Abstract:
  Three-body decay is a rare decay mode observed in a handful of unbound rare
isotopes. The angular and energy correlations between emitted nucleons are of
particular interest, as they provide invaluable information on the interplay
between structure and reaction aspects of the nuclear open quantum system. To
study the mechanism of two-nucleon emission, we developed a time-dependent
approach that allows us to probe emitted nucleons at long times and large
distances. We successfully benchmarked the new method against the Green's
function approach and applied it to low-energy two-proton and two-neutron
decays. In particular, we studied the interplay between initial-state
nucleon-nucleon correlations and final-state interaction. We demonstrated that
the time evolution of the two-nucleon wave function is strongly impacted by the
diproton/dineutron dynamics and that the correlations between emitted nucleons
provide invaluable information on the dinucleon structure in the initial-state.


Abstract:
  Responding rapidly to a patient who is demonstrating signs of imminent
clinical deterioration is a basic tenet of patient care. This gave rise to a
patient safety intervention philosophy known as a Rapid Response System (RRS),
whereby a patient who meets a pre-determined set of criteria for imminent
clinical deterioration is immediately assessed and treated, with the goal of
mitigating the deterioration and preventing intensive care unit (ICU) transfer,
cardiac arrest, or death. While RRSs have been widely adopted, multiple
systematic reviews have failed to find evidence of their effectiveness.
Typically, RRS criteria are simple, expert (consensus) defined rules that
identify significant physiologic abnormalities or are based on clinical
observation.
  If one can find a pattern in the patient's data earlier than the onset of the
physiologic derangement manifest in the current criteria, intervention
strategies might be more effective. In this paper, we apply machine learning to
electronic medical records (EMR) to infer if patients are at risk for clinical
deterioration. Our models are more sensitive and offer greater advance
prediction time compared with existing rule-based methods that are currently
utilized in hospitals. Our results warrant further testing in the field; if
successful, hospitals can integrate our approach into their existing IT systems
and use the alerts generated by the model to prevent ICU transfer, cardiac
arrest, or death, or to reduce the ICU length of stay.


Abstract:
  Flows through porous media can carry suspended and dissolved materials. These
sediments may deposit inside the pore-space and alter its geometry. In turn,
the changing pore structure modifies the preferential flow paths, resulting in
a strong coupling between structural modifications and transport
characteristics. Here, we compare two different models that lead to channel
obstruction as a result of subsequent deposition. The first model randomly
obstructs pore-throats across the porous medium, while in the second model the
pore-throat with the highest flow rate is always obstructed first. By
subsequently closing pores, we find that the breakdown of the permeability
follows a power-law scaling, whose exponent depends on the obstruction model.
The pressure jumps that occur during the obstruction process also follow a
power-law distribution with the same universal scaling exponent as the
avalanche size distribution of invasion percolation, independent of the model.
This result suggests that the clogging processes and invasion percolation may
belong to the same universality class.


Abstract:
  The search for novel magnetic quantum phases, phenomena and functional
materials has been guided by relativistic magnetic-symmetry groups in coupled
spin and real space from the dawn of the field in 1950s to the modern era of
topological matter. However, the magnetic groups cannot disentangle
non-relativistic phases and effects, such as the recently reported
unconventional spin physics in collinear antiferromagnets from the typically
weak relativistic spin-orbit coupling phenomena. Here we discover that more
general spin symmetries in decoupled spin and crystal space categorize
non-relativistic collinear magnetism in three phases: conventional ferromagnets
and antiferromagnets, and a third distinct phase combining zero net
magnetization with an alternating spin-momentum locking in energy bands, which
we dub "altermagnetic". For this third basic magnetic phase, which is omitted
by the relativistic magnetic groups, we develop a spin-group theory describing
six characteristic types of the altermagnetic spin-momentum locking. We
demonstrate an extraordinary spin-splitting mechanism in altermagnetic bands
originating from a local electric crystal field, which contrasts with the
conventional magnetic or relativistic splitting by global magnetization or
inversion asymmetry. Based on first-principles calculations, we identify
altermagnetic candidates ranging from insulators and metals to a parent crystal
of cuprate superconductor. Our results underpin emerging research of quantum
phases and spintronics in high-temperature magnets with light elements,
vanishing net magnetization, and strong spin-coherence.


Abstract:
  We consider the derivative NLS equation in one spatial dimension, which is
known to be completely integrable. We prove that the orbits of $L^2$ bounded
and equicontinuous sets of initial data remain bounded and equicontinuous, not
only under this flow, but under the entire hierarchy. This allows us to remove
the small-data restriction from prior conservation laws and global
well-posedness results.


Abstract:
  It is shown that if the dual of a Banach space satisfies Property($K^*$) then
$R(X) \leq 1 + \frac{\displaystyle 1}{\displaystyle 1 + r_{X^*}(1)} < 2$ where
$r_{X^*}(c)$ is Opial's modulus for $X^*.$ Thus $X$ has the weak fixed point
property.


Abstract:
  In this paper we propose the PCP-like theorem for sub-linear time
inapproximability. Abboud et al. have devised the distributed PCP framework for
proving sub-quadratic time inapproximability. Here we try to go further in this
direction. Staring from SETH, we first find a problem denoted as Ext-$k$-SAT,
which can not be computed in linear time, then devise an efficient MA-like
protocol for this problem. To use this protocol to prove the sub-linear time
inapproximability of other problems, we devise a new kind of reduction denoted
as Ext-reduction, and it is different from existing reduction techniques. We
also define two new hardness class, the problems in which can be computed in
linear-time, but can not be efficiently approximated in sub-linear time. Some
problems are shown to be in the newly defined hardness class.


Abstract:
  With the evolution of the concept of Speaker diarization using LSTM, it is
relatively easier to understand the speaker identities for specific segments of
input audio stream data than manually tagging the data. With such a concept, it
is highly desirable to consider the possibility of using the identified speaker
identities to aid in recognizing the Speaker States in a conversation. In this
study, the Markov Chains are used to identify and update the Speaker States for
the next conversations between the same set of speakers, to enable
identification of their states in the most natural and long conversations. The
model is based on several audio samples from natural conversations of three or
greater than three speakers in two datasets with overall total error
percentages for recognized states being lesser than or equal to 12%. The
findings imply that the proposed extension to the Speaker diarization is
effective to predict the states for a conversation.


Abstract:
  High-fidelity parametric gates have been demonstrated with superconducting
qubits via rf flux modulation of the qubit frequency. The modulation however
leads to renormalization of the bare qubit-qubit coupling, thereby reducing the
gate speed. Here, we realize a parametric-resonance gate, which is activated by
bringing the average frequency of the modulated qubit in resonance with a
static-frequency qubit while approximately retaining the bare qubit-qubit
coupling. The activation of parametric-resonance gates does not depend on the
frequency of modulation, allowing us to choose the modulation frequencies and
avoid frequency collisions. Moreover, we show that this approach is compatible
with tunable coupler architectures, which reduce always-on residual couplings.
Using these techniques, we demonstrate iSWAP and CZ gates between two qubits
coupled via a tunable coupler with average process fidelities as high as
$99.3\%$ and $97.9\%$, respectively. The flexibility in activating
parametric-resonance gates combined with a tunable coupler architecture
provides a pathway for building large-scale quantum computers.


Abstract:
  In Ni80Fe20/Au/Co/Au deposited on silicon substrate the interaction between
spin waves and surface acoustic waves is observed by Brillion light scattering
spectroscopy. We show that by changing the number of receptions in the
multilayer we can tune the dispersion of surface acoustic waves (SAWs) not
affecting significantly fundamentals spin wave (SW) mode. As a result, we can
control (activate or deactivate) the magnetoelastic interaction between
fundamental SW mode and SAWs by the thickness of the magnetostrictive
multilayer.


Abstract:
  In the last three decades, powerful computer-assisted techniques have been
developed in order to validate a posteriori numerical solutions of semilinear
elliptic problems of the form $\Delta u +f(u,\nabla u) = 0$. By studying a well
chosen fixed point problem defined around the numerical solution, these
techniques make it possible to prove the existence of a solution in an explicit
(and usually small) neighborhood the numerical solution. In this work, we
develop a similar approach for a broader class of systems, including nonlinear
diffusion terms of the form $\Delta \Phi(u)$. In particular, this enables us to
obtain new results about steady states of a cross-diffusion system from
population dynamics: the (non-triangular) SKT model. We also revisit the idea
of automatic differentiation in the context of computer-assisted proof, and
propose an alternative approach based on differential-algebraic equations.


Abstract:
  We prove D.Gaiotto's conjecture about geometric Satake equivalence for
quantum supergroup
  $U_q({\mathfrak{gl}}(N-1|N))$ for generic $q$. The equivalence goes through
the category of factorizable
  sheaves.


Abstract:
  Conferences are a mainstay of most scientific disciplines, where scientists
of all career stages come together to share cutting-edge ideas and approaches.
If you do research, chances are you will attend one or more of these meetings
in your career. Conferences are a microcosm of their discipline, and while
conferences offer different perspectives in different disciplines, they all
offer experiences that range from a casual chat waiting in line for coffee to
watching someone present their groundbreaking, hot-off-the-press research. The
authors of this piece have attended our fair share of conferences and have
collectively mentored hundreds of students in understanding the "unwritten
rules" and pro-tips of conference attendance. As you head to your first
scientific conference, these rules will help you navigate the conference
environment and make the most of your experience.
  We have also developed a web portal which contains far more information about
these rules, tables of professional societies and conferences in different
disciplines, and other resources that may come in handy for first-time
conference attendees and their mentors. We encourage any reader to use, adapt,
and contribute to these materials.


Abstract:
  The spectral form factor (SFF), characterizing statistics of energy
eigenvalues, is a key diagnostic of many-body quantum chaos. In addition,
partial spectral form factors (pSFFs) can be defined which refer to subsystems
of the many-body system. They provide unique insights into energy eigenstate
statistics of many-body systems, as we show in an analysis on the basis of
random matrix theory and of the eigenstate thermalization hypothesis. We
propose a protocol which allows the measurement of SFF and pSFFs in quantum
many-body spin models, within the framework of randomized measurements. Aimed
to probe dynamical properties of quantum many-body systems, our scheme employs
statistical correlations of local random operations which are applied at
different times in a single experiment. Our protocol provides a unified testbed
to probe many-body quantum chaotic behavior, thermalization and many-body
localization in closed quantum systems which we illustrate with simulations for
Hamiltonian and Floquet many-body spin-systems.


