Abstract:
  Explainable Artificial Intelligence (XAI) is a rising field in AI. It aims to
produce a demonstrative factor of trust, which for human subjects is achieved
through communicative means, which Machine Learning (ML) algorithms cannot
solely produce, illustrating the necessity of an extra layer producing support
to the model output. When approaching the medical field, we can see challenges
arise when dealing with the involvement of human-subjects, the ideology behind
trusting a machine to tend towards the livelihood of a human poses an ethical
conundrum - leaving trust as the basis of the human-expert in acceptance to the
machines decision. The aim of this paper is to apply XAI methods to demonstrate
the usability of explainable architectures as a tertiary layer for the medical
domain supporting ML predictions and human-expert opinion, XAI methods produce
visualization of the feature contribution towards a given models output on both
a local and global level. The work in this paper uses XAI to determine feature
importance towards high-dimensional data-driven questions to inform
domain-experts of identifiable trends with a comparison of model-agnostic
methods in application to ML algorithms. The performance metrics for a
glass-box method is also provided as a comparison against black-box capability
for tabular data. Future work will aim to produce a user-study using metrics to
evaluate human-expert usability and opinion of the given models.


Abstract:
  Individual mobility is driven by demand for activities with diverse
spatiotemporal patterns, but existing methods for mobility prediction often
overlook the underlying activity patterns. To address this issue, this study
develops an activity-based modeling framework for individual mobility
prediction. Specifically, an input-output hidden Markov model (IOHMM) framework
is proposed to simultaneously predict the (continuous) time and (discrete)
location of an individual's next trip using transit smart card data. The
prediction task can be transformed into predicting the hidden activity duration
and end location. Based on a case study of Hong Kong's metro system, we show
that the proposed model can achieve similar prediction performance as the
state-of-the-art long short-term memory (LSTM) model. Unlike LSTM, the proposed
IOHMM model can also be used to analyze hidden activity patterns, which
provides meaningful behavioral interpretation for why an individual makes a
certain trip. Therefore, the activity-based prediction framework offers a way
to preserve the predictive power of advanced machine learning methods while
enhancing our ability to generate insightful behavioral explanations, which is
useful for enhancing situational awareness in user-centric transportation
applications such as personalized traveler information.


Abstract:
  $\alpha$-cluster structures above double shell closures are among the
cornerstones for nuclear $\alpha$-cluster physics. Semi-microscopic cluster
models (SMCMs) are important theoretical models to study their properties. A
crucial ingredient of SMCM is the effective potential between the alpha cluster
and the doubly magic nucleus. We derive new double-folding potentials between
$\alpha$ clusters and doubly magic nuclei from soft local chiral
nucleon-nucleon potentials given by chiral effective field theory ($\chi$EFT)
at the next-to-next-to-leading order. The $\alpha$-cluster structures in
${}^{8}\text{Be}$, ${}^{20}\text{Ne}$, ${}^{44,52}\text{Ti}$, and
${}^{212}\text{Po}$ are explored to validate these new double-folding
potentials. The $\alpha$ decay of ${}^{104}\text{Te}$ is also studied in the
light of recent experimental results. Our study shows that double-folding
potentials from $\chi$EFT are the new reliable effective potentials for the
SMCM approach to $\alpha$-cluster structures above double shell closures, with
both conceptual and phenomenological merits.


Abstract:
  We investigate stabilizers of finite sets of rational points in Cantor space
for the Higman-Thompson groups $V_{n,r}$. We prove that the pointwise
stabilizer is an iterated ascending HNN extension of $V_{n,q}$ for any $q\geq
1$. We also prove that the commutator subgroup of the pointwise stabilizer is
simple, and we compute the abelianization. Finally, for each $n$ we classify
such pointwise stabilizers up to isomorphism.


Abstract:
  Animal cells are active, contractile objects. While bioassays address the
molecular characterization of cell contractility, the mechanical
characterization of the active forces in cells remains challenging. Here by
confronting theoretical analysis, finite element modeling and experiments, we
analyze the relevance of existing mechanical approaches and show that their
complementarity allows us to characterize both the resistive and the active
components of the intracellular stresses that build up following cell adhesion.
By highlighting their linear correlation, we show that these mechanical
approaches allow us to localize the stress generators in the cells, give an
upper bound to their size, and demonstrate that they are mechanically linked to
the substrate at a very local scale.


Abstract:
  Parents - particularly moms - increasingly consult social media for support
when taking decisions about their young children, and likely also when advising
other family members such as elderly relatives. Minimizing malignant online
influences is therefore crucial to securing their assent for policies ranging
from vaccinations, masks and social distancing against the pandemic, to
household best practices against climate change, to acceptance of future 5G
towers nearby. Here we show how a strengthening of bonds across online
communities during the pandemic, has led to non-Covid-19 conspiracy theories
(e.g. fluoride, chemtrails, 5G) attaining heightened access to mainstream
parent communities. Alternative health communities act as the critical conduits
between conspiracy theorists and parents, and make the narratives more
palatable to the latter. We demonstrate experimentally that these
inter-community bonds can perpetually generate new misinformation, irrespective
of any changes in factual information. Our findings show explicitly why
Facebook's current policies have failed to stop the mainstreaming of
non-Covid-19 and Covid-19 conspiracy theories and misinformation, and why
targeting the largest communities will not work. A simple yet exactly solvable
and empirically grounded mathematical model, shows how modest tailoring of
mainstream communities' couplings could prevent them from tipping against
establishment guidance. Our conclusions should also apply to other social media
platforms and topics.


Abstract:
  In two dimensional isotropic scale invariant theories, the time scaling of
the entanglement entropy of a segment is fixed via the conformal symmetry. We
consider scale invariance in a more general sense and show that in integrable
theories that the scale invariance is anisotropic between time and space,
parametrized by $z$, most of the entanglement is carried by the slow modes for
$z>1$. At early times entanglement grows linearly due to the contribution of
the fast modes, before smoothly entering a slow mode regime where it grows
forever with $t^{\frac{1}{1-z}}$. The slow mode regime admits a logarithmic
enhancement in bosonic theories. We check our analytical results against
numerical simulations in corresponding fermionic and bosonic lattice models
finding extremely good agreement. We show that in these non-relativistic
theories that the slow modes are dominant, local quantum information is
universally scrambled in a stronger way compared to their relativistic
counterparts.


Abstract:
  Preserving customers' expectations and understanding factors affecting their
purchasing decisions would significantly affect designing effective marketing
and advertising strategies. However, constantly and swiftly changing the
customers' interests and consumption behaviors, make it inevitable to utilize
the sophisticated tools and approaches based on advanced technologies. Among
them, neuromarketing by measuring the customers' physiological and neural
signals, studying the consumers' cognitive, and affective responses to
marketing stimulus, provides deep insight into the customers' motivations,
preferences, and decisions. Recently, the Internet of Everything (IoE) has
brought many new opportunities to the industry and has attracted the attention
of many researchers in recent years. The main objective of this paper is to
address how the Internet of Everything (IoE) would empower neuromarketing
techniques. In particular, applications of IoE gadgets and devices in eleven
groups of neuromarketing techniques are discussed to present numerous solutions
that would help meet this goal. Moreover, we present an in-depth understanding
of current research issues as well as emerging trends.


Abstract:
  The intense emission of 511 keV photons from the Galactic center and within
terrestrial thunderstorms is attributed to the formation of parapositronia'
clouds. Unbound electron-positron pairs and positronia can be created by strong
electromagnetic fields produced in interactions of electrically charged
objects, in particular, in collisions of heavy nuclei. Kinematics of this
process favours abundant creation of the unbound electron-positron pairs with
very small masses and the confined parapositronia states which decay directly
to two 511 keV quanta. Therefore we propose to consider interactions of
electromagnetic fields of colliding heavy ions as a source of low-mass pairs
which can transform to 511 keV quanta. Intensity of their creation is enlarged
by the factor Z$^4$ (Z is the electric charge of a heavy ion) compared to
protons with Z=1. These processes are especially important at very high
energies of nuclear collisions because their cross sections increase
proportionally to cube of the logarithm of energy and can even exceed the cross
sections of strong interactions which may not increase faster than the squared
logarithm of energy. Moreover, production of extremely low-mass $e^+e^-$-pairs
in ultraperipheral nuclear collisions is strongly enhanced due to the
Sommerfeld-Gamow-Sakharov (SGS) factor which accounts for mutual Coulomb
attraction of non-relativistic electrons to positrons in case of low
pair-masses. This attraction may lead to their annihilation and, therefore, to
the increased intensity of 511 keV photons. It is proposed to confront the
obtained results to forthcoming experimental data at NICA collider.


Abstract:
  Most prior works on deep learning-based wireless device classification using
radio frequency (RF) data apply off-the-shelf deep neural network (DNN) models,
which were matured mainly for domains like vision and language. However,
wireless RF data possesses unique characteristics that differentiate it from
these other domains. For instance, RF data encompasses intermingled time and
frequency features that are dictated by the underlying hardware and protocol
configurations. In addition, wireless RF communication signals exhibit
cyclostationarity due to repeated patterns (PHY pilots, frame prefixes, etc.)
that these signals inherently contain. In this paper, we begin by explaining
and showing the unsuitability as well as limitations of existing DNN feature
design approaches currently proposed to be used for wireless device
classification. We then present novel feature design approaches that exploit
the distinct structures of the RF communication signals and the spectrum
emissions caused by transmitter hardware impairments to custom-make DNN models
suitable for classifying wireless devices using RF signal data. Our proposed
DNN feature designs substantially improve classification robustness in terms of
scalability, accuracy, signature anti-cloning, and insensitivity to environment
perturbations. We end the paper by presenting other feature design strategies
that have great potentials for providing further performance improvements of
the DNN-based wireless device classification, and discuss the open research
challenges related to these proposed strategies.


