networks are capable of providing high-speed network access by
exploiting higher frequency spectrum; high-throughout satellite communication
services are also adopted to achieve pervasive coverage in remote and isolated
areas. In order to enable seamless access, Integrated Satellite-Terrestrial
Communication Networks (ISTCN) has emerged as an important research area. ISTCN
aims to provide high speed and pervasive network services by integrating
broadband terrestrial mobile networks with satellite communication networks. As
terrestrial mobile networks began to use higher frequency spectrum (between
3GHz to 40GHz) which overlaps with that of satellite communication (4GHz to
8GHz for C band and 26GHz to 40GHz for Ka band), there are opportunities and
challenges. On one hand, satellite terminals can potentially access terrestrial
networks in an integrated manner; on the other hand, there will be more
congestion and interference in this spectrum, hence more efficient spectrum
management techniques are required. In this paper, we propose a new technique
to improve spectrum sharing performance by introducing Non-orthogonal Frequency
Division Multiplexing (NOMA) and Cognitive Radio (CR) in the spectrum sharing
of ISTCN. In essence, NOMA technology improves spectrum efficiency by allowing
different users to transmit on the same carrier and distinguishing users by
user power levels while CR technology improves spectrum efficiency through
dynamic spectrum sharing. Furthermore, some open researches and challenges in
ISTCN will be discussed.


Abstract:
  Camera-based passive dietary intake monitoring is able to continuously
capture the eating episodes of a subject, recording rich visual information,
such as the type and volume of food being consumed, as well as the eating
behaviours of the subject. However, there currently is no method that is able
to incorporate these visual clues and provide a comprehensive context of
dietary intake from passive recording (e.g., is the subject sharing food with
others, what food the subject is eating, and how much food is left in the
bowl). On the other hand, privacy is a major concern while egocentric wearable
cameras are used for capturing. In this paper, we propose a privacy-preserved
secure solution (i.e., egocentric image captioning) for dietary assessment with
passive monitoring, which unifies food recognition, volume estimation, and
scene understanding. By converting images into rich text descriptions,
nutritionists can assess individual dietary intake based on the captions
instead of the original images, reducing the risk of privacy leakage from
images. To this end, an egocentric dietary image captioning dataset has been
built, which consists of in-the-wild images captured by head-worn and
chest-worn cameras in field studies in Ghana. A novel transformer-based
architecture is designed to caption egocentric dietary images. Comprehensive
experiments have been conducted to evaluate the effectiveness and to justify
the design of the proposed architecture for egocentric dietary image
captioning. To the best of our knowledge, this is the first work that applies
image captioning to dietary intake assessment in real life settings.


Abstract:
  With the advancement of the accelerator systems and the requirements of high
luminosity particle beams to reach different physics goals, detectors with good
position resolution and high rate handling capability have become essential for
designing any High Energy Physics (HEP) experiments. The Gas Electron
Multiplier (GEM) detectors are widely used in many HEP experiments as a
tracking device because of their good spatial resolution and rate handling
capability. The presence of the dielectric medium inside the active volume of
the GEM detector changes its behaviour when exposed to external radiation. This
mechanism is commonly referred as the charging-up effect. In this article, the
effect of the charging-up phenomenon and the initial polarisation effect of the
dielectric on the gain of the chamber are reported for a single mask triple GEM
chamber with Ar/CO2 gas mixture.


Abstract:
  We study the regret of reinforcement learning from offline data generated by
a fixed behavior policy in an infinite-horizon discounted Markov decision
process (MDP). While existing analyses of common approaches, such as fitted
$Q$-iteration (FQI), suggest a $O(1/\sqrt{n})$ convergence for regret,
empirical behavior exhibits much faster convergence. In this paper, we present
a finer regret analysis that exactly characterizes this phenomenon by providing
fast rates for the regret convergence. First, we show that given any estimate
for the optimal quality function $Q^*$, the regret of the policy it defines
converges at a rate given by the exponentiation of the $Q^*$-estimate's
pointwise convergence rate, thus speeding it up. The level of exponentiation
depends on the level of noise in the decision-making problem, rather than the
estimation problem. We establish such noise levels for linear and tabular MDPs
as examples. Second, we provide new analyses of FQI and Bellman residual
minimization to establish the correct pointwise convergence guarantees. As
specific cases, our results imply $O(1/n)$ regret rates in linear cases and
$\exp(-\Omega(n))$ regret rates in tabular cases.


Abstract:
  The comb-like spectrum of a white light-illuminated Fabry-P\'{e}rot etalon
can serve as a cost-effective and stable reference for precise Doppler
measurements. Understanding the stability of these devices across their broad
(100's of nm) spectral bandwidths is essential to realize their full potential
as Doppler calibrators. However, published descriptions remain limited to small
bandwidths or short timespans. We present a $\sim6$ month broadband stability
monitoring campaign of the Fabry-P\'{e}rot etalon system deployed with the
near-infrared Habitable Zone Planet Finder spectrograph (HPF). We monitor the
wavelengths of each of $\sim3500$ resonant modes measured in HPF spectra of
this Fabry-P\'{e}rot etalon (free spectral range = 30 GHz, bandwidth = 820 -
1280 nanometers), leveraging the accuracy and precision of an electro-optic
frequency comb reference. These results reveal chromatic structure in the
Fabry-P\'{e}rot mode locations and in their evolution with time. We measure an
average drift on the order of 2 cm s $^{-1}$ d$^{-1}$, with local departures up
to $\pm5$ cm s $^{-1}$ d$^{-1}$. We discuss these behaviors in the context of
the Fabry-P\'{e}rot etalon mirror dispersion and other optical properties of
the system, and the implications for the use of similar systems for precise
Doppler measurements. Our results show that this system supports the wavelength
calibration of HPF at the $\lesssim10$ cm s $^{-1}$ level over a night and at
the $\lesssim30$ cm s $^{-1}$ level over $\sim10$ d. Our results also highlight
the need for long-term and spectrally-resolved study of similar systems that
will be deployed to support Doppler measurement precision approaching $\sim10$
cm s $^{-1}$.


Abstract:
  This paper explores the calibration of a classifier output score in binary
classification problems. A calibrator is a function that maps the arbitrary
classifier score, of a testing observation, onto $[0,1]$ to provide an estimate
for the posterior probability of belonging to one of the two classes.
Calibration is important for two reasons; first, it provides a meaningful
score, that is the posterior probability; second, it puts the scores of
different classifiers on the same scale for comparable interpretation. The
paper presents three main contributions: (1) Introducing multi-score
calibration, when more than one classifier provides a score for a single
observation. (2) Introducing the idea that the classifier scores to a
calibration process are nothing but features to a classifier, hence proposing
expanding the classifier scores to higher dimensions to boost the calibrator's
performance. (3) Conducting a massive simulation study, in the order of 24,000
experiments, that incorporates different configurations, in addition to
experimenting on two real datasets from the cybersecurity domain. The results
show that there is no overall winner among the different calibrators and
different configurations. However, general advices for practitioners include
the following: the Platt's
calibrator~\citep{Platt1999ProbabilisticOutputsForSupport}, a version of the
logistic regression that decreases bias for a small sample size, has a very
stable and acceptable performance among all experiments; our suggested
multi-score calibration provides better performance than single score
calibration in the majority of experiments, including the two real datasets. In
addition, expanding the scores can help in some experiments.


Abstract:
  We trained deep neural networks (DNNs) as a function of the neutrino energy
density, flux, and the fluid velocity to reproduce the Eddington tensor for
neutrinos obtained in our first-principles core-collapse supernova (CCSN)
simulations. Although the moment method, which is one of the most popular
approximations for neutrino transport, requires a closure relation, none of the
analytical closure relations commonly employed in the literature captures all
aspects of the neutrino angular distribution in momentum space. In this paper,
we developed a closure relation by using the DNN that takes the neutrino energy
density, flux, and the fluid velocity as the input and the Eddington tensor as
the output. We consider two kinds of DNNs: a conventional DNN named a
component-wise neural network (CWNN) and a tensor-basis neural network (TBNN).
We found that the diagonal component of the Eddington tensor is reproduced
better by the DNNs than the M1-closure relation especially for low to
intermediate energies. For the off-diagonal component, the DNNs agree better
with the Boltzmann solver than the M1 closure at large radii. In the comparison
between the two DNNs, the TBNN has slightly better performance than the CWNN.
With the new closure relations at hand based on the DNNs that well reproduce
the Eddington tensor with much smaller costs, we opened up a new possibility
for the moment method.


Abstract:
  We show that a suitable weak solution to the incompressible Navier-Stokes
equations on ${\mathbb{R}^3\times(-1,1)}$ is regular on
$\mathbb{R}^3\times(0,1]$ if $\partial_3 u $ belongs to $M^{2p/(2p-3),\alpha }
((-1,0);L^p (\mathbb{R}^3 ))$ for any $\alpha >1$ and $p\in (3/2,\infty)$,
which is a logarithmic-type variation of a Morrey space in time. For each
$\alpha >1$ this space is, up to a logarithm, critical with respect to the
scaling of the equations, and contains all spaces $L^q ((-1,0);L^p
(\mathbb{R}^3 ))$ that are subcritical, that is for which $2/q+3/p<2$.