Abstract:
  Feature attribution is often loosely presented as the process of selecting a
subset of relevant features as a rationale of a prediction. Task-dependent by
nature, precise definitions of "relevance" encountered in the literature are
however not always consistent. This lack of clarity stems from the fact that we
usually do not have access to any notion of ground-truth attribution and from a
more general debate on what good interpretations are. In this paper we propose
to formalise feature selection/attribution based on the concept of relaxed
functional dependence. In particular, we extend our notions to the
instance-wise setting and derive necessary properties for candidate selection
solutions, while leaving room for task-dependence. By computing ground-truth
attributions on synthetic datasets, we evaluate many state-of-the-art
attribution methods and show that, even when optimised, some fail to verify the
proposed properties and provide wrong solutions.


Abstract:
  Virtualization of computing and networking, IT-OT convergence, cybersecurity
and AI-based enhancement of autonomy are significantly increasing the
complexity of CPS and CPSoS. New challenges have emerged to demonstrate that
these systems are safe and secure. We emphasize the role of control and
emerging fields therein, like symbolic control or set-based fault-tolerant and
decentralized control, to address safety. We have chosen three open
verification problems we deem central in cost-effective development and
certification of safety critical CPSoS. We review some promising threads of
research that could lead in the long term to a scalable and powerful
verification strategy. Its main components are set-based and invariant-based
design, contracts, adversarial testing, algorithmic geometry of dynamics, and
probabilistic estimation derived from compositional massive testing. To explore
these orientations in collaborative projects, and to promote them in
certification arenas, we propose to continue and upgrade an open innovation
drone-based use case that originated from a collaborative research project in
aeronautic certification reformation


Abstract:
  PatchMatch based Multi-view Stereo (MVS) algorithms have achieved great
success in large-scale scene reconstruction tasks. However, reconstruction of
texture-less planes often fails as similarity measurement methods may become
ineffective on these regions. Thus, a new plane hypothesis inference strategy
is proposed to handle the above issue. The procedure consists of two steps:
First, multiple plane hypotheses are generated using filtered initial depth
maps on regions that are not successfully recovered; Second, depth hypotheses
are selected using Markov Random Field (MRF). The strategy can significantly
improve the completeness of reconstruction results with only acceptable
computing time increasing. Besides, a new acceleration scheme similar to
dilated convolution can speed up the depth map estimating process with only a
slight influence on the reconstruction. We integrated the above ideas into a
new MVS pipeline, Plane Hypothesis Inference Multi-view Stereo (PHI-MVS). The
result of PHI-MVS is validated on ETH3D public benchmarks, and it demonstrates
competing performance against the state-of-the-art.


Abstract:
  Despite the close connection between exploration and sample efficiency, most
state of the art reinforcement learning algorithms include no considerations
for exploration beyond maximizing the entropy of the policy. In this work we
address this seeming missed opportunity. We observe that the most common
formulation of directed exploration in deep RL, known as bonus-based
exploration (BBE), suffers from bias and slow coverage in the few-sample
regime. This causes BBE to be actively detrimental to policy learning in many
control tasks. We show that by decoupling the task policy from the exploration
policy, directed exploration can be highly effective for sample-efficient
continuous control. Our method, Decoupled Exploration and Exploitation Policies
(DEEP), can be combined with any off-policy RL algorithm without modification.
When used in conjunction with soft actor-critic, DEEP incurs no performance
penalty in densely-rewarding environments. On sparse environments, DEEP gives a
several-fold improvement in data efficiency due to better exploration.


Abstract:
  In this study we present a compressible test-field method (CTFM) for
computing $\alpha$ effect and turbulent magnetic diffusivity tensors, as well
as those relevant for mean ponderomotive force and mass source, applied to the
full MHD equations. We describe the theoretical background of the method, and
compare it to the quasi-kinematic test-field method (QKTFM), and to the
previously studied variant working in simplified MHD (SMHD). We present several
test cases using velocity and magnetic fields of the Roberts geometry, and also
compare with the imposed-field method. We show that in all cases investigated,
the CTFM gives results in agreement with the imposed field method. Some
deviations between CTFM and SMHD variant exist. As a relevant physical
application, we study non-helically forced shear flows, which exhibit
large-scale dynamo (LSD) action, and present a re-analysis of low Reynolds
number, moderate shear systems, where we previously neglected the pressure
gradient in the momentum equation, and found no coherent shear-current effect.
Another key difference is that in the earlier study we used magnetic forcing to
mimic small-scale dynamo (SSD) action, while here it is self-consistently
driven by purely kinetic forcing. We still find no coherent shear-current
effect, but do recover strong large-scale dynamo (LSD) action, that, according
to our analysis, is driven through the incoherent effects.


Abstract:
  The vibrational dynamics of a model polymer glass is studied by Molecular
Dynamics simulations. The focus is on the "soft" monomers with high
participation to the lower-frequency vibrational modes contributing to the
thermodynamic anomalies of glasses. To better evidence their role, the
threshold to qualify monomers as soft is made severe, allowing for the use of
systems with limited size. A marked tendency of soft monomers to form
quasi-local clusters involving up to 15 monomers is evidenced. Each chain
contributes to a cluster up to about three monomers and a single cluster
involves monomer belonging to about 2-3 chains. Clusters with monomers
belonging to a single chain are rare. The open and tenuous character of the
clusters is revealed by their fractal dimension $d_f < 2$. The inertia tensor
of the soft clusters evidences their strong anisotropy in shape and remarkable
linear correlation of the two largest eigenvalues. Owing to the limited size of
the system, finite-size effects, as well as dependence of the results on the
adopted polymer length, cannot be ruled out.


Abstract:
  Finding Hemisystems is a challenging problem and just few examples arising
from the Hermitian surface are known. A recent method to obtain Hemisystems is
based on using maximal curves. Along this side of research, we provide new
examples of Hemisystems in $PG(3,p^2)$, for each prime of the form $p=1+4a^2$,
with $ a $ integer. Last, we use these results to obtain two weight linear
codes and strongly regular graphs.


Abstract:
  The ability to realize nonlinear controllers with formal guarantees on
dynamic robotic systems has the potential to enable more complex robotic
behaviors -- yet, realizing these controllers is often practically challenging.
To address this challenge, this paper presents the end-to-end realization of
dynamic bipedal locomotion on an underactuated bipedal robot via hybrid zero
dynamics and control Lyapunov functions. A compliant model of Cassie is
represented as a hybrid system to set the stage for a trajectory optimization
framework. With the goal of achieving a variety of walking speeds in all
directions, a library of compliant walking motions is compiled and then
parameterized for efficient use within real-time controllers. Control Lyapunov
functions, which have strong theoretic guarantees, are synthesized to leverage
the gait library and coupled with inverse dynamics to obtain optimization-based
controllers framed as quadratic programs. It is proven that this controller
provably achieves stable locomotion; this is coupled with a theoretic analysis
demonstrating useful properties of the controller for tuning and
implementation. The proposed theoretic framework is practically demonstrated on
the Cassie robot, wherein 3D walking is achieved through the use of
optimization-based torque control. The experiments highlight robotic walking at
different speeds and terrains, illustrating the end-to-end realization of
theoretically justified nonlinear controllers on dynamic underactuated robotic
systems.


Abstract:
  Quasar luminosity function (QLF) shows the active galactic nucleus (AGN)
demography as a result of the combination of the growth and the evolution of
black holes, galaxies, and dark matter halos along the cosmic time. The recent
wide and deep surveys have improved the census of high-redshift quasars, making
it possible to construct reliable ultraviolet (UV) QLFs at $2\lesssim
z\lesssim6$ down to $M_{1450}=-23$ mag. By parameterizing these up-to-date
observed UV QLFs that are the most extensive in both luminosity and survey area
coverage at a given redshift, we show that the UV QLF has a universal shape,
and their evolution can be approximated by a pure density evolution (PDE). In
order to explain the observed QLF, we construct a model QLF employing the halo
mass function, a number of empirical scaling relations, and the Eddington ratio
distribution. We also include the outshining of AGN over its host galaxy, which
made it possible to reproduce a moderately flat shape of the faint end of the
observed QLF (slope of $\sim-1.1$). This model successfully explains the
observed PDE behavior of UV QLF at $z>2$, meaning that the QLF evolution at
high redshift can be understood under the framework of halo mass function
evolution. The importance of the outshining effect in our model also implies
that there could be a hidden population of faint AGNs ($M_{1450}\gtrsim-24$
mag), which are buried under their host galaxy light.


Abstract:
  Superconducting technology makes it possible to build a high energy $e^+e^-$
linear collider with energy recovery (ERLC) and reusable beams. To avoid
parasitic collisions inside the linacs, a twin (dual) LC is proposed. In this
article, I consider the principle scheme of the collider and estimate the
achievable luminosity, which is limited by collision effects and available
power. Such a collider can operate in a duty cycle (DC) and in a continuous
(CW) modes, if sufficient power. With current SC Nb technology ($T=1.8$ K,
$f_{\rm RF}=1.3$ GHz, used for ILC) and with power $P= 100$ MW, a luminosity $L
\sim 0.33 \times10^{36}\,\rm cm^{-2}s^{-1}$ is possible at the Higgs factory
with $2E_0=250$ GeV. Using superconductors operating at 4.5 K with high $Q_0$
values, such as Nb$_3$Sn, and $f_{\rm RF}=0.65$ GHz, the luminosity can reach
$L \sim 1.4 \times10^{36} \,\rm cm^{-2}s^{-1}$ at $2E_0=250$ GeV (with P=100
MW) and $L \sim 0.8 \times 10^{36}\,\rm cm^{-2}s^{-1}$ at $2E_0=500$ GeV (with
P=150 MW), which is almost two orders of magnitude greater than at the ILC,
where the beams are used only once. This technology requires additional efforts
to obtain the required parameters and reliably operation. Such a collider would
be the best machine for precision Higgs studies, including the measurement of
Higgs self-coupling.