Abstract:
  Rowhammer attacks that corrupt level-1 page tables to gain kernel privilege
are the most detrimental to system security and hard to mitigate. However,
recently proposed software-only mitigations are not effective against such
kernel privilege escalation attacks. In this paper, we propose an effective and
practical software-only defense, called SoftTRR, to protect page tables from
all existing rowhammer attacks on x86. The key idea of SoftTRR is to refresh
the rows occupied by page tables when a suspicious rowhammer activity is
detected. SoftTRR is motivated by DRAM-chip-based target row refresh (ChipTRR)
but eliminates its main security limitation (i.e., ChipTRR tracks a limited
number of rows and thus can be bypassed by many-sided hammer). Specifically,
SoftTRR protects an unlimited number of page tables by tracking memory accesses
to the rows that are in close proximity to page-table rows and refreshing the
page-table rows once the tracked access count exceeds a pre-defined threshold.
We implement a prototype of SoftTRR as a loadable kernel module, and evaluate
its security effectiveness, performance overhead, and memory consumption. The
experimental results show that SoftTRR protects page tables from real-world
rowhammer attacks and incurs small performance overhead as well as memory cost.


Abstract:
  This paper presents a clustering algorithm that is an extension of the
Category Trees algorithm. Category Trees is a clustering method that creates
tree structures that branch on category type and not feature. The development
in this paper is to consider a secondary order of clustering that is not the
category to which the data row belongs, but the tree, representing a single
classifier, that it is eventually clustered with. Each tree branches to store
subsets of other categories, but the rows in those subsets may also be related.
This paper is therefore concerned with looking at that second level of
clustering between the other category subsets, to try to determine if there is
any consistency over it. It is argued that Principal Components may be a
related and reciprocal type of structure, and there is an even bigger question
about the relation between exemplars and principal components, in general. The
theory is demonstrated using the Portugal Forest Fires dataset as a case study.
The Category Trees are then combined with other Self-Organising algorithms from
the author and it is suggested that they all belong to the same family type,
which is an Entropy-style of classifier.


Abstract:
  Model-based planning and prospection are widely studied in both cognitive
neuroscience and artificial intelligence (AI), but from different perspectives
- and with different desiderata in mind (biological realism versus scalability)
that are difficult to reconcile. Here, we introduce a novel method to plan in
large POMDPs - Active Tree Search - that combines the normative character and
biological realism of a leading planning theory in neuroscience (Active
Inference) and the scalability of Monte-Carlo methods in AI. This unification
is beneficial for both approaches. On the one hand, using Monte-Carlo planning
permits scaling up the biologically grounded approach of Active Inference to
large-scale problems. On the other hand, the theory of Active Inference
provides a principled solution to the balance of exploration and exploitation,
which is often addressed heuristically in Monte-Carlo methods. Our simulations
show that Active Tree Search successfully navigates binary trees that are
challenging for sampling-based methods, problems that require adaptive
exploration, and the large POMDP problem Rocksample. Furthermore, we illustrate
how Active Tree Search can be used to simulate neurophysiological responses
(e.g., in the hippocampus and prefrontal cortex) of humans and other animals
that contain large planning problems. These simulations show that Active Tree
Search is a principled realisation of neuroscientific and AI theories of
planning, which offers both biological realism and scalability.


Abstract:
  In this paper we provide further studies of the Fenchel duality theory in the
general frame work of locally convex topological vector (LCTV) spaces. We prove
the validity of the Fenchel strong duality under some qualification conditions
via generalized relative interiors imposed on the epigraphs and the domains of
the functions involved. Our results directly generalize the classical
Fenchel-Rockafellar theorem on strong duality from finite dimensions to LCTV
spaces.


Abstract:
  Optimization of the intermetallic layer thickness and the suppression of
interfacial defects are key elements to improve the load bearing capacity of
dissimilar joints. However, till date we do not have a systematic tool to
investigate the dissimilar joints and the intermetallic properties produced by
a welding condition. Friction Melt Bonding (FMB) is a recently developed
technique for joining dissimilar metals that also does not exempt to these
challenges. The FMB of DP980 and Al6061-T6 is investigated using a new physical
simulation tool, based on Gleeble thermo-mechanical simulator, to understand
the effect of individual parameter on the intermetallic formation. The proposed
method demonstrates its capability in reproducing the intermetallic
characteristics, including the thickness of intermetallic bonding layer, the
morphology and texture of its constituents (Fe2Al5 and Fe4Al13), as well as
their nanohardness and reduced modulus. The advantages of physical simulation
tool can enable novel developing routes for the development of dissimilar metal
joining processes and facilitate to reach the requiring load bearing capacity
of the joints.


Abstract:
  In this paper we discuss a sequential algorithm for the computation of a
minimum-time speed profile over a given path, under velocity, acceleration and
jerk constraints. Such a problem arises in industrial contexts such as
automated warehouses, where LGVs need to perform assigned tasks as fast as
possible in order to increase productivity. It can be reformulated as an
optimization problem with a convex objective function, linear velocity and
acceleration constraints, and non-convex jerk constraints, which, thus,
represent the main source of difficulty. While existing non-linear programming
(NLP) solvers can be employed for the solution of this problem, it turns out
that the performance and robustness of such solvers can be enhanced by the
sequential line-search algorithm proposed in this paper. At each iteration a
feasible direction, with respect to the current feasible solution, is computed,
and a step along such direction is taken in order to compute the next iterate.
The computation of the feasible direction is based on the solution of a
linearized version of the problem, and the solution of the linearized problem,
through an approach which strongly exploits its special structure, represents
the main contribution of this work. The efficiency of the proposed approach
with respect to existing NLP solvers is proved through different computational
experiments.


Abstract:
  Under structural conditions which are almost optimal, we derive a
quantitative version of boundary estimate then prove existence of solutions to
Dirichlet problem for a class of fully nonlinear elliptic equations on
Hermitian manifolds.


Abstract:
  The development of Graph Neural Networks (GNNs) has led to great progress in
machine learning on graph-structured data. These networks operate via diffusing
information across the graph nodes while capturing the structure of the graph.
Recently there has also seen tremendous progress in quantum computing
techniques. In this work, we explore applications of multi-particle quantum
walks on diffusing information across graphs. Our model is based on learning
the operators that govern the dynamics of quantum random walkers on graphs. We
demonstrate the effectiveness of our method on classification and regression
tasks.


Abstract:
  Backstepping based controller and observer models were designed for higher
order linear and nonlinear Schr\"odinger equations on a finite interval in Part
I of this study where the controller was assumed to be acting from the left
endpoint of the medium. In this companion paper, we further the analysis by
considering boundary controller(s) acting at the right endpoint of the domain.
It turns out that the problem is more challenging in this scenario as the
associated boundary value problem for the backstepping kernel becomes
overdetermined and lacks a smooth solution. The latter is essential to switch
back and forth between the original plant and the so called target system. To
overcome this difficulty we rely on the strategy of using an imperfect kernel,
namely one of the boundary conditions in kernel PDE model is disregarded. The
drawback is that one loses rapid stabilization in comparison with the left
endpoint controllability. Nevertheless, the exponential decay of the $L^2$-norm
with a certain rate still holds. The observer design is associated with new
challenges from the point of view of wellposedness and one has to prove
smoothing properties for an associated initial boundary value problem with
inhomogeneous boundary data. This problem is solved by using Laplace transform
in time. However, the Bromwich integral that inverts the transformed solution
is associated with certain analyticity issues which are treated through a
subtle analysis. Numerical algorithms and simulations verifying the theoretical
results are given.


Abstract:
  After a short revision of the historical milestones on normal numbers, we
introduce the Borel numbers as the reals admitting a probability function on
their different bases representations. In this setting, we provide two
probabilistic characterizations of normality based on the stochastic
independence of their digits. Finally, we define the pseudonormality condition,
which is satisfied by normal numbers and may be evaluated in a finite number of
steps.


Abstract:
  Various forms of exact laws governing magnetohydrodynamic (MHD) turbulence
have been derived either in the incompressibility limit, or for isothermal
compressible flows. Here we propose a more general method that allows us to
obtain such laws for any turbulent isentropic flow (i.e., constant entropy). We
demonstrate that the known MHD exact laws (incompressible and isothermal) and
the new (polytropic) one can be obtained as specific cases of the general law
when the corresponding closure equation is stated. We also recover all known
exact laws of hydrodynamic (HD) turbulence (incompressible, isothermal and
polytropic) from this law in the limit ${\bf B}=0$. We furthermore show that
the difference between the two forms (isothermal and polytropic) of the MHD
exact laws of interest in this work resides in some of the source terms and in
the explicit form of the flux term that depends on internal energy. Finally, we
apply these two forms to Parker Solar Probe (PSP) data taken in the inner
heliosphere to highlight how the different closure equations affect the energy
cascade rate estimates.


Abstract:
  Blockchain like Bitcoin and Ethereum suffer from scalability issues. Sharding
is one of the most promising and leading solutions to scale blockchain. The
basic idea behind sharding is to divide the blockchain network into multiple
committees, where each processing a separate set of transactions, rather than
the entire network processes all transactions. In this paper, we propose a
probabilistic approach to analyze the security of sharding-based blockchain
protocols. Based on this approach, we investigate the threat of Sybil attacks
in these protocols. The key contribution of our paper is a tractable
probabilistic approach to accurately compute the failure probability that at
least one committee fails and ultimately compute the probability of a
successful attack. To show the effectiveness of our approach, we conduct a
numerical and comparative analysis of the proposed approach with existing
approaches.


Abstract:
  By Liouville's theorem, in dimensions 3 or more conformal transformations
form a finite-dimensional group, an apparent drastic departure from the
2-dimensional case. We propose a derived enhancement of the conformal Lie
algebra which is an infinite-dimensional dg-Lie algebra incorporating not only
symmetries but also deformations of the conformal structure. Our approach is
based on (derived) deformation theory of the ambitwistor space of complex
null-geodesics.


Abstract:
  We construct a family of growing finite bounded degree rooted graphs, $G_n$,
in which the mixing time for simple random walk, starting at the root, is order
$\log |G_n|$. Yet after a quasi - isometry, the ratio of $|G_n|$ over the
mixing time grows arbitrarily slow to infinity.


Abstract:
  We are producing high statistics 250 GeV common MC samples for the ILD
physics study using the latest generator, simulation, and reconstruction
packages. In the production, we utilize ILCDirac distributed computing
environment. The estimated resource requirements, the current status and
prospects of the production are presented.