Abstract:
  Resource-disaggregated data centres (RDDC) propose a resource-centric, and
high-utilisation architecture for data centres (DC), avoiding resource
fragmentation and enabling arbitrarily sized resource pools to be allocated to
tasks, rather than server-sized ones. RDDCs typically impose greater demand on
the network, requiring more infrastructure and increasing cost and power, so
new resource allocation algorithms that co-manage both server and networks
resources are essential to ensure that allocation is not bottlenecked by the
network, and that requests can be served successfully with minimal networking
resources. We apply reinforcement learning (RL) to this problem for the first
time and show that an RL policy based on graph neural networks can learn
resource allocation policies end-to-end that outperform previous
hand-engineered heuristics by up to 22.0\%, 42.6\% and 22.6\% for acceptance
ratio, CPU and memory utilisation respectively, maintain performance when
scaled up to RDDC topologies with $10^2\times$ more nodes than those seen
during training and can achieve comparable performance to the best baselines
while using $5.3\times$ less network resources.


Abstract:
  Vortex induced vibration (VIV) occurs when vortex shedding frequency falls
close to the natural frequency of a structure. Investigation on VIV is of great
value in disaster mitigation, energy extraction and other applications.
Following recent development in machine learning on VIV in laminar flow, this
study extends it to the turbulent region by employing the state-of-the-art
parameterised Navier-Stokes equations based physics informed neural network
(PNS-PINN). Turbulent flow with Reynolds number $Re$ = $10^4$, passing through
a cylinder undergoing VIV motion, was considered as an example. Within the
PNS-PINN, an artificial viscosity $\nu_t$ is introduced in the Navier-Stokes
equations and treated as a hidden output variable. A recently developed
Navier-Stokes equations based PINN, termed NSFnets (Jin \textit{et al.}, J
COMPUT PHYS 426: 109951, 2021), was also considered for comparison. Training
datasets of scattered velocity and dye trace concentration snapshots, from
computational fluid dynamics (CFD) simulations, were prepared for both the
PNS-PINN and NSFnets. Results show that, compared with the NSFnets, the
PNS-PINN is more effective in inferring and reconstructing turbulent flow under
VIV circumstances. The PNS-PINN also shows the capability to deal with unsteady
and multi-scale flows in VIV. Inspired by the Reynolds-Averaged Navier-Stokes
(RANS) formulation, by implanting an additional artificial viscosity, the
PNS-PINN is free of complex turbulence model closure, thus may be applied for
more general and complex turbulent flows.


Abstract:
  We develop a framework for model checking infinite-state systems by
automatically augmenting them with auxiliary variables, enabling
quantifier-free induction proofs for systems that would otherwise require
quantified invariants. We combine this mechanism with a counterexample-guided
abstraction refinement scheme for the theory of arrays. Our framework can thus,
in many cases, reduce inductive reasoning with quantifiers and arrays to
quantifier-free and array-free reasoning. We evaluate the approach on a wide
set of benchmarks from the literature. The results show that our implementation
often outperforms state-of-the-art tools, demonstrating its practical
potential.


Abstract:
  This work discusses an extension to conventional low-coherence interferometry
by the introduction of dispersion-encoding. The extension facilitates the
measurement of surface height profiles with sub-nm resolution. The selection of
a dispersive element for encoding allows for tuning of the axial measurement
range and resolution of the setup. The approach is theoretically designed and
implemented for applications such as surface profilometry, the characterization
of polymeric cross-linking and as a tool for the determination of layer
thicknesses in thin-film processing. During the characterization of the
implemented setup, it was shown that an axial measurement range of 79.91 $\mu
m$ with a resolution of 0.1 nm was achievable in the evaluation of surface
profiles. Simultaneously, profiles of up to 1.5 mm length could be obtained
without the need for mechanical scanning. This marked a significant improvement
in relation to state-of-the-art technologies in terms of dynamic range. It was
also shown that axial and lateral measurement range can be decoupled partially.
Additionally, functional parameters such as surface roughness were
characterized with the same tool. The characterization of the degree of
polymeric cross-linking was performed as a function of the refractive index.
Here, the refractive index could be acquired in a spatially-resolved manner
with an index resolution of down to $3.36 \pm 10^{-5}$. This was achieved by
the development of a novel mathematical analysis approach. For the acquisition
of layer thicknesses of thin-films, an advanced setup was developed which could
be used to characterize the thickness thin-films and its (flexible) substrate
simultaneously.


Abstract:
  Where do cosmic X-rays come from? Every new unidentified X-ray source could
potentially revolutionize our understanding of the universe. The international
collaborative astronomy project EXTraS aimed at automatically classifying new
sources of X-ray emission (e.g., stars or galaxies) in the large observation
database of the X-ray satellite XMM-Newton. Because data archives have reached
dimensions of big data astronomers used different machine-learning (ML) random
forest decision tree algorithms that performed the classification process. In
this bachelor thesis in information design, I was interested in the challenge
to visualize these big data sets and the results of the ML algorithms in an
interactive and intuitive way to facilitate the visual exploration of its
internal structures and relationships. The VIRTUAL DATA COSMOS is an
interactive data visualization tool in virtual reality (VR) for scientists to
explore multidimensional data sets.


Abstract:
  It was established by Boalch that Euler continuants arise as Lie group valued
moment maps for a class of wild character varieties described as moduli spaces
of points on $\mathbb{P}^1$ by Sibuya. Furthermore, Boalch noticed that these
varieties are multiplicative analogues of certain Nakajima quiver varieties
originally introduced by Calabi, which are attached to the quiver $\Gamma_n$ on
two vertices and $n$ equioriented arrows. In this article, we go a step further
by unveiling that the Sibuya varieties can be understood using noncommutative
quasi-Poisson geometry modeled on the quiver $\Gamma_n$. We prove that the
Poisson structure carried by these varieties is induced, via the
Kontsevich-Rosenberg principle, by an explicit Hamiltonian double quasi-Poisson
algebra defined at the level of the quiver $\Gamma_n$ such that its
noncommutative multiplicative moment map is given in terms of Euler
continuants. This result generalises the Hamiltonian double quasi-Poisson
algebra associated with the quiver $\Gamma_1$ by Van den Bergh. Moreover, using
the method of fusion, we prove that the Hamiltonian double quasi-Poisson
algebra attached to $\Gamma_n$ admits a factorisation in terms of $n$ copies of
the algebra attached to $\Gamma_1$.


Abstract:
  The Gibbs-Thomson (GT) equation describes the shift of the crystallization
temperature for a confined fluid with respect to the bulk as a function of pore
size. While this century old relation is successfully used to analyze
experiments, its derivations found in the literature often rely on nucleation
theory arguments (i.e. kinetics instead of thermodynamics) or fail to state
their assumptions, therefore leading to similar but different expressions.
Here, we revisit the derivation of the GT equation to clarify the system
definition, corresponding thermodynamic ensemble, and assumptions made along
the way. We also discuss the role of the thermodynamic conditions in the
external reservoir on the final result. We then turn to numerical simulations
of a model system to compute independently the various terms entering in the GT
equation, and compare the predictions of the latter with the melting
temperatures determined under confinement by means of hyper-parallel tempering
grand canonical Monte Carlo simulations. We highlight some difficulties related
to the sampling of crystallization under confinement in simulations. Overall,
despite its limitations, the GT equation may provide an interesting alternative
route to predict the melting temperature in large pores, using molecular
simulations to evaluate the relevant quantities entering in this equation. This
approach could for example be used to investigate the nanoscale capillary
freezing of ionic liquids recently observed experimentally between the tip of
an Atomic Force Microscope and a substrate.


Abstract:
  Recently, we have introduced and modified two graph-decomposition theorems
based on a new graph product, motivated by applications in the context of
synchronising periodic real-time processes. This vertex-removing synchronised
product (VRSP), is based on modifications of the well-known Cartesian product,
and is closely related to the synchronised product due to Wohrle and Thomas.
Here, we recall the definition of the VRSP and the two modified
graph-decompositions and introduce three new graph-decomposition theorems. The
first new theorem decomposes a graph with respect to the semicomplete bipartite
subgraphs of the graph. For the second new theorem, we introduce a matrix
graph, which is used to decompose a graph in a manner similar to the
decomposition of graphs using the Cartesian product. In the third new theorem,
we combine these two types of decomposition. Ultimately, the goal of these
graph-decomposition theorems is to come to a prime-graph decomposition.


Abstract:
  Frictional sliding is an intrinsically complex phenomenon, emerging from the
interplay between driving forces, elasto-frictional instabilities, interfacial
nonlinearity and dissipation, material inertia and bulk geometry. We show that
homogeneous rate-and-state dependent frictional systems, driven at a prescribed
boundary velocity -- as opposed to a prescribed stress -- in a range where the
frictional interface is rate-weakening, generically host self-healing slip
pulses, a sliding mode not yet fully understood. Such velocity-driven
frictional systems are then shown to exhibit coarsening dynamics saturated at
the system length in the sliding direction, independently of the system's
height, leading to steadily propagating pulse trains. The latter may be viewed
as a propagating phase-separated state, where slip and stick characterize the
two phases. While pulse trains' periodicity is coarsening-limited by the
system's length, the single pulse width, characteristic slip velocity and
propagation speed exhibit rich properties, which are comprehensively understood
using theory and extensive numerics. Finally, we show that for sufficiently
small system heights, pulse trains are accompanied by periodic
elasto-frictional instabilities.