Abstract:
  Automatic learning algorithms for improving the image quality of diagnostic
B-mode ultrasound (US) images have been gaining popularity in the recent past.
In this work, a novel convolutional neural network (CNN) is trained using time
of flight corrected in-vivo receiver data of plane wave transmit to produce
corresponding high-quality minimum variance distortion less response (MVDR)
beamformed image. A comprehensive performance comparison in terms of
qualitative and quantitative measures for fully connected neural network
(FCNN), the proposed CNN architecture, MVDR and Delay and Sum (DAS) using the
dataset from Plane wave Imaging Challenge in Ultrasound (PICMUS) is also
reported in this work. The CNN architecture could leverage the spatial
information and will be more region adaptive during the beamforming process.
This is evident from the improvement seen over the baseline FCNN approach and
conventional MVDR beamformer, both in resolution and contrast with an
improvement of 6 dB in CNR using only zero-angle transmission over the
baseline. With the observed reduction in the requirement of number of angles to
produce similar image metrics would prove advantageous in providing a
possibility for higher frame rates.


Abstract:
  For many households, investing for retirement is one of the most significant
decisions and is fraught with uncertainty. In a classic study in behavioral
economics, Benartzi and Thaler (1999) found evidence using bar charts that
investors exhibit myopic loss aversion in retirement decisions: Investors
overly focus on the potential for short-term losses, leading them to invest
less in riskier assets and miss out on higher long-term returns. Recently,
advances in uncertainty visualizations have shown improvements in
decision-making under uncertainty in a variety of tasks. In this paper, we
conduct a controlled and incentivized crowdsourced experiment replicating
Benartzi and Thaler (1999) and extending it to measure the effect of different
uncertainty representations on myopic loss aversion. Consistent with the
original study, we find evidence of myopic loss aversion with bar charts and
find that participants make better investment decisions with longer evaluation
periods. We also find that common uncertainty representations such as interval
plots and bar charts achieve the highest mean expected returns while other
uncertainty visualizations lead to poorer long-term performance and strong
effects on the equity premium. Qualitative feedback further suggests that
different uncertainty representations lead to visual reasoning heuristics that
can either mitigate or encourage a focus on potential short-term losses. We
discuss implications of our results on using uncertainty visualizations for
retirement decisions in practice and possible extensions for future work.


Abstract:
  COVID-19 testing, the cornerstone for effective screening and identification
of COVID-19 cases, remains paramount as an intervention tool to curb the spread
of COVID-19 both at local and national levels. However, the speed at which the
pandemic struck and the response was rolled out, the widespread impact on
healthcare infrastructure, the lack of sufficient preparation within the public
health system, and the complexity of the crisis led to utter confusion among
test-takers. Invasion of privacy remains a crucial concern. The user experience
of test takers remains low. User friction affects user behavior and discourages
participation in testing programs. Test efficacy has been overstated. Test
results are poorly understood resulting in inappropriate follow-up
recommendations. Herein, we review the current landscape of COVID-19 testing,
identify four key challenges, and discuss the consequences of the failure to
address these challenges. The current infrastructure around testing and
information propagation is highly privacy-invasive and does not leverage
scalable digital components. In this work, we discuss challenges complicating
the existing covid-19 testing ecosystem and highlight the need to improve the
testing experience for the user and reduce privacy invasions. Digital tools
will play a critical role in resolving these challenges.


Abstract:
  We prove new results on splitting Brauer classes by genus 1 curves, settling
in particular the case of degree 7 classes over global fields. Though our
method is cohomological in nature, and proceeds by considering the more
difficult problem of splitting $\mu_N$-gerbes, we use crucial input from the
arithmetic of modular curves and explicit $N$-descent on elliptic curves.


Abstract:
  With the powerful learning ability of deep convolutional networks, deep
clustering methods can extract the most discriminative information from
individual data and produce more satisfactory clustering results. However,
existing deep clustering methods usually ignore the relationship between the
data. Fortunately, the graph convolutional network can handle such
relationship, opening up a new research direction for deep clustering. In this
paper, we propose a cross-attention based deep clustering framework, named
Cross-Attention Fusion based Enhanced Graph Convolutional Network (CaEGCN),
which contains four main modules: the cross-attention fusion module which
innovatively concatenates the Content Auto-encoder module (CAE) relating to the
individual data and Graph Convolutional Auto-encoder module (GAE) relating to
the relationship between the data in a layer-by-layer manner, and the
self-supervised model that highlights the discriminative information for
clustering tasks. While the cross-attention fusion module fuses two kinds of
heterogeneous representation, the CAE module supplements the content
information for the GAE module, which avoids the over-smoothing problem of GCN.
In the GAE module, two novel loss functions are proposed that reconstruct the
content and relationship between the data, respectively. Finally, the
self-supervised module constrains the distributions of the middle layer
representations of CAE and GAE to be consistent. Experimental results on
different types of datasets prove the superiority and robustness of the
proposed CaEGCN.


Abstract:
  Cosmic-ray muons can be used for the non-destructive imaging of spent nuclear
fuel in sealed dry storage casks. The scattering data of the muons after
traversing provides information on the thereby penetrated materials. Based on
these properties, we investigate and discuss the theoretical feasibility of
detecting single missing fuel rods in a sealed cask for the first time. We
perform simulations of a vertically standing generic cask model loaded with
fuel assemblies from a pressurized water reactor and muon detectors placed
above and below the cask. By analysing the scattering angles and applying a
significance ratio based on the Kolmogorov-Smirnov test statistic we conclude
that missing rods can be reliably identified in a reasonable measuring time
period depending on their position in the assembly and cask, and on the angular
acceptance criterion of the primary, incoming muons.


Abstract:
  We are interested in describing the infected size of the SIS Epidemic model
using Birth-Death Markov process. The Susceptible-Infected-Susceptible (SIS)
model is defined within a population of constant size $M$; the size is kept
constant by replacing each death with a newborn healthy individual. The life
span of each individual in the population is modelled by an exponential
distribution with parameter $\alpha$; and the disease spreads within the
population is modelled by a Poisson process with a rate $\lambda_{I}$.
$\lambda_{I}=\beta I(1-\frac{I}{M}) $ is similar to the instantaneous rate in
the logistic population growth model. The analysis is focused on the disease
outbreak, where the reproduction number $R=\frac{\beta}{\alpha} $ is greater
than one. As methodology, we use both numerical and analytical approaches. The
analysis relies on the stationary distribution for Birth and Death Markov
process. The numerical approach creates sample path simulations into order show
the infected size dynamics, and the relationship between infected size and $R$.
As $M$ becomes large, some stable statistical characteristics of the infected
size distribution can be deduced. And the infected size is shown analytically
to follow a normal distribution with mean $(1-\frac{1}{R}) M$ and Variance
$\frac{M}{R} $.


Abstract:
  Suppose we observe an infinite series of coin flips $X_1,X_2,\ldots$, and
wish to sequentially test the null that these binary random variables are
exchangeable. Nonnegative supermartingales (NSMs) are a workhorse of sequential
inference, but we prove that they are powerless for this problem. First,
utilizing a geometric concept called fork-convexity (a sequential analog of
convexity), we show that any process that is an NSM under a set of
distributions, is also necessarily an NSM under their "fork-convex hull".
Second, we demonstrate that the fork-convex hull of the exchangeable null
consists of all possible laws over binary sequences; this implies that any NSM
under exchangeability is necessarily nonincreasing, hence always yields a
powerless test for any alternative. Since testing arbitrary deviations from
exchangeability is information theoretically impossible, we focus on Markovian
alternatives. We combine ideas from universal inference and the method of
mixtures to derive a "safe e-process", which is a nonnegative process with
expectation at most one under the null at any stopping time, and is upper
bounded by a martingale, but is not itself an NSM. This in turn yields a level
$\alpha$ sequential test that is consistent; regret bounds from universal
coding also demonstrate rate-optimal power. We present ways to extend these
results to any finite alphabet and to Markovian alternatives of any order using
a "double mixture" approach. We provide an array of simulations, and give
general approaches based on betting for unstructured or ill-specified
alternatives. Finally, inspired by Shafer, Vovk, and Ville, we provide
game-theoretic interpretations of our e-processes and pathwise results.


Abstract:
  Entity synonyms discovery is crucial for entity-leveraging applications.
However, existing studies suffer from several critical issues: (1) the input
mentions may be out-of-vocabulary (OOV) and may come from a different semantic
space of the entities; (2) the connection between mentions and entities may be
hidden and cannot be established by surface matching; and (3) some entities
rarely appear due to the long-tail effect. To tackle these challenges, we
facilitate knowledge graphs and propose a novel entity synonyms discovery
framework, named \emph{KGSynNet}. Specifically, we pre-train subword embeddings
for mentions and entities using a large-scale domain-specific corpus while
learning the knowledge embeddings of entities via a joint TransC-TransE model.
More importantly, to obtain a comprehensive representation of entities, we
employ a specifically designed \emph{fusion gate} to adaptively absorb the
entities' knowledge information into their semantic features. We conduct
extensive experiments to demonstrate the effectiveness of our \emph{KGSynNet}
in leveraging the knowledge graph. The experimental results show that the
\emph{KGSynNet} improves the state-of-the-art methods by 14.7\% in terms of
hits@3 in the offline evaluation and outperforms the BERT model by 8.3\% in the
positive feedback rate of an online A/B test on the entity linking module of a
question answering system.


Abstract:
  Silicon detection is a mature technology for registering the passage of
charged particles. At the same time it continues to evolve toward increasing
radiation tolerance as well as precision and adaptability. For these reasons it
is likely to remain a critical element of detection systems associated with
extra-terrestrial exploration. Silicon sensor leakage current and depletion
voltage depend upon the integrated fluence received by the sensor, and upon its
thermal history during and after the irradiation process. For minimal
assumptions on shielding and hence on particle energy spectrum, and using
published data on Martian ground temperature, we predict the leakage current
density and the depletion voltage, as a function of time, of silicon sensors
deployed continuously on the Mars surface for a duration of up to 28
Earth-years, for several sensor geometries and a worst-case temperature
scenario.