Abstract:
  A scheme is presented to catalyze nuclear fusion by the excitation of the
localized surface plasmon resonance (LSPR) of an Au nanobipyramid (NBP) by
ultrafast (femtosecond) laser pulses. The effect utilizes the exceptionally
high electric field enhancement provided by the Au NBP LSPR, localized to nm^3
volumes and fs time scales, to produce a ponderomotive force that in turn
provides for screening energies near the Au NBP tip. Discrete-dipole
approximation (DDA) simulations are presented to support the proposed scheme.
Calculations made with conservative parameters suggest that the effect should
be observable in a laboratory setting using commercially available ultrafast
lasers.


Abstract:
  Real time monitoring using in situ sensors is becoming a common approach for
measuring water quality within watersheds. High frequency measurements produce
big data sets that present opportunities to conduct new analyses for improved
understanding of water quality dynamics and more effective management of rivers
and streams. Of primary importance is enhancing knowledge of the relationships
between nitrate, one of the most reactive forms of inorganic nitrogen in the
aquatic environment, and other water quality variables. We analysed high
frequency water quality data from in situ sensors deployed in three sites from
different watersheds and climate zones within the National Ecological
Observatory Network, USA. We used generalised additive mixed models to explain
the nonlinear relationships at each site between nitrate concentration and
conductivity, turbidity, dissolved oxygen, water temperature, and elevation.
Temporal auto correlation was modelled with an auto regressive moving average
model and we examined the relative importance of the explanatory variables.
Total deviance explained by the models was high for all sites. Although
variable importance and the smooth regression parameters differed among sites,
the models explaining the most variation in nitrate contained the same
explanatory variables. This study demonstrates that building a model for
nitrate using the same set of explanatory water quality variables is
achievable, even for sites with vastly different environmental and climatic
characteristics. Applying such models will assist managers to select cost
effective water quality variables to monitor when the goals are to gain a
spatially and temporally in depth understanding of nitrate dynamics and adapt
management plans accordingly.


Abstract:
  With the advent of neuromorphic hardware, spiking neural networks can be a
good energy-efficient alternative to artificial neural networks. However, the
use of spiking neural networks to perform computer vision tasks remains
limited, mainly focusing on simple tasks such as digit recognition. It remains
hard to deal with more complex tasks (e.g. segmentation, object detection) due
to the small number of works on deep spiking neural networks for these tasks.
The objective of this paper is to make the first step towards modern computer
vision with supervised spiking neural networks. We propose a deep convolutional
spiking neural network for the localization of a single object in a grayscale
image. We propose a network based on DECOLLE, a spiking model that enables
local surrogate gradient-based learning. The encouraging results reported on
Oxford-IIIT-Pet validates the exploitation of spiking neural networks with a
supervised learning approach for more elaborate vision tasks in the future.


Abstract:
  We discuss how to use correlations between different physical observables to
improve recently obtained thermodynamics bounds, notably the
fluctuation-response inequality (FRI) and the thermodynamic uncertainty
relation (TUR). We show that increasing the number of measured observables will
always produce a tighter bound. This tighter bound becomes particularly useful
if one of the observables is a conserved quantity, whose expectation is
invariant under a given perturbation of the system. For the case of the TUR, we
show that this applies to any function of the state of the system. We
demonstrate our finding on a model of the F1-ATPase molecular motor, where we
obtain a bound that is more than a factor of 2 tighter than the one given by
the TUR.


Abstract:
  We explore the tunneling transport properties of a quantum dot embedded in an
optical microcavity and coupled to a semiconductor-superconductor
one-dimensional nanowire (Majorana nanowire) hosting Majorana zero modes (MZMs)
at their edges. Conductance profiles reveal that strong light-matter coupling
can be employed to distinguish between the cases of highly nonlocal MZMs,
overlapped MZMs and quasi-MZMs. Moreover, we show that it is possible to access
the degree of Majorana nonlocality (topological quality factor) by changing the
dot spectrum through photon-induced transitions tuned by an external pump
applied to the microcavity.


Abstract:
  Bi-2212 is the only high-field, high-temperature superconductor (HTS) capable
of reaching a critical current density $J_{\text{c}}$(16 T, 4.2 K) of 6500
$\mathrm{A\cdot mm^{-2}}$ in the highly desirable round wire (RW) form.
However, state-of-the-art Bi-2212 conductors still have a critical current
density ($J_{\text{c}}$) to depairing current density ($J_{\text{d}}$) ratio
around 20 to 30 times lower than that of state-of-the-art $\mathrm{Nb-Ti}$ or
REBCO. Previously, we have shown that recent improvements in Bi-2212 RW
$J_{\text{c}}$ are due to improved connectivity associated with optimization of
the heat treatment process, and most recently due to a transition to a finer
and more uniform powder manufactured by Engi-Mat. One quantitative measure of
connectivity may be the critical current ($I_{\text{c}}$) distribution, since
the local $I_{\text{c}}$ in a wire can vary along the length due to variable
vortex-microstructure interactions and to factors such as filament shape
variations, grain-to-grain connectivity variations and blocking secondary phase
distributions. Here we compare $\sim$ 0.1 m length $I_{\text{c}}$ distributions
of Bi-2212 RWs with recent state-of-the-art very high-$J_{\text{c}}$ Engi-Mat
powder and lower $J_{\text{c}}$ and older Nexans granulate powder. We do find
that the $I_{\text{c}}$ spread for Bi-2212 wires is about twice the relative
standard of high-$J_{\text{c}}$ $\mathrm{Nb-Ti}$ well below $H_{\text{irr}}$.
We do not yet see any obvious contribution of the Bi-2212 anisotropy to the
$I_{\text{c}}$ distribution and are rather encouraged that these Bi-2212 round
wires show relative $I_{\text{c}}$ distributions not too far from
high-$J_{\text{c}}$ $\mathrm{Nb-Ti}$ wires.


Abstract:
  The three Laser Interferometer Space Antenna (LISA) spacecraft are going to
be placed in a triangular formation in an Earth-trailing or Earth-leading
orbit. They will be launched together on a single rocket and transferred to
that science orbit using Solar Electric Propulsion. Since the transfer $\Delta
v$ depends on the chosen science orbit, both transfer and science orbit have
been optimised together. For a thrust level of 90 mN, an allocation of 1092 m/s
per spacecraft is sufficient for an all-year launch in 2034. For every launch
month a dedicated science orbit is designed with a corner angle variation of
close to $60^\circ \pm 1.0^\circ$ and an arm length rate of maximum 10 m/s.
Moreover, a detailed navigation analysis of the science orbit insertion and the
impact on insertion errors on the constellation stability has been conducted.
The analysis shows that Range/Doppler measurements together with a series of
correction manoeuvres at the beginning of the science orbit phase can reduce
insertion dispersions to a level where corner angle variations remain at about
$60^\circ \pm 1.1^\circ$ at $99\%$ C.L.. However, the situation can become
significantly worse if the self-gravity accelerations acting during the science
orbit phase are not sufficiently characterised prior to science orbit
insertion.


Abstract:
  We propose a new single photon source based on the principle of active
multiplexing of heralded single photons which, unlike previously reported
architecture, requires a limited amount of physical resources. We discuss both
its feasibility and the purity and indistinguishability of single photons as
function of the key parameters of a possible implementation.


Abstract:
  We present a new approach for the mechanically consistent modelling and
simulation of fluid-structure interactions with contact. The fundamental idea
consists of combining a relaxed contact formulation with the modelling of
seepage through a porous layer of co-dimension 1 during contact. For the
latter, a Darcy model is considered in a thin porous layer attached to a solid
boundary in the limit of infinitesimal thickness. In combination with a
relaxation of the contact conditions the computational model is both
mechanically consistent and simple to implement. We analyse the approach in
detailed numerical studies with both thick- and thin-walled solids, within a
fully Eulerian and an immersed approach for the fluid-structure interaction and
using fitted and unfitted finite element discretisations.


Abstract:
  Therapeutics machine learning is an emerging field with incredible
opportunities for innovatiaon and impact. However, advancement in this field
requires formulation of meaningful learning tasks and careful curation of
datasets. Here, we introduce Therapeutics Data Commons (TDC), the first
unifying platform to systematically access and evaluate machine learning across
the entire range of therapeutics. To date, TDC includes 66 AI-ready datasets
spread across 22 learning tasks and spanning the discovery and development of
safe and effective medicines. TDC also provides an ecosystem of tools and
community resources, including 33 data functions and types of meaningful data
splits, 23 strategies for systematic model evaluation, 17 molecule generation
oracles, and 29 public leaderboards. All resources are integrated and
accessible via an open Python library. We carry out extensive experiments on
selected datasets, demonstrating that even the strongest algorithms fall short
of solving key therapeutics challenges, including real dataset distributional
shifts, multi-scale modeling of heterogeneous data, and robust generalization
to novel data points. We envision that TDC can facilitate algorithmic and
scientific advances and considerably accelerate machine-learning model
development, validation and transition into biomedical and clinical
implementation. TDC is an open-science initiative available at
https://tdcommons.ai.


Abstract:
  Bayesian hierarchical mixture clustering (BHMC) is an interesting model that
improves on the traditional Bayesian hierarchical clustering approaches.
Regarding the parent-to-node diffusion in the generative process, BHMC replaces
the conventional Gaussian-to-Gaussian (G2G) kernels with a Hierarchical
Dirichlet Process Mixture Model (HDPMM). However, the drawback of the BHMC lies
in that it might obtain comparatively high nodal variance in the higher levels
(i.e., those closer to the root node). This can be interpreted as that the
separation between the nodes, in particular those in the higher levels, might
be weak. Attempting to overcome this drawback, we consider a recent inferential
framework named posterior regularization, which facilitates a simple manner to
impose extra constraints on a Bayesian model to address some weakness of the
original model. Hence, to enhance the separation of clusters, we apply
posterior regularization to impose max-margin constraints on the nodes at every
level of the hierarchy. In this paper, we illustrate how the framework
integrates with the BHMC and achieves the desired improvements over the
original model.