Abstract:
  To ease the burden of labeling, unsupervised domain adaptation (UDA) aims to
transfer knowledge in previous and related labeled datasets (sources) to a new
unlabeled dataset (target). Despite impressive progress, prior methods always
need to access the raw source data and develop data-dependent alignment
approaches to recognize the target samples in a transductive learning manner,
which may raise privacy concerns from source individuals. Several recent
studies resort to an alternative solution by exploiting the well-trained
white-box model from the source domain, yet, it may still leak the raw data
through generative adversarial learning. This paper studies a practical and
interesting setting for UDA, where only black-box source models (i.e., only
network predictions are available) are provided during adaptation in the target
domain. To solve this problem, we propose a new two-step knowledge adaptation
framework called DIstill and fine-tuNE (DINE). Taking into consideration the
target data structure, DINE first distills the knowledge from the source
predictor to a customized target model, then fine-tunes the distilled model to
further fit the target domain. Besides, neural networks are not required to be
identical across domains in DINE, even allowing effective adaptation on a
low-resource device. Empirical results on three UDA scenarios (i.e.,
single-source, multi-source, and partial-set) confirm that DINE achieves highly
competitive performance compared to state-of-the-art data-dependent approaches.
Code is available at \url{https://github.com/tim-learn/DINE/}.


Abstract:
  We study the dynamics of a small number of impurity particles coupled to the
ideal Fermi gas in a $d$-dimensional box. The impurities interact with the
fermions via a two-body potential $\lambda v(x)$ where $\lambda$ is a coupling
constant and $v(x)$ for instance a screened Coulomb potential. After taking the
large-volume limit at positive Fermi momentum $k_F$ we consider the regime of
high density of the fermions, that is, $k_F$ large compared to one. For
coupling constants that scale like $\lambda^2 \sim k_F^{(2-d)}$ we show that
the impurity particles effectively decouple from the fermions but evolve with
an attractive pair interaction among each other which is induced by
fluctuations in the Fermi gas.


Abstract:
  We perform a covariant (Lagrangian) quantization of perturbative gravity in
the background of a Schwarzschild black hole. The key tool is a decomposition
of the field into spherical harmonics. We fix Regge-Wheeler gauge for modes
with angular momentum quantum number $l \geq 2$, while for low multipole modes
with $l$ $=$ $0$ or $1$ -- for which Regge-Wheeler gauge is inapplicable -- we
propose a set of gauge fixing conditions which are 2D background covariant and
perturbatively well-defined. We find that the corresponding Faddeev-Popov
ghosts are non-propagating for the $l\geq2$ modes, but are in general
nontrivial for the low multipole modes with $l = 0,1$. However, in
Schwarzschild coordinates, all time derivatives acting on the ghosts drop from
the action and the low multipole ghosts have instantaneous propagators. Up to
possible subtleties related to quantizing gravity in a space with a horizon,
Faddeev's theorem suggests the possibility of an underlying canonical
(Hamiltonian) quantization with a manifestly ghost-free Hilbert space.


Abstract:
  Although deep neural networks hold the state-of-the-art in several remote
sensing tasks, their black-box operation hinders the understanding of their
decisions, concealing any bias and other shortcomings in datasets and model
performance. To this end, we have applied explainable artificial intelligence
(XAI) methods in remote sensing multi-label classification tasks towards
producing human-interpretable explanations and improve transparency. In
particular, we utilized and trained deep learning models with state-of-the-art
performance in the benchmark BigEarthNet and SEN12MS datasets. Ten XAI methods
were employed towards understanding and interpreting models' predictions, along
with quantitative metrics to assess and compare their performance. Numerous
experiments were performed to assess the overall performance of XAI methods for
straightforward prediction cases, competing multiple labels, as well as
misclassification cases. According to our findings, Occlusion, Grad-CAM and
Lime were the most interpretable and reliable XAI methods. However, none
delivers high-resolution outputs, while apart from Grad-CAM, both Lime and
Occlusion are computationally expensive. We also highlight different aspects of
XAI performance and elaborate with insights on black-box decisions in order to
improve transparency, understand their behavior and reveal, as well, datasets'
particularities.


Abstract:
  In a time division broadcast positioning system (TDBPS), a user device (UD)
determines its position by obtaining sequential time-of-arrival (TOA) or
pseudorange measurements from signals broadcast by multiple synchronized base
stations (BSs). The existing localization method using sequential pseudorange
measurements and a linear clock drift model for the TDPBS, namely LSPM-D, does
not compensate the position displacement caused by the UD movement and will
result in position error. In this paper, depending on the knowledge of the UD
velocity, we develop a set of optimal localization methods for different cases.
First, for known UD velocity, we develop the optimal localization method,
namely LSPM-KVD, to compensate the movement-caused position error. We show that
the LSPM-D is a special case of the LSPM-KVD when the UD is stationary with
zero velocity. Second, for the case with unknown UD velocity, we develop a
maximum likelihood (ML) method to jointly estimate the UD position and
velocity, namely LSPM-UVD. Third, in the case that we have prior distribution
information of the UD velocity, we present a maximum a posteriori (MAP)
estimator for localization, namely LSPM-PVD. We derive the Cramer-Rao lower
bound (CRLB) for all three estimators and analyze their localization error
performance. We show that the position error of the LSPM-KVD increases as the
assumed known velocity deviates from the true value. As expected, the LSPM-KVD
has the smallest position error while the LSPM-PVD and the LSPM-UVD are more
robust when the prior knowledge of the UD velocity is limited. Numerical
results verify the theoretical analysis on the optimality and the positioning
accuracy of the proposed methods.


Abstract:
  Modeling various aspects that make a music piece unique is a challenging
task, requiring the combination of multiple sources of information. Deep
learning is commonly used to obtain representations using various sources of
information, such as the audio, interactions between users and songs, or
associated genre metadata. Recently, contrastive learning has led to
representations that generalize better compared to traditional supervised
methods. In this paper, we present a novel approach that combines multiple
types of information related to music using cross-modal contrastive learning,
allowing us to learn an audio feature from heterogeneous data simultaneously.
We align the latent representations obtained from playlists-track interactions,
genre metadata, and the tracks' audio, by maximizing the agreement between
these modality representations using a contrastive loss. We evaluate our
approach in three tasks, namely, genre classification, playlist continuation
and automatic tagging. We compare the performances with a baseline audio-based
CNN trained to predict these modalities. We also study the importance of
including multiple sources of information when training our embedding model.
The results suggest that the proposed method outperforms the baseline in all
the three downstream tasks and achieves comparable performance to the
state-of-the-art.


Abstract:
  PageRank (PR) is a fundamental tool for assessing the relative importance of
the nodes in a network. In this paper, we propose a measure, weighted PageRank
(WPR), extended from the classical PR for weighted, directed networks with
possible non-uniform node-specific information that is dependent or independent
of network structure. A tuning parameter leveraging node degree and strength is
introduced. An efficient algorithm based on R program has been developed for
computing WPR in large-scale networks. We have tested the proposed WPR on
widely used simulated network models, and found it outperformed other competing
measures in the literature. By applying the proposed WPR to the real network
data generated from World Input-Output Tables, we have seen the results that
are consistent with the global economic trends, which renders it a preferred
measure in the analysis.


Abstract:
  Background: Distinction of IDH mutant and wildtype GBMs is challenging on
MRI, since conventional imaging shows considerable overlap. While few studies
employed deep-learning in a mixed low/high grade glioma population, a
GBM-specific model is still lacking in the literature. Our objective was to
develop a deep-learning model for IDH prediction in GBM by using Convoluted
Neural Networks (CNN) on multiparametric MRI. Methods: We included 100 adult
patients with pathologically proven GBM and IDH testing. MRI data included:
morphologic sequences, rCBV and ADC maps. Tumor area was obtained by a bounding
box function on the axial slice with widest tumor extension on T2 images and
was projected on every sequence. Data was split into training and test (80:20)
sets. A 4 block 2D - CNN architecture was implemented for IDH prediction on
every MRI sequence. IDH mutation probability was calculated with softmax
activation function from the last dense layer. Highest performance was
calculated accounting for model accuracy and categorical cross-entropy loss
(CCEL) in the test cohort. Results: Our model achieved the following
performance: T1 (accuracy 77%, CCEL 1.4), T2 (accuracy 67%, CCEL 2.41), FLAIR
(accuracy 77%, CCEL 1.98), MPRAGE (accuracy 66%, CCEL 2.55), rCBV (accuracy
83%, CCEL 0.64). ADC achieved lower performance. Conclusion: We built a
GBM-tailored deep-learning model for IDH mutation prediction, achieving
accuracy of 83% with rCBV maps. High predictivity of perfusion images may
reflect the known correlation between IDH, hypoxia inducible factor (HIF) and
neoangiogenesis. This model may set a path for non-invasive evaluation of IDH
mutation in GBM.


