Abstract:
  Femtosecond laser pulse systems allows to modify in a precise and permanent
way the optical properties of atransparent materials. This process enables the
direct writing of guiding structures in materials, commonlyknown as waveguides,
which are the base for optical circuit fabrication.It is our interest to study
the main characteristics of the waveguides manufactured by the laser
micro-machining technique. Here, an analysis of the resulting refractive index
profile has been carried out. Thischaracteristic is essential for the design
and simulation of integrated optical circuits. In particular we havedeveloped
our research on the study of light coupling in a pair of type II waveguides
made in Lithium Niobate(LiNbO3).These experimental backgrounds provide us with
elements to adjust and test the retrieved profile.Taking into account different
distance between tracks and writing energies, it is well known that the
couplinglength changes and the coupling ratio too. Then this study allows us to
reconstruct the refractive index profileaccording to its manufacturing
conditions. Modeling of the refractive index distribution profile is a key
parameterto perform beam propagation mode simulations (BPM) to achieve more
realistic results. So, by means of thismethod it is possible to obtain a
general procedure to describe the characteristics of these kinds of
waveguides.As a model test, integrated waveguides were built to corroborate
their light coupling. In a first stage it is designedthrough BPM simulations
then it is manufactured in an X-cut LiNbO3crystal in order to check its
operationaccording to the simulations carried out


Abstract:
  In this chapter, we reflect on the use of Artificial Intelligence (AI) and
its acceptance in clinical environments. We develop a general view of
hindrances for clinical acceptance in the form of a pipeline model combining AI
and clinical practise. We then link each challenge to the relevant stage in the
pipeline and discuss the necessary requirements in order to overcome each
challenge. We complement this discussion with an overview of opportunities for
AI, which we currently see at the periphery of clinical workflows.


Abstract:
  We show that a non-Hermitian operator with a tridiagonal matrix
representation in a finite-dimensional vector space is similar to an Hermitian
operator. The required condition is sufficient and simple examples show that it
is not necessary. We derive quite general features of the eigenvalues and
eigenvectors for a somewhat particular case.


Abstract:
  The gas mass fraction in galaxy clusters has been widely used to determine
cosmological parameters. This method assumes that the ratio of the cluster gas
mass fraction to the cosmic baryon fraction ($\gamma(z)$) is constant as a
function of redshift. In this work, we look for a time evolution of $\gamma(z)$
at $R_{500}$ by using both the SPT-SZ and Planck Early SZ (ESZ) cluster data,
in a model-independent fashion without any explicit dependence on the
underlying cosmology. For this calculation, we use a non-parametric functional
form for the Hubble parameter obtained from Gaussian Process regression using
cosmic chronometers. We parameterize $\gamma(z)$ as: $\gamma(z)=
\gamma_0(1+\gamma_1 z)$ to constrain the redshift evolution. We find
contradictory results between both the samples. For SPT-SZ, $\gamma (z)$
decreases as a function of redshift (at more than 5$\sigma$), whereas a
positive trend with redshift is found for Planck ESZ data (at more than
4$\sigma$). We however find that the $\gamma_1$ values for a subset of SPT-SZ
and Planck ESZ clusters between the same redshift interval agree to within
$1\sigma$. When we allow for a dependence on the halo mass in the evolution of
the gas depletion factor, the $4-5\sigma$ discrepancy reduces to $2\sigma$.


Abstract:
  Regulatory radio spectrum management is evolving from traditional static
frequency allocation and assignment schemes towards dynamic spectrum management
and access schemes. This evolution is necessitated by a number of factors
including underutilization of licensed spectrum bands, changing market and
technological developments and increased demand for spectrum for emerging
applications in multimedia communications, internet-of-things and fifth
generation (5G) wireless networks. In simple terms dynamic spectrum management
involves allowing unlicensed users known as secondary users (SUs) to access the
licensed spectrum of a licensed user also known as primary user (PU). This is
primarily achieved using spectrum sharing schemes that leverage spectrum
database and cognitive radio techniques. However, the use of spectrum database
and cognitive radio techniques faces reliability, security and privacy concerns
for spectrum sharing. There is also a need to support other requirements of
dynamic spectrum management such as secondary spectrum trading market and
dynamic spectrum access coordination. In this work, we review the use of
blockchains for enabling spectrum sharing and other aspects of dynamic spectrum
management. The review covers the use of blockchain to record spectrum
management information such as spectrum sensing results and spectrum auction
transactions in a secure manner. The article also covers the use of smart
contracts to support complex service-levelagreements (SLAs) between network
operators which is key to supporting a self-organized secondary spectrum
sharing market and enforcement of regulatory policies. A taxonomy of the
intersection between blockchain and various concepts of dynamic spectrum
management is also provided


Abstract:
  Natural language video localization (NLVL), which aims to locate a target
moment from a video that semantically corresponds to a text query, is a novel
and challenging task. Toward this end, in this paper, we present a
comprehensive survey of the NLVL algorithms, where we first propose the
pipeline of NLVL, and then categorize them into supervised and
weakly-supervised methods, following by the analysis of the strengths and
weaknesses of each kind of methods. Subsequently, we present the dataset,
evaluation protocols and the general performance analysis. Finally, the
possible perspectives are obtained by summarizing the existing methods.


Abstract:
  The discrete baroclinic modes of quasigeostrophic theory are incomplete and
the incompleteness manifests as a loss of information in the projection
process. The incompleteness of the baroclinic modes is related to the presence
of two previously unnoticed stationary step-wave solutions of the Rossby wave
problem with flat boundaries. These step-waves are the limit of surface
quasigeostrophic waves as boundary buoyancy gradients vanish. A complete normal
mode basis for quasigeostrophic theory is obtained by considering the
traditional Rossby wave problem with prescribed buoyancy gradients at the lower
and upper boundaries. The presence of these boundary buoyancy gradients
activates the previously inert boundary degrees of freedom. These Rossby waves
have several novel properties such as the presence of multiple modes with no
internal zeros, a finite number of modes with negative norms, and their
vertical structures form a basis capable of representing any quasigeostrophic
state with a differentiable series expansion. Using this complete basis, we are
able to obtain a differentiable series expansion to the potential vorticity of
Bretherton (with Dirac delta contributions). We also examine the
quasigeostrophic vertical velocity modes and derive a complete basis for such
modes as well. A natural application of these modes is the development of a
weakly non-linear wave-interaction theory of geostrophic turbulence that takes
topography into account.


Abstract:
  Opioid and substance misuse is rampant in the United States today, with the
phenomenon known as the opioid crisis. The relationship between substance use
and mental health has been extensively studied, with one possible relationship
being substance misuse causes poor mental health. However, the lack of evidence
on the relationship has resulted in opioids being largely inaccessible through
legal means. This study analyzes the substance misuse posts on social media
with the opioids being sold through crypto market listings. We use the Drug
Abuse Ontology, state-of-the-art deep learning, and BERT-based models to
generate sentiment and emotion for the social media posts to understand user
perception on social media by investigating questions such as, which synthetic
opioids people are optimistic, neutral, or negative about or what kind of drugs
induced fear and sorrow or what kind of drugs people love or thankful about or
which drug people think negatively about or which opioids cause little to no
sentimental reaction. We also perform topic analysis associated with the
generated sentiments and emotions to understand which topics correlate with
people's responses to various drugs. Our findings can help shape policy to help
isolate opioid use cases where timely intervention may be required to prevent
adverse consequences, prevent overdose-related deaths, and worsen the epidemic.


Abstract:
  The advantages of evolutionary algorithms with respect to traditional methods
have been greatly discussed in the literature. While particle swarm optimizers
share such advantages, they outperform evolutionary algorithms in that they
require lower computational cost and easier implementation, involving no
operator design and few coefficients to be tuned. However, even marginal
variations in the settings of these coefficients greatly influence the dynamics
of the swarm. Since this paper does not intend to study their tuning,
general-purpose settings are taken from previous studies, and virtually the
same algorithm is used to optimize a variety of notably different problems.
Thus, following a review of the paradigm, the algorithm is tested on a set of
benchmark functions and engineering problems taken from the literature. Later,
complementary lines of code are incorporated to adapt the method to
combinatorial optimization as it occurs in scheduling problems, and a real case
is solved using the same optimizer with the same settings. The aim is to show
the flexibility and robustness of the approach, which can handle a wide variety
of problems.


Abstract:
  High Definition (HD) maps are maps with precise definitions of road lanes
with rich semantics of the traffic rules. They are critical for several key
stages in an autonomous driving system, including motion forecasting and
planning. However, there are only a small amount of real-world road topologies
and geometries, which significantly limits our ability to test out the
self-driving stack to generalize onto new unseen scenarios. To address this
issue, we introduce a new challenging task to generate HD maps. In this work,
we explore several autoregressive models using different data representations,
including sequence, plain graph, and hierarchical graph. We propose HDMapGen, a
hierarchical graph generation model capable of producing high-quality and
diverse HD maps through a coarse-to-fine approach. Experiments on the Argoverse
dataset and an in-house dataset show that HDMapGen significantly outperforms
baseline methods. Additionally, we demonstrate that HDMapGen achieves high
scalability and efficiency.


Abstract:
  We study the set $\mathcal{S}$ of odd positive integers $n$ with the property
${2n}/{\sigma(n)} - 1 = 1/x$, for positive integer $x$, i.e., the set that
relates to odd perfect and odd "spoof perfect" numbers. As a consequence, we
find that if $D=pq$ denotes a spoof odd perfect number other than Descartes'
example, with pseudo-prime factor $p$, then $q>10^{12}$. Furthermore, we find
irregularities in the ending digits of integers $n\in\mathcal{S}$ and study
aspects of its density, leading us to conjecture that the amount of numbers in
$\mathcal{S}$ below $k$ is $\sim10 \log(k)$.