Abstract:
  We investigated daily COVID-19 cases and deaths in the 337 lower tier local
authority regions in England and Wales to better understand how the disease
propagated over a 15-month period. Population density scaling models revealed
residual variance and skewness to be sensitive indicators of the dynamics of
propagation. Lockdowns and schools reopening triggered increased variance
indicative of outbreaks with local impact and country scale heterogeneity.
University reopening and December holidays triggered reduced variance
indicative of country scale homogenisation which reached a minimum in
mid-January 2021. Homogeneous propagation was associated with better
correspondence with normally distributed residuals while heterogeneous
propagation was more consistent with skewed models. Skewness varied from
strongly negative to strongly positive revealing an unappreciated feature of
community propagation. Hot spots and super-spreading events are well understood
descriptors of regional disease dynamics that would be expected to be
associated with positively skewed distributions. Positively skewed behaviour
was observed; however, negative skewness indicative of cold-spots and
super-isolation dominated for approximately 8 months during the period of
study. In contrast, death metrics showed near constant behaviour in scaling,
variance, and skewness metrics over the full period with rural regions
preferentially affected, an observation consistent with regional age
demographics in England and Wales. Regional positions relative to density
scaling laws were remarkably persistent after the first 5-9 days of the
available data set. The determinants of this persistent behaviour probably
precede the pandemic and remain unchanged.


Abstract:
  Non-linearity of a Boolean function indicates how far it is from any linear
function. Despite there being several strong results about identifying a linear
function and distinguishing one from a sufficiently non-linear function, we
found a surprising lack of work on computing the non-linearity of a function.
The non-linearity is related to the Walsh coefficient with the largest absolute
value; however, the naive attempt of picking the maximum after constructing a
Walsh spectrum requires $\Theta(2^n)$ queries to an $n$-bit function. We
improve the scenario by designing highly efficient quantum and randomised
algorithms to approximate the non-linearity allowing additive error, denoted
$\lambda$, with query complexities that depend polynomially on $\lambda$. We
prove lower bounds to show that these are not very far from the optimal ones.
The number of queries made by our randomised algorithm is linear in $n$,
already an exponential improvement, and the number of queries made by our
quantum algorithm is surprisingly independent of $n$. Our randomised algorithm
uses a Goldreich-Levin style of navigating all Walsh coefficients and our
quantum algorithm uses a clever combination of Deutsch-Jozsa, amplitude
amplification and amplitude estimation to improve upon the existing quantum
versions of the Goldreich-Levin technique.


Abstract:
  Whether the number of beta-steps in the lambda-calculus can be taken as a
reasonable time cost model (that is, polynomially related to the one of Turing
machines) is a delicate problem, which depends on the notion of evaluation
strategy. Since the nineties, it is known that weak (that is, out of
abstractions) call-by-value evaluation is a reasonable strategy while L\'evy's
optimal parallel strategy, which is strong (that is, it reduces everywhere), is
not. The strong case turned out to be subtler than the weak one. In 2014
Accattoli and Dal Lago have shown that strong call-by-name is reasonable, by
introducing a new form of useful sharing and, later, an abstract machine with
an overhead quadratic in the number of beta-steps.
  Here we show that also strong call-by-value evaluation is reasonable for
time, via a new abstract machine realizing useful sharing and having a linear
overhead. Moreover, our machine uses a new mix of sharing techniques, adding on
top of useful sharing a form of implosive sharing, which on some terms brings
an exponential speed-up. We give examples of families that the machine executes
in time logarithmic in the number of beta-steps.


Abstract:
  In this article, we construct a Stratonovich solution for the stochastic wave
equation in spatial dimension $d \leq 2$, with time-independent noise and
linear term $\sigma(u)=u$ multiplying the noise. The noise is spatially
homogeneous and its spectral measure satisfies an integrability condition which
is stronger than Dalang's condition. We give a probabilistic representation for
this solution, similar to the Feynman-Kac-type formula given in Dalang, Mueller
and Tribe (2008) for the solution of the stochastic wave equation with
spatially homogeneous Gaussian noise, that is white in time. We also give the
chaos expansion of the Stratonovich solution and we compare it with the chaos
expansion of the Skorohod solution from Balan, Chen and Chen (2020).


Abstract:
  Bifurcating Markov chains (BMC) are Markov chains indexed by a full binary
tree representing the evolution of a trait along a population where each
individual has two children. We provide a central limit theorem for additive
functionals of BMC under $L^2$-ergodic conditions with three different regimes.
This completes the pointwise approach developed in a previous work. As
application, we study the elementary case of symmetric bifurcating
autoregressive process, which justify the non-trivial hypothesis considered on
the kernel transition of the BMC. We illustrate in this example the phase
transition observed in the fluctuations.


Abstract:
  Bassino et al. 2010 and Regnier et al. 1998 showed the generating functions
of the distributions of the number of the occurrences of words (distributions
of words for short) in finite string in the form of rational functions. However
the coefficients of the expansion of the rational functions are complicated and
we do not have a simple formula of the exact distributions of words from
rational functions. In this paper we study the finite dimensional generating
functions of the distribution of nonoverlapping words for each fixed sample
size and show the explicit formula of the distributions of words for Bernoulli
model. We demonstrate that 1) the tests based on the distributions of words
reject the random number generator in BSD Library with p-value almost zero and
2) computation of the distributions of words in the human DNA size strings.


Abstract:
  The optimized effective potential (OEP) method presents an unambiguous way to
construct the Kohn-Sham potential corresponding to a given diagrammatic
approximation for the exchange-correlation functional. The OEP from the
random-phase approximation (RPA) has played an important role ever since the
conception of the OEP formalism. However, the solution of the OEP equation is
computationally fairly expensive and has to be done in a self-consistent way.
So far, large scale solid state applications have therefore been performed only
using the quasiparticle approximation (QPA), neglecting certain dynamical
screening effects. We obtain the exact RPA-OEP for 15 semiconductors and
insulators by direct solution of the linearized Sham-Schl\"uter equation. We
investigate the accuracy of the QPA on Kohn-Sham band gaps and dielectric
constants, and comment on the issue of self-consistency.


Abstract:
  Information sharing is vital in resisting cyberattacks, and the volume and
severity of these attacks is increasing very rapidly. Therefore responders must
triage incoming warnings in deciding how to act. This study asked a very
specific question: "how can the addition of confidence information to alerts
and warnings improve overall resistance to cyberattacks." We sought, in
particular, to identify current practices, and if possible, to identify some
"best practices." The research involved literature review and interviews with
subject matter experts at every level from system administrators to persons who
develop broad principles of policy. An innovative Modified Online Delphi Panel
technique was used to elicit judgments and recommendations from experts who
were able to speak with each other and vote anonymously to rank proposed
practices.


Abstract:
  The immersed boundary (IB) method is a non-body conforming approach to
fluid-structure interaction (FSI) that uses an Eulerian description of the
momentum, viscosity, and incompressibility of a coupled fluid-structure system
and a Lagrangian description of the deformations, stresses, and resultant
forces of the immersed structure. Integral transforms with Dirac delta function
kernels couple Eulerian and Lagrangian variables. In practice, discretizations
of these integral transforms use regularized delta function kernels, and
although a number of different types of regularized delta functions have been
proposed, there has been limited prior work to investigate the impact of the
choice of kernel function on the accuracy of the methodology. This work
systematically studies the effect of the choice of regularized delta function
in several fluid-structure interaction benchmark tests using the immersed
finite element/difference (IFED) method, which is an extension of the IB method
that uses finite element structural discretizations combined with a Cartesian
grid finite difference method for the incompressible Navier-Stokes equations.
Further, many IB-type methods evaluate the delta functions at the nodes of the
structural mesh, and this requires the Lagrangian mesh to be relatively fine
compared to the background Eulerian grid to avoid leaks. The IFED formulation
offers the possibility to avoid leaks with relatively coarse structural meshes
by evaluating the delta function on a denser collection of interaction points.
This study investigates the effect of varying the relative mesh widths of the
Lagrangian and Eulerian discretizations. Although this study is done within the
context of the IFED method, the effect of different kernels could be important
not just for this method, but also for other IB-type methods more generally.


Abstract:
  This paper presents a novel method for embedding transfer, a task of
transferring knowledge of a learned embedding model to another. Our method
exploits pairwise similarities between samples in the source embedding space as
the knowledge, and transfers them through a loss used for learning target
embedding models. To this end, we design a new loss called relaxed contrastive
loss, which employs the pairwise similarities as relaxed labels for
inter-sample relations. Our loss provides a rich supervisory signal beyond
class equivalence, enables more important pairs to contribute more to training,
and imposes no restriction on manifolds of target embedding spaces. Experiments
on metric learning benchmarks demonstrate that our method largely improves
performance, or reduces sizes and output dimensions of target models
effectively. We further show that it can be also used to enhance quality of
self-supervised representation and performance of classification models. In all
the experiments, our method clearly outperforms existing embedding transfer
techniques.


Abstract:
  In the present study, Kolmogorov flow represents the stationary sinusoidal
solution $(\sin y,0)$ to a two-dimensional spatially periodic Navier-Stokes
system, driven by an external force. This system admits the additional
non-stationary solution $(\sin y,0)+e^{-\nu t} (\sin y,0)$, which tends
exponentially to the Kolmogorov flow at the minimum decay rate determined by
the viscosity $\nu$. Enhanced damping or enhanced dissipation of the problem is
obtained by presenting higher decay rate for the difference between a solution
and the non-stationary basic solution. Moreover, for the understanding of the
metastability problem in an explicit manner, a variety of exact solutions are
presented to show enhanced and unenhanced dampings.


Abstract:
  The new measurement of the muon's anomalous magnetic moment released by the
Muon g-2 experiment at Fermilab sets strong constraints on the properties of
many new particles. Using an effective field theory approach to the
interactions of higher-spin fields, we evaluate the contribution of an
electrically neutral and colour singlet spin-3/2 fermion to $(g-2)_\mu$ and
derive the corresponding constraints on its mass and couplings. These
constraints are then compared with the ones on spin-1/2 fermions, such as the
vector-like leptons that are predicted by various extensions of the Standard
Model, the excited leptons which appear in composite models, as well as the
charginos and neutralinos of supersymmetric theories. Unlike these new spin-1/2
fermions, the spin-3/2 particles generate only small contributions to the muon
anomalous magnetic moment.