Abstract:
  Contemporary business innovation relies increasingly on information and
communications technology (ICT) solutions. As ICT initiatives are generally
implemented via projects the management of ICT projects has come under
increasing scrutiny. ICT projects continue to fail; as a result, while research
in ICT project management has indeed increased, many challenges for research
and practice remain. Many studies have addressed the execution and management
of ICT projects and the many factors that might relate to project outcomes.
Very few, however, have considered ICT project initiation and the crucial
decisions made at that very early, pre-life cycle stage. The primary intent of
this research is therefore to investigate ICT projects with a particular focus
on their initiation. In doing so we wished to understand why ICT projects are
started, and how they are moved from idea or proposal to supported reality. A
combination of semi-structured interviews and the repertory grid data
collection and analysis method was employed to investigate and validate the
motivating factors that influence individual IT Managers' project initiation
decisions and the methods they use to transition from idea to enacted project.
Our results showed that there are indeed multiple underlying reasons for the
decisions made at this early stage and that there are some especially common
decision drivers. Some were expected, in the sense that they mapped to
recommended best practice. For instance, most projects are motivated by a
desire to achieve efficiencies or cost savings, and their potential tends to be
assessed using cost benefit analysis. Other results were more surprising -
competitor pressure was not a common driver for ICT project initiation in our
analysis. Unsurprisingly, formal evaluation methods are more frequently used to
assess project proposals when those projects are larger and higher profile.
(Abridged)


Abstract:
  How can multiple humans interact with multiple robots? The goal of our
research is to create an effective interface that allows multiple operators to
collaboratively control teams of robots in complex tasks. In this paper, we
focus on a key aspect that affects our exploration of the design space of
human-robot interfaces -- inter-human communication. More specifically, we
study the impact of direct and indirect communication on several metrics, such
as awareness, workload, trust, and interface usability. In our experiments, the
participants can engage directly through verbal communication, or indirectly by
representing their actions and intentions through our interface. We report the
results of a user study based on a collective transport task involving 18 human
subjects and 9 robots. Our study suggests that combining both direct and
indirect communication is the best approach for effective multi-human /
multi-robot interaction.


Abstract:
  Runaway stars can result from core-collapse supernovae in multiple stellar
systems. If the supernova disrupts the system, the companion gets ejected with
its former orbital velocity. A clear identification of a runaway star can yield
the time and place of the explosion as well as orbital parameters of the
pre-supernova binary system. Previous searches have mostly considered O- and
B-type stars as runaway stars because they are always young in absolute terms
(not much older than the lifetime of the progenitor) and can be detected up to
larger distances. We present here a search for runaway stars of all spectral
types. For late-type stars, a young age can be inferred from the lithium test.
We used Gaia data to identify and characterise runaway star candidates in
nearby supernova remnants, obtained spectra of 39 stars with UVES at the VLT
and HDS at the Subaru telescope and found a significant amount of lithium in
the spectra of six dwarf stars. We present the spectral analysis, including
measurements of radial velocities, atmospheric parameters and lithium
abundances. Then we estimate the ages of our targets from the
Hertzsprung-Russell diagram and with the lithium test, present a selection of
promising runaway star candidates and draw constraints on the number of ejected
runaway stars compared to model expectations.


Abstract:
  Modern processors can offer hardware primitives that allow a process to run
in isolation. These primitives implement a trusted execution environment (TEE)
in which a program can run such that the integrity and confidentiality of its
execution are guaranteed. Intel's Software Guard eXtensions (SGX) is an example
of such primitives and its isolated processes are called \emph{enclaves}. These
guarantees, however, can be easily thwarted if the enclave has not been
properly designed. Its interface with the untrusted software stack is arguably
the largest attack surface that adversaries can exploit; unintended
interactions with untrusted code can expose the enclave to memory corruption
attacks, for instance. In this paper, we propose a notion of an \emph{orderly}
enclave which splits its behaviour into several execution phases each of which
imposes a set of restrictions on accesses to untrusted memory, phase
transitions and registers sanitisation. A violation to these restrictions
indicates an undesired behaviour which could be harnessed to perpetrate attacks
against the enclave. We also introduce \Analyser{}: a tool that uses symbolic
execution to carry out the validation of an enclave against our notion of an
orderly enclave; in this process, it also looks for some typical
memory-corruption vulnerabilities. We discuss how our approach can prevent and
flag enclave vulnerabilities that have been identified in the literature.
Moreover, we have evaluated how our approach fares in the analysis of some
practical enclaves. \Analyser{} was able to identify real vulnerabilities on
these enclaves which have been acknowledged and fixed by their maintainers.


Abstract:
  This abstract seeks to introduce the ISSAC community to the DEWCAD project,
which is based at Coventry University and the University of Bath, in the United
Kingdom. The project seeks to push back the Doubly Exponential Wall of
Cylindrical Algebraic Decomposition, through the integration of SAT/SMT
technology, the extension of Lazard projection theory, and the development of
new algorithms based on CAD technology but without producing CADs themselves.
The project also seeks to develop applications of CAD and will focus on
applications in the domains of economics and bio-network analysis.


Abstract:
  This paper presents a hierarchical motion planner for planning the
manipulation motion to repose long and heavy objects considering external
support surfaces. The planner includes a task level layer and a motion level
layer. We formulate the manipulation planning problem at the task level by
considering grasp poses as nodes and object poses for edges. We consider
regrasping and constrained in-hand slip (drooping) during building graphs and
find mixed regrasping and drooping sequences by searching the graph. The
generated sequences autonomously divide the object weight between the arm and
the support surface and avoid configuration obstacles. Cartesian planning is
used at the robot motion level to generate motions between adjacent critical
grasp poses of the sequence found by the task level layer. Various experiments
are carried out to examine the performance of the proposed planner. The results
show improved capability of robot arms to manipulate long and heavy objects
using the proposed planner. Our contribution is we initially develop a
graph-based planning system that reasons both in-hand and regrasp manipulation
motion considering external supports. On one hand, the planner integrates
regrasping and drooping to realize in-hand manipulation with external support.
On the other hand, it switches states by releasing and regrasping objects when
the object is in stably placed. The search graphs' nodes could be retrieved
from remote cloud servers that provide a large amount of pre-annotated data to
implement cyber intelligence.


Abstract:
  Recently, there has been great success in applying deep neural networks on
graph structured data. Most work, however, focuses on either node- or
graph-level supervised learning, such as node, link or graph classification or
node-level unsupervised learning (e.g. node clustering). Despite its wide range
of possible applications, graph-level unsupervised learning has not received
much attention yet. This might be mainly attributed to the high representation
complexity of graphs, which can be represented by n! equivalent adjacency
matrices, where n is the number of nodes. In this work we address this issue by
proposing a permutation-invariant variational autoencoder for graph structured
data. Our proposed model indirectly learns to match the node ordering of input
and output graph, without imposing a particular node ordering or performing
expensive graph matching. We demonstrate the effectiveness of our proposed
model on various graph reconstruction and generation tasks and evaluate the
expressive power of extracted representations for downstream graph-level
classification and regression.


Abstract:
  The magnetic fields of accretion disks play an important role in studying
their evolution. We may assume that its generation is connected to the dynamo
mechanism, which is similar with that in the galactic disks. Here, we propose a
model of the magnetic field of the accretion disk that uses the same approaches
that have been used for galaxies. It is necessary to obtain the field, which is
expected to be less than the equipartition value, and without destroying the
disk. To do so, it is necessary to formulate the basic properties of the
ionized medium and to estimate the parameters governing the dynamo. We used the
no-z approximation that has been developed for thin disks. We also take
different boundary conditions that can change the value of the field
significantly. We show that the magnetic field strictly depends on the boundary
conditions. Taking zero conditions and the fixed magnetic field condition on
the inner boundary, which are connected to the physical properties of the
accretion disk, we can avoid solutions that are greater than the equipartition
field.


Abstract:
  The use of probe molecules to extract the local dynamical and structural
properties of complex dynamical systems is an age-old technique both in
simulations and experiments. A lot of important information which is not
immediately accessible from the bulk measurements can be accessed via these
local measurements. Still, a detailed understanding of how a probe particle's
dynamics are affected by the surrounding liquid medium is not very well
understood, especially in the supercooled temperature regime. This work shows
how translational dynamics of a rod-like particle immersed in a supercooled
liquid can give us information on the growth of the correlation length scale
associated with dynamical heterogeneity and the multi-body static correlations
in the medium. A unified scaling theory rationalizes all the observed results
leading to the development of a novel yet simple method that is accessible in
experiments to measure the growth of these important length-scales in molecular
glass-forming liquids.


Abstract:
  We show within the slave-boson mean field approximation that the
two-dimensional t-J model has an intrinsic instability toward forming a
quasi-one-dimensional (q-1d) Fermi surface. This q-1d state competes with, and
is overcome by, the d-wave pairing state for a realistic parameter choice.
However, we find that a small spatial anisotropy in t and J exposes the q-1d
instability which has been hidden behind the d-wave pairing state, and brings
about the coexistence with the d-wave pairing. We argue that this coexistence
can be realized in La_{2-x}Sr_{x}CuO_4 systems


Abstract:
  The role of a new form of dynamic interaction is explored in a network of
generic identical oscillators. The proposed design of dynamic coupling
facilitates the onset of a plethora of asymptotic states including synchronous
states, amplitude death states, oscillation death states, a mixed state
(complete synchronized cluster and small amplitude unsynchronized domain), and
bistable states (coexistence of two attractors). The dynamical transitions from
the oscillatory to death state are characterized using an average temporal
interaction approximation, which agrees with the numerical results in temporal
interaction. A first-order phase transition behavior may change into a
second-order transition in spatial dynamic interaction solely depending on the
choice of initial conditions in the bistable regime. However, this possible
abrupt first-order like transition is completely non-existent in the case of
temporal dynamic interaction. Besides the study on periodic Stuart-Landau
systems, we present results for paradigmatic chaotic model of R\"ossler
oscillators and Mac-arthur ecological model.