Abstract:
  In this paper we study the distributed average consensus problem in
multi-agent systems with directed communication links that are subject to
quantized information flow. Specifically, we present and analyze a distributed
averaging algorithm which operates exclusively with quantized values (i.e., the
information stored, processed and exchanged between neighboring agents is
subject to deterministic uniform quantization) and relies on event-driven
updates (e.g., to reduce energy consumption, communication bandwidth, network
congestion, and/or processor usage). The main idea of the proposed algorithm is
that each node (i) models its initial state as two quantized fractions which
have numerators equal to the node's initial state and denominators equal to
one, and (ii) transmits one fraction randomly while it keeps the other stored.
Then, every time it receives one or more fractions, it averages their
numerators with the numerator of the fraction it stored, and then transmits
them to randomly selected out-neighbors. We characterize the properties of the
proposed distributed algorithm and show that its execution, on any static and
strongly connected digraph, allows each agent to reach in finite time a fixed
state that is equal (within one quantisation level) to the average of the
initial states. We extend the operation of the algorithm to achieve finite-time
convergence in the presence of a dynamic directed communication topology
subject to some connectivity conditions. Finally, we provide examples to
illustrate the operation, performance, and potential advantages of the proposed
algorithm. We compare against state-of-the-art quantized average consensus
algorithms and show that our algorithm's convergence speed significantly
outperforms most existing protocols.


Abstract:
  The behaviour of information cascades (such as retweets) has been modelled
extensively. While point process-based generative models have long been in use
for estimating cascade growths, deep learning has greatly enhanced diverse
feature integration. We observe two significant temporal signals in cascade
data that have not been emphasized or reported to our knowledge. First, the
popularity of the cascade root is known to influence cascade size strongly; but
the effect falls off rapidly with time. Second, there is a measurable positive
correlation between the novelty of the root content (with respect to a
streaming external corpus) and the relative size of the resulting cascade.
Responding to these observations, we propose GammaCas, a new cascade growth
model as a parametric function of time, which combines deep influence signals
from content (e.g., tweet text), network features (e.g., followers of the root
user), and exogenous event sources (e.g., online news). Specifically, our model
processes these signals through a customized recurrent network, whose states
then provide the parameters of the cascade rate function, which is integrated
over time to predict the cascade size. The network parameters are trained
end-to-end using observed cascades. GammaCas outperforms seven recent and
diverse baselines significantly on a large-scale dataset of retweet cascades
coupled with time-aligned online news -- it beats the best baseline with an
18.98% increase in terms of Kendall's $\tau$ correlation and $35.63$ reduction
in Mean Absolute Percentage Error. Extensive ablation and case studies unearth
interesting insights regarding retweet cascade dynamics.


Abstract:
  Acoustic emission analysis is a promising technique to investigate the
physiological events leading to drought-induced injuries and mortality.
However, the nature and the source of the acoustic emissions are not fully
understood and make the use of this technique difficult as a direct measure of
the loss of xylem hydraulic conductance. In this study, acoustic emissions were
recorded during severe dehydration in lavender plants and compared to the
dynamics of embolism development and cell lysis. The timing and characteristics
of acoustic signals from two independent recording systems were compared by
principal component analysis. In parallel, changes in water potential, branch
diameter, loss of hydraulic conductance and electrolyte leakage were measured
to quantify drought-induced damages. Two distinct phases of acoustic emissions
were observed during dehydration: the first one associated with a rapid loss of
diameter and a significant increase in loss of xylem conductance (90%) and the
second one with a significant increase in electrolyte leakage and slower
diameter changes. This second phase corresponds to a complete loss of recovery
capacity. The acoustic signals of both phases were discriminated by the third
and fourth principal components. The loss of hydraulic conductance during the
first acoustic phase suggests the hydraulic origin of these signals (i.e.
cavitation events). For the second phase, the signals showed much higher
variability between plants and acoustic systems suggesting that the sources of
these signals may be plural, although likely including cellular damage. A
simple algorithm was developed to discriminate hydraulic-related acoustic
signals from other sources, allowing the reconstruction of dynamic hydraulic
vulnerability curves. However, hydraulic failure precedes cellular damage and
lack of whole plant recovery is associated to these latter.


Abstract:
  We investigate the problem of active learning in the streaming setting in
non-parametric regimes, where the labels are stochastically generated from a
class of functions on which we make no assumptions whatsoever. We rely on
recently proposed Neural Tangent Kernel (NTK) approximation tools to construct
a suitable neural embedding that determines the feature space the algorithm
operates on and the learned model computed atop. Since the shape of the label
requesting threshold is tightly related to the complexity of the function to be
learned, which is a-priori unknown, we also derive a version of the algorithm
which is agnostic to any prior knowledge. This algorithm relies on a regret
balancing scheme to solve the resulting online model selection problem, and is
computationally efficient. We prove joint guarantees on the cumulative regret
and number of requested labels which depend on the complexity of the labeling
function at hand. In the linear case, these guarantees recover known minimax
results of the generalization error as a function of the label complexity in a
standard statistical learning setting.


Abstract:
  We show that the physical conditions which induce the Thakurta metric,
recently studied by B{\oe}hm {\it et al.} in the context of time-dependent
black hole masses, correspond to a single accreting black hole in the entire
Universe filled with isotropic non-interacting dust. In such a case, the
physics of black hole accretion is not local but tied to the properties of the
whole Universe. We show that radiation, primordial black holes or particle dark
matter cannot produce the specific energy flux required for supporting the mass
growth of Thakurta black holes. In particular, this solution does not apply to
black hole binaries. We conclude that cosmological black holes and their mass
growth cannot be described by the Thakurta metric, and thus existing
constraints on the primordial black hole abundance from the LIGO-Virgo and the
CMB measurements remain valid.


Abstract:
  We propose a simple yet effective framework for instance and panoptic
segmentation, termed CondInst (conditional convolutions for instance and
panoptic segmentation). In the literature, top-performing instance segmentation
methods typically follow the paradigm of Mask R-CNN and rely on ROI operations
(typically ROIAlign) to attend to each instance. In contrast, we propose to
attend to the instances with dynamic conditional convolutions. Instead of using
instance-wise ROIs as inputs to the instance mask head of fixed weights, we
design dynamic instance-aware mask heads, conditioned on the instances to be
predicted. CondInst enjoys three advantages: 1.) Instance and panoptic
segmentation are unified into a fully convolutional network, eliminating the
need for ROI cropping and feature alignment. 2.) The elimination of the ROI
cropping also significantly improves the output instance mask resolution. 3.)
Due to the much improved capacity of dynamically-generated conditional
convolutions, the mask head can be very compact (e.g., 3 conv. layers, each
having only 8 channels), leading to significantly faster inference time per
instance and making the overall inference time almost constant, irrelevant to
the number of instances. We demonstrate a simpler method that can achieve
improved accuracy and inference speed on both instance and panoptic
segmentation tasks. On the COCO dataset, we outperform a few state-of-the-art
methods. We hope that CondInst can be a strong baseline for instance and
panoptic segmentation. Code is available at: https://git.io/AdelaiDet


Abstract:
  Domain adaptation is to transfer the shared knowledge learned from the source
domain to a new environment, i.e., target domain. One common practice is to
train the model on both labeled source-domain data and unlabeled target-domain
data. Yet the learned models are usually biased due to the strong supervision
of the source domain. Most researchers adopt the early-stopping strategy to
prevent over-fitting, but when to stop training remains a challenging problem
since the lack of the target-domain validation set. In this paper, we propose
one efficient bootstrapping method, called Adaboost Student, explicitly
learning complementary models during training and liberating users from
empirical early stopping. Adaboost Student combines the deep model learning
with the conventional training strategy, i.e., adaptive boosting, and enables
interactions between learned models and the data sampler. We adopt one adaptive
data sampler to progressively facilitate learning on hard samples and aggregate
"weak" models to prevent over-fitting. Extensive experiments show that (1)
Without the need to worry about the stopping time, AdaBoost Student provides
one robust solution by efficient complementary model learning during training.
(2) AdaBoost Student is orthogonal to most domain adaptation methods, which can
be combined with existing approaches to further improve the state-of-the-art
performance. We have achieved competitive results on three widely-used scene
segmentation domain adaptation benchmarks.


Abstract:
  Analyzing the effects of ocean eddies is important in oceanology for gaining
insights into transport of energy and biogeochemical particles. We present an
application of statistical visualization algorithms for the analysis of the Red
Sea eddy simulation ensemble. Specifically, we demonstrate the applications of
statistical volume rendering and statistical Morse complex summary maps to a
velocity magnitude field for studying the eddy positions in the flow dataset.
In statistical volume rendering, we model per-voxel data uncertainty using
noise models, such as parametric and nonparametric, and study the propagation
of uncertainty into the volume rendering pipeline. In the statistical Morse
complex summary maps, we derive histograms charactering uncertainty of gradient
flow destinations to understand Morse complex topological variations across the
ensemble. We demonstrate the utility of our statistical visualizations for an
effective analysis of the potential eddy positions and their spatial
uncertainty.


Abstract:
  We prove that, for almost all $r \leq N^{1/2}/\log^{O(1)}N$, for any given
$b_1 \mod r$ with $(b_1, r) = 1$, and for almost all $b_2 \mod r$ with $(b_2,
r) = 1$, we have that almost all natural numbers $2n \leq N$ with $2n \equiv
b_1 + b_2 \mod r$ can be written as the sum of two prime numbers $2n = p_1 +
p_2$, where $p_1 \equiv b_1 \mod r$ and $p_2 \equiv b_2 \mod r$. This improves
the previous result which required $r \leq N^{1/3}/\log^{O(1)}N$ instead of $r
\leq N^{1/2}/\log^{O(1)}N$. We also improve some other results concerning
variations of the problem.