Abstract:
  In this paper, we propose a novel four-stage data augmentation approach to
ResNet-Conformer based acoustic modeling for sound event localization and
detection (SELD). First, we explore two spatial augmentation techniques, namely
audio channel swapping (ACS) and multi-channel simulation (MCS), to deal with
data sparsity in SELD. ACS and MDS focus on augmenting the limited training
data with expanding direction of arrival (DOA) representations such that the
acoustic models trained with the augmented data are robust to localization
variations of acoustic sources. Next, time-domain mixing (TDM) and
time-frequency masking (TFM) are also investigated to deal with overlapping
sound events and data diversity. Finally, ACS, MCS, TDM and TFM are combined in
a step-by-step manner to form an effective four-stage data augmentation scheme.
Tested on the Detection and Classification of Acoustic Scenes and Events
(DCASE) 2020 data sets, our proposed augmentation approach greatly improves the
system performance, ranking our submitted system in the first place in the SELD
task of DCASE 2020 Challenge. Furthermore, we employ a ResNet-Conformer
architecture to model both global and local context dependencies of an audio
sequence to yield further gains over those architectures used in the DCASE 2020
SELD evaluations.


Abstract:
  The derivative nonlinear Schrodinger (DNLS) equation is the canonical model
for dynamics of nonlinear waves in plasma physics and optics. We study exact
solutions describing rogue waves on the background of periodic standing waves
in the DNLS equation. We show that the space-time localization of a rogue wave
is only possible if the periodic standing wave is modulationally unstable. If
the periodic standing wave is modulationally stable, the rogue wave solutions
degenerate into algebraic solitons propagating along the background and
interacting with the periodic standing waves. Maximal amplitudes of rogue waves
are found analytically and confirmed numerically.


Abstract:
  A major challenge for physically unconstrained gaze estimation is acquiring
training data with 3D gaze annotations for in-the-wild and outdoor scenarios.
In contrast, videos of human interactions in unconstrained environments are
abundantly available and can be much more easily annotated with frame-level
activity labels. In this work, we tackle the previously unexplored problem of
weakly-supervised gaze estimation from videos of human interactions. We
leverage the insight that strong gaze-related geometric constraints exist when
people perform the activity of "looking at each other" (LAEO). To acquire
viable 3D gaze supervision from LAEO labels, we propose a training algorithm
along with several novel loss functions especially designed for the task. With
weak supervision from two large scale CMU-Panoptic and AVA-LAEO activity
datasets, we show significant improvements in (a) the accuracy of
semi-supervised gaze estimation and (b) cross-domain generalization on the
state-of-the-art physically unconstrained in-the-wild Gaze360 gaze estimation
benchmark. We open source our code at
https://github.com/NVlabs/weakly-supervised-gaze.


Abstract:
  With the approval of vaccines for the coronavirus disease by many countries
worldwide, most developed nations have begun, and developing nations are
gearing up for the vaccination process. This has created an urgent need to
provide a solution to optimally distribute the available vaccines once they are
received by the authorities. In this paper, we propose a clustering-based
solution to select optimal distribution centers and a Constraint Satisfaction
Problem framework to optimally distribute the vaccines taking into
consideration two factors namely priority and distance. We demonstrate the
efficiency of the proposed models using real-world data obtained from the
district of Chennai, India. The model provides the decision making authorities
with optimal distribution centers across the district and the optimal
allocation of individuals across these distribution centers with the
flexibility to accommodate a wide range of demographics.


Abstract:
  Modern wake word detection systems usually rely on neural networks for
acoustic modeling. Transformers has recently shown superior performance over
LSTM and convolutional networks in various sequence modeling tasks with their
better temporal modeling power. However it is not clear whether this advantage
still holds for short-range temporal modeling like wake word detection.
Besides, the vanilla Transformer is not directly applicable to the task due to
its non-streaming nature and the quadratic time and space complexity. In this
paper we explore the performance of several variants of chunk-wise streaming
Transformers tailored for wake word detection in a recently proposed LF-MMI
system, including looking-ahead to the next chunk, gradient stopping, different
positional embedding methods and adding same-layer dependency between chunks.
Our experiments on the Mobvoi wake word dataset demonstrate that our proposed
Transformer model outperforms the baseline convolution network by 25% on
average in false rejection rate at the same false alarm rate with a comparable
model size, while still maintaining linear complexity w.r.t. the sequence
length.


Abstract:
  There is increasing interest in developing personalized Task-oriented
Dialogue Systems (TDSs). Previous work on personalized TDSs often assumes that
complete user profiles are available for most or even all users. This is
unrealistic because (1) not everyone is willing to expose their profiles due to
privacy concerns; and (2) rich user profiles may involve a large number of
attributes (e.g., gender, age, tastes, . . .). In this paper, we study
personalized TDSs without assuming that user profiles are complete. We propose
a Cooperative Memory Network (CoMemNN) that has a novel mechanism to gradually
enrich user profiles as dialogues progress and to simultaneously improve
response selection based on the enriched profiles. CoMemNN consists of two core
modules: User Profile Enrichment (UPE) and Dialogue Response Selection (DRS).
The former enriches incomplete user profiles by utilizing collaborative
information from neighbor users as well as current dialogues. The latter uses
the enriched profiles to update the current user query so as to encode more
useful information, based on which a personalized response to a user request is
selected.
  We conduct extensive experiments on the personalized bAbI dialogue benchmark
datasets. We find that CoMemNN is able to enrich user profiles effectively,
which results in an improvement of 3.06% in terms of response selection
accuracy compared to state-of-the-art methods. We also test the robustness of
CoMemNN against incompleteness of user profiles by randomly discarding
attribute values from user profiles. Even when discarding 50% of the attribute
values, CoMemNN is able to match the performance of the best performing
baseline without discarding user profiles, showing the robustness of CoMemNN.


Abstract:
  This work proposes a scheme that allows learning complex multi-agent
behaviors in a sample efficient manner, applied to 2v2 soccer. The problem is
formulated as a Markov game, and solved using deep reinforcement learning. We
propose a basic multi-agent extension of TD3 for learning the policy of each
player, in a decentralized manner. To ease learning, the task of 2v2 soccer is
divided in three stages: 1v0, 1v1 and 2v2. The process of learning in
multi-agent stages (1v1 and 2v2) uses agents trained on a previous stage as
fixed opponents. In addition, we propose using experience sharing, a method
that shares experience from a fixed opponent, trained in a previous stage, for
training the agent currently learning, and a form of frame-skipping, to raise
performance significantly. Our results show that high quality soccer play can
be obtained with our approach in just under 40M interactions. A summarized
video of the resulting game play can be found in https://youtu.be/f25l1j1U9RM.


Abstract:
  This article is a survey of our recent work on the connections between
Koba-Nielsen amplitudes and local zeta functions (in the sense of Gel'fand,
Weil, Igusa, Sato, Bernstein, Denef, Loeser, etc.). Our research program is
motivated by the fact that the p-adic strings seem to be related in some
interesting ways with ordinary strings. For instance, connections through the
adelic relations and through the limit when p tends to 1. Gerasimov and
Shatashvili studied the limit p tends to 1 of the p-adic effective action
introduced by Brekke, Freund, Olson and Witten. They showed that this limit
gives rise to a boundary string field theory, which was previously proposed by
Witten in the context of background independent string theory. Explicit
computations in the cases of 4 and 5 points show that the Feynman amplitudes at
the tree level of the Gerasimov-Shatashvili Lagrangian are related with the
limit p tends to 1 of the p-adic Koba-Nielsen amplitudes. At a mathematical
level, this phenomenon is deeply connected with the topological zeta functions
introduced by Denef and Loeser. A Koba-Nielsen amplitude is just a new type of
local zeta function, which can be studied by using embedded resolution of
singularities. In this way, one shows the existence of a meromorphic
continuations for the Koba-Nielsen amplitudes as functions of the kinematic
parameters. The Koba-Nielsen local zeta functions are algebraic-geometric
integrals that can be defined over arbitrary local fields (for instance R, C,
Q_{p}, F_{p}((T)), and it is completely natural to expect connections between
these objects. The limit p tends to one of the Koba-Nielsen amplitudes give
rise to a new amplitudes which we have called Denef-Loeser amplitudes. Along
the article, we have emphasized the explicit calculations in the cases of 4 and
5 points.


Abstract:
  Let $\mathfrak{g}$ be a hyperbolic Kac-Moody algebra of rank $2$, and let
$\lambda$ be an arbitrary integral weight. We denote by $\mathbb{B}(\lambda)$
the crystal of all Lakshmibai-Seshadri paths of shape $\lambda$. Let
$V(\lambda)$ be the extremal weight module of extremal weight $\lambda$
generated by the (cyclic) extremal weight vector $v_\lambda$ of weight
$\lambda$, and let $\mathcal{B}(\lambda)$ be the crystal basis of $V(\lambda)$
with $u_\lambda \in \mathcal{B}(\lambda)$ the element corresponding to
$v_\lambda$. We prove that the connected component $\mathcal{B}_0(\lambda)$ of
$\mathcal{B}(\lambda)$ containing $u_\lambda$ is isomorphic, as a crystal, to
the connected component $\mathbb{B}_0(\lambda)$ of $\mathbb{B}(\lambda)$
containing the straight line $\pi_\lambda$. Furthermore, we prove that if
$\lambda$ satisfies a special condition, then the crystal basis
$\mathcal{B}(\lambda)$ is isomorphic, as a crystal, to the crystal
$\mathbb{B}(\lambda)$. As an application of these results, we obtain an
algorithm for computing the number of elements of weight $\mu$ in
$\mathcal{B}(\Lambda_1-\Lambda_2)$, where $\Lambda_1, \Lambda_2$ are the
fundamental weights, in the case that $\mathfrak{g}$ is symmetric.