Abstract:
  The recent advent of noisy intermediate-scale quantum devices, especially
near-term quantum computers, has sparked extensive research efforts concerned
with their possible applications. At the forefront of the considered approaches
are variational methods that use parametrized quantum circuits. The classical
and quantum Fisher information are firmly rooted in the field of quantum
sensing and have proven to be versatile tools to study such parametrized
quantum systems. Their utility in the study of other applications of noisy
intermediate-scale quantum devices, however, has only been discovered recently.
Hoping to stimulate more such applications, this article aims to further
popularize classical and quantum Fisher information as useful tools for
near-term applications beyond quantum sensing. We start with a tutorial that
builds an intuitive understanding of classical and quantum Fisher information
and outlines how both quantities can be calculated on near-term devices. We
also elucidate their relationship and how they are influenced by noise
processes. Next, we give an overview of the core results of the quantum sensing
literature and proceed to a comprehensive review of recent applications in
variational quantum algorithms and quantum machine learning.


Abstract:
  The construction of catalogues of a particular type of galaxy can be
complicated by interlopers contaminating the sample. In spectroscopic galaxy
surveys this can be due to the misclassification of an emission line; for
example in the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) low
redshift [OII] emitters may make up a few percent of the observed Ly${\alpha}$
emitter (LAE) sample. The presence of contaminants affects the measured
correlation functions and power spectra. Previous attempts to deal with this
using the cross-correlation function have assumed sources at a fixed redshift,
or not modelled evolution within the adopted redshift bins. However, in
spectroscopic surveys like HETDEX, where the contamination fraction is likely
to be redshift dependent, the observed clustering of misclassified sources will
appear to evolve strongly due to projection effects, even if their true
clustering does not. We present a practical method for accounting for the
presence of contaminants with redshift-dependent contamination fractions and
projected clustering. We show using mock catalogues that our method, unlike
existing approaches, yields unbiased clustering measurements from the upcoming
HETDEX survey in scenarios with redshift-dependent contamination fractions
within the redshift bins used. We show our method returns auto-correlation
functions with systematic biases much smaller than the statistical noise for
samples with at least as high as 7 per cent contamination. We also present and
test a method for fitting for the redshift-dependent interloper fraction using
the LAE-[OII] galaxy cross-correlation function, which gives less biased
results than assuming a single interloper fraction for the whole sample.


Abstract:
  Stochastic modelling of complex systems plays an essential, yet often
computationally intensive role across the quantitative sciences. Recent
advances in quantum information processing have elucidated the potential for
quantum simulators to exhibit memory advantages for such tasks. Heretofore, the
focus has been on lossless memory compression, wherein the advantage is
typically in terms of lessening the amount of information tracked by the model,
while -- arguably more practical -- reductions in memory dimension are not
always possible. Here we address the case of lossy compression for quantum
stochastic modelling of continuous-time processes, introducing a method for
coarse-graining in quantum state space that drastically reduces the requisite
memory dimension for modelling temporal dynamics whilst retaining near-exact
statistics. In contrast to classical coarse-graining, this compression is not
based on sacrificing temporal resolution, and brings memory-efficient,
high-fidelity stochastic modelling within reach of present quantum
technologies.


Abstract:
  Many distributed optimization algorithms achieve existentially-optimal
running times, meaning that there exists some pathological worst-case topology
on which no algorithm can do better. Still, most networks of interest allow for
exponentially faster algorithms. This motivates two questions: (1) What network
topology parameters determine the complexity of distributed optimization? (2)
Are there universally-optimal algorithms that are as fast as possible on every
topology?
  We resolve these 25-year-old open problems in the known-topology setting
(i.e., supported CONGEST) for a wide class of global network optimization
problems including MST, $(1+\varepsilon)$-min cut, various approximate shortest
paths problems, sub-graph connectivity, etc.
  In particular, we provide several (equivalent) graph parameters and show they
are tight universal lower bounds for the above problems, fully characterizing
their inherent complexity. Our results also imply that algorithms based on the
low-congestion shortcut framework match the above lower bound, making them
universally optimal if shortcuts are efficiently approximable. We leverage a
recent result in hop-constrained oblivious routing to show this is the case if
the topology is known -- giving universally-optimal algorithms for all above
problems.


Abstract:
  High-fidelity single- and two-qubit gates are essential building blocks for a
fault-tolerant quantum computer. While there has been much progress in
suppressing single-qubit gate errors in superconducting qubit systems,
two-qubit gates still suffer from error rates that are orders of magnitude
higher. One limiting factor is the residual ZZ-interaction, which originates
from a coupling between computational states and higher-energy states. While
this interaction is usually viewed as a nuisance, here we experimentally
demonstrate that it can be exploited to produce a universal set of fast single-
and two-qubit entangling gates in a coupled transmon qubit system. To implement
arbitrary single-qubit rotations, we design a new protocol called the two-axis
gate that is based on a three-part composite pulse. It rotates a single qubit
independently of the state of the other qubit despite the strong ZZ-coupling.
We achieve single-qubit gate fidelities as high as 99.1% from randomized
benchmarking measurements. We then demonstrate both a CZ gate and a CNOT gate.
Because the system has a strong ZZ-interaction, a CZ gate can be achieved by
letting the system freely evolve for a gate time $t_g=53.8$ ns. To design the
CNOT gate, we utilize an analytical microwave pulse shape based on the SWIPHT
protocol for realizing fast, low-leakage gates. We obtain fidelities of 94.6%
and 97.8% for the CNOT and CZ gates respectively from quantum progress
tomography.


Abstract:
  A model is constructed to describe the thermal-field emission of electrons
from a three-dimensional ($3$D) topological semimetal hosting Dirac/Weyl
node(s). The traditional thermal-field electron emission model is generalised
to accommodate the $3$D non-parabolic energy band structures in the topological
Dirac/Weyl semimetals, such as cadmium arsenide (\ch{Cd3As2}), sodium
bismuthide (\ch{Na3Bi}), tantalum arsenide (\ch{TaAs}) and tantalum phosphide
(\ch{TaP}). Due to the unique Dirac cone band structure, an unusual dual-peak
feature is observed in the total energy distribution (TED) spectrum. This
non-trivial dual-peak feature, absent from traditional materials, plays a
critical role in manipulating the TED spectrum and the magnitude of the
emission current. At zero temperature limit, a new scaling law for pure field
emission is derived and it is different from the well-known Fowler-Nordheim
(FN) law. This model expands the recent understandings of electron emission
studied for the Dirac $2$D materials into the $3$D regime, and thus offers a
theoretical foundation for the exploration in using topological semimetals as
novel electrodes.


Abstract:
  In this paper, we consider a single-cell multi-user orthogonal frequency
division multiple access (OFDMA) network with one unmanned aerial vehicle
(UAV), which works as an amplify-and-forward relay to improve the
quality-of-service (QoS) of the user equipments (UEs) in the cell edge. Aiming
to improve the throughput while guaranteeing the user fairness, we jointly
optimize the communication mode, subchannel allocation, power allocation, and
UAV trajectory, which is an NP-hard problem. To design the UAV trajectory and
resource allocation efficiently, we first decompose the problem into three
subproblems, i.e., mode selection and subchannel allocation, trajectory
optimization, and power allocation, and then solve these subproblems
iteratively. Simulation results show that the proposed algorithm outperforms
the random algorithm and the cellular scheme.


Abstract:
  We develop new strategies to build numerical relativity surrogate models for
eccentric binary black hole systems, which are expected to play an increasingly
important role in current and future gravitational-wave detectors. We introduce
a new surrogate waveform model, \texttt{NRSur2dq1Ecc}, using 47 nonspinning,
equal-mass waveforms with eccentricities up to $0.2$ when measured at a
reference time of $5500M$ before merger. This is the first waveform model that
is directly trained on eccentric numerical relativity simulations and does not
require that the binary circularizes before merger. The model includes the
$(2,2)$, $(3,2)$, and $(4,4)$ spin-weighted spherical harmonic modes. We also
build a final black hole model, \texttt{NRSur2dq1EccRemnant}, which models the
mass, and spin of the remnant black hole. We show that our waveform model can
accurately predict numerical relativity waveforms with mismatches $\approx
10^{-3}$, while the remnant model can recover the final mass and dimensionless
spin with absolute errors smaller than $\approx 5 \times 10^{-4}M$ and $\approx
2 \times10^{-3}$ respectively. We demonstrate that the waveform model can also
recover subtle effects like mode-mixing in the ringdown signal without any
special ad-hoc modeling steps. Finally, we show that despite being trained only
on equal-mass binaries, \texttt{NRSur2dq1Ecc} can be reasonably extended up to
mass ratio $q\approx3$ with mismatches $\simeq 10^{-2}$ for eccentricities
smaller than $\sim 0.05$ as measured at a reference time of $2000M$ before
merger. The methods developed here should prove useful in the building of
future eccentric surrogate models over larger regions of the parameter space.


Abstract:
  Research on autonomous driving is advancing dramatically and requires new
data and techniques to progress even further. To reflect this pressure, we
present an extension of our recent work - the Brno Urban Dataset (BUD). The new
data focus on winter conditions in various snow-covered environments and
feature additional LiDAR and radar sensors for object detection in front of the
vehicle. The improvement affects the old data as well. We provide YOLO
detection annotations for all old RGB images in the dataset. The detections are
further also transferred by our original algorithm into the infra-red (IR)
images, captured by the thermal camera. To our best knowledge, it makes this
dataset the largest source of machine-annotated thermal images currently
available. The dataset is published under MIT license on
https://github.com/Robotics-BUT/Brno-Urban-Dataset.