Abstract:
  Because of the invisible human keypoints in images caused by illumination,
occlusion and overlap, it is likely to produce unreasonable human pose
prediction for most of the current human pose estimation methods. In this
paper, we design a novel generative adversarial network (GAN) to improve the
localization accuracy of visible joints when some joints are invisible. The
network consists of two simple but efficient modules, Cascade Feature Network
(CFN) and Graph Structure Network (GSN). First, the CFN utilizes the prediction
maps from the previous stages to guide the prediction maps in the next stage to
produce accurate human pose. Second, the GSN is designed to contribute to the
localization of invisible joints by passing message among different joints.
According to GAN, if the prediction pose produced by the generator G cannot be
distinguished by the discriminator D, the generator network G has successfully
obtained the underlying dependence of human joints. We conduct experiments on
three widely used human pose estimation benchmark datasets, LSP, MPII and COCO,
whose results show the effectiveness of our proposed framework.


Abstract:
  In the quests to uncovering the fundamental structures that underlie some of
the asymptotic Swampland conjectures we initiate the general study of
asymptotic period vectors of Calabi- Yau manifolds. Our strategy is to exploit
the constraints imposed by completeness, symmetry, and positivity, which are
formalized in asymptotic Hodge theory. We use these general principles to study
the periods near any boundary in complex structure moduli space and explain
that near most boundaries leading exponentially suppressed corrections must be
present for consistency. The only exception are period vectors near the
well-studied large complex structure point. Together with the classification of
possible boundaries, our procedure makes it possible to construct general
models for these asymptotic periods. The starting point for this construction
is the sl(2)-data classifying the boundary, which we use to construct the
asymptotic Hodge decomposition known as the nilpotent orbit. We then use the
latter to determine the asymptotic period vector. We explicitly carry out this
program for all possible one- and two-moduli boundaries in Calabi-Yau
threefolds and write down general models for their asymptotic periods.


Abstract:
  Multi-connectivity facilitates higher throughput, shorter delay, and lower
outage probability for a user in a wireless network. Considering these
promises, a rationale policy for a network operator would be to implement
multi-connectivity for all of its users. In this paper, we investigate whether
the promises of multi-connectivity also hold in such a setting where all users
of a network are connected through multiple links. In particular, we consider a
network where every user connects to its k closest base stations. Using a
framework of stochastic geometry and probability theory, we obtain analytic
expressions for per-user throughput and outage probability of $k$-connectivity
networks under several failure models. In contrast to the conclusions of
previous research, our analysis shows that per-user throughput decreases with
increasing k. However, multi-connected networks are more resilient against
failures than single connected networks as reflected with lower outage
probability and lead to higher fairness among the users. Consequently, we
conclude that rather than implementing multi-connectivity for all users, a
network operator should consider it for its users who would benefit from
additional links the most, e.g., cell edge users.


Abstract:
  In this paper we introduce the concept of Wiener-Luxemburg amalgam spaces
which are a modification of the more classical Wiener amalgam spaces intended
to address some of the shortcomings the latter face in the context of
rearrangement-invariant Banach function spaces.
  We introduce the Wiener-Luxemburg amalgam spaces and study their properties,
including (but nor limited to) their normability, embeddings between them and
their associate spaces. We also study amalgams of quasi-Banach function spaces
and introduce a necessary generalisation of the concept of associate spaces. We
then apply this general theory to resolve the question whether the
Hardy-Littlewood-P\'{o}lya principle holds for all r.i. quasi-Banach function
spaces. Finally, we illustrate the asserted shortcomings of Wiener amalgam
spaces by providing counterexamples to certain properties of Banach function
spaces as well as rearrangement invariance.


Abstract:
  The use of lattice tensor representations is explored to investigate the
lattice Landau gauge gluon propagator for the pure SU(3) Yang-Mills gauge
theory in 4D. The analysis of several tensor bases allows to quantify the
completeness of the various tensor bases considered, the deviations of the
lattice results from the continuum theory due to the lattice artefacts and
estimate the theoretical uncertainty in the propagator. Furthermore, our
analysis tests continuum based relations with the lattice data and show that
the lattice Landau gauge gluon propagator is described by a unique form factor,
as in the continuum formulation.


Abstract:
  We study the problem of maximizing a monotone $k$-submodular function $f$
under a knapsack constraint, where a $k$-submodular function is a natural
generalization of a submodular function to $k$ dimensions. We present a
deterministic $(\frac12-\frac{1}{2e})$-approximation algorithm that evaluates
$f$ $O(n^5k^4)$ times.


Abstract:
  Given two sources of evidence about a latent variable, one can combine the
information from both by multiplying the likelihoods of each piece of evidence.
However, when one or both of the observation models are misspecified, the
distributions will conflict. We study this problem in the setting with two
conflicting reward functions learned from different sources. In such a setting,
we would like to retreat to a broader distribution over reward functions, in
order to mitigate the effects of misspecification. We assume that an agent will
maximize expected reward given this distribution over reward functions, and
identify four desiderata for this setting. We propose a novel algorithm,
Multitask Inverse Reward Design (MIRD), and compare it to a range of simple
baselines. While all methods must trade off between conservatism and
informativeness, through a combination of theory and empirical results on a toy
environment, we find that MIRD and its variant MIRD-IF strike a good balance
between the two.


Abstract:
  Power networks, responsible for transporting electricity across large
geographical regions, are complex infrastructures on which modern life
critically depend. Variations in demand and production profiles, with
increasing renewable energy integration, as well as the high voltage network
technology, constitute a real challenge for human operators when optimizing
electricity transportation while avoiding blackouts. Motivated to investigate
the potential of AI methods in enabling adaptability in power network
operation, we have designed a L2RPN challenge to encourage the development of
reinforcement learning solutions to key problems present in the next-generation
power networks. The NeurIPS 2020 competition was well received by the
international community attracting over 300 participants worldwide.
  The main contribution of this challenge is our proposed comprehensive
'Grid2Op' framework, and associated benchmark, which plays realistic sequential
network operations scenarios. The Grid2Op framework, which is open-source and
easily re-usable, allows users to define new environments with its companion
GridAlive ecosystem. Grid2Op relies on existing non-linear physical power
network simulators and let users create a series of perturbations and
challenges that are representative of two important problems: a) the
uncertainty resulting from the increased use of unpredictable renewable energy
sources, and b) the robustness required with contingent line disconnections. In
this paper, we give the competition highlights. We present the benchmark suite
and analyse the winning solutions, including one super-human performance
demonstration. We propose our organizational insights for a successful
competition and conclude on open research avenues. Given the challenge success,
we expect our work will foster research to create more sustainable solutions
for power network operations.


Abstract:
  In this paper, we investigate the relation between Bachelier and
Black-Scholes models driven by the infinitely divisible inverse subordinators.
Such models, in contrast to their classical equivalents, can be used in markets
where periods of stagnation are observed. We introduce the subordinated
Cox-Ross-Rubinstein model and prove that the price of the underlying in that
model converges in distribution to the price of underlying in the subordinated
Black-Scholes model defined in [25]. Motivated by this fact we price the
selected option contracts using the binomial trees. The results are compared to
other numerical methods.


Abstract:
  Majority-SAT is the problem of determining whether an input $n$-variable
formula in conjunctive normal form (CNF) has at least $2^{n-1}$ satisfying
assignments. Majority-SAT and related problems have been studied extensively in
various AI communities interested in the complexity of probabilistic planning
and inference. Although Majority-SAT has been known to be PP-complete for over
40 years, the complexity of a natural variant has remained open:
Majority-$k$SAT, where the input CNF formula is restricted to have clause width
at most $k$.
  We prove that for every $k$, Majority-$k$SAT is in P. In fact, for any
positive integer $k$ and rational $\rho \in (0,1)$ with bounded denominator, we
give an algorithm that can determine whether a given $k$-CNF has at least $\rho
\cdot 2^n$ satisfying assignments, in deterministic linear time (whereas the
previous best-known algorithm ran in exponential time). Our algorithms have
interesting positive implications for counting complexity and the complexity of
inference, significantly reducing the known complexities of related problems
such as E-MAJ-$k$SAT and MAJ-MAJ-$k$SAT. At the heart of our approach is an
efficient method for solving threshold counting problems by extracting
sunflowers found in the corresponding set system of a $k$-CNF.
  We also show that the tractability of Majority-$k$SAT is somewhat fragile.
For the closely related GtMajority-SAT problem (where we ask whether a given
formula has greater than $2^{n-1}$ satisfying assignments) which is known to be
PP-complete, we show that GtMajority-$k$SAT is in P for $k\le 3$, but becomes
NP-complete for $k\geq 4$. These results are counterintuitive, because the
``natural'' classifications of these problems would have been PP-completeness,
and because there is a stark difference in the complexity of GtMajority-$k$SAT
and Majority-$k$SAT for all $k\ge 4$.


Abstract:
  Knee arthroscopy is a minimally invasive surgical (MIS) procedure which is
performed to treat knee-joint ailment. Lack of visual information of the
surgical site obtained from miniaturized cameras make this surgical procedure
more complex. Knee cavity is a very confined space; therefore, surgical scenes
are captured at close proximity. Insignificant context of knee atlas often
makes them unrecognizable as a consequence unintentional tissue damage often
occurred and shows a long learning curve to train new surgeons. Automatic
context awareness through labeling of the surgical site can be an alternative
to mitigate these drawbacks. However, from the previous studies, it is
confirmed that the surgical site exhibits several limitations, among others,
lack of discriminative contextual information such as texture and features
which drastically limits this vision task. Additionally, poor imaging
conditions and lack of accurate ground-truth labels are also limiting the
accuracy. To mitigate these limitations of knee arthroscopy, in this work we
proposed a scene segmentation method that successfully segments multi
structures.


Abstract:
  This paper investigates the problem of providing ultra-reliable and
energy-efficient virtual reality (VR) experiences for wireless mobile users. To
ensure reliable ultra-high-definition (UHD) video frame delivery to mobile
users and enhance their immersive visual experiences, a coordinated multipoint
(CoMP) transmission technique and millimeter wave (mmWave) communications are
exploited. Owing to user movement and time-varying wireless channels, the
wireless VR experience enhancement problem is formulated as a
sequence-dependent and mixed-integer problem with a goal of maximizing users'
feeling of presence (FoP) in the virtual world, subject to power consumption
constraints on access points (APs) and users' head-mounted displays (HMDs). The
problem, however, is hard to be directly solved due to the lack of users'
accurate tracking information and the sequence-dependent and mixed-integer
characteristics. To overcome this challenge, we develop a parallel echo state
network (ESN) learning method to predict users' tracking information by
training fresh and historical tracking samples separately collected by APs.
With the learnt results, we propose a deep reinforcement learning (DRL) based
optimization algorithm to solve the formulated problem. In this algorithm, we
implement deep neural networks (DNNs) as a scalable solution to produce integer
decision variables and solving a continuous power control problem to criticize
the integer decision variables. Finally, the performance of the proposed
algorithm is compared with various benchmark algorithms, and the impact of
different design parameters is also discussed. Simulation results demonstrate
that the proposed algorithm is more 4.14% energy-efficient than the benchmark
algorithms.


Abstract:
  We provide explicit generators and relations for the affine Kac-Moody groups,
as well as a realization of them as (twisted) loop groups by means of Galois
descent considerations.


Abstract:
  We introduce the problem of robust subgroup discovery, i.e., finding a set of
interpretable descriptions of subsets that 1) stand out with respect to one or
more target attributes, 2) are statistically robust, and 3) non-redundant. Many
attempts have been made to mine either locally robust subgroups or to tackle
the pattern explosion, but we are the first to address both challenges at the
same time from a global modelling perspective. First, we formulate the broad
model class of subgroup lists, i.e., ordered sets of subgroups, for univariate
and multivariate targets that can consist of nominal or numeric variables, and
that includes traditional top-1 subgroup discovery in its definition. This
novel model class allows us to formalise the problem of optimal robust subgroup
discovery using the Minimum Description Length (MDL) principle, where we resort
to optimal Normalised Maximum Likelihood and Bayesian encodings for nominal and
numeric targets, respectively. Second, as finding optimal subgroup lists is
NP-hard, we propose SSD++, a greedy heuristic that finds good subgroup lists
and guarantees that the most significant subgroup found according to the MDL
criterion is added in each iteration, which is shown to be equivalent to a
Bayesian one-sample proportions, multinomial, or t-test between the subgroup
and dataset marginal target distributions plus a multiple hypothesis testing
penalty. We empirically show on 54 datasets that SSD++ outperforms previous
subgroup set discovery methods in terms of quality and subgroup list size.


Abstract:
  We study the phase behaviour of a quasi-two dimensional cholesteric liquid
crystal shell. We characterise the topological phases arising close to the
isotropic-cholesteric transition, and show that they differ in a fundamental
way from those observed on a flat geometry. For spherical shells, we discover
two types of quasi-two dimensional topological phases: finite quasicrystals and
amorphous structures, both made up by mixtures of polygonal tessellations of
half-skyrmions. These structures generically emerge instead of regular double
twist lattices because of geometric frustration, which disallows a regular
hexagonal tiling of curved space. For toroidal shells, the variations in the
local curvature of the surface stabilises heterogeneous phases where
cholesteric patterns coexist with hexagonal lattices of half-skyrmions.
Quasicrystals, amorphous and heterogeneous structures could be sought
experimentally by self assembling cholesteric shells on the surface of emulsion
droplets.


Abstract:
  A systematic study of nonlocal and size effects on the energy transfer of a
dipole (e.g. a molecule or a quantum dot) induced by the proximity of a metal
slab is presented. Nonlocal effects are accounted for using the hydrodynamic
model (HDM). We derive a general relation that connects the energy transfer
rate to the linear charge density-density response function of the slab. This
function is explicitly evaluated for the HDM and the local Drude model. We show
that a thin metal slab can support a series of higher-frequency surface plasma
wave (SPW) modes in addition to the normal SPW modes thanks to the nonlocal
effects. These modes markedly alter the response and the energy transfer
process, as revealed in the structure of the energy transfer rate in the
parameter space. Our findings are important for applications such as the
recently developed metal-induced energy transfer imaging, which relies on
accurate modeling of the energy transfer rate.


Abstract:
  In April 2021, the European Commission proposed a Regulation on Artificial
Intelligence, known as the AI Act. We present an overview of the Act and
analyse its implications, drawing on scholarship ranging from the study of
contemporary AI practices to the structure of EU product safety regimes over
the last four decades. Aspects of the AI Act, such as different rules for
different risk-levels of AI, make sense. But we also find that some provisions
of the Draft AI Act have surprising legal implications, whilst others may be
largely ineffective at achieving their stated goals. Several overarching
aspects, including the enforcement regime and the risks of maximum
harmonisation pre-empting legitimate national AI policy, engender significant
concern. These issues should be addressed as a priority in the legislative
process.


Abstract:
  We introduce SelfExplain, a novel self-explaining model that explains a text
classifier's predictions using phrase-based concepts. SelfExplain augments
existing neural classifiers by adding (1) a globally interpretable layer that
identifies the most influential concepts in the training set for a given sample
and (2) a locally interpretable layer that quantifies the contribution of each
local input concept by computing a relevance score relative to the predicted
label. Experiments across five text-classification datasets show that
SelfExplain facilitates interpretability without sacrificing performance. Most
importantly, explanations from SelfExplain show sufficiency for model
predictions and are perceived as adequate, trustworthy and understandable by
human judges compared to existing widely-used baselines.


Abstract:
  Radhakrishnan \emph{et.al} [Phys. Rev. Lett. \textbf{124}, 110401 (2020)]
proposed quantum discord to multipartite systems and derived explicit formulae
for any states. These results are significant in capturing quantum correlations
for multi-qubit systems. In this paper, we evaluate the geometric measure of
multipartite quantum discord and obtain results for a large family of
multi-qubit states. %Intriguingly, our results demonstrate that for the family
of states, multipartite geometric discord decreases exponentially as the number
of parties increases while multipartite quantum discord not,therefore, we
conclude that multipartite geometric discord could not reflect multipartite
quantum discord in all cases. Furthermore, we investigated the dynamic behavior
of geometric discord for the family of two-, three- and four-qubit states under
phase noise acting on the first qubit. And we discover that sudden change of
multipartite geometric discord can appear when phase noise act only on one part
of the two-, three- and four-qubit states.


Abstract:
  We study an anisotropic, possibly non-homogeneous version of the evolution
$p$-Laplacian equation when fast diffusion holds in all directions. We develop
the basic theory and prove symmetrization results from which we derive $L^1$ to
$L^\infty$ estimates. We prove the existence of a self-similar fundamental
solution of this equation in the appropriate exponent range, and uniqueness in
a smaller range. We also obtain the asymptotic behaviour of finite mass
solutions in terms of the self-similar solution. Positivity, decay rates as
well as other properties of the solutions are derived. The combination of
self-similarity and anisotropy is not common in the related literature. It is
however essential in our analysis and creates mathematical difficulties that
are solved for fast diffusions.


Abstract:
  Multi-agent influence diagrams (MAIDs) are a popular form of graphical model
that, for certain classes of games, have been shown to offer key complexity and
explainability advantages over traditional extensive form game (EFG)
representations. In this paper, we extend previous work on MAIDs by introducing
the concept of a MAID subgame, as well as subgame perfect and trembling hand
perfect equilibrium refinements. We then prove several equivalence results
between MAIDs and EFGs. Finally, we describe an open source implementation for
reasoning about MAIDs and computing their equilibria.


Abstract:
  In existing linear response theories for adiabatically driven cyclic heat
engines, Onsager symmetry is identified only phenomenologically, and a relation
between global and local Onsager coefficients, defined over one cycle and at
any instant of a cycle, respectively, is not derived. To address this
limitation, we develop a linear response theory for the speed of adiabatically
changing parameters and temperature differences in generic Gaussian heat
engines obeying Fokker--Planck dynamics. We establish a hierarchical
relationship between the global linear response relations, defined over one
cycle of the heat engines, and the local ones, defined at any instant of the
cycle. This yields a detailed expression for the global Onsager coefficients in
terms of the local Onsager coefficients. Moreover, we derive an efficiency
bound, which is tighter than the Carnot bound, for adiabatically driven linear
irreversible heat engines based on the detailed global Onsager coefficients.
Finally, we demonstrate the application of the theory using the simplest
stochastic Brownian heat engine model.


Abstract:
  We observationally examine cosmological models based on primordial power
spectra with quantized wavevectors. Introducing a linearly quantized power
spectrum with $k_0=3.225\times10^{-4}\mathrm{Mpc}^{-1}$ and spacing $\Delta k =
2.257 \times 10^{-4} \mathrm{Mpc}^{-1}$ provides a better fit to the Planck
2018 observations than the concordance baseline, with $\Delta \chi^2 = -8.55$.
Extending the results of Lasenby et al [1], we show that the requirement for
perturbations to remain finite beyond the future conformal boundary in a
universe containing dark matter and a cosmological constant results in a
linearly quantized primordial power spectrum. It is found that the infrared
cutoffs for this future conformal boundary quantized cosmology do not provide
cosmic microwave background power spectra compatible with observations, but
future theories may predict more observationally consistent quantized spectra.


Abstract:
  This paper presents a temporal classification method for all three subtasks
of symbol segmentation, symbol recognition and relation classification in
online handwritten mathematical expressions (HMEs). The classification model is
trained by multiple paths of symbols and spatial relations derived from the
Symbol Relation Tree (SRT) representation of HMEs. The method benefits from
global context of a deep bidirectional Long Short-term Memory network, which
learns the temporal classification directly from online handwriting by the
Connectionist Temporal Classification loss. To recognize an online HME, a
symbol-level parse tree with Context-Free Grammar is constructed, where symbols
and spatial relations are obtained from the temporal classification results. We
show the effectiveness of the proposed method on the two latest CROHME
datasets.


Abstract:
  22. Shortening acquisition time and reducing the motion-artifact are two of
the most critical issues in MRI. As a promising solution, high-quality MRI
image restoration provides a new approach to achieve higher resolution without
costing additional acquisition time or modification on the pulse sequences.
Recently, as to the rise of deep learning, convolutional neural networks have
been proposed for super-resolution (SR) image generation and motion-artifact
reduction (MAR) for MRI. Recent studies suggest that using perceptual feature
space loss and k space loss to capture the perceptual information and
high-frequency information of images, respectively. However, the quality of
reconstructed SR and MAR MR images is limited because the most important
details of the informative area in the MR image, the edges and the structure,
cannot be very well restored. Besides, lots of the SR approaches are trained by
using low-resolution images generated by applying bicubic or blur-downscale
degradation, which cannot represent the real process of MRI measurement. Such
inconsistencies lead to performance degradation in the reconstruction of SR
images as well. This study reveals that using the L1 loss of SSIM and gradient
map edge quality loss could force the deep learning model to focus on studying
the features of edges and structure details of MR images, thus generating SR
images with more accurate, fruitful information and reduced motion-artifact. We
employed a state-of-the-art model, RCAN, as the network framework in both SR
and MAR tasks, trained the model by using low-resolution images and
motion-artifact affected images which were generated by emulating how they are
measured in the real MRI measurement to ensure the model can be easily applied
in the practical clinic environment, and verified the trained model could work
fairly well.


Abstract:
  Halide perovskites perform remarkably in optoelectronic devices including
tandem photovoltaics. However, this exceptional performance is striking given
that perovskites exhibit deep charge carrier traps and spatial compositional
and structural heterogeneity, all of which should be detrimental to
performance. Here, we resolve this long-standing paradox by providing a global
visualisation of the nanoscale chemical, structural and optoelectronic
landscape in halide perovskite devices, made possible through the development
of a new suite of correlative, multimodal microscopy measurements combining
quantitative optical spectroscopic techniques and synchrotron nanoprobe
measurements. We show that compositional disorder dominates the optoelectronic
response, while nanoscale strain variations even of large magnitude (~1 %) have
only a weak influence. Nanoscale compositional gradients drive carrier
funneling onto local regions associated with low electronic disorder, drawing
carrier recombination away from trap clusters associated with electronic
disorder and leading to high local photoluminescence quantum efficiency. These
measurements reveal a global picture of the competitive nanoscale landscape,
which endows enhanced defect tolerance in devices through spatial chemical
disorder that outcompetes both electronic and structural disorder.


Abstract:
  Image matting refers to the estimation of the opacity of foreground objects.
It requires correct contours and fine details of foreground objects for the
matting results. To better accomplish human image matting tasks, we propose the
Cascade Image Matting Network with Deformable Graph Refinement, which can
automatically predict precise alpha mattes from single human images without any
additional inputs. We adopt a network cascade architecture to perform matting
from low-to-high resolution, which corresponds to coarse-to-fine optimization.
We also introduce the Deformable Graph Refinement (DGR) module based on graph
neural networks (GNNs) to overcome the limitations of convolutional neural
networks (CNNs). The DGR module can effectively capture long-range relations
and obtain more global and local information to help produce finer alpha
mattes. We also reduce the computation complexity of the DGR module by
dynamically predicting the neighbors and apply DGR module to higher--resolution
features. Experimental results demonstrate the ability of our CasDGR to achieve
state-of-the-art performance on synthetic datasets and produce good results on
real human images.


Abstract:
  We consider the problem of dividing limited resources to individuals arriving
over $T$ rounds. Each round has a random number of individuals arrive, and
individuals can be characterized by their type (i.e. preferences over the
different resources). A standard notion of `fairness' in this setting is that
an allocation simultaneously satisfy envy-freeness and efficiency. The former
is an individual guarantee, requiring that each agent prefers their own
allocation over the allocation of any other; in contrast, efficiency is a
global property, requiring that the allocations clear the available resources.
For divisible resources, when the number of individuals of each type are known
upfront, the above desiderata are simultaneously achievable for a large class
of utility functions. However, in an online setting when the number of
individuals of each type are only revealed round by round, no policy can
guarantee these desiderata simultaneously, and hence the best one can do is to
try and allocate so as to approximately satisfy the two properties.
  We show that in the online setting, the two desired properties (envy-freeness
and efficiency) are in direct contention, in that any algorithm achieving
additive counterfactual envy-freeness up to a factor of $L_T$ necessarily
suffers a efficiency loss of at least $1 / L_T$. We complement this uncertainty
principle with a simple algorithm, HopeGuardrail, which allocates resources
based on an adaptive threshold policy and is able to achieve any
fairness-efficiency point on this frontier. In simulation results, our
algorithm provides allocations close to the optimal fair solution in hindsight,
motivating its use in practical applications as the algorithm is able to adapt
to any desired fairness efficiency trade-off.


Abstract:
  Video instance segmentation (VIS) aims to segment and associate all instances
of predefined classes for each frame in videos. Prior methods usually obtain
segmentation for a frame or clip first, and merge the incomplete results by
tracking or matching. These methods may cause error accumulation in the merging
step. Contrarily, we propose a new paradigm -- Propose-Reduce, to generate
complete sequences for input videos by a single step. We further build a
sequence propagation head on the existing image-level instance segmentation
network for long-term propagation. To ensure robustness and high recall of our
proposed framework, multiple sequences are proposed where redundant sequences
of the same instance are reduced. We achieve state-of-the-art performance on
two representative benchmark datasets -- we obtain 47.6% in terms of AP on
YouTube-VIS validation set and 70.4% for J&F on DAVIS-UVOS validation set. Code
is available at https://github.com/dvlab-research/ProposeReduce.


Abstract:
  To approximately compute the non-relativistic ground state of an electrically
non-neutral star, an exactly solvable model was recently introduced, and partly
solved, by Krivoruchenko, Nadyozhin, and Yudin. The model generalizes the
well-known Lane--Emden equation of a polytropic gas ball of index $n=1$ to a
two-fluid setting. Here its complete solution is presented in terms of simple
elementary functions; it is also generalized to a more-than-two-fluid setting
where it remains exactly solvable. It is shown that, given the number of
nuclei, a maximal negatively and a maximal positively charged solution exists,
plus a continuous family of solutions which interpolates between these
extremes. Numerical comparisons show that this exactly solvable model captures
the qualitative behavior of the more physical model it is supposed to
approximate. Furthermore, it correctly answers the question: how non-neutral
can the star be? The answer is independent of the speed of light $c$ and the
Planck quantum $h$. It supports Penrose's weak cosmic censorship hypothesis, in
the sense that the bounds on the excess charge are compatible with the bound on
the charge of a Reissner--Weyl--Nordstr\"om black hole.


Abstract:
  We show that compositions of time-reversal and spatial symmetries, also known
as the magnetic-space-group symmetries, protect topological invariants as well
as surface states that are distinct from those of all preceding topological
states. We obtain, by explicit and exhaustive construction, the topological
classification of electronic band insulators that are magnetically ordered for
each one of the 1421 magnetic space groups in three dimensions. We have also
computed the symmetry-based indicators for each nontrivial class, and, by doing
so, establish the complete mapping from symmetry representations to topological
invariants.


Abstract:
  Reinforcement learning agents have demonstrated remarkable achievements in
simulated environments. Data efficiency poses an impediment to carrying this
success over to real environments. The design of data-efficient agents calls
for a deeper understanding of information acquisition and representation. We
develop concepts and establish a regret bound that together offer principled
guidance. The bound sheds light on questions of what information to seek, how
to seek that information, and it what information to retain. To illustrate
concepts, we design simple agents that build on them and present computational
results that demonstrate improvements in data efficiency.


Abstract:
  Inference is the task of drawing conclusions about unobserved variables given
observations of related variables. Applications range from identifying diseases
from symptoms to classifying economic regimes from price movements.
Unfortunately, performing exact inference is intractable in general. One
alternative is variational inference, where a candidate probability
distribution is optimized to approximate the posterior distribution over
unobserved variables. For good approximations, a flexible and highly expressive
candidate distribution is desirable. In this work, we use quantum Born machines
as variational distributions over discrete variables. We apply the framework of
operator variational inference to achieve this goal. In particular, we adopt
two specific realizations: one with an adversarial objective and one based on
the kernelized Stein discrepancy. We demonstrate the approach numerically using
examples of Bayesian networks, and implement an experiment on an IBM quantum
computer. Our techniques enable efficient variational inference with
distributions beyond those that are efficiently representable on a classical
computer.


Abstract:
  Context. Studying gas chemistry in protoplanetary disks is key to
understanding the process of planet formation. Sulfur chemistry in particular
is poorly understood in interstellar environments, and the location of the main
reservoirs remains unknown. Protoplanetary disks in Taurus are ideal targets
for studying the evolution of the composition of planet forming systems.
  Aims. We aim to elucidate the chemical origin of sulfur-bearing molecular
emission in protoplanetary disks, with a special focus on H$_2$S emission, and
to identify candidate species that could become the main molecular sulfur
reservoirs in protoplanetary systems.
  Methods. We used IRAM 30m observations of nine gas-rich young stellar objects
(YSOs) in Taurus to perform a survey of sulfur-bearing and oxygen-bearing
molecular species. In this paper we present our results for the CS 3-2 ($\nu_0$
= 146.969 GHz), H$_2$CO 2$_{11}$-1$_{10}$ ($\nu_0$ = 150.498 GHz), and H$_2$S
1$_{10}$-1$_{01}$ ($\nu_0$ = 168,763 GHz) emission lines.
  Results. We detected H$_2$S emission in four sources out of the nine
observed, significantly increasing the number of detections toward YSOs. We
also detected H$_2$CO and CS in six out of the nine. We identify a tentative
correlation between H$_2$S 1$_{10}$-1$_{01}$ and H$_2$CO 2$_{11}$-1$_{10}$ as
well as a tentative correlation between H$_2$S 1$_{10}$-1$_{01}$ and H$_2$O
8$_{18}$-7$_{07}$. By assuming local thermodynamical equilibrium, we computed
column densities for the sources in the sample, with N(o-H$_2$S) values ranging
between $2.6\times10^{12}$ cm$^{-2}$ and $1.5\times10^{13}$ cm$^{-2}$.


Abstract:
  Titanium-based shape memory alloys, such as Ti2448, have attracted enormous
attention owing to their unique thermomechanical properties and potential
biomedical applications. In this study, we develop a polycrystalline phase
field to investigate the grain size dependence of the martensitic
transformation and associated mechanical properties of nanograined Ti-Nb
alloys. It is shown that a reduction of the average grain size strengthens the
suppression of the martensitic transformation (MT), leading to an increase of
the transformation stress, shrinkage of the stress hysteresis, and elimination
of residual strain. The time-temperature-transformation curves of nano-grained
Ti-Nb alloys with different average grain sizes are obtained and the validity
of Hall-Petch relation is also confirmed in all studied grain sizes.
Furthermore, when the average grain size becomes ultrasmall, both the
temperature- and stress-induced MTs show the continuous second-order phase
transition behavior. These superior transformation characteristics are
attributed to the high density of grain boundaries and the related dominant
role of the gradient energy at the nanoscale. Our results have profound
implications for the design and control of the properties in nano-grained shape
memory alloys.


Abstract:
  Well-designed molecular representations (fingerprints) are vital to combine
medical chemistry and deep learning. Whereas incorporating 3D geometry of
molecules (i.e. conformations) in their representations seems beneficial,
current 3D algorithms are still in infancy. In this paper, we propose a novel
molecular representation algorithm which preserves 3D conformations of
molecules with a Molecular Hamiltonian Network (HamNet). In HamNet, implicit
positions and momentums of atoms in a molecule interact in the Hamiltonian
Engine following the discretized Hamiltonian equations. These implicit
coordinations are supervised with real conformations with translation- &
rotation-invariant losses, and further used as inputs to the Fingerprint
Generator, a message-passing neural network. Experiments show that the
Hamiltonian Engine can well preserve molecular conformations, and that the
fingerprints generated by HamNet achieve state-of-the-art performances on
MoleculeNet, a standard molecular machine learning benchmark.


Abstract:
  This paper introduces a model of a stylized organization that is comprised of
several departments that autonomously allocate tasks. To do so, the departments
either take short-sighted decisions that immediately maximize their utility or
take long-sighted decisions that aim at minimizing the interdependencies
between tasks. The organization guides the departments' behavior by either an
individualistic, a balanced, or an altruistic linear incentive scheme. Even if
tasks are perfectly decomposable, altruistic incentive schemes are preferred
over individualistic incentive schemes since they substantially increase the
organization's performance. Interestingly, if altruistic incentive schemes are
effective, short-sighted decisions appear favorable since they do not only
increase performance in the short run but also result in significantly higher
performances in the long run.


Abstract:
  Machine learning techniques are gaining attention in the context of intrusion
detection due to the increasing amounts of data generated by monitoring tools,
as well as the sophistication displayed by attackers in hiding their activity.
However, existing methods often exhibit important limitations in terms of the
quantity and relevance of the generated alerts. Recently, knowledge graphs are
finding application in the cybersecurity domain, showing the potential to
alleviate some of these drawbacks thanks to their ability to seamlessly
integrate data from multiple domains using human-understandable vocabularies.
We discuss the application of machine learning on knowledge graphs for
intrusion detection and experimentally evaluate a link-prediction method for
scoring anomalous activity in industrial systems. After initial unsupervised
training, the proposed method is shown to produce intuitively well-calibrated
and interpretable alerts in a diverse range of scenarios, hinting at the
potential benefits of relational machine learning on knowledge graphs for
intrusion detection purposes.


Abstract:
  In this paper, we aim at improving the computational efficiency of graph
convolutional networks (GCNs) for learning on point clouds. The basic graph
convolution that is typically composed of a $K$-nearest neighbor (KNN) search
and a multilayer perceptron (MLP) is examined. By mathematically analyzing the
operations there, two findings to improve the efficiency of GCNs are obtained.
(1) The local geometric structure information of 3D representations propagates
smoothly across the GCN that relies on KNN search to gather neighborhood
features. This motivates the simplification of multiple KNN searches in GCNs.
(2) Shuffling the order of graph feature gathering and an MLP leads to
equivalent or similar composite operations. Based on those findings, we
optimize the computational procedure in GCNs. A series of experiments show that
the optimized networks have reduced computational complexity, decreased memory
consumption, and accelerated inference speed while maintaining comparable
accuracy for learning on point clouds. Code will be available at
\url{https://github.com/ofsoundof/EfficientGCN.git}.


Abstract:
  In this paper, we present machine learning models based on random forest
classifiers, support vector machines, gradient boosted decision trees, and
artificial neural networks to predict participation in cancer screening
programs in South Korea. The top performing model was based on gradient boosted
decision trees and achieved an area under the receiver operating characteristic
curve (AUC-ROC) of 0.8706 and average precision of 0.8776. The results of this
study are encouraging and suggest that with further research, these models can
be directly applied to Korea's healthcare system, thus increasing participation
in Korea's National Cancer Screening Program.


Abstract:
  While age of Information (AoI) has gained importance as a metric
characterizing the fresh-ness of information in information-update systems and
time-critical applications, most previous studies on AoI have been theoretical.
In this chapter, we compile a set of recent works reporting API measurements in
real-life networks and experimental testbeds, and investigating practical
issues such as synchronization, the role of various transport layer protocols,
congestion control mechanisms, application of machine learning for adaptation
to network conditions, and device related bottlenecks such as limited
processing power.


Abstract:
  We explore experimentally a quantum metamaterial based on a superconducting
chip with 25 frequency-tunable transmon qubits coupled to a common coplanar
resonator. The collective bright and dark modes are probed via the microwave
response, i.e., by measuring the transmission amplitude of an external
microwave signal. All qubits have individual control and readout lines. Their
frequency tunability allows to change the number N of resonantly coupled qubits
and also to introduce a disorder in their excitation frequencies with
preassigned distributions. While increasing N, we demonstrate the expected
$N^{1/2}$ scaling law for the energy gap (Rabi splitting) between bright modes
around the cavity frequency. By introducing a controllable disorder and
averaging the transmission amplitude over a large number of realizations, we
demonstrate a decay of mesoscopic fluctuations which mimics an approach towards
the thermodynamic limit. The collective bright states survive in the presence
of disorder when the strength of individual qubit coupling to the cavity
dominates over the disorder strength.


Abstract:
  A series invariant for a certain class of closed 3-manifolds associated with
a type I Lie superalgebra sl(m|n) was introduced recently. We find a q-series
for the other Lie superalgebra of the same type of the minimum rank.


Abstract:
  Statistical techniques used in air pollution modelling usually lack the
possibility to understand which predictors affect air pollution in which
functional form; and are not able to regress on exceedances over certain
thresholds imposed by authorities directly. The latter naturally induce
conditional quantiles and reflect the seriousness of particular events. In the
present paper we focus on this important aspect by developing quantile
regression models further. We propose a general Bayesian effect selection
approach for additive quantile regression within a highly interpretable
framework. We place separate normal beta prime spike and slab priors on the
scalar importance parameters of effect parts and implement a fast Gibbs
sampling scheme. Specifically, it enables to study quantile-specific covariate
effects, allows these covariates to be of general functional form using
additive predictors, and facilitates the analysts' decision whether an effect
should be included linearly, non-linearly or not at all in the quantiles of
interest. In a detailed analysis on air pollution data in Madrid (Spain) we
find the added value of modelling extreme nitrogen dioxide (NO2) concentrations
and how thresholds are driven differently by several climatological variables
and traffic as a spatial proxy. Our results underpin the need of enhanced
statistical models to support short-term decisions and enable local authorities
to mitigate or even prevent exceedances of NO2 concentration limits.


Abstract:
  Multicavity Klystron (MCK) is a high power microwave (HPM) vacuum electronic
device used to amplify radio-frequency (RF) signals with numerous applications,
including radar, radio navigation, space communication, television, radio
repeaters, and charged particle accelerators. The microwave-generating
interactions in klystrons take place in resonant cavities at discrete locations
along the beam. Importantly, there is no electromagnetic coupling between
cavities, they are coupled only by the bunched electron beam, which drifts from
one cavity to the next. We advance here an analytic theory of MCKs operating in
voltage amplification mode associated with the maximal gain. This theory
features in particular exact formulas for the MCK instability frequencies, its
dispersion relations and optimal values of the MCK parameters providing for
maximal gain.


Abstract:
  Indexing large-scale databases in main memory is still challenging today.
Learned index structures -- in which the core components of classical indexes
are replaced with machine learning models -- have recently been suggested to
significantly improve performance for read-only range queries. However, a
recent benchmark study shows that learned indexes only achieve limited
performance improvements for real-world data on modern hardware. More
specifically, a learned model cannot learn the micro-level details and
fluctuations of data distributions thus resulting in poor accuracy; or it can
fit to the data distribution at the cost of training a big model whose
parameters cannot fit into cache. As a consequence, querying a learned index on
real-world data takes a substantial number of memory lookups, thereby degrading
performance. In this paper, we adopt a different approach for modeling a data
distribution that complements the model fitting approach of learned indexes. We
propose Shift-Table, an algorithmic layer that captures the micro-level data
distribution and resolves the local biases of a learned model at the cost of at
most one memory lookup. Our suggested model combines the low latency of lookup
tables with learned indexes and enables low-latency processing of range
queries. Using Shift-Table, we achieve a speedup of 1.5X to 2X on real-world
datasets compared to trained and tuned learned indexes.


Abstract:
  The rapid development of artificial intelligence, especially deep learning
technology, has advanced autonomous driving systems (ADSs) by providing precise
control decisions to counterpart almost any driving event, spanning from
anti-fatigue safe driving to intelligent route planning. However, ADSs are
still plagued by increasing threats from different attacks, which could be
categorized into physical attacks, cyberattacks and learning-based adversarial
attacks. Inevitably, the safety and security of deep learning-based autonomous
driving are severely challenged by these attacks, from which the
countermeasures should be analyzed and studied comprehensively to mitigate all
potential risks. This survey provides a thorough analysis of different attacks
that may jeopardize ADSs, as well as the corresponding state-of-the-art defense
mechanisms. The analysis is unrolled by taking an in-depth overview of each
step in the ADS workflow, covering adversarial attacks for various deep
learning models and attacks in both physical and cyber context. Furthermore,
some promising research directions are suggested in order to improve deep
learning-based autonomous driving safety, including model robustness training,
model testing and verification, and anomaly detection based on cloud/edge
servers.


Abstract:
  Cybercriminals are rapidly developing new malicious tools that leverage
artificial intelligence (AI) to enable new classes of adaptive and stealthy
attacks. New defensive methods need to be developed to counter these threats.
Some cybersecurity professionals are speculating AI will enable corresponding
new classes of active cyber defence measures -- is this realistic, or currently
mostly hype? The Alan Turing Institute, with expert guidance from the UK
National Cyber Security Centre and Defence Science Technology Laboratory,
published a research roadmap for AI for ACD last year. This position paper
updates the roadmap for two of the most promising AI approaches --
reinforcement learning and causal inference - and describes why they could help
tip the balance back towards defenders.


Abstract:
  Let $p$ be a prime and $G$ a finite group. A complex character of $G$ is
called almost $p$-rational if its values belong to a cyclotomic field
$\mathbb{Q}(e^{2\pi i/n})$ for some $n\in \mathbb{Z}^+$ prime to $p$ or
precisely divisible by $p$. We prove that, in contrast to usual $p$-rational
characters, there are always "many" almost $p$-rational irreducible characters
in finite groups. We obtain both explicit and asymptotic bounds for the number
of almost $p$-rational irreducible characters of $G$ in terms of $p$. In fact,
motivated by the McKay-Navarro conjecture, we obtain the same bound for the
number of such characters of $p'$-degree and prove that, in the minimal
situation, the number of almost $p$-rational irreducible $p'$-characters of $G$
coincides with that of $N_G(P)$ for $P\in\mathrm{Syl}_p(G)$. Lastly, we propose
a new way to detect the cyclicity of Sylow $p$-subgroups of a finite group $G$
from its character table, using almost $p$-rational irreducible $p'$-characters
and the blockwise refinement of the McKay-Navarro conjecture.


Abstract:
  Purpose: The purpose of this work is to develop a new approach for high
spatial resolution dosimetry based on Raman micro-spectroscopy scanning of
radiochromic film (RCF). We generate dose calibration curves over an extended
dose range from 0-50 Gy and with improved sensitivity to low (<2 Gy) doses, in
addition to evaluating uncertainties. Methods: Samples of RCF (EBT3) were
irradiated at a broad dose-range of 0.03 Gy-50 Gy. Raman spectra were acquired
with a custom-built Raman micro-spectroscopy setup involving a 500 mW,
multimode 785 nm laser. The depth of focus of 34 um enabled the concurrent
collection of Raman spectra from the RCF active layer and the polyester
laminate. The pre-processed Raman spectra were normalized to the intensity of
the 1614 cm-1 Raman peak from the polyester laminate that was unaltered by
radiation. The experimental, fitting and total dose uncertainty was determined
across the entire dose range for the dosimetry system of Raman
micro-spectroscopy and RCF. Results: High resolution in the dose response of
the RCF, even down to 0.03 Gy, was obtained in this study. The dynamic range of
the calibration curves based on all three Raman peaks in the RCF extended up to
50 Gy with no saturation. At a spatial resolution of 30X30 um^2, the total
uncertainty in estimating dose in the 0.5 Gy to 50 Gy dose range was [6 - 9]%
for all three Raman calibration curves. This consisted of the experimental
uncertainty of [5 - 8]%, and the fitting uncertainty of [2.5 - 4.5]%.
Conclusions: The high spatial resolution experimental dosimetry technique based
on Raman micro-spectroscopy and RCF presented here, could become useful for
applications in, as well as for applications based on small field dosimetry.


Abstract:
  In this work, optimized size distribution and optical properties in colloidal
synthesis of gold nanoparticles (GNPs) were obtained using a proposed
ultrasonic Tuerkevich-Frens method. The effect of three ultrasound (20 kHz)
irradiation powers has been analyzed as size and shape control parameter. The
GNPs colloidal solutions were obtained from chloroauric acid (HAuCl$_{4}$) and
trisodium citrate ($\rm C_{6}H_{5}Na_{3}O_{7}\cdot 2H_{2}O$) under continuous
irradiation for 1 hour without any heat or stirring. The surface plasmon
resonance (SPR) was monitored in the UV-Vis spectra every 10 minutes to found
the optimal time for localized SPR wavelength ($\lambda_{\rm LSPR}$) and the
210 sample procedure reduces the $\lambda_{\rm LSPR}$ localization to 20
minutes, while 150 and 60 samples show $\lambda_{\rm LSPR}$ in 60 minutes. The
nucleation and growth of GNPs showed changes in shape and size distribution,
which were associated with physical (cavitation, temperature) and chemical
(radical generation, pH) conditions in the solution. The results showed
quasispherical GNPs as pentakis dodecahedron ($\lambda_{\rm LSPR}$=560 nm),
triakis icosahedron ($\lambda_{\rm LSPR}$=535 nm), and tetrakis hexahedron
($\lambda_{\rm LSPR}$= 525 nm) in a size range from 12-16 nm. US irradiation
induced a disproportionation process, electrons of AuCl$_{2}^-$ rapidly
exchanged through the gold surface. After AuCl$_{4}^-$ and Cl$^-$ are desorbed
and a complex tetrachloroaurate is recycled for the two-electron reduction by
citrate, aurophilic interaction between complexes AuCl$_{2}^-$, electrons
exchange and gold seeds, the deposition of new gold atoms on the surface
promoting the growth of GNPs. These mechanisms are enhanced by the cavitation
effects and transmitted energy into the solution, showing that the plasmonic
response from our nanoparticles can be tuned with this simple method and
minimum intrumentation.


Abstract:
  The negatively charged silicon vacancy [V$_\text{Si}(-)$] in silicon carbide
(SiC) is a paramagnetic and optically active defect in hexagonal SiC.
V$_\text{Si}(-)$ defect possesses $S = 3/2$ spin with long spin coherence time
and can be optically manipulated even at room temperature. Recently, electron
spin resonance signals have been observed besides the signals associated with
the V$_\text{Si}(-)$ defects in the 4H polytype of SiC. The corresponding
centers share akin properties to those of the V$_\text{Si}(-)$ defects and thus
they may be promising candidates for quantum technology applications. However,
the exact origin of the new signals is unknown. In this paper we report
V$_\text{Si}(-)$-related pair defect models as possible candidates for the
unknown centers. We determine the corresponding electronic structures and
magneto-optical properties as obtained by density functional theory (DFT)
calculations. We propose models for the recently observed electron paramagnetic
resonance centers with predicting their optical signals for identification in
future experiments.


Abstract:
  The UK has had a volatile political environment for some years now, with
Brexit and leadership crises marking the past five years. With this work, we
wanted to understand more about how the global health emergency, COVID-19,
influences the amount, type or topics of abuse that UK politicians receive when
engaging with the public. With this work, we wanted to understand more about
how the global health emergency, COVID-19, influences the amount, type or
topics of abuse that UK politicians receive when engaging with the public. This
work covers the period of June to December 2020 and analyses Twitter abuse in
replies to UK MPs. This work is a follow-up from our analysis of online abuse
during the first four months of the COVID-19 pandemic in the UK. The paper
examines overall abuse levels during this new seven month period, analyses
reactions to members of different political parties and the UK government, and
the relationship between online abuse and topics such as Brexit, government's
COVID-19 response and policies, and social issues. In addition, we have also
examined the presence of conspiracy theories posted in abusive replies to MPs
during the period. We have found that abuse levels toward UK MPs were at an
all-time high in December 2020 (5.4% of all reply tweets sent to MPs). This is
almost 1% higher that the two months preceding the General Election. In a
departure from the trend seen in the first four months of the pandemic, MPs
from the Tory party received the highest percentage of abusive replies from
July 2020 onward, which stays above 5% starting from September 2020 onward, as
the COVID-19 crisis deepened and the Brexit negotiations with the EU started
nearing completion.


Abstract:
  We introduce Combinatory Homomorphic Automatic Differentiation (CHAD), a
principled, pure, provably correct define-then-run method for performing
forward- and reverse-mode automatic differentiation (AD) on programming
languages with expressive features. It implements AD as a compositional,
type-respecting source-code transformation that generates purely functional
code. This code transformation is principled in the sense that it is the unique
homomorphic (structure preserving) extension to expressive languages of the
well-known and unambiguous definitions of AD for a first-order functional
language. Correctness of the method follows by a (compositional) logical
relations argument that shows that the semantics of the syntactic derivative is
the usual calculus derivative of the semantics of the original program.
  In their most elegant formulation, the transformations generate code with
linear types. However, the code transformations can be implemented in a
standard functional language lacking linear types: while the correctness proof
requires tracking of linearity, the actual transformations do not. In fact,
even in a standard functional language, we can get all of the type-safety that
linear types give us: we can implement all linear types used to type the
transformations as abstract types, by using a basic module system.
  In this paper, we detail the method when applied to a simple higher-order
language for manipulating statically sized arrays. However, we explain how the
methodology applies, more generally, to functional languages with other
expressive features. Finally, we discuss how the scope of CHAD extends beyond
applications in AD to other dynamic program analyses that accumulate data in a
commutative monoid.


Abstract:
  The Massachusetts Attorney General issued an Enforcement Notice in 2016 to
announce a new interpretation of a key phrase in the state's assault weapons
ban. The Enforcement Notice increased sales of tagged assault rifles by 616% in
the first 5 days, followed by a 9% decrease over the next three weeks. Sales of
Handguns and Shotguns did not change significantly. Tagged assault rifle sales
fell 28-30% in 2017 compared to previous years, suggesting that the Enforcement
Notice reduced assault weapon sales but also that many banned weapons continued
to be sold. Tagged assault rifles sold most in 2017 in zip codes with higher
household incomes and proportions of white males. Overall, the results suggest
that the firearm market reacts rapidly to policy changes and partially complies
with firearm restrictions.


Abstract:
  A singularly perturbed system for doubly diffusive convection equations,
called the artificial compressible system, is considered on a two-dimensional
infinite layer for a parameters range where the Hopf bifurcation occurs in the
corresponding incompressible system. The spectrum of the linearized operator in
a time periodic function space is investigated in detail near the bifurcation
point when the singular perturbation parameter is small. The results of this
paper are the basis of the study of the nonlinear Hopf bifurcation problem and
the singular limit of the time periodic bifurcating solutions.


Abstract:
  We classify the elements of $W(\tilde{A}_n)$ by giving a canonical reduced
expression for each, using basic tools among which affine length. We give some
direct consequences for such a canonical form: a description of left
multiplication by a simple reflection, a study of the right descent set, and a
proof that the affine length is preserved along the tower of affine Coxeter
groups of type $\tilde A$, which implies in particular that the corresponding
tower of affine Hecke algebras is a faithful tower.


Abstract:
  Interpreters facilitate multi-lingual meetings but the affordable set of
languages is often smaller than what is needed. Automatic simultaneous speech
translation can extend the set of provided languages. We investigate if such an
automatic system should rather follow the original speaker, or an interpreter
to achieve better translation quality at the cost of increased delay.
  To answer the question, we release Europarl Simultaneous Interpreting Corpus
(ESIC), 10 hours of recordings and transcripts of European Parliament speeches
in English, with simultaneous interpreting into Czech and German. We evaluate
quality and latency of speaker-based and interpreter-based spoken translation
systems from English to Czech. We study the differences in implicit
simplification and summarization of the human interpreter compared to a machine
translation system trained to shorten the output to some extent. Finally, we
perform human evaluation to measure information loss of each of these
approaches.


Abstract:
  In recent years, the robotics community has made substantial progress in
robotic manipulation using deep reinforcement learning (RL). Effectively
learning of long-horizon tasks remains a challenging topic. Typical RL-based
methods approximate long-horizon tasks as Markov decision processes and only
consider current observation (images or other sensor information) as input
state. However, such approximation ignores the fact that skill-sequence also
plays a crucial role in long-horizon tasks. In this paper, we take both the
observation and skill sequences into account and propose a
skill-sequence-dependent hierarchical policy for solving a typical long-horizon
task. The proposed policy consists of a high-level skill policy (utilizing
skill sequences) and a low-level parameter policy (responding to observation)
with corresponding training methods, which makes the learning much more
sample-efficient. Experiments in simulation demonstrate that our approach
successfully solves a long-horizon task and is significantly faster than
Proximal Policy Optimization (PPO) and the task schema methods.


Abstract:
  We consider the lattice Higgs model on $\mathbb{Z}^4$, with structure group
given by $ \mathbb{Z}_n $ for $ n \geq 2 $. We compute the expected value of
the Wilson loop observable to leading order when the gauge coupling constant
and hopping parameter are both sufficiently large. The leading order term is
expressed in terms of a quantity arising from the related but much simpler $
\mathbb{Z}_n $ model, which reduces to the Ising model when $n=2$. As part of
the proof, we construct a coupling between the lattice Higgs model and the $
\mathbb{Z}_n $ model.


Abstract:
  Spacetime theories obtained from perturbative string theory constructions are
automatically free of perturbative anomalies, but it is not settled whether
they are always free of global anomalies. Here we discuss a possible
$\mathbb{Z}_{24}$-valued pure gravitational anomaly of heterotic
compactifications down to two spacetime dimensions, and point out that it can
be shown to vanish using the theory of topological modular forms, assuming the
validity of the Stolz-Teichner conjecture.


Abstract:
  Reinforcement learning in cooperative multi-agent settings has recently
advanced significantly in its scope, with applications in cooperative
estimation for advertising, dynamic treatment regimes, distributed control, and
federated learning. In this paper, we discuss the problem of cooperative
multi-agent RL with function approximation, where a group of agents
communicates with each other to jointly solve an episodic MDP. We demonstrate
that via careful message-passing and cooperative value iteration, it is
possible to achieve near-optimal no-regret learning even with a fixed constant
communication budget. Next, we demonstrate that even in heterogeneous
cooperative settings, it is possible to achieve Pareto-optimal no-regret
learning with limited communication. Our work generalizes several ideas from
the multi-agent contextual and multi-armed bandit literature to MDPs and
reinforcement learning.


Abstract:
  While traditional approaches to prevent the carbuncle phenomenon in gas
dynamics simulations increase the viscosity on entropy and shear waves near
shocks, it was quite recently suggested to instead decrease the viscosity on
the acoustic waves for low Mach numbers. The goal is to achieve what, in this
paper, we call Mach number consistency: for all waves, the numerical viscosity
decreases with the same order of the Mach number when the Mach number tends to
zero. We take the simple approach that was used for the proof of concept
together with the simple model for the increased numerical viscosity on linear
waves and investigate the possibilities of combining both in an adaptive manner
while locally maintaining Mach number consistency.


Abstract:
  Graph convolutional networks (GCNs) have achieved great success in dealing
with data of non-Euclidean structures. Their success directly attributes to
fitting graph structures effectively to data such as in social media and
knowledge databases. For image processing applications, the use of graph
structures and GCNs have not been fully explored. In this paper, we propose a
novel encoder-decoder network with added graph convolutions by converting
feature maps to vertexes of a pre-generated graph to synthetically construct
graph-structured data. By doing this, we inexplicitly apply graph Laplacian
regularization to the feature maps, making them more structured. The
experiments show that it significantly boosts performance for image restoration
tasks, including deblurring and super-resolution. We believe it opens up
opportunities for GCN-based approaches in more applications.


Abstract:
  In this paper, we prove that the ratio of the modulus of the iterates of two
points in an escaping Fatou component may be bounded even if the orbit of the
component contains an infinite modulus annulus sequence and this case cannot
happen when the maximal modulus of the meromorphic function is large enough.
Therefore, we extend the related results for entire functions to ones for
meromorphic functions with infinitely many poles. And we investigate the fast
escaping Fatou components of meromorphic functions defined in [44] in terms of
the Nevanlinna characteristic instead of the maximal modulus in [12] and show
that the multiply-connected wandering domain is a part of the fast escaping set
under a growth condition of the maximal modulus. Finally we give examples of
wandering domains escaping at arbitrary fast rate and slow rate.


Abstract:
  This paper studies the effect of the order of depth of mention on nested
named entity recognition (NER) models. NER is an essential task in the
extraction of biomedical information, and nested entities are common since
medical concepts can assemble to form larger entities. Conventional NER systems
only predict disjointed entities. Thus, iterative models for nested NER use
multiple predictions to enumerate all entities, imposing a predefined order
from largest to smallest or smallest to largest. We design an order-agnostic
iterative model and a procedure to choose a custom order during training and
prediction. To accommodate for this task, we propose a modification of the
Transformer architecture to take into account the entities predicted in the
previous steps. We provide a set of experiments to study the model's
capabilities and the effects of the order on performance. Finally, we show that
the smallest to largest order gives the best results.


Abstract:
  In this work, we present a class of relativistic and well-behaved solution to
Einstein's field equations for anisotropic matter distribution. We perform our
analysis by using the Buchdahl ansatz for the metric function grr. Three
different classes of new exact solution are found for anisotropy factor(delta).
We have analyzed our model with various physical aspects such as pressure
(radial as well as transverse), energy density, anisotropy factor, mass,
compactness parameter, adiabatic index(gamma) and surface redshift. A graphical
analysis of energy conditions, TOV equation and causality condition indicates
the model are well behaved. The physical acceptability of the model has
verified by considering compact objects with similar mass and radii, such as 4U
1820-30,Vela X-1, PSR J1614-2230, LMC X-4, SMC X-1, 4U 1538-52, Her X-1, Cyg
X-2, PSR B1913+16 and PSR J1903+327.


Abstract:
  Intelligent mobile robots are critical in several scenarios. However, as
their computational resources are limited, mobile robots struggle to handle
several tasks concurrently and yet guaranteeing real-timeliness. To address
this challenge and improve the real-timeliness of critical tasks under resource
constraints, we propose a fast context-aware task handling technique. To
effectively handling tasks in real-time, our proposed context-aware technique
comprises of three main ingredients: (i) a dynamic time-sharing mechanism,
coupled with (ii) an event-driven task scheduling using reactive programming
paradigm to mindfully use the limited resources; and, (iii) a lightweight
virtualized execution to easily integrate functionalities and their
dependencies. We showcase our technique on a Raspberry-Pi-based robot with a
variety of tasks such as Simultaneous localization and mapping (SLAM), sign
detection, and speech recognition with a 42% speedup in total execution time
compared to the common Linux scheduler.


Abstract:
  Two-dimensional transition metal dichalcogenides (TMD) offer a unique
platform for creating van-der-Waals heterojunctions with fascinating physical
properties and promising applications in optoelectronics and valleytronics.
Because of their typical type-II band alignment, photoexcited electrons and
holes can separate via interfacial charge transfer. To understand the nature
and the dynamics of this charge transfer is of utmost importance for the design
and efficiency of potential devices. However, systematic studies concerning the
influence of the stacking angle on the charge transfer remain sparse. Here, we
apply time- and polarization resolved second-harmonic imaging microscopy to
investigate the charge-transfer dynamics for three MoS$_2$/WSe$_2$
heterostructures with different stacking angles at a previously unattainable
time-resolution of $\approx$ 6 fs. For 1.70 eV excitation energy, electron
transfer from WSe$_2$ to MoS$_2$ is found to depend considerably on the
stacking angle with the fastest transfer time observed to be as short as 12 fs.
At 1.85 eV excitation energy, ultrafast hole transfer from MoS$_2$ to
hybridized states at the $\Gamma$-point or to the K-points of WSe$_2$ has to be
considered. Surprisingly, the corresponding decay dynamics show only a minor
stacking-angle dependence indicating that radiative recombination of indirect
$\Gamma$-K excitons becomes the dominant decay route for all samples.


Abstract:
  Commercially available low-cost particulate matter (PM) sensors provide
output as total or size-specific particle counts and mass concentrations. These
quantities are not measured directly but are estimated by the original
equipment manufacturers' (OEM) proprietary algorithms and have inherent
limitations since particle scattering depends on their composition, size,
shape, and complex index of refraction (CRI). Hence, there is a need to
characterize and calibrate their performance under a controlled environment. We
present calibration algorithms for Plantower PMS A003 sensor as a function of
particle size and concentration. A standardized experimental protocol was used
to control the PM level, environmental conditions and to evaluate
sensor-to-sensor reproducibility. The calibration was based on tests when PMS
A003 were exposed to different polydisperse standardized testing aerosols. The
results suggested particle size distribution from PMS A003 was shifted compared
to reference instrument measures. For calibration of number concentration,
linear model without adjusting aerosol properties corrects the raw PMS A003
measurement for specific size bins with normalized mean absolute error within
4.0% of the reference instrument. Although the Bayesian Information Criterion
suggests that models adjusting for particle optical properties and relative
humidity are technically superior, they should be used with caution as the
particle properties used in fitting were within a narrow range for challenge
aerosols. The calibration models adjusted for particle CRI and density account
for non-linearity in the OEM's mass concentrations estimates and demonstrated
lower error. These results have significant implications for using PMS A003 in
high concentration environments, including indoor air quality and
occupational/industrial exposure assessments, wildfire smoke, or near-source
monitoring scenarios.


Abstract:
  Stochastic averaging for a class of backward stochastic differential
equations driven by both standard and fractional Brownian motions (SFrBSDEs in
short), is investigated. An averaged SFrBSDEs for the original SFrBSDEs is
proposed, and their solutions are quantitatively compared. Under some
appropriate assumptions, the solutions to original systems can be approximated
by the solutions to averaged stochastic systems in the sense of mean square and
also in probability.


Abstract:
  This article discusses the problem of handwriting recognition in Kazakh and
Russian languages. This area is poorly studied since in the literature there
are almost no works in this direction. We have tried to describe various
approaches and achievements of recent years in the development of handwritten
recognition models in relation to Cyrillic graphics. The first model uses deep
convolutional neural networks (CNNs) for feature extraction and a fully
connected multilayer perceptron neural network (MLP) for word classification.
The second model, called SimpleHTR, uses CNN and recurrent neural network (RNN)
layers to extract information from images. We also proposed the Bluechet and
Puchserver models to compare the results. Due to the lack of available open
datasets in Russian and Kazakh languages, we carried out work to collect data
that included handwritten names of countries and cities from 42 different
Cyrillic words, written more than 500 times in different handwriting. We also
used a handwritten database of Kazakh and Russian languages (HKR). This is a
new database of Cyrillic words (not only countries and cities) for the Russian
and Kazakh languages, created by the authors of this work.


Abstract:
  Reconfigurable intelligent surface (RIS) provides a promising way to build
the programmable wireless transmission environments in the future. Owing to the
large number of reflecting elements used at the RIS, joint optimization for the
active beamforming at the transmitter and the passive reflector at the RIS is
usually complicated and time-consuming. To address this problem, this paper
proposes a low-complexity joint beamforming and reflecting algorithm based on
fractional programing (FP). Specifically, we first consider a RIS-aided
multi-user communication system with perfect channel state information (CSI)
and formulate an optimization problem to maximize the sum rate of all users.
Since the problem is nonconvex, we decompose the original problem into three
disjoint subproblems. By introducing favorable auxiliary variables, we derive
the closed-form expressions of the beamforming vectors and reflecting matrix in
each subproblem, leading to a joint beamforming and reflecting algorithm with
low complexity. We then extend our approach to handle the case when
transmitter-RIS and RIS-receiver channels are not perfect and develop
corresponding low-complexity joint beamforming and reflecting algorithm with
practical channel estimation. Simulation results have verified the
effectiveness of the proposed algorithms as compared to various benchmark
schemes.


Abstract:
  This paper studies intelligent reflecting surface (IRS) assisted active
device detection. Since the locations of the devices are a priori unknown,
optimal IRS beam alignment is not possible and a worst-case design for a given
coverage area is developed. To this end, we propose a generalized likelihood
ratio test (GLRT) detection scheme and an IRS phase-shift design that minimizes
the worst-case probability of misdetection. In addition to the proposed
optimization-based phase-shift design, we consider two alternative suboptimal
designs based on closed-form expressions for the IRS phase shifts. Our
performance analysis establishes the superiority of the optimization-based
design, especially for large coverage areas. Furthermore, we investigate the
impact of scatterers on the proposed line-of-sight based design using
simulations.


Abstract:
  We explore the usage of meta-learning to derive the causal direction between
variables by optimizing over a measure of distribution simplicity. We
incorporate a stochastic graph representation which includes latent variables
and allows for more generalizability and graph structure expression. Our model
is able to learn causal direction indicators for complex graph structures
despite effects of latent confounders. Further, we explore robustness of our
method with respect to violations of our distributional assumptions and data
scarcity. Our model is particularly robust to modest data scarcity, but is less
robust to distributional changes. By interpreting the model predictions as
stochastic events, we propose a simple ensemble method classifier to reduce the
outcome variability as an average of biased events. This methodology
demonstrates ability to infer the existence as well as the direction of a
causal relationship between data distributions.


Abstract:
  Intrusion Detection Systems (IDS) enhanced with Machine Learning (ML) have
demonstrated the capacity to efficiently build a prototype of "normal" cyber
behaviors in order to detect cyber threats' activity with greater accuracy than
traditional rule-based IDS. Because these are largely black boxes, their
acceptance requires proof of robustness to stealthy adversaries. Since it is
impossible to build a baseline from activity completely clean of that of
malicious cyber actors (outside of controlled experiments), the training data
for deployed models will be poisoned with examples of activity that analysts
would want to be alerted about. We train an autoencoder-based anomaly detection
system on network activity with various proportions of malicious activity mixed
in and demonstrate that they are robust to this sort of poisoning.


Abstract:
  GW170817 has led to the first example of multi-messenger astronomy with
observations from gravitational wave interferometers and electromagnetic
telescopes combined to characterise the source. However, detections of the
early inspiral phase by the gravitational wave detectors would allow the
observation of the earlier stages of the merger in the electromagnetic band,
improving multi-messenger astronomy and giving access to new information. In
this paper, we introduce a new machine-learning-based approach to produce
early-warning alerts for an inspiraling binary neutron star system, based only
on the early inspiral part of the signal. We give a proof of concept to show
the possibility to use a combination of small convolutional neural networks
trained on the whitened detector strain in the time domain to detect and
classify early inspirals. Each of those is targeting a specific range of chirp
masses dividing the binary neutron star category into three sub-classes: light,
intermediate and heavy. In this work, we focus on one LIGO detector at design
sensitivity and generate noise from the design power spectral density. We show
that within this setup it is possible to produce an early alert up to 100
seconds before the merger for the best-case scenario. We also present some
future upgrades that will enhance the detection capabilities of our
convolutional neural networks. Finally, we also show that the current number of
detections for a realistic binary neutron star population is comparable to that
of matched filtering and that there is a high probability to detect GW170817-
and GW190425-like events at design sensitivity.


Abstract:
  The world ocean plays a key role in redistributing heat in the climate system
and hence in regulating Earth's climate. Yet statistical analysis of ocean heat
transport suffers from partially incomplete large-scale data intertwined with
complex spatio-temporal dynamics, as well as from potential model
misspecification. We present a comprehensive spatio-temporal statistical
framework tailored to interpolating the global ocean heat transport using
in-situ Argo profiling float measurements. We formalize the statistical
challenges using latent local Gaussian process regression accompanied by a
two-stage fitting procedure. We introduce an approximate
Expectation-Maximization algorithm to jointly estimate both the mean field and
the covariance parameters, and refine the potentially under-specified mean
field model with a debiasing procedure. This approach provides data-driven
global ocean heat transport fields that vary in both space and time and can
provide insights into crucial dynamical phenomena, such as El Ni{\~n}o \& La
Ni{\~n}a, as well as the global climatological mean heat transport field, which
by itself is of scientific interest. The proposed framework and the Argo-based
estimates are thoroughly validated with state-of-the-art multimission satellite
products and shown to yield realistic subsurface ocean heat transport
estimates.


Abstract:
  Existing generalization measures that aim to capture a model's simplicity
based on parameter counts or norms fail to explain generalization in
overparameterized deep neural networks. In this paper, we introduce a new,
theoretically motivated measure of a network's simplicity which we call
prunability: the smallest \emph{fraction} of the network's parameters that can
be kept while pruning without adversely affecting its training loss. We show
that this measure is highly predictive of a model's generalization performance
across a large set of convolutional networks trained on CIFAR-10, does not grow
with network size unlike existing pruning-based measures, and exhibits high
correlation with test set loss even in a particularly challenging double
descent setting. Lastly, we show that the success of prunability cannot be
explained by its relation to known complexity measures based on models' margin,
flatness of minima and optimization speed, finding that our new measure is
similar to -- but more predictive than -- existing flatness-based measures, and
that its predictions exhibit low mutual information with those of other
baselines.


Abstract:
  The timing performance of silicon sensors bump-bonded to Timepix3 ASICs is
investigated, prior to and after different types of irradiation up to $8 \times
10^{15} 1 \mathrm{\,Me\kern -0.1em V} \mathrm{ \,n_{eq}} {\mathrm{
\,cm}}^{-2}$. The sensors have been tested with a beam of charged particles in
two different configurations, perpendicular to and almost parallel to the
incident beam. The second approach, known as the grazing angles method, is
shown to be a powerful method to investigate not only the charge collection,
but also the time-to-threshold properties as a function of the depth at which
the charges are liberated.


Abstract:
  Reinforcement learning (RL) is a foundation of learning in biological systems
and provides a framework to address numerous challenges with real-world
artificial intelligence applications. Efficient implementations of RL
techniques could allow for agents deployed in edge-use cases to gain novel
abilities, such as improved navigation, understanding complex situations and
critical decision making. Towards this goal, we describe a flexible
architecture to carry out reinforcement learning on neuromorphic platforms.
This architecture was implemented using an Intel neuromorphic processor and
demonstrated solving a variety of tasks using spiking dynamics. Our study
proposes a usable energy efficient solution for real-world RL applications and
demonstrates applicability of the neuromorphic platforms for RL problems.


Abstract:
  Two novel nonlinear mode coupling processes for reversed shear Alfven
eigenmode (RSAE) nonlinear saturation are proposed and investigated. In the
first process, RSAE nonlinearly couples to a co-propagating toroidal Alfven
eigenmode (TAE) with the same toroidal and poloidal mode numbers, and generates
a geodesic acoustic mode (GAM). In the second process, RSAE couples to a
counter-propagating TAE and generates an ion acoustic wave quasi-mode (IAW).
The condition for the two processes to occur is favored during current ramp.
Both processes contribute to effectively saturate the Alfvenic instabilities,
as well as nonlinearly transfer of energy from energetic fusion alpha particles
to fuel ions in burning plasmas.