Abstract:
  The reading of arbitrarily-shaped text has received increasing research
attention. However, existing text spotters are mostly built on two-stage
frameworks or character-based methods, which suffer from either Non-Maximum
Suppression (NMS), Region-of-Interest (RoI) operations, or character-level
annotations. In this paper, to address the above problems, we propose a novel
fully convolutional Point Gathering Network (PGNet) for reading
arbitrarily-shaped text in real-time. The PGNet is a single-shot text spotter,
where the pixel-level character classification map is learned with proposed
PG-CTC loss avoiding the usage of character-level annotations. With a PG-CTC
decoder, we gather high-level character classification vectors from
two-dimensional space and decode them into text symbols without NMS and RoI
operations involved, which guarantees high efficiency. Additionally, reasoning
the relations between each character and its neighbors, a graph refinement
module (GRM) is proposed to optimize the coarse recognition and improve the
end-to-end performance. Experiments prove that the proposed method achieves
competitive accuracy, meanwhile significantly improving the running speed. In
particular, in Total-Text, it runs at 46.7 FPS, surpassing the previous
spotters with a large margin.


Abstract:
  Within the context of social balance theory, much attention has been paid to
the attainment and stability of unipolar or bipolar societies. However,
multipolar societies are commonplace in the real world, despite the fact that
the mechanism of their emergence is much less explored. Here, we investigate
the evolution of a society of interacting agents with friendly (positive) and
enmity (negative) relations into a final stable multipolar state. Triads are
assigned energy according to the degree of tension they impose on the network.
Agents update their connections in order to decrease the total energy (tension)
of the system, on average. Our approach is to consider a variable energy
$\epsilon\in[0,1]$ for triads which are entirely made of negative relations. We
show that the final state of the system depends on the initial density of the
friendly links $\rho_0$. For initial densities greater than an $\epsilon$
dependent threshold $\rho^c_0(\epsilon)$ unipolar (paradise) state is reached.
However, for $\rho_0 \leq \rho^c_0(\epsilon)$ multi-polar and bipolar states
can emerge. We observe that the number of stable final poles increases with
decreasing $\epsilon$ where the first transition from bipolar to multipolar
society occurs at $\epsilon^*\approx 0.67$. We end the paper by providing a
mean-field calculation that provides an estimate for the critical ($\epsilon$
dependent) initial positive link density, which is consistent with our
simulations.


Abstract:
  Recently, several deep learning models have been proposed for 3D human pose
estimation. Nevertheless, most of these approaches only focus on the
single-person case or estimate 3D pose of a few people at high resolution.
Furthermore, many applications such as autonomous driving or crowd analysis
require pose estimation of a large number of people possibly at low-resolution.
In this work, we present PandaNet (Pose estimAtioN and Dectection Anchor-based
Network), a new single-shot, anchor-based and multi-person 3D pose estimation
approach. The proposed model performs bounding box detection and, for each
detected person, 2D and 3D pose regression into a single forward pass. It does
not need any post-processing to regroup joints since the network predicts a
full 3D pose for each bounding box and allows the pose estimation of a possibly
large number of people at low resolution. To manage people overlapping, we
introduce a Pose-Aware Anchor Selection strategy. Moreover, as imbalance exists
between different people sizes in the image, and joints coordinates have
different uncertainties depending on these sizes, we propose a method to
automatically optimize weights associated to different people scales and joints
for efficient training. PandaNet surpasses previous single-shot methods on
several challenging datasets: a multi-person urban virtual but very realistic
dataset (JTA Dataset), and two real world 3D multi-person datasets (CMU
Panoptic and MuPoTS-3D).


Abstract:
  Conductance of the edge modes as well as conductance across the
co-propagating edge modes around the \nu = 4/3, 5/3 and 2 quantum Hall states
are measured by individually exciting the modes. Temperature dependent
equilibration rates of the outer unity conductance edge mode are presented for
different filling fractions. We find that the equilibration rate of the outer
unity conductance mode at \nu = 2 is higher and more temperature sensitive
compared to the mode at fractional filling 5/3 and 4/3. At lowest temperature,
equilibration length of the outer unity conductance mode tends to saturate with
lowering filling fraction \nu by increasing magnetic field B. We speculate this
saturating nature of equilibration length is arising from an interplay of
Coulomb correlation and spin orthogonality.


Abstract:
  Manual labelling of training examples is common practice in supervised
learning. When the labelling task is of non-trivial difficulty, the supplied
labels may not be equal to the ground-truth labels, and label noise is
introduced into the training dataset. If the manual annotation is carried out
by multiple experts, the same training example can be given different class
assignments by different experts, which is indicative of label noise. In the
framework of model-based classification, a simple, but key observation is that
when the manual labels are sampled using the posterior probabilities of class
membership, the noisy labels are as valuable as the ground-truth labels in
terms of statistical information. A relaxation of this process is a random
effects model for imperfect labelling by a group that uses approximate
posterior probabilities of class membership. The relative efficiency of
logistic regression using the noisy labels compared to logistic regression
using the ground-truth labels can then be derived. The main finding is that
logistic regression can be robust to label noise when label noise and
classification difficulty are positively correlated. In particular, when
classification difficulty is the only source of label errors, multiple sets of
noisy labels can supply more information for the estimation of a classification
rule compared to the single set of ground-truth labels.


Abstract:
  A broad range of dynamical systems involve multi-body interactions, or group
interactions, which may not be encoded in traditional graphical structures. In
this work, we focus on a canonical example from opinion dynamics, the Majority
Rule, and investigate the possibility to represent and analyse the system by
means of hypergraphs. We explore the formation of consensus and restrict our
attention to interaction groups of size $3$, in order to simplify our analysis
from a combinatorial perspective. We propose different types of hypergraph
models, incorporating modular structure or degree heterogeneity, and recast the
dynamics in terms of Fokker-Planck equations, which allows us to predict the
transient dynamics toward consensus. Numerical simulations show a very good
agreement between the stochastic dynamics and theoretical predictions for large
population sizes.


Abstract:
  Grauert showed that it is possible to construct complete K\"{a}hler metrics
on the complement of complex analytic sets in a domain of holomorphy. In this
note, we study the holomorphic sectional curvatures of such metrics on the
complement of a principal divisor in $\mathbb{C}^n$, $n \ge 1$. In addition, we
also study how this metric and its holomorphic sectional curvature behaves when
the corresponding principal divisors vary continuously.


Abstract:
  Let $G$ be an almost simple sporadic group and let $H$ be a soluble subgroup
of $G$. In this paper we prove that there exists $x,y \in G$ such that $H \cap
H^x \cap H^y=1$, which is equivalent to the bound $b(G,H) \leqslant 3$ with
respect to the base size of $G$ on the set of cosets of $H$. This bound is best
possible. In this setting, our main result establishes a strong form of a more
general conjecture of Vdovin on the intersection of conjugate soluble subgroups
of finite groups. The proof uses a combination of computational and
probabilistic methods.


Abstract:
  Linear forms in logarithms have an important role in the theory of
Diophantine equations. In this article, we prove explicit $p$-adic lower bounds
for linear forms in $p$-adic logarithms of rational numbers using Pad\'e
approximations of the second kind.


Abstract:
  Although the idea that there is a maximum force in nature seems untenable, we
explore whether this concept can make sense in the restricted context of black
holes. We discuss uniformly accelerated and cosmological black holes and we
find that, although a maximum force acting on these black holes can in
principle be introduced, this concept is rather tautological.


Abstract:
  We consider the usual causal structure $(I^+,J^+)$ on a spacetime, and a
number of alternatives based on Minguzzi's $D^+$ and Sorkin and Woolgar's
$K^+$, in the case where the spacetime metric is continuous, but not
necessarily smooth. We compare the different causal structures based on three
key properties, namely the validity of the push-up lemma, the openness of
chronological futures, and the existence of limit causal curves. Recall that if
the spacetime metric is smooth, $(I^+,J^+)$ satisfies all three properties, but
that in the continuous case, the push-up lemma fails. Among the proposed
alternative causal structures, there is one that satisfies push-up and open
futures, and one that has open futures and limit curves. Furthermore, we show
that spacetimes with continuous metrics do not, in general, admit a causal
structure satisfying all three properties at once.


Abstract:
  A reputable social media or review account can be a good cover for spamming
activities. It has become prevalent that spammers buy/sell such accounts openly
on the Web. We call these sold/bought accounts the changed-hands (CH) accounts.
They are hard to detect by existing spam detection algorithms as their spamming
activities are under the disguise of clean histories. In this paper, we first
propose the problem of detecting CH accounts, and then design an effective
detection algorithm which exploits changes in content and writing styles of
individual accounts, and a proposed novel feature selection method that works
at a fine-grained level within each individual account. The proposed method not
only determines if an account has changed hands, but also pinpoints the change
point. Experimental results with online review accounts demonstrate the high
effectiveness of our approach.


Abstract:
  Aims. The connection between initial disc conditions and final orbital and
physical properties of planets is not well-understood. In this paper, we
numerically study the formation of planetary systems via pebble accretion and
investigate the effects of disc properties such as masses, dissipation
timescales, and metallicities on planet formation outcomes. Methods. We
improved the N-body code SyMBA that was modified by taking account of new
planet-disc interaction models and type II migration. We adopted the 'two-alpha
disc' model to mimic the effects of both the standard disc turbulence and the
mass accretion driven by the magnetic disc wind. Results. We successfully
reproduced the overall distribution trends of semi-major axes, eccentricities,
and planetary masses of extrasolar giant planets. We find that, when planet
formation happens fast enough, giant planets are fully grown (Jupiter mass or
higher) and are distributed widely across the disc. On the other hand, when
planet formation is limited by the disc's dissipation, discs generally form
low-mass cold Jupiters (CJs). Our simulations also naturally explain why hot
Jupiters (HJs) tend to be alone and how the observed eccentricity-metallicity
trends arise. The low-metallicity discs tend to form nearly circular and
coplanar HJs in situ, because planet formation is slower than high-metallicity
discs, and thus protoplanetary cores migrate significantly before gas
accretion. The high-metallicity discs, on the other hand, generate HJs in situ
or via tidal circularisation of eccentric orbits. Both pathways usually involve
dynamical instabilities, and thus HJs tend to have broader eccentricity and
inclination distributions. When giant planets with very wide orbits
('super-cold Jupiters') are formed, we find that they often belong to
metal-rich stars, have eccentric orbits, and tend to have (~80%) companions
interior to their orbits.