Abstract:
  Identifying the failure modes of cloud computing systems is a difficult and
time-consuming task, due to the growing complexity of such systems, and the
large volume and noisiness of failure data. This paper presents a novel
approach for analyzing failure data from cloud systems, in order to relieve
human analysts from manually fine-tuning the data for feature engineering. The
approach leverages Deep Embedded Clustering (DEC), a family of unsupervised
clustering algorithms based on deep learning, which uses an autoencoder to
optimize data dimensionality and inter-cluster variance. We applied the
approach in the context of the OpenStack cloud computing platform, both on the
raw failure data and in combination with an anomaly detection pre-processing
algorithm. The results show that the performance of the proposed approach, in
terms of purity of clusters, is comparable to, or in some cases even better
than manually fine-tuned clustering, thus avoiding the need for deep domain
knowledge and reducing the effort to perform the analysis. In all cases, the
proposed approach provides better performance than unsupervised clustering when
no feature engineering is applied to the data. Moreover, the distribution of
failure modes from the proposed approach is closer to the actual frequency of
the failure modes.


Abstract:
  We study the error of the number of unimodular lattice points that fall into
a dilated and translated parallelogram. By using an article from Skriganov, we
see that this error can be compared to an ergodic sum that involves the
discrete geodesic flow over the space of unimodular lattices. With the right
normalization, we show, by using tools from a previous work of Fayad and
Dolgopyat, that a certain point process converges in law towards a Poisson
process and deduce that the ergodic sum converges towards a Cauchy centered law
when the unimodular lattice is distributed according to the normalized Haar
measure. Strong from this experience, we apply the same kind of approach, with
more difficulties, to the study of the asymptotic behaviour of the error and
show that this error, normalized by $\log(t)$ with $t$ the factor of dilatation
of the parallelogram, also converges in law towards a Cauchy centered law when
the dilatation parameter tends to infinity and when the lattice and the vector
of translation are random. In a next article, we will show that, in the case of
a ball in dimension $d$ superior or equal to $2$, the error, normalized by
$t^{\frac{d-1}{2}}$ with $t$ the factor of dilatation of the ball, converges in
law when $t \rightarrow \infty$ and the limit law admits a moment of order $1$.


Abstract:
  Smart contracts are self-enforcing agreements that are employed to exchange
assets without the approval of trusted third parties. This feature has
encouraged various sectors to make use of smart contracts when transacting.
Experience shows that many deployed contracts are vulnerable to exploitation
due to their poor design, which allows attackers to steal valuable assets from
the involved parties. Therefore, an assessment approach that allows developers
to recognise the consequences of deploying vulnerable contracts is needed. In
this paper, we propose a debt-aware approach for assessing security design
vulnerabilities in smart contracts. Our assessment approach involves two main
steps: (i) identification of design vulnerabilities using security analysis
techniques and (ii) an estimation of the ramifications of the identified
vulnerabilities leveraging the technical debt metaphor, its principal and
interest. We use examples of vulnerable contracts to demonstrate the
applicability of our approach. The results show that our assessment approach
increases the visibility of security design issues. It also allows developers
to concentrate on resolving smart contract vulnerabilities through technical
debt impact analysis and prioritisation. Developers can use our approach to
inform the design of more secure contracts and for reducing unintentional debts
caused by a lack of awareness of security issues.


Abstract:
  Consider the puzzle: given a number, remove $k$ digits such that the
resulting number is as large as possible. Various techniques were employed to
derive a linear-time solution to the puzzle: predicate logic was used to
justify the structure of a greedy algorithm, a dependently-typed proof
assistant was used to give a constructive proof of the greedy condition, and
equational reasoning was used to calculate the greedy step as well as the
final, linear-time optimisation.


Abstract:
  In this note, we show how a continuous action of the Morava stabilizer group
$\mathbb G_n$ on the Lubin-Tate spectrum $E_n$, satisfying the conclusion
$E_n^{h\mathbb G_n}\simeq L_{K(n)} S$ of the Devinatz-Hopkins Theorem, may be
obtained by monodromy on the stack of oriented deformations of formal groups in
the context of formal spectral algebraic geometry.


Abstract:
  We implement and benchmark the frozen core approximation, a technique
commonly adopted in electronic structure theory to reduce the computational
cost by means of mathematically fixing the chemically inactive core electron
states. The accuracy and efficiency of this approach are well controlled by a
single parameter, the number of frozen orbitals. Explicit corrections for the
frozen core orbitals and the unfrozen valence orbitals are introduced,
safeguarding against seemingly minor numerical deviations from the assumed
orthonormality conditions of the basis functions. A speedup of over two-fold
can be achieved for the diagonalization step in all-electron density-functional
theory simulations containing heavy elements, without any accuracy degradation
in terms of the electron density, total energy, and atomic forces. This is
demonstrated in a benchmark study covering 103 materials across the periodic
table, and a large-scale simulation of CsPbBr3 with 2,560 atoms. Our study
provides a rigorous benchmark of the precision of the frozen core approximation
(sub-meV per atom for frozen core orbitals below -200 eV) for a wide range of
test cases and for chemical elements ranging from Li to Po. The algorithms
discussed here are implemented in the open-source Electronic Structure
Infrastructure software package.


Abstract:
  We propose a new viewpoint on Hilbert scales extending them by means of all
Hilbert spaces that are interpolation ones between spaces on the scale. We
prove that this extension admits an explicit description with the help of
$\mathrm{OR}$-varying functions of the operator generating the scale. We also
show that this extended Hilbert scale is obtained by the quadratic
interpolation (with function parameter) between the above spaces and is closed
with respect to the quadratic interpolation between Hilbert spaces. We give
applications of the extended Hilbert scale to interpolational inequalities,
generalized Sobolev spaces, and spectral expansions induced by abstract and
elliptic operators.


Abstract:
  In this paper, we introduce a new large-scale face database from KIST,
denoted as K-FACE, and describe a novel capturing device specifically designed
to obtain the data. The K-FACE database contains more than 1 million
high-quality images of 1,000 subjects selected by considering the ratio of
gender and age groups. It includes a variety of attributes, including 27 poses,
35 lighting conditions, three expressions, and occlusions by the combination of
five types of accessories. As the K-FACE database is systematically constructed
through a hemispherical capturing system with elaborate lighting control and
multiple cameras, it is possible to accurately analyze the effects of factors
that cause performance degradation, such as poses, lighting changes, and
accessories. We consider not only the balance of external environmental
factors, such as pose and lighting, but also the balance of personal
characteristics such as gender and age group. The gender ratio is the same,
while the age groups of subjects are uniformly distributed from the 20s to 50s
for both genders. The K-FACE database can be extensively utilized in various
vision tasks, such as face recognition, face frontalization, illumination
normalization, face age estimation, and three-dimensional face model
generation. We expect systematic diversity and uniformity of the K-FACE
database to promote these research fields.


Abstract:
  A distinguishing feature of active particles is the nature of the
non-equilibrium noise driving their dynamics. Control of these noise properties
is, therefore, of both fundamental and applied interest. We demonstrate
emergent tuning of the active noise of a granular self-propelled particle by
confining it to a quasi one-dimensional channel. We find that this particle,
moving like an active Brownian particle (ABP) in two-dimensions, displays
run-and-tumble (RTP) characteristics in confinement. We show that the dynamics
of the relative orientation co-ordinate of the particle maps to that of a
Brownian particle in a periodic potential subject to a constant force, in
analogy to the dynamics of a molecular motor. This mapping captures the
essential statistical characteristics of the one-dimensional RTP motion.
Specifically, our theoretical analysis is in agreement with the empirical
distributions of the relative orientation co-ordinate and the run-times
(tumble-rates) of the particle. Finally, we explicitly control these emergent
run-and-tumble like noise parameters by external driving. Altogether, our work
illustrates geometry-induced tuning of the active dynamics of self-propelled
units thus suggesting an independent route to harness their internal dynamics.


Abstract:
  Two-photon excitation (TPE) proceeds via a "virtual" pathway, which depends
on the accessibility of one or more intermediate states, and, in the case of
non-centrosymmetric molecules, an additional "dipole" pathway involving the
off-resonance dipole-allowed one-photon transitions and the change in the
permanent dipole moment between the initial and final states. Here, we control
the quantum interference between these two optical excitation pathways by using
phase-shaped femtosecond laser pulses. We find enhancements by a factor of up
to 1.75 in the two-photon-excited fluorescence of the photobase FR0-SB in
methanol after taking into account the longer pulse duration of the shaped
laser pulses. Simulations taking into account the different responses of the
virtual and dipole pathways to external fields and the effect of pulse shaping
on two-photon transitions are found to be in good agreement with our
experimental measurements. The observed quantum control of TPE in condensed
phase may lead to enhanced signal at a lower intensity in two-photon
microscopy, multiphoton-excited photoreagents, and novel spectroscopic
techniques that are sensitive to the magnitude of the contributions from the
virtual and dipole pathways to multiphoton excitations.


Abstract:
  Recently, Abebe et al. (KDD 2018) and Chan et al. (WWW 2019) have considered
an opinion dynamics optimization problem that is based on a popular model for
social opinion dynamics, in which each agent has some fixed innate opinion, and
a resistance that measures the importance it places on its innate opinion;
moreover, the agents influence one another's opinions through an iterative
process. Under certain conditions, this iterative process converges to some
equilibrium opinion vector. Previous works gave an efficient local search
algorithm to solve the unbudgeted variant of the problem, for which the goal is
to modify the resistance of any number of agents (within some given range) such
that the sum of the equilibrium opinions is minimized. On the other hand, it
was proved that the $L_0$-budgeted variant is NP-hard, where the $L_0$-budget
is a restriction given upfront on the number of agents whose resistance may be
modified.
  Inspired by practical situations in which the effort to modify an agent's
resistance increases with the magnitude of the change, we propose the
$L_1$-budgeted variant, in which the $L_1$-budget is a restriction on the sum
of the magnitudes of the changes over all agents' resistance parameters. In
this work, we show that the $L_1$-budgeted variant is NP-hard via a reduction
from vertex cover. However, contrary to the $L_0$-budgeted variant, a very
technical argument is needed to show that the optimal solution can be achieved
by focusing the given $L_1$-budget on as small a number of agents as possible,
as opposed to spreading the budget over a large number of agents.