Abstract:
  A common observation regarding adversarial attacks is that they mostly give
rise to false activation at the penultimate layer to fool the classifier.
Assuming that these activation values correspond to certain features of the
input, the objective becomes choosing the features that are most useful for
classification. Hence, we propose a novel approach to identify the important
features by employing counter-adversarial attacks, which highlights the
consistency at the penultimate layer with respect to perturbations on input
samples. First, we empirically show that there exist a subset of features,
classification based in which bridge the gap between the clean and robust
accuracy. Second, we propose a simple yet efficient mechanism to identify those
features by searching the neighborhood of input sample. We then select features
by observing the consistency of the activation values at the penultimate layer.


Abstract:
  In this paper, we study the exact dynamics of open quantum systems to the
case with periodic driving field. It is shown that different from the static
adjustment of the system on-site energy that can either generate or destroy the
dissipationless localized bound states, the periodic driving can either
preserve the existed localized bound states or destroy some of them but cannot
generate new localized bound states. With the picture of energy transfer
involved with the driving field, we find the condition for the survival of the
localized bound states when the driving amplitude is weak. For the strong
driving case, the condition breaks down because of the strong energy
renormalization to the originally existed localized bound states. These
properties of decoherence dynamics may help in controlling the quantum state
against decoherence for the sake of its sensitivity to the fundamental
frequency of the driving field.


Abstract:
  This Overview, Design Concepts, and Details Protocol (ODD) provides a
detailed description of an agent-based model (ABM) that was developed to
simulate hospitalizations during the COVID-19 pandemic. Using the descriptions
of submodels, provided parameters, and the links to data sources, modelers will
be able to replicate the creation and results of this model.


Abstract:
  Non-intrusive load monitoring (NILM) is essential for understanding
customer's power consumption patterns and may find wide applications like
carbon emission reduction and energy conservation. The training of NILM models
requires massive load data containing different types of appliances. However,
inadequate load data and the risk of power consumer privacy breaches may be
encountered by local data owners during the NILM model training. To prevent
such potential risks, a novel NILM method named Fed-NILM which is based on
Federated Learning (FL) is proposed in this paper. In Fed-NILM, local model
parameters instead of local load data are shared among multiple data owners.
The global model is obtained by weighted averaging the parameters. Experiments
based on two measured load datasets are conducted to explore the generalization
ability of Fed-NILM. Besides, a comparison of Fed-NILM with locally-trained
NILMs and the centrally-trained NILM is conducted. The experimental results
show that Fed-NILM has superior performance in scalability and convergence.
Fed-NILM outperforms locally-trained NILMs operated by local data owners and
approximates the centrally-trained NILM which is trained on the entire load
dataset without privacy protection. The proposed Fed-NILM significantly
improves the co-modeling capabilities of local data owners while protecting
power consumers' privacy.


Abstract:
  Virtualization technology is nowadays adopted in security-critical embedded
systems to achieve higher performance and more design flexibility. However, it
also comes with new security threats, where attackers leverage timing covert
channels to exfiltrate sensitive information from a partition using a trojan.
  This paper presents a novel approach for the experimental assessment of
timing covert channels in embedded hypervisors, with a case study on security
assessment of a commercial hypervisor product (Wind River VxWorks MILS), in
cooperation with a licensed laboratory for the Common Criteria security
certification. Our experimental analysis shows that it is indeed possible to
establish a timing covert channel, and that the approach is useful for system
designers for assessing that their configuration is robust against this kind of
information leakage.


Abstract:
  Recommender systems (RS) have started to employ knowledge distillation, which
is a model compression technique training a compact model (student) with the
knowledge transferred from a cumbersome model (teacher). The state-of-the-art
methods rely on unidirectional distillation transferring the knowledge only
from the teacher to the student, with an underlying assumption that the teacher
is always superior to the student. However, we demonstrate that the student
performs better than the teacher on a significant proportion of the test set,
especially for RS. Based on this observation, we propose Bidirectional
Distillation (BD) framework whereby both the teacher and the student
collaboratively improve with each other. Specifically, each model is trained
with the distillation loss that makes to follow the other's prediction along
with its original loss function. For effective bidirectional distillation, we
propose rank discrepancy-aware sampling scheme to distill only the informative
knowledge that can fully enhance each other. The proposed scheme is designed to
effectively cope with a large performance gap between the teacher and the
student. Trained in the bidirectional way, it turns out that both the teacher
and the student are significantly improved compared to when being trained
separately. Our extensive experiments on real-world datasets show that our
proposed framework consistently outperforms the state-of-the-art competitors.
We also provide analyses for an in-depth understanding of BD and ablation
studies to verify the effectiveness of each proposed component.


Abstract:
  There is a growing interest in Behavior Trees (BTs) as a tool to describe and
implement robot behaviors. BTs were devised in the video game industry and
their adoption in robotics resulted in the development of ad-hoc libraries to
design and execute BTs that fit complex robotics software architectures.
  While there is broad consensus on how BTs work, some characteristics rely on
the implementation choices done in the specific software library used.
  In this letter, we outline practical aspects in the adoption of BTs and the
solutions devised by the robotics community to fully exploit the advantages of
BTs in real robots. We also overview the solutions proposed in open-source
libraries used in robotics, we show how BTs fit in robotic software
architecture, and we present a use case example.


Abstract:
  The use of millimeter waves for wireless communications is one of the main
technological innovations of 5G systems with respect to previous generations of
cellular systems. Their consideration, however, has been up to now mainly
restricted to the case in which analog or, at most, hybrid analog-digital
beamforming structures were used, thus posing a limitation on the multiplexing
capabilities and peak data rates that could be theoretically achieved at these
frequencies. Recent progress in the field of electronics, however, has made the
energy consumption of digital beamforming structures at least on par with that
of analog beamforming, thus redeeming them from the ghetto they had been placed
in over the last year. Digital beamforming, coupled with the use of large
antenna arrays at both sides of the communication link, promises thus to be one
of the secret weapons of future 6G networks, capable of unleashing
unprecedented values of spectral and energy efficiency for ultra-broadband
connectivity.


Abstract:
  The Ensemble Kalman Filter (EnKF) has achieved great successes in data
assimilation in atmospheric and oceanic sciences, but its failure in
convergence to the right filtering distribution precludes its use for
uncertainty quantification. We reformulate the EnKF under the framework of
Langevin dynamics, which leads to a new particle filtering algorithm, the
so-called Langevinized EnKF. The Langevinized EnKF inherits the
forecast-analysis procedure from the EnKF and the use of mini-batch data from
the stochastic gradient Langevin-type algorithms, which make it scalable with
respect to both the dimension and sample size. We prove that the Langevinized
EnKF converges to the right filtering distribution in Wasserstein distance
under the big data scenario that the dynamic system consists of a large number
of stages and has a large number of samples observed at each stage. We
reformulate the Bayesian inverse problem as a dynamic state estimation problem
based on the techniques of subsampling and Langevin diffusion process. We
illustrate the performance of the Langevinized EnKF using a variety of
examples, including the Lorenz-96 model, high-dimensional variable selection,
Bayesian deep learning, and Long Short Term Memory (LSTM) network learning with
dynamic data.


Abstract:
  We introduce dissipative solutions to the compressible Navier-Stokes system
with potential temperature transport motivated by the concept of Young
measures. We prove their global-in-time existence by means of convergence
analysis of a mixed finite element-finite volume method. If a classical
solution to the compressible Navier-Stokes system with potential temperature
transport exists, we prove the strong convergence of numerical solutions. Our
results hold for the full range of adiabatic indices including the physically
relevant cases in which the existence of global-in-time weak solutions is open.


Abstract:
  A graph neural network (GNN) enables deep learning on structured graph data.
There are two major GNN training obstacles: 1) it relies on high-end servers
with many GPUs which are expensive to purchase and maintain, and 2) limited
memory on GPUs cannot scale to today's billion-edge graphs. This paper presents
Dorylus: a distributed system for training GNNs. Uniquely, Dorylus can take
advantage of serverless computing to increase scalability at a low cost.
  The key insight guiding our design is computation separation. Computation
separation makes it possible to construct a deep, bounded-asynchronous pipeline
where graph and tensor parallel tasks can fully overlap, effectively hiding the
network latency incurred by Lambdas. With the help of thousands of Lambda
threads, Dorylus scales GNN training to billion-edge graphs. Currently, for
large graphs, CPU servers offer the best performance-per-dollar over GPU
servers. Just using Lambdas on top of CPU servers offers up to 2.75x more
performance-per-dollar than training only with CPU servers. Concretely, Dorylus
is 1.22x faster and 4.83x cheaper than GPU servers for massive sparse graphs.
Dorylus is up to 3.8x faster and 10.7x cheaper compared to existing
sampling-based systems.


Abstract:
  The problem of dynamic prediction with time-dependent covariates, given by
biomarkers, repeatedly measured over time, has received much attention over the
last decades. Two contrasting approaches have become in widespread use. The
first is joint modelling, which attempts to jointly model the longitudinal
markers and the event time. The second is landmarking, a more pragmatic
approach that avoids modelling the marker process. Landmarking has been shown
to be less efficient than correctly specified joint models in simulation
studies, when data are generated from the joint model. When the mean model is
misspecified, however, simulation has shown that joint models may be inferior
to landmarking.
  The objective of this paper is to develop methods that improve the predictive
accuracy of landmarking, while retaining its relative simplicity and
robustness. We start by fitting a working longitudinal model for the biomarker,
including a temporal correlation structure. Based on that model, we derive a
predictable time-dependent process representing the expected value of the
biomarker after the landmark time, and we fit a time-dependent Cox model based
on the predictable time-dependent covariate. Dynamic predictions based on this
approach for new patients can be obtained by first deriving the expected values
of the biomarker, given the measured values before the landmark time point, and
then calculating the predicted probabilities based on the time-dependent Cox
model.
  We illustrate the approach in predicting overall survival in liver cirrhosis
patients based on prothrombin index.


Abstract:
  Imitation learning (IL) algorithms use expert demonstrations to learn a
specific task. Most of the existing approaches assume that all expert
demonstrations are reliable and trustworthy, but what if there exist some
adversarial demonstrations among the given data-set? This may result in poor
decision-making performance. We propose a novel general frame-work to directly
generate a policy from demonstrations that autonomously detect the adversarial
demonstrations and exclude them from the data set. At the same time, it's
sample, time-efficient, and does not require a simulator. To model such
adversarial demonstration we propose a min-max problem that leverages the
entropy of the model to assign weights for each demonstration. This allows us
to learn the behavior using only the correct demonstrations or a mixture of
correct demonstrations.


Abstract:
  We consider the problem of designing video games (modeled here by choosing
the structure of a linear program solved by players) so that players with
different resources play diverse strategies. In particular, game designers hope
to avoid scenarios where players use the same ``weapons'' or ``tactics'' even
as they progress through the game. We model this design question as a choice
over the constraint matrix $A$ and cost vector $c$ that seeks to maximize the
number of possible supports of unique optimal solutions (what we call loadouts)
of Linear Programs $\max\{c^\top x \mid Ax \le b, x \ge 0\}$ with nonnegative
data considered over all resource vectors $b$. We provide an upper bound on the
optimal number of loadouts and provide a family of constructions that have an
asymptotically optimal number of loadouts. The upper bound is based on a
connection between our problem and the study of triangulations of point sets
arising from polyhedral combinatorics, and specifically the combinatorics of
the cyclic polytope. Our asymptotically optimal construction also draws
inspiration from the properties of the cyclic polytope. Our construction
provides practical guidance to game designers seeking to offer a diversity of
play for their plays.


Abstract:
  We investigate further the properties of axially symmetrical
monopole-antimonopole pair in the standard Weinberg-Salam theory. These
numerical solutions behave quite differently from their counterparts in the
SU(2) Yang-Mills-Higgs theory as the poles are now bounded by a flux string.
Our results confirm that the energy of these solutions resides in a range of
13.17 - 21.02 TeV. In addition, we found strong evidence suggesting this
configuration is time-dependent and discovered an energy oscillation phenomenon
between the monopole and anti-monopole, which is associated with the symmetry
of the solution. Finally, we calculate numerically the magnetic charge of the
solution and confirm that its value is indeed
$\frac{4\pi}{e}\sin^2\theta_{\scalebox{.5} {W}}$, as predicted by Y. Nambu in
the 1970s. Counterintuitively, the magnetic charge is spread out and
distributed along the string instead of concentrated near the poles.


Abstract:
  Ultra-short period (USP) planets are an enigmatic subset of exoplanets
defined by having orbital periods $<$ 1 day. It is still not understood how USP
planets form, or to what degree they differ from planets with longer orbital
periods. Most USP planets have radii $<$ 2 $R_{\oplus}$, while planets that
orbit further from their star extend to Jupiter size ($>$ 10 $R_{\oplus}$).
Several theories attempt to explain the formation and composition of USP
planets: they could be remnant cores of larger gas giants that lost their
atmospheres due to photo-evaporation or Roche lobe overflow, or they could have
developed through mass accretion in the innermost part of the protoplanetary
disk. The radius and mass distribution of USP planets could provide important
clues to distinguish between potential formation mechanisms. In this study, we
first verify and update the Kepler catalog of USP planet host star properties,
incorporating new data collected by the Gaia mission where applicable. We then
use the transit depths measured by Kepler to derive a radius distribution and
present occurrence rates for USP planets. Using spherical and tidally distorted
planet models, we then derive a mass distribution for USP planets. Comparisons
between the updated USP planet mass distribution and simulated planetary
systems offer further insights into the formation and evolutionary processes
shaping USP planet populations.


Abstract:
  We illustrate with several new applications the power and elegance of the
Bendixson Dulac theorem to obtain upper bounds of the number of limit cycles
for several families of planar vector fields. In some cases we propose to use a
function related with the curvature of the orbits of the vector field as a
Dulac function. We get some general results for Lienard type equations and for
rigid planar systems. We also present a remarkable phenomenon: for each integer
m greater than one, we provide a simple one parametric differential system for
which we prove that it has limit cycles only for the values of the parameter in
a subset of an interval that decreases exponentially when m grows. One of the
strengths of the results presented in this work is that although they are
obtained with simple calculations, that can be easily checked by hand, they
improve and extend previous studies. Another one is that, for certain systems,
it is possible to reduce the question of the number of limit cycles to the
study of the shape of a planar curve and the sign of an associated function in
one or two variables.


Abstract:
  In the interaction between control and mathematics, mathematical tools are
fundamental for all the control methods, but it is unclear how control impacts
mathematics. This is the first part of our paper that attempts to give an
answer with focus on solving linear algebraic equations (LAEs) from the
perspective of systems and control, where it mainly introduces the
controllability-based design results. By proposing an iterative method that
integrates a learning control mechanism, a class of tracking problems for
iterative learning control (ILC) is explored for the problem solving of LAEs. A
trackability property of ILC is newly developed, by which analysis and
synthesis results are established to disclose the equivalence between the
solvability of LAEs and the controllability of discrete control systems. Hence,
LAEs can be solved by equivalently achieving the perfect tracking tasks of
resulting ILC systems via the classic state feedback-based design and analysis
methods. It is shown that the solutions for any solvable LAE can all be
calculated with different selections of the initial input. Moreover, the
presented ILC method is applicable to determining all the least squares
solutions of any unsolvable LAE. In particular, a deadbeat design is
incorporated to ILC such that the solving of LAEs can be completed within
finite iteration steps. The trackability property is also generalized to
conventional two-dimensional ILC systems, which creates feedback-based methods,
instead of the common used contraction mapping-based methods, for the design
and convergence analysis of ILC.


Abstract:
  The presented report evaluates Contextualizing Hate Speech Classifiers with
Post-hoc Explanation paper within the scope of ML Reproducibility Challenge
2020. Our work focuses on both aspects constituting the paper: the method
itself and the validity of the stated results. In the following sections, we
have described the paper, related works, algorithmic frameworks, our
experiments and evaluations.


Abstract:
  Despite their outstanding accuracy, semi-supervised segmentation methods
based on deep neural networks can still yield predictions that are considered
anatomically impossible by clinicians, for instance, containing holes or
disconnected regions. To solve this problem, we present a Context-aware Virtual
Adversarial Training (CaVAT) method for generating anatomically plausible
segmentation. Unlike approaches focusing solely on accuracy, our method also
considers complex topological constraints like connectivity which cannot be
easily modeled in a differentiable loss function. We use adversarial training
to generate examples violating the constraints, so the network can learn to
avoid making such incorrect predictions on new examples, and employ the
Reinforce algorithm to handle non-differentiable segmentation constraints. The
proposed method offers a generic and efficient way to add any constraint on top
of any segmentation network. Experiments on two clinically-relevant datasets
show our method to produce segmentations that are both accurate and
anatomically-plausible in terms of region connectivity.


Abstract:
  In this work, we propose an ensemble forecasting approach based on randomized
neural networks. Improved randomized learning streamlines the fitting abilities
of individual learners by generating network parameters in accordance with the
data and target function features. A pattern-based representation of time
series makes the proposed approach suitable for forecasting time series with
multiple seasonality. We propose six strategies for controlling the diversity
of ensemble members. Case studies conducted on four real-world forecasting
problems verified the effectiveness and superior performance of the proposed
ensemble forecasting approach. It outperformed statistical models as well as
state-of-the-art machine learning models in terms of forecasting accuracy. The
proposed approach has several advantages: fast and easy training, simple
architecture, ease of implementation, high accuracy and the ability to deal
with nonstationarity and multiple seasonality in time series.


Abstract:
  When the transition temperature of a continuous phase transition is tuned to
absolute zero, new ordered phases and physical behaviour emerge in the vicinity
of the resulting quantum critical point. Sr3Ru2O7 can be tuned through quantum
criticality with magnetic field at low temperature. Near its critical field Bc
it displays the hallmark T-linear resistivity and a T log(1/T) electronic heat
capacity behaviour of strange metals. However, these behaviours have not been
related to any critical fluctuations. Here we use inelastic neutron scattering
to reveal the presence of collective spin fluctuations whose relaxation time
and strength show a nearly singular variation with magnetic field as Bc is
approached. The large increase in the electronic heat capacity and entropy near
Bc can be understood quantitatively in terms of the scattering of conduction
electrons by these spin-fluctuations. On entering the spin density wave (SDW)
phase present near Bc, the fluctuations become stronger suggesting that the SDW
order is stabilised through an "order-by-disorder" mechanism.


Abstract:
  We study the large time behavior of small data solutions to the
Vlasov-Navier-Stokes system set on $\Omega \times \mathbb{R}^3$, for a smooth
bounded domain $\Omega$ of $\mathbb{R}^3$, with homogeneous Dirichlet boundary
condition for the fluid and absorption boundary condition for the kinetic
phase. We prove that the fluid velocity homogenizes to $0$ while the
distribution function concentrates towards a Dirac mass in velocity centered at
$0$, with an exponential rate. The proof, which follows the methods introduced
in [Han-Kwan - Moussa - Moyano, arXiv:1902.03864v2], requires a careful
analysis of the boundary effects. We also exhibit examples of classes of
initial data leading to a variety of asymptotic behaviors for the kinetic
density, from total absorption to no absorption at all.


Abstract:
  The nature of the three narrow $P_c$ states, i.e., $P_c(4312)$, $P_c(4440)$
and $P_c(4457)$, is under intense discussion since their discovery from the
updated analysis by LHCb. In this work we extend our previous coupled-channel
approach [Phys. Rev. Lett. \bf{124}, 072001 (2020)] by including the
$\Lambda_c\bar{D}^{(*)}$ and $\eta_cp$ as explicitly in addition to the $J/\psi
p$, as required by unitarity and heavy quark spin symmetry (HQSS). Since
inelastic parameters are very badly constrained by the current data, three
schemes are considered: (a) scheme I with pure contact interactions and without
the $\Lambda_c\bar D^{(*)}$ interactions, (b) scheme II, where the one-pion
exchange is added to scheme I, and (c) scheme III, where the $\Lambda_c \bar
D^{(*)}$ are included in addition. It is shown that to obtain cutoff
independent results, OPE in the multichannel system is to be supplemented with
$S$-$D$ mixing contact terms. We demonstrate that the experimental data for the
$J/\psi p$ invariant mass distribution are consistent with the interpretation
of the $P_c(4312)$ and $P_c(4440)/P_c(4457)$ as $\Sigma_c\bar{D}$ and $\Sigma_c
\bar{D}^{*}$ hadronic molecules, respectively, and that the data show clear
evidence for a new narrow $P_c(4380)$, as a $\Sigma_c^*\bar D$ molecule, which
should exist as a consequence of HQSS. While two equally good solutions are
found in scheme I, only one of these solutions with the quantum numbers of the
$P_c(4440)$ and $P_c(4457)$ being $J^P=3/2^-$ and $1/2^-$, respectively,
survives the requirement of regulator independence once the OPE is included.
Moreover, we predict the line shapes in the elastic and inelastic channels and
demonstrate that those related to the $P_c(4440)$ and the $P_c(4457)$ in the
corresponding $\Sigma_c^{(*)}\bar{D}$ and $\eta_cp$ mass distributions allow
one to confirm the quantum numbers given above, once the data are available.


Abstract:
  In this work we introduce a concept of expansiveness for actions of connected
Lie groups. We study some of its properties and investigate some implications
of expansiveness. We study the centralizer of expansive actions and introduce
CW-expansiveness for pseudo-group actions. As an application, we prove
positiveness of geometric entropy for expansive foliations and expansive group
actions.


Abstract:
  The growth of meromorphic solutions of linear difference equations containing
Askey-Wilson divided difference operators is estimated. The $\varphi$-order is
used as a general growth indicator, which covers the growth spectrum between
the logarithmic order $\rho_{\log}(f)$ and the classical order $\rho(f)$ of a
meromorphic function $f$.


Abstract:
  The two-parameter Poisson--Dirichlet diffusion takes values in the infinite
ordered simplex and has a two-parameter Poisson-Dirichlet stationary
distribution ${\cal PD}(\alpha, \theta)$ . The dual process is in terms of a
line-of-descent process which tracks the evolution of non-mutant frequencies
from time zero, and Remarkably does not depend on the parameter $\alpha$.
Methods of proof use the sampling distributions associated to the two-parameter
Poisson-Dirichlet process. We also exploit a connection between the sampling
distribution and a generalized Blackwell and MacQueen P{\'o}lya urn model to
obtain a direct derivation of the transition density.


Abstract:
  We report on the controlled switching of domain wall (DW) magnetization in
aligned stripe domain structures, stabilized in [Co (0.44 nm)/Pt (0.7 nm)]$_X$
($X = 48$, 100, 150) multilayers with perpendicular magnetic anisotropy. The
switching process, induced by an external magnetic field, is monitored by
measuring the evolution of the in-plane magnetization. % We show that the
remanent in-plane magnetization originates from the polarization of the
Bloch-type DWs. With micromagnetic simulations, we reveal that the reversal of
the DW polarization is the result of the emergence and collapse of horizontal
Bloch lines within the DWs at particular strengths of the external magnetic
field, applied opposite to the DW polarization. Our findings are relevant for
DW-based magnonics and bubble skyrmion applications in magnetic multilayers.


Abstract:
  Macular holes are a common eye condition which result in visual impairment.
We look at the application of deep convolutional neural networks to the problem
of macular hole segmentation. We use the 3D U-Net architecture as a basis and
experiment with a number of design variants. Manually annotating and measuring
macular holes is time consuming and error prone. Previous automated approaches
to macular hole segmentation take minutes to segment a single 3D scan. Our
proposed model generates significantly more accurate segmentations in less than
a second. We found that an approach of architectural simplification, by greatly
simplifying the network capacity and depth, exceeds both expert performance and
state-of-the-art models such as residual 3D U-Nets.


Abstract:
  Many effective field theories describing gravity cannot arise from an
underlying theory based on Riemann geometry or its extensions to include
torsion and nonmetricity but may instead emerge from another geometry or may
have a nongeometric basis. The Lagrange density for a broad class of such
theories is investigated. The action for fermions coupled to gravity is
linearized about a Minkowski background and is found to include terms
describing small deviations from Lorentz invariance and gravitational gauge
invariance. The corresponding nonrelativistic hamiltonian is derived at second
order in the fermion momentum. The implications for laboratory experiments and
astrophysical observations with fermions are studied, with primary focus on
anomalous spin-gravity couplings. First constraints on some coefficients are
extracted from existing data obtained via measurements at different potentials,
comparisons of gravitational accelerations, interferometric methods, and
investigations of gravitational bound states. Some prospects for future
experimental studies are discussed.


Abstract:
  We discuss the model robustness of the infinite two-dimensional square grid
with respect to symmetry breakings due to the presence of defects, that is,
lacks of finitely or infinitely many edges. Precisely, we study how these
topological perturbations of the square grid affect the so-called dimensional
crossover identified in [Adami et al. 2019]. Such a phenomenon has two
evidences: the coexistence of the one and the two-dimensional Sobolev
inequalities and the appearence of a continuum of $L^2$-critical exponents for
the ground states at fixed mass of the nonlinear Schr\"odinger equation. From
this twofold perspective, we investigate which classes of defects do preserve
the dimensional crossover and which classes do not.


Abstract:
  Enumerating integral orbits in prehomogeneous vector spaces plays an
important role in arithmetic statistics. We describe a method of proving
subconvexity of the zeta function enumerating the integral orbits, illustrated
by proving a subconvex estimate for the Shintani $\zeta$ function enumerating
class numbers of binary cubic forms. The method also obtains a subconvex
estimate for the zeta function twisted by a Maass cusp form.


Abstract:
  A widely used tool for analysing the Covid-19 pandemic is the standard SIR
model. It seems often to be used as a black box, not taking into account that
this model was derived as a special case of the seminal Kermack-McKendrick
theory from 1927. This is our starting point. We explain the setup of the
Kermack-McKendrick theory (passing to a discrete approach) and use medical
information for specializing to a model which we call {\em adapted
K-McK-model}. This includes effects of vaccination, mass testing and mutants.
We demonstrate the use of the model by applying it to the development in
Germany. As a striking application we demonstrate that a comparatively mild
intervention reducing the time until quarantine by one day leads to a drastic
improvement. A similar effect can be obtained by certain mass testings as we
will demonstrate. We discuss possibilities to apply the model both for
predictions and as an analysis tool. We compare the adapted K-McK-model to the
standard SIR model and observe condiderable differences if the contact rates
are not constant. Finally we compare the model reproduction rate with the
empirical reproduction rate determined by the Robert Koch-Institut.


Abstract:
  By a combined study with first-principles calculations and symmetry analysis,
we theoretically investigate the electronic properties of monolayer
MoSi$_2$N$_4$. While the spin-orbital coupling results in bands splitting, the
horizontal mirror symmetry locks the spin polarization along z-direction. In
addition, a three-band tight-binding model is constructed to describe the
low-energy quasi-particle states of monolayer MoSi$_2$N$_4$, which can be
generalized to strained MoSi$_2$N$_4$ and its derivatives. The calculations
using the tight-binding model show an undamped $\sqrt{q}$-dependent plasmon
mode that agrees well with the results of first-principles calculations. Our
model can be extended to be suitable for future theoretical and numerical
studies of low-energy properties in MoSi$_2$N$_4$ family materials.
Furthermore, the study of electronic properties of monolayer MoSi$_2$N$_4$
paves a way for its applications in spintronics and plasmonics.


Abstract:
  Stars in the mass range from 8 to 10 solar masses are expected to produce one
of two types of supernovae (SNe), either electron-capture supernovae (ECSNe) or
core-collapse supernovae (CCSNe), depending on their previous evolution. Either
of the associated progenitors retain extended and massive hydrogen-rich
envelopes, the observables of these SNe are, therefore, expected to be similar.
In this study we explore the differences in these two types of SNe.
Specifically, we investigate three different progenitor models: a
solar-metallicity ECSN progenitor with an initial mass of 8.8 solar masses, a
zero-metallicity progenitor with 9.6 solar masses, and a solar-metallicity
progenitor with 9 solar masses, carrying out radiative transfer simulations for
these progenitors. We present the resulting light curves for these models. The
models exhibit very low photospheric velocity variations of about 2000 km/s,
therefore, this may serve as a convenient indicator of low-mass SNe. The ECSN
has very unique light curves in broad bands, especially the U band, and does
not resemble any currently observed SN. This ECSN progenitor being part of a
binary will lose its envelope for which reason the light curve becomes short
and undetectable. The SN from the 9.6 solar masses progenitor exhibits also
quite an unusual light curve, explained by the absence of metals in the initial
composition. The artificially iron polluted 9.6 solar masses model demonstrates
light curves closer to normal SNe IIP. The SN from the 9 solar masses
progenitor remains the best candidate for so-called low-luminosity SNe IIP like
SN 1999br and SN 2005cs.


Abstract:
  In this paper, we study communication efficient distributed algorithms for
distributionally robust federated learning via periodic averaging with adaptive
sampling. In contrast to standard empirical risk minimization, due to the
minimax structure of the underlying optimization problem, a key difficulty
arises from the fact that the global parameter that controls the mixture of
local losses can only be updated infrequently on the global stage. To
compensate for this, we propose a Distributionally Robust Federated Averaging
(DRFA) algorithm that employs a novel snapshotting scheme to approximate the
accumulation of history gradients of the mixing parameter. We analyze the
convergence rate of DRFA in both convex-linear and nonconvex-linear settings.
We also generalize the proposed idea to objectives with regularization on the
mixture parameter and propose a proximal variant, dubbed as DRFA-Prox, with
provable convergence rates. We also analyze an alternative optimization method
for regularized cases in strongly-convex-strongly-concave and non-convex (under
PL condition)-strongly-concave settings. To the best of our knowledge, this
paper is the first to solve distributionally robust federated learning with
reduced communication, and to analyze the efficiency of local descent methods
on distributed minimax problems. We give corroborating experimental evidence
for our theoretical results in federated learning settings.


Abstract:
  We model the dynamics of the cryptocurrency (CC) asset class via a stochastic
volatility with correlated jumps (SVCJ) model with rolling-window parameter
estimates. By analyzing the time-series of parameters, stylized patterns are
observable which are robust to changes of the window size and supported by
cluster analysis. During bullish periods, volatility stabilizes at low levels
and the size and volatility of jumps in mean decreases. In bearish periods
though, volatility increases and takes longer to return to its long-run trend.
Furthermore, jumps in mean and jumps in volatility are independent. With the
rise of the CC market in 2017, a level shift of the volatility of volatility
occurred. All codes are available on Quantlet.com.


Abstract:
  Quantum emitters (QEs) in two-dimensional transition metal dichalcogenides
(2D TMDCs) have advanced to the forefront of quantum communication and
transduction research due to their unique potentials in accessing valley
pseudo-spin degree of freedom (DOF) and facile integration into
quantum-photonic, electronic and sensing platforms via the
layer-by-layer-assembly approach. To date, QEs capable of operating in O-C
telecommunication bands have not been demonstrated in TMDCs. Here we report a
deterministic creation of such telecom QEs emitting over the 1080 to 1550 nm
wavelength range via coupling of 2D molybdenum ditelluride (MoTe2) to strain
inducing nano-pillar arrays. Our Hanbury Brown and Twiss experiment conducted
at 10 K reveals clear photon antibunching with 90% single photon purity.
Ultra-long lifetimes, 4-6 orders of magnitude longer than that of the 2D
exciton, are also observed. Polarization analysis further reveals that while
some QEs display cross-linearly polarized doublets with ~1 meV splitting
resulting from the strain induced anisotropic exchange interaction, valley
degeneracy is preserved in other QEs. Valley Zeeman splitting as well as
restoring of valley symmetry in cross-polarized doublets are observed under 8T
magnetic field. In contrast to other telecom QEs, our QEs which offer the
potential to access valley DOF through single photons, could lead to
unprecedented advantages in optical fiber-based quantum networks.


Abstract:
  In the presence of both space and time reversal symmetries, an s-wave A1g
superconducting state is usually topologically trivial. Here we demonstrate
that an exception can take place in a type of nonsymmorphic lattice structures.
We specify the demonstration in a system with a centrosymmetric space group
P4/nmm, the symmetry group that governs iron-based superconductors, by showing
the existence of a second-order topological state protected by a mirror
symmetry. The topological superconductivity is featured by 2Z degenerate Dirac
cones on the (1,0) edge, and Z pairs of Majorana modes at the intersection
between the (1,1) and (1,-1) edges. The topological invariance and Fermi
surface criterion for the topological state are provided. Moreover, we point
out that the previously proposed s-wave state in iron-based superconductors,
which features a sign-changed superconducting order parameter between two
electron pockets, is such a topological state. Thus, these results not only
open a new route to pursue topological superconductivity, but also establish a
measurable quantity to settle one long-lasting debate on the pairing nature of
iron-based superconductors.


Abstract:
  Automated audio captioning (AAC) aims at generating summarizing descriptions
for audio clips. Multitudinous concepts are described in an audio caption,
ranging from local information such as sound events to global information like
acoustic scenery. Currently, the mainstream paradigm for AAC is the end-to-end
encoder-decoder architecture, expecting the encoder to learn all levels of
concepts embedded in the audio automatically. This paper first proposes a topic
model for audio descriptions, comprehensively analyzing the hierarchical audio
topics that are commonly covered. We then explore a transfer learning scheme to
access local and global information. Two source tasks are identified to
respectively represent local and global information, being Audio Tagging (AT)
and Acoustic Scene Classification (ASC). Experiments are conducted on the AAC
benchmark dataset Clotho and Audiocaps, amounting to a vast increase in all
eight metrics with topic transfer learning. Further, it is discovered that
local information and abstract representation learning are more crucial to AAC
than global information and temporal relationship learning.


Abstract:
  A long-standing question in the theory of measures of noncompactness is that
for the Kuratowski measure of noncompactness $\alpha$ defined on a metric space
$M$, and for every bounded subset $B\subset M$, is there a countable subset
$B_0\subset B$ such that $\alpha(B_0)=\alpha(B)$? In this paper, we give an
affirmative answer to the question above. It is done by showing that for each
nonempty set $B$ of a Banach space, there is a countable subset $B_0\subset B$
so that $B$ is strongly finitely representable in $B_0$, and that there is a
free ultrafilter $\mathcal U$ so that $B$ is affinely isometric to a subset of
the ultrapower $[{\rm co}(B_0)]_\mathcal U$ of ${\rm co}(B_0)$.


Abstract:
  The main contributions of this paper are the proposition and the convergence
analysis of a class of inertial projection-type algorithm for solving
variational inequality problems in real Hilbert spaces where the underline
operator is monotone and uniformly continuous. We carry out a unified analysis
of the proposed method under very mild assumptions. In particular, weak
convergence of the generated sequence is established and nonasymptotic $O(1/n)$
rate of convergence is established, where $n$ denotes the iteration counter. We
also present some experimental results to illustrate the profits gained by
introducing the inertial extrapolation steps.


Abstract:
  Recent works in medical image segmentation have actively explored various
deep learning architectures or objective functions to encode high-level
features from volumetric data owing to limited image annotations. However, most
existing approaches tend to ignore cross-volume global context and define
context relations in the decision space. In this work, we propose a novel
voxel-level Siamese representation learning method for abdominal multi-organ
segmentation to improve representation space. The proposed method enforces
voxel-wise feature relations in the representation space for leveraging limited
datasets more comprehensively to achieve better performance. Inspired by recent
progress in contrastive learning, we suppressed voxel-wise relations from the
same class to be projected to the same point without using negative samples.
Moreover, we introduce a multi-resolution context aggregation method that
aggregates features from multiple hidden layers, which encodes both the global
and local contexts for segmentation. Our experiments on the multi-organ dataset
outperformed the existing approaches by 2% in Dice score coefficient. The
qualitative visualizations of the representation spaces demonstrate that the
improvements were gained primarily by a disentangled feature space.


Abstract:
  What makes an artificial neural network easier to train and more likely to
produce desirable solutions than other comparable networks? In this paper, we
provide a new angle to study such issues under the setting of a fixed number of
model parameters which in general is the most dominant cost factor. We
introduce a notion of variability and show that it correlates positively to the
activation ratio and negatively to a phenomenon called {Collapse to Constants}
(or C2C), which is closely related but not identical to the phenomenon commonly
known as vanishing gradient. Experiments on a styled model problem empirically
verify that variability is indeed a key performance indicator for fully
connected neural networks. The insights gained from this variability study will
help the design of new and effective neural network architectures.


Abstract:
  This paper describes the use of Distributed Ledger Technologies as a mean to
enforce social contracts and to orchestrate the behaviour of agents in a smart
city environment. Specifically, we present a scheme to price personalised risk
in sharing economy applications. We provide proofs for the convergence of the
proposed stochastic system and we validate our approach through the use of
extensive Monte Carlo simulations.