Abstract:
  Uncertainty quantification for deep neural networks has recently evolved
through many techniques. In this work, we revisit Laplace approximation, a
classical approach for posterior approximation that is computationally
attractive. However, instead of computing the curvature matrix, we show that,
under some regularity conditions, the Laplace approximation can be easily
constructed using the gradient second moment. This quantity is already
estimated by many exponential moving average variants of Adagrad such as Adam
and RMSprop, but is traditionally discarded after training. We show that our
method (L2M) does not require changes in models or optimization, can be
implemented in a few lines of code to yield reasonable results, and it does not
require any extra computational steps besides what is already being computed by
optimizers, without introducing any new hyperparameter. We hope our method can
open new research directions on using quantities already computed by optimizers
for uncertainty estimation in deep neural networks.


Abstract:
  Background: Conferences bring scientists together and provide one of the most
timely means for disseminating new ideas and cutting-edge works.The importance
of conferences in scientific areas is testified by quantitative indicators. In
Computer Science, for instance, almost two out of three papers published on
Scopus are conference papers. Objective/Purpose: The main goal of this paper is
to investigate a novel research question: is there any correlation between the
impact of a scientific conference and the venue where it took place? Approach:
In order to measure the impact of conferences we conducted a large scale
analysis on the bibliographic data extracted from 3,838 Computer Science
conference series and over 2.5 million papers spanning more than 30 years of
research. To quantify the "touristicity" of a venue we exploited some
indicators such as the size of the Wikipedia page for the city hosting the
venue and other indexes from reports of the World Economic Forum.
Results/Findings: We found out that the two aspects are related, and the
correlation with conference impact is stronger when considering country-wide
touristic indicators, such as the Travel&Tourism Competitiveness Index.
More-over the almost linear correlation with the Tourist Service Infrastructure
index attests the specific importance of tourist/accommodation facilities in a
given country. Conclusions: This is the first attempt to focus on the
relationship of venue characteristics to conference papers. The results open up
new possibilities, such as allowing conference organizers and authors to
estimate in advance the impact of conferences, thus supporting them in their
decisions.


Abstract:
  In some Internet of Things (IoT) applications, multi-path propagation is a
main constraint of communication channel. Recently, the chaotic baseband
wireless communication system (CBWCS) is promising to eliminate the
inter-symbol interference (ISI) caused by multipath propagation. However, the
current technique is only capable of removing the partial effect of ISI, due to
only past decoded bits are available for the suboptimal decoding threshold
calculation. However, the future transmitting bits also contribute to the
threshold. The unavailable future information bits needed by the optimal
decoding threshold are an obstacle to further improve the bit error rate (BER)
performance. Different from the previous method using echo state network (ESN)
to predict one future information bit, the proposed method in this paper
predicts the optimal threshold directly using ESN. The proposed ESN-based
threshold prediction method simplifies the symbol decoding operation by
removing the threshold calculation from the transmitting symbols and channel
information, which achieves better BER performance as compared to the previous
method. The reason for this superior result lies in two folds, first, the
proposed ESN is capable of using more future symbols information conveyed by
the ESN input to get more accurate threshold; second, the proposed method here
does not need to estimate the channel information using Least Square method,
which avoids the extra error caused by inaccurate channel information
estimation. By this way, the calculation complexity is decreased as compared to
the previous method. Simulation results and experiment based on a wireless
open-access research platform under a practical wireless channel, show the
effectiveness and superiority of the proposed method.


Abstract:
  In many decision-making tasks, some specific actions are limited in their
frequency or total amounts, such as "fire" in the gunfight game and "buy/sell"
in the stock trading. We name such actions as "sparse action". Sparse action
often plays a crucial role in achieving good performance. However, their
Q-values, estimated by \emph{classical Bellman update}, usually suffer from a
large estimation error due to the sparsity of their samples. The \emph{greedy}
policy could be greatly misled by the biased Q-function and takes sparse action
aggressively, which leads to a huge sub-optimality. This paper constructs a
reference distribution that assigns a low probability to sparse action and
proposes a regularized objective with an explicit constraint to the reference
distribution. Furthermore, we derive a regularized Bellman operator and a
regularized optimal policy that can slow down the propagation of error and
guide the agent to take sparse action more carefully. The experiment results
demonstrate that our method achieves state-of-the-art performance on typical
sparse action tasks.


Abstract:
  This paper is build around the stationary anisotropic Stokes and
Navier-Stokes systems with an $L^\infty$-tensor coefficient satisfying an
ellipticity condition in terms of symmetric matrices in ${\mathbb R}^{n\times
n}$ with zero matrix traces. We analyze, in $L^2$-based Sobolev spaces, the
non-homogeneous boundary value problems of Dirichlet-transmission type for the
anisotropic Stokes and Navier-Stokes systems in a compressible framework in a
bounded Lipschitz domain with a Lipschitz interface in ${\mathbb R}^n$, $n\ge
2$ ($n=2,3$ for the nonlinear problems). The transversal interface intersects
the boundary of the Lipschitz domain. First, we use a mixed variational
approach to prove well-posedness results for the linear anisotropic Stokes
system. Then we show the existence of a weak solution for the nonlinear
anisotropic Navier-Stokes system by implementing the Leray-Schauder fixed point
theorem and using various results and estimates from the linear case, as well
as the Leray-Hopf and some other norm inequalities. Explicit conditions for
uniqueness of solutions to the nonlinear problems are also provided.


Abstract:
  In this work, the branching ratio $\mathcal{B}[B\to K\chi_{c0}(2P)]$ is
extracted for the first time through the fit fractions in the newly measured
$B\to KD\bar{D}$ process by LHCb. With the rescattering mechanism, this
extracted branching ratio is reproduced well. Our study further enforces the
role of the newly measured $B\to KD\bar{D}$ process to establish the
$\chi_{c0}(2P)$ charmonium state, which is a crucial step when constructing
charmonium family.


Abstract:
  QED cascades in a strong electromagnetic field of optical range and arbitrary
configuration are considered. A general expression for short-time dependence of
the key electron quantum dynamical parameter is derived, allowing to generalize
the effective threshold condition of QED cascade onset. The generalized theory
is applied to selfsustained cascades in a single focused laser pulse. According
to numerical simulations, if a GeV electron bunch is used as a seed, an
ordinary cascade can be converted into the selfsustained one. As an
application, it would be also possible to produce this way bright collimated
photon beams with up to GeV photon energies.


Abstract:
  For a separable Hamiltonian, there are two fundamental, time-symmetric,
second-order velocity-Verlet (VV) and position-Verlet (PV) symplectic
integrators. Similarly, there are two VV and PV version of exact energy
conserving algorithms for solving magnetic field trajectories. For a constant
magnetic field, both algorithms can be further modified so that their
trajectories are exactly on the gyro-circle. The magnetic PV integrator then
becomes the well known Boris solver, while VV yields a second, previously
unknown, Boris-type algorithm. Remarkably, the required on-orbit modification
is a reparametrization of the time step, reminiscent of the Ge-Marsden theorem.


Abstract:
  In this paper, we study the generalization properties of Model-Agnostic
Meta-Learning (MAML) algorithms for supervised learning problems. We focus on
the setting in which we train the MAML model over $m$ tasks, each with $n$ data
points, and characterize its generalization error from two points of view:
First, we assume the new task at test time is one of the training tasks, and we
show that, for strongly convex objective functions, the expected excess
population loss is bounded by ${\mathcal{O}}(1/mn)$. Second, we consider the
MAML algorithm's generalization to an unseen task and show that the resulting
generalization error depends on the total variation distance between the
underlying distributions of the new task and the tasks observed during the
training process. Our proof techniques rely on the connections between
algorithmic stability and generalization bounds of algorithms. In particular,
we propose a new definition of stability for meta-learning algorithms, which
allows us to capture the role of both the number of tasks $m$ and number of
samples per task $n$ on the generalization error of MAML.


Abstract:
  This paper intends to apply the Hidden Markov Model into stock market and and
make predictions. Moreover, four different methods of improvement, which are
GMM-HMM, XGB-HMM, GMM-HMM+LSTM and XGB-HMM+LSTM, will be discussed later with
the results of experiment respectively. After that we will analyze the pros and
cons of different models. And finally, one of the best will be used into stock
market for timing strategy.


Abstract:
  We decompose the monthly aa-index of cycles 10-23 using principal component
analysis (PCA). We show that the first component (PC1) is related to solar
cycle, and accounts for 41.5 % of the variance of the data. The second
component (PC2) is related to 22-year Hale cycle, and explains 23.6% of the
variance of the data. The PC1 time series of aa cycles 10-23 has only one peak
in its power spectrum at the period 10.95 years, which is the average solar
cycle period for the interval SC10-SC23. The PC2 time series of the same cycles
has a clear peak at period 21.90 (Hale cycle) and a smaller peak at 3/4 of that
period. We also study the principal component of sunspot numbers (SSN) for
cycles 10-23, and compare mutual behavior of the PC2 components of aa-index and
SSN PCA analyses. We note that they are in the same phase in all other cycles
than Solar Cycles 15 and 20. The aa cycle 20 also differs from other even aa
cycles in its shape, especially in anomalously high peaks during its descending
phase. Even though there is a coherence in the PC2 time series phases of
aa-index and sunspot number, this effect is too small to cause all of the
difference of the shape of even and odd aa cycles. We estimate that 30% of the
shape of the PC2 component of aa-index is due to sunspot number and the rest to
other recurrent events in Sun/solar-wind. The first maximum of aa-data (typical
to odd cycles), during sunspot maximum, has been shown to be related to coronal
mass ejections (CME), while the second maximum (typical to even cycles) in the
declining phase of cycle, is probably related to high-speed streams (HSS). The
last events increase the activity level such that the minimum between even-odd
cycle-pair is always higher than the minimum between succeeding odd-even cycle
pair.