Abstract:
  We consider the extremal values of the stationary distribution of sparse
directed random graphs with given degree sequences and their relation to the
extremal values of the in-degree sequence. The graphs are generated by the
directed configuration model. Under the assumption of bounded
$(2+\eta)$-moments on the in-degrees and of bounded out-degrees, we obtain
tight comparisons between the maximum value of the stationary distribution and
the maximum in-degree. Under the further assumption that the order statistics
of the in-degrees have a power-law behavior, we show that the extremal values
of the stationary distribution also have a power-law behavior with the same
index. In the same setting, we prove that these results extend to the PageRank
scores of the random digraph, thus confirming a version of the so-called
power-law hypothesis. Along the way, we establish several facts about the
model, including the mixing time cutoff and the characterization of the typical
values of the stationary distribution, which were previously obtained under the
assumption of bounded in-degrees.


Abstract:
  Robust and accurate localization is an essential component for robotic
navigation and autonomous driving. The use of cameras for localization with
high definition map (HD Map) provides an affordable localization sensor set.
Existing methods suffer from pose estimation failure due to error prone data
association or initialization with accurate initial pose requirement. In this
paper, we propose a cost-effective vehicle localization system with HD map for
autonomous driving that uses cameras as primary sensors. To this end, we
formulate vision-based localization as a data association problem that maps
visual semantics to landmarks in HD map. Specifically, system initialization is
finished in a coarse to fine manner by combining coarse GPS (Global Positioning
System) measurement and fine pose searching. In tracking stage, vehicle pose is
refined by implicitly aligning the semantic segmentation result between image
and landmarks in HD maps with photometric consistency. Finally, vehicle pose is
computed by pose graph optimization in a sliding window fashion. We evaluate
our method on two datasets and demonstrate that the proposed approach yields
promising localization results in different driving scenarios. Additionally,
our approach is suitable for both monocular camera and multi-cameras that
provides flexibility and improves robustness for the localization system.


Abstract:
  The transport coefficients for dilute granular gases of inelastic and rough
hard disks or spheres with constant coefficients of normal ($\alpha$) and
tangential ($\beta$) restitution are obtained in a unified framework as
functions of the number of translational ($d_t$) and rotational ($d_r$) degrees
of freedom. The derivation is carried out by means of the Chapman--Enskog
method with a Sonine-like approximation in which, in contrast to previous
approaches, the reference distribution function for angular velocities does not
need to be specified. The well-known case of purely smooth $d$-dimensional
particles is recovered by setting $d_t=d$ and formally taking the limit $d_r\to
0$. In addition, previous results [G. M. Kremer, A. Santos, and V. Garz\'o,
Phys. Rev. E 90, 022205 (2014)] for hard spheres are reobtained by taking
$d_t=d_r=3$, while novel results for hard-disk gases are derived with the
choice $d_t=2$, $d_r=1$. The singular quasismooth limit ($\beta\to -1$) and the
conservative Pidduck's gas ($\alpha=\beta=1$) are also obtained and discussed.


Abstract:
  While Artificial Neural Networks (ANNs) have yielded impressive results in
the realm of simulated intelligent behavior, it is important to remember that
they are but sparse approximations of Biological Neural Networks (BNNs). We go
beyond comparison of ANNs and BNNs to introduce principles from BNNs that might
guide the further development of ANNs as embodied neural models. These
principles include representational complexity, complex network
structure/energetics, and robust function. We then consider these principles in
ways that might be implemented in the future development of ANNs. In
conclusion, we consider the utility of this comparison, particularly in terms
of building more robust and dynamic ANNs. This even includes constructing a
morphology and sensory apparatus to create an embodied ANN, which when
complemented with the organizational and functional advantages of BNNs unlocks
the adaptive potential of lifelike networks.


Abstract:
  Infrared quasi-stellar objects (IR QSOs) are a rare subpopulation selected
from ultraluminous infrared galaxies (ULIRGs) and have been regarded as
promising candidates of ULIRG-to-optical QSO transition objects. Here we
present NOEMA observations of the CO(1-0) line and 3 mm continuum emission in
an IR QSO IRAS F07599+6508 at $z=0.1486$, which has many properties in common
with Mrk 231. The CO emission is found to be resolved with a major axis of
$\sim$6.1 kpc that is larger than the size of $\sim$4.0 kpc derived for 3 mm
continuum. We identify two faint CO features located at a projected distance of
$\sim$11.4 and 19.1 kpc from the galaxy nucleus, respectively, both of which
are found to have counterparts in the optical and radio bands and may have a
merger origin. A systematic velocity gradient is found in the CO main
component, suggesting that the bulk of molecular gas is likely rotationally
supported. Based on the radio-to-millimeter spectral energy distribution and IR
data, we estimate that about 30$\%$ of the flux at 3 mm arises from free-free
emission and infer a free-free-derived star formation rate of 77 $M_\odot\ {\rm
yr^{-1}}$, close to the IR estimate corrected for the AGN contribution. We find
a high-velocity CO emission feature at the velocity range of about -1300 to
-2000 km s$^{-1}$. Additional deep CO observations are needed to confirm the
presence of a possible very high-velocity CO extension of the OH outflow in
this IR QSO.


Abstract:
  We present here the results of broadband spectral analysis of low-mass X-ray
binary and a black hole candidate 4U 1957+11. The source was observed nine
times with the Nuclear Spectroscopic Telescope Array (NuSTAR) between 2018
September and 2019 November. During these observations, the spectral state of
4U 1957+11 evolved marginally. The disc dominant spectra are well described
with a hot, multicolour disc blackbody with disc temperature varying in the
range $kT_{\rm in} \sim$ 1.35-1.86 keV and a non-thermal component having a
steep slope ($\Gamma =$ 2-3). A broad Fe emission line feature (5-8 keV) was
observed in the spectra of all the observations. The relativistic disc model
was used to study the effect of distance, inclination, and the black hole mass
on its spin. Simulations indicate a higher spin for smaller distances and lower
black hole mass. At smaller distances and higher mass, spin is maximum and
almost independent of the distance. An inverse correlation exists between the
spin and the spectral hardening factor for all the cases. The system prefers a
moderate spin of about 0.85 for black hole masses between 4-6 M_sun for a 7 kpc
distance.


Abstract:
  Deep learning has demonstrated radiograph screening performances that are
comparable or superior to radiologists. However, recent studies show that deep
models for thoracic disease classification usually show degraded performance
when applied to external data. Such phenomena can be categorized into shortcut
learning, where the deep models learn unintended decision rules that can fit
the identically distributed training and test set but fail to generalize to
other distributions. A natural way to alleviate this defect is explicitly
indicating the lesions and focusing the model on learning the intended
features. In this paper, we conduct extensive retrospective experiments to
compare a popular thoracic disease classification model, CheXNet, and a
thoracic lesion detection model, CheXDet. We first showed that the two models
achieved similar image-level classification performance on the internal test
set with no significant differences under many scenarios. Meanwhile, we found
incorporating external training data even led to performance degradation for
CheXNet. Then, we compared the models' internal performance on the lesion
localization task and showed that CheXDet achieved significantly better
performance than CheXNet even when given 80% less training data. By further
visualizing the models' decision-making regions, we revealed that CheXNet
learned patterns other than the target lesions, demonstrating its shortcut
learning defect. Moreover, CheXDet achieved significantly better external
performance than CheXNet on both the image-level classification task and the
lesion localization task. Our findings suggest improving annotation granularity
for training deep learning systems as a promising way to elevate future deep
learning-based diagnosis systems for clinical usage.


Abstract:
  Schr\"odinger operators with periodic (possibly complex-valued) potentials
and discrete periodic operators (possibly with complex-valued entries) are
considered, and in both cases the computational spectral problem is
investigated: namely, under what conditions can a "one-size-fits-all" algorithm
for computing their spectra be devised? It is shown that for periodic banded
matrices this can be done, as well as for Schr\"odinger operators with periodic
potentials that are sufficiently smooth. In both cases implementable algorithms
are provided, along with examples. For certain Schr\"odinger operators whose
potentials may diverge at a single point (but are otherwise well-behaved) it is
shown that there does not exist such an algorithm, though it is shown that the
computation is possible if one allows for two successive limits.


Abstract:
  Bi-level optimization model is able to capture a wide range of complex
learning tasks with practical interest. Due to the witnessed efficiency in
solving bi-level programs, gradient-based methods have gained popularity in the
machine learning community. In this work, we propose a new gradient-based
solution scheme, namely, the Bi-level Value-Function-based Interior-point
Method (BVFIM). Following the main idea of the log-barrier interior-point
scheme, we penalize the regularized value function of the lower level problem
into the upper level objective. By further solving a sequence of differentiable
unconstrained approximation problems, we consequently derive a sequential
programming scheme. The numerical advantage of our scheme relies on the fact
that, when gradient methods are applied to solve the approximation problem, we
successfully avoid computing any expensive Hessian-vector or Jacobian-vector
product. We prove the convergence without requiring any convexity assumption on
either the upper level or the lower level objective. Experiments demonstrate
the efficiency of the proposed BVFIM on non-convex bi-level problems.


Abstract:
  State-of-the-art summarization systems are trained and evaluated on massive
datasets scraped from the web. Despite their prevalence, we know very little
about the underlying characteristics (data noise, summarization complexity,
etc.) of these datasets, and how these affect system performance and the
reliability of automatic metrics like ROUGE. In this study, we manually analyze
600 samples from three popular summarization datasets. Our study is driven by a
six-class typology which captures different noise types (missing facts,
entities) and degrees of summarization difficulty (extractive, abstractive). We
follow with a thorough analysis of 27 state-of-the-art summarization models and
5 popular metrics, and report our key insights: (1) Datasets have distinct data
quality and complexity distributions, which can be traced back to their
collection process. (2) The performance of models and reliability of metrics is
dependent on sample complexity. (3) Faithful summaries often receive low scores
because of the poor diversity of references. We release the code, annotated
data and model outputs.


Abstract:
  We show that combining randomized measurement protocols with importance
sampling allows for characterizing entanglement in significantly larger quantum
systems and in a more efficient way than in previous work. A drastic reduction
of statistical errors is obtained using classical techniques of
machine-learning and tensor networks using partial information on the quantum
state. In present experimental settings of engineered many-body quantum systems
this effectively doubles the (sub-)system sizes for which entanglement can be
measured. In particular, we show an exponential reduction of the required
number of measurements to estimate the purity of product states and GHZ states.


Abstract:
  We present an infrared photodetector leveraging an all-epitaxial device
architecture consisting of a 'designer' plasmonic metal integrated with a
quantum-engineered detector structure, all in a mature III-V semiconductor
material system. Incident light is coupled into surface plasmon-polariton modes
at the detector/'designer' metal interface, and the strong confinement of these
modes allows for a sub-diffractive ($\sim \lambda_0 / 33$) detector absorber
layer thickness, effectively decoupling the detector's absorption efficiency
and dark current. We demonstrate high-performance detectors operating at
non-cryogenic temperatures (T = 195 K), without sacrificing external quantum
efficiency, and superior to well established and commercially-available
detectors. This work provides a practical and scalable plasmonic optoelectronic
device architecture with real world mid-infrared applications.


Abstract:
  We examine the composition of barium stars in the context of mass transfer
from an asymptotic giant branch (AGB) companion. We accrete between 0.01 and
0.5 M$_\odot$ of AGB ejecta on to low mass companions of [Fe/H] = -0.25 at the
ages expected for the end of the lives of AGB stars of 2.5, 3 and 4M$_\odot$.
In each case, we form a star of 2.5 M$_\odot$ which is thought to be a typical
barium star mass. We discuss the extent of dilution of accreted material as the
star evolves, and describe the impact on the surface abundances. For accretion
from a 2.5\ms\ primary, if the secondary's initial mass is 2.45 M$_\odot$ or
more, accretion takes place when the secondary is undergoing core helium
burning. Using data from the sample of De Castro et al., we attempt to fit the
observed properties of 74 barium giants using the models we have computed. We
find that all but six of these objects are best fit using ejecta from 2.5
M$_\odot$ (32 objects) or 3 M$_\odot$ (36 objects) AGB stars. Higher accretion
masses are typically required when accreting from a lower mass companion. We
find accretion masses that are broadly consistent with recent hydrodynamical
simulations of wind mass transfer, though the accretion efficiency is toward
the upper limit found in these simulations. For the 18 stars with reported
orbital periods, we find no strong correlations between period and accretion
mass.


Abstract:
  Negative ions have been detected in abundance in recent years by spacecraft
across the solar system. These detections were, however, made by instruments
not designed for this purpose and, as such, significant uncertainties remain
regarding the prevalence of these unexpected plasma components. In this
article, the phenomenon of photodetachment is examined and experimentally and
theoretically derived cross-sections are used to calculate photodetachment
rates for a range of atomic and molecular negative ions subjected to the solar
photon spectrum. These rates are applied to negative ions outflowing from
Europa, Enceladus, Titan, Dione and Rhea and their trajectories are traced to
constrain source production rates and the extent to which negative ions are
able to pervade the surrounding space environments. Predictions are also made
for further negative ion populations in the outer solar system with Triton used
as an illustrative example. This study demonstrates how, at increased
heliocentric distances, negative ions can form stable ambient plasma
populations and can be exploited by future missions to the outer solar system.


Abstract:
  The main focus of this work is a novel framework for the joint reconstruction
and segmentation of parallel MRI (PMRI) brain data. We introduce an image
domain deep network for calibrationless recovery of undersampled PMRI data. The
proposed approach is the deep-learning (DL) based generalization of local
low-rank based approaches for uncalibrated PMRI recovery including CLEAR [6].
Since the image domain approach exploits additional annihilation relations
compared to k-space based approaches, we expect it to offer improved
performance. To minimize segmentation errors resulting from undersampling
artifacts, we combined the proposed scheme with a segmentation network and
trained it in an end-to-end fashion. In addition to reducing segmentation
errors, this approach also offers improved reconstruction performance by
reducing overfitting; the reconstructed images exhibit reduced blurring and
sharper edges than independently trained reconstruction network.


Abstract:
  In this paper, we consider a variational formulation for the Dirichlet
problem of the wave equation with zero boundary and initial conditions, where
we use integration by parts in space and time. To prove unique solvability in a
subspace of $H^1(Q$) with $Q$ being the space-time domain, the classical
assumption is to consider the right-hand side $f$ in $L^2(Q)$. Here, we analyze
a generalized setting of this variational formulation, which allows us to prove
unique solvability also for $f$ being in the dual space of the test space,
i.e., the solution operator is an isomorphism between the ansatz space and the
dual of the test space. This new approach is based on a suitable extension of
the ansatz space to include the information of the differential operator of the
wave equation at the initial time $t=0$. These results are of utmost importance
for the formulation and numerical analysis of unconditionally stable space-time
finite element methods, and for the numerical analysis of boundary element
methods to overcome the well-known norm gap in the analysis of boundary
integral operators.


Abstract:
  Let the diameter cover number, $D^t_r(G)$, denote the least integer $d$ such
that under any $r$-coloring of the edges of the graph $G$, there exists a
collection of $t$ monochromatic subgraphs of diameter at most $d$ such that
every vertex of $G$ is contained in at least one of the subgraphs. We explore
the diameter cover number with two colors and two subgraphs when $G$ is a
complete multipartite graph with at least three parts. We determine exactly the
value of $D_2^2(G)$ for all complete tripartite graphs $G$, and almost all
complete multipartite graphs with more than three parts.


Abstract:
  The analysis of record-breaking events is of interest in fields such as
climatology, hydrology, economy or sports. In connection with the record
occurrence, we propose three distribution-free statistics for the changepoint
detection problem. They are CUSUM-type statistics based on the upper and/or
lower record indicators which occur in a series. Using a version of the
functional central limit theorem, we show that the CUSUM-type statistics are
asymptotically Kolmogorov distributed. The main results under the null
hypothesis are based on series of independent and identically distributed
random variables, but a statistic to deal with series with seasonal component
and serial correlation is also proposed. A Monte Carlo study of size, power and
changepoint estimate has been performed. Finally, the methods are illustrated
by analyzing the time series of temperatures at Madrid, Spain. The $\textsf{R}$
package $\texttt{RecordTest}$ publicly available on CRAN implements the
proposed methods.


Abstract:
  We describe the population-based SEIR (susceptible, exposed, infected,
removed) model developed by the Irish Epidemiological Modelling Advisory Group
(IEMAG), which advises the Irish government on COVID-19 responses. The model
assumes a time-varying effective contact rate (equivalently, a time-varying
reproduction number) to model the effect of non-pharmaceutical interventions. A
crucial technical challenge in applying such models is their accurate
calibration to observed data, e.g., to the daily number of confirmed new cases,
as the past history of the disease strongly affects predictions of future
scenarios. We demonstrate an approach based on inversion of the SEIR equations
in conjunction with statistical modelling and spline-fitting of the data, to
produce a robust methodology for calibration of a wide class of models of this
type.


Abstract:
  In this paper, we compute normalized Laplacian spectra of central graph of a
regular graph, central vertex join and central edge join of two regular graphs.
Also, we determine their Kemeny's constant and degree Kirchhoff index.


Abstract:
  We present a new adaptive parallel algorithm for the challenging problem of
multi-dimensional numerical integration on massively parallel architectures.
Adaptive algorithms have demonstrated the best performance, but efficient
many-core utilization is difficult to achieve because the adaptive work-load
can vary greatly across the integration space and is impossible to predict a
priori. Existing parallel algorithms utilize sequential computations on
independent processors, which results in bottlenecks due to the need for data
redistribution and processor synchronization. Our algorithm employs a
high-throughput approach in which all existing sub-regions are processed and
sub-divided in parallel. Repeated sub-region classification and filtering
improves upon a brute-force approach and allows the algorithm to make efficient
use of computation and memory resources. A CUDA implementation shows orders of
magnitude speedup over the fastest open-source CPU method and extends the
achievable accuracy for difficult integrands. Our algorithm typically
outperforms other existing deterministic parallel methods.


Abstract:
  We show that log-concavity is the weakest power concavity preserved by the
Dirichlet heat flow in $N$-dimensional convex domains, where $N\ge 2$ (indeed,
we prove that starting with a negative power concave initial datum may result
in losing immediately any reminiscence of concavity). Jointly with what we
already know, i.e. that log-concavity is the strongest power concavity
preserved by the Dirichlet heat flow, we see that log-concavity is indeed the
only power concavity preserved by the Dirichlet heat flow.


Abstract:
  The temporal Talbot effect refers to the periodic revivals of a pulse train
propagating in a dispersive medium, and is a temporal analog of the spatial
Talbot effect with group-velocity dispersion in time replacing diffraction in
space. Because of typically large temporal Talbot lengths, this effect has been
observed to date in only single-mode fibers, rather than with freely
propagating fields in bulk dispersive media. Here we demonstrate for the first
time the temporal Talbot effect in free space by employing dispersive
space-time wave packets, whose spatio-temporal structure induces group-velocity
dispersion of controllable magnitude and sign in free space.


Abstract:
  Thermal face imagery, which captures the naturally emitted heat from the
face, is limited in availability compared to face imagery in the visible
spectrum. To help address this scarcity of thermal face imagery for research
and algorithm development, we present the DEVCOM Army Research Laboratory
Visible-Thermal Face Dataset (ARL-VTF). With over 500,000 images from 395
subjects, the ARL-VTF dataset represents, to the best of our knowledge, the
largest collection of paired visible and thermal face images to date. The data
was captured using a modern long wave infrared (LWIR) camera mounted alongside
a stereo setup of three visible spectrum cameras. Variability in expressions,
pose, and eyewear has been systematically recorded. The dataset has been
curated with extensive annotations, metadata, and standardized protocols for
evaluation. Furthermore, this paper presents extensive benchmark results and
analysis on thermal face landmark detection and thermal-to-visible face
verification by evaluating state-of-the-art models on the ARL-VTF dataset.