Abstract:
  We present the model of an ultrasensitive mid-infrared (mid-IR) photodetector
operating in the mid-wavelength infrared (MWIR) and long-wavelength infrared
(LWIR) domains consisting of a hybrid heterostructure made of nanopatterned
graphene (NPG) and vanadium dioxide (VO$_2$) which exhibits a large
responsivity of $R\sim 10^4$ V/W, a detectivity exceeding $D^*\sim 10^{10}$ J,
and a sensitivity in terms of noise-equivalent power $\mathrm{NEP}\sim 100$
fW/$\sqrt{\rm Hz}$ close to room temperature by taking advantage of the phase
change of a thin VO$_2$ film. Our proposed photodetector can reach an
absorption of nearly 100\% in monolayer graphene due to localized surface
plasmons (LSPs) around the patterned circular holes. The geometry of the
nanopattern and an electrostatic gate potential can be used to tune the
absorption peak in the mid-IR regime between 3 and 12 $\mu$m. After the photon
absorption by the NPG sheet and the resulting phase change of VO$_2$ from
insulating to metallic phase the applied bias voltage $V_b$ triggers a current
through the VO$_2$ sheet, which can be detected electronically in about 1 ms,
shorter than the detection times of current VO$_2$ bolometers. Our envisioned
mid-IR photodetector reaches detectivities of cryogenically cooled HgCdTe
photodetectors and sensitivities larger than VO$_2$ microbolometers while
operating close to room temperature.


Abstract:
  In this article, we construct a class of explicit, smooth and spherically
symmetric solutions to the asymptotically flat vacuum constraint equations
which have ADM mass of arbitrary sign ($- \infty$, negative, zero, positive).
As a direct consequence, there exist asymptotically flat vacuum initial data
sets whose metrics are exactly negative mass Schwarzschild outside a given
ball. We emphasize that our result does not contradict the spacetime positive
energy theorem proven by Eichmair, instead it shows that the decay rate at
infinity of the symmetric $(0,2)$-tensor $k$ stated in the theorem is sharp.
The key argument we use in the article is classical, based on the conformal
method, in which the conformal equations are equivalently transformed into a
single nonlinear equation of functions of one variable.


Abstract:
  Transformer-based language models benefit from conditioning on contexts of
hundreds to thousands of previous tokens. What aspects of these contexts
contribute to accurate model prediction? We describe a series of experiments
that measure usable information by selectively ablating lexical and structural
information in transformer language models trained on English Wikipedia. In
both mid- and long-range contexts, we find that several extremely destructive
context manipulations -- including shuffling word order within sentences and
deleting all words other than nouns -- remove less than 15% of the usable
information. Our results suggest that long contexts, but not their detailed
syntactic and propositional content, are important for the low perplexity of
current transformer language models.


Abstract:
  Semantic segmentation has a wide array of applications ranging from
medical-image analysis, scene understanding, autonomous driving and robotic
navigation. This work deals with medical image segmentation and in particular
with accurate polyp detection and segmentation during colonoscopy examinations.
Several convolutional neural network architectures have been proposed to
effectively deal with this task and with the problem of segmenting objects at
different scale input. The basic architecture in image segmentation consists of
an encoder and a decoder: the first uses convolutional filters to extract
features from the image, the second is responsible for generating the final
output. In this work, we compare some variant of the DeepLab architecture
obtained by varying the decoder backbone. We compare several decoder
architectures, including ResNet, Xception, EfficentNet, MobileNet and we
perturb their layers by substituting ReLU activation layers with other
functions. The resulting methods are used to create deep ensembles which are
shown to be very effective. Our experimental evaluations show that our best
ensemble produces good segmentation results by achieving high evaluation scores
with a dice coefficient of 0.884, and a mean Intersection over Union (mIoU) of
0.818 for the Kvasir-SEG dataset. To improve reproducibility and research
efficiency the MATLAB source code used for this research is available at
GitHub: https://github.com/LorisNanni.


Abstract:
  We investigate the gravitational wave spectrum resulted from the cosmological
first-order phase transition. We compare two models; one is a scalar field
model without gravitation, while the other is a scalar field model with
gravitation. Based on the sensitivity curves of the LISA space-based
interferometer on the stochastic gravitational-wave background, we compare the
difference between the gravitational wave spectra of the former and the latter
cases resulted from the bubble collision process. Especially, we calculated the
speed of the bubble wall before collision for the two models numerically. We
show that the difference between the amplitudes of those spectra can clearly
distinguish between the two models. We expect that the LISA with Signal to
Noise Ratio =10 could observe the spectrum as the fast first-order phase
transition.


Abstract:
  Deep learning models for automatic readability assessment generally discard
linguistic features traditionally used in machine learning models for the task.
We propose to incorporate linguistic features into neural network models by
learning syntactic dense embeddings based on linguistic features. To cope with
the relationships between the features, we form a correlation graph among
features and use it to learn their embeddings so that similar features will be
represented by similar embeddings. Experiments with six data sets of two
proficiency levels demonstrate that our proposed methodology can complement
BERT-only model to achieve significantly better performances for automatic
readability assessment.


Abstract:
  Recapturing attack can be employed as a simple but effective anti-forensic
tool for digital document images. Inspired by the document inspection process
that compares a questioned document against a reference sample, we proposed a
document recapture detection scheme by employing Siamese network to compare and
extract distinct features in a recapture document image. The proposed algorithm
takes advantages of both metric learning and image forensic techniques. Instead
of adopting Euclidean distance-based loss function, we integrate the forensic
similarity function with a triplet loss and a normalized softmax loss. After
training with the proposed triplet selection strategy, the resulting feature
embedding clusters the genuine samples near the reference while pushes the
recaptured samples apart. In the experiment, we consider practical domain
generalization problems, such as the variations in printing/imaging devices,
substrates, recapturing channels, and document types. To evaluate the
robustness of different approaches, we benchmark some popular off-the-shelf
machine learning-based approaches, a state-of-the-art document image detection
scheme, and the proposed schemes with different network backbones under various
experimental protocols. Experimental results show that the proposed schemes
with different network backbones have consistently outperformed the
state-of-the-art approaches under different experimental settings.
Specifically, under the most challenging scenario in our experiment, i.e.,
evaluation across different types of documents that produced by different
devices, we have achieved less than 5.00% APCER (Attack Presentation
Classification Error Rate) and 5.56% BPCER (Bona Fide Presentation
Classification Error Rate) by the proposed network with ResNeXt101 backbone at
5% BPCER decision threshold.


Abstract:
  Pixelwise annotation of image sequences can be very tedious for humans.
Interactive video object segmentation aims to utilize automatic methods to
speed up the process and reduce the workload of the annotators. Most
contemporary approaches rely on deep convolutional networks to collect and
process information from human annotations throughout the video. However, such
networks contain millions of parameters and need huge amounts of labeled
training data to avoid overfitting. Beyond that, label propagation is usually
executed as a series of frame-by-frame inference steps, which is difficult to
be parallelized and is thus time consuming. In this paper we present a graph
neural network based approach for tackling the problem of interactive video
object segmentation. Our network operates on superpixel-graphs which allow us
to reduce the dimensionality of the problem by several magnitudes. We show,
that our network possessing only a few thousand parameters is able to achieve
state-of-the-art performance, while inference remains fast and can be trained
quickly with very little data.


Abstract:
  Recommendation algorithms have been pointed out as one of the major culprits
of misinformation spreading in the digital sphere. However, it is still unclear
how these algorithms really propagate misinformation, e.g., it has not been
shown which particular recommendation approaches are more prone to suggest
misinforming items, or which internal parameters of the algorithms could be
influencing more on their misinformation propagation capacity.
  Motivated by this fact, in this paper we present an analysis of the effect of
some of the most popular recommendation algorithms on the spread of
misinformation in Twitter. A set of guidelines on how to adapt these algorithms
is provided based on such analysis and a comprehensive review of the research
literature. A dataset is also generated and released to the scientific
community to stimulate discussions on the future design and development of
recommendation algorithms to counter misinformation. The dataset includes
editorially labelled news items and claims regarding their misinformation
nature.


Abstract:
  Let $X$ be a Banach space and $Y \subseteq X$ be a closed subspace. We prove
that if the quotient $X/Y$ is weakly Lindel\"{o}f determined or weak Asplund,
then for every $w^*$-convergent sequence $(y_n^*)_{n\in \mathbb N}$ in $Y^*$
there exist a subsequence $(y_{n_k}^*)_{k\in \mathbb N}$ and a $w^*$-convergent
sequence $(x_k^*)_{k\in \mathbb N}$ in $X^*$ such that $x_k^*|_Y=y_{n_k}^*$ for
all $k\in \mathbb N$. As an application we obtain that $Y$ is Grothendieck
whenever $X$ is Grothendieck and $X/Y$ is reflexive, which answers a question
raised by Gonz\'{a}lez and Kania.


Abstract:
  Deformable object manipulation (DOM) is an emerging research problem in
robotics. The ability to manipulate deformable objects endows robots with
higher autonomy and promises new applications in the industrial, services, and
healthcare sectors. However, compared to rigid object manipulation, the
manipulation of deformable objects is considerably more complex, and is still
an open research problem. Addressing DOM challenges demand breakthroughs in
almost all aspects of robotics, namely hardware design, sensing, (deformation)
modeling, planning, and control. In this article, we review recent advances and
highlight the main challenges when considering deformation in each sub-field. A
particular focus of our paper lies in the discussions of these challenges and
proposing future directions of research.


Abstract:
  In-situ etching using Ga flux in an ultra-high vacuum environment like MBE is
introduced as a method to make high aspect ratio 3 dimensional structures in
$\beta$-Ga2O3. Etching of $\beta$-Ga2O3 due to excess Ga adatoms on the
epilayer surface had been viewed as non-ideal for epitaxial growth especially
since it results in plateauing and lowering of growth rate. In this study, we
use this well-known reaction from epitaxial growth to intentionally etch
$\beta$-Ga2O3. We demonstrate etch rate ranging from 2.9 nm/min to 30 nm/min
with the highest reported etch rate being only limited by the highest Ga flux
used. Patterned in-situ etching is also demonstrated and used to study the
effect of fin orientation on the sidewall profiles and dopant (Si) segregation
on the etched surface. Using in-situ Ga etching, we also demonstrate 150 nm
wide fins and 200 nm wide nano pillars with high aspect ratio. This new etching
method could enable future development of highly scaled vertical and lateral 3D
devices in $\beta$-Ga2